Interdependence of plankton spatial distribution and plancton biomass temporal oscillations: mathematical simulation

The dynamics of aquatic biological communities in a patchy environment is of great interest in respect to interrelations between phenomena at various spatial and time scales. To study the complex plankton dynamics in relation to variations of such a biologically essential parameter as the fish predation rate, we use a simple reaction-diffusion model of trophic interactions between phytoplankton, zooplankton, and fish. We suggest that plankton is distributed between two habitats one of which is fish-free due to hydrological inhomogeneity, while the other is fish-populated. We show that temporal variations in the fish predation rate do not violate the strong correspondence between the character of spatial distribution of plankton and changes of plankton biomass in time: regular temporal oscillations of plankton biomass correspond to large-scale plankton patches, while chaotic oscillations correspond to small-scale plankton patterns. As in the case of the constant fish predation rate, the chaotic plankton dynamics is characterized by coexistence of the chaotic attractor and limit cycle.     

Pattern formation and chaos in spatial ecological public goodsgames

Cooperators and defectors can coexist in ecological public goods games. When the game is played in two-dimensional continuous space, a reaction diffusion model produces highly irregular dynamics, in which cooperators and defectors survive in ever-changing configurations (Wakano et al., 2009. Spatial dynamics of ecological public goods. Proc. Natl. Acad. Sci. 106, 7910-7914). The dynamics is related to the formation of Turing patterns, but the origin of the irregular dynamics is not well understood. In this paper, we present a classification of the spatio-temporal dynamics based on the dispersion relation, which reveals that the spontaneous pattern formation can be attributed to the dynamical interplay between two linearly unstable modes: temporal instability arising from a Hopf-bifurcation and spatial instability arising from a Turing-bifurcation. Moreover, we provide a detailed analysis of the highly irregular dynamics through Fourier analysis, the break-down of symmetry, the maximum Lyapunov exponent, and the excitability of the reaction-term dynamics. All results clearly support that the observed irregular dynamics qualifies as spatio-temporal chaos. A particularly interesting type of chaotic dynamics, which we call intermittent bursts, clearly demonstrates the effects of the two unstable modes where (local) periods of stasis alternate with rapid changes that may induce local extinction.     

Dispersal, colored environmental noise, and spatial synchrony in population dynamics: analyzing a discrete host–parasitoid population model

Spatial synchrony is common, and its influences and causes have attracted the interest of ecologists. Spatially correlated environmental noise, dispersal, and trophic interactions have been considered as the causes of spatial synchrony. In this study, we developed a spatially structured population model, which is described by coupled-map lattices. Our recent investigation showed that trophic correlation of environmental noise was another important factor that affects spatial synchrony. As a supplement, we considered the influence of the color of the environmental noise on the spatial synchrony in this study. The noise color refers to the temporal correlation in the time series data of the noise, and is expressed as the degree of (first-order) autocorrelation for autoregressive noise. Patterns of spatial synchrony were considered for stable, periodic (quasi-periodic), and chaotic population dynamics. Numerical simulations verified that the color of the environmental noise is another mechanism that causes spatial synchrony. Generally, the effect of the color of the noise on the synchrony is dependent on the type of dynamics (stable, cyclic, chaotic) present in the population. For cyclic dynamics, simulation results clearly demonstrate that reddened noise has higher synchrony than white noise. The importance of our research is that it enriches the theory of potential causes of spatial synchrony.     

Chaotic dynamics and fractal space in biochemistry: simplicity underlies complexity

In this work we attempt to analyze the coupling between the dynamics of biochemical reactions (especially chaotic dynamics), and the geometry of cytoarchitecture (especially fractal ultrastructure), because of its importance and consequences for the ultradian dynamic behaviour of cells. Fractal geometry in intracellular macromolecular assemblies suggests that chaotic dynamics occur during their organization. Non-linear interactions in and between spatial and temporal domains and over wide ranges of scales underlie the emergent properties of complex biological systems.     

