Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters

IF 5.8 2区 环境科学与生态学 Q1 ECOLOGY
Loukas Katikas , Sofia Reizopoulou , Paraskevi Drakopoulou , Vassiliki Vassilopoulou
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引用次数: 0

Abstract

Marine habitat mapping is an essential tool for planning conservation efforts and sustainable management of marine activities. High spatial resolution in marine habitat maps is of utmost importance, as it may encompass more detail in imagery and reveal important biotopes. This level of detail supports directing monitoring and analysis efforts for effective implementation of European Union (EU) environmental policies and provides more relevant advice for robust decision-making under sectorial policies (e.g., the Common Fisheries Policy) and more integrated policies (e.g., marine spatial planning). In this study, sea bottom type data recorded during national monitoring of commercial fishing vessel operations and fishery surveys in the Greek Seas were used. These data were then assigned to the EU EMODnet seabed habitats using local ecological knowledge. Two machine-learning algorithms, i.e., random forest classifier (RFC) and gradient boosting classifier, were trained on the entire national-scale dataset and subsequently applied to assess their performance in predicting habitat types in the Saronikos Gulf (regional scale) using various environmental factors as predictors. The borderline synthetic minority oversampling technique was applied to manage inherent data class imbalances. A validation dataset and georeferenced data from previous studies were used to compare the accuracy and predictive performance of the models. Using this approach, the Saronikos Gulf was enriched with five more habitat types than visualised in the EMODnet portal, which also filled habitat gaps in areas where no data existed. Results from application of the RFC-Borderline Smote (BS) model (62 % accuracy, 0.51 kappa score) were then used to address conservation planning commitments recently made by the Greek government. The vast majority of marine seabed priority habitats in the study area appeared to fall outside the borders of the current Natura 2000 sites, which served as the baseline for the declared trawl bans in Greek waters, following the provisions of the EU Marine Action Plan.
利用渔业相关数据、科学专业知识和机器学习来改善地中海东北部水域的海洋栖息地测绘
海洋生境测绘是规划养护工作和海洋活动可持续管理的重要工具。海洋栖息地地图的高空间分辨率至关重要,因为它可以在图像中包含更多细节并揭示重要的生物群落。这种详细程度支持指导监测和分析工作,以便有效执行欧洲联盟(欧盟)的环境政策,并为根据部门政策(例如共同渔业政策)和更综合的政策(例如海洋空间规划)作出强有力的决策提供更相关的咨询意见。在这项研究中,使用了在国家监测商业渔船作业和希腊海渔业调查期间记录的海底类型数据。然后利用当地生态知识将这些数据分配给欧盟EMODnet海底栖息地。随机森林分类器(RFC)和梯度增强分类器这两种机器学习算法在整个国家尺度数据集上进行了训练,随后应用于评估它们在预测萨罗尼科斯湾(区域尺度)栖息地类型方面的表现,使用各种环境因素作为预测因子。采用边界合成少数过采样技术来处理固有的数据类不平衡。使用验证数据集和先前研究的地理参考数据来比较模型的准确性和预测性能。使用这种方法,萨罗尼科斯湾比EMODnet门户网站中可视化的栖息地类型多了五种,这也填补了没有数据的地区的栖息地空白。RFC-Borderline Smote (BS)模型的应用结果(准确率为62%,kappa分数为0.51)随后被用于解决希腊政府最近做出的保护规划承诺。根据欧盟海洋行动计划的规定,研究区域的绝大多数海洋海底优先栖息地似乎不在当前的Natura 2000站点的边界之内,该站点作为宣布在希腊水域禁止拖网的基线。
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来源期刊
Ecological Informatics
Ecological Informatics 环境科学-生态学
CiteScore
8.30
自引率
11.80%
发文量
346
审稿时长
46 days
期刊介绍: The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.
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