Prediction of spatial distribution characteristics of ecosystem functions based on a minimum data set of functional traits of desert plants

Yudong Chen; Jinlong Wang; Lamei Jiang; Hanpeng Li; Hengfang Wang; Guanghui Lv; Xiaotong Li

doi:10.3389/fpls.2023.1131778

Prediction of spatial distribution characteristics of ecosystem functions based on a minimum data set of functional traits of desert plants

Front Plant Sci. 2023 Jun 2:14:1131778. doi: 10.3389/fpls.2023.1131778. eCollection 2023.

Authors

Yudong Chen^{1

2

3}, Jinlong Wang^{1

2

3}, Lamei Jiang^{1

2

3}, Hanpeng Li^{1

2

3}, Hengfang Wang^{1

2

3}, Guanghui Lv^{1

2

3}, Xiaotong Li^{1

2

3}

Affiliations

¹ College of Ecology and Environment, Xinjiang University, Urumqi, China.
² Key Laboratory of Oasis Ecology of Education Ministry, Xinjiang University, Urumqi, China.
³ Xinjiang Jinghe Observation and Research Station of Temperate Desert Ecosystem, Ministry of Education, Jinghe, China.

Abstract

The relationship between plant functional traits and ecosystem function is a hot topic in current ecological research, and community-level traits based on individual plant functional traits play important roles in ecosystem function. In temperate desert ecosystems, which functional trait to use to predict ecosystem function is an important scientific question. In this study, the minimum data sets of functional traits of woody (wMDS) and herbaceous (hMDS) plants were constructed and used to predict the spatial distribution of C, N, and P cycling in ecosystems. The results showed that the wMDS included plant height, specific leaf area, leaf dry weight, leaf water content, diameter at breast height (DBH), leaf width, and leaf thickness, and the hMDS included plant height, specific leaf area, leaf fresh weight, leaf length, and leaf width. The linear regression results based on the cross-validations (FTEI_{W - L}, FTEI_{A - L}, FTEI_{W - NL}, and FTEI_{A - NL}) for the MDS and TDS (total data set) showed that the R² (coefficients of determination) for wMDS were 0.29, 0.34, 0.75, and 0.57, respectively, and those for hMDS were 0.82, 0.75, 0.76, and 0.68, respectively, proving that the MDSs can replace the TDS in predicting ecosystem function. Then, the MDSs were used to predict the C, N, and P cycling in the ecosystem. The results showed that non-linear models RF and BPNN were able to predict the spatial distributions of C, N and P cycling, and the distributions showed inconsistent patterns between different life forms under moisture restrictions. The C, N, and P cycling showed strong spatial autocorrelation and were mainly influenced by structural factors. Based on the non-linear models, the MDSs can be used to accurately predict the C, N, and P cycling, and the predicted values of woody plant functional traits visualized by regression kriging were closer to the kriging results based on raw values. This study provides a new perspective for exploring the relationship between biodiversity and ecosystem function.

Keywords: arid regions; random forest; regression kriging; semi-variable functions; spatial variation.

Grants and funding

This study was financially supported by National Natural Science Foundation of China (No.42171026), Xinjiang Uygur Autonomous Region innovation environment Construction special project & Science and technology innovation base construction project (PT2107), and Excellent doctoral research and innovation Project of Xinjiang University (XJU2022BS060).