breed [males male]
breed [females female]

globals
 [
  ; The following are all controled by sliders/switches on the interface tab:
   ; carrying-capacity  ; The number of individuals in the population
   ; mutant-proportion   ; The proportion of individuals that carry one new mutation in their genome, every generation
   ; genome-length   ; The number of loci in each chromosome
   ; preference-strength   ; The factor determining the addition of each preference mutation to the strength of the preference (i.e the slope of the probability to mate function)
   ; recombination    ; Determines whether or not the genome goes through recombination
   ; selection-coefficient   ; Determines selection force against the AB genotype. Equals the proportion of individuals with an AB genotype that die during selection.
   ; max-matching-attempts   ; The maximum number of females that a male can refuse to mate with before he is taken out of the mating pool.
                               ;This variable is included to prevent the simulations from getting stuck because none of the females are suitable for a single male.
   genotyped-AA ; An agentset that holds all turtles with an AA genotype
   genotyped-AB ; An agentset that holds all turtles with an AB genotype
   genotyped-BB ; An agentset that holds all turtles with a BB genotype

   ; Creating agent sets comprised of all turtles that have at least one nuetral / preference locus in their genome, separately for each genotype and locus type (Pa-preference for AA; Pb-preference for BB):
  AA-turtles-with-Pa-loci
  AA-turtles-with-Pb-loci
  AA-turtles-with-nuetral-loci
  BB-turtles-with-Pa-loci
  BB-turtles-with-Pb-loci
  BB-turtles-with-nuetral-loci
  AB-turtles-with-Pa-loci
  AB-turtles-with-Pb-loci
  AB-turtles-with-nuetral-loci

; For analysing the simulation results:
  how-many-AA-mated-with-AA   ; Holds a new value every generation
  how-many-BB-mated-with-BB   ; -"-
  how-many-mating-pairs ; -"-
  proportion-unmated-males; -"-

 ]
patches-own []
turtles-own
 [
  homolog1
  homolog2
  mating-status ; Can be one of: 'unmated', 'mated' or 'no mate found'
  genotype ; Determined based on the letters at the first locus of homolog1 and homolog2 (either: AA, AB or BB)
  mating-preference ; Determined by the sum of Pa and Pb mutations in the genome.
                      ; Pa mutations (preference for AA genotype) are summed positively and Pb mutations (preference for BB genotype) are summed negatively
                      ; For example, if an individual has 1 Pa and 1 Pb in his genome, they cancel each other out and his mating preference will equal zero (no mating preference)
  prob-AA   ; The probability to mate with an AA genotyped individual. Calculated from the values of mating-preference and preference strength.
  prob-BB   ; The probability to mate with a BB genotyped individual. -"-
  Pa-count  ; Counts the number of Pa mutations in the entire genome (both chromosomes)
  Pb-count  ; Counts the number of Pb mutations in the entire genome (both chromosomes)
  nuetral-count ; Counts the number of nuetral mutations in the entire genome (both chromosomes)
  Pa-average-distance ; holds the average distance of all preference for AA loci from the trait locus in both chromosomes
  Pb-average-distance ; holds the average distance of all preference for BB loci from the trait locus in both chromosomes
  nuetral-average-distance ; holds the average distance of all nuetral loci from the trait locus in both chromosomes

;  subpopulation  ; Removed. Used only for the non-spatial model version. Can be either: '1' or '2'. Divides the individuals into two subpopulations. Selection favors A alleles in subpopulation '1' and B alleles in subpopulation '2'.
 ]

to setup
  ca
  set how-many-AA-mated-with-AA []
  set how-many-BB-mated-with-BB []
  set how-many-mating-pairs []
  set proportion-unmated-males []

  ask patches
  [if pxcor > (max-pxcor - 1) / 2 [set pcolor white]]  ; Splits the arena into two separate habitats. Selection gives an advantage to the AA genotype in the white habitat and to the BB genotype in the black habitat

  create-turtles carrying-capacity
  [setxy random-xcor random-ycor
   set size 1.5
   set mating-status "unmated"

 ; Setting each of the two homologous chromosomes so that the first gene, controling the trait, will be randomly assigned one of the triat alleles (A or B):
   set homolog1 n-values genome-length ["-"]
    set homolog1 replace-item 0 homolog1 (one-of (list "A" "B"))
   set homolog2 n-values genome-length ["-"]
    set homolog2 replace-item 0 homolog2 (one-of (list "A" "B"))
   set genotype word (item 0 homolog1) (item 0 homolog2)
    if genotype = "BA" [set genotype "AB"]  ; Unifies all heterozygotes under the genotype AB

 ; Homozygotes for A will be blue, homozygotes for B will be yellow, and heterozygotes will be green (intermediate between blue and yellow):
   if genotype = "AA" [set color blue]
   if genotype = "AB" [set color green]
   if genotype = "BB" [set color yellow]

  ]

  set genotyped-AA turtles with [genotype = "AA"]
  set genotyped-AB turtles with [genotype = "AB"]
  set genotyped-BB turtles with [genotype = "BB"]

;   output-print word "number of A alleles on homolog1: " count turtles with [(item 0 homolog1) = "A"]
;   output-print word "number of B alleles on homolog1: " count turtles with [(item 0 homolog1) = "B"]
;   output-print word "number of A alleles on homolog2: " count turtles with [(item 0 homolog2) = "A"]
;   output-print word "number of B alleles on homolog2: " count turtles with [(item 0 homolog2) = "B"]

;Initializing the population to be half males half females:
  ask n-of (carrying-capacity / 2) turtles [set breed males]
  ask turtles with [breed != males] [set breed females]


; Adding mutations that are either random ("0") or that control mating preferences (Pa, preference for A or Pb, preference for B):
   ; The mutations occur in random individuals, at random places throughout the genome, at a rate derived from the 'mutant-proportion' variable (see 'globals')
   ; A mutation can occur in all loci except the first locus, that controls the trait

  ask n-of (carrying-capacity * mutant-proportion) turtles
  [let mutation-place ((random (genome-length - 1)) + 1)  ; randomly choosing a locus within the chromosome, excluding the first locus, that controls the trait.
            ; ('genome-length' - 1) reduces the number of places from which to choose by one, and then +1 shifts the places to exclude zero but include the last place in the list.
   ; randomly choosing one of the two chromosomes to add the mutation to:
    ifelse (random 2) = 0 ; randomly chooses 0 or 1, then if equals 0 adds the mutation to homolog1 and if equals 1 adds it to homolog2
      [set homolog1 replace-item mutation-place homolog1 (one-of (list "0" "Pa" "Pb"))
  ;      output-print (word "ind. " who ", homolog 1: " homolog1)
      ]
      [set homolog2 replace-item mutation-place homolog2 (one-of (list "0" "Pa" "Pb"))
  ;      output-print (word "ind. " who ", homolog 2: " homolog2)
      ]
  ]


