Integrating GIS and homing experiments to study avian movement costs

Forest cover reduction may affect movements of forest animals, but resistance to animal movements in and out of forests remains unknown despite its importance for modelling. We tested whether ovenbird (Seiurus aurocapilla), a forest-interior songbird, responds similarly to the amount of forest cover while moving locally (~2 km) and over entire landscapes (~25 km). We compared spatially-explicit simulations to field data to address the issue of resistance to movement in open areas. We caught, banded and translocated 143 territorial males 0.8–27 km away from their territory early in the breeding season. Seventy-eight percent and 50% of translocated males returned (homed) within 10 days following “local” and “landscape” translocations respectively. Independent of translocation distance, homing times increased with decreasing forest in the landscape. With a Geographic Information System (GIS), we simulated “least-cost” paths that homing ovenbirds would ideally take, when resistance to movement in open areas ranged 1–1000 times the resistance to movement in forest. The length, the cumulative cost, and variability of simulated least-cost movement paths increased with increasing resistance in open areas. With landscape translocations, least-cost path length explained homing time better than Euclidean distance, and based on an information-theoretic approach, resistance to movement was estimated to be 27 times greater in open areas than in forests (95% confidence interval: 16–45). However, least-cost path length did not perform better than Euclidean distance with local translocations, and the cumulative cost of least-cost paths was not associated to homing time in either translocation scale. We conclude that resistance to animal movements in open areas can be addressed by a combination of GIS modelling and translocation experiments, and is between one and two orders of magnitude greater than resistance to movements in forests, in the case of ovenbirds.     

Least-cost modelling on irregular landscape graphs

Least-cost modelling is becoming widely used in landscape ecology to examine functional connectivity. Traditionally the least-cost modelling algorithm creates a regularly structured landscape graph for connectivity analysis by converting all the cells from a cost-surface into vertices in a landscape graph. However, use of a regular landscape graph is problematic as it: contains a great deal of redundant information that in turn increases processing times, is constructed in a deterministic manner that precludes examination of the effects of graph structure on connectivity measures, and is known to produce results with directional bias. I present, and provide Python code for, an algorithm to produce an irregular landscape graph from a cost-surface. Tests demonstrate that comparable results to those of the traditional regular landscape graph approach can be achieved, while at the same time reducing computational expense, enabling variations in graph structure to be incorporated into an analysis, and avoiding directional bias. Therefore, this approach may allow for more robust ecological decision-making when examining matters of functional connectivity using least-cost modelling.     

Circuit theory emphasizes the importance of edge-crossing decisions in dispersal-scale movements of a forest passerine

Measuring landscape connectivity in ways that reflect an animal’s propensity or reluctance to move across a given landscape is key for planning effective conservation strategies. Resistance distance, based on circuit theory, is one such measure relevant for modeling how broad-scale animal movements over long time periods may lead to gene flow across the landscape. Despite the success of circuit theory in landscape genetic studies, its applicability to model finer-scale processes such as the movement patterns of individual animals within their breeding grounds (e.g., while prospecting for territories) has yet to be tested. Here, we applied both circuit models and least-cost models to understand the relationship between landscape connectivity and return time of Ovenbirds (Seiurus aurocapilla) that had been translocated at least 20 km from their home territory near Québec City, Canada. Using an iterative optimization process, we derived resistance values for three cover types (forest, edge, and open) that resulted in resistance distance values that best explained Ovenbird return times. We also identified the cover-type resistance values that yielded length of least-cost path estimates that best explained return times of the translocated birds. The circuit theory and least-cost path methods were equally supported by the data despite being based on different sets of resistance values. The optimal resistance values for calculating resistance distance indicated that for Ovenbirds, traversing a given distance of edge habitat presented a substantially greater resistance than that of open areas. On the other hand, optimized resistances of edge and open were very similar for calculating length of least-cost path. The circuit theory approach suggested that for an Ovenbird moving through fragmented habitat, the number of forest-open transitions (i.e., edge-crossings) that an individual must make is critical to understanding return times after translocation. The least-cost path approach, on the other hand, suggested that the birds strongly avoid all open areas, regardless of size. Circuit theory offers an important new approach for understanding landscapes from the perspective of individuals moving within their breeding range, at finer spatial scales and shorter time scales than have been previously considered.     

