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0
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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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64
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14 make_option(c("-m", "--num_columns"), action="store", dest="num_columns", type="integer", help="Total number of columns in the temperature dataset"),
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13
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15 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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16 make_option(c("-o", "--output"), action="store", dest="output", help="Output dataset"),
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17 make_option(c("-p", "--oviposition"), action="store", dest="oviposition", type="integer", help="Adjustment for oviposition rate"),
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18 make_option(c("-q", "--photoperiod"), action="store", dest="photoperiod", type="double", help="Critical photoperiod for diapause induction/termination"),
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19 make_option(c("-s", "--replications"), action="store", dest="replications", type="integer", help="Number of replications"),
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20 make_option(c("-t", "--se_plot"), action="store", dest="se_plot", help="Plot SE"),
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22
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21 make_option(c("-v", "--input"), action="store", dest="input", help="Temperature data for selected location"),
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22 make_option(c("-y", "--young_nymph_accum"), action="store", dest="young_nymph_accum", type="integer", help="Adjustment of DD accumulation (egg->young nymph)")
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23 )
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24
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25 parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
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26 args <- parse_args(parser, positional_arguments=TRUE)
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27 opt <- args$options
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28
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64
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29 add_daylight_length = function(latitude, temperature_data_matrix, num_days) {
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46
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30 # Return a vector of daylight length (photoperido profile) for
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31 # the number of days specified in the input temperature data
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32 # (from Forsythe 1995).
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33 p = 0.8333
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34 daylight_length_vector <- NULL
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35 for (i in 1:num_days) {
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36 # Get the day of the year from the current row
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37 # of the temperature data for computation.
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64
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38 doy <- temperature_data_matrix[i, 4]
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46
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39 theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (doy - 186)))
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40 phi <- asin(0.39795 * cos(theta))
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41 # Compute the length of daylight for the day of the year.
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42 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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43 }
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64
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44 # Append daylight_length_vector as a new column to temperature_data_matrix.
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66
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45 cbind(temperature_data_matrix, newColumn=daylight_length_vector)
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68
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46 # Return the altered temperature_data_matrix.
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47 temperature_data_matrix
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48 }
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49
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64
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50 get_temperature_at_hour = function(latitude, temperature_data_matrix, row, num_days) {
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26
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51 # Base development threshold for Brown Marmolated Stink Bug
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52 # insect phenology model.
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46
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53 # TODO: Pass insect on the command line to accomodate more
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54 # the just the Brown Marmolated Stink Bub.
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55 threshold <- 14.17
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56
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57 # Input temperature currently has the following columns.
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58 # # LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX
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59 # Minimum temperature for current row.
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64
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60 dnp <- temperature_data_matrix[row, 5]
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61 # Maximum temperature for current row.
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64
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62 dxp <- temperature_data_matrix[row, 6]
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46
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63 # Mean temperature for current row.
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64 dmean <- 0.5 * (dnp + dxp)
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47
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65 # Initialize degree day accumulation
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66 dd <- 0
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46
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67 if (dxp < threshold) {
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68 dd <- 0
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69 }
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70 else {
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26
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71 # Initialize hourly temperature.
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72 T <- NULL
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73 # Initialize degree hour vector.
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74 dh <- NULL
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46
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75 # Daylight length for current row.
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64
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76 y <- temperature_data_matrix[row, 7]
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46
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77 # Darkness length.
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78 z <- 24 - y
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79 # Lag coefficient.
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80 a <- 1.86
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81 # Darkness coefficient.
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82 b <- 2.20
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26
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83 # Sunrise time.
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84 risetime <- 12 - y / 2
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26
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85 # Sunset time.
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86 settime <- 12 + y / 2
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87 ts <- (dxp - dnp) * sin(pi * (settime - 5) / (y + 2 * a)) + dnp
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88 for (i in 1:24) {
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89 if (i > risetime && i < settime) {
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90 # Number of hours after Tmin until sunset.
