Identifying barriers. Studies focused on understanding barriers to gene flow were among the first to incorporate landscape data in genetic analyses (Manel et al. 2003). Initial applications were directed toward identification of major breaks in genetic structure and visual association with landscape features (Monmonier 1973; Dupanloup et al. 2002). More recently, identification of barriers been extended to address more specific ecological and conservation questions, including: identifying linear features that act as barriers to gene flow, barriers in aquatic systems, cryptic barriers and barriers in relation to disease spread.
Linear features, such as rivers, mountain ridges, roads and anthropogenic habitat fragmentation, are the most obvious testable barriers to gene flow and frequently considered in landscape genetics studies. The effects of putative barriers on gene flow varies by species, ranging from no discernable effect on genetic structure (Gauffre et al. 2008), to extreme effects (Funk et al. 2005). Rivers have been identified as a barrier to gene flow in several taxonomic groups including small mammals, turtles and deer (Lugon-Moulin & Hausser 2002; Coulon et al. 2006; Mockford et al. 2007), but may actually facilitate gene flow in amphibians (Spear et al. 2005) and transgenic crops (Cureton et al. 2006). Major ridgelines are barriers to gene flow in amphibians (Funk et al. 1999, 2005) and meso-mammals (Zalewski et al. 2009), but may not be barriers to small mammals (Lugon-Moulin & Hausser 2002). Unlike these natural features which vary in effect by taxonomic group, roads and other human development have been identified as barriers across several taxonomic groups, including carnivores (Riley et al. 2006; Millions & Swanson 2007), ungulates (Epps et al. 2005; Kuehn et al. 2007; Perez-Espona et al. 2008) and amphibians (Manier & Arnold 2006; Murphy et al. 2010).
Clearly defined barriers are also present in aquatic systems. Dams are major barriers to gene flow in aquatic dependent species such as fish, even when passage systems are in place (Taylor et al. 2003; Wofford et al. 2005; Deiner et al. 2007; Raeymaekers et al. 2008; Skalski et al. 2008). In addition, natural physical barriers such as waterfalls were important for shaping genetic structure patterns in yellow perch (Perca flavescens; Leclerc et al. 2008), westslope cutthroat trout (Taylor et al. 2003), bull trout, (Salvelinus confluentus; Costello et al. 2003; Whiteley et al. 2006), coastal cutthroat trout (Oncorhynchus clarki clarki;Wofford et al. 2005), mountain whitefish (Prosopium williamsoni; Whiteley et al. 2006) and steelhead and rainbow trout (Oncorhynchus mykiss; Deiner et al. 2007). Interestingly, anthropogenic development along waterways can also be a barrier to gene flow, possibly because of changes in water temperature or chemistry (Wilcock et al. 2007). In addition, fragmentation of breeding habitat may have an additive effect with physical structures such as dams (Leclerc et al. 2008) and roads (Balkenhol & Waits 2009).
Barriers to gene flow can also be less obvious than distinct linear features or physical structures. Unsuitable natural habitat may be a significant barrier to gene flow (McRae & Beier 2007), as in the case of savanna habitats that fragment lemur populations (Radespiel et al. 2008) or dry grassland habitat fragmenting salamander populations (Rittenhouse & Semlitsch 2006). In addition, climate gradients have been shown to be cryptic barriers to gene flow, producing unexpected sharp breaks in genetic structure possibly because of species’ environmental tolerance limits. This phenomenon has been observed in the apparently continuous habitat of Northern temperate regions (Geffen et al. 2004; Carmichael et al. 2007) and in salt-water habitat where cetaceans have abrupt discontinuities in genetic structure (Fontaine et al. 2007). In some cases, weakly genetically structured populations may be separated by the confluence of several features of low permeability, as in the combined effects of a river, highways and canals on roe deer (Capreolus capreolus) gene flow in France (Coulon et al. 2006).
Identification of barriers to gene flow can also help predict the geographic nature of disease spread or assist with management. Barriers can quarantine disease within a limited geographic area (Rees et al. 2008). Rivers restrict spread of chronic wasting disease in deer (Blanchong et al. 2008) and control rabies outbreaks by acting as natural barriers to raccoon dispersal (Real & Biek 2007; Blanchong et al. 2008; Cullingham et al. 2009). In the example of rabies, understanding the spatio-temporal nature of disease occurrence combined with identification of barriers can result in effective vaccination strategies. That is, because rivers act as barriers, control strategies were focused on using vaccinated raccoon bait in areas where landscape variables facilitated dispersal and gene flow (Real & Biek 2007).
