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Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

BOTANICAL RESEARCH AND PRACTICES

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WILD PLANTS: IDENTIFICATION, USES AND CONSERVATION

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Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

BOTANICAL RESEARCH AND PRACTICES

WILD PLANTS: IDENTIFICATION, USES AND CONSERVATION

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RYAN E. DAVIS EDITOR

Nova Science Publishers, Inc. New York Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

Copyright © 2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers‘ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data Wild plants : identification, uses and conservation / editor: Ryan E. Davis. p. cm. Includes bibliographical references and index. ISBN 978-1-62257-136-9 (E-Book) 1. Plants--Identification. 2. Plants, Useful. 3. Plant conservation. I. Davis, Ryan E. QK97.5.W55 2011 580--dc22 2011005854

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CONTENTS Preface Chapter 1

Identification of Plant Species Using Traditional and Molecular-Based Methods Nadia Haider

1

Chapter 2

Antioxidants from Wild Plants: Sources, Features and Assays Maria S. Gião, A. Catarina Guedes and F. Xavier Malcata

Chapter 3

Wild Plant Seeds Identification through Image and Linear Discriminant Analysis Oscar Grillo and Gianfranco Venora

105

Landscape Genetics of Fagus Sylvatica in One of its Glacial Refuge Areas Giovanni Figliuolo

149

Importance of Dominant Plant Species for Ecological Interactions in Forest Soil and Litter: Example from the Heron Wood Reserve, Dawyck Botanic Garden, Scotland V. Krivtsov, S. J. J. Walker, R. Watling, A. Garside and M. J. Richardson

179

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i

Chapter 5

Chapter 6

An Overview on the Human Exploitation of Sicilian Native Edible Plants Salvatore Pasta, Giuseppe Garfì, Francesca La Bella, Juliane Rühl and Francesco Carimi

Chapter 7

Wild Rice: Identification, Uses and Conservation Jin Quan Li

Chapter 8

Persian Shallot (Allium Hirtifolium Boiss): An Endangered Wild Plant R. Ebrahimi, Z. Zamani, M. R. Hassandokht and A. Kashi

Index Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

63

195

269

289

305

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PREFACE In this book, the authors present topical research in the study of the identification, uses and conservation of wild plants. Topics discussed include identification of plant species using traditional and molecular-based methods; extracts from wild plants that possess antioxidant capacity; wild plant seed identification through image and linear discriminant analysis; the keystone tree species of Fagus sylvatica in the glacial refuge area of southern Europe and how dominant plant species influence the patterns of ecological interactions. Chapter 1 - Identification of experimental material is one of the most fundamental requirements of all areas of biological science. The discovery and naming of living organisms (any life form including plant, animal or microbe) has attracted a lot of attention throughout history. Identification is ‗the process of assigning a specimen to a (pre-existing) taxon‘. Plant species have been regarded as a basic unit of biodiversity and it is the level at which most of the evolutionary studies have focused. The need for plant species identification is both varied and widespread, and includes applications for plant breeding, agricultural seed industry, food processing, conservation biology, forensic analysis and many other aspects of plant science. Traditionally, identification of plant species has relied heavily on morphological characters. For the most part, however, vegetative traits are highly variable between individuals and often too plastic (plasticity: the deviation of the mean phenotype of a genotype (the sum total of the genes contained in the chromosomes of the eukaryotes) within an environment from the mean phenotype of that across all environments) to be used for identification at the species level. The value of floral features for diagnosis is constrained, however, by their absence from the plant during much of the year and unavailability of flowers in small plant species. There are many instances when the amount of material available is insufficient to enable identification using the traditional strategies. The use of molecular approaches for species identification, therefore, is the most attractive strategy. Genetic variation in plants has been reported at all taxonomic levels. This variation can be exploited for the identification of plants at all taxonomic ranks. The development of various molecular techniques that generate molecular markers has made it possible to accurately identify plants. These techniques either exploit differences at the level of the DNA or of the protein encoded by it. In this chapter, few of those techniques and traditional methods that can be used for identification plant species will be discussed, and examples of employing them for identification of plant species will be provided. Chapter 2 - Antioxidants have been classically employed in the food industry and elsewhere, mainly as preservatives. Further to this deed, antioxidants are nowadays

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Ryan E. Davis

increasingly sought as components of the diet owing to their benefits upon human health – via protection of cells against oxidative stress, which might otherwise lead to cell damage and death; coronary heart diseases, ulcers, cancers and neurogenerative diseases – besides overall ageing, are but a few examples of diseases and health conditions that can be prevented (or, at least, delayed) via regular and balanced ingestion of antioxidants. Antioxidants can be chemically synthesized, or else extracted from biological samples – especially from plants. Natural antioxidants have been in greater and greater demand, in response to a more environmentally-aware consumer population. The most abundant antioxidants in plants are polyphenols; deprived of nitrogen, these arenes substituted by hydroxyl groups possess more than one phenol ring, and are mainly generated via the shikimate or the acetate pathways. This chapter reviews the most important literature sources pertaining to wild plants from which extracts have been obtained that possess Chapter 3 - High biodiversity assures the ecosystems aptitude to adapt to environmental changes, guaranteeing ecological balance and future life. Unfortunately today, biological diversity faces many threats throughout the world and, as a consequence, the loss of wild plant biodiversity is constantly increasing. Extinction is the gravest aspect of the biodiversity crisis. It is irreversible. While extinction is a natural process, human impacts have elevated the rate of extinction by at least a thousand times than the natural rate. It was worked out that, in the last 100 years, human activity has caused between fifty and one thousand times more extinctions than would have happened due to natural processes. In order to spark off processes of conservation and preservation, the in situ and ex situ conservation concepts were introduced by the Convention on Biological Diversity (CBD), ratified in 1993 by European Union. In addition to preserve existing genetic resources, the conservation allows the study and the development of new cultivars during genetic improvement processes, it provides populations for reintroduction and repopulation programs of degraded habitats, and then it permits industry, agriculture and scientific research to use essential for future progress. Finally, the ex situ conservation allows to study the best strategies to apply at the in situ conservation of threatened species (Bacchetta, 2011a). All that is possible thanks to the activities of structures more and more widespread such as the germplasm banks, whose function is not only to preserve threatened species, but also to store, by long-term techniques, seeds, spore, woods, tissues and any other structures that make up the genetic biodiversity of the planet. Chapter 4 - Fagus sylvatica is a keystone species shaping the most important natural and quasi-natural ecosystems of the mountains in the Mediterranean area. This work has the aims to evaluate the genetic structure of beech in the southernmost distribution areas and map relevant sub-populations for conservation genetics. The methods used are based on landscape genetics. Landscape genetic maps were generated from 7 microsatellite loci using 37 subpopulations sampled in southern Italy and two control subpopulations from Norway and Sicily. Ecological sources of variation were also recorded and evaluated. Genetic disequilibrium increased from the sub-population to the whole population. The significant differentiation among sub-population for nuclear markers was consistent with the outcrossing breeding system. Two main clusters spatially distributed according to a contact zone migration model were inferred. Chloroplast haplotype richness decreased when moving northward and was independent of the sub-population sample size. Nuclear allelic richness was evenly distributed and correlated with both gene diversity (He) and sub-population size. Sub-populations bearing both a low heterozygote deficit and high chloroplast haplotype

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Preface

iii

richness were mapped in multiple sites, were spatially marginal, and interpreted as being associated with -glacial niches. Southern Italy is not a homogeneous area in terms of beech genetic diversity. The assessed spatial genetic pattern can better direct both the beech-wood management design and the strategies of genetic conservation. Chapter 5 - Analysis of a dataset obtained from a monitoring programme at the Heron Wood Reserve (Scotland, UK), focuses on the differences in certain properties of soil and forest litter and patterns of ecosystem dynamics in plots dominated by differing vegetation types, especially the arborescent beech (Fagus sylvatica) and birch (Betula pendula x B. pubescens), and the grass Holcus lanatus. A number of properties show some considerable differences in relation to the habitats dominated by different plant species. For example, pH in the grassland and beech-dominated habitat was significantly lower than in the habitats dominated by birch and mixed vegetation. The highest soil ergosterol was found in the beechdominated habitat, and it was significantly different from grass and birch dominated habitats. By contrast, the numbers of forest floor layer bacteria in the beech-dominated plots were significantly lower than in the other habitats. Some remarkable differences have also been found as regards forest litter composition and moisture, the community saprotrophs and sheathing mycorrhizas. The ecological patterns are further complicated by animals, and are exemplified here by discussing the role of nutrient inputs due to the mammalian droppings characterised by a succession of the coprophilous mycota. The discussion concentrates on how the dominant plant species influence the patterns of ecological interactions observed. Chapter 6 - Sicily and its satellite islets host a rich vascular flora, including almost 3,000 native plant species and subspecies; in addition, due to its central position in the Mediterranean, the island has played and still plays a key-role in connecting both plant and human populations of neighbouring Mediterranean countries. The high plant biodiversity is due to a number of factors, such as geographical setting, geological history, soil-type diversity, bioclimatic variability and natural and human disturbance history. Among this flora, many plants, mostly herbs and sub-shrubs, have been used by local people since ancient times for various purposes, mainly as food and/or medicine. The long lasting history of exploitation, deeply permeated with the strong influence from external civilisations, has given rise to a rich inheritance of knowledge that indissolubly has bound biological and cultural (e.g. ethnic and/or linguistic) aspects, resulting in a remarkable bio-cultural diversity within the island territory. In the present chapter, we provide an updated list of Sicilian autochthonous edible plants, giving supplementary information on their vernacular names and uses, in addition to the eco-geography of some rare or endemic species. Emphasis is placed on some differences in plant naming and uses within the regional territory, probably due to different cultural influences, mostly deriving from Greek, Latin and Arab languages. Moreover, the local richness in wild relatives of food crops and the large number of foodmedicines among locally gathered plants is highlighted. The study allowed for the identification of more than 250 wild edible plants that are known through an unexpectedly vast number of vernacular plant names and used in many preparations. This suggests an extremely complex and intriguing history of exploitation, quite afar to be acquainted. Moreover, it could significantly contribute to the conservation and valorisation of the rich bio-cultural inheritance of Sicily. Chapter 7 - Wild plants, which are wild species closely related to cultivar, are a neglected global natural resource, yet they make a concrete contribution to global wealth creation and food security (Maxted et al. 2007). Identification, uses and conservation for wild plants are of

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importance for their sustainable use in plant breeding. The current rapidly development of bioinformatics, genomics, and molecular biology as well as conventional breeding methods provides useful means to mine the desirable genes in wild plants. Rice (Oryza sativa L.) is the most important human food crop in the world. As a model plant of cereal family, two rice genome sequence map have been generated (Goff et al., 2002; Yu et al., 2002). The genus Oryza consists of two cultivated rice (O. sativa and O. glaberrima) and 21 wild species (Khush, 1997; Vaughan et al., 2003). The wild rice species offer a largely untapped resource of agriculturally important genes that have the potential to solve many of the problems in rice production which we face today such as yield, drought and salt tolerance and disease and insect resistance. To unlock the genetic potential of wild rice a project entitled the ―Oryza Map Alignment Project‖ (OMAP) had been constructed to sequence 11 wild rice species comprise nine different genome types and include six diploid genomes (AA, BB, CC, EE, FF and GG) and four tetrapliod genomes (BBCC, CCDD, HHKK and HHJJ) (Wing et al. 2005). The project provides a research platform to study evolution, development, genome organization, polyploidy, domestication, gene regulatory networks and crop improvement. Therefore, in this charter, cultivated rice (Oryza sativa) and its wild relatives were used as a case for demonstration of identification, use and conservation of wild plants. Chapter 8 - Persian shallot (Allium hirtifolium Boiss.), a bulb producing plant from Alliaceae, is a wildly growing plant collected for its bulbs. Bulbs of Persian shallot, called "Mooseer" in Farsi, are oval, white skinned, usually of one and rarely of two main bulbs and are completely different from common shallot (Allium ascalonicum). Mooseer is a nutritive plant with special taste and its dried bulb slices are used as an additive to yogurt and also pickling mixtures. Its powder is used as a tasty additive or spice for foods in Iran. In addition, it has crucial medicinal effects; The mean dry matter of Mooseer was higher (36.71%) than other alliums except garlic. Mooseer was rich in Cu as well as Zn and Mn elements. Also, its linolenic acid (ω3) and linoleic acid (ω6) were higher than common shallot and onion. As some of the edible vegetable alliums such as mooseer are indigenous (native and endemic) to Iran, and do not exist in other parts of the world, their conservation is become imperative.

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In: Wild Plants: Identification, Uses and Conservation ISBN 978-1-61209-966-8 Editor: Ryan E. Davis, pp. 1-62 © 2011 Nova Science Publishers, Inc.

Chapter 1

IDENTIFICATION OF PLANT SPECIES USING TRADITIONAL AND MOLECULAR-BASED METHODS Nadia Haider* Department of Molecular Biology and Biotechnology, AECS, Damascus, Syria

ABSTRACT

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Identification of experimental material is one of the most fundamental requirements of all areas of biological science. The discovery and naming of living organisms (any life form including plant, animal or microbe) has attracted a lot of attention throughout history. Identification is ‗the process of assigning a specimen to a (pre-existing) taxon‘. Plant species have been regarded as a basic unit of biodiversity and it is the level at which most of the evolutionary studies have focused. The need for plant species identification is both varied and widespread, and includes applications for plant breeding, agricultural seed industry, food processing, conservation biology, forensic analysis and many other aspects of plant science. Traditionally, identification of plant species has relied heavily on morphological characters. For the most part, however, vegetative traits are highly variable between individuals and often too plastic (plasticity: the deviation of the mean phenotype of a genotype (the sum total of the genes contained in the chromosomes of the eukaryotes) within an environment from the mean phenotype of that across all environments) to be used for identification at the species level. The value of floral features for diagnosis is constrained, however, by their absence from the plant during much of the year and unavailability of flowers in small plant species. There are many instances when the amount of material available is insufficient to enable identification using the traditional strategies. The use of molecular approaches for species identification, therefore, is the most attractive strategy. Genetic variation in plants has been reported at all taxonomic levels. This variation can be exploited for the identification of plants at all taxonomic ranks. The development of various molecular techniques that generate molecular markers has made it possible to accurately identify plants. These techniques either exploit differences at the level of the DNA or of the *

Department of Molecular Biology and Biotechnology, AECS, P.O. Box 6091, Damascus, Syria. E-mail: [email protected]. Tel. 00963-11-2132581,2,3. Fax 00963-11-6112289.

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2

Nadia Haider protein encoded by it. In this chapter, few of those techniques and traditional methods that can be used for identification plant species will be discussed, and examples of employing them for identification of plant species will be provided.

INTRODUCTION Systematics and Taxonomy

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There has been considerable debate over the formal definition of the terms systematics, phylogenetics, classification and taxonomy (e.g., De Queiroz and Donoghue, 1988). In Greek terminology, the word taxonomy means ‗putting in order‘ whereas systematics means ‗putting together‘. Accordingly, Stace (1989) and many others preferred to treat both terms synonymously. Stace (1989) defined taxonomy as ‗the study and description of the variation of organisms, the investigation of the causes and consequences of this variation, and the manipulation of the data obtained to produce a system of classification‘. However, only a few workers (e.g., Mason in 1950, cited in Sivarajan and Robson, 1991) have accepted the treatment of taxonomy as a term with a wider concept than systematics. Stuessy (1979), Small (1989), Winston (1999) and Kazlev (2002) took a different view and preferred to use systematics as the broad term describing the discipline (see Fig.1 for an example).

Figure 1. Relationships among and within the three main aspects of systematics (after Stuessy, 1979).

Kazlev (2002) defined systematics as ‗the branch of biology that deals with classifying living beings: the diversity and interrelationships of living beings, both present day organisms ("neontology") and prehistoric ones ("palaeontology")‘, and divided the concept into three Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

Identification of Plant Species Using Traditional and Molecular-Based Methods

3

subdisciplines; phylogenetics, taxonomy and classification. Taxonomy was defined as ‗the describing and naming new taxa‘. Although in practice the distinction between these two terms is not always sharp, in this chapter taxonomy will be adopted as a subdivision of the broader term systematics. Savolainen and Chase (2003) believe that plant systematics is one of the most active areas of biology because of marked progress in molecular phylogenetics during recent decades. The three main aspects associated with taxonomy, namely nomenclature, classification and identification are described below.

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Nomenclature The discovery and naming of living organisms (any life form including plant, animal or microbe) has attracted a lot of attention throughout history. The term ‗Nomenclature‘ covers the system of naming organisms, through the construction, interpretation and application of the regulations that govern this system (Stace, 1989). The species rank forms only part of a hierarchy in which individuals that share characters in common are grouped together, with position in the hierarchy being set by the level of variability. The hierarchical level containing the least variability is assigned the lowest taxonomic rank; ‗form‘. Groups of forms are progressively amalgamated into broader associations at the next taxonomic rank in the sequence; variety, subspecies, species, genus, tribe, family, order, class, division and Kingdom (Stace, 1989). The species rank has particular importance in the hierarchy (Stuessy, 1990) and is the standard rank against which others are gauged. This importance is reflected in the highly regulated process of naming a new species (nomenclature), with the new names at the species rank requiring a description of phenotypic attributes in Latin, the designation of a type specimen (a reference individual belonging to the species) as well as the creation of a new binomial and authority. The type specimens of higher ranks relate back to that of a designated (type) species. Uniform and internationally acceptable principles for naming plants were drafted in a series of meetings of the International (Botanical) Congress and first released in a formal publication entitled International Code of Botanical Nomenclature (ICBN), whose rules were developed at the International Congress held in Cambridge (England) in 1930. These rules, however, were derived in turn from previous ones, the earliest of which emanated from the Paris Congress in 1867. Names were needed to avoid descriptive phrases to refer to organisms and facilitate communications (Sivarajan and Robson, 1991). For example, in 1738, Linnaeus named a species ‗Plantago foliis ovatis glabris‘. When the number of plant species increased, however, this phrase became insufficient for the definite recognition of the taxon. So in 1753, Linnaeus named it as ‗Plantago foliis ovatis glabris, undo scapo tereti, spica flosculis imbricate’. Such names proved unpractical, therefore from 1753 Linnaeus used the binomial (binary) system of nomenclature, and for the Plantago species he gave Plantago major (Stace, 1989). Usually, the names assigned to plants are either Latin or from some other language and then ‗Latinised‘. In text, names are written in italics for the purpose of clarity and normally should be accompanied by the authority (not italicised) (e.g., Tritcum aestivum L.). The inclusion of authority avoids ambiguity as can occur when two or more species have been given the same name (only one of which, generally the earliest, is valid). For example, there

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are two concepts of Festuca glauca: the valid name described by Villars (F. glauca Vill.) and that described by Lamarck (F. glauca Lam.). The scientific name of a species always consists of two parts (binomial phrase); the first is the generic name (usually singular and starting with a capital letter), and the second is the species epithet (starting with a small letter). The latter can be one of the following: 1) an adjective that agrees with the generic name, and that refers to a distinguishing character of the species or to its origin (e.g., Rosa alba and Ulmus americana), 2) a noun is an apposition that agrees with the generic name but not necessarily agreeing with it in gender (as in Pyrus malus), 3) a noun in the genitive singular or plural such as when a species is named in honour of one or more persons (as in Carex davisii that named for a Mr. Davis), and 4) a common name in the genitive plural; this usually describes some thing about the species habitat (as in Carex paludosum meaning ―of the swamps‖) (Porter, 1967). Some of the names are made up of a random combination of letters as long as they can be pronounceable. This is the case in groups that are composed of a large number of species (Winston, 1999). According to the latter, name must be more than one letter long and cannot contain any diacritical marks.

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Classification Stace (1989) defined classification (as a process) as ‗the production of a logical system of categories, each containing any number of organisms, which allows easier reference to its components (kinds of organisms)‘. However, as an object, he defined classification as ‗that system itself, of which there are many sorts‘. Most systems of classification group species into a hierarchical series of successively larger groups (ranks) that culminate with a single group that includes all plants. The ICBN recognizes 12 main ranks in the hierarchy with the Kingdom as the largest one and the form as the smallest (Fig. 2). This number can be doubled by the addition of subcategories below each rank (e.g., subdivision).

Figure 2. Ranks of the classification hierarchy recognised by the ICBN. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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Identification of Plant Species Using Traditional and Molecular-Based Methods

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The origin of man‘s propensity to classify organisms into groups can be traced back to prehistoric times (Sivarajan and Robson, 1991) and where the ability to discriminate between the various types of plants and animals was useful for survival. Early recognition of groups at that time was mainly based on their gross morphological similarities and dissimilarities. This is called ‗folk systematics‘ and these provide classifications that arise in both primitive and civilised communities through need and without the influence of science. The fundamental taxa recognised in folk systematics may often but not always correspond with species described scientifically (Berlin, 1973). The introduction of printing into Europe allowed the production of the ‗herbals‘ that were concerned with the classification of plants that are valuable to mankind either as food or medicines. Ancient Greeks were the first people to provide a written record of plant classifications in the form of herbals, such as the Codex of Dioscorides introduced in the middle ages. Many other early herbals were discovered and improved by taxonomists of Europe during that period (Porter, 1967). During the transition period of taxonomy, geographical exploration led to the discovery of new species and genera. Therefore, botanists developed a new system of classification that was regarded as ‗artificial‘ because it was developed primarily for the ease of identification regardless of evolutionary origin or genetic relationships (Porter, 1967). During this period, the founder of modern taxonomy, Carolus Linnaeus (1707-1778) classified large numbers of both animals and plants according to their reproductive morphology. He described this as the ‗Sexual System‘ of classification and was constructed entirely for the purpose of identification. Although the system was highly arbitrary (Sivarajan and Robson, 1991), its clear utility and simplicity meant that it rapidly gained widespread popularity. A major drawback of Linnaeus‘s sexual system of classification was that it lacked ‗predictive value‘; with obviously different species sometimes being grouped together whilst similar sister species could equally be separated. For instance, the genera Salvia, Anthoxanthum and Circaea were grouped into one class (Diandria) since they all have only two stamens. At present, however, these genera are placed in separate families (Lamiaceae, Poaceae and Onagraceae, respectively), the remainder of each of these modern families appeared in separate classes (Octandria, Didynamia and Ttriandria). In spite of these limitations, two works Linnaeus produced using the system Genera plantarum and Species Plantarum have proved to be of seminal scientific importance. Adanson (1727-1806) first proposed the idea that a great number of characters covering all aspects of plants should be used equally in classification. In this way, groups of organisms are established on the basis of their overall similarity to each other. This ethos is known as the ‗phenetic‘ system of classification. Wide ranges of sources of data are used when compiling such classification systems, which typically include information from plant structure and anatomy, cytology, morphology, genetics and biochemistry. However, it soon became apparent that there are many problems associated with the estimation of phenetic relationships. The incongruence between classifications based on different organs or life history stages proved particularly germane, as were the observation that different classifications may result from using different clustering methods. The landmark work of Charles Darwin in 1859 entitled ‗The origin of species‘ has stimulated many publications aimed at establishing the relationships between organisms on the basis of their evolutionary relationships. Interestingly, his theory of evolution did not have an immediate impact on plant classification strategies used and only gradually emerged as the

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predominant method of classification (Stuessy, 1990). Classifications based on shared evolutionary pathways are known as ‗phylogenetic‘ (phyletic) classifications, a concept that was founded by Hennig (1966). In contrast to the artificial classification systems aimed at enhancing identification, phylogenetic classifications are considered ‗natural‘ since it primarily reflects the phylogeny (evolutionary history and relationships of a group of taxa) of the plant (Porter, 1967). Intriguingly, there is no particular taxonomic method implied in this system. Currently, cladistics is the most popular paradigm of phylogenetic classification in biological taxonomy and has become widely accepted as the primary means for describing evolutionary relationships. Kitching et al. (1998) define cladistics as ‗a method of classification that groups taxa hierarchally into discrete sets and subsets‘. The authors believe that this system of classification can be used for the organisation of any kind of comparative data (e.g., linguistics) but it has been implied primarily in the field of biological systematics. This form of phylogenetic classification (i.e., cladistics) does imply a specific methodology or approach in deriving and constructing the classification. The system of cladistics is usually represented by the branching patterns of a family tree forming evolutionary clades (a single phylogenetic lineage) produced by ancestor-descendent divergent speciation (Morrison, 1993). According to Kitching et al. (1998), the primary aim of cladistic analysis is the establishment of relationships between sister groups (share common direct ancestor). The authors also reported that the principles of cladistics were developed by Willi Hennig in 1950, but it was not until 1966 that his work began to reach a lot of biologists. Some of the basic principles of cladistic theory proposed by Hennig are: 1) relationships are clearly defined in terms of common ancestry, 2) relationships are determined by shared derived characters, 3) cladogram (unrooted tree) is the branching diagram that express relationships and character distribution. Cladogram does not, however, imply ancestry and descent relationships. Evolutionary trees that are usually constructed from cladograms do so employing a time axis. Several trees may be compatible with one cladogram. Weighting characters to be included in the cladogram are aimed to improve the chances of getting a closer estimate to the true cladistic relationships using those characters (Sneath and Sokal, 1973). The three approaches of classification mentioned above can be used alone or in combination.

IDENTIFICATION OF PLANT SPECIES Identification of experimental material is one of the most fundamental requirements of all areas of biological science. According to Dallwitz (1992), identification is ‗the process of assigning a specimen to a (pre-existing) taxon‘. Winston (1999) defined identification as ‗refereeing a species to a previously classified and named group‘. The author argued that many biologists are confused between the description and diagnosis of a species. He pointed out that diagnosis differs from description in that the latter has no clear or definite limit of characters that can be used to establish it, whereas the diagnosis includes just those characters that are needed to distinguish one species from all others. Hence, diagnosis of a species can be regarded as a part of its description.

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For diagnostic purposes, species can be considered as groups of individuals that are organised into populations and that share a combination of diagnostic characters not found outside the group. A full description of such characters is an integral part of the process of naming a new species. In practice, a character is any feature of an organism that can be measured, counted or assessed (Sivarajan and Robson, 1991) and typically includes morphological and anatomical traits but may also encompass biochemical, physiological and cytological features. The manner in which a feature varies between organisms can be broadly categorised into two main classes; qualitative and quantitative. Characters exhibiting qualitative variation show an abrupt change of phenotype among groups of individuals. For instance, members of the species Festuca vivipara L. can be distinguished from other species of the genus, Festuca L. by the presence of adventitious bulbils in the place of floral parts. Clear qualitative differences like this have great value for diagnostic purposes and so are favoured for identification whenever they are available. The majority of characters, however, show a more continuous pattern of variation and so differences between groups are more a matter of degree. These traits are said to show a quantitative pattern of variation. Their use generally requires either careful measurement or counting. Members of the species Festuca brevipila Tracey, for example, possess awns in the range 1.4-2.5 mm compared to those of F. lemanii (Bastard) that have shorter awns of only 0.3-1.8 mm (Wilkinson and Stace, 1989). The two also differ slightly in the number of adaxial leaf furrows, with 4-6 in the former and 2-4 in the latter. In this case, the character has only limited use for diagnosis. Clearly therefore, not all characters are equally informative; good characters are deemed as those that do not exhibit wide variation within the target group and yet are highly correlated with the group (Sivarajan and Robson, 1991). The most useful characters for species diagnosis are those that remain constant within the species, but differ between species (Winston, 1999). Such traits should also be quick and easy to record. Two sources of information have been generally utilized for this purpose; gross morphological features and anatomical traits. Other sources of such information include pollen characters, chromosome characteristics and phytochemical characters.

Morphological Characters Traditionally, species identification has relied heavily on morphological characters (Rout et al., 2003). For example, Wagner (1996) found that the main morphological characters that can be considered to discriminate between wild and cultivated apple are the hairiness of inferior leaf surfaces, presence of thorns on twigs and form, colour and taste of the fruits. Similarly, Riaz et al. (2007) used different morphological characteristics (flowers, leaves, rose hips and plant length) for characterisation of plants that belong to two wild Rosa species (Rosa webbiana and R. brunonii). In 2008, Blas et al. selected 28 discriminant morphological characters to identify the wild Arracacia species from Peru. Among morphological characters, however, vegetative characteristics have been used only sparingly for the discrimination of species of angiosperm. For example, Morris et al. (1996) concluded that the occurrence of the Dendrobiinae species across a range of ecological situations is broadly reflected by the variability in their vegetative features. In this instance, features of the leaf have value for species diagnosis. The shape and length of non-

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glandular hairs have been also proved useful in identifying crude drugs from genus Leonurus (Chao et al., 1999). For the most part, however, vegetative traits are highly variable between individuals and often too plastic (plasticity: the deviation of the mean phenotype of a genotype (the sum total of the genes contained in the chromosomes of the eukaryotes) within an environment from the mean phenotype of that across all environments) to be used for identification at the species level. Barp et al. (2006) reported that leaf morphology may vary considerably even within a branch of Passiflora suberosa plants. Very recently, López et al. (2010) also belive that foliar plasticity in response to ontogeny, location within the plant and environmental changes is widespread among long-lived organisms. The variability of floral characters, and associated features like fruits and seeds, is in stark contrast to this. Flowers often lack variation in the basic structure between members of the same species, but frequently vary between species. It is for this reason that the floral parts are the most widely used for species identification purposes in Floras (e.g., Poaceae, Ranunculaceae, Asteraceae etc.). The value of floral features for diagnosis is constrained, however, by their absence from the plant during much of the year and unavailability of flowers in small plant species (e.g,. mosses and algae). The problem is made greater in plant groups that lack useful variability in the non-sexual parts. The lack of informative morphological characters outside the flowering period is particularly acute in higher plant families such as the Poaceae and Juncaceae, and also in larger groupings of the lower plants, including mosses and pteridophytes. The fact that aspects of plant phenotype can be affected by changes in the environment can also cause problems for identification purposes. For instance, Wissemann (2000) reported continuous and wide variance in the genus Rosa for many morphological characters. The author postulated that this variation is heavily influenced by environmental fluctuations and so can be attributed to phenotypic plasticity (the ratio of plastic variance to total phenotypic variance; flexibility). Similarly, Szczepaniak et al. (2002) noted that the excessive plasticity of diagnostic morphological characters considerably blurs the boundaries between subspecies of Elymus repens (L.) Gould (Poaceae). Conklin et al. (2009) also reported that the paucity of diagnostic morphological characters for identification and high morphological plasticity within the genera Eucheuma and Kappaphycus has led to confusion about the distributions and spread of three introduced eucheumoid species in Hawaii.

Anatomical Characters The absence of reliable vegetative features in many plant groups creates the need for an alternative source of reliable and persistent characters. This need led to the examination of anatomical features (the internal form and structure of plant organs), particularly those of the vegetative organs. According to Stuessy (1990), the use of such features for the purpose of plant identification dates back to Bureau (in 1864). There are many sources of anatomical characters that have been used for diagnosis, including leaf and root sections (Hussin and Sani, 1998; Morris et al., 1996), leaf or stem epidermal structures (Davis and Barnett, 1997) and wood anatomy (Archer and Vanwyk, 1993; Callado and Costa, 1997). Of these, leaves are regarded as the most anatomically varied organs among angiosperms (cited in Stuessy, 1990). In a study carried out by Appezzato-da-Gloria et al. (1997), the foliar anatomy of five species of Aristolochia (Aristolochiaceae) proved useful for their diagnosis, and the authors

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proposed an artificial key for the identification of these species based on diagnostic characters analysed. Anackov et al. (2008) also observed clear distinction in the leaf anatomy between the Salvia species (Salvia bertolonii Vis. and Salvia pratensis L. (Sect. Plethiosphace, Lamiaceae) they investigated. To distinguish 11 species of Paris collected from the western Sichuan province of China, Xue et al. (2009) systematically described and illustrated their microscopic features. The differences found among species analysed were great enough that the identity of most material could be easily determined. The key limitation restricting the usefulness of anatomical features lies in the need for a relatively high magnification to observe most features (excluding some trichome features) and the tendency for some traits to vary with developmental age. Anatomical features can also be sensitive to environmental change. For instance, Ramesarfortner et al. (1995) examined phenotypic plasticity in several anatomical features of leaves that were previously thought to differ diagnostically between four species of arctic Festuca (F. baffinensis, F. brachyphylla, F. edlundiae and F. hyperborean). The authors found that when these four species were subjected to the same conditions of temperature and water treatments, all produced leaves with similar anatomical structures. They concluded that most of the anatomical differences between these species largely represent a component of plasticity rather than species-specific adaptations. Accordingly, the authors indicated that anatomical features should be used with caution for the purposes of identification. Davis and Barnett (1997) confirmed that when they observed that stomatal number and distribution in species belonging to the genus Galanthus L. (Amaryllidaceae J. St.-Hil.) is at least partially controlled by environmental factors and the stage of growth. In another study, the leaf anatomy of plants growing in three different habitats (a dry site in the Antarctic tundra, a wet site in a zone exposed to sea spray and a greenhouse) were investigated by Wanowska et al. (2005). The authors found that the anatomical features of the leaf of Deschampsia antarctica (Poaceae) change under stress conditions. In a very recent study, López, et al. (2010) compared contrasted populations of Pinus canariensis grown in five sites inside and outside the natural distribution area of the species in order to quantify the phenotypic variation in needle morphology and anatomy in response to a climate gradient. Results revealed that most needle and growth traits were strongly affected by site.

Pollen Characters Species-specific pollen patterns are established early in meiosis (Sheldon and Dickinson, 1983). Basic differences in pollen structure (such as shape, size, exine sculpturing and number of germinal pores) are correlated with the mode of pollination in a particular plant group, and have a strong tendency towards consistency within that group. For this reason, pollen has long been viewed as having value for species identification purposes. According to Stuessy (1990), the first attempt to use pollen features for taxon diagnosis was carried out by the Englishman, John Lindley (1799-1865). A considerable overlap in mean and maximum pollen diameter as well as in a value called the axis/pore ratio (long axis length divided by the diameter of the pore present on the grains) occurs between teosinte and maize (cited in Holst et al., 2007).

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The diagnostic information offered by pollen grains proved to be valuable at many taxonomic ranks. For instance, Szibor et al. (1998) believe that because pollen wall patterns are also so diverse and often so characteristic of each species, they have long been used for taxonomic classification and even for forensic identifications. Dessein et al. (2002) also showed that several pollen characteristics (size, aperture morphology and tectum peculiarities) have value for identification of species belonging to the genus Spermacoce (Rubiaceae). The seven species of Chrysanthemum examined by Meo and Khan (2006) using light and scanning electron microscopy revealed that there is a great range of variation of pollen size of potential taxonomic value for identification and delimitation of species within the genus. The same study demonstrated the potential of pollen studies in distinguishing some taxonomic groups in Anthemideae. Pollen morphology has also been investigated in three wild medicinal plant species [Fagonia cretica L. (Zygophyllaceae); Peganum harmala L. (Peganaceae) and Scorzonera undulata Vahl (Asteraceae)] growing in Egypt (Abdel-Hafez and El Naggar, 2006). Even at the genus level, Holst et al. (2007) listed several studies that were able to differentiate between Zea and the closely related genus Tripsacum, when Tripsacum pollen was found to be smaller. Stuessy (1990), however, argued that caution must be exercised when features of pollen structure are used for taxon discrimination since the chemical processing of these grains and nutritional factors may alter pollen size. Beyers and Marais (1998) attributed the polymorphism in pollen size between three species of the genus Lachnaea (Thymelaeaceae) to temporary ecological conditions. These observations led the authors to conclude that palynological characters have no diagnostic value for the discrimination of species they targeted. Holst et al. (2007) examined the promise, potential importance and pitfalls of distinguishing teosinte, maize and the closest wild relatives of the genus Zea, members of the genus Tripsacum, by using pollen, starch grain and phytolith analysis. Starch grains and phytoliths were revealed to be more useful than pollen in discriminating wild from domesticated maize.

Chromosomes Characteristics The first decades of the 20th century witnessed a shift from descriptive methods for plant identification to experimental strategies. Chromosomes contain the genetic material responsible for maintaining reproductive barriers and the integrity of species and other taxa. They were reported to be the most useful for diagnosis at the specific level due to their close relationship with reproductive factors. In this regard, it is worth noting that all lower plants are haploids and/or diploids (possess two copies of the basic chromosome number), whereas higher plants include diploids, polyploids (with more than two copies of the chromosome basic number; constituting 30-70% of angiosperms) and aneuploids (vary in single or few chromosomes only) as well as haploids in some species. Interspecifc chromosomal variations have been reported in the genus Mikania (Ruas and Ruas, 1987; Ruas and Aguiar-Perecin, 1997). Among characteristics of chromosomes, the most widely used for the diagnosis of species are: 1) the chromosome number (in somatic cells, denoted 2n or in meiocytes, denoted n) and 2) chromosome structure (Karyology). For instance, Bayly et al. (2000) used chromosome

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number as a key feature to differentiate Hebe parviflora from H. stenophylla (the former is tetraploid and the latter is diploid). In many cases, however, polyploidy can arise spontaneously and have no taxonomic or diagnostic value. For example, the haploid and the diploid races of Pellia epiphylla are rarely separated taxonomically (Stace, 1989) neither are diploid and tetraploid cytotypes (populations or infraspecific taxa having different chromosome number or morphology) of Avena strigosa Schreb. (2n=14, 28) (Stace, 1997). Chromosome number can also vary in a less discrete fashion. For instance, Poa pratensis L. has chromosome numbers varying from 50-124, and Clytonia virginica (Portulacaceae) has chromosome number varying between 12 and 72 (Lewis, 1970). Other chromosome features such as chromosome behaviour during meiosis, DNA content and chromosomes banding pattern can be useful in plant identification. In 1926, Delaunay introduced the concept of ‗karyotype‘ and defined it as ‗a group of individuals resembling each other in the number, size and form of their chromosomes‘. A karyotype describes the phenotypic aspects of the chromosome complement of a species in terms of number, size, arm ratio (or centromere position), and other landmark features of its chromosomes. It has been revealed that the basic chromosome number is of importance to determine the systematic position of a taxon at high taxonomic levels (Raven, 1975). This was confirmed by Ghanbari et al. (online reference) when they found that the chromosome counts in the genus Rosa (Rosaceae) was various and useful in its taxonomy. The speciation in this genus is also believed to be accompanied by variation in chromosome size (Creomonini et al., 1993). Karyotypes are dynamic structures evolving through numerical and structural changes (for a comprehensive overview on chromosomal changes in plants, see Levin, 2002). Pimenov et al. (2003) reviewed the karyotype formulae of some 300 Apioideae species. It was noted by Fregonezi et al. (2004) that the presence of chromosomal markers in cultivated and wild species of the family Asteraceae has contributed to understanding the karyotypic organization and has also provided useful information for taxonomic applications. The characters of karyotype have been accepted to be fairly constant and species-specific, and have been used, therefore, as a molecular marker in plant identification. Indeed, the number, size and arm ratio of chromosome complements may differ even between closely related taxa (Iovene et al., 2008). In 2008, Mandáková and Lysak studied karyotype evolution in x=7 crucifer species (Brassicaceae) using comparative chromosome painting (CCP). Iovene et al. (2008) believe that although several karyotypes of leading Apioideae crops such as carrot, celery and coriander have been published, the karyological data of the wild relatives of these crop species (including wild Daucus species), however, are scarce or nonexistent. They also mentioned that genome studies at the chromosome level in both Daucus and its family have been based primarily on chromosome counts and morphology. Based on chromosome measurement and FISH-based chromosome landmarks, the authors compared and grouped karyotypes of Daucus species using numerical taxonomy approaches. However, they reported that "a serious weakness of karyotype analysis is the paucity of chromosome markers, which has limited the identification of the chromosomal changes responsible for the extant karyotypes". They also stated that this shortcoming can be overcome using two other main strategies in order to track the evolutionary events that led to the present karyotypes. These are comparative genetic mapping and cross-species or comparative chromosome painting. For example, based on a comparative RFLP map of a wild rice, O. officinalis and O. sativa, comparative analyses of karyotypes of O. officinalis were

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demonstrated firstly by fluorescent in situ hybridization (FISH) using a BAC clone and an RFLP marker from O. sativa as probes (Hong et al., 2009). A similar karyotype, however, does not always indicate affinity. For instance, Rheo discolor has an extremely variable karyotype (Darlington, 1937) as does Elymus striatulus (Gramineae) (Heneen and Runemark, 1972). For these reasons, coupled with the skill and time requirement to record cytological features, the latter generally have little utility for species diagnosis. The sole exception lies in the use of flow cytometry for the distinction of closely related species with different ploidy levels. For example, Wilkinson et al. (2000) used flow cytometry to differentiate between Brassica rapa (2n=20) and B. napus (2n=38). Karyotype evolution in species with identical chromosome number but belonging to distinct phylogenetic clades is a long-standing question of plant biology (Mandáková and Lysak, 2008). Karyotype analysis is also useful in understanding phylogenetical relationships, but before this can be attempted it is necessary to have a clear picture of the basic karyotype of all species of interest (Simak, 1966). Added to that, a better understanding of the genomic relationships of species in a genus will aid valuable conservation and efficient utilization of plant genetic resources. Therefore, Lee and Chung (2008) investigated the genomic relationships among the species of Paphiopedilum by FISH using both total genomic DNA (GISH) and 45S ribosomal RNA genes (rDNA) as probes. More recently, the karyotypes of four South American species of Cestrum (C. capsulare, C. corymbosum, C. laevigatum and C. megalophylum) were studied using conventional staining, C-CMA/DAPI chromosome banding and FISH with 45S and 5S rDNA probes (Fernandes et al., 2009). The authors believe that such information is useful in evolutionary studies of this group and allows the development of new directions in the elucidation of karyotype differentiation in Cestrum.

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Phytochemical Characters Phytochemical characters of plants first attracted attention for plant identification purposes in the early 1960s. Phytochemical data belong to two main types; micromolecular and macromolecular (proteins and nucleic acids, discussed later). The development of new analytical techniques simplified and accelerated the process of plant identification based on differential phytochemical profiles (Alston, 1967). However, the earliest comparative studies of plant chemistry were those of Abott, who examined the distribution of saponin in plants in 1886 (cited in Stuessy, 1990). Phytochemical information has proved useful for the differentiation of plants at all levels of the taxonomic hierarchy. Advantages of using different chemical profiles in different species lie in the consistency of some compounds within species, ease of assay and unambiguity in scoring. Tofern et al. (1999) used lolines for the diagnosis of several grass species. These chemicals are only known from certain grass genera (e.g., Festuca) and the genus Adenocarpus (Fabaceae). NFormylloline (loline alkaloid), that was detected in the roots and aerial vegetative parts of Argyreia mollis, was shown to be diagnostic for this species within this genus and 14 other Convolvulaceae genera. In 2002, Binns et al. determined quantitative phytochemical variation from roots and inflorescences of native plant populations in the genus Echinacea. The authors concluded that baseline phytochemical data and chromatographic profiles for all types of wild Echinacea may be used for germplasm identification, protection of wild stands and crop

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improvement. Using phytoliths, Holst et al. (2007) were also able to identify teosinte, maize and Tripsacum in Mesoamerica. Generally, the need for specialist equipment and knowledge, the protracted time requirement for analysis and sensitivity of phytochemical profiles to environmental influences mean that the discipline has relative little utility for routine or large-scale species identification. As discussed above, there are usually several characters that can prove valuable for the discrimination of similar plant species. The number of characters and character states required for diagnosis increases as the number of possible identification outcomes increases. In cases where there are many species to be distinguished, therefore, there is a need to organise character state comparisons in such a way that diagnosis is achieved with a high level of accuracy and minimum effort. This process can be effected in several ways as outlined below.

Early Methods of Species Diagnosis Expert determination was perhaps the most widely used of the early strategies implied for species identification and is still in use today. Contemporary experts on the diagnosis of plant species usually specialise on a limited number of plant groups. This means that there are relatively few experts for any group in any one geographic area. It follows that the key limitation in the use of experts for diagnosis lies in their limited availability. For this reason, most experts prepare detailed treatments of their study group including monographs, revisions and synopses in order to facilitate diagnosis. It is usual for these works to contain extensive descriptions of all taxa covered. A similarly descriptive approach for identification is contained in the many Floras that are available for all parts of the world. In spite of the great reliability that extensive descriptions offer in terms of accuracy of diagnosis, it nevertheless presents problems by requiring either the valuable time of experts or painstaking comparisons of extensive lists of character states. The problem is worse, when there are no experts or associated monographs available for a certain taxa of plants, or when characters or parts of the plants expert need to make their determination are lacking from the material in question. Another traditional approach of identification is the recognition approach. According to Morse (1971), this approach is based on the extensive past experience of the identifier of a plant group with that group. However, such identifiers are not available for all groups of plants and hence, this approach will have no value for the diagnosis of a wide range of plant groups. A third method is to identify plants by the comparison of an unknown plant with named specimens, photographs, illustrations or descriptions. Although this approach has been found useful for many plant groups, its reliability is controlled by the accuracy and authenticity of the specimens, illustrations or descriptions used. Moreover, it cannot be applied sometimes due to the lack of suitable materials for comparison or the unavailability of all characters or parts of the plant in question required for complete and informative comparison. Even when such materials and characters are available, it is a very time consuming procedure. The limitations in these holistic approaches for identification gave rise to the need for a more structured approach for the comparison of character traits in different species.

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The Diagnostic Keys There are many factors that limit the usefulness of holistic comparisons for identification. These include the fact that the many characters included are not ranked to indicate their value for diagnosis. Hence, many redundant characters are included that have no diagnostic value for any particular pairwise comparison. This can cause confusion and may lead the reader towards misdiagnosis, especially where some useful characters are unavailable (e.g., because of season). A second major constraint lies in the time required to effect comparisons between all character traits across all possible species. In cases where there are large numbers of possibilities, this need can render identification an impractical prospect. The imposition of structure and ranking of characters that are compared in order to reach a diagnosis can overcome many of these drawbacks. This process can be completed most effectively through the construction of diagnosis keys. The need for keys is particularly acute where identification of specimens includes all members of extremely divergent groups, such as the angiosperms, where the holistic approaches are impractical because of their high diversity and polythetic nature. The use of the diagnostic keys (taxonomic keys or determinators) for the identification of plants (and other organisms) is by far the most widely used method and does not require the time, experience or materials involved in comparison and recognition methods. Diagnostic keys are generally devices consisting of a series of contrasting or contradictory statements or propositions requiring the identifier to make comparisons and decisions based on statements in the key as related to the material to be identified (Quick, 1997). Diagnostic keys can be grouped into two broad types: 1) single-entry (dichotomous or sequential) keys and 2) multiple-entry keys (polyclaves). Both display only the diagnostic features and so the taxon in question can be identified without the need to refer to exhaustive descriptions that can contain a high proportion of redundant information (Geneve et al., 1997). The two key types differ mainly in the way in which differences in the features involved are arranged and presented to effect identification. The morphological traits targeted for use in keys tend to be those that vary in a qualitative rather than quantitative manner. The intra-taxon variability of characters used in keys is also an important consideration in keys construction. To avoid confusion caused by that, characters are coded as either variables or as (nearly) constant, leaving selection of a variability threshold to the researcher preparing the data (Morse, 1971). The first dichotomous keys clearly designed for identification purposes were those of Lamarck in his ‗Flore Francaise‘ in 1778. In dichotomous keys, the order in which the couplets (each is a pair of statements (leads) describes states of one character) appear in the key is of critical importance for minimising the information required for diagnosis. The number of possible identifications is progressively narrowed while working through the couplets of the key until all possibilities are eliminated except the single taxon the material is deemed to represent (Quick, 1997). Dichotomous keys constitute of two categories based on the way they are written: 1) bracketed keys and 2) indented keys. These two differ only in that, in the former the two leads of each couplet appear together, while in the later all possibilities linked to the first lead are described before the second is mentioned (Fig. 3). The choice between using either of these two layouts is to some extent a matter of a personal preference. Indented keys, however, are preferable only for short keys as the space requirements of a large key increasing become impractical.

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Figure 3. Two layouts of (indented and bracketed) a dichotomous key (after Stace, 1989). In both, same 5 species (in bold) are keyed out using identical data represented by couplets 1-5.

The use of dichotomous keys requires that the specimens under consideration possess most or all characters that are used in the key. Where specimens do not possess all of the characters needed to complete passage through the key, however, the user may be forced to proceed by guessing unknown character states for the specimen in question (Duncan and Meacham, 1986). Generally, keys constructed for a large number of species (e.g., all species in a family) are based on several organs of the plant. The keys constructed by Hubbard (1968) and Stace (1997) for the grass family (Poaceae) provide an example of such keys. However, for a small number of species, organ-specific (e.g., leaves and flowers) keys are common. For instance, Stace (1997) constructed inflorescence-based keys for five Vulpia species. It follows that closely related taxa may not always occupy adjacent positions in the key and therefore their positions in the key bears no 'a priori' indication of the evolutionary distance between the taxa being distinguished. Such keys, therefore, are termed 'artificial keys'. In multiple entry keys, the user has the choice in each stage to select the characteristics for use in identifying the specimen in question, taking his selection from a predefined list of characters. For each character, the user classifies the specimen into a group of taxa according to which a possible alternate character state is manifested in the specimen being identified. This process is continually repeated until a tentative identification is made. Sokal and Sneath (1966), and Williams (1967) first noted the possibility of using computerizing polyclave keys for identification, an approach that has recently found in favour for handling large numbers of species (see below). These types of keys allow the user to select the most obvious or available characters first, and to choose unusual attributes that help to eliminate majority of the taxa. In this sense, they provide a more flexible tool for identification than dichotomous keys. On the other hand, characters are often given more or less equal weight and so there is generally no control to guide the user towards the most reliable diagnostic features. It is also possible to generate two or more different diagnoses when atypical specimens such as hybrids are examined. Care needs to be exercised in the construction of such keys to overcome this problem. This contrasts to dichotomous keys present where such specimens are usually unresolved.

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Data Matrices and on-Line Identification Programs In a hierarchical taxonomic matrix system, each taxon-description line may refer to another complete matrix differentiating subordinate taxa, permitting programs to work down through the hierarchy such as one uses a key to families, then a key to genera, and then to species (Dallwitz, 1992). On-line identification programs are based on the step-by-step elimination as in diagnostic keys. They work in a question-answer manner between the identifier and the machine, and the user is free to choose characters for the elimination of species. These programs require the use of a computer and a high degree of interaction (Pankhurst, 1974). Bonnie and Snow (2002) created an on-line identification key to the vascular plants of the Laramie Foothills region in North Central Colorado using Lucid Professional software. Computer-Assisted Diagnosis in Plants The possibility of computerizing key construction has been mentioned frequently since the early 1960s. Efficient keys are those that can provide the simplest and most free route to any answer. Meeting these needs, however, for a large group of plants may place heavy effort on the classifier (Hall, 1970). A practical solution for such case would be the use of computers to process, store and retrieve diagnostic keys (Morse et al., 1968). This need led to the creation of the first computer-assisted identification programs in the beginning of the 1970s (Forget et al., 1986). Pankhurst (1974) provided a comparison of some of these programs. Computerised diagnosis usually implies the principles of multi-access keys rather than sequential keys, and most modern programs can accommodate for atypical phenotypes in one or two of the many characters involved. Bonnie and Snow (2002) recognised five advantages in the construction of diagnostic keys by computer compared to manual diagnosis: 1) once the data has been collected, the production of keys is simple, 2) keys can be easily revised if errors are found or new taxa or characters need to be added, 3) the probability of incorrect identifications is reduced, 4) characters of the specimen to be identified can be entered in any order, and 5) the user can confirm the correct identification of the unknown specimen by calling up digital images and text boxes that further describe the taxon. Dallwitz (1974) created a computer program called ‗KEY‘ for generating identification keys. The author reported its flexibility, reliability, speed, minimising the number of characters used, low cost, and minimising the intra-taxon variation. Duncan and Meacham (1986) described the application of a general-purpose multiple-entry key algorithm called ‗MEKA‘.

Computer-Stored Keys Computers can be used to step through a traditional dichotomous key in a way that the computer asks a question, waits for an answer, and then proceeds to the next couplet. Keys of this sort offer no direct advantage over printed keys and have the limitation of requiring using the computer for the performance of each identification (Morse, 1975). On the other hand, the advent of laptop, palm top and notepad computer hardware may overcome this limitation and

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present significant advantage over hard-copy keys for distinguishing between large numbers of samples.

Computer-Constructed Keys

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Traditional dichotomous identification keys can readily be constructed from machinereadable files of taxonomic information. The difficulty of this topic lies in the collection of data and coding. The commonest approach to key construction by computer is a recursive algorithm that repeatedly divides sets of taxa into pairs of mutually exclusive subsets on the basis of one or more taxonomic characters (Morse, 1975). Westfall et al. (1986) described a new method of identification combining features of polyclave and dichotomous keys. It is based on presence or absence of characters, and is presented in the form of a matrix with the characters in rows and the taxa in columns. The ‗PHYTOTAB‘ program package is used to order the matrix in order to enable identification. This method combines the flexibility of the former and the ease of reproduction of the latter, making it efficient for rapid identification. All strategies of identification discussed above are based only on morphological characters. Whilst morphological descriptions form the basis for the definition of species and all other taxonomic ranks, there are many instances when the amount of material available is insufficient to enable identification using any of the above strategies. This may be the case at certain times of the year, or where only one plant part is available or even when the material is no longer in a recognisable form (e.g., in processed foodstuffs). This may render diagnosis using morphological traits impossible and necessitate alternative means of sample discrimination. In such circumstances, the use of molecular approaches for the recognition of taxa often represents the most attractive strategy.

THE NEED FOR MOLECULAR IDENTIFICATION OF PLANT SPECIES The need for plant species identification is both varied and widespread, and includes applications for plant breeding, agricultural seed industry, food processing, conservation biology, forensic analysis and many other aspects of plant science. The level of precision required varies according to type of application. For instance, in plant breeding, Poulsen et al. (1996) argued that it is essential to accurately identify the parent species of pre-breeding program prior to performing any cross-pollination. Hybridisation between different outcrossing plant species has been reported to occur naturally and numerous studies have attempted to characterise or quantify the occurrence of spontaneous hybrids in natural habitats (e.g., Stace, 1975). In works of this kind, it is clearly important to be able to distinguish both parental species from the hybrid and also from each other. Interest in the quantification of interspecific hybridisation has increased dramatically with the commercial cultivation of genetically modified (GM) crops, where attention is focussed on the ecological consequences of transgene recruitment by wild relatives of GM crops through gene flow (Lavigne et al., 2002; Haider et al., 2009).

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In 2003, Hammer et al. estimated plant accessions conserved in gene banks worldwide as six million. The authors believe that all of these accessions belong to a very limited number of species, and that approximately 15% are wild relatives of crop species and weeds. The authors reviewed the most important aspects of plant biodiversity for conservation. Molecular markers allow the characterisation of genetic diversity in germplasm collections of endangered plant species. This, in turn, helps accomplish successful conservation of the species of interest. Therefore, Segarra-Moragues et al. (2005) confirmed that genetic fingerprinting of germplasm accessions of Borderea chouardii (Dioscoreaceae), one of the most critically endangered Iberian plants, can aid for better species conservation. Börner (2006) also reported that although wild ancestors have continued to persist in regions where their domestication took place, there is a great risk of loss of the genetic variability of cultivated plants and their wild relatives in response to changes in the environmental conditions and cultural practices. Hence, accurate identification of plant species is essential for the maintenance of germplasm collections (Bechmann et al., 1999). Wild species are sources of resistance genes for various biotic and abiotic stresses. Hence, they are considered as a genetic pool for the improvement of crop species. Reliable species diagnosis is also necessary for the conservation and use of plant genetic resources that are essential for the efficient maintenance and improvement of agricultural and forestry crops (Karp et al., 1997; Rout et al., 2003) since many wild relatives of crops are threatened and endangered to extinction (Prance, 1997). For instance, Page (1994) surveyed the conservation status of rare and endangered conifers and associated temperate rainforest tree species by providing a scientifically sound strategy to minimise further genetic erosion (loss of genetic diversity) of an already fragmented wild tree resource. Ozden-Tokatli et al. (2010) reported that in order to minimise the genetic erosion of Pistacia germplasm, national and international (especially IPGRI and IFAR) institutions have initiated projects proposing to characterize, collect and conserve Pistacia germplasm. Karp et al. (1997) stated that accurate genetic distinction of all species is required in order to place the highest estimate on the most genetically distinct groups in conservation. Muller (2002) presented a case study that discussed the management practices required to ensure conservation of rare and locally threatened plant species in grasslands, and Reinhammar et al. (2002) focussed on the conservation of an endangered grassland plant species; Pseudorchis albida. Molecular data have additionally been of great value for ensuring identity and integrity of accessions (a group of similar plants received from a single source at a single time) held within a collection does not change through unwanted gene flow or by genetic drift after regeneration by seed (Karp et al., 1997). Bechmann et al. (1999) believed that identification of mislabelled or unintentionally duplicated material within and between germplasm collections is a fundamental issue for germplasm conservation. Furthermore, when germplasm collections are stored in genebanks as seeds, the characterisation of seeds and checking the seeds contamination is widely viewed as important (Ford-Lloyd et al., 1997). In situ conservation efforts (conservation of species within their natural habitats) equally have need for reliable species identification (Brush, 1995). There is increasing interest in this strategy particularly to protect wild relatives of crop species (Hawtin and Hodgkin, 1997) largely because issues of mislabelling, inadvertent seed mixing and artificial selection are less of an issue for this type of conservation measure. Tewksbury et al. (1999) suggested that insufficient effort has been expended to protect wild species in their natural habitats by in situ

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conservation. There is, therefore, a great need to identify all species comprising habitats containing targeted species for conservation so that their abundance can be readily monitored over time (Karp et al., 1997). Yuan et al. (2010) believe that the wild resources of the medicinal plant, Scutellaria baicalensis (Lamiaceae) still need to be protected in situ and the evolutionary consequences of extensive seed exchange mediated by human being should be monitored carefully. Maxted et al. (1997) defines ex situ conservation as ‗the maintenance of components of biological diversity outside their natural habitats‘. A comprehensive international ex situ program aims to conserve crop and wild germplasms in genebanks (as accessions) and botanical gardens (contain representatives of populations lost in the wild) (Brush, 1995). McGregor et al. (2002) analysed the wild potato germplasm of the series Acaulia with molecular markers and discussed its implications for ex situ conservation. Li and Pritchard (2009) believe that ex situ seed storage underpins global agriculture and food supplies and enables the conservation of thousands of wild species of plants within national and international facilities. Malus sylvestris Mill., the apple species native to Europe, is one of the most endangered tree species in Lithuania; those that remain are very scattered. Therefore, the construction of a gene bank that can be used as a new interbreeding population is seen as a necessary step for its future conservation (Coart et al., 2003). The distinction between species helps checking the true identity of material entering the genebank. Martin et al. (1997), for example, found Oryza meridionalis to be misidentified when they compared it with material reserved in a genebank. One of the main objectives of ex situ conservation is minimising genetic erosion on crops gene pools (Brush, 1995). Maunder et al. (2001) highlighted the dangers of hybridisation in ex situ collections. Available strategies for maintenance and management of germplasm collections are reviewed by Börner (2006), considering modern biotechnologies (in vitro and cryopreservation). Li and Pritchard (2009) stated that the assumptions, costs, risks and scientific challenges associated with ex situ plant conservation depend on the species, the methods employed and the desired storage time. The authors reviewed the science and economics of ex situ plant conservation.

Molecular Tools Used for Plant Species Identification Genetic variation in plants has been reported at all taxonomic levels. This variation can be exploited for the identification of plants at all taxonomic ranks (Powell et al., 1996b). Blaxter (2003) stated that ‗it is impossible to describe biological diversity with traditional approaches. Molecular methods are the way forward especially, perhaps, in the form of DNA barcodes‘ (discussed later). The development of various molecular techniques that generate molecular markers has made it possible to accurately identify plants. These techniques either exploit differences at the level of the deoxyribonucleic acids (DNA) or of the protein encoded by it. Wagner (1996) reviewed morphological discrimination between wild and cultivated apple trees. Coart (2003), however, concluded that the resolution of assignment of individuals to the wild and/or cultivated gene pool reached by molecular markers is much higher when molecular markers revealed a very clear differentiation among the wild gene pool, edible and ornamental apple cultivars.

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Molecular markers are biochemical constituents (e.g., secondary metabolites in plants) and macromolecules, viz. proteins and DNA that play a very important role in plant taxonomy, physiology, embryology, breeding, ecology, genetic fingerprinting, genetic engineering etc. (Mittal and Dubey, 2009).

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Molecular Marker Methods Based on Protein Analysis Proteins are the primary product of genes. When the nucleotide sequence of the DNA changes, so too do the proteins banding patterns (IPGR and Cornell University, 2003). Seed storage proteins and isozymes (described below) have been the most popular protein-based markers used for plant identification. Seed storage proteins are defined by Higgins (1984) as those proteins that occur in large amounts in the developing seeds, and that provide nitrogen for early seedling growth when hydrolysed. Electrophoresis is a chromatographic technique for separating mixtures of ionic compounds. It has been adapted as a common tool for biochemical analysis (IPGR and Cornell University, 2003). The diversity of such proteins has long been used for the identification of a range of plant species. For example, Moller and Spoor (1993) distinguished three Lolium species analysed based on the seed protein banding patterns when fractionated by Polyacrylamide Gel Electrophoresis (PAGE). Bianchi-Hall et al. (1993) also analyzed 55 accessions of wild peanuts (Arachis spp.) introduced from South America for seed storage protein composition using sodium dodecyl sulphate (SDS)-PAGE electrophoresis. The study showed that great diversity exists for protein profiles and seed storage proteins have potential for aiding species classification and for serving as markers for interspecific hybridization studies. In order to study genetic variation of wild wheat relatives, electrophoretic patterns of seed storage proteins, the high-molecular-weight glutenins and gliadins from about 12 wild species and some check improved cultivars were fractionated by SDS-PAGE and Acid-PAGE. The electrophoresis proved to be a suitable method to discriminate wheat species (Sofalian and Valizadeh, 2009). This category of proteins, however, has been used principally for the discrimination of cultivars rather than species. For example, Cai and Bullen (1992) were able to differentiate cultivars in the forage crop timothy (Phleum pratense L.) using SDS-PAGE analysis of their seed storage proteins. Seed storage proteins have proved to be of only limited value for identification of plant species due to: 1) the long time required for protein separation (typically 5-6 h) for the routine and large-scale identification, 2) the polymorphism they present is often due to a very low number of loci, and most importantly 3) band profiles can vary under the influence of different environmental conditions (Gepts, 1995). Indeed, Blanco et al. (1996) reported that the number and size of Quantitative Trait Loci (QTLs) controlling seed storage proteins concentration in kernels of durum wheat (Triticum turgidum var. durum) varied widely across environments. Such sensitivity of phenotype to environmental changes clearly compromises the utility of storage proteins for the purposes of reliable diagnosis. In a recent study carried out by Gao et al. (2010), the authors concluded that the main drawback of SDS-PAGE was its overestimation of molecular mass and incorrect identification of HMW-GS due to its low resolution. However, they believe that it is suitable for large-scale and high-throughput HMW-GS screening for breeding programs, especially

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when the glutenin composition is clear in the breeding material due to the advantages of technical simplicity and low requirements of equipment. Enzymes electrophoresis can directly reveal genetic polymorphism through demonstrating the multiple forms of a specific enzyme (IPGR and Cornell University, 2003). The use of electrophoresis analysis of multi-locus isozymes (a class of multiple, separable forms of enzymes (= isoenzymes) occurring within the same organism and having similar or identical catalytic activities) has been reported for the assessment of overall genetic differences and similarities between plant species and identification of various plant taxa. Starch gel electrophoresis is the preferred matrix for isozyme electrophoresis. The use of isozymes markers (introduced by Hunter and Markert in 1957) for identification purposes has proved valuable since these molecules do not significantly converge in evolutionary terms but rather change at a steady rate (Lowenstein, 1985). For example, isozyme patterns and their genetic control in three Centrosema species (Leguminosae) were described by Penteado et al. (1997). Apavatjrut et al. (1999) similarly used isozyme analysis to aid the reliable distinction between some Curcuma L. species. In 2000, Lange et al. concluded that isozyme patterns, especially when several systems are employed, are reliable and useful biochemical markers for the taxonomic delimitation and characterization of Trifolium germplasm when they observed that the species groupings are consistent with traditional taxonomic species delimitation. Vab den Heede et al. (2002), likewise, used the approach to differentiate between Asplenium cyprium and A. lolegnamense. The authors reported that isozyme polymorphism and variability within and between populations and species were relatively high and allowed discrimination among species. The applications of isozymes in studies on plant genetic diversity are reviewed by IPGRI and Cornell University (2003). Recently, Petrokas and Stanys (2008) revealed the isozyme resemblances in the leaf peroxidase of wild apple and defined the traits related to the identification of Malus sylvestris Mill. It was possible to generate species-specific peroxidase markers for this species. In a study carried out by ONeill and Mathias (1995), isozymes also proved valuable genetic markers for the identification of hybrid plant species in Brassica L. Similarly, Hirose et al. (1993) identified and estimated genetic variation of the hybrids between cultivated tetraploid common buckwheat (Fagopyrum esculentum, 2n=32) and the wild perennial species (F. cymosum, 2n=32) based on esterase isozyme analysis. Although the technique of isozyme analysis is relatively easy, robust and highly reproducible (IPGRI and Cornell University, 2003), inexpensive and allows simultaneous analysis of several individuals and isozyme systems, it has inherent problems (Brown and Weir, 1983) that limit its value for large-scale applications such as: 1) there are only a limited number of reliable isozyme systems available (Stuessy, 1990), 2) some species can prove problematic to assay, 3) it is phenotype-based, 4) relatively few biochemical assays are available to detect enzymes (IPGRI and Cornell University, 2003), and 5) isozymes are available only in small numbers (Torres et al., 1993). Added to that isozyme analysis is usually a time-consuming process (extraction of enzymes and staining reactions) and illsuited for the simultaneous analysis of many samples (Ballve et al., 1995). Furthermore, inferring homology between species examined in isozymes analysis depends on comigration of isozymes through the gel matrix (Arnold and Emms, 1998). This assumption is not always justified. More importantly, Delavega (1996) stated that metabolic processes are controlled by enzymes, influenced by the environment and used to react in response to it. Henry (1999)

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confirmed that when he found out that isozyme analysis can be sensitive to environmental variation. This point is aptly illustrated by the work of Asins et al. (1995), who evaluated isozymes for the identification of Citrus species and detected differences within some trees attributable mainly to the position of the leaf relative to the sunlight and the age of the leaf.

DNA-Based Molecular Methods of Plant Identification

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Other than the nuclear DNA (nDNA) in plant cells, the plant cytoplasm, which has effects on the morphology, physiology and reproduction of the plant (Ogihara and Tsunewaki, 1988), contains two genomes, namely the mitochondrial and chloroplast genomes. Thus at the DNA level, plants differ from animals in the possession of the chloroplast DNA (cpDNA) (Fig. 4) and in that their mitochondrial DNA (mtDNA) is much larger and structurally more variable.

Figure 4. Diagram of chloroplast genome representing most land plants (after Soltis et al., 1992). This genome is circular and constitutes of a large single copy (LSC), a small single copy (SSC), and two inverted repeats (IRs). The locations of some chloroplast genes are shown on the genome.

The nuclear genome contrasts strikingly with the chloroplast genome. The inheritance of cpDNA is clonal and it is most commonly inherited from only one parent, almost always through the maternal parent in angiosperms (Fig. 5) (e.g., Kengyilia, Zhang et al., 2009), and the paternal parent in gymnosperms such as red and black spruce (Picea) (Bobola et al., 1996). Paternal inheritance of the cpDNA, however, has been observed in some angiosperm plant species such as species of genera Actinidia (Testolin and Cipriani, 1997), Larrea (Zygophyllaceae), Medicago, Turnera, Pharbitis and Actinidia as stated by Yang et al. (2000). In a number of flowering plants, however, both parents contribute. In most plants species and even in some cultivated crops, the inheritance mode of chloroplasts is not defined yet (see Scott and Wilkinson, 1999). The maternal inheritance of cpDNA means that the latter is moved only by seed and not by pollen and therefore being highly structured compared to the nDNA (Petit et al., 1993). Owing to this mode of inheritance, lower effective population sizes and the potential for periodic selection (Maruyama and Birky, 1991), cpDNA experience different patterns of

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genetic differentiation compared to nuclear genes (Birky, 1988). It has a much lower mutation rate than plant nuclear genome, and therefore is conservative in its evolution since it is a slowly evolving in terms of gene rearrangement and primary nucleotide sequence (Curtis and Clegg, 1984) compared to both the nuclear and mitochondrial genomes (Wolfe et al., 1987). However, different portions of the chloroplast genome evolve at different rates. It has also been reported that rates of chloroplast genome evolution appear to be heterogeneous among plant lineages (Clegg et al., 1994). Thus, overall, the chloroplast genome varies little among angiosperms in terms of size, structure and gene content. It is also not influenced by polyploidy and gene duplication that are widespread features of the nuclear genomes of plants (Tanksley and Pichersky, 1988). Further, the high rate of recombination which is a feature of the nuclear genome is rare or absent in cpDNA (Harris and Ingram, 1991). DNA-based molecular methods provide unlimited numbers of potential heritable markers (Weising et al., 1995) that are extremely useful in assessment of genetic diversity and hence, can be exploited for plant species identification. These techniques have advantages over traditional methods in that: 1) environmental and developmental influences on the phenotype of the plant examined are not a problem since its genotype is analysed directly and since uniformity of DNA sequence that exists within an individual largely remains fixed, regardless of the environmental conditions, 2) the fact that different regions of DNA evolve at different rates means that appropriate regions can be chosen for a given study (e.g., highly variable for cultivar identification) (Weising et al., 1995), 3) they provide a seemingly unlimited number of detectable polymorphisms between species, 4) a wide variety of techniques have been developed, 5) markers generated show a considerable range of discriminatory power and appear to evolve in a correlated way (Weising et al., 1995), and 6) these markers are often codominantly inherited and developmentally stable. Properties of molecular markers discussed above make these markers extremely useful and in many ways preferable to morphological or protein markers (Weigand et al., 1993).

Figure 5. Diagrammatic illustration of inheritance of nuclear and chloroplast DNAs in plants.

Theoretically, the analysis of DNA can identify plant taxa down to the level of individual genotypes that in the most extreme cases differ by only a single base pair (Henry, 1999). Moreover, it can provide a significant tool for a reliable and precise identification of plants. All attempts to use molecular techniques based on DNA for plant identification purposes seek

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to exploit variation in DNA sequence or combinations of sequences that collectively are able to uniquely distinguish the targeted plant taxon. These techniques can be broadly grouped into two categories: 1) fingerprinting (multi- and single-locus) and 2) sequencing.

Multi-Locus Systems of DNA Fingerprinting

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Systems of fingerprinting utilise DNA from several regions in the genome. Most methods of DNA fingerprinting rely on the comparison of a series of bands (band profile) generated when DNA fragments of different sizes are migrated through a matrix in the presence of an electric field (electrophoresis). Differences in the number and position of individual fragments arise from differences in fragment size and sometimes sequence, and the variable presence of specific fragments between individuals. Diagnosis is based on comparison of band profiles for the shared presence of variable bands. There are numerous techniques that can be used to generate band profiles. These techniques vary in their sensitivity, cost, reliability, reproducibility and type of data they generate. The aim of all systems is to reduce the complexity of the genome into ‗polled‘ fragments of DNA or DNA sequences that differ between individuals (Caetano-Anolle's, 1996). Coyle et al. (2001) reviewed a case in which plant DNA fingerprinting was employed to match seedpods to a tree under which a body was discovered. DNA-based techniques used to generate diagnostic band profiles include several multilocus systems that are based on either DNA-DNA hybridisation (Southern hybridisation) or Polymerase Chain Reaction (PCR). DNA-DNA hybridisation techniques aim to exploit variations in the lengths of immobilised restricted DNA fragments that are able to bind to a labelled fragment of DNA known as a probe. The most widely used approach is called Restriction Fragment Length Polymorphism (see below).

Restriction Fragment Length Polymorphism (RFLP) A scheme illustrating the RFLP technique is shown in Figure 6. RFLP is based on the hybridisation of a short and labelled piece of DNA (the probe) to a target DNA that has been cut by a restriction enzyme, separated on the basis of size by electrophoresis and immobilised onto a nylon or nitrocellulose membrane. The probe can be either single-copy DNA clones or multiple/repetitive DNA sequences such as SSRs, rDNAs (described later). The patterns generated can be used to differentiate species from one another (Ludwig, online reference). Assessment of variation using RFLP can overcome some of limitations of protein analysis as RFLP loci are assessed at the DNA level and are not limited to specific classes of sequences. The reliability and diagnostic value of the approach relies on the specificity with which the probe cross-hybridises to the target DNA. Variation between individuals arises from the presence or absence of restriction sites flanking specific sequences targeted by the probe DNA. These differences can result from base substitution in a restriction enzyme site, or deletions or insertions between sites, and they give rise to detectable differences in the DNA length when cleaved with restriction endonucleases (Geneve et al., 1997). RFLP has been widely used to quantify genetic variability in a diverse array of plant groups (Powell et al., 1996b), and was the first DNA-based technique to be commonly applied for plant identification purposes. These early studies mimicked the use of multi-locus

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probes for the fingerprinting of human DNA samples (Jeffreys et al., 1985) and so tended to use sequences that are highly repeated in the plant genome (Henry, 1997). Smith and Register (1998) stated that RFLP can allow more discriminative and faster identification of plant taxa comparing to traditional methods and isozymes. Mita et al. (1991), however, were unable to differentiate between Beta webbiana and B. procumbens using this approach, and so suggested that the two species should be merged. RFLP variability was also studied in 14 wild Arachis species accessions, using random genomic clones from a Pst I library (Kochert, et al., 1991). Large number of RFLP loci have now been characterized in Eucalyptus and can be utilized in studies of diversity in the genus (Byrne et al., 1998). Using RFLP analysis, Lu et al. (2002) assessed genetic differentiation of wild relatives of rice.

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Figure 6. General protocol of RFLP (after Karp et al., 1997). The process of reprobing the filter is optional.

Although RFLP is highly reproducible and generates co-dominant markers, it is limited by the need for a suitable DNA probe and for large quantities (generally 10-20 µg per digest) of high quality DNA in a form that can be digested by selected restriction enzymes (Henry, 1997). It is also laborious and so is poorly suited for large-scale operations (Scott et al., 1996).

Analysis of Nuclear Ribosomal DNA (nrDNA) Nuclear ribosomal genes are arranged in tandem arrays of several hundreds to several thousands copies within the nuclear genome. rDNA modules may occur at one or several chromosomal sites (Rogers and Bendish, 1987). Each repeat unit is composed of a highly conserved coding sequence of total length about 6 kilo bases (kb), plus shorter and more variable noncoding spacer regions (Dayhoff and Eck, 1968). There are two groups of nuclear ribosomal genes found useful for the diagnosis of plant taxa. The first is the 5S rDNA gene that is located at dispersed sites across the genome. The coding regions of this gene are separated by noncoding spacer sequences (nontranscribed spacers (NTS)), which have proved to be highly informative for plant diagnosis. These spacers vary both in length and sequence. These features have allowed their use for the distinction of groups of plants at various taxonomic levels. Species-specific PCR products

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(see below) can usually be obtained by amplifying across the variable spacer regions using primers that target the conserved sequence in the coding regions. Ford et al. (1997) stated that molecular markers specific for the 5S rRNA NTS region might be very valuable for the identification of species in which the length of the NTS is conserved. The second group of rDNA genes comprises the 18S, 26S and 5.8S genes. These have a high copy number within the nuclear genome and this property can aid analysis of difficult or degraded samples. The small subunit 18S is associated with the large subunit 26S genes and these two are separated with a smaller 5.8S gene. There are two short internal transcribed spacers (ITS) between the three genes (Fig. 7). The arrays are generally restricted to a small number of regions on the genome. rDNA genes can be analysed by RFLP using a DNA probe that contains homologies to these repetitive genomic sequences and hybridises to all such sequences and thereby simultaneously reveals restriction fragment changes at members of these repeats. Using RFLP on rDNA, Bhatia et al. (1996) generated markers that were able to distinguish closely related species such as those belonging to the genus Brassica. There is, however, a difficulty in interpreting genetic markers provided by rDNAs multigene family as this involves understanding the degree to which mechanisms of concerted evolution may have homogenized the repeated DNA sequences (Ohta and Dover, 1983). In spite of the valuable contribution of ribosomal genes for discrimination of plant species, Acevedo et al. (2002) detected changes in their organisation and nucleolar protein components in sugarcane (Saccharum officanarum L. cv. Cristalina). The authors showed a high plasticity of the nucleolar domains in response to cell activation. More significantly, most variation is levied at the level of the DNA sequence and so requires relatively slow and expensive sequencing reactions to effect identification. The advent of Polymerase Chain Reaction (PCR) by Saiki et al. (1988) yielded entirely new technologies for the assessment of polymorphisms among the DNA sequences of different plants (Powell et al., 1996b). PCR overcame many of the innate problems that prevent the automated use of RFLP for plant diagnosis. Using PCR, small regions of the target DNA (template DNA) are replicated using a cyclical reaction that exploits variation in reaction temperature (Figure 8).

Figure 7. Schematic diagram of the organisation of the repeat unit of 18S, 5.8S and 26S nrDNA in plants showing the ITS1 and ITS2 regions (after Soltis et al., 1998).

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Figure 8. Schematic representation of Polymerase Chain Reaction (after Judd et al., 1999). Targeted DNA region is amplified after several cycles of denaturation, primer annealing and new DNA strand synthesis by DNA Taq polymerase.

PCR has several advantages over hybridisation-based systems for plant identification including sensitivity, specificity, speed, ease of application and efficiency with much lower amounts of DNA of a much lower quality. More importantly, dry and processed materials can be used for PCR although fresh samples are usually preferred. PCR facilitated the automation of DNA sequencing (Bock and Slightom, 1995) that also has many implications for plant identification (discussed later). Several PCR-based techniques have been developed to detect variation between plants and animals. Examples of these are Randomly Amplified Polymorphic DNA (RAPD), Inter-Simple Sequence Repeat (ISSR), and Amplified Fragment Length Polymorphism (AFLP). The characteristics of each of these are described below.

Randomly Amplified Polymorphic DNA (RAPD) Analysis In the early 1990s, a novel PCR-based strategy wasAintroduced for DNA fingerprinting. This method involves the use of single oligonucleotide primers of arbitrary sequence to amplify DNA fragments that are randomly distributed throughout the genome (Micheli et al., 1994). The protocol initially gained a lot of popularity because it is cheap, quick and requires little in the way of infrastructure or expertise, small quantities of DNA. Moreover, it does not involve radioactive detection, and does not require prior knowledge of the target DNA sequence (Culham and Grant, 1999). The use of arbitrary oligonucleotide primers can be a good choice when there is no prior information about the genome sequence of the sample in study. Three variations of the basic approach have been described: 1) Arbitrarily Primed PCR (AP-PCR) (Welsh and McClelland, 1990), 2) Randomly Amplified Polymorphic DNA (RAPD) (Williams et al., 1990), and 3) DNA Amplification Fingerprinting (DAF) (Caetano-Anolles et al., 1991). These protocols are essentially identical but arguably differ slightly in the length of primer used, amplification conditions, and separation and visualisation of the amplification products. In this chapter, APPCR, RAPD and DAF will be considered as synonyms and the most widely used RAPD name will be applied hereafter.

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RAPD technique exponentially amplifies small portions of the target genome by PCR using short oligonucleotide fragments that are generally 10 bases in length and of arbitrary sequence. Sets of arbitrary primers are now available commercially and are useful for screening of plant genomes. The many products generated by the procedure are usually separated by agarose gel electrophoresis to generate diagnostic band profiles that are then used for identification purposes (Figure 9).

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Figure 9. Schematic representation of RAPD. One arbitrary primer is used in PCR and generated amplicons are visualised on agarose gel where only dominant alleles can be detected.

RAPD was the first PCR-based technique to be applied widely for the identification of plant germplasm. Wachira et al. (1997) compared several plant species using RAPD analysis and reported that bands were frequently constant within species. By comparing RAPD fingerprints of wild and cultivated barley, markers were identified among the set of amplified DNA fragments which could be used to distinguish wild and cultivated Hordeum species (Reddy and Soliman, 1997). There are several other instances where RAPD has been used for species identification including lentils (Lens culinaris ssp. culinaris) (Ford et al., 1997), Botrychium species (Swartz and Brunsfeld, 2002), beans, peas and other legumes (Weder, 2002), basils (Ocimum spp.) (Satovic et al., 2002) and Dalbergia (Rout et al., 2003). In addition, RAPD analysis was also useful in the determination of the genetic origin of Iris nelsonii (Arnold, 1993) and sea beet (Beta vulgaris ssp. maritima) at Germany's Baltic Sea coast (Driessen et al., 2001). In a study carried out by Xu et al. (2000), Vigna angularisspecific RAPD bands were believed to have potential value for in situ conservation of this species. RAPD fingerprinting also allowed Nowbuth et al. (2005) to rapidly evaluate the level of genetic diversity that would be otherwise difficult to assess using the limited number of morphological markers present among closely related Anthurium cultivars. Recently, Alyev et al. (2007) were able to genetically identify diploid and tetraploid wheat species with RAPD markers. More recently, DNA-based markers, using RAPD technique, have been developed to distinguish Ipomoea mauritiana from the other Vidari candidates when a putative 600-bp polymorphic sequence, specific to I. mauritiana was identified by Devaiah et al. (2010). Because of the ease in using RAPD for the assessment of genetic variation, simplicity of use, and the low cost and the small amount of template DNA required, numerous publications

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have demonstrated the utility of RAPD markers for the analysis of the genetic diversity among plant cultivars and their identification. Examples are cultivars of Heliconia L. (Kumar et al., 1998), potato (Demeke et al., 1993), Cannabis sativa L. (Forapani et al., 2001), Ficus carica L. (Aka-Kaçar et al., 2003) and Phoenix dactylifera L. (El-Tarras et al., 2007). RAPD was also proved useful for the identification of mislabelled germplasm accessions of Poa pratensis L. (Johnson et al., 2002). Therefore, the utilization of RAPD markers could potentially have a higher priority for studying genetic diversity and molecular characterization of plant cultivars. Description of cultivar identities (such as those of potato) by RAPD could be useful for cultivar registration and germplasm maintenance programs (Mori et al., 1993). In 2008, Muthusamy et al. proved the efficiency of RAPD markers system in accessing genetic variation of rice bean (Vigna umbellata) landraces. The main problem associated with RAPD analysis lies in the lack of reproducibility of the amplification profiles, which are influenced by minor changes to the protocol such a template DNA isolation or degradation, the reaction mixture composition, PCR conditions and product detection (Geneve et al., 1997). Weder (2002) examined the influence of experimental conditions on the reproducibility of RAPD band profiles for the species identification of two food legumes (soybeans and lentils) and two cereals (wheat and rye), and observed variable amplification of some products. Accordingly, he advocated that all RAPD analyses should be performed in duplicate. Another disadvantage of RAPD for diagnostic applications lies in the fact that homology between bands from different profiles is generally inferred only by band comigration. This can lead to erroneous interpretation where heterologous amplicons share approximately the same size (Arnold and Emms, 1998). The problem of comigration becomes more acute as the number of amplicons within a profile increases.

Inter-Simple Sequence Repeat-PCR (ISSR-PCR) Inter-Simple Sequence Repeat-PCR (ISSR-PCR) is a multi-locus approach that exploits the abundance of Simple Sequence Repeats (SSRs) (described later) motifs in eukaryotic organisms. ISSR-PCR uses primers that are complementary to SSRs sequences with or without short (1-3 bp) oligonucleotide ‗anchors‘ at the 3` or 5` end of the primer that discontinues the repeat array (e.g., CACACACACAGT). The PCR yields many products of variable length. The anchors ensure that primers target either terminus of the SSR. As a result, the method has also been described as ‗anchored SSR-PCR‘ analysis (Zietkiewicz et al., 1994). Products from a 5`-anchored primer include the targeted SSR sequences themselves and the region of DNA between them whereas those from 3`-anchored primer consist primarily of the region between targeted SSRs. The resulting amplified fragments are resolved on agarose or polyacrylamide gels (Wolf and Liston, 1998) or exceptionally on lowtemperature precast polyacrylamide gels (Charters et al, 1996). The number and size of these fragments can be used as a basis for DNA fingerprinting. ISSR was proved useful for the identification of cultivars. For instance, fast and reliable strawberry cultivar identification using ISSR amplification was proved by Arnau et al., (2003). ISSR variation was also investigated by Essadki et al. (2006) in olive-tree cultivars from Morocco and other Western Countries of the Mediterranean Basin. Some other examples are those of Rajapakse and Ballard (1997), Charters et al. (1996) and Pasakinskiene et al. (2000).

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However, only a very limited number of studies have used ISSR for species identification. For instance, Salimath et al. (1995) were able to determine the allopolyploid origin of Eleusine coracana using ISSR markers. Similarly, Raina et al. (2001) used ISSR and RAPD markers to indicate that Arachis villosa and A. ipaensis are the diploid wild progenitors of tetraploid Arachis species. In 2002, ISSR has been proved useful by Benharrat et al. for the identification of Orobanche species. Gao et al. (2006) also revealed ISSR as a useful analysis for identification and determination of genetic relationships in the genus Populus.

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Amplified Fragment Length Polymorphism (AFLP) Amplified Fragment Length Polymorphism (AFLP) analysis, originally known as Selective Restriction Fragment Amplification (SRFA, Zabeau and Vos, 1993), is a system that is equally applicable to all species and was developed by Vos et al. (1995). It combines DNA restriction digestion and DNA fragment amplification by PCR. This methodology begins with the digestion of total genomic DNA with two endonucleases (a rare cutter and a frequent one). The restriction site sequence at the end of the fragments is insufficient for primer design. Therefore, restriction is followed by ligation of short DNA oligonucleotides (adaptors) to the ends of the cut fragments to provide known sequences for subsequent PCR amplification (Karp et al., 1997). Two successive PCR amplifications follow using different sets of primers that are designed so that they incorporate the known adaptor sequence plus 1, 2 or 3 (more than 3 results with non-specific PCR) additional base pairs that target unknown sequence internal to the restriction sites (these are known as selective nucleotides) (Fig. 10).

Figure 10. Diagrammatic representation of the AFLP technique (after Wolf and Liston, 1998). Genomic DNA is restricted and then linkers (adaptors) are ligated to both ends of generated restriction fragments to allow a preselective PCR that is followed by a selective amplification.

The first amplification is a pre-selection stage and amplifies a subset of restriction fragments that have the linked primer sequences for the double digest and plus a single specific nucleotide in the original fragment sequence. This reduces the pool of fragments from the original mixture (Wolf and Liston, 1998). In the second amplification step, the same selective nucleotide plus one or two additional ones are used to make the amplification even

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more selective and reduce the final pool to a manageable number of fragments for analysis following fractionation (generally 50-100 fragments). The fragments produced are usually separated on polyacrylamide gels (Arnold and Emmes, 1998). The technique has been adapted to florescent, non-radioactive and silver staining procedures (Karp et al., 1997). To develop a convenient and reliable technique for rapid genetic characterisation of plants, Ranamukhaarachchi et al. (2000) modified the standard AFLP technique to include one restriction enzyme, one adaptor and primer, formamide to generate more intense and uniform bands and agarose gel electrophoresis. Using three AFLP primer combinations, the 716 markers generated were suffiecient to discriminate all Alstroemeria species analysed by Han et al. (2000). Quagliaro et al. (2001), similarly, employed AFLP markers for identification of wild and cultivated sunflower for breeding purposes. AFLP analysis also appeared to be an efficient method to verify taxonomic classification and to identify redundancies in the wild potato germplasm of the series Acaulia (McGregor et al., 2002). In 2005, Hawkins et al. investigated the possibility of identifying AFLP genetic markers that are diagnostic for the species G. tomentosum Nuttall ex Seeman and the closely related species G. hirsutum. The authors believe that such markers could be used in future studies to detect introgression between the two species. Recently, Misra et al. (2010) used AFLP to produce DNA fingerprints for six Swertia species (S. chirayita, S. angustifolia, S. bimaculata, S. ciliata, S. cordata and S. alata) that are most commonly used in the herbal trade. However, as discussed above for RAPD and ISSR, AFLP has been proved more useful for cultivar identification. For example, Geuna et al. (2003) used AFLP markers for cultivar identification in apricot. Similarly, Ĉurn et al. (2002) and Èron et al. (2006) identified oilseed rape cultivars using AFLP markers. In 2009, El-Khishin et al. used AFLP for molecular characterization of banana cultivars (Musa spp.) from Egypt. AFLP has several advantages: 1) the ability to resolve tens to hundreds of loci from a single reaction (Arnold and Emms, 1998), 2) no prior sequence information is required for the analysis, 3) a large number of polymorphic bands are produced, 4) the technique is highly reproducible and sensitive, and 5) standardized kits are commercially available. AFLP, however, has several disadvantages such as: 1) generated markers are dominantly inherited, 2) homology again being inferred from band comigration, and 3) the large number of steps required to produce results (Wolf and Liston, 1998). Added to these, a problem of reliability could arise if partial digestion occurred in the first step of the protocol, which leads to the production of artifactual fragments during the subsequent amplification steps. Clearly, all multi-locus molecular techniques have major practical limitation in that generally it remains unknown which bands in a fingerprint profile belong to which loci (array), so allelism is not established (Dayhoff and Eck, 1968). More importantly, complex band profiles generated are all difficult to standardise and to score; rendering accurate comparisons between laboratories virtually impossible. Such genetic complexity of multilocus fingerprints, however, is an advantage that facilitates individual or cultivar diagnosis. Studies that exploited this complexity in multi-locus markers for discrimination of plant individuals have used ISSR (Li and Ge, 2001), AFLP (Danquah et al., 2002), and RAPD that enabled the identification of resistant individuals of tomato to spotted wilt virus (SWV) (Chague et al., 1996), and bean (Phaseolus vulgaris L.) to mosaic virus (Johnson et al., 1997).

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Single or Oligo-Locus Systems of DNA Fingerprinting The shortcomings of multi-locus DNA fingerprinting methods have led to an increasing interest in developing systems that generate simple band profiles that originate from a single locus and that are relatively easy to score automatically. As a consequence, techniques that generate such simple band profiles have been developed in recent years. The most widely used of these is based on the variation in size of Simple Sequence Repeats (SSRs) (described below).

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Simple Sequence Repeat-PCR (SSR-PCR) Simple Sequence Repeats (SSRs) (or microsatellites; the term was first coined by Litt et al., 1989) consist of tandem arrays of repeats of a simple motif of bases. For instance, a repeat of the AGT motif in noncoding DNA may read TTAGCTCAGTAGTAGTAGTAGTAGTTA. The repeated motif can comprise 1-6 bases, termed as mono-di-, tri- tetra-, penta- and hexanucleotide repeat motifs that are arranged throughout the genomes of most eukaryotic species (Powell et al., 1996a). The number of motif units in the repeat array often varies among individuals and this generates PCR products of differing length that provide the basis for the polymorphism exploited by SSR-PCR. Such Simple Sequence Length Polymorphism (SSLP) (Fig. 11) can be detected using primers for PCR that target sites that flank the SSR in question (Ishii and McCouch, 2000). Developed primer pairs for different microsatellite loci in different plant species, can be used to investigate the level of polymorphism (Mittal and Dubey, 2009). However, since microsatellites frequently occur outside of coding regions, primers are often species-specific, and thus for each species of interest the entire process for SSR marker development must be repeated (Kuhn et al., 2008).

Figure 11. The generation of SSR length polymorphism (after Cregan et al., 1994) Two primers that are specific to the flanking regions of the targeted SSR are used for the amplification of the latter. Generated amplification profiles are then detected on polyacrylamide gel to detect size variation between two genotypes.

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Since microsatellites and AFLP analyses are available, most researchers focus on those methods for which any kind of tissue (including processed tissue) can be used. In addition, they are easier to use and less expensive than allozyme electrophoresis and the results are comparable between different laboratories (Ludwig, online reference). SSR-based genetic markers have not been utilised widely for plant species diagnosis, although they are being actively developed for the majority of crop plant species (Ramsay et al., 1999). Examples of studies that used SSRs for the identification of plant species are those of Suliman-Pollatschek et al. (2002) (Lycopersicon), Lowe et al. (2002) (U-triangle Brassica species), and Cuadrado and Jouve (2002) (Secale). Pasakinskiene et al. (2000) did generate distinctive species-specific SSR profiles to distinguish four grass species namely, Lolium multiflorum, L. perenne, Festruca pratensis and F. arundinacea. Genetic diversity of wild and cultivated Rubus species in Colombia was also investigated by Marulanda et al. (2007) using AFLP and SSR markers. All species evaluated produced very specific banding patterns, differentiating them from the others. AFLP and SSR data generated produced bands exclusive to each of the species R. robustus, R. urticifolius, R. glaucus and R. rosifolius. The SSR markers also differentiated diploid and tetraploid genotypes of R. glaucus. In another study, Eurlings et al. (2010) developed polymorphic microsatellites for forensic identification of agarwood (Aquilaria crassna). The loci characterized in this study are believed to provide a starting point for forensic identification of traded material and certification of sustainably produced agarwood. However, McGregor et al. (2000) reported problems in relating SSRs banding patterns to individual loci and alleles for polyploid genomes. The uniqueness and value of microsatellites arises from several characteristics, such as 1) detection of multiallelic variation, 2) hypervariable (i.e., have a high information content), 3) co-dominant transmission, 4) ease of detection by PCR, 5) relative abundance with uniform genome coverage, and 6) requirement of a small amount of DNA as a starting material (Mittal and Dubey, 2009). The major limitation on the use of SSRs technique for plant identification, however, is the difficulty of cloning and sequencing of the regions flanking the SSR. Difficulty can also arise in interpreting some banding patterns, especially on agarose gels. Additionally, the SSR primers do not always display a high level of polymorphism between species (Henry, 1997), and primers designed for a SSR in one species frequently fail to amplify a product when applied to another. Most importantly, the highly variable nature of SSRs means that there is a strong likelihood that the variation detected will not be speciesspecific (Arnold and Emms, 1998) which limits their usefulness for species identification. For instance, SSRs have proved useful for the diagnosis of individuals of Eucalyptus (Brondani et al., 1998) and Beta vulgaris L. (Morchen et al., 1996), inbred lines of Sorghum bicolour (Taramino et al., 1997), accessions of Malus hupehensis (Damp.) Rehd. (Rahman and Rajora, 2002), genotypes of sunflower (Helianthus annuus L.) (Paniego el al., 2002), and cultivars of tomato (Lycopersicon esculentum) (Suliman-Pollatschek et al., 2002), apple (Goulao and Oliveira, 2001) and Populus (Rahman and Rajora, 2002). Fluorescent SSLPs have also been used to differentiate between known Basmati rice cultivars and likely adulterants (Bligh, 2000). Highly polymorphic markers like SSRs can even be informative for the individualization of plant germplasm resources that have become important in the present day scenario for their proper management and utilization, as well as for intellectual property right (IPR) protection. Microsatellites developed from the sequenced chloroplast genome of the plant species are commonly referred to as the chloroplast microsatellites (cpSSRs). Polymorphism in cpSSRs

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is found out in the conserved and variable regions of the chloroplast genome (Mittal and Dubey, 2009). Ogliari et al. (2000) reported that these serve as a common tool for collaborative research by acting as universal genetic markers because they can be easily shared between laboratories. An example of the usefulness of cpSSRs for identification of plant species was provided by the Final report of CITES-financed pilot project (2006) when five consensus cpSSRs, that were amplified using primers developed by Chung et al. (2003), were sequenced for different species of Aquilaria (A. crassna, A. malaccensis and A. rugosa) and Gyrinops ledermanii. SSR regions sequenced showed sufficient variation for identification of plant samples analyzed to species level. CpSSRs have also been proved to be of considerable value in numerous studies of plant population structure, diversity, differentiation and paternity analysis (Deguilloux et al., 2003). Of other single-locus techniques that can be used for genotyping of plant species and their identification is the PCR-based Single Strand Confirmation Polymorphism (SSCP), which is based on the electrophoresis of single-stranded (ss) DNA fragments of suitable size through a non-denaturating polyacrylamide gel, followed by visualization (Sunnucks et al., 2000). SSCP markers are polymorphic and co-dominant. They can be screened by mobility differences in a variety of low- to high-throughput electrophoretic techniques, including capillary array electrophoresis (CAE) (Kuhn et al., 2008). Ludwig (online reference) reported that SSCP method is only valuable for short PCR fragments (normally 0 if the trend is shared to low values of the distribution (positive symmetry). In figure 25 an illustrative plot is reported. Kurtosis (K) is referred to the degree of peakness (or flattening) relative of intensity distribution, that is, the concentration or dispersion of values around the median value (formula 12):

(12) where, also in this case, μi is the ith central moment of the intensity distribution. The intensity distribution is called “mesokurtic” if this parameter assumes value 0; “platikurtic” if the Kurtosis value results < -3, and in this case the curve appears flattened because the intensity values are concentred in the two tails of the plot; and “leptokurtic” if this parameter assumes

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value > 3, and the curve appears peaked because the intensity values are concentred around the higher value. Figure 26 shows a descriptive plot of kurtosis.

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Figure 25. Skewness plot. In blue, a curve with skewness value of 0,1; in red, a curve with skewness value of 0,25.

Figure 26. Kurtosis plot. In blue, a leptokurtic curve; in red, a mesokurtic curve.

Energy (E) measures the strength of intensity increase and it is referred to grey level values of ROI pixels (formula 13):

(13) Entropy (H) measures the dispersion strength, that may be also understood as degree of chaos or disorganization of density values within the ROI (formula 14):

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(14) where , and Z is the number of the grey levels in the image. Finally, Density sum (Dsum) and Square density sum (SqDsum) are parameters related to the sum and the square sum of density values of each single pixel composing the object. Obviously a lot of others morphological and colorimetric/densitometric parameters exist and can be used to extract information from a digital image, but as discussed above, only the features used in our works are here introduced and explained. In table 3 all the selected parameters measured for each seed, are reported.

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Table 3. List of the measured morphometric and colorimetric features

A P Pconv PCrof Pconv /PCrof Dmax Dmin Fr Sf Rf Ecd EAmax EAmin Rmean Rsd Gmean Gsd Bmean Bsd Hmean Hsd Lmean Lsd Smean Ssd Dmean Dsd S K H E Dsum SqDsum

Feature Area Perimeter Convex perimeter Crofton‘s perimeter Perimeter ratio Max diameter Min diameter Feret ratio Shape factor Roundness factor Eq. circular diameter Maximum ellipse axis Minimum ellipse axis Mean red channel Red std. deviation Mean green channel Green std. deviation Mean blue channel Blue std. deviation Mean hue channel Hue std. deviation Mean lightness ch. Lightness std. dev. Mean saturation ch. Saturation std. dev. Mean density Density std. deviation Skewness Kurtosis Energy Entropy Density sum Square density sum

Description Seed area (mm2) Seed perimeter (mm) Convex perimeter of the seed (mm) Crofton‘s perimeter of the seed (mm) Ratio between convex and Crofton‘s perimeters Maximum diameter of the seed (mm) Minimum diameter of the seed (mm) Ratio between minimum and maximum diameters Seed shape descriptor (normalized value) Seed roundness descriptor (normalized value) Diameter of a circle with equivalent area (mm) Maximum axis of an ellipse with equivalent area (mm) Minimum axis of an ellipse with equivalent area (mm) Red channel mean value of seed pixels (grey levels) Red channel standard deviation of seed pixels Green channel mean value of seed pixels (grey levels) Green channel standard deviation of seed pixels Blue channel mean value of seed pixels (grey levels) Blue channel standard deviation of seed pixels Hue channel mean value of seed pixels (grey levels) Hue channel standard deviation of seed pixels Lightness channel mean value of seed pixels (grey levels) Lightness channel standard deviation of seed pixels Saturation channel mean value of seed pixels (grey levels) Saturation channel standard deviation of seed pixels Density channel mean value of seed pixels (grey levels) Density channel standard deviation of seed pixels Asymmetry degree of intensity values distribution (grey levels) Peakness degree of intensity values distribution (densitometric units) Measure of the increasing intensity power (densitometric units) Dispersion power (bit) Sum of density values of the seed pixels (grey levels) Sum of the squares of density values (grey levels)

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3. STATISTICS When information about a certain phenomenon are collected, they may represent an awful lot of raw data. Hence, the first problem to deal with is to summarize this great amount of raw data in few numbers or significative indicators, using graphical or numerical methods, that are able to describe the whole quantity of data without modify the overall meaning. In the statistic field, the aptitude to reorganize the raw data in this way is defined descriptive statistic. Many times, the simple description of the raw data could not be enough, and though could not represent the real goal of the statistical survey. Moreover, especially in the scientific research, the employ of a suitable treatment of data is very important, in order to overcome all the problems due to the experimental error, that is the cluster of the variations led by non-controlled factors, whose effects are overlapped to that one of the studied factor. One of the aims of the seed characterization consists in the implementation of statistical classifiers able to recognize and discriminate seeds belonging to different botanical ranks. In order to achieve this goal, the more significant morpho-colorimetric features measured by image analysis, should be used to describe size, shape and colour of each analysed seed, to identify and classify them on the basis of these morphological and colorimetric parameters.

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3.1. Linear Discriminant Analysis Sometimes, analyzing the data of various hundreds of groups of objects, in this case families, genera and species of seeds, the principal practical limitation of the pattern recognition is the high-dimensionality of the dataset. In the past several decades, many dimensionality reduction techniques have been proposed. The Linear Discriminant Analysis (LDA) (Fukunaga, 1990) is one of the most popular supervised methods for linear dimensionality reduction. It has been proven to be very powerful in many practical applications. The LDA is a multivariate statistical analysis and allows to analyze simultaneously measurements of many characters (qualitative and/or quantitative variables) from many samples. Fundamentally, this kind of statistical analysis aims to summarize the cases and simplify their structure to obtain the most correct grouping of them. The LDA is a very well-known method for dimensionality reduction and classification that projects highdimensional data onto a low-dimensional space where the data achieve maximum class separability (Fukunaga, 1990; Duda et al., 2000; Hastie et al., 2001). The derived features in LDA, also called discriminant functions, are linear combinations of the original features, where the coefficients are from the transformation matrix. The optimal projection or transformation in classical LDA is obtained by minimizing the withinclass distance and maximizing the between-class distance simultaneously, thus achieving maximum class discrimination. Calling J this objective, the original LDA formulation, also known as the Fisher Linear Discriminant Analysis (FLDA) (Fisher, 1936; 1940), that deals with binary-class classifications, can be described by the following formula (15):

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(15) where w is a linear transformation matrix, Sb is the between-class scatter matrix and Sw is the within-class scatter matrix. As discussed above, the purpose of the LDA is to maximize the between-class scatter, minimizing, at the same time, the within-class scatter. The two scatter matrices, Sb (betweenclass) and Sw (within-class), are defined as:

(16)

(17) where c is the number of classes; mi and pi are the mean vector and a priori probability of

m  i1 pi mi

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c

class i , respectively; is the total mean vector; Si is the covariance matrix of class i. Generally, to obtain the discriminant functions and consequently classify objects (seeds in this case) into one of two or more groups, on the base of a set of features that describe the objects (e.g. area, perimeter, red, green or blue channel, etc.), it is need to assign an object to one of a number of predetermined groups, based on observations made on the object. It is important to note that the groups are known or predetermined a priori. So, it is possible to summarize that the basic tasks of the LDA are two:  

to detect set of features that better can determine group membership of the object; to identify the classification model (or rule) that better can separate the groups.

The first of these two purposes, the detection of feature set, is a process of variables selection by steps, that allows to define the LDA method as stepwise Linear Discriminant Analysis (sLDA). Using this method, only the best features for the identification of the different seed samples were detected, in order to implement a statistical classifier able to discriminate and classify the seeds, on the basis of morpho-colorimetric features. When several variables are available, the stepwise method can be useful by automatically selecting the best characters on the basis of three statistical variables: Tolerance, F-to-enter and F-toremove. The Tolerance value indicates the proportion of a variable variance not accounted for by other independent variables in the equation. A variable with very low Tolerance value provides little information to a model. F-to-enter and F-to-remove values define the power of each variable in the model and they are useful to describe what happens if a variable is insert and removed, respectively, from the current model. This method starts with a model that does not include any of the variables. At each step, the variable with the largest F-to-enter value that exceeds the entry criteria chosen (F ≥ x) is added to the model. The variables left out of

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the analysis at the last step have F-to-enter values smaller than x, so no more are added. The process was automatically stopped when no remaining variables increased the discrimination ability. (Bacchetta et al., 2011a; Venora et al., 2009a; 2009b). The second purpose of the LDA concerns the model or rule of classification to predict the membership of a new object on the base of the model. This approach is commonly used for the classification/identification of unknown groups characterized by quantitative and qualitative variables (Fisher, 1936; 1940). This method requires a teaching procedure that use information derived by previous identified sample groups (also called training set) allowing to develop and to teach all the classifiers used in the study. In a Linear Discriminant Analysis, the class categories or the groups that represent what it is looking for, are called dependent variable; while each measured feature, that describes the analysed object, is statistically defined independent variable. Hence, in our case, the analysed objects are seeds, the dependent variable is the considered taxonomic rank (family, genera or species) of the germplasm accessions, while the descriptive features (area, perimeter, red, green or blue channel, etc.) are the independent variables. Thus, the LDA finds a set of discriminant functions, whose values are as close as possible within groups and as far apart as possible between groups. A discriminant function (fni), that above was defined as a linear combination of the discriminating variables, has the following mathematical form (18):

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(18) where (fni) is the value (or score) on the canonical discriminant function for case i in the group n; x is the value on discriminant variable for the same case in the same group; and a is the eigenvalue which produce the desired characteristics in the function. The coefficients a1, a2, a3, …, ak, for the first discriminant function are derived so as to maximize the differences between the group means. At the same way, the coefficients a1, a2, a3, …, ak, for the second discriminant function are also derived in order to maximize the difference between the group means, but they are subject to the constraint that the values, on the second discriminant function, are not correlated with the values on the first discriminant function, and so on for the discriminant function that follow. In geometrical terms, the second discriminant function is orthogonal to the first, and the third discriminant function is orthogonal to the second, and so on. The maximum number of unique functions that can be derived is equal to the number of groups minus one or equal to the number of discriminating variables. Summarizing, the discriminant functions were selected so that:  

f1 reflects, as much as possible, the differences between the groups; f2 reflects, as much as possible, the differences between the groups, not highlighted by f1;



f3 reflects, as much as possible, the differences between the groups, not highlighted by f1 and f2;

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Wild Plant Seeds Identification through Image and Linear Discriminant Analysis 

135

… and so on.

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Each linear discriminant function explains a certain percentage of the total variance (or variability) of the cases, and all of them explain the 100% of the variability. Generally, it is desirable that the first two discriminant functions explain variability levels higher than 6070%. The simplest case of a LDA assumes that the groups are linearly separable. It occurs when the groups can be separated by a linear combination of features that describe the objects. If the independent variables are only two, the separator between object groups is simply a line; if the features are three, the separator is a plane; while if the number of independent variables is more than three, the separator become a hyper-plane. In this last case, the great utility of linear dimensionality reduction of the LDA method, is evident, and can be better explained taking advantage of graphical representations. Figure 27 shows a graphical representation of a case in which objects belonging to three different groups (dependent variables) are detected by only two discriminant functions.

Figure 27. Bi-dimensional plot of a Linear Discriminant Analysis.

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Figure 28. Three-dimensional graphic representation of a LDA.

In figure 28, a 3D plot shows the scores of three of the discriminating functions used to distinguish the objects belonging to five different groups. The principal advantage of the multidimensional plot is in the possibility to represent graphically the discriminant scores in a biggest space than a classical Cartesian plane. In this way, it is simplest to visually appreciate the distances among groups. When different groups of objects have to be discriminated and only two discriminant functions are available, it is possible to insert a third value that allows to draw a multidimensional plot, the Mahalanobis distance. This is a measure introduced by the Indian statistician Prasanta Chandra Mahalanobis (1936) and it is based on correlations among variables by which different patterns can be identified and analysed. It determines similarity of an unknown sample set to a known one. In other words, Mahalanobis distance is a measure of distance between two data points in the space defined by two or more discriminant functions; a high value indicates that a particular case includes extreme values for one or more independent variables and it can be considered not similar to other cases (Bacchetta et al., 2008). Formally, the Mahalanobis distance from a group of values with mean μ=(μ1,μ2,μ3,…μn)T, and covariance matrix S for a multivariate vector x=(x1,x2,x3,…xn)T is defined as (De Maesschalck et al., 2000):

DM ( x)  ( x   )T S 1 ( x   )

(19)

A major drawback of LDA is that it often suffers from the small sample size problem when dealing with the high dimensional data. When there are not enough training samples, Sw (in the 15) may become singular and it is difficult to compute the LDA vectors. Several Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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approaches have been proposed to address this problem (Liu et al., 1992; Belhumeur et al., 1997; Chen et al., 2000; Yu and Yang, 2001), but a common problem with all these proposed variant LDA approaches is that they all lose some discriminative information in the high dimensional space. Anyway, the stepwise LDA method has been applied successfully in many different applications (Swets and Weng, 1996; Venora et al., 2007; 2009a; Bacchetta et al., 2008).

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3.2. The Cross-Validation Sometimes, it is convenient to apply the cross-validation procedure, also called rotation estimation (Picard and Cook, 1984; Kohavi, 1995), both to evaluate the performance and to validate any classifier, and to avoid problems and/or mistakes that might arise on account of seed samples not enough numerically representative. Indeed, this procedure is usually applied for small amount of data, in lack of a broad group of new unknown cases (test set). It tests the individual cases and classifies them on the basis of all the others (SPSS Application Guide, 1999). The most common types of cross-validation are three. The repeated random sub-sampling validation, is a method that randomly splits the dataset into training and test (or validation) set. For each such split, the model is fit to the training set of data, and predictive accuracy is assessed using the test set of data. The results are then averaged over the splits. The advantage of this method is that the proportion of the training/test split is not dependent on the number of iterations (as it occurs for the k-fold cross validation type). The disadvantage of this method is that it is very expensive from the point of view of the optimal use of the available dataset. In K-fold cross-validation, another very common type of cross-validation, the original sample is partitioned into K subsamples. One of the K subsamples is put aside as the test dataset to validate the model, and the remaining K−1 subsamples are used as training set. The cross-validation process is then repeated K times (the folds), with each of the K subsamples used exactly once as the validation data. Then, the K results from the folds can be averaged (or otherwise combined) to produce a single estimation. The advantage of this method is that all cases are used for both training and validation, and each case is used for validation exactly once, but as hinted above, the ratio between the split training set and the test set, is closely related to the number K of process iterations. The third common type of cross-validation is the leave-one-out cross-validation (LOOCV). As the name suggests, it involves using a single case from the original sampleset as the validation dataset, and the remaining cases as the training set. This is repeated such that each case in the sampleset is used once as the test set. This is the same as a K-fold crossvalidation with K being equal to the number of cases in the original sample. Unfortunately, the leave-one-out cross-validation is often computationally expensive because of the large number of times the training process is repeated.

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4. RESULTS Hereafter, some our achievements are reported. Applying this method, Grillo et al. (2010), validated the statistical classification system explored in two previous works (Bacchetta et al., 2008; Mattana et al., 2008) by the implementation of a general database and the elaboration of a dedicated seed classifier for each of ten families representative of the Mediterranean vascular flora (Apiaceae, Asteraceae, Boraginaceae, Brassicaceae, Caryophyllaceae, Cistaceae, Fabaceae, Lamiaceae, Poaceae and Scrophulariaceae), achieving performance ranging from 90.4% to 98.5% and from 87.8% to 98.3% at genus and taxon levels, respectively. Moreover, in the same paper, a first application of this technique to gymnosperm seeds was also presented, implementing a statistical classifier for the genus Juniperus. The percentage of correct identification of the species belonging to this genus is reported in table 6. Using the images of 1,700 seeds of 8 different taxa, the cross-validated classification performance was 93.6%. Figure 31 shows the first three function scores used to distinguish the taxa belonging to the genus, explaining 96.4% of the variability. These findings for the genus Juniperus suggest that this seed classifier technology, based on quick and cheap acquisitions of morphometric and colorimetric feature measurements of seed accessions upon their entry to a seed bank, is also reliable for gymnosperms. In another recent work, Bacchetta et al. (2011b) proved how Astragalus terraccianoi Vals. and A. tegulensis Bacch. andand Brullo shows an interspecific differentiation as previously highlighted by Bacchetta andand Brullo (2010), confirming the taxonomic distance between these taxa and validating A. tegulensis as a new species from Sardinia, inside the Sect. Melanocercis Bunge. Moreover, the comparison of these two species with A. thermensis Vals. confirmed the morphological distance and the differences previously highlighted by Valsecchi (1994). Using this model, 99.8% of the cross-validated test set samples were correctly classified (Table 4). Only one seed of A. terraccianoi was wrongly identified as A. thermensis, but not one of A. thermensis and A. tegulensis seeds was misclassified. Figure 29 shows the graphic representation of the discriminant functions scores and the Mahalanobis distances belonging to Astragalus discriminated species. It is possible to notice graphically too, how the studied species were significantly scattered . Table 4. Cross-validated percentages of correct classification for the Astragalus species classifier. In brackets, the number of seeds Species

A. terraccianoi A. thermensis Vals. Vals.

Astragalus terraccianoi Vals.

99.6% (238)

Astragalus thermensis Vals. Astragalus tegulensis Bacch. & Brullo

A. tegulensis Total Bacch. & Brullo

0.4 (1)

239

100.0% (200)

200 100.0% (100)

Overall

(Bacchetta et al., 2011b).

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100 99.8% (539)

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139

(Bacchetta et al., 2011b). Figure 29. Graphic representation of the discriminating functions scores for Astragalus species.

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To evaluate the inter-population variability of A. terraccianoi, the seeds of three populations of this species (Figure 30), were also analysed giving a performance of 92.5%, with the Corsican population being well differentiated from the Sardinian ones (Table 5). From this comparison it is fair to think that the Corsican population really is a well defined population, not only geographically but also morpho-colorimetrically. Instead, the two Sardinian populations, although distinguishable, are not enough isolated or enough geographically far between them to express their phenotypic differences, or maybe they are not two different populations.

(Bacchetta et al., 2011b). Figure 30. Graphic representation of the discriminating functions scores for Astragalus terraccianoi populations. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

140

Oscar Grillo and Gianfranco Venora Table 5. Cross-validated percentages of correct classification for the Astragalus terraccianoi populations classifier. In brackets, the number of seeds A. terraccianoi Stintino - Sardinia 95.0% (95)

A. terraccianoi Ajaccio - Corsica

A. terraccianoi Alghero - Sardinia 5.0 (5)

100.0% (100)

33.3 (13)

66.7% (26)

(Bacchetta et al., 2011b).

Table 6. Cross validated performances of the classifier developed for the genus Juniperus. In brackets, the number of seeds Taxa

1

J. communis L. subsp. communis (1)

84.0% (84)

2

3 1.0% (1)

97. 3.0% 0% (3) (97)

J. phoenicea L. subsp. phoenicea (2)

J. phoenicea L. subsp. turbinata (Guss.) 0.4% Nyman (3) (2)

J. communis subsp. alpina Ĉelak (5)

13.0% (26)

14.0 % (7)

J. oxycedrus L. subsp. oxycedrus (6)

0.5% (1)

8.0% (16)

J. sabina L. (7)

4.0% (4)

5 15.0 % (15)

6

7

8

Total (100) (100)

96.6% (483)

J. oxycedrus L. subsp. macrocarpa (Sibth. & Sm.) Neilr. (4) Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

4

1.4% 1.6% (7) (8) 100. 0% (400)

(500)

(400) 80.0 % (160)

86.5 3.5 1.5% % % (3) (173) (7) 94. 2.0% 0% (2) (94)

J. thurifera L. (8)

Overall

(Grillo et al., 2010).

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(200) (200) (100) 100. 0% (100) (100) 93.6% (1700)

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141

(Grillo et al., 2010).

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Figure 31. Graphic representation of the discriminating function scores for the genus Juniperus.

In order to contribute to the assessment of the taxonomic position of five taxa of Lavatera (L. agrigentina, L. flava, L. triloba subsp. triloba, L. triloba subsp. pallescens and L. triloba subsp. minoricensis) within the L. triloba aggregate by investigating the morphometric and colorimetric features of their seeds, and to prove once again that morpho-colorimetric traits of seeds, measured with accurate, reliable and repeatable methods, as image analysis, can be considered a valid tool to identify also taxa of lower ranks within difficult to distinguish species aggregates, in another recent work, Bacchetta et al. (2011a) presented the results about the first approach to investigate taxonomic relationships within this aggregate by seed phenetic characterization. One of the most interesting results of this work, concerns the great differentiation between the studied Iberian and Sardinian populations of Lavatera triloba, and the relative similarity among these two populations and the L. flava collected in Morocco (Table 7). As discussed by the authors, isolation and genetic divergences of the species in Sardinia may explain these outcomes, supported also by a more marked distance from L. flava with respect to the Iberian populations (Figure 32). Table 7. Cross-validated classification performance of Lavatera flava and L. triloba subsp. triloba populations. In brackets, the number of seeds Taxon L. flava (Morocco) L. triloba subsp. triloba (Spain) L. triloba subsp. triloba (Sardinia)

L. flava (Morocco) 75.1% (136)

L. triloba subsp. triloba (Spain) 18.8% (34)

L. triloba subsp. triloba (Sardinia) 6.1% (11)

3.8% (24)

95.1% (594)

1.1% (7)

625

1.0% (4)

1.0% (4)

98.0% (382)

390

Overall

(Bacchetta et al., 2011a). Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

Total 181

93.0% (1196)

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Oscar Grillo and Gianfranco Venora

(Bacchetta et al., 2011a). Figure 32. Discriminant analysis of L. triloba subsp. triloba vs. L. flava.

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CONCLUSIONS The emphasis of this chapter was on a current and increasing interest for image analysis application on seed identification. This work was performed on the fundamentals of image analysis to well understand how the elaboration and processing techniques of digital images can be applied and which kind of problems they can solve, giving a detailed argumentation concerning the applied methodologies and the used tools, and explaining how the recorded data were treated to develop statistical classifiers able to identify and classify plant specie seeds. The achieved results confirms that this innovative kind of identification system, which method was specifically developed to identify wild species seeds, and that requires only a few seconds for scanning and measurement operations, proved to be a quick, repeatable, reliable and non destructive method. It does not require any chemical reagents, expensive analytical consumables or high priced physical preparation of samples, hence it is a very cheap method. This precise and accurate identification system it was only possible thanks to efficient and useful cooperation between taxonomists and image analysis specialists. The expert, practical experience in such different fields allowed the development of a system so complex in its structure and so simple in the use. Indeed, having a broad database of morpho-colorimetric seed features for an adequate amount of families, genera and species would enable the identification of taxa already present in the database. In this way, this innovative tool would open new perspectives in plant taxonomy, but also offer the opportunity for germplasm banks to make identifications in a standard, speedy way.

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In addition, the availability of morpho-colorimetric data should be helpful for ecological and/or archeobotanical studies such as the prediction of seed persistence in the soil.

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In: Wild Plants: Identification, Uses and Conservation ISBN 978-1-61209-966-8 Editor: Ryan E. Davis, pp.149-177 © 2011 Nova Science Publishers, Inc.

Chapter 4

LANDSCAPE GENETICS OF FAGUS SYLVATICA IN ONE OF ITS GLACIAL REFUGE AREAS Giovanni Figliuolo Dept. Biologia – Università degli Studi della Basilicata Via Ateneo Lucano, Potenza - Italy

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ABSTRACT Fagus sylvatica is a keystone species shaping the most important natural and quasinatural ecosystems of the mountains in the Mediterranean area. This work has the aims to evaluate the genetic structure of beech in the southernmost distribution areas and map relevant sub-populations for conservation genetics. The methods used are based on landscape genetics. Landscape genetic maps were generated from 7 microsatellite loci using 37 sub-populations sampled in southern Italy and two control subpopulations from Norway and Sicily. Ecological sources of variation were also recorded and evaluated. Genetic disequilibrium increased from the sub-population to the whole population. The significant differentiation among sub-population for nuclear markers was consistent with the outcrossing breeding system. Two main clusters spatially distributed according to a contact zone migration model were inferred. Chloroplast haplotype richness decreased when moving northward and was independent of the sub-population sample size. Nuclear allelic richness was evenly distributed and correlated with both gene diversity (He) and sub-population size. Sub-populations bearing both a low heterozygote deficit and high chloroplast haplotype richness were mapped in multiple sites, were spatially marginal, and interpreted as being associated with -glacial niches. Southern Italy is not a homogeneous area in terms of beech genetic diversity. The assessed spatial genetic pattern can better direct both the beech-wood management design and the strategies of genetic conservation.

Keywords: Fagus sylvatica, conservation genetics, microsatellite, haplotype.

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INTRODUCTION Fagus sylvatica L. is the keystone tree species in the southernmost European distribution area, shaping the ecosystems of important protected mountain areas comprising both Natura 2000 sites and National Parks. The conservation value of beech in the Mediterranean region is further increased by the species‘ role in providing several important ecosystem services (Millennium Ecosystem Assessment 2005). Several papers have described (Comps et al. 2001; Vettori et al. 2004; Buiteveld et al. 2007) and reviewed (Magri et al. 2006) the phylogenetics and paleogeography of F. sylvatica. Nonetheless, the site-specific pattern of genetic variation needs to be accurately described with the genetic data along with the location of the late glacial hot spots of genetic diversity. The landscape genetics of F. sylvatica, described through allelic variations at marker loci, could be useful for identifying suitable sub-populations for conservation and seed sampling (Petit et al. 1998). The distribution areas of F. sylvatica range from Sicily (south) to the south of England and Norway (north) and from Spain (west) to Poland and Ukraine (east). Beech-wood stands perform as a late-succession stage of the temperate Mediterranean mountains, extending, on average, from 800 to 1500 m a.s.l. (warm Fagetum Pavari‘s sub-zone) and from 1500 to 1800 m a.s.l. (cold sub zone) (Pavari, 1916; Pignatti, 1998). The altimetry location of the species can show outliers depending either on latitude - the upper limit increases by 110 m per 1 latitude degree decrease – or on the occurrence of site-specific humid ecological niches (Pignatti, 1998). Beech forests during the most recent glaciations (70-15,000 yr. BP) survived in several different places (South France, Pyrenees, Istria, Slovenia, Balkans and Apennines) (Magri et al. 2006; Magri, 2008). The target area in this study is the Fagetum landscape of southern Italy nested in Zone 1 - corresponding to the range of beech inferred from the fossil pollen of the last ice age - as mapped by Comps et al. (2001). In this area, data from Monticchio volcanic lake sediments (Vulture, southern Italy) show 20-30% of beech pollen records from the 100,000-80,000 yr. BP period, and lower but significant values (< 10-20%) from the earliest late glacial (10,000 yr. BP) to the present (Watts et al. 1996; Allen et al. 2000). These data indicate that the present distribution of F. sylvatica in this sub-region must be the outcome of the re-colonization that started after the last ice age. The modern distribution of F. sylvatica over the studied region is about 8.4% of the total forest land-cover with a distribution of 2.1% between 400-800 m of altitude, 32.3% between 800-1000 m, 55.1% between 1200-1600 m, 10.4% between 1600-2000 m, and just 0.1% above 2000 m (Costantini et al. 2006). The island model of gene flow could be helpful to make inferences about the spatial variation of species given that F. sylvatica distribution is fragmented throughout the Fagetum phytoclimatic belt of the studied target zone. This model requires that each sub-population is equally accessible by genes from all the remaining sub-populations and that equilibrium between losses of variation due to drift within islands, and variation gains by migration are achieved (Wright 1951; Slatkin 1987). However in such a fragmented landscape, the geneflow that occurred since the earliest late glacial could have been limited by geological gaps (especially valleys and canyons) between the mountainous beech-wood corridors, whereas human activities (site specific management and land take) could have played a significant role in terms of fragmentation increase.

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F. sylvatica is a barochorous wind-pollinated diploid (2n = 2X = 24) monoecious tree species (Ahuja 1991) with a low (0% – 10%) self-pollinating rate (Merzeau et al. 1994) and a high nuclear genetic variation within sub-populations. Nuclear microsatellite markers within a 30 m range reveal a strong family genetic structure (Vornam 2004) while AFLP markers, due to their efficient genome sampling, extend the family genetic structure within a range of 110 m (Jump and Peñuelas 2007). Chloroplast microsatellite loci (maternally inherited), over a wide latitudinal range, show a high differentiation (Gst = 0.78) (Magri et al. 2006) and a decreasing trend in haplotype richness from south to north, supporting the paradigm ‗southern richness versus northern purity‘ (Hewitt 2000). The geographical distribution of haplotypes depicts a central-European allele common elsewhere, while several differentiated haplotypes are specific to the Balkans, Southern France, Italy and Spain (Magri et al. 2006). In addition, in Italy, the estimated genetic differentiation among sub-populations for chloroplast markers is high (Gst = 0.855) with an increased haplotype diversity in southern Italy (Vettori et al. 2004). Based on the Intergovernmental Panel on Climate Change (IPCC) forecast for the next 50-100 years, the species could risk geographic isolation and local extinction at lower latitudes (IPCC 2002). Given the economic and ecological functions (springs of drinking water, CO2 and H2O capture, soil formation and protection, local climate mitigation, biomass production, wild focal species conservation and landscape wilderness promotion), a better understanding of the global and local driving forces and pressures on such keystone species could be achieved by adopting a landscape genetics approach (Manel et al. 2003; Figliuolo and Celano 2008). Landscape genetics provides detailed eco-geographical sources of variation (hidden barriers to gene flow and ecological factors), taking into account populations, sub-populations and the genetic and ecological information associated with each individual (Manel et al. 2003). Using this analytical method, individual trees are tracked and mapped using a geographical information system. F. sylvatica, based on its reproduction system, spatial distribution, availability of independent datasets, could be a useful long-living tree species to clarify patterns of postglacial colonization (Taberlet 1998; Widmer and Lexer 2001). The aim of this study is evaluate the genetic structure of beech in the southernmost distribution areas and map relevant sub-populations for conservation genetics. From the spatial patterns of genetic variation it will be possible to identify marginal genetically relevant sub-populations, seed sources with high heterozygosis and hot spots of genetic diversity. In addition, it is proposed the use of landscape genetics to improve the in situ conservation of beech-wood.

MATERIAL AND METHOD Sampling Design Beech-woods are located on the entire mountain range of the Lucanian Apennine (Basilicata), Pollino National Park (Basilicata and northern Calabria), Cilento National Park (Campania) and Foresta Umbra (Gargano National Park, Apulia) (Figure 2). On the above mountain ranges, the sampling transects targeted 12 Natura 2000 sites. Controls were sampled in Norway (Stavanger, the northernmost site) and Sicily (Etna volcano, the southernmost

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site). Overall, 37 sub-populations were analyzed. The whole population was made up of 890 trees and each sub-population, on average, had a sample size of n = 24. In this study the concept of sub-population is related to both ecologically isolated units and woods managed by different administrations. Genotype sampling (years: 2005-2007) was systematic and stratified by generation. In order to avoid the effect of family sub-structure, the distance between each sampled plant within transect was approximately 150 - 200 m. The subpopulations were also grouped on the basis of ecological barriers to gene flow (e.g. valleys) in four sub-regions (South, Centre, West and North of the Apennine range) overlapping the regional boundaries of the Basilicata, Campania and Calabria (Figure 3). Individual trees - in field geo-referenced (coordinate system: WAGS-84; N-33) and described with their associated ecological variables (penology, exposition, altitude, forest composition and structure) - represented the base level of the sampling hierarchy. Two age strata were sampled: The old growth generation (high trunks or at least 80-150 years old coppiced plants) and, if present, the next sexually reproduced generation (plants on average aged between 3-10 years). Leaves or buds for DNA isolation were harvested from each individual tree. The genetic database has been independently analysed by students in order to elaborate graduation thesis (Biancone, 2007; Cioffi 2007 and Marcantonio 2007).

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Molecular Analysis Total genomic DNA was purified from buds or leaves using Trans-Prep chemistry and ABI PRISM 6100 Nucleic-Acid prep Station (Applied Biosystems). An average concentration of 200 ng/ul of total genomic DNA was obtained. DNA concentration and quality were assessed both by gel electrophoresis, spectrophotometry and PCR reaction across multiple loci. Out of five selected microsatellite loci, one (MFC5) was isolated by Tanaka et al. (1999) and four (FS1-03, FS1-15, FS3-04 and FS4-46) by Pastorelli et al. (2003). Two chloroplast microsatellite loci (Cmcs3 and Cmcs12) were isolated by Sebastiani et al. (2004). Three out of five nuclear microsatellites, (FS1-03, FS3-04 and FS4-46) were mapped markers located on LG1-F, LG3-M and LG11-M chromosomes respectively (Scalfi et al. 2004). DNA from null genotypes (showed only by locus FS4-46) was amplified twice using one of the four loci showing a positive reaction as controls. The forward microsatellite primer was labelled in 5‘ position with different dyes (6-Fam, Vic and Ned). At 5‘ end of the reverse primer a tail bearing the GTGTCTT sequence was added in order to reduce the plus-A effect. PCR reactions for nuclear loci were carried out in a final volume of 25 ul, containing 20-30 ng of the target DNA, buffer 1X (200 mM Tris-HCl, pH 8.4, 500 mM KCl), 2 mM of MgCl2, 0.2 mM of dNTPs (Invitrogen), 0.4 uM of each primer (Applied Biosystem), 1 U of Taq DNA polymerase (Invitrogen). Termocycler was programmed as follows: 5 minutes of DNA denaturation (95°C), 30 cycles at 1 minute of DNA denaturation (95°C), 1 minute of annealing (60°C), 1 minute of extension (72°C) with a final extension of 8 minutes (72°C). DNA at the two chloroplast loci (Cmcs3 and Cmcs12) was amplified with small modifications (0.2 uM of each primer, 35 cycles, annealing temperatures at 52°C and a final extension for 5 minutes at 72°C) of the basic protocol (Sebastiani et al. 2004). Capillary electrophoresis carried out using the 3130 sequencer (Applied Biosystems) adopting the standard running conditions: 1-1.5 ul of PCR products were diluted in 10 ul of formamide; DNA denaturation occurred at 95°C for 3-5 minutes, cooled on ice for 5-10

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minutes and then loaded on microplates before processing was begun. Each peak was converted to molecular weight using Gene-Mapper 3.7 software (Applied Biosystems) by applying the microsatellite default full range and local Southern options.

Statistical Analysis

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Analysis of the genetic data set was achieved either adopting a Bayesian method by assigning the inferred ancestry probabilistically to each individual (Pritchard et al. 2000) and with a top-down approach with a-priori fixed sources of variation (Weir 1996).

