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5
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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
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6 make_option(c("--adult_mortality"), action="store", dest="adult_mortality", type="integer", help="Adjustment rate for adult mortality"),
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7 make_option(c("--adult_accumulation"), action="store", dest="adult_accumulation", type="integer", help="Adjustment of degree-days accumulation (old nymph->adult)"),
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8 make_option(c("--egg_mortality"), action="store", dest="egg_mortality", type="integer", help="Adjustment rate for egg mortality"),
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9 make_option(c("--input"), action="store", dest="input", help="Temperature data for selected location"),
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10 make_option(c("--insect"), action="store", dest="insect", help="Insect name"),
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11 make_option(c("--insects_per_replication"), action="store", dest="insects_per_replication", type="integer", help="Number of insects with which to start each replication"),
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12 make_option(c("--location"), action="store", dest="location", help="Selected location"),
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13 make_option(c("--min_clutch_size"), action="store", dest="min_clutch_size", type="integer", help="Adjustment of minimum clutch size"),
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14 make_option(c("--max_clutch_size"), action="store", dest="max_clutch_size", type="integer", help="Adjustment of maximum clutch size"),
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15 make_option(c("--nymph_mortality"), action="store", dest="nymph_mortality", type="integer", help="Adjustment rate for nymph mortality"),
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16 make_option(c("--old_nymph_accumulation"), action="store", dest="old_nymph_accumulation", type="integer", help="Adjustment of degree-days accumulation (young nymph->old nymph)"),
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17 make_option(c("--num_days"), action="store", dest="num_days", type="integer", help="Total number of days in the temperature dataset"),
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18 make_option(c("--output"), action="store", dest="output", help="Output dataset"),
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19 make_option(c("--oviposition"), action="store", dest="oviposition", type="integer", help="Adjustment for oviposition rate"),
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20 make_option(c("--photoperiod"), action="store", dest="photoperiod", type="double", help="Critical photoperiod for diapause induction/termination"),
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21 make_option(c("--replications"), action="store", dest="replications", type="integer", help="Number of replications"),
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22 make_option(c("--std_error_plot"), action="store", dest="std_error_plot", help="Plot Standard error"),
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23 make_option(c("--young_nymph_accumulation"), action="store", dest="young_nymph_accumulation", type="integer", help="Adjustment of degree-days accumulation (egg->young nymph)")
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5
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24 )
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25
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26 parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
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27 args <- parse_args(parser, positional_arguments=TRUE)
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28 opt <- args$options
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29
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30 add_daylight_length = function(temperature_data_frame, num_columns, num_rows) {
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31 # Return a vector of daylight length (photoperido profile) for
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32 # the number of days specified in the input temperature data
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33 # (from Forsythe 1995).
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34 p = 0.8333
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35 latitude <- temperature_data_frame$LATITUDE[1]
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36 daylight_length_vector <- NULL
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37 for (i in 1:num_rows) {
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38 # Get the day of the year from the current row
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39 # of the temperature data for computation.
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40 doy <- temperature_data_frame$DOY[i]
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41 theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (doy - 186)))
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42 phi <- asin(0.39795 * cos(theta))
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43 # Compute the length of daylight for the day of the year.
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44 darkness_length <- 24 / pi * acos((sin(p * pi / 180) + sin(latitude * pi / 180) * sin(phi)) / (cos(latitude * pi / 180) * cos(phi)))
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45 daylight_length_vector[i] <- 24 - darkness_length
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5
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46 }
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47 # Append daylight_length_vector as a new column to temperature_data_frame.
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48 temperature_data_frame[, num_columns+1] <- daylight_length_vector
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49 return(temperature_data_frame)
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50 }
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51
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6
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52 dev.egg = function(temperature) {
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53 dev.rate = -0.9843 * temperature + 33.438
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54 return(dev.rate)
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55 }
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56
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57 dev.emerg = function(temperature) {
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58 emerg.rate <- -0.5332 * temperature + 24.147
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59 return(emerg.rate)
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60 }
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61
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62 dev.old = function(temperature) {
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63 n34 <- -0.6119 * temperature + 17.602
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64 n45 <- -0.4408 * temperature + 19.036
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65 dev.rate = mean(n34 + n45)
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66 return(dev.rate)
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67 }
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68
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69 dev.young = function(temperature) {
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70 n12 <- -0.3728 * temperature + 14.68
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71 n23 <- -0.6119 * temperature + 25.249
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72 dev.rate = mean(n12 + n23)
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73 return(dev.rate)
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74 }
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75
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76 get_temperature_at_hour = function(latitude, temperature_data_frame, row, num_days) {
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77 # Base development threshold for Brown Marmolated Stink Bug
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78 # insect phenology model.
