cvasi (version 1.4.0)

DEB_abj: DEB_abj

Description

Creates a DEB abj scenario. The abj model with type M acceleration is like model std, but acceleration occurs between birth and metamorphosis (V1-morph). Isomorphy is assumed before and after acceleration. Metamorphosis is before puberty and occurs at maturity E_Hj, which might or might not correspond with changes in morphology. The abj model is a one-parameter extension of model std (DEB Wiki).

Usage

DEB_abj()

Value

an S4 object of type DebAbj

Simulation output

Simulation results will contain the state variables. It is possible to amend the output of simulate() with additional model quantities that are not state variables, for e.g. debugging purposes or to analyze model behavior. To enable or disable additional outputs, use the optional argument nout of simulate(). As an example, set nout=2 to enable reporting of the acceleration factor (MV) and the mobilization flux (pC). Set nout=0 to disable additional outputs (default).

The available output levels are as follows:

• nout >= 1: MV acceleration factor (-)

• nout >= 2: pC mobilization flux (J/d)

• nout >= 3: pA assimilation flux (J/d)

• nout >= 4: pJ energy invested in maturity flux (J/d)

Solver settings

The arguments to ODE solver deSolve::ode() control how model equations are numerically integrated. The settings influence stability of the numerical integration scheme as well as numerical precision of model outputs. Generally, the default settings as defined by deSolve are used, but all deSolve settings can be modified in cvasi workflows by the user, if needed. Please refer to e.g. simulate() on how to pass arguments to deSolve in cvasi workflows.

Details

State variables

The following list describes the default names and standard units of the model's state variables:

• L, structural length (cm)

• E, energy reserve (J)

• H, energy invested in maturity (J)

• R, reproduction buffer (J)

• cV, internal concentration (C)

• Lmax, maximum structural length (cm)

All state variables are initialized with zero. See set_init() on how to set the initial state.

Parameters

The following model parameters are required:

• p_M, vol-spec somatic maintenance (J/d.cm^3)

• v, energy conductance (cm/d)

• k_J, maturity maint rate coefficient (1/d)

• p_Am, surface-area specific maximum assimilation rate (J/d.cm^2)

• kap, allocation fraction to soma (-)

• E_G, spec cost for structure (J/cm^3)

• f, scaled functional response (-)

• E_Hj, maturity at metamorphosis (J)

• E_Hp, maturity at puberty (J)

• kap_R, reproduction efficiency (-)

• L_b, structural length at birth (cm)

• L_j, structural length at metamorphosis (cm)

• ke, elimination rate constant (d-1)

• c0, no-effect concentration sub-lethal (C)

• cT, tolerance concentration (C)

• MoA, mode of action switch (-)

Mode of Actions

Any combination of the following mode of actions (MoA) can be considered by the model:

• MoA = 1: effect on feeding

• MoA = 2: effect on maintenance costs

• MoA = 4: effect on overhead costs for making an egg

• MoA = 8: hazard during oogenesis

• MoA = 16: energy conductance

To activate more than one MoA, simply add up the corresponding codes. To disable all MoAs, set the parameter to zero. See also set_mode_of_action().

Effects

The state variables L (structural length) and R (reproduction buffer) are set as effect endpoints by default. All state variables are available as potential endpoints. The list of considered endpoints can be modified by using set_endpoints().

To calculate effects, each DEB scenario is simulated twice: One simulation which considers exposure to a toxicant and one simulation without exposure, i.e. a control. See also effect().

See Also

Other DEB models: DEB-models, DEBtox()

Examples

# Create an abj scenario from scratch and simulate it
DEB_abj() %>%
  set_init(c(L=0.02,E=0.1,H=0.01)) %>%
  set_param(c(p_M=3000,v=0.02,k_J=0.6,p_Am=300,kap=0.9,E_G=4000,f=1,
              E_Hj=0.05,E_Hp=0.3,kap_R=0.9,ke=1,c0=0,cT=1,L_b=0.02,
              L_j=0.04,MoA=0)) %>%
  set_exposure(no_exposure()) %>%
  set_times(0:10) %>%
  simulate()

# Print information about sample scenario 'americamysis'
americamysis

# Simulate 'americamysis' scenario
americamysis %>% simulate()