Multiscale assessment of functional connectivity: Landscape genetics of eastern indigo snakes in an anthropogenically fragmented landscape in central Florida

Abstract

Landscape features can strongly influence gene flow and the strength and direction of these effects may vary across spatial scales. However, few studies have evaluated methodological approaches for selecting spatial scales in landscape genetics analyses, in part because of computational challenges associated with optimizing landscape resistance surfaces (LRS). We used the federally threatened eastern indigo snake (Drymarchon couperi) in central Florida as a case study with which to compare the importance of landscape features and their scales of effect in influencing gene flow. We used genetic algorithms (ResistanceGA) to empirically optimize LRS using categorical land cover surfaces, multiscale resource selection surfaces (RSS), and four combinations of landscape covariates measured at multiple spatial scales (multisurface multiscale LRS). We compared LRS where scale was selected using pseudo-and full optimization. Multisurface multiscale LRS received more empirical support than LRS optimized from categorical land cover surfaces or RSS. Multiscale LRS with scale selected using full optimization generally outperformed those with scale selected using pseudo-optimization. Multiscale LRS with large spatial scales (1200-1800 m) received the most empirical support. Our results highlight the importance of considering landscape features across multiple spatial scales in landscape genetic analyses, particularly broad scales relative to species movement potential. Different effects of scale on home range-level movements and dispersal could explain weak associations between habitat suitability and gene flow in other studies. Our results also demonstrate the importance of large tracts of undeveloped upland habitat with heterogenous vegetation communities and low urbanization for promoting indigo snake connectivity.

Roo-tine Obstacles: Uncovering the spatio-genetic barriers for Eastern Grey Kangaroos (Macropus giganteus) in an urbanising landscape

“…Specifically, we found that changes in vegetation health and moisture content were scale-dependent, meaning that broad and fine-scale drivers show significant variability when assessed through space and time. This result is particularly noteworthy, as most landscape genetics studies that address this topic pertain to carnivores, amphibian, or reptile species (Bauder et al, 2021;Rezaei et al, 2022;Sadoti et al, 2017) Urban infrastructure, including buildings and major roadways, significantly disrupts wildlife dispersal in urban landscapes by segmenting habitats and creating physical and behavioural barriers (Baguette & Van Dyck, 2007;Bennett & Saunders, 2010;Fischer & Lindenmayer, 2007;Schaal, 1980;Schlaepfer et al, 2018). Our study within the rapidly urbanising Sunshine Coast reveals that these infrastructures significantly impact the gene flow capabilities between surveyed M. giganteus populations.…”

Quantifying uncertainty in inferences of landscape genetic resistance due to choice of individual‐based genetic distance metric

“…Given the lack of convergence between optimised resistance surfaces in the BOARS and FOXES datasets, we cannot confidently identify accurate resistance surfaces for them. One limitation of the present study is the single spatial scale used in analyses, as spatial scale can influence the inferred effects of landscape variables in landscape genetic studies (Angelone et al, 2011; Bauder et al, 2021). However, as OMFRSs did not converge at the spatial scale analysed here, we assume a similar influence of genetic distance metrics on results also at other scales.…”

Inferring population connectivity in eastern massasauga rattlesnakes (Sistruruscatenatus) using landscape genetics

“…However, these limitations are inherent in most landscape studies due to both the cost and difficulty of developing fine‐grained data layers and standardizing variables across multiple sites. Second, our resistance maps were parameterized using land‐cover and elevation data estimated over coarse spatial scales; the choice of scale can affect analyses and resistance surfaces and should be considered depending on data availability (Bauder et al, 2021; Winiarski et al, 2020). Third, our analyses are focused on landscapes occupied by S. catenatus in Ohio, and so the degree to which the results can be generalized to other populations is unknown.…”