The role of temperature on clustering behavior and mating opportunity in Bonin flying foxes

Social thermoregulation is an important adaptation for animals living in cold environments, especially for those with high surface area-to-volume ratios. Huddling behavior can influence animal dispersion patterns. However, little attention has been paid to the relationship between huddling behavior and social organization, including mating systems. We studied aggregations of Bonin flying foxes Pteropus pselaphon on Chichijima Island in Japan, which is located at one of the northern limits of their geographical range. Bonin flying foxes aggregate in arboreal roosts, where they form temporary clusters within the colony during winter months. Specifically, we hypothesized that the occurrence and intensity of clustering behavior are affected by ambient temperature; therefore, we examined the relationship between cluster size and ambient temperature. We also investigated the age and sex compositions of clusters and mating behavior to gain insight into the mating system of this species. Field observations showed that P. pselaphon clusters were roughly separated into three groups: males, females, and subadults. Statistical analyses showed that ambient temperature had significant negative effects on the proportion of individuals that formed clusters for all groups. Cluster size decreased significantly with increasing ambient temperature in female and subadult groups. Some female clusters were composed of a single male and multiple females. Marked males were observed excluding other males from the periphery of female clusters, and they monopolized copulations with clustering females. Our findings suggest that clustering plays an important warming role in the species, and that this behavior is exploited by males resulting in a social system that is best described as a form of female-defense polygyny.     

Hierarchical structure and the influence of individual attributes in the captive squirrel monkey (<Emphasis Type="Italic">Saimiri collinsi</Emphasis>)

The dominance structure of primate social groups varies widely. In addition to the groups’ composition, intrinsic attributes such as sex, body size and life experience are important factors that can affect hierarchical dominance relations. All primates are social animals, and the social environment has a direct influence on the physiological conditions of vital systems such as immunological, reproductive and cardiovascular systems. In this study, we analyze the hierarchical structure of Saimiri collinsi in captivity, including the hierarchical structure type, the influence of individual intrinsic characteristics (sex, age, weight and origin—born in captivity or in the wild) based on the prior-attributes model, the relation between agonistic behavior frequency and hierarchical position, and hierarchy steepness, which represents the dominance gradient. We found that the group order was characterized by a partial hierarchy: a dominance position could be occupied by more than one individual simultaneously, including individuals of both sexes. Intrinsic characteristics had no influence on hierarchical structure, with the exception of the male in the highest hierarchical position, which had a markedly larger body than all other group members. Thus, the prior-attributes model did not apply to hierarchical formation of S. collinsi in captivity. Only the frequency of agonistic behavior of males correlated with their hierarchical position, and they differed from all other group members in their more aggressive behavior. The steepness between adjacent positions along the dominance gradient was significant only between the dominant male and the next individual in the group, with a smooth gradient between the other positions in the rank. As the access to resources is directly related to hierarchical dominance, a smooth dominance gradient is to be expected in species that form very large groups, such as wild Saimiri populations.     

Activated dynamics and timescale separation within the landscape paradigm: signature of complexity, diversity and glassiness

The landscape paradigm has become a widespread picture within the realm of complex systems. Complex systems include a great variety of systems, ranging from glasses to biopolymers, which display a common dynamical behavior. Within this framework, the dynamics of a such a system can be envisioned as the search it performs on its (potential energy) landscape. This approach rests on the belief that the relaxation behavior depends only on generic features, irrespective of specific details and lies on the validity of a timescale separation scenario computationally corroborated but not properly validated yet form first principles. In this work we shall show that the prevalence of activated dynamics over other kinds of mechanisms determines the emergence of complex dynamical behavior. Thus, complexity and diversity are not intrinsic properties of a system but depend on the kind of exploration of the landscape. We shall focus mainly on an ample generic context (complex hierarchical systems which have been used as models of glasses, spin glasses and biopolymers) and a specific one (model glass formers). For the last case we shall be able to reveal (in mechanistic terms) the microscopic rationale for the occurrence of timescale separation. Furthermore, we shall explore the connections between these two up to now mostly unrelated contexts and the relation to a variational principle, and we shall reveal the conditions for the applicability of the landscape approach.     

