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comparison bmsb.R @ 22:a5f80d53feee draft

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author greg
date Tue, 16 Aug 2016 14:17:46 -0400
parents ce78cd25b873
children 08cb8c7228c2
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21:ce78cd25b873 22:a5f80d53feee
1 #!/usr/bin/env Rscript 1 #!/usr/bin/env Rscript
2 2
3 options_list <- list( 3 options_list <- list(
4 make_option(c("-i", "--input_temperatures"), action="store", help="Input temperatures csv file"),
4 make_option(c("-s", "--save_log"), action="store_true", default=FALSE, help="Save R logs"), 5 make_option(c("-s", "--save_log"), action="store_true", default=FALSE, help="Save R logs"),
5 make_option(c("-m", "--output_r_log"), help="Output dataset for R logs"), 6 make_option(c("-m", "--output_r_log"), action="store", help="Output dataset for R logs"),
6 make_option(c("-o", "--output"), help="Output dataset") 7 make_option(c("-o", "--output"), action="store", help="Output dataset")
7 ) 8 )
8 9
9 parser <- OptionParser(usage="%prog [options] file", options_list) 10 parser <- OptionParser(usage="%prog [options] file", options_list)
10 args <- parse_args(parser, positional_arguments=TRUE) 11 args <- parse_args(parser, positional_arguments=TRUE)
11 opt <- args$options 12 opt <- args$options
12 13
13 14
14 daylength = function(L){ 15 if (opt$save_log) {
15 # from Forsythe 1995 16 rlogf <- file(opt$output_r_log, open="wt")
16 p = 0.8333 17 } else {
17 dl <- NULL 18 # Direct R messaging to a temporary file.
18 for (i in 1:365) { 19 rlogf <- file("tmpRLog", open="wt")
19 theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (i - 186)))
20 phi <- asin(0.39795 * cos(theta))
21 dl[i] <- 24 - 24/pi * acos((sin(p * pi/180) + sin(L * pi/180) * sin(phi))/(cos(L * pi/180) * cos(phi)))
22 }
23 # return a vector of daylength in 365 days
24 dl
25 } 20 }
21 sink(file=rlogf, type=c("output", "message"), append=FALSE, split=FALSE)
26 22
27 23 tempdata <- read.csv(opt$input_temperatures)
28 # source("daylength.R") 24 save(tempdata, file=opt$output)
29 hourtemp = function(L,date){
30 # L = 37.5 specify this in main program
31 # base development threshold for BMSB
32 threshold <- 12.7
33 # threshold2 <- threshold/24 degree hour accumulation
34 #expdata <- tempdata[1:365,11:13] # Use daily max, min, mean
35 # daily minimum
36 dnp <- expdata[date,2]
37 # daily maximum
38 dxp <- expdata[date,3]
39 dmean <- 0.5 * (dnp + dxp)
40 #if (dmean>0) {
41 #dnp <- dnp - k1 * dmean
42 #dxp <- dxp + k2 * dmean
43 #} else {
44 #dnp <- dnp + k1 * dmean
45 #dxp <- dxp - k2 * dmean
46 #}
47 dd <- 0 # initialize degree day accumulation
48
49 if (dxp<threshold) {
50 dd <- 0
51 } else {
52 # extract daylength data for entire year
53 dlprofile <- daylength(L)
54 # initialize hourly temperature
55 T <- NULL
56 #initialize degree hour vector
57 dh <- NULL
58 # calculate daylength in given date
59 # date <- 200
60 y <- dlprofile[date]
61 # night length
62 z <- 24 - y
63 # lag coefficient
64 a <- 1.86
65 # night coefficient
66 b <- 2.20
67 #import raw data set
68 #tempdata <- read.csv("tempdata.csv")
69 # Should be outside function otherwise its redundant
70 # sunrise time
71 risetime <- 12 - y/2
72 # sunset time
73 settime <- 12 + y/2
74 ts <- (dxp - dnp) * sin(pi * (settime-5)/(y + 2 * a)) + dnp
75 for (i in 1:24) {
76 if (i > risetime && i < settime) {
77 # number of hours after Tmin until sunset
78 m <- i - 5
79 T[i] = (dxp - dnp) * sin(pi * m/(y + 2 * a)) + dnp
80 if (T[i]<8.4) {
81 dh[i] <- 0
82 } else {
83 dh[i] <- T[i] - 8.4
84 }
85 } else if (i>settime) {
86 n <- i - settime
87 T[i] = dnp + (ts - dnp) * exp(-b * n/z)
88 if (T[i]<8.4) {
89 dh[i] <- 0
90 } else {
91 dh[i] <- T[i] - 8.4
92 }
93 } else {
94 n <- i + 24 - settime
95 T[i] = dnp + (ts - dnp) * exp(-b * n / z)
96 if (T[i]<8.4) {
97 dh[i] <- 0
98 } else {
99 dh[i] <- T[i] - 8.4
100 }
101 }
102 }
103 dd <- sum(dh) / 24
104 }
105 return = c(dmean, dd)
106 return
107 }
108
109
110 mortality.egg = function(temperature) {
111 if (temperature < 12.7) {
112 mort.prob = 1
113 } else {
114 # 100% mortality if <12.7
115 mort.prob = 0.8 - temperature / 40
116 if (mort.prob<0) {
117 mort.prob = 0.01
118 }
119 }
120 return = mort.prob
121 return
122 }
123
124
125 mortality.nymph = function(temperature) {
126 if (temperature<12.7) {
127 mort.prob = 0.03
128 } else {
129 # at low temperature
130 mort.prob = -temperature * 0.0008 + 0.03
131 }
132 return = mort.prob
133 return
134 }
135
136
137 mortality.adult = function(temperature) {
138 if (temperature < 12.7) {
139 mort.prob = 0.002
140 } else {
141 mort.prob = -temperature * 0.0005 + 0.02
142 }
143 return = mort.prob
144 return
145 }
146
147
148 # model initialization
149 # TODO: add tool params for the following options.
150 # start with 1000 individuals
151 n <- 1000
152 # Generation, Stage, DD, T, Diapause
153 vec.ini <- c(0,3,0,0,0)
154 # overwintering, previttelogenic, DD = 0, T = 0, no-diapause
155 vec.mat <- rep(vec.ini,n)
156 # complete matrix for the population
157 vec.mat <- t(matrix(vec.mat, nrow = 5))
158 # latitude for Asheville NC
159 L <- 35.58
160 # complete photoperiod profile in a year, requires daylength function
161 ph.p <- daylength(L)
162
163 # load temperature data@location/year
164 load("asheville2014.Rdat")
165
166 # time series of population size
167 tot.pop <- NULL
168
169 # gen.0 pop size
170 gen0.pop <- rep(0, 365)
171 gen1.pop <- rep(0, 365)
172 gen2.pop <- rep(0, 365)
173
174 # aggregate
175 S0 <- S1 <- S2 <- S3 <- S4 <- S5 <- rep(0, 365)
176 g0.adult <- g1.adult <- g2.adult <- rep(0, 365)
177
178 # birth death adults
179 N.newborn <- N.death <- N.adult <- rep(0, 365)
180
181 # degree-day accumulation
182 dd.day <- rep(0, 365)
183
184 # start tick
185 ptm <- proc.time()
186
187 for (n.sim in 1:1000) {
188 # loop through 1000 simulations
189 for (day in 1:365) {
190 # loop through 365 day/yr
191 photoperiod <- ph.p[day]
192 # photoperiod in the day
193 temp.profile <- hourtemp(L,day)
194 # temperature profile
195 mean.temp <- temp.profile[1]
196 # mean temp
197 dd.temp <- temp.profile[2]
198 # degree-day
199 dd.day[day] <- dd.temp
200 death.vec <- NULL
201 # trash bin for death
202 birth.vec <- NULL
203 # record new born
204 for (i in 1:n) {
205 # loop through all individual
206 vec.ind <- vec.mat[i,]
207 # find individual record
208 # first of all, still alive?
