Identification of landscape features influencing gene flow: How useful are habitat selection models?

Abstract

Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modeling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider explicit assumptions and spatial scales of measurement. We calculated pairwise genetic distance among 301 Dall's sheep (Ovis dalli dalli) in southcentral Alaska using an intensive noninvasive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important during summer (rugged terrain, mid‐range elevation) were not influential to effective dispersal. This work underscores that consideration of both habitat selection and landscape genetics models may be useful in developing management strategies to both meet the immediate survival of a species and allow for long‐term genetic connectivity.

Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range‐wide gene flow in sage‐grouse

“…These results suggest that thresholds in habitat indices may be an effective approach for other species. Yet, in some cases, continuous habitat surfaces have performed better than discrete values (Hagerty et al., 2011), or individual landscape predictors have performed better than combined habitat indices altogether (Roffler et al., 2016; Wasserman, Cushman, Schwartz, & Wallin, 2010). A weak association between habitat and functional connectivity is likely when there are large differences between daily use and dispersal habitat or when one or a few landscape components are the primary drivers of functional connectivity.…”

“…(2010) and Roffler et al. (2016) only tested habitat resistance surfaces with a direct linear relationship between habitat and landscape resistance. In contrast, discrete values (tested here and by Wang et al., 2008 and by Row et al., 2010) or the exponential relationships between habitat and resistance (Row et al., 2015) often appear to provide better model fit.…”

“…It is also hard to compare between studies when different approaches are used to derive resistance surfaces. For example, both Wasserman et al (2010) and Roffler et al (2016) only tested habitat resistance surfaces with a direct linear relationship between habitat and landscape resistance.…”

“…If structural connectivity quantified using habitat indices proves to be a strong predictor of functional connectivity (Row, Blouin-Demers, & Lougheed, 2010;Row et al, 2015;Wang, Yang, Bridgman, & Lin, 2008), identifying regions of importance to maintaining connectivity and predicting impacts from changes to habitat suitability become more clear (Roever et al, 2013). However, in some cases, the relationship between habitat indices and functional connectivity can be weak (Roffler et al, 2016). This lack of relationship is likely the result of a mismatch between dispersal and the habitats modeled in the indices (Ribe, Morganti, Hulse, & Shull, 1998;Spear, Balkenhol, Fortin, McRae, & Scribner, 2010).…”

Quantifying the climatic niche of symbiont partners in a lichen symbiosis indicates mutualist‐mediated niche expansions

“…To characterize the geographic distribution of molecular variance among these haplotypes, we used a regression-based framework to describe the genetic distances -using Kimura's 2-parameter distance -with spatial predictor variables that were generated using Moran's eigenvector maps (MEM; Borcard andLegendre 2002, Griffith andPeres-Neto 2006), as implemented in the 'MEMGENE' R package (Galpern et al 2014). This approach detects spatial neighborhoods in distancebased data and has been widely used in spatial ecological and genetic contexts (Dray et al 2012, Manel et al 2012, Wagner and Fortin 2013, Roffler et al 2016). The resulting scores for each individual haplotype on the MEM-variables were then used to visualize the spatial genetic structure in our data.…”