ecoidx: Marine ecological indicators R package for CA Current IEA

#' CCIEA Anthropogenic Drivers
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#' Seafood consumption (total): source data: Data can be found in NOAA's annual Fisheries of the United States reports (http://www.st.nmfs.noaa.gov/st1/publications.html)., additional calculations: Seafood demand was measured as total consumption or utilization of edible and non-edible fisheries products across the entire United States. We used data from the entire United States as seafood from the California Current is consumed and utilized at national, and even international scales.; Seafood consumption (per capita): source data: Data can be found in NOAA's annual Fisheries of the United States reports (http://www.st.nmfs.noaa.gov/st1/publications.html)., additional calculations: Seafood demand was measured as total consumption or utilization of edible and non-edible fisheries products across the entire United States. We used data from the entire United States as seafood from the California Current is consumed and utilized at national, and even international scales.; Coastal pelagic species (w/o squid) landings CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/); Coastal pelagic species (w/o squid) landings coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Coastal pelagic species (without market squid (Loligo opalescens)) landings (1000's of metric tons) on the U.S. West Coast. Coastal pelagic species include Pacific herring (Clupea harengus pallasii), round herring (Etrumeus teres), chub mackerel (Scomber japonicus), jack mackerel (Trachurus symmetricus), northern anchovy (Engraulis mordax), Pacific bonito (Sarda chiliensis), Pacific sardine (Sardinops sagax), and unspecified mackerel.; Coastal pelagic species (w/o squid) landings OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Coastal pelagic species (without market squid (Loligo opalescens)) landings (1000's of metric tons) in Oregon. Coastal pelagic species include Pacific herring (Clupea harengus pallasii), round herring (Etrumeus teres), chub mackerel (Scomber japonicus), jack mackerel (Trachurus symmetricus), northern anchovy (Engraulis mordax), Pacific bonito (Sarda chiliensis), Pacific sardine (Sardinops sagax), and unspecified mackerel.; Coastal pelagic species (w/o squid) landings WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/); Coastal pelagic species (w/o squid) revenue CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Coastal pelagic species (without market squid (Loligo opalescens)) revenue (millions of 2015 dollars) in California. Coastal pelagic species include Pacific herring (Clupea harengus pallasii), round herring (Etrumeus teres), chub mackerel (Scomber japonicus), jack mackerel (Trachurus symmetricus), northern anchovy (Engraulis mordax), Pacific bonito (Sarda chiliensis), Pacific sardine (Sardinops sagax), and unspecified mackerel.; Coastal pelagic species (w/o squid) revenue coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Coastal pelagic species (without market squid (Loligo opalescens)) revenue (millions of 2015 dollars) on the U.S. West Coast. Coastal pelagic species include Pacific herring (Clupea harengus pallasii), round herring (Etrumeus teres), chub mackerel (Scomber japonicus), jack mackerel (Trachurus symmetricus), northern anchovy (Engraulis mordax), Pacific bonito (Sarda chiliensis), Pacific sardine (Sardinops sagax), and unspecified mackerel.; Coastal pelagic species (w/o squid) revenue OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Coastal pelagic species (without market squid (Loligo opalescens)) revenue (millions of 2015 dollars) in Oregon. Coastal pelagic species include Pacific herring (Clupea harengus pallasii), round herring (Etrumeus teres), chub mackerel (Scomber japonicus), jack mackerel (Trachurus symmetricus), northern anchovy (Engraulis mordax), Pacific bonito (Sarda chiliensis), Pacific sardine (Sardinops sagax), and unspecified mackerel.; Coastal pelagic species (w/o squid) revenue WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Coastal pelagic species (without market squid (Loligo opalescens)) revenue (millions of 2015 dollars) in Washington. Coastal pelagic species include Pacific herring (Clupea harengus pallasii), round herring (Etrumeus teres), chub mackerel (Scomber japonicus), jack mackerel (Trachurus symmetricus), northern anchovy (Engraulis mordax), Pacific bonito (Sarda chiliensis), Pacific sardine (Sardinops sagax), and unspecified mackerel.; Crab landings CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Crab landings (1000's of metric tons) in California. Crab species include Dungeness (Metacarcinus magister), tanner (Chionoecetes spp.), rock (Cancer spp.) and unspecified crabs.; Crab landings coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Crab landings (1000's of metric tons) on the U.S. West Coast. Crab species include Dungeness (Metacarcinus magister), tanner (Chionoecetes spp.), rock (Cancer spp.) and unspecified crabs.; Crab landings OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Crab landings (1000's of metric tons) in Oregon. Crab species include Dungeness (Metacarcinus magister), tanner (Chionoecetes spp.), rock (Cancer spp.) and unspecified crabs.; Crab landings WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Crab landings (1000's of metric tons) in Washington. Crab species include Dungeness (Metacarcinus magister), tanner (Chionoecetes spp.), rock (Cancer spp.) and unspecified crabs.; Crab revenue CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Crab revenue (millions of 2015 dollars) in California. Crab species include Dungeness (Metacarcinus magister), tanner (Chionoecetes spp.), rock (Cancer spp.) and unspecified crabs.; Crab revenue coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Crab revenue (millions of 2015 dollars) on the U.S. West Coast. Crab species include Dungeness (Metacarcinus magister), tanner (Chionoecetes spp.), rock (Cancer spp.) and unspecified crabs.; Crab revenue OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Crab revenue (millions of 2015 dollars) in Oregon. Crab species include Dungeness (Metacarcinus magister), tanner (Chionoecetes spp.), rock (Cancer spp.) and unspecified crabs.; Crab revenue WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Crab revenue (millions of 2015 dollars) in Washington. Crab species include Dungeness (Metacarcinus magister), tanner (Chionoecetes spp.), rock (Cancer spp.) and unspecified crabs.; Nutrient Input: source data: We used county-level data from 1987 - 2006 and state-level data from 2007 - 2010 from the U.S. Geological Survey (Ruddy et al. 2006, Gronberg and Spahr 2012; http://water.usgs.gov/lookup/getspatial?sir2012-5207_county_fertilizer) and nationwide data (1945 - 2001;  Ruddy et al. (2006)) to develop an index for the California Current across the longer time series., additional calculations: Nutrient input was measured using a normalized index of total nitrogen and phosphorus input from agricultural fertilizers used within watersheds that drain into the California Current.; Finfish Aquaculture: source data: Washington Department of Fish & Wildlife, Commercial Harvest Data Team, additional calculations: Finfish aquaculture was measured as the production of finfish from aquaculture operations that are located in marine waters. Only the State of Washington currently operates net-pen farms for Atlantic salmon (Salmo salar).; Groundfish landings (w/o hake) CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Groundfish (without Pacific hake (Merluccius productus)) landings (1000's of metric tons) in California. Groundfish taxa include flatfishes, rockfishes and abundant demersal roundfishes.; Groundfish landings (w/o hake) coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Groundfish (without Pacific hake (Merluccius productus)) landings (1000's of metric tons) on the U.S. West Coast. Groundfish taxa include flatfishes, rockfishes and abundant demersal roundfishes.; Groundfish landings (w/o hake) OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Groundfish (without Pacific hake (Merluccius productus)) landings (1000's of metric tons) in Oregon. Groundfish taxa include flatfishes, rockfishes and abundant demersal roundfishes.; Groundfish landings (w/o hake) WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Groundfish (without Pacific hake (Merluccius productus)) landings (1000's of metric tons) in Washington. Groundfish taxa include flatfishes, rockfishes and abundant demersal roundfishes.; Groundfish revenue (w/o hake) CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Groundfish (without Pacific hake (Merluccius productus)) revenue (millions of 2015 dollars) in California. Groundfish taxa include flatfishes, rockfishes and abundant demersal roundfishes.; Groundfish revenue (w/o hake) coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Groundfish (without Pacific hake (Merluccius productus)) revenue (millions of 2015 dollars) on the U.S. West Coast. Groundfish taxa include flatfishes, rockfishes and abundant demersal roundfishes.; Groundfish revenue (w/o hake) OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Groundfish (without Pacific hake (Merluccius productus)) revenue (millions of 2015 dollars) in Oregon. Groundfish taxa include flatfishes, rockfishes and abundant demersal roundfishes.; Groundfish revenue (w/o hake) WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Groundfish (without Pacific hake (Merluccius productus)) revenue (millions of 2015 dollars) in Washington. Groundfish taxa include flatfishes, rockfishes and abundant demersal roundfishes.; Bottom trawl contact with seafloor habitat (weighted): source data: Distances were summed within physiographic depth, habitat and ecoregion categories from logbook data provided by the West Coast Groundfish Observer Program at the Northwest Fisheries Science Center and is comparable to the data developed for NOAA's Essential Fish Habitat 5-year Synthesis Review in 2013 (maps and data available: http://efh-catalog.coas.oregonstate.edu/overview/, additional calculations: Habitat modification was measured using the total distance disturbed by trawling and fixed (longlines and pots) gear. Straight line distances between start and end points for trawling gear and between set and retrieval points for fixed gear were calculated for each gear type and weighted by the gear type's impact to the bottom habitat and by the type of habitat. These weightings come from NOAA's 5-year Synthesis Review of West Coast Groundfish (Supplemental Table A3a.2); Highly migratory species landings CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Highly migratory species landings (1000's of metric tons) in California. Highly migratory species primarily consist of tunas (Thunnus spp), swordfish (Xiphias gladius) and pelagic sharks (e.g., blue (Prionace glauca), thresher (Alopias spp), and shortfin mako (Isurus oxyrinchus).; Highly migratory species landings coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Highly migratory species landings (1000's of metric tons) on the U.S. West Coast. Highly migratory species primarily consist of tunas (Thunnus spp), swordfish (Xiphias gladius) and pelagic sharks (e.g., blue (Prionace glauca), thresher (Alopias spp), and shortfin mako (Isurus oxyrinchus).; Highly migratory species landings OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Highly migratory species landings (1000's of metric tons) in Oregon. Highly migratory species primarily consist of tunas (Thunnus spp), swordfish (Xiphias gladius) and pelagic sharks (e.g., blue (Prionace glauca), thresher (Alopias spp), and shortfin mako (Isurus oxyrinchus).; Highly migratory species landings WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Highly migratory species landings (1000's of metric tons) in Washington. Highly migratory species primarily consist of tunas (Thunnus spp), swordfish (Xiphias gladius) and pelagic sharks (e.g., blue (Prionace glauca), thresher (Alopias spp), and shortfin mako (Isurus oxyrinchus).; Highly migratory species revenue CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Highly migratory species revenue (millions of 2015 dollars) in California. Highly migratory species primarily consist of tunas (Thunnus spp), swordfish (Xiphias gladius) and pelagic sharks (e.g., blue (Prionace glauca), thresher (Alopias spp), and shortfin mako (Isurus oxyrinchus).; Highly migratory species revenue coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Highly migratory species revenue (millions of 2015 dollars) on the U.S. West Coast. Highly migratory species primarily consist of tunas (Thunnus spp), swordfish (Xiphias gladius) and pelagic sharks (e.g., blue (Prionace glauca), thresher (Alopias spp), and shortfin mako (Isurus oxyrinchus).; Highly migratory species revenue OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Highly migratory species revenue (millions of 2015 dollars) in Oregon. Highly migratory species primarily consist of tunas (Thunnus spp), swordfish (Xiphias gladius) and pelagic sharks (e.g., blue (Prionace glauca), thresher (Alopias spp), and shortfin mako (Isurus oxyrinchus).; Highly migratory species revenue WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Highly migratory species revenue (millions of 2015 dollars) in Washington. Highly migratory species primarily consist of tunas (Thunnus spp), swordfish (Xiphias gladius) and pelagic sharks (e.g., blue (Prionace glauca), thresher (Alopias spp), and shortfin mako (Isurus oxyrinchus).; Oil And Gas Activity: source data: Oil production data come from annual reports of the California State Department of Conservation's Division of oil, gas, and geothermal resources (ftp://ftp.consrv.ca.gov/../pub/oil/annual_reports/), while natural gas production come from the U.S. Energy Information Administration (http://www.eia.gov/dnav/ng/ng_prod_sum_dcu_rcatf_a.htm)., additional calculations: Oil and gas activities were measured using a normalized index combining the production of oil and natural gas occurring in offshore sites of California.; Other species landings CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Other species landings (1000's of metric tons) in California. Other species include several taxa, but consists primarily of red sea urchin (Stronglyocentrotus franciscanus) and hagfish (Eptatretus spp.).; Other species landings coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Other species landings (1000's of metric tons) on the U.S. West Coast. Other species include several taxa, but consists primarily of red sea urchin (Stronglyocentrotus franciscanus) and hagfish (Eptatretus spp.).; Other species landings OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Other species landings (1000's of metric tons) in Oregon. Other species include several taxa, but consists primarily of red sea urchin (Stronglyocentrotus franciscanus) and hagfish (Eptatretus spp.).; Other species landings WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Other species landings (1000's of metric tons) in Washington. Other species include several taxa, but consists primarily of red sea urchin (Stronglyocentrotus franciscanus) and hagfish (Eptatretus spp.).; Other species revenue CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Other species revenue (millions of 2015 dollars) in California. Other species include several taxa, but consists primarily of red sea urchin (Stronglyocentrotus franciscanus) and hagfish (Eptatretus spp.).; Other species revenue coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Other species revenue (millions of 2015 dollars) on the U.S. West Coast. Other species include several taxa, but consists primarily of red sea urchin (Stronglyocentrotus franciscanus) and hagfish (Eptatretus spp.).; Other species revenue OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Other species revenue (millions of 2015 dollars) in Oregon. Other species include several taxa, but consists primarily of red sea urchin (Stronglyocentrotus franciscanus) and hagfish (Eptatretus spp.).; Other species revenue WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Other species revenue (millions of 2015 dollars) in Washington. Other species include several taxa, but consists primarily of red sea urchin (Stronglyocentrotus franciscanus) and hagfish (Eptatretus spp.).; Pacific hake landings CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Pacific hake (Merluccius productus)) landings (1000's of metric tons) in California. Pacific hake landings include data from shoreside and at-sea processors.; Pacific hake landings coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Pacific hake (Merluccius productus)) landings (1000's of metric tons) on the U.S. West Coast. Pacific hake landings include data from shoreside and at-sea processors.; Pacific hake landings OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Pacific hake (Merluccius productus)) landings (1000's of metric tons) in Oregon. Pacific hake landings include data from shoreside and at-sea processors.; Pacific hake landings WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Pacific hake (Merluccius productus)) landings (1000's of metric tons) in Washington. Pacific hake landings include data from shoreside and at-sea processors.; Pacific hake revenue CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Pacific hake (Merluccius productus)) revenue (millions of 2015 dollars) in California. Pacific hake landings include data from shoreside and at-sea processors.; Pacific hake revenue coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Pacific hake (Merluccius productus)) revenue (millions of 2015 dollars) on the U.S. West Coast. Pacific hake landings include data from shoreside and at-sea processors.; Pacific hake revenue OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Pacific hake (Merluccius productus)) revenue (millions of 2015 dollars) in Oregon. Pacific hake landings include data from shoreside and at-sea processors.; Pacific hake revenue WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Pacific hake (Merluccius productus)) revenue (millions of 2015 dollars) in Washington. Pacific hake landings include data from shoreside and at-sea processors.; Recreational landings CA: source data: Recreational Fisheries Information Network (RecFIN; http://www.recfin.org/), additional calculations: Total landings of all species from recreational fisheries in California from www.recfin.org using weight of catch type "A + B1" metric tons.; Recreational landings coastwide: source data: Recreational Fisheries Information Network (RecFIN; http://www.recfin.org/), additional calculations: Total landings of all species from recreational fisheries from www.recfin.org using weight of catch type "A + B1" metric tons.; Recreational landings OR: source data: Recreational Fisheries Information Network (RecFIN; http://www.recfin.org/), additional calculations: Total landings of all species from recreational fisheries in Oregon from www.recfin.org using weight of catch type "A + B1" metric tons.; Recreational landings WA: source data: Recreational Fisheries Information Network (RecFIN; http://www.recfin.org/), additional calculations: Total landings of all species from recreational fisheries in Washington from www.recfin.org using weight of catch type "A + B1" metric tons.; Salmon commercial landings CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Salmon landings (1000's of metric tons) in California. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon commercial landings coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Salmon landings (1000's of metric tons) on the U.S. West Coast. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon commercial landings OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Salmon landings (1000's of metric tons) in Oregon. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon commercial landings WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Salmon landings (1000's of metric tons) in Washington. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon recreational landings CA: source data: Recreational Fisheries Information Network (RecFIN; http://www.recfin.org/), additional calculations: Salmon landings (1000's of metric tons) in California. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon recreational landings coastwide: source data: Recreational Fisheries Information Network (RecFIN; http://www.recfin.org/), additional calculations: Salmon landings (1000's of metric tons) on the U.S. West Coast. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon recreational landings OR: source data: Recreational Fisheries Information Network (RecFIN; http://www.recfin.org/), additional calculations: Salmon landings (1000's of metric tons) in Oregon. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon recreational landings WA: source data: Recreational Fisheries Information Network (RecFIN; http://www.recfin.org/), additional calculations: Salmon landings (1000's of metric tons) in Washington. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon revenue CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Salmon revenue (millions of 2015 dollars) in California. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon revenue coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Salmon revenue (millions of 2015 dollars) on the U.S. West Coast. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon revenue OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Salmon revenue (millions of 2015 dollars) in Oregon. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Salmon revenue WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Salmon revenue (millions of 2015 dollars) in Washington. Salmon landings primarily consist of Chinook (Oncorhynchus tshawytscha), but also includes chum (O. keta), coho (O. kisutch), pink (O. gorbuscha) and sockeye (O. nerka) and steelhead (O. mykiss) .; Shellfish Aquaculture: source data: Data were retrieved and summed together from Washington Department of Fish and Wildlife's Commercial Harvest Data Team, Oregon Department of Agriculture and the California Department of Fish and Game., additional calculations: Shellfish aquaculture was measured as the estimated production of shellfish from aquaculture operations in the states of Washington, Oregon and California.; Commercial shipping - distance: source data: Domestic vessel data from U.S. Army Corps of Engineers Navigation Data Center (New Orleans, LA) and foreign vessel data from http://www.ndc.iwr.usace.army.mil/data/dataclen.htm ., additional calculations: Commercial shipping activity (distance) was measured as the distance traveled by commercial vessels during transit within waters of the California Current. Distance traveled was calculated using distance traveled within the California Current while in transit between shipping and receiving ports; Shrimp landings CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Shrimp landings (1000's of metric tons) in California. Shrimp landings consist primarily of Pacific pink shrimp (Pandalus jordani).; Shrimp landings coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Shrimp landings (1000's of metric tons) on the U.S. West Coast. Shrimp landings consist primarily of Pacific pink shrimp (Pandalus jordani).; Shrimp landings OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Shrimp landings (1000's of metric tons) in Oregon. Shrimp landings consist primarily of Pacific pink shrimp (Pandalus jordani).; Shrimp landings WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Shrimp landings (1000's of metric tons) in Washington. Shrimp landings consist primarily of Pacific pink shrimp (Pandalus jordani).; Shrimp revenue CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Shrimp revenue (millions of 2015 dollars) in California. Shrimp landings consist primarily of Pacific pink shrimp (Pandalus jordani).; Shrimp revenue coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Shrimp revenue (millions of 2015 dollars) on the U.S. West Coast. Shrimp landings consist primarily of Pacific pink shrimp (Pandalus jordani).; Shrimp revenue OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Shrimp revenue (millions of 2015 dollars) in Oregon. Shrimp landings consist primarily of Pacific pink shrimp (Pandalus jordani).; Shrimp revenue WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Shrimp revenue (millions of 2015 dollars) in Washington. Shrimp landings consist primarily of Pacific pink shrimp (Pandalus jordani).; Market squid landings CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Market squid (Loligo opalescens) landings (1000's of metric tons) in California.; Market squid landings coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/); Market squid landings OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/); Market squid revenue CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/); Market squid revenue coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/); Market squid revenue OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/); Total Fisheries landings CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Combined commercial and recreational fisheries landings (1000's of metric tons) in California.; Total Fisheries landings coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Combined commercial and recreational fisheries landings (1000's of metric tons) on the U.S. West Coast.; Total Fisheries landings OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Combined commercial and recreational fisheries landings (1000's of metric tons) in Oregon.; Total Fisheries landings WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Combined commercial and recreational fisheries landings (1000's of metric tons) in Washington.; Commercial fisheries revenue CA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Combined commercial and recreational fisheries revenue (millions of 2015 dollars) in California.; Commercial fisheries revenue coastwide: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Combined commercial and recreational fisheries revenue (millions of 2015 dollars) on the U.S. West Coast.; Commercial fisheries revenue OR: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Combined commercial and recreational fisheries revenue (millions of 2015 dollars) in Oregon.; Commercial Fisheries revenue WA: source data: Pacific Fisheries Information Network (PacFIN; http://pacfin.psmfc.org/), additional calculations: Combined commercial and recreational fisheries revenue (millions of 2015 dollars) in Washington.;
#'
#' @format A data frame with 3670 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-7.889184E8, 1.5778368E9\]}
#'   \item{consumption_fish}{Seafood consumption (total) (Millions of metric tons) \[4.482482, 10.0965\]}
#'   \item{consumption_per_cap}{Seafood consumption (per capita) (kg) \[20.95595, 39.28107\]}
#'   \item{cps_landings_ca}{Coastal pelagic species (w/o squid) landings CA (1000s metric tons) \[9.179026, 116.2179\]}
#'   \item{cps_landings_coastwide}{Coastal pelagic species (w/o squid) landings coastwide (1000s metric tons) \[10.1784, 146.6982\]}
#'   \item{cps_landings_or}{Coastal pelagic species (w/o squid) landings OR (1000s metric tons) \[0.1021285, 45.57781\]}
#'   \item{cps_landings_wa}{Coastal pelagic species (w/o squid) landings WA (1000s metric tons) \[0.2823894, 36.98861\]}
#'   \item{cps_revenue_ca}{Coastal pelagic species (w/o squid) revenue CA (Millions $ (year 2015)) \[1.492483, 66.92373\]}
#'   \item{cps_revenue_coastwide}{Coastal pelagic species (w/o squid) revenue coastwide (Millions $ (year 2015)) \[1.655624, 69.18495\]}
#'   \item{cps_revenue_or}{Coastal pelagic species (w/o squid) revenue OR (Millions $ (year 2015)) \[0.007071201, 10.42128\]}
#'   \item{cps_revenue_wa}{Coastal pelagic species (w/o squid) revenue WA (Millions $ (year 2015)) \[0.1544515, 9.577675\]}
#'   \item{crab_landings_ca}{Crab landings CA (1000s metric tons) \[1.777526, 15.26896\]}
#'   \item{crab_landings_coastwide}{Crab landings coastwide (1000s metric tons) \[4.784686, 41.72063\]}
#'   \item{crab_landings_or}{Crab landings OR (1000s metric tons) \[1.720652, 15.29602\]}
#'   \item{crab_landings_wa}{Crab landings WA (1000s metric tons) \[1.286507, 14.47761\]}
#'   \item{crab_revenue_ca}{Crab revenue CA (Millions $ (year 2015)) \[11.14968, 111.6288\]}
#'   \item{crab_revenue_coastwide}{Crab revenue coastwide (Millions $ (year 2015)) \[29.94091, 252.6162\]}
#'   \item{crab_revenue_or}{Crab revenue OR (Millions $ (year 2015)) \[11.44846, 77.55869\]}
#'   \item{crab_revenue_wa}{Crab revenue WA (Millions $ (year 2015)) \[7.342773, 106.0389\]}
#'   \item{fertilizer_applications}{Nutrient Input (Normalized index) \[-1.867494, 1.818944\]}
#'   \item{finfish_production}{Finfish Aquaculture (1000s mt) \[0.01149992, 10.43139\]}
#'   \item{groundfish_landings_ca}{Groundfish landings (w/o hake) CA (1000s metric tons) \[5.847436, 52.65097\]}
#'   \item{groundfish_landings_coastwide}{Groundfish landings (w/o hake) coastwide (1000s metric tons) \[23.25325, 124.748\]}
#'   \item{groundfish_landings_or}{Groundfish landings (w/o hake) OR (1000s metric tons) \[9.699529, 41.12125\]}
#'   \item{groundfish_landings_wa}{Groundfish landings (w/o hake) WA (1000s metric tons) \[3.268318, 30.97578\]}
#'   \item{groundfish_revenue_ca}{Groundfish revenue (w/o hake) CA (Millions $ (year 2015)) \[13.45095, 76.30337\]}
#'   \item{groundfish_revenue_coastwide}{Groundfish revenue (w/o hake) coastwide (Millions $ (year 2015)) \[34.74943, 168.6599\]}
#'   \item{groundfish_revenue_or}{Groundfish revenue (w/o hake) OR (Millions $ (year 2015)) \[18.45586, 56.10078\]}
#'   \item{groundfish_revenue_wa}{Groundfish revenue (w/o hake) WA (Millions $ (year 2015)) \[2.842623, 42.45563\]}
#'   \item{habitat_modification}{Bottom trawl contact with seafloor habitat (weighted) (Weighted 1000s km) \[66.89688, 373.32\]}
#'   \item{hms_landings_ca}{Highly migratory species landings CA (1000s metric tons) \[0.6113654, 52.93386\]}
#'   \item{hms_landings_coastwide}{Highly migratory species landings coastwide (1000s metric tons) \[7.694124, 54.95815\]}
#'   \item{hms_landings_or}{Highly migratory species landings OR (1000s metric tons) \[0.4897378, 4.879423\]}
#'   \item{hms_landings_wa}{Highly migratory species landings WA (1000s metric tons) \[0.06658192, 10.78039\]}
#'   \item{hms_revenue_ca}{Highly migratory species revenue CA (Millions $ (year 2015)) \[2.833692, 149.0757\]}
#'   \item{hms_revenue_coastwide}{Highly migratory species revenue coastwide (Millions $ (year 2015)) \[25.0612, 155.5917\]}
#'   \item{hms_revenue_or}{Highly migratory species revenue OR (Millions $ (year 2015)) \[1.877474, 21.98329\]}
#'   \item{hms_revenue_wa}{Highly migratory species revenue WA (Millions $ (year 2015)) \[0.2299453, 32.39646\]}
#'   \item{oil_gas_production}{Oil And Gas Activity (Normalized index) \[-2.330674, 1.640257\]}
#'   \item{other_species_landings_ca}{Other species landings CA (1000s metric tons) \[2.618598, 26.98719\]}
#'   \item{other_species_landings_coastwide}{Other species landings coastwide (1000s metric tons) \[5.138479, 37.44385\]}
#'   \item{other_species_landings_or}{Other species landings OR (1000s metric tons) \[0.4558114, 4.70202\]}
#'   \item{other_species_landings_wa}{Other species landings WA (1000s metric tons) \[1.004735, 7.22235\]}
#'   \item{other_species_revenue_ca}{Other species revenue CA (Millions $ (year 2015)) \[23.10604, 82.84975\]}
#'   \item{other_species_revenue_coastwide}{Other species revenue coastwide (Millions $ (year 2015)) \[28.95057, 107.5516\]}
#'   \item{other_species_revenue_or}{Other species revenue OR (Millions $ (year 2015)) \[1.278496, 10.40565\]}
#'   \item{other_species_revenue_wa}{Other species revenue WA (Millions $ (year 2015)) \[2.083458, 17.45447\]}
#'   \item{pacific_hake_landings_ca}{Pacific hake landings CA (1000s metric tons) \[0.003771167, 135.1784\]}
#'   \item{pacific_hake_landings_coastwide}{Pacific hake landings coastwide (1000s metric tons) \[69.49753, 354.2313\]}
#'   \item{pacific_hake_landings_or}{Pacific hake landings OR (1000s metric tons) \[14.8115, 241.9501\]}
#'   \item{pacific_hake_landings_wa}{Pacific hake landings WA (1000s metric tons) \[6.960765, 142.5702\]}
#'   \item{pacific_hake_revenue_ca}{Pacific hake revenue CA (Millions $ (year 2015)) \[1.79688E-4, 2.37041\]}
#'   \item{pacific_hake_revenue_coastwide}{Pacific hake revenue coastwide (Millions $ (year 2015)) \[1.015924, 76.73247\]}
#'   \item{pacific_hake_revenue_or}{Pacific hake revenue OR (Millions $ (year 2015)) \[1.84902E-4, 60.24329\]}
#'   \item{pacific_hake_revenue_wa}{Pacific hake revenue WA (Millions $ (year 2015)) \[0.03069159, 39.40967\]}
#'   \item{recreational_landings_ca}{Recreational landings CA (1000s metric tons) \[3.675944, 6.593854\]}
#'   \item{recreational_landings_coastwide}{Recreational landings coastwide (1000s metric tons) \[5.084461, 8.823516\]}
#'   \item{recreational_landings_or}{Recreational landings OR (1000s metric tons) \[0.5492349, 1.255881\]}
#'   \item{recreational_landings_wa}{Recreational landings WA (1000s metric tons) \[0.450543, 1.257387\]}
#'   \item{salmon_com_landings_ca}{Salmon commercial landings CA (1000s metric tons) \[6.09329E-4, 7.70475\]}
#'   \item{salmon_com_landings_coastwide}{Salmon commercial landings coastwide (1000s metric tons) \[3.172211, 37.98297\]}
#'   \item{salmon_com_landings_or}{Salmon commercial landings OR (1000s metric tons) \[0.4526994, 8.078773\]}
#'   \item{salmon_com_landings_wa}{Salmon commercial landings WA (1000s metric tons) \[1.465571, 29.93654\]}
#'   \item{salmon_rec_landings_ca}{Salmon recreational landings CA (1000s fish) \[0.0, 398.0\]}
#'   \item{salmon_rec_landings_coastwide}{Salmon recreational landings coastwide (1000s fish) \[47.0, 774.0\]}
#'   \item{salmon_rec_landings_or}{Salmon recreational landings OR (1000s fish) \[6.0, 305.0\]}
#'   \item{salmon_rec_landings_wa}{Salmon recreational landings WA (1000s fish) \[23.0, 323.0\]}
#'   \item{salmon_revenue_ca}{Salmon revenue CA (Millions $ (year 2015)) \[0.0, 93.52464\]}
#'   \item{salmon_revenue_coastwide}{Salmon revenue coastwide (Millions $ (year 2015)) \[22.90256, 345.7938\]}
#'   \item{salmon_revenue_or}{Salmon revenue OR (Millions $ (year 2015)) \[2.574897, 86.96242\]}
#'   \item{salmon_revenue_wa}{Salmon revenue WA (Millions $ (year 2015)) \[5.292395, 188.2471\]}
#'   \item{shellfish_production}{Shellfish Aquaculture (metric tons) \[5.537665, 11.91462\]}
#'   \item{shipping_distance_traveled}{Commercial shipping - distance (Millions of km) \[17.68676, 31.40238\]}
#'   \item{shrimp_landings_ca}{Shrimp landings CA (1000s metric tons) \[0.5452471, 9.112365\]}
#'   \item{shrimp_landings_coastwide}{Shrimp landings coastwide (1000s metric tons) \[5.660932, 47.73951\]}
#'   \item{shrimp_landings_or}{Shrimp landings OR (1000s metric tons) \[2.249487, 24.30083\]}
#'   \item{shrimp_landings_wa}{Shrimp landings WA (1000s metric tons) \[1.424119, 19.11796\]}
#'   \item{shrimp_revenue_ca}{Shrimp revenue CA (Millions $ (year 2015)) \[4.988947, 20.19256\]}
#'   \item{shrimp_revenue_coastwide}{Shrimp revenue coastwide (Millions $ (year 2015)) \[16.18392, 113.3358\]}
#'   \item{shrimp_revenue_or}{Shrimp revenue OR (Millions $ (year 2015)) \[5.739861, 70.92217\]}
#'   \item{shrimp_revenue_wa}{Shrimp revenue WA (Millions $ (year 2015)) \[4.210262, 36.33729\]}
#'   \item{squid_landings_ca}{Market squid landings CA (1000s metric tons) \[0.5640231, 130.8447\]}
#'   \item{squid_landings_coastwide}{Market squid landings coastwide (1000s metric tons) \[0.9934503, 130.8523\]}
#'   \item{squid_landings_or}{Market squid landings OR (1000s metric tons) \[0.0, 4.670769\]}
#'   \item{squid_revenue_ca}{Market squid revenue CA (Millions $ (year 2015)) \[0.7664008, 84.71667\]}
#'   \item{squid_revenue_coastwide}{Market squid revenue coastwide (Millions $ (year 2015)) \[1.27265, 84.71667\]}
#'   \item{squid_revenue_or}{Market squid revenue OR (Millions $ (year 2015)) \[0.0, 5.999833\]}
#'   \item{total_fisheries_landings_ca}{Total Fisheries landings CA (1000s metric tons) \[51.75612, 302.277\]}
#'   \item{total_fisheries_landings_coastwide}{Total Fisheries landings coastwide (1000s metric tons) \[337.4346, 569.5555\]}
#'   \item{total_fisheries_landings_or}{Total Fisheries landings OR (1000s metric tons) \[68.72131, 290.9624\]}
#'   \item{total_fisheries_landings_wa}{Total Fisheries landings WA (1000s metric tons) \[72.43497, 177.1615\]}
#'   \item{total_fisheries_revenue_ca}{Commercial fisheries revenue CA (Millions $ (year 2015)) \[137.713, 374.8401\]}
#'   \item{total_fisheries_revenue_coastwide}{Commercial fisheries revenue coastwide (Millions $ (year 2015)) \[367.6378, 884.9662\]}
#'   \item{total_fisheries_revenue_or}{Commercial fisheries revenue OR (Millions $ (year 2015)) \[84.34836, 215.3039\]}
#'   \item{total_fisheries_revenue_wa}{Commercial Fisheries revenue WA (Millions $ (year 2015)) \[116.7023, 315.2471\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_AC/index.html}
#' @concept dataset_erddap
"cciea_AC"