Bottom-Up Analysis Based on the observed genetic data, a Bayesian analysis of nuclear genetic data was adopted to assign each individual on the basis of their inferred ancestry probabilistically to one or more clusters (Pritchard et al. 2000). The software Structure 2.2 available from http://www.stats.ox.ac.uk/~pritch/home.html implements a parametric model-based clustering method to use multi-locus genotype data to infer population structures and assign individuals to sub-populations using both maximum-likelihood and Bayesian computations (Pritchard et al. 2000). The model assumes there are K sub-populations or clusters (where K may be unknown), each of which is characterised by a set of allele frequencies at each locus, and individuals are probabilistically assigned to two or more populations if their genotypes indicate that they are admixed. The model assumes no linkage between markers, no HardyWeinberg equilibrium and no mutation processes (for details see Pritchard et al. 2000). Data analysis was set as advised in the Structure 2.2 user manual (Pritchard and Wen 2003). The admixture model and the option of correlated allele frequencies between populations was chosen, as this is considered the best configuration by Falush et al. (2003) in cases of subtle population structures. In addition, the degree of admixture alpha was inferred from the data and lambda was set to one. The length of burn-in was 30,000 over 20 different runs. True numbers of sub-populations (K) are often identified using the maximal value of the log probability of data (L (K)) returned by Structure (Ciofi et al. 2002; Vernesi et al. 2003; Hampton et al. 2004). However following Evanno et al‘s methodology (2005), we observed that once the real K was reached, starting from a given K, L (K) increased slightly and the variance between runs increased (Figure 2). Thus to be confident that the indicated clusters were the real ones, we computed the second order rate of change (ΔK) based on the rate of change in L(K) between successive K values, as proposed by Evanno et al. (2005). In addition, the true number of groups was confirmed by the symmetric pattern of the aposteriori classification of the whole set of sub-populations (Pritchard and Wen 2003). Top Down Analysis Analysis of Variance and Genetic Parameter Estimation Genetic Data Analysis (GDA) software was used for top-down analyses (Lewis and Zaykin 2001). The analysis of variance was carried out with either three or two-level hierarchy using the degrees of freedom available from the whole population (890 individuals,

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37 sub-populations and 4 geographic sub-regions partitioned among two genetic clusters) or the restricted dataset (whole population minus sub-populations from Etna, Foresta Umbra and Norway) (Weir and Cokerham 1984; Weir 1996; Lewis and Zaykin 2001). The dependent variable was represented by the allelic frequency at five different diploid microsatellite loci. The following F-statistics were estimated: θ (or Fst), F (or FIT) and f (or FIS). The expected value of θ corresponds to Wright‘s Fst statistics (Wright, 1951) and measures relationships between alleles in the same population, relative to zero relationships between alleles of different populations (Weir 1996). The θ significance was assessed by bootstrapping over loci (Lewis and Zaykin 2001). The effect of ecological factors (slope exposition, stand composition, altitude layer, lithologic substrate) and generation of the trees (maternal vs. filial) on genetic differentiation was also tested using GDA as well as the following genetic parameters: the expected heterozygosity (He), the observed heterozygosity (Ho), the fixation index (Fis), the mean number of alleles (A), and the genetic differentiation between sub-populations (θp). Confidence intervals for θ (= Fst) were calculated using the bootstrap method implemented in GDA and linkage disequilibrium was measured using a chi-square estimate based on the exact test (Lewis and Zaykin 2001). The population parameters He, Ho and θ are unbiased for sample size. The observed heterozygosity (Ho) per individual was analysed as dependent variable with one-way analysis of variance (Proc GLM in SAS-stat software) using a protected Fisher‘s LSD.05 for mean separation. Genetic (nuclear Bayesian genetic cluster, haplotype group, generation type) and ecologic factors (pedologic substrate, sub-region, sub-population, slope exposition and beech dominance) were considered as independent sources of variation (SAS 1993).

Correlation and Correspondence Analysis Allelic richness per sub-population was also standardised using the rarefaction method (Hurlbert 1971; Petit et al. 1998) with the goal to avoid the effect of sample size on allele richness. Correlation analyses (Proc corr) and contingency tables of the allelic frequencies by sub-population were performed using SAS-Stat software. Finally, correspondence analysis (Proc corresp) was used to find associations between ecologic factors and genetic groups (SAS 1993).

Gene-Flow Estimation The gene flow estimation for diploid data, based on the hypothesis that the allelic effect is neutral, was indirectly computed as: Nm=(1-Fst)/4Fst; where Nm is the migration rate based on the island model and Fst the estimated differentiation index among sub-populations (Slatkin 1987). The nuclear gene-flow was computed mainly with the aim of obtaining a qualitative picture of the most likely principal components of pollen migration paths given that the biological assumptions underlying the model are unrealistic for diploid data collected within a narrow geographical range (Whitlock and Mccauley 1998).

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Landscape Maps The direct representation of genetic variation and gene-flow was achieved using geographic maps generated by Arc-Gis software (Esri 2004). Separately were plotted haplotype frequencies by sub-population, individual haplotype and the proportion of nuclear inferred ancestry per individual (q1 for cluster 1) (Figures 1, 3, 4 and 5).

RESULT Polymorphism

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At each of the five microsatellite loci, diploid genotypes were obtained from an average of 887 plants distributed over 37 woods (a-priori sub-populations), resulting in the scoring of a total of 86 allelic products and a number of alleles by locus ranging from 5 to 30 (Table 1). The alleles were widely distributed in all sub-populations with the exception of 5 out of 86 alleles that were private alleles found at a very low frequency (p < 0.026) in five different sub-populations. Allelic richness was evenly distributed across the landscape. The average expected heterozygosity by locus (He = 0.816) and the observed heterozygosity (Ho = 0.572) were consistent with the heterozygote deficit (Fis = 0.298) and thus with the Hardy-Weinberg equilibrium expectations (Table 1). By excluding FS4-46 locus, bearing null genotypes at a frequency of 19%, the mean value of the fixation index decreased from 0.298 to 0.236. The standardised number of alleles per nuclear loci increased by increasing the sub-population size (r = 0.88; P < 0.0001) (Table 2). In addition, the expected heterozygosity (He) correlated highly with the standardised average number of alleles per sub-population (r = 0.82; P < 0.0001). Table 1. Unbiased estimates of the genetic parameters for 5 microsatellite loci and 37 Fagus sylvatica sub-populations sampled in southern Italy

FS4-46 FS1-03 FS1-15 FS3-04 MFC5 Average Subpopulations Sub-regions

Sample size (n) 889 887 889 886 883 887 24

Allele number (A) 13 21 17 5 30 17 8

Expected heterozygosity (He) 0.847 0.819 0.895 0.588 0.930 0.816 0.774

Observed heterozygosity (Ho) 0.395 0.562 0.725 0.558 0.620 0.572 0.577

Fixation index (Fis) 0.533 0.313 0.189 0.050 0.332 0.298 0.269

Genetic differentiation (Fst) 0.087 0.024 0.025 0.006 0.055 0.039

127

11

0.771

0.538

0.304

0.014

Table 2. Significant correlations between different genetic parameters Type of correlation Std number of chloroplast alleles vs. Latitude Std number of nuclear alleles vs. Sample size Std number of nuclear alleles vs. He % q1 individual genome vs. Haplotype

r - 0.56 + 0.89 + 0.82 + 0.27

P < 0.0004 < 0.0001 < 0.0001 < 0.0001

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Genetic Disequilibrium

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An increasing Hardy-Weinberg equilibrium trend was demonstrated, from the whole population (no loci in equilibrium) to the four sub-populations defined on the basis of the ecological barriers (1 locus in equilibrium/sub-population), and finally for each of the 36 subpopulations separately tested (from 1 to 5 loci in equilibrium/sub-population).

Figure 1. Geographic distribution of Fagus sylvatica chloroplast haplotypes (pie charts). In the main picture the chart diameter is proportional to the sample size (e.g. minimum size, n = 8 individuals, for Vetrice and Crocetta vs. maximum size, n = 82 for Serra Calvello S-W). The heavy green pattern indicates the broad-leaf wood dominated by F. sylvatica within the lighter green pattern of the forestal land cover. The white pattern crossed by rivers localizes the main valleys reminiscent of late quaternary lakes. On the top right the distribution of H2 from Sicily to Norway is depicted. Arrows indicate the gene-flow quantified in terms of average number of individuals migrating per generation (Nm) based on nuclear markers (pollen flow). Dotted circles indicate sub-populations close to the Hardy-Weinberg equilibrium.

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All pair-wise comparisons among loci showed a significant departure from the HardyWeinberg expectation (P < 0.05) with respect to the whole population. Only locus FS3-04 was found in equilibrium in the North, West and Center sub-regions, whereas linkage equilibrium was detected for FS446/FS115 and FS115/MFC5 in the Northern, FS103/FS115 and FS103/FS304 in the Southern, FS103/FS304, FS115/MFC5 and FS304/MFC5 in the Western sub-regions, respectively. The mountainous range of the Central sub-region, due to its significant linkage disequilibrium among loci, is probably one of the last to be colonised after the last glacial age. With regard to the sub-population, Vulture volcano held the maximum equilibrium value (5 loci and 10 pair-wise locus-comparisons) whereas M. Madonna Viggiano exhibited the minimum (1 locus and 2 pair-wise locus-comparisons). Overall the number of loci in HardyWeinberg equilibrium together with the increasing number of locus combinations in equilibrium per sub-population was consistent with the heterozygote shortage and the low fixation indices reported in Table 3 and Figure 1.

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Genetic Differentiation The Bayesian bottom-up analysis conducted with Structure (software application) also highlighted the presence of two different genotype clusters (Figure 2). After excluding the sub-populations (out-groups) from Etna, Foresta Umbra and Norway, genetic differentiation among sub-regions was lower (Fst = 0.014) than the differentiation among 34 subpopulations (Fst = 0.037) (Tables 4). These estimates indicate that, as expected for an open pollinated tree species, the within sub-population nuclear genetic variance was significantly higher than the between sub-population variance (Table 4). The model of analysis of variance which includes the genetic clusters (inferred with Structure software) as source of variation, improves the parameter estimates by increasing the variance values explained by subpopulation genetic differentiation (Table 4).

Figure 2. Values of the likelihood function [L(K)] and rate of change indicated by ΔK with respect to the number of clusters (K). The detection of the true number of groups (K =2) corresponds to the maximum value of ΔK.

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Table 3. Geographic position, sub-population sample sizes, nuclear genetic parameters along with chloroplast haplotype distribution by sub-population

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5 Nuclear microsatellite loci

Sub-population

Long.

Lat.

Sample size (n)

North Basilicata Vulture, Monticchio lakes (Atella, Rionero in Vulture, PZ) Paratiello (Muro Lucano, PZ) Tre Confini (Bella, PZ) Santa Croce (San Fele, PZ) San Cataldo (Bella, PZ) Carmine and Caruso (Avigliano, PZ) Vetrice (Balvano, PZ) Li Foy (Picerno, Ruoti, PZ) Center Basilicata Cerchiara (Tito, PZ) Crocetta (Abriola, PZ) Sasso Costara (Sasso di C., PZ) Pierfaone (Abriola, PZ) Pascoleto (Marsico Nuovo, PZ) Serra Calvello N (Calvello, PZ) Raimondo (Calvello, PZ) Serra Calvello-S-W Calvelluzzo (Marsico N., Calvello, PZ) Campo Imperatore (Marsico Vetere, PZ) Saraceno (Calvello, PZ) M. Viggiano (Marsico V., Viggiano, PZ) Pilato (Viggiano, PZ)

553369

4532915

166 16

Expected Heterozyg osity (He) 0.802 0.727

535133 545923 549758 555945 562752

4509306 4515874 4514923 4510398 4512759

47 10 15 10 17

0.806 0.793 0.793 0.800 0.784

0.634 0.580 0.600 0.660 0.588

0.214 0.268 0.243 0.175 0.250

10.4 6.8 7.8 7.2 8

546077 558900

4499414 4501436

559622 567178 560365 563486 564949 565070 565252 566846

4487408 4488049 4482100 4483734 4480868 4480458 4480075 4477092

8 44 390 10 8 12 48 17 30 18 68

0.770 0.806 0.802 0.783 0.765 0.756 0.788 0.774 0.807 0.803 0.786

0.750 0.591 0.581 0.620 0.525 0.617 0.567 0.576 0.527 0.700 0.588

0.026 0.267 0.275 0.208 0.314 0.184 0.280 0.255 0.348 0.128 0.252

569767

4473920

26

0.776

0.600

571303 573420

4473879 4470838

10 41

0.756 0.821

573837

4471590

29

0.802

Standardized alleles/locus A[6]

Observed Heterozygosity (Ho)

Fixation index (Fis)

Alleles/loc us (A)

0.581 0.675

0.275 0.074

14.8 7.8

3.01

2 Chloroplast microsatellite loci Standardized Haplotype alleles/locus [type(number)] A[6] 1.02

3.40 3.28 3.36 3.38 3.31

H1(12);H2(3); H4(1) H1(23); H2(24) H1(1);H2(9) H1(14) H2(1) H2(10) H2(17)

6 10.8 15.2 6.2 6.2 7.4 11 7.6 10 9.6 12.2

3.06 3.33 3.38 3.20 3.23 3.21 3.22 3.20 3.11 3.26

H2(7); H4(1) H2(44) H1(10) H1(8) H1(12) H1(48) H1(17) H1(30) H1(18) H1(68)

0.62 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.227

9.8

3.23

H1(26)

0.00

0.620 0.556

0.180 0.322

6.2 11.2

3.22 3.34

H1(10) H1(24);H2(17)

0.00 0.93

0.538

0.329

9.4

3.25

H1(3);H2(25);

0.62

0.95 0.50 0.33 0.00 0.00

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Standardized alleles/locus A[6]

2 Chloroplast microsatellite loci Standardized Haplotype alleles/locus [type(number)] A[6]

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5 Nuclear microsatellite loci Expected Heterozyg osity (He)

Observed Heterozygosity (Ho)

Fixation index (Fis)

Alleles/loc us (A)

Sub-population

Long.

Lat.

Sample size (n)

Caldarosa (Viggiano, PZ) Pietra Iaccata (Laurenzana, Corleto, PZ) Faggeta di Moliterno (Moliterno, PZ) South Basilicata Raparo (Spinoso, PZ) Gurmara (Lagonegro, PZ) Sirino (Lagonegro, PZ) Alpi (Castelsaraceno, PZ) La Spina (Lauria, PZ) Pollino E (Terranova di P., PZ) Pollino W (Rotonda, PZ, Morano, CZ) West (Cilento) Maddalena (Brienza, PZ Padula, SA) Alburni (Petina, SA)

577636 580675

4473860 4472640

32 15

0.813 0.759

0.588 0.643

0.277 0.145

10.8 7.8

3.37 2.95

H3(1) H2(32) H1(5); H6 (10)

0.00 0.92

568452

4456745

27

0.757

0.556

0.266

8.8

3.03

H2(2); H4(25)

0.34

582120 566724 570948 584419 579229 604020 594712

4451433 4443966 4443843 4442888 4432173 4424951 4419152

151 18 19 11 17 23 30 33

0.804 0.767 0.762 0.697 0.744 0.794 0.788 0.775

0.551 0.567 0.589 0.509 0.462 0.470 0.673 0.527

0.315 0.261 0.227 0.270 0.379 0.408 0.146 0.320

14.2 8.2 8.4 4.8 6.2 8 9.8 9.4

3.11 3.04 2.59 2.93 3.16 3.33 3.20

552640

4475775

135 24

0.794 0.791

0.539 0.483

0.321 0.389

13.2 8.4

3.21

531985

4484740

47

0.782

0.536

0.314

11.4

3.20

Cervati (Sanza, SA) Gelbison (Novi Velia, SA)

539926 528682

4460158 4451663

46 18

0.782 0.801

0.574 0.533

0.266 0.334

11.4 8.8

3.18 3.38

Etna (Sicily) Foresta Umbra (Apulia) Stavanger (Norway) Population mean

491448 582423 615384 -

4184518 4630674 4515303 -

11 21 12 887

0.747 0.795 0.631 0.816

0.675 0.448 0.350 0.573

0.096 0.437 0.445 0.298

5.4 8.4 4.4 17.2

2.94 3.17 2.18 -

H2(18) H2(17); H3(2) H2(11) H1(5); H2(13) H1(2); H2(21) H3(30) H2(10);H3(13); H4(10) H1(24) H1(10); H2(37) H1(5); H2(1); H3(7); H5(33) H2(15);H4(3) H2(1); H3(2); H6(4); H7(2); H8(2) H2(21) H2 (12) -

0.00 0.47 0.00 0.85 0.39 0.00 1.65 0.00 0.72 1.14 0.65

2.59 0.00 0.00 -

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160

Giovanni Figliuolo

Table 4. Three-level hierarchy analysis of variance using as inputs: 889 individuals (diploid status at 5 different microsatellite loci), 34 sub-populations, 4 sub-regions (a) and 2 genetic clusters (b). Out-groups are the sub-populations geographically distant (Foresta Umbra, Stavanger, Etna) from the target area (N, C, W and S sub-regions). Each reported degree of relatedness (f, F, θs and θp) is statistically significant (P < 0.05) (a) ANOVA using sub-regions as nesting source of variation f F θs = Fst (within (between individuals (between subindividuals) within subpopulations within populations) sub-regions) 0.27 0.30 0.039 0.26 0.29 0.037

θp = Fst (between subregions)

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Whole dataset 0.014 Whole dataset minus 0.014 out-groups (b) ANOVA using genetic clusters detected by Structure software as nesting source of variation f F θs = Fst θp = Fst (within (between individuals (between sub(between clusters) individuals) within subpopulations within populations) genetic clusters) Whole dataset 0.27 0.31 0.048 0.026 Whole dataset minus 0.28 0.30 0.046 0.027 out-groups

The average fixation index per sub-population (excluding the out-groups) was Fis = 0.261; the maximum values of the fixation index were found in the Umbra Forest (Fis = 0.437) and in Stavanger (Norway) (Fis = 0.445) (Table 3). Sub-populations consistently closer to the genetic equilibrium, according to the assessed level of Hardy Weinberg equilibrium, were Monticchio-Vulture (Fis = 0.074), Mount Vetrice (Fis = 0.026), Pietra Iaccata in Corleto (Fis = 0.042) and Etna (Fis = 0.096) (Table 3). All the other subpopulations showed Fis values close to the average (Table 3). The observed heterozygosity of the northernmost isolated populations - Foresta Umbra (south Italy) and Stavanger (Norway) were consistently low (Ho = 0.45 and Ho = 0.35 respectively) along with a correlated high inbreeding (Table 3).

Chloroplast Genetic Diversity Microsatellite sequences at loci Cmcs3 and Cmcs12, already characterised by Sebastiani et al. (2004) in Castanea sativa as uni-nucleotide repeats (GenBank Acc. No. AY497342 and AY497349), highlighted three different alleles per locus in our population. The two loci produced fragment sizes of 181-183 bp and 246-248 bp respectively. Overall, eight different haplotypes were generated (Figure 1). Differentiation among sub-populations performed higher (Fst = 0.709; upper CI0.05 = 0.735, lower CI0.05 = 0.647) than the differentiation among sub-regions (Fst = 0.338). The chloroplast haplotype number decreased when moving northward (r = -0.54; P < 0.0005) and did not correlate with sample size. A significant relationship between chloroplast haplotype distribution and nuclear genetic diversity was confirmed by the significant correlation (r = 0.28; P < 0.0001) between chloroplast haplotype and the quantitative value represented by q1 (percentage of nuclear inferred ancestry) estimated with the Bayesian analysis (Table 2).

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161

Landscape Genetics of Fagus Sylvatica in One of its Glacial Refuge Areas

Parental vs. Filial Generation Twenty-one out of 37 sub-populations were made up of the old maternal generation together with the last filial generation (Table 5). Linkage disequilibrium analysis showed that the filial generation had two locus-combinations in equilibrium (4 combinations out of 10) more than the parental one (2 out of 10) along with a small decrease in the fixation index (from Fis = 0.30 to Fis = 0.27). In eight sub-populations no genetic differentiation between generations was observed (Fst = 0). Table 5. Geographical sub-region, sub-population, sample size, nuclear genetic parameters along with Wright’s differentiation index per group composed of maternal (M) and filial (F) generations Sub-region

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NORTH

CENTER

SOUTH

WEST

CONTROL

Sub-population (M = maternal; F = filial) Paratiello M Paratiello F Li Foy M Li Foy F Crocetta M Crocetta F Pierfaone M Pierfaone F Pascoleto M Pascoleto F Serra Calvello (N) M Serra Calvello (N) F Raimondo M Raimondo F Serra Calvello (SW) M Serra Calvello (SW) F Campo Imperatore M Campo Imperatore F Saraceno M Saraceno F Madonna Viggiano M Madonna Viggiano F Pilato M Pilato F Caldarosa M Caldarosa F Faggeta Moliterno M Faggeta Moliterno F La Spina M La Spina F Pollino M Pollino F Maddalena M Maddalena F Alburni M Alburni F Cervati M Cervati F Gelbison M Gelbison F Foresta Umbra M Foresta Umbra F

Sample size (n) 31 16 30 14 4 4 24 24 9 8 15 15 9 9 40 28 13 13 5 5 24 17 18 11 16 16 15 12 17 6 19 14 18 6 40 7 40 6 11 7 15 6

Allele number (A) 9.8 8.4 9.4 8.4 4.2 4.4 9.8 9.2 6.8 5.2 7.8 9 7.2 6.6 11 10.4 7.6 7.4 4.6 4.6 9.6 8.4 8.4 6.8 9.2 8 7.4 6.4 6.4 6 8.2 7.2 7.6 4.4 11.2 5.2 11 4.8 7 6 7.8 4.2

Observed heterozygosity (Ho) 0.613 0.675 0.597 0.586 0.500 0.550 0.536 0.608 0.644 0.500 0.485 0.573 0.688 0.711 0.625 0.536 0.585 0.615 0.680 0.560 0.533 0.588 0.555 0.509 0.625 0.550 0.533 0.583 0.400 0.667 0.463 0.614 0.478 0.500 0.550 0.457 0.590 0.467 0.509 0.571 0.467 0.400

Fixation index (Fis) 0.239 0.164 0.264 0.280 0.355 0.320 0.321 0.208 0.218 0.293 0.398 0.296 0.156 0.089 0.214 0.309 0.239 0.197 0.117 0.268 0.356 0.282 0.293 0.335 0.231 0.309 0.297 0.080 0.490 0.206 0.405 0.221 0.409 0.345 0.299 0.385 0.257 0.320 0.376 0.296 0.431 0.439

Differentia tion (θp = Fst) 0.004

Lower c.i. 0.05

Upper c.i. 0.05

0

0.029

0

-

-

0

-

-

0.015

0

0.039

0.026

0.006

0.046

0

-

-

0.018

0

0.067

0.003

0

0.018

0.029

0

0.066

0

-

-

0

-

-

0.07

0

0.218

0.027

0

0.081

0.136

0

0.373

0.0003

0

0.025

0

-

-

0

-

-

0.029

0

0.083

0.021

0.007

0.036

0

-

-

0.034

0

0.075

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162

Giovanni Figliuolo

A small reduction in the observed heterozygosity, as indicated by the significant Fst, was recorded in five sub-populations, while in eight remaining woods the filial generation had a considerable heterozygosity increase (Table 5). Among these sub-populations, the filial generation differentiation was the maximum (Fst = 0.136) in Moliterno beech-wood with a lower heterozygote deficit (Fis = 0.08) than the mother plants (Fis = 0.297).

Effect of Ecology By comparing different types of ecological sources of variation, the overall differentiation index (Fst) between the different levels of each ecological source of variation was low (from 0.003 to 0.0009) compared to the differentiation among eco-geographic subpopulations (Fst = 0.039) (Table 6). Although significant, the level of differentiation based on ecological factors was on average lower than the differentiation due to sub-population spatial structure by a factor ranging from 10 to 100. Nonetheless the altimetry layers and the geological substrate were associated with higher differentiation indices (Table 6).

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Table 6. Wright’s differentiation index along with 95% probability of confidence intervals estimated considering different levels for each ecological factor as sources of variation Source of variation

Sub-group

SLOPE EXPOSITION

NE-E NW-N SW-W

Sample size (n) 249 134 334

SE-S

149

STAND STRUCTURE

Beech dominant Mixed wood

617 272

ALTIMETRY LAYER

above 1500 m between 1500 m –1001 m below 1000 m

140 603

Sandstone

37

Calcareous Calcareous-Sandstone Calcareous- Siliceous

509 16 85

Pudding-stone Siliceous Volcanic

22 181 27

LITHOLOGIC SUBSTRATE

88

θp = Fst 0.000371 Lower c.i. 0.05 = -0.00057 Upper c.i. 0.05 = 0.00125 0.000971 Lower c.i 0.05 = -0.00046 Upper c.i 0.05 = 0.002019 0.003376 Lower c.i. 0.05 = 0.000557 Upper c.i. 0.05 = 0.007

0.006621 Lower c.i. 0.05 = 0.00328 Upper c.i. 0.05 = 0.00964

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Landscape Genetics of Fagus Sylvatica in One of its Glacial Refuge Areas

163

Table 7. F test and protected Fisher’s LSD.05 as output of one-way analysis of variance with dependent variable the individual observed heterozygosity (Ho) and several sources of variation SOURCE OF VARIATION Genetic cluster Pedologenetic substrate Slope exposition Dominance Generation Haplotype group Sub-regions Sub-population

Pr > F 0.235 0.254 0.020 * 0.284 0.810 0.597 0.020 * 0.009 **

LSD.05 0.028 0.102 0.050 (W>E>N>S) 0.029 0.031 0.102 0.044 (N>C>S>W) 0.147

Genetic differentiation between sub-populations is also demonstrated with one-way analysis of variance (Table 7). The significant effect (P < 0.01) of sub-popopulation on the average values of Ho is consistent with the analysis of the population genetic structure (Table 4). Differentiation is higher at the level of sub-population (P < 0.01) than at sub-region level (P < 0.05). Also, the analysis of variance showed a significant effect of slope exposition on the Ho values: genotypes sampled on the west-exposed slopes showed a significantly higher heterozygosity (Table 7).

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Landscape Genetics Map The geographical map of the nuclear genetic structure is depicted by the individuals (stars) coloured with a specific intensity-gradient correlated with the proportion of the inferred nuclear ancestry computed using Structure (Figure 3). Tree distribution across the landscape is in accordance with the presence of two different clusters (K = 2) (Figures 2 and 3, Table 6), which correlate with the geography and with the chloroplast haplotype distribution. The first cluster of genotypes targeted the mountain corridors on the western and northern side of the region whereas the second cluster was mainly in the central zone (Figures 3 and 4). The average gene-flow between sub-populations, indirectly estimated using the Fst values, was Nm = 15 migrating individuals per generation. A decreasing trend of gene flow was computed south-north, with the maximum value between Mount Etna and southern Basilicata (Nm = 25) and the minimum value (Nm = 8) between the northern sub-region and the Foresta Umbra within the target zone (Figure 3). The western path of pollen flow was between mount Gelbison and Cervati toward Alburni massif (southern Campania) to northern Basilicata (Paratiello, Vetrice, S. Croce, S. Cataldo and Vulture) (Figure 1). The central path was from Pollino massif to La Spina, Alpi, Raparo, Volturino-Crocetta and Li Foy mountains (Figure 1).

Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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Table 8. Differentiation index (θp = Fst) between sub-populations of Fagus sylvatica sampled across the Fagetum phytoclimatic belt in southern Italy. Sub-populations bold marked belong to cluster 1 detected with the Bayesian analysis 1. Pascoleto 2. Caldarosa 3. Serra Calvello, Calv. 4. Campo Imperatore 5. Crocetta 6. Saraceno 7. M. Viggiano 8. Pilato 9. Moliterno 10. Bosco Raimondo 11. Pierfaone 12. Sasso Costara 13. Serra Calvello N 14. Cerchiara 15. Iaccata sud 16. Iaccata nord 17. Maddalena 18. Paratiello 19. Santa Croce 20. San Cataldo 21. Tre Confini 22. Vulture, Mont.lakes 23. Li Foy 24. Vetrice 25. Carmine, Caruso 26. Pollino W 27. La Spina 28. Sirino 29. Raparo 30. Gurmara 31. Pollino E 32. Alpi 33. Alburni 34. Cervati 35. Gelbison 36. Foresta Umbra 37. Stavanger 38. Etna

0 0.0158

0

0.0176 0.0021 0.0272 0.0127 0.0091 0.0184 0.0338 0.0343 0.0139 0.0377 0.0172 0.0173 0.0693 0.0504 0.0700 0.0637 0.0575 0.0342 0.0709

0.0089 0.0158 0.0358 0.0012 0 0.0259 0.0214 0.0022 0.0004 0.0145 0.0017 0.0217 0.0652 0.0279 0.0629 0.0419 0.0600 0.0347 0.0422

0 0.0047 0.0154 0.0000 0.0028 0.0088 0.0059 0.0110 0.0008 0.0091 0.0045 0.0146 0.0577 0.0505 0.0538 0.0510 0.0616 0.0435 0.0652

0 0.0371 0.0149 0.0086 0.0262 0.0213 0.0307 0.0102 0.0166 0.0229 0.0142 0.0743 0.0715 0.0770 0.0672 0.0727 0.0467 0.0699

0 0 0.0226 0.0000 0.0040 0.0210 0.0170 0.0429 0.0150 0.0377 0.0344 0.0508 0.0463 0.0676 0.0752 0.0535 0.0769

0 0.0030 0.0042 0.0064 0.0020 0 0.0227 0 0.0180 0.0458 0.0487 0.0612 0.0585 0.0619 0.0338 0.0668

0 0.0100 0.0133 0.0026 0.0022 0.0049 0 0.0212 0.0425 0.0166 0.0475 0.0344 0.0381 0.0285 0.0448

0 0.0237 0.0176 0.0122 0.0315 0.0034 0.0184 0.0285 0.0310 0.0249 0.0377 0.0289 0.0297 0.0556

0 0.0121 0.0106 0.0246 0.0240 0.0467 0.0493 0.0664 0.0661 0.0646 0.0765 0.0508 0.0776

0 0.0019 0.0134 0.0018 0.0425 0.0415 0.0296 0.0519 0.0365 0.0587 0.0345 0.0450

0 0.0038 0.0012 0.0079 0.0535 0.0284 0.0536 0.0388 0.0539 0.0276 0.0433

0 0.0057 0.0274 0.0783 0.0489 0.0777 0.0557 0.0677 0.0489 0.0599

0 0.0083 0.0484 0.0308 0.0477 0.0411 0.0449 0.0241 0.0440

0 0.0827 0.0693 0.0564 0.0535 0.0670 0.0332 0.0541

0 0.0074 0.0345 0.0338 0.0281 0.0188 0.0574

0 0.0220 0 0.0122 0.0135 0.0154

0 0.0158 0.0096 0.0365 0.0352

0 0.0031 0.0033 0.0025

0 0.0005 0.0160

0.0678 0.0241 0.0833 0.0452 0.0328 0.0552 0.0414 0.0519 0.0624 0.0034 0.0806 0.0648 0.0621 0.0594 0.0650 0.1077 0.0486

0.0578 0.0119 0.0440 0.0418 0.0543 0.0487 0.0576 0.0534 0.0694 0.0133 0.0573 0.0486 0.0462 0.0467 0.0559 0.0856 0.0597

0.0585 0.0105 0.0683 0.0470 0.0593 0.0561 0.0166 0.0587 0.0473 0.0133 0.0598 0.0373 0.0371 0.0407 0.0564 0.0625 0.0678

0.0735 0.0288 0.0817 0.0594 0.0660 0.0713 0.0304 0.0722 0.0773 0.0110 0.0900 0.0590 0.0626 0.0624 0.0767 0.0937 0.0745

0.0859 0 0.0945 0.0583 0.0543 0.0534 0.0513 0.0560 0.0440 0.0276 0.0541 0.0644 0.0432 0.0398 0.0499 0.0970 0.0432

0.0682 0.0023 0.0732 0.0410 0.0356 0.0435 0.0486 0.0518 0.0539 0.0028 0.0577 0.0561 0.0421 0.0576 0.0591 0.0720 0.0489

0.0417 0.0042 0.0470 0.0303 0.0524 0.0457 0.0417 0.0387 0.0542 0.0052 0.0449 0.0319 0.0287 0.0343 0.0477 0.0764 0.0455

0.0452 0.0058 0.0600 0.0329 0.0475 0.0430 0.0345 0.0405 0.0185 0.0197 0.0485 0.0210 0.0249 0.0187 0.0221 0.0616 0.0446

0.0744 0.0110 0.0751 0.0620 0.0789 0.0650 0.0376 0.0630 0.0539 0.0229 0.0567 0.0530 0.0437 0.0491 0.0595 0.0795 0.0715

0.0456 0.0085 0.0353 0.0417 0.0573 0.0434 0.0685 0.0441 0.0588 0.0219 0.0430 0.0390 0.0256 0.0360 0.0566 0.0696 0.0602

0.0422 0.0077 0.0461 0.0346 0.0505 0.0496 0.0283 0.0447 0.0550 0.0069 0.0548 0.0368 0.0329 0.0437 0.0594 0.0692 0.0522

0.0541 0.0180 0.0674 0.0605 0.0896 0.0705 0.0493 0.0616 0.0870 0.0170 0.0728 0.0488 0.0494 0.0601 0.0778 0.0759 0.0839

0.0491 0.0000 0.0507 0.0283 0.0492 0.0436 0.0311 0.0436 0.0429 0.0107 0.0541 0.0329 0.0308 0.0353 0.0438 0.0676 0.0497

0.0701 0.0164 0.0699 0.0322 0.0404 0.0607 0.0254 0.0714 0.0598 0.0058 0.0806 0.0412 0.0491 0.0580 0.0665 0.1067 0.0544

0.0325 0.0411 0.0469 0.0194 0.0421 0.0145 0.1097 0.0144 0.0270 0.0555 0 0.0362 0.0098 0.0390 0.0329 0.0932 0.0103

0.0017 0.0350 0.0357 0.0045 0.0298 0.0051 0.1250 0 0.0619 0.0396 0 0.0204 0.0018 0.0167 0.0396 0.0910 0.0193

0.0408 0.0477 0.0385 0.0384 0.0593 0.0274 0.0812 0.0402 0.0274 0.0599 0.0378 0.0135 0.0177 0 0.0061 0.0743 0.0556

0.0115 0.0468 0.0050 0.0184 0.0473 0.0143 0.0928 0.0111 0.0411 0.0505 0.0226 0.0114 0.0085 0.0102 0.0345 0.0825 0.0362

0.0224 0.0582 0.0329 0.0147 0.0460 0.0142 0.0976 0.0143 0.0248 0.0555 0.0396 0.0183 0.0255 0.0147 0.0068 0.0778 0.0383

ib/multco/detail.action?docID=3021288.

1 0 0

2 0

0.0199 0.0411 0 0 0.0109 0 0.0827 0 0.0271 0.0319 0.0145 0.0209 0.0199 0.0240 0.0385 0.0908 0.0013 20

0.0373 0.0515 0 0.0070 0.0432 0.0025 0.1071 0.0097 0.0645 0.0616 0.0153 0.0238 0.0180 0.0218 0.0605 0.1211 0.0338 21

3

0 0.0589 0.0329 0.0249 0.0498 0.0303 0.1192 0.0082 0.0566 0.0433 0.0378 0.0210 0.0159 0.0386 0.0533 0.0647 0.0441 22

4

0 0.0611 0.0415 0.0599 0.0529 0.0420 0.0495 0.0492 0.0124 0.0463 0.0405 0.0290 0.0365 0.0497 0.0897 0.0432 23

5

0 0.0067 0.0618 0 0.1321 0.0245 0.0744 0.0565 0.0264 0.0286 0.0210 0.0312 0.0676 0.1053 0.0406 24

6

0 0.0201 0.0045 0.0835 0.0116 0.0273 0.0391 0.0219 0.0160 0.0122 0.0308 0.0492 0.0793 0.0070 25

7

0 0.0127 0.0787 0.0246 0.0364 0.0461 0.0458 0.0591 0.0477 0.0630 0.0710 0.1105 0.0180 26

8

0 0.0903 0.0053 0.0304 0.0490 0.0082 0.0333 0.0186 0.0251 0.0526 0.0910 0.0119 27

9

0 0.1054 0.0671 0.0507 0.1121 0.0608 0.0652 0.0808 0.0896 0.1134 0.1272 28

10

0 0.0466 0.0448 0.0076 0.0364 0.0158 0.0287 0.0510 0.0963 0.0121 29

11

0 0.0708 0.0505 0.0152 0.0325 0.0215 0.0142 0.0610 0.0405 30

12

0 0.0614 0.0522 0.0454 0.0571 0.0651 0.0961 0.0477 31

13

0 0.0340 0.0141 0.0326 0.0566 0.1195 0.0110 32

14

15

16

17

18

19

0 0.0049 0.0030 0.0268 0.0608 0.0551 33

0 0.0109 0.0446 0.0639 0.0396 34

0 0.0114 0.0707 0.0635 35

0 0.0670 0.0632 36

0 0.1153 37

0 38

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20. San Cataldo 21. Tre Confini 22. Vulture, Mont. Lakes 23. Li Foy 24. Vetrice 25. Caruso, Carmine 26. Pollino W 27. La Spina 28. Sirino 29. Raparo 30. Gurmara 31. Pollino E 32. Alpi 33. Alburni 34. Cervati 35. Gelbison 36. Foresta Umbra 37. Stavanger 38. Etna

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Figure 3. Geographical distribution of the individual plant (star) nuclear estimated ancestry (proportional to the intensity of star color) across the core area of the Region. Two clusters capture the whole population. Cluster one is depicted with the maximum intensity of red color indicating the upper limit of the estimated ancestry (99%); yellow indicates a mixed ancestry from both clusters, whereas the maximum intensity of the green color indicates the lower limit (1%) of the estimated ancestry. The symmetric proportion of the estimated ancestry could be inferred for each individual that is part of cluster two (central range of the region). Top right shows the geographic homogeneous sub-regions of Fagus sylvatica. The main direction of gene-flow, dictated by seed migration, is depicted with arrows labeled with the average population size of migrants per generation (Nm) inferred from nuclear genetic differentiation among sub-populations.

By contrasting cytoplasm vs. nuclear genetic diversity it would seem that the Li Foy beech-wood ancestry was the result of seed-flow from the northern side, given the lack of evidence of the maternal lineage migration from the south (Crocetta vs. Mounts Li Foy and Crocetta vs. Vetrice), and the result of pollen flow from the south (Figures 3 and 4). Pollino massif with the presence of haplotype H2, H3 and H4 on the western side (Figure 1) is

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genetically linked with the southern side of the Campania Region (Mount Cervati and Gelbison) and with southern Basilicata (La Spina, Alpi and Raparo Mounts (Nm > 30). The central part of the region was genetically homogeneous except for its south-eastern side (Pilato – Pietra Iaccata). The beech-wood of Sirino showed a gene-flow lower than the neighbouring woods. The beech-woods of the Maddalena mountain range, between the di Diano valley and the Agri valley, were differentiated from the central Basilicata beech-woods and genetically more similar to the sub-populations of the Western path (Cilento National Park) (Figures 3 and 4). This pattern indicates that the gene flow of F. sylvatica in southern Italy over the last 12,000 years has been limited mainly by deep valley oriented from SE to NW. Seed migration occurred through contact zones along the main Apennine chains (Figures 1, 3 and 4).

Figure 4. Chloroplast haplotype geographic distribution of each tree (star) across the core area of the Region. Maternal haplotype transition from marginal sites to the actual maximum density tree distribution is according to a contact zone seed migration path.

A clear trend of a decrease in richness from the south (5 different haplotypes) to the north (1 haplotype) was revealed at a macro scale (Figure 1, Table 2). Haplotype H2 was the most Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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common (p = 0.41) and widely distributed (from Sicily to Norway) and H1 was equally common (p = 0.42), but widely distributed only over the Basilicata landscape (Figures 1 and 4). The canopy structure of sites with high chloroplast haplotype richness was characterised by low density woods, mixed with more thermophilous species (Figure 7). By overlapping geographic nuclear and chloroplast genetic variation it would seem that the most probable F. sylvatica sub-populations linked with the original glacial refugia could be: Etna, western side of Pollino, Cervati, Pietra Iaccata-Caldarosa, Vulture, Vetrice and, probably, Faggeta Moliterno.

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Site Specific Genetic Map Figure 5 shows the individual tree heterozygosity at Laghi Monticchio (Vulture volcano) and the genetic spatial distribution of locus Fs4-46 on Mounts Li Foy. The right hand side of the map depicts the beech haplotype and the individual heterozygosity of individuals present in a mixed broad-leaf wood only on the western-exposed slope inside the old crater (200,000 yr. BP) of the Vulture volcano. Beech trees in this area are located at an average altitude of 665 m. Such an outlier altitude location is due to the specific oceanic climate favoured by the humidity arising from the two volcanic lakes (Laghi Monticchio) and because of the protection, provided by the crater ridge, from dry-warm (from S-SE) and dry-cold winds (from N-NE). This site is of special interest for biology conservation studies; here are present individuals bearing three different maternal lineages (H1, H2 and H4 haplotype), a variable level of individual heterozygosity (from Ho = 0.4 to Ho = 0.8) and a Hardy-Weinberg equilibrium. Beech on mount Li Foy are dominant and distributed from 950 m (northern exposed slopes) to 1367 m; here the population is fixed for chloroplast haplotype H2 (from the northern side of the sub-Region) and shows a prevalence of null genotypes for locus Fs446 on the southernmost mountain side. The nuclear allele distribution is probably due to the most recent pollen-flow from the south.

Figure 5. Site specific landscape genetic map is reported for Mount Li Foy (left) and Monticchio lakes within the crater of the Vulture volcano (right). The stars indicate the position and the maternal haplotype of each Fagus sylvatica tree, and the numbers indicate the diploid genotype of the microsatellite locus Fs4-46 (M.nt Li Foy) or the individual observed heterozygosity (Monticchio lakes).

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Ecologic Diversity

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The sampling transects captured a wide range of ecologic diversity within a heterogeneous environment that can be decomposed in terms of differences in altimetry layers, slope exposition, geologic substrates, stand composition, wood structure and management type. Overall the 74.9% of individuals were from the altimetry range 1000-1500 m a.s.l.; the 16.7% from mountain slopes above 1500 m, while the 8.3% only from ecologic niches between 590 and 990 m a.s.l. (e.g. Vulture volcanic lakes, S. Cataldo and Foresta Umbra). Beech individuals were mainly from pure stands (63.9%) with the remaining population (37%) from woods composed by mixed tree stands. The 34% of the individuals were from woods composed by the dominant beech associated with minor different companion species and only the 3% of the beech population was represented by rare trees within woods dominated by Quercus cerris. Ilex aquifolium, as a companion species, was widely distributed but almost never was found on the south-exposed slopes (the opposite was observed for Pinus nigra). Rare and locally distributed companion trees were: Prunus cocomilia, Sorbus aria, Malus sylvestris, Pyrus piraster, Taxus baccata, Abies alba, Carpinus betulus, Ostrya carpinifolia, Quercus frainetto and Salix caprea.

Figure 6. Scatter diagram of the first and second dimension calculated by correspondence analysis describing the relationships among soil pedogenetic substrate (from calcareous to volcanic), genetic cluster (sub-population 1 or 2) and beech-wood composition (mixed or pure stand).

Lower altimetry layers (below 1000 m) were mainly associated with vegetation transitional zones capturing the following long living plant associations: Fagus sylvatica Acer spp - Castanea sativa; Fagus sylvatica – Alnus cordata - Quercus pubescens; Fagus Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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sylvatica - Quercus cerris - Abies alba - Populus nigra. A unique association was detected either on M.nt La Spina (Pinus nigra - Fagus sylvatica – Juniperus spp - Alnus cordata – Salvia officinalis) or on M.nt Etna (Castanea sativa - Fagus sylvatica - Genista aethnensis – Berberis aethnensis). The sampled beech individuals were mainly located on the north exposed slopes (50%), followed by the south (21%), east (12%), west (8%) and high plane (9%). The 67% of the pedologic substrate targeted by the sampled genotypes was calcareous, the 11% siliceous, 10% siliceous-calcareous, 6% arenaceous, 3% with conglomerates and 3% volcanic. The most meaningful output from multivariate correspondence analysis was the association between calcareous pedologic substrate, pure beech-wood stands and genetic cluster number 1. Mixed stands were mainly associated with the genetic group number 2 and with siliceous soils, volcanic or with a mixed structure (Figure 6). The calcareous geologic substrate is typical of the western side of the Region and of the medium-high altimetry sites of the center. While the siliceous or mixed substrates often are represented by strata covering the basal belt of the Apennine slopes. The volcanic soils are specific of two sites: Etna and Vulture. The first, being the southernmost site for beech, is characterized both by high altitude and a milder climate than in the core zone. The second (Vulture) is a site with beech present at low altitude protected by an old volcano cratere.

CONCLUSION

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Spatial vs Temporal Scale Genetic disequilibrium of F. sylvatica in southern Italy increases by increasing the geographical range of species distribution within a spatial scale of about 20,000 Km2. In fact several sub-populations were either close to the genetic equilibrium (Fis = 0.09 Etna; Fis = 0.07 Vulture; Fis = 0.14 Pietra Iaccata; Fis = 0.03 Vetrice) or rich in chloroplast haplotypes, whereas the geographic sub-populations (North, West, Center and South) showed an intermediate level of equilibrium and just the overall dataset showed the maximum disequilibrium. This trend is also supported by higher differentiation indices among subpopulations than among geographic sub-populations. The Bayesian analysis confirmed a subtle population structure characterised by two main clusters of nuclear genotypes along with a clear distribution of chloroplast haplotype richness. The migration patterns and the evidence of multiple hot spots of haplotype maternal diversity are consistent with the findings inferred from paleobotanic data (Magri et al. 2006, Magri 2008) and from the differentiation estimates previously reported for populations scattered across the Italian peninsula (Vettori et al. 2004). It is still not known from where the widely distributed haplotype H2 migrated to Norway. The original source of this haplotype may be the Balkans, given that the southern Italian haplotypes may share their ancestry with Balkan‘s haplotypes (Magri et al. 2006, Gömöry et al. 2007). Nonetheless the geographic scale of the depicted genetic structure must be related to the temporal scale. Almost all the sub-populations (those with haplotype richness) that can be associated with the original pre-glacial sites (valleys and canyons during the cold ages) are

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currently found at higher altitudes (mountain ranges and oceanic low-altitude niches). Some sub-populations may have continued to occupy the pre-glacial sites (e.g. craters of the Vulture volcano). These sub-populations experienced more overlapping generations than the late colonisers distributed on the upper mountain belt and alpine layer. The main reason for the lack of genetic equilibrium can be understood given that since 12,000 years B.P. less than 60120 generations have elapsed (by fixing 100-200 years per generation). Ecological gaps represented by deep valleys helped to limit random seed and pollen dispersal. The presence of two genotype clusters together with sub-populations bearing high values of the fixation index suggests that spatial genetic variation is unlikely to be explained by the random dispersal of genetic variants (see also Wang 2004). In addition, within a changing environment, processes such as land use and pressures on the forests could have played a significant effect on population genetics. Within this real context the island model of gene flow can just perform as model to make alternative hypothesis. The low level of observed heterozygosity in Foresta Umbra and Stavanger, and the allelic richness decrease in chloroplast markers when moving northwards validate the general model and predict a higher intra-specific diversity in the southernmost areas compared with the northernmost recently re-colonised areas (Taberlet 1998). Nonetheless, this study demonstrates that the southernmost areas are not represented by genetically homogeneous populations. Note that the indirect method to compute gene flow produces estimates over a long time period that are not consistent with the actual gene flow (Slatkin 1987). Theoretically when the population size is small, the genetic drift should be stronger. Within a very small population, in a few generations an allele could be lost or could be completely fixed (Weir 1996). With values of Nm < 1 the genetic drift will lead to a significant differentiation between subpopulations (Slatkin 1987). Based on the average values of gene flow estimated with the whole dataset, the effect of genetic drift can be ruled out. However, at sub-population level, the rare haplotypes could be lost by drift alone or as consequence of antropogenic pressures (e.g. climate change, artificial selection, habitat loss and fragmentation). In this study maximum haplotype richness across mountain slopes (Cervati, Vetrice, Caldarosa-Pietra Iaccata, west side of Pollino massif) and volcanoes (Etna and Vulture) was observed as marginal to the central core of the contemporary maximum species density (central subregion marked by H1) (Figure 2). Such marginal sites are often characterised by a weak maritime climate as demonstrated by the Mediterranean flora growing close to the sampled beech trees (data not shown). Thus the sites close to the beech glacial refugia - 12,000 yr. BP were the most suited climatically for beech forests - at the present, because of the increasing Mediterraneanisation processes of the southern Italian peninsula, probably are unsuited for F. sylvatica (Birks 2008). Our sampling design (plants spaced on average about 200 m) enabled us to avoid the effect of the family genetic sub-structure, due to the clustering of related genotypes after mating among related parents on the estimates of genetic diversity. Family sub-structure is the consequence, at a wood level, of an average within a wood migration distance of about 50 m (Muller-Starch and Starke 1993). The spatial distribution of nuclear genetic variants in this research is thus mainly affected by the long-term effects of gene flow. The low correlation between nuclear and chloroplast genetic diversity and the non significant correlation between He and latitude, may be due to both a gradual rarefaction of the likely original nuclear differentiation and to the spatial conservation of chloroplast diversity. Genetic

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homogenisation, in the absence of recent bottleneck effects, is expected to be more evident in a small geographic region located in the species‘ centre of origin than all over Europe. In addition, in this study we did not observe the diverging trend between heterozygosis and allelic richness detected by Comps et al. (2001) using a large genetic database of the European beech.

Heterozygosity vs Adaptation

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Inbreeding does not affect the current marginal small sub-populations (e.g. Etna, Vulture, Vetrice and Pietra Iaccata). This could probably be explained by the fact that the present subpopulations, which are genetically closer to the pre-glacial sites, are represented by the fittest genotypes (with a high individual heterozygosity), which are suited to less oceanic environments. Pollen flow efficiency among isolated trees within these marginal subpopulations and the high number of generations elapsed in the relicts from glacial refugia could have favoured genetic equilibrium and survival of the fittest phenotypes within the context of a changing environment. Inbreeding, on the other hand, could have been promoted by the differential clear cuttings of neighbouring woods over the last few centuries. For instance, coppiced woods from one municipality may not have pollinated old growth woods growing in the neighbouring municipalities. Thus management versus protection could explain the differential pattern of heterozygosis found in the filial generation of some woods. We also detected a weak genetic differentiation among different altitudinal layers probably explained by the prevalent effect of slope exposition on local climate rather than by altitude (Pomarico et al. 2007). An accurate assessment of the site-specific effect of factors, such as natural selection, management, canopy structure and wood composition on the trend of heterozygosity would entail a specific and more powerful sampling design.

Sampling Landscape genetics based on neutral or quasi-neutral markers proved to be an efficient method to clarify both spatial patterns of variation and species phylogeny. In this study, it was not possible to use candidate gene sequences having a major effect on the phenotype of adaptive traits. By developing such markers it may well be possible to find more significant relationships between ecological sources of variation (altitude, soil, climate, exposition and forest composition) and genetic diversity. We did not use a high number of DNA markers (e.g. AFLPs) to find associations, as by increasing the number of markers (which were not always independent) type I statistical errors for the detected associations are also high. In addition, for long-living tree species it is not efficient to score adaptive quantitative traits in situ because of their high phenotypic plasticity. Therefore the implementation of sampling strategies must consider the assumption that genetic variation for quantitative traits is distributed as the variation of qualitative traits (Marshall and Brown 1975). It follows that in natural populations a seed collection based on chloroplast and nuclear marker diversity could be also diversified for quantitative traits. In order to avoid the effects of an inbreeding depression we recommend seed sampling across sub-populations marked by both high heterozigosity (Ho) and low heterozygote shortages (Fis).

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By overlapping landscape and genetic diversity maps it is also possible to identify sites that have already experienced the selection pressure forecast by the IPCC and thus bear adapted genes or gene-complexes. A stringent sampling strategy for land re-forestation, could be adopted by collecting seeds from genetically appropriate seed provenances. A less stringent sampling procedure could be achieved with a spaced seed sampling within the homogeneous sub-regions depicted in this research. Seed core samples complementing genotypes from allelic rich sites with genotypes from genetically unique populations could also be produced (Petit et al. 1998). It is believed that the identification of sites that are close to the most probable glacial niches along with the location of genetically homogeneous areas and the possibility of tracking the individual trees bearing the maximum level of heterozygosity could lead to seed collecting for multiple purposes, for example either site specific mitigations or long-term forest mountain corridor restoration.

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Conservation From a conservation genetics point of view, landscape genetic maps merge phylogeography and population genetics to design and monitor better regional reserves (Avise, 1994). In the studied area, both the advanced Mediterraneanisation processes and the ecological isolation of the sub-populations capturing allelic richness raise conservation issues. The eruption of Etna in 2002 damaged the beech-wood sampled for this study considerably, and there has been a significant human impact at several sites. These sites (e.g. Etna, Vulture, Pietra Iaccata, Vetrice), because of their small demographic sizes, merit high priority conservation efforts. In southern Italy the biodiversity promoted by the keystone species F. sylvatica is more at risk than commonly perceived because mountain slopes are now more accessible than they were in the past (Figure 7). In addition, the ecological integrity of the F. sylvatica associated-ecosystems ensure stability and the persistence of several wild species of interest for E.U. (within and outside Natura 2000 sites) and provides the most important associated ecosystem services (Myers 1996).

Figure 7. Scattered plants of Fagus sylvatica (dark green) between Genista aetnensis (yellow) and Castanea sativa (light green) on the summit of Etna volcano (Sicily) and above an altitude of 2000 m. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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Figure 8. Effects of Fagus sylvatica clear cutting in terms of soil erosion and ecosystem degradation on the slopes of Mount Cervati (Campania). Photo taken by Cioffi.

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Management From a management point of view, the appropriate use of landscape genetic maps can direct both specific seed collecting methods and more sustainable management practices (Rajora and Mosseler, 2001). Landscape genetic maps can also help the putting into practice the regulations about the use and trade of forestry plant materials by establishing a list of the regional sources of seed provenance. Corridors and forest restoration over the Fagetum strip have often been achieved with seeds from un-characterized genetic sources or by planting alien species (Pseudotsuga duglasia, Cedrus spp., Abies spp and Pinus spp.). Such practices should be avoided to favor the persistence and stability of communities and ecosystem patterns (Higgins et al. 1999). During sampling trips it was also observed that beech plants from nurseries planted at the margins of natural stands were 15-20 days late in budding compared with the native plants. Such plants are unable to escape the harmful effects of dry winds in the spring from the south. The introduction of beech seed materials in the Mediterranean zone from northern latitudes could be deleterious, while the fitness of plant materials from the southernmost sites introduced in the northern regions, given the current GCC, needs assessing. A man-made beech-wood offset, simulating beech migration trends, using the most potentially adapted phenotypes (heterozygous genotypes selected on the basis of landscape genetic maps) could act as a buffer against on-going environmental pressures.

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Pritchard JK, Stephens P., Donnelly P. (2000). Inference of population structure using multilocus genotype data. Genetics, 155: 945-956. Pritchard JK, Wen W (2003). Documentation for STRUCTURE software: Version 2. Available from http://pritch.bsd.uchicago.edu. Rajora OP, Mosseler A. (2001). Challenges and opportunities for conservation of forest genetic resources. Euphytica 118: 197-212. SAS (1993) SAS/STAT, user‘s guide, vol 2. SAS Institute Inc., Cary, NC. Scalfi M, Troggio M, Piovani P, Leopardi S, Magnaschi G, Vendramin GG, Menozzi P (2004) A RAPD, AFLP and SSR linkage map, and QTL analysis in European beech (Fagus selvatica L.). Theor. Appl. Genet.. 108: 433-441. Sebastiani F, Carnevale S, Vendramin GG (2004) A new set of mono and dinucleotide chloroplast microsatellites in Fagaceae. Molecular Ecology Notes 4: 259-261. Slatkin M (1987) Gene flow and the geographic structure of natural populations. Science 236: 787-792. Taberlet P (1998) Biodiversity at the intraspecific level: The comparative phylogeographic approach. Journal of Biotechnology 64, 91-100. Tanaka K, Tsumura Y, Nakamura T (1999) Development and polymorphism of microsatellite markers for Fagus crenata and the closely related species F. japonica. Theor. Appl. Genet. 99: 11-15. Vernesi C, Crestanello B, Pecchioli E et al. (2003). The genetic impact of demographic decline and reintroduction in the wild boar (Sus scrofa): a micro satellite analysis. Molecular Ecology 12: 585-595. Vettori C, Vendramin GG, Anzi M, Pastorelli R, Paffetti D, Giannini R (2004) Geographic distribution of chloroplast variation in Italian populations of beech (Fagus sylvatica L.) Theor. Appl. Genet 109: 1-9. Vornam B, Decarli N, Gailing O (2004) Spatial distribution of genetic variation in a natural beech stand (Fagus sylvatica L.) based on microsatellite markers. Conserv. Genet. 5: 561–570. Wang KS (2004) Gene Flow in European beech (Fagus sylvatica L.) Genetica 122: 105-113. Watts WA, Allen JRM, Huntley JB, Fritz SC (1996) Vegetation history and climate of the last 15,000 years at laghi di Monticchio, Southern Italy. Quarernary Science Reviews 15: 113-132; Weir BS (1996) Genetic data analysis II. Sinawer Associates, Inc., Sunderland Mass, CA, USA. Weir BS, Cockerham C (1984) Estimating F-statistics for the analysis of population structure. Evolution 38(6): 1358-1370. Whitlock MC, Mccauley DE (1998). Indirect measures of gene flow and migration: Fst ≠ 1/(4Nm+1). Heredity 82: 117-125. Widmer A, Lexer C (2001) Glacial refugia: sanctuaries for allelic richness, but not for gene diversity. Trends in Ecology and Evolution Vol. 16 (6): 267-269. Wright S (1951) The genetical structure of populations. Annals of Eugenetics 15: 323–354.

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In: Wild Plants: Identification, Uses and Conservation ISBN 978-1-61209-966-8 Editor: Ryan E. Davis, pp. 179-194 © 2011 Nova Science Publishers, Inc.

Chapter 5

IMPORTANCE OF DOMINANT PLANT SPECIES FOR ECOLOGICAL INTERACTIONS IN FOREST SOIL AND LITTER: EXAMPLE FROM THE HERON WOOD RESERVE, DAWYCK BOTANIC GARDEN, SCOTLAND

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1

V. Krivtsov*,1,6, S. J. J. Walker2,†, R. Watling3,‡, A. Garside4 and M. J. Richardson5

CECS, The Crew Building, King‘s Buildings, University of Edinburgh, Edinburgh, UK 2 SIMBIOS, School of Science and Engineering, University of Abertay Dundee, Dundee, Scotland, UK 3 Caledonian Mycological Enterprises, Edinburgh, Scotland, UK 4 National Museum of Scotland, Edinburgh, Scotland, UK 5 165 Braid Road, Edinburgh EH10 6JE, Scotland, UK 6 Department of Ecology, Kharkov State University, Kharkov, USSR, Ukraine

ABSTRACT Analysis of a dataset obtained from a monitoring programme at the Heron Wood Reserve (Scotland, UK), focuses on the differences in certain properties of soil and forest litter and patterns of ecosystem dynamics in plots dominated by differing vegetation types, especially the arborescent beech (Fagus sylvatica) and birch (Betula pendula x B. pubescens), and the grass Holcus lanatus. A number of properties show some considerable differences in relation to the habitats dominated by different plant species. For example, pH in the grassland and beech-dominated habitat was significantly lower *

CECS, The Crew Building, King‘s Buildings, University of Edinburgh, West Mains Road, Edinburgh EH9 3JN, UK. Also at: Department of Ecology, Kharkov State University, 4 Svobody Square, Kharkov 310077, USSR, Ukraine. Email [email protected]. † SIMBIOS, School of Science and Engineering, University of Abertay Dundee, Bell Street, Dundee DD1 1HG, Scotland, UK. ‡ Caledonian Mycological Enterprises, 26 Blinkbonny Av., Edinburgh EH4 3HU, EH4 3HU, Scotland, UK.

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V. Krivtsov, S. J. J. Walker, R. Watling et al. than in the habitats dominated by birch and mixed vegetation. The highest soil ergosterol was found in the beech-dominated habitat, and it was significantly different from grass and birch dominated habitats. By contrast, the numbers of forest floor layer bacteria in the beech-dominated plots were significantly lower than in the other habitats. Some remarkable differences have also been found as regards forest litter composition and moisture, the community saprotrophs and sheathing mycorrhizas. The ecological patterns are further complicated by animals, and are exemplified here by discussing the role of nutrient inputs due to the mammalian droppings characterised by a succession of the coprophilous mycota. The discussion concentrates on how the dominant plant species influence the patterns of ecological interactions observed.

Keywords: Plant ecology, indirect interactions, soil, field layer, litter composition, saprotrophs, mycorrhiza, plant-animal interactions, coprophilous fungi, Oryctolagus cuniculus.

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INTRODUCTION Species of beech (Fagus) and birch (Betula) are important plants in temperate woodland and forest ecosystems, and have been considered in studies related to patterns of ecological interactions in relation to environmental change (Lindroth 2010; Matyssek et al. 2010). Holcus lanatus is a widespread grassland species, which has been the focus of previous research on ecological interactions (Macel et al. 2007; Thomsen et al. 2006). Heron Wood Reserve is an important site specially designated to study ecological interactions between higher plants, fungi and other biota (Watling 2004). Earlier research at the sanctuary concentrated on complex interactions involving fungi, bacteria and invertebrates in soil and forest litter (Krivtsov et al. 2006a; Krivtsov et al. 2007a; Krivtsov et al. 2007b; Krivtsov et al. 2001a; Krivtsov et al. 2001b; Krivtsov et al. 2001c; Krivtsov et al. 2004a; Krivtsov et al. 2003a; Krivtsov et al. 2003b; Krivtsov et al. 2005; Krivtsov et al. 2004b). The purpose of this paper is to analyse a dataset obtained from a monitoring programme at the site. It focuses on the differences in certain properties of soil and forest litter, and the ecosystem dynamics in the plots dominated by differing vegetation, which includes the trees beech (Fagus sylvatica L.) and birch (Betula pendula Roth. x B. pubescens Ehrh.), and the grass Holcus lanatus L. The discussion concentrates on how these important plant species influence the patterns of ecological interactions observed.

MATERIALS AND METHODS In order to study ecological interactions between plants and other biota, a 1ha site has been delineated (Watling 2004) within the sanctuary at Heron Wood Reserve (situated in the Dawyck Botanic Garden, Peeblesshire, Scotland). In each quarter of this biodiversity site, two smaller plots of 100 m2 in size have been designated, making eight subplots (i.e. sites) in all (a plan adopted from earlier biodiversity studies undertaken in the UK, including Scotland, by the Forestry Commission).

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Soil moisture content (%)

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Figures 1-8. Mean values of measured properties. The letters indicate ANOVA subsets at 95% confidence interval. 1) soil moisture; 2) pH; 3) SOM, 4) root content; 5) indication of soil microbial biomass (difference in the absorbance at 280 nm of fumigated and unfumigated samples); 6) soil ergosterol; 7) soil glomalin; 8) litter moisture.

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The sampling sites were positioned in a range of woodland habitats (variously dominated by beech, Fagus sylvatica; birch, Betula pendula x pubescens, and oak Quercus petraea (Mattuschla) Liebl.) and a clear area covered with grass (dominated by Holcus lanatus). Composite samples of forest floor (consisting predominantly of forest litter, hence throughout this paper these terms may be used interchangeably) were collected monthly between September and December 2001, each month 32 samples: eight subplots, four replicates from each) using a plastic sampling frame (10 x 15 cm). The sampling depth was variable (depending on the thickness of the litter cover), and was determined by the lower limit of the fragmentation layer. After removing the litter layer, soil cores were also taken from each sampling point. The exact positions of sampling points were determined a priori using a random number generator. In the laboratory, the samples were hand sorted into various components. Moisture content was determined via weight loss after drying for 48 h at 80°C. The litter composition was determined on a per cent (weight by weight) basis. Ergosterol (an indicator of fungal biomass), bacterial numbers, and a number of soil properties were determined using standard methods. Fungal sporomes were regularly observed throughout the study period. Three collections of rabbit pellets were also collected during the study period and incubated in damp chambers to record the composition and succession of the coprophilous mycota.

RESULTS

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Soil Figures 1 – 7 display a number of variables monitored in the soil layer of the Heron Wood Reserve from September to December 2001. Soil moisture content increased significantly over the period, from a mean of 31% in September to 38% in December (Figure 1). Mean soil pH fluctuated from 3.2 to 3.4 (Figure 2), but differences were not significant. Soil organic content was higher in September and November (15.5%) than in October and December (13.5%), but differences were not significant (Figure 3). Root content was greatest in September (6.6mg / g dwt soil) and lowest in December (4.0mg / g dwt soil), but with no significant differences between them (Figure 4). The highest microbial biomass was recorded in November (Figure 5), with the greatest difference in absorbance at 280nm of 0.302, a significant difference when compared individually to the other three months. A decrease in the mean monthly soil ergosterol content was recorded from September to October, followed by an increase through to December (Figure 6). Soil glomalin concentration showed only a limited fluctuation (Figure 7).

Forest Floor Layer Figures 8 – 10 show the moisture content, fungal biomarker ergosterol, and bacteria numbers in the forest floor layer of the study area from September to December 2001. Litter moisture levels were lower in September and November (65%) than in October and December (Figure 8), and significantly higher in December (73%). Forest floor litter

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ergosterol content declined significantly from September (369g / g dwt litter) to November/December (mean 174g / g dwt litter) (Figure 9). Bacterial numbers in the forest floor layer fluctuated over the period, but with no significant differences (Figure 10).

