Intraspecific Competition Model-CNDD

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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 conspecific negative density dependence (CNND) can maintain the species richness at local spatial scales (i.e. tree neighborhood). This model illustrates the CNND (intraspecific 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 conspecifics around the focal tree in its neighborhood (i.e. surrounding eight patches) exceeds a certain number (CND-sp) then the focal tree dies due to intense CNND, 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 conspecifics, 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.

  1. Community size: J = (w1 + 1)2.

  2. Initial abundance of species: 1 (N1). CND-sp1 is the maximum number of conspecifics that can be allowed in the eight neigboring patches for a stable community. For example if CND-sp1 is equal to 5 then focal species-1 has 6, 7, or 8 conspecific neighbors then the focal species-1 has to die.

  3. Conspecific density on focal species-1: (CND-sp1).

  4. Conspecific density on focal species-2: (CND-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 CNND leave many vacant sites.

THINGS TO TRY

Reduce the threshold values (i.e. reduce CND-sp1 or CND-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 CND-sp1 and CND-sp2 are equal.

EXTENDING THE MODEL

The model is extended by incorperating stochastic death process. Certain number of trees die (K) randomly. 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.

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Click to Run Model 

globals [J N2 empty-patch-random K1 K2 K3]
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]
  stochastic-death
  update-plots
  tick
end 

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

to species2-die
  ask species2 [
    let count-neighbor-species2 count species2-on neighbors
    if (count-neighbor-species2 > CND-sp2) [
      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 < CND-sp1) [
        ask one-of species1 [
          hatch 1
          setxy [pxcor] of empty-patch-random [pycor] of empty-patch-random
        ]
      ]
      if (species2-count < CND-sp2) [
        ask one-of species2 [
          hatch 1
          setxy [pxcor] of empty-patch-random [pycor] of empty-patch-random
        ]
      ]
    ]
  ]
end 

to stochastic-death
  if-else (non-overlapping-genrations? = true) [
    set K J
    setup
  ]
  [
    set K3 K
    while [K3 > 0][
      ask one-of turtles [
        die
      ]
      set K3 K3 - 1
    ]
  ]
end 


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

There are 3 versions of this model.

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Ruwan Punchi-Manage almost 2 years ago Web version Download this version
Ruwan Punchi-Manage almost 2 years ago Web version Download this version
Ruwan Punchi-Manage almost 2 years ago Initial upload Download this version

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