#' Dissolved Oxygen
#'
#' Source Data: Dr. Bill Peterson, NOAA (bill.peterson@noaa.gov); https://www.nwfsc.noaa.gov/research/divisions/fe/estuarine/oeip/index.cfm; Source Data: Isaac Schroeder (NOAA; isaac.schroeder@noaa.gov); derived from CalCOFI surveys (http://calcofi.org/)
#'
#' @format A data frame with 5401 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-6.31152E8, 1.6067808E9\]}
#'   \item{location}{Location () \[\]}
#'   \item{dissolved_oxygen}{ (ml/L) \[0.62, 6.66876\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_DO/index.html}
#' @concept dataset_erddap
"cciea_OC_DO"


#' Oceanic Nino Index
#'
#' Oceanic Nino Index From NOAA Climate Prediction Center (CPC). Three month running mean of NOAA ERSST.V4 SST anomalies in the Nino 3.4 region (5N-5S, 120-170W), based on changing base period which onsist of multiple centered 30-year base periods. These 30-year base periods will be used to calculate the anomalies for successive 5-year periods in the historical record.  Data from: https://www.cpc.ncep.noaa.gov/data/indices/
#'
#' @format A data frame with 854 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-6.31152E8, 1.6145568E9\]}
#'   \item{ONI}{Oceanic Nino Index () \[-2.03, 2.64\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_ONI/index.html}
#' @concept dataset_erddap
"cciea_OC_ONI"


#' Seabird Prey Size
#'
#' Data from Oikonos Ecosystem Knowledge Ano Nuevo Seabird Conservation and Restoration Project; contact Ryan Carle (ryan@oikonos.org) before citing or distributing these data. Annual mean fork length of anchoy calculated from bill loads of returning adults to the colony at Ano Nuevo Island, CA.
#'
#' @format A data frame with 27 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[7.258464E8, 1.5778368E9\]}
#'   \item{fork_length}{Fork Length (mm) \[86.0, 152.0\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_B_LEN/index.html}
#' @concept dataset_erddap
"cciea_B_LEN"


