Spatiotemporal changes of ecosystem health and the impact of its driving factors on the Loess Plateau in China

• A framework for assessing ecosystem health (EH) with soil erosion has been proposed. • Exploring the driving relationship using machine learning and interpretability models. • The driving factors exhibit a nonlinear relationship with EH. The Loess Plateau is renowned for its fragile ecosystem, and understanding the changes and driving impacts of ecosystem health (EH) is crucial for formulating environmental protection policies for the region. Soil erosion, as a key limiting factor of the region’s ecosystem, must be considered when evaluating EH in this area. Based on this idea, a new framework for assessing EH is proposed. The efficient machine learning model (light gradient boosting machine model) and the variable explanation model (SHapley Additive exPlanation model) are combined to quantify the functional relationships between various driving factors and EH, thereby exploring the impacts of climatic factors, socioeconomic development (SED), and ecological restoration projects (ERP) on EH. The study found that: (1) From 1995 to 2020, EH in the Loess Plateau increased from 0.42 to 0.58, and the forms of spatial clustering changed. Moreover, there are significant differences in the speed and patterns of EH improvement in different regions, especially after 2010. (2) The importance of precipitation (PRE), SED, ERP, and temperature (TEM) is 33 %, 26 %, 24 %, and 17 %, respectively. The driving impacts exhibit non-monotonic polynomial relationships of third, fourth, second, and fourth degrees, indicating that the mechanisms through which EH is influenced by these factors can change. (3) Counties with higher response to PRE (>0.32) are mainly located in the central region. Counties with higher response to SED (>0.30) are primarily in areas with higher population and urbanization. Counties with higher response to ERP (>0.36) are mainly in the eastern region. Counties with higher response to TEM (>0.26) are primarily in the southern and western regions. The results of this paper provide new insights for EH research in areas with fragile ecosystems and are of significant importance for the ecological civilization construction of the Loess Plateau.

Authors

Institutions

Publication Details

Journal
Ecological Indicators
Published
2024-12-27
DOI
https://doi.org/10.1016/j.ecolind.2024.113020
Citations
18
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
5.09

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatiotemporal changes of ecosystem health and the impact of its driving factors on the Loess Plateau in China

Guangchao Li, Ninglei Ouyang, Qiqi Du, Wei Nie et al.
18 citations
Ecological Indicators
Land Use and Ecosystem Services
5.09
article

Spatiotemporal changes of ecosystem health and the impact of its driving factors on the Loess Plateau in China

Guangchao Li, Ninglei Ouyang, Qiqi Du, Wei Nie, Taixia Wu, Yang Liu, Xuepeng Zhang, Peng Gou
article en
18 citations

Abstract

• A framework for assessing ecosystem health (EH) with soil erosion has been proposed. • Exploring the driving relationship using machine learning and interpretability models. • The driving factors exhibit a nonlinear relationship with EH. The Loess Plateau is renowned for its fragile ecosystem, and understanding the changes and driving impacts of ecosystem health (EH) is crucial for formulating environmental protection policies for the region. Soil erosion, as a key limiting factor of the region’s ecosystem, must be considered when evaluating EH in this area. Based on this idea, a new framework for assessing EH is proposed. The efficient machine learning model (light gradient boosting machine model) and the variable explanation model (SHapley Additive exPlanation model) are combined to quantify the functional relationships between various driving factors and EH, thereby exploring the impacts of climatic factors, socioeconomic development (SED), and ecological restoration projects (ERP) on EH. The study found that: (1) From 1995 to 2020, EH in the Loess Plateau increased from 0.42 to 0.58, and the forms of spatial clustering changed. Moreover, there are significant differences in the speed and patterns of EH improvement in different regions, especially after 2010. (2) The importance of precipitation (PRE), SED, ERP, and temperature (TEM) is 33 %, 26 %, 24 %, and 17 %, respectively. The driving impacts exhibit non-monotonic polynomial relationships of third, fourth, second, and fourth degrees, indicating that the mechanisms through which EH is influenced by these factors can change. (3) Counties with higher response to PRE (>0.32) are mainly located in the central region. Counties with higher response to SED (>0.30) are primarily in areas with higher population and urbanization. Counties with higher response to ERP (>0.36) are mainly in the eastern region. Counties with higher response to TEM (>0.26) are primarily in the southern and western regions. The results of this paper provide new insights for EH research in areas with fragile ecosystems and are of significant importance for the ecological civilization construction of the Loess Plateau.

Ecological IndicatorsVol. 170
Hohai University (CN), Jiaxing University (CN), Henan Normal University (CN)
National Natural Science Foundation of China, Hohai University, Department of Science and Technology of Inner Mongolia
Life in Land
Openalex Percentile: Top 4%
Land Use and Ecosystem Services
5.09
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.