Biology subjects

Subbey, S.

Publications and source records attributed to Subbey, S..

5 recordsLinked to original sources

bioRxiv2024

Evaluating the Fidelity of Data-Driven Predator-Prey Models: A Dynamical Systems Analysis

In empirical predator-prey systems, understanding the inherent dynamics typically comes from analyzing a structural model fitted to observation data. However, determining an appropriate model structure and its parameters is often complex and highly uncertain. A promising alternative is to learn the model structure directly from time series data of both predator and prey. This study explores the capability of a data-driven algorithm, Sparse Identification of Nonlinear Dynamics (SINDy), to accurately capture the dynamics of a predator-prey system. We apply SINDy to derive a Learned Model (LM) from data generated by a Reference Model (RM), whose predator-prey dynamics are well understood. The study compares the dynamics of the LM to the RM using criteria such as equilibrium points, stability, sensitivity, and bifurcation analysis. Our results demonstrate general consistency between the RM and LM dynamics, though notable differences remain. We discuss the implications of these differences in the broader context of using learned models to uncover the inherent drivers of predator-prey dynamics and ecological implications. 2010 MSC37M05, 37N25, 92B05, 92D25, 92D40, 65L05, 37G15 HighlightsO_LIEvaluates data-driven models ability to replication of predator-prey dynamics using SINDy framework C_LIO_LILearned Model captures core dynamics but shows parameter sensitivity and bifurcation differences C_LIO_LIAnalysis reveals need for extensive data for effective model learning C_LIO_LISuggests combining data-driven methods with biological priors to improve model accuracy C_LI

ecology↗

bioRxiv2024

Discrete Time Modeling of Stock-Recruit Relationships with Life-History Stanzas

The stock-recruit relationship is a foundational concept in fisheries science, bridging the connection between parental populations (stock) and progeny (recruits). Traditional approaches describe this relationship using closedform analytical functions, which represent only a restricted subset of the broader class of possibilities. This paper advocates for a novel approach that integrates discrete time modeling with a life-history cycle framework, incorporating distinct stanzas and developmental processes. By breaking down the life cycle into identifiable stages, we capture the step-wise progression of life history traits and the factors influencing recruitment outcomes. Through numerical simulations, we explore the advantages of this approach, including complexity handling, dynamic behavior modeling, and scenario exploration. Our simulation results show that we are able to generate a broad spectrum of stock-recruit relationships (including the traditional ones), which best reflect variability observed in nature. We demonstrate how this framework allows for the identification of critical stages, and integration of various factors that influence recruitment. This holistic approach enhances our comprehension of the intricate interactions shaping stock-recruit relationships and advances our understanding of sustainable population dynamics. HighlightsO_LIA novel multi-stage life-cycle model is presented. C_LIO_LIModel simulations reveal three different Stock-Recruitment (SR) patterns. C_LIO_LIOur approach contributes to enhanced understanding of SR relationships. C_LI

ecology↗

bioRxiv2023

Mathematical Preconditions for Existence of the Stock-Recruitment Function

For marine species, several life stages link parents to their progeny (recruits), through a process referred to as recruitment. Current stock recruitment (SR) functions encapsulate this multi-stage relationship in a closed-form mathematical expression, which explicitly relates the biomass of parents, to the number of recruits. This functional relationship (between parents and recruits) is required for management purposes. No study, however, has investigated the conditions that validate the existence of the SR functions when all life history stages are incorporated into a model. In this study, we represent the processes leading to recruitment by a stage-and age-structured discrete-time population dynamic model. We show that in general, a SR function does not correctly represent the parent-progeny relationship. A valid relationship must incorporate information across the complete life cycle over several time periods. For populations with simple life cycle history, a functional relationship may result, which is not necessarily consistent with current SR functions. We present a brief discussion of the relevance of our results to effective management of fisheries.

ecology↗

bioRxiv2020

Population Dynamic Regulators In An Empirical Predator-Prey System

This paper investigates stability conditions of an empirical predator-prey system using a model that includes a single delay term,{tau} , in description of the predator dynamics. We derive theoretical conditions on{tau} , in terms of other model parameters, and determine how changes in these conditions define different stability regimes of the system. We derive optimal model parameters by fitting model to empirical data, using unconstrained optimization. The optimization results are combined with those from the theoretical analysis, to make inference about the empirical system stability. Our results show that Hopf bifurcation occurs in the predatory-prey system when{tau} exceeds a theoretically derived value{tau} * > 0. This value represents the critical time for prey availability in advance of the optimal predator growth period. Set into an ecological context, our findings provide mathematical evidence for validity of the match-mismatch hypothesis, for this particular species.

ecology↗

bioRxiv2020

Crowding Effects In An Empirical Predator-Prey System

AO_SCPLOWBSTRACTC_SCPLOWThis paper uses a Lotka-Volterra (predator-prey) modeling framework to investigate the dynamical link between the biomass of an empirical predator, and that of its prey. We use a system of ordinary (ODE) differential equations to describe the system dynamics, and derive theoretical conditions for stability, in terms of system parameters. We derive the empirical system parameters by fitting the ODE system to empirical data, using non-constrained optimization. We present results to show that the predator biomass is regulated by that of the prey. Furthermore, that the system dynamics is subject to Hopf bifurcation, conditioned on independent second-order terms in the ODE system. In ecological terms, the findings translate into evidence for existence of population crowding (density) effects.

ecology↗