A hierarchical distance sampling approach to estimating mortality rates from opportunistic carcass surveillance data
Article first published online: 10 JAN 2013
© 2013 The Authors. Methods in Ecology and Evolution © 2013 British Ecological Society
Methods in Ecology and Evolution
Volume 4, Issue 4, pages 361–369, April 2013
How to Cite
Bellan, S. E., Gimenez, O., Choquet, R., Getz, W. M. (2013), A hierarchical distance sampling approach to estimating mortality rates from opportunistic carcass surveillance data. Methods in Ecology and Evolution, 4: 361–369. doi: 10.1111/2041-210x.12021
- Issue published online: 2 APR 2013
- Article first published online: 10 JAN 2013
- Manuscript Accepted: 30 OCT 2012
- Manuscript Received: 5 JUN 2012
- Chang-Lin Tien Environmental Fellowship
- Andrew and Mary Thompson Rocca Scholarships
- Edna and Yoshinori Tanada Fellowship
- James S. McDonnell
- NIH. Grant Number: GM83863
- distance sampling;
- hierarchical model;
- opportunistic surveillance
- Distance sampling is widely used to estimate the abundance or density of wildlife populations. Methods to estimate wildlife mortality rates have developed largely independently from distance sampling, despite the conceptual similarities between estimation of cumulative mortality and the population density of living animals. Conventional distance sampling analyses rely on the assumption that animals are distributed uniformly with respect to transects and thus require randomised placement of transects during survey design. Because mortality events are rare, however, it is often not possible to obtain precise estimates in this way without infeasible levels of effort. A great deal of wildlife data, including mortality data, are available via road-based surveys. Interpreting these data in a distance sampling framework requires accounting for the non-uniformity of sampling. In addition, analyses of opportunistic mortality data must account for the decline in carcass detectability through time. We develop several extensions to distance sampling theory to address these problems.
- We build mortality estimators in a hierarchical framework that integrates animal movement data, surveillance effort data and motion-sensor camera trap data, respectively, to relax the uniformity assumption, account for spatiotemporal variation in surveillance effort and explicitly model carcass detection and disappearance as competing ongoing processes.
- Analysis of simulated data showed that our estimators were unbiased and that their confidence intervals had good coverage.
- We also illustrate our approach on opportunistic carcass surveillance data acquired in 2010 during an anthrax outbreak in the plains zebra of Etosha National Park, Namibia.
- The methods developed here will allow researchers and managers to infer mortality rates from opportunistic surveillance data.