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85
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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_accum"), action="store", dest="adult_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("-e", "--location"), action="store", dest="location", help="Selected location"),
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10 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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11 make_option(c("-i", "--max_clutch_size"), action="store", dest="max_clutch_size", type="integer", help="Adjustment of maximum clutch size"),
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12 make_option(c("-j", "--nymph_mort"), action="store", dest="nymph_mort", type="integer", help="Adjustment rate for nymph mortality"),
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13 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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14 make_option(c("-n", "--num_days"), action="store", dest="num_days", type="integer", help="Total number of days in the temperature dataset"),
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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("-v", "--input"), action="store", dest="input", help="Temperature data for selected location"),
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21 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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22 )
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23
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24 parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
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25 args <- parse_args(parser, positional_arguments=TRUE)
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26 opt <- args$options
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27
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28 parse_input_data = function(input_file, num_rows) {
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29 # Read in the input temperature datafile into a data frame.
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30 temperature_data_frame <- read.csv(file=input_file, header=T, strip.white=TRUE, sep=",")
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31 num_columns <- dim(temperature_data_frame)[2]
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32 if (num_columns == 6) {
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33 # The input data has the following 6 columns:
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34 # LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX
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35 # Set the column names for access when adding daylight length..
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36 colnames(temperature_data_frame) <- c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX")
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37 # Add a column containing the daylight length for each day.
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38 temperature_data_frame <- add_daylight_length(temperature_data_frame, num_columns, num_rows)
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39 # Reset the column names with the additional column for later access.
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40 colnames(temperature_data_frame) <- c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX", "DAYLEN")
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41 }
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42 return(temperature_data_frame)
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43 }
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44
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45 add_daylight_length = function(temperature_data_frame, num_columns, num_rows) {
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46 # Return a vector of daylight length (photoperido profile) for
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47 # the number of days specified in the input temperature data
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48 # (from Forsythe 1995).
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49 p = 0.8333
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50 latitude <- temperature_data_frame$LATITUDE[1]
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51 daylight_length_vector <- NULL
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52 for (i in 1:num_rows) {
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53 # Get the day of the year from the current row
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54 # of the temperature data for computation.
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55 doy <- temperature_data_frame$DOY[i]
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56 theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (doy - 186)))
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57 phi <- asin(0.39795 * cos(theta))
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58 # Compute the length of daylight for the day of the year.
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59 daylight_length_vector[i] <- 24 - (24 / pi * acos((sin(p * pi / 180) + sin(latitude * pi / 180) * sin(phi)) / (cos(latitude * pi / 180) * cos(phi))))
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60 }
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61 # Append daylight_length_vector as a new column to temperature_data_frame.
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62 temperature_data_frame[, num_columns+1] <- daylight_length_vector
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63 return(temperature_data_frame)
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64 }
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65
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66 get_temperature_at_hour = function(latitude, temperature_data_frame, row, num_days) {
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67 # Base development threshold for Brown Marmolated Stink Bug
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68 # insect phenology model.
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69 # TODO: Pass insect on the command line to accomodate more
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70 # the just the Brown Marmolated Stink Bub.
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71 threshold <- 14.17
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72 # Minimum temperature for current row.
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73 dnp <- temperature_data_frame$TMIN[row]
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74 # Maximum temperature for current row.
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75 dxp <- temperature_data_frame$TMAX[row]
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76 # Mean temperature for current row.
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77 dmean <- 0.5 * (dnp + dxp)
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78 # Initialize degree day accumulation
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79 dd <- 0
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80 if (dxp < threshold) {
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81 dd <- 0
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82 }
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83 else {
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84 # Initialize hourly temperature.
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85 T <- NULL
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86 # Initialize degree hour vector.
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87 dh <- NULL
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88 # Daylight length for current row.
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89 y <- temperature_data_frame$DAYLEN[row]
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90 # Darkness length.
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91 z <- 24 - y
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92 # Lag coefficient.
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93 a <- 1.86
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94 # Darkness coefficient.
