The stability of trophic mass-balance models of marine ecosystems: a comparative analysis

Dynamic simulations of 18

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mass-balance marine trophic models are used to explore the stability of systems when briefly impacted by a fishery on the key ‘wasp–waist' populations occurring at intermediate trophic levels. The results are related to different ecosystem goal functions previously identified as representative of three attributes of ecosystems development: community complexity, homeostasis and energetics. System recovery time, the time required for all functional groups to returns to baseline level, and here used as a measure of model stability, was inversely correlated to Finn's Cycling Index, i.e. to the fraction of ecosystem throughput that is recycled, and to the mean length of trophic pathways in the systems. Systems with higher capacity to recycle detritus are systems with a higher ability to recover from perturbations. The results are in agreement with the E.P. Odum's theory of ecosystem development, where recycling is interpreted as a chief positive feedback mechanism that contributes to stability in the mature systems by preventing overshoots and destructive oscillations due to external impacts.     

Ecological modelling Internet resources

Internet resources are grouped into registers and sources of ecological models (6 addresses), sources of documents (7), sources of data (5), initiatives (5), societies (4), journals (4) frequently asked questions (7), extensive list of links to Web pages (4), search engines (5) and Usenet groups (10). At present, Internet resources are not replacing classical libraries but should be considered as complementary sources of information.     

Integrated models to support multiobjective ecological restoration decisions

Many objectives motivate ecological restoration, including improving vegetation condition, increasing the range and abundance of threatened species, and improving species richness and diversity. Although models have been used to examine the outcomes of ecological restoration, few researchers have attempted to develop models to account for multiple, potentially competing objectives. We developed a combined state‐and‐transition, species‐distribution model to predict the effects of restoration actions on vegetation condition and extent, bird diversity, and the distribution of several bird species in southeastern Australian woodlands. The actions reflected several management objectives. We then validated the models against an independent data set and investigated how the best management decision might change when objectives were valued differently. We also used model results to identify effective restoration options for vegetation and bird species under a constrained budget. In the examples we evaluated, no one action (improving vegetation condition and extent, increasing bird diversity, or increasing the probability of occurrence for threatened species) provided the best outcome across all objectives. In agricultural lands, the optimal management actions for promoting the occurrence of the Brown Treecreeper (Climacteris picumnus), an iconic threatened species, resulted in little improvement in the extent of the vegetation and a high probability of decreased vegetation condition. This result highlights that the best management action in any situation depends on how much the different objectives are valued. In our example scenario, no management or weed control were most likely to be the best management options to satisfy multiple restoration objectives. Our approach to exploring trade‐offs in management outcomes through integrated modeling and structured decision‐support approaches has wide application for situations in which trade‐offs exist between competing conservation objectives.     

Analysis and prediction of ecological and ecosystem dynamics with response structure models

A method of directly using data concerning species' responses to various combinations of levels of environmental factors is outlined. Examples of its utilization to analyze (and predict) the dynamics of the ecological processes of population growth, interspecific competition, and predation are given for simple systems; a simulation is presented wherein all of these processes interact within a simple ecosystem. A technique of summarizing the pattern and detail of variation of levels of combination of important environmental factors is also introduced. Together these methods permit application of real world information about the components of the biological community and the environment toward the modeling of ecosystems and the making of predictions concerning them to whatever degree of accuracy which may be useful.There are several major advantages of this approach: (1) It makes maximum use of real world data; the limits of its precision are determined only by the adequacy of the data available (hypothetical values also may be used where real data are unavailable and/or where such hypothetical values may be useful in model and theory development). (2) It is powerful (and applicable to the dynamics of diverse kinds of systems), mathematically very simple, and has important advantages in that non-linearities, thresholds, etc. are automatically and accurately taken into account. (3) It can be expressed numerically or graphically, and therefore is especially helpful in permitting visualization of ecological processes and the results of their action.     

