Combining state and transition models with dynamic Bayesian networks

Bashari et al. (2009) propose combining state and transition models (STMs) with Bayesian networks for decision support tools where the focus is on modelling the system dynamics. There is already an extension of Bayesian networks - so-called dynamic Bayesian networks (DBNs) - for explicitly modelling systems that change over time, that has also been applied in ecological modelling. In this paper we propose a combination of STMs and DBNs that overcome some of the limitations of Bashari et al.’s approach including providing an explicit representation of the next state, while retaining its advantages, such an the explicit representation of transitions. We then show that the new model can be applied iteratively to predict into the future consistently with different time frames. We use Bashari et al.’s rangeland management problem as an illustrative case study. We present a comparative complexity analysis of the different approaches, based on the structure inherent in the problem being modelled. This analysis showed that any models that explicitly represent all the transitions only remain tractable when there are natural constraints in the domain. Thus we recommend modellers should analyse these aspects of their problem before deciding whether to use the framework.     

Deriving state-and-transition models from an image series of grassland pattern dynamics

We present how state-and-transition models (STMs) may be derived from image data, providing a graphical means of understanding how ecological dynamics are driven by complex interactions among ecosystem events. A temporal sequence of imagery of fine scale vegetation patterning was acquired from close range photogrammetry (CRP) of 1 m quadrats, in a long term monitoring project of Themeda triandra (Forsskal) grasslands in north western Australia. A principal components scaling of image metrics calculated on the imagery defined the state space of the STM, and thereby characterised the different patterns found in the imagery. Using the state space, we were able to relate key events (i.e. fire and rainfall) to both the image data and aboveground biomass, and identified distinct ecological ‘phases’ and ‘transitions’ of the system. The methodology objectively constructs a STM from imagery and, in principle, may be applied to any temporal sequence of imagery captured in any event-driven system. Our approach, by integrating image data, addresses the labour constraint limiting the extensive use of STMs in managing vegetation change in arid and semiarid rangelands.     

Indicators of ecosystem function identify alternate states in the sagebrush steppe

Models of ecosystem change that incorporate nonlinear dynamics and thresholds, such as state-and-transition models (STMs), are increasingly popular tools for land management decision-making. However, few models are based on systematic collection and documentation of ecological data, and of these, most rely solely on structural indicators (species composition) to identify states and transitions. As STMs are adopted as an assessment framework throughout the United States, finding effective and efficient ways to create data-driven models that integrate ecosystem function and structure is vital. This study aims to (1) evaluate the utility of functional indicators (indicators of rangeland health, IRH) as proxies for more difficult ecosystem function measurements and (2) create a data-driven STM for the sagebrush steppe of Colorado, USA, that incorporates both ecosystem structure and function. We sampled soils, plant communities, and IRH at 41 plots with similar clayey soils but different site histories to identify potential states and infer the effects of management practices and disturbances on transitions. We found that many IRH were correlated with quantitative measures of functional indicators, suggesting that the IRH can be used to approximate ecosystem function. In addition to a reference state that functions as expected for this soil type, we identified four biotically and functionally distinct potential states, consistent with the theoretical concept of alternate states. Three potential states were related to management practices (chemical and mechanical shrub treatments and seeding history) while one was related only to ecosystem processes (erosion). IRH and potential states were also related to environmental variation (slope, soil texture), suggesting that there are environmental factors within areas with similar soils that affect ecosystem dynamics and should be noted within STMs. Our approach generated an objective, data-driven model of ecosystem dynamics for rangeland management. Our findings suggest that the IRH approximate ecosystem processes and can distinguish between alternate states and communities and identify transitions when building data-driven STMs. Functional indicators are a simple, efficient way to create data-driven models that are consistent with alternate state theory. Managers can use them to improve current model-building methods and thus apply state-and-transition models more broadly for land management decision-making.     

