Exercise 2: Lake Phyto- and Zooplankton Model
ETH Zurich Course 701-0426-00L: Modelling Aquatic Ecosystems (Schuwirth)
March 6, 2024
Goals:
- Understand the process table notation introduced in section 4.1 of the manuscript.
- Understand the formulation of biogeochemical and ecological process rates introduced in section 4.2.
- Increase your familiarity with the R package
ecosim. - Understand the behaviour of the solutions of the lake phyto- and zooplankton model described in section 11.2 under constant and seasonally varying environmental conditions.
Task 1: Model with constant driving forces
Study the implementation of the model described in section 11.2 for constant driving forces. Run the model implementation below and interpret the behavior of the solutions.
# Model with constant driving forces
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# load required packages:
if ( !require("deSolve") ) {install.packages("deSolve"); library("deSolve") }## Loading required package: deSolve## Warning: package 'deSolve' was built under R version 4.3.2if ( !require("ecosim") ) {install.packages("ecosim"); library("ecosim") }## Loading required package: ecosim## Warning: package 'ecosim' was built under R version 4.3.2## Loading required package: stoichcalc# definition of model parameters:
param <- list(k.gro.ALG = 0.5, # 1/d
k.gro.ZOO = 0.4, # m3/gDM/d
k.death.ALG = 0.1, # 1/d
k.death.ZOO = 0.05, # 1/d
K.HPO4 = 0.002, # gP/m3
Y.ZOO = 0.2, # gDM/gDM
alpha.P.ALG = 0.003, # gP/gDM
A = 5e+006, # m2
h.epi = 5, # m
Q.in = 5, # m3/s
C.HPO4.in = 0.04, # gP/m3
C.HPO4.ini = 0.04, # gP/m3
C.ALG.ini = 0.1, # gDM/m3
C.ZOO.ini = 0.1) # gDM/m3- Next, we define the processes of growth and death of algae and zooplankton as objects of the class
process}of the packageecosim. Each process is defined by its name, rate, and stoichiometry. The rate is defined as an expression that can use parameters (defined for the object of classsystembelow), concentrations defined in objects of classreactor}that are part of the object of classsystem}`. To define the stoichiometry, a named list of expressions must be provided that identifies the substance or organism concentrations as the names and contains the stoichiometric coefficients as expressions.
# definition of transformation processes
# growth of algae:
gro.ALG <- new(Class = "process",
name = "Growth of algae",
rate = expression(k.gro.ALG
*C.HPO4/(K.HPO4+C.HPO4)
*C.ALG),
stoich = list(C.ALG = expression(1), # gDM/gDM
C.HPO4 = expression(-alpha.P.ALG))) # gP/gDM
# death of algae: TO BE COMPLETED
death.ALG <- new(Class = "process",
name = "Death of algae",
rate = expression(k.death.ALG*C.ALG),
stoich = list(C.ALG = expression(-1))) # gDM/gDM
# growth of zooplankton:
gro.ZOO <- new(Class = "process",
name = "Growth of zooplankton",
rate = expression(k.gro.ZOO
*C.ALG
*C.ZOO),
stoich = list(C.ZOO = expression(1), # gDM/gDM
C.ALG = expression(-1/Y.ZOO))) # gP/gDM
# death of zooplankton: TO BE COMPLETED
death.ZOO <- new(Class = "process",
name = "Death of zooplankton",
rate = expression(k.death.ZOO*C.ZOO),
stoich = list(C.ZOO = expression(-1))) # gDM/gDM- Next, we define the mixed box describing the epliminion of the lake as an object of the class
reactorof the packageecosim.
# definition of reactor to describe the epilimnion of the lake: TO BE COMPLETED
epilimnion <-
new(Class = "reactor",
name = "Epilimnion",
volume.ini = expression(A*h.epi),
conc.pervol.ini = list(C.HPO4 = expression(C.HPO4.ini), # gP/m3
C.ALG = expression(C.ALG.ini), # gDM/m3
C.ZOO = expression(C.ZOO.ini)), # gDM/m3
inflow = expression(Q.in*86400), # m3/d
inflow.conc = list(C.HPO4 = expression(C.HPO4.in),
C.ALG = 0,
C.ZOO = 0),
outflow = expression(Q.in*86400),
processes = list(gro.ALG,death.ALG,gro.ZOO,death.ZOO))Finally, we combine the reactor, the parameters, and the desired output times in an object of class system of the package ecoval.