#' Seabird diet
#'
#' No CC Rhinoceros auklet diet: Data from Washington Rhinoceros Auklet Ecology Project; contact tom.good@noaa.gov before citing or distributing these data. Diets of rhinoceros auklet chicks (% occurrence) calculated from bill loads of returning adults to the colony at Destruction Island, WA. No CC Common murre diet. Data from Hatfield Marine Science Center Seabird Oceanography Lab Yaquina Head Seabird Studies; contact Robert Suryan (rob.suryan@oregonstate.edu) before citing or distributing these data. Diets of common murre chicks (% occurrence) observed as bill loads of returning adults to colonies at Yaquina Head, OR. Ce CC Rhinoceros auklet diet: Data from Oikonos Ecosystem Knowledge Ano Nuevo Seabird Conservation and Restoration Project; contact Ryan Carle (ryan@oikonos.org) before citing or distributing these data. Diets of rhinoceros auklet chicks (% occurrence) calculated from bill loads of returning adults to the colony at Ano Nuevo Island, CA. Ce CC: Brandt's cormorant diet. Data from Point Blue Conservation Science collected on Southeast Farallon Island in collaboration with the Farallon Islands National Wildlife Refuge (USFWS); contact Dr. Jaime Jahncke (jjahncke@pointblue.org) before citing or distributing these data. Diet is percent occurrence of fish species in the diets of adult birds that are provisioning chicks calculated from bill loads of adults returning to the colony at Southeast Farallon Island, CA.
#'
#' @format A data frame with 518 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[7.258464E8, 1.5778368E9\]}
#'   \item{percent_diet}{Percent Observed Diet () \[0.0, 81.9\]}
#'   \item{diet_species_cohort}{Diet (Bird species cohort) () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_B_AS_DIET/index.html}
#' @concept dataset_erddap
"cciea_B_AS_DIET"


#' Aragonite Saturation
#'
#' Source Data: Data are derived from methods in Juranek et al. 2009: Juranek, L.W., Feely, R.A., Peterson, W.T., Alin, S.R., Hales, B., Lee, K., Sabine, C.L. and Peterson, J., 2009. A novel method for determination of aragonite saturation state on the continental shelf of central Oregon using multi-parameter relationships with hydrographic data. Geophysical Research Letters, 36(24). Additional Calculations: Data are derived from methods in Juranek et al. 2009: Juranek, L.W., Feely, R.A., Peterson, W.T., Alin, S.R., Hales, B., Lee, K., Sabine, C.L. and Peterson, J., 2009. A novel method for determination of aragonite saturation state on the continental shelf of central Oregon using multi-parameter relationships with hydrographic data. Geophysical Research Letters, 36(24).
#'
#' @format A data frame with 949 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.836128E8, 1.6067808E9\]}
#'   \item{location}{Location () \[\]}
#'   \item{aragonite_saturation}{ (relative to 1) \[0.4761976, 2.132431\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_ARG/index.html}
#' @concept dataset_erddap
"cciea_OC_ARG"


#' Chinook Abundance, OR/WA/ID
#'
#' Source Data: Various; see Wells et al. 2014, Table S5. For Oregon, Idaho, and Washington ESUs, data were obtained from the NWFSC's "Salmon Population Summary" database (https://www.webapps.nwfsc.noaa.gov/sps), with additional data for Oregon Coast coho salmon (Oregon Department of Fish and Wildlife, https://oregonstate.edu/dept/ODFW/spawn/data.htm), and from PFMC (2012) for the Upper Columbia Summer/Fall-run Chinook Salmon. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Spring/Summer-run Chinook salmon to the Snake River system (for list of tributaries, see Wells et al. 2014, Table S5). Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S5. For Oregon, Idaho, and Washington ESUs, data were obtained from the NWFSC's "Salmon Population Summary" database (https://www.webapps.nwfsc.noaa.gov/sps), with additional data for Oregon Coast coho salmon (Oregon Department of Fish and Wildlife, https://oregonstate.edu/dept/ODFW/spawn/data.htm), and from PFMC (2012) for the Upper Columbia Summer/Fall-run Chinook Salmon. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Chinook salmon to the lower Columbia River system (for list of tributaries, see Wells et al. 2014, Table S5). Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S5. For Oregon, Idaho, and Washington ESUs, data were obtained from the NWFSC's "Salmon Population Summary" database (https://www.webapps.nwfsc.noaa.gov/sps), with additional data for Oregon Coast coho salmon (Oregon Department of Fish and Wildlife, https://oregonstate.edu/dept/ODFW/spawn/data.htm), and from PFMC (2012) for the Upper Columbia Summer/Fall-run Chinook Salmon. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Spring-run Chinook salmon to the upper Columbia River system (based on the Entiat, Methow, and Wenatchee Rivers; see Wells et al. 2014, Table S5). Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S5. For Oregon, Idaho, and Washington ESUs, data were obtained from the NWFSC's "Salmon Population Summary" database (https://www.webapps.nwfsc.noaa.gov/sps), with additional data for Oregon Coast coho salmon (Oregon Department of Fish and Wildlife, https://oregonstate.edu/dept/ODFW/spawn/data.htm), and from PFMC (2012) for the Upper Columbia Summer/Fall-run Chinook Salmon. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Chinook salmon to the Willamette River system (based on Clackamas and McKenzie River Spring-run Chinook salmon; see Wells et al. 2014, Table S5). Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S5. For Oregon, Idaho, and Washington ESUs, data were obtained from the NWFSC's "Salmon Population Summary" database (https://www.webapps.nwfsc.noaa.gov/sps), with additional data for Oregon Coast coho salmon (Oregon Department of Fish and Wildlife, https://oregonstate.edu/dept/ODFW/spawn/data.htm), and from PFMC (2012) for the Upper Columbia Summer/Fall-run Chinook Salmon. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Fall-run Chinook salmon to the lower mainstem Snake River system. Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation.
#'
#' @format A data frame with 394 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[0.0, 1.5463008E9\]}
#'   \item{population}{Population () \[\]}
#'   \item{abundance_anomaly}{Abundance anomaly (Abundance anomaly) \[-1.438184, 3.748215\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_SM_ORWA_CH_ABND/index.html}
#' @concept dataset_erddap
"cciea_SM_ORWA_CH_ABND"


#' Coastal pelagics-Simp
#'
#' See: https://www.noaa.gov/iea/Assets/iea/california/Report/pdf/Ecological%20Integrity%20Status%20CCIEA%202012.pdf
#'
#' @format A data frame with 15 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.836128E8, 1.325376E9\]}
#'   \item{simpson_diversity}{Simpson Diversity (1-lambda) \[0.3012306, 0.4404312\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_CP_SIMP/index.html}
#' @concept dataset_erddap
"cciea_EI_CP_SIMP"


#' Coastal pelagics-Spp No
#'
#' See: https://www.noaa.gov/iea/Assets/iea/california/Report/pdf/Ecological%20Integrity%20Status%20CCIEA%202012.pdf
#'
#' @format A data frame with 15 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.836128E8, 1.325376E9\]}
#'   \item{species_number}{Species Number (No. of species) \[2.530864, 4.763441\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_CP_SPNO/index.html}
#' @concept dataset_erddap
"cciea_EI_CP_SPNO"


#' Coho Abundance, California
#'
#' Source Data: Various; see Wells et al. 2014, Table S3. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin spawners from Huntley Park (Rogue River). Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S3. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement (redd counts) by natural origin spawners in Lagunitas Creek. Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation.
#'
#' @format A data frame with 115 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[5.364576E8, 1.5463008E9\]}
#'   \item{population}{Population () \[\]}
#'   \item{abundance_anomaly}{Abundance anomaly (Abundance anomaly) \[-1.519987, 3.885922\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_SM_CA_CO_ABND/index.html}
#' @concept dataset_erddap
"cciea_SM_CA_CO_ABND"


#' Groundfish Abundance Index
#'
#' Source Data: Groundfish data are from the NMFS U.S. West Coast Groundfish Bottom Trawl Survey (https://www.nwfsc.noaa.gov/research/divisions/fram/groundfish/bottom_trawl.cfm) and were provided by Dr. Todd Hay and Ms. Beth Horness (NOAA). Additional Calculations: Reference Cope and Haltuch (2014) CCIEA PHASE III REPORT 2013: ECOSYSTEM COMPONENTS - GROUNDFISH report for full explanation of what sources of information were used for each species.
#'
#' @format A data frame with 247 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.0413792E9, 1.4200704E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{abundance_index}{ (abundance index) \[297.1226, 138590.0\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_GF_ABND/index.html}
#' @concept dataset_erddap
"cciea_GF_ABND"


#' Groundfish Simpson diversity
#'
#' Source Data: Groundfish data are from the NMFS U.S. West Coast Groundfish Bottom Trawl Survey (https://www.nwfsc.noaa.gov/research/divisions/fram/groundfish/bottom_trawl.cfm) and were downloaded from the FRAM data warehouse at https://www.nwfsc.noaa.gov/data/map with the following API query:Â  https://www.nwfsc.noaa.gov/data/api/v1/source/trawl.catch_fact/selection.csv?variables=program,trawl_id,date_dim$year,date_dim$yyyymmdd,vessel,performance,year_stn_invalid,depth_m,latitude_dd,longitude_dd,scientific_name,common_name,species_category,partition,total_catch_numbers,total_catch_wt_kg,cpue_kg_per_ha_der,cpue_numbers_per_ha_der,"actual_station_design_dim$station_invalid_survey_year", "actual_station_design_dim$reason_station_invalid","reason_stn_invalid (target station)", "year_stn_invalid" Additional Calculations:  Simpson diversity (1-Î») for West Coast groundfishes. For details, see Tolimieri et al. 2014 (https://www.noaa.gov/iea/CCIEA-Report/pdf/index.html)
#'
#' @format A data frame with 192 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.0413792E9, 1.5147648E9\]}
#'   \item{population}{Population () \[\]}
#'   \item{simpson_diversity}{ () \[0.6001309, 0.7265539\]}
#'   \item{Seup}{Confidence Interval, Upper () \[0.6113339, 0.7355888\]}
#'   \item{Selo}{Confidence Interval, Lower () \[0.588928, 0.7175189\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_SIMP/index.html}
#' @concept dataset_erddap
"cciea_EI_SIMP"


#' Groundfish Stock Status
#'
#' Source Data: Dr. Jason Cope (NOAA; jason.cope@noaa.gov), derived from NMFS stock assessments (https://www.nwfsc.noaa.gov/research/divisions/fram/popeco/assessment.cfm). Additional Calculations: Reference Cope and Haltuch (2014) CCIEA PHASE III REPORT 2013: ECOSYSTEM COMPONENTS - GROUNDFISH report for full explanation of what sources of information were used for each species.
#'
#' @format A data frame with 7714 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-2.9663712E9, 1.4200704E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{relative_stock_status}{ (relative biomass) \[0.0498108, 1.25279\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_GF_STAT/index.html}
#' @concept dataset_erddap
"cciea_GF_STAT"


#' Groundfish species richness
#'
#' Source Data: Groundfish data are from the NMFS U.S. West Coast Groundfish Bottom Trawl Survey (https://www.nwfsc.noaa.gov/research/divisions/fram/groundfish/bottom_trawl.cfm) and were downloaded from the FRAM data warehouse at https://www.nwfsc.noaa.gov/data/map with the following API query:Â  https://www.nwfsc.noaa.gov/data/api/v1/source/trawl.catch_fact/selection.csv?variables=program,trawl_id,date_dim$year,date_dim$yyyymmdd,vessel,performance,year_stn_invalid,depth_m,latitude_dd,longitude_dd,scientific_name,common_name,species_category,partition,total_catch_numbers,total_catch_wt_kg,cpue_kg_per_ha_der,cpue_numbers_per_ha_der,"actual_station_design_dim$station_invalid_survey_year", "actual_station_design_dim$reason_station_invalid","reason_stn_invalid (target station)", "year_stn_invalid" Additional Calculations:  Species richness for groundfishes on the West Coast from the West Coast Groundfish Bottom Trawl Survey.  Data underwent sample-based rarefaction and were then scaled to 3900 individuals to produce richness estimates. For details, see Tolimieri et al. 2014 (https://www.noaa.gov/iea/CCIEA-Report/pdf/index.html)
#'
#' @format A data frame with 192 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.0413792E9, 1.5147648E9\]}
#'   \item{population}{Population () \[\]}
#'   \item{species_richness}{ () \[28.58534, 40.22564\]}
#'   \item{Seup}{Confidence Interval, Upper () \[31.00103, 43.00691\]}
#'   \item{Selo}{Confidence Interval, Lower () \[26.16965, 37.62311\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_SP_RICH/index.html}
#' @concept dataset_erddap
"cciea_EI_SP_RICH"


#' Habitat Compression Index
#'
#' In eastern boundary upwelling ecosystems the spatial footprint of cool upwelled water is regularly demarcated by the differential boundary of warmer oceanic water offshore from cooler coastal water, with upwelling conditions varying with latitude.   Therefore, the goal of the habitat compression index (HCI) is to track the area of cool surface waters as an index of potential 'upwelling habitat' for assessing the spatio-temporal aspects of upwelling.  Upwelling patterns of cold nutrient-rich water are clearly assessed by models and satellite observations and classified spatially by monitoring SST values less than and equal to a monthly resolved temperature threshold.  The HCI tracks the amount of area, determined by the number of grid cells in the model with 2 m surface temperature values less than the monthly temperature threshold, therefore the time series reflects the area of cool water adjacent to the coastline and provides a measure for how compressed cool surface temperatures may be in a particular month.  In this study, we extracted modeled 2 m temperature fields over the domain of each of four regions for each month and tracked the amount of area with temperature values less than and equal to a monthly temperature threshold, resulting in monthly time series starting January 1980.  Monthly temperature thresholds for a given month is the spatial average of 2 m temperature grid cells between the indicated latitude boundaries for each region and from shore out to 75 km for the time period 1980-2010. Cool expansion periods are defined as months with areas exceeding +1 standard deviation (SD) of the full time series, limited cool habitat where area of cool water is less than -1 SD, and periods of habitat compression when the area of cool water falls between the long-term monthly mean and -1 SD.
#'
#' @format A data frame with 1984 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.155328E8, 1.6172352E9\]}
#'   \item{hci_regn1}{Habitat Compression Index, 43.5-48N (fraction below monthly threshold) \[0.0, 1.0\]}
#'   \item{hci_regn2}{Habitat Compression Index, 40-43.5N (fraction below monthly threshold) \[0.0, 1.0\]}
#'   \item{hci_regn3}{Habitat Compression Index, 35.5-40N (fraction below monthly threshold) \[0.0, 1.0\]}
#'   \item{hci_regn4}{Habitat Compression Index, 30-35.5N (fraction below monthly threshold) \[0.0, 0.9744409\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_HCI/index.html}
#' @concept dataset_erddap
"cciea_EI_HCI"


