OHI Science | Citation policy
[REFERENCE RMD FILE: https://cdn.rawgit.com/OHI-Science/ohiprep/master/globalprep/np/v2016/targetharvest_dataprep.html]
This analysis converts FAO capture production data into the OHI 2016 targeted harvest pressure data.
Corrected a couple typos in the targeted species master list.
http://www.fao.org/fishery/statistics/software/fishstatj/en#downlApp
Release date: March 2016 FAO Global Capture Production Quantity 1950_2014
Downloaded: July 29 2016
Description: Quantity (tonnes) of fisheries capture for each county, species, year.
Time range: 1950-2014
# load libraries, set directories
library(ohicore) #devtools::install_github('ohi-science/ohicore@dev')
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(stringr)
library(tidyr)
## comment out when knitting
#setwd("globalprep/prs_targetedharvest/v2016")
### Load FAO-specific user-defined functions
source('../../../src/R/fao_fxn.R') # function for cleaning FAO files
source('../../../src/R/common.R') # directory locationsThis includes the FAO capture production data and a list of the “target” species.
## FAO capture production data
fis_fao_csv = read.csv(file.path(dir_M, 'git-annex/globalprep/_raw_data/FAO_capture/d2016/Global_capture_production_Quantity_1950-2014.csv'))
# species list
sp2grp = read.csv('raw/species2group.csv') %>%
filter(incl_excl == 'include') %>%
select(target, species); head(sp2grp)## target species
## 1 cetacean Atlantic spotted dolphin
## 2 cetacean Atlantic white-sided dolphin
## 3 cetacean Australian snubfin dolphin
## 4 cetacean Baird's beaked whale
## 5 cetacean Baleen whales nei
## 6 cetacean Beaked whales neim <- fis_fao_csv %>%
rename(country = Country..Country.,
species = Species..ASFIS.species.,
area = Fishing.area..FAO.major.fishing.area.,
Unit = Measure..Measure.) %>%
select(-Unit)
m <- m %>%
gather("year", "value", 4:(ncol(m))) %>%
mutate(year = gsub("X", "", year)) %>%
fao_clean_data() ## Warning: attributes are not identical across measure variables; they will
## be droppedm <- m %>%
mutate(species = as.character(species)) %>%
mutate(species = ifelse(species == "Henslow\x92s swimming crab", "Henslow's swimming crab", species))This analysis only includes target species. The warning messages need to be checked and, if necessary, changes should be made to the raw/species2group.csv
# check for discrepancies in species list
## seals are no longer included:
spgroups = sort(as.character(unique(m$species)))
groups = c('turtle', 'seal', 'whale', 'sea lion', 'dolphin', 'porpoise')
for (group in groups) { #group='dolphin'
possibles <- spgroups[grep(group, spgroups)]
d_missing_l = setdiff(possibles, sp2grp$species)
if (length(d_missing_l)>0){
cat(sprintf("\nMISSING in the lookup the following species in target='%s'.\n %s\n",
group, paste(d_missing_l, collapse='\n ')))
