Genetic admixture between captive-bred and wild individuals affects patterns of dispersal in a brown trout (Salmo trutta) population

Genetic admixture between captive-bred and wild individuals has been demonstrated to affect many individual traits, although little is known about its potential influence on dispersal, an important trait governing the eco-evolutionary dynamics of populations. Here, we quantified and described the spatial distribution of genetic admixture in a brown trout (Salmo trutta) population from a small watershed that was stocked until 1999, and then tested whether or not individual dispersal parameters were related to admixture between wild and captive-bred fish. We genotyped 715 fish at 17 microsatellite loci sampled from both the mainstream and all populated tributaries, as well as 48 fish from the hatchery used to stock the study area. First, we used Bayesian clustering to infer local genetic structure and to quantify genetic admixture. We inferred first generation migrants to identify dispersal events and test which features (genetic admixture, sex and body length) affected dispersal parameters (i.e. probability to disperse, distance of dispersal and direction of the dispersal event). We identified two genetic clusters in the river basin, corresponding to wild fish on the one hand and to fish derived from the captive strain on the other hand, allowing us to define an individual gradient of admixture. Individuals with a strong assignment to the captive strain occurred almost exclusively in some tributaries, and were more likely to disperse towards a tributary than towards a site of the mainstream. Furthermore, dispersal probability increased as the probability of assignment to the captive strain increased, and individuals with an intermediate level of admixture exhibited the lowest dispersal distances. These findings show that various dispersal parameters may be biased by admixture with captive-bred genotypes, and that management policies should take into account the differential spread of captive-bred individuals in wild populations

Kin-dependent dispersal influences relatedness and genetic structuring in a lek system

Kin selection and dispersal play a critical role in the evolution of cooperative breeding systems. Limited dispersal increases relatedness in spatially structured populations (population viscosity), with the result that neighbours tend to be genealogical relatives. Yet the increase in neighbours’ fitness-related performance through altruistic interaction may also result in habitat saturation and thus exacerbate local competition between kin. Our goal was to detect the footprint of kin selection and competition by examining the spatial structure of relatedness and by comparing non-effective and effective dispersal in a population of a lekking bird, Tetrao urogallus. For this purpose, we analysed capture–recapture and genetic data collected over a 6-year period on a spatially structured population of T. urogallus in France. Our findings revealed a strong spatial structure of relatedness in males. They also indicated that the population viscosity could allow male cooperation through two non-exclusive mechanisms. First, at their first lek attendance, males aggregate in a lek composed of relatives. Second, the distance corresponding to non-effective dispersal dramatically outweighed effective dispersal distance, which suggests that dispersers incur high post-settlement costs. These two mechanisms result in strong population genetic structuring in males. In females, our findings revealed a lower level of spatial structure of relatedness and genetic structure in respect to males. Additionally, non-effective dispersal and effective dispersal distances in females were highly similar, which suggests limited post- settlement costs. These results indicate that kin-dependent dispersal decisions and costs have a genetic footprint in wild populations and are factors that may be involved in the evolution of cooperative courtship

Data from: Contribution of spatial heterogeneity in effective population sizes to the variance in pairwise measures of genetic differentiation

