Algae_TKTD {cvasi}

Algae model with exponential growth, forcings (P, I) and scaled damage

Description

The model is a mechanistic combined toxicokinetic-toxicodynamic (TK/TD) and growth model for algae. The model simulates the development of algal biomass under laboratory and environmental conditions. The growth of the algae population is simulated on the basis of growth rates, which are dependent on environmental conditions (radiation, temperature and phosphorus). The model is a variant of the Algae_Weber() model (Weber 2012) as cited in EFSA TKTD opinion (2018). This Algae model, Algae_TKTD(), provides an additional possibility (probit) to simulate the dose-response curve and considers a scaled internal damage instead of the external concentration.

Usage

Algae_TKTD()

Value

an S4 object of type AlgaeTKTDScenario

State variables

The model has four state variables:

• A, Biomass (ug fresh wt)

• Q, Mass of phosphorous internal (mg P/ug fresh wt)

• P, Mass of phosphorous external (mg P/L)

• Dw, Damage concentration (ug/L)

Model parameters

• Growth model

  • mu_max, Maximum growth rate (d-1)

  • Q_min, Minimum intracellular P (mg P/ug fresh wt)

  • Q_max, Maximum intracellular P (mg P/ug fresh wt)

  • v_max, Maximum P-uptake rate at non-limited growth (mg P/ug fresh wt/d)

  • k_s, Half-saturation constant for extracellular P (mg P/L)

  • m_max, Natural mortality rate (1/d)

  • I_opt, Optimum light intensity for growth (uE/m²/s)

  • T_opt, Optimum temperature for growth (°C)

  • T_max, Maximum temperature for growth (°C)

  • T_min, Minimum temperature for growth (°C)

• Concentration response (Toxicodynamics)

  • EC_50, Effect concentration of 50% inhibition of growth rate (ug L-1)

  • b, slope of concentration effect curve at EC_50 (-)

  • dose_resp, shape of the dose response curve (0 = logit, 1 = probit)

• External concentration (Toxicokinetics)

  • kD, dominant rate constant (used in Algae_TKTD()only) (d-1)

Forcings

Besides exposure events (Cw), the Algae model requires two environmental properties as time-series input: Irradiance (I, uE/m²/s) and temperature (T_act, deg C). Forcings time-series are represented by data.frame objects consisting of two columns. The first for time and the second for the environmental factor in question. The input format for all forcings is a list of the data frames.

Simulation output

Simulation results will contain the state variables Biomass (A), mass of internal phosphorous (Q), mass of external phosphorous (P) and the damage concentration (Dw). The derivatives are also available as additional output.

• nout >= 4

  • dA, biomass derivative (µg)

  • dQ, internal phosphorous derivative (mg P/ug fresh wt)

  • dP, external phosphorous derivative (mg P L-1)

  • dDw, damage concentration derivative (ug L-1)

References

Weber D, Schaeffer D, Dorgerloh M, Bruns E, Goerlitz G, Hammel K, Preuss TG and Ratte HT, 2012. Combination of a higher-tier flow-through system and population modeling to assess the effects of time-variable exposure of isoproturon on the green algae Desmodesmus subspictatus and Pseudokirchneriella subcapitata. Environmental Toxicology and Chemistry, 31, 899-908. doi:10.1002/etc.1765

See Also

Scenarios, Transferable

Other algae models: Algae-models, Algae_Simple(), Algae_Weber()