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.

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