1. Do microenvironments govern macroecology? Frank W. Davis 1, John Dingman 3, Alan Flint 3, Lorrie Flint 3, Janet Franklin 4, Alex Hall 5, Lee Hannah 6, Sean McKnight 2, Max Moritz 7, Malcolm North 8, Kelly Redmond 9, Helen Regan 10, Peter Slaughter 2, Anderson Shepard 2, Lynn Sweet 2 and Alexandra Syphard 11 1 University of California, Santa Barbara; 2 Earth Research Institute, University of California, Santa Barbara; 3 US Geological Survey, California Water Science Center; 4 School of Geographic Sciences, Arizona State University; 5 Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles; 6 Conservation International; 7 Department of Environmental Science Policy and Management, University of California, Berkeley; 8 USDA Forest Service, Pacific Southwest Research Station; 9 Western Regional Climate Center, Desert Research Institute, University of Nevada, Reno; 10 University of California, Riverside; 11 Conservation Biology Institute Overview Question 1. What is the distribution of microenvironments in mountain landscapes in California under current climate? Develop a physical model that represents the topographic, energy budget, and hydrologic drivers under current climate, describing the microenvironments of each study area Survey microenvironments in the landscapes of our study sites, using remote sensing and field surveys Measure conditions in microenvironments to validate inputs and outputs of the physical model Develop mechanistic, species-specific models of habitat suitability of microenvironments. Question 2. How does climate change affect species occupancy of microenvironments? Project future distributions of microenvironments using the physical model driven by GCM simulations downscaled through both dynamic and statistical methods Determine species sensitivities to climate change in the establishment phase through experimental manipulation Model habitat suitability of future microenvironments Question 3. How are the macroscale dynamics of species distribution, abundance and diversity response to climate change altered by microenvironments? Model population responses to climate change incorporating microenvironments Model community/population dynamics in response to climate change incorporating microenvironments Model the frequency of disturbance (fire) relevant to creation of establishment phase microenvironments Abstract The aim of this research is to measure and model microenvironment controls on tree species establishment and population dynamics in order to predict regional range dynamics under projected future climate for four dominant tree species across four study sites in the Sierra Nevada and Coast Ranges of California. In a novel combination of site trials, physical models, distribution models and population models, our design incorporates measured (rather than inferred) species' tolerances relevant to microenvironments at spatial scales that vary over five orders of magnitude (30m-3000km). Our tools will be reciprocal transplant experiments, species trait-based distribution models, field surveys, population models and biogeographic models of climate change. Biological studies will be coordinated with and informed by detailed, multiscale measurement and modeling of climate and soil factors related to temperature and moisture regimes. Our approach is an integrated multi-scale modeling framework that allows us to bridge scales from micro- to macro-, incorporating experimental results and field observations in an iterative process of refinement. The advantages of such a system in climate change analyses have long been recognized (Root and Schneider 1995). Our approach will contribute to understanding microenvironment effects on macroecology. Funding, Support and Cooperation Common GardensStudy Site LocationsMicroclimate Sensor Network Specific Project Challenges Organizing and making a very large number of sensor data files available for use by the project team Cross-scale integration of biological and physical processes Downscaling 69 global climate models to very fine scales includes both scientific (e.g., coupling dynamical and statistical approaches) and data management challenges Study Species Montane Study Sites: Tejon Ranch Teakettle Experimental Forest Foothill Study Sites: Tejon Ranch San Joaquin Experimental Rangeland 6 common gardens per site: North slope, south and valley bottoms Temperature, relative humidity, precipitation, wind, soil moisture and insolation recorded Plantings of study species to measure establishment Grid of 20 temperature sensors measuring mean surface temperature at the 30 m scale of digital terrain data Containing plantings of all species and provenances Experiments on Species Establishment Many studies investigating the potential responses of tree species to climate change rely on temperature tolerances inferred from ecotonal or range boundaries. Field trials using seeds of different geographic provenance provide a more consistent basis for investigating microclimatic and genetic controls on tree species establishment. Array of sensors sampling landscape heterogeneity across approximately 2 km per site estimate mean surface temperature at the scale of available coarse climate grids and at the scale of a dynamic regional climate model (1-3 km 2 ) Data will be used to parameterize: Species Distribution Models Spatially explicit population and landscape simulation models Linking fine scale climate variation and local population dynamics to landscape and regional patterns of species distribution under alternative climate scenarios Ensemble Hydrologic Modeling Comparison of 69 monthly projectionsComparison of 69 monthly projections (precipitation, Tavg, Tmax, Tmin) (precipitation, Tavg, Tmax, Tmin)  23 models and 3 emissions scenarios:  23 models and 3 emissions scenarios: A2, B1, and A1B, downscaled to 800 m A2, B1, and A1B, downscaled to 800 m using BCCA methodology using BCCA methodology Climate projections furtherClimate projections further downscaled to 270-m for downscaled to 270-m for hydrologic model application hydrologic model application Apply projections to Basin CharacterizationApply projections to Basin Characterization Model to produce hydrologic variables in Model to produce hydrologic variables in response to climate change response to climate change Grid-based input and output dataGrid-based input and output data Monthly or daily time stepMonthly or daily time step Relies on hourly model to calculateRelies on hourly model to calculate potential evapotranspiration from solar potential evapotranspiration from solar radiation and topographic shading radiation and topographic shading Calculates recharge, runoff, actual ET, climatic water deficit (CWD), snow accumulation and melt. Calculates recharge, runoff, actual ET, climatic water deficit (CWD), snow accumulation and melt. CWD = PET – AETCWD = PET – AET Apply multivariate analysis with abiotic explanatory variables (water and energy balance) to predict the future distribution of endemic flora species.Apply multivariate analysis with abiotic explanatory variables (water and energy balance) to predict the future distribution of endemic flora species. Reference: Flint and Flint (2012) Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis. Ecological Processes 1:1 pp. 1-15