Transcript Do microenvironments govern macroecology?

Do microenvironments govern macroecology?
Frank W. Davis1, John Dingman3, Alan Flint3, Lorrie Flint3, Janet Franklin4, Alex Hall5, Lee Hannah6, Sean McKnight2, Max Moritz7, Malcolm North8,
Kelly Redmond9, Helen Regan10, Peter Slaughter2, Anderson Shepard2, Lynn Sweet2 and Alexandra Syphard11
1University
of California, Santa Barbara; 2Earth Research Institute, University of California, Santa Barbara; 3US Geological Survey, California Water Science Center;
4School of Geographic Sciences, Arizona State University; 5Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles; 6Conservation International;
7Department of Environmental Science Policy and Management, University of California, Berkeley; 8USDA Forest Service, Pacific Southwest Research Station;
9Western Regional Climate Center, Desert Research Institute, University of Nevada, Reno; 10University of California, Riverside; 11Conservation Biology Institute
Overview
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Question 1. What is the distribution of microenvironments in mountain landscapes
in California under current climate?
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Develop a physical model that represents the topographic, energy budget,
and hydrologic drivers under current climate, describing the
microenvironments of each study area
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Survey microenvironments in the landscapes of our study sites, using remote
sensing and field surveys
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Measure conditions in microenvironments to validate inputs and outputs of
the physical model
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Develop mechanistic, species-specific models of habitat suitability of
microenvironments.
Question 2. How does climate change affect species occupancy of
microenvironments?
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Project future distributions of microenvironments using the physical model
driven by GCM simulations downscaled through both dynamic and statistical
methods
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Determine species sensitivities to climate change in the establishment phase
through experimental manipulation
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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?
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Model population responses to climate change incorporating
microenvironments
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Model community/population dynamics in response to climate change
incorporating microenvironments
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Model the frequency of disturbance (fire) relevant to creation of
establishment phase microenvironments
Abstract
Ensemble Hydrologic Modeling
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.
• Comparison of 69 monthly projections
(precipitation, Tavg, Tmax, Tmin)
23 models and 3 emissions scenarios:
A2, B1, and A1B, downscaled to 800 m
using BCCA methodology
• Climate projections further
downscaled to 270-m for
hydrologic model application
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 Site Locations
Common Gardens
Microclimate Sensor
Network
• Apply projections to Basin Characterization
Model to produce hydrologic variables in
response to climate change
• Grid-based input and output data
• Monthly or daily time step
• Relies on hourly model to calculate
potential evapotranspiration from solar
radiation and topographic shading
Calculates recharge, runoff, actual ET,
climatic water deficit (CWD), snow
accumulation and melt.
• CWD = PET – AET
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.
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
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 km2)
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
• Apply multivariate analysis with abiotic explanatory variables (water and
energy balance) to predict the future distribution of endemic flora species.
Funding, Support and Cooperation