R/aegis_ts_db.R
In jae0/emgis: Package that interacts with stmv.
aegis_ts_db = function( db="", ref.year=2008 ) {
if (db %in% c("climate", "climate.redo" ) ) {
fn = file.path( project.datadirectory("aegis"), "data", "climate.rdata")
if (db=="climate") {
load(fn)
return (climate)
}
# old method ... manual db updates
# climate = get.climate.data( file.path( project.datadirectory("aegis"), "data", "oceanclimate.csv"))
seaice = osd.data( source="seaice.refresh" ) # daily
icebergs = osd.data( source="icebergs.refresh" ) # monthly
rivsum = osd.data( source="freshwater.runoff.quebec.refresh" ) # monthly
hudsonbay = osd.data( source="freshwater.runoff.into.hudsonbay.refresh" ) # monthly
stjohn = osd.data( source="freshwater.discharge.stjohn.refresh" ) # monthly
t.sydney = osd.data( source="t.sydney.ns.refresh" ) # monthly air tempratures at Sydney NS
t.sable = osd.data( source="t.sable.ns.refresh" ) # monthly air tempratures at Sydney NS
t.halifax = osd.data( source="t.halifax.ns.refresh" ) # monthly air tempratures at Sydney NS
sable = osd.data(source="sable.meteorological.refresh" ) # EC climate/meteorological data
nao = osd.data(source="nao.refresh" )
halifax.harbour = osd.data(source="halifax.harbour.sst.refresh" )
gulf.stream.front = osd.data(source="gulf.stream.front.refresh" )
ss.slope.front = osd.data(source="ss.slope.front.refresh" )
# ... to do:
# missaine bottom /top
# emerald bottom / top
# oxygen
# nitrate,
# chla
# etc.
# http://www.meds-sdmm.dfo-mpo.gc.ca/zmp/main_zmp_e.html
seaice = osd.data( source="seaice" )
seaice = tapply( seaice$sa.km2, years(seaice$date), sum, na.rm=T)
seaice = data.frame( seaice.sa.km2=as.vector( seaice), yr = as.numeric( rownames( seaice ) ) )
icebergs = osd.data( source="icebergs" )
icebergs = tapply( icebergs$n.icebergs, years(icebergs$date), sum, na.rm=T )
icebergs = data.frame( n.icebergs=as.vector( icebergs ), yr = as.numeric( rownames( icebergs ) ) )
rivsum = osd.data( source="freshwater.runoff.quebec" )
rivsum = tapply( rivsum$discharge.m3.sec, years(rivsum$date), sum, na.rm=T)
rivsum = data.frame( rivsum.discharge.m3.sec=as.vector( rivsum ), yr = as.numeric( rownames( rivsum ) ) )
rivsum = rivsum[ -which(rivsum$yr==max(rivsum$yr)) ,] # the last year is a partial record
hudsonbay = osd.data( source="freshwater.runoff.into.hudsonbay" )
hudsonbay = tapply( hudsonbay$discharge.m3.sec, years(hudsonbay$date), sum, na.rm=T)
hudsonbay = data.frame( hudsonbay.discharge.m3.sec=as.vector(hudsonbay), yr=as.numeric( rownames( hudsonbay ) ) )
hudsonbay = hudsonbay[ -which(hudsonbay$yr==max(hudsonbay$yr)) ,] # the last year is a partial record
stjohn = osd.data( source="freshwater.discharge.stjohn" )
stjohn = tapply( stjohn$discharge.m3.sec, years(stjohn$date), sum, na.rm=T)
stjohn = data.frame( stjohn.discharge.m3.sec=as.vector(stjohn), yr=as.numeric( rownames( stjohn ) ) )
stjohn = stjohn[ -which(stjohn$yr==min(stjohn$yr)) ,] # the first year is a partial record
stjohn = stjohn[ -which(stjohn$yr==max(stjohn$yr)) ,] # the last year is a partial record
t.sydney = osd.data( source="t.sydney.ns" )
t.sydney = tapply( t.sydney$t.sydney.C, years(t.sydney$date), sum, na.rm=T)
t.sydney = data.frame( t.sydney.C=as.vector(t.sydney), yr=as.numeric( rownames( t.sydney ) ) )
t.sable = osd.data( source="t.sable.ns" )
t.sable = tapply( t.sable$t.sable.C, years(t.sable$date), sum, na.rm=T)
t.sable = data.frame( t.sable.C=as.vector(t.sable), yr=as.numeric( rownames( t.sable ) ) )
t.halifax = osd.data( source="t.halifax.ns" )
t.halifax = tapply( t.halifax$t.halifax.C, years(t.halifax$date), sum, na.rm=T)
t.halifax = data.frame( t.halifax.C=as.vector(t.halifax), yr=as.numeric( rownames( t.halifax ) ) )
sable = osd.data(source="sable.meteorological" )
sable.rad = tapply( sable$rad.global.kJ.m2, sable$year, mean, na.rm=T ) * 24 * 365 # hourly mean .. convert to yearly total
sable.rad = data.frame( sable.rad.global.kJ.m2=as.vector(sable.rad), yr=as.numeric( rownames( sable.rad ) ) )
sable.rad = sable.rad[ which(sable.rad$sable.rad.global.kJ.m2 > 3750000 ) ,] # obvious outliers
sable.rain = tapply( sable$rain.mm, sable$year, mean, na.rm=T ) * 24 *365
sable.rain = data.frame( sable.rain.mm=as.vector(sable.rain), yr=as.numeric( rownames( sable.rain ) ) )
sable = merge( sable.rain, sable.rad, by="yr" )
nao = osd.data(source="nao" )
nao$yr = years( nao$date)
nao$date = NULL
halifax.harbour = osd.data(source="halifax.harbour.sst" )
halifax.harbour = tapply( halifax.harbour$halifax.sst, years(halifax.harbour$date), sum, na.rm=T)
halifax.harbour = data.frame( halifax.harbour.sst=as.vector(halifax.harbour), yr=as.numeric( rownames( halifax.harbour ) ) )
gulf.stream.front = osd.data(source="gulf.stream.front" )
gulf.stream.front = gulf.stream.front[ which ( gulf.stream.front$lon == -62 ) , ]
gulf.stream.front = gulf.stream.front[ which (! ( gulf.stream.front$lat < 0 )) , ]
gulf.stream.front.mean = tapply( gulf.stream.front$lat, years(gulf.stream.front$date), mean, na.rm=T)
gulf.stream.front.mean = data.frame( gulf.stream.front.lon62.lat.mean=as.vector( gulf.stream.front.mean ), yr=as.numeric( rownames( gulf.stream.front.mean ) ) )
gulf.stream.front.sd = tapply( gulf.stream.front$lat, years(gulf.stream.front$date), sd, na.rm=T)
gulf.stream.front.sd = data.frame( gulf.stream.front.lon62.lat.sd=as.vector( gulf.stream.front.sd ), yr=as.numeric( rownames( gulf.stream.front.sd ) ) )
gulf.stream.front = merge( gulf.stream.front.mean, gulf.stream.front.sd, by="yr" )
ss.slope.front = osd.data(source="ss.slope.front" )
ss.slope.front = ss.slope.front[ which ( ss.slope.front$lon == -62 ) , ]
ss.slope.front = ss.slope.front[ which (! ( ss.slope.front$lat < 0 )) , ]
ss.slope.front.mean = tapply( ss.slope.front$lat, years(ss.slope.front$date), mean, na.rm=T)
ss.slope.front.mean = data.frame( ss.slope.front.lon62.lat.mean=as.vector( ss.slope.front.mean ), yr=as.numeric( rownames( ss.slope.front.mean ) ) )
ss.slope.front.sd = tapply( ss.slope.front$lat, years(ss.slope.front$date), sd, na.rm=T)
ss.slope.front.sd = data.frame( ss.slope.front.lon62.lat.sd=as.vector( ss.slope.front.sd ), yr=as.numeric( rownames( ss.slope.front.sd ) ) )
ss.slope.front = merge( ss.slope.front.mean, ss.slope.front.sd, by="yr" )
climate = merge( seaice, icebergs, by="yr", all=T )
climate = merge( climate, rivsum, by="yr", all=T )
climate = merge( climate, hudsonbay, by="yr", all=T )
climate = merge( climate, stjohn, by="yr", all=T )
climate = merge( climate, t.sydney, by="yr", all=T )
climate = merge( climate, t.sable, by="yr", all=T )
climate = merge( climate, t.halifax, by="yr", all=T )
climate = merge( climate, sable, by="yr", all=T )
climate = merge( climate, nao, by="yr", all=T )
climate = merge( climate, halifax.harbour, by="yr", all=T )
climate = merge( climate, gulf.stream.front, by="yr", all=T )
climate = merge( climate, ss.slope.front, by="yr", all=T )
save(climate, file=fn, compress=T )
return(climate)
}
if ( db %in% c("cpi", "cpi.redo" ) ) {
cpidir = file.path( project.datadirectory("aegis"), "data", "CPI" )
fn = file.path( cpidir, "cpi.csv")
if (db=="cpi") {
cpi = read.csv( fn, sep=",", header=T )
cpi$cpi = cpi$cpi / cpi$cpi[ which(cpi$yr==ref.year) ]
return(cpi)
}
# read stored data
cpi = read.table( fn, sep=",", header=T )
cpi$cpi = cpi$cpi / cpi$cpi[ which(cpi$yr==ref.year) ]
# update data
o = readLines( fn )
print( "Incomplete:: need an automated update process" )
# save()
return()
}
if (db %in% c("landings" ) ) {
##### Landings and landed values of fish ... DFO /economics stats
# load landings and landed values from CSV files
# these have been exported from spreadsheets that need to be updated
# need an automated update mechanism
infile = file.path( project.datadirectory("aegis"), "data", "fish", "landings.all.modern.csv")
ld = read.csv( infile, header=T, strip.white=T, stringsAsFactors=F ) # mt.live
colnames(ld) = tolower(colnames(ld))
ld$type = tolower( ld$type )
for ( i in c("ns", "nb", "pei", "qc", "nfld", "atlantic", "bc", "canada" ) ) {
ld[,i] = as.numeric(ld[,i])
iii = which(! is.finite( ld[,i] ) )
if (length(iii)>0) ld[iii,i] = 0
}
return (ld)
}
if (db %in% c("landedvalue" ) ) {
##### Landings and landed values of fish ... DFO /economics stats
# load landings and landed values from CSV files
# these have been exported from spreadsheets that need to be updated
# need an automated update mechanism
infile = file.path( project.datadirectory("aegis"), "data", "fish", "landedvalue.all.modern.csv")
lv = read.csv( infile, header=T, strip.white=T, stringsAsFactors=F ) # K dollar
colnames(lv) = tolower(colnames(lv))
lv$type = tolower( lv$type )
for ( i in c("ns", "nb", "pei", "quebec", "nfld.", "atlantic", "bc", "canada" ) ) {
lv[,i] = as.numeric(lv[,i])
iii = which(! is.finite( lv[,i] ) )
if (length(iii)>0) lv[iii,i] = 0
}
return (lv )
}
if (db=="landedvalue.annual") {
lv = aegis_ts_db( db="landedvalue", ref.year=ref.year ) # 1000 $ (CAD, inflation adjusted to refyear )
lv$ns = adjust.inflation( x=lv$ns, yr=lv$year, reference.year=ref.year )
lv$atlantic = adjust.inflation( x=lv$atlantic, yr=lv$year, reference.year=ref.year )
ns = as.data.frame.matrix( xtabs( ns~ year+type, lv ) )
names(ns) = paste( "landedvalue.ns", names(ns), sep="." )
ns$yr = as.numeric( rownames(ns))
atlantic = as.data.frame.matrix( xtabs( atlantic~ year+type, lv ) )
names(atlantic) = paste( "landedvalue.atlantic", names(atlantic), sep="." )
atlantic$yr = as.numeric( rownames(atlantic))
landedvalue = merge( ns, atlantic, by="yr", sort=T )
return(landedvalue)
}
if (db=="landings.annual") {
ld = aegis_ts_db( db="landings", ref.year=ref.year ) ## metric tons
ns = as.data.frame.matrix( xtabs( ns~ year+type, ld ) )
names(ns) = paste( "landings.ns", names(ns), sep="." )
ns$yr = as.numeric( rownames(ns))
atlantic = as.data.frame.matrix( xtabs( atlantic~ year+type, ld ) )
names(atlantic) = paste( "landings.atlantic", names(atlantic), sep="." )
atlantic$yr = as.numeric( rownames(atlantic))
landings = merge( ns, atlantic, by="yr", sort=T )
return(landings)
}
if (db=="landedvalue.archive") {
data.file = file.path( project.datadirectory("aegis"), "data", "landedvalues.csv")
landedvalue = read.table(file=data.file, sep=";", header=T, as.is=T, strip.white=T)
return (landedvalue)
}
if (db=="landings.archive") {
data.file=file.path( project.datadirectory("aegis"), "data", "landings.csv")
landings = read.table(file=data.file, sep=";", header=T, as.is=T, strip.white=T)
return(landings)
}
if (db %in% c("landings.ns") ) {
ld = aegis_ts_db( db="landings" ) # metric tons
vars = c("year", "type", "ns")
to.extract = c("groundfish~total", "pelagic~total", "shellfish~total", "other~total" )
ld = ld[ which(ld$type %in% to.extract), vars ]
return (ld)
}
if (db %in% c("landedvalue.ns") ) {
lv = aegis_ts_db( db="landedvalue", ref.year=ref.year )
vars = c("year", "type", "ns")
to.extract = c("groundfish~total", "pelagic~total", "shellfish~total", "other~total" )
lv = lv[ which(lv$type %in% to.extract), vars ] # CAN
lv$ns = adjust.inflation( x=lv$ns, yr=lv$year, reference.year=ref.year )
return (lv)
}
if (db %in% c("demographics.redo" ) ) {
### goto:: http://www.gov.ns.ca/finance/communitycounts/dataview.asp?gnum=pro9012&gnum2=pro9012&chartid=&whichacct=&year2=&mapid=&ptype=>ype=&yearid=2006&acctype=0&gname=&dcol=&group=&group1=&group2=&group3=&gview=3&table=table_d17&glevel=pro
infile = file.path( project.datadirectory("aegis"), "data", "human.csv")
demogr = read.table(file=infile, sep=",", header=T, as.is=T, strip.white=T, row.names = NULL)
demogr$PCB.sealblubber = as.numeric( demogr$PCB.sealblubber )
