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A notable example is the 2008 Wenchuan earthquake, which induced numerous co\u2010seismic landslides and created widespread bare areas that have hindered vegetation recovery. Understanding post\u2010disruption vegetation recovery patterns and their climatic drivers is therefore essential for targeted restoration efforts. This study establishes an integrated framework combining nonlinear trajectory analysis with Bayesian probabilistic inference to quantify vegetation recovery dynamics and identify key climatic controls. Using Landsat time\u2010series data (2008\u20132023), we applied a Generalized Additive Model (GAM)\u2010based nonlinear trajectory approach to extract recovery trajectories, identify trajectory\u2010derived recovery inflection points marking the transition from accelerated to decelerated recovery, and quantify spatial variations in recovery rates. A Bayesian Network (BN) was then used to quantify the influence of ecological memory and environmental drivers on recovery regimes, revealing critical temperature\u2013moisture\u2013radiation configurations and enabling scenario\u2010based evaluation of climatic constraints on regional recovery potential. GAM results indicate that most areas reached a critical recovery inflection point within 5\u20137\u2009years, while delayed recovery was mainly observed in high\u2010elevation, rugged terrain. The BN further identified that a synergistic \u201cwarm\u2013moist\u2013moderate light\u201d regime is necessary for fast recovery. Scenario simulations highlighted clear spatial differences in limiting factors: solar radiation is the dominant constraint in rugged highlands, whereas temperature and precipitation exert stronger controls in low\u2010elevation or moderately disturbed areas. This spatial variability suggests that restoration interventions should be tailored to local conditions to maximize efficiency. Overall, this study provides a transferable framework for diagnosing environmental constraints and guiding restoration strategies to enhance ecosystem recovery capacity in disaster\u2010prone mountainous landscapes.<\/jats:p>","DOI":"10.1002\/ece3.74117","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T08:35:20Z","timestamp":1785314120000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unraveling Spatiotemporal Heterogeneity and Driving Mechanism of Vegetation Recovery in Co\u2010Seismic Landslide Areas via Nonlinear Trajectories and Bayesian Networks"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7600-6488","authenticated-orcid":false,"given":"Mingxuan","family":"Wan","sequence":"first","affiliation":[{"name":"Institute of Mountain Hazards and Environment Chinese Academy of Sciences  Chengdu China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment Chinese Academy of Sciences  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiujiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment Chinese Academy of Sciences  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9991-8275","authenticated-orcid":false,"given":"Yanqing","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment Chinese Academy of Sciences  Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junli","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment Chinese Academy of Sciences  Chengdu China"},{"name":"University of Chinese Academy of Sciences  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envsoft.2011.06.004"},{"key":"e_1_2_12_3_1","first-page":"D05109","article-title":"Crop Evapotranspiration\u2010Guidelines for Computing Crop Water Requirements\u2010FAO Irrigation and Drainage Paper 56","volume":"300","author":"Allen R. 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