Network thinking in ecology and evolution

Although pairwise interactions have always had a key role in ecology and evolutionary biology, the recent increase in the amount and availability of biological data has placed a new focus on the complex networks embedded in biological systems. The increased availability of computational tools to store and retrieve biological data has facilitated wide access to these data, not just by biologists but also by specialists from the social sciences, computer science, physics and mathematics. This fusion of interests has led to a burst of research on the properties and consequences of network structure in biological systems. Although traditional measures of network structure and function have started us off on the right foot, an important next step is to create biologically realistic models of network formation, evolution, and function. Here, we review recent applications of network thinking to the evolution of networks at the gene and protein level and to the dynamics and stability of communities. These studies have provided new insights into the organization and function of biological systems by applying existing techniques of network analysis. The current challenge is to recognize the commonalities in evolutionary and ecological applications of network thinking to create a predictive science of biological networks.     

Is sociality required for the evolution of communicative complexity? Evidence weighed against alternative hypotheses in diverse taxonomic groups

Complex social communication is expected to evolve whenever animals engage in many and varied social interactions; that is, sociality should promote communicative complexity. Yet, informal comparisons among phylogenetically independent taxonomic groups seem to cast doubt on the putative role of social factors in the evolution of complex communication. Here, we provide a formal test of the sociality hypothesis alongside alternative explanations for the evolution of communicative complexity. We compiled data documenting variations in signal complexity among closely related species for several case study groups--ants, frogs, lizards and birds--and used new phylogenetic methods to investigate the factors underlying communication evolution. Social factors were only implicated in the evolution of complex visual signals in lizards. Ecology, and to some degree allometry, were most likely explanations for complexity in the vocal signals of frogs (ecology) and birds (ecology and allometry). There was some evidence for adaptive evolution in the pheromone complexity of ants, although no compelling selection pressure was identified. For most taxa, phylogenetic null models were consistently ranked above adaptive models and, for some taxa, signal complexity seems to have accumulated in species via incremental or random changes over long periods of evolutionary time. Becoming social presumably leads to the origin of social communication in animals, but its subsequent influence on the trajectory of signal evolution has been neither clear-cut nor general among taxonomic groups.     

The Industrial Ecology of Emerging Technologies

The modern world increasingly reflects human activities, to the point that many scientists are referring to this era as the "Anthropocene," the Age of Humans. A major domain of human activity involves sociotechnical systems, which can be characterized as occurring in constellations of coevolving technological, cultural, institutional, economic, and psychological systems lasting over many decades. The current constellation, still in its early stages of development, brings together five powerful technology systems—nanotechnology, biotechnology, robotics, information and communication technology, and cognitive science—that are even more complex than historical precedents because they enable not just far more powerful capabilities to design domains external to humans but also the potential to design individual humans themselves. Understanding the implications of this sociotechnical landscape for industrial ecology suggests profound theoretical challenges as well as important new areas of research.     

合成微生物体系研究进展

合成微生物体系作为自下而上构建的人工合成微生物群落,相比于自然微生物群落具有复杂度低及可控性、可操作性强等特点。其作为新兴的生物技术,综合借鉴了合成生物学、系统生物学、生物进化等知识,通过合理的设计、规划与调控,成为研究微生物生态学理论的实验平台,以及验证已知理论的微生物系统。本文首先简单介绍了合成微生物体系的概念及其由来,阐述了其基本构建原则,随后介绍了其生态学理论基础,并总结概括了近年来的实际应用,最后提出合成微生物体系的发展前景,包括需要设计构建更为复杂的人工合成微生物群落,以及优化生态模型。     

Towards a general theory of adaptive walks on rugged landscapes

Adaptive evolution, to a large extent, is a complex combinatorial optimization process. In this article we take beginning steps towards developing a general theory of adaptive "walks" via fitter variants in such optimization processes. We introduce the basic idea of a space of entities, each a 1-mutant neighbor of many other entities in the space, and the idea of a fitness ascribed to each entity. Adaptive walks proceed from an initial entity, via fitter neighbors, to locally or globally optimal entities that are fitter than their neighbors. We develop a general theory for the number of local optima, lengths of adaptive walks, and the number of alternative local optima accessible from any given initial entity, for the baseline case of an uncorrelated fitness landscape. Most fitness landscapes are correlated, however. Therefore we develop parts of a universal theory of adaptation on correlated landscapes by adaptive processes that have sufficient numbers of mutations per individual to "jump beyond" the correlation lengths in the underlying landscape. In addition, we explore the statistical character of adaptive walks in two independent complex combinatorial optimization problems, that of evolving a specific cell type in model genetic networks, and that of finding good solutions to the traveling salesman problem. Surprisingly, both show similar statistical features, encouraging the hope that a general theory for adaptive walks on correlated and uncorrelated landscapes can be found. In the final section we explore two limits to the efficacy of selection. The first is new, and surprising: for a wide class of systems, as the complexity of the entities under selection increases, the local optima that are attainable fall progressively closer to the mean properties of the underlying space of entities. This may imply that complex biological systems, such as genetic regulatory systems, are "close" to the mean properties of the ensemble of genomic regulatory systems explored by evolution. The second limit shows that with increasing complexity and a fixed mutation rate, selection often becomes unable to pull an adapting population to those local optima to which connected adaptive walks via fitter variants exist. These beginning steps in theory development are applied to maturation of the immune response, and to the problem of radiation and stasis. Despite the limitations of the adaptive landscape metaphor, we believe that further development along the lines begun here will prove useful.     

