cvasi-1-manual.md

User Manual

N. Kehrein 24 October, 2024

• Required skills
• How to install
• Modeling and assessment workflow
  • Overview
  • Setting up a scenario
  • Running a simulation
  • Deriving assessment endpoints
• Data format of model inputs
• How to derive model inputs
• Description of model outputs
  • Simulation results
  • Effect levels
  • Effect profiles
• References

The cvasi package is a software library that extends the features of the programming language R by routines for ecotox effect modeling. The routines are intended to be used for scientific purposes as well as in the context of regulatory risk assessments. cvasi implements model equations and default parameters to simulate a set of toxicokinetic-toxicodynamic (TKTD) models. The models are intended to assess the effects of substance exposure on effect endpoints, such as growth and reproduction, for a range of standard test species.

The package offers a standardized framework which can be extended by (almost) any TKTD model that can be implemented in R. Its modeling workflows are designed to be self-explanatory and as intuitive as possible to make modeling approaches more accessible. In addition, the framework’s routines are optimized to reduce the computing time required for extensive risk assessments.

Required skills

The cvasi package and its routines can be used by anyone with basic to intermediate knowledge of the R programming language. The routines can be used in a procedural fashion, commonly referred to as base R, in which function statements are executed, assigned to a variable, and then passed on to the next function:

library(cvasi)

## Sample base R workflow ##
# Create a scenario object of model GUTS-RED-IT
my_it <- GUTS_RED_IT()
# Set model parameters
my_it <- set_param(my_it, c(kd=1.2, hb=0, alpha=9.2, beta=4.3))
# Print scenario details
my_it
#> 'GUTS-RED-IT' scenario
#> param: kd=1.2, hb=0, alpha=9.2, beta=4.3
#> init : D=0, H=0
#> endpt: L
#> times: none
#> forcs: none
#> expsr:
#> >> exposure series is empty

A more advanced approach is to use the tidyr syntax (cf. Wickham & Grolemund 2017) which uses verb-like statements to modify data. tidyr syntax can be used to chain functions calls which pass the result of one function on to the next:

## Example 'tidyr' workflow ##
# the pipeline (%>%) symbol passes results to the next statement
GUTS_RED_IT() %>%
  set_param(c(kd=1.2, hb=0, alpha=9.2, beta=4.3))
#> 'GUTS-RED-IT' scenario
#> param: kd=1.2, hb=0, alpha=9.2, beta=4.3
#> init : D=0, H=0
#> endpt: L
#> times: none
#> forcs: none
#> expsr:
#> >> exposure series is empty

Both workflow styles achieve the same result, but tidyr is terser, especially if several operations are performed on the same object. tidyr style is the recommended way to interact with the cvasi package.

How to install

The simplest way to install the cvasi package is by downloading it from CRAN:

The latest development version can be installed from GitHub:

install.packages("remotes", dependencies=TRUE)
remotes::install_github("cvasi-tktd/cvasi", dependencies=TRUE)

The package and its source code is also available on GitHub.

Modeling and assessment workflow

Overview

The central concept within the cvasi package is the scenario. It encapsulates all key properties to run a simulation such as initial state and model parameters. The scenario can be thought of as the collection of data and parameters necessary to reproduce a certain study condition or situation. The set of available scenario properties depends on the model type but may include:

• Initial state,
• Model parameters,
• Assessment parameters,
• Exposure time-series, and
• Environmental factor time-series

Scenario properties can be modified using intuitively named tidyr verbs: in general, each verb accepts a scenario as an argument and returns a modified scenario as the result. For instance, the set_param() function will update a scenario’s parameters to new values:

library(cvasi)

# Create a new GUTS-RED-IT scenario and set its model parameters
GUTS_RED_IT() %>%
  set_param(c(kd=1.2, hb=0, alpha=9.2, beta=4.3))
#> 'GUTS-RED-IT' scenario
#> param: kd=1.2, hb=0, alpha=9.2, beta=4.3
#> init : D=0, H=0
#> endpt: L
#> times: none
#> forcs: none
#> expsr:
#> >> exposure series is empty

