Climate and urbanization data
We included four climate and one urbanization variable in our modeling
framework. The climate variables were year-specific and included mean
annual temperature, annual precipitation, temperature seasonality, and
precipitation seasonality. We obtained annual mean values of the maximum
2-m air temperature data and annual precipitation values for North
America at a 1-km spatial resolution for the five years of our study
from Daymet (Thornton
et al., 2016a). We generated annual temperature and precipitation
seasonality using the monthly maximum temperature and precipitation
summaries provided by Daymet at a 1-km spatial resolution
(Thornton et al.,
2016b). Temperature seasonality was calculated as the standard
deviation of the monthly maximum temperature values for the
corresponding year, and precipitation seasonality was the coefficient of
variation of the monthly precipitation values for the corresponding
year.
We used estimated human population density for the year 2020 as a proxy
for urbanization and obtained this data from the Center for
International Earth Science Information Network, which provides global
estimates of population density at a 0.25 degree resolution
(~27-km; CIESIN, 2017). Global estimates of human
population density are only available on five-year intervals, so we
choose to only use the 2020 estimates as a proxy for urbanization given
our sampling temporal extent. Year-specific changes in human population
density are minimal compared to the variation across space, and
therefore should have minimal impact on statistical models.