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79 threshold <- 14.17
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80 # Minimum temperature for current row.
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81 curr_min_temp <- temperature_data_frame$TMIN[row]
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82 # Maximum temperature for current row.
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83 curr_max_temp <- temperature_data_frame$TMAX[row]
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84 # Mean temperature for current row.
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85 curr_mean_temp <- 0.5 * (curr_min_temp + curr_max_temp)
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86 # Initialize degree day accumulation
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87 averages <- 0
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88 if (curr_max_temp < threshold) {
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89 averages <- 0
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5
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90 }
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91 else {
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92 # Initialize hourly temperature.
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93 T <- NULL
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94 # Initialize degree hour vector.
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95 dh <- NULL
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96 # Daylight length for current row.
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97 y <- temperature_data_frame$DAYLEN[row]
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98 # Darkness length.
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99 z <- 24 - y
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100 # Lag coefficient.
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101 a <- 1.86
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102 # Darkness coefficient.
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103 b <- 2.20
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104 # Sunrise time.
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105 risetime <- 12 - y / 2
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106 # Sunset time.
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107 settime <- 12 + y / 2
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108 ts <- (curr_max_temp - curr_min_temp) * sin(pi * (settime - 5) / (y + 2 * a)) + curr_min_temp
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109 for (i in 1:24) {
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110 if (i > risetime && i < settime) {
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111 # Number of hours after Tmin until sunset.
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112 m <- i - 5
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113 T[i] = (curr_max_temp - curr_min_temp) * sin(pi * m / (y + 2 * a)) + curr_min_temp
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114 if (T[i] < 8.4) {
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115 dh[i] <- 0
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116 }
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117 else {
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118 dh[i] <- T[i] - 8.4
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119 }
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120 }
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121 else if (i > settime) {
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122 n <- i - settime
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123 T[i] = curr_min_temp + (ts - curr_min_temp) * exp( - b * n / z)
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124 if (T[i] < 8.4) {
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125 dh[i] <- 0
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126 }
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127 else {
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128 dh[i] <- T[i] - 8.4
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129 }
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130 }
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131 else {
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132 n <- i + 24 - settime
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133 T[i] = curr_min_temp + (ts - curr_min_temp) * exp( - b * n / z)
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134 if (T[i] < 8.4) {
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135 dh[i] <- 0
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136 }
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137 else {
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138 dh[i] <- T[i] - 8.4
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139 }
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140 }
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141 }
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142 averages <- sum(dh) / 24
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143 }
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144 return(c(curr_mean_temp, averages))
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145 }
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146
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147 mortality.adult = function(temperature) {
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148 if (temperature < 12.7) {
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149 mortality.probability = 0.002
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150 }
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151 else {
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152 mortality.probability = temperature * 0.0005 + 0.02
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153 }
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154 return(mortality.probability)
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155 }
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156
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157 mortality.egg = function(temperature) {
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158 if (temperature < 12.7) {
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159 mortality.probability = 0.8
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160 }
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161 else {
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162 mortality.probability = 0.8 - temperature / 40.0
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163 if (mortality.probability < 0) {
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164 mortality.probability = 0.01
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165 }
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166 }
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167 return(mortality.probability)
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168 }
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169
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170 mortality.nymph = function(temperature) {
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171 if (temperature < 12.7) {
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172 mortality.probability = 0.03
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173 }
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174 else {
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175 mortality.probability = temperature * 0.0008 + 0.03
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176 }
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177 return(mortality.probability)
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178 }
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179
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180 parse_input_data = function(input_file, num_rows) {
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181 # Read in the input temperature datafile into a data frame.
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182 temperature_data_frame <- read.csv(file=input_file, header=T, strip.white=TRUE, sep=",")
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183 num_columns <- dim(temperature_data_frame)[2]
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184 if (num_columns == 6) {
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185 # The input data has the following 6 columns:
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186 # LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX
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187 # Set the column names for access when adding daylight length..
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188 colnames(temperature_data_frame) <- c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX")
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189 # Add a column containing the daylight length for each day.
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190 temperature_data_frame <- add_daylight_length(temperature_data_frame, num_columns, num_rows)
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191 # Reset the column names with the additional column for later access.