If a scenario is properly defined, it can be passed to dedicated functions that, for instance, run a simulation or derive assessment endpoints. These higher-level functions do not return a scenario object but a table of e.g. simulation results for that particular scenario. As an example, simulating the sample scenario minnow_it will run a GUTS-RED-IT model using substance-specific parameters:

# Example GUTS-RED-IT scenario derived from an acute fish toxicity study
# of the fathead minnow and Chlorpyrifos (Geiger et al. 1988)
minnow_it %>%
  simulate()
#>   time         D      H         S
#> 1    0 0.0000000 0.0000 1.0000000
#> 2    1 0.7075891 0.0017 0.9982869
#> 3    2 0.9144958 0.0034 0.9965618
#> 4    3 0.9749972 0.0051 0.9948550
#> 5    4 0.9926889 0.0068 0.9931605

The table returned by simulate() contains the value of each state variable at the requested output time points. In case of the GUTS-RED-IT model, this comprises the state variables D (scaled damage) and H (cumulative hazard). Effect levels, effect profiles, and dose-response graphs can be derived in a similar manner. Please refer to EFSA PPR Panel (2018) or the package help for more information on GUTS-RED type models:

# Access the package help on GUTS-RED type models
?"GUTS-RED-models"

Setting up a scenario

A scenario is created by first calling a constructor function that is dedicated to a particular model. The cvasi package supports multiple TKTD models, for instance:

• Reduced General Unified Threshold models of Survival (GUTS-RED)
  • GUTS_RED_SD()
  • GUTS_RED_IT()
• Macrophyte models
  • Lemna_Schmitt(), published by Schmitt et al. (2013)
  • Lemna_SETAC(), published by Klein et al. (2021)
  • Myrio(), an adaption of Klein et al. (2021) to Myriophyllum at Tier 2C
• Algae models
  • Algae_Weber(), published by Weber et al. (2012)
  • Algae_TKTD(), based on Weber et al. (2012), but with scaled damage
  • Algae_Simple(), simplified growth model without external forcing variables
• Dynamic Energy Budget (DEB) models
  • DEB_abj(), the abj model with type M acceleration
  • DEBtox(), as implemented in the DEBtox2019 module of BYOM

The listed functions create objects that will carry all data and settings to fully describe a scenario. In case of a GUTS-RED-IT scenario, one needs to provide at least the four model parameters and an exposure time-series to fully define a scenario:

# Define an exposure time-series
myexposure <- data.frame(time=c(0, 1, 1.01, 5),
                         conc=c(10, 10, 0, 0))

# Create and parameterize a scenario
GUTS_RED_IT() %>%
  set_param(c(kd=1.2, hb=0, alpha=9.2, beta=4.3)) %>%
  set_exposure(myexposure) -> myscenario

One can examine the main scenario properties by passing the scenario to the console like in the next example. The console will print the model name, model parameters (param), initial state (init), enabled effect endpoints (endpt), output times (times), environmental forcings (forcs), and exposure series (expsr). Depending on model type, the scenario may carry additional properties that are not necessarily displayed in the console output.

# Print information about the scenario
myscenario
#> 'GUTS-RED-IT' scenario
#> param: kd=1.2, hb=0, alpha=9.2, beta=4.3
#> init : D=0, H=0
#> endpt: L
#> times: [0,5] n=4, regular
#> forcs: none
#> expsr:
#>   time conc
#> 1 0.00   10
#> 2 1.00   10
#> 3 1.01    0
#> 4 5.00    0

Several set_*() verbs are available to modify scenario properties, such as:

• set_tag(), to assign a custom tag to identify the scenario
• set_init(), to set the initial state
• set_param(), to modify model parameters
• set_times(), to define output time points
• set_forcings(), to set environmental factors (i.e. forcings)
• set_exposure(), to set an exposure time-series
• set_endpoints(), to enable/disable assessment endpoints
• set_transfer(), to define transfer intervals of Lemna fronds
• set_moa(), to select a model’s mode of action
• set_window(), to control moving exposure windows

Please note that set_exposure() will, by default, use the exposure series’ time points also as output time points. If this behavior is undesired, it can be disabled by setting the argument reset_times=FALSE:

