Otolith data are collected to measure age and growth of larval pollock. They are used to determine whether growth rate, hatch data and/or temperature influence fish size.

Zooplankton data are abundance by taxanomic group (to species where possible), stage, size and sex. Zooplankton sorting is performed at The Polish Plankton Sorting Institute in Szcecin, Poland. The zooplankton protocol was changed in 2012 to include a wider range of taxaonomic categories for all study areas and to finer taxanomic resolution where possible. Limited biomass information is available using literature values for micrograms carbon of select copepod species. Various gears with different mesh sizes are used to asses different size classes of zooplankton including 20/60cm paired bongos, Tucker, Multinet, MOCNESS, Methot, CalVET and microzooplankton nets hung on nisken bottles.

The data set focuses on the diets of young of the year Gadus chalcogrammus from the Eastern Gulf of Alaska and the South Eastern Bering Sea. Diet data is available in select years and locations for year one Gadus chalcogrammus and juvenile Mallotus villosus, Thaleichthys pacificus, Lepidopsetta polyxystra, Limanda aspera, Gadus chalcogrammus, Atheresthes stomias, Hippoglossoides elassodon and Atheresthes evermanni. Diet data currently in EcoDAAT are from 1993-2010. The early 1990’s ,except for 1993, are focused on transects extending of the Pribilof islands. From 2000-2010 sampling occurred in the Eastern Gulf of Alaska, except for 2008 when sampling occurred in the South Eastern Bering Sea. Currently there are 166 prey taxa or groups identified within the diets of all fish. The majority of identifications being: large copepod, small copepod, and euphausiid.

Data collected on this cruise included the following: We conducted a juvenile fish and benthic fish prey survery in the eastern Bering Sea (61 3-meter beam trawls, 64 modified tucker epibenthic plankton sled tows, 54 van veen benthic grab collections).

Data collected on this cruise included the following: We conducted a juvenile fish and benthic fish prey survery in the eastern Bering Sea (61 3-meter beam trawls, 64 modified tucker epibenthic plankton sled tows, 54 van veen benthic grab collections).

A total of 105 stations were occupied. There were two sample grids (southeast Alaska and Yakutat Bay) and two transects in the vicinity of Kayak Island. At each station we sampled using paired 20 and 60 cm Bongo frames (150 and 500 micron mesh nets, respectively) and a Sameoto neuston sampler (500 micron mesh net) to estimate the abundance of zoo- and ichthyoplankton. A SeaBird SeaCat (SBE 19 plus) was used with the bongo frames to determine the depth of the samplers in real time and to measure temperature and conductivity. At a few selected stations depth-stratified plankton were obtained with a 1 meter squared MOCNESS (500 micron mesh nets) and at other selected stations microzooplankton were sampled with vertical tows of a CalVET net (53 micron mesh).

A total of 105 stations were occupied. There were two sample grids (southeast Alaska and Yakutat Bay) and two transects in the vicinity of Kayak Island. At each station we sampled using paired 20 and 60 cm Bongo frames (150 and 500 micron mesh nets, respectively) and a Sameoto neuston sampler (500 micron mesh net) to estimate the abundance of zoo- and ichthyoplankton. A SeaBird SeaCat (SBE 19 plus) was used with the bongo frames to determine the depth of the samplers in real time and to measure temperature and conductivity. At a few selected stations depth-stratified plankton were obtained with a 1 meter squared MOCNESS (500 micron mesh nets) and at other selected stations microzooplankton were sampled with vertical tows of a CalVET net (53 micron mesh).

The dataset contains records of fish eggs and larvae collected during FOCI assessment surveys. Records include all data pertinent to identify where specimens were collected (lat, lon, date, gear used, max depth of gear, water depth). Specific data on specimens includes scientific name, stage of development, number collected (whole numbers and CPUE), lengths of larvae, and diameters and stages of eggs. In addition, there are comments that explain any irregularities that may have occurred during sample collection; depending on the reason data is being extracted, comments may indicate a sample is not suitable for consideration.

A comprehensive species list of marine invertebrates of Alaska has been lacking. The checklist of Austin (1985) treated the marine invertebrates of the southern coast of Alaska to California and since then many new species have been described, many range extensions have been discovered, and considerable changes in higher-level systematics have been made. The checklist we compiled lists over 3,500 species and includes the currently accepted scientific name and its significant synonyms, common names, type localities, geographic and depth distributions, a general statement of abundance in Alaska when known (e.g., rare, uncommon, common, abundant), and general remarks. This checklist will serve as a foundation for future species-specific research. Updated species lists are necessary to reflect the current state of biodiversity knowledge and are thus essential for conservation planning and management. To monitor and predict future changes to marine life, the distribution and abundance of marine species need to be better understood, and this can only be achieved with reliable identifications based on a sound taxonomy. The current status and future directions of Alaskan marine invertebrate biodiversity are briefly discussed.

The GOA/AI Bottom Trawl Estimate database contains abundance estimates for the Alaska Biennial Bottom Trawl Surveys conducted in the Gulf of Alaska and the Aleutian Islands in alternate years. The estimates build upon raw and summary data available from the RACEBASE database and include calculated catch-per-unit-effort (cpue)s for principal species of groundfish and key invertebrates for each survey region. The cpues are averaged by survey strata, and then average cpues are multiplied by stratum areas which results in estimates of biomass and numerical abundance. Length and age data are combined with abundance to estimate the population at length and sex and population at age and sex.