Meeting Abstract
64.6 Monday, Jan. 6 09:15 Bringing regional projections of climate and landscape to the organismal level LEVY, O*; BUCKLEY, BB; KEITT, TH; ANGILLETTA, MJ; Arizona State University, Tempe, AZ; University of North Carolina at Chapel Hill, Chapel Hill, NC; The University of Texas at Austin, Austin, TX; Arizona State University, Tempe, AZ levyofi@gmail.com
Organisms are the fundamental components of ecological systems. Organisms interact with microenvironments and respond to microclimatic changes. Macroclimate, on the other hand, operates and changes at spatial scales well beyond any organism. Climatic data used to predict biological responses are temporally or spatially aggregated. Hence, these data obscure natural variation that occurs among microhabitats as well as climatic extremes, both of which are important for accurate forecasting. To resolve this problem, we used the Community Earth System Model to predict 30 years of current and future climates around the globe. We then used the Weather Research and Forecasting model to downscale these climatic predictions to spatial and temporal resolutions of 36 km and 1 h, respectively. Finally, we modeled changes in vegetation cover caused by climate change. To simulate fine-scale climatic variation, we used the model output and heat transfer functions to calculate ambient, surface, and soil temperatures for various levels of shade and heights above or below ground. We applied this climatic downscaling to predict operative temperatures for a widespread species of lizards, Sceloporus undulatus. We implemented an individual-based model where lizards forage, grow, and reproduce based on their thermal tolerance and predicted climates. Lizards can buffer climate change by thermoregulating behaviorally, but this capacity varies dramatically among locations. Since embryos cannot thermoregulate, the timing and location of nesting should shift differently among locations, given predicted changes in soil temperatures. Our downscaling approach should enable one to make more accurate predictions about the biological impacts of climate change.
