Extinction risk assessment
Population growth and temperature increases in China have been highly
correlated in the last few hundred years, which leads to uncertainty
regarding which variable plays a stronger role in local extinctions. In
total, 604 occurrence records (87% of the historical observations) of
Chinese pangolin were documented in 1970-2000 across China, and more
detailed climatic data are available for this period. Therefore, we
lowered the timescale and combined 19 climatic variables, anthropogenic
variables from HYDE, elevation and identified extinction records of
Chinese pangolin to construct a model to assess extinction risk with
MaxEnt. We compared those occurrences with the current distribution
range of Chinese pangolins assessed by the IUCN expert group and
identified 94 occurrences outside the distribution area (IUCN, 2019;
Fig. 2). We collected 162 rescue and observation records of Chinese
pangolins during 2000-2020 from the wildlife recuse departments, news
reports and GBIF database in China. We set up circular buffer zones
(r=50 km) according to extant occurrences of Chinese pangolin to exclude
the potential distribution area (Fig. 2). Considering the rapid
development of wildlife monitoring technology and emphasis on
biodiversity conservation of the Chinese government, 220 occurrences out
of buffer zones were supposed to be extinct (Fig. 2). In total, 314
occurrence records out of distribution range and buffer zones were
considered extinction locations (Fig. 2). Those occurrences were
dispersed (no highly spatial autocorrelation) based on the analysis of
Ripley’s K function. The elevation data were derived from the SRTM.
Higher-resolution and multidimensional climate data from 1970-2000 were
available from WorldClim (download fromhttps://worldclim.org/). These
biological variables are related to various aspects of temperature and
precipitation affecting the geographic distribution of Chinese pangolins
and their prey (mainly ants and termites).
The resolution of the environmental variables was uniform at 5 arc
minutes, and each grid retained only one occurrence record. We input
extinction records and environmental variables into MaxEnt (version
3.4.1) (Phillips et al., 2006) and ran the Model 25 iterations
(preexperiment) to exclude insignificant variables with 0% contribution
and 0 permutation importance value. To avoid multicollinearity, we
calculated the Pearson correlation coefficient (r) between variables;
when r >0.7, the variable with the lower contribution rate
was discarded. Finally, eight variables, including population density,
elevation, cropland, grazing, temperature seasonality (bio4), mean
temperature of the driest quarter (bio9), precipitation seasonality
(bio15) and precipitation of the warmest quarter (bio19), were used to
construct the model.
We ran the Algorithm 100 times, and the average of the predicted results
was output in logistic. Maximum training sensitivity plus specificity
was used as the threshold value to distinguish extinction, and Nature
Breaks methods were used to further assess the levels of extinction
risk. Finally, we extracted the extinction risk of extant populations of
Chinese pangolins according to the risk map.