# Update the exposure time-series but keep former output time points
myscenario %>%
  set_noexposure()
#> 'GUTS-RED-IT' scenario
#> param: kd=1.2, hb=0, alpha=9.2, beta=4.3
#> init : D=0, H=0
#> endpt: L
#> times: [0,5] n=4, regular
#> forcs: none
#> expsr: no exposure
#>   time conc
#> 1    0    0

Note that times still has the original four elements although the assigned exposure series has only a single entry. Output time points can be explicitly set with set_times(). For deriving effect endpoints, the simulated time frame can be split up into moving exposure windows of a certain length using set_window(). A more complex scenario setup may look like the following workflow:

# Selected model parameters
myparam <- c(p_M=3211, v=0.023, k_J=0.63)
# Initial body length L
myinit <- c(L=0.02)
# Constant non-zero exposure
myexposure <- data.frame(time=0, conc=1.72)

DEB_abj() %>%
  set_param(myparam) %>%
  set_init(myinit) %>%
  set_exposure(myexposure, reset_times=FALSE) %>%
  set_times(0:10) %>%              # Output times 0,1,2,...,10
  set_mode_of_action(4) %>%        # Method of Action #4 to be activated
  set_window(length=3, interval=1) # Using moving exposure windows of length 3 days
#> 'DEB_abj' scenario
#> param: p_M=3211, v=0.023, k_J=0.63, MoA=4
#> init : L=0.02, E=0, H=0, R=0, cV=0, Lmax=0
#> endpt: L, R
#> times: [0,10] n=11, regular
#> forcs: none
#> expsr:
#>   time conc
#> 1    0 1.72

Running a simulation

After a scenario is fully set up, it can be passed to simulate() to return model results:

# Example scenario of the Lemna TKTD model
metsulfuron %>%
  set_times(0:7) %>%
  simulate()
#>   time       BM E    M_int     C_int  FrondNo
#> 1    0 50.00000 1   0.0000 0.0000000 500000.0
#> 2    1 52.15858 1 223.0745 0.2560989 521585.8
#> 3    2 52.43495 1 369.6344 0.4221192 524349.5
#> 4    3 52.33917 1 463.0261 0.5297392 523391.7
#> 5    4 52.17923 1 521.8927 0.5989176 521792.3
#> 6    5 52.00016 1 558.6268 0.6432808 520001.6
#> 7    6 51.81403 1 581.2187 0.6717006 518140.3
#> 8    7 51.63740 1 474.0716 0.5497473 516374.0

simulate() returns the model state for each output time point. In this example, it returns results for the period from zero to seven with an equi-distant time step length of one. The temporal resolution of results can be increased by specifying additional output times:

# Same simulation period, but with a smaller step length of 0.1
metsulfuron %>%
  set_times(seq(0, 7, 0.1)) %>%
  simulate() %>%
  tail()
#>    time       BM E    M_int     C_int  FrondNo
#> 66  6.5 51.72111 1 556.4478 0.6442288 517211.1
#> 67  6.6 51.70319 1 544.1623 0.6302236 517031.9
#> 68  6.7 51.68570 1 529.6855 0.6136648 516857.0
#> 69  6.8 51.66877 1 513.1159 0.5946630 516687.7
#> 70  6.9 51.65259 1 494.5480 0.5733237 516525.9
#> 71  7.0 51.63740 1 474.0716 0.5497473 516374.0

The resulting table now contains ten times as many rows because we decreased the step length by a factor of ten but simulated the same period. The temporal resolution of simulations can have an influence on results, i.e. the numerical values of the returned variables. These differences usually originate from small numerical errors introduced by the solver of the model’s Ordinary Differential Equations. Generally, parameters such as the step-length in time can have influence on the precision of simulation results.