demogr$fish_harvesters_ns [ which( demogr$year %in% c(2003: 2007) ) ] = demogr$fish_harvesters_ns[ which(demogr$year==2002) ]
return( demogr )
}
if ( db %in% c("GDP") ) {
# GDP of NS from various sources, esp::
# http://www.gov.ns.ca/econ/businessclimate/bci/indicator_view.asp?IndicatorID=19
GDP = data.frame( cbind(
yr = c( 1999:2007,2009:2013),
percap = c( 24688, 26400, 27799, 28980, 30807, 31828, 33414, 33942, 35337,34753,35806,36073,35950,36042 )
) ) # reference year= 2004
GDP$percap = adjust.inflation( x=GDP$percap, yr=GDP$yr, reference.year=2004, reverse=T ) # return to an unadjusted state
#GDP$percap = adjust.inflation( x=GDP$percap, yr=GDP$yr, reference.year=ref.year )
# historical from GPI NS report scaled ... need to rescale ... already adjusted
gg = data.frame( cbind(
yr = c(1980:1998),
percap = c(
100, 113.5, 118.9, 124.7, 130.6, 134.3, 139.9, 145.1, 147.8, 149.7, 147.4, 143.6,
145.4, 144.7, 145.4, 147.8, 146.1, 148.1, 153.7)
))
gg$percap = gg$percap / gg$percap[ which(gg$yr==1998)] # rescale to reference of 1998
gg$percap = gg$percap * GDP$percap[ which(GDP$yr==1999) ]
GDP = rbind( gg, GDP )
return (GDP)
}
if (db %in% c("economic.data","economic.data.redo" ) ) {
data.file=file.path( project.datadirectory("aegis"), "data", "economics.csv")
economics = read.table(file=data.file, sep=",", header=T, as.is=T, strip.white=T)
to.keep = c( "yr", "No.of.vessels.4vw", "Commercial.Licences.ns", "No.Fish.harvesters", "No.Fish.processors",
"Employment.per.landedvalue.n.per.millions1997CAD", "Employment.per.landings.n.per.mt", "GDP.Fish.Processing.millions.2005CAD",
"GDP.Fishing.hunt.trap.millions.2002CAD", "GDP.NS.millions.2002CAD", "GDP.fishing.percent", "GDP.Offshore.Oil.Gas.millions.2005CAD" )
economics = economics[, to.keep ]
economics$No.of.vessels.4vw = as.numeric( economics$No.of.vessels.4vw )
economics$No.Fish.processors = as.numeric( economics$No.Fish.processors )
return(economics)
}
if (db %in% c("percapita.landedvalue") ) {
lvy = as.data.frame.table( tapply( lv$ns*1000, lv$year, sum ) )
names(lvy) = c("year", "landedvalue" )
lvy$year = as.numeric( as.character( lvy$year ) )
# load number of fishers
demogr = aegis_ts_db( db="demographics" )
lvy = merge( lvy, demogr, by="year" )
lvy$fish_harvesters_ns [ which( lvy$year %in% c(2003: 2007) ) ] = lvy$fish_harvesters_ns[ which(lvy$year==2002) ]
lvy$percapita.landedvalue = lvy$landedvalue / lvy$fish_harvesters_ns
return (lvy)
}
if (db %in% c("shrimp.timeseries", "shrimp.timeseries.redo") ) {
# shrimp: directly contributed by Peter Koeller, but apparently available via VDC
# data from Peter Koeller's aegis
fn = file.path( project.datadirectory("aegis"), "data", "shrimp.rdata" )
if (db=="shrimp.timeseries") {
load( fn )
return ( shrimp )
}
shrimp = read.csv( file.path( project.datadirectory("aegis"), "data", "ESS_shrimp.csv"), header=T, stringsAsFactors=FALSE, na.strings=c("NA", "NAN", "NaN"))
names(shrimp) = c("yr", "shrimp.abundance.index", "shrimp.size.sexchange.mm", "shrimp.exploitation.index", "shrimp.size.female", "shrimp.capelin.index" )
# rv_cpue -- cpue of shrimp from dedicated shrimp trawls
# sex_mm -- size at sex change -- lower size at sex change when growth rates are higher (density, temperature)
# count -- number of shrimp per pound --- mean size estimate
# exp_tot -- exploitation rates index
# fem_size -- size of females -- index of exploitation of largest shrimp
# ssjuly -- july SST in shrimp fishing areas "shrimp.sst.july"
# capelin -- abundance of capelin -- a cold-adapted species
save( shrimp, file=fn, compress=T )
return (shrimp)
}
if (db %in% c("groundfish.timeseries", "groundfish.timeseries.redo" ) ) {
outfn = file.path( project.datadirectory("aegis"), "data", "groundfish.ts.rdata" )
if ( db=="groundfish.timeseries" ) {
load(outfn)
return(Z)
}
p = aegis_parameters(DS="groundfish")
# data from groundfish data series
set = groundfish.db( p=p, DS="set" ) ## TODO --- convert to aegis_db
variables = c( groundfish.variablelist("all"), "area")
byyear = ts.getdata(set=set, fname="byear.4vw", from.file=F, variables=variables, plottimes="annual", regions="nafo.4vw", custom="normal")
yrs = sort( unique( byyear$yr ) )
vars = sort( unique( byyear$variable ) )
nyrs = length(yrs)
nvars = length(vars)
Z = matrix(NA, ncol=nvars, nrow=nyrs)
for (y in 1:nyrs) {
for (v in 1:nvars) {
i = which( byyear$yr==yrs[y] & byyear$variable==vars[v])
if ( length(i)>0 ) {
g = byyear[ i, "mean" ]
Z[y,v] = g
}
}}
Z = as.data.frame(Z)
colnames(Z) = paste("groundfish.stratified.mean", vars, sep="." )
Z$yr = yrs
save( Z, file=outfn, compress=T )
return (Z)
}
if (db %in% c("species.area", "species.area.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "species.area.ts.rdata" )
if ( db=="species.area" ) {
load(outfn)
return(res)
}
p = spatial_parameters( spatial.domain="SSE" )
fns = list.files( file.path( project.datadirectory("aegis"), "data", "SSE","complete" ) )
yrs = substring( fns, 4,7)
yrs = as.numeric(yrs)
yrs = sort( yrs[ which( is.finite( yrs) ) ] )
res = data.frame( yr=yrs, C=NA, Z=NA, Npred=NA )
for ( i in 1:length(yrs) ) {
H = aegis_db( DS="complete", p=p, year=yrs[i] )
res$C[i] = mean( H$C )
res$Z[i] = mean( H$Z )
#res$sar.rsq[i] = mean( H$sar.rsq )
res$Npred[i] = mean( H$Npred )
}
save( res, file=outfn, compress=T )
return (res)
}
if (db %in% c("sizespectrum", "sizespectrum.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "sizespectrum.ts.rdata" )
if ( db=="sizespectrum" ) {
load(outfn)
return(res)
}
p = spatial_parameters( spatial.domain="SSE" )
fns = list.files( file.path( project.datadirectory("aegis"), "data", "SSE",'complete' ) )
yrs = substring( fns, 4,7)
yrs = as.numeric(yrs)
yrs = sort( yrs[ which( is.finite( yrs) ) ] )
res = data.frame( yr=yrs, nss.rsquared=NA, nss.b1=NA, nss.shannon=NA )
for ( i in 1:length(yrs) ) {
H = aegis_db( DS="complete", p=p, year=yrs[i] )
res$nss.rsquared[i] = mean( H$nss.rsquared )
# res$nss.b0[i] = mean( H$nss.b0 )
res$nss.b1[i] = mean( H$nss.b1 )
res$nss.shannon[i] = mean( H$nss.shannon )
}
save( res, file=outfn, compress=T )
return (res)
}
if (db %in% c("metabolism", "metabolism.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "metabolism.ts.rdata" )
if ( db=="metabolism" ) {
load(outfn)
return(res)
}
p = spatial_parameters( spatial.domain="SSE" )
fns = list.files( file.path( project.datadirectory("aegis"), "data", "SSE","complete" ) )
yrs = substring( fns, 4,7)
yrs = as.numeric(yrs)
yrs = sort( yrs[ which( is.finite( yrs) ) ] )
res = data.frame( yr=yrs, smr=NA, meanlen=NA, meanwgt=NA )
for ( i in 1:length(yrs) ) {
H = aegis_db( DS="complete", p=p, year=yrs[i] )
#res$mr[i] = mean( H$mr )
res$smr[i] = mean( H$smr )
res$meanlen[i] = mean( H$len )
res$meanwgt[i] = mean( H$mass )
res$tmean[i] = mean( H$tmean )
}
save( res, file=outfn, compress=T )
return (res)
}
if (db %in% c("species.composition", "species.composition.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "species.composition.ts.rdata" )
if ( db=="species.composition" ) {
load(outfn)
return(res)
}
p = spatial_parameters( spatial.domain="SSE" )
fns = list.files( file.path( project.datadirectory("aegis"), "data", "SSE","complete" ) )
yrs = substring( fns, 4,7)
yrs = as.numeric(yrs)
yrs = sort( yrs[ which( is.finite( yrs) ) ] )
res = data.frame( yr = yrs, ca1=NA, ca2=NA)#, pca1=NA, pca2=NA )
for ( i in 1:length(yrs) ) {
H = aegis_db( DS="complete", p=p, year=yrs[i] )
res$ca1[i] = mean( H$ca1 )
res$ca2[i] = mean( H$ca2 )
#res$pca1[i] = mean( H$pca1 )
#res$pca2[i] = mean( H$pca2 )
}
save( res, file=outfn, compress=T )
return (res)
}
if (db %in% c("snowcrab.timeseries", "snowcrab.timeseries.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "snowcrab.ts.rdata" )
if ( db=="snowcrab.timeseries") {
load(outfn)
return(res)
}
p = bio.snowcrab::load.environment()
# trawl data
tsdata = snowcrab.timeseries.db( DS="biologicals" ) # this returns 1.96SE as "se"
tsdata = tsdata[ tsdata$region=="cfaall", ]
yrs = sort( unique( tsdata$year ) )
vars = sort( unique( tsdata$variable ) )
nyrs = length(yrs)
nvars = length(vars)
x = matrix(NA, ncol=nvars, nrow=nyrs)
for (y in 1:nyrs) {
for (v in 1:nvars) {
i = which( tsdata$year==yrs[y] & tsdata$variable==vars[v])
if ( length(i)>0 ) {
g = tsdata[ i, "mean" ]
if( length (g) > 1) {
print( "Duplicates found:")
print( tsdata[i,] )
g = g[1]
}
x[y,v] = g
}
}}
x = as.data.frame(x)
colnames(x) = paste("snowcrab.geometricmean", vars, sep="." )
x$yr = yrs
# abundance estimated from interpolation
#turned off for 2014
# p = parallel_run( p=p, runindex= list(y=p$years.to.model, v=c("R0.mass", "R1.no", "R2.no", "totno.female.berried", "totno.female.imm", "totno.female.mat") ) )
#K = interpolation.db( DS="interpolation.simulation", p=p ) # to return the saved file
#yrs = sort( unique( K$yr ) )
#vars = sort( unique( K$vars ) )
#nyrs = length(yrs)
#nvars = length(vars)
#Z = matrix(NA, ncol=nvars, nrow=nyrs)
#for (y in 1:nyrs) {
#for (v in 1:nvars) {
# i = which( K$yr==yrs[y] & K$vars==vars[v])
# if ( length(i)>0 ) {
# g = sum(K[ i, "total" ], na.rm=T) # sum over all areas
# Z[y,v] = g
# }
#}}
#Z = Z
#Z = as.data.frame(Z)
#
#colnames(Z) = paste("snowcrab.kriged", vars, sep="." )
#Z$yr = yrs
# fishery data
res = get.fishery.stats.by.region()
res$landings = res$landings / 1000
res$effort = res$effort / 1000
# rownames(res) = yrs
colnames(res) = paste("snowcrab.fishery", colnames(res), sep="." )
names(res)[which(names(res)=="snowcrab.fishery.yr") ] = "yr"
res = merge(res, x, by="yr", all=T, sort=T)
res = merge(res, Z, by="yr", all=T, sort=T)
res = res[ which(res$yr <= max(Z$yr)) , ]
# exploitation rates
# post-2002 surveys have surveys in autumn
res$snowcrab.exploitation.index = NA
i = which(res$yr >= 2002 )
j = which(res$yr < 2002 )
res$snowcrab.exploitation.index[i] = res$snowcrab.fishery.landings[i] / (res$snowcrab.kriged.R0.mass[i] + res$snowcrab.fishery.landings[i] )
res$snowcrab.exploitation.index[j] = res$snowcrab.fishery.landings[j] / res$snowcrab.kriged.R0.mass[j]
# temperatures
td = NULL
for (yy in 1970:max(p$yrs) ) {
td = rbind( td, snowcrab_stmv ( DS="K", p=p, v="R0.mass", y=yy ) )
}
tmean = as.data.frame.matrix ( xtabs( temp.region~yr+region, td ) )
names( tmean ) = paste( "tmean.snowcrab", names( tmean ), sep="." )
tmean$yr = as.numeric( rownames( tmean ) )
tsa = as.data.frame.matrix ( xtabs( sa.region ~ yr+region, td ) )
tsa$cfaall = tsa$cfa4x+ tsa$cfanorth + tsa$cfasouth
names( tsa ) = paste( "sa.snowcrab", names( tsa ), sep="." )
tsa$yr = as.numeric( rownames( tsa ) )
res = merge( res, tmean, by="yr", all=T, sort=T)
res = merge( res, tsa, by="yr", all=T, sort=T)
save( res, file=outfn, compress=T )
return (res)
}
if (db=="seal.timeseries") {
data.file = file.path( project.datadirectory("aegis"), "data", "seals.csv")
seal = read.table( data.file, sep=",", header=T, as.is=T, strip.white=T)
return(seal)
}
if (db=="plankton.timeseries") {
data.file = file.path( project.datadirectory("aegis"), "data", "plankton.csv")
plankton = read.table( data.file, sep=";", header=T, as.is=T, strip.white=T)
return(plankton)
}
if (db %in% c("aegis.all", "aegis.all.glue") ) {
fn = file.path( project.datadirectory("aegis") , "data", "aegis.all.rdata" )
if (db=="aegis.all" ) {
load(fn)
return (aegis)
}
# refresh the survey data
groundfish = aegis_ts_db( db="groundfish.timeseries" )
snowcrab = aegis_ts_db( db="snowcrab.timeseries")
climate = aegis_ts_db (db="climate" )
shrimp = aegis_ts_db( db="shrimp.timeseries")
sar = aegis_ts_db( db="species.area" )
nss = aegis_ts_db( db="sizespectrum" )
metab = aegis_ts_db( db="metabolism" )
sc = aegis_ts_db( db="species.composition" )
economics = aegis_ts_db( db="economic.data" )
