1. Technological advancements in data collection and analysis are producing a new generation of ecological data. Among these, computer vision (CV) has received increased attention for its robust capabilities for rapidly processing large volumes of digital imagery.
2. In marine ecosystems, the study of fish connectivity provides fundamental information for assessing fisheries stocks, designing and implementing protected areas and understanding the impact of habitat loss. While the field of fish connectivity has benefited from technological advancements, the extent to which novel techniques, such as CV, have been utilized has not been assessed. To inform future directions and developments, this study reviewed the current use of CV in fish connectivity research, quantified how the implementation of such technology in fish connectivity research compared with other areas of marine research and described how this field could benefit from CV.
3. The review found that the use of remote camera systems in fish connectivity research is increasing, but the implementation of automated analysis of digital imagery has been slow. Successful implementation and expansion of CV frameworks in aquaculture and coral reef ecology suggest that CV techniques could greatly benefit fish connectivity research.
4. A case study of potential use of CV in fish connectivity research, scaling up optimal foraging models to predict marine population connectivity, highlights how beneficial it could be.
5. The capacity for CV techniques to be adopted alongside traditional approaches, the unparalleled speed, accuracy and reliability of these approaches and the benefits of being able to study ecosystems along multiple spatial–temporal scales, all make CV a valuable tool for assessing connectivity. Ultimately, these technologies can assist data‐driven decisions that directly influence the health and productivity of marine ecosystems.

中文翻译：

---

技术的缓慢发展：鱼类种群连通性中的计算机视觉技术

1. 数据收集和分析的技术进步正在产生新一代的生态数据。其中，计算机视觉（CV）以其快速处理大量数字图像的强大功能而受到越来越多的关注。
2. 在海洋生态系统中，鱼类连通性研究为评估渔业种群，设计和实施保护区以及了解生境丧失的影响提供了基本信息。尽管鱼类连通性领域受益于技术进步，但尚未评估使用新技术（例如CV）的程度。为了为未来的方向和发展提供信息，本研究回顾了CV在鱼类连通性研究中的当前应用，量化了与海洋研究的其他领域相比，在鱼类连通性研究中这种技术的实施情况，并描述了该领域如何从CV中受益。
3. 该评论发现，在鱼类连通性研究中越来越多地使用远程相机系统，但是数字图像自动分析的实施进展缓慢。在水产养殖和珊瑚礁生态学中成功实施和扩展CV框架表明，CV技术可以极大地促进鱼类连通性研究。
4. 在鱼类连通性研究中潜在使用CV的案例研究中，扩大了最佳觅食模型以预测海洋种群连通性，凸显了它可能带来的好处。
5. 与传统方法一起采用CV技术的能力，这些方法无与伦比的速度，准确性和可靠性以及能够在多个时空尺度上研究生态系统的好处，所有这些都使CV成为评估连通性的宝贵工具。最终，这些技术可以帮助以数据为依据的决策直接影响海洋生态系统的健康和生产力。