Theoretical Ecology Group

 
UNIVERSITETET I BERGEN
Institutt for biologi
 
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Animal Decision Making

Currently, a major activity in the group is to investigate the pathways from (sensory) information to decisions and behaviour in animals. We call this pathway the proximate architecture for decision-making, and we study its effect through evolutionary simulation models.
We first describe this research program, and then our history leading up to it.

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The Proximate Architecture for Decision-Making

The model of the Proximate Architecture for Decision-Making (Giske & al, 2013)

We try to develop tools that mimic the proximate architecture for decision-making of an animal (often a fish) and thereby to understand what decisions it makes. The popular science text Oceans of Emotions can give an easy intro to the thinking. A first attempt at this was Giske & al. (2003), but Eliassen et al (2016) and Andersen et al (2016) give a more formal yet strongly simplified model of behavioural architecture and their ecological effects. Giske & al. (2014) shows that this architecture also impacts the genetic diversity of populations, and hence their evolvability.

Core References

Giske J, Eliassen S, Fiksen Ø, Jakobsen PJ, Aksnes DL, Jørgensen C, Mangel M. 2013.
Effects of the emotion system on adaptive behavior
American Naturalist. 182: 689-703. [ doi:10.1086/673533 ] [ open access ] [ pdf ] [ online appendix ] [ popularized version ]
Giske J, Eliassen S, Fiksen Ø, Jakobsen PJ, Aksnes DL, Mangel M, Jørgensen C. 2014.
The emotion system promotes diversity and evolvability
Proceedings of the Royal Society B. 281: 20141096. [ doi:10.1098/rspb.2014.1096 ] [ open access ] [ pdf ] [ online appendix ] [ fortran code for model at datadryad.org ]
Eliassen S, Andersen BS, Jørgensen C, Giske J. 2016.
From sensing to emergent adaptations: Modelling the proximate architecture for decision-making
Ecological Modelling. 326: 90-100. [ doi:10.1016/j.ecolmodel.2015.09.001 ] [ open access ] [ pdf ]
Andersen BS, Jørgensen C, Eliassen S, Giske J. 2016.
The proximate architecture for decision-making in fish
Fish and Fisheries. 17: 680-695. [ doi:10.1111/faf.12139 ] [ open access ] [ pdf ]

 

Early work in the research group

Since 1990 we have been involved in modeling decision-making, particularly in fish and plankton. Much of this work is based on a theoretically derived model for visual range of aquatic organisms (Aksnes & Giske 1993, Aksnes & Utne 1997), which again has allowed calculations of feeding rates and predation risks (Giske & al. 1994, Fiksen & al. 2002).

We have been using Life History Theory (Aksnes & Giske 1990, Giske & Aksnes 1992, Salvanes & al. 1994, Giske & Salvanes 1995, Eiane & al. 1998), Game Theory (Giske & al. 1997) and State-Dependent Optimization (Giske & al. 1992, Rosland & Giske 1994, 1997, Fiksen & Giske 1995, Fiksen 1997, Rosland 1997, Fiksen & Carlotti 1998, Kirby & al. 2000) to model both short-term and life-history decisions, and participated in development and standardization of Individual Based Modelling (IBM; Grimm et al. 2006, 2010, Stillman et al. 2016). More recently, we have been using Genetic Algorithms to evolve adaptive behaviors in IBMs, either directly as life-history decision genes, neural networks of brains, or decisions coming out from the proximate architecture for behavioural control of the individuals. We have also performed field and lab studies to test assumptions in the models.

Core References

Aksnes DL, Giske J. 1990.
Habitat profitability in pelagic environments
Marine Ecology-Progress Series. 64: 209-215. [ pdf ]
Aksnes DL, Utne ACW. 1997.
A revised model of visual range in fish
Sarsia. 82: 137-147. [ pdf ]
Giske J, Huse G, Fiksen Ø. 1998.
Modelling spatial dynamics of fish
Reviews in Fish Biology and Fisheries. 8: 57-91. [ pdf ]

 

 

Learning and Sociality

In a series of papers (Eliassen & al. 2006, 2007, 2009) we have studied what conditions favour evolution of learning, and under which circumstances the learning strategy is better or worse than the non-learning alternative. Our key assumption is that individual learning through exploration incurs a time-cost relative to the innate or genetically fixed strategy. Some situations allow coexistence of learners and non-learners in the same population, while life expectancy may be an important determinant for the adaptive value of learning.

