|
|
|
|
|
|
|
|
|
|
|
|
| require(maps)
|
| require(mapdata)
|
| require(maptools)
|
| require(rgdal)
|
| require(rgeos)
|
| require(raster)
|
| require(data.table)
|
| require(beanplot)
|
| require(RColorBrewer)
|
| require(lme4)
|
| require(ggsci)
|
|
|
| se <- function(x,na.rm=FALSE){
|
| if(!na.rm){
|
| return(sd(x, na.rm=FALSE)/sqrt(length(x)))
|
| }
|
| if(na.rm){
|
| return(sd(x, na.rm=TRUE)/sqrt(sum(!is.na(x))))
|
| }
|
| }
|
|
|
|
|
|
|
| color.bar <- function(lut, min, max=-min, nticks=11, ticks=seq(min, max, len=nticks), title='') {
|
| scale = (length(lut)-1)/(max-min)
|
|
|
| plot(c(0,10), c(min,max), type='n', bty='n', xaxt='n', xlab='', yaxt='n', ylab='', main=title)
|
| axis(4, ticks, las=1)
|
| for (i in 1:(length(lut)-1)) {
|
| y = (i-1)/scale + min
|
| rect(0,y,10,y+1/scale, col=lut[i], border=NA)
|
| }
|
| }
|
|
|
|
|
|
|
|
|
|
|
|
|
| presmap1 <- fread('temp/presmap_Atl_rcp26_2007-2020.csv.gz', drop = 1)
|
| presmap2 <- fread('temp/presmap_Pac_rcp26_2007-2020.csv.gz', drop = 1)
|
| biomap1 <- fread('temp/biomassmap_Atl_rcp26_2007-2020.csv.gz', drop = 1)
|
| biomap2 <- fread('temp/biomassmap_Pac_rcp26_2007-2020.csv.gz', drop = 1)
|
| nspp <- presmap1[, length(unique(spp))] + presmap2[, length(unique(spp))] + biomap1[, length(unique(spp))] + biomap2[, length(unique(spp))]
|
| nspp
|
| 8*2*4*nspp
|
|
|
| rm(presmap1, presmap2, biomap1, biomap2)
|
|
|
|
|
| networks <- fread('temp/wdpaturnbyMPAbymod.csv.gz', drop = 1)
|
| networks[, length(unique(WDPA_PID)), by = network]
|
|
|
|
|
|
|
|
|
|
|
| turn <- fread('output/turnover_by_CMSPgrid.csv', drop = 1)
|
|
|
|
|
| regiongrid <- fread('output/region_grid.csv.gz', drop = 1)
|
| regiongrid[longrid > 0, longrid := longrid - 360]
|
| regiongrid[longrid < 0, longrid2 := longrid + 360]
|
|
|
|
|
| turn <- merge(turn, regiongrid, by = c('latgrid', 'longrid'), all.x = TRUE)
|
|
|
|
|
|
|
| regs <- c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf')
|
| cexs <- c(ebs = 0.3, goa = 0.3, bc = 0.5, wc = 0.35, gmex = 0.33, seus = 0.4, neus = 0.3, maritime = 0.25, newf = 0.13)
|
| regsnice = c('Eastern Bering Sea', 'Gulf of Alaska', 'British Columbia', 'West Coast U.S.', 'Gulf of Mexico', 'Southeast U.S.', 'Northeast U.S.', 'Maritimes', 'Newfoundland')
|
| regsniceabbrev = c('(EBS)', '(GoA)', '(BC)', '(WC)', '(GMex)', '(SEUS)', '(NEUS)', '(Mar)', '(Newf)')
|
| ylabs = c('Latitude (°N)', '', '', '', 'Latitude (°N)', '', '', 'Latitude (°N)', '')
|
| xlabs = c('', '', '', 'Longitude (°E)', '', '', 'Longitude (°E)', 'Longitude (°E)', 'Longitude (°E)')
|
| ylims = list(ebs = c(51,62.5), goa = c(54, 61), bc = c(48, 54), wc = c(32.2, 48.5), gmex = c(24,30.5), seus = c(25, 35.5), neus = c(35, 45), maritime = c(41, 52), newf = c(42, 62))
|
| xlims = list(ebs = c(-179.5,-155), goa = c(-156, -133), bc = c(-136, -122), wc = c(-126.5, -117), gmex = c(-97.5,-81), seus = c(-82, -74), neus = c(-76.5, -66), maritime = c(-69, -53), newf = c(-68, -43))
|
| pos <- c(ebs = 'right', goa = 'left', bc = 'left', wc = 'left', gmex = 'left', seus = 'right', neus = 'right', maritime = 'right', newf = 'left')
|
| bcol <- 'dark grey'
|
| yfrac <- c(0.1, 0.1, 0.1, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1)
|
| mfgs <- list(ebs = c(1,2), goa = c(1,3), bc = c(2,1), wc = c(1,4), gmex = c(2,2), seus = c(2,3), neus = c(3,1), maritime = c(3,2), newf = c(3,3))
|
| cols <- brewer.pal(9, 'GnBu')
|
| colfun <- colorRamp(colors = cols)
|
| colpal <- colorRampPalette(colors = cols)
|
|
|
|
|
|
|
|
|
| jpeg(width = 8.7/2.54, height = 6/2.54, file = 'figures/Fig1_study_regions.jpg', units = 'in', res = 1200, quality = 100)
|
| par(mai=c(0.15, 0.08, 0.15, 0.1), omi=c(0.15, 0.2, 0, 0), tck=-0.06, mgp=c(1.2,0.4,0), las=1, cex.main=0.5, cex.axis=0.5)
|
| layout(mat=matrix(c(1,2,3,5,4,6,7,5,8,9,10,11), byrow=TRUE, nrow=3))
|
|
|
|
|
| turn[!is.na(beta_sor), plot(1, 1, xlab='', ylab='', main='', xaxt='n', xlim = c(178,310), ylim = c(24, 62))]
|
| axis(1, mgp=c(1.2, 0.02, 0), at=c(200,250,300), labels=c(-160, -110, -60))
|
| map('world2Hires', add=TRUE, xlim=c(170,320), col=bcol, lwd=0.2, resolution=0, fill=FALSE, wrap=TRUE)
|
| turn[!is.na(beta_sor), points(longrid2, latgrid, col=rgb(colfun(beta_sor), maxColorValue = 255), pch=15, cex=0.1)]
|
|
|
|
|
| for(i in 1:length(regs)){
|
| cat(paste0(regs[i], ' '))
|
| inds <- turn[, region==regs[i] & !is.na(beta_sor)]
|