}
}##
## MISSING in the lookup the following species in target='turtle'.
## Chinese softshell turtle
## River and lake turtles nei
##
## MISSING in the lookup the following species in target='seal'.
## Baikal seal
## Bearded seal
## Caspian seal
## Grey seal
## Harbour seal
## Harp seal
## Hooded seal
## Larga seal
## Leopard seal
## New Zealand fur seal
## Northern fur seal
## Ribbon seal
## Ringed seal
## South African fur seal
## South American fur seal
##
## MISSING in the lookup the following species in target='whale'.
## Velvet whalefish
##
## MISSING in the lookup the following species in target='sea lion'.
## New Zealand sea lion
## Steller sea lion
##
## MISSING in the lookup the following species in target='dolphin'.
## Common dolphinfish# check for species in lookup not found in data
l_missing_d = setdiff(sp2grp$species, spgroups)
if (length(l_missing_d)>0){
cat(sprintf('\nMISSING: These species in the lookup are not found in the FAO data \n'))
print(l_missing_d)
}
## filter data to include only target species ----
m2 = m %>%
filter(species %in% sp2grp$species)
unique(m2$area) # confirm these are all marine## [1] Marine areas outside the Antarctic Antarctic areas nei
## [3] Atlantic, Western Central Atlantic, Eastern Central
## [5] Atlantic, Southwest Pacific, Northwest
## [7] Pacific, Southeast Mediterranean and Black Sea
## [9] Pacific, Western Central Atlantic, Northeast
## [11] Atlantic, Northwest Pacific, Eastern Central
## [13] Indian Ocean, Eastern Indian Ocean, Western
## [15] Pacific, Southwest Pacific, Northeast
## 29 Levels: Africa - Inland waters ... Pacific, Western Central# spread wide to expand years
m_w = m2 %>%
spread(year, value) %>%
left_join(sp2grp, by='species'); head(m_w)## Warning in left_join_impl(x, y, by$x, by$y, suffix$x, suffix$y): joining
## factor and character vector, coercing into character vector## country species area 1950
## 1 Argentina Baleen whales nei Marine areas outside the Antarctic NA
## 2 Argentina Blue whale Antarctic areas nei 7
## 3 Argentina Bottlenose dolphin Marine areas outside the Antarctic NA
## 4 Argentina Burmeister's porpoise Marine areas outside the Antarctic NA
## 5 Argentina Commerson's dolphin Marine areas outside the Antarctic NA
## 6 Argentina Common dolphin Marine areas outside the Antarctic NA
## 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 19 2 2 9 4 2 1 1 1 6 0 0 0 0
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978
## 1 NA NA NA NA NA 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 NA NA NA NA NA 0 0 0 0 0 0 0 0 0
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA 0 0 0 0 0 0 0 0 0
## 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992
## 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## 4 NA NA NA NA NA 0 0 0 0 0 0 0 0 1
## 5 NA NA NA NA NA 0 12 212 37 40 16 12 24 14
## 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 2007 2008 2009 2010 2011 2012 2013 2014 target
## 1 0 0 0 0 0 0 0 0 cetacean
## 2 0 0 0 0 0 0 0 0 cetacean
## 3 0 0 0 0 0 0 0 0 cetacean
## 4 0 0 0 0 5 0 0 0 cetacean
## 5 0 0 21 21 11 0 0 0 cetacean
## 6 1 0 0 0 1 0 0 0 cetacean# gather long by target
m_l = m_w %>%
select(-area) %>%
gather(year, value, -country, -species, -target, na.rm=T) %>%
mutate(year = as.integer(as.character(year))) %>%
arrange(country, target, year); head(m_l)## country species target year value
