5
+ − 1 #!/usr/bin/env Rscript
+ − 2
+ − 3 suppressPackageStartupMessages(library("optparse"))
+ − 4
+ − 5 option_list <- list(
6
+ − 6 make_option(c("--adult_mortality"), action="store", dest="adult_mortality", type="integer", help="Adjustment rate for adult mortality"),
+ − 7 make_option(c("--adult_accumulation"), action="store", dest="adult_accumulation", type="integer", help="Adjustment of degree-days accumulation (old nymph->adult)"),
+ − 8 make_option(c("--egg_mortality"), action="store", dest="egg_mortality", type="integer", help="Adjustment rate for egg mortality"),
+ − 9 make_option(c("--input"), action="store", dest="input", help="Temperature data for selected location"),
+ − 10 make_option(c("--insect"), action="store", dest="insect", help="Insect name"),
+ − 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"),
+ − 12 make_option(c("--location"), action="store", dest="location", help="Selected location"),
+ − 13 make_option(c("--min_clutch_size"), action="store", dest="min_clutch_size", type="integer", help="Adjustment of minimum clutch size"),
+ − 14 make_option(c("--max_clutch_size"), action="store", dest="max_clutch_size", type="integer", help="Adjustment of maximum clutch size"),
+ − 15 make_option(c("--nymph_mortality"), action="store", dest="nymph_mortality", type="integer", help="Adjustment rate for nymph mortality"),
+ − 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)"),
+ − 17 make_option(c("--num_days"), action="store", dest="num_days", type="integer", help="Total number of days in the temperature dataset"),
+ − 18 make_option(c("--output"), action="store", dest="output", help="Output dataset"),
+ − 19 make_option(c("--oviposition"), action="store", dest="oviposition", type="integer", help="Adjustment for oviposition rate"),
+ − 20 make_option(c("--photoperiod"), action="store", dest="photoperiod", type="double", help="Critical photoperiod for diapause induction/termination"),
+ − 21 make_option(c("--replications"), action="store", dest="replications", type="integer", help="Number of replications"),
+ − 22 make_option(c("--std_error_plot"), action="store", dest="std_error_plot", help="Plot Standard error"),
+ − 23 make_option(c("--young_nymph_accumulation"), action="store", dest="young_nymph_accumulation", type="integer", help="Adjustment of degree-days accumulation (egg->young nymph)")
5
+ − 24 )
+ − 25
8
+ − 26 parser <- OptionParser(usage="%prog [options] file", option_list=option_list);
+ − 27 args <- parse_args(parser, positional_arguments=TRUE);
+ − 28 opt <- args$options;
5
+ − 29
+ − 30 add_daylight_length = function(temperature_data_frame, num_columns, num_rows) {
+ − 31 # Return a vector of daylight length (photoperido profile) for
+ − 32 # the number of days specified in the input temperature data
+ − 33 # (from Forsythe 1995).
8
+ − 34 p = 0.8333;
+ − 35 latitude = temperature_data_frame$LATITUDE[1];
+ − 36 daylight_length_vector = NULL;
5
+ − 37 for (i in 1:num_rows) {
+ − 38 # Get the day of the year from the current row
+ − 39 # of the temperature data for computation.
8
+ − 40 doy = temperature_data_frame$DOY[i];
+ − 41 theta = 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (doy - 186)));
+ − 42 phi = asin(0.39795 * cos(theta));
5
+ − 43 # Compute the length of daylight for the day of the year.
8
+ − 44 darkness_length = 24 / pi * acos((sin(p * pi / 180) + sin(latitude * pi / 180) * sin(phi)) / (cos(latitude * pi / 180) * cos(phi)));
+ − 45 daylight_length_vector[i] = 24 - darkness_length;
5
+ − 46 }
+ − 47 # Append daylight_length_vector as a new column to temperature_data_frame.
8
+ − 48 temperature_data_frame[, num_columns+1] = daylight_length_vector;
+ − 49 return(temperature_data_frame);
5
+ − 50 }
+ − 51
6
+ − 52 dev.egg = function(temperature) {
8
+ − 53 dev.rate = -0.9843 * temperature + 33.438;
+ − 54 return(dev.rate);
6
+ − 55 }
+ − 56
+ − 57 dev.emerg = function(temperature) {
8
+ − 58 emerg.rate = -0.5332 * temperature + 24.147;
+ − 59 return(emerg.rate);
6
+ − 60 }
+ − 61
+ − 62 dev.old = function(temperature) {
8
+ − 63 n34 = -0.6119 * temperature + 17.602;
+ − 64 n45 = -0.4408 * temperature + 19.036;
+ − 65 dev.rate = mean(n34 + n45);
+ − 66 return(dev.rate);
6
+ − 67 }
+ − 68
+ − 69 dev.young = function(temperature) {
8
+ − 70 n12 = -0.3728 * temperature + 14.68;
+ − 71 n23 = -0.6119 * temperature + 25.249;
+ − 72 dev.rate = mean(n12 + n23);
+ − 73 return(dev.rate);
+ − 74 }
+ − 75
+ − 76
+ − 77 get_date_labels = function(temperature_data_frame, num_rows) {
+ − 78 # Keep track of the years to see if spanning years.
