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comparison insect_phenology_model.R @ 8:37f1ad91a949 draft

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author greg
date Tue, 13 Feb 2018 13:53:37 -0500
parents fe3f86012394
children 61bc6bd8807d
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7:ad26f07a7dd8 8:37f1ad91a949
21 make_option(c("--replications"), action="store", dest="replications", type="integer", help="Number of replications"), 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"), 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)") 23 make_option(c("--young_nymph_accumulation"), action="store", dest="young_nymph_accumulation", type="integer", help="Adjustment of degree-days accumulation (egg->young nymph)")
24 ) 24 )
25 25
26 parser <- OptionParser(usage="%prog [options] file", option_list=option_list) 26 parser <- OptionParser(usage="%prog [options] file", option_list=option_list);
27 args <- parse_args(parser, positional_arguments=TRUE) 27 args <- parse_args(parser, positional_arguments=TRUE);
28 opt <- args$options 28 opt <- args$options;
29 29
30 add_daylight_length = function(temperature_data_frame, num_columns, num_rows) { 30 add_daylight_length = function(temperature_data_frame, num_columns, num_rows) {
31 # Return a vector of daylight length (photoperido profile) for 31 # Return a vector of daylight length (photoperido profile) for
32 # the number of days specified in the input temperature data 32 # the number of days specified in the input temperature data
33 # (from Forsythe 1995). 33 # (from Forsythe 1995).
34 p = 0.8333 34 p = 0.8333;
35 latitude <- temperature_data_frame$LATITUDE[1] 35 latitude = temperature_data_frame$LATITUDE[1];
36 daylight_length_vector <- NULL 36 daylight_length_vector = NULL;
37 for (i in 1:num_rows) { 37 for (i in 1:num_rows) {
38 # Get the day of the year from the current row 38 # Get the day of the year from the current row
39 # of the temperature data for computation. 39 # of the temperature data for computation.
40 doy <- temperature_data_frame$DOY[i] 40 doy = temperature_data_frame$DOY[i];
41 theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (doy - 186))) 41 theta = 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (doy - 186)));
42 phi <- asin(0.39795 * cos(theta)) 42 phi = asin(0.39795 * cos(theta));
43 # Compute the length of daylight for the day of the year. 43 # Compute the length of daylight for the day of the year.
44 darkness_length <- 24 / pi * acos((sin(p * pi / 180) + sin(latitude * pi / 180) * sin(phi)) / (cos(latitude * pi / 180) * cos(phi))) 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 45 daylight_length_vector[i] = 24 - darkness_length;
46 } 46 }
47 # Append daylight_length_vector as a new column to temperature_data_frame. 47 # Append daylight_length_vector as a new column to temperature_data_frame.
48 temperature_data_frame[, num_columns+1] <- daylight_length_vector 48 temperature_data_frame[, num_columns+1] = daylight_length_vector;
49 return(temperature_data_frame) 49 return(temperature_data_frame);
50 } 50 }
51 51
52 dev.egg = function(temperature) { 52 dev.egg = function(temperature) {
53 dev.rate = -0.9843 * temperature + 33.438 53 dev.rate = -0.9843 * temperature + 33.438;
54 return(dev.rate) 54 return(dev.rate);
55 } 55 }
56 56
57 dev.emerg = function(temperature) { 57 dev.emerg = function(temperature) {
58 emerg.rate <- -0.5332 * temperature + 24.147 58 emerg.rate = -0.5332 * temperature + 24.147;
59 return(emerg.rate) 59 return(emerg.rate);
60 } 60 }
61 61
62 dev.old = function(temperature) { 62 dev.old = function(temperature) {
63 n34 <- -0.6119 * temperature + 17.602 63 n34 = -0.6119 * temperature + 17.602;
64 n45 <- -0.4408 * temperature + 19.036 64 n45 = -0.4408 * temperature + 19.036;
65 dev.rate = mean(n34 + n45) 65 dev.rate = mean(n34 + n45);
66 return(dev.rate) 66 return(dev.rate);
67 } 67 }
68 68
69 dev.young = function(temperature) { 69 dev.young = function(temperature) {
70 n12 <- -0.3728 * temperature + 14.68 70 n12 = -0.3728 * temperature + 14.68;
71 n23 <- -0.6119 * temperature + 25.249 71 n23 = -0.6119 * temperature + 25.249;
72 dev.rate = mean(n12 + n23) 72 dev.rate = mean(n12 + n23);
73 return(dev.rate) 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$DATE[i]);
85 items = strsplit(date, "-")[[1]];
86 month = items[2];
87 month_label = month.abb[as.integer(month)];
88 if (!identical(current_month_label, month_label)) {
89 month_labels[length(month_labels)+1] = month_label;
90 current_month_label = month_label;
91 }
92 }
93 return(c(unlist(month_labels)));
74 } 94 }
75 95
76 get_temperature_at_hour = function(latitude, temperature_data_frame, row, num_days) { 96 get_temperature_at_hour = function(latitude, temperature_data_frame, row, num_days) {
77 # Base development threshold for Brown Marmolated Stink Bug 97 # Base development threshold for Brown Marmorated Stink Bug
78 # insect phenology model. 98 # insect phenology model.
79 threshold <- 14.17 99 threshold = 14.17;
80 # Minimum temperature for current row. 100 # Minimum temperature for current row.
81 curr_min_temp <- temperature_data_frame$TMIN[row] 101 curr_min_temp = temperature_data_frame$TMIN[row];
82 # Maximum temperature for current row. 102 # Maximum temperature for current row.
83 curr_max_temp <- temperature_data_frame$TMAX[row] 103 curr_max_temp = temperature_data_frame$TMAX[row];
84 # Mean temperature for current row. 104 # Mean temperature for current row.
85 curr_mean_temp <- 0.5 * (curr_min_temp + curr_max_temp) 105 curr_mean_temp = 0.5 * (curr_min_temp + curr_max_temp);
86 # Initialize degree day accumulation 106 # Initialize degree day accumulation
87 averages <- 0 107 averages = 0;
88 if (curr_max_temp < threshold) { 108 if (curr_max_temp < threshold) {
89 averages <- 0 109 averages = 0;
90 } 110 }
91 else { 111 else {
92 # Initialize hourly temperature. 112 # Initialize hourly temperature.
93 T <- NULL 113 T = NULL;
94 # Initialize degree hour vector. 114 # Initialize degree hour vector.
95 dh <- NULL 115 dh = NULL;
96 # Daylight length for current row. 116 # Daylight length for current row.
