{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T00:15:26Z","timestamp":1789172126902,"version":"build-2803163510"},"reference-count":76,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Global Change Biology"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Measurement of methane fluxes (FCH\n                    <jats:sub>4<\/jats:sub>\n                    ) from natural systems, such as wetlands, has lagged far behind carbon dioxide fluxes. Short and fragmented wetland FCH\n                    <jats:sub>4<\/jats:sub>\n                    data limit our ability to assess its long\u2010term dynamics and potential climate feedbacks. Extrapolating short\u2010term FCH\n                    <jats:sub>4<\/jats:sub>\n                    records to recent decades remains challenging for both process\u2010based models and data\u2010driven machine learning (ML) approaches. Here, we develop a knowledge\u2010guided ML framework that integrates eddy covariance (EC) FCH\n                    <jats:sub>4<\/jats:sub>\n                    observations, field warming experiments, and biogeochemical knowledge to reconstruct the long\u2010term FCH\n                    <jats:sub>4<\/jats:sub>\n                    budgets and trends. Focusing on the 11 longest EC monitoring sites in the AmeriFlux network, we found considerable variability in multi\u2010decadal trends of wetland FCH\n                    <jats:sub>4<\/jats:sub>\n                    , with increases up to 14% per decade from 2000 to 2024. We also found that the strength of these increasing trends declines from high to low latitudes, highlighting the vulnerability of northern wetlands. This work presents novel and robust reconstructions of long\u2010term wetland FCH\n                    <jats:sub>4<\/jats:sub>\n                    , offering critical benchmark datasets for bottom\u2010up ecosystem models and advancing fundamental understanding of wetland biogeochemistry.\n                  <\/jats:p>","DOI":"10.1111\/gcb.70899","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:02:38Z","timestamp":1778752958000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010Decadal Dynamics of Wetland Methane Emissions Revealed by Knowledge\u2010Guided Machine Learning"],"prefix":"10.1111","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2441-944X","authenticated-orcid":false,"given":"Qing","family":"Zhu","sequence":"first","affiliation":[{"name":"Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory  Berkeley California 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