# definition of the system consisting of a single reactor:
# observe the time frame of the simulation
system.11.2.a <- new(Class = "system",
name = "Lake",
reactors = list(epilimnion),
param = param,
t.out = seq(0,2*365,by=1)) Note that this object contains all definitions of the configuration of reactors (in this case just a single one), the processes active in each reactor, the model parameters, and the output time points. Any simulations carried out will refer to the definitions in this object, and not to the external variables that we used to set up the elements of the system.
- Perform a simulation and store the results by using the function
calcresof the packageecosim:
# perform simulation:
res.11.2.a <- calcres(system.11.2.a)
# plot results with default options:
plotres(res.11.2.a)
# variables in a vector 'c()' are plotted in the same graph
plotres(res=res.11.2.a,colnames=c("C.ALG", "C.HPO4","C.ZOO"))
# variables in a list 'list()' are plotted in different graphs
plotres(res=res.11.2.a,colnames=list("C.ALG", "C.HPO4", "C.ZOO"))
# combination of the two
plotres(res=res.11.2.a,colnames=list("C.HPO4",c("C.ALG", "C.ZOO")))
# plot and save as pdf
plotres(res = res.11.2.a,
colnames = list("C.HPO4",c("C.ALG", "C.ZOO")),
file = "exercise_2_results_a.pdf",
width = 10,
height = 5)## png
## 2Task 2: Model with seasonally varying driving forces
Study the implementation of the model extension to seasonally varying driving forces. First, run the model implementation below.
We adapt system definitions:
# Model with seasonally varying driving forces
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# copy the previous system definition:
system.11.2.b <- system.11.2.a
# extend model parameters:
param <- c(param,
list(beta.ALG = 0.046, # 1/degC
beta.ZOO = 0.08, # 1/degC
T0 = 20, # degC
K.I = 30, # W/m2
lambda.1 = 0.10, # 1/m
lambda.2 = 0.10, # m2/gDM
t.max = 230, # d
I0.min = 25, # W/m2
I0.max = 225, # W/m2
T.min = 5, # degC
T.max = 25)) # degC
# extend growth of algae and zooplankton by considering environmental factors:
gro.ALG.ext <-
new(Class = "process",
name = "Growth of algae extended",
rate = expression(k.gro.ALG
*exp(beta.ALG*(T-T0))
*C.HPO4/(K.HPO4+C.HPO4)
*log((K.I+I0)
/(K.I+I0*exp(-(lambda.1+lambda.2*C.ALG)*h.epi)))
/((lambda.1+lambda.2*C.ALG)*h.epi)
*C.ALG),
stoich = list(C.ALG = 1, # gDM/gDM
C.HPO4 = expression(-alpha.P.ALG))) # gP/gDM
gro.ZOO.ext <-
new(Class = "process",
name = "Growth of zooplankton",
rate = expression(k.gro.ZOO
*exp(beta.ZOO*(T-T0))
*C.ALG
*C.ZOO),
stoich = list(C.ZOO = expression(1), # gDM/gDM
C.ALG = expression(-1/Y.ZOO))) # gP/gDM
# re-define processes in the reactor "epilimnion":
epilimnion@processes <- list(gro.ALG.ext,death.ALG,gro.ZOO.ext,death.ZOO)
# make environmental conditions (light and temperature) time dependent:
epilimnion@cond <- list(I0 = expression(0.5*(I0.min+I0.max)+
0.5*(I0.max-I0.min)*
cos(2*pi/365.25*(t-t.max))), # W/m2
T = expression(0.5*(T.min+T.max)+
0.5*(T.max-T.min)*
cos(2*pi/365.25*(t-t.max)))) # degC
# plot the environmental conditions
t <- seq(1,2*365) # for two years
I0 <- numeric(0)
T <- numeric(0)
for(i in 1:length(t))
{
I0[i] <- eval(epilimnion@cond$I0, envir=c(param, t=t[i]))
T[i] <- eval(epilimnion@cond$T, envir=c(param, t=t[i]))
}
par(mfrow=c(1,2),xaxs="i",yaxs="i",mar=c(4.5,4.5,2,1.5)+0.1)
plot(t, I0, type="l")
plot(t, T, type="l")
# re-define the reactor "epilimnion" in the system definition:
system.11.2.b@reactors <- list(epilimnion)
# increase algal growth rate to compensate for new limitations:
param$k.gro.ALG <- 0.8
# replace parameters in the system definition:
system.11.2.b@param <- paramSecond, redo the simulations and plot and interpret the behavior of the solutions.