Figures 9-10. Mean monthly ergosterol and bacteria levels. The letters indicate ANOVA subsets at 95% confidence interval. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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Table 1. List of fungi observed in the Heron Wood Reserve during the study period Basidiomycotina Amanita crocea (Quél.) Singer ass. with Betula A. muscaria (L.) Lamb. ass. with Betula A. rubescens Pers. ass. with Fagus Amphinema byssoides (Fr.) J. Erikss. Armillaria gallica Marxm. and Romagn. on Betula poles Bjerkandera adusta (Willd.) P. Karst. on fallen Betula pole Boletus calopus Pers. ass. with Fagus B. chrysenteron Bull. ass. with Betula B. ferrugineus Schaeff. ass. with Fagus B. luridiformis Rostk. ass. with Fagus Cantharellus cibarius Fr. ass. with Fagus Chalciporus piperatus (Bull.) Bataille ass. with Fagus Clitocybe metachroa (Fr.) P. Kumm. under Fagus Collybia butyracea (Bull.) P. Kumm. under Fagus C. confluens (Pers.) P. Kumm. under Betula and Fagus C. dryophila (Bull.) P. Kumm. under Betula C. peronata (Bolton) P. Kumm. under Fagus Conocybe velutipes (Velen.) Hauskn. and Svrĉek amongst grass Cortinarius glandicolor (Fr.) Fr. ass. with Fagus C. hinnuleus (Sowerby) Fr. ass. with Fagus C. obtusus (Fr.) Fr. ass. with Fagus C. ringens (Pers.) Fr. ass. with Fagus C. stillatitius Fr. ass. with Fagus C. torvus (Fr.) Fr. ass. with Fagus C. umbrinolens P.D. Orton ass. with Fagus Dacrymyces stillatus Nees on decorticated Fagus branch Fistulina hepatica (Schaeff.) With. wound on standing Quercus Gloeocystidiellum porosum (Berk. and Curtis) Donk on decorticated Fagus branch Hydnum repandum L. ass. with Fagus Hyphoderma praetermissum (P. Karst.) J. Erikss. and Strid on fallen branch H. roseocremeum (Bres.) Donk on Fagus branch Hyphodontia quercina (Pers.) J. Erikss. on fallen Quercus branch Hypholoma fasciculare (Huds.) P. Kumm. on Betula pole Inocybe assimilata Britzelm. ass. with Fagus I. geophylla (Fr.) P. Kumm. ass. with Fagus I. petiginosa (Fr.) Gillet ass. with Fagus Laccaria amethystea Cooke ass. with Fagus L. laccata (Scop.) Cooke ass. with Betula and Fagus Lactarius blennius (Fr.) Fr. ass. with Fagus L. tabidus Fr. ass. with Betula L. turpis (Weinm.) Fr. ass. with Betula Lycoperdon perlatum Pers. under Fagus Marasmius setosus (Sowerby) Noordel. on cast Fagus leaves Mycena filopes (Bull.) P. Kumm. on small diameter woody litter M. galopus (Pers.) P. Kumm. on soil under Fagus M. haematopus (Pers.) P. Kumm. on fallen Quercus trunk M. inclinata (Fr.) Quél. on fallen Quercus trunk M. pelianthina (Fr.) Quél. under Fagus M. pura (Pers.) P. Kumm. under Betula

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M. vitilis (Fr.) P. Kumm. under Fagus and Betula Oudemansiella mucida (Schrad.) Hoehn. on Fagus branches Panellus serotinus (Pers.) Kuhner on Quercus branch Paxillus involutus (Batsch) Fr. ass. with Betula Phellinus ferreus (Pers.) Bourdot and Galzin on fallen Quercus trunk Physisporinus sanguinolentus (Alb. and Schwein.) Pilát on soil under Fagus Psathyrella piluliformis (Bull.) P. D. Orton on fallen Quercus trunk Radulomyces confluens (Fr.) M. P. Christ. on unidentified frondose branches Russula curtipes F.H.Møller and Jul. Schaff. ass. with Fagus R. cyanoxantha (Schaeff.) Fr. ass. with Fagus R. fellea (Fr.) Fr. ass. with Fagus R. grata Britzelm. ass. with Betula and Fagus R. nigricans (Bull.) Fr. ass. with Fagus R. nobilis Velen. ass. with Fagus R. ochroleuca Pers. ass. with Betula and Fagus Schizopora paradoxa (Schrad.) Donk on Betula branch Stereum gausapatum (Fr.) Fr. on Quercus branches S. hirsutum (Willd.) Gray on Betula and Fagus branches S. rugosum (Pers.) Fr. on Betula pole S. sanguinolentum (Alb. and Schwein.) Fr. on Pinus sylvestris branch Tricholoma columbetta (Fr.) P. Kumm. ass. with Fagus T. sciodes (Pers.) C. Martin ass. with Fagus T. ustale (Fr.) P. Kumm. ass. with Fagus Tulasnella violea (Quél.) Bourdot and Galzin on decorticated Fagus branch Vuilleminia comedens (Nees) Maire on Fagus branch Ascomycotina Ascocoryne sarcoides (Jacq.) Groves and Wilson on decorticated Fagus branch Ascodichaena rugosa Butin (as asexual Polymorphum quercinum (Pers.) Chev.) at the base of Fagus tree trunks and on Betula and Quercus branches Bisporella citrina (Batsch) Korf and Carpenter on decorticated Fagus branches Diaporthe leiphaemia (Fr.) Sacc. (as asexual Phomopsis quercina (Sacc.) Hoehn. on Quercus twigs Diatrypella favacea (Fr. ) de Not. on Betula poles Helvella lacunosa Afzel. under Fagus Microglossum viride (Pers.) Gillet in moss under Betula Nectria cinnabarina (Tode) Fr. (as asexual stage Tubercularia) on branhces Peziza arvensis Boud. in leaf-litter under Fagus Quaternaria quaternata (Pers.) J. Schröt. on Fagus branches Rhytisma acerinum (Pers.) Fr. on cast Acer pseudoplatanus leaves (blown into area) Xylaria hypoxylon (L.) Grev.on Betula root Mitosporic Fungi Fusicoccum galericulatum Sacc. on Fagus twigs Fusidium griseum Link on Betula leaves Zygomycotina Syzygites megalocarpus Ehrenb. on Mycena inclinata

The fungi found in the sampling plots during the study period are given in Table 1. Fungal observations followed an expected pattern (Figure 11), with a peak in September followed by a decline to December. This decrease was accompanied by the concurrent dramatic changes in the soil and litter ergosterol levels (see Figures 6 and 9).

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On incubating the rabbit droppings a characteristic succession of fungi was recorded: firstly the specialist coprophilous zygomycetes Pilobolus and Pilaira, followed by ascomycetous flask- and disc-fungi, and basidiomycetes. This succession can be seen in the field but it is often interrupted by micro-climate changes and physical disturbance, and is often repeated when a sample has been collected in the field, dried and rehydrated for incubation at a later date. Some of these fungi have their own associated fungal parasites e.g. Piptocephalis repens on mucoraceous fungi and Unguiculella tityri on the schizothecioid Podospora spp. A list of the species recorded during the study period is presented below (Table 2). Table 2. Coprophilous fungi and their parasites recorded from three collections of rabbit pellets collected and incubated in damp chambers during the study period Ascomycotina

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Arnium leporinum (Cain) N. Lundq. and J.C. Krug Ascobolus crenulatus P. Karst. Ascobolus stictoideus Speg. Ascozonus monascus Brumm. and M. J. Richardson Phomatospora coprophila M. J. Richardson Podospora decipiens (G. Winter ex Fuckel) Niessl Podospora pleiospora (G. Winter) Niessl Podospora tetraspora (G. Winter) Cain Podospora vesticola (Berk. and Broome) Cain and J.H. Mirza ex Kobayasi Saccobolus versicolor (P. Karst.) P. Karst. Sporormiella intermedia (Auersw.) S.I. Ahmed and Cain Thelebolus stercoreus Tode Unguiculella tityri (Velen.) Huhtinen and Spooner Basidiomycotina

Coprinellus cordisporus (T. Gibbs) Watling and M.J. Richardson Coprinopsis stercorea (Fr.) Redhead, Vilgalys and Moncalvo Parasola misera (P. Karst.) Redhead, Vilgalys and Hopple Zygomycotina

Pilaira moreaui Y.Ling Pilobolus crystallinus var. crystallinus (F.H. Wigg.) Tode Piptocephalis repens Tiegh. and G. Le Monn.

Soil and Forest Floor Layer Properties in Different Habitats There are considerable differences in some soil and forest floor layer parameters in relation to the habitats dominated by different plant species. pH in the grassland and beechdominated habitat was significantly lower than in habitats dominated by birch and mixed vegetation (Figure 12). The highest soil ergosterol was found in the beech-dominated habitat (Figure 13), and it was significantly different from grass and birch dominated habitats. The highest numbers of soil bacteria were found in the plots with mixed vegetation, but differences were not significant (Figure 14). The highest ergosterol in the forest floor layer was in the grassland plot, but differences were not significant (Figure 15). The highest

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numbers of forest floor layer bacteria were also in the grassland plots and numbers in the beech plots were significantly lower than in the other habitats (Figure 16).

Figures 11-12. 11) Total observations of fungal sporomes in the sanctuary; 12) soil pH vs dominant vegetation. Outliers are labelled with the sample numbers.

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Figures 13 – 16. Measured levels of decomposers (bacteria and ergosterol, an indicator of fungal biomass) in habitats with differing dominant vegetation. Outliers are labelled with the sample numbers. 13) soil ergosterol; 14) soil bacteria; 15) litter ergosterol; 16) litter bacteria.

Forest Floor Composition The selected components of forest floor composition in the eight sampling plots are presented in Figure 17. It should be noted that the forest floor mainly consisted of seeds and plant litter, e.g. leaves, wood (i.e. branches and twigs), and unidentifiable fragments. In certain plots, however, moss and grass featured prominently, and mainly as living components. There was a great deal of variation between the plots, with unidentified fragments generally comprising the greatest fraction. Seeds were found in the greatest quantity in beech plots. The wood component of the litter remained fairly constant in all the plots except the one dominated by grass. Beech leaves were mainly restricted to plots dominated by beech trees, although beech leaves were found in smaller quantities in all plots. Birch leaves were mainly found in plots containing birch or birch with beech trees. Small

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quantities of birch leaves were, however, also occasionally found in other plots. Grass dominated one plot but was present in smaller quantities in birch plots. The dynamics of the forest floor composition is exemplified below by selected plots with typical dominant vegetation. In particular, the change in litter density displayed with litter components for beech (plot 1), and birch (plot 5) from September to December 2001 is presented in Figures 18 and 19 respectively.

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Figure 17. Overall forest floor composition (NB selected components only) in the eight plots for September – December 2001.

Figure 18. Forest floor composition in plot 1 (dominated by beech). The pie charts display proportions of the litter components by dry mass. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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In the beech plot the litter density was variable each month, although the proportions of components remained similar with the exception of beech leaves, which varied. In October, the general increase in litter density was paralleled with an increase in seeds. Similarly, the increase in December was coupled with an increase in wood.

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Figure 19. Forest floor composition in plot 5 (dominated by birch). The pie charts display proportions of the litter components by dry mass.

In the grass plot the greatest components each month were roots and grass (data not shown). Unlike the other plots, unidentified fragments did not comprise a considerable proportion of the forest floor. As the litter density increased in October to November, beech, birch and oak leaves were present, thus showing an influence of surrounding areas. The proportion of moss peaked in December while the proportion of roots peaked in November. In the birch plot, litter density peaked in December with a corresponding peak in unidentified fragments. The predominance of fragments was similar to a beech plot, where fragments and seeds comprised the majority of components by dry mass, while a birch plot had a greater number of components than either a mixed birch/beech or the grass plot. Fragments fraction comprised nearly three quarters of the litter density in September but was lower in October and November, finally increasing in December.

DISCUSSION The results presented here indicate that the patterns of ecological interrelations of both soil and litter subsystems are heavily influenced by the dominant plant species. In particular, beech-dominated sites have lower pH and moisture content, and a considerably thicker cover of undecomposed litter. The results are in line with those from previous investigations of interactions between forest litter composition and other ecosystem components (Krivtsov et al. 2005; Krivtsov et al. 2006b) and the findings that N availability under beech trees is considerably lower than under birch trees due to the slow decomposition of the beech leaves

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and twigs, and that the decomposition in the beech-dominated habitats is mainly governed by the fungal pathway, whilst in the birch-dominated plots the bacterial pathway is considerably more prominent (Krivtsov et al., in press). It should be noted, however, that the availability of nutrients affects soil microorganisms in a complex way, both by directly influencing soil properties and also by influencing vegetation growth and community composition and thus the quality and quantity of litter inputs (Manning et al. 2006). The interaction of direct and indirect effects is complex, however, and their relative effects are not easily discernable (Krivtsov 2008; Manning et al. 2008). For example, Bardgett et al. (1999) suggest that following N enrichment, in the short term, the abundance and activity of soil microorganisms in upland grasslands are regulated more by plant species traits than by a direct effect of nitrogen. These effects were attributed to variations amongst plant species in root exudation patterns and/or efficiency of nutrient acquisition (Bardgett et al. 1999). Innes et al. (2004), however, conclude that effects of plant species on microbial properties differ markedly in soils of differing fertility, making general predictions about how individual plants impact on soil properties difficult to make (Innes et al. 2004). The appearance of fruiting structures of fungi, both saprotrophs and sheathing mycorrhizas, also differ from one plot to another. This might be expected in the case of the latter group because of the close relationship between fungus species and tree. Thus, Lactarius blennius and Russula nobilis are only found with beech whereas Lactarius turpis only fruits under birch. Some species it is true are catholic in their fruiting patterns and may be found not only associated with beech or birch but with many other, often exotic, tree species in the policies surrounding the study area, e.g. Russula ochroleuca. Unfortunately we know little about the distribution of these fungi below ground in the study area. As far as the saprotrophs are concerned care in attributing association must be used, as some fungi found in the birch-dominated plots are attached to wind distributed beech leaves hidden in the ground layer. Such a case is Marasmius setosus. On the other hand, Rhytisma acerinum, known to grow on leaves of Acer pseudoplatanus L., is found in the reserve on sycamore leaves stuck amongst tillers of Holcus lanatus. Mycena pelianthina is a feature of beechwoods, especially in the south of England, growing on compacted beech leaves; it is rarely found in Scotland. One of its centres of distribution in southern Scotland is, however, amongst the beech plots in the study area but over a ten-year period it has gradually spread to birch dominated plots growing on matted leaves of both tree species. Equally the lignicolous Oudemansiella mucida, found commonly in the crowns and on the trunks and fallen branches of beech in the study area has been also found but rarely on fallen twigs of birch. Specialization is also seen amongst the fungi associated with the solitary oak trees found in some of the plots, especially if there are wind-thrown branches and limbs fallen on the ground. Thus the mushroom, Panellus serotinus, and bracket fungi Dichomitus campestris (Quél.) Domański and Orlicz and Phellinus ferreus are only found on oak. Amongst 4,000 records, made up of 231 fungal species from the study area and from a total of over 16,000 records for the whole of the Dawyck Garden, over half of those species in some way are specialists. They are either specific to an associated higher plant taxon, or to the kind of substrate, viz. diameter of woody debris, whether leaf-midrib or -lamina, or associated bryophytes and grasses (Watling 2010). It should be noted that although the study area is within a much larger fenced area that encompasses a wide range of exotic trees and shrubs, various herbivores, e.g. rabbits

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(Oryctolagus cuniculus), hares (Lepus europaeus), squirrels (Sciurus vulgaris and Neosciurus carolinensis) and deer (Sika, Cervus nippon, and roe, Capreolus capreolus) are still able to browse undisturbed. Their feeding habits include a full range of plant material and their droppings are cast in small discrete collections of pellets. Nutrients increase below these droppings and this encourages more luxuriant plant growth. A second and new factor, however, superimposes itself on the organismal patterns; this is the appearance of coprophiles, a group of specialised organisms – fungi, insects, other invertebrates, and no doubt other micro-organisms. The list of coprophilous fungi recorded from three collections of rabbit pellets over the study period gives an indication of the biodiversity of this secondary habitat. Seventeen dung samples, from all species mentioned above, collected and incubated from 1998-2006, produced 199 records of approximately 60 species. The results of this study highlighted the complexity of factors influencing temporal dynamics and spatial variability of ecological interactions and the importance of the dominant plant species in that respect. The results are also relevant to the issues of fire ecology and vegetation flammability (Krivtsov et al. 2009).

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REFERENCES Bardgett R. D., Mawdsley J. L., Edwards S., Hobbs P. J., Rodwell J. S. and Davies W. J. (1999) Plant species and nitrogen effects on soil biological properties of temperate upland grasslands. Functional Ecology 13: 650-660. Innes L., Hobbs P. J. and Bardgett R. D. (2004) The impacts of individual plant species on rhizosphere microbial communities in soils of different fertility. Biology and Fertility of Soils 40: 7-13. Krivtsov V. (2008) Indirect effects in ecology. In: Encyclopedia of Ecology (ed. S. E. Jorgensen) pp. 1948-1958. Elsevier. Krivtsov V., Bezginova T., Salmond R., Liddell K., Garside A., Thompson J., Palfreyman J. W., Staines H. J., Brendler A., Griffiths B. and Watling R. (2006a) Ecological interactions between fungi, other biota and forest litter composition in a unique Scottish woodland. Forestry 79: 201-216. Krivtsov V., Brendler A., Watling R., Liddell K. and Staines H. J. (2007a) Some aspects of forest soil and litter ecology in the Dawyck Cryptogamic Sanctuary with a particular reference to fungi. Acta Ecologica Sinica 27: 813-834. Krivtsov V., Garside A., Bezginova T., Thompson J., Palfreyman J. W., Salmond R., Liddell K., Brendler A., Griffiths B. S., Watling R. and Staines H. J. (2006b) Ecological study of the forest litter meiofauna of a unique Scottish woodland. Animal Biology 56: 69-93. Krivtsov V., Garside A., Brendler A., Liddell K., Griffiths B. S. and Staines H. J. (2007b) A study of population numbers and ecological interactions of soil and forest floor microfauna. Animal Biology 57: 467-484. Krivtsov V., Garside A., Thompson J., Bezginova T., Salmond R., Liddell K., Griffiths B., Staines H., Watling R. and Palfreyman J. (2001a) Interrelations between soil nematodes, bacteria, fungi and protozoa in the 'Dawyck cryptogamic sanctuary' in winter. In: IV International Nematology Symposium pp. 64 - 65, 173.

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Importance of Dominant Plant Species for Ecological Interactions …

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Krivtsov V., Garside, A., Thompson, J., Bezginova, T., Salmond, R., Liddell, K., Griffiths, B.S., Staines, H.J., Watling, R. and Palfreyman, J.W. (2001b) Interrrelations between soil nematodes, bacteria, fungi and protozoa in the "Dawyck cryptogamic sanctuary" in winter. Russian Journal of Nematology 9: 150-151. Krivtsov V., Griffiths B. S., Salmond R., Liddell K., Garside A., Bezginova T., Thompson J. A., Staines H. J., Watling R. and Palfreyman J. W. (2004a) Some aspects of interrelations between fungi and other biota in forest soil. Mycological Research 108: 933-946. Krivtsov V., Illian J. B., Liddell K., Garside A., Bezginova T., Salmond R., Thompson J., Griffiths B., Staines H. J., Watling R., Brendler A. and Palfreyman J. W. (2003a) Some aspects of complex interactions involving soil mesofauna: analysis of the results from a Scottish woodland. Ecological Modelling 170: 441-452. Krivtsov V., Liddell K., Bezginova T., Salmond R., Garside A., Thompson J., Palfreyman J. W., Staines H. J., Watling R., Brendler A. and Griffiths B. (2003b) Ecological interactions of heterotrophic flagellates, ciliates and naked amoebae in forest litter of the Dawyck Cryptogamic Sanctuary (Scotland, UK). European Journal of Protistology 39: 183-198. Krivtsov V., Liddell K., Bezginova T., Salmond R., Staines H. J., Watling R., Garside A., Thompson J. A., Griffiths B. S. and Brendler A. (2005) Forest litter bacteria: Relationships with fungi, microfauna, and litter composition over a winter-spring period. Polish Journal of Ecology 53: 383-394. Krivtsov V., Liddell K., Salmond R., Garside A., Thompson J., Bezginova T., Griffiths B., Staines H. J., Watling R. and Palfreyman J. W. (2001c) Analysis of microbial interactions in forest soil. In: The first workshop on information technologies application to problems of biodiversity and dynamics of ecosystems in north Eurasia (WITA), Novosibirsk. Krivtsov V., Vigy O., Legg C., Curt T., Rigolot E., Lecomte I., Jappiot M., Lampin-Maillet C., Fernandes P. and Pezzatti G. B. (2009) Fuel modelling in terrestrial ecosystems: An overview in the context of the development of an object-orientated database for wild fire analysis. Ecological Modelling 220: 2915-2926. Krivtsov V., Walker S. J. J., Staines H. J., Watling R., Burt-Smith G. and Garside A. (2004b) Integrative analysis of ecological patterns in an untended temperate woodland utilising standard and customised software. Environmental Modelling and Software 19: 325-335. Lindroth R. L. (2010) Impacts of Elevated Atmospheric CO2 and O3 on Forests: Phytochemistry, Trophic Interactions, and Ecosystem Dynamics. Journal of Chemical Ecology 36: 2-21. Macel M., Lawson C. S., Mortimer S. R., Smilauerova M., Bischoff A., Cremieux L., Dolezal J., Edwards A. R., Lanta V., Bezemer T. M., van der Putten W. H., Igual J. M., Rodriguez-Barrueco C., Muller-Scharer H. and Steinger T. (2007) Climate vs. soil factors in local adaptation of two common plant species. Ecology 88: 424-433. Manning P., Newington J. E., Robson H. R., Saunders M., Eggers T., Bradford M. A., Bardgett R. D., Bonkowski M., Ellis R. J., Gange A. C., Grayston S. J., Kandeler E., Marhan S., Reid E., Tscherko D., Godfray H. C. J. and Rees M. (2006) Decoupling the direct and indirect effects of nitrogen deposition on ecosystem function. Ecology Letters 9: 1015-1024. Manning P., Saunders M., Bardgett R. D., Bonkowski M., Bradford M. A., Ellis R. J., Kandeler E., Marhan S. and Tscherko D. (2008) Direct and indirect effects of nitrogen deposition on litter decomposition. Soil Biology and Biochemistry 40: 688-698.

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Matyssek R., Karnosky D. F., Wieser G., Percy K., Oksanen E., Grams T. E. E., Kubiske M., Hanke D. and Pretzsch H. (2010) Advances in understanding ozone impact on forest trees: Messages from novel phytotron and free-air fumigation studies. Environmental Pollution 158: 1990-2006. Thomsen M. A., D'Antonio C. M., Suttle K. B. and Sousa W. P. (2006) Ecological resistance, seed density and their interactions determine patterns of invasion in a California coastal grassland. Ecology Letters 9: 160-170. Watling R. (2004) Dawyck Botanic Garden: the Heron Wood cryptogamic project. Botanical Journal of Scotland 56: 109 - 118. Watling R. (2010) Ten Years in a Cryptogamic Sanctuary. Fungi 3: 52-55.

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In: Wild Plants: Identification, Uses and Conservation ISBN 978-1-61209-966-8 Editor: Ryan E. Davis, pp. 195-268 © 2011 Nova Science Publishers, Inc.

Chapter 6

AN OVERVIEW ON THE HUMAN EXPLOITATION OF SICILIAN NATIVE EDIBLE PLANTS

1

Salvatore Pasta1, Giuseppe Garfì1, Francesca La Bella1, Juliane Rühl2 and Francesco Carimi1 Istituto di Genetica Vegetale del Consiglio Nazionale delle Ricerche UOS di Palermo, Corso Calatafimi 414, I-90129 Palermo, Italy 2 EnBioTech s.r.l., Via Aquileia 34/B, I-90144 Palermo

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ABSTRACT Sicily and its satellite islets host a rich vascular flora, including almost 3,000 native plant species and subspecies; in addition, due to its central position in the Mediterranean, the island has played and still plays a key-role in connecting both plant and human populations of neighbouring Mediterranean countries. The high plant biodiversity is due to a number of factors, such as geographical setting, geological history, soil-type diversity, bioclimatic variability and natural and human disturbance history. Among this flora, many plants, mostly herbs and sub-shrubs, have been used by local people since ancient times for various purposes, mainly as food and/or medicine. The long lasting history of exploitation, deeply permeated with the strong influence from external civilisations, has given rise to a rich inheritance of knowledge that indissolubly has bound biological and cultural (e.g. ethnic and/or linguistic) aspects, resulting in a remarkable bio-cultural diversity within the island territory. In the present chapter, we provide an updated list of Sicilian autochthonous edible plants, giving supplementary information on their vernacular names and uses, in addition to the eco-geography of some rare or endemic species. Emphasis is placed on some differences in plant naming and uses within the regional territory, probably due to different cultural influences, mostly deriving from Greek, Latin and Arab languages. Moreover, the local richness in wild relatives of food crops and the large number of food-medicines among locally gathered plants is highlighted. The study allowed for the identification of more than 250 wild edible plants that are known through an unexpectedly vast number of vernacular plant names and used in many preparations. This suggests an extremely complex and intriguing history of exploitation, quite afar to be acquainted. Moreover, it could significantly contribute to the conservation and valorisation of the rich bio-cultural inheritance of Sicily.

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Keywords: Bio-cultural diversity, Ethnobotany, Food plants, Mediterranean Basin, Traditional knowledge, Vernacular plant names.

1. SICILY, A LAND OF DIVERSITIES

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By 2050, the world will need to produce twice as much food as was produced at the beginning of this century. However, the reduction of available lands for agriculture, in addition to the forecasted climate change, will be a new challenge for farmers in coming years (FAO, 2010). Better conservation and use of food plants will be crucial to help farmers adapt to current and upcoming challenges. In the light of these considerations, the traditional use of non-cultivated food plants may represent a valuable supplementary food source for the present and future generations, and thus preservation of knowledge on plant identities and uses are of major concern. In Sicily, the use of wild plants in the human diet dates back to very ancient times, and still retains a certain importance in rural communities. Moreover, the cultural inheritance in this regard is extremely rich and diversified for a number of reasons. Firstly, one must consider the floristic richness of Sicily and circumsicilian islets. Falling within the so-called ―Tyrrhenian area‖, one of the most important hot spots of plant diversity on a global scale (Médail and Quézel, 1997; Myers et al., 2000), the island hosts 2,962 native vascular plants, including 331 endemics, while the total amount of introduced plants (archaeophytes and neophytes) comprises about 470 different entities (Table 1) (Pasta, 1997; Giardina et al., 2007). Table 1. Selected information on the vascular flora of Sicily and its satellite islets. Lycophyta, Monilophyta, Pinophyta, Gnetophyta and Spermatophyta according to APG (2009). IT = Infrageneric Taxa (species + subspecies)

Native Families Native Genera Endemic Genera Native IT Sicilian endemic IT Native IT living on satellite islets IT strictly endemic of satellite islets Naturalized alien IT

Spermatophyta Dicots Monocots 82 28 539 164 1 0 2,212 678 262 67

Lycophyta

Monilophyta

Pinophyta

Gnetophyta

Total

2 2 0 6 1

11 19 0 54 0

3 4 0 9 1

1 1 0 3 0

3

17

4

2

ca. 1,100

ca. 400

ca. 1,530

0

0

0

0

43

10

53

3

3

3

0

378

84

471

127 729 1 2,962 331

In regard to this remarkable biodiversity, a primary role must be ascribed to the natural/environmental features of Sicily, such as its geographical setting, geological history, soil-type variety and bioclimatic variability. For instance, the elevation of the main island (Figure 1), ranging from the coastline to over 3,300 metres above sea level, offers a wide variety of habitats and bioclimates (Drago, 2002) and has led to the abundance of a widely heterogeneous mosaic of plant communities. Also, its complex geological history accounts for the present array of outcropping mother rocks and large variety of soils (AA. VV., 1996),

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whereas the main palaeoclimatic and geodynamic events, e.g. the Pleistocenic glaciations and/or the alternation of submersion/emersion phases since at least the Oligocene period (28 million years ago) (Ragazzi et al., 2003) involved faunal and floristic migrations and exchanges with the nearby continental areas through temporary terrestrial bridges.

Figure 1. Elevation map of Sicily and circum-sicilian archipelagos. The administrative boundaries of provinces are abbreviated as follows: Ag: Agrigento, Cl: Caltanissetta, Ct: Catania, En: Enna, Me: Messina, Pa: Palermo, Rg: Ragusa, Sr: Syracuse, Tp: Trapani. Pelagie Islands (Lampedusa and Linosa) belongs to Ag; Egadi Islands (Favignana, Levanzo, Marettimo) and Pantelleria Island to Tp; Ustica Island to Pa; Eolie Islands (Alicudi, Filicudi, Lipari, Panarea, Salina, Stromboli and Vulcano) to Me.

In addition to these factors, biotic disturbance, including grazing wild fauna and human impact, has also been important in driving diversity. Up to now, five different faunal Pleistocenic complexes have been recorded in Sicily, occurring from the early Pleistocene to the Late Glacial periods (Bonfiglio et al., 2002). Most show a more or less sharp trophic unbalance due to an irregular presence of a scarce pool of carnivores. Under low predation pressure, herbivores could have experienced notable demographic booms, so it appears likely that large areas of Sicily were already characterized by open habitats even before the first occasional (~35,000 years ago) and definitive (~20,000 years ago) colonization by humans (Tusa, 1994; Mussi, 2001). Recent paleobotanical investigations (Sadori et al., 2008; Stika et al., 2008; Noti et al., 2009; Tinner et al., 2009) also suggest that from the beginning of the Holocene (~10,000 years ago) onward, human impact has been the main factor responsible for the final opening of the Sicilian landscape. Human activities (burning, clearing, cutting, farming, ploughing, etc.) not only fostered the success of many allochthonous pioneers and helio-xerophilous plants inadvertently introduced with crop species (now named ―archaeophytes‖), but also shaped the regional vegetal landscape giving rise to a complicated mosaic of prevalently open habitats dominated by sub-shrubs and grasses (Guarino, 2006).

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Indeed, wild food plants, mainly represented by herbs and/or small shrubs, could have found optimal conditions thus favouring interest from the gathering human communities since the early stages of civilisation. Within this framework, due to its position in the middle of the Mediterranean Basin, Sicily has historically played and still plays a very important role as ‗stepping stone‘ not only for the spreading of plants, but for human migrations as well. Consequently, as far as the present investigation is concerned, the island has acted as a veritable melting pot for the ethnobotanical knowledge of Mediterranean people. Each civilisation or domination has left its own traces either through the use or introduction of plants formerly unknown or by giving them vernacular names, which can vary considerably for the same species depending on linguistic influence on local dialects. Such occurrences inevitably underline the value of cultural (e.g. ethnical and linguistic) diversity, which is inextricably linked to biological diversity (UNESCO, 2010b). The year 2010 can be seen as the year that celebrated the diversity of life on earth in all its forms, but it has also raised concerns about the unprecedented biological and cultural changes that are taking place, including loss of species. The impact of reductions in bio-cultural diversity on the resilience of our planetary system is profound (UNESCO, 2010b). In the current global change context, the loss of biological diversity, with the simultaneous loss of languages, knowledge systems and specific ways of life, has resulted in new challenges for coupled social-ecological systems. To address these challenges, it is critical that the links between biological and cultural diversity encompassing, inter alia, languages as repositories of knowledge and practices, tangible and intangible heritages related to nature, modes of subsistence, economic and social relations and belief systems - are taken into consideration in policy development at all scales. This chapter is an attempt to provide a comprehensive overview of the rich cultural heritage related to the use of wild food plants that has developed over the centuries in Sicily. In the purely conservative perspective, it also aims, by analysing and clarifying the etymological meaning of their vernacular names, at organising an updated plant check-list in a logical fashion, thus contributing to univocal classification in order to reduce the risks of misidentification.

2. HUMANS IN THE MEDITERRANEAN BASIN AND THEIR DIET Before the domestication of plants and animals, Homo sapiens was dependent on foods that were available within his own territory. The fact that humans have survived under extremely different climate and ecological conditions, with highly variable dietary patterns, confirms the remarkable biological and cultural resilience of our species and our ability to exploit the large variability of resources in nature (Willett, 2006). From ancient times, the Mediterranean Basin has been a hub for different migration pathways and commercial routes, acting as a cultural and ethnic crossing point (Arnaldi, 2002). As elsewhere in the Mediterranean, the heterogeneity of cultures and populations migrating to and out of Sicily supported the transfer of knowledge about plant uses, thereby increasing the available food options. During the last decades, the nutritional regime of traditional Mediterranean diets has become the object of a growing interest. On 29th August 2009, Greece, Italy, Morocco and

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Spain, jointly presented the Nomination of the Mediterranean Diet for its inscription on the ―Representative List of Intangible Cultural Heritage of Humanity‖ to the UNESCO. From the description presented by the ―Intergovernmental Committee for the Safeguarding of the Intangible Cultural Heritage‖, it emerged that the Mediterranean diet consists in ―a set of skills, knowledge, practices and traditions ranging from the landscape to the table, including the crops, harvesting, fishing, conservation, processing, preparation and, particularly, consumption of food. The Mediterranean diet is characterized by a nutritional model remained constant over time and space, consisting mainly of olive oil, cereals, fresh or dried fruit and vegetables, a moderate amount of fish, dairy and meat, and many condiments and spices, all accompanied by wine or infusions, always respecting the beliefs of each community‖. Moreover, ―the system is rooted in respect for the territory and biodiversity, and ensures the conservation and development of traditional activities and crafts linked to fishing and farming in the Mediterranean communities‖ (UNESCO, 2010a). As can be observed in the former statements, proteins and fats from animal derivation are sufficiently represented, although foods from plants constitute a significant proportion of the diet. Among these, some wild herbs, aromatic plants and spices (e.g. asparagus, wild beet chart, saffron, wild cardoon, fennel young shoots and seeds, capers, etc.) are often irreplaceable ingredients for the most peculiar and genuine regional or local dishes. The final decision for the inclusion of the Mediterranean diet in the List of Intangible Cultural Heritage of Humanity was made at the Fifth Session of the ―Convention for the Safeguarding of the Intangible Cultural Heritage‖, held in Nairobi, Kenya, 15-19 November 2010. This resolution, with all its implications from biological, ethnic and linguistic points of view, represents a veritable milestone in programs of conservation of bio-cultural diversity, contributing at providing new ways to adapt to change, articulate traditional knowledge and create institutions to deal with the challenges, opportunities and threats posed by modern changes.

3. A CHECK-LIST OF SICILIAN EDIBLE PLANTS: INVESTIGATION APPROACH AND SOURCES In the present chapter, only data concerning the autochthonous plants and the archaeophytes gathered as food or for aromatic uses were considered. Native plants collected for other purposes, as well as archaeophytes and neophytes, were excluded. Synthetic information on the most important features of Sicilian edible wild plants was drawn from the literature and oral sources (Table 2), considering the following data: 1) scientific name according to Giardina et al. (2007); 2) family according to APG (2009); 3) Sicilian vernacular name(s); 4) status: native (N) vs. uncertain (U) according to Conti et al. (2005); 5) life form sensu Raunkiær (1934); 6) Edible part(s), whose abbreviations follow the scheme proposed by Lentini and Venza (2007), i.e.: aerial parts (a-p), basal rosettes (b-r), bulbs (bu), flowers (fl), flower buds (fl-b), fruits (fr), inflorescences (infl), leaves (le), roots/tubers (ro), pseudo-fruits (ps-fr), seeds (se), tender parts (t-p), tender stems (t-st), stem juice (st-j), whole plant (w-p), young shoots (y-s); 7) traditional food use, according to information from Mortillaro (1881), Arcidiacono and Pavone (1995), Lentini and Venza

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(2007) and other authors. A preliminary list of the origin of the vernacular names of some Sicilian wild edible plants is summarised in Table 3. To improve the accuracy of the list, we first checked for the wild food plants quoted in the following sources: some of the most ancient botany treatises of Sicily, including Cupani (1713) and Ucria (1789); the main Sicilian dictionaries of the XIX century, i.e. Traina (1868) and Mortillaro (1881); the ethnological works of Calcara (1851), Salamone Marino (1897) and Pitrè (1939), or appearing within the volumes edited by the ‗Centro di Studi Filologici e Linguistici siciliani‘ (Piccitto, 1977; Tropea, 1985, 1988, 1990, 1997; Trovato, 2002); the botanic dictionaries of Penzig (1924), Provitina (1986, 1990), Pirrone (1990) and Hammer and Laghetti (2006). Etymologies mainly issue from Giarrizzo (1989). Further information was obtained from Rocci (1980), for Greek words, and Bustamante Costa (2009), for Arab ones. Finally, many recent ethnobotanical contributions on Sicilian edible plants (Sommier, 1908; Di Martino, 1970; Barbagallo and Furnari, 1970; Galt and Galt, 1978; Lentini, 1989, 2000, 2002; Raimondo and Lentini, 1990; Lentini and Raimondo, 1992; Lentini et al., 1993; Ilardi and Raimondo, 1994; Arcidiacono and Pavone, 1995; Amico and Sorce, 1997; Catanzaro, 2002; Arcidiacono, 2002; Lo Cascio and Navarra, 2003; Napoli and Giglio, 2002; Di Pede, 2002; Ruta, 2006; Arcidiacono et al., 2007) were consulted and some oral sources were also taken into account. In the following paragraphs, special attention is paid to four interesting topics: i) phytonyms, ii) main wild edible plants, iii) food-medicines and iv) underutilized plants.

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4. PHYTONYMS: HANDLE WITH CARE A multitude of people from different origins and cultures have colonised or dominated Sicily during its millennial history. From time to time, Sicani (probably from northern Africa), Siculi (an Indo-European population from the Italian peninsula), Elymians (coming from the Near East), Punics (from north Africa), Greeks, Romans, Byzantines, Arabs, Normans (from north-western Europe), Swabians (from Germany), Angevins (from France), Aragoneses (from Spain), Spaniards and Bourbons (from Spain) (Tusa, 1994; Rickards et al., 1998) have all profoundly influenced the usages and customs of the Sicilian inhabitants, sometimes simply dominating the region, and at othertimes integrating their knowledge with the local culture. As a result, the current Sicilian dialect is a linguistic mixture of words with quite different origins. Due to the different degree of local influence of each domination, plant vernacular naming may differ notably within the regional territory and cases of synonymy are quite common. In order to ascertain the correct identification of each taxonomic entity, it was firstly necessary to check for the univocal correspondence vernacular name / taxon for the Sicilian wild edible plants reported in the specialistic literature. Additionally, the etymological analysis of the vernacular names provided a valuable contribution to their proper attribution. The research enabled the compilation of a check-list (Table 2) of nearly 2000 vernacular names referring to 224 edible plants plus 30 taxa, whose vernacular names and/or systematic affinities suggest an alimentary use (at least in the past).

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Table 2. List of the popular names and the main uses of Sicilian edible plants Scientific name according to Giardina et al. (2007) Alliaria petiolata (M. Bieb.) Cavara & Grande

Family according to APG (2009) Brassicaceae

Allium ampeloprasum L.

Amaryllidaceae

Allium nigrum L.

Amaryllidaceae

Allium roseum L.

Amaryllidaceae

Allium triquetrum L.

Amaryllidaceae

Ammi majus L.

Apiaceae

Apium graveolens L.

Apiaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Pedi d'asinu 3. Agghialòra (Penzig, 1924), Aggialòra vera (Pasqualino, 1783-1795), Erva masticògna (Piccitto, 1977) 1. Porru, Porro, Purretta 2. Agghiàstru, Agghiu porru, Aggioru, Cipuddàzzu, Ghiàstru, Porru, Puorru sarvaggiu, Purri, Purru, Purriètti 3. Cipuddàzzu, Agghiu porru, Aggiòru, Agghiàstru, Ghiàstru (Arcidiacono and Pavone, 1995); Purru, Purrèttu (Penzig, 1924)

2. Porra 3. Agghiu d'i siminati, Cipuddàzza (Piccitto, 1977); Cipudda fètida (Penzig, 1924); Cipuddùzza, Purrazzèddu (Pirrone, 1990) 2. Porru 3. Agghiu sarvaggiu, Purrièttu (Pirrone, 1990) 1. Agghialòri, Purretti, Porri sarvaggi 2. Porrua, Pràs 3. Agghialòra, Agghialòri, Aggièddu sarvaggiu (Piccitto, 1977); Prazzìddi (Penzig, 1924) 2. Sberra, Caliota 3. Cariota (Giarrizzo,1989); Cicònia, Cicòria (Traina, 1868); Ènniri cu‘ fogghi larghi, Erva annettadenti, Galiòtu (Piccitto, 1977); Galiòtu cu‘ fogghi larghi, Sponza (Penzig, 1924), Sponza di gesumìnu, Sponza di gersimìnu (Pirrone, 1990); Ènniri (Provitina, 1990) 1. Accia sarvaggia, Accia 2. Accia 3. Accia, Accia sarbaggia (Traina, 1868); Acci (Amico and Sorce, 1997); Acciu, Appiu (Piccitto, 1977); Gàccia (Pirrone, 1990); Ràdichi d‘acci (Salamone Marino, 1897)

Status

Life form

Edible part(s)

Traditional food use in Sicily

N

H bienn

le, fr

Substitute of garlic

N

G bulb

bu, le, t-p

N

G bulb

bu

bu: steamed and seasoned with oil, salt and vinegar or preserved in oil; grounded and mixed with flour, eggs, parsley and cheese, it is used to prepare meat balls to fry; roasted on ashes and seasoned with oil and salt. t-p ("‘u tènniru") and bu ("‘a testa") are used to season soups, sauces, chicken broth and veal. Le: used to add aroma to omelettes and to the "olive cunzate" Fried in oil and added to the tomato sauce to intesify its flavour

N

G bulb

bu

As seasoning

N

G bulb

bu

Seasoning for salads and traditional dishes with cheese and olives

N

T scap

le

Raw or boiled and seasoned with oil and lemon

U

H bienn

t-st

As flavour for salads, meat soups and vegetables; it is consumed cut in cubes and mixed with goat cheese

ib/multco/detail.action?docID=3021288.

Table 2. (Continued) Scientific name according to Giardina et al. (2007)

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Apium nodiflorum (L.) Lag.

Family according to APG (2009) Apiaceae

Arabis collina Ten.

Brassicaceae

Arabis hirsuta (L.) Scop.

Brassicaceae

Arabis turrita L.

Brassicaceae

Arbutus unedo L.

Ericaceae

Asparagus acutifolius L.

Asparagaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Scavùni 2. Crisciùni, Scavùna 3. Scravùni (Lentini, 1989); Crisciùni (Penzig, 1924); Erva cannedda (Piccitto, 1977) 3. Razzi sarvaggi (Provitina, 1990; Pirrone, 1990) 3. Razzi (Provitina, 1990; Pirrone, 1990) 2. Mazarèddra duci 3. Bbalichièddu, Cavulèdda (Pirrone, 1990), Razzi (Pirrone, 1990; Provitina, 1990) 1. Mbriàcula 2. Corbezzulu, ‘Mbriacotti, ‘Mbriachèddi, Acùmmaru 3. Agugùmara (Piccitto, 1977); Agùmara, Agùmaru (Pasqualino, 1783-1795); Òmmari, Vòmmira, 'Mbriàculi (Raimondo and Lentini 1990); 'Mbriacòtta, Armuìni, Armuìnu, Armulìna, Arvulu miraculi, Aùmaru, Aùmmaru, Aùmmiru, Bbazzarìnu, Ciràsa marina (Piccitto, 1977); Iarmalìni (Pasqualino, 1783-1795); Lughi (Penzig, 1924); 'Briàca, 'Mbrichèdda, 'Mbriachètta, 'Mbriachiddàra, 'Mbriacula, 'Mbriàculu, Miràculu, Peri di miràculi, 'Mmiràculu, 'Mmirìaca, 'Mmbriachèdda (Tropea, 1985); 'Mpriachedda, Mmiriàculi, 'Mmràcula, 'Mpriacula, 'Mpriàculu (Pirrone, 1990); Fraulùni (D. Brolo, pers. comm.) 1. Sparacogna 2. Aspàraci, Spàraci, Spàraciu finu, Spàraci sarbaggiu, Spàracia, Sparaciàra, Sparacièddru, Spàracio, Spàraciu sarvaggiu, Sparacògna, Spràngiu, Spinapùcciu, Spinapùlici, Spinipùggiu, Spàraci servaggi 3. Sparagògna (Giarrizzo, 1989); Spàraciu nivuru (Raimondo and Lentini, 1990); Spàraciu niuru (Traina, 1868), Sparacògna (Lentini, 1989), Spinapùlici, Spinapùgghiu, Spinipùggiu, Spinapùcciu, Spàraciu spinusu, Spàraciu sarbaggiu, Spàraciu, Sparaciàru, Sparaciàra, Sparaciù, Spàracia, Spini di Bamminu, Spàraciu scuru (Arcidiacono and Pavone, 1995); Spàriciu

Status

Life form

Edible part(s)

N

H scap

a-p

N

H ros

a-p?

N

H scap

a-p?

N

H scap

a-p

Seasoned with oil, lemon and salt

N

P scap

fr

The raw fruits are consumed or in readymade marmalades jams with 1 kg of mature fruits, 600 gr of sugar, ½ lemon, ½ glass of maraschino cherries and ½ glass of water

N

Ch frut

y-s

Boiled like vegetables in an omelette. Panfried with onion and oil, in order to add aroma to the tomato sauce or as seasoning for risottos

Traditional food use in Sicily Boiled raw in salads, seasoned with oil and salt

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Asparagus albus L.

Asparagaceae

Asparagus aphyllus L.

Asparagaceae

Asparagus horridus L.

Asparagaceae

Asphodeline lutea (L.) Rchb.

Xanthorrhoeaceae

Asphodelus ramosus L.

Xanthorrhoeaceae

Astragalus boeticus L.

Fabaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors (Iacono, 2009); Spina di sùrci (Galt and Galt, 1978) 2. Spàraci, Spàraciu spinosu, Sparacògna, Spàraciu jancu 3. Spàraciu biancu (Raimondo and Lentini, 1990); Spàraciu jancu o vrancu, Spàriciu jancu o vrancu (Penzig, 1924); Spàraciu (D. Brolo, pers. comm.) 3. Spàraciu nìuru (Provitina, 1990) 3. Spàraciu arrianu, Sparacògna sarvaggia, Spàriciu spinusu (Penzig, 1924), Spàriciu marinu (Provitina, 1990) 1. Garùfu, Zubbi 2. Baffalùpo, Battagghiòri, Garufi, Musulùchi, Puddicìna, Scannabbeccu, Puddicinu, Zzubbi, Baffalùchi 3. Garufu (Giarrizzo, 1989); Zzubbi, Battagghiori, Scornabeccu, Scannabeccu (Arcidiacono and Pavone, 1995); Aruffi (Ilardi and Raimondo, 1994); Puddicìnu (Lentini, 1989); Arùffu, Arùfu, Bbafalùcu (Penzig, 1924); Bbèccu, Garùffu, Garùfi, Garùfu (Tropea, 1985); Mafalùcu, Vavalùcu (Pirrone, 1990); Musulùccu (Provitina, 1986); Maialuffu (G. Garfì, pers. record) 1. Arvuzzi, Cucuncèddu, Purràzza 2. Agghiu porru, Purrazzu 3. Arvuzzi, Purrazzu, Cucunceddu (Giarrizzo, 1989); Purracca (Lentini and Raimondo, 1992); Firringhèddu, Agghiu porru, Purrazzu (Lo Cascio and Navarra, 2003); Abbruzza, Aliùzzu, Alivùzzu, Arivùzzu, Arbuluzzu, Arvùzzi ramùsi (Penzig, 1924); Aulivuzzèddu, Aulivùzzu, Bbastùni di Sanciusèppi (Piccitto, 1977); Bbruvàca* (Tropea, 1988); Bbeccu, Burraccia, Burrazza (Penzig, 1924); Burrazzèdda, Cannariddàra, Cannilèra, Cipuddàzzu, Cucuncèdda, Cuncucèddu minuri, Cucuncèddu, Cucunzèddu, Erva burraccia (Piccitto, 1977); Purràzza, Purràzzi (Pirrone, 1990) 3. Caffè missicanu (Penzig, 1924)

Status

Life form

Edible part(s)

N

Ch frut

y-s

N

Ch frut

y-s

N

Ch frut

y-s

Boiled and seasoned with oil and lemon, scrambled with eggs

N

G rhiz

fl, a-p, y-s

Fl are eaten boiled and seasoned with oil, lemon, salt and pepper or covered with flour and fried; the tender stem is used for the preparation of omelettes together with asparagus; when fried it is added to the sauce for extra flavour. The sprouts harvested before full bloom are peeled, cooked and fried with eggs

N

G rhiz

ro

The underground parts are eaten boiled and with a bit of olive oil

N

T scap

se

Toasted to obtain a substitute of coffee (Pirrone, 1990)

Traditional food use in Sicily

Boiled and seasoned with oil and lemon, scrambled with eggs

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Athamanta sicula L.

Family according to APG (2009) Apiaceae

Barbarea vulgaris R. Br.

Brassicaceae

Bellis perennis L.

Asteraceae

Berula erecta (Hudson) Coville

Apiaceae

Beta maritima L.

Chenopodiaceae

Beta vulgaris L.

Chenopodiaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Daucu, Pedi di nigghiu, Daucu cretico, Pastinaca salvatica 3. Petrafènnula (Giarrizzo, 1989); Càriu, Cincinnàura (Piccitto, 1977); Dàucu creticu, Spezzapètri (Penzig, 1924); Nigghiu (Pirrone, 1990); Ped'i nigghiu (Cupani, 1713) 1. Làssana 3. Caulicèddi (Pirrone, 1990); Caulicèddi di crapa, Càvulu sarvaggiu (Pasqualino, 1783-1795); Erva di Santa Bbàrbara (Piccitto, 1977); Làssana, Làssani, Ravanastri, Ruca sarvaggia (Penzig, 1924) 2. Erva di primu ciùri, Erba bianca, Jancuzzu, Primusciùri 3. Jancùzzu, Primusciùri, Erba di primu ciuri, Erba bianca (Arcidiacono and Pavone, 1995); Bbellis, Ciùri bbiancu, Erva di primiciùri (Piccitto, 1977); Zzita, Faurèdda d'u Signuruzzu, Lastra, Maicìddu, Maiulìna, Maggherita, Margarita di campagna, Margarita sìmplici, Margherita, Margirita, Maricarìta, Munachièddu, Primu sciùri (Penzig, 1924) 3. Scavùni (Penzig, 1924), Schigghioni (Provitina, 1990; Pirrone, 1990) 2. Aggìti, Agìri, Billètti, Geri, Giri, Sarchi, Sècala, Seghila, Zarca, Zarchi, Zarchitèdda 3. Aìti, Gira (Giarrizzo, 1989); Sècala, Sècala sarbaggia, Sèghila, Gira, Giri, Geri (Arcidiacono and Pavone, 1995); Zarchi sarbaggi (Lentini, 1989); Agghìti (Iacono, 2009)

1. Gira 2. Giri, Zàlaca, Zarca 3. Bilètti, Bletta, Britta (Pirrone, 1990); Giri, Zarchi, Sàlica, Gida (Lentini, 2000): Aìta, Aìta sarvaggia, Aiticèdda sarvaggia, Bbletti, Bbritti, Carota, Caròtuli, Gira, Gira russa, Gira di ripi di mari, Sàlica, Sarchi, Sécali, Séchila sarvaggia, Sècala, Sècala zarca (Penzig, 1924)

Status

Life form

Edible part(s)

N

H ros

ro

N

H scap

a-p

N

H ros

b-r

N

G rhiz

a-p?

N

H scap

le

U

H scap

a-p

Traditional food use in Sicily

It is used to be added in vegetable soups in times of poverty or lack of other vegetables

Boiled and seasoned with olive oil and lemon; fried with garlic, bread crumbs, cheese and parsley; browned with tomatoes and garlic, chili pepper and small pieces of cheese; boiled and fried as filling for "focacce scacciate"; fried with potatoes for preparing the filling of the "cuddiruni", a typical kind of pizza Boiled and seasoned with olive oil, salt and black pepper; fried "ngranciata" (= browned) with garlic, olive oil and tomatoes; "maritata" (= married) with other wild vegetables or as ingredient for soups. It can also be boiled and fried with potatoes and onions and used for

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors

Status

Life form

Edible part(s)

Traditional food use in Sicily

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preparing the filling of the "cuddiruni", a typical kind of pizza of the province of Messina Biscutella maritima Ten.

Brassicaceae

3. Cassatèddi, Ucchialèddi, Ucchialèddi di Santa Lucia (Penzig, 1924)

N

T scap

a-p

Borago officinalis L.

Boraginaceae

1. Vurrània 2. Burrània, Vurrània, Urrània 3. Vurràina (Salamone Marino, 1897), Vurrània (Calcara, 1851); Bburraina (Arcidiacono and Pavone, 1995); Burrània (Piccitto, 1977); Burràina (Penzig, 1924); Purràina, Urràina, Urrània (Salamone Marino, 1897); Vurràina c'u ciuri azòlu (Cupani, 1713)

N

T scap

le, fl, a-p

The basal leaves are eaten boiled and seasoned with olive oil, lemon, and salt as vegetables; in soups or in omelettes; also boiled, minced and mixed with water and flour, are used to prepare "tagghiarini virdi" (= green tagliatelle). In the Etna region, during spring, people collect the upper parts ("'i spicuna") in order to prepare dishes of vegetables and soups such as the "paparotta"

Brassica fruticulosa Cirillo

Brassicaceae

2. Calicèddu, Caulicèddu, Calucèddu, Cauricèllu, Cavulicèddu, Colicèddu, Coricèllu, Qualicèddu, Quaricèllu, Rapuddi 3. Calucèddu, Caulicèddu, Cavulicèddu, Calicèddu, Qualicèddu, Colicèddu, Cauricèllu, Quaricèllu, Cavuricèllu, Coricèllo (Arcidiacono and Pavone, 1995); Caulicèddi, Caulicèddi di Missina (Piccitto, 1977); Caulicèddi di crapa (Pasqualino, 1783-1795); Cauliceddi di vigna (Pirrone, 1990); Rapuddi (Lo Cascio and Navarra, 2003)

N

H caesp

b-r, y-s, infl

Very widespread and commonly consumed in the Etna region, where it is eaten fried with garlic, oil and chili pepper, above all as side dish to pork sausages; consumed also in the Eolian islands. Le and fl eaten both raw and cooked

Brassica incana Ten.

Brassicaceae

N

Ch suffr

le, infl

Brassica nigra (L.) Koch

Brassicaceae

3. Amarèddi (Pirrone, 1990) 1. Sinàpa 2. Cavulicèddu niuru, Qualèddu, Làssani, Mazzarèdda amara, Mazzarèddi, Qualèdda, Senàpa, Sinàpu 3. Mazarèdda (Tropea, 1985); Sanàpu, Sinàpa masculina o sarvaggia (Penzig, 1924); Sinàpa nìura (Pirrone, 1990); Sinàpi (Lentini & Raimondo, 1992)

U

T scap

a-p, b-r

The lower part, still tender, is eaten boiled and seasoned with olive oil and lemon or cooked and fried with oil, garlic and tomatoes like side dish of typical meatbased meals. The tender parts, boiled and seasoned with olive oil or added to the soup, are used as a laxative

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Brassica rapa L. subsp. campestris (L.) Clapham

Family according to APG (2009) Brassicaceae

Brassica rapa L. subsp. rapa

Brassicaceae

Brassica rupestris Raf. subsp. rupestris

Brassicaceae

Brassica tournefortii Gouan

Brassicaceae

Bunias erucago L.

Brassicaceae

Cakile maritima Scop.

Brassicaceae

Calamintha nepeta (L.) Savi s.l.

Lamiaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Cavuliceddi veri, Cavuliceddi di vigna, Cavuliceddi, Cauliceddi 2. Alàssanu sarbaggiu, Cavulàzzi, Sciurìddi, Cavulèdda, Cavulicèddi, Cavulicèddu amaru, Mazzarèddi, Spicùna sarbaggia, Qualèddu, Spicùni, Àssani, Lapisani 3. Cavulàzzu (Lentini and Raimondo, 1992); Calùzzi, Qualùzzi (Lentini, 2000); Caulicèddi, Caulicèddi di Missina, Caulicèddi pisciacani, Caulicìddu, Cavulicèddi, Cavulicièddi, Cavulicìddi, Cavulièddu, Càvulu forti (Piccitto, 1977); Càvulu trunzu o Càvulu di trunzu (Pirrone, 1990) 3. Mazzarèddi (Lentini, 1989)

3. Caulicèddu, Càulu di rocca o sarvaggiu (Penzig, 1924); Cìmati? (Giarrizzo, 1989) 2. Musulùchi 2. Catanzìculi, Cicòina, Cicònia, Erba stidda, Cicòira, Mazzarèlli, Triulìddi 3. Cicònia, Cicòni di vigna, Cicoina di vigna, Spinàcia sarbaggia, Cicòira, Mazzarèlli, Cicoina, Erba-stidda, Erba stilla, Cicòina sarbaggia, Catanzìculi, Triuliddi (Arcidiacono and Pavone, 1995); Arùca (Piccitto, 1977); Cicòria sarvaggia (Pirrone, 1990); Ggirgira* (Tropea, 1988) 3. Arùca marina (Traina, 1868); Arùcula di mari, Cavulu sarvaggiu (Piccitto, 1977) 1. Nipitedda 2. Amintàstru, Nièputa, Nieputèdda, Nipitèdda, Niputèdda (Lentini, 2000)

Status

Life form

Edible part(s)

U

H bienn

b-r, infl

The lower part, still tender, is eaten boiled and seasoned with olive oil and lemon or cooked and fried with oil, garlic and tomatoes. The young flowers (sciuriddi) are eaten boiled with salty water and mixed with eggs or fried in the pan

U

T scap

b-r, inf

The lower part, still tender, is eaten boiled and seasoned with olive oil and lemon or cooked and fried with oil, garlic and tomatoes. The young flowers ("sciuriddi") are eaten boiled with salty water and mixed with eggs or fried in the pan

N

Ch suffr

le, infl

N

T scap

le

Boiled and seasoned with olive oil and lemon

N

T scap

le

Boiled and seasoned with olive oil and salt

N

T scap

a-p

N

H scap

le, y-s

Traditional food use in Sicily

As seasoning for omelettes or other meals and specially the boiled "babbaluceddi" (white escargots). A typical dish of the

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 3. Nieputìdda (Lo Cascio and Navarra, 2003); Erva di funci (Piccitto, 1977); Nipitèdda, Nippitèdda (Penzig, 1924); Niputèdda (Pirrone, 1990); Amintastrèddu (D. Brolo, pers. comm.)

Status

Life form

Edible part(s)

Capparis ovata Desf.

Capparaceae

2. Càppiru, Chiàpparu sarbaggiu, Chiàppara, Chiapparèdda liscia, Ciàppari 3. Chiàppara (Amico and Sorce, 1997); Càppiru, Chiàppara (Penzig, 1924); Chiàppara cirasola, Ciàppara, Ciapparàzza, Ciapparèdda, Ciàppiru (Pirrone, 1990)

N

Ch suffr

fl-b, le, y-s

Capparis spinosa L.

Capparaceae

1. Chiappara, Chiappari 2. Chiàppaira, Chiàppara, Chiàppare, Chiàppari, Chiapparìna, Chiàpparo, Chiàpparu, Chiàpparu manzu, Chiàppuli, Ciàppiru, Ciàppari 3. Chiàppara (Amico and Sorce, 1997); Càppiru, Chiàppara (Penzig, 1924); Chiàppara cirasola, Ciàppara, Ciapparàzza, Ciapparèdda (Pirrone, 1990), Ciàppiru (Pirrone, 1990; Iacono, 2009))

U

NP scand

fl-b, le, fr

Capsella bursa-pastoris (L.) Medik.

Brassicaceae

N

T scap

a-p

Cardamine hirsuta L.

Brassicaceae

3. Bbursa, Bbursa di picuràru (Piccitto, 1977); Bbursa pasturi, Calatafàna, Menzalunèdda (Piccitto, 1977); Làssina, Làssinu, Mastròzzu sarvaggiu, Vurza di picuraru (Pirrone, 1990); Vurza di pasturi (Penzig, 1924), Urzapasturi (Salamone Marino, 1897) 3. Aruculicèdda sarvaggia, Cardàmina, Crisciuneddu d''i mura, Erva di San Marcu (Piccitto, 1977)

N

T scap

a-p?

Traditional food use in Sicily Eolian Islands the "nieputiddata" is prepared by mixing the plant with garlic, pestle and mortar and cooking it with oil, tomatoes, adding eggs, some other vegetables and stale bread. Also used to aromatise omelettes The flower buds are preserved in salt, vinegar or olive oil and added in order to season salads during the summer or also sauces and various meals. The leaves and the young parts are boiled in salty water and seasoned with salt, oil and vinegar and are added to tomato salad. With olives and tuna fish, capers are used to prepare pasta sauces Before consuming the parts of the plant you need to "cure" them by boiling and changing the water for at least 2-3 days in order to eliminate a bitter substance; they are preserved under salt or vinegar. The leaves, boiled or seasoned with oil, salt, oregano and vinegar are eaten as vegetables; the small fruits ("cucunci"), kept in salty water, are used to season many dishes; the shoots are eaten boiled with oil and lemon or in omelettes. With capers, olives, garlic, oil, broccoli, chilli peppers, carrots and celery is prepared a traditional meal, the "stemperata" Boiled or in infusion

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Table 2. (Continued)

Cardaria draba (L.) Desv.

Family according to APG (2009) Brassicaceae

Carduus argyroa Biv.

Asteraceae

Carduus pycnocephalus L.

Asteraceae

Carlina gummifera (L.) Less.

Asteraceae

Carlina hispanica Lam. subsp. globosa (Arcangeli) Meusel & Kästner

Asteraceae

Carlina sicula Ten. subsp. sicula

Asteraceae

Carrichtera annua (L.) DC.

Brassicaceae

Carthamus lanatus L.

Asteraceae

Carthamus pinnatus Desf.

Asteraceae

Celtis australis L.

Ulmaceae

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Scientific name according to Giardina et al. (2007)

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 3. Aruchèdda (Penzig, 1924); Erva pipirìna (Piccitto, 1977) 2. Napordi d'acqua 3. Napordi d'acqua (Lentini, 1989) 2. Scoddi 3. Magnazzu, Saittùni (Penzig, 1924); Cardùna? (Salamone Marino, 1897) 1. Cacucciulìdda, Carlina, Agghiàru, Masticògna 2. Masticògna 3. Masticogna (Penzig, 1924); Mastica sarvaggia, Erva masticogna (Piccitto, 1977); Masticògnu, Masticuògna, Masticuògnu (Pirrone, 1990) 3. Mazzacugghiuna, Mazzacani, Mazzacugghiuni (Arcidiacono and Pavone, 1995); Bbuttasumèri, Cardunàzzu (Piccitto, 1977 sub C. corymbosa); Cardogna (Penzig, 1924); Panicàudu (Provitina, 1990 sub C. corymbosa) 2. Carlina siciliana 3. Panicàudu (Pirrone, 1990)

3. Mastruzzu sarvaggiu (Penzig, 1924) 2. Vavanàzzi 3. Carduni 'nfiliniatu o ri spina (Piccitto, 1977) 2. Carduncèllu 2. Càccamo, Milicùccu, Milikùkku

Status

Life form

Edible part(s)

N

H scap

a-p?

N

T scap

t-st

Boiled in salty water or fried in butter or with eggs

N

T scap

le

Fried with butter or with eggs

N

H ros

infl

The inflorescences ("cacocciulidda") are consumed simply boiled or in a traditional stew seasoned with garlic, parsley and cheese

N

H ros

a-p

The stems should be washed once collected, cut into segments of 5-8 cm, then blanched and dressed with oil and vinegar

N

H ros

a-p

N

T scap

a-p?

It is eaten boiled and the trunks ("trunzi"), boiled and seasoned with oil and vinegar, are well appreciated for their peculiar taste similar to artichocke and hazelnuts NS

N

H bienn

t-st

Raw as vegetable

N

H ros

a-p

Fried with the batter or with eggs

U

P scap

fr

Eaten fresh or dried

Traditional food use in Sicily

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.

Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 3. Zafarem, Calambèrsa (Pirrone, 1990); Fafa reca, Favaracàru, Favaricàru (Pitrè, 1939); Favarècu, Fadarècu, Minicùccu, Milicuccàru, Middicuccàru (Pirrone, 1990); Favaràcchiu, Favaràggiu (Pitrè, 1939); Càccamu, Middicuccu (Giarrizzo, 1989;); Millicùccu (Arcidiacono, 2002); Càccamu nìuru, Menicùccu (Penzig, 1924); Farfarèca (Tropea, 1985); Milincùccu, Millicucchi, Mimicùccu (Piccitto, 1977)

Centaurea calcitrapa L. s.l.

Asteraceae

Centaurea sicula L.

Status

Life form

Edible part(s)

1. Aprocchi, Gattareddi 2. Apròcchiu, Abbròcciu, Grap l'occhi, Occhi 'n Cristu 3. Aprocchiu (Giarrizzo, 1989); Ambretta, Apròcchia, Appròcchiu, Appruòccu, Apuòrchiu, Apuòrciu, Erva Gattarèdda (Piccitto, 1977); Apruòcchiu, Gattarèddi (Pirrone, 1990); Appròcchi cu' ciuri russi (Cupani, 1713); Apùrchiu? (Amico and Sorce, 1997)

N

H bienn

le

Boiled and seasoned with olive oil and lemon, then eaten as refreshing food

Asteraceae

2. Appròcchiu 3. Buttùni d'oru (Pirrone, 1990)

N

H bienn

w-p

Boiled and seasoned with oil, lemon and salt

Centaurea solstitialis L. subsp. schouwii (DC.) Dostál

Asteraceae

1. Apròcchiu fimminedda 2. Gattarèdda 3. Apròcchi fimineddi (Traina, 1868), Scibbilichìsi (Giarrizzo, 1989); Xiaccabulìci (S. Pasta, pers. record); Buttuneddi d'oru (D. Brolo, pers. comm.)

N

H bienn

w-p

Boiled and seasoned with oil, lemon and salt

Centranthus ruber (L.) DC.

Caprifoliaceae

N

Ch suffr

le

Raw are used to make tasty salads

Ceratonia siliqua L.

Fabaceae

U

P scap

fr

The flesh of the sweet fruit were used to be eaten from poor people and today is still appreciated from the children living in the countryside. The flour is used to season the wine

Chamaemelum fuscatum (Brot.) Vasc.