土地利用、覆被变化（LUCC）与环境变化关系研究进展

土地利用、覆被变化（LUCC）作为环境变化的主要原因之一,已成为全球变化研究的前沿和热点问题。文章总结了国内外 LUCC 与环境变化关系的主要研究成果和方法,继而从气候、碳循环、土壤环境、水环境以及生态环境对土地利用方式的限制等方面概括了 LUCC 与环境变化之间的关系。LUCC 通过改变大气成分和下垫面性质对气候造成影响;影响着陆地生态系统的碳循环;改变土壤的理化性质,带来土壤污染、土壤养分迁移等土壤质量问题;并且引起水体的非点源污染,影响区域的产水量和水循环。同时,环境变化对LUCC具有限制作用。不仅通过特定的气候环境直接限制土地的利用方式;还间接通过借助人类生态环境意识的改变,实现对区域土地利用强度与方式的约束。LUCC既是全球环境变化的原因,也是全球环境变化的结果。LUCC 与生态环境之间存在着复杂的、非线性的动态反馈关系。进一步探讨了当前 LUCC 与环境变化关系的主要研究方向和相关研究方法,针对目前存在的缺乏统一的指标体系,研究区域、时空尺度单一,以单要素静态研究为主,实验研究相对薄弱以及动态模拟不够等问题,提出加强跨学科综合交叉研究、注重多尺度探讨LUCC的环境效应、构建一个 LUCC 环境效应研究的统一指标体系及加强“3S”技术与模拟模型的融合等建议。为寻求更科学更合理的土地利用方式提供了基础信息。     

Empirical assessment of state-and-transition models with a long-term vegetation record from the Sonoran Desert

Resilience-based frameworks, including state-and-transition models (STM), are being increasingly called upon to inform policy and guide ecosystem management, particularly in rangelands. Yet, multiple challenges impede their effective implementation: (1) paucity of empirical tests of resilience concepts, such as alternative states and thresholds, and (2) heavy reliance on expert models, which are seldom tested against empirical data. We developed an analytical protocol to identify unique plant communities and their transitions, and applied it to a long-term vegetation record from the Sonoran Desert (1953-2009). We assessed whether empirical trends were consistent with resilience concepts, and evaluated how they may inform the construction and interpretation of expert STMs. Seven statistically distinct plant communities were identified based on the cover of 22 plant species in 68 permanent transects. We recorded 253 instances of community transitions, associated with changes in species composition between successive samplings. Expectedly, transitions were more frequent among proximate communities with similar species pools than among distant communities. But unexpectedly, communities and transitions were not strongly constrained by soil type and topography. Only 18 transitions featured disproportionately large compositional turnover (species dissimilarity ranged between 0.54 and 0.68), and these were closely associated with communities that were dominated by the common shrub (burroweed, Haplopappus tenuisecta); indicating that only some, and not all, communities may be prone to large compositional change. Temporal dynamics in individual transects illustrated four general trajectories: stability, nondirectional drift, reversibility, and directional shifts that were not reversed even after 2-3 decades. The frequency of transitions and the accompanying species dissimilarity were both positively correlated with fluctuation in precipitation, indicating that climatic drivers require more attention in STMs. Many features of the expert models, including the number of communities and participant species, were consistent with empirical trends, but expert models underrepresented recent increases in cacti while overemphasizing the introduced Lehmann's lovegrass (Eragrostis lehmanniana). Quantification of communities and transitions within long-term vegetation records presents several quantitative metrics such as transition frequency, magnitude of accompanying compositional change, presence of unidirectional trajectories, and lack of reversibility within various timescales, which can clarify resilience concepts and inform the construction and interpretation of STMs.     

Methods to summarize change among land categories across time intervals

Time-series maps have become more detailed in terms of numbers of categories and time points. Our paper proposes methods for raster datasets where detailed analysis of all categorical transitions would be initially overwhelming. We create two measurements: Incidents and States. The former is the number of times a pixel’s category changes across time intervals; the latter is the number of categories that a pixel represents across time points. The combinations of Incidents and States summarize change trajectories. We also describe categorical transitions in terms of annual flow matrices, which quantify the additional information generated by intermediate time points within the temporal extent. Our approach summarizes change at the pixel and landscape levels in ways that communicate where and how categories transition over time. These methods are useful to detect hotspots of change and to consider whether the apparent changes are real or due to map error.     