Evaluating the ecological benefits of wildfire by integrating fire and ecosystem simulation models

Fire managers are now realizing that wildfires can be beneficial because they can reduce hazardous fuels and restore fire-dominated ecosystems. A software tool that assesses potential beneficial and detrimental ecological effects from wildfire would be helpful to fire management. This paper presents a simulation platform called FLEAT (Fire and Landscape Ecology Assessment Tool) that integrates several existing landscape- and stand-level simulation models to compute an ecologically based measure that describes if a wildfire is moving the burning landscape towards or away from the historical range and variation of vegetation composition. FLEAT uses a fire effects model to simulate fire severity, which is then used to predict vegetation development for 1, 10, and 100 years into the future using a landscape simulation model. The landscape is then simulated for 5000 years using parameters derived from historical data to create an historical time series that is compared to the predicted landscape composition at year 1, 10, and 100 to compute a metric that describes their similarity to the simulated historical conditions. This tool is designed to be used in operational wildfire management using the LANDFIRE spatial database so that fire managers can decide how aggressively to suppress wildfires. Validation of fire severity predictions using field data from six wildfires revealed that while accuracy is moderate (30-60%), it is mostly dictated by the quality of GIS layers input to FLEAT. Predicted 1-year landscape compositions were only 8% accurate but this was because the LANDFIRE mapped pre-fire composition accuracy was low (21%). This platform can be integrated into current readily available software products to produce an operational tool for balancing benefits of wildfire with potential dangers.     

Testing ecological models: the meaning of validation

The ecological literature reveals considerable confusion about the meaning of validation in the context of simulation models. The confusion arises as much from semantic and philosophical considerations as from the selection of validation procedures. Validation is not a procedure for testing scientific theory or for certifying the ‘truth’ of current scientific understanding, nor is it a required activity of every modelling project. Validation means that a model is acceptable for its intended use because it meets specified performance requirements.Before validation is undertaken, (1) the purpose of the model, (2) the performance criteria, and (3) the model context must be specified. The validation process can be decomposed into several components: (1) operation, (2) theory, and (3) data. Important concepts needed to understand the model evaluation process are verification, calibration, validation, credibility, and qualification. These terms are defined in a limited technical sense applicable to the evaluation of simulation models, and not as general philosophical concepts. Different tests and standards are applied to the operational, theoretical, and data components. The operational and data components can be validated; the theoretical component cannot.The most common problem with ecological and environmental models is failure to state what the validation criteria are. Criteria must be explicitly stated because there are no universal standards for selecting what test procedures or criteria to use for validation. A test based on comparison of simulated versus observed data is generally included whenever possible. Because the objective and subjective components of validation are not mutually exclusive, disagreements over the meaning of validation can only be resolved by establishing a convention.     

基于生态系统管理的湿地概念生态模型研究

湿地是地球上的一种重要生态系统,基于生态系统管理理念,进行湿地保护与管理,既是湿地科学发展的必然结果.也是当前湿地保护与管理的客观需求.湿地生态模型是以湿地生态系统作为研究对象的模型,是对湿地生态系统组成、结构、过程和功能进行简化、类比或抽象,是用来反映湿地生态系统各种过程和关系的定性或定量化工具.湿地概念生态模型是各类湿地生态模型中最基本的类型,是对湿地生态系统组成及其相互关系的一种简约的定性表达,特别是指人类活动影响下湿地生态系统因子变化及其相互关系的概念性表达.湿地概念生态模型构建的主要目的是旨在识别人类活动对湿地生态系统的驱动与胁迫,这些驱动与胁迫产生的一系列生态效应,以及湿地生态系统对此所表现出来的特征.湿地生态系统是一个多层次、多因子组成的,结构复杂、功能多样、具有多向反馈和调节机制的复杂大系统或巨系统.影响系统状态或驱动系统变化的因子众多,既有来自系统内部的、也有来自系统外部的,它们对系统造成的影响往往具有联动关系和因果效应.湿地概念生态模型就是在生态系统管理理论指导下,将这些系统因子及其关系抽象并提取出来,以"驱动-胁迫-效应-表征"为主线,判断系统变化与演化背后存在的因果关系,构建能够反映系统变化与演化特征和规律的结构性关系网络模型.湿地概念生态模型研究的意义在于在科学与决策之间架起一座桥梁,为实施湿地生态保护与管理提供指导,同时为建立湿地数量化模型奠定基础.     

