Nils Olav Handegard
Subjectarea
My research interests are application of new methodology and data processing methods to marine ecology and fisheries oceanography. I am interested in how we can use sensors to observe behavioral responses in fishes, and how to apply these techniques on the assessment of anthropogenic stressors on behavior and distribution of fish and zooplankton. I am also interested in how these behavioral changes may bias scientific surveys. I have an interest in behavioral ecology with an emphasis on the observing methods. This has led to several publications in theoretical and applied ecology. I also have an interest in machine learning and deep learning algorithms, and I have an interest in how these methods can be applied to ecology and fisheries oceanography. I have an interest in e-science, data management and data processing, and I have led projects and worked in close collaboration with our data center on data processing pipelines for fisheries advice and other advisory products.
I have leadership experience from international organizations as well IMR. I have been involved in the International Council for Exploration of the Sea (ICES) through a range of working groups, including leading the Fisheries Acoustics Science and Technology group (WGFAST) and the steering group overseeing ICES coordinated scientific surveys. I have been part of the science leadership group in the organization and have actively contributed to the ICES strategic plan. I have extensive project management experience, leading large interdisciplinary projects.
I am currently leading the CRIMAC SFI (https://crimac.no/)
Curriculum Vitae
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WORK EXPERIENCE |
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05.2013 – Ongoing |
Principal Scientist, Institute of marine research, Bergen Norway, Norway |
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I continued working on acoustic methods, but with more emphasis towards e-science, including machine learning algorithms and data infrastructures to support scientific computing. I also took on several leadership positions in international organizations, and I was active in developing the previous ICES strategic plan. See my publication list for an overview of the science production.
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01.2010 - 07.2010 |
Visiting scholar, Department of Ecology and Evolutionary Biology, Princeton University I worked with one of the best groups worldwide on collective behavior, and I used acoustic methods to extract schooling behavior and predator prey interactions using acoustics. This collaboration led to my most highly cited paper.
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10.2007 - 12.2007 |
Acting Head of research group “Observation methodology”, Institute of Marine Research, Norway
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11.2005 - 10.2006 |
Visiting Scientist, Alaska Fisheries Science Center, NOAA, Seattle, WA, USA I worked on fish avoidance observations and I developed methods to track fish inside trawls using an acoustic camera. I also established strong links between IMR to NOAA.
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11.2005 - 10.2006 |
Visiting Scholar, School of Oceanography, University of Washington, USA I worked on algorithms to use split beam echosounders to observe fish in dense schools.
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08.2004 - 04.2013 |
Research scientist, Institute of Marine Research, Norway I worked on wide range of projects. Initially I focused mainly on how to observe biases in scientific surveys and developed data processing methods to extract fish behavioral information from acoustic data. See my publication list for an overview of the science production.
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06.2001 - 08.2001 |
Young Scientists Summer Program, International Institute for Applied Systems Analysis, Austria A summer school project where I developed a quantitative genetics model for how fishing could lead to behavioral changes in cod.
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08.2000 - 12.2000 |
Project Scientist, Nansensenteret I worked as a project scientist at the “Fisheries Earth Observations and Prediction project. The main task was to manage automated data flows of large volumes of satellite data, as well as data analyzes.
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02.1998 - 01.2000 |
ICT consultant, Institute of Marine Research, Norway (part time, full time summer 1998) I worked on the Y2K problem and made an inventory of programs and service that could potentially be affected by the Y2K problem, including statements from vendors and service providers on Y2K compliance from their programs and services.
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EDUCATION |
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01.2001 - 06.2004 |
Dr. Scient. Applied mathematics, Institute of mathematics, University of Bergen, Norway |
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06.1997 - 01.2000 |
Cand. Scient. applied Mathematics, Universitetet i Bergen, Norway |
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08.1993 - 06.1996 |
Mechanical engineering, Sør-Trøndelag University College, Norway |
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SUPERVISION |
2 MSc students and 4 PhD students.
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AWARDS |
Nominated to the best paper at the Institute of Marine Research 2013 (out if 3 papers): Handegard, N.O. et al. (2013) Towards an acoustic-based coupled observation and modelling system for monitoring and predicting ecosystem dynamics of the open ocean, Fish and Fisheries 14(4): 605-615, doi:10.1111/j.1467-2979.2012.00480.x
Co-author on the paper that won the best presentation at the 2010 ICES Annual Science Conference: Lehodey, P. et al. (2010). “Towards Operational Management Of Pelagic Ecosystems”, ICES CM 2010/A:04
Best paper at the Institute of Marine Research 2007: Ona, Egil and Godø, Olav Rune and Handegard, Nils Olav and Hjellvik, Vidar and Patel, Ruben and Pedersen, Geir (2007). "Silent research vessels are not quiet", J. Acoust. Soc. Am, Vol 121, JASA Express letters online
Best presentation at the 2004 ICES Annual Science Conference: Handegard, N.O. and Tjøstheim, D. "The effective swept volume of a bottom trawl", ICES CM 2004/R:07, ICES Annual Science conference, Vigo Sep 2004.
