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.     

Spatial pattern analysis in forest dynamics: deviation from power law and direction of regeneration waves

Grid-based models have been used to understand spatial heterogeneity of the vegetation height in forests and to analyze spatio-temporal dynamics of the forest regeneration process. In this report, we present two methods of identifying lattice models when spatio-temporal data are given. The first method detects directionality of regeneration waves based on the timing of local disturbance events. The second evaluates the forest pattern by recording the fraction of high and low vegetation areas at multiple spatial scales. We illustrate these methods by applying them to patterns generated using three simple stochastic lattice models: (1) two-state model, distinguishing sites with high and low vegetation, (2) three-state model, in which sites can be in an additional disturbed state, and (3) Shimagare model, which considers a continuous range of states. The combination of the two methods provides efficient means of distinguishing the models. The first method has a more direct ecological meaning, while the second is useful when forest data are limited in time.     

基于个体的集水区森林动态模型

研制了基于个体的在景观尺度上运行的森林动态模型CFDM(individual-based catchmentscale forest dynamic model),介绍了CFDM模型的研制和检验过程,以及GIS技术的应用、气候和生理因子的参数化.对卧龙保护区一个139hm2小集水区进行了400年的模拟(包括地形数据的预处理、改进的山地小气候模型的运算和森林动态模拟).结果表明:CFDM模型能够描述林木个体的生理过程与空间分布,进而实现对整个集水区范围内森林的空间格局及其动态的模拟.     

Individual-based modelling in ecology: what makes the difference?

Is individual-based modelling really a new approach in ecology? A large part of the uncertainty surrounding this question is a consequence of imprecisely delimited boundaries between classical and individual-based modelling. Genuine 'individual-based' models describe a population made up of individuals that may differ from one another; they also describe changes in numbers of individuals rather than in the population density, and take resource dynamics explicitly into account. Individual-based models that fulfil these criteria will not characterize ecological systems as 'stable' systems in their ideal form, with equilibrium states represented by points in the phase space.     

Inferring the dynamics of a spatial epidemic from time-series data

Spatial interactions are key determinants in the dynamics of many epidemiological and ecological systems; therefore it is important to use spatio-temporal models to estimate essential parameters. However, spatially-explicit data sets are rarely available; moreover, fitting spatially-explicit models to such data can be technically demanding and computationally intensive. Thus non-spatial models are often used to estimate parameters from temporal data. We introduce a method for fitting models to temporal data in order to estimate parameters which characterise spatial epidemics. The method uses semi-spatial models and pair approximation to take explicit account of spatial clustering of disease without requiring spatial data. The approach is demonstrated for data from experiments with plant populations invaded by a common soilborne fungus, Rhizoctonia solani. Model inferences concerning the number of sources of disease and primary and secondary infections are tested against independent measures from spatio-temporal data. The applicability of the method to a wide range of host-pathogen systems is discussed.     

Epidemiological landscape models reproduce cyclic insect outbreaks

Forest insect outbreaks can have large impacts on ecosystems and understanding the underlying ecological processes is critical for their management. Current process-based modeling approaches of insect outbreaks are often based on population processes operating at small spatial scales (i.e. within individual forest stands). As such, they are difficult to parameterize and offer limited applicability when modeling and predicting outbreaks at the landscape level where management actions take place. In this paper, we propose a new process-based landscape model of forest insect outbreaks that is based on stand defoliation, the Forest-Infected-Recovering-Forest (FIRF) model. We explore both spatially-implicit (mean field equations with global dispersal) and spatially-explicit (cellular automata with limited dispersal between neighboring stands) versions of this model to assess the role of dispersal in the landscape dynamics of outbreaks. We show that density-dependent dispersal is necessary to generate cyclic outbreaks in the spatially-implicit version of the model. The spatially-explicit FIRF model with local and stochastic dispersal displays cyclic outbreaks at the landscape scale and patchy outbreaks in space, even without density-dependence. Our simple, process-based FIRF model reproduces large scale outbreaks and can provide an innovative approach to model and manage forest pests at the landscape scale.     