Atrazine leaching into groundwater: comparison of five simulation models

Five deterministic models (BAM, GLEAMS, HYDRUS, MOUSE and TETrans) were compared to evaluate, in first order of approximation, their ability to predict different soil vulnerabilities with respect to the atrazine leachability towards shallow groundwater. Two environmental scenarios were defined on the basis of the measured groundwater contamination. The former scenario corresponded to a vulnerable sandy loam soil, the latter to a moderately vulnerable sandy loam-organic soil. The pesticide was applied on both soils for corn weed control in early June 1986, while the groundwater samplings were made in February and September 1987. The small atrazine transport into groundwater through the sandy loam-organic soil, as it was detected from the groundwater sampling in late winter, was simulated only by TET rans model. With respect to the sandy loam soil vulnerability, satisfactory simulations were performed by both TETrans and HYDRUS. The applied herbicide was leached throughout this soil towards groundwater both in February and in September 1987. Conversely, atrazine leaching simulated by GLEAMS, MOUSE and BAM compared less consistently with the measured groundwater contaminations.     

State-of-the-art of ecological modelling with emphasis on development of structural dynamic models

The paper deals with two major problems in ecological modelling today, namely how to get reliable parameters? and how to build ecosystem properties into our models? The use of new mathematical tools to answer these questions is mentioned briefly, but the main focus of the paper is on development of structural dynamic models which are models using goal functions to reflect a current change of the properties of the biological components in the models. These changes of the properties are due to the enormous adaptability of the biological components to the prevailing conditions. All species in an ecosystem attempt to obtain most biomass, i.e. to move as far away as possible from thermodynamic equilibrium which can be measured by the thermodynamic concept exergy. Consequently, exergy has been proposed as a goal function in ecological models with dynamic structure, meaning currently changed properties of the biological components and in model language currently changed parameters. An equation to compute an exergy index of a model is presented. The theoretical considerations leading to this equation are not presented here but references to literature where the basis theory can be found are given. Two case studies of structural dynamic modelling are presented: a shallow lake where the structural dynamic changes have been determined before the model was developed, and the application of biomanipulation in lake management, where the structural dynamic changes are generally known. Moreover. it is also discussed how the same idea of using exergy as a goal function in ecological modelling may be applied to facilitate the estimation of parameters.     

基于自然灾害的北京幅综合生态风险评价

经过20多年的发展,风险源评价从单一风险源扩展到多风险源,风险受体从单一受体发展到多受体,评价尺度也从种群、生态系统扩展到区域和景观水平。但总体说来,对多风险源、多过程的多个生态系统层次的风险评价尚不成熟。运用数字高程模型、干燥度分布图、植被指数、人口密度以及8种自然灾害风险源频率分布图、土地利用图、植被类型图、陆地生态系统生态资产分布图等数据,基于ArcGIS 9.2平台,综合考虑生态的脆弱性,风险源的发生频率,受体的暴露水平、危害程度等,对多风险源、多个生态系统综合的北京幅生态风险进行评价。旨在对区域综合生态风险评价方法进行探讨。评价结果显示：（1）高风险区主要为本区环渤海湾沿岸,包括天津市,河北唐山、沧州,山东滨州、东营、潍坊等沿海地带;以及本图幅西部、北部的太行山地,包括河北省、北京市及图幅西部的山西、河南部分地区;（2）较低生态风险区所占比例较高,达42.55%,可见降低生态风险有很大的潜力;（3）高强度的人类开发,尤其是在滨海地区围海造陆,将会导致沿海地区生态风险更高。     

青岛市生态足迹动态研究

利用生态足迹和生态承载力度量和评价1995—2004年间的青岛市可持续发展程度。结果发现,随着经济的发展和社会的进步青岛市的生态足迹显著上升,各类用地都呈现增加趋势,耕地增加程度最大,而水域则表现出不稳定;承载力明显下降,其中主要是耕地承载力下降,林地、水域等其他用地则有相对的提高。生态足迹和承载力的变化造成生态赤字进一步加大。将单位生态足迹内产生的GDP作为生态效率,可以发现青岛市的生态效率呈现增加趋势,表明青岛市的资源利用效率明显增加。按照1995到2004年的发展趋势,青岛在未来十年的生态赤字将进一步加大,通过科学技术提高生态效率是青岛降低生态赤字的主要途径。文章首次分析了青岛市的生态足迹动态,提出了降低生态赤字的途径,为青岛市的可持续发展提供了理论参考。     

