Zhipang Huang , Yuling Chen , Haitao Yang , Yihao Fang , Kai Cheng , Hongcan Guan , Cyril C. Grueter , Wen Xiao , Qinghua Guo
{"title":"高海拔环境下森林垂直结构与黑黑金丝猴栖息地偏好的关系","authors":"Zhipang Huang , Yuling Chen , Haitao Yang , Yihao Fang , Kai Cheng , Hongcan Guan , Cyril C. Grueter , Wen Xiao , Qinghua Guo","doi":"10.1016/j.ecoinf.2025.103269","DOIUrl":null,"url":null,"abstract":"<div><div>Understanding how wildlife behavior relates to habitat characteristics is essential for ecology and conservation, particularly in remote, structurally complex landscapes. In this study, we examine seasonal and behavior-specific habitat preferences in black-and-white snub-nosed monkeys (<em>Rhinopithecus bieti</em>), an endangered primate endemic to high-elevation forests in Yunnan, China. We combined long-term behavioral observations (2008–2018) with UAV-based LiDAR data on forest structure (collected in 2022–2023), and camera trap data on human and wildlife activity (2017–2018), to assess how these factors are associated with space use across different behaviors and seasons. Using machine learning models, we identified structural and disturbance-related attributes that were consistently associated with <em>R. bieti</em> behavior at the home-range scale. Vertical forest structure, particularly canopy height and vegetation layering, showed strong associations with foraging and sleeping locations across both wet and dry seasons. These patterns varied depending on behavioral context, supporting the idea that <em>R. bieti</em> adjusts its space use in response to seasonal and structural variation. Human and livestock presence were also negatively associated with habitat use during feeding and movement. While our findings align with established ecological expectations for semi-arboreal primates, they provide one of the first fine-scale, spatially explicit analyses of habitat preferences in <em>R. bieti</em>. We acknowledge the temporal mismatch between datasets as a limitation, and interpret our results conservatively as correlational. Nonetheless, this study highlights the value of combining behavioral, structural, and disturbance data to inform habitat conservation in montane forest ecosystems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103269"},"PeriodicalIF":7.3000,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Associations between forest vertical structure and habitat preferences of black-and-white snub-nosed monkeys (Rhinopithecus bieti) in high-elevation environments\",\"authors\":\"Zhipang Huang , Yuling Chen , Haitao Yang , Yihao Fang , Kai Cheng , Hongcan Guan , Cyril C. Grueter , Wen Xiao , Qinghua Guo\",\"doi\":\"10.1016/j.ecoinf.2025.103269\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Understanding how wildlife behavior relates to habitat characteristics is essential for ecology and conservation, particularly in remote, structurally complex landscapes. In this study, we examine seasonal and behavior-specific habitat preferences in black-and-white snub-nosed monkeys (<em>Rhinopithecus bieti</em>), an endangered primate endemic to high-elevation forests in Yunnan, China. We combined long-term behavioral observations (2008–2018) with UAV-based LiDAR data on forest structure (collected in 2022–2023), and camera trap data on human and wildlife activity (2017–2018), to assess how these factors are associated with space use across different behaviors and seasons. Using machine learning models, we identified structural and disturbance-related attributes that were consistently associated with <em>R. bieti</em> behavior at the home-range scale. Vertical forest structure, particularly canopy height and vegetation layering, showed strong associations with foraging and sleeping locations across both wet and dry seasons. These patterns varied depending on behavioral context, supporting the idea that <em>R. bieti</em> adjusts its space use in response to seasonal and structural variation. Human and livestock presence were also negatively associated with habitat use during feeding and movement. While our findings align with established ecological expectations for semi-arboreal primates, they provide one of the first fine-scale, spatially explicit analyses of habitat preferences in <em>R. bieti</em>. We acknowledge the temporal mismatch between datasets as a limitation, and interpret our results conservatively as correlational. Nonetheless, this study highlights the value of combining behavioral, structural, and disturbance data to inform habitat conservation in montane forest ecosystems.</div></div>\",\"PeriodicalId\":51024,\"journal\":{\"name\":\"Ecological Informatics\",\"volume\":\"90 \",\"pages\":\"Article 103269\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2025-06-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Informatics\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S157495412500278X\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S157495412500278X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Associations between forest vertical structure and habitat preferences of black-and-white snub-nosed monkeys (Rhinopithecus bieti) in high-elevation environments
Understanding how wildlife behavior relates to habitat characteristics is essential for ecology and conservation, particularly in remote, structurally complex landscapes. In this study, we examine seasonal and behavior-specific habitat preferences in black-and-white snub-nosed monkeys (Rhinopithecus bieti), an endangered primate endemic to high-elevation forests in Yunnan, China. We combined long-term behavioral observations (2008–2018) with UAV-based LiDAR data on forest structure (collected in 2022–2023), and camera trap data on human and wildlife activity (2017–2018), to assess how these factors are associated with space use across different behaviors and seasons. Using machine learning models, we identified structural and disturbance-related attributes that were consistently associated with R. bieti behavior at the home-range scale. Vertical forest structure, particularly canopy height and vegetation layering, showed strong associations with foraging and sleeping locations across both wet and dry seasons. These patterns varied depending on behavioral context, supporting the idea that R. bieti adjusts its space use in response to seasonal and structural variation. Human and livestock presence were also negatively associated with habitat use during feeding and movement. While our findings align with established ecological expectations for semi-arboreal primates, they provide one of the first fine-scale, spatially explicit analyses of habitat preferences in R. bieti. We acknowledge the temporal mismatch between datasets as a limitation, and interpret our results conservatively as correlational. Nonetheless, this study highlights the value of combining behavioral, structural, and disturbance data to inform habitat conservation in montane forest ecosystems.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.