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We evaluated whether high\u2010resolution digital surface models (DSMs) (\u22641\u00a0m) based on data from an unmanned aerial vehicle (UAV) enhanced species distribution models (SDMs) relative to medium\u2011resolution satellite\u2010based ALOS World 3D (AW3D) DSMs (\u226530\u00a0m) for Fran\u00e7ois\u2019 langur (\n                    <jats:italic>Trachypithecus francoisi<\/jats:italic>\n                    ). This endangered primate represents an ideal focal species for evaluating SDM performance because its habitat consists of topographically complex karst mountains. Using a systematic comparative framework, we built 290 random forest SDMs across multiple spatial resolutions (3\u2013150\u00a0m) and sample sizes (30\u2013122 occurrence records). We assessed model accuracy with the area under the receiver operating characteristic curve (AUC\u2010ROC) and the area under the precision\u2010recall curve (AUC\u2010PR). The UAV DSM improved model performance across all metrics, particularly identification of microhabitat. Performance improvements were scale dependent and most pronounced at fine spatial resolutions of 3\u201310\u00a0m. Improvement was less at coarser resolutions above 100\u00a0m, where topographic aggregation reduced fine\u2010feature discrimination. The UAV\u2010based models were more accurate than AW3D\u2010based models even with fewer presence records, indicating greater robustness in data\u2010limited mountainous surveys. Slope\u2010related predictors derived from UAV DSM were more important than those derived from AW3D DSM, suggesting that high\u2010resolution elevation data more effectively resolve terrain features relevant to species habitat selection. The UAV DSM showed wildlife corridors in steep terrain that did not appear in the AW3D DSM. Given that contemporary conservation planning in mountainous protected areas predominantly relies on medium\u2010resolution elevation data, we recommend implementing UAV structure from motion photogrammetry as a cost\u2010effective approach for generating detailed elevation data to enhance biodiversity management in topographically complex landscapes.\n                  <\/jats:p>","DOI":"10.1111\/cobi.70274","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:56:07Z","timestamp":1776081367000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Use of drone\u2010derived high\u2010resolution elevation data to improve species distribution models for a primate in mountainous areas"],"prefix":"10.1111","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1982-171X","authenticated-orcid":false,"given":"Ke","family":"Wen","sequence":"first","affiliation":[{"name":"Guangxi 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EJDrones Technology Corporation LTD Nanning China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanyan","family":"Zhao","sequence":"additional","affiliation":[{"name":"GXAS Laboratory of Forest Protection, Restoration and Ecological Utilization, Institute of Eco\u2010Environmental Research Guangxi Academy of Sciences  Nanning China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yubiao","family":"Lyu","sequence":"additional","affiliation":[{"name":"GXAS Laboratory of Forest Protection, Restoration and Ecological Utilization, Institute of Eco\u2010Environmental Research Guangxi Academy of Sciences  Nanning China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qixian","family":"Zou","sequence":"additional","affiliation":[{"name":"Mayanghe National Nature Reserve Administration  Tongren China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minhao","family":"Chen","sequence":"additional","affiliation":[{"name":"GXAS 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