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# INPUT
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# INPUT
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During simulations, three temporal series of environmental features influenced the life of the individuals: the river temperature, the river flow and the marine growth conditions (a variable synthesizing marine environment effects on growth, see [section](oceanclimate)). These series were observed data for the parameter- ization of the model. Additional simulations utilized a set of the 3 series generated from stochastic climate models (see Sections 2.1.7.9 and 2.1.7.10).
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During simulations, three temporal series of environmental features influenced the life of the individuals: the river temperature, the river flow and the marine growth conditions (a variable synthesizing marine environment effects on growth, see [section](oceanclimate)).
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These series can be observed data for the parameterization of the model (see [section](riverclimate)). To do so,
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use the R functions called in the [demoIbasam](IBASAM/IBASAM/R/demoIbasam.R) function:
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``` R
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#mm <- river_climate_model(nYears + 1, CC_Temp, CC_Amp)
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Reset_environment()
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Prepare_environment_vectors(mm$temperatures, mm$logrelflow)
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setup_environment_parameters(def$envParam)
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```
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with *mm* being a data frame containing daily water temperatures (in Celsius) and water flow (log of m^3/s) over the period to be simulated (years*365).
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Additional simulations utilize a set of the 3 series generated from stochastic climate models (see [river climate](riverclimate) and [ocean climate](oceanclimate) sections).
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1. Initialization
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1. Initialization
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At the beginning of each simulation (a 1st of April) a virtual population was created with individuals from each life stages to represent a standard population from the Scorff river (Caudal and Prévost, 2008): 1.2 emerging juvenile/m2 of riffle and rapid equiva- lent area (RA), 1.32 parr of 1 year/100 m2 RA, 3.61 smolt running to sea/100 m2 RA, 3.61 grilse/1000 m2 RA (1 sea-winter anadromous adult) and 0.6 multiple sea winter individuals/1000 m2 RA. Each individual’s state variable was initialized by drawing from prob- ability distributions describing their variability (see Appendix A). The first simulation step was then a summer.
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At the beginning of each simulation (a 1st of April) a virtual population was created with individuals from each life stages to represent a standard population from the Scorff river (Caudal and Prévost, 2008): 1.2 emerging juvenile/m2 of riffle and rapid equivalent area (RA), 1.32 parr of 1 year/100 m2 RA, 3.61 smolt running to sea/100 m2 RA, 3.61 grilse/1000 m2 RA (1 sea-winter anadromous adult) and 0.6 multiple sea winter individuals/1000 m2 RA. Each individual’s state variable was initialized by drawing from prob- ability distributions describing their variability (see Appendix A). The first simulation step was then a summer.
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2. Model parameterization
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2. Model parameterization
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... | @@ -33,4 +46,4 @@ demoIbasam(nYears, CC_Temp, CC_Amp, plotting = TRUE, window = FALSE, returning = |
... | @@ -33,4 +46,4 @@ demoIbasam(nYears, CC_Temp, CC_Amp, plotting = TRUE, window = FALSE, returning = |
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* nYears: number of years to simulate.
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* nYears: number of years to simulate.
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* CC_Temp: increase of average water temperature (in degrees Celsius) over the simulation (ex: CC_Temp = 3).
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* CC_Temp: increase of average water temperature (in degrees Celsius) over the simulation (ex: CC_Temp = 3).
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* CC_Amp: increase of amplitude of water flow over the simulation (ex: CC_Amp = 1.25, i.e. +25%). |
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* CC_Amp: increase of amplitude of water flow over the simulation (ex: CC_Amp = 1.25, i.e. +25%). |
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\ No newline at end of file |