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91 m <- i - 5
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92 T[i] = (dxp - dnp) * sin(pi * m / (y + 2 * a)) + dnp
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93 if (T[i] < 8.4) {
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94 dh[i] <- 0
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95 }
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96 else {
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97 dh[i] <- T[i] - 8.4
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98 }
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99 }
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100 else if (i > settime) {
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101 n <- i - settime
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102 T[i] = dnp + (ts - dnp) * exp( - b * n / z)
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103 if (T[i] < 8.4) {
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104 dh[i] <- 0
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105 }
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106 else {
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107 dh[i] <- T[i] - 8.4
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108 }
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109 }
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110 else {
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111 n <- i + 24 - settime
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112 T[i]=dnp + (ts - dnp) * exp( - b * n / z)
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113 if (T[i] < 8.4) {
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114 dh[i] <- 0
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115 }
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116 else {
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117 dh[i] <- T[i] - 8.4
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118 }
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119 }
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120 }
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121 dd <- sum(dh) / 24
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122 }
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123 return=c(dmean, dd)
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124 return
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125 }
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126
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127 dev.egg = function(temperature) {
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128 dev.rate= -0.9843 * temperature + 33.438
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129 return = dev.rate
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130 return
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131 }
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132
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133 dev.young = function(temperature) {
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134 n12 <- -0.3728 * temperature + 14.68
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135 n23 <- -0.6119 * temperature + 25.249
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136 dev.rate = mean(n12 + n23)
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137 return = dev.rate
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138 return
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139 }
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140
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141 dev.old = function(temperature) {
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142 n34 <- -0.6119 * temperature + 17.602
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143 n45 <- -0.4408 * temperature + 19.036
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144 dev.rate = mean(n34 + n45)
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145 return = dev.rate
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146 return
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147 }
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148
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149 dev.emerg = function(temperature) {
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150 emerg.rate <- -0.5332 * temperature + 24.147
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151 return = emerg.rate
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152 return
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153 }
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154
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155 mortality.egg = function(temperature) {
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156 if (temperature < 12.7) {
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157 mort.prob = 0.8
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158 }
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159 else {
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160 mort.prob = 0.8 - temperature / 40.0
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161 if (mort.prob < 0) {
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162 mort.prob = 0.01
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163 }
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164 }
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165 return = mort.prob
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166 return
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167 }
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168
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169 mortality.nymph = function(temperature) {
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170 if (temperature < 12.7) {
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171 mort.prob = 0.03
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172 }
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173 else {
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174 mort.prob = temperature * 0.0008 + 0.03
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175 }
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176 return = mort.prob
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177 return
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178 }
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179
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180 mortality.adult = function(temperature) {
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181 if (temperature < 12.7) {
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182 mort.prob = 0.002
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183 }
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184 else {
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185 mort.prob = temperature * 0.0005 + 0.02
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186 }
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187 return = mort.prob
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188 return
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189 }
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190
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22
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191 # Read in the input temperature datafile into a Data Frame object.
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44
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192 # The input data currently must have 6 columns:
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193 # LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX
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194 temperature_data_matrix <- read.csv(file=opt$input, header=T, strip.white=TRUE, sep=",")
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195 start_date <- temperature_data_matrix[1, 3]
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196 end_date <- temperature_data_matrix[opt$num_days, 3]
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197 latitude <- temperature_data_matrix[1, 1]
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68
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198 temperature_data_matrix <- add_daylight_length(latitude, temperature_data_matrix, opt$num_days)
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199
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200 cat("Number of days: ", opt$num_days, "\n")
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201 cat(temperature_data_matrix)
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202
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203 # Initialize matrix for results from all replications.
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204 S0.rep <- S1.rep <- S2.rep <- S3.rep <- S4.rep <- S5.rep <- matrix(rep(0, opt$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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26
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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$num_days)
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227
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22
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228 # All the days included in the input temperature dataset.
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229 for (row in 1:opt$num_days) {
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230 # Get the integer day of the year for the current row.