There are several ways to increase the precision of simulation results without modifying a scenario’s output times. One option is to decrease the solver’s maximum step length in time by setting the optional argument hmax. The smaller hmax, the more precise the results:

# Original simulation period, but with a maximum solver step length of hmax=0.01
metsulfuron %>%
  set_times(0:7) %>%
  simulate(hmax=0.01)
#>   time       BM E    M_int     C_int  FrondNo
#> 1    0 50.00000 1   0.0000 0.0000000 500000.0
#> 2    1 52.15858 1 223.0745 0.2560989 521585.8
#> 3    2 52.43495 1 369.6344 0.4221192 524349.5
#> 4    3 52.33917 1 463.0261 0.5297392 523391.7
#> 5    4 52.17923 1 521.8927 0.5989176 521792.3
#> 6    5 52.00016 1 558.6268 0.6432808 520001.6
#> 7    6 51.81403 1 581.2187 0.6717006 518140.3
#> 8    7 51.63740 1 474.0716 0.5497473 516374.0

Although very effective, reducing hmax to small values may lead to inefficiently long runtimes of simulations. If numerical precision is an issue, it is advisable to also test other solver settings such as absolute and relative error tolerances (atol and rtol) or to switch to another solver method. Numerical precision and stability is a complex and advanced topic, please refer to the manual of simulate() for additional information:

?cvasi::simulate

Deriving assessment endpoints

Simulation results are the basis for deriving assessment endpoints such as effect levels and effect profiles. The cvasi package implements routines to derive these endpoints efficiently independent of the actual model that is being assessed. Hence, if a model is available within the package framework, it can be used to derive assessment endpoints. As an example, the following statement derives effect levels for a sample GUTS-RED-IT scenario:

# GUTS-RED-IT scenario of the fathead minnow and chlorpyrifos
minnow_it %>% effect()
#> # A tibble: 1 × 4
#>   scenario           L L.dat.start L.dat.end
#>   <list>         <dbl>       <dbl>     <dbl>
#> 1 <GutsRdIt> 0.0000630           0         4

The lethality endpoint L has a value of 6.3e-5 which means that lethality has only marginally increased by 0.0063% compared to a control scenario without exposure to the contaminant. The columns L.dat.start and L.dat.end denote the period for which the effect level was observed. In case of GUTS-RED models, this usually refers to the whole simulation period. For certain assessments it may be necessary to evaluate all exposure periods of a certain length within the original series separately. This approach is referred to as a moving exposure window:

# Setting up a custom scenario with a total simulation period of 14 days and
# an exposure window length of 7 to assess a trapezoidal exposure pattern
americamysis %>%
  set_window(7) %>%
  set_exposure(data.frame(t=c(0,3,4,7,8), c=c(0,0,3,3,0))) %>%
  set_times(0:14) -> mydeb

# Derive maximum effect level of all exposure windows
mydeb %>% effect()
#> # A tibble: 1 × 7
#>   scenario      L L.dat.start L.dat.end     R R.dat.start R.dat.end
#>   <list>    <dbl>       <dbl>     <dbl> <dbl>       <dbl>     <dbl>
#> 1 <DebAbj> 0.0521           2         9     0           0         7

The americamysis scenario provides two effect endpoints: structural length (L) and reproduction (R). Exposure to the simulated toxicant results at maximum in a 5.21% decrease in body length compared to control scenarios. The maximum effect was observed in the exposure window starting at time 2.0 and ending at 9.0, cf. columns L.dat.start and L.dat.end. The toxicant had no effect on reproduction due to the simulation being too short for reproduction to occur.

Scenarios can also be assessed regarding to so-called effect profiles, or EPx values for short. An EPx value quantifies the multiplication factor that is necessary to produce an effect level of x% for that particular scenario. As an example, an EP50 of 17.0 would mean that the scenario’s exposure series needs to be multiplied by a factor of 17 to observe a 50% effect. The function epx() can derive effect profiles efficiently with arbitrary precision:

# Restrict assessed endpoints to structural length (L)
mydeb %>% 
  set_endpoints("L") %>%
  epx()
#> # A tibble: 1 × 3
#>   scenario L.EP10 L.EP50
#>   <list>    <dbl>  <dbl>
#> 1 <DebAbj>   1.16   1.71

In this example, a factor of 1.16 resulted in 10% effect on structural length and a factor of 1.71 in 50% effect. epx() provides means to control the precision of derived EPx values, the range of tested multiplication factors, factor cutoff thresholds, and debugging capabilities. To examine how a particular EPx value was derived, simply set the argument verbose=TRUE:

# Examine how the EP20 value is derived
minnow_it %>% epx(level=20, verbose=TRUE)
#> epx: screening multiplication factors
#>   start: 10
#>   L.EP20: 1 <<<
#>   L.EP20: 5.5
#>   L.EP20: 7.75
#>   L.EP20: 6.625
#>   L.EP20: 7.1875
#>   L.EP20: 6.90625
#>   L.EP20: 6.765625
#>   L.EP20: 6.8359375
#>   L.EP20: 6.80078125
#> # A tibble: 1 × 2
#>   scenario   L.EP20
#>   <list>      <dbl>
#> 1 <GutsRdIt>   6.80

In this example, the algorithm of epx() first tests a multiplication factor of 10 and then continues testing additional factors using a binary search algorithm until a factor is found that causes 20% effect.

