Bauder, J.M., M.L. Allen, T.J. Benson, C.A. Miller, and K.W. Stodola. 2021. An approach for using multiple indices for monitoring long-term trends of mesopredators at broad spatial scales. Biodiversity and Conservation 30:3529-3547. doi.org/10.1007/s10531-021-02259-8
Abstract
Indices of relative abundance are routinely used to monitor and manage wildlife, yet all indices contain observation error and have unknown relationships with true abundance. State-space models (SSM) allow estimation of observation error while concordance in trends among multiple indices from different sampling methods may reflect true trends in abundance. We used multiple decades of data from roadkill surveys, nocturnal spotlight surveys, and observations from hunters along with trapper harvest data for six mesopredators in Illinois, USA, to evaluate concordance (i.e., similarity in trend direction and magnitude) across count- and harvest-based indices, while controlling for observation error using Bayesian SSM. We assumed that increased concordance among trends from different sampling methods would increasingly mirror trends in true abundance. We observed positive trends for raccoon and coyote, negative trends for gray and red fox, and stable trends for skunk, consistent with spatiotemporal patterns of distribution and abundance of these species within midwestern USA. Concordance among count-based indices and harvest-based indices adjusted for temporal changes in trapper numbers was generally high. In contrast, total annual trapper harvest often showed discordance with other trends. Sampling variability was similar across methods but was highest across the shortest time series highlighting the importance of methodologically or analytically controlling for sampling variability. Our results suggest that concordant broad-scale (e.g., statewide) trends in index data may be best used for evaluating relatively general trends and using relatively drastic changes as justification for more in-depth studies.