Exponential Signaling Gain at the Receptor Level Enhances Signal-to-Noise Ratio in Bacterial Chemotaxis

Cellular signaling systems show astonishing precision in their response to external stimuli despite strong fluctuations in the molecular components that determine pathway activity. To control the effects of noise on signaling most efficiently, living cells employ compensatory mechanisms that reach from simple negative feedback loops to robustly designed signaling architectures. Here, we report on a novel control mechanism that allows living cells to keep precision in their signaling characteristics – stationary pathway output, response amplitude, and relaxation time – in the presence of strong intracellular perturbations. The concept relies on the surprising fact that for systems showing perfect adaptation an exponential signal amplification at the receptor level suffices to eliminate slowly varying multiplicative noise. To show this mechanism at work in living systems, we quantified the response dynamics of the E. coli chemotaxis network after genetically perturbing the information flux between upstream and downstream signaling components. We give strong evidence that this signaling system results in dynamic invariance of the activated response regulator against multiplicative intracellular noise. We further demonstrate that for environmental conditions, for which precision in chemosensing is crucial, the invariant response behavior results in highest chemotactic efficiency. Our results resolve several puzzling features of the chemotaxis pathway that are widely conserved across prokaryotes but so far could not be attributed any functional role.     

Slime mold inspired routing protocols for wireless sensor networks

Many biological systems are composed of unreliable components which self-organize effectively into systems that achieve a balance between efficiency and robustness. One such example is the true slime mold Physarum polycephalum which is an amoeba-like organism that seeks and connects food sources and efficiently distributes nutrients throughout its cell body. The distribution of nutrients is accomplished by a self-assembled resource distribution network of small tubes with varying diameter which can evolve with changing environmental conditions without any global control. In this paper, we exploit two different mechanisms of the slime mold??s tubular network formation process via laboratory experiments and mathematical behavior modeling to design two corresponding localized routing protocols for wireless sensor networks (WSNs) that take both efficiency and robustness into account. In the first mechanism of path growth, slime mold explores its immediate surroundings to discover and connect new food sources during its growth cycle. We adapt this mechanism for a path growth routing protocol by treating data sources and sinks as singular potentials to establish routes from the sinks to all the data sources. The second mechanism of path evolution is the temporal evolution of existing tubes through nonlinear feedback in order to distribute nutrients efficiently throughout the organism. Specifically, the diameters of tubes carrying large fluxes of nutrients grow to expand their capacities, and tubes that are not used decline and disappear entirely. We adapt the tube dynamics of the slime mold for a path evolution routing protocol. In our protocol, we identify one key adaptation parameter to adjust the tradeoff between efficiency and robustness of network routes. Through extensive realistic network simulations and ideal closed form or numerical computations, we validate the effectiveness of both protocols, as well as the efficiency and robustness of the resulting network connectivity.     

Induction Kinetics of a Conditional pH Stress Response System in Escherichia coli

The analysis of stress response systems in microorganisms can reveal molecular strategies for regulatory control and adaptation. In this study, we focused on the Cad module, a subsystem of Escherichia coli’s response to acidic stress that is conditionally activated at low pH only when lysine is available. When expressed, the Cad system counteracts the elevated H⁺ concentration by converting lysine to cadaverine under the consumption of H⁺ and exporting cadaverine in exchange for external lysine. Surprisingly, the cad operon displays a transient response, even when the conditions for its induction persist. To quantitatively characterize the regulation of the Cad module, we experimentally recorded and theoretically modeled the dynamics of important system variables. We established a quantitative model that adequately describes and predicts the transient expression behavior for various initial conditions. Our quantitative analysis of the Cad system supports negative feedback by external cadaverine as the origin of the transient response. Furthermore, the analysis puts causal constraints on the precise mechanism of signal transduction via the regulatory protein CadC.     

Collective motion of bacteria in two dimensions

Collective motion can be observed in biological systems over a wide range of length scales, from large animals to bacteria. Collective motion is thought to confer an advantage for defense and adaptation. A central question in the study of biological collective motion is how the traits of individuals give rise to the emergent behavior at population level. This question is relevant to the dynamics of general self-propelled particle systems, biological self-organization, and active fluids. Bacteria provide a tractable system to address this question, because bacteria are simple and their behavior is relatively easy to control. In this mini review we will focus on a special form of bacterial collective motion, i.e., bacterial swarming in two dimensions. We will introduce some organization principles known in bacterial swarming and discuss potential means of controlling its dynamics. The simplicity and controllability of 2D bacterial behavior during swarming would allow experimental examination of theory predictions on general collective motion.     

Aggregation error in nonlinear ecological models

Conditions are presented for accurately representing the dynamics of several components of a nonlinear ecological system by a single state variable. For mass-balance ecosystem models, only two conditions exist that permit such aggregation without introducing error into the model. Under the first condition, components can be lumped if their environmental losses (e.g. respiration or migration) are characterized by identical linear functions. Under the second condition, the components must maintain constant proportionality throughout their transient behavior. If these conditions are violated, it is not possible to aggregate without error. However, it is shown that aggregation error can still be minimized. The theorems developed here extend related results derived for the general theory of systems and have the advantage of generality, being applicable to nonlinear models of considerable complexity.     

