Aurélie Coulon

Bio: Aurélie Coulon is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Population & Biological dispersal. The author has an hindex of 24, co-authored 56 publications receiving 3662 citations. Previous affiliations of Aurélie Coulon include PSL Research University & University of Montpellier.

Costs of dispersal

Abstract: Dispersal costs can be classified into energetic, time, risk and opportunity costs and may be levied directly or deferred during departure, transfer and settlement. They may equally be incurred during life stages before the actual dispersal event through investments in special morphologies. Because costs will eventually determine the performance of dispersing individuals and the evolution of dispersal, we here provide an extensive review on the different cost types that occur during dispersal in a wide array of organisms, ranging from micro-organisms to plants, invertebrates and vertebrates. In general, costs of transfer have been more widely documented in actively dispersing organisms, in contrast to a greater focus on costs during departure and settlement in plants and animals with a passive transfer phase. Costs related to the development of specific dispersal attributes appear to be much more prominent than previously accepted. Because costs induce trade-offs, they give rise to covariation between dispersal and other life-history traits at different scales of organismal organisation. The consequences of (i) the presence and magnitude of different costs during different phases of the dispersal process, and (ii) their internal organisation through covariation with other life-history traits, are synthesised with respect to potential consequences for species conservation and the need for development of a new generation of spatial simulation models.

Landscape connectivity influences gene flow in a roe deer population inhabiting a fragmented landscape: an individual-based approach.

Abstract: Changes in agricultural practices and forest fragmentation can have a dramatic effect on landscape connectivity and the dispersal of animals, potentially reducing gene flow within populations. In this study, we assessed the influence of woodland connectivity on gene flow in a traditionally forest-dwelling species--the European roe deer--in a fragmented landscape. From a sample of 648 roe deer spatially referenced within a study area of 55 x 40 km, interindividual genetic distances were calculated from genotypes at 12 polymorphic microsatellite loci. We calculated two geographical distances between each pair of individuals: the Euclidean distance (straight line) and the 'least cost distance' (the trajectory that maximizes the use of wooded corridors). We tested the correlation between genetic pairwise distances and the two types of geographical pairwise distance using Mantel tests. The correlation was better using the least cost distance, which takes into account the distribution of wooded patches, especially for females (the correlation was stronger but not significant for males). These results suggest that in a fragmented woodland area roe deer dispersal is strongly linked to wooded structures and hence that gene flow within the roe deer population is influenced by the connectivity of the landscape.

Statistical methods in spatial genetics.

Abstract: The joint analysis of spatial and genetic data is rapidly becoming the norm in population genetics. More and more studies explicitly describe and quantify the spatial organization of genetic variation and try to relate it to underlying ecological processes. As it has become increasingly difficult to keep abreast with the latest methodological developments, we review the statistical toolbox available to analyse population genetic data in a spatially explicit framework. We mostly focus on statistical concepts but also discuss practical aspects of the analytical methods, highlighting not only the potential of various approaches but also methodological pitfalls.

Genetic structure is influenced by landscape features: empirical evidence from a roe deer population.

Abstract: The delimitation of population units is of primary importance in population management and conservation biology. Moreover, when coupled with landscape data, the description of population genetic structure can provide valuable knowledge about the permeability of landscape features, which is often difficult to assess by direct methods (e.g. telemetry). In this study, we investigated the genetic structuring of a roe deer population which recently recolonized a fragmented landscape. We sampled 1148 individuals from a 40 × 55-km area containing several putative barriers to deer movements, and hence to gene flow, namely a highway, rivers and several canals. In order to assess the effect of these landscape features on genetic structure, we implemented a spatial statistical model known as GENELAND which analyses genetic structure, explicitly taking into account the spatial nature of the problem. Two genetic units were inferred, exhibiting a very low level of differentiation ( F ST = 0.008). The location of their boundaries suggested that there are no absolute barriers in this study area, but that the combination of several landscape features with low permeability can lead to population differentiation. Our analysis hence suggests that the landscape has a significant influence on the structuring of the population under study. It also illustrates the use of GENELAND as a powerful method to infer population structure, even in situations of young populations exhibiting low genetic differentiation.

Identifying future research needs in landscape genetics: Where to from here?

Abstract: Landscape genetics is an emerging interdisciplinary field that combines methods and concepts from population genetics, landscape ecology, and spatial statistics. The interest in landscape genetics is steadily increasing, and the field is evolving rapidly. We here outline four major challenges for future landscape genetic research that were identified during an international landscape genetics workshop. These challenges include (1) the identification of appropriate spatial and temporal scales; (2) current analytical limitations; (3) the expansion of the current focus in landscape genetics; and (4) interdisciplinary communication and education. Addressing these research challenges will greatly improve landscape genetic applications, and positively contribute to the future growth of this promising field.

Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Genetic monitoring as a promising tool for conservation and management

Abstract: In response to ever-increasing anthropogenic changes to natural ecosystems, regional, national and international organizations have established guidelines for monitoring biological diversity. Most monitoring programs, however, do not take full advantage of the potential afforded by molecular genetic markers, which can provide information relevant to both ecological and evolutionary time frames, while costing less and being more sensitive and reliable than traditional monitoring approaches. As several molecular and computational approaches are relatively new, many technical and theoretical issues remain to be resolved. Here, we illustrate how DNA and population genetic data can provide valuable information, often unattainable via other approaches, for monitoring species of management, conservation and ecological interest.

Isolation by resistance.

Abstract: Despite growing interest in the effects of landscape heterogeneity on genetic structuring, few tools are available to incorporate data on landscape composition into population genetic studies. Analyses of isolation by distance have typically either assumed spatial homogeneity for convenience or applied theoretically unjustified distance metrics to compensate for heterogeneity. Here I propose the isolation-by-resistance (IBR) model as an alternative for predicting equilibrium genetic structuring in complex landscapes. The model predicts a positive relationship between genetic differentiation and the resistance distance, a distance metric that exploits precise relationships between random walk times and effective resistances in electronic networks. As a predictor of genetic differentiation, the resistance distance is both more theoretically justified and more robust to spatial heterogeneity than Euclidean or least cost path-based distance measures. Moreover, the metric can be applied with a wide range of data inputs, including coarse-scale range maps, simple maps of habitat and nonhabitat within a species' range, or complex spatial datasets with habitats and barriers of differing qualities. The IBR model thus provides a flexible and efficient tool to account for habitat heterogeneity in studies of isolation by distance, improve understanding of how landscape characteristics affect genetic structuring, and predict genetic and evolutionary consequences of landscape change.