ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms

Peterman, William E.

doi:10.1111/2041-210x.12984

Cited by 276 publications

(367 citation statements)

References 35 publications

(56 reference statements)

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“…And (3) which landscape features or combination of features have the largest influence on patterns of gene flow (dispersal)? We used optimization methods for the prediction of patterns of genetic distance using ResistanceGA (Peterman, 2018) to determine the best resistance values for each landscape layer individually, and for all of them combined. We conduct bootstrapping and model selection analyses on genetic and landscape resistance distance matrices to determine the best predictors of gene flow patterns and to infer likely dispersal vectors and habitat features that may facilitate or impede seed dispersal.…”

Section: R E S E a R C H A R T I C L Ementioning

confidence: 99%

“…We used these three landscape layers (habitat, vole runways, and flower presence) and the cpDNA haplotype genetic distance matrix to estimate the effects of landscape features on patterns of seed-mediated gene flow using the optimization methods of ResistanceGA (Peterman, 2018). Layers were analyzed as independent surfaces as well as multi-surface RasterStack objects (Hijmans et al, 2019) to consider two-and three-way interactions among all landscape features (Table 1).…”

Section: Landscape Genetics Analysesmentioning

confidence: 99%

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Fine‐scale habitat heterogeneity and vole runways influence seed dispersal in Plagiobothrys nothofulvus

Grasty

Thompson

Hendrickson

et al. 2020

American J of Botany

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Premise Seed dispersal allows plants to colonize new sites and contributes to gene flow among populations. Despite its fundamental importance to ecological and evolutionary processes, our understanding of seed dispersal is limited due to the difficulty of directly observing dispersal events. This is particularly true for the majority of plant species that are considered to have gravity as their primary dispersal mechanism. The potential for long‐distance movement of gravity‐dispersed seeds by secondary dispersal vectors is rarely evaluated. Methods We employ whole‐genome assays of maternally inherited cpDNA in Plagiobothrys nothofulvus to resolve patterns of genetic variation due to effective (realized) seed dispersal within a 16 hectare prairie that is characterized by a mosaic of habitat types. We evaluate the effects of microgeographic landscape features extracted from micro‐UAV aerial surveys on patterns of seed dispersal using landscape genetics methods. Results We found evidence of high resistance to seed‐mediated gene flow (effective dispersal) within patches of Plagiobothrys nothofulvus, and strong genetic structure over distances of less than 20 m. Geographic distance was a poor predictor of dispersal distance, while landscape features had stronger influences on patterns of dispersal (distance and direction of seed movement). Patterns of dispersal were best predicted by the combined distribution of flower patches, habitat type, and the network of vole runways, with the latter explaining the largest proportion of variation in the model. Conclusions Our results suggest that primary dispersal occurs mostly within microhabitats and infrequent secondary dispersal may occur over longer distances due to the activity of small mammals and other vertebrates.

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Section: R E S E a R C H A R T I C L Ementioning

confidence: 99%

Section: Landscape Genetics Analysesmentioning

confidence: 99%

Fine‐scale habitat heterogeneity and vole runways influence seed dispersal in Plagiobothrys nothofulvus

Grasty

Thompson

Hendrickson

et al. 2020

American J of Botany

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“…We used this dataset because it contained comparable genetic data for a diverse group of species (Meirmans, Goudet, IntraBioDiv Consortium, & Gaggiotti, 2011). Of the 27 sampled species, we used only nine species for our analysis because we excluded (a) species that primarily use wind for dispersal (Meirmans et al, 2011;Thiel-Egenter et al, 2009) as contemporary landscape genetics methods are ill-suited for asymmetric gene flow (e.g., Peterman, 2018), (b) species with insufficient occurrence data for habitat modelling (i.e., fewer than 100 occurrence records remaining after data cleaning; Cerastium uniflorum and Loiseleuria procumbens), and (c) species for which we were unable to K E Y W O R D S alpine plants, connectivity, gene flow, integer programming, landscape genetics, optimization, spatial prioritization, systematic conservation planning generate resistance maps that adequately explained their genetic variation (R 2 β statistic less than 0.35; Gentiana nivalis; see below for more information). All analyses were performed using the R statistical environment ( version 3.4.4; R Core Team, 2017).…”

Section: Data and Study Areamentioning

confidence: 99%

“…Resistance maps-broadly speaking, heat maps where greater values indicate areas with greater impediments to gene flow-are commonly used to describe how different areas affect gene flow for a particular species (e.g., Burkhart et al, 2017;Dudaniec et al, 2016). Today, advanced methods use genetic data, models, and optimization algorithms (e.g., Peterman, 2018) to transform maps describing the distribution of landscape features (e.g., land cover classes or environmental conditions; Dudaniec et al, 2016) into resistance maps (but see Cushman & Landguth, 2010) which, in turn, can be used to predict the effects of habitat modification on gene flow (Ruiz-Lopez et al, 2016). Although resistance maps are often used to identify wildlife corridors (e.g., Dilkina et al, 2017), they are rarely used to guide the configuration of protected area systems (Keller, Holderegger, van Strien, & Bolliger, 2015).…”

