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Population size estimation techniques, such as multiple-systems or capture-recapture estimation, typically require multiple samples from the study population, in addition to the information on which individuals are included in which samples. In many contexts, these samples come from existing data sources that contain certain information on the individuals but no unique identifiers. The goal of record linkage and duplicate detection techniques is to identify unique individuals across and within samples based on the information collected on them, which might correspond to basic partial identifiers, such as given and family name, and other demographic information. Therefore, record linkage and duplicate detection are often needed to generate the input for a given population size estimation technique that a researcher might want to use. Linkage decisions, however, are subject to uncertainty when partial identifiers are limited or contain errors and missingness, and therefore, intuitively, uncertainty in the linkage and deduplication process should somehow be taken into account in the stage of population size estimation.
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Zero‐inflated count distributions for capture–mark–reencounter data
Abstract 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.
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- Award ID(s):
- 2209765
- PAR ID:
- 10520343
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Ecology and Evolution
- Volume:
- 12
- Issue:
- 9
- ISSN:
- 2045-7758
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1002/ece3.9274
