Multi-species genetic connectivity in a terrestrial habitat network

Abstract

BackgroundHabitat fragmentation reduces genetic connectivity for multiple species, yet conservation efforts tend to rely heavily on single-species connectivity estimates to inform land-use planning. Such conservation activities may benefit from multi-species connectivity estimates, which provide a simple and practical means to mitigate the effects of habitat fragmentation for a larger number of species. To test the validity of a multi-species connectivity model, we used neutral microsatellite genetic datasets of Canada lynx (Lynx canadensis), American marten (Martes americana), fisher (Pekania pennanti), and southern flying squirrel (Glaucomys volans) to evaluate multi-species genetic connectivity across Ontario, Canada.ResultsWe used linear models to compare node-based estimates of genetic connectivity for each species to point-based estimates of landscape connectivity (current density) derived from circuit theory. To our knowledge, we are the first to evaluate current density as a measure of genetic connectivity. Our results depended on landscape context: habitat amount was more important than current density in explaining multi-species genetic connectivity in the northern part of our study area, where habitat was abundant and fragmentation was low. In the south however, where fragmentation was prevalent, genetic connectivity was correlated with current density. Contrary to our expectations however, locations with a high probability of movement as reflected by high current density were negatively associated with gene flow. Subsequent analyses of circuit theory outputs showed that high current density was also associated with high effective resistance, underscoring that the presence of pinch points is not necessarily indicative of gene flow.ConclusionsOverall, our study appears to provide support for the hypothesis that landscape pattern is important when habitat amount is low. We also conclude that while current density is proportional to the probability of movement per unit area, this does not imply increased gene flow, since high current density tends to be a result of neighbouring pixels with high cost of movement (e.g., low habitat amount). In other words, pinch points with high current density appear to constrict gene flow.Electronic supplementary materialThe online version of this article (10.1186/s40462-017-0112-2) contains supplementary material, which is available to authorized users.

Human footprint differentially impacts genetic connectivity of four wide-ranging mammals in a fragmented landscape

“…It is no surprise therefore that research on connectivity conservation has markedly increased over the last decade (Correa Ayram et al, 2016). However, connectivity studies in mammals have largely focused on a single focal species, usually a large predator (Beier et al, 2008; Segelbacher et al, 2010; McRae et al, 2012; Dudaniec et al, 2013; Joshi et al, 2013; execept in a few recent cases- Dudaniec et al, 2016; Wultsch et al, 2016; Marrotte et al, 2017). Conservation strategies based on information from a single species may not effectively capture varied ecological requirements for dispersal of other sympatric species (Brodie et al, 2015; Gangadharan et al, 2016).…”

“…While opinion articles emphasize the importance of multi-species landscape genetics studies when developing guidelines for landscape level conservation and management (Keller et al, 2015; Richardson et al, 2016), relatively few studies have compared connectivity for multiple species (eg. Engler et al, 2014; Dudaniec et al, 2016; Wultsch et al, 2016; Marrotte et al, 2017).…”

Human footprint differentially impacts genetic connectivity of four wide‐ranging mammals in a fragmented landscape

“…It is no surprise therefore that research on connectivity conservation has markedly increased over the last decade (Correa Ayram, Mendoza, Etter, & Salicrup, 2016). However, connectivity studies in mammals have largely focused on a single focal species, usually a large predator (Beier, Majka, & Spencer, 2008;Segelbacher et al, 2010;McRae, Hall, Beier, & Theobald, 2012;Dudaniec et al, 2013;Joshi, Vaidyanathan, Mondol, Edgaonkar, & Ramakrishnan, 2013; except in a few recent cases- Dudaniec et al, 2016;Wultsch et al, 2016;Marrotte et al, 2017). Conservation strategies based on information from a single species may not effectively capture varied ecological requirements for dispersal of other sympatric species (Brodie et al, 2015;Gangadharan, Vaidyanathan, & Clair, 2016).…”

“…For long-term persistence, areas that facilitate dispersal of multiple species need to be protected. While opinion articles emphasize the importance of multispecies landscape genetics studies when developing guidelines for landscape-level conservation and management (Keller, Holderegger, Strien, & Bolliger, 2015;Richardson, Brady, Wang, & Spear, 2016), relatively few studies have compared connectivity for multiple species (e.g., Dudaniec et al, 2016;Engler, Balkenhol, Filz, Habel, & Rödder, 2014;Marrotte et al, 2017;Wultsch et al, 2016).…”

Operationalizing ecological connectivity in spatial conservation planning with Marxan Connect

“…In turn, methods for estimating ecological connectivity are also advancing, and new conservation planning tools are quickly emerging to capitalize on these new data and methods (Saura & Pascual-Hortal 2007; Beger et al 2010b; White et al 2014). Examples of connectivity data that have been incorporated in conservation applications include: gene flow (Beger et al 2014; Marrotte et al 2017), dynamic distributions and migratory bottlenecks on migratory pathways (Iwamura et al 2013; Runge et al 2016), maximizing larval flow (Magris et al 2016; D’Aloia et al 2017), ontogenetic shifts in habitat use (Brown et al 2016; Weeks 2017), ensuring the movement of adult individuals pathways (Beger et al 2015; Mazor et al 2016; Pereira, Saura & Jordán 2017; Zeller et al 2018), and maintaining fisheries benefits (Daigle, Monaco & Elgin 2017; Krueck et al 2017). Despite these efforts, connectivity is not commonly being incorporated in on-the-ground decision making for planning (Beger et al 2010a; Barnes et al 2018; Balbar, unpublished data).…”