Growth forest

Alejandro Morales & Ana Ernst

Centre for Crop Systems Analysis - Wageningen University

TL;DR

Now we want to implement a more extended functionality of our Forest!

• Growth rules, based on information stored in organs (dimensions, carbon assimilation)
• Update dimensions in function of assimilation
• Compute sink strength
• Merge Scenes
• Generate forest on grid and retrieve canopy-level data (e.g., LAI)

In this example we extend the binary forest example to have more complex, time- dependent development and growth based on carbon allocation. For simplicity, the model assumes a constant relative growth rate at the plant level to compute the biomass increment. In the next example this assumption is relaxed by a model of radiation use efficiency. When modelling growth from carbon allocation, the biomass of each organ is then translated in to an area or volume and the dimensions of the organs are updated accordingly (assuming a particular shape).

The following packages are needed:

using VirtualPlantLab, ColorTypes
using Base.Threads: @threads
using Plots
import Random
using FastGaussQuadrature
using Distributions
Random.seed!(123456789)
import GLMakie

Model definition

Node types

The data types needed to simulate the trees are given in the following module. The differences with respect to the previous example are:

- Meristems do not produce phytomers every day
- A relative sink strength approach is used to allocate biomass to organs
- The geometry of the organs is updated based on the new biomass
- Bud break probability is a function of distance to apical meristem

# Data types
module TreeTypes
    using VirtualPlantLab
    using Distributions
    # Meristem
    Base.@kwdef mutable struct Meristem <: VirtualPlantLab.Node
        age::Int64 = 0   # Age of the meristem
    end
    # Bud
    struct Bud <: VirtualPlantLab.Node end
    # Node
    struct Node <: VirtualPlantLab.Node end
    # BudNode
    struct BudNode <: VirtualPlantLab.Node end
    # Internode (needs to be mutable to allow for changes over time)
    Base.@kwdef mutable struct Internode <: VirtualPlantLab.Node
        age::Int64 = 0         ## Age of the internode
        biomass::Float64 = 0.0 ## Initial biomass
        length::Float64 = 0.0  ## Internodes
        width::Float64  = 0.0  ## Internodes
        sink::Exponential{Float64} = Exponential(5)
    end
    # Leaf
    Base.@kwdef mutable struct Leaf <: VirtualPlantLab.Node
        age::Int64 = 0         ## Age of the leaf
        biomass::Float64 = 0.0 ## Initial biomass
        length::Float64 = 0.0  ## Leaves
        width::Float64 = 0.0   ## Leaves
        sink::Beta{Float64} = Beta(2,5)
    end
    # Graph-level variables -> mutable because we need to modify them during growth
    Base.@kwdef mutable struct treeparams
        # Variables
        biomass::Float64 = 2e-3 ## Current total biomass (g)
        # Parameters
        RGR::Float64 = 1.0   ## Relative growth rate (1/d)
        IB0::Float64 = 1e-3  ## Initial biomass of an internode (g)
        SIW::Float64 = 0.1e6 ## Specific internode weight (g/m3)
        IS::Float64  = 15.0  ## Internode shape parameter (length/width)
        LB0::Float64 = 1e-3  ## Initial biomass of a leaf
        SLW::Float64 = 100.0 ## Specific leaf weight (g/m2)
        LS::Float64  = 3.0   ## Leaf shape parameter (length/width)
        budbreak::Float64 = 1/0.5 ## Bud break probability coefficient (in 1/m)
        plastochron::Int64 = 5 ## Number of days between phytomer production
        leaf_expansion::Float64 = 15.0 ## Number of days that a leaf expands
        phyllotaxis::Float64 = 140.0
        leaf_angle::Float64 = 30.0
        branch_angle::Float64 = 45.0
    end
end

import .TreeTypes

Geometry

The methods for creating the geometry and color of the tree are the same as in the previous example. Create geometry + color for the internodes

function VirtualPlantLab.feed!(turtle::Turtle, i::TreeTypes.Internode, vars)
    # Rotate turtle around the head to implement elliptical phyllotaxis
    rh!(turtle, vars.phyllotaxis)
    HollowCylinder!(turtle, length = i.length, height = i.width, width = i.width,
                move = true, colors = RGB(0.5,0.4,0.0))
    return nothing
end

# Create geometry + color for the leaves
function VirtualPlantLab.feed!(turtle::Turtle, l::TreeTypes.Leaf, vars)
    # Rotate turtle around the arm for insertion angle
    ra!(turtle, -vars.leaf_angle)
    # Generate the leaf
    Ellipse!(turtle, length = l.length, width = l.width, move = false,
             colors = RGB(0.2,0.6,0.2))
    # Rotate turtle back to original direction
    ra!(turtle, vars.leaf_angle)
    return nothing
end

# Insertion angle for the bud nodes
function VirtualPlantLab.feed!(turtle::Turtle, b::TreeTypes.BudNode, vars)
    # Rotate turtle around the arm for insertion angle
    ra!(turtle, -vars.branch_angle)
end