## 1 Argentina Blue whale cetacean 1950 7
## 2 Argentina Fin whale cetacean 1950 503
## 3 Argentina Humpback whale cetacean 1950 12
## 4 Argentina Minke whale cetacean 1950 0
## 5 Argentina Sei whale cetacean 1950 372
## 6 Argentina Sperm whale cetacean 1950 52# explore Japan[210]
m_l %>%
group_by(country, target, year) %>%
summarize(value = sum(value)) %>%
filter(country == 'Japan', target == 'cetacean', year >= 2000) ## Source: local data frame [15 x 4]
## Groups: country, target [1]
##
## country target year value
## <fctr> <fctr> <int> <dbl>
## 1 Japan cetacean 2000 19396
## 2 Japan cetacean 2001 19072
## 3 Japan cetacean 2002 19268
## 4 Japan cetacean 2003 17955
## 5 Japan cetacean 2004 16736
## 6 Japan cetacean 2005 17083
## 7 Japan cetacean 2006 15374
## 8 Japan cetacean 2007 14173
## 9 Japan cetacean 2008 10161
## 10 Japan cetacean 2009 12324
## 11 Japan cetacean 2010 7489
## 12 Japan cetacean 2011 3853
## 13 Japan cetacean 2012 2651
## 14 Japan cetacean 2013 3439
## 15 Japan cetacean 2014 3320# summarize totals per region per year
m_sum = m_l %>%
group_by(country, year) %>%
summarize(value = sum(value, na.rm=TRUE)) %>%
filter(value != 0) %>%
ungroup(); head(m_sum) ## # A tibble: 6 × 3
## country year value
## <fctr> <int> <dbl>
## 1 Argentina 1950 946
## 2 Argentina 1951 796
## 3 Argentina 1952 798
## 4 Argentina 1953 678
## 5 Argentina 1954 1083
## 6 Argentina 1955 947m_sum <- m_sum %>%
mutate(country = as.character(country)) %>%
mutate(country = ifelse(country == "C\xf4te d'Ivoire", "Ivory Coast", country))
### Function to convert to OHI region ID
m_sum_rgn <- name_2_rgn(df_in = m_sum,
fld_name='country',
flds_unique=c('year'))##
## These data were removed for not having any match in the lookup tables:
##
## Other nei
## 1
##
## These data were removed for not being of the proper rgn_type (eez,ohi_region) or mismatching region names in the lookup tables:
## < table of extent 0 x 0 >##
## DUPLICATES found. Consider using collapse2rgn to collapse duplicates (function in progress).## # A tibble: 2 × 1
## country
## <chr>
## 1 Guadeloupe
## 2 Martinique# these are duplicates for the same region
filter(m_sum_rgn, country %in% c("Guadeloupe", "Martinique"))## # A tibble: 57 × 5
## country year value rgn_id rgn_name
## <chr> <int> <dbl> <int> <chr>
## 1 Guadeloupe 1970 0.1 140 Guadeloupe and Martinique
## 2 Guadeloupe 1971 0.1 140 Guadeloupe and Martinique
## 3 Guadeloupe 1972 0.1 140 Guadeloupe and Martinique
## 4 Guadeloupe 1973 0.1 140 Guadeloupe and Martinique
## 5 Guadeloupe 1974 0.1 140 Guadeloupe and Martinique
## 6 Guadeloupe 1975 0.1 140 Guadeloupe and Martinique
## 7 Guadeloupe 1976 0.1 140 Guadeloupe and Martinique
## 8 Guadeloupe 1977 20.0 140 Guadeloupe and Martinique
## 9 Guadeloupe 1978 20.0 140 Guadeloupe and Martinique
## 10 Guadeloupe 1979 10.0 140 Guadeloupe and Martinique
## # ... with 47 more rows# They will be summed:
m_sum_rgn <- m_sum_rgn %>%
group_by(rgn_id, rgn_name, year) %>%
summarize(value = sum(value)) %>%
ungroup()For each scenario identify the maximum year, rescale and save the pressure layer.
# identify max and min years for each scenario and overall
maxyear_all = max(m_sum_rgn$year, na.rm=T)
scenario_maxyear = c('eez2016' = maxyear_all,
'eez2015' = maxyear_all - 1,
'eez2014' = maxyear_all - 2,
'eez2013' = maxyear_all - 3,
