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Wednesday, April 11 • 1:30pm - 1:45pm
MODELLING CLIMATE AS PROCESS DRIVERS: Predicting Microclimate Temperatures in the Great Smoky Mountains Using Statistical Downscaling and LiDAR-derived Vegetation Information

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AUTHORS: Samuel Stickley*, Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign; Jennifer Fraterrigo, Department of Natural Resources and Environmental Sciences and Program in Ecology, Evolution, and Conservation Biology, University of Illinois at Urbana-Champaign

ABSTRACT: Predicting microclimate temperatures over large extents is imperative for understanding how organisms with limited mobility may respond to climate change. In forests, vegetation structure and topographic factors can strongly influence near-surface temperature, resulting in a spatial mismatch between regional climate and the microclimate experienced by ground-dwelling organisms in particular. The direction and magnitude of such effects on near-surface temperatures are not well understood, however, making microclimate prediction at the forest floor challenging. We demonstrate an approach to downscaling regional temperatures to a fine-scale (< 3 m) for Great Smoky Mountains National Park (Tennessee and North Carolina) using previously collected temperature data from a spatially distributed network of microclimate loggers. We used linear mixed models within an information theoretic framework to assess the effects of regional weather station temperature measurements, LiDAR-derived vegetation characteristics and GIS-derived environmental characteristics on air temperature near the forest floor during the surface-active period of plethodontid salamanders from 2006-2010. The best supported models included regional weather station data, solar insolation, vegetation density at heights below 15 m, Soil Topographic Index (a proxy for cold-air drainage) and distance to stream. These findings reveal that the interactive effects of vegetation density at lower heights with certain topographic factors are important to consider when predicting microclimate temperatures in forested landscapes. In addition to increasing our understanding of microclimatic variation across heterogeneous landscapes, our results provide a fine-scale dataset of temperature near the forest floor for future use in modeling organismal responses to changes in microclimatic regimes.

Wednesday April 11, 2018 1:30pm - 1:45pm CDT
LaSalle 1 (7th Floor)