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A practical method to control spatiotemporal confounding in environmental impact studies.

MethodsX·2018
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Development of a protocol for environmental impact studies using causal modelling.

Rezvan Hatami1

  • 1Department of Ecology, Environment and Evolution, La Trobe University, Bundoora 3086, Victoria, Australia.

Water Research
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Causal modeling effectively infers wastewater impacts on aquatic ecosystems by addressing spatiotemporal confounding. This method reveals seasonal shifts in macroinvertebrate communities due to water quality changes, aiding environmental risk assessment.

Keywords:
CausalityConfoundingEnvironmentFreshwaterModelling

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Area of Science:

  • Environmental Science
  • Ecology
  • Water Resource Management

Background:

  • Assessing human impacts on natural resources is challenged by land use versus natural spatiotemporal variation.
  • Current freshwater assessments struggle to resolve spatiotemporal confounding in study designs and statistical analyses.
  • Observational studies often neglect the period-by-location interaction when assessing impacts.

Purpose of the Study:

  • To address spatiotemporal confounding in environmental impact assessments.
  • To infer causal effects of wastewater on biotic ecosystems using causal modeling.
  • To improve freshwater assessments and support environmental management decisions.

Main Methods:

  • Employed causal modeling integrating statistical analysis and causation theory.
  • Collected benthic macroinvertebrate and environmental data upstream and downstream of a wastewater discharge point over 1.5 years.
  • Utilized Structural Equation Modelling (SEM) and distance-based redundancy analysis (dbRDA) for model building and hypothesis testing.

Main Results:

  • Causal modeling successfully inferred the effects of wastewater effluent on macroinvertebrate communities.
  • Observed seasonal shifts in macroinvertebrate community composition linked to water quality degradation post-discharge.
  • Identified key determinants including chlorophyll a, total organic carbon, zinc, conductivity, and temperature interactions.

Conclusions:

  • Causal modeling is a valuable tool for environmental impact studies, effectively handling spatial and temporal confounding.
  • The approach facilitates communication between scientists and resource managers.
  • Supports informed management decision-making for environmental risk assessment.