Asteraceae

2. Pasqua russa, Pasqua bianca, Valeriana 3. Baddariàna russa (Penzig, 1924); Badda-riàna (Salomone Marino, 1897); Ciocca di muru, Ciocca ri muru, Sapunàra, Fricchi Fracchi (Pirrone, 1990) 1. Carrubba 2. Carruba, Carrubbu, Cicibanoz§ 3. Carrùi, iarrùba (Giarrizzo, 1989); Arrùbba, Arrùbbu, Arvulu di carrùbbi, Carrùa, Carrùbbi, Carrùbba, Carrubbàra, Pedi di carrùbba (Penzig, 1924); Carruàdda, Carrùva, Harrùbba, Pedi di carrùbbi (Pirrone, 1990) 2. Cacumidda, Cucumidda, Pan'i cavaddu 3. Finòcchiu marinu (D. Brolo, pers. comm.)

N

T scap

t-p

Raw or boiled and seasoned with oil and lemon

Traditional food use in Sicily

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Table 2. (Continued)

Chamaerops humilis L.

Family according to APG (2009) Arecaceae

Chenopodium album L.

Chenopodiaceae

Chenopodium ambrosioides L.

Chenopodiaceae

Chondrilla juncea L.

Asteraceae

Cichorium intybus L.

Asteraceae

Clematis vitalba L.

Ranunculaceae

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Scientific name according to Giardina et al. (2007)

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Ciafagghiùni 2. Giummàrra, Safaggùni, Scupàzzu 3. Ciafàggiu, Ciafagghiùni, Ciafagghiùni spinusi (Penzig, 1924); Ciafagliùni, Ggiafagliùni, Ggirbigghiùni (Traina, 1868); Giafaggiùni, Giummàra, Ggiummàrra, Giummàri (Penzig, 1924); Giummarùni, Erv'i scùpi, Scupàra, Scuparìna, Scupazzu (Pirrone, 1990); Ddùmmi (fruits: Giarrizzo, 1989) 2. Erva fitenti 3. Brittìcula spica o c'u fogghiu majuri (Cupani, 1713); Inìsca (Pirrone, 1990); Ainìsca (Arcidiacono and Pavone, 1995) 1. Erva tè siciliana 3. Erva siciliana (Penzig, 1924); Erva tè siciliana (Traina, 1868); Tè siciliano (Penzig, 1924) 2. Cud'e attu, Cudidda, Curi 'i suggi, Curìdda, Cutulèdda, Cutulìdda, Inestruòla, Inestruòra, Inistròra 3. Cudìdda, Curìdda, Cutulìdda, Cud'e attu, Cuturìdda, Inestruòla, Inestruòra, Inistròra, Cur'i suggi, Cutulèdda (Arcidiacono and Pavone, 1995); Caccialièbbri, Caccialèpri (Pirrone, 1990) 1. Cicoria 2. Cicòira, Cicòria, Cicòria amara, Cicòria catalogna, Cicoria di campagna, Cicoria di chianca 3. Nirvia (ms), Cicòria (Amico and Sorce, 1997); Cicònia (Arcidiacono, 2002); Cicòria amara (Catanzaro, 2002); Cardìdda ( Galt and Galt, 1978); Cicòina (Piccitto, 1977); Cicònia rizza, Cicònia di chiana (Penzig, 1924); Cicòria (Traina, 1868); Cicunièdda, Cicuòria, Scalòra (Pirrone, 1990) 2. Liara, Liareddi, Ligara, Mitabbi, Mitarbi, Vitalba, Viterbi 3. Ligàra (Calcara, 1851), Vitàrdi (A. Guella, pers. comm.), Vitàrda (Calcara, 1851); Mitàrbi, Liàra, Vitèrbi, Mitàbbi (Arcidiacono and Pavone, 1995); Liareddi (Lentini and

Status

Life form

Edible part(s)

N

NP caesp

y-s, t-p

U

T scap

w-p

U

T scap

le?

Boiled to obtain a substitute of tea ( cfr. Mortillaro, 1881)

N

H ros

a-p

During winter the basal part ("'a 'zzotta") is collected; during spring the floral axis ("'u giummu") and in the summer the buds of the branches ("'i taddi") are eaten cooked, raw or used like asparagus for preparing tasty omelettes

U

H scap

w-p

Raw or cooked, seasoned with oil, salt and lemon; in soup, in stew or fried with garlic and oil. The water in which the vegetable is cooked is drunk because of its positive effects on kidneys and liver. Furthermore it "cleans" the stomach and refreshes the intestine

N

P lian

y-s (otherwise poisonous)

Boiled and dripped, in order to eliminate a toxic and bitter substance, they are used to prepare omelettes, fillings and salads

Traditional food use in Sicily In Mazzarino and in Riesi, in the Caltanissetta province the bud was considered to be a delicious food which was eaten for Christmas. Nowadays, in Vittoria (Ragusa province) the buds and the lower tender parts are eaten raw in salads, seasoned with oil, salt, lemon and vinegar Boiled or fried in the pan with olive oil, garlic and pepper. It is also used to prepare "risotti" and several fillings

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Corylus avellana L.

Betulaceae

Crataegus monogyna Jacq.

Rosaceae

Crepis bursifolia L.

Asteraceae

Crepis leontodontoides All.

Asteraceae

Crepis vesicaria L. subsp. vesicaria

Asteraceae

Crithmum maritimum L.

Apiaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors Raimondo, 1992); Liàra, Liarèra, Lifara, Ligàna, Vitàrba, Viticèdda, Vraca di cuccu (Penzig, 1924); Ligàru, Ligunìa, Liunìa, Mitàrba, Mitàrva (Traina, 1868); Pitàlba (Salamone Marino, 1897); Vitùrbu (Pirrone, 1990) 2. Nucidda 3. Nizzòi, Nizzòli (Giarrizzo, 1989); Nucidda (Calcara, 1851); Nucidda sarvaggia (Penzig, 1924); Nucidda ghiandulàra (Pirrone, 1990)

Status

Life form

Edible part(s)

N

P scap

fr

2. Br'zulinu, Brizzulina, Brizzulino, Bruzzellinu, Russulina, Ursuliddru, Vrizzulina, Zinzuli 3. Nzìnzuli (Lentini, 1989); Azzalòra sarvaggia, Azzanèdda (Piccitto, 1977); Brizzulìnu, Bbrunzulìnu, Bbruzzulìnu, Cacariddàru, Iancuspìnu, Pedi di fruttu santu, Zìsima sarvaggia (Pirrone, 1990); Russulìddi (Di Martino, 1970); Ràttaculu (D. Brolo, pers. comm.) 2. Ricuttella, Rizzarella, Rizzaredda 3. Ricuttella, Rizzarella, Rizzaredda (Arcidiacono and Pavone, 1995) 3. Rizzaredda (http://www.caitaormina.it/pagine/erbedelletna.htm) 2. Cicoria amara, Cicoria missinìsa, Cicuriuni, Erba d'acieddri, Occh'i pirnici 3. Cicònia (Arcidiacono, 2002); Latti d'acieddu, Cardiddùzzi (Lentini, 2002); Cicòria vessicaria (Penzig, 1924); Lattuchèdda di lu Signuri (Tropea, 1985); Lattuchèdda 'i Diu (Pirrone, 1990); Denti di liùni (D. Brolo, pers. comm.)

N

P caesp

fr

They are widely used in the confectionary field in order to produce sweets. In Bisacquino (Palermo province) the 6th of January toasted and blessed nuts are distributed to all the families as sign of good auspice Fresh or in a sweet jam

N

H ros

le

Boiled and seasoned with oil and lemon

N

H bienn

le

Boiled and seasoned with oil and lemon

N

H bienn

a-p

Boiled and seasoned with oil and lemon

1. Erva di lu pitittu, Finocchiu marinu 2. Finucchieddu di mari, Finucchieddu marinu 3. Crìttimu (Pirrone, 1990); Erva di lu pitìttu (Pasqualino, 1783-1795); Finòcchiu marinu, Finòcciu marinu (Penzig, 1924)

N

Ch suffr

le, se

Used as seasoning

Traditional food use in Sicily

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Table 2. (Continued)

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Scientific name according to Giardina et al. (2007) Crocus longiflorus Raf.

Family according to APG (2009) Iridaceae

Cynara cardunculus L.

Asteraceae

Cynara scolymus L.

Asteraceae

Cyperus esculentus L.

Cyperaceae

Daucus carota L. subsp. carota

Apiaceae

Daucus carota L. subsp. maximus (Desf.) Ball

Apiaceae

Descurainia sophia (L.)

Brassicaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 3. Cersavòi (Giarrizzo, 1989); Zafaràna sarvaggia (Calcara, 1851); Crocu sicilianu (Piccitto, 1977); Zafaràna, Zafaranèdda (Pirrone, 1990) 2. Cacocciula sarbaggia, Cacocciuliddu sarbaggiu, Cacocciuliddu spinusu, Cacocciulu sarbaggiu, Cacucciuliddu, Carcocciula sarvaggia, Carduna servaggi, Carduni, Carduni centutesti, Carduni amaru, Caccucciuledda, Carduni sarvaggiu 3. Carduna di fiu, Carduna amari (Amico and Sorce, 1997); Cacòcciulu sarbaggiu (Lentini, 1989); Cacòcciula di spina, Cacòcciula di muntagna, Cacòcciula di vintùra, Cacòrciulu jancu, Carduni sarvaggi, Cuzzulìna (Piccitto, 1977) 3. Caccioffulu, Capòcciula (ms); Cardùna, Cacòcciula (Amico and Sorce, 1997); Carciòffula, Caccòrcila, Cacòcciula, Cacuòccila, Cacuòccima (Piccitto, 1977)

1. Cabbasisa, Trasi 3. Cìparu, zìparu (Giarrizzo, 1989); Cabbasìsi di Trapani (Cupani, 1713); Bbascìsi, Bbasìsi (Traina, 1868); Cabbasìsi (Penzig, 1924); Ciparèddu (Piccitto, 1977); 'Nziparèddu (Pirrone, 1990) 3. Pedi di gaddu (Lentini, 1989); Bastunàca, Frastunàca, Vastunàca sarvaggia (Penzig, 1924) 3. Cuda di gattu (Lentini, 1989) -

Status

Life form

Edible part(s)

N

G bulb

fl

Stamens are gathered in order to obtain a good substitute of saffron

N

H scap

t-st, fl-b

t.st are cooked and seasoned with oil, lemon and vinegar but also fried with eggs and parsley, boiled, stewed with onions, tomatoes and cheese; fried. fl.b: cut in the middle and roasted or cut in small slices and seasoned with oil and vinegar; boiled and seasoned with oil and lemon; mixed with flour and fried; in omelettes or stewed with potatoes

N

H scap

fl-b

U

G rhiz

ro

t.st cooked and seasoned with oil, lemon and vinegar but also fried with eggs and parsley, boiled, stewed with onions, tomatoes and cheese; fried. fl.b: cut in the middle and roasted or cut in small slices and seasoned with oil and vinegar; boiled and seasoned with oil and lemon; mixed with flour and fried; in omelettes or stewed with potatoes Stewed to eat or mashed to obtain the sweet liquid "cabbasisata"

N

T scap

le, ro

N

T scap

a-p

N

T scap

a-p?

Traditional food use in Sicily

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Scientific name according to Giardina et al. (2007) Prantl

Family according to APG (2009)

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 3. Arùca sarvaggia, Erva falcòna (Penzig, 1924)

Status

Life form

Edible part(s)

2. Erva cavulàra, Cavulicèddi (Lentini, 1989) 2. Ciuri bianchi, Finacciòlu, Finacciuòla, Finacciuòlu, Lariani, Làssanu d'aglia, Pissineddi, Razzi, Razzina, Ruca, Sinicciòla, Ruca, Sanacciòla, Sanacciòli, Sanacciòlu, Senàpa, Sinacciòla, Sinacciùddu, Sinacciùlu 3. Sinacciòla (Lentini, 1989); Caulicèddi di Missina (Penzig, 1924), Caulicèddi pisciacani, Ciurìddi (Piccitto, 1977); Finacciòlu o Sinacciòlu (Pirrone, 1990); Pisciacàni (Salamone Marino, 1897)

N

Ch suffr

a-p

Boiled and seasoned with oil, garlic and chilli peppers

N

T scap

a-p

Raw in salad; boiled and seasoned with oil and lemon, salt and pepper; fried and scrambled with eggs; fried in "balls" with eggs and cheese, tomato sauce and chilli peppers. Medicine-food appreciated despite its bitter flavour

Diplotaxis crassifolia (Raf.) DC.

Brassicaceae

Diplotaxis erucoides (L.) DC.

Brassicaceae

Diplotaxis muralis (L.) DC.

Brassicaceae

3. Erva diàvula, Erva diàula, Erva diàulu, Erva diaulìna (Piccitto, 1977)

N

T scap

a-p

Diplotaxis tenuifolia (L.) DC.

Brassicaceae

2. Ruca 3. Arùca sarvaggia (Pirrone, 1990)

N

H scap

le

Eruca sativa L.

Brassicaceae

1. Arùca 3. Arùca (Giarrizzo, 1989); Arùca, Arùca d'i mònaci, Arùca sarvaggia, Aruchèdda sarvaggia, Camèa (Piccitto, 1977); Arùcula (Pirrone, 190); Arùnca (Salamone Marino, 1987); Ruca (Penzig, 1924)

U

T scap

le

Erucastrum virgatum (C. Presl) C. Presl subsp. virgatum Erygium campestre L.

Brassicaceae

2. Sinàpi 3. Càvulu sarvaggiu (Penzig, 1924) 1. Panicàudu 2. Insalata ru riàvuliu, Panicàllu, N'zalata du diàvulu, N'zalata du scessu, Panicàudu, Panicàuru 3. Boncàudu (Penzig, 1924); Boncòddu (Traina, 1868); Muanàzzu (Pirrone, 1990); Erva di funci, Panicàuru (Piccitto, 1977)

N

Ch suffr

a-p

N

G rhiz

le, y-s

Apiaceae

Traditional food use in Sicily

Eaten in salads with oil and lemon

Boiled and fried with oil, eggs and also with tomatoes, garlic and small cheese pieces Raw in salads with salt, oil, lemon and vinegar. In the Agrigento province it is used like medicine-food for its appetizing and laxative properties

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Fedia graciliflora Fischer & C.A. Meyer

Family according to APG (2009) Caprifoliaceae

Ferula communis L.

Apiaceae

Ficus carica L. var. caprificus Risso

Moraceae

Foeniculum vulgare Mill. subsp. piperitum (Ucria) Bég.

Apiaceae

Foeniculum vulgare Mill. subsp. vulgare

Apiaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Lattuchedda modda 2. Lattucheddra ri maio, Lattuchedda modda, Spazzaquartara, Maggio, Peri ri ciocca, Peri ciocca, Ervi moddi, Spezzaquartari 3. Lattuchedda 'i San Giuseppi (Raimondo and Lentini, 1990); Lattuchedd'i maio (Lentini and Raimondo, 1992); Ciòcca, Lattuchedda modda (Pirrone, 1990); Baddariàna rumpiquartàri, Ciòcca di vignali (Piccitto, 1977); Cugnimòddi (Traina, 1868); Ervi moddi, Pascuzza, Rumpiquartàri (Penzig, 1924); Insalàta d'i mònaci (Provitina, 1986) 1. Ferra, Erva di la virtù 2. Fella, Ferla, Ferra 3. Ferla (Giarrizzo, 1989); Ferra (Tropea, 1985); Erva di la virtù (Piccitto, 1977); Fella grossa, Firrazzèddu, Firruzzèddu, Firrazzuòlu, Frièula (Pirrone, 1990); Ferra majuri (Tropea, 1985) 2. Ficara, Fichera, Ficu, Ticchiàra 3. Duccàra, Nàccaru, Nàccara, 'Nnàccara (Giarrizzo, 1989); Ficu, Bìfari (Amico and Sorce, 1997); Ddicchàra, Ficàra sarvaggia, Ficaràzza sarvaggia, Ficaràzzu (Pirrone, 1990); Duccèra (Traina, 1868); Ticchiàra (Penzig, 1924) 1. Finocchiu d'asinu, Finocchiu sarvaggiu fitenti 2. Finucchieddru silvaticu 3. Finocchieddu o Finocchiu rizzu, Finocchieddu 'i timpa (Arcidiacono and Pavone, 1995); Finucchiastru (Ilardi and Raimondo, 1994); Finòcchiu o Finucchièddu di muntagna, Finuccèddu sarvaggiu, Finucchiastru (Pirrone, 1990); Finòcchiu sarvaggiu fitenti (Traina, 1868) 1. Finocchiu di muntagna, Finocchiu 'ngranatu 2. Finichieddu sarbaggiu, Finocchi, Finocchiu, Finocchiu di campagna, Finocchiu duci, Finocchiu 'ngranatu, Finocchiu sarbaggiu, Finucchieddu di giru, Finucchieddru rizzu, Finocchieddu 'i timpa 3. Finocchiu (Amico and Sorce, 1997); Finocchiu sarbaggiu (Lentini, 1989); Fiòcchiu d'asinu (Pasqualino, 1783-1795); Finòcchiu di muntagna, Finòcchiu di timpa, Finocchiu 'ngranatu, Finocchiu rizzu, Finuccièddu di timpa, Finùgghiti (Pirrone, 1990)

Status

Life form

Edible part(s)

N

T scap

w-p

Raw and seasoned with oil and lemon or fried in the pan with eggs

N

H scap

NS

Although known as toxic for grazing cattle, it is indicated by Arcidiacono (2002) as edible plant in the area of Bronte (Catania province)

U

P scap

fr

N

H scap

se

Used fresh, dried or to prepare "viscotta di ficu" (figs biscuits) and jams. During the Christmas period they are also used to prepare "buccellati", sweets filled with figs-jam or "purciddrane" made out of flour, dough and dried figs Aromatic seasoning used for many traditional dishes

N

H scap

se, a-p

Traditional food use in Sicily

The tender parts of the plant are eaten boiled and seasoned with oil and lemon or mixed with vegetables and soups, omelettes, pasta, tomato sauces. They are also an ingredient of the "pasta 'cchi sardi" or "pasta n'casciata" prepared with onions, pine nuts, raisins, sardines and saffron. The tender parts are also used to season the "maccu ri favi", a traditional Sicilian meal with broad beans, onion, tomato and

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Fragaria vesca L.

Rosaceae

Fraxinus angustifolia Vahl

Oleaceae

Fraxinus ornus L.

Oleaceae

Galactites tomentosa Moench

Geum urbanum L.

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors

Status

Life form

Edible part(s)

Traditional food use in Sicily olive oil. From that it derives the proverb "essere favi e finocchi" to indicate two things which get along very good together. Very tasty are the fried "balls" of wild fennel Raw or mixed with pother fruits to make cakes

3. Fràula (Calcara, 1851); Fràula (Traina, 1868); Fràula sarvaggia, Fraulàra, Fràgula, Fràgulu, Fraulèdda, Fràulu, Fràvila, Fràvula (Pirrone, 1990) 1. Fràscinu di manna 2. Manna, Fràssanu 3. Amèddu (Traina, 1868); Amiddèu majuri, Carrubbàru, Dàrdaru, Fràscianu, Fràscina, Fràsciunu, Fràsinu, Muddìa sarvaggia, Muddìvaru, Muddrìu (Pirrone, 1990); Muddìnu (Tropea, 1985) 1. Fràscinu 2. Manna, Amollèo 3. Muddacchina (Giarrizzo, 1989); Middèu, Muddèu, Muddìu (Tropea, 1985), Muddia, Amoddei, Carabillò, Dàrdaru, Dàrdanu (Giarrizzo, 1989), Fràscinu di manna, Fràssanu, Fràssunu, Muddìa, Muddì, Lardinèddu? (Pirrone, 1990), Amiddèu (Piccitto, 1977); Mullìa (Tropea, 1985); Amànnu (S. Pasta, pers. record)

N

H rept

fr

N

P scap

st-j

N

P scap

st-j

Asteraceae

3. Scalèra (S. Pasta, pers. record); Apròcchiu fimminedda (Penzig, 1924); Lapròcchia, Cacucciulìdda sarvaggia, Spina janca (Pirrone, 1990); Cardunàzzu (Penzig, 1924); Cardunèddu fimminèdda, Cardunèddu jancu (Piccitto, 1977); Luciàna (Tropea, 1985); Spina janca cu 'i ciuri viuletti (Provitina, 1986); Calazìta, Carrazìtula (Giarrizzo, 1989); Cardunazzu cu' ciuri vranchi (Provitina, 1990)

N

T scap

t-st

After being skinned, it is eaten cooked and seasoned with oil and salt

Rosaceae

1. Gariofillata 3. Cariufillàta (Piccitto, 1977); Garofarària, Garufulàta (Penzig, 1924); Erva binirìtta (Traina, 1868); Erva di San Binirìttu, Ervarriàli (Piccitto, 1977)

N

H scap

y-s

Used to aromatise meat, sauces and salads

The "manna" obtained by scatching the bark of this tree during summer season comes from the dessiccation of a sweet liquid. It is used to sweeten some traditional cakes of Madonie Mts (Palermo province) The "manna" obtained by scatching the bark of this tree during summer season comes from the dessiccation of a sweet liquid. It is used to sweeten some traditional cakes of Madonie Mts (Palermo province)

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Glebionis coronarium L.

Family according to APG (2009) Asteraceae

Glycyrrhyza glabra L.

Fabaceae

Helichrysum italicum (Roth) G. Don fil. s.l.

Asteraceae

Helminthotheca echioides (L.) J. Holub

Asteraceae

Hermodactylus tuberosus (L.) Mill.

Iridaceae

Himantoglossum robertianum (Loisel.) P. Delforge

Orchidaceae

Hirschfeldia incana (L.) Lagrèze-Fossat

Brassicaceae

Hyoseris radiata L.

Asteraceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Ciuri di cacamaiu (Lentini, 1989), Maiazzu, Maiu 3. Cucuddi (Giarrizzo, 1989); Erba di luci (Galt and Galt, 1978); Ciùri o Sciùri di maju (Penzig, 1924); Cucùdda, Cucùddi, Màiu (Pirrone, 1990); Màju cu 'i fogghi turchigni (Provitina, 1986); Maju giarnu (Penzig, 1924) 1. Rivulìzzia 2. Niculìzia, Nigulìzia, Niquirìzia, Rigolìzia 3. Niculìzzia (Traina, 1868); Niculìzia, Rigulìzza (Giarrizzo, 1989); Rigulìzia, Riculìzia (Calcara, 1851); Azzuculìzia, Licurìzza, Livurìzza, Licurìzzia, Lignèddu duci (Pirrone, 1990); Glisirrìza (Penzig, 1924) 2. Rosamarina sarvaggia masculina (Lentini, 1989) 2. Asparèdda, Spinèdda, Spirèdda 3. Asprèdda (Amico and Sorce, 1997); Aspirèlla, Aspurèlla (Arcidiacono, 2002); Asparèdda (Pirrone, 1990); Aspirèdda (Traina, 1868); Spirèdda (Penzig, 1924), Cracchiòla (D. Brolo, pers. comm.). 1. Ermodàttilu 2. Buttùni di iaddu, Cantaliaddi, Cantajàddu, Canta addu, Castagnottu, Cricch'ê addu, Pizzicaladdi, Sucamèli 3. Pizzicalàddi, Cricch'ê addu, Buttùni di jaddu, Sucamèli, Castagnòttu, Cantaliàddi, Cantajàddu, Canta-àddu (Arcidiacono and Pavone, 1995); Crìcchia di gaddu (Pirrone, 1990) 2. Patatara, Per'i patatara 3. Gaddùzzi, Lapùzzi (Tropea, 1985); Satiriùni (Di Pede, 2002) 2. Àssini biancu, Sanapieddu duci 3. Làssimi (Arcidiacono, 2002): Mazzareddi (Lentini, 2000); Amarèddi (Pirrone, 1990); Làssani, Pisciacani veri (Provitina, 1990) 1. Radicchia

Status

Life form

Edible part(s)

N

T scap

t-st, fl

After being skinned, it is eaten both raw and cooked seasoned with oil and salt

N

G rhiz

ro

The root is chewed and has a sweet taste. It is used to prepare delicious candies

N

Ch suffr

le

It is used to season meat, fish and potatoes

N

H bienn

t-p

Raw, seasoned with oil and salt

N

G rhiz

ro

In Catania province: eaten roasted or boiled after peeling, seasoned with oil and lemon

N

G bulb

ro

N

T scap

infl

Washed, cut in slices and roasted on the barbecue, seasoned with salt and oil, red chilli pepper, black pepper or rosemary, or just boiled The tender flowers called "sciuritti" are eaten boiled or scrambled with eggs, fried, stewed with onions, tomatoes and peas.

N

H ros

b-r, y-s

Traditional food use in Sicily

Boiled and seasoned with oil and lemon;

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Hypochoeris cretensis (L.) Chaub. & Bory

Asteraceae

Hypochoeris glabra L.

Asteraceae

Hypochoeris laevigata L.

Asteraceae

Hypochoeris radicata L. subsp. heterocarpa (Moris) Arcang.

Asteraceae

Isatis tinctoria L. s.l.

Brassicaceae

Juncus acutus L.

Juncaceae

Kundmannia sicula (L.) DC.

Apiaceae

Lactuca muralis (L.) Gaertn.

Asteraceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Attalebbra, Buttuni ri gallo, Cardèddi di serpi, Cicuriùni, Cuddurèdda, Cuddurùni, Erba duci, Perigallu, Pidicùdda, Tarassaco 3. Erba duci, Buttùni ri gallu, Perigàllu, Cuddurèdda, Cuddurùni (Arcidiacono and Pavone, 1995); Radicchiu, Cicuriùni (Lentini, 2000); Bbirbicìna (Pirrone, 1990); Ganga* (Tropea, 1988); Taràssacu fimminèdda (D. Brolo, pers. comm.) 2. Cìtula duci 2. Razza 2. Cosc'i vecchia, Coscia vecchia, Cost'i vecchia, Costa ri vecchia, Cucummarèddu, Erva rassùdda, Micci scarola, Scaranzìnzuli, Cazzicatùmmuli, Scarla, Scarri 3. Cosc'i vecchia, Cost'i vecchia, Costa ri vecchia, Cost'i vecchi, Costa vecchia, Cosciavecchia (Arcidiacono and Pavone, 1995); Scarri (Lentini and Raimondo, 1992) 3. Cavulucarammu, Maju, Calacarammu, Cavulu di carammu (Arcidiacono and Pavone, 1995); Caràmmu, Caulucaràmmu, Guàdu (Tropea, 1985); Guàdu sarvaggiu, Vadu (Penzig, 1924)

Status

Life form

Edible part(s)

Traditional food use in Sicily in soups with other vegetables or fried in the pan. The tender buds were also eaten raw by shepherds and peasants

N

H ros

a-p

Raw and seasoned with oil and lemon

N

T ros

a-p

Raw and seasoned with oil and lemon

N

H ros

a-p

N

H ros

a-p

U

H scap

infl

Fried in the pan or boiled with the "cicoina" (Urospermum dalechampi) and seasoned with salt and olive oil Boiled together with Urospermum dalechampii seasoned with oil or fried in the pan with oil, garlic and other ingredients. Widely used in the Etna region (Catania province). t-st and infl, called "micc'i scalora" are eaten like asparagus The inflorescences boiled and seasoned with oil and lemon, or as an ingredient in fried

3. Juncu (Traina, 1868); Junci di liari (Provitina, 1990; Pirrone, 1990), Juncu puncenti (Penzig, 1924) 3. Anìtu, Pedi di nigghiu (Penzig, 1924)

N

H caesp

y-s

Boiled

N

H bienn

b-r

Boiled and seasoned with olive oil and salt

1. Cardedda di muru 3. Cardedda di muru (Penzig, 1924)

N

H scap

le

Eaten like salad

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Lactuca serriola L.

Family according to APG (2009) Asteraceae

Lactuca viminea (L.) Presl.

Asteraceae

Lamium flexuosum Ten.

Lamiaceae

Lathyrus articulatus L.

Fabaceae

Lathyrus cicera L.

Fabaceae

Lathyrus clymenum L.

Fabaceae

Lathyrus ochrus (L.) DC.

Fabaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Lattuca sarbaggia 3. Lattuca sarbaggia (Arcidiacono, 2002); Lattùca sarbaggia (Piccitto, 1977); Lattùca sarvaggia, Lattùca sarvaggia vilinùsa (Pirrone, 1990); Lattùca spinusa (Penzig, 1924) 2. Cacciacunigghia, Caccialèpri, Pirnici amara, Caranzìcula, Cardèdda di petra, Ervascursùni, Evva di scussùni, Gattaru, Lattughèdda du Signuri, Ntossicacèddi, Pieririnigghiu, Pisciacunigghia, Scursunàra, Virinèlla 3. Evvascursuni, Evva di scursuni, Scursunara, Pisciacunigghia, Pisciacunigghiu, Caranzinzula, Cacciacunigghia, Caccialepri, Pirnici amara, Cardedda di petra, Pieri ri nigghiu, Per'i nigghiu, Virinella, Guttaru, Lattughedda d''u Signuri, 'ntossicaceddi, Scursunera, Evva di scussuni (Arcidiacono and Pavone, 1995); Ciconia rizza (Arcidiacono, 2002) 2. ‘Nzinzili 2. Fajòru, Fasòla, Fasòli, Fasulèdda, Fasulèdda sarbaggia, Pusèdda sarbaggia, Vizza 3. Fasulèdda sarbaggia, Vizza, Pusèdda sarbaggia, Pusèdda sabbaggia, Fajoru, Pusèddu serbaggiu, Fasola, Fasulèdda (Arcidiacono and Pavone, 1995). 3. Cicerca sarvaggia, Cicirumignu sarvaggiu (Pirrone, 1990) 2. Pisèddu sarbaggiu 3. Casulèdda, Pisèdda sarvaggia (Pirrone, 1990); Fasòla sarvaggia (Penzig, 1924); Fasulàzza (D. Brolo, pers. comm.) 1. Fasuledda sarvaggia 2. Pisèddu sarbaggiu, Fasòlu sarbaggiu 3. Fasuledda sarvaggia, Fasuòlu (Pirrone, 1990); Fasòla* (Tropea, 1988), Fasulazza, Fasulèdda sarvaggia, Fasulìna sarvaggia, Favòccia (Tropea, 1985); Fasulazzi (Penzig, 1924)

Status

Life form

Edible part(s)

Traditional food use in Sicily

N

T scap

le

Eaten like salad

N

H bienn

b-r

Mostly collected before blooming, it is eaten boiled or in salads, also seasoned with oil. T-st called ―cimuzzu‖ is cooked like asparagus

N

H scap

fl

N

T scap

se

The children use to suck the flowers called ―sucameli‖ because of their sweet flavour The seeds are cooked like the peas when they are tender and can be also eaten raw

N

T scap

se

N

T scap

se

N

T scap

se

The seeds are cooked like the peas when they are tender and can be also eaten raw. Boiled and seasoned with tomato, oil, salt and pepper; in omelettes or stewed with onions, oil and parsley Boiled and seasoned with tomato, oil, salt and pepper; in omelettes or stewed with onions

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Scientific name according to Giardina et al. (2007) Lathyrus odoratus L.

Family according to APG (2009) Fabaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Fasòlu sarbaggiu, Pisèddu sarbaggiu, Pisiddùzzu sarvaggiu -

Lathyrus sativus L.

Fabaceae

Lathyrus sylvestris L.

Status

Life form

Edible part(s)

N

T scap

se

Boiled and seasoned with tomato, oil, salt and pepper; in omelettes or stewed with onions

3. Chèrchiri (Piccitto, 1977); Anchi di vecchia, Anga di vecchia, Angùzza, Anch‘i vecchia, Chièrchira, Chèrchira, Cicirimigna, Cicèrcula, Ciciècca, Ciciècculu, Cicièrcula, Cicirimìgnu, Faravècchia, Fasòla, Fasuòla, Favècchia, Rivighi (Pirrone, 1990); Cicèrchia, Cicèrcu, Cicirucèrcu (Piccitto, 1977)

U

T scap

se

Boiled and seasoned with tomato, oil, salt and pepper; in omelettes or stewed with onions

Fabaceae

2. Cessavuoi, Gelsaù 3. Fasòla sarvaggia (Tropea, 1985); Fasulùni, Vaialòra (Pirrone, 1990); Pusiddàzza (Penzig, 1924)

N

H scand

infl

Boiled and seasoned with oil and lemon; scrambled with eggs and cheese; fried in the pan with onions and eaten as delicious side dishes

Laurus nobilis L.

Lauraceae

1. Addàuru 2. Addàgaru, Addàura, Addàuro, Addàuru, Addàvuru, Dadàr, Dàuru 3. Addàuro, Làuru (Giarrizzo, 1989); Addàgaru, Addàgru, Addàguru, Addàiri, Addàvitu, Allàuru, Allàvuru, Allòru, Ddàuru, Ddàvuru (Pirrone, 1990), Addàuru (Traina, 1868);

N

P scap

le

The dried leaves are used to season ragout and other dishes, especially meat and game dishes or figs and mustard. In Bisacquino it is used to make a traditional "rosolio", produced with 9-10 green leaves left in alcool at 90°, sirup, 1 liter of water and 700 g of sugar. It is used as digestive after the meals

Lavandula stoechas L.

Lamiaceae

1. Erva di Palermu 3. Harhala*, Harhalèdda*, Harhara* (Tropea, 1988)

N

Ch frut

fl

The flowers can be used to flavour the white wine, jellies and desserts

Leontodon tuberosus L.

Asteraceae

1. Erva di pirnici 2. Occhiu di pinnici, Occh'i pinnici, Lattughedda 3. Occhiu di pinnici, Occhiu pinnici, Lattughedda (Arcidiacono and Pavone, 1995); Cracchiòla (D. Brolo, pers. comm.); Coscia di vecchia, Iscila, Jeràciu, Mustazzuòlu (Provitina, 1990)

N

G rhiz

le

Boiled and seasoned with olive oil

Traditional food use in Sicily

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Leopoldia comosa (L.) Parl.

Family according to APG (2009) Asparagaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Agghiòru niuru, Cipuddàzza, Cipuddàzzu, Cipuddrùzza, Trubittùni, Cipuddùzza sarvaggia, Cipuddùzzu, Purràzzu 3. Cipuddùzzu, Purràzzu, Agghiòru neru, Cipuddàzzu, Cipuddàzza (Arcidiacono and Pavone, 1995); Cipuddètta (Arcidiacono, 2002); Bbirrittèddi,Cipuddùzza calabbrìsa (Pirrone, 1990); Bburrittèddi (Piccitto, 1977); Jacìntu campagnòlu, Jacìntu sarvaggiu (Penzig, 1924)

Lepidium graminifolium L. subsp. graminifolium

Brassicaceae

Lepidium latifolium L.

Brassicaceae

Lobularia maritima (L.) Desv.

Brassicaceae

Lotus cytisoides L.

Fabaceae

Lotus edulis L.

Fabaceae

Lycium europaeum L.

Solanaceae

3. Mastrùzzu sarvaggiu (Piccitto, 1977) 1. Erva pipirìtu 3. Erva mustarda (Piccitto, 1977); Pipirìtu (Cupani, 1713), (Penzig, 1924) 3. Crisciùni di rocca, Erva di suli, Scuffièddi (Penzig, 1924); Ciùri bbiàncu (Piccitto, 1977) 3. Pecuredda (Lo Cascio and Navarra, 2003); Curnìcchia di rripi (Piccitto, 1977) 3. Cannavùci (Sommier, 1908); Camùciu, Cannaùci, Caramùci, Curnicèddi di mari, Giugguòlu (Piccitto, 1977); Carmùci, Carnabbùsci, Carnanùcci, Carnaùci, Carnavùci, Cramùci, Curnìcchia, Curnìcchia di li pruna, Erva curnìcchia, Curnicèddi, Piseddu sarvaggiu (Penzig, 1924), Curnicièddi di manciàri (Pasqualino, 1783-1795), Cirazzùni (Traina, 1868), Siliqua curnùta (Pirrone, 1990), Grampùci* (Tropea, 1988) 2. Spina santa, Spinasanta 3. Spinasanta (Arcidiacono, 2002); Spina santa, Spinu Cristu,

Status

Life form

Edible part(s)

N

G bulb

bu

N

H scap

a-p?

The bulb must be collected before the bloom and it is eaten boiled or fried like seasoning in omelettes, soups or sauces; it can also be cooked in the oven, roasted or preserved in vinegar becoming a delicious appetizer. According to an ancient recipe of Ragalna (Catania province), the bulbs boiled in salty water, mashed and mixed with minced meat, eggs and cheese are used to prepare meat balls NS

U

H scap

a-p

Used as flavouring

N

H scap

a-p?

N

Ch suffr

fr

Eaten after boiling (Lo Cascio and Navarra, 2003)

N

T scap

fr

Eaten after boiling (Lo Cascio and Navarra, 2003)

U

NP scand

y-s

Boiled and seasoned with oil, lemon and pepper or scrambled with eggs, cheese and onions

Traditional food use in Sicily

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Malva cretica Cav.

Malvaceae

Malva multiflora (Cav.) Soldano, Banfi & Galasso

Malvaceae

Malva nicaeensis All.

Malvaceae

Malva parviflora L.

Malvaceae

Malva sylvestris L.

Malvaceae

Malva trimestris (L.) Salisb.

Malvaceae

Melissa officinalis L. subsp. altissima (Sibth. & Sm.) Arcang.

Lamiaceae

Mentha × piperita L.

Lamiaceae

Mentha aquatica L.

Lamiaceae

Mentha longifolia (L.) Hudson

Lamiaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors Tammuscèddu (Pirrone, 1990), Vùsciu spinusu (Penzig, 1924) 3. Marvùni (Tropea, 1985) 2. Marba, Marva 3. Marva (Pirrone, 1990) 3. Panicèdda (Penzig, 1924); Pani-panùzzi (Pirrone, 1990) 1. Marva 2. Marba, Marva, Mavva, Mavvàscu 3. Ggiràna, Màibba, Màiva, Màivva, Màula, Màrua, Màruva, Màvra (Pirrone, 1990); Màlva (Tropea, 1985); Màvva, Màrva, Màrba, Mavvàscu, Màriva (Arcidiacono and Pavone, 1995) 2. Marba, Bianca russina janca 3. Marbùni, Marvùni (Tropea, 1985) 1. Melìssa 3. Aranciàta (Penzig, 1924); Amènta citràna, Amènta d'api (Piccitto, 1977), Melissa (Tropea, 1985); Menta d'apa, Menta di lapa, Menta di milìssa, Citrunèdda, Citrunèlla, Citrunìlla, Milìssa, Mintàstru (Pirrone, 1990) 1. Amenta pipirìta 3. Menta pipirìta, Mintàstru (Pirrone, 1990) 2. Amenta 3. Balsamìta acquatica, Balsamìta d'acqua (Penzig, 1924), Bbàrsamita acquatica, Bbarsamìta nobili (Penzig, 1924), Mintàstru d'acqua (Pirrone, 1990) 1. Amintastru, Mintastru cu oduri d'amenta 3. Amintàstru (Traina, 1868); Menta sarvaggia, Menta di vadduni (Tropea, 1985), Mentàstru (Penzig, 1924), Mintàstru (Pirrone, 1990)

Status

Life form

Edible part(s)

Traditional food use in Sicily

N

T scap

le

Boiled and seasoned with oil

N

H bienn

le

Boiled and seasoned with oil

N

T scap

le

Raw in salads seasoned with lemon and oil

N

T scap

le

Boiled and seasoned with oil

N

H scap

le

Boiled and seasoned with oil. The tender fruits called "panuzzi d'u Signuri" are eaten as a pastime during country walks

N

T scap

le

Boiled and seasoned with oil and salt

N

H scap

le

Used fresh and dry as aromatising for typical dishes

U

H scap

le

They are used both fresh and dry as aromatising for typical dishes

U

G rhiz

le

They are used both fresh and dry as aromatising for typical dishes

N

H scap

le

They are used both fresh and dry as aromatising for typical dishes

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Mentha pulegium L.

Family according to APG (2009) Lamiaceae

Mentha spicata L. subsp. glabrata (Lej. & Curt.) Lebeau Mentha spicata L. subsp. spicata

Lamiaceae

Mentha suaveolens Ehrh.

Lamiaceae

Mespilus germanica L.

Rosaceae

Micromeria juliana (L.) Rchb.

Lamiaceae

Moricandia arvensis (L.) DC.

Brassicaceae

Myrtus communis L.

Myrtaceae

Lamiaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Amenta puleju, Puleju 2. Amenta, Menta 3. Amenta pulèia, Amenta pulèiu (Traina, 1868); Menta pulèu (Tropea, 1985); Pilìu, Pulèju, Pulèo, Pulèu (Penzig, 1924), Puìia (Pirrone, 1990) 2. Menta 1. Amenta 2. Amintastru, Scordiu, Mintuzza, Menta 3. Amènta, Menta dumèstica (Penzig, 1924)

1. Amintastru, Mintastru 2. Amenta, Amenta sarvaggia, Amintàstru, Mintàstru 3. Amintàstru, Mintàstru, Amintàstru cu odùri d'amenta, Mintàstru cu odùri d'amenta (Traina, 1868); Bbalsamìta nobili (Cupani, 1713), Mintàstra (Tropea, 1985) 2. Nèspula d'invernu 3. Nèspula (Calcara, 1851); Amèdda, Amèddi, A'rvulu, Nèspula, Pedi di nèspula d'immernu, Chiapùni, Chippùta, Cùncuma (Piccitto, 1977), Nèspula, Nèspula d'invernu, Nespularu (Penzig, 1924), Nèspula cciappùni, Nièspula (Penzig, 1924), Nièspula agghiaffùni, Nièspulu (Pirrone, 1990); Amièddu (Provitina, 1990) 3. Erva di San Giulianu (Piccitto, 1977); Spezziu di povir'omu (Penzig, 1924) 2. Càvulu sarvaggiu 3. Garòfalu sarvaggiu (Penzig, 1924) 1. Murtidda 2. Mirtu, Murtidda, Murtiddra

Status

Life form

Edible part(s)

N

G rhiz

le

They are used both fresh and dry as aromatising for typical dishes

U

H scap

le

They are used both fresh and dry as aromatising for typical dishes

U

H scap

le

N

H scap

le

N

P scap

fr

Fresh leaves are used for the preparation of refreshing drinks or even for "rosolio" mint (liqueur), for "salmoriglio", a sauce prepared with olive oil, garlic, lemon juice, mint, oregano, laurel to aromatise meat and fish, as seasoning for stewed broad beans grounded with garlic, bread crumbs, salt and vinegar It is used to aromatise grilled food, sauces and salads. With 20-30 leaves left and soaked in alcohol for 8 days, filtered and with the adjunction of 500 gr of sugar you can obtain an excellent liqueur It is eaten as a fruit

N

Ch suffr

fl?

Used as flavouring

N

T scap

a-p

It is eaten cooled down and seasoned with oil and salt

N

P scap

fr, le, fl

Fresh fr are used to prepare jam; fl are eaten; le are used to aromatise olives in

Traditional food use in Sicily

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Narcissus tazetta L. subsp. tazetta

Amaryllidaceae

Nasturtium officinale R. Br.

Brassicaceae

Notobasis syriaca (L.) Cass.

Asteraceae

Olea europaea L. var. sylvestris (Mill.) Lehr.

Oleaceae

Onopordum horridum Viv.

Asteraceae

Onopordum illyricum L.

Asteraceae

Orchis cfr. palustris Jacq.

Orchidaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 3. Agrùmulu (Giarrizzo, 1989); Marcìta (Catanzaro, 2002); Murtìdda, Murtìdda rizza, Murtìdda rumana (Tropea, 1985), Muittìdda, Muittìddu, Mutiddàra, Muttìlla (Pirrone, 1990) 2. Agghi porri 3. Cipudduzza, Narcìsu (Traina, 1868); Coddu di gamìddu (Penzig, 1924); Ggìgghiu d'immèrnu o d'invièrnu, Narcìssu (Pirrone, 1990); Purràzza (Provitina, 1986); Tazzetta ggiàrna (Di Martino, 1970) 1. Crisciuni 2. Crisciùna, Crisciùni, Scavùni 3. Crisciùni (Calcara, 1851); Calunèddu, Crisciunèddu, Crisciùni, Mastròzzu (Penzig, 1924); Nastrùzzu, Scaùni, Scavùni (Pirrone, 1990) 2. Piscialàsinu, Lamànna 3. Lapuòrdu (Lentini, 2000); Spina jànca (Pirrone, 1990) 1. Agghiastru 3. Agghiàstru (Salamone Marino, 1897); Agghiastrèddu, Agliastrèddu, Aulivàstru, Gghiàstru, Ggiastròlu, Livastru (Pirrone, 1990); Agliàstru, Alivàstru, Aulivastrèddu (Piccitto, 1977) 2. Napòrdu 3. Napòrdu (Giarrizzo, 1989) 2. Cacocciulìdda sarvaggia, Cardu anapòrdu, Muìni, Munacèddi, Munachèddu, Muni, Napuòrdu, Napùrda, Piddònicu, Scaddallàsinu, Trimazzi, Trummazzi, Zanùri, Napòrdu (Lentini, 1989) 3. Trimazzi, Scaddallasinu, Muni, Muìni, Munaceddu, Piddònicu, Trummazzi (Arcidiacono and Pavone, 1995); Apuòrdu, Munacèdiu, Munacèdu, Munacìddu, Napuòrdu, Napordu (Pirrone, 1990); Napòrdu (Cupani, 1713) 3. sub O. morio L.: Gadduzzi d'acqua (Calcara, 1851); Addùzzi d'acqua (Piccitto, 1977); Ciùri comu gadduzzi d'acqua russi (Ucria, 1789); Lapuzzi (Pirrone, 1990)

Status

Life form

Edible part(s)

Traditional food use in Sicily brine

N

G bulb

infl

Blanched and seasoned with oil, lemon and salt or cooked with dry broad beans

N

H scap

a-p, le

a-p eaten blanched or raw in salads and seasoned with oil and salt

N

T scap

t-st

Raw with bread and goat cheese

N

P scap

fr

Oil was extracted from Lampedusa's wild olives. Virgin oil is used as a holy oil

N

H bienn

t-st

Eaten cooled down and seasoned with oil and salt

N

H bienn

t-st

Eaten cooled down and seasoned with oil and salt; with flour and fried; or battered with eggs

N

G bulb

ro

Stewed and boiled; rich of starch (Calcara, 1851)

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Origanum majorana L.

Family according to APG (2009) Lamiaceae

Origanum onites L.

Lamiaceae

Origanum vulgare L. subsp. viridulum (MartrinDonos) Nyman

Lamiaceae

Origanum vulgare L. subsp. vulgare

Lamiaceae

Phagnalon saxatile (L.) Cass. s.l.

Asteraceae

Phlomis fruticosa L.

Lamiaceae

Piptatherum miliaceum (L.) Cosson subsp. miliaceum Plantago afra L. subsp. afra

Poaceae

Plantago coronopus L.

Plantaginaceae

Plantaginaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Majurana 2. Maiurana, Majurana, Maggiorana 3. Arìanu, Arìganu, Majuràna (Piccitto, 1977) 3. Riganèddu (Penzig, 1924) 1. Rìganu 2. Arìanu, Arìfinu, Arìganu, Arrìano, Arrìanu, Rèniu, Rìanu, Rigane§, Rìganu sarvaggiu, Rìuni 3. Arìanu (Giarrizzo, 1989); Arìanu, Arìganu (Amico and Sorce, 1997); Arìniu (Ilardi and Raimondo, 1994); Arìganu (Penzig, 1924); Aricanèddu (Pirrone, 1990)

2. Rìanu, Rìcunu 3. Arìanu (Piccitto, 1977); Arìcunu, Arièfinu, Arìfinu, Aiènu, Arìfunu, Agrìganu (Tropea, 1985); Arìgunu, Arìinu, Arrìanu, Arrìniu, Arrìunu, Rrìanu, Rriènu, Rrìganu, Arìcinu, Arìunu, Arrìnu, Rrìunu (Penzig, 1924) 2. Rosamarina sarvaggia fimminina 3. Semprivìvu sarvaggiu (Penzig, 1924) 1. Sarviùni 2. Salvia, Sarbia, Sarvia sarvaggia, Sucamèli 3. Capùtria, Caputriàra (Piccitto, 1977), Cutrùpia, Cuda di liùni, Salviùni, Sàrvia sarvàggia (Pirrone, 1990); Savviùni (Penzig, 1924) 2. Fiurbugliùni 3. Sanguinàra, Ciacciarèddu (Pirrone, 1990) 1. Pissillium, Erva d'i purci 3. Erva d'i purci (Pasqualino, 1783-1795), Musca canina (Tropea, 1985); Pisillìna (Penzig, 1924), Pissìlliu (Traina, 1868), Psìlliu (Pirrone, 1990); Psìlliu 'ntagghiatièddu, Sìllium Sìlliu fruticùsu (Penzig, 1924) 1. Cornopu, Cornopiu, Erva di stidda

Status

Life form

Edible part(s)

Traditional food use in Sicily

N

H scap

le

Used to season meat, fish or salads

U

Ch suffr

le

N

H scap

le

N

H scap

le

N

Ch suffr

le

Used as seasoning for meat and fish dishes and also for potatoes

N

NP caesp

le, fl

N

H caesp

se

Le: used to aromatise meat; browned in butter or in lard they make an excellent pasta seasoning. In the olden days, children used to suck the flowers due to their sweet taste In Licata (AG province) they are considered a very good food for pregnant women

N

T scap

le

N

T ros

b-r

The leaves are used to season meat, fish or salads and to prepare a typical condiment used together with onions and goat cheese, oregano and anchovies to bake a traditional pizza of the Trapani province called "rianata". It is considered a medicine-food because it helps with the digestion The leaves are used to season meat, fish or salads

Boiled and seasoned with oil and salt

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Scientific name according to Giardina et al. (2007) subsp. coronopus

Family according to APG (2009)

Plantago lagopus L.

Plantaginaceae

Plantago lanceolata L.

Plantaginaceae

Plantago major L. s.l.

Plantaginaceae

Plantago serraria L.

Plantaginaceae

Portulaca oleracea L.

Portulacaceae

Prasium majus L.

Lamiaceae

Prunus mahaleb L. s.l. (incl. P. cupaniana Guss.)

Rosaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 3. Caronòpu veru (Penzig, 1924); Caranòbbiu, Corunòpia, Carunòpiu, Cornu cirvìnu, Cuda di atta (Piccitto, 1977); Erva stidda (Pasqualino, 1783-1795) 2. Cutidduzzi (Lentini and Raimondo, 1992) 3. Gattarèddi, Cuda di gatta (Piccitto, 1977); Erva stidda (Pasqualino, 1783-1795; Traina, 1868; Penzig, 1924) 3. Lanzafina (Giarrizzo, 1989); Centunèrvi, Centunèrbi (Pirrone, 1990), Centunèrvi strittu (Penzig, 1924) 1. Centunervi, Lanza fina 3. Centunèrvi (Di Martino, 1970); Centunièrvi, Piantàggini maggiuri (Pirrone, 1990), Erva di Sant'Antoniu (Penzig, 1924); Lanzafìna (Tropea, 1985) 2. Cutidduzzu 3. Erva stiddària (Traina, 1868) 2. Burdulàca*, Cucciàra, Pirciddana, Porcellana, Purciddana, Puccillana, Purciddana, Purcillana 3. Pucciddana, Puccillana, Purcillana (Arcidiacono and Pavone, 1995); Purciddana (Di Martino, 1970); Bburdulàca* (Tropea, 1988); Erva d'i porci (Piccitto, 1977); Gamarrunèddu marinu (Penzig, 1924); Purciàca, Prucciàca (Pirrone, 1990)

Status

Life form

Edible part(s)

Traditional food use in Sicily

N

T ros

b-r

Boiled and seasoned with oil and salt

N

H ros

le

N

H ros

le

N

H ros

le

N

T scap

t-p

2. Camedriu biancu, Erva thè, Menta sarvaggia, Tè nustrali 3. Camèdriu bbiancu, Tè sicilianu (Penzig, 1924)

N

Ch frut

le

Used for preparing drinks with a diuretic and thirst-quenching effect in Chiusa Sclafani

1. Amarena di Madunia 3. Agghiandri d'inguantari, A'rvulu di Santa Lucia, Ciràsa di Santa Lucia, Ggirasèddi ri Santa Lucia, Ciràsa sarvaggia, Ggiràsa sarvaggia, Cirasèdda (Piccitto, 1977); Amarèna/i di Busàmmara, Amarèna/i di Madunìa, Ciràsa lampàsa, Ceràsu purganti, Lampàsa purganti (Penzig, 1924); Amarèna/i di muntagna (Pirrone, 1990)

N

P scap

fr

Eaten raw

Boiled, drained and browned in garlic and oil, adding bread-crumbs and a drop of vinegar Used to prepare salads with tomatoes, capers and cucumbers or as an ingredient for delicious soups

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Table 2. (Continued)

Prunus spinosa L.

Family according to APG (2009) Rosaceae

Pyrus spinosa Forssk.

Rosaceae

Raphanus raphanistrum L. subsp. landra (Moretti) Bonnier

Brassicaceae

Raphanus raphanistrum L. subsp. raphanistrum

Brassicaceae

Rapistrum rugosum (L.) J. Bergeret subsp. orientale (L.) Arcang. Rapistrum rugosum (L.) Roth subsp. rugosum

Brassicaceae

Reichardia picroides (L.)

Asteraceae

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Scientific name according to Giardina et al. (2007)

Brassicaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Atrignu, Brugnòle, Trigne, Pruna, Prunu sarvaggiu, Vrignòla 3. Atrigna, Brignolu (Giarrizzo, 1989); Trigna (S. Pasta, pers. record); Brignoru (Arcidiacono, 2002); Addagnòlu, Agragnòlu, Ardignòlu, Astrigna, Atrìgna sarvaggia, Atrignèddu sarvaggiu, Atrìgni, Atrìgnu, Bbrignòli, Brugnàla, Brugnolàra, Bbrugnuòlu, Pirùni ardignòlu, Prignòlu, Prunàra sarvaggia, Prunu di San Petru (Pirrone, 1990); Atrìgna (Traina, 1868); Atrignòla (Pasqualino, 1783-1795); Atrignòlu (Traina, 1868), Bbrignulàru, Prunu atrignòlu (Piccitto, 1977), Bbrugnòlu (Penzig, 1924), Prunu atrìgnu (Penzig, 1924); Rragnòlu (Tropea, 1985) 2. Pràinu , Piràinu, Pràniu (Lentini and Raimondo, 1992) 3. Piràinu (Giarrizzo, 1989); Aràstru (Pirrone, 1990); Piru sarvaggiu (Penzig, 1924) 2. Alàssani, Amarèddi, Mazzarèdda, Razza, Spicunèdda di làssanu, Vrucculùni 3. Radicèdda, Ràzza (Pirrone, 1990) 1. Ramurazza, Razzi, Cavulicèddu 2. Razza, Razza ruci, Lapistra, Razza duci, Razzi, Raricèdda, Sanapièddu duci, Tadduzzu sanàpa, Zarra amara, Zazzu 3. Razza, Razza ruci (Arcidiacono and Pavone, 1995); Aràzzu (Lentini, 2000); Caulicèddi veri (Piccitto, 1977), Caulicèddu, Ciurìddi, 'Ngrassapòrci, Ràdicia, Ramuràzza majuri, Ràdicia sarvaggia, Radicèdda, Ràfanu sarvaggiu, Razza, Razzi (Penzig, 1924); Rapìsti nìuri (Pirrone, 1990) 3. Sinàpa spagnola (Provitina, 1990) 2. Mazzarèddra, Sinapìna 3. Làssanu jancu, Razza (Pirrone, 1990)

1. Lattilebbra

Status

Life form

Edible part(s)

Traditional food use in Sicily

N

P caesp

fr

Eaten raw during the autumn

N

P scap

fr

Eaten as fresh fruits

N

T scap

a-p

U

T scap

y-s, t-st

Eaten boiled and seasoned with oil and lemon; in soups together with other vegetables; stewed in tomato sauces and in soups The young shoots are eaten salted, in the pan with garlic and peppers; the tender stalk is eaten raw as a side dish with sausages

U

T scap

a-p

U

T scap

a-p

N

H scap

b-r

Eaten cooled down and fried with peas and onions; boiled and seasoned with oil and lemon. Added to tomato sauces for the preparation of "'u sucu ccu 'a mazzaredda" excellent as a pasta seasoning Eaten both raw and cooked, on its own

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

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Roth

Rhagadiolus stellatus (L.) Gaertn.

Asteraceae

Ridolfia segetum Moris

Apiaceae

Rorippa sylvestris (L.) Besser

Brassicaceae

Rosa canina L.

Rosaceae

Rosmarinus officinalis L.

Lamiaceae

Rubia peregrina L. s.l.

Rubiaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Insalatèdda, Lattughèddra, Coccialèbbra, Caccialèbbra, Pirnìci dùci, Erva di pinnìci, Giallèpura, Caccianèpura, Caccialièpura, Caccialèbbru, Caccialèpri, Scaccialèbbra, Caccialèpre, Cattalèbbra, Scarcìtula, Cacazzìna, Lazzìni 3. Lattilèbbra (Tropea, 1985; Giarrizzo, 1989); Evva di pinnici, Evva-pinnici, Caccialepri, Caccialebbra, Caccialebbri, Pirnici, Pirnici duci, Caccianèpura, Caccialèpura, Giallepura, Gallepura, Caccialebbru, Scaccialebbra, Scacciacalèbbra (Arcidiacono and Pavone, 1995); Antilebbra di campagna, Attalièbbra, Carazzìtula, Erva latti d'aceddu, Latti d'aceddu (Piccitto, 1977); Attalèbbra, Caccialèbbri, Caranzìtula, Cattilèbbra (Pirrone, 1990); Latti d'oceddu (Penzig, 1924); Atilèbbra di campagna (Penzig, 1924); Latticèbbra (Tropea, 1985); Lazzìni (Lo Cascio and Navarra, 2003) 3. Denti di liuni (Penzig, 1924) 2. Finocchiu anìtu 3. Anìtru, Finucciàzzu (Pirrone, 1990) 3. Arùca sarvaggia picciridda (Penzig, 1924) 2. Giarrauta 3. Giarrauta (Arcidiacono, 2002); Grattacùlu (Pasqualino, 1783-1795); Gullenza (Tropea, 1985); Ingànnula, Rosa a cincu pàmpini (Penzig, 1924); Rosa sarvaggia (Traina, 1868); Rusarèdda sarvaggia, Sponza di rrosi, Ugnu di attu (Pirrone, 1990); Ruvèttu masculu (Penzig, 1924), 'Ntuppaculu (G. Garfì, pers. record)

Status

Life form

Edible part(s)

Traditional food use in Sicily or "maritata" (married) to other vegetables as "cardedda (Sonchus sp.), cicoina (Urospermum dalechampii), scursunara (Lactuca viminea) and cutulidda (Chondrilla juncea)" and seasoned with oil and lemon. Considered a medicinal-food, it is eaten for its refreshing, emollient and diuretic effect and is used in Canicattì, Campobello di Licata and Ravanusa

N

T scap

a-p?

N

T scap

a-p

N

H scap

le?

N

NP scand

ps-fr

Eaten as a fruit

2. Rosamarina, Rosmarina, Rosmarinu, Rusamarinu 3. Rosamarina (Traina, 1868); Rrosamarina (Pirrone, 1990)

N

Ch frut

le

Used to aromatise meat, potatoes, bread and focacce like the "vucciddati" that are prepared on the 19th of March in the honour of Saint Joseph

2. Rascalingua 3. Battilingua, Ruggiàstra (Pirrone, 1990

N

P lian

fr

Eaten fresh or as a jam and also to prepare a delicate rosolio

Eaten raw in salads

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Rubus idaeus L.

Family according to APG (2009) Rosaceae

Rubus ulmifolius Schott

Rosaceae

Rumex acetosa L.

Polygonaceae

Rumex bucephalophorus L. s.l.

Polygonaceae

Rumex crispus L.

Polygonaceae

Rumex intermedius DC.

Polygonaceae

Rumex patientia L.

Polygonaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 3. Ruvettu di San Franciscu (Cupani, 1713), Frambuà (Calcara, 1851); Amurèddu (Pirrone, 1990) 2. Rivèttu, Ròvu, Ruètto, Ruèttu, Ruìtti, Runzi, Ruvèttu, Ruvièttu, Ruvìtti 3. Dudda (Giarrizzo, 1989: fruit); Amurèdda (Giarrizzo, 1989); Ruvièttu, Amarèddi (Amico and Sorce, 1997); Ruvèttu (Arcidiacono, 2002); Amurèdda niura (Traina, 1868), Amurèddi (Penzig, 1924), Gèusu di càia, Mararuvèttu, Marèdda, Murèdda, Murèra, Muridda, Muriddàzzu, Peri di amareddi, Peri d'amureddi (Pirrone, 1990); Cirasèdda (Piccitto, 1977), Murèdda di sipàla, Murèddi, Murièddi, Murèddu, Murèddu di pala, Murella (Tropea, 1985) 1. Acitusèlla, Acìtula 3. Acitàzzu, Acitùsa, Erva acitùsa, Acitusèddu, A'ghira edduci, A'ghiru e-dduci, Agraedduci, Arbulùzzu, Arieddùci, Ariu e dduci, Aureddùci, Calacìtra, Calacìtru, Caracìtula, Cataciètala, Gracìtula, Iacreddùci, Iaureddùci, Lapàzza, Lapàzzu (Pirrone, 1990); A'vuru e duci (Provitina, 1986); Acitusèlla (Piccitto, 1977), Agradùci (Traina, 1868) 3. Agru duci, Acìtula, Acitusèdda (Penzig, 1924); Agru-duci cu' fogghi picciriddi (Provitina, 1986); Agracìtula, Caracìtula, Racinèda di lu Signuruzzu (Pirrone, 1990), Ciaturrina (Piccitto, 1977); Maracìtula (Tropea, 1985); Hurrisa* (Tropea, 1988) 2. Apàzzu, Aùru acìtu, Lapàzzu 3. Acitàzzu (Pirrone, 1990) 1. Lapàzzu 3. A'rvulu di pacenza, Erva paciènzia (Piccitto, 1977), Ggìra

Status

Life form

Edible part(s)

N

NP scand

fr

Eaten fresh or as a jam and also to prepare a delicate jèlée

N

NP scand

fr, le

Fr eaten fresh or as a jam. In Licata, le used to prpare a liqueur

U

H scap

a-p

Used to prepare salads

N

T scap

a-p

N

H scap

a-p

N

H scap

a-p

U

H scap

a-p

Traditional food use in Sicily

The tender aerial parts are used boiled and seasoned with oil and lemon

NS

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors sarvaggia (Penzig, 1924); Lapàzza, Lapàzzu (Pirrone, 1990); Lapàzziu, Lappàzziu, Lappùzziu (Tropea, 1985)

Rumex pulcher L. s.l.

Polygonaceae

Rumex scutatus L.

Status

Life form

Edible part(s)

3. Lapàzza, Lapazzèddu rizzu (Penzig, 1924), Lappanàzzu, Lingua di cani (Provitina, 1990; Pirrone, 1990)

N

H scap

a-p

Polygonaceae

1. Acitùsa ortensi cu fogghi rotunni 2. Acìtula, Acìtura, Ocìtura, Citulìdda 3. Acìtula, Acìtura, Ocìtura, Citulìdda (Arcidiacono and Pavone, 1995); Acìtula di sciara (Piccitto, 1977), Acitàzzu (Pirrone, 1990), Acitùzza cu' fogghi rutùnni (Traina, 1868)

N

H scap

a-p

It is chewed in order to extract the thirst quenching juice. It is also used to give flavour to salads

Rumex thyrsoides Desf.

Polygonaceae

2. Acìtura 3. Acìtura (Arcidiacono, 2002)

N

H scap

a-p

The tender aerial parts are used boiled and seasoned with oil and lemon

Ruscus aculeatus L.

Asparagaceae

1. Spinapurci, Spàraci scuparìni 2. Bammuschìtta, Bammuscìttu, Buscu, Puci pulci, Spina scuparìna, Grattacùla, Scuparìni, Spinaprùci, Spinapùrci, Sinèddu, Sparabusch, Sparacèddi, Spàraci, Spàrac'i trona sarvaggi, Spàraci di scupazzu, Spàraci scuparìni, Spàraciu d'i vadduna, Spàraciu sarvaggiu, Spàraciu 'mpiriali, Sparacògna, Spinocciuòli, Taddisprùni 3. Mursiddina?, Spinapuci (Giarrizzo, 1989); Spina purci, Rascogni (Cupani, 1713); Cafè sicilianu o Bruscu (Calcara, 1851); Sparacògna, Spinapùlici, Spàraciu 'mpiriali, Spàraciu di vadduna, Taddispruni, Bammuschitta, Spinapurci, Bammuscittu, Spinapruci (Arcidiacono and Pavone, 1995); Spàraciu rùsculu (Ilardi and Raimondo, 1994); Bascògni, Erva bbrusca (Piccitto, 1977), Bbruscu, Spinapùrci (Salamone Marino, 1897); Cafè sicilianu, Tammuscèddu, Tammuscèttu (Traina, 1868), Scuparìna, Spinapùlici (Pirrone, 1990), Spinapùcci (Di Martino, 1970), Puncipulci, Puncisurci, Rascògnu, Spinafrùtici, Spinèdda (Penzig, 1924)

N

Ch frut

ro, y-s, se

y-s: boiled and seasoned with oil and lemon, scrambled with eggs and cheese or in an omelette. A traditional Palermitan dish, called in dialect "sparaci di scupazzi impurrazzati", is prepared by wrapping the "sparaci" in the "purrazzu" leaves and by cooking them in tomato sauce. The seeds and the bulbs are roasted as a substitute of coffee (Calcara, 1851)

Ruscus hypoglossum L.

Asparagaceae

2. Spàraci di tronu 3. Erva di trònu (Pasqualino, 1783-1795), Spàraciu americanu, Spàraciu di tronu, Spàraciu di Spagna (Penzig, 1924)

U

Ch frut

y-s

The young shoots are boiled and seasoned with oil and lemon, scrambled with eggs, in omelettes or as an asparagus cream

Traditional food use in Sicily

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Ruscus hypophyllum L.