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PROFESSIONAL SERVICE |
Board member in «Havforskerlaget», union for scientists (2018-ongoiing)
Norwegian representative in the ICES Science Committee (2012-ongoing)
Chair of the ICES SCICOM steering group on Ecosystem Surveys, Science and Technology (2012-2016)
Chair of the “MAAS group” in the Cliotop programme
Chair of the ICES Working Group on Fisheries Acoustics Science and Technology 2010-2013
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GRANTS AND PROJECT MANAGEMENT |
I have led more than 15 projects at IMR, including both large and complex projects. I have received funding from the Ministry of fisheries and commerce, the Norwegian Research Council, Fiskerinæringens Havbruksfond, and EU Horizon 2020 program. I have led work packages in EU projects, and I have received a highly competitive FriPro grant from the Research Council of Norway.
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SELECTED GRANTS |
Sea2Data 2010-2020, a project for improving the institutes’ infrastructure for data from scientific surveys. Funded by the Ministry of Fisheries and coastal affairs.
FriBio project “CollPen - Collective behaviour of penned herring: Observing the collective behaviour and investigating the effect of various sound stimuli” from 2011-2014. Funded by the highly competitive “FRIBIO” program at the Norwegian Research Council.
Strategic institute program “Adaptive management of living marine resources by integrating different data sources and key ecological processes (ADMAR): A joint effort by IMR and CEES” from 2010-2015. Funded by the Norwegian Research Council.
“Ecosystem dynamics and fish stocks (EcoFish) from 2007-2010. This is an interdisciplinary strategic institute program funded by the research council of Norway. Funded by the Norwegian Research Council.
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PEER REVIEW PUBLICATIONS |
I have 54 peer review publications in the primary literature, covering several fields including ecology, fisheries and acoustics. Several publications are in top journals within the respective fields, including Current Biology, Fish and Fisheries and The Journal of the Acoustical Society of America. I have two highly cited papers in the field, one paper that won the best publication at IMR, and one that was nominated (1 out of 3) for the same price.
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LIST OF PUBLICATIONS |
[54] Brautaset, O., Waldeland, A. U., Johnsen, E., Malde, K., Eikvil, L., Salberg, A.-B., & Handegard, N. O. (2020). Acoustic classification in multifrequency echosounder data using deep convolutional neural networks. ICES Journal of Marine Science. https://doi.org/10.1093/icesjms/fsz235
[53] Myers, S. C., Thorsen, A., Smoliński, S., Godiksen, J. A., Malde, K., & Handegard, N. O. (2020). An efficient protocol and data set for automated otolith image analysis. Geoscience Data Journal, n/a(n/a). https://doi.org/10.1002/gdj3.86
[51] Fields, D. M., Handegard, N. O., Dalen, J., Eichner, C., Malde, K., Karlsen, Ø., … Browman, H. I. (2019). Airgun blasts used in marine seismic surveys have limited effects on mortality, and no sublethal effects on behaviour or gene expression, in the copepod Calanus finmarchicus. ICES Journal of Marine Science, 76(7), 2033–2044. https://doi.org/10.1093/icesjms/fsz126
[50] Johnsen, E., Totland, A., Skålevik, Å., Holmin, A. J., Dingsør, G. E., Fuglebakk, E., & Handegard, N. O. (2019). StoX: An open source software for marine survey analyses. Methods in Ecology and Evolution, 10(9), 1523–1528. https://doi.org/10.1111/2041-210X.13250
[49] Tenningen, M., Pobitzer, A., Handegard, N. O., & de Jong, K. (2019). Estimating purse seine volume during capture: implications for fish densities and survival of released unwanted catches. ICES Journal of Marine Science, 76(7), 2481–2488. https://doi.org/10.1093/icesjms/fsz119
[48] Snowden, D., Tsontos, V. M., Handegard, N. O., Zarate, M., O’ Brien, K., Casey, K. S., … Arms, S. C. (2019). Data Interoperability Between Elements of the Global Ocean Observing System. Frontiers in Marine Science, 6. https://doi.org/10.3389/fmars.2019.00442
[47] Allken, V., Handegard, N. O., Rosen, S., Schreyeck, T., Mahiout, T., & Malde, K. (2019). Fish species identification using a convolutional neural network trained on synthetic data. ICES Journal of Marine Science, 76(1), 342–349. https://doi.org/10.1093/icesjms/fsy147
[46] Anders, N., Howarth, K., Totland, B., Handegard, N. O., Tenningen, M., & Breen, M. (2019). Effects on individual level behaviour in mackerel (Scomber scombrus) of sub-lethal capture related stressors: Crowding and hypoxia. PLOS ONE, 14(3), e0213709. https://doi.org/10.1371/journal.pone.0213709
[45] Garcia, H. A., Zhu, C., Schinault, M. E., Kaplan, A. I., Handegard, N. O., Godø, O. R., … Ratilal, P. (2019). Temporal–spatial, spectral, and source level distributions of fin whale vocalizations in the Norwegian Sea observed with a coherent hydrophone array. ICES Journal of Marine Science, 76(1), 268–283. https://doi.org/10.1093/icesjms/fsy127
[44] Malde, K., Handegard, N. O., Eikvil, L., & Salberg, A.-B. (2019). Machine intelligence and the data-driven future of marine science. ICES Journal of Marine Science. https://doi.org/10.1093/icesjms/fsz057
