Testing the precision and sensitivity of density estimates obtained with a camera-trap method revealed limitations and opportunities

The use of camera traps in ecology helps affordably address questions
about the distribution and density of cryptic and mobile species. The
Random encounter model (REM) is a camera-trap method that has been
developed to estimate population densities using unmarked individuals.
However, few studies have evaluated its reliability in the field,
especially considering that this method relies on parameters obtained from
collared animals (i.e. average speed, in km/h), which can be difficult to
acquire at low cost and effort. Our objectives were to (1) assess the
reliability of this camera-trap method and (2) evaluate
the influence of parameters coming from different populations on density
estimates. We estimated a reference density of black bears (Ursus
americanus) in Forillon National Park (Québec, Canada) using a spatial
capture-recapture estimator based on hair-snag stations. We calculated
average speed using telemetry data acquired from four different bear
populations located outside our study area and estimated densities using
the REM. The reference density, determined with a Bayesian spatial
capture-recapture model, was 2.87 individuals/10km2 [95% CI: 2.41–3.45],
which was slightly lower (although not significatively different) than the
different densities estimated using REM (ranging from 4.06–5.38
bears/10km2 depending on the average speed value used). Average speed
values obtained from different populations had minor impacts on REM
estimates when the difference in average speed between populations was
low. Bias in speed values for slow-moving species had more influence on
REM density estimates than for fast-moving species. We pointed out that a
potential overestimation of density occurs when average speed is
underestimated, i.e. using GPS telemetry locations with large fix-rate
intervals. Our study suggests that REM could be an affordable alternative
to conventional spatial capture-recapture, but highlights the need for
further research to control for potential bias associated with speed
values determined using GPS telemetry data.