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Modeling Bird Distribution Responses to Climate Change:
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Modeling Bird Distribution Responses to Climate Change:
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California supports high biodiversity and many plants and animals found nowhere else due to its large geographic extent and highly varied habitats. Meanwhile, rapid human population growth is resulting in large–scale landscape changes throughout the state. Thus many of California’s ecosystems are particularly vulnerable to future changes in the global climate such as increased temperatures and changing precipitation patterns.
The development of sophisticated species distribution modeling techniques provides an opportunity to examine the potential effects of these changes on bird communities. Using these modeling approaches, we are relating bird data to environmental layers to generate robust predictions of current (1971–2000) and projected future species occurrence. Future bird distributions are based on regional climate model projections for the periods 2038–2070 (IPCC Scenario A2).
Bird species distributions were created using the Maxent modeling technique: Maxent (Phillips et al. 2006), which is able to model non-linear responses to environmental variables. Map values represent the predicted habitat suitability; the higher the values, the higher the suitability (Phillips and Dudik 2008).
Because bird distributions are greatly affected by the availability of suitable habitat, we used general vegetation type as an input to our bird models. We used the Random Forest (Breiman 2001) modeling technique to develop models and generate future projections of vegetation.
| All environmental variables where manipulated and model map layers created within ArcGIS 9.2 (ESRI 2009) which was made available through a grant from the ESRI Conservation Program. |
Models were developed using presence information from a compilation of different bird survey datasets from PRBO Conservation Science, the Redwood Sciences Laboratory (RSL), Klamath Bird Observatory (KBO), The California Natural Diversity Database (CNDDB) and USGS Breeding Bird Survey (BBS), and eBird. The combination of these datasets is made possible by the California Avian Data Center (CADC) a node of the Avian Knowledge Network (AKN). Each of these sources have a high spatial accuracy, collectively cover all major geographical regions of the state. Bird data were filtered to remove non-breeding records.
Current climate data were based on monthly climate means for the years 1971-2000 taken from the PRISM dataset (Daly et al. 1994) and transformed into standard bioclimatic variables (Nix 1986). Future climate scenarios were based on projections from 30-km resolution regional climate models (Snyder and Sloan 2005, Pal et al. 2007) driven by one of two global climate models: 1) NCAR CCSM3.0: National Center for Atmospheric Research Community Climate System Model, averaged for the years 2038-2069 (478-610 ppm CO2); 2) GFDL CM2.1: Geophysical Fluid Dynamics Laboratory Coupled Climate Model, averaged for the years 2038-2070 (478-615 ppm CO2);
Vegetation, soil, and stream data consisted of land cover data from the California Gap Analysis project (Davis et al. 1998), modified SSURGO soil attributes (Miller and White 1998), and streams from the National Hydrography Dataset. Final input variables (Table 1) were chosen based on their predictive abilities and biological relevance.
Maps for all species are available for download from the "Individual Species Models" link on the Environmental Change Network download page (login required). For more information about these models please contact Sam Veloz sveloz@prbo.org or Dennis Jongsomjit djongsomjit@prbo.org of PRBO Conservation Science.
Table 1. Environmental variables used in distributional models.
| Variable | Definition | Model(s) |
|---|---|---|
| Annual mean temperature | The average temperature in a year. | Bird, Vegetation |
| Mean diurnal range | A measure of the average temperature change within each month. | Bird, Vegetation |
| Isothermality | A measure of the temperature changes throughout a year. | Bird, Vegetation |
| Temperature seasonality | A measure of the variation in temperature throughout a year. | Bird, Vegetation |
| Mean temperature of the warmest quarter | The average temperature for the three warmest consecutive months in a year. | Bird, Vegetation |
| Annual precipitation | The total rainfall in a year. | Bird, Vegetation |
| Precipitation seasonality | A measure of the variation in rainfall throughout a year. | Bird, Vegetation |
| Precipitation of driest quarter | Total rainfall amount for the three driest consecutive months in a year. | Bird, Vegetation |
| Vegetation | Grouped vegetation types taken from the California Wildlife Habitat Relationships classification scheme. | Bird |
| Distance to stream | A measure of the distance to the nearest stream (1/distance in meters). | Bird |
| Solar radiation | Average yearly solar radiation (watt-hours / m2) | Vegetation |
| Slope | Maximum rate of change in elevation (degrees) | Vegetation |
| Soil pH | Soil acidity/alkalinity (pH) | Vegetation |
| Soil permeability | Soil permeability (inches of H20 held / inch of soil) | Vegetation |
| Soil available water capacity (AWC) | Soil available water capacity (inches of H20 / hour) | Vegetation |
Breiman, L. 2001. Random Forests. Machine Learning 45:5-32.
Daly, C., Neilson, R. P. & Phillips, D. L. 1994. A statistical topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33, 140-158.
Davis F.W., D.M. Stoms, & A.D Hollander, et al. 1998. The California Gap Analysis Project — Final Report. University of California, Santa Barbara, CA.
ESRI, (Environmental Systems Research Institute). 2009. ArcGIS 9.2. Redlands, CA.
Miller, D. A., and R. A. White. 1998. A conterminous United States multi-layer soil characteristics data set for regional climate and hydrology modeling. Earth Interactions 2:1-26.
Nix, H. 1986. A biogeographic analysis of Australian elapid snakes. Pages 4-15 in R. Longmore, editor. Atlas of Elapid Snakes of Australia. Australian Government Public Service, Canberra, Australia.
Phillips, S.J., R.P. Anderson, & R.E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling. 190:231-59.
Phillips, S.J. & M. Dudik. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161-175.
Snyder, M. A., J. L. Bell, , L. C. Sloan, P. B. Duffy, & B. Govindasamy. 2002. Climate responses to a doubling of atmospheric carbon dioxide for a climatically vulnerable region. Geophysical Research Letters 29, 9-1 to 9-4.
Snyder, M. A. & L. C. Sloan, 2005. Transient future climate over the western United States using a regional climate model. Earth Interactions 9, 1-21.