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      Environmental drivers and the distribution of cold-water corals in the global ocean

      , , , , , ,
      Frontiers in Marine Science
      Frontiers Media SA

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          Abstract

          Species distribution models (SDMs) are useful tools for describing and predicting the distribution of marine species in data-limited environments. Outputs from SDMs have been used to identify areas for spatial management, analyzing trawl closures, quantitatively measuring the risk of bottom trawling, and evaluating protected areas for improving conservation and management. Cold-water corals are globally distributed habitat-forming organisms that are vulnerable to anthropogenic impacts and climate change, but data deficiency remains an ongoing issue for the effective spatial management of these important ecosystem engineers. In this study, we constructed 11 environmental seabed variables at 500 m resolution based on the latest multi-depth global datasets and high-resolution bathymetry. An ensemble species distribution modeling method was used to predict the global habitat suitability for 10 widespread cold-water coral species, namely, 6 Scleractinian framework-forming species and 4 large gorgonian species. Temperature, depth, salinity, terrain ruggedness index, carbonate saturation state, and chlorophyll were the most important factors in determining the global distributions of these species. The Scleractinian Madrepora oculata showed the widest niche breadth, while most other species demonstrated somewhat limited niche breadth. The shallowest study species, Oculina varicosa, had the most distinctive niche of the group. The model outputs from this study represent the highest-resolution global predictions for these species to date and are valuable in aiding the management, conservation, and continued research into cold-water coral species.

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          Random Forests

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            SciPy 1.0: fundamental algorithms for scientific computing in Python

            SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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              Greedy function approximation: A gradient boosting machine.

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                Author and article information

                Journal
                Frontiers in Marine Science
                Front. Mar. Sci.
                Frontiers Media SA
                2296-7745
                October 13 2023
                October 13 2023
                : 10
                Article
                10.3389/fmars.2023.1217851
                58d4e282-af41-47de-80b9-0ece5431fa8b
                © 2023

                Free to read

                https://creativecommons.org/licenses/by/4.0/

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