| turn[inds, plot(longrid, latgrid, col = rgb(colfun(beta_sor), maxColorValue = 255), pch = 15, cex = cexs[i], xlab = '', ylab = '', xlim = xlims[[i]], ylim = ylims[[i]],
|
| main = paste(regsnice[i], '\n', regsniceabbrev[i], sep=''), xaxt='n')]
|
| axis(1, mgp=c(1.2, 0.02, 0))
|
| map('worldHires',add=TRUE, col=bcol, fill=TRUE, border=FALSE, resolution=0)
|
| }
|
|
|
| mtext('Longitude (°E)', side = 1, outer = TRUE, cex = 0.5)
|
| mtext('Latitude (°N)', side = 2, outer = TRUE, cex = 0.5, las = 0, line = 0.7)
|
|
|
|
|
|
|
| par(mgp=c(2,0.5,0), mai=c(0.1, 0.3, 0.1, 0.4), tcl=-0.1)
|
| color.bar(colpal(100), min = 0, max = 1, nticks = 5, title = 'Turnover')
|
|
|
|
|
| dev.off()
|
|
|
|
|
|
|
|
|
|
|
| goalsmetbymod1 <- fread('output/goalsmetbymod_hist_all.csv', drop = 1)
|
| goalsmetbymod2 <- fread('output/goalsmetbymod_2per_all.csv', drop = 1)
|
| goalsmetbymod1 <- goalsmetbymod1[modeltype == 'testing', ]
|
| goalsmetbymod2 <- goalsmetbymod2[modeltype == 'testing', ]
|
| setkey(goalsmetbymod1, 'region', 'rcp', 'model', 'year_range')
|
| setkey(goalsmetbymod2, 'region', 'rcp', 'model', 'year_range')
|
| goalsmetbymod1[, type := 'hist']
|
| goalsmetbymod2[, type := '2per']
|
| goalsmetbymod <- rbind(goalsmetbymod1, goalsmetbymod2)
|
| goalsmetbymod[, type := factor(type, levels = c('hist', '2per'))]
|
|
|
| myregs <- c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf')
|
| regnames = c('Eastern Bering Sea', 'Gulf of Alaska', 'British Columbia', 'West Coast U.S.', 'Gulf of Mexico', 'Southeast U.S.', 'Northeast U.S.', 'Maritimes', 'Newfoundland')
|
| mods <- sort(unique(goalsmetbymod1$model))
|
| rcps <- sort(unique(goalsmetbymod1$rcp))
|
|
|
|
|
|
|
| goalsmetbymod[year_range == '2041-2060', .(mean = mean(pmetstrict), sd = sd(pmetstrict)), by = c('type')]
|
|
|
| goalsmetbymod[year_range == '2081-2100' & rcp == '26', .(mean = mean(pmetstrict), sd = sd(pmetstrict)), by = c('type')]
|
| goalsmetbymod[year_range == '2081-2100' & rcp == '85', .(mean = mean(pmetstrict), sd = sd(pmetstrict)), by = c('type')]
|
|
|
|
|
|
|
| goalsmetbymod[year_range == '2081-2100', .(prop = 1 - sum(pmetstrict > 0.7)/.N), by = c('type')]
|
|
|
|
|
| goalsmetbymod[year_range == '2081-2100', .(mean = mean(pmet), meanstrict = mean(pmetstrict)), by= c('region', 'type')]
|
| goalsmetbymod[year_range == '2081-2100', .(mean=mean(pmet), meanstrict = mean(pmetstrict)),
|
| by= c('region', 'model')][, .(mean = mean(mean), se = se(mean), meanstrict = mean(meanstrict),
|
| sestrict = se(meanstrict)), by = 'region']
|
|
|
|
|
| mod <- glmer(cbind(nmet, nmet/pmet - nmet) ~ type + (1|region/rcp/model/year_range), data=goalsmetbymod, family='binomial')
|
| summary(mod)
|
| nrow(goalsmetbymod)
|
| cc <- confint(mod, parm="beta_")
|
| ctab <- cbind(est = fixef(mod), cc)
|
| rtab <- exp(ctab)
|
| print(rtab, digits = 3)
|
|
|
|
|
| summ <- goalsmetbymod[, .(mid = mean(mid), mean = mean(pmetstrict), lb = mean(pmetstrict) - sd(pmetstrict), ub = mean(pmetstrict) + sd(pmetstrict)), by = c('year_range', 'region', 'type')]
|
| colmat <- t(col2rgb(brewer.pal(6, 'PuOr')))
|
| cols <- rgb(red=colmat[,1], green=colmat[,2], blue=colmat[,3], alpha=c(255, 90, 200, 200, 90, 255), maxColorValue=255)
|
| yaxts <- c('s', 'n', 'n', 's', 'n', 'n', 's', 'n', 'n')
|
| xaxts <- c('n', 'n', 'n', 'n', 'n', 'n', 's', 's', 's')
|
| outfile <- 'figures/Fig2_prioritizr_goalsmetbymod.pdf'; presentonly <- FALSE
|
|
|
| outfile
|
| ylims <- c(0, 1)
|
|
|
|
|
|
|
| pdf(width=8.7/2.54, height=8.7/2.54, file=outfile)
|
| par(mfrow=c(3,3), mai=c(0.05, 0.05, 0.2, 0.05), omi=c(0.4,0.4,0,0), cex.main=0.8, cex.axis=0.6, tcl=-0.15, mgp=c(1.6,0.4,0), las = 1)
|
|
|
| for (i in 1:length(myregs)) {
|
| plot(0, 0, xlab='', ylab='', ylim=ylims, xlim=c(2030,2090), main=regnames[i], yaxt='n', xaxt='n')
|
|
|
| if(yaxts[i]=='s'){
|
| axis(2, mgp=c(2,0.4,0))
|
| } else {
|
| axis(2, labels=FALSE)
|
| }
|
|
|
| if(xaxts[i]=='s'){
|
| axis(1, mgp=c(2,0.1,0))
|
| } else {
|
| axis(1, labels=FALSE)
|
| }
|
|
|
|
|
| for(k in 1:length(mods)) {
|
| for(j in 1:length(rcps)) {
|
| goalsmetbymod1[model == mods[k] & rcp == rcps[j] & region==myregs[i] & year_range != '2007-2020',
|
| points(mid, pmetstrict, type='l', pch=16, lwd = 0.5, col=cols[2])]
|
| }
|
| }
|
|
|
| if(!presentonly){
|
| for(k in 1:length(mods)){
|
| for(j in 1:length(rcps)){
|
| goalsmetbymod2[model == mods[k] & rcp == rcps[j] & region==myregs[i] & year_range != '2007-2020',
