Shale Pettit Rosen
Subjectarea
Observation methods (underwater cameras)
Trawl selectivity
Fish behaviour
Sampling for scientific surveys
Curriculum Vitae
Name: Shale Pettit Rosen
Job title: Researcher, Research Group Fish Capture
Telephone number: (+47) 944 83 404
E-mail: shale.rosen@hi.no
Researcher unique identifier(s): ORCID 0000-0002-5857-1637, ResearcherID E-9444-2017
EDUCATION
06.2009 - 11.2013 PhD, Biology, 26.11.13, Department of Biology, University of Bergen, Norway
08.2007 - 05.2009 MSc, Fisheries biology and management, University of Bergen, Norway
08.1994 - 05.1998 BA, Geology with concentration in environmental science, Department of Geology, Haverford College, USA
WORK EXPERIENCE
04.2017 - Ongoing Researcher, Research and development, trawl technology, Institute of Marine Research, Norway. Development and testing of trawls for research and commercial fisheries.
01.2014 - 03.2017 Postdoctoral Researcher, Trawl and underwater camera technology, Institute of Marine Research, Norway. Trawl and underwater stereo camera technology, image analysis
06.2009 - 12.2013 Researcher / PhD student, Research, Scantrol AS, Norway. Development of underwater camera system for use inside fishing trawls
01.2001 - 07.2007 Collaborative Research Operations Manager, Research, Gulf of Maine Research Institute, USA. Management of research activities and safety on board commercial fishing boats, acoustic fisheries surveys, design and testing of fishing gear for sustainable fisheries.
Publications
Academic articles
2023
How to obtain clear images from in-trawl cameras near the seabed? A case study from the Barents Sea demersal fishing grounds
Fisheries Research
268
2023
How to obtain clear images from in-trawl cameras near the seabed? A case study from the Barents Sea demersal fishing grounds
Fisheries Research
268
2022
A catch limitation device to avoid excessive catches in the blue whiting (Micromesistius poutassou) Northeast Atlantic pelagic trawl fishery
Frontiers in Marine Science
9
2021
A real-world dataset and data simulation algorithm for automated fish species identification
Geoscience Data Journal
2021
A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images
ICES Journal of Marine Science
78
p. 3780-3792
2021
Development and evaluation of dynamic catch control devices in the demersal seine fisheries for cod (Gadus morhua)
Journal of Ocean Technology
16
p. 92-106
2021
A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images
ICES Journal of Marine Science
78(10)
p. 3780-3792
2021
A real-world dataset and data simulation algorithm for automated fish species identification
Geoscience Data Journal
2021
Development and Evaluation of Dynamic Catch Control Devices in the Demersal Seine Fisheries for Cod (Gadus morhua)
Journal of Ocean Technology
16(2)
p. 92-106
2019
Automatic segmentation of fish using deep learning with application to fish size measurement
ICES Journal of Marine Science
2018
Fish species identification using a convolutional neural network trained on synthetic data
ICES Journal of Marine Science
76
p. 342-349
2018
Species-specific residence times in the aft part of a pelagic survey trawl: implications for inference of pre-capture spatial distribution using the Deep Vision system
ICES Journal of Marine Science
75
p. 1393-1404
2014
Deep vision: An in-trawl stereo camera makes a step forward in monitoring the pelagic community
PLOS ONE
9
2013
DeepVision in-trawl imaging: Sampling the water column in four dimensions
Fisheries Research
148
p. 64-73
2013
DeepVision: a stereo camera system provides highly accurate counts and lengths of fish passing inside a trawl
Canadian Journal of Fisheries and Aquatic Sciences
70
p. 1456-1467
2012
The reactions of shoaling adult cod to a pelagic trawl: implications for commercial trawling
ICES Journal of Marine Science
69
p. 303-312
2008
Trophic relationships and oceanography on and around a small offshore bank