Beyond the Food Web Connections to a Deeper Industrial Ecology

Industrial ecology is a school of thought based, in part, upon a simple analogy between industrial systems and ecological systems in terms of their material and energy flows. This article argues for a more sophisticated connection between these diverse systems based on the fact that they are all complex self-organizing systems, operating far from thermodynamic equilibrium. As such, industrial and ecological systems have in common certain constraints and dynamic properties that move beyond the central metaphor of industrial ecology and could align these systems under a more comprehensive analytical framework. If incorporated at a fundamental level, the complex systems framework could add depth and sophistication to the field of industrial ecology.     

Summary and perspective on evolutionary ecology of fishes

This special issue of Evolutionary Ecology provides ten papers that have been presented at a conference on Evolutionary Ecology of Fishes in 2009. In addition to briefly summarizing the main content of the papers which is related to adaptive radiations, processes of ecological divergence, and fisheries-induced evolution, we review and synthesize in short the recent advance in studies on evolutionary ecology of fishes. We conclude that fishes are excellent model systems to study evolutionary ecology of animals, and suggest three promising new research avenues; (1) the contribution of behavioural processes to evolution, in particular the consideration of animal personalities and predator–prey interactions, (2) metabolic physiology and parasite-host interactions as new niche dimensions to be considered for adaptive diversification, and (3) the opportunities for mechanistic understanding of adaptation and speciation emerging from new genetic tools.     

Evolutionary engineering of Corynebacterium glutamicum

A unique feature of biotechnology is that we can harness the power of evolution to improve process performance. Rational engineering of microbial strains has led to the establishment of a variety of successful bioprocesses, but it is hampered by the overwhelming complexity of biological systems. Evolutionary engineering represents a straightforward approach for fitness‐linked phenotypes (e.g., growth or stress tolerance) and is successfully applied to select for strains with improved properties for particular industrial applications. In recent years, synthetic evolution strategies have enabled selection for increased small molecule production by linking metabolic productivity to growth as a selectable trait. This review summarizes the evolutionary engineering strategies performed with the industrial platform organism Corynebacterium glutamicum. An increasing number of recent studies highlight the potential of adaptive laboratory evolution (ALE) to improve growth or stress resistance, implement the utilization of alternative carbon sources, or improve small molecule production. Advances in next‐generation sequencing and automation technologies will foster the application of ALE strategies to streamline microbial strains for bioproduction and enhance our understanding of biological systems.     

Interactions matter—complexity in landscapes and ecosystems

In this review we argue that theories and methodology arising from the field of complex systems form a new paradigm for ecology. Patterns and processes resulting from interactions between individuals, populations, species and communities in landscapes are the core topic of ecology. These interactions form complex networks, which are the subject of intense research in complexity theory, informatics and statistical mechanics. This research has shown that complex natural networks often share common structures such as loops, trees and clusters. The observed structures contribute to widespread processes including feedback, non-linear dynamics, criticality and self-organisation. Simulation modelling is a key tool in studying complex networks and has become popular in ecology, especially in adaptive management. Important techniques include cellular automata and individual-based models. The complex systems paradigm has led to advances in landscape ecology, including a deeper understanding of the dynamics of spatial pattern formation, habitat fragmentation, epidemic processes, and genetic variation. Network analysis reveals that underlying patterns of interactions, such as small worlds and clusters, in food webs and ecosystems have strong implications for their stability and dynamics. These investigations illustrate how complexity theory and associated methodologies are transforming ecological research, providing new perspectives on old questions as well as raising many new ones.     