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192 colnames(temperature_data_frame) <- c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX", "DAYLEN")
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193 }
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194 return(temperature_data_frame)
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5
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195 }
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196
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197 render_chart = function(chart_type, insect, location, latitude, start_date, end_date, days, maxval, plot_std_error,
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198 group1, group2, group3, group1_std_error, group2_std_error, group3_std_error) {
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199 if (chart_type == "pop_size_by_life_stage") {
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200 title <- paste(insect, ": Total pop. by life stage :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ")
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201 legend_text <- c("Egg", "Nymph", "Adult")
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202 columns <- c(4, 2, 1)
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203 } else if (chart_type == "pop_size_by_generation") {
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204 title <- paste(insect, ": Total pop. by generation :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ")
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205 legend_text <- c("P", "F1", "F2")
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206 columns <- c(1, 2, 4)
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207 } else if (chart_type == "adult_pop_size_by_generation") {
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208 title <- paste(insect, ": Adult pop. by generation :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ")
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209 legend_text <- c("P", "F1", "F2")
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210 columns <- c(1, 2, 4)
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211 }
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212 plot(days, group1, main=title, type="l", ylim=c(0, maxval), axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
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213 legend("topleft", legend_text, lty=c(1, 1, 1), col=columns, cex=3)
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214 lines(days, group2, lwd=2, lty=1, col=2)
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215 lines(days, group3, lwd=2, lty=1, col=4)
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216 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"))
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217 axis(2, cex.axis=3)
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218 if (plot_std_error==1) {
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219 # Standard error for group1.
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220 lines(days, group1+group1_std_error, lty=2)
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221 lines (days, group1-group1_std_error, lty=2)
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222 # Standard error for group2.
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223 lines(days, group2+group2_std_error, col=2, lty=2)
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224 lines(days, group2-group2_std_error, col=2, lty=2)
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225 # Standard error for group3.
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226 lines(days, group3+group3_std_error, col=4, lty=2)
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227 lines(days, group3-group3_std_error, col=4, lty=2)
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228 }
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229 }
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230
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231 temperature_data_frame <- parse_input_data(opt$input, opt$num_days)
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232 # All latitude values are the same, so get the value from the first row.
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233 latitude <- temperature_data_frame$LATITUDE[1]
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234
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6
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235 # Initialize matrices.
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236 Eggs.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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237 YoungNymphs.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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238 OldNymphs.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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239 Previtellogenic.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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240 Vitellogenic.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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241 Diapausing.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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242
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243 newborn.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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244 adult.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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245 death.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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246
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247 P.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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248 P_adults.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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249 F1.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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250 F1_adults.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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251 F2.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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252 F2_adults.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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253
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254 population.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications)
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255
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6
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256 # Process replications.
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257 for (N.replications in 1:opt$replications) {
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258 # Start with the user-defined number of insects per replication.
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259 num_insects <- opt$insects_per_replication
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260 # Generation, Stage, degree-days, T, Diapause.
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261 vector.ini <- c(0, 3, 0, 0, 0)
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262 # Overwintering, previttelogenic, degree-days=0, T=0, no-diapause.
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263 vector.matrix <- rep(vector.ini, num_insects)
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5
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264 # Complete matrix for the population.
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265 vector.matrix <- base::t(matrix(vector.matrix, nrow=5))
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5
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266 # Time series of population size.
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267 Eggs <- rep(0, opt$num_days)
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268 YoungNymphs <- rep(0, opt$num_days)
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269 OldNymphs <- rep(0, opt$num_days)
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270 Previtellogenic <- rep(0, opt$num_days)
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271 Vitellogenic <- rep(0, opt$num_days)
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272 Diapausing <- rep(0, opt$num_days)
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273
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274 N.newborn <- rep(0, opt$num_days)
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275 N.adult <- rep(0, opt$num_days)
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276 N.death <- rep(0, opt$num_days)
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277
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278 overwintering_adult.population <- rep(0, opt$num_days)
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279 first_generation.population <- rep(0, opt$num_days)
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280 second_generation.population <- rep(0, opt$num_days)
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281
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282 P.adult <- rep(0, opt$num_days)
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283 F1.adult <- rep(0, opt$num_days)
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284 F2.adult <- rep(0, opt$num_days)
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285
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286 total.population <- NULL
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287
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288 averages.day <- rep(0, opt$num_days)
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5
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289 # All the days included in the input temperature dataset.
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290 for (row in 1:opt$num_days) {
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291 # Get the integer day of the year for the current row.