Ecological effects associated with land-use change in China's southwest agricultural landscape

Research on land-use and land-cover change, with associated effects on the ecoenvironment, is a key to understanding global change. The concept of 'ecosystem services' is also a hot issue in ecology and ecological economics. In this study, ecosystem service values are used to assess the ecological values of corresponding land-use types, so as to evaluate the ecological effects of regional land-use change. China's southwest agricultural landscape has unique ecological functions; but it also belongs to an ecologically fragile zone. Consequently, land-use change and associated ecological effects must be monitored to assure sustainable development of this area. Based on TM images in 1988, 1994 and 1999, the landscape classification maps of Yongsheng County were assessed using supervised classification and interactive modification methods. The transition matrix of land-use types and three indices of spatial patterns, patch-level, class-level and landscape-level indices, were calculated using models and GIS to examine the spatial patterns and dynamics of land use in the study area. The results show the influences of human activities and natural environmental elements, and that unused land has decreased rapidly, together with a continuous increase in forest during the past 11 years. There are also frequent intermediate transitions between farmland and unused land. An index for landscape diversity shows a tendency to increase, indicating that the proportions of each landscape element tended to average. Also, the decrease in the fractal dimension of unused land reveals that the effects of human activity are increasing. Ecological value calculations show that land-use change in Yongsheng County from 1988–1999 has resulted in positive ecological effects, with distinct spatial differences.     

Conversion or conservation? Understanding wetland change in northwest Costa Rica.

Wetlands are more threatened than any other ecosystem type, with losses exceeding 50% of their original extent worldwide. Despite the small portion of the Earth's surface that they comprise, wetlands contribute significantly to global ecosystem services. In this study, we tested the hypothesis that the location and rate of change in wetland amount in the Tempisque Basin of northwest Costa Rica is predictable from landscape setting. Our results demonstrate that a strong potential exists for developing predictive models of wetland conversion based on an understanding of wetland location and surrounding trends of land use. We found that topography was the single most important predictor of wetland conversion in this area, entraining other conversion processes, and that spatial patterns of wetland loss could consistently be predicted from landscape-level variables. Areas with highest probabilities of conversion were found in the most accessible, non-protected regions of the landscape. While Palo Verde National Park made a substantial contribution to wetland conservation, our results highlight the dependence of lower-lying protected areas on upland processes, adding a little-addressed dimension of complexity to the dialogue about protected area management. Conservation strategies aimed at reducing wetland loss in tropical habitats will benefit from careful analysis of the dominant land use system(s) at a relatively broad scale, and the subsequent development of management and policy responses that take into account dynamic opportunities and constraints in the landscape.     

The effects of landscape structure on space competition and alternative stable states

Many species that compete for space live on heterogeneous landscapes and interact at local scales. The quality, amount, and structure of landscapes may have considerable impact on the ability of species to compete or coexist, yet basic models of space competition do not include that level of detail. We model space competition between two species with positive feedback through recruitment facilitation, which creates the potential for alternative stable states to occur. We compare the predictions of a spatially implicit model with a simulation model that includes explicit space and landscape structure. We create structured landscapes in which we specify the amount of habitat and degree of fragmentation and ask how landscape structure, dispersal strategy, and scale affect the presence of alternative stable states, or bistability. We find that structured landscapes can reduce the range of parameter values that lead to bistability in our model, but they do not eliminate bistability. The type of landscape and the dispersal distance for each species also influence the amount of environmental change needed for abrupt community shifts to occur. Coexistence of the two competitors is possible under certain conditions when connectivity is low. Consequently, landscape structure may lead to considerable disparity between the predictions of simple models and actual dynamics on complex landscapes during environmental change.     

Incorporating spatial autocorrelation into cellular automata model: An application to the dynamics of Chinese tamarisk (Tamarix chinensis Lour.)