Structural complexity analysis for industrial ecosystems: A case study on LuBei industrial ecosystem in China

Natural ecosystems and human societies have evolved in many diverse ways and they are both complex systems. Our learning from the structure complexity of natural ecosystems can help us to redesign the structure of industrial system. Thus the materials and energy efficiency of industrial systems can be improved well to achieve the sustainable goals. In this paper, Structural Analysis Method for Industrial Ecosystems (SAMIE) is introduced and applied in the analysis of the structure complexity and efficiency of the industrial ecosystems. The industrial ecosystem is analyzed based on the industrial species’ classification, which is analogous to the natural ecosystem. A set of indicators are developed to evaluate the industrial system, in order to explore the problems of structural complexity, identify the limiting factors of industrial ecosystem evolution, and strengthen the capacity of adaptation and self-organization. A case study on LuBei industrial ecosystem in China has been selected to apply the SAMIE approach.     

Next-generation approaches to the microbial ecology of food fermentations

Food fermentations have enhanced human health since the dawn of time and remain a prevalent means of food processing and preservation. Due to their cultural and nutritional importance, many of these foods have been studied in detail using molecular tools, leading to enhancements in quality and safety. Furthermore, recent advances in high-throughput sequencing technology are revolutionizing the study of food microbial ecology, deepening insight into complex fermentation systems. This review provides insight into novel applications of select molecular techniques, particularly next-generation sequencing technology, for analysis of microbial communities in fermented foods. We present a guideline for integrated molecular analysis of food microbial ecology and a starting point for implementing next-generation analysis of food systems.     

Industrial Ecology and Competitiveness

In the emerging field of industrial ecology one of the unsettled questions is the degree to which design for the environment, closing energy and materials loops, and other industrial ecology concepts apply at the firm level. In this article we examine this issue with a particular focus on whether industrial ecology can guide company strategy and efforts to enhance competitiveness.
We conclude that industrial ecology thinking will often be useful for firms seeking to improve their resource productivity and thus their competitiveness. The systems perspective that industrial ecology promotes can help companies find ways to add value or reduce costs both within their own production processes and up and down the supply chain. But industrial ecology cannot always be counted upon to yield competitive advantage at the firm level. In some cases, the cost of closing loops will exceed the benefits. In other cases, regulatory requirements do not fully internalize environmental costs, and thus polluting firms may gain temporary or permanent cost advantages relative to companies that attempt to eliminate all emissions. Finally, because industrial ecology focuses attention on materials and energy flows, it may not optimize other variables that contribute to competitiveness within the corporate setting.     

Technologies to support the creation of complex systems models--using StarLogo software with students

Research on complex, adaptive systems has made significant advances in recent years in the study of natural and social phenomena that exhibit random variation and selection, resulting in learning or evolution. Unfortunately, students (including K-12, undergraduate and graduate) in most biology programs have little opportunity to explore complex systems during the course of their studies. StarLogo and the Adventures in Modeling Curriculum [Adventures in Modeling: Exploring Complex, Dynamic Systems with StarLogo. Teachers College Press, New York] provide an easily accessible entry point into complex systems modeling for students and other novice modelers. These specialized tools can provide powerful insights into the dynamics of systems and create opportunities to explore challenging and meaningful domains in the biological sciences. Specific applications to epidemiological and ecological systems are explored, including the often debated topic of the evolution of reduced attack rates in predator-prey systems.     

Mensch and Mesh

This article discusses several relationships between technologies, industries, and socioeconomic institutions that are central to the emerging field of industrial ecology but as of yet have found little recognition. Special attention is given to the history of changes in the complexity of technologies and socioeconomic institutions, methods for dealing with this complexity conceptually and in the context of decision making, and interrelationships between technology and policy choice at various levels of system organization. On the basis of that discussion, new roles for systems thinking and modeling, systems engineering, and technology and industrial policy are identified to promote the development of industrial ecosystems that minimize their environmental impacts.     

Graph models of habitat mosaics

Graph theory is a body of mathematics dealing with problems of connectivity, flow, and routing in networks ranging from social groups to computer networks. Recently, network applications have erupted in many fields, and graph models are now being applied in landscape ecology and conservation biology, particularly for applications couched in metapopulation theory. In these applications, graph nodes represent habitat patches or local populations and links indicate functional connections among populations (i.e. via dispersal). Graphs are models of more complicated real systems, and so it is appropriate to review these applications from the perspective of modelling in general. Here we review recent applications of network theory to habitat patches in landscape mosaics. We consider (1) the conceptual model underlying these applications; (2) formalization and implementation of the graph model; (3) model parameterization; (4) model testing, insights, and predictions available through graph analyses; and (5) potential implications for conservation biology and related applications. In general, and for a variety of ecological systems, we find the graph model a remarkably robust framework for applications concerned with habitat connectivity. We close with suggestions for further work on the parameterization and validation of graph models, and point to some promising analytic insights.     