# hand constructed and updated ..
plankton = aegis_ts_db( db="plankton.timeseries" )
human = aegis_ts_db( db="demographics.redo" )
climate = aegis_ts_db (db="climate" )
seals = aegis_ts_db( db="seal.timeseries" )
landedvalue = aegis_ts_db( db="landedvalue.annual", ref.year=2008 )
landings = aegis_ts_db( db="landings.annual" )
# merge all data
out = merge( snowcrab, shrimp, by = "yr", all=T )
out = merge( out, groundfish, by="yr", all=T )
out = merge( out, plankton, by="yr", all=T )
out = merge( out, human, by="yr", all=T )
out = merge( out, climate, by="yr", all=T )
out = merge( out, economics, by="yr", all=T )
out = merge( out, seals, by="yr", all=T )
out = merge( out, landings, by="yr", all=T )
out = merge( out, landedvalue, by="yr", all=T )
out = merge( out, sar, by="yr", all=T )
out = merge( out, nss, by="yr", all=T )
out = merge( out, metab, by="yr", all=T )
out = merge( out, sc, by="yr", all=T )
data = out[ order(out$yr), ]
rownames(data) = data$yr
colnames(data) = gsub( "[_~]", ".", tolower(colnames(data) ) )
landings.Atlantic = c( "landingsCod.Atlantic.mt.live","landingsHaddock.Atlantic.mt.live","landingsRedfish.spp.Atlantic.mt.live",
"landingsHalibut.Atlantic.mt.live","landingsFlatfishes.Atlantic.mt.live","landingsGreenland.turbot.Atlantic.mt.live",
"landingsPollock.Atlantic.mt.live","landingsHake.Atlantic.mt.live","landingsCusk.Atlantic.mt.live","landingsCatfish.Atlantic.mt.live",
"landingsSkate.Atlantic.mt.live","landingsDogfish.Atlantic.mt.live","landingsOther.Atlantic.mt.live",
"landingstotal.groundfish.Atlantic.mt.live","landingsHerring.Atlantic.mt.live","landingsMackerel.Atlantic.mt.live",
"landingsSwordfish.Atlantic.mt.live","landingsTuna.Atlantic.mt.live","landingsAlewife.Atlantic.mt.live",
"landingsEel.Atlantic.mt.live","landingsSalmon.Atlantic.mt.live","landingsSmelt.Atlantic.mt.live","landingsCapelin.Atlantic.mt.live",
"landingsOther.finifish.Atlantic.mt.live","landingsTOTAL.pelagic.Atlantic.mt.live","landingsClams.quahaug.Atlantic.mt.live",
"landingsOyster.1.Atlantic.mt.live","landingsScallop.2.Atlantic.mt.live","landingsSquid.Atlantic.mt.live",
"landingsMussel.3.Atlantic.mt.live","landingsLobster.Atlantic.mt.live","landingsShrimp.Atlantic.mt.live",
"landingsCrab.Queen.Atlantic.mt.live","landingsCrab.Other.Atlantic.mt.live","landingsSea.urchin.Atlantic.mt.live",
"landingsOther.shellfish.Atlantic.mt.live","landingsTOTAL.shellfish.Atlantic.mt.live",
"landingsTOTAL.fish.shellfish.Atlantic.mt.live","landingsMarine.plants.Atlantic.mt.live","landingsLumpfish.roe.Atlantic.mt.live",
"landingsMiscellaneous.other.Atlantic.mt.live","landingsTotal.misc.Atlantic.mt.live","landingstotal.all.seafood.Atlantic.mt.live"
)
landings.NS = c( "landingsCod.ns.mt.live","landingsHaddock.ns.mt.live","landingsRedfish.spp.ns.mt.live",
"landingsHalibut.ns.mt.live","landingsFlatfishes.ns.mt.live","landingsGreenland.turbot.ns.mt.live",
"landingsPollock.ns.mt.live","landingsHake.ns.mt.live","landingsCusk.ns.mt.live","landingsCatfish.ns.mt.live",
"landingsSkate.ns.mt.live","landingsDogfish.ns.mt.live","landingsOther.ns.mt.live",
"landingstotal.groundfish.ns.mt.live","landingsHerring.ns.mt.live","landingsMackerel.ns.mt.live",
"landingsSwordfish.ns.mt.live","landingsTuna.ns.mt.live","landingsAlewife.ns.mt.live",
"landingsEel.ns.mt.live","landingsSalmon.ns.mt.live","landingsSmelt.ns.mt.live","landingsCapelin.ns.mt.live",
"landingsOther.finifish.ns.mt.live","landingsTOTAL.pelagic.ns.mt.live","landingsClams.quahaug.ns.mt.live",
"landingsOyster.1.ns.mt.live","landingsScallop.2.ns.mt.live","landingsSquid.ns.mt.live",
"landingsMussel.3.ns.mt.live","landingsLobster.ns.mt.live","landingsShrimp.ns.mt.live",
"landingsCrab.Queen.ns.mt.live","landingsCrab.Other.ns.mt.live","landingsSea.urchin.ns.mt.live",
"landingsOther.shellfish.ns.mt.live","landingsTOTAL.shellfish.ns.mt.live",
"landingsTOTAL.fish.shellfish.ns.mt.live","landingsMarine.plants.ns.mt.live","landingsLumpfish.roe.ns.mt.live",
"landingsMiscellaneous.other.ns.mt.live","landingsTotal.misc.ns.mt.live","landingstotal.all.seafood.ns.mt.live"
)
landings.totals.NS = c( "landingstotal.groundfish.ns.mt.live", "landingsTOTAL.pelagic.ns.mt.live", "landingsTOTAL.shellfish.ns.mt.live",
"landingsTotal.misc.ns.mt.live", "landingstotal.all.seafood.ns.mt.live", "landings_grd.4vw", "landings_pel.4vw", "landing_inv.4vw" )
landings.totals.Atlantic = c( "landingstotal.groundfish.Atlantic.mt.live", "landingsTOTAL.pelagic.Atlantic.mt.live",
"landingsTOTAL.shellfish.Atlantic.mt.live","landingsTotal.misc.Atlantic.mt.live", "landingstotal.all.seafood.Atlantic.mt.live" )
landedvalue.NS = c("landedvalue.NS.Cod", "landedvalue.NS.Haddock", "landedvalue.NS.Redfish.spp.","landedvalue.NS.Halibut",
"landedvalue.NS.Flatfishes", "landedvalue.NS.Greenland.turbot","landedvalue.NS.Pollock","landedvalue.NS.Hake",
"landedvalue.NS.Cusk","landedvalue.NS.Catfish","landedvalue.NS.Skate","landedvalue.NS.Dogfish","landedvalue.NS.Other.groundfish",
"landedvalue.NS.TOTAL.groundfish","landedvalue.NS.Herring","landedvalue.NS.Mackerel","landedvalue.NS.Swordfish","landedvalue.NS.Tuna",
"landedvalue.NS.Alewife","landedvalue.NS.Eel","landedvalue.NS.Salmon","landedvalue.NS.Smelt","landedvalue.NS.Capelin",
"landedvalue.NS.Other.pelagics","landedvalue.NS.TOTAL.pelagics","landedvalue.NS.Clams.quahaug","landedvalue.NS.Oyster.1",
"landedvalue.NS.Scallop.2","landedvalue.NS.Squid","landedvalue.NS.Mussel.3","landedvalue.NS.Lobster","landedvalue.NS.Shrimp",
"landedvalue.NS.Crab.Queen","landedvalue.NS.Crab.Other","landedvalue.NS.Sea.urchin","landedvalue.NS.Other.shellfish",
"landedvalue.NS.TOTAL.shellfish","landedvalue.NS.TOTAL.shellandfinfish","landedvalue.NS.Marine.plants","landedvalue.NS.Lumpfish.roe",
"landedvalue.NS.Miscellaneous","landedvalue.NS.Total.miscellaneous","landedvalue.NS.total.all"
)
landedvalue.Atlantic = c( "landedvalue.Atlantic.Cod","landedvalue.Atlantic.Haddock","landedvalue.Atlantic.Redfish.spp.",
"landedvalue.Atlantic.Halibut","landedvalue.Atlantic.Flatfishes","landedvalue.Atlantic.Greenland.turbot","landedvalue.Atlantic.Pollock",
"landedvalue.Atlantic.Hake","landedvalue.Atlantic.Cusk","landedvalue.Atlantic.Catfish","landedvalue.Atlantic.Skate",
"landedvalue.Atlantic.Dogfish","landedvalue.Atlantic.Other.groundfish","landedvalue.Atlantic.TOTAL.groundfish",
"landedvalue.Atlantic.Herring","landedvalue.Atlantic.Mackerel","landedvalue.Atlantic.Swordfish","landedvalue.Atlantic.Tuna",
"landedvalue.Atlantic.Alewife","landedvalue.Atlantic.Eel","landedvalue.Atlantic.Salmon","landedvalue.Atlantic.Smelt",
"landedvalue.Atlantic.Capelin","landedvalue.Atlantic.Other.pelagics","landedvalue.Atlantic.TOTAL.pelagics",
"landedvalue.Atlantic.Clams.quahaug","landedvalue.Atlantic.Oyster1","landedvalue.Atlantic.Scallop.2","landedvalue.Atlantic.Squid",
"landedvalue.Atlantic.Mussel.3","landedvalue.Atlantic.Lobster","landedvalue.Atlantic.Shrimp","landedvalue.Atlantic.Crab.Queen",
"landedvalue.Atlantic.Crab.Other","landedvalue.Atlantic.Sea.urchin","landedvalue.Atlantic.Other.shellfish",
"landedvalue.Atlantic.TOTAL.shellfish","landedvalue.Atlantic.TOTAL.shellandfinfish","landedvalue.Atlantic.Marine.plants",
"landedvalue.Atlantic.Lumpfish.roe","landedvalue.Atlantic.Miscellaneous","landedvalue.Atlantic.Total.miscellaneous",
"landedvalue.Atlantic.total.all"
)
landedvalue.totals.NS = c( "landedvalue.NS.TOTAL.groundfish", "landedvalue.NS.TOTAL.pelagics", "landedvalue.NS.TOTAL.shellfish",
"landedvalue.NS.Total.miscellaneous", "landedvalue.NS.total.all", "landval_grd.4vw", "landval_pel.4vw", "landval_inv.4vw" )
landedvalue.totals.Atlantic = c( "landedvalue.Atlantic.TOTAL.groundfish", "landedvalue.Atlantic.TOTAL.pelagics",
"landedvalue.Atlantic.TOTAL.shellfish", "landedvalue.Atlantic.Total.miscellaneous", "landedvalue.Atlantic.total.all" )
human = c( "No.of.vessels.4vw","Commercial.Licences.ns","No.Fish.harvesters","No.Fish.processors",
"Employment.per.landedvalue.n.per.millions1997CAD","Employment.per.landings.n.per.mt","GDP.Fish.Processing.millions.2005CAD",
"GDP.Fishing.hunt.trap.millions.2002CAD","GDP.NS.millions.2002CAD","GDP.fishing.percent","GDP.Offshore.Oil.Gas.millions.2005CAD",
"Population.NovaScotia","Unemployment.rate","Demographics.0_19","Demographics.20_64","Demographics.65plus","no.highschool",
"university","Nwells.drilled","seismic.2D","seismic.3D","PCB.sealblubber","PCB.Atlantic.Puffin.ppm.wet","No.Shellfish.closures",
"No.Cruise.ships.Halifax","area_trawled.4vw"
)
climate = c( "snowcrab.bottom.temperature", "snowcrab.bottom.temperature.sd", "snowcrab.bottom.habitat.area",
"ice_coverage.km.2", "T_bottom_emerald", "T_bottom_misaine", "SST_halifax", "vol_source_cil", "CIL.min.T",
"Gulf.Stream.front.Ref.62lon", "Shelf.front.Ref.62lon", "NAO.index.mbar", "T_sable_annual", "storms",
"groundfish.stratified.mean.oxyml", "groundfish.stratified.mean.sal", "groundfish.stratified.mean.temp", "nitrate"
)
snowcrab = c( "snowcrab.fishery.landings", #"snowcrab.exploitation.index",
"snowcrab.geometricmean.cw.male.mat.mean" , "snowcrab.geometricmean.cw.fem.mat.mean",
"snowcrab.geometricmean.R0.mass", "snowcrab.geometricmean.R1.no", "snowcrab.geometricmean.ma13.no", "snowcrab.geometricmean.ma12.no",
"snowcrab.geometricmean.ma11.no", "snowcrab.geometricmean.ma10.no", "snowcrab.geometricmean.ma9.no",
"snowcrab.geometricmean.fa10.no", "snowcrab.geometricmean.fa9.no", "snowcrab.geometricmean.fa8.no", "snowcrab.geometricmean.fa7.no",
"snowcrab.geometricmean.totno.female.mat", "snowcrab.geometricmean.totno.female.imm"
)
ecosystem = c( "seals.total", "groundfish.stratified.mean.shannon", "shrimp.capelin.index",
"shrimp.size.female", "shrimp.exploitation.index", "shrimp.size.sexchange.mm", "shrimp.abundance.index",
"cpr_colour", "cpr_diatoms", "cpr_dino", "cpr_cf1_4", "cpr_cf5_6", "cpr_ch", "cpr_para_pseudo",