[43] Moen, E., Handegard, N. O., Allken, V., Albert, O. T., Harbitz, A., & Malde, K. (2018). Automatic interpretation of otoliths using deep learning. PLOS ONE, 13(12), e0204713. https://doi.org/10.1371/journal.pone.0204713
[42] Proud, R., Handegard, N. O., Kloser, R. J., Cox, M. J., & Brierley, A. S. (2018). From siphonophores to deep scattering layers: uncertainty ranges for the estimation of global mesopelagic fish biomass. ICES Journal of Marine Science. https://doi.org/10.1093/icesjms/fsy037 Highly Cited in Field
[41] Vatnehol, S., & Handegard, N. O. (2018). Echo integration using non-vertical sonar beams: The bias caused by non-uniform distribution of fish within the echo beam. The Journal of the Acoustical Society of America, 144(4), 2160–2165. https://doi.org/10.1121/1.5057120
[40] Vatnehol, S., Peña, H., & Handegard, N. O. (2018). A method to automatically detect fish aggregations using horizontally scanning sonar. ICES Journal of Marine Science, 75(5), 1803–1812. https://doi.org/10.1093/icesjms/fsy029
[39] Zhu, C., Garcia, H., Kaplan, A., Schinault, M., Handegard, N., Godø, O., … Ratilal, P. (2018). Detection, Localization and Classification of Multiple Mechanized Ocean Vessels over Continental-Shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing. Remote Sensing, 10(11), 1699. https://doi.org/10.3390/rs10111699
[38] Handegard, N. O., Tenningen, M., Howarth, K., Anders, N., Rieucau, G., & Breen, M. (2017). Effects on schooling function in mackerel of sub-lethal capture related stressors: Crowding and hypoxia. PLOS ONE, 12(12), e0190259. https://doi.org/10.1371/journal.pone.0190259
[37] Handegard, N. O., Holmin, A. J., & Rieucau, G. (2017). Method to observe large scale behavioural waves propagating through fish schools using 4D sonar. ICES Journal of Marine Science, 74(3), 804–812. https://doi.org/10.1093/icesjms/fsw211
[36] Kruusmaa, M., Rieucau, G., Montoya, J. C. C., Markna, R., & Handegard, N. O. (2016). Collective responses of a large mackerel school depend on the size and speed of a robotic fish but not on tail motion. Bioinspiration & Biomimetics, 11(5), 056020. https://doi.org/10.1088/1748-3190/11/5/056020
[35] Skern-Mauritzen, M., Ottersen, G., Handegard, N. O., Huse, G., Dingsør, G. E., Stenseth, N. C., & Kjesbu, O. S. (2016). Ecosystem processes are rarely included in tactical fisheries management. Fish and Fisheries, 17(1), 165–175. http://doi.org/10.1111/faf.12111 Highly Cited in Field
[34] Handegard, N. O., Boswell, K., De Robertis, A., Macaulay, G. J., Rieucau, G., & Sivle, L. D. (2016). Investigating the Effect of Tones and Frequency Sweeps on the Collective Behavior of Penned Herring (Clupea harengus). Advances in Experimental Medicine and Biology, 875, 391–398. http://doi.org/10.1007/978-1-4939-2981-8_46
[33] Trenkel, V. M., Handegard, N. O., & Weber, T. C. (2016). Observing the ocean interior in support of integrated management.ICES Journal of Marine Science: Journal Du Conseil, fsw132. https://doi.org/10.1093/icesjms/fsw132
[32] Rieucau, G., Holmin, A. J., Castillo, J. C., Couzin, I. D., & Handegard, N. O. (2016). School level structural and dynamic adjustments to risk promote information transfer and collective evasion in herring. Animal Behaviour, 117, 69–78. http://doi.org/10.1016/j.anbehav.2016.05.002
[31] Rieucau, G., Sivle, L. D., & Handegard, N. O. (2015). Herring perform stronger collective evasive reactions when previously exposed to killer whales calls. Behavioral Ecology, arv186. http://doi.org/10.1093/beheco/arv186
[30] Ressler, P. H., Dalpadado, P., Macaulay, G. J., Handegard, N., & SkernMauritzen, M. (2015). Acoustic surveys of euphausiids and models of baleen whale distribution in the Barents Sea. Marine Ecology Progress Series, 527, 13–29. http://doi.org/10.3354/meps11257
[29] Godø, O. R., Handegard, N. O., Browman, H. I., Macaulay, G. J., Kaartvedt, S., Giske, J., … Johnsen, E. (2014). Marine ecosystem acoustics (MEA): quantifying processes in the sea at the spatio-temporal scales on which they occur. ICES Journal of Marine Science: Journal Du Conseil, 71(8), 2357–2369. http://doi.org/10.1093/icesjms/fsu116
[28] Rieucau, G., Boswell, K. M., De Robertis, A., Macaulay, G. J., & Handegard, N. O. (2014). Experimental Evidence of Threat-Sensitive Collective Avoidance Responses in a Large Wild-Caught Herring School. PLoS ONE, 9(1), e86726. http://doi.org/10.1371/journal.pone.0086726
[27] Blanchard, A., Hauge, K. H., Andersen, G., Fosså, J. H., Grøsvik, B. E., Handegard, N. O., … Vikebø, F. (2014). Harmful routines? Uncertainty in science and conflicting views on routine petroleum operations in Norway. Marine Policy, 43, 313–320. http://doi.org/10.1016/j.marpol.2013.07.001
[26] Rieucau, G., De Robertis, A., Boswell, K. M., & Handegard, N. O. (2014). School density affects the strength of collective avoidance responses in wild-caught Atlantic herring Clupea harengus: a simulated predator encounter experiment. Journal of Fish Biology, 85(5), 1650–1664. http://doi.org/10.1111/jfb.12520
[25] Handegard, N. O., Robertis, A. D., Rieucau, G., Boswell, K., & Macaulay, G. J. (2014). The reaction of a captive herring school to playbacks of a noise-reduced and a conventional research vessel. Canadian Journal of Fisheries and Aquatic Sciences,72(4), 491–499. http://doi.org/10.1139/cjfas-2014-0257
[24] Rieucau, G., Fernö, A., Ioannou, C. C., & Handegard, N. O. (2014). Towards of a firmer explanation of large shoal formation, maintenance and collective reactions in marine fish. Reviews in Fish Biology and Fisheries, 25(1), 21–37. http://doi.org/10.1007/s11160-014-9367-5
[23] Peña, H., Handegard, N. O., & Ona, E. (2013). Feeding herring schools do not react to seismic air gun surveys. ICES Journal of Marine Science: Journal Du Conseil, 70(6), 1174–1180. http://doi.org/10.1093/icesjms/fst079
[22] De Robertis, A., & Handegard, N. O. (2013). Fish avoidance of research vessels and the efficacy of noise-reduced vessels: a review. ICES Journal of Marine Science, 70(1), 34–45. http://doi.org/10.1093/icesjms/fss155
[21] Handegard, N. O., Tronstad, T. V., & Hovem, J. M. (2013). Evaluating the effect of seismic surveys on fish — the efficacy of different exposure metrics to explain disturbance. Canadian Journal of Fisheries and Aquatic Sciences, 70(9), 1271–1277. http://doi.org/10.1139/cjfas-2012-0465
[20] Handegard, N. O., Buisson, L. du, Brehmer, P., Chalmers, S. J., De Robertis, A., Huse, G., … Godø, O. R. (2013). Towards an acoustic-based coupled observation and modelling system for monitoring and predicting ecosystem dynamics of the open ocean. Fish and Fisheries, 14(4), 605–615. 1 of 3 that were nominated to the best paper at IMR in 2013. http://doi.org/10.1111/j.1467-2979.2012.00480.x
[19] Sivle, L. D., Kvadsheim, P. H., Ainslie, M. A., Solow, A., Handegard, N. O., Nordlund, N., & Lam, F.-P. A. (2012). Impact of naval sonar signals on Atlantic herring (Clupea harengus) during summer feeding. ICES Journal of Marine Science: Journal Du Conseil, 69(6), 1078–1085. http://doi.org/10.1093/icesjms/fss080
[18] Doksaeter, L., Handegard, N. O., Godo, O. R., Kvadsheim, P. H., & Nordlund, N. (2012). Behavior of captive herring exposed to naval sonar transmissions (1.0–1.6 kHz) throughout a yearly cycle. The Journal of the Acoustical Society of America,131(2), 1632–1642. http://doi.org/10.1121/1.3675944
[17] Holmin, A. J., Handegard, N. O., Korneliussen, R. J., & Tjostheim, D. (2012). Simulations of multi-beam sonar echos from schooling individual fish in a quiet environment. The Journal of the Acoustical Society of America, 132(6), 3720–3734. http://doi.org/10.1121/1.4763981
[16] Handegard, N. O., Boswell, K. M., Ioannou, C. C., Leblanc, S. P., Tjøstheim, D. B., & Couzin, I. D. (2012). The Dynamics of Coordinated Group Hunting and Collective Information Transfer among Schooling Prey. Current Biology, 22(13), 1213–1217. http://doi.org/10.1016/j.cub.2012.04.050 See dispatch by Graeme Ruxton (http://www.sciencedirect.com/science/article/pii/S0960982212005349) and feature by Michael Gross (http://www.cell.com/current-biology/fulltext/S0960-9822(12)00639-2) , and video abstract (http://www.youtube.com/watch?v=e_NVDz7XzIo).
[15] Bazazi, S., Pfennig, K., Handegard, N., & Couzin, I. (2012). Vortex formation and foraging in polyphenic spadefoot toad tadpoles. Behavioral Ecology and Sociobiology, 66(6), 879–889. http://doi.org/10.1007/s00265-012-1336-1
[14] Doksaeter, L., Godo, O. R., Handegard, N. O., Kvadsheim, P. H., Lam, F.-P. A., Donovan, C., & Miller, P. J. O. (2009). Behavioral responses of herring (Clupea harengus) to 1–2 and 6–7 kHz sonar signals and killer whale feeding sounds. The Journal of the Acoustical Society of America, 125(1), 554–564. http://doi.org/10.1121/1.3021301
[13] Handegard, N. O., Pedersen, G., & Brix, O. (2009). Estimating tail-beat frequency using split-beam echosounders. ICES Journal of Marine Science, 66, 1252–1258. https://doi.org/10.1093/icesjms/fsp003
[12] Pedersen, G., Handegard, N. O., & Ona, E. (2009). Lateral-aspect target-strength measurements of in situ herring (Clupea harengus). ICES Journal of Marine Science, 66, 1191–1196. http://doi.org/10.1093/icesjms/fsp121
[11] Handegard, N. O., & Tjøstheim, D. (2009). The sampling volume of trawl and acoustics: estimating availability probabilities from observations of tracked individual fish. Canadian Journal of Fisheries & Aquatic Sciences, 66, 425–438. http://doi.org/10.1139/F09-004
[10] Handegard, N. O., & Williams, K. (2008). Automated tracking of fish in trawls using the DIDSON (dual frequency identification sonar). ICES Journal of Marine Science, 65, 636–644. http://doi.org/10.1093/icesjms/fsn029
[9] Hjellvik, V., Handegard, N. O., & Ona, E. (2008). Correcting for vessel avoidance in acoustic abundance estimates for herring using a generalized linear model. ICES Journal of Marine Science, 65. http://doi.org/doi:10.1093/icesjms/fsn082
[8] Handegard, N. O. (2007). Observing individual fish behavior in fish aggregations: Tracking in dense aggregations. The Journal of the Acoustical Society of America, 122(1), 177–187. http://doi.org/10.1121/1.2739421
[7] Ona, E., Godø, O. R., Handegard, N. O., Hjellvik, V., Patel, R., & Pedersen, G. (2007). Silent research vessels are not quiet. The Journal of the Acoustical Society of America, 121, EL145–EL150. http://doi.org/10.1121/1.2710741 . This paper won the best paper award at the Institute of Marine Research in 2007.
