Ecosystem engineering as an energy transfer process: a simple agent-based model

Ecosystem engineers are defined as organisms who modulate the availability of resources for themselves and other organisms by physically changing the environment. Ecosystem engineering is a well-recognised ecological interaction, but there is a limited number of general models due to the recent development of the field. Agent-based models are often used to study how organisms respond to changing environments and are suitable for modelling ecosystem engineering. To our knowledge, agent-based methodology has not yet been used to model ecosystem engineering. In this paper, we develop a simple agent-based population dynamics model of ecosystem engineering as an energy transfer process. We apply energy budget approach to conceptually explain how ecosystem engineers transfer energy to the environment and define various types of energy transfers relative to their effects on the engineers and other organisms. We simulate environments with various levels of resource abundance and compare the results of the model without ecosystem engineering agents to the model with ecosystem engineering agents. We find that in environments with higher levels of resources, the presence of ecosystem engineers increases the average carrying capacity and the strength of population fluctuations, while in environments with lower levels of resources, ecosystem engineering mitigates fluctuations, increases average carrying capacity and makes environments more resilient. Finally, we discuss about the further application of agent-based modelling for the theoretical and experimental development of the ecosystem engineering concept.     

Modelling evolutionary cell behaviour using neural networks: Application to tumour growth

In this paper, we present a modelling framework for cellular evolution that is based on the notion that a cell’s behaviour is driven by interactions with other cells and its immediate environment. We equip each cell with a phenotype that determines its behaviour and implement a decision mechanism to allow evolution of this phenotype. This decision mechanism is modelled using feed-forward neural networks, which have been suggested as suitable models of cell signalling pathways. The environmental variables are presented as inputs to the network and result in a response that corresponds to the phenotype of the cell. The response of the network is determined by the network parameters, which are subject to mutations when the cells divide. This approach is versatile as there are no restrictions on what the input or output nodes represent, they can be chosen to represent any environmental variables and behaviours that are of importance to the cell population under consideration. This framework was implemented in an individual-based model of solid tumour growth in order to investigate the impact of the tissue oxygen concentration on the growth and evolutionary dynamics of the tumour. Our results show that the oxygen concentration affects the tumour at the morphological level, but more importantly has a direct impact on the evolutionary dynamics. When the supply of oxygen is limited we observe a faster divergence away from the initial genotype, a higher population diversity and faster evolution towards aggressive phenotypes. The implementation of this framework suggests that this approach is well suited for modelling systems where evolution plays an important role and where a changing environment exerts selection pressure on the evolving population.     

Ecosystem productivity is associated with bacterial phylogenetic distance in surface marine waters

Understanding the link between community diversity and ecosystem function is a fundamental aspect of ecology. Systematic losses in biodiversity are widely acknowledged but the impact this may exert on ecosystem functioning remains ambiguous. There is growing evidence of a positive relationship between species richness and ecosystem productivity for terrestrial macro‐organisms, but similar links for marine micro‐organisms, which help drive global climate, are unclear. Community manipulation experiments show both positive and negative relationships for microbes. These previous studies rely, however, on artificial communities and any links between the full diversity of active bacterial communities in the environment, their phylogenetic relatedness and ecosystem function remain hitherto unexplored. Here, we test the hypothesis that productivity is associated with diversity in the metabolically active fraction of microbial communities. We show in natural assemblages of active bacteria that communities containing more distantly related members were associated with higher bacterial production. The positive phylogenetic diversity–productivity relationship was independent of community diversity calculated as the Shannon index. From our long‐term (7‐year) survey of surface marine bacterial communities, we also found that similarly, productive communities had greater phylogenetic similarity to each other, further suggesting that the traits of active bacteria are an important predictor of ecosystem productivity. Our findings demonstrate that the evolutionary history of the active fraction of a microbial community is critical for understanding their role in ecosystem functioning.     