"groundfish.stratified.mean.totwgt.elasmobranchs", "groundfish.stratified.mean.totwgt.gadoid",
"groundfish.stratified.mean.totwgt.flatfish", "groundfish.stratified.mean.ntaxa.elasmobranchs",
"groundfish.stratified.mean.ntaxa.large.demersal", "groundfish.stratified.mean.ntaxa.small.demersal",
"groundfish.stratified.mean.ntaxa.large.pelagic", "groundfish.stratified.mean.ntaxa.small.pelagic",
"groundfish.stratified.mean.ntaxa.flatfish", "groundfish.stratified.mean.rmean.elasmobranchs",
"groundfish.stratified.mean.rmean.gadoid", "groundfish.stratified.mean.rmean.flatfish",
"groundfish.stratified.mean.rmean.large.demersal", "groundfish.stratified.mean.rmean.small.demersal",
"groundfish.stratified.mean.rmean.large.pelagic", "groundfish.stratified.mean.rmean.small.pelagic",
"groundfish.stratified.mean.mmean.elasmobranchs",
"groundfish.stratified.mean.mmean.gadoid", "groundfish.stratified.mean.mmean.flatfish",
"groundfish.stratified.mean.mmean.large.demersal", "groundfish.stratified.mean.mmean.small.demersal",
"groundfish.stratified.mean.mmean.large.pelagic", "groundfish.stratified.mean.mmean.small.pelagic",
"groundfish.stratified.mean.smrT", "groundfish.stratified.mean.sar.rsq",
"groundfish.stratified.mean.nss.rsquared.all.50km.50day", "groundfish.stratified.mean.Z", "groundfish.stratified.mean.C",
"groundfish.stratified.mean.Npred"
)
keyfactors = c(
"landings.ns.all.grand.total",
"landings.ns.groundfish.total",
"landings.ns.pelagic.total",
"landings.ns.shellfish.total",
"landings.ns.other.total",
"landings.ns.shellfish.crab.queen",
"landedvalue.ns.groundfish.total",
"landedvalue.ns.pelagic.total",
"landedvalue.ns.shellfish.total",
"landedvalue.ns.other.total",
"landedvalue.ns.all.grand.total",
"landedvalue.ns.shellfish.crab.queen",
"population.novascotia", "demographics.65plus", "university", "no.shellfish.closures",
"employment.per.landedvalue.n.per.millions1997cad", "employment.per.landings.n.per.mt",
"gdp.ns.millions.2002cad", "gdp.fish.processing.millions.2005cad", "gdp.fishing.hunt.trap.millions.2002cad",
"gdp.offshore.oil.gas.millions.2005cad", "fish.harvesters.ns",
"snowcrab.geometricmean.cw.fem.mat.mean", "snowcrab.geometricmean.totno.female.mat", "snowcrab.geometricmean.totno.female.imm",
"snowcrab.geometricmean.cw.male.mat.mean",
"snowcrab.geometricmean.R0.mass", "snowcrab.geometricmean.R1a.no", "snowcrab.fishery.landings",
"tmean", "gulf.stream.front.lon62.lat.mean",
"sa.snowcrab.cfaall",
"nao",
"groundfish.stratified.mean.oxyml", "groundfish.stratified.mean.temp", "groundfish.stratified.mean.sal",
"nwells.drilled","seismic.2d","seismic.3d","pcb.sealblubber","pcb.atlantic.puffin.ppm.wet",
"halifax.harbour.sst", "t.sable.c", "ss.slope.front.lon62.lat.mean", "seaice.sa.km2",
"seals.total", "shrimp.capelin.index", "shrimp.abundance.index",
"cpr.diatoms", "groundfish.stratified.mean.shannon", "cpr.colour",
"groundfish.stratified.mean.totwgt.capelin",
"groundfish.stratified.mean.totwgt.cod",
"groundfish.stratified.mean.totwgt.elasmobranchs",
"groundfish.stratified.mean.totwgt.gadoid",
"groundfish.stratified.mean.totwgt.flatfish",
"groundfish.stratified.mean.totwgt.large.demersal",
"groundfish.stratified.mean.totwgt.small.demersal",
"groundfish.stratified.mean.totwgt.large.pelagic",
"groundfish.stratified.mean.totwgt.small.pelagic",
"cpr.cf1.4","cpr.cf5.6", "cpr.dino",
"smr", "ca1", "ca2", "meanwgt", "nss.b1", "nss.rsquared",
# "groundfish.stratified.mean.rmean.small.pelagic",
# "groundfish.stratified.mean.rmean.small.demersal",
#"groundfish.stratified.mean.rmean.flatfish",
#"groundfish.stratified.mean.rmean.elasmobranchs",
#"groundfish.stratified.mean.rmean.gadoid",
#"groundfish.stratified.mean.rmean.large.demersal",
#"groundfish.stratified.mean.rmean.large.pelagic",
"z",
"c",
"npred"
)
keyfactors = setdiff( keyfactors, c("landingsTotal.misc.ns.mt.live","landedvalue.NS.Total.miscellaneous" ) )
to.log = c("snowcrab.geometricmean.totno.female.mat", "snowcrab.geometricmean.totno.female.imm" ,
"snowcrab.geometricmean.r0.mass", "snowcrab.geometricmean.r1.no" ,"snowcrab.fishery.landings", "seaice.sa.km2", "seals.total")
keyfactors.names = matrix( c(
"population.novascotia", "NS: Population size",
"demographics.65plus", "NS: % 65 and older",
"university", "NS: % attending university",
"no.shellfish.closures", "No. shellfish closures",
"employment.per.landedvalue.n.per.millions1997cad", "Employment per total landed value",
"employment.per.landings.n.per.mt", "Employment per total landings",
"gdp.ns.millions.2002cad", "GDP: NS total",
"gdp.fish.processing.millions.2005cad", "GDP: fish processing",
"gdp.fishing.hunt.trap.millions.2002cad", "GDP: fishing and hunting",
"gdp.offshore.oil.gas.millions.2005cad", "GDP: oil and gas",
"fish.harvesters.ns", "No. fish harvesters",
"snowcrab.geometricmean.cw.fem.mat.mean", "Snow crab: mature female mean size",
"snowcrab.geometricmean.totno.female.mat", "Snow crab: mature female abundance",
"snowcrab.geometricmean.totno.female.imm", "Snow crab: immature female abundance",
"snowcrab.geometricmean.cw.male.mat.mean", "Snow crab: mature male mean size",
"snowcrab.geometricmean.r0.mass", "Snow crab: mature male biomass",
"snowcrab.geometricmean.r1.no", "Snow crab: male recruitment",
"snowcrab.fishery.landings", "Snow crab: landings",
"tmean", "SSE: temperature mean",
"sa.snowcrab.cfaall","Snow crab: habitat area",
"gulf.stream.front.lon62.lat.mean", "Gulf stream front: lat@-62 lon",
"nao","NAO index",
"groundfish.stratified.mean.oxyml","RV: bottom oxygen",
"groundfish.stratified.mean.temp","RV: bottom temperature",
"groundfish.stratified.mean.sal", "RV: bottom salinity",
"halifax.harbour.sst","Temperature: SST Halifax",
"t.sable.c","Temperature: Sable Is.",
"ss.slope.front.lon62.lat.mean" , "Shelf front: lat@-62 lon",
"seaice.sa.km2","Ice coverage",
"smr","RV: groundfish SMR",
"mr","RV: groundfish MR",
"seals.total", "Seal abundance",
"shrimp.capelin.index", "Shrimp: capelin abundance index",
"shrimp.abundance.index","Shrimp: abundance index",
"cpr.diatoms","CPR: diatoms",
"groundfish.stratified.mean.shannon","RV: Shannon index",
"cpr.colour","CPR: colour",
"groundfish.stratified.mean.rmean.small.pelagic","RV: condition small pelagics",
"groundfish.stratified.mean.rmean.small.demersal", "RV: condition small demersals",
"groundfish.stratified.mean.rmean.flatfish","RV: condition flatfish",
"groundfish.stratified.mean.rmean.elasmobranchs","RV: condition elasmobranchs",
"groundfish.stratified.mean.rmean.gadoid","RV: condition gadoids",
"groundfish.stratified.mean.rmean.large.demersal","RV: condition large demersals",
"groundfish.stratified.mean.rmean.large.pelagic","RV: condition large pelagics",
"z","RV: species-area exponent",
"c","RV: species-area intercept",
"ca1","RV: species-composition axis 1",
"ca2","RV: species-composition axis 2",
"nss.b1", "RV: Normailised size spectra slope",
"nss.rsquared", "RV: Normalised size spectra R-square",
"meanwgt","RV: Mean body weight",
"groundfish.stratified.mean.totwgt.capelin","RV: biomass capelin",
"groundfish.stratified.mean.totwgt.cod", "RV: biomass cod",
"groundfish.stratified.mean.totwgt.elasmobranchs", "RV: biomass elasmobranchs",
"groundfish.stratified.mean.totwgt.gadoid","RV: biomass gadoids",
"groundfish.stratified.mean.totwgt.flatfish", "RV: biomass flatfish",
"groundfish.stratified.mean.totwgt.large.demersal","RV: biomass large demersals",
"groundfish.stratified.mean.totwgt.small.demersal", "RV: biomass small demersals",
"groundfish.stratified.mean.totwgt.large.pelagic", "RV: biomass large pelagics",
"groundfish.stratified.mean.totwgt.small.pelagic", "RV: biomass small pelagics",
"npred","RV: No. taxa predicted",
"landings.ns.all.grand.total", "Landings: all",
"landings.ns.groundfish.total", "Landings: groundfish",
"landings.ns.pelagic.total", "Landings: pelagic",
"landings.ns.shellfish.total", "Landings: shellfish",
"landings.ns.other.total", "Landings: misc",
"landings.ns.shellfish.crab.queen", "Landings: snow crab",
"landedvalue.ns.groundfish.total", "Landed value: groundfish",
"landedvalue.ns.pelagic.total", "Landed value: pelagics",
"landedvalue.ns.shellfish.total", "Landed value: shellfish",
"landedvalue.ns.other.total", "Landed value: misc",
"landedvalue.ns.all.grand.total", "Landed value: all",
"landedvalue.ns.shellfish.crab.queen", "Landed value: snow crab",
"nwells.drilled", "No. wells drilled",
"seismic.2d", "Seismic 2D; km",
"seismic.3d", "Seismic 3D, km^2",
"pcb.sealblubber", "PCBs: seals",
"pcb.atlantic.puffin.ppm.wet", "PCBs: puffins",
"cpr.cf1.4","CPR: Calanus finmarchicus 1-4",
"cpr.cf5.6","CPR: Calanus finmarchicus 5-6",
"cpr.dino","CPR: dinoflagellates"
), ncol=2, byrow=T )
aegis = list(data=data,
landings.NS=landings.NS,
landings.Atlantic=landings.Atlantic,
landings.totals.NS=landings.totals.NS,
landings.totals.Atlantic=landings.totals.Atlantic,
landedvalue.NS = landedvalue.NS,
landedvalue.Atlantic=landedvalue.Atlantic,
landedvalue.totals.Atlantic=landedvalue.totals.Atlantic,
landedvalue.totals.NS=landedvalue.totals.NS,
snowcrab=snowcrab, human=human, ecosystem=ecosystem, climate=climate,
keyfactors=keyfactors, to.log=to.log,
keyfactors.names =keyfactors.names
)
save( aegis , file=fn, compress=T )
return ( aegis )
}
return ("Data not found")
# aegis_subset = function( X, type ) {
# varlist = X[[ type ]]
# varlist = intersect( names(X$data), varlist)
# dat = X$data[, varlist ]
# for (i in 1:length( X$to.log)) dat[,i] = log10(dat[,i] + 1)
# if ( type=="keyfactors" ) {
# lkup = X$keyfactors.names
# for (i in 1:length( varlist )) {
# j = NULL
# j = which( tolower( lkup[,1]) == tolower( varlist[i]) )
# if (length(j)==1) names(dat)[i] = lkup[ j, 2]
# }
# }
# return (dat)
# }
}
jae0/emgis documentation built on May 28, 2019, 9:58 p.m.