+ − 79 month_labels = list();
+ − 80 current_month_label = NULL;
+ − 81 for (i in 1:num_rows) {
+ − 82 # Get the year and month from the date which
+ − 83 # has the format YYYY-MM-DD.
+ − 84 date = format(temperature_data_frame$DATE[i]);
+ − 85 items = strsplit(date, "-")[[1]];
+ − 86 month = items[2];
+ − 87 month_label = month.abb[as.integer(month)];
+ − 88 if (!identical(current_month_label, month_label)) {
+ − 89 month_labels[length(month_labels)+1] = month_label;
+ − 90 current_month_label = month_label;
+ − 91 }
+ − 92 }
+ − 93 return(c(unlist(month_labels)));
6
+ − 94 }
+ − 95
5
+ − 96 get_temperature_at_hour = function(latitude, temperature_data_frame, row, num_days) {
8
+ − 97 # Base development threshold for Brown Marmorated Stink Bug
5
+ − 98 # insect phenology model.
8
+ − 99 threshold = 14.17;
5
+ − 100 # Minimum temperature for current row.
8
+ − 101 curr_min_temp = temperature_data_frame$TMIN[row];
5
+ − 102 # Maximum temperature for current row.
8
+ − 103 curr_max_temp = temperature_data_frame$TMAX[row];
5
+ − 104 # Mean temperature for current row.
8
+ − 105 curr_mean_temp = 0.5 * (curr_min_temp + curr_max_temp);
5
+ − 106 # Initialize degree day accumulation
8
+ − 107 averages = 0;
6
+ − 108 if (curr_max_temp < threshold) {
8
+ − 109 averages = 0;
5
+ − 110 }
+ − 111 else {
+ − 112 # Initialize hourly temperature.
8
+ − 113 T = NULL;
5
+ − 114 # Initialize degree hour vector.
8
+ − 115 dh = NULL;
5
+ − 116 # Daylight length for current row.
8
+ − 117 y = temperature_data_frame$DAYLEN[row];
5
+ − 118 # Darkness length.
8
+ − 119 z = 24 - y;
5
+ − 120 # Lag coefficient.
8
+ − 121 a = 1.86;
5
+ − 122 # Darkness coefficient.
8
+ − 123 b = 2.20;
5
+ − 124 # Sunrise time.
8
+ − 125 risetime = 12 - y / 2;
5
+ − 126 # Sunset time.
8
+ − 127 settime = 12 + y / 2;
+ − 128 ts = (curr_max_temp - curr_min_temp) * sin(pi * (settime - 5) / (y + 2 * a)) + curr_min_temp;
5
+ − 129 for (i in 1:24) {
+ − 130 if (i > risetime && i < settime) {
+ − 131 # Number of hours after Tmin until sunset.
8
+ − 132 m = i - 5;
+ − 133 T[i] = (curr_max_temp - curr_min_temp) * sin(pi * m / (y + 2 * a)) + curr_min_temp;
5
+ − 134 if (T[i] < 8.4) {
8
+ − 135 dh[i] = 0;
5
+ − 136 }
+ − 137 else {
8
+ − 138 dh[i] = T[i] - 8.4;
5
+ − 139 }
+ − 140 }
6
+ − 141 else if (i > settime) {
8
+ − 142 n = i - settime;
+ − 143 T[i] = curr_min_temp + (ts - curr_min_temp) * exp( - b * n / z);
5
+ − 144 if (T[i] < 8.4) {
8
+ − 145 dh[i] = 0;
5
+ − 146 }
+ − 147 else {
8
+ − 148 dh[i] = T[i] - 8.4;
5
+ − 149 }
+ − 150 }
+ − 151 else {
8
+ − 152 n = i + 24 - settime;
+ − 153 T[i] = curr_min_temp + (ts - curr_min_temp) * exp( - b * n / z);
5
+ − 154 if (T[i] < 8.4) {
8
+ − 155 dh[i] = 0;
5
+ − 156 }
+ − 157 else {
8
+ − 158 dh[i] = T[i] - 8.4;
5
+ − 159 }
+ − 160 }
+ − 161 }
8
+ − 162 averages = sum(dh) / 24;
5
+ − 163 }
6
+ − 164 return(c(curr_mean_temp, averages))
5
+ − 165 }
+ − 166
6
+ − 167 mortality.adult = function(temperature) {
+ − 168 if (temperature < 12.7) {
8
+ − 169 mortality.probability = 0.002;
6
+ − 170 }
+ − 171 else {
8
+ − 172 mortality.probability = temperature * 0.0005 + 0.02;
6
+ − 173 }
+ − 174 return(mortality.probability)
5
+ − 175 }
+ − 176
+ − 177 mortality.egg = function(temperature) {
+ − 178 if (temperature < 12.7) {
8
+ − 179 mortality.probability = 0.8;
5
+ − 180 }
+ − 181 else {
8
+ − 182 mortality.probability = 0.8 - temperature / 40.0;
6
+ − 183 if (mortality.probability < 0) {
8
+ − 184 mortality.probability = 0.01;
5
+ − 185 }
+ − 186 }
6
+ − 187 return(mortality.probability)
5
+ − 188 }
+ − 189
+ − 190 mortality.nymph = function(temperature) {
+ − 191 if (temperature < 12.7) {
8
+ − 192 mortality.probability = 0.03;
5
+ − 193 }
+ − 194 else {
8
+ − 195 mortality.probability = temperature * 0.0008 + 0.03;
5
+ − 196 }
8
+ − 197 return(mortality.probability);
6
+ − 198 }
+ − 199
+ − 200 parse_input_data = function(input_file, num_rows) {
+ − 201 # Read in the input temperature datafile into a data frame.
8
+ − 202 temperature_data_frame = read.csv(file=input_file, header=T, strip.white=TRUE, sep=",");
+ − 203 num_columns = dim(temperature_data_frame)[2];
6
+ − 204 if (num_columns == 6) {
+ − 205 # The input data has the following 6 columns:
+ − 206 # LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX
+ − 207 # Set the column names for access when adding daylight length..
8
+ − 208 colnames(temperature_data_frame) = c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX");
6
+ − 209 # Add a column containing the daylight length for each day.
8
+ − 210 temperature_data_frame = add_daylight_length(temperature_data_frame, num_columns, num_rows);
6
+ − 211 # Reset the column names with the additional column for later access.
8
+ − 212 colnames(temperature_data_frame) = c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX", "DAYLEN");
6
+ − 213 }
8
+ − 214 return(temperature_data_frame);
5
+ − 215 }
+ − 216
8
+ − 217
6
+ − 218 render_chart = function(chart_type, insect, location, latitude, start_date, end_date, days, maxval, plot_std_error,
8
+ − 219 group1, group2, group3, group1_std_error, group2_std_error, group3_std_error, date_labels) {
6
+ − 220 if (chart_type == "pop_size_by_life_stage") {
8
+ − 221 title = paste(insect, ": Total pop. by life stage :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ");
+ − 222 legend_text = c("Egg", "Nymph", "Adult");
+ − 223 columns = c(4, 2, 1);
6
+ − 224 } else if (chart_type == "pop_size_by_generation") {
8
+ − 225 title = paste(insect, ": Total pop. by generation :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ");
+ − 226 legend_text = c("P", "F1", "F2");
+ − 227 columns = c(1, 2, 4);
6
+ − 228 } else if (chart_type == "adult_pop_size_by_generation") {
8
+ − 229 title = paste(insect, ": Adult pop. by generation :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ");
+ − 230 legend_text = c("P", "F1", "F2");
+ − 231 columns = c(1, 2, 4);
5
+ − 232 }
8
+ − 233 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);
+ − 234 legend("topleft", legend_text, lty=c(1, 1, 1), col=columns, cex=3);
+ − 235 lines(days, group2, lwd=2, lty=1, col=2);
+ − 236 lines(days, group3, lwd=2, lty=1, col=4);
+ − 237 axis(1, at=c(1:length(date_labels)) * 30 - 15, cex.axis=3, labels=date_labels);
+ − 238 axis(2, cex.axis=3);
6
+ − 239 if (plot_std_error==1) {
+ − 240 # Standard error for group1.
8
+ − 241 lines(days, group1+group1_std_error, lty=2);
+ − 242 lines(days, group1-group1_std_error, lty=2);
6
+ − 243 # Standard error for group2.
8
+ − 244 lines(days, group2+group2_std_error, col=2, lty=2);
+ − 245 lines(days, group2-group2_std_error, col=2, lty=2);
6
+ − 246 # Standard error for group3.
8
+ − 247 lines(days, group3+group3_std_error, col=4, lty=2);
+ − 248 lines(days, group3-group3_std_error, col=4, lty=2);
5
+ − 249 }
+ − 250 }
+ − 251
8
+ − 252 temperature_data_frame = parse_input_data(opt$input, opt$num_days);
6
+ − 253 # All latitude values are the same, so get the value from the first row.
