集合生态系统研究15年回顾与展望

集合生态系统(meta-ecosystem)由法国的Loreau教授等于2003年提出,是指"由跨生态系统边界的物质流、能量流和生物体流所连接起来的一系列生态系统的集合",是对只关注生物体(organism)迁移交换的集合种群(meta-population)和集合群落(meta-community)概念的外推,也是为了给生态系统空间异质性研究提供一个重要的分析路径,对研究和理解生态系统的结构、过程、功能和异质性具有重要意义。通过对相关文献的梳理分析,简述了集合生态系统研究的基本状况,分析了对集合生态系统概念的狭义和广义两种理解,指出了探讨集合生态系统结构的两个方向,构建了分析集合生态系统研究的六维整体框架,综述了研究集合生态系统的两类方法,探讨了经验化的集合生态系统(empirical meta-ecosystem)的3种空间结构和两种构建路径。将集合生态系统概念和理论引入流域复合生态系统(integrated watershed ecosystem)的分析,为流域生态学研究提供新的概念框架。     

Coupled land use and ecological models reveal emergence and feedbacks in socio‐ecological systems

Understanding the dynamics of socio‐ecological systems is crucial to the development of environmentally sustainable practices. Models of social or ecological sub‐systems have greatly enhanced such understanding, but at the risk of obscuring important feedbacks and emergent effects. Integrated modelling approaches have the potential to address this shortcoming by explicitly representing linked socio‐ecological dynamics. We developed a socio‐ecological system model by coupling an existing agent‐based model of land‐use dynamics and an individual‐based model of demography and dispersal. A hypothetical case‐study was established to simulate the interaction of crops and their pollinators in a changing agricultural landscape, initialised from a spatially random distribution of natural assets. The bi‐directional coupled model predicted larger changes in crop yield and pollinator populations than a unidirectional uncoupled version. The spatial properties of the system also differed, the coupled version revealing the emergence of spatial land‐use clusters that neither supported nor required pollinators. These findings suggest that important dynamics may be missed by uncoupled modelling approaches, but that these can be captured through the combination of currently‐available, compatible model frameworks. Such model integrations are required to further fundamental understanding of socio‐ecological dynamics and thus improve management of socio‐ecological systems.     

Hierarchy measure for complex networks

Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.     

Applying stable isotopes to examine food‐web structure: an overview of analytical tools

Stable isotope analysis has emerged as one of the primary means for examining the structure and dynamics of food webs, and numerous analytical approaches are now commonly used in the field. Techniques range from simple, qualitative inferences based on the isotopic niche, to Bayesian mixing models that can be used to characterize food‐web structure at multiple hierarchical levels. We provide a comprehensive review of these techniques, and thus a single reference source to help identify the most useful approaches to apply to a given data set. We structure the review around four general questions: (1) what is the trophic position of an organism in a food web?; (2) which resource pools support consumers?; (3) what additional information does relative position of consumers in isotopic space reveal about food‐web structure?; and (4) what is the degree of trophic variability at the intrapopulation level? For each general question, we detail different approaches that have been applied, discussing the strengths and weaknesses of each. We conclude with a set of suggestions that transcend individual analytical approaches, and provide guidance for future applications in the field.     

Modeling the Effect of Amino Acids and Copper on Monoclonal Antibody Productivity and Glycosylation: A Modular Approach

In manufacturing monoclonal antibodies (mAbs), it is crucial to be able to predict how process conditions and supplements affect productivity and quality attributes, especially glycosylation. Supplemental inputs, such as amino acids and trace metals in the media, are reported to affect cell metabolism and glycosylation; quantifying their effects is essential for effective process development. We aim to present and validate, through a commercially relevant cell culture process, a technique for modeling such effects efficiently. While existing models can predict mAb production or glycosylation dynamics under specific process configurations, adapting them to new processes remains challenging, because it involves modifying the model structure and often requires some mechanistic understanding. Here, a modular modeling technique for adapting an existing model for a fed-batch Chinese hamster ovary (CHO) cell culture process without structural modifications or mechanistic insight is presented. Instead, data is used, obtained from designed experimental perturbations in media supplementation, to train and validate a supplemental input effect model, which is used to “patch” the existing model. The combined model can be used for model-based process development to improve productivity and to meet product quality targets more efficiently. The methodology and analysis are generally applicable to other CHO cell lines and cell types.     

