Table 2.

Major categories of work that could improve the long term application, usability, and interpretation by risk assessment practitoners.

Research area	Description
Uses of SSD	Collate and review the uses of SSDs for purposes other than estimating the HC5 (e.g. using the entire SSD for probabilistic risk assessment and deriving other values (say HC50) for trigger management action).
Ecology	Investigate whether an approach which allows better extrapolate to all ecosystems is viable.
	Compare trait-based SSDs with traditional strictly taxonomic-based SSDs, and to define what traits are most relevant to SSD generation. Alternative approaches should be explored, including focusing on sensitive taxa rather than broadly populating an SSD. However, there is uncertainty of what the sensitive taxa will be for many substances. A sensitive species approach may require novel methods development, including integrating chemical structure, genomic, traits and MOA information.
	Compare SSD-based approaches to the use of generic AF values under different scenarios of data richness, and the need to explore uncertainty in relaxed (10 species/8 taxa group) requirements versus AF uncertainty and conservatism. Determination of the ecology and composition of representative ecosystems should inform requirements for taxa composition in SSDs. SSD-based estimates determined from various approaches and data richness scenarios should be compared to field data, and field monitoring should be performed to verify SSD-based predictions of community level effects.
	(Further) Develop a model that takes account of the number and type of species in a community and that shows the consequences/reliability of the results. Establish what validity criteria are needed.
	Determine what additional ecological knowledge needs to be included to add value for the risk assessors.
Guidelines	Develop a formal and transparent decision tree approach that is inclusive of the available data, and that considers the generic or specific use of SSDs in environmental protection and management.
	Develop guidelines on how to deal with data quality (of the input data on species sensitivities, or sometimes functions sensitivities).
	Develop guidance on the use of non-standard test species.
	Develop guidance on which methods and tools can be used to generate SSDs – this requires sensitivity analysis, identification of causes of differences, etc.
Model development and validation	Investigate the limitations of the models and whether they are fit for the purpose for which they are used.
	Evaluate the viable methods for incorporating all relevant data in SSDs
	Further validation of SSDs derived from laboratory data against field and mesocosm studies is required, as is guidance on the different approaches (including their limitations) that can be taken.
	Further validation for extrapolations that are in relevant models (i.e. hSSD and Web-ICE) and of consequences for HC5 uncertainty.
	Validation of hSSD scenario-specific HC5s relative to the field and/or mesocosm studies.
	Critically review whether any of the growing amount of information types about chemicals and their impacts that is now available should be used to inform SSD development, application, and interpretation, including for example knowledge of omics, mechanisms, chemical properties, and exposure scenarios.
Toxicity data	Research is needed to determine how best to use available data (e.g. strict standardization criteria with resulting loss of species diversity or use weighting based on data quality). The focus of SSD development has been on acute toxicity data, and chronic toxicity estimation approaches will need the same level of evaluation (e.g. minimum data sets, acute to chronic ratio estimation, lowest toxicity value approaches). Develop better application of toxicological data in SSDs, e.g. using more chronic data, mechanistic understanding. Develop methods to expand on data availability by adding less strictly selected input data and putting less weight on their inclusion, based on reliability of data.
	Develop methodology to improve the use of predictive modelling to overcome limited data sets. The applicability of toxicity extrapolation method should be further validated for acute effects, and should also be evaluated for chronic effects. Develop and extend software tools to add the capacity to predict chronic toxicity and approaches applicable to other environmental compartments (such as sediment, soil and air) both remain significant research needs.
	Investigate the value of including microorganisms in SSDs to protect ecosystem functions e.g. when assessing the ecological risk of fungicides, investigate the effects of including various fungal species in the test battery and incorporating their data into the SSD; Microorganisms should be considered in the HCx derivation but development is currently hindered by the lack of available approved testing procedures for different groups of microorganisms.
Mechanistic understanding	Investigate whether critical body residue (CBR)-based SSDs could be developed.
	MOA (mode of action) is an important determinant of species sensitivity. Research is needed to determine linkages between MOA and SSD composition requirements. Investigate whether it is possible to treat MoA in the statistical models in the same way taxonomic distance is being used? (In particular, is this feasible for Web-ICE and hSSD?)
Uncertainty	Develop an understanding of uncertainties within the assessment that are currently unquantifiable. Studies should be conducted to identify the magnitude of the uncertainty of various components of the SSD methodology. Uncertainty may be related to lack of data, (non)representativity of data, mode of action considerations, and many other aspects of real exposure situations. An understanding of the mathematical magnitude of uncertainty alone may not be enough as it is possible that large sources of error may have little ecological importance, and vice-versa. Research should then be focused on reducing the uncertainty of the most important sources uncertainty in the SSD methodology. The group felt that uncertainty-driven research would be an important means to improve SSDs and maximize their usefulness in a cost-efficient manner. An uncertainty driven research agenda is also likely to increase uptake of the other methods that can be used in combination with SSDs e.g. QSARs, Web-ICE.
	A simple example of uncertainty-driven research would be the selection of chemicals (or species) to be used in ecotoxicity tests. If the toxicity of a chemical to a large number of species belonging to different taxonomic groups has been determined then the need for further research for that chemical may be low compared to a chemical that has been the subject of no or minimal toxicity testing. Another example is that very few SSDs have been conducted for non-chemical stressors (e.g. temperature, salinity) or the combined action of chemical and non-chemical stressors. Conducting such research could dramatically reduce uncertainty in the ecological relevance of single chemical SSDs, and place the risks posed by chemicals into a more meaningful context that addresses all possible pressures.