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231 doy <- temperature_data_matrix[row, 4]
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232 # Photoperiod in the day.
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233 photoperiod <- temperature_data_matrix[row, 7]
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234 temp.profile <- get_temperature_at_hour(latitude, temperature_data_matrix, row, opt$num_days)
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235 mean.temp <- temp.profile[1]
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236 dd.temp <- temp.profile[2]
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237 dd.day[row] <- dd.temp
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238 # Trash bin for death.
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239 death.vec <- NULL
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26
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240 # Newborn.
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241 birth.vec <- NULL
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242
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243 # All individuals.
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244 for (i in 1:n) {
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245 # Find individual record.
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246 vec.ind <- vec.mat[i,]
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26
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247 # First of all, still alive?
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248 # Adjustment for late season mortality rate.
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249 if (latitude < 40.0) {
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250 post.mort <- 1
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251 day.kill <- 300
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252 }
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253 else {
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254 post.mort <- 2
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255 day.kill <- 250
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256 }
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257 if (vec.ind[2] == 0) {
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26
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258 # Egg.
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259 death.prob = opt$egg_mort * mortality.egg(mean.temp)
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260 }
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261 else if (vec.ind[2] == 1 | vec.ind[2] == 2) {
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262 death.prob = opt$nymph_mort * mortality.nymph(mean.temp)
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263 }
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264 else if (vec.ind[2] == 3 | vec.ind[2] == 4 | vec.ind[2] == 5) {
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26
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265 # For adult.
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266 if (doy < day.kill) {
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267 death.prob = opt$adult_mort * mortality.adult(mean.temp)
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268 }
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269 else {
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270 # Increase adult mortality after fall equinox.
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271 death.prob = opt$adult_mort * post.mort * mortality.adult(mean.temp)
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272 }
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273 }
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274 # (or dependent on temperature and life stage?)
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275 u.d <- runif(1)
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276 if (u.d < death.prob) {
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277 death.vec <- c(death.vec, i)
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278 }
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279 else {
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280 # Aggregrate index of dead bug.
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281 # Event 1 end of diapause.
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282 if (vec.ind[1] == 0 && vec.ind[2] == 3) {
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283 # Overwintering adult (previttelogenic).
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284 if (photoperiod > opt$photoperiod && vec.ind[3] > 68 && doy < 180) {
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285 # Add 68C to become fully reproductively matured.
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286 # Transfer to vittelogenic.
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287 vec.ind <- c(0, 4, 0, 0, 0)
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288 vec.mat[i,] <- vec.ind
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289 }
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290 else {
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291 # Add to dd.
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292 vec.ind[3] <- vec.ind[3] + dd.temp
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293 # Add 1 day in current stage.
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294 vec.ind[4] <- vec.ind[4] + 1
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295 vec.mat[i,] <- vec.ind
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296 }
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297 }
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298 if (vec.ind[1] != 0 && vec.ind[2] == 3) {
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299 # Not overwintering adult (previttelogenic).
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300 current.gen <- vec.ind[1]
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301 if (vec.ind[3] > 68) {
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302 # Add 68C to become fully reproductively matured.
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303 # Transfer to vittelogenic.
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304 vec.ind <- c(current.gen, 4, 0, 0, 0)
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305 vec.mat[i,] <- vec.ind
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306 }
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307 else {
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26
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308 # Add to dd.
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309 vec.ind[3] <- vec.ind[3] + dd.temp
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26
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310 # Add 1 day in current stage.
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311 vec.ind[4] <- vec.ind[4] + 1
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312 vec.mat[i,] <- vec.ind
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313 }
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314 }
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315
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26
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316 # Event 2 oviposition -- where population dynamics comes from.
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317 if (vec.ind[2] == 4 && vec.ind[1] == 0 && mean.temp > 10) {
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318 # Vittelogenic stage, overwintering generation.