Rule-based computing system for microbial interactions and communications: evolution in virtual bacterial populations

We have developed a novel rule-based computing system of microbial interactions and communications, referred to as COSMIC-Rules, for simulating evolutionary processes within populations of virtual bacteria. The model incorporates three levels: the bacterial genome, the bacterial cell and an environment inhabited by such cells. The virtual environment in COSMIC-Rules can contain multiple substances, whose relative toxicity or nutrient status is specified by the genome of the bacterium. Each substance may be distributed uniformly or in a user-defined manner. The organisms in COSMIC-Rules possess individually-defined physical locations, size, cell division status and genomes. Genes and/or gene systems are represented by abstractions that may summate sometimes complex phenotypes. Central to COSMIC-Rules is a simplified representation of bacterial species, each containing a functional genome including, where desired, extrachromosomal elements such as plasmids and/or bacteriophages. A widely applicable computer representation of biological recognition systems based on bit string matching is essential to the model. This representation permits, for example, the modelling of protein-protein interactions, receptor-ligand interactions and DNA-DNA transactions. COSMIC-Rules is intended to inform studies on bacterial adaptation and evolution, and to predict behaviour of populations of pathogenic bacteria and their viruses. The framework is constructed for parallel execution across a large number of machines and efficiently utilises a 64 processor development cluster. It will run on any Grid system and has successfully tested simulations with millions of bacteria, of multiple species and utilising multiple substrates. The model may be used for large-scale simulations where a genealogical record for individual organisms is required.     

Modelling spatial dynamics of fish

Our ability to model spatial distributions of fish populations is reviewed by describing the available modelling tools. Ultimate models of the individual's motivation for behavioural decisions are derived from evolutionary ecology. Mechanistic models for how fish sense and may respond to their surroundings are presented for vision, olfaction, hearing, the lateral line and other sensory organs. Models for learning and memory are presented, based both upon evolutionary optimization premises and upon neurological information processing and decision making. Functional tools for modelling behaviour and life histories can be categorized as belonging to an optimization or an adaptation approach. Among optimization tools, optimal foraging theory, life history theory, ideal free distribution, game theory and stochastic dynamic programming are presented. Among adaptation tools, genetic algorithms and the combination with artificial neural networks are described. The review advocates the combination of evolutionary and neurological approaches to modelling spatial dynamics of fish.     

Adaptive dynamics of Lotka-Volterra systems with trade-offs: the role of interspecific parameter dependence in branching

We determine the adaptive dynamics of a general Lotka-Volterra system containing an intraspecific parameter dependency--in the form of an explicit functional trade-off between evolving parameters--and interspecific parameter dependencies--arising from modelling species interactions. We develop expressions for the fitness of a mutant strategy in a multi-species resident environment, the position of the singular strategy in such systems and the non-mixed second-order partial derivatives of the mutant fitness. These expressions can be used to determine the evolutionary behaviour of the system. The type of behaviour expected depends on the curvature of the trade-off function and can be interpreted in a biologically intuitive manner using the rate of acceleration/deceleration of the costs implicit in the trade-off function. We show that for evolutionary branching to occur we require that one (or both) of the traded-off parameters includes an interspecific parameter dependency and that the trade-off function has weakly accelerating costs. This could have important implications for understanding the type of mechanisms that cause speciation. The general theory is motivated by using adaptive dynamics to examine evolution in a predator-prey system. The applicability of the general theory as a tool for examining specific systems is highlighted by calculating the evolutionary behaviour in a three species (prey-predator-predator) system.     

The sociobiological perspectives in the study of human evolution

The idea of a new multilevel approach to an understanding of regularities of evolution and its consequences for the study of human evolution is analysed. Three levels of evolutionary process are defined: (1) genetic level-the basic one. Memory of this level is coded, fixed, collected and translated by means of chemical structures (mainly of nucleic acids). The super-organismic system is the population. Classic natural selection functioned on this level. (2) The epigenetic level had originated with the origin of multicellular organisms and is relatively self-dependent. Regularities of this level allow the organisms to vary their phenotypes within the limits of reaction norms corresponding to the actual environment. The superorganismic system is the “family group”. Sexual selection functioned on this level and influenced the genetic one indirectly. (3) The behavioural level had originated on the high stage of evolution with the origin of species that are able to adapt themselves by behaviour. Their own means of coding, fixation, collection and translation of information have originated from symbolic signals (sounds, smells, postures, gestures, etc.) used for communication (signal or social heredity). The super-organismic system is society. Group selection functioned on this level. Sociobiology as a science is defined as “the systematic study of the biological basis of all forms of social behaviour, including sexual and parental behaviour; in all kinds of organisms including man” (Wilson, 1978, p. 10), and has become the necessary tool for the study of human evolution beginning from its origin. To kill an error is as good a service as, and sometimes even better than, the establishment of a new truth or fact.Charles Darwin     

Agent-based modelling as scientific method: a case study analysing primate social behaviour