Finding the functional grain: comparing methods for scaling resistance surfaces

The influence of landscape features on the movement of an organism between two point locations is often measured as an effective distance. Typically, raster models of landscape resistance are used to calculate effective distance. Because organisms may experience landscape heterogeneity at different scales (i.e. functional grains), using a raster with too fine or too coarse a spatial grain (i.e. analysis grain) may lead to inaccurate estimates of effective distance. We adopted a simulation approach where the true functional grain and effective distance for a theoretical organism were defined and the analysis grains of landscape connectivity models were systematically changed. We used moving windows and grains of connectivity, a recently introduced landscape graph method that uses an irregular tessellation of the resistance surface to coarsen the landscape data. We then used least-cost path metrics to measure effective distance and found that matching the functional and analysis grain sizes was most accurate at recovering the expected effective distance, affirming the importance of multi-scale analysis. Moving window scaling with a maximum function (win.max) performed well when the majority of landscape structure influencing connectivity consisted of high resistance features. Moving window scaling with a minimum function (win.min) performed well when the relevant landscape structure consisted of low resistance regions. The grains of connectivity method performed well under all scenarios, avoiding an a priori choice of window function, which may be challenging in complex landscapes. Appendices are provided that demonstrate the use of grains of connectivity models.     

Maintaining or restoring connectivity of modified landscapes: evaluating the least-cost path model with multiple sources of ecological information

Habitat connectivity is an important element of functioning landscapes for mobile organisms. Maintenance or creation of movement corridors is one conservation strategy for reducing the negative effects of habitat fragmentation. Numerous spatial models exist to predict the location of movement corridors. Few studies, however, have investigated the effectiveness of these methods for predicting actual movement paths. We used an expert-based model and a resource selection function (RSF) to predict least-cost paths of woodland caribou. Using independent data for model evaluation, we found that the expert-based model was a poor predictor of long-distance animal movements; in comparison, the RSF model was effective at predicting habitat selection by caribou. We used the Path Deviation Index (PDI), cumulative path cost, and sinuosity to quantitatively compare the spatial differences between inferred caribou movement paths and predicted least-cost paths, and quasi-random null models of directional movement. Predicted movement paths were on average straighter than inferred movement paths for collared caribou. The PDI indicated that the least-cost paths were no better at predicting the inferred paths than either of two null models—straight line paths and randomly generated paths. We found statistically significant differences in cumulative cost scores for the main effects of model and path type; however, post-hoc comparisons were non-significant suggesting no difference among inferred, random, and predicted least cost paths. Paths generated from an expert based cost surface were more sinuous than those premised on the RSF model, but neither differed from the inferred path. Although our results are specific to one species, they highlight the importance of model evaluation when planning for habitat connectivity. We recommend that conservation planners adopt similar techniques when validating the effectiveness of movement corridors for other populations and species.     

Improved connectivity analysis using multiple low-cost paths to evaluate habitat for the endangered San Martin titi monkey (Plecturocebus oenanthe) in north-central Peru

Context

Graph-theoretic evaluations of habitat connectivity often rely upon least-cost path analyses to evaluate connectedness of habitat patches, based on an underlying cost surface. We present two improvements upon these methods.

Objectives

As a case study to test these methods, we evaluated habitat connectivity for the endangered San Martin titi monkey (Plecturocebus oenanthe) in north-central Peru, to prioritize habitat patches for conservation.

Methods

First, rather than using a single least-cost path between habitat patches, we analyzed multigraphs made up of multiple low-cost paths. This allows us to differentiate between patches connected through a single narrow corridor, and patches connected by a wide swath of traversable land. We evaluate potential movement pathways by iteratively removing paths and recomputing connectivity metrics. Second, instead of performing a sensitivity analysis by varying costs uniformly across the landscape, we generated landscapes with spatially varying costs.

