Duration : 15 month (+ 3 months depending on additional funding)
Lab: UMR DECOD , Ifremer Lorient, France
Background and objectives
Previous projects from our lab have enabled, on one hand, the development of a 3D simulation environment for fishing gear (FineLab) and on the other hand, to collect fine-scale data on fish behavior inside trawls (Projects : ESCAPE, Game of Trawls, MarineBeacon, etc.). The objective of this open position is to combine the two approaches by developing an Individual-Based Modelling (IBM) framework to study fish-gear interactions, as illustrated in Figure 1. This work will goes beyond traditional modelling approaches (e.g. [1–4]) as it enables us to manipulate internal (e.g. swimming speed, sensory organs and stress) and external (e.g. fishing gear volume, turbulence, and mesh size) variables. IBM models are powerful because they can flexibly represent observed natural processes, such as swimming and escape behaviours. They can they can study the link between individual behaviours and the resulting collective dynamics. This quantitative approach could enable us to estimate the main parameters of gear selectivity, such as the 50% escape size or the ‘selection range’ [5], depending on the environment, the species present, and their specific behaviours, which has never been undertaken so far.
Behaviour modelling will partly be informed by the results obtained in other ESCAPE project work packages (funded by the FFP), in which several behavioural archetypes are currently being defined. Group effects, which enhance the effectiveness of escape behaviours in the presence of predators and are similar to escape behaviours, will be studied. One hypothesis to be tested is that the temporal dynamics of escape attempts may be influenced by the dynamics of fish arrival in the extension and codend. Certain mechanical aspects may also be considered, particularly the relationship between mesh deformation when fish pass through it and their morphological (cross-section) and behavioural (penetration angle and acceleration) characteristics.
Figure 1. Left: photograph illustrating how the trawl net reduces fish swimming speed and maintains them at the mouth level of the net. Right: simulation depicting fish movement within the extension and codend of the trawl net
Main tasks
While the project’s primary goal is to improve our understanding of the behavioural characteristics underlying swimming and escape behaviours in a trawling environment, this position’s main challenge will be integrating this knowledge and data into an IBM model.
He or she will improve the existing Qt C++ code to implement behavioural rules and to optimise computing time. Metrics will be defined to compare modelling and experimental local results. The behavioural rule parameters will be optimised to minimise objective functions based on modelling and experimental overall results.
This work should be promoted through at least one A-level scientific publication.
Skills
A Master’s or engineer degree in Quantitative Ecology or a PhD in Ecology or Applied Mathematics.
Good skills in C++ and Qt code writing
Good skills in numerical simulation and computation time optimisation
Experienced in optimisation using specific algorithms (e.g. genetic algorithms)
Experience with IBM models is desirable, particularly in the field of ecology
Experience with a graphics framework such as OpenSceneGraph would be an advantage
Interpersonal skills: ability to integrate into the team and project; ability to work independently.
Applications
CV, cover letter, and letter of recommendation to be sent to Marianne.robert@ifremer.fr
Applications will be reviewed as they are received.
Desired start date: first quarter of 2026
References
1. Kim Y-H, Wardle CS. Basic modelling of fish behaviour in a towed trawl based on chaos in decision-making. Fisheries Research. 2005;73: 217–229.
2. Sala A, Priour D, Herrmann B. Experimental and theoretical study of red mullet (Mullus barbatus) selectivity in codends of Mediterranean bottom trawls. Aquat Living Resour. 2006;19: 317–327.
3.Krag LA, Herrmann B, Karlsen JD. Inferring Fish Escape Behaviour in Trawls Based on Catch Comparison Data: Model Development and Evaluation Based on Data from Skagerrak, Denmark (vol 9, e88819, 2014). Plos One. 2014;9: e100605.
4. Grimaldo E, Sistiaga M, Herrmann B, Larsen RB, Brinkhof J, Tatone I. Improving release efficiency of cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) in the Barents Sea demersal trawl fishery by stimulating escape behaviour. Can J Fish Aquat Sci. 2018;75: 402–416.
5. Wileman DA, Ferro RST, Fonteyne R, Millar RB. Manual of methods of measuring the selectivity of towed fishing gears. ICES Cooperative Research Report 215, 126p. 1996
6. Pitcher TJ. The Behaviour of Teleost Fishes. Springer US; 1986
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