|
| points(mid, pmetstrict, type='l', pch=16, lwd = 0.5, col=cols[5])]
|
| }
|
| }
|
| }
|
|
|
|
|
| summ[region==myregs[i] & mid > 2014 & type == 'hist', polygon(c(mid, rev(mid)), c(lb, rev(ub)), col = cols[3], border = NA)]
|
| if(!presentonly) summ[region==myregs[i] & mid > 2014 & type == '2per', polygon(c(mid, rev(mid)), c(lb, rev(ub)), col = cols[4], border = NA)]
|
|
|
|
|
| summ[region==myregs[i] & mid > 2014 & type == 'hist', lines(mid, mean, col = cols[1], lwd = 2)]
|
| if(!presentonly) summ[region==myregs[i] & mid > 2014 & type == '2per', lines(mid, mean, col = cols[6], lwd = 2)]
|
|
|
| }
|
|
|
| mtext(side=1, text='Year', line=1.6, outer=TRUE)
|
| mtext(side=2, text='Fraction goals met', line=1.8, outer=TRUE, las = 0)
|
|
|
| dev.off()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| folder <- 'output/prioritizr_runs'
|
| runnames <- list.files(path = folder, pattern = 'solution')
|
|
|
| consplans <- vector('list', length(runnames))
|
| for(i in 1:length(consplans)){
|
| consplans[[i]] <- fread(paste0(folder, '/', runnames[i]), drop = 1)
|
| consplans[[i]]$region <- gsub('solution_|2per_|hist_|.csv', '', runnames[i])
|
| consplans[[i]]$type <- gsub('solution_|_ebs|_goa|_bc|_wc|_gmex|_seus|_neus|_maritime|_newf|.csv', '', runnames[i])
|
| }
|
| consplans <- rbindlist(consplans)
|
| regs <- c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf')
|
|
|
|
|
| nrow(consplans)
|
| consplans[, .N, by = region]
|
|
|
|
|
| consplans[ , zone := as.numeric(NA)]
|
| consplans[solution_1_conservation == 1 , zone := 1]
|
| consplans[solution_1_fishery == 1 , zone := 2]
|
| consplans[solution_1_energy == 1 , zone := 3]
|
| consplans[solution_1_conservation == 0 & solution_1_fishery == 0 & solution_1_energy == 0 , zone := 4]
|
|
|
|
|
| consplansw <- dcast(consplans, latgrid + longrid + region ~ type, value.var = 'zone')
|
| consplansw[, change := hist != `2per`]
|
|
|
| consplansw[, .(nchange = sum(change), ntot = .N, pchange = sum(change)/.N), by = region] # average in each region
|
| consplansw[, .(nchange = sum(change), ntot = .N, pchange = sum(change)/.N),
|
| by = region][, .(avepchange = mean(nchange/ntot), sd = sd(nchange/ntot))] # average proportion across regions
|
|
|
|
|
| # Make matrix of proportion in each zone in each region, for barplot
|
| mathist <- matrix(NA, nrow = 4, ncol=length(regs))
|
| colnames(mathist) <- regs
|
| rownames(mathist) <- c('conservation', 'fishery', 'energy', 'free')
|
| mat2per <- mathist
|
| mathistraw <- mathist
|
| mat2perraw <- mathist
|
| for(i in 1:length(regs)){
|
| for(j in 1:4){
|
| mathist[j,i] <- consplansw[region == regs[i], sum(hist == j)/.N]
|
| mat2per[j,i] <- consplansw[region == regs[i], sum(`2per` == j)/.N]
|
| mathistraw[j,i] <- consplansw[region == regs[i], sum(hist == j)]
|
| mat2perraw[j,i] <- consplansw[region == regs[i], sum(`2per` == j)]
|
| }
|
| }
|
| colSums(mathist)
|
| colSums(mat2per)
|
| colSums(mathistraw)
|
| colSums(mat2perraw)
|
|
|
| l <- list(mathist=mathist, mat2per=mat2per)
|
| mat <- do.call(cbind, l)[,order(sequence(sapply(l, ncol)))]
|
|
|
| # change in zones: 2per - hist
|
| mat2perraw - mathistraw # number
|
| round(mat2per - mathist,3) # fraction
|
| range(mat2per[4,] - mathist[4,]) # fractional decrease in free space (e.g., increase in plan area)
|
| mean(mat2per[4,] - mathist[4,]) # mean fractional decrease in free space (e.g., increase in plan area)
|
| sd(mat2per[4,] - mathist[4,])/sqrt(9) # SE fractional decrease in free space (e.g., increase in plan area)
|
|
|
|
|
| # modify to trick barplot into letting me use 8 colors instead of 4
|
| # see http://r.789695.n4.nabble.com/barplot-colors-td4662538.html
|
| matmod <- cbind(c(mat[,1], rep(0,nrow(mat))), c(rep(0,nrow(mat)), mat[,2]))
|
| for(i in seq(3,ncol(mat),by=2)){
|
| matmod <- cbind(matmod, c(mat[,i], rep(0,nrow(mat))), c(rep(0,nrow(mat)), mat[,i+1]))
|
| }
|
|
|
| # plot
|
| regnames = c('EBS', 'GoA', 'BC', 'WC', 'GMex', 'SEUS', 'NEUS', 'Mar', 'Newf')
|
| cols <- brewer.pal(8, 'RdYlBu')
|
| cols <- cols[c(1:4, 8:5)]
|
|
|
| # quartz(width=8.7/2.54, height=5/2.54)
|
| pdf(width=8.7/2.54, height=10/2.54, file='figures/Fig3_planareas.pdf')
|
| #png(width=8.7/2.54, height=10/2.54, units = 'in', res = 300, file='figures/Fig3_planareas.png')
|
| par(mfrow = c(2, 1), mai=c(0.2, 0.75, 0.1, 0.1), mgp=c(1.8, 0.4, 0), tcl=-0.2, las=1, cex.axis=0.8)
|
