Projected impacts of future climate change on the aboveground biomass of seagrasses at global scale

IF 8.2 1区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Lidiane P. Gouvêa , Dorte Krause-Jensen , Carlos M. Duarte , Jorge Assis
{"title":"Projected impacts of future climate change on the aboveground biomass of seagrasses at global scale","authors":"Lidiane P. Gouvêa ,&nbsp;Dorte Krause-Jensen ,&nbsp;Carlos M. Duarte ,&nbsp;Jorge Assis","doi":"10.1016/j.scitotenv.2025.178680","DOIUrl":null,"url":null,"abstract":"<div><div>Seagrasses are crucial marine ecosystems that have experienced declines due to anthropogenic and climate change impacts. The projected future climate change suggests additional seagrass losses, but no global-scale estimates are currently available on the potential changes in aboveground biomass of seagrasses. We modelled and quantified the current potential aboveground biomass (AGB) of seagrasses on the global scale and projected future AGB under contrasting Shared Socioeconomic Pathway (SSP) scenarios, from low emissions (SSP1–1.9) to high emissions (SSP3–7.0 and SSP5–8.5). A machine learning algorithm (Boosted Regression Trees) fitted a comprehensive AGB dataset against biological and anthropogenic meaningful predictors. The model performed with high accuracy (deviance explained: 0.83), highlighting the role of genus and temperature conditions in defining global AGB patterns. The model estimated a present-day average AGB of 133.83 gDW·m<sup>2</sup> (DW, dry weight) and a total global AGB of 0.0673 Pg DW. Future projections were highly dependent on the emission scenario, with losses in AGB ranging between 4.25 % and 9.25 % and in overall AGB between 9.96 % and 10.26 % across scenarios. Particularly, the higher emission scenario projected severe regional losses along the coastlines of the Tropical Eastern Pacific, the Eastern Indo-Pacific, the Temperate Northern Pacific, and the Tropical Atlantic, and gains along the Temperate Southern Africa and the Arctic regions. Our global estimates underline that fulfilling the Paris Agreement, as well as conserving and monitoring populations most affected by combined anthropogenic pressures would help to limit seagrass AGB declines, thereby supporting the multiple ecological services of seagrasses.</div></div>","PeriodicalId":422,"journal":{"name":"Science of the Total Environment","volume":"966 ","pages":"Article 178680"},"PeriodicalIF":8.2000,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science of the Total Environment","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0048969725003146","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Seagrasses are crucial marine ecosystems that have experienced declines due to anthropogenic and climate change impacts. The projected future climate change suggests additional seagrass losses, but no global-scale estimates are currently available on the potential changes in aboveground biomass of seagrasses. We modelled and quantified the current potential aboveground biomass (AGB) of seagrasses on the global scale and projected future AGB under contrasting Shared Socioeconomic Pathway (SSP) scenarios, from low emissions (SSP1–1.9) to high emissions (SSP3–7.0 and SSP5–8.5). A machine learning algorithm (Boosted Regression Trees) fitted a comprehensive AGB dataset against biological and anthropogenic meaningful predictors. The model performed with high accuracy (deviance explained: 0.83), highlighting the role of genus and temperature conditions in defining global AGB patterns. The model estimated a present-day average AGB of 133.83 gDW·m2 (DW, dry weight) and a total global AGB of 0.0673 Pg DW. Future projections were highly dependent on the emission scenario, with losses in AGB ranging between 4.25 % and 9.25 % and in overall AGB between 9.96 % and 10.26 % across scenarios. Particularly, the higher emission scenario projected severe regional losses along the coastlines of the Tropical Eastern Pacific, the Eastern Indo-Pacific, the Temperate Northern Pacific, and the Tropical Atlantic, and gains along the Temperate Southern Africa and the Arctic regions. Our global estimates underline that fulfilling the Paris Agreement, as well as conserving and monitoring populations most affected by combined anthropogenic pressures would help to limit seagrass AGB declines, thereby supporting the multiple ecological services of seagrasses.

Abstract Image

求助全文
约1分钟内获得全文 求助全文
来源期刊
Science of the Total Environment
Science of the Total Environment 环境科学-环境科学
CiteScore
17.60
自引率
10.20%
发文量
8726
审稿时长
2.4 months
期刊介绍: The Science of the Total Environment is an international journal dedicated to scientific research on the environment and its interaction with humanity. It covers a wide range of disciplines and seeks to publish innovative, hypothesis-driven, and impactful research that explores the entire environment, including the atmosphere, lithosphere, hydrosphere, biosphere, and anthroposphere. The journal's updated Aims & Scope emphasizes the importance of interdisciplinary environmental research with broad impact. Priority is given to studies that advance fundamental understanding and explore the interconnectedness of multiple environmental spheres. Field studies are preferred, while laboratory experiments must demonstrate significant methodological advancements or mechanistic insights with direct relevance to the environment.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信