Relative Bycatch:Target Catch Probability Product (daily), EcoCast Project, Lon0360

The Relative Bycatch:Target Catch Probability Product is produced using a data-driven, multi-species predictive habitat modelling framework. First, boosted regression tree models were fit to determine the habitat preferences of the target species, broadbill swordfish (Xiphias gladius), and three bycatch-sensitive species that interact with the California drift gillnet fishery (leatherback sea turtle (Dermochelys coricea), blue shark (Prionace glauca), California sea lion (Zalophus californianus)). Then, individual species weightings were set to reflect the level of bycatch and management concern for each species. Prediction layers for each species were then combined into a single surface by multiplying the layer by the species weighting, summing the layers, and then re-calculating the range of values in the final predictive surface from -1 (low catch & high bycatch probabilities) to 1 (high catch & low bycatch probabilities).
acknowledgment=We thank the scientific teams and all those who supported animal tagging efforts in addition to the SWFSC fisheries observer program that collected bycatch data aboard drift gillnet vessels. We are grateful to the numerous captains and crews who provided ship time and logistical support, and NOAA regional managers including Heidi Taylor and Tina Fahy that provided feedback and support along the way. We also thank Lucie Hazen at Stanford’s Center for Ocean Solutions for logistical and meeting support towards achieving the NASA project goals. This project also was a brain-child of the late Dave Foley whose career was dedicated to incorporate oceanographic data into fisheries management.
cdm_data_type=Grid
comment=Match up the time from this dataset to the time in the EcoCast Inputs dataset (https://oceanview.pfeg.noaa.gov/erddap/tabledap/ecocast_inputs) the obtain species weightings and enviromental data dates used to generate the EcoCast Maps
contributor_name=Elliott L. Hazen, Dana K. Briscoe, Heather Welch, Steven J. Bograd, Dale Robinson, Tomo Eguchi, Heidi Dewar, Suzy Kohin, Daniel P. Costa, Scott R. Benson (NOAA Southwest Fisheries Science Center / University of California Santa Cruz), Rebecca Lewison (San Diego State University), Helen Bailey (University of Maryland Center for Environmental Science), Sara M. Maxwell (Old Dominion University), Larry B. Crowder (Stanford University)
contributor_role=Co-PIs
Conventions=CF-1.6, ACDD-1.3, COARDS
Easternmost_Easting=244.32899808000002
geospatial_lat_max=47.0653
geospatial_lat_min=29.652364600000002
geospatial_lat_resolution=0.24875621999999997
geospatial_lat_units=degrees_north
geospatial_lon_max=244.32899808000002
geospatial_lon_min=228.4086
geospatial_lon_resolution=0.2487562200000002
geospatial_lon_units=degrees_east
infoUrl=https://coastwatch.pfeg.noaa.gov/ecocast
institution=NOAA NMFS SWFSC ERD
keywords_vocabulary=NASA Global Change Master Directory (GCMD) Keywords, Version 7.0.0
nameing_authority=gov.noaa.pfeg.coastwatch
Northernmost_Northing=47.0653
project=EcoCast
references=https://coastwatch.pfeg.noaa.gov/ecocast/, https://heatherwelch.shinyapps.io/ecocastapp/
source_data=CoastWatch West Coast ERDDAP (ncdcOwDly_LonPM180, jplUKMO_OSTIAv201), CMEMS (SEALEVEL_GLO_SLA_MAP_L4_NRT_OBSERVATIONS_008_026), AVISO+ (msla), NOAA Coral Reef Watch (CRW_SST)
sourceUrl=https://oceanview.pfeg.noaa.gov/erddap/
Southernmost_Northing=29.652364600000002
standard_name_vocabulary=CF Standard Name Table v70
time_coverage_end=2024-12-11T12:00:00Z
time_coverage_resolution=PD1
time_coverage_start=2018-05-09T12:00:00Z
Westernmost_Easting=228.4086

本数据集之相对误捕：目标捕捞概率乘积，系基于数据驱动、多物种预测生境模型框架制作而成。首先，采用提升回归树模型对目标物种——宽吻鯛（Xiphias gladius）及其与加州流刺网渔业互动的三个误捕敏感物种（革龟 Dermochelys coricea、蓝鲨 Prionace glauca、加州海狮 Zalophus californianus）的生境偏好进行拟合。随后，为每个物种设定个体权重，以反映各物种误捕及管理关注程度。接着，通过将预测层与物种权重相乘、汇总各层，并重新计算最终预测表面值域，从-1（低捕捞量与高误捕概率）至1（高捕捞量与低误捕概率），将各物种的预测层合并为单一表面。