Functional–structural plant models: a growing paradigm for plant studies

A number of research groups in various areas of plant biology as well as computer science and applied mathematics have addressed modelling the spatiotemporal dynamics of growth and development of plants. This has resulted in development of functional–structural plant models (FSPMs). In FSPMs, the plant structure is always explicitly represented in terms of a network of elementary units. In this respect, FSPMs are different from more abstract models in which a simplified representation of the plant structure is frequently used (e.g. spatial density of leaves, total biomass, etc.). This key feature makes it possible to build modular models and creates avenues for efficient exchange of model components and experimental data. They are being used to deal with the complex 3-D structure of plants and to simulate growth and development occurring at spatial scales from cells to forest areas, and temporal scales from seconds to decades and many plant generations. The plant types studied also cover a broad spectrum, from algae to trees. This special issue of Annals of Botany features selected papers on FSPM topics such as models of morphological development, models of physical and biological processes, integrated models predicting dynamics of plants and plant communities, modelling platforms, methods for acquiring the 3-D structures of plants using automated measurements, and practical applications for agronomic purposes.     

Predicting Forest Microclimate in Heterogeneous Landscapes

Forest microclimate plays an integral role in ecosystem processes, yet a predictive understanding of its spatial and temporal variability in heterogeneous landscapes is largely lacking. In this study, we used regression kriging (RK) to analyze the degree to which physiographic versus ecological variables influence spatio-temporal variation in understory microclimate conditions. We monitored understory temperature in 200 forest plots within a 274 km² environmentally heterogeneous region in northern California (0.55 obs/km²). For each plot location, we measured four physiographic influences (elevation, coastal proximity, potential solar radiation, topographic wetness index) and three ecological drivers (forest patch size, proximity to forest edge, tree abundance). Temperature observations were aggregated to three time scales (hourly, daily, and monthly) to examine temporal variability in microclimate dynamics and its effect on spatial prediction. The obtained prediction models included both physiographic and vegetative effects, although the relative importance of individual effects varied greatly between the different models. Across time scales, elevation and coastal proximity had the most consistent physiographic effects on temperature, followed by the vegetative effects of forest patch size and distance to forest edge. RK captured significantly more landscape-scale variability in understory temperature than a regression-only approach with considerably better model performance at hourly and daily time scales than at a monthly scale. Using varied sampling density scenarios our results also suggest that predictive accuracy drops considerably at densities less than 0.34 obs/km². This research illustrates how geospatial and statistical modeling can be used to distinguish physiographic versus ecological effects on microclimate dynamics and elucidates the spatial and temporal scales that these processes operate.     

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.     

森林流域生态水文过程动力学机制与模拟研究进展

水文过程是联系气候变化和森林生态系统时空变化的关键因素．未来的气候变化和人类大尺度活动将影响森林流域生态系统与水文过程的变化,森林流域生态与水文过程耦合、生态系统水文过程动力学机制研究在认识和调控生态资源及其合理利用、区域生态恢复,以及社会经济可持续方面均具有重要意义．森林流域生态系统中的水文过程可分为降雨截留、蒸散和产汇流过程．森林流域生态与水文耦合过程边界条件的确定,土壤表面特定水文过程的参数化,降雨、土壤水分运动和植被的动态耦合作用,分布式模型的应用以及森林生态水文过程动力学机制与调控等将是今后生态水文动力学过程研究的重点,综述了森林流域生态水文过程动力学机制与调控的研究进展。     

辽东山区次生林木本植物空间分布

森林木本植物的空间格局有助于揭示群落结构的形成机制与潜在的生态学过程,且对林分经营具有一定指导意义。在0—50 m尺度范围内综合分析了辽东山区4 hm2温带次生林样地多度10的树种空间格局。研究发现:(1)在完全随机零模型下,大部分树种呈现聚集格局,聚集格局树种的比例随尺度增加而降低;在32 m的较大尺度下,随尺度增加,随机和规则格局成为树种分布的主要形式;(2)在异质性泊松过程零模型下,55.9%的树种呈现随机格局,其余大部分树种在10 m的尺度下呈现聚集格局,且随尺度增加,规则格局成为主要形式;(3)在完全随机零模型下,树种属性(林层、径级和多度)显著地影响种群聚集度,而在异质性泊松过程零模型下,树种属性对种群聚集度不存在显著影响。综上,生境异质性、扩散限制和树种属性部分解释了辽东山区次生林木本植物空间分布格局,相对而言,生境异质性的效应更为突出。研究结果有助于揭示次生林群落生物多样性的维持机制。     

Models of patch dynamics in tropical forests

Recent interest in the ecology and evolution of metapopulations and conservation of fragmented populations has stimulated the development of models that combine patch and population dynamics in tropical forests. One approach uses matrix models that are actual metapopulation or multi-regional demographic models. Another approach uses computer simulations to model forest succession based on the behavior of individual trees. We review applications of both types of models and suggest new combined modelling approaches.     

