景观生态学的学科前沿与发展战略

根据美国景观生态学2001年年会关于景观生态学的关键议题和研究领域特别专题的讨论,综合16位国际知名专家所发表的关于深化理论、整合协调和方法改进等方面的意见,深入论述了景观生态学的6项关键议题和10个优先研究领域。包括通过景观镶嵌体的生态流,土地利用和土地覆被变化的过程、机制,非线性动态和景观复杂性,尺度转换方法论的发展,景观指标与生态过程,人类活动与景观生态,景观格局的优化,景观保育与可持续性,资料获取与准确性评价。并对构建中国景观生态学的理论框架提出了初步的想法,即发展以格局——过程关系为中心的生态空间理论,以人类活动有序化为中心的景观生态建设理论,以发挥景观多重价值为中心的景观规划理论。     

Simulation modelling of ecological hierarchies in constructive dynamical systems

Organized complexity is a characteristic feature of ecological systems with heterogeneous components interacting at several spatio-temporal scales. The hierarchy theory is a powerful epistemological framework to describe such systems by decomposing them vertically into levels and horizontally into holons. It was at first developed in a temporal and functional perspective and then, in the context of landscape ecology, extended to a spatial and structural approach. So far, most ecological applications of this theory were restricted to observational purposes, using multi-scale analysis to describe hierarchies. In spite of an increasing attention to dynamics of hierarchically structured ecological systems, current simulation models are still very limited in their representation of self-organization in complex adaptive systems. An ontological conceptualization of the hierarchy theory is outlined, focusing on key concepts, such as levels of organization and the compound and component faces of the holons. Various existing formalisms are currently used in simulation modelling, such as system dynamics, discrete event and agent based paradigms. Their ability to express the hierarchical organization of dynamical ecological systems is discussed. It turns out that a multi-modelling approach linking all these formalisms and oriented toward the specification of a constructive dynamical system would be able to express the dynamical structure of the hierarchy (creation, destruction and change of holons) and the functional and structural links between levels of organization.     

Spatio-temporal development of forests – current trends in field methods and models

We present a critical review of current trends in research of spatio-temporal development of forests. The paper addresses (1) field methods for the development of spatially-explicit models of forest dynamics and their integration in models of forest dynamics, (2) strengths and limitations of traditional patch models versus spatially-explicit, individual-based models, and (3) the potential for moment-based methods in the analysis of forest dynamics. These topics are discussed with reference to their potential for solving open questions in the studies of forest dynamics. The study of spatio-temporal processes provides a link between pattern and process in plant communities, and plays a crucial role in understanding ecosystem dynamics. In the last decade, the development of spatially-explicit, individual-based models shifted the focus of forest dynamics modelling from the dynamics of discrete patches to the interactions among individual organisms, thus encapsulating the theory of “neighbourhood” dynamics. In turn, the stochastic properties and the complexity of spatially-explicit, individual-based models gave rise to the development of a new suite of so-called moment-based models. These new models describe the dynamics of individuals and of pairs of individuals in terms of their densities, thus directly capturing second-order information on spatial structure. So far, this approach has not been applied to forests; we indicate extensions needed for such applications. Moment-based models may be an important complement to spatially explicit individual-based models in developing a general spatial theory of forest dynamics. However, both kinds of models currently focus on fine scales, whereas a critical issue in forest dynamics is to understand the interaction of fine-scale processes with coarser-scale disturbances. To obtain a more complete picture of forest dynamics, the relevant links and interactions between fine-, intermediate-, and coarse-scale processes ought to be identified. Intensive links between modelling work and field studies designed across different scales are a promising means to create a new perspective on forest dynamics.     

Spatial self-organization in a cyclic resource-species model

Biological communities are remarkable in their ability to form cooperative ensembles that lead to coexistence through various types of niche partitioning, usually intimately tied to spatial structure. This is especially true in microbial settings where differential expression and regulation of genes allows members of a given species to alter their lifestyle so as to fill a functional role within the community. The resulting species interactions can involve feedback, as in the case of some bacterial consortia that participate in the cooperative degradation of a given resource in a succession of steps and in such a way that certain "later" species provide catalytic support for the primary degrader. We seek to capture the essential features of such spatially extended biological systems by introducing a lattice-based stochastic spatial model (interacting particle system) with cyclic local dynamics. Here, a given site progresses through a sequence of resource and species states in a prescribed order. Furthermore, this succession of states (at a site) is assumed to form a cyclic pattern due to a natural feedback mechanism. We explore conditions under which all the species are able to coexist and consider the extent to which this coexistence requires the development of spatio-temporal patterns, including spiral waves. This self-organization, if it occurs, results when synchronization of the dynamics at the microscopic level leads to macroscopic patterns. These patterns result in consumer-driven resource fluctuations that generate a form of spatio-temporal niche partitioning. As with most models of this complexity, we employ a mixture of mathematical analysis and simulations to develop an understanding of the resulting dynamics.     

