Populations and Evolution

New submissions

• New submissions
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New submissions for Fri, 3 May 24

[1]  arXiv:2405.00817 [pdf, other]

Title: Chaotic behavior in Lotka-Volterra and May-Leonard models of biodiversity

Authors: D. Bazeia, M. Bongestab, B. F. de Oliveira

Comments: 8 pages, 7 figures, to appear in Chaos: An Interdisciplinary Journal of Nonlinear Science

Subjects: Statistical Mechanics (cond-mat.stat-mech); Physics and Society (physics.soc-ph); Populations and Evolution (q-bio.PE)

Quantification of chaos is a challenging issue in complex dynamical systems. In this paper, we discuss the chaotic properties of generalized Lotka-Volterra and May-Leonard models of biodiversity, via the Hamming distance density. We identified chaotic behavior for different scenarios via the specific features of the Hamming distance and the method of q-exponential fitting. We also investigated the spatial autocorrelation length to find the corresponding characteristic length in terms of the number of species in each system. In particular, the results concerning the characteristic length are in good accordance with the study of the chaotic behavior implemented in this work.

[2]  arXiv:2405.01015 [pdf, other]

Title: Network reconstruction via the minimum description length principle

Authors: Tiago P. Peixoto

Comments: 17 pages, 10 figures

Subjects: Machine Learning (stat.ML); Machine Learning (cs.LG); Social and Information Networks (cs.SI); Data Analysis, Statistics and Probability (physics.data-an); Populations and Evolution (q-bio.PE)

A fundamental problem associated with the task of network reconstruction from dynamical or behavioral data consists in determining the most appropriate model complexity in a manner that prevents overfitting, and produces an inferred network with a statistically justifiable number of edges. The status quo in this context is based on $L_{1}$ regularization combined with cross-validation. As we demonstrate, besides its high computational cost, this commonplace approach unnecessarily ties the promotion of sparsity with weight "shrinkage". This combination forces a trade-off between the bias introduced by shrinkage and the network sparsity, which often results in substantial overfitting even after cross-validation. In this work, we propose an alternative nonparametric regularization scheme based on hierarchical Bayesian inference and weight quantization, which does not rely on weight shrinkage to promote sparsity. Our approach follows the minimum description length (MDL) principle, and uncovers the weight distribution that allows for the most compression of the data, thus avoiding overfitting without requiring cross-validation. The latter property renders our approach substantially faster to employ, as it requires a single fit to the complete data. As a result, we have a principled and efficient inference scheme that can be used with a large variety of generative models, without requiring the number of edges to be known in advance. We also demonstrate that our scheme yields systematically increased accuracy in the reconstruction of both artificial and empirical networks. We highlight the use of our method with the reconstruction of interaction networks between microbial communities from large-scale abundance samples involving in the order of $10^{4}$ to $10^{5}$ species, and demonstrate how the inferred model can be used to predict the outcome of interventions in the system.

[3]  arXiv:2405.01437 [pdf, other]

Title: Two competing populations with a common environmental resource

Authors: Keith Paarporn, James Nelson

Comments: Submission to CDC 24

Subjects: Computer Science and Game Theory (cs.GT); Systems and Control (eess.SY); Populations and Evolution (q-bio.PE)

Feedback-evolving games is a framework that models the co-evolution between payoff functions and an environmental state. It serves as a useful tool to analyze many social dilemmas such as natural resource consumption, behaviors in epidemics, and the evolution of biological populations. However, it has primarily focused on the dynamics of a single population of agents. In this paper, we consider the impact of two populations of agents that share a common environmental resource. We focus on a scenario where individuals in one population are governed by an environmentally "responsible" incentive policy, and individuals in the other population are environmentally "irresponsible". An analysis on the asymptotic stability of the coupled system is provided, and conditions for which the resource collapses are identified. We then derive consumption rates for the irresponsible population that optimally exploit the environmental resource, and analyze how incentives should be allocated to the responsible population that most effectively promote the environment via a sensitivity analysis.

Replacements for Fri, 3 May 24

[4]  arXiv:2109.07645 (replaced) [pdf, ps, other]

Title: Two-type branching processes with immigration, and the structured coalescents

Authors: María Emilia Caballero, Adrián González Casanova, José Luis Pérez

Subjects: Populations and Evolution (q-bio.PE); Probability (math.PR)

[5]  arXiv:2403.12684 (replaced) [pdf, other]

Title: Concepts and methods for predicting viral evolution

Authors: Matthijs Meijers, Denis Ruchnewitz, Jan Eberhardt, Malancha Karmakar, Marta Łuksza, Michael Lässig

Comments: 30 pages, 6 figures

Subjects: Populations and Evolution (q-bio.PE)

[6]  arXiv:2404.19041 (replaced) [pdf, other]

Title: Stochastic dynamics of two-compartment models with regulatory mechanisms for hematopoiesis

Authors: Ren-Yi Wang, Marek Kimmel, Guodong Pang

Subjects: Populations and Evolution (q-bio.PE)

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• Replacements