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Replication Data for: Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Semiparametric model selection for identification of environmental covariates related to adult groundfish catches and weights
 
Identifier https://doi.org/10.7910/DVN/WNCYJ3
 
Creator Correia, Hannah
 
Publisher Harvard Dataverse
 
Description The data for this study were obtained from the National Oceanic and Atmospheric Administration (NOAA) for download to the public. The annual longline survey of the Marine Ecology and Stock Assessment (MESA) Program drops baited lines at specific locations (“stations”) off the coast of Alaska to catch groundfish species along the entire coast [1]. A catch per unit effort (CPUE) is calculated for each species within each geographic area [2]. MESA data were acquired for the years of 1979 through 2012. Air temperature in degrees Celsius (ATMP), sea level pressure in hPa (PRES), wind speed in meters per second averaged over eight-minute periods (WSPD), sea surface temperature in degrees Celsius (WTMP), and the average height in meters of the highest one-third of all waves in 20-minute sampling periods (WVHT) measured daily from buoys in the Gulf of Alaska were obtained from the National Data Buoy Center and summarized by monthly means [3]. Temperature in degrees Celsius measured at the sea floor (hereafter bottom temperature) was obtained from the AFSC Resource Assessment and Conservation Engineering (RACE) Division's bottom trawl surveys [4]. Zooplankton biomass volume given in number per cubic meter (hereafter plankton) were obtained from the NOAA's Coastal and Oceanic Plankton Ecology, Production, and Observation Database [5]. Alkalinity (Alk), chlorophyll (Chl), nitrate (NO3), dissolved oxygen (Oxy), phosphate (Phos), and silicate (Sil) concentrations at depths of 75, 400, and 900 meters were obtained from the NOAA's World Ocean Database [6]. For all environmental variables, a seasonal amplitude was then calculated for each survey year for all locations. For physical variables ATMP, PRES, WSPD, WTMP, WVHT and bottom temperature, seasonal amplitude was defined as the mean of June, July, and August averages minus the mean of December, January, and February averages. Seasonal amplitudes for chemical and biological variables including zooplankton, Chl, Alk, NO3, Oxy, Phos, Sal, and Sil were calculated as the mean of August, September, and October averages minus the mean of March, April, and May averages. Lags of five years were used for environmental variables in models with sablefish, rockfish, or Pacific cod CPUEs and weights as responses. Lags of 10 years were used for environmental variables in models with responses of Pacific halibut CPUE and weight. [var]_samp5 = [variable] measures lagged by five years. [var]_samp10 = [variable] measures lagged by ten years.

For all other variables, refer to https://apps-afsc.fisheries.noaa.gov/maps/longline/glossary.html

[1] AFSC, NOAA MESA: Longline survey, Online (2015). Available at http://www.afsc.noaa.gov/ABL/MESA/mesa_sfs_ls.php, Accessed: 2014-04-14. [2] K. Echave, C. Rodgveller, and S. Shotwell, Calculation of the geographic area sizes used to create population indices for the alaska fisheries science center longline survey, Tech. Rep. NMFS-AFSC-253, National Oceanic and Atmospheric Administration (NOAA), 2013. NOAA Technical Memorandum NMFS-AFSC-253. [3] National Data Buoy Center, Meteorological and oceanographic data col- lected from the national data buoy center coastal-marine automated network (c-man) and moored (weather) buoys., Online (2018). Available at https://accession.nodc.noaa.gov/NDBC-CMANWx. [4] Alaska Fisheries Science Center, AFSC/RACE/GAP: RACEBASE Database, Online (2019). Available at http://www.afsc.noaa.gov/RACE/groundfish/survey_data/ default.htm. [5] T. O’Brien, Copepod: The global plankton database. a review of the 2007 database contents and new quality control methodology., Tech. Rep. NOAA Tech. Memo. NMFS-F/ST-34, U.S. Dep. Commerce (2007). [6] T. Boyer, J. I. Antonov, O. K. Baranova, C. Coleman, H. E. Garcia, A. Grodsky, D. R. Johnson, R. A. Locarnini, A. V. Mishonov, T. O’Brien, C. Paver, J. Reagan, D. Seidov, I. V. Smolyar, M. M. Zweng, World Ocean Database 2013, Tech. rep., National Oceanographic Data Center, Ocean Climate Laboratory, NOAA (2013). http://doi.org/10. 7289/V5NZ85MT.
 
Subject Medicine, Health and Life Sciences
CPUE, groundfish, Alaska
 
Language English
 
Date 2020-08-29
 
Contributor Correia, Hannah