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• W4384484839 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> Trait-based approaches are of increasing concern in predicting vegetation changes and linking ecosystem structure to functions at large scales. However, a critical challenge for such approaches is acquiring spatially continuous plant functional trait distribution. Here, eight key plant functional traits were selected to represent two-dimensional spectrum of plant form and function, including leaf area (LA), leaf dry matter content (LDMC), leaf N concentration (LNC), leaf P concentration (LPC), plant height, seed mass (SM), specific leaf area (SLA) and wood density (WD). A total of 52477 trait measurements of 4291 seed plant species were collected from 1541 sampling sites in China and were used to generate a spatial plant functional trait dataset (1 km), together with environmental variables and vegetation indices based on two machine learning models (random forest and boosted regression trees). The two models showed a good accuracy in estimating WD, LPC and SLA, with average R² values ranging from 0.45 to 0.66. In contrast, both the two models had a weak performance in estimating SM and LDMC, with average R² values below 0.25. Meanwhile, LA, SM and plant height showed considerable differences between two models in some regions. To obtain the optimal estimates, a weighted average algorithm was further applied to merge the predictions of the two models to derive the final spatial plant functional trait dataset. The optimal estimates showed that climatic effects were more important than those of edaphic factors in predicting the spatial distribution of plant functional traits. Estimates of plant functional traits in northeast China and the Qinghai-Tibet Plateau had relatively high uncertainties due to sparse samplings, implying a need of more observations in these regions in future. Our trait dataset could provide critical support for trait-based vegetation models and allows exploration into the relationships between vegetation characteristics and ecosystem functions at large scales. The eight plant functional traits datasets for China with 1 km spatial resolution are now available at <a href=https://figshare.com/s/c527c12d310cb8156ed2 target=_blank rel=noopener>https://figshare.com/s/c527c12d310cb8156ed2</a> (An et al., 2023)." @default.
• W4384484839 created "2023-07-17" @default.
• W4384484839 date "2023-07-17" @default.
• W4384484839 modified "2023-07-17" @default.
• W4384484839 title "Comment on essd-2023-121" @default.
• W4384484839 doi "https://doi.org/10.5194/essd-2023-121-rc3" @default.
• W4384484839 hasPublicationYear "2023" @default.
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