[6] Skaret, G., Slotte, A., Handegard, N. O., Axelsen, B. E., & Jørgensen, R. (2006). Pre-spawning herring in a protected area showed only moderate reaction to a surveying vessel. Fisheries Research, 78(2–3), 359–367. http://doi.org/10.1016/j.fishres.2005.11.007
[5] Handegard, N. O., Patel, R., & Hjellvik, V. (2005). Tracking individual fish from a moving platform using a split-beam transducer. The Journal of the Acoustical Society of America, 118(4), 2210–2223. http://doi.org/10.1121/1.2011410
[4] Handegard, N. O., & Tjøstheim, D. (2005). When fish meets a trawling vessel: examining the behaviour of gadoids using a free floating buoy and acoustic split-beam tracking. Canadian Journal of Fisheries and Aquatic Sciences, 62, 2409–2422. http://doi.org/10.1139/F05-131 The content of this paper won the best presentation at the ICES ASC 2004
[3] Patel, R., Handegard, N. O., & Godø, O. R. (2004). Behaviour of herring (Clupea harengus L.) to an approaching AUV. ICES Journal of Marine Science, 61, 1044–1049. http://doi.org/10.1016/j.icesjms.2004.07.002
[2] Jørgensen, R., Handegard, N. O., Gjøsæter, H., & Slotte, A. (2004). Possible vessel avoidance behaviour of capelin in a feeding area and on a spawning ground. Fisheries Research, 69, 251–261. http://doi.org/10.1016/j.fishres.2004.04.012
[1] Handegard, N. O., Michalsen, K., & Tjøstheim, D. (2003). Avoidance behaviour in cod (Gadus morhua) to a bottom-trawling vessel. Aquatic Living Resources, 16, 265–270. http://doi.org/10.1016/S0990-7440(03)00020-2
Publications
Academic articles
2024
Self-supervised feature learning for acoustic data analysis
Ecological Informatics
84
2024
Uncrewed surface vehicles (USVs) as platforms for fisheries and plankton acoustics
ICES Journal of Marine Science
2024
DIB-X: Formulating Explainability Principles for a Self-Explainable Model Through Information Theoretic Learning
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
2024
Broadband backscattering by Atlantic herring (Clupea harengus L.) differs when measured from a research vessel vs. a silent uncrewed surface vehicle
ICES Journal of Marine Science
2024
Environmental impact on visual perception modulates behavioral responses of schooling fish to looming predators
Journal of Experimental Biology
227
2023
Applications and analytical approaches using imaging sonar for quantifying behavioural interactions among aquatic organisms and their environment
ICES Journal of Marine Science
2023
Quantitative processing of broadband data as implemented in a scientific split-beam echosounder
Methods in Ecology and Evolution
2023
Age interpretation of cod otoliths using deep learning
Ecological Informatics
2023
Addressing class imbalance in deep learning for acoustic target classification
ICES Journal of Marine Science
p. 1-15
2023
Machine learning in marine ecology: an overview of techniques and applications
ICES Journal of Marine Science
80
p. 1829-1853
2023
Machine learning in marine ecology: an overview of techniques and applications
ICES Journal of Marine Science
80
p. 1829-1853
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
Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data
IEEE Journal of Oceanic Engineering
48
p. 384-400
2023
Characterizing coastal cod vocalization using a towed hydrophone array
ICES Journal of Marine Science
80
p. 1727-1745
2023
Behavioural responses of wild, spawning Atlantic cod (Gadus morhua L.) to seismic airgun exposure
ICES Journal of Marine Science
80
p. 1052-1065
2023
Diversity and seasonal development of large zooplankton along physical gradients in the Arctic Barents Sea
Progress in Oceanography
216
2023
Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data
IEEE Journal of Oceanic Engineering
48
p. 384-400
2023
Deep Semi-Supervised Semantic Segmentation in Multi-Frequency Echosounder Data
IEEE Journal of Oceanic Engineering
2022
Evaluation of echosounder data preparation strategies for modern machine learning models
Fisheries Research
254
2022
Spawning Atlantic cod (Gadus morhua L.) exposed to noise from seismic airguns do not abandon their spawning site