| barplot(height=matmod, space=rep(c(1,0), ncol(mathist)), xaxt='n', col=cols, ylab='Proportion', xlim=c(1.5,40))
|
| axis(1, at=seq(2,26,by=3), labels=NA, cex.axis=0.8, las=2, mgp=c(1.8, 0.5, 0))
|
| mtext(side = 3, text = 'a)', adj = -0.3, line = -0.5, las = 1, cex = 1)
|
| legend(x=28.5, y=1, fill=cols[8:5], legend=c('Free', 'Energy', 'Fishing', 'Conservation'), cex=0.5, bty='n')
|
| legend(x=27, y=1, fill=cols[4:1], legend=rep("",4), cex=0.5, bty='n')
|
|
|
| par(mai=c(0.8, 0.75, 0.05, 0.1), mgp = c(1.8, 0.4, 0))
|
| consplansw[, .(pchange = sum(change)/.N),
|
| by = region][, barplot(height = pchange, space = 2, xaxt = 'n', yaxt = 'n', col = 'black',
|
| ylab = 'Proportion\nchanged', xlim = c(2,40), ylim = c(0, 0.35))]
|
| axis(1, at = seq(2.5,26.5,by=3), labels=regnames, cex.axis=0.8, las=2, mgp=c(1.8, 0.5, 0))
|
| axis(2, at = seq(0, 0.3, by = 0.1))
|
| mtext(side = 3, text = 'b)', adj = -0.3, line = -0.5, las = 1, cex = 1)
|
|
|
| dev.off()
|
|
|
| # make a table instead
|
| round(mathist[c('conservation', 'fishery', 'energy', 'free'), c('ebs', 'ai', 'goa', 'wc', 'gmex', 'neus', 'scot', 'sgulf', 'newf')],2)
|
|
|
| round(mat2per[c('conservation', 'fishery', 'energy', 'free'), c('ebs', 'ai', 'goa', 'wc', 'gmex', 'neus', 'scot', 'sgulf', 'newf')],2)
|
|
|
| round(rowMeans(mathist[c('conservation', 'fishery', 'energy', 'free'), c('ebs', 'ai', 'goa', 'wc', 'gmex', 'neus', 'scot', 'sgulf', 'newf')]),2)
|
| round(rowMeans(mat2per[c('conservation', 'fishery', 'energy', 'free'), c('ebs', 'ai', 'goa', 'wc', 'gmex', 'neus', 'scot', 'sgulf', 'newf')]),2)
|
|
|
|
|
|
|
| ################################################
|
| # Fig. 4 Plot the prioritizr efficiency frontier
|
| ################################################
|
| # read in prioritizr solutions
|
| frontierall <- fread('temp/frontierall_2019-12-31_075440.csv', drop = 1)
|
| frontierall <- frontierall[budget == 0.75, ]
|
| setkey(frontierall, region, budget, presweight)
|
|
|
| # definition of planning features by region
|
| sppfiles <- list.files(path = 'output/prioritizr_runs/', pattern = 'spp_*', full.names = TRUE)
|
| spps <- fread(sppfiles[1], drop = 1)
|
| spps[, region := gsub('/|output|prioritizr_runs|spp_|\\.csv', '', sppfiles[1])]
|
| for(i in 2:length(sppfiles)){
|
| temp <- fread(sppfiles[i], drop = 1)
|
| temp[, region := gsub('/|output|prioritizr_runs|spp_|\\.csv', '', sppfiles[i])]
|
| spps <- rbind(spps, temp)
|
| }
|
| rm(temp)
|
| ngoals <- spps[name != 'energy', .(ngoals = .N), by = region]
|
| setkey(ngoals, region)
|
|
|
| # set up region order and add ngoals
|
| frontierall[, region := factor(region, levels = c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf'))] # set order
|
| frontierall <- merge(frontierall, ngoals, by = 'region')
|
|
|
| # how many not optimal?
|
| frontierall[, .(notopt = sum(status != 'OPTIMAL'), total = .N), by = region]
|
|
|
|
|
| # set up goals as a % of total
|
| frontierall[, ':='(presperc = presgoals/ngoals, futperc = futgoals/ngoals)]
|
|
|
| # quick plot as a check
|
| #require(ggplot2)
|
| #ggplot(frontierall, aes(x = presperc, y = futperc, group = budget, color = budget)) +
|
| # geom_path(size = 0.4) +
|
| # geom_point(size = 0.3) +
|
| # facet_wrap(~ region, nrow = 3, scales = 'free')
|
|
|
| # Drop non-frontier points (automated)
|
| # If two points share the same futperc, it chooses the one with the higher presperc (or vice versa)
|
| frontierall[, todrop := 0]
|
| for(i in 1:nrow(frontierall)){
|
| thisreg <- frontierall[i, region]
|
| thispresperc <- frontierall[i, presperc]
|
| k1 <- frontierall[, presperc == thispresperc & region == thisreg]
|
| if(length(unique(frontierall[k1, futperc])) > 1){
|
| mx <- frontierall[k1, max(futperc)]
|
| frontierall[presperc == thispresperc & futperc != mx & region == thisreg & presweight != 0 & presweight != 100, todrop := 1]
|
| }
|
|
|
| thisfutperc <- frontierall[i, futperc]
|
| k2 <- frontierall[, futperc == thisfutperc & region == thisreg]
|
| if(length(unique(frontierall[k2, presperc])) > 1){
|
| mx <- frontierall[k2, max(presperc)]
|
| frontierall[futperc == thisfutperc & presperc != mx & region == thisreg & presweight != 0 & presweight != 100, todrop := 1]
|
| }
|
| }
|
|
|
| ggplot(frontierall[todrop == 0,], aes(x = presperc, y = futperc, group = budget, color = budget)) +
|
| geom_path(size = 0.4) +
|
| geom_point(size = 0.3) +
|
| facet_wrap(~ region, nrow = 3, scales = 'free')
