基于文化与政治生态学的社会生态系统演进机制研究 ——以杭州与西湖为例

科学认识社会生态系统演进机制是对其进行有效管理的重要基础。以文化与政治生态学为理论基础,提出适合杭州—西湖社会生态系统的综合研究框架,识别了杭州—西湖社会生态系统的5个演进阶段,并分析出系统演进中的3种潜在状态。自然、政治、经济以及社会驱动力是影响杭州—西湖社会生态系统的主导因素,人类行为影响整个生态系统中扰动的频率、大小和形式并改变西湖生态系统的结构与功能,进而影响西湖为城市提供生态系统服务的潜能。在不同历史时期,基于自然、社会、经济、文化等多层面的需求,西湖在不同系统状态下为城市供给不同类别和质量的生态系统服务,总体而言供给与调节服务比例逐渐下降,文化服务逐渐上升,并且后者逐步成为最主要的生态系统服务类别。杭州与西湖在长期的互馈共生中建立了社会生态系统的自适应性调节机制,其背后的生态智慧可为现代风景园林规划提供重要启示。     

Time-dependent regimes of a tourism-based social–ecological system: Period-doubling route to chaos

The period-doubling route to chaos has occupied a prominent position and it is still object of great interest among the different complex phenomena observed in nonlinear dynamical systems. The reason of such interest is that such route to chaos has been observed in many physical, chemical and ecological models when they change over from simple periodic to complex aperiodic motion. In interlinked social–ecological systems (SESs) there might be an apparent great ability to cope with change and adapt if analysed only in their social dimension. However, such an adaptation may be at the expense of changes in the capacity of ecosystems to sustain the adaptation and it could affect the quality of ecosystem goods and services since it could degrade natural renewable and non-renewable resources and generate traps and breakpoints in the whole SES eventually leading to chaotic behaviour. This paper is rooted in previous results on modelling tourism-based SESs, only recently object of theoretical investigations, focusing on the dynamics of the coexistence between mass-tourists and eco-tourists. Here we describe a finer scale analysis of time-dependent regimes in the ranges of the degradation coefficient (bifurcation parameter), for which the system can exhibit coexistence. This bifurcation parameter is determined by objective changes in the real world in the quality of ecosystem goods and services together with whether and how such changes are perceived by different tourist typologies. Varying the bifurcation parameter, the dynamical system may in fact evolve toward an aperiodical dynamical state in many ways, showing that there could be different scenarios for the transition to chaos. This paper provides a further evidence for the period-doubling route to chaos with reference to tourism-based socio-ecological models, and for a period locking behaviour, where a small variation in the bifurcation parameter can lead to alternating regular and chaotic dynamics. Moreover, for many models undergoing chaos via period-doubling, it has been showed that structural perturbations with real ecological justification, may break and reverse the expected period-doublings, hence inhibiting chaos. This feature may be of a certain relevance also in the context of adaptive management of tourism-based SESs: these period-doubling reversals might in fact be used to control chaos, since they potentially act in way to suppress possibly dangerous fluctuations.     

Living within dynamic social-ecological freshwater systems: System parameters and the role of ecological engineering

The objective of ecological engineering is to design sustainable ecosystems that integrate human communities and their natural environment for the benefit of both. In this paper, we illustrate how social-ecological modeling can be used as a tool to clarify this objective at a landscape scale for freshwater systems. Coupled social-ecological systems (SESs) are open, dynamic systems subject to both ecological and socioeconomic perturbations. Here we demonstrate the interactive effects of social and technological uncertainties on SES dynamics over time. Additionally, we integrate research on ecosystem stability, social-ecological modeling, and ecological engineering to offer guidance for research at the human-environment interface. Based on a case study of Lake Erie's Sandusky watershed, we use an integrated human-biophysical model to investigate the influence of two parameters on SES dynamics: (1) regional societal preferences that impact watershed management and (2) technological innovation that alters agricultural nutrient efficiency. Our results illustrate ways in which SES dynamics and optimum management strategies depend on societal preferences within the region, indicating a key area of uncertainty for future investigation. As guidance for SES restoration, our model results also illustrate the conditions under which technological change that increases nutrient efficiency on farms can and cannot create a win-win, or increase both human welfare and SES resistance to eutrophication simultaneously. Using these results, we elucidate the value of ecological engineering and offer guidance for assessments of ecological engineering projects using social-ecological modeling.     

