Polishing Nutrient Intake.

docs/src/example/paradox_enrichment.md

@@ -174,8 +174,8 @@ df = DataFrame(;
 
 # Run simulations: for each carrying capacity we compute the equilibrium biomass.
 for K in K_values
-    environment = Environment(foodweb; K)
-    params = ModelParameters(foodweb; functional_response, environment)
+    producer_growth = LogisticGrowth(foodweb; K)
+    params = ModelParameters(foodweb; functional_response, producer_growth)
     B0 = rand(2) # Inital biomass.
     solution = simulate(params, B0; tmax, verbose)
     extrema = biomass_extrema(solution, "10%")

docs/src/man/modelparameters.md

@@ -3,15 +3,15 @@
 Once the [`FoodWeb`](@ref) is created,
 you still have to attribute values to the system parameters.
 To navigate easily through the parameters,
-they are split in 4 different fields depending on their nature:
+they are split into 4 different fields depending on their nature:
 
   - [`BioRates`](@ref) contains species biological rates,
     *e.g.* producer intrinsic growth rates
   - [`Environment`](@ref) contains environmental variables,
     *e.g.* temperature
   - [`FunctionalResponse`](@ref) contains functional response parameters,
     *e.g.* the hill exponent
-  - [`producer_growth`](@ref) contains producers growth model parameters,
+  - [`producer_growth`](@ref) contains producer growth model parameters,
     *e.g.* the carrying capacity
 
 All the fields are stored within the same object [`ModelParameters`](@ref).
@@ -123,34 +123,49 @@ can be given to [`BioRates`](@ref) as seen previously.
 biorates = BioRates(foodweb; x = rate) # custom metabolic demand
 ```
 
-## Producers growth
+## Producer growth
 
 Two models are available for producers growth:
 
-- a logistic growth model, used by default and a
-- a nutrient intake model.
+  - a logistic growth model, used by default, and a
+  - a nutrient intake model.
 
 In both models, the producer biomasses $B_p$ grow following the general equation:
 
 $$
 growth = r_i * B_i * G_i
 $$
 
-where $r_i$ is producer's $i$ intrinsic growth rate, $B_i$ its biomass and $G_i$ its net growth term. The latter can take different forms depending on the growth model in use.
+Where $r_i$ is producer $i$'s intrinsic growth rate, $B_i$ is its biomass
+and $G_i$ is its net growth term.
+The latter can take different forms depending on the growth model in use.
 
-The model is controlled by the field `producer_growth` in model parameters. 
+The form of producer growth term is controlled by
+the field `producer_growth` in model parameters.
 
 ### The logistic model
 
-In the logistic model, $G_i$ takes the general form 
+In the logistic model, $G_i$ takes the general form:
 
 $$
 G_i = 1 - \frac{s}{K_i}
 $$
 
-where the numerator $s$ can take the simplest form $s = B_i$ (default) or include a matrix $\alpha$ describing the relative strength of the inter- vs intra-specific competition among producer. In this case, $s = \sum\alpha_{ij}B_j$ The matrix $\alpha$ should have as many rows and columns as there are producers in the food web. When the intra-specific competition is $1$ ($\alpha_{ii} = 1$), then species pairing with values below $1$ ($\alpha_{ij} < 1$) describe something akin to facilitation and values above $1$ ($\alpha_{ij} > 1$) describe a stronger inter-specific competition (that would lead to competitive exclusion in the absence of other processes). Note that when all non diagonal values are set to $0$, then $s = \sum\alpha_{ij}B_j = B_j$ and the two models are equivalent.
+Where the numerator $s$ can take the simplest form $s = B_i$ (default),
+or include a matrix $a$ describing the relative strength of the inter- vs
+intra-specific competition among producers.
+In this case, $s = \sum a_{ij}B_j$.
 
-When setting up the simulations, the logistic model is controlled as followed: 
+The matrix $a$ should have as many rows
+and columns as there are producers in the food web.
+When the intra-specific competition is $1$ ($a_{ii} = 1$),
+then species pairs with values below $1$ ($a_{ij} < 1$) describes something akin to
+facilitation and values above $1$ ($a_{ij} > 1$) describe stronger inter-specific
+competition (that would lead to competitive exclusion in the absence of other processes).
+Note that when all non-diagonal values are set to $0$,
+then $s = \sum\alpha_{ij}B_j = B_j$ and the two models are equivalent.
+
+When setting up the simulations, the logistic model is controlled as followed:
 
 ```
 julia> foodweb = FoodWeb([0 0 0; 0 0 0; 1 1 0])
@@ -161,10 +176,10 @@ FoodWeb of 3 species:
   method: unspecified
   species: [s1, s2, s3]
 
