About

Alexandru Hening is an Associate Professor in the Department of Mathematics at Texas A&M University College of Arts and Sciences. His research focuses on applied mathematics and interdisciplinary research, with particular interests in probability theory, stochastic processes, and mathematical biology. He is engaged in exploring complex systems through probabilistic models, contributing to the understanding of biological phenomena and other interdisciplinary applications.

Research topics

• Mathematics
• Statistical physics
• Ecology
• Applied mathematics
• Computer science

Selected publications

• Population size in stochastic discrete-time ecological dynamics

  Journal of Mathematical Biology · 2025-09-10 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Dynamics of stochastic microorganism flocculation models

  ArXiv.org · 2025-11-17

  preprintOpen access1st authorCorresponding

  In this paper we study the dynamics of stochastic microorganism flocculation models. Given the strong influence of environmental and seasonal fluctuations that are present in these models, we propose a stochastic model that includes multiple layers of stochasticity, from small Brownian fluctuations, to possibly large changes due to environmental `shifts'. We are able to give a full classification of the asymptotic behavior of these models. New techniques had to be developed to prove the persistence and extinction of the process as the system is not in Kolmogorov form and, as a result, the analysis is significantly more involved.

  PublisherOA PDFDOI

• Population size in stochastic discrete-time ecological dynamics

  ArXiv.org · 2025-07-12

  preprintOpen access1st authorCorresponding

  We study how environmental stochasticity influences the long-term population size in certain one- and two-species models. The difficulty is that even when one can prove that there is persistence, it is usually impossible to say anything about the invariant probability measure which describes the persistent species. We are able to circumvent this problem for some important ecological models by noticing that the per-capita growth rates at stationarity are zero, something which can sometimes yield information about the invariant probability measure. For more complicated models we use a recent result by Cuello to explore how small noise influences the population size. We are able to show that environmental fluctuations can decrease, increase, or leave unchanged the expected population size. The results change according to the dynamical model and, within a fixed model, also according to which parameters (growth rate, carrying capacity, etc) are affected by environmental fluctuations. Moreover, we show that not only do things change if we introduce noise differently in a model, but it also matters what one takes as the deterministic `no-noise' baseline for comparison.

  PublisherOA PDFDOI

• Population dynamics under demographic and environmental stochasticity

  The Annals of Applied Probability · 2024-12-01

  article1st authorCorresponding
  PublisherDOI

• Long-term behavior of stochastic SIQRS epidemic models

  arXiv (Cornell University) · 2024-06-03

  preprintOpen access1st authorCorresponding

  In this paper we analyze and classify the dynamics of SIQRS epidemiological models with susceptible, infected, quarantined, and recovered classes, where the recovered individuals can become reinfected. We are able to treat general incidence functional responses. Our models are more realistic than what has been studied in the literature since they include two important types of random fluctuations. The first type is due to small fluctuations of the various model parameters and leads to white noise terms. The second type of noise is due to significant environment regime shifts in the that can happen at random. The environment switches randomly between a finite number of environmental states, each with a possibly different disease dynamic. We prove that the long-term fate of the disease is fully determined by a real-valued threshold $λ$. When $λ< 0$ the disease goes extinct asymptotically at an exponential rate. On the other hand, if $λ> 0$ the disease will persist indefinitely. We end our analysis by looking at some important examples where $λ$ can be computed explicitly, and by showcasing some simulation results that shed light on real-world situations.

  PublisherOA PDFDOI

• Stability and Fluctuations in Complex Ecological Systems

  arXiv (Cornell University) · 2023-06-12

  preprintOpen access

  From 08-12 August, 2022, 32 individuals participated in a workshop, Stability and Fluctuations in Complex Ecological Systems, at the Lorentz Center, located in Leiden, The Netherlands. An interdisciplinary dialogue between ecologists, mathematicians, and physicists provided a foundation of important problems to consider over the next 5-10 years. This paper outlines eight areas including (1) improving our understanding of the effect of scale, both temporal and spatial, for both deterministic and stochastic problems; (2) clarifying the different terminologies and definitions used in different scientific fields; (3) developing a comprehensive set of data analysis techniques arising from different fields but which can be used together to improve our understanding of existing data sets; (4) having theoreticians/computational scientists collaborate closely with empirical ecologists to determine what new data should be collected; (5) improving our knowledge of how to protect and/or restore ecosystems; (6) incorporating socio-economic effects into models of ecosystems; (7) improving our understanding of the role of deterministic and stochastic fluctuations; (8) studying the current state of biodiversity at the functional level, taxa level and genome level.

