2.1 Data of macaques and variables
The project includes datasets of geographic distributions of the fossil macaques found in the Pleistocene. They comprised Macacayoungi , M. jiangchuanensis , M.anderssoni , M. mulatta , M. robustus , and M. spp., and eight extant species in mainland China –M. mulatta , M. arctoides , M. leonina ,M. assamensis , M . thibetana , M. munzala and M. leucogenys , and another one in Taiwan Island (M. cyclopis ). The database was collected from a broad literature review of academic journals, government annals, and archives, magazines, and books in Chinese (Please see the details in Supplementary information). Unfortunately, it is difficult, if not impossible, to define the relationship between fossils taxa and extant crown species – they are extensively overlapped in distribution, so all the taxa were analyzed at the genus (Macaca ) other than the species level.
Nineteen variables are relevant to climatic, ecological, and environmental alterations,Bioclimatic variables (BC). They were extracted from the WorldClim database (Fick and Hijmans, 2017), which have been regarded to drive animals’ geographic distribution and evolutionary development significantly (Virkkala and Lehikoinen, 2017), especially regarding mammals (Sharma et al., 2019). As addressed above, like other primates (colobines and apes), macaques in East Asia started continental dispersion and radiation in the Late Miocene and Early Pliocene, about 5-6 Mya, from Western China. Severe climate changes drove such processes during the glaciation of the Quaternary (Otto-Bliesner et al., 2006; Li et al., 2020). Thus, to have an integral comprehension of distribution changes from the Quaternary, the blooming period of the Asian macaques (Zhang et al., 2022), the fossil distribution of the macaques was analyzed also.
We used the shared social-economic pathways (SSPs) to analyze BC variables to predict the prospective distribution profile in the 2050s. Such a method has successfully been applied in predicting global temperature changes, referring to different trajectories and greenhouse gas (GHG) parameters in the 21st century (Riahi et al., 2017). Two different SSPs were considered – SSP5 assuming continuous accelerated greenhouse emission (GHG) and SSP2, presuming a moderate emission level following the proposed reduction of GHG emissions (Riahi et al., 2017).
Land use variables (LU) were from Land-use Harmonization (https://luh.umd.edu/data.shtml), a database of LUH2 v2h covering 850-2015, and a future land-use dataset (LUH2 v2f) for CMIP6, including the period 2015-2100 (Hurtt et al., 2011). Considering the consistency of climate scenarios, we used SSP2 and SSP5 strategies in this study. We analyzed eight variables for 1970-2000 and 2041-2060 to demonstrate the current and future distribution models for the 2050s.
The human population variables (HP) were downloaded from Spatial Population Scenarios for all five SSPs with decadal intervals and 0.125-degree resolution (Jones and O’Neill, 2016). We obtained population distribution data for 2000 and proposed the two scenarios (SSP2 and SSP5) to calculate population density and distribution in the 2050s.