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95 b <- 2.20
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96 # Sunrise time.
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97 risetime <- 12 - y / 2
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98 # Sunset time.
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99 settime <- 12 + y / 2
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100 ts <- (dxp - dnp) * sin(pi * (settime - 5) / (y + 2 * a)) + dnp
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101 for (i in 1:24) {
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102 if (i > risetime && i < settime) {
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103 # Number of hours after Tmin until sunset.
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104 m <- i - 5
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105 T[i] = (dxp - dnp) * sin(pi * m / (y + 2 * a)) + dnp
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106 if (T[i] < 8.4) {
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107 dh[i] <- 0
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108 }
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109 else {
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110 dh[i] <- T[i] - 8.4
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111 }
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112 }
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113 else if (i > settime) {
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114 n <- i - 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 dh[i] <- 0
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118 }
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119 else {
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120 dh[i] <- T[i] - 8.4
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121 }
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122 }
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123 else {
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124 n <- i + 24 - settime
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125 T[i]=dnp + (ts - dnp) * exp( - b * n / z)
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126 if (T[i] < 8.4) {
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127 dh[i] <- 0
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128 }
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129 else {
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130 dh[i] <- T[i] - 8.4
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131 }
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132 }
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133 }
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134 dd <- sum(dh) / 24
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135 }
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136 return(c(dmean, dd))
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137 }
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138
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139 dev.egg = function(temperature) {
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140 dev.rate= -0.9843 * temperature + 33.438
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141 return(dev.rate)
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142 }
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143
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144 dev.young = function(temperature) {
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145 n12 <- -0.3728 * temperature + 14.68
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146 n23 <- -0.6119 * temperature + 25.249
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147 dev.rate = mean(n12 + n23)
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148 return(dev.rate)
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149 }
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150
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151 dev.old = function(temperature) {
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152 n34 <- -0.6119 * temperature + 17.602
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153 n45 <- -0.4408 * temperature + 19.036
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154 dev.rate = mean(n34 + n45)
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155 return(dev.rate)
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156 }
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157
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158 dev.emerg = function(temperature) {
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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 }
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162
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163 mortality.egg = function(temperature) {
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164 if (temperature < 12.7) {
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165 mort.prob = 0.8
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166 }
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167 else {
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168 mort.prob = 0.8 - temperature / 40.0
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169 if (mort.prob < 0) {
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170 mort.prob = 0.01
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171 }
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172 }
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173 return(mort.prob)
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174 }
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175
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176 mortality.nymph = function(temperature) {
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177 if (temperature < 12.7) {
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178 mort.prob = 0.03
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179 }
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180 else {
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181 mort.prob = temperature * 0.0008 + 0.03
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182 }
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183 return(mort.prob)
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184 }
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185
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186 mortality.adult = function(temperature) {
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187 if (temperature < 12.7) {
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188 mort.prob = 0.002
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189 }
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190 else {
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191 mort.prob = temperature * 0.0005 + 0.02
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192 }
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193 return(mort.prob)
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194 }
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195
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196 temperature_data_frame <- parse_input_data(opt$input, opt$num_days)
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197 # All latitude values are the same,
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198 # so get the value from the first row.
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199 latitude <- temperature_data_frame$LATITUDE[1]
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200
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201 cat("Number of days: ", opt$num_days, "\n")
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202
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203 # Initialize matrix for results from all replications.
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204 S0.rep <- S1.rep <- S2.rep <- S3.rep <- S4.rep <- S5.rep <- matrix(rep(0, opt$num_days * opt$replications), ncol = opt$replications)
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205 newborn.rep <- death.rep <- adult.rep <- pop.rep <- g0.rep <- g1.rep <- g2.rep <- g0a.rep <- g1a.rep <- g2a.rep <- matrix(rep(0, opt$num_days * opt$replications), ncol=opt$replications)
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206
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207 # Loop through replications.
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208 for (N.rep in 1:opt$replications) {
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209 # During each replication start with 1000 individuals.
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210 # TODO: user definable as well?