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292 doy <- temperature_data_frame$DOY[row]
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293 # Photoperiod in the day.
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294 photoperiod <- temperature_data_frame$DAYLEN[row]
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295 temp.profile <- get_temperature_at_hour(latitude, temperature_data_frame, row, opt$num_days)
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296 mean.temp <- temp.profile[1]
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6
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297 averages.temp <- temp.profile[2]
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298 averages.day[row] <- averages.temp
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5
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299 # Trash bin for death.
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6
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300 death.vector <- NULL
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5
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301 # Newborn.
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6
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302 birth.vector <- NULL
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5
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303 # All individuals.
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6
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304 for (i in 1:num_insects) {
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305 # Individual record.
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306 vector.individual <- vector.matrix[i,]
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307 # Adjustment for late season mortality rate (still alive?).
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5
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308 if (latitude < 40.0) {
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309 post.mortality <- 1
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5
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310 day.kill <- 300
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311 }
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312 else {
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6
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313 post.mortality <- 2
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5
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314 day.kill <- 250
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315 }
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6
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316 if (vector.individual[2] == 0) {
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5
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317 # Egg.
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6
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318 death.probability = opt$egg_mortality * mortality.egg(mean.temp)
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5
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319 }
|
|
6
|
320 else if (vector.individual[2] == 1 | vector.individual[2] == 2) {
|
|
|
321 death.probability = opt$nymph_mortality * mortality.nymph(mean.temp)
|
|
5
|
322 }
|
|
6
|
323 else if (vector.individual[2] == 3 | vector.individual[2] == 4 | vector.individual[2] == 5) {
|
|
|
324 # Adult.
|
|
5
|
325 if (doy < day.kill) {
|
|
6
|
326 death.probability = opt$adult_mortality * mortality.adult(mean.temp)
|
|
5
|
327 }
|
|
|
328 else {
|
|
|
329 # Increase adult mortality after fall equinox.
|
|
6
|
330 death.probability = opt$adult_mortality * post.mortality * mortality.adult(mean.temp)
|
|
5
|
331 }
|
|
|
332 }
|
|
6
|
333 # Dependent on temperature and life stage?
|
|
5
|
334 u.d <- runif(1)
|
|
6
|
335 if (u.d < death.probability) {
|
|
|
336 death.vector <- c(death.vector, i)
|
|
|
337 }
|
|
5
|
338 else {
|
|
6
|
339 # End of diapause.
|
|
|
340 if (vector.individual[1] == 0 && vector.individual[2] == 3) {
|
|
5
|
341 # Overwintering adult (previttelogenic).
|
|
6
|
342 if (photoperiod > opt$photoperiod && vector.individual[3] > 68 && doy < 180) {
|
|
5
|
343 # Add 68C to become fully reproductively matured.
|
|
|
344 # Transfer to vittelogenic.
|
|
6
|
345 vector.individual <- c(0, 4, 0, 0, 0)
|
|
|
346 vector.matrix[i,] <- vector.individual
|
|
5
|
347 }
|
|
|
348 else {
|
|
6
|
349 # Add to # Add average temperature for current day.
|
|
|
350 vector.individual[3] <- vector.individual[3] + averages.temp
|
|
5
|
351 # Add 1 day in current stage.
|
|
6
|
352 vector.individual[4] <- vector.individual[4] + 1
|
|
|
353 vector.matrix[i,] <- vector.individual
|
|
5
|
354 }
|
|
|
355 }
|
|
6
|
356 if (vector.individual[1] != 0 && vector.individual[2] == 3) {
|
|
5
|
357 # Not overwintering adult (previttelogenic).
|
|
6
|
358 current.gen <- vector.individual[1]
|
|
|
359 if (vector.individual[3] > 68) {
|
|
5
|
360 # Add 68C to become fully reproductively matured.
|
|
|
361 # Transfer to vittelogenic.
|
|
6
|
362 vector.individual <- c(current.gen, 4, 0, 0, 0)
|
|
|
363 vector.matrix[i,] <- vector.individual
|
|
5
|
364 }
|
|
|
365 else {
|
|
6
|
366 # Add average temperature for current day.
|
|
|
367 vector.individual[3] <- vector.individual[3] + averages.temp
|
|
5
|
368 # Add 1 day in current stage.
|
|
6
|
369 vector.individual[4] <- vector.individual[4] + 1
|
|
|
370 vector.matrix[i,] <- vector.individual
|
|
5
|
371 }
|
|
|
372 }
|
|
6
|
373 # Oviposition -- where population dynamics comes from.