A scientific methodology in general should provide two things: first, a means of explanation and, second, a mechanism for improving that explanation. Agent-based modelling (ABM) is a method that facilitates exploring the collective effects of individual action selection. The explanatory force of the model is the extent to which an observed meta-level phenomenon can be accounted for by the behaviour of its micro-level actors. This article demonstrates that this methodology can be applied to the biological sciences; agent-based models, like any other scientific hypotheses, can be tested, critiqued, generalized or specified. We review the state of the art for ABM as a methodology for biology and then present a case study based on the most widely published agent-based model in the biological sciences: Hemelrijk's DomWorld, a model of primate social behaviour. Our analysis shows some significant discrepancies between this model and the behaviour of the macaques, the genus used for our analysis. We also demonstrate that the model is not fragile: its other results are still valid and can be extended to compensate for these problems. This robustness is a standard advantage of experiment-based artificial intelligence modelling techniques over analytic modelling.     

全球变化对资源环境及生态系统影响的生态学理论基础

随着全球变化研究的深入,面向社会可持续发展的全球变化风险与应对、全球变化对资源环境要素时空配置与生态系统的影响评估等应用性问题正成为全球变化领域的新趋势。基于生态学范畴,本文重点梳理了资源环境的涵义及其构成要素：资源是自养生物利用无机物制造有机物及能量和物质在生物间传递过程中所消耗的一切实体,包括无机资源(太阳辐射、CO₂、O₂、水和矿质元素等)和有机资源(作为其他生物的食物资源)两类,而环境不能被生物有机体消耗或用竭。此外,阐述了全球变化组成及其引发的资源环境要素变化特征,以及当前关于生态系统对全球变化响应的研究进展,以期从生态学原理角度科学认知全球变化对资源环境及生态系统的影响过程与机制,为全球变化风险应对实践提供生态学理论基础。     

Learning and evolution in bacterial taxis: an operational amplifier circuit modeling the computational dynamics of the prokaryotic 'two component system' protein network

Adaptive behavior in unicellular organisms (i.e., bacteria) depends on highly organized networks of proteins governing purposefully the myriad of molecular processes occurring within the cellular system. For instance, bacteria are able to explore the environment within which they develop by utilizing the motility of their flagellar system as well as a sophisticated biochemical navigation system that samples the environmental conditions surrounding the cell, searching for nutrients or moving away from toxic substances or dangerous physical conditions. In this paper we discuss how proteins of the intervening signal transduction network could be modeled as artificial neurons, simulating the dynamical aspects of the bacterial taxis. The model is based on the assumption that, in some important aspects, proteins can be considered as processing elements or McCulloch-Pitts artificial neurons that transfer and process information from the bacterium's membrane surface to the flagellar motor. This simulation of bacterial taxis has been carried out on a hardware realization of a McCulloch-Pitts artificial neuron using an operational amplifier. Based on the behavior of the operational amplifier we produce a model of the interaction between CheY and FliM, elements of the prokaryotic two component system controlling chemotaxis, as well as a simulation of learning and evolution processes in bacterial taxis. On the one side, our simulation results indicate that, computationally, these protein 'switches' are similar to McCulloch-Pitts artificial neurons, suggesting a bridge between evolution and learning in dynamical systems at cellular and molecular levels and the evolutive hardware approach. On the other side, important protein 'tactilizing' properties are not tapped by the model, and this suggests further complexity steps to explore in the approach to biological molecular computing.     

Effect of variable surrounding on species creation

We constructed a model of speciation from evolution in an ecosystem consisting of a limited amount of energy recources. The species possesses genetic information, which is inherited according to the rules of the Penna model of genetic evolution. The increase in the number of the individuals of each species depends on the quality of their genotypes and the available energy resources. The decrease in number of the individuals results from genetic death or maximum-age reaching by the individual. The amount of energy resources is represented by a solution of the differential logistic equation, where the growth rate of the amount of the energy resources has been modified to include the number of individuals from all species in the ecosystem under consideration. The fluctuating surrounding is modelled with the help of the function V(x, t) = 1/4 x4 + 1/2 b(t)x2, where x represents phenotype and the coefficient b(t) shows the cos(omega t) time dependence. The closer the value x of an individual to the minimum of V(x, t), the better adapted its genotype to the surrounding. We observed that the life span of the organisms strongly depends on the value of the frequency omega. It becomes shorter the more frequent the changes of the surrounding. However, there is a tendency for the species that have a higher value of the reproduction age aR to win the competition with the other species. Another observation is that small evolutionary changes of the inherited genetic information lead to spontaneous bursts of the evolutionary activity when many new species may appear in a short period.     