Results

This approach produced a more informative assessment of connectivity than standard graph analyses. Of the 4340 habitat patches considered across the landscape, we identified the most important 100, those frequently ranked highly through repeated network modifications, for multiple metrics and cost surfaces.

Conclusions

These methods represent a novel approach for assessing connectivity, better accounting for spatial configurations of habitat patches and uncertainty in cost surfaces. The ability to identify habitat patches with more possible routes to other patches is of interest for resiliency planning and prioritization in the face of continued habitat loss and climate change. These methods should be broadly applicable to conservation planning for other wildlife species.

     

Incorporating landscape elements into a connectivity measure: a case study for the Speckled wood butterfly (Pararge aegeria L.)

In spatial studies of populations, Euclidean distance is commonly used to measure the structural connectivity between habitat patches. The role of the matrix on patch connectivity is thereby ignored. However, the importance of the matrix for (dispersal) movement is increasingly being acknowledged. Our study compared the cost-distance measure with the Euclidean distance. The cost-distance is a simple GIS-calculated connectivity measure that incorporates the resistance of the landscape matrix to movement behaviour. We used presence-absence data from a field study on the Speckled wood butterfly in two Belgian landscapes. Logistic regression revealed that the cost-distance measure had a significantly better predictive power than the Euclidean distance. This result was consistent for all the six sets of different matrix resistance values. In our study the cost-distance proves to be a better connectivity measure than the Euclidean distance. This revised version was published online in July 2006 with corrections to the Cover Date.     

Beyond the least-cost path: evaluating corridor redundancy using a graph-theoretic approach

The impact of the landscape matrix on patterns of animal movement and population dynamics has been widely recognized by ecologists. However, few tools are available to model the matrix’s influence on the length, relative quality, and redundancy of dispersal routes connecting habitat patches. Many GIS software packages can use land use/land cover maps to identify the route of least resistance between two points—the least-cost path. The limitation of this type of analysis is that only a single path is identified, even though alternative paths with comparable costs might exist. In this paper, we implemented two graph theory methods that extend the least-cost path approach: the Conditional Minimum Transit Cost (CMTC) tool and the Multiple Shortest Paths (MSPs) tool. Both methods enable the visualization of multiple dispersal routes that, together, are assumed to form a corridor. We show that corridors containing alternative dispersal routes emerge when favorable habitat is randomly distributed in space. As clusters of favorable habitat start forming, corridors become less redundant and dispersal bottlenecks become visible. Our approach is illustrated using data from a real landscape in the Brazilian Atlantic forest. We explored the effect of small, localized disturbance on dispersal routes linking conservation units. Simulated habitat destruction caused the appearance of alternative dispersal routes, or caused existing corridors to become narrower. These changes were observed even in the absence of significant differences in the length or cost of least-cost paths. Last, we discuss applications to animal movement studies and conservation initiatives.     

Making molehills out of mountains: landscape genetics of the Mojave desert tortoise

Heterogeneity in habitat often influences how organisms traverse the landscape matrix that connects populations. Understanding landscape connectivity is important to determine the ecological processes that influence those movements, which lead to evolutionary change due to gene flow. Here, we used landscape genetics and statistical models to evaluate hypotheses that could explain isolation among locations of the threatened Mojave desert tortoise (Gopherus agassizii). Within a causal modeling framework, we investigated three factors that can influence landscape connectivity: geographic distance, barriers to dispersal, and landscape friction. A statistical model of habitat suitability for the Mojave desert tortoise, based on topography, vegetation, and climate variables, was used as a proxy for landscape friction and barriers to dispersal. We quantified landscape friction with least-cost distances and with resistance distances among sampling locations. A set of diagnostic partial Mantel tests statistically separated the hypotheses of potential causes of genetic isolation. The best-supported model varied depending upon how landscape friction was quantified. Patterns of genetic structure were related to a combination of geographic distance and barriers as defined by least-cost distances, suggesting that mountain ranges and extremely low-elevation valleys influence connectivity at the regional scale beyond the tortoises’ ability to disperse. However, geographic distance was the only influence detected using resistance distances, which we attributed to fundamental differences between the two ways of quantifying friction. Landscape friction, as we measured it, did not influence the observed patterns of genetic distances using either quantification. Barriers and distance may be more valuable predictors of observed population structure for species like the desert tortoise, which has high dispersal capability and a long generation time.     