Marine Ecosystems as Complex Adaptive Systems: Emergent Patterns,Critical Transitions,and Public Goods

Complex adaptive systems provide a unified framework for explaining ecosystem phenomena. In the past 20 years, complex adaptive systems have been sharpened from an abstract concept into a series of tools that can be used to solve concrete problems. These advances have been led by the development of new techniques for coupling ecological and evolutionary dynamics, for integrating dynamics across multiple scales of organization, and for using data to infer the complex interactions among different components of ecological systems. Focusing on the development and usage of these new methods, we discuss how they have led to an improved understanding of three universal features of complex adaptive systems, emergent patterns; tipping points and critical phenomena; and cooperative behavior. We restrict our attention primarily to marine ecosystems, which provide numerous successful examples of the application of complex adaptive systems. Many of these are currently undergoing dramatic changes due to anthropogenic perturbations, and we take the opportunity to discuss how complex adaptive systems can be used to improve the management of public goods and to better preserve critical ecosystem services.     

Fostering Industrial Symbiosis With Agent-Based Simulation and Participatory Modeling

The sciences of industrial ecology, complex systems, and adaptive management are intimately related, since they deal with flows and dynamic interdependencies between system elements of various kinds. As such, the tool kit of complex systems science could enrich our understanding of how industrial ecosystems might evolve over time. In this article, I illustrate how an important tool of complex systems science— agent-based simulation —can help to identify those potential elements of an industrial ecosystem that could work together to achieve more eco-efficient outcomes. For example, I show how agent-based simulation can generate cost-efficient energy futures in which groups of firms behave more eco-efficiently by introducing strategically located clusters of renewable, low-emissions, distributed generation. I then explain how role-playing games and participatory modeling can build trust and reduce conflict about the sharing of common-pool resources such as water and energy among small clusters of evolving agents. Collective learning can encourage potential industrial partners to gradually cooperate by exchanging by-products and/or sharing common infrastructure by dint of their close proximity. This kind of coevolutionary learning, aided by participatory modeling, could help to bring about industrial symbiosis.     

Framework for Applying a Complex Adaptive System Approach to Model the Operation of Eco‐Industrial Parks

Models of eco‐industrial parks (EIPs) might help us transform our production systems by fostering the emergence of sustainable EIPs since such models have the potential to support the decision‐making processes of cooperative companies that participate and to decrease operational uncertainties. In this article, a conceptual framework for modeling the operation of EIPs is presented. The framework is underpinned by complex adaptive systems theory, industrial ecology, and an analysis of the experiences of existing EIPs. The proposed framework draws on the observed strengths of two types of industrial symbiosis models—planned eco‐industrial parks (PEIPs) and EIPs that developed through self‐organizing symbiosis (SOS)—as well as their observed weaknesses and the features of complex adaptive systems. From this analysis, five key properties to be modeled are deduced: functionality, reliability, life span, theoretical knowledge, and adaptability. It is proposed that the properties of functionality and theoretical knowledge are determined by the goals of the EIP and its member companies, while the property of adaptability is determined by the understanding that the companies in an EIP have of the environment surrounding the EIP, while the properties of reliability and life span are determined by the internal and external relationships of the companies that make up an EIP.     

Would Industrial Ecology Exist without Sustainability in the Background?

Industrial ecology rests historically—even in a short lifetime of 15 years or so—on the metaphorical power of natural ecosystems. Its evolution parallels the rise of concerns over unsustainability, that is, the threats to our world's ability to support human life the emergence of sustainability as a normative goal on a global scale. This article examines the relationships between industrial ecology and sustainability and argues that, in its historical relationship to classical ecology models, the field lacks power to address the full range of goals of sustainability, however defined. The classical ecosystem analogy omits aspects of human social and cultural life central to sustainability. But by moving beyond this model to more recent ecosystem models based on complexity theory, the field can expand its purview to address sustainability more broadly and powerfully. Complexity models of living systems can also ground alternative normative models for sustainability as an emergent property rather than the output of a mechanistic economic model for society's workings.     

Community genetics: what have we accomplished and where should we be going?

Research in community genetics seeks to understand how the dynamic interplay between ecology and evolution shapes simple and complex communities and ecosystems. A community genetics perspective, however, may not be necessary or informative for all studies and systems. To better understand when and how intraspecific genetic variation and microevolution are important in community and ecosystem ecology, we suggest future research should focus on three areas: (i) determining the relative importance of intraspecific genetic variation compared with other ecological factors in mediating community and ecosystem properties; (ii) understanding the importance of microevolution in shaping ecological dynamics in multi-trophic communities; and (iii) deciphering the phenotypic and associated genetic mechanisms that drive community and ecosystem processes. Here, we identify key areas of research that will increase our understanding of the ecology and evolution of complex communities but that are currently missing in community genetics. We then suggest experiments designed to meet these current gaps.