rscripts/ProductivityIndex_Species.R
In emilyhmarkowitz/FisheriesEconomicProductivityIndex: What the Package Does (One Line, Title Case)
####***Intitial edit of commerical.data######
commercial.data <- read.csv(file = paste0(dir.data, "/fis_foss_coastal_ws-foss_landings_mv.csv"))
commercial.data<-commercial.data[, c("TSN", "STATE_NAME", "YEAR", "TS_AFS_NAME", "POUNDS",
"DOLLARS", "COLLECTION")]#, "REGION_ID")]
commercial.data<-commercial.data[commercial.data$COLLECTION %in% "Commercial",]
commercial.data$COLLECTION<-NULL
names(commercial.data)<-c("TSN", "State", "year", "CommonName", "POUNDS", "DOLLARS")
commercial.data<-commercial.data[commercial.data$year %in% minyr:maxyr,]
#####***State Codes#####
commercial.data$State<-simpleCap(commercial.data$State)
commercial.data$State[commercial.data$State %in% "Florida-east"]<-"East Florida"
commercial.data$State[commercial.data$State %in% "Florida-west"]<-"West Florida"
commercial.data$State[commercial.data$State %in% "Hawaii"]<-"Hawai`i"
# commercial.data<-merge(x = statereg, y = commercial.data, by = "State")
######***Apply Updated TSN Codes############
fisc_itis_history<-read.csv(file = paste0(dir.data, "fis_foss-fisc_itis_history.csv"))
fisc_itis_history<-fisc_itis_history[,c("NEW_ITIS", "OLD_ITIS")]
commercial.data$TSN.old<-commercial.data$TSN
for (i in 1:length(unique(fisc_itis_history$OLD_ITIS))){
old<-unique(fisc_itis_history$OLD_ITIS)[i]
new<-unique(fisc_itis_history$NEW_ITIS[fisc_itis_history$OLD_ITIS %in% old])
#Find the correct common name
common<-unique(commercial.data$CommonName[commercial.data$TSN %in% c(new)])
common<-unique(commercial.data$CommonName[commercial.data$TSN %in% c(old, new)])
common<-common[is.na(as.numeric(common))]
if (length(common) %in% 0) {
common<-""
} else if (length(common)>0) {
common.new<-unique(commercial.data$CommonName[commercial.data$TSN %in% c(new)])
if (length(common.new)>0){
common<-common.new[1]
} else if (length(common.new)>0) {
common<-common[1]
}
}
commercial.data$CommonName[commercial.data$TSN %in% c(old, new)]<-common
#Get most up to date TSN number
commercial.data$TSN[commercial.data$TSN %in% old]<-new
}
## Load and minorly edit NMFSToTSNTranslation Reference Data
NMFSToTSNTranslation <- read.csv(paste0(dir.data, '/NMFS to TSN translation.csv'),
header=TRUE, stringsAsFactors = FALSE)
names(NMFSToTSNTranslation)<-c("TSN", "CommonName", "SCIENTIFIC_NAME", "NMFS", "Reported.by.Sources")
NMFSToTSNTranslation<-NMFSToTSNTranslation[, names(NMFSToTSNTranslation) %in%
c("TSN", "CommonName", "SCIENTIFIC_NAME", "NMFS", "Reported.by.Sources")]
TSNHistory<-read.csv(paste0(dir.data, 'fis_foss-fisc_itis_history.csv'),
header=TRUE, stringsAsFactors = FALSE)
TSNHistory<-TSNHistory[,c("OLD_ITIS", "NEW_ITIS")]
for (i in 1:nrow(TSNHistory)){
if (sum(NMFSToTSNTranslation$TSN %in% TSNHistory$OLD_ITIS[i])>1) {
# print(i)
NMFSToTSNTranslation$TSN[NMFSToTSNTranslation$TSN %in% TSNHistory$OLD_ITIS[i]]<-TSNHistory$NEW_ITIS[i]
}
}
a<-NMFSToTSNTranslation[!(NMFSToTSNTranslation$NMFS %in%
names(table(NMFSToTSNTranslation$NMFS)[table(NMFSToTSNTranslation$NMFS)>1])),]
b<-NMFSToTSNTranslation[NMFSToTSNTranslation$NMFS %in%
names(table(NMFSToTSNTranslation$NMFS)[table(NMFSToTSNTranslation$NMFS)>1]),]
cc<-c()
for (i in 1:length(unique(b$NMFS))){
nmfs<-unique(b$NMFS)[i]
if ( sum(grepl(pattern = "CLS:88", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T))>0) {
bb<-b[which(b$NMFS %in% nmfs)[grep(pattern = "CLS:88", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T)],]
} else if ( sum(grepl(pattern = "SWFSC:9", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T))>0 ) {
bb<-b[which(b$NMFS %in% nmfs)[!(grepl(pattern = "SWFSC:9", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T))],]
} else if ( sum(grepl(pattern = "HQHIST:10", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T))>0 ) {
bb<-b[which(b$NMFS %in% nmfs)[grep(pattern = "HQHIST:10", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T)],]
}
cc<-rbind.data.frame(cc, bb)
}
NMFSToTSNTranslation<-rbind.data.frame(a, cc)
NMFSToTSNTranslation$Reported.by.Sources<-NULL
## Load and minorly edit species.codes Reference Data
# species.codes <- read.csv(paste0(dir.data, '/olo_species_codes.csv'), header=TRUE, stringsAsFactors = FALSE)
# species.codes<-unique(species.codes)
# species.codes$AFS_NAME<-trimws(species.codes$AFS_NAME)
# species.codes$FUS_NAME[which(species.codes$AFS_NAME=="POLLOCK, WALLEYE")]<-"Pollock, Alaska"
# species.codes$NAME[which(species.codes$AFS_NAME=="POLLOCK, WALLEYE")]<-"Pollock,Alaska"
# species.codes$AFS_NAME[which(species.codes$AFS_NAME=="POLLOCK, WALLEYE")]<-"POLLOCK, ALASKA"
# ####NMFSToITIS###
# NMFSToITIS<-NMFSToTSNTranslation
# NMFSToITIS0<-data.frame()
# for (i in 1:length(unique(NMFSToITIS$NMFS))) {
# id<-unique(NMFSToITIS$NMFS)[i]
# temp<-NMFSToITIS[NMFSToITIS$NMFS %in% id, ]
# if (nrow(temp)>1) {
# NMFSToITIS0<-rbind.data.frame(NMFSToITIS0, temp[1,])
# } else {
# NMFSToITIS0<-rbind.data.frame(NMFSToITIS0, temp)
# }
# }
# NMFSToITIS<-NMFSToITIS0
# NMFSToITIS$Sources<-"NMFSToTSNTranslation"
#
#
# NMFSToITIS0<-cbind.data.frame("TSN" = NA,
# "CommonName" = (species.codes$AFS_NAME[!(species.codes$species %in%
# unique(NMFSToITIS$NMFS))]), "SCIENTIFIC_NAME" = NA,
# "NMFS" = (species.codes$species[!(species.codes$species %in%
# unique(NMFSToITIS$NMFS))]),
#
# "Sources" = "species.codes")
#
# NMFSToITIS<-rbind.data.frame(NMFSToITIS,
# unique(NMFSToITIS0))
#
#
# NMFSToITIS0<-cbind.data.frame("TSN" = NA,
# "CommonName" = NA,
# "SCIENTIFIC_NAME" = NA,
# "NMFS" = unique(commercial.data.old$SPECIES[!(commercial.data.old$SPECIES %in% unique(NMFSToITIS$NMFS))]),
# "Sources" = "commercial.data.old")
# NMFSToITIS<-rbind.data.frame(NMFSToITIS,
# NMFSToITIS0)
####ITISToNMFS###
ITISToNMFS<-NMFSToTSNTranslation
ITISToNMFS0<-data.frame()
for (i in 1:length(unique(ITISToNMFS$TSN))) {
id<-unique(ITISToNMFS$TSN)[i]
temp<-ITISToNMFS[ITISToNMFS$TSN %in% id, ]
if (nrow(temp)>1) {
ITISToNMFS0<-rbind.data.frame(ITISToNMFS0, temp[1,])
} else {
ITISToNMFS0<-rbind.data.frame(ITISToNMFS0, temp)
}
}
ITISToNMFS<-ITISToNMFS0
ITISToNMFS$Sources<-"NMFSToTSNTranslation"
ITISToNMFS0<-cbind.data.frame("TSN" = unique(commercial.data$TSN[!(commercial.data$TSN %in%
unique(ITISToNMFS$TSN))]),
"CommonName" = NA,
"SCIENTIFIC_NAME" = NA,
"NMFS" = NA,
"Sources" = "commercial.data.new")
ITISToNMFS<-rbind.data.frame(ITISToNMFS,
ITISToNMFS0)
###***Merge data####
commercial.data<-commercial.data[!(commercial.data$TSN %in% c(0)),]
commercial.data<-aggregate.data.frame(x = commercial.data[,names(commercial.data) %in%
c("POUNDS", "DOLLARS")],
by = list("TSN" = commercial.data$TSN,
"State" = commercial.data$State,
"year" = commercial.data$year),
FUN = sum, na.rm = T)
commercial.data<-merge(x = commercial.data,
y = ITISToNMFS[,c("TSN", "CommonName")],
by = "TSN")
commercial.data$CommonName<-trimws(commercial.data$CommonName)
commercial.data<-merge(x = commercial.data,
y = statereg,
by = "State")
######***GENERAL########
SpCodeName.General<-list(
"All" = c(unique(commercial.data$TSN)),
"Finfish" = 914179, # Infraphylum Gnathostomata
"Shellfish" = c(82696, # Phylum Arthropoda – Artrópode, arthropodes, arthropods
69458), # Phylum Mollusca – mollusques, molusco, molluscs, mollusks
###
########********Crustaceans#####
###
'Arthropoda' = 82696, # Phylum Arthropoda – Artrópode, arthropodes, arthropods
'American lobster' = 97314, #Species: Homarus americanus H. Milne Edwards, 1837 – valid
'Shrimp' = c(96106, # Infraorder Caridea Dana, 1852
83688, # Order Anostraca G. O. Sars, 1867 – brine shrimp, fairy shrimp
621194, # Superfamily Callianassoidea Dana, 1852
97294, # Infraorder Stenopodidea Claus, 1872
95600), # Suborder Dendrobranchiata Bate, 1888
'Mantis Shrimp' = 99140, # Order Stomatopoda Latreille, 1817 – mantis shrimp, mantis shrimps
'Penaeidae Shirmp' = 95602, # Family Penaeidae Rafinesque, 1815 – penaeid shrimps, crevettes pénaéïdes
'Crab'= c(#95599, # Order Decapoda Latreille, 1802 – crabs, crayfishes, lobsters, prawns, shrimp, crabes, crevettes, écrevisses, homards # Has previously also included all decapods
98276, #Infraorder Brachyura Latreille, 1802 – short-tailed crabs, true crabs
97698), # Infraorder Anomura MacLeay, 1838
'Sea urchins' = 157821, #Class Echinoidea Leske, 1778 – heart urchins, sand dollars, sea urchins, châtaignes de mer, clypéastres, oursins, bolacha da praia, equinóide, ouriço do mar, heart urchins, sand dollars, sea urchins, urchins
'Pac Spiny lobster' = 97650, # Species Panulirus interruptus (J. W. Randall, 1840) – California spiny lobster
'Crawfish' = 97306, # Superfamily Astacoidea Latreille, 1802 – crayfishes
'Lobsters' = c(97660, # Family Scyllaridae Latreille, 1825 – slipper lobsters
97646), # Family Palinuridae Latreille, 1802 – spiny lobsters
'Stone crab' = 98810, # Genus Menippe De Haan, 1833
'Blue crab' = 98696, # Species Callinectes sapidus M. J. Rathbun, 1896 – blue crab, bluepoint
'Caribbean Spiny lobster' = 97648, # Species Panulirus argus (Latreille, 1804) – Caribbean spiny lobster
'Red Crab' = 620992, # Species Chaceon quinquedens (S. I. Smith, 1879) – red deepsea crab
'Horseshoe crab' = 82703, # Species Limulus polyphemus (Linnaeus, 1758) – Atlantic horseshoe crab, American horseshoe crab
'Abalone' = 69492, # Family Haliotidae Rafinesque, 1815 – abalone # 'Abalone*',
'Cockle' = 80865, # Family Cardiidae Lamarck, 1809 # 'Cockle',
'Limpets' = 566854, # Order Patellogastropoda # 'LIMPETS',
'Periwinkles' = 70395, # Genus Littorina Ferussac, 1822 # 'PERIWINKLES',
###
########********Other############
###
'Bloodworms' = 66107, # Species Glycera dibranchiata Ehlers, 1868
'Coral' = 51938, # Class Anthozoa Ehrenberg, 1834 – corals, flower animals, sea anemones, anémones de mer, coraux, água viva, anêmona, antozoário, caravela, corais, gorgônia
###
########********General Fish##############
###
#######All
'Flounders' = c(553179, # Family Paralichthyidae – flétans de sable, lenguados areneros, sand flounders, lefteye flounders
172714, # Family Bothidae – lefteyed flounders, lefteye flounders, lenguados chuecos, turbots flounders
172859), # Family Pleuronectidae – halibuts, dabs, righteyed flounders, platijas, plies, righteye flounders
'Righteyed flounders' = 172859, # Family Pleuronectidae – halibuts, dabs, righteyed flounders, platijas, plies, righteye flounders
'Flatfish' = c(172707), #Suborder Pleuronectoidei
'Dolphinfish' = 168790, # Genus Coryphaena Linnaeus, 1758
'Cusk-eel' = 164839,
'Menhaden' = 161731, #Genus Brevoortia Gill, 1861 – menhadens
'Herring' = 551153, # Subfamily Clupeinae – herrings, sardines, sprats
'Atlantic Herring' = 161722, # Species Clupea harengus Linnaeus, 1758 – Baltic herring, herring, hareng atlantique, Atlantic herring
'Hogchoker' = 172982, # Species Trinectes maculatus (Bloch & Schneider, 1801) – hogchoker, suela tortilla, Hogchoker
'Plaice' = 172900, # Genus Pleuronectes Linnaeus, 1758 – plaices, northern flounders
'Swordfish' = 172480, #Family Xiphiidae – swordfishes, espadas, espadons
'Goosefish' = c(#164500, #Species Lophius gastrophysus Miranda Ribeiro, 1915 – blackfin goosefish, rape pescador
164498), # Genus Lophius Linnaeus, 1758
'Sablefish' = 167123, # Species Anoplopoma fimbria (Pallas, 1814) – sablefish, bacalao negro
'Snappers' = 168845, # Family Lutjanidae – sea perches, snappers, perches de mer, vivaneaux, fusiliers, pargos y huachinangos
'Albacore tuna' = 172419, # Species Thunnus alalunga (Bonnaterre, 1788) – atún blanco, albacore, longfinned albacore, albacora, germon
'Bluefin tuna' = 172421, # Species Thunnus thynnus (Linnaeus, 1758) – bluefin tuna, atún aleta azul, atún, horse mackerel, northern bluefin tuna, Atlantic bluefin tuna, thon rouge
'Tunas' = c(638252, # Tribe Thunnini Starks, 1910
-172454, # Genus Auxis Cuvier, 1829 – frigate mackerels, frigate tunas
-172459), # Genus Allothunnus Serventy, 1948
'Mullets' = 170333, # Family Mugilidae – mullets, grey mullets, lisas, muges
'Pollock' = 164726, # Genus Pollachius Nilsson, 1832 – pollocks
'Salmon' = c(161996, # Species Salmo salar Linnaeus, 1758 – Atlantic salmon, saumon atlantique
161975, # Species Oncorhynchus gorbuscha (Walbaum, 1792) – pink salmon, humpback, humpbacked salmon, saumon rose
161976, # Species Oncorhynchus keta (Walbaum in Artedi, 1792) – chum salmon
161977, # Species Oncorhynchus kisutch (Walbaum, 1792) – coho salmon, salmón plateado, saumon coho, silver salmon
161979, # Species Oncorhynchus nerka (Walbaum in Artedi, 1792) – blueback salmon, kokanee, red salmon, sockeye salmon, saumon rouge
161980 # Species Oncorhynchus tshawytscha (Walbaum in Artedi, 1792) – Chinook salmon, salmón boquinegra, king salmon, saumon chinook
),
'Ladyfish' = 161110, # Genus Elops Linnaeus, 1766 – ladyfishes
'Oilfish' = 172364, # Species Ruvettus pretiosus Cocco, 1833 – oilfish, escolar, escolar clavo
'Anchovies' = 553173, # Family Engraulidae – anchois, anchoas, anchovies
'Croaker' = c(169258, # Genus Bairdiella Gill, 1861 – mademoiselle, striped croakers
169282, # Genus Micropogonias Bonaparte, 1831 – finebarbel croakers
169357, # Genus Cheilotrema Tschudi, 1846
169256, # Genus Genyonemus Gill, 1861
169359, # Genus Roncador Jordan and Gilbert, 1880
169297), # Genus Umbrina Cuvier, 1816 – croakers, roncadores
'American John Dory' = 166284, # Species Zenopsis conchifera (Lowe, 1852) – buckler dory, San Pedro plateado, American john dory
'Grenadiers' = 165332, # Family Macrouridae Gilbert and Hubbs, 1916 – grenadiers, rattails, grenadiers, marcrouridés, granaderos
'Lumpfish' = 167611, # Genus Cyclopterus Linnaeus, 1758 – lumpfishes
'Octopus' = 82590, # Family Octopodidae D'Orbigny, 1839-1842 in Férussac and D'Orbigny, 1834-1848
'Pomfrets' = 170288, # Genus Brama Bloch and Schneider, 1801 – pomfrets
'Sardine' = c(161728, # Genus Sardinops Hubbs, 1929
161812, # Genus Sardina Antipa, 1904