Core Reference

Eliassen S, Jørgensen C, Mangel M, Giske J. 2007.
Exploration or exploitation: life expectancy changes the value of learning in foraging strategies
Oikos. 116: 513-523. [ doi:10.1111/j.2006.0030-1299.15462.x ] [ pdf ]

 

 

Evolving Life Histories by Genetic Algorithms

The Genetic Algorithm has been a much used research tool in the group for more than a decade. While several papers have used the GA to evolve adaptive values of neural networks or animal decison architectures, the GA can also be used directly to model the major life history decisions in an organism, as done by Fiksen (2000) for the copepod Calanus finmarchicus. This method was also used by Strand & al. (2002) for the mesopelagic fish Maurolicus muelleri.

Core Reference

Fiksen Ø. 2000.
The adaptive timing of diapause - a search for evolutionarily robust strategies in Calanus finmarchicus
ICES Journal of Marine Science. 57: 1825-1833. [ pdf ]

 

 

The ING Method

The ING is a method for evolving (by a genetic algorithm) flexible adaptive behaviour (controlled by a neural network) in individuals. The background for wanting to develop this tool was the complexity of decisions often faced by organisms. The classical tools in optimization and game are very good at solving specific aspects of adaptive behavior, but by focussing on this single aspect: Life History Theory is a good tool for studies of long-term strategic decisions (and also has been used for the short-term by implicitly assuming constant motivations), Game Theory is good for studies of conflict and cooperation between organisms, and State-Dependent Optimization is good for modelling short-term fluctuations in motivation driven by changes in the (physiological) state of the organism. Through this new method we wanted an agent to be able to consider all these aspects simultaneously (Giske & al. 1998, Huse & Giske 1998).

ING consists of an Individual-Based Model where the decisions in each individual is controlled by its Artificial Neural Network which again is inherited from the parents and evolved by a Genetic Algorithm. Its ability to find the optimal solution has been studied by comparing with dynamic programming (Huse & al. 1999). The tool has been used to model capelin distribution in the Barents Sea (Huse & Giske 1998) and vertical migration in mesopelagic fish (Strand & al. 2002).

However, while ING is able to evolve adaptive solutions to very complex situations, the method does not consider the ability of the organisms to find these solutions. This is the main reason we continued thinking, and arrived at the proximate architecture for decision-making.

Core References

Huse G, Giske J. 1998.
Ecology in Mare Pentium: An individual-based spatio-temporal model for fish with adapted behaviour
Fisheries Research. 37: 163-178. [ pdf ]
Strand E, Huse G, Giske J. 2002.
Artificial evolution of life history and behavior
American Naturalist. 159: 624-644. [ pdf ]

 

 

Implementing Decision Modules in Simulation Models

These theoretical models have also been used in applied models (Fiksen & MacKenzie 2002, Fiksen & al. 2007, Kristiansen & al. 2007, Fouzai et al. 2015), and applied models have been used to compare and test goodness of fit of different behavioural models in applied ecological situations (Vikebø & al. 2007, Kristiansen & al. 2009). We also work on making efficient simplified model formulations (Castellani et al. 2013, Fiksen & Opdal 2015, Sainmont & al. 2015). This is a step from individual behaviour to population dynamics, and maybe a step towards ecosystem and fisheries management tools.

 

Core References

Fiksen Ø, Opdal AF. 2015.
Optimality and rule-based models for larval fish behavior
Vie et Milieu. 65: 115-120. [ open access ] [ pdf ]
Kristiansen T, Jørgensen C, Lough RG, Vikebø F, Fiksen Ø. 2009.
Modeling rule-based behavior: habitat selection and the growth-survival trade-off in larval cod
Behavioral Ecology. 20: 490-500. [ doi:10.1093/beheco/arp023 ] [ pdf ]
Vikebø F, Jørgensen C, Kristiansen T, Fiksen Ø. 2007.
Drift, growth and survival of larval Northeast Arctic cod with simple rules of behaviour
Marine Ecology-Progress Series. 347: 207-219\r\n. [ doi:10.3354/meps06979 ] [ open access ] [ pdf ]

 

 

Field and Lab Studies

In connection with the development of the modelling tools, we have also utilised field and lab studies either to test model assumptions or to study behaviours that models should be able to capture. Our most common field studies include ocean optics, vertical behaviour and life cycles of mesopelagic fishes (Kaartvedt & al. 1988, 2005, Giske & al. 1990, Giske & Aksnes 1992, Balino & Aksnes 1993, Rasmussen & Giske 1994, Goodson & al. 1995, Aksnes & al. 2009, Staby & Aksnes 2011, Staby et al. 2013, Irigoien et al. 2014, Prihartato et al. 2015, Folkvord et al. 2016, Norheim et al. 2016, Røstad et al. 2016) and jellies (Eiane & al. 1999, Ugland et al. 2014, Haraldsson et al. 2014) but also laboratory investigations of vision in fish (Utne & al. 1993, Utne & Aksnes 1994).