Creative dynamics approach to neural intelligence

The thrust of this paper is to introduce and discuss a substantially new type of dynamical system for modelling biological behavior. The approach was motivated by an attempt to remove one of the most fundamental limitations of artificial neural networks — their rigid behavior compared with even simplest biological systems. This approach exploits a novel paradigm in nonlinear dynamics based upon the concept of terminal attractors and repellers. It was demonstrated that non-Lipschitzian dynamics based upon the failure of Lipschitz condition exhibits a new qualitative effect — a multi-choice response to periodic external excitations. Based upon this property, a substantially new class of dynamical systems — the unpredictable systems — was introduced and analyzed. These systems are represented in the form of coupled activation and learning dynamical equations whose ability to be spontaneously activated is based upon two pathological characteristics. Firstly, such systems have zero Jacobian. As a result of that, they have an infinite number of equilibrium points which occupy curves, surfaces or hypersurfaces. Secondly, at all these equilibrium points, the Lipschitz conditions fails, so the equilibrium points become terminal attractors or repellers depending upon the sign of the periodic excitation. Both of these pathological characteristics result in multi-choice response of unpredictable dynamical systems. It has been shown that the unpredictable systems can be controlled by sign strings which uniquely define the system behaviors by specifying the direction of the motions in the critical points. By changing the combinations of signs in the code strings the system can reproduce any prescribed behavior to a prescribed accuracy. That is why the unpredictable systems driven by sign strings are extremely flexible and are highly adaptable to environmental changes. It was also shown that such systems can serve as a powerful tool for temporal pattern memories and complex pattern recognition. It has been demonstrated that new architecture of neural networks based upon non-Lipschitzian dynamics can be utilized for modelling more complex patterns of behavior which can be associated with phenomenological models of creativity and neural intelligence.     

Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii

Aggregation of algae, mainly diatoms, is an important process in marine systems leading to the settling of particulate organic carbon predominantly in the form of marine snow. Exudation products of phytoplankton form transparent exopolymer particles (TEP), which acts as the glue for particle aggregation. Heterotrophic bacteria interacting with phytoplankton may influence TEP formation and phytoplankton aggregation. This bacterial impact has not been explored in detail. We hypothesized that bacteria attaching to Thalassiosira weissflogii might interact in a yet-to-be determined manner, which could impact TEP formation and aggregate abundance. The role of individual T. weissflogii-attaching and free-living new bacterial isolates for TEP production and diatom aggregation was investigated in vitro. T. weissflogii did not aggregate in axenic culture, and striking differences in aggregation dynamics and TEP abundance were observed when diatom cultures were inoculated with either diatom-attaching or free-living bacteria. The data indicated that free-living bacteria might not influence aggregation whereas bacteria attaching to diatom cells may increase aggregate formation. Interestingly, photosynthetically inactivated T. weissflogii cells did not aggregate regardless of the presence of bacteria. Comparison of aggregate formation, TEP production, aggregate sinking velocity and solid hydrated density revealed remarkable differences. Both, photosynthetically active T. weissflogii and specific diatom-attaching bacteria were required for aggregation. It was concluded that interactions between heterotrophic bacteria and diatoms increased aggregate formation and particle sinking and thus may enhance the efficiency of the biological pump.     

On the problems of the evolutionary optimization of life history. II. To justification of optimization criterion for nonlinear Leslie model

The paper considers the problems in the adaptive evolution of life-history traits for individuals in the nonlinear Leslie model of age-structured population. The possibility to predict adaptation results as the values of organism’s traits (properties) that provide for the maximum of a certain function of traits (optimization criterion) is studied. An ideal criterion of this type is Darwinian fitness as a characteristic of vital success of an individual. Criticism of the optimization approach is associated with the fact that it does not take into account the changes in the environment (in a broad sense) caused by evolution, thereby leading to losses in the adequacy of the criterion. In addition, the justification for this criterion under stationary conditions is not usually rigorous. It has been suggested to overcome these objections in terms of the adaptive dynamics theory using the concept of invasive fitness. The reasons are given that favor the application of the average number of offspring for an individual, R _L , as an optimization criterion in the nonlinear Leslie model. According to the theory of quantitative genetics, the selection for fertility (that is, for a set of correlated quantitative traits determined by both multiple loci and the environment) leads to an increase in R _L . In terms of adaptive dynamics, the maximum R _L corresponds to the evolutionary stability and, in certain cases, convergent stability of the values for traits. The search for evolutionarily stable values on the background of limited resources for reproduction is a problem of linear programming.     