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211 n <- 1000
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212 # Generation, Stage, DD, T, Diapause.
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213 vec.ini <- c(0, 3, 0, 0, 0)
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214 # Overwintering, previttelogenic, DD=0, T=0, no-diapause.
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215 vec.mat <- rep(vec.ini, n)
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216 # Complete matrix for the population.
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217 vec.mat <- base::t(matrix(vec.mat, nrow=5))
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218 # Time series of population size.
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219 tot.pop <- NULL
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220 gen0.pop <- rep(0, opt$num_days)
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221 gen1.pop <- rep(0, opt$num_days)
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222 gen2.pop <- rep(0, opt$num_days)
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223 S0 <- S1 <- S2 <- S3 <- S4 <- S5 <- rep(0, opt$num_days)
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224 g0.adult <- g1.adult <- g2.adult <- rep(0, opt$num_days)
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225 N.newborn <- N.death <- N.adult <- rep(0, opt$num_days)
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226 dd.day <- rep(0, opt$num_days)
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227 # All the days included in the input temperature dataset.
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228 for (row in 1:opt$num_days) {
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229 # Get the integer day of the year for the current row.
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230 doy <- temperature_data_frame$DOY[row]
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231 # Photoperiod in the day.
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232 photoperiod <- temperature_data_frame$DAYLEN[row]
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233 temp.profile <- get_temperature_at_hour(latitude, temperature_data_frame, row, opt$num_days)
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234 mean.temp <- temp.profile[1]
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235 dd.temp <- temp.profile[2]
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236 dd.day[row] <- dd.temp
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237 # Trash bin for death.
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238 death.vec <- NULL
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239 # Newborn.
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240 birth.vec <- NULL
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241 # All individuals.
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242 for (i in 1:n) {
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243 # Find individual record.
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244 vec.ind <- vec.mat[i,]
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245 # First of all, still alive?
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246 # Adjustment for late season mortality rate.
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247 if (latitude < 40.0) {
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248 post.mort <- 1
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249 day.kill <- 300
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250 }
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251 else {
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252 post.mort <- 2
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253 day.kill <- 250
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254 }
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255 if (vec.ind[2] == 0) {
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256 # Egg.
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257 death.prob = opt$egg_mort * mortality.egg(mean.temp)
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258 }
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259 else if (vec.ind[2] == 1 | vec.ind[2] == 2) {
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260 death.prob = opt$nymph_mort * mortality.nymph(mean.temp)
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261 }
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262 else if (vec.ind[2] == 3 | vec.ind[2] == 4 | vec.ind[2] == 5) {
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263 # For adult.
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264 if (doy < day.kill) {
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265 death.prob = opt$adult_mort * mortality.adult(mean.temp)
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266 }
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267 else {
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268 # Increase adult mortality after fall equinox.
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269 death.prob = opt$adult_mort * post.mort * mortality.adult(mean.temp)
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270 }
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271 }
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272 # (or dependent on temperature and life stage?)
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273 u.d <- runif(1)
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274 if (u.d < death.prob) {
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275 death.vec <- c(death.vec, i)
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276 }
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277 else {
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278 # Aggregrate index of dead bug.
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279 # Event 1 end of diapause.
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280 if (vec.ind[1] == 0 && vec.ind[2] == 3) {
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281 # Overwintering adult (previttelogenic).
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282 if (photoperiod > opt$photoperiod && vec.ind[3] > 68 && doy < 180) {
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283 # Add 68C to become fully reproductively matured.
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284 # Transfer to vittelogenic.
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285 vec.ind <- c(0, 4, 0, 0, 0)
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286 vec.mat[i,] <- vec.ind
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287 }
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288 else {
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289 # Add to dd.
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290 vec.ind[3] <- vec.ind[3] + dd.temp
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291 # Add 1 day in current stage.
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292 vec.ind[4] <- vec.ind[4] + 1
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293 vec.mat[i,] <- vec.ind
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294 }
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295 }
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296 if (vec.ind[1] != 0 && vec.ind[2] == 3) {
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297 # Not overwintering adult (previttelogenic).
