A REVIEW OF METHODS TO ESTIMATE AND MONITOR MOOSE DENSITY AND ABUNDANCE

Abstract

Acquiring accurate and precise population parameters is fundamental to the ecological understanding and management and conservation of moose (Alces alces). Moose density is challenging to measure and often estimated using winter aerial surveys; however, numerous alternative approaches exist including harvest analysis, public observation, unpiloted aerial system (UAS) surveys, and camera trapping. Given recent developments in a number of field and analytical techniques, there is value in reviewing and synthesizing the strengths and limitations of monitoring methods to best evaluate their respective tradeoffs in management scenarios. We reviewed 89 studies that included 131 estimates or indices of moose density. As expected, aerial surveys were the most common method of obtaining a moose density estimate (58%) followed by use of public data (e.g., harvest records = 27%); more recent studies employed novel methods including UAS. Most estimates (64%) failed to account for imperfect detection of moose (i.e., “sightability”) and this tendency has not improved over time. Density estimates ranged from <0.1 to 10.6 moose/km² (average = 0.7) and population precision, as measured by the 90% confidence interval, ranged from 6.5 to 120.0% of the density estimate (average = 37.4%). Correlations among estimates obtained for the same populations varied widely, with R² values ranging from 0.02 to 0.99 (average = 0.58). Our review indicates that: 1) methods to estimate moose density have been dominated by aerial surveys but are diversifying, 2) precision of density estimates has been highly variable and on average lower than broadly accepted target benchmarks, and 3) many methods did not account for sightability and presumably underestimated moose density. We reflect on these trends and discuss how emerging methods, including camera trapping, UAS surveys, and integrated population modeling (IPM) can complement and improve traditional approaches. We suggest that no single “best” method exists, but rather the best method is one that accounts for sightability bias and yields target precision at reasonable cost, which vary by jurisdiction and goal.