4 DISCUSSION
4.1 Influence of environmental variables
In the field of Francois’ langur ecology, habitat selection and use have
been an important research topic for researchers. The factors of habitat
selection have been divided into biological, geographical, and
anthropogenic factors (Han et al., 2023). Our results suggested that
bioclimatic variables made the largest contribution to the distribution
of Francois’ langur, vegetation, water source and anthropogenic
variables were also important factors influencing the distribution of
Francois’ langur. As demonstrated by numerous previous studies,, the
distribution of Francois’ langur was largely restricted to areas
characterized by karst topography, limestone cliffs, and caves with
mixed conifer-broadleaf forests as they provided quality food resources
and cover for predator avoidance (Wang et al., 2011; Wen et al., 2010;
Zeng et al., 2013). Additionally, its distribution was concurrently
influenced by various factors, including temperature, precipitation and
vegetation (Enstam & Isbell, 2004; Han et al., 2011).
We used the MaxEnt model to construct patterns of habitat suitability
for the Francois’ langur in China. According to our model prediction,
temperature-associated bioclimatic variables were of great importance in
predicting habitat suitability for Francois’ langur, while
precipitation-associated bioclimatic variables were the most important
habitat predictor for Francois’ langur. Numerous studies have shown that
in recent years the frequency of precipitation in southwest China has
decreased, while the intensity, total precipitation, and occurrence of
extreme rainstorm have increased (Yan et al., 2023; Lu et al., 2022;
Ding, 2014; Feng et al., 2012). As a result, river water level have
risen, and the original valley area with food and habitat caves for
Francois’ langur have been flooded, posing a direct threat to the living
habitat of Francois’ langur. In addition, temperature and precipitation
have a great influence on the growth rates of plant species, and fruits,
seeds, and especially young leaves are the main food of Francois’
langur. Therefore, temperature and precipitation indirectly determined
the distribution of Francois’ langur by affecting the availability of
potential food resources (Huang et al., 2008; Zhou et al., 2011; Zhou et
al., 2006). Based on the response curves in our study, the most suitable
distribution of Francois’ langur exhibited the following temperature
characteristics: temperature annual range of 21-23℃, mean of monthly
temperature range (max temp-min temp) of < 6℃, and minimum
temperature of coldest month of 9-11℃. The precipitation seasonality
(coefficient of variation) and precipitation of coldest quarter of
Francois’ langur’s most suitable distribution were < 65 mm and
70-80 mm, respectively. This result is consistent with the climatic
conditions of the extant range of Francois’ langur (Han & Hu, 2010; Li
et al., 2019; Niu et al., 2016).
Vegetation-related variable, the normalized difference vegetation index,
was another important variable. Francois’ langur prefers areas with
relatively high NDVI, which is consistent with Han et al. (2023), Niu et
al. (2016), Zeng et al. (2013) and Zeng (2011), who showed that
Francois’ langur habitat was mainly in the valley areas on both sides of
the rivers, covered by evergreen broad-leaf forests with higher tree
canopy density and vegetation cover, which provided availability of
better food resources and suitable cover for predator avoidance.
Meanwhile, distance to rivers and altitude were the less important
variables. Previous studies have indicated that Francois’ langur was
primarily distributed in valleys characterized by dominant evergreen
broadleaf forests. These areas offer abundant food and water sources,
and culverts and karst caves along rivers provide enough suitable
sleeping sites for Francois’ langur (Han et al., 2011; Hu, 2011; Wang et
al., 2011). In addition, the elevation of Francois’ langur distribution
ranges from 300 to 1800m, with seasonal changes in climate and food (Li
et al., 2019; Li et al., 2015).
For the anthropogenic variables, distance to roads (Including railways)
was found to be the more important variable compared to distance to
housing. Most previous studies have assessed the impact of linear
infrastructure on wild animals and have typically shown that
distribution decreases as road density increases (Céline Clauzel et al.,
2013; Clauzel et al., 2015). Roads are likely to act as barriers to
wildlife movement (Coffin, 2007; Zhu et al., 2011). Therefore, roads may
have negative impact on the activity and distribution of Francois’
langur. In this study, distance to roads was positively correlated with
the occurrence probability of Francois’ langur. However, distance to
housing had a limited influence on the distribution prediction of
Francois’ langur. This finding aligns with Han et al. (2023), Zeng et
al. (2013), Wang et al. (2011), who reported that Francois’ langur has
become accustomed to human disturbances, especially, crops grown on
agricultural land may supply the food resources for Francois’ langurs
(Han et al., 2023).
4.2 Characteristics of the current potential distribution
Our model estimated potential suitable habitat of 144207.44
km2 for Francois’ langur across the range, based on
the Max TSS and average TPT thresholds in the MaxEnt output. Hu et al.
