Synchrony in tritrophic food chain metacommunities

Generalized synchronization in a tritrophic food web metacommunity

Complete synchronization among the metacommunity is known to elevate the risk of their extinction due to stochasticity and other environmental perturbations. Owing to the inherent heterogeneous nature of the metacommunity, we demonstrate the emergence of generalized synchronization among the patches of dispersally connected tritrophic food web using the framework of an auxiliary system approach and the mutual false nearest neighbor. We find that the critical value of the dispersal rate increases significantly with the size of the metacommunity for both unidirectional and bidirectional dispersals, which in turn corroborates that larger metacommunities are more stable than smaller ones. Further, we find that the critical value of the dispersal for the onset of generalized synchronization is smaller(larger) for bidirectional dispersal than that for unidirectional dispersal for smaller(larger) metacommunities. Most importantly, complete synchronization error remains finite even after the onset of generalized synchronization in a wider range of dispersal rate elucidating that the latter can serve as an early warning signal for the extinction of the metacommunity.

Effectiveness of phase synchronization in chaotic food chain model with refugia and Allee effects during seasonal fluctuations

Seasonal effects powerfully shape the population dynamics with periodic climate changes because species naturally adjust their dynamics with seasonal variations. In response to these effects, sometimes population dynamics exhibit synchrony or generate chaos. However, synchronized dynamics enhance species' persistence in naturally unstable environments; thus, it is imperative to identify parameters that alter the dynamics of an ecosystem and bring it into synchrony. This study examines how ecological parameters enable species to adapt their dynamics to seasonal changes and achieve phase synchrony within ecosystems. For this, we incorporate seasonal effects as a periodic sinusoidal function into a tri-trophic food chain system where two crucial bio-controlling parameters, Allee and refugia effects, are already present. First, it is shown that the seasonal effects disrupt the limit cycle and bring chaos to the system. Further, we perform rigorous mathematical analysis to perform the dynamical and analytical properties of the nonautonomous version of the system. These properties include sensitive dependence on initial condition (SDIC), sensitivity analysis, bifurcation results, the positivity and boundedness of the solution, permanence, ultimate boundedness, and extinction scenarios of species. The SDIC characterizes the presence of chaotic oscillations in the system. Sensitivity analysis determines the parameters that significantly affect the outcome of numerical simulations. The bifurcation study concerning seasonal parameters shows a higher dependency of species on the frequency of seasonal changes than the severity of the season. The bifurcation study also examines the bio-controlling parameters and reveals various dynamic states within the system, such as fold, transcritical branch points, and Hopf points. Moreover, the mathematical analysis of our seasonally perturbed system reveals the periodic coexistence of all species and a globally attractive solution under certain parametric constraints. Finally, we examine the role of essential parameters that contribute to phase synchrony. For this, we numerically investigate the defining role of the coupling dimension coefficient, bio-controlling parameters, and other parameters associated with seasonality. This study infers that species can tune their dynamics to seasonal effects with low seasonal frequency, whereas the species' tolerance for the severity of seasonal effects is relatively high. The research also sheds light on the correlation between the degree of phase synchrony, prey biomass levels, and the severity of seasonal forcing. This study offers valuable insights into the dynamics of ecosystems affected by seasonal perturbations, with implications for conservation and management strategies.

When multilayer links exchange their roles in synchronization

Real world networks contain multiple layers of links whose interactions can lead to extraordinary collective dynamics, including synchronization. The fundamental problem of assessing how network topology controls synchronization in multilayer networks remains open due to serious limitations of the existing stability methods. Towards removing this obstacle, we propose an approximation method which significantly enhances the predictive power of the master stability function for stable synchronization in multilayer networks. For a class of saddle-focus oscillators, including Rössler and piecewise linear systems, our method reduces the complex stability analysis to simply solving a set of linear algebraic equations. Using the method, we analytically predict surprising effects due to multilayer coupling. In particular, we prove that two coupling layers-one of which would alone hamper synchronization and the other would foster it-reverse their roles when used in a multilayer network. We also analytically demonstrate that increasing the size of a globally coupled layer, that in isolation would induce stable synchronization, makes the multilayer network unsynchronizable.