喀斯特常绿落叶阔叶林死亡个体空间分布格局及生境关联

树木死亡在森林动态中发挥着重要作用, 环境因子是影响树木死亡的重要因素。为了阐明喀斯特常绿落叶阔叶林死亡个体空间分布格局及其与环境的相关性, 本研究以木论25 ha森林动态监测样地的两次木本植物调查数据为基础, 分析了死亡个体的物种组成、径级结构、分布格局及其与生境的关联。结果表明, 样地内共有死亡个体17,306株, 隶属57科130属194种; 其中, 死亡率排名前三的科和属分别为八角枫科、蔷薇科、大风子科以及山麻杆属(Alchornea)、火棘属(Pyracantha)、八角枫属(Alangium); 在物种水平上, 山麻杆(Alchornea trewioides)、毛桐(Mallotus barbatus)、野桐(Mallotus japonicus)等死亡率较高。样地内死亡个体的平均胸径为3.83 cm, 最大47.11 cm; 径级分布呈倒“J”型。从分布格局来看, 样地内大部分死亡个体在0-50 m的尺度上呈聚集分布, 部分大径级树木死亡个体趋向于随机分布。生境关联分析发现, 个体死亡率与山顶、陡坡、缓坡、洼地呈正关联的物种数分别为41种、13种、41种和38种, 呈负关联的物种数分别为38种、67种、33种和10种, 与各生境无关联的物种数分别为4种、3种、9种和35种; 多度排名前20的物种其个体死亡率均与4种生境类型呈现出正关联或者负关联; 不同径级个体死亡率都与山顶生境呈正关联, 与缓坡生境呈负关联, 其中5 cm ≤ DBH < 10 cm和10 cm ≤ DBH < 15 cm个体死亡率均与洼地生境呈正关联; DBH ≥ 15 cm个体死亡率与陡坡生境呈正关联, 与洼地生境呈负关联。在喀斯特亚热带常绿落叶阔叶林中, 死亡个体空间分布格局总体表现为聚集分布, 树木死亡受生境作用的影响较大。     

人工林与天然林破碎化过程差异对比——以美国华盛顿州和密西西比州为例

森林损失和破碎化一直是国际社会普遍关注的重大环境问题之一。根据Forman景观变化包括穿孔、分割、破碎化、收缩和消失5种空间过程的理论,利用ArcGIS Modeler建立森林破碎化过程模型用以明确描述森林景观破碎化的空间过程和生态进程。基于NLCD2001、2006、2011 3期数据,以美国华盛顿州和密西西比州为研究区,利用森林破碎化过程模型,将森林损失斑块分为4种破碎化过程(分割类型因其线状特征被归入破碎化类型),对比分析天然林和人工林的破碎化过程在时空上的差异性。研究表明天然林破碎化斑块多分布于城市/森林、耕地/森林、以及灌木/森林的交界处,而人工林破碎化斑块分布格局较为零散;天然林中破碎化斑块和收缩斑块大多发生在上阶段收缩、破碎化以及穿孔斑块的边缘,而人工林中4种空间过程的承接关系不像天然林那么明显,但两者整体上都呈现相似的"收缩-消失-穿孔/破碎化"变化规律,主要表现为收缩类型占主导然后慢慢消退,穿孔和破碎化逐渐增多占据新的主导。     

A size-distribution-based model of forest dynamics along a latitudinal environmental gradient

A geographically extended model of the dynamics of tree size structure of forests is proposed to simulate the change of forest zonation along latitude in response to global environmental change. To predict the response of forests to global change, it is necessary to construct functional models of forest tree populations. The size-structure-based model requires far less memory and steps of calculation compared with individual-based models, and it is easy to incorporate the dimension of geographic locations into the model to describe large-scale dynamics of forest-type distributions. The effect of increasing size growth rate, expected from increasing atmospheric carbon dioxide, was diminished at the stand-level basal area density, because of regulation by one-sided competition. Model simulations of a century-long global warming at around 3 °C predicted that (1) biomass changed in resident forests rather simultaneously in response to warming, and that (2) there was a considerable time lag in movement at the boundaries of different forest types, particularly under the existence of resident forest types that would be finally replaced. It required several thousand years after a century-long warming spell for forest types to attain new steady-state distributions after shifting. As a consequence, global warming created a zigzag pattern of biomass distribution along a latitudinal gradient, i.e., an increase in the cooler-side boundary of forest types and a decrease in the warmer-side boundary.     