Model-checking ecological state-transition graphs

Model-checking is a methodology developed in computer science to automatically assess the dynamics of discrete systems, by checking if a system modelled as a state-transition graph satisfies a dynamical property written as a temporal logic formula. The dynamics of ecosystems have been drawn as state-transition graphs for more than a century, ranging from state-and-transition models to assembly graphs. Model-checking can provide insights into both empirical data and theoretical models, as long as they sum up into state-transition graphs. While model-checking proved to be a valuable tool in systems biology, it remains largely underused in ecology apart from precursory applications.This article proposes to address this situation, through an inventory of existing ecological STGs and an accessible presentation of the model-checking methodology. This overview is illustrated by the application of model-checking to assess the dynamics of a vegetation pathways model. We select management scenarios by model-checking Computation Tree Logic formulas representing management goals and built from a proposed catalogue of patterns. In discussion, we sketch bridges between existing studies in ecology and available model-checking frameworks. In addition to the automated analysis of ecological state-transition graphs, we believe that defining ecological concepts with temporal logics could help clarify and compare them.     

Spatial modelling of succession-disturbance dynamics in forest ecosystems: Concepts and examples

Over the last few decades it has become increasingly obvious that disturbance, whether natural or anthropogenic in origin, is ubiquitous in ecosystems. Disturbance-related processes are now considered to be important determinants of the composition, structure and function of ecological systems. However, because disturbance and succession processes occur across a wide range of spatio-temporal scales their empirical investigation is difficult. To counter these difficulties much use has been made of spatial modelling to explore the response of ecological systems to disturbance(s) occurring at spatial scales from the individual to the landscape and above, and temporal scales from minutes to centuries. Here we consider such models by contrasting two alternative motivations for their development and use: prediction and exploration, with a focus on forested ecosystems. We consider the two approaches to be complementary rather than competing. Predictive modelling aims to combine knowledge (understanding and data) with the goal of predicting system dynamics; conversely, exploratory models focus on developing understanding in systems where uncertainty is high. Examples of exploratory modelling include model-based explorations of generic issues of criticality in ecological systems, whereas predictive models tend to be more heavily data-driven (e.g. species distribution models). By considering predictive and exploratory modelling alongside each other, we aim to illustrate the range of methods used to model succession and disturbance dynamics and the challenges involved in the model-building and evaluation processes in this arena.     

A general framework for neutral models of community dynamics

Neutral models of community dynamics are a powerful tool for ecological research, but their applications are currently limited to unrealistically simple types of dynamics and ignore much of the complexity that characterize natural ecosystems. Here, we present a new analytical framework for neutral models that unifies existing models of neutral communities and extends the applicability of existing models to a much wider spectrum of ecological phenomena. The new framework extends the concept of neutrality to fitness equivalence and in spite of its simplicity explains a wide spectrum of empirical patterns of species diversity including positive, negative and unimodal productivity–diversity relationships; gradual and highly delayed declines in species diversity with habitat loss; and positive and negative responses of species diversity to habitat heterogeneity. Surprisingly, the abundance distribution in all of these cases is given by the dispersal limited multinomial (DLM), the abundance distribution in Hubbell's zero-sum model, showing DLM's robustness and demonstrating that it cannot be used to infer the underlying community dynamics. These results support the hypothesis that ecological communities are regulated by a limited set of fundamental mechanisms much simpler than could be expected from their immense complexity.     

What kind of spatial and temporal details are required in models of heterogeneous systems?

Spatial and temporal heterogeneity can make ecological systems hard to understand and model. We propose a simple classification of the types of spatial and temporal complexity contained in ecological systems, and describe the kinds of data and models needed to account for each. We classify ecological systems by the presence of heterogeneity at the scale of study, the nature of their dynamics (linear vs non-linear), attributes of the patches that constitute the heterogeneous system, and the presence and directionality of interactions among patches. Heterogeneity in space and time are nearly equivalent in our framework. Advanced modeling skills are necessary to create appropriate mathematical representations of highly complex systems (with non-linear dynamics, patches with more than one kind of important attribute, or interactive patches). Simple models can work well when the scale of heterogeneity is much finer than the scale of observation, when low precision is sufficient, when patches interact only weakly, or when empirical approaches are used to fit functions and constants. Having a way to classify complexity in space and time in ecological systems should help ecologists to select modeling approaches consistent with their abilities and goals.     

The ecological and evolutionary implications of merging different types of networks

Interactions among species drive the ecological and evolutionary processes in ecological communities. These interactions are effectively key components of biodiversity. Studies that use a network approach to study the structure and dynamics of communities of interacting species have revealed many patterns and associated processes. Historically these studies were restricted to trophic interactions, although network approaches are now used to study a wide range of interactions, including for example the reproductive mutualisms. However, each interaction type remains studied largely in isolation from others. Merging the various interaction types within a single integrative framework is necessary if we want to further our understanding of the ecological and evolutionary dynamics of communities. Dividing the networks up is a methodological convenience as in the field the networks occur together in space and time and will be linked by shared species. Herein, we outline a conceptual framework for studying networks composed of more than one type of interaction, highlighting key questions and research areas that would benefit from their study.     

