Biology subjects

Greenwood, P. E.

Publications and source records attributed to Greenwood, P. E..

3 recordsLinked to original sources

bioRxiv2026

Intermittent precipitation and spatial Allee effects drive irregular vegetation patterns in semiarid ecosystems

Vegetation in semi-arid ecosystems frequently organizes into spatially heterogeneous mosaics that regulate ecosystem functioning, productivity, and resilience. These patterns arise from local biological interactions, including facilitation among neighboring plants and competition for limiting resources. Classical theoretical approaches have attributed such organization to scale-dependent feedbacks, predicting regular spatial patterns and abrupt transitions to collapse. However, growing empirical and theoretical evidence reveal that environmental variability and demographic stochasticity can fundamentally reshape spatial organization, driving irregular clusters, dynamic mosaics, and gradual rather than catastrophic vegetation declines. In drylands, rainfall variability is a dominant source of environmental forcing: precipitation typically occurs in short, irregular pulses that transiently enhance survival and recruitment before competitive interactions again dominate. Near persistence thresholds, ecosystem dynamics are therefore governed not only by average climatic conditions but also by the timing and spatial coincidence of favorable events. Under these conditions, positive density dependence and local facilitation can critically determine whether vegetation patches persist, expand, or collapse. Here, we develop an individual-based model that integrates intermittent precipitation with local Allee effects to examine how stochastic rainfall shapes spatial organization and persistence. We show that the interaction between pulsed resource availability and density-dependent survival generates irregular cluster structures and strongly modulates extinction risk, with resilience emerging from local spatial covariance and neighborhood density rather than from total biomass alone. These results highlight the importance of individual-level, stochastic processes in determining ecosystem resilience and provide a mechanistic basis for understanding vegetation persistence under increasing climatic variability.

ecology↗

bioRxiv2025

Noisy neural systems with static and dynamic Hopf bifurcation parameters

Stochastic neural oscillations may be quasi-cycles or stochastic limit cycles. Here we discuss how and when these arise in stochastic dynamical systems with Hopf bifurcations, and point to relevant mathematical results. We describe a new type of oscillation, quasi-limit-cycles, which are limit cycles stirred up by noise during time periods called bifurcation delays in deterministic models. We define a class of models of neural activity all of which display Hopf bifurcations in their base (fixed bifurcation parameter) deterministic form. We then introduce a two-time, slow-fast, version of our model class, in which the bifurcation parameter, instead of being fixed, changes on a time scale slower than the time scale of the state variables. We demonstrate the effects of the slowly changing bifurcation parameter on the deterministic dynamics of the state variables, in particular delayed bifurcation. We find a new understanding about the length of the delay. Most importantly, we display with simulations the effect of noise on the dynamics of both base system and slow-fast system models in our class. Adding noise to a slow-fast model eliminates the bifurcation delay and induces what we call quasi-limit-cycles during the delay period. We measure the sizes of the quasi-limit-cycles and show that they are closely related to the sizes of the limit cycles that would arise from the same values of the bifurcation parameter in the deterministic base system. We conclude that, given the similarities of the dynamics of these models under moderate noise, there is little reason to favour one model over another when studying the behaviour of large groups of neurons, i.e., when used as neural mass models. Author summaryRecordings of brain activity display noisy periodic oscillations. Many computational models of this oscillatory activity have what is called a Hopf bifurcation. This is a point in the dynamic phase space at which solutions to the deterministic versions of the models change from having a stable fixed point to having a stable limit cycle. In this paper we define a new class of models of oscillatory brain activity all of which have Hopf bifurcations. We compute both deterministic and stochastic path solutions to the models. These solutions give additional insight into an intriguing phenomenon of these models when a parameter slowly changes, called delayed bifurcation, as well as expanding our knowledge of their stochastic dynamics. In particular, we define and measure a new type of noisy oscillation, called quasi-limit-cycles, that occur during a bifurcation delay in stochastic solutions. The stochastic versions of these models are roughly equally useful as neural mass models.

neuroscience↗

bioRxiv2023

Attentional selection and communication through coherence: Scope and limitations.

Synchronous neural oscillations are strongly associated with a variety of perceptual, cognitive, and behavioural processes. It has been proposed that the role of the synchronous oscillations in these processes is to facilitate information transmission between brain areas, the communication through coherence, or CTC hypothesis.The details of how this mechanism would work, however, and its causal status, are still unclear. Here we investigate computationally a proposed mechanism for selective attention that directly implicates the CTC as causal. The mechanism involves alpha band (about 10 Hz) oscillations, originating in the pulvinar nucleus of the thalamus, being sent to communicating cortical areas, organizing gamma (about 40 Hz) oscillations there, and thus facilitating phase coherence and communication between them. This is proposed to happen contingent on control signals sent from higher-level cortical areas to the thalamic reticular nucleus, which controls the alpha oscillations sent to cortex by the pulvinar. We studied the scope of this mechanism in parameter space, and limitations implied by this scope, using a computational implementation of our conceptual model. Our results indicate that, although the CTC-based mechanism can account for some effects of top-down and bottom-up attentional selection, its limitations indicate that an alternative mechanism, in which oscillatory coherence is caused by communication between brain areas rather than being a causal factor for it, might operate in addition to, or even instead of, the CTC mechanism. Author summaryThe ability to select some stimulus or stimulus location from all of those available to our senses is critical to our ability to navigate our complex biological and social niche, and to organize appropriate actions to accomplish our goals, namely to survive and to reproduce. We study here a possible mechanism by which the brain could accomplish attentional selection. We show that the interaction between 10 Hz and 40 Hz neural oscillations can provide increases in coherence and information transmission between computationally modelled cortical areas. Our results support the idea that synchronization between oscillations facilitates communication between cortical areas. Limitations and sensitivities to the model, however, also point to the idea that the relation between coherence and communication could go in the opposite direction in some cases, particularly when attention is not involved.

neuroscience↗