Discovering Ecological Relationships in Flowing Freshwater Ecosystems

Frontiers in Ecology and Evolution

Leo Posthuma¹  Michiel C. Zijp¹  Tom Claassen²  Konrad P. Mielke²  Tom Heskes²  Mark A. J. Huijbregts³  Aafke M. Schipper⁴ 

[1] Centre for Sustainability, Environment and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, Netherlands;Department of Data Science, Institute for Computing and Information Sciences, Radboud University Nijmegen, Nijmegen, Netherlands;Department of Environmental Science, Radboud Institute for Biological and Environmental Sciences, Radboud University Nijmegen, Nijmegen, Netherlands;Planbureau voor de Leefomgeving (PBL) Netherlands Environmental Assessment Agency, The Hague, Netherlands;

关键词: biodiversity;    causal discovery;    causal relationships;    Fast Causal Inference;    rivers;    IBI;   

DOI  :  10.3389/fevo.2021.782554

来源: DOAJ

【 摘 要 】

Knowledge of ecological responses to changes in the environment is vital to design appropriate measures for conserving biodiversity. Experimental studies are the standard to identify ecological cause-effect relationships, but their results do not necessarily translate to field situations. Deriving ecological cause-effect relationships from observational field data is, however, challenging due to potential confounding influences of unmeasured variables. Here, we present a causal discovery algorithm designed to reveal ecological relationships in rivers and streams from observational data. Our algorithm (a) takes into account the spatial structure of the river network, (b) reveals the complete network of ecological relationships, and (c) shows the directions of these relationships. We apply our algorithm to data collected in the US state of Ohio to better understand causes of reductions in fish and invertebrate community integrity. We found that nitrogen is a key variable underlying fish and invertebrate community integrity in Ohio, likely negatively impacting both. We also found that fish and community integrity are each linked to one physical habitat quality variable. Our algorithm further revealed a split between physical habitat quality and water quality variables, indicating that causal relations between these groups of variables are likely absent. Our approach is able to reveal networks of ecological relationships in rivers and streams based on observational data, without the need to formulate a priori hypotheses. This is an asset particularly for diagnostic assessments of the ecological state and potential causes of biodiversity impairment in rivers and streams.

【 授权许可】

Unknown