|
|
|
374 if (vector.individual[2] == 4 && vector.individual[1] == 0 && mean.temp > 10) {
|
|
5
|
375 # Vittelogenic stage, overwintering generation.
|
|
6
|
376 if (vector.individual[4] == 0) {
|
|
5
|
377 # Just turned in vittelogenic stage.
|
|
6
|
378 num_insects.birth = round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size))
|
|
5
|
379 }
|
|
|
380 else {
|
|
|
381 # Daily probability of birth.
|
|
|
382 p.birth = opt$oviposition * 0.01
|
|
|
383 u1 <- runif(1)
|
|
|
384 if (u1 < p.birth) {
|
|
6
|
385 num_insects.birth = round(runif(1, 2, 8))
|
|
5
|
386 }
|
|
|
387 }
|
|
6
|
388 # Add average temperature for current day.
|
|
|
389 vector.individual[3] <- vector.individual[3] + averages.temp
|
|
5
|
390 # Add 1 day in current stage.
|
|
6
|
391 vector.individual[4] <- vector.individual[4] + 1
|
|
|
392 vector.matrix[i,] <- vector.individual
|
|
|
393 if (num_insects.birth > 0) {
|
|
5
|
394 # Add new birth -- might be in different generations.
|
|
6
|
395 new.gen <- vector.individual[1] + 1
|
|
5
|
396 # Egg profile.
|
|
6
|
397 new.individual <- c(new.gen, 0, 0, 0, 0)
|
|
|
398 new.vector <- rep(new.individual, num_insects.birth)
|
|
5
|
399 # Update batch of egg profile.
|
|
6
|
400 new.vector <- t(matrix(new.vector, nrow=5))
|
|
5
|
401 # Group with total eggs laid in that day.
|
|
6
|
402 birth.vector <- rbind(birth.vector, new.vector)
|
|
5
|
403 }
|
|
|
404 }
|
|
6
|
405 # Oviposition -- for generation 1.
|
|
|
406 if (vector.individual[2] == 4 && vector.individual[1] == 1 && mean.temp > 12.5 && doy < 222) {
|
|
5
|
407 # Vittelogenic stage, 1st generation
|
|
6
|
408 if (vector.individual[4] == 0) {
|
|
5
|
409 # Just turned in vittelogenic stage.
|
|
6
|
410 num_insects.birth = round(runif(1, 2+opt$min_clutch_size, 8+opt$max_clutch_size))
|
|
5
|
411 }
|
|
|
412 else {
|
|
|
413 # Daily probability of birth.
|
|
|
414 p.birth = opt$oviposition * 0.01
|
|
|
415 u1 <- runif(1)
|
|
|
416 if (u1 < p.birth) {
|
|
6
|
417 num_insects.birth = round(runif(1, 2, 8))
|
|
5
|
418 }
|
|
|
419 }
|
|
6
|
420 # Add average temperature for current day.
|
|
|
421 vector.individual[3] <- vector.individual[3] + averages.temp
|
|
5
|
422 # Add 1 day in current stage.
|
|
6
|
423 vector.individual[4] <- vector.individual[4] + 1
|
|
|
424 vector.matrix[i,] <- vector.individual
|
|
|
425 if (num_insects.birth > 0) {
|
|
5
|
426 # Add new birth -- might be in different generations.
|
|
6
|
427 new.gen <- vector.individual[1] + 1
|
|
5
|
428 # Egg profile.
|
|
6
|
429 new.individual <- c(new.gen, 0, 0, 0, 0)
|
|
|
430 new.vector <- rep(new.individual, num_insects.birth)
|
|
5
|
431 # Update batch of egg profile.
|
|
6
|
432 new.vector <- t(matrix(new.vector, nrow=5))
|
|
5
|
433 # Group with total eggs laid in that day.
|
|
6
|
434 birth.vector <- rbind(birth.vector, new.vector)
|
|
5
|
435 }
|
|
|
436 }
|
|
6
|
437 # Egg to young nymph.
|
|
|
438 if (vector.individual[2] == 0) {
|
|
|
439 # Add average temperature for current day.
|
|
|
440 vector.individual[3] <- vector.individual[3] + averages.temp
|
|
|
441 if (vector.individual[3] >= (68+opt$young_nymph_accumulation)) {
|
|
|
442 # From egg to young nymph, degree-days requirement met.
|
|
|
443 current.gen <- vector.individual[1]
|
|
5
|
444 # Transfer to young nymph stage.