8
+ − 254 latitude = temperature_data_frame$LATITUDE[1];
+ − 255 num_columns = dim(temperature_data_frame)[2];
+ − 256 date_labels = get_date_labels(temperature_data_frame, opt$num_days);
5
+ − 257
6
+ − 258 # Initialize matrices.
8
+ − 259 Eggs.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 260 YoungNymphs.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 261 OldNymphs.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 262 Previtellogenic.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 263 Vitellogenic.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 264 Diapausing.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
5
+ − 265
8
+ − 266 newborn.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 267 adult.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 268 death.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
6
+ − 269
8
+ − 270 P.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 271 P_adults.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 272 F1.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 273 F1_adults.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 274 F2.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
+ − 275 F2_adults.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
6
+ − 276
8
+ − 277 population.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
5
+ − 278
6
+ − 279 # Process replications.
+ − 280 for (N.replications in 1:opt$replications) {
+ − 281 # Start with the user-defined number of insects per replication.
8
+ − 282 num_insects = opt$insects_per_replication;
6
+ − 283 # Generation, Stage, degree-days, T, Diapause.
8
+ − 284 vector.ini = c(0, 3, 0, 0, 0);
6
+ − 285 # Overwintering, previttelogenic, degree-days=0, T=0, no-diapause.
8
+ − 286 vector.matrix = rep(vector.ini, num_insects);
5
+ − 287 # Complete matrix for the population.
8
+ − 288 vector.matrix = base::t(matrix(vector.matrix, nrow=5));
5
+ − 289 # Time series of population size.
8
+ − 290 Eggs = rep(0, opt$num_days);
+ − 291 YoungNymphs = rep(0, opt$num_days);
+ − 292 OldNymphs = rep(0, opt$num_days);
+ − 293 Previtellogenic = rep(0, opt$num_days);
+ − 294 Vitellogenic = rep(0, opt$num_days);
+ − 295 Diapausing = rep(0, opt$num_days);
6
+ − 296
8
+ − 297 N.newborn = rep(0, opt$num_days);
+ − 298 N.adult = rep(0, opt$num_days);
+ − 299 N.death = rep(0, opt$num_days);
6
+ − 300
8
+ − 301 overwintering_adult.population = rep(0, opt$num_days);
+ − 302 first_generation.population = rep(0, opt$num_days);
+ − 303 second_generation.population = rep(0, opt$num_days);
6
+ − 304
8
+ − 305 P.adult = rep(0, opt$num_days);
+ − 306 F1.adult = rep(0, opt$num_days);
+ − 307 F2.adult = rep(0, opt$num_days);
6
+ − 308
8
+ − 309 total.population = NULL;
6
+ − 310
8
+ − 311 averages.day = rep(0, opt$num_days);
5
+ − 312 # All the days included in the input temperature dataset.
+ − 313 for (row in 1:opt$num_days) {
+ − 314 # Get the integer day of the year for the current row.
8
+ − 315 doy = temperature_data_frame$DOY[row];
5
+ − 316 # Photoperiod in the day.
8
+ − 317 photoperiod = temperature_data_frame$DAYLEN[row];
+ − 318 temp.profile = get_temperature_at_hour(latitude, temperature_data_frame, row, opt$num_days);
+ − 319 mean.temp = temp.profile[1];
+ − 320 averages.temp = temp.profile[2];
+ − 321 averages.day[row] = averages.temp;
5
+ − 322 # Trash bin for death.
8
+ − 323 death.vector = NULL;
5
+ − 324 # Newborn.
8
+ − 325 birth.vector = NULL;
5
+ − 326 # All individuals.
6
+ − 327 for (i in 1:num_insects) {
+ − 328 # Individual record.
8
+ − 329 vector.individual = vector.matrix[i,];
6
+ − 330 # Adjustment for late season mortality rate (still alive?).
5
+ − 331 if (latitude < 40.0) {
8
+ − 332 post.mortality = 1;
+ − 333 day.kill = 300;
5
+ − 334 }
+ − 335 else {
8
+ − 336 post.mortality = 2;
+ − 337 day.kill = 250;
5
+ − 338 }
6
+ − 339 if (vector.individual[2] == 0) {
5
+ − 340 # Egg.
8
+ − 341 death.probability = opt$egg_mortality * mortality.egg(mean.temp);
5
+ − 342 }
6
+ − 343 else if (vector.individual[2] == 1 | vector.individual[2] == 2) {
8
+ − 344 death.probability = opt$nymph_mortality * mortality.nymph(mean.temp);
5
+ − 345 }
6
+ − 346 else if (vector.individual[2] == 3 | vector.individual[2] == 4 | vector.individual[2] == 5) {
+ − 347 # Adult.