介绍一种建立池塘能流模型的方法

系统生态学是对生物群落的生存、成长和死亡进行宏观研究。其系统方法论有多种处理问题的方法,特别是框图模型的应用尤其广泛,如在海洋、经济、城市、水体和陆地生态系统均有应用,但仅局限于画画框图讨论而已,并没有指出其应用的实际意义。还有一种系统动力学框图,属臆造模型。     

Neural networks for perceptual processing: from simulation tools to theories

Neural networks are modelling tools that are, in principle, able to capture the input-output behaviour of arbitrary systems that may include the dynamics of animal populations or brain circuits. While a neural network model is useful if it captures phenomenologically the behaviour of the target system in this way, its utility is amplified if key mechanisms of the model can be discovered, and identified with those of the underlying system. In this review, we first describe, at a fairly high level with minimal mathematics, some of the tools used in constructing neural network models. We then go on to discuss the implications of network models for our understanding of the system they are supposed to describe, paying special attention to those models that deal with neural circuits and brain systems. We propose that neural nets are useful for brain modelling if they are viewed in a wider computational framework originally devised by Marr. Here, neural networks are viewed as an intermediate mechanistic abstraction between 'algorithm' and 'implementation', which can provide insights into biological neural representations and their putative supporting architectures.     

资源生态化利用中的生物加工过程

自然的生态系统目前正受到现代生产方式的严重挑战,其结果造成能源短缺,资源匮乏,环境污染等问题,对人类生存构成危机。人类需要一种遵循地球生态系统规律的,自然与社会环境协调发展的生态化超现代化生产方式。其核心技术是资源生态化利用。对资源生态化系统中宏观的、介观的和微观的化学和生化过程问题进行初步探讨：宏观尺度上的生态平衡、物质与能量的循环转化,介观尺度上物种进化、繁殖与死亡,生物食物铁的形成,微观尺度上生物体内代谢过程中的物质转化与传递等。资源利用生态化的基础是生物加工过程,因此实现资源生态化利用不仅要效法自然的生态系统,还要注重现代工程技术、现代生物技术在生态化系统应用中理论和技术的创新。合理利用生物加工过程可以解决人类面临的资源、能源、环境与健康等重大问题,并实现可持续发展。     

Trophic-dynamic considerations in relating species diversity to ecosystem resilience

Complexity in the networks of interactions among and between the living and abiotic components forming ecosystems confounds the ability of ecologists to predict the economic consequences of perturbations such as species deletions in nature. Such uncertainty hampers prudent decision making about where and when to invest most intensively in species conservation programmes. Demystifying ecosystem responses to biodiversity alterations may be best achieved through the study of the interactions allowing biotic communities to compensate internally for population changes in terms of contributing to ecosystem function, or their intrinsic functional redundancy. Because individual organisms are the biologically discrete working components of ecosystems and because environmental changes are perceived at the scale of the individual, a mechanistic understanding of functional redundancy will hinge upon understanding how individuals' behaviours influence population dynamics in the complex community setting. Here, I use analytical and graphical modelling to construct a conceptual framework for predicting the conditions under which varying degrees of interspecific functional redundancy can be found in dynamic ecosystems. The framework is founded on principles related to food web successional theory, which provides some evolutionary insights for mechanistically linking functional roles of discrete, interacting organisms with the dynamics of ecosystems because energy is the currency both for ecological fitness and for food web commerce. Net productivity is considered the most contextually relevant ecosystem process variable because of its socioeconomic significance and because it ultimately subsumes all biological processes and interactions. Redundancy relative to productivity is suggested to manifest most directly as compensatory niche shifts among adaptive foragers in exploitation ecosystems, facilitating coexistence and enhancing ecosystem recovery after disturbances which alter species' relative abundances, such as extinctions. The framework further explicates how resource scarcity and environmental stochasticity may constitute 'ecosystem legacies' influencing the emergence of redundancy by shaping the background conditions for foraging behaviour evolution and, consequently, the prevalence of compensatory interactions. Because it generates experimentally testable predictions for a priori hypothesis testing about when and where varying degrees of functional redundancy are likely to be found in food webs, the framework may be useful for advancing toward the reliable knowledge of biodiversity and ecosystem function relations necessary for prudent prioritization of conservation programmes. The theory presented here introduces explanation of how increasing diversity can have a negative influence on ecosystem sustainability by altering the environment for biotic interactions and thereby changing functional compensability among biota--under particular conditions.     