生态系统服务建模技术研究进展

在生态系统服务评估模型的数量、类型及应用大量增加的背景下,为将生态系统服务评估有效整合到决策中,系统比较、甄别不同建模工具并筛选出适合决策需求的生态系统服务评估和模拟方法尤为必要。因此,归纳并总结了国内外现有的生态系统服务评估模型的建模技术,包括:相关关系法、生物-物理过程法以及专家知识法;分别对其原理、差异、优缺点以及适用性进行了详尽阐释。大多数相关模型侧重于统计关系,相对容易创建和扩展,适用于生态系统服务的初始评估;生物-物理过程模型难以构建且不易获取,但提供了探索人-地系统相互作用和长期变化的有效机制;专家知识法有效结合了多种类型的知识体系,关注人类社会与自然系统之间反馈和交互动态的系统整合,但当评估地点发生变化时难以验证。在此基础上,介绍了基于上述3种建模技术的典型生态系统服务综合评估模型的发展和应用现状。各类建模技术面临着实用性和科学准确性之间的权衡。通过对不同建模技术的梳理与整合分析旨在提升当前生态系统服务研究的决策支撑能力,并为国内相关研究提供参考和借鉴。     

森林-农业景观格局变化的社会-生态系统模拟模型方法进展

社会-生态系统（SES）模拟模型是景观格局分析和决策的有效工具,能表征景观格局变化的社会-生态效应及景观决策的复杂反馈机制。文献综述了森林-农业景观格局的SES模型方法进展发现：（1）多数模型对景观过程与社会经济决策的反馈关系分析不足;（2）应集成多种情景模拟和景观效应分析方法,完善现有SES模型的理论方法基础;（3）通过集成格局优化模型和自主体模型会有效改进SES模型功能,具体途径包括：集成情景-生态效应的景观格局模拟方法、完善景观决策的理论基础、加强集成模型的不确定性分析、降低模型复杂性和综合定性-定量数据等。研究结果有助于理解多尺度森林-农业景观格局在社会-生态系统中的重要作用,能更好地支持跨学科集成模型开发与应用。     

Testing,tracing and isolation in compartmental models

Existing compartmental mathematical modelling methods for epidemics, such as SEIR models, cannot accurately represent effects of contact tracing. This makes them inappropriate for evaluating testing and contact tracing strategies to contain an outbreak. An alternative used in practice is the application of agent- or individual-based models (ABM). However ABMs are complex, less well-understood and much more computationally expensive. This paper presents a new method for accurately including the effects of Testing, contact-Tracing and Isolation (TTI) strategies in standard compartmental models. We derive our method using a careful probabilistic argument to show how contact tracing at the individual level is reflected in aggregate on the population level. We show that the resultant SEIR-TTI model accurately approximates the behaviour of a mechanistic agent-based model at far less computational cost. The computational efficiency is such that it can be easily and cheaply used for exploratory modelling to quantify the required levels of testing and tracing, alone and with other interventions, to assist adaptive planning for managing disease outbreaks.     

Integrating modelling of biodiversity composition and ecosystem function

There is increasing reliance on ecological models to improve our understanding of how ecological systems work, to project likely outcomes under alternative global change scenarios and to help develop robust management strategies. Two common types of spatiotemporally explicit ecological models are those focussed on biodiversity composition and those focussed on ecosystem function. These modelling disciplines are largely practiced separately, with separate literature, despite growing evidence that natural systems are shaped by the interaction of composition and function. Here we call for the development of new modelling approaches that integrate composition and function, accounting for the important interactions between these two dimensions, particularly under rapid global change. We examine existing modelling approaches that have begun to combine elements of composition and function, identifying their potential contribution to fully integrated modelling approaches. The development and application of integrated models of composition and function face a number of important challenges, including biological data limitations, system knowledge and computational constraints. We suggest a range of promising avenues that could help researchers overcome these challenges, including the use of virtual species, macroecological relationships and hybrid correlative‐mechanistic modelling. Explicitly accounting for the interactions between composition and function within integrated modelling approaches has the potential to improve our understanding of ecological systems, provide more accurate predictions of their future states and transform their management. Synthesis There is increasing attention from researchers and policy makers around the world on both assessing and projecting the state of the planet's biodiversity, its ecosystems and the essential services they provide to society. However, existing modelling approaches largely ignore the interactions between biodiversity composition and ecosystem function. We highlight the key challenges and potential solutions to developing integrated models of composition and function. Such models will require a new effort and focus from ecologists, yet the benefits are likely to be substantial, including better informing the management of natural systems at regional, national and international scales.     