|
|
|
| # Plot %goals met for each weighting and region
|
| #colmat <- t(col2rgb(brewer.pal(9, 'Set1')))
|
| #cols <- rgb(red=colmat[,1], green=colmat[,2], blue=colmat[,3], alpha=c(255,255,255,255), maxColorValue=255)
|
| cols <- pal_lancet(alpha = 1)(9)
|
|
|
| yaxts <- c('s', 'n', 'n', 's', 'n', 'n', 's', 'n', 'n')
|
| xaxts <- c('n', 'n', 'n', 'n', 'n', 'n', 's', 's', 's')
|
| myregs <- c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf')
|
| regnames = c('Eastern Bering Sea', 'Gulf of Alaska', 'British Columbia', 'West Coast U.S.', 'Gulf of Mexico', 'Southeast U.S.', 'Northeast U.S.', 'Maritimes', 'Newfoundland')
|
| buds <- 0.75 # the budgets to plot
|
|
|
| #png('figures/Fig4_prioritizr_frontiers.png', height = 4, width = 6, units = 'in', res = 300)
|
| pdf('figures/Fig4_prioritizr_frontiers.pdf', height = 4, width = 6)
|
| layout(matrix(c(1,4,4,4,2,4,4,4,3,4,4,4), byrow = TRUE, ncol = 4))
|
|
|
| par(mai=c(0.1, 0.1, 0.1, 0.05), cex.main = 1, cex.axis = 0.8, tcl = -0.3, mgp=c(2,0.5,0))
|
| plot(x = c(0, 1, 1), y = c(1, 1, 0), type = 'l', bty = 'l', lwd = 2, xaxt = 'n', yaxt = 'n', xlab = '', ylab = '', xlim = c(-0.25, 1.25), ylim = c(-0.25, 1.25))
|
| plot(x = c(0, 1), y = c(1, 0), type = 'l', bty = 'l', lwd = 2, xaxt = 'n', yaxt = 'n', xlab = '', ylab = '', xlim = c(-0.25, 1.25), ylim = c(-0.25, 1.25))
|
|
|
| x1 <- seq(0, 1, length = 100)
|
| plot(x = x1, y = sqrt(1 - x1^2), type = 'l', bty = 'l', lwd = 2, xaxt = 'n', yaxt = 'n', xlab = '', ylab = '', xlim = c(-0.25, 1.25), ylim = c(-0.25, 1.25))
|
|
|
| par(mai=c(0.7, 0.7, 0.2, 0.05))
|
| plot(0, 0, type = 'o', bty = 'l', pch = 16, col = 'white', xlab='Present goals met (proportion)', ylab='Future goals met (proportion)', ylim = c(0.2, 1), xlim = c(0.3, 1), main='')
|
| for (i in 1:length(myregs)) { # for each region
|
| frontierall[region == myregs[i] & budget == buds & todrop == 0, lines(presperc, futperc, type = 'l', pch = 16, col = cols[i])]
|
| }
|
|
|
| legend('bottomleft', legend = regnames, col = cols, lty = 1, cex = 0.7, title = 'Regions')
|
|
|
| dev.off()
|
|
|
|
|
| ###############################################
|
| ## Fig. 5 Management area species gains and losses plot and stats
|
| ###############################################
|
| wdpaturnbyMPAbymod <- fread('temp/wdpaturnbyMPAbymod.csv.gz', drop = 1) # individual MPA results
|
| wdpaturnbynetbymod <- fread('temp/wdpaturnbynetbymod.csv.gz') # network results
|
|
|
| # Prep
|
| # reshape to long format
|
| wdpaturnbyMPAbymodl <- melt(wdpaturnbyMPAbymod, id.vars = c('WDPA_PID', 'network'),
|
| measure.vars = patterns('ninit|nfinal|nshared|nlost|ngained')) # convert to long
|
| wdpaturnbyMPAbymodl[, c('var', 'rcp', 'model') := tstrsplit(variable, "\\.", fixed = FALSE)][, variable := NULL] # extract rcp and GMC number from the col name
|
| wdpaturnbyMPAbymodl <- dcast(wdpaturnbyMPAbymodl, WDPA_PID + network + rcp + model ~ var) # group ninit -> nshared as separate columns
|
|
|
| wdpaturnbynetbymodl <- melt(wdpaturnbynetbymod, id.vars = c('network'),
|
| measure.vars = patterns('ninit|nfinal|nshared|nlost|ngained')) # convert to long
|
| wdpaturnbynetbymodl[, c('var', 'rcp', 'model') := tstrsplit(variable, "\\.", fixed = FALSE)][, variable := NULL] # extract rcp and GMC number from the col name
|
| wdpaturnbynetbymodl <- dcast(wdpaturnbynetbymodl, network + rcp + model ~ var) # group ninit -> nshared as separate columns
|
|
|
| # calculate means within models/rcps (across regions)
|
| means <- wdpaturnbyMPAbymodl[, .(flost = mean(nlost/ninit), fgained = mean(ngained/nfinal), beta_sor=mean(2*nshared/(2*nshared + ngained + nlost))),
|
| by=c('rcp', 'model')]
|
|
|
| # calculate network means within climate models/rcps
|
| means.net <- wdpaturnbynetbymodl[, .(flost = mean(nlost/ninit), fgained = mean(ngained/nfinal), beta_sor=mean(2*nshared/(2*nshared + ngained + nlost))),
|
| by=c('rcp', 'model')]
|
|
|
| # calculate means within models/rcps for individual MPAs in the networks
|
| means.net.indiv <- wdpaturnbyMPAbymodl[!is.na(network), .(flost = mean(nlost/ninit), fgained = mean(ngained/nfinal), beta_sor=mean(2*nshared/(2*nshared + ngained + nlost))),
|
| by=c('rcp', 'model')]
|
|
|
| # combine network and individual MPA results
|
| means.net$type <- 'net'
|
| means.net.indiv$type <- 'ind'
|
| means.net <- rbind(means.net, means.net.indiv)
|
|
|
| # Statistics for individual and networks of management zones