 

References

Aksnes DL, Giske J. 1990.
Habitat profitability in pelagic environments
Marine Ecology-Progress Series. 64: 209-215. [ pdf ]
Aksnes DL, Giske J. 1993.
A theoretical model of aquatic visual feeding
Ecological Modelling. 67: 233-250. [ pdf ]
Aksnes DL, Utne ACW. 1997.
A revised model of visual range in fish
Sarsia. 82: 137-147. [ pdf ]
Aksnes DL, Dupont N, Staby A, Fiksen Ø, Kaartvedt S, Aure J. 2009.
Coastal water darkening and implications for mesopelagic regime shifts in Norwegian fjords
Marine Ecology-Progress Series. 387: 39-49. [ open access ]
Andersen BS, Jørgensen C, Eliassen S, Giske J. 2016.
The proximate architecture for decision-making in fish
Fish and Fisheries. 17: 680-695. [ doi:10.1111/faf.12139 ] [ open access ] [ pdf ]
Baliño BM, Aksnes DL. 1993.
Winter distribution and migration of the sound-scattering layers, zooplankton and micronekton in Masfjorden, Western Norway
Marine Ecology-Progress Series. 102: 35-50. [ pdf ]
Castellani M, Rosland R, Urtizberea A, Fiksen Ø. 2013.
A mass-balanced pelagic ecosystem model with size-structured behaviourally adaptive zooplankton and fish
Ecological Modelling. 251: 54-63. [ doi:10.1016/j.ecolmodel.2012.12.007 ] [ pdf ]
Eiane K, Aksnes DL, Ohman MD. 1998.
Advection and zooplankton fitness
Sarsia. 83: 87-93. [ pdf ]
Eiane K, Aksnes DL, Bagøien E, Kaartvedt S. 1999.
Fish or jellies - a question of visibility?
Limnology and Oceanography. 44: 1352-1357. [ pdf ]
Eliassen S, Jørgensen C, Giske J. 2006.
Co-existence of learners and stayers maintains the advantage of social foraging
Evolutionary Ecology Research. 8: 1311-1324. [ pdf ]
Eliassen S, Jørgensen C, Mangel M, Giske J. 2007.
Exploration or exploitation: life expectancy changes the value of learning in foraging strategies
Oikos. 116: 513-523. [ doi:10.1111/j.2006.0030-1299.15462.x ] [ pdf ]
Eliassen S, Jørgensen C, Mangel M, Giske J. 2009.
Quantifying the adaptive value of learning in foraging behaviour
American Naturalist. 174: 478-489. [ doi:10.1086/605370 ] [ pdf ]
Eliassen S, Andersen BS, Jørgensen C, Giske J. 2016.
From sensing to emergent adaptations: Modelling the proximate architecture for decision-making
Ecological Modelling. 326: 90-100. [ doi:10.1016/j.ecolmodel.2015.09.001 ] [ open access ] [ pdf ]
Fiksen Ø. 1997.
Allocation patterns and diel vertical migration: Modeling the optimal Daphnia
Ecology. 78: 1446-1456. [ pdf ]
Fiksen Ø. 2000.
The adaptive timing of diapause - a search for evolutionarily robust strategies in Calanus finmarchicus
ICES Journal of Marine Science. 57: 1825-1833. [ pdf ]
Fiksen Ø, Carlotti F. 1998.
A model of optimal life history and diel vertical migration in Calanus finmarchicus
Sarsia. 83: 129-147. [ pdf ]
Fiksen Ø, Giske J. 1995.
Vertical distribution and population dynamics of copepods by dynamic optimization
Ices Journal of Marine Science. 52: 483-503. [ pdf ]
Fiksen Ø, Mackenzie BR. 2002.
Process-based models of feeding and prey selection in larval fish
Marine Ecology-Progress Series. 243: 151-164. [ pdf ]
Fiksen Ø, Opdal AF. 2015.
Optimality and rule-based models for larval fish behavior
Vie et Milieu. 65: 115-120. [ open access ] [ pdf ]
Fiksen Ø, Aksnes DL, Flyum MH, Giske J. 2002.
The influence of turbidity on growth and survival of fish larvae: a numerical analysis