Predictive models in spatially and temporally variable freshwater systems

Abstract This paper discusses the relationships between scaling and predictability in ecosystems. The logical basis of ecosystem modelling is explored using ideas first developed in complexity theory and analogies with the behaviour of complex adaptive systems. Any ecological model is a scale-dependent entity and both empirical and dynamic models of freshwater systems have their strengths and weaknesses. The logical basis of modelling using functional groups is explored. I conclude that such an approach can be justified and that such models have predictive power. Any predictive model of freshwater systems must take the major scales of external (atmospheric and catchment) forcing into account as well as the scales of key processes in the ecosystem itself. The importance of so-called ‘pink noise’ spectra, which arise both from external forcing and the internal dynamics of dynamic systems, is noted. The key scales of pattern and process in freshwater ecosystems are discussed in relation to the properties of the major functional groups. In order to have predictive power, I conclude that models of freshwater systems must include sediment exchanges and the properties of aquatic macrophytes as well as water column interactions and the pelagic components. When viewed at the scale of functional groups and the major biogeochemical processes, freshwater ecosystems may not be as complex as is often assumed.     

Modeling Metal Flow Systems

Substance flow analysis (SFA) is a frequently used industrial ecology technique for studying societal metal flows, but it is limited in its ability to inform us about future developments in metal flow patterns and how we can affect them. Equation‐based simulation modeling techniques, such as dynamic SFA and system dynamics, can usefully complement static SFA studies in this respect, but they are also restricted in several ways. The objective of this article is to demonstrate the ability of agent‐based modeling to overcome these limitations and its usefulness as a tool for studying societal metal flow systems. The body of the article summarizes the parallel implementation of two models—an agent‐based model and a system dynamics model—both addressing the following research question: What conditions foster the development of a closed‐loop flow network for metals in mobile phones? The results from in silico experimentation with these models highlight three important differences between agent‐based modeling (ABM) and equation‐based modeling (EBM) techniques. An analysis of how these differences affected the insights that could be extracted from the constructed models points to several key advantages of ABM in the study of metal flow systems. In particular, this analysis suggests that a key advantage of the ABM technique is its flexibility to enable the representation of societal metal flow systems in a more native manner. This added flexibility endows modelers with enhanced leverage to identify options for steering metal flows and opens new opportunities for using the metaphor of an ecosystem to understand metal flow systems more fully.     

Hybrid simulation modelling of networks of heterogeneous care homes and the inter-facility spread of Covid-19 by sharing staff

Although system dynamics [SD] and agent-based modelling [ABM] have individually served as effective tools to understand the Covid-19 dynamics, combining these methods in a hybrid simulation model can help address Covid-19 questions and study systems and settings that are difficult to study with a single approach. To examine the spread and outbreak of Covid-19 across multiple care homes via bank/agency staff and evaluate the effectiveness of interventions targeting this group, we develop an integrated hybrid simulation model combining the advantages of SD and ABM. We also demonstrate how we use several approaches adapted from both SD and ABM practices to build confidence in this model in response to the lack of systematic approaches to validate hybrid models. Our modelling results show that the risk of infection for residents in care homes using bank/agency staff was significantly higher than those not using bank/agency staff (Relative risk [RR] 2.65, 95% CI 2.57–2.72). Bank/agency staff working across several care homes had a higher risk of infection compared with permanent staff working in a single care home (RR 1.55, 95%CI 1.52–1.58). The RR of infection for residents is negatively correlated to bank/agency staff’s adherence to weekly PCR testing. Within a network of heterogeneous care homes, using bank/agency staff had the most impact on care homes with lower intra-facility transmission risks, higher staff-to-resident ratio, and smaller size. Forming bubbles of care homes had no or limited impact on the spread of Covid-19. This modelling study has implications for policy makers considering developing effective interventions targeting staff working across care homes during the ongoing and future pandemics.     