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298 current.gen <- vec.ind[1]
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299 if (vec.ind[3] > 68) {
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300 # Add 68C to become fully reproductively matured.
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301 # Transfer to vittelogenic.
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302 vec.ind <- c(current.gen, 4, 0, 0, 0)
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303 vec.mat[i,] <- vec.ind
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304 }
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305 else {
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306 # Add to dd.
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307 vec.ind[3] <- vec.ind[3] + dd.temp
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308 # Add 1 day in current stage.
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309 vec.ind[4] <- vec.ind[4] + 1
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310 vec.mat[i,] <- vec.ind
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311 }
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312 }
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313 # Event 2 oviposition -- where population dynamics comes from.
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314 if (vec.ind[2] == 4 && vec.ind[1] == 0 && mean.temp > 10) {
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315 # Vittelogenic stage, overwintering generation.
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316 if (vec.ind[4] == 0) {
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317 # Just turned in vittelogenic stage.
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318 n.birth=round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size))
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319 }
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320 else {
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321 # Daily probability of birth.
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322 p.birth = opt$oviposition * 0.01
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323 u1 <- runif(1)
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324 if (u1 < p.birth) {
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325 n.birth=round(runif(1, 2, 8))
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326 }
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327 }
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328 # Add to dd.
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329 vec.ind[3] <- vec.ind[3] + dd.temp
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330 # Add 1 day in current stage.
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331 vec.ind[4] <- vec.ind[4] + 1
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332 vec.mat[i,] <- vec.ind
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333 if (n.birth > 0) {
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334 # Add new birth -- might be in different generations.
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335 new.gen <- vec.ind[1] + 1
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336 # Egg profile.
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337 new.ind <- c(new.gen, 0, 0, 0, 0)
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338 new.vec <- rep(new.ind, n.birth)
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339 # Update batch of egg profile.
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340 new.vec <- t(matrix(new.vec, nrow=5))
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341 # Group with total eggs laid in that day.
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342 birth.vec <- rbind(birth.vec, new.vec)
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343 }
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344 }
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345 # Event 2 oviposition -- for generation 1.
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346 if (vec.ind[2] == 4 && vec.ind[1] == 1 && mean.temp > 12.5 && doy < 222) {
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347 # Vittelogenic stage, 1st generation
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348 if (vec.ind[4] == 0) {
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349 # Just turned in vittelogenic stage.
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350 n.birth=round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size))
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351 }
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352 else {
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353 # Daily probability of birth.
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354 p.birth = opt$oviposition * 0.01
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355 u1 <- runif(1)
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356 if (u1 < p.birth) {
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357 n.birth = round(runif(1, 2, 8))
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358 }
|
|
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359 }
|
|
|
360 # Add to dd.
|
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|
361 vec.ind[3] <- vec.ind[3] + dd.temp
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362 # Add 1 day in current stage.
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363 vec.ind[4] <- vec.ind[4] + 1
|
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|
364 vec.mat[i,] <- vec.ind
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365 if (n.birth > 0) {
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366 # Add new birth -- might be in different generations.
|
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367 new.gen <- vec.ind[1] + 1
|
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|
368 # Egg profile.
|
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|
369 new.ind <- c(new.gen, 0, 0, 0, 0)
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370 new.vec <- rep(new.ind, n.birth)
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371 # Update batch of egg profile.
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372 new.vec <- t(matrix(new.vec, nrow=5))
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373 # Group with total eggs laid in that day.
|
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374 birth.vec <- rbind(birth.vec, new.vec)
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375 }
|
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376 }
|
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377 # Event 3 development (with diapause determination).
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378 # Event 3.1 egg development to young nymph (vec.ind[2]=0 -> egg).
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379 if (vec.ind[2] == 0) {
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380 # Egg stage.
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381 # Add to dd.
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382 vec.ind[3] <- vec.ind[3] + dd.temp
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383 if (vec.ind[3] >= (68 + opt$young_nymph_accum)) {
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384 # From egg to young nymph, DD requirement met.
