Interspecific Competition-HNDD

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WHAT IS IT?

It is assumed that two species cannot coexist in the same environment (Gauss 1932). The competition among two species out compete one of the species and only one species survive in the long run. If two species are equally competitive then stochastic drift determines the species abundances, the stochastic drift caused community to reach monodominance. However, we have seen two species managed to coexist in the same environment. It is assumed that ecological processes such as heterospecific negative density dependence (HNND) can maintain the species richness at local spatial scales (i.e. tree neighborhood). This model illustrates the HNND (interspecific competition) in sessile organisms (e.g. trees).

HOW IT WORKS

The model has two species (sp₁ and sp₂). The initial abundance of the species are N₁ and N₂. The community has a size J. Each patch contains maximum one individual. Initially, community is saturated. Therefore, initial abundance of the species-2 is, N₂ = J - N₁. Two species placed in the community randomly at the beginning. Each time a tree is selected (a focal tree). If the number of heterospecifics around the focal tree in its neighborhood (i.e. surrounding eight patches) exceeds a certain number (HND-sp) then the focal tree dies due to intense HNND, otherwise focal tree is survived. This process creates empty patches in the community. These empty patches will be filled by the individuals from one of the species. However, the filling process should not violate the above rule that we mentioned (i.e. The number of heterospecifics, in the surrounding eight patches, should not exceeds the threshold value. If it exceeds the threshold value vacant site cannot be filled and should remained empty).

HOW TO USE IT

The model has four parameters.

Community size: J = (w₁ + 1)².
Initial abundance of species: 1 (N₁). HND-sp₁ is the maximum number of conspecifics that can be allowed in the eight neigboring patches for a stable community. Note: For example if HND-sp1 is equals to 5 then focal species-1 has 6, 7, or 8 heterospecific neighbors then the focal species-1 has to die.
Heterospecific density on focal species-1: (HND-sp₁).
Heterospecific density on focal species-2: (HND-sp₂).

The other parameter K, controls the stochastic drift, set to be 0. For more details about the parameter K refer the section "extending the model".

THINGS TO NOTICE

Stong HNND leave many vacant sites.

THINGS TO TRY

Reduce the threshold values (i.e. reduce HND-sp1 or HND-sp2 or both) to see the number of vacant sites increases.

One of the assumptions in neutral model is that "functional equivalence". Model becomes a neutral model when the HND-sp1 and HND-sp2 are equal.

EXTENDING THE MODEL

The model is extended by incorperating stochastic death process. Certain number of trees die (K) randomly in each time step. Maximum number of deaths per time step is equal to J - 1. When K = J the model becomes a non-overlapping genrations model.

NETLOGO FEATURES

RELATED MODELS

Gause, G.F. (1932). "Experimental studies on the struggle for existence: 1. Mixed population of two species of yeast". Journal of Experimental Biology, 9: 389–402.

CREDITS AND REFERENCES

Sareena, B. & Punchi-Manage, R. (2023l). NetLogo Interspecific Competition Model. http://netlogo/models/NetLogo-Interspecific-Competition-HNND-Model.

Please cite the NetLogo software as:

Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.

Comments and Questions

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Click to Run Model

globals [J N2 empty-patch-random K1 K2]
breed [species1 a-species1]
breed [species2 a-species2]
turtles-own [species]
patches-own [random-number]

to setup
  clear-all
  set J (w1 + 1) * (w1 + 1)
  resize-world 0 w1 0 w1
  set N2 J - N1
  set K1 N1
  set K2 J - N1
  while [K1 > 0] [
    set empty-patch-random one-of patches with [count turtles-here = 0]
    create-species1 1 [
      setxy [pxcor] of empty-patch-random [pycor] of empty-patch-random
      set species 1
      set size 1
      set shape "circle"
      set color orange
    ]
    set K1 K1 - 1
  ]
  while [K2 > 0] [
    set empty-patch-random one-of patches with [count turtles-here = 0]
    create-species2 1 [
      setxy [pxcor] of empty-patch-random [pycor] of empty-patch-random
      set species 2
      set size 1
      set shape "circle"
      set color blue
    ]
    set K2 K2 - 1
  ]
  reset-ticks
end 

to go
  species1-die
  species2-die
  ask turtles [species-birth]
  update-plots
  tick
end 

to species1-die
  ask species1 [
    let count-neighbors-species1 count species2-on neighbors
    if (count-neighbors-species1 > HND-sp2) [
      die
    ]
  ]
end 

to species2-die
  ask species2 [
    let count-neighbors-species2 count species1-on neighbors
    if (count-neighbors-species2 > HND-sp1) [
      die
    ]
  ]
end 

to species-birth
  let empty-patches count patches with [count turtles-here = 0]
  if (empty-patches > 0) [
    set empty-patch-random one-of patches with [count turtles-here = 0]
    ask empty-patch-random [
      let species1-count count species1-on neighbors
      let species2-count count species2-on neighbors
      if (species1-count < HND-sp1) [
        ask one-of species1 [
          hatch 1
          setxy [pxcor] of empty-patch-random [pycor] of empty-patch-random
        ]
      ]
      if (species2-count < HND-sp2) [
        ask one-of species2 [
          hatch 1
          setxy [pxcor] of empty-patch-random [pycor] of empty-patch-random
        ]
      ]
    ]
  ]
end 


; Copyright 2023l Sareena, B. & Ruwan Punchi-Manage.
; * Corresponding Email: spunchi@sci.pdn.ac.lk
; See Info tab for full copyright and license.

There is only one version of this model, created almost 2 years ago by Ruwan Punchi-Manage.

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