161762), # Genus Sardinella Valenciennes in Cuvier and Valenciennes, 1847
'Shad' = c(161701, # Genus Alosa Linck, 1790 – river herrings
161736), # Genus Dorosoma Rafinesque, 1820 – gizzard shads
'Thornyhead' = 166782, # 'Genus Sebastolobus Gill, 1881
###
########********Atlantic Fish####
###
'American eel' = 161127, # Species Anguilla rostrata (Lesueur, 1817) – American eel, anguila, anguila americana, anguille d'Amérique
'Atlantic Cod' = 164712, # Species Gadus morhua Linnaeus, 1758 – morue de l'Atlantique, bacalao del Atlántico, cod, rock cod, morue franche, Atlantic cod
'Atlantic haddock' = 164744, # Species Melanogrammus aeglefinus (Linnaeus, 1758) – haddock, aiglefin
'Haddock' = 164744, # Species Melanogrammus aeglefinus (Linnaeus, 1758) – haddock, aiglefin
'Atlantic mackerel' = 172414, #Species Scomber scombrus Linnaeus, 1758 – caballa del Atlántico, maquereau commun, maquereau bleu, Atlantic mackerel
'Atlantic pollock' = 164727, #Species Pollachius virens (Linnaeus, 1758) – pollock, coalfish, carbonero, lieu noir, saithe, goberge
'Atlantic croaker' = 169283, # Species Micropogonias undulatus (Linnaeus, 1766) – Atlantic croaker, roncadina, gurrubata
'Atlantic Wolffish' = 171336, # Genus Anarhichas Linnaeus, 1758 – Atlantic wolffishes
'Alewife' = 161706, # Species Alosa pseudoharengus (Wilson, 1811) – alewife, kyak, gaspareau, bigeye herring, branch herring, freshwater herring, gray herring, white herring, sawbelly, grayback
'Black sea bass' = 167687, # Species Centropristis striata (Linnaeus, 1758) – black sea bass
'Porgies' = 169206, # Genus Pagrus Cuvier, 1816
'Striped bass' = 167680, # Species Morone saxatilis (Walbaum, 1792) – rockfish, striped bass, lobina estriada, bar rayé
'Spot' = 169267, # VSpecies Leiostomus xanthurus Lacepède, 1802 – spot, croca
'King mackerel' = 172435, # Species Scomberomorus cavalla (Cuvier, 1829) – king mackerel, sierra, carito, carite lucio, thazard serra
'Scups' = 169181, # Genus Stenotomus Gill, 1865
'Spanish mackerel' = 172436, # Species Scomberomorus maculatus (Mitchill, 1815) – serrucho, sierra común, carite Atlántico, thazard Atlantique, Atlantic Spanish mackerel, Spanish mackerel
'Tilefish' = 168537, #Family Malacanthidae – tilefishes, blanquillos, tiles
'Black drum' = 169288, # Species Pogonias cromis (Linnaeus, 1766) – black drum, corvina negra, tambor negro
'Groupers' = 643094, # Tribe Epinephelini
'Gag grouper' = 167759, # 167759 – Mycteroperca microlepis (Goode and Bean, 1879) – valid – abadejo, charcoal belly, gag
'Red grouper' = 167702, # Species Epinephelus morio (Valenciennes in Cuvier and Valenciennes, 1828) – red grouper, cherna americana
'Red snapper' = 168853, # Species Lutjanus campechanus (Poey, 1860) – red snapper, pargo colorado, huachinango del Golfo, northern red snapper
'Atlantic Bonito' = 172409, # Species Sarda sarda (Bloch, 1793) – bonito, bonito del Atlántico, bonite à dos rayé, bonito Atlántico, bloater, bone jack, Boston mackerel, common bonito, Atlantic bonito
'Billfishes' = 172486, # Family Istiophoridae – billfishes, sailfishes, marlins, spearfishes, picudos, voiliers
'White perch' = 167678, #Species Morone americana (Gmelin, 1789) – white perch, baret
'Vermilion snapper' = 168909, # Rhomboplites aurorubens (Cuvier in Cuvier and Valenciennes, 1829) – vermilion snapper, cotorro, besugo
'Weakfish' = 169241, #Species Cynoscion regalis (Bloch and Schneider, 1801) – weakfish, gray trout, sea trout
'Butterfish' = 172567, # Species Peprilus triacanthus (Peck, 1804) – palometa estrecha, butterfish, stromatée à fossettes
'Offshore Hake' = 164793, # Species Merluccius albidus (Mitchill, 1818) – offshore hake, offshore whiting
'Blueline Tilefish' = 168543, # Species Caulolatilus microps Goode and Bean, 1878 – blueline tilefish, blanquillo lucio
'Golden Tilefish' = 168546, # Species Lopholatilus chamaeleonticeps Goode and Bean, 1879 – blue tilefish, tilefish, conejo amarillo
'Monkfish' = 164499 , #Species Lophius americanus Valenciennes in Cuvier and Valenciennes, 1837 – goosefish, monkfish, baudroie d'Amérique
'Acadian Redfish' = 166774, # Species Sebastes fasciatus Storer, 1854 – Acadian redfish, Labrador redfish, Acadian rockfish, sébaste acadien
'American Plaice Flounder' = 172877, # Species Hippoglossoides platessoides (Fabricius, 1780) – American plaice, plie canadienne, American dab, Canadian plaice, dab, American Plaice
'Bluefish' = 168559, # Species Pomatomus saltatrix (Linnaeus, 1766) – bluefish, anjova
###
########********Pacific Fish############
###
'Halibut' = c(172931, # Genus Hippoglossus Cuvier, 1816 – halibuts
172929), # Genus Reinhardtius Gill, 1861
'Pacific halibut' = 172932, #Species: Hippoglossus stenolepis Schmidt, 1904 – valid
'Atlantic halibut' = 172933, # Species Hippoglossus hippoglossus (Linnaeus, 1758) – Atlantic halibut, flétan atlantique, Atlantic Halibut
'Summer flounder' = 172735, #Species Paralichthys dentatus (Linnaeus, 1766) – summer flounder, fluke, cardeau d'été, Summer Flounder
'Winter Flounder' = 172905, # Species Pseudopleuronectes americanus (Walbaum, 1792) – plie rouge, blackback, Georges Bank flounder, lemon sole, rough flounder, winter flounder, Winter Flounder
'Witch Flounder' = 172873, #Species Glyptocephalus cynoglossus (Linnaeus, 1758) – witch flounder, gray sole, plie grise, Witch Flounder
'Yellowtail Flounder' = 172909, #Species Limanda ferruginea (Storer, 1839) – limande à queue jaune, rusty flounder, yellowtail flounder, Yellowtail Flounder
'Sand Dab Flounder' = 172746, #Species Scophthalmus aquosus (Mitchill, 1815) – windowpane, brill, sand dab, spotted flounder, turbot de sable, Windowpane
'Windowpane Flounder' = 172746, #Species Scophthalmus aquosus (Mitchill, 1815) – windowpane, brill, sand dab, spotted flounder, turbot de sable, Windowpane
'Pacific salmon' = c(
# 161974 # Genus Oncorhynchus Suckley, 1861 – Pacific salmon
161975, # Species Oncorhynchus gorbuscha (Walbaum, 1792) – pink salmon, humpback, humpbacked salmon, saumon rose
161976, # Species Oncorhynchus keta (Walbaum in Artedi, 1792) – chum salmon
161977, # Species Oncorhynchus kisutch (Walbaum, 1792) – coho salmon, salmón plateado, saumon coho, silver salmon
161979, # Species Oncorhynchus nerka (Walbaum in Artedi, 1792) – blueback salmon, kokanee, red salmon, sockeye salmon, saumon rouge
161980 # Species Oncorhynchus tshawytscha (Walbaum in Artedi, 1792) – Chinook salmon, salmón boquinegra, king salmon, saumon chinook
# 161989 # Species Oncorhynchus mykiss (Walbaum, 1792) – rainbow trout, trucha arcoiris, steelhead, truite arc-en-ciel, redband trout
),
'Ocean pout' = 630979, # Species Zoarces americanus (Bloch and Schneider, 1801) – ocean pout, loquette d'Amérique
'Pacific cod' = 164711, #Species Gadus macrocephalus Tilesius, 1810 – morue du Pacifique, bacalao del Pacifico, Pacific cod
# 'Pacific herring' = 551209, #Species Clupea pallasii Valenciennes in Cuvier and Valenciennes, 1847 – arenque del Pacífico, Pacific herring
'Pacific hake (whiting)' = 164792, #Species Merluccius productus (Ayres, 1855) – North Pacific hake, whiting, Pacific hake, merluza norteña, Pacific whiting
'Pacific sardine' = 161729,
'Pacific sanddab' = 172716, # Species Citharichthys sordidus (Girard, 1854) – Pacific sanddab, lenguado moteado, limande sordide, Pacific Sanddab
'Arrowtooth flounder' = 172862, # Species Atheresthes stomias (Jordan & Gilbert, 1880) – arrowtooth flounder, Arrowtooth Flounder, plie à grande bouche
'Sole' = 172980, # Family Soleidae – soles, soles, suelas soles
'Yellow sole' = 172907, # Species Limanda aspera (Pallas, 1814) – yellowfin sole, limande à nageoires jaunes, Yellowfin Sole
'Alaska Pollock' = c(#164722, #invalid - subsequent name/combination
934083), # Species Gadus chalcogrammus Pallas, 1814 – Walleye
'Rockfish' = 166705, # Genus Sebastes Cuvier, 1829 – rockfishes, rockcod, rosefishes
'Atka mackerel'= 167119, #Genus Pleurogrammus Gill, 1861 – atka mackerels
'Hake' = c(164729, #Genus Urophycis Gill, 1863 – codlings
164790), # Genus Merluccius Rafinesque, 1810 – hakes
'White hake' = 164732, # Species Urophycis tenuis (Mitchill, 1814) – white hake, mud hake, merluche blanche
'Red hake' = c(164730 # Species Urophycis chuss (Walbaum, 1792) – red hake, squirrel hake, merluche-écureuil
),#164729), # Genus Urophycis Gill, 1863 – codlings # Toledo, includes other hake
'Silver hake' = c(164791 #Species Merluccius bilinearis (Mitchill, 1814) – silver hake, merlu argenté
), # 164790), # Genus Merluccius Rafinesque, 1810 – hakes # Toledo, includes other hake
'Lingcod' = 167116, # Species Ophiodon elongatus Girard, 1854 – lingcod, molva
'White seabass' = 169387, # Species Atractoscion nobilis (Ayres, 1860) – white seabass, corvina cabaicucho
###
########********Cartilagenous Fish###########
###
'Skate' = 160845, # Family Rajidae Blainville, 1816 – rayas, rays, skates, raies
'Skates' = 160845, # Family Rajidae Blainville, 1816 – rayas, rays, skates, raies
'Dogfish Shark' = 160604, # Family Squalidae Blainville, 1816 – chiens de mer, cazones aguijones, dogfish sharks, spiny dogfishes
'Spiny dogfish' = 160617, #Species Squalus acanthias Linnaeus, 1758 – cazón espinoso común, piked dogfish, spiny dogfish, galludo espinoso, aiguillat commun, dogfish, grayfish, spurdog
'Sharks' = c(563987 # Superorder Euselachii #TOLEDO - some of these are sawfish, rays, and etc.
# 551500, # Order Carcharhiniformes – ground sharks
# 159788, # Order Heterodontiformes – bullhead sharks
# 159810, # Order Hexanchiformes Compagno, 1973 – cow sharks, frilled sharks
# 159851, # Order Lamniformes – mackerel sharks
# 551499, # Order Orectolobiformes – gatas nodrizas, requins-tapis, tiburones tapiceros, carpet sharks
# 551498, # Order Pristiophoriformes – saw sharks
# 160602, #Order Squaliformes Compagno, 1973 – dogfish sharks
# 563990 # Order Squatiniformes
),
'Thresher' = 159915, # Genus Alopias Rafinesque, 1810
"Rosette skate" = 564136,# Species Leucoraja garmani (Whitley, 1939) – rosette skate
'Little Skate' = 564130,#Species Leucoraja erinacea (Mitchill, 1825) – raie-hérisson, common skate, little skate, summer skate
'Winter Skate' = 564145,#Species Leucoraja ocellata (Mitchill, 1815) – raie tachetée, big skate, eyed skate, winter skate
'Barndoor Skate' = 564139,#Species Dipturus laevis (Mitchill, 1818) – grande raie, barndoor skate
'Smooth Skate' = 564151,# Species Malacoraja senta (Garman, 1885) – raie à queue de velours, smooth skate
'Thorny Skate' = 564149,#Species Amblyraja radiata (Donovan, 1808) – raie épineuse, starry skate, thorny skate
'Clearnose Skate' = 160855,#Species Raja eglanteria Bosc in Lacepède, 1800 – raya naricita, clearnose skate
###
########********Reef Fish############
###
'Bigeye' = 168176, # Family Priacanthidae – bigeyes, catalufas, beauclaires, catalufas
'Butterflyfishes' = 169554, # Family Chaetodontidae – butterflyfishes, peces mariposa, poissons-papillons
'Coney' = 167740, # Species Cephalopholis fulva (Linnaeus, 1758) – guatívere, cabrilla roja, coney
'Gag' = 167759, # Species Mycteroperca microlepis (Goode and Bean, 1879) – gag, abadejo, charcoal belly
'Glasseye snapper' = 168181, # Species Heteropriacanthus cruentatus (Lacepède, 1801) – catalufa espinosa, glasseye snapper, catalufa roquera
'Graysby' = 181220, # Species Cephalopholis cruentata (Lacepède, 1802) – enjambre, cherna enjambre, graysby
# 'Groupers' = 551018, # Subfamily Epinephelinae
'Hogfish' = c(170565, # Genus Lachnolaimus Cuvier, 1829
170482), # Genus Bodianus Bloch, 1790 – hogfishes
'Jacks' = c(168605, # Genus Caranx Lacepède, 1801 – cavallas, crevallies, bigeye jacks,
168688), # Genus Seriola Cuvier, 1816 – amberfishes, amberjacks, yellowtails
'Green Jobfish' = 168926, # Species Aprion virescens Valenciennes in Cuvier and Valenciennes, 1830 – blue-gray snapper, green jobfish
'Mackerel' = 638253, # Tribe Scombrini Bonaparte, 1831 – mackerels
'Pompano' = 168707, # Genus Trachinotus Lacepède, 1801 – pompanos
'Blackbelly Rosefish' = 166787, # Species Helicolenus dactylopterus (Delaroche, 1809) – blackbelly rosefish
'Runners' = 168737, # Genus Elagatis Bennett, 1840 – runners
'Scamp' = 167763, # 'Species Mycteroperca phenax Jordan and Swain, 1884 – scamp, abadejo garropa
'Scorpionfish' = 166704, # Family Scorpaenidae – firefishes, goblinfishes, rockfishes, scorpionfishes, escorpiones y rocotes, rascasses, scorpènes, scorpénidés
'Sheepshead' = 169189, # Species Archosargus probatocephalus (Walbaum, 1792) – sheepshead, sargo chopa
'Snapper' = 168845, # Family Lutjanidae – sea perches, snappers, perches de mer, vivaneaux, fusiliers, pargos y huachinangos
'Spanish flag' = 167798, # Species Gonioplectrus hispanus (Cuvier in Cuvier and Valenciennes, 1828) – Spanish flag, biajaiba de lo alto, cherna bandera
'Squirrelfishes' = 166170, # 'Family Holocentridae – soldierfishes, squirrelfishes, candiles, marignans
'Surgeonfishes' = 172250, # Family Acanthuridae – surgeonfishes, tangs, cirujanos, poissons-chirurgiens
'Tilefish' = 168537, # Family Malacanthidae – tilefishes, blanquillos, tiles
'Wenchman' = c(168913, # 'Species Pristipomoides aquilonaris (Goode and Bean, 1896) – wenchman, huachinango navaja
168914), # Species Pristipomoides freemani Anderson, 1966 – yelloweye wenchman, slender wenchman
# 'YELLOWTAIL'
###
########********Mollescus#############
###
'Mollusca' = 69458, # Phylum Mollusca – mollusques, molusco, molluscs, mollusks
'Sea scallop' = 79718, #Species Placopecten magellanicus (Gmelin, 1791) – sea scallop
'Squid' = 555706, # Superorder Decabrachia Boettger, 1952
'Oysters' = c(79777, # Family Spondylidae Gray, 1826
79857, # Family Gryphaeidae Vyalov, 1936
79866), # Family Ostreidae Rafinesque, 1815
'Clams' = 80384, #Order Veneroida H. Adams and A. Adams, 1856
'Mussels' = c(79451, #Family Mytilidae Rafinesque, 1815
79913), # Family Unionidae Rafinesque, 1820 # TOLEDO - why include freshwater spp?