Temporal-to-spatial dynamic mapping,flexible recognition,and temporal correlations in an olfactory cortex model

 This paper proposes temporal-to-spatial dynamic mapping inspired by neural dynamics of the olfactory cortex. In our model the temporal structure of olfactory-bulb patterns is mapped to the spatial dynamics of the ensemble of cortical neurons. This mapping is based on the following biological mechanism: while anterior part of piriform cortex can be excited by the afferent input alone, the posterior areas require both afferent and association signals, which are temporally correlated in a specific way. One of the functional types of the neurons in our model corresponds to the cortical spatial dynamics and encodes odor components, and another represents temporal activity of association-fiber signals, which, we suggest, may be relevant to the encoding of odor concentrations. The temporal-to-spatial mapping and distributed representation of the model enable simultaneous rough cluster classification and fine recognition of patterns within a cluster as parts of the same dynamic process. The model is able to extract and segment the components of complex odor patterns which are spatiotemporal sequences of neural activity. Received: 16 October 2001 / Accepted in revised form: 7 February 2002     

Monitoring temporal trends in spatially structured populations: how should sampling effort be allocated between space and time?

Estimating temporal trends in spatially structured populations has a critical role to play in understanding regional changes in biological populations and developing management strategies. Designing effective monitoring programmes to estimate these trends requires important decisions to be made about how to allocate sampling effort among spatial replicates (i.e. number of sites) and temporal replicates (i.e. how often to survey) to minimise uncertainty in trend estimates. In particular, the optimal mix of spatial and temporal replicates is likely to depend upon the spatial and temporal correlations in population dynamics. Although there has been considerable interest in the ecological literature on understanding spatial and temporal correlations in species’ population dynamics, little attention has been paid to its consequences for monitoring design. We address this issue using model‐based survey design to identify the optimal allocation of sampling effort among spatial and temporal replicates for estimating population trends under different levels of spatial and temporal correlation. Based on linear trends, we show that how we should allocate sampling effort among spatial and temporal replicates depends crucially on the spatial and temporal correlations in population dynamics, environmental variation, observation error and the spatial variation in temporal trends. When spatial correlation is low and temporal correlation is high, the best option is likely to be to sample many sites infrequently, particularly when observation error and/or spatial variation in temporal trends are high. When spatial correlation is high and temporal correlation is low, the best option is likely to be to sample few sites frequently, particularly when observation error and/or spatial variation in temporal trends are low. When abundances are spatially independent, it is always preferable to maximise spatial replication. This provides important insights into how spatio‐temporal monitoring programmes should be designed to estimate temporal trends in spatially structured populations.     

A structured-population model of Proteus mirabilis swarm-colony development

In this paper we present continuous age- and space-structured models and numerical computations of Proteus mirabilis swarm-colony development. We base the mathematical representation of the cell-cycle dynamics of Proteus mirabilis on those developed by Esipov and Shapiro, which are the best understood aspects of the system, and we make minimum assumptions about less-understood mechanisms, such as precise forms of the spatial diffusion. The models in this paper have explicit age-structure and, when solved numerically, display both the temporal and spatial regularity seen in experiments, whereas the Esipov and Shapiro model, when solved accurately, shows only the temporal regularity. The composite hyperbolic-parabolic partial differential equations used to model Proteus mirabilis swarm-colony development are relevant to other biological systems where the spatial dynamics depend on local physiological structure. We use computational methods designed for such systems, with known convergence properties, to obtain the numerical results presented in this paper.     

Evolutionary branching of dispersal strategies in structured metapopulations

 Dispersal polymorphism and evolutionary branching of dispersal strategies has been found in several metapopulation models. The mechanism behind those findings has been temporal variation caused by cyclic or chaotic local dynamics, or temporally and spatially varying carrying capacities. We present a new mechanism: spatial heterogeneity in the sense of different patch types with sufficient proportions, and temporal variation caused by catastrophes. The model where this occurs is a generalization of the model by Gyllenberg and Metz (2001). Their model is a size-structured metapopulation model with infinitely many identical patches. We present a generalized version of their metapopulation model allowing for different types of patches. In structured population models, defining and computing fitness in polymorphic situations is, in general, difficult. We present an efficient method, which can be applied also to other structured population or metapopulation models. Received: 6 March 2001 / Revised version: 12 February 2002 / Published online: 17 July 2002     