Misspecification of within-area exposure distribution in ecological Poisson models

Ecological studies enable investigation of geographic variations in exposure to environmental variables, across groups, in relation to health outcomes measured on a geographic scale. Such studies are subject to ecological biases, including pure specification bias which arises when a nonlinear individual exposure-risk model is assumed to apply at the area level. Introduction of the within-area variance of exposure should induce a marked reduction in this source of ecological bias. Assuming several measurements per area of exposure and no confounding risk factors, we study the model including the within-area exposure variability when Gaussian within-area exposure distribution is assumed. The robustness is assessed when the within-area exposure distribution is misspecified. Two underlying exposure distributions are studied: the Gamma distribution and an unimodal mixture of two Gaussian distributions. In case of strong ecological association, this model can reduce the bias and improve the precision of the individual parameter estimates when the within-area exposure means and variances are correlated. These different models are applied to analyze the ecological association between radon concentration and childhood acute leukemia in France.

Definition and calculation of uncertainty in ecological process models

We present a method of multi-criteria assessment for the analysis of process model uncertainty that combines analysis of model structure, parameters and data requirements. There are three components in calculation and definition of uncertainty.

(1)

Assessment criteria: Uncertainty in a process model is reduced as the model can simultaneously simulate an increased number of assessment criteria selected to test specific aspects of the theory being investigated, and within acceptable limits set for those criteria. This reduces incomplete specification of the model—the characteristic that a model may explain some, but not all, of the observed features of a phenomenon. The calculation required is computation of the Pareto set which provides the list of simultaneously achieved criteria within specified ranges.     

Conditioning uncertainty in ecological models: Assessing the impact of fire management strategies

A simple simulation model has been used to investigate whether large fires in Mediterranean regions are a result of extreme weather conditions or the cumulative effect of a policy of fire suppression over decades. The model reproduced the fire regime characteristics for a wide variety of regions of Mediterranean climate in California, France and Spain. The Generalised Likelihood Uncertainty Estimation (GLUE) methodology was used to assess the possibility of multiple model parameter sets being consistent with the available calibration data. The resulting set of behavioural models was used to assess uncertainty in the predictions. The results suggested that (1) for a given region, the total area burned is much the same whether suppression or prescribed fire policies are used or not; however fire suppression enhances fire intensity and prescribed burning reduces it; (2) the proportion of large fires can be reduced, but not eliminated, using prescribed fires, especially in areas which have the highest proportion of large fires.     

Sensitivity of ecological models to their climate drivers: statistical ensembles for forcing.

Global and regional numerical models for terrestrial ecosystem dynamics require fine spatial resolution and temporally complete historical climate fields as input variables. However, because climate observations are unevenly spaced and have incomplete records, such fields need to be estimated. In addition, uncertainty in these fields associated with their estimation are rarely assessed. Ecological models are usually driven with a geostatistical model's mean estimate (kriging) of these fields without accounting for this uncertainty, much less evaluating such errors in terms of their propagation in ecological simulations. We introduce a Bayesian statistical framework to model climate observations to create spatially uniform and temporally complete fields, taking into account correlation in time and space, spatial heterogeneity, lack of normality, and uncertainty about all these factors. A key benefit of the Bayesian model is that it generates uncertainty measures for the generated fields. To demonstrate this method, we reconstruct historical monthly precipitation fields (a driver for ecological models) on a fine resolution grid for a climatically heterogeneous region in the western United States. The main goal of this work is to evaluate the sensitivity of ecological models to the uncertainty associated with prediction of their climate drivers. To assess their numerical sensitivity to predicted input variables, we generate a set of ecological model simulations run using an ensemble of different versions of the reconstructed fields. We construct such an ensemble by sampling from the posterior predictive distribution of the climate field. We demonstrate that the estimated prediction error of the climate field can be very high. We evaluate the importance of such errors in ecological model experiments using an ensemble of historical precipitation time series in simulations of grassland biogeochemical dynamics with an ecological numerical model, Century. We show how uncertainty in predicted precipitation fields is propagated into ecological model results and that this propagation had different modes. Depending on output variable, the response of model dynamics to uncertainty in inputs ranged from uncertainty in outputs that matched that of inputs to those that were muted or that were biased, as well as uncertainty that was persistent in time after input errors dropped.     

