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Climate change is arguably the greatest challenge to conservation of our time. Most vulnerability assessments rely on past and current species distributions to predict future persistence but ignore species' abilities to disperse through landscapes, which may be particularly important in fragmented habitats and crucial for long-term persistence in changing environments. Landscape genetic approaches explore the interactions between landscape features and gene flow and can clarify how organisms move among suitable habitats, but have suffered from methodological uncertainties. We used a landscape genetic approach to determine how landscape and climate-related features influence gene flow for American pikas ( Ochotona princeps) in Crater Lake National Park. Pikas are heat intolerant and restricted to cool microclimates; thus, range contractions have been predicted as climate changes. We evaluated the correlation between landscape variables and genetic distance using partial Mantel tests in a causal modelling framework, and used spatially explicit simulations to evaluate methods of model optimization including a novel approach based on relative support and reciprocal causal modelling. We found that gene flow was primarily restricted by topographic relief, water and west-facing aspects, suggesting that physical restrictions related to small body size and mode of locomotion, as well as exposure to relatively high temperatures, limit pika dispersal in this alpine habitat. Our model optimization successfully identified landscape features influencing resistance in the simulated data for this landscape, but underestimated the magnitude of resistance. This is the first landscape genetic study to address the fundamental question of what limits dispersal and gene flow in the American pika. [ABSTRACT FROM AUTHOR]

Landscape resistance reflects how difficult it is for genes to move across an area with particular attributes (e.g. land cover, slope). An increasingly popular approach to estimate resistance uses Mantel and partial Mantel tests or causal modelling to relate observed genetic distances to effective distances under alternative sets of resistance parameters. Relatively few alternative sets of resistance parameters are tested, leading to relatively poor coverage of the parameter space. Although this approach does not explicitly model key stochastic processes of gene flow, including mating, dispersal, drift and inheritance, bias and precision of the resulting resistance parameters have not been assessed. We formally describe the most commonly used model as a set of equations and provide a formal approach for estimating resistance parameters. Our optimization finds the maximum Mantel r when an optimum exists and identifies the same resistance values as current approaches when the alternatives evaluated are near the optimum. Unfortunately, even where an optimum existed, estimates from the most commonly used model were imprecise and were typically much smaller than the simulated true resistance to dispersal. Causal modelling using Mantel significance tests also typically failed to support the true resistance to dispersal values. For a large range of scenarios, current approaches using a simple correlational model between genetic and effective distances do not yield accurate estimates of resistance to dispersal. We suggest that analysts consider the processes important to gene flow for their study species, model those processes explicitly and evaluate the quality of estimates resulting from their model. [ABSTRACT FROM AUTHOR]

Understanding how specific environmental factors shape gene flow while disentangling their importance relative to the effects of geographical isolation is a major question in evolutionary biology and a specific goal of landscape genetics. Here, we combine information from nuclear microsatellite markers and ecological niche modelling to study the association between climate and spatial genetic structure and variability in Engelmann oak ( Quercus engelmannii), a wind-pollinated species with high potential for gene flow. We first test whether genetic diversity is associated with climatic niche suitability and stability since the Last Glacial Maximum (LGM). Second, we use causal modelling to analyse the potential influence of climatic factors (current and LGM niche suitability) and altitude in the observed patterns of genetic structure. We found that genetic diversity is negatively associated with local climatic stability since the LGM, which may be due to higher immigration rates in unstable patches during favourable climatic periods and/or temporally varying selection. Analyses of spatial genetic structure revealed the presence of three main genetic clusters, a pattern that is mainly driven by two highly differentiated populations located in the northern edge of the species distribution range. After controlling for geographic distance, causal modelling analyses showed that genetic relatedness decreases with the environmental divergence among sampling sites estimated as altitude and current and LGM niche suitability. Natural selection against nonlocal genotypes and/or asynchrony in reproductive phenology may explain this pattern. Overall, this study suggests that local environmental conditions can shape patterns of genetic structure and variability even in species with high potential for gene flow and relatively small distribution ranges. [ABSTRACT FROM AUTHOR]

