%0 Journal Article %1 durban_multisite_2005 %A Durban, J. W. %A Elston, D. A. %A Ellifrit, D. K. %A Dickson, E. %A Hammond, P. S. %A Thompson, P. M. %D 2005 %J Marine Mammal Science %K Bayesian Carlo, Markov Model Monte averaging, capture-recapture, chain inference, model population selection, size %N 1 %P 80--92 %R 10.1111/j.1748-7692.2005.tb01209.x %T Multisite mark-recapture for cetaceans: population estimates with Bayesian model averaging %U http://onlinelibrary.wiley.com/doi/10.1111/j.1748-7692.2005.tb01209.x/abstract %V 21 %X Mark-recapture techniques are widely used to estimate the size of wildlife populations. However, in cetacean photo-identification studies, it is often impractical to sample across the entire range of the population. Consequently, negatively biased population estimates can result when large portions of a population are unavailable for photographic capture. To overcome this problem, we propose that individuals be sampled from a number of discrete sites located throughout the population's range. The recapture of individuals between sites can then be presented in a simple contingency table, where the cells refer to discrete categories formed by combinations of the study sites. We present a Bayesian framework for fitting a suite of log-linear models to these data, with each model representing a different hypothesis about dependence between sites. Modeling dependence facilitates the analysis of opportunistic photo-identification data from study sites located due to convenience rather than by design. Because inference about population size is sensitive to model choice, we use Bayesian Markov chain Monte Carlo approaches to estimate posterior model probabilities, and base inference on a model-averaged estimate of population size. We demonstrate this method in the analysis of photographic mark-recapture data for bottlenose dolphins from three coastal sites around NE Scotland.

@article{durban_multisite_2005, abstract = {Mark-recapture techniques are widely used to estimate the size of wildlife populations. However, in cetacean photo-identification studies, it is often impractical to sample across the entire range of the population. Consequently, negatively biased population estimates can result when large portions of a population are unavailable for photographic capture. To overcome this problem, we propose that individuals be sampled from a number of discrete sites located throughout the population's range. The recapture of individuals between sites can then be presented in a simple contingency table, where the cells refer to discrete categories formed by combinations of the study sites. We present a Bayesian framework for fitting a suite of log-linear models to these data, with each model representing a different hypothesis about dependence between sites. Modeling dependence facilitates the analysis of opportunistic photo-identification data from study sites located due to convenience rather than by design. Because inference about population size is sensitive to model choice, we use Bayesian Markov chain Monte Carlo approaches to estimate posterior model probabilities, and base inference on a model-averaged estimate of population size. We demonstrate this method in the analysis of photographic mark-recapture data for bottlenose dolphins from three coastal sites around NE Scotland.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Durban, J. W. and Elston, D. A. and Ellifrit, D. K. and Dickson, E. and Hammond, P. S. and Thompson, P. M.}, biburl = {https://www.bibsonomy.org/bibtex/26022d9b73a42a615c1ab82cfcc953e98/yourwelcome}, doi = {10.1111/j.1748-7692.2005.tb01209.x}, interhash = {36f1eed559f7ca5ae55384518c8f5150}, intrahash = {6022d9b73a42a615c1ab82cfcc953e98}, issn = {1748-7692}, journal = {Marine Mammal Science}, keywords = {Bayesian Carlo, Markov Model Monte averaging, capture-recapture, chain inference, model population selection, size}, language = {en}, number = 1, pages = {80--92}, shorttitle = {Multisite {Mark}-{Recapture} for {Cetaceans}}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Multisite mark-recapture for cetaceans: population estimates with {Bayesian} model averaging}, url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1748-7692.2005.tb01209.x/abstract}, urldate = {2013-01-22}, volume = 21, year = 2005 }