;  output-print word "number of females: " count females
;  output-print word "number of males: " count males

  ask turtles
  [
    set nuetral-count (length (filter [i -> i = "0"] homolog1) + length (filter [i -> i = "0"] homolog2))
    set Pa-count (length (filter [i -> i = "Pa"] homolog1) + length (filter [i -> i = "Pa"] homolog2))
    set Pb-count (length (filter [i -> i = "Pb"] homolog1) + length (filter [i -> i = "Pb"] homolog2))
    set mating-preference (Pa-count - Pb-count)
    set prob-AA (1 / (1 + e ^ (- preference-strength * mating-preference))) ; Calculate the Probability to mate with an AA genotype female
    set prob-BB (1 - prob-AA)                                               ; Calculate the Probability to mate with a BB genotype female

  ; Creating a list that holds the distances of all nuetral mutations from the trait locus in both chromosomes, and then calculating the average for the list:
    let nuetral-distances (list)
    ifelse nuetral-count > 0
      [let locus 0
        foreach homolog1 [i -> ifelse i = "0"
                          [ifelse empty? nuetral-distances
                            [set nuetral-distances (list locus)]
                            [set nuetral-distances fput locus nuetral-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
       set locus 0
        foreach homolog2 [i -> ifelse i = "0"
                          [ifelse empty? nuetral-distances
                            [set nuetral-distances (list locus)]
                            [set nuetral-distances fput locus nuetral-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
      set nuetral-average-distance mean nuetral-distances
      ]
      [set nuetral-average-distance []]

  ; Creating a list that holds the distances of all preference for AA loci from the trait locus in both chromosomes, and then calculating the average for the list:
    let Pa-distances (list)
    ifelse Pa-count > 0
      [let locus 0
        foreach homolog1 [i -> ifelse i = "Pa"
                          [ifelse empty? Pa-distances
                            [set Pa-distances (list locus)]
                            [set Pa-distances fput locus Pa-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
       set locus 0
        foreach homolog2 [i -> ifelse i = "Pa"
                          [ifelse empty? Pa-distances
                            [set Pa-distances (list locus)]
                            [set Pa-distances fput locus Pa-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
      set Pa-average-distance mean Pa-distances
      ]
      [set Pa-average-distance []]

  ; Creating a list that holds the distances of all preference for BB loci from the trait locus in both chromosomes, and then calculating the average for the list:
    let Pb-distances (list)
    ifelse Pb-count > 0
      [let locus 0
        foreach homolog1 [i -> ifelse i = "Pb"
                          [ifelse empty? Pb-distances
                            [set Pb-distances (list locus)]
                            [set Pb-distances fput locus Pb-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
       set locus 0
        foreach homolog2 [i -> ifelse i = "Pb"
                          [ifelse empty? Pb-distances
                            [set Pb-distances (list locus)]
                            [set Pb-distances fput locus Pb-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
      set Pb-average-distance mean Pb-distances
      ]
      [set Pb-average-distance []]


;   output-print homolog1
;   output-print homolog2
;   output-print genotype
;   output-print mating-preference

  ]

  set AA-turtles-with-Pa-loci genotyped-AA with [Pa-count > 0]
  set AA-turtles-with-Pb-loci genotyped-AA with [Pb-count > 0]
  set AA-turtles-with-nuetral-loci genotyped-AA with [nuetral-count > 0]
  set BB-turtles-with-Pa-loci genotyped-BB with [Pa-count > 0]
  set BB-turtles-with-Pb-loci genotyped-BB with [Pb-count > 0]
  set BB-turtles-with-nuetral-loci genotyped-BB with [nuetral-count > 0]
  set AB-turtles-with-Pa-loci genotyped-AB with [Pa-count > 0]
  set AB-turtles-with-Pb-loci genotyped-AB with [Pb-count > 0]
  set AB-turtles-with-nuetral-loci genotyped-AB with [nuetral-count > 0]

  reset-ticks
end

to go
  tick
  find-mate
  reproduce
  end-generation
  recombinate
  mutate
  select
  regulate-population-size

; Outputing the genomes of all individuals:
; if ticks = 250 ; or ticks = 800
;  [
;  output-print word "ticks: " ticks
;    output-print word "average distance of Pa mutations to A: " mean [Pa-average-distance] of AA-turtles-with-Pa-loci
;  output-print word "average distance of Pb mutations to B: " mean [Pb-average-distance] of BB-turtles-with-Pb-loci
;   ask turtles
;    [let place 0
;      while [place < 100]
;      [ifelse place = 99
;        [output-print item place homolog1]
;        [output-type word item place homolog1 ","]
;       set place place + 1
;      ]
;     set place 0
;      while [place < 100]
;      [ifelse place = 99
;        [output-print item place homolog2]
;        [output-type word item place homolog2 ","]
;       set place place + 1
;      ]
;    ]
;    if ticks = 250 [stop]
; ]

; output-print "" ; to make a clear separation between generations in the output window
end

to find-mate   ;;; THIS IS THE MOST TIME CONSUMING PROCEDURE ;;;
; reset-timer ; used to test which procedures are slowing the model down
  set how-many-mating-pairs 0
  let unmated-males males with [mating-status = "unmated"]
  let unmated-females females with [mating-status = "unmated"]
  while [(count unmated-males > 0) and (count unmated-females > 0)]  ; Continue matching males with females until either males or females have all found or attempted to find a mate
   [ask one-of unmated-males
     [let non-matches 0
       while [mating-status = "unmated"]
        [let female-candidate one-of unmated-females
         set prob-AA (1 / (1 + e ^ (- preference-strength * mating-preference))) ; Calculate the Probability to mate with an AA genotype female
         set prob-BB (1 - prob-AA)                                               ; Calculate the Probability to mate with a BB genotype female

         if [genotype] of female-candidate = "AA"
          [
           ifelse random-float 1 < prob-AA
           [create-link-with female-candidate
            set mating-status "mated"
              set unmated-males unmated-males with [mating-status = "unmated"]  ; quicker than filtering through the entire male angentset
            ask female-candidate [set mating-status "mated"]
              set unmated-females unmated-females with [mating-status = "unmated"] ; quicker than filtering through the entire female agentset
            set how-many-mating-pairs how-many-mating-pairs + 1
             ; For testing:
             ; output-print (word "female genotype" ";" [genotype] of female-candidate ";" "mating preference" ";" mating-preference ";" "chosen")
           ]
           [set non-matches non-matches + 1
            if non-matches = max-matching-attempts [set mating-status "no mate found"] ; After reaching the maximum number of matching attempts, the male is taken out of the mating pool, to prevent the simulation run from getting stuck
              set unmated-males unmated-males with [mating-status = "unmated"]
             ; For testing:
             ; output-print (word "female genotype" ";" [genotype] of female-candidate ";" "mating preference" ";" mating-preference ";" "rejected")
           ]
          ]

