Elsevier

Biological Conservation

Volume 113, Issue 3, October 2003, Pages 351-365
Biological Conservation

Spatial analysis of genetic diversity as a tool for plant conservation

https://doi.org/10.1016/S0006-3207(03)00122-8Get rights and content

Abstract

Development of suitable approaches to the analysis of genetic diversity in a spatial context, where factors such as pollination, seed dispersal, breeding system, habitat heterogeneity and human influence are appropriately integrated, can provide new insights in the understanding of the mechanisms of maintenance and dynamics of populations. In this sense, it is important to recognise that patterns and processes may take place at different scales at the same time, and that the scales of a study must be chosen in accordance with the objectives pursued. Apart from conventional approaches to genetic structure, spatial autocorrelation and related techniques, such as Mantel test, correlograms, Mantel correlograms, join-counts, variograms and point pattern analysis, can detect and characterise the existence of spatial genetic structures and lead the way to discussing the environmental and biological factors responsible for them. An alternative way of including spatial variability in modelling approaches that deal with genetic patterns or processes is through the use of constrained ordinations. Although scarcely used at present, these methodologies have great applicability in conservation biology and can lead a way to an effective integration of genetic, demographic and ecological perspectives.

Introduction

During the last few decades we have witnessed an intense academic debate between plant conservation biologists about which is the most appropriate way to approach a conservation problem—the so called ecological or the genetic approach (Schemske et al., 1994). In the course of this long debate, several authors have pointed out that not only is knowledge of the amount of genetic diversity critical for a correct diagnosis of the status, threats and viability of populations (Frankham, 1995), but also the spatial distribution of this diversity (Falk & Holsinger, 1991, Dunham et al., 1999). This recognition falls within a more general statement in which geneticists have recognized the importance of the interaction between genome and environment in time and space so as to better understand evolution (Berry, 1989), thus connecting genetics and ecology (Jelinski, 1997). Genetic diversity may appear spatially structured at different scales, such as population, subpopulation or among neighbouring individuals. This spatial distribution is necessarily a product of environmental influences, including human activities (Knowles et al., 1992), life story traits and demographic past history of the plant species (Loveless & Hamrick, 1984, Slatkin, 1985). Thus, knowledge of spatial genetic structures provides a valuable tool for inferring these causal factors and also the underlying genetic processes such as differential selective pressures, gene flow and drift (Nevo et al., 1986, Barbujani, 1987, Epperson, 1993, Bjornstad et al., 1995). Consequently, information about dispersal, pollinator behaviour, breeding system, safe site availability for establishment and other processes operating and structuring populations at such scales can be derived from spatially explicit approaches (Peakall and Beattie, 1995). Knowledge of all these features represents a key priority for conservation managers. Surprisingly, however, most genetic studies on endangered plants either lack explicit spatial considerations or analyse them only at scales where biological interpretations at the population level are not straightforward.

Although there is considerable agreement that there is no single “correct” scale at which to describe populations, since the processes that originate the spatial patterns may operate at different scales (Levin, 1992), most of the available numerical methods used in population genetics have been applied at medium and large scales (population or larger geographical approaches). The spatial association of environmental and genetic variables (Hedrick, 1986) and the spatial patterning of plant genetic diversity (Heywood, 1991) have been extensively studied at these medium or large scales. However, abundant evidence has been reported in which plant populations exhibit genetic micro-differentiation, spatially closer individuals being genetically more alike than individuals at some distance (Sokal et al., 1989, Epperson, 1993). Even at extremely fine scales, spatial genetic structures have been detected in some plant populations (Turkington & Harper, 1979, Epperson & Clegg, 1986, Wagner et al., 1991, Tani et al., 1998), although, in other cases, random or near random distributions have been found (Waser, 1987, Epperson & Allard, 1989, Leonardi et al., 1995). Therefore, the challenge that arises when studying the genetic structure of populations of rare plants is not only choosing the scale of description, but rather recognising that change may be taking place at several scales at the same time (McCue et al., 1996).

There is an urgent need to integrate the knowledge derived from genetic, demographic and ecological approaches to species conservation in order to be able to formulate management strategies that take into account all the different considerations. Spatial analysis techniques are a meeting point for all these three approaches and thereby progress on this direction is likely to facilitate a much sought comprehensive and integrated outlook in conservation biology.

Section snippets

Objectives

The purpose of this work is to present methods that can be applied to the study of spatial genetic structures in endangered plants, both at broad and narrow scales, and that may be helpful in interpreting key biological processes which affect the viability of the populations. A second matter which is also posed is the treatment of genetic spatial variability in the context of hypothesis testing and its use as a tool in conservation biology.

Traditionally genetic concerns have been put on one

Conventional approaches to genetic structure

Traditionally, population genetic structures within a species have been primarily studied by testing departures of allele frequencies from panmictic expectations or testing for heterogeneity in allele frequencies among populations or other spatial subdivisions (Heywood, 1991). This implies that the genetic information at the individual level is necessarily merged into clusters of a different spatial or biological nature (populations, subpopulations…) and that the relative frequencies are

Molecular markers and nature of genetic data

A wide choice of genetic markers is now available to the conservation biologist (see review by Haig, 1998). Since the 1960s, allozymes have been succesfully used to characterise genetic diversity of rare plant species (Hamrick and Godt, 1990), although sometimes they have not shown enough discriminatory power to distinguish between individuals (Brauner et al., 1992, Buso et al., 1998). In the past decade, DNA techniques have gained ground, especially those based on the polymerase chain reaction

Measuring spatial autocorrelation of genetic data

Genetic data frequently present spatial autocorrelation, that is, the association of values of one geographically distributed variable with the values of the same variable at all other localities. In these cases, it is possible to predict the values of a genetic variable at some points of the space from the known values of the variable at other known sampling points.

Hypothesis testing and partialling out spatial information

Theoretical and empirical work is rapidly advancing a set of hypothesis tests that use spatial patterns to study some genetic processes such as gene flow or natural selection (Epperson, 1990). The evaluation of the effect of environmental conditions on spatial genetic structure can be viewed as a problem of spatial covariation (Legendre and Fortin, 1989). This type of covariation can efficiently be approached by means of constrained ordinations considering the spatial location of each

Past and present applications and future possibilities

Spatial statistical methods provide plant conservation biologists with powerful tools for measuring the structure of genetic diversity of target plants. However, a quick review of current literature shows that the use of these techniques is low among genetic conservationists and even among population geneticists. We have reviewed papers approaching spatial genetic structures in plant species and limited our search to articles dealing with spatial autocorrelation and related techniques. The

Acknowledgements

We are grateful to J. Heywood and D. Gömöry for their very useful comments and suggestions. This work has been partially financed by REN 2000-0254-P4-03 project of the Spanish Ministry of Science and Technology.

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