finalize SAU rec landings

indicator_data/SAU_USVI/readme.txt → indicator_data/SAU/SAUreadme.txt

@@ -1,9 +1,9 @@
 
-Downloaded 22-December-2022
+Downloaded 08-February-2024
 
 CATCH DATA, STOCK STATUS, MARINE TROPHIC INDEX, MULTINATIONAL FOOTPRINT, AND ASSOCIATED METHODS
 
-Data version 50.0
+Data version 50.1
 
 Please note that the data (‘reconstructed data’) are a combination of official reported data and reconstructed estimates of unreported data (including major discards).
 

indicator_processing/automated_download/ACE_index_Carib.R

@@ -8,29 +8,32 @@
 # ACE index calculations done according to methods from: 
 # GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L19702, doi:10.1029/2011GL049265, 2011
 
-# data source: https://www.ncdc.noaa.gov/ibtracs/index.php?name=ib-v4-access 
+# data source: https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ 
 # (download .csv file for N. Atlantic basin)
 
 # specification file and libraries -----------------------------
 rm(list = ls())
 library(maps)
 library(plotTimeSeries)
 
-load("spec_file.RData")
+#load("spec_file.RData")
+
+# define years  --------------------------------
+styear <- 1961
+enyear <- 2023
 
 # download data directly from site -----------------------------
 options(download.file.method="libcurl")
 
+url <- "https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ibtracs.NA.list.v04r00.csv"
 url <- "https://www.ncei.noaa.gov/data/international-best-track-archive-for-climate-stewardship-ibtracs/v04r00/access/csv/ibtracs.NA.list.v04r00.csv"
 
-download.file(url = url, destfile = "../indicator_data/ibtracs.csv")
-dat <- read.csv( "../indicator_data/ibtracs.csv", skip = 2, header = F)
-datn <- read.csv( "../indicator_data/ibtracs.csv", skip = 0, header = T)
+download.file(url = url, destfile = "C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_data/ibtracs.csv")
+dat <- read.csv("C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_data/ibtracs.csv", skip = 2, header = F)
+datn <- read.csv("C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_data/ibtracs.csv", skip = 0, header = T)
 names(dat) <- names(datn)
 
-# define years and cut columns --------------------------------
-styear <- 1961
-enyear <- terminal_year
+# cut columns --------------------------------
 
 head(dat)
 dat <- dat[, 1:17]
@@ -111,7 +114,7 @@ nam <- unique(d2$uniq)
 length(nam)
 
 dev.off()
-par(mfrow = c(7, 8), mex = 0.3)
+par(mfrow = c(8, 8), mex = 0.3)
 for (i in 1:length(nam)) {
   map('world', add=F, fill=T, col=8,  xlim=c(-72, -62), ylim=c(15, 22))
   ptcol <- as.numeric(strftime(d2$tim[which(d2$uniq == nam[i])], format = "%j"))
@@ -149,7 +152,9 @@ indnames <- data.frame(matrix(labs, nrow = 3, byrow = F))
 s <- list(labels = indnames, indicators = inddata, datelist = datdata) #, ulim = ulidata, llim = llidata)
 class(s) <- "indicatordata"
 
-plotIndicatorTimeSeries(s)
+setwd("C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_plots/")
+
+plotIndicatorTimeSeries(s, widadj = 0.5, outtype = "png")
 
 inddata <- s
 save(inddata, file = "C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_objects/ACEindex.RData")

indicator_processing/automated_download/DHW.R

@@ -3,6 +3,9 @@
 rm(list = ls())
 options(download.file.method="libcurl")
 
+styear <- 2012
+enyear <- 2023
+
 # download data and calculate mean -------------------
 
 for (i in 1:2) {
@@ -13,7 +16,7 @@ for (i in 1:2) {
   download.file(url = url, destfile = "C:/Users/mandy.karnauskas/Downloads/dhw.txt")
   d <- read.table("C:/Users/mandy.karnauskas/Downloads/dhw.txt", skip = 21, header = T)
 