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aegis_ts_db = function( db="", ref.year=2008 ) {
if (db %in% c("climate", "climate.redo" ) ) {
fn = file.path( project.datadirectory("aegis"), "data", "climate.rdata")
if (db=="climate") {
load(fn)
return (climate)
}
# old method ... manual db updates
# climate = get.climate.data( file.path( project.datadirectory("aegis"), "data", "oceanclimate.csv"))
seaice = osd.data( source="seaice.refresh" ) # daily
icebergs = osd.data( source="icebergs.refresh" ) # monthly
rivsum = osd.data( source="freshwater.runoff.quebec.refresh" ) # monthly
hudsonbay = osd.data( source="freshwater.runoff.into.hudsonbay.refresh" ) # monthly
stjohn = osd.data( source="freshwater.discharge.stjohn.refresh" ) # monthly
t.sydney = osd.data( source="t.sydney.ns.refresh" ) # monthly air tempratures at Sydney NS
t.sable = osd.data( source="t.sable.ns.refresh" ) # monthly air tempratures at Sydney NS
t.halifax = osd.data( source="t.halifax.ns.refresh" ) # monthly air tempratures at Sydney NS
sable = osd.data(source="sable.meteorological.refresh" ) # EC climate/meteorological data
nao = osd.data(source="nao.refresh" )
halifax.harbour = osd.data(source="halifax.harbour.sst.refresh" )
gulf.stream.front = osd.data(source="gulf.stream.front.refresh" )
ss.slope.front = osd.data(source="ss.slope.front.refresh" )
# ... to do:
# missaine bottom /top
# emerald bottom / top
# oxygen
# nitrate,
# chla
# etc.
# http://www.meds-sdmm.dfo-mpo.gc.ca/zmp/main_zmp_e.html
seaice = osd.data( source="seaice" )
seaice = tapply( seaice$sa.km2, years(seaice$date), sum, na.rm=T)
seaice = data.frame( seaice.sa.km2=as.vector( seaice), yr = as.numeric( rownames( seaice ) ) )
icebergs = osd.data( source="icebergs" )
icebergs = tapply( icebergs$n.icebergs, years(icebergs$date), sum, na.rm=T )
icebergs = data.frame( n.icebergs=as.vector( icebergs ), yr = as.numeric( rownames( icebergs ) ) )
rivsum = osd.data( source="freshwater.runoff.quebec" )
rivsum = tapply( rivsum$discharge.m3.sec, years(rivsum$date), sum, na.rm=T)
rivsum = data.frame( rivsum.discharge.m3.sec=as.vector( rivsum ), yr = as.numeric( rownames( rivsum ) ) )
rivsum = rivsum[ -which(rivsum$yr==max(rivsum$yr)) ,] # the last year is a partial record
hudsonbay = osd.data( source="freshwater.runoff.into.hudsonbay" )
hudsonbay = tapply( hudsonbay$discharge.m3.sec, years(hudsonbay$date), sum, na.rm=T)
hudsonbay = data.frame( hudsonbay.discharge.m3.sec=as.vector(hudsonbay), yr=as.numeric( rownames( hudsonbay ) ) )
hudsonbay = hudsonbay[ -which(hudsonbay$yr==max(hudsonbay$yr)) ,] # the last year is a partial record
stjohn = osd.data( source="freshwater.discharge.stjohn" )
stjohn = tapply( stjohn$discharge.m3.sec, years(stjohn$date), sum, na.rm=T)
stjohn = data.frame( stjohn.discharge.m3.sec=as.vector(stjohn), yr=as.numeric( rownames( stjohn ) ) )
stjohn = stjohn[ -which(stjohn$yr==min(stjohn$yr)) ,] # the first year is a partial record
stjohn = stjohn[ -which(stjohn$yr==max(stjohn$yr)) ,] # the last year is a partial record
t.sydney = osd.data( source="t.sydney.ns" )
t.sydney = tapply( t.sydney$t.sydney.C, years(t.sydney$date), sum, na.rm=T)
t.sydney = data.frame( t.sydney.C=as.vector(t.sydney), yr=as.numeric( rownames( t.sydney ) ) )
t.sable = osd.data( source="t.sable.ns" )
t.sable = tapply( t.sable$t.sable.C, years(t.sable$date), sum, na.rm=T)
t.sable = data.frame( t.sable.C=as.vector(t.sable), yr=as.numeric( rownames( t.sable ) ) )
t.halifax = osd.data( source="t.halifax.ns" )
t.halifax = tapply( t.halifax$t.halifax.C, years(t.halifax$date), sum, na.rm=T)
t.halifax = data.frame( t.halifax.C=as.vector(t.halifax), yr=as.numeric( rownames( t.halifax ) ) )
sable = osd.data(source="sable.meteorological" )
sable.rad = tapply( sable$rad.global.kJ.m2, sable$year, mean, na.rm=T ) * 24 * 365 # hourly mean .. convert to yearly total
sable.rad = data.frame( sable.rad.global.kJ.m2=as.vector(sable.rad), yr=as.numeric( rownames( sable.rad ) ) )
sable.rad = sable.rad[ which(sable.rad$sable.rad.global.kJ.m2 > 3750000 ) ,] # obvious outliers
sable.rain = tapply( sable$rain.mm, sable$year, mean, na.rm=T ) * 24 *365
sable.rain = data.frame( sable.rain.mm=as.vector(sable.rain), yr=as.numeric( rownames( sable.rain ) ) )
sable = merge( sable.rain, sable.rad, by="yr" )
nao = osd.data(source="nao" )
nao$yr = years( nao$date)
nao$date = NULL
halifax.harbour = osd.data(source="halifax.harbour.sst" )
halifax.harbour = tapply( halifax.harbour$halifax.sst, years(halifax.harbour$date), sum, na.rm=T)
halifax.harbour = data.frame( halifax.harbour.sst=as.vector(halifax.harbour), yr=as.numeric( rownames( halifax.harbour ) ) )
gulf.stream.front = osd.data(source="gulf.stream.front" )
gulf.stream.front = gulf.stream.front[ which ( gulf.stream.front$lon == -62 ) , ]
gulf.stream.front = gulf.stream.front[ which (! ( gulf.stream.front$lat < 0 )) , ]
gulf.stream.front.mean = tapply( gulf.stream.front$lat, years(gulf.stream.front$date), mean, na.rm=T)
gulf.stream.front.mean = data.frame( gulf.stream.front.lon62.lat.mean=as.vector( gulf.stream.front.mean ), yr=as.numeric( rownames( gulf.stream.front.mean ) ) )
gulf.stream.front.sd = tapply( gulf.stream.front$lat, years(gulf.stream.front$date), sd, na.rm=T)
gulf.stream.front.sd = data.frame( gulf.stream.front.lon62.lat.sd=as.vector( gulf.stream.front.sd ), yr=as.numeric( rownames( gulf.stream.front.sd ) ) )
gulf.stream.front = merge( gulf.stream.front.mean, gulf.stream.front.sd, by="yr" )
ss.slope.front = osd.data(source="ss.slope.front" )
ss.slope.front = ss.slope.front[ which ( ss.slope.front$lon == -62 ) , ]
ss.slope.front = ss.slope.front[ which (! ( ss.slope.front$lat < 0 )) , ]
ss.slope.front.mean = tapply( ss.slope.front$lat, years(ss.slope.front$date), mean, na.rm=T)
ss.slope.front.mean = data.frame( ss.slope.front.lon62.lat.mean=as.vector( ss.slope.front.mean ), yr=as.numeric( rownames( ss.slope.front.mean ) ) )
ss.slope.front.sd = tapply( ss.slope.front$lat, years(ss.slope.front$date), sd, na.rm=T)
ss.slope.front.sd = data.frame( ss.slope.front.lon62.lat.sd=as.vector( ss.slope.front.sd ), yr=as.numeric( rownames( ss.slope.front.sd ) ) )
ss.slope.front = merge( ss.slope.front.mean, ss.slope.front.sd, by="yr" )
climate = merge( seaice, icebergs, by="yr", all=T )
climate = merge( climate, rivsum, by="yr", all=T )
climate = merge( climate, hudsonbay, by="yr", all=T )
climate = merge( climate, stjohn, by="yr", all=T )
climate = merge( climate, t.sydney, by="yr", all=T )
climate = merge( climate, t.sable, by="yr", all=T )
climate = merge( climate, t.halifax, by="yr", all=T )
climate = merge( climate, sable, by="yr", all=T )
climate = merge( climate, nao, by="yr", all=T )
climate = merge( climate, halifax.harbour, by="yr", all=T )
climate = merge( climate, gulf.stream.front, by="yr", all=T )
climate = merge( climate, ss.slope.front, by="yr", all=T )
save(climate, file=fn, compress=T )
return(climate)
}
if ( db %in% c("cpi", "cpi.redo" ) ) {
cpidir = file.path( project.datadirectory("aegis"), "data", "CPI" )
fn = file.path( cpidir, "cpi.csv")
if (db=="cpi") {
cpi = read.csv( fn, sep=",", header=T )
cpi$cpi = cpi$cpi / cpi$cpi[ which(cpi$yr==ref.year) ]
return(cpi)
}
# read stored data
cpi = read.table( fn, sep=",", header=T )
cpi$cpi = cpi$cpi / cpi$cpi[ which(cpi$yr==ref.year) ]
# update data
o = readLines( fn )
print( "Incomplete:: need an automated update process" )
# save()
return()
}
if (db %in% c("landings" ) ) {
##### Landings and landed values of fish ... DFO /economics stats
# load landings and landed values from CSV files
# these have been exported from spreadsheets that need to be updated
# need an automated update mechanism
infile = file.path( project.datadirectory("aegis"), "data", "fish", "landings.all.modern.csv")
ld = read.csv( infile, header=T, strip.white=T, stringsAsFactors=F ) # mt.live
colnames(ld) = tolower(colnames(ld))
ld$type = tolower( ld$type )
for ( i in c("ns", "nb", "pei", "qc", "nfld", "atlantic", "bc", "canada" ) ) {
ld[,i] = as.numeric(ld[,i])
iii = which(! is.finite( ld[,i] ) )
if (length(iii)>0) ld[iii,i] = 0
}
return (ld)
}
if (db %in% c("landedvalue" ) ) {
##### Landings and landed values of fish ... DFO /economics stats
# load landings and landed values from CSV files
# these have been exported from spreadsheets that need to be updated
# need an automated update mechanism
infile = file.path( project.datadirectory("aegis"), "data", "fish", "landedvalue.all.modern.csv")
lv = read.csv( infile, header=T, strip.white=T, stringsAsFactors=F ) # K dollar
colnames(lv) = tolower(colnames(lv))
lv$type = tolower( lv$type )
for ( i in c("ns", "nb", "pei", "quebec", "nfld.", "atlantic", "bc", "canada" ) ) {
lv[,i] = as.numeric(lv[,i])
iii = which(! is.finite( lv[,i] ) )
if (length(iii)>0) lv[iii,i] = 0
}
return (lv )
}
if (db=="landedvalue.annual") {
lv = aegis_ts_db( db="landedvalue", ref.year=ref.year ) # 1000 $ (CAD, inflation adjusted to refyear )
lv$ns = adjust.inflation( x=lv$ns, yr=lv$year, reference.year=ref.year )
lv$atlantic = adjust.inflation( x=lv$atlantic, yr=lv$year, reference.year=ref.year )
ns = as.data.frame.matrix( xtabs( ns~ year+type, lv ) )
names(ns) = paste( "landedvalue.ns", names(ns), sep="." )
ns$yr = as.numeric( rownames(ns))
atlantic = as.data.frame.matrix( xtabs( atlantic~ year+type, lv ) )
names(atlantic) = paste( "landedvalue.atlantic", names(atlantic), sep="." )
atlantic$yr = as.numeric( rownames(atlantic))
landedvalue = merge( ns, atlantic, by="yr", sort=T )
return(landedvalue)
}
if (db=="landings.annual") {
ld = aegis_ts_db( db="landings", ref.year=ref.year ) ## metric tons
ns = as.data.frame.matrix( xtabs( ns~ year+type, ld ) )
names(ns) = paste( "landings.ns", names(ns), sep="." )
ns$yr = as.numeric( rownames(ns))
atlantic = as.data.frame.matrix( xtabs( atlantic~ year+type, ld ) )
names(atlantic) = paste( "landings.atlantic", names(atlantic), sep="." )
atlantic$yr = as.numeric( rownames(atlantic))
landings = merge( ns, atlantic, by="yr", sort=T )
return(landings)
}
if (db=="landedvalue.archive") {
data.file = file.path( project.datadirectory("aegis"), "data", "landedvalues.csv")
landedvalue = read.table(file=data.file, sep=";", header=T, as.is=T, strip.white=T)
return (landedvalue)
}
if (db=="landings.archive") {
data.file=file.path( project.datadirectory("aegis"), "data", "landings.csv")
landings = read.table(file=data.file, sep=";", header=T, as.is=T, strip.white=T)
return(landings)
}
if (db %in% c("landings.ns") ) {
ld = aegis_ts_db( db="landings" ) # metric tons
vars = c("year", "type", "ns")
to.extract = c("groundfish~total", "pelagic~total", "shellfish~total", "other~total" )
ld = ld[ which(ld$type %in% to.extract), vars ]
return (ld)
}
if (db %in% c("landedvalue.ns") ) {
lv = aegis_ts_db( db="landedvalue", ref.year=ref.year )
vars = c("year", "type", "ns")
to.extract = c("groundfish~total", "pelagic~total", "shellfish~total", "other~total" )
lv = lv[ which(lv$type %in% to.extract), vars ] # CAN
lv$ns = adjust.inflation( x=lv$ns, yr=lv$year, reference.year=ref.year )
return (lv)
}
if (db %in% c("demographics.redo" ) ) {
### goto:: http://www.gov.ns.ca/finance/communitycounts/dataview.asp?gnum=pro9012&gnum2=pro9012&chartid=&whichacct=&year2=&mapid=&ptype=>ype=&yearid=2006&acctype=0&gname=&dcol=&group=&group1=&group2=&group3=&gview=3&table=table_d17&glevel=pro
infile = file.path( project.datadirectory("aegis"), "data", "human.csv")
demogr = read.table(file=infile, sep=",", header=T, as.is=T, strip.white=T, row.names = NULL)
demogr$PCB.sealblubber = as.numeric( demogr$PCB.sealblubber )