Giants on the landscape: modelling the abundance of megaherbivorous dinosaurs of the Morrison Formation (Late Jurassic,western USA)

The ecosystem impact of megaherbivorous dinosaurs of the Morrison Formation would have depended on their abundance (number of animals per unit of habitat area) on the landscape. We constrain Morrison megaherbivore abundance by modelling dinosaur abundance in terms of carrying capacity (K), average body mass (ABM) and animal's energy needs. Two kinds of model are presented: ‘demand-side’ models that estimate K in terms of the aggregate energy demand of the dinosaur community, and ‘supply-side’ models that estimate K in terms of retrodicted primary productivity. Baseline values of K, ABM and energy needs for the models are further derived from comparisons with modern large herbivores, and from the composition of the megaherbivore fauna from a particular stratigraphic interval of the Morrison, but in all models a broad range of fractions and multiples of these baseline parameters are considered. ‘Best-guess’ estimates of Morrison megaherbivore abundance suggest an upper limit of a few hundred animals across all taxa and size classes per square kilometre, and up to a few tens of individuals of large subadults and adults.     

A network-patch methodology for adapting agent-based models for directly transmitted disease to mosquito-borne disease

Mosquito-borne diseases cause significant public health burden and are widely re-emerging or emerging. Understanding, predicting, and mitigating the spread of mosquito-borne disease in diverse populations and geographies are ongoing modelling challenges. We propose a hybrid network-patch model for the spread of mosquito-borne pathogens that accounts for individual movement through mosquito habitats, extending the capabilities of existing agent-based models (ABMs) to include vector-borne diseases. The ABM are coupled with differential equations representing ‘clouds’ of mosquitoes in patches accounting for mosquito ecology. We adapted an ABM for humans using this method and investigated the importance of heterogeneity in pathogen spread, motivating the utility of models of individual behaviour. We observed that the final epidemic size is greater in patch models with a high risk patch frequently visited than in a homogeneous model. Our hybrid model quantifies the importance of the heterogeneity in the spread of mosquito-borne pathogens, guiding mitigation strategies.     

Phylogeography of speciation: allopatric divergence and secondary contact between outcrossing and selfing Clarkia

The origins of hybrid zones between parapatric taxa have been of particular interest for understanding the evolution of reproductive isolation and the geographic context of species divergence. One challenge has been to distinguish between allopatric divergence (followed by secondary contact) versus primary intergradation (parapatric speciation) as alternative divergence histories. Here, we use complementary phylogeographic and population genetic analyses to investigate the recent divergence of two subspecies of Clarkia xantiana and the formation of a hybrid zone within the narrow region of sympatry. We tested alternative phylogeographic models of divergence using approximate Bayesian computation (ABC) and found strong support for a secondary contact model and little support for a model allowing for gene flow throughout the divergence process (i.e. primary intergradation). Two independent methods for inferring the ancestral geography of each subspecies, one based on probabilistic character state reconstructions and the other on palaeo-distribution modelling, also support a model of divergence in allopatry and range expansion leading to secondary contact. The membership of individuals to genetic clusters suggests geographic substructure within each taxon where allopatric and sympatric samples are primarily found in separate clusters. We also observed coincidence and concordance of genetic clines across three types of molecular markers, which suggests that there is a strong barrier to gene flow. Taken together, our results provide evidence for allopatric divergence followed by range expansion leading to secondary contact. The location of refugial populations and the directionality of range expansion are consistent with expectations based on climate change since the last glacial maximum. Our approach also illustrates the utility of combining phylogeographic hypothesis testing with species distribution modelling and fine-scale population genetic analyses for inferring the geography of the divergence process.     

From theory to practice in pattern-oriented modelling: identifying and using empirical patterns in predictive models

To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.     

Modelling Southern Ocean ecosystems: krill, the food-web, and the impacts of harvesting

The ecosystem approach to fisheries recognises the interdependence between harvested species and other ecosystem components. It aims to account for the propagation of the effects of harvesting through the food-web. The formulation and evaluation of ecosystem-based management strategies requires reliable models of ecosystem dynamics to predict these effects. The krill-based system in the Southern Ocean was the focus of some of the earliest models exploring such effects. It is also a suitable example for the development of models to support the ecosystem approach to fisheries because it has a relatively simple food-web structure and progress has been made in developing models of the key species and interactions, some of which has been motivated by the need to develop ecosystem-based management. Antarctic krill, Euphausia superba, is the main target species for the fishery and the main prey of many top predators. It is therefore critical to capture the processes affecting the dynamics and distribution of krill in ecosystem dynamics models. These processes include environmental influences on recruitment and the spatially variable influence of advection. Models must also capture the interactions between krill and its consumers, which are mediated by the spatial structure of the environment. Various models have explored predator-prey population dynamics with simplistic representations of these interactions, while others have focused on specific details of the interactions. There is now a pressing need to develop plausible and practical models of ecosystem dynamics that link processes occurring at these different scales. Many studies have highlighted uncertainties in our understanding of the system, which indicates future priorities in terms of both data collection and developing methods to evaluate the effects of these uncertainties on model predictions. We propose a modelling approach that focuses on harvested species and their monitored consumers and that evaluates model uncertainty by using alternative structures and functional forms in a Monte Carlo framework.     

