Structured machine learning modeling to support conservation of deep-sea benthic biodiversity.
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| Natura: | Artículo científico |
| Lingua: | en |
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Conservation biology : the journal of the Society for Conservation Biology
2026
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| author | Fonseca, Gustavo Vieira, Danilo C Carneiro, Juliane C Carreira, Renato S Ceccopieri, Milena Corbisier, Thais N Dalto, Adriana Galindo Figueiredo, Alberto G Gallucci, Fabiane Gheller, Paula de Jesus, Simone Brito Lavrado, Helena Passeri Lazzari, Letícia Marcon, Eduardo Hilzendeger Moreira, Daniel Leite de Moura, Rafael Bendayan Pape, Ellen Santarosa, Ana Cláudia Aoki Dos Santos Filho, João Regis de Mello E Sousa, Silvia Helena Vicente, Thaisa Marques Yaginuma, Luciana Erika Yamashita, Cinthia Watanabe, Wandrey |
| author_facet | Fonseca, Gustavo Vieira, Danilo C Carneiro, Juliane C Carreira, Renato S Ceccopieri, Milena Corbisier, Thais N Dalto, Adriana Galindo Figueiredo, Alberto G Gallucci, Fabiane Gheller, Paula de Jesus, Simone Brito Lavrado, Helena Passeri Lazzari, Letícia Marcon, Eduardo Hilzendeger Moreira, Daniel Leite de Moura, Rafael Bendayan Pape, Ellen Santarosa, Ana Cláudia Aoki Dos Santos Filho, João Regis de Mello E Sousa, Silvia Helena Vicente, Thaisa Marques Yaginuma, Luciana Erika Yamashita, Cinthia Watanabe, Wandrey Fonseca, Gustavo Vieira, Danilo C Carneiro, Juliane C Carreira, Renato S Ceccopieri, Milena Corbisier, Thais N Dalto, Adriana Galindo Figueiredo, Alberto G Gallucci, Fabiane Gheller, Paula de Jesus, Simone Brito Lavrado, Helena Passeri Lazzari, Letícia Marcon, Eduardo Hilzendeger Moreira, Daniel Leite de Moura, Rafael Bendayan Pape, Ellen Santarosa, Ana Cláudia Aoki Dos Santos Filho, João Regis de Mello E Sousa, Silvia Helena Vicente, Thaisa Marques Yaginuma, Luciana Erika Yamashita, Cinthia Watanabe, Wandrey |
| collection | PubMed - marine biology |
| contents | Structured machine learning modeling to support conservation of deep-sea benthic biodiversity. Fonseca, Gustavo Vieira, Danilo C Carneiro, Juliane C Carreira, Renato S Ceccopieri, Milena Corbisier, Thais N Dalto, Adriana Galindo Figueiredo, Alberto G Gallucci, Fabiane Gheller, Paula de Jesus, Simone Brito Lavrado, Helena Passeri Lazzari, Letícia Marcon, Eduardo Hilzendeger Moreira, Daniel Leite de Moura, Rafael Bendayan Pape, Ellen Santarosa, Ana Cláudia Aoki Dos Santos Filho, João Regis de Mello E Sousa, Silvia Helena Vicente, Thaisa Marques Yaginuma, Luciana Erika Yamashita, Cinthia Watanabe, Wandrey Biodiversity monitoring programs need to deliver accurate, timely, and actionable predictions. To establish a predictive monitoring program for deep-sea benthos of the Santos Basin, Brazil, we developed a two-stage structured model that allowed comparison of biodiversity predictions obtained from environmental simulations (2M-Sim). We also modeled the environmental variables as a function of spatial and temporal variables and compared this model's predictions with predictions obtained from real environmental data (2M). We built unstructured models (1M) as references to evaluate whether the proposed structured approach was reliable. We expected no significant differences between 1M and 2M or between 2M and 2M-Sim. Data were obtained from 100 stations at depths of 25-2400 m during two surveys (2019 and 2021). In our model framework, we used 12 benthic macro- and meiofaunal variables, 44 sediment and water column environmental variables, and four spatial and temporal variables. We applied a random forest algorithm to the structured and unstructured models. All comparisons were performed with 20% of the dataset set aside for validation. The average accuracy was 72%, 69%, and 68% for the 1M, 2M, and 2M-Sim models, respectively. Accuracies of 2M ranged from 38% to 84% and were generally higher for macrofauna. The observed accuracy loss from 1M to 2M (3%) and from 2M to 2M-Sim (1%) was not significant for any biodiversity variable. The 2M model identified 30 significant environmental variables; bottom water parameters and sedimentary phytopigment and carbonate concentrations were the best predictors. Our approach supports biodiversity conservation by optimizing data needs and future sampling and by guiding data-driven management decisions for benthic biodiversity. |
