Trace-Contrast Models for Capture–Recapture Without Capture Histories

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 estimat...

Full description

Bibliographic Details
Published in:Statistical Science
Main Authors: Fewster, R. M., Stevenson, B. C., Borchers, D. L.
Format: Text
Language:English
Published: The Institute of Mathematical Statistics 2016
Subjects:
Camera-traps
mark recapture
natural tags
Neyman–Scott process
palm likelihood estimation
Rattus species
New Zealand
Rattus rattus
Online Access:http://projecteuclid.org/euclid.ss/1464105041
https://doi.org/10.1214/16-STS551
Description
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.

Similar Items