A Full-Capture Hierarchical Bayesian Model of Pollock's Closed Robust Design and Application to Dolphins

Autor: Michael Krützen, Kenneth H. Pollock, Krista Nicholson, Lars Bejder, Simon Allen, R.W. Rankin
Přispěvatelé: University of Zurich, Rankin, Robert W
Rok vydání: 2016
Předmět:
10207 Department of Anthropology
0106 biological sciences
bottlenose dolphin
hierarchical Bayes
Computer science
Bayesian inference
Bayesian probability
2306 Global and Planetary Change
Inference
Ocean Engineering
Aquatic Science
Oceanography
010603 evolutionary biology
01 natural sciences
multimodel inference
010104 statistics & probability
2312 Water Science and Technology
1910 Oceanography
Statistics
Range (statistics)
Bayesian hierarchical modeling
Marine Science
0101 mathematics
Bayesian average
2212 Ocean Engineering
Water Science and Technology
abundance
Global and Planetary Change
1104 Aquatic Science
300 Social sciences
sociology & anthropology
2301 Environmental Science (miscellaneous)
individual heterogeneity
Variable-order Bayesian network
mark recapture
Bayesian statistics
detection probability
Zdroj: Frontiers in Marine Science. 3
ISSN: 2296-7745
DOI: 10.3389/fmars.2016.00025
Popis: 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 modeling 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 (CMR) practitioners.
Databáze: OpenAIRE
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