         if [genotype] of female-candidate = "BB"
          [
           ifelse random-float 1 < prob-BB
           [create-link-with female-candidate
            set mating-status "mated"
              set unmated-males unmated-males with [mating-status = "unmated"]
            ask female-candidate [set mating-status "mated"]
              set unmated-females unmated-females with [mating-status = "unmated"]
            set how-many-mating-pairs how-many-mating-pairs + 1
             ; For testing:
             ; output-print (word "female genotype" ";" [genotype] of female-candidate ";" "mating preference" ";" mating-preference ";" "chosen")
           ]
           [set non-matches non-matches + 1
            if non-matches = max-matching-attempts [set mating-status "no mate found"] ; After reaching the maximum number of matching attempts, the male is taken out of the mating pool, to prevent the simulation run from getting stuck
              set unmated-males unmated-males with [mating-status = "unmated"]
             ; For testing:
             ; output-print (word "female genotype" ";" [genotype] of female-candidate ";" "mating preference" ";" mating-preference ";" "rejected")
           ]
          ]

         if [genotype] of female-candidate = "AB"
          [
           ifelse random-float 1 < 0.5
           [create-link-with female-candidate
            set mating-status "mated"
              set unmated-males unmated-males with [mating-status = "unmated"]
            ask female-candidate [set mating-status "mated"]
              set unmated-females unmated-females with [mating-status = "unmated"]
            set how-many-mating-pairs how-many-mating-pairs + 1
             ; For testing:
             ; output-print (word "female genotype" ";" [genotype] of female-candidate ";" "mating preference" ";" mating-preference ";" "chosen")
           ]
           [set non-matches non-matches + 1
             if non-matches = max-matching-attempts [set mating-status "no mate found"] ; After reaching the maximum number of matching attempts, the male is taken out of the mating pool, to prevent the simulation run from getting stuck
              set unmated-males unmated-males with [mating-status = "unmated"]
              ; For testing:
              ; output-print (word "female genotype" ";" [genotype] of female-candidate ";" "mating preference" ";" mating-preference ";" "rejected")
           ]
          ]

       ;  output-print (word "no. of Pa mutations" ";" Pa-count ";" "no. of Pb mutations" ";" Pb-count ";" "mating preference" ";" mating-preference ";" "Prob-AA" ";" Prob-AA ";" "Prob-BB" ";" Prob-BB)

        ]
     ]

   ]
;  output-print word "mated males: " count males with [mating-status = "mated"]
;  output-print word "mated females: " count females with [mating-status = "mated"]
;  output-print word "unmated males: " count males with [mating-status = "unmated"]
;  output-print word "unmated females: " count females with [mating-status = "unmated"]
;  output-print word "males without a match: " count males with [mating-status = "no mate found"]

  ask turtles [if mating-status = "unmated" [set mating-status "no mate found"]]  ; To make sure the mating status of the entire generation is either "mated" or "no mate found"
                                                                                   ; so that when the new generation is produced (and are all "unmated") they can be distinguished from the old generation
 ; For testing the proportion of non-mated males:
  set proportion-unmated-males (count males with [mating-status = "no mate found"]) / count males

 ; To test which procedures are slowing down the model:
;  output-print word "find-mate procedure duration: " timer
end


to reproduce
; reset-timer ; used to test which procedures are slowing the model down

  set how-many-AA-mated-with-AA 0  ; For model result analysis
  set how-many-BB-mated-with-BB 0  ; -"-

  let mating-male []
  let mating-female []
  ask males with [mating-status = "mated"]
   [set mating-male self
    ask my-links [set mating-female other-end]  ; setting the female partner to be the female at the other end of the link, i.e. the female the male chose to mate with during the 'find-mate' procedure
  ; For testing that the pairs are found successfully:
  ;  let new-color random 140
  ;  set color new-color
  ;  ask mating-female [set color new-color]

  ; For model result analysis:
      if [genotype] of mating-male = "AA" and [genotype] of mating-female = "AA" [set how-many-AA-mated-with-AA (how-many-AA-mated-with-AA + 1)]
      if [genotype] of mating-male = "BB" and [genotype] of mating-female = "BB" [set how-many-BB-mated-with-BB (how-many-BB-mated-with-BB + 1)]

  ; Each pair produces 4 offspring, two males and two females:
    hatch-males 2
      [set homolog1 one-of list ([homolog1] of mating-male) ([homolog2] of mating-male)      ; homolog1 will be from paternal origin (randomly chosen from the father's two homologous chromosomes)
       set homolog2 one-of list ([homolog1] of mating-female) ([homolog2] of mating-female)  ; homolog2 will be from maternal origin (randomly chosen from the mother's two homologous chromosomes)
       set mating-status "unmated"
;       set subpopulation one-of list "1" "2"
;       setxy random-xcor random-ycor    ; cancelled, to keep the offspring in the same habitat as the male
       set size 1.5

    ; For testing that the offspring are produced properly:
     ; output-print word "male homolog1: " [homolog1] of mating-male
     ; output-print word "male homolog2: " [homolog2] of mating-male
     ; output-print word "female homolog1: " [homolog1] of mating-female
     ; output-print word "female homolog2: " [homolog2] of mating-female
     ; output-print word "offspring homolog1: " homolog1
     ; output-print word "offspring homolog2: " homolog2
     ; output-print word "offspring mating-preference: " mating-preference
     ; output-print " "
      ]

     hatch-females 2
      [set homolog1 one-of list ([homolog1] of mating-male) ([homolog2] of mating-male)      ; homolog1 will be from paternal origin (randomly chosen from the father's two homologous chromosomes)
       set homolog2 one-of list ([homolog1] of mating-female) ([homolog2] of mating-female)  ; homolog2 will be from maternal origin (randomly chosen from the mother's two homologous chromosomes)
       set mating-status "unmated"
;       set subpopulation one-of list "1" "2"
;       setxy random-xcor random-ycor  ; cancelled, to keep the offspring in the same habitat as the male
       set size 1.5
      ]
   ]
; To test which procedures are slowing down the model:
;  output-print word "reproduce procedure duration: " timer
end


to end-generation
; reset-timer ; used to test which procedures are slowing the model down

  ask turtles [if mating-status = "mated" or mating-status = "no mate found" [die]]  ;  Ends the previous generation, as only the offspring now have the status "unmated"
; To test which procedures are slowing down the model:
;  output-print word "end-generation procedure duration: " timer
end

to recombinate  ;;; THIS IS THE THIRD MOST TIME CONSUMING PROCEDURE ;;;
; reset-timer ; used to test which procedures are slowing the model down

  if recombination = true
  [ask turtles
    [let cross-over-point random genome-length
     let temp-list homolog2   ; sets the temporary list to be a copy of homolog2
   ; For testing the recombination proccess:
    ; output-print word "initial homolog1: " homolog1
    ; output-print word "initial homolog2: " homolog2

   ; Transfer of first chromosome genes, beyond the cross-over point, to the temporary list for storage.
    ; The temporary list will then hold the future homolog2 sequence:
     let current-gene 0
     foreach temp-list
      [
       ifelse current-gene < cross-over-point
        [set current-gene current-gene + 1]
        [set temp-list replace-item current-gene temp-list (item current-gene homolog1)
         set current-gene current-gene + 1]
      ]

   ; Transfer of second chromosome genes, beyond the cross-over point, to the first chromosome:
     set current-gene 0
     foreach homolog1
      [
       ifelse current-gene < cross-over-point
        [set current-gene current-gene + 1]
        [set homolog1 replace-item current-gene homolog1 (item current-gene homolog2)
         set current-gene current-gene + 1]
      ]

   ; Transfer of first chromosome genes, that are stored in the temporary list, to the second chromosome:
     set homolog2 temp-list

   ; For testing the recombination proccess:
    ; output-print word "cross-over point: " cross-over-point
    ; output-print word "final homolog1: " homolog1
    ; output-print word "final homolog2: " homolog2
    ; output-print " "

    ]
  ]

 set genotyped-AA turtles with [genotype = "AA"]
 set genotyped-AB turtles with [genotype = "AB"]
 set genotyped-BB turtles with [genotype = "BB"]

; To test which procedures are slowing down the model:
;  output-print word "recombinate procedure duration: " timer
end


to mutate
; reset-timer ; used to test which procedures are slowing the model down

 ask n-of (count turtles * mutant-proportion) turtles
  [let mutation-place ((random (genome-length - 1)) + 1)  ; randomly choosing a locus within the chromosome, excluding the first locus, that controls the trait.
            ; ('genome-length' - 1) reduces the number of places from which to choose by one, and then +1 shifts the places to exclude zero but include the last place in the list.
   ; output-print word "old homolog1: " homolog1
   ; output-print word "old homolog2: " homolog2

  ; randomly choosing one of the two chromosomes to add the mutation to:
    ifelse (random 2) = 0 ; randomly chooses 0 or 1, then if equals 0 adds the mutation to homolog1 and if equals 1 adds it to homolog2
      [set homolog1 replace-item mutation-place homolog1 (one-of (list "0" "Pa" "Pb"))]
      [set homolog2 replace-item mutation-place homolog2 (one-of (list "0" "Pa" "Pb"))]

   ; output-print word "new homolog1: " homolog1
   ; output-print word "new homolog2: " homolog2
   ; output-print " "

  ]

  ask turtles
  [set genotype word (item 0 homolog1) (item 0 homolog2)
         if genotype = "BA" [set genotype "AB"]  ; Unifies all heterozygotes under the genotype AB
         if genotype = "AA" [set color blue]
         if genotype = "AB" [set color green]
         if genotype = "BB" [set color yellow]

    set nuetral-count (length (filter [i -> i = "0"] homolog1) + length (filter [i -> i = "0"] homolog2))
    set Pa-count (length (filter [i -> i = "Pa"] homolog1) + length (filter [i -> i = "Pa"] homolog2))
    set Pb-count (length (filter [i -> i = "Pb"] homolog1) + length (filter [i -> i = "Pb"] homolog2))
    set mating-preference (Pa-count - Pb-count)

  ; Creating a list that holds the distances of all nuetral mutations from the trait locus in both chromosomes, and then calculating the average for the list:
    let nuetral-distances (list)
    ifelse nuetral-count > 0
      [let locus 0
        foreach homolog1 [i -> ifelse i = "0"
                          [ifelse empty? nuetral-distances
                            [set nuetral-distances (list locus)]
                            [set nuetral-distances fput locus nuetral-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
       set locus 0
        foreach homolog2 [i -> ifelse i = "0"
                          [ifelse empty? nuetral-distances
                            [set nuetral-distances (list locus)]
                            [set nuetral-distances fput locus nuetral-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
      set nuetral-average-distance mean nuetral-distances
      ]
      [set nuetral-average-distance []]

  ; Creating a list that holds the distances of all preference for AA loci from the trait locus in both chromosomes, and then calculating the average for the list:
    let Pa-distances (list)
    ifelse Pa-count > 0
      [let locus 0
        foreach homolog1 [i -> ifelse i = "Pa"
                          [ifelse empty? Pa-distances
                            [set Pa-distances (list locus)]
                            [set Pa-distances fput locus Pa-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
       set locus 0
        foreach homolog2 [i -> ifelse i = "Pa"
                          [ifelse empty? Pa-distances
                            [set Pa-distances (list locus)]
                            [set Pa-distances fput locus Pa-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
      set Pa-average-distance mean Pa-distances
      ]
      [set Pa-average-distance []]

  ; Creating a list that holds the distances of all preference for BB loci from the trait locus in both chromosomes, and then calculating the average for the list:
    let Pb-distances (list)
    ifelse Pb-count > 0
      [let locus 0
        foreach homolog1 [i -> ifelse i = "Pb"
                          [ifelse empty? Pb-distances
                            [set Pb-distances (list locus)]
                            [set Pb-distances fput locus Pb-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
       set locus 0
        foreach homolog2 [i -> ifelse i = "Pb"
                          [ifelse empty? Pb-distances
                            [set Pb-distances (list locus)]
                            [set Pb-distances fput locus Pb-distances]
                                set locus locus + 1
                          ]
                          [set locus locus + 1]
                         ]
      set Pb-average-distance mean Pb-distances
      ]
      [set Pb-average-distance []]
  ]

  set AA-turtles-with-Pa-loci genotyped-AA with [Pa-count > 0]
  set AA-turtles-with-Pb-loci genotyped-AA with [Pb-count > 0]
  set AA-turtles-with-nuetral-loci genotyped-AA with [nuetral-count > 0]
  set BB-turtles-with-Pa-loci genotyped-BB with [Pa-count > 0]
  set BB-turtles-with-Pb-loci genotyped-BB with [Pb-count > 0]
  set BB-turtles-with-nuetral-loci genotyped-BB with [nuetral-count > 0]
  set AB-turtles-with-Pa-loci genotyped-AB with [Pa-count > 0]
  set AB-turtles-with-Pb-loci genotyped-AB with [Pb-count > 0]
  set AB-turtles-with-nuetral-loci genotyped-AB with [nuetral-count > 0]

; To test which procedures are slowing down the model:
;  output-print word "mutate procedure duration: " timer
end

to select
; reset-timer ; used to test which procedures are slowing the model down

;   if ticks = 1 [output-print "pcolor;pre-AA;pre-AB;pre-BB;post-AA;post-AB;post-BB" ]
;  output-type (word "black;" count turtles with [genotype = "AA" and pcolor = black] ";" count turtles with [genotype = "AB" and pcolor = black] ";" count turtles with [genotype = "BB" and pcolor = black] ";" )

   ask n-of (selection-coefficient * count turtles with [genotype = "AA" and pcolor = black]) turtles with [genotype = "AA" and pcolor = black] [die]
   ask n-of (selection-coefficient * count turtles with [genotype = "AB" and pcolor = black]) turtles with [genotype = "AB" and pcolor = black] [die]
;  output-print (word count turtles with [genotype = "AA" and pcolor = black] ";" count turtles with [genotype = "AB" and pcolor = black] ";" count turtles with [genotype = "BB" and pcolor = black] )
;  output-type (word "white;" count turtles with [genotype = "AA" and pcolor = white] ";" count turtles with [genotype = "AB" and pcolor = white] ";" count turtles with [genotype = "BB" and pcolor = white] ";" )
   ask n-of (selection-coefficient * count turtles with [genotype = "BB" and pcolor = white]) turtles with [genotype = "BB" and pcolor = white] [die]
   ask n-of (selection-coefficient * count turtles with [genotype = "AB" and pcolor = white]) turtles with [genotype = "AB" and pcolor = white] [die]
;  output-print (word count turtles with [genotype = "AA" and pcolor = white] ";" count turtles with [genotype = "AB" and pcolor = white] ";" count turtles with [genotype = "BB" and pcolor = white] )

; To test which procedures are slowing down the model:
;  output-print word "select procedure duration: " timer
end

to regulate-population-size  ; Kills turtles in excess of the carrying capacity, in each of the two habitats seperately
; reset-timer ; used to test which procedures are slowing the model down

  let white-habitat-pop-size count turtles with [pcolor = white]
   if white-habitat-pop-size >= (carrying-capacity / 2)
    [let chance-to-die-white (white-habitat-pop-size - (carrying-capacity / 2)) / white-habitat-pop-size
     ask turtles with [pcolor = white]
     [if random-float 1 < chance-to-die-white [die] ]
    ]
;  output-print word "white habitat pop size: " count turtles with [pcolor = white]

  let black-habitat-pop-size count turtles with [pcolor = black]
   if black-habitat-pop-size >= (carrying-capacity / 2)
    [let chance-to-die-black (black-habitat-pop-size - (carrying-capacity / 2)) / black-habitat-pop-size
     ask turtles with [pcolor = black]
     [if random-float 1 < chance-to-die-black [die] ]
    ]
;  output-print word "black habitat pop size: " count turtles with [pcolor = black]

; To test which procedures are slowing down the model:
;  output-print word "regulate-population-size procedure duration: " timer
end
@#$#@#$#@
GRAPHICS-WINDOW
33
15
457
440
-1
-1
13.0
1
10
1
1
1
0
1
1
1
0
31
0
31
1
1
1
ticks
30.0

BUTTON
1075
35
1138
68
NIL
setup
NIL
1
T
OBSERVER
NIL
NIL
NIL
NIL
1

BUTTON
1159
35
1222
69
NIL
go
T
1
T
OBSERVER
NIL
NIL
NIL
NIL
1

BUTTON
1248
36
1311
69
step
go
NIL
1
T
OBSERVER
NIL
NIL
NIL
NIL
1

SLIDER
1109
93
1281
126
carrying-capacity
carrying-capacity
2
10000
10000.0
2
1
NIL
HORIZONTAL

SLIDER
1109
183
1281
216
genome-length
genome-length
0
100
100.0
1
1
NIL
HORIZONTAL

SLIDER
1108
136
1280
169
mutant-proportion
mutant-proportion
1 / carrying-capacity
1
0.01
1 / carrying-capacity
1
NIL
HORIZONTAL

SLIDER
1111
227
1283
260
preference-strength
preference-strength
0
6
0.3
0.1
1
NIL
HORIZONTAL

SLIDER
1109
478
1290
511
max-matching-attempts
max-matching-attempts
0
100
2.0
1
1
NIL
HORIZONTAL

SWITCH
1128
282
1261
315
recombination
recombination
0
1
-1000

SLIDER
1112
324
1284
357
selection-coefficient
selection-coefficient
0
1
0.5
0.1
1
NIL
HORIZONTAL

PLOT
490
526
750
676
Histogram - mating preference
mating preference
Number of individuals
0.0
10.0
0.0
70.0
true
false
"set-plot-x-range -20 20\nset-plot-y-range 0 70" ""
PENS
"default" 1.0 0 -13345367 true "" "histogram [mating-preference] of turtles with [genotype = \"AA\"]"
"pen-1" 1.0 0 -1184463 true "" "histogram [mating-preference] of turtles with [genotype = \"BB\"]"

PLOT
487
326
793
506
Mating Preference
Number of Generations
Average mating preference
0.0
3.0
0.0
1.0
true
false
"" ""
PENS
"default" 1.0 0 -13345367 true "" "plot mean [mating-preference] of turtles with [genotype = \"AA\"]"
"pen-1" 1.0 0 -10899396 true "" "plot mean [mating-preference] of turtles with [genotype = \"AB\"]"
"pen-2" 1.0 0 -1184463 true "" "plot mean [mating-preference] of turtles with [genotype = \"BB\"]"

PLOT
506
11
706
161
Genotype abundance
Number of generations
Genotype abundance
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"default" 1.0 0 -13345367 true "" "plot count turtles with [genotype = \"AA\"]"
"pen-1" 1.0 0 -10899396 true "" "plot count turtles with [genotype = \"AB\"]"
"pen-2" 1.0 0 -1184463 true "" "plot count turtles with [genotype = \"BB\"]"

PLOT
722
10
973
160
How many genotype-matching matings?
Number of generations
Number of inter-trait matings
0.0
10.0
10.0
20.0
true
false
"" ""
PENS
"AA mating with AA" 1.0 0 -13345367 true "" "plot how-many-AA-mated-with-AA"
"BB mating with BB" 1.0 0 -1184463 true "" "plot how-many-BB-mated-with-BB"

PLOT
552
171
946
321
Average distance of Preference loci
Number of generations
Number of individuals with preference for AA
0.0
10.0
0.0
10.0
true
false
"" ""
PENS
"AA" 1.0 0 -13345367 true "" "plot mean [Pa-average-distance] of AA-turtles-with-Pa-loci "
"BB" 1.0 0 -1184463 true "" "plot mean [Pb-average-distance] of BB-turtles-with-Pb-loci "

OUTPUT
1340
37
1761
508
11

@#$#@#$#@
## How to use the model

Sliders/switches on the interface tab:
* carrying-capacity - the number of individuals in the population.
* mutant-proportion - the proportion of individuals that carry one new mutation in their genome, every generation
* genome-length - the number of loci in each chromosome.
* preference-strength - the factor determining the addition of each preference mutation to the strength of the preference (i.e the slope of the probability to mate function).
* recombination - determines whether or not the genome goes through recombination.
* selection-coefficient - determines selection force against maladaptive genotypes. Equals the proportion of maladaptive individuals that die during selection.
* max-matching-attempts - the maximum number of females that a male can refuse to mate with before he is taken out of the mating pool. This variable is included to prevent the simulations from getting stuck because none of the females are suitable for a single male.

## Comparison to the physical_linkage_one_trait_locus model

This model is an extention of the physical_linkage_one_trait_locus model, in which the ecological trait is controlled by a single locus. In this model, the ecological trait is controlled by two separate loci with epistatic interactions.
The model description specified below is of the one trait locus model (see ODD below). The differences in the current model compared to the one trait locus model are specified here:
The genetic basis of the trait in the model is similar to that of wing colour pattern in the two sympatric species Heliconius melpomene and H. cydno, where the optix and cortex loci control red and white forewing bands respectively (Naisbit et al., 2003) . To simplify the model description, we hereafter refer to colour pattern as the ecological trait, but the model could equally apply to a wide range of ecological traits.
The two colour loci in this model are located at opposite ends of the chromosome. The ‘Red’ locus is located at the first position within the chromosome and the ‘White’ locus is located at the last position within the chromosome. The colour loci each have two possible alleles which account for the presence (‘R’ and ‘W’ alleles) and absence (‘r’ and ‘w’ alleles) of a red and white band in the forewing, respectively. The alleles for presence of colour bands are dominant over those for absence. The presence of one colour band does not replace the presence of the other, resulting in a possible intermediate red-white band, characteristic of hybrids of H. melpomene and H. cydno. Together, there are nine possible genotypes at the two colour loci, and four possible colour phenotypes. The representation of genotypes and phenotypes in the model are based on descriptions of the genetic basis of forewing colour pattern in H. melpomene, H. cydno and their hybrids (Naisbit et al., 2003).  
Similar to the previous model, selection favours the red phenotype in one habitat and the white phenotype in the other, reflecting separate predators in each habitat, that learn to avoid different wing colour patterns (Mallet and Barton, 1989). The intermediate red-white phenotype and the no-colour phenotype are maladaptive in both habitats. 
Randomly placed mutations cause mating preferences for either red or white phenotypes, similar to the preference for AA or A’A’ phenotypes in the model with one locus controlling the ecological trait. Neutral mutations, which do not contribute to mating preferences, are added as a reference. The probability of mating with an individual of red or white phenotype is dependent on the number of preference loci and is calculated in the same way as in the model with one locus controlling the ecological trait. All individuals exhibit an intermediate mating preference for the red-white and no-colour phenotypes with a constant mating probability of 0.5. All other processes were identical to the model with one locus controlling the ecological trait.

##  

Complete model description following the ODD (Overview, Design concepts and Details) protocol for individual-based models (Grimm et al., 2006, 2010):
## Purpose 
The model was designed to explore evolutionary changes in the relative location of loci contributing to reproductive isolation, as a result of selection pressure against intermediate phenotypes. The model is inspired by the colour patterns and mating behaviours of Heliconius butterflies but applies for a wide range of biological systems. 
## Entities, state variables, and scales
The model consists of 10000 individuals with an even male:female ratio. Individuals are diploid with a single pair of homologous chromosomes, each containing a sequence of 100 loci, defined together as the “genome”. The first locus controls an ecological trait, for which there are two possible alleles, with an initially equal frequency in the population: A or A’. The alleles are codominant and therefore the three possible genotypes produce three separate phenotypes: AA, A’A’ homozygotes or an intermediate, heterozygote AA’ phenotype. The ecological trait is subject to selection, but also serves as a mating cue and can therefore be considered a “magic trait” (Servedio et al. 2011).
Mutations that cause a preference to mate with either AA or A’A’ phenotypes (preference loci), as well as neutral mutations for comparison, occur at random across the 100 loci in one percent of individuals every generation. The higher the number of preference loci for AA in the genome, compared to the number of  A’A’ preference loci, the higher the probability to mate with an individual of AA phenotype (see Submodels section for detailed description).
The modelled environment comprises two habitats, such that selection favours AA genotype in one habitat and A’A’ genotype in the other. Both habitats are maladaptive for the heterozygote AA’ phenotype. Selection is modelled to reflect a scenario of two separate phenotypic optima, with sub-optimal hybrid phenotypes. Habitats are represented in the model in a non-spatial manner. Individuals remain in the habitat to which they are initially assigned.
Time steps in the model correspond to discrete, non-overlapping generations. Each simulation was run for 3000 generations.
## Process overview and scheduling
Each generation, several stages are executed in the following order: (1) formation of mating pairs, (2) reproduction, (3) recombination of offspring chromosomes and addition of randomly placed neutral mutations or mutations causing mating preference, (5) ecological selection, and (6) density dependent regulation of population size. For details on each process see ‘Submodels’ section.    
## Design concepts
Basic principles. 
The model design is based on several Basic principles: (i) selection favours distinct phenotypes of the ecological trait and acts against intermediate phenotypes that arise when separate phenotypes mate, thereby promoting assortative mating based on the trait phenotype. Therefore the trait is regarded as a ‘magic trait’, which is subject to divergent ecological selection and also contributes to non-random mating (Servedio et al., 2011). Specifically,. (ii) Genetic elements that control the trait evolve first, followed by the evolution of genetic elements that control mating preferences, as is assumed to have occurred in Heliconius (Jiggins et al., 2004). The latter can potentially evolve at various places within the genome, and within various distances from trait loci. (iii) Recombination has the potential to break up associations between specific trait and preference alleles (Felsenstein, 1981). Therefore, the physical distance between trait and preference alleles within the genome plays an important role in the development of assortative mating. 
Emergence. 
Given the disadvantage of offspring with an intermediate phenotype, assortative mating is expected to emerge. Mutations that cause mating preference for the AA or A’A’ genotypes are expected to accumulate on chromosomes that carry the A or A’ allele, respectively. However, it is not straight forward under which conditions selection will favour genomes in which preference loci accumulate nearby the trait locus.
Stochasticity. 
The model includes several procedures that are determined stochastically, to represent random events that take place in reality. These include matching of individuals with potential mating pairs, choice of two of the four parent chromosomes which will be passed on to each of their offspring separately, the cross-over point for recombination, individual genomes that undergo a mutation (either neutral or one that contributes to mating preference), and the location of each mutation within the genome.
Alongside these central stochastic procedures, there are several components of the model that are determined stochastically to maintain events or behaviours at a specified frequency. These include the initial distribution of individuals among the two habitats, determining the trait locus alleles of initial individuals, the decision whether to mate with a potential mating partner, and death due to selection and due to density dependent population size regulation. 
Observation. 
The average distance of each type of preference loci and of neutral mutations from the trait locus is recorded for each individual across both chromosomes. The values are then averaged across all individuals, separately for individuals of AA and A’A’ phenotype, at every generation. 
The proportion of phenotypically matching mating pairs, in which both male and female are of AA phenotype or of A’A’ phenotype, is recorded every generation, as a measure of assortative mating.
## Initialization
At the beginning of each simulation, 10000 individuals, half male and half female, are randomly assigned to one of the two habitats. The alleles at the trait locus on each of the two chromosomes of each individual are randomly chosen at an equal probability for the A and A’ alleles. Neutral mutations or ones that cause mating preferences are then randomly added to one percent of the population.  
## Input data
The model does not use input data to represent time-varying processes.
## Submodels

Formation of mating pairs:
Males and females from both habitats are mixed into the same mating pool and have an equal probability to be paired with each other, reflecting a scenario of unrestricted movement between habitats. The mating choice is made by the female, however since there are no other differences between males and females, the model can also reflect a scenario in which males make the mating choice. Each female is paired with a random male and will mate with him at a probability that depends on the strength of her preference for his phenotype (see below). If a female decides not to mate, she is paired sequentially with a maximum of ten random males until she mates. If she does not mate with the tenth male with which she is paired, she is taken out of the mating pool and does not reproduce. Limiting the number of males with which a female is paired avoids simulations from running endlessly. See supplementary material (???) for details on the sensitivity of model results to changes in the maximum number of males with which a female is paired.    The probability of a female to mate with a male with which she is paired is described by the following equations:
P_A=1/(1+e^(-d∙pf) )
P_A'=1-P_A
Where PA and PA’ are the probabilities to mate with a male of phenotype AA and A’A’, respectively. d is the difference between the number of AA preference loci and the number of A’A’ preference loci in the genome. The higher the number of AA preference loci, compared to A’A’ preference loci, the larger the probability to mate with an AA phenotyped individual, and vice versa. A preference strength factor (pf) determines how strong the contribution of each additional preference locus is.
The probability to mate with a male of  AA’ phenotype is constant for all individuals and equals 0.5. This represents a case in which hybrids, represented by the heterozygotes in this model, exhibit intermediate phenotypes which are partially attractive to individuals who prefer to mate with one of the distinct phenotypes, represented as homozygotes in this model. Robustness of model results to changes in this basic assumption are detailed in the supplementary material (S3.1).  
The preference strength factor (pf) is used to control the rate at which strong mating preferences accumulate across the population. Varying the value of pf allows testing the influence of mating preference strength on the development of physical linkage between trait and preference loci.      

Reproduction:
Only individuals who have found a mating partner in the previous procedure will reproduce. Each mating pair produces four offspring, two males and two females. The number of offspring was chosen to keep the size of the new generation above carrying capacity, before selection and density dependent regulation take place, to avoid population collapse. 
Each offspring receives one paternal and one maternal chromosome, randomly chosen from the two chromosomes of each parent. Offspring are assigned the same habitat as their mother, to ensure that the mating choice has a direct influence on the survival of offspring and is therefore subject to natural selection. Following reproduction, the parent generation dies.

Offspring recombination and mutation:
Each offspring genome undergoes recombination between the two homologous chromosomes. Recombination occurs at one, randomly chosen, cross-over point within the chromosomes. The content of the “genome” sequence after the cross-over point is exchanged between the two homologous chromosomes.
Recombination can break apart linkage disequilibrium between traits and mating preferences, both in the model and in reality. It is therefore the force that drives physical linkage between loci controlling the trait and loci controlling mating preferences.
After recombination is completed, one percent of individuals in the population undergo a mutation at a single position within their genome. The mutations cause a preference to mate with either AA or A’A’ phenotypes (preference loci), or do not have any influence on mating preferences (neutral loci). The mutations are placed at random locations within the genome and therefore within random distances from the first locus that controls the ecological trait. See supplementary material (S2.3) for details on the sensitivity of model results to changes in the percent of individuals that undergo mutations every generation.  
We chose to add randomly placed mutations that cause mating preferences based on the assumption that physical linkage is formed by co-option of genes that are already within physical linkage with the trait locus, rather than by transposition of genetic elements that control preference from other regions in the genome. In Heliconius, for example, there is no evidence of transposition around colour pattern genes, or chromosomal inversions that might be involved in maintaining species barriers among Heliconius melpomene and H. cydno (The Heliconius Genome Consortium, 2012; Davey et al., 2017). 

Ecological selection:
Selection favours AA phenotype in one habitat and A’A’ phenotype in the second habitat. Phenotypes that are not favoured in each of the habitats are subject to selection. Selection is modelled to reflect a scenario of two separate phenotypic optima, with sub-optimal hybrid phenotypes. The strength of selection is fixed per simulation and controlled by a selection coefficient (s), which determines the proportion of maladapted individuals that will die due to selection. Robustness of model results to changes in the relative selection level against heterozygote, intermediate, phenotypes are detailed in the supplementary material (S3.2).

