Nansen Guest Lecture 14.11.2024

Dr. Sam Subbey, Institute of Marine Research, Bergen

“Population Dynamic Regulators in an Empirical Predator-Prey (Capelin-Zooplankton) System”

This talk is divided into two parts: theoretical and applied.

In the theoretical part, Subbey will discuss the stability conditions of an empirical predator-prey system, based on a model that incorporates a single delay term, τ, in the description of predator dynamics. He will present results from the theoretical analysis of τ in relation to other model parameters and demonstrate how variations in these parameters define different stability regimes for the system.

In the application part, Subbey will present results from fitting the model to empirical data (from the Barents Sea) using unconstrained optimization. He will show how these optimization results are integrated with the theoretical analysis to make inferences about the stability of the empirical system. Specifically, he will demonstrate that there is a threshold value of τ, which represents the critical time for prey availability before the optimal period for predator growth. Placed in an ecological context, these findings provide mathematical support for the match-mismatch hypothesis in this particular species.

Keywords: Lokta-Volterra, Predator-prey, Hopf bifurcation, Match-mismatch, Barents Sea, Capelin, Zooplankton

When and where?

Thursday, 14.11.24, 11:15 – 11:55.

Copernicus lecture room, 1st floor, Nansen Center, Jahnebakken 3, Bergen

About Dr. Sam Subbey

Sam Subbey is a Principal Scientist at the Institute of Marine Research (IMR) in Bergen and an Adjunct Professor in computations at the Western Norway University of Applied Science (HVL). A Fulbright alumnus, he serves on the Editorial Board of the ICES Journal of Marine Science. With a background in mathematics, his expertise lies in optimization and solving inverse and ill-posed problems. His recent work focuses on using mathematical models for understanding empirical data, prediction, and planning, with a particular emphasis on optimization algorithms for parameter estimation and uncertainty quantification.