Marine Ecology Progress Series
363
p. 15-28
2007
Current status of mobile and static sampling gears used in resource surveys
ICES Journal of Marine Science
64
p. 1607-1609
Other journal contributions
2011
Subsea stereo computer vision with Scantrol Deep Vision
Journal of Ocean Technology
6
p. 72-73
Scientific lectures
2024
A deep learning-based method to identify and count small pelagic and mesopelagic fishes from trawl camera images
2024
Advancing the interpretation of fisheries acoustics with in-trawl cameras: A survey method for mesopelagic resources
2022
Combining behavioral observations with machine learning results to improve count estimates of small pelagic and mesopelagic fish in a video trawl
2022
Test of Deep Vision in-trawl camera during 2021 IESSNS (IMR, Norway)
2022
Deep Vision stereo in-trawl camera system
2022
Using deep learning models to count and identify fish species from in-trawl images
2021
Combined trawl-mounted optic and acoustic methods to study the mesopelagic ecosystem
2020
Application of simultaneous trawl-mounted optic and acoustic methods to study the mesopelagic ecosystem
2019
Norwegian fisheries and fisheries research: Technological advances in fishing gears for sustainability
2019
New Norwegian fishing gear technology in the climate change era
2019
Using a CNN trained on synthetic data for fish species identification
2019
Using a CNN trained on synthetic data for fish species identification
2019
CRISP - eight years of innovation in fisheries technology
2017
Deep Vision: Stereo bilder i trål
2017
Deep Vision in trawl fish sampling: mesopelagic applications
2017
CRISP - med øyne i dypet
2017
Bycatch Reduction Devices (BRDs) and Alternative rigging and operation Pre-catch monitoring: acoustic methods for fish targeting
Advanced Course: TECHNOLOGICAL SOLUTIONS FOR REDUCTION OF DISCARDS IN FISHERIES, The International Centre for Advanced Mediterranean Agronomi
2016
Deep Vision, in situ video sampling for species and sizes during pelagic and demersal trawling
Seminar series, groundfish research group Institute of Marine Research
2016
Deep Vision ‐ In situ fish sampling
INMARTECH 2016
2016
Deep Vision: non-extractive sampling using cameras inside trawls
7th World Fisheries Congress
2015
Trials of ruffled small-meshed inner nets in a pelagic survey trawl
ICES-FAO Working Group on Fishing Technology and Fish Behaviour (WGFTFB)
2015
Deep Vision - non-extractive fish sampling for stock surveillance
Transatlantic Science Week 2015
2015
Using action cameras to quantify fish behaviour and passages rate inside a trawl
POLSHIFT Poleward shifts in the pelagic complex, and effect of climate change
2015
Development of a standarized surface trawling swept area abundance technique for estimation of Northeast Atlantic mackerel
POLSHIFT Poleward shifts in the pelagic complex, and effect of climate change
2014
Excess Fish Exclusion Device (ExFED): How to passively release fish at depth during trawling
2014
Excess Fish Exclusion Device: Passive System Releases Fish at Depth During Trawling
2014
Inexpensive action cameras quantify fish behaviour and passage rates inside a survey trawl
ICES-FAO Working Group on Fishing Technology and Fish Behaviour (WGFTFB)
2014
Deep vision stereo camera system to identify, count, measure, and map a variety of species during trawling
ICES-FAO Working Group on Fishing Technology and Fish Behaviour (WGFTFB)
2013
DeepVision: Using in-Trawl Images to Improve Sampling
2013
Deep Vision: Precision Measurements of Fish Inside a Trawl
Ukens Orientering, Institute of Marine Research
2013
DeepVision: Using in-Trawl Images to Improve Sampling
Gulf of Maine Research Institute, USA
2013
DeepVision: an in-trawl stereo camera makes a step forward in monitoring the pelagic community
The 16th Russian-Norwegian Symposium, Sochi, Russia
2012
Biology seminar
2012
DeepVision: Improving surveys through identification and measurement of fish inside a trawl.
