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f4a0962 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | # evaluate randomly selected protected areas against shifts in species distribution
# calculate species gains, losses, turnover, etc.
############
## Flags
############
# choose the rcps (get to choose two)
rcps <- c(26, 85)
# select initial and final timeperiod for these grids
periods <- c('2007-2020', '2081-2100')
# network parameters
temps <- c(0.01, seq(0.1, 1,length.out = 10)) # how many steps of temperature extent (0 to 100%)
sizes <- c(0.01, seq(0.05, 0.5, length.out = 10)) # which steps of area (could be 0 to 100%).
nreps <- 3 # how many repeats at each combination of lat and size
####################
## helper functions
####################
require(data.table)
##################################################
# Generate random MPA networks
# Across gradients of area and temperature extent
##################################################
clim <- fread('output/climatology.csv.gz', drop = 1) # climatology from Morley et al. 2018
regions <- fread('output/region_grid.csv.gz', drop = 1) # CMSP region definitions from 5.0_define_CMSP.r
# round to analysis grids (0.25) and aggregate
clim[, lat := floor(latClimgrid/0.25)*0.25 + 0.25/2]
clim[, lon := floor(lonClimgrid/0.25)*0.25 + 0.25/2]
clim <- clim[, .(sbt = mean(sbt)), by = .(lat, lon)]
# merge climatology and region: the planning units
clim <- merge(clim, regions[, .(latgrid, longrid, region)], by.x = c('lat', 'lon'), by.y = c('latgrid', 'longrid'))
# the regions to analyze
regs <- as.character(regions[,sort(unique(region))])
# data frame to store results
randMPAs <- as.data.table(expand.grid(region = regs, tempstep = temps, sizestep = sizes, repnum = 1:nreps, temprng = NA_real_,
size = NA_real_, meanturnIndiv = NA_real_, sdturnIndiv = NA_real_, netwrkturn = NA_real_,
sdnetwrkturn = NA_real_))
# a slow loop, especially on higher values of j and k
for(i in 1:length(regs)){
print(regs[i])
# set up the set of planning units (lat, lon, sbt)
punits <- clim[region == regs[i],]
tx <- diff(range(punits$sbt, na.rm = TRUE)) # the range of temperatures in the region
nu <- sum(!duplicated(punits[, .(lat, lon)])) # the number of planning units
if(nu != nrow(punits)) stop(paste0('i=', i, '. Duplicated entries in punits'))
# loads presence/absence data for each species/model/rcp
if(regs[i] %in% c('ebs', 'goa', 'bc', 'wc')) ocean <- 'Pac'
if(regs[i] %in% c('gmex', 'seus', 'neus', 'maritime', 'newf')) ocean <- 'Atl'
for (j in 1:length(rcps)){
for(k in 1:length(periods)){
cat(paste0('\tLoading rcp', rcps[j], ' ocean', ocean, ' period', periods[k], '\n'))
prestemp <- fread(cmd = paste0('gunzip -c temp/presmap_', ocean, '_rcp', rcps[j], '_', periods[k], '.csv.gz'), drop = 1)
if(j == 1 & k == 1){
presmap <- prestemp
} else {
presmap <- rbind(presmap, prestemp)
}
}
}
rm(prestemp)
# create and evaluate the random networks
for(j in 1:length(temps)){ # for each temperature step
cat(paste('\nj=',j, sep=''))
for(k in 1:length(sizes)){ # for each size step
cat(paste('\tk=',k,': ', sep=''))
for(r in 1:nreps){ # for each repeat
cat(paste('r=',r,' '))
# randomly designate a first planning unit as MPA
punits[, sel := FALSE]
punits[sample(1:.N,1) , sel := TRUE] # pick a first MPA
ft <- punits[sel == TRUE, sbt] # the temperature
# calculate nearby planning units from first MPA (based on temp extent)
punits[, nearby := FALSE] # initialize "nearby" flag (within a certain fraction of lat extent)
