Lawson Christopher L, Chartrand Kathryn M, Roelfsema Chris M, Kolluru Aruna, Mumby Peter J
Marine Spatial Ecology Laboratory, Centre for Conservation and Biodiversity Science, School of the Environment, The University of Queensland, Brisbane, Australia.
Centre for Tropical Water and Aquatic Ecosystem Research, James Cook University, Cairns, Australia.
Environ Monit Assess. 2025 Jun 27;197(7):814. doi: 10.1007/s10661-025-14261-6.
Coral reef managers require various forms of data. While monitoring is typically the preserve of scientists, there is an increasing need to collect larger scale, up-to-date data to prioritise limited conservation resources. Citizen science combined with novel technology may achieve data collection at the required scale, but the accuracy and feasibility of new tools must be assessed. Here, we show that a citizen science program that collects large field-of-view benthic images and analyses them using a combination of deep learning and online citizen scientists can produce accurate benthic cover estimates of key coral groups. The deep learning and citizen scientist analysis methods had different but complementary strengths depending on coral category. When the best performing analysis method was used for each category in all images, mean estimates from 8086 images of percent benthic cover of branching Acropora, plating Acropora, and massive-form coral were ~ 99% accurate compared to expert assessment, and > 95% accurate at all coral cover ranges tested. Site-level accuracy of 95% was attainable with 18-80 images. Power analyses showed that up to 114 images per site were needed to detect a 10% absolute difference in coral cover per category (power = 0.8). However, estimates of 'all other coral' as a single category achieved 95% accuracy at only 60% of sites and for images with 10-30% coral cover. Overall, emerging technology and citizen science present an attainable tool for collecting inexpensive, widespread data that can complement higher resolution survey programs or be an accessible tool for locations with limited scientific or conservation resources.
珊瑚礁管理者需要各种形式的数据。虽然监测通常是科学家的工作，但越来越需要收集更大规模、最新的数据，以便对有限的保护资源进行优先排序。公民科学与新技术相结合可能会实现所需规模的数据收集，但必须评估新工具的准确性和可行性。在这里，我们表明，一个公民科学项目收集大视野底栖生物图像，并结合深度学习和在线公民科学家对其进行分析，可以得出关键珊瑚群体准确的底栖生物覆盖率估计值。深度学习和公民科学家分析方法根据珊瑚类别具有不同但互补的优势。当在所有图像中对每个类别使用表现最佳的分析方法时，与专家评估相比，来自8086张图像的分支鹿角珊瑚、板状鹿角珊瑚和块状珊瑚的底栖生物覆盖率百分比的平均估计值约为99%准确，并且在所有测试的珊瑚覆盖率范围内准确率均超过95%。每个站点使用18 - 80张图像可实现95%的站点级准确率。功效分析表明，每个站点最多需要114张图像才能检测到每个类别珊瑚覆盖率10%的绝对差异（功效 = 0.8）。然而，将“所有其他珊瑚”作为一个单一类别进行估计时，仅在60%的站点以及珊瑚覆盖率为10 - 30%的图像中达到了95%的准确率。总体而言，新兴技术和公民科学提供了一种可实现的工具，用于收集廉价、广泛的数据，这些数据可以补充高分辨率调查项目，或者成为科学或保护资源有限地区可获取的工具。