|
|
6
|
445 vector.individual <- c(current.gen, 1, 0, 0, 0)
|
|
5
|
446 }
|
|
|
447 else {
|
|
|
448 # Add 1 day in current stage.
|
|
6
|
449 vector.individual[4] <- vector.individual[4] + 1
|
|
5
|
450 }
|
|
6
|
451 vector.matrix[i,] <- vector.individual
|
|
5
|
452 }
|
|
6
|
453 # Young nymph to old nymph.
|
|
|
454 if (vector.individual[2] == 1) {
|
|
|
455 # Add average temperature for current day.
|
|
|
456 vector.individual[3] <- vector.individual[3] + averages.temp
|
|
|
457 if (vector.individual[3] >= (250+opt$old_nymph_accumulation)) {
|
|
|
458 # From young to old nymph, degree_days requirement met.
|
|
|
459 current.gen <- vector.individual[1]
|
|
5
|
460 # Transfer to old nym stage.
|
|
6
|
461 vector.individual <- c(current.gen, 2, 0, 0, 0)
|
|
5
|
462 if (photoperiod < opt$photoperiod && doy > 180) {
|
|
6
|
463 vector.individual[5] <- 1
|
|
5
|
464 } # Prepare for diapausing.
|
|
|
465 }
|
|
|
466 else {
|
|
|
467 # Add 1 day in current stage.
|
|
6
|
468 vector.individual[4] <- vector.individual[4] + 1
|
|
5
|
469 }
|
|
6
|
470 vector.matrix[i,] <- vector.individual
|
|
|
471 }
|
|
|
472 # Old nymph to adult: previttelogenic or diapausing?
|
|
|
473 if (vector.individual[2] == 2) {
|
|
|
474 # Add average temperature for current day.
|
|
|
475 vector.individual[3] <- vector.individual[3] + averages.temp
|
|
|
476 if (vector.individual[3] >= (200+opt$adult_accumulation)) {
|
|
|
477 # From old to adult, degree_days requirement met.
|
|
|
478 current.gen <- vector.individual[1]
|
|
|
479 if (vector.individual[5] == 0) {
|
|
|
480 # Previttelogenic.
|
|
|
481 vector.individual <- c(current.gen, 3, 0, 0, 0)
|
|
5
|
482 }
|
|
|
483 else {
|
|
|
484 # Diapausing.
|
|
6
|
485 vector.individual <- c(current.gen, 5, 0, 0, 1)
|
|
5
|
486 }
|
|
|
487 }
|
|
|
488 else {
|
|
|
489 # Add 1 day in current stage.
|
|
6
|
490 vector.individual[4] <- vector.individual[4] + 1
|
|
5
|
491 }
|
|
6
|
492 vector.matrix[i,] <- vector.individual
|
|
5
|
493 }
|
|
6
|
494 # Growing of diapausing adult (unimportant, but still necessary).
|
|
|
495 if (vector.individual[2] == 5) {
|
|
|
496 vector.individual[3] <- vector.individual[3] + averages.temp
|
|
|
497 vector.individual[4] <- vector.individual[4] + 1
|
|
|
498 vector.matrix[i,] <- vector.individual
|
|
5
|
499 }
|
|
|
500 } # Else if it is still alive.
|
|
|
501 } # End of the individual bug loop.
|
|
6
|
502
|
|
|
503 # Number of deaths.
|
|
|
504 num_insects.death <- length(death.vector)
|
|
|
505 if (num_insects.death > 0) {
|
|
|
506 # Remove record of dead.
|
|
|
507 vector.matrix <- vector.matrix[-death.vector, ]
|
|
5
|
508 }
|
|
6
|
509 # Number of births.
|
|
|
510 num_insects.newborn <- length(birth.vector[,1])
|
|
|
511 vector.matrix <- rbind(vector.matrix, birth.vector)
|
|
5
|
512 # Update population size for the next day.
|
|
6
|
513 num_insects <- num_insects - num_insects.death + num_insects.newborn
|
|
5
|
514
|
|
|
515 # Aggregate results by day.
|
|
6
|
516 # Egg population size.
|
|
|
517 Eggs[row] <- sum(vector.matrix[,2]==0)
|
|
|
518 # Young nymph population size.
|
|
|
519 YoungNymphs[row] <- sum(vector.matrix[,2]==1)
|
|
|
520 # Old nymph population size.
|
|
|
521 OldNymphs[row] <- sum(vector.matrix[,2]==2)
|
|
|
522 # Previtellogenic population size.
