Aanleiding van het project
Since it is impossible to experimentally determine the sensitivity of all the species present in any ecosystem to all chemicals to which they can possibly be exposed, we rely on cross-species extrapolation of chemical sensitivity. Such extrapolation requires on the one hand a mechanistic understanding of how a chemical can cause damage, and on the other hand a mechanistic understanding of which factors cause that one species responds differently to a chemical than another species.
Van den Berg et al. (2019) developed an R pipeline that provides predictive aquatic macroinvertebrate trait models for a diverse set of pre-defined Modes of Action (MOAs). This pipeline was later extended to also include relatedness-based predictors, since other studies found that both traits and relatedness explained a unique part of sensitivity. Advantages of this pipeline include reproducibility (all analyses are 100% reproducible, permitting clear communication of modelling decisions and outcomes), flexibility (the pipeline can be adjusted to different organism groups), and speed (models can be compiled in a few hours). However, so far this R pipeline has been constructed for research purposes only, and still requires fluency in R to make changes to its functionality and applicability. For instance, adjusting the taxonomic focus of the models would require making changes in multiple parts of the R pipeline. Therefore, further development of this pipeline by transforming it into a flexible tool would make it directly usable for incorporation into risk assessment, and can additionally allow the assessment of important aspects influencing sensitivity models (e.g. exposure duration, effect endpoints). Once the tool has been developed further and evaluated on important sensitivity modelling aspects, the next step is to demonstrate how the tool can be applied in risk assessment. The development of case studies as well as validation of the models the tool delivers is crucial to create under
Doel van het project
This project has two main objectives: i) to transform the R pipeline developed by Van den Berg et al. (2019) into a user-friendly tool that can be applied to multiple aquatic taxonomic and chemical groups, and can simultaneously be used to evaluate important modelling aspects for the application of trait- and lineage-based models, and ii) to deliver a set of case studies and model validation studies that demonstrate how this tool can directly be applied for aquatic risk assessment purposes and to proof the validity of the constructed models. To ensure that both objectives are addressed with equal importance, the project is split into two separate phases. Phase 1 is concerned with objective i, improving and evaluating the tool on both technical and scientific aspects, whilst phase 2 is concerned with objective ii, constructing case- and validation-studies that demonstrate the application and validity of the tool and the models for aquatic risk assessment purposes. The phases are subdivided into four working packages (WPs), which will be described in more detail in the section on ‘Geplande activiteiten’.
Omschrijving van de activiteiten
It is impossible to experimentally determine the sensitivity of all species present in rivers, lakes and streams everywhere around the world to all chemicals to which they can possibly be exposed. Therefore, a valuable method is to extrapolate the known chemical sensitivity of species, who have been tested in the lab, to species never tested before. This method is also known as cross-species extrapolation of chemical sensitivity and is able to predict the sensitivity of whole communities to chemicals. Creating a cross-species extrapolation modelling tool, flexible to different taxonomic and chemical groups, would allow for its incorporation into aquatic risk assessment, where it can assist in the evaluation and protection of water quality. This project, therefore, has two main objectives: i) to transform an already existing cross-species extrapolation modelling pipeline into a user-friendly tool that can construct predictive models for multiple aquatic taxonomic and chemical groups, and ii) to deliver a set of case studies that demonstrate how this tool can directly be applied for aquatic water quality and risk assessment purposes
We expect that this tool can be used to evaluate important modelling aspects for the application of trait- and lineage-based models (e.g. exposure duration, effect endpoints), and facilitates their incorporation into risk assessment.
The main innovation of this project lies in the use and exploration of all toxicity data collected over time, and the possibility to flexibly select, evaluate and incorporate various important aspects of these data into statistical models. This combination of flexibility and big data will assist in obtaining new mechanistic information to understand the sensitivity process better, which can strongly stimulate fundamental research into the ecological and chemical process of sensitivity, as well as contribute to the transition to a zero animal-testing society.
First we explain the activities performed under this project, along with their type (industrial, fundamental, or experimental), and the role of the involved parties within these activities. Next, we provide two tables containing more detailed information on the budget spend on each activity, and a calculation of the percentage PPS financing.