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Leveraging a machine learning approach, and utilizing multi-source satellite remote-sensing and field-collected sPlotOpen measurements data, we generated the first global community-level map of CSR functional strategy variations. Results show that S-selected strategies are globally dominant (C:S:R\u00a0=\u00a023.66:62.40:13.94%), with substantial spatial variations across biomes. This variability is strongly influenced by climatic variables (e.g. mean annual precipitation, diurnal temperature range) and soil properties (e.g. cation exchange capacity, total nitrogen). Future projections show that climate change favours S- (+0.33%) and R- (+0.31%) at the expense of C-selected strategy (\u22120.64%), alongside marked biome-specific shifts. Despite potential underestimation of localized climate uncertainties, these findings provide critical insights into global plant community dynamics under challenging abiotic conditions.<\/jats:p>","DOI":"10.1093\/nsr\/nwag416","type":"journal-article","created":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T14:34:01Z","timestamp":1784039641000},"source":"Crossref","is-referenced-by-count":0,"title":["Facing a harsh climate: terrestrial plant functional strategies in a changing world"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0116-6179","authenticated-orcid":false,"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Biocontrol, School of Ecology, Shenzhen Campus of Sun Yat-sen University , Shenzhen 518107 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