Effects of different matrix representations and connectivity measures on habitat network assessments

Assessing landscape connectivity is important to understand the ecology of landscapes and to evaluate alternative conservation strategies. The question is though, how to quantify connectivity appropriately, especially when the information available about the suitability of the matrix surrounding habitat is limited. Our goal here was to investigate the effects of matrix representation on assessments of the connectivity among habitat patches and of the relative importance of individual patches for the connectivity within a habitat network. We evaluated a set of 50 × 50 km² test areas in the Carpathian Mountains and considered three different matrix representations (binary, categorical and continuous) using two types of connections among habitat patches (shortest lines and least-cost paths). We compared connections, and the importance of patches, based on (1) isolation, (2) incidence-functional, and (3) graph measures. Our results showed that matrix representation can greatly affect assessments of connections (i.e., connection length, effective distance, and spatial location), but not patch prioritization. Although patch importance was not much affected by matrix representation, it was influenced by the connectivity measure and its parameterization. We found the biggest differences in the case of the integral index of connectivity and equally weighted patches, but no consistent pattern in response to changing dispersal distance. Connectivity assessments in more fragmented landscapes were more sensitive to the selection of matrix representation. Although we recommend using continuous matrix representation whenever possible, our results indicated that simpler matrix representations can be also used as a proxy to delineate those patches that are important for overall connectivity, but not to identify connections among habitat patches.     

Linking movement behavior and fine-scale genetic structure to model landscape connectivity for bobcats (Lynx rufus)

Spatial heterogeneity can constrain the movement of individuals and consequently genes across a landscape, influencing demographic and genetic processes. In this study, we linked information on landscape composition, movement behavior, and genetic differentiation to gain a mechanistic understanding of how spatial heterogeneity may influence movement and gene flow of bobcats in the agricultural landscape of Iowa (USA). We analyzed movement paths of 23 animals to parameterize landscape resistance surfaces, applied least cost path analysis to generate measures of effective geographic distance between DNA collection locations of 625 bobcats, and tested the correlation between genetic distance and the different models of geographic distance. We found that bobcats showed a strong preference for forest over any other habitat type, and that incorporating information on habitat composition both along the path and in the surrounding landscape provided the best model of movement. Measures of effective geographic distance were significantly correlated with genetic distance, but not once the effects of Euclidean distance were accounted for. Thus, despite the impact of habitat composition on movement behavior, we did not detect a signature of a landscape effect in genetic structure. Our results are consistent with the issue of limiting factors: the high uniformity of forest fragmentation across southern Iowa, the primary study area, results in a landscape resistance pattern virtually indistinguishable from the isolation-by-distance pattern. The northern portion of the state, however, is predicted to pose a high level of resistance to bobcat movement, which may impede the regional genetic connectivity of populations across the Midwest.     

