Chengfeng Meng , Hao Zhou , Xichuan Liu , Jiao Zheng , Jun Wang , Qingwei Zeng , Shuai Hu
{"title":"泛北极生态系统陆地植被碳动态的主要气候驱动因素辨析","authors":"Chengfeng Meng , Hao Zhou , Xichuan Liu , Jiao Zheng , Jun Wang , Qingwei Zeng , Shuai Hu","doi":"10.1016/j.ecoinf.2025.103267","DOIUrl":null,"url":null,"abstract":"<div><div>Pan-Arctic terrestrial ecosystems have experienced widespread greening over the past few decades due to climate warming. However, the dynamic variations and climatic causes of such greening in the pan-Arctic are still unclear due to limitations in high-latitude ground-based measurements. The study first evaluated the applicability of three satellite-based vegetation indices (VIs) for pan-Arctic carbon dynamics in previous decades compared with multisource observations. Three VIs presented significant increasing trends up to 0.0005–0.0017 yr<sup>−1</sup> for pan-Arctic ecosystems in the past 23 years, and near-infrared reflectance of vegetation (NIRv) could better capture pan-Arctic terrestrial carbon dynamic variations (e.g., increasing trends) than other VIs among most vegetation types, such as grasslands. The separate contributions of climatic factors to satellite-based NIRv variability were further quantified using partial correlation and ridge regression analysis over pan-Arctic ecosystems from 2001 to 2023. Carbon dioxide (CO<sub>2</sub>) and air temperature (AT) presented positive partial correlations with the satellite-based NIRv over entire pan-Arctic ecosystems, due to fertilization and warming effects. However, the vapor pressure deficit (VPD) showed positive (negative) partial correlations with NIRv variations in high (low) pan-Arctic ecosystems because of diverse stomatal responses to air dryness. Therefore, increases in VPD contributed 11.2 % of the pan-Arctic NIRv variability, although positive (negative) effects were detected for high (low) pan-Arctic ecosystems. These findings highlight the advantages of the NIRv for representing terrestrial carbon dynamics in fragile ecosystems (e.g., the pan-Arctic) and reveal the diverse effects of the regional climate (e.g., air dryness) on pan-Arctic ecosystems under climate warming.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103267"},"PeriodicalIF":7.3000,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Distinguishing the main climatic drivers of terrestrial vegetation carbon dynamics in pan-Arctic ecosystems\",\"authors\":\"Chengfeng Meng , Hao Zhou , Xichuan Liu , Jiao Zheng , Jun Wang , Qingwei Zeng , Shuai Hu\",\"doi\":\"10.1016/j.ecoinf.2025.103267\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Pan-Arctic terrestrial ecosystems have experienced widespread greening over the past few decades due to climate warming. However, the dynamic variations and climatic causes of such greening in the pan-Arctic are still unclear due to limitations in high-latitude ground-based measurements. The study first evaluated the applicability of three satellite-based vegetation indices (VIs) for pan-Arctic carbon dynamics in previous decades compared with multisource observations. Three VIs presented significant increasing trends up to 0.0005–0.0017 yr<sup>−1</sup> for pan-Arctic ecosystems in the past 23 years, and near-infrared reflectance of vegetation (NIRv) could better capture pan-Arctic terrestrial carbon dynamic variations (e.g., increasing trends) than other VIs among most vegetation types, such as grasslands. The separate contributions of climatic factors to satellite-based NIRv variability were further quantified using partial correlation and ridge regression analysis over pan-Arctic ecosystems from 2001 to 2023. Carbon dioxide (CO<sub>2</sub>) and air temperature (AT) presented positive partial correlations with the satellite-based NIRv over entire pan-Arctic ecosystems, due to fertilization and warming effects. However, the vapor pressure deficit (VPD) showed positive (negative) partial correlations with NIRv variations in high (low) pan-Arctic ecosystems because of diverse stomatal responses to air dryness. Therefore, increases in VPD contributed 11.2 % of the pan-Arctic NIRv variability, although positive (negative) effects were detected for high (low) pan-Arctic ecosystems. These findings highlight the advantages of the NIRv for representing terrestrial carbon dynamics in fragile ecosystems (e.g., the pan-Arctic) and reveal the diverse effects of the regional climate (e.g., air dryness) on pan-Arctic ecosystems under climate warming.</div></div>\",\"PeriodicalId\":51024,\"journal\":{\"name\":\"Ecological Informatics\",\"volume\":\"90 \",\"pages\":\"Article 103267\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2025-06-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Informatics\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574954125002766\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Informatics","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574954125002766","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECOLOGY","Score":null,"Total":0}
Distinguishing the main climatic drivers of terrestrial vegetation carbon dynamics in pan-Arctic ecosystems
Pan-Arctic terrestrial ecosystems have experienced widespread greening over the past few decades due to climate warming. However, the dynamic variations and climatic causes of such greening in the pan-Arctic are still unclear due to limitations in high-latitude ground-based measurements. The study first evaluated the applicability of three satellite-based vegetation indices (VIs) for pan-Arctic carbon dynamics in previous decades compared with multisource observations. Three VIs presented significant increasing trends up to 0.0005–0.0017 yr−1 for pan-Arctic ecosystems in the past 23 years, and near-infrared reflectance of vegetation (NIRv) could better capture pan-Arctic terrestrial carbon dynamic variations (e.g., increasing trends) than other VIs among most vegetation types, such as grasslands. The separate contributions of climatic factors to satellite-based NIRv variability were further quantified using partial correlation and ridge regression analysis over pan-Arctic ecosystems from 2001 to 2023. Carbon dioxide (CO2) and air temperature (AT) presented positive partial correlations with the satellite-based NIRv over entire pan-Arctic ecosystems, due to fertilization and warming effects. However, the vapor pressure deficit (VPD) showed positive (negative) partial correlations with NIRv variations in high (low) pan-Arctic ecosystems because of diverse stomatal responses to air dryness. Therefore, increases in VPD contributed 11.2 % of the pan-Arctic NIRv variability, although positive (negative) effects were detected for high (low) pan-Arctic ecosystems. These findings highlight the advantages of the NIRv for representing terrestrial carbon dynamics in fragile ecosystems (e.g., the pan-Arctic) and reveal the diverse effects of the regional climate (e.g., air dryness) on pan-Arctic ecosystems under climate warming.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.