'eez2012' = maxyear_all - 4)
minyear_all = scenario_maxyear[length(scenario_maxyear)]
# calculate and save for each scenario
for (i in 1:length(names(scenario_maxyear))) { # i=1
maxyear = scenario_maxyear[i]
scen = as.character(str_extract(names(scenario_maxyear)[i], "\\d{4}"))
m_f = m_sum_rgn %>%
filter(year >= minyear_all & year <= maxyear) %>%
mutate(score = value / quantile(value, 0.95, na.rm = T)) %>% # * 1.10: don't multiply by 1.10 since comparing to the max across all scenarios
mutate(score = ifelse(score>1, 1, score))
head(m_f); summary(m_f)
m_f_max = m_f %>%
filter(value == max(value, na.rm = TRUE))
m_f_quantile_95 <- quantile(m_f$value, 0.95, na.rm=TRUE)
message(sprintf('\n%s pressures scores for %d regions are rescaled to the 95th quantile in harvest since %s (%d-%d):',
names(scenario_maxyear)[i], length(unique(m_f$rgn_id)), names(minyear_all), minyear_all, maxyear))
message(sprintf('%s in %s: %d marine mammals and sea turtles harvested, and the 95th quantile is: %s ',
m_f_max$rgn_name, m_f_max$year, m_f_max$value, m_f_quantile_95))
# output displayed below
m_f = m_f %>%
filter(year == maxyear) %>%
select(rgn_id, pressure_score = score) %>%
arrange(rgn_id); head(m_f); summary(m_f)
# any regions that did not have a catch should have score = 0
rgns = rgn_master %>%
filter(rgn_typ == "eez") %>%
select(rgn_id = rgn_id_2013) %>%
filter(rgn_id < 255) %>%
unique() %>%
arrange(rgn_id)
m_f_fin = rgns %>%
left_join(m_f) %>%
mutate(pressure_score = ifelse(is.na(pressure_score), 0, pressure_score)) %>%
arrange(rgn_id); head(m_f_fin); summary(m_f_fin)
filesave = sprintf('rgn_fao_targeted_%sa.csv', scen)
write.csv(m_f_fin, sprintf('output/fao_targeted_%s.csv', scen), row.names = FALSE)
m_f_fin_gf <- m_f_fin %>%
mutate(gapfill = 0) %>%
select(rgn_id, gapfill)
write.csv(m_f_fin_gf, sprintf('output/fao_targeted_gf_%s.csv', scen), row.names = FALSE)
}##
## eez2016 pressures scores for 30 regions are rescaled to the 95th quantile in harvest since eez2012 (2010-2014):## Japan in 2010: 7489 marine mammals and sea turtles harvested, and the 95th quantile is: 3528.5## Joining, by = "rgn_id"##
## eez2015 pressures scores for 30 regions are rescaled to the 95th quantile in harvest since eez2012 (2010-2013):## Japan in 2010: 7489 marine mammals and sea turtles harvested, and the 95th quantile is: 3684.5## Joining, by = "rgn_id"##
## eez2014 pressures scores for 30 regions are rescaled to the 95th quantile in harvest since eez2012 (2010-2012):## Japan in 2010: 7489 marine mammals and sea turtles harvested, and the 95th quantile is: 3701.35## Joining, by = "rgn_id"##
## eez2013 pressures scores for 29 regions are rescaled to the 95th quantile in harvest since eez2012 (2010-2011):## Japan in 2010: 7489 marine mammals and sea turtles harvested, and the 95th quantile is: 3718.2## Joining, by = "rgn_id"##
## eez2012 pressures scores for 23 regions are rescaled to the 95th quantile in harvest since eez2012 (2010-2010):## Japan in 2010: 7489 marine mammals and sea turtles harvested, and the 95th quantile is: 3278.6## Joining, by = "rgn_id"The data from last year and this year should be the same unless there were changes to underlying FAO data or the master species list.
In this case, all of the regions looked very similar except region 141 (Faeroe Islands). This was due to a change in the FAO data.