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385 current.gen <- vec.ind[1]
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386 # Transfer to young nymph stage.
|
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387 vec.ind <- c(current.gen, 1, 0, 0, 0)
|
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388 }
|
|
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389 else {
|
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390 # Add 1 day in current stage.
|
|
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391 vec.ind[4] <- vec.ind[4] + 1
|
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392 }
|
|
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393 vec.mat[i,] <- vec.ind
|
|
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394 }
|
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|
395 # Event 3.2 young nymph to old nymph (vec.ind[2]=1 -> young nymph: determines diapause).
|
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396 if (vec.ind[2] == 1) {
|
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397 # young nymph stage.
|
|
|
398 # add to dd.
|
|
|
399 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
400 if (vec.ind[3] >= (250 + opt$old_nymph_accum)) {
|
|
|
401 # From young to old nymph, dd requirement met.
|
|
|
402 current.gen <- vec.ind[1]
|
|
|
403 # Transfer to old nym stage.
|
|
|
404 vec.ind <- c(current.gen, 2, 0, 0, 0)
|
|
|
405 if (photoperiod < opt$photoperiod && doy > 180) {
|
|
|
406 vec.ind[5] <- 1
|
|
|
407 } # Prepare for diapausing.
|
|
|
408 }
|
|
|
409 else {
|
|
|
410 # Add 1 day in current stage.
|
|
|
411 vec.ind[4] <- vec.ind[4] + 1
|
|
|
412 }
|
|
|
413 vec.mat[i,] <- vec.ind
|
|
|
414 }
|
|
|
415 # Event 3.3 old nymph to adult: previttelogenic or diapausing?
|
|
|
416 if (vec.ind[2] == 2) {
|
|
|
417 # Old nymph stage.
|
|
|
418 # add to dd.
|
|
|
419 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
420 if (vec.ind[3] >= (200 + opt$adult_accum)) {
|
|
|
421 # From old to adult, dd requirement met.
|
|
|
422 current.gen <- vec.ind[1]
|
|
|
423 if (vec.ind[5] == 0) {
|
|
|
424 # Non-diapausing adult -- previttelogenic.
|
|
|
425 vec.ind <- c(current.gen, 3, 0, 0, 0)
|
|
|
426 }
|
|
|
427 else {
|
|
|
428 # Diapausing.
|
|
|
429 vec.ind <- c(current.gen, 5, 0, 0, 1)
|
|
|
430 }
|
|
|
431 }
|
|
|
432 else {
|
|
|
433 # Add 1 day in current stage.
|
|
|
434 vec.ind[4] <- vec.ind[4] + 1
|
|
|
435 }
|
|
|
436 vec.mat[i,] <- vec.ind
|
|
|
437 }
|
|
|
438 # Event 4 growing of diapausing adult (unimportant, but still necessary).
|
|
|
439 if (vec.ind[2] == 5) {
|
|
|
440 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
441 vec.ind[4] <- vec.ind[4] + 1
|
|
|
442 vec.mat[i,] <- vec.ind
|
|
|
443 }
|
|
|
444 } # Else if it is still alive.
|
|
|
445 } # End of the individual bug loop.
|
|
|
446 # Find how many died.
|
|
|
447 n.death <- length(death.vec)
|
|
|
448 if (n.death > 0) {
|
|
|
449 vec.mat <- vec.mat[-death.vec, ]
|
|
|
450 }
|
|
|
451 # Remove record of dead.
|
|
|
452 # Find how many new born.
|
|
|
453 n.newborn <- length(birth.vec[,1])
|
|
|
454 vec.mat <- rbind(vec.mat, birth.vec)
|
|
|
455 # Update population size for the next day.
|
|
|
456 n <- n - n.death + n.newborn
|
|
|
457
|
|
|
458 # Aggregate results by day.
|
|
|
459 tot.pop <- c(tot.pop, n)
|
|
|
460 # Egg.
