# A first model of meta population In this script, we model the evolution of a population where agents are simply defined by their position on some landscape. We implement the simplest possible birth and death function. ## The landscape Let's start by a linear landscape. We define a discrete segment of length 9, with reflecting boundary conditions. In fact, reflecting boundary conditions are implemented for any finite space. !!! warning "1D Reflections" As of v1, only reflections on one dimensional space are implemented. We have a two dimensional reflection method that will be released in the future. There are two ways of implementing a linear landscape. The first one uses a DiscreteSegment while the second relies on LightGraphs.jl library. Both are *almost* equivalent. ### DiscreteSegment julia using ABMEv nodes = 10 mysegment = DiscreteSegment(1,nodes) wholespace = (mysegment,)  ### grid julia using ABMEv, LightGraphs nodes = 10 g = grid([nodes,1]) mysegmentgraph = GraphSpace(g) wholespace = (mysegmentgraph,)  !!! warning "Space tuple" Notice that the whole space should be a *tuple of spaces*. Even where there is only one sub vector space as here, you need to have brackets and comma around the unit vector space. Here is how you can visualise the landscape. julia using GraphPlot gplot(g, collect(1:nodes), collect(1:nodes))  ## Defining competition processes We propose that any individual have a constant birth rate, and competes with all the individuals present in the same patch. Let i \in \N,x_i \in \{1,2,\dots,9\}. The competition pressure experience by individual i is such that math d(x_i) = \sum_j \delta(x_i-x_j)  where \delta is the dirac function. In this way, we recover a logistic growth function for subpopulation within a patch. julia K0 = 1000 # We will have in total 1000 individuals b(X,t) = 1 / nodes d(X,Y,t) = (X[1] ≈ Y[1]) / K0  At equilibrium, population size in each deme will reach K0 / nodes. !!! warning "Time dependency" Even though time is not used, you have to write birth and death functions with time dependency. ## Dispersal We assume that anytime an offspring is born, it is given a chance to move (\mu = 1). julia mu = [1.] D = (1.5,)  ## Running the world We initialise the world with initial population of size K_0 / 9 located on patch 5. We keep track of individuals' ancestors by setting ancestors=true. Because we wish to use Gillepsie algorithm, we need rates=true as agents' internal birth and death rates are updated at every time step. !!! note "Warning" rates treatment is something we might implement in the library internals. julia using UnPack# useful macro @pack! NMax = 2000 tend = 300. p = Dict{String,Any}();@pack! p = d,b,D,mu,NMax myagents = [Agent(myspace,(5,),ancestors=true,rates=true) for i in 1:K0/nodes] w0 = World(myagents,myspace,p,0.) @time sim = run!(w0,Gillepsie(),tend)  This is the simplest run you can do. Now time to more interesting things ## Analysis ### Size of the world Let's verify that the population's growth is logistic. We will plot the population size over time. To do so, one need to define dt_saving < tend to save every dt_saving time steps of the world. julia myagents = [Agent(wholespace,(5,),ancestors=true,rates=true) for i in 1:K0/nodes] w0 = World(myagents,wholespace,p,0.) # we need to reinitialise the world @time sim = run!(w0,Gillepsie(),tend,dt_saving=2.) wsize = [length(w) for w in sim[:]] using Plots Plots.plot(get_tspan(sim),wsize, label = "", ylabel = "Metapopulation size", xlabel ="time", grid = false)  ![](../assets/tutorials/delta_comp_wsize.png) !!! notes "Callbacks" Note that one could also use a callback function to obtain time series of size of the world computed at simulation time. See [Simulation page](../manual/simulation.md). ### Position through time One might be tempted to plot the agents position for some time steps. julia Plots.plot(sim, label = "", ylabel = "Geographical position", grid = false, markersize = 10)  ![delta_comp_pos](../assets/tutorials/delta_comp_pos.png)