'Blue mussel' = 79454, #Species Mytilus edulis Linnaeus, 1758 – edible blue mussel, blue mussel
'Conchs' = 72554, # Family Strombidae Rafinesque, 1815 (Conches)
'Whelks' = 74069, # Family Melongenidae Gill, 1867 (Whelks)
'Snails' = 72878, # Family Naticidae Guilding, 1834 (Snails)
'Loligo squid' = 82370, # Genus Loligo Lamarck, 1798
'Illex Squid' = 82520, # Genus Illex Steenstrup, 1880
'Atlantic surf clam' = 80944, #Species Spisula solidissima (Dillwyn, 1817) – Atlantic surfclam
'Arctic surf (Stimpson) clam' = 80983, #Species Mactromeris polynyma (Stimpson, 1860) – Arctic surfclam
'Softshell clam' = 81692, # Species Mya arenaria Linnaeus, 1758 – softshell clam, softshell
"Scallops" = 79611, # Family Pectinidae Rafinesque, 1815
'Eastern oyster' = 79872, # Species Crassostrea virginica (Gmelin, 1791) – eastern oyster
'Quahog clams' = 81495, # Genus Mercenaria Schumacher, 1817
'Ocean quahog clam' = 81343, # Species Arctica islandica (Linnaeus, 1767) – ocean quahog
'Snails' = 69459, # Class Gastropoda Cuvier, 1797 – gastropods, slugs, snails, escargots, gastéropodes, limaces, caracol, caramujo, lesma
###
########********Hawai`i##########
###
'Lobsters (*ula*)' = c(206946), # Superfamily Palinuroidea Latreille, 1802
'Dolphinfish (*mahimahi*)' = 168790, # Genus Coryphaena Linnaeus, 1758
"Marlin (*a'u*)" = c(#172486, # Family Istiophoridae – billfishes, sailfishes, marlins, spearfishes, picudos, voiliers TOLEDO
172490), #Genus Makaira Lacepède, 1802 – marlins, blue marlin
'Moonfish (*opah*)' = 166326, # Species Lampris guttatus (Brünnich, 1788) – opah, opah, pez mariposa
'Pomfrets (*Monchong*)' = 170287, #Family Bramidae – pomfrets, castagnoles, tristones
'Scad (*opelu*)' = c(168723, # Genus Decapterus Bleeker, 1851 – mackerel scads, round scads
168585, # Genus Trachurus Rafinesque, 1810 – saurels
168676), # Genus Selar Bleeker, 1851 – bigeyed scads, goggle-eyes, gogglers
'Hawaii Snappers' = c(168181, #Heteropriacanthus cruentatus (Lacepède, 1801) – catalufa espinosa, glasseye snapper, catalufa roquera
168845), #Family Lutjanidae – sea perches, snappers, perches de mer, vivaneaux, fusiliers, pargos y huachinangos
'Wahoo (*ono*)' = 172451, #Species Acanthocybium solandri (Cuvier in Cuvier and Valenciennes, 1832) – peto, wahoo, thazard-bâtard
###
########********Freshwater################
###
'True perches' = 168356, # Family Percidae – true perches, perches, percas, perches et dards, perches
'Quillback' = 163916, # ' Genus Carpiodes Rafinesque, 1820 – quillbacks, carpsuckers
'Silversides' = 630579, # Family Atherinopsidae – charales y pejerreyes, New World silversides, poissons d'argent
'Sturgeon' = 161064, # Family Acipenseridae – sturgeons, esturgeons, esturiones
'Suckers' = 163892, # Family Catostomidae – suckers, matalotes, catostomes
'Sunfishes' = 168093, # Family Centrarchidae – sunfishes, achigans et crapets, lobinas ####TOLEDO this is what data uses, but should be more specific
'Tilapias' = c(170014, # Genus Oreochromis Günther, 1889 – blue tilapias
169809, # Genus Tilapia Smith, 1840 – African mouthbrooders, speckled tilapias
553244), # Genus Sarotherodon Rüppell, 1852 – brushtooth tilapias
'Trout' = c(61989, # Species Oncorhynchus mykiss (Walbaum, 1792) – rainbow trout, trucha arcoiris, steelhead, truite arc-en-ciel, redband trout
161987, # Species Oncorhynchus aguabonita (Jordan, 1892) – golden trout, California golden trout
161981, # Species Oncorhynchus apache (Miller, 1972) – Apache trout, Arizona trout
623485, # Species Oncorhynchus iwame Kimura and Nakamura, 1961, Iwame trout
161992, # Species Oncorhynchus chrysogaster (Needham and Gard, 1964) – trucha dorada mexicana, Mexican golden trout
161983, # Species Oncorhynchus clarkii (Richardson, 1836) – cutthroat trout, trucha degollada, truite fardée
914076, # Species Oncorhynchus penshinensis (Pallas, 1814), Kamchatkan rainbow trout
161985), # Species Oncorhynchus gilae (Miller, 1950) – Gila trout
# 'TURTLE*',
'Walleye' = c(650173, # Species Sander vitreus (Mitchill, 1818) – walleye, doré jaune
168508), # invalid - subsequent name/combination Valid Name: Sander vitreus (Mitchill, 1818
'Whitefish' = 623284, # Subfamily Coregoninae
'Rock Bass' = 168097, # Species Ambloplites rupestris (Rafinesque, 1817) – rock bass, crapet de roche
'Bowfin' = 161100, # Order Amiiformes – bowfin
'Buffalofishes' = 163954, # Genus Ictiobus Rafinesque, 1820 – buffalo suckers, buffalo fishes
'Burbot' = 164725,# Species Lota lota (Linnaeus, 1758) – burbot, eelpout, lotte
'Carp' = c(163690, # Genus Hypophthalmichthys Bleeker, 1860 – bighead carps
163349, # Genus Carassius Nilsson, 1832 – Crucian carps
163678, # Genus Cirrhinus Oken, 1817
163343, # Genus Cyprinus Linnaeus, 1758 – common carps
638893, # Genus Mylopharyngodon Peters, 1881
163536), # Genus Ctenopharyngodon Steindachner, 1866 – grass carps herbívora
'Freshwater Catfish' = c(163995, # Ictaluridae family 163995
164066), # Siluridae family 164066
'Chubs' = c(163541, # Genus Gila Baird and Girard, 1853 – western chubs
163495, # Genus Hybopsis Agassiz, 1854 – bigeye chubs
163374, # Genus Semotilus Rafinesque, 1820 – creek chubs
163562, # Genus Hemitremia Cope, 1870 – flame chub
163881, # Genus Platygobio Gill, 1863 – flathead chubs
163391, # Genus Nocomis Girard, 1856 – hornyhead chubs
163534, # Genus Couesius Jordan, 1878 – lake chubs
163566, # Genus Iotichthys Jordan and Evermann, 1896 – least chubs
163878, # Genus Oregonichthys Hubbs in Schultz, 1929 – Oregon chubs
687586, # Genus Petroleuciscus Bogutskaya, 2002
163819, # Genus Erimystax Jordan, 1882 – slender chubs
638870, # Genus Algansea Girard, 1856
639152, # Genus Siphateles Cope, 1883 – tui chubs
913987, # Species Lepidomeda copei (Jordan and Gilbert, 1881) – leatherside chub, northern leatherside chub
163863), # Genus Macrhybopsis Cockerell and Allison, 1909 – blacktail chubs
'Carppie' = 168165, # Genus Pomoxis Rafinesque, 1818 – crappies
'Crayfishes or crawfishes' = 97306,# Superfamily Astacoidea Latreille, 1802 – crayfishes
'Freshwater Drum' = 169363, # Genus Aplodinotus Rafinesque, 1819 – river drums, thunderpumpers, freshwater drums
# 'FINFISHES, FW, OTHER',
'Gars' = 650224, # Order Lepisosteiformes
# 'GOLDFISH', # covered by carps
'Mooneyes' = 161903, # Family Hiodontidae – mooneyes, laquaiches, ojos de luna
'Catfish' = c(163992), #, Siluriformes order
###
########********Inshore and Miscellaneous############
###
'Stingrays' = 649685, # Order Myliobatiformes – stingrays
'Alligator* Land' = c(201897, # Species Atractosteus spatula (Lacepède, 1803) – alligator gar, catán
668671, # Species Macrochelys temminckii (Troost in Harlan, 1835) – Alligator Snapping Turtle
551771), # Species Alligator mississippiensis (Daudin, 1802) – American Alligator, Aligator americano, alligator, gator, Florida alligator, Mississippi alligator, Louisiana alligator
# 'Bass',
'Echinoderm' = 156857, # Phylum Echinodermata Bruguière, 1791 [ex Klein, 1734] – echinoderms, échinodermes, bolacha da praia, equinoderma, equinodermata, estrela do mar, ouriço do mar
'Frogs' = 173423, # Order Anura – Frogs, perereca, rã, sapo, crapauds, grenouilles, Toads
# 'HERRING, PACIFIC, ROE ON KELP',
'Jellyfish' = 51483, # Class Scyphozoa Götte, 1887 – jellyfish, cup animals, jellyfishes, méduses, água viva, Cifozoa, cifozoário, meduza
# 'LEATHER-BACK',
'Permit' = 168709, # Species Trachinotus falcatus (Linnaeus, 1758) – permit, pámpano, pámpano palometa
'Stingrays' = 160960, # Genus Gymnura van Hasselt, 1823 – stingrays, butterfly rays
'Sea Cucumber' = 158140, # Class Holothuroidea – holotúria, pepino do mar, cocombres de mer, sea cucumbers
'Seaweed' = 660055, # Class Phaeophyceae
'Sponges' = 46861, # Phylum Porifera Grant, 1836 – sponges, éponges, esponja, porifero
'Starfish' = 156862, # Class Asteroidea de Blainville, 1830 – étoiles de mer, stelléridés, estrela do mar, sea stars, starfishes, starfish, estrella de mar
'Rock bass' = 168097, # Species Ambloplites rupestris (Rafinesque, 1817) – rock bass, crapet de roche
###
########********Bait############
###
'Ballyhoo' = 165459, # Genus Hemiramphus Cuvier, 1816
# 'FINFISHES, UNC BAIT AND ANIMAL FOOD',
'Mummichog' = 165647, # Species Fundulus heteroclitus (Linnaeus, 1766) – mummichog, choquemort
'Sandworms' = 65891 # Species Neanthes virens (Sars, 1835)
)
######***STATE and UNITED STATES########
### Species names and codes for the state tables
#########******United States#############
SpCodeName<-list(
"United States" = list('American lobster' = SpCodeName.General$`American lobster`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Menhaden' = SpCodeName.General$Menhaden,
'Pacific halibut' = SpCodeName.General$`Pacific halibut`,
'Pacific salmon' = SpCodeName.General$`Pacific salmon`,
'Sablefish' = SpCodeName.General$Sablefish,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Shrimp' = SpCodeName.General$Shrimp,
'Tunas' = SpCodeName.General$Tunas,
'Alaska pollock' = SpCodeName.General$`Alaska Pollock`),
#####******North Pacific Region#####
#Alaska
"Alaska" = list('Atka mackerel'= SpCodeName.General$`Atka mackerel`,
'Crab'= SpCodeName.General$Crab,
'Flatfish' = SpCodeName.General$Flatfish,
'Pacific cod' = SpCodeName.General$`Pacific cod`,
'Pacific halibut' = SpCodeName.General$`Pacific halibut`,
'Pacific herring' = SpCodeName.General$`Herring`,
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Alaska Pollock' = SpCodeName.General$`Alaska Pollock`),
"North Pacific"= list('Atka mackerel'= SpCodeName.General$`Atka mackerel`,
'Crab'= SpCodeName.General$Crab,
'Flatfish' = SpCodeName.General$Flatfish,
'Pacific cod' = SpCodeName.General$`Pacific cod`,
'Pacific halibut' = SpCodeName.General$`Pacific halibut`,
'Pacific herring' = SpCodeName.General$`Herring`,
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Alaska Pollock' = SpCodeName.General$`Alaska Pollock`
),
#####******Pacific Region#####
"Pacific" = list('Albacore tuna' = SpCodeName.General$`Albacore tuna`,
'Crab' = SpCodeName.General$Crab,
'Flatfish' = SpCodeName.General$Flatfish,
'Pacific hake (whiting)' = SpCodeName.General$`Pacific hake (whiting)`,
'Other shellfish' = c(SpCodeName.General$Arthropoda,
SpCodeName.General$Mollusca,
-(SpCodeName.General$Crab),
-(SpCodeName.General$Shrimp),
-(SpCodeName.General$Squid)),
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Shrimp' = SpCodeName.General$Shrimp,
'Squid' = SpCodeName.General$Squid),
#California
"California" = list('Crab'= SpCodeName.General$Crab,
'Pacific sardine' = SpCodeName.General$`Pacific sardine`,
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Sea urchins' = SpCodeName.General$`Sea urchins`,
'Shrimp' = SpCodeName.General$Shrimp,
'Spiny lobster' = SpCodeName.General$`Pac Spiny lobster`,
'Squid' = SpCodeName.General$Squid,
'Swordfish' = SpCodeName.General$Swordfish),
## Oregon
"Oregon" = list('Albacore tuna' = SpCodeName.General$`Albacore tuna`,
'Crab' = SpCodeName.General$Crab,
'Flatfish' = SpCodeName.General$Flatfish,
'Pacific hake (whiting)' = SpCodeName.General$`Pacific hake (whiting)`,
'Oysters' = SpCodeName.General$Oysters,
'Pacific sardine' = SpCodeName.General$`Pacific sardine`,
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Shrimp' = SpCodeName.General$Shrimp
),
#Washington
"Washington" = list('Albacore tuna' = SpCodeName.General$`Albacore tuna`,
'Clams' = SpCodeName.General$Clams,
'Crab' = SpCodeName.General$Crab,
'Pacific hake (whiting)' = SpCodeName.General$`Pacific hake (whiting)`,
'Pacific halibut' = SpCodeName.General$`Pacific halibut`,
'Mussels' = SpCodeName.General$Mussels,
'Oysters' = SpCodeName.General$Oysters,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Shrimp' = SpCodeName.General$Shrimp
),
########******Western Pacific Region (Hawaii)#######
########Hawaii
"Hawai`i" = list('Lobsters (*ula*)' = SpCodeName.General$`Lobsters (*ula*)`,
'Dolphinfish (*mahimahi*)' = SpCodeName.General$`Dolphinfish (*mahimahi*)`,
"Marlin (*a'u*)" = SpCodeName.General$`Marlin (*a'u*)`,
'Moonfish (*opah*)' = SpCodeName.General$`Moonfish (*opah*)`,
'Pomfrets (*Monchong*)' = SpCodeName.General$`Pomfrets (*Monchong*)`,
'Scad (*opelu*)' = SpCodeName.General$`Scad (*opelu*)`,
'Snappers' = SpCodeName.General$`Hawaii Snappers`,
'Swordfish (*mekajiki*)' = SpCodeName.General$Swordfish,
'Tunas (*aku*)' = SpCodeName.General$Tunas,
'Wahoo (*ono*)' = SpCodeName.General$`Wahoo (*ono*)`
),
"Western Pacific (Hawai`i)" = list('Lobsters (*ula*)' = SpCodeName.General$`Lobsters (*ula*)`,
'Dolphinfish (*mahimahi*)' = SpCodeName.General$`Dolphinfish (*mahimahi*)`,
"Marlin (*a'u*)" = SpCodeName.General$`Marlin (*a'u*)`,
'Moonfish (*opah*)' = SpCodeName.General$`Moonfish (*opah*)`,
'Pomfrets (*Monchong*)' = SpCodeName.General$`Pomfrets (*Monchong*)`,
'Scad (*opelu*)' = SpCodeName.General$`Scad (*opelu*)`,
'Snappers' = SpCodeName.General$`Hawaii Snappers`,
'Swordfish (*mekajiki*)' = SpCodeName.General$Swordfish,
'Tunas (*aku*)' = SpCodeName.General$Tunas,
'Wahoo (*ono*)' = SpCodeName.General$`Wahoo (*ono*)`
),
########******New England Region########
"New England" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic mackerel' = SpCodeName.General$`Atlantic mackerel`,
'Bluefin tuna' = SpCodeName.General$`Bluefin tuna`,
'Cod and haddock' = c(SpCodeName.General$`Atlantic Cod`,
SpCodeName.General$`Atlantic haddock`),
'Flounders' = SpCodeName.General$Flounders,
'Goosefish' = SpCodeName.General$Goosefish,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Squid' = SpCodeName.General$Squid),
## Connecticut
"Connecticut"=list('American lobster' = SpCodeName.General$`American lobster`,
'Summer flounder' = SpCodeName.General$`Summer flounder`,
'Other flounders' = c(SpCodeName.General$Flounders,
-(SpCodeName.General$`Summer flounder`)), #exclude
'Goosefish' = SpCodeName.General$Goosefish,
'Red hake' = SpCodeName.General$`Silver hake`,
'Silver hake' = SpCodeName.General$`Red hake`,
'Scups or Porgies' = c(SpCodeName.General$Scups,
SpCodeName.General$Porgies),
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Whelks and Conchs' = c(SpCodeName.General$Conchs,
SpCodeName.General$Whelks,
SpCodeName.General$Snails),
'loligo squid' = SpCodeName.General$`Loligo squid`
),
## Maine
"Maine" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Bloodworms' = SpCodeName.General$Bloodworms,
'Blue mussel' = SpCodeName.General$`Blue mussel`,
'Cod and haddock' = c(SpCodeName.General$`Atlantic Cod`,
SpCodeName.General$`Atlantic haddock`),
'Goosefish' = SpCodeName.General$Goosefish,
'Ocean quahog clam' = SpCodeName.General$`Ocean quahog clam`,
'Pollock' = SpCodeName.General$`Atlantic pollock`,
'Sea urchins' = SpCodeName.General$`Sea urchins`,
'Softshell clam' = SpCodeName.General$`Softshell clam`
),
## Massachusetts
"Massachusetts" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic mackerel' = SpCodeName.General$`Atlantic mackerel`,
'Other Clams' = c(SpCodeName.General$Clams, #TOLEDO check
-(SpCodeName.General$`Eastern oyster`), #exclude
-(SpCodeName.General$`Ocean quahog clam`), #exclude
-(SpCodeName.General$`Sea scallop`)), #exclude
'Cod and haddock' = c(SpCodeName.General$`Atlantic Cod`,
SpCodeName.General$`Atlantic haddock`),
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Flounders' = SpCodeName.General$Flounders,
'Goosefish' = SpCodeName.General$Goosefish,
'Ocean quahog clam' = SpCodeName.General$`Ocean quahog clam`,
'Sea scallop' = SpCodeName.General$`Sea scallop`),
## New Hampshire
"New Hampshire" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic cod' = SpCodeName.General$`Atlantic Cod`,
'Goosefish' = SpCodeName.General$Goosefish,
'Haddock' = SpCodeName.General$`Atlantic haddock`,
'Hake' = SpCodeName.General$Hake,
'Pollock' = SpCodeName.General$`Atlantic pollock`,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Shrimp' = SpCodeName.General$Shrimp,
'Spiny dogfish' = SpCodeName.General$`Spiny dogfish`),
## Rhode Island
"Rhode Island" = list('Other flounders' = c(SpCodeName.General$Flounders,
-(SpCodeName.General$`Summer flounder`)), #exclude
'American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic mackerel' = SpCodeName.General$`Atlantic mackerel`,
'Goosefish' = SpCodeName.General$Goosefish,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Scups and porgies' = c(SpCodeName.General$Scups,
SpCodeName.General$Porgies), #changed from "or" to "and"
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Squid' = SpCodeName.General$Squid,
'Summer flounder' = SpCodeName.General$`Summer flounder`
),
########******Mid-Atlantic Region########
"Mid-Atlantic" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic surf clam' = SpCodeName.General$`Atlantic surf clam`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Menhaden' = SpCodeName.General$Menhaden,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Squid' = SpCodeName.General$Squid,
'Striped bass' = SpCodeName.General$`Striped bass`,
'Summer flounder' = SpCodeName.General$`Summer flounder`),
# Delaware
"Delaware"=list('American eel' = SpCodeName.General$`American eel`,
'Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Spot' = SpCodeName.General$Spot,
'Striped bass' = SpCodeName.General$`Striped bass`,
'Weakfish' = SpCodeName.General$Weakfish,
'Whelks' = SpCodeName.General$Whelks
),
# Maryland
"Maryland"=list('Atlantic croaker' = SpCodeName.General$`Atlantic croaker`,
'Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Clams or bivalves' = SpCodeName.General$Clams, #TOLEDO Should just be called "Calms"
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Menhaden' = SpCodeName.General$Menhaden,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Striped bass' = SpCodeName.General$`Striped bass`,
'Summer flounder' = SpCodeName.General$`Summer flounder`,
'White perch' = SpCodeName.General$`White perch`
),
# New Jersey
"New Jersey"=list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic mackerel' = SpCodeName.General$`Atlantic mackerel`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Goosefish' = SpCodeName.General$Goosefish,
'Ocean quahog and surf clams' = c(SpCodeName.General$`Ocean quahog clam`#,
# SpCodeName.General$`Atlantic surf clam` # Shouldn't this be here too?
),
#Shouldnt this be 'Quahog clam' = 81495?
'Quahog clam' = c(SpCodeName.General$`Ocean quahog clam`, #Isn't this redundant?
SpCodeName.General$`Arctic surf (Stimpson) clam`, #TOLEDO, check, only time called
SpCodeName.General$`surf clams`),
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Summer flounder' = SpCodeName.General$`Summer flounder`
),
# New York
"New York"=list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic surf clam' = SpCodeName.General$`Atlantic surf clam`,
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Summer flounder' = SpCodeName.General$`Summer flounder`,
'Loligo squid' = SpCodeName.General$`Loligo squid`,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Scups and porgies' = c(SpCodeName.General$Scups,
SpCodeName.General$Porgies),
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Softshell clam' = SpCodeName.General$`Softshell clam`,
'Tilefishes' = SpCodeName.General$Tilefish),
# Virginia
"Virginia"=list('Atlantic croaker' = SpCodeName.General$`Atlantic croaker`,
'Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Goosefish' = SpCodeName.General$Goosefish,
'Menhaden' = SpCodeName.General$Menhaden,
'Oysters' = SpCodeName.General$Oysters,
'Sea Scallop' = SpCodeName.General$`Sea scallop`,
'Spot' = SpCodeName.General$Spot,
'Striped bass' = SpCodeName.General$`Striped bass`,
'Summer flounder' = SpCodeName.General$`Summer flounder`
),
########******South Atlantic Region########
"South Atlantic" = list('Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Flounders' = SpCodeName.General$Flounders,
'Groupers' = SpCodeName.General$Groupers,
'King mackerels' = SpCodeName.General$`King mackerel`,
'Oysters' = SpCodeName.General$Oysters,
'Shrimp' = SpCodeName.General$Shrimp,
'Snappers' = SpCodeName.General$Snappers,
'Swordfish' = SpCodeName.General$Swordfish,
'Tunas' = SpCodeName.General$Tunas),
# East Florida
"East Florida"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Groupers' = SpCodeName.General$Groupers,
'King mackerel' = SpCodeName.General$`King mackerel`,
'Lobsters' = SpCodeName.General$Lobsters,
'Sharks' = SpCodeName.General$Sharks,
'Shrimp' = SpCodeName.General$Shrimp,
'Snappers' = SpCodeName.General$Snappers,
'Spanish mackerel' = SpCodeName.General$`Spanish mackerel`,
'Swordfish' = SpCodeName.General$Swordfish
),
# st.sp.codes[["East Florida"]][[4]] <- c(1939, 1940) # East Florida, king mackerel
# Georgia
# groupers: 1410:1430, 1850, 3800, 4740 groupers
"Georgia"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Grouper' = SpCodeName.General$Groupers,
'Shrimp' = SpCodeName.General$Shrimp,
'Snails (conchs)' = c(SpCodeName.General$Conchs), #TOLEDO was snails - why?!
