Flexible behavioral capture-recapture modeling

We develop alternative strategies for building and fitting parametric capture–recapture models for closed populations which can be used to address a better understanding of behavioral patterns. In the perspective of transition models, we first rely on a conditional probability parameterization. A la...

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Bibliographic Details
Published in:Biometrics 2016-03, Vol.72 (1), p.125-135
Main Authors: Alunni Fegatelli, Danilo, Tardella, Luca
Format: Article
Language:English
Subjects:
Algorithms
Animal behavior
Animals
Behavior, Animal - physiology
Behavioral response
BIOMETRIC METHODOLOGY
Biometry - methods
Capture-recapture studies
Censuses
Computer Simulation
Conditional probability
Data Interpretation, Statistical
Datasets
Effect Modifier, Epidemiologic
Generalized linear models
Humans
Mark-recapture
Markov models
Memory effect
Memory-related summary statistics
Models, Statistical
Parameterization
Population Density
Population size
Regression analysis
Reproducibility of Results
Sample Size
Sensitivity and Specificity
Statistical analysis
Statistical models
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Summary:We develop alternative strategies for building and fitting parametric capture–recapture models for closed populations which can be used to address a better understanding of behavioral patterns. In the perspective of transition models, we first rely on a conditional probability parameterization. A large subset of standard capture–recapture models can be regarded as a suitable partitioning in equivalence classes of the full set of conditional probability parameters. We exploit a regression approach combined with the use of new suitable summaries of the conditioning binary partial capture histories as a device for enlarging the scope of behavioral models and also exploring the range of all possible partitions. We show how one can easily find unconditional MLE of such models within a generalized linear model framework. We illustrate the potential of our approach with the anlaysis of some known datasets and a simulation study.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12417