Invasive species impacts on ecosystem structure and function: A comparison of Oneida Lake, New York, USA, before and after zebra mussel invasion

Exotic species invasion is widely considered to affect ecosystem structure and function. Yet, few contemporary approaches can assess the effects of exotic species invasion at such an inclusive level. Our research presents one of the first attempts to examine the effects of an exotic species at the ecosystem level in a quantifiable manner. We used ecological network analysis (ENA) and a social network analysis (SNA) method called cohesion analysis to examine the effect of zebra mussel (Dreissena polymorpha) invasion on the Oneida Lake, New York, USA, food web. We used ENA to quantify ecosystem function through an analysis of food web carbon transfer that explicitly incorporated flow over all food web paths (direct and indirect). The cohesion analysis assessed ecosystem structure through an organization of food web members into subgroups of strongly interacting predators and prey. Our analysis detected effects of zebra mussel invasion throughout the entire Oneida Lake food web, including changes in trophic flow efficiency (i.e., carbon flow among trophic levels) and alterations of food web organization (i.e., paths of carbon flow) and ecosystem activity (i.e., total carbon flow). ENA indicated that zebra mussels altered food web function by shunting carbon from pelagic to benthic pathways, increasing dissipative flow loss, and decreasing ecosystem activity. SNA revealed the strength of zebra mussel perturbation as evidenced by a reorganization of food web subgroup structure, with a decrease in importance of pelagic pathways, a concomitant rise of benthic pathways, and a reorganization of interactions between top predator fish. Together, these analyses allowed for a holistic understanding of the effects of zebra mussel invasion on the Oneida Lake food web.     

Invasive species impacts on ecosystem structure and function: A comparison of the Bay of Quinte, Canada, and Oneida Lake, USA, before and after zebra mussel invasion

As invasion rates of exotic species increase, an ecosystem level understanding of their impacts is imperative for predicting future spread and consequences. We have previously shown that network analyses are powerful tools for understanding the effects of exotic species perturbation on ecosystems. We now use the network analysis approach to compare how the same perturbation affects another ecosystem of similar trophic status. We compared food web characteristics of the Bay of Quinte, Lake Ontario (Canada), to previous research on Oneida Lake, New York (USA) before and after zebra mussel (Dreissena polymorpha) invasion. We used ecological network analysis (ENA) to rigorously quantify ecosystem function through an analysis of direct and indirect food web transfers. We used a social network analysis method, cohesion analysis (CA), to assess ecosystem structure by organizing food web members into subgroups of strongly interacting predators and prey. Together, ENA and CA allowed us to understand how food web structure and function respond simultaneously to perturbation. In general, zebra mussel effects on the Bay of Quinte, when compared to Oneida Lake, were similar in direction, but greater in magnitude. Both systems underwent functional changes involving focused flow through a small number of taxa and increased use of benthic sources of production; additionally, both systems structurally changed with subgroup membership changing considerably (33% in Oneida Lake) or being disrupted entirely (in the Bay of Quinte). However, the response of total ecosystem activity (as measured by carbon flow) differed between both systems, with increasing activity in the Bay of Quinte, and decreasing activity in Oneida Lake. Thus, these analyses revealed parallel effects of zebra mussel invasion in ecosystems of similar trophic status, yet they also suggested that important differences may exist. As exotic species continue to disrupt the structure and function of our native ecosystems, food web network analyses will be useful for understanding their far-reaching effects.     

On the consequences of aggregation and balancing of networks on system properties derived from ecological network analysis

In the ecological network analysis (ENA) of complex flow food webs the assumption is often made that the models characterizing the flows and stocks of ecosystems occur in a steady state where inflows equals outflows. An assessment of the system indices derived from ENA of six balanced and unbalanced system models, respectively, indicate to differences between indices. The aggregation of highly articulated flow models into models with fewer compartments also has drastic effects on the system metrics, particularly on the information indices.     

Ecological network analysis for water use systems—A case study of the Yellow River Basin

Ecological network analysis (ENA) is introduced in this paper as a promising approach to study water use systems. Information indices from ENA involving total system throughput (TST), ascendency and overhead are calculated here. Two related aspects including organization inherent in system structures and synthesized water use intensity related with sustainable development of water use systems are analyzed. The indices of ascendency and overhead are applied for analyzing and characterizing water use network organization. For comparison of sustainability of water use systems from integrated aspects of environment, society and economy and based on TST, a new indicator termed as total system throughput intensity (TSTI) is constructed incorporating parameters of land, precipitation, population, GDP and environmental flow, which can be used as a measure of sustainability in terms of synthesized water use intensity. The Yellow River Basin in China during 1998–2006 is chosen as the case