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Activity Number: 332
Type: Invited
Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #314412 View Presentation
Title: Improving Inference by Using Times-to-Detection on Ecological Surveys
Author(s): David Borchers* and Roland Langrock and Greg Distiller and Martin Cox
Companies: University of St. Andrews and University of St. Andrews and University of St. Andrews and Australian Antarctic Division
Keywords: wildlife survey ; survival model ; Poisson process ; distance sampling ; capture-recapture ; occupancy models
Abstract:

In this talk we develop models for closed-population wildlife surveys that incorporate time-to-detection data. We show that many of the most widely-used wildlife survey models, including removal method models, distance sampling models, capture-recapture models and some occupancy models, can be viewed as survival or recurrent event models. We investigate what is gained by using time-to-detection data in cases in which it is traditionally neglected. In the case of distance sampling, something close to time-to-detection data was used until the early 1980s; we look at the reasons its use was discontinued, and suggest that it is time to reconsider its use. In the case of capture-recapture surveys, the advent of continuously-sampling detectors like camera traps and spatially explicit capture-recapture methods motivates the use of time-to-detection data, while time-to-detection data are also shown to be useful in occupancy modelling.


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