comparison insect_phenology_model.R @ 5:1878a03f9c9f draft

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4:e7b1fc0133bb

    make_option(c("-p", "--oviposition"), action="store", dest="oviposition", type="integer", help="Adjustment for oviposition rate"),
    make_option(c("-q", "--photoperiod"), action="store", dest="photoperiod", type="double", help="Critical photoperiod for diapause induction/termination"),
    make_option(c("-s", "--replications"), action="store", dest="replications", type="integer", help="Number of replications"),
    make_option(c("-t", "--se_plot"), action="store", dest="se_plot", help="Plot SE"),
    make_option(c("-v", "--input"), action="store", dest="input", help="Temperature data for selected location"),
    make_option(c("-y", "--young_nymph_accum"), action="store", dest="young_nymph_accum", type="integer", help="Adjustment of DD accumulation (egg->young nymph)")
)

parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
args <- parse_args(parser, positional_arguments=TRUE)
opt <- args$options

get_daylight_length = function(latitude, temperature_data, num_days)
{
    # Return a vector of daylight length (photoperido profile) for
    # the number of days specified in the input temperature data
    # (from Forsythe 1995).
    p = 0.8333
    daylight_length_vector <- NULL
    for (i in 1:num_days) {
        # Get the day of the year from the current row
        # of the temperature data for computation.
        doy <- temperature_data[i, 4]
        theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (doy - 186)))
        phi <- asin(0.39795 * cos(theta))
        # Compute the length of daylight for the day of the year.
        daylight_length_vector[i] <- 24 - (24 / pi * acos((sin(p * pi / 180) + sin(latitude * pi / 180) * sin(phi)) / (cos(latitude * pi / 180) * cos(phi))))
    }
    daylight_length_vector
}

get_temperature_at_hour = function(latitude, temperature_data, daylight_length_vector, row, num_days)
{
    # Base development threshold for Brown Marmolated Stink Bug
    # insect phenology model.
    # TODO: Pass insect on the command line to accomodate more
    # the just the Brown Marmolated Stink Bub.
    threshold <- 14.17

    # Input temperature currently has the following columns.
    # # LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX
    # Minimum temperature for current row.
    dnp <- temperature_data[row, 5]
    # Maximum temperature for current row.
    dxp <- temperature_data[row, 6]
    # Mean temperature for current row.
    dmean <- 0.5 * (dnp + dxp)
    # Initialize degree day accumulation
    dd <- 0
    if (dxp < threshold) {

        # Initialize hourly temperature.
        T <- NULL
        # Initialize degree hour vector.
        dh <- NULL
        # Daylight length for current row.
        y <- daylight_length_vector[row]
        # Darkness length.
        z <- 24 - y
        # Lag coefficient.
        a <- 1.86
        # Darkness coefficient.

                }
            }
        }
        dd <- sum(dh) / 24
    }
    return=c(dmean, dd)
    return
}

dev.egg = function(temperature)
{
    dev.rate= -0.9843 * temperature + 33.438
    return = dev.rate
    return
}

dev.young = function(temperature)
{
    n12 <- -0.3728 * temperature + 14.68
    n23 <- -0.6119 * temperature + 25.249
    dev.rate = mean(n12 + n23)
    return = dev.rate
    return
}

dev.old = function(temperature)
{
    n34 <- -0.6119 * temperature + 17.602
    n45 <- -0.4408 * temperature + 19.036
    dev.rate = mean(n34 + n45)
    return = dev.rate
    return
}

dev.emerg = function(temperature)
{
    emerg.rate <- -0.5332 * temperature + 24.147
    return = emerg.rate
    return
}

mortality.egg = function(temperature)
{
    if (temperature < 12.7) {
        mort.prob = 0.8
    }
    else {
        mort.prob = 0.8 - temperature / 40.0
        if (mort.prob < 0) {
            mort.prob = 0.01
        }
    }
    return = mort.prob
    return
}

mortality.nymph = function(temperature)
{
    if (temperature < 12.7) {
        mort.prob = 0.03
    }
    else {
        mort.prob = temperature * 0.0008 + 0.03
    }
    return = mort.prob
    return
}

mortality.adult = function(temperature)
{
    if (temperature < 12.7) {
        mort.prob = 0.002
    }
    else {
        mort.prob = temperature * 0.0005 + 0.02
    }
    return = mort.prob
    return
}

