One hundred years of climate change in Mexico

A map of the biogeographic provinces of Mexico (Fig 2) was obtained from the Comisión Nacional para el Conocimiento y Uso de la Biodiversidad [57]. This regionalization was based on the distributional pattern of four taxonomic groups (vascular plants, amphibians, reptiles, and mammals) and the main morpho-tectonic features of Mexico [56]. Each unit represents a relatively homogeneous area with high levels of endemic species sharing similar historical, physiographic, climatic, edaphic, and vegetation features [57]. Climatic changes between time periods We estimated annual and seasonal changes of maximum temperature, minimum temperature and precipitation, at three scales: Mexico, the three biogeographic regions (i.e., Nearctic, Neo- tropical and the Transition Zone) and the 19 biogeographic provinces, using as source data the monthly ANUSPLIN interpolated climate surfaces (grid points). For estimating changes from t1-1940 to t2-1970 and from t2-1970 to t3-2000 at the country level and the three regions, first, we calculated descriptive statistics using the grid point values of the raster surfaces to summarize temperature and precipitation changes. We also estimated extreme warming and cooling at the 90th and 10th percentile distribution of the grid point val- ues of temperature changes and the median fractional increases and decreases in precipitation. Then, for the 19 provinces, we calculated seasonal changes from t1-1940 to t2-1970 and from t2-1970 to t3-2000 using the average values of the grid points of the climate surfaces calculated

with the software Earth Trends Modeler (ETM) of Idrisi Selva [52]. Seasons were defined as: (i) December, January, February (DJF); (ii) March, April, May (MAM); (iii) June, July, August (JJA); and (iv) September, October, November (SON). Changes in precipitation were esti- mated in millimeters (mm) and percentage (%), and changes in temperature in Celsius degrees (˚C). To visualize a monotonic upward or downward change through time in the interpolated cli- mate surfaces of annual precipitation (Bio 12), maximum temperature of the warmest month (Bio 05) and minimum temperature of the coldest month (Bio 06), we produced a map show- ing the consistency of the sign between periods as follows: at each grid point, we obtained the difference between t2-1970 to t1-1940 and between t3-2000 to t2-1970, if the sign was consistent between these two transitions we classified them either as Positive or Negative, otherwise, we classified them as Not consistent. Finally, we performed a Pettitt Test for Change-point Detection on annual averages time- series of all individual weather stations used for creating the climate surfaces across the coun- try [58]. The Pettitt test is a non-parametric analysis for detecting inhomogeneities in a time- series and identifying the possible year of breakpoint [59]. Inhomogeneities in climatic time- series may occur owing to climatic factors, or due to instrumental, observer, or positional changes in the stations, and they may distort the signal of climatic change [60]. We used the pettitt.test function [61] implement in the R software [51]. Results

Climate surfaces Diagnostic measures obtained from ANUSPLIN indicated that model fit of splines varied across historical time slices. The average ratio of the signal to the number of data points was <0.5 for monthly temperatures and precipitation, mostly for t2-1970 and t3-2000 (S1 Table in S1 File). For t1-1940, only a small number of weather stations (<900) had available precipita- tion data. Thus, we eliminated only those stations with higher residual error (>1) to maintain the largest number of stations (>800). The average signal for precipitation was above the per- mitted threshold (values greater than about half the number of data points), indicating that the PLOS ONE Historical climate change in Mexico PLOS ONE | https://doi.org/10.1371/journal.pone.0209808 July 16, 2020 7 / 19