A methodological framework for integrating computational fluid dynamics and ecological models applied to juvenile freshwater mussel dispersal in the Upper Mississippi River

Interdisciplinary research in hydraulics and ecology for river management and restoration must integrate processes that occur over a wide range of spatial and temporal scales, which presents a challenge to ecohydraulics modelers. Computational fluid dynamics (CFD) models are being more widely used to determine flow fields for ecohydraulics applications. In the Upper Mississippi River (UMR), the mussel dynamics model was developed as a tool for management and conservation of freshwater mussels (Unionidae), which are benthic organisms, imperiled in North America, that are inextricably linked with the hydraulics of river flow. We updated the juvenile dispersal component of the mussel dynamics model by using stochastic Lagrangian particle tracking in a three dimensional flow field output from CFD models of reaches in the UMR. We developed a methodological framework to integrate hydrodynamic data with the mussel dynamics model, and we demonstrate the use of the juvenile dispersal model employed within the methodological framework in two reaches of the UMR. The method was used to test the hypothesis that impoundment affects the relationship of some hydraulic parameters with juvenile settling distribution. Simulation results were consistent with this hypothesis, and the relationships of bed shear stress and Froude number with juvenile settling were altered by impoundment most likely through effects on local hydraulics. The methodological framework is robust, integrates Eulerian and Lagrangian reference frameworks, and incorporates processes over a wide range of temporal and spatial scales, from watershed scale hydrologic processes (decades), to reach scale (km) processes that occur over hours or days, and turbulent processes on spatial scales of meter to millimeter and times scales of seconds. The methods are presently being used to assess the impacts of pre- and early post-settlement processes on mussel distributions, including the effects of bed shear stress, and the sensitivity of the location of the host fish when juveniles excyst, on juvenile settling distribution.     

Measuring impacts on species with models and metrics of varying ecological and computational complexity

Approaches to assess the impacts of landscape disturbance scenarios on species range from metrics based on patterns of occurrence or habitat to comprehensive models that explicitly include ecological processes. The choice of metrics and models affects how impacts are interpreted and conservation decisions. We explored the impacts of 3 realistic disturbance scenarios on 4 species with different ecological and taxonomic traits. We used progressively more complex models and metrics to evaluate relative impact and rank of scenarios on the species. Models ranged from species distribution models that relied on implicit assumptions about environmental factors and species presence to highly parameterized spatially explicit population models that explicitly included ecological processes and stochasticity. Metrics performed consistently in ranking different scenarios in order of severity primarily when variation in impact was driven by habitat amount. However, they differed in rank for cases where dispersal dynamics were critical in influencing metapopulation persistence. Impacts of scenarios on species with low dispersal ability were better characterized using models that explicitly captured these processes. Metapopulation capacity provided rank orders that most consistently correlated with those from highly parameterized and data-rich models and incorporated information about dispersal with little additional computational and data cost. Our results highlight the importance of explicitly considering species’ ecology, spatial configuration of habitat, and disturbance when choosing indicators of species persistence. We suggest using hybrid approaches that are a mixture of simple and complex models to improve multispecies assessments.     

Implications of ecological data constraints for integrated policy and livelihoods modelling: An example from East Kalimantan, Indonesia

Policy and human livelihoods modelling increasingly demands integrated research which requires ecological expertise. However, contributions from ecologists are often based on sparse data. Rather than discounting such data, in this paper we demonstrate how ecological modelling can effectively contribute to the development of policy recommendations in spite of data constraints. In a petrol subsidy analysis in East Kalimantan, Indonesia, we accounted for ecological data uncertainty by (a) assuming large parameter value ranges and (b) conducting a robustness test for policy recommendations. In addition to data scarcity, counter-intuitive results emerged emphasising the need for model validation. These counter-intuitive results indicated that decreasing petrol prices led to increased poverty. This informed a policy recommendation to prevent the reduction of petrol prices below IDR 5500 per litre. Using two key livelihood resources (fish and honey), we found that while a traditional sensitivity analysis suggested highly robust results, a robustness test indicated that policy recommendations would change if the incorrectness of parameter values approached 50%. The results show that ecological modelling can contribute effectively in spite of sparse data to guide policy, as well as identifying future research priorities.