arXiv Quantitative Biology#science
Assessing global drivers of forest transpiration using clustered machine learning modelstranslating…
Factuality: 88/100USACornell University
arXiv:2605.22755v1 Announce Type: new
Abstract: Understanding the environmental drivers of forest transpiration is critical for improving global predictions of water availability and ecosystem health. Due to many competing controls on plant water stress and ecosystem transpiration, however, these drivers may vary widely across tree species which have adapted hydraulically to local climate conditions. Here, clustered machine learning models were used to analyze global drivers of forest transpira