|
| # means and SE
|
| means.net[, .(flost = mean(flost), flost.sd = sd(flost), fgained = mean(fgained), fgained.sd = sd(fgained),
|
| beta_sor_diss = mean(1 - beta_sor), beta_sor_diss.sd = sd(1 - beta_sor)),
|
| by = c('type', 'rcp')]
|
|
|
| sort(means.net[type == 'ind', beta_sor] - means.net[type == 'net', beta_sor]) # sorted differences: all negative
|
|
|
| # test
|
| t.test(means.net[type == 'ind', beta_sor], means.net[type == 'net', beta_sor]) # parametric
|
| wilcox.test(means.net[type == 'ind', beta_sor], means.net[type == 'net', beta_sor]) # non-parametric
|
|
|
| # MLPA example
|
| wdpaturnbyMPAbymodl[network == 'mlpa', .(flost = mean(nlost/ninit), fgained = mean(ngained/nfinal), beta_sor = 1 - mean(2*nshared/(2*nshared + ngained + nlost))),
|
| by = .(rcp, model)][, .(flost = mean(flost), fgained = mean(fgained), beta_sor = mean(beta_sor))] # individual MPAs
|
| wdpaturnbynetbymodl[network == 'mlpa', .(flost = mean(nlost/ninit), fgained = mean(ngained/nfinal), beta_sor = 1 - mean(2*nshared/(2*nshared + ngained + nlost)))] # individual MPAs
|
|
|
|
|
|
|
| # Plot of mean MPA change and network change
|
| cols <- list(c('#67a9cf','white','black','#2166ac'), c('#ef8a62', 'white','black','#b2182b')) # colors from Colorbrewer2 7-class RdBu
|
| # quartz(width=8.7/2.54,height=8.7/2.54)
|
| pdf(width=8.7/2.54, height=8.7/2.54, file='figures/Fig5_MPA_turnover.pdf')
|
| #png(width=8.7/2.54, height=8.7/2.54, units = 'in', res = 300, file='figures/Fig5_MPA_turnover.png')
|
|
|
| par(mfrow=c(2,2), las=2, mai=c(0.5,0.45,0.1, 0.05), omi=c(0,0,0,0), mgp=c(1.6,0.4,0), tcl=-0.2, cex.axis=0.8)
|
|
|
| beanplot(flost ~ type + rcp, data = means.net, what = c(0,1,1,0), side = 'both', col = cols, border = NA, wd = 0.18, handlelog = FALSE,
|
| names = c('RCP 2.6', 'RCP 8.5'), las = 1,
|
| at = c(1, 3), log = "", ylim = c(0, 0.5), xlim = c(0, 4), cut = 0.01, ylab = 'Fraction lost')
|
| mtext(side = 3, text = 'a)', adj = -0.4, line = -0.4, las = 1, cex = 0.9)
|
|
|
| beanplot(fgained ~ type + rcp, data = means.net, what = c(0,1,1,0), side = 'both', col = cols, border = NA, wd = 0.18, handlelog = FALSE,
|
| names = c('RCP 2.6', 'RCP 8.5'), las = 1,
|
| at = c(1, 3.2), log = "", ylim = c(0, 0.6), xlim = c(0, 4.5), cut = 0.01, ylab = 'Fraction gained')
|
| mtext(side = 3, text = 'b)', adj = -0.4, line = -0.4, las = 1, cex = 0.9)
|
|
|
| beanplot(I(1-beta_sor) ~ type + rcp, data = means.net, what = c(0,1,1,0), side = 'both', col = cols, border = NA, wd = 0.18, handlelog = FALSE,
|
| names = c('RCP 2.6', 'RCP 8.5'), las = 1,
|
| at = c(1, 3), log = "", ylim = c(0, 1), xlim = c(0, 4), cut = 0.01, ylab = 'Dissimilarity') # flost
|
| mtext(side = 3, text = 'c)', adj = -0.4, line = -0.4, las = 1, cex = 0.9)
|
|
|
| plot(1, 1, bty = 'n', xaxt = 'n', yaxt = 'n', col = 'white', xlab = '', ylab = '')
|
| legend('center', legend = c('Individual', 'Network'), title = 'Type', col = c(cols[[1]][1], cols[[2]][1]), lty = 1, lwd = 10, bty = 'n')
|
|
|
| dev.off()
|
|
|
|
|
|
|
| ###################################################
|
| ## Fig. S1 Efficiency frontiers for other budgets
|
| ###################################################
|
|
|
| # read in prioritizr solutions
|
| frontier1 <- fread('temp/frontierall_2019-12-22_071607.csv', drop = 1) # 50% budget
|
| frontier2 <- fread('temp/frontierall_2019-12-31_075440.csv', drop = 1) # 75% and 90% budgets
|
| frontier2 <- frontier2[budget == 0.9, ]
|
| frontierall <- rbind(frontier1, frontier2)
|
| setkey(frontierall, region, budget, presweight)
|
|
|
| # definition of planning features by region
|
| sppfiles <- list.files(path = 'output/prioritizr_runs/', pattern = 'spp_*', full.names = TRUE)
|
| spps <- fread(sppfiles[1], drop = 1)
|
| spps[, region := gsub('/|output|prioritizr_runs|spp_|\\.csv', '', sppfiles[1])]
|
| for(i in 2:length(sppfiles)){
|
| temp <- fread(sppfiles[i], drop = 1)
|
| temp[, region := gsub('/|output|prioritizr_runs|spp_|\\.csv', '', sppfiles[i])]
|
| spps <- rbind(spps, temp)
|
| }
|
| rm(temp)
|
| ngoals <- spps[name != 'energy', .(ngoals = .N), by = region]
|
| setkey(ngoals, region)
|
|
|
| # set up region order and add ngoals
|
| frontierall[, region := factor(region, levels = c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf'))] # set order
|
| frontierall <- merge(frontierall, ngoals, by = 'region')
|
|
|
| # how many not optimal?
