Stéphanie Manel

Landscape genetics: combining landscape ecology and population genetics

Understanding the processes and patterns of gene flow and local adaptation requires a detailed knowledge of how landscape characteristics structure populations. This understanding is crucial, not only for improving ecological knowledge, but also for managing properly the genetic diversity of threatened and endangered populations. For nearly 80 years, population geneticists have investigated how physiognomy and other landscape features have influenced genetic variation within and between populations. They have relied on sampling populations that have been identified beforehand because most population genetics methods have required discrete populations. However, a new approach has emerged for analyzing spatial genetic data without requiring that discrete populations be identified in advance. This approach, landscape genetics, promises to facilitate our understanding of how geographical and environmental features structure genetic variation at both the population and individual levels, and has implications for ecology, evolution and conservation biology. It differs from other genetic approaches, such as phylogeography, in that it tends to focus on processes at finer spatial and temporal scales. Here, we discuss, from a population genetic perspective, the current tools available for conducting studies of landscape genetics.

Statistical analysis of amplified fragment length polymorphism data : a toolbox for molecular ecologists and evolutionists

Recently, the amplified fragment length polymorphism (AFLP) technique has gained a lot of popularity, and is now frequently applied to a wide variety of organisms. Technical specificities of the AFLP procedure have been well documented over the years, but there is on the contrary little or scattered information about the statistical analysis of AFLPs. In this review, we describe the various methods available to handle AFLP data, focusing on four research topics at the population or individual level of analysis: (i) assessment of genetic diversity; (ii) identification of population structure; (iii) identification of hybrid individuals; and (iv) detection of markers associated with phenotypes. Two kinds of analysis methods can be distinguished, depending on whether they are based on the direct study of band presences or absences in AFLP profiles (‘band‐based’ methods), or on allelic frequencies estimated at each locus from these profiles (‘allele frequency‐based’ methods). We investigate the characteristics and limitations of these statistical tools; finally, we appeal for a wider adoption of methodologies borrowed from other research fields, like for example those especially designed to deal with binary data.

Ten years of landscape genetics

Landscape genetics is now ten years old. It has stimulated research into the effect of landscapes on evolutionary processes. This review describes the main topics that have contributed most significantly to the progress of landscape genetics, such as conceptual and methodological developments in spatial and temporal patterns of gene flow, seascape genetics, and landscape genomics. We then suggest perspectives for the future, investigating what the field will contribute to the assessment of global change and conservation in general and to the management of tropical and urban areas in particular. To address these urgent topics, future work in landscape genetics should focus on a better integration of neutral and adaptive genetic variation and their interplay with species distribution and the environment.

Applications of landscape genetics in conservation biology: concepts and challenges

Landscape genetics plays an increasingly important role in the management and conservation of species. Here, we highlight some of the opportunities and challenges in using landscape genetic approaches in conservation biology. We first discuss challenges related to sampling design and introduce several recent methodological developments in landscape genetics (analyses based on pairwise relatedness, the application of Bayesian methods, inference from landscape resistance and a shift from population-based to individual-based analyses). We then show how simulations can foster the field of landscape genetics and, finally, elaborate on technical developments in sequencing techniques that will dramatically improve our ability to study genetic variation in wild species, opening up new and unprecedented avenues for genetic analysis in conservation biology.

Perspectives on the use of landscape genetics to detect genetic adaptive variation in the field

Understanding the genetic basis of species adaptation in the context of global change poses one of the greatest challenges of this century. Although we have begun to understand the molecular basis of adaptation in those species for which whole genome sequences are available, the molecular basis of adaptation is still poorly understood for most non‐model species. In this paper, we outline major challenges and future research directions for correlating environmental factors with molecular markers to identify adaptive genetic variation, and point to research gaps in the application of landscape genetics to real‐world problems arising from global change, such as the ability of organisms to adapt over rapid time scales. High throughput sequencing generates vast quantities of molecular data to address the challenge of studying adaptive genetic variation in non‐model species. Here, we suggest that improvements in the sampling design should consider spatial dependence among sampled individuals. Then, we describe available statistical approaches for integrating spatial dependence into landscape analyses of adaptive genetic variation.