Spatial and temporal correlations of spike trains in frog retinal ganglion cells

For a neuron, firing activity can be in synchrony with that of others, which results in spatial correlation; on the other hand, spike events within each individual spike train may also correlate with each other, which results in temporal correlation. In order to investigate the relationship between these two phenomena, population neurons’ activities of frog retinal ganglion cells in response to binary pseudo-random checker-board flickering were recorded via a multi-electrode recording system. The spatial correlation index (SCI) and temporal correlation index (TCI) were calculated for the investigated neurons. Statistical results showed that, for a single neuron, the SCI and TCI values were highly related—a neuron with a high SCI value generally had a high TCI value, and these two indices were both associated with burst activities in spike train of the investigated neuron. These results may suggest that spatial and temporal correlations of single neuron’s spiking activities could be mutually modulated; and that burst activities could play a role in the modulation. We also applied models to test the contribution of spatial and temporal correlations for visual information processing. We show that a model considering spatial and temporal correlations could predict spikes more accurately than a model does not include any correlation.     

Synchrony of spatial populations induced by colored environmental noise and dispersal

Spatial synchrony of oscillating populations has been observed in many ecological systems, and its influences and causes have attracted the interest of ecologists. Spatially correlated environmental noises, dispersal, and trophic interactions have been considered as the causes of spatial synchrony. In this study, we develop a spatially structured population model, which is described by coupled-map lattices and incorporates both dispersal and colored environmental noise. A method for generating time series with desired spatial correlation and color is introduced. Then, we use these generated time series to analyze the influence of noise color on synchrony in population dynamics. The noise color refers to the temporal correlation in the time series data of the noise, and is expressed as the degree of (first-order) autocorrelation for autoregressive noise. Patterns of spatial synchrony are considered for stable, periodic and chaotic population dynamics. Numerical simulations verify that environmental noise color has a major influence on the level of synchrony, which depends strongly on how noise is introduced into the model. Furthermore, the influence of noise color also depends on patterns of dispersal between local populations. In addition, the desynchronizing effect of reddened noise is always weaker than that of white noise. From our results, we notice that the role of reddened environmental noise on spatial synchrony should be treated carefully and cautiously, especially for the spatially structured populations linked by dispersal.     

Heterogeneity of the spatial distribution of the primordial organic substance as an initial stage of biological evolution

An approach to describe the emergence of dynamics of polymerization/depolymerization of some spatially distributed prebiological structures has been analyzed, and two phases of the development of the system have been identified. In the first phase, polymerization of organic monomers occurs under the influence of external factors, and in the second one depolymerization takes place. Both processes are accompanied by “diffusive mixing” of reaction products. The dynamic equations of the system are presented. Numerical examination of the space nonuniform solution of model equations has shown that, in conditions of low stability of uniform space distribution, these solutions resolve into a number of discrete peaks of nonzero density, which are isolated from each other by free space. Such nonuniform distributions are stable when close to the bifurcation point; yet in other conditions, they can lose their stability, which entails a more pronounced nonuniformity of space dynamics. Thus, interaction of polymerization/depolymerization processes results in chaotic self-organization and leads to origination of complex and inhomogeneous (patchy) spatial structures. These structures in physical space can reflect the emergence of the spatial nonuniformity in prebiological associations, while in the distributive space of characters they can correspond to the initial steps of emergence of the first discrete domains fixed in biological evolution.     

Spatial pattern switching enables cyclic evolution in spatial epidemics

Infectious diseases often spread as spatial epidemic outbreak waves. A number of model studies have shown that such spatial pattern formation can have important consequences for the evolution of pathogens. Here, we show that such spatial patterns can cause cyclic evolutionary dynamics in selection for the length of the infectious period. The necessary reversal in the direction of selection is enabled by a qualitative change in the spatial pattern from epidemic waves to irregular local outbreaks. The spatial patterns are an emergent property of the epidemic system, and they are robust against changes in specific model assumptions. Our results indicate that emergent spatial patterns can act as a rich source for complexity in pathogen evolution.     

Emergent Features Due to Grid-Cell Biology: Synchronisation in Biophysical Models

Modelling studies of upper ocean phenomena, such as that of the spatial and temporal patchiness in plankton distributions, typically employ coupled biophysical models, with biology in each grid-cell represented by a plankton ecosystem model. It has not generally been considered what impact the choice of grid-cell ecosystem model, from the many developed in the literature, might have upon the results of such a study. We use the methods of synchronisation theory, which is concerned with ensembles of interacting oscillators, to address this question, considering the simplest possible case of a chain of identically represented interacting plankton grid-cells. It is shown that the ability of the system to exhibit stably homogeneous (fully synchronised) dynamics depends crucially upon the choice of biological model and number of grid-cells, with dynamics changing dramatically at a threshold strength of mixing between grid-cells. Consequently, for modelling studies of the ocean the resolution chosen, and therefore number of grid-cells used, could drastically alter the emergent features of the model. It is shown that chaotic ecosystem dynamics, in particular, should be used with care.     

Influence of consumer mutual interference on the stabilization of a three-level trophic system

In this paper, we present a three-level (food–prey–predator) trophic food chain which includes consumer mutual interference (MIF). In contrast with other analyses, we consider the effect of both prey and predator MIF on the dynamics of a three-level trophic system. MIF is generally considered to exert a stabilizing effect on population dynamics based on the predator–prey model. However, results from analytical and numerical simulations utilizing a simple three-species food chain model suggest that while the addition of prey MIF to the model provides a stabilizing influence, as the chaotic dynamics collapse to a stable steady state, adding only predator MIF to the model can only stabilize the system at intermediate MIF values. The three-species trophic food chain is also stabilized when combination of both prey and predator MIF is added to the model. Our work serves to provide insight into the effects of MIF in the real world.     

Weevils and camellias in a Darwin’s race: model system for the study of eco-evolutionary interactions between species

Organisms are surrounded by their predators, parasites, hosts, and mutualists, being involved in reciprocal adaptation processes with such “biotic environment”. The concept of “coevolution”, therefore, provides a basis for the comprehensive understanding of evolutionary and ecological dynamics in biological communities and ecosystems. Recent studies have shown that coevolutionary processes are spatially heterogeneous and that traits mediating interspecific interactions can evolve rapidly in natural communities. Here, I discuss factors promoting the geographic differentiation of coevolutionary interactions, the spatial scales of the geographic structuring, and the pace of coevolutionary changes, reviewing findings in the arms race coevolution involving a long-mouthed weevil and its host camellia plant. Evolutionary, ecological, and population genetic studies on the system illuminated that viewpoints from the aspect of “coevolving biosphere” were important for predicting how ongoing anthropogenic change in global environment alter the spatiotemporal dynamics of biological communities.     

Simple models for complex systems: exploiting the relationship between local and global densities

Simple temporal models that ignore the spatial nature of interactions and track only changes in mean quantities, such as global densities, are typically used under the unrealistic assumption that individuals are well mixed. These so-called mean-field models are often considered overly simplified, given the ample evidence for distributed interactions and spatial heterogeneity over broad ranges of scales. Here, we present one reason why such simple population models may work even when mass-action assumptions do not hold: spatial structure is present but it relates to global densities in a special way. With an individual-based predator–prey model that is spatial and stochastic, and whose mean-field counterpart is the classic Lotka–Volterra model, we show that the global densities and densities of pairs (or spatial covariances) establish a bi-power law at the stationary state and also in their transient approach to this state. This relationship implies that the dynamics of global densities can be written simply as a function of those densities alone without invoking pairs (or higher order moments). The exponents of the bi-power law for the predation rate exhibit a remarkable robustness to changes in model parameters. Evidence is presented for a connection of our findings to the existence of a critical phase transition in the dynamics of the spatial system. We discuss the application of similar modified mean-field equations to other ecological systems for which similar transitions have been described, both in models and empirical data.     

Application of chaotic dynamics in a recurrent neural network to control: hardware implementation into a novel autonomous roving robot

Originating from a viewpoint that complex/chaotic dynamics would play an important role in biological system including brains, chaotic dynamics introduced in a recurrent neural network was applied to control. The results of computer experiment was successfully implemented into a novel autonomous roving robot, which can only catch rough target information with uncertainty by a few sensors. It was employed to solve practical two-dimensional mazes using adaptive neural dynamics generated by the recurrent neural network in which four prototype simple motions are embedded. Adaptive switching of a system parameter in the neural network results in stationary motion or chaotic motion depending on dynamical situations. The results of hardware implementation and practical experiment using it show that, in given two-dimensional mazes, the robot can successfully avoid obstacles and reach the target. Therefore, we believe that chaotic dynamics has novel potential capability in controlling, and could be utilized to practical engineering application.     

