Considering spatial and temporal scale in landscape‐genetic studies of gene flow

Abstract

Landscape features exist at multiple spatial and temporal scales, and these naturally affect spatial genetic structure and our ability to make inferences about gene flow. This article discusses how decisions about sampling of genotypes (including choices about analytical methods and genetic markers) should be driven by the scale of spatial genetic structure, the time frame that landscape features have existed in their current state, and all aspects of a species' life history. Researchers should use caution when making inferences about gene flow, especially when the spatial extent of the study area is limited. The scale of sampling of the landscape introduces different features that may affect gene flow. Sampling grain should be smaller than the average home-range size or dispersal distance of the study organism and, for raster data, existing research suggests that simplifying the thematic resolution into discrete classes may result in low power to detect effects on gene flow. Therefore, the methods used to characterize the landscape between sampling sites may be a primary determinant for the spatial scale at which analytical results are applicable, and the use of only one sampling scale for a particular statistical method may lead researchers to overlook important factors affecting gene flow. The particular analytical technique used to correlate landscape data and genetic data may also influence results; common landscape-genetic methods may not be suitable for all study systems, particularly when the rate of landscape change is faster than can be resolved by common molecular markers.

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

“…Despite the potential implications, the output from functional connectivity analyses is often omitted from conservation plans because they are not carried out in ways that are conducive to management objectives (Keller, Holderegger, van Strien, & Bolliger, 2015). For example, both the spatial extent of the study area and the resolution of landscape variables can influence the estimated importance of landscape features to functional connectivity (Anderson et al., 2010), which makes extrapolation across scales and comparison among studies challenging. Thus, conducting research at scales too large or too small for planning tools will hinder implementation (Keller et al., 2015).…”

Beyond the snapshot: Landscape genetic analysis of time series data reveal responses of American black bears to landscape change

“…(2011) found higher correlations between land cover and black bear gene flow in landscapes where forest cover was highly fragmented compared to landscapes of contiguous forest. Yet, the absence of a landscape effect on 2002 SGS may reflect a time lag between when landscape change occurs and when SGS response to landscape change becomes evident (Anderson et al., 2010; Epps & Keyghobadi, 2015). However, when dispersal rates and distances are large, as exhibited in the NLP black bear population (Draheim, 2015; Draheim et al., 2016; Moore et al., 2014), shorter or no time lags are expected (Epps & Keyghobadi, 2015).…”

“…However, when dispersal rates and distances are large, as exhibited in the NLP black bear population (Draheim, 2015; Draheim et al., 2016; Moore et al., 2014), shorter or no time lags are expected (Epps & Keyghobadi, 2015). Also, legacy effects of historical landscape processes may be reduced using genetic markers with higher mutations rates (i.e., microsatellites) that reach mutation–drift equilibrium quickly and genetic measures that respond rapidly to changes in connectivity (e.g., Dps) (Anderson et al., 2010). A number of simulation studies have found landscape effects on SGS could be detected with relatively short time spans (Cushman & Landguth, 2010; Landguth et al., 2010; Murphy, Evans, Cushman, & Storfer, 2008).…”

“…However, further inference about the effects of specific biological and ecological processes that shape SGS is often limited because sampling is typically conducted at a single point in time (Goetze, Andrews, Peijnenburg, Portner, & Norton, 2015). These “snapshots” of data only measure populations in their current state (Anderson et al., 2010; Martensen, Saura, & Fortin, 2017; Schwartz, Luikart, & Waples, 2007). Therefore, inferences can be problematic when studying long‐lived and iteroparous species such as black bears ( Ursus americanus ) that are sensitive to landscape features (i.e., land cover) that are expected to undergo future modification (Cushman, McKelvey, Hayden, & Schwartz, 2006; Cushman, Wasserman, Landguth, & Shirk, 2013).…”

“…Despite these challenges, time series landscape genetic studies are valuable as natural ecosystems are spatially heterogeneous and landscape composition/configurations can change over time, sometimes drastically so, due to natural and anthropogenic factors (Bolliger et al., 2010; Spear et al., 2010). Furthermore, there may be a discord between the time genetic processes occur (over generations) and the landscape change that reflect contemporary landscape effects (Anderson et al., 2010). Explicitly adding a temporal component in landscape genetics analyses may provide valuable additional resolution on directional trends in gene flow (Martensen et al., 2017; Wagner & Fortin, 2013).…”