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Reviewing seas of data: Integrating image‐based bio‐logging and artificial intelligence to enhance marine conservation.

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Title: Reviewing seas of data: Integrating image‐based bio‐logging and artificial intelligence to enhance marine conservation.
Authors: Chimienti, Marianna, Kato, Akiko, Seydi, Vahid, Schoombie, Stefan, Hinke, Jefferson T., Joy, Ruth, Lidgard, Damian C., Oosthuizen, W. Chris, Papastamatiou, Yannis P., Ropert‐Coudert, Yan, Takahashi, Akinori, Thiebot, Jean‐Baptiste, Visani, Muriel, Coustaty, Mickael, Jeanniard‐du‐Dot, Tiphaine
Source: Methods in Ecology & Evolution; Feb2026, Vol. 17 Issue 2, p272-290, 19p
Subject Terms: ARTIFICIAL intelligence, COMPUTER vision, ANIMAL tagging, ENVIRONMENTAL sciences, COOPERATION, IMAGE processing, MARINE resources conservation, DATA integration
Abstract: Conservation of marine ecosystems can be improved through a better understanding of ecosystem functioning, particularly the cryptic underwater behaviours and interactions of marine predators. Image‐based bio‐logging devices (including images, videos and active acoustic) are increasingly used to monitor wildlife movements, foraging behaviours and their environment, but generate complex datasets needing efficient analytical tools.We review advances in image‐based bio‐logging technology for ecological studies on marine fauna. Emphasis is placed on the diversity of data collected, merging research questions, challenges in image processing, and integration of Artificial Intelligence (AI) methods. Image‐based system issues, such as exposure, focus, blurriness, colour balance, moving background, perspective and scale variability are even more challenging in underwater images where conditions change constantly and cannot be controlled. We list computer vision tools and algorithms available for analyses of underwater images, including enhanced tracking algorithms that recognise objects and treat images as a time series.Although AI and computer vision methods offer ample and robust analytical solutions for (semi‐) automated image processing, their uptake by marine ecologists has been slow. Collaboration among ecologists, modellers, statisticians, engineers and computer scientists is needed to integrate ecological questions, data selection and computational methodology. We propose a four‐phase framework for image data processing and analysis (video checking and manipulation, image processing, image labelling and model development) accompanied by detailed python code. We also outline the additional complications in aligning the diverse scalar movement metrics from bio‐loggers along with image‐based data, such as acceleration, depth and location, which typically are collected at different resolutions. Building analytical frameworks for on‐board image data collection (e.g. lightweight models) is also explored.We advocate for a collaborative research community at the Ecology‐AI interface, emphasising sharing and exchange of both data and tools to drive cross‐disciplinary innovation. Beyond the Ecology‐AI interface, we pave the path for the application of insights from image‐based bio‐logging technology enabling collaboration among scientists, conservation managers, and policymakers. Systematic applications of computer vision tools to image‐based bio‐logging technology will enhance the power these data hold, informing about the status of marine ecosystems, testing and developing ecological theory and aiding conservation. [ABSTRACT FROM AUTHOR]
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Database: Complementary Index
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Abstract:Conservation of marine ecosystems can be improved through a better understanding of ecosystem functioning, particularly the cryptic underwater behaviours and interactions of marine predators. Image‐based bio‐logging devices (including images, videos and active acoustic) are increasingly used to monitor wildlife movements, foraging behaviours and their environment, but generate complex datasets needing efficient analytical tools.We review advances in image‐based bio‐logging technology for ecological studies on marine fauna. Emphasis is placed on the diversity of data collected, merging research questions, challenges in image processing, and integration of Artificial Intelligence (AI) methods. Image‐based system issues, such as exposure, focus, blurriness, colour balance, moving background, perspective and scale variability are even more challenging in underwater images where conditions change constantly and cannot be controlled. We list computer vision tools and algorithms available for analyses of underwater images, including enhanced tracking algorithms that recognise objects and treat images as a time series.Although AI and computer vision methods offer ample and robust analytical solutions for (semi‐) automated image processing, their uptake by marine ecologists has been slow. Collaboration among ecologists, modellers, statisticians, engineers and computer scientists is needed to integrate ecological questions, data selection and computational methodology. We propose a four‐phase framework for image data processing and analysis (video checking and manipulation, image processing, image labelling and model development) accompanied by detailed python code. We also outline the additional complications in aligning the diverse scalar movement metrics from bio‐loggers along with image‐based data, such as acceleration, depth and location, which typically are collected at different resolutions. Building analytical frameworks for on‐board image data collection (e.g. lightweight models) is also explored.We advocate for a collaborative research community at the Ecology‐AI interface, emphasising sharing and exchange of both data and tools to drive cross‐disciplinary innovation. Beyond the Ecology‐AI interface, we pave the path for the application of insights from image‐based bio‐logging technology enabling collaboration among scientists, conservation managers, and policymakers. Systematic applications of computer vision tools to image‐based bio‐logging technology will enhance the power these data hold, informing about the status of marine ecosystems, testing and developing ecological theory and aiding conservation. [ABSTRACT FROM AUTHOR]
ISSN:2041210X
DOI:10.1111/2041-210x.70063