'Snappers' = SpCodeName.General$Snappers
),
# North Carolina
"North Carolina"=list('Atlantic croaker' = SpCodeName.General$`Atlantic croaker`,
'Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Flounders' = SpCodeName.General$Flounders,
'Groupers' = SpCodeName.General$Groupers,
'King mackerel' = SpCodeName.General$`King mackerel`,
'Shrimp' = SpCodeName.General$Shrimp,
'Snappers' = SpCodeName.General$Snappers,
'Tunas' = SpCodeName.General$Tunas
),
# South Carolina
"South Carolina"=list('Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Groupers' = SpCodeName.General$Groupers,
'Oysters' = SpCodeName.General$Oysters,
'Sharks' = SpCodeName.General$Sharks,
'Shrimp' = SpCodeName.General$Shrimp,
'Snappers' = SpCodeName.General$Snappers,
'Swordfish' = SpCodeName.General$Swordfish,
'Tilefish' = SpCodeName.General$Tilefish),
########******Gulf of Mexico Region########
"Gulf of Mexico" = list('Blue crab' = SpCodeName.General$`Blue crab`,
'Crawfish' = SpCodeName.General$Crawfish,
'Groupers' = SpCodeName.General$Groupers,
'Menhaden' = SpCodeName.General$Menhaden,
'Mullets' = SpCodeName.General$Mullets,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp,
'Spiny lobster' = SpCodeName.General$`Caribbean Spiny lobster`,
'Tunas' = SpCodeName.General$Tunas),
## Alabama
"Alabama"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Flounders' = SpCodeName.General$Flatfish,
'Menhaden' = SpCodeName.General$Menhaden,
'Mullets' = SpCodeName.General$Mullets,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Sharks' = SpCodeName.General$Sharks,
'Shrimp' = SpCodeName.General$Shrimp,
'Spanish mackerel' = SpCodeName.General$`Spanish mackerel`,
'Vermilion snapper' = SpCodeName.General$`Vermilion snapper`
),
## West Florida
"West Florida" = list('Blue crab' = SpCodeName.General$`Blue crab`,
'Gag' = SpCodeName.General$Gag,
'Lobsters' = SpCodeName.General$Lobsters,
'Mullet' = SpCodeName.General$Mullets,
'Oyster' = SpCodeName.General$Oysters,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Red grouper' = SpCodeName.General$`Red grouper`,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp,
'Stone crab' = SpCodeName.General$`Stone crab`
),
## Louisiana
"Louisiana"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Crawfish' = SpCodeName.General$Crawfish,
'King mackerel' = SpCodeName.General$`King mackerel`,
'Menhaden' = SpCodeName.General$Menhaden,
'Mullets' = SpCodeName.General$Mullets,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp,
'Tunas' = SpCodeName.General$Tunas,
'Vermilion snapper' = SpCodeName.General$`Vermilion snapper`
),
## Mississippi
"Mississippi"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Flounders' = SpCodeName.General$Flounders,
'Menhaden' = SpCodeName.General$Menhaden,
'Mullets' = SpCodeName.General$Mullets,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp
),
## Texas
"Texas"=list('Atlantic croaker' = SpCodeName.General$`Atlantic croaker`,
'Black drum' = SpCodeName.General$`Black drum`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Flounders' = SpCodeName.General$Flounders,
'Groupers' = SpCodeName.General$Groupers,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp,
'Tunas' = SpCodeName.General$Tunas,
'Vermilion snapper' = SpCodeName.General$`Vermilion snapper`)
)
#####***spcat.list#####
spcat.list<-list("General" = SpCodeName.General,
"Areas" = SpCodeName)
itis_reclassify<-function(tsn, categories, missing.name){
# Find which codes are in which categories
tsn0<-as.numeric(tsn)[!(is.na(tsn))]
tsn.indata<-classification(sci_id = tsn0, db = 'itis')
tsn.indata<-tsn.indata[!(names(tsn.indata) %in% 0)]
valid0<- sciname<-category0<-bottomrank<-sppname<- TSN<-c()
TSN<-c()
bottomrank<-c()
category0<-c()
sciname<-c()
valid0<-c()
for (i in 1:length(categories)) {
a<-list.search(lapply(X = tsn.indata, '[', 3), categories[i][[1]] %in% . )
# for (ii in 1:length(categories[i][[1]])) {
# a<-c(a, list.search(lapply(X = tsn.indata, '[', 3), categories[i][[1]][[ii]] %in% . ))
# }
if (length(a)!=0) {
sppcode<-names(a)
sppcode<-gsub(pattern = "[a-zA-Z]+", replacement = "", x = sppcode)
sppcode<-gsub(pattern = "\\.", replacement = "", x = sppcode)
for (ii in 1:length(sppcode)) {
TSN<-c(TSN, sppcode[ii])
bottomrank<-c(bottomrank, tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]]$rank[
nrow(tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]])])
category0<-c(category0, names(categories[i]))
sciname<-c(sciname, tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]]$name[
nrow(tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]])])
valid0<-c(valid0,
ifelse(nrow(tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]])>1,
"valid", "invalid"))
}
}
}
df.out<-data.frame(TSN = TSN,
category = category0,
valid = valid0,
rank = bottomrank,
sciname = sciname )
return(list("df.out" = df.out,
"tsn.indata" = tsn.indata))
}
spp_reclassify<-function(landings.df, spcat.list, place){
####
####FINFISH AND SHELFISH AND OTHER TOTALS
###
temp<-landings.df
total.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum,na.rm=TRUE)
#Finfish
temp<-landings.df[landings.df$OFS %in% "F",]
if (nrow(temp)!=0) {
finfish.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum, na.rm=TRUE)
if (sum(!(min(landings.df$year):max(landings.df$year) %in% finfish.df$year)) >0){
finfish.df<-rbind.data.frame(finfish.df,
cbind.data.frame("year" = (min(landings.df$year):max(landings.df$year))[!(min(landings.df$year):max(landings.df$year) %in% other.df$year)],
"POUNDS" = 0,
"DOLLARS" = 0))
landings.df<-landings.df[order(landings.df$year),]
}
} else {
finfish.df<-data.frame(year = min(landings.df$year):max(landings.df$year),
POUNDS = 0,
DOLLARS = 0)
}
#Shellfish
temp<-landings.df[landings.df$OFS %in% "S",]
if (nrow(temp)!=0) {
shellfish.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum,na.rm=TRUE)
if (sum(!(min(landings.df$year):max(landings.df$year) %in% shellfish.df$year)) >0){
shellfish.df<-rbind.data.frame(shellfish.df,
cbind.data.frame("year" = (min(landings.df$year):max(landings.df$year))[!(min(landings.df$year):max(landings.df$year) %in% other.df$year)],
"POUNDS" = 0,
"DOLLARS" = 0))
shellfish.df<-shellfish.df[order(shellfish.df$year),]
}
} else {
shellfish.df<-data.frame(year = min(landings.df$year):max(landings.df$year),
POUNDS = 0,
DOLLARS = 0)
}
#Other
temp<-landings.df[landings.df$OFS %in% "O",]
if (nrow(temp)!=0) {
other.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum,na.rm=TRUE)
if (sum(!(min(landings.df$year):max(landings.df$year) %in% other.df$year)) > 0){
other.df<-rbind.data.frame(other.df,
cbind.data.frame("year" = (min(landings.df$year):max(landings.df$year))[
!(min(landings.df$year):max(landings.df$year) %in% other.df$year)],
"POUNDS" = 0,
"DOLLARS" = 0))
other.df<-other.df[order(other.df$year),]
}
} else {
other.df<-data.frame(year = min(landings.df$year):max(landings.df$year),
POUNDS = 0,
DOLLARS = 0)
}
land.df<-land.tot<-cbind.data.frame("Total" = total.df$POUNDS,
"Finfish" = finfish.df$POUNDS,
"Shellfish" = shellfish.df$POUNDS,
"Other" = other.df$POUNDS,
"Key Species" = NA)
rev.df<-rev.tot<-cbind.data.frame("Total" = total.df$DOLLARS,
"Finfish" = finfish.df$DOLLARS,
'Shellfish' = shellfish.df$DOLLARS,
"Other" = other.df$DOLLARS,
"Key Species" = NA)
price.df<-price.tot<-cbind.data.frame("Key Species" = rep_len(x = NA, length.out = nrow(rev.df)))
####
####SPECIES
###
uniquespp<-data.frame()
if (!(is.na(spcat.list))) {
temp<-itis_reclassify(tsn = as.numeric(paste(unique(landings.df$TSN))),
categories = spcat.list$Areas[place][[1]],
missing.name="Uncategorized")
tsn.id<-temp[1][[1]]
# tsn.id<-tsn.id[!(tsn.id$category %in% c("Other", "Uncategorized")),
# c("TSN", "category")]
tsn.id<-tsn.id[!(tsn.id$category %in% c("Uncategorized", "Other")),]
land.df<-data.frame(t(land.df))
rev.df<-data.frame(t(rev.df))
price.df<-data.frame(t(price.df))
#Add species to tables
land.df$Footnotes<-rev.df$Footnotes<-price.df$Footnotes<-""
for (i in 1:length(names(spcat.list$Areas[place][[1]]))) {
spp0<-sort(names(spcat.list$Areas[place][[1]]))[i]
temp<-landings.df[landings.df$TSN %in% tsn.id$TSN[tsn.id$category %in% spp0],]
# temp<-temp[!(is.na(temp$POUNDS) & is.na(temp$DOLLARS)), ]
#FOOTNOTES
Footnotes<-""
#Species that should be included in this sum and footnote
sppno<-as.numeric(unlist(spcat.list$Areas[place][[1]][spp0]))
spp<-tsn.indata.foot<-classification(sci_id = sppno[sppno>0], db = 'itis')
sppnames<-taxspp(spp = tsn.indata.foot, spp0, landings.df)
species<-sppnames$species
notspecies<-sppnames$notspecies
spp00<-strsplit(x = spp0, split = ",")
spp00<-strsplit(x = spp00[[length(spp00)]], split = "and")
spp00<-gsub(x = unlist(spp00), pattern = "and ", replacement = "", ignore.case = T)
spp00<-gsub(x = unlist(spp00), pattern = "& ", replacement = "", ignore.case = T)
spp00<-trimws(spp00)
if (!(is.null(species)) && !(tolower(species) %in% tolower(spp00))) { #If species names the few specific species in the group, then dont footnote
Footnotes<-""
#If a specific species (and not a gorup) #No Footnote required
} else if (length(spp) == 1 & !(spp[[1]]$rank[nrow(spp[[1]])] %in% "species") ) { #If there is only 1 code and its not a specific species
Footnotes<-paste0("This species group includes species within the ",
paste0(ifelse(spp[[1]]$rank[nrow(spp[[1]])] %in% "genus",
paste0("*", (spp[[1]]$name[nrow(spp[[1]])]), "*"),
tolower(spp[[1]]$name[nrow(spp[[1]])])) ,
" ", spp[[1]]$rank[nrow(spp[[1]])]), ".")
} else if (length(spp) > 1) {
if (length(species)>0 & length(notspecies)>0) {
Footnotes<-paste0("This species group includes species within the ",
funct_list(notspecies), " and ", funct_list(species) , ".")
} else if (length(species)==0 & length(notspecies)>0) {
Footnotes<-paste0("This species group includes species within the ",
funct_list(notspecies), ".")
} else if (length(species)>0 & length(notspecies)==0) {
Footnotes<-paste0("This species group includes ",
funct_list(species) , ".")
}
}
#Species that should be excluded from this sum and footnote
if (sum(sppno<0)>0) {
spp<-tsn.indata.foot<-classification(sci_id = (sppno[sppno<0])*-1, db = 'itis')
sppnames<-taxspp(spp = tsn.indata.foot, spp0, landings.df)
species<-sppnames$species
notspecies<-sppnames$notspecies
if (length(tsn.indata.foot)>1) {
if (length(species)>0 & length(notspecies)>0) {
Footnotes<-paste0(Footnotes, " This species group excludes species within the ",
funct_list(notspecies), " and, specifically, ", funct_list(species) , ".")
} else if (length(species)==0 & length(notspecies)>0) {
Footnotes<-paste0(Footnotes, " This species group excludes species within the ",
funct_list(notspecies), ".")
} else if (length(species)>0 & length(notspecies)==0) {
Footnotes<-paste0(Footnotes, " This species group excludes ",
funct_list(species) , ".")
}
}
}
#COMPILE TABLES
if (nrow(temp) %in% 0) {
land.df<-rbind.data.frame(land.df,
c(rep_len(x = NA, length.out = (ncol(land.df)-1)), Footnotes))
rev.df<-rbind.data.frame(rev.df,
c(rep_len(x = NA, length.out = (ncol(land.df)-1)), Footnotes))
price.df<-rbind.data.frame(price.df,
c(rep_len(x = NA, length.out = (ncol(land.df)-1)), Footnotes))
} else {
#Unique Species
uniquespp0<-unique(temp[,c("TSN", "CommonName")])
uniquespp0$Category<-spp0
uniquespp0$CommonName1<-funct_list(spp00)
uniquespp<-rbind.data.frame(uniquespp, uniquespp0)
temp.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum,na.rm=TRUE)
#make sure columns match
if (sum((!(min(landings.df$year):max(landings.df$year) %in% temp.df$year)) >0)) {
yr0<-(min(landings.df$year):max(landings.df$year))[!(min(landings.df$year):max(landings.df$year) %in% temp.df$year)]
temp.df<-rbind.data.frame(temp.df,
cbind.data.frame('year' = yr0,
'POUNDS' = rep_len(x = NA, length.out = length(yr0)),
'DOLLARS' = rep_len(x = NA, length.out = length(yr0))))
}
temp.df<-temp.df[order(temp.df$year, decreasing = F),]
land.df<-rbind.data.frame(land.df,
c(temp.df$POUNDS, Footnotes))
rev.df<-rbind.data.frame(rev.df,
c(temp.df$DOLLARS, Footnotes))
price.df<-rbind.data.frame(price.df,
c(temp.df$POUNDS/temp.df$DOLLARS, Footnotes))
}
rownames(land.df)[length(rownames(land.df))]<-
rownames(rev.df)[length(rownames(rev.df))]<-
rownames(price.df)[length(rownames(price.df))]<-tolower2(spp0, capitalizefirst = T)
}
uniquespp$Area<-place
uniquespp$SciName<-""
a<-classification(sci_id = uniquespp$TSN, db = 'itis')
for (ii in 1:length(uniquespp)){
if (a[ii][[1]]$rank[nrow(a[ii][[1]])] %in% "species") {
uniquespp$SciName[ii]<-a[ii][[1]]$name[nrow(a[ii][[1]])]
} else {
uniquespp$SciName[ii]<-paste0(a[ii][[1]]$name[nrow(a[ii][[1]])], " ",
a[ii][[1]]$rank[nrow(a[ii][[1]])])
}
}
land.df<-cbind.data.frame(rownames(land.df), land.df)
rev.df<-cbind.data.frame(rownames(rev.df), rev.df)
price.df<-cbind.data.frame(rownames(price.df), price.df)
} else {
land.df<-t(land.df)
land.df<-cbind.data.frame("keyspecies" = rownames(land.df)[-nrow(land.df)],
land.df[-nrow(land.df),],
Footnotes = NA)
rev.df<-t(rev.df)
rev.df<-cbind.data.frame("keyspecies" = rownames(rev.df)[-nrow(rev.df)],
rev.df[-nrow(rev.df),],
Footnotes = NA)
price.df<-t(price.df)
price.df<-cbind.data.frame("keyspecies" = NA,
price.df,
Footnotes = NA)
}
colnames(land.df)<-colnames(rev.df)<-colnames(price.df)<-c("keyspecies",
as.character(min(landings.df$year):
max(landings.df$year)),
"Footnotes")
#####footnote with species
return(list("revenue" = rev.df,
"landings" = land.df,
"price" = price.df,
"uniquespp" = uniquespp))
}
temp<-itis_reclassify(tsn = as.numeric(paste0(unique(commercial.data$TSN))),
categories = list("Shellfish" = spcat.list$General$Shellfish,
"Finfish" = spcat.list$General$Finfish),
missing.name="Uncategorized")
tsn.id<-temp[1][[1]]
if (sum(tsn.id$category %in% c("Other", "Uncategorized"))>0) {
tsn.id<-tsn.id[!(tsn.id$category %in% c("Other", "Uncategorized")),
c("TSN", "category")]
}
taxspp<-function(spp, spp0, landings.df){
species<-c()
notspecies<-c()
for (ii in 1:length(spp)){
if (spp[[ii]]$rank[nrow(spp[[ii]])] %in% "species") {
#Find common name
a<-unique(landings.df$CommonName[landings.df$TSN %in% as.numeric(names(spp[ii]))])
a<-a[is.na(as.numeric(a))]
# SPECIES_CROSS_REFERENCE <- read.csv(paste0(
# 'C:/Users/Emii/Documents/Homework/FEUS/2018/FEUS2018Commercial/data/SPECIES_CROSS_REFERENCE.csv'),
# header=TRUE, stringsAsFactors = FALSE)
# a<-unique(SPECIES_CROSS_REFERENCE$AFS_NAME[SPECIES_CROSS_REFERENCE$ITIS %in% as.numeric(names(spp[ii]))])
if (!(length(a) %in% 0)) {
b<-strsplit(x = a, split = ",")[[1]]
# b<-gsub(pattern = ", Na", replacement = "", x = b)
c0<-ifelse(length(b) > 1, paste0(b[2], " ", b[1]), b)
c0<-gsub(pattern = " ", replacement = " ", x = c0)
c0<-tolower2(c0, capitalizefirst = T)
c0<-gsub(pattern = "spp", replacement = "spp.", x = c0)
c0<-ifelse(grepl(pattern = "spp.", x = c0) &
grepl(pattern = spp0, x = c0, ignore.case = T),
gsub(pattern = spp0, replacement = "", x = c0, ignore.case = T),
c0)
# c0<-ifelse(bottomrank[i] %in% "genus" & !(grepl(pattern = "spp.", x = c0)),
# paste0(c0, " spp."), c0)
c0<-trimws(c0)
c0<-tolower2(c0, capitalizefirst = F)
# commonname[i]<-c0
species<-c(species,
paste0(tolower2(unique(c0))))
}
}
if (!(spp[[ii]]$rank[nrow(spp[[ii]])] %in% "species")) {
notspecies<-c(notspecies,
paste0(ifelse(spp[[ii]]$rank[nrow(spp[[ii]])] %in% "genus",
paste0("*", (spp[[ii]]$name[nrow(spp[[ii]])]), "*"),
tolower(spp[[ii]]$name[nrow(spp[[ii]])])) ,
" ", spp[[ii]]$rank[nrow(spp[[ii]])]))
}
}
return(list("species" = species,
"notspecies" = notspecies))
}
emilyhmarkowitz/FisheriesEconomicProductivityIndex documentation built on June 13, 2025, 10:31 a.m.