|
| frontierall[, .(notopt = sum(status != 'OPTIMAL'), total = .N), by = region]
|
|
|
|
|
| # set up goals as a % of total
|
| frontierall[, ':='(presperc = presgoals/ngoals, futperc = futgoals/ngoals)]
|
|
|
| # quick plot as a check
|
| require(ggplot2)
|
| ggplot(frontierall, aes(x = presperc, y = futperc, group = budget, color = region)) +
|
| geom_path(size = 0.4) +
|
| geom_point(size = 0.3) +
|
| facet_wrap(~ budget, nrow = 3, scales = 'free')
|
|
|
| # Drop non-frontier points (automated)
|
| # If two points share the same futperc, it chooses the one with the higher presperc (or vice versa)
|
| frontierall[, todrop := 0]
|
| for(i in 1:nrow(frontierall)){
|
| thisreg <- frontierall[i, region]
|
| thisbud <- frontierall[i, budget]
|
| thispresperc <- frontierall[i, presperc]
|
| k1 <- frontierall[, presperc == thispresperc & region == thisreg & budget == thisbud]
|
| if(length(unique(frontierall[k1, futperc])) > 1){
|
| mx <- frontierall[k1, max(futperc)]
|
| frontierall[presperc == thispresperc & futperc < mx & region == thisreg & presweight != 0 & presweight != 100, todrop := 1]
|
| }
|
|
|
| thisfutperc <- frontierall[i, futperc]
|
| k2 <- frontierall[, futperc == thisfutperc & region == thisreg & budget == thisbud]
|
| if(length(unique(frontierall[k2, presperc])) > 1){
|
| mx <- frontierall[k2, max(presperc)]
|
| frontierall[futperc == thisfutperc & presperc < mx & region == thisreg & presweight != 0 & presweight != 100, todrop := 1]
|
| }
|
| }
|
|
|
| ggplot(frontierall[todrop == 0, ], aes(x = presperc, y = futperc, group = budget, color = region)) +
|
| geom_path(size = 0.4) +
|
| geom_point(size = 0.3) +
|
| facet_wrap(~ budget, nrow = 3, scales = 'free')
|
|
|
|
|
| # Plot %goals met for each weighting and region
|
| #colmat <- t(col2rgb(brewer.pal(9, 'Set1')))
|
| #cols <- rgb(red=colmat[,1], green=colmat[,2], blue=colmat[,3], alpha=c(255,255,255,255), maxColorValue=255)
|
| cols <- pal_lancet(alpha = 1)(9)
|
|
|
| yaxts <- c('s', 'n', 'n', 's', 'n', 'n', 's', 'n', 'n')
|
| xaxts <- c('n', 'n', 'n', 'n', 'n', 'n', 's', 's', 's')
|
| myregs <- c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf')
|
| regnames = c('Eastern Bering Sea', 'Gulf of Alaska', 'British Columbia', 'West Coast U.S.', 'Gulf of Mexico', 'Southeast U.S.', 'Northeast U.S.', 'Maritimes', 'Newfoundland')
|
| buds <- 0.5 # the budgets to plot
|
|
|
| png('figures/FigS1_prioritizr_frontiers.png', height = 8, width = 6, units = 'in', res = 300)
|
| par(mfrow = c(2,1), mai=c(0.7, 0.7, 0.2, 0.05), cex.main = 1, cex.axis = 0.8, tcl = -0.3, mgp=c(2,0.5,0), las = 1)
|
|
|
| plot(0, 0, type = 'o', pch = 16, col = 'white', bty = 'l', xlab='Present goals met (proportion)', ylab='Future goals met (proportion)', ylim = c(0.2, 1), xlim = c(0.3, 1), main='')
|
| for (i in 1:length(myregs)) { # for each region
|
| frontierall[region == myregs[i] & budget == 0.5 & todrop == 0, lines(presperc, futperc, type = 'l', pch = 16, col = cols[i])]
|
| }
|
|
|
| legend('bottomleft', legend = regnames, col = cols, lty = 1, cex = 0.7, title = 'Regions')
|
|
|
| plot(0, 0, type = 'o', pch = 16, col = 'white', bty = 'l', xlab='Present goals met (proportion)', ylab='Future goals met (proportion)', ylim = c(0.2, 1), xlim = c(0.3, 1), main='')
|
| for (i in 1:length(myregs)) { # for each region
|
| frontierall[region == myregs[i] & budget == 0.9 & todrop == 0, lines(presperc, futperc, type = 'l', pch = 16, col = cols[i])]
|
| }
|
|
|
| dev.off()
|
|
|
|
|
|
|
| #################################################
|
| ## Fig. S2: Simulated management area networks
|
| #################################################
|
| randMPAs <- fread('output/randMPAs_byBT.csv', drop = 1) # read in the simulations
|
| stats <- fread('output/MPA_network_stats.csv', drop = 1)
|
|
|
| regs <- sort(unique(randMPAs$region))
|
|
|
|
|
| # plot netwrkturn as color dots (initial plot)
|
| colrmp <- colorRamp(brewer.pal(11, name='Spectral'))
|
|
|
| par(mfrow=c(3,3))
|
| for(i in 1:length(regs)){
|
| inds <- randMPAs$region==regs[i]
|
| plot(randMPAs$size[inds], randMPAs$temprng[inds], pch=16, col=rgb(colrmp(randMPAs$netwrkturn[inds]), maxColorValue=256), main=regs[i])
|
| }
|
|
|
|
|
| # plot netwrkturn as averages within grid squares
|
| colrmp <- colorRamp(rev(brewer.pal(11, name='Spectral')))
|
| colpal <- colorRampPalette(rev(brewer.pal(11, name='Spectral')))
|
| xlabs <- c('', '', '', '', '', '', 'Proportion of area in network', 'Proportion of area in network', 'Proportion of area in network')
|
| ylabs <- c('Proportion of thermal\nrange in network', '', '', 'Proportion of thermal\nrange in network', '', '', 'Proportion of thermal\nrange in network', '', '')
|
| regs <- c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf') # set plot order
|
| regsnice = c('Eastern Bering Sea', 'Gulf of Alaska', 'British Columbia', 'West Coast U.S.', 'Gulf of Mexico', 'Southeast U.S.', 'Northeast U.S.', 'Maritimes', 'Newfoundland')
|
|
|
| szs <- seq(min(randMPAs$size, na.rm=TRUE), max(randMPAs$size, na.rm=TRUE), length.out=10)
|
| rngs <- seq(min(randMPAs$temprng, na.rm=TRUE), max(randMPAs$temprng, na.rm=TRUE), length.out=10)
|
| szstep <- diff(szs)[1]
|
| tempstep <- diff(rngs)[1]
|
|
|
| gridave <- expand.grid(region = regs, size = szs, temprng = rngs, ave = NA)
|
| for(i in 1:nrow(gridave)){
|
| inds <- randMPAs[, region == gridave$region[i] & abs(temprng - gridave$temprng[i]) < tempstep/2 & abs(size - gridave$size[i]) < szstep]
|
| gridave$ave[i] <- mean(randMPAs$netwrkturn[inds])