|
|
|
461 s0 <- sum(vec.mat[,2] == 0)
|
|
|
462 # Young nymph.
|
|
|
463 s1 <- sum(vec.mat[,2] == 1)
|
|
|
464 # Old nymph.
|
|
|
465 s2 <- sum(vec.mat[,2] == 2)
|
|
|
466 # Previtellogenic.
|
|
|
467 s3 <- sum(vec.mat[,2] == 3)
|
|
|
468 # Vitellogenic.
|
|
|
469 s4 <- sum(vec.mat[,2] == 4)
|
|
|
470 # Diapausing.
|
|
|
471 s5 <- sum(vec.mat[,2] == 5)
|
|
|
472 # Overwintering adult.
|
|
|
473 gen0 <- sum(vec.mat[,1] == 0)
|
|
|
474 # First generation.
|
|
|
475 gen1 <- sum(vec.mat[,1] == 1)
|
|
|
476 # Second generation.
|
|
|
477 gen2 <- sum(vec.mat[,1] == 2)
|
|
|
478 # Sum of all adults.
|
|
|
479 n.adult <- sum(vec.mat[,2] == 3) + sum(vec.mat[,2] == 4) + sum(vec.mat[,2] == 5)
|
|
|
480
|
|
|
481 # Generation 0 pop size.
|
|
|
482 gen0.pop[row] <- gen0
|
|
|
483 gen1.pop[row] <- gen1
|
|
|
484 gen2.pop[row] <- gen2
|
|
|
485
|
|
|
486 S0[row] <- s0
|
|
|
487 S1[row] <- s1
|
|
|
488 S2[row] <- s2
|
|
|
489 S3[row] <- s3
|
|
|
490 S4[row] <- s4
|
|
|
491 S5[row] <- s5
|
|
|
492
|
|
|
493 g0.adult[row] <- sum(vec.mat[,1] == 0)
|
|
|
494 g1.adult[row] <- 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))
|
|
|
495 g2.adult[row] <- 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))
|
|
|
496
|
|
|
497 N.newborn[row] <- n.newborn
|
|
|
498 N.death[row] <- n.death
|
|
|
499 N.adult[row] <- n.adult
|
|
|
500 } # end of days specified in the input temperature data
|
|
|
501
|
|
|
502 dd.cum <- cumsum(dd.day)
|
|
|
503
|
|
|
504 # Collect all the outputs.
|
|
|
505 S0.rep[,N.rep] <- S0
|
|
|
506 S1.rep[,N.rep] <- S1
|
|
|
507 S2.rep[,N.rep] <- S2
|
|
|
508 S3.rep[,N.rep] <- S3
|
|
|
509 S4.rep[,N.rep] <- S4
|
|
|
510 S5.rep[,N.rep] <- S5
|
|
|
511 newborn.rep[,N.rep] <- N.newborn
|
|
|
512 death.rep[,N.rep] <- N.death
|
|
|
513 adult.rep[,N.rep] <- N.adult
|
|
|
514 pop.rep[,N.rep] <- tot.pop
|
|
|
515 g0.rep[,N.rep] <- gen0.pop
|
|
|
516 g1.rep[,N.rep] <- gen1.pop
|
|
|
517 g2.rep[,N.rep] <- gen2.pop
|
|
|
518 g0a.rep[,N.rep] <- g0.adult
|
|
|
519 g1a.rep[,N.rep] <- g1.adult
|
|
|
520 g2a.rep[,N.rep] <- g2.adult
|
|
|
521 }
|
|
|
522
|
|
|
523 # Data analysis and visualization can currently
|
|
|
524 # plot only within a single calendar year.
|
|
|
525 # TODO: enhance this to accomodate multiple calendar years.
