Mapping forest temperature buffering from LiDAR

Ecological research on the responses of biotic systems to climate change is largely based on coarse-gridded climate data, derived from standardized meteorological weather stations. These stations measure temperatures in open areas at 1.2 to 2 m above ground covered by short grass, thus measuring macroclimatic conditions. Those macroclimate conditions are, however, not representative for the microclimates most organisms experience. In closed canopy forests, for instance, hot and cold temperature extremes are buffered by several degrees and forests thus provide microclimatic shelters for many forest-dwelling species under climate change. Yet, with increasing forest disturbances opening up forest canopies globally, the future buffering capacity of forests remains uncertain. We aim at closing this knowledge gap by modelling the temperature buffering capacity of forests from remote sensing data. Models were based on boosted regression trees and a set of forest structural and topographic predictors derived from an airborne LiDAR acquisition. Buffering capacity was estimated from in situ microclimatic loggers across 150 plots and a network of meteorological weather stations in the Berchtesgaden National Park – a 20,000 ha landscape located in southern Germany. Spatial models of temperature buffering yielded high predictive accuracies, ranging from R2=0.62 to R2=0.74 depending on the month of observation. Forest structure was consistently more important than topography in explaining temperature buffering. Spatial predictions of temperature buffering revealed a clear elevational gradient, with less buffering in higher-elevation forests (open canopy structures) compared to low-elevation forests (mostly closed canopies). We also found strong variation in temperature buffering over the successional trajectory, with no or even inverted buffering in disturbed sites and a recovery of the buffering capacity within 30 years after disturbance on most sites. Our results will help better understanding the impacts of climate change on forest dwelling species by improving species distribution models and other models of key life history traits. Our approach further provides the ability to be expanded to other regions.