new <- read.csv("output/fao_targeted_2015.csv")
old <- read.csv("../v2015/data/rgn_fao_targeted_2015a.csv") %>%
select(rgn_id, pressure_score_old=pressure_score) %>%
left_join(new, by="rgn_id")
old## rgn_id pressure_score_old pressure_score
## 1 1 0.0000000000 0.0000000000
## 2 2 0.0000000000 0.0000000000
## 3 3 0.0000000000 0.0000000000
## 4 4 0.0000000000 0.0000000000
## 5 5 0.0000000000 0.0000000000
## 6 6 0.0000000000 0.0000000000
## 7 7 0.0000000000 0.0000000000
## 8 8 0.0000000000 0.0000000000
## 9 9 0.0000000000 0.0000000000
## 10 10 0.0000000000 0.0000000000
## 11 11 0.0000000000 0.0000000000
## 12 12 0.0000000000 0.0000000000
## 13 13 0.0000000000 0.0000000000
## 14 14 0.0000000000 0.0000000000
## 15 15 0.0000000000 0.0000000000
## 16 16 0.0071067144 0.0067851812
## 17 17 0.0000000000 0.0000000000
## 18 18 0.0000000000 0.0000000000
## 19 19 0.0000000000 0.0000000000
## 20 20 0.5472170106 0.5224589497
## 21 21 0.0000000000 0.0000000000
## 22 24 0.0000000000 0.0000000000
## 23 25 0.0000000000 0.0000000000
## 24 26 0.0000000000 0.0000000000
## 25 28 0.0000000000 0.0000000000
## 26 29 0.0000000000 0.0000000000
## 27 30 0.0000000000 0.0000000000
## 28 31 0.0000000000 0.0000000000
## 29 32 0.0000000000 0.0000000000
## 30 33 0.0000000000 0.0000000000
## 31 34 0.0000000000 0.0000000000
## 32 35 0.0000000000 0.0000000000
## 33 36 0.0000000000 0.0000000000
## 34 37 0.0000000000 0.0000000000
## 35 38 0.0000000000 0.0000000000
## 36 39 0.0000000000 0.0000000000
## 37 40 0.0000000000 0.0000000000
## 38 41 0.0000000000 0.0000000000
## 39 42 0.0000000000 0.0000000000
## 40 43 0.0000000000 0.0000000000
## 41 44 0.0000000000 0.0000000000
## 42 45 0.0000000000 0.0000000000
## 43 46 0.0000000000 0.0000000000
## 44 47 0.0000000000 0.0000000000
## 45 48 0.0000000000 0.0000000000
## 46 49 0.0000000000 0.0000000000
## 47 50 0.0000000000 0.0000000000
## 48 51 0.0000000000 0.0000000000
## 49 52 0.0000000000 0.0000000000
## 50 53 0.0000000000 0.0000000000
## 51 54 0.0000000000 0.0000000000
## 52 55 0.0000000000 0.0000000000
## 53 56 0.0000000000 0.0000000000
## 54 57 0.0000000000 0.0000000000
## 55 58 0.0000000000 0.0000000000
## 56 59 0.0048325658 0.0046139232
## 57 60 0.0000000000 0.0000000000
## 58 61 0.0000000000 0.0000000000
## 59 62 0.0000000000 0.0000000000
## 60 63 0.0000000000 0.0000000000
## 61 64 0.0000000000 0.0000000000
## 62 65 0.0000000000 0.0000000000
## 63 66 0.0000000000 0.0000000000
## 64 67 0.0000000000 0.0000000000
## 65 68 0.0000000000 0.0000000000
## 66 69 0.0000000000 0.0000000000
## 67 70 0.0000000000 0.0000000000
## 68 71 0.0000000000 0.0000000000
## 69 72 0.0000000000 0.0000000000
## 70 73 0.2132014327 0.2035554349
## 71 74 0.0000000000 0.0000000000
## 72 75 0.0000000000 0.0000000000
## 73 76 0.0000000000 0.0000000000
## 74 77 0.0000000000 0.0000000000
## 75 78 0.0000000000 0.0000000000
## 76 79 0.0000000000 0.0000000000
## 77 80 0.0000000000 0.0000000000
## 78 81 0.0000000000 0.0000000000
## 79 82 0.0000000000 0.0000000000
## 80 84 0.0000000000 0.0000000000
## 81 85 0.0000000000 0.0000000000
## 82 86 0.0000000000 0.0000000000
## 83 88 0.0000000000 0.0000000000
## 84 89 0.0000000000 0.0000000000
## 85 90 0.0000000000 0.0000000000
## 86 91 0.0000000000 0.0000000000
## 87 92 0.0000000000 0.0000000000
## 88 93 0.0000000000 0.0000000000
## 89 94 0.0000000000 0.0000000000
## 90 95 0.0000000000 