Hydrobiologia. 484: 49-59. [ pdf ]
Fiksen Ø, Jørgensen C, Kristiansen T, Vikebø F, Huse G. 2007.
Linking behavioural ecology and oceanography: larval behaviour determines growth, mortality and dispersal
Marine Ecology-Progress Series. 347: 195-205. [ doi:10.3354/meps06978 ] [ open access ] [ pdf ]
Folkvord A, Gundersen G, Albretsen A, Asplin L, Kaartvedt S, Giske J. 2016.
Impact of hatch date on early life growth and survival of Mueller’s pearlside (Maurolicus muelleri) larvae and life-history consequences
Canadian Journal of Fisheries and Aquatic Sciences. 73: 163-176. [ doi:10.1139/cjfas-2015-0040 ] [ open access ] [ pdf ]
Giske J, Aksnes DL. 1992.
Ontogeny, season and trade-offs: vertical distribution of the mesopelagic fish Maurolicus muelleri
Sarsia. 77: 253-261. [ pdf ]
Giske J, Aksnes DL, Baliño BM, Kaartvedt S, Lie U, Nordeide JT, Salvanes AGV, Wakili SM, Aadnesen A. 1990.
Vertical distribution and trophic interactions of zooplankton and fish in Masfjorden, Norway
Sarsia. 75: 65-81. [ pdf ]
Giske J, Skjoldal HR, Aksnes DL. 1992.
A conceptual model of distribution of capelin in the Barents Sea
Sarsia. 77: 147-156. [ pdf ]
Giske J, Aksnes DL, Fiksen Ø. 1994.
Visual predators, environmental variables and zooplankton mortality risk
Vie et Milieu. 44: 1-9. [ pdf ]
Giske J, Rosland R, Berntsen J, Fiksen Ø. 1997.
Ideal free distribution of copepods under predation risk
Ecological Modelling. 95: 45-59. [ pdf ]
Giske J, Huse G, Fiksen Ø. 1998.
Modelling spatial dynamics of fish
Reviews in Fish Biology and Fisheries. 8: 57-91. [ pdf ]
Giske J, Mangel M, Jakobsen P, Huse G, Wilcox C, Strand E. 2003.
Explicit trade-off rules in proximate adaptive agents
Evolutionary Ecology Research. 5: 835-865. [ pdf ]
Giske J, Eliassen S, Fiksen Ø, Jakobsen PJ, Aksnes DL, Jørgensen C, Mangel M. 2013.
Effects of the emotion system on adaptive behavior
American Naturalist. 182: 689-703. [ doi:10.1086/673533 ] [ open access ] [ pdf ] [ online appendix ] [ popularized version ]
Giske J, Eliassen S, Fiksen Ø, Jakobsen PJ, Aksnes DL, Mangel M, Jørgensen C. 2014.
The emotion system promotes diversity and evolvability
Proceedings of the Royal Society B. 281: 20141096. [ doi:10.1098/rspb.2014.1096 ] [ open access ] [ pdf ] [ online appendix ] [ fortran code for model at datadryad.org ]
Giske J, Salvanes AGV. 1995.
Why pelagic planktivores should be unselective feeders
Journal of Theoretical Biology. 173: 41-50. [ pdf ]
Goodson MS, Giske J, Rosland R. 1995.
Growth and ovarian development of Maurolicus muelleri during spring
Marine Biology. 124: 185-195. [ pdf ]
Grimm V, Berger U, Bastiansen F, Eliassen S, Ginot V, Giske J, Goss-Custard J, Grand T, Heinz SK, Huse G, Huth A, Jepsen JU, Jørgensen C, Mooij WM, Müller B, Pe\’er G, Piou C, Railsback SF, Robbins AM, Robbins MM, Rossmanith E, Rüger N, Strand E, Souissi S, Stillman RA, Vabø R, Visser U, DeAngelis DL. 2006.
A standard protocol for describing individual-based and agent-based models
Ecological Modelling. 198: 115-156. [ doi:10.1016/j.ecolmodel.2006.04.023 ] [ pdf ]
Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF. 2010.
The ODD protocol: A review and first update
Ecological Modelling. 221: 2760-2768. [ doi:10.1016/j.ecolmodel.2010.08.019 ] [ pdf ]
Haraldsson M, Båmstedt U, Tiselius P, Titelman J, Aksnes DL. 2014.