Estimating landscape resistance to dispersal

Dispersal is an inherently spatial process that can be affected by habitat conditions in sites encountered by dispersers. Understanding landscape resistance to dispersal is important in connectivity studies and reserve design, but most existing methods use resistance functions with cost parameters that are subjectively chosen by the investigator. We develop an analytic approach allowing for direct estimation of resistance parameters that folds least cost path methods typically used in simulation approaches into a formal statistical model of dispersal distributions. The core of our model is a frequency distribution of dispersal distances expressed as least cost distance rather than Euclidean distance, and which includes terms for feature-specific costs to dispersal and sex (or other traits) of the disperser. The model requires only origin and settlement locations for multiple individuals, such as might be obtained from mark–recapture studies or parentage analyses, and maps of the relevant habitat features. To evaluate whether the model can estimate parameters correctly, we fit our model to data from simulated dispersers in three kinds of landscapes (in which resistance of environmental variables was categorical, continuous with a patchy configuration, or continuous in a trend pattern). We found maximum likelihood estimators of resistance and individual trait parameters to be approximately unbiased with moderate sample sizes. We applied the model to a small grizzly bear dataset to demonstrate how this approach could be used when the primary interest is in the prediction of costs and found that estimates were consistent with expectations based on bear ecology. Our method has important practical applications for testing hypotheses about dispersal ecology and can be used to inform connectivity planning efforts, via the resistance estimates and confidence intervals, which can be used to create a data-driven resistance surface.     

Prioritization of habitat patches for landscape connectivity conservation differs between least-cost and resistance distances

Context

Methods quantifying habitat patch importance for maintaining habitat network connectivity have been emphasized in helping to prioritize conservation actions. Functional connectivity is accepted as depending on landscape resistance, and several measures of functional inter-patch distance have been designed. However, how the inter-patch distance, i.e., based on least-cost path or multiple paths, influences the identification of key habitat patches has not been explored.

Objectives

We compared the prioritization of habitat patches according to least-cost distance (LCD) and resistance distance (RD), using common binary and probabilistic connectivity metrics.

Methods

Our comparison was based on a generic functional group of forest mammals with different dispersal distances, and was applied to two landscapes differing in their spatial extent and fragmentation level.

Results

We found that habitat patch prioritization did not depend on distance type when considering the role of patch as contributing to dispersal fluxes. However, the role of patch as a connector facilitating dispersal might be overestimated by LCD-based indices compared with RD for short- and medium-distance dispersal. In particular, when prioritization was based on dispersal probability, the consideration of alternatives routes identified the connectors that probably provided functional connectivity for species in the long term. However, the use of LCD might help identify landscape areas that need critical restoration to improve individual dispersal.

Conclusions

Our results provide new insights about the way that inter-patch distance is viewed changes the evaluation of functional connectivity. Accordingly, prioritization methods should be carefully selected according to assumptions about population functioning and conservation aims.

     

The sensitivity of least-cost habitat graphs to relative cost surface values

Maintaining and restoring connectivity among high-quality habitat patches is recognized as an important goal for the conservation of animal populations. To provide an efficient measure of potential connectivity pathways in heterogeneous landscapes, least-cost route analysis has been combined with graph-theoretical techniques. In this study we use spatially explicit least-cost habitat graphs to examine how matrix quality and spatial configuration influence assessments of habitat connectivity. We generated artificial landscapes comprised of three landcover types ranked consistently from low to high quality: inhospitable matrix, hospitable matrix, and habitat. We controlled the area and degree of fragmentation of each landcover in a factorial experiment for a total of 20 combinations replicated 100 times. In each landscape we compared eight sets of relative landcover qualities (cost values of 1 for habitat, between 1.5 and 150 for hospitable matrix, and 3–10,000 for inhospitable matrix). We found that the spatial location of least-cost routes was sensitive to differences in relative cost values assigned to landcover types and that the degree of sensitivity depended on the spatial structure of the landscape. Highest sensitivity was found in landscapes with fragmented habitat and between 20 and 50% hospitable matrix; sensitivity decreased as habitat fragmentation decreased and the amount of hospitable matrix increased. As a means of coping with this sensitivity, we propose identifying multiple low-cost routes between pairs of habitat patches that collectively delineate probable movement zones. These probable movement zones account for uncertainty in least-cost routes and may be more robust to variation in landcover cost values.     

Influence of separating home range and dispersal movements on characterizing corridors and effective distances

Context

Corridors are usually delineated as areas of minimum cumulative resistance to movement through a resistance surface and characterized by their effective distance (accumulated resistance along the least-cost path). The results of these assessments depend on resistance values, which are typically derived from the inverse of habitat suitability models or from presence data of individuals within their home ranges, rather than from data on dispersal or exploratory movements.