-julia> logmod = LogisticGrowth(foodweb) #default behavior
+julia> logmod = LogisticGrowth(foodweb) # Default behaviour.
 LogisticGrowth:
   Kᵢ - carrying capacity: [1.0, 1.0, nothing]
-  α - competition: (3, 3) sparse matrix
+  a - competition: (3, 3) sparse matrix
 
 julia> #Note: This is equivalent to ModelParameters(foodweb):
 julia> p = ModelParameters(foodweb, producer_growth = logmod)
@@ -177,34 +192,44 @@ ModelParameters{BioenergeticResponse, LogisticGrowth}:
   temperature_response: NoTemperatureResponse
 ```
 
-which allows to control the value of the carrying capacities and the matrix of competition: 
+which allows for control over the value of the carrying capacities
+and the matrix of competition:
 
 ```
 julia> logmod = LogisticGrowth(foodweb, K = [10, 5, nothing], αij = 0.8)
 LogisticGrowth:
   Kᵢ - carrying capacity: [10.0, 5.0, nothing]
-  α - competition: (3, 3) sparse matrix
+  a - competition: (3, 3) sparse matrix
 ```
 
-### The nutrient intake model 
+### The nutrient intake model
 
-The nutrient intake model is implemented following Brose et al., 2005. In this model, the net growth terms depends on nutrients $l$ concentration ($N_l$) and take the form: 
+The nutrient intake model is implemented following Brose et al., 2005.
+In this model, the net growth terms depends on nutrient $l$'s concentration ($N_l$)
+and take the form:
 
 $$
 G_i(N) = MIN(\frac{N_l}{K_{li} + N_l}, ...)
 $$
 
-where $K_{li}$ describes the species-specific half saturation densities for each nutrient $l$. In the absence of other factors it describes the producers hierarchy of competition (lower values means higher intake efficiency).
+where $K_{li}$ describes the species-specific half saturation densities
+for each nutrient $l$.
+In the absence of other factors it describes the producers hierarchy of competition
+(lower values means higher intake efficiency).
 
-The nutrients concentrations are described as: 
+The nutrients concentrations are described as:
 
 $$
 dN_l/dt = D(S_l - N_l)-\sum^n_{i = 1}(C_{li}G_i(N)B_i)
 $$
 
-where $D$ is the system turnover (default is 0.25), $S_l$ is nutrient $l$'s supply concentration and $C_{li}$ is the concentration of the nutrients in the producers biomass (the higher it is the most needed the nutrient is for the species growth).
+where $D$ is the system turnover (default is 0.25), $S_l$ is
+nutrient $l$'s supply concentration and $C_{li}$ is the concentration of
+the nutrients in the producers biomass
+(the higher it is the more essential the nutrient is for the species growth).
 
-As for the logistic model, the nutrient intake model's behavior is controlled through the use of the `producer_growth` field: 
+As with the logistic model, the nutrient intake model's behaviour is controlled
+through the use of the `producer_growth` field:
 
 ```
 julia> foodweb = FoodWeb([0 0 0; 0 0 0; 1 1 0])
@@ -223,7 +248,8 @@ NutrientIntake - 2 nutrients:
   Kₗᵢ - half sat. densities: (S, n) matrix
 ```
 
-By default, the models has 2 nutrients, but has for every other parameters, this can be changed: 
+By default, the models has 2 nutrients, but as for every other parameters,
+this can be changed:
 
 ```
 julia> ni4 = NutrientIntake(foodweb, n = 4)
@@ -234,7 +260,7 @@ NutrientIntake - 4 nutrients:
   Kₗᵢ - half sat. densities: (S, n) matrix
 ```
 
-It's then passed to `ModelParameter`:
+This is then passed to `ModelParameter`:
 
 ```
 julia> p = ModelParameters(foodweb, producer_growth = ni2)
@@ -256,13 +282,13 @@ in which case the parameters take default values.
 
 ```@example econetd
 foodweb = FoodWeb([0 0 0; 0 0 0; 0 1 0]; Z = 10)
-environment = Environment(foodweb)
+environment = Environment()
 ```
 
 By default the temperature is set to 293.15 K.
 
 ```@example econetd
-environment.K
+environment.T
 ```
 
 However, these default values can be changed
@@ -271,7 +297,7 @@ For the temperature
 you just have to provide the scalar corresponding to the wanted temperature.
 
 ```@example econetd
-environment = Environment(foodweb; T = 273.15);
+environment = Environment(; T = 273.15);
 environment.T
 ```
 

src/EcologicalNetworksDynamics.jl

@@ -24,7 +24,6 @@ by writing `?<function_name>` in a Julia REPL.
 """
 module EcologicalNetworksDynamics
 
-# Dependencies
 import DifferentialEquations.Rodas4
 import DifferentialEquations.SSRootfind
 import DifferentialEquations.Tsit5
@@ -39,27 +38,22 @@ using Statistics
 using Decimals
 using SciMLBase
 