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319 if (vec.ind[4] == 0) {
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320 # Just turned in vittelogenic stage.
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321 n.birth=round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size))
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322 }
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323 else {
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26
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324 # Daily probability of birth.
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325 p.birth = opt$oviposition * 0.01
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326 u1 <- runif(1)
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327 if (u1 < p.birth) {
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328 n.birth=round(runif(1, 2, 8))
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329 }
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330 }
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26
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331 # Add to dd.
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332 vec.ind[3] <- vec.ind[3] + dd.temp
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26
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333 # Add 1 day in current stage.
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334 vec.ind[4] <- vec.ind[4] + 1
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335 vec.mat[i,] <- vec.ind
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336 if (n.birth > 0) {
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26
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337 # Add new birth -- might be in different generations.
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338 new.gen <- vec.ind[1] + 1
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26
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339 # Egg profile.
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340 new.ind <- c(new.gen, 0, 0, 0, 0)
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341 new.vec <- rep(new.ind, n.birth)
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26
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342 # Update batch of egg profile.
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343 new.vec <- t(matrix(new.vec, nrow=5))
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26
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344 # Group with total eggs laid in that day.
|
|
0
|
345 birth.vec <- rbind(birth.vec, new.vec)
|
|
|
346 }
|
|
|
347 }
|
|
|
348
|
|
26
|
349 # Event 2 oviposition -- for gen 1.
|
|
47
|
350 if (vec.ind[2] == 4 && vec.ind[1] == 1 && mean.temp > 12.5 && doy < 222) {
|
|
26
|
351 # Vittelogenic stage, 1st generation
|
|
0
|
352 if (vec.ind[4] == 0) {
|
|
26
|
353 # Just turned in vittelogenic stage.
|
|
0
|
354 n.birth=round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size))
|
|
|
355 }
|
|
|
356 else {
|
|
26
|
357 # Daily probability of birth.
|
|
0
|
358 p.birth = opt$oviposition * 0.01
|
|
|
359 u1 <- runif(1)
|
|
|
360 if (u1 < p.birth) {
|
|
|
361 n.birth = round(runif(1, 2, 8))
|
|
|
362 }
|
|
|
363 }
|
|
26
|
364 # Add to dd.
|
|
0
|
365 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
26
|
366 # Add 1 day in current stage.
|
|
0
|
367 vec.ind[4] <- vec.ind[4] + 1
|
|
|
368 vec.mat[i,] <- vec.ind
|
|
|
369 if (n.birth > 0) {
|
|
26
|
370 # Add new birth -- might be in different generations.
|
|
0
|
371 new.gen <- vec.ind[1] + 1
|
|
26
|
372 # Egg profile.
|
|
0
|
373 new.ind <- c(new.gen, 0, 0, 0, 0)
|
|
|
374 new.vec <- rep(new.ind, n.birth)
|
|
26
|
375 # Update batch of egg profile.
|
|
0
|
376 new.vec <- t(matrix(new.vec, nrow=5))
|
|
26
|
377 # Group with total eggs laid in that day.
|
|
0
|
378 birth.vec <- rbind(birth.vec, new.vec)
|
|
|
379 }
|
|
|
380 }
|
|
|
381
|
|
26
|
382 # Event 3 development (with diapause determination).
|
|
|
383 # Event 3.1 egg development to young nymph (vec.ind[2]=0 -> egg).
|
|
0
|
384 if (vec.ind[2] == 0) {
|
|
26
|
385 # Egg stage.
|
|
|
386 # Add to dd.
|
|
0
|
387 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
388 if (vec.ind[3] >= (68 + opt$young_nymph_accum)) {
|
|
26
|
389 # From egg to young nymph, DD requirement met.
|
|
0
|
390 current.gen <- vec.ind[1]
|
|
26
|
391 # Transfer to young nymph stage.
|
|
0
|
392 vec.ind <- c(current.gen, 1, 0, 0, 0)
|
|
|
393 }
|
|
|
394 else {
|
|
26
|
395 # Add 1 day in current stage.
