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


Zong, Shuo; Brantschen, Jeanine; Zhang, Xiaowei; Albouy, Camille; Valentini, Alice; Zhang, Heng; Altermatt, Florian; Pellissier, Loïc (2024). Combining environmental DNA with remote sensing variables to map fish species distributions along a large river. Remote Sensing in Ecology and Conservation, 10(2):220-235.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Evolutionary Biology and Environmental Studies
08 Research Priority Programs > Global Change and Biodiversity
Dewey Decimal Classification:590 Animals (Zoology)
570 Life sciences; biology
Scopus Subject Areas:Life Sciences > Ecology, Evolution, Behavior and Systematics
Physical Sciences > Ecology
Physical Sciences > Computers in Earth Sciences
Physical Sciences > Nature and Landscape Conservation
Uncontrolled Keywords:Nature and Landscape Conservation, Computers in Earth Sciences, Ecology, Ecology, Evolution, Behavior and Systematics
Language:English
Date:1 April 2024
Deposited On:24 Jan 2024 09:09
Last Modified:30 Jun 2024 01:37
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:2056-3485
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1002/rse2.366
Project Information:
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)