demogr$fish_harvesters_ns [ which( demogr$year %in% c(2003: 2007) ) ] = demogr$fish_harvesters_ns[ which(demogr$year==2002) ]
return( demogr )
}
if ( db %in% c("GDP") ) {
# GDP of NS from various sources, esp::
# http://www.gov.ns.ca/econ/businessclimate/bci/indicator_view.asp?IndicatorID=19
GDP = data.frame( cbind(
yr = c( 1999:2007,2009:2013),
percap = c( 24688, 26400, 27799, 28980, 30807, 31828, 33414, 33942, 35337,34753,35806,36073,35950,36042 )
) ) # reference year= 2004
GDP$percap = adjust.inflation( x=GDP$percap, yr=GDP$yr, reference.year=2004, reverse=T ) # return to an unadjusted state
#GDP$percap = adjust.inflation( x=GDP$percap, yr=GDP$yr, reference.year=ref.year )
# historical from GPI NS report scaled ... need to rescale ... already adjusted
gg = data.frame( cbind(
yr = c(1980:1998),
percap = c(
100, 113.5, 118.9, 124.7, 130.6, 134.3, 139.9, 145.1, 147.8, 149.7, 147.4, 143.6,
145.4, 144.7, 145.4, 147.8, 146.1, 148.1, 153.7)
))
gg$percap = gg$percap / gg$percap[ which(gg$yr==1998)] # rescale to reference of 1998
gg$percap = gg$percap * GDP$percap[ which(GDP$yr==1999) ]
GDP = rbind( gg, GDP )
return (GDP)
}
if (db %in% c("economic.data","economic.data.redo" ) ) {
data.file=file.path( project.datadirectory("aegis"), "data", "economics.csv")
economics = read.table(file=data.file, sep=",", header=T, as.is=T, strip.white=T)
to.keep = c( "yr", "No.of.vessels.4vw", "Commercial.Licences.ns", "No.Fish.harvesters", "No.Fish.processors",
"Employment.per.landedvalue.n.per.millions1997CAD", "Employment.per.landings.n.per.mt", "GDP.Fish.Processing.millions.2005CAD",
"GDP.Fishing.hunt.trap.millions.2002CAD", "GDP.NS.millions.2002CAD", "GDP.fishing.percent", "GDP.Offshore.Oil.Gas.millions.2005CAD" )
economics = economics[, to.keep ]
economics$No.of.vessels.4vw = as.numeric( economics$No.of.vessels.4vw )
economics$No.Fish.processors = as.numeric( economics$No.Fish.processors )
return(economics)
}
if (db %in% c("percapita.landedvalue") ) {
lvy = as.data.frame.table( tapply( lv$ns*1000, lv$year, sum ) )
names(lvy) = c("year", "landedvalue" )
lvy$year = as.numeric( as.character( lvy$year ) )
# load number of fishers
demogr = aegis_ts_db( db="demographics" )
lvy = merge( lvy, demogr, by="year" )
lvy$fish_harvesters_ns [ which( lvy$year %in% c(2003: 2007) ) ] = lvy$fish_harvesters_ns[ which(lvy$year==2002) ]
lvy$percapita.landedvalue = lvy$landedvalue / lvy$fish_harvesters_ns
return (lvy)
}
if (db %in% c("shrimp.timeseries", "shrimp.timeseries.redo") ) {
# shrimp: directly contributed by Peter Koeller, but apparently available via VDC
# data from Peter Koeller's aegis
fn = file.path( project.datadirectory("aegis"), "data", "shrimp.rdata" )
if (db=="shrimp.timeseries") {
load( fn )
return ( shrimp )
}
shrimp = read.csv( file.path( project.datadirectory("aegis"), "data", "ESS_shrimp.csv"), header=T, stringsAsFactors=FALSE, na.strings=c("NA", "NAN", "NaN"))
names(shrimp) = c("yr", "shrimp.abundance.index", "shrimp.size.sexchange.mm", "shrimp.exploitation.index", "shrimp.size.female", "shrimp.capelin.index" )
# rv_cpue -- cpue of shrimp from dedicated shrimp trawls
# sex_mm -- size at sex change -- lower size at sex change when growth rates are higher (density, temperature)
# count -- number of shrimp per pound --- mean size estimate
# exp_tot -- exploitation rates index
# fem_size -- size of females -- index of exploitation of largest shrimp
# ssjuly -- july SST in shrimp fishing areas "shrimp.sst.july"
# capelin -- abundance of capelin -- a cold-adapted species
save( shrimp, file=fn, compress=T )
return (shrimp)
}
if (db %in% c("groundfish.timeseries", "groundfish.timeseries.redo" ) ) {
outfn = file.path( project.datadirectory("aegis"), "data", "groundfish.ts.rdata" )
if ( db=="groundfish.timeseries" ) {
load(outfn)
return(Z)
}
p = aegis_parameters(DS="groundfish")
# data from groundfish data series
set = groundfish.db( p=p, DS="set" ) ## TODO --- convert to aegis_db
variables = c( groundfish.variablelist("all"), "area")
byyear = ts.getdata(set=set, fname="byear.4vw", from.file=F, variables=variables, plottimes="annual", regions="nafo.4vw", custom="normal")
yrs = sort( unique( byyear$yr ) )
vars = sort( unique( byyear$variable ) )
nyrs = length(yrs)
nvars = length(vars)
Z = matrix(NA, ncol=nvars, nrow=nyrs)
for (y in 1:nyrs) {
for (v in 1:nvars) {
i = which( byyear$yr==yrs[y] & byyear$variable==vars[v])
if ( length(i)>0 ) {
g = byyear[ i, "mean" ]
Z[y,v] = g
}
}}
Z = as.data.frame(Z)
colnames(Z) = paste("groundfish.stratified.mean", vars, sep="." )
Z$yr = yrs
save( Z, file=outfn, compress=T )
return (Z)
}
if (db %in% c("species.area", "species.area.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "species.area.ts.rdata" )
if ( db=="species.area" ) {
load(outfn)
return(res)
}
p = spatial_parameters( spatial.domain="SSE" )
fns = list.files( file.path( project.datadirectory("aegis"), "data", "SSE","complete" ) )
yrs = substring( fns, 4,7)
yrs = as.numeric(yrs)
yrs = sort( yrs[ which( is.finite( yrs) ) ] )
res = data.frame( yr=yrs, C=NA, Z=NA, Npred=NA )
for ( i in 1:length(yrs) ) {
H = aegis_db( DS="complete", p=p, year=yrs[i] )
res$C[i] = mean( H$C )
res$Z[i] = mean( H$Z )
#res$sar.rsq[i] = mean( H$sar.rsq )
res$Npred[i] = mean( H$Npred )
}
save( res, file=outfn, compress=T )
return (res)
}
if (db %in% c("sizespectrum", "sizespectrum.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "sizespectrum.ts.rdata" )
if ( db=="sizespectrum" ) {
load(outfn)
return(res)
}
p = spatial_parameters( spatial.domain="SSE" )
fns = list.files( file.path( project.datadirectory("aegis"), "data", "SSE",'complete' ) )
yrs = substring( fns, 4,7)
yrs = as.numeric(yrs)
yrs = sort( yrs[ which( is.finite( yrs) ) ] )
res = data.frame( yr=yrs, nss.rsquared=NA, nss.b1=NA, nss.shannon=NA )
for ( i in 1:length(yrs) ) {
H = aegis_db( DS="complete", p=p, year=yrs[i] )
res$nss.rsquared[i] = mean( H$nss.rsquared )
# res$nss.b0[i] = mean( H$nss.b0 )
res$nss.b1[i] = mean( H$nss.b1 )
res$nss.shannon[i] = mean( H$nss.shannon )
}
save( res, file=outfn, compress=T )
return (res)
}
if (db %in% c("metabolism", "metabolism.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "metabolism.ts.rdata" )
if ( db=="metabolism" ) {
load(outfn)
return(res)
}
p = spatial_parameters( spatial.domain="SSE" )
fns = list.files( file.path( project.datadirectory("aegis"), "data", "SSE","complete" ) )
yrs = substring( fns, 4,7)
yrs = as.numeric(yrs)
yrs = sort( yrs[ which( is.finite( yrs) ) ] )
res = data.frame( yr=yrs, smr=NA, meanlen=NA, meanwgt=NA )
for ( i in 1:length(yrs) ) {
H = aegis_db( DS="complete", p=p, year=yrs[i] )
#res$mr[i] = mean( H$mr )
res$smr[i] = mean( H$smr )
res$meanlen[i] = mean( H$len )
res$meanwgt[i] = mean( H$mass )
res$tmean[i] = mean( H$tmean )
}
save( res, file=outfn, compress=T )
return (res)
}
if (db %in% c("species.composition", "species.composition.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "species.composition.ts.rdata" )
if ( db=="species.composition" ) {
load(outfn)
return(res)
}
p = spatial_parameters( spatial.domain="SSE" )
fns = list.files( file.path( project.datadirectory("aegis"), "data", "SSE","complete" ) )
yrs = substring( fns, 4,7)
yrs = as.numeric(yrs)
yrs = sort( yrs[ which( is.finite( yrs) ) ] )
res = data.frame( yr = yrs, ca1=NA, ca2=NA)#, pca1=NA, pca2=NA )
for ( i in 1:length(yrs) ) {
H = aegis_db( DS="complete", p=p, year=yrs[i] )
res$ca1[i] = mean( H$ca1 )
res$ca2[i] = mean( H$ca2 )
#res$pca1[i] = mean( H$pca1 )
#res$pca2[i] = mean( H$pca2 )
}
save( res, file=outfn, compress=T )
return (res)
}
if (db %in% c("snowcrab.timeseries", "snowcrab.timeseries.redo" )) {
outfn = file.path( project.datadirectory("aegis"), "data", "snowcrab.ts.rdata" )
if ( db=="snowcrab.timeseries") {
load(outfn)
return(res)
}
p = bio.snowcrab::load.environment()
# trawl data
tsdata = snowcrab.timeseries.db( DS="biologicals" ) # this returns 1.96SE as "se"
tsdata = tsdata[ tsdata$region=="cfaall", ]
yrs = sort( unique( tsdata$year ) )
vars = sort( unique( tsdata$variable ) )
nyrs = length(yrs)
nvars = length(vars)
x = matrix(NA, ncol=nvars, nrow=nyrs)
for (y in 1:nyrs) {
for (v in 1:nvars) {
i = which( tsdata$year==yrs[y] & tsdata$variable==vars[v])
if ( length(i)>0 ) {
g = tsdata[ i, "mean" ]
if( length (g) > 1) {
print( "Duplicates found:")
print( tsdata[i,] )
g = g[1]
}
x[y,v] = g
}
}}
x = as.data.frame(x)
colnames(x) = paste("snowcrab.geometricmean", vars, sep="." )
x$yr = yrs
# abundance estimated from interpolation
#turned off for 2014
# p = parallel_run( p=p, runindex= list(y=p$years.to.model, v=c("R0.mass", "R1.no", "R2.no", "totno.female.berried", "totno.female.imm", "totno.female.mat") ) )
#K = interpolation.db( DS="interpolation.simulation", p=p ) # to return the saved file
#yrs = sort( unique( K$yr ) )
#vars = sort( unique( K$vars ) )
#nyrs = length(yrs)
#nvars = length(vars)
#Z = matrix(NA, ncol=nvars, nrow=nyrs)
#for (y in 1:nyrs) {
#for (v in 1:nvars) {
# i = which( K$yr==yrs[y] & K$vars==vars[v])
# if ( length(i)>0 ) {
# g = sum(K[ i, "total" ], na.rm=T) # sum over all areas
# Z[y,v] = g
# }
#}}
#Z = Z
#Z = as.data.frame(Z)
#
#colnames(Z) = paste("snowcrab.kriged", vars, sep="." )
#Z$yr = yrs
# fishery data
res = get.fishery.stats.by.region()
res$landings = res$landings / 1000
res$effort = res$effort / 1000
# rownames(res) = yrs
colnames(res) = paste("snowcrab.fishery", colnames(res), sep="." )
names(res)[which(names(res)=="snowcrab.fishery.yr") ] = "yr"
res = merge(res, x, by="yr", all=T, sort=T)
res = merge(res, Z, by="yr", all=T, sort=T)
res = res[ which(res$yr <= max(Z$yr)) , ]
# exploitation rates
# post-2002 surveys have surveys in autumn
res$snowcrab.exploitation.index = NA
i = which(res$yr >= 2002 )
j = which(res$yr < 2002 )
res$snowcrab.exploitation.index[i] = res$snowcrab.fishery.landings[i] / (res$snowcrab.kriged.R0.mass[i] + res$snowcrab.fishery.landings[i] )
res$snowcrab.exploitation.index[j] = res$snowcrab.fishery.landings[j] / res$snowcrab.kriged.R0.mass[j]
# temperatures
td = NULL
for (yy in 1970:max(p$yrs) ) {
td = rbind( td, snowcrab_stmv ( DS="K", p=p, v="R0.mass", y=yy ) )
}
tmean = as.data.frame.matrix ( xtabs( temp.region~yr+region, td ) )
names( tmean ) = paste( "tmean.snowcrab", names( tmean ), sep="." )
tmean$yr = as.numeric( rownames( tmean ) )
tsa = as.data.frame.matrix ( xtabs( sa.region ~ yr+region, td ) )
tsa$cfaall = tsa$cfa4x+ tsa$cfanorth + tsa$cfasouth
names( tsa ) = paste( "sa.snowcrab", names( tsa ), sep="." )
tsa$yr = as.numeric( rownames( tsa ) )
res = merge( res, tmean, by="yr", all=T, sort=T)
res = merge( res, tsa, by="yr", all=T, sort=T)
save( res, file=outfn, compress=T )
return (res)
}
if (db=="seal.timeseries") {
data.file = file.path( project.datadirectory("aegis"), "data", "seals.csv")
seal = read.table( data.file, sep=",", header=T, as.is=T, strip.white=T)
return(seal)
}
if (db=="plankton.timeseries") {
data.file = file.path( project.datadirectory("aegis"), "data", "plankton.csv")
plankton = read.table( data.file, sep=";", header=T, as.is=T, strip.white=T)
return(plankton)
}
if (db %in% c("aegis.all", "aegis.all.glue") ) {
fn = file.path( project.datadirectory("aegis") , "data", "aegis.all.rdata" )
if (db=="aegis.all" ) {
load(fn)
return (aegis)
}
# refresh the survey data
groundfish = aegis_ts_db( db="groundfish.timeseries" )
snowcrab = aegis_ts_db( db="snowcrab.timeseries")
climate = aegis_ts_db (db="climate" )
shrimp = aegis_ts_db( db="shrimp.timeseries")
sar = aegis_ts_db( db="species.area" )
nss = aegis_ts_db( db="sizespectrum" )
metab = aegis_ts_db( db="metabolism" )
sc = aegis_ts_db( db="species.composition" )
economics = aegis_ts_db( db="economic.data" )