|
|
|
523 Previtellogenic[row] <- sum(vector.matrix[,2]==3)
|
|
|
524 # Vitellogenic population size.
|
|
|
525 Vitellogenic[row] <- sum(vector.matrix[,2]==4)
|
|
|
526 # Diapausing population size.
|
|
|
527 Diapausing[row] <- sum(vector.matrix[,2]==5)
|
|
5
|
528
|
|
6
|
529 # Newborn population size.
|
|
|
530 N.newborn[row] <- num_insects.newborn
|
|
|
531 # Adult population size.
|
|
|
532 N.adult[row] <- sum(vector.matrix[,2]==3) + sum(vector.matrix[,2]==4) + sum(vector.matrix[,2]==5)
|
|
|
533 # Dead population size.
|
|
|
534 N.death[row] <- num_insects.death
|
|
|
535
|
|
|
536 total.population <- c(total.population, num_insects)
|
|
|
537
|
|
|
538 # Overwintering adult population size.
|
|
|
539 overwintering_adult.population[row] <- sum(vector.matrix[,1]==0)
|
|
|
540 # First generation population size.
|
|
|
541 first_generation.population[row] <- sum(vector.matrix[,1]==1)
|
|
|
542 # Second generation population size.
|
|
|
543 second_generation.population[row] <- sum(vector.matrix[,1]==2)
|
|
5
|
544
|
|
6
|
545 # P adult population size.
|
|
|
546 P.adult[row] <- sum(vector.matrix[,1]==0)
|
|
|
547 # F1 adult population size.
|
|
|
548 F1.adult[row] <- sum((vector.matrix[,1]==1 & vector.matrix[,2]==3) | (vector.matrix[,1]==1 & vector.matrix[,2]==4) | (vector.matrix[,1]==1 & vector.matrix[,2]==5))
|
|
|
549 # F2 adult population size
|
|
|
550 F2.adult[row] <- sum((vector.matrix[,1]==2 & vector.matrix[,2]==3) | (vector.matrix[,1]==2 & vector.matrix[,2]==4) | (vector.matrix[,1]==2 & vector.matrix[,2]==5))
|
|
|
551 } # End of days specified in the input temperature data.
|
|
5
|
552
|
|
6
|
553 averages.cum <- cumsum(averages.day)
|
|
5
|
554
|
|
6
|
555 # Define the output values.
|
|
|
556 Eggs.replications[,N.replications] <- Eggs
|
|
|
557 YoungNymphs.replications[,N.replications] <- YoungNymphs
|
|
|
558 OldNymphs.replications[,N.replications] <- OldNymphs
|
|
|
559 Previtellogenic.replications[,N.replications] <- Previtellogenic
|
|
|
560 Vitellogenic.replications[,N.replications] <- Vitellogenic
|
|
|
561 Diapausing.replications[,N.replications] <- Diapausing
|
|
|
562
|
|
|
563 newborn.replications[,N.replications] <- N.newborn
|
|
|
564 adult.replications[,N.replications] <- N.adult
|
|
|
565 death.replications[,N.replications] <- N.death
|
|
|
566
|
|
|
567 P.replications[,N.replications] <- overwintering_adult.population
|
|
|
568 P_adults.replications[,N.replications] <- P.adult
|
|
|
569 F1.replications[,N.replications] <- first_generation.population
|
|
|
570 F1_adults.replications[,N.replications] <- F1.adult
|
|
|
571 F2.replications[,N.replications] <- second_generation.population
|
|
|
572 F2_adults.replications[,N.replications] <- F2.adult
|
|
|
573
|
|
|
574 population.replications[,N.replications] <- total.population
|
|
5
|
575 }
|
|
|
576
|
|
6
|
577 # Mean value for eggs.
|
|
|
578 eggs <- apply(Eggs.replications, 1, mean)
|
|
|
579 # Standard error for eggs.
|
|
|
580 eggs.std_error <- apply(Eggs.replications, 1, sd) / sqrt(opt$replications)
|
|
|
581
|
|
|
582 # Mean value for nymphs.
|
|
|
583 nymphs <- apply((YoungNymphs.replications+OldNymphs.replications), 1, mean)
|
|
|
584 # Standard error for nymphs.
|
|
|
585 nymphs.std_error <- apply((YoungNymphs.replications+OldNymphs.replications) / sqrt(opt$replications), 1, sd)
|
|
5
|
586
|
|
6
|
587 # Mean value for adults.
