[MARMAM] Tutorial: Bayesian Mark-Recapture in R/JAGS

Robert Rankin robertw.rankin at gmail.com
Wed Feb 24 09:21:49 PST 2016

As a follow-up to a presentation at the 2015 SMM Bienniel Conference, I am
happy to share an online demonstration of Bayesian Mark-Recapture using R
and JAGS, available on github. The demonstration presents various Robust
Design temporary emigration models, as discussed in a forthcoming paper
Rankin et al. 2016 (see below for link to presentation, pre-release draft,
and github URL).

The demo has both "Fixed effects" models and as well as a Hierarchical
Bayesian version which offers many advantages familiar to users of Program
MARK, such as: i) individual heterogeneity, ii) recruitment processes, and
iii) an alternative type of 'model parsimony' similar to multimodel
inference by model averaging.

HB is an automatic "Occam's Razor" which helps prevent over-parametrization
and smooths over many of the instabilities encountered in Frequentist CMR
and AIC-based model averaging, especially for sparse dolphin data. Also,
Bayesian models are fully-probabilistic, intuitive, and provide exact
inference under low-sample sizes.

Link to the demonstration with R and JAGS code (including real bottlenose
photo-ID data): https://github.com/faraway1nspace/PCRD_JAGS_demo/

Link to SMM presentation on Hierarchical Bayesian PCRD:

See github Readme file for more information on Bayesian PCRD.

Abstract of forthcoming paper accompanying the online tutorial:

We present a Hierarchical Bayesian version of Pollock's Closed Robust
Design for studying the survival, temporary-migration, and abundance of
marked animals. Through simulations and analyses of a bottlenose dolphin
photo-identification dataset, we compare several estimation frameworks,
including Maximum Likelihood estimation (ML), model-averaging by AICc, as
well as Bayesian and Hierarchical Bayesian (HB) procedures. Our results
demonstrate a number of advantages of the Bayesian framework over other
popular methods. First, for simple fixed-effect models, we show the
near-equivalence of Bayesian and ML point-estimates and
confidence/credibility intervals. Second, we demonstrate how there is an
inherent correlation among temporary-migration and survival parameter
estimates in the PCRD, and while this can lead to serious convergence
issues and singularities among MLEs, we show that the Bayesian estimates
were more reliable. Third, we demonstrate that a Hierarchical Bayesian
model with carefully thought-out hyperpriors, can lead to similar parameter
estimates and conclusions as multi-model inference by AICc model-averaging.
This latter point is especially interesting for mark-recapture
practitioners, for whom model-uncertainty and multi-model inference have
become a major preoccupation. Lastly, we extend the Hierarchical Bayesian
PCRD to include full-capture histories (i.e., by modelling a recruitment
process) and individual-level heterogeneity in detection probabilities,
which can have important consequences for the range of phenomena studied by
the PCRD, as well as lead to large differences in abundance estimates. For
example, we estimate 8%-24% more bottlenose dolphins in the western gulf of
Shark Bay than previously estimated by ML and AICc-based model-averaging.
Other important extensions are discussed. Our Bayesian PCRD models are
written in the BUGS-like JAGS language for easy dissemination and
customization by the community of capture-mark-recapture practitioners.

Robert Rankin
Ph.D. Candidate
Murdoch University Cetacean Research Unit, School of Veterinary & Life
Sciences, 90 South Street, Murdoch WA 6150
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