Circuit theory predicts gene flow in plant and animal populations

Abstract

Maintaining connectivity for broad-scale ecological processes like dispersal and gene flow is essential for conserving endangered species in fragmented landscapes. However, determining which habitats should be set aside to promote connectivity has been difficult because existing models cannot incorporate effects of multiple pathways linking populations. Here, we test an ecological connectivity model that overcomes this obstacle by borrowing from electrical circuit theory. The model vastly improves gene flow predictions because it simultaneously integrates all possible pathways connecting populations. When applied to data from threatened mammal and tree species, the model consistently outperformed conventional gene flow models, revealing that barriers were less important in structuring populations than previously thought. Circuit theory now provides the best-justified method to bridge landscape and genetic data, and holds much promise in ecology, evolution, and conservation planning.Gulo gulo ͉ isolation by resistance ͉ landscape connectivity ͉ Swietenia macrophylla ͉ landscape genetics P reserving and restoring connectivity for broad-scale ecological processes, such as dispersal and gene flow, has become a major conservation priority (1, 2). Conservation organizations are investing considerable resources-and asking governments to do the same-to set aside land to promote connectivity (3). A major impediment to this goal is the difficulty in predicting how different land use, climate change, or reserve design scenarios will affect connectivity, and conservation planning decisions are often made without quantifying benefits for the ecological processes they are meant to conserve. If scarce conservation dollars are to be spent effectively, conservation biologists need clear, efficient, and reliable tools relating landscape composition and pattern to important ecological processes (4).Gene flow is a critical ecological process with conservation benefits ranging from promoting the persistence of small populations to spreading adaptive traits in changing environments (5-8). Because of these important ecological and evolutionary roles, a new and rapidly growing field-landscape genetics-is primarily dedicated to understanding and predicting how landscape characteristics affect gene flow (5). By combining genetic, computational, and spatial analytic tools unavailable a decade ago, the interdisciplinary field has yielded insights relevant not only to conservation (e.g., refs. 8 and 9), but to fields such as evolution (10), infectious disease ecology (11), and population ecology (12).Yet progress in all of these fields has been hampered by a lack of models capable of predicting gene flow from landscape structure. As a result, typical landscape genetic analyses simply detect genetic discontinuities and propose ad hoc explanations based on coincident landscape features, rather than testing a priori predictions of how such features are expected to influence genetic structure. This hinders hypothesis testing and leaves conserva...

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

“…Landscape surfaces were coded such that each pixel was assigned a value representing resistance to gene flow. Subsequently, we used the gdistance package in R to quantify pairwise resistance between two groups as the expected correlate to the amount of gene flow between two groups (i.e., higher pairwise resistance equates to lower expected movement; McRae, 2006; McRae & Beier, 2007). Pairwise effective resistance values are a function of the flow of current—representative of gene flow—and therefore incorporate geographic distance (McRae & Beier, 2007).…”

“…All raster surfaces were resampled using bilinear interpolation to a resolution of 1.2 km. Overall, changes in resolution should not have a strong effect on pairwise resistance values (McRae & Beier, 2007; Row et al., 2015). In all cases, we compare results to resistances derived from an undifferentiated landscape in which all cells had a resistance of one.…”

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

“…CIRCUITSCAPE incorporates circuit theory to quantify the total landscape resistance between individuals via multiple potential paths of least resistance (McRae & Beier, 2007). We used Mantel (Mantel, 1967) and partial Mantel tests (Smouse, Long, & Sokal, 1986) to correlate genetic and geographic/resistance distances.…”

Rural avenues as dispersal corridors for the vulnerable saproxylic beetle Elater ferrugineus in a fragmented agricultural landscape

“…This approach takes into account the effect of all alternative pathways between habitat patches, which is especially appropriate when the specific aim is to assess gene flow across a landscape over many generations. Several studies indicated that resistance distances may allow for better assessment of genetic isolation-by-distance than the usual Euclidean or least-cost-path distance because the metric is based on assumptions about the impact of many alternative routes in the entire landscape on the dispersal of individuals, and consequently on the chances of gene flow (McRae and Beier 2007;Schwartz et al 2009;Lozier et al 2013). Despite the theoretical superiority of isolation-by-resistance approach over distance measures assuming a single optimal path, the performance of the these approaches have rarely been evaluated with empirical data sets at finer spatial scales (Row et al 2010).…”