Spatial autocorrelation (SAC) is frequently encountered in most spatial data in ecology. Cellular automata (CA) models have been widely used to simulate complex spatial phenomena. However, little has been done to examine the impact of incorporating SAC into CA models. Using image-derived maps of Chinese tamarisk (Tamarix chinensis Lour.), CA models based on ordinary logistic regression (OLCA model) and autologistic regression (ALCA model) were developed to simulate landscape dynamics of T. chinensis. In this study, significant positive SAC was detected in residuals of ordinary logistic models, whereas non-significant SAC was found in autologistic models. All autologistic models obtained lower Akaike's information criterion corrected for small sample size (AICc) values than the best ordinary logistic models. Although the performance of ALCA models only satisfied the minimum requirement, ALCA models showed considerable improvement upon OLCA models. Our results suggested that the incorporation of the autocovariate term not only accounted for SAC in model residuals but also provided more accurate estimates of regression coefficients. The study also found that the neglect of SAC might affect the statistical inference on underlying mechanisms driving landscape changes and obtain false ecological conclusions and management recommendations. The ALCA model is statistically sound when coping with spatially structured data, and the adoption of the ALCA model in future landscape transition simulations may provide more precise probability maps on landscape transition, better model performance and more reasonable mechanisms that are responsible for landscape changes.     

广州东进扩张下的萝岗区土地利用与景观动态分析

以研究区1979年MSS1、990年LandsatTM、2000年LandsatETM以及2006年Aster 4个时段的卫星遥感影像为基础数据,运用RS与GIS技术手段,结合FRAGSTATS景观格局分析软件,对萝岗区近30年来的土地利用与景观格局变化进行了定量分析。研究表明：研究区未利用土地、园地及耕地面积大幅减少,林地与城镇用地面积显著增加。1979—2006年期间,2000年是一个分水岭,2000年前景观的连接度下降,破碎程度增加,景观斑块的形状趋于复杂化,景观结构组成的复杂性也趋于增加。2000年后各指数呈现了逆转的趋势,表明萝岗区为建设成为国际化生态型新城区,在后期的城市化进程中特别重视了生态环境的保护及建设。     

Multiscale advanced raster map analysis system: Definition, design and development

This paper brings together a multidisciplinary initiative to develop advanced statistical and computational techniques for analyzing, assessing, and extracting information from raster maps. This information will provide a rigorous foundation to address a wide range of applications including disease mapping, emerging infectious diseases, landscape ecological assessment, land cover trends and change detection, watershed assessment, and map accuracy assessment. It will develop an advanced map analysis system that integrates these techniques with an advanced visualization toolbox, and use the system to conduct large case studies using rich sets of raster data, primarily from remotely sensed imagery. As a result, it will be possible to study and evaluate raster maps of societal, ecological, and environmental variables to facilitate quantitative characterization and comparative analysis of geospatial trends, patterns, and phenomena. In addition to environmental and ecological studies, these techniques and tools can be used for policy decisions at national, state, and local levels, crisis management, and protection of infrastructure. Geospatial data form the foundation of an information-based society. Remote sensing has been a vastly under-utilized resource involving a multi-million dollar investment at the national levels. Even when utilized, the credibility has been at stake, largely because of lack of tools that can assess, visualize, and communicate accuracy and reliability in timely manner and at desired confidence levels. Consider an imminent 21st century scenario: What message does a multi-categorical map have about the large landscape it represents? And at what scale, and at what level of detail? Does the spatial pattern of the map reveal any societal, ecological, environmental condition of the landscape? And therefore can it be an indicator of change? How do you automate the assessment of the spatial structure and behavior of change to discover critical areas, hot spots, and their corridors? Is the map accurate? How accurate is it? How do you assess the accuracy of the map? How do we evaluate a temporal change map for change detection? What are the implications of the kind and amount of change and accuracy on what matters, whether climate change, carbon emission, water resources, urban sprawl, biodiversity, indicator species, human health, or early warning? And with what confidence? The proposed research initiative is expected to find answers to these questions and a few more that involve multi-categorical raster maps based on remote sensing and other geospatial data. It includes the development of techniques for map modeling and analysis using Markov Random Fields, geospatial statistics, accuracy assessment and change detection, upper echelons of surfaces, advanced computational techniques for geospatial data mining, and advanced visualization techniques.     

Linking species- and ecosystem-level impacts of climate change in lakes with a complex and a minimal model

To study the interaction between species- and ecosystem-level impacts of climate change, we focus on the question of how climate-induced shifts in key species affect the positive feedback loops that lock shallow lakes either in a transparent, macrophyte-dominated state or, alternatively, in a turbid, phytoplankton-dominated state. We hypothesize that climate warming will weaken the resilience of the macrophyte-dominated clear state. For the turbid state, we hypothesize that climate warming and climate-induced eutrophication will increase the dominance of cyanobacteria. Climate change will also affect shallow lakes through a changing hydrology and through climate change-induced eutrophication. We study these phenomena using two models, the full ecosystem model PCLake and a minimal dynamic model of lake phosphorus dynamics. Quantitative predictions with the complex model show that changes in nutrient loading, hydraulic loading and climate warming can all lead to shifts in ecosystem state. The minimal model helped in interpreting the non-linear behaviour of the complex model. The main output parameters of interest for water quality managers are the critical nutrient loading at which the system will switch from clear to turbid and the much lower critical nutrient loading – due to hysteresis – at which the system switches back. Another important output parameter is the chlorophyll-a level in the turbid state. For each of these three output parameters we performed a sensitivity analysis to further understand the dynamics of the complex model PCLake. This analysis showed that our model results are most sensitive to changes in temperature-dependence of cyanobacteria, planktivorous fish and zooplankton. We argue that by combining models at various levels of complexity and looking at multiple aspects of climate changes simultaneously we can develop an integrated view of the potential impact of climate change on freshwater ecosystems.     

Fish reintroductions reveal smooth transitions between lake community states

Whether communities respond smoothly or discontinuously to changing environmental conditions has important consequences for the preservation and restoration of ecosystems. Theory shows that communities may exhibit a variety of responses to environmental change, including abrupt transitions due to the existence of alternate states. However, there have been few opportunities to look for such transitions in nature. Here, we examine the impact of a two-orders-of-magnitude decrease and then increase in planktivore abundance in Wintergreen lake (Michigan, USA), caused by the extinction and reintroduction of two dominant fish species (largemouth bass, Micropterus salmoides, and bluegill, Lepomis macrochirus). Over a 16 + yr period of slow change from high planktivory to low planktivory back to high planktivory, the zooplankton community changed smoothly and predictably between states. In years of low planktivory, the zooplankton assemblage was dominated by a single, large, cladoceran species, Daphnia pulicaria, whereas in years of high planktivory, D. pulicaria disappeared and was replaced by a suite of small-bodied cladocerans. We quantified the multivariate change in zooplankton community dissimilarity and found that community state smoothly tracked changes in planktivore density in both a forward and backward direction. Thus, there was little evidence of discontinuity in this system where transitions are strongly driven by planktivory.     

Exploration of spatial morphometry and socioeconomic variables in modelling urban land use change

The incorporation of land use (LU) data with socioeconomic data is a main issue in modelling. This is as a result of difference in data model and scale. This study proposed and tested the change–pattern approach, which allows the incorporation of these data sets in modelling LU change. Focusing on LU dynamics for a selected part of the Thames Gateway within the City of London, the approach tested two different methods of input selection for the modelling operations. Variables selected from these two methods serve as inputs into several neural networks tested in order to identify the direction of change for each of the LU types within the study area. The result shows that direction of LU change across the study area could be identified when spatial morphology of the area and socioeconomic variables are considered. Some classes of change could be identified fairly accurately using landscape metrics indicating level of fragmentation, extent of LU patches, shape complexity of LU patches in combination with some socioeconomic variables.     

From teleconnection to telecoupling: taking stock of an emerging framework in land system science

Land use change is influenced by a complexity of drivers that transcend spatial, institutional and temporal scales. The analytical framework of telecoupling has recently been proposed in land system science to address this complexity, particularly the increasing importance of distal connections, flows and feedbacks characterising change in land systems. This framework holds important potential for advancing the analysis of land system change. In this article, we review the state of the art of the telecoupling framework in the land system science literature. The article traces the development of the framework from teleconnection to telecoupling and presents two approaches to telecoupling analysis currently proposed in the literature. Subsequently, we discuss a number of analytical challenges related to categorisation of systems, system boundaries, hierarchy and scale. Finally, we propose approaches to address these challenges by looking beyond land system science to theoretical perspectives from economic geography, social metabolism studies, political ecology and cultural anthropology.     