#' Highly Migratory Species
#'
#' This index represents modeled female spawning potential biomass from the latest (2017) stock assessment report, completed through the International Scientific Committee for Tuna and Tuna-like Species in the North Pacific Ocean (ISC). North Pacific albacore are considered to be one stock throughout the North Pacific Ocean, although some studies have suggested that a northern and southern stock may be present within the assessment area. They are fished throughout their range by multiple countries, mostly using surface gear (troll and pole and line), as well as pelagic longlines and other gears. Their population dynamics are assessed using an age-, length- and sex-structured model (Stock Synthesis v3). The full assessment is available from http://isc.fra.go.jp/reports/stock_assessments.html.; Estimates of recruitment are derived from the assessment model. The latest stock assessment report  was completed through the Inter-American Tropical Tuna Commission (IATTC), using Stock Synthesis V3. The assessment assumes that there is one stock of bigeye in the eastern Pacific. However, the assessment report acknowledges that recent tagging research suggests that bigeye undertake extensive longitudinal movements, which may be at odds with this assumption. The latest assessment report is available from http://www.iattc.org/Meetings/Meetings2017/SAC08/PDFs/SAC-08-04a-BET-Assessment-of-bigeye-2016.pdf .; This series shows the modeled female spawning stock biomass index from the 2017 stock assessment, which was an update of the 2016 full assessment. Yellowfin tuna are assessed through the Inter-American Tropical Tuna Commission (IATTC), using Stock Synthesis V3. They are assumed to comprise one stock throughout the Pacific, although tagging data suggest considerable regional fidelity. In the eastern Pacific, they are primarily fished in tropical waters, from Baja California south. The latest assessment report is available from http://www.iattc.org/Meetings/Meetings2017/SAC08/PDFs/SAC-08-04b-YFT-Assessment-of-yellowfin-2016.pdf.; Estimates of annual recruitment are derived from the stock assessment model. The modeled spawning stock biomass from the latest (2016) stock assessment report was completed through the International Scientific Committee for Tuna and Tuna-like Species in the North Pacific Ocean (ISC). Blue marlin are considered to be one stock throughout the Pacific Ocean, and the majority of catch is from pelagic longlines. Their population dynamics are assessed using an age-, length- and sex-structured model (Stock Synthesis v3). The full assessment is available from http://isc.fra.go.jp/reports/stock_assessments.html.; This index represents modeled  female spawning stock biomass from the latest (2016) stock assessment report, completed through the International Scientific Committee for Tuna and Tuna-like Species in the North Pacific Ocean (ISC). Blue marlin are considered to be one stock throughout the Pacific Ocean, and the majority of catch is from pelagic longlines. Their population dynamics are assessed using an age-, length- and sex-structured model (Stock Synthesis v3). The full assessment is available from http://isc.fra.go.jp/reports/stock_assessments.html.; Annual recruitment is derived from the stock assessment model, and is primarily indexed by catches from troll fisheries on age-0 juvenile fish near Japan.  m the latest (2016) stock assessment rep was complted byleted through the International Scientific Committee for Tuna and Tuna-like Species in the North Pacific Ocean (ISC). Pacific bluefin are considered to be one stock throughout the Pacific Ocean, and are fished throughout their range by multiple countries and fishing gears. At present, the majority are caught by purse seine gear. Their population dynamics are assessed using a fully integrated age-structured model (Stock Synthesis v3). The full assessment is available from http://isc.fra.go.jp/reports/stock_assessments.html .; Estimates of recruitment are derived from the assessment model. Skipjack tuna are assumed to comprise one contiguous stock throughout the Pacific ocean. In the eastern Pacific, they are primarily fished in tropical waters, using purse seine gear. Skipjack are difficult to assess with standard stock assessment methods, due to high and variable productivity, and uncertainties in natural mortality and growth. This species is thus assessed using a simple model which generates indicators of biomass, recruitment and exploitation rate, and compares these to historically observed values (Maunder & Deriso 2007). The stock assessment is completed through the Inter-American Tropical Tuna Commission (IATTC). The relative biomass index shown is from the latest update assessment, including data up to 2016, which is available from http://www.iattc.org/Meetings/Meetings2017/SAC08/PDFs/SAC-08-04c-SKJ-Status-of-skipjack-2016.pdf.; This index represents modeled spawning stock biomass from the latest (2016) stock assessment report, completed through the International Scientific Committee for Tuna and Tuna-like Species in the North cific Ocean (ISC). Pacific bluefin are considered to be one stock throughout the Pacific Ocean, and are fished throughout their range by multiple countries and fishing gears. At present, the majority are caught by purse seine gear. Their population dynamics are assessed using a fully integrated age-structured model (Stock Synthesis v3). The full assessment is available from http://isc.fra.go.jp/reports/stock_assessments.html.; Skipjack tuna are assumed to comprise one contiguous stock throughout the Pacific ocean. In the eastern Pacific, they are primarily fished in tropical waters, using purse seine gear. Skipjack are difficult to assess with standard stock assessment methods, due to high and variable productivity, and uncertainties in natural mortality and growth. This species is thus assessed using a simple model which generates indicators of biomass, recruitment and exploitation rate, and compares these to historically observed values (Maunder & Deriso 2007). The stock assessment is completed through the Inter-American Tropical Tuna Commission (IATTC). The relative biomass index shown is from the latest update assessment, including data up to 2016, which is available from http://www.iattc.org/Meetings/Meetings2017/SAC08/PDFs/SAC-08-04c-SKJ-Status-of-skipjack-2016.pdf.; These indices represent modeled exploitable biomass for the western central and eastern Pacific swordfish stocks, from the latest (2014) stock assessment report, completed through the International Scientific Committee for Tuna and Tuna-like Species in the North Pacific Ocean (ISC). Swordfish are considered to comprise two stocks in the North Pacific. The western and central Pacific stock is located throughout the entire North Pacific, except for off Baja California, and central and south America, which is occupied by the eastern Pacific stock. The highest catches are from pelagic longline gears. The population dynamics of both stocks are assessed using a Bayesian state-space generalized surplus production model. The  full assessment is available from http://isc.fra.go.jp/reports/stock_assessments.html.; Estimates of annual recruitment are derived from the stock assessment model.Â The latest (2017) stock assessment report was completed through the International Scientific Committee for Tuna and Tuna-like Species in the North Pacific Ocean (ISC). North Pacific albacore are considered to be one stock throughout the North Pacific Ocean, although some studies have suggested that a northern and southern stock may be present within the assessment area. They are fished throughout their range by multiple countries, mostly using surface gear (troll and pole and line), as well as pelagic longlines and other gears. Their population dynamics are assessed using an age-, length- and sex-structured model (Stock Synthesis v3). The full assessment is available from http://isc.fra.go.jp/reports/stock_assessments.html; This index shows modeled female spawning stock biomass of bigeye tuna from the latest stock assessment report, which was completed through the Inter-American Tropical Tuna Commission (IATTC), using Stock Synthesis V3. The assessment assumes that there is one stock of bigeye in the eastern Pacific. However, the assessment report acknowledges that recent tagging research suggests that bigeye undertake extensive longitudinal movements, which may be at odds with this assumption. The latest assessment report is available from http://www.iattc.org/Meetings/Meetings2017/SAC08/PDFs/SAC-08-04a-BET-Assessment-of-bigeye-2016.pdf.; Estimates of recruitment are derived from the assessment model. The 2017 stock assessment was an update of the 2016 full assessment. Yellowfin tuna are assessed through the Inter-American Tropical Tuna Commission (IATTC), using Stock Synthesis V3. They are assumed to comprise one stock throughout the Pacific, although tagging data suggest considerable regional fidelity. In the eastern Pacific, they are primarily fished in tropical waters, from Baja California south. The latest assessment report is available from http://www.iattc.org/Meetings/Meetings2017/SAC08/PDFs/SAC-08-04b-YFT-Assessment-of-yellowfin-2016.pdf.;
#'
#' @format A data frame with 2851 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-5.99616E8, 1.5778368E9\]}
#'   \item{albacore_tuna_recruitment}{Albacore tuna recruitment (thousands of fish) \[112811.0, 256880.0\]}
#'   \item{albacore_tuna_recruitment_SEup}{Albacore tuna recruitment SEup (thousands of fish) \[141942.1, 295771.1\]}
#'   \item{albacore_tuna_recruitment_SElo}{Albacore tuna recruitment SElo (thousands of fish) \[83679.9, 217988.9\]}
#'   \item{albacore_tuna_recruitment_SEtype}{Albacore tuna recruitment SE type () \[\]}
#'   \item{albacore_tuna_spawning_stock_biomass}{Albacore tuna spawning stock biomass (metric tonnes) \[52465.5, 86714.6\]}
#'   \item{albacore_tuna_spawning_stock_biomass_SEup}{Albacore tuna spawning stock biomass SEup (metric tonnes) \[66807.3, 108882.4\]}
#'   \item{albacore_tuna_spawning_stock_biomass_SElo}{Albacore tuna spawning stock biomass SElo (metric tonnes) \[38123.7, 64546.8\]}
#'   \item{albacore_tuna_spawning_stock_biomass_SEtype}{Albacore tuna spawning stock biomass SE type () \[\]}
#'   \item{bigeye_tuna_recruitment_optimistic}{Bigeye tuna recruitment optimistic (thousands of fish) \[25361.0, 62830.0\]}
#'   \item{bigeye_tuna_recruitment_optimistic_SEup}{Bigeye tuna recruitment optimistic SEup (thousands of fish) \[35741.0, 91464.1\]}
#'   \item{bigeye_tuna_recruitment_optimistic_SElo}{Bigeye tuna recruitment optimistic SElo (thousands of fish) \[17645.0, 42846.0\]}
#'   \item{bigeye_tuna_recruitment_optimistic_SEtype}{Bigeye tuna recruitment optimistic SE type () \[\]}
#'   \item{bigeye_tuna_recruitment_pessimistic}{Bigeye tuna recruitment pessimistic (thousands of fish) \[17413.0, 42614.0\]}
#'   \item{bigeye_tuna_recruitment_pessimistic_SEup}{Bigeye tuna recruitment pessimistic SEup (thousands of fish) \[20315.0, 52259.9\]}
#'   \item{bigeye_tuna_recruitment_pessimistic_SElo}{Bigeye tuna recruitment pessimistic SElo (thousands of fish) \[14327.6, 36075.0\]}
#'   \item{bigeye_tuna_recruitment_pessimistic_SEtype}{Bigeye tuna recruitment pessimistic SE type () \[\]}
#'   \item{bigeye_tuna_spawning_stock_biomass_optimistic}{Bigeye tuna spawning stock biomass optimistic (metric tonnes) \[223595.3, 365256.0\]}
#'   \item{bigeye_tuna_spawning_stock_biomass_optimistic_SEup}{Bigeye tuna spawning stock biomass optimistic SEup (metric tonnes) \[405962.0, 668900.0\]}
#'   \item{bigeye_tuna_spawning_stock_biomass_optimistic_SElo}{Bigeye tuna spawning stock biomass optimistic SElo (metric tonnes) \[62773.0, 112185.0\]}
#'   \item{bigeye_tuna_spawning_stock_biomass_optimistic_SEtype}{Bigeye tuna spawning stock biomass optimistic SE type () \[\]}
#'   \item{bigeye_tuna_spawning_stock_biomass_pessimistic}{Bigeye tuna spawning stock biomass pessimistic (metric tonnes) \[59567.01, 101975.1\]}
#'   \item{bigeye_tuna_spawning_stock_biomass_pessimistic_SEup}{Bigeye tuna spawning stock biomass pessimistic SEup (metric tonnes) \[97979.0, 147028.0\]}
#'   \item{bigeye_tuna_spawning_stock_biomass_pessimistic_SElo}{Bigeye tuna spawning stock biomass pessimistic SElo (metric tonnes) \[31691.0, 64157.0\]}
#'   \item{bigeye_tuna_spawning_stock_biomass_pessimistic_SEtype}{Bigeye tuna spawning stock biomass pessimistic SE type () \[\]}
#'   \item{bluefin_tuna_recruitment}{Bluefin tuna recruitment (thousands of fish) \[3194.88, 34812.8\]}
#'   \item{bluefin_tuna_recruitment_SEup}{Bluefin tuna recruitment SEup (thousands of fish) \[4149.436, 39464.52\]}
#'   \item{bluefin_tuna_recruitment_SElo}{Bluefin tuna recruitment SElo (thousands of fish) \[2240.324, 30194.73\]}
#'   \item{bluefin_tuna_recruitment_SEtype}{Bluefin tuna recruitment SE type () \[\]}
#'   \item{bluefin_tuna_spawning_stock_biomass}{Bluefin tuna spawning stock biomass (metric tonnes) \[10837.1, 156302.0\]}
#'   \item{bluefin_tuna_spawning_stock_biomass_SEup}{Bluefin tuna spawning stock biomass SEup (metric tonnes) \[12789.73, 212828.6\]}
#'   \item{bluefin_tuna_spawning_stock_biomass_SElo}{Bluefin tuna spawning stock biomass SElo (metric tonnes) \[8884.47, 99775.4\]}
#'   \item{bluefin_tuna_spawning_stock_biomass_SEtype}{Bluefin tuna spawning stock biomass SE type () \[\]}
#'   \item{blue_marlin_recruitment}{Blue marlin recruitment (thousands of fish) \[589.3, 1181.18\]}
#'   \item{blue_marlin_recruitment_SEup}{Blue marlin recruitment SEup (thousands of fish) \[705.14, 1399.42\]}
#'   \item{blue_marlin_recruitment_SElo}{Blue marlin recruitment SElo (thousands of fish) \[473.46, 964.91\]}
#'   \item{blue_marlin_recruitment_SEtype}{Blue marlin recruitment SE type () \[\]}
#'   \item{blue_marlin_spawning_stock_biomass}{Blue marlin spawning stock biomass (metric tonnes) \[20972.0, 71806.5\]}
#'   \item{blue_marlin_spawning_stock_biomass_SEup}{Blue marlin spawning stock biomass SEup (metric tonnes) \[23548.58, 91587.2\]}
#'   \item{blue_marlin_spawning_stock_biomass_SElo}{Blue marlin spawning stock biomass SElo (metric tonnes) \[18395.42, 52025.8\]}
#'   \item{blue_marlin_spawning_stock_biomass_SEtype}{Blue marlin spawning stock biomass SE type () \[\]}
#'   \item{skipjack_tuna_biomass}{Skipjack tuna relative biomass index (metric tonnes) \[0.4711875, 2.244901\]}
#'   \item{skipjack_tuna_biomass_SEup}{Skipjack tuna relative biomass index SEup (metric tonnes) \[\]}
#'   \item{skipjack_tuna_biomass_SElo}{Skipjack tuna relative biomass index SElo (metric tonnes) \[\]}
#'   \item{skipjack_tuna_biomass_SEtype}{Skipjack tuna relative biomass index SE type () \[\]}
#'   \item{skipjack_tuna_recruitment}{Skipjack tuna recruitment index (thousands of fish) \[0.4760525, 2.503387\]}
#'   \item{skipjack_tuna_recruitment_SEup}{Skipjack tuna recruitment index SEup (thousands of fish) \[\]}
#'   \item{skipjack_tuna_recruitment_SElo}{Skipjack tuna recruitment index SElo (thousands of fish) \[\]}
#'   \item{skipjack_tuna_recruitment_SEtype}{Skipjack tuna recruitment index SE type () \[\]}
#'   \item{swordfish_eastern_pacific_exploitable_total_biomass}{Eastern Pacific exploitable swordfish total biomass (metric tonnes) \[31510.0, 67070.0\]}
#'   \item{swordfish_eastern_pacific_exploitable_total_biomass_SEup}{Eastern Pacific exploitable swordfish total biomass SEup (metric tonnes) \[42580.0, 89380.0\]}
#'   \item{swordfish_eastern_pacific_exploitable_total_biomass_SElo}{Eastern Pacific exploitable swordfish total biomass SElo (metric tonnes) \[20440.0, 44760.0\]}
#'   \item{swordfish_eastern_pacific_exploitable_total_biomass_SEtype}{Eastern Pacific exploitable swordfish total biomass SE type () \[\]}
#'   \item{swordfish_western_central_pacific_exploitable_recruitment}{Western Central Pacific swordfish recruitment (thousands of fish) \[401.0, 1241.0\]}
#'   \item{swordfish_western_central_pacific_exploitable_recruitment_SEup}{Western Central Pacific swordfish recruitment SEup (thousands of fish) \[487.0, 1856.0\]}
#'   \item{swordfish_western_central_pacific_exploitable_recruitment_SElo}{Western Central Pacific swordfish recruitment SElo (thousands of fish) \[315.0, 949.0\]}
#'   \item{swordfish_western_central_pacific_exploitable_recruitment_SEtype}{Western Central Pacific swordfish recruitment SE type () \[\]}
#'   \item{swordfish_western_central_pacific_spawning_stock_biomass}{Western Central Pacific swordfish spawning stock biomass (metric tonnes) \[17191.0, 44100.0\]}
#'   \item{swordfish_western_central_pacific_spawning_stock_biomass_SEup}{Western Central Pacific swordfish spawning stock biomass SEup (metric tonnes) \[21959.0, 52738.0\]}
#'   \item{swordfish_western_central_pacific_spawning_stock_biomass_SElo}{Western Central Pacific swordfish spawning stock biomass SElo (metric tonnes) \[12423.0, 35462.0\]}
#'   \item{swordfish_western_central_pacific_spawning_stock_biomass_SEtype}{Western Central Pacific swordfish spawning stock biomass SE type () \[\]}
#'   \item{yellowfin_tuna_recruitment}{Yellowfin tuna recruitment (thousands of fish) \[598155.6, 2035279.0\]}
#'   \item{yellowfin_tuna_recruitment_SEup}{Yellowfin tuna recruitment SEup (thousands of fish) \[911885.0, 3308169.0\]}
#'   \item{yellowfin_tuna_recruitment_SElo}{Yellowfin tuna recruitment SElo (thousands of fish) \[246326.1, 1244589.0\]}
#'   \item{yellowfin_tuna_recruitment_SEtype}{Yellowfin tuna recruitment SE type () \[\]}
#'   \item{yellowfin_tuna_spawning_stock_biomass_index}{Yellowfin tuna spawning stock biomass (metric tonnes) \[6244.701, 26607.24\]}
#'   \item{yellowfin_tuna_spawning_stock_biomass_index_SEup}{Yellowfin tuna spawning stock biomass SEup (metric tonnes) \[13145.74, 66526.3\]}
#'   \item{yellowfin_tuna_spawning_stock_biomass_index_SElo}{Yellowfin tuna spawning stock biomass SElo (metric tonnes) \[2892.493, 8664.75\]}
#'   \item{yellowfin_tuna_spawning_stock_biomass_index_SEtype}{Yellowfin tuna spawning stock biomass SE type () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HMS/index.html}
#' @concept dataset_erddap
"cciea_HMS"


#' Multivariate ENSO Index
#'
#' This calculation is an attempt to monitor ENSO by basing the Multivariate ENSO Index (MEI) on the six main observed variables over the tropical Pacific. These six variables are: sea-level pressure, zonal and meridional components of the surface wind, sea surface temperature, surface air temperature, and total cloudiness fraction of the sky. These observations have been collected and published in ICOADS for many years. The MEI is computed separately for each of twelve sliding bi-monthly seasons (Dec/Jan, Jan/Feb,..., Nov/Dec). After spatially filtering the individual fields into clusters (Wolter, 1987), the MEI is calculated as the first unrotated Principal Component (PC) of all six observed fields combined. This is accomplished by normalizing the total variance of each field first, and then performing the extraction of the first PC on the co-variance matrix of the combined fields (Wolter and Timlin, 1993). In order to keep the MEI comparable, all seasonal values are standardized with respect to each season and to the 1950-93 reference period.
#'
#' @format A data frame with 827 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-6.31152E8, 1.5410304E9\]}
#'   \item{MEI}{MEI Index () \[-2.247, 3.008\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_MEI/index.html}
#' @concept dataset_erddap
"cciea_OC_MEI"


#' North Pacific High Area
#'
#' Variations in large-scale atmospheric forcing influence upwelling dynamics and ecosystem productivity in the California Current System. The area of the North Pacific High (NPH) is characterized by areal extent of the 1020 Pa isobar (Schroeder et al., doi:10.1002/grl.50100). The area of the NPH are calculated from monthly mean sea level pressure (SLP) fields created by the U.S. Navy Fleet Numerical Meterology and Oceanography Center (FNMOC). Monthly SLP data available at https://upwell.pfeg.noaa.gov/erddap/index.html , search for Dataset ID: erdlasFnWPr. The area is the areal extent of the 1020 hPa contour for a given month. The NRT ROMS temperature data downloaded from the UCSC website (http://oceanmodeling.pmc.ucsc.edu:8080/thredds) has grid points with 2 m temperature data reported as Not-A-Number, these values are 2 m temperatu. Tdata at these grid points are obtained by extrapolating the data to 2 m depth using the interp1d routine from the Python library scipy.interpolate sub-package.
#'
#' @format A data frame with 652 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.46944E7, 1.6172352E9\]}
#'   \item{nph_area}{Monthly North Pacific High Area (10^6 km^2) \[0.0, 7.810526\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_NPH/index.html}
#' @concept dataset_erddap
"cciea_OC_NPH"