|
|
|
588 adults <- apply((Previtellogenic.replications+Vitellogenic.replications+Diapausing.replications), 1, mean)
|
|
|
589 # Standard error for adults.
|
|
|
590 adults.std_error <- apply((Previtellogenic.replications+Vitellogenic.replications+Diapausing.replications), 1, sd) / sqrt(opt$replications)
|
|
|
591
|
|
|
592 # Mean value for P.
|
|
|
593 P <- apply(P.replications, 1, mean)
|
|
|
594 # Standard error for P.
|
|
|
595 P.std_error <- apply(P.replications, 1, sd) / sqrt(opt$replications)
|
|
5
|
596
|
|
6
|
597 # Mean value for P adults.
|
|
|
598 P_adults <- apply(P_adults.replications, 1, mean)
|
|
|
599 # Standard error for P_adult.
|
|
|
600 P_adults.std_error <- apply(P_adults.replications, 1, sd) / sqrt(opt$replications)
|
|
|
601
|
|
|
602 # Mean value for F1.
|
|
|
603 F1 <- apply(F1.replications, 1, mean)
|
|
|
604 # Standard error for F1.
|
|
|
605 F1.std_error <- apply(F1.replications, 1, sd) / sqrt(opt$replications)
|
|
5
|
606
|
|
6
|
607 # Mean value for F1 adults.
|
|
|
608 F1_adults <- apply(F1_adults.replications, 1, mean)
|
|
|
609 # Standard error for F1 adult.
|
|
|
610 F1_adults.std_error <- apply(F1_adults.replications, 1, sd) / sqrt(opt$replications)
|
|
|
611
|
|
|
612 # Mean value for F2.
|
|
|
613 F2 <- apply(F2.replications, 1, mean)
|
|
|
614 # Standard error for F2.
|
|
|
615 F2.std_error <- apply(F2.replications, 1, sd) / sqrt(opt$replications)
|
|
|
616
|
|
|
617 # Mean value for F2 adults.
|
|
|
618 F2_adults <- apply(F2_adults.replications, 1, mean)
|
|
|
619 # Standard error for F2 adult.
|
|
|
620 F2_adults.std_error <- apply(F2_adults.replications, 1, sd) / sqrt(opt$replications)
|
|
|
621
|
|
|
622 # Display the total number of days in the Galaxy history item blurb.
|
|
|
623 cat("Number of days: ", opt$num_days, "\n")
|
|
5
|
624
|
|
|
625 dev.new(width=20, height=30)
|
|
|
626
|
|
|
627 # Start PDF device driver to save charts to output.
|
|
|
628 pdf(file=opt$output, width=20, height=30, bg="white")
|
|
6
|
629 par(mar=c(5, 6, 4, 4), mfrow=c(3, 1))
|
|
5
|
630
|
|
6
|
631 # Data analysis and visualization plots only within a single calendar year.
|
|
|
632 days <- c(1:opt$num_days)
|
|
|
633 start_date <- temperature_data_frame$DATE[1]
|
|
|
634 end_date <- temperature_data_frame$DATE[opt$num_days]
|
|
5
|
635
|
|
6
|
636 # Subfigure 1: population size by life stage.
|
|
|
637 maxval <- max(eggs+eggs.std_error, nymphs+nymphs.std_error, adults+adults.std_error)
|
|
|
638 render_chart("pop_size_by_life_stage", opt$insect, opt$location, latitude, start_date, end_date, days, maxval,
|
|
|
639 opt$std_error_plot, adults, nymphs, eggs, adults.std_error, nymphs.std_error, eggs.std_error)
|
|
|
640 # Subfigure 2: population size by generation.
|
|
|
641 maxval <- max(F2)
|
|
|
642 render_chart("pop_size_by_generation", opt$insect, opt$location, latitude, start_date, end_date, days, maxval,
|
|
|
643 opt$std_error_plot, P, F1, F2, P.std_error, F1.std_error, F2.std_error)
|
|
|
644 # Subfigure 3: adult population size by generation.
|
|
|
645 maxval <- max(F2_adults) + 100
|
|
|
646 render_chart("adult_pop_size_by_generation", opt$insect, opt$location, latitude, start_date, end_date, days, maxval,
|
|
|
647 opt$std_error_plot, P_adults, F1_adults, F2_adults, P_adults.std_error, F1_adults.std_error, F2_adults.std_error)
|
|
5
|
648
|
|
|
649 # Turn off device driver to flush output.
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|
|
650 dev.off()
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