Life-history stages and behavior influence demographic classification of moose captured on remote cameras

Moose (Alces alces) are a photogenic species found across boreal and
semi-boreal forests of the Northern Hemisphere. Previous studies have used
demographic data from remote cameras to estimate demographic parameters
and population dynamics. A primary assumption is that these age and sex
classes are accurately classified. However, numerous factors can influence
the ability of observers to identify age and sex classes of moose captured
on cameras. We used data from 84 cameras from a 3-year period (19 Nov
2021–30 Apr 2024) in northern Maine, USA, to evaluate how temporal,
environmental, site-level, and endogenous factors influence the ability of
observers to classify age and sex classes of moose. Using Bayesian
categorical regression models, we found that temporal variability,
position and proximity of moose from cameras, and the behavior of moose
influenced our ability to identify age and sex classes. This information
can be used to decide which periods to use data for population modeling
and how to design studies to reduce the amount of uncertainty associated
with different age and sex classes. We anticipate that our approach could
also be used for other species whose age and sex classes can be
differentiated using remote cameras.