# Read in the input temperature datafile into a Data Frame object.
# The input data currently must have 6 columns:
# LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX
temperature_data <- read.csv(file=opt$input, header=T, strip.white=TRUE, sep=",")
start_date <- temperature_data[1, 3]
end_date <- temperature_data[opt$num_days, 3]
latitude <- temperature_data[1, 1]
daylight_length_vector <- get_daylight_length(latitude, temperature_data, opt$num_days)

cat("Number of days: ", opt$num_days, "\n")

# Initialize matrix for results from all replications.
S0.rep <- S1.rep <- S2.rep <- S3.rep <- S4.rep <- S5.rep <- matrix(rep(0, opt$num_days * opt$replications), ncol = opt$replications)
newborn.rep <- death.rep <- adult.rep <- pop.rep <- g0.rep <- g1.rep <- g2.rep <- g0a.rep <- g1a.rep <- g2a.rep <- matrix(rep(0, opt$num_days * opt$replications), ncol=opt$replications)

# loop through replications
for (N.rep in 1:opt$replications) {
    # During each replication start with 1000 individuals.
    # TODO: user definable as well?
    n <- 1000
    # Generation, Stage, DD, T, Diapause.

    gen2.pop <- rep(0, opt$num_days)
    S0 <- S1 <- S2 <- S3 <- S4 <- S5 <- rep(0, opt$num_days)
    g0.adult <- g1.adult <- g2.adult <- rep(0, opt$num_days)
    N.newborn <- N.death <- N.adult <- rep(0, opt$num_days)
    dd.day <- rep(0, opt$num_days)

    # All the days included in the input temperature dataset.
    for (row in 1:opt$num_days) {
        # Get the integer day of the year for the current row.
        doy <- temperature_data[row, 4]
        # Photoperiod in the day.
        photoperiod <- daylight_length_vector[row]
        temp.profile <- get_temperature_at_hour(latitude, temperature_data, daylight_length_vector, row, opt$num_days)
        mean.temp <- temp.profile[1]
        dd.temp <- temp.profile[2]
        dd.day[row] <- dd.temp
        # Trash bin for death.
        death.vec <- NULL
        # Newborn.
        birth.vec <- NULL

        # All individuals.
        for (i in 1:n) {
            # Find individual record.
            vec.ind <- vec.mat[i,]
            # First of all, still alive?

                        # Add 1 day in current stage.
                        vec.ind[4] <- vec.ind[4] + 1
                        vec.mat[i,] <- vec.ind
                    }
                }

                # Event 2 oviposition -- where population dynamics comes from.
                if (vec.ind[2] == 4 && vec.ind[1] == 0 && mean.temp > 10) {
                    # Vittelogenic stage, overwintering generation.
                    if (vec.ind[4] == 0) {
                        # Just turned in vittelogenic stage.

                        new.vec <- t(matrix(new.vec, nrow=5))
                        # Group with total eggs laid in that day.
                        birth.vec <- rbind(birth.vec, new.vec)
                    }
                }

                # Event 2 oviposition -- for gen 1.
                if (vec.ind[2] == 4 && vec.ind[1] == 1 && mean.temp > 12.5 && doy < 222) {
                    # Vittelogenic stage, 1st generation
                    if (vec.ind[4] == 0) {
                        # Just turned in vittelogenic stage.
                        n.birth=round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size))

                        new.vec <- t(matrix(new.vec, nrow=5))
                        # Group with total eggs laid in that day.
                        birth.vec <- rbind(birth.vec, new.vec)
                    }
                }

                # Event 3 development (with diapause determination).
                # Event 3.1 egg development to young nymph (vec.ind[2]=0 -> egg).
                if (vec.ind[2] == 0) {
                    # Egg stage.
                    # Add to dd.

                        # Add 1 day in current stage.
                        vec.ind[4] <- vec.ind[4] + 1
                    }
                    vec.mat[i,] <- vec.ind
                }

                # Event 3.2 young nymph to old nymph (vec.ind[2]=1 -> young nymph: determines diapause).
                if (vec.ind[2] == 1) {
                    # young nymph stage.
                    # add to dd.
                    vec.ind[3] <- vec.ind[3] + dd.temp

                        # Add 1 day in current stage.
                        vec.ind[4] <- vec.ind[4] + 1
                    }
                    vec.mat[i,] <- vec.ind
                }  

                # Event 3.3 old nymph to adult: previttelogenic or diapausing?
                if (vec.ind[2] == 2) {
                    # Old nymph stage.
                    # add to dd.
                    vec.ind[3] <- vec.ind[3] + dd.temp

                        # Add 1 day in current stage.
                        vec.ind[4] <- vec.ind[4] + 1
                    }
                    vec.mat[i,] <- vec.ind
                }

                # Event 4 growing of diapausing adult (unimportant, but still necessary).
                if (vec.ind[2] == 5) {
                    vec.ind[3] <- vec.ind[3] + dd.temp
                    vec.ind[4] <- vec.ind[4] + 1
                    vec.mat[i,] <- vec.ind
                }
            } # Else if it is still alive.
        } # End of the individual bug loop.

        # Find how many died.
        n.death <- length(death.vec)
        if (n.death > 0) {
            vec.mat <- vec.mat[-death.vec, ]
        }

}

# Data analysis and visualization can currently
# plot only within a single calendar year.
# TODO: enhance this to accomodate multiple calendar years.
n.yr <- 1
day.all <- c(1:opt$num_days * n.yr)

# mean value for adults
sa <- apply((S3.rep + S4.rep + S5.rep), 1, mean)

pdf(file=opt$output, width=20, height=30, bg="white")

par(mar = c(5, 6, 4, 4), mfrow=c(3, 1))

# Subfigure 1: population size by life stage
title <- paste("BSMB total population by life stage :", opt$location, ": Lat:", latitude, ":", start_date, "to", end_date, sep=" ")
plot(day.all, sa, main=title, type="l", ylim=c(0, max(se + se.se, sn + sn.se, sa + sa.se)), axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
# Young and old nymphs.
lines(day.all, sn, lwd=2, lty=1, col=2)
# Eggs
lines(day.all, se, lwd=2, lty=1, col=4)

    lines (day.all, se + se.se, col=4, lty=2)
    lines (day.all, se - se.se, col=4, lty=2)
}

# Subfigure 2: population size by generation
title <- paste("BSMB total population by generation :", opt$location, ": Lat:", latitude, ":", start_date, "to", end_date, sep=" ")
plot(day.all, g0, main=title, type="l", ylim=c(0, max(g2)), axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
lines(day.all, g1, lwd = 2, lty = 1, col=2)
lines(day.all, g2, lwd = 2, lty = 1, col=4)
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"))
axis(2, cex.axis=3)

    lines (day.all, g2+g2.se, col=4, lty=2)
    lines (day.all, g2-g2.se, col=4, lty=2)
}

# Subfigure 3: adult population size by generation
title <- paste("BSMB adult population by generation :", opt$location, ": Lat:", latitude, ":", start_date, "to", end_date, sep=" ")
plot(day.all, g0a, ylim=c(0, max(g2a) + 100), main=title, type="l", axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
lines(day.all, g1a, lwd = 2, lty = 1, col=2)
lines(day.all, g2a, lwd = 2, lty = 1, col=4)
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"))
axis(2, cex.axis=3)

5:1878a03f9c9f

    make_option(c("-p", "--oviposition"), action="store", dest="oviposition", type="integer", help="Adjustment for oviposition rate"),
    make_option(c("-q", "--photoperiod"), action="store", dest="photoperiod", type="double", help="Critical photoperiod for diapause induction/termination"),
    make_option(c("-s", "--replications"), action="store", dest="replications", type="integer", help="Number of replications"),
    make_option(c("-t", "--se_plot"), action="store", dest="se_plot", help="Plot SE"),
    make_option(c("-v", "--input"), action="store", dest="input", help="Temperature data for selected location"),
    make_option(c("-y", "--young_nymph_accum"), action="store", dest="young_nymph_accum", type="integer", help="Adjustment of DD accumulation (egg->young nymph)"),
    make_option(c("-x", "--insect"), action="store", dest="insect", help="Insect name")
)

parser <- OptionParser(usage="%prog [options] file", option_list=option_list)
args <- parse_args(parser, positional_arguments=TRUE)
opt <- args$options

parse_input_data = function(input_file, num_rows) {
    # Read in the input temperature datafile into a data frame.
    temperature_data_frame <- read.csv(file=input_file, header=T, strip.white=TRUE, sep=",")
    num_columns <- dim(temperature_data_frame)[2]
    if (num_columns == 6) {
        # The input data has the following 6 columns:
        # LATITUDE, LONGITUDE, DATE, DOY, TMIN, TMAX
        # Set the column names for access when adding daylight length..
        colnames(temperature_data_frame) <- c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX")
        # Add a column containing the daylight length for each day.
        temperature_data_frame <- add_daylight_length(temperature_data_frame, num_columns, num_rows)
        # Reset the column names with the additional column for later access.
        colnames(temperature_data_frame) <- c("LATITUDE","LONGITUDE", "DATE", "DOY", "TMIN", "TMAX", "DAYLEN")
    }
    return(temperature_data_frame)
}

add_daylight_length = function(temperature_data_frame, num_columns, num_rows) {
    # Return a vector of daylight length (photoperido profile) for
    # the number of days specified in the input temperature data
    # (from Forsythe 1995).
    p = 0.8333
    latitude <- temperature_data_frame$LATITUDE[1]
    daylight_length_vector <- NULL
    for (i in 1:num_rows) {
        # Get the day of the year from the current row
        # of the temperature data for computation.
        doy <- temperature_data_frame$DOY[i]
        theta <- 0.2163108 + 2 * atan(0.9671396 * tan(0.00860 * (doy - 186)))
        phi <- asin(0.39795 * cos(theta))
        # Compute the length of daylight for the day of the year.
        daylight_length_vector[i] <- 24 - (24 / pi * acos((sin(p * pi / 180) + sin(latitude * pi / 180) * sin(phi)) / (cos(latitude * pi / 180) * cos(phi))))
    }
    # Append daylight_length_vector as a new column to temperature_data_frame.
    temperature_data_frame[, num_columns+1] <- daylight_length_vector
    return(temperature_data_frame)
}

get_temperature_at_hour = function(latitude, temperature_data_frame, row, num_days) {
    # Base development threshold for Brown Marmolated Stink Bug
    # insect phenology model.
    # TODO: Pass insect on the command line to accomodate more
    # the just the Brown Marmolated Stink Bub.
    threshold <- 14.17
    # Minimum temperature for current row.
    dnp <- temperature_data_frame$TMIN[row]
    # Maximum temperature for current row.
    dxp <- temperature_data_frame$TMAX[row]
    # Mean temperature for current row.
    dmean <- 0.5 * (dnp + dxp)
    # Initialize degree day accumulation
    dd <- 0
    if (dxp < threshold) {

        # Initialize hourly temperature.
        T <- NULL
        # Initialize degree hour vector.
        dh <- NULL
        # Daylight length for current row.
        y <- temperature_data_frame$DAYLEN[row]
        # Darkness length.
        z <- 24 - y
        # Lag coefficient.
        a <- 1.86
        # Darkness coefficient.

                }
            }
        }
        dd <- sum(dh) / 24
    }
    return(c(dmean, dd))
}

dev.egg = function(temperature) {
    dev.rate= -0.9843 * temperature + 33.438
    return(dev.rate)
}

dev.young = function(temperature) {
    n12 <- -0.3728 * temperature + 14.68
    n23 <- -0.6119 * temperature + 25.249
    dev.rate = mean(n12 + n23)
    return(dev.rate)
}

dev.old = function(temperature) {
    n34 <- -0.6119 * temperature + 17.602
    n45 <- -0.4408 * temperature + 19.036
    dev.rate = mean(n34 + n45)
    return(dev.rate)
}

dev.emerg = function(temperature) {
    emerg.rate <- -0.5332 * temperature + 24.147
    return(emerg.rate)
}

mortality.egg = function(temperature) {
    if (temperature < 12.7) {
        mort.prob = 0.8
    }
    else {
        mort.prob = 0.8 - temperature / 40.0
        if (mort.prob < 0) {
            mort.prob = 0.01
        }
    }
    return(mort.prob)
}

mortality.nymph = function(temperature) {
    if (temperature < 12.7) {
        mort.prob = 0.03
    }
    else {
        mort.prob = temperature * 0.0008 + 0.03
    }
    return(mort.prob)
}

mortality.adult = function(temperature) {
    if (temperature < 12.7) {
        mort.prob = 0.002
    }
    else {
        mort.prob = temperature * 0.0005 + 0.02
    }
    return(mort.prob)
}

temperature_data_frame <- parse_input_data(opt$input, opt$num_days)
# All latitude values are the same,
# so get the value from the first row.
latitude <- temperature_data_frame$LATITUDE[1]

cat("Number of days: ", opt$num_days, "\n")

# Initialize matrix for results from all replications.
S0.rep <- S1.rep <- S2.rep <- S3.rep <- S4.rep <- S5.rep <- matrix(rep(0, opt$num_days * opt$replications), ncol = opt$replications)
newborn.rep <- death.rep <- adult.rep <- pop.rep <- g0.rep <- g1.rep <- g2.rep <- g0a.rep <- g1a.rep <- g2a.rep <- matrix(rep(0, opt$num_days * opt$replications), ncol=opt$replications)

# Loop through replications.
for (N.rep in 1:opt$replications) {
    # During each replication start with 1000 individuals.
    # TODO: user definable as well?
    n <- 1000
    # Generation, Stage, DD, T, Diapause.

    gen2.pop <- rep(0, opt$num_days)
    S0 <- S1 <- S2 <- S3 <- S4 <- S5 <- rep(0, opt$num_days)
    g0.adult <- g1.adult <- g2.adult <- rep(0, opt$num_days)
    N.newborn <- N.death <- N.adult <- rep(0, opt$num_days)
    dd.day <- rep(0, opt$num_days)
    # All the days included in the input temperature dataset.
    for (row in 1:opt$num_days) {
        # Get the integer day of the year for the current row.
        doy <- temperature_data_frame$DOY[row]
        # Photoperiod in the day.
        photoperiod <- temperature_data_frame$DAYLEN[row]
        temp.profile <- get_temperature_at_hour(latitude, temperature_data_frame, row, opt$num_days)
        mean.temp <- temp.profile[1]
        dd.temp <- temp.profile[2]
        dd.day[row] <- dd.temp
        # Trash bin for death.
        death.vec <- NULL
        # Newborn.
        birth.vec <- NULL
        # All individuals.
        for (i in 1:n) {
            # Find individual record.
            vec.ind <- vec.mat[i,]
            # First of all, still alive?

                        # Add 1 day in current stage.
                        vec.ind[4] <- vec.ind[4] + 1
                        vec.mat[i,] <- vec.ind
                    }
                }
                # Event 2 oviposition -- where population dynamics comes from.
                if (vec.ind[2] == 4 && vec.ind[1] == 0 && mean.temp > 10) {
                    # Vittelogenic stage, overwintering generation.
                    if (vec.ind[4] == 0) {
                        # Just turned in vittelogenic stage.

                        new.vec <- t(matrix(new.vec, nrow=5))
                        # Group with total eggs laid in that day.
                        birth.vec <- rbind(birth.vec, new.vec)
                    }
                }
                # Event 2 oviposition -- for generation 1.
                if (vec.ind[2] == 4 && vec.ind[1] == 1 && mean.temp > 12.5 && doy < 222) {
                    # Vittelogenic stage, 1st generation
                    if (vec.ind[4] == 0) {
                        # Just turned in vittelogenic stage.
                        n.birth=round(runif(1, 2 + opt$min_clutch_size, 8 + opt$max_clutch_size))

                        new.vec <- t(matrix(new.vec, nrow=5))
                        # Group with total eggs laid in that day.
                        birth.vec <- rbind(birth.vec, new.vec)
                    }
                }
                # Event 3 development (with diapause determination).
                # Event 3.1 egg development to young nymph (vec.ind[2]=0 -> egg).
                if (vec.ind[2] == 0) {
                    # Egg stage.
                    # Add to dd.

                        # Add 1 day in current stage.
                        vec.ind[4] <- vec.ind[4] + 1
                    }
                    vec.mat[i,] <- vec.ind
                }
                # Event 3.2 young nymph to old nymph (vec.ind[2]=1 -> young nymph: determines diapause).
                if (vec.ind[2] == 1) {
                    # young nymph stage.
                    # add to dd.
                    vec.ind[3] <- vec.ind[3] + dd.temp

                        # Add 1 day in current stage.
                        vec.ind[4] <- vec.ind[4] + 1
                    }
                    vec.mat[i,] <- vec.ind
                }  
                # Event 3.3 old nymph to adult: previttelogenic or diapausing?
                if (vec.ind[2] == 2) {
                    # Old nymph stage.
                    # add to dd.
                    vec.ind[3] <- vec.ind[3] + dd.temp

                        # Add 1 day in current stage.
                        vec.ind[4] <- vec.ind[4] + 1
                    }
                    vec.mat[i,] <- vec.ind
                }
                # Event 4 growing of diapausing adult (unimportant, but still necessary).
                if (vec.ind[2] == 5) {
                    vec.ind[3] <- vec.ind[3] + dd.temp
                    vec.ind[4] <- vec.ind[4] + 1
                    vec.mat[i,] <- vec.ind
                }
            } # Else if it is still alive.
        } # End of the individual bug loop.
        # Find how many died.
        n.death <- length(death.vec)
        if (n.death > 0) {
            vec.mat <- vec.mat[-death.vec, ]
        }

}

# Data analysis and visualization can currently
# plot only within a single calendar year.
# TODO: enhance this to accomodate multiple calendar years.
start_date <- temperature_data_frame$DATE[1]
end_date <- temperature_data_frame$DATE[opt$num_days]

n.yr <- 1
day.all <- c(1:opt$num_days * n.yr)

# mean value for adults
sa <- apply((S3.rep + S4.rep + S5.rep), 1, mean)

pdf(file=opt$output, width=20, height=30, bg="white")

par(mar = c(5, 6, 4, 4), mfrow=c(3, 1))

# Subfigure 1: population size by life stage
title <- paste(opt$insect, ": Total pop. by life stage :", opt$location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ")
plot(day.all, sa, main=title, type="l", ylim=c(0, max(se + se.se, sn + sn.se, sa + sa.se)), axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
# Young and old nymphs.
lines(day.all, sn, lwd=2, lty=1, col=2)
# Eggs
lines(day.all, se, lwd=2, lty=1, col=4)

    lines (day.all, se + se.se, col=4, lty=2)
    lines (day.all, se - se.se, col=4, lty=2)
}

# Subfigure 2: population size by generation
title <- paste(opt$insect, ": Total pop. by generation :", opt$location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ")
plot(day.all, g0, main=title, type="l", ylim=c(0, max(g2)), axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
lines(day.all, g1, lwd = 2, lty = 1, col=2)
lines(day.all, g2, lwd = 2, lty = 1, col=4)
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"))
axis(2, cex.axis=3)

    lines (day.all, g2+g2.se, col=4, lty=2)
    lines (day.all, g2-g2.se, col=4, lty=2)
}

# Subfigure 3: adult population size by generation
title <- paste(opt$insect, ": Adult pop. by generation :", opt$location, ": Lat:", latitude, ":", start_date, "-", end_date, sep=" ")
plot(day.all, g0a, ylim=c(0, max(g2a) + 100), main=title, type="l", axes=F, lwd=2, xlab="", ylab="", cex=3, cex.lab=3, cex.axis=3, cex.main=3)
lines(day.all, g1a, lwd = 2, lty = 1, col=2)
lines(day.all, g2a, lwd = 2, lty = 1, col=4)
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"))
axis(2, cex.axis=3)