Water balance and topography predict fire and forest structure patterns

• Examined effects of the mountainous environmental mosaic in Yosemite, USA.
• Examined environment effects on fire number, burn severity, and forest structure.
• Environmental mosaic predicted majority of fire and forest structure pattern.
• Forest structure continued to vary with environmental conditions following fire.
• Site water balance explained most of variation in fire and structure.

Mountainous topography creates fine-scale environmental mosaics that vary in precipitation, temperature, insolation, and slope position. This mosaic in turn influences fuel accumulation and moisture and forest structure. We studied these the effects of varying environmental conditions across a 27,104 ha landscape within Yosemite National Park, California, USA, on the number of fires and burn severity (measured from Landsat data for 1984–2010) and on canopy cover at two heights (>2 m and 2–8 m) and dominant tree height (measured with airborne LiDAR data). We used site water balance (actual evapotranspiration and climatic water deficit) and topography (slope position, slope, and insolation) as environmental predictors. Random forest modeling showed that environmental conditions predicted substantial portions of the variations in fire and forest structure: e.g., 85–93% of the variation in whether a location did not burn, burned once, or burned twice; 64% of the variation in the burn severity; and 72% of the variation in canopy cover >2 m for unburned forests, 64% for once-burned forests, and 59% for twice-burned forests. Environmental conditions also predicted a substantial portion of forest structure following one and two fires, even though the post-fire forest structures were substantially different than pre-fire structures. This suggests a feedback mechanism in which local fire regimes and pre-fire forest structures are related to local environments, and their interaction produces post-fire structures also related to local environments. Among environmental predictors, water balance had the greatest explanatory power, followed by slope position, and then by slope and insolation. Managers could use our methods to help select reference areas that match environmental conditions, identify areas at risk for fires that endanger critical habitat or other resources, and identify climate analog areas to help anticipate and plan for climate change.