Review of forest landscape models: Types, methods, development and applications

Forest landscape models simulate forest change through time using spatially referenced data across a broad spatial scale (i.e. landscape scale) generally larger than a single forest stand. Spatial interactions between forest stands are a key component of such models. These models can incorporate other spatio-temporal processes such as natural disturbances (e.g. wildfires, hurricanes, outbreaks of native and exotic invasive pests and diseases) and human influences (e.g. harvesting and commercial thinning, planting, fire suppression). The models are increasingly used as tools for studying forest management, ecological assessment, restoration planning, and climate change. In this paper, we define forest landscape models and discuss development, components, and types of the models. We also review commonly used methods and approaches of modeling forest landscapes, their application, and their strengths and weaknesses. New developments in computer sciences, geographic information systems (GIS), remote sensing technologies, decision-support systems, and geo-spatial statistics have provided opportunities for developing a new generation of forest landscape models that are increasingly valuable for ecological research, restoration planning and resource management.     

Review of forest landscape models: Types, methods, development and applications

Forest landscape models simulate forest change through time using spatially referenced data across a broad spatial scale (i.e. landscape scale) generally larger than a single forest stand. Spatial interactions between forest stands are a key component of such models. These models can incorporate other spatio-temporal processes such as natural disturbances (e.g. wildfires, hurricanes, outbreaks of native and exotic invasive pests and diseases) and human influences (e.g. harvesting and commercial thinning, planting, fire suppression). The models are increasingly used as tools for studying forest management, ecological assessment, restoration planning, and climate change. In this paper, we define forest landscape models and discuss development, components, and types of the models. We also review commonly used methods and approaches of modeling forest landscapes, their application, and their strengths and weaknesses. New developments in computer sciences, geographic information systems (GIS), remote sensing technologies, decision-support systems, and geo-spatial statistics have provided opportunities for developing a new generation of forest landscape models that are increasingly valuable for ecological research, restoration planning and resource management.     

Semivariograms from a forest transect gap model compared with remotely sensed data

Abstract. A spatially linked version of a forest gap model, ZELIG, parameterized for the H. J. Andrews Experimental Forest, Oregon, was used to generate structural properties (i.e. biomass, leaf area, and maximum tree height) of young (80 yr), mature (140 yr), and old-growth (450 yr) Pseudotsuga menziesii (Douglas fir) forests. Semivariograms were produced at 10 and 30 m resolution to describe the spatio-temporal patterns of variation of the simulated structural features along a 5 km transect of contiguous 10 m x 10 m grid cells. These semivariograms from the simulations were compared with semivariograms from matrices of pixel digital values obtained from aerial videography of similarly aged stands. Although autocorrelative spatial patterning was absent from both the remotely sensed imagery (except at < 20 m for the 450 yr stand) and the model output, the pixel-to-pixel and plot-to-plot variances exhibited similar patterns across the chronosequence at both resolutions. This suggests that gap models are able to capture temporal aspects of landscape dynamics associated with canopy texture of Pacific Northwest forests.     

微生物相互作用中的空间自组织

微生物在全球生态系统中占据着重要地位, 其中一个重要的研究领域是微生物与环境(包括无机环境与生物环境)之间的相互作用。在生态相互作用过程中, 微生物常常通过自组织形成特定的空间模式。微生物的空间模式在种群稳定性、群落动态变化以及维持合作行为方面具有重要作用。本文中, 我们梳理了当下对微生物空间自组织及其所形成的空间模式的研究内容, 首先介绍什么是空间自组织, 再根据生态相互作用类型对自组织的空间模式进行描述, 其中重点讨论合作与竞争中的空间模式, 接着关注微生物空间自组织的过程, 最后我们指出空间自组织对整个群体的结构和功能稳定具有重要意义。研究微生物种群间相互作用中的空间模式, 有助于探索维持合作行为的新机制, 进而为微生物共生系统的构建提供新的理解。     

Spatial epidemiology: an emerging (or re-emerging) discipline

Spatial epidemiology is the study of spatial variation in disease risk or incidence. Several ecological processes can result in strong spatial patterns of such risk or incidence: for example, pathogen dispersal might be highly localized, vectors or reservoirs for pathogens might be spatially restricted, or susceptible hosts might be clumped. Here, we briefly describe approaches to spatial epidemiology that are spatially implicit, such as metapopulation models of disease transmission, and then focus on research in spatial epidemiology that is spatially explicit, such as the creation of risk maps for particular geographical areas. Although the spatial dynamics of infectious diseases are the subject of intensive study, the impacts of landscape structure on epidemiological processes have so far been neglected. The few studies that demonstrate how landscape composition (types of elements) and configuration (spatial positions of those elements) influence disease risk or incidence suggest that a true integration of landscape ecology with epidemiology will be fruitful.     