Synchronizability of directed networks: The power of non-existent ties

The understanding of how synchronization in directed networks is influenced by structural changes in network topology is far from complete. While the addition of an edge always promotes synchronization in a wide class of undirected networks, this addition may impede synchronization in directed networks. In this paper, we develop the augmented graph stability method, which allows for explicitly connecting the stability of synchronization to changes in network topology. The transformation of a directed network into a symmetrized-and-augmented undirected network is the central component of this new method. This transformation is executed by symmetrizing and weighting the underlying connection graph and adding new undirected edges with consideration made for the mean degree imbalance of each pair of nodes. These new edges represent "non-existent ties" in the original directed network and often control the location of critical nodes whose directed connections can be altered to manipulate the stability of synchronization in a desired way. In particular, we show that the addition of small-world shortcuts to directed networks, which makes "non-existent ties" disappear, can worsen the synchronizability, thereby revealing a destructive role of small-world connections in directed networks. An extension of our method may open the door to studying synchronization in directed multilayer networks, which cannot be effectively handled by the eigenvalue-based methods.

Spatial disease dynamics of free-living pathogens under pathogen predation

The epidemiological dynamics of potentially free-living pathogens are often studied with respect to a specific pathogen species (e.g., cholera) and most studies concentrate only on host-pathogen interactions. Here we show that metacommunity-level interactions can alter conventional spatial disease dynamics. We introduce a pathogen eating consumer species and investigate a deterministic epidemiological model of two habitat patches, where both patches can be occupied by hosts, pathogens, and consumers of free-living pathogens. An isolated habitat patch shows periodic disease outbreaks in the host population, arising from cyclic consumer-pathogen dynamics. On the other hand, consumer dispersal between the patches generate asymmetric disease prevalence, such that the host population in one patch stays disease-free, while disease outbreaks occur in the other patch. Such asymmetry can also arise with host dispersal, where infected hosts carry pathogens to the other patch. This indirect movement of pathogens causes also a counter-intuitive effect: decreasing morbidity in a focal patch under increasing pathogen immigration. Our results underline that community-level interactions influence disease dynamics and consistent spatial asymmetry can arise also in spatially homogeneous systems.

Non-equilibrium spatial dynamics of ecosystems

Ecological systems show tremendous variability across temporal and spatial scales. It is this variability that ecologists try to predict and that managers attempt to harness in order to mitigate risk. However, the foundations of ecological science and its mainstream agenda focus on equilibrium dynamics to describe the balance of nature. Despite a rich body of literature on non-equilibrium ecological dynamics, we lack a well-developed set of predictions that can relate the spatiotemporal heterogeneity of natural systems to their underlying ecological processes. We argue that ecology needs to expand its current toolbox for the study of non-equilibrium ecosystems in order to both understand and manage their spatiotemporal variability. We review current approaches and outstanding questions related to the study of spatial dynamics and its application to natural ecosystems, including the design of reserves networks. We close by emphasizing the importance of ecosystem function as a key component of a non-equilibrium ecological theory, and of spatial synchrony as a central phenomenon for its inference in natural systems.

Complex response of a food-web module to symmetric and asymmetric migration between several patches

We investigate the stability of a diamond food-web module on two patches coupled by migration in terms of robustness, which is the proportion of surviving species in the system. The parameters are chosen such that the dynamics on an isolated patch have a periodic attractor with all four species present as well as an attractor where the prey that is preferred by the top predator dies out. The migration rate and the migration bias between the two patches are varied, resulting in a surprisingly complex relation between migration rate and robustness. In particular, while the degree of synchronization usually increases with increasing migration rate, robustness can increase as well as decrease. We find that the main results also hold when the number of patches is larger. Different types of connectivity patterns between patches can lead to different extent of migration bias if the migration rate out of each patch is the same.

Biomedical signal and image processing

Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses. Traditionally, these two cultural aspects were separated, with the first one more oriented to physiological issues and how to model them and the second one more dedicated to the development of processing tools or algorithms to enhance useful information from clinical data. A practical consequence was that those who did models did not do signal processing and vice versa. However, in recent years,the need for closer integration between signal processing and modeling of the relevant biological systems emerged very clearly [1], [2]. This is not only true for training purposes(i.e., to properly prepare the new professional members of BME) but also for the development of newly conceived research projects in which the integration between biomedical signal and image processing (BSIP) and modeling plays a crucial role. Just to give simple examples, topics such as brain–computer machine or interfaces,neuroengineering, nonlinear dynamical analysis of the cardiovascular (CV) system,integration of sensory-motor characteristics aimed at the building of advanced prostheses and rehabilitation tools, and wearable devices for vital sign monitoring and others do require an intelligent fusion of modeling and signal processing competences that are certainly peculiar of our discipline of BME.