2.3 MaxEnt modelling
We used the open source software package MaxEnt v.3.4.4, which can be
downloaded from http://biodiversity
informatics.amnh.org/open_source/maxent/ (Sun et al., 2020). Francois’
langur occurrence records and environmental variables were imported into
MaxEnt. All models were set random seeds and used 75% of the record
data as the training set and the remaining 25% as the test set with 10
replicates and bootstrap as replicated run type (Wang et al., 2020),
while the number of modelling iterations was set at 10000 to allow the
models to have adequate time for convergence (Young et al., 2011). The
jackknife test procedure was used to evaluate the relative importance of
each environmental variable. The response curves of environmental
variables were used to test the correlation between the variables and
the occurrence probability of Francois’ langur. Logistic was selected to
output results. The default options were used for the other parameters.
2.4 Model evaluation and validation
Receiver operating characteristic (ROC) curve analysis is an effective
method for evaluating the accuracy of species distribution models (Liu
et al., 2022; Wang et al., 2007). We used the area under the ROC curve
(AUC), which is a threshold-independent discrimination metric that
represents the probability that a random presence or absence is
correctly assigned by the model (Phillips et al., 2006). The AUC values
range from 0.5 to 1.0, with AUC > 0.9 indicating excellent
performance (Janitza et al., 2013).
The mean value of the 10 replicates from the model was taken as the
distribution result, and the value represent habitat suitability index
(HSI), which was between 0 and 1. The ASCII output file from MaxEnt was
transformed into a raster layer, which was reclassified using the
Spatial Analyst tool. At present, the classification of suitable habitat
areas is largely based on experience, and there is no unified standard
(Sun et al., 2020). We used the Jenks’ natural breaks classification in
ArcGIS combined with maximum training sensitivity plus specificity (Max
TSS), balance training omission, predicted area and threshold value
(average TPT) in the MaxEnt output to reclassify the data into four
suitability areas for Francois’ langur: high habitat suitability,
moderate habitat suitability, low habitat suitability, and unsuitable
habitat. The spatial analysis tool in ArcGIS 10.2 was used to calculate
the areas and proportions of each suitability area.
2.5 Changes in potential distribution and centroid shifts
To construct the core distribution shifts and potential area changes
under each future climate model, binary suitable or unsuitable maps were
produced using ArcGIS 10.2 to analyse the changes in centroid and
potential distributions based on the threshold of unsuitable area. By
comparing the suitable/unsuitable potential distributions in different
time periods, a total of four types of distribution changes were
determined, namely, contraction, expansion, no change and no occupancy
(Jose & Po, 2020; Liu et al., 2022). Moreover, because the potential
distribution of Francois’ langur was scattered and its outline was
irregular, centroid shift analysis was conducted based on the potential
distribution under different time periods using SDMtoolbox v2.4 (Brown,
2014) to visually show the changing trends of the potential core
distribution of Francois’ langur.