R Package Documentation
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####***Intitial edit of commerical.data######
commercial.data <- read.csv(file = paste0(dir.data, "/fis_foss_coastal_ws-foss_landings_mv.csv"))
commercial.data<-commercial.data[, c("TSN", "STATE_NAME", "YEAR", "TS_AFS_NAME", "POUNDS",
"DOLLARS", "COLLECTION")]#, "REGION_ID")]
commercial.data<-commercial.data[commercial.data$COLLECTION %in% "Commercial",]
commercial.data$COLLECTION<-NULL
names(commercial.data)<-c("TSN", "State", "year", "CommonName", "POUNDS", "DOLLARS")
commercial.data<-commercial.data[commercial.data$year %in% minyr:maxyr,]
#####***State Codes#####
commercial.data$State<-simpleCap(commercial.data$State)
commercial.data$State[commercial.data$State %in% "Florida-east"]<-"East Florida"
commercial.data$State[commercial.data$State %in% "Florida-west"]<-"West Florida"
commercial.data$State[commercial.data$State %in% "Hawaii"]<-"Hawai`i"
# commercial.data<-merge(x = statereg, y = commercial.data, by = "State")
######***Apply Updated TSN Codes############
fisc_itis_history<-read.csv(file = paste0(dir.data, "fis_foss-fisc_itis_history.csv"))
fisc_itis_history<-fisc_itis_history[,c("NEW_ITIS", "OLD_ITIS")]
commercial.data$TSN.old<-commercial.data$TSN
for (i in 1:length(unique(fisc_itis_history$OLD_ITIS))){
old<-unique(fisc_itis_history$OLD_ITIS)[i]
new<-unique(fisc_itis_history$NEW_ITIS[fisc_itis_history$OLD_ITIS %in% old])
#Find the correct common name
common<-unique(commercial.data$CommonName[commercial.data$TSN %in% c(new)])
common<-unique(commercial.data$CommonName[commercial.data$TSN %in% c(old, new)])
common<-common[is.na(as.numeric(common))]
if (length(common) %in% 0) {
common<-""
} else if (length(common)>0) {
common.new<-unique(commercial.data$CommonName[commercial.data$TSN %in% c(new)])
if (length(common.new)>0){
common<-common.new[1]
} else if (length(common.new)>0) {
common<-common[1]
}
}
commercial.data$CommonName[commercial.data$TSN %in% c(old, new)]<-common
#Get most up to date TSN number
commercial.data$TSN[commercial.data$TSN %in% old]<-new
}
## Load and minorly edit NMFSToTSNTranslation Reference Data
NMFSToTSNTranslation <- read.csv(paste0(dir.data, '/NMFS to TSN translation.csv'),
header=TRUE, stringsAsFactors = FALSE)
names(NMFSToTSNTranslation)<-c("TSN", "CommonName", "SCIENTIFIC_NAME", "NMFS", "Reported.by.Sources")
NMFSToTSNTranslation<-NMFSToTSNTranslation[, names(NMFSToTSNTranslation) %in%
c("TSN", "CommonName", "SCIENTIFIC_NAME", "NMFS", "Reported.by.Sources")]
TSNHistory<-read.csv(paste0(dir.data, 'fis_foss-fisc_itis_history.csv'),
header=TRUE, stringsAsFactors = FALSE)
TSNHistory<-TSNHistory[,c("OLD_ITIS", "NEW_ITIS")]
for (i in 1:nrow(TSNHistory)){
if (sum(NMFSToTSNTranslation$TSN %in% TSNHistory$OLD_ITIS[i])>1) {
# print(i)
NMFSToTSNTranslation$TSN[NMFSToTSNTranslation$TSN %in% TSNHistory$OLD_ITIS[i]]<-TSNHistory$NEW_ITIS[i]
}
}
a<-NMFSToTSNTranslation[!(NMFSToTSNTranslation$NMFS %in%
names(table(NMFSToTSNTranslation$NMFS)[table(NMFSToTSNTranslation$NMFS)>1])),]
b<-NMFSToTSNTranslation[NMFSToTSNTranslation$NMFS %in%
names(table(NMFSToTSNTranslation$NMFS)[table(NMFSToTSNTranslation$NMFS)>1]),]
cc<-c()
for (i in 1:length(unique(b$NMFS))){
nmfs<-unique(b$NMFS)[i]
if ( sum(grepl(pattern = "CLS:88", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T))>0) {
bb<-b[which(b$NMFS %in% nmfs)[grep(pattern = "CLS:88", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T)],]
} else if ( sum(grepl(pattern = "SWFSC:9", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T))>0 ) {
bb<-b[which(b$NMFS %in% nmfs)[!(grepl(pattern = "SWFSC:9", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T))],]
} else if ( sum(grepl(pattern = "HQHIST:10", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T))>0 ) {
bb<-b[which(b$NMFS %in% nmfs)[grep(pattern = "HQHIST:10", x = b$Reported.by.Sources[b$NMFS %in% nmfs], ignore.case = T)],]
}
cc<-rbind.data.frame(cc, bb)
}
NMFSToTSNTranslation<-rbind.data.frame(a, cc)
NMFSToTSNTranslation$Reported.by.Sources<-NULL
## Load and minorly edit species.codes Reference Data
# species.codes <- read.csv(paste0(dir.data, '/olo_species_codes.csv'), header=TRUE, stringsAsFactors = FALSE)
# species.codes<-unique(species.codes)
# species.codes$AFS_NAME<-trimws(species.codes$AFS_NAME)
# species.codes$FUS_NAME[which(species.codes$AFS_NAME=="POLLOCK, WALLEYE")]<-"Pollock, Alaska"
# species.codes$NAME[which(species.codes$AFS_NAME=="POLLOCK, WALLEYE")]<-"Pollock,Alaska"
# species.codes$AFS_NAME[which(species.codes$AFS_NAME=="POLLOCK, WALLEYE")]<-"POLLOCK, ALASKA"
# ####NMFSToITIS###
# NMFSToITIS<-NMFSToTSNTranslation
# NMFSToITIS0<-data.frame()
# for (i in 1:length(unique(NMFSToITIS$NMFS))) {
# id<-unique(NMFSToITIS$NMFS)[i]
# temp<-NMFSToITIS[NMFSToITIS$NMFS %in% id, ]
# if (nrow(temp)>1) {
# NMFSToITIS0<-rbind.data.frame(NMFSToITIS0, temp[1,])
# } else {
# NMFSToITIS0<-rbind.data.frame(NMFSToITIS0, temp)
# }
# }
# NMFSToITIS<-NMFSToITIS0
# NMFSToITIS$Sources<-"NMFSToTSNTranslation"
#
#
# NMFSToITIS0<-cbind.data.frame("TSN" = NA,
# "CommonName" = (species.codes$AFS_NAME[!(species.codes$species %in%
# unique(NMFSToITIS$NMFS))]), "SCIENTIFIC_NAME" = NA,
# "NMFS" = (species.codes$species[!(species.codes$species %in%
# unique(NMFSToITIS$NMFS))]),
#
# "Sources" = "species.codes")
#
# NMFSToITIS<-rbind.data.frame(NMFSToITIS,
# unique(NMFSToITIS0))
#
#
# NMFSToITIS0<-cbind.data.frame("TSN" = NA,
# "CommonName" = NA,
# "SCIENTIFIC_NAME" = NA,
# "NMFS" = unique(commercial.data.old$SPECIES[!(commercial.data.old$SPECIES %in% unique(NMFSToITIS$NMFS))]),
# "Sources" = "commercial.data.old")
# NMFSToITIS<-rbind.data.frame(NMFSToITIS,
# NMFSToITIS0)
####ITISToNMFS###
ITISToNMFS<-NMFSToTSNTranslation
ITISToNMFS0<-data.frame()
for (i in 1:length(unique(ITISToNMFS$TSN))) {
id<-unique(ITISToNMFS$TSN)[i]
temp<-ITISToNMFS[ITISToNMFS$TSN %in% id, ]
if (nrow(temp)>1) {
ITISToNMFS0<-rbind.data.frame(ITISToNMFS0, temp[1,])
} else {
ITISToNMFS0<-rbind.data.frame(ITISToNMFS0, temp)
}
}
ITISToNMFS<-ITISToNMFS0
ITISToNMFS$Sources<-"NMFSToTSNTranslation"
ITISToNMFS0<-cbind.data.frame("TSN" = unique(commercial.data$TSN[!(commercial.data$TSN %in%
unique(ITISToNMFS$TSN))]),
"CommonName" = NA,
"SCIENTIFIC_NAME" = NA,
"NMFS" = NA,
"Sources" = "commercial.data.new")
ITISToNMFS<-rbind.data.frame(ITISToNMFS,
ITISToNMFS0)
###***Merge data####
commercial.data<-commercial.data[!(commercial.data$TSN %in% c(0)),]
commercial.data<-aggregate.data.frame(x = commercial.data[,names(commercial.data) %in%
c("POUNDS", "DOLLARS")],
by = list("TSN" = commercial.data$TSN,
"State" = commercial.data$State,
"year" = commercial.data$year),
FUN = sum, na.rm = T)
commercial.data<-merge(x = commercial.data,
y = ITISToNMFS[,c("TSN", "CommonName")],
by = "TSN")
commercial.data$CommonName<-trimws(commercial.data$CommonName)
commercial.data<-merge(x = commercial.data,
y = statereg,
by = "State")
######***GENERAL########
SpCodeName.General<-list(
"All" = c(unique(commercial.data$TSN)),
"Finfish" = 914179, # Infraphylum Gnathostomata
"Shellfish" = c(82696, # Phylum Arthropoda – Artrópode, arthropodes, arthropods
69458), # Phylum Mollusca – mollusques, molusco, molluscs, mollusks
###
########********Crustaceans#####
###
'Arthropoda' = 82696, # Phylum Arthropoda – Artrópode, arthropodes, arthropods
'American lobster' = 97314, #Species: Homarus americanus H. Milne Edwards, 1837 – valid
'Shrimp' = c(96106, # Infraorder Caridea Dana, 1852
83688, # Order Anostraca G. O. Sars, 1867 – brine shrimp, fairy shrimp
621194, # Superfamily Callianassoidea Dana, 1852
97294, # Infraorder Stenopodidea Claus, 1872
95600), # Suborder Dendrobranchiata Bate, 1888
'Mantis Shrimp' = 99140, # Order Stomatopoda Latreille, 1817 – mantis shrimp, mantis shrimps
'Penaeidae Shirmp' = 95602, # Family Penaeidae Rafinesque, 1815 – penaeid shrimps, crevettes pénaéïdes
'Crab'= c(#95599, # Order Decapoda Latreille, 1802 – crabs, crayfishes, lobsters, prawns, shrimp, crabes, crevettes, écrevisses, homards # Has previously also included all decapods
98276, #Infraorder Brachyura Latreille, 1802 – short-tailed crabs, true crabs
97698), # Infraorder Anomura MacLeay, 1838
'Sea urchins' = 157821, #Class Echinoidea Leske, 1778 – heart urchins, sand dollars, sea urchins, châtaignes de mer, clypéastres, oursins, bolacha da praia, equinóide, ouriço do mar, heart urchins, sand dollars, sea urchins, urchins
'Pac Spiny lobster' = 97650, # Species Panulirus interruptus (J. W. Randall, 1840) – California spiny lobster
'Crawfish' = 97306, # Superfamily Astacoidea Latreille, 1802 – crayfishes
'Lobsters' = c(97660, # Family Scyllaridae Latreille, 1825 – slipper lobsters
97646), # Family Palinuridae Latreille, 1802 – spiny lobsters
'Stone crab' = 98810, # Genus Menippe De Haan, 1833
'Blue crab' = 98696, # Species Callinectes sapidus M. J. Rathbun, 1896 – blue crab, bluepoint
'Caribbean Spiny lobster' = 97648, # Species Panulirus argus (Latreille, 1804) – Caribbean spiny lobster
'Red Crab' = 620992, # Species Chaceon quinquedens (S. I. Smith, 1879) – red deepsea crab
'Horseshoe crab' = 82703, # Species Limulus polyphemus (Linnaeus, 1758) – Atlantic horseshoe crab, American horseshoe crab
'Abalone' = 69492, # Family Haliotidae Rafinesque, 1815 – abalone # 'Abalone*',
'Cockle' = 80865, # Family Cardiidae Lamarck, 1809 # 'Cockle',
'Limpets' = 566854, # Order Patellogastropoda # 'LIMPETS',
'Periwinkles' = 70395, # Genus Littorina Ferussac, 1822 # 'PERIWINKLES',
###
########********Other############
###
'Bloodworms' = 66107, # Species Glycera dibranchiata Ehlers, 1868
'Coral' = 51938, # Class Anthozoa Ehrenberg, 1834 – corals, flower animals, sea anemones, anémones de mer, coraux, água viva, anêmona, antozoário, caravela, corais, gorgônia
###
########********General Fish##############
###
#######All
'Flounders' = c(553179, # Family Paralichthyidae – flétans de sable, lenguados areneros, sand flounders, lefteye flounders
172714, # Family Bothidae – lefteyed flounders, lefteye flounders, lenguados chuecos, turbots flounders
172859), # Family Pleuronectidae – halibuts, dabs, righteyed flounders, platijas, plies, righteye flounders
'Righteyed flounders' = 172859, # Family Pleuronectidae – halibuts, dabs, righteyed flounders, platijas, plies, righteye flounders
'Flatfish' = c(172707), #Suborder Pleuronectoidei
'Dolphinfish' = 168790, # Genus Coryphaena Linnaeus, 1758
'Cusk-eel' = 164839,
'Menhaden' = 161731, #Genus Brevoortia Gill, 1861 – menhadens
'Herring' = 551153, # Subfamily Clupeinae – herrings, sardines, sprats
'Atlantic Herring' = 161722, # Species Clupea harengus Linnaeus, 1758 – Baltic herring, herring, hareng atlantique, Atlantic herring
'Hogchoker' = 172982, # Species Trinectes maculatus (Bloch & Schneider, 1801) – hogchoker, suela tortilla, Hogchoker
'Plaice' = 172900, # Genus Pleuronectes Linnaeus, 1758 – plaices, northern flounders
'Swordfish' = 172480, #Family Xiphiidae – swordfishes, espadas, espadons
'Goosefish' = c(#164500, #Species Lophius gastrophysus Miranda Ribeiro, 1915 – blackfin goosefish, rape pescador
164498), # Genus Lophius Linnaeus, 1758
'Sablefish' = 167123, # Species Anoplopoma fimbria (Pallas, 1814) – sablefish, bacalao negro
'Snappers' = 168845, # Family Lutjanidae – sea perches, snappers, perches de mer, vivaneaux, fusiliers, pargos y huachinangos
'Albacore tuna' = 172419, # Species Thunnus alalunga (Bonnaterre, 1788) – atún blanco, albacore, longfinned albacore, albacora, germon
'Bluefin tuna' = 172421, # Species Thunnus thynnus (Linnaeus, 1758) – bluefin tuna, atún aleta azul, atún, horse mackerel, northern bluefin tuna, Atlantic bluefin tuna, thon rouge
'Tunas' = c(638252, # Tribe Thunnini Starks, 1910
-172454, # Genus Auxis Cuvier, 1829 – frigate mackerels, frigate tunas
-172459), # Genus Allothunnus Serventy, 1948
'Mullets' = 170333, # Family Mugilidae – mullets, grey mullets, lisas, muges
'Pollock' = 164726, # Genus Pollachius Nilsson, 1832 – pollocks
'Salmon' = c(161996, # Species Salmo salar Linnaeus, 1758 – Atlantic salmon, saumon atlantique
161975, # Species Oncorhynchus gorbuscha (Walbaum, 1792) – pink salmon, humpback, humpbacked salmon, saumon rose
161976, # Species Oncorhynchus keta (Walbaum in Artedi, 1792) – chum salmon
161977, # Species Oncorhynchus kisutch (Walbaum, 1792) – coho salmon, salmón plateado, saumon coho, silver salmon
161979, # Species Oncorhynchus nerka (Walbaum in Artedi, 1792) – blueback salmon, kokanee, red salmon, sockeye salmon, saumon rouge
161980 # Species Oncorhynchus tshawytscha (Walbaum in Artedi, 1792) – Chinook salmon, salmón boquinegra, king salmon, saumon chinook
),
'Ladyfish' = 161110, # Genus Elops Linnaeus, 1766 – ladyfishes
'Oilfish' = 172364, # Species Ruvettus pretiosus Cocco, 1833 – oilfish, escolar, escolar clavo
'Anchovies' = 553173, # Family Engraulidae – anchois, anchoas, anchovies
'Croaker' = c(169258, # Genus Bairdiella Gill, 1861 – mademoiselle, striped croakers
169282, # Genus Micropogonias Bonaparte, 1831 – finebarbel croakers
169357, # Genus Cheilotrema Tschudi, 1846
169256, # Genus Genyonemus Gill, 1861
169359, # Genus Roncador Jordan and Gilbert, 1880
169297), # Genus Umbrina Cuvier, 1816 – croakers, roncadores
'American John Dory' = 166284, # Species Zenopsis conchifera (Lowe, 1852) – buckler dory, San Pedro plateado, American john dory
'Grenadiers' = 165332, # Family Macrouridae Gilbert and Hubbs, 1916 – grenadiers, rattails, grenadiers, marcrouridés, granaderos
'Lumpfish' = 167611, # Genus Cyclopterus Linnaeus, 1758 – lumpfishes
'Octopus' = 82590, # Family Octopodidae D'Orbigny, 1839-1842 in Férussac and D'Orbigny, 1834-1848
'Pomfrets' = 170288, # Genus Brama Bloch and Schneider, 1801 – pomfrets
'Sardine' = c(161728, # Genus Sardinops Hubbs, 1929
161812, # Genus Sardina Antipa, 1904
161762), # Genus Sardinella Valenciennes in Cuvier and Valenciennes, 1847