1. Pairwise measures of neutral genetic differentiation are supposed to contain information about past and on-going dispersal events and are thus often used as dependent variables in correlative analyses to elucidate how neutral genetic variation is affected by landscape connectivity. However, spatial heterogeneity in the intensity of genetic drift, stemming from variations in population sizes, may inflate variance in measures of genetic differentiation and lead to erroneous or incomplete interpretations in terms of connectivity. Here, we tested the efficiency of two distance-based metrics designed to capture the unique influence of spatial heterogeneity in local drift on genetic differentiation. These metrics are easily computed from estimates of effective population sizes or from environmental proxies for local carrying capacities, and allow us to introduce the hypothesis of Spatial-Heterogeneity-in-Effective-Population-Sizes (SHNe). SHNe can be tested in a way similar to isolation-by-distance or isolation-by-resistance within the classical landscape genetics hypothesis-testing framework. 2. We used simulations under various models of population structure to investigate the reliability of these metrics to quantify the unique contribution of SHNe in explaining patterns of genetic differentiation. We then applied these metrics to an empirical genetic dataset obtained for a freshwater fish (Gobio occitaniae). 3. Simulations showed that SHNe explained up to 60% of variance in genetic differentiation (measured as Fst) in the absence of gene flow, and up to 20% when migration rates were as high as 0.10. Furthermore, one of the two metrics was particularly robust to uncertainty in the estimation of effective population sizes (or proxies for carrying capacity). In the empirical dataset, the effect of SHNe on spatial patterns of Fst was five times higher than that of isolation-by-distance, uniquely contributing to 41% of variance in pairwise Fst. Taking the influence of SHNe into account also allowed decreasing the signal-to-noise ratio, and improving the upper estimate of effective dispersal distance. 4. We conclude that the use of SHNe metrics in landscape genetics will substantially improve the understanding of evolutionary drivers of genetic variation, providing substantial information as to the actual drivers of patterns of genetic differentiation in addition to traditional measures of Euclidean distance or landscape resistance

Étude de l’efficacité de la restauration de la continuité écologique utilisant la différenciation génétique au sein de populations de poissons

Suite à la Loi sur l'Eau et les Milieux Aquatiques de 2006, les aménagements situés sur les cours d'eau classés en liste 2 et entravant la continuité écologique doivent être équipés ou gérés de manière à rétablir le franchissement des poissons. Compte tenu du coût de ces opérations, les maîtres d'ouvrage chargés de la mise en œuvre technique de la restauration de la continuité doivent bénéficier d'outils de diagnostic de la franchissabilité des obstacles fiables afin d'optimiser la planification des actions. Les outils de génétique des populations offrent l'opportunité de développer de tels indicateurs, mais cela nécessite des développements pour gagner en précision et en opérationnalité. En particulier, un indicateur génétique (le F-index) a récemment été développé et appliqué, mais certaines de ses limitations pourraient être levées en utilisant des données génomiques issues du séquençage nouvelle génération (SNG). Ainsi, l'objectif principal de cette note technique est de présenter le développement d'un nouvel indicateur génomique de fragmentation et de tester son efficacité dans un contexte réel de fragmentation et de restauration de la continuité écologique. Dans unpremier temps, un travail de simulations numériques a permis de développer analytiquement un F-index basé sur l'utilisation de données génomiques sous la forme de milliers de marqueurs SNP (Single Nucleotide Polymorphisms). Dans un second temps, une autre série de simulations a montré que, comme attendu, le F-index "génomique" était plus précis (moins de variabilité dans les estimations) et permettait un meilleur diagnostic que le F-index "classique" (basé sur des marqueurs "microsatellites"). Enfin, sur la base de sept cas d'étude empirique sur les rivières Cher et Sarthe, nous avons démontré : (i) que les conclusions obtenues entre le F-index génomique et le F-index classique étaient globalement cohérentes, mais que certaines situations contrastées soulevaient les limites et forces de chacune des approches, (ii) que certaines conclusions basées sur les approches moléculaires n'étaient pas cohérentes avec les dires d'experts, montrant la complémentarité des approches mais aussi la difficulté d'estimer convenablement le phénomène complexe qu'est le franchissement d'un obstacle (que ce soit d’un point de vue génétique ou physique), et (iii) que le F-index génomique, bien que prometteur, n'est pas encore opérationnel et nécessite d'homogénéiser et de rendre plus répétables les procédures de séquençage et de bio-informatiques nécessaires à l'obtention des données génomiques. Pour conclure, nous discutons des résultats obtenus au regard des difficultés rencontrées, des limites et avantages de l'approche génomique, et des futurs travaux qui pourraient améliorer la précision de l'approche génétique

Pairwise matrices of landscape distances

Pairwise inter-individual landscape distances for the 669 individuals retained in main analyses

