Stemflow has been increasingly recognized as an indispensable component in water and nutrient budgets within vegetated ecosystems. Here we aim to quantify the stemflow percentage (St, %) of incident precipitation (i.e. stemflow production) at a global scale, and to provide a systematic evaluation on how biotic and abiotic factors affect St.
We compiled a global stemflow dataset from 234 peer-reviewed papers, which included 488 observations of St and the related biotic (stand characteristics) and abiotic factors (climate variables) at 283 sites within terrestrial woody plant ecosystems. We explored the global pattern of St and performed a machine learning method (boosted regression trees) to model the effects of biotic and abiotic variables on St.
Globally, the median (interquartile range, IQR) St was 2.7% (1.0–6.3%). We found that St in arid zones (type B in the Köppen-Geiger climate classification) was significantly higher (p <0.01) than in other climate types, and we also detected a significant difference (p <0.01) in St between trees (median: 2.4%; IQR: 1.0–5.3%) and shrubs (median: 7.2%; IQR: 5.2–11.9%). Predictor variables that substantially accounted for the explained deviance of the final model included vegetation height (27.0%), mean annual precipitation (16.1%), mean annual temperature (14.4%), stand density (10.8%), stand age (8.9%) and bark type (5.5%). In contrast, leaf area index, diameter at breast height, basal area, phenology type, life-form and leaf type were classified as low importance.
We performed a literature search on Web of Science to compile suitable studies published before 2020 to assess the effects of bi-otic and abiotic variables on global stemflow production. Through searching using the keyword ‘stemflow’, 1,126 articles from 64 coun-tries were generated and con-sidered to be potentially suitable.We reviewed each article to determine whether the studies met the following criteria.
We mainly used boosted regression trees (BRT) analysis to evalu-ate the effects of individual predictor variables on St, with MAP andMAT grouping into the abiotic factors and 10 stand metrics groupinginto the biotic factors. BRT is a machine learning method,which combines the strengths of two algorithms: regression trees and boosting.
Moreover, because the data were not normally distributed ac-cording to the Shapiro– Wilk test (p < .05), we compared the differ-ences in St between climate types,between trees and shrubs, and between bark types using a Kruskal– Wallis rank sum test, and mul-tiple comparisons were done using Dunn’s test with the ‘dunn.test’.
Our synthesis has important implications for understanding the roles of climate variables and stand characteristics in stemflow pro-duction of woody plants at a global scale. Stemflow, relatively under- represented in rainfall partitioning studies, has gained increasing attention in recent years, and is now recognized as an indispensable component in water and nutrient cycles within vegetated ecosys-tems. As such, a global synthesis of stemflow production in ter-restrial woody ecosystems as provided here will aid in an unbiased estimation in water and nutrient budgets. Future studies are encour-aged to include other plant life-forms such as herbs and crops
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CSTR:11738.11.NCDC.ZENODO.DB3958.2023
10.5061/dryad.j3tx95xcs