Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration
Soil respiration (Rs) represents the largest carbon flux from land to the atmosphere and is important for assessing the terrestrial carbon cycle and studying climate change. In this study, we propose an interpretable machine learning prediction of global Rs (IMPGRs) based on explainable artificial i...
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Elsevier
2025-08-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25006806 |
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| author | Junjie Jiang Lingxia Feng Junguo Hu Chao Zhu |
| author_facet | Junjie Jiang Lingxia Feng Junguo Hu Chao Zhu |
| author_sort | Junjie Jiang |
| collection | DOAJ |
| description | Soil respiration (Rs) represents the largest carbon flux from land to the atmosphere and is important for assessing the terrestrial carbon cycle and studying climate change. In this study, we propose an interpretable machine learning prediction of global Rs (IMPGRs) based on explainable artificial intelligence (XAI) technology to interpret tree-integrated global Rs prediction models, explore the factors driving global Rs responses, and predict their distribution patterns. The IMPGRs model showed superior performance in predicting Rs, capturing two meaningful non-linear (‘J’- and ‘U’-type) relationships between Rs and environmental variables. We found that a soil temperature of 20.9°C represented an important threshold for the ‘thermal adaptation’ of Rs. Moreover, this phenomenon varied significantly across climatic zones and ecosystems, and the threshold was positively correlated with precipitation. The response of global Rs to the leaf area index (LAI) was not a simple positive correlation, and contrasting results were observed both inside and outside the Tropic of Cancer. Global Rs values (688.43 g C m−2 year−1) and their distribution were predicted using IMPGRs, with forest soils releasing the most carbon dioxide (CO2; 42.84 Pg C year−1) and accounting for 45.7 % of the global Rs. Additionally, we found significant biases in the annual Rs calculated by area weighting based on climate and ecosystem classifications because these factors characterise spatial heterogeneity differently. Such dynamics should be considered when modelling global Rs and analysing the results because they can help improve the estimation accuracy of global Rs prediction models. |
| format | Article |
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| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
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| series | Ecological Indicators |
| spelling | doaj-art-ce5080726d714c9da9569b82fb133ebb2025-08-20T03:29:35ZengElsevierEcological Indicators1470-160X2025-08-0117711375010.1016/j.ecolind.2025.113750Interpretable machine learning unveils threshold responses and spatial patterns of global soil respirationJunjie Jiang0Lingxia Feng1Junguo Hu2Chao Zhu3College of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; State Forestry Administration Key Laboratory of Forestry Sensing Technology and Intelligent Equipment, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research, Zhejiang A&F University, Hangzhou, Zhejiang 311300, ChinaCollege of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; State Forestry Administration Key Laboratory of Forestry Sensing Technology and Intelligent Equipment, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research, Zhejiang A&F University, Hangzhou, Zhejiang 311300, ChinaCollege of Chemistry and Materials Engineering, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; State Forestry Administration Key Laboratory of Forestry Sensing Technology and Intelligent Equipment, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; Key Laboratory of Carbon Cycling in Forest Ecosystems and Carbon Sequestration of Zhejiang Province, Zhejiang A&F University, Lin’an, Zhejiang 311300, China; Corresponding author at: College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China.College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; State Forestry Administration Key Laboratory of Forestry Sensing Technology and Intelligent Equipment, Zhejiang A&F University, Hangzhou, Zhejiang 311300, China; Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research, Zhejiang A&F University, Hangzhou, Zhejiang 311300, ChinaSoil respiration (Rs) represents the largest carbon flux from land to the atmosphere and is important for assessing the terrestrial carbon cycle and studying climate change. In this study, we propose an interpretable machine learning prediction of global Rs (IMPGRs) based on explainable artificial intelligence (XAI) technology to interpret tree-integrated global Rs prediction models, explore the factors driving global Rs responses, and predict their distribution patterns. The IMPGRs model showed superior performance in predicting Rs, capturing two meaningful non-linear (‘J’- and ‘U’-type) relationships between Rs and environmental variables. We found that a soil temperature of 20.9°C represented an important threshold for the ‘thermal adaptation’ of Rs. Moreover, this phenomenon varied significantly across climatic zones and ecosystems, and the threshold was positively correlated with precipitation. The response of global Rs to the leaf area index (LAI) was not a simple positive correlation, and contrasting results were observed both inside and outside the Tropic of Cancer. Global Rs values (688.43 g C m−2 year−1) and their distribution were predicted using IMPGRs, with forest soils releasing the most carbon dioxide (CO2; 42.84 Pg C year−1) and accounting for 45.7 % of the global Rs. Additionally, we found significant biases in the annual Rs calculated by area weighting based on climate and ecosystem classifications because these factors characterise spatial heterogeneity differently. Such dynamics should be considered when modelling global Rs and analysing the results because they can help improve the estimation accuracy of global Rs prediction models.http://www.sciencedirect.com/science/article/pii/S1470160X25006806Global soil respirationInterpretable prediction modelResponse mechanismDistribution patternNon-linear relationshipsEstimation bias |
| spellingShingle | Junjie Jiang Lingxia Feng Junguo Hu Chao Zhu Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration Ecological Indicators Global soil respiration Interpretable prediction model Response mechanism Distribution pattern Non-linear relationships Estimation bias |
| title | Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration |
| title_full | Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration |
| title_fullStr | Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration |
| title_full_unstemmed | Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration |
| title_short | Interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration |
| title_sort | interpretable machine learning unveils threshold responses and spatial patterns of global soil respiration |
| topic | Global soil respiration Interpretable prediction model Response mechanism Distribution pattern Non-linear relationships Estimation bias |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25006806 |
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