The effect of landscape heterogeneity on host–parasite dynamics

Environmental heterogeneity has been shown to have a profound effect on population dynamics and biological invasions, yet the effect of its spatial structure on the dynamics of disease invasion in a spatial host–parasite system has received little attention. Here we explore the effect of environment heterogeneity using the pair approximation and the stochastic spatially explicit simulation in which the lost patches are clustered in a fragmented landscape. The intensity of fragmentation is defined by the amount and spatial autocorrelation of the lost habitat. More fragmented landscape (high amount of habitat loss, low clustering of lost patches) was shown to be detrimental to the parasitic disease invasion and transmission, which implies that the potential of using artificial disturbances as a disease-control agency in biological conservation and management. Two components of the spatial heterogeneity (the amount and spatial autocorrelation of the lost habitat) formed a trade-off in determining the host–parasite dynamics. An extremely high degree of habitat loss was, counter-intuitively, harmful to the host. These results enrich our understanding of eco-epidemiological, host–parasite systems, and suggest the possibility of using the spatial arrangement of habitat patches as a conservation tool for guarding focal species against parasitic infection and transmission.     

Relative spike timing in stochastic oscillator networks of the Hermissenda eye

The role of relative spike timing on sensory coding and stochastic dynamics of small pulse-coupled oscillator networks is investigated physiologically and mathematically, based on the small biological eye network of the marine invertebrate Hermissenda. Without network interactions, the five inhibitory photoreceptors of the eye network exhibit quasi-regular rhythmic spiking; in contrast, within the active network, they display more irregular spiking but collective network rhythmicity. We investigate the source of this emergent network behavior first analyzing the role of relative input to spike–timing relationships in individual cells. We use a stochastic phase oscillator equation to model photoreceptor spike sequences in response to sequences of inhibitory current pulses. Although spike sequences can be complex and irregular in response to inputs, we show that spike timing is better predicted if relative timing of spikes to inputs is accounted for in the model. Further, we establish that greater noise levels in the model serve to destroy network phase-locked states that induce non-monotonic stimulus rate-coding, as predicted in Butson and Clark (J Neurophysiol 99:146–154, 2008a; J Neurophysiol 99:155–165, 2008b). Hence, rate-coding can function better in noisy spiking cells relative to non-noisy cells. We then study how relative input to spike–timing dynamics of single oscillators contribute to network-level dynamics. Relative timing interactions in the network sharpen the stimulus window that can trigger a spike, affecting stimulus encoding. Also, we derive analytical inter-spike interval distributions of cells in the model network, revealing that irregular Poisson-like spike emission and collective network rhythmicity are emergent properties of network dynamics, consistent with experimental observations. Our theoretical results generate experimental predictions about the nature of spike patterns in the Hermissenda eye.     

Cross–Scale Interactions and Changing Pattern–Process Relationships: Consequences for System Dynamics

Cross–scale interactions refer to processes at one spatial or temporal scale interacting with processes at another scale to result in nonlinear dynamics with thresholds. These interactions change the pattern–process relationships across scales such that fine-scale processes can influence a broad spatial extent or a long time period, or broad-scale drivers can interact with fine-scale processes to determine system dynamics. Cross–scale interactions are increasing recognized as having important influences on ecosystem processes, yet they pose formidable challenges for understanding and forecasting ecosystem dynamics. In this introduction to the special feature, “Cross–scale interactions and pattern–process relationships”, we provide a synthetic framework for understanding the causes and consequences of cross–scale interactions. Our framework focuses on the importance of transfer processes and spatial heterogeneity at intermediate scales in linking fine- and broad-scale patterns and processes. Transfer processes and spatial heterogeneity can either amplify or attenuate system response to broad-scale drivers. Providing a framework to explain cross–scale interactions is an important step in improving our understanding and ability to predict the impacts of propagating events and to ameliorate these impacts through proactive measures.