Usually the competition between species is viewed as a “all or nothing” instantaneous process. However, observing nature we realize that plants with high capacity for colonization generally have high metabolic rates (respiration, photosynthesis and allocation of reproductive tissue). These high rates allow for the plants to grow and reproduce more quickly, which may give them an extra advantage in competitive interaction. Imagine a forest where a clearing was opened by a fallen tree and both species, the best competitor and the better disperser reach it at the same time. In this situation, imagining that the best competitor will immediately remove the other does not seem very reasonable, simply because there is no resource limitation yet. On the other hand, the species that have the highest growth rate can reproduce before the resource limitation occurs and it is driven to local extinction by competition.
This period, before the environmental resource reduction, creates an ephemeral niche that was called by Pacala and Rees (1998) the “succession niche”. These authors developed a simple model to test their ideas. They have established five possible states in the system:
Given these states the succession process can have several possible trajectories:
Let's create our model. For simplicity, rather than we model each species, we model the state and its transitions in a similar way that we model the states of individuals in a population: remember the matrix models of Leslie and Lefkovich from the earlier classes?! Look at the diagram below to understand the state transitions:
This model has four parameters: $c$, $\alpha$, $m$ and $\gamma$:
With these four parameters, we can model the variation of the proportion of states over time, with the expressions that appear in the transitions from the figure above. Solid lines indicate increase in proportion and broken lines decreases in proportion. For example, variation in the SUSCEPTIBLE state is given by:
$$ (dS)/dt = [c(S + R + M)]V - [αc(M+E)]S - gS - mS $$
The following parameters can be changed:
option | parameter | effect |
---|---|---|
data set | R object | stores the simulation results |
Simulation Arena Condition | Basic simulation parameters | |
Maximum time | tmax | number of model iterations to be run |
columns | cl | number of columns in the habitat |
rows | rw | number of rows in the habitat |
Initial Stages Proportions | Initial state | |
Early Stage | er | fraction of patches initially occupied by sp2 |
Susceptible | /sc | fraction of patches initially with sp1, and that may be invaded by sp2 |
Mixed | mx | fraction of patches initially with both species |
Resistant | rs | fraction of patches initially with sp1, and that may not be colonized sp2 |
Colonization rates | Colonization parameters | |
Better competitor | c1 | sp1 colonization coefficient |
Poor competitor | c2 | sp2 colonization coefficient |
General Parameters | General parameters | |
Competitive exclusion | ec | probability of transition from SC and MX to RS |
Disturbance | dst | proportion of patches that is kept empty |
Testing with a high rate of competitive exclusion and low disturbance:
tmax=50, rw=100, cl=100, c1=0.2, c2=0.8, ec=0.5, dst=0.04, er=0.08, sc=0.02, mx=0, rs=0,
Now, let's simulate some scenarios to see what happens:
What is the biological interpretation for your results? Connect your answer to: