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33
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1 #!/usr/bin/env Rscript
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2
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3 suppressPackageStartupMessages(library("optparse"))
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4
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5 option_list <- list(
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6 make_option(c("-a", "--adult_mort"), action="store", dest="adult_mort", type="integer", help="Adjustment rate for adult mortality"),
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7 make_option(c("-b", "--adult_nymph_accum"), action="store", dest="adult_nymph_accum", type="integer", help="Adjustment of DD accumulation (old nymph->adult)"),
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8 make_option(c("-c", "--egg_mort"), action="store", dest="egg_mort", type="integer", help="Adjustment rate for egg mortality"),
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9 make_option(c("-d", "--latitude"), action="store", dest="latitude", type="double", help="Latitude of selected location"),
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10 make_option(c("-e", "--location"), action="store", dest="location", help="Selected location"),
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11 make_option(c("-f", "--min_clutch_size"), action="store", dest="min_clutch_size", type="integer", help="Adjustment of minimum clutch size"),
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12 make_option(c("-g", "--max_clutch_size"), action="store", dest="max_clutch_size", type="integer", help="Adjustment of maximum clutch size"),
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13 make_option(c("-j", "--nymph_mort"), action="store", dest="nymph_mort", type="integer", help="Adjustment rate for nymph mortality"),
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14 make_option(c("-k", "--old_nymph_accum"), action="store", dest="old_nymph_accum", type="integer", help="Adjustment of DD accumulation (young nymph->old nymph)"),
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15 make_option(c("-o", "--output"), action="store", dest="output", help="Output dataset"),
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16 make_option(c("-p", "--oviposition"), action="store", dest="oviposition", type="integer", help="Adjustment for oviposition rate"),
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17 make_option(c("-q", "--photoperiod"), action="store", dest="photoperiod", type="double", help="Critical photoperiod for diapause induction/termination"),
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18 make_option(c("-s", "--replications"), action="store", dest="replications", type="integer", help="Number of replications"),
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19 make_option(c("-t", "--se_plot"), action="store", dest="se_plot", help="Plot SE"),
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20 make_option(c("-u", "--year"), action="store", dest="year", type="integer", help="Starting year"),
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21 make_option(c("-v", "--temperature_dataset"), action="store", dest="temperature_dataset", help="Temperature data for selected location"),
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22 make_option(c("-y", "--young_nymph_accum"), action="store", dest="young_nymph_accum", type="integer", help="Adjustment of DD accumulation (egg->young nymph)")
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23 )
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24
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25 parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
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26 args <- parse_args(parser, positional_arguments=TRUE)
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27 opt <- args$options
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28
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29 data.input=function(loc, start.yr, temperature.dataset)
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30 {
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31 expdata <- matrix(rep(0, 365 * 3), nrow=365)
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32 # replace 2004 with start. yr
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33 yr <- start.yr
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34 namedat <- paste(loc, yr, ".Rdat", sep="")
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35 temp.data <- read.csv(file=temperature.dataset, header=T)
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36
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37 expdata[,1] <- c(1:365)
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38 save(expdata, file=namedat)
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39 namedat
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40 }
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41
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42 daylength=function(L)
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43 {
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44 # from Forsythe 1995
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45 p=0.8333
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46 dl <- NULL
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47 for (i in 1:365)
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48 {
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49 theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (i - 186)))
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50 phi <- asin(0.39795 * cos(theta))
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51 dl[i] <- 24 - 24 / pi * acos((sin(p * pi / 180) + sin(L * pi / 180) * sin(phi)) / (cos(L * pi / 180) * cos(phi)))
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52 }
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53 dl # return a vector of daylength in 365 days
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54 }
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55
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56 hourtemp=function(L, date, temperature_file_path)
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57 {
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58 load(temperature_file_path)
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59 threshold <- 14.17 # base development threshold for BMSB
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60 dnp <- expdata[date, 2] # daily minimum
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61 dxp <- expdata[date, 3] # daily maximum
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62 dmean <- 0.5 * (dnp + dxp)
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63 dd <- 0 # initialize degree day accumulation
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64
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65 if (dxp<threshold)
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66 {
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67 dd <- 0
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68 }
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69 else
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70 {
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71 dlprofile <- daylength(L) # extract daylength data for entire year
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72 T <- NULL # initialize hourly temperature
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73 dh <- NULL #initialize degree hour vector
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74 # date <- 200
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75 y <- dlprofile[date] # calculate daylength in given date
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76 z <- 24 - y # night length
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77 a <- 1.86 # lag coefficient
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78 b <- 2.20 # night coefficient
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79 #tempdata <- read.csv("tempdata.csv") #import raw data set
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80 # Should be outside function otherwise its redundant
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81 risetime <- 12 - y / 2 # sunrise time
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82 settime <- 12 + y / 2 # sunset time
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83 ts <- (dxp - dnp) * sin(pi * (settime - 5) / (y + 2 * a)) + dnp
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84 for (i in 1:24)
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85 {
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86 if (i > risetime && i<settime)
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87 {
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88 m <- i - 5 # number of hours after Tmin until sunset
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89 T[i]=(dxp - dnp) * sin(pi * m / (y + 2 * a)) + dnp
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90 if (T[i]<8.4)
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91 {
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92 dh[i] <- 0
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93 }
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94 else
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95 {
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96 dh[i] <- T[i] - 8.4
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97 }
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98 }
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99 else if (i > settime)
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100 {
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101 n <- i - settime
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102 T[i]=dnp + (ts - dnp) * exp( - b * n / z)
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103 if (T[i]<8.4)
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104 {
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105 dh[i] <- 0
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106 }
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107 else
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108 {
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109 dh[i] <- T[i] - 8.4
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110 }
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111 }
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112 else
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113 {
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114 n <- i + 24 - settime
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115 T[i]=dnp + (ts - dnp) * exp( - b * n / z)
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116 if (T[i]<8.4)
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117 {
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118 dh[i] <- 0
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119 }
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120 else
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121 {
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122 dh[i] <- T[i] - 8.4
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123 }
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124 }
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125 }
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126 dd <- sum(dh) / 24
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127 }
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128 return=c(dmean, dd)
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129 return
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130 }
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131
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132 dev.egg = function(temperature)
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133 {
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134 dev.rate= -0.9843 * temperature + 33.438
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135 return = dev.rate
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136 return
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137 }
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138
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139 dev.young = function(temperature)
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140 {
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141 n12 <- -0.3728 * temperature + 14.68
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142 n23 <- -0.6119 * temperature + 25.249
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143 dev.rate = mean(n12 + n23)
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144 return = dev.rate
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145 return
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146 }
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147
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148 dev.old = function(temperature)
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149 {
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150 n34 <- -0.6119 * temperature + 17.602
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151 n45 <- -0.4408 * temperature + 19.036
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152 dev.rate = mean(n34 + n45)
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153 return = dev.rate
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154 return
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155 }
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156
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157 dev.emerg = function(temperature)
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158 {
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159 emerg.rate <- -0.5332 * temperature + 24.147
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160 return=emerg.rate
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161 return
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162 }
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163
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164 mortality.egg=function(temperature)
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165 {
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166 if (temperature<12.7)
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167 {
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168 mort.prob = 0.8
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169 }
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170 else
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171 {
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172 mort.prob = 0.8 - temperature / 40.0
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173 if (mort.prob<0)
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174 {
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175 mort.prob=0.01
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176 }
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177 }
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178 return=mort.prob
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179 return
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180 }
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181
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182 mortality.nymph=function(temperature)
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183 {
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184 if (temperature<12.7)
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185 {
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186 mort.prob=0.03
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187 }
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188 else
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189 {
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190 mort.prob=temperature * 0.0008 + 0.03
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191 }
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192 return=mort.prob
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193 return
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194 }
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195
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196 mortality.adult=function(temperature)
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197 {
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198 if (temperature<12.7)
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199 {
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200 mort.prob=0.002
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201 }
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202 else
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203 {
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204 mort.prob=temperature * 0.0005 + 0.02
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205 }
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206 return=mort.prob
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207 return
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208 }
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209
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210 # Read in the input temperature datafile
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211 temperature_file_path <- data.input(opt$location, opt$year, opt$temperature_dataset)
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212
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213 # Initialize matrix for results from all replications
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214 S0.rep <- S1.rep <- S2.rep <- S3.rep <- S4.rep <- S5.rep <- matrix(rep(0, 365 * opt$replications), ncol = opt$replications)
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215 newborn.rep <- death.rep <- adult.rep <- pop.rep <- g0.rep <- g1.rep <- g2.rep <- g0a.rep <- g1a.rep <- g2a.rep <- matrix(rep(0, 365 * opt$replications), ncol=opt$replications)
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216
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217 # loop through replications
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218 for (N.rep in 1:opt$replications)
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219 {
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220 # during each replication
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221 # start with 1000 individuals -- user definable as well?
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222 n <- 1000
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223 # Generation, Stage, DD, T, Diapause
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224 vec.ini <- c(0, 3, 0, 0, 0)
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225 # overwintering, previttelogenic, DD=0, T=0, no-diapause
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226 vec.mat <- rep(vec.ini, n)
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227 # complete matrix for the population
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228 vec.mat <- t(matrix(vec.mat, nrow=5))
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229 # complete photoperiod profile in a year, requires daylength function
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230 ph.p <- daylength(opt$latitude)
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231
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232 # time series of population size
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233 tot.pop <- NULL
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234 # gen.0 pop size
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235 gen0.pop <- rep(0, 365)
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236 gen1.pop <- rep(0, 365)
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237 gen2.pop <- rep(0, 365)
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238 S0 <- S1 <- S2 <- S3 <- S4 <- S5 <- rep(0, 365)
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239 g0.adult <- g1.adult <- g2.adult <- rep(0, 365)
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240 N.newborn <- N.death <- N.adult <- rep(0, 365)
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241 dd.day <- rep(0, 365)
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242
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243 # start tick
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244 ptm <- proc.time()
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245
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246 # all the days
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247 for (day in 1:365)
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248 {
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249 # photoperiod in the day
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250 photoperiod <- ph.p[day]
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251 temp.profile <- hourtemp(opt$latitude, day, temperature_file_path)
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252 mean.temp <- temp.profile[1]
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253 dd.temp <- temp.profile[2]
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254 dd.day[day] <- dd.temp
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255 # trash bin for death
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256 death.vec <- NULL
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257 # new born
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258 birth.vec <- NULL
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259
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260 # all individuals
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261 for (i in 1:n)
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262 {
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263 # find individual record
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264 vec.ind <- vec.mat[i,]
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265 # first of all, still alive?
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266 # adjustment for late season mortality rate
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267 if (opt$latitude < 40.0)
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268 {
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269 post.mort <- 1
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270 day.kill <- 300
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271 }
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272 else
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273 {
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274 post.mort <- 2
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275 day.kill <- 250
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276 }
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277 if (vec.ind[2] == 0)
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278 {
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279 # egg
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280 death.prob = opt$egg_mort * mortality.egg(mean.temp)
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281 }
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282 else if (vec.ind[2] == 1 | vec.ind[2] == 2)
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283 {
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284 death.prob = opt$nymph_mort * mortality.nymph(mean.temp)
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285 }
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286 else if (vec.ind[2] == 3 | vec.ind[2] == 4 | vec.ind[2] == 5)
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287 {
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288 # for adult
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289 if (day < day.kill)
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290 {
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291 death.prob = opt$adult_mort * mortality.adult(mean.temp)
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292 }
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293 else
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294 {
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295 # increase adult mortality after fall equinox
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296 death.prob = opt$adult_mort * post.mort * mortality.adult(mean.temp)
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297 }
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298 }
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299 # (or dependent on temperature and life stage?)
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300 u.d <- runif(1)
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301 if (u.d < death.prob)
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302 {
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303 death.vec <- c(death.vec, i)
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304 }
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305 else
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306 {
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307 # aggregrate index of dead bug
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308 # event 1 end of diapause
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309 if (vec.ind[1] == 0 && vec.ind[2] == 3)
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310 {
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311 # overwintering adult (previttelogenic)
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312 if (photoperiod > opt$photoperiod && vec.ind[3] > 68 && day < 180)
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313 {
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314 # add 68C to become fully reproductively matured
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315 # transfer to vittelogenic
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316 vec.ind <- c(0, 4, 0, 0, 0)
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317 vec.mat[i,] <- vec.ind
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318 }
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319 else
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320 {
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321 # add to DD
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322 vec.ind[3] <- vec.ind[3] + dd.temp
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323 # add 1 day in current stage
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324 vec.ind[4] <- vec.ind[4] + 1
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325 vec.mat[i,] <- vec.ind
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326 }
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327 }
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328 if (vec.ind[1] != 0 && vec.ind[2] == 3)
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329 {
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330 # NOT overwintering adult (previttelogenic)
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331 current.gen <- vec.ind[1]
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332 if (vec.ind[3] > 68)
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333 {
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334 # add 68C to become fully reproductively matured
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335 # transfer to vittelogenic
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336 vec.ind <- c(current.gen, 4, 0, 0, 0)
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337 vec.mat[i,] <- vec.ind
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338 }
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339 else
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340 {
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341 # add to DD
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342 vec.ind[3] <- vec.ind[3] + dd.temp
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343 # add 1 day in current stage
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344 vec.ind[4] <- vec.ind[4] + 1
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345 vec.mat[i,] <- vec.ind
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346 }
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347 }
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348
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349 # event 2 oviposition -- where population dynamics comes from
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350 if (vec.ind[2] == 4 && vec.ind[1] == 0 && mean.temp > 10)
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351 {
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352 # vittelogenic stage, overwintering generation
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353 if (vec.ind[4] == 0)
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354 {
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355 # just turned in vittelogenic stage
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356 n.birth=round(runif(1, 2 + min.ovi.adj, 8 + max.ovi.adj))
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357 }
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358 else
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359 {
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360 # daily probability of birth
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361 p.birth = opt$oviposition * 0.01
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362 u1 <- runif(1)
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363 if (u1 < p.birth)
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364 {
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365 n.birth=round(runif(1, 2, 8))
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366 }
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367 }
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368 # add to DD
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369 vec.ind[3] <- vec.ind[3] + dd.temp
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370 # add 1 day in current stage
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371 vec.ind[4] <- vec.ind[4] + 1
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372 vec.mat[i,] <- vec.ind
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373 if (n.birth > 0)
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374 {
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375 # add new birth -- might be in different generations
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376 # generation + 1
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377 new.gen <- vec.ind[1] + 1
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378 # egg profile
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379 new.ind <- c(new.gen, 0, 0, 0, 0)
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380 new.vec <- rep(new.ind, n.birth)
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381 # update batch of egg profile
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382 new.vec <- t(matrix(new.vec, nrow=5))
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383 # group with total eggs laid in that day
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384 birth.vec <- rbind(birth.vec, new.vec)
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385 }
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386 }
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387
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388 # event 2 oviposition -- for gen 1.
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389 if (vec.ind[2] == 4 && vec.ind[1] == 1 && mean.temp > 12.5 && day < 222)
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390 {
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391 # vittelogenic stage, 1st generation
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392 if (vec.ind[4] == 0)
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393 {
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394 # just turned in vittelogenic stage
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395 n.birth=round(runif(1, 2 + min.ovi.adj, 8 + max.ovi.adj))
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396 }
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397 else
|
|
|
398 {
|
|
|
399 # daily probability of birth
|
|
|
400 p.birth = opt$oviposition * 0.01
|
|
|
401 u1 <- runif(1)
|
|
|
402 if (u1 < p.birth)
|
|
|
403 {
|
|
|
404 n.birth = round(runif(1, 2, 8))
|
|
|
405 }
|
|
|
406 }
|
|
|
407 # add to DD
|
|
|
408 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
409 # add 1 day in current stage
|
|
|
410 vec.ind[4] <- vec.ind[4] + 1
|
|
|
411 vec.mat[i,] <- vec.ind
|
|
|
412 if (n.birth > 0)
|
|
|
413 {
|
|
|
414 # add new birth -- might be in different generations
|
|
|
415 # generation + 1
|
|
|
416 new.gen <- vec.ind[1] + 1
|
|
|
417 # egg profile
|
|
|
418 new.ind <- c(new.gen, 0, 0, 0, 0)
|
|
|
419 new.vec <- rep(new.ind, n.birth)
|
|
|
420 # update batch of egg profile
|
|
|
421 new.vec <- t(matrix(new.vec, nrow=5))
|
|
|
422 # group with total eggs laid in that day
|
|
|
423 birth.vec <- rbind(birth.vec, new.vec)
|
|
|
424 }
|
|
|
425 }
|
|
|
426
|
|
|
427 # event 3 development (with diapause determination)
|
|
|
428 # event 3.1 egg development to young nymph (vec.ind[2]=0 -> egg)
|
|
|
429 if (vec.ind[2] == 0)
|
|
|
430 {
|
|
|
431 # egg stage
|
|
|
432 # add to DD
|
|
|
433 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
434 if (vec.ind[3] >= (68 + dd.adj1))
|
|
|
435 {
|
|
|
436 # from egg to young nymph, DD requirement met
|
|
|
437 current.gen <- vec.ind[1]
|
|
|
438 # transfer to young nym stage
|
|
|
439 vec.ind <- c(current.gen, 1, 0, 0, 0)
|
|
|
440 }
|
|
|
441 else
|
|
|
442 {
|
|
|
443 # add 1 day in current stage
|
|
|
444 vec.ind[4] <- vec.ind[4] + 1
|
|
|
445 }
|
|
|
446 vec.mat[i,] <- vec.ind
|
|
|
447 }
|
|
|
448
|
|
|
449 # event 3.2 young nymph to old nymph (vec.ind[2]=1 -> young nymph: determines diapause)
|
|
|
450 if (vec.ind[2] == 1)
|
|
|
451 {
|
|
|
452 # young nymph stage
|
|
|
453 # add to DD
|
|
|
454 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
455 if (vec.ind[3] >= (250 +dd.adj2))
|
|
|
456 {
|
|
|
457 # from young to old nymph, DD requirement met
|
|
|
458 current.gen <- vec.ind[1]
|
|
|
459 # transfer to old nym stage
|
|
|
460 vec.ind <- c(current.gen, 2, 0, 0, 0)
|
|
|
461 if (photoperiod < opt$photoperiod && day > 180)
|
|
|
462 {
|
|
|
463 vec.ind[5] <- 1
|
|
|
464 } # prepare for diapausing
|
|
|
465 }
|
|
|
466 else
|
|
|
467 {
|
|
|
468 # add 1 day in current stage
|
|
|
469 vec.ind[4] <- vec.ind[4] + 1
|
|
|
470 }
|
|
|
471 vec.mat[i,] <- vec.ind
|
|
|
472 }
|
|
|
473
|
|
|
474 # event 3.3 old nymph to adult: previttelogenic or diapausing?
|
|
|
475 if (vec.ind[2] == 2)
|
|
|
476 {
|
|
|
477 # old nymph stage
|
|
|
478 # add to DD
|
|
|
479 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
480 if (vec.ind[3] >= (200 + dd.adj3))
|
|
|
481 {
|
|
|
482 # from old to adult, DD requirement met
|
|
|
483 current.gen <- vec.ind[1]
|
|
|
484 if (vec.ind[5] == 0)
|
|
|
485 {
|
|
|
486 # non-diapausing adult -- previttelogenic
|
|
|
487 vec.ind <- c(current.gen, 3, 0, 0, 0)
|
|
|
488 }
|
|
|
489 else
|
|
|
490 {
|
|
|
491 # diapausing
|
|
|
492 vec.ind <- c(current.gen, 5, 0, 0, 1)
|
|
|
493 }
|
|
|
494 }
|
|
|
495 else
|
|
|
496 {
|
|
|
497 # add 1 day in current stage
|
|
|
498 vec.ind[4] <- vec.ind[4] + 1
|
|
|
499 }
|
|
|
500 vec.mat[i,] <- vec.ind
|
|
|
501 }
|
|
|
502
|
|
|
503 # event 4 growing of diapausing adult (unimportant, but still necessary)##
|
|
|
504 if (vec.ind[2] == 5)
|
|
|
505 {
|
|
|
506 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
507 vec.ind[4] <- vec.ind[4] + 1
|
|
|
508 vec.mat[i,] <- vec.ind
|
|
|
509 }
|
|
|
510 } # else if it is still alive
|
|
|
511 } # end of the individual bug loop
|
|
|
512
|
|
|
513 # find how many died
|
|
|
514 n.death <- length(death.vec)
|
|
|
515 if (n.death > 0)
|
|
|
516 {
|
|
|
517 vec.mat <- vec.mat[-death.vec, ]
|
|
|
518 }
|
|
|
519 # remove record of dead
|
|
|
520 # find how many new born
|
|
|
521 n.newborn <- length(birth.vec[,1])
|
|
|
522 vec.mat <- rbind(vec.mat, birth.vec)
|
|
|
523 # update population size for the next day
|
|
|
524 n <- n - n.death + n.newborn
|
|
|
525
|
|
|
526 # aggregate results by day
|
|
|
527 tot.pop <- c(tot.pop, n)
|
|
|
528 # egg
|
|
|
529 s0 <- sum(vec.mat[,2] == 0)
|
|
|
530 # young nymph
|
|
|
531 s1 <- sum(vec.mat[,2] == 1)
|
|
|
532 # old nymph
|
|
|
533 s2 <- sum(vec.mat[,2] == 2)
|
|
|
534 # previtellogenic
|
|
|
535 s3 <- sum(vec.mat[,2] == 3)
|
|
|
536 # vitellogenic
|
|
|
537 s4 <- sum(vec.mat[,2] == 4)
|
|
|
538 # diapausing
|
|
|
539 s5 <- sum(vec.mat[,2] == 5)
|
|
|
540 # overwintering adult
|
|
|
541 gen0 <- sum(vec.mat[,1] == 0)
|
|
|
542 # first generation
|
|
|
543 gen1 <- sum(vec.mat[,1] == 1)
|
|
|
544 # second generation
|
|
|
545 gen2 <- sum(vec.mat[,1] == 2)
|
|
|
546 # sum of all adults
|
|
|
547 n.adult <- sum(vec.mat[,2] == 3) + sum(vec.mat[,2] == 4) + sum(vec.mat[,2] == 5)
|
|
|
548 # gen.0 pop size
|
|
|
549 gen0.pop[day] <- gen0
|
|
|
550 gen1.pop[day] <- gen1
|
|
|
551 gen2.pop[day] <- gen2
|
|
|
552 S0[day] <- s0
|
|
|
553 S1[day] <- s1
|
|
|
554 S2[day] <- s2
|
|
|
555 S3[day] <- s3
|
|
|
556 S4[day] <- s4
|
|
|
557 S5[day] <- s5
|
|
|
558 g0.adult[day] <- sum(vec.mat[,1] == 0)
|
|
|
559 g1.adult[day] <- sum((vec.mat[,1] == 1 & vec.mat[,2] == 3) | (vec.mat[,1] == 1 & vec.mat[,2] == 4) | (vec.mat[,1] == 1 & vec.mat[,2] == 5))
|
|
|
560 g2.adult[day] <- sum((vec.mat[,1]== 2 & vec.mat[,2] == 3) | (vec.mat[,1] == 2 & vec.mat[,2] == 4) | (vec.mat[,1] == 2 & vec.mat[,2] == 5))
|
|
|
561
|
|
|
562 N.newborn[day] <- n.newborn
|
|
|
563 N.death[day] <- n.death
|
|
|
564 N.adult[day] <- n.adult
|
|
|
565 #print(c(N.rep, day, n, n.adult))
|
|
|
566 } # end of 365 days
|
|
|
567
|
|
|
568 dd.cum <- cumsum(dd.day)
|
|
|
569 # collect all the outputs
|
|
|
570 S0.rep[,N.rep] <- S0
|
|
|
571 S1.rep[,N.rep] <- S1
|
|
|
572 S2.rep[,N.rep] <- S2
|
|
|
573 S3.rep[,N.rep] <- S3
|
|
|
574 S4.rep[,N.rep] <- S4
|
|
|
575 S5.rep[,N.rep] <- S5
|
|
|
576 newborn.rep[,N.rep] <- N.newborn
|
|
|
577 death.rep[,N.rep] <- N.death
|
|
|
578 adult.rep[,N.rep] <- N.adult
|
|
|
579 pop.rep[,N.rep] <- tot.pop
|
|
|
580 g0.rep[,N.rep] <- gen0.pop
|
|
|
581 g1.rep[,N.rep] <- gen1.pop
|
|
|
582 g2.rep[,N.rep] <- gen2.pop
|
|
|
583 g0a.rep[,N.rep] <- g0.adult
|
|
|
584 g1a.rep[,N.rep] <- g1.adult
|
|
|
585 g2a.rep[,N.rep] <- g2.adult
|
|
|
586 }
|
|
|
587
|
|
|
588 # save(dd.day, dd.cum, S0.rep, S1.rep, S2.rep, S3.rep, S4.rep, S5.rep, newborn.rep, death.rep, adult.rep, pop.rep, g0.rep, g1.rep, g2.rep, g0a.rep, g1a.rep, g2a.rep, file=opt$output)
|
|
|
589 # maybe do not need to export this bit, but for now just leave it as-is
|
|
|
590 # do we need to export this Rdat file?