Minireviews: Neighborhood Effects, Disturbance Severity, and Community Stability in Forests

A theoretical framework and conceptual model for temporal stability of forest tree-species composition was developed based on a synthesis of existing studies. The model pertains primarily to time periods of several tree lifetimes (several hundred to a few thousand years) at the neighborhood and stand spatial scales (0.01–10 ha), although a few extensions to the landscape scale are also made. The cusp catastrophe was chosen to illustrate compositional dynamics at the stand level for jack pine, northern hardwood, and white pine forests in the Great Lakes Region of the United States and for tropical rainforests in the northern Amazon basin. The models feature a response surface (degree of dominance by late-successional species) that depends on two variables: type of neighborhood effects of the dominant tree species and severity of disturbances. Neighborhood effects are processes that affect the chance of a species replacing itself at the time of disturbance (they can be positive, neutral, or negative) and are of two types: overstory–undestory effects, such as the presence of advanced reproduction; and disturbance-activated effects, such as serotinous seed rain. Disturbance severity is the proportion of trees killed during a disturbance. Interactions between neighborhood effects and disturbance severity can lead to either punctuated stability (dramatic but infrequent change in composition, in those forests dominated by species with positive neighborhood effects) or succession (continuous change, in those forests dominated by species with neutral-negative neighborhood effects). We propose that neighborhood effects are a major organizing factor in forest dynamics that provide a link across spatial scales between individual trees and disturbance/patch dynamics at the stand and landscape scales. Received 23 June 1998; accepted 16 December 1998.     

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.     

Multiple successional pathways in human‐modified tropical landscapes: new insights from forest succession,forest fragmentation and landscape ecology research

Old‐growth tropical forests are being extensively deforested and fragmented worldwide. Yet forest recovery through succession has led to an expansion of secondary forests in human‐modified tropical landscapes (HMTLs). Secondary forests thus emerge as a potential repository for tropical biodiversity, and also as a source of essential ecosystem functions and services in HMTLs. Such critical roles are controversial, however, as they depend on successional, landscape and socio‐economic dynamics, which can vary widely within and across landscapes and regions. Understanding the main drivers of successional pathways of disturbed tropical forests is critically needed for improving management, conservation, and restoration strategies. Here, we combine emerging knowledge from tropical forest succession, forest fragmentation and landscape ecology research to identify the main driving forces shaping successional pathways at different spatial scales. We also explore causal connections between land‐use dynamics and the level of predictability of successional pathways, and examine potential implications of such connections to determine the importance of secondary forests for biodiversity conservation in HMTLs. We show that secondary succession (SS) in tropical landscapes is a multifactorial phenomenon affected by a myriad of forces operating at multiple spatio‐temporal scales. SS is relatively fast and more predictable in recently modified landscapes and where well‐preserved biodiversity‐rich native forests are still present in the landscape. Yet the increasing variation in landscape spatial configuration and matrix heterogeneity in landscapes with intermediate levels of disturbance increases the uncertainty of successional pathways. In landscapes that have suffered extensive and intensive human disturbances, however, succession can be slow or arrested, with impoverished assemblages and reduced potential to deliver ecosystem functions and services. We conclude that: (i) succession must be examined using more comprehensive explanatory models, providing information about the forces affecting not only the presence but also the persistence of species and ecological groups, particularly of those taxa expected to be extirpated from HMTLs; (ii) SS research should integrate new aspects from forest fragmentation and landscape ecology research to address accurately the potential of secondary forests to serve as biodiversity repositories; and (iii) secondary forest stands, as a dynamic component of HMTLs, must be incorporated as key elements of conservation planning; i.e. secondary forest stands must be actively managed (e.g. using assisted forest restoration) according to conservation goals at broad spatial scales.