9. Effects Assessment
9.9. Modelling approaches to Surrogate Reference Tier and Recovery
Currently, the biological level at which the model should be targeted is the population. As can be seen from the general framework (see Figure 13 and Section7.3), population modelling is considered to be an evaluation that integrates the effects of multiple exposures over time into the lower tier tests.
Since recovery is an integral part of the population response, once we apply the systems approach we no longer need to consider long-term recovery explicitly, since if this does not occur, the population will decline. However, for some SPGs it might be necessary to consider short-term recovery, e.g. to maintain a ecosystem service needed at shorter time-scales.
Which type of model is appropriate for the systems approach to population modelling will depend on what data are available, and the modelling question. For in-soil organisms, spatial dynamics are not considered as being important, hence a greater range of model types can be considered than would be the case should action at a distance be important in the assessment.
Depending on model formulation, the model can be generic or specific to, e.g. a given species type, chemical type, landscape type. The domain of applicability of the model, including the extent of acceptable extrapolations, should be described by the modeller (EFSA PPR Panel, 2014b).
Since population modelling is considered to be an integration of multiple exposures over time into the lower tier tests, refinement of the models is permissible only by refinement of the exposure. This strategy is designed to prevent the population modelling becoming highly complex from a regulatory perspective by limiting its use to the preselected species and scenarios used for tier 1. This means that the models will need to be designed to work with the standard data from the submission dossier as the inputs, and only these values will be modified by the user, i.e. users will not be able to modify the ecological processes or parameter values for behaviour and ecology of the chosen species.
9.9.1. Overview of different types of effect models for populations
The types of model used in population-level risk assessment have been classified and reviewed, e.g. by Munns et al. (2008) and briefly by EFSA PPR Panel, (2014b). Here, we give only an outline summary. There are three main types of model in use; these are: scalar (unstructured), structured (e.g. matrix) and individual-based modelling (IBM, also known as agent-based modelling (ABM)). The model types differ in the degree of detail in the representation of the population and processes that can be represented.
Scalar models – These are the simplest form in which all individuals are treated as identical. They do not differ in age or in any other characteristic and so the population can be fully represented by a single scalar variable representing population size. Density dependence can be incorporated in scalar models, but in general they lack the necessary descriptive power to cope with multiple applications that might affect different stages of an animal’s life-cycle differently.
Structured-population models – these are often implemented as matrix models. Distinction can be made between individuals in different categories, e.g. ages, sizes. The population is divided into age or size classes (e.g. juveniles and adults) and this gives the population structure, its structure being described by the numbers in each class. All individuals within an age or size class are treated as identical, but there may be variation between the classes in their survival or reproductive rates per unit time. The characteristics of the individuals in each class are entered into the cells of a matrix, and this allows computation of how population structure changes as time progresses. Models of this form can be quite complex.
Individual-based models – In individual-based models (IBMs or ABMs), each individual animal in a population is modelled separately. Individuals may interact with their environment (e.g. depleting food resources by feeding) and with each other (e.g. predation, reproductive behaviour). Individuals should differ in their characteristics, such as their age and size and energy reserves, and each acts according to its modelled needs, e.g. for food, or a mate, or to care for its offspring.
9.9.2. Considerations of models used for in-soil populations
Scalar models are probably too simple to be employed to model a system with complicated, temporally variable inputs and differential effects on life-stages, but structured population models will often be suitable for this purpose. If complex, these models do suffer from issues of mathematical tractability, but require fewer parameters and therefore fewer explicit assumptions compared with IBMs. Model-development time is also shorter. These models would therefore be most suitable in cases where general assumptions need to be made in the face of uncertain data. A further advantage is that, since these models are mathematical rather than logical, uncertainty is also easier to quantify.
IBMs provide the most detailed description of the effects of chemicals on populations, and generally use more parameters than other model types. The advantages of IBMs are that they can represent the effects of chemicals applied in environments that may change seasonally and can incorporate feedback loops and therefore include more complex ecological interactions than is possible with other model types. IBMs therefore provide the richest potential for prediction if the relevant mechanisms are included (Topping et al., 2015b). IBMs do not suffer from mathematical tractability issues, but they do require greater input of resources to development and testing than the other model types. IBMs have a further distinct advantage in that they can incorporate TK/TD models directly.
