25 Apr 2023
 | 25 Apr 2023
Status: this preprint is currently under review for the journal ESSD.

Spatial mapping of key plant functional traits in terrestrial ecosystems across China

Nannan An, Nan Lu, Weiliang Chen, Yongzhe Chen, Hao Shi, Fuzhong Wu, and Bojie Fu

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 (An et al., 2023).

Nannan An et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-121', Anonymous Referee #1, 25 May 2023
    • AC3: 'Reply on RC1', Nannan An, 20 Oct 2023
  • RC2: 'Comment on essd-2023-121', Anonymous Referee #2, 14 Jun 2023
    • AC1: 'Reply on RC2', Nannan An, 20 Oct 2023
  • RC3: 'Comment on essd-2023-121', Anonymous Referee #3, 17 Jul 2023
  • RC4: 'Comment on essd-2023-121', Anonymous Referee #4, 17 Aug 2023
    • AC2: 'Reply on RC4', Nannan An, 20 Oct 2023
  • AC4: 'Comment on essd-2023-121', Nannan An, 20 Oct 2023

Nannan An et al.

Nannan An et al.


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Short summary
This study generated a spatially continuous plant functional trait dataset (1 km) in China in combination with field observations, environmental variables and vegetation indices using machine learning methods. Results showed that wood density, leaf P concentration, plant height and specific leaf area showed good accuracy with average R2 of higher than 0.45. This dataset could provide data support for developments of earth system models to predict vegetation distribution and ecosystem functions.