Although identifying barriers to gene flow provides insights into species’ ecology, understanding complex relationships between gene flow and landscape condition requires more in-depth analyses (Cushman et al. 2006; Murphy et al. 2008; Cushman & Landguth 2010). For example, in grey wolves no statistical relationship was seen between genetic distance and hypothesized barriers, but a statistically significant relationship was observed between genetic distance and continuous landscape variables (Pilot et al. 2006). As many high-dispersing species may not have obvious barriers to gene flow over the spatial scale of typical landscape genetic studies, it is important to identify the relative influence of landscape variables on the spatial distribution of genetic variation.
Influence of landscape variables and configuration on genetic variation. In terrestrial systems, topographic relief was shown to negatively influence gene flow in many species, including most species of amphibians (Funk et al. 2005; Spear et al. 2005; Spear & Storfer 2008; Murphy et al. 2010), small mammals including voles (Berthier et al. 2004), red deer (Cervus elaphus; Pérez-Espona et al. 2008), wax palms (Ceroxylon echinulatum; Trénel et al. 2008) and golden-brown mouse lemurs (Microcebus ravelobensis; Radespiel et al. 2008). Elevational gradients resulted in reduced gene flow among high and low altitude populations of Columbia spotted frogs (Rana luteiventris; Funk et al. 2005) and long-toed salamanders (Ambystoma macrodactylum; Giordano et al. 2007). However, landscape features had little effect on gene flow and population genetic structure of several species of birds, including black-capped vireos (Vireo atricapilla; Barr et al. 2008) and Darwin’s finches (Petren et al. 2005). In red-tailed hawks (Buteo jamaicensis), however, some populations appeared separated by mountain ranges such as the Sierra Nevada, while other mountain ranges had little effect (Hull et al. 2008). Habitat preferences affected gene flow more appreciably, as evidenced by limited dispersal between Mediterranean central California habitats and more xeric inland west and southern habitats (Hull et al. 2008).
Seasonality can also affect gene flow. As an example, fisher (Martes pennant) population connectivity was inversely correlated with snow depth (Garroway et al. 2008). In the alpine snowbed herb, Primula cunefolia, flowering time at different altitudes was significantly affected by timing of snowmelt, and flowering segregation, in turn, led to fine-scale spatial genetic structuring (Hirao & Kudo 2008). Similar results were also seen in two of three snowbed herb species in a previous study (Hirao & Kudo 2004).
Effects of habitat fragmentation and land use change have been the focus of a large number of terrestrial landscape genetic studies, and fragmentation has reduced gene flow in many species, including: formica ants (Mäki-Petäys et al. 2005), common frogs (Rana temporaria; Johannson et al. 2005), alpine butterflies (Parnassius smintheus; Keyghobadi et al. 2005) and golden cheeked warblers (Dendroica chrysoparia), but not Amazon liverworts (Radula flaccid; Zartman et al. 2006), the tree species, Sorbus aucuparia (Bacles et al. 2004) or the yellow-footed antechinus (Antechinus flavipes; Lada et al. 2008). An example of one study suggesting negative effects of habitat fragmentation showed lower genetic variation in habitat patches less than 35 years old versus those greater than 35 years old in the forest herb, Primula elatior (Jacquemyn et al. 2004). A study in fragments vs. closed forest of Andean oak (Quercus humboldtii) showed detrimental effects of contemporary habitat on genetic variability (Fernandez & Sork 2007). Latitude and spatial other factors affected the distribution of genetic variation in the endangered California valley oak (Quercus lobata Née), leading to the recommendation of maintenance of corridors in reserve design and prioritization of genetically diverse populations to maintain evolutionary potential (Grivet et al. 2008).
Fragmented landscapes also affected dispersal movements of species. For example, gene flow among European roe deer populations more closely followed woodland corridors than disturbed habitat patches in a fragmented landscape (Coulon et al. 2004). Similarly, gene flow of Rocky Mountain tailed frogs (Ascaphus montanus) more closely followed riparian corridors in deforested landscapes than naturally regenerated post-fire landscapes (Spear & Storfer 2010). In addition, although habitat fragmentation did not appreciably alter gene flow in the yellow-footed antechinus, dispersal was reduced through agricultural landscapes (Lada et al. 2008). Habitat fragmentation also apparently did not affect gene flow in coyotes (Canis latrans) which were not affected by obvious anthropogenic barriers such as roads; rather gene flow appeared restricted by habitat-specific breaks and natal site fidelity (Sacks et al. 2004). Results of habitat fragmentation for the forest herb, Geum urbanum, were mixed. Gene flow remained high between primary and secondary growth (i.e. post-harvest) forest fragments, but small populations were genetically more diverged than large populations (Venderpitte et al. 2007). Overall, given the variability in effects of habitat fragmentation on genetic variation and gene flow, studies that test the effects of fragmentation should be conducted before conservation and management decisions are implemented.
Landscape genetics studies have also been useful for revealing counterintuitive features that facilitate gene flow. For example, although rivers were thought as an a priori barrier to gene flow among populations of blotched tiger salamanders (Ambystoma tigrinum melanostictum) in Yellowstone National Park, they were actually found to facilitate gene flow (Spear et al. 2005). Upon further consideration, this result made sense because occasional flooding can allow enough dispersal across rivers to positively affect gene flow. Similarly, flooding apparently increased gene flow in the herb, Primula sieboldii (Kitamoto et al. 2005). In addition, counter intuitively, two studies in Yellowstone revealed that post 1988 fire-regenerated habitat facilitated gene flow among tiger salamanders (Spear et al. 2005) and boreal toads (Bufo boreas; Murphy et al. 2010). Although previously thought of as an impediment to gene flow, fire-regenerated shrub habitat probably provides shade closer to the ground than forested habitat, thereby facilitating amphibian dispersal.
In freshwater systems, landscape genetic studies revealed the importance of including both historic and contemporary landscape variables in explanatory models. For example, glacial history was important for explaining current levels of genetic diversity and structure in bull trout (Salvelinus confluentus; Costello et al. 2003), coastal cutthroat trout (Wofford et al. 2005), mountain whitefish (Whiteley et al. 2006), and brook charr (Salvelinus fontinalis; Angers et al. 1999). In brook charr, genetic structure was more consistent with historical hydrological structure predicted based on geomorphological and biogeographical models than current landscape structure (Poissant et al. 2005).
In river and spring systems, landscape genetic models that included the drainage pattern, direction and/or speed of water flow best explained genetic structure patterns in zooplankton (Michels et al. 2001), brook charr (Angers et al. 1999), and aquatic snails (Wilmer et al. 2008). In addition, dynamic and sporadic events such as flooding were critical for explaining genetic structure (Wilmer et al. 2008). The landscape variables of slope, elevation and temperature were important predictor variables in multiple studies. For example, fish populations at higher elevations had lower levels of genetic diversity and lower levels of gene flow with other populations (Angers et al. 1999; Castric et al. 2001; Narum et al. 2008), and gene flow was negatively correlated with change in elevation between sampling sites for the stream salamander, (Gyrinophilus porphyriticus;Lowe et al. 2006). In addition, water temperature was the best predictor variable explaining genetic structure and variation among Atlantic salmon (Salmo salar) along the North American Atlantic coast (Dionne et al. 2008) and steelhead trout in the Pacific Northwest (Narum et al. 2008).
Anthropogenic impacts were associated with decreased gene flow and genetic diversity in multiple river systems. In particular, dams, tunnels and weirs were found to impede gene flow in yellow perch (Leclerc et al. 2008), westslope cutthroat trout (Taylor et al. 2003), three-spined stickleback (Gasterosteus aculeatus; Raeymaekers et al. 2008), bull trout (Costello et al. 2003) and coastal cutthroat trout (Wofford et al. 2005). Pulp mill impacts were also found to increase genetic structure in redbreast sunfish (Lepomis auritus; Theodorakis et al. 2006) and three-spined stickleback (Raeymaekers et al. 2008). A similar study focused in a coastal saltwater system showed that copper mine waste outputs restricted gene flow among populations of the kelp, Lessonia nigrescens in Chile (Faugeron et al. 2005).
In marine environments, landscape genetic studies have demonstrated that genetic diversity and structure are often best explained by analyses that include ocean currents (blue whiting, Micromesistius poutassou; Was et al. 2008; and kelp bass Paralabrax clathratus, Selkoe et al. 2010) and models of simulated transport of larvae (sea scallop, Placopecten magellanicus; Kenchington et al. 2006, and the intertidal barnacle, Balanus glandula; Galindo et al. 2010). In other studies, ocean temperatures were the most important predictor variable of genetic structure in the sea urchin, Centrostephanus rodgersii (Banks et al. 2007) while both temperature and salinity were key variables for explaining genetic structure patterns in Atlantic cod (Gadus morhua; Case et al. 2005). In a multi-species analysis along the California coast, cumulative kelp cover, a measure of habitat quality, was the most important variable for explaining genetic diversity and structure in kelp bass, Kellet’s whelk (Kelletia kelletii) and California spiny lobster (Panulirus interruptus; Selkoe et al. 2010).