'Shad' = c(161701, # Genus Alosa Linck, 1790 – river herrings
161736), # Genus Dorosoma Rafinesque, 1820 – gizzard shads
'Thornyhead' = 166782, # 'Genus Sebastolobus Gill, 1881
###
########********Atlantic Fish####
###
'American eel' = 161127, # Species Anguilla rostrata (Lesueur, 1817) – American eel, anguila, anguila americana, anguille d'Amérique
'Atlantic Cod' = 164712, # Species Gadus morhua Linnaeus, 1758 – morue de l'Atlantique, bacalao del Atlántico, cod, rock cod, morue franche, Atlantic cod
'Atlantic haddock' = 164744, # Species Melanogrammus aeglefinus (Linnaeus, 1758) – haddock, aiglefin
'Haddock' = 164744, # Species Melanogrammus aeglefinus (Linnaeus, 1758) – haddock, aiglefin
'Atlantic mackerel' = 172414, #Species Scomber scombrus Linnaeus, 1758 – caballa del Atlántico, maquereau commun, maquereau bleu, Atlantic mackerel
'Atlantic pollock' = 164727, #Species Pollachius virens (Linnaeus, 1758) – pollock, coalfish, carbonero, lieu noir, saithe, goberge
'Atlantic croaker' = 169283, # Species Micropogonias undulatus (Linnaeus, 1766) – Atlantic croaker, roncadina, gurrubata
'Atlantic Wolffish' = 171336, # Genus Anarhichas Linnaeus, 1758 – Atlantic wolffishes
'Alewife' = 161706, # Species Alosa pseudoharengus (Wilson, 1811) – alewife, kyak, gaspareau, bigeye herring, branch herring, freshwater herring, gray herring, white herring, sawbelly, grayback
'Black sea bass' = 167687, # Species Centropristis striata (Linnaeus, 1758) – black sea bass
'Porgies' = 169206, # Genus Pagrus Cuvier, 1816
'Striped bass' = 167680, # Species Morone saxatilis (Walbaum, 1792) – rockfish, striped bass, lobina estriada, bar rayé
'Spot' = 169267, # VSpecies Leiostomus xanthurus Lacepède, 1802 – spot, croca
'King mackerel' = 172435, # Species Scomberomorus cavalla (Cuvier, 1829) – king mackerel, sierra, carito, carite lucio, thazard serra
'Scups' = 169181, # Genus Stenotomus Gill, 1865
'Spanish mackerel' = 172436, # Species Scomberomorus maculatus (Mitchill, 1815) – serrucho, sierra común, carite Atlántico, thazard Atlantique, Atlantic Spanish mackerel, Spanish mackerel
'Tilefish' = 168537, #Family Malacanthidae – tilefishes, blanquillos, tiles
'Black drum' = 169288, # Species Pogonias cromis (Linnaeus, 1766) – black drum, corvina negra, tambor negro
'Groupers' = 643094, # Tribe Epinephelini
'Gag grouper' = 167759, # 167759 – Mycteroperca microlepis (Goode and Bean, 1879) – valid – abadejo, charcoal belly, gag
'Red grouper' = 167702, # Species Epinephelus morio (Valenciennes in Cuvier and Valenciennes, 1828) – red grouper, cherna americana
'Red snapper' = 168853, # Species Lutjanus campechanus (Poey, 1860) – red snapper, pargo colorado, huachinango del Golfo, northern red snapper
'Atlantic Bonito' = 172409, # Species Sarda sarda (Bloch, 1793) – bonito, bonito del Atlántico, bonite à dos rayé, bonito Atlántico, bloater, bone jack, Boston mackerel, common bonito, Atlantic bonito
'Billfishes' = 172486, # Family Istiophoridae – billfishes, sailfishes, marlins, spearfishes, picudos, voiliers
'White perch' = 167678, #Species Morone americana (Gmelin, 1789) – white perch, baret
'Vermilion snapper' = 168909, # Rhomboplites aurorubens (Cuvier in Cuvier and Valenciennes, 1829) – vermilion snapper, cotorro, besugo
'Weakfish' = 169241, #Species Cynoscion regalis (Bloch and Schneider, 1801) – weakfish, gray trout, sea trout
'Butterfish' = 172567, # Species Peprilus triacanthus (Peck, 1804) – palometa estrecha, butterfish, stromatée à fossettes
'Offshore Hake' = 164793, # Species Merluccius albidus (Mitchill, 1818) – offshore hake, offshore whiting
'Blueline Tilefish' = 168543, # Species Caulolatilus microps Goode and Bean, 1878 – blueline tilefish, blanquillo lucio
'Golden Tilefish' = 168546, # Species Lopholatilus chamaeleonticeps Goode and Bean, 1879 – blue tilefish, tilefish, conejo amarillo
'Monkfish' = 164499 , #Species Lophius americanus Valenciennes in Cuvier and Valenciennes, 1837 – goosefish, monkfish, baudroie d'Amérique
'Acadian Redfish' = 166774, # Species Sebastes fasciatus Storer, 1854 – Acadian redfish, Labrador redfish, Acadian rockfish, sébaste acadien
'American Plaice Flounder' = 172877, # Species Hippoglossoides platessoides (Fabricius, 1780) – American plaice, plie canadienne, American dab, Canadian plaice, dab, American Plaice
'Bluefish' = 168559, # Species Pomatomus saltatrix (Linnaeus, 1766) – bluefish, anjova
###
########********Pacific Fish############
###
'Halibut' = c(172931, # Genus Hippoglossus Cuvier, 1816 – halibuts
172929), # Genus Reinhardtius Gill, 1861
'Pacific halibut' = 172932, #Species: Hippoglossus stenolepis Schmidt, 1904 – valid
'Atlantic halibut' = 172933, # Species Hippoglossus hippoglossus (Linnaeus, 1758) – Atlantic halibut, flétan atlantique, Atlantic Halibut
'Summer flounder' = 172735, #Species Paralichthys dentatus (Linnaeus, 1766) – summer flounder, fluke, cardeau d'été, Summer Flounder
'Winter Flounder' = 172905, # Species Pseudopleuronectes americanus (Walbaum, 1792) – plie rouge, blackback, Georges Bank flounder, lemon sole, rough flounder, winter flounder, Winter Flounder
'Witch Flounder' = 172873, #Species Glyptocephalus cynoglossus (Linnaeus, 1758) – witch flounder, gray sole, plie grise, Witch Flounder
'Yellowtail Flounder' = 172909, #Species Limanda ferruginea (Storer, 1839) – limande à queue jaune, rusty flounder, yellowtail flounder, Yellowtail Flounder
'Sand Dab Flounder' = 172746, #Species Scophthalmus aquosus (Mitchill, 1815) – windowpane, brill, sand dab, spotted flounder, turbot de sable, Windowpane
'Windowpane Flounder' = 172746, #Species Scophthalmus aquosus (Mitchill, 1815) – windowpane, brill, sand dab, spotted flounder, turbot de sable, Windowpane
'Pacific salmon' = c(
# 161974 # Genus Oncorhynchus Suckley, 1861 – Pacific salmon
161975, # Species Oncorhynchus gorbuscha (Walbaum, 1792) – pink salmon, humpback, humpbacked salmon, saumon rose
161976, # Species Oncorhynchus keta (Walbaum in Artedi, 1792) – chum salmon
161977, # Species Oncorhynchus kisutch (Walbaum, 1792) – coho salmon, salmón plateado, saumon coho, silver salmon
161979, # Species Oncorhynchus nerka (Walbaum in Artedi, 1792) – blueback salmon, kokanee, red salmon, sockeye salmon, saumon rouge
161980 # Species Oncorhynchus tshawytscha (Walbaum in Artedi, 1792) – Chinook salmon, salmón boquinegra, king salmon, saumon chinook
# 161989 # Species Oncorhynchus mykiss (Walbaum, 1792) – rainbow trout, trucha arcoiris, steelhead, truite arc-en-ciel, redband trout
),
'Ocean pout' = 630979, # Species Zoarces americanus (Bloch and Schneider, 1801) – ocean pout, loquette d'Amérique
'Pacific cod' = 164711, #Species Gadus macrocephalus Tilesius, 1810 – morue du Pacifique, bacalao del Pacifico, Pacific cod
# 'Pacific herring' = 551209, #Species Clupea pallasii Valenciennes in Cuvier and Valenciennes, 1847 – arenque del Pacífico, Pacific herring
'Pacific hake (whiting)' = 164792, #Species Merluccius productus (Ayres, 1855) – North Pacific hake, whiting, Pacific hake, merluza norteña, Pacific whiting
'Pacific sardine' = 161729,
'Pacific sanddab' = 172716, # Species Citharichthys sordidus (Girard, 1854) – Pacific sanddab, lenguado moteado, limande sordide, Pacific Sanddab
'Arrowtooth flounder' = 172862, # Species Atheresthes stomias (Jordan & Gilbert, 1880) – arrowtooth flounder, Arrowtooth Flounder, plie à grande bouche
'Sole' = 172980, # Family Soleidae – soles, soles, suelas soles
'Yellow sole' = 172907, # Species Limanda aspera (Pallas, 1814) – yellowfin sole, limande à nageoires jaunes, Yellowfin Sole
'Alaska Pollock' = c(#164722, #invalid - subsequent name/combination
934083), # Species Gadus chalcogrammus Pallas, 1814 – Walleye
'Rockfish' = 166705, # Genus Sebastes Cuvier, 1829 – rockfishes, rockcod, rosefishes
'Atka mackerel'= 167119, #Genus Pleurogrammus Gill, 1861 – atka mackerels
'Hake' = c(164729, #Genus Urophycis Gill, 1863 – codlings
164790), # Genus Merluccius Rafinesque, 1810 – hakes
'White hake' = 164732, # Species Urophycis tenuis (Mitchill, 1814) – white hake, mud hake, merluche blanche
'Red hake' = c(164730 # Species Urophycis chuss (Walbaum, 1792) – red hake, squirrel hake, merluche-écureuil
),#164729), # Genus Urophycis Gill, 1863 – codlings # Toledo, includes other hake
'Silver hake' = c(164791 #Species Merluccius bilinearis (Mitchill, 1814) – silver hake, merlu argenté
), # 164790), # Genus Merluccius Rafinesque, 1810 – hakes # Toledo, includes other hake
'Lingcod' = 167116, # Species Ophiodon elongatus Girard, 1854 – lingcod, molva
'White seabass' = 169387, # Species Atractoscion nobilis (Ayres, 1860) – white seabass, corvina cabaicucho
###
########********Cartilagenous Fish###########
###
'Skate' = 160845, # Family Rajidae Blainville, 1816 – rayas, rays, skates, raies
'Skates' = 160845, # Family Rajidae Blainville, 1816 – rayas, rays, skates, raies
'Dogfish Shark' = 160604, # Family Squalidae Blainville, 1816 – chiens de mer, cazones aguijones, dogfish sharks, spiny dogfishes
'Spiny dogfish' = 160617, #Species Squalus acanthias Linnaeus, 1758 – cazón espinoso común, piked dogfish, spiny dogfish, galludo espinoso, aiguillat commun, dogfish, grayfish, spurdog
'Sharks' = c(563987 # Superorder Euselachii #TOLEDO - some of these are sawfish, rays, and etc.
# 551500, # Order Carcharhiniformes – ground sharks
# 159788, # Order Heterodontiformes – bullhead sharks
# 159810, # Order Hexanchiformes Compagno, 1973 – cow sharks, frilled sharks
# 159851, # Order Lamniformes – mackerel sharks
# 551499, # Order Orectolobiformes – gatas nodrizas, requins-tapis, tiburones tapiceros, carpet sharks
# 551498, # Order Pristiophoriformes – saw sharks
# 160602, #Order Squaliformes Compagno, 1973 – dogfish sharks
# 563990 # Order Squatiniformes
),
'Thresher' = 159915, # Genus Alopias Rafinesque, 1810
"Rosette skate" = 564136,# Species Leucoraja garmani (Whitley, 1939) – rosette skate
'Little Skate' = 564130,#Species Leucoraja erinacea (Mitchill, 1825) – raie-hérisson, common skate, little skate, summer skate
'Winter Skate' = 564145,#Species Leucoraja ocellata (Mitchill, 1815) – raie tachetée, big skate, eyed skate, winter skate
'Barndoor Skate' = 564139,#Species Dipturus laevis (Mitchill, 1818) – grande raie, barndoor skate
'Smooth Skate' = 564151,# Species Malacoraja senta (Garman, 1885) – raie à queue de velours, smooth skate
'Thorny Skate' = 564149,#Species Amblyraja radiata (Donovan, 1808) – raie épineuse, starry skate, thorny skate
'Clearnose Skate' = 160855,#Species Raja eglanteria Bosc in Lacepède, 1800 – raya naricita, clearnose skate
###
########********Reef Fish############
###
'Bigeye' = 168176, # Family Priacanthidae – bigeyes, catalufas, beauclaires, catalufas
'Butterflyfishes' = 169554, # Family Chaetodontidae – butterflyfishes, peces mariposa, poissons-papillons
'Coney' = 167740, # Species Cephalopholis fulva (Linnaeus, 1758) – guatívere, cabrilla roja, coney
'Gag' = 167759, # Species Mycteroperca microlepis (Goode and Bean, 1879) – gag, abadejo, charcoal belly
'Glasseye snapper' = 168181, # Species Heteropriacanthus cruentatus (Lacepède, 1801) – catalufa espinosa, glasseye snapper, catalufa roquera
'Graysby' = 181220, # Species Cephalopholis cruentata (Lacepède, 1802) – enjambre, cherna enjambre, graysby
# 'Groupers' = 551018, # Subfamily Epinephelinae
'Hogfish' = c(170565, # Genus Lachnolaimus Cuvier, 1829
170482), # Genus Bodianus Bloch, 1790 – hogfishes
'Jacks' = c(168605, # Genus Caranx Lacepède, 1801 – cavallas, crevallies, bigeye jacks,
168688), # Genus Seriola Cuvier, 1816 – amberfishes, amberjacks, yellowtails
'Green Jobfish' = 168926, # Species Aprion virescens Valenciennes in Cuvier and Valenciennes, 1830 – blue-gray snapper, green jobfish
'Mackerel' = 638253, # Tribe Scombrini Bonaparte, 1831 – mackerels
'Pompano' = 168707, # Genus Trachinotus Lacepède, 1801 – pompanos
'Blackbelly Rosefish' = 166787, # Species Helicolenus dactylopterus (Delaroche, 1809) – blackbelly rosefish
'Runners' = 168737, # Genus Elagatis Bennett, 1840 – runners
'Scamp' = 167763, # 'Species Mycteroperca phenax Jordan and Swain, 1884 – scamp, abadejo garropa
'Scorpionfish' = 166704, # Family Scorpaenidae – firefishes, goblinfishes, rockfishes, scorpionfishes, escorpiones y rocotes, rascasses, scorpènes, scorpénidés
'Sheepshead' = 169189, # Species Archosargus probatocephalus (Walbaum, 1792) – sheepshead, sargo chopa
'Snapper' = 168845, # Family Lutjanidae – sea perches, snappers, perches de mer, vivaneaux, fusiliers, pargos y huachinangos
'Spanish flag' = 167798, # Species Gonioplectrus hispanus (Cuvier in Cuvier and Valenciennes, 1828) – Spanish flag, biajaiba de lo alto, cherna bandera
'Squirrelfishes' = 166170, # 'Family Holocentridae – soldierfishes, squirrelfishes, candiles, marignans
'Surgeonfishes' = 172250, # Family Acanthuridae – surgeonfishes, tangs, cirujanos, poissons-chirurgiens
'Tilefish' = 168537, # Family Malacanthidae – tilefishes, blanquillos, tiles
'Wenchman' = c(168913, # 'Species Pristipomoides aquilonaris (Goode and Bean, 1896) – wenchman, huachinango navaja
168914), # Species Pristipomoides freemani Anderson, 1966 – yelloweye wenchman, slender wenchman
# 'YELLOWTAIL'
###
########********Mollescus#############
###
'Mollusca' = 69458, # Phylum Mollusca – mollusques, molusco, molluscs, mollusks
'Sea scallop' = 79718, #Species Placopecten magellanicus (Gmelin, 1791) – sea scallop
'Squid' = 555706, # Superorder Decabrachia Boettger, 1952
'Oysters' = c(79777, # Family Spondylidae Gray, 1826
79857, # Family Gryphaeidae Vyalov, 1936
79866), # Family Ostreidae Rafinesque, 1815
'Clams' = 80384, #Order Veneroida H. Adams and A. Adams, 1856
'Mussels' = c(79451, #Family Mytilidae Rafinesque, 1815
79913), # Family Unionidae Rafinesque, 1820 # TOLEDO - why include freshwater spp?