| format | Artículo científico |
| id | pubmed_41808427 |
| institution | PubMed |
| language | en |
| publishDate | 2026 |
| publisher | Conservation biology : the journal of the Society for Conservation Biology |
| record_format | pubmed |
| spellingShingle | Structured machine learning modeling to support conservation of deep-sea benthic biodiversity. Fonseca, Gustavo Vieira, Danilo C Carneiro, Juliane C Carreira, Renato S Ceccopieri, Milena Corbisier, Thais N Dalto, Adriana Galindo Figueiredo, Alberto G Gallucci, Fabiane Gheller, Paula de Jesus, Simone Brito Lavrado, Helena Passeri Lazzari, Letícia Marcon, Eduardo Hilzendeger Moreira, Daniel Leite de Moura, Rafael Bendayan Pape, Ellen Santarosa, Ana Cláudia Aoki Dos Santos Filho, João Regis de Mello E Sousa, Silvia Helena Vicente, Thaisa Marques Yaginuma, Luciana Erika Yamashita, Cinthia Watanabe, Wandrey Structured machine learning modeling to support conservation of deep-sea benthic biodiversity. Fonseca, Gustavo Vieira, Danilo C Carneiro, Juliane C Carreira, Renato S Ceccopieri, Milena Corbisier, Thais N Dalto, Adriana Galindo Figueiredo, Alberto G Gallucci, Fabiane Gheller, Paula de Jesus, Simone Brito Lavrado, Helena Passeri Lazzari, Letícia Marcon, Eduardo Hilzendeger Moreira, Daniel Leite de Moura, Rafael Bendayan Pape, Ellen Santarosa, Ana Cláudia Aoki Dos Santos Filho, João Regis de Mello E Sousa, Silvia Helena Vicente, Thaisa Marques Yaginuma, Luciana Erika Yamashita, Cinthia Watanabe, Wandrey Biodiversity monitoring programs need to deliver accurate, timely, and actionable predictions. To establish a predictive monitoring program for deep-sea benthos of the Santos Basin, Brazil, we developed a two-stage structured model that allowed comparison of biodiversity predictions obtained from environmental simulations (2M-Sim). We also modeled the environmental variables as a function of spatial and temporal variables and compared this model's predictions with predictions obtained from real environmental data (2M). We built unstructured models (1M) as references to evaluate whether the proposed structured approach was reliable. We expected no significant differences between 1M and 2M or between 2M and 2M-Sim. Data were obtained from 100 stations at depths of 25-2400 m during two surveys (2019 and 2021). In our model framework, we used 12 benthic macro- and meiofaunal variables, 44 sediment and water column environmental variables, and four spatial and temporal variables. We applied a random forest algorithm to the structured and unstructured models. All comparisons were performed with 20% of the dataset set aside for validation. The average accuracy was 72%, 69%, and 68% for the 1M, 2M, and 2M-Sim models, respectively. Accuracies of 2M ranged from 38% to 84% and were generally higher for macrofauna. The observed accuracy loss from 1M to 2M (3%) and from 2M to 2M-Sim (1%) was not significant for any biodiversity variable. The 2M model identified 30 significant environmental variables; bottom water parameters and sedimentary phytopigment and carbonate concentrations were the best predictors. Our approach supports biodiversity conservation by optimizing data needs and future sampling and by guiding data-driven management decisions for benthic biodiversity. |
| title | Structured machine learning modeling to support conservation of deep-sea benthic biodiversity. |
| url | https://pubmed.ncbi.nlm.nih.gov/41808427/ |