Spatiotemporal simulation of sustainable development based on ecosystem services under climate change

become a focal point of research. Climate change profoundly influences the structure and function of ecosystems by altering temperature, precipitation patterns, and the frequency of extreme weather events [2]. Such changes can lead to a loss of biodiversity, a decline in ecosys- tem services (ESs), and negative impacts on human societies [3]. For instance, the rise in global temperatures and the increased occurrence of extreme weather events due to climate change have destroyed habitats for many species, affecting the stability and function of ecosystems [4]. Additionally, climate change has disrupted global precipitation patterns, leading to droughts or flooding in various regions and exacerbating the vulnerability of ecosystems [5]. The karst region of southeastern Yunnan, one of China’s three major karst centers, boasts unique geo- graphical and ecological characteristics, including complex terrain, diverse biotic communi- ties, and a distinctive hydrological system [6]. The region primarily experiences tropical and subtropical monsoon climates, with annual precipitation ranging from 1000 to 2000 millime- ters and an average annual temperature between 14 and 24 degrees Celsius [7]. However, this region also faces challenges from climate change, significantly impacting ESs. Presently, research on climate change primarily focuses on concepts [8], formation mecha- nisms [9], spatiotemporal evolution [10], trends [11], and comprehensive governance [12]. Some scholars have also conducted studies on extracting climate change information, explor-

ing the interrelations between climate change and natural and anthropogenic factors [13], sim- ulating future climate change scenarios [14], and introducing key technologies and experiences for climate change mitigation [15]. Chang et al. (2024) examined the interactions among landscape patterns, climate change, and ESs, highlighting their impacts on regional sus- tainability [16]. Assessing the effects of climate change and land use changes (LUCs) on ESs through these models can effectively evaluate the impacts on the supply and function of ESs [17]. Climate change, by altering temperature, precipitation, and extreme weather events, influences the spatial distribution and function of ESs [18]. For example, Wen et al. (2024) investigated the spatial and seasonal impacts of future climate and LUCs on ESs, finding that these changes significantly affect the supply and function of regional ESs [19]. The temperature rise and precipitation changes caused by climate change lead to a loss of biodiversity, ecosys- tem degradation, and a weakening of ES functions [20]. These changes further impact the reg- ulatory and productive capacities of ecosystems, posing a threat to human well-being [21]. In studies examining the relationship between ESs and sustainability, there is a common focus on the impacts of climate change on ESs, such as biodiversity, regulatory functions of ecosystems, and threats to human well-being [22]. However, despite providing valuable insights, there are still some shortcomings. For instance, many studies concentrate on single ecosystems or spe-

cific regions, lacking a comprehensive consideration of interactions between different ecosys- tems [23]. Additionally, existing research often fails to adequately explore how changes in ESs under different socio-economic contexts affect the achievement of SDGs [24]. This study aims to address these gaps by comprehensively considering the dynamic relationships among cli- mate change, LUCs, and ESs. Unlike existing research, this study not only focuses on changes in the supply and function of ESs but also delves into how these changes impact sustainability across different regions and socio-economic contexts. Employing an interdisciplinary approach, combining socio-economic data with ecological models, this study strives to com- prehensively assess the critical role of ESs in achieving sustainable development goals (SDGs). Existing research primarily focuses on the assessment of ESs in karst regions [25], but stud- ies comprehensively addressing the impacts of climate change are relatively limited. This study innovatively integrates multi-source data and models to systematically evaluate the spatial dis- tribution and dynamic changes of ESs and SDGs under the backdrop of climate change in the karst region of southeastern Yunnan. This fills the current research gap and provides scientific evidence for the management of ESs and sustainable development strategies in the region. PLOS ONE Sustainable development simulation under climate change: A spatiotemporal ecosystem services approach PLOS ONE | https://doi.org/10.1371/journal.pone.0316605 February 4, 2025 2 / 19

NDVI Dataset, also covering China, is available in raster format with a spatial resolution of 30 meters, categorized under China’s vegetation index remote sensing inversion data, and identified by DOI 10. 12078/2022030801. (2)Regarding natural data, the 30-meter resolution DEM data comes from the Geospatial Data Cloud (http://www.gscloud.cn). Furthermore, data on soil erodibility, rooting depth, and plant available water capacity are sourced from the International Soil Reference and Information Centre (https://data.isric.org), with a resolution of 1 kilometer. Social data, covering population and GDP, are sourced from the Resource and Environment Science and Data Center (https:// www.resdc.cn/) for the year 2020, in raster format, with a spatial resolution of 1 kilometer, categorized under socioeconomic data. The GDP data is identified by DOI 10.12078/2017121102, while the population distribution dataset also in raster format with a spatial resolution of 1 kilometer, is identified by DOI 10.12078/2017121101. (3)Meteorological data, including precipitation, temperature, and evapotranspiration, are obtained from the National Earth System Science Data Center (http://www. geodata.cn), with a temporal resolution of monthly. Additionally, future climate scenario data is downloaded from IPCC (https://esgf-node.llnl.gov). For more detailed information on other data, please contact the corresponding author via email: chenguoping@kust.edu.cn. Funding: This study was supported by the Basic Research Program of Yunnan Province (Grant No. 202201AU070112), led by Professor G.C., and the

National Natural Science Foundation of China (Grant No. 42301304), also led by Professor G.C. Competing interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.