97 y <- temperature_data_frame$DAYLEN[row] 117 y = temperature_data_frame$DAYLEN[row];
98 # Darkness length. 118 # Darkness length.
99 z <- 24 - y 119 z = 24 - y;
100 # Lag coefficient. 120 # Lag coefficient.
101 a <- 1.86 121 a = 1.86;
102 # Darkness coefficient. 122 # Darkness coefficient.
103 b <- 2.20 123 b = 2.20;
104 # Sunrise time. 124 # Sunrise time.
105 risetime <- 12 - y / 2 125 risetime = 12 - y / 2;
106 # Sunset time. 126 # Sunset time.
107 settime <- 12 + y / 2 127 settime = 12 + y / 2;
108 ts <- (curr_max_temp - curr_min_temp) * sin(pi * (settime - 5) / (y + 2 * a)) + curr_min_temp 128 ts = (curr_max_temp - curr_min_temp) * sin(pi * (settime - 5) / (y + 2 * a)) + curr_min_temp;
109 for (i in 1:24) { 129 for (i in 1:24) {
110 if (i > risetime && i < settime) { 130 if (i > risetime && i < settime) {
111 # Number of hours after Tmin until sunset. 131 # Number of hours after Tmin until sunset.
112 m <- i - 5 132 m = i - 5;
113 T[i] = (curr_max_temp - curr_min_temp) * sin(pi * m / (y + 2 * a)) + curr_min_temp 133 T[i] = (curr_max_temp - curr_min_temp) * sin(pi * m / (y + 2 * a)) + curr_min_temp;
114 if (T[i] < 8.4) { 134 if (T[i] < 8.4) {
115 dh[i] <- 0 135 dh[i] = 0;
116 } 136 }
117 else { 137 else {
118 dh[i] <- T[i] - 8.4 138 dh[i] = T[i] - 8.4;
119 } 139 }
120 } 140 }
121 else if (i > settime) { 141 else if (i > settime) {
122 n <- i - settime 142 n = i - settime;
123 T[i] = curr_min_temp + (ts - curr_min_temp) * exp( - b * n / z) 143 T[i] = curr_min_temp + (ts - curr_min_temp) * exp( - b * n / z);
124 if (T[i] < 8.4) { 144 if (T[i] < 8.4) {
125 dh[i] <- 0 145 dh[i] = 0;
126 } 146 }
127 else { 147 else {
128 dh[i] <- T[i] - 8.4 148 dh[i] = T[i] - 8.4;
129 } 149 }
130 } 150 }
131 else { 151 else {
132 n <- i + 24 - settime 152 n = i + 24 - settime;
133 T[i] = curr_min_temp + (ts - curr_min_temp) * exp( - b * n / z) 153 T[i] = curr_min_temp + (ts - curr_min_temp) * exp( - b * n / z);
134 if (T[i] < 8.4) { 154 if (T[i] < 8.4) {
135 dh[i] <- 0 155 dh[i] = 0;
136 } 156 }
137 else { 157 else {
138 dh[i] <- T[i] - 8.4 158 dh[i] = T[i] - 8.4;
139 } 159 }
140 } 160 }
141 } 161 }
142 averages <- sum(dh) / 24 162 averages = sum(dh) / 24;
143 } 163 }
144 return(c(curr_mean_temp, averages)) 164 return(c(curr_mean_temp, averages))
145 } 165 }
146 166
147 mortality.adult = function(temperature) { 167 mortality.adult = function(temperature) {
148 if (temperature < 12.7) { 168 if (temperature < 12.7) {
149 mortality.probability = 0.002 169 mortality.probability = 0.002;
150 } 170 }
151 else { 171 else {
152 mortality.probability = temperature * 0.0005 + 0.02 172 mortality.probability = temperature * 0.0005 + 0.02;
153 } 173 }
154 return(mortality.probability) 174 return(mortality.probability)
155 } 175 }
156 176
157 mortality.egg = function(temperature) { 177 mortality.egg = function(temperature) {
158 if (temperature < 12.7) { 178 if (temperature < 12.7) {
159 mortality.probability = 0.8 179 mortality.probability = 0.8;
160 } 180 }
161 else { 181 else {
162 mortality.probability = 0.8 - temperature / 40.0 182 mortality.probability = 0.8 - temperature / 40.0;
163 if (mortality.probability < 0) { 183 if (mortality.probability < 0) {
164 mortality.probability = 0.01 184 mortality.probability = 0.01;
165 } 185 }
166 } 186 }
167 return(mortality.probability) 187 return(mortality.probability)
168 } 188 }
169 189
170 mortality.nymph = function(temperature) { 190 mortality.nymph = function(temperature) {
171 if (temperature < 12.7) { 191 if (temperature < 12.7) {
172 mortality.probability = 0.03 192 mortality.probability = 0.03;
173 } 193 }
174 else { 194 else {
175 mortality.probability = temperature * 0.0008 + 0.03 195 mortality.probability = temperature * 0.0008 + 0.03;
176 } 196 }
177 return(mortality.probability) 197 return(mortality.probability);
178 } 198 }
179 199
180 parse_input_data = function(input_file, num_rows) { 200 parse_input_data = function(input_file, num_rows) {
181 # Read in the input temperature datafile into a data frame. 201 # Read in the input temperature datafile into a data frame.
182 temperature_data_frame <- read.csv(file=input_file, header=T, strip.white=TRUE, sep=",") 202 temperature_data_frame = read.csv(file=input_file, header=T, strip.white=TRUE, sep=",");
183 num_columns <- dim(temperature_data_frame)[2] 203 num_columns = dim(temperature_data_frame)[2];
184 if (num_columns == 6) { 204 if (num_columns == 6) {
185 # The input data has the following 6 columns: 205 # The input data has the following 6 columns:
186 # LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX 206 # LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX
187 # Set the column names for access when adding daylight length.. 207 # Set the column names for access when adding daylight length..
188 colnames(temperature_data_frame) <- c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX") 208 colnames(temperature_data_frame) = c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX");
189 # Add a column containing the daylight length for each day. 209 # Add a column containing the daylight length for each day.
190 temperature_data_frame <- add_daylight_length(temperature_data_frame, num_columns, num_rows) 210 temperature_data_frame = add_daylight_length(temperature_data_frame, num_columns, num_rows);
191 # Reset the column names with the additional column for later access. 211 # Reset the column names with the additional column for later access.