#' Scavenger biomass ratio
#'
#' Source Data: Groundfish data are from the NMFS U.S. West Coast Groundfish Bottom Trawl Survey (https://www.nwfsc.noaa.gov/research/divisions/fram/groundfish/bottom_trawl.cfm) and were downloaded from the FRAM data warehouse at https://www.nwfsc.noaa.gov/data/map with the following API query:Â  https://www.nwfsc.noaa.gov/data/api/v1/source/trawl.catch_fact/selection.csv?variables=program,trawl_id,date_dim$year,date_dim$yyyymmdd,vessel,performance,year_stn_invalid,depth_m,latitude_dd,longitude_dd,scientific_name,common_name,species_category,partition,total_catch_numbers,total_catch_wt_kg,cpue_kg_per_ha_der,cpue_numbers_per_ha_der,"actual_station_design_dim$station_invalid_survey_year", "actual_station_design_dim$reason_station_invalid","reason_stn_invalid (target station)", "year_stn_invalid" Additional Calculations:  Ratio of groundfish and crab scavengers to total biomass for the West Coast shelf and slope. For details, see Tolimieri et al. 2014 (https://www.noaa.gov/iea/CCIEA-Report/pdf/index.html)
#'
#' @format A data frame with 156 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.0413792E9, 1.4200704E9\]}
#'   \item{population}{Population () \[\]}
#'   \item{biomass_ratio}{ () \[0.1095965, 0.3513092\]}
#'   \item{Seup}{Confidence Interval, Upper () \[0.1182098, 0.3672911\]}
#'   \item{Selo}{Confidence Interval, Lower () \[0.1009832, 0.3353272\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_SCAV_RAT/index.html}
#' @concept dataset_erddap
"cciea_EI_SCAV_RAT"


#' Seabird Productivity
#'
#' Data from Hatfield Marine Science Center Seabird Oceanography Lab Yaquina Head Seabird Studies; contact Robert Suryan (rob.suryan@oregonstate.edu) before citing or distributing these data.
#'
#' @format A data frame with 80 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.1676096E9, 1.5778368E9\]}
#'   \item{productivity_anomaly}{Productivity Anomaly () \[-0.969, 1.511\]}
#'   \item{species_cohort}{Species cohort () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_B_PR_ANOM/index.html}
#' @concept dataset_erddap
"cciea_B_PR_ANOM"


#' Seabird beached mortality
#'
#' Annual mean encounter rates (bird carcasses/km surveyed) aggregated from October to February (inclusive, with years labelled according to the convention that Oct 2014 to Feb 2015 are labelled as 2014) for each surveyed beach location, and then averaged across all beaches surveyed in that year. No CC: Data from the Coastal Observation and Seabird Survey Team (COASST), who coordinate a team of trained volunteers that collect effort-controlled survey data on an approximately monthly basis, recording beached bird numbers and identity from survey locations in Northern California through to Northern Washington and into Alaska and the Bering Sea. Contact COASST (https://depts.washington.edu/coasst/) for details on calculations before citing or distributing these data. So/Ce CC: Data from BeachCombers, who coordinate a team of trained volunteers that collect effort-controlled survey data on an approximately monthly basis, recording beached bird numbers and identity from survey locations in Central/Southern California. Contact BeachCombers (https://www.mlml.calstate.edu/beachcombers/) for details on calculations before citing or distributing these data. Ce CC: Data from BeachWatch, who coordinate a team of trained volunteers that collect effort-controlled survey data on an approximately monthly basis, recording beached bird numbers and identity from survey locations in Central California. Contact BeachWatch (https://farallones.noaa.gov/science/beachwatch.html) for details on calculations before citing or distributing these data.
#'
#' @format A data frame with 876 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[7.573824E8, 1.5778368E9\]}
#'   \item{encounter_rate}{Encounter Rate () \[0.0, 31.652\]}
#'   \item{species_cohort}{Species (region, season) () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_B_B_MORT/index.html}
#' @concept dataset_erddap
"cciea_B_B_MORT"


#' Snow Water Equivalent
#'
#' Snow-water equivalent data were derived from the California Department of Water Resources snow survey (https://cdec.water.ca.gov/) and the Natural Resources Conservation Service's SNOTEL sites in WA, OR, CA and ID (https://www.wcc.nrcs.usda.gov/snow/). Anomalies of April 1 snow-water equivalents (SWE) for the CCE, calculated as an area-weighted average of data from 5 ecoregions. SWE is a measure of the total water available in snowpack. Measurements on April 1st are considered the best indicator of maximum extent of snowpack.
#'
#' @format A data frame with 1332 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-1.6094592E9, 1.5778368E9\]}
#'   \item{SWE_anomaly}{1 Apr SWE Anomaly (Annual Anomaly) \[-2.187893, 2.455789\]}
#'   \item{Seup}{95% credible interval upper bound (Annual Anomaly) \[-1.721963, 3.500461\]}
#'   \item{Selo}{95% credible interval lower bound (Annual Anomaly) \[-2.957523, 2.095322\]}
#'   \item{location}{Location () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HB_SWE/index.html}
#' @concept dataset_erddap
"cciea_HB_SWE"


#' Whale Entanglements
#'
#' Whale Entanglements
#'
#' @format A data frame with 420 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[9.466848E8, 1.5778368E9\]}
#'   \item{species}{Species () \[\]}
#'   \item{number_of_entanglements}{Number of Entanglements () \[0.0, 55.0\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_WH_ENT/index.html}
#' @concept dataset_erddap
"cciea_EI_WH_ENT"


#' California Current Cetacean Abundance
#'
#' Bottlenose dolphin abundance CA coastal stock: Estimates from mark-resighting analysis; see Dudzik 1999, Weller et al. 2016. Abundance is calculated from closed population models and averaged over 2 or 3 year intervals. X-axis last year used for each period.  Abundance of short-beaked common dolphin, Dall's porpoise, Fin whale, Blue whale, Humpback whale: Estimates from ship-based line transect surveys; see Barlow 2016. Gray whale abundance: Estimates from shore-based counts using baysian model; see Durban et al. 2015, Durban et al. 2017. Southern resident killer whale abundance: Estimates from vessel and shore-based surveys. No error; counts have no error assoicated with them
#'
#' @format A data frame with 402 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.46944E7, 1.4832288E9\]}
#'   \item{abundance}{Abundance () \[71.0, 1427576.0\]}
#'   \item{Seup}{95% credible interval upper bound () \[390.0, 39210.0\]}
#'   \item{Selo}{95% credible interval lower bound () \[230.0, 24420.0\]}
#'   \item{CV}{Coefficient of Variation () \[0.044, 0.82\]}
#'   \item{common_name}{Common name () \[\]}
#'   \item{scientific_name}{Scientific name () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_MM_cetacean/index.html}
#' @concept dataset_erddap
"cciea_MM_cetacean"


#' Chinook Abundance, California
#'
#' Source Data: Various; see Wells et al. 2014, Table S3. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Spring-run Chinook spawners to the Central Valley system. Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S3. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement (based on carcass surveys) by natural origin Winter-run Chinook spawners to the Central Valley system. Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S3. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Fall Run Chinook to the Klamath and Trinity River systems. Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S3. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Fall-run Chinook spawners to the Central Valley system, based on the following watersheds: Antelope Creek, Battle Creek, Big Chico Creek, Butte Creek, Clear Creeek, Cottonwood Creek, Deer Creek, Feather River hatchery, and Mill Creek. Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S3. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Late Fall-run Chinook spawners to the Central Valley system. Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S3. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin Fall-run Chinook to the Klamath and Trinity River systems. Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation., Source Data: Various; see Wells et al. 2014, Table S3. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement (live/dead counts) by natural origin spawners from the California Coast, based on the following watersheds: Tomki Creek, Cannon Creek, and Sprowl Creek. Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation.
#'
#' @format A data frame with 490 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.1536E7, 1.5463008E9\]}
#'   \item{population}{Population () \[\]}
#'   \item{abundance_anomaly}{Abundance anomaly (Abundance anomaly) \[-1.671656, 3.423461\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_SM_CA_CH_ABND/index.html}
#' @concept dataset_erddap
"cciea_SM_CA_CH_ABND"


#' Chinook Condition, California
#'
#' Source Data: Various; see Wells et al. 2014, Table S4. Additional Calculations: Proportion of natural-origin spawners, computed for a single population as the fraction NN/NT, where NN is the number of naturally-origin spawners, and NT is the total number of spawners. Population fractions were then averaged across the populations within the ESU, weighted by total spawner abundance., Source Data: Various; see Wells et al. 2014, Table S4. Additional Calculations: Population growth rate, estimated as the ratio of the 4-year running mean of spawning escapement in one year to the 4-year running mean for the previous year, Source Data: Various; see Wells et al. 2014, Table S4. Additional Calculations: Age-structure diversity, computed as Shannon's diversity index of spawner age for each population within each year. The indices were then averaged across populations, weighted by total spawner abundance.
#'
#' @format A data frame with 506 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[0.0, 1.4516064E9\]}
#'   \item{population}{Population () \[\]}
#'   \item{condition}{ (Population growth rate) \[-1.41059, 4.28817\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_SM_CA_CH_CND/index.html}
#' @concept dataset_erddap
"cciea_SM_CA_CH_CND"


#' Chinook Condition, Oregon/Washington
#'
#' Source Data: Various; see Wells et al. 2014, Table S6. For Oregon and Washington ESUs, data were obtained from the NWFSC's "Salmon Population Summary" database (https://www.webapps.nwfsc.noaa.gov/sps), with additional data for Oregon Coast coho salmon (Oregon Department of Fish and Wildlife, https://oregonstate.edu/dept/ODFW/spawn/data.htm), and from PFMC (2012) for the Upper Columbia Summer/Fall-run Chinook Salmon. Additional Calculations: Age-structure diversity, computed as Shannon's diversity index of spawner age for each population within each year. The indices were then averaged across populations, weighted by total spawner abundance.
#'
#' @format A data frame with 853 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[0.0, 1.3885344E9\]}
#'   \item{population}{Population/Season () \[\]}
#'   \item{cond_pct_nat}{Proportion of natural fish to total fish returning () \[0.0, 100.0\]}
#'   \item{cond_pop_gr}{Proportional change in abundance between cohorts () \[0.4510503, 2.383912\]}
#'   \item{cond_age_div}{Age Diversity Shannon-Weaver () \[0.1894317, 1.450296\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_SM_ORWA_CH_CND/index.html}
#' @concept dataset_erddap
"cciea_SM_ORWA_CH_CND"


#' Coho Abundance, Oregon/Washington
#'
#' Source Data: Various; see Wells et al. 2014, Table S5. For Oregon and Washington ESUs, data were obtained from the NWFSC's "Salmon Population Summary" database (https://www.webapps.nwfsc.noaa.gov/sps), with additional data for Oregon Coast coho salmon (Oregon Department of Fish and Wildlife, https://oregonstate.edu/dept/ODFW/spawn/data.htm), and from PFMC (2012) for the Upper Columbia Summer/Fall-run Chinook Salmon. Additional Calculations: Abundance indices are calculated as longterm anomalies (observed mean/standard deviation) of annual escapement by natural origin coho salmon in the lower Columbia River ESU (Clackamas and Sandy Rivers; see Wells et al. 2014, Table S5). Data series for multiple subpopulations were standardized by subtracting the series mean and dividing by the series standard deviation. If a consolidated index for the stock was needed we computed an annual weighted average of the standardized series, with weights proportional to the average abundance for each subpopulation.
#'
#' @format A data frame with 88 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[7.573824E8, 1.5463008E9\]}
#'   \item{population}{Population/Season () \[\]}
#'   \item{abundance_anomaly}{Abundance anomaly (observed mean/standard deviation) () \[-1.150923, 3.129896\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_SM_ORWA_CO_ABND/index.html}
#' @concept dataset_erddap
"cciea_SM_ORWA_CO_ABND"


#' Coho Condition, Oregon/Washington
#'
#' Source Data:  Various; see Wells et al. 2014, Table S6. For Oregon and Washington ESUs, data were obtained from the NWFSC's "Salmon Population Summary" database (https://www.webapps.nwfsc.noaa.gov/sps), with additional data for Oregon Coast coho salmon (Oregon Department of Fish and Wildlife, https://oregonstate.edu/dept/ODFW/spawn/data.htm), and from PFMC (2012) for the Upper Columbia Summer/Fall-run Chinook Salmon. Additional Calculations: Proportion of natural-origin spawners, computed for a single population as the fraction NN/NT, where NN is the number of naturally-origin spawners, and NT is the total number of spawners. Population fractions were then averaged across the populations within the ESU, weighted by total spawner abundance.
#'
#' @format A data frame with 187 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.262304E8, 1.3885344E9\]}
#'   \item{population}{Population/Season () \[\]}
#'   \item{cond_pct_nat}{Proportion of natural fish to total fish returning () \[0.9189854, 99.35072\]}
#'   \item{cond_pop_gr}{Proportional change in abundance between cohorts () \[0.4980789, 2.20522\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_SM_ORWA_CO_CND/index.html}
#' @concept dataset_erddap
"cciea_SM_ORWA_CO_CND"


#' Commercial Fishing Engagement Index
#'
#' Commercial fishing engagement data were provided by Dr. Karma Norman (NOAA), and are derived from state reported fish ticket data as maintained by the Pacific Fishery Information Network (PacFIN) (https://pacfin.psmfc.org/).
#'
#' @format A data frame with 1457 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.1045376E9, 1.5147648E9\]}
#'   \item{fishing_engagement}{Commercial fishing engagement index (Index) \[-0.19823, 18.59184\]}
#'   \item{location}{Location () \[\]}
#'   \item{region}{Region () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HD_Fish_Eng/index.html}
#' @concept dataset_erddap
"cciea_HD_Fish_Eng"


#' Commercial Fishing Reliance Index
#'
#' Commercial fishing  data were provided by Dr. Karma Norman (NOAA), and are derived from state reported fish ticket data as maintained by the Pacific Fishery Information Network (PacFIN) (https://pacfin.psmfc.org/).
#'
#' @format A data frame with 1845 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.1045376E9, 1.5147648E9\]}
#'   \item{fishing_reliance}{Commercial fishing reliance index (Index) \[-0.941, 33.18725\]}
#'   \item{location}{Location () \[\]}
#'   \item{region}{Region () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HD_Fish_Rel/index.html}
#' @concept dataset_erddap
"cciea_HD_Fish_Rel"


#' Euphausia pacifica (krill) lengths
#'
#' Krill (Euphausia pacifica) data were provided by Dr. Eric Bjorkstedt (eric.bjorkstedt@noaa.gov), NMFS/SWFSC and Humboldt State University (HSU), and R. Robertson, Cooperative Institute for Marine Ecosystems and Climate (CIMEC) at HSU. Krill were collected at monthly intervals from the Trinidad Head Hydrographic Line (https://swfsc.noaa.gov/textblock.aspx?Division=FED&ParentMenuId=54&id=17306). Krill body length (BL) was measured in mm from the back of the eye to base of the telson.
#'
#' @format A data frame with 520 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.19448E9, 1.5978816E9\]}
#'   \item{mean_length}{Carapace Length (mm) \[7.457964, 17.27011\]}
#'   \item{std_dev}{Standard Deviation (mm) \[0.0, 3.874455\]}
#'   \item{Seup}{Confidence Interval, Upper () \[8.44838, 19.4095\]}
#'   \item{Selo}{Confidence Interval, Lower () \[6.331795, 15.25163\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_KRILLEN/index.html}
#' @concept dataset_erddap
"cciea_EI_KRILLEN"


#' Finfish scavengers:total biomass ratio
#'
#' Source Data: Groundfish data are from the NMFS U.S. West Coast Groundfish Bottom Trawl Survey (https://www.nwfsc.noaa.gov/research/divisions/fram/groundfish/bottom_trawl.cfm) and were downloaded from the FRAM data warehouse at https://www.nwfsc.noaa.gov/data/map with the following API query:Â  https://www.nwfsc.noaa.gov/data/api/v1/source/trawl.catch_fact/selection.csv?variables=program,trawl_id,date_dim$year,date_dim$yyyymmdd,vessel,performance,year_stn_invalid,depth_m,latitude_dd,longitude_dd,scientific_name,common_name,species_category,partition,total_catch_numbers,total_catch_wt_kg,cpue_kg_per_ha_der,cpue_numbers_per_ha_der,"actual_station_design_dim$station_invalid_survey_year", "actual_station_design_dim$reason_station_invalid","reason_stn_invalid (target station)", "year_stn_invalid" Additional Calculations:  Data are area-weighted average crab:finfish biomass ratios from the southern US border to the northern US border. For details, see Tolimieri et al. 2014 (https://www.noaa.gov/iea/CCIEA-Report/pdf/index.html)
#'
#' @format A data frame with 204 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.0413792E9, 1.5463008E9\]}
#'   \item{population}{Population () \[\]}
#'   \item{biomass_ratio}{ () \[0.03598382, 0.3129661\]}
#'   \item{Seup}{Confidence Interval, Upper () \[0.04058898, 0.4040344\]}
#'   \item{Selo}{Confidence Interval, Lower () \[0.02916655, 0.2584166\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_CRAB_FINF/index.html}
#' @concept dataset_erddap
"cciea_EI_CRAB_FINF"


