Capture history analysis for spatial wildlife surveys with detection times

2026-07-30

AI Summary

This study addresses the frequent underutilization of temporal and spatial information in existing wildlife survey methods, which often impedes accurate estimation of population parameters. Building upon counting process theory, the authors develop a unified maximum likelihood estimation framework applicable to timestamped data from proximity detectors, single-catch traps, multi-catch traps, and removal-type traps. The proposed framework not only resolves several open statistical challenges inherent in single-catch and removal surveys but also introduces novel methodology for analyzing multi-catch trap data. Through extensive simulations, the approach demonstrates robust performance, and its practical utility is confirmed by a successful application to real-world single-catch trap data on brushtail possums in New Zealand, yielding substantially improved accuracy in population parameter estimates.

Abstract

Many wildlife surveys of species with individually identifiable animals are based on the analysis of the capture histories of individual animals. However, not many of these take account of both the animals' capture times and their capture locations. In this work, we develop a maximum likelihood estimator for surveys of individually identifiable animals using proximity detectors, multi-catch traps, single-catch traps, and traps that remove animals from the population, when capture times are known. We do this using the counting process theory at the foundation of event history (or survival) analysis. The work provides a unifying framework for such surveys, resolves open problems for single-catch traps and removal surveys, and introduces a new statistical method for multi-catch traps. We test the new methods by simulation and we present an analysis of possums in New Zealand caught with single-catch traps.

Problem

• capture history
• spatial wildlife surveys
• detection times
• individually identifiable animals
• capture locations

Innovation

• capture-recapture
• spatial capture history
• maximum likelihood estimation
• counting process theory
• single-catch traps