|
|
|
526 start_date <- temperature_data_frame$DATE[1]
|
|
|
527 end_date <- temperature_data_frame$DATE[opt$num_days]
|
|
|
528
|
|
|
529 n.yr <- 1
|
|
|
530 day.all <- c(1:opt$num_days * n.yr)
|
|
|
531
|
|
|
532 # mean value for adults
|
|
|
533 sa <- apply((S3.rep + S4.rep + S5.rep), 1, mean)
|
|
|
534 # mean value for nymphs
|
|
|
535 sn <- apply((S1.rep + S2.rep), 1,mean)
|
|
|
536 # mean value for eggs
|
|
|
537 se <- apply(S0.rep, 1, mean)
|
|
|
538 # mean value for P
|
|
|
539 g0 <- apply(g0.rep, 1, mean)
|
|
|
540 # mean value for F1
|
|
|
541 g1 <- apply(g1.rep, 1, mean)
|
|
|
542 # mean value for F2
|
|
|
543 g2 <- apply(g2.rep, 1, mean)
|
|
|
544 # mean value for P adult
|
|
|
545 g0a <- apply(g0a.rep, 1, mean)
|
|
|
546 # mean value for F1 adult
|
|
|
547 g1a <- apply(g1a.rep, 1, mean)
|
|
|
548 # mean value for F2 adult
|
|
|
549 g2a <- apply(g2a.rep, 1, mean)
|
|
|
550
|
|
|
551 # SE for adults
|
|
|
552 sa.se <- apply((S3.rep + S4.rep + S5.rep), 1, sd) / sqrt(opt$replications)
|
|
|
553 # SE for nymphs
|
|
|
554 sn.se <- apply((S1.rep + S2.rep) / sqrt(opt$replications), 1, sd)
|
|
|
555 # SE for eggs
|
|
|
556 se.se <- apply(S0.rep, 1, sd) / sqrt(opt$replications)
|
|
|
557 # SE value for P
|
|
|
558 g0.se <- apply(g0.rep, 1, sd) / sqrt(opt$replications)
|
|
|
559 # SE for F1
|
|
|
560 g1.se <- apply(g1.rep, 1, sd) / sqrt(opt$replications)
|
|
|
561 # SE for F2
|
|
|
562 g2.se <- apply(g2.rep, 1, sd) / sqrt(opt$replications)
|
|
|
563 # SE for P adult
|
|
|
564 g0a.se <- apply(g0a.rep, 1, sd) / sqrt(opt$replications)
|
|
|
565 # SE for F1 adult
|
|
|
566 g1a.se <- apply(g1a.rep, 1, sd) / sqrt(opt$replications)
|
|
|
567 # SE for F2 adult
|
|
|
568 g2a.se <- apply(g2a.rep, 1, sd) / sqrt(opt$replications)
|
|
|
569
|
|
|
570 dev.new(width=20, height=30)
|
|
|
571
|
|
|
572 # Start PDF device driver to save charts to output.
|
|
|
573 pdf(file=opt$output, width=20, height=30, bg="white")
|
|
|
574
|
|
|
575 par(mar = c(5, 6, 4, 4), mfrow=c(3, 1))
|
|
|
576
|
|
|
577 # Subfigure 1: population size by life stage
|
|
|
578 title <- paste("BSMB total population by life stage :", opt$location, ": Lat:", latitude, ":", start_date, "to", end_date, sep=" ")
|
|
|
579 plot(day.all, sa, main=title, type="l", ylim=c(0, max(se + se.se, sn + sn.se, sa + sa.se)), axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
|
|
|
580 # Young and old nymphs.
