ESTIMATING MOOSE ABUNDANCE AND TRENDS IN NORTHEASTERN WASHINGTON STATE: INDEX COUNTS, SIGHTABILITY MODELS, AND REDUCING UNCERTAINTY

Richard B. Harris, Michael Atamian, Howard Ferguson, Ilai Keren

Abstract


The state of Washington was historically considered to be unoccupied by moose (Alces alces) with initial colonization in the 1920s primarily in the northeastern quarter of the state. All evidence indicates a steadily increasing population since, with moose and moose hunting now firmly established. Given the expectation that Washington's moose population will face increasing challenges in the coming decades, our monitoring objective is to move from index-counts to valid estimates of abundance. We documented environmental covariates as an adjunct to simple counts from annual helicopter-based surveys in 2002–2012, and examined the performance of existing moose sightability models on these data. While acknowledging our inability to compare modeled estimates with actual abundance, we reasoned that if existing models converged on similar results, this would suggest that moose sightability is a sufficiently general phenomenon that the cost of developing a specific local model might not be justified. However, despite using similar covariates, the sightability models applied to our data produced widely disparate abundances and estimates with poor precision. Specifically, where coniferous forest cover renders expected detection probability low, sightability models tend to behave erratically. We also used covariate data bearing on sampling variation to refine our estimate of population trend. Multiple regression analyses revised the linear rate of increase associated with the raw counts of the instantaneous rate of growth, r = 0.084 (SE = 0.019) to an adjusted estimate of r = 0.077 (SE = 0.075). While incapable of transforming an index into a population estimate, accounting for variables likely to affect raw counts may be useful to refine estimates of trend. The use of an approach that avoids the autocorrelation inherent in a simple regression of counts on time better reflects true uncertainty.

Keywords


Population Demograpy

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