#' Groundfish mean trophic level
#'
#' Source Data: Groundfish data are from the NMFS U.S. West Coast Groundfish Bottom Trawl Survey (https://www.nwfsc.noaa.gov/research/divisions/fram/groundfish/bottom_trawl.cfm) and were downloaded from the FRAM data warehouse at https://www.nwfsc.noaa.gov/data/map with the following API query:Â  https://www.nwfsc.noaa.gov/data/api/v1/source/trawl.catch_fact/selection.csv?variables=program,trawl_id,date_dim$year,date_dim$yyyymmdd,vessel,performance,year_stn_invalid,depth_m,latitude_dd,longitude_dd,scientific_name,common_name,species_category,partition,total_catch_numbers,total_catch_wt_kg,cpue_kg_per_ha_der,cpue_numbers_per_ha_der,"actual_station_design_dim$station_invalid_survey_year", "actual_station_design_dim$reason_station_invalid","reason_stn_invalid (target station)", "year_stn_invalid" Additional Calculations:  Area-weighted mean trophic level (MTL) for West Coast groundfishes. MTL was calculated for survey data without adjusting for sampling effort in different depth x latitude strata. For details, see Tolimieri et al. 2014 (https://www.noaa.gov/iea/CCIEA-Report/pdf/index.html)
#'
#' @format A data frame with 192 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.0413792E9, 1.5147648E9\]}
#'   \item{population}{Population () \[\]}
#'   \item{mean_trophic_level}{ () \[3.614351, 3.773371\]}
#'   \item{Seup}{Confidence Interval, Upper () \[3.623129, 3.786633\]}
#'   \item{Selo}{Confidence Interval, Lower () \[3.605017, 3.760109\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_MTL/index.html}
#' @concept dataset_erddap
"cciea_EI_MTL"


#' Jellyfish Biomass, Central Californial
#'
#' Source Data: Dr. John Field (NOAA; john.field@noaa.gov) from the SWFSC Rockfish Recruitment and Ecosystem Assessment Survey (https://swfsc.noaa.gov/textblock.aspx?Division=FED&ParentMenuId=54&id=20615). Additional Calculations: Samples represent catch (individuals) per standard 15 minute trawl (CPUE). Data are log(CPUE+1) transformed; Geometric means calculated on non-zero data.
#'
#' @format A data frame with 98 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.31152E8, 1.4200704E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{abundance}{Mean CPUE (ln(catch+1)) \[0.0, 1.382158\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_JELLY_CC/index.html}
#' @concept dataset_erddap
"cciea_EI_JELLY_CC"


#' Monthly Mean Crab Landings 1990-2015
#'
#' Pending
#'
#' @format A data frame with 24 rows and 6 variables:
#' \describe{
#'   \item{month}{ () \[\]}
#'   \item{month_number}{ () \[1, 12\]}
#'   \item{landings}{Landings (monthly mean) 1990-2015 (tons) \[0.1127933, 356.5503\]}
#'   \item{fish_tickets}{Fish Tickets (monthly mean0 1990-2015 (counts) \[1.222222, 326.8811\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_AC_mn/index.html}
#' @concept dataset_erddap
"cciea_AC_mn"


#' North Pacific Gyre Oscillation Index
#'
#' The North Pacific Gyre Oscillation (NPGO) is a climate pattern that emerges as the 2nd dominant mode of sea surface height variability (2nd EOF SSH) in the Northeast Pacific. The NPGO is significantly correlated with previously unexplained fluctuations of salinity, nutrients and chlorophyll-a measured in long-term observations in the California Current (CalCOFI) and Gulf of Alaska (Line P). We use the term NPGO because its fluctuations reflect changes in the intensity of the central and eastern branches of the North Pacific gyre circulations as evident from the NPGO SSHa anomalies. Fluctuations in the NPGO are driven by regional and basin-scale variations in wind-driven upwelling and horizontal advection- the fundamental processes controlling salinity [figure] and nutrient [figure] concentrations. Nutrient fluctuations drive concomitant changes in phytoplankton concentrations, and may force similar variability in higher trophic levels. The NPGO thus provides a strong indicator of fluctuations in the mechanisms driving planktonic ecosystem dynamics.
#'
#' @format A data frame with 847 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-6.31152E8, 1.5935616E9\]}
#'   \item{NPGO}{NPGO Index () \[-3.646516, 2.956047\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_NPGO/index.html}
#' @concept dataset_erddap
"cciea_OC_NPGO"


#' Northern copepod biomass anomaly 44.6N
#'
#' See: https://www.noaa.gov/iea/Assets/iea/california/Report/pdf/Ecological%20Integrity%20Status%20CCIEA%202012.pdf
#'
#' @format A data frame with 213 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.309088E8, 1.4148E9\]}
#'   \item{biomass_anomaly}{Biomass Anomaly () \[-1.541188, 1.013472\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_CBAN/index.html}
#' @concept dataset_erddap
"cciea_OC_CBAN"


#' Pacific Decadal Oscillation Index
#'
#' Updated standardized values for the PDO index, derived as the leading PC of monthly SST anomalies in the North Pacific Ocean, poleward of 20N. The monthly mean global average SST anomalies are removed to separate this pattern of variability from any "global warming" signal that may be present in the data.
#'
#' @format A data frame with 1456 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-2.2089888E9, 1.6172352E9\]}
#'   \item{PDO}{PDO Index () \[-3.6, 3.51\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_PDO/index.html}
#' @concept dataset_erddap
"cciea_OC_PDO"


#' Relative humpback whale abundance
#'
#' Regional estimate of Relative humpback whale abundance values from surveys conducted by the Farallon Institute OR the RREAS surveys (or combination).
#'
#' @format A data frame with 42 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.204544E8, 1.5463008E9\]}
#'   \item{humpback_rate}{Relative humpback abundance (index) \[0.0, 17.2\]}
#'   \item{humpback_anomaly}{Relative humpback abundance anomaly (index) \[-6.3381, 10.8619\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_MM_HB_REL/index.html}
#' @concept dataset_erddap
"cciea_MM_HB_REL"


#' SST Buoy 46014 (39.2N 124.0W)
#'
#' The National Data Buoy Center (NDBC) distributes meteorological data from moored buoys maintained by NDBC and others. The data is from NOAA NDBC. It has been reformatted by NOAA Coastwatch, West Coast Node and then monthly averaged. This dataset has both historical data and near real time data.
#'
#' @format A data frame with 412 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.549312E8, 1.5989184E9\]}
#'   \item{SST}{Sea Surface Temperature (degree_C) \[9.142, 16.114\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_SST2/index.html}
#' @concept dataset_erddap
"cciea_OC_SST2"


#' SST Buoy 46025 (33.7N 119.1W)
#'
#' The National Data Buoy Center (NDBC) distributes meteorological data from moored buoys maintained by NDBC and others. The data is from NOAA NDBC. It has been reformatted by NOAA Coastwatch, West Coast Node and then monthly averaged. This dataset has both historical data and near real time data.
#'
#' @format A data frame with 448 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.864672E8, 1.6198272E9\]}
#'   \item{SST}{Sea Surface Temperature (degree_C) \[12.83, 22.44\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_SST3/index.html}
#' @concept dataset_erddap
"cciea_OC_SST3"


#' SST Buoy 46050 (44.6N 124.5W)
#'
#' The National Data Buoy Center (NDBC) distributes meteorological data from moored buoys maintained by NDBC and others. The data is from NOAA NDBC. It has been reformatted by NOAA Coastwatch, West Coast Node and then monthly averaged. This dataset has both historical data and near real time data.
#'
#' @format A data frame with 300 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.889536E8, 1.6198272E9\]}
#'   \item{SST}{Sea Surface Temperature (degree_C) \[8.744, 17.749\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_SST1/index.html}
#' @concept dataset_erddap
"cciea_OC_SST1"


#' Seabird at-sea density anomaly
#'
#' Data are shipboard counts, transformed as ln(bird density/km2 +1) and expressed as an anomaly of log density relative to the long-term mean. So CC: Data are from CalCOFI surveys (http://calcofi.org/field-work/underway-observations/380-bird-observations.html), courtesy of Dr. Bill Sydeman of the Farallon Institute (wsydeman@faralloninstitute.org). Ce CC: Data are from the SWFSC Rockfish Recruitment and Ecosystem Assessment Survey (https://swfsc.noaa.gov/textblock.aspx?Division=FED&ParentMenuId=54&id=20615). No CC: Data are shipboard counts, transformed as ln(bird density/km2 +1) and expressed as an anomaly of log density relative to the long-term mean.
#'
#' @format A data frame with 423 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[5.364576E8, 1.5463008E9\]}
#'   \item{density_anomaly}{Density Anomaly () \[-2.107251, 1.742638\]}
#'   \item{species_cohort}{Species (season) () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_B_AS_DENS/index.html}
#' @concept dataset_erddap
"cciea_B_AS_DENS"


#' Southern copepod biomass anomaly 44.6N
#'
#' See: https://www.noaa.gov/iea/Assets/iea/california/Report/pdf/Ecological%20Integrity%20Status%20CCIEA%202012.pdf
#'
#' @format A data frame with 213 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.309088E8, 1.4148E9\]}
#'   \item{biomass_anomaly}{Biomass Anomaly () \[-0.606, 0.714\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_CBAS/index.html}
#' @concept dataset_erddap
"cciea_OC_CBAS"


#' Spring Transition Index (STI)
#'
#' Spring Transition Index
#'
#' @format A data frame with 648 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.46944E7, 1.5778368E9\]}
#'   \item{sti}{Spring Transition Index (yearday) \[1.0, 174.0\]}
#'   \item{latitude}{Latitude (degrees_north) \[33, 48\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_STI/index.html}
#' @concept dataset_erddap
"cciea_OC_STI"


#' Total Upwelling Magnitude Index (TUMI)
#'
#' Total Upwelling Magnitude Index
#'
#' @format A data frame with 648 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.46944E7, 1.5778368E9\]}
#'   \item{tumi}{Total Upwelling Magnitude Index (m^3/s/100m coastline) \[564.6994, 55104.24\]}
#'   \item{latitude}{Latitude (degrees_north) \[33, 48\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_TUMI/index.html}
#' @concept dataset_erddap
"cciea_OC_TUMI"


#' Upwelling Index, 33N 119W, monthly
#'
#' Upwelling index computed from 1-degree FNMOC sea level pressure at 33 degrees of latitude. The coastal Upwelling Index is an index of the strength of the wind forcing on the ocean which has been used in many studies of the effects of ocean variability on the reproductive and recruitment success of many fish and invertebrate species.
#'
#' @format A data frame with 1956 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.46944E7, 1.6172352E9\]}
#'   \item{upwelling_index}{Upwelling Index, 33N 119W, monthly (m^3/s/100m coastline) \[-39.515, 292.556\]}
#'   \item{nobs}{Number of oberservations in month () \[87, 124\]}
#'   \item{stdev}{Standard deviation (m^3/s/100m coastline) \[23.955, 337.93\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_UI3/index.html}
#' @concept dataset_erddap
"cciea_OC_UI3"


#' Upwelling Index, 39N 125W, monthly
#'
#' Upwelling index computed from 1-degree FNMOC sea level pressure at 39 degrees of latitude. The coastal Upwelling Index is an index of the strength of the wind forcing on the ocean which has been used in many studies of the effects of ocean variability on the reproductive and recruitment success of many fish and invertebrate species.
#'
#' @format A data frame with 1956 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.46944E7, 1.6172352E9\]}
#'   \item{upwelling_index}{Upwelling Index, 39N 125W, monthly (m^3/s/100m coastline) \[-282.4014, 448.8212\]}
#'   \item{nobs}{Number of oberservations in month () \[87, 124\]}
#'   \item{stdev}{Standard deviation (m^3/s/100m coastline) \[39.227, 670.585\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_UI2/index.html}
#' @concept dataset_erddap
"cciea_OC_UI2"


#' Upwelling Index, 45N 125W, monthly
#'
#' Upwelling index computed from 1-degree FNMOC sea level pressure 45 degrees of latitude. The coastal Upwelling Index is an index of the strength of the wind forcing on the ocean which has been used in many studies of the effects of ocean variability on the reproductive and recruitment success of many fish and invertebrate species.
#'
#' @format A data frame with 1956 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.46944E7, 1.6172352E9\]}
#'   \item{upwelling_index}{Upwelling Index, 45N 125W, monthly (m^3/s/100m coastline) \[-395.7448, 93.0324\]}
#'   \item{nobs}{Number of oberservations in month () \[87, 124\]}
#'   \item{stdev}{Standard deviation (m^3/s/100m coastline) \[23.007, 659.207\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_UI1/index.html}
#' @concept dataset_erddap
"cciea_OC_UI1"


#' 2020 Forage Biomass, California Current Central
#'
#' Source Data: Dr. John Field (NOAA; john.field@noaa.gov) from the SWFSC Rockfish Recruitment and Ecosystem Assessment Survey (https://swfsc.noaa.gov/textblock.aspx?Division=FED&ParentMenuId=54&id=20615). Additional Calculations: Samples represent catch (individuals) per standard 15 minute trawl (CPUE). Data are log(CPUE+1) transformed; Geometric means calculated on non-zero data.
#'
#' @format A data frame with 1512 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.31152E8, 1.5463008E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{mean_cpue}{Mean CPUE (ln(catch+1)) \[0.0, 10.85264\]}
#'   \item{Seup}{Confidence Interval, Upper () \[0.0, 11.18415\]}
#'   \item{Selo}{Confidence Interval, Lower () \[-4.05304E-10, 10.52114\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_FBC_2020/index.html}
#' @concept dataset_erddap
"cciea_EI_FBC_2020"


#' 2020 Forage Biomass, California Current North
#'
#' Source Data: Dr. Brian Burke (NOAA; brian.burke@noaa.gov); derived from surface trawls taken during NOAA Northwest Fisheries Science Center Juvenile Salmon & Ocean Ecosystem Survey (JSOES).  Additional calculations by Cheryl Morgan (OSU - CIMRS; cheryl.morgan@oregonstate.edu). Partial funding is from the Bonneville Power Administration (1998-014-00). Additional Calculations: Jellyfish data from 1998 were not reliably recorded and are not included in this analysis. To be included in this analysis, stations must have been 1) sampled during the day time, 2) on the continental shelf (greater than 200 m water depth), and 3) sampled during at least half of the years of the JSOES effort. Sampling occurs from the northern tip of Washington (48N 13.7') down to Newport, Oregon (44N 40.0') in late June. A Nordic 264 rope trawl (Nor'Eastern Trawl Systems, Bainbridge Island, WA) is towed at the surface (upper 20 m) for 15 - 30 min at approximately 6.5 km/hr. The total abundance for each nekton species caught in each haul was either determined directly or estimated from the total weight of the species in a catch and the weight and number of individuals in a subsample of that catch.  Trawl catches were standardized to linear density by dividing catch of each species at a station by the distance between the start- and endpoints of the tow as determined by a global positioning system receiver and log10 transformed (Log10(no. km-1+ 1)).
#'
#' @format A data frame with 952 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.836128E8, 1.5463008E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{mean_density}{Mean Density (Linear density (Log10(no. km-1+ 1)).) \[0.0, 1.9612\]}
#'   \item{Seup}{Confidence Interval, Upper () \[0.0, 2.099\]}
#'   \item{Selo}{Confidence Interval, Lower () \[-4.1E-4, 1.824\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_FBN_2020/index.html}
#' @concept dataset_erddap
"cciea_EI_FBN_2020"


#' 2020 Forage Biomass, California Current South
#'
#' Source Data: Dr. Andrew Thompson (NOAA; andrew.thompson@noaa.gov); derived from spring CalCOFI surveys (https://calcofi.org/) Additional Calculations: Larval fish data summed across all stations of the CalCOFI survey in spring (units are in number under 10 sq. m of surface area; ln(abundance+1)).
#'
#' @format A data frame with 683 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[2.524608E8, 1.5463008E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{relative_abundance}{Relative abundance (ln(abundance+1)) \[0.0, 4.627175\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_FBS_2020/index.html}
#' @concept dataset_erddap
"cciea_EI_FBS_2020"


#' California Current Marine Heatwave Event List
#'
#' Marine heatwaves, or MHWs, occur when ocean temperatures are much warmer than usual for an extended period of time; they are specifically defined by differences in expected temperatures for the location and time of year. MHWs are a growing field of study worldwide because of their effects on ecosystem structure, biodiversity, and regional economies. Developed by oceanographers from NOAA Fisheries' Southwest Fisheries Science Center as an experimental tool for natural resource managers, the California Current MHW Tracker is a program designed to understand, describe, and provide a historical context for the 2014-16 blob. It also produces a range of indices that could help forecast or predict future MHWs expected to impact our coast.
#'
#' @format A data frame with 2891 rows and 5 variables:
#' \describe{
#'   \item{time}{Start time (seconds since 1970-01-01T00:00:00Z) \[3.874176E8, 1.6003872E9\]}
#'   \item{blob_id}{Blob Id () \[\]}
#'   \item{max_area}{Maximum Area (km^2) \[402937.0, 9759080.0\]}
#'   \item{duration}{Duration (days) \[0, 757\]}
#'   \item{max_intensity}{Maximum Intensity (STDEVs from normal) \[1.43471, 2.56513\]}
#'   \item{mean_intensity}{Mean Intensity (STDEVs from normal) \[1.43471, 2.41716\]}
#'   \item{min_dist_to_coast}{Minimum distance to coast (km) \[5.95226, 2772.68\]}
#'   \item{max_index}{Maximum MHW index () \[0.0, 0.771722\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_MHW_EV/index.html}
#' @concept dataset_erddap
"cciea_OC_MHW_EV"


#' California Current Marine Heatwave, Daily
#'
#' Marine heatwaves, or MHWs, occur when ocean temperatures are much warmer than usual for an extended period of time; they are specifically defined by differences in expected temperatures for the location and time of year. MHWs are a growing field of study worldwide because of their effects on ecosystem structure, biodiversity, and regional economies. Developed by oceanographers from NOAA Fisheries' Southwest Fisheries Science Center as an experimental tool for natural resource managers, the California Current MHW Tracker is a program designed to understand, describe, and provide a historical context for the 2014-16 blob. It also produces a range of indices that could help forecast or predict future MHWs expected to impact our coast.
#'
#' @format A data frame with 106582 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.786912E8, 1.6025904E9\]}
#'   \item{area_1}{Area of MHW Feature 1 (km^2) \[-999.0, 9759084.0\]}
#'   \item{intensity_1}{Intensity of MHW Feature 1 (STDEVs from normal) \[0.5671552, 3.806598\]}
#'   \item{area_2}{Area of MHW Feature 2 (km^2) \[-999.0, 3410075.0\]}
#'   \item{intensity_2}{Intensity of MHW Feature 2 (STDEVs from normal) \[0.0, 3.764146\]}
#'   \item{area_3}{Area of MHW Feature 3 (km^2) \[-999.0, 1570442.0\]}
#'   \item{intensity_3}{Intensity of MHW Feature 3 (STDEVs from normal) \[-0.1109982, 3.856909\]}
#'   \item{area_4}{Area of MHW Feature 4 (km^2) \[-999.0, 798923.1\]}
#'   \item{intensity_4}{Intensity of MHW Feature 4 (STDEVs from normal) \[-0.1767007, 3.873491\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_MHW/index.html}
#' @concept dataset_erddap
"cciea_OC_MHW"