5
+ − 348 if (doy < day.kill) {
8
+ − 349 death.probability = opt$adult_mortality * mortality.adult(mean.temp);
5
+ − 350 }
+ − 351 else {
+ − 352 # Increase adult mortality after fall equinox.
8
+ − 353 death.probability = opt$adult_mortality * post.mortality * mortality.adult(mean.temp);
5
+ − 354 }
+ − 355 }
6
+ − 356 # Dependent on temperature and life stage?
8
+ − 357 u.d = runif(1);
6
+ − 358 if (u.d < death.probability) {
8
+ − 359 death.vector = c(death.vector, i);
6
+ − 360 }
5
+ − 361 else {
6
+ − 362 # End of diapause.
+ − 363 if (vector.individual[1] == 0 && vector.individual[2] == 3) {
5
+ − 364 # Overwintering adult (previttelogenic).
6
+ − 365 if (photoperiod > opt$photoperiod && vector.individual[3] > 68 && doy < 180) {
5
+ − 366 # Add 68C to become fully reproductively matured.
+ − 367 # Transfer to vittelogenic.
8
+ − 368 vector.individual = c(0, 4, 0, 0, 0);
+ − 369 vector.matrix[i,] = vector.individual;
5
+ − 370 }
+ − 371 else {
6
+ − 372 # Add to # Add average temperature for current day.
8
+ − 373 vector.individual[3] = vector.individual[3] + averages.temp;
5
+ − 374 # Add 1 day in current stage.
8
+ − 375 vector.individual[4] = vector.individual[4] + 1;
+ − 376 vector.matrix[i,] = vector.individual;
5
+ − 377 }
+ − 378 }
6
+ − 379 if (vector.individual[1] != 0 && vector.individual[2] == 3) {
5
+ − 380 # Not overwintering adult (previttelogenic).
8
+ − 381 current.gen = vector.individual[1];
6
+ − 382 if (vector.individual[3] > 68) {
5
+ − 383 # Add 68C to become fully reproductively matured.
+ − 384 # Transfer to vittelogenic.
8
+ − 385 vector.individual = c(current.gen, 4, 0, 0, 0);
+ − 386 vector.matrix[i,] = vector.individual;
5
+ − 387 }
+ − 388 else {
6
+ − 389 # Add average temperature for current day.
8
+ − 390 vector.individual[3] = vector.individual[3] + averages.temp;
5
+ − 391 # Add 1 day in current stage.
8
+ − 392 vector.individual[4] = vector.individual[4] + 1;
+ − 393 vector.matrix[i,] = vector.individual;
5
+ − 394 }
+ − 395 }
6
+ − 396 # Oviposition -- where population dynamics comes from.
+ − 397 if (vector.individual[2] == 4 && vector.individual[1] == 0 && mean.temp > 10) {
5
+ − 398 # Vittelogenic stage, overwintering generation.
6
+ − 399 if (vector.individual[4] == 0) {
5
+ − 400 # Just turned in vittelogenic stage.
8
+ − 401 num_insects.birth = round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size));
5
+ − 402 }
+ − 403 else {
+ − 404 # Daily probability of birth.
8
+ − 405 p.birth = opt$oviposition * 0.01;
+ − 406 u1 = runif(1);
5
+ − 407 if (u1 < p.birth) {
8
+ − 408 num_insects.birth = round(runif(1, 2, 8));
5
+ − 409 }
+ − 410 }
6
+ − 411 # Add average temperature for current day.
8
+ − 412 vector.individual[3] = vector.individual[3] + averages.temp;
5
+ − 413 # Add 1 day in current stage.
8
+ − 414 vector.individual[4] = vector.individual[4] + 1;
+ − 415 vector.matrix[i,] = vector.individual;
6
+ − 416 if (num_insects.birth > 0) {
5
+ − 417 # Add new birth -- might be in different generations.
8
+ − 418 new.gen = vector.individual[1] + 1;
5
+ − 419 # Egg profile.
8
+ − 420 new.individual = c(new.gen, 0, 0, 0, 0);
+ − 421 new.vector = rep(new.individual, num_insects.birth);
5
+ − 422 # Update batch of egg profile.
8
+ − 423 new.vector = t(matrix(new.vector, nrow=5));
5
+ − 424 # Group with total eggs laid in that day.
8
+ − 425 birth.vector = rbind(birth.vector, new.vector);
5
+ − 426 }
+ − 427 }
6
+ − 428 # Oviposition -- for generation 1.
+ − 429 if (vector.individual[2] == 4 && vector.individual[1] == 1 && mean.temp > 12.5 && doy < 222) {
5
+ − 430 # Vittelogenic stage, 1st generation
6
+ − 431 if (vector.individual[4] == 0) {
5
+ − 432 # Just turned in vittelogenic stage.
