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Saturday, June 1
Computational Statistics
Computational Statistics E-Posters
Sat, Jun 1, 9:30 AM - 10:30 AM
Grand Ballroom Foyer

A Model Based Data Fusion Algorithm using Bayesian Hierarchal Modeling for Density Estimation of Rare Species (306285)

*Purna Gamage, Wake Forest University 
Souparno Ghosh, Texas Tech University 
Philip Gipson, Texas Tech University 
Greg Pavur, Texas Tech University 

Keywords: Spatial Capture recapture, swift fox, MCMC

Estimating relative abundance of a species is one of the most important problems arising in ecology. Traditionally, such estimates are obtained using capture-mark- recapture methodologies. Non-invasive procedures, for example, camera trap surveys have also been used extensively. However, such methodologies are not efficient when the focal species is relatively rare and exhibits cryptic behavior.

Over the past decade, scent detection dogs were extensively trained to identify the scats of focal species and they have been used to perform scat surveys to assess occurrence of that species in particular geographical region. Besides detection of presence, the relative abundance can also be estimated from DNA analysis of the collected scats. But, Scat-dog survey followed with the DNA analysis is very expensive. Camera traps can be use to cover a large area. Therefore, the dogs can be used on the high dense areas discovered by the camera trap survey.

In this study, a data fusion technique is developed to combine camera trap survey and scat surveys to draw inference on the density of the target species. The major challenge lies in developing a coherent model that can handle the discrete sampling protocol induced by camera traps and the continuous search paths of scat surveys.

A Bayesian hierarchical extension of Spatial Capture recapture method(SCR) was used, to combine these two types of datasources. In constructing the Joint Model, both the SECR models for marked individuals by Borchers and Efford and the Spatially Explicit model for unmarked/partially marked populations by Chandler and Royle were used. Two distinct Metropolis-within-Gibbs MCMC algorithms were developed for this model to estimate the population size.

This model is applied to estimate the density of swift foxes during a study conducted in west Texas, by the Swift Fox Team in the Natural Resource Management Department , collaborated with Mathematics and Statistics Department at Texas Tech University.