Predicting and indexing ungulate browse intensity from local to regional scales
College of Forest Resources and Environmental Science
The intensity of browsing by white-tailed deer (Odocoileus virginianus; hereafter deer) can be influenced directly by deer densities and food availability, and indirectly by landscape characteristics that influence these factors. However, the variables and spatial scales that influence browsing intensity likely differ in landscapes with varying composition and land use. Furthermore, because high browsing intensity can influence the structure and function of forests, determining the most effective and efficient indices of browsing intensity can aid forest management decisions. To evaluate differences in browsing intensity, we used Bayesian hierarchical models to estimate the probability of browsing for twigs from non-avoided species based upon woodlot and landscape-scale variables, which were calculated within 200, 500, 1000, and 1500 m buffers. We then evaluated the efficacy of the twig age, oak sentinel, and herbaceous indicator methods of indexing browsing intensity by correlating these to browsing intensity and deer density. We also evaluated the expense and required work time associated with each index method. Food availability seemed to drive browsing intensities in our study, however, deer density was also important in the region with the lowest cover of forest. When meaningful, landscape characteristics fit data best at the 500 m buffer size. The twig age method showed the strongest correlations to both browsing intensity and deer density and was among the most efficient methods, suggesting it is a reliable index of browsing intensity within Indiana and similar regions. Together, our results highlight that landscape characteristics can mediate the relationship between deer and forest plant communities, which emphasizes the need to tailor management actions to variable landscapes that may occur within a single state or region. Additionally, our results suggest that the twig age method is the most efficient and effective index to monitor browse intensity in agriculturally dominated landscapes, such as those of the Central Hardwood Forest Region.
Webster, C. R.,
Predicting and indexing ungulate browse intensity from local to regional scales.
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/17370