Mapping fish species distributions in River Rhone using environmental DNA and remote sensing

Biodiversity loss in freshwater 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 are critical for effective measures. However, high-resolution biodiversity maps still need to be improved, especially for large riverine systems. Coupling data from the latest global satellite sensors with broad-scale environmental DNA (eDNA) and machine learning could enable fast and precise mapping of the distribution of river organisms. Here, we investigated the potential for combining these methods using a unique fish eDNA data set sampled along the entire 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 part (blue) and the surrounding terrestrial landscape of the river (green). We combined these variables with eDNA-based presence and absence data on 29 fish species and used three models to assess environmental suitability for these species. Most models showed good performance, indicating that ecological variables derived from remote sensing can provide valuable information on the ecological determinants of fish species distributions. Variable importance analyses showed that the blue variables (water temperature, water quality, water clarity) had stronger associations than the green 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 its outlet into the Mediterranean Sea in southern France. Our study demonstrates the feasibility of combining remote sensing and eDNA to map species distributions in large rivers; this method can be up-scaled to any large river worldwide. Hence, in the future, the approach presented here could be used to predict precise biodiversity distributions in rivers to help design conservation schemes.