Applying deep learning to quantify drivers of long-term ecological change in a Swedish marine protected area

Applying deep learning to quantify drivers of long-term ecological change in a Swedish marine protected area | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 21 April 2025 V1 Latest version Share on Applying deep learning to quantify drivers of long-term ecological change in a Swedish marine protected area Authors : Christian Nilsson 0009-0005-6356-2152 [email protected] , Søren Faurby 0000-0002-2974-2628 , Emil Burman , Jurie Germishuys , and Matthias Obst 0000-0003-0264-9631 Authors Info & Affiliations https://doi.org/10.22541/au.174522631.15993508/v1 Published Ecology and Evolution Version of record Peer review timeline 501 views 253 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract “‘latex Recent advances in remotely operated vehicle technology and automated processing of visual data through deep-learning approaches have enabled us to track long-term ecological trends at marine rock walls. Here, we trained a deep-learning based object-detection model to classify prominent benthic invertebrate fauna on a slope/wall-section of the Koster fjord, part of the Swedish marine protected area Kosterhavet National Park. The model was applied to footage of the study site from 1997-2023, from which depth ranges and relative abundances of 17 invertebrate taxa were extracted, generating 72,369 occurrence records. The object-detection model was deemed reliable for its purpose with modeled depth distributions aligning with previously documented occurrences. Community structure was found to change along the study site’s depth gradient, with a higher taxon diversity at greater depths. Significant temporal increases in overall abundance across all depths were found in eight taxa and significant decreases in five taxa. The overall community structure shifted toward a higher abundance of small, heat-tolerant suspension-feeders. Temperature preference and size were found to be significant drivers behind taxon-specific abundance change. The documented loss over time of large, heat-sensitive taxa suggests that ongoing temperature increases are a likely cause for the altered community structure. However, a widespread trend of increasing abundance was noted throughout the remaining community, including species sensitive to trawling. This suggests that while species sensitive to climate change may disappear from the area, the remaining benthic community benefits from the protection measures in the national park. Our study demonstrates the application potential of video surveillance and deep learning technology, and we recommend the implementation of standardized video monitoring in adaptive management of marine ecosystems. Supplementary Material File (manuscript.docx) Download 129.11 KB Information & Authors Information Version history V1 Version 1 21 April 2025 Peer review timeline Published Ecology and Evolution Version of Record 2 Sep 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecology and Evolution Keywords description ecosystem ecosystem ecology invertebrate marine method development multiple statistical Authors Affiliations Christian Nilsson 0009-0005-6356-2152 [email protected] University of Gothenburg Department of Marine Sciences View all articles by this author Søren Faurby 0000-0002-2974-2628 University of Gothenburg Department of Biological and Environmental Sciences View all articles by this author Emil Burman University of Gothenburg Department of Marine Sciences View all articles by this author Jurie Germishuys Combine AB View all articles by this author Matthias Obst 0000-0003-0264-9631 University of Gothenburg Department of Marine Sciences View all articles by this author Metrics & Citations Metrics Article Usage 501 views 253 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Christian Nilsson, Søren Faurby, Emil Burman, et al. Applying deep learning to quantify drivers of long-term ecological change in a Swedish marine protected area. Authorea . 21 April 2025. DOI: https://doi.org/10.22541/au.174522631.15993508/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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