Spatial mapping of key plant functional traits in terrestrial ecosystems across China
Abstract. 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 https://figshare.com/s/c527c12d310cb8156ed2 (An et al., 2023).
Nannan An et al.
Status: open (until 02 Jul 2023)
- RC1: 'Comment on essd-2023-121', Anonymous Referee #1, 25 May 2023 reply
Nannan An et al.
Nannan An et al.
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This is a well-prepared manuscript. The authors focused on mapping several key plant functional traits in China by integrating three kinds of machine learning algorithms and climate, soil, and vegetation variables. Comprehensive experiments were implemented and all necessary technical details were properly introduced. It could be of great interest to those who are interested in trait ecology, and global vegetation modeling. However, at present, some technical details need to be added and the language of the paper needs to be further improved. In its current form, major revisions are needed before this manuscript could be accepted, thereby further improving the quality and legibility of this manuscript. The main comments are as follows: