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Copy pathDDRP_v2_funcs.R
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DDRP_v2_funcs.R
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# Functions used for DDRP_v2.R -----
#
# This file must accompany DDRP_v2.R; it contains the majority of functions
# needed to run the program.
#
# Log of most recent changes -----
# 4/19/24: Added Hawaii to extents, and fixed error in adult PEM labels when
# OW stage is egg ("a0" was skipped, and instead called "a1") in Daily Loop.
# Also added bg = "white" to ggsave for the PlotMap function.
# 10/2/23: Clarified that "obligate_diapause" parameter cannot be in a parameter
# file unless the species actually does have obligate diapause. This should maybe
# be changed (i.e., have it all parameter files) but would require changing all
# species parameter files created to date (except for EAB)
# 1/24/23: Modified "Base_map" function so that maps for North America included
# both state and world boundaries in image file (PNG) outputs
# 11/10/22: Add formula to compute a lognormal distribution and skewed normal
# distribution of emergence times. Also fixed mistake in PEM colors due to
# incorrect ordering of factors.
# 9/29/22: Changed PEM color palettes (8/25 + 9/29) & fixed bug w/ ordering of key
# 7/2/22: Edit CombineMaps function to increase tolerance in merge raster function
# 6/29/22: Added features for processing and mapping E-OBS and CDAT data
# 3/18/22: Fixed bug in code to plot pest event maps
# 10/26/20: Fixed code that allows for different DD calc methods to be used and
# removed "SimpDD" calculation (need to add Sine DD calc method later)
# 8/15/20: Fixed bug in Stage Count plotting function
# 7/22/20: Minor edits to RegCluster function
# 7/16/20: Improved some legend colors to better discern categories
# 6/26/20: Added "StageCount" summary maps, improved map legends, cleaned
# up PlotMap function
# 4/24/20: Changed the parameter name "chill stress" to "cold stress"
# 3/31/20: Fixed issue w/ PEMs showing Dec of previous year
# 2/25/20: Minor edits to PEM legend colors so weeks of year are always
# assigned the same color
# 2/18/20: Changed RegCluster back from FORK (to default = PSOCK) due to
# apparent issues on Hopper (cannot open connection issues whenever the
# process required access to numerous raster files). Updated Cut_interval func.
# 2/15/20: Removed EXCl1 and EXCL2 steps for NumGen and Lifestage in
# Daily Loop function because those raster bricks are not used - it is more
# memory efficient to calculate them in the Data Processing section
# 2/12/20: streamlined Rast_Subs_Excl functions; added Weight_rasts function
# 2/7/20: fixed minor bugs in PlotMap function & changed a color
# 1/28/20: detect CRS from template data, not hard-code it
# 1/23/20: changed RegCluster func; fixed memory issues
# 1/3/20: changed naming of clim. suit. output files
# 12/9/19: had to fix code in PEM plotting - error for some spp
#
# Issues to resolve: boundary of CONUS doesn't line up completely with raster
# May not be an issue, but the color key in PEMs is really convoluted - should
# look into simplifying the code if possible.
#### (1). Assign_extent: assign geographic extent ####
# Add new extent definitions here for use in models and plots
# Set up regions
# Use switch() (works like a single use hash)
Assign_extent <- function(region_param = paste0(region_param)) {
REGION <- switch(region_param,
"CONUS" = extent(-125.0, -66.5, 24.54, 49.4),
"WEST" = extent(-125.0, -102, 31.1892, 49.4),
"EAST" = extent(-106.8, -66.5, 24.54, 49.4),
"MIDWEST" = extent(-104.2, -87, 30, 49.3),
# Daymet - midwest and northeast plus some of Canada
"NORTHPLUS" = extent(-106, -64, 36, 52),
"NORTHWEST" = extent(-125.1, -103.8, 40.6, 49.15),
"SOUTHWEST" = extent(-124.6, -101.5, 31.2, 42.3),
"SOUTHCENTRAL" = extent(-83.6, -78.3, 31.8, 35.3),
"NORTHCENTRAL" = extent(-104.3, -80.2, 35.7, 49.4),
"SOUTHEAST" = extent(-107.1, -75.0, 24.54, 39.6),
"NORTHEAST" = extent(-84.2, -64.3, 36.9, 48.1),
"N_AMERICA" = extent(-159.8597, -48.08864, 18.57516, 61.04535),
#"N_AMERICA" = extent(-106, -70, 44, 56),
"EUROPE" = extent(-11, 45, 35.5, 71.5),
"CHINA" = extent(70, 140, 15, 55),
"AL" = extent(-88.5294, -84.7506, 30.1186, 35.1911),
"AR" = extent(-94.8878, -89.5094, 32.8796, 36.6936),
"AZ" = extent(-115, -108.98, 31.2, 37),
"CA" = extent(-124.6211, -113.7428, 32.2978, 42.2931),
"CO" = extent(-109.2625, -101.8625, 36.7461, 41.2214),
"CT" = extent(-73.7700, -71.7870, 40.9529, 42.0355),
"DL" = extent(-76.1392, -74.1761, 38.3508, 39.9919),
"FL" = extent(-87.8064, -79.9003, 24.54, 31.1214),
"GA" = extent(-85.7850, -80.5917, 30.1767, 35.1594),
"HI" = extent(-178.335, -154.8068, 18.910, 28.402),
"IA" = extent(-96.8617, -89.9697, 40.1147, 43.7353),
"ID" = extent(-117.3917, -110.6167, 41.4500, 49.15),
"IL" = extent(-91.5897, -87.0461, 36.8903, 42.6375),
"IN" = extent(-88.1686, -84.4686, 37.7836, 41.9794),
"KS" = extent(-102.3342, -94.1756, 36.6369, 40.2836),
"KY" = extent(-89.3581, -81.8425, 36.4208, 39.3347),
"LA" = extent(-94.3019, -88.7758, 28.8333, 33.2994),
"MA" = extent(-73.5639, -69.7961, 41.1689, 42.9525),
"MD" = extent(-79.7014, -74.8833, 37.0631, 39.9075),
"ME" = extent(-71.4056, -66.6667, 42.9525, 47.5228),
"MI" = extent(-90.5542, -82.3047, 41.6311, 47.5739),
"MN" = extent(-97.4000, -89.3786, 43.2550, 49.4),
"MO" = extent(-95.8803, -88.9883, 35.8822, 40.7058),
"MS" = extent(-91.7475, -87.8522, 29.9842, 35.2631),
"MT" = extent(-116.3667, -103.8250, 44.0667, 49.15),
"NC" = extent(-84.44092, -75.3003, 33.6829, 36.6461),
"ND" = extent(-104.2708, -96.3075, 45.6403, 49.15),
"NE" = extent(-104.3553, -95.0464, 39.7506, 43.2022),
"NH" = extent(-72.6617, -70.6142, 42.6256, 45.4700),
"NJ" = extent(-75.9175, -73.1892, 38.8944, 41.5806),
"NM" = extent(-109.2942, -102.6383, 31.1892, 37.2000),
"NV" = extent(-120.3358, -113.6803, 34.7356, 42.2981),
"NY" = extent(-80.0867, -71.7381, 40.4828, 45.1692),
"OH" = extent(-85.0439, -80.2464, 38.2797, 42.0217),
"OK" = extent(-103.2850, -94.1964, 33.3839, 37.2850),
"OR" = extent(-124.7294, -116.2949, 41.7150, 46.4612),
"PA" = extent(-80.7672, -74.5033, 39.4694, 42.5094),
"RI" = extent(-71.8628, -71.1206, 41.1463, 42.0188),
"SC" = extent(-83.6422, -78.3275, 31.8814, 35.3811),
"SD" = extent(-104.3553, -96.0806, 42.3050, 46.2050),
"TN" = extent(-90.3239, -81.5047, 34.5578, 37.1125),
"TX" = extent(-107.1592, -93.2411, 25.8614, 36.7200),
"UT" = extent(-114.2925, -108.7450, 36.7778, 42.2347),
"VA" = extent(-83.8322, -75.6200, 36.3892, 39.7886),
"VT" = extent(-73.6747, -71.4108, 42.5886, 45.1956),
"WA" = extent(-124.9585, -116.8364, 45.4554, 49.15),
"WI" = extent(-93.1572, -86.6822, 42.2733, 46.9914),
"WV" = extent(-82.8783, -77.5114, 37.1158, 40.7836),
"WY" = extent(-111.6167, -103.7333, 40.6667, 45.4833))
return(REGION)
}
# autoStopCluster <- function(cl) {
# stopifnot(inherits(cl, "cluster"))
# env <- new.env()
# env$cluster <- cl
# attr(cl, "gcMe") <- env
# reg.finalizer(env, function(e) {
# message("Finalizing cluster ...")
# #message(capture.output(print(e$cluster)))
# try(parallel::stopCluster(e$cluster), silent = FALSE)
# message("Finalizing cluster ... done")
# })
# cl
# }
#### (2). Base_map: base map for summary plots ####
# Base features used for all summary (PNG) maps in "PlotMap" function
# The "coord_quickmap" function allows the state/region of interest to be plotted
# geom_raster is faster than geom_tile
# Input data are in a data frame (= df) format
Base_map <- function(df) {
# Data to use for base map (i.e. target region - will usually be US states)
if (region_param %in% c("EUROPE", "CHINA")) {
base_map <- map_data("world")
} else if (region_param == "N_AMERICA") {
# Combine world and state data (add 1627 to group numbers for state data
# [max group # in world data] so that there's no overlapping group numbers
# between data frames)
states_df <- map_data("state") %>% mutate(group = group + 1627)
base_map <- bind_rows(map_data("world"), states_df)
} else {
base_map <- map_data("state")
}
# Base map plot
p <- ggplot(base_map, aes(x = long, y = lat)) +
geom_raster(data = df, aes(x = x, y = y, fill = value)) +
geom_path(aes(group = group), color = "black", lwd = 0.4) +
coord_quickmap(xlim = c(REGION@xmin, REGION@xmax),
ylim = c(REGION@ymin, REGION@ymax), expand = FALSE)
}
#### (3). CohortDistrib: cohort emergence distribution ####
# This is an approximation from GAM predictions.
CohortDistrib <- function(dist, numstage, perc) {
ReturnClosestValue <- function(dist, xval) {
out <- dist$CDF[which(dist$x > xval)[1]]
}
# Vector with emergence times (in degree-days) for each cohort
low <- (1 - perc)/2
high <- 1 - (1 - perc) / 2
low <- dist$x[which(dist$CDF > low)[1]]
high <- dist$x[which(dist$CDF > high)[1]]
bounds <- seq(low, high, length.out = numstage + 1)
means <- (bounds[1:numstage] + bounds[2:(numstage + 1)]) / 2
# Vector of weights: the proportion of the population made up by each cohort
wts <- diff(sapply(X = bounds, FUN = ReturnClosestValue, dist = dist), lag = 1)
# Return results as a data frame
return(data.frame(means, wts))
}
#### (4). CombineMaps: merge raster tiles ####
# Merge tiles back together for CONUS or EAST
# SpaDES.tools requires 'sf' package (needs GDAL update), so just use
# "raster::merge"
CombineMaps <- function(brick_files, type, cohort) {
# Search for file type in brick file list
fls_by_type <- brick_files[grep(pattern = paste0(type, "_"), x = brick_files,
fixed = TRUE)]
# Merge tiles back together for the input cohort
fls_by_cohort <- fls_by_type[grep(pattern = paste0("cohort", cohort),
x = fls_by_type, fixed = TRUE)]
mrgd <- Reduce(function(...)raster::merge(..., tolerance = 1),
lapply(fls_by_cohort, brick))
#mrgd <- Reduce(raster::merge, lapply(fls_by_cohort, brick))
writeRaster(mrgd, filename = paste0(type, "_cohort", cohort, "_all"),
overwrite = TRUE, datatype = "INT2S", format = "GTiff")
}
#### (5). Cond: if then else (conditional) ####
# If then else raster function [sim. to GRASS r.mapcalc if (x,a,b)]
Cond <- function(condition, trueValue, falseValue) {
return(condition * trueValue + (!condition) * falseValue)
}
#### (6). CohortVals: simple way to assign weights for substages (cohorts) ####
# Choose number of substages (= numstage) and the decimal (0, 1) (= perc)
# of population to include
# Outputs standard deviations from mean and weights for substage
CohortVals <- function(numstage, perc) {
low <- qnorm((1 - perc)/2)
high <- qnorm(1 - (1 - perc) / 2)
bounds <- seq(low, high, length.out = numstage + 1)
means <- (bounds[1:numstage] + bounds[2:(numstage + 1)]) / 2
weights <- diff(pnorm(bounds), lag = 1)
return(data.frame(means, weights))
}
#### (7). Colfunc: gradient color ramp for summary maps ####
# Creates a gradient color ramp consisting of n colors
# x and y are color1 and color2
Colfunc <- function(x, y, n) {
colorRampPalette(c(x, y))(n)
}
#### (8). ConvDF: convert raster to data frame ####
# First convert raster (= rast) to a spatial pixels data frame and
# then to a data frame
ConvDF <- function(rast) {
spdf <- as(rast, "SpatialPixelsDataFrame")
df <- as.data.frame(spdf)
colnames(df) <- c("value", "x", "y")
return(df)
}
#### (9). Cut_bins: classify data into bins for plotting ####
# Classify data (= df) so it can be visualized in categories
# (e.g., 1-10, 11-20, 21-30)
Cut_bins <- function(df, breaks) {
df$value_orig <- df$value # Keep old value so can sort factors against it
# Round up max value to highest number divisible by 10
df$value[df$value == max(df$value)] <- 10 * ceiling(max(df$value)/10)
# Cut values into bins and format results
df2 <- df %>% mutate(value = cut_interval(df$value, n = breaks),
value = gsub("[()]|\\[|\\]", "", value),
bin1 = ceiling(as.numeric(str_split_fixed(value, ",", 2)[,1])),
bin2 = ceiling(as.numeric(str_split_fixed(value, ",", 2)[,2])),
value = paste(bin1, bin2, sep = "-"))
return(df2)
}
# For data potentially ranging from 0 - 100 (Lifestage pop. size),
# bins them by groups of 10 always starting at 0 and ending at 100.
# TO DO: see about combining the two "Cut_bin" functions; simplify
Cut_bins2 <- function(df) {
df$value_orig <- df$value # Keep old value so can sort factors against it
df <- mutate(df, value = ifelse(value < 10, "0-10",
ifelse(value < 20, "10-20", ifelse(value < 30, "20-30",
ifelse(value < 40, "30-40", ifelse(value < 50, "40-50",
ifelse(value < 60, "50-60", ifelse(value < 70, "60-70",
ifelse(value < 80, "70-80", ifelse(value < 90, "80-90",
"90-100"))))))))))
return(df)
}
#### (10). DailyLoop: daily loop model ####
# The daily loop is the daily time step used to generate all results for DDRP
# The loop is run for each cohort (= cohort) in parallel. If the study region
# is CONUS or EAST, then each of 4 tiles (= tile_num) are run in parallel as
# well. The template (= template) is needed for setting up data sets to
# populate during the model run, and to convert matrix outputs into raster
# format.
DailyLoop <- function(cohort, tile_num, template) {
# Generate a daily log file for each cohort -
# this is useful only for trouble-shooting
# if (region_param %in% c("CONUS", "EAST")) {
# daily_logFile <- paste0("Daily_loop_cohort", cohort, "_",
# tile_num, ".txt")
# } else {
# daily_logFile <- paste0("Daily_loop_cohort", cohort, ".txt")
# }
#### * Initialize rasters for all variables ####
# Main rasters - these are built upon within daily loop
DDaccum <- as.matrix(template)
# Track total degree days accumulated, for a single Lifestage (larvae)
DDtotal <- as.matrix(template)
# Track Lifestage for each cell per day (all cells start in OW = stage1)
Lifestage <- as.matrix(template) + 1
# Track voltinism per cell per day, starting at 0
NumGen <- as.matrix(template)
# Additional rasters - created depending on input setting
if (exclusions_stressunits) {
# Create masks for each variable
coldmask <- as.matrix(template) # Daily cold units mask
coldstress <- as.matrix(template) # Count of daily cold units
coldstressTHRESH <- as.matrix(template) # Mask for coldstrs units thres
coldstressTHRESH <- coldstress_threshold # Mask for coldstrs units thres
coldunitsCUM <- as.matrix(template) # Cumulative cold units
coldstressMAX1 <- as.matrix(template) # Max cold before most die
coldstressMAX1 <- coldstress_units_max1 # Max cold before most die
coldstressMAX2 <- as.matrix(template) # Max cold before all die
coldstressMAX2 <- coldstress_units_max2 # Max cold before all die
coldEXCL <- as.matrix(template) # Cold stress exclusion
heatmask <- as.matrix(template) # Daily heat stress units mask
heatstress <- as.matrix(template) # Count of daily heat stress units
heatstressTHRESH <- as.matrix(template) # Mask for heatstress units thres
heatstressTHRESH <- heatstress_threshold # Mask for heatstress units thres
heatunitsCUM <- as.matrix(template) # Cumulative heat stress units
heatstressMAX1 <- as.matrix(template) # Max heat before most die
heatstressMAX1 <- heatstress_units_max1 # Max heat before most die
heatstressMAX2 <- as.matrix(template) # Max heat before all die
heatstressMAX2 <- heatstress_units_max2 # Max heat before all die
heatEXCL <- as.matrix(template) # Heat stress exclusions
AllEXCL <- as.matrix(template) # Combined stress exclusions
}
if (pems) {
if (mapE == 1 & eggEventDD) {
# Event DD for all must be specified in spp.params file for this to run
if (PEMnumgens > 0) {
# Egg DOYs for when cumDDs > eggEvent threshold OW generation
PEMe0 <- as.matrix(template)
# Egg DOYs for when cumDDs > eggEvent threshold 1st Gen
PEMe1 <- as.matrix(template)
}
if (PEMnumgens > 1) {
# Egg DOYs for when cumDDs > eggEvent threshold 2nd Gen
PEMe2 <- as.matrix(template)
}
if (PEMnumgens > 2) {
# Egg DOYs for when cumDDs > eggEvent threshold 3rd Gen
PEMe3 <- as.matrix(template)
}
if (PEMnumgens > 3) {
# Egg DOYs for when cumDDs > eggEvent threshold 4th Gen
PEMe4 <- as.matrix(template)
}
}
if (mapL == 1 & larvaeEventDD) {
if (PEMnumgens > 0) {
# Larval DOYs for when cumDDs > larvaeEvent threshold OW generation
PEMl0 <- as.matrix(template)
# Larval DOYs for when cumDDs > larvaeEvent threshold 1st Gen
PEMl1 <- as.matrix(template)
}
if (PEMnumgens > 1) {
# Larval DOYs for when cumDDs > larvaeEvent threshold 2nd Gen
PEMl2 <- as.matrix(template)
}
if (PEMnumgens > 2) {
# Larval DOYs for when cumDDs > larvaeEvent threshold 3rd Gen
PEMl3 <- as.matrix(template)
}
if (PEMnumgens > 3) {
# Larval DOYs for when cumDDs > larvaeEvent threshold 4th Gen
PEMl4 <- as.matrix(template)
}
}
if (mapP == 1 & pupaeEventDD) {
if (PEMnumgens > 0) {
# Pupal DOYs for when cumDDs > pupalEvent threshold OW generation
PEMp0 <- as.matrix(template)
# Pupal DOYs for when cumDDs > pupalEvent threshold 1st Gen
PEMp1 <- as.matrix(template)
}
if (PEMnumgens > 1) {
# Pupal DOYs for when cumDDs > pupalEvent threshold 2nd Gen
PEMp2 <- as.matrix(template)
}
if (PEMnumgens > 2) {
# Pupal DOYs for when cumDDs > pupalEvent threshold 3rd Gen
PEMp3 <- as.matrix(template)
}
if (PEMnumgens > 3) {
# Pupal DOYs for when cumDDs > pupalEvent threshold 4th Gen
PEMp4 <- as.matrix(template)
}
}
if (mapA == 1 & adultEventDD) {
if (PEMnumgens > 0) {
# Adult DOYs for when cumDDs > adultEvent threshold OW generation
PEMa0 <- as.matrix(template)
# Adult DOYs for when cumDDs > adultEvent threshold 1st Gen
PEMa1 <- as.matrix(template)
}
if (PEMnumgens > 1) {
# Adult DOYs for when cumDDs > adultEvent threshold 2nd Gen
PEMa2 <- as.matrix(template)
}
if (PEMnumgens > 2) {
# Adult DOYs for when cumDDs > adultEvent threshold 3rd Gen
PEMa3 <- as.matrix(template)
}
if (PEMnumgens > 3) {
# Adult DOYs for when cumDDs > adultEvent threshold 4th Gen
PEMa4 <- as.matrix(template)
}
}
}
#### * Step through days ####
# tryCatch(
for (d in 1:length(sublist)) {
#cat("\n\ndoy: ", sublist[d], "\n", file=daily_logFile, append=TRUE)
stage_dd_cohort <- stage_dd[as.integer(cohort), ]
# Get temperature matrices for the day
if (region_param %in% c("CONUS", "EAST", "N_AMERICA", "EUROPE", "CHINA")) {
tmax <- as.numeric(tmax_list[[tile_num]][[d]])
tmin <- as.numeric(tmin_list[[tile_num]][[d]])
} else {
tmax <- as.numeric(tmax_list[[d]])
tmin <- as.numeric(tmin_list[[d]])
}
# Assign each raster cell to a Lifestage
# These three matrices assign physiological parameters by cell
ls_ldt <- stage_ldt[Lifestage]
ls_udt <- stage_udt[Lifestage]
ls_dd <- stage_dd_cohort[Lifestage]
# Accumulate total DDs across year, using larvae Lifestage
# Can not use DDaccum because this one has values reset when progress = 1
ls_ldt_larv <- stage_ldt[which(stgorder == "L")]
ls_udt_larv <- stage_udt[which(stgorder == "L")]
# Calculate stage-specific degree-days for each cell per day
if (calctype == "average") {
dd_tmp <- AvgDD(tmax, tmin, ls_ldt, ls_udt)
dd_tmp_larv <- AvgDD(tmax, tmin, ls_ldt_larv, ls_udt_larv)
} else if (calctype == "triangle") {
dd_tmp <- TriDD(tmax, tmin, ls_ldt, ls_udt)
dd_tmp_larv <- TriDD(tmax, tmin, ls_ldt_larv, ls_udt_larv)
}
# Accumulate degree days
DDaccum <- DDaccum + dd_tmp
# Calculate total degree days for larvae
DDtotal <- DDtotal + dd_tmp_larv
# Climate stress exclusions - results will be same for all cohorts,
# so just calculate exclusions for cohort 1
if (exclusions_stressunits) {
# Cold stress accumulation
# Make today's cold mask and calculate today's cold stress DDs
coldmask <- tmin < coldstressTHRESH
coldstress <- coldmask * abs(coldstressTHRESH - tmin)
coldunitsCUM <- coldunitsCUM + coldstress
# ASSUME NEW -2=severe -1=mod 0=none throughout
coldEXCL <- Cond(coldunitsCUM >= coldstressMAX2, -2,
Cond(coldunitsCUM >= coldstressMAX1, -1, 0))
# Heat stress accumulation
# Make today's heat mask and calculate today's heat stress DDs
heatmask <- tmax > heatstressTHRESH
heatstress <- heatmask * abs(tmax - heatstressTHRESH)
heatunitsCUM <- heatunitsCUM + heatstress
heatEXCL <- Cond(heatunitsCUM >= heatstressMAX2, -2,
Cond(heatunitsCUM >= heatstressMAX1, -1, 0))
AllEXCL <- Cond((coldEXCL == 0) & (heatEXCL == 0), 0,
Cond((coldEXCL == -1) & (heatEXCL >= -1),-1,
Cond((coldEXCL >= -1) & (heatEXCL == -1), -1, -2)))
}
# Calculate pest events
# DOYs for when cumDDs > event threshold for a given generation
if (pems) {
# The OW pest event is a percentage of the OW stage DDs
# (e.g. OWEventP = 0.5 would be 50% of the OWlarvaeDD, or half-way
# through OWlarvaeDD). It is specified in the species param file.
OWEventDD <- stage_dd_cohort[1] * OWEventP
# Egg PEMs
if (mapE == 1 & eggEventDD) {
if (PEMnumgens > 0) {
if (owstage == "OE") {
# If owstage = egg, then eggs finish developing after the DD of OWegg
# for that cohort (do NOT use OWeggDD from param file - this is for
# DDRP v2)
PEMe0 <- Cond(PEMe0 == 0 & NumGen == 0 & (DDaccum >= OWEventDD),
d * (Lifestage == which(stgorder == "OE")), PEMe0)
}
# Egg DOYs for when cumDDs > eggEvent threshold 1st gen
PEMe1 <- Cond(PEMe1 == 0 & NumGen == 1 & (DDaccum >= eggEventDD),
d * (Lifestage == which(stgorder == "E")), PEMe1)
}
if (PEMnumgens > 1) {
# Egg DOYs for when cumDDs > eggEvent threshold 2nd gen
PEMe2 <- Cond(PEMe2 == 0 & NumGen == 2 & (DDaccum >= eggEventDD),
d * (Lifestage == which(stgorder == "E")), PEMe2)
}
if (PEMnumgens > 2) {
# Egg DOYs for when cumDDs > eggEvent threshold 3rd gen
PEMe3 <- Cond(PEMe3 == 0 & NumGen == 3 & (DDaccum >= eggEventDD),
d * (Lifestage == which(stgorder == "E")), PEMe3)
}
if (PEMnumgens > 3) {
# Egg DOYs for when cumDDs > eggEvent threshold 4th gen
PEMe4 <- Cond(PEMe4 == 0 & NumGen == 4 & (DDaccum >= eggEventDD),
d * (Lifestage == which(stgorder == "E")), PEMe4)
}
}
# Larvae PEMs
if (mapL == 1 & larvaeEventDD) {
if (PEMnumgens > 0) {
if (owstage == "OL") {
# If owstage = larvae, then larvae finish developing after the DD of
# OWlarvae for that cohort (do NOT use OWlarvaeDD from species
# param file - this is for DDRP v2 only)
PEMl0 <- Cond(PEMl0 == 0 & NumGen == 0 & (DDaccum >= OWEventDD),
d * (Lifestage == which(stgorder == "OL")), PEMl0)
# If owstage = egg, then larvae of this OW gen will have to go through
# full development
} else if (owstage %in% c("OE")) {
PEMl0 <- Cond(PEMl0 == 0 & NumGen == 0 & (DDaccum >= larvaeEventDD),
d * (Lifestage == which(stgorder == "L")), PEMl0)
}
# Larvae DOYs for when cumDDs > larvaeEvent threshold 1st gen
PEMl1 <- Cond(PEMl1 == 0 & NumGen == 1 & (DDaccum >= larvaeEventDD),
d * (Lifestage == which(stgorder == "L")), PEMl1)
}
if (PEMnumgens > 1) {
# Larvae DOYs for when cumDDs > larvaeEvent threshold 2nd gen
PEMl2 <- Cond(PEMl2 == 0 & NumGen == 2 & (DDaccum >= larvaeEventDD),
d * (Lifestage == which(stgorder == "L")), PEMl2)
}
if (PEMnumgens > 2) {
# Larvae DOYs for when cumDDs > larvaeEvent threshold 3rd gen
PEMl3 <- Cond(PEMl3 == 0 & NumGen == 3 & (DDaccum >= larvaeEventDD),
d * (Lifestage == which(stgorder == "L")), PEMl3)
}
if (PEMnumgens > 3) {
# Larvae DOYs for when cumDDs > larvaeEvent threshold 4th gen
PEMl4 <- Cond(PEMl4 == 0 & NumGen == 4 & (DDaccum >= larvaeEventDD),
d * (Lifestage == which(stgorder == "L")), PEMl4)
}
}
# Pupae PEMs
if (mapP == 1 & pupaeEventDD) {
if (PEMnumgens > 0) {
if (owstage == "OP") {
# If owstage = pupae, then pupae finish developing after the DD of
# OWpupae for that cohort (do NOT use OWpupaeDD from species param
# file - this is for DDRP v2)
PEMp0 <- Cond(PEMp0 == 0 & NumGen == 0 & (DDaccum >= OWEventDD),
d * (Lifestage == which(stgorder == "OP")), PEMp0)
} else if (owstage %in% c("OE", "OL")) {
# If owstage = egg or larvae, then pupae of the OW gen will have to
# go through full development
PEMp0 <- Cond(PEMp0 == 0 & NumGen == 0 & (DDaccum >= pupaeEventDD),
d * (Lifestage == which(stgorder == "P")), PEMp0)
}
# Pupae DOYs for when cumDDs > pupaeEvent threshold 1st gen
PEMp1 <- Cond(PEMp1 == 0 & NumGen == 1 & (DDaccum >= pupaeEventDD),
d * (Lifestage == which(stgorder == "P")), PEMp1)
}
if (PEMnumgens > 1) {
# Pupae DOYs for when cumDDs > pupaeEvent threshold 2nd gen
PEMp2 <- Cond(PEMp2 == 0 & NumGen == 2 & (DDaccum >= pupaeEventDD),
d * (Lifestage == which(stgorder == "P")), PEMp2)
}
if (PEMnumgens > 2) {
# Pupae DOYs for when cumDDs > pupaeEvent threshold 3rd gen
PEMp3 <- Cond(PEMp3 == 0 & NumGen == 3 & (DDaccum >= pupaeEventDD),
d * (Lifestage == which(stgorder == "P")), PEMp3)
}
if (PEMnumgens > 3) {
# Pupae DOYs for when cumDDs > pupaeEvent threshold 4th gen
PEMp4 <- Cond(PEMp4 == 0 & NumGen == 4 & (DDaccum >= pupaeEventDD),
d * (Lifestage == which(stgorder == "P")), PEMp4)
}
}
# Adult PEMs
if (mapA == 1 & adultEventDD) {
if (PEMnumgens > 0) {
if (owstage == "OA") {
# If owstage = adult, then adults finish developing after the DD of
# OWadult for that cohort (do NOT use OWadultDD from species param
# file - this is for DDRP v2)
PEMa0 <- Cond(PEMa0 == 0 & NumGen == 0 & (DDaccum >= OWEventDD),
d * (Lifestage == which(stgorder == "OA")), PEMa0)
} else if (owstage %in% c("OE", "OL", "OP")) {
# If owstage = egg, larvae, or pupae, then adults of the OW gen will
# have to go through full development
PEMa0 <- Cond(PEMa0 == 0 & NumGen == 0 & (DDaccum >= adultEventDD),
d * (Lifestage == which(stgorder == "A")), PEMa0)
}
# Adult DOYs for when cumDDs > adultEvent threshold 1st gen
PEMa1 <- Cond(PEMa1 == 0 & NumGen == 1 & (DDaccum >= adultEventDD),
d * (Lifestage == which(stgorder == "A")), PEMa1)
}
if (PEMnumgens > 1) {
# Adult DOYs for when cumDDs > adultEvent threshold 2nd gen
PEMa2 <- Cond(PEMa2 == 0 & NumGen == 2 & (DDaccum >= adultEventDD),
d * (Lifestage == which(stgorder == "A")), PEMa2)
}
if (PEMnumgens > 2) {
# Adult DOYs for when cumDDs > adultEvent threshold 3rd gen
PEMa3 <- Cond(PEMa3 == 0 & NumGen == 3 & (DDaccum >= adultEventDD),
d * (Lifestage == which(stgorder == "A")), PEMa3)
}
if (PEMnumgens > 3) {
# Adult DOYs for when cumDDs > adultEvent threshold 4th gen
PEMa4 <- Cond(PEMa4 == 0 & NumGen == 4 & (DDaccum >= adultEventDD),
d * (Lifestage == which(stgorder == "A")), PEMa4)
}
}
}
# Calculate Lifestage progression: Is accumulation > Lifestage requirement
# (0 = FALSE, 1 = TRUE)? If species has obligate diapause, then don't
# allow it to progress past the 1st gen overwintering stage.
# This configuration assumes that a parameter file has an "obligate_diapause"
# only if that species actually does have obligate diapause.
if (exists("obligate_diapause")) {
if (obligate_diapause == 1) {
if (owstage == "OL") {
progress <- Cond(NumGen <= 1 & Lifestage != which(stgorder == "L"),
as.integer(DDaccum >= ls_dd), 0)
} else if (owstage == "OP") {
progress <- Cond(NumGen <= 1 & Lifestage != which(stgorder == "P"),
as.integer(DDaccum >= ls_dd), 0)
} else if (owstage == "OA") {
progress <- Cond(NumGen <= 1 & Lifestage != which(stgorder == "A"),
as.integer(DDaccum >= ls_dd), 0)
} else if (owstage == "OE") {
progress <- Cond(NumGen <= 1 & Lifestage != which(stgorder == "E"),
as.integer(DDaccum >= ls_dd), 0)
}
}
# If no obligate diapause, then there are no limits on no. of generations
} else {
progress <- as.integer(DDaccum >= ls_dd)
}
# If reproductive adult stage progresses, then that cell has oviposition
# and the generation count increases. If species has OW adults, then need
# to change stage value to "adult" to allow NumGen to increase when it
# progresses. "OA" = 1, and "adult" = 5 for species with OW adults
if (owstage == "OA") {
Lifestage2 <- Lifestage
Lifestage2[Lifestage2 == 1] <- 5
NumGen <- NumGen + (progress == 1 & Lifestage2 == 5)
} else {
# Value for "adult" varies depending on OW stage
NumGen <- NumGen + (progress == 1 & Lifestage == which(stgorder == "A"))
}
#cat("No. gen (max): ", max(NumGen, na.rm=T), "\n", file=daily_logFile,
#append=TRUE)
# If progress is 1, then there is progression to the next life stage
Lifestage <- Lifestage + progress
# Reset the DDaccum cells to zero for cells that progressed to next stage
DDaccum <- DDaccum - (progress * ls_dd)
#cat("DDaccum (max): ", max(DDaccum, na.rm=T), "\n", file=daily_logFile,
#append=TRUE)
# Reassign cells that progressed past end of stgorder to first non-OW stage
Lifestage <- Cond(Lifestage == (length(stgorder) + 1), 2, Lifestage)
#cat("Lifestage (max): ", max(Lifestage, na.rm=T), file=daily_logFile,
#append=TRUE)
#### * Save data for certain days, specified by sampling frequency ####
# Data from last sampling day of year is also saved
if (sublist[d] %in% sample_pts) {
#cat("sampled", sublist[d])
# Convert Lifestage and Numgen matrices to rasters and put into a brick
mat_list <- list(Lifestage, NumGen, DDtotal)
ext <- as.data.frame(as.matrix(extent(template)))
rast_list <- lapply(mat_list, Mat_to_rast, ext = ext, template = template)
names(rast_list) <- c("Lifestage_rast", "NumGen_rast", "DDtotal_rast")
#cat("\n\n### Adding layers to Lifestage brick for cohort", cohort, ":
#doy =", sublist[d], "\n", file=daily_logFile, append=TRUE)
# Lifestage brick
if (!exists("Lifestage_brick")) {
Lifestage_brick <- brick(rast_list$Lifestage_rast, crs = crs)
} else {
Lifestage_brick <- addLayer(Lifestage_brick, rast_list$Lifestage_rast)
}
#cat("### Adding layers to NumGen brick for cohort", cohort, ": doy =",
#sublist[d], "\n", file=daily_logFile, append=TRUE)
# NumGen brick
if (!exists("NumGen_brick")) {
NumGen_brick <- brick(rast_list$NumGen_rast, crs = crs)
} else {
NumGen_brick <- addLayer(NumGen_brick, rast_list$NumGen_rast)
}
# Only need to do DDtotal brick for a single cohort, because it will be
# the same across all cohorts
if (cohort == 1) {
#cat("### Adding layers to DDtotal brick for cohort", cohort, ": doy =",
# sublist[d], file=daily_logFile, append=TRUE)
if (!exists("DDtotal_brick")) {
DDtotal_brick <- brick(rast_list$DDtotal_rast, crs = crs)
} else {
DDtotal_brick <- addLayer(DDtotal_brick, rast_list$DDtotal_rast)
}
}
rm(rast_list) # Free up memory
# If exclusions_stressunits = 1, then do the same thing for LifestageEXCL
# and NumGenEXCL, after they are calculated
if (exclusions_stressunits) {
# Do the same for cold/heat units and cold/heat exclusion, but just
# for cohort 1, because results will be same for all cohorts
if (cohort == 1) {
# Convert matrices to rasters and put them into a raster brick
mat_list3 <- list(coldunitsCUM, coldEXCL, heatunitsCUM,
heatEXCL, AllEXCL)
ext <- as.data.frame(as.matrix(extent(template)))
rast_list3 <- lapply(mat_list3, Mat_to_rast, ext = ext,
template = template)
names(rast_list3) <- c("coldunitsCUM_rast", "coldEXCL_rast",
"heatunitsCUM_rast", "heatEXCL_rast", "AllEXCL_rast")
#cat("\n### Adding layers to Cold Stress Units brick for cohort",
#cohort, ": doy =", sublist[d], "\n", file=daily_logFile, append=TRUE)
# Cold stress unit accumulation brick
if (!exists("coldunitsCUM_brick")) {
coldunitsCUM_brick <- brick(rast_list3$coldunitsCUM_rast,
crs = crs)
} else {
coldunitsCUM_brick <- addLayer(coldunitsCUM_brick,
rast_list3$coldunitsCUM_rast)
}
#cat("### Adding layers to Cold Stress Exclusion brick for cohort",
#cohort, ": doy =", sublist[d], "\n", file=daily_logFile, append=TRUE)
# Cold stress exclusion brick
if (!exists("coldEXCL_brick")) {
coldEXCL_brick <- brick(rast_list3$coldEXCL_rast, crs = crs)
} else {
coldEXCL_brick <- addLayer(coldEXCL_brick,
rast_list3$coldEXCL_rast)
}
#cat("### Adding layers to Heat Stress Units brick for cohort",
#cohort, ": doy =", sublist[d], "\n", file=daily_logFile, append=TRUE)
# Heat stress unit accumulation brick
if (!exists("heatunitsCUM_brick")) {
heatunitsCUM_brick <- brick(rast_list3$heatunitsCUM_rast, crs = crs)
} else {
heatunitsCUM_brick <- addLayer(heatunitsCUM_brick,
rast_list3$heatunitsCUM_rast)
}
#cat("### Adding layers to Heat Stress Exclusion brick for cohort",
#cohort, ": doy =", sublist[d], "\n", file=daily_logFile, append=TRUE)
# Heat stress exclusion brick
if (!exists("heatEXCL_brick")) {
heatEXCL_brick <- brick(rast_list3$heatEXCL_rast, crs = crs)
} else {
heatEXCL_brick <- addLayer(heatEXCL_brick, rast_list3$heatEXCL_rast)
}
#cat("### Adding layers to All Stress Exclusion brick for cohort",
# cohort, ": doy =", sublist[d], file=daily_logFile, append=TRUE)
# All stress exclusion brick (cold stress + heat stress exclusions)
if (!exists("AllEXCL_brick")) {
AllEXCL_brick <- brick(rast_list3$AllEXCL_rast, crs = crs)
} else {
AllEXCL_brick <- addLayer(AllEXCL_brick, rast_list3$AllEXCL_rast)
}
rm(rast_list3) # Free up memory
}
}
}
}#,
# error = function(e) {
# # Add error message to the error log file
# write(toString(e), msg, append=TRUE)
# }
# )
### * Daily loop done - save raster bricks ###
# Save non-optional raster bricks = Lifestage and NumGen
SaveRaster(Lifestage_brick, cohort, tile_num, "Lifestage", "INT1U")
SaveRaster(NumGen_brick, cohort, tile_num, "NumGen", "INT1U")
# Save DDtotal brick (only for cohort 1)
if (cohort == 1) {
SaveRaster(DDtotal_brick, cohort, tile_num, "DDtotal", "INT2S")
}
# Free up memory
rm(Lifestage_brick, NumGen_brick, DDtotal_brick)
# If Pest Event Maps are specified (pems = 1), then convert PEM matrices
# to rasters and save them
if (pems) {
pem_list <- mget(ls(pattern = "PEMe|PEMl|PEMp|PEMa"))
# Convert each matrix in the list to a raster and save it
for (i in 1:length(pem_list)) {
pem_mat <- pem_list[[i]]
pem_rast <- Mat_to_rast(pem_mat, ext, template)
if (region_param %in% c("CONUS", "EAST", "N_AMERICA", "EUROPE", "CHINA")) {
SaveRaster(pem_rast, cohort, tile_num, names(pem_list[i]), "INT2U")
} else {
SaveRaster(pem_rast, cohort, NA, names(pem_list[i]), "INT2U")
}
}
}
rm(list = ls(pattern = "PEM|pem_list|pem_rast")) # Free up memory
# If exclusions_stressunits = 1, then save stress unit and exclusions bricks
if (exclusions_stressunits) {
# Cold and heat stress unit and exclusion bricks will be the same for
# all cohorts, so take only 1st one
if (cohort == 1) {
stress_excl_brick_list <- c(coldunitsCUM_brick, coldEXCL_brick,
heatunitsCUM_brick, heatEXCL_brick, AllEXCL_brick)
names(stress_excl_brick_list) <- c("Cold_Stress_Units",
"Cold_Stress_Excl", "Heat_Stress_Units", "Heat_Stress_Excl",
"All_Stress_Excl")
# Save each raster brick product in the list
for (i in 1:length(stress_excl_brick_list)) {
brk <- stress_excl_brick_list[[i]]
SaveRaster(brk, cohort, tile_num,
paste0(names(stress_excl_brick_list[i])), "INT2S")
}
# Free up memory
rm(stress_excl_brick_list, coldunitsCUM_brick, coldEXCL_brick,
heatunitsCUM_brick, heatEXCL_brick, AllEXCL_brick)
}
#cat("\n### Finished climate exclusions and stress units raster output
#cohort", cohort, "###\n\n", file = Model_rlogging, append=TRUE)
}
# Remove .xml files generated w/ .tif files for certain raster bricks
# Haven't yet figured out a way to prevent these from being created. The
# only solution I have found is to change a GDAL setting.
delfiles <- dir(pattern = "*xml")
suppressWarnings(file.remove(delfiles))
gc() # Clear items from the environment - is this necessary?
}
#### (11). Degree Day calculation methods ####
# Equations used to calculate degree-days
# tmax = max. temp. data; tmin = min. temp data; LDT = lower developmental
# temperature threshold; UDT = upper developmental temperature threshold
# Averaging DD Calc method (max + min/2 - tlow) but with horizontal
# (substitution) upper threshold:
AvgDD <- function(tmax, tmin, LDT, UDT) {
return(Cond(tmax < LDT, 0, Cond(tmin > UDT, 0,
Cond(tmax > UDT, (UDT + tmin)/2 - LDT,
Cond((tmax + tmin)/2 - LDT < 0, 0,
(tmax + tmin)/2 - LDT)))))
}
# Single triangle with upper threshold (Sevachurian et al. 1977) -
# also a good substitution for the single sine method
TriDD <- function(tmax, tmin, LDT, UDT) {
tmax <- Cond(tmax == tmin, tmax + 0.01, tmax)
Tmp1 = 6 * ((tmax - LDT) * (tmax - LDT))/(tmax - tmin)
Tmp2 = 6 * ((tmax - UDT) * (tmax - UDT))/(tmax - tmin)
Cond(tmax < LDT, 0,
Cond(tmin >= UDT, UDT - LDT,
Cond((tmax < UDT) & (tmin <= LDT), Tmp1/12,
Cond((tmin <= LDT) & (tmax >= UDT), (Tmp1 - Tmp2)/12,
Cond((tmin > LDT) & (tmax >= UDT),
6 * (tmax + tmin - 2 * LDT)/12 - (Tmp2/12),
Cond((tmin > LDT) & (tmax < UDT),
6 * (tmax + tmin - 2 * LDT)/12, 0))))))
}
#### (12). Density for the skewed normal distribution ####
# A function implemented by Diethelm Wuertz
# Description: Compute the density function of the skew normal distribution
# Arguments:
# x - a numeric vector of quantiles.
# mean, sd, xi - location parameter, scale parameter, and skewness parameter.
# Source code from "fGarch" R package: https://rdrr.io/cran/fGarch/src/R/dist-snorm.R
dsnorm <- function(x, mean, sd, xi, log = FALSE) {
# Params:
if (length(mean) == 3) {
xi = mean[3]
sd = mean[2]
mean = mean[1]
}
# Function to shift and scale the distribution
.dsnorm <- function(x, xi) {
# A function implemented by Diethelm Wuertz
# Description:
# Compute the density function of the "normalized" skew
# normal distribution
# Standardize:
m1 = 2/sqrt(2*pi)
mu = m1 * (xi - 1/xi)
sigma = sqrt((1-m1^2)*(xi^2+1/xi^2) + 2*m1^2 - 1)
z = x*sigma + mu
# Compute:
Xi = xi^sign(z)
g = 2 / (xi + 1/xi)
Density = g * dnorm(x = z/Xi)
# Return Value:
Density * sigma
}
# Shift and Scale:
result = .dsnorm(x = (x-mean)/sd, xi = xi) / sd
# Log:
if(log) result = log(result)
# Return Value:
result
}
#### (13). ExtractBestPRISM: get best PRISM/NMME file from directory ####
# Take .bil files from PRISM or NMME (= forecast_data) yearly directories
# (= files). The function returns the best data for each day. If data are
# from a leap year, then leap day data may be removed or kept (= keep_leap).
ExtractBestPRISM <- function(files, forecast_data, keep_leap) {
numsplits <- str_count(string = files[1], pattern = "/")
pfile <- str_split(string = files, pattern = coll("/"), numsplits) %>%
purrr::map(numsplits)
qa <- str_split(string = pfile, pattern = coll("_"), 6) %>% purrr::map(3) %>%
unlist()
# Extract dates - the dates will always be the largest number, so sort and
# then take first element of vector
dates <- regexpr(pattern = "[0-9]{8}", text = files)
df <- data.frame(dates = regmatches(files, dates),
quality = substr(qa, start = 1, stop = 4),
rownum = 1:length(qa))
# Added to Tyson's version of this function - ability to choose PRISM vs.
# NMME for future dates
if (forecast_data == "PRISM") {
df <- mutate(df, rank = ifelse(quality == "stab", 1,
ifelse(quality == "prov", 2,
ifelse(quality == "earl", 3,
ifelse(quality == "10yr", 4,
ifelse(quality == "30yr", 5,
ifelse(quality == "nmme", 6, NA)))))))
} else if (dat == "NMME") {
df <- mutate(df, rank = ifelse(quality == "stab", 1,
ifelse(quality == "prov", 2,
ifelse(quality == "earl", 3,
ifelse(quality == "nmme", 4,
ifelse(quality == "10yr", 5,
ifelse(quality == "30yr", 6, NA)))))))
}
# Sorting backwards matches data hierarchy
# If PRISM, then stable > provisional > early > 10yr > 30yr > nmme
# If NMME, then stable > provisional > early > nmme > 10yr > 30yr
df2 <- df %>% group_by(dates) %>%
dplyr::filter(rank == min(rank)) %>%
dplyr::filter(1:n() == 1)
best <- files[df2$rownum]
dates <- regexpr(pattern = "[0-9]{8}", text = best)
fileorder <- order(regmatches(best, dates))
files <- best[fileorder]
# Remove leap day (2/29) if the start_year is not a leap year, or if it is