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.