Data from: Generalized spatial mark-resight models with an application to grizzly bears

1. The high cost associated with capture-recapture studies presents a
major challenge when monitoring and managing wildlife populations.
Recently-developed spatial mark-resight (SMR) models were proposed as a
cost-effective alternative because they only require a single marking
event. However, existing SMR models ignore the marking process and make
the tenuous assumption that marked and unmarked populations have the same
encounter probabilities. This assumption will be violated in most
situations because the marking process results in different spatial
distributions of marked and unmarked animals. 2. We developed a
generalized SMR model that includes sub-models for the marking and
resighting processes, thereby relaxing the assumption that marked and
unmarked populations have the same spatial distributions and encounter
probabilities. 3. Our simulation study demonstrated that conventional SMR
models produce biased density estimates with low credible interval
coverage when marked and unmarked animals had differing spatial
distributions. In contrast, generalized SMR models produced unbiased
density estimates with correct credible interval coverage in all
scenarios. 4. We applied our SMR model to grizzly bear (Ursus arctos) data
where the marking process occurred along a transportation route through
Banff and Yoho National Parks, Canada. Twenty-two grizzly bears were
trapped, fitted with radio-collars, and then detected along with unmarked
bears on 214 remote cameras. Closed population density estimates
(posterior median + 1 SD) averaged from 2012 to 2014 were much lower for
conventional SMR models (7.4 + 1.0 bears per 1,000 km2) than for
generalized SMR models (12.4 + 1.5). When compared to previous DNA-based
estimates, conventional SMR estimates erroneously suggested a 51% decline
in density. Conversely, generalized SMR estimates were similar to previous
estimates, indicating that the grizzly bear population was relatively
stable. 5. Synthesis and application. Conventional SMR models that ignore
the marking process should only be used when marked and unmarked animals
share the same spatial distribution, such as when a subset of the
population has natural marks. Generalized SMR models that include the
marking process are much more widely applicable. They represent a
promising new approach for reducing the costs of studies aimed at
understanding spatial and temporal variation in density.24-May-2017