8
+ − 433 num_insects.birth = round(runif(1, 2+opt$min_clutch_size, 8+opt$max_clutch_size));
5
+ − 434 }
+ − 435 else {
+ − 436 # Daily probability of birth.
8
+ − 437 p.birth = opt$oviposition * 0.01;
+ − 438 u1 = runif(1);
5
+ − 439 if (u1 < p.birth) {
8
+ − 440 num_insects.birth = round(runif(1, 2, 8));
5
+ − 441 }
+ − 442 }
6
+ − 443 # Add average temperature for current day.
8
+ − 444 vector.individual[3] = vector.individual[3] + averages.temp;
5
+ − 445 # Add 1 day in current stage.
8
+ − 446 vector.individual[4] = vector.individual[4] + 1;
+ − 447 vector.matrix[i,] = vector.individual;
6
+ − 448 if (num_insects.birth > 0) {
5
+ − 449 # Add new birth -- might be in different generations.
8
+ − 450 new.gen = vector.individual[1] + 1;
5
+ − 451 # Egg profile.
8
+ − 452 new.individual = c(new.gen, 0, 0, 0, 0);
+ − 453 new.vector = rep(new.individual, num_insects.birth);
5
+ − 454 # Update batch of egg profile.
8
+ − 455 new.vector = t(matrix(new.vector, nrow=5));
5
+ − 456 # Group with total eggs laid in that day.
8
+ − 457 birth.vector = rbind(birth.vector, new.vector);
5
+ − 458 }
+ − 459 }
6
+ − 460 # Egg to young nymph.
+ − 461 if (vector.individual[2] == 0) {
+ − 462 # Add average temperature for current day.
8
+ − 463 vector.individual[3] = vector.individual[3] + averages.temp;
6
+ − 464 if (vector.individual[3] >= (68+opt$young_nymph_accumulation)) {
+ − 465 # From egg to young nymph, degree-days requirement met.
8
+ − 466 current.gen = vector.individual[1];
5
+ − 467 # Transfer to young nymph stage.
8
+ − 468 vector.individual = c(current.gen, 1, 0, 0, 0);
5
+ − 469 }
+ − 470 else {
+ − 471 # Add 1 day in current stage.
8
+ − 472 vector.individual[4] = vector.individual[4] + 1;
5
+ − 473 }
8
+ − 474 vector.matrix[i,] = vector.individual;
5
+ − 475 }
6
+ − 476 # Young nymph to old nymph.
+ − 477 if (vector.individual[2] == 1) {
+ − 478 # Add average temperature for current day.
8
+ − 479 vector.individual[3] = vector.individual[3] + averages.temp;
6
+ − 480 if (vector.individual[3] >= (250+opt$old_nymph_accumulation)) {
+ − 481 # From young to old nymph, degree_days requirement met.
8
+ − 482 current.gen = vector.individual[1];
5
+ − 483 # Transfer to old nym stage.
8
+ − 484 vector.individual = c(current.gen, 2, 0, 0, 0);
5
+ − 485 if (photoperiod < opt$photoperiod && doy > 180) {
8
+ − 486 vector.individual[5] = 1;
5
+ − 487 } # Prepare for diapausing.
+ − 488 }
+ − 489 else {
+ − 490 # Add 1 day in current stage.
8
+ − 491 vector.individual[4] = vector.individual[4] + 1;
5
+ − 492 }
8
+ − 493 vector.matrix[i,] = vector.individual;
6
+ − 494 }
+ − 495 # Old nymph to adult: previttelogenic or diapausing?
+ − 496 if (vector.individual[2] == 2) {
+ − 497 # Add average temperature for current day.
8
+ − 498 vector.individual[3] = vector.individual[3] + averages.temp;
6
+ − 499 if (vector.individual[3] >= (200+opt$adult_accumulation)) {
+ − 500 # From old to adult, degree_days requirement met.
8
+ − 501 current.gen = vector.individual[1];
6
+ − 502 if (vector.individual[5] == 0) {
+ − 503 # Previttelogenic.
8
+ − 504 vector.individual = c(current.gen, 3, 0, 0, 0);
5
+ − 505 }
+ − 506 else {
+ − 507 # Diapausing.
8
+ − 508 vector.individual = c(current.gen, 5, 0, 0, 1);
5
+ − 509 }
+ − 510 }
+ − 511 else {
+ − 512 # Add 1 day in current stage.
8
+ − 513 vector.individual[4] = vector.individual[4] + 1;
5
+ − 514 }
8
+ − 515 vector.matrix[i,] = vector.individual;
5
+ − 516 }
6
+ − 517 # Growing of diapausing adult (unimportant, but still necessary).
+ − 518 if (vector.individual[2] == 5) {
8
+ − 519 vector.individual[3] = vector.individual[3] + averages.temp;
+ − 520 vector.individual[4] = vector.individual[4] + 1;
+ − 521 vector.matrix[i,] = vector.individual;
5
+ − 522 }
+ − 523 } # Else if it is still alive.