Please note: Depending on model and chosen parameters, it may be impossible to calculate EPx values (for selected effect levels), because the model or organism cannot reach the required state. As an example: If the simulated period of a DEB_abj scenario is too short, reproduction cannot occur. Therefore, effects on reproduction will always be zero independent of the exposure level. Consequently, EPx values for the reproduction endpoint will not be available.

Data format of model inputs

Input data for the cvasi package relies on common R data structures and imposes only minimal requirements to set up scenarios:

1. Data representing numerical values should be represented by numerical types. Practically, this means that numerical scenario properties such as output time points must not be passed as string literals even if the string may be representing a number. Due to R being a dynamically typed language, parameter values and their types may have to be checked on import by the user.
2. Time-series data must be represented by a data.frame with exactly two numerical columns: The first column for time, the second for the series’ value. The column names are irrelevant but sensible names may help documenting the data. Constant values can be represented by time-series with only a single row or entry.

Therefore, it should be possible to apply the package’s routines on existing datasets with minimal effort. Please refer to Wickham and Grolemund (2017) on how to implement plain and consistent processes to import and transform data using tidyr syntax.

cvasi does support loading data from common sources, such as the exposure model TOXSWA, to ease data processing. Notable file formats and data structures that are supported are exposure time-series from the model FOCUS TOXSWA, as well as fitted GUTS-RED model parameter sets from morse.

How to derive model inputs

In general, all models require parameter values specific to the simulated species and substance. Some scenario types contain default values for selected model parameters as recommended by model authors. Scenarios providing default values include:

• Lemna_Schmitt() and derived models are parameterized according to Schmitt et al. (2013)
• Lemna_SETAC(), Myrio(), and derived models are parameterized as recommended by Klein et al. (2021)

In addition, some models require values for environmental factors such as temperature and nutrient concentrations. These values or time-series are specific to a certain scenario and must be chosen accordingly.

Parameters of TKTD sub-models are substance-specific and are usually obtained by fitting model parameters to observed data. The cvasi package supports this process with the function calibrate() which uses a frequentist approach for parameter fitting. However, parameters can also be derived by other means. Generally, the user is responsible for choosing model parameters and the package does not impose any restrictions in this regard.

The package function calibrate() supports the following operation modes:

• Fitting of a single parameter or multiple parameters simultaneously
• Fit to one or more independent datasets
• Weighting of datasets
• Choosing the error function which is minimized
• Selecting one of multiple optimization algorithms

Parameter fitting builds upon the R function stats::optim() and it exposes full control over the fitting process. In this context, a dataset is represented by a calibration set object which consists of a scenario, observed data, and an optional weighting term. Please refer to the Modeling Howto for a description of the fitting process using calibrate().

As an alternative for GUTS-RED type models, model parameters can also be obtained using the morse package. It uses a Bayesian approach for parameter fitting.

Description of model outputs

The cvasi package does not use or define any custom file formats for model outputs but strictly relies on common R data structures. Simulation results and assessment endpoints are returned in tabular formats which can easily be serialized to common file formats such as .csv or Microsoft Excel files for further processing and data storage.

The following subsections will describe in detail the return values of common package routines for simulating and assessing scenarios.