ICES Journal of Marine Science
79
p. 2697-2708
2021
Anthropogenic Marsh Impoundments Alter Collective Tendency in Schooling Fish
Estuaries and Coasts
p. 1-10
2021
A real-world dataset and data simulation algorithm for automated fish species identification
Geoscience Data Journal
2021
Automatic interpretation of salmon scales using deep learning
Ecological Informatics
63
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
Semi-supervised target classification in multi-frequency echosounder data
ICES Journal of Marine Science
78
p. 2615-2627
2020
Evaluating acoustic-trawl survey strategies using an end to end ecosystem model
ICES Journal of Marine Science
2020
Evaluating acoustic-trawl survey strategies using an end-to-end ecosystem model
ICES Journal of Marine Science
2020
Environmental context elicits behavioural modification of collective state in schooling fish
Animal Behaviour
165
p. 107-116
2020
Acoustic classification in multifrequency echosounder data using deep convolutional neural networks
ICES Journal of Marine Science
77
p. 1391-1400
2020
Machine intelligence and the data-driven future of marine science
ICES Journal of Marine Science
2019
An efficient protocol and data set for automated otolith image analysis
Geoscience Data Journal
p. 1-9
2019
Airgun blasts used in marine seismic surveys have limited effects on mortality, and no sublethal effects on behaviour or gene expression, in the copepod Calanus finmarchicus
ICES Journal of Marine Science
76
p. 2033-2044
2019
StoX: An open source software for marine survey analyses
Methods in Ecology and Evolution
10
p. 1523-1528
2019
From siphonophores to deep scattering layers: Uncertainty ranges for the estimation of global mesopelagic fish biomass
ICES Journal of Marine Science
76
p. 718-733
2019
Effects on individual level behaviour in mackerel (Scomber scombrus) of sub-lethal capture related stressors: Crowding and hypoxia
PLOS ONE
14
2019
Temporal-spatial, spectral, and source level distributions of fin whale vocalizations in the Norwegian Sea observed with a coherent hydrophone array
ICES Journal of Marine Science
76
p. 268-283
2019
Data interoperability between elements of the global ocean observing system
Frontiers in Marine Science
6:442
p. 1-15
2019
Estimating purse seine volume during capture: implications for fish densities and survival of released unwanted catches
ICES Journal of Marine Science
2018
Detection, Localization and Classification of Multiple Mechanized Ocean Vessels over Continental-Shelf Scale Regions with Passive Ocean Acoustic Waveguide Remote Sensing
Remote Sensing
10
2018
Fish species identification using a convolutional neural network trained on synthetic data
ICES Journal of Marine Science
76
p. 342-349
2018
Echo integration using non-vertical sonar beams: The bias caused by non-uniform distribution of fish within the echo beam
Journal of the Acoustical Society of America
144
p. 2160-2165
2018
A method to automatically detect fish aggregations using horizontally scanning sonar
ICES Journal of Marine Science
75
p. 1803-1812
2018
Automatic interpretation of otoliths using deep learning
PLOS ONE
13
p. 1-14
2017
Effects on schooling function in mackerel of sub-lethal capture related stressors: Crowding and hypoxia
PLOS ONE
12
p. 1-16
2017
The loss of schooling function in mackerel in response to crowding but not hypoxia
PLOS ONE
2017
Method to observe large scale behavioural waves propagating through fish schools using 4D sonar
ICES Journal of Marine Science
74
p. 804-812
2016
Herring perform stronger collective evasive reactions when previously exposed to killer whales calls
Behavioral Ecology
27
p. 538-544
2016
School level structural and dynamic adjustments to risk promote information transfer and collective evasion in herring
Animal Behaviour
117
p. 69-78
2016
Mackerel behaviour and seismic signals – a net pen study. Rapport fra Havforskningen nr 19 2016. ISSN 1893-4536.
Rapport fra havforskningen
19
2016
Investigating the Effect of Tones and Frequency Sweeps on the Collective Behavior of Penned Herring (Clupea harengus).