+ − 524 } # End of the individual bug loop.
6
+ − 525
+ − 526 # Number of deaths.
8
+ − 527 num_insects.death = length(death.vector);
6
+ − 528 if (num_insects.death > 0) {
+ − 529 # Remove record of dead.
8
+ − 530 vector.matrix = vector.matrix[-death.vector,];
5
+ − 531 }
6
+ − 532 # Number of births.
8
+ − 533 num_insects.newborn = length(birth.vector[,1]);
+ − 534 vector.matrix = rbind(vector.matrix, birth.vector);
5
+ − 535 # Update population size for the next day.
8
+ − 536 num_insects = num_insects - num_insects.death + num_insects.newborn;
5
+ − 537
+ − 538 # Aggregate results by day.
6
+ − 539 # Egg population size.
8
+ − 540 Eggs[row] = sum(vector.matrix[,2]==0);
6
+ − 541 # Young nymph population size.
8
+ − 542 YoungNymphs[row] = sum(vector.matrix[,2]==1);
6
+ − 543 # Old nymph population size.
8
+ − 544 OldNymphs[row] = sum(vector.matrix[,2]==2);
6
+ − 545 # Previtellogenic population size.
8
+ − 546 Previtellogenic[row] = sum(vector.matrix[,2]==3);
6
+ − 547 # Vitellogenic population size.
8
+ − 548 Vitellogenic[row] = sum(vector.matrix[,2]==4);
6
+ − 549 # Diapausing population size.
8
+ − 550 Diapausing[row] = sum(vector.matrix[,2]==5);
5
+ − 551
6
+ − 552 # Newborn population size.
8
+ − 553 N.newborn[row] = num_insects.newborn;
6
+ − 554 # Adult population size.
8
+ − 555 N.adult[row] = sum(vector.matrix[,2]==3) + sum(vector.matrix[,2]==4) + sum(vector.matrix[,2]==5);
6
+ − 556 # Dead population size.
8
+ − 557 N.death[row] = num_insects.death;
6
+ − 558
8
+ − 559 total.population = c(total.population, num_insects);
6
+ − 560
+ − 561 # Overwintering adult population size.
8
+ − 562 overwintering_adult.population[row] = sum(vector.matrix[,1]==0);
6
+ − 563 # First generation population size.
8
+ − 564 first_generation.population[row] = sum(vector.matrix[,1]==1);
6
+ − 565 # Second generation population size.
8
+ − 566 second_generation.population[row] = sum(vector.matrix[,1]==2);
5
+ − 567
6
+ − 568 # P adult population size.
8
+ − 569 P.adult[row] = sum(vector.matrix[,1]==0);
6
+ − 570 # F1 adult population size.
8
+ − 571 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));
6
+ − 572 # F2 adult population size
8
+ − 573 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));
6
+ − 574 } # End of days specified in the input temperature data.
5
+ − 575
8
+ − 576 averages.cum = cumsum(averages.day);
5
+ − 577
6
+ − 578 # Define the output values.
8
+ − 579 Eggs.replications[,N.replications] = Eggs;
+ − 580 YoungNymphs.replications[,N.replications] = YoungNymphs;
+ − 581 OldNymphs.replications[,N.replications] = OldNymphs;
+ − 582 Previtellogenic.replications[,N.replications] = Previtellogenic;
+ − 583 Vitellogenic.replications[,N.replications] = Vitellogenic;
+ − 584 Diapausing.replications[,N.replications] = Diapausing;
6
+ − 585
8
+ − 586 newborn.replications[,N.replications] = N.newborn;
+ − 587 adult.replications[,N.replications] = N.adult;
+ − 588 death.replications[,N.replications] = N.death;
6
+ − 589
8
+ − 590 P.replications[,N.replications] = overwintering_adult.population;
+ − 591 P_adults.replications[,N.replications] = P.adult;
+ − 592 F1.replications[,N.replications] = first_generation.population;
+ − 593 F1_adults.replications[,N.replications] = F1.adult;
+ − 594 F2.replications[,N.replications] = second_generation.population;
+ − 595 F2_adults.replications[,N.replications] = F2.adult;
6
+ − 596
8
+ − 597 population.replications[,N.replications] = total.population;
5
+ − 598 }
+ − 599
6
+ − 600 # Mean value for eggs.
8
+ − 601 eggs = apply(Eggs.replications, 1, mean);
6
+ − 602 # Standard error for eggs.
8
+ − 603 eggs.std_error = apply(Eggs.replications, 1, sd) / sqrt(opt$replications);
6
+ − 604
+ − 605 # Mean value for nymphs.
