[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124951-en":3,"doc-seo-124951-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124951,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Modelling global mesozooplankton biomass using machine learning","Mesozooplankton link primary producers to higher trophic levels and strongly influence marine food webs, biological carbon pumps, and fishery resources, yet global biomass patterns and drivers remain unclear. The study compares boosted regression trees, random forest, artificial neural networks, and support vector machines to model spatiotemporal mesozooplankton biomass using compiled observations and contemporaneous satellite environmental predictors. Random forest shows the highest accuracy (R2=0.57, RMSE=0.39) and better alignment with observations. Outputs highlight surface chlorophyll a as a key correlate and enable an emergent scaling constraint for ecosystem model validation, with a predicted 3% global biomass decrease by century’s end under business-as-usual scenarios.","Progress in Oceanography 229 (2024) 103371  \nContents lists available at ScienceDirect  \nProgress in Oceanography  \njournal [homepage:](homepage: www.elsevier.com/locate/pocean)[ www.elsevier.com/locate/pocean](homepage: www.elsevier.com/locate/pocean)  \n| Modelling global mesozooplankton biomass using machine learning Kailin Liu a, Zhimeng Xu b, Xin Liu a, Bangqin Huang a, Hongbin Liu c, Bingzhang Chend,*\u003Cbr>a State Key Laboratory of Marine Environmental Science / Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies / College of Environment & Ecology, Xiamen University, Xiamen, China\u003Cbr>b Haide College, Ocean University of China, Qingdao, China\u003Cbr>c Department of Ocean Science, Hong Kong University of Science and Technology, Hong Kong, SAR, China d Department of Mathematics and Statistics, University of Strathclyde, Glasgow, United Kingdom |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: Mesozooplankton Data-driven model Spatiotemporal pattern Random forest Monthly climatology |  | Mesozooplankton are a crucial link between primary producers and higher trophic levels and play a vital role in marine food webs, biological carbon pumps, and sustaining fishery resources. However, the global distribution of mesozooplankton biomass and the relevant controlling mechanisms remain elusive. We compared four machine learning algorithms (Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine) to model the spatiotemporal distributions of global mesozooplankton biomass. These algorithms were trained on a compiled dataset of published mesozooplankton biomass observations with corresponding environmental predictors from contemporaneous satellite observations (temperature, chlorophyll, salinity, and mixed layer depth). We found that Random Forest achieved the best predictive accuracy with R2 and RMSE (Root Mean Standard Error) of 0.57 and 0.39, respectively. Also, the global distribution of mesozooplankton biomass predicted by the Random Forest model was more consistent with the observational data than other models. We used the Random Forest model to create a global map of mesozooplankton biomass which serves as a reference for validating process-based ecosystem models. The model outputs confirm that environmental factors, especially surface Chl a, a proxy for prey availability, significantly correlate with the spatiotemporal distribution of mesozooplankton biomass. The scaling relationship between the mesozooplankton biomass and Chl a can be used asan emergent constraint for model validation and development. Moreover, our model predicts that the global total mesozooplankton biomass will decrease by 3% by the end of this century under the “business-as-usual” scenarios, potentially reducing fishery production and carbon sequestration. Our study contributes to predicting global mesozooplankton biomass and provides deep insights into the underlying environmental impacts on the distribution of mesozooplankton biomass. |\n\n1. Introduction  \nMesozooplankton, defined as zooplankton with a size range of 0.2–20 mm (Sieburth et al., 1978), mainly consist of crustacean plankton such as copepods (Sommer & Stibor, 2002). They prey on microzooplankton (\u003C 200 μm), large phytoplankton, and detritus, acting as a vital link between the microbial food web and the classic food chain that transfers energy and materials from primary producers to higher trophic levels (Ikeda, 1985; Steinberg & Landry, 2017). Mesozooplankton play a crucial role in marine biological carbon pumps because their faecal pellets account for a large part of passive carbon export (i.e., gravitational carbon pump), and their migration drives active transport of carbon that also contributes to the total carbon export  \n(Nowicki et al., 2022). In addition to the central biogeochemical and ecological roles, mesozooplankton have socio-economic interests, as they are essential food sources for c","cbCaiaTd4ePUXc4k","https://ap.wps.com/l/cbCaiaTd4ePUXc4k","pdf",12255396,1,17,"English","en",105,"# Introduction\n## Ecological and biogeochemical importance of mesozooplankton\n## Challenges in estimating global biomass\n## Role of data-driven machine learning","[{\"question\":\"Which machine learning algorithms are compared for modelling mesozooplankton biomass?\",\"answer\":\"The study compares Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine.\"},{\"question\":\"Why does surface chlorophyll a matter in the model results?\",\"answer\":\"The model outputs show environmental factors—especially surface Chl a, used as a proxy for prey availability—significantly correlate with the spatiotemporal biomass distribution.\"},{\"question\":\"How does the random forest model contribute to ecosystem model validation?\",\"answer\":\"The random forest predictions are used to produce a global biomass map, and the scaling relationship between biomass and Chl a is proposed as an emergent constraint for validating process-based ecosystem models.\"}]","Modelling global mesozooplankton biomass using machine learning | 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machine learning algorithms are compared for modelling mesozooplankton biomass?","Question",{"text":75,"@type":76},"The study compares Boosted Regression Trees, Random Forest, Artificial Neural Network, and Support Vector Machine.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why does surface chlorophyll a matter in the model results?",{"text":80,"@type":76},"The model outputs show environmental factors—especially surface Chl a, used as a proxy for prey availability—significantly correlate with the spatiotemporal biomass distribution.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the random forest model contribute to ecosystem model validation?",{"text":84,"@type":76},"The random forest predictions are used to produce a global biomass map, and the scaling relationship between biomass and Chl a is proposed as an emergent constraint for validating process-based ecosystem 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