(2011) showed that Francois’ langur was restricted to only five isolated
sites (Dashahe, Baiqing, Mayanghe, Kuankuoshui and Yezhong) with a total
area of about 912 km2 in Guizhou.Chen (2006)
estimated potential Francois’ langur habitat in Guangxi at approximately
3216.23 km2. Han and Hu (2010) confirmed that the
distribution area of Francois’ langur in Chongqing Jinfo Mountain was
about 50 km2. Our model predicted the largest
proportion of Francois’ langur habitat in China than the total habitat
in three provinces (Guizhou, Guangxi, and Chongqing) in previous
studies, which were based on survey records or estimates at a small
scale, such as one or more nature reserves. Importantly, our model
analysis was based on a machine-learning algorithm, species records from
the entire range in China, and a set of environmental variables
including climatic factors. Furthermore, our model prediction for the
Francois’ langur was based on a larger and more comprehensive scale than
prior methods (Chen, 2006; Han et al., 2011, 2023; Hu et al., 2011).
Our model predicted a total of 34265.96 km2 of
moderate- and high-suitability habitat for Francois’ langur, which only
accounted for 23.76% of predicted suitable habitat. The moderate- and
high-suitability habitats, primarily located in southwest Guangxi, east
of Chongqing and the Guizhou-Chongqing border, was nearly 10 times the
area of previous surveys (Chen, 2006; Han & Hu, 2010; Hu et al., 2011).
However, the low suitable habitat was distributed over a larger area as
fragmentation, which is consistent with Guan et al. (2022) and Niu et
al. (2016), who suggested that the Francois’ langur habitats presented
severe fragmentation, complex patch shape, weak patch aggregation, and
decentralization, and typically showed that habitat area decreased and
further fragmented with increasing human activities. The current
potential distribution in our study basically covered and was larger
than the historical distribution areas of Francois’ langur recorded in
“A guide to the mammals of China” (Smith et al., 2010) and “China’ s
mammal diversity and geographic distribution” (Jiang et al., 2015). Our
results can provide a scientific reference for further field survey on
the distribution of Francois’ langur in China.
4.3 Distribution change in the future
Our results indicate that, compared to the current potential habitat,
the suitable habitats of Francois’ langur will decrease in all future
periods under climate change. In the future climate prediction, the
temperature in southwest China are expected to continue increasing, and
show a more obvious difference in the increase between the northern and
southern regions (Yang et al., 2022; Zhang, 2020).Climate change will
lead to changes in species habitat, such as increased precipitation and
more extreme precipitation events (Feng et al., 2023; Yue, 2022). From
2021 to 2100, the area of suitable habitat will greatly contracted,
especially in the medium- and high-suitability areas along the border
between Guizhou and Chongqing. Moreover, most of the patchy areas of
low-suitability will cease to exist. The centroid of habitat shifted to
the southeast at a rate of approximately 2.84km/year from current to
2100. Previous studies have indicated that changes in annual
precipitation, annual mean temperature, precipitation and temperature
seasonality, as well as land use/ land cover changes, could reduce
suitable habitat for the large mammals such as Asiatic black bear
(Ursus thibetanus ), Asian elephant (Elephas maximus ) and
western hoolock gibbon (Hoolock hoolock ), and thus increase their
extinction risk (Deb, 2016). In addition, our results are consistent
with the description that primates and other large mammals are highly
dependent on a complete forest ecosystem for their food and security
requirements, and habitat fragmentation will accelerate local population
extinctions (Guan et al., 2022). For Francois’ langur, habitat
fragmentation caused by climate change and anthropogenic disturbance
would be the most important factor in changing the distribution of this
species.
4.4 Conservation recommendations
Our results showed that the current predicted habitats of Francois’
langur were over more than the history areas, but the distribution
habitats of Francois’ langur would be threatened to contract and
fragment dramatically in the future. We suggest to expand the survey
range of their habitat, reduce human disturbance and enhance habitat
connectivity by establishing ecological corridor especially the border
of Chongqing and Guizhou and further protection on these areas.
Moreover, conservation of the Francois’ langur should not be based only
on the existing distribution areas, but should also focus on
conservation beyond existing habitats, especially the expansion areas in
the study, which should be considered to join the national programme as
protected areas.
AUTHOR CONTRIBUTIONS
Yaqiong Wan: Conceptualization (lead); data curation (equal);
formal analysis (equal); writing - original draft (lead); writing -
review and editing (equal). Luanxin Li: Data curation (equal);
writing - original draft (equal). Jiang Zhou: Investigation
(equal); writing - review and editing (equal). Yue Ma: Data
curation (equal); writing - original draft (equal). Yanjing
Zhang: data curation (equal); methodology (equal). Yan Liu:writing - review and editing (equal). Jiaqi Li: Supervision
(equal); writing - review and editing (equal). Wei Liu:Supervision (equal); writing - review and editing (equal).
ACKNOWLEDGMENTS
We thank the nature reserves for their support to collect data: Guangxi
Xialei Nature Reserve, Guangxi Encheng Nature Reserve, Guangxi Chongzuo
White-Headed Langur Nature Reserve, Guangxi Nonggang National Nature
Reserve, Guizhou Dashahe Nature Reserve, Guizhou Yezhong Nature Reserve,
Guizhou Mayanghe National Nature Reserve, Guizhou Kuankuoshui National
Nature Reserve, and Chongqing Jinfoshan National Nature Reserve.
APPENDIXES
APPENDIX 1: Pearson’ s correlation coefficients between 19 climate
variables, and intensity of color represents greater positive (blue) or
negative correlation (orange).