Advances in Experimental Medicine and Biology
875
p. 391-398
2016
Observing the ocean interior in support of integrated management
ICES Journal of Marine Science
73
p. 1947-1954
2016
Collective responses of a large mackerel school depend on the size and speed of a robotic fish but not on tail motion
Bioinspiration & Biomimetics
11
2015
Ecosystem processes are rarely included in tactical fisheries management
Fish and Fisheries
17
p. 165-175
2015
Acoustic surveys of euphausiids and models of baleen whale distribution in the Barents Sea
Marine Ecology Progress Series
527
p. 13-29
2015
The reaction of a captive herring school to playbacks of a noise-reduced and a conventional research vessel
Canadian Journal of Fisheries and Aquatic Sciences
72
p. 491-499
2014
Marine ecosystem acoustics (MEA): Quantifying processes in the sea at the spatio-temporal scales on which they occur
ICES Journal of Marine Science
71
p. 2357-2369
2014
School density affects the strength of collective avoidance responses in wild-caught Atlantic herring (Clupea harengus): a simulated-predator encounter experiment
Journal of Fish Biology
85
p. 1650-1664
2014
Towards of a firmer explanation of large shoal formation, maintenance and collective reactions in marine fish
Reviews in Fish Biology and Fisheries
25
p. 21-37
2014
Experimental evidence of threat-sensitive collective avoidance responses in a large wild-caught herring school
PLOS ONE
9
2014
Harmful routines? Uncertainty in science and conflicting views on routine petroleum operations in Norway
Marine Policy
43
p. 313-320
2013
Feeding herring schools do not react to seismic air gun surveys
ICES Journal of Marine Science
70
p. 1174-1180
2013
Evaluating the effect of seismic surveys on fish — the efficacy of different exposure metrics to explain disturbance
Canadian Journal of Fisheries and Aquatic Sciences
70
p. 1271-1277
2013
Towards an acoustic-based coupled observation and modelling system for monitoring and predicting ecosystem dynamics of the open ocean
Fish and Fisheries
14
p. 605-615
2013
Fish avoidance of research vessels and the efficacy of noise-reduced vessels: a review
ICES Journal of Marine Science
70
p. 34-45
2012
Behavioral Responses of Captive Herring to Sonar Signals (1-1.6 kHz) of a Naval Frigate Throughout a Yearly Cycle
Journal of the Acoustical Society of America
131
p. 1632-1642
2012
Simulations of multi-beam sonar echos from schooling individual fish in a quiet environment
Journal of the Acoustical Society of America
132
p. 3720-3734
2012
Behavior of captive herring exposed to naval sonar transmissions (1.0-1.6 kHz) throughout a yearly cycle
Journal of the Acoustical Society of America
131
p. 1632-1642
2012
Vortex formation and foraging in polyphenic spadefoot toad tadpoles
Behavioral Ecology and Sociobiology
66
p. 879-889
2012
Impact of naval sonar signals on Atlantic herring (Clupea harengus) during summer feeding
ICES Journal of Marine Science
69
p. 1078-1085
2012
The dynamics of coordinated group hunting and collective information transfer among schooling prey
Current Biology
22
p. 1213-1217
2010
Effekter av seismiske undersøkelser på fiskefordeling og fangstrater for garn og line i Vesterålen sommeren 2009
Fisken og Havet
2
2009
Lateral-aspect, target-strength measurements of in situ herring (Clupea harengus)
ICES Journal of Marine Science
66
p. 1191-1196
2009
The sampling volume of trawl and acoustics: estimating availability probabilities from observations of tracked individual fish
Canadian Journal of Fisheries and Aquatic Sciences
66
p. 425-438
2009
Estimating tail-beat frequency using split-beam echosounders
ICES Journal of Marine Science
66
p. 1252-1258
2009
Behavioral responses of herring (Clupea harengus) to 1-2 and 6-7 kHz sonar signals and killer whale feeding sounds
Journal of the Acoustical Society of America
125
p. 554-564
2006
Pre-spawning herring in a protected area showed only moderate reaction to a surveying vessel
Fisheries Research
78
p. 359-367
2005
When fish meet a trawling vessel: examining the behaviour of gadoids using a free-floating buoy and acoustic split-beam tracking
Canadian Journal of Fisheries and Aquatic Sciences
62
p. 2409-2422
2004
Possible vessel avoidance behaviour of capelin in a feeding area and on a spawning ground
Fisheries Research
69
p. 251-261
2003
Avoidance behaviour in cod (Gadus morhua) to a bottom-trawling vessel
Aquatic Living Resources
16
p. 265-270
Other journal contributions
2021
Fisken som leikar i havet
Fiskeribladet
2021
Betre ekkolodd vil gi betre forståing av våre marine økosystem
Skipsrevyen
Scientific lectures
2024
Presentation of SFI CRIMAC
2024
Visual Intelligence – Marine science. Visual intelligence days.
2024
Analysing large amounts of echosounder data using cloud based data access combined with deep learning models
2024
Identifying size and behavior of Mueller’s Pearlside (Maurolicus muelleri) by combining measured broadband frequency responses with backscattering models and in-trawl observations
2024
Examples of AI and Machine Learning methods applied to data from marine ecosystems
2024
Fisheries acoustics and deep learning
2024
Exploring the impact of Fourier window lengthon target strength frequency response estimation in broadband echosounders
2024
Behaviour and platform dependent broadband backscattering by physostome fish (Clupea harengus L.) revisited
2024
Testing acoustic data quality and performance on Sounder USV
2024
Annotation-Free Feature Learning for Improved Acoustic Target Classification
2024
Unraveling Acoustic Signal Patterns in Fisheries Through DINO-Based Self-Supervised Learning
2023
Learning from weakly labelled marine acoustic data
2023
A story about data extraction and deep learning applied to fishery acoustic data
2023
Towards a multi-platform armada strategy for ecosystem based marine surveys
2023
Armadastrategi og digitalisering
2023
IMRs strategy for unmanned vehicles
2023
Visual Intelligence – Marine science
2023
Resolving and characterizing nearby targets by means of broadband acoustics
2023
Investigation of broadband acoustic to resolve and identify nearby targets using different pulse durations
2023
Investigation of broadband acoustic to resolve and identify nearby targets using different pulse durations
2023
Investigation of broadband acoustic to resolve and identify nearby targets using different pulse durations
2023
Behaviour dependent broadband backscattering by physostomous fish (Clupea harengus L.)
2023
Resolving and characterizing nearby targets by means of broadband acoustics
2023
Resolving and characterizing nearby targets by means of broadband acoustics
2022
Deep learning models for fisheries acoustic target classification
2022
Using deep learning models to count and identify fish species from in-trawl images
2022
Selecting maximally informative frequency subsets for acoustic surveys
2022
Developing and deploying machine learning methods for acoustic data
2022
Monitoring salmon with broad band echo sounders – investigate acoustic parameters as indicators for welfare
2022
Quantitative processing of broadband data as implemented in a scientific split-beam echosounder
2022
Behavioural responses of spawning Atlantic cod (Gadus morhua) to seismic airgun exposure.
