Structured machine learning modeling to support conservation of deep‐sea benthic biodiversity

Pape, E.

doi:/10.1111/cobi.70255

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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 (2026). Structured machine learning modeling to support conservation of deep‐sea benthic biodiversity. Conserv. Biol. 40(4): e70255. https://dx.doi.org/10.1111/cobi.70255

In: Conservation Biology. Wiley: Boston, Mass.. ISSN 0888-8892; e-ISSN 1523-1739

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Abstract

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.

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