|
|
0
|
396 vec.ind[4] <- vec.ind[4] + 1
|
|
|
397 }
|
|
|
398 vec.mat[i,] <- vec.ind
|
|
|
399 }
|
|
|
400
|
|
26
|
401 # Event 3.2 young nymph to old nymph (vec.ind[2]=1 -> young nymph: determines diapause).
|
|
0
|
402 if (vec.ind[2] == 1) {
|
|
26
|
403 # young nymph stage.
|
|
|
404 # add to dd.
|
|
0
|
405 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
406 if (vec.ind[3] >= (250 + opt$old_nymph_accum)) {
|
|
26
|
407 # From young to old nymph, dd requirement met.
|
|
0
|
408 current.gen <- vec.ind[1]
|
|
26
|
409 # Transfer to old nym stage.
|
|
0
|
410 vec.ind <- c(current.gen, 2, 0, 0, 0)
|
|
47
|
411 if (photoperiod < opt$photoperiod && doy > 180) {
|
|
0
|
412 vec.ind[5] <- 1
|
|
26
|
413 } # Prepare for diapausing.
|
|
0
|
414 }
|
|
|
415 else {
|
|
26
|
416 # Add 1 day in current stage.
|
|
0
|
417 vec.ind[4] <- vec.ind[4] + 1
|
|
|
418 }
|
|
|
419 vec.mat[i,] <- vec.ind
|
|
|
420 }
|
|
|
421
|
|
26
|
422 # Event 3.3 old nymph to adult: previttelogenic or diapausing?
|
|
0
|
423 if (vec.ind[2] == 2) {
|
|
26
|
424 # Old nymph stage.
|
|
|
425 # add to dd.
|
|
0
|
426 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
427 if (vec.ind[3] >= (200 + opt$adult_accum)) {
|
|
26
|
428 # From old to adult, dd requirement met.
|
|
0
|
429 current.gen <- vec.ind[1]
|
|
|
430 if (vec.ind[5] == 0) {
|
|
26
|
431 # Non-diapausing adult -- previttelogenic.
|
|
0
|
432 vec.ind <- c(current.gen, 3, 0, 0, 0)
|
|
|
433 }
|
|
|
434 else {
|
|
26
|
435 # Diapausing.
|
|
0
|
436 vec.ind <- c(current.gen, 5, 0, 0, 1)
|
|
|
437 }
|
|
|
438 }
|
|
|
439 else {
|
|
26
|
440 # Add 1 day in current stage.
|
|
0
|
441 vec.ind[4] <- vec.ind[4] + 1
|
|
|
442 }
|
|
|
443 vec.mat[i,] <- vec.ind
|
|
|
444 }
|
|
|
445
|
|
26
|
446 # Event 4 growing of diapausing adult (unimportant, but still necessary).
|
|
0
|
447 if (vec.ind[2] == 5) {
|
|
|
448 vec.ind[3] <- vec.ind[3] + dd.temp
|
|
|
449 vec.ind[4] <- vec.ind[4] + 1
|
|
|
450 vec.mat[i,] <- vec.ind
|
|
|
451 }
|
|
26
|
452 } # Else if it is still alive.
|
|
|
453 } # End of the individual bug loop.
|
|
0
|
454
|
|
26
|
455 # Find how many died.
|
|
0
|
456 n.death <- length(death.vec)
|
|
|
457 if (n.death > 0) {
|
|
|
458 vec.mat <- vec.mat[-death.vec, ]
|
|
|
459 }
|
|
26
|
460 # Remove record of dead.
|
|
|
461 # Find how many new born.
|
|
0
|
462 n.newborn <- length(birth.vec[,1])
|
|
|
463 vec.mat <- rbind(vec.mat, birth.vec)
|
|
26
|
464 # Update population size for the next day.
|
|
0
|
465 n <- n - n.death + n.newborn
|
|
|
466
|
|
26
|
467 # Aggregate results by day.