|
| }
|
|
|
|
|
| png(units='in', res=300, width=7, height=6, file='figures/FigS2_randMPAs.png')
|
| par(mgp=c(2,0.5,0), mai=c(0.2, 0.3, 0.3, 0.1), omi=c(0.25, 0.3, 0, 0), xpd=NA, tcl=-0.3, las=1)
|
| layout(matrix(1:18, nrow = 3, byrow = TRUE), widths = c(6, 3, 6, 3, 6, 3), heights = c(1, 1, 1))
|
| for(i in 1:length(regs)){
|
| # construct matrix for plotting dissimilarity and scale to 0-1
|
| inds <- gridave$region == regs[i] & !is.na(gridave$ave)
|
| thisdat <- gridave[inds,]
|
| minz <- min(floor(thisdat$ave*10)/10)
|
| maxz <- max(ceiling(thisdat$ave*10)/10)
|
| thisdat$newz <- thisdat$ave - minz
|
| thisdat$newz <- thisdat$newz/(maxz - minz)
|
| mat <- as.data.frame(dcast(as.data.table(thisdat), temprng ~ size, value.var='newz'))
|
| row.names(mat) <- mat$temprng
|
| mat <- t(as.matrix(mat[,2:ncol(mat)])) # transpose because of the way image handles matrices
|
|
|
| # plot
|
| par(mgp=c(2,0.5,0), mai=c(0.2, 0.3, 0.3, 0.05), tcl=-0.3)
|
| image(z=mat, x=sort(unique(thisdat$size)), y=sort(unique(thisdat$temprng)), col=colpal(100),
|
| main=regsnice[i], xlab=xlabs[i], ylab=ylabs[i])
|
|
|
| # plot NA as grey
|
| matna <- mat
|
| matna[is.na(mat)] <- 1
|
| matna[!is.na(mat)] <- NA
|
| image(z=matna, x=sort(unique(thisdat$size)), y=sort(unique(thisdat$temprng)), col='grey', add=TRUE)
|
|
|
| # add dot for empirical network
|
| inds2 <- as.character(stats$region) == regs[i]
|
| if(sum(inds2) > 0){
|
| points(stats$fracsize[inds2], stats$fractemp[inds2], pch=10, cex=2, col='white')
|
| }
|
|
|
| # add color bar in next plot space
|
| par(mgp=c(2,0.5,0), mai=c(0.1, 0.05, 0.3, 0.5), tcl=-0.1)
|
| color.bar(colpal(100), min = minz, max = maxz, nticks = 5)
|
| # color.bar(colpal(100), min = 0, max = 0.4, nticks = 5)
|
|
|
|
|
| }
|
|
|
| dev.off()
|
|
|
|
|
|
|
| #################################################################################
|
| ## Fig. S3: Compare trawl observations vs. SDM predictions in management areas
|
| #################################################################################
|
| wdpa_by_spp_obs_reg <- fread('temp/wdpa_trawlsppobs_byreg.csv.gz', drop = 1) # trawl survey observations of species in MPAs
|
| mpatrawl <- fread('output/MPAvstrawl_NPV_PPV.csv', drop = 1) # NPV and PPV calculations
|
|
|
| cols = brewer.pal(4, 'Paired')
|
|
|
| png(units='in', res=300, width=8, height=4, file='figures/FigS3_MPA_trawl.png')
|
|
|
| par(mfrow = c(1,2), mai = c(1, 1, 0.3, 0.3))
|
| wdpa_by_spp_obs_reg[, hist(nhaul, breaks = seq(0, 450, by = 4), col = 'grey', xlab = 'Number of hauls per management area', main = '', ylab = '')]
|
| mtext(side = 2, 'Frequency', line = 2.8, las = 0)
|
| mtext(side = 3, 'a)', line = -0.5, at = -120, cex = 1.5)
|
|
|
| plot(1000, 1000, xlim = c(1, 100), ylim = c(0, 1), log = 'x', xlab = 'Minimum hauls per management area', ylab = 'Predictive value', las = 1, bty = 'n')
|
| thresh[, polygon(c(min, rev(min)), c(npv+npvse, rev(npv-npvse)), col = cols[1], border = NA)]
|
| thresh[, polygon(c(min, rev(min)), c(ppv+ppvse, rev(ppv-ppvse)), col = cols[3], border = NA)]
|
| thresh[, lines(min, npv, type = 'l', col = cols[2])]
|
| thresh[, lines(min, ppv, type ='l', col = cols[4])]
|
| mtext(side = 3, 'b)', line = -0.5, at = 0.25, cex = 1.5)
|
|
|
| dev.off()
|
|
|
| # overall values (for text)
|
| thresh[min == 1, ]
|
|
|
|
|
|
|
|
|
|
|
|
|
| ##############################################################
|
| ## Table S1 problem definitions for planning in each region
|
| ## Also stats for text (# planning units total)
|
| ##############################################################
|
| # Read in plans and species
|
| folder <- 'output/prioritizr_runs'
|
|
|
| runnames1 <- list.files(path = folder, pattern = 'solution')
|
| consplans <- vector('list', length(runnames1))
|
| for(i in 1:length(consplans)){
|
| consplans[[i]] <- fread(paste0(folder, '/', runnames1[i]), drop = 1)
|
| consplans[[i]]$region <- gsub('solution_|2per_|hist_|.csv', '', runnames[i])
|
| consplans[[i]]$type <- gsub('solution_|_ebs|_goa|_bc|_wc|_gmex|_seus|_neus|_maritime|_newf|.csv', '', runnames[i])
|
| }
|
| consplans <- rbindlist(consplans)
|
|
|
| runnames2 <- list.files(path = folder, pattern = 'spp')
|
| spps <- vector('list', length(runnames2))
|
| for(i in 1:length(spps)){
|
| spps[[i]] <- fread(paste0(folder, '/', runnames2[i]), drop = 1)
|
| spps[[i]]$region <- gsub('spp_|.csv', '', runnames2[i])
|
| }
|
| spps <- rbindlist(spps)
|
| spps[, type := 'conservation']
|
| spps[grepl('fishery', name), type := 'fishery']
|
| spps[grepl('energy', name), type := 'energy']
|
|
|
| # How many planning units and in each region?
|
| nrow(consplans)
|
| area <- consplans[type == 'hist', .(area = .N), by = .(region)] # per region
|
| area
|
|
|
| # How many goals of each type in each region?
|
| spps[, .N, by = .(region, type)]
|
| cons <- spps[type == 'conservation', .(conservation = .N), by = .(region)]
|
| fish <- spps[type == 'fishery', .(fishery = .N), by = .(region)]
|
| spps[type == 'energy', .N, by = .(region)]
|
|
|
| # combine into table
|
| tables1 <- merge(area, cons, by = 'region')
|
| tables1 <- merge(tables1, fish, by = 'region')
|
|
|
| # order W to E
|
| ord <- data.table(region = c('ebs', 'goa', 'bc', 'wc', 'gmex', 'seus', 'neus', 'maritime', 'newf'), order = 1:9)
|
| tables1 <- merge(tables1, ord)
|
| setorder(tables1, order)
|
|
|
| # write out
|
| write.csv(tables1[, .(region, area, conservation, fishery)], 'tables/tableS1.csv', row.names = FALSE)
|
|
|