The ecological effect of phenotypic plasticity — Analyzing complex interaction networks (COIN) with agent-based models

Analyzing complex dynamics of ecological systems is complicated by two important facts: First, phenotypic plasticity allows individual organisms to adapt their reaction norms in terms of morphology, anatomy, physiology and behavior to changing local environmental conditions and trophic relationships. Secondly, individual reactions and ecological dynamics are often determined by indirect interactions through reaction chains and networks involving feedback processes.

We present an agent-based modeling framework which allows to represent and analyze ecological systems that include phenotypic changes in individual performances and indirect interactions within heterogeneous and temporal changing environments. We denote this structure of interacting components as COmplex Interaction Network (COIN).

Three examples illustrate the potential of the system to analyze complex ecological processes that incorporate changing phenotypes on the individual level:

• A model on fish population dynamics of roach (Rutilus rutilus) leads to a differentiation in fish length resulting in a conspicuous distribution that influences reproduction capability and thus indirectly the fitness.

• Modeling the reproduction phase of the passerine bird Erithacus rubecula (European Robin) illustrates variation in the behavior of higher organisms in dependence of environmental factors. Changes in reproduction success and in the proportion of different activities are the results.

• The morphological reaction of plants to changes in fundamental environmental parameters is illustrated by the black alder (Alnus glutinosa) model. Specification of physiological processes and the interaction structure on the level of modules allow to represent the reaction to changes in irradiance and temperature accurately.

Applying the COIN-approach, individual plasticity emerges as a structural and functional implication in a self-organized manner. The examples illustrate the potential to integrate existing approaches to represent detailed and complex traits for higher order organisms and to combine ecological and evolutionary aspects.

Dynamics of Reciprocal Pulsed Subsidies in Local and Meta-Ecosystems

Temporally variable and reciprocal subsidies between ecosystems are ubiquitous. These spatial flows can generate a suite of direct and indirect effects in local and meta-ecosystems. The focus of most subsidy research, however, has been on the response of consumers in recipient ecosystems to constant subsidies over very short or very long time scales. We derive a meta-ecosystem model to explicitly consider the dynamic feedbacks between local ecosystems coupled through reciprocal pulsed subsidies. We predict oscillating reinforcing and dampening effects of reciprocal pulsed herbivore flows. Maximum reinforcing effects between reciprocal pulsed herbivore flows occur when these flows are in phase with the dynamics of neighboring predators. This prediction is robust to a range of pulse quantities and frequencies. Reciprocal pulsed herbivore subsidies lead to spatial and temporal variability in the strength of trophic cascades in local and meta-ecosystems but these cascading effects are the strongest when reciprocal pulsed subsidies are temporally concentrated. When predators demonstrate a behavioral response to prey abundance, reciprocal pulsed subsidies dampen the strength of local trophic cascades but lead to strong trophic cascades across local ecosystems. The timing of reciprocal pulsed subsidies is a critical component that determines the cascading effects of spatial flows. We show that spatial and temporal variabilities in resources and consumers can have a significant influence on the strength of cascading trophic interactions; therefore, our ability to detect and understand trophic cascades may depend on the scale of inquiry of ecological studies.     

The interplay between determinism and stochasticity in childhood diseases

An important issue in the history of ecology has been the study of the relative importance of deterministic forces and processes noise in shaping the dynamics of ecological populations. We address this question by exploring the temporal dynamics of two childhood infections, measles and whooping cough, in England and Wales. We demonstrate that epidemics of whooping cough are strongly influenced by stochasticity; fully deterministic approaches cannot achieve even a qualitative fit to the observed data. In contrast, measles dynamics are extremely well explained by a deterministic model. These differences are shown to be caused by their contrasting responses to dynamical noise due to different infectious periods.     

Guiding conformation space search with an all-atom energy potential

The most significant impediment for protein structure prediction is the inadequacy of conformation space search. Conformation space is too large and the energy landscape too rugged for existing search methods to consistently find near-optimal minima. To alleviate this problem, we present model-based search, a novel conformation space search method. Model-based search uses highly accurate information obtained during search to build an approximate, partial model of the energy landscape. Model-based search aggregates information in the model as it progresses, and in turn uses this information to guide exploration toward regions most likely to contain a near-optimal minimum. We validate our method by predicting the structure of 32 proteins, ranging in length from 49 to 213 amino acids. Our results demonstrate that model-based search is more effective at finding low-energy conformations in high-dimensional conformation spaces than existing search methods. The reduction in energy translates into structure predictions of increased accuracy.     

