Spatial analysis of genetic diversity as a tool for plant conservation

Escudero, Adrián; Iriondo, José María; Torres, Elena

doi:10.1016/s0006-3207(03)00122-8

Cited by 191 publications

(157 citation statements)

References 130 publications

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“…Empirical assessment of genetic variability is therefore essential for successful management of endangered plants (e.g. Escudero et al 2003). Our analysis of genetic variation within and between subpopulations in A. fulva indicates that despite the evident clonal reproduction and recent decrease in the number of populations, the species has maintained a high level of genetic and genotypic diversity and large effective neighbourhood size at the Liminka Bay area.…”

Section: Conservation Implicationsmentioning

confidence: 91%

Genetic structure and gene flow in an endangered perennial grass, Arctophila fulva var. pendulina

et al. 2005

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Arctophila fulva var. pendulina is a rare endemic perennial grass confined to seashore and riverbank meadows around the Bothnian Bay, the northernmost part of the Baltic Sea. The number of A. fulva populations has decreased during the last few decades in Finland and Sweden, and nowadays there are only eight populations left in the drainage area of the Bothnian Bay. We investigated the distribution of genetic variation within and between six subpopulations in the largest remaining population at Liminka Bay, Finland, using amplified fragment length polymorphism (AFLP) markers. Relatively high amounts of variation were found in the subpopulations, the mean Nei's expected heterozygosity being typical (0.267) for an outcrossing species. Despite the fact that no seedlings or viable seeds of A. fulva have been found in the previous field studies, the observed high genotypic diversity suggested that sexual reproduction has played an important role at some time during the history of the studied A. fulva population. Analysis of population structure revealed a low level of genotypic differentiation (U ST =0.046) between subpopulations, and also significant sub-structuring within subpopulations. Isolation-by-distance between subpopulations was present on scales larger than 1 km. The overall pattern of genetic variation within and between subpopulations suggest that the population has characters of both stepping-stone and metapopulation models. Because our results suggested that subpopulations are more or less ephemeral, the conservation and management effort in this species should be targeted to conservation of the required habitat of the species instead of extant subpopulations.

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Section: Conservation Implicationsmentioning

confidence: 91%

Genetic structure and gene flow in an endangered perennial grass, Arctophila fulva var. pendulina

et al. 2005

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“…Existen abundantes ejemplos basados aplicados al estudio espacial de la variabilidad genética (Sokal y Oden 1978a, 1978bSakai y Oden, 1983;Heywood, 1991;Epperson y Li, 1996;Escudero et al, 2003). También se han producido avances en el estudio de la sincronía espaciotemporal de fenómenos ecológicos como la vecería o la dispersión de plagas (Koenig, 2000;Buonacorssi et al, 2001;Bjørnstad et al, 2002).…”

Section: Análisis Univariable De Variables Cuantitativasunclassified

Spatial surface-pattern analyses and boundary detection techniques applied in forest ecology

Camarero

Rozas

2006

For. syst.

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ResumenEn este trabajo se revisan los métodos de análisis univariable y multivariable de los patrones de superficies y de detección de fronteras más utilizados en ecología forestal. El patrón de superficies es un patrón espacial continuo definido por las posiciones de los puntos (árboles) en el espacio y una o varias variables asociadas a cada punto. Se ilustran métodos útiles para describir patrones espaciales e inferir los procesos que los generaron. Se muestra el fundamento estadístico y ejemplos aplicados de métodos de análisis univariables para variables binarias (conteo contiguo) y cuantitativas (correlogramas de Moran y Geary, semivariogramas, dimensión fractal). Se detalla el cálculo e interpretación de métodos multivariables para la descripción de patrones de superficies (correlograma y test de Mantel) y su relación con los métodos de ordenación. Finalmente, se muestran ejemplos de métodos para la detección de fronteras. La mayor parte de los patrones reales analizados provienen de bosques de Pinus uncinata del límite altitudinal superior en los Pirineos o bien de una población relíctica. Se discuten las ventajas y desventajas de cada metodología y sus aplicaciones en ecología forestal.Palabras clave: autocorrelación espacial, correlograma, semivariograma, ordenación, fronteras Abstract Spatial surface-pattern analyses and boundary detection techniques applied in forest ecologyWe review methods for uni-and multivariate surface pattern analysis and boundary detection used in forest ecology. A continuous surface pattern is defined as the locations of points (trees) in the space and the associated variable or variables. We illustrate useful methods to describe spatial patterns and infer the generating processes. We show the statistical basis and applied examples of univariate methods for binary (join counts) and quantitative variables (Moran and Geary correlograms, semivariograms, fractal dimension). We explain the calculus and interpretation of multivariate methods to describe surface patterns (Mantel test and correlogram) and their relationships with ordination methods. Finally, we show examples of techniques for boundary detection. Most analysed patterns corresponded to Pinus uncinata forests from the upper altitudinal limit in the Pyrenees or from a relict population. We discuss the advantages and disadvantages of each methodology and their applications in forest ecology.

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“…Multivariate analyses (also called ordinations in reduced space) such as principal component analysis (Pearson, 1901) have been shown to be efficient in extracting information from genetic markers (CavalliSforza, 1966;Johnson et al, 1969;Smouse et al, 1982) because of their ability to summarize multivariate genetic information into a few synthetic variables. From these early applications to current innovative developments Pavoine and Bailly, 2007;Jombart et al, 2008), these methods have proven to be useful in various fields, such as human genetics (Menozzi et al, 1978;Bertranpetit and Cavalli-Sforza, 1991;Cavalli-Sforza et al, 1993), conservation (MoazamiGoudarzi et al, 1997;Escudero et al, 2003;Laloë et al, 2007), phylogeography (Hanotte et al, 2002;Matsuoka et al, 2002;Ciofi et al, 2006), landscape genetics (Angers et al, 1999;McRae et al, 2005) and the identification of adaptations (Johnson et al, 1969;Mulley et al, 1979;Barker et al, 1986).…”

Section: Introductionmentioning

confidence: 99%

Genetic markers in the playground of multivariate analysis

2009

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Multivariate analyses such as principal component analysis were among the first statistical methods employed to extract information from genetic markers. From their early applications to current innovations, these approaches have proven to be efficient for the analysis of the genetic variability in various contexts such as human genetics, conservation and adaptation studies. However, because multivariate analysis is a wide and diversified area of statistics, choosing a method appropriate to both the data and to the question being asked can be difficult. Moreover, some particularities of genetic markers need to be taken into account when using multivariate methods. As a consequence, multivariate analyses are often used as black boxes, which results in frequent mistakes in the literature. In this review, we provide a critical analysis of the application of multivariate methods to genetic markers, using a general framework that unifies all these methods for the sake of clarity. First, we focus on some common mistakes in these applications and ways to avoid these pitfalls. We then detail the most critical particularities of allele frequencies that demand adaptations of multivariate methods, and we propose solutions to the subsequent problems. Finally, we tackle several questions of interest in which multivariate analysis has a great role to play, such as the study of the typological coherence of different genetic markers, or the investigation of spatial genetic patterns.

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Spatial analysis of genetic diversity as a tool for plant conservation

Cited by 191 publications

References 130 publications

Genetic structure and gene flow in an endangered perennial grass, Arctophila fulva var. pendulina

Genetic structure and gene flow in an endangered perennial grass, Arctophila fulva var. pendulina

Spatial surface-pattern analyses and boundary detection techniques applied in forest ecology

Genetic markers in the playground of multivariate analysis

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