#' Coastal community fishery dependence index
#'
#' Source Data: Fishery dependence data were provided by Dr. Karma Norman (NOAA), and are derived from state reported fish ticket data as maintained by the Pacific Fishery Information Network (PacFIN) (https://pacfin.psmfc.org/). Additional Calculations: The fishing dependence composite index is based on commercial fishing engagement (a measure of the fishing activity in a community) and commercial fishing reliance (fishing activity relative to population size).
#'
#' @format A data frame with 200 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[9.466848E8, 1.3885344E9\]}
#'   \item{fishing_dependence}{Fishery Dependence Index () \[-0.279896, 32.81\]}
#'   \item{location}{Location () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HD_Fish_Dep/index.html}
#' @concept dataset_erddap
"cciea_HD_Fish_Dep"


#' Coastal community social vulnerability index
#'
#' Source Data: Community social vulnerability index (CSVI) data were provided by Dr. Karma Norman (NOAA), and are derived from the American Community Survey (ACS) associated with the US Census (https://factfinder.census.gov/faces/nav/jsf/pages/index.xhtml).  Although decennial census data represent an estimate at a specific date, the ACS data are period estimates, collected over an entire year and are potentially averaged over varying time periods, depending on the size of the geographic area (U.S. Census Bureau, 2009).  Given that communities of interest include geographic areas with populations less than 20,000, ACS data at the Census-Designate Place level or for place-based communities, are averaged over a five-year period., Additional Calculations:  The Community Social Vulnerability Index (CSVI) is derived from social vulnerability indices (e.g., personal disruption, poverty, population composition, housing characteristics, housing disruption, labor force structure, and natural resource labor force).
#'
#' @format A data frame with 1491 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.230768E9, 1.5147648E9\]}
#'   \item{social_vulnerability_index}{Community Social Vulnerability Index () \[-5.977, 9.320572\]}
#'   \item{location}{Location () \[\]}
#'   \item{region}{Region () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HD_Soc_Vuln_Indx/index.html}
#' @concept dataset_erddap
"cciea_HD_Soc_Vuln_Indx"


#' Copepod Indices, Northern California Current
#'
#' Source Data: Dr. Bill Peterson, NOAA (bill.peterson@noaa.gov); https://www.nwfsc.noaa.gov/research/divisions/fe/estuarine/oeip/index.cfm Additional Calculations: Copepod species richness anomaly: Monthly anomaly of copepod species richness in the Northern California Current off Newport, Oregon, 1996-present.; Total Copepod Biomass: Total biomass of copepods off Newport, Oregon, 1996-present.; Southern copepod biomass anomaly 44.6N: Monthly anomalies of the southern copepod biomass from 1996-present in waters off Newport, OR. See Fisher et al. 2015 for methods.; Copepod Community Composition Index: Monthly anomaly of the copepod community composition off Newport, Oregon, 1996-present from MDS. See Keister et al. 2011 for methods.; Northern copepod biomass anomaly 44.6N: Monthly anomalies of the northern copepod biomass from 1996-present in waters off Newport, OR. See Fisher et al. 2015 for methods.;
#'
#' @format A data frame with 821 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.335872E8, 1.5989184E9\]}
#'   \item{copepod_species_richness_anomaly}{Copepod species richness anomaly (anomaly) \[-7.194445, 11.20833\]}
#'   \item{northern_copepod_biomass_anomaly}{Northern copepod biomass anomaly 44.6N (mg C -m3) \[-1.578806, 1.0801\]}
#'   \item{southern_copepod_biomass_anomaly}{Southern copepod biomass anomaly 44.6N (mg C -m3) \[-0.6561004, 0.8078717\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_COP/index.html}
#' @concept dataset_erddap
"cciea_EI_COP"


#' Extratropical-based Northern Oscillation Index
#'
#' Northern Oscillation Index - The NOI (extratropical-based Northern Oscillation Index is an index of midlatitude climate fluctuations that shows interesting relationships with fluctuations in marine ecosystems and populations. It reflects the variability in equatorial and extratropical teleconnections and represent a wide range of local and remote climate signals.
#'
#' @format A data frame with 2643 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-6.929712E8, 1.6211664E9\]}
#'   \item{NOI}{Extratropical-based Northern Oscillation Index (Index) \[-12.164, 8.678\]}
#'   \item{SOIX}{Extratropical-bassed Southern Oscillation Index (Index) \[-8.9, 8.661\]}
#'   \item{SOI}{Southern Oscillation Inde (Index) \[-6.898, 5.355\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_NOI/index.html}
#' @concept dataset_erddap
"cciea_OC_NOI"


#' Forage Biomass, California Current Central
#'
#' Source Data: Dr. John Field (NOAA; john.field@noaa.gov) from the SWFSC Rockfish Recruitment and Ecosystem Assessment Survey (https://swfsc.noaa.gov/textblock.aspx?Division=FED&ParentMenuId=54&id=20615). Additional Calculations: Samples represent catch (individuals) per standard 15 minute trawl (CPUE). Data are log(CPUE+1) transformed; Geometric means calculated on non-zero data.
#'
#' @format A data frame with 496 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.31152E8, 1.5778368E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{mean_cpue}{Mean CPUE (ln(catch+1)) \[-2.404505, 2.47525\]}
#'   \item{Seup}{Confidence Interval, Upper () \[\]}
#'   \item{Selo}{Confidence Interval, Lower () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_FBC/index.html}
#' @concept dataset_erddap
"cciea_EI_FBC"


#' Forage Biomass, California Current North
#'
#' Source Data: Dr. Brian Burke (NOAA; brian.burke@noaa.gov); derived from surface trawls taken during NOAA Northwest Fisheries Science Center Juvenile Salmon & Ocean Ecosystem Survey (JSOES).  Additional calculations by Cheryl Morgan (OSU - CIMRS; cheryl.morgan@oregonstate.edu). Partial funding is from the Bonneville Power Administration (1998-014-00). Additional Calculations: Jellyfish data from 1998 were not reliably recorded and are not included in this analysis. To be included in this analysis, stations must have been 1) sampled during the day time, 2) on the continental shelf (greater than 200 m water depth), and 3) sampled during at least half of the years of the JSOES effort. Sampling occurs from the northern tip of Washington (48N 13.7') down to Newport, Oregon (44N 40.0') in late June. A Nordic 264 rope trawl (Nor'Eastern Trawl Systems, Bainbridge Island, WA) is towed at the surface (upper 20 m) for 15 - 30 min at approximately 6.5 km/hr. The total abundance for each nekton species caught in each haul was either determined directly or estimated from the total weight of the species in a catch and the weight and number of individuals in a subsample of that catch.  Trawl catches were standardized to linear density by dividing catch of each species at a station by the distance between the start- and endpoints of the tow as determined by a global positioning system receiver and log10 transformed (Log10(no. km-1+ 1)).
#'
#' @format A data frame with 1088 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.836128E8, 1.5778368E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{mean_density}{Mean Density (Linear density (Log10(no. km-1+ 1)).) \[0.0, 1.963\]}
#'   \item{Seup}{Confidence Interval, Upper () \[0.0, 2.096\]}
#'   \item{Selo}{Confidence Interval, Lower () \[0.0, 1.83\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_FBN/index.html}
#' @concept dataset_erddap
"cciea_EI_FBN"


#' Forage Biomass, California Current South
#'
#' Source Data: Dr. Andrew Thompson (NOAA; andrew.thompson@noaa.gov); derived from spring CalCOFI surveys (https://calcofi.org/) Additional Calculations: Larval fish data summed across all stations of the CalCOFI survey in spring (units are in number under 10 sq. m of surface area; ln(abundance+1)).
#'
#' @format A data frame with 495 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.836128E8, 1.5778368E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{relative_abundance}{Relative abundance (ln(abundance+1)) \[0.0, 10.48216\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_FBS/index.html}
#' @concept dataset_erddap
"cciea_EI_FBS"


#' Jellyfish Biomass, Oregon and Washington
#'
#' Source Data: Dr. Ric Brodeur (NOAA; rick.brodeur@noaa.gov), derived from surface trawls conducted as part of the BPA Plume Survey. Additional calculations by C. Barcelo (OSU). Additional Calculations: Geometric mean catch per unit effort (CPUE, #/sq. km) from Newport, OR (44.6 deg N, 124.0 deg W) to Father and Son, WA (48.24 deg. N, 124.7 deg W) by year; Numbers of individuals were recorded for each species caught in each haul and were standardized by the horizontal distance sampled by the towed net as CPUE. Yearly abundance data were obtained by combining (summing) the standardized count data of each species captured during June for each year. All CPUE estimates were log transformed.
#'
#' @format A data frame with 72 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[9.151488E8, 1.4516064E9\]}
#'   \item{species_group}{Species Group () \[\]}
#'   \item{mean_cpue}{Mean CPUE (ln(catch+1)) \[0.48, 10919.47\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_JELLY_OW/index.html}
#' @concept dataset_erddap
"cciea_EI_JELLY_OW"


#' Length of Upwelling Season Index (LUSI)
#'
#' Length of Upwelling Season Index
#'
#' @format A data frame with 648 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.46944E7, 1.5778368E9\]}
#'   \item{lusi}{Length of Upwelling Season Index (days) \[58.0, 365.0\]}
#'   \item{latitude}{Latitude (degrees_north) \[33, 48\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_LUSI/index.html}
#' @concept dataset_erddap
"cciea_OC_LUSI"


#' Meridional Wind Buoy 46014 (39.2N 124.0W)
#'
#' The National Data Buoy Center (NDBC) distributes meteorological data from moored buoys maintained by NDBC and others. The data is from NOAA NDBC. It has been reformatted by NOAA Coastwatch, West Coast Node and then monthly averaged. This dataset has both historical data and near real time data.
#'
#' @format A data frame with 441 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.549312E8, 1.6172352E9\]}
#'   \item{vwnd}{Wind Speed, Meridional (m s-1) \[-7.22959, 5.5367\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_VWND2/index.html}
#' @concept dataset_erddap
"cciea_OC_VWND2"


#' Meridional Wind Buoy 46025 (33.7N 119.1W)
#'
#' The National Data Buoy Center (NDBC) distributes meteorological data from moored buoys maintained by NDBC and others. The data is from NOAA NDBC. It has been reformatted by NOAA Coastwatch, West Coast Node and then monthly averaged. This dataset has both historical data and near real time data.
#'
#' @format A data frame with 449 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.864672E8, 1.6198272E9\]}
#'   \item{vwnd}{Wind Speed, Meridional (m s-1) \[-2.906052, 0.8122222\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_VWND3/index.html}
#' @concept dataset_erddap
"cciea_OC_VWND3"


#' Meridional Wind Buoy 46050 (44.6N 124.5W)
#'
#' The National Data Buoy Center (NDBC) distributes meteorological data from moored buoys maintained by NDBC and others. The data is from NOAA NDBC. It has been reformatted by NOAA Coastwatch, West Coast Node and then monthly averaged. This dataset has both historical data and near real time data.
#'
#' @format A data frame with 304 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.889536E8, 1.6198272E9\]}
#'   \item{vwnd}{Wind Speed, Meridional (m s-1) \[-6.163844, 8.036128\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_VWND1/index.html}
#' @concept dataset_erddap
"cciea_OC_VWND1"


#' Monthly Coastal Upwelling Transport Index (CUTI)
#'
#' CUTI provides estimates of vertical transport near the coast (i.e., upwelling/downwelling). It was developed as a more accurate alternative to the previously available Bakun Index.
#'
#' @format A data frame with 2406 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[5.692032E8, 1.6210368E9\]}
#'   \item{cuti}{Coastal Upwelling Transport Index (m^2 s^(-1)) \[-1.252, 2.751\]}
#'   \item{latitude}{Latitude (degrees_north) \[33, 45\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_CUTI/index.html}
#' @concept dataset_erddap
"cciea_OC_CUTI"


#' Monthly Sea Level: San Diego CA (32.7N 117.2W)
#'
#' The California Current Large Marine Ecosystem (CCLME) is primarily driven by bottom-up physical oceanographic processes, thus understanding trends in the physical state can inform our knowledge of ecosystem processes and management of ecosystem services.
#'
#' @format A data frame with 6571 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-2.0184768E9, 1.4501376E9\]}
#'   \item{sea_level}{Sea Level (mm) \[1766.71, 2282.7\]}
#'   \item{std_error}{Standard Error (mm) \[0.0, 0.0\]}
#'   \item{station}{ () \[\]}
#'   \item{latitude}{Latitude (degrees_north) \[32.7, 32.7\]}
#'   \item{longitude}{Longitude (degrees_east) \[117.2, 117.2\]}
#'   \item{depth}{Depth (m) \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_SL3/index.html}
#' @concept dataset_erddap
"cciea_OC_SL3"


#' Monthly Sea Level: South Beach OR (44.6N 124.0W)
#'
#' The California Current Large Marine Ecosystem (CCLME) is primarily driven by bottom-up physical oceanographic processes, thus understanding trends in the physical state can inform our knowledge of ecosystem processes and management of ecosystem services.
#'
#' @format A data frame with 2930 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.34848E7, 1.4501376E9\]}
#'   \item{sea_level}{Sea Level (mm) \[2522.033, 3236.0\]}
#'   \item{std_error}{Standard Error (mm) \[0.0, 0.0\]}
#'   \item{station}{ () \[\]}
#'   \item{latitude}{Latitude (degrees_north) \[44.6, 44.6\]}
#'   \item{longitude}{Longitude (degrees_east) \[124.0, 124.0\]}
#'   \item{depth}{Depth (m) \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_SL1/index.html}
#' @concept dataset_erddap
"cciea_OC_SL1"


#' North Pacific High Area, January - February Mean
#'
#' Variations in large-scale atmospheric forcing influence upwelling dynamics and ecosystem productivity in the California Current System.The area of the North Pacific High (NPH) is characterized by areal extent of the 1020 Pa isobar. Winter values (January - February mean) of the NPH area can be used as an ecosystem pre-conditioning index (Schroeder et al., doi:10.1002/grl.50100).The area of the NPH are calculated from monthly mean sea level pressure (SLP) fields created by the U.S. Navy Fleet Numerical Meterology and Oceanography Center (FNMOC). Monthly SLP data available at https://upwell.pfeg.noaa.gov/erddap/index.html , search for Dataset ID: erdlasFnWPr. The area is the areal extent of the 1020 hPa contour for a given month. The January - February mean is the average of the January and February areas for a given year. The NRT ROMS temperature data downloaded from the UCSC website (http://oceanmodeling.pmc.ucsc.edu:8080/thredds) has grid points with 2 m temperature data reported as Not-A-Number, these values are  2 m temperatu. Tdata at these grid points are obtained by extrapolating the data to 2 m depth using the interp1d routine from the Python library scipy.interpolate sub-package.
#'
#' @format A data frame with 55 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-9.46944E7, 1.6094592E9\]}
#'   \item{nph_area}{North Pacific High Area, Jan-Feb Mean (10^6 km^2) \[0.00561367, 5.806523\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_NPH_JF/index.html}
#' @concept dataset_erddap
"cciea_OC_NPH_JF"


#' Streamflow - Annual 1-day maximum flow anomaly
#'
#' Streamflow is measured using active USGS gages (https://waterdata.usgs.gov/nwis/sw) with records that meet or exceed 30 years in duration.Average daily values from 213 gages were used to calculate annual 1-day maximum flows.  These indicators correspond to flow parameters to which salmon populations are most sensitive.  Standardized anomalies of time series from individual gages were then averaged to obtain weighted averages for ecoregions (for which HUC-8 area served as a weighting factor) and for the entire California current (weighted by ecoregion area).
#'
#' @format A data frame with 960 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.471552E8, 1.5778368E9\]}
#'   \item{flow_anomaly_1_day_max}{Annual 1-day maximum flow anomaly (Annual Anomaly) \[-1.457172, 2.296121\]}
#'   \item{Seup}{95% credible interval upper bound (Annual Anomaly) \[-1.059154, 2.656894\]}
#'   \item{Selo}{95% credible interval lower bound (Annual Anomaly) \[-1.855576, 1.93781\]}
#'   \item{location}{Location () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HB_FLO1/index.html}
#' @concept dataset_erddap
"cciea_HB_FLO1"


#' Streamflow - Annual 7-day minimum flow anomaly
#'
#' Streamflow is measured using active USGS gages (https://waterdata.usgs.gov/nwis/sw) with records that meet or exceed 30 years in duration.Average daily values from 213 gages were used to calculate annual 7-day minimum flows.  These indicators correspond to flow parameters to which salmon populations are most sensitive.  Standardized anomalies of time series from individual gages were then averaged to obtain weighted averages for ecoregions (for which HUC-8 area served as a weighting factor) and for the entire California current (weighted by ecoregion area).
#'
#' @format A data frame with 960 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.471552E8, 1.5778368E9\]}
#'   \item{flow_anomaly_7_day_min}{Annual 7-day minimum flow anomaly (Annual Anomaly) \[-1.140393, 1.916366\]}
#'   \item{Seup}{95% credible interval upper bound (Annual Anomaly) \[-0.7810067, 2.257912\]}
#'   \item{Selo}{95% credible interval lower bound (Annual Anomaly) \[-1.502816, 1.575647\]}
#'   \item{location}{Location () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HB_FLO7/index.html}
#' @concept dataset_erddap
"cciea_HB_FLO7"


