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Clayton, GA, United States

Steen D.A.,Auburn University | Barbour M.,Auburn University | McClure C.J.W.,Peregrine Fund | Wray K.P.,Florida State University | And 2 more authors.
Copeia | Year: 2015

The Harlequin Coralsnake (Micrurus fulvius) is an iconic and imperiled species of the southeastern United States, but we know little of its ecology and natural history. We used our field notes on incidentally observed coralsnakes within three large, protected areas in Georgia and Florida (Apalachicola National Forest, Eglin Air Force Base, and Fort Stewart Military Installation) to generate information related to the habitat preferences of individual animals. We generated random location points in each of our study areas and compared the landscape-scale habitats surrounding them to the habitats surrounding coralsnake location points. We obtained evidence that coralsnakes exhibit hierarchal (i.e., multiscale) habitat selection. Specifically, coralsnakes were found in areas with more sandy soils (250 m scale) and scrub/shrub habitat (500 m scale) than random points across the landscape. Our study generates novel habitat information for a poorly known species. © 2015 by the American Society of Ichthyologists and Herpetologists. Source

Graves T.A.,Northern Arizona University | Wasserman T.N.,Northern Arizona University | Ribeiro M.C.,Claro | Ribeiro M.C.,Virginia Commonwealth University | And 8 more authors.
Landscape Ecology | Year: 2012

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. © 2011 Springer Science+Business Media B.V. (outside the USA). Source

Bauder J.M.,University of Massachusetts Amherst | Breininger D.R.,NASA | Bolt M.R.,NASA | Legare M.L.,Merritt Island National Wildlife Refuge | And 2 more authors.
Wildlife Research | Year: 2015

Context Despite the diversity of available home range estimators, no single method performs equally well in all circumstances. It is therefore important to understand how different estimators perform for data collected under diverse conditions. Kernel density estimation is a popular approach for home range estimation. While many studies have evaluated different kernel bandwidth selectors, few studies have compared different formulations of the bandwidth matrix using wildlife telemetry data. Additionally, few studies have compared the performance of kernel bandwidth selectors using VHF radio-telemetry data from small-bodied taxa. Aims In this study, we used eight different combinations of bandwidth selectors and matrices to evaluate their ability to meet several criteria that could be potentially used to select a home range estimator. Methods We used handheld VHF telemetry data from two species of snake displaying non-migratory and migratory movement patterns. We used subsampling to estimate each estimator's sensitivity to sampling duration and fix rate and compared home range size, the number of disjunct volume contours and the proportion of telemetry fixes not included in those contours among estimators. Key Results We found marked differences among bandwidth selectors with regards to our criteria but comparatively little difference among bandwidth matrices for a given bandwidth selector. Least-squares cross-validation bandwidths exhibited near-universal convergence failure whereas likelihood cross-validation bandwidths showed high sensitivity to sampling duration and fix rate. The reference, plug-in and smoothed cross-validation bandwidths were more robust to variation in sampling intensity, with the former consistently producing the largest estimates of home range size. Conclusions Our study illustrates the performance of multiple kernel bandwidth estimators for estimating home ranges with datasets typical of many small-bodied taxa. The reference and plug-in bandwidths with an unconstrained bandwidth matrix generally had the best performance. However, our study concurs with earlier studies indicating that no single home range estimator performs equally well in all circumstances. Implications Although we did not find strong differences between bandwidth matrices, we encourage the use of unconstrained matrices because of their greater flexibility in smoothing data not parallel to the coordinate axes. We also encourage researchers to select an estimator suited to their study objectives and the life history of their study organism. © 2015 CSIRO. Source

Stevenson D.J.,Orianne Society | Greer G.,Greg Greer Enterprises Inc. | Elliott M.J.,065 U.S. Highway 278 SE
Southeastern Naturalist | Year: 2012

Abstract Field collections made by the authors in pineland ecosystems in southern Georgia during 2011 significantly expand the previously published range limits of the scorpion Centruroides hentzi in Georgia. We commonly found specimens beneath the exfoliating bark of Pinus palustris (Longleaf Pine) and P. elliottii (Slash Pine) snags, stumps, and logs in sandhills and pine flatwoods habitats, documenting this scorpion from 50 sites in 34 south Georgia counties, and extending the known range of C. hentzi 150 km north (from near Waycross, Ware County, GA) to Statesboro, Bulloch County, GA. Our collections indicate that the species is widespread in pine-dominated uplands throughout much of the lower and middle Coastal Plain of southern Georgia. We comment on the life history, ecology, and habitat requirements of the species based on this survey and the existing literature. In Georgia, C. hentzi is a characteristic associate of Longleaf Pine and Slash Pine ecosystems, is often locally abundant, and is part of an arthropod-vertebrate food web that includes the endangered Picoides borealis (Red-cockaded Woodpecker). Source

Richardson J.L.,Providence College | Brady S.P.,Dartmouth College | Wang I.J.,University of California at Berkeley | Spear S.F.,Orianne Society
Molecular Ecology | Year: 2016

The field of landscape genetics has been evolving rapidly since its emergence in the early 2000s. New applications, techniques and criticisms of techniques appear like clockwork with each new journal issue. The developments are an encouraging, and at times bewildering, sign of progress in an exciting new field of study. However, we suggest that the rapid expansion of landscape genetics has belied important flaws in the development of the field, and we add an air of caution to this breakneck pace of expansion. Specifically, landscape genetic studies often lose sight of the fundamental principles and complex consequences of gene flow, instead favouring simplistic interpretations and broad inferences not necessarily warranted by the data. Here, we describe common pitfalls that characterize such studies, and provide practical guidance to improve landscape genetic investigation, with careful consideration of inferential limits, scale, replication, and the ecological and evolutionary context of spatial genetic patterns. Ultimately, the utility of landscape genetics will depend on translating the relationship between gene flow and landscape features into an understanding of long-term population outcomes. We hope the perspective presented here will steer landscape genetics down a more scientifically sound and productive path, garnering a field that is as informative in the future as it is popular now. © 2016 John Wiley & Sons Ltd. Source

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