Ecological Infrastructure as a framework for mapping ecosystem services for place-based conservation and management

Ecosystem services are an important nexus between people and nature. Nevertheless, their inclusion in place-based conservation and management is limited also because they are often intangible. The Ecological Infrastructure (EI) concept is a promising framework to address this, but a clear definition and mapping approach is still missing. We aim to analyse the uses of EI and to distil a definition and recommendations for using EI as a framework for mapping ecosystem services. A semi-systematic review of peer-reviewed and grey literature was conducted to examine: (1) perceptions of what constitutes EI (n = 117), and (2) EI mapping approaches (n = 51). The main interpretations of EI indicated that it should be natural or naturally functioning (56%); deliver multiple services (75%); and benefit humans (64%) and biodiversity (36%). EI was thus defined as ‘natural and naturally functioning ecological systems or networks of ecological systems that deliver multiple services to humans and enable biodiversity persistence’. Studies have used simple proxies, e.g., land cover, to identify EI, sometimes combined with service-specific variables. To evaluate EI performance (26% of studies), modelling all three ecosystem service aspects (capacity, flow, and demand) was considered appropriate. EI prioritisation (50% of studies) as part of a systematic spatial prioritisation process was recommended. Sixteen recommendations for mapping EI for inclusion in place-based conservation and management were developed. We illustrate how EI can be used to integrate ecosystem services into conservation and management in three real-world applications. The EI-based framework is a promising approach and supports the new ‘people and nature’ era in conservation.     

Revisiting the Holy Grail: using plant functional traits to understand ecological processes

One of ecology's grand challenges is developing general rules to explain and predict highly complex systems. Understanding and predicting ecological processes from species' traits has been considered a ‘Holy Grail’ in ecology. Plant functional traits are increasingly being used to develop mechanistic models that can predict how ecological communities will respond to abiotic and biotic perturbations and how species will affect ecosystem function and services in a rapidly changing world; however, significant challenges remain. In this review, we highlight recent work and outstanding questions in three areas: (i) selecting relevant traits; (ii) describing intraspecific trait variation and incorporating this variation into models; and (iii) scaling trait data to community‐ and ecosystem‐level processes. Over the past decade, there have been significant advances in the characterization of plant strategies based on traits and trait relationships, and the integration of traits into multivariate indices and models of community and ecosystem function. However, the utility of trait‐based approaches in ecology will benefit from efforts that demonstrate how these traits and indices influence organismal, community, and ecosystem processes across vegetation types, which may be achieved through meta‐analysis and enhancement of trait databases. Additionally, intraspecific trait variation and species interactions need to be incorporated into predictive models using tools such as Bayesian hierarchical modelling. Finally, existing models linking traits to community and ecosystem processes need to be empirically tested for their applicability to be realized.     

Development of an Agent-Based Model (ABM) to Simulate the Immune System and Integration of a Regression Method to Estimate the Key ABM Parameters by Fitting the Experimental Data

Agent-based models (ABM) and differential equations (DE) are two commonly used methods for immune system simulation. However, it is difficult for ABM to estimate key parameters of the model by incorporating experimental data, whereas the differential equation model is incapable of describing the complicated immune system in detail. To overcome these problems, we developed an integrated ABM regression model (IABMR). It can combine the advantages of ABM and DE by employing ABM to mimic the multi-scale immune system with various phenotypes and types of cells as well as using the input and output of ABM to build up the Loess regression for key parameter estimation. Next, we employed the greedy algorithm to estimate the key parameters of the ABM with respect to the same experimental data set and used ABM to describe a 3D immune system similar to previous studies that employed the DE model. These results indicate that IABMR not only has the potential to simulate the immune system at various scales, phenotypes and cell types, but can also accurately infer the key parameters like DE model. Therefore, this study innovatively developed a complex system development mechanism that could simulate the complicated immune system in detail like ABM and validate the reliability and efficiency of model like DE by fitting the experimental data.     

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.