To summarize, the effects of a wide variety of landscape variables on population genetic structure have been considered. Several landscape variables such as elevation, ridgelines and topographic relief limited gene flow in several, but not all terrestrial species. In aquatic species, drainage structure, slope, elevation and temperature were important explanatory variables. Anthropogenic features, such as deforestation, agricultural development, damming and other types of waterway manipulation decreased gene flow or affected movement pathways in some species, but had little to no effect on others. Some features, such as rivers and post-fire regenerated habitats facilitated gene flow, contrary to expectations. Taken together, this wide variety of studies suggest that, while there are some generalities, effects of landscape variables and habitat fragmentation vary among species, highlighting the need for species-specific studies.
Spatial and temporal scales. Determining the relative influence of contemporary vs. historic landscape features on gene flow is critical for understanding processes associated with anthropogenic landscape change (Keyghobadi et al. 2005; Storfer et al. 2007; Pavlacky et al. 2009). Yet, few landscape genetic studies have examined how landscape configuration at different time scales has influenced genetic structure, and therefore it is difficult to make broad conclusions. In addition, studies have defined the scale of ‘historical’ differently, from years (Orsini et al. 2008) to decades (Honnay et al. 2006; Spear & Storfer 2008) to prehistory (Vandergast et al. 2007). The need to account for historic effects in landscape genetic studies is likely highly dependent on both choice of molecular marker and the rate of landscape change, and thus is not necessarily generalizable across species and study areas. Two different methods have primarily been used to correlate current genetic data with reconstructed historic landscapes: correlating genetic data separately with landscape configurations from different time points (Orsini et al. 2008; Spear & Storfer 2008) or by creating regression models based on historic landscape, and then correlating the residuals from that model with the current landscape to account for previously unexplained variation (Vandergast et al. 2007; see also Dyer et al. 2010).
Vandergast et al. (2007) found evidence of both prehistoric and contemporary effects of landscape on Jerusalem cricket (Stenopelmatus mahogani) using mtDNA. Spear & Storfer (2008) examined correlation of current and historic patterns of timber harvest on Coastal tailed frog (Ascaphus truei) gene flow, which correlated strongly with harvest pattern from 20 years ago. This time lag may be due to the speed in which timber harvest can alter landscape structure, as well as the delay detection of a genetic signature of reduced dispersal because of a long generation time in Coastal tailed frogs. Similarly, Orsini et al. (2008) did not find a significant correlation of gene flow among populations of the Glanville fritillary butterfly (Melitaea cinxia) current fragmented landscape fragments; rather, genetic structure was explained better by past events. In contrast, the contemporary landscape explained slightly more variation in genetic differentiation than a reconstructed historic landscape for the rainforest dwelling logrunner (Orthonyx temminckii; Pavlacky et al. 2009). Specifically, landscape heterogeneity facilitated gene flow before European settlement, but contemporary deforestation was shown to be the most important impediment to logrunner gene flow.
The effects of spatial scale have been investigated less frequently in landscape genetics studies. Yet, the spatial scale of variables used, the spatial scale of their influence on the species of interest, and functional scale of the species itself are all important considerations (Storfer et al. 2007). As an example, the relative influence of independent variables at different spatial scales can be tested to establish the most appropriate spatial scale to include in a model (Cushman et al. 2006; Murphy et al. 2010). Murphy et al. (2010) investigated scale of the landscape influencing connectivity for boreal toads (Bufo boreas) in Yellowstone by testing the influence of landscape variables at multiple bandwidths (from 30 to 960 m) connecting sites. They found habitat permeability and topographic roughness influenced population connectivity at fine scales, ridgelines were important at broad scales, and temperature and moisture had effects across spatial scales. In contrast, no effect of spatial scale was found in the spotted frog (Rana luteiventris; Murphy et al. 2010), possibly because of a smaller study area of that in boreal toads. Although small in size, genetic diversity of a beetle (Carabus auratus) was highly correlated with broad-scale availability of grasslands suggesting that functional connectivity does not necessarily operate at small spatial scales for small species (Sander et al. 2006).
In sum, few landscape genetics studies have been conducted across a variety of spatial scales or considered effects of temporal variation. Yet, with recent technological advances, we are increasingly able to collect large amounts of molecular data in short periods of time. Effects of spatial scale should be considered because the limited studies that do exist suggest different landscape features affect genetic variation at different spatial scales. As a result, studies that occur in a single area or in across a limited spatial scale may be limited in their inference regarding the effects of landscape variables on genetic connectivity. In addition, with increased global development, land use change and climate change, understanding effects of temporal scale variation will become increasingly important. As an example, knowledge of the effects of previous development actions may allow simulation testing of landscape genetic effects of alternative proposed future development actions to facilitate management recommendations.