Objective

Evaluate the extent to which corridor delineation and effective distance estimates may vary depending on whether home range locations or dispersal data are used to characterize species habitat selection and landscape resistance to movement.

Methods

We analyzed a large telemetry dataset (GPS collars) for the endangered Iberian lynx. We modeled corridors and effective distances three ways: (1) considering only GPS locations within home ranges, (2) considering only locations in dispersal or exploratory movements outside home ranges, and (3) considering all locations together.

Results

Delineated least-cost corridors followed similar trajectories and sometimes overlapped in the three models. The estimated effective distances were 42 % lower in the dispersal-based model than in the model based solely on home range use.

Conclusions

Models derived exclusively from locations within home ranges may provide lower connectivity estimates than models derived from dispersal locations, affecting estimates of resistance to move between habitat areas, even when the most likely movement routes are similar. Although dispersal data are costly to gather, they potentially provide more realistic assessments of the actual isolation of populations in heterogeneous landscapes.

     

Towards a measure of functional connectivity: local synchrony matches small scale movements in a woodland edge butterfly

This study investigates the sensitivity of local synchrony to movement patterns of the Ringlet butterfly (Aphantopus hyperantus). We examine whether population synchrony, describing the correlated fluctuations of conspecific populations, may prove an effective surrogate measure for monitoring functional connectivity in this species without the requirement of exhaustive sampling. We compared the effect on population synchrony of two different distance measures, direct (Euclidean) distance and distance via woodland rides and edges, and also of habitat matrix composition. Population synchrony of A. hyperantus was calculated as the pairwise correlation between population time-series using 20?years of data from UK butterfly monitoring scheme transects. Local population synchrony was better explained by distance via woodland edges than direct distance, especially for woodland-dominated transects. These results are consistent with mark-recapture data previously collected on the Ringlet butterfly. The results indicate a sensitivity of population synchrony to butterfly local dispersal behaviour, particularly, to the use of habitat corridors and other functional dispersal routes. Population synchrony is considered to have potential as a surrogate measure of functional connectivity. With development, this method could become a valuable conservation tool for identifying important landscape features which promote species?? connectivity.     

The influence of landscape characteristics and home-range size on the quantification of landscape-genetics relationships

A common approach used to estimate landscape resistance involves comparing correlations of ecological and genetic distances calculated among individuals of a species. However, the location of sampled individuals may contain some degree of spatial uncertainty due to the natural variation of animals moving through their home range or measurement error in plant or animal locations. In this study, we evaluate the ways that spatial uncertainty, landscape characteristics, and genetic stochasticity interact to influence the strength and variability of conclusions about landscape-genetics relationships. We used a neutral landscape model to generate 45 landscapes composed of habitat and non-habitat, varying in percent habitat, aggregation, and structural connectivity (patch cohesion). We created true and alternate locations for 500 individuals, calculated ecological distances (least-cost paths), and simulated genetic distances among individuals. We compared correlations between ecological distances for true and alternate locations. We then simulated genotypes at 15 neutral loci and investigated whether the same influences could be detected in simple Mantel tests and while controlling for the effects of isolation-by-distance using the partial Mantel test. Spatial uncertainty interacted with the percentage of habitat in the landscape, but led to only small reductions in correlations. Furthermore, the strongest correlations occurred with low percent habitat, high aggregation, and low to intermediate levels of cohesion. Overall genetic stochasticity was relatively low and was influenced by landscape characteristics.     