Dynamics control of semantic processes in a hierarchical associative memory

 A neural mechanism for control of dynamics and function of associative processes in a hierarchical memory system is demonstrated. For the representation and processing of abstract knowledge, the semantic declarative memory system of the human brain is considered. The dynamics control mechanism is based on the influence of neuronal adaptation on the complexity of neural network dynamics. Different dynamical modes correspond to different levels of the ultrametric structure of the hierarchical memory being invoked during an associative process. The mechanism is deterministic but may also underlie free associative thought processes. The formulation of an abstract neural network model of hierarchical associative memory utilizes a recent approach to incorporate neuronal adaptation. It includes a generalized neuronal activation function recently derived by a Hodgkin-Huxley-type model. It is shown that the extent to which a hierarchically organized memory structure is searched is controlled by the neuronal adaptability, i.e. the strength of coupling between neuronal activity and excitability. In the brain, the concentration of various neuromodulators in turn can regulate the adaptability. An autonomously controlled sequence of bifurcations, from an initial exploratory to a final retrieval phase, of an associative process is shown to result from an activity-dependent release of neuromodulators. The dynamics control mechanism may be important in the context of various disorders of the brain and may also extend the range of applications of artificial neural networks. Received: 19 April 1995/Accepted in revised form: 8 August 1995     

Agent-Based Analysis of Biomass Feedstock Production Dynamics

The success of the bioenergy sector based on lignocellulosic feedstock will require a sustainable and resilient transition from the current agricultural system focused on food crops to one also producing energy crops. The dynamics of this transition are not well understood. It will be driven significantly by the collective participation, behavior, and interaction of various stakeholders such as farmers within the production system. The objective of this work is to study the system dynamics through the development and application of an agent-based model using the theory of complex adaptive systems. Farmers and biorefinery, two key stakeholders in the system, are modeled as independent agents. The decision making of each agent as well as its interaction with other agents is modeled using a set of rules reflecting the economic, social, and personal attributes of the agent. These rules and model parameters are adapted from literature. Regulatory mechanisms such as Biomass Crop Assistance Program are embedded in the decision-making process. The model is then used to simulate the production of Miscanthus as an energy crop in Illinois. Particular focus has been given on understanding the dynamics of Miscanthus adaptation as an agricultural crop and its impact on biorefinery capacity and contractual agreements. Results showed that only 60% of the maximum regional production capacity could be reached, and it took up to 15 years to establish that capacity. A 25% reduction in the land opportunity cost led to a 63% increase in the steady- state productivity. Sensitivity analysis showed that higher initial conversion of land by farmers to grow energy crop led to faster growth in regional productivity.     

A reexamination of stability in randomly varying versus deterministic environments with comments on the stochastic theory of limiting similarity

The focus of this paper is a general relationship proposed by May (Amer. Natur. 107 (1973)) between the stability properties of stochastic models incorporating environmental variation and the stability properties of the deterministic models from which they are derived. The concepts of stochastic stability underlying this conjectured relationship are discussed and compared to the standard definitions of deterministic stability as well as alternative criteria for stability in stochastic models. It is shown by example that May's qualitative stability criterion does not ensure stability in any sense unless restrictive conditions on the form of the model are satisfied. Even under these conditions, the criterion, which is based on linearization, generally provides information only about the local dynamics of multispecies models. The applicability of such information to stochastic limiting similarity theory is discussed and alternative methods of analysis are proposed.     

The balance between adaptability and adaptation.

In his 1983 book, Adaptability, Michael Conrad explored the quantitative relationship between adaptability and adaptation using the conditional 'entropy' of information theory as his primary tool. The conditional entropy can be used to estimate the connectivity of the network of system exchanges, a key indicator of system stability. In fact, the May-Wigner criterion for the stability of linear dynamical systems can be recast using the conditional entropy to help identify the boundary along which adaptability and adaptation are exactly in balance-the 'edge of chaos' as it is popularly known. Real data on networks of ecosystem flows indicate that in general these systems do not exist nigh upon the edge of chaos, but rather they populate a much wider 'window of vitality' that exists between the realms of chaotic and deterministic dynamics. It appears that the magnitudes of network flows within this region are distributed in power law fashion. The theory also suggests that an absolute limit to the connectivity of natural self-organizing systems exists, at approximately 3.015 effective connections per node.     