punits[abs(sbt-ft)/tx <= temps[j], nearby := TRUE] # mark some as nearby in temperature space
# add more MPAs until size criterion met or all nearby units selected
fracwholearea <- 1/nu
fracnearbyarea <- 1/sum(punits$nearby)
while(fracwholearea <= sizes[k] & fracnearbyarea <= 1 & sum(punits$nearby & !punits$sel)>0 ) {
newunit <- punits[, sample(which(nearby & !sel), 1)] # select a new unit
punits[newunit, sel:= TRUE] # assign as selected
fracwholearea <- sum(punits$sel)/nu
fracnearbyarea <- sum(punits$sel)/sum(punits$nearby)
minsbtsel <- punits[sel == TRUE, min(sbt)]
maxsbtsel <- punits[sel == TRUE, max(sbt)]
punits[, nearby := ((sbt - minsbtsel)/tx <= temps[j] & sbt >= minsbtsel) | ((maxsbtsel - sbt)/tx < temps[j] & sbt <= maxsbtsel)] # measure nearby from the min temp if new potential temps are > min, and measure from max temp if new potential temps are < max
}
# merge selected MPA list with species data
thesesppbymod <- merge(punits[sel == TRUE, .(lat, lon)], presmap, by.x = c('lat', 'lon'), by.y = c('latgrid', 'longrid'), all.x = TRUE)
# calculate change in poccur for each species/model/rcp/grid
thesesppbymod <- dcast(thesesppbymod, spp + rcp + model + lat + lon ~ year_range, value.var = 'poccur') # reshape to wide format
thesesppbymod[, dpoccur := get(periods[2]) - get(periods[1])] # calculate the change
thesesppbymod[, pshared := get(periods[2]) * get(periods[1])] # calculate the probability of being shared
# basic calcs for this random network
ind <- randMPAs[, which(region == regs[i] & tempstep == temps[j] & sizestep == sizes[k] & repnum == r)]
randMPAs[ind, temprng := punits[sel == TRUE, (max(sbt)-min(sbt))/tx]]
randMPAs[ind, size := punits[, sum(sel)/nu]]
# evaluate ecological turnover for each individual MPA
mpatempstats <- thesesppbymod[, .(nshared = sum(pshared)), by = .(lat, lon, rcp, model)]
mpatempstats <- merge(mpatempstats, thesesppbymod[dpoccur > 0, .(ngained = sum(dpoccur)), by = .(lat, lon, rcp, model)])
mpatempstats <- merge(mpatempstats, thesesppbymod[dpoccur < 0, .(nlost = - sum(dpoccur)), by = .(lat, lon, rcp, model)])
mpatempstats[, beta_sor := 1 - 2*nshared / (2*nshared + nlost + ngained)] # sorenson dissimilarity
randMPAs$meanturnIndiv[ind] <- mpatempstats[, mean(beta_sor)]
randMPAs$sdturnIndiv[ind] <- mpatempstats[, .(beta_sor = mean(beta_sor)), by = .(lat, lon)][, sd(beta_sor)] # sd across models and rcps
# ecological turnover for the entire network
thesesppbymodnet <- thesesppbymod[, .(pinit = 1 - prod(1 - get(periods[1])), pfinal = 1 - prod(1 - get(periods[2]))), by = .(spp, rcp, model)] # aggregate probability for each species across the full network at each time period
thesesppbymodnet[, dpoccur := pfinal - pinit] # calculate the change
thesesppbymodnet[, pshared := pfinal * pinit] # calculate the probability of being shared
mpatempstatsnet <- thesesppbymodnet[, .(nshared = sum(pshared)), by = .(rcp, model)]
mpatempstatsnet <- merge(mpatempstatsnet, thesesppbymodnet[dpoccur > 0, .(ngained = sum(dpoccur)), by = .(rcp, model)])
mpatempstatsnet <- merge(mpatempstatsnet, thesesppbymodnet[dpoccur < 0, .(nlost = - sum(dpoccur)), by = .(rcp, model)])
mpatempstatsnet[, beta_sor := 1 - 2*nshared / (2*nshared + nlost + ngained)] # sorenson dissimilarity
randMPAs$netwrkturn[ind] <- mpatempstatsnet[, mean(beta_sor)]
randMPAs$sdnetwrkturn[ind] <- mpatempstatsnet[, sd(beta_sor)] # sd across models and rcps
}
}
}
cat('\n')
}
# write out
write.csv(randMPAs, file='output/randMPAs_byBT.csv')
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