Solving multidimensional bioeconomic problems with singular-perturbation reduction methods: Application to managing pest resistance to pesticidal crops

We investigate the application of ‘singular-perturbation’ reduction methods from the dynamical-systems literature to solve continuous-time multidimensional bioeconomic models resulting from integrating economics with increasingly complex biological structures. These methods reduce multidimensional solution space to the lower-dimensional subspace confining long-term dynamics. They arise naturally in problems with state variables evolving on widely disparate time scales. In particular, we demonstrate how the methods reduce the solution space of a linear-control specification—characterized by two state variables adjusting at widely disparate rates—to a single differential equation in the slow variable. All other system variables are determined by algebraic equations. We apply singular-perturbation methods to investigate the optimal management of pest resistance to pesticidal crops. The pest population evolves on a fast-time scale, while the population's genetic composition evolves on a slow-time scale. In comparison with past work, we can more fully characterize the continuous-time dynamics associated with a complex genetic specification.     

Local disturbance cycles and the maintenance of heterogeneity across scales in marine metapopulations

In marine systems, the occurrence and implications of disturbance-recovery cycles have been revealed at the landscape level, but only in demographically open or closed systems where landscape-level dynamics are assumed to have no feedback effect on regional dynamics. We present a mussel metapopulation model to elucidate the role of landscape-level disturbance cycles for regional response of mussel populations to onshore productivity and larval transport. Landscape dynamics are generated through spatially explicit rules, and each landscape is connected to its neighbor through unidirectional larval dispersal. The role of landscape disturbance cycles in the regional system behavior is elucidated (1) in demographically open vs. demographically coupled systems, in relation to (2) onshore reproductive output and (3) the temporal scale of landscape disturbance dynamics. By controlling for spatial structure at the landscape and metapopulation levels, we first demonstrate the interaction between landscape and oceanographic connectivity. The temporal scale of disturbance cycles, as controlled by mussel colonization rate, plays a critical role in the regional behavior of the system. Indeed, fast disturbance cycles are responsible for regional synchrony in relation to onshore reproductive output. Slow disturbance cycles, however, lead to increased robustness to changes in productivity and to demographic coupling. These testable predictions indicate that the occurrence and temporal scale of local disturbance-recovery dynamics can drive large-scale variability in demographically open systems, and the response of metapopulations to changes in nearshore productivity.     

辽西大凌河流域土地利用变化及驱动力分析

从政策、流域综合治理、经济发展和技术进步、农民认知态度等4方面对影响大凌河流域土地利用变化的驱动力进行了分析。同时运用农户问卷调查和驱动力分析结果,选取影响耕地变化的社会经济和人口因子,运用主成分分析和多元迭代回归分析确定影响耕地变化的主要因子,并拟合出耕地变化的最优度模型。研究结果表明:在1987—2002年期间,农田和未利用荒地面积在不断减小,而林地、果园、草地在不断增加,但1995年后变化边际度大大减小;主成分分析表明影响土地利用变化主要影响因子是农业人口(A-POP)、总人口(T-POP)、农村经济收入(TIRE)、农林牧渔收入(IAFAF)和第三产业总产值(GTI);多元迭代回归分析表明耕地面积变化的最优回归模型中主变量是农业人口(A-POP)、总人口(T-POP)、农村经济收入(TIRE),这些变量能够解释95.1%的耕地变化。     

Increased spatial variance accompanies reorganization of two continental shelf ecosystems

Phase transitions between alternate stable states in marine ecosystems lead to disruptive changes in ecosystem services, especially fisheries productivity. We used trawl survey data spanning phase transitions in the North Pacific (Gulf of Alaska) and the North Atlantic (Scotian Shelf) to test for increases in ecosystem variability that might provide early warning of such transitions. In both time series, elevated spatial variability in a measure of community composition (ratio of cod [Gadus sp.] abundance to prey abundance) accompanied transitions between ecosystem states, and variability was negatively correlated with distance from the ecosystem transition point. In the Gulf of Alaska, where the phase transition was apparently the result of a sudden perturbation (climate regime shift), variance increased one year before the transition in mean state occurred. On the Scotian Shelf, where ecosystem reorganization was the result of persistent overfishing, a significant increase in variance occurred three years before the transition in mean state was detected. However, we could not reject the alternate explanation that increased variance may also have simply been inherent to the final stable state in that ecosystem. Increased variance has been previously observed around transition points in models, but rarely in real ecosystems, and our results demonstrate the possible management value in tracking the variance of key parameters in exploited ecosystems.