# hand constructed and updated ..
plankton = aegis_ts_db( db="plankton.timeseries" )
human = aegis_ts_db( db="demographics.redo" )
climate = aegis_ts_db (db="climate" )
seals = aegis_ts_db( db="seal.timeseries" )
landedvalue = aegis_ts_db( db="landedvalue.annual", ref.year=2008 )
landings = aegis_ts_db( db="landings.annual" )
# merge all data
out = merge( snowcrab, shrimp, by = "yr", all=T )
out = merge( out, groundfish, by="yr", all=T )
out = merge( out, plankton, by="yr", all=T )
out = merge( out, human, by="yr", all=T )
out = merge( out, climate, by="yr", all=T )
out = merge( out, economics, by="yr", all=T )
out = merge( out, seals, by="yr", all=T )
out = merge( out, landings, by="yr", all=T )
out = merge( out, landedvalue, by="yr", all=T )
out = merge( out, sar, by="yr", all=T )
out = merge( out, nss, by="yr", all=T )
out = merge( out, metab, by="yr", all=T )
out = merge( out, sc, by="yr", all=T )
data = out[ order(out$yr), ]
rownames(data) = data$yr
colnames(data) = gsub( "[_~]", ".", tolower(colnames(data) ) )
landings.Atlantic = c( "landingsCod.Atlantic.mt.live","landingsHaddock.Atlantic.mt.live","landingsRedfish.spp.Atlantic.mt.live",
"landingsHalibut.Atlantic.mt.live","landingsFlatfishes.Atlantic.mt.live","landingsGreenland.turbot.Atlantic.mt.live",
"landingsPollock.Atlantic.mt.live","landingsHake.Atlantic.mt.live","landingsCusk.Atlantic.mt.live","landingsCatfish.Atlantic.mt.live",
"landingsSkate.Atlantic.mt.live","landingsDogfish.Atlantic.mt.live","landingsOther.Atlantic.mt.live",
"landingstotal.groundfish.Atlantic.mt.live","landingsHerring.Atlantic.mt.live","landingsMackerel.Atlantic.mt.live",
"landingsSwordfish.Atlantic.mt.live","landingsTuna.Atlantic.mt.live","landingsAlewife.Atlantic.mt.live",
"landingsEel.Atlantic.mt.live","landingsSalmon.Atlantic.mt.live","landingsSmelt.Atlantic.mt.live","landingsCapelin.Atlantic.mt.live",
"landingsOther.finifish.Atlantic.mt.live","landingsTOTAL.pelagic.Atlantic.mt.live","landingsClams.quahaug.Atlantic.mt.live",
"landingsOyster.1.Atlantic.mt.live","landingsScallop.2.Atlantic.mt.live","landingsSquid.Atlantic.mt.live",
"landingsMussel.3.Atlantic.mt.live","landingsLobster.Atlantic.mt.live","landingsShrimp.Atlantic.mt.live",
"landingsCrab.Queen.Atlantic.mt.live","landingsCrab.Other.Atlantic.mt.live","landingsSea.urchin.Atlantic.mt.live",
"landingsOther.shellfish.Atlantic.mt.live","landingsTOTAL.shellfish.Atlantic.mt.live",
"landingsTOTAL.fish.shellfish.Atlantic.mt.live","landingsMarine.plants.Atlantic.mt.live","landingsLumpfish.roe.Atlantic.mt.live",
"landingsMiscellaneous.other.Atlantic.mt.live","landingsTotal.misc.Atlantic.mt.live","landingstotal.all.seafood.Atlantic.mt.live"
)
landings.NS = c( "landingsCod.ns.mt.live","landingsHaddock.ns.mt.live","landingsRedfish.spp.ns.mt.live",
"landingsHalibut.ns.mt.live","landingsFlatfishes.ns.mt.live","landingsGreenland.turbot.ns.mt.live",
"landingsPollock.ns.mt.live","landingsHake.ns.mt.live","landingsCusk.ns.mt.live","landingsCatfish.ns.mt.live",
"landingsSkate.ns.mt.live","landingsDogfish.ns.mt.live","landingsOther.ns.mt.live",
"landingstotal.groundfish.ns.mt.live","landingsHerring.ns.mt.live","landingsMackerel.ns.mt.live",
"landingsSwordfish.ns.mt.live","landingsTuna.ns.mt.live","landingsAlewife.ns.mt.live",
"landingsEel.ns.mt.live","landingsSalmon.ns.mt.live","landingsSmelt.ns.mt.live","landingsCapelin.ns.mt.live",
"landingsOther.finifish.ns.mt.live","landingsTOTAL.pelagic.ns.mt.live","landingsClams.quahaug.ns.mt.live",
"landingsOyster.1.ns.mt.live","landingsScallop.2.ns.mt.live","landingsSquid.ns.mt.live",
"landingsMussel.3.ns.mt.live","landingsLobster.ns.mt.live","landingsShrimp.ns.mt.live",
"landingsCrab.Queen.ns.mt.live","landingsCrab.Other.ns.mt.live","landingsSea.urchin.ns.mt.live",
"landingsOther.shellfish.ns.mt.live","landingsTOTAL.shellfish.ns.mt.live",
"landingsTOTAL.fish.shellfish.ns.mt.live","landingsMarine.plants.ns.mt.live","landingsLumpfish.roe.ns.mt.live",
"landingsMiscellaneous.other.ns.mt.live","landingsTotal.misc.ns.mt.live","landingstotal.all.seafood.ns.mt.live"
)
landings.totals.NS = c( "landingstotal.groundfish.ns.mt.live", "landingsTOTAL.pelagic.ns.mt.live", "landingsTOTAL.shellfish.ns.mt.live",
"landingsTotal.misc.ns.mt.live", "landingstotal.all.seafood.ns.mt.live", "landings_grd.4vw", "landings_pel.4vw", "landing_inv.4vw" )
landings.totals.Atlantic = c( "landingstotal.groundfish.Atlantic.mt.live", "landingsTOTAL.pelagic.Atlantic.mt.live",
"landingsTOTAL.shellfish.Atlantic.mt.live","landingsTotal.misc.Atlantic.mt.live", "landingstotal.all.seafood.Atlantic.mt.live" )
landedvalue.NS = c("landedvalue.NS.Cod", "landedvalue.NS.Haddock", "landedvalue.NS.Redfish.spp.","landedvalue.NS.Halibut",
"landedvalue.NS.Flatfishes", "landedvalue.NS.Greenland.turbot","landedvalue.NS.Pollock","landedvalue.NS.Hake",
"landedvalue.NS.Cusk","landedvalue.NS.Catfish","landedvalue.NS.Skate","landedvalue.NS.Dogfish","landedvalue.NS.Other.groundfish",
"landedvalue.NS.TOTAL.groundfish","landedvalue.NS.Herring","landedvalue.NS.Mackerel","landedvalue.NS.Swordfish","landedvalue.NS.Tuna",
"landedvalue.NS.Alewife","landedvalue.NS.Eel","landedvalue.NS.Salmon","landedvalue.NS.Smelt","landedvalue.NS.Capelin",
"landedvalue.NS.Other.pelagics","landedvalue.NS.TOTAL.pelagics","landedvalue.NS.Clams.quahaug","landedvalue.NS.Oyster.1",
"landedvalue.NS.Scallop.2","landedvalue.NS.Squid","landedvalue.NS.Mussel.3","landedvalue.NS.Lobster","landedvalue.NS.Shrimp",
"landedvalue.NS.Crab.Queen","landedvalue.NS.Crab.Other","landedvalue.NS.Sea.urchin","landedvalue.NS.Other.shellfish",
"landedvalue.NS.TOTAL.shellfish","landedvalue.NS.TOTAL.shellandfinfish","landedvalue.NS.Marine.plants","landedvalue.NS.Lumpfish.roe",
"landedvalue.NS.Miscellaneous","landedvalue.NS.Total.miscellaneous","landedvalue.NS.total.all"
)
landedvalue.Atlantic = c( "landedvalue.Atlantic.Cod","landedvalue.Atlantic.Haddock","landedvalue.Atlantic.Redfish.spp.",
"landedvalue.Atlantic.Halibut","landedvalue.Atlantic.Flatfishes","landedvalue.Atlantic.Greenland.turbot","landedvalue.Atlantic.Pollock",
"landedvalue.Atlantic.Hake","landedvalue.Atlantic.Cusk","landedvalue.Atlantic.Catfish","landedvalue.Atlantic.Skate",
"landedvalue.Atlantic.Dogfish","landedvalue.Atlantic.Other.groundfish","landedvalue.Atlantic.TOTAL.groundfish",
"landedvalue.Atlantic.Herring","landedvalue.Atlantic.Mackerel","landedvalue.Atlantic.Swordfish","landedvalue.Atlantic.Tuna",
"landedvalue.Atlantic.Alewife","landedvalue.Atlantic.Eel","landedvalue.Atlantic.Salmon","landedvalue.Atlantic.Smelt",
"landedvalue.Atlantic.Capelin","landedvalue.Atlantic.Other.pelagics","landedvalue.Atlantic.TOTAL.pelagics",
"landedvalue.Atlantic.Clams.quahaug","landedvalue.Atlantic.Oyster1","landedvalue.Atlantic.Scallop.2","landedvalue.Atlantic.Squid",
"landedvalue.Atlantic.Mussel.3","landedvalue.Atlantic.Lobster","landedvalue.Atlantic.Shrimp","landedvalue.Atlantic.Crab.Queen",
"landedvalue.Atlantic.Crab.Other","landedvalue.Atlantic.Sea.urchin","landedvalue.Atlantic.Other.shellfish",
"landedvalue.Atlantic.TOTAL.shellfish","landedvalue.Atlantic.TOTAL.shellandfinfish","landedvalue.Atlantic.Marine.plants",
"landedvalue.Atlantic.Lumpfish.roe","landedvalue.Atlantic.Miscellaneous","landedvalue.Atlantic.Total.miscellaneous",
"landedvalue.Atlantic.total.all"
)
landedvalue.totals.NS = c( "landedvalue.NS.TOTAL.groundfish", "landedvalue.NS.TOTAL.pelagics", "landedvalue.NS.TOTAL.shellfish",
"landedvalue.NS.Total.miscellaneous", "landedvalue.NS.total.all", "landval_grd.4vw", "landval_pel.4vw", "landval_inv.4vw" )
landedvalue.totals.Atlantic = c( "landedvalue.Atlantic.TOTAL.groundfish", "landedvalue.Atlantic.TOTAL.pelagics",
"landedvalue.Atlantic.TOTAL.shellfish", "landedvalue.Atlantic.Total.miscellaneous", "landedvalue.Atlantic.total.all" )
human = c( "No.of.vessels.4vw","Commercial.Licences.ns","No.Fish.harvesters","No.Fish.processors",
"Employment.per.landedvalue.n.per.millions1997CAD","Employment.per.landings.n.per.mt","GDP.Fish.Processing.millions.2005CAD",
"GDP.Fishing.hunt.trap.millions.2002CAD","GDP.NS.millions.2002CAD","GDP.fishing.percent","GDP.Offshore.Oil.Gas.millions.2005CAD",
"Population.NovaScotia","Unemployment.rate","Demographics.0_19","Demographics.20_64","Demographics.65plus","no.highschool",
"university","Nwells.drilled","seismic.2D","seismic.3D","PCB.sealblubber","PCB.Atlantic.Puffin.ppm.wet","No.Shellfish.closures",
"No.Cruise.ships.Halifax","area_trawled.4vw"
)
climate = c( "snowcrab.bottom.temperature", "snowcrab.bottom.temperature.sd", "snowcrab.bottom.habitat.area",
"ice_coverage.km.2", "T_bottom_emerald", "T_bottom_misaine", "SST_halifax", "vol_source_cil", "CIL.min.T",
"Gulf.Stream.front.Ref.62lon", "Shelf.front.Ref.62lon", "NAO.index.mbar", "T_sable_annual", "storms",
"groundfish.stratified.mean.oxyml", "groundfish.stratified.mean.sal", "groundfish.stratified.mean.temp", "nitrate"
)
snowcrab = c( "snowcrab.fishery.landings", #"snowcrab.exploitation.index",
"snowcrab.geometricmean.cw.male.mat.mean" , "snowcrab.geometricmean.cw.fem.mat.mean",
"snowcrab.geometricmean.R0.mass", "snowcrab.geometricmean.R1.no", "snowcrab.geometricmean.ma13.no", "snowcrab.geometricmean.ma12.no",
"snowcrab.geometricmean.ma11.no", "snowcrab.geometricmean.ma10.no", "snowcrab.geometricmean.ma9.no",
"snowcrab.geometricmean.fa10.no", "snowcrab.geometricmean.fa9.no", "snowcrab.geometricmean.fa8.no", "snowcrab.geometricmean.fa7.no",
"snowcrab.geometricmean.totno.female.mat", "snowcrab.geometricmean.totno.female.imm"
)
ecosystem = c( "seals.total", "groundfish.stratified.mean.shannon", "shrimp.capelin.index",
"shrimp.size.female", "shrimp.exploitation.index", "shrimp.size.sexchange.mm", "shrimp.abundance.index",
"cpr_colour", "cpr_diatoms", "cpr_dino", "cpr_cf1_4", "cpr_cf5_6", "cpr_ch", "cpr_para_pseudo",
