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Assessing the robustness of time‐to‐event models for estimating unmarked wildlife abundance using remote cameras
Author(s) -
Loonam Kenneth E.,
Lukacs Paul M.,
Ausband David E.,
Mitchell Michael S.,
Robinson Hugh S.
Publication year - 2021
Publication title -
ecological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.864
H-Index - 213
eISSN - 1939-5582
pISSN - 1051-0761
DOI - 10.1002/eap.2388
Subject(s) - robustness (evolution) , abundance (ecology) , abundance estimation , wildlife , event (particle physics) , cluster analysis , population , mark and recapture , computer science , ecology , artificial intelligence , biology , physics , biochemistry , demography , quantum mechanics , sociology , gene
Recently developed methods, including time‐to‐event and space‐to‐event models, estimate the abundance of unmarked populations from encounter rates with camera trap arrays, addressing a gap in noninvasive wildlife monitoring. However, estimating abundance from encounter rates relies on assumptions that can be difficult to meet in the field, including random movement, population closure, and an accurate estimate of movement speed. Understanding how these models respond to violation of these assumptions will assist in making them more applicable in real‐world settings. We used simulated walk models to test the effects of violating the assumptions of the time‐to‐event model under four scenarios: (1) incorrectly estimating movement speed, (2) violating closure, (3) individuals moving within simplified territories (i.e., movement restricted to partially overlapping circles), (4) and individuals clustering in preferred habitat. The time‐to‐event model was robust to closure violations, territoriality, and clustering when cameras were placed randomly. However, the model failed to estimate abundance accurately when movement speed was incorrectly estimated or cameras were placed nonrandomly with respect to habitat. We show that the time‐to‐event model can provide unbiased estimates of abundance when some assumptions that are commonly violated in wildlife studies are not met. Having a robust method for estimating the abundance of unmarked populations with remote cameras will allow practitioners to monitor a more diverse array of populations noninvasively. With the time‐to‐event model, placing cameras randomly with respect to animal movement and accurately estimating movement speed allows unbiased estimation of abundance. The model is robust to violating the other assumptions we tested.