Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci USA

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Proceedings of the National Academy of Sciences (Impact Factor: 9.81). 01/2008; 104(50):19885-90. DOI: 10.1073/pnas.0706568104
Source: PubMed

ABSTRACT Maintaining connectivity for broad-scale ecological processes like dispersal and gene flow is essential for conserving endangered species in fragmented landscapes. However, determining which habitats should be set aside to promote connectivity has been difficult because existing models cannot incorporate effects of multiple pathways linking populations. Here, we test an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model vastly improves gene flow predictions because it simultaneously integrates all possible pathways connecting populations. When applied to data from threatened mammal and tree species, the model consistently outperformed conventional gene flow models, revealing that barriers were less important in structuring populations than previously thought. Circuit theory now provides the best-justified method to bridge landscape and genetic data, and holds much promise in ecology, evolution, and conservation planning.

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Available from: Brad Mcrae, Aug 28, 2015
    • "Circuitscape has so far been mainly used as a descriptive tool to identify landscape variables affecting genetic connectivity or movement corridors between populations (e.g. Castillo et al., 2014; McRae and Beier, 2007). Its use as a predictive tool has only been explored in a couple of recent studies, albeit not within the landscape genetics framework which allows testing whether the landscape variable actually affects movement in the form of gene flow. "
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    ABSTRACT: Understanding how biodiversity will respond to future climate change is a major conservation and societal challenge. Climate change is predicted to force many species to shift their ranges in pursuit of suitable conditions. This study aims to use landscape genetics, the study of the effects of environmental heterogeneity on the spatial distribution of genetic variation, as a predictive tool to assess how species will shift their ranges to track climatic changes and inform conservation measures that will facilitate movement. The approach is based on three steps: 1) using Species Distribution Models (SDMs) to predict suitable ranges under future climate change, 2) using the landscape genetics framework to identify landscape variables that impede or facilitate movement, and 3) extrapolating the effect of landscape connectivity on range shifts in response to future climate change. I show how this approach can be implemented using the publicly available genetic dataset of the grey long-eared bat, Plecotus austriacus, in the Iberian Peninsula. Forest cover gradient was the main landscape variable affecting genetic connectivity between colonies. Forest availability is likely to limit future range shifts in response to climate change, primarily over the central plateau, but important range shift pathways have been identified along the eastern and western coasts. I provide outputs that can be directly used by conservation managers and review the viability of the approach. Using landscape genetics as a predictive tool in combination with SDMs enables the identification of potential pathways, whose loss can affect the ability of species to shift their range into future climatically suitable areas, and the appropriate conservation management measures to increase landscape connectivity and facilitate movement.
    Ecological Informatics 06/2015; DOI:10.1016/j.ecoinf.2015.05.007 · 1.98 Impact Factor
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    • "By running electrical current between nodes (lek groups), the program calculates pairwise electrical resistance (measured in ohms) between locations (McRae 2006; McRae et al. 2008). Current flow and random walkers through electrical networks have a strong relationship (McRae and Beier 2007), and thus, circuit theory has been widely applied to predict patterns of dispersal and gene flow and identify corridors in ecological landscapes (e.g., Schwartz et al. 2009; Row et al. 2010; Moore et al. 2011). "
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    ABSTRACT: Given the significance of animal dispersal to population dynamics and geographic variability, understanding how dispersal is impacted by landscape patterns has major ecological and conservation importance. Speaking to the importance of dispersal, the use of linear mixed models to compare genetic differentiation with pairwise resistance derived from landscape resistance surfaces has presented new opportunities to disentangle the menagerie of factors behind effective dispersal across a given landscape. Here, we combine these approaches with novel resistance surface parameterization to determine how the distribution of high- and low-quality seasonal habitat and individual landscape components shape patterns of gene flow for the greater sage-grouse (Centrocercus urophasianus) across Wyoming. We found that pairwise resistance derived from the distribution of low-quality nesting and winter, but not summer, seasonal habitat had the strongest correlation with genetic differentiation. Although the patterns were not as strong as with habitat distribution, multivariate models with sagebrush cover and landscape ruggedness or forest cover and ruggedness similarly had a much stronger fit with genetic differentiation than an undifferentiated landscape. In most cases, landscape resistance surfaces transformed with 17.33-km-diameter moving windows were preferred, suggesting small-scale differences in habitat were unimportant at this large spatial extent. Despite the emergence of these overall patterns, there were differences in the selection of top models depending on the model selection criteria, suggesting research into the most appropriate criteria for landscape genetics is required. Overall, our results highlight the importance of differences in seasonal habitat preferences to patterns of gene flow and suggest the combination of habitat suitability modeling and linear mixed models with our resistance parameterization is a powerful approach to discerning the effects of landscape on gene flow.
    Ecology and Evolution 04/2015; DOI:10.1002/ece3.1479 · 1.66 Impact Factor
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    • "However, species do not disperse randomly through the habitat matrix in most landscapes, but use species-specific corridors for migration, which are determined by habitat preferences and dispersal abilities of the respective study species (Engler et al., 2014). Therefore , least-cost path analyses or the calculation of isolation by resistance using circuit theory (McRae & Beier, 2007) might be ecologically much more meaningful than those based on linear geographic "
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    ABSTRACT: Population genetic research has transformed from the study of genetic structures into a much wider and highly multidisciplinary research field. In this contribution, we outline the limitations of classic population genetic data and highlight the potential of combining molecular data with additional environmental, ecological and biological data sets in multidisciplinary approaches. The combination of data sets from various fields allows a more comprehensive understanding of extrinsic and intrinsic evolutionary processes affecting populations such as the distinction between genetic drift and natural selection. The integration of population size estimates and demographic dynamics as well as individual behaviour (embracing aspects like dispersal behaviour) allows for testing of adaptations to local environments and understanding time-lags frequently observed in molecular data. Modelling approaches are integrated at increasing rates into population genetic research to identify potential barriers and corridors. In our review, we therefore highlight multiple synergistic effects when combining population genetic data with morphological, ecological, behavioural data and modelling. We outline strengths and limitations and indicate future possibilities and challenges. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, ●●, ●●–●●.
    Biological Journal of the Linnean Society 03/2015; 115(1). DOI:10.1111/bij.12481 · 2.54 Impact Factor
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