0.0000000000
## 91 96 0.0000000000 0.0000000000
## 92 97 0.0000000000 0.0000000000
## 93 98 0.0000000000 0.0000000000
## 94 99 0.0000000000 0.0000000000
## 95 100 0.0000000000 0.0000000000
## 96 101 0.0000000000 0.0000000000
## 97 102 0.0000000000 0.0000000000
## 98 103 0.0000000000 0.0000000000
## 99 104 0.0000000000 0.0000000000
## 100 105 0.0000000000 0.0000000000
## 101 106 0.0000000000 0.0000000000
## 102 107 0.0000000000 0.0000000000
## 103 108 0.0000000000 0.0000000000
## 104 110 0.0000000000 0.0000000000
## 105 111 0.0000000000 0.0000000000
## 106 112 0.0000000000 0.0000000000
## 107 113 0.0000000000 0.0000000000
## 108 114 0.0000000000 0.0000000000
## 109 115 0.0000000000 0.0000000000
## 110 116 0.0000000000 0.0000000000
## 111 117 0.0000000000 0.0000000000
## 112 118 0.0000000000 0.0000000000
## 113 119 0.0000000000 0.0000000000
## 114 120 0.0000000000 0.0000000000
## 115 121 0.0000000000 0.0000000000
## 116 122 0.0000000000 0.0000000000
## 117 123 0.0000000000 0.0000000000
## 118 124 0.0000000000 0.0000000000
## 119 125 0.0008528057 0.0008142217
## 120 126 0.0000000000 0.0000000000
## 121 127 0.0011370743 0.0010856290
## 122 129 0.0000000000 0.0000000000
## 123 130 0.0000000000 0.0000000000
## 124 131 0.0000000000 0.0000000000
## 125 132 0.0000000000 0.0000000000
## 126 133 0.0000000000 0.0000000000
## 127 134 0.0000000000 0.0000000000
## 128 135 0.0000000000 0.0000000000
## 129 136 0.0000000000 0.0000000000
## 130 137 0.0000000000 0.0000000000
## 131 138 0.0002842686 0.0002714072
## 132 139 0.0000000000 0.0000000000
## 133 140 0.0000000000 0.0000000000
## 134 141 0.0002842686 0.4179671597
## 135 143 0.0480413895 0.0903786131
## 136 144 0.0000000000 0.0000000000
## 137 145 1.0000000000 1.0000000000
## 138 146 0.0000000000 0.0000000000
## 139 147 0.0000000000 0.0000000000
## 140 148 0.0000000000 0.0000000000
## 141 149 0.0000000000 0.0000000000
## 142 150 0.0000000000 0.0000000000
## 143 151 0.0000000000 0.0000000000
## 144 152 0.0000000000 0.0000000000
## 145 153 0.0000000000 0.0000000000
## 146 154 0.0000000000 0.0000000000
## 147 155 0.0000000000 0.0000000000
## 148 156 0.0000000000 0.0000000000
## 149 157 0.0000000000 0.0000000000
## 150 158 0.0000000000 0.0000000000
## 151 159 0.0000000000 0.0000000000
## 152 161 0.0000000000 0.0000000000
## 153 162 0.0105179373 0.0100420681
## 154 163 0.0153505032 0.0268693174
## 155 164 0.0000000000 0.0000000000
## 156 166 0.0000000000 0.0000000000
## 157 167 0.0000000000 0.0000000000
## 158 168 0.0000000000 0.0000000000
## 159 169 0.0000000000 0.0000000000
## 160 171 0.0011370743 0.0010856290
## 161 172 0.0000000000 0.0000000000
## 162 173 0.0000000000 0.0000000000
## 163 174 0.0000000000 0.0000000000
## 164 175 0.0000000000 0.0000000000
## 165 176 0.0025584172 0.0024426652
## 166 177 0.0000000000 0.0000000000
## 167 178 0.0000000000 0.0000000000
## 168 179 0.0000000000 0.0000000000
## 169 180 0.0162033089 0.0157416203
## 170 181 0.0000000000 0.0000000000
## 171 182 0.0005685372 0.0005428145
## 172 183 0.0000000000 0.0000000000
## 173 184 0.0000000000 0.0000000000
## 174 185 0.0000000000 0.0000000000
## 175 186 0.0000000000 0.0000000000
## 176 187 0.0000000000 0.0000000000
## 177 188 0.0000000000 0.0000000000
## 178 189 0.0000000000 0.0000000000
## 179 190 0.0000000000 0.0000000000
## 180 191 0.0000000000 0.0000000000
## 181 192 0.0000000000 