Evidence of diel vertical migration in Mnemiopsis leidyi
PLoS ONE. 9: e86595. [ doi:10.1371/journal.pone.0086595 ] [ open access ] [ pdf ]
Huse G, Giske J. 1998.
Ecology in Mare Pentium: An individual-based spatio-temporal model for fish with adapted behaviour
Fisheries Research. 37: 163-178. [ pdf ]
Huse G, Strand E, Giske J. 1999.
Implementing behaviour in individual-based models using neural networks and genetic algorithms
Evolutionary Ecology. 13: 469-483. [ pdf ]
Heuschele J, Eliassen S, Kiørboe T. 2013.
Optimal mate choice patterns in pelagic copepods
Oecologia. 172: 399-408. [ doi:10.1007/s00442-012-2516-4 ] [ pdf ]
Irigoien X, Klevjer TA, Røstad A, Martinez U, Boyra G, Acuña JL, Bode A, Echevarria F, Gonzalez-Gordillo JI, Hernandez-Leon S, Agusti S, Aksnes DL, Duarte CM, Kaartvedt S. 2014.
Large mesopelagic fishes biomass and trophic efficiency in the open ocean
Nature Communications. 5: 3271. [ doi:10.1038/ncomms4271 ] [ online appendix ] [ online ]
Jørgensen C, Holt RE. 2013.
Natural mortality: its ecology, how it shapes fish life histories, and why it may be increased by fishing
Journal of Sea Research. 75: 8-18. [ doi:10.1016/j.seares.2012.04.003 ] [ open access ] [ pdf ]
Kaartvedt S, Aksnes DL, Aadnesen A. 1988.
Winter distribution of macroplankton and micronekton in Masfjorden, western Norway
Marine Ecology-Progress Series. 45: 45-55. [ pdf ]
Kaartvedt S, Rostad A, Fiksen Ø, Melle W, Torgersen T, Breien MT, Klevjer TA. 2005.
Piscivorous fish patrol krill swarms
Marine Ecology-Progress Series. 299: 1-5. [ pdf ]
Kirby DS, Fiksen Ø, Hart PJB. 2000.
A dynamic optimisation model for the behaviour of tunas at ocean fronts
Fisheries Oceanography. 9: 328-342. [ pdf ]
Kristiansen T, Fiksen Ø, Folkvord A. 2007.
Modelling feeding, growth, and habitat selection in larval Atlantic cod (Gadus morhua): observations and model predictions in a macrocosm environment\r\n
Canadian Journal of Fisheries and Aquatic Sciences. 64: 136-151. [ pdf ]
Kristiansen T, Jørgensen C, Lough RG, Vikebø F, Fiksen Ø. 2009.
Modeling rule-based behavior: habitat selection and the growth-survival trade-off in larval cod
Behavioral Ecology. 20: 490-500. [ doi:10.1093/beheco/arp023 ] [ pdf ]
Norheim E, Klevjer TA, Aksnes DL. 2016.
Evidence for light-controlled migration amplitude of a sound scattering layer in the Norwegian Sea
Marine Ecology-Progress Series. 551: 45-52. [ doi:10.3354/meps11731 ] [ open access ] [ pdf ]
Prihartato PK, Aksnes DL, Kaartvedt S. 2015.
Seasonal patterns in the nocturnal distribution and behavior of the mesopelagic fish Maurolicus muelleri at high latitudes
Marine Ecology-Progress Series. 521: 189-200. [ doi:10.3354/meps11139 ] [ open access ] [ pdf ]
Rasmussen OI, Giske J. 1994.
Life-history parameters and vertical distribution of Maurolicus muelleri in Masfjorden in summer
Marine Biology. 120: 649-664. [ pdf ]
Rosland R. 1997.
Optimal responses to environmental and physiological constraints: Evaluation of a model for a planktivore
Sarsia. 82: 113-128. [ pdf ]
Rosland R, Giske J. 1994.
A dynamic optimization model of the diel vertical distribution of a pelagic planktivorous fish
Progress in Oceanography. 34: 1-43. [ pdf ]
Rosland R, Giske J. 1997.
A dynamic model for the life history of Maurolicus muelleri, a pelagic planktivorous fish
