Zero‐inflated count distributions for capture–mark–reencounter data

• Published in: Ecology and evolution 2022-09, Vol.12 (9), p.e9274-n/a
• Main Authors: Riecke, Thomas V., Gibson, Daniel, Sedinger, James S., Schaub, Michael
• Format: Article
• Language: English
• Subjects: Applied Ecology; Availability; Bayesian; Capture-recapture studies; capture–mark–recapture; Conservation biology; Demographics; Demography; Emigration; Estimates; Estimation; gamma‐Poisson; Heterogeneity; individual heterogeneity; Information processing; Life History Ecology; mark‐resight; Mathematical models; Organisms; Parameter estimation; Parameters; Poisson distribution; Population Ecology; Probability; robust design; Statistical analysis; temporary emigration; zero‐inflation
• ISSN: 2045-7758; 2045-7758
• DOI: 10.1002/ece3.9274

The estimation of demographic parameters is a key component of evolutionary demography and conservation biology. Capture–mark–recapture methods have served as a fundamental tool for estimating demographic parameters. The accurate estimation of demographic parameters in capture–mark–recapture studies depends on accurate modeling of the observation process. Classic capture–mark–recapture models typically model the observation process as a Bernoulli or categorical trial with detection probability conditional on a marked individual's availability for detection (e.g., alive, or alive and present in a study area). Alternatives to this approach are underused, but may have great utility in capture–recapture studies. In this paper, we explore a simple concept: in the same way that counts contain more information about abundance than simple detection/non‐detection data, the number of encounters of individuals during observation occasions contains more information about the observation process than detection/non‐detection data for individuals during the same occasion. Rather than using Bernoulli or categorical distributions to estimate detection probability, we demonstrate the application of zero‐inflated Poisson and gamma‐Poisson distributions. The use of count distributions allows for inference on availability for encounter, as well as a wide variety of parameterizations for heterogeneity in the observation process. We demonstrate that this approach can accurately recover demographic and observation parameters in the presence of individual heterogeneity in detection probability and discuss some potential future extensions of this method. In this paper we explore a simple concept: in the same way that counts provide more information about abundance than detection/non‐detection data, counts of the number of observations of uniquely marked individuals can provide more information about demographic parameters than detection/non‐detection data. Zero‐inflated parameterizations of capture–recapture models can decrease runtime, and improve the estimation of heterogeneity in detection probability among individuals.