Regression commonality analyses on hierarchical genetic distances

International audienceLandscape genetics is emerging as an important way of supporting decision-making in landscape management, in response to the deterioration of matrix permeability due to habitat loss and fragmentation. In line with unremitting methodological developments in landscape genetics, a new analytical procedure was recently proposed as a way of evaluating the effects of landscape gradients on genetic structures. This procedure is based on the computation of inter-individual hierarchical genetic distances (HGD), a metric of genetic differentiation taking into account the hierarchical structure in populations as inferred from clustering algorithms. HGD can be used as dependent variables in multivariate regressions to assess the effects of various landscape predictors on spatial patterns of genetic differentiation. However, multicollinearity may obscure the interpretation of multivariate regressions. We illustrate how regression commonality analyses (CA), a detailed variance partitioning procedure that can be used to deal with multicollinearity issues, can thoroughly improve our understanding of landscape connectivity when HGD are used as a dependent variable, with the red deer Cervus elaphus as an empirical example. Using logistic regression commonality analyses on HGD, we showed that semi-natural open areas, transportation infrastructures and, to a lesser extent, urban areas and rivers, were associated with an increase in hierarchical genetic differentiation in red deer. Regressions based on HGD provided detailed results that could not have been obtained with regressions based on standard genetic distances, with notably additional insights as to the possible influence of linear features such as roads and highways on landscape connectivity. Furthermore, CA helped identify synergistic associations among variables as well as suppressors, thus resolving inconsistencies among hierarchical levels and revealing spurious correlations that may have gone unnoticed in the course of classical regression analyses. We thus recommend the use of regression commonality analysis on hierarchical genetic distances as a promising statistical tool for landscape geneticists

Data from: Regression commonality analyses on hierarchical genetic distances.

Landscape genetics is emerging as an important way of supporting decision-making in landscape management, in response to the deterioration of matrix permeability due to habitat loss and fragmentation. In line with unremitting methodological developments in landscape genetics, a new analytical procedure was recently proposed as a way of evaluating the effects of landscape gradients on genetic structures. This procedure is based on the computation of inter-individual hierarchical genetic distances (HGD), a metric of genetic differentiation taking into account the hierarchical structure in populations as inferred from clustering algorithms. HGD can be used as dependent variables in multivariate regressions to assess the effects of various landscape predictors on spatial patterns of genetic differentiation. However, multicollinearity may obscure the interpretation of multivariate regressions. We illustrate how regression commonality analyses (CA), a detailed variance partitioning procedure that can be used to deal with multicollinearity issues, can thoroughly improve our understanding of landscape connectivity when HGD are used as a dependent variable, with the red deer (Cervus elaphus) as an empirical example. Using logistic regression commonality analyses on HGD, we showed that semi-natural open areas, transportation infrastructures and, to a lesser extent, urban areas and rivers, were associated with an increase in hierarchical genetic differentiation in red deer. Regressions based on HGD provided detailed results that could not have been obtained with regressions based on standard genetic distances, with notably additional insights as to the possible influence of linear features such as roads and highways on landscape connectivity. Furthermore, CA helped identify synergistic associations among variables as well as suppressors, thus resolving inconsistencies among hierarchical levels and revealing spurious correlations that may have gone unnoticed in the course of classical regression analyses. We thus recommend the use of regression commonality analysis on hierarchical genetic distances as a promising statistical tool for landscape geneticists

Pairwise matrices of genetic distances

Pairwise inter-individual classical and hierarchical genetic distances for the 669 individuals retained in main analyses

Genetic erosion reduces biomass temporal stability in wild fish populations

Abstract Genetic diversity sustains species adaptation. However, it may also support key ecosystems functions and services, for example biomass production, that can be altered by the worldwide loss of genetic diversity. Despite extensive experimental evidence, there have been few attempts to empirically test whether genetic diversity actually promotes biomass and biomass stability in wild populations. Here, using long-term demographic wild fish data from two large river basins in southwestern France, we demonstrate through causal modeling analyses that populations with high genetic diversity do not reach higher biomasses than populations with low genetic diversity. Nonetheless, populations with high genetic diversity have much more stable biomasses over recent decades than populations having suffered from genetic erosion, which has implications for the provision of ecosystem services and the risk of population extinction. Our results strengthen the importance of adopting prominent environmental policies to conserve this important biodiversity facet