Fisheries Oceanography. 6: 19-34. [ pdf ]
Røstad A, Kaartvedt S, Aksnes DL. 2016.
Light comfort zones of mesopelagic acoustic scattering layers in two contrasting optical environments
Deep Sea Research Part I: Oceanographic Research Papers. 113: 1-6. [ doi:10.1016/j.dsr.2016.02.020 ] [ open access ] [ pdf ]
Sainmont J, Andersen KH, Thygesen UH, Fiksen Ø, Visser AW. 2015.
An effective algorithm for approximating adaptive behavior in seasonal environments
Ecological Modelling. 311: 20-30. [ pdf ]
Salvanes AGV, Giske J, Nordeide JT. 1994.
A life-history approach to habitat shifts for coastal cod (Gadus morhua L.)
Aquaculture and Fisheries Management. 25 (Suppl 1): 215-228. [ pdf ]
Staby A, Aksnes DL. 2011.
Follow the light – diurnal and seasonal variations in vertical distribution of the mesopelagic fish Maurolicus muelleri
Marine Ecology-Progress Series. 422: 265-273. [ doi:10.3354/meps08938 ] [ pdf ]
Staby A, Srisomwong J, Rosland R. 2013.
Variation in DVM behaviour of juvenile and adult pearlside (Maurolicus muelleri) linked to feeding strategies and related predation risk
Fisheries Oceanography. 22: 90-101. [ doi:10.1111/fog.12012 ] [ pdf ]
Stillman RA, Railsback SF, Giske J, Berger U, Grimm V. 2015.
Making predictions in a changing world: the benefits of individual-based ecology
BioScience. 65: 140-150. [ doi:10.1093/biosci/biu192 ] [ open access ] [ pdf ]
Strand E, Huse G, Giske J. 2002.
Artificial evolution of life history and behavior
American Naturalist. 159: 624-644. [ pdf ]
Ugland KI, Aksnes DL, Klevjer TA, Titelman J, Kaartvedt S. 2014.
Lévy night flights by the jellyfish Periphylla periphylla
Marine Ecology-Progress Series. 513: 121-130. [ doi:10.3354/meps10942 ] [ open access ] [ pdf ]
Utne ACW, Aksnes DL. 1994.
An experimental study on the influence of feeding versus predation risk in the habitat choice of juvenile and adult two-Spotted goby Gobiusculus flavescens (Fabricius)
Journal of Experimental Marine Biology and Ecology. 179: 69-79. [ pdf ]
Utne ACW, Aksnes DL, Giske J. 1993.
Food, predation risk and shelter - an experimental study on the distribution of adult two-spotted Goby Gobiusculus flavescens (Fabricius)
Journal of Experimental Marine Biology and Ecology. 166: 203-216. [ pdf ]
Vikebø F, Jørgensen C, Kristiansen T, Fiksen Ø. 2007.
Drift, growth and survival of larval Northeast Arctic cod with simple rules of behaviour
Marine Ecology-Progress Series. 347: 207-219\r\n. [ doi:10.3354/meps06979 ] [ open access ] [ pdf ]
Visser AW, Fiksen Ø. 2013.
Optimal foraging in marine ecosystem models: selectivity, profitability and switching
Marine Ecology-Progress Series. 473: 91-101. [ doi:10.3354/meps10079 ] [ pdf ]

 

 
Professor
Dag L. Aksnes
Researcher
Sergey Budaev
PhD Student
Ryan J. Dillon
Associate Professor
Sigrunn Eliassen
PhD student
Johanna Fall
Group Leader, Professor
Øyvind Fiksen
PhD Student
Nadia Fouzai
Professor
Jarl Giske
PhD Student
Camilla Håkonsrud Jensen
PhD Student
Judy Jinn
Professor
Christian Jørgensen
PhD Student
Tom J. Langbehn
Postdoc
Christian Lindemann
PhD Student
Gabriella Ljungström
Adjunct Professor
Marc Mangel
Postdoc
Adèle Mennerat
Postdoc
Anders F. Opdal
PhD student
Nicolas J. I. Rodriguez
PhD Student
Jacqueline Weidner
PhD Student
Johanna Myrseth Aarflot

Alumni