Connecting diversity and process: Eukaryotic phytoplankton community dynamics across the Yangtze River using environmental DNA

Ecological Indicators, vol. 178, pp. 114026

Abstract

Rivers are collectors of both water and environmental changes in a catchment area, and act as dispersal pathways for biodiversity. But it remains unclear whether, and how strongly, environmental changes and dispersal dynamics shape the phytoplankton communities, especially within large river ecosystems. This study utilised environmental DNA (eDNA) to assess how water quality, land use, and geographical distance affect deterministic community assembly processes of eukaryotic phytoplankton across the entire Yangtze River—the world’s third–largest river—in the dry and wet season. We found significant differences in phytoplankton community composition over time and space, with more phyla detected during the dry season, and α diversity increased from upstream to downstream. Generalist species’ composition varied significantly across geographic regions, land use types, and environmental gradients during the wet season, whereas endemic species were primarily affected by local environmental conditions during the dry season. Stochastic processes dominated community assembly during both seasons, with dispersal limitation having a stronger influence than drift, particularly in the dry season. In contrast, deterministic processes had minimal effects, with α diversity showing weak responses to individual factors. By integrating eDNA–based biodiversity data with physicochemical and geospatial factors, we provide a comprehensive view of ecosystem conditions and highlight the need for region-specific conservation strategies in large, human-impacted rivers. It also indicates the predominant role of stochastic processes in shaping phytoplankton communities and suggests further investigation into the effects of hydrological factors.

Authors 5

1. Yun Shao Aachen Institute of Environmental Science

   Leiden University · Chinese Research Academy of Environmental Sciences

   Affiliation as printed

   Institute of Environmental Science (CML), Leiden University, Leiden 2333 CC, the Netherlands

   Research Institute for Environmental Innovation (Tianjin Binhai), Tianjin 300450, PR China

   State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, PR China

2. Kathryn A. Stewart Aachen Institute of Environmental Science

   Leiden University

   Affiliation as printed

   Institute of Environmental Science (CML), Leiden University, Leiden 2333 CC, the Netherlands

3. Alena S. Gsell Aachen Institute of Environmental Science

   Leiden University · Netherlands Institute of Ecology

   Affiliation as printed

   Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO–KNAW), Wageningen 6708 PB, the Netherlands

   Institute of Environmental Science (CML), Leiden University, Leiden 2333 CC, the Netherlands

4. Shuping Wang

   Chinese Research Academy of Environmental Sciences

   Affiliation as printed

   State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, PR China

5. Zhenguang Yan corresponding

   Chinese Research Academy of Environmental Sciences · Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

   Affiliation as printed

   Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 511458, PR China

   State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, PR China