Family according to APG (2009) Asparagaceae

Salvia officinalis L.

Lamiaceae

Salvia sclarea L.

Lamiaceae

Sambucus nigra L.

Caprifoliaceae

Sanguisorba minor Scop. s.l.

Rosaceae

Scolymus grandiflorus Desf.

Asteraceae

Scolymus hispanicus L.

Asteraceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Sparaciu di tronu, Sparaciu di Spagna, Erva di tronu 2. Spàraciu 'mpriacu, Spàraciu 'mpiriali, Spàraciu di Spagna, Spàraciu di salamunia, Spàraciu di bordura 3. Spàraciu 'mpriacu, Spàraciu 'mpiriali, Spàraciu di Spagna, Spàraciu di salamunìa, Spàraciu di bordura (Arcidiacono and Pavone, 1995) 1. Sarvia 2. Sarvia 2. Erva muscatiddara, Muscaiddara, Salvia 3. Matricàla (Giarrizzo, 1989)

Status

Life form

Edible part(s)

U

Ch frut

y-s

The young shoots are boiled and seasoned with oil and lemon, scrambled with eggs, in omelettes or as an asparagus cream

U

Ch frut

le

N

H bienn

le

2. Savuiccu, Savùco, Sammùccu, Savùcu 3. Savùcu (Giarrizzo, 1989), Scassu (G. Garfì, pers. record)

N

P scap

fl, fr

The fresh or dry leaves are used to aromatise various main dishes like roasts and sauces The fresh or dry leaves are used to aromatise various main dishes. Their decoction was used to soak wine barrels and to give the wine a nice aroma Both fr and fl are eaten; the latter iced or fried in butter, powdered with a coating of sugar. In CL dry fl were eaten with bread as snack

3. Pimpinedda (Calcara, 1851), Aròciula, Cèusu (Piccitto, 1977), Gièusu, Sanguisorba (Pirrone, 1990), Pampinèdda (Salamone Marino, 1897); Pampinèdda di campagna (Penzig, 1924) 1. Scoddi, Scolimo 2. Papellu, Rattamèli, Scoddi, Scoddu, Scolli, Scuòddi, Scuòddu, Scuèdda, Scuddu, Zammuri di campagna 3. Zammurri di campagna, Rattamèli, Scolli, Scoddi (Arcidiacono and Pavone, 1995); Scuoddi (Lentini and Raimondo, 1992); Scalìmbru, Scòddu (Giarrizzo, 1989); Lamanna (Tropea, 1985); Lamantìnu (Pirrone, 1990) 1. Scoddi, Scolimo 2. Scoddu, Spina bianca (Lentini, 1989; Lentini and Venza, 2007) 3. Scalìmbru, Scòddu (Giarrizzo, 1989); Maccarrùni di chiana, Scallièmmiru (Pirrone, 1990); Saittùni, Scalìmmiri, Scoddi di ripi di mari, Scuòddu (Penzig, 1924); Scoddi (Traina, 1868; Penzig, 1924)

N

H scap

le

N

H scap

le, t-st

Boiled and seasoned with oil and lemon; scrambled or in an omelette with eggs, in batter or in a salad

N

H bienn

t-st

Cooked or raw and seasoned with oil and lemon; fried in butter, scrambled with eggs

Traditional food use in Sicily

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Scientific name according to Giardina et al. (2007) Scolymus maculatus L.

Family according to APG (2009) Asteraceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Scoddi, Scolimo 2. Scoddu, Scuòddu, Scuòddo 3. Lamàgnu (Pirrone, 1990); Lamànna (Pasqualino, 17831795); Scoddi, Scoddi di curmi (Penzig, 1924)

Scorzonera calcitrapifolia Vahl

Asteraceae

Scorzonera cana (C.A. Mey.) Griseb.

Status

Life form

Edible part(s)

N

T scap

t-st

3. Latti di lepri (Provitina, 1990; Pirrone, 1990)

N

H bienn

le?, a-p?

Asteraceae

3. Benedìciti (http://www.caitaormina.it/pagine/erbedelletna.htm)

N

H scap

le, a-p

Scorzonera deliciosa Guss.

Asteraceae

N

G bulb

ro

Scorzonera laciniata L.

Asteraceae

N

H bienn

le, a-p

Senecio aquaticus Hill. subsp. erraticus (Bertol.) Tourlet

Asteraceae

N

H scap

le?

Senecio vulgaris L.

Asteraceae

N

T scap

le?

Silene vulgaris (Moench) Garcke subsp. angustifolia (Mill.) Hayek

Caryophyllaceae

3. Scursunèra (Calcara, 1851) 1. Erva di gnàgnaru pilusa; Scursunèra 3. Erva di gnàgnaru (Pirrone, 1990); Erva di gnàgnaru pilusa (Traina, 1868); Sfardata (Penzig, 1924); Truffuni? (S. Pasta, pers. record) 3. Erva rapudda, Rapudda (Penzig, 1924); Erva chitàrra, Erva di San Giacumu, Erva di Sagnàbbicu, Erva di Sampètru (Piccitto, 1977) 3. Erva d'i caddìddi, Erva di li cardìddi, Mancialèbbri (Tropea, 1985); Mangia-lebbra (Provitina, 1990; Pirrone, 1990); Sinèciu (Penzig, 1924) 2. Calicèdda di muru, Campanèdda, Cannatèdda, Cannatèlla, Erba du priricatùri, Priricatùri, Erba ru priricatùri, Scaf'ti'nfrunti 3. Cunigghiàra (Lo Cascio and Navarra, 2003); Cannatèdda, Campanèdda, Erba pridicatùra, Cannatèlla, Erba d'u pridicatùri, Priricatùri, Erva ru priricatùri, Ebba priricatùra, Calicèdda 'i mura, Calicèdda di muru (Arcidiacono and Pavone, 1995)

N

H scap

y-s, fl-b

Traditional food use in Sicily Cooked or raw and seasoned with oil and lemon

Used as sweetening (Calcara, 1851)

NS

The tender "spicuneddi" (y-s), harvested in spring before full bloom, are eaten boiled in "misticanza" (= mixture) with other vegetables or, alternatively, cooled down and scrambled with eggs. Boiled, mashed and mixed with eggs, pecorino cheese and black pepper are used to prepare delicious meat balls

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Silene vulgaris (Moench.) Garcke subsp. vulgaris

Family according to APG (2009) Caryophyllaceae

Silybum marianum (L.) Gaertner

Asteraceae

Sinapis alba L. subsp. alba

Brassicaceae

Sinapis alba L. subsp. dissecta (Lag.) Bonnier

Brassicaceae

Sinapis arvensis L.

Brassicaceae

Sinapis pubescens L.

Brassicaceae

Sisymbrium irio L.

Brassicaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Calicèdda di muru, Cannatèlla, Ebba priricatùra, Erba d'u pridicatùri, Erba pridicatùra, Campanèdda, Priricatùri 3. Benalbu, Caulicèddi di vigna, Erva di pridicatùri (Penzig, 1924); Calatafàna (Traina, 1868); Cannatèdda (Penzig, 1924); Erva di cuccu, Erva di purcèddu (Piccitto, 1977); Pizza-parrini (Pirrone, 1990) 1. Muganazza 2. Cardu marianu, Cardunazzu, Cardunciu, Crox, Mianu, Piscia l'asinu 3. Battilana, Cardalana (Penzig, 1924); Cardunazzu, Miànu, Magunazzu, Magunazzi veri, Muganazza, Muganazzi, Muganazzu, Munganazzu (Tropea, 1985); Mugnazzi (Pasqualino, 1783-1795)

2. Làssani, Mazzarèddu, Arazzi 3. Amarèddu bbiancu (Piccitto, 1977); Mustarda, Sinàpi d'ortu, Sinapièddu di linu (Penzig, 1924); Amarèddu jancu, Qualàzza, Qualàzzi, Sanàpa, Sanapèdda (Pirrone, 1990) 3. Sinacciòlu di linu (Penzig, 1924) 2. Alàssani, Qualeddu 3. Sinàpi (Arcidiacono, 2002); Amarèddi, Alàssani, Alàssanu, Alàssina, Amareddu jancu, Làssina, Làassinu, Làssanu, Sanapùni, Sinapùni (Pirrone, 1990); Alàssana (Piccitto, 1977); Pisciacàni, Sinàpa masculina, Sinàpa sarvaggia, Sinapàzzu (Penzig, 1924) 3. Alùca, Làssanu (Pirrone, 1990); Lapàzzani (Tropea, 1985); Mazzarèddi amari, Sinacciòla, Sinàpa fimminedda (Penzig, 1924) 2. Approcchiu

Status

Life form

Edible part(s)

N

H scap

y-s, fl-b

Cooled down, chopped up and mixed with eggs and cheese, they are used to prepare the exquisite meat-balls with a "nannata" (fried fish) taste; boiled in a soup with other vegetables or browned. Also used to aromatise omelettes

N

H bienn

a-p

N

T scap

a-p

Raw in salads, battered, stewed like the artichokes, fried with garlic and chili pepper, tomato and goat "tuma" (=local fresh cheese); cooked in the oven with garlic, oil and crumbledbread. In Campobello di Licata the peasants used to suck the stem in order to quench their thirst during the working hours Eaten like vegetables, boiled with oil and lemon

N

T scap

a-p

Eaten like vegetables, boiled with oil and lemon

N

T scap

a-p

Eaten like vegetable soup seasoned with oil and lemon; like side dish for sausages after being fried in the pan with garlic, oil and chilli pepper

N

H scap

a-p

Eaten like vegetables, boiled with oil and lemon

U

T scap

le

Raw in salad or boiled and seasoned with oil, lemon and salt

Traditional food use in Sicily

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 3. Lapazzani (Penzig, 1924); Làssina, Làssinu (Tropea, 1985); Mazzarèddi amari, Mazzarèddu, Pisciacani (Penzig, 1924)

Sisymbrium officinale L.

Brassicaceae

Smilax aspera L.

Status

Life form

Edible part(s)

2. Làssani duci, Làssinu di sceccu, Lassanèddi, Mazzarèddri, Sciurìti, Ciùnciuli 3. Amarèlli (Arcidiacono, 2002); Mazzarèddi (Ilardi and Raimondo, 1994); Alàssana di sceccu, Cacacàni, Cavulicèddi (Pirrone, 1990); Amarèddi (Piccitto, 1977); Làssina, Làssinu (Tropea, 1985)

U

T scap

infl

The inflorescences called "sciuriti" are eaten boiled and seasoned with oil and lemon, in soups, battered, or fried with oil and garlic

Smilacaceae

1. Salsa siciliana 2. Cafarètra, Cannacìtura, Gratta culu, Liara, Sarsa siciliana, Sciracàusi, Erva serretta, Raja, Strazzacammìsi, Ugna di attu 3. Gratta culu, Raia (Giarrizzo, 1989); Salsa siciliana, Sàusa siciliana, Unguèddi (Calcara, 1851); Cannacìtura, Strazzacammìsi, Ugnu ri attu, Ugna di attu (Arcidiacono and Pavone, 1995); Arrascacùlu (Ilardi and Raimondo, 1994); Ciàchi-ciùcha* (Galt and Galt, 1978); Sarsa siciliana (Salamone Marino, 1897); Arèddira spinusa (Piccitto, 1977); Frisèddi (Traina, 1868); Lèria, Liàra, Liiàra, Raja, Raja vera, Ugnèddi (Pirrone, 1990); Erva siciliana, Stràssacausi, Stràzzacausi (Penzig, 1924)

U

P lian

y-s

Eaten after being "curati" ("washed") and seasoned with oil and lemon; also used like ingredient for omelettes

Smyrnium olusatrum L.

Apiaceae

2. Accia sarvaggia, Làccia sarvaggia, Lisciàndru, Lisciàntru 3. Casèsi, Lisciàndru (Penzig, 1924); Lèstrica, Lisciàndra, Lisciandrèddu, Lisciandrèttu (Tropea, 1985); Lisciànnaru (Pirrone, 1990)

N

H bienn

y-s, fl-b

Raw in salad and as seasoning for soups as substitute for celery

Smyrnium perfoliatum L.

Apiaceae

N

H bienn

NS

NS

Sonchus asper (L.) Hill. subsp. glaucescens (Jordan) Ball Sonchus arvensis L.

Asteraceae

3. Lisciandrèddu (Penzig, 1924) 2. Cardèdda 3. Cardèdda (Provitina, 1990); Cardèdda di sceccu (Piccitto, 1977)

N

H scap

a-p

Eaten in soup or raw in salad seasoned with oil and lemon

N

T scap

a-p

Asteraceae

Traditional food use in Sicily

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Sonchus asper L. subsp. asper

Family according to APG (2009) Asteraceae

Sonchus oleraceus L.

Asteraceae

Sonchus tenerrimus L.

Asteraceae

Sorbus domestica L.

Rosaceae

Sulla coronaria (L.) Medik.

Fabaceae

Tamus communis L.

Dioscoreaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Cardèdda, Cardèdda mascula, Cardèdda di cani, Cardèdda di puorci, Cardèdda spinusa, Cardèdda niura, Cardèdda russa 3. Cardèdda spinusa, Cardèdda masculina (Lentini, 1989), Cardiddàstra, Cardinnàstra (Pirrone, 1990); Cardiddàzza (Penzig, 1924); Cardèdda russa, Cardèdda nìura, Cardèdda ri porci, Cardèdda mascula (Arcidiacono and Pavone, 1995); Cardiddùni (D. Brolo, pers. comm.) 1. Cardedda, Cicerbita 2. Cardèdda, Cardèdda bianca, Cardèdda fimmina, Cardèdda fimminedda, Cardèdda janca, Cardèdda liscia, Cardèdda scucivola, Cardèddra, Scarola 3. Cardèdda liscia, Cardèdda, Cardèdda bianca, Cardèdda janca, Cardèdda fimminina (Arcidiacono and Pavone, 1995); Cardèdda fimminedda (Arcidiacono and Pavone, 1995); Cardèddra, Cardiddùni (Pirrone, 1990); Cardèdda d'invernu (Penzig, 1924); Cicerbita (Traina, 1868) 2. Cardèdda, Cardèdda scucìvola, Cardèlla, Kardèdda, Spargola, Cardèdda fimminina 3. Cardèdda di muru (Penzig, 1924) 2. Zorba 3. Sciòrbu (Giarrizzo, 1989); Anzuòrba (Pirrone, 1990); Sciòrba, Sorbàra, Zzorba, Zzorbi, Zorbi di Catania (Penzig, 1924); Sorbu (Provitina, 1986) 2. Sudda, Suddra 3. Sudda (Traina, 1868); Curnicchia (Pirrone, 1990); Erva sudda (Piccitto, 1977); Sudda caprina (Penzig, 1924) 2. Spàraciu arrampicusu, Spàrac'i cuccu, Spàraci di serpa, Spàrac'i serpi, Spàraciu impiriali, Spàraciu di lupu, Spàraciu di tronu, Sparacògni, Tamuscèddi, Viddicèddu, Virricèddu, Viticèddu 3. Ragùnia? (Giarrizzo, 1989); Spàraci di cannitu, Spàraci di donna (Cupani, 1713), Viticedda (Calcara, 1851); Sparaciu 'mpiriali, Sparacognu, Virriceddu, Viticeddu, Viddiceddu,

Status

Life form

Edible part(s)

N

T scap

a-p

They are eaten in soup or raw in salad, seasoned with oil and lemon

N

T scap

a-p

Eaten in soup or raw in salad, boiled and fried in the pan like seasoning for tasty omelettes

N

H scap

a-p

Eaten in soups or raw in salad, seasoned with oil

U

P scap

fr

Eaten raw

U

T scap

a-p

Boiled, seasoned with oil and lemon or scrambled with eggs and onions

N

G bulb

y-s

Only cooked (toxic if eaten raw!) seasoned with oil and lemon; browned with oil and onions to season omelettes and risotti; fried also with eggs and cheese; cooked with tomato sauce

Traditional food use in Sicily

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Scientific name according to Giardina et al. (2007)

Family according to APG (2009)

Tanacetum vulgare L.

Asteraceae

Taraxacum minimum (Guss.) N. Terracc.

Asteraceae

Taraxacum officinale Weber

Asteraceae

Tetragonolobus conjugatus (L.) Link

Fabaceae

Tetragonolobus purpureus Moench

Fabaceae

Teucrium fruticans L.

Lamiaceae

Teucrium scordium L. cfr. subsp. scordioides (Schreber) Arcang. Thlaspi perfoliatum L. subsp. perfoliatum

Lamiaceae

Brassicaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors Sparacuogna, Sparacògni, Sparacògna (Arcidiacono and Pavone, 1995); Spàraciu ri tronu (Ilardi and Raimondo, 1994); Sparaciu di cucca (Lentini, 2000); Erva di la fata (Piccitto, 1977); Pedi di liufànti, Spàraci nìuri (Pirrone, 1990) 2. Tannavìra 3. Tannavìda (Giarrizzo, 1989); Amènta rrumana (Pasqualino, 1783-1795), Amènta samparigghia (Pirrone, 1990); Atanàsia, Erva di la principissa (Piccitto, 1977); Erva atanàsia, Erva di li vermi (Penzig, 1924), Erva d'i vermi (Pirrone, 1990); Tanacìtu (Traina, 1868) 3. Cuddu cudduzzu (Penzig, 1924); Cicòria sarvaggia (Pirrone, 1990; Provitina, 1990) 2. Tarassacu, Erba di pirnici, Erba piscialetto 3. Cardèdda di sprivèri (Piccitto, 1977); Cicuriùni, Laprùcchi, Mancialèbbri, Minni di vacca, Rrampa di cuccu (Pirrone, 1990); Denti di liuni, Erva di pirnìci, Taràssacu (Penzig, 1924)

3. Cascitèddi (Pirrone, 1990; Provitina, 1990); Cricchia di 'addu (D. Brolo, pers. comm.) 3. Cascitèddi, Tavuliddùna ca si màncianu (Provitina, 1990); Cassatèddi (D. Brolo, pers. comm.); Tavulettìni (Penzig, 1924) 2. Ricuttedda 3. Alivedda, Mulinàru, Vranculidda (Pirrone, 1990); Alivètta, Caca aucèddi (Penzig, 1924) 2. Scòrdiu 3. Scòrdiu (Penzig, 1924) -

Status

Life form

Edible part(s)

Traditional food use in Sicily

N

H scap

le

Used both as a spice and seasoning

N

H ros

a-p

N

H ros

a-p

N

T scap

fr?

N

T scap

fr

N

Ch frut

le

Boiled and seasoned with oil and salt

N

H scap

le

Used to prepare aromatic wines, liqueurs, aperitifs and digestives

N

T scap

a-p

Fresh leaves are eaten raw or cooked, in omelettes or browned in a frying pan. The buds are kept in salt, like capers, and then used to flavour main courses. The boiled root, when seasoned with olive oil, becomes a tasty dish with a wonderful aroma

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Thymbra capitata (L.) Cav.

Family according to APG (2009) Lamiaceae

Thymus spinulosus Ten.

Lamiaceae

Tolpis virgata (Desf.) Bertol. subsp. quadriaristata (Biv.) Giardina & Raimondo Tragopogon crocifolius L. subsp. nebrodensis (Guss.) Raimondo

Asteraceae

Tragopogon porrifolius L. subsp. australis (Jordan) Br.-Bl.

Asteraceae

Tragopogon porrifolius L. subsp. porrifolius

Asteraceae

Urospermum dalechampii (L.) F.W. Schmidt

Asteraceae

Urospermum picroides

Asteraceae

Asteraceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Satureddu, Satareddu 2. Santarièdda, Santarièddru, Satarèdda, Satarèddu, Sitarèdda, Timu 3. Sataredda (Giarrizzo, 1989); Arìganu sarvaggiu (Galt and Galt, 1978); Riganèdda (Cupani, 1713); Riganèddu (Traina, 1868); Riinèddu, Santarièddu, Satarèdda di Satiru, Satarèddu, Satira, Satru, Saturèdda, Saturèddu, Sautarèdda, Tuminèddu (Penzig, 1924); Tumminèdda (Cupani, 1713) 2. Timu 3. Timu (Pirrone, 1990); Sirpìllu muntagnòlu (Penzig, 1924) 2. Erba janca, Gallinella, Lattuchèdda, Scalurèdda 3. Lattuchedda, Scaluredda, Erba janca, Gallinella (Arcidiacono and Pavone, 1995) 2. Barbabècchi, Brambàscu, Cuttunèddu, Erva di San Petru, Lattaròli, Pampasciùscia, Latti d'aceddu, Pedi di lupu, Percia cannèdda, Pistalacèddi, Stuppacannèdda 3. Varvabèccu (Giarrizzo, 1989); Latti d'aceddu (http://www.caitaormina.it/pagine/erbedelletna.htm) 2. Erba di gnagnaru pilusa, Pedi di gaddru, Perciacannèdda, Reggia, Varva di beccu 1. Sassifraga 3. Pampasciuna, Cuttuneddu, Stuppacannedda, Erba di San Petru, Pedi di lupu, Latti d'aceddu, Pistalaceddi, Latti d'aceddi, Brambascu, Barbabecchi, Lattaroli (Arcidiacono and Pavone, 1995); Perciacannedda (Lentini, 2000) 2. Cartèdda, Cicòria sarvaggia, Scursunèra giarna, Cuòst'i porci, Cicòina 3. Scursunèra giarna (Penzig, 1924)

-

Status

Life form

Edible part(s)

N

Ch suffr

le

Used to aromatise traditional "focaccia" bread, tinned olives and salads with sardines, fish and meat dishes

N

Ch rept

le

Used as aromatic spices

N

H scap

b-r

Eaten after having been boiled and seasoned with oil

N

T scap

a-p

Eaten boiled in soups or raw in salads, seasoned with oil and salt

N

H bienn

a-p, ro

The root is eaten after having been boiled; the stem and tender leaves are eaten raw in salads or boiled

N

H bienn

infl

Eaten in winter, boiled in salads (Mortillaro, 1881)

N

H scap

le

N

T scap

le

Very appreciated in countrymen's culinary tradition, due to its bitter taste and the fleshy consistency of its leaves that are eaten boiled or seasoned with oil or browned with garlic, oil and peppers Eaten raw in soup or cooked in salads,

Traditional food use in Sicily

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Scientific name according to Giardina et al. (2007) (L.) F.W. Schmidt

Family according to APG (2009)

Urtica dioica L.

Urticaceae

Urtica membranacea Poir.

Urticaceae

Urtica urens L.

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 2. Caccialèbbra, Bukeriepur§ 3. Attarèbbula (Pirrone, 1990); Cardiddàzza spinusa (Penzig, 1924) 1. Ardicula fimminedda cu li rappi e fogghi granni 3. Addìca, Ardìca, Ardìcula, Ardìcura, Firdìca, Firdìcula, Furdìca, Iardìca, Lardìca, Larzìca, Lardìca sarvaggia, Lardìchi masculini, Lurdìca, Lurdìcula (Pirrone, 1990); Ardìca masculina, Ardicàstra, Ardìcula fimminedda (Penzig, 1924); Firrìcu, Furdìcula, Urdìca (Giarrizzo, 1989), Trìula ammarretta (G. Garfì, pers. record)

Status

Life form

Edible part(s)

Traditional food use in Sicily seasoned with oil, salt and pepper

N

H scap

le

2. Addrìcula, Ardìcula, Ardìchi, Lurida, Ziculièdda 3. Ardìca di vadduni (Piccitto, 1977)

N

T scap

le

The leaves harvested before full blooming and cooked in boiling water, are eaten both on their own and "maritate" (= married, together with) with other vegetables, seasoned with oil and lemon or used for the preparation of risottos and soups

Urticaceae

1. Ardìcula, Ardìcula fimminedda, Ardìcula cu li spiculìddi 2. Arzìcula, Ardìca 3. Ardìca (Piccitto, 1977); Ardìca fimminedda, Ardiculèdda fimminedda (Penzig, 1924); Ardìcula, Ardìcura, Ddiìcara, Ddichèdda, Ddrìca, Ddìcula (Pirrone, 1990)

N

T scap

le

The leaves harvested before full bloom and cooked in boiling water, are eaten both on their own and "maritate" (= married, together with) with other vegetables, seasoned with oil and lemon or they are used for the preparation of risottos and soups

Valerianella eriocarpa Desf.

Caprifoliaceae

N

T scap

w-p

Raw in salads and seasoned with oil, salt and vinegar

Veronica cfr. anagallisaquatica L.

Plantaginaceae

2. Gaddinedda, Per'i ciocca, Lattuchedda 2. Crisciùni, Scavùni 3. Bbeccalunga; Bbeccambunga (Piccitto, 1977); Crisciunèddu, Erva di tràcina (Pirrone, 1990)

N

H scap

le

Raw in salads and seasoned with oil and salt

Vicia narbonensis L.

Fabaceae

3. Favaccia, Favi stritti, Favetta (Provitina, 1990); Bbezza (Piccitto, 1977); Fava sarbaggia (Penzig, 1924)

U

T scap

fr?

NS

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Table 2. (Continued) Scientific name according to Giardina et al. (2007) Vicia cfr. sativa L.

Family according to APG (2009) Fabaceae

Vitex agnus-castus L.

Verbenaceae

Vitis vinifera L. subsp. sylvestris (C.C. Gmelin) Hegi

Vitaceae

Sicilian vernacular name(s) reported by: 1. Mortillaro (1881); 2. Lentini & Venza (2007); 3. other Authors 1. Cuculiddi di frumentu, Vizza 3. Castagghiùna, Cuculiddi di furmentu (Traina, 1868); Cucculìddi di frumentu, Fasùla, Fasulàzzu, Fasulèddu, Fasulìdda, Fasulièddu, Fazulèddu, Fazulùni, Fasùlu sarvaggiu, Fasuòlu, Filu Nìuru, Vizza favalòra (Pirrone, 1990); Vizza (Penzig, 1924) 3. Làganu (Cupani, 1713); Lignu castu (Traina, 1868); Agnucàstu (Piccitto, 1977); Hiascùni (Pirrone, 1990); Làcanu, Làgomu (Penzig, 1924); Làgaru (Tropea, 1985) 2. Racìna, Vigna, Viti, Zuccu 3. Vitùsa (Giarrizzo, 1989); Prèula sarvaggia (Pirrone, 1990)

Status

Life form

Edible part(s)

Traditional food use in Sicily

N

T scap

fr?

NS

N

P caesp

infl?

NS

N

P lian

fr

Eaten raw in Neolithic period, as assessed from archaelogical remains from Uzzo Cave, Trapani province (Costantini, 1989; Leighton, 1993, 1999)

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* vernacular names of Berberian origin typical to the Pantescan community; §: vernacular names from Albanian communities; ?: use not ascertained; NS: not specified.

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An Overview on the Human Exploitation of Sicilian Native Edible Plants

239

Table 3. An overview of the Sicilian phytonyms on edible plants that can be undoubtfully ascribed to the language of one of the colonizers of the island, i.e. Greek (ancient and °modern), Latin, Arab Linguistic origin

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Greek

Latin

Vernacular names, original name/root and botanical identity acùmmaru (θόκαξνο: Arbutus unedo); - agrùmulu, armuìna, aròmulu (αγξηνκήινλ = wild fruit: Myrtus communis, Arbutus unedo and Crataegus spp., respectively); - aíti, brìtti, bilètti (βιίηνλ: Beta spp.); - arìganu, arìanu (νξίγαλνλ = Origanum spp.); càccamu (θαθθαβία°: Celtis australis); - calazìta, carrazìtula (γαιαθηίο, γαιαηζίδα°: Galactites tomentosa); - cannavùçi, cornabùsci (θαξλαβνύθηνλ: Lotus spp.); caranòbbiu (θνξωλόπνπο: Plantago coronopus); - cariòta (θάξνλ: Ammi majus); caulicèddu, qualèddi, qualùzzi (dim. θαπιόο = stem: Brassica); - cìmati (θύκαηα = tips: wild Brassica); - cucùddi (θνύθπιινλ = head: Glebionis coronaria); - cucùnci, cucuncèddi (dim. θόθθνο = round fruit: fruits of Capparis spp. and Asphodelus spp., respectively); - favaràggi (θαύιαη ξάγεο = little berries = Celtis australis); - glisirrìza (γιπθύξξηδα = sweet root: Glycyrrhiza glabra); - làcanu, làganu, làgaru, làgomu (άγλνο = pure: Vitex agnus-castus); - lapàzzu (ιάπαζνλ: Rumex spp.); - lapisànu, làssanu (ιαπάζζεηλ = to evacuate: Barbarea vulgaris, Brassica rapa subsp. campestris, Capsella bursa-pastoris, Hirschfeldia incana, Raphanus raphanistrum, Rapistrum rugosum); - masticògna (κάζηαμ = a chew of food: Carlina gummifera); - middicùccu, milicùccu, minicùccu (κέιη + θόθθνο = sweet grain: Celtis australis); - middèu, muddìu, mullìa (κειία: Fraxinus spp.); - muganàzza (ακύζζω = to scratch: Silybum marianum); mursiddìna (κπξζίλε άγξηα = wild mirtle: Ruscus aculeatus); - napòrdu (νλνπόξδνλ: Onopordon spp.); - pras, prazzìddi (πξάζνλ = leek: Allium triquetrum); - psìlliu (ψύιιηνλ: P. afra subsp. afra); - racogna, ragunìa (ξάθνο: Smilax aspera); - rafanèddu (dim. ξάθαλνο: Raphanus spp.); - ramuràzza (αξκνξάθηα = radish: Raphanus raphanistrum L. subsp. raphanistrum); - satarèdda, satarèddu (ζαηύξηνλ: Satureja spp.); - scalìmbru (αζθόιπκπξνο, ζθνιύκπξη: Scolymus spp.); - scòddu (ζθόιπκνο: Scolymus spp.); - sècala, sècara, sèchili, sìchili (ζηθειή = Sicilian: Beta vulgaris subsp. maritima); - sìlipu (ζίιιπβνλ: Silybum marianum); - sparacògna, sparagògna (αζπαξαγόληα: Asparagus spp.); - taddùzzu (dim. ζαιιόο = sprout, shoot: Raphanus raphanistrum); tannavìda (δαλαίο: Tanacetum vulgare); - tuminèddu (dim. ζύκνλ: Coridothymus capitatus); - zannuru (θπλάξα?: Cynara scolymus) àccia (Apium graveolens); - acìtula (acidula: Rumex spp.); - addàuru, làuru (Laurus nobilis); - agghiàstru (oleaster: Olea europaea var. oleaster); - amurèdda (dim. mora: Rubus spp. berries); - ardìcula, urdìca (ardere = to burn: Urtica spp.); - arùca (eruca: Eruca sativa); - arvuzzi (albucius, dim. albus: Asphodelus spp.); - atrìgna (ater = dark: Prunus spinosa); - attalèbbra, caccialebbri, lattilebbra (lactis herba = milky grass: Hyoseris radiata and Reichardia picroides); - bbezza, vezza, vizza (vicia: Vicia cfr. sativa); - bràscu (Brassica spp.); - buda, burda (buda: Typha spp.); - burdulàca (Portulaca oleracea); - cacòcciula (dim. caput: Cynara scolymus); - cersavoi (helvus? = yellow: Crocus longiflorus); - ciafagghiùni, safagghiùni (cephalo, -nis: Chamaerops humilis); - cìparu, ciparèddu (Cyperus spp.); - lapìstra, rapìsta (Rapistrum rugosum); lisciàntru (olus atrum = dark vegetable: Smyrnium olusatrum); - matricàla (matricalis: Salvia sclarea and Tanacetum parthenium); - ‘mbriàcula (ebriacus = drunk: Arbutus unedo); - mitàrbi, vitàrdi (Clematis vitalba); - nipitèdda (dim. nepeta: Calamintha nepeta); - petrafènnula (petra findula = cracked stone or stonecracker: Athamanta sicula); - piràinu (piraneus = pear-like: Pyrus amygdaliformis); - pulèu (pulegium: Mentha pulegium); - purrazzu (porraceus = leek-like: Asphodelus ramosus); - ràdicia, razza, razzi (radix = root: Arabis turrita, Hypochoeris laevigata, Raphanus raphanistrum, Rapistrum rugosum, Sinapis alba); -

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Linguistic origin

Latin

Arab

French Spanish

Vernacular names, original name/root and botanical identity rosamarina (rosmarinus: Rosmarinus officinalis); - rapùdda (rapula = little turnip: Brassica fruticulosa); - saùcu, savùcu (sabucus: Sambucus nigra); - scalèri (escarius, escalis = edible: Galactites tomentosa); - scòrdiu (scordium: Teucrium spp., Mentha spp.); - sinacciòla, sinàpa (sinapis: Brassica nigra, Diplotaxis erucoides and Sinapis arvensis); - spinapuci (spinare = to sting + podex = bottom: Ruscus aculeatus); - sulla, sudda (sylla: Sulla coronaria); - tamusceddi (dim. tamus: Tamus communis); vastunàca (pastinaca: Daucus carota subsp. carota); - viticeddi (dim. vitis: Tamus communis); - vurràina, vurrània (borago: Borago officinalis) cabbasìsi (habb = berry + azīz = renowned: Cyperus esculentus); - carciòffulu (kharshūf: Cynara scolymus); - carrùbba, iarrùbba (kharrūb, jarrūb: Ceratonia siliqua); - diccàra, duccàra, nàccara, ticchiàra (dukkār: Ficus carica var. caprificus); - ddùmma (daum, dūm: fruits of Chamaerops humilis); - firrìcu, furdìcula, furrìchi, furrihi*, hurrihi* (hurrīc: Urtica spp.); - garùfu (garūf: Aspholine lutea); - giummàrra (jemmār: Chamaerops humilis); - musulùghi (maslūq? = cooked, boiled: Asphodeline lutea); nubbia, nuvea (nawāyah = grain, hard seed: carob-tree or date-alm seed); - sàlica, zàlica, zàrca (salqa: Beta spp.); - satarèdda, satarèddu (sa‘tar, Coridothymus capitatus); - ùrfaru (‘usfur: Crocus spp.); - zaitùni (zaytūn: Olea europaea var. sylvestris); - zzafarànu (azzafran = saffron: Crocus spp.); - zzubbu (zubb = penis: Asphodeline lutea) brignòlu (pruneau: Prunus spinosa); - frambuà (framboise: Rubus idaeus); - rigolìzia (réglisse: Glycyrrhiza glabra); - sìgra (sègle: Secale cereale) cardedda (cardeta^ = little cardoon: Carduus sp.?); - mastròzzu (mastuerzo: Nasturtium officinale); - panicàudu (panical^: Eryngium campestre)

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(*Pantescan phytonyms mostly deriving from Berberian dialects: see Tropea, 1988), French, Spanish (Castilian and ^Catalan). Dim.: diminutive of.

The results also led to unexpected findings: although Spanish domination was the longest (more than four centuries) after the Roman Empire, only three terms used for edible plants have an origin that can be univocally referred to the Castilian (Table 3). Instead, 41 phytonyms show a clear Latin origin, 36 directly issue from Greek, while 16 and 4 derive from Arab and French, respectively. This supports the assumption that most of the edible plants listed were already known since antiquity (and then already recognized by their vernacular name), hence well before the first Spaniards colonized the island (14th century). The relatively high occurrence of Greek-derived plant names is not surprising if we consider that the first Greek colonies in Magna Graecia date back to the VIII century BC, and that the Hellenistic cultural influence lasted nearly 1,000 years in Sicily, i.e. until the beginning of Christianisation (2nd century AD); moreover, the Byzantines, who spoke a Greek-derived language, ruled the island for an additional three centuries (between 535 and 830 AD). However, caution should be used with phytonyms since their phonetics can lead to misinterpretation; in other cases, it may be quite intriguing to unravel a complicated or unusual history. For example, according to Bustamante Costa (2009), the Sicilian term ―sècala‖, actually indicating Beta vulgaris s.l. (and not Secale sp.), comes from the Greek ―sikelé‖ and simply means ―Sicilian‖. Over time it was gradually modified by the Arab (―assilqa”) and Spanish (―acelga‖) languages, leading to the current phytonyms sècala, sèchili, sìchili. Furthermore, as the European and southwest Asian names for Borago europaea sound

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very similar - e.g. German: ―borretsch‖, French: ―bourrache‖, Croatian: ―borač‖, Greek: ―borántza‖, Bulgarian: ―porech‖ and Hebrew: ―bora‖ - many authors assess that they all come from the Latin ―burra‖, i.e. rough woolen material, due to its hairiness, or from the Arab ―abu ’arak‖ = ―father of perspiration‖, for its property of inducing sweat. On the other hand, as similar names also exist among people that speak non-indoeuropean languages and who interacted only occasionally with Romans and Arabs (Basque: ―borrai‖ and Hungarian: ―borrágófü‖), a more ancient root could be invoked. Other Sicilian phytonyms have a clear Arab origin but may have originated after being ―filtered‖ by Spanish terms: this is the case, for instance, for saffron and carob-tree. The former comes from the classic Arab ―azzafaran‖, becoming ―azafrán‖ in Spanish and finally ―zzafarànu‖ in Sicilian. The second has a more complex derivation, and its root originates from the classic Arab ―al-jarnuba‖, becoming ―aljarruba‖ in Andalusian Arab, ―algarróbo‖ in Spanish, finally resulting in the Sicilian ―jarrùbbu‖, ―carrùbbu‖. Moreover, it is possible that plants whose Sicilian vernacular names show even more than 3-4 roots have a very ancient history of exploitation on the island. This is the case, for instance, for Celtis australis, Beta vulgaris s.l., Ammi majus, Arbutus unedo, Asparagus spp., Asphodeline lutea, Chamaerops humilis, Galactites tomentosa, Lactuca viminea, Mespilus germanica, Onopordon spp., Rosa canina, Ruscus aculeatus, Scolymus spp., Silene vulgaris s.l., Sinapis spp., Smilax aspera, Tamus communis, Tanacetum vulgare and Tragopogon spp.

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5. EDIBLE WILD PLANTS AND CROP WILD RELATIVES The Mediterranean Basin and southwest Asia are the centre of origin and/or differentiation of many edible plants and crops. This is particularly true if we consider woody species, large families like Fabaceae (Lathyrus, Pisum, Vicia, etc.) and Poaceae (Avena, Hordeum, Secale, etc.), or large genera like Allium. The vast amount of information available for woody species (e.g. Amygdalus, Corylus, Fraxinus, Malus, Olea, Prunus, Pyrus, etc.) and the two above-mentioned families would require a special treatment; in addition, for many species no traditional alimentary use has been found. All the 254 edible plants considered in this research are Magnoliophyta; they belong to 38 different families and 148 distinct genera. The most representative families are: Asteraceae (62 taxa, 33 genera), Brassicaceae (45, 27), Lamiaceae (25, 14), Fabaceae (17, 8), Apiaceae (16, 12) and Rosaceae (12, 10). The origin of 39 of the taxa is uncertain, but could be archaeophytes. The remaining 215 taxa probably are native; two of these - Carlina sicula subsp. sicula and Tragopogon crocifolius subsp. nebrodensis - are endemic to Sicily, and 6 live only in central-southern Italy and Sicily (Biscutella maritima, Brassica rupestris subsp. rupestris, Carlina hispanica subsp. globosa, Crepis bursifolia, Scorzonera deliciosa and Tolpis virgata subsp. quadriaristata); finally, Crocus longiflorus, Helichrysum italicum and Sonchus asper subsp. glaucescens grow exclusively in the central Mediterranean area. The majority of the edible plants are herbs (Therophytes, T: 29.1%; Hemicryptophytes, H: 40.9%; Geophytes, G: 8.3%), or shrubs and sub-shrubs (Chamaephytes, Ch: 10.2%); Nanophanerophytes (NP) and the Phanerophytes (P) represented 2.8% and 8.7%, respectively.

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While some brief information is given about crop wild relatives and the families of Fabaceae and Poaceae, the main focus is placed on the most important wild food plants included in the genera Allium, Asparagus, Beta, Brassica, Capparis, Cichorium, Cynara and Foeniculum.

Allium With more than 200 different taxa, the Mediterranean area is one of the main centres of differentiation for the genus Allium (De Wilde-Duyfjes, 1977; Stearn, 1978; Pastor and Valdés, 1983; Ozhatay, 1990). In Sicily, 39 different Allium taxa thrive, and 10 are endemic to the island: A. agrigentinum Brullo and Pavone, A. castellanense (Garbari, Miceli and Raimondo) Brullo, Guglielmo, Pavone and Salmeri, A. franciniae Brullo and Pavone, A. hemisphaericum (Sommier) Brullo, A. lehmani Lojac., A. lopadusanum Bartolo, Brullo and Pavone, A. nebrodense Guss., A. obtusiflorum DC., A. panormitanum Brullo, Pavone and Salmeri and A. pelagicum Brullo, Pavone and Salmeri. Three cultivated species (A. cepa, A. schoenoprasum and A. sativum) and four wild species (A. ampeloprasum, A. nigrum, A. roseum and A. triquetrum) are used in many Sicilian recipes (Table 2). Although the understanding of the phylogenetic relationships within the garlic group is improving (Hirschegger et al., 2010), further investigations are still needed for some of the local wild relatives of the domesticated forms, such as A. commutatum Guss. and A. hemisphaericum.

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Asparagus Following Bozzini (1959), Sicily hosts 5-6 native Asparagus species. The most common are A. acutifolius, widespread in all Mediterranean countries, where it typically grows within evergreen maquis and forest communities, and A. albus, better adapted to summer thermohydric stress. The shoots of both species are eaten raw, boiled and seasoned with oil and lemon or scrambled with eggs (Lentini and Venza, 2007). Also, A. horridus L. (= A. stipularis Forssk.), a southern Mediterranean salt-tolerant shrub showing a scattered distribution in sunny and arid coastal habitats, especially on marls and clays, is eaten near Catania (Arcidiacono and Pavone, 1995). Other Asparagus species are less known: A. pastorianus Webb and Berth., a close relative of A. albus (Valdés, 1979), has been reported only for southwest Sicily (Trapani and Agrigento province) (Pignatti, 1982; Raimondo and Bazan, 2008), while the distribution of A. aphyllus L., often confused with A. acutifolius, is still uncertain, although it seems to share the same habitat of A. albus. Finally, Pignatti (1982) treated A. aetnensis (Tornabene, 1856) as a hybrid or an intermediate between A. officinalis L. (an escaped archaeophyte) and A. tenuifolius Lam.. Other authors (Raimondo et al., 1994; Scoppola et al., 2003) consider it a valid species, once endemic of Mt. Etna (Catania province), but it is now probably extinct. No information is available on the gathering of these species, probably because they are rare and poorly studied.

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Beta As elsewhere in Europe and in southern Italy, the cultivated varieties of the common beet chart, Beta vulgaris L., probably underwent introgression because its wild relatives, B. maritima L., B. macrocarpa Guss. and B. patellaris Moq. (recorded only in Linosa island) grow near cultivated areas (Hammer et al., 1987a; Jarvis and Hodgkin, 1999). The leaves of B. maritima are eaten boiled and seasoned with olive oil and lemon juice, or fried with garlic, bread crumbs, cheese and parsley, or browned with tomatoes and garlic, chili pepper and small pieces of cheese, or boiled and fried as filling for some typical kinds of pizza like ‗girate‘, in Ragusa and Syracuse provinces, and the ‗cuddurùni‘, in Agrigento province.

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Brassica At a European level, only 10 species within the entire Brassica sect. Brassica are recognized (Snogerup et al., 1990; Diederichsen, 2001), with a distribution ranging from the centre-western Mediterranean Basin to the European Atlantic coasts. Actually, the botanical classification of infrageneric taxa is still the object of lively debate. Gladis and Hammer (2001) proposed to treat all wild and cultivated cabbages as subspecies or varieties within one single species (Brassica oleracea). In fact, this section represents a cytodeme (Harberd, 1972) in which the cultivated forms of B. oleracea and wild species are partially interfertile (Snogerup, 1979, 1980; Kianian and Quiros 1992; Böthmer et al., 1995). In any case, the conservation of wild relatives is highly advisable for the genetic improvement of domesticated cultivars (Geraci et al., 2000, 2004). Sicily is considered an important centre of differentiation for wild species belonging to the same cytodeme of Brassica oleracea L. (Raimondo, 1997). According to Sicilian authors (Raimondo et al., 1991; Raimondo and Mazzola, 1997; Geraci et al., 2001; Raimondo and Geraci, 2002), Sicily hosts some 18 wild cabbages. Eight of these, included within Brassica sect. Brassica and characterized by the chromosomal number 2n = 18, are Sicilian endemics: B. macrocarpa Guss., B. villosa Biv. subsp. villosa, B. villosa Biv. subsp. drepanensis (Caruel) Raimondo and Mazzola, B. villosa Biv. subsp. bivoniana (Mazzola and Raimondo) Raimondo and Mazzola, B. villosa Biv. subsp. tinei (Lojac.) Raimondo and Mazzola, B. rupestris Raf. subsp. hispida Raimondo and Mazzola, B. rupestris Raf. subsp. brevisiliqua Raimondo and Mazzola. Three other Brassica species are very rare in Italy: B. incana Ten., B. insularis Moris and B. amplexicaulis (Desf.) Pomel. Their habitats consist of limestone cliffs (Figure 2) from the coastline up to 1,000-1,200 m a.s.l.. Many populations are exiguous and have a scattered distribution, principally due to the limited extension of the cliffs, human disturbance and competition by other rupicolous species; hence, it is not surprising that they are often endangered or threatened (Raimondo et al., 1994). All these Sicilian taxa are of significant interest because they can hybridize with the cultivated varieties, and thus represent an important germplasm pool for the genetic enhancement of cultivated forms (Kianian and Quiros 1992; Böthmer et al., 1995).

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Figure 2. Brassica villosa Biv. subsp. drepanensis (Caruel) Raimondo and Mazzola. Sicily hosts many native wild cabbages which are consumed since ancient times by local people.

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Capparis The genus Capparis grows on both the tropical and the subtropical regions of the old and new world and includes some 250 species (Fici, 2001). According to Heywood (1993), the populations present in the European continent can be confined within only one species, Capparis spinosa L., with two subspecies: subsp. spinosa (spiny) and subsp. rupestris (Sibth. and Sm.) Nyman (spineless). However, recent investigations disagree with this classification. For instance, Inocencio et al. (2006) consider Capparis ovata Desf. (= C. sicula Veillard), showing the habit of a perennial herb and widespread on the salty clays of central and southern Sicily, to be one of the ancestors of the Caper bush (C. spinosa). The relationship between human beings and capers is very old, and up to now these perennial shrubs that are extremely resistant to drought stress are found throughout the Mediterranean Basin. They have long been used as a condiment in the Mediterranean diet, being highly appreciated for their pungent and bitter flavour (Alvarruiz et al., 1990; Inocencio et al., 2000). The first traces can be found in the lower Mesolithic as testified by remains of capers at archaeological sites (Hansen, 1991). The finding of carbonized flower buds and unripe fruits in a jar (dated to about 2400-1400 BC) at the site of Tell es-Sweyhat, Syria, suggests that capers have been used at least since that time as a condiment (Van Zeist and Bakker-Heeres, 1985). Caper is a typical orphan crop, i.e. a crop usually not traded internationally but which can play an important role in regional food security (Gruère et al., 2006). In fact, if properly promoted it could provide a stronger impulse to the economic development of the small Mediterranean islands. Customarily, its use consists of harvesting the flower buds, which are utilized in many traditional Mediterranean dishes. This is the reason why this shrub has become a valuable and important crop of great economic importance in the Mediterranean

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Basin for both the local market and export over the last decades. In Sicily, the caper has been cultivated mainly at Pantelleria and the Aeolian Islands, where it is historically the main economic crop (Giuffrida et al., 2002). The importance of capers is related to their contribution to the food industry, but has medicinal and cosmetic uses as well (Zargari, 1986; Al-Said et al., 1988; Bonina et al., 2002). However, it remains particularly important as a typical component of the Mediterranean diet and quite recently a new trend in caper use has appeared, mostly regarding the use of shoot tips and small unripe fruits (‗cucunci‘) collected at different developmental stages (Ozcan and Chalchat, 2007).

Cichorium Although four different Cichorium taxa live in Sicily, namely C. intybus L. subsp. intybus, C. intybus L. subsp. glabratum (C. Presl) Wagenitz and Bedarff, C. endivia L. subsp. pumilum (Jacq.) Coutinho and C. spinosum L. (Wagenitz and Bedarff, 1989), only the first is collected for use in local cuisine. It is eaten raw or cooked, seasoned with oil, salt and lemon juice or in soup, stewed or fried with garlic and oil (Lentini and Venza, 2007).

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Cynara Three native taxa refer to the genus Cynara grow in Sicily: C. cardunculus L. subsp. cardunculus, C. cardunculus L. subsp. scolymus (L.) Hegi and C. cardunculus L. subsp. flavescens Wiklund. Wild cardoon, C. cardunculus subsp. cardunculus (incl. var. sylvestris (Lam.) Fiori) (Figure 3), native to the Mediterranean Basin, is a non-domesticated vigorous perennial plant with a rosette of large spiny leaves and branched flowering stems. It is considered as the ancestor of both the globe artichoke (subsp. scolymus (L.) Hegi) and the leafy or cultivated cardoon (var. altilis DC.) (Zohary and Basnizki, 1975). Its high local ecomorphological and genetic richness has been confirmed by the recent description of the variety zingaroensis Raimondo and Domina, an endemic to Trapani Mts. (Robba and Raimondo, 2003; Raimondo et al., 2004). Ancient classifications considered the cultivated artichoke as a separate species (C. scolymus L.). Successively, studies based on morphological and isozyme analyses (Wiklund, 1992; Basnizki and Zohary, 1994; Rottenberg et al., 1996) have confirmed the classification proposed by Fiori and Béguinot (1904), which included cultivated artichoke, leafy cardoon and wild cardoon in the single species C. cardunculus L.. Some authors suggest that the domestication of artichoke took place around the beginning of the first millennium (Foury, 1989; Pignone and Sonnante, 2004), while the domestication of cardoon occurred during the first half of the second millennium. According to a recent investigation (Pignone and Sonnante, 2004), artichoke was probably domesticated in Sicily. This theory agrees with the high inter- and intra-populational genetic diversity of the local populations recorded by Portis et al. (2005). Another hypothesis, however, suggested that cardoon was likely domesticated in the western Mediterranean basin within Spain and France (Sonnante et al., 2007a).

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Globe artichoke is considered an important plant for the agricultural economy of southern Europe and is widely cultivated for its edible immature inflorescences, called ‗capitula‘ or ‗heads‘. Leafy cardoon is cultivated for its fleshy stems and leaf stalks and has some regional importance in Italy, Spain and southern France (Sonnante et al, 2007b). Moreover, the tender stems and the flower buds of the wild cardoon are used in numerous local recipes.

Figure 3. Cynara cardunculus L. subsp. cardunculus. Sicily and its satellite islets may be the centre of origin of cultivated cardoons and artichokes.

Foeniculum The two subspecies of fennel present in Sicily, Foeniculum vulgare L. subsp. vulgare and subsp. piperitum (Ucria) Bég., often live together and are gathered for eating. The first is used in many recipes: its tender parts are used to make balls, to prepare liquors, omelettes, pasta, tomato sauce or are consumed boiled and seasoned with oil and lemon juice or mixed with other vegetables and soups. It is one of the main ingredients of the typical Sicilian dishes ‗pasta ch’i sardi‘ (pasta with sardines), prepared together with onions, pine nuts, raisins, sardines and saffron, and ‗maccu ri favi‘ (Lentini and Venza, 2007), a sort of velouté of broad beans, onion, tomato, olive oil and ricotta fresh cheese. The seeds of both subspecies are used for seasoning many traditional foods such as sausages.

Woody Species Many of the taxa cited within this paragraph are not listed in Table 2 because at present either an official use is not known or since a vernacular name does not suggest their use in the

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past. Nevertheless, they are worthy of mention because of their importance as wild relatives of crops. Amygdalus webbii Spach, the ancestor of cultivated almonds, was probably introduced during Greek colonization and still grows near former Greek colonies in southeast and southwest Sicily (Alberghina, 1978; Marcenò et al., 1995; Minissale et al., 2003; Scuderi and Pasta, 2009). Hazelnut (Corylus avellana L.) has long been considered as an introduced species, but recent genetic (Boccacci and Botta, 2009) and archaeobotanic investigations (Noti et al., 2009; Tinner et al., 2009) suggest that it is native to Sicily. The same is true for the wild olive, Olea europaea L. var. sylvestris (Mill.) Lehr., although proper olive cultivation seems to have begun only with Greek colonizers (Stika et al., 2008). Among the three native ash trees, the endemic and extremely localized Fraxinus excelsior L. subsp. siciliensis (Ilardi and Raimondo, 1999; Schicchi et al., 2007b) has no traditional use, while several local varietes of F. ornus L. and F. angustifolia Vahl have been exploited for centuries to extract the so-called ‗manna‘, a sweet product with many medicinal properties (Raimondo et al., 1981; Crescimanno et al., 1993; Mazzola et al., 2006; Schicchi et al., 2007a). Wild apples in Sicily are represented by Malus sylvestris Mill. and the recently discovered M. crescimannoi (Raimondo, 2008), a quite distinct species endemic to northeast Sicily. The Sicilian populations of Pyrus show a high morphological heterogeneity. Accordingly, this prompted some authors to describe 3 endemic new microspecies close to P. communis L., i.e. P. vallis-demonis (Raimondo and Schicchi, 2004), P. castribonensis (Raimondo et al., 2006b) and P. sicanorum (Raimondo et al., 2006a). Two Prunus are undoubtedly native to Sicily: P. spinosa L. and P. mahaleb L. subsp. prostrata (Lojac.) Raimondo and Spadaro, endemic of the summit areas of Etna, Sicani and Madonie Mts. The latter may be considered one of the most interesting wild relatives of the cherry tree.

Fabaceae Among the 20 taxa of Lathyrus growing in Sicily, L. sativus L. is worthy of mention. It was probably domesticated 7,000-8,000 years ago in centre-eastern Mediterranean area and then spread over southern Europe and Asia (Lioi et al., 2010). In Sicily, the seeds of L. ochrus (L.) DC. and L. cicera L., both cultivated as food or fodder throughout Mediterranean countries (Heywood et al., 2007), and those of L. clymenum L., L. articulatus L. and L. odoratus L., are eaten raw or boiled and seasoned with tomato, oil, salt and pepper; otherwise they are consumed in omelettes or stewed with onions, oil and parsley, while the inflorescences of L. sylvestris L. are eaten boiled and seasoned with oil and lemon juice, scrambled with eggs and cheese or pan-fried with onions (Lentini and Venza, 2007). Other wild peas growing in Sicily may play an important role for crop improvement, like L. gorgoni Parl., a central-eastern Mediterranean pea with a gene pool similar to that of L. sativus (Heywood et al., 2007).

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Pisum biflorum Raf. (= P. elatius M. Bieb.), one of the few wild relatives of P. sativum L. (Samec and Našinec, 1996), is quite common in Sicily, generally in partially shaded plant communities. The genus Vicia is well represented in Sicily, with 45 taxa (Allkin, 1984). Notwithstanding, there is no recent record of their alimentary use on the island. Nevertheless, the vernacular names of some species (Table 2) suggest that - at least in the past - they were gathered for human consumption. This is probably true for V. narbonensis L., a relative of the cultivated broad bean (V. faba L.), and for several other wild vetchs, like V. angustifolia L., V. incisa M. Bieb. and V. sativa L.

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Poaceae Although in Sicily local alimentary use of wild oats, barleys and ryes has never been recorded, the Sicilian taxa might represent an important source of biotic and abiotic resistance for genetic improvement programs of several cultivated crops of Poaceae family, especially for the increased fitness of certain entities for harsh environments. According to the most recent surveys on the Mediterranean species of the genus Avena (Baum, 1977; Rocha Afonso, 1980; Scholz, 1991; Romero-Zarco, 1990, 1994), Sicily hosts 11 taxa. Nine presumed to be native: A. barbata Link subsp. barbata, A. barbata subsp. lusitanica (Tab.-Mor.) Romero-Zarco, A. x haussknechtii Nesvsky, A. hirtula Lag., A. ludoviciana Durieu, A. madritensis Baum, A. sterilis L., A. wiestii Steudel and A. insularis Ladizinsky. The latter, recently described as endemic to the central sector of the island (Ladizinsky, 1998, 1999; Ladizinsky and Jellen, 2003), is a tetraploid oat that probably contributed to the evolution of the cultivated hexaploids A. fatua L. and A. sativa L. (Shelukhina et al., 2007; Peng et al., 2010). Hordeum is also well represented in Sicily, with seven taxa; three are archaeophytes, namely H. vulgare L., H. distichon L. and H. secalinum Schreber (Hammer, 1984). Among the native barleys, H. bulbosum L. prefers the xeric grasslands on the hills, H. leporinum Link thrives in the very xeric and disturbed areas, H. geniculatum All. is found in the clay and salty soils of the inland, whereas H. marinum Hudson lives in the coastal meadows. Due to their higher salt-tolerance, breeding programs involving them have been planned to improve the performance of barley (Garthwaite et al., 2005) and wheat (Colmer et al., 2006) in marginal soils. Secale strictum (C. Presl) C. Presl subsp. strictum (= S. montanum Guss.), the only native rye of Sicily, is linked to the xeric montane grasslands of the island. All recent contributions about this rye underline its probably ancient origin and its close relationship with S. cereale L. (Hammer et al., 1987b; Hammer, 1990; Frederiksen and Petersen, 1998; De Bustos and Jouve, 2002; Cuadrado and Jouve, 2002).

6. FOOD OR MEDICINES? Nearly all plants contain secondary metabolites, i.e. biochemical compounds that enable them to struggle against herbivores and pathogens or face abiotic stress. Many of these

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products also have important pharmacological properties and, in some cases, it is difficult to assess if a plant has been harvested for alimentary or medical purposes. Therefore, it can be assumed that many wild plants have been gathered at least since Neolithic times as ―foodmedicines‖ or ―nutraceuticals‖ (Rivera et al., 2006). Nearly half of the edible plant taxa listed in Table 2 are known to have at least one medicinal property (PFAF, 2006; Lentini and Venza, 2007; Campanili, 2009), and almost all those cited in the previous paragraph are also used as natural remedies. Among the most extensively exploited food-medicines figure annual and biennial cabbages (genus Brassica), like B. fruticulosa, very common in sandy volcanic soils (Catania province, Pantelleria and Aeolian Islands), B. nigra (probably an archaeophyte), B. rapa subsp. campestris and B. tournefortii, all frequent in cereal crops and in fallows (Branca and Iapichino, 1997). Many vernacular names used for these wild cabbages, e.g. ―cauliceddi‖ or ―làssani‖, are derived from Greek. The first is simply a diminutive form from ―kaulós‖ (= stem) (Maggioni et al., 2010), whereas the second derives from ―lapázein‖ (= to soften, to purge) due to the medicinal (mainly stomachic and laxative) properties these species possess (Hedrick, 1972). Their leaves and tender parts are consumed boiled and seasoned with olive oil and lemon juice, and eaten as side dish of typical meat-based meals or cooked and fried with oil, garlic and tomatoes (Lentini and Venza, 2007). Moreover, all members of the Brassica group are generally rich in glucosinolates (Branca et al., 2002) that are already known to play an important role in reducing the risk of cancer (Kohlmeier and Su, 1997; Shapiro et al., 1998; Cohen et al., 2000). Beneficial effects on intestinal function are also recognised for species of the genera Asparagus (purifying and refreshing effect), Beta (cooling medical food in soups against constipation) and Cichorium, whose cooking water cleanses the stomach and allays the intestine, in addition to aiding kidney and liver functions (Lentini and Venza, 2007). Capparis, besides its use as flavouring, has been used since late antiquity in traditional phytomedicine (Rivera et al., 2003) and is still utilized for its antifungal, anti-inflammatory, antidiabetic and antihyperlipidaemic properties (Tesoriere et al., 2007). Cynara also have non-food utilizations since different parts of the plants are a source of antioxidant compounds, such as luteolin and dicaffeoylquinic acids (cynarin) (Gebhardt, 1997; Di Venere et al., 2005), and the roots are rich in inulin, an oligosaccharide known to have a positive effect on human intestinal flora (Raccuia and Melilli, 2004; Raccuia et al., 2005). Locally (e.g. in Agrigento province), the tender stems and the flower buds of the wild cardoon figure in dozens of local recipes, but due to their properties (hypoglycaemic effect, liver protection, etc.) they are also used as a food-medicine (Lentini and Venza, 2007). Finally, Foeniculum should also be considered as food-medicine due to their widespread utilization and to the large variety of chemical compounds that they contain (Tanira et al., 1996; Muckensturm et al., 1997; Conforti et al., 2006; Wright et al., 2007; Napoli et al., 2010). A number of other plant genera may be included among nutraceuticals. For example, many wild Asteraceae have a long history of exploitation as edible plants. According to local uses, the entire aerial part or the peeled stems of many thistles referred to as the genera Carduus, Carlina, Carthamus, Centaurea, Galactites, Onopordum, Scolymus, and Silybum are consumed as food in many (mostly southern) Mediterranean or southwestern Asian countries, but recent biochemical investigations have indicated that this group of wild herbs is an essential source of inulin.

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The use of representants of the genus Scolymus as food plants is one of the most widespread and known since ancient times in Sicily and the Mediterranean area. Already Theophrastus and Pliny said that these thistles were eaten as a vegetable (Hedrick, 1972). It is probably not by chance that their most common vernacular name ―scoddu‖ derivates from the Greek name ―skólymos‖. Three native species of Scolymus grow on the island: S. grandiflorus, S. hispanicus and S. maculatus (Vásquez, 2000). In Sicily, the tender stems and leaves of all species are eaten boiled and seasoned with oil and lemon juice, scrambled or with an egg omelette, in batter or as a salad. They are eaten raw in salads, fried with garlic, chilli pepper, tomato and ‗tuma‘ (a local fresh cheese) or even cooked in the oven with garlic, oil and crumbled-bread only in Trapani, Palermo and Agrigento provinces (Lentini and Venza, 2007). In the Agrigento province, S. hispanicus is also considered a medicinal food for its digestive action (Lentini, 1989); interestingly, Tunisians believe that it stimulates good liver function (Lemordant et al., 1977). The traditional use of Silybum marianum in health care is well-known. As well as the origin of its scientific epithet, also its specific destination is probably linked to a legend: Mary was feeding the infant Jesus when some of her milk spilt onto the thistle at her feet, causing the characteristic white markings of the leaves. Consistently with the ancient ‗doctrine of signatures‘, which states that nature provides clues to the therapeutic use of a plant, Silybum seeds were consumed by European wet nurses and nursing mothers to improve the quality of their breast milk (Fletcher, 1991; Mars, 1997; Chopra and Simon, 2000). From this legend, the common or local names ‗Maria‘s milk thistle‘ and ‗spina bianca‘ (= white spine) arose. Recent scientific investigations have documented the valuable properties of this thistle as a medicine against hepatic diseases; in particular, milk thistle is claimed to be beneficial in the treatment of liver disease and immune disorders, and may also have anti-cancer activity (Kittur et al., 2002). Among the Asteraceae, also Carlina is worthy of particular mention. There are nine Carlina taxa that grow in Sicily; among these both C. nebrodensis DC. and C. hispanica subsp. globosa are endemic to southern Italy and Sicily (Meusel et al., 1996). To our knowledge, only C. sicula subsp. sicula (strictly endemic), C. globosa subsp. hispanica and C. gummifera are gathered for eating. Even if common on the island, C. sicula lacks an own vernacular name; in some places it is called ―panicaudu‖, actually referring to another spiny plant, Eryngium campestre (Pirrone, 1990; Provitina, 1990), probably due to confusion. Its trunks (―trunzi‖) are boiled and seasoned with oil and vinegar and are very valued for their peculiar taste similar to artichoke (Lentini and Venza, 2007). The tender sprouts and the young leaves of C. gummifera - the ―glue thistle‖ - are stewed as an ingredient in some traditional dishes typical among Mediterranean and Saharian ethnic groups. The plant is also known for its medicinal (antipyretic, diuretic, purgative and emetic) properties (Larrey and Pageaux, 1995; Vallejo Villalobos et al., 2008). In the whole Italy, it is still used against parasite worms in folk veterinary medicine (Viegi et al., 2003). Moreover, in the past C. gummifera was chewed by women from Malta to enhance salivation in flax fibre processing (Cassar-Pullicino, 1961); this use explains one of its most widespread vernacular names, ―masticogna‖, i.e. ―gummy, chewable thing‖. However, glue thistle can be very dangerous, as already Theophrastos (1949) pointed out some 2,300 years ago. Its toxicity resides in atractyloside and carboxyatractyloside, two diterpenoid glucosides that inhibit mitochondrial oxidative phosphorylation (Daniele et al., 2005). These compounds may stop glycogen synthesis and therefore cause often fatal liver disease (Larrey and Pageaux, 1995; Hamouda et

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al., 2004; Zaim et al., 2008) and serious kidney damage (Nogué et al., 1992; Hopps et al., 1997). In order to reduce its toxicity, in Sicily the inflorescence (―cacocciulidda‖ = little artichoke) is never consumed raw, but boiled or stewed and seasoned with garlic, parsley and cheese (Lentini and Venza, 2007). Carduus is represented in Sicily by 9 taxa (Kazmi, 1964), and the region is probably one of the main diversity hot-spots for this genus. Two endemics live on the island: Carduus nutans L. subsp. siculus (Franco) Greuter, common in the pastures of hilly and mountain areas, and Carduus rugulosus Guss., which probably became extinct in the last century. Sicilian people gather two species of Carduus (C. argyroa and C. pycnocephalus), which are eaten boiled in salty water or fried in butter or with eggs (Lentini and Venza, 2007). Centaurea (the genus of cornflower) also contributes notably to Sicilian global biodiversity as it is one of the most representative genera of the Sicilian vascular flora, enclosing 36 taxa, 15 of which are strictly endemic to the island (Colombo and Marcenò, 1984; Bancheva et al., 2006). Only the three species Centaurea calcitrapa, C. sicula and C. solstitialis subsp. schouwii are known as edible, and their young leaves are eaten cool after boiling briefly and seasoning with olive oil and lemon juice. The tender stems of two different species of Carthamus, i.e. C. lanatus subsp. lanatus (widespread in Europe, North Africa and southwestern Asia) and C. pinnatus subsp. pinnatus (North Africa, Sicily and Malta) are eaten raw as vegetables (Lentini and Venza, 2007). Two other Carthamus, i.e. C. dentatus (Forssk.) Vahl and C. lanatus L. subsp. baeticus (Boiss. and Reuter) Nyman, are known as edible plants, but are now very rare in Europe and have no longer been found in Sicily since the last century (Hanelt, 1963; López González, 1990). Notobasis syriaca is a very vigorous (up to 3 m high) thistle whose tender stems are eaten raw with bread and ‗tuma‘ in Agrigento, Caltanissetta and Palermo provinces (Lentini and Venza, 2007). Onopordum horridum and O. illyricum can also reach a height of 2-3 m. Their most common vernacular name, ―napordu‖, directly originates from ancient Greek. They are eaten cool and seasoned with oil and salt, with flour and fried, or battered with eggs.

