1 INTRODUCTION
Climate change is one of the most significant contemporary threats to biodiversity worldwide (Isaac & Williams, 2007; Jing et al., 2022) and is already affecting species and ecosystems on a global scale, and these effects are projected to become more rapid and extensive (Field et al., 2014). In particular, many species that are already vulnerable, such as those with limited geographical range and/or restricted habitat requirements, could find their available habitat area reduced or eliminated (Hobbs et al., 2009). And these endangered species may be threatened by a combination of disturbances associated with climate change (such as temperature or precipitation) and ongoing anthropogenic activities (such as habitat fragmentation, pollution, urbanization) (Holling, 1973). For example, deforestation and forest degradation are threatening the survival of about half of all bamboo species worldwide, and climate change may present an additional significant threat, especially, many bamboo species provide essential food and shelter for wildlife species, including one of the most endangered species, the giant panda (Tuanmu et al., 2013). Many species are responding to changing environments by shifting their geographical ranges (Chen et al., 2011; Parmesan, 2006).
Francois’ langur (Trachypithecus francoisi ) is an endangered species endemic to the limestone forests of the tropical and subtropical zones of northern Vietnam and southwestern China (Nadler et al., 2004), with a current estimated global population of approximately 2,000 individuals (Wang et al., 2011; Zeng et al., 2013). In China, the distribution of Francois’ langur was largely restricted to areas characterized by karst topography at altitudes of 120-1,800 m in Guangxi, Guizhou and Chongqing (Han et al., 2021). These areas have some unique features that differentiate them from mountain forests, such as steep cliffs, which cover about 10-20% of the area, lack of surface water (Huang, 2002), and relatively poor, but more diverse vegetation (Zhou et al., 2013). Due to forest loss, habitat destruction and fragmentation, some Francois’ langur populations have been locally extirpated from some parts of their historic range (Li et al., 2007; Zeng et al., 2013). For instance, this species has become restricted to only five isolated sites in Guizhou (Hu et al., 2011). Although the Francois’ langur was listed as endangered on the Red List of the International Union for Conservation of Nature (IUCN, 2020), and was considered a first-grade protected wildlife species in China (Niu et al., 2016; Wang et al., 2011; Wen et al., 2010), little was known about its habitat use and factors affecting habitat selection or association, except for some roost selection and behavioral ecology, moreover, previous studies were limited to specific nature reserves or locations (Hu et al., 2011; Li et al., 2020; Liu & Bhumpakphan, 2020; Zhou et al., 2013, 2018).
Climate change effects on distribution of species, particularly rare and endangered ones, have attracted increasing attention, and predicting how these species will react to climate change is a major current challenge in ecology. Some species, including plants (Mahmoodi et al., 2022; Pan et al., 2020; Wu et al., 2016; Zhang et al., 2020), freshwater fishes (Chu et al. 2005), mammals (Wilkening et al., 2019), coral reefs (Sill & Dawson, 2021) and invertebrates (Martín & Abellán, 2022), have been predicted distribution affected by the climate change with some model methods, such as bioclimatic prediction system (BioCLIM), random forest model (RFM) and maximum entropy models (MaxEnt) (Phillips et al., 2004, 2006). Among these models, MaxEnt is the most accurate and commonly used model (Bellard et al., 2018; Tsoar et al., 2007). The MaxEnt is an open-source software package for modeling species niches and distributions through the application of a machine-learning technique. It combines the current distribution data of the species with the background environmental variables, associates the known occurrences with a set of unique environmental conditions and then predicts the probability of presence for unknown locations via the use of multiple algorithms, and evaluates the importance of environmental variables using jackknife (Yin et al., 2022). Thus, MaxEnt can be applied to well predict the distribution of Francois’ langur as well as habitat selection.
In this study, MaxEnt was applied to construct the potential distributions of Francois’ langur. The primary objectives were as follows: (1) to model current potential distribution of the Francois’ langur habitat across the entire range in China, (2)to explore the relationships among the potential distribution patterns and environments of the Francois’ langur and determine relevant influencing variables, (3)to predict potential distribution in the future different periods, and (4)to evaluate how the climate change in the future affect the potential distribution of this species. Additionally, the study aims to provide suggestions and new insights for the biodiversity conservation of rare species.