|
|
0
|
468 tot.pop <- c(tot.pop, n)
|
|
26
|
469 # Egg.
|
|
0
|
470 s0 <- sum(vec.mat[,2] == 0)
|
|
26
|
471 # Young nymph.
|
|
0
|
472 s1 <- sum(vec.mat[,2] == 1)
|
|
26
|
473 # Old nymph.
|
|
0
|
474 s2 <- sum(vec.mat[,2] == 2)
|
|
26
|
475 # Previtellogenic.
|
|
0
|
476 s3 <- sum(vec.mat[,2] == 3)
|
|
26
|
477 # Vitellogenic.
|
|
0
|
478 s4 <- sum(vec.mat[,2] == 4)
|
|
26
|
479 # Diapausing.
|
|
0
|
480 s5 <- sum(vec.mat[,2] == 5)
|
|
26
|
481 # Overwintering adult.
|
|
0
|
482 gen0 <- sum(vec.mat[,1] == 0)
|
|
26
|
483 # First generation.
|
|
0
|
484 gen1 <- sum(vec.mat[,1] == 1)
|
|
26
|
485 # Second generation.
|
|
0
|
486 gen2 <- sum(vec.mat[,1] == 2)
|
|
26
|
487 # Sum of all adults.
|
|
0
|
488 n.adult <- sum(vec.mat[,2] == 3) + sum(vec.mat[,2] == 4) + sum(vec.mat[,2] == 5)
|
|
47
|
489
|
|
|
490 # Generation 0 pop size.
|
|
|
491 gen0.pop[row] <- gen0
|
|
|
492 gen1.pop[row] <- gen1
|
|
|
493 gen2.pop[row] <- gen2
|
|
0
|
494
|
|
47
|
495 S0[row] <- s0
|
|
|
496 S1[row] <- s1
|
|
|
497 S2[row] <- s2
|
|
|
498 S3[row] <- s3
|
|
|
499 S4[row] <- s4
|
|
|
500 S5[row] <- s5
|
|
|
501
|
|
|
502 g0.adult[row] <- sum(vec.mat[,1] == 0)
|
|
|
503 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))
|
|
|
504 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))
|
|
|
505
|
|
|
506 N.newborn[row] <- n.newborn
|
|
|
507 N.death[row] <- n.death
|
|
|
508 N.adult[row] <- n.adult
|
|
13
|
509 } # end of days specified in the input temperature data
|
|
0
|
510
|
|
|
511 dd.cum <- cumsum(dd.day)
|
|
47
|
512
|
|
26
|
513 # Collect all the outputs.
|
|
0
|
514 S0.rep[,N.rep] <- S0
|
|
|
515 S1.rep[,N.rep] <- S1
|
|
|
516 S2.rep[,N.rep] <- S2
|
|
|
517 S3.rep[,N.rep] <- S3
|
|
|
518 S4.rep[,N.rep] <- S4
|
|
|
519 S5.rep[,N.rep] <- S5
|
|
|
520 newborn.rep[,N.rep] <- N.newborn
|
|
|
521 death.rep[,N.rep] <- N.death
|
|
|
522 adult.rep[,N.rep] <- N.adult
|
|
|
523 pop.rep[,N.rep] <- tot.pop
|
|
|
524 g0.rep[,N.rep] <- gen0.pop
|
|
|
525 g1.rep[,N.rep] <- gen1.pop
|
|
|
526 g2.rep[,N.rep] <- gen2.pop
|
|
|
527 g0a.rep[,N.rep] <- g0.adult
|
|
|
528 g1a.rep[,N.rep] <- g1.adult
|
|
|
529 g2a.rep[,N.rep] <- g2.adult
|
|
|
530 }
|
|
|
531
|
|
44
|
532 # Data analysis and visualization can currently
|
|
46
|
533 # plot only within a single calendar year.
|
|
|
534 # TODO: enhance this to accomodate multiple calendar years.