Simulation results

The return value of simulate() is a data.frame containing the value of each state variable at each requested output time. The first column represents time, followed by columns for each state variable:

## Display selected scenario properties
metsulfuron@times  # Output times
#>  [1]  0  1  2  3  4  5  6  7  8  9 10 11 12 13 14
metsulfuron@init   # Initial state
#>    BM     E M_int 
#>    50     1     0

## Simulate the sample scenario
metsulfuron %>% simulate()
#>    time       BM E     M_int      C_int  FrondNo
#> 1     0 50.00000 1   0.00000 0.00000000 500000.0
#> 2     1 52.15858 1 223.07447 0.25609887 521585.8
#> 3     2 52.43495 1 369.63440 0.42211917 524349.5
#> 4     3 52.33917 1 463.02605 0.52973924 523391.7
#> 5     4 52.17923 1 521.89269 0.59891761 521792.3
#> 6     5 52.00016 1 558.62681 0.64328081 520001.6
#> 7     6 51.81403 1 581.21867 0.67170059 518140.3
#> 8     7 51.63740 1 474.07158 0.54974731 516374.0
#> 9     8 51.61806 1 301.84519 0.35015964 516180.6
#> 10    9 52.32264 1 192.18727 0.21994721 523226.4
#> 11   10 54.34850 1 122.36718 0.13482205 543485.0
#> 12   11 57.08426 1  77.91217 0.08172830 570842.6
#> 13   12 59.99459 1  49.60731 0.04951275 599945.9
#> 14   13 62.96113 1  31.58537 0.03003980 629611.3
#> 15   14 65.95879 1  20.11066 0.01825733 659587.9

The model state in the first row is, by definition, equal to the initial state of the scenario. The simulation result may contain additional columns that are derived variables or intermediate model variables. In case of the metsulfuron scenario, the internal toxicant concentration (C_int) was derived from BM, M_int, and model parameters. The number of fronds (FrondNo) was derived from BM and model parameters. Additional outputs such as C_int and FrondNo are included in results for user convenience and to support model understanding. Additional outputs can be disabled by setting the optional argument nout=0:

metsulfuron %>%
  simulate(nout=0) %>%
  head(5)
#>   time       BM E    M_int
#> 1    0 50.00000 1   0.0000
#> 2    1 52.15858 1 223.0745
#> 3    2 52.43495 1 369.6344
#> 4    3 52.33917 1 463.0261
#> 5    4 52.17923 1 521.8927

Intermediate model variables can be enabled in the same manner:

metsulfuron %>%
  simulate(nout=8) %>%
  head(5)
#>   time       BM E    M_int     C_int  FrondNo   C_int_u BM_fresh k_phot_eff k_resp_eff     f_Eff P_up_eff
#> 1    0 50.00000 1   0.0000 0.0000000 500000.0 0.0000000 835.0000 0.07548561 0.02030631 1.0000000   0.0054
#> 2    1 52.15858 1 223.0745 0.2560989 521585.8 0.3414652 871.0482 0.03746128 0.02030631 0.5049205   0.0054
#> 3    2 52.43495 1 369.6344 0.4221192 524349.5 0.5628256 875.6636 0.01993781 0.02030631 0.2693322   0.0054
#> 4    3 52.33917 1 463.0261 0.5297392 523391.7 0.7063190 874.0641 0.01760496 0.02030631 0.2376344   0.0054
#> 5    4 52.17923 1 521.8927 0.5989176 521792.3 0.7985568 871.3931 0.01699676 0.02030631 0.2291285   0.0054

Effect levels

The return value of effect() is a data.frame containing the effect level of each selected endpoint. The first column, scenario, contains the original scenario which was the basis of the assessment. Followed by the effect level and the exposure window in which the maximum effect was observed:

metsulfuron %>%
  set_times(0:7) %>%  # restrict scenario to the period [0,7]
  effect()
#> # A tibble: 1 × 7
#>   scenario      BM BM.dat.start BM.dat.end     r r.dat.start r.dat.end
#>   <list>     <dbl>        <dbl>      <dbl> <dbl>       <dbl>     <dbl>
#> 1 <LmnSchmS> 0.268            0          7 0.906           0         7

Effect levels are reported as a fraction relative to a control scenario. Effects can be converted to percentages by multiplying the value with 100 (%). For scenarios with moving exposure windows disabled, the reported window will cover the entire simulation period. If multiple windows result in identical maximum effect levels, the first matching window will be reported. The table’s content can be reduced to endpoints only by setting the argument ep_only=TRUE.