|
|
|
591
|
|
|
592 # Data analysis and visualization
|
|
|
593 # default: plot 1 year of result
|
|
|
594 # but can be expanded to accommodate multiple years
|
|
|
595 n.yr <- 1
|
|
|
596 day.all <- c(1:365 * n.yr)
|
|
|
597
|
|
|
598 # mean value for adults
|
|
|
599 sa <- apply((S3.rep + S4.rep + S5.rep), 1, mean)
|
|
|
600 # mean value for nymphs
|
|
|
601 sn <- apply((S1.rep + S2.rep), 1,mean)
|
|
|
602 # mean value for eggs
|
|
|
603 se <- apply(S0.rep, 1, mean)
|
|
|
604 # mean value for P
|
|
|
605 g0 <- apply(g0.rep, 1, mean)
|
|
|
606 # mean value for F1
|
|
|
607 g1 <- apply(g1.rep, 1, mean)
|
|
|
608 # mean value for F2
|
|
|
609 g2 <- apply(g2.rep, 1, mean)
|
|
|
610 # mean value for P adult
|
|
|
611 g0a <- apply(g0a.rep, 1, mean)
|
|
|
612 # mean value for F1 adult
|
|
|
613 g1a <- apply(g1a.rep, 1, mean)
|
|
|
614 # mean value for F2 adult
|
|
|
615 g2a <- apply(g2a.rep, 1, mean)
|
|
|
616
|
|
|
617 # SE for adults
|
|
|
618 sa.se <- apply((S3.rep + S4.rep + S5.rep), 1, sd) / sqrt(opt$replications)
|
|
|
619 # SE for nymphs
|
|
|
620 sn.se <- apply((S1.rep + S2.rep) / sqrt(opt$replications), 1, sd)
|
|
|
621 # SE for eggs
|
|
|
622 se.se <- apply(S0.rep ,1 ,sd) / sqrt(opt$replications)
|
|
|
623 # SE value for P
|
|
|
624 g0.se <- apply(g0.rep, 1, sd) / sqrt(opt$replications)
|
|
|
625 # SE for F1
|
|
|
626 g1.se <- apply(g1.rep, 1, sd) / sqrt(opt$replications)
|
|
|
627 # SE for F2
|
|
|
628 g2.se <- apply(g2.rep, 1, sd) / sqrt(opt$replications)
|
|
|
629 # SE for P adult
|
|
|
630 g0a.se <- apply(g0a.rep, 1, sd) / sqrt(opt$replications)
|
|
|
631 # SE for F1 adult
|
|
|
632 g1a.se <- apply(g1a.rep, 1, sd) / sqrt(opt$replications)
|
|
|
633 # SE for F2 adult
|
|
|
634 g2a.se <- apply(g2a.rep, 1, sd) / sqrt(opt$replications)
|
|
|
635
|
|
|
636 dev.new(width = 9, height = 9)
|
|
|
637
|
|
|
638 # Start PDF device driver to save charts to output.