'Blue mussel' = 79454, #Species Mytilus edulis Linnaeus, 1758 – edible blue mussel, blue mussel
'Conchs' = 72554, # Family Strombidae Rafinesque, 1815 (Conches)
'Whelks' = 74069, # Family Melongenidae Gill, 1867 (Whelks)
'Snails' = 72878, # Family Naticidae Guilding, 1834 (Snails)
'Loligo squid' = 82370, # Genus Loligo Lamarck, 1798
'Illex Squid' = 82520, # Genus Illex Steenstrup, 1880
'Atlantic surf clam' = 80944, #Species Spisula solidissima (Dillwyn, 1817) – Atlantic surfclam
'Arctic surf (Stimpson) clam' = 80983, #Species Mactromeris polynyma (Stimpson, 1860) – Arctic surfclam
'Softshell clam' = 81692, # Species Mya arenaria Linnaeus, 1758 – softshell clam, softshell
"Scallops" = 79611, # Family Pectinidae Rafinesque, 1815
'Eastern oyster' = 79872, # Species Crassostrea virginica (Gmelin, 1791) – eastern oyster
'Quahog clams' = 81495, # Genus Mercenaria Schumacher, 1817
'Ocean quahog clam' = 81343, # Species Arctica islandica (Linnaeus, 1767) – ocean quahog
'Snails' = 69459, # Class Gastropoda Cuvier, 1797 – gastropods, slugs, snails, escargots, gastéropodes, limaces, caracol, caramujo, lesma
###
########********Hawai`i##########
###
'Lobsters (*ula*)' = c(206946), # Superfamily Palinuroidea Latreille, 1802
'Dolphinfish (*mahimahi*)' = 168790, # Genus Coryphaena Linnaeus, 1758
"Marlin (*a'u*)" = c(#172486, # Family Istiophoridae – billfishes, sailfishes, marlins, spearfishes, picudos, voiliers TOLEDO
172490), #Genus Makaira Lacepède, 1802 – marlins, blue marlin
'Moonfish (*opah*)' = 166326, # Species Lampris guttatus (Brünnich, 1788) – opah, opah, pez mariposa
'Pomfrets (*Monchong*)' = 170287, #Family Bramidae – pomfrets, castagnoles, tristones
'Scad (*opelu*)' = c(168723, # Genus Decapterus Bleeker, 1851 – mackerel scads, round scads
168585, # Genus Trachurus Rafinesque, 1810 – saurels
168676), # Genus Selar Bleeker, 1851 – bigeyed scads, goggle-eyes, gogglers
'Hawaii Snappers' = c(168181, #Heteropriacanthus cruentatus (Lacepède, 1801) – catalufa espinosa, glasseye snapper, catalufa roquera
168845), #Family Lutjanidae – sea perches, snappers, perches de mer, vivaneaux, fusiliers, pargos y huachinangos
'Wahoo (*ono*)' = 172451, #Species Acanthocybium solandri (Cuvier in Cuvier and Valenciennes, 1832) – peto, wahoo, thazard-bâtard
###
########********Freshwater################
###
'True perches' = 168356, # Family Percidae – true perches, perches, percas, perches et dards, perches
'Quillback' = 163916, # ' Genus Carpiodes Rafinesque, 1820 – quillbacks, carpsuckers
'Silversides' = 630579, # Family Atherinopsidae – charales y pejerreyes, New World silversides, poissons d'argent
'Sturgeon' = 161064, # Family Acipenseridae – sturgeons, esturgeons, esturiones
'Suckers' = 163892, # Family Catostomidae – suckers, matalotes, catostomes
'Sunfishes' = 168093, # Family Centrarchidae – sunfishes, achigans et crapets, lobinas ####TOLEDO this is what data uses, but should be more specific
'Tilapias' = c(170014, # Genus Oreochromis Günther, 1889 – blue tilapias
169809, # Genus Tilapia Smith, 1840 – African mouthbrooders, speckled tilapias
553244), # Genus Sarotherodon Rüppell, 1852 – brushtooth tilapias
'Trout' = c(61989, # Species Oncorhynchus mykiss (Walbaum, 1792) – rainbow trout, trucha arcoiris, steelhead, truite arc-en-ciel, redband trout
161987, # Species Oncorhynchus aguabonita (Jordan, 1892) – golden trout, California golden trout
161981, # Species Oncorhynchus apache (Miller, 1972) – Apache trout, Arizona trout
623485, # Species Oncorhynchus iwame Kimura and Nakamura, 1961, Iwame trout
161992, # Species Oncorhynchus chrysogaster (Needham and Gard, 1964) – trucha dorada mexicana, Mexican golden trout
161983, # Species Oncorhynchus clarkii (Richardson, 1836) – cutthroat trout, trucha degollada, truite fardée
914076, # Species Oncorhynchus penshinensis (Pallas, 1814), Kamchatkan rainbow trout
161985), # Species Oncorhynchus gilae (Miller, 1950) – Gila trout
# 'TURTLE*',
'Walleye' = c(650173, # Species Sander vitreus (Mitchill, 1818) – walleye, doré jaune
168508), # invalid - subsequent name/combination Valid Name: Sander vitreus (Mitchill, 1818
'Whitefish' = 623284, # Subfamily Coregoninae
'Rock Bass' = 168097, # Species Ambloplites rupestris (Rafinesque, 1817) – rock bass, crapet de roche
'Bowfin' = 161100, # Order Amiiformes – bowfin
'Buffalofishes' = 163954, # Genus Ictiobus Rafinesque, 1820 – buffalo suckers, buffalo fishes
'Burbot' = 164725,# Species Lota lota (Linnaeus, 1758) – burbot, eelpout, lotte
'Carp' = c(163690, # Genus Hypophthalmichthys Bleeker, 1860 – bighead carps
163349, # Genus Carassius Nilsson, 1832 – Crucian carps
163678, # Genus Cirrhinus Oken, 1817
163343, # Genus Cyprinus Linnaeus, 1758 – common carps
638893, # Genus Mylopharyngodon Peters, 1881
163536), # Genus Ctenopharyngodon Steindachner, 1866 – grass carps herbívora
'Freshwater Catfish' = c(163995, # Ictaluridae family 163995
164066), # Siluridae family 164066
'Chubs' = c(163541, # Genus Gila Baird and Girard, 1853 – western chubs
163495, # Genus Hybopsis Agassiz, 1854 – bigeye chubs
163374, # Genus Semotilus Rafinesque, 1820 – creek chubs
163562, # Genus Hemitremia Cope, 1870 – flame chub
163881, # Genus Platygobio Gill, 1863 – flathead chubs
163391, # Genus Nocomis Girard, 1856 – hornyhead chubs
163534, # Genus Couesius Jordan, 1878 – lake chubs
163566, # Genus Iotichthys Jordan and Evermann, 1896 – least chubs
163878, # Genus Oregonichthys Hubbs in Schultz, 1929 – Oregon chubs
687586, # Genus Petroleuciscus Bogutskaya, 2002
163819, # Genus Erimystax Jordan, 1882 – slender chubs
638870, # Genus Algansea Girard, 1856
639152, # Genus Siphateles Cope, 1883 – tui chubs
913987, # Species Lepidomeda copei (Jordan and Gilbert, 1881) – leatherside chub, northern leatherside chub
163863), # Genus Macrhybopsis Cockerell and Allison, 1909 – blacktail chubs
'Carppie' = 168165, # Genus Pomoxis Rafinesque, 1818 – crappies
'Crayfishes or crawfishes' = 97306,# Superfamily Astacoidea Latreille, 1802 – crayfishes
'Freshwater Drum' = 169363, # Genus Aplodinotus Rafinesque, 1819 – river drums, thunderpumpers, freshwater drums
# 'FINFISHES, FW, OTHER',
'Gars' = 650224, # Order Lepisosteiformes
# 'GOLDFISH', # covered by carps
'Mooneyes' = 161903, # Family Hiodontidae – mooneyes, laquaiches, ojos de luna
'Catfish' = c(163992), #, Siluriformes order
###
########********Inshore and Miscellaneous############
###
'Stingrays' = 649685, # Order Myliobatiformes – stingrays
'Alligator* Land' = c(201897, # Species Atractosteus spatula (Lacepède, 1803) – alligator gar, catán
668671, # Species Macrochelys temminckii (Troost in Harlan, 1835) – Alligator Snapping Turtle
551771), # Species Alligator mississippiensis (Daudin, 1802) – American Alligator, Aligator americano, alligator, gator, Florida alligator, Mississippi alligator, Louisiana alligator
# 'Bass',
'Echinoderm' = 156857, # Phylum Echinodermata Bruguière, 1791 [ex Klein, 1734] – echinoderms, échinodermes, bolacha da praia, equinoderma, equinodermata, estrela do mar, ouriço do mar
'Frogs' = 173423, # Order Anura – Frogs, perereca, rã, sapo, crapauds, grenouilles, Toads
# 'HERRING, PACIFIC, ROE ON KELP',
'Jellyfish' = 51483, # Class Scyphozoa Götte, 1887 – jellyfish, cup animals, jellyfishes, méduses, água viva, Cifozoa, cifozoário, meduza
# 'LEATHER-BACK',
'Permit' = 168709, # Species Trachinotus falcatus (Linnaeus, 1758) – permit, pámpano, pámpano palometa
'Stingrays' = 160960, # Genus Gymnura van Hasselt, 1823 – stingrays, butterfly rays
'Sea Cucumber' = 158140, # Class Holothuroidea – holotúria, pepino do mar, cocombres de mer, sea cucumbers
'Seaweed' = 660055, # Class Phaeophyceae
'Sponges' = 46861, # Phylum Porifera Grant, 1836 – sponges, éponges, esponja, porifero
'Starfish' = 156862, # Class Asteroidea de Blainville, 1830 – étoiles de mer, stelléridés, estrela do mar, sea stars, starfishes, starfish, estrella de mar
'Rock bass' = 168097, # Species Ambloplites rupestris (Rafinesque, 1817) – rock bass, crapet de roche
###
########********Bait############
###
'Ballyhoo' = 165459, # Genus Hemiramphus Cuvier, 1816
# 'FINFISHES, UNC BAIT AND ANIMAL FOOD',
'Mummichog' = 165647, # Species Fundulus heteroclitus (Linnaeus, 1766) – mummichog, choquemort
'Sandworms' = 65891 # Species Neanthes virens (Sars, 1835)
)
######***STATE and UNITED STATES########
### Species names and codes for the state tables
#########******United States#############
SpCodeName<-list(
"United States" = list('American lobster' = SpCodeName.General$`American lobster`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Menhaden' = SpCodeName.General$Menhaden,
'Pacific halibut' = SpCodeName.General$`Pacific halibut`,
'Pacific salmon' = SpCodeName.General$`Pacific salmon`,
'Sablefish' = SpCodeName.General$Sablefish,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Shrimp' = SpCodeName.General$Shrimp,
'Tunas' = SpCodeName.General$Tunas,
'Alaska pollock' = SpCodeName.General$`Alaska Pollock`),
#####******North Pacific Region#####
#Alaska
"Alaska" = list('Atka mackerel'= SpCodeName.General$`Atka mackerel`,
'Crab'= SpCodeName.General$Crab,
'Flatfish' = SpCodeName.General$Flatfish,
'Pacific cod' = SpCodeName.General$`Pacific cod`,
'Pacific halibut' = SpCodeName.General$`Pacific halibut`,
'Pacific herring' = SpCodeName.General$`Herring`,
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Alaska Pollock' = SpCodeName.General$`Alaska Pollock`),
"North Pacific"= list('Atka mackerel'= SpCodeName.General$`Atka mackerel`,
'Crab'= SpCodeName.General$Crab,
'Flatfish' = SpCodeName.General$Flatfish,
'Pacific cod' = SpCodeName.General$`Pacific cod`,
'Pacific halibut' = SpCodeName.General$`Pacific halibut`,
'Pacific herring' = SpCodeName.General$`Herring`,
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Alaska Pollock' = SpCodeName.General$`Alaska Pollock`
),
#####******Pacific Region#####
"Pacific" = list('Albacore tuna' = SpCodeName.General$`Albacore tuna`,
'Crab' = SpCodeName.General$Crab,
'Flatfish' = SpCodeName.General$Flatfish,
'Pacific hake (whiting)' = SpCodeName.General$`Pacific hake (whiting)`,
'Other shellfish' = c(SpCodeName.General$Arthropoda,
SpCodeName.General$Mollusca,
-(SpCodeName.General$Crab),
-(SpCodeName.General$Shrimp),
-(SpCodeName.General$Squid)),
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Shrimp' = SpCodeName.General$Shrimp,
'Squid' = SpCodeName.General$Squid),
#California
"California" = list('Crab'= SpCodeName.General$Crab,
'Pacific sardine' = SpCodeName.General$`Pacific sardine`,
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Sea urchins' = SpCodeName.General$`Sea urchins`,
'Shrimp' = SpCodeName.General$Shrimp,
'Spiny lobster' = SpCodeName.General$`Pac Spiny lobster`,
'Squid' = SpCodeName.General$Squid,
'Swordfish' = SpCodeName.General$Swordfish),
## Oregon
"Oregon" = list('Albacore tuna' = SpCodeName.General$`Albacore tuna`,
'Crab' = SpCodeName.General$Crab,
'Flatfish' = SpCodeName.General$Flatfish,
'Pacific hake (whiting)' = SpCodeName.General$`Pacific hake (whiting)`,
'Oysters' = SpCodeName.General$Oysters,
'Pacific sardine' = SpCodeName.General$`Pacific sardine`,
'Rockfish' = SpCodeName.General$Rockfish,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Shrimp' = SpCodeName.General$Shrimp
),
#Washington
"Washington" = list('Albacore tuna' = SpCodeName.General$`Albacore tuna`,
'Clams' = SpCodeName.General$Clams,
'Crab' = SpCodeName.General$Crab,
'Pacific hake (whiting)' = SpCodeName.General$`Pacific hake (whiting)`,
'Pacific halibut' = SpCodeName.General$`Pacific halibut`,
'Mussels' = SpCodeName.General$Mussels,
'Oysters' = SpCodeName.General$Oysters,
'Sablefish' = SpCodeName.General$Sablefish,
'Salmon' = SpCodeName.General$`Pacific salmon`,
'Shrimp' = SpCodeName.General$Shrimp
),
########******Western Pacific Region (Hawaii)#######
########Hawaii
"Hawai`i" = list('Lobsters (*ula*)' = SpCodeName.General$`Lobsters (*ula*)`,
'Dolphinfish (*mahimahi*)' = SpCodeName.General$`Dolphinfish (*mahimahi*)`,
"Marlin (*a'u*)" = SpCodeName.General$`Marlin (*a'u*)`,
'Moonfish (*opah*)' = SpCodeName.General$`Moonfish (*opah*)`,
'Pomfrets (*Monchong*)' = SpCodeName.General$`Pomfrets (*Monchong*)`,
'Scad (*opelu*)' = SpCodeName.General$`Scad (*opelu*)`,
'Snappers' = SpCodeName.General$`Hawaii Snappers`,
'Swordfish (*mekajiki*)' = SpCodeName.General$Swordfish,
'Tunas (*aku*)' = SpCodeName.General$Tunas,
'Wahoo (*ono*)' = SpCodeName.General$`Wahoo (*ono*)`
),
"Western Pacific (Hawai`i)" = list('Lobsters (*ula*)' = SpCodeName.General$`Lobsters (*ula*)`,
'Dolphinfish (*mahimahi*)' = SpCodeName.General$`Dolphinfish (*mahimahi*)`,
"Marlin (*a'u*)" = SpCodeName.General$`Marlin (*a'u*)`,
'Moonfish (*opah*)' = SpCodeName.General$`Moonfish (*opah*)`,
'Pomfrets (*Monchong*)' = SpCodeName.General$`Pomfrets (*Monchong*)`,
'Scad (*opelu*)' = SpCodeName.General$`Scad (*opelu*)`,
'Snappers' = SpCodeName.General$`Hawaii Snappers`,
'Swordfish (*mekajiki*)' = SpCodeName.General$Swordfish,
'Tunas (*aku*)' = SpCodeName.General$Tunas,
'Wahoo (*ono*)' = SpCodeName.General$`Wahoo (*ono*)`
),
########******New England Region########
"New England" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic mackerel' = SpCodeName.General$`Atlantic mackerel`,
'Bluefin tuna' = SpCodeName.General$`Bluefin tuna`,
'Cod and haddock' = c(SpCodeName.General$`Atlantic Cod`,
SpCodeName.General$`Atlantic haddock`),
'Flounders' = SpCodeName.General$Flounders,
'Goosefish' = SpCodeName.General$Goosefish,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Squid' = SpCodeName.General$Squid),
## Connecticut
"Connecticut"=list('American lobster' = SpCodeName.General$`American lobster`,
'Summer flounder' = SpCodeName.General$`Summer flounder`,
'Other flounders' = c(SpCodeName.General$Flounders,
-(SpCodeName.General$`Summer flounder`)), #exclude
'Goosefish' = SpCodeName.General$Goosefish,
'Red hake' = SpCodeName.General$`Silver hake`,
'Silver hake' = SpCodeName.General$`Red hake`,
'Scups or Porgies' = c(SpCodeName.General$Scups,
SpCodeName.General$Porgies),
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Whelks and Conchs' = c(SpCodeName.General$Conchs,
SpCodeName.General$Whelks,
SpCodeName.General$Snails),
'loligo squid' = SpCodeName.General$`Loligo squid`
),
## Maine
"Maine" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Bloodworms' = SpCodeName.General$Bloodworms,
'Blue mussel' = SpCodeName.General$`Blue mussel`,
'Cod and haddock' = c(SpCodeName.General$`Atlantic Cod`,
SpCodeName.General$`Atlantic haddock`),
'Goosefish' = SpCodeName.General$Goosefish,
'Ocean quahog clam' = SpCodeName.General$`Ocean quahog clam`,
'Pollock' = SpCodeName.General$`Atlantic pollock`,
'Sea urchins' = SpCodeName.General$`Sea urchins`,
'Softshell clam' = SpCodeName.General$`Softshell clam`
),
## Massachusetts
"Massachusetts" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic mackerel' = SpCodeName.General$`Atlantic mackerel`,
'Other Clams' = c(SpCodeName.General$Clams, #TOLEDO check
-(SpCodeName.General$`Eastern oyster`), #exclude
-(SpCodeName.General$`Ocean quahog clam`), #exclude
-(SpCodeName.General$`Sea scallop`)), #exclude
'Cod and haddock' = c(SpCodeName.General$`Atlantic Cod`,
SpCodeName.General$`Atlantic haddock`),
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Flounders' = SpCodeName.General$Flounders,
'Goosefish' = SpCodeName.General$Goosefish,
'Ocean quahog clam' = SpCodeName.General$`Ocean quahog clam`,
'Sea scallop' = SpCodeName.General$`Sea scallop`),
## New Hampshire
"New Hampshire" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic cod' = SpCodeName.General$`Atlantic Cod`,
'Goosefish' = SpCodeName.General$Goosefish,
'Haddock' = SpCodeName.General$`Atlantic haddock`,
'Hake' = SpCodeName.General$Hake,
'Pollock' = SpCodeName.General$`Atlantic pollock`,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Shrimp' = SpCodeName.General$Shrimp,
'Spiny dogfish' = SpCodeName.General$`Spiny dogfish`),
## Rhode Island
"Rhode Island" = list('Other flounders' = c(SpCodeName.General$Flounders,
-(SpCodeName.General$`Summer flounder`)), #exclude
'American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic mackerel' = SpCodeName.General$`Atlantic mackerel`,
'Goosefish' = SpCodeName.General$Goosefish,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Scups and porgies' = c(SpCodeName.General$Scups,
SpCodeName.General$Porgies), #changed from "or" to "and"
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Squid' = SpCodeName.General$Squid,
'Summer flounder' = SpCodeName.General$`Summer flounder`
),
########******Mid-Atlantic Region########
"Mid-Atlantic" = list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic surf clam' = SpCodeName.General$`Atlantic surf clam`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Menhaden' = SpCodeName.General$Menhaden,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Squid' = SpCodeName.General$Squid,
'Striped bass' = SpCodeName.General$`Striped bass`,
'Summer flounder' = SpCodeName.General$`Summer flounder`),
# Delaware
"Delaware"=list('American eel' = SpCodeName.General$`American eel`,
'Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Spot' = SpCodeName.General$Spot,
'Striped bass' = SpCodeName.General$`Striped bass`,
'Weakfish' = SpCodeName.General$Weakfish,
'Whelks' = SpCodeName.General$Whelks
),
# Maryland
"Maryland"=list('Atlantic croaker' = SpCodeName.General$`Atlantic croaker`,
'Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Clams or bivalves' = SpCodeName.General$Clams, #TOLEDO Should just be called "Calms"
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Menhaden' = SpCodeName.General$Menhaden,
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Striped bass' = SpCodeName.General$`Striped bass`,
'Summer flounder' = SpCodeName.General$`Summer flounder`,
'White perch' = SpCodeName.General$`White perch`
),
# New Jersey
"New Jersey"=list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic herring' = SpCodeName.General$`Herring`,
'Atlantic mackerel' = SpCodeName.General$`Atlantic mackerel`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Goosefish' = SpCodeName.General$Goosefish,
'Ocean quahog and surf clams' = c(SpCodeName.General$`Ocean quahog clam`#,
# SpCodeName.General$`Atlantic surf clam` # Shouldn't this be here too?
),
#Shouldnt this be 'Quahog clam' = 81495?
'Quahog clam' = c(SpCodeName.General$`Ocean quahog clam`, #Isn't this redundant?
SpCodeName.General$`Arctic surf (Stimpson) clam`, #TOLEDO, check, only time called
SpCodeName.General$`surf clams`),
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Summer flounder' = SpCodeName.General$`Summer flounder`
),
# New York
"New York"=list('American lobster' = SpCodeName.General$`American lobster`,
'Atlantic surf clam' = SpCodeName.General$`Atlantic surf clam`,
'Eastern oyster' = SpCodeName.General$`Eastern oyster`,
'Summer flounder' = SpCodeName.General$`Summer flounder`,
'Loligo squid' = SpCodeName.General$`Loligo squid`,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Scups and porgies' = c(SpCodeName.General$Scups,
SpCodeName.General$Porgies),
'Sea scallop' = SpCodeName.General$`Sea scallop`,
'Softshell clam' = SpCodeName.General$`Softshell clam`,
'Tilefishes' = SpCodeName.General$Tilefish),
# Virginia
"Virginia"=list('Atlantic croaker' = SpCodeName.General$`Atlantic croaker`,
'Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Goosefish' = SpCodeName.General$Goosefish,
'Menhaden' = SpCodeName.General$Menhaden,
'Oysters' = SpCodeName.General$Oysters,
'Sea Scallop' = SpCodeName.General$`Sea scallop`,
'Spot' = SpCodeName.General$Spot,
'Striped bass' = SpCodeName.General$`Striped bass`,
'Summer flounder' = SpCodeName.General$`Summer flounder`
),
########******South Atlantic Region########
"South Atlantic" = list('Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Flounders' = SpCodeName.General$Flounders,
'Groupers' = SpCodeName.General$Groupers,
'King mackerels' = SpCodeName.General$`King mackerel`,
'Oysters' = SpCodeName.General$Oysters,
'Shrimp' = SpCodeName.General$Shrimp,
'Snappers' = SpCodeName.General$Snappers,
'Swordfish' = SpCodeName.General$Swordfish,
'Tunas' = SpCodeName.General$Tunas),
# East Florida
"East Florida"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Groupers' = SpCodeName.General$Groupers,
'King mackerel' = SpCodeName.General$`King mackerel`,
'Lobsters' = SpCodeName.General$Lobsters,
'Sharks' = SpCodeName.General$Sharks,
'Shrimp' = SpCodeName.General$Shrimp,
'Snappers' = SpCodeName.General$Snappers,
'Spanish mackerel' = SpCodeName.General$`Spanish mackerel`,
'Swordfish' = SpCodeName.General$Swordfish
),
# st.sp.codes[["East Florida"]][[4]] <- c(1939, 1940) # East Florida, king mackerel
# Georgia
# groupers: 1410:1430, 1850, 3800, 4740 groupers
"Georgia"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Grouper' = SpCodeName.General$Groupers,
'Shrimp' = SpCodeName.General$Shrimp,
'Snails (conchs)' = c(SpCodeName.General$Conchs), #TOLEDO was snails - why?!
