Biodiversity loss in river ecosystems is much faster and more severe than in terrestrial systems, and spatial conservation and restoration plans are needed to halt this erosion. Reliable and highly resolved data on the state of and change in biodiversity and species distributions are critical for effective measures. However, high-resolution maps of fish distribution remain limited for large riverine systems. Coupling data from global satellite sensors with broad-scale environmental DNA (eDNA) and machine learning could enable rapid and precise mapping of the distribution of river organisms. Here, we investigated the potential for combining these methods using a fish eDNA dataset from 110 sites sampled along the full length of the Rhone River in Switzerland and France. Using Sentinel 2 and Landsat 8 images, we generated a set of ecological variables describing both the aquatic and the terrestrial habitats surrounding the river corridor. We combined these variables with eDNA-based presence and absence data on 29 fish species and used three machine-learning models to assess environmental suitability for these species. Most models showed good performance, indicating that ecological variables derived from remote sensing can approximate the ecological determinants of fish species distributions, but water-derived variables had stronger associations than the terrestrial variables surrounding the river. The species range mapping indicated a significant transition in the species occupancy along the Rhone, from its source in the Swiss Alps to outlet into the Mediterranean Sea in southern France. Our study demonstrates the feasibility of combining remote sensing and eDNA to map species distributions in a large river. This method can be expanded to any large river to support conservation schemes.

中文翻译：

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将环境 DNA 与遥感变量相结合，绘制大河沿岸鱼类物种分布图

河流生态系统的生物多样性丧失比陆地系统的生物多样性丧失更快、更严重，需要制定空间保护和恢复计划来阻止这种侵蚀。关于生物多样性和物种分布的状况和变化的可靠且高度解析的数据对于采取有效措施至关重要。然而，大型河流系统的高分辨率鱼类分布图仍然有限。将全球卫星传感器的数据与大规模环境 DNA (eDNA) 和机器学习相结合，可以快速、精确地绘制河流生物的分布图。在这里，我们利用从瑞士和法国罗讷河全长 110 个地点采样的鱼类 eDNA 数据集，研究了将这些方法结合起来的可能性。使用 Sentinel 2 和 Landsat 8 图像，我们生成了一组生态变量来描述河流走廊周围的水生和陆地栖息地。我们将这些变量与 29 种鱼类基于 eDNA 的存在和缺失数据相结合，并使用三种机器学习模型来评估这些物种的环境适宜性。大多数模型表现出良好的性能，表明来自遥感的生态变量可以近似鱼类物种分布的生态决定因素，但来自水的变量比河流周围的陆地变量具有更强的关联性。物种范围测绘表明，从瑞士阿尔卑斯山的源头到法国南部的地中海出口，罗讷河沿岸的物种占有率发生了重大转变。我们的研究证明了将遥感和 eDNA 结合起来绘制大河物种分布图的可行性。该方法可以扩展到任何大型河流以支持保护计划。