Section: Introductionmentioning

confidence: 99%

Conventional methods for enhancing connectivity in conservation planning do not always maintain gene flow

Hanson

Fuller

Rhodes

2019

Journal of Applied Ecology

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Protected area systems need to conserve species in places with suitable habitat that are configured to facilitate gene flow. Since genetic data require considerable resources to obtain, many proxy methods have been developed to generate plans for protected area systems (prioritizations) that facilitate gene flow without needing genetic data. However, the effectiveness of such methods—such as minimising fragmentation or enforcing contiguity among priority areas—remains largely untested. We investigated the ability of prioritizations to maintain gene flow when they are generated using conventional methods for promoting connectivity. Using existing environmental, genetic, and occurrence datasets, we created maps of habitat suitability and resistance to gene flow for nine alpine plant species. Next, we generated multispecies prioritizations that secured 10% of the suitable habitat for each species and attempted to maintain gene flow by (a) penalizing fragmentation, (b) representing species in contiguous areas of suitable habitat, and (c) representing species in contiguous areas with minimal resistance to gene flow as modelled from genetic data. We found that prioritizations generated using fragmentation penalties failed to represent seven of the nine species in areas that would maintain high levels of gene flow. Similarly, prioritizations that represented species in contiguous areas of suitable habitat were unable to maintain high levels of gene flow for six species—potentially because a few areas with high resistance can disrupt gene flow throughout an entire prioritization. Although prioritizations generated using genetic data successfully maintained gene flow, they also selected over three times more land than other prioritizations, suggesting that failing to account for gene flow when setting priorities may underestimate the scale of conservation action required. Synthesis and applications. We found that conventional methods for enhancing connectivity in conservation planning, such as spatially clustering priority areas or providing connected sections of suitable habitat, were generally unable to maintain high levels of gene flow. Our results suggest that conservation plans could be substantially improved by directly using genetic data, although whether this is a good choice for a particular situation will also depend on the costs of obtaining these data.

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“…Land cover types occupying <5% of the study area were reclassified as "other." We followed the framework of optimization and selection of resistance surfaces using the "ResistanceGA" package (Peterman, 2018) in R. This method uses a genetic algorithm (GA; Scrucca, 2013)…”

Section: Landscape Genetics Analysesmentioning

confidence: 99%

Contrasting effects of habitat discontinuity on three closely related fungivorous beetle species with diverging host‐use patterns and dispersal ability

Kobayashi

Sota

2019

Ecology and Evolution

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Understanding how landscape structure influences biodiversity patterns and ecological processes are essential in ecological research and conservation practices. Forest discontinuity is a primary driver affecting the population persistence and genetic structure of forest‐dwelling species. However, the actual impacts on populations are highly species‐specific. In this study, we tested whether dispersal capability and host specialization are associated with susceptibility to forest discontinuity using three closely related, sympatric fungivorous ciid beetle species (two host specialists, Octotemnus assimilis and O . crassus ; one host generalist, O . kawanabei ). Landscape genetic analyses and the estimation of effective migration surfaces (EEMS) method consistently demonstrated contrasting differences in the relationships between genetic structure and configuration of forest land cover. Octotemnus assimilis , one of the specialists with a presumably higher dispersal capability due to lower wing loading, lacked a definite spatial genetic structure in our study landscape. The remaining two species showed clear spatial genetic structure, but the results of landscape genetic analyses differed between the two species: while landscape resistance appeared to describe the spatial genetic structure of the specialist O . crassus , genetic differentiation of the generalist O . kawanabei was explained by geographic distance alone. This finding is consistent with the prediction that nonforest areas act more strongly as barriers between specialist populations. Our results suggest that differences in host range can influence the species‐specific resistance to habitat discontinuity among closely related species inhabiting the same landscape.

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ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms

Cited by 276 publications

References 35 publications

Fine‐scale habitat heterogeneity and vole runways influence seed dispersal in Plagiobothrys nothofulvus

Fine‐scale habitat heterogeneity and vole runways influence seed dispersal in Plagiobothrys nothofulvus

Conventional methods for enhancing connectivity in conservation planning do not always maintain gene flow

Contrasting effects of habitat discontinuity on three closely related fungivorous beetle species with diverging host‐use patterns and dispersal ability

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