Mapping Landscape Resistance to Identify Corridors and Barriers for Elephant Movement in Southern Africa

Mapping Landscape Resistance to Identify Corridors and Barriers for Elephant Movement in Southern Africa ..

Spatially-explicit estimation of Wright's neighborhood size in continuous populations

Effective population size (Ne) is an important parameter in conservation genetics because it quantifies a population’s capacity to resist loss of genetic diversity due to inbreeding and drift. The classical approach to estimate Ne from genetic data involves grouping sampled individuals into discretely defined subpopulations assumed to be panmictic. Importantly, this assumption does not capture the continuous nature of populations genetically isolated by distance. Alternative approaches based on Wright’s genetic neighborhood concept quantify the local number of breeding individuals (NS) in a continuous population (as opposed to the global Ne). However, they do not reflect the potential for NS to vary spatially nor do they account for the resistance of a heterogeneous landscape to gene flow (isolation by resistance). Here, we describe an application of Wright’s neighborhood concept that provides spatially-explicit estimates of local NS from genetic data in continuous populations isolated by distance or resistance. We delineated local neighborhoods surrounding each sampled individual based on sigma (), a measure of the local extent of breeding. When was known, the linkage disequilibrium method applied to local neighborhoods produced unbiased estimates of NS that were highly variable across the landscape. NS near the periphery or areas surrounded by high resistance was as much as an order of magnitude lower compared to the center, raising the potential for a spatial component to extinction vortex dynamics in continuous populations. When is not known, it may be estimated from genetic data, but two methods we evaluated identified analysis extents that produced considerable bias or error in the estimate of NS. When is known or accurately estimated, and the assumptions of Wright’s neighborhood are met, the method we describe provides spatially explicit information regarding short-term genetic processes that may inform conservation genetic analyses and management

Why Did the Bear Cross the Road? Comparing the Performance of Multiple Resistance Surfaces and Connectivity Modeling Methods

There have been few assessments of the performance of alternative resistance surfaces, and little is known about how connectivity modeling approaches differ in their ability to predict organism movements. In this paper, we evaluate the performance of four connectivity modeling approaches applied to two resistance surfaces in predicting the locations of highway crossings by American black bears in the northern Rocky Mountains, USA. We found that a resistance surface derived directly from movement data greatly outperformed a resistance surface produced from analysis of genetic differentiation, despite their heuristic similarities. Our analysis also suggested differences in the performance of different connectivity modeling approaches. Factorial least cost paths appeared to slightly outperform other methods on the movement-derived resistance surface, but had very poor performance on the resistance surface obtained from multi-model landscape genetic analysis. Cumulative resistant kernels appeared to offer the best combination of high predictive performance and sensitivity to differences in resistance surface parameterization. Our analysis highlights that even when two resistance surfaces include the same variables and have a high spatial correlation of resistance values, they may perform very differently in predicting animal movement and population connectivity

Proteomic characterization of thiazolidinedione regulation of obese adipose secretome in Zucker obese rats

Signaling molecules released by adipose tissue have been implicated in inflammation, adipocyte dysfunction and systemic insulin resistance. In this study, we used 2-D LC-MS/MS and quantitative proteomics approaches to characterize the obese adipose secretory proteins that are responsive to the thiazolidinediones class of PPAR-γ agonizts. We first showed the differential secretion profiling between obese and lean adipose tissue; 87 proteins were detected from the conditioned medium of adipose tissue of Zucker obese rats compared with 31 from lean rats. A total of 57 proteins comprising immune factors, inflammatory molecules, collagens, proteases, and extracellular matrix proteins were detected from obese, but not lean adipose tissue. More importantly, a quantitative proteomics approach using ^(18)O proteolytic labeling allowed quantification of the difference in the secretion levels of 77 proteins, and thiazolidinediones treatment suppressed the secretion of most of the obese adipose tissue secretome, thus resembling a lean tissue. We have demonstrated an application of identifying the obese adipose secretome and characterizing the regulation of adipose secretion in obesity and insulin resistance. Our data provide the first evidence of changes in adipose secretion in obesity at a global level and show that such changes are correlated with systemic insulin resistance