Development

The meristem rule is now parameterized by the initial states of the leaves and internodes and will only be triggered every X days where X is the plastochron. Create right side of the growth rule (parameterized by the initial states of the leaves and internodes)

function create_meristem_rule(vleaf, vint)
    meristem_rule = Rule(TreeTypes.Meristem,
                        lhs = mer -> mod(data(mer).age, graph_data(mer).plastochron) == 0,
                        rhs = mer -> TreeTypes.Node() +
                                     (TreeTypes.Bud(),
                                     TreeTypes.Leaf(biomass = vleaf.biomass,
                                                    length  = vleaf.length,
                                                    width   = vleaf.width)) +
                                     TreeTypes.Internode(biomass = vint.biomass,
                                                         length  = vint.length,
                                                         width   = vint.width) +
                                     TreeTypes.Meristem())
end

The bud break probability is now a function of distance to the apical meristem rather than the number of internodes. An adhoc traversal is used to compute this length of the main branch a bud belongs to (ignoring the lateral branches).

# Compute the probability that a bud breaks as function of distance to the meristem
function prob_break(bud)
    # We move to parent node in the branch where the bud was created
    node =  parent(bud)
    # Extract the first internode
    child = filter(x -> data(x) isa TreeTypes.Internode, children(node))[1]
    data_child = data(child)
    # We measure the length of the branch until we find the meristem
    distance = 0.0
    while !isa(data_child, TreeTypes.Meristem)
        # If we encounter an internode, store the length and move to the next node
        if data_child isa TreeTypes.Internode
            distance += data_child.length
            child = children(child)[1]
            data_child = data(child)
        # If we encounter a node, extract the next internode
        elseif data_child isa TreeTypes.Node
                child = filter(x -> data(x) isa TreeTypes.Internode, children(child))[1]
                data_child = data(child)
        else
            error("Should be Internode, Node or Meristem")
        end
    end
    # Compute the probability of bud break as function of distance and
    # make stochastic decision
    prob =  min(1.0, distance*graph_data(bud).budbreak)
    return rand() < prob
end

# Branch rule parameterized by initial states of internodes
function create_branch_rule(vint)
    branch_rule = Rule(TreeTypes.Bud,
            lhs = prob_break,
            rhs = bud -> TreeTypes.BudNode() +
                         TreeTypes.Internode(biomass = vint.biomass,
                                             length  = vint.length,
                                             width   = vint.width) +
                         TreeTypes.Meristem())
end

Growth

We need some functions to compute the length and width of a leaf or internode from its biomass

function leaf_dims(biomass, vars)
    leaf_biomass = biomass
    leaf_area    = biomass/vars.SLW
    leaf_length  = sqrt(leaf_area*4*vars.LS/pi)
    leaf_width   = leaf_length/vars.LS
    return leaf_length, leaf_width
end

function int_dims(biomass, vars)
    int_biomass = biomass
    int_volume  = biomass/vars.SIW
    int_length  = cbrt(int_volume*4*vars.IS^2/pi)
    int_width   = int_length/vars.IS
    return int_length, int_width
end

Each day, the total biomass of the tree is updated using a simple RGR formula and the increment of biomass is distributed across the organs proportionally to their relative sink strength (of leaves or internodes).

The sink strength of leaves is modelled with a beta distribution scaled to the leaf_expansion argument that determines the duration of leaf growth, whereas for the internodes it follows a negative exponential distribution. The pdf function computes the probability density of each distribution which is taken as proportional to the sink strength (the model is actually source-limited since we imposed a particular growth rate).

sink_strength(leaf, vars) = leaf.age > vars.leaf_expansion ? 0.0 :
                            pdf(leaf.sink, leaf.age/vars.leaf_expansion)/100.0
plot(0:1:50, x -> sink_strength(TreeTypes.Leaf(age = x), TreeTypes.treeparams()),
     xlabel = "Age", ylabel = "Sink strength", label = "Leaf")

sink_strength(int) = pdf(int.sink, int.age)
plot!(0:1:50, x -> sink_strength(TreeTypes.Internode(age = x)), label = "Internode")

Now we need a function that updates the biomass of the tree, allocates it to the different organs and updates the dimensions of said organs. For simplicity, we create the functions leaves() and internodes() that will apply the queries to the tree required to extract said nodes:

get_leaves(tree) = apply(tree, Query(TreeTypes.Leaf))
get_internodes(tree) = apply(tree, Query(TreeTypes.Internode))

The age of the different organs is updated every time step:

function age!(all_leaves, all_internodes, all_meristems)
    for leaf in all_leaves
        leaf.age += 1
    end
    for int in all_internodes
        int.age += 1
    end
    for mer in all_meristems
        mer.age += 1
    end
    return nothing
end

The daily growth is allocated to different organs proportional to their sink strength.