192 colnames(temperature_data_frame) <- c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX", "DAYLEN") 212 colnames(temperature_data_frame) = c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX", "DAYLEN");
193 } 213 }
194 return(temperature_data_frame) 214 return(temperature_data_frame);
195 } 215 }
216
196 217
197 render_chart = function(chart_type, insect, location, latitude, start_date, end_date, days, maxval, plot_std_error, 218 render_chart = function(chart_type, insect, location, latitude, start_date, end_date, days, maxval, plot_std_error,
198 group1, group2, group3, group1_std_error, group2_std_error, group3_std_error) { 219 group1, group2, group3, group1_std_error, group2_std_error, group3_std_error, date_labels) {
199 if (chart_type == "pop_size_by_life_stage") { 220 if (chart_type == "pop_size_by_life_stage") {
200 title <- paste(insect, ": Total pop. by life stage :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ") 221 title = paste(insect, ": Total pop. by life stage :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ");
201 legend_text <- c("Egg", "Nymph", "Adult") 222 legend_text = c("Egg", "Nymph", "Adult");
202 columns <- c(4, 2, 1) 223 columns = c(4, 2, 1);
203 } else if (chart_type == "pop_size_by_generation") { 224 } else if (chart_type == "pop_size_by_generation") {
204 title <- paste(insect, ": Total pop. by generation :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ") 225 title = paste(insect, ": Total pop. by generation :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ");
205 legend_text <- c("P", "F1", "F2") 226 legend_text = c("P", "F1", "F2");
206 columns <- c(1, 2, 4) 227 columns = c(1, 2, 4);
207 } else if (chart_type == "adult_pop_size_by_generation") { 228 } else if (chart_type == "adult_pop_size_by_generation") {
208 title <- paste(insect, ": Adult pop. by generation :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ") 229 title = paste(insect, ": Adult pop. by generation :", location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ");
209 legend_text <- c("P", "F1", "F2") 230 legend_text = c("P", "F1", "F2");
210 columns <- c(1, 2, 4) 231 columns = c(1, 2, 4);
211 } 232 }
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) 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);
213 legend("topleft", legend_text, lty=c(1, 1, 1), col=columns, cex=3) 234 legend("topleft", legend_text, lty=c(1, 1, 1), col=columns, cex=3);
214 lines(days, group2, lwd=2, lty=1, col=2) 235 lines(days, group2, lwd=2, lty=1, col=2);
215 lines(days, group3, lwd=2, lty=1, col=4) 236 lines(days, group3, lwd=2, lty=1, col=4);
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")) 237 axis(1, at=c(1:length(date_labels)) * 30 - 15, cex.axis=3, labels=date_labels);
217 axis(2, cex.axis=3) 238 axis(2, cex.axis=3);
218 if (plot_std_error==1) { 239 if (plot_std_error==1) {
219 # Standard error for group1. 240 # Standard error for group1.
220 lines(days, group1+group1_std_error, lty=2) 241 lines(days, group1+group1_std_error, lty=2);
221 lines (days, group1-group1_std_error, lty=2) 242 lines(days, group1-group1_std_error, lty=2);
222 # Standard error for group2. 243 # Standard error for group2.
223 lines(days, group2+group2_std_error, col=2, lty=2) 244 lines(days, group2+group2_std_error, col=2, lty=2);
224 lines(days, group2-group2_std_error, col=2, lty=2) 245 lines(days, group2-group2_std_error, col=2, lty=2);
225 # Standard error for group3. 246 # Standard error for group3.
226 lines(days, group3+group3_std_error, col=4, lty=2) 247 lines(days, group3+group3_std_error, col=4, lty=2);
227 lines(days, group3-group3_std_error, col=4, lty=2) 248 lines(days, group3-group3_std_error, col=4, lty=2);
228 } 249 }
229 } 250 }
230 251
231 temperature_data_frame <- parse_input_data(opt$input, opt$num_days) 252 temperature_data_frame = parse_input_data(opt$input, opt$num_days);
232 # All latitude values are the same, so get the value from the first row. 253 # All latitude values are the same, so get the value from the first row.
233 latitude <- temperature_data_frame$LATITUDE[1] 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);
234 257
235 # Initialize matrices. 258 # Initialize matrices.
236 Eggs.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications) 259 Eggs.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
237 YoungNymphs.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);
238 OldNymphs.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);
239 Previtellogenic.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);
240 Vitellogenic.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);
241 Diapausing.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);
242 265
243 newborn.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications) 266 newborn.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
244 adult.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);
245 death.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);
246 269
247 P.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications) 270 P.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
248 P_adults.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);
249 F1.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);
250 F1_adults.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);
251 F2.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);
252 F2_adults.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);
253 276
254 population.replications <- matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications) 277 population.replications = matrix(rep(0, opt$num_days*opt$replications), ncol=opt$replications);
255 278
256 # Process replications. 279 # Process replications.
257 for (N.replications in 1:opt$replications) { 280 for (N.replications in 1:opt$replications) {
258 # Start with the user-defined number of insects per replication. 281 # Start with the user-defined number of insects per replication.
259 num_insects <- opt$insects_per_replication 282 num_insects = opt$insects_per_replication;
260 # Generation, Stage, degree-days, T, Diapause. 283 # Generation, Stage, degree-days, T, Diapause.
261 vector.ini <- c(0, 3, 0, 0, 0) 284 vector.ini = c(0, 3, 0, 0, 0);
262 # Overwintering, previttelogenic, degree-days=0, T=0, no-diapause. 285 # Overwintering, previttelogenic, degree-days=0, T=0, no-diapause.
263 vector.matrix <- rep(vector.ini, num_insects) 286 vector.matrix = rep(vector.ini, num_insects);
264 # Complete matrix for the population. 287 # Complete matrix for the population.
265 vector.matrix <- base::t(matrix(vector.matrix, nrow=5)) 288 vector.matrix = base::t(matrix(vector.matrix, nrow=5));
266 # Time series of population size. 289 # Time series of population size.
267 Eggs <- rep(0, opt$num_days) 290 Eggs = rep(0, opt$num_days);
268 YoungNymphs <- rep(0, opt$num_days) 291 YoungNymphs = rep(0, opt$num_days);
269 OldNymphs <- rep(0, opt$num_days) 292 OldNymphs = rep(0, opt$num_days);
270 Previtellogenic <- rep(0, opt$num_days) 293 Previtellogenic = rep(0, opt$num_days);
271 Vitellogenic <- rep(0, opt$num_days) 294 Vitellogenic = rep(0, opt$num_days);
272 Diapausing <- rep(0, opt$num_days) 295 Diapausing = rep(0, opt$num_days);
273 296
274 N.newborn <- rep(0, opt$num_days) 297 N.newborn = rep(0, opt$num_days);
275 N.adult <- rep(0, opt$num_days) 298 N.adult = rep(0, opt$num_days);
276 N.death <- rep(0, opt$num_days) 299 N.death = rep(0, opt$num_days);
277 300
278 overwintering_adult.population <- rep(0, opt$num_days) 301 overwintering_adult.population = rep(0, opt$num_days);
279 first_generation.population <- rep(0, opt$num_days) 302 first_generation.population = rep(0, opt$num_days);
280 second_generation.population <- rep(0, opt$num_days) 303 second_generation.population = rep(0, opt$num_days);
281 304
282 P.adult <- rep(0, opt$num_days) 305 P.adult = rep(0, opt$num_days);
283 F1.adult <- rep(0, opt$num_days) 306 F1.adult = rep(0, opt$num_days);
284 F2.adult <- rep(0, opt$num_days) 307 F2.adult = rep(0, opt$num_days);
285 308
286 total.population <- NULL 309 total.population = NULL;
287 310
288 averages.day <- rep(0, opt$num_days) 311 averages.day = rep(0, opt$num_days);
289 # All the days included in the input temperature dataset. 312 # All the days included in the input temperature dataset.
290 for (row in 1:opt$num_days) { 313 for (row in 1:opt$num_days) {
291 # Get the integer day of the year for the current row. 314 # Get the integer day of the year for the current row.
292 doy <- temperature_data_frame$DOY[row] 315 doy = temperature_data_frame$DOY[row];
293 # Photoperiod in the day. 316 # Photoperiod in the day.
294 photoperiod <- temperature_data_frame$DAYLEN[row] 317 photoperiod = temperature_data_frame$DAYLEN[row];
295 temp.profile <- get_temperature_at_hour(latitude, temperature_data_frame, row, opt$num_days) 318 temp.profile = get_temperature_at_hour(latitude, temperature_data_frame, row, opt$num_days);
296 mean.temp <- temp.profile[1] 319 mean.temp = temp.profile[1];
297 averages.temp <- temp.profile[2] 320 averages.temp = temp.profile[2];
298 averages.day[row] <- averages.temp 321 averages.day[row] = averages.temp;
299 # Trash bin for death. 322 # Trash bin for death.
300 death.vector <- NULL 323 death.vector = NULL;
301 # Newborn. 324 # Newborn.
302 birth.vector <- NULL 325 birth.vector = NULL;
303 # All individuals. 326 # All individuals.
304 for (i in 1:num_insects) { 327 for (i in 1:num_insects) {
305 # Individual record. 328 # Individual record.
306 vector.individual <- vector.matrix[i,] 329 vector.individual = vector.matrix[i,];
307 # Adjustment for late season mortality rate (still alive?). 330 # Adjustment for late season mortality rate (still alive?).
308 if (latitude < 40.0) { 331 if (latitude < 40.0) {
309 post.mortality <- 1 332 post.mortality = 1;
310 day.kill <- 300 333 day.kill = 300;
311 } 334 }
312 else { 335 else {
313 post.mortality <- 2 336 post.mortality = 2;
314 day.kill <- 250 337 day.kill = 250;
315 } 338 }
316 if (vector.individual[2] == 0) { 339 if (vector.individual[2] == 0) {
317 # Egg. 340 # Egg.
318 death.probability = opt$egg_mortality * mortality.egg(mean.temp) 341 death.probability = opt$egg_mortality * mortality.egg(mean.temp);
319 } 342 }
320 else if (vector.individual[2] == 1 | vector.individual[2] == 2) { 343 else if (vector.individual[2] == 1 | vector.individual[2] == 2) {
321 death.probability = opt$nymph_mortality * mortality.nymph(mean.temp) 344 death.probability = opt$nymph_mortality * mortality.nymph(mean.temp);
322 } 345 }
323 else if (vector.individual[2] == 3 | vector.individual[2] == 4 | vector.individual[2] == 5) { 346 else if (vector.individual[2] == 3 | vector.individual[2] == 4 | vector.individual[2] == 5) {
324 # Adult. 347 # Adult.
325 if (doy < day.kill) { 348 if (doy < day.kill) {
326 death.probability = opt$adult_mortality * mortality.adult(mean.temp) 349 death.probability = opt$adult_mortality * mortality.adult(mean.temp);
327 } 350 }
328 else { 351 else {
329 # Increase adult mortality after fall equinox. 352 # Increase adult mortality after fall equinox.
330 death.probability = opt$adult_mortality * post.mortality * mortality.adult(mean.temp) 353 death.probability = opt$adult_mortality * post.mortality * mortality.adult(mean.temp);
331 } 354 }
332 } 355 }
333 # Dependent on temperature and life stage? 356 # Dependent on temperature and life stage?
334 u.d <- runif(1) 357 u.d = runif(1);
335 if (u.d < death.probability) { 358 if (u.d < death.probability) {
336 death.vector <- c(death.vector, i) 359 death.vector = c(death.vector, i);
337 } 360 }
338 else { 361 else {
339 # End of diapause. 362 # End of diapause.
340 if (vector.individual[1] == 0 && vector.individual[2] == 3) { 363 if (vector.individual[1] == 0 && vector.individual[2] == 3) {
341 # Overwintering adult (previttelogenic). 364 # Overwintering adult (previttelogenic).
342 if (photoperiod > opt$photoperiod && vector.individual[3] > 68 && doy < 180) { 365 if (photoperiod > opt$photoperiod && vector.individual[3] > 68 && doy < 180) {
343 # Add 68C to become fully reproductively matured. 366 # Add 68C to become fully reproductively matured.
344 # Transfer to vittelogenic. 367 # Transfer to vittelogenic.
345 vector.individual <- c(0, 4, 0, 0, 0) 368 vector.individual = c(0, 4, 0, 0, 0);
346 vector.matrix[i,] <- vector.individual 369 vector.matrix[i,] = vector.individual;
347 } 370 }
348 else { 371 else {
349 # Add to # Add average temperature for current day. 372 # Add to # Add average temperature for current day.
350 vector.individual[3] <- vector.individual[3] + averages.temp 373 vector.individual[3] = vector.individual[3] + averages.temp;
351 # Add 1 day in current stage. 374 # Add 1 day in current stage.
352 vector.individual[4] <- vector.individual[4] + 1 375 vector.individual[4] = vector.individual[4] + 1;
353 vector.matrix[i,] <- vector.individual 376 vector.matrix[i,] = vector.individual;
354 } 377 }
355 } 378 }
356 if (vector.individual[1] != 0 && vector.individual[2] == 3) { 379 if (vector.individual[1] != 0 && vector.individual[2] == 3) {
357 # Not overwintering adult (previttelogenic). 380 # Not overwintering adult (previttelogenic).
358 current.gen <- vector.individual[1] 381 current.gen = vector.individual[1];
359 if (vector.individual[3] > 68) { 382 if (vector.individual[3] > 68) {
360 # Add 68C to become fully reproductively matured. 383 # Add 68C to become fully reproductively matured.
361 # Transfer to vittelogenic. 384 # Transfer to vittelogenic.
362 vector.individual <- c(current.gen, 4, 0, 0, 0) 385 vector.individual = c(current.gen, 4, 0, 0, 0);
363 vector.matrix[i,] <- vector.individual 386 vector.matrix[i,] = vector.individual;
364 } 387 }
365 else { 388 else {
366 # Add average temperature for current day. 389 # Add average temperature for current day.
367 vector.individual[3] <- vector.individual[3] + averages.temp 390 vector.individual[3] = vector.individual[3] + averages.temp;
368 # Add 1 day in current stage. 391 # Add 1 day in current stage.
369 vector.individual[4] <- vector.individual[4] + 1 392 vector.individual[4] = vector.individual[4] + 1;
370 vector.matrix[i,] <- vector.individual 393 vector.matrix[i,] = vector.individual;
371 } 394 }
372 } 395 }
373 # Oviposition -- where population dynamics comes from. 396 # Oviposition -- where population dynamics comes from.
374 if (vector.individual[2] == 4 && vector.individual[1] == 0 && mean.temp > 10) { 397 if (vector.individual[2] == 4 && vector.individual[1] == 0 && mean.temp > 10) {
375 # Vittelogenic stage, overwintering generation. 398 # Vittelogenic stage, overwintering generation.
376 if (vector.individual[4] == 0) { 399 if (vector.individual[4] == 0) {
377 # Just turned in vittelogenic stage. 400 # Just turned in vittelogenic stage.
378 num_insects.birth = round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size)) 401 num_insects.birth = round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size));
379 } 402 }
380 else { 403 else {
381 # Daily probability of birth. 404 # Daily probability of birth.
382 p.birth = opt$oviposition * 0.01 405 p.birth = opt$oviposition * 0.01;
383 u1 <- runif(1) 406 u1 = runif(1);
384 if (u1 < p.birth) { 407 if (u1 < p.birth) {
385 num_insects.birth = round(runif(1, 2, 8)) 408 num_insects.birth = round(runif(1, 2, 8));
386 } 409 }
387 } 410 }
388 # Add average temperature for current day. 411 # Add average temperature for current day.
389 vector.individual[3] <- vector.individual[3] + averages.temp 412 vector.individual[3] = vector.individual[3] + averages.temp;
390 # Add 1 day in current stage. 413 # Add 1 day in current stage.
391 vector.individual[4] <- vector.individual[4] + 1 414 vector.individual[4] = vector.individual[4] + 1;
392 vector.matrix[i,] <- vector.individual 415 vector.matrix[i,] = vector.individual;
393 if (num_insects.birth > 0) { 416 if (num_insects.birth > 0) {
394 # Add new birth -- might be in different generations. 417 # Add new birth -- might be in different generations.
395 new.gen <- vector.individual[1] + 1 418 new.gen = vector.individual[1] + 1;
396 # Egg profile. 419 # Egg profile.
397 new.individual <- c(new.gen, 0, 0, 0, 0) 420 new.individual = c(new.gen, 0, 0, 0, 0);
398 new.vector <- rep(new.individual, num_insects.birth) 421 new.vector = rep(new.individual, num_insects.birth);
399 # Update batch of egg profile. 422 # Update batch of egg profile.
400 new.vector <- t(matrix(new.vector, nrow=5)) 423 new.vector = t(matrix(new.vector, nrow=5));
401 # Group with total eggs laid in that day. 424 # Group with total eggs laid in that day.
402 birth.vector <- rbind(birth.vector, new.vector) 425 birth.vector = rbind(birth.vector, new.vector);
403 } 426 }
404 } 427 }
405 # Oviposition -- for generation 1. 428 # Oviposition -- for generation 1.
406 if (vector.individual[2] == 4 && vector.individual[1] == 1 && mean.temp > 12.5 && doy < 222) { 429 if (vector.individual[2] == 4 && vector.individual[1] == 1 && mean.temp > 12.5 && doy < 222) {
407 # Vittelogenic stage, 1st generation 430 # Vittelogenic stage, 1st generation
408 if (vector.individual[4] == 0) { 431 if (vector.individual[4] == 0) {
409 # Just turned in vittelogenic stage. 432 # Just turned in vittelogenic stage.
410 num_insects.birth = round(runif(1, 2+opt$min_clutch_size, 8+opt$max_clutch_size)) 433 num_insects.birth = round(runif(1, 2+opt$min_clutch_size, 8+opt$max_clutch_size));
411 } 434 }
412 else { 435 else {
413 # Daily probability of birth. 436 # Daily probability of birth.
414 p.birth = opt$oviposition * 0.01 437 p.birth = opt$oviposition * 0.01;
415 u1 <- runif(1) 438 u1 = runif(1);
416 if (u1 < p.birth) { 439 if (u1 < p.birth) {
417 num_insects.birth = round(runif(1, 2, 8)) 440 num_insects.birth = round(runif(1, 2, 8));
418 } 441 }
419 } 442 }
420 # Add average temperature for current day. 443 # Add average temperature for current day.
421 vector.individual[3] <- vector.individual[3] + averages.temp 444 vector.individual[3] = vector.individual[3] + averages.temp;
422 # Add 1 day in current stage. 445 # Add 1 day in current stage.
423 vector.individual[4] <- vector.individual[4] + 1 446 vector.individual[4] = vector.individual[4] + 1;
424 vector.matrix[i,] <- vector.individual 447 vector.matrix[i,] = vector.individual;
425 if (num_insects.birth > 0) { 448 if (num_insects.birth > 0) {
426 # Add new birth -- might be in different generations. 449 # Add new birth -- might be in different generations.
427 new.gen <- vector.individual[1] + 1 450 new.gen = vector.individual[1] + 1;
428 # Egg profile. 451 # Egg profile.
429 new.individual <- c(new.gen, 0, 0, 0, 0) 452 new.individual = c(new.gen, 0, 0, 0, 0);
430 new.vector <- rep(new.individual, num_insects.birth) 453 new.vector = rep(new.individual, num_insects.birth);
431 # Update batch of egg profile. 454 # Update batch of egg profile.
432 new.vector <- t(matrix(new.vector, nrow=5)) 455 new.vector = t(matrix(new.vector, nrow=5));
433 # Group with total eggs laid in that day. 456 # Group with total eggs laid in that day.
434 birth.vector <- rbind(birth.vector, new.vector) 457 birth.vector = rbind(birth.vector, new.vector);
435 } 458 }
436 } 459 }
437 # Egg to young nymph. 460 # Egg to young nymph.
438 if (vector.individual[2] == 0) { 461 if (vector.individual[2] == 0) {
439 # Add average temperature for current day. 462 # Add average temperature for current day.
440 vector.individual[3] <- vector.individual[3] + averages.temp 463 vector.individual[3] = vector.individual[3] + averages.temp;
441 if (vector.individual[3] >= (68+opt$young_nymph_accumulation)) { 464 if (vector.individual[3] >= (68+opt$young_nymph_accumulation)) {
442 # From egg to young nymph, degree-days requirement met. 465 # From egg to young nymph, degree-days requirement met.
443 current.gen <- vector.individual[1] 466 current.gen = vector.individual[1];
444 # Transfer to young nymph stage. 467 # Transfer to young nymph stage.
445 vector.individual <- c(current.gen, 1, 0, 0, 0) 468 vector.individual = c(current.gen, 1, 0, 0, 0);
446 } 469 }
447 else { 470 else {
448 # Add 1 day in current stage. 471 # Add 1 day in current stage.
449 vector.individual[4] <- vector.individual[4] + 1 472 vector.individual[4] = vector.individual[4] + 1;
450 } 473 }
451 vector.matrix[i,] <- vector.individual 474 vector.matrix[i,] = vector.individual;
452 } 475 }
453 # Young nymph to old nymph. 476 # Young nymph to old nymph.
454 if (vector.individual[2] == 1) { 477 if (vector.individual[2] == 1) {
455 # Add average temperature for current day. 478 # Add average temperature for current day.
456 vector.individual[3] <- vector.individual[3] + averages.temp 479 vector.individual[3] = vector.individual[3] + averages.temp;
457 if (vector.individual[3] >= (250+opt$old_nymph_accumulation)) { 480 if (vector.individual[3] >= (250+opt$old_nymph_accumulation)) {
458 # From young to old nymph, degree_days requirement met. 481 # From young to old nymph, degree_days requirement met.
459 current.gen <- vector.individual[1] 482 current.gen = vector.individual[1];
460 # Transfer to old nym stage. 483 # Transfer to old nym stage.
461 vector.individual <- c(current.gen, 2, 0, 0, 0) 484 vector.individual = c(current.gen, 2, 0, 0, 0);
462 if (photoperiod < opt$photoperiod && doy > 180) { 485 if (photoperiod < opt$photoperiod && doy > 180) {
463 vector.individual[5] <- 1 486 vector.individual[5] = 1;
464 } # Prepare for diapausing. 487 } # Prepare for diapausing.
465 } 488 }
466 else { 489 else {
467 # Add 1 day in current stage. 490 # Add 1 day in current stage.
468 vector.individual[4] <- vector.individual[4] + 1 491 vector.individual[4] = vector.individual[4] + 1;
469 } 492 }
470 vector.matrix[i,] <- vector.individual 493 vector.matrix[i,] = vector.individual;
471 } 494 }
472 # Old nymph to adult: previttelogenic or diapausing? 495 # Old nymph to adult: previttelogenic or diapausing?
473 if (vector.individual[2] == 2) { 496 if (vector.individual[2] == 2) {
474 # Add average temperature for current day. 497 # Add average temperature for current day.
475 vector.individual[3] <- vector.individual[3] + averages.temp 498 vector.individual[3] = vector.individual[3] + averages.temp;
476 if (vector.individual[3] >= (200+opt$adult_accumulation)) { 499 if (vector.individual[3] >= (200+opt$adult_accumulation)) {
477 # From old to adult, degree_days requirement met. 500 # From old to adult, degree_days requirement met.
478 current.gen <- vector.individual[1] 501 current.gen = vector.individual[1];
479 if (vector.individual[5] == 0) { 502 if (vector.individual[5] == 0) {
480 # Previttelogenic. 503 # Previttelogenic.
481 vector.individual <- c(current.gen, 3, 0, 0, 0) 504 vector.individual = c(current.gen, 3, 0, 0, 0);
482 } 505 }
483 else { 506 else {
484 # Diapausing. 507 # Diapausing.
485 vector.individual <- c(current.gen, 5, 0, 0, 1) 508 vector.individual = c(current.gen, 5, 0, 0, 1);
486 } 509 }
487 } 510 }
488 else { 511 else {
489 # Add 1 day in current stage. 512 # Add 1 day in current stage.
490 vector.individual[4] <- vector.individual[4] + 1 513 vector.individual[4] = vector.individual[4] + 1;
491 } 514 }
492 vector.matrix[i,] <- vector.individual 515 vector.matrix[i,] = vector.individual;
493 } 516 }
494 # Growing of diapausing adult (unimportant, but still necessary). 517 # Growing of diapausing adult (unimportant, but still necessary).
495 if (vector.individual[2] == 5) { 518 if (vector.individual[2] == 5) {
496 vector.individual[3] <- vector.individual[3] + averages.temp 519 vector.individual[3] = vector.individual[3] + averages.temp;
497 vector.individual[4] <- vector.individual[4] + 1 520 vector.individual[4] = vector.individual[4] + 1;
498 vector.matrix[i,] <- vector.individual 521 vector.matrix[i,] = vector.individual;
499 } 522 }
500 } # Else if it is still alive. 523 } # Else if it is still alive.
501 } # End of the individual bug loop. 524 } # End of the individual bug loop.
502 525
503 # Number of deaths. 526 # Number of deaths.
504 num_insects.death <- length(death.vector) 527 num_insects.death = length(death.vector);
505 if (num_insects.death > 0) { 528 if (num_insects.death > 0) {
506 # Remove record of dead. 529 # Remove record of dead.
507 vector.matrix <- vector.matrix[-death.vector, ] 530 vector.matrix = vector.matrix[-death.vector,];
508 } 531 }
509 # Number of births. 532 # Number of births.
510 num_insects.newborn <- length(birth.vector[,1]) 533 num_insects.newborn = length(birth.vector[,1]);
511 vector.matrix <- rbind(vector.matrix, birth.vector) 534 vector.matrix = rbind(vector.matrix, birth.vector);
512 # Update population size for the next day. 535 # Update population size for the next day.
513 num_insects <- num_insects - num_insects.death + num_insects.newborn 536 num_insects = num_insects - num_insects.death + num_insects.newborn;
514 537
515 # Aggregate results by day. 538 # Aggregate results by day.