|
|
|
581 lines(day.all, sn, lwd=2, lty=1, col=2)
|
|
|
582 # Eggs
|
|
|
583 lines(day.all, se, lwd=2, lty=1, col=4)
|
|
|
584 axis(1, at=c(1:12) * 30 - 15, cex.axis=3, labels=c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))
|
|
|
585 axis(2, cex.axis=3)
|
|
|
586 leg.text <- c("Egg", "Nymph", "Adult")
|
|
|
587 legend("topleft", leg.text, lty=c(1, 1, 1), col=c(4, 2, 1), cex=3)
|
|
|
588 if (opt$se_plot == 1) {
|
|
|
589 # Add SE lines to plot
|
|
|
590 # SE for adults
|
|
|
591 lines (day.all, sa + sa.se, lty=2)
|
|
|
592 lines (day.all, sa - sa.se, lty=2)
|
|
|
593 # SE for nymphs
|
|
|
594 lines (day.all, sn + sn.se, col=2, lty=2)
|
|
|
595 lines (day.all, sn - sn.se, col=2, lty=2)
|
|
|
596 # SE for eggs
|
|
|
597 lines (day.all, se + se.se, col=4, lty=2)
|
|
|
598 lines (day.all, se - se.se, col=4, lty=2)
|
|
|
599 }
|
|
|
600
|
|
|
601 # Subfigure 2: population size by generation
|
|
|
602 title <- paste("BSMB total population by generation :", opt$location, ": Lat:", latitude, ":", start_date, "to", end_date, sep=" ")
|
|
|
603 plot(day.all, g0, main=title, type="l", ylim=c(0, max(g2)), axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
|
|
|
604 lines(day.all, g1, lwd = 2, lty = 1, col=2)
|
|
|
605 lines(day.all, g2, lwd = 2, lty = 1, col=4)
|
|
|
606 axis(1, at=c(1:12) * 30 - 15, cex.axis=3, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))
|
|
|
607 axis(2, cex.axis=3)
|
|
|
608 leg.text <- c("P", "F1", "F2")
|
|
|
609 legend("topleft", leg.text, lty=c(1, 1, 1), col=c(1, 2, 4), cex=3)
|
|
|
610 if (opt$se_plot == 1) {
|
|
|
611 # Add SE lines to plot
|
|
|
612 # SE for adults
|
|
|
613 lines (day.all, g0+g0.se, lty=2)
|
|
|
614 lines (day.all, g0-g0.se, lty=2)
|
|
|
615 # SE for nymphs
|
|
|
616 lines (day.all, g1+g1.se, col=2, lty=2)
|
|
|
617 lines (day.all, g1-g1.se, col=2, lty=2)
|
|
|
618 # SE for eggs
|
|
|
619 lines (day.all, g2+g2.se, col=4, lty=2)
|
|
|
620 lines (day.all, g2-g2.se, col=4, lty=2)
|
|
|
621 }
|
|
|
622
|
|
|
623 # Subfigure 3: adult population size by generation
|
|
|
624 title <- paste("BSMB adult population by generation :", opt$location, ": Lat:", latitude, ":", start_date, "to", end_date, sep=" ")
|
|
|
625 plot(day.all, g0a, ylim=c(0, max(g2a) + 100), main=title, type="l", axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
|
|
|
626 lines(day.all, g1a, lwd = 2, lty = 1, col=2)
|
|
|
627 lines(day.all, g2a, lwd = 2, lty = 1, col=4)
|
|
|
628 axis(1, at=c(1:12) * 30 - 15, cex.axis=3, labels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))
|
|
|
629 axis(2, cex.axis=3)
|
|
|
630 leg.text <- c("P", "F1", "F2")
|
|
|
631 legend("topleft", leg.text, lty=c(1, 1, 1), col=c(1, 2, 4), cex=3)
|
|
|
632 if (opt$se_plot == 1) {
|
|
|
633 # Add SE lines to plot
|
|
|
634 # SE for adults
|
|
|
635 lines (day.all, g0a+g0a.se, lty=2)
|
|
|
636 lines (day.all, g0a-g0a.se, lty=2)
|
|
|
637 # SE for nymphs
|
|
|
638 lines (day.all, g1a+g1a.se, col=2, lty=2)
|
|
|
639 lines (day.all, g1a-g1a.se, col=2, lty=2)
|
|
|
640 # SE for eggs
|
|
|
641 lines (day.all, g2a+g2a.se, col=4, lty=2)
|
|
|
642 lines (day.all, g2a-g2a.se, col=4, lty=2)
|
|
|
643 }
|
|
|
644
|
|
|
645 # Turn off device driver to flush output.
|
|
|
646 dev.off()
|