Toxicokinetic/toxicodynamic models (TK/TD models)– these are not population models but work at the individual level. In these models, the uptake of a chemical and its distribution between the organs of the body and its subsequent biotransformation and elimination processes are collectively referred to as toxicokinetics. Also, modelled are the effects of the chemical where it causes harm within the body, with consequences on individual performances and or life-cycle trait values, referred to as toxicodynamics.
These models can be used to refine an exotoxicological assessment when exposure over time is considered variable and important (e.g. in the case of vertical movement in earthworms).
9.9.3. Issues arising in selection of modelling focus
Depending on the species and spatiotemporal scales selected the modelling approach may vary considerably. For example, Reed et al. (2016) illustrate the use of two rather different soil organism models (for earth works and Collembola), each addressing different specific characteristics. Prior to making a decision on species and scales it is not possible to recommend details of the modelling approach, therefore the Panel provides general guidelines only.
Selection of species – there is no a priori reason for selection of one species over another in terms of population modelling. Therefore, species selection should be justified in terms of representativity and expected vulnerability based on demographic and ecotoxicological traits. The following demographic traits are relevant to the assessment of population recovery (Rubach et al., 2011):
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Life span;•
Survival to reproduction;•
Generation time (i.e. the interval between reproductive events);•
Voltinism (i.e. the number of reproductive events per year);•
Number of offspring (i.e. clutch size per reproductive event).In addition, the ecotoxicological components of life-stage sensitivity and methods of incorporating multiple and long-term exposure need to be considered.
Spatial and temporal variation – for both the surrogate reference tier models and recovery, it is necessary to have a realistic, worst-case scenario of exposure during time and space. Since we do not consider action at a distance to have an appreciable influence on the assessment, spatial dynamics may not be necessary in most cases. However, it should be noted that spatial variation is scale dependent, and in the case where the in-field pattern of toxicant causes spatial heterogeneity at a scale commensurate with driving population processes potentially altering population dynamics, then this should be considered by the model (e.g. Meli et al., 2013). In the case of vertical movement, however, the same argument applies to in-soil organisms as to NTAs in space (see EFSA PPR Panel, 2015a), and integration of exposure and effects may need to be a dynamic modelling process rather than a combination of statistical distributions. In this case, TK/TD modelling will be needed. In order to maximise usefulness by integration with long-term factors and to prevent creation of further parallel tests, however, the TK/TD modelling should be integrated into population models.
9.9.4. Model development
Model development should follow the guidelines given by EFSA PPR Panel (2014b). This involves the following steps implemented in a modelling cycle, which is repeated until the model is considered to perform satisfactorily compared to predefined criteria (adapted from EFSA PPR Panel, 2014b):
1) The problem formulation sets the scene for the use of the model within the environmental risk assessment. It therefore needs to explain clearly how the modelling fits into the risk assessment and how it can be used to address protection goals. In all cases, for the evaluation of in-soil population impacts, the critical issue is the development of a baseline model that represents the state of the population under normal, realistic worst-case conditions, but without the stressor to be evaluated. Evaluation of the model will initially be based on the baseline, and only later on the implementation of the regulated stressor.
2) Model formulation. Based on the problem definition, a conceptual model is designed. The conceptual model provides a general and qualitative description of the system to be modelled. It characterises the environmental and biological processes and their interactions and interdependencies.
3) Model formalisation. In this step, model variables and parameters are defined and linked together into mathematical equations or algorithms. The result of this step is called the formal model.
4) Model implementation. In the following step, the formal model is transferred into a computer model by implementing the model equations into computer code. The computer code should be verified to check if it correctly represents the conceptual and formal model.
5) Model set up. In this step, model parameter values are estimated and the computer model is combined with one or more environmental scenarios. The result is called the regulatory model.
Note that the regulatory model includes both the computer model and the environmental scenarios. Model analysis, including sensitivity analysis, uncertainty analysis and comparison with observed data, is an essential part of the procedure to set up the regulatory model.
In all steps, documentation of the procedure should be provided, and both the model and the computer code or mathematical equations should be documented and explained clearly for risk managers.