Density dependent regulation:
Each of the two habitats has a carrying capacity of 5000 individuals. The probability of each individual to die due to density dependent regulation equals the number of access individuals in the respective habitat divided by the total number of individuals in that habitat. Separate density dependent regulation of population size in each habitat is based on the assumption that individuals depend on separate, limited, resources in each habitat.
@#$#@#$#@
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@#$#@#$#@
NetLogo 6.1.1
@#$#@#$#@
@#$#@#$#@
@#$#@#$#@
<experiments>
  <experiment name="Steps until - 9-1-19" repetitions="10" runMetricsEveryStep="false">
    <setup>setup</setup>
    <go>go</go>
    <exitCondition>how-many-AA-mated-with-BB + how-many-BB-mated-with-AA &lt; 0.01 * how-many-mating-pairs</exitCondition>
    <metric>count genotyped-AA</metric>
    <metric>count genotyped-AB</metric>
    <metric>count genotyped-BB</metric>
    <metric>how-many-AA-mated-with-BB</metric>
    <metric>how-many-BB-mated-with-AA</metric>
    <metric>how-many-mating-pairs</metric>
    <metric>mean [prob-AA] of genotyped-AA</metric>
    <metric>mean [prob-BB] of genotyped-BB</metric>
    <metric>mean [Pa-count] of genotyped-AA</metric>
    <metric>mean [Pa-count] of genotyped-BB</metric>
    <metric>mean [Pb-count] of genotyped-AA</metric>
    <metric>mean [Pb-count] of genotyped-BB</metric>
    <metric>population-mean-distance-of-closest-Pa</metric>
    <metric>population-mean-distance-of-closest-Pb</metric>
    <metric>population-mean-average-distance-of-Pa</metric>
    <metric>population-mean-average-distance-of-Pb</metric>
    <metric>minimum-population-average-distance-of-Pa</metric>
    <metric>minimum-population-average-distance-of-Pb</metric>
    <metric>step-of-minimum-distance-of-Pa</metric>
    <metric>step-of-minimum-distance-of-Pb</metric>
    <enumeratedValueSet variable="carrying-capacity">
      <value value="10000"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="preference-strength">
      <value value="0.3"/>
      <value value="0.6"/>
      <value value="1.2"/>
      <value value="2.4"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="selection-coefficient">
      <value value="0.8"/>
      <value value="0.9"/>
      <value value="1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="max-matching-attempts">
      <value value="10"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="recombination">
      <value value="true"/>
      <value value="false"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="mutant-proportion">
      <value value="0.01"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="genome-length">
      <value value="100"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="All steps" repetitions="10" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="3000"/>
    <metric>count genotyped-AA</metric>
    <metric>count genotyped-AB</metric>
    <metric>count genotyped-BB</metric>
    <metric>how-many-mating-pairs</metric>
    <metric>how-many-AA-mated-with-AA</metric>
    <metric>how-many-BB-mated-with-BB</metric>
    <metric>mean [prob-AA] of genotyped-AA</metric>
    <metric>mean [prob-BB] of genotyped-AA</metric>
    <metric>mean [prob-AA] of genotyped-BB</metric>
    <metric>mean [prob-BB] of genotyped-BB</metric>
    <metric>standard-deviation [prob-AA] of genotyped-AA</metric>
    <metric>standard-deviation [prob-BB] of genotyped-AA</metric>
    <metric>standard-deviation [prob-AA] of genotyped-BB</metric>
    <metric>standard-deviation [prob-BB] of genotyped-BB</metric>
    <metric>mean [Pa-count] of genotyped-AA</metric>
    <metric>mean [Pb-count] of genotyped-AA</metric>
    <metric>mean [nuetral-count] of genotyped-AA</metric>
    <metric>mean [Pa-count] of genotyped-BB</metric>
    <metric>mean [Pb-count] of genotyped-BB</metric>
    <metric>mean [nuetral-count] of genotyped-BB</metric>
    <metric>standard-deviation [Pa-count] of genotyped-AA</metric>
    <metric>standard-deviation [Pb-count] of genotyped-AA</metric>
    <metric>standard-deviation [nuetral-count] of genotyped-AA</metric>
    <metric>standard-deviation [Pa-count] of genotyped-BB</metric>
    <metric>standard-deviation [Pb-count] of genotyped-BB</metric>
    <metric>standard-deviation [nuetral-count] of genotyped-BB</metric>
    <metric>mean [Pa-average-distance] of AA-turtles-with-Pa-loci</metric>
    <metric>mean [Pb-average-distance] of AA-turtles-with-Pb-loci</metric>
    <metric>mean [nuetral-average-distance] of AA-turtles-with-nuetral-loci</metric>
    <metric>mean [Pa-average-distance] of BB-turtles-with-Pa-loci</metric>
    <metric>mean [Pb-average-distance] of BB-turtles-with-Pb-loci</metric>
    <metric>mean [nuetral-average-distance] of BB-turtles-with-nuetral-loci</metric>
    <metric>standard-deviation [Pa-average-distance] of AA-turtles-with-Pa-loci</metric>
    <metric>standard-deviation [Pb-average-distance] of AA-turtles-with-Pb-loci</metric>
    <metric>standard-deviation [nuetral-average-distance] of AA-turtles-with-nuetral-loci</metric>
    <metric>standard-deviation [Pa-average-distance] of BB-turtles-with-Pa-loci</metric>
    <metric>standard-deviation [Pb-average-distance] of BB-turtles-with-Pb-loci</metric>
    <metric>standard-deviation [nuetral-average-distance] of BB-turtles-with-nuetral-loci</metric>
    <metric>count AA-turtles-with-Pa-loci</metric>
    <metric>count AA-turtles-with-Pb-loci</metric>
    <metric>count AA-turtles-with-nuetral-loci</metric>
    <metric>count BB-turtles-with-Pa-loci</metric>
    <metric>count BB-turtles-with-Pb-loci</metric>
    <metric>count BB-turtles-with-nuetral-loci</metric>
    <enumeratedValueSet variable="carrying-capacity">
      <value value="10000"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="preference-strength">
      <value value="0.3"/>
      <value value="0.6"/>
      <value value="1.2"/>
      <value value="2.4"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="selection-coefficient">
      <value value="0.5"/>
      <value value="0.6"/>
      <value value="0.7"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="max-matching-attempts">
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    <enumeratedValueSet variable="mutant-proportion">
      <value value="0.01"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="recombination">
      <value value="true"/>
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    <enumeratedValueSet variable="genome-length">
      <value value="100"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="distance vs. number - 13-2-20" repetitions="20" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="1000"/>
    <metric>count genotyped-AA</metric>
    <metric>count genotyped-AB</metric>
    <metric>count genotyped-BB</metric>
    <metric>how-many-AA-mated-with-BB</metric>
    <metric>how-many-BB-mated-with-AA</metric>
    <metric>how-many-mating-pairs</metric>
    <metric>mean [prob-AA] of genotyped-AA</metric>
    <metric>mean [prob-BB] of genotyped-BB</metric>
    <metric>count turtles with [prob-AA &gt; 0.5]</metric>
    <metric>count turtles with [prob-AA &gt; 0.9]</metric>
    <metric>count turtles with [prob-BB &gt; 0.5]</metric>
    <metric>count turtles with [prob-BB &gt; 0.9]</metric>
    <metric>mean A-chromosome-Pa-count</metric>
    <metric>standard-deviation A-chromosome-Pa-count</metric>
    <metric>mean B-chromosome-Pb-count</metric>
    <metric>standard-deviation B-chromosome-Pb-count</metric>
    <metric>mean [Pa-count] of genotyped-AA</metric>
    <metric>mean [Pa-count] of genotyped-BB</metric>
    <metric>mean [Pb-count] of genotyped-AA</metric>
    <metric>mean [Pb-count] of genotyped-BB</metric>
    <metric>population-mean-distance-of-closest-Pa</metric>
    <metric>population-mean-distance-of-closest-Pb</metric>
    <metric>population-mean-average-distance-of-Pa</metric>
    <metric>population-stdev-average-distance-of-Pa</metric>
    <metric>population-count-average-distance-of-Pa</metric>
    <metric>population-mean-average-distance-of-Pb</metric>
    <metric>population-stdev-average-distance-of-Pb</metric>
    <metric>population-count-average-distance-of-Pb</metric>
    <enumeratedValueSet variable="carrying-capacity">
      <value value="10000"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="preference-strength">
      <value value="0.3"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="selection-coefficient">
      <value value="1"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="max-matching-attempts">
      <value value="10"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="mutant-proportion">
      <value value="0.01"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="recombination">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="genome-length">
      <value value="100"/>
    </enumeratedValueSet>
  </experiment>
  <experiment name="testing-max-mating" repetitions="1" runMetricsEveryStep="true">
    <setup>setup</setup>
    <go>go</go>
    <timeLimit steps="1000"/>
    <metric>proportion-unmated-males</metric>
    <metric>count males</metric>
    <enumeratedValueSet variable="carrying-capacity">
      <value value="10000"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="preference-strength">
      <value value="0.3"/>
      <value value="2.4"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="selection-coefficient">
      <value value="0.5"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="max-matching-attempts">
      <value value="2"/>
      <value value="4"/>
      <value value="6"/>
      <value value="8"/>
      <value value="10"/>
      <value value="12"/>
      <value value="14"/>
      <value value="16"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="recombination">
      <value value="true"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="mutant-proportion">
      <value value="0.01"/>
    </enumeratedValueSet>
    <enumeratedValueSet variable="genome-length">
      <value value="100"/>
    </enumeratedValueSet>
  </experiment>
</experiments>
@#$#@#$#@
@#$#@#$#@
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