How (not) to model autonomous behaviour

Autonomous systems are the result of self-sustaining processes of constitution of an identity under precarious circumstances. They may transit through different modes of dynamical engagement with their environment, from committed ongoing coping to open susceptibility to external demands. This paper discusses these two statements and presents examples of models of autonomous behaviour using methods in evolutionary robotics. A model of an agent capable of issuing self-instructions demonstrates the fragility of modelling autonomy as a function rather than as a property of a system's organization. An alternative model of behavioural preference based on homeostatic adaptation avoids this problem by establishing a mutual constraining between lower-level processes (neural dynamics and sensorimotor interaction) and higher-level metadynamics (experience-dependent, homeostatic triggering of local plasticity and re-organization). The results of these models are lessons about how strong autonomy should be approached: neither as a function, nor as a matter of external vs. internal determination.     

Investigating the properties of bio-chemical networks of artificial organisms with opposing behaviours

Organisms, be it singled-celled organisms or multi-cellular organisms, are constantly faced with opposing objectives requiring different sets of behaviours. These behaviours can be classified into two, predatory behaviours or anti-prey behaviours, with one set of behaviours causing an opposite effect to the other. A healthy organism aims to achieve its equilibrium state or to be in homeostasis. Homeostasis is achieved when a balance between the two opposing behaviours is created and maintained. This raises some questions: is there an innate mechanism that encodes for these categories of behaviours? Is there also an innate mechanism(s) that resolves conflicts and allows switching between these two opposing behaviours? If we consider artificial organisms as single-celled organisms, how do the organisms’ gene regulatory network, metabolic network and/or signalling network (their biochemical networks) maintain homeostasis of the organisms? This paper investigates the properties of the networks of best evolved artificial organisms, in order to help answer these questions, and guide the evolutionary development of controllers for artificial systems.     

Species interactions alter evolutionary responses to a novel environment

Studies of evolutionary responses to novel environments typically consider single species or perhaps pairs of interacting species. However, all organisms co-occur with many other species, resulting in evolutionary dynamics that might not match those predicted using single species approaches. Recent theories predict that species interactions in diverse systems can influence how component species evolve in response to environmental change. In turn, evolution might have consequences for ecosystem functioning. We used experimental communities of five bacterial species to show that species interactions have a major impact on adaptation to a novel environment in the laboratory. Species in communities diverged in their use of resources compared with the same species in monocultures and evolved to use waste products generated by other species. This generally led to a trade-off between adaptation to the abiotic and biotic components of the environment, such that species evolving in communities had lower growth rates when assayed in the absence of other species. Based on growth assays and on nuclear magnetic resonance (NMR) spectroscopy of resource use, all species evolved more in communities than they did in monocultures. The evolutionary changes had significant repercussions for the functioning of these experimental ecosystems: communities reassembled from isolates that had evolved in polyculture were more productive than those reassembled from isolates that had evolved in monoculture. Our results show that the way in which species adapt to new environments depends critically on the biotic environment of co-occurring species. Moreover, predicting how functioning of complex ecosystems will respond to an environmental change requires knowing how species interactions will evolve.     

A generation‐time effect on the rate of molecular evolution in bacteria

Molecular evolutionary rate varies significantly among species and a strict global molecular clock has been rejected across the tree of life. Generation time is one primary life‐history trait that influences the molecular evolutionary rate. Theory predicts that organisms with shorter generation times evolve faster because of the accumulation of more DNA replication errors per unit time. Although the generation‐time effect has been demonstrated consistently in plants and animals, the evidence of its existence in bacteria is lacking. The bacterial phylum Firmicutes offers an excellent system for testing generation‐time effect because some of its members can enter a dormant, nonreproductive endospore state in response to harsh environmental conditions. It follows that spore‐forming bacteria would—with their longer generation times—evolve more slowly than their nonspore‐forming relatives. It is therefore surprising that a previous study found no generation‐time effect in Firmicutes. Using a phylogenetic comparative approach and leveraging on a large number of Firmicutes genomes, we found sporulation significantly reduces the genome‐wide spontaneous DNA mutation rate and protein evolutionary rate. Contrary to the previous study, our results provide strong evidence that the evolutionary rates of bacteria, like those of plants and animals, are influenced by generation time.