PRECISION OF MOOSE DENSITY ESTIMATES DERIVED FROM STRATIFICATION SURVEY DATA

Abstract

Stratified random block (SRB) surveys are commonly used to monitor moose abundance. However, SRB surveys are expensive and time consuming, hence few areas can be surveyed annually and successive surveys in an area are infrequent. We investigated the potential for using only the stratification portion of the SRB survey technique to monitor moose abundance. Our objective was to determine how precisely moose density could be predicted from stratification data. Densities predicted from stratification data were compared with densities estimated from SRB surveys. A simple regression model demonstrated that moose seen per minute on the stratification surveys explains 81% of the variance in moose density. When applied to new data, the regression model predicted moose density with a 90% confidence interval of ± 72 moose per 1,000 km². Changes in predicted moose density in excess of about 120 moose per 1,000 km² are statistically significant (P < 0.05). Moose densities predicted from stratification data were not significantly different from SRB estimates in 6 test cases (P > 0.05), but fell outside the 90% confidence intervals of the SRB estimates in 4 of the 6 test cases. Management applications for moose density estimates derived from stratification survey data are discussed.