Variation in the hatching behaviour of Nematodirus battus: Polymorphic bet hedging?

Previous work on the transmission dynamics of Nematodirus battus, an important nematode parasite of farmed ruminants in temperate regions, suggests that it operates a bet-hedging strategy. Hatching of cold-sensitised eggs is concentrated in spring, while alternative hatching of non-cold-sensitised eggs in autumn mitigates the risk of poor conditions for hatching in spring or host absence during peak larval availability. Isolates from Scotland showed much less propensity to hatch without chilling than the previously characterised isolate from southern England. Nematodirus battus eggs from a hill farm in Scotland showed intermediate proportions of non-chilled hatching, perhaps related to unpredictability of climate at higher altitudes. Geographic polymorphism in larval behaviour appears to be present in the form of differing chilling requirements for egg hatching. Since bet-hedging through trait diversification is a plausible and demonstrated strategy for coping with environmental unpredictability, it is a likely target for adaptation to climate change. Predictions of disease epidemiology in a changing climate should incorporate parasite adaptation, but further theoretical and empirical characterisations of likely evolutionary responses are needed before this is possible for the most economically important systems.     

Computational Models of Consumer Confidence from Large-Scale Online Attention Data: Crowd-Sourcing Econometrics

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation''s citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.     

Evolution of specialization and ecological character displacement of herbivores along a gradient of plant quality

We study the combined evolutionary dynamics of herbivore specialization and ecological character displacement, taking into account foraging behavior of the herbivores, and a quality gradient of plant types. Herbivores can adapt by changing two adaptive traits: their level of specialization in feeding efficiency and their point of maximum feeding efficiency along the plant gradient. The number of herbivore phenotypes, their levels of specialization, and the amount of character displacement among them are the result of the evolutionary dynamics, which is driven by the underlying population dynamics, which in turn is driven by the underlying foraging behavior. Our analysis demonstrates broad conditions for the diversification of a herbivore population into many specialized phenotypes, for basically any foraging behavior focusing use on highest gains while also including errors. Our model predicts two characteristic phases in the adaptation of herbivore phenotypes: a fast character-displacement phase and a slow coevolutionary niche-shift phase. This two-phase pattern is expected to be of wide relevance in various consumer-resource systems. Bringing together ecological character displacement and the evolution of specialization in a single model, our study suggests that the foraging behavior of herbivorous arthropods is a key factor promoting specialist radiation.     

Modeling the C. elegans nematode and its environment using a particle system

A particle system, as understood in computer science, is a novel technique for modeling living organisms in their environment. Such particle systems have traditionally been used for modeling the complex dynamics of fluids and gases. In the present study, a particle system was devised to model the movement and feeding behavior of the nematode Caenorhabditis elegans in three different virtual environments: gel, liquid, and soil. The results demonstrate that distinct movements of the nematode can be attributed to its mechanical interactions with the virtual environment. These results also revealed emergent properties associated with modeling organisms within environment-based systems.     

Computational nature of human adaptive control during learning of reaching movements in force fields

Learning to make reaching movements in force fields was used as a paradigm to explore the system architecture of the biological adaptive controller. We compared the performance of a number of candidate control systems that acted on a model of the neuromuscular system of the human arm and asked how well the dynamics of the candidate system compared with the movement characteristics of 16 subjects. We found that control via a supra-spinal system that utilized an adaptive inverse model resulted in dynamics that were similar to that observed in our subjects, but lacked essential characteristics. These characteristics pointed to a different architecture where descending commands were influenced by an adaptive forward model. However, we found that control via a forward model alone also resulted in dynamics that did not match the behavior of the human arm. We considered a third control architecture where a forward model was used in conjunction with an inverse model and found that the resulting dynamics were remarkably similar to that observed in the experimental data. The essential property of this control architecture was that it predicted a complex pattern of near-discontinuities in hand trajectory in the novel force field. A nearly identical pattern was observed in our subjects, suggesting that generation of descending motor commands was likely through a control system architecture that included both adaptive forward and inverse models. We found that as subjects learned to make reaching movements, adaptation rates for the forward and inverse models could be independently estimated and the resulting changes in performance of subjects from movement to movement could be accurately accounted for. Results suggested that the adaptation of the forward model played a dominant role in the motor learning of subjects. After a period of consolidation, the rates of adaptation in the internal models were significantly larger than those observed before the memory had consolidated. This suggested that consolidation of motor memory coincided with freeing of certain computational resources for subsequent learning. Received: 01 January 1998 / Accepted in revised form: 26 January 1999