Reliable interpretation of landscape genetic analyses depends on statistical methods that have high power to identify the correct process driving gene flow while rejecting incorrect alternative hypotheses. Little is known about statistical power and inference in individual-based landscape genetics. Our objective was to evaluate the power of causal-modelling with partial Mantel tests in individual-based landscape genetic analysis. We used a spatially explicit simulation model to generate genetic data across a spatially distributed population as functions of several alternative gene flow processes. This allowed us to stipulate the actual process that is in action, enabling formal evaluation of the strength of spurious correlations with incorrect models. We evaluated the degree to which naïve correlational approaches can lead to incorrect attribution of the driver of observed genetic structure. Second, we evaluated the power of causal modelling with partial Mantel tests on resistance gradients to correctly identify the explanatory model and reject incorrect alternative models. Third, we evaluated how rapidly after the landscape genetic process is initiated that we are able to reliably detect the effect of the correct model and reject the incorrect models. Our analyses suggest that simple correlational analyses between genetic data and proposed explanatory models produce strong spurious correlations, which lead to incorrect inferences. We found that causal modelling was extremely effective at rejecting incorrect explanations and correctly identifying the true causal process. We propose a generalized framework for landscape genetics based on analysis of the spatial genetic relationships among individual organisms relative to alternative hypotheses that define functional relationships between landscape features and spatial population processes. [ABSTRACT FROM AUTHOR]

Habitat fragmentation may strongly reduce individuals’ dispersal among resource patches and hence influence population distribution and persistence. We studied the impact of landscape heterogeneity on the dispersal of the golden-crowned sifaka ( Propithecus tattersalli), an endangered social lemur species living in a restricted and highly fragmented landscape. We combined spatial analysis and population genetics methods to describe population units and identify the environmental factors which best predict the rates and patterns of genetic differentiation within and between populations. We used non-invasive methods to genotype 230 individuals at 13 microsatellites in all the main forest fragments of its entire distribution area. Our analyses suggest that the Manankolana River and geographical distance are the primary structuring factors, while a national road crossing the region does not seem to impede gene flow. Altogether, our results are in agreement with a limited influence of forest habitat connectivity on gene flow patterns (except for North of the species’ range), suggesting that dispersal is still possible today among most forest patches for this species. Within forest patches, we find that dispersal is mainly among neighbouring social groups, hence confirming previous behavioural observations. [ABSTRACT FROM AUTHOR]

Phenotypic and genetic divergence can be influenced by a variety of factors, including sexual and natural selection, genetic drift and geographic isolation. Investigating the roles of these factors in natural systems can provide insight into the relative influences of allopatric and ecological modes of biological diversification in nature. The strawberry poison frog, Dendrobates pumilio, presents an excellent opportunity for this kind of research, displaying a diverse array of colour morphs and inhabiting a heterogeneous landscape that includes oceanic islands, fragmented rainforest patches and wide expanses of suitable habitat. In this study, we use 15 highly polymorphic microsatellite loci to estimate population structure and gene flow among populations from across the range of D. pumilio and a causal modelling framework to statistically test 12 hypotheses regarding the geographic and phenotypic variables that explain genetic differentiation within this system. Our results demonstrate that the genetic distance between populations is most strongly associated with differences in dorsal coloration. Previous experimental studies have shown that phenotypic differences can result in sexual and natural selection against non-native phenotypes, and our results now show that these forces lead to genetic isolation between different colour morphs in the wild, presenting a potential case of incipient speciation through selection. [ABSTRACT FROM AUTHOR]

Understanding genetic variation for complex traits in heterogeneous environments is a fundamental problem in biology. In this issue of Molecular Ecology, Fournier-Level et al. () analyse quantitative trait loci ( QTL) influencing ecologically important phenotypes in mapping populations of Arabidopsis thaliana grown in four habitats across its native European range. They used causal modelling to quantify the selective consequences of life history and morphological traits and QTL on components of fitness. They found phenology QTL colocalizing with known flowering time genes as well as novel loci. Most QTL influenced fitness via life history and size traits, rather than QTL having direct effects on fitness. Comparison of phenotypes among environments found no evidence for genetic trade-offs for phenology or growth traits, but genetic trade-offs for fitness resulted because flowering time had opposite fitness effects in different environments. These changes in QTL effects and selective consequences may maintain genetic variation among populations. [ABSTRACT FROM AUTHOR]