Trace-contrast models for capture-recapture without capture histories. (English) Zbl 1442.62211

Fewster, R. M.; Stevenson, B. C.; Borchers, D. L.

Stat. Sci. 31, No. 2, 245-258 (2016).

Summary: Capture-recapture studies increasingly rely upon natural tags that allow animals to be identified by features such as coat markings, DNA profiles, acoustic profiles, or spatial locations. These innovations greatly increase the number of capture samples achievable and enable capture-recapture estimation for many inaccessible and elusive species. However, natural features are invariably imperfect as indicators of identity. Drawing on the recently developed Palm likelihood approach to parameter estimation in clustered point processes, we propose a new estimation framework based on comparing pairs of detections, which we term the trace-contrast framework. Importantly, no reconstruction of capture histories is needed. We show that we can achieve accurate, precise, and computationally fast inference. We illustrate the methods with a camera-trap study of a partially marked population of ship rats (Rattus rattus) in New Zealand.

MSC:

62M30	Inference from spatial processes
62F10	Point estimation
62P10	Applications of statistics to biology and medical sciences; meta analysis

Keywords:

camera-traps; mark recapture; natural tags; Neyman-Scott process; Palm likelihood estimation; rattus species

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