miguelppais_EcoModDay-netlogo/src/EcoModDay.nlogo
extensions [csv matrix]

globals[
  night
  wind

  ;regression coefficients:
  intercept
  c_age
  c_height
  c_sex
  c_wind
  c_night
  c_interference
  R^2
  r ; logistic growth rate
]

breed[indicators indicator]

breed[predators predator]

breed[preys prey]

patches-own[
  nutrients
]

predators-own[
  energy
  height
  sex
  age
  success-rate
]

preys-own[
  energy

]

to startup
  ifelse user-yes-or-no? "Hello! Welcome to the Ecological Modelling Day ABM! Did you put the input.csv file on the same folder as the model?" [
    user-message "Ok, the model will now extract the data from the csv and calculate a linear model of the variable effects on predation success. Everytime you press setup the model will recalculate the coefficients. Have fun!"
    setup]
  [user-message "Ok, so close the model and go do that first!" stop]
end


to setup
  ca
  stop-inspecting-dead-agents
  set-default-shape predators "person"
  set-default-shape preys "circle"
  import-data-and-calculate-effects
  generate-landscape
  populate-landscape
  reset-ticks
end


to go
  if not any? predators [user-message "All predators are dead" stop]
  if not any? preys [user-message "There is no prey left..." stop]
  update-wind
  if ticks mod 48 = 0 [update-light]
  ask predators [
    check-starvation
    ifelse any? preys-here [hunt] [move]
    reproduce-predator
  ]

  ask preys [
    check-starvation
    move
    forage
    reproduce-prey
  ]

  ask patches [regrow]



  tick
end


;; OBSERVER PROCEDURES

to import-data-and-calculate-effects
  let data remove-item 0 (csv:from-file "input.csv" ";") ;import and clip header
  set data matrix:from-column-list data ; transpose data so that matrix rows are now the variables
  set data matrix:to-row-list data ; convert back to list
  repeat 5 [set data remove-item 6 data] ; remove the attempts columns (we don't need them)
  set data matrix:from-column-list data
  matrix:swap-columns data 6 0
  ;show data  ; debug
  let regression matrix:regress data ; multiple regression with 7 explanatory vars
  ;show regression ; debug
  set intercept item 0 (item 0 regression)
  set c_age item 6 (item 0 regression)
  set c_height item 1 (item 0 regression)
  set c_sex item 2 (item 0 regression)
  set c_wind item 3 (item 0 regression)
  set c_night item 4 (item 0 regression)
  set c_interference item 5 (item 0 regression)
  set R^2 item 0 (item 1 regression)
end


to generate-landscape
  create-indicators 1 [  ; create the wind symbol
    setxy 28 28
    set size 2
    set shape "arrow"
    set color black
    set label word "wind " wind
  ]
  create-indicators 1 [ ; create the sun/moon symbol
    setxy 2 28
    set size 3
    set shape "sun"
    set color yellow
  ]

  set night 0
  set wind 0
  ask patches [
    set nutrients 10
    update-color
  ]
end

to populate-landscape
  create-predators initial-number-of-predators [
    set energy 100
    set sex one-of [1 2]
    move-to one-of patches
    set height (random-between 150 200)
    set size height / 200
    set success-rate fixed-success-rate ;starting conditions
    ifelse sex = 2 [set color blue] [set color pink]
  ]

  create-preys initial-number-of-prey [
    set color white
    set size 0.3
    set energy 100
    move-to one-of patches


  ]

end




;; TURTLE PROCEDURES

to check-starvation
  if energy < 0 [die]
end

to move
  move-to one-of neighbors
  set energy energy - movement-cost
end

to reproduce-prey
if energy > 200 [
    set energy energy - 100
    hatch 1 [
      set energy 100
    ]
  ]
end

to reproduce-predator
if energy > 200 [
    set energy energy - 100
    hatch 1 [
      set energy 100
      set sex one-of [1 2]
      ifelse sex = 2 [set color blue] [set color pink]
      set height (random-between 150 200)
      set size height / 200
    ]
  ]
end

to hunt
  if any? preys-here [
    let interfered 0
    if any? predators-here [set interfered 1]
    let wind-convert (wind / 8)  ; wind will be 1 only at strength 8, otherwise we scale it down
    ifelse fixed-success-rate? [set success-rate fixed-success-rate] [set success-rate (intercept + (c_age * age)  + (c_height * height) + (c_sex * sex) + (c_night * night) + (c_interference * interfered) + (c_wind * wind-convert))]
    let food one-of preys-here
    if bernoulli success-rate [
      ask food [die]
      set energy energy + energy-from-prey
    ]
  ]
end

to forage
  if ( nutrients >= energy-from-nutrients ) [
    set energy energy + energy-from-nutrients
    set nutrients nutrients - energy-from-nutrients
    ask patch-here [update-color]
  ]
end

to update-wind
  set wind wind + random-between -1 1
  if wind > 8 [set wind 8]
  if wind < 0 [set wind 0]
  ask indicator 0 [
    set heading heading + random-between -2 2
    set label word "wind " wind
  ]
end

to update-light
  ifelse night = 1 [
    ask indicator 1 [set shape "sun" set color yellow]
    set night 0
  ] [
    ask indicator 1 [set shape "moon" set color gray]
    set night 1
  ]

end

;; PATCH PROCEDURES

to regrow
  set nutrients nutrients + nutrients-growth-rate
  if nutrients > 10.0 [set nutrients 10.0]
  update-color
end

to update-color
  set pcolor scale-color green (10 - nutrients) -10 20
end

to sexual-reproduction


end

;; REPORTERS

to-report random-between [low high]
  report random (high - low + 1) + low
  end

to-report bernoulli [p]
  report random-float 1 < p
end
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set initial-number-of-predators 50\nset initial-number-of-prey 200\nset nutrients-growth-rate 1.5\nset movement-cost 0.5\nset energy-from-prey 4\nset energy-from-nutrients 2.6\nset fixed-success-rate? false\nset fixed-success-rate 0.5
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Agent-based predator-prey model  -  Ecological Modelling Day, FCUL
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Author:\nMiguel Pessanha Pais, MARE (mppais@fc.ul.pt)
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@#$#@#$#@
## WHAT IS IT?

This is a simple predator-prey model created to support an activity for the Ecological Modelling Day at the Faculty of Sciences, Univeristy of Lisbon.

It uses data gathered from people performing a challenge (throwing a ball at a hole) to determine the predation success rate. Several external factors are acting upon the person, namely night, wind and interference from another person. The effects of these factors are combined using a multiple linear regression.

## HOW IT WORKS



## HOW TO USE IT



## THINGS TO NOTICE

Notice how the average predation success changes with external factors, also notice how the populations of prey, predators and "grass" change and oscillate with time.

Notice what's happenning at different trime scales. The individual movement and preying decisions, the day and night cycle, the population cycles. How are they affected by variables?

## THINGS TO TRY

Change a few variables and try to see how it affects population changes. Can you keep the system going or does it collapse? Why did it collapse? Turn off "update changes" to run the model at maximum speed.

## EXTENDING THE MODEL

The model can be extended to include evolution of the most successful attributes.

## NETLOGO FEATURES

This model uses the csv extension to import data from a csv file and the matrix extension to convert the csv data to a matrix format and perform an ordinary least squares regression.

## RELATED MODELS

This model uses concepts from predator-prey models found in the model library, particularly the model from chapter 4 of the ABM book.

## CREDITS AND REFERENCES

(c) Miguel Pessanha Pais 2019

Model created under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License. For more info go to https://creativecommons.org/licenses/by-nc-sa/4.0/
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