effect() can also be instructed to return the effect levels of all assessed exposure windows by setting the argument max_only=FALSE:

metsulfuron %>%
  set_window(length=7, interval=1) %>%   # enable moving exposure windows
  effect(max_only=FALSE)                 # return effects of all windows
#> # A tibble: 8 × 5
#>   scenario          BM         r dat.start dat.end
#>   <list>         <dbl>     <dbl>     <dbl>   <dbl>
#> 1 <LmnSchmS>  2.68e- 1  9.06e- 1         0       7
#> 2 <LmnSchmS>  2.65e- 1  8.95e- 1         1       8
#> 3 <LmnSchmS>  2.48e- 1  8.29e- 1         2       9
#> 4 <LmnSchmS>  2.05e- 1  6.69e- 1         3      10
#> 5 <LmnSchmS>  1.47e- 1  4.63e- 1         4      11
#> 6 <LmnSchmS>  7.39e- 2  2.23e- 1         5      12
#> 7 <LmnSchmS>  3.15e- 3  9.18e- 3         6      13
#> 8 <LmnSchmS> -6.66e-16 -1.78e-15         7      14

In few cases, effect() may report spurious non-zero effect levels that are very close to zero. These can either be avoided by increasing the numerical precision of simulations or by setting a marginal effect threshold which instructs effect() to zero any effect below that threshold. A warning will be shown if a marginal effect threshold greater than 1% is chosen. Applying a threshold of 1% to the previous example results in zeroing the effect in the last and second to last exposure window but leaving the remaining results unchanged:

metsulfuron %>%
  set_window(length=7, interval=1) %>%         # enable moving exposure windows
  effect(max_only=FALSE, marginal_effect=0.01) # return effects of all windows
#> # A tibble: 8 × 5
#>   scenario       BM     r dat.start dat.end
#>   <list>      <dbl> <dbl>     <dbl>   <dbl>
#> 1 <LmnSchmS> 0.268  0.906         0       7
#> 2 <LmnSchmS> 0.265  0.895         1       8
#> 3 <LmnSchmS> 0.248  0.829         2       9
#> 4 <LmnSchmS> 0.205  0.669         3      10
#> 5 <LmnSchmS> 0.147  0.463         4      11
#> 6 <LmnSchmS> 0.0739 0.223         5      12
#> 7 <LmnSchmS> 0      0             6      13
#> 8 <LmnSchmS> 0      0             7      14

Effect profiles

The return value of epx() is a data.frame containing the effect profile (EPx) of each selected endpoint and effect level. The first column, scenario, contains the original scenario which was the basis of the assessment. Followed by the derived effect profiles, i.e. the multiplication factors to achieve x% effect:

metsulfuron %>% epx()
#> Warning in epx(.): Some scenarios have failed (n=1), result will include NAs
#>   **  multiplication factor out of range: BM.EP50 > 1e+30
#> # A tibble: 1 × 6
#>   scenario   BM.EP10 r.EP10 BM.EP50 r.EP50 error                                                 
#>   <list>       <dbl>  <dbl>   <dbl>  <dbl> <chr>                                                 
#> 1 <LmnSchmS>   0.395  0.325      NA  0.880 " multiplication factor out of range: BM.EP50 > 1e+30"

For the sample metsulfuron scenario, a factor of 0.395 would need to be applied to the exposure series to achieve an effect level of 10% on biomass (BM). An effect level of 50% on biomass is unattainable with the tested factors ranging from 1e-30 to 1e30 which is demonstrated by the warning message and the NA value for BM.EP50.

The table’s content can be reduced to endpoints only by setting the argument ep_only=TRUE. The range of tested factors can be controlled by setting the arguments min_factor and max_factor. Multiplication factors exceeding this range will raise a warning and NA will be returned for that EPx. Finding multiplication factors can be cut short if a certain margin of safety is reached by using the argument factor_cutoff: if the algorithm identifies that an EPx greater or equal to factor_cutoff is required, then factor_cutoff is reported as the result. This approach may be used to save computational resources for long running models if the exact EPx value above the cutoff is not of interest.

The precision of EPx values is controlled by the argument effect_tolerance and is given as the upper absolute error threshold of effects that is deemed acceptable. The default value of 0.001 ensures that a derived EPx will result in an effect of x% ± 0.1. Decreasing the effect_tolerance will result in additional model iterations and longer runtime.

