ecocast
eng
UTF8
dataset
service
Lynn deWitt
NOAA Fisheries/SWFSC/ERD
+1 831 420 3668
110 Shaffer Rd
Santa Cruz
CA
95960
USA
lynn.dewitt@noaa.gov
pointOfContact
2024-03-28
ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for Imagery and Gridded Data
ISO 19115-2:2009(E)
3
column
65
0.24875621999999997
row
71
0.24875621999999997
temporal
1989
93397.18309859154
area
Relative Bycatch:Target Catch Probability Product (daily), EcoCast Project
2024-03-28
creation
oceanview.pfeg.noaa.gov
ecocast
NOAA NMFS SWFSC ERD
heather.welch@noaa.gov ; erd.data@noaa.gov
https://coastwatch.pfeg.noaa.gov/ecocast
information
web browser
Background Information
Background information from the source
information
originator
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
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).
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.
NOAA NMFS SWFSC ERD
heather.welch@noaa.gov ; erd.data@noaa.gov
https://coastwatch.pfeg.noaa.gov/ecocast
information
web browser
Background Information
Background information from the source
information
pointOfContact
blue shark
California
California sea lion
Dermochelys coricea
dynamic ecosystem management
Ecocast
leatherback sea turtle
NOAA
ocean
Prionace glauca
Southwest fisheries
SWFSC
Zalophus californianus
theme
Earth Science > Oceans > Marine Environment Monitoring
Earth Science > Human Dimensions > Sustainability > Environmental Sustainability
Earth Science > Human Dimensions > Environmental Governance/Management > Environmental Regulations
Earth Science > Biological Classification > Animals/Vertebrates > Reptiles > Turtles
Earth Science > Biological Classification > Animals/Vertebrates > Mammals > Carnivores > Seals/Sea Lions/Walruses
Earth Science > Biological Classification > Animals/Vertebrates > Fish > Sharks/Rays/Chimaeras
Earth Science > Biological Classification > Animals/Vertebrates > Fish > Ray-Finned Fishes
theme
GCMD Science Keywords
EcoCast
project
time
latitude
longitude
theme
CF Standard Name Table v70
These data are available for use without restriction. Please acknowledge the use of these data by citing the following publications: Hazen et al. Sustaining pelagic fisheries: An Eco-Informatic solution to fisheries bycatch. In prep. Welch et al. Practical considerations for operationalizing dynamic management tools. In prep.). The data may be used and redistributed for free but are not intended or legal use, since they may contain inaccuracies. Neither the data contributor, ERD, NOAA, nor the United States Government, nor any of their employees or contractors, makes any warranty, express or implied, including warranties of merchantability and fitness for a particular purpose, or assumes any legal liability for the accuracy, completeness, or usefulness, of this information.
EcoCast
largerWorkCitation
project
Unidata Common Data Model
Grid
largerWorkCitation
project
eng
geoscientificInformation
1
-131.5914
-115.671005
29.652365
47.0653
seconds
2018-05-09T12:00:00Z
2024-03-27T12:00:00Z
Relative Bycatch:Target Catch Probability Product (daily), EcoCast Project
2024-03-28
creation
NOAA NMFS SWFSC ERD
heather.welch@noaa.gov ; erd.data@noaa.gov
https://coastwatch.pfeg.noaa.gov/ecocast
information
web browser
Background Information
Background information from the source
information
originator
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
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).
ERDDAP griddap
1
-131.5914
-115.671005
29.652365
47.0653
seconds
2018-05-09T12:00:00Z
2024-03-27T12:00:00Z
tight
ERDDAPgriddapDatasetQueryAndAccess
https://oceanview.pfeg.noaa.gov/erddap/griddap/ecocast
ERDDAP:griddap
ERDDAP-griddap
ERDDAP's griddap service (a flavor of OPeNDAP) for gridded data. Add different extensions (e.g., .html, .graph, .das, .dds) to the base URL for different purposes.
download
Relative Bycatch:Target Catch Probability Product (daily), EcoCast Project
2024-03-28
creation
NOAA NMFS SWFSC ERD
heather.welch@noaa.gov ; erd.data@noaa.gov
https://coastwatch.pfeg.noaa.gov/ecocast
information
web browser
Background Information
Background information from the source
information
originator
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
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).
OPeNDAP
1
-131.5914
-115.671005
29.652365
47.0653
seconds
2018-05-09T12:00:00Z
2024-03-27T12:00:00Z
tight
OPeNDAPDatasetQueryAndAccess
https://oceanview.pfeg.noaa.gov/erddap/griddap/ecocast
OPeNDAP:OPeNDAP
OPeNDAP
An OPeNDAP service for gridded data. Add different extensions (e.g., .html, .das, .dds) to the base URL for different purposes.
download
Relative Bycatch:Target Catch Probability Product (daily), EcoCast Project
2024-03-28
creation
NOAA NMFS SWFSC ERD
heather.welch@noaa.gov ; erd.data@noaa.gov
https://coastwatch.pfeg.noaa.gov/ecocast
information
web browser
Background Information
Background information from the source
information
originator
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
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).
Open Geospatial Consortium Web Map Service (WMS)
1
-131.5914
-115.671005
29.652365
47.0653
seconds
2018-05-09T12:00:00Z
2024-03-27T12:00:00Z
tight
GetCapabilities
https://oceanview.pfeg.noaa.gov/erddap/wms/ecocast/request?service=WMS&version=1.3.0&request=GetCapabilities
OGC:WMS
OGC-WMS
Open Geospatial Consortium Web Map Service (WMS)
download
physicalMeasurement
ecocast
double
EcoCast Relative Bycatch-Target Catch Probability Product
Lynn deWitt
NOAA Fisheries/SWFSC/ERD
+1 831 420 3668
110 Shaffer Rd
Santa Cruz
CA
95960
USA
lynn.dewitt@noaa.gov
distributor
OPeNDAP
DAP/2.0
https://oceanview.pfeg.noaa.gov/erddap/griddap/ecocast.html
order
Data Subset Form
ERDDAP's version of the OPeNDAP .html web page for this dataset. Specify a subset of the dataset and download the data via OPeNDAP or in many different file types.
download
https://oceanview.pfeg.noaa.gov/erddap/griddap/ecocast.graph
order
Make-A-Graph Form
ERDDAP's Make-A-Graph .html web page for this dataset. Create an image with a map or graph of a subset of the data.
mapDigital
This record was created from dataset metadata by ERDDAP Version 2.18