0.0000000000
## 182 193 0.0000000000 0.0000000000
## 183 194 0.0000000000 0.0000000000
## 184 195 0.0000000000 0.0000000000
## 185 196 0.0000000000 0.0000000000
## 186 197 0.0000000000 0.0000000000
## 187 198 0.0008528057 0.0000000000
## 188 199 0.0000000000 0.0000000000
## 189 200 0.0000000000 0.0000000000
## 190 202 0.0000000000 0.0000000000
## 191 203 0.0000000000 0.0000000000
## 192 204 0.0000000000 0.0000000000
## 193 205 0.0000000000 0.0000000000
## 194 206 0.0000000000 0.0000000000
## 195 207 0.0000000000 0.0000000000
## 196 208 0.0000000000 0.0000000000
## 197 209 0.0000000000 0.0000000000
## 198 210 0.9775996361 0.9333695210
## 199 212 0.0000000000 0.0000000000
## 200 213 0.0000000000 0.0000000000
## 201 214 0.0000000000 0.0000000000
## 202 215 0.0000000000 0.0000000000
## 203 216 0.0056853715 0.0054281449
## 204 218 0.0008528057 0.0008142217
## 205 219 0.0000000000 0.0000000000
## 206 220 0.0000000000 0.0000000000
## 207 221 0.0000000000 0.0000000000
## 208 222 0.0000000000 0.0000000000
## 209 223 0.1688555347 0.1612159045
## 210 224 0.0000000000 0.0000000000
## 211 227 0.0000000000 0.0000000000
## 212 228 0.0000000000 0.0000000000
## 213 231 0.0002842686 0.0002714072
## 214 232 0.0000000000 0.0000000000
## 215 237 0.0000000000 0.0000000000
## 216 244 0.0000000000 0.0000000000
## 217 245 0.0000000000 0.0000000000
## 218 247 0.0000000000 0.0000000000
## 219 248 0.0000000000 0.0000000000
## 220 249 0.0000000000 0.0000000000
## 221 250 0.0000000000 0.0000000000plot(pressure_score ~ pressure_score_old, data=old)
abline(0, 1, col="red")
## look at the species in the 2016 data:
filter(m, country=="Faroe Islands") %>%
filter(year == 2013) %>%
filter(value>0)## country species
## 1 Faroe Islands Angler(=Monk)
## 2 Faroe Islands Argentines
## 3 Faroe Islands Atlantic bluefin tuna
## 4 Faroe Islands Atlantic cod
## 5 Faroe Islands Atlantic cod
## 6 Faroe Islands Atlantic halibut
## 7 Faroe Islands Atlantic halibut
## 8 Faroe Islands Atlantic herring
## 9 Faroe Islands Atlantic horse mackerel
## 10 Faroe Islands Atlantic mackerel
## 11 Faroe Islands Atlantic redfishes nei
## 12 Faroe Islands Atlantic redfishes nei
## 13 Faroe Islands Atlantic white-sided dolphin
## 14 Faroe Islands Atlantic wolffish
## 15 Faroe Islands Atlantic wolffish
## 16 Faroe Islands Black scabbardfish
## 17 Faroe Islands Blue ling
## 18 Faroe Islands Blue whiting(=Poutassou)
## 19 Faroe Islands Capelin
## 20 Faroe Islands Common dab
## 21 Faroe Islands Common dolphin
## 22 Faroe Islands Dealfish
## 23 Faroe Islands Dogfish sharks nei
## 24 Faroe Islands European hake
## 25 Faroe Islands European plaice
## 26 Faroe Islands European sprat
## 27 Faroe Islands Freshwater fishes nei
## 28 Faroe Islands Gadiformes nei
## 29 Faroe Islands Gadiformes nei
## 30 Faroe Islands Greenland cod
## 31 Faroe Islands Greenland halibut
## 32 Faroe Islands Greenland halibut
## 33 Faroe Islands Grey gurnard
## 34 Faroe Islands Haddock
## 35 Faroe Islands Lanternfishes nei
## 36 Faroe Islands Leafscale gulper shark
## 37 Faroe Islands Lemon sole
## 38 Faroe Islands Ling
## 39 Faroe Islands Long-finned pilot whale
## 40 Faroe Islands Lumpfish(=Lumpsucker)
## 41 Faroe Islands Marine crabs nei
## 42 Faroe Islands Marine fishes nei
## 43 Faroe Islands Moras nei
## 44 Faroe Islands Northern prawn
## 45 Faroe Islands Northern prawn
## 46 