Predicting Forest Microclimate in Heterogeneous Landscapes

Forest microclimate plays an integral role in ecosystem processes, yet a predictive understanding of its spatial and temporal variability in heterogeneous landscapes is largely lacking. In this study, we used regression kriging (RK) to analyze the degree to which physiographic versus ecological variables influence spatio-temporal variation in understory microclimate conditions. We monitored understory temperature in 200 forest plots within a 274 km² environmentally heterogeneous region in northern California (0.55 obs/km²). For each plot location, we measured four physiographic influences (elevation, coastal proximity, potential solar radiation, topographic wetness index) and three ecological drivers (forest patch size, proximity to forest edge, tree abundance). Temperature observations were aggregated to three time scales (hourly, daily, and monthly) to examine temporal variability in microclimate dynamics and its effect on spatial prediction. The obtained prediction models included both physiographic and vegetative effects, although the relative importance of individual effects varied greatly between the different models. Across time scales, elevation and coastal proximity had the most consistent physiographic effects on temperature, followed by the vegetative effects of forest patch size and distance to forest edge. RK captured significantly more landscape-scale variability in understory temperature than a regression-only approach with considerably better model performance at hourly and daily time scales than at a monthly scale. Using varied sampling density scenarios our results also suggest that predictive accuracy drops considerably at densities less than 0.34 obs/km². This research illustrates how geospatial and statistical modeling can be used to distinguish physiographic versus ecological effects on microclimate dynamics and elucidates the spatial and temporal scales that these processes operate.