function grow!(tree, all_leaves, all_internodes)
    # Compute total biomass increment
    tvars = data(tree)
    ΔB    = tvars.RGR*tvars.biomass
    tvars.biomass += ΔB
    # Total sink strength
    total_sink = 0.0
    for leaf in all_leaves
        total_sink += sink_strength(leaf, tvars)
    end
    for int in all_internodes
        total_sink += sink_strength(int)
    end
    # Allocate biomass to leaves and internodes
    for leaf in all_leaves
        leaf.biomass += ΔB*sink_strength(leaf, tvars)/total_sink
    end
    for int in all_internodes
        int.biomass += ΔB*sink_strength(int)/total_sink
    end
    return nothing
end

Finally, we need to update the dimensions of the organs. The leaf dimensions are

function size_leaves!(all_leaves, tvars)
    for leaf in all_leaves
        leaf.length, leaf.width = leaf_dims(leaf.biomass, tvars)
    end
    return nothing
end
function size_internodes!(all_internodes, tvars)
    for int in all_internodes
        int.length, int.width = int_dims(int.biomass, tvars)
    end
    return nothing
end

Daily step

All the growth and developmental functions are combined together into a daily step function that updates the forest by iterating over the different trees in parallel.

get_meristems(tree) = apply(tree, Query(TreeTypes.Meristem))
function daily_step!(forest)
    @threads for tree in forest
        # Retrieve all the relevant organs
        all_leaves = get_leaves(tree)
        all_internodes = get_internodes(tree)
        all_meristems = get_meristems(tree)
        # Update the age of the organs
        age!(all_leaves, all_internodes, all_meristems)
        # Grow the tree
        grow!(tree, all_leaves, all_internodes)
        tvars = data(tree)
        size_leaves!(all_leaves, tvars)
        size_internodes!(all_internodes, tvars)
        # Developmental rules
        rewrite!(tree)
    end
end

Initialization

The trees are initialized in a regular grid with random values for the initial orientation and RGR:

RGRs = rand(Normal(0.3,0.01), 10, 10)
histogram(vec(RGRs))

orientations = [rand()*360.0 for i = 1:2.0:20.0, j = 1:2.0:20.0]
histogram(vec(orientations))

origins = [Vec(i,j,0) for i = 1:2.0:20.0, j = 1:2.0:20.0];
nothing #hide

The following initalizes a tree based on the origin, orientation and RGR:

function create_tree(origin, orientation, RGR)
    # Initial state and parameters of the tree
    vars = TreeTypes.treeparams(RGR = RGR)
    # Initial states of the leaves
    leaf_length, leaf_width = leaf_dims(vars.LB0, vars)
    vleaf = (biomass = vars.LB0, length = leaf_length, width = leaf_width)
    # Initial states of the internodes
    int_length, int_width = int_dims(vars.LB0, vars)
    vint = (biomass = vars.IB0, length = int_length, width = int_width)
    # Growth rules
    meristem_rule = create_meristem_rule(vleaf, vint)
    branch_rule   = create_branch_rule(vint)
    axiom = T(origin) + RH(orientation) +
            TreeTypes.Internode(biomass = vint.biomass,
                             length  = vint.length,
                             width   = vint.width) +
            TreeTypes.Meristem()
    tree = Graph(axiom = axiom, rules = (meristem_rule, branch_rule),
                 data = vars)
    return tree
end

Visualization

As in the previous example, it makes sense to visualize the forest with a soil tile beneath it. Unlike in the previous example, we will construct the soil tile using a dedicated graph and generate a Scene object which can later be merged with the rest of scene generated in daily step:

Base.@kwdef struct Soil <: VirtualPlantLab.Node
    length::Float64
    width::Float64
end
function VirtualPlantLab.feed!(turtle::Turtle, s::Soil, vars)
    Rectangle!(turtle, length = s.length, width = s.width, colors = RGB(255/255, 236/255, 179/255))
end
soil_graph = RA(-90.0) + T(Vec(0.0, 10.0, 0.0)) + ## Moves into position
             Soil(length = 20.0, width = 20.0) ## Draws the soil tile
soil = Mesh(Graph(axiom = soil_graph));
render(soil, axes = false)

And the following function renders the entire scene (notice that we need to use display() to force the rendering of the scene when called within a loop or a function):

function render_forest(forest, soil)
    mesh = Mesh(vec(forest)) ## create mesh from forest
    mesh = Mesh([mesh, soil]) ## merges the two scenes
    render(mesh)
end

Retrieving canopy-level data

We may want to extract some information at the canopy level such as LAI. This is best achieved with a query:

function get_LAI(forest)
    LAI = 0.0
    @threads for tree in forest
        for leaf in get_leaves(tree)
            LAI += leaf.length*leaf.width*pi/4.0
        end
    end
    return LAI/400.0
end

Simulation

We can now create a forest of trees on a regular grid:

forest = create_tree.(origins, orientations, RGRs);
render_forest(forest, soil)
for i in 1:50
    daily_step!(forest)
end
render_forest(forest, soil)

And compute the leaf area index:

get_LAI(forest)

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