7. UNDERUTILIZED EDIBLE PLANTS, A RESOURCE TO BE (RE-)EXPLORED Some edible plant species could be of greater potential interest although currently show a very narrow distribution range and/or niche width within the Sicilian territory, or in the past they were eaten in only a few places or even they appear not quite attractive (e.g. Chenopodium spp., Urtica spp.) (Figures 4 and 5). Some of these plants are commented upon below. Alliaria petiolata is used as a substitute for garlic in Sicily and elsewhere in the Mediterranean area (Vokou et al., 1993) and central Europe (Allen and Hatfield, 2003). Its Sicilian vernacular name ―agghialòra‖ (i.e. ―garlic-like‖) has been documented by Pasqualino (1783-1795), Penzig (1924) and Provitina (1990), whereas it is indicated as ―pedi d’asinu‖ (i.e. donkey foot) in Mortillaro (1881). The gathering and consumption of its raw leaves are restricted (Branca and Iapichino, 1997) to the hilly and mountain areas of the island where it grows (Giardina et al., 2007).

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Figure 4. Chenopodium murale L.. Notwithstanding its ruderal behaviour and its vernacular name ―erba fitenti‖ (= stinking herb) this herb is often consumed in eastern Sicily.

Figure 5. Urtica urens L.. After cooking the stinging-nettle is harmless and may be used to prepare many local dishes.

To our knowledge, Diplotaxis crassifolia, a wild rocket endemic to northwest Africa and Sicily, is used in Agrigento province where it is very common on gypsum outcrops, but not elsewhere in Sicily, although it also grows in Trapani, Palermo, Caltanissetta, Enna and Catania provinces. The aerial part of this sub-shrub, locally called ―erva cavulara‖ (i.e. ―cabbage herb‖) and ―cavuliceddi‖ (i.e. ―little cabbage‖) is eaten boiled and seasoned with oil, garlic and chilli pepper (Lentini, 1989; Lentini and Venza, 2007). Another gathered

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Brassicaceae, Erucastrum virgatum subsp. virgatum is endemic to Basilicata, Calabria and Sicily (Pignatti, 1982), where it grows only on the coastal cliffs of Messina province. Interestingly, Crepis bursifolia, a hawksbeard endemic to southern Italy and Sicily, where it is common in all provinces, is used only in Catania province, where it is called ―ricuttedda‖ or ―rizzaredda‖; its leaves are eaten boiled and seasoned with oil and lemon juice (Lentini and Venza, 2007). Similarly, the composite Tolpis virgata subsp. quadriaristata also lives on the Aeolian Islands, Pantelleria and Agrigento province, but it is consumed only in Catania province, where it is very common (Arcidiacono and Pavone, 1995).

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CONCLUDING REMARKS AND FUTURE RESEARCH ADDRESSES The results presented here should be considered as the first version of a much-needed multidisciplinary research programme focusing on the heritage of edible plants in Sicily. Major effort is still required to harmonize the available information, mostly published in Italian and in regional or local publications that are often difficult to find and consult. The actual number of used plants and their uses is likely underestimated because the ethnobotanical investigation network is not uniform and does not adequately cover past and present knowledge. Adding to current knowledge on popular names and uses will require a multidisciplinary team including several additional specialists (e.g. cooks, gatherers, philologists, biochemists, historians, botanists, etc.), with the aim to avoid ―communication gaps‖ (e.g. incorrect plant classification by non-botanists, erroneous transcription of vernacular names, etc.) that can cause misunderstanding about the original alimentary and therapeutic uses of plants. Interestingly, many plants employed for food or aroma elsewhere in Europe (Hedrick, 1972; Allen and Hatfield, 2004; Tardío et al., 2006), in the Mediterranean area (Vokou et al., 1993; Rivera et al., 2006; Leonti et al., 2006) and in several Italian regions (Aliotta, 1987; Camarda, 1990; Manzi, 1999; Pieroni, 2000; Guarrera, 2003; Pieroni et al., 2002, 2005; Passalacqua et al., 2006; Dreon and Paoletti, 2009; Campanili, 2009; De Natale et al., 2009; Nebel and Heinrich, 2009), such as Allium ursinum L., Artemisia arborescens L., Eryngium maritimum L., Juniperus spp., Rosa spp., Salicornia spp., Salsola kali L., Stellaria media (L.) Vill., Suaeda maritima (L.) Dumort., etc., are not gathered in Sicily (Arcidiacono and Pavone, 1995). On the other hand, Sicilians developed many original recipes or uses not only for endemic plants, but also for many other edible plants consumed in other Mediterranean countries, which have probably been gathered since the very first stages of agriculture by Neolithic farmers, as they behaved as weeds. This is particularly true for many Asteraceae of the subfamily Cichorioideae, which play a very useful role as food-medicines (Leonti et al., 2006). Considering popular medicine, Leonti et al. (2010) highlighted the anomalous ‗insularity‘ of Sicilian plant uses with rigorous statistical procedures: although Campania and Sardinia share a higher number of plant species with Sicily than with each other, their medicinal floras are more similar between themselves than those in Sicily. This pattern is also valid if edible plants are taken into account. It has been proposed (Leonti et al., 2009) that the popular use of plants in Sardinia and Sicily depends, more than direct experience and oral transmission,

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upon the local retention of information reported by ancient classical authors, such as Dioscorides. Furthermore, Leonti et al. (2010) pointed out that it is more correct to speak about ‗contemporary plant use‘ than about ‗traditional plant knowledge‘. This suggests that the present neighbouring regional uses represent only part of the information contained in ancient texts, and explains why there are so many local differences in current plant uses. Accordingly, in future investigations ancient texts and toponyms should also be considered to provide a complete record of all ethnobotanical data available (De Natale et al., 2009). The linguistic analysis of Sicilian phytonyms must be considered far from comprehensive: for example, some correspondences between the terms used in the different Sicilian provinces and their colonization history must be still verified. Moreover, future contributions should be enlarged to the neighbouring regional dialects and toponyms. In fact, a high number of terms referring to edible plants with a clearly Greek origin has been recorded in both Sicily (see Table 3) and southern Calabria (Nebel and Heinrich, 2009). As a consequence of ongoing social and economical changes in the Mediterranean area, within the next two decades most of the cultural and technical heritage concerning Mediterranean edible plants and agro-sylvo-pastoral systems will likely cease to exist. In this perspective, ethnobotanical data collection and retrieval is as important as wild germplasm collection and conservation (Hammer et al., 1987b, 1990, 1992, 1999; Cohen et al., 1991; Hammer and Perrino, 1995; Laghetti et al., 1996, 1998a-b, 2002; Valdés et al., 1997; Meilleur and Hodgkin, 2004; La Mantia and Pasta, 2005; Hammer and Laghetti, 2006; Hajjar and Hodgkin, 2007; Heywood et al., 2007; La Mantia et al., 2007; Maxted et al., 2007, 2008a-b; Moore et al., 2008; Khoury et al., 2010). Actions in this direction should fully accomplish the main objectives of the recent International Conference on Cultural and Biological Diversity: Diversity for Development - Development for Diversity, sponsored by UNESCO (2010b), namely: a) to bring together civil society, representatives of indigenous and local communities, policy makers, scientists and intergovernmental and development cooperation agencies, b) to exchange knowledge and practices linking biological and cultural diversity, and c) to provide elements for a programme of work to be jointly implemented by UNESCO, the CBD and other partners. In the perspective of upgrading this rich patrimony of biodiversity, a significant amount of local plant resources appear underutilized or not properly valorized (Padulosi and Pignone, 1997; Padulosi et al., 2003; Melilli et al., 2010). For example, in Sicily there are many species of the genera Daucus (wild carrots) and Linum (wild flaxes), but there has been no applied research on their potential uses. The same is also true locally considering the high rate of intraspecific diversity, for instance in Brassica rapa (Crouch et al., 1995), Cynara (Raccuia et al., 2004; Portis et al., 2005; Mauro et al., 2009) etc.. More research should be directed towards this area, not only because local knowledge is disappearing, but also because there are too few efforts to exploit this information within modern agricultural systems, to restore old varieties or to improve modern cultivated varieties. Finally, recent studies confirm the hypothesis that, beyond satisfying nutritional needs, the diet may modulate various functions in the human body and play detrimental or beneficial roles in some diseases. In this regard, ‗functional foods‘ are of special interest. The concept of functional food was first mentioned in 1984 by Japanese scientists, who pointed out the integrated relationships among nutrition, sensory satisfaction, fortification and modulation of physiological systems (Siro et al., 2008). Therefore, functional food - launched in Europe since the mid-1990s - plays a specific role by increasing the physical and mental well-being

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of consumers (Roberfroid, 2000; Menrad, 2003). The growing interest of consumers in functional foods has brought about a rise in demand for functional ingredients obtained using ‗natural‘ processes and, as ascertained in several cultures, wild plants can represent an important source for functional foods (Pieroni et al., 2002; Ogle et al., 2003). In Sicily, to date little attention has been paid to the significance of functional foods, despite a rich tradition of popular phytotherapeutical remedies. It is desirable that the knowledge accumulated over the course of centuries on the alimentary-medicinal use of wild plants can provide such an important ‗added value‘ to the future development of this type of dietary approach.

ACKNOWLEDGMENTS The authors are grateful to Mr. Antonino (―Nino‖) Guella (Giuliana, Palermo Province), Dr. Tommaso La Mantia (DCA Department, University of Palermo) and Mr. Domenico Brolo (Partinico, Palermo Province) for communicating personal data on edible plant vernacular names and uses; to Prof. Joaquín Bustamante Costa (University of Cádiz, Spain) for providing interesting papers on Arab phytonyms; to Dr. Elisabetta Oddo (Botany Sciences Department, University of Palermo) for supporting reference collection; to prof. Martin Schnittler (University of Greifswald, Germany) for providing some photos.

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REFERENCES AA. VV., 1996 - Linee-guida del Piano Territoriale Paesistico Regionale.- Regione Siciliana, Assessorato BB.CC.AA. e P.I., Palermo. Alberghina O., 1978 - Il mandorlo spontaneo (Amygdalus webbii, Spach) della Sicilia sudorientale.- Tecnica Agricola (Catania), 30(6): 385-393. Aliotta G., 1987 - Edible wild plants of Italy.- Inform. Bot. Ital., 19: 17-30. Allen D.E., Hatfield G. (eds.), 2004 - Medicinal plants in folk tradition: an ethnobotany of Britain and Ireland.- Allen and Hatfield, 437 pp. Allkin R., 1984 - Computer management of taxonomic data with examples for Sicilian Viciaeae (Leguminosae).- Webbia, 38(1): 577-583. Al-Said M.S., Abdelsattar E.A., Khalifa S.I., El-Feraly F.S., 1988 - Isolation and identification of an anti-inflammatory principle from Capparis spinosa.- Pharmazie, 43: 640-641. Alvarruiz A., Rodrigo M., Miquel J., Giner V., Feria A., Vila R., 1990 - Influence of brining and packing conditions on product quality of Capers.- J. Food Sci., 55: 196-198. Amico F.P., Sorce E.G., 1997 - Medicinal plants and phytotherapy in Mussomeli area (Caltanissetta, Sicily, Italy).- Fitoterapia, 68: 143-159. APG (Angiosperm Phylogeny Group), 2009 - An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG III.- Bot. J. Linn. Soc., 161: 105-121. Arcidiacono S., 2002 - Flora popolare nel territorio di Bronte (CT).- In: ―Etnobotanica nella Provincia di Catania‖, Atti Conv. ―Andar per verdure‖, Linguaglossa, Nuova Zangara Stampa Editrice.

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Chapter 7

WILD RICE: IDENTIFICATION, USES AND CONSERVATION Jin Quan Li South China Agricultural University, China

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INTRODUCTION Wild plants, which are wild species closely related to cultivar, are a neglected global natural resource, yet they make a concrete contribution to global wealth creation and food security (Maxted et al. 2007). Identification, uses and conservation for wild plants are of importance for their sustainable use in plant breeding. The current rapidly development of bioinformatics, genomics, and molecular biology as well as conventional breeding methods provides useful means to mine the desirable genes in wild plants. Rice (Oryza sativa L.) is the most important human food crop in the world. As a model plant of cereal family, two rice genome sequence map have been generated (Goff et al., 2002; Yu et al., 2002). The genus Oryza consists of two cultivated rice (O. sativa and O. glaberrima) and 21 wild species (Khush, 1997; Vaughan et al., 2003). The wild rice species offer a largely untapped resource of agriculturally important genes that have the potential to solve many of the problems in rice production which we face today such as yield, drought and salt tolerance and disease and insect resistance. To unlock the genetic potential of wild rice a project entitled the ―Oryza Map Alignment Project‖ (OMAP) had been constructed to sequence 11 wild rice species comprise nine different genome types and include six diploid genomes (AA, BB, CC, EE, FF and GG) and four tetrapliod genomes (BBCC, CCDD, HHKK and HHJJ) (Wing et al. 2005). The project provides a research platform to study evolution, development, genome organization, polyploidy, domestication, gene regulatory networks and crop improvement. Therefore, in this charter, cultivated rice (Oryza sativa) and its wild relatives were used as a case for demonstration of identification, use and conservation of wild plants.

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Jin Quan Li

WILD SPECIES IN ORYZA GENUS

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The genus Oryza comprises 2 cultivated rice, O. sativa L. (Fig. 1) and O. glaberrima Steud., and 21 wild related species distributed in the tropical and the sub-tropical areas of the earth (Vaughan et al. 2003, Khush 1997). Most of those species have been assigned to nine genome types, AA, BB, CC, EE, FF, GG, BBCC, CCDD, and HHJJ, among which fifteen are diploid species and seven tetraploid species (Table 1). The classification of genome types were determined based on cytogenetic analysis (Morinaga 1964), genomic DNA hybridization (Aggarwal et al. 1997), rDNA spacer (Cordesse et al. 1992), transposons (Kanazawa et al. 2000, Iwamoto et al. 1999, Motohashi et al. 1997) and DNA sequence comparison of two nuclear Adh genes (Ge et al. 1999). Among them, three wild species is indigenous to China, i.e. O. rufipogenrufipogon, O. officinalis, and O. meyeriana (Figure 1).

Figure 1. Cultivated rice (Oryza sativa L., upper left) and three wild rice species, i.e. common wild rice (O. rufipogenrufipogon, upper right), O. meyeriana (bottom left), and O. officinalis (bottom right) (photographed by Jin Quan Li).

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Wild Rice

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The growing habitat for these wild species are diverse, including river or stream sides, upland, deepwater, forest floor under the semi shade environment, ponds, rock pools, and nearby of cultivar fields (Table 1, Fig. 2). Table 1 Oryza species: their chromosome number, genome group and usual habitat. (Adapted from Vaughan et al. 2003)

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Section complex Chromosome species number Oryza sativa complex O. sativa 24 O. 24 rufipogenrufipogon O. glaberrima 24 O. barthii 24 O. longistaminata 24 O. meridionalis 24 O. glumaepatula 24 O. officinalis complex O.officinalsis 24 O.minuta 48 O. rhizomatis 24 O. eichingeri 24 O. malapuzhaensis 48 O. punctata 24 O. latifolia 48 O. alta 48 O. grandiglumis 48 O. australiensis 24 O. ridleyi complex O. schlechteri 48 O. ridleyi 48 O. longiglumis 48 O. granulata complex O. granulate 24 O. meyeriana 24 O. brachyantha 24

Genome type

Usual habitat

AA AA

Upland to deepwater; open Annual or perennial, Seansonally dry or deepwater and wet year round; open Upland to deepwater; open Seasonally dry; open Seasonally dry to deepwater; open Seasonally dry; open Inundated areas that become seasonally dry; open

AA AA AA AA AA

CC BBCC CC CC BBCC BB,BBCC CCDD CCDD CCDD EE

Seasonally dry; open Stream sides; semi shade Seasonally dry; open Stream sides, forest floor; semi shade Seasonally dry forest pools; shade (Diploid) seasonally dry; open (Tetraploid) forest floor; semi shade Seasonally dry; open Seasonally inundated; open Seasonally inundated; open Seasonally dry; open

Unknown HHJJ HHJJ

River banks; open Seasonally inundated forest floor; shade Seasonally inundated forest floor; shade

GG GG FF

Forest floor; shade Forest floor; shade Rock pools; open

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272 meyeriana (bottom)

Jin Quan Li

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Figure 2. The habitat for common wild rice (O. rufipogenrufipogon) in China. Upper left is a pond near a streamside and cultivar fields, upper right is a seasonally dry pond in the semi hill, bottom left is a downstream of the dam, and bottom right is riverside (The first, third, and fourth pictures were taken by Jiu Huan Feng and the second Xiang Dong Liu).

THE CROSSABILITY AND HYBRID FERTILITY BETWEEN CULTIVATED RICE AND WILD RICE Interspecific reproductive barrier is a critical bottleneck restricting genes transferred from wild plants to cultivars. The interspecific reproductive barrier was examined between cultivated rice (Oryza sativa) and seven species with AA genome of Oryza geneus (Li et al., 2007). The results indicated that all crosses between O. sativa (including indica and japonica subspecies) and different species with AA genome produced certain seed setting rates, which varied from 7.78% to 51.11% (Figure 3), suggesting that crossability were not the main barriers influencing on the gene flow between the cultivated rice and other species with AA genome. The pollen fertility and spikelet fertility of interspecific hybrids F1 between O.sativa and O. rufipongon or O. nivara varied from 33.6%-89.9%. However, the interspecific hybrids F1 between O. sativa and other species excluding O. rufipongon and O. nivara possessed very low or even none pollen fertility and spikelet fertility (Fig. 3). Therefore, F1 hybrid sterility was the main reproductive barrier influencing on the gene flow within AA genome.

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Wild Rice

Figure 3. The crossability (upper) and F1 hybrid fertility (bottom) between the cultivated rice (Oryza sativa) and other rice species with AA genome (Li et al. 2007). Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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Strong reproductive barriers exist between cultivar and non-AA genome wild rice species. The cytological mechanisms of interspecific incrossability and hybrid sterility between O. sativa and O. alta (with CCDD genome) or O. meyeriana (with GG genome) were studied by Fu et al. (2007, 2009). No hybrids through conventional reproduction would obtain for these crosses. The main reason for the incrossability was (i) hybrid embryo inviability, presenting as embryo development stagnation and degeneration since 3 days after anthesis, thus no mature hybrid seeds would be obtained; (ii) embryo sac incompatibility that caused fertilization barriers of variable severity such as non-fertilization, fertilization stagnation and egg cell single-fertilization. For some crosses between cultivar and non-AA genome wild rice, hybrid plants could be obtained via culturing in vitro to rescue the young embryos (Figure 4) but high sterility was observed for the F1 plants. F1 hybrid sterility included both embryo sac sterility and pollen sterility. The hybrid embryo sac was completely sterile and exhibited mainly embryo sac degeneration. Hybrid pollen was also sterile and mainly typical abortive.

Figure 4. Hybrid plants between cultivated rice and O. officinalis (with CC genome) via culturing in vitro to rescue the young embryos (photographed by Jin Quan Li).

THREE LEVELS OF GENE POOL FOR RICE According the theory of Harlan and de Wet (1971), the cultivar and its wild relatives could be characterized by assigning taxa to primary, secondary and tertiary gene pools. The three levels of gene pool for cultivated rice are: (i) primary pool (GP-1), which consists of O. sativa, O. rufipogenrufipogon, O. nivara, and the weedy forms of O. sativa; (ii) secondary pool (GP-2), which consists of the species with AA genome, i.e. O. glaberrima, O. barthii, O. longistaminata, O. meridonalis, and O. glumaepalua, (iii) tertiary pool (GP-3), including the wild species with non-AA genome and the species in related to genera of tribe Oryzeae, which has very strong reproductive barriers with cultivated rice. The three gene pools provide the basis for applying these wild species in the genetic improvement of cultivar.

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THE BENEFICIAL CHARACTERS IDENTIFIED IN WILD RICE More than 28 beneficial characters have been identified within the wild rice species, which are important for cultivated rice improvement (Table 2). Table 2. Some beneficial characters identified in wild rice (He et al. 2002) Character Resistance to disease Bacterial blight Rice blast Virus Sheath blight Bacterial leaf streak Resistance to insect Nilaparvata lugens

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Sogatella fucifera Leafhopper Borer Cnaphalocrocis medinalis Rice gall midge Rice thrips Tolerance to abiotic stress Salt Drought Submergence tolerance Elongation Seedling cold tolerance Cold tolerance in winter Shade Acid sulphate soils Cytoplasm male sterility Stigma stretching out Sensitive to light period Early maturity High protein content Yield related tratis Biomass Dwarfness

Wild rice species O. rhizonatis, O. eichingeri, O. longistaminata, O. australiensis, O. ridleyi, O. barthii, O. malamluzhaesis, O. meyeriana, O. rufipogenrufipogon, O. officinalis O. nivara, O. longistaminata, O. officinalis, O. minuta, O. malamluzhaesis, O. ridleyi, O. rufipogenrufipogon O. officinalis, O. latifolia, O. ridleyi, O. nivara, O. barthii, O. rufipogenrufipogon O. rufipogenrufipogon, O. minuta O. rufipogenrufipogon O. rhizonatis, O. eichingeri, O. officinalis, O. rufipogenrufipogon, O. minuta, O .australiensis, O. latifolia, O. ridleyi, O. nivara, O. alta, O. brachyantha O. officinalis, O. latifolia, O. punctata, O. eichingeri, O. minuta O. barthii, O. officinalis, O. eichinger, O. minuta O. rufipogenrufipogon, O. alta, O. brachyantha ,O. ridleyi O. rufipogenrufipogon, O. nivara, O. punctata, O. brachyantha, O. officinalis O. rufipogenrufipogon, O. officinalis, O. brachyantha, O. ridleyi O. officinalis O. eichinger, O. officinalis, O. punctata O. rufipogenrufipogon, O. longistaminata, O. australienlis O. rufipogenrufipogon O. rufipogenrufipogon O. rufipogenrufipogon, O. officinalis O. rufipogenrufipogon, O. rufipogenrufipogon, O. longistaminata, O. officinalis O. longiglumis, O. granulate, O. meyeriana O. rufipogenrufipogon O. rufipogenrufipogon, O. nivara O. longistaminata, O. barthii, O. punctata O. rufipogenrufipogon O. australienlis O. rufipogenrufipogon, O. officinalis O. rufipogenrufipogon O. alta, O. grandiglumis O. nivara

These traits are (i) resistance to disease, such as resistance to bacterial blight, blast, virus, sheath blight, and bacterial leaf streak; (ii) resistance to insect, such as resistance to Nilaparvata lugens, Sogatella fucifera, Leafhopper, Borer, Cnaphalocrocis medinalis, rice gall midge, and rice thrips; (iii) tolerance to abiotic stress, such as tolerance to cold, drought,

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submergence, shade, and acid sulphate soils; (iv) yield-enhancing traits, good quality traits, high biomass, and cytoplasm male sterility. Almost every wild species has their own valuable traits to improve the cultivars. In this chapter, we described some cases for the identification of the novel genes in wild rice.

IDENTIFICATION OF QTLS FOR YIELD RELATED TRAITS IN A BC2F2 POPULATION BETWEEN O. SATIVA AND O. RUFIPOGON We first developed an advanced backcross population, i.e. a BC2F2 population from an interspecific cross between a cultivated rice variety Guanglu-ai-4 (Orya sativa ssp. indica) and a wild rice line from O. rufipogon using Guanglu-ai-4 as the maternal parent as well as the recurrent parent. The substitution segments were analyzed for the BC2F1s using 241 polymorphic simple sequence repeat (SSR) markers. 11

22 2

1

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RM462 RM462 0.0 0.0 0.0 RM462 RM323 RM323 20.2 20.220.2 RM323 RM220 RM220 24.4 24.424.4 RM220 RM259 RM259 glw1 glw1 gwp1 gwp1 RM259 gw1 gwp1 38.8 38.838.8 gwpi1-1 gwpi1-1 gwpi1-1 RM243 RM243 42.4 42.442.4 RM243 RM140 RM140 64.4 64.464.4 RM140 RM493 RM493 64.9 64.964.9 RM493 RM562 RM562 72.8 72.872.8 RM562 RM513 gwpi1-2 RM513 gwpi1-2 gwpi1-2 73.4 73.473.4 RM513 PSM367 PSM367 83.7 83.783.7 PSM367 RM488 RM488 102.3 102.3 102.3 RM488

0.0 0.00.0 8.9 8.98.9 13.4 13.4 13.4 17.6 17.6 17.6 26.9 26.9 26.9 33.6 33.6 33.6 50.3 50.3 50.3

33 3

RM109 RM109 RM109 RM211 RM211 RM211 RM279 RM279 pl2 pl2pl2 RM279 RM423 RM423 RM423 RM8 RM8 RM8 RM492 RM492 RM492 RM521 RM521 RM521

RM475 RM475 RM475 RM263 RM263 RM263 RM526 RM526 RM526 RM525 RM525 gw2 gw2 glw2 glw2 RM525 gw2 glw2 RM6 RM6 RM6 RM530 RM530 gwp2 gwp2 gwpi2 gwpi2 RM530 gwp2 gwpi2 130.2 130.2 130.2 sd2 sd2sd2 spp2 spp2 spp2 RM240 RM240 135.5 RM240 135.5 135.5 RM138 RM138 157.9 157.9 157.9 RM138 80.5 80.5 80.5 103.9 103.9 103.9 109.3 109.3 109.3 118.1 118.1 118.1 125.6 125.6 125.6

1.1 1.11.1 2.7 2.72.7 11.5 11.5 11.5 20.3 20.3 20.3

5

PSM301 PSM301 PSM301 2.0 RM60 RM60 RM60 RM231 RM231 RM231 25.0 RM489 RM489 RM489 27.0 42.2 48.3 55.0 60.7 67.5 70.5 77.4

120.4 120.4 120.4 128.0 128.0 128.0 128.3 128.3 128.3 139.5 139.5 139.5

99.0 RM135 RM135 RM135 110.4 RM55 RM55 RM55 116.5 RM203 RM203 RM203 123.0 RM520 RM520 RM520

164.4 164.4 164.4

PSM381 PSM381 PSM381

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RM507 RM548 RM574 PSM363 RM169 RM249 RM509 RM146 RM430 RM459 gwp5 pl5 spp5 RM421 RM274 RM31 PSM123

0.0

60.4 70.1

121.2

PSM371 PSM371 181.8 PSM371 181.8 181.8 5 5

2.02.0 25.0 25.0 27.0 27.0 42.2 42.2 48.3 48.3 55.0 55.0 60.7 60.7 67.5 67.5 70.5 70.5 77.4 77.4 99.0 99.0 110.4 110.4 116.5 116.5 123.0 123.0

8 8

RM507 RM507 RM548 RM548 RM574 RM574 PSM363 PSM363 RM169 RM169 ph5-2 ph5-2 RM249 RM249 RM509 RM509 RM146 RM146 RM430 RM430 RM459 RM459 gwp5 gwp5 pl5pl5 spp5 spp5 RM421 RM421 RM274 RM274 RM31 RM31 PSM123 PSM123

0.00.0

60.4 60.4 70.1 70.1

121.2 121.2

RM506 RM506

RM339 RM339 RM42 RM42 RM515 RM515

PSM396 PSM396

9 9

0.00.0 20.7 20.7 30.6 30.6 33.0 33.0 40.7 40.7 42.5 42.5 45.2 45.2 50.7 50.7 55.3 55.3 65.1 65.1 76.9 76.9 78.0 78.0 83.2 83.2 91.5 91.5 93.5 93.5

PSM156 PSM156 RM219 RM219 PSM157 PSM157 PSM158 PSM158 RM105 RM105 PSM160 PSM160 PSM161 PSM161 RM566 RM566 RM434 RM434 gwp9 gwp9 sd9sd9 gwpi9 gwpi9 gpp9 gpp9 spp9 spp9 RM257 RM257 RM328 RM328 RM201 RM201 RM215 RM215 RM245 RM245 RM205 RM205

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0.0 0.3 19.8 20.3 30.5

PSM408 RM286 PSM175 RM167 pl11 RM120

49.6 69.3 70.3 72.6 78.8

RM202 RM287 RM209 RM229 RM457

117.9

PSM176

Figure 5. The distribution of QTLs for traits associated with yield on the chromosomes. The white bar represents the genetic background of Oryza sativa and black bar the introgressed segment from O. rufipogon (Cheng et al. 2006). Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

9

RM506

RM339 RM42 RM515

PSM396

0.0 20.7 30.6 33.0 40.7 42.5 45.2 50.7 55.3 65.1 76.9 78.0 83.2 91.5 93.5

PSM RM PSM PSM RM PSM PSM RM RM gw RM RM RM RM RM RM

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Twenty substitution segments were identified on seven chromosomes including chromosomes 1, 2, 3, 5, 8, 9 and 11, and covered 16. 21 % of the rice haploid genome (Figure 5). Using the BC2F2 as mapping population, a total of 20 QTLs for eight traits associated with yield were identified, and of which 55 % had positive effects, which indicating that the alleles from wild rice could increase the performance of cultivar (Cheng et al. 2006).

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IDENTIFYING NEUTRAL GENES AT THE POLLEN STERILITY LOCI IN CULTIVATED RICE WITH O. RUFIPOGON ORIGIN Pollen sterility is commonly found in the intra-specific hybrids of O. sativa ssp. indica and O. sativa ssp. japonica rice, which is one of the main constrains for the utilization of strong heterosis between indica and japonica rice. Six loci (i.e. Sa, Sb, Sc, Sd, Se, and Sf) controlling the pollen sterility of F1 between indica and japonica have been identified and mapped or fine mapped by the previous studies. Neutral alleles at each locus are potential to overcome the F1 pollen sterility associated with the locus. Therefore, exploitation and utilization of neutral alleles are of significant importance. For Sb locus, a japonica rice Taichung65 (T65) with the genotype of SbjSbj and its near-isogenic line E2 with the genotype of SbiSbi were used as two genetic tester lines, where i and j referred to an indica and japonica allele at this locus. A total of 12 different accessions of O. rufipogon were crossed with the two tester lines and examined the allele at Sb locus. The pollen fertility of two F1s from one accession of the wild rice (GZW099) with both T65 and E2 were rather high, i.e. 89.22±1.07% and 85.65±1.05%, respectively, and showed non-significant difference between these two crosses (Table 3). Four SSR markers closely linked to the Sb locus were used to examine the segregation of the genotypes in both F2 populations. The segregation of the genotypes agreed with the expected Mendelian ratio (1:2:1) and their related pollen fertility showed non-significant difference, suggesting that no interaction existed between the alleles at the Sb locus of GZW099 and T65 or E2. Evidentially, a neutral allele (named Sbn) was identified from GZW099. Similarly, another accession of O. rufipogon was identified carrying neutral alleles Sdn and Sen at Sd and Se loci. These gene resources are helpful to overcome the sterility of indica-japonica hybrids (Shi et al. 2009, Liu, 2009). Table 3. Pollen fertility and seed setting rate for Taichung 65 (T65), E2, GZW099, and their F1s (Shi et al. 2009) Parents or crosses Pollen fertility (%) (mean±S.E.)

Seed setting rate (%) (mean±S.E.)

T65

93.33±1.08

95.32±0.53

E2

94.81±1.61

94.56±2.25

GZW099

94.24±0.84

85.20±0.51

T65×E2

58.53±2.86

74.28±2.55

T65×GZW099

89.22±1.07

86.86±1.25

E2×GZW099

85.65±1.05

86.34±0.47

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IDENTIFYING NEW GERMPLASM RESOURCES CONTAINING S5 N GENE FROM WILD RICE WITH FUNCTIONAL MOLECULAR MARKERS Female sterility, i.e. embryo sac abortion is one of the most important reasons that cause the hybrid sterility between indica and japonica cultivars. It is well known that widecompatible gene (S5n) can overcome the embryo sac sterility which caused by S5 locus in the indica-japonica hybrid. But most of the germplasm resources contain S5n genes were from cultivated rice. Chen et al (2008) had cloned the S5n gene and revealed that the S5n gene had 136bp DNA sequence deletion compared to indica and japonica varieties. Therefore, according to the deletion sequence of S5n DNA sequence, one functional marker ―S5-t2‖ was designed to screen the germplasm resources containing S5n gene (Fig. 6). Among 441 accessions of O. rufipogon, 18 accessions were found containing S5n gene. The deleted sequence at the examined gene region was the same as those known cultivars containing wide-compatible gene (S5n) (Wei et al. 2010). M GQ NT TZ 1

2

3 4

5

6 7

8

9 10 11 12 13 14 15 16

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Figure 6. The genotypes at S5n locus amplified with the functional molecular marker S5-t2f/S5-t2r for O. rufipogon accessions and the controlling cultivated rice, where 1-16 were the 16 accessions of O. rufipogon, NT is an indica rice and TZ a japonica rice, GQ is a cultivar with S5n gene, M is the standard DNA (provided by Youxin Yang).

WILD RICE AS IMPORTANT GERMPLASM RESOURCES IN CULTIVATED RICE IMPROVEMENT Wild rice has played great importance on the genetic improvement of cultivated rice, mainly due to that they have the elite genes which do not exist in the cultivars. As earlier as 1926-1933, Professor Ying Ting (Fig. 7), an academician of Chinese academy of science and the pioneer in the wild rice germplasm collection, conservation, research, and utilization in China, had made use of the natural hybrids from common wild rice (O. rufipogon) indigenous to the marshes of Shee New Wei, east suburbs of Guangzhou city, China (Li et al. 2009). He bred a new variety Yatsen No. 1 which was the first variety involving wild rice in the world. The variety was integrated with the excellent characters from cultivated rice and wild rice, as it having high yield, growing vigorously and special tolerance in the lower temperature, as well as in the extremely hot temperature and in the bad soil. Over a half century, at least 95 varieties of eight generations were derived from Yatsen No. 1, which were bred by Chinese rice breeder and farmers through pedigree selection, hybridization breeding and radioactive mutation breeding (Table 4). The total cultivated area for these varieties were more than 8.246 million hm2. Thus huge economic and social benefits were produced. It is an unusual fact in the history of rice breeding, which indicates that O. rufipogon is the important material to broaden the genetic diversity of rice varieties.

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Wild Rice Table 4. Summary of the varieties derived from Yatsen No.1 Breeding method Pedigree selection Hybrid breeding Radioactive mutation Sum

1 1 0 0 1

Generation after Yatsen No. 1 2 3 4 5 6 7 2 11 13 14 5 0 0 10 15 10 5 1 0 0 5 2 0 0 2 21 33 26 10 1

8 0 1 0 1

Sum

Percentage

46 42 7 95

48.4% 44.2% 7.4% 100%

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Figure 7. Professor Ying Ting (1888-1964), an academician of Chinese Academy of Science and a pioneer in wild rice germplasm resources in China, who bred rice cultivar Yatsen No. 1 from natural hybrid of O. rufipogon during 1926-1933, which was the first variety involving wild rice in the world (provided by Academician and professor Yong Gen Lu).

Another successful example of utilizing the wild Oryza species is hybrid rice, where the cytoplasm male sterility (CMS) gene was introduced from the perennial common wild rice (O. rufipogon) found in Hainan province, China, and subsequently the most essential CMS system of hybrid rice was developed (Yuan 1993). It launched the fast development of the high-yielding hybrid rice in 1970s in China as well as later in the world. The third prominent example is the varieties of grassy stunt virus-resistant rice, where the virus-resistant gene was incorporated from one accession of the wild rice O. nivara collected from India (Khush 1977). Other agronomically beneficial traits in the wild rice, such as rice tungro virus resistance, bacterial leaf blight (Xa21 gene) resistance and acid sulfate soil tolerance, have played important roles in rice breeding (Song et al. 2005).

CONSTRUCTION OF CHROMOSOME SINGLE SEGMENT SUBSTITUTION LINE WITH WILD RICE AS DONOR PARENTS Due to the offspring between cultivar and wild rice segregated wildly, and the useful genes always linked with unfavorable genes, it might be an efficient way to make use of the exotic genes from wild rice through construction of chromosome single segment substitution

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line (SSSL) with wild rice as donor parents. SSSLs are those plant lines which have the most of the entire genetic background of recipient parent (usually an excellent cultivated variety) and only a segment introgressed from the donor parent plants through multi-generations of backcrossing and molecular-assisted selection (MAS). The SSSLs are useful for QTL identification, QTL fine mapping, QTL interactions, evaluating of wild germplasm and transferring of novel alleles into the elite varieties. The idea is that: (i) any morphological or physiological significant variation between the recipient parent and the SSSLs is mainly due to the introgressed segment, thus the QTLs could be mapped; (ii) one can breed excellent variety if the SSSLs is better than the recipient parent; (iii) one can gather the beneficial QTLs by combining different SSSLs and breed a super varieties. Fig. 8 shows the strategy of construction for SSSL with wild rice (O. rufipogon) native to Gaozhou, China as donor parent and an excellent rice cultivar Yuexianzhan or Huajingxian 74 as recipient and recurrent parent. A total of 29 SSSLs have been constructed (Zhao et al. 2010, Wang, 2010). The length of substituted segments varied from 4.45 to 53.05 cM, which covered 26.9% of the rice genome. Nine of them were shown on Fig. 9 and Fig. 10.

Figure 8. The strategy of constructing chromosome single segment substitution line (SSSL) with wild rice (O. rufipogon) native to Gaozhou as donor parent, where Yuexianzhan is an excellent rice cultivar.

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With these SSSLs, a neutral gene San at the pollen sterility locus Sa was identified with above mentioned test crossing method where the SSSL carrying the segments of locus Sa from wild rice was used as the tested line instead of wild rice itself. The identified SSSL could be directly severed as an important germplasm resource to overcome the pollen sterility at locus Sa in indica-japonica hybrid breeding because it carried the novel gene with the genetic background of cultivar (Wang, 2010). A primary QTL analysis detected five QTLs controlling some agriculturally important traits. These included two QTLs (Pss-2-1 and Pss-11-1) for seed set rate, one QTL(Lfl-6-1) for flag leaf length, one QTL(Wfl-6-1) for flag leaf width, and one QTL(WRfl-2-2) for flag leaf length-width Among them, and Wfl-6-1 6, as Chr.1 Chr.1 ratio.Chr.1 Chr.2 Lfl-6-1 Chr.2 Chr.3were located Chr.3 in chromosome Chr.10 Chr.11 its previous reported position. Pss-2-1 and Pss-11-1 were minor QTLs. WRfl-2-2 was a new QTL which has not been reported (Wang, 2010).

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Chr.1

Chr.1

Chr.1

Chr.2

Chr.2

Chr.3

Chr.3

Chr.10

Chr.11

Figure 9. Chromosomal position of the substituted segment (black bar) in the nine SSSLs (Zhao et al. 2010), where Chr.1, Chr.2, Chr.3, Chr.10, and Chr. 11 are the rice Chromosomes. The right side of chromosome is SSR markers and the left side their genetic map distance.

Figure 10. Hybrid F1s between rice cultivar and O. rufipogon (left) , and the plants with chromosome single segment substitution (right) (photographed by Jin Quan Li). Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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TRANSFERRING STRESS TOLERANCE GENE THROUGH A TAC LIBRARY OF O. OFFICINALIS As described before, strong interspecific reproductive isolation limits the transfer of favorable genes from non-AA genome wild rice species into cultivated rice. Therefore, using a TAC (Transformation-competent artificial chromosome) library of O. officinalis, research was attempted to get some clones with genes for resistance and transform the cultivated rice with these clones (Liu 2010). Four transcription factors (AP2/EREBP, bZIP, NAC, and WRKY) related to the response to stress were used to design primers and screen the TAC library. When the clones were picked out, they were transformed into a cultivar (Nipponbare) by the Agrobacterium-mediated transformation method to develop stress-tolerant rice plants. Among the transgenic plants, the performance of drought tolerance index for lines TL1-TL5 showed significant higher than that of the recipient parent Nipponbare and the donor parent O. officinalis (Table 5). It provides an efficient way to make use of the elite genes hiding in the non-AA genome of wild rice.

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Table 5. The performance of index for drought tolerance for O. officinalis, a recipient cultivar Nipponbare, and the transgenic lines (TL1-TL5), where the different alphabets show significant difference (p < 0.05) between two lines (Liu 2010) Varities or lines O. officinalis Nipponbare TL1 TL2 TL3 TL4 TL5

Index for drought tolerance 4.53 a 31.17 b 53.84 cd 56.24 cd 59.52 cd 68.08 de 91.31 e

THE IMPORTANCE FOR CONSERVATION OF WILD RICE During millions of years of evolution and genetic adaptation to environments, the wild relatives have accumulated abundant genetic diversity and many novel genes which were lack off in cultivars and might be beneficial to the improvement of cultivated species. However, in practice, people would ususlly pay more importance to breeding than to germplasm resources, and more importance to utilization of germplasm resources than to conservation of them. For example, most of the populations of wild species have disappeared in China over the last three decades, mainly caused by habitat loss, fragmentation and other human disturbances (Song et al. 2005). Unfortunately, the decline of existing populations still continues. Therefore, conservation of the wild Oryza species is essential for the world‘s sustainable food supply and becomes increasingly important for continued availability and sustainable use of these valuable genetic resources.

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TWO APPROACHES FOR CONSERVATION OF WILD SPECIES There are two basic approaches for germplasm resources conservation— ex situ and in situ conservation. Ex situ conservation is an approach that collecting the genetic resources (seeds, pollen, sperm, or individual organisms) from the original habitats or natural environments and conserved in the seed bank, conservation base, etc. The advantage for this approach is that it can easily conserve a lot of genetic resources with a low cost. However, in relation to evolution, ex situ conservation is static; thus, it may reduce the adaptive potential of the wild species (Bellon et al. 1998; Morishima 1998). In situ conservation is a method that is used to preserve the integrity of genetic resources by conserving them within the evolutionary dynamic ecosystems of their original habitats or natural environments. In contrast to the ex situ approach, this method is intended for preserving wild species or populations in a dynamic way using a continuing evolutionary process (Morishima 1998; Lu 1999).

EX SITU CONSERVATION OF WILD RICE IN CHINA

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The National Crop Gene Bank of China preserves more than 5000 O. rufipogon accessions in the seed form. There are two National Wild Rice Nurseries built in the 1980s in Guangzhou, Guangdong Province and Nanning, Guangxi Chuang Municipality in China.

Figure 11. The ex situ conservation base for Oryza genus germplasm resources, located in South China Agricultural University, Guangzhou, China, where more than 2000 accessions of samples including all the 23 species in the Oryza genus are conserved as a living stock. Upper left is the appearance of the base, upper right is the population zone with blocks, bottom left is the cultivated zone with pots, and bottom right is the collection of wild rice seeds (photographed by in Quan Li).

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They keep more than 8000 accessions of O. rufipogon samples as well as other wild rice species as a living stock. Some university and local agricultural research institute are also keeping a large number of wild rice samples. For example, the ex situ conservation base for Oryza genus germplasm resources (Figure 11) located in South China Agricultural University, Guangzhou, China, where more than 2000 accessions of samples including all the 23 species in the Oryza genus are conserved as a living stock, with part of which were derived from professor Ying Ting‘s collection.

IN SITU CONSERVATION OF WILD RICE IN CHINA

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To date, a total of 10 in situ conservation bases for wild rice have been established in China, i.e. Dongxiang (Jiangxi Province), Chaling (Hunan Province), Zhangpu (Fujian Province), Yuanjian (Yunnan Province), Gaozhou (Guangdong province) (Fig. 12), Zhengcheng (Guangdong Province) (Figure 12), Yulin (Guangxi Chuang Municipality), Wuxuan (Guangxi Chuang Municipality) and Qionghai (Hainan Province) (Song et al. 2005).

Figure 12. in situ conservation base for O. rufipogon, which located in Gaozhou (left) and Zengcheng (right), Guangdong province, China, which is also a good teching practice site as shown by the right figure that professor Yong Ggen Lu brought his master and PhD. students as well as his assistances to the in situ conservation base for practive practice of one course (provided by Academician and professor Yong Gen Lu).

PROSPECTS FOR CONSERVATION, IDENTIFICATION AND USE FOR WILD RICE The accession numbers of wild species both in ex situ and in situ conservation base are huge. Therefore, to make efficient use and conservation of wild rice, a strategy with four steps is proposed. The first step is to construct a core collection to achieve the maximum diversity with minimum accessions of wild rice, so that the core collection could be easily conserved and studied efficiently. The second step is to map desirable QTL with combining linkage mapping and association mapping in a large scale and high resolution. The third step is to

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develop near isogenic lines to verify and fine map these QTLs. Finally, cloning desirable genes and making use of them in cultivated rice breeding would be possible.

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REFERENCES Aggarwal, R. K., D. S. Brar and G. S. Khush. 1997. Two new genomes in the Oryza complex identified on the basis of molecular divergence analysis using total genomic DNA hybridization. Mol. Gen. Genet. 254: 1-12. Bellon M. R., Brar D. S., Lu B. R. & Pham J. L. 1998. Rice genetic resources. In: Dwoling N. G., Greenfield S. M. & Fischer K. S. (eds). Sustainability of Rice in the Global Food System, Chapter 16. Pacific Basin Study Center and International Rice Research Institute, Manila, pp. 251–283. Chen JJ, Ding JH, Ouyang YD, et al. 2008. A triallelic system of S5 is a major regulator of the reproductive barrier and compatibility of indica–japonica hybrids in rice. Proc Natl Acad Sci USA.105:11436-11441. Cheng GP, Feng JH, Liang GH, Liu XD, Li JQ. 2006. Identification of QTLs for agronomic traits associated with yield in a BC2F2 population between Oryza sativa and Oryza rufipogon. Chinese J Rice Sci, 20: 553-556. Cordesse F, Grellet F, Reddy AS and Delseny M. 1992. Genome specificity of rDNA Cytogenetics” Tsunoda, S. and N. Takahashi (eds.), Elsevier, Amsterdam. 91-103. Fu XL, Lu YG, Liu XD, et al. 2007. Cytological mechanisms of interspecific incrossability and hybrid sterility between Oryza sativa L. and O. alta Swallen. Chinese science bulletin, 52: 755-765. Fu XL, Lu YG, Liu XD, et al. 2009. Crossability barriers in the interspecific hybridization between Oryza sativa and O. meyeriana. Journal of integrative plant biology, 51: 21-28. Ge S, Sang T, Lu BR and Hong DY. 1999. Phylogeny of rice genomes with emphasis on origins of allotetraploid species. Proc. Natl. Acad. Sci. USA 96: 14400-14405. Goff SA, Ricke D, Lan TH, et al. 2002. A draft sequence of the rice genome (Oryza sativa L. ssp. japonica). Science 296: 92–100. Harlan JR and de Wet JMJ. 1971. Toward a rational classification of cultivated plants. Taxon, 20: 509-517. He GQ. 2002. Minging and transferring of the beneficial genes in wild rice. IN: Lue LJ, Ying CS, Tang SX (eds) Rice germplasm resources. Wuhan: Scientific technology press of Hubei, 279 Iwamoto M, Nagashima H, Nagamine T, et al. 1999. A Tourist element in the 5‘-flanking region of the catalase gene CatA reveals evolutionary relationships among Oryza species with various genome types. Mol. Gen. Genet. 262: 493-500. Kanazawa A, Akimoto M, Morishima H and Shimamoto Y. 2000. The distribution of Khush GS. 1977. Disease and insect resistance in rice. Advances in Agronomics 29: 265–361. Khush G. S. 1977.Disease and insect resistance in rice. Advances in Agronomics 29: 265– 361. Khush GS. 1997. Origin, dispersal, cultivation and variation of rice. Plant Mol. Biol. 35: 25– 34.

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Li JQ, Lu YG, Feng JH, et al. 2007. The crossability and F1 hybrid fertility between Oryza sativa and other AA genome species. Journal of plant genetic resources, 8: 1-6 Li JQ, Yang XQ, Lu YG. 2009. The reviews and enlightenments on the breeding and popularization of rice variety Yatsen No.1 and its derived ones. Journal of plant genetic resources, 10: 317-323. Liu B. 2009. Identifying neutral allele at pollen-sterility loci (Sd and Se) in cultivated rice with Oryza rufipogon origin. Master thesis, South China Agricultural University, Guangzhou, China Liu R. 2010. Germplasm innovation for rice by TAC library of Oryza officinalis Wall. PhD. Thesis, South China Agricultural University, Guangzhou, China. Lu BR. 1999. Need to wild rice species in Nepal. International Rice Research Notes 24: 41. Maxted N, Iriondo JM, Ford-LIoyd BV, Dulloo E, Kell SP, Turok J. 2007. Crop wild relative conservation and use. CABI International, Oxfordshire OX10 8DE. Ministry of Agriculture, Forestry and Fisheries and National Institute of Agrobiological Resources, Tsukuba Japan, pp. 31– 42. Morinaga T. 1964 Cytological investigations on Oryza species. In “Rice Genetics and Cytogenetics” Tsunoda, S. and N. Takahashi (eds.), Elsevier, Amsterdam. 91-103. Morishima H. 1998. Conservation and genetic characterization of plant genetic resources. In: Seko H, Vaughan DA, Okuno K, Shirata K & Ebana K (eds). Plant Genetic resources: Characterization and Evaluation. Research Council Secretariat of the Ministry of Agriculture, Forestry and Fisheries and National Institute of Agrobiological resources, Tsukuba Japan, 31– 42. Motohashi R, Mochizuki K, Ohtsubo E and Ohtsubo H. 1997. Structures and distribution of p-SINE1 members in rice genomes. Theor. Appl. Genet. 95: 359-368. Shi LG, Liu XD, Liu B, Zhao XJ, Wang L, Li JQ, Lu YG. 2009. Identifying neutral allele Sb at pollen-sterility loci in cultivated rice with Oryza rufipogon origin. Chinese Sci Bull, 54: 2967-2974 Song ZP, Li B, Chen JK, Lu BR. 2005. Genetic diversity and conservation of common wild rice (Oryza rufipogon) in China. Plant species biology, 20: 83-92. Vaughan, D.A., Morishima, H. and Kadowaki, K. 2003. Diversity in the Oryza genus. Curr. Opin. Plant Biol. 6: 139–146. Wang L. 2010. Construction of single segment substitution lines of Oryza rufipogon Griff. indigenous to Gaozhou of Guangdong province and identify neutral gene of pollen sterility at Sa locus. Master thesis, South China Agricultural University, Guangzhou, China. Wei C M, Wang L, Yang Y X, et al. 2010. Identification of an S5n allele in Oryza rufipogon Griff. and its effect on embryo sac fertility. Chinese Sci Bull, 55: 1007-1014 Wing RA, Ammiraju JSS, Luo M, et al. 2005. The Oryza Map Alignment Project: the golden path ot unlocking the genetic potential of wild rice species. Plant molecular biology, 59: 53-62 Yu J, Hu S, Wang J, et al. 2002. A draft sequence of the rice genome (Oryza sativa L. ssp. indica). Science 296: 79–92. Yuan LP. 1993. Advantages of and constraints to the use of hybrid rice varieties. In: Wilson K. J. (ed.). International Workshop on Apomixis in Rice. Hunan Hybrid Rice Research Center, Changsha, China.

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Zhao XJ, Liu XD, Li JQ, Lu YG. 2010. Construction of single segment substitution lines in rice by using common wild rice (Oryza rufipogon) as donor parent. Chin J Rice Sci, 24(2): 210-214.

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In: Wild Plants: Identification, Uses and Conservation ISBN 978-1-61209-966-8 Editor: Ryan E. Davis, pp. 289-304 © 2011 Nova Science Publishers, Inc.

Chapter 8

PERSIAN SHALLOT (ALLIUM HIRTIFOLIUM BOISS): AN ENDANGERED WILD PLANT R. Ebrahimi, Z. Zamani, M. R. Hassandokht and A. Kashi Department of Horticultural Science, University of Tehran, Karaj, Iran

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ABSTRACT Persian shallot (Allium hirtifolium Boiss.), a bulb producing plant from Alliaceae, is a wildly growing plant collected for its bulbs. Bulbs of Persian shallot, called "Mooseer" in Farsi, are oval, white skinned, usually of one and rarely of two main bulbs and are completely different from common shallot (Allium ascalonicum). Mooseer is a nutritive plant with special taste and its dried bulb slices are used as an additive to yogurt and also pickling mixtures. Its powder is used as a tasty additive or spice for foods in Iran. In addition, it has crucial medicinal effects; The mean dry matter of Mooseer was higher (36.71%) than other alliums except garlic. Mooseer was rich in Cu as well as Zn and Mn elements. Also, its linolenic acid (ω3) and linoleic acid (ω6) were higher than common shallot and onion. As some of the edible vegetable alliums such as mooseer are indigenous (native and endemic) to Iran, and do not exist in other parts of the world, their conservation is become imperative.

INTRODUCTION Wild plant species are very useful in breeding because they serve as important sources of resistance to adverse environmental conditions such as salinity, drought, pests and diseases. Many factors affect the loss of genetic resources. These include industrial development, mechanization, replacement of local and traditional varieties with new inbred varieties, changes in cultivation techniques, and control of wild species as weeds. These factors lead to genetic extinction of valuable materials which are used in breeding. The richest source of genetic diversity of each species is its center of origin, which is the geographic region where the species originated. This center is often the center of maximum diversity where the different genotypes are found. Iran, due to its special geographical

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location, is considered one of the richest sources of genetic material of plants and plant diversity, including many Persian shallots. Iran might hold the first place in the world in terms of edible plant species. The edible alliums include some of man s most ancient cultivated crops. Depictions of onion bulbs and models of garlic bulbs which date back more than 5000 years have been found in Egypt. The cultivated crops evolved from wild relatives that grow in the mountainous regions of central Asia. Many species of wild alliums are edible and some are still collected for food, but only a few species are commercially cultivated as crops [7]. The botanical classification of alliums has recently been reviewd and summarized by Hanelt (1990). The genus occupies the following taxonomic context [7, 17]: Class: Monocotyledones Superorder: Liliflorae Order: Asparagales Family: Alliaceae Tribe: Alliae Genus: Allium

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There are more than 500 species within the genus Allium. In table 1 the most important species of alliums is given. Vegetable alliums are largely Asian in origin with important species found across the continent, stretching from eastern China to the Mediterranean regions. Afghanistan, Iran and western Pakistan are major genetic diversity areas. The mediterranean basin is considered a secondary center. Many wild alliums, for example A. amplectens and A. anceps in northern California are also found in other areas [23] Hanelt (1990) classified Allium genus to two sub-genus and each genus contains important which summarized in table 2 [7, 17]. Table 1. Scientific and common name of some vegetable alliums [10, 20, 23] Scientific name A. cepa var. cepa A. cepa var. aggregatum A. cepa var. solanina A. cepa var. perutile A. cepa var. ascalonicum A. bulbiferum A. ampeloprasum A. ampeloprasum var. aegiptiacum A. ampeloprasum var. holmerise A. ampeloprasum var. sativum A. chinese A. sativum var. sativum A. tuberosum A. hirtifolium Allium sp.

Common name Onion Multiplier onion Potato onion Ever ready onion Shallot Topset onion Leek Kurrat Great- headed garlic Pearl onion Rakkyo Garlic Chinese chives Persian shallot Persian leek

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Persian Shallot (Allium Hirtifolium Boiss) Table 2. Classification of Allium genus [7, 10] Sub-genus Rhizirideum Rhizirideum Rhizirideum Rhizirideum Rhizirideum Allium Allium Allium

Division Rhizirideum Schoenoperasum cepa cepa cepa Allium Allium Allium

Species A. tuberosum A. schoenoprasum A. cepa A. fistulosum A. chinese A. ampeloprasum A. ampeloprasum A. hirtifolium

Common name Chinese chives Chives Onion, Shallot Japanese bunching onion Rakkyo Leek, Kurrat, Great headed garlic Pearl onion Persian shallot

The taste and odor characteristics of the vegetable alliums are their major attribute. Other features are the umble inflorescence, flowers with nectaries, a three-chambered ovary and a basic choromosome number of 8 for the cultivated species. Differences in flowering habit, floral morphology, leaves, scapes, storage organs and flavor are useful for species identification (Table 3). Table 3. Characteristics useful in identifying vegetables alliums [10, 12, 13, 23]

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Allium

Onion

Diploid chromosome number 16

Garlic

16

Bulbs, foliage leaf bases and foliage blades Cloves

Leek and kurrat Great-headed garlic Japanese bunching onion Chives

32

Pseudostem

48

Rakkyo

16, 24 or 32

Chinese chives

32

Shallot

16

Kurrat Persian shallot

32 16

Cloves few and large Pseudostem, foliage leaf bases and leaf blades Foliage leaf blades and leaf bases Bulbs, swollen foliage leaf bases Foliage leaves, scape and flower buds Bulbs, foliage leaf bases and foliage blades Pseudostem Bulbs, foliage leaf bases and foliage blades

16

16, 24 or 32

Usual edible portions

Usual flower color

Bulbs formed

Bulbils in inflorescence

White, green striped

Yes

Absent in most cvs.

Lavender to pale green and white White to purple

Yes

Very common

No

Sometimes

White to purple

Yes

Usually not

Pale yellow to white

No

Absent in most cvs.

Purple or rose, rarely white Rose-purple

No

Rarely

Yes

No

White

No

No

White, green striped

Yes

Absent in most cvs.

White to purple Purple

No Yes

Sometimes Rarely

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PERSIAN SHALLOT (ALLIUM HIRTIFOLIUM BOISS) A: Origin and Distribution of Presian Shallot Persian shallot (Allium hirtifolium Boiss.) is native and endemic plant in Iran [19, 22]. It grows wildly in mountainous areas, in the highlands of Markazi, Kermanshah, Kurdistan, Hamedan, Chahar Mahal and Bakhtiari, Kohkiluyeh and Boyer Ahmad, Lorestan, Isfahan and Fars provinces [16, 22]. Persian shallot called mooseer in Iran. This species is different from cultivated shallot in Europe and America. Persian shallot has some similarities to a kind of shallot in Germany, called Russian shallots (Russische Eschlauch) [25].

B: Botany Characteristics

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Persian shallot (A. hirtifolium Boiss.) is a perennial plant belonging to Alliaceae. This plant belongs to monocotyledons and grows wildly (Figure 1).The leaves are broad and similar to leek. Leaves have white trichomes (Figure 2). The edible parts of Persian shallot are leaves and bulbs. Each plant has 1-2 large bulbs and some bulblets sticking to the main bulb (Figure 3). Bulb contains a thin external skin which has a protective role. The color of bulb is dark yellow to pale. Bulb diameter varies between 2.5 to 4 cm. The inflorescence is umbel and the length of scape is 80-120 cm (Figure 4). Each plant usually consists of one flower stem, but occasionally two flower stems are also seen. Spath has two lobes and the length is 3 cm. The spaths are green when they emerge, but their color change to light green or whitish upon opening. All populations have anthocyanin at the base of the inflorescence and peduncle. Based on differences in time of flowering, the populations can be categorized into early and late flowering.

Figure 1. The overall appearance of mooseer plant.

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Persian Shallot (Allium Hirtifolium Boiss)

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Figure 2. Trichome in Persian shallot leave.

Figure 3. Cluster form of Persian shallot.

Figure 4. Umbel Inflorescence in Persian shallot. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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Umble is spherical and flower peduncles are 3.5-5 cm long. Perianth is purple and rarely white. Corolla is half drawn. Each flower has three carpels and every carpel has two ovules (Figure 5, 6). Flowers are complete and have three petals, six filaments and three tepals (Figure 7, 8) [10, 19]. Filaments are 6-7 mm in length and their base destination more or less distinct and triangular shape. Anthers are about 2 mm in length. Seeds form in capsules and each capsule has 5-8 mm diameter (Figure 9) [21, 22]. Seeds are black and the weight of a lot of 1000 seeds ranges from 4.4 to 7 grams. Some populations have bulbils in the inflorescence which can be used in propagation (Figure 10). Shallots are cross pollination and they are pollinated by insects. All plants do not form flower stem which could be attributed to size and environmental conditions. Since the scape uses plant reserves, the absence of flower stem is useful to produce the bulbs, resulting in the formation of bigger bulbs. Cutting of flower stem immediately after emergence leads to increas in bulb weight.

Figure 5. Seed formation in mooseer.

Figure 6. Three carpels in every flower. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

Persian Shallot (Allium Hirtifolium Boiss)

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Figure 7. Complete flower in Persian shallot.

Figure 8. Filaments structure in Persian shallot.

Figure 9. Seed formation in capsules. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

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Figure 10. Bulbil formation in inflorescence of Persian shallot.

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C: Harvesting, Storage and Consumption of Persian Shallot The Persian shallot is harvested when the foliage is dried and fallen off. At this time Persian shallot is quite ripe and can be harvested. They usually grow in mountainous areas, in soils 20-30 cm deep. Crop destructions by grazing cattle are likely, and can result in yield losses. The selection of growing sites is done by the local people, who choose and their choices are influenced by the ease with which the site can be access during harvesting. Harvesting is done using, and harvesting time varies from region to region depending on climatic conditions. In the southern provinces, mooseers are harvested in May, whiles in temperate and cold region, harvest takes place in late spring and summer [10, 13]. Mooseers are dried following harvest, and can store at room temperature for up to a year if well dried. Mooseers from South of Iran, have been observed to store for longer compared with those from temperate and cold regions. This could be attributed to differences in morphology of the plant. The mooseer in warm regions (southern provinces) seems to have a thinner neck and thus, they have less gas exchange with the surroundings and consequently less transpiration [10]. Persian shallot is consumed in a dried and powder form in many parts of the country. The bulbs are, thus, purposely chopped and dried in the shade. They are soaked in water prior to meal so as to remove the bitter taste. Persian shallot has diverse uses. It is used as pickles, and can be served with yogurt [11].

NUTRITIONAL VALUE OF PERSIAN SHALLOT AND OTHER VEGETABLE ALLIUMS The highest and lowest water content are related to Chinese chives and garlic, respectively. In terms of calories, shallot bulbs have the highest and garlic leaves have the lowest value. Garlic bulbs and Chinese chives bulbs have the highest and lowest

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carbohydrate, respectively. The highest and lowest protein were measured in onion and garlic bulbs. Shallot leaf and bulb contain the highest and lowest fat percentage, respectively. Shallot leaf contains the highest amount of fiber in 100 grams fresh edible part. Shallot leaf containing 86 mg calcium per 100 grams fresh edible parts, has the highest amount of calcium among vegetable alliums. Garlic bulb also has the largest amount of phosphorus, potassium, sodium and magnesium. Shallot leaves having 3.7 mg of iron per 100 grams of edible fresh part is the first rank among vegetable alliums. Table 4 presents essential fatty acids of some vegetable alliums. According to this table vegetable alliums have different amounts of essential fatty acids and chives and leek have the highest amount of linoleic acid (omega-6) and linolenic acid (omega-3). Based on studies, 80 percent of fatty acids in onion, garlic and leek are four most important essential fatty acid linolenic acid (3-7%), linoleic acid (46-53%), palmitic acid (2023%) and oleic acid (4-13%) [27]. Vegetable alliums have less than 0.7 g fat per 100 g fresh weight. The skin of colored onion bulbs has 10 times more lipid than that of garlic skins [1, 2]. More than 90% of vegetables weight contains water [23, 24]. Therefore, the bulb of mooseer with an average of 30% dry matter is a valuable vegetable for production of dried products such as shallot powder. The dry matter content of shallot is slightly less than that of garlic (36%) which has the highest dry matter among vegetables, but it is higher than that of other vegetable alliums [23, 27]. The average ash of mooseer is more than two times of onion (0.4 g per 100 g fresh weight), more than 1.8 times of shallot (0.8 g per 100 g fresh weight) and more than 1.2 times of garlic (1.3 g per 100 g fresh weight). Potassium content of mooseer is 1.2 times of onion and less than the amount reported in garlic and shallot [24]. However, the sodium content of mooseer is less than the amount reported in the onion, garlic, leek, shallot and chives [24]. The average amount of magnesium in mooseer is less than that of all vegetable alliums [24]. The average iron content of mooseer is 1.4 times of the amount reported in the onion (0.4 mg per 100 g fresh weight) and less than the amounts reported in the garlic, leek, shallot and chives [23]. Based on the results of our studies, different populations have different amounts of magnesium, zinc, iron, copper and sodium and could be recommended for cultivation in different regions if they are adapted to these areas [11]. Table 4. Essential fatty acids of some important alliums (mg per 100 g fresh weight) [11, 27] Allium Onion Leek Garlic Chives Shallot Persian shallot

C18:3 4 99 20 15 2 4.96

C18:2 13 67 229 225 37 26.66

C18:1 13 4 11 95 14 22.23

C18:0 0 0 0 9 1 5.07

C16:0 34 38 87 103 15 19.54

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Fatty acids of mooseer were determined using gas chromatography. The important fatty acids of mooseer include linolenic acid (ω3) (C18: 3), linoleic acid (ω6) (C18: 2), palmitic acid (C16: 0), Palmitoleic acid (C16: 1), stearic acid (C18: 0) and oleic acid (C18: 1). The average amount of palmitic acid in Persian shallot is less than that of garlic and chives (respectively 87 and 103 mg per100 gram fresh weight) and 3.8 times more than this fatty acid in shallot (15 mg per 100 g fresh weight). The average amount of stearic acid in mooseer is more than that of all vegetable alliums and 15 times of shallot. The average amount of oleic acid in Persian shallot is more than that of onion, leek, garlic and shallot, but less than the amount reported in the chives (95 mg per 100 g fresh weight) [11, 27]. The average amount of linolenic acid in Persian shallot is less than the amount measured in garlic, chives and leek and seven times more than this amount reported in shallot (2 mg per 100 g fresh weight). Although the average amount of linoleic acid in mooseer is less than that of leek, garlic and chieves, it is two times more than that of shallot. Hence, the populations of Persian shallot with linolenic (omega-3) and linoleic acid (omega-6) are considered a valuable source of essential fatty acids [11, 27]. The main fatty acids in leek parenchymatous tissue were palmitic, linoleic and linolenic acids [8]. Vegetable alliums are rich source of antioxidants and vitamins D, C, B, A, beta carotene and essential amino acids [8]. Most of the amino acids commonly found in proteins have been detected in onions and Japanese bunching onion. They contain relatively large amounts of arginine and glutamic acid, and these may be important N reserves. Onion skins contain large amounts of pectin and are suitable sources for the extraction of pectin substances for processing. The colour of red onions is due to anthocyanins of known chemical structure. Flavonols are found, particularly in the outer skins of yellow onions and 14 types are listed by Fenwick and Hanley (1990a), including many with sugar residues attached to the central flavonol ring structure [15]. A number of phenolic substances have been isolated, including protocatechuic acid, from the outer skins of coloured onions. This is absent from the skin of white onions causing them to be more susceptible to attack from onion smudge [7].

