A functional response model fit to empirical consumption data - Mahmood Jokar; Natoya O. A. S. Jourdain; Sondre Hølleland; Bjarte Bogstad; Sam Subbey (Reviews in Fish Biology and Fisheries, 2026)
The anticipated empirical consumption level of a target (prey) species by its predators is an important variable when implementing the constant biomass escapement strategy in fisheries management. For a single predator, the projection of empirical consumption will depend, among others, on projected distribution in space–time of the predator, degree of predator overlap with prey, and considerations of fluctuating environmental variables (e.g., temperature) that may affect the consumption rate. Hence, the projected empirical consumption is usually uncertain, as it involves the integration of uncertain space-time data and high-throughput models and sub-models. The inclusion of such modeling frameworks into an overall assessment model is usually non-trivial. The goal of this paper is to approximate estimates of the empirical consumption rate of capelin by cod in the Barents Sea, using an analytical functional response (FR) model. The empirical consumption rate is estimated by integrating projected space-time (biotic and abiotic) data with a cod stomach evacuation model. We consider the time series of empirical consumption rates as observation data, and use an optimization procedure to estimate parameters of the FR model. We assess the model performance by the degree to which derived uncertainty envelopes encompass the input data, and evaluate its prediction ability using data that were excluded in the parameter estimation process. Our results show that the FR model provides good approximations to estimates of empirical consumption. A major advantage of the FR modeling framework is that it facilitates hypothesis testing, uncertainty quantification, and seamless integration of the FR model into an overall stock assessment model for capelin.

Insights into population dynamics of fish populations using growth transition matrices - Mahmood Jokar; Sam Subbey (Ecological Modelling, 2026)

A machine learning framework for modeling Barents Sea Capelin spawning migration - Salah Alrabeei; Talal Rahman; Sam Subbey (Reviews in Fish Biology and Fisheries, 2025)
This paper presents a modeling framework for simulating capelin spawning migration in the Barents Sea by integrating artificial neural networks (ANNs), a genetic algorithm (GA), an individual-based model (IBM), and environmental variables. ANNs determine movement direction based on inputs like temperature and ocean currents and are trained using a GA whose fitness function dynamically adapts to temperature and proximity to spawning routes. The model successfully reproduces the southeastward migration observed in 2019, aligning with historical spawning sites along northern Norway’s eastern coast. Validation against empirical data confirms its accuracy, and results highlight the importance of the adaptive fitness function in such learning-based models. The proposed model is also compared to three alternatives based on passive swimming, gradient detection, and restricted-area search. It outperforms them by accurately replicating migration patterns, with most simulated fish reaching spawning sites. In contrast, the alternative models often failed due to current-induced drift, particularly in the gradient detection and restricted-area search approaches.

Small fish, big implications: considerations for an ecosystem approach to capelin fisheries management - Warsha Singh; John T. Trochta; Hannah M. Murphy; David W. McGowan; Aaron T. Adamack; Mayumi L. Arimitsu; Birkir Barðarson; Höskuldur Björnsson; Bjarte Bogstad; Mathieu Boudreau; Catherine Chambers; Harald Gjøsæter; et al. (Reviews in Fish Biology and Fisheries, 2025)

Exploring multistability in marine ecosystems: Insights from food web model dynamics - A.S. Frank; Andrea Pinke; Elizabeth Read; Susanna Röblitz; Susmita Sadhu; Sam Subbey (Ecological Modelling, 2025)

Multiple Patterns of Food Web Dynamics Revealed by a Minimal Non-deterministic Model - Ulf Lindstrøm; Benjamin Planque; Sam Subbey (Ecosystems, 2017)

A tool for analyzing information from data storage tags: the continuous wavelet transform (CWT) - Sam Subbey; Kathrine Michalsen; Geir Kjetil Nilsen (Reviews in Fish Biology and Fisheries, 2008)

Reservoir description using dynamic parameterisation selection with a combined stochastic and gradient search - Lars Kristian Nielsen; Sam Subbey; Mike Christie; Trond Mannseth (Computational Geosciences, 2006)