Browsing Pressure Modelling: Spatial Prediction of Browsing Probabilities

The global forestry discourse frequently highlights the issue of ungulate browsing, which can significantly impact tree regeneration and tree species composition by inhibition of growth and elimination of certain, particularly ecologically valuable, tree species. The forestry field often utilizes the percentage of browsed trees within a specific area, ranging from single hunting grounds to broader provincial scales, as a metric of browsing intensity. This measure correlates with ungulate density, which is known to vary across landscapes, rendering spatially averaged browsing percentages less useful for silvicultural decisions even with accurate results. Addressing this gap, we utilized a GLMM with random effects to assess tree specific browsing pressure more appropriately. We incorporated data from two adjacent areas in the northeastern limestone Alps, focussing on the four important tree species in the region (Abies alba, Acer pseudoplatanus, Fagus sylvatica, and Picea abies). We analyzed data collected with distinct methodologies for the two regions, respectively, Austrian Federal Game Impact Monitoring and Austrian Regeneration and Browsing Monitoring of Federal Forests. Overall, the data documented browsing occurrence on 8933 trees over 632 sampling plots totalling 55,000 hectares. By comparing various models, including those with spatial considerations, we found that treating sampling plot location as a latent state variable improved the model fit and allowed prediction of browsing probability on a landscape scale. This study underlines the value of incorporating spatial elements into models for assessing browsing pressure and its spatial variations, thereby facilitating more informed silvicultural decisions.