MEDICINAL VALUE OF PERSIAN SHALLOT AND OTHER VEGETABLE ALLIUMS Alliums have a long history of medicinal use and are ascribed to cure a wide range of ailments in traditional medical writings. Scientific studies have shown considerable pharmacological effects which have, in some cases, been attributable to specific molecular structures mostly derived from the flavour inducing sulfur compounds [7]. There has been particular interest in benefits in the prevention and treatment of atherosclerosis and coronary heart disease, as these diseases are probably the most serious medical problem in the wealthiest parts of the world [7]. Numerous tests of garlic and onion extracts in vitro have demonstrated that they can inhibit the aggregation of human blood platelets to form the clots which have the potential for arterial blocking. The combination of three molecules of allicin has been shown to produce a substance called ajoene which is at least as potent as aspirin in preventing the aggregation of blood platelets [6].

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Allicin is strongly antibacterial and antifungal. Other thiosulfinates and thiosulfonates present in garlic juice also have antimicrobial activity which is attributed to their interaction with SH groups in the target microorganisms. In general, extracts of garlic are more powerfully antimicrobial than extracts of onion [7]. Persian shallot is an important medicinal plant used for lowering blood pressure, treatment of rheumatism, healing wounds and sputum. Besides the bulb, the leaves of mooseer are also consumed [23]. The effect of aqueous extract of mooseer on the bacterium Pseudomonas aeroginosa (PTCC1074) was investigated. This study showed that mooseer has an inhibitory effect on the growth of the bacteria. The extract of mooseer has antibacterial effects on gram positive and gram negative bacteria which were attributed to its thiosulfunate compounds [4]. Another research on the antimicrobial properties of mooseer extract showed that dried and autoclaved extracts of mooseer have more antibacterial effects as compare to that of garlic and onion. This property is stable at temperature of 121°C [3]. The extract of mooseer has the immune effects in animals such as mice. The amount of allicin and thiosulfunate in mooseer is respectively 0.53% and 0.37% [18]. Anti-parasitic property of Persian shallots was compared with metronidazole. The results suggest that the in vitro growth of Trichomonas vaginalis was inhibited within a shorter period and at lower temperature [26]. A number of sterols and saponins have been extracted including one aginosid, a steroidal saponin which exhibits growth inhibitory activity against leek moth, an important pest. Several other unusual complex chemical structures have been isolated from alliums including prostaglandins [5].

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CONCLUSION Studies showed that the populations of Persian shallots have higher dry matter among most vegetables. They contain essential fatty acids such as linolenic acid (omega-3), linoleic acid (omega-6), and important minerals such as potassium, sodium, magnesium, iron, copper, zinc and manganese. They contain more essential fatty acids than European shallots. They are, therefore, recommended as good source of omega 3, omega 6 and minerals for human nutrition. Another benefit of Persian shallot is that its dry matter content is high (30%) compared with about 10% in most vegetables. This makes Persian shallot a good material for production of powder. Studies of different populations of Persian shallots revealed similarities and differences [9]. Majority of the populations are upright types while a relatively few are the running types. Leaves are light green in color and in most populations have trichome. The populations differ in leaf number, leaf length and number of flower stems and flowers. More leaves are found (up to 20) on plants in the main area than on plants in other regions (at most seven leaves). Additionally, leaf length of plant in the main area appears longer, and plants mostly bear two flower stems. Ability to bear flowers differs among the populations with some flowering easily. Flowering is different among different populations. Some have higher ability to produce flowers. The flowering stem is solid in the beginning and it becomes hollow with time.

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Changes in climatic conditions (temperature, light and relative humidity) in the main region may be responsible for the above different characteristics of the populations. RAPD analysis revealed high genetic diversity among Persian shallot populations and results showed that RAPD is a useful technique for genetic diversity of Persian shallot [9, 14]. Low temperature may be a crucial factor accounting for the differences as the plant is mainly distributed in cold regions. Qualitative and quantitative studies have revealed that Persian shallots differ significantly in many traits. The studies show that Persian shallots differ from their European relatives. Based on the results of qualitative and quantitative traits measured, the Persian shallot with the scientific name Allium hirtifolium Boiss. is completely different from the European shallot with the scientific name Allium ascalonicum (Table 5). This difference could be attributed to the main habitat of these two species. The Persian shallot originates from cold and mountainous regions of Iran, while European shallot mainly grows in tropical regions [7, 8, 10, 23, 24]. Some morphological characterists of Persian shallot and the European shallot are shown in figures 11 to 16. Table 5. Comparison of traits of Persian shallot with shallot [7, 8, 10, 23, 24] Traits Scientific name Leaf condition

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Flower color Color of bulb skin Bulb color Cluster Presence of leaf trichome Presence of leaf anthocyanine

Shallot Allium ascalonicum Narrow, Long, Hollow and similar to onion White White or light violent, similar to onion White, Violent, Purple, Yellow Present Without trichome

Persian shallot Allium hirtifolium Boiss. Broad, Long, Solid and similar to leek Purple or violent Light yellow

Without anthocyanine

With anthocyanine

White Rare or sometimes With trichome

Figure 11. Shallot bulb. Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

Persian Shallot (Allium Hirtifolium Boiss)

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Figure 12. Persian shallot bulb.

Figure 13. Scale of shallot bulb.

Figure 14. Cross section of Persian shallot bulb.

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Figure 15. Inflorescence of shallot.

Figure 16. Inflorescence of Persian shallot.

As some of the edible vegetable alliums such as mooseer are indigenous (native and endemic) to Iran, and do not exist in other parts of the world, their conservation is become imperative. The plant is almost become an endangered species due to its overexploitation by human in the nature. A step towards successful conservation of Persian shallot will be to study its modes of propagation, which may be sexual or asexual (vegetative). These are currently been studied by authors, and we hope our results will enable us gain insights into how this valuable vegetable can be propagated and conserved.

ACKNOWLEDGMENT I wish to thank Dr. Negin Ebrahimi, Dr. Patrick Norshie and Mr. Mohsen Ebrahimi for providing various facilities in the preparation of this manuscript.

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[10] [11] [12] [13] [14] [15] [16]

Abdel-Fattah, A.F. and Edress, M. (1972a). Chemical investigations on some constituents of pigmented onion skins. Journal of Food Science and Agriculture, 22: 298. Abdel-Fattah, A.F. and Edress, M. (1972b). A study on the composition of garlic skins and the structural features of the isolated pectic acid. Journal of Food Science and Agriculture, 23: 871. Amin, M. and Kapadnis, B.P. (2005). Heat stable antimicrobial activity of Allium ascalonicum against bacteria and fungi. Indian Journal of Experimental Biology, 43: 751-754. Ashrafi, F., Akhavan Sepahi, A. and Kazemzadeh, A. (2004). Effect of aqueous extract of shallot (Allium ascalonicum) on inhibition of growth of Pseudomonas aeroginosa. Iranian Journal of Pharmaceutical Research, 2: 71-76. Attrep, K.A., Mariuani, J.M. and Attrep, M.Jr. (1973). The search for prostaglandin A1 in onion. Lipids, 8: 484. Block, E. (1985). The chemistry of onions and garlic. Scientific American, 252: 94-99. Brewster, J.L. (1994). Onions and Other Vegetable Alliums. CAB International, Cambridge University Press, United Kingdom, 238 pp. Brewster, J.L. and Rabinowitch, H.D. (1990). Onions and Allied Crops. V. III. CRC Press, Boca Raton, Florida, 265 pp. Ebrahimi, R., Zamani, Z. and Kashi, A. (2009). Genetic diversity evaluation of wild Persian shallot (Allium hirtifolium Boiss.) using morphological and RAPD markers. Scientia Horticulturae, 119, 345-351. Ebrahimi, R., Zamani, Z. and Kashi, A. (2008a). Genetic diversity of Iranian shallot genotypes (Allium hirtifolium Boiss.) using morphological traits. Iranian Journal of Agricultural Science, 39 (1), 151-154. Ebrahimi, R., Zamani, Z., Kashi, A. and Jabbari, A. (2008b). Comparison of fatty acids, mineral elements of 17 Iranian shallot landraces (Allium hirtifolium Boiss.). Journal of Food Technology, 5 (1), 61-68. Ebrahimi, R., Zamani, Z., Kashi, A. and Omidi, M. (2008c). Determination of ploidy level in Persian shallot (Allium hirtifolium Boiss.). The First Congress of Cytotechnology and Its Applications, Mashad, Iran, November 11-12, p, 41. Ebrahimi, R., Zamani, Z. and Kashi, A. (2007a). Geographical distribution, bulb traits and descriptor preparing for Iranian shallot (Allium hirtifolium Boiss.). 2 th. Symposium Cell and Molecular Biology, Kerman, Iran, pp: 378-379. Ebrahimi, R., Zamani, Z. and Kashi, A. (2007b). Genetic diversity evaluation of Iranian shallot landraces using RAPD markers. 2th. Symposium Cell and Molecular Biology, Kerman, Iran, pp: 476-477. Fenwick, G.R. and Hanley, A.B. (1990). Chemical composition. In: Rabinowitch, H.D. and Brewster, J. L. (Eds.) Onions and Allied crops Vol. 3. CRC Press, Boca Raton, Florida, 17-31 pp. Ghahreman, A. (1984). Color Atlas of Iranian Plants. Institute of Forestries and Grasslands, Botany Division, No. 5, 512 pp.

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[17] Hanelt, P. (1990). Taxonomy, Evolution and History. In: Rabinowithch, H.D. and Brewster, J.L. (Eds.), Onion and Allied Crops. Vol. I. CRC press, Boca Raton, Florida, 1-26 pp. [18] Jafarian, A., Ghannadi, A. and Elyasi, F. (2003). The effects of Allium hirtifolium Boiss. on cell-mediated immune response in mice. Iranian Journal of Pharmaceutical Research, 51-55. [19] Mobin, S. (1975). Iranian Plants, Flora of Vascular Plants. Vol. I, Tehran University Press, Tehran, Iran. 502 pp. [20] Mousavi, A., Kashi, A., Davoodi, D. and Sanei Shariatpanahi, M. (2006). Characterization of an Allium cultivated in Iran: The Persian Leek. Belgian Journal of Botany, 139(1), 115-123. [21] Parsa, A. (1988). Flora of Iran. Vol 5., Danech Press, Tehran, Iran, 322 pp. [22] Rechinger, K.H. (1984). Flore Iranica, Alliaceae. Vol 76, Akademische Druck-u Verlagsanstalt, Graz–Austria, 85 pp. [23] Rubatzky, V.E. and Yamaguchi, M. (1997). World Vegetables Principles, Production and Nutritive Values. Second Edition Chapman and Hall, International Thompson Publishing, New York. 843 pp. [24] Salunkhe, D.K. and Kadam, S.S. (1998). Handbook of Vegetable Science and Technology. Marcel Dekker, Inc. 721 pp. [25] Sheibani, H. (1982). Horticulture, Vol. III: Olericulture. Sepehr Publication, Tehran, Iran. 332 pp. [26] Taran, M., Rezaeian, M. and Izaddoost, M. (2006). In vitro anthitrichomona activity of Allium hirtifolium (Persian shallot) in comparison with metronidazole. Iranian Journal of Public Health, 35 (1): 92- 94. [27] US Department of Agriculture, Agriculture Research Service, USDA. (2006). National Nutrient Database for Standard Reference. Nutrient Laboratory Home Page http://www.nol.gov/fnic/foodcomp/cgi-bin

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INDEX 2 20th century, 10, 86

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A absorption spectroscopy, 90 access, 16, 34, 262, 267, 268, 296 accessions, 18, 19, 20, 25, 29, 33, 49, 58, 134, 138, 258, 277, 278, 283, 284 accounting, 300 acetic acid, 87 acetonitrile, 87 acetylcholinesterase, 79, 95 acid, x, 64, 65, 67, 68, 69, 70, 71, 72, 73, 77, 78, 80, 81, 85, 86, 87, 89, 93, 96, 97, 98, 102, 276, 279, 289, 297, 298, 299, 303 acidic, 86, 87 acquisitions, 138 adaptation, 106, 193, 282 adaptations, 9 adenine, 34, 72 adhesion, 82, 101 adipose, 65 adipose tissue, 65 ADP, 82, 103 adsorption, 70 Afghanistan, 290 Africa, 200, 251, 252, 257, 265 age, 9, 22, 42, 71, 150, 152, 157, 266 agencies, 254 aggregation, 82, 101, 298 agricultural seed industry, vii, 1, 17 agriculture, viii, 19, 105, 196, 253 Agrobacterium, 282 albumin, 69, 90 aldehydes, 67 algae, 8

algorithm, 16, 17, 121, 128, 147 alien species, 174 alkalinity, 86 alkaloids, 60 alkenes, 67 allele, 52, 151, 153, 154, 168, 171, 175, 277, 286 allopolyploid, 30 almonds, 247 alternative hypothesis, 171 amino, 67, 71, 72, 298 amino acid, 67, 71, 72, 298 amino acids, 71, 72, 298 amylase, 56 anatomy, 5, 8, 9, 43, 45, 47, 54 ancestors, 18, 244, 268 anemia, 65 angiosperm, 7, 22, 43, 47, 57, 74 annealing, 27, 152 ANOVA, 160, 181, 183 anthocyanin, 292 anti-cancer, 250 anticonvulsant, 80, 96, 101 anti-inflammatory agents, 68 antioxidant, vii, 63, 66, 67, 68, 69, 70, 71, 72, 79, 80, 81, 82, 83, 84, 85, 86, 88, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 249, 256, 257, 267 antioxidants, vii, 63, 65, 66, 67, 68, 69, 71, 72, 80, 81, 84, 85, 86, 87, 88, 89, 92, 93, 94, 95, 96, 97, 98, 100, 101, 298 antipyretic, 250 aorta, 100 apples, 73, 247 aptitude, viii, 105, 132 Arabidopsis thaliana, 43 arginine, 298 arithmetic, 113, 129 artificial intelligence, 121 ascorbic acid, 70, 71, 86, 87, 88

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Index

Asia, 241, 247, 251, 290 Asian countries, 249 asparagus, 199, 203, 210, 217, 218, 229, 230 assessment, 21, 23, 26, 28, 34, 35, 46, 53, 60, 122, 141, 172, 262 asymmetry, 129 atherosclerosis, 298 atoms, 64 attribution, 200 Austria, 304 authentication, 39, 61 authenticity, 13 authority, 3 automation, 27, 37, 106

blood plasma, 69 blood pressure, 77, 78, 299 bonds, 67 brain, 82, 101 branching, 6 Brazil, 43 breast milk, 250 breeding, vii, viii, x, 1, 17, 20, 31, 39, 42, 56, 149, 248, 269, 278, 279, 281, 282, 285, 286, 289 Britain, 255 budding, 174 bulb producing plant, x, 289 burn, 153, 239 bursa, 207, 239 by-products, 66

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B BAC, 12 backcross, 276 bacteria, ix, 70, 180, 182, 183, 186, 188, 192, 193, 299, 303 bacterium, 299 Balkans, 150, 151, 170 banks, viii, 18, 105, 142, 271 barriers, 10, 151, 152, 156, 272, 274, 285 base, 23, 24, 30, 35, 36, 37, 55, 56, 67, 88, 133, 134, 144, 152, 184, 283, 284, 292, 294 base pair, 23, 30, 67 Belgium, 46 belief systems, 198 benefits, viii, 63, 259, 263, 298 benzene, 74 benzoyl peroxide, 82, 101 beverages, 70, 71, 97 bilirubin, 69, 72, 90 bioavailability, 91, 96, 99 biochemistry, 5 biodiversity, vii, viii, ix, 1, 18, 39, 41, 47, 50, 52, 56, 57, 105, 173, 176, 180, 192, 193, 195, 196, 199, 251, 254, 260, 262 biodiversity crisis, viii, 105 bioinformatics, x, 269 biological activities, 95 biological activity, 88 biological samples, viii, 63 biological systems, 63, 64, 90, 98 biomarkers, 89, 90 biomass, 151, 181, 182, 188, 276 biomolecules, 90 biosynthesis, 98 biotechnology, 45 biotic, 18, 42, 60, 197, 248 blood, 69, 77, 78, 82, 101, 298, 299

C Ca2+, 67 cabbage, 73, 252 calcium, 297 calibration, 117, 118, 147 cancer, 66, 71, 83, 94, 99, 249, 266 candidates, 28 cannabis, 53 capillary, 34, 81, 97, 100 capsule, 294 carbohydrate, 297 carbohydrates, 65 carbon, 66, 67, 74, 75, 79, 80, 81, 93, 95, 102 carbon atoms, 67, 74 carbon dioxide, 80, 93, 95 carbon tetrachloride, 79, 81, 102 carboxylic acid, 85 carcinogenesis, 70 cardiac muscle, 53 cardiovascular disease, 66 carnivores, 197 carob, 240, 241 carotene, 298 carotenoids, 71, 72 case study, 18, 49, 53, 54, 58, 63, 175, 257 cation, 64, 100 cattle, 214, 296 C-C, 12, 48 cDNA, 34, 36 cell culture, 98 cell line, 89 cell lines, 89 cell membranes, 69, 70 Central Europe, 46 centromere, 11 certification, 33

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Index chain propagation, 68 challenges, 19, 196, 198, 199 changing environment, 171, 172 chaos, 130 chaperones, 90 character traits, 13, 14 cheese, 201, 204, 208, 212, 213, 219, 220, 223, 224, 229, 231, 232, 234, 243, 246, 247, 250, 251 chemical, 10, 12, 66, 69, 72, 76, 80, 82, 91, 92, 94, 98, 101, 142, 249, 257, 258, 298, 299 chemical characteristics, 69 chemical structures, 69, 299 chemicals, 12 chemiluminescence, 84 chemoprevention, 70 chemotherapy, 71 chicken, 201 children, 209, 218, 224 China, 9, 49, 62, 269, 270, 272, 278, 279, 280, 282, 283, 284, 286, 290 chlorine, 102 chloroplast, viii, 22, 23, 33, 38, 40, 41, 44, 46, 47, 49, 50, 51, 52, 55, 58, 60, 61, 62, 149, 151, 152, 155, 156, 158, 160, 163, 168, 170, 171, 172, 177, 256 Chloroplast haplotype richness, viii, 149 chromatographic technique, 20 chromatography, 87, 100, 298 chromosome, 7, 10, 11, 12, 45, 48, 52, 271, 279, 280, 281, 282, 291 civil society, 254 clarity, 3 classes, 5, 7, 24, 74, 123, 133 classification, 2, 3, 4, 5, 6, 10, 20, 31, 39, 44, 46, 61, 75, 106, 107, 108, 132, 133, 134, 138, 140, 141, 144, 145, 147, 153, 198, 243, 244, 245, 253, 255, 266, 270, 285, 290 cleaning, 126 cleavage, 36, 55, 64, 67 climate, 9, 53, 151, 168, 170, 171, 172, 177, 186, 196, 198 climate change, 171, 186, 196 clinical trials, 99 clone, 12 cloning, 33, 36, 47, 285 clustering, 5, 153, 171 clusters, viii, 149, 153, 154, 157, 160, 163, 166, 170, 171, 175 CO2, 92, 151, 193 cocoa, 71, 73 code generation, 128 coding, 17, 25, 32, 40, 58 coffee, 73, 203, 229

307

collaboration, 36 Colombia, 33, 53 colon, 91, 97 colon cancer, 97 colonization, 150, 151, 175, 197, 247, 254 color, 65, 110, 112, 117, 144, 145, 166, 291, 292, 299, 300 colorectal cancer, 260 commercial, 17, 76, 198, 260 communication, 98, 253 communities, 5, 76, 174, 196, 198, 199, 238, 242, 248, 254 community, ix, 40, 180, 191, 199, 238 comparative analysis, 49, 261 compatibility, 285 competition, 243 compilation, 200 complement, 11, 40 complex interactions, 180, 193 complex numbers, 128 complexity, 24, 31, 87, 118, 122, 192 composition, ix, 20, 21, 29, 43, 79, 80, 81, 82, 90, 92, 94, 95, 96, 97, 98, 99, 101, 102, 103, 152, 154, 169, 172, 180, 182, 188, 189, 190, 191, 192, 193, 257, 263, 303 compound identification, 86 compounds, 12, 20, 66, 68, 69, 70, 71, 72, 74, 79, 80, 81, 82, 84, 88, 89, 91, 92, 94, 95, 96, 97, 99, 100, 101, 248, 249, 250, 298, 299 compression, 111, 112, 128 computation, 117, 128 computer, 16, 17, 47, 50, 54, 61, 106, 107, 108, 109, 111, 117, 118, 143, 144, 145, 146 computer image analysis, 144 computer systems, 118 computing, 106 condensation, 72 configuration, 108, 117, 153 Congress, 3, 303 conifer, 55 conjugation, 87 consensus, 34, 46, 84 conservation, vii, viii, ix, x, 1, 12, 17, 18, 19, 28, 35, 42, 45, 53, 54, 55, 56, 58, 60, 61, 105, 149, 150, 151, 168, 171, 173, 175, 176, 195, 196, 199, 243, 254, 257, 259, 260, 261, 262, 263, 269, 278, 282, 283, 284, 286, 289, 302 constipation, 249 constituents, 20, 67, 70, 81, 91, 97, 104, 258, 303 construction, 3, 14, 15, 16, 17, 19, 54, 279, 280 consumers, 116, 255 consumption, 71, 95, 199, 248, 251, 260 contamination, 18

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308

Index

contingency, 154 contour, 121, 128, 143 controversial, 41 convention, 109, 110 Convention on Biological Diversity (CBD), viii, 105 cooking, 206, 229, 249, 252 cooling, 249 cooperation, 142, 254 coordination, 71 copper, 65, 67, 81, 87, 297, 299 coronary heart disease, viii, 63, 298 correlation, 100, 144, 155, 160, 171 correlations, 136, 155 cosmetic, 245 cost, 16, 24, 28, 35, 37, 38, 42, 108, 112, 283 Costa Rica, 40 cotton, 50 coumarins, 72 covering, 5, 116, 170 criticism, 41 crop, x, 11, 12, 18, 19, 20, 33, 42, 45, 52, 56, 107, 197, 242, 244, 247, 259, 260, 261, 262, 263, 269 crops, ix, 11, 17, 18, 19, 22, 42, 52, 195, 199, 241, 247, 248, 249, 262, 266, 290, 303 cross-validation, 137, 145 crowns, 191 cryopreservation, 19 cultivars, viii, 19, 20, 28, 29, 31, 33, 35, 45, 46, 47, 48, 49, 52, 54, 55, 57, 58, 79, 81, 94, 96, 105, 147, 243, 256, 260, 264, 272, 276, 278, 282 cultivation, 17, 62, 247, 285, 289, 297 cultural heritage, 76, 198, 268 cultural influence, ix, 195, 240 cultural practices, 18 culture, 89, 107, 200, 260, 268 culture media, 89 cure, 207, 298 cut-flower, 55 cycles, 27, 152 cycling, 71 Cyprus, 60 cytochrome, 39, 66 cytokines, 90 cytology, 5, 47, 57 cytometry, 12 cytoplasm, 22, 166, 276, 279 cytosine, 34 cytotoxicity, 67, 88

D damages, 67, 88 data analysis, 177

data collection, 254 data set, 153 database, 36, 39, 58, 138, 142, 152, 172, 193 DCA, 255 decay, 66 decoding, 110 decomposition, 84, 190, 193 defects, 106, 107, 108, 112, 113, 123 deficit, viii, 79, 99, 149, 155, 162 degradation, 29, 65, 87, 112, 174 denaturation, 27, 152 dendritic cell, 79, 103 density values, 129, 130, 131 deoxyribonucleic acid, 19 deoxyribose, 87, 96 Department of Agriculture, 256, 304 dependent variable, 134, 135, 154, 163 deposition, 193 depression, 172 depth, 108, 109, 110, 111, 182 derivatives, 72, 79, 81, 94, 95 desorption, 36 destruction, 90 detectable, 23, 24 detection, 27, 29, 33, 34, 36, 37, 43, 49, 59, 85, 87, 95, 100, 108, 133, 157 deviation, vii, 1, 8, 131 diet, viii, 63, 71, 89, 196, 199, 244, 245, 254, 268 diffusion, 88, 92 digestion, 30, 31, 38, 91, 98, 224, 267 digital cameras, 106 dilation, 122, 123 dimensionality, 132, 135 diploid, x, 11, 28, 30, 33, 44, 151, 154, 155, 160, 168, 269, 270 direct action, 66 discontinuity, 118 discriminant analysis, vii discrimination, 7, 10, 13, 17, 19, 20, 21, 26, 31, 39, 44, 53, 132, 134, 146 disease gene, 36 diseases, viii, 63, 66, 77, 78, 250, 254, 289, 298 disequilibrium, viii, 149, 154, 157, 161, 170 disorder, 79, 99 dispersion, 129, 130 distinctness, 59 distortions, 118 distribution, viii, 6, 9, 12, 91, 129, 149, 150, 151, 156, 158, 160, 163, 166, 167, 168, 170, 171, 175, 176, 177, 191, 242, 243, 251, 264, 265, 268, 276, 285, 286, 303 diuretic, 79, 93, 97, 225, 226, 250 divergence, 35, 38, 39, 44, 175, 285

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Index diversification, 258 diversity, viii, ix, 2, 14, 19, 20, 25, 29, 33, 34, 36, 38, 39, 43, 44, 45, 47, 50, 53, 56, 105, 149, 151, 169, 170, 171, 172, 175, 177, 195, 196, 197, 198, 199, 251, 254, 256, 257, 262, 264, 267, 284, 286, 289, 300, 303 DNA, vii, 1, 11, 12, 19, 20, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 65, 66, 67, 87, 88, 89, 90, 96, 100, 152, 172, 257, 270, 278, 285 DNA damage, 87, 88, 89, 90, 96 DNA polymerase, 67, 152 DNA sequencing, 27, 34, 35, 38, 58 DNAs, 23, 60, 61 domestication, x, 18, 61, 198, 245, 262, 266, 269 dominance, 154 dominant allele, 28 double bonds, 67 dough, 214 draft, 285, 286 drinking water, 151 Drosophila, 100 drought, x, 146, 244, 269, 275, 282, 289 drugs, 8, 45, 66 dry matter, x, 289, 297, 299 drying, 81, 92, 97, 182 dyes, 152

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E ecological information, 151 ecological systems, 198 ecology, 20, 59, 176, 180, 192 economic development, 244 economics, 19, 53 ecosystem, ix, 150, 173, 174, 179, 180, 190, 193 ecosystems aptitude, viii, 105 egg, 250, 274 Egypt, 10, 31, 42, 47, 290 elaboration, 118, 138, 142 electric field, 24 electromagnetic, 107 electron, 71, 84 electrons, 64, 66 electrophoresis, 20, 21, 24, 28, 31, 33, 34, 37, 38, 43, 54, 55, 67, 81, 87, 97, 100, 152 ELISA, 90 elucidation, 12 embryo sac, 274, 278, 286 embryology, 20 emission, 85 encoding, 49, 128

309

endangered species, 302 energy, 66, 95 enforcement, 46 engineering, 20, 106, 144 England, 3, 150, 176, 191 entropy, 145 environment, vii, 1, 8, 21, 47, 64, 84, 87, 91, 121, 145, 169, 264, 271 environmental change, viii, 8, 9, 20, 105, 180 environmental conditions, 18, 20, 23, 289, 294 environmental factors, 9 environmental influences, 13 enzyme, 21, 37, 38, 70, 89, 90 enzyme induction, 90 enzymes, 21, 38, 66, 67, 68, 69, 70, 71, 72, 89, 90, 91 epidemiologic studies, 71 epidemiology, 99 equilibrium, 71, 150, 153, 155, 156, 157, 160, 161, 168, 170, 171, 172 equipment, 13, 21, 85 erosion, 18, 19, 122 error detection, 40 erythrocytes, 88 ESI, 95 essential fatty acids, 65, 297, 298, 299 ester, 88 ethanol, 80, 82, 92, 93, 99 ethnic groups, 250 eukaryotic, 29, 32, 89 eukaryotic cell, 89 Eurasia, 193 Europe, vii, 5, 19, 172, 200, 243, 246, 247, 251, 253, 254, 257, 260, 262, 292 European Union, viii, 105 evidence, 43, 48, 57, 60, 143, 166, 170, 257, 260, 268 evolution, x, 5, 11, 12, 23, 26, 35, 40, 46, 49, 53, 55, 57, 59, 98, 175, 248, 257, 259, 262, 266, 269, 282, 283 excitation, 85 excretion, 91, 266 execution, 107 experimental condition, 29 expertise, 27 exploitation, ix, 195, 241, 249, 257, 277 exposure, 90, 92 extinction, viii, 18, 42, 105, 151, 289 extraction, 21, 79, 80, 81, 82, 92, 93, 94, 96, 97, 98, 102, 144, 298 extracts, vii, viii, 63, 70, 71, 79, 80, 81, 82, 83, 86, 88, 92, 93, 94, 95, 97, 99, 101, 102, 103, 258, 298, 299

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Index

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F Fagus sylvatica, vii, viii, ix, 149, 150, 155, 156, 164, 166, 168, 169, 173, 174, 175, 176, 177, 179, 180, 182 families, 5, 8, 16, 40, 47, 55, 74, 75, 132, 138, 142, 144, 211, 241, 242, 255 farmers, 196, 253, 278 fat, 297 fatty acids, 67, 297, 298, 299, 303 fauna, 197 ferrous ion, 86 fertility, 191, 192, 260, 272, 273, 277, 286 fertilization, 274 fiber, 297 filters, 108, 112, 113, 114, 119, 145 fingerprints, 28, 31, 57 fish, 199, 207, 216, 222, 224, 232, 236 fishing, 199 fitness, 174, 248 fixation, 154, 155, 157, 160, 161, 171 flammability, 192 flank, 32 flavonoids, 68, 75, 79, 80, 87, 95, 98, 99 flavonol, 298 flavor, 65, 263, 291 flavour, 201, 203, 213, 218, 219, 229, 235, 244, 298 flexibility, 8, 16, 17, 106, 112 flight, 36 flora, ix, 44, 59, 138, 144, 171, 195, 196, 251, 256, 262, 263, 264, 265, 266 flour, 201, 203, 205, 209, 212, 214, 223, 251 flowering period, 8 flowers, vii, 1, 7, 8, 15, 74, 77, 79, 199, 206, 216, 218, 219, 224, 291, 299 fluctuations, 8 fluid, 80, 81, 82, 92, 94, 95, 99, 100 fluid extract, 80, 81, 82, 92, 94, 95, 99, 100 fluorescence, 48, 84, 85, 90, 96, 97, 147 food, vii, ix, 1, 5, 17, 19, 29, 63, 65, 70, 84, 91, 93, 99, 101, 144, 195, 196, 198, 199, 200, 201, 202, 204, 206, 208, 209, 210, 212, 213, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 239, 242, 244, 245, 247, 249, 250, 253, 254, 258, 261, 263, 264, 265, 266, 269, 282, 290 food industry, vii, 63, 245 food products, 70 food security, ix, 244, 269 food spoilage, 70 Ford, 18, 26, 28, 39, 40, 48, 50, 53, 56, 58, 259, 262, 286 forensic analysis, vii, 1, 17 forest ecosystem, 180

forest restoration, 174 formamide, 31, 152 formation, 65, 67, 68, 72, 85, 86, 89, 90, 151, 294, 295, 296 formula, 124, 125, 126, 127, 129, 130, 132 fragments, 24, 27, 28, 29, 30, 31, 34, 36, 57, 188, 190 France, v, 54, 150, 151, 195, 200, 245, 246, 256 free radicals, 68, 72, 84, 86 freedom, 153 fruits, 7, 8, 73, 92, 101, 191, 199, 202, 207, 210, 215, 221, 226, 239, 240, 244, 245, 263 functional food, 100, 254, 262, 265 funding, 41 fungi, 74, 80, 95, 180, 184, 185, 186, 191, 192, 193, 303 fungus, 191 furan, 75

G gel, 21, 28, 31, 32, 34, 37, 38, 42, 43, 55, 67, 87, 152 gene expression, 43, 68, 90 gene pool, 19, 247, 266, 274 genes, vii, x, 1, 8, 12, 18, 20, 22, 23, 25, 26, 34, 35, 36, 38, 40, 42, 52, 56, 57, 59, 150, 173, 269, 270, 272, 276, 278, 279, 282, 285 genetic background, 276, 280, 281 genetic code, 36 genetic diversity, ix, 18, 21, 23, 28, 29, 34, 40, 51, 52, 55, 57, 58, 60, 62, 149, 150, 151, 160, 166, 171, 172, 173, 175, 245, 258, 278, 282, 289, 290, 300 genetic drift, 18, 171 genetic marker, 21, 26, 31, 33, 34, 57, 176 genetics, viii, 5, 36, 54, 55, 56, 149, 150, 151, 171, 172, 173, 176 genome, x, 11, 22, 23, 24, 25, 26, 27, 28, 33, 35, 36, 38, 39, 40, 48, 52, 58, 59, 62, 151, 155, 269, 270, 271, 272, 273, 274, 277, 280, 282, 285, 286 genomics, x, 36, 269 genotype, vii, 1, 8, 23, 36, 153, 157, 168, 171, 175, 176, 277 genotyping, 34, 36, 42, 52, 56, 59, 62 genus, x, 3, 7, 8, 9, 10, 11, 12, 25, 26, 30, 45, 46, 47, 49, 52, 57, 58, 60, 61, 138, 140, 141, 242, 244, 245, 248, 249, 250, 251, 256, 257, 260, 263, 268, 269, 270, 283, 284, 286, 290, 291 geography, ix, 163, 195, 265 geological history, ix, 195, 196 geometry, 125, 146 Germany, 28, 47, 62, 200, 255, 292 global scale, 196

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311

Index glucose, 72 glucoside, 73 glucosinolates, 249, 256, 266 glue, 250 glutamic acid, 298 glutathione, 68, 71, 72, 90 glycogen, 250 glycoside, 72 glycosylation, 72 grading, 146 grass, ix, 12, 15, 33, 77, 80, 179, 180, 182, 184, 186, 188, 190, 239 grasses, 56, 191, 197 grasslands, 18, 54, 191, 192, 248, 259 grazing, 197, 214, 296 Greece, 198, 268 Greeks, 5, 200 green revolution, 46 greenhouse, 9 group membership, 133 grouping, 132 growth, 9, 20, 53, 65, 91, 95, 152, 172, 191, 299, 303 Guangdong, 283, 284, 286 Guangzhou, 278, 283, 284, 286 guanine, 34 guessing, 15 guidance, 89 gymnosperm, 138

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H habitat, ix, 4, 171, 179, 186, 192, 242, 271, 272, 282, 300 habitats, viii, ix, 9, 18, 60, 105, 179, 182, 186, 188, 191, 196, 197, 242, 243, 283 hair, 65 haploid, 11, 277 haplotypes, 151, 156, 160, 167, 170, 171 harmful effects, 174 harvesting, 82, 93, 199, 244, 296 Hawaii, 8, 46 healing, 76, 299 health, viii, 63, 65, 66, 76, 77, 91, 96, 98, 250, 257 health care, 250 health condition, viii, 63, 66 health risks, 65 height, 110, 251 hemp, 48 hepatocytes, 82, 88, 103 hepatotoxicity, 79, 81, 82, 102, 103 herbal teas, 98 Heron Wood Reserve, v, ix, 179, 180, 182, 184 heterogeneity, 91, 198, 247

heterosis, 277 heterozygote, viii, 149, 155, 157, 162, 172 highlands, 292 histidine, 88 histogram, 112, 115, 116, 119, 120, 144, 145, 146 history, vii, ix, 1, 3, 5, 6, 39, 41, 175, 176, 177, 195, 196, 200, 240, 241, 249, 254, 261, 263, 265, 278, 298 HIV, 81, 93 HIV-1, 81, 93 HLS, 106, 111, 129 Holocene, 197, 263, 266, 267 homeostasis, 89 homogeneity, 116, 129 host, ix, 195 hot spots, 50, 150, 151, 170, 196 hotspots, 40, 52, 263 HSB, 111 hub, 198 hue, 131 human, viii, ix, x, 19, 25, 36, 59, 63, 66, 70, 79, 88, 91, 92, 95, 97, 103, 105, 106, 107, 150, 173, 195, 196, 197, 198, 243, 244, 248, 249, 254, 263, 269, 282, 298, 299, 302 human activity, viii, 105 human body, 91, 254 human genome, 36 human health, viii, 63 human resources, 263 humidity, 168, 300 Hunter, 21 hybrid, 17, 21, 43, 60, 242, 272, 273, 274, 278, 279, 281, 285, 286 hybridization, 20, 39, 40, 270, 278, 285 hydrogen, 64, 71, 84, 93, 108 hydrogen atoms, 64 hydrogen peroxide, 93 hydrolysis, 92 hydroperoxides, 69 hydrophobicity, 91 hydroxyl, viii, 63, 67, 72, 85, 87, 88, 96 hydroxyl groups, viii, 63, 72, 87 hyperactivity, 79, 99 hypertrophic cardiomyopathy, 62 hypotensive, 92 hypothesis, 52, 154, 245, 254, 262

I ideal, 108, 126, 127, 128 identification, vii, ix, x, 1, 3, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 27, 28, 29, 30, 31, 33, 34, 35, 38, 39, 40, 41, 42,

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Index

43, 44, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 60, 61, 79, 80, 95, 100, 106, 107, 133, 134, 138, 142, 143, 145, 173, 195, 200, 255, 266, 269, 276, 280, 291 identification problem, 56 identity, 9, 18, 19, 35, 239, 240 illumination, 108, 116, 117 image, vii, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 126, 128, 129, 131, 132, 141, 142, 143, 144, 145, 146, 147 image analysis, 106, 107, 111, 112, 117, 121, 126, 132, 141, 142, 143, 144, 145, 147 images, 16, 106, 107, 108, 109, 111, 112, 113, 115, 116, 117, 118, 120, 121, 122, 123, 128, 138, 142, 145, 146 immune disorders, 250 immune response, 304 immunomodulatory, 80, 96 improvements, 85 in situ hybridization, 12, 48, 52 in vitro, 19, 45, 53, 70, 79, 81, 82, 87, 88, 89, 90, 93, 95, 98, 101, 103, 274, 298, 299 in vivo, 66, 67, 79, 82, 87, 89, 90, 95, 102, 103, 104, 256 inbreeding, 160, 172 incidence, 71 incompatibility, 274 independent variable, 133, 134, 135, 136 indexing, 117 India, 145, 279 indirect effect, 191, 193 individualization, 33 individuals, vii, 1, 3, 7, 8, 11, 19, 21, 24, 31, 32, 33, 34, 36, 42, 91, 153, 156, 160, 163, 168, 169, 170, 175 induction, 43, 67, 85, 90 induction period, 85 industry, vii, viii, 1, 17, 86, 93, 105 inefficiency, 49 inferences, 106, 150, 263 inflammation, 66, 76, 82, 93 infrastructure, 27 ingestion, viii, 63, 66, 92 ingredients, 72, 199, 217, 246, 255 inheritance, ix, 22, 23, 60, 61, 195, 196, 260 inhibition, 79, 84, 95, 303 inhibitor, 67 initiation, 68 injury, 66 insects, 72, 192, 258, 294 insertion, 36, 37 inspections, 106, 107

inspectors, 106 institutions, 18, 199 integration, 144 integrity, 10, 18, 67, 173, 283 intellectual property, 33 intensity values, 115, 129, 131 interference, 85 international standards, 109 interrelations, 190, 193 intervention, 103, 117 intestinal flora, 249 intestinal tract, 66 intestine, 91, 210, 249 intoxication, 263, 268 introns, 40 invertebrates, 180, 192 ions, 71, 87 IPR, 33 Iran, x, 289, 290, 292, 296, 300, 302, 303, 304 Iraq, 256 Ireland, 255 iron, 67, 68, 87, 88, 89, 91, 297, 299 islands, 150, 197, 205, 244, 259 isoflavonoids, 76 isolation, 29, 34, 55, 60, 99, 141, 151, 152, 173, 282 isoprene, 67 isozyme, 21, 43, 50, 56, 60, 245 isozymes, 20, 21, 25, 42, 43, 256 issues, 18, 36, 86, 91, 173, 192 Italy, viii, 105, 149, 150, 151, 155, 160, 164, 167, 170, 173, 175, 176, 177, 195, 198, 241, 243, 246, 250, 253, 255, 257, 258, 259, 261, 263, 264, 266, 267, 268

J Japan, 286 Jordan, 233, 236

K kaempferol, 80, 99 karyotype, 11, 12, 42, 49, 51, 53 Kenya, 199, 268 keratinocytes, 88 kidney, 82, 104, 249, 251 kidneys, 210 kinetics, 86

L lactase, 91

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Index lakes, 156, 158, 164, 168, 169 landscape, viii, 149, 150, 151, 155, 163, 168, 173, 174, 175, 176, 197, 199, 260 Landscape genetic maps, viii, 149, 174 languages, ix, 195, 198, 240 laptop, 16 LDL, 67 lead, viii, 14, 29, 63, 65, 66, 84, 87, 89, 91, 92, 171, 173, 240, 267, 289 leakage, 70 learning, 144 legend, 250 legume, 144 lesions, 65 lifetime, 88, 104 light, 10, 43, 65, 85, 108, 116, 117, 121, 173, 196, 275, 292, 299, 300 lignans, 72 lignin, 72 limestone, 243 linguistics, 6 linoleic acid, x, 289, 297, 298, 299 lipid oxidation, 65, 68 lipid peroxidation, 67, 69, 85, 88 lipids, 65, 67, 85 lipoproteins, 67 liquid chromatography, 36, 61, 67, 87, 99 Listeria monocytogenes, 70 Lithuania, 19, 103 liver, 81, 82, 91, 98, 104, 210, 249, 250 liver disease, 250 localization, 69, 121 loci, viii, 20, 24, 25, 31, 32, 33, 35, 38, 40, 44, 55, 149, 150, 151, 152, 154, 155, 156, 157, 158, 160, 175, 277, 286 locus, 21, 24, 29, 31, 32, 34, 35, 37, 40, 49, 152, 153, 155, 156, 157, 158, 160, 161, 168, 277, 278, 281, 286 Louisiana, 38, 43 LSD, 154, 163 lumen, 91 Luo, 46, 286 lymphocytes, 79, 97 lysine, 91

M macromolecules, 20 magnesium, 297, 299 magnetic field, 108 majority, 7, 15, 33, 111, 190, 241 man, 5, 174, 290

313

management, ix, 18, 19, 33, 48, 54, 149, 150, 169, 172, 174, 175, 255, 258, 260 manganese, 299 manipulation, 2 manufactured goods, 106 mapping, 11, 36, 45, 49, 52, 59, 60, 107, 144, 277, 280, 284 marketing, 262, 266 Mars, 250, 262 MAS, 280 mass, 36, 56, 81, 87, 92, 97, 189, 190 mass spectrometry, 36, 56, 81, 97 materials, 13, 14, 27, 85, 92, 108, 174, 289 maternal inheritance, 22, 60 maternal lineage, 166, 168 matrix, 16, 17, 21, 24, 36, 47, 68, 76, 91, 92, 109, 128, 133, 136 matrix metalloproteinase, 68 matter, 7, 14, 100, 297, 299 measurement, 7, 11, 109, 110, 119, 121, 123, 125, 142, 144, 147 measurements, 102, 106, 107, 123, 125, 126, 127, 132, 138, 144 meat, 199, 201, 205, 215, 216, 219, 220, 222, 224, 227, 231, 232, 236, 249, 267 media, 253 median, 114, 129 medical, 249, 298 medicine, ix, 76, 77, 78, 97, 98, 195, 213, 224, 249, 250, 253, 259, 263, 268 Mediterranean, viii, ix, 29, 42, 48, 63, 76, 77, 138, 144, 149, 150, 171, 174, 175, 195, 196, 198, 199, 241, 242, 243, 244, 245, 247, 248, 249, 250, 251, 253, 254, 259, 260, 261, 262, 263, 264, 265, 268, 290 Mediterranean countries, ix, 77, 195, 242, 247, 253, 260 meiosis, 9, 11 melanin, 72 melanoma, 81, 97 melting, 36, 198 membership, 134 membrane permeability, 91 membranes, 67, 88 memory, 117 meta-analysis, 260 metabolism, 65, 66, 72, 91, 96, 266 metabolites, 20, 72, 90, 98, 248 metabolized, 91 metal complexes, 71 metal ion, 68, 70, 87 metal ions, 68, 87 metalloproteinase, 97

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Index

metals, 64, 71, 87, 89 methanol, 81, 83, 86, 87, 92, 94 methodology, 6, 30, 104, 153 methylation, 91 mice, 80, 81, 82, 94, 97, 98, 101, 103, 299, 304 microbial communities, 192 microorganisms, 70, 191, 299 microsatellite loci, viii, 32, 55, 149, 151, 152, 154, 155, 158, 160 microsatellites, 32, 33, 34, 47, 48, 52, 152, 177, 256 microscopy, 61, 116, 146 Microsoft, 112 migrants, 166 migration, viii, 149, 150, 154, 166, 167, 170, 171, 174, 177, 198 misconceptions, 41 Missouri, 43, 57, 260, 262 misunderstanding, 253 mitochondria, 88, 90 mitochondrial DNA, 22, 60 mitosis, 67 mixing, 18, 92, 206 model system, 88 modelling, 193 models, 144, 290 modifications, 152 modules, 25 moisture, ix, 180, 181, 182, 190 moisture content, 182, 190 molecular biology, x, 37, 50, 269, 286 molecular mass, 20 molecular structure, 87, 298 molecular weight, 49, 69, 153 molecules, 21, 72, 298 monolayer, 89 Morocco, 29, 47, 48, 141, 198 morphology, 5, 8, 9, 10, 11, 22, 44, 53, 54, 57, 61, 107, 119, 144, 146, 291, 296 morphometric, 59, 131, 138, 141 mortality, 71 mosaic, 31, 196, 197 motif, 32 MRI, 108 mtDNA, 22, 38, 39, 41 multidimensional, 136 murder, 59 mutation, 23, 36, 61, 66, 88, 153, 278, 279 mutation rate, 23 mutations, 37, 52, 67, 88, 89, 96 mycorrhiza, 180

N NAD, 72 NADH, 72 naming, vii, ix, 1, 3, 7, 195, 200 native species, 250 natural compound, 70 natural habitats, 17, 18, 19 natural resources, 107 natural selection, 43, 172 Nepal, 286 neural network, 145 neural networks, 145 neurological disease, 71 neurons, 260 neutral, 154, 172, 277, 281, 286 New Zealand, 44 NH2, 67 niacin, 91 nicotinamide, 72 nitrogen, viii, 20, 63, 65, 66, 67, 72, 191, 192, 193 N-N, 168 North Africa, 251 North America, 54 Norway, viii, 149, 150, 151, 154, 156, 157, 159, 160, 168, 170, 263 Nuclear allelic richness, viii, 149 nuclear genome, 22, 23, 25, 26, 39, 40, 59 nuclei, 108 nucleic acid, 12, 45 nucleotide sequence, 20, 23, 34, 38, 40 nucleotides, 30, 34, 37 nucleus, 88 null, 152, 155, 168 nurses, 250 nursing, 250 nutraceutical, 91 nutrient, ix, 180, 191 nutrients, 91, 191 nutrition, 254, 299

O objectivity, 107 OH, 64, 67, 91 oil, ix, 43, 79, 80, 81, 82, 83, 93, 94, 95, 98, 99, 101, 102, 104, 195, 196, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 242, 245, 246, 247, 249, 250, 251, 252, 253, 263 oilseed, 31, 45, 46, 48, 58, 61

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Index oleic acid, 297, 298 oligosaccharide, 249 olive oil, 73, 199, 203, 204, 205, 206, 207, 209, 210, 214, 217, 219, 222, 235, 243, 246, 249, 251 omega-3, 297, 298, 299 operating system, 112 operations, 25, 112, 119, 122, 123, 142 operon, 88 opportunities, 176, 199 orchid, 35, 38 organ, 15 organelles, 88, 89 organism, 7, 21, 34, 89, 90, 91 organs, 5, 8, 15, 95, 291 overlap, 9 overlay, 119 ox, 153 oxidation, 64, 65, 66, 67, 68, 75, 84, 85, 89 oxidative damage, 68, 82, 87, 89, 90, 93, 100, 103 oxidative reaction, 65 oxidative stress, viii, 63, 66, 67, 79, 82, 89, 90, 97, 98, 101, 258 oxygen, 64, 65, 66, 68, 71, 75, 84, 85, 90, 96, 100 oxygen consumption, 85 ozone, 102, 194

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P Pacific, 285 pain, 76 Pakistan, 54, 290 parameter estimates, 157 parasite, 250 parasites, 101, 186 parents, 22, 171, 280 partition, 118 pasta, 207, 214, 224, 226, 246 pastures, 251 pathogenesis, 67 pathogens, 43, 70, 248 pathways, viii, 6, 63, 72, 198 pattern recognition, 106, 132, 143, 144 PCR, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 36, 37, 38, 43, 45, 47, 48, 52, 54, 55, 58, 60, 61, 152 pedigree, 278 penis, 240 peptides, 66, 71 percolation, 92 permeability, 67, 70 peroxidation, 67, 90 peroxide, 64, 87 Persian shallot, x, 289, 290, 291, 292, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304

315

Peru, 7, 48 pests, 289 pH, ix, 86, 87, 92, 152, 179, 181, 182, 186, 187, 190 pharmaceutical, 93, 100 pharmacogenomics, 46 phenol, viii, 63, 72, 79, 95, 96 phenolic compounds, 71, 72, 73, 79, 80, 81, 99, 100, 103 phenotype, vii, 1, 7, 8, 20, 21, 23, 172 phenotypes, 16, 59, 172, 174 phenylalanine, 74 phosphorus, 297 phosphorylation, 250 photographs, 13 photolysis, 65 phycoerythrin, 85 Physiological, 102 physiology, 20, 22 phytomedicine, 249 phytotherapy, 255 picture processing, 112 pigs, 175 plant breeding, vii, x, 1, 17, 56, 269 plant growth, 192 plasma levels, 91 plasma proteins, 85 plasticity, vii, 1, 8, 9, 26, 43, 53, 57, 172 plastics, 64 plastid, 40, 48, 50 platelet aggregation, 82, 93, 103 platelets, 298 platform, x, 269 playing, 64 ploidy, 12, 303 point mutation, 36 Poland, 150 polar, 87, 97 policy, 198, 254 policy makers, 254 pollen, 7, 9, 10, 22, 42, 51, 52, 58, 150, 154, 156, 163, 166, 168, 171, 266, 272, 274, 277, 281, 283, 286 pollination, 9, 17, 294 polyacrylamide, 29, 31, 32, 34, 67 polymerase, 27, 58 polymorphism, 10, 20, 21, 32, 33, 35, 38, 42, 46, 48, 49, 50, 54, 55, 56, 59, 60, 177 polymorphisms, 23, 26, 46, 55, 58, 61 polypeptides, 65 polyphenols, viii, 63, 81, 83, 88, 91, 92, 96, 98, 101, 103 polyploid, 33 polyploidy, x, 11, 23, 40, 269

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Index

ponds, 271 pools, 271, 274 population, viii, 19, 22, 34, 36, 41, 44, 63, 139, 149, 150, 152, 153, 154, 155, 156, 157, 158, 160, 161, 162, 163, 166, 168, 169, 170, 171, 173, 175, 176, 177, 192, 200, 265, 276, 277, 283, 285 population size, viii, 22, 149, 155, 166, 171 population structure, 34, 153, 170, 175, 176, 177 porosity, 144 Portugal, 63, 95, 100, 101, 102 potassium, 85, 297, 299 potassium persulfate, 85 potato, 19, 29, 31, 47, 53, 54 potential benefits, 41 poultry, 144 poverty, 61, 204 predation, 197 predictive accuracy, 137 preparation, 112, 142, 199, 203, 222, 226, 237, 302 preservation, viii, 55, 105, 196 prevention, 70, 298 principles, 3, 6, 16 prior knowledge, 27 probability, 16, 38, 58, 133, 153, 162 probe, 24, 25, 26, 36, 48, 84, 85 process duration, 92 producers, 76 pro-inflammatory, 83, 94 project, x, 34, 48, 194, 269 proliferation, 42, 71, 89 propagation, 68, 294, 302 prophylactic, 101 prophylaxis, 100 prostaglandins, 299 prostate cancer, 257 protection, viii, 12, 33, 63, 89, 151, 168, 172, 249 protective role, 292 protein analysis, 24 protein components, 26 protein oxidation, 67 proteinase, 67 proteins, 12, 20, 42, 45, 50, 54, 65, 66, 67, 71, 72, 86, 87, 90, 95, 199, 298 purification, 92 purity, 52, 58, 151

Q qualitative differences, 7 quality assurance, 106 quality control, 107, 144, 147 quantification, 17, 79, 86, 87, 98, 99 quasi-natural ecosystems, viii, 149

quercetin, 80, 87, 89, 91, 101 quinone, 103

R radiation, 72 Radiation, 55 radical formation, 88 radicals, 64, 66, 67, 68, 85, 86, 88, 89, 96 rainforest, 18 rape, 31, 45, 46, 48, 58, 61 reactant, 86 reaction temperature, 26 reactions, 21, 26, 34, 35, 64, 65, 66, 68, 69, 84, 86, 88, 94, 96, 98, 152 reactive oxygen, 65, 66, 69, 94 reagents, 37, 86, 102, 142 receptors, 67 recognition, 3, 5, 13, 14, 17, 37, 121, 143, 144, 147 recombination, 23, 40, 53 recommendations, 91 reconstruction, 108, 123 red blood cells, 88, 98 red wine, 71, 73 reducing sugars, 86 regeneration, 18 regulations, 3, 112, 174 reintroduction, viii, 105, 177 relatives, ix, x, 10, 11, 17, 18, 20, 25, 42, 52, 53, 60, 195, 242, 243, 247, 248, 257, 259, 260, 262, 265, 268, 269, 274, 282, 290, 300 reliability, 13, 16, 24, 31, 107 repair, 67 replication, 67 reproduction, 17, 22, 151, 274 requirements, vii, 1, 6, 14, 21 researchers, 33 reserves, 173, 294, 298 residues, 298 resilience, 198 resistance, x, 18, 42, 49, 51, 55, 92, 194, 248, 269, 275, 279, 282, 285, 289 resolution, 19, 20, 41, 55, 56, 109, 110, 111, 123, 124, 199, 284 resources, viii, 12, 18, 19, 33, 42, 45, 48, 50, 105, 176, 198, 254, 257, 258, 259, 260, 261, 277, 278, 279, 282, 283, 284, 285, 286, 289 respiratory disorders, 76 response, viii, 8, 9, 18, 21, 26, 63, 82, 101, 117, 282 restoration, 173 restriction enzyme, 24, 25, 31, 37 Rhodophyta, 46 ribosomal RNA, 12

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Index rice field, 47 rings, 74, 87 risk, 18, 71, 151, 173, 249, 257, 260 risks, 19, 198 RNA, 57 robotics, 107 ROI, 119, 129, 130 ROOH, 64, 68 room temperature, 87, 296 root, 8, 42, 181, 184, 191, 216, 235, 236, 239, 240, 241 roots, 12, 44, 190, 199, 241, 249 rose hips, 7 routes, 198 routines, 109, 122 Royal Society, 45, 58, 143 rules, 3, 122

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S salinity, 289 salt tolerance, x, 257, 269 sanctuaries, 177 saponin, 12, 299 satellite islets, ix, 195, 196, 246 saturation, 110, 131 savings, 117 scaling, 116, 128 scanning electron microscopy, 10 scatter, 133 scavengers, 69, 71, 88, 93 science, vii, 1, 5, 6, 17, 19, 41, 53, 106, 107, 117, 123, 144, 145, 263, 265, 268, 278, 285 scientific investigations, 250 sea level, 196 secretion, 82, 101 sedative, 96 sediments, 150 seed, vii, 1, 17, 18, 19, 20, 22, 42, 44, 45, 48, 52, 54, 56, 58, 98, 104, 124, 125, 129, 131, 132, 133, 137, 138, 141, 142, 143, 144, 146, 150, 151, 166, 167, 171, 172, 173, 174, 194, 240, 256, 272, 277, 281, 283 segregation, 277 selectivity, 92 sensitivity, 13, 20, 24, 27 sensors, 108 sequencing, 24, 26, 33, 34, 35, 36, 37, 38, 39, 42, 44, 52 serum, 84, 85 services, 150, 173, 176 set theory, 122 shade, 271, 276, 296

317

shape, 7, 9, 91, 105, 106, 107, 123, 126, 127, 128, 131, 132, 143, 144, 147, 294 sheathing mycorrhizas, ix, 180, 191 shoot, 239, 245 shoots, 199, 207, 226, 229, 230, 242 shortage, 157 showing, 26, 89, 152, 190, 242, 244 shrubs, ix, 191, 195, 197, 241, 244 side chain, 67, 88 silver, 31 simulation, 175 single-nucleotide polymorphism, 53 skewness, 130 skin, 73, 292, 297, 298, 300 smoothing, 112, 113, 115, 145 SNP, 36, 37, 38, 42, 52, 53, 56, 58, 59, 62 social benefits, 278 social relations, 198 sodium, 20, 67, 297, 299 software, 16, 106, 153, 154, 155, 157, 160, 175, 176, 193 soil erosion, 174 solid state, 108 solubility, 84, 91 solution, 16, 86, 116 solvents, 92 somatic cell, 10 South Africa, 40, 98 South America, 12, 20 soybeans, 29 Spain, 94, 141, 150, 151, 199, 200, 245, 246, 255, 267 specialists, 41, 142, 191, 253 specialty crop, 52 speciation, 6, 11, 43, 46, 257 specifications, 107, 117 spectrophotometry, 87, 90, 152 spectroscopy, 87, 90 sperm, 283 spin, 88, 108 spine, 250 spore, viii, 105 sputum, 299 stability, 79, 92, 99, 102, 173, 174 stabilization, 71 stamens, 5 standard deviation, 129, 131 standardization, 84 starch, 10, 43, 51, 223 stars, 163, 168 state, 13, 15, 42, 75 states, 13, 14, 15, 34, 250 statistics, 145, 154, 177

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318

Index

sterile, 274 sterols, 299 stomach, 210, 249 storage, 19, 20, 42, 44, 45, 54, 65, 72, 79, 92, 102, 109, 111, 112, 147, 291 stress, 9, 67, 89, 90, 242, 244, 248, 275, 282 stretching, 275, 290 structural changes, 11, 67 structural characteristics, 126 structure, viii, 5, 8, 9, 10, 14, 23, 43, 49, 74, 75, 76, 87, 88, 117, 128, 132, 142, 149, 151, 152, 162, 163, 168, 169, 170, 171, 172, 175, 176, 177, 264, 295, 298 subgroups, 75 subsistence, 198, 257, 267 substitution, 24, 36, 61, 75, 276, 277, 279, 280, 281, 286, 287 substitutions, 40, 71, 88 substrate, 70, 84, 154, 162, 163, 169, 170, 191 substrates, 169, 170 subtraction, 116 succession, ix, 150, 180, 182, 186 sugarcane, 26, 42 sulfate, 67, 279 sulfites, 71 sulfur, 298 Sun, 49, 107, 112, 114, 115, 144, 147 supplementation, 104 suppliers, 76 survival, 5, 71, 172 survivors, 55 suspensions, 81, 101 sweat, 241 symmetry, 126, 129 symptoms, 35, 79, 99 synthesis, 27, 72, 92, 250 Syria, 1, 50, 60, 244

T T cell, 79, 101 tannins, 72 tar, 240 target, 7, 24, 26, 27, 28, 29, 30, 32, 36, 37, 68, 69, 87, 91, 92, 117, 150, 152, 160, 163, 299 target zone, 150, 163 taxa, 3, 5, 6, 10, 11, 13, 15, 16, 17, 21, 23, 25, 39, 41, 138, 141, 142, 200, 241, 242, 243, 245, 246, 247, 248, 249, 250, 251, 274 taxonomy, 2, 3, 5, 6, 11, 20, 41, 54, 58, 59, 61, 142, 259, 265, 268 techniques, vii, viii, 1, 12, 19, 23, 24, 27, 31, 32, 34, 36, 37, 42, 47, 50, 53, 58, 86, 90, 92, 97, 105,

106, 107, 112, 118, 121, 132, 142, 143, 145, 146, 147, 175, 289 technologies, 26, 36, 38, 51, 58, 193 technology, 41, 49, 51, 106, 115, 138, 263, 285 technology transfer, 263 temperature, 9, 29, 65, 85, 91, 92, 278, 299, 300 terrestrial ecosystems, 193 territory, ix, 195, 198, 199, 200, 251 test data, 137 testing, 58, 88, 89 textbooks, 121 texture, 65, 118, 121, 144 therapeutic use, 250, 253 therapy, 100 thermal analysis, 264 thermal decomposition, 85 thermodynamic equilibrium, 92 threats, viii, 105, 199 three-dimensional space, 123 threshold level, 121 thrombin, 82, 103 thrombosis, 103 thymine, 34 tissue, 33, 90, 298 tones, 110, 117 tortoises, 175 toxic aldehydes, 67 toxicity, 81, 91, 93, 250 trade, 31, 53, 174 traditions, 199, 259, 264 training, 134, 136, 137 traits, vii, 1, 7, 8, 9, 14, 17, 21, 55, 141, 172, 191, 261, 275, 276, 277, 279, 281, 285, 300, 303 transcription, 253, 282 transcription factors, 282 transformation, 49, 132, 133, 282 transformation matrix, 132, 133 transgene, 17, 49, 61 transition metal, 87 transition metal ions, 87 transition period, 5 translation, 128 transmission, 33, 50, 55, 253 transpiration, 296 transport, 91 treatment, 2, 61, 82, 83, 89, 94, 101, 132, 241, 250, 298, 299 tumor, 95 tundra, 9 Turkey, 263 tyrosine, 72, 86

Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,

319

Index

U Ukraine, 150, 179 ulcer, 68 ultrasound, 80, 93, 102 ultrastructure, 60 UN, 263 underlying mechanisms, 89 UNESCO, 198, 199, 254, 267, 268 uniform, 31, 33, 115, 116, 118, 120, 253 United Kingdom (UK), ix, 48, 49, 59, 96, 100, 143, 144, 145, 179, 180, 193, 262, 303 United States (USA), 43, 50, 51, 52, 55, 56, 60, 61, 103, 144, 146, 147, 175, 176, 177, 285 universality, 40 uric acid, 69, 70, 72 USDA, 51, 304 USSR, 179 UV, 87

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V validation, 80, 100, 137 variables, 14, 91, 109, 110, 111, 128, 132, 133, 134, 135, 136, 152, 182 variations, 10, 24, 27, 36, 41, 132, 144, 150, 191 varieties, 48, 57, 107, 123, 144, 146, 243, 254, 278, 279, 280, 286, 289 vector, 34, 86, 133, 136 vegetables, 100, 101, 199, 201, 202, 204, 205, 206, 207, 214, 216, 226, 231, 232, 237, 246, 251, 256, 264, 266, 291, 297, 299 vegetation, ix, 144, 169, 179, 180, 186, 187, 188, 189, 191, 192, 263 Vietnam, 263 vision, 106, 107, 108, 111, 117, 143, 144, 145, 146, 147 visualization, 34, 109 Vitamin C, 68 vitamin E, 85 vitamins, 65, 69, 71, 72, 89, 298

W Washington, 176, 259 water, 9, 72, 80, 84, 85, 92, 99, 144, 175, 202, 205, 206, 207, 208, 210, 219, 220, 237, 249, 251, 296, 297 wavelengths, 87 weakness, 11 wealth, ix, 269 wear, 117 weight loss, 182 well-being, 176, 255 Western countries, 48, 71 wild type, 264 wilderness, 151 wildlife, 175 wood, ix, 8, 78, 81, 149, 150, 151, 156, 162, 166, 168, 169, 170, 171, 172, 173, 174, 188, 190 woodland, 180, 182, 192, 193 workers, 2 working hours, 232 worldwide, 18 worms, 250 wound healing, 82, 102

X XML, 262

Y yield, x, 72, 80, 82, 92, 93, 94, 99, 264, 269, 276, 277, 278, 285, 296

Z zinc, 297, 299 Zone 1, 150

Wild Plants: Identification, Uses and Conservation : Identification, Uses and Conservation, edited by Ryan E. Davis, Nova Science Publishers,