Abstract
Over a century of fire suppression activities have altered the structure and composition of mixed conifer forests throughout the western United States. In the absence of fire, fuels have accumulated in these forests causing concerns over the potential for catastrophic wildfires. Fuel reduction treatments are being used on federal and state lands to reduce the threat of wildfire by mechanically removing biomass. Although these treatments result in a reduction in fire hazard, their impact on wildlife is less clear. We use a multi-species occupancy modeling approach to build habitat-suitability models for 46 upland forest birds found in the Lake Tahoe Basin in the Sierra Nevada based on forest structure and abiotic variables. Using a Bayesian hierarchical framework, we predict species-specific and community-level responses to changes in forest structure and make inferences about responses of important avian foraging guilds. Disparities within and among foraging group responses to canopy cover, tree size and shrub cover emphasized the complexities in managing forests to meet biodiversity goals. Based on our species-specific model results, we predicted changes in species richness and community similarity under forest prescriptions representing three management practices: no active management, a typical fuel reduction treatment that emphasizes spacing between trees, and a thinning prescription that creates structural heterogeneity. Simulated changes to structural components of the forest analogous to management practices to reduce fuel loads clearly affected foraging groups differentially despite variability in responses within guilds. Although species richness was predicted to decrease slightly under both simulated fuels reduction treatments, the prescription that incorporated structural heterogeneity retained marginally higher species richness. The composition of communities supported by different management alternatives was influenced by urbanization and management practice, emphasizing the importance of creating heterogeneity at the landscape scale.
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