-  d <- d[which(d$YYYY >=1985 & d$YYYY <= 2021), ]
+  d <- d[which(d$YYYY >= styear & d$YYYY <= 2023), ]
 
   head(d)
   d$yrmon <- paste0(d$YYYY, sprintf("%02.f", d$MM))

indicator_processing/automated_download/sst.R

@@ -7,11 +7,15 @@ rm(list = ls())
 #devtools::install_github("mdsumner/ncdf4")
 #library(ncdf4)
 
+# define years  --------------------------------
+styear <- 1982
+enyear <- 2023
+
 # empty data  -------------------------------------------------
 dat <- data.frame(row.names = c("year", "mon", "PR_mean", "PR_min", "PR_max", "VI_mean", "VI_min", "VI_max"))
 
 # download by year to avoid timeout errors --------------------
-for (yr in 1982:2021) { 
+for (yr in styear:enyear) { 
 
 # url from ERDDAP for OISST, download and read ----------------
   url <- paste0("https://coastwatch.pfeg.noaa.gov/erddap/griddap/ncdcOisst21Agg.csv?sst[(", yr, "-01-01T12:00:00Z):1:(", yr, "-12-31T12:00:00Z)][(0.0):1:(0.0)][(17):1:(19)][(292):1:(296)]")

indicator_processing/non_automated/CRMP_compile.R

@@ -0,0 +1,140 @@
+
+## compile TCRMP and PRCRMP into data plots
+## M. Karnauskas 2/8/2024
+#
+# run PRCRMP_benthic.R, TCRMP_benthic.R, PRCRMP_fish.R and TCRMP_fish.R first
+# 
+
+rm(list = ls())
+
+library(plotTimeSeries)
+
+# load coral indicator data -------------------------
+
+setwd("C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_data/")
+
+load("PRCRMP/coralspprich_PR.RData")
+PRsr <- out1
+load("PRCRMP/percoralcov_PR.RData") 
+PRcc <- out1
+load("TCRMP/coralspprich_USVI.RData")
+VIsr <- out1
+load("TCRMP/percoralcov_USVI.RData") 
+VIcc <- out1
+
+# extract years from linear mixed model ----------------------
+
+y1 <- as.numeric(gsub("YEAR", "", rownames(summary(PRsr)$coef)))
+y2 <- as.numeric(gsub("YEAR", "", rownames(summary(PRcc)$coef)))
+y3 <- as.numeric(gsub("SampleYear", "", rownames(summary(VIsr)$coef)))
+y4 <- as.numeric(gsub("SampleYear", "", rownames(summary(VIcc)$coef)))
+
+y1 == y2
+y3 == y4
+yrs <- (min(c(y1, y2, y3, y4))) : (max(c(y1, y2, y3, y4)))
+
+# extract coefficients from linear mixed model ----------------------
+
+mat <- data.frame(matrix(data = NA, nrow = length(yrs), ncol = 4))
+rownames(mat) <- yrs
+mat[match(y1, yrs), 1] <- summary(PRsr)$coef[, 1] 
+mat[match(y2, yrs), 2] <- summary(PRcc)$coef[, 1] 
+mat[match(y3, yrs), 3] <- summary(VIsr)$coef[, 1] 
+mat[match(y4, yrs), 4] <- summary(VIcc)$coef[, 1] 
+
+matse <- data.frame(matrix(data = NA, nrow = length(yrs), ncol = 4))
+rownames(matse) <- yrs
+matse[match(y1, yrs), 1] <- summary(PRsr)$coef[, 2] 
+matse[match(y2, yrs), 2] <- summary(PRcc)$coef[, 2] 
+matse[match(y3, yrs), 3] <- summary(VIsr)$coef[, 2] 
+matse[match(y4, yrs), 4] <- summary(VIcc)$coef[, 2] 
+
+# format indicator object --------------------
+
+datdata <- yrs
+inddata <- data.frame(mat)
+ulidata <- data.frame(mat + matse)
+llidata <- data.frame(mat - matse)
+labs <- c("Coral species richness" , "average number per transect", "Puerto Rico",
+          "Percent coral cover" , "percent cover", "Puerto Rico",