"groundfish.stratified.mean.totwgt.elasmobranchs", "groundfish.stratified.mean.totwgt.gadoid",
"groundfish.stratified.mean.totwgt.flatfish", "groundfish.stratified.mean.ntaxa.elasmobranchs",
"groundfish.stratified.mean.ntaxa.large.demersal", "groundfish.stratified.mean.ntaxa.small.demersal",
"groundfish.stratified.mean.ntaxa.large.pelagic", "groundfish.stratified.mean.ntaxa.small.pelagic",
"groundfish.stratified.mean.ntaxa.flatfish", "groundfish.stratified.mean.rmean.elasmobranchs",
"groundfish.stratified.mean.rmean.gadoid", "groundfish.stratified.mean.rmean.flatfish",
"groundfish.stratified.mean.rmean.large.demersal", "groundfish.stratified.mean.rmean.small.demersal",
"groundfish.stratified.mean.rmean.large.pelagic", "groundfish.stratified.mean.rmean.small.pelagic",
"groundfish.stratified.mean.mmean.elasmobranchs",
"groundfish.stratified.mean.mmean.gadoid", "groundfish.stratified.mean.mmean.flatfish",
"groundfish.stratified.mean.mmean.large.demersal", "groundfish.stratified.mean.mmean.small.demersal",
"groundfish.stratified.mean.mmean.large.pelagic", "groundfish.stratified.mean.mmean.small.pelagic",
"groundfish.stratified.mean.smrT", "groundfish.stratified.mean.sar.rsq",
"groundfish.stratified.mean.nss.rsquared.all.50km.50day", "groundfish.stratified.mean.Z", "groundfish.stratified.mean.C",
"groundfish.stratified.mean.Npred"
)
keyfactors = c(
"landings.ns.all.grand.total",
"landings.ns.groundfish.total",
"landings.ns.pelagic.total",
"landings.ns.shellfish.total",
"landings.ns.other.total",
"landings.ns.shellfish.crab.queen",
"landedvalue.ns.groundfish.total",
"landedvalue.ns.pelagic.total",
"landedvalue.ns.shellfish.total",
"landedvalue.ns.other.total",
"landedvalue.ns.all.grand.total",
"landedvalue.ns.shellfish.crab.queen",
"population.novascotia", "demographics.65plus", "university", "no.shellfish.closures",
"employment.per.landedvalue.n.per.millions1997cad", "employment.per.landings.n.per.mt",
"gdp.ns.millions.2002cad", "gdp.fish.processing.millions.2005cad", "gdp.fishing.hunt.trap.millions.2002cad",
"gdp.offshore.oil.gas.millions.2005cad", "fish.harvesters.ns",
"snowcrab.geometricmean.cw.fem.mat.mean", "snowcrab.geometricmean.totno.female.mat", "snowcrab.geometricmean.totno.female.imm",
"snowcrab.geometricmean.cw.male.mat.mean",
"snowcrab.geometricmean.R0.mass", "snowcrab.geometricmean.R1a.no", "snowcrab.fishery.landings",
"tmean", "gulf.stream.front.lon62.lat.mean",
"sa.snowcrab.cfaall",
"nao",
"groundfish.stratified.mean.oxyml", "groundfish.stratified.mean.temp", "groundfish.stratified.mean.sal",
"nwells.drilled","seismic.2d","seismic.3d","pcb.sealblubber","pcb.atlantic.puffin.ppm.wet",
"halifax.harbour.sst", "t.sable.c", "ss.slope.front.lon62.lat.mean", "seaice.sa.km2",
"seals.total", "shrimp.capelin.index", "shrimp.abundance.index",
"cpr.diatoms", "groundfish.stratified.mean.shannon", "cpr.colour",
"groundfish.stratified.mean.totwgt.capelin",
"groundfish.stratified.mean.totwgt.cod",
"groundfish.stratified.mean.totwgt.elasmobranchs",
"groundfish.stratified.mean.totwgt.gadoid",
"groundfish.stratified.mean.totwgt.flatfish",
"groundfish.stratified.mean.totwgt.large.demersal",
"groundfish.stratified.mean.totwgt.small.demersal",
"groundfish.stratified.mean.totwgt.large.pelagic",
"groundfish.stratified.mean.totwgt.small.pelagic",
"cpr.cf1.4","cpr.cf5.6", "cpr.dino",
"smr", "ca1", "ca2", "meanwgt", "nss.b1", "nss.rsquared",
# "groundfish.stratified.mean.rmean.small.pelagic",
# "groundfish.stratified.mean.rmean.small.demersal",
#"groundfish.stratified.mean.rmean.flatfish",
#"groundfish.stratified.mean.rmean.elasmobranchs",
#"groundfish.stratified.mean.rmean.gadoid",
#"groundfish.stratified.mean.rmean.large.demersal",
#"groundfish.stratified.mean.rmean.large.pelagic",
"z",
"c",
"npred"
)
keyfactors = setdiff( keyfactors, c("landingsTotal.misc.ns.mt.live","landedvalue.NS.Total.miscellaneous" ) )
to.log = c("snowcrab.geometricmean.totno.female.mat", "snowcrab.geometricmean.totno.female.imm" ,
"snowcrab.geometricmean.r0.mass", "snowcrab.geometricmean.r1.no" ,"snowcrab.fishery.landings", "seaice.sa.km2", "seals.total")
keyfactors.names = matrix( c(
"population.novascotia", "NS: Population size",
"demographics.65plus", "NS: % 65 and older",
"university", "NS: % attending university",
"no.shellfish.closures", "No. shellfish closures",
"employment.per.landedvalue.n.per.millions1997cad", "Employment per total landed value",
"employment.per.landings.n.per.mt", "Employment per total landings",
"gdp.ns.millions.2002cad", "GDP: NS total",
"gdp.fish.processing.millions.2005cad", "GDP: fish processing",
"gdp.fishing.hunt.trap.millions.2002cad", "GDP: fishing and hunting",
"gdp.offshore.oil.gas.millions.2005cad", "GDP: oil and gas",
"fish.harvesters.ns", "No. fish harvesters",
"snowcrab.geometricmean.cw.fem.mat.mean", "Snow crab: mature female mean size",
"snowcrab.geometricmean.totno.female.mat", "Snow crab: mature female abundance",
"snowcrab.geometricmean.totno.female.imm", "Snow crab: immature female abundance",
"snowcrab.geometricmean.cw.male.mat.mean", "Snow crab: mature male mean size",
"snowcrab.geometricmean.r0.mass", "Snow crab: mature male biomass",
"snowcrab.geometricmean.r1.no", "Snow crab: male recruitment",
"snowcrab.fishery.landings", "Snow crab: landings",
"tmean", "SSE: temperature mean",
"sa.snowcrab.cfaall","Snow crab: habitat area",
"gulf.stream.front.lon62.lat.mean", "Gulf stream front: lat@-62 lon",
"nao","NAO index",
"groundfish.stratified.mean.oxyml","RV: bottom oxygen",
"groundfish.stratified.mean.temp","RV: bottom temperature",
"groundfish.stratified.mean.sal", "RV: bottom salinity",
"halifax.harbour.sst","Temperature: SST Halifax",
"t.sable.c","Temperature: Sable Is.",
"ss.slope.front.lon62.lat.mean" , "Shelf front: lat@-62 lon",
"seaice.sa.km2","Ice coverage",
"smr","RV: groundfish SMR",
"mr","RV: groundfish MR",
"seals.total", "Seal abundance",
"shrimp.capelin.index", "Shrimp: capelin abundance index",
"shrimp.abundance.index","Shrimp: abundance index",
"cpr.diatoms","CPR: diatoms",
"groundfish.stratified.mean.shannon","RV: Shannon index",
"cpr.colour","CPR: colour",
"groundfish.stratified.mean.rmean.small.pelagic","RV: condition small pelagics",
"groundfish.stratified.mean.rmean.small.demersal", "RV: condition small demersals",
"groundfish.stratified.mean.rmean.flatfish","RV: condition flatfish",
"groundfish.stratified.mean.rmean.elasmobranchs","RV: condition elasmobranchs",
"groundfish.stratified.mean.rmean.gadoid","RV: condition gadoids",
"groundfish.stratified.mean.rmean.large.demersal","RV: condition large demersals",
"groundfish.stratified.mean.rmean.large.pelagic","RV: condition large pelagics",
"z","RV: species-area exponent",
"c","RV: species-area intercept",
"ca1","RV: species-composition axis 1",
"ca2","RV: species-composition axis 2",
"nss.b1", "RV: Normailised size spectra slope",
"nss.rsquared", "RV: Normalised size spectra R-square",
"meanwgt","RV: Mean body weight",
"groundfish.stratified.mean.totwgt.capelin","RV: biomass capelin",
"groundfish.stratified.mean.totwgt.cod", "RV: biomass cod",
"groundfish.stratified.mean.totwgt.elasmobranchs", "RV: biomass elasmobranchs",
"groundfish.stratified.mean.totwgt.gadoid","RV: biomass gadoids",
"groundfish.stratified.mean.totwgt.flatfish", "RV: biomass flatfish",
"groundfish.stratified.mean.totwgt.large.demersal","RV: biomass large demersals",
"groundfish.stratified.mean.totwgt.small.demersal", "RV: biomass small demersals",
"groundfish.stratified.mean.totwgt.large.pelagic", "RV: biomass large pelagics",
"groundfish.stratified.mean.totwgt.small.pelagic", "RV: biomass small pelagics",
"npred","RV: No. taxa predicted",
"landings.ns.all.grand.total", "Landings: all",
"landings.ns.groundfish.total", "Landings: groundfish",
"landings.ns.pelagic.total", "Landings: pelagic",
"landings.ns.shellfish.total", "Landings: shellfish",
"landings.ns.other.total", "Landings: misc",
"landings.ns.shellfish.crab.queen", "Landings: snow crab",
"landedvalue.ns.groundfish.total", "Landed value: groundfish",
"landedvalue.ns.pelagic.total", "Landed value: pelagics",
"landedvalue.ns.shellfish.total", "Landed value: shellfish",
"landedvalue.ns.other.total", "Landed value: misc",
"landedvalue.ns.all.grand.total", "Landed value: all",
"landedvalue.ns.shellfish.crab.queen", "Landed value: snow crab",
"nwells.drilled", "No. wells drilled",
"seismic.2d", "Seismic 2D; km",
"seismic.3d", "Seismic 3D, km^2",
"pcb.sealblubber", "PCBs: seals",
"pcb.atlantic.puffin.ppm.wet", "PCBs: puffins",
"cpr.cf1.4","CPR: Calanus finmarchicus 1-4",
"cpr.cf5.6","CPR: Calanus finmarchicus 5-6",
"cpr.dino","CPR: dinoflagellates"
), ncol=2, byrow=T )
aegis = list(data=data,
landings.NS=landings.NS,
landings.Atlantic=landings.Atlantic,
landings.totals.NS=landings.totals.NS,
landings.totals.Atlantic=landings.totals.Atlantic,
landedvalue.NS = landedvalue.NS,
landedvalue.Atlantic=landedvalue.Atlantic,
landedvalue.totals.Atlantic=landedvalue.totals.Atlantic,
landedvalue.totals.NS=landedvalue.totals.NS,
snowcrab=snowcrab, human=human, ecosystem=ecosystem, climate=climate,
keyfactors=keyfactors, to.log=to.log,
keyfactors.names =keyfactors.names
)
save( aegis , file=fn, compress=T )
return ( aegis )
}
return ("Data not found")
# aegis_subset = function( X, type ) {
# varlist = X[[ type ]]
# varlist = intersect( names(X$data), varlist)
# dat = X$data[, varlist ]
# for (i in 1:length( X$to.log)) dat[,i] = log10(dat[,i] + 1)
# if ( type=="keyfactors" ) {
# lkup = X$keyfactors.names
# for (i in 1:length( varlist )) {
# j = NULL
# j = which( tolower( lkup[,1]) == tolower( varlist[i]) )
# if (length(j)==1) names(dat)[i] = lkup[ j, 2]
# }
# }
# return (dat)
# }
}
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