Presentation Abstract

Title: Using Stream Distance To Estimate Connectivity, Movement And Density Of American Mink In A River Network Using Spatial Capture-recapture Models
Session Title: New Technology and Applications
Session Number: 70
Session Time: Wednesday, Oct 09, 2013, 8:30 AM -12:20 PM
Presentation Time: Wednesday, Oct 09, 2013, 11:00 AM -11:20 AM
Presentation Number: 8
Author(s): Chris S. Sutherland1, J. Andrew Royle2, Angela K. Fuller3, 1New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources, Cornell University, Ithaca, NY, 2USGS Patuxent Wildlife Research Center, Laurel, MD, 3U.S. Geological Survey, New York Cooperative Fish and Wildlife Research Unit, Department of Natural Resources, Cornell University, Ithaca, NY, Contact: cs922@cornell.edu
Abstract Body: The manner in which animals utilize a landscape is determined largely by the configuration of suitable habitat. As such, Euclidean distance measures may not be sufficient to adequately characterize landscape connectivity. This is particularly true for riparian species such as American mink Neovison vison whose movements are often restricted to linear corridors (i.e. stream and rivers). The challenge therefore is to incorporate such ‘ecological distances’ into analyses of animal movements and subsequent density estimates. Spatial Capture Recapture models (SCR) describe how the detectability of individuals decrease the farther their activity centers are from a ‘trap’. This requires detections of unique individuals at multiple spatial locations; the distances between those locations are typically measured using Euclidean distances. We conducted a non-invasive genetic study that sampled scat to identify individual mink. We developed an ecologically relevant distance metric, calculated as the stream distance between detections, to characterize river network connectivity and estimate mink density. We demonstrate that ecologically meaningful measures of landscape connectivity can be incorporated into the SCR framework when landscapes are highly heterogeneous or contain movement barriers or corridors. The use of such ‘ecological distances’ can reduce the potential for biased estimates of density.



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