Relationship between resource selection, distribution, and abundance: a test with implications to theory and conservation
Much of applied and theoretical ecology is concerned with the interactions of habitat quality, animal distribution, and population abundance. We tested a technique that uses resource selection functions (RSF) to scale animal density to the relative probability of selecting a patch of habitat. Following an accurate survey of a reference block, the habitat-based density estimator can be used to predict population abundance for other areas with no or unreliable survey data. We parameterized and tested the technique using multiple years of radiotelemetry locations and survey data collected for woodland caribou across four landscape-level survey blocks. The habitat-based density estimator performed poorly. Predictions were no better than those of a simple area estimator and in some cases deviated from the observed by a factor of 10. We developed a simulation model to investigate factors that might influence prediction success. We experimentally manipulated population density, caribou distribution, ability of animals to track carrying capacity, and precision of the estimation equation. Our simulations suggested that interactions between population density, the size of the reference block, and the pattern of distribution can lead to large discrepancies between observed and predicted population numbers. Over- or undermatching patch carrying capacity and precision of the estimator can influence predictions, but the effect is much less extreme. Although there is some empirical and theoretical evidence to support a relationship between animal abundance and resource selection, our study suggests that a number of factors can seriously confound these relationships. Habitat-based density estimators might be effective where a stable, isolated population at equilibrium is used to generate predictions for areas with similar population parameters and ecological conditions.
KeywordsCaribou Estimation Habitat Population Resource selection function
We thank M. Boyce, M. Gillingham, D. Heard, and S. Nielsen for guidance on an earlier version of this manuscript. We thank G. Watts for providing survey data. Two anonymous reviewers provided constructive comments that greatly improved the final paper.
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