# redo simulations and plot results:
res.11.2.b <- calcres(system.11.2.b)
plotres(res=res.11.2.b, colnames=list("C.HPO4",c("C.ALG", "C.ZOO")))
plotres(res=res.11.2.b[1:365,], colnames=list("C.HPO4",c("C.ALG", "C.ZOO")))
plotres(res = res.11.2.b, # plot to pdf file
colnames = list("C.HPO4",c("C.ALG","C.ZOO")),
file = "exercise_2_results_b.pdf",
width = 10,
height = 5)## png
## 2We compare the two simulations.
# comparison of the two simulations:
plotres(res = list(const=res.11.2.a,dyn=res.11.2.b),
colnames = list("C.HPO4","C.ALG","C.ZOO"))
plotres(res = list(const=res.11.2.a,dyn=res.11.2.b),
colnames = list("C.HPO4","C.ALG","C.ZOO"),
file = "exercise_2_results_b.pdf",
width = 10,
height = 8)## png
## 2
Task 3: Sensitivity analyis for constant driving forces
Fill in the missing terms and perform a sensitivity analysis of the model results to the parameters
\(C_{in, HPO^{2-}_{4}}\), \(Q_{in}\), \(k_{gro,ALG}\), \(k_{death,ALG}\), \(k_{gro,ZOO}\), \(k_{death,ZOO}\), and \(Y_{ZOO}\)
by modifying their values by factors of 2 and 1/2 for the model under constant driving forces using the function calcsens. Interpret the results. Hint: look at the solution of the Task 5 of Exercise 1.
# Sensitivity analysis with constant driving forces
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# calculate results of sensitivity analysis: TO BE COMPLETED
res.11.2.a.sens <- calcsens(system.11.2.a,
param.sens=c("C.HPO4.in",
"k.gro.ZOO",
"k.death.ZOO",
"k.gro.ALG",
"k.death.ALG",
"K.HPO4",
"Y.ZOO",
"alpha.P.ALG",
"Q.in"))
# plot results:
plotres(res = res.11.2.a.sens,
colnames = list("C.HPO4","C.ALG", "C.ZOO"))
plotres(res = res.11.2.a.sens,
colnames = list("C.HPO4","C.ALG", "C.ZOO"),
file = "exercise_2_results_a_sens.pdf",
width = 10,
height = 8)## png
## 2
Task 4 - Homework: Sensitivity analyis for seasonally varying driving forces
Do a sensitivity analysis for seasonally varying driving forces and discuss the differences to the case with constant driving forces.
# calculate results of sensitivity analysis:
res.11.2.b.sens <- calcsens(system.11.2.b,
param.sens=c("C.HPO4.in",
"k.gro.ZOO",
"k.death.ZOO",
"k.gro.ALG",
"k.death.ALG",
"K.HPO4",
"Y.ZOO",
"alpha.P.ALG",
"Q.in"))
# plot results:
plotres(res = res.11.2.b.sens,
colnames = list("C.HPO4","C.ALG", "C.ZOO"))
plotres(res = res.11.2.b.sens,
colnames = list("C.HPO4","C.ALG", "C.ZOO"),
file = "exercise_2_results_b_sens.pdf",
width = 10,
height = 8)## png
## 2
Theory questions
Are the algae concentrations controlled bottom-up (by phosphate limitation) or top-down (by grazing of zooplankton)?
What is the reason for oscillating concentrations under constant driving forces? What happens when you introduce periodic driving forces?
What are the main deficits of the model compared to a real lake?
What is your expectation regarding the response of the model to the change in each parameter, does the result match your expectation and can you explain the observed changes?