Methods

Eco(toxico)logical experiments & surveys

To adress the lab-to-field gap, we use a gradient of systems characterised by different levels of ecological and environmental complexity and generalisability

Image credit: B. Bimah

Lab experiments

Simplified but controlled lab experiments to minimise the effect of potential confounders and obtain generalisable results

Image credit: T. Macher

Mesocosm experiments

Capturing part of real-world complexity while keeping the power of controlled conditions and replication, mesocosm systems provide generalisable results but at much higher costs than lab experiments

Image credit: R. Schäfer

Field surveys

Capturing real-world complexity, field surveys provide ground-truth to other experimental systems, but can typically only provide associations not causality

Linking ecological scales

A major challenge of ecotoxicology is linking biomolecular changes driven by toxicants to higher biological and ecological levels

Individual level

Determination of energetic and stress biomarkers as well as the identification of molecular mechanisms using omics approaches

Population & community level

Determination of changes in populations and communities of microbes, especially aquatic fungi, and stream macroinvertebrates using traditional (i.e., morphology-based) and molecular tools (i.e., DNA-metabarcoding based)

Ecosystem function

Determination of e.g., leaf decomposition and primary production based on changes in biomass or by using enzyamtic assays

Ecosystem evaluation

Biological parameters

  • Community composition of macroinvertebrates and microorganisms
  • Trait composition and functional diversity

Chemical parameters

  • Water quality monitoring
  • Chemical sampling (grab, event, passive sampling)
  • Chemical analysis of organic toxicants

Data analysis

Data compilation & statistics

  • Univariate and multivariate statistics

  • Machine learning

  • Big data compilation, management and analysis

Ecological & ecotoxicological modelling

  • Process-based, mechanistic, and inferential modelling 

  • Model evaluation and uncertainty quantification

  • Geospatial modelling (incl. remote sensing data)