8
+ − 606 nymphs = apply((YoungNymphs.replications+OldNymphs.replications), 1, mean);
6
+ − 607 # Standard error for nymphs.
8
+ − 608 nymphs.std_error = apply((YoungNymphs.replications+OldNymphs.replications) / sqrt(opt$replications), 1, sd);
5
+ − 609
6
+ − 610 # Mean value for adults.
8
+ − 611 adults = apply((Previtellogenic.replications+Vitellogenic.replications+Diapausing.replications), 1, mean);
6
+ − 612 # Standard error for adults.
8
+ − 613 adults.std_error = apply((Previtellogenic.replications+Vitellogenic.replications+Diapausing.replications), 1, sd) / sqrt(opt$replications);
6
+ − 614
+ − 615 # Mean value for P.
8
+ − 616 P = apply(P.replications, 1, mean);
6
+ − 617 # Standard error for P.
8
+ − 618 P.std_error = apply(P.replications, 1, sd) / sqrt(opt$replications);
5
+ − 619
6
+ − 620 # Mean value for P adults.
8
+ − 621 P_adults = apply(P_adults.replications, 1, mean);
6
+ − 622 # Standard error for P_adult.
8
+ − 623 P_adults.std_error = apply(P_adults.replications, 1, sd) / sqrt(opt$replications);
6
+ − 624
+ − 625 # Mean value for F1.
8
+ − 626 F1 = apply(F1.replications, 1, mean);
6
+ − 627 # Standard error for F1.
8
+ − 628 F1.std_error = apply(F1.replications, 1, sd) / sqrt(opt$replications);
5
+ − 629
6
+ − 630 # Mean value for F1 adults.
8
+ − 631 F1_adults = apply(F1_adults.replications, 1, mean);
6
+ − 632 # Standard error for F1 adult.
8
+ − 633 F1_adults.std_error = apply(F1_adults.replications, 1, sd) / sqrt(opt$replications);
6
+ − 634
+ − 635 # Mean value for F2.
8
+ − 636 F2 = apply(F2.replications, 1, mean);
6
+ − 637 # Standard error for F2.
8
+ − 638 F2.std_error = apply(F2.replications, 1, sd) / sqrt(opt$replications);
6
+ − 639
+ − 640 # Mean value for F2 adults.
8
+ − 641 F2_adults = apply(F2_adults.replications, 1, mean);
6
+ − 642 # Standard error for F2 adult.
8
+ − 643 F2_adults.std_error = apply(F2_adults.replications, 1, sd) / sqrt(opt$replications);
6
+ − 644
+ − 645 # Display the total number of days in the Galaxy history item blurb.
8
+ − 646 cat("Number of days: ", opt$num_days, "\n");
5
+ − 647
8
+ − 648 dev.new(width=20, height=30);
5
+ − 649
+ − 650 # Start PDF device driver to save charts to output.
8
+ − 651 pdf(file=opt$output, width=20, height=30, bg="white");
+ − 652 par(mar=c(5, 6, 4, 4), mfrow=c(3, 1));
5
+ − 653
6
+ − 654 # Data analysis and visualization plots only within a single calendar year.
8
+ − 655 days = c(1:opt$num_days);
+ − 656 start_date = temperature_data_frame$DATE[1];
+ − 657 end_date = temperature_data_frame$DATE[opt$num_days];
5
+ − 658
6
+ − 659 # Subfigure 1: population size by life stage.
8
+ − 660 maxval = max(eggs+eggs.std_error, nymphs+nymphs.std_error, adults+adults.std_error);
6
+ − 661 render_chart("pop_size_by_life_stage", opt$insect, opt$location, latitude, start_date, end_date, days, maxval,
8
+ − 662 opt$std_error_plot, adults, nymphs, eggs, adults.std_error, nymphs.std_error, eggs.std_error, date_labels);
6
+ − 663 # Subfigure 2: population size by generation.
8
+ − 664 maxval = max(F2);
6
+ − 665 render_chart("pop_size_by_generation", opt$insect, opt$location, latitude, start_date, end_date, days, maxval,
8
+ − 666 opt$std_error_plot, P, F1, F2, P.std_error, F1.std_error, F2.std_error, date_labels);
6
+ − 667 # Subfigure 3: adult population size by generation.
8
+ − 668 maxval = max(F2_adults) + 100;
6
+ − 669 render_chart("adult_pop_size_by_generation", opt$insect, opt$location, latitude, start_date, end_date, days, maxval,
8
+ − 670 opt$std_error_plot, P_adults, F1_adults, F2_adults, P_adults.std_error, F1_adults.std_error, F2_adults.std_error,
+ − 671 date_labels);
5
+ − 672
+ − 673 # Turn off device driver to flush output.
8
+ − 674 dev.off();