-# Convenience aliases.
 const Solution = SciMLBase.AbstractODESolution
 
-# Include scripts
 include(joinpath(".", "macros.jl"))
 include(joinpath(".", "inputs/foodwebs.jl"))
 include(joinpath(".", "inputs/nontrophic_interactions.jl"))
 include(joinpath(".", "inputs/functional_response.jl"))
 include(joinpath(".", "inputs/biological_rates.jl"))
 include(joinpath(".", "inputs/environment.jl"))
 include(joinpath(".", "inputs/temperature_dependent_rates.jl"))
-#include(joinpath(".", "inputs/producer_competition.jl"))
-include(joinpath(".", "inputs/nutrient_intake.jl"))
 include(joinpath(".", "inputs/producer_growth.jl"))
 include(joinpath(".", "model/model_parameters.jl"))
+include(joinpath(".", "model/producer_growth.jl"))
 include(joinpath(".", "model/set_temperature.jl"))
-#include(joinpath(".", "model/productivity.jl"))
 include(joinpath(".", "model/consumption.jl"))
 include(joinpath(".", "model/metabolic_loss.jl"))
-include(joinpath(".", "model/dbdt.jl"))
-include(joinpath(".", "model/nutrient_dynamics.jl"))
+include(joinpath(".", "model/dudt.jl"))
 include(joinpath(".", "model/generate_dbdt.jl"))
 include(joinpath(".", "model/simulate.jl"))
 include(joinpath(".", "model/effect_nti.jl"))
@@ -70,7 +64,6 @@ include(joinpath(".", "measures/utils.jl"))
 include(joinpath(".", "utils.jl"))
 include(joinpath(".", "formatting.jl"))
 
-# Export public functions
 export @check_between
 export @check_greater_than
 export @check_in
@@ -100,7 +93,6 @@ export DefaultGrowthParams
 export DefaultMaxConsumptionParams
 export DefaultMetabolismParams
 export DefaultMortalityParams
-export DefaultNIntakeParams
 export draw_asymmetric_links
 export draw_symmetric_links
 export efficiency
@@ -129,8 +121,12 @@ export get_parameters
 export handling_time
 export homogeneous_preference
 export interaction_names
+export is_boostable
 export is_success
 export is_terminated
+export ispredator
+export isprey
+export isproducer
 export Layer
 export LinearResponse
 export living_species
@@ -149,6 +145,8 @@ export NIntakeParams
 export nontrophic_adjacency_matrix
 export NonTrophicIntensity
 export NoTemperatureResponse
+export nutrient_indices
+export nutrient_richness
 export NutrientIntake
 export population_stability
 export potential_competition_links
@@ -167,15 +165,17 @@ export shannon_diversity
 export simpson
 export simulate
 export species_cv
+export species_indices
 export species_persistence
 export species_richness
 export synchrony
 export TemperatureResponse
 export top_predators
 export total_biomass
+export total_richness
 export trophic_classes
 export trophic_levels
 export trophic_structure
 export weighted_mean_trophic_level
 
-end
+end

src/inputs/environment.jl

@@ -1,31 +1,25 @@
-#### Type definition ####
 mutable struct Environment
-    T::Union{Int64,Float64}
+    T::Float64
 end
-#### end ####
 
-#### Type display ####
 """
 One line Environment display.
 """
 function Base.show(io::IO, environment::Environment)
     T = environment.T
-    print(io, "Environment(" * "T=$(T)K)")
+    print(io, "Environment(T=$(T)K)")
 end
 
 """
 Multiline Environment display.
 """
 function Base.show(io::IO, ::MIME"text/plain", environment::Environment)
-
-    # Display output
     println(io, "Environment:")
     print(io, "  T: $(environment.T) Kelvin")
 end
-#### end ####
 
 """
-    Environment(foodweb, T=293.15)
+    Environment(; T=293.15)
 
 Create environmental parameters of the system.
 
@@ -38,23 +32,15 @@ The environmental parameters are:
 # Examples
 
 ```jldoctest
-julia> foodweb = FoodWeb([0 0 0; 0 0 0; 1 1 0]); # species 1 & 2 producers, 3 consumer
-
-julia> environment = Environment(foodweb) # default behaviour
-Environment:
-  T: 293.15 Kelvin
+julia> e = Environment() # Default behaviour.
+       e.T == 293.15 # Kelvin
+true
 
-
-julia> Environment(foodweb; T = 300.15).T # change temperature
-Environment:
-  T: 300.15 Kelvin
+julia> e = Environment(; T = 300.0)
+       e.T == 300.0
+true
 ```
 
 See also [`ModelParameters`](@ref).
 """
-function Environment(
-    net::EcologicalNetwork;
-    T::Real = 293.15,
-) where {Tp<:Real}
-    Environment(T)
-end
+Environment(; T = 293.15) = Environment(T)