|
|
|
639 pdf(file=opt$output, height=20, width=20, bg="white")
|
|
|
640
|
|
|
641 par(mar = c(5, 6, 4, 4), mfrow=c(3, 1))
|
|
|
642
|
|
|
643 # Subfigure 2: population size by life stage
|
|
|
644 plot(day.all, sa, main = "Total Population Size by Life Stage", type = "l", ylim = c(0, max(se + se.se, sn + sn.se, sa + sa.se)), axes = F, lwd = 2, xlab = "", ylab = "Number", cex = 2, cex.lab = 2, cex.axis = 2, cex.main = 2)
|
|
|
645 # Young and old nymphs
|
|
|
646 lines(day.all, sn, lwd = 2, lty = 1, col = 2)
|
|
|
647 # Eggs
|
|
|
648 lines(day.all, se, lwd = 2, lty = 1, col = 4)
|
|
|
649 axis(1, at = c(1:12) * 30 - 15, cex.axis = 2, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))
|
|
|
650 axis(2, cex.axis = 2)
|
|
|
651 leg.text <- c("Egg", "Nymph", "Adult")
|
|
|
652 legend("topleft", leg.text, lty = c(1, 1, 1), col = c(4, 2, 1), cex = 2)
|
|
|
653 if (opt$se_plot == 1)
|
|
|
654 {
|
|
|
655 # add SE lines to plot
|
|
|
656 # SE for adults
|
|
|
657 lines (day.all, sa + sa.se, lty = 2)
|
|
|
658 lines (day.all, sa - sa.se, lty = 2)
|
|
|
659 # SE for nymphs
|
|
|
660 lines (day.all, sn + sn.se, col = 2, lty = 2)
|
|
|
661 lines (day.all, sn - sn.se, col = 2, lty = 2)
|
|
|
662 # SE for eggs
|
|
|
663 lines (day.all, se + se.se, col = 4, lty = 2)
|
|
|
664 lines (day.all, se - se.se, col = 4, lty = 2)
|
|
|
665 }
|
|
|
666
|
|
|
667 # Subfigure 3: population size by generation
|
|
|
668 plot(day.all, g0, main = "Total Population Size by Generation", type = "l", ylim = c(0, max(g2)), axes = F, lwd = 2, xlab = "", ylab = "Number", cex = 2, cex.lab = 2, cex.axis = 2, cex.main = 2)
|
|
|
669 lines(day.all, g1, lwd = 2, lty = 1, col = 2)
|
|
|
670 lines(day.all, g2, lwd = 2, lty = 1, col = 4)
|
|
|
671 axis(1, at = c(1:12) * 30 - 15, cex.axis = 2, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))
|
|
|
672 axis(2, cex.axis = 2)
|
|
|
673 leg.text <- c("P", "F1", "F2")
|
|
|
674 legend("topleft", leg.text, lty = c(1, 1, 1), col =c(1, 2, 4), cex = 2)
|
|
|
675 if (opt$se_plot == 1)
|
|
|
676 {
|
|
|
677 # add SE lines to plot
|
|
|
678 # SE for adults
|
|
|
679 lines (day.all, g0 + g0.se, lty = 2)
|
|
|
680 lines (day.all, g0 - g0.se, lty = 2)
|
|
|
681 # SE for nymphs
|
|
|
682 lines (day.all, g1 + g1.se, col = 2, lty = 2)
|
|
|
683 lines (day.all, g1 - g1.se, col = 2, lty = 2)
|
|
|
684 # SE for eggs
|
|
|
685 lines (day.all, g2 + g2.se, col = 4, lty = 2)
|
|
|
686 lines (day.all, g2 - g2.se, col = 4, lty = 2)
|
|
|
687 }
|
|
|
688
|
|
|
689 # Subfigure 4: adult population size by generation
|
|
|
690 plot(day.all, g0a, ylim = c(0, max(g2a) + 100), main = "Adult Population Size by Generation", type = "l", axes = F, lwd = 2, xlab = "Year", ylab = "Number", cex = 2, cex.lab = 2, cex.axis = 2, cex.main = 2)
|
|
|
691 lines(day.all, g1a, lwd = 2, lty = 1, col = 2)
|
|
|
692 lines(day.all, g2a, lwd = 2, lty = 1, col = 4)
|
|
|
693 axis(1, at = c(1:12) * 30 - 15, cex.axis = 2, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))
|
|
|
694 axis(2, cex.axis = 2)
|
|
|
695 leg.text <- c("P", "F1", "F2")
|
|
|
696 legend("topleft", leg.text, lty = c(1, 1, 1), col = c(1, 2, 4), cex = 2)
|
|
|
697 if (opt$se_plot == 1)
|
|
|
698 {
|
|
|
699 # add SE lines to plot
|
|
|
700 # SE for adults
|
|
|
701 lines (day.all, g0a + g0a.se, lty = 2)
|
|
|
702 lines (day.all, g0a - g0a.se, lty = 2)
|
|
|
703 # SE for nymphs
|
|
|
704 lines (day.all, g1a + g1a.se, col = 2, lty = 2)
|
|
|
705 lines (day.all, g1a - g1a.se, col = 2, lty = 2)
|
|
|
706 # SE for eggs
|
|
|
707 lines (day.all, g2a + g2a.se, col = 4, lty = 2)
|
|
|
708 lines (day.all, g2a - g2a.se, col = 4, lty = 2)
|
|
|
709 }
|
|
|
710
|
|
|
711 # Turn off device driver to flush output.
|
|
|
712 dev.off()
|
