Using multilevel models to identify drivers of landscape‐genetic structure among management areas

Dudaniec, Rachael Y.; Rhodes, Jonathan R.; Wilmer, Jessica Worthington; Lyons, Mitchell; Lee, Kristen E.; McAlpine, Clive; Frederick, Carrick

doi:10.1111/mec.12359

Cited by 60 publications

(72 citation statements)

References 86 publications

(105 reference statements)

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“…Furthermore, our approach suggested that the ideal sampling bandwidth for eastern chipmunks in this landscape was in segments approximately 400 m wide because performance (i.e., model fit statistics) occurred at 400 m wide for both logistic and multiple regression models. Landscape variables can have variable effects according to spatial scales (e.g., [15], [16], [75]), so our method provides a way to disentangle multiple influences on gene flow across spatial scales. Taken together, this approach is highly useful for informing management about how to maintain connectivity in fragmented landscapes because it can predict how landscape variables impact gene flow without a priori parameterization and explicitly incorporates spatial scale.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Permeability of Landscape Features to Animal Movement: Using Genetic Structure to Infer Functional Connectivity

et al. 2015

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Human-altered environments often challenge native species with a complex spatial distribution of resources. Hostile landscape features can inhibit animal movement (i.e., genetic exchange), while other landscape attributes facilitate gene flow. The genetic attributes of organisms inhabiting such complex environments can reveal the legacy of their movements through the landscape. Thus, by evaluating landscape attributes within the context of genetic connectivity of organisms within the landscape, we can elucidate how a species has coped with the enhanced complexity of human altered environments. In this research, we utilized genetic data from eastern chipmunks (Tamias striatus) in conjunction with spatially explicit habitat attribute data to evaluate the realized permeability of various landscape elements in a fragmented agricultural ecosystem. To accomplish this we 1) used logistic regression to evaluate whether land cover attributes were most often associated with the matrix between or habitat within genetically identified populations across the landscape, and 2) utilized spatially explicit habitat attribute data to predict genetically-derived Bayesian probabilities of population membership of individual chipmunks in an agricultural ecosystem. Consistency between the results of the two approaches with regard to facilitators and inhibitors of gene flow in the landscape indicate that this is a promising new way to utilize both landscape and genetic data to gain a deeper understanding of human-altered ecosystems.

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Section: Discussionmentioning

confidence: 99%

Assessing the Permeability of Landscape Features to Animal Movement: Using Genetic Structure to Infer Functional Connectivity

et al. 2015

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“…Using simulated data, Peterman (2014) has demonstrated that genetic algorithms are a powerful optimization framework for accurately parameterizing resistance surfaces in a landscape genetic analysis. The use of genetic algorithms for resistance surface optimization provides significant advances over existing approaches, which require a priori determination of the direction of the resistance relationship and assess only a limited range of potential parameter space (for example, Dudaniec et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A novel landscape genetic approach demonstrates the effects of human disturbance on the Udzungwa red colobus monkey (Procolobus gordonorum)

et al. 2015

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A comprehensive understanding of how human disturbance affects tropical forest ecosystems is critical for the mitigation of future losses in global biodiversity. Although many genetic studies of tropical forest fragmentation have been conducted to provide insight into this issue, relatively few have incorporated landscape data to explicitly test the effects of human disturbance on genetic differentiation among populations. In this study, we use a newly developed landscape genetic approach that relies on a genetic algorithm to simultaneously optimize resistance surfaces to investigate the effects of human disturbance in the Udzungwa Mountains of Tanzania, which is an important part of a universally recognized biodiversity hotspot. Our study species is the endangered Udzungwa red colobus monkey (Procolobus gordonorum), which is endemic to the Udzungwa Mountains and a known indicator species that thrives in large and well-protected blocks of old growth forest. Population genetic analyses identified significant population structure among Udzungwa red colobus inhabiting different forest blocks, and Bayesian cluster analyses identified hierarchical structure. Our new method for creating composite landscape resistance models found that the combination of fire density on the landscape and distance to the nearest village best explains the genetic structure observed. These results demonstrate the effects that human activities are having in an area of high global conservation priority and suggest that this ecosystem is in a precarious state. Our study also illustrates the ability of our novel landscape genetic method to detect the impacts of relatively recent landscape features on a long-lived species.

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“…The direct effects of roads are wide ranging and include the destruction and modification of habitat [2], the modification of animal behaviour [3], the fragmentation of habitat by the formation of barriers [4], [5] and vehicle collisions [6]. Roads also indirectly affect wildlife populations by increasing human access to previously inaccessible areas and changing land use patterns [2], [7].…”

Section: Introductionmentioning

confidence: 99%

A Few Large Roads or Many Small Ones? How to Accommodate Growth in Vehicle Numbers to Minimise Impacts on Wildlife

Rhodes

Lunney

Callaghan³

et al. 2014

PLoS ONE

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Roads and vehicular traffic are among the most pervasive of threats to biodiversity because they fragmenting habitat, increasing mortality and opening up new areas for the exploitation of natural resources. However, the number of vehicles on roads is increasing rapidly and this is likely to continue into the future, putting increased pressure on wildlife populations. Consequently, a major challenge is the planning of road networks to accommodate increased numbers of vehicles, while minimising impacts on wildlife. Nonetheless, we currently have few principles for guiding decisions on road network planning to reduce impacts on wildlife in real landscapes. We addressed this issue by developing an approach for quantifying the impact on wildlife mortality of two alternative mechanisms for accommodating growth in vehicle numbers: (1) increasing the number of roads, and (2) increasing traffic volumes on existing roads. We applied this approach to a koala (Phascolarctos cinereus) population in eastern Australia and quantified the relative impact of each strategy on mortality. We show that, in most cases, accommodating growth in traffic through increases in volumes on existing roads has a lower impact than building new roads. An exception is where the existing road network has very low road density, but very high traffic volumes on each road. These findings have important implications for how we design road networks to reduce their impacts on biodiversity.

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Using multilevel models to identify drivers of landscape‐genetic structure among management areas

Cited by 60 publications

References 86 publications

Assessing the Permeability of Landscape Features to Animal Movement: Using Genetic Structure to Infer Functional Connectivity

Assessing the Permeability of Landscape Features to Animal Movement: Using Genetic Structure to Infer Functional Connectivity

A novel landscape genetic approach demonstrates the effects of human disturbance on the Udzungwa red colobus monkey (Procolobus gordonorum)

A Few Large Roads or Many Small Ones? How to Accommodate Growth in Vehicle Numbers to Minimise Impacts on Wildlife

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