|
|
|
535 n.yr <- 1
|
|
|
536 day.all <- c(1:opt$num_days * n.yr)
|
|
0
|
537
|
|
|
538 # mean value for adults
|
|
|
539 sa <- apply((S3.rep + S4.rep + S5.rep), 1, mean)
|
|
|
540 # mean value for nymphs
|
|
|
541 sn <- apply((S1.rep + S2.rep), 1,mean)
|
|
|
542 # mean value for eggs
|
|
|
543 se <- apply(S0.rep, 1, mean)
|
|
|
544 # mean value for P
|
|
|
545 g0 <- apply(g0.rep, 1, mean)
|
|
|
546 # mean value for F1
|
|
|
547 g1 <- apply(g1.rep, 1, mean)
|
|
|
548 # mean value for F2
|
|
|
549 g2 <- apply(g2.rep, 1, mean)
|
|
|
550 # mean value for P adult
|
|
|
551 g0a <- apply(g0a.rep, 1, mean)
|
|
|
552 # mean value for F1 adult
|
|
|
553 g1a <- apply(g1a.rep, 1, mean)
|
|
|
554 # mean value for F2 adult
|
|
|
555 g2a <- apply(g2a.rep, 1, mean)
|
|
|
556
|
|
|
557 # SE for adults
|
|
|
558 sa.se <- apply((S3.rep + S4.rep + S5.rep), 1, sd) / sqrt(opt$replications)
|
|
|
559 # SE for nymphs
|
|
|
560 sn.se <- apply((S1.rep + S2.rep) / sqrt(opt$replications), 1, sd)
|
|
|
561 # SE for eggs
|
|
|
562 se.se <- apply(S0.rep, 1, sd) / sqrt(opt$replications)
|
|
|
563 # SE value for P
|
|
|
564 g0.se <- apply(g0.rep, 1, sd) / sqrt(opt$replications)
|
|
|
565 # SE for F1
|
|
|
566 g1.se <- apply(g1.rep, 1, sd) / sqrt(opt$replications)
|
|
|
567 # SE for F2
|
|
|
568 g2.se <- apply(g2.rep, 1, sd) / sqrt(opt$replications)
|
|
|
569 # SE for P adult
|
|
|
570 g0a.se <- apply(g0a.rep, 1, sd) / sqrt(opt$replications)
|
|
|
571 # SE for F1 adult
|
|
|
572 g1a.se <- apply(g1a.rep, 1, sd) / sqrt(opt$replications)
|
|
|
573 # SE for F2 adult
|
|
|
574 g2a.se <- apply(g2a.rep, 1, sd) / sqrt(opt$replications)
|
|
|
575
|
|
48
|
576 dev.new(width=20, height=30)
|
|
0
|
577
|
|
|
578 # Start PDF device driver to save charts to output.
|
|
48
|
579 pdf(file=opt$output, width=20, height=30, bg="white")
|
|
0
|
580
|
|
|
581 par(mar = c(5, 6, 4, 4), mfrow=c(3, 1))
|
|
|
582
|
|
54
|
583 # Subfigure 1: population size by life stage
|
|
|
584 title <- paste("BSMB total population by life stage :", opt$location, ": Lat:", latitude, ":", start_date, "to", end_date, sep=" ")
|
|
55
|
585 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)
|
|
31
|
586 # Young and old nymphs.
