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Raw Climatic Data

DAYMET data were produced from models and algorithms imposed onto daily outputs of ground weather stations throughout North America in order to produce a continuous surface dataset (Thornton et al. 2018; Thornton et al. 1997). The monthly values for minimum and maximum air temperatures and water vapor pressure used for our analysis were generated by averaging the daily DAYMET output values. Monthly precipitation totals were used in our analysis summed from daily DAYMET output values. The spatial resolution for all NASA DAYMET data is 1km x 1km.

Data was downloaded in GeoTIFF format for North America directly from The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) which serves as a data center for the NASA Earth Observing System Data and Information System (EOSDIS). Downloaded rasters were multispectral GeoTIFF rasters comprised of 12 bands (one for each month). Each individual band contained monthly data for one of the environmental parameters: Average air temperature, Precipitation, Maximum air temperature, Minimum air temperature, Water vapor pressure. We conducted a batch download for a total of 68 multispectral GeoTIFF rasters containing more than 3.5 GB of environmental data.

Multispectral GeoTIFF rasters were then separated into single band rasters for each parameter and month; new monthly single band rasters were then generated including only data for Maricopa County, Arizona, utilizing ESRI ArcMAP version 10.6.0.8321.

Bioclimatic Variables

Species distribution are driven by both climatic and non-climatic variables (O’Donnell and Ignizio 2012). In order to understand the impact of climatic variables on species, researchers have developed bioclimatic predictors to explain climatic seasonality. These bioclimatic predictors can be used in an interdisciplinary way to look at nuances of climate impacts instead of utilizing only average and total parameters like temperature and precipitation (Nix 1986; Hijmans 2004).

Once climactic variables were extracted for Maricopa County from NASA DAYMET data, bioclimatic predictors were generated using the biovars function of the dismo package on Program R version 3.5.1 (Hijmans 2017, R Core Team 2017). Quarters referred to any 3 months in consecutive order and November and December data were analyzed with January and February data of the same calendar year in order to analyze three months. The bioclimatic predictors were generated for each calendar year from 2000 to 2016 for a total of 323 single band rasters. The explanation for each raster calculation is shown in Table 2 (Nix 1986; Hijmans 2004; O’Donnell and Ignizio 2012).

References

Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A., 2004. The WorldClim interpolated global terrestrial climate surfaces. Version 1.3.

Hijmans, Robert J., 2017, Program R Dismo Package: Species Distribution Modeling: Berkeley, Calif, Museum of Vertebrate Zoology, University of California at Berkeley. Available at http://www.worldclim.org/bioclim.

Nix, Henry A., 1986, A biogeographic analysis of Australian elapid snakes, in Longmore, Richard, ed., Atlas of elapid snakes of Australia: Canberra, Australian Flora and Fauna Series 7, Australian Government Publishing Service, p. 4‒15.

O’Donnell, M.S., and Ignizio, D.A. 2012. Bioclimatic Predictors for Supporting Ecological Applications in the Conterminous United States. U.S Geol. Surv. Data Ser. 691: 10.

R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

Thornton, M.M., P.E. Thornton, Y. Wei, B.W. Mayer, R.B. Cook, and R.S. Vose. 2018. Daymet: Monthly Climate Summaries on a 1-km Grid for North America, Version 3. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1345

Thornton, P.E., Running, S.W., White, M.A. 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology 190: 214 - 251. https://doi.org/10.1016/S0022-1694(96)03128-9