Monitoring the ecological status of natural habitats is crucial to the conservation process, as it enables the implementation of efficient conservation policies. Nowadays, it is increasingly possible to automate species identification, given the availability of very large image databases and state-of-the-art computational power which makes the training of automated machine learning-based classification models an increasingly viable tool for monitoring marine habitats. Coralligenous reefs are an underwater habitat of particular importance, found in the Mediterranean. This habitat is of a similar biocomplexity to coral reefs. They have been monitored in French waters since 2010 using manually annotated photo quadrats (RECOR monitoring network). Based on the large database of annotations accumulated therein, we have trained convolutional neural networks to automatically recognise coralligenous species using the data gathered from photo quadrats. Previous studies conducted on similar habitats performed well, but were only able to consider a limited number of classes, resulting in a very coarse description of these often-complex habitats. We therefore designed a custom network based on off-the-shelf architectures which is able to discriminate between 61 classes with 72.59% accuracy. Our results showed that confusion errors were for the most part taxonomically coherent, showing accuracy performances of 84.47% when the task was simplified to 15 major categories, thereby outperforming the human accuracy previously recorded in a similar study. In light of this, we built a semi-automated tool to reject unsure results and reduce error risk, for when a higher level of accuracy is required. Finally, we used our model to assess the biodiversity and ecological status of coralligenous reefs with the Coralligenous Assemblage Index (CAI) and the Shannon Index. Our results showed that whilst the prediction of the CAI was only moderately accurate (pearson correlation between observed and predicted CAI = 0.61), the prediction of Shannon Index was more accurate (pearson correlation = 0.74). In conclusion, it will be argued that the approach outlined by this study offers a cost and time-effective tool for the analysis of coralligenous assemblages which is suitable for integration into a large-scale monitoring network of this habitat.

监测自然栖息地的生态状况对于保护过程至关重要，因为它可以实施有效的保护政策。如今，鉴于非常大的图像数据库的可用性和先进的计算能力，使物种识别自动化变得越来越可能，这使基于机器学习的自动分类模型的训练成为监视海洋生境的日益可行的工具。珊瑚礁是在地中海发现的特别重要的水下栖息地。该栖息地与珊瑚礁具有相似的生物复杂性。自2010年以来，已在法国水域使用手动标注的照片四方仪（RECOR监测网络）对其进行了监测。基于其中积累的大型注释数据库，我们已经训练了卷积神经网络，以使用从照片四边形收集的数据自动识别珊瑚源物种。先前在类似栖息地上进行的研究效果很好，但只能考虑有限的类别，因此对这些经常复杂的栖息地进行了非常粗略的描述。因此，我们基于现成的体系结构设计了一个自定义网络，该网络能够以72.59％的准确度区分61个类别。我们的结果表明，混淆错误在很大程度上是分类学上一致的，当任务简化为15个主要类别时，其准确度性能为84.47％，从而胜过以前在类似研究中记录的准确度。有鉴于此，我们构建了一个半自动化的工具来拒绝不确定的结果并降低错误风险，当需要更高级别的精度时。最后，我们使用我们的模型通过珊瑚组合指数（CAI）和香农指数评估了珊瑚礁的生物多样性和生态状况。我们的结果表明，尽管对CAI的预测只有中等准确度（观察到的CAI与预测的CAI之间的皮尔逊相关系数= 0.61），但对香农指数的预测更为准确（皮尔逊相关系数= 0.74）。总之，将有争议的是，这项研究概述的方法为分析珊瑚组合提供了一种成本和时间效益高的工具，适合整合到该生境的大规模监测网络中。我们使用我们的模型通过珊瑚组合指数（CAI）和香农指数评估了珊瑚礁的生物多样性和生态状况。我们的结果表明，尽管对CAI的预测只有中等准确度（观察到的CAI与预测的CAI之间的皮尔逊相关系数= 0.61），但对香农指数的预测更为准确（皮尔逊相关系数= 0.74）。总之，将有争议的是，这项研究概述的方法为分析珊瑚组合提供了一种成本和时间效益高的工具，适合整合到该生境的大规模监测网络中。我们使用我们的模型通过珊瑚组合指数（CAI）和香农指数评估了珊瑚礁的生物多样性和生态状况。我们的结果表明，尽管对CAI的预测只有中等准确度（观察到的CAI与预测的CAI之间的皮尔逊相关系数= 0.61），但对香农指数的预测更为准确（皮尔逊相关系数= 0.74）。总之，将有争议的是，这项研究概述的方法为分析珊瑚组合提供了一种成本和时间效益高的工具，适合整合到该生境的大规模监测网络中。Shannon指数的预测更为准确（皮尔森相关系数= 0.74）。总之，将有争议的是，这项研究概述的方法为分析珊瑚组合提供了一种成本和时间效益高的工具，适合整合到该生境的大规模监测网络中。Shannon指数的预测更为准确（皮尔森相关系数= 0.74）。总之，将有争议的是，这项研究概述的方法为分析珊瑚组合提供了一种成本和时间效益高的工具，适合整合到该生境的大规模监测网络中。