Are potential natural vegetation maps a meaningful alternative to neutral landscape models?

Abstract. In this paper, we present a short overview of neutral landscape models traditionally adopted in the landscape ecological literature to differentiate landscape patterns that are the result of simple random processes from patterns that are generated from more complex ecological processes. Then, we present another family of models based on Tuxen’ s definition of potential natural vegetation that play an important role, especially in Europe, for landscape planning and management. While neutral landscape models by their very nature do not take into account vegetation dynamics, nor abiotic constraints to vegetation distribution, the concept of potential natural vegetation includes the effects of vegetation dynamics in a spatially explicit manner. Therefore, we believe that distribution maps of potential natural vegetation may represent an ecological meaningful alternative to neutral landscape models for evaluating the effects of landscape structure on ecological processes.     

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.     

Diffusion-driven instabilities and emerging spatial patterns in patchy landscapes

Spatial variation in population densities across a landscape is a feature of many ecological systems, from self-organised patterns on mussel beds to spatially restricted insect outbreaks. It occurs as a result of environmental variation in abiotic factors and/or biotic factors structuring the spatial distribution of populations. However the ways in which abiotic and biotic factors interact to determine the existence and nature of spatial patterns in population density remain poorly understood. Here we present a new approach to studying this question by analysing a predator–prey patch-model in a heterogenous landscape. We use analytical and numerical methods originally developed for studying nearest-neighbour (juxtacrine) signalling in epithelia to explore whether and under which conditions patterns emerge. We find that abiotic and biotic factors interact to promote pattern formation. In fact, we find a rich and highly complex array of coexisting stable patterns, located within an enormous number of unstable patterns. Our simulation results indicate that many of the stable patterns have appreciable basins of attraction, making them significant in applications. We are able to identify mechanisms for these patterns based on the classical ideas of long-range inhibition and short-range activation, whereby landscape heterogeneity can modulate the spatial scales at which these processes operate to structure the populations.     

Moving beyond abundance distributions: neutral theory and spatial patterns in a tropical forest

Assessing the relative importance of different processes that determine the spatial distribution of species and the dynamics in highly diverse plant communities remains a challenging question in ecology. Previous modelling approaches often focused on single aggregated forest diversity patterns that convey limited information on the underlying dynamic processes. Here, we use recent advances in inference for stochastic simulation models to evaluate the ability of a spatially explicit and spatially continuous neutral model to quantitatively predict six spatial and non-spatial patterns observed at the 50 ha tropical forest plot on Barro Colorado Island, Panama. The patterns capture different aspects of forest dynamics and biodiversity structure, such as annual mortality rate, species richness, species abundance distribution, beta-diversity and the species–area relationship (SAR). The model correctly predicted each pattern independently and up to five patterns simultaneously. However, the model was unable to match the SAR and beta-diversity simultaneously. Our study moves previous theory towards a dynamic spatial theory of biodiversity and demonstrates the value of spatial data to identify ecological processes. This opens up new avenues to evaluate the consequences of additional process for community assembly and dynamics.     

From diversity indices to community assembly processes: a test with simulated data

Ecological theory suggests that spatial distribution of biodiversity is strongly driven by community assembly processes. Thus the study of diversity patterns combined with null model testing has become increasingly common to infer assembly processes from observed distributions of diversity indices. However, results in both empirical and simulation studies are inconsistent. The aim of our study is to determine with simulated data which facets of biodiversity, if any, may unravel the processes driving its spatial patterns, and to provide practical considerations about the combination of diversity indices that would produce significant and congruent signals when using null models. The study is based on simulated species’ assemblages that emerge under various landscape structures in a spatially explicit individual‐based model with contrasting, predefined assembly processes. We focus on four assembly processes (species‐sorting, mass effect, neutral dynamics and competition colonization trade‐off) and investigate the emerging species’ distributions with varied diversity indices (alpha, beta and gamma) measured at different spatial scales and for different diversity facets (taxonomic, functional and phylogenetic). We find that 1) the four assembly processes result in distinct spatial distributions of species under any landscape structure, 2) a broad range of diversity indices allows distinguishing between communities driven by different assembly processes, 3) null models provide congruent results only for a small fraction of diversity indices and 4) only a combination of these diversity indices allows identifying the correct assembly processes. Our study supports the inference of assembly processes from patterns of diversity only when different types of indices are combined. It highlights the need to combine phylogenetic, functional and taxonomic diversity indices at multiple spatial scales to effectively infer underlying assembly processes from diversity patterns by illustrating how combination of different indices might help disentangling the complex question of coexistence.     