Molecular dynamics simulations of a guaiacyl beta-O-4 lignin model compound: examination of intramolecular hydrogen bonding and conformational flexibility

The dynamical conformational behavior of a guaiacyl beta-O-4 lignin model compound has been investigated by molecular simulations. The potential energy surface of the molecule in vacuum has been examined by means of an adiabatic map, showing a large accessible conformational space with multiple energy minima separated by low barriers. Molecular dynamics simulations have been performed in vacuum and with explicit solvent molecules for 10 and 2.1 ns, respectively. Molecular dynamics trajectories recorded in vacuum have shown the molecule to be flexible and to visit a large number of conformations. Many intramolecular H-bonds have been observed, existing for more than 90% of the total simulation time. The presence of explicit solvent molecules induces a significant broadening of some regions of the accessible conformational space and also largely reduces the statistical significance of intramolecular H-bonding. Intramolecular H-bonds observed in vacuum do not persist significantly and are preferentially exchanged with intermolecular H-bonds to the surrounding solvent molecules. The theoretical results are in good agreement with experimental NMR data that do not support the existence of strong and persistent intramolecular H-bonds in solution but instead indicate that H-bonds to solvent predominate. Finally, both molecular modeling and NMR approaches predict the guaiacyl beta-O-4 structure to be flexible and indicate that intramolecular H-bonds are not strong and persistent enough to confer rigidity to the molecule in solution.     

Understanding and predicting effects of modified interactions through a qualitative analysis of community structure

Models of ecological communities are traditionally based on relationships between pairs of species, where the strengths of per capita interactions are fixed and independent of population abundance. A growing body of literature, however; describes interactions whose strength is modified by the density of either a third species or by one of the species involved in a pairwise interaction. These modified interactions have been treated as indirect effects, and the terminology addressing them is diverse and overlapping. In this paper we develop a general analytical framework based on a qualitative analysis of community structure to account for the consequence of modified interactions in complex ecological communities. Modified interactions are found to create both direct and indirect effects between species. The sign of a direct effect can change in some instances depending on the magnitude of a key variable or parameter, which leads to a threshold change in system structure and dynamics. By considering alternative structures of a community, we extend our ability to model perturbations that move the system far from a previous equilibrium. Using specific examples, we reinterpret existing results, develop hypotheses to guide experiments or management interventions, and explore the role of modified interactions and positive feedback in creating and maintaining alternative stable states. Through a qualitative analysis of community structure, system feedback is demonstrated as being key in understanding and predicting the dynamics of complex ecological communities.     

Using network models to approximate spatial point-process models

Spatial effects are fundamental to ecological and epidemiological systems, yet the incorporation of space into models is potentially complex. Fixed-edge network models (i.e. networks where each edge has the same fixed strength of interaction) are widely used to study spatial processes but they make simplistic assumptions about spatial scale and structure. Furthermore, it can be difficult to parameterize such models with empirical data. By comparison, spatial point-process models are often more realistic than fixed-edge network models, but are also more difficult to analyze. Here we develop a moment closure technique that allows us to define a fixed-edge network model which predicts the prevalence and rate of epidemic spread of a continuous spatial point-process epidemic model. This approach provides a systematic method for accurate parameterization of network models using data from continuously distributed populations (such as data on dispersal kernels). Insofar as point-process models are accurate representations of real spatial biological systems, our example also supports the view that network models are realistic representations of space.     

城市地域生态调控的空间途径——以深圳市为例

城市地域的可持续发展必须以生态环境的可持续发展为前提和保障。景观生态学主要研究地表各种景观空间格局与生态过程及其相互作用,其原理和规律为在实践中通过优化景观格局,提高生态系统稳定性提供了理论框架。以深圳市为例,尝试综合运用生态敏感性与适宜性分析和景观格局整体优化的方法,通过对生态系统的格局分析与问题诊断,构建合理、稳定的生态系统结构,保障生态系统服务功能的正常发挥。案例研究将深圳市域的整体生态结构分为自然生态空间、城镇发展空间和生态廊道3部分。城镇发展空间具有文化支持的生态功能,自然生态空间具有环境服务和生物生产的生态功能;生态廊道主要起到加强生态联系,提高生态系统稳定性,并防止城市发展空间无序蔓延的功能。城镇景观斑块镶嵌于自然生态景观基质中,并通过多种类型的生态廊道相连,形成区域生态系统的良性循环。案例研究表明,景观生态规划方法的综合应用,可以将景观生态学原理和规律比较合理的运用于实践。