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Walter Applied Spatial Ecology Laboratory

Our research focuses on conducting wildlife research, both basic and applied, on large ecological datasets that provide an unique opportunity to explicitly incorporate sources of spatial and temporal variability into understanding motivations for an organism’s movements, resource selection, subpopulation structuring, or presence in a landscape.

These large-scale ecological datasets provide a powerful tool for addressing research questions at a regional level that can also be evaluated by future researchers. Within this broader framework, the primary research interests that direct the types of projects in the lab are: (1) methodologies for spatial analysis of mammalian and avian datasets to determine ecological drivers of movements, resource selection, and presence/absence; (2) monitoring occurrences of disease to link demographic and environmental influences on disease transmission and spread, and (3) landscape genetics of various taxa for monitoring of population size, relatedness, and subpopulation structuring across ecosystems and barriers to gene flow.

We are housed in the Department of Ecosystem Science and Management at Pennsylvania State University.

Walter Lab News

New publications on sampling for CWD
June 30, 2016
RAMALT in captive deer and elk
Updated Manual of Applied Spatial Ecology now online along with example datasets
February 2, 2016
Manual updates and example datasets are available for downloading
New publication on spatial modeling of CWD
February 2, 2016
Because CWD is a new and emerging disease with a spatial distribution that had yet to be assessed in the Northeast, we examined demographic, environmental, and spatial effects to determine how each related to this spatial distribution. The objectives of our study were to identify environmental and spatial effects that best described the spatial distribution of CWD in free-ranging white-tailed deer and identify areas that support deer that are at risk for CWD infection in the Northeast. We used Bayesian hierarchical modeling that incorporated demographic covariates, such as sex and age, along with environmental covariates, which included elevation, slope, riparian corridor, percent clay, and 3 landscapes (i.e., developed, forested, open).