  PublisherOA PDFDOI

• Stochastic nutrient-plankton models

  arXiv (Cornell University) · 2023-03-13

  preprintOpen access1st authorCorresponding

  We analyze plankton-nutrient food chain models composed of phytoplankton, herbivorous zooplankton and a limiting nutrient. These models have played a key role in understanding the dynamics of plankton in the oceanic layer. Given the strong environmental and seasonal fluctuations that are present in the oceanic layer, we propose a stochastic model for which we are able to fully classify the longterm behavior of the dynamics. In order to achieve this we had to develop new analytical techniques, as the system does not satisfy the regular dissipativity conditions and the analysis is more subtle than in other population dynamics models.

  PublisherOA PDFDOI

• Coevolution of Patch Selection in Stochastic Environments

  The American Naturalist · 2023-03-20 · 3 citations

  articleCorresponding

  AbstractSpecies interact in landscapes where environmental conditions vary in time and space. This variability impacts how species select habitat patches. Under equilibrium conditions, evolution of this patch selection can result in ideal free distributions where per capita growth rates are zero in occupied patches and negative in unoccupied patches. These ideal free distributions, however, do not explain why species occupy sink patches, why competitors have overlapping spatial ranges, or why predators avoid highly productive patches. To understand these patterns, we solve for coevolutionarily stable strategies (coESSs) of patch selection for multispecies stochastic Lotka-Volterra models accounting for spatial and temporal heterogeneity. In occupied patches at the coESS, we show that the differences between the local contributions to the mean and the variance of the long-term population growth rate are equalized. Applying this characterization to models of antagonistic interactions reveals that environmental stochasticity can partially exorcize the ghost of competition past, select for new forms of enemy-free and victimless space, and generate hydra effects over evolutionary timescales. Viewing our results through the economic lens of modern portfolio theory highlights why the coESS for patch selection is often a bet-hedging strategy coupling stochastic sink populations. Our results highlight how environmental stochasticity can reverse or amplify evolutionary outcomes as a result of species interactions or spatial heterogeneity.

  PublisherDOI

• Stochastic nutrient-plankton models

  Journal of Differential Equations · 2023-09-13 · 4 citations

  article1st authorCorresponding
  PublisherDOI

• Random Switching in an Ecosystem with Two Prey and One Predator

  SIAM Journal on Mathematical Analysis · 2023-02-01 · 2 citations

  article1st authorCorresponding

  In this paper, we study the long-term dynamics of two prey species and one predator species. In the deterministic setting, if we assume the interactions are of Lotka–Volterra type (competition or predation), the long-term behavior of this system is well known. However, nature is usually not deterministic. All ecosystems experience some type of random environmental fluctuations. We incorporate these into a natural framework as follows. Suppose the environment has two possible states. In each of the two environmental states the dynamics is governed by a system of Lotka–Volterra ODEs. The randomness comes from spending an exponential amount of time in each environmental state and then switching to the other one. We show how this random switching can create very interesting phenomena. In some cases the randomness can facilitate the coexistence of the three species even though coexistence is impossible in each of the two environmental states. In other cases, even though there is coexistence in each of the two environmental states, switching can lead to the loss of one or more species. We look into how predators and environmental fluctuations can mediate coexistence among competing species.

  PublisherDOI

Recent grants

• Collaborative Research: Population Dynamics in Random Environments: Theory and Approximation

  NSF · $145k · 2019–2021

Frequent coauthors

• Dang Nguyen

  Harvard University

  43 shared

• Nhu N. Nguyen

  University of Rhode Island

  22 shared

• Nguyen Trong Hieu

  Vietnam National University, Hanoi

  17 shared

• Dang H. Nguyen

  University of Alabama

  16 shared

• Sergiu C. Ungureanu

  13 shared

• George Yin

  Shandong University

  13 shared

• Ky Tran

  SUNY Korea

  10 shared

• Nguyen Huu Du

  VNU University of Science

  8 shared