Animal behavior, cost-based corridor models, and real corridors

Corridors are popular conservation tools because they are thought to allow animals to safely move between habitat fragments, thereby maintaining landscape connectivity. Nonetheless, few studies show that mammals actually use corridors as predicted. Further, the assumptions underlying corridor models are rarely validated with field data. We categorized corridor use as a behavior, to identify animal-defined corridors, using movement data from fishers (Martes pennanti) tracked near Albany, New York, USA. We then used least-cost path analysis and circuit theory to predict fisher corridors and validated the performance of all three corridor models with data from camera traps. Six of eight fishers tracked used corridors to connect the forest patches that constitute their home ranges, however the locations of these corridors were not well predicted by the two cost-based models, which together identified only 5 of the 23 used corridors. Further, camera trap data suggest the cost-based corridor models performed poorly, often detecting fewer fishers and mammals than nearby habitat cores, whereas camera traps within animal-defined corridors recorded more passes made by fishers, carnivores, and all other non-target mammal groups. Our results suggest that (1) fishers use corridors to connect disjunct habitat fragments, (2) animal movement data can be used to identify corridors at local scales, (3) camera traps are useful tools for testing corridor model predictions, and (4) that corridor models can be improved by incorporating animal behavior data. Given the conservation importance and monetary costs of corridors, improving and validating corridor model predictions is vital.     

Population genetic structure and landscape connectivity of the Eastern Yellowbelly Racer (<Emphasis Type="Italic">Coluber constrictor flaviventris</Emphasis>) in the contiguous tallgrass prairie of northeastern Kansas,USA

The tallgrass prairie of North America has undergone widespread habitat loss and fragmentation (<4% remains). The Flint Hills region of Kansas and Oklahoma is the largest tallgrass prairie remaining and therefore provides an opportunity to study the population genetic structure of grassland species in a relatively contiguous landscape and set a baseline for evaluating changes when the habitat is fragmented. We adopted a landscape genetics approach to identify how landscape structure affected dispersal, population genetic structure, and landscape connectivity of the Eastern Yellowbelly Racer (Coluber constrictor flaviventris) across a 13,500-km² landscape in northeastern Kansas, USA. The racer population had high allelic diversity, high heterozygosity, and was maintaining migration-drift equilibrium. Autocorrelation between genetic and geographic distance revealed that racers exhibited restricted dispersal within 3 km, and isolation-by-distance. Significant isolation-by-distance occurred at broad regional scales (>100 km), but because of sufficient gene flow between locations, we were unable to define discrete subpopulations using Bayesian clustering analyses. Resistance distance, which considers the permeability of habitats, did not explain significant variation in genetic distance beyond Euclidean distance alone, suggesting that racers are not currently influenced by landscape composition. In northeastern Kansas, racers appear to be an abundant and continuously distributed snake that perceives the landscape as well connected with no cover type currently impeding snake dispersal or gene flow.     

Modeling occupancy dynamics of a rare species,Franklin’s ground squirrel,with limited data: are simple connectivity metrics adequate?

Conservation of populations in fragmented habitats is often based on spatially realistic metapopulation theory, which predicts negative relationships between patch extinction and area and patch colonization and isolation. Cost-distance metrics have been developed to integrate habitat quality into measures of connectivity, and thus may improve predictive power of the area-isolation paradigm. Few studies use empirical data to compare predictive performance of complex cost-distance metrics to simple metrics relying on Euclidean distances. We used 3 years of presence–absence data to examine relative influence of habitat quality, habitat area, and connectivity on occupancy and extinction rates for Poliocitellus franklinii (Franklin’s ground squirrel), a rare grassland species of conservation concern. We calculated connectivity using nearest-neighbor (NN) and incidence function model (IFM) metrics based on Euclidean and cost-distances. Habitat quality, area, and connectivity were all positive predictors for occupancy, but only isolation was a positive predictor of extinction. P. franklinii does not appear to be a tallgrass prairie obligate, but the species distribution is limited by isolation of suitable grassland habitat. A simple NN metric measuring Euclidean distance between a target area and nearest occupied source outperformed IFM (Euclidean and cost-distance) in predicting occupancy and extinction for P. franklinii. Although NN metrics are criticized for considering only the contribution of the source nearest to a target, this simplicity may be acceptable when measuring connectivity for rare species with few occupied habitat patches within dispersal distance.