+          "Coral species richness" , "average number per transect", "USVI", 
+          "Percent coral cover" , "percent cover", "USVI")
+indnames <- data.frame(matrix(labs, nrow = 3, byrow = F))
+s <- list(labels = indnames, indicators = inddata, datelist = datdata, ulim = ulidata, llim = llidata)
+class(s) <- "indicatordata"
+
+setwd("C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_plots/") 
+
+plotIndicatorTimeSeries(s, coltoplot = 1:4, plotrownum = 2, sublabel = T, sameYscale = F, 
+                        widadj = 1, hgtadj = 1, trendAnalysis = T, type = "allLines", CItype = "band", outtype = "png")
+
+inddata <- s
+save(inddata, file = "C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_objects/coral_spprichness_cover.RData")
+
+
+
+## compile TCRMP and PRCRMP into data plots
+## M. Karnauskas 2/8/2024
+#
+# run PRCRMP_benthic.R, TCRMP_benthic.R, PRCRMP_fish.R and TCRMP_fish.R first
+# 
+
+rm(list = ls())
+
+library(plotTimeSeries)
+
+# load fish indicator data -------------------------
+
+setwd("C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_data/")
+
+load("PRCRMP/fish_density_PR.RData")
+PR <- fin
+load("TCRMP/fish_density_USVI.RData") 
+VI <- findens
+load("TCRMP/slopeSizeSpec_USVI.RData") 
+sl <- fin
+
+# extract years ----------------------
+
+PR$yrs == VI$yrs
+PR$yrs == sl$yrs
+yrs <- (min(c(PR$yrs, VI$yrs, sl$yrs))) : (max(c(PR$yrs, VI$yrs, sl$yrs)))
+
+# extract coefficients from linear mixed model ----------------------
+
+mat <- data.frame(matrix(data = NA, nrow = length(yrs), ncol = 3))
+rownames(mat) <- yrs
+mat[match(PR$yrs, yrs), 1] <- PR$ind_norm
+mat[match(VI$yrs, yrs), 2] <- VI$ind_norm
+mat[match(sl$yrs, yrs), 3] <- sl$stind
+
+matse <- data.frame(matrix(data = NA, nrow = length(yrs), ncol = 3))
+rownames(matse) <- yrs
+matse[match(PR$yrs, yrs), 1] <- PR$indse_norm
+matse[match(VI$yrs, yrs), 2] <- VI$indse_norm
+matse[match(sl$yrs, yrs), 3] <- sl$stindse
+
+# format indicator object --------------------
+
+datdata <- yrs
+inddata <- data.frame(mat)
+ulidata <- data.frame(mat + matse)
+llidata <- data.frame(mat - matse)
+labs <- c("Commercial fish density" , "average number per transect", "Puerto Rico",
+          "Commercial fish density" , "average number per transect", "USVI", 
+          "Slope of the size spectrum" , "slope of log distribution", "USVI")
+indnames <- data.frame(matrix(labs, nrow = 3, byrow = F))
+s <- list(labels = indnames, indicators = inddata, datelist = datdata, ulim = ulidata, llim = llidata)
+class(s) <- "indicatordata"
+
+setwd("C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_plots/") 
+
+plotIndicatorTimeSeries(s, coltoplot = 1:3, plotrownum = 3, sublabel = T, sameYscale = F, 
+                        widadj = 1, hgtadj = 1, trendAnalysis = T, type = "allLines", CItype = "band", outtype = "png")
+
+inddata <- s
+save(inddata, file = "C:/Users/mandy.karnauskas/Desktop/Caribbean-ESR/indicator_objects/fish_density.RData")
+
+#########################  END  ############################
+
+