'Snappers' = SpCodeName.General$Snappers
),
# North Carolina
"North Carolina"=list('Atlantic croaker' = SpCodeName.General$`Atlantic croaker`,
'Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Flounders' = SpCodeName.General$Flounders,
'Groupers' = SpCodeName.General$Groupers,
'King mackerel' = SpCodeName.General$`King mackerel`,
'Shrimp' = SpCodeName.General$Shrimp,
'Snappers' = SpCodeName.General$Snappers,
'Tunas' = SpCodeName.General$Tunas
),
# South Carolina
"South Carolina"=list('Black sea bass' = SpCodeName.General$`Black sea bass`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Clams' = SpCodeName.General$Clams,
'Groupers' = SpCodeName.General$Groupers,
'Oysters' = SpCodeName.General$Oysters,
'Sharks' = SpCodeName.General$Sharks,
'Shrimp' = SpCodeName.General$Shrimp,
'Snappers' = SpCodeName.General$Snappers,
'Swordfish' = SpCodeName.General$Swordfish,
'Tilefish' = SpCodeName.General$Tilefish),
########******Gulf of Mexico Region########
"Gulf of Mexico" = list('Blue crab' = SpCodeName.General$`Blue crab`,
'Crawfish' = SpCodeName.General$Crawfish,
'Groupers' = SpCodeName.General$Groupers,
'Menhaden' = SpCodeName.General$Menhaden,
'Mullets' = SpCodeName.General$Mullets,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp,
'Spiny lobster' = SpCodeName.General$`Caribbean Spiny lobster`,
'Tunas' = SpCodeName.General$Tunas),
## Alabama
"Alabama"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Flounders' = SpCodeName.General$Flatfish,
'Menhaden' = SpCodeName.General$Menhaden,
'Mullets' = SpCodeName.General$Mullets,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Sharks' = SpCodeName.General$Sharks,
'Shrimp' = SpCodeName.General$Shrimp,
'Spanish mackerel' = SpCodeName.General$`Spanish mackerel`,
'Vermilion snapper' = SpCodeName.General$`Vermilion snapper`
),
## West Florida
"West Florida" = list('Blue crab' = SpCodeName.General$`Blue crab`,
'Gag' = SpCodeName.General$Gag,
'Lobsters' = SpCodeName.General$Lobsters,
'Mullet' = SpCodeName.General$Mullets,
'Oyster' = SpCodeName.General$Oysters,
'Quahog clam' = SpCodeName.General$`Quahog clam`,
'Red grouper' = SpCodeName.General$`Red grouper`,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp,
'Stone crab' = SpCodeName.General$`Stone crab`
),
## Louisiana
"Louisiana"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Crawfish' = SpCodeName.General$Crawfish,
'King mackerel' = SpCodeName.General$`King mackerel`,
'Menhaden' = SpCodeName.General$Menhaden,
'Mullets' = SpCodeName.General$Mullets,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp,
'Tunas' = SpCodeName.General$Tunas,
'Vermilion snapper' = SpCodeName.General$`Vermilion snapper`
),
## Mississippi
"Mississippi"=list('Blue crab' = SpCodeName.General$`Blue crab`,
'Flounders' = SpCodeName.General$Flounders,
'Menhaden' = SpCodeName.General$Menhaden,
'Mullets' = SpCodeName.General$Mullets,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp
),
## Texas
"Texas"=list('Atlantic croaker' = SpCodeName.General$`Atlantic croaker`,
'Black drum' = SpCodeName.General$`Black drum`,
'Blue crab' = SpCodeName.General$`Blue crab`,
'Flounders' = SpCodeName.General$Flounders,
'Groupers' = SpCodeName.General$Groupers,
'Oysters' = SpCodeName.General$Oysters,
'Red snapper' = SpCodeName.General$`Red snapper`,
'Shrimp' = SpCodeName.General$Shrimp,
'Tunas' = SpCodeName.General$Tunas,
'Vermilion snapper' = SpCodeName.General$`Vermilion snapper`)
)
#####***spcat.list#####
spcat.list<-list("General" = SpCodeName.General,
"Areas" = SpCodeName)
itis_reclassify<-function(tsn, categories, missing.name){
# Find which codes are in which categories
tsn0<-as.numeric(tsn)[!(is.na(tsn))]
tsn.indata<-classification(sci_id = tsn0, db = 'itis')
tsn.indata<-tsn.indata[!(names(tsn.indata) %in% 0)]
valid0<- sciname<-category0<-bottomrank<-sppname<- TSN<-c()
TSN<-c()
bottomrank<-c()
category0<-c()
sciname<-c()
valid0<-c()
for (i in 1:length(categories)) {
a<-list.search(lapply(X = tsn.indata, '[', 3), categories[i][[1]] %in% . )
# for (ii in 1:length(categories[i][[1]])) {
# a<-c(a, list.search(lapply(X = tsn.indata, '[', 3), categories[i][[1]][[ii]] %in% . ))
# }
if (length(a)!=0) {
sppcode<-names(a)
sppcode<-gsub(pattern = "[a-zA-Z]+", replacement = "", x = sppcode)
sppcode<-gsub(pattern = "\\.", replacement = "", x = sppcode)
for (ii in 1:length(sppcode)) {
TSN<-c(TSN, sppcode[ii])
bottomrank<-c(bottomrank, tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]]$rank[
nrow(tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]])])
category0<-c(category0, names(categories[i]))
sciname<-c(sciname, tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]]$name[
nrow(tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]])])
valid0<-c(valid0,
ifelse(nrow(tsn.indata[names(tsn.indata) %in% sppcode[ii]][[1]])>1,
"valid", "invalid"))
}
}
}
df.out<-data.frame(TSN = TSN,
category = category0,
valid = valid0,
rank = bottomrank,
sciname = sciname )
return(list("df.out" = df.out,
"tsn.indata" = tsn.indata))
}
spp_reclassify<-function(landings.df, spcat.list, place){
####
####FINFISH AND SHELFISH AND OTHER TOTALS
###
temp<-landings.df
total.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum,na.rm=TRUE)
#Finfish
temp<-landings.df[landings.df$OFS %in% "F",]
if (nrow(temp)!=0) {
finfish.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum, na.rm=TRUE)
if (sum(!(min(landings.df$year):max(landings.df$year) %in% finfish.df$year)) >0){
finfish.df<-rbind.data.frame(finfish.df,
cbind.data.frame("year" = (min(landings.df$year):max(landings.df$year))[!(min(landings.df$year):max(landings.df$year) %in% other.df$year)],
"POUNDS" = 0,
"DOLLARS" = 0))
landings.df<-landings.df[order(landings.df$year),]
}
} else {
finfish.df<-data.frame(year = min(landings.df$year):max(landings.df$year),
POUNDS = 0,
DOLLARS = 0)
}
#Shellfish
temp<-landings.df[landings.df$OFS %in% "S",]
if (nrow(temp)!=0) {
shellfish.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum,na.rm=TRUE)
if (sum(!(min(landings.df$year):max(landings.df$year) %in% shellfish.df$year)) >0){
shellfish.df<-rbind.data.frame(shellfish.df,
cbind.data.frame("year" = (min(landings.df$year):max(landings.df$year))[!(min(landings.df$year):max(landings.df$year) %in% other.df$year)],
"POUNDS" = 0,
"DOLLARS" = 0))
shellfish.df<-shellfish.df[order(shellfish.df$year),]
}
} else {
shellfish.df<-data.frame(year = min(landings.df$year):max(landings.df$year),
POUNDS = 0,
DOLLARS = 0)
}
#Other
temp<-landings.df[landings.df$OFS %in% "O",]
if (nrow(temp)!=0) {
other.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum,na.rm=TRUE)
if (sum(!(min(landings.df$year):max(landings.df$year) %in% other.df$year)) > 0){
other.df<-rbind.data.frame(other.df,
cbind.data.frame("year" = (min(landings.df$year):max(landings.df$year))[
!(min(landings.df$year):max(landings.df$year) %in% other.df$year)],
"POUNDS" = 0,
"DOLLARS" = 0))
other.df<-other.df[order(other.df$year),]
}
} else {
other.df<-data.frame(year = min(landings.df$year):max(landings.df$year),
POUNDS = 0,
DOLLARS = 0)
}
land.df<-land.tot<-cbind.data.frame("Total" = total.df$POUNDS,
"Finfish" = finfish.df$POUNDS,
"Shellfish" = shellfish.df$POUNDS,
"Other" = other.df$POUNDS,
"Key Species" = NA)
rev.df<-rev.tot<-cbind.data.frame("Total" = total.df$DOLLARS,
"Finfish" = finfish.df$DOLLARS,
'Shellfish' = shellfish.df$DOLLARS,
"Other" = other.df$DOLLARS,
"Key Species" = NA)
price.df<-price.tot<-cbind.data.frame("Key Species" = rep_len(x = NA, length.out = nrow(rev.df)))
####
####SPECIES
###
uniquespp<-data.frame()
if (!(is.na(spcat.list))) {
temp<-itis_reclassify(tsn = as.numeric(paste(unique(landings.df$TSN))),
categories = spcat.list$Areas[place][[1]],
missing.name="Uncategorized")
tsn.id<-temp[1][[1]]
# tsn.id<-tsn.id[!(tsn.id$category %in% c("Other", "Uncategorized")),
# c("TSN", "category")]
tsn.id<-tsn.id[!(tsn.id$category %in% c("Uncategorized", "Other")),]
land.df<-data.frame(t(land.df))
rev.df<-data.frame(t(rev.df))
price.df<-data.frame(t(price.df))
#Add species to tables
land.df$Footnotes<-rev.df$Footnotes<-price.df$Footnotes<-""
for (i in 1:length(names(spcat.list$Areas[place][[1]]))) {
spp0<-sort(names(spcat.list$Areas[place][[1]]))[i]
temp<-landings.df[landings.df$TSN %in% tsn.id$TSN[tsn.id$category %in% spp0],]
# temp<-temp[!(is.na(temp$POUNDS) & is.na(temp$DOLLARS)), ]
#FOOTNOTES
Footnotes<-""
#Species that should be included in this sum and footnote
sppno<-as.numeric(unlist(spcat.list$Areas[place][[1]][spp0]))
spp<-tsn.indata.foot<-classification(sci_id = sppno[sppno>0], db = 'itis')
sppnames<-taxspp(spp = tsn.indata.foot, spp0, landings.df)
species<-sppnames$species
notspecies<-sppnames$notspecies
spp00<-strsplit(x = spp0, split = ",")
spp00<-strsplit(x = spp00[[length(spp00)]], split = "and")
spp00<-gsub(x = unlist(spp00), pattern = "and ", replacement = "", ignore.case = T)
spp00<-gsub(x = unlist(spp00), pattern = "& ", replacement = "", ignore.case = T)
spp00<-trimws(spp00)
if (!(is.null(species)) && !(tolower(species) %in% tolower(spp00))) { #If species names the few specific species in the group, then dont footnote
Footnotes<-""
#If a specific species (and not a gorup) #No Footnote required
} else if (length(spp) == 1 & !(spp[[1]]$rank[nrow(spp[[1]])] %in% "species") ) { #If there is only 1 code and its not a specific species
Footnotes<-paste0("This species group includes species within the ",
paste0(ifelse(spp[[1]]$rank[nrow(spp[[1]])] %in% "genus",
paste0("*", (spp[[1]]$name[nrow(spp[[1]])]), "*"),
tolower(spp[[1]]$name[nrow(spp[[1]])])) ,
" ", spp[[1]]$rank[nrow(spp[[1]])]), ".")
} else if (length(spp) > 1) {
if (length(species)>0 & length(notspecies)>0) {
Footnotes<-paste0("This species group includes species within the ",
funct_list(notspecies), " and ", funct_list(species) , ".")
} else if (length(species)==0 & length(notspecies)>0) {
Footnotes<-paste0("This species group includes species within the ",
funct_list(notspecies), ".")
} else if (length(species)>0 & length(notspecies)==0) {
Footnotes<-paste0("This species group includes ",
funct_list(species) , ".")
}
}
#Species that should be excluded from this sum and footnote
if (sum(sppno<0)>0) {
spp<-tsn.indata.foot<-classification(sci_id = (sppno[sppno<0])*-1, db = 'itis')
sppnames<-taxspp(spp = tsn.indata.foot, spp0, landings.df)
species<-sppnames$species
notspecies<-sppnames$notspecies
if (length(tsn.indata.foot)>1) {
if (length(species)>0 & length(notspecies)>0) {
Footnotes<-paste0(Footnotes, " This species group excludes species within the ",
funct_list(notspecies), " and, specifically, ", funct_list(species) , ".")
} else if (length(species)==0 & length(notspecies)>0) {
Footnotes<-paste0(Footnotes, " This species group excludes species within the ",
funct_list(notspecies), ".")
} else if (length(species)>0 & length(notspecies)==0) {
Footnotes<-paste0(Footnotes, " This species group excludes ",
funct_list(species) , ".")
}
}
}
#COMPILE TABLES
if (nrow(temp) %in% 0) {
land.df<-rbind.data.frame(land.df,
c(rep_len(x = NA, length.out = (ncol(land.df)-1)), Footnotes))
rev.df<-rbind.data.frame(rev.df,
c(rep_len(x = NA, length.out = (ncol(land.df)-1)), Footnotes))
price.df<-rbind.data.frame(price.df,
c(rep_len(x = NA, length.out = (ncol(land.df)-1)), Footnotes))
} else {
#Unique Species
uniquespp0<-unique(temp[,c("TSN", "CommonName")])
uniquespp0$Category<-spp0
uniquespp0$CommonName1<-funct_list(spp00)
uniquespp<-rbind.data.frame(uniquespp, uniquespp0)
temp.df<-aggregate(temp[,names(temp) %in% c("POUNDS", "DOLLARS")] ,
by=list(year=temp$year),
FUN=sum,na.rm=TRUE)
#make sure columns match
if (sum((!(min(landings.df$year):max(landings.df$year) %in% temp.df$year)) >0)) {
yr0<-(min(landings.df$year):max(landings.df$year))[!(min(landings.df$year):max(landings.df$year) %in% temp.df$year)]
temp.df<-rbind.data.frame(temp.df,
cbind.data.frame('year' = yr0,
'POUNDS' = rep_len(x = NA, length.out = length(yr0)),
'DOLLARS' = rep_len(x = NA, length.out = length(yr0))))
}
temp.df<-temp.df[order(temp.df$year, decreasing = F),]
land.df<-rbind.data.frame(land.df,
c(temp.df$POUNDS, Footnotes))
rev.df<-rbind.data.frame(rev.df,
c(temp.df$DOLLARS, Footnotes))
price.df<-rbind.data.frame(price.df,
c(temp.df$POUNDS/temp.df$DOLLARS, Footnotes))
}
rownames(land.df)[length(rownames(land.df))]<-
rownames(rev.df)[length(rownames(rev.df))]<-
rownames(price.df)[length(rownames(price.df))]<-tolower2(spp0, capitalizefirst = T)
}
uniquespp$Area<-place
uniquespp$SciName<-""
a<-classification(sci_id = uniquespp$TSN, db = 'itis')
for (ii in 1:length(uniquespp)){
if (a[ii][[1]]$rank[nrow(a[ii][[1]])] %in% "species") {
uniquespp$SciName[ii]<-a[ii][[1]]$name[nrow(a[ii][[1]])]
} else {
uniquespp$SciName[ii]<-paste0(a[ii][[1]]$name[nrow(a[ii][[1]])], " ",
a[ii][[1]]$rank[nrow(a[ii][[1]])])
}
}
land.df<-cbind.data.frame(rownames(land.df), land.df)
rev.df<-cbind.data.frame(rownames(rev.df), rev.df)
price.df<-cbind.data.frame(rownames(price.df), price.df)
} else {
land.df<-t(land.df)
land.df<-cbind.data.frame("keyspecies" = rownames(land.df)[-nrow(land.df)],
land.df[-nrow(land.df),],
Footnotes = NA)
rev.df<-t(rev.df)
rev.df<-cbind.data.frame("keyspecies" = rownames(rev.df)[-nrow(rev.df)],
rev.df[-nrow(rev.df),],
Footnotes = NA)
price.df<-t(price.df)
price.df<-cbind.data.frame("keyspecies" = NA,
price.df,
Footnotes = NA)
}
colnames(land.df)<-colnames(rev.df)<-colnames(price.df)<-c("keyspecies",
as.character(min(landings.df$year):
max(landings.df$year)),
"Footnotes")
#####footnote with species
return(list("revenue" = rev.df,
"landings" = land.df,
"price" = price.df,
"uniquespp" = uniquespp))
}
temp<-itis_reclassify(tsn = as.numeric(paste0(unique(commercial.data$TSN))),
categories = list("Shellfish" = spcat.list$General$Shellfish,
"Finfish" = spcat.list$General$Finfish),
missing.name="Uncategorized")
tsn.id<-temp[1][[1]]
if (sum(tsn.id$category %in% c("Other", "Uncategorized"))>0) {
tsn.id<-tsn.id[!(tsn.id$category %in% c("Other", "Uncategorized")),
c("TSN", "category")]
}
taxspp<-function(spp, spp0, landings.df){
species<-c()
notspecies<-c()
for (ii in 1:length(spp)){
if (spp[[ii]]$rank[nrow(spp[[ii]])] %in% "species") {
#Find common name
a<-unique(landings.df$CommonName[landings.df$TSN %in% as.numeric(names(spp[ii]))])
a<-a[is.na(as.numeric(a))]
# SPECIES_CROSS_REFERENCE <- read.csv(paste0(
# 'C:/Users/Emii/Documents/Homework/FEUS/2018/FEUS2018Commercial/data/SPECIES_CROSS_REFERENCE.csv'),
# header=TRUE, stringsAsFactors = FALSE)
# a<-unique(SPECIES_CROSS_REFERENCE$AFS_NAME[SPECIES_CROSS_REFERENCE$ITIS %in% as.numeric(names(spp[ii]))])
if (!(length(a) %in% 0)) {
b<-strsplit(x = a, split = ",")[[1]]
# b<-gsub(pattern = ", Na", replacement = "", x = b)
c0<-ifelse(length(b) > 1, paste0(b[2], " ", b[1]), b)
c0<-gsub(pattern = " ", replacement = " ", x = c0)
c0<-tolower2(c0, capitalizefirst = T)
c0<-gsub(pattern = "spp", replacement = "spp.", x = c0)
c0<-ifelse(grepl(pattern = "spp.", x = c0) &
grepl(pattern = spp0, x = c0, ignore.case = T),
gsub(pattern = spp0, replacement = "", x = c0, ignore.case = T),
c0)
# c0<-ifelse(bottomrank[i] %in% "genus" & !(grepl(pattern = "spp.", x = c0)),
# paste0(c0, " spp."), c0)
c0<-trimws(c0)
c0<-tolower2(c0, capitalizefirst = F)
# commonname[i]<-c0
species<-c(species,
paste0(tolower2(unique(c0))))
}
}
if (!(spp[[ii]]$rank[nrow(spp[[ii]])] %in% "species")) {
notspecies<-c(notspecies,
paste0(ifelse(spp[[ii]]$rank[nrow(spp[[ii]])] %in% "genus",
paste0("*", (spp[[ii]]$name[nrow(spp[[ii]])]), "*"),
tolower(spp[[ii]]$name[nrow(spp[[ii]])])) ,
" ", spp[[ii]]$rank[nrow(spp[[ii]])]))
}
}
return(list("species" = species,
"notspecies" = notspecies))
}
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