Climate change is already creating novel environmental conditions. Responding proactively and effectively to its impacts in target habitats will rely increasingly on predictive ecological models. A major challenge of using spatially-explicit models is spatial mismatch: the area represented by each grid square is typically much larger than both the scale at which microclimate conditions vary and at which most organisms experience their environments. This ongoing project combines emerging methods in remote sensing with mechanistic modeling to map the 3D microclimate landscape and quantify how spatial resolution affects our ability to detect microclimate-scale variation. We are using terrain layers derived from unoccupied aerial systems (UAS) to drive a series of microclimate and biophysical models, predicting hourly, above and below-ground temperatures, and their physiological implications for a model organism, at cm2-scale spatial resolutions. Then, we are comparing predictions with those generated using coarser-resolution (1m2 - 10,000km2), freely downloadable terrain data to determine how spatial resolution affects the accuracy, strength, and direction of model predictions.
Results/Conclusions
Increasing the spatial resolution of the microclimate model increases our ability to detect heterogeneity in thermal conditions and, to a point, model accuracy. Most spatially-explicit ecological models, such as species distribution models, are driven by relatively coarse-resolution data. However, our results so far suggest that coarse models can mask variation in key microclimate components that determine the physiological outcomes of organisms’ interactions with their environments. Coarse-resolution models may be unable to detect microrefugia, the patches of microclimatic space in which species can persist during periods of otherwise unsuitable climatic conditions, and microclimatic range edges, the dynamic boundaries of microclimatic space that could indicate the potential for range shifts or act as barriers to dispersal. Although coarse-resolution spatial data are free and widely available, they may be insufficient for building realistic models and should be used with caution. Collection of spatial data that more realistically represent the microclimate landscape with which organisms actually interact should be a high research priority.