516 # Egg population size. 539 # Egg population size.
517 Eggs[row] <- sum(vector.matrix[,2]==0) 540 Eggs[row] = sum(vector.matrix[,2]==0);
518 # Young nymph population size. 541 # Young nymph population size.
519 YoungNymphs[row] <- sum(vector.matrix[,2]==1) 542 YoungNymphs[row] = sum(vector.matrix[,2]==1);
520 # Old nymph population size. 543 # Old nymph population size.
521 OldNymphs[row] <- sum(vector.matrix[,2]==2) 544 OldNymphs[row] = sum(vector.matrix[,2]==2);
522 # Previtellogenic population size. 545 # Previtellogenic population size.
523 Previtellogenic[row] <- sum(vector.matrix[,2]==3) 546 Previtellogenic[row] = sum(vector.matrix[,2]==3);
524 # Vitellogenic population size. 547 # Vitellogenic population size.
525 Vitellogenic[row] <- sum(vector.matrix[,2]==4) 548 Vitellogenic[row] = sum(vector.matrix[,2]==4);
526 # Diapausing population size. 549 # Diapausing population size.
527 Diapausing[row] <- sum(vector.matrix[,2]==5) 550 Diapausing[row] = sum(vector.matrix[,2]==5);
528 551
529 # Newborn population size. 552 # Newborn population size.
530 N.newborn[row] <- num_insects.newborn 553 N.newborn[row] = num_insects.newborn;
531 # Adult population size. 554 # Adult population size.
532 N.adult[row] <- sum(vector.matrix[,2]==3) + sum(vector.matrix[,2]==4) + sum(vector.matrix[,2]==5) 555 N.adult[row] = sum(vector.matrix[,2]==3) + sum(vector.matrix[,2]==4) + sum(vector.matrix[,2]==5);
533 # Dead population size. 556 # Dead population size.
534 N.death[row] <- num_insects.death 557 N.death[row] = num_insects.death;
535 558
536 total.population <- c(total.population, num_insects) 559 total.population = c(total.population, num_insects);
537 560
538 # Overwintering adult population size. 561 # Overwintering adult population size.
539 overwintering_adult.population[row] <- sum(vector.matrix[,1]==0) 562 overwintering_adult.population[row] = sum(vector.matrix[,1]==0);
540 # First generation population size. 563 # First generation population size.
541 first_generation.population[row] <- sum(vector.matrix[,1]==1) 564 first_generation.population[row] = sum(vector.matrix[,1]==1);
542 # Second generation population size. 565 # Second generation population size.
543 second_generation.population[row] <- sum(vector.matrix[,1]==2) 566 second_generation.population[row] = sum(vector.matrix[,1]==2);
544 567
545 # P adult population size. 568 # P adult population size.
546 P.adult[row] <- sum(vector.matrix[,1]==0) 569 P.adult[row] = sum(vector.matrix[,1]==0);
547 # F1 adult population size. 570 # 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)) 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));
549 # F2 adult population size 572 # 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)) 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));
551 } # End of days specified in the input temperature data. 574 } # End of days specified in the input temperature data.
552 575
553 averages.cum <- cumsum(averages.day) 576 averages.cum = cumsum(averages.day);
554 577
555 # Define the output values. 578 # Define the output values.
556 Eggs.replications[,N.replications] <- Eggs 579 Eggs.replications[,N.replications] = Eggs;
557 YoungNymphs.replications[,N.replications] <- YoungNymphs 580 YoungNymphs.replications[,N.replications] = YoungNymphs;
558 OldNymphs.replications[,N.replications] <- OldNymphs 581 OldNymphs.replications[,N.replications] = OldNymphs;
559 Previtellogenic.replications[,N.replications] <- Previtellogenic 582 Previtellogenic.replications[,N.replications] = Previtellogenic;
560 Vitellogenic.replications[,N.replications] <- Vitellogenic 583 Vitellogenic.replications[,N.replications] = Vitellogenic;
561 Diapausing.replications[,N.replications] <- Diapausing 584 Diapausing.replications[,N.replications] = Diapausing;
562 585
563 newborn.replications[,N.replications] <- N.newborn 586 newborn.replications[,N.replications] = N.newborn;
564 adult.replications[,N.replications] <- N.adult 587 adult.replications[,N.replications] = N.adult;
565 death.replications[,N.replications] <- N.death 588 death.replications[,N.replications] = N.death;
566 589
567 P.replications[,N.replications] <- overwintering_adult.population 590 P.replications[,N.replications] = overwintering_adult.population;
568 P_adults.replications[,N.replications] <- P.adult 591 P_adults.replications[,N.replications] = P.adult;
569 F1.replications[,N.replications] <- first_generation.population 592 F1.replications[,N.replications] = first_generation.population;
570 F1_adults.replications[,N.replications] <- F1.adult 593 F1_adults.replications[,N.replications] = F1.adult;
571 F2.replications[,N.replications] <- second_generation.population 594 F2.replications[,N.replications] = second_generation.population;
572 F2_adults.replications[,N.replications] <- F2.adult 595 F2_adults.replications[,N.replications] = F2.adult;
573 596
574 population.replications[,N.replications] <- total.population 597 population.replications[,N.replications] = total.population;
575 } 598 }
576 599
577 # Mean value for eggs. 600 # Mean value for eggs.
578 eggs <- apply(Eggs.replications, 1, mean) 601 eggs = apply(Eggs.replications, 1, mean);
579 # Standard error for eggs. 602 # Standard error for eggs.
580 eggs.std_error <- apply(Eggs.replications, 1, sd) / sqrt(opt$replications) 603 eggs.std_error = apply(Eggs.replications, 1, sd) / sqrt(opt$replications);
581 604
582 # Mean value for nymphs. 605 # Mean value for nymphs.
583 nymphs <- apply((YoungNymphs.replications+OldNymphs.replications), 1, mean) 606 nymphs = apply((YoungNymphs.replications+OldNymphs.replications), 1, mean);
584 # Standard error for nymphs. 607 # Standard error for nymphs.
585 nymphs.std_error <- apply((YoungNymphs.replications+OldNymphs.replications) / sqrt(opt$replications), 1, sd) 608 nymphs.std_error = apply((YoungNymphs.replications+OldNymphs.replications) / sqrt(opt$replications), 1, sd);
586 609
587 # Mean value for adults. 610 # Mean value for adults.
588 adults <- apply((Previtellogenic.replications+Vitellogenic.replications+Diapausing.replications), 1, mean) 611 adults = apply((Previtellogenic.replications+Vitellogenic.replications+Diapausing.replications), 1, mean);
589 # Standard error for adults. 612 # Standard error for adults.
590 adults.std_error <- apply((Previtellogenic.replications+Vitellogenic.replications+Diapausing.replications), 1, sd) / sqrt(opt$replications) 613 adults.std_error = apply((Previtellogenic.replications+Vitellogenic.replications+Diapausing.replications), 1, sd) / sqrt(opt$replications);
591 614
592 # Mean value for P. 615 # Mean value for P.
593 P <- apply(P.replications, 1, mean) 616 P = apply(P.replications, 1, mean);
594 # Standard error for P. 617 # Standard error for P.
595 P.std_error <- apply(P.replications, 1, sd) / sqrt(opt$replications) 618 P.std_error = apply(P.replications, 1, sd) / sqrt(opt$replications);
596 619
597 # Mean value for P adults. 620 # Mean value for P adults.
598 P_adults <- apply(P_adults.replications, 1, mean) 621 P_adults = apply(P_adults.replications, 1, mean);
599 # Standard error for P_adult. 622 # Standard error for P_adult.
600 P_adults.std_error <- apply(P_adults.replications, 1, sd) / sqrt(opt$replications) 623 P_adults.std_error = apply(P_adults.replications, 1, sd) / sqrt(opt$replications);
601 624
602 # Mean value for F1. 625 # Mean value for F1.
603 F1 <- apply(F1.replications, 1, mean) 626 F1 = apply(F1.replications, 1, mean);
604 # Standard error for F1. 627 # Standard error for F1.
605 F1.std_error <- apply(F1.replications, 1, sd) / sqrt(opt$replications) 628 F1.std_error = apply(F1.replications, 1, sd) / sqrt(opt$replications);
606 629
607 # Mean value for F1 adults. 630 # Mean value for F1 adults.
608 F1_adults <- apply(F1_adults.replications, 1, mean) 631 F1_adults = apply(F1_adults.replications, 1, mean);
609 # Standard error for F1 adult. 632 # Standard error for F1 adult.
610 F1_adults.std_error <- apply(F1_adults.replications, 1, sd) / sqrt(opt$replications) 633 F1_adults.std_error = apply(F1_adults.replications, 1, sd) / sqrt(opt$replications);
611 634
612 # Mean value for F2. 635 # Mean value for F2.
613 F2 <- apply(F2.replications, 1, mean) 636 F2 = apply(F2.replications, 1, mean);
614 # Standard error for F2. 637 # Standard error for F2.
615 F2.std_error <- apply(F2.replications, 1, sd) / sqrt(opt$replications) 638 F2.std_error = apply(F2.replications, 1, sd) / sqrt(opt$replications);
616 639
617 # Mean value for F2 adults. 640 # Mean value for F2 adults.
618 F2_adults <- apply(F2_adults.replications, 1, mean) 641 F2_adults = apply(F2_adults.replications, 1, mean);
619 # Standard error for F2 adult. 642 # Standard error for F2 adult.
620 F2_adults.std_error <- apply(F2_adults.replications, 1, sd) / sqrt(opt$replications) 643 F2_adults.std_error = apply(F2_adults.replications, 1, sd) / sqrt(opt$replications);
621 644
622 # Display the total number of days in the Galaxy history item blurb. 645 # Display the total number of days in the Galaxy history item blurb.
623 cat("Number of days: ", opt$num_days, "\n") 646 cat("Number of days: ", opt$num_days, "\n");
624 647
625 dev.new(width=20, height=30) 648 dev.new(width=20, height=30);
626 649
627 # Start PDF device driver to save charts to output. 650 # Start PDF device driver to save charts to output.
628 pdf(file=opt$output, width=20, height=30, bg="white") 651 pdf(file=opt$output, width=20, height=30, bg="white");
629 par(mar=c(5, 6, 4, 4), mfrow=c(3, 1)) 652 par(mar=c(5, 6, 4, 4), mfrow=c(3, 1));
630 653
631 # Data analysis and visualization plots only within a single calendar year. 654 # Data analysis and visualization plots only within a single calendar year.
632 days <- c(1:opt$num_days) 655 days = c(1:opt$num_days);
633 start_date <- temperature_data_frame$DATE[1] 656 start_date = temperature_data_frame$DATE[1];
634 end_date <- temperature_data_frame$DATE[opt$num_days] 657 end_date = temperature_data_frame$DATE[opt$num_days];
635 658
636 # Subfigure 1: population size by life stage. 659 # Subfigure 1: population size by life stage.
637 maxval <- max(eggs+eggs.std_error, nymphs+nymphs.std_error, adults+adults.std_error) 660 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, 661 render_chart("pop_size_by_life_stage", opt$insect, opt$location, latitude, start_date, end_date, days, maxval,
639 opt$std_error_plot, adults, nymphs, eggs, adults.std_error, nymphs.std_error, eggs.std_error) 662 opt$std_error_plot, adults, nymphs, eggs, adults.std_error, nymphs.std_error, eggs.std_error, date_labels);
640 # Subfigure 2: population size by generation. 663 # Subfigure 2: population size by generation.
641 maxval <- max(F2) 664 maxval = max(F2);
642 render_chart("pop_size_by_generation", opt$insect, opt$location, latitude, start_date, end_date, days, maxval, 665 render_chart("pop_size_by_generation", opt$insect, opt$location, latitude, start_date, end_date, days, maxval,
643 opt$std_error_plot, P, F1, F2, P.std_error, F1.std_error, F2.std_error) 666 opt$std_error_plot, P, F1, F2, P.std_error, F1.std_error, F2.std_error, date_labels);
644 # Subfigure 3: adult population size by generation. 667 # Subfigure 3: adult population size by generation.
645 maxval <- max(F2_adults) + 100 668 maxval = max(F2_adults) + 100;
646 render_chart("adult_pop_size_by_generation", opt$insect, opt$location, latitude, start_date, end_date, days, maxval, 669 render_chart("adult_pop_size_by_generation", opt$insect, opt$location, latitude, start_date, end_date, days, maxval,
647 opt$std_error_plot, P_adults, F1_adults, F2_adults, P_adults.std_error, F1_adults.std_error, F2_adults.std_error) 670 opt$std_error_plot, P_adults, F1_adults, F2_adults, P_adults.std_error, F1_adults.std_error, F2_adults.std_error,
671 date_labels);
648 672
649 # Turn off device driver to flush output. 673 # Turn off device driver to flush output.
650 dev.off() 674 dev.off();