The algorithm’s inner workings can be examined by setting the argument verbose=TRUE:

metsulfuron %>%
  set_endpoints("BM") %>%
  epx(verbose=TRUE)
#> epx: screening multiplication factors
#>   start: 10
#>   BM.EP10: 1 <<<
#>   BM.EP10: 0.1 <<<
#>   BM.EP10: 0.55
#>   BM.EP10: 0.325
#>   BM.EP10: 0.4375
#>   BM.EP10: 0.38125
#>   BM.EP10: 0.409375
#>   BM.EP10: 0.3953125
#>   BM.EP50: 100 >>>
#>   BM.EP50: 1000 >>>
#>   BM.EP50: 10000 >>>
#>   BM.EP50: 1e+05 >>>
#>   BM.EP50: 1e+06 >>>
#>   BM.EP50: 1e+07 >>>
#>   BM.EP50: 1e+08 >>>
#>   BM.EP50: 1e+09 >>>
#>   BM.EP50: 1e+10 >>>
#>   BM.EP50: 1e+11 >>>
#>   BM.EP50: 1e+12 >>>
#>   BM.EP50: 1e+13 >>>
#>   BM.EP50: 1e+14 >>>
#>   BM.EP50: 1e+15 >>>
#>   BM.EP50: 1e+16 >>>
#>   BM.EP50: 1e+17 >>>
#>   BM.EP50: 1e+18 >>>
#>   BM.EP50: 1e+19 >>>
#>   BM.EP50: 1e+20 >>>
#>   BM.EP50: 1e+21 >>>
#>   BM.EP50: 1e+22 >>>
#>   BM.EP50: 1e+23 >>>
#>   BM.EP50: 1e+24 >>>
#>   BM.EP50: 1e+25 >>>
#>   BM.EP50: 1e+26 >>>
#>   BM.EP50: 1e+27 >>>
#>   BM.EP50: 1e+28 >>>
#>   BM.EP50: 1e+29 >>>
#>   BM.EP50: 1e+30 >>>
#>   BM.EP50: 1e+31 >>>
#> Warning in epx(., verbose = TRUE): Some scenarios have failed (n=1), result will include NAs
#>   **  multiplication factor out of range: BM.EP50 > 1e+30
#> # A tibble: 1 × 4
#>   scenario   BM.EP10 BM.EP50 error                                                 
#>   <list>       <dbl>   <dbl> <chr>                                                 
#> 1 <LmnSchmS>   0.395      NA " multiplication factor out of range: BM.EP50 > 1e+30"

In this example, the binary search finds a BM.EP10 with acceptable precision in less than ten iterations. For the BM.EP50, the algorithm re-uses the knowledge gained from previously performed calculations but is ultimately unable to find a suitable multiplication factor within the allowed range of multiplication factors.

References

• EFSA PPR Panel (EFSA Panel on Plant Protection Products and their Residues), Ockleford C., Adriaanse P., Berny P., et al., 2018: Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms. EFSA Journal 2018; 16(8):5377, 188 pp. DOI: 10.2903/j.efsa.2018.5377
• Geiger D.L., Call D.J., and Brooke L.T., 1988: Acute toxicities of organic chemicals to fathead minnows (Pimephales promelas): Volume IV, pp. 195-197. University of Wisconsin-Superior, Center for Lake Superior Environmental Studies. ISBN 9780961496838.
• Klein J., Cedergreen N., Heine S., Reichenberger S., Rendal C., Schmitt W., Hommen U., 2021: Refined description of the Lemna TKTD growth model based on Schmitt et al. (2013) - equation system and default parameters. Report of the working group Lemna of the SETAC Europe Interest Group Effect Modeling. Version 1, uploaded on 22. Sept. 2021. https://www.setac.org/group/effect-modeling.html
• Schmitt W., Bruns E., Dollinger M., and Sowig P., 2013: Mechanistic TK/TD-model simulating the effect of growth inhibitors on Lemna populations. Ecol Model 255, pp. 1-10. DOI: 10.1016/j.ecolmodel.2013.01.017
• Wickham H. and Grolemund G., 2017: R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (1st ed.). O’Reilly Media. https://r4ds.had.co.nz