Detecting selection along environmental gradients: analysis of eight methods and their effectiveness for outbreeding and selfing populations

Thanks to genome‐scale diversity data, present‐day studies can provide a detailed view of how natural and cultivated species adapt to their environment and particularly to environmental gradients. However, due to their sensitivity, up‐to‐date studies might be more sensitive to undocumented demographic effects such as the pattern of migration and the reproduction regime. In this study, we provide guidelines for the use of popular or recently developed statistical methods to detect footprints of selection. We simulated 100 populations along a selective gradient and explored different migration models, sampling schemes and rates of self‐fertilization. We investigated the power and robustness of eight methods to detect loci potentially under selection: three designed to detect genotype–environment correlations and five designed to detect adaptive differentiation (based on FST or similar measures). We show that genotype–environment correlation methods have substantially more power to detect selection than differentiation‐based methods but that they generally suffer from high rates of false positives. This effect is exacerbated whenever allele frequencies are correlated, either between populations or within populations. Our results suggest that, when the underlying genetic structure of the data is unknown, a number of robust methods are preferable. Moreover, in the simulated scenario we used, sampling many populations led to better results than sampling many individuals per population. Finally, care should be taken when using methods to identify genotype–environment correlations without correcting for allele frequency autocorrelation because of the risk of spurious signals due to allele frequency correlations between populations.

Utility of computer simulations in landscape genetics

Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to processes by combining complex and realistic life histories, behaviours, landscape features and genetic data. Central to landscape genetics is the connection of spatial patterns of genetic variation to the usually highly stochastic space–time processes that create them over both historical and contemporary time periods. The field should benefit from a shift to computer simulation approaches, which enable incorporation of demographic and environmental stochasticity. A key role of simulations is to show how demographic processes such as dispersal or reproduction interact with landscape features to affect probability of site occupancy, population size, and gene flow, which in turn determine spatial genetic structure. Simulations could also be used to compare various statistical methods and determine which have correct type I error or the highest statistical power to correctly identify spatio‐temporal and environmental effects. Simulations may also help in evaluating how specific spatial metrics may be used to project future genetic trends. This article summarizes some of the fundamental aspects of spatial–temporal population genetic processes. It discusses the potential use of simulations to determine how various spatial metrics can be rigorously employed to identify features of interest, including contrasting locus‐specific spatial patterns due to micro‐scale environmental selection.

Common factors drive adaptive genetic variation at different spatial scales in Arabis alpina

A major challenges facing landscape geneticists studying adaptive variation is to include all the environmental variables that might be correlated with allele frequencies across the genome. One way of identifying loci that are possibly under selection is to see which ones are associated with environmental gradient or heterogeneity. Since it is difficult to measure all environmental variables, one may take advantage of the spatial nature of environmental filters to incorporate the effect of unaccounted environmental variables in the analysis. Assuming that the spatial signature of these variables is broad‐scaled, broad‐scale Moran’s eigenvector maps (MEM) can be included as explanatory variables in the analysis as proxies for unmeasured environmental variables. We applied this approach to two data sets of the alpine plant Arabis alpina. The first consisted of 140 AFLP loci sampled at 130 sites across the European Alps (large scale). The second one consisted of 712 AFLP loci sampled at 93 sites (regional scale) in three mountain massifs (local scale) of the French Alps. For each scale, we regressed the frequencies of each AFLP allele on a set of eco‐climatic and MEM variables as predictors. Twelve (large scale) and 11% (regional scale) of all loci were detected as significantly correlated to at least one of the predictors ( > 0.5), and, except for one massif, 17% at the local scale. After accounting for spatial effects, temperature and precipitation were the two major determinants of allele distributions. Our study shows how MEM models can account for unmeasured environmental variation in landscape genetics models.

Landscape genetics of plants

Landscape genetics is the amalgamation of landscape ecology and population genetics to help with understanding microevolutionary processes such as gene flow and adaptation. In this review, we examine why landscape genetics of plants lags behind that of animals, both in number of studies and consideration of landscape elements. The classical landscape distance/resistance approach to study gene flow is challenging in plants, whereas boundary detection and the assessment of contemporary gene flow are more feasible. By contrast, the new field of landscape genetics of adaptive genetic variation, establishing the relationship between adaptive genomic regions and environmental factors in natural populations, is prominent in plant studies. Landscape genetics is ideally suited to study processes such as migration and adaptation under global change.

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods’ effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance.