100 years of anthropogenic impact causes changes in freshwater functional biodiversity

Niamh Eastwood*, Jiarui Zhou, Romain Derelle, Mohamed Abou-Elwafa Abdallah, William A Stubbings, Yunlu Jia, Sarah E Crawford, Thomas A Davidson, John K Colbourne, Simon Creer, Holly Bik, Henner Hollert, Luisa Orsini, Detlef Weigel

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Despite efforts from scientists and regulators, biodiversity is declining at an alarming rate. Unless we find transformative solutions to preserve biodiversity, future generations may not be able to enjoy nature’s services. We have developed a conceptual framework that establishes the links between biodiversity dynamics and abiotic change through time and space using artificial intelligence. Here, we apply this framework to a freshwater ecosystem with a known history of human impact and study 100 years of community-level biodiversity, climate change and chemical pollution trends. We apply explainable network models with multimodal learning to community-level functional biodiversity measured with multilocus metabarcoding, to establish correlations with biocides and climate change records. We observed that the freshwater community assemblage and functionality changed over time without returning to its original state, even if the lake partially recovered in recent times. Insecticides and fungicides, combined with extreme temperature events and precipitation, explained up to 90% of the functional biodiversity changes. The community-level biodiversity approach used here reliably explained freshwater ecosystem shifts. These shifts were not observed when using traditional quality indices (e.g. Trophic Diatom Index). Our study advocates the use of high-throughput systemic approaches on long-term trends over species-focused ecological surveys to identify the environmental factors that cause loss of biodiversity and disrupt ecosystem functions.
Original languageEnglish
Article numberRP86576
Number of pages28
JournaleLife
Volume12
DOIs
Publication statusPublished - 7 Nov 2023

Bibliographical note

Acknowledgements:
We thank Kerry Walsh and Glenn Watts, the UK Environment Agency, for helpful discussions on the application of the approach presented here within regulatory frameworks. The metabarcoding data were generated by EnviSion, BioSeqencing and BioComputing at the University of Birmingham (https://www.envision-service.com/). The DDT chemical data were generated by the GEES Mass Spectrometry Facility at the University of Birmingham. Sediment sampling and dating was completed by Goldsmith Ecology, Somerset. We thank Stephen Kissane for technical assistance in generating high throughput sequencing data, Dr Xiaojing Li for helpful discussions on functional analysis and Chantal Jackson for the artwork of Figure 1. This work was funded by the Alan Turing Institute (under EPSRC grant R-BIR-001); and the NERC highlights grant LOFRESH (NE/N005716/1). NE is supported by the Biotechnology and Biological Sciences Research Council (Midlands Integrative Biosciences Training Partnership (MIBTP; BB/M01116X/1)). LO and HH have been supported by the RobustNature Cluster of Excellence Initiative (internal prefunding of Goethe University Frankfurt).

Keywords

  • functional biodiversity
  • None
  • multilocus metabarcoding
  • machine learning
  • freshwater
  • sedaDNA

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