Data from: using camera traps and N-mixture models to estimate population abundance: model selection really matters

Estimating the abundance or density of wildlife populations is a critical
part of species conservation and management, but estimates can vary
greatly in precision and accuracy according to the data collection and
statistical methods, sampling and ecological variation, and sample size.
N-mixture models are a common method which has been applied to a wide
range of taxa for estimating population abundance from non-invasive data
representing the distribution of the species. We used population estimates
from an aerial survey of moose and videos from camera traps to assess the
sensitivity of N-mixture models to ecological conditions, the spatial
scale at which they were measured, the criteria used to define independent
detections, and model choice based on the common statistical criterion of
parsimony. The most parsimonious N-mixture models were considerably
biased, producing implausibly large and considerably imprecise estimates
of the abundance of moose. Most of the other models produced estimates of
abundance that were ecologically realistic and relatively accurate. The
accuracy of population estimates produced by N-mixture models were not
overly sensitive to the formulation of models, the scale at which
ecological conditions were measured, or the criteria used to define
independent detection and by extension sample size. Our results suggest
that parsimony was a poor measure of the predictive accuracy of the
population estimates produced with the N-mixture model. Collecting and
processing data from the aerial survey was less expensive and took less
time, but data from camera traps can provide valuable information on
behavior of the target species as well as insights into multiple species
in the community.