|
|
|
587 lines(day.all, sn, lwd=2, lty=1, col=2)
|
|
0
|
588 # Eggs
|
|
31
|
589 lines(day.all, se, lwd=2, lty=1, col=4)
|
|
54
|
590 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"))
|
|
|
591 axis(2, cex.axis=3)
|
|
0
|
592 leg.text <- c("Egg", "Nymph", "Adult")
|
|
48
|
593 legend("topleft", leg.text, lty=c(1, 1, 1), col=c(4, 2, 1), cex=3)
|
|
0
|
594 if (opt$se_plot == 1) {
|
|
48
|
595 # Add SE lines to plot
|
|
0
|
596 # SE for adults
|
|
31
|
597 lines (day.all, sa + sa.se, lty=2)
|
|
|
598 lines (day.all, sa - sa.se, lty=2)
|
|
0
|
599 # SE for nymphs
|
|
31
|
600 lines (day.all, sn + sn.se, col=2, lty=2)
|
|
48
|
601 lines (day.all, sn - sn.se, col=2, lty=2)
|
|
0
|
602 # SE for eggs
|
|
31
|
603 lines (day.all, se + se.se, col=4, lty=2)
|
|
48
|
604 lines (day.all, se - se.se, col=4, lty=2)
|
|
0
|
605 }
|
|
|
606
|
|
54
|
607 # Subfigure 2: population size by generation
|
|
|
608 title <- paste("BSMB total population by generation :", opt$location, ": Lat:", latitude, ":", start_date, "to", end_date, sep=" ")
|
|
55
|
609 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)
|
|
56
|
610 lines(day.all, g1, lwd = 2, lty = 1, col=2)
|
|
|
611 lines(day.all, g2, lwd = 2, lty = 1, col=4)
|
|
54
|
612 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"))
|
|
|
613 axis(2, cex.axis=3)
|
|
0
|
614 leg.text <- c("P", "F1", "F2")
|
|
54
|
615 legend("topleft", leg.text, lty=c(1, 1, 1), col=c(1, 2, 4), cex=3)
|
|
0
|
616 if (opt$se_plot == 1) {
|
|
48
|
617 # Add SE lines to plot
|
|
0
|
618 # SE for adults
|
|
56
|
619 lines (day.all, g0+g0.se, lty=2)
|
|
|
620 lines (day.all, g0-g0.se, lty=2)
|
|
0
|
621 # SE for nymphs
|
|
56
|
622 lines (day.all, g1+g1.se, col=2, lty=2)
|
|
|
623 lines (day.all, g1-g1.se, col=2, lty=2)
|
|
0
|
624 # SE for eggs
|
|
56
|
625 lines (day.all, g2+g2.se, col=4, lty=2)
|
|
|
626 lines (day.all, g2-g2.se, col=4, lty=2)
|
|
0
|
627 }
|
|
|
628
|
|
54
|
629 # Subfigure 3: adult population size by generation
|
|
|
630 title <- paste("BSMB adult population by generation :", opt$location, ": Lat:", latitude, ":", start_date, "to", end_date, sep=" ")
|
|
55
|
631 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)
|
|
56
|
632 lines(day.all, g1a, lwd = 2, lty = 1, col=2)
|
|
|
633 lines(day.all, g2a, lwd = 2, lty = 1, col=4)
|
|
54
|
634 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"))
|
|
|
635 axis(2, cex.axis=3)
|
|
0
|
636 leg.text <- c("P", "F1", "F2")
|
|
54
|
637 legend("topleft", leg.text, lty=c(1, 1, 1), col=c(1, 2, 4), cex=3)
|
|
0
|
638 if (opt$se_plot == 1) {
|
|
48
|
639 # Add SE lines to plot
|
|
0
|
640 # SE for adults
|
|
56
|
641 lines (day.all, g0a+g0a.se, lty=2)
|
|
|
642 lines (day.all, g0a-g0a.se, lty=2)
|
|
0
|
643 # SE for nymphs
|
|
56
|
644 lines (day.all, g1a+g1a.se, col=2, lty=2)
|
|
|
645 lines (day.all, g1a-g1a.se, col=2, lty=2)
|
|
0
|
646 # SE for eggs
|
|
56
|
647 lines (day.all, g2a+g2a.se, col=4, lty=2)
|
|
|
648 lines (day.all, g2a-g2a.se, col=4, lty=2)
|
|
0
|
649 }
|
|
|
650
|
|
|
651 # Turn off device driver to flush output.
|
|
|
652 dev.off()
|