Multilevel Selection in Models of Prebiotic Evolution II: A Direct Comparison of Compartmentalization and Spatial Self-Organization

Multilevel selection has been indicated as an essential factor for the evolution of complexity in interacting RNA-like replicator systems. There are two types of multilevel selection mechanisms: implicit and explicit. For implicit multilevel selection, spatial self-organization of replicator populations has been suggested, which leads to higher level selection among emergent mesoscopic spatial patterns (traveling waves). For explicit multilevel selection, compartmentalization of replicators by vesicles has been suggested, which leads to higher level evolutionary dynamics among explicitly imposed mesoscopic entities (protocells). Historically, these mechanisms have been given separate consideration for the interests on its own. Here, we make a direct comparison between spatial self-organization and compartmentalization in simulated RNA-like replicator systems. Firstly, we show that both mechanisms achieve the macroscopic stability of a replicator system through the evolutionary dynamics on mesoscopic entities that counteract that of microscopic entities. Secondly, we show that a striking difference exists between the two mechanisms regarding their possible influence on the long-term evolutionary dynamics, which happens under an emergent trade-off situation arising from the multilevel selection. The difference is explained in terms of the difference in the stability between self-organized mesoscopic entities and externally imposed mesoscopic entities. Thirdly, we show that a sharp transition happens in the long-term evolutionary dynamics of the compartmentalized system as a function of replicator mutation rate. Fourthly, the results imply that spatial self-organization can allow the evolution of stable folding in parasitic replicators without any specific functionality in the folding itself. Finally, the results are discussed in relation to the experimental synthesis of chemical Darwinian systems and to the multilevel selection theory of evolutionary biology in general. To conclude, novel evolutionary directions can emerge through interactions between the evolutionary dynamics on multiple levels of organization. Different multilevel selection mechanisms can produce a difference in the long-term evolutionary trend of identical microscopic entities.     

生态学系统的空间异质性

空间异质性是生态学系统的一个普遍牧场生,生态学家对它在生态学中的重要性已取得了比以往更深刻的认识。试图从空间异质性的含义,空间异质性与尺度和等级的关系,空间异质性的定量描述,空间异质性对生物和非生物过程的影响,以及空间异质性的动态等5个方面综述了有关空间异质性的生态学研究的新进展。     

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。     

Interactive controls of herbivory and fluvial dynamics on landscape vegetation patterns on the Tanana River floodplain, interior Alaska

Aim  We examined the interactive effects of mammalian herbivory and fluvial dynamics on vegetation dynamics and composition along the Tanana River in interior Alaska.
Location  Model parameters were obtained from field studies along the Tanana River, Alaska between Fairbanks (64°50.50' N, 147°43.30' W) and Manley Hot Springs (65°0.0' N, 150°36.0' W).
Methods  We used a spatially explicit model of landscape dynamics (ALFRESCO) to simulate vegetation changes on a 1-year time-step. The model was run for 250 years and was replicated 100 times.
Results  Increases in herbivory decreased the proportion of early successional vegetation and increased the proportion of late successional vegetation on the simulated landscape. Erosion and accretion worked as antagonists to herbivory, increasing the amount of early successional vegetation and decreasing the amount of late successional vegetation. However, the interactive effects of herbivory and erosion/accretion were especially important in determining system response, particularly in early seral vegetation types. High erosion rates, when coupled with low herbivory, greatly increased the proportion of willow on the landscape. When coupled with high herbivory, however, they greatly increased the proportion of alder on the landscape. At low levels of herbivory, alder abundance peaked at intermediate levels of erosion/accretion.
Main conclusions  Neither erosion/accretion nor herbivory produced consistent landscape patterns that could be predicted independently of the other. These findings underscore the importance of the interactive effects of biotic and abiotic disturbances in shaping large-scale landscape vegetation patterns in boreal floodplain ecosystems – systems traditionally thought to be driven primarily by abiotic disturbance alone.