Faroe Islands Northern shortfin squid
## 47 Faroe Islands Norway lobster
## 48 Faroe Islands Norway pout
## 49 Faroe Islands Orange roughy
## 50 Faroe Islands Pollack
## 51 Faroe Islands Porbeagle
## 52 Faroe Islands Portuguese dogfish
## 53 Faroe Islands Queen scallop
## 54 Faroe Islands Rays and skates nei
## 55 Faroe Islands Roundnose grenadier
## 56 Faroe Islands Roundnose grenadier
## 57 Faroe Islands Saithe(=Pollock)
## 58 Faroe Islands Turbot
## 59 Faroe Islands Tusk(=Cusk)
## 60 Faroe Islands Whelk
## 61 Faroe Islands Whiting
## 62 Faroe Islands Witch flounder
## 63 Faroe Islands Wolffishes(=Catfishes) nei
## 64 Faroe Islands Wolffishes(=Catfishes) nei
## area year value
## 1 Atlantic, Northeast 2013 608.0
## 2 Atlantic, Northeast 2013 14306.0
## 3 Atlantic, Northeast 2013 1.0
## 4 Atlantic, Northeast 2013 29290.0
## 5 Atlantic, Northwest 2013 3156.0
## 6 Atlantic, Northeast 2013 103.0
## 7 Atlantic, Northwest 2013 10.0
## 8 Atlantic, Northeast 2013 115552.0
## 9 Atlantic, Northeast 2013 51.0
## 10 Atlantic, Northeast 2013 144626.0
## 11 Atlantic, Northeast 2013 3460.0
## 12 Atlantic, Northwest 2013 200.0
## 13 Marine areas outside the Antarctic 2013 430.0
## 14 Atlantic, Northeast 2013 66.0
## 15 Atlantic, Northwest 2013 5.0
## 16 Atlantic, Northeast 2013 425.0
## 17 Atlantic, Northeast 2013 1089.0
## 18 Atlantic, Northeast 2013 82938.0
## 19 Atlantic, Northeast 2013 29361.0
## 20 Atlantic, Northeast 2013 18.0
## 21 Marine areas outside the Antarctic 2013 1.0
## 22 Atlantic, Northeast 2013 24.0
## 23 Atlantic, Northeast 2013 280.0
## 24 Atlantic, Northeast 2013 3.0
## 25 Atlantic, Northeast 2013 172.0
## 26 Atlantic, Northeast 2013 510.0
## 27 Europe - Inland waters 2013 0.1
## 28 Atlantic, Northeast 2013 1320.0
## 29 Atlantic, Northwest 2013 0.1
## 30 Atlantic, Northeast 2013 2.0
## 31 Atlantic, Northeast 2013 3378.0
## 32 Atlantic, Northwest 2013 302.0
## 33 Atlantic, Northeast 2013 5.0
## 34 Atlantic, Northeast 2013 5578.0
## 35 Atlantic, Northeast 2013 1456.0
## 36 Atlantic, Northeast 2013 28.0
## 37 Atlantic, Northeast 2013 121.0
## 38 Atlantic, Northeast 2013 5208.0
## 39 Marine areas outside the Antarctic 2013 1109.0
## 40 Atlantic, Northeast 2013 1.0
## 41 Atlantic, Northeast 2013 31.0
## 42 Atlantic, Northeast 2013 0.1
## 43 Atlantic, Northeast 2013 0.1
## 44 Atlantic, Northeast 2013 3660.0
## 45 Atlantic, Northwest 2013 592.0
## 46 Atlantic, Northeast 2013 50.0
## 47 Atlantic, Northeast 2013 44.0
## 48 Atlantic, Northeast 2013 336.0
## 49 Atlantic, Northeast 2013 2.0
## 50 Atlantic, Northeast 2013 10.0
## 51 Atlantic, Northeast 2013 17.0
## 52 Atlantic, Northeast 2013 52.0
## 53 Atlantic, Northeast 2013 5300.0
## 54 Atlantic, Northeast 2013 224.0
## 55 Atlantic, Northeast 2013 19.0
## 56 Atlantic, Northwest 2013 1.0
## 57 Atlantic, Northeast 2013 29704.0
## 58 Atlantic, Northeast 2013 0.1
## 59 Atlantic, Northeast 2013 2631.0
## 60 Atlantic, Northeast 2013 53.0
## 61 Atlantic, Northeast 2013 483.0
## 62 Atlantic, Northeast 2013 1.0
## 63 Atlantic, Northeast 2013 251.0
## 64 Atlantic, Northwest 2013 26.0## look at the catch in the 2015 data:
old <- read.csv(file.path(dir_M, "git-annex/globalprep/_raw_data/FAO_capture/d2015/FAO_captureproduction_1950_2013.csv")) %>%
filter(Country..Country.=="Faroe Islands") %>%
select(Country..Country., Common_Name_ASFIS_species, X2013)