Little information exists on the roost habitat characteristics of reproductive forest bats; hence, we used radiotelemetry to locate 121 roosts of 8 species of bats in 2 study areas on the Coconino National Forest (CNF), northern Arizona, during 1993-95. Only pregnant or lactating bats were examined in the study. Ninetyseven (80%) roosts were in ponderosa pine (Pinus ponderosa) snags. Snags used by bats were larger in diameter at breast height (dbh) and were more likely to have exfoliating bark (bark peeling away from the snag, thus creating space between the bark and the snag) than random snags in both areas. In both study areas, roost snags were surrounded by forest with higher tree densities, greater tree species diversity, and trees had larger basal areas than forest surrounding random snags. Forests immediately surrounding roost snags also had higher densities of snags and logs than random snag areas. In the southern study area, roost snags were located closer to water than random snags and were more likely near the tops of slopes. Roost snags in the northern study area were on steeper slopes and were less likely within a recently harvested area. Radiomarked bats frequently used multiple roosts: 37 of 76 (49%) bats used ?2 snags during the study. We recommend preserving all large snags with exfoliating bark and suggest steps to ensure that sufficient numbers of such snags are maintained for roosting bats in the future.

Miller, Carol and Landres, Peter. 2004. Exploring information needs for wildland fire and fuels management.

Spatially explicit information on the probability of burning is necessary for virtually all strategic fire and fuels management planning activities, including conducting wildland fire risk assessments, optimizing fuel treatments, and prevention planning. Predictive models providing a reliable estimate of the annual likelihood of fire at each point on the landscape have enormous potential to support strategic fire and fuels management planning decisions, especially when combined with information on the values at risk and the expected fire impacts. To this end, a spatially-explicit modelling technique, termed 'burn probability' (BP) modelling, has been developed to simulate fires as a function of the physical factors that drive their spread -fuels, weather, and topographyusing the most sophisticated landscape-scale fire spread algorithms available. Despite several applications of the BP technique, much remains to be learned about their predictive ability. To achieve this goal, we are conducting experiments to not only unearth new discoveries about the complexities of fireenvironment relationships, but also to test and compare the relevance and accuracy of modelling approaches.

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