#' California Current Marine Heatwave, EEZ regions Daily
#'
#' Marine heatwaves, or MHWs, occur when ocean temperatures are much warmer than usual for an extended period of time; they are specifically defined by differences in expected temperatures for the location and time of year. MHWs are a growing field of study worldwide because of their effects on ecosystem structure, biodiversity, and regional economies. Developed by oceanographers from NOAA Fisheries' Southwest Fisheries Science Center as an experimental tool for natural resource managers, the California Current MHW Tracker is a program designed to understand, describe, and provide a historical context for the 2014-16 blob. It also produces a range of indices that could help forecast or predict future MHWs expected to impact our coast.
#'
#' @format A data frame with 345316 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.786912E8, 1.609416E9\]}
#'   \item{heatwave_cover}{Heatwave cover (%) \[0.0, 100.0\]}
#'   \item{intensity}{Intensity (degree_C) \[1.290018, 3.802803\]}
#'   \item{distance}{Distance (km) \[0.5110544, 3201.752\]}
#'   \item{avedegday}{Cumulative Intensity (degree_C) \[0.0, 1087.539\]}
#'   \item{avedegdayclim}{Average Day Climatology (degree_C) \[35.65968, 138.4911\]}
#'   \item{region}{Region () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_MHW_regions/index.html}
#' @concept dataset_erddap
"cciea_OC_MHW_regions"


#' Monthly Sea Level: San Francisco CA (37.8N 122.5W)
#'
#' The California Current Large Marine Ecosystem (CCLME) is primarily driven by bottom-up physical oceanographic processes, thus understanding trends in the physical state can inform our knowledge of ecosystem processes and management of ecosystem services.
#'
#' @format A data frame with 6950 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-2.2077792E9, 1.4501376E9\]}
#'   \item{sea_level}{Sea Level (mm) \[2466.067, 3081.964\]}
#'   \item{std_error}{Standard Error (mm) \[0.0, 0.0\]}
#'   \item{station}{ () \[\]}
#'   \item{latitude}{Latitude (degrees_north) \[37.8, 37.8\]}
#'   \item{longitude}{Longitude (degrees_east) \[122.5, 122.5\]}
#'   \item{depth}{Depth (m) \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_SL2/index.html}
#' @concept dataset_erddap
"cciea_OC_SL2"


#' Personal use landings data, Washington and California
#'
#' Personal use landings data are from PacFIN (https://pacfin.psmfc.org), and were compiled by Dr. Melissa Poe (NOAA, Washington Sea Grant). Catch of all species retained for personal use  by commercial operators from 1990 - 2014, in tons (2000 lbs). Data are from landings in 139 of 350 ports in Washington and California.
#'
#' @format A data frame with 100 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.31152E8, 1.3885344E9\]}
#'   \item{personal_catch}{Personal Catch (tons) \[22.825, 3579.716\]}
#'   \item{use_type}{Use Type () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HD_PU/index.html}
#' @concept dataset_erddap
"cciea_HD_PU"


#' Rockfish Recruitment and Ecosystem Assessement Survey, Biodiversity: CCIEA format
#'
#' Biodiversity indices (total abundance, species richness, Shannon-Weaver diversity, Pielou's evenness) of all taxa (also young-of-the-year rockfish and groundfish taxa, and forage taxa) caught in the RREAS (Rockfish Recruitment and Ecosystem Assessment Survey) mid-water trawl. This is a regional average of all net haul stations located from Point Reyes to Monterey CA.
#'
#' @format A data frame with 2088 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.31152E8, 1.5147648E9\]}
#'   \item{metric}{ () \[\]}
#'   \item{timeseries}{ () \[\]}
#'   \item{data}{ () \[0.03166048, 21.98214\]}
#'   \item{error}{ () \[0.05746754, 13.34593\]}
#'   \item{SEup}{ () \[0.08912803, 27.93191\]}
#'   \item{SElo}{ () \[-1.839871, 16.03238\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_RREAS_diversity_list/index.html}
#' @concept dataset_erddap
"cciea_EI_RREAS_diversity_list"


#' Streamflow - August Mean Maximum stream temperature
#'
#' Streamflow is measured using active USGS gages (https://waterdata.usgs.gov/nwis/sw) with records that meet or exceed 30 years in duration.Average daily values from 213 gages were used to calculate annual 7-day minimum flows.  These indicators correspond to flow parameters to which salmon populations are most sensitive.  Standardized anomalies of time series from individual gages were then averaged to obtain weighted averages for ecoregions (for which HUC-8 area served as a weighting factor) and for the entire California current (weighted by ecoregion area).
#'
#' @format A data frame with 680 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.471552E8, 1.5778368E9\]}
#'   \item{aug_mean_max}{August Mean Maximum stream temperature - SW Ecoregion (Annual Anomaly) \[14.60329, 23.80615\]}
#'   \item{Seup}{95% credible interval upper bound (Annual Anomaly) \[15.42901, 25.04026\]}
#'   \item{Selo}{95% credible interval lower bound (Annual Anomaly) \[13.76901, 22.56459\]}
#'   \item{location}{Location () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HB_AUGMX/index.html}
#' @concept dataset_erddap
"cciea_HB_AUGMX"


#' California sea lion pup survey, San Miguel Island, California.
#'
#' California sea lion pups are counted at San Miguel Island, California. Live pups are counted at the end of July each year, after all pups have been born. Pups are weighed in September or October each year and weights are adjusted using a mixed effects model to a 1 October weighing date. Growth rates are predicted from a longitudinal sample of uniquely marked pups that are captured in October and again the following February. Growth rates are modeled using a mixed effects model and standard errors are estimated using bootstrap
#'
#' @format A data frame with 137 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.520768E8, 1.5778368E9\]}
#'   \item{live_pup_count}{Sea lion pup count, San Miguel Isl. (count) \[9428.0, 27148.0\]}
#'   \item{live_pup_count_se}{Sea lion pup count standard error, San Miguel Isl. (count) \[51.99038, 719.5551\]}
#'   \item{mean_weight}{Female sea lion pup weight index (kg) \[11.95362, 20.63407\]}
#'   \item{mean_weight_se}{Female sea lion pup weight index standard error (kg) \[0.1044493, 0.1937013\]}
#'   \item{mean_growth_rate}{Female sea lion pup growth rate (kg/day) \[0.01268848, 0.09503055\]}
#'   \item{mean_growth_rate_se}{Female sea lion pup growth rate standard error (kg/day) \[0.001847422, 0.01116279\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_MM_pup_count/index.html}
#' @concept dataset_erddap
"cciea_MM_pup_count"


#' Domoic acid levels in razor clams and other species, West Coast
#'
#' Data on domoic acid concentrations in razor clams are from Ms. Audrey Coyne (audrey.coyne@doh.wa.gov; Washington State Department of Health); these data are compiled from tests conducted by a variety of Tribal, State, and County partners on Washington beaches. Domoic acid levels are reported in ppm. Sample testing frequency is irregular as it depends on the timing of proposed recreational razor clamming digs by Washington State Department of Fish and Wildlife and prevalence of recent detections. We present data as maximum monthly values compiled from samples in a variety of locations on the Washington State outer coast beaches.
#'
#' @format A data frame with 96670 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[-3.643488E8, 1.6093728E9\]}
#'   \item{site}{Site Name () \[\]}
#'   \item{domoic_acid_concentration}{Domoic Acid Concentration (ppm) \[0.0, 730.0\]}
#'   \item{county}{County () \[\]}
#'   \item{region}{Region () \[\]}
#'   \item{species}{Species () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_DOMACID/index.html}
#' @concept dataset_erddap
"cciea_EI_DOMACID"


#' Monthly Biologically Effective Upwelling Transport Index (BEUTI)
#'
#' BEUTI provides estimates of vertical nitrate flux near the coast (i.e., the amount of nitrate upwelled/downwelled), which may be more relevant than upwelling strength when considering some biological responses.
#'
#' @format A data frame with 2406 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[5.692032E8, 1.6210368E9\]}
#'   \item{beuti}{Biologically Effective Upwelling Transport Index (mmol s^-1 m^-1) \[-22.082, 57.187\]}
#'   \item{latitude}{Latitude (degrees_north) \[33, 45\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_OC_BEUTI/index.html}
#' @concept dataset_erddap
"cciea_OC_BEUTI"


#' Monthly Maximum Domoic acid levels in dungeness crab, West Coast
#'
#' Data on domoic acid concentrations in razor clams are from Ms. Audrey Coyne (audrey.coyne@doh.wa.gov; Washington State Department of Health); these data are compiled from tests conducted by a variety of Tribal, State, and County partners on Washington beaches. Domoic acid levels are reported in ppm. Sample testing frequency is irregular as it depends on the timing of proposed recreational razor clamming digs by Washington State Department of Fish and Wildlife and prevalence of recent detections. We present data as maximum monthly values compiled from samples in a variety of locations on the Washington State outer coast beaches.
#'
#' @format A data frame with 424 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.323616E8, 1.6079904E9\]}
#'   \item{region}{Region () \[\]}
#'   \item{domoic_acid_concentration}{Maximum Domoic Acid Concentration (ppm) \[0.0, 270.0\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_DOMACID_MON_crab/index.html}
#' @concept dataset_erddap
"cciea_EI_DOMACID_MON_crab"


#' Monthly Maximum Domoic acid levels in razor clams, West Coast
#'
#' Data on domoic acid concentrations in razor clams are from Ms. Audrey Coyne (audrey.coyne@doh.wa.gov; Washington State Department of Health); these data are compiled from tests conducted by a variety of Tribal, State, and County partners on Washington beaches. Domoic acid levels are reported in ppm. Sample testing frequency is irregular as it depends on the timing of proposed recreational razor clamming digs by Washington State Department of Fish and Wildlife and prevalence of recent detections. We present data as maximum monthly values compiled from samples in a variety of locations on the Washington State outer coast beaches.
#'
#' @format A data frame with 1520 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.270912E8, 1.6079904E9\]}
#'   \item{region}{Region () \[\]}
#'   \item{domoic_acid_concentration}{Maximum Domoic Acid Concentration (ppm) \[0.0, 450.0\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_DOMACID_MON_clam/index.html}
#' @concept dataset_erddap
"cciea_EI_DOMACID_MON_clam"


#' Streamflow - Annual 1-day maximum flow anomaly, Chinook ESU's
#'
#' Streamflow is measured using active USGS gages (https://waterdata.usgs.gov/nwis/sw) with records that meet or exceed 30 years in duration.Average daily values from 213 gages were used to calculate annual 1-day maximum flows.  These indicators correspond to flow parameters to which salmon populations are most sensitive.  Standardized anomalies of time series from individual gages were then averaged to obtain weighted averages for ecoregions (for which HUC-8 area served as a weighting factor) and for the entire California current (weighted by ecoregion area).
#'
#' @format A data frame with 2560 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.471552E8, 1.5778368E9\]}
#'   \item{flow_anomaly_1_day_max}{Annual 1-day maximum flow anomaly (Annual Anomaly) \[-1.902059, 3.428976\]}
#'   \item{Seup}{Confidence Interval, Upper (Annual Anomaly) \[-1.681833, 3.731741\]}
#'   \item{Selo}{Confidence Interval, Lower (Annual Anomaly) \[-2.152806, 3.126341\]}
#'   \item{location}{Location () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HB_FLO1_CH/index.html}
#' @concept dataset_erddap
"cciea_HB_FLO1_CH"


#' Streamflow - Annual 7-day minimum flow anomaly, Chinook ESU's
#'
#' Streamflow is measured using active USGS gages (https://waterdata.usgs.gov/nwis/sw) with records that meet or exceed 30 years in duration.Average daily values from 213 gages were used to calculate annual 7-day minimum flows.  These indicators correspond to flow parameters to which salmon populations are most sensitive.  Standardized anomalies of time series from individual gages were then averaged to obtain weighted averages for ecoregions (for which HUC-8 area served as a weighting factor) and for the entire California current (weighted by ecoregion area).
#'
#' @format A data frame with 2560 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.471552E8, 1.5778368E9\]}
#'   \item{flow_anomaly_7_day_min}{Annual 7-day minimum flow anomaly (Annual Anomaly) \[-1.466948, 2.954578\]}
#'   \item{Seup}{Confidence Interval, Upper (Annual Anomaly) \[-0.9782854, 3.31243\]}
#'   \item{Selo}{Confidence Interval, Lower (Annual Anomaly) \[-1.982343, 2.596406\]}
#'   \item{location}{Location () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HB_FLO7_CH/index.html}
#' @concept dataset_erddap
"cciea_HB_FLO7_CH"


#' Annual Effective Shannon Index (ESI) for various fleet groupings and West Coast Ports
#'
#' Indices for annual average Effective Shannon Index (ESI) values for various fleet groupings and West Coast Ports Data derived from Pacific Fisheries Information Network (PacFIN; https://pacfin.psmfc.org/) and Alaska Fisheries Information Network (AKFIN; https://www.akfin.org/). Aggregation and manipulaton of vessel level annual revenue data to create effective shannon index. For details, see: Kasperski, S. and D.S. Holland 2013. Income Diversification and Risk for Fishermen. Proceedings of the National Academy of Science. 100(6):2076-2081.  doi: 10.1073/pnas.1212278110; Holland, D.S. and S. Kasperski 2016. The Impact of Access Restrictions on Fishery Income Diversification of US West Coast Fishermen. Forthcoming in Coastal Management.
#'
#' @format A data frame with 2698 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[3.471552E8, 1.5463008E9\]}
#'   \item{average_ESI}{Average ESI (Average ESI) \[1.25, 9.44\]}
#'   \item{vessel_category}{Vessel category () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_HD_ESI_VESS/index.html}
#' @concept dataset_erddap
"cciea_HD_ESI_VESS"


#' Rockfish Recruitment and Ecosystem Assessement Survey, Biodiversity: Grid format
#'
#' Biodiversity indices (total abundance, species richness, Shannon-Weaver diversity, Pielou's evenness) of all taxa (also young-of-the-year rockfish and groundfish taxa, and forage taxa) caught in the RREAS (Rockfish Recruitment and Ecosystem Assessment Survey) mid-water trawl. This is a regional average of all net haul stations located from Point Reyes to Monterey CA.
#'
#' @format A data frame with 783 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[6.31152E8, 1.5147648E9\]}
#'   \item{taxa}{Taxa () \[\]}
#'   \item{abundance}{Total Abundance (ln(CPUE + 1)) \[1.390595, 12.2398\]}
#'   \item{abundance_error}{Abundance Error (ln(CPUE + 1)) \[1.744331, 13.34593\]}
#'   \item{richness}{Species Richness (number of species) \[1.5, 21.98214\]}
#'   \item{richness_error}{Species Richness Error (number of species) \[1.453661, 7.003501\]}
#'   \item{diversity}{Shannon-Weaver Diversity () \[0.0696443, 1.690858\]}
#'   \item{diversity_error}{Shannon-Weaver Diversity Error () \[0.1325587, 0.8078451\]}
#'   \item{evenness}{Pielou's Evenness () \[0.03166048, 0.7764255\]}
#'   \item{evenness_error}{Pielou's Evenness Error () \[0.05746754, 0.4711267\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/cciea_EI_RREAS_diversity_grid/index.html}
#' @concept dataset_erddap
"cciea_EI_RREAS_diversity_grid"


#' Newport Hydrographic Line Nitrate, NH25
#'
#' Newport Hydrographic Line Nitrate, NH25
#'
#' @format A data frame with 498 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[1.0997856E9, 1.4998176E9\]}
#'   \item{station}{Station () \[\]}
#'   \item{depth}{Depth (m) \[150, 150\]}
#'   \item{nitrate}{Nitrate (no3+no2) () \[4.5802, 36.2214\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/newportNitrate/index.html}
#' @concept dataset_erddap
"newportNitrate"


#' Newport Hydrographic Line Dissolved Oxygen and Aragonite Saturation, NH05 and NH25
#'
#' Newport Hydrographic Line Dissolved Oxygen and Aragonite Saturation, NH05 and NH25
#'
#' @format A data frame with 2656 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[9.019296E8, 1.5094944E9\]}
#'   \item{station}{Station () \[\]}
#'   \item{depth}{Depth (m) \[40, 150\]}
#'   \item{temperature}{Water Temperature (degrees C) \[6.832, 14.66645\]}
#'   \item{salinity}{Salinity (PSU) \[31.8645, 33.9962\]}
#'   \item{density}{Density () \[24.2092, 26.648\]}
#'   \item{oxygen}{Oxygen () \[0.54, 6.9\]}
#'   \item{dissolved_oxygen}{Dissolved Oxygen (umol/kg) \[23.4929, 300.5841\]}
#'   \item{aragonite_saturation}{Aragonite Saturation () \[0.4761976, 2.132431\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/newportDOArag/index.html}
#' @concept dataset_erddap
"newportDOArag"


#' Newport Hydrographic Line CTD Data, NH05 and NH25
#'
#' Newport Hydrographic Line CTD Data, NH05 and NH25
#'
#' @format A data frame with 1002090 rows and 5 variables:
#' \describe{
#'   \item{time}{Time (seconds since 1970-01-01T00:00:00Z) \[8.589024E8, 1.5099264E9\]}
#'   \item{station}{Station () \[\]}
#'   \item{depth}{Depth (m) \[0, 743\]}
#'   \item{temperature}{Water Temperature (degrees C) \[5.7691, 18.2575\]}
#'   \item{salinity}{Salinity (PSU) \[23.8151, 34.7945\]}
#'   \item{density}{Density () \[17.1925, 27.1704\]}
#'   \item{oxygen}{Oxygen () \[0.2496, 12.01967\]}
#'   \item{latitude}{Latitude (degrees_north) \[44.391, 44.70633\]}
#'   \item{longitude}{Longitude (degrees_east) \[-124.6712, -124.106\]}
#'   \item{project}{Project () \[\]}
#' }
#' @source \url{https://oceanview.pfeg.noaa.gov/erddap/info/newportCTD/index.html}
#' @concept dataset_erddap
"newportCTD"
marinebon/ecoidx documentation built on Jan. 19, 2022, 1:46 p.m.
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marinebon/ecoidx
Marine ecological indicators R package for CA Current IEA

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In marinebon/ecoidx: Marine ecological indicators R package for CA Current IEA

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Marine ecological indicators R package for CA Current IEA

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In marinebon/ecoidx: Marine ecological indicators R package for CA Current IEA
