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

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

Estimates of gene flow resulting from landscape resistance inferences frequently inform conservation management decision-making processes. Therefore, results must be robust across approaches and reflect real-world gene flow instead of methodological artefacts. Here, we tested the impact of 32 individual-based genetic distance metrics on the robustness and accuracy of landscape resistance modelling results. We analysed three empirical microsatellite datasets and 36 simulated datasets that varied in landscape resistance and genetic spatial autocorrelation. We used ResistanceGA to generate optimised multi-feature resistance surfaces for each of these datasets using 32 different genetic distance metrics. Results of the empirical dataset demonstrated that the choice of genetic distance metric can have strong impacts on inferred optimised resistance surfaces. Simulations showed accurate parametrisation of resistance surfaces across most genetic distance metrics only when a small number of environmental features was impacting gene flow. Landscape scenarios with many features impacting gene flow led to a generally poor recovery of true resistance surfaces. Simulation results also emphasise that choosing a genetic distance metric should not be based on marginal R2 -based model fit. Until more robust methods are available, resistance surfaces can be optimised with different genetic distance metrics and the convergence of results needs to be assessed via pairwise matrix correlations. Based on the results presented here, high correlation coefficients across different genetic distance categories likely indicate accurate inference of true landscape resistance. Most importantly, empirical results should be interpreted with great caution, especially when they appear counter-intuitive in light of the ecology of a species.

An evaluation of isolation by distance and isolation by resistance on genetic structure of the Persian squirrel (Sciurus anomalus) in the Zagros forests of Iran

For the conservation of wild species, it is important to understand how landscape change and land management can affect gene flow and movement. Landscape genetic analyses provide a powerful approach to infer effects of various landscape factors on gene flow, thereby informing conservation actions. The Persian squirrel is a keystone species in the woodlands and oak forests of Western Asia, where it has experienced recent habitat loss and fragmentation. We conducted landscape genetic analyses of individuals sampled in the northern Zagros Mountains of Iran (provinces of Kurdistan, Kermanshah, and Ilam), focusing on the evaluation of isolation by distance (IBD) and isolation by resistance (IBR), using 16 microsatellite markers. The roles of geographical distance and landscape features including roads, rivers, developed areas, farming and agriculture, forests, lakes, plantation forests, rangelands, shrublands, and rocky areas of varying canopy cover, and swamp margins on genetic structure were quantified using individual-based approaches and resistance surface modeling. We found a significant pattern of IBD but only weak support for an effect of forest cover on genetic structure and gene flow. It seems that geographical distance is an important factor limiting the dispersal of the Persian squirrel in this region. The results of the current study inform ongoing conservation programs for the Persian squirrel in the Zagros oak forest.

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

Assessing the environmental factors that influence the ability of a threatened species to move through a landscape can be used to identify conservation actions that connect isolated populations. However, direct observations of species' movement are often limited, making the development of alternate approaches necessary. Here we use landscape genetic analyses to assess the impact of landscape features on the movement of individuals between local populations of a threatened snake, the eastern massasauga rattlesnake (Sistrurus catenatus). We linked connectivity data with habitat information from two landscapes of similar size: a large region of unfragmented habitat and a previously studied fragmented landscape consisting of isolated patches of habitat. We used this analysis to identify features of the landscape where modification or acquisition would enhance population connectivity in the fragmented region. We found evidence that current connectivity was impacted by both contemporary land-cover features, especially roads, and inherent landscape features such as elevation. Next, we derived estimates of expected movement ability using a recently developed pedigree-based approach and least-cost paths through the unfragmented landscape. We then used our pedigree and resistance map to estimate resistance polygons of the potential extent for S. catenatus movement in the fragmented landscape. These polygons identify possible sites for future corridors connecting currently isolated populations in this landscape by linking the impact of future habitat modification or land acquisition to dispersal ability in this species. Overall, our study shows how modeling landscape resistance across differently fragmented landscapes can identify habitat features that affect contemporary movement in threatened species in fragmented landscapes and how this information can be used to guide mitigation actions whose goal is to connect isolated populations.

An integration method to predict the impact of urban land use change on green space connectivity under different development scenarios using a case study of Nanjing, China

Urbanization leads to land use change and fragmentation of green patches, affecting natural habitats and their connectivity. Scientific prediction and analysis of the impact of future land use change on green space connectivity are an effective tool for planning and evaluating urban sustainable development, especially for ecological protection in rapidly developing areas. In this study, an integrated method is proposed that uses the CA-Markov method and combines a morphological spatial pattern analysis (MSPA) with a graph theory analysis to jointly evaluate the impact of land use change on the habitat connectivity index under different urban development scenarios from two aspects of structural and functional connectivity. Using China's rapidly developing Nanjing as the study area, the land use changes under four scenarios in 2030 are forecast, and the connectivity index is analyzed. The results showed that only under the ecological land protection scenario will forest and grassland increase, but the strong barrier effect is still brought about by urban expansion from the analysis of the structural connectivity. At the level of functional connectivity, we identified the important connecting patches and future change trends of species with different diffusion distances. In addition, we identified the key connecting patches (i.e., stepping stones) and changes and suggested giving priority to the protection of these patches. This method can be applied to other rapidly developing cities, and the conclusions can be used as a spatial explicit tool for urban green space and land use planning.