2012
DeepVision: Using in-Trawl Images to Improve Sampling
Research Group seminar series, Institute of Marine Research
2012
DeepVision Pilot: species & length of fish in the trawl
Biology seminar, University of Bergen
2012
DeepVision: Improving surveys through identification and measurement of fish inside a trawl
2010
CatchMeter: Video Monitoring and Real-Time Species and Length Analysis of Fish In a Trawl
ICES-FAO Working Group on Fishing Technology and Fish Behaviour (WGFTFB)
2009
Pelagic Trawling for Cod, Haddock and Saithe in the Barents Sea: Shifting effort to reduce impact on the seabed.
Ocean and Coastal Areas Conference, Asker, NORWAY
2008
Pelagic Trawling for Cod, Haddock and Saithe in the Barents Sea
Nor-Fishing Technology Conference
Poster
2023
Counting fish with in-trawl cameras: Problems and solutions
2018
Distribution and composition of mesopelagic macroplankton and micronekton in the North-east Atlantic.
2018
Distribution and composition of mesopelagic macroplankton and micronekton in the North-east Atlantic
2008
The Behaviour of Cod When Approaching and Entering a Pelagic Trawl
Nor-Fishing Technology Conference
Reports and dissertations
2025
International Bottom Trawl Survey Working Group (IBTSWG; outputs from 2024 meeting)
ICES Scientific Reports
7
2024
Nytt konsept for krok og forsyn til linefiske
Rapport fra havforskningen
2023
CRIMAC cruise report: Development of acoustic and optic methods for underwater target calssification - G.O. Sars 22.11 - 03.12 2022
Toktrapport
2023
Test av snurrevad til fiske etter makrell - Toktrapport M/S Brattholm høsten 2022
Toktrapport
2023
CRIMAC cruise report: Development of acoustic and optic methods for underwater target calssification - G.O. Sars 22.11 - 03.12 2022
2022
International Bottom Trawl Survey Working Group (IBTSWG)
ICES Scientific Reports
4
2022
ICES 2022 - International Bottom Trawl Survey Working Group (IBTSWG)
2022
Workshop on the Further Development of the New IBTS Gear (WKFDNG)
2021
International Bottom Trawl Survey Working Group (IBTSWG)
2021
CRIMAC cruise report 2020116
2019
Fangstkontroll i fisket etter kolmule (Toktrapport nr. 11-2019 frå MS Vendla) -
2019
Fangstkontroll i kolmuletrål - Forstudie — Fiskeforsøk på Porcupinebanken i 2018
2018
Fangstkontroll i snurrevad Sluttrapport til Fiskeri- og Havbruksnæringens Forskningsfond
2016
Science, Technology, and Society Initiative to Minimize Unwanted Catches in European Fisheries - Final report
2016
CRISP- Toktrapport Testing trålutstyr og instrumentering G. O. Sars 1.-11. mars 2015
2016
Nettløsninger foran og bak Deep Vison systemet
2016
A comparison of abundance, distribution and behavior of Northeast Atlantic mackerel (Scomber scombrus L.) during curved and straight forward trawling.
2016
Selektiv flyndresnurrevad. Rapport fra forsøk med en lavåpnings snurrevad for å skille torsk fra flatfisk
Rapport fra havforskningen
27-2016
2015
Makrell: ICES godkjenner økt bestandsestimat fra ny trålmetode
Havforskningsrapporten 2015
p. 105-107
2014
The behaviour of Atlantic cod, haddock and redfish in the aft belly section of a demersal trawl: implications for the interpretation of the information from the Deep Vision
2014
Kan forske på fangsten uten forskningsfangsten
Havforsknings NYTT
11-2014
2013
Giving eyes to pelagic trawls. Acoustic and optical techniques measure behaviour, species, and sizes of fish in situ
PhD Thesis, University of Bergen, Norway
2012
Survey Report from the Joint Norwegian/Russian Ecosystem Survey in the Barents Sea, August – October 2012
IMR/PINRO Joint Report Series
2/2012
p. Section 10.2: Arctic and boreal benthic process and function (ArcProFun) and Deep Sea (sic.) Vision
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