2021
Combined trawl-mounted optic and acoustic methods to study the mesopelagic ecosystem
2021
Using model averaging ensembles in semantic segmentation of marine echosounder data for acoustic classification of species
2021
CRIMAC - Center for Research-based Innovation in Marine Acoustic Abundance Estimation and Backscatter Classification
2021
Direct measurements of the migration speed of spawning herring
2019
The COGMAR project
2019
Machine Learning
2019
The IMR deep vision pipeline for the Herring survey
2019
Automated acoustic data processing with deep learning
2019
Using a CNN trained on synthetic data for fish species identification
2019
Using a CNN trained on synthetic data for fish species identification
2018
Sea2Data - from acquisition to advice
2018
Drowning in data: Can deep learning approaches be the solution?
2017
Challenges and lessons learned in fisheries advice work, and the complex interplay between science, method development and people management
2016
Sea2Data - from data acquisition to advice
2015
Lyd i havet
2015
Using acoustics to observe fine scale schooling behaviour in open ocean and controlled net pen experiments
2014
Snakkar fisk i stim med kvarandre?
2014
The reaction of a captive herring school to playbacks of a noisy and a conventional research vessel
2014
Risk perception affects the schooling dynamic collective anti-predator responses in herring
2013
"StoX" - an open source approach to survey calculations
2013
Sounds in fishes and the environmental impact of noise – the Norwegian experience
2013
A framework for data flow from observations to advice – the development of a new, efficient data infrastructure at IMR
2013
Integrated Ecosystem Observations
2013
A multifrequency acoustic index of Barents Sea euphausiid (krill) biomass?
2013
Investigating the effect of tones and frequency sweeps on the collective behavior of penned herring (Clupea harengus)
2013
An overview of underwater acoustics applied to observe fish behaviour at the Institute of Marine Research
2010
Sea2Data - Havforskingsinstituttets datainfrastrukturprosjekt for feltdata
2010
Behaviorural responses of captive herring to sonar signals (1-1.6 kHz) of a naval frigate throughout a yearly cycle
2003
Avoidance behaviour in cod (Gadus morhua) to a bottom-trawling vessel
Poster
2023
Classifying fish with broadband acoustics; behaviour and platform dependent broadband backscattering by physostomous fish (Clupea harengus L.). From Echosounders to the Cloud: Transforming Acoustic Data to Information.
2023
Direct measurements of the migration speed of spawning herring
2023
Learning from weakly labelled marine acoustic data
2023
Deep Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data
2023
Mitigating Class-Imbalance in Acoustic Target Classification for Fisheries via Similarity-Based Data Selection
2023
Similarity-Based Data Selection to Improve Automatic Acoustic Target Classification
2023
Deep Learning for Acoustic Target Classification: Addressing Class-Imbalance with a Similarity-Based Sampling Approach
2023
Centre for research based innovation in marine acoustic abundance estimation and backscatter classification (CRIMAC)
2022
Improving marine acoustic target classification with context information
2022
Explaining Marine Acoustic Target Classification in Multi-channel Echosounder Data using Self-attention Mask, Information-Bottleneck, and Mask Prior
2021
Improving marine acoustic target classification with context information
2021
Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data
2020
Automatic interpretation of salmon scales using deep learning
2018
Towards seamless integration of data and processing software: data products generated from machine-to-machine interfaces
2017
Automatisk klassifisering av svømming i not
Reports and dissertations
2024
Survey report for CRIMAC SFI 2023
Toktrapport
2023
The One Ocean Expedition: Science and Sailing for the Ocean We Want
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
2022
Joint survey report for CRIMAC SFI and IMR sampling gear project
2022
CRIMAC - Annual Report 2021
2022
Selecting Maximally Informative Frequency Subsets for Acoustic Surveys
2021
CRIMAC - Annual Report 2020
2021
Fisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acoustics
2021
Final report for the REDUS project - Reduced Uncertainty in Stock Assessment
2021
CRIMAC cruise report 2020116
2019
Machine learning to improve marine science for the sustainability of living ocean resources: Report from the 2019 Norway - U.S. Workshop
2018
PELFOSS (PELagic Fish Observation System Simulator)
2016
Korleis kan vi dokumentera at fisken overlever slipping frå not?
2000
Simulering av torsk (Gadus Morhua) sin reaksjon på fartøystøy
Net reports
Net reports
- Survey report for CRIMAC SFI 2023
- The One Ocean Expedition: Science and Sailing for the Ocean We Want
- CRIMAC cruise report: Development of acoustic and optic methods for underwater target calssification
- Joint survey report for CRIMAC SFI and IMR sampling gear project
- Fiskeriakustikk og akustisk målklassifisering
- Final report for the REDUS project
News
News
- (23.10.2023) Unmanned vehicles to meet growing need for research data
- (14.01.2022) Computer learns to identify fish species in trawl images
- (12.08.2020) Artificial intelligence is becoming important to marine research
- (11.05.2020) Researchers have trained artificial intelligence in identifying schools of sandeel
- (07.08.2019) Seismic surveys do not significantly harm Calanus finmarchicus
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