209 if (vec.ind[2] == 0) {
210 # egg
211 death.prob = mortality.egg(mean.temp)
212 } else if (vec.ind[2] == 1 | vec.ind[2] == 2) {
213 # nymph
214 death.prob = mortality.nymph(mean.temp)
215 } else if (vec.ind[2] == 3 | vec.ind[2] == 4 | vec.ind[2] == 5) {
216 # for adult
217 death.prob = mortality.adult(mean.temp)
218 }
219 u.d <- runif(1)
220 if (u.d<death.prob) {
221 death.vec <- c(death.vec,i)
222 } else {
223 # aggregrate index of dead bug
224 # event 1 end of diapause
225 if (vec.ind[1] == 0 && vec.ind[2] == 3) {
226 # overwintering adult (previttelogenic)
227 if (photoperiod>13.5 && vec.ind[3] > 68 && day < 180) {
228 # add 68C to become fully reproductively matured
229 # transfer to vittelogenic
230 vec.ind <- c(0,4,0,0,0)
231 vec.mat[i,] <- vec.ind
232 } else {
233 # add to DD
234 vec.ind[3] <- vec.ind[3] + dd.temp
235 vec.ind[4] <- vec.ind[4] + 1 # add 1 day in current stage
236 vec.mat[i,] <- vec.ind
237 }
238 }
239 if (vec.ind[1]!=0 && vec.ind[2] == 3) {
240 # NOT overwintering adult (previttelogenic)
241 current.gen <- vec.ind[1]
242 if (vec.ind[3]>68) {
243 # add 68C to become fully reproductively matured
244 # transfer to vittelogenic
245 vec.ind <- c(current.gen,4,0,0,0)
246 vec.mat[i,] <- vec.ind
247 } else {
248 # add to DD
249 vec.ind[3] <- vec.ind[3] + dd.temp
250 # add 1 day in current stage
251 vec.ind[4] <- vec.ind[4] + 1
252 vec.mat[i,] <- vec.ind
253 }
254 }
255 # event 2 oviposition -- where population dynamics comes from
256 # vittelogenic stage, overwintering generation
257 if (vec.ind[2] == 4 && vec.ind[1] == 0 && mean.temp>10) {
258 if (vec.ind[4] == 0) {
259 # just turned in vittelogenic stage
260 n.birth = round(runif(1,10,20))
261 } else {
262 p.birth = 1/4/75
263 # prob of birth
264 u1 <- runif(1)
265 if (u1<p.birth) {
266 n.birth = n.birth
267 }
268 }
269 # add to DD
270 vec.ind[3] <- vec.ind[3] + dd.temp
271 # add 1 day in current stage
272 vec.ind[4] <- vec.ind[4] + 1
273 vec.mat[i,] <- vec.ind
274 if (n.birth>0) {
275 # add new birth -- might be in different generations
276 # generation + 1
277 new.gen <- vec.ind[1] + 1
278 # egg profile
279 new.ind <- c(new.gen,0,0,0,0)
280 new.vec <- rep(new.ind,n.birth)
281 # update batch of egg profile
282 new.vec <- t(matrix(new.vec,nrow = 5))
283 # group with total eggs laid in that day
284 birth.vec <- rbind(birth.vec,new.vec)
285 }
286 }
287 # event 2 oviposition -- for gen 1.
288 if (vec.ind[2] == 4 && vec.ind[1] == 1 && mean.temp>12.5 && day<222) {
289 # vittelogenic stage, 1st generation
290 if (vec.ind[4] == 0) {
291 # just turned in vittelogenic stage
292 n.birth = round(runif(1,10,20))
293 } else {
294 p.birth = 1/4/75
295 u1 <- runif(1)
296 if (u1<p.birth) {
297 n.birth = n.birth
298 }
299 }
300 # add to DD
301 vec.ind[3] <- vec.ind[3] + dd.temp
302 # add 1 day in current stage
303 vec.ind[4] <- vec.ind[4] + 1
304 vec.mat[i,] <- vec.ind
305 if (n.birth>0) {
306 # add new birth -- might be in different generations
307 # generation + 1
308 new.gen <- vec.ind[1] + 1
309 # egg profile
310 new.ind <- c(new.gen,0,0,0,0)
311 new.vec <- rep(new.ind,n.birth)
312 # update batch of egg profile
313 new.vec <- t(matrix(new.vec,nrow = 5))
314 # group with total eggs laid in that day
315 birth.vec <- rbind(birth.vec,new.vec)
316 }
317 }
318 # event 3 development (with diapause determination)
319 # event 3.1 egg development to young nymph (vec.ind[2] = 0 -> egg)
320 # egg stage
321 if (vec.ind[2] == 0) {
322 # add to DD
323 vec.ind[3] <- vec.ind[3] + dd.temp
324 # from egg to young nymph
325 if (vec.ind[3] >= 53.30 && -0.9843 * dd.temp + 33.438>0) {
326 current.gen <- vec.ind[1]
327 # transfer to young nym stage
328 vec.ind <- c(current.gen,1,0,0,0)
329 } else {
330 # add 1 day in current stage
331 vec.ind[4] <- vec.ind[4] + 1
332 }
333 vec.mat[i,] <- vec.ind
334 }
335 # event 3.2 young nymph to old nymph (vec.ind[2] = 1 -> young nymph: determines diapause)
336 # young nymph stage
337 if (vec.ind[2] == 1) {
338 # add to DD
339 vec.ind[3] <- vec.ind[3] + dd.temp
340 # from young to old nymph
341 if (vec.ind[3] >= 537/2 && -0.45 * dd.temp + 18>0) {
342 current.gen <- vec.ind[1]
343 # transfer to old nym stage
344 vec.ind <- c(current.gen,2,0,0,0)
345 # prepare for diapausing
346 if (photoperiod<13.5 && day > 180) {
347 vec.ind[5] <- 1
348 }
349 } else {
350 # add 1 day in current stage
351 vec.ind[4] <- vec.ind[4] + 1
352 }
353 vec.mat[i,] <- vec.ind
354 }
355 # event 3.3 old nymph to adult: previttelogenic or diapausing?
356 # old nymph stage
357 if (vec.ind[2] == 2) {
358 # add to DD
359 vec.ind[3] <- vec.ind[3] + dd.temp
360 # from old to adult
361 if (vec.ind[3] >= 537/2 && -0.50 * dd.temp + 22>0) {
362 current.gen <- vec.ind[1]
363 # non-diapausing adult -- previttelogenic
364 if (vec.ind[5] == 0) {
365 vec.ind <- c(current.gen,3,0,0,0)
366 # diapausing
367 } else {
368 vec.ind <- c(current.gen,5,0,0,1)
369 }
370 } else {
371 # add 1 day in current stage
372 vec.ind[4] <- vec.ind[4] + 1
373 }
374 vec.mat[i,] <- vec.ind
375 }
376 # event 4 growing of diapausing adult (unimportant, but still necessary)##
377 if (vec.ind[2] == 5) {
378 vec.ind[3] <- vec.ind[3] + dd.temp
379 vec.ind[4] <- vec.ind[4] + 1
380 vec.mat[i,] <- vec.ind
381 }
382 } # else if it is still alive
383 } # end of the individual bug loop
384 # find how many died
385 n.death <- length(death.vec)
386 if (n.death>0) {
387 vec.mat <- vec.mat[-death.vec, ]
388 }
389 # remove record of dead
390 # find how many new born
391 n.newborn <- length(birth.vec[,1])
392 vec.mat <- rbind(vec.mat,birth.vec)
393 # update population size for the next day
394 n <- n-n.death + n.newborn
395
396 # aggregate results by day
397 tot.pop <- c(tot.pop,n)
398 # egg
399 s0 <- sum(vec.mat[,2] == 0)
400 # young nymph
401 s1 <- sum(vec.mat[,2] == 1)
402 # old nymph
403 s2 <- sum(vec.mat[,2] == 2)
404 # previtellogenic
405 s3 <- sum(vec.mat[,2] == 3)
406 # vitellogenic
407 s4 <- sum(vec.mat[,2] == 4)
408 # diapausing
409 s5 <- sum(vec.mat[,2] == 5)
410 # overwintering adult
411 gen0 <- sum(vec.mat[,1] == 0)
412 # first generation
413 gen1 <- sum(vec.mat[,1] == 1)
414 # second generation
415 gen2 <- sum(vec.mat[,1] == 2)
416 # sum of all adults
417 n.adult <- sum(vec.mat[,2] == 3) + sum(vec.mat[,2] == 4) + sum(vec.mat[,2] == 5)
418 # gen.0 pop size
419 gen0.pop[day] <- gen0
420 gen1.pop[day] <- gen1
421 gen2.pop[day] <- gen2
422 S0[day] <- s0
423 S1[day] <- s1
424 S2[day] <- s2
425 S3[day] <- s3
426 S4[day] <- s4
427 S5[day] <- s5
428 g0.adult[day] <- sum(vec.mat[,1] == 0)
429 g1.adult[day] <- 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))
430 g2.adult[day] <- 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))
431 N.newborn[day] <- n.newborn
432 N.death[day] <- n.death
433 N.adult[day] <- n.adult
434 }
435 #print(n.sim)
436 }
437
438 proc.time() - ptm
439 dd.cum <- cumsum(dd.day)
440 save(dd.day, dd.cum, S0, S1, S2, S3, S4, S5, N.newborn, N.death, N.adult, tot.pop, gen0.pop, gen1.pop, gen2.pop, g0.adult, g1.adult, g2.adult, file=opt$output)