Using Resource Selection Functions to Improve Estimation of Elk Population Numbers

Abstract Stratification is commonly used to improve sampling efficiency of aerial surveys of ungulate populations with strata typically based on a priori information, such as preflight animal observations or vegetation attributes as surrogates for animal densities. We evaluated the usefulness of stratifying survey units for elk (Cervus elaphus) in the Rocky Mountain foothills of Alberta, Canada, using a resource selection function (RSF). We compared precision and design efficiency (DEFF) of population estimates from stratification approaches based on an RSF model to the past approach using amount of forest cover. We used a sample of telemetry relocations taken over a 3-year period from 165 elk, rarified to times of the day and months of the year when aerial surveys are conducted, to develop the RSF. We then used the top RSF model, based on Akaike's Information Criterion, to derive the average RSF value for an 8-km2 survey unit. Using survey data from the first year, we evaluated binning schemes to define RSF-oriented strata based on poststratification and showed that Jenks natural breaks in the RSF values provided the greatest improvement in DEFF and increased precision, compared to 2 other stratification schemes. We then used this approach with data from 2 additional surveys to find that stratification by RSF consistently improves relative precision and design efficiency of elk population estimates, whether we employ pre- or poststratification. Where a RSF is available it could be used as a surrogate for animal densities when conducting stratified sampling for population surveys.

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