Combining environmental DNA with remote sensing variables to map fish species distributions along a large river

Abstract

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.

eDITH: An R‐package to spatially project eDNA‐based biodiversity across river networks with minimal prior information

“…In either case, the aforementioned assumptions on production and decay rates (see Section 2.1.1) must hold. In principle, it is also possible to estimate the spatial distribution of terrestrial taxa (Lyet et al, 2021;Zhang et al, 2023;Zong et al, 2023), provided that the assumption of proportionality between DNA production rate and taxon density holds. In this case, the spatial unit where predictions are performed is the subcatchment, that is, the portion of land that directly drains towards the associated reach.…”

eDITH: an R-package to spatially project eDNA-based biodiversity across river networks with minimal prior information

“…In either case, the aforementioned assumptions on production and decay rates (see DNA production and decay rates ) must hold. In principle, it is also possible to estimate the spatial distribution of terrestrial taxa (Lyet et al, 2021; Zong et al, 2023; Zhang et al, 2023), provided that the assumption of proportionality between DNA production rate and taxon density holds. In this case, the spatial unit where predictions are performed is the subcatchment, i.e., the portion of land that directly drains towards the associated reach.…”