Canadian Forest Service Publications

Estimating moose (Alces alces) occurrence and abundance from remotely derived environmental indicators. 2014. Michaud, J.S., Coops, N.C., Andrew, M.E., Wulder, M.A., Brown, G.S. Rickbeil, G.J.M. Remote Sensing of Environment. 152: 190-201.

Year: 2014

Issued by: Pacific Forestry Centre

Catalog ID: 35698

Language: English

Availability: PDF (request by e-mail)

Available from the Journal's Web site.
DOI: 10.1016/j.rse.2014.06.005

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Understanding species–habitat relationships is critical for wildlife management, providing information on habitat requirements, distribution, and potential land use impacts. In this paper, we examined occurrence and abundance–habitat relationships for moose (Alces alces), a species of economic and ecological importance, across its range in southern and central Ontario. We (1) evaluated and tested competing hypotheses for predicting moose distribution and abundance, and (2) examined the ability of remotely derived environmental indicators to characterize and extrapolate moose habitat throughout the Ontario moose range. To do so, remotely sensed environmental indicators – including vegetation productivity calculated using the Dynamic Habitat Index (DHI), land cover, topography, snow cover, and natural and anthropogenic disturbances – were used to estimate moose occurrence and abundance derived from moose aerial survey data. A 2-step Hurdle model was used to accommodate for zero-inflated data, separately modeling moose occurrence and abundance in a common model framework. Our results indicate that remotely sensed indicators are able to estimate moose occurrence with a moderate degree of certainty; however, these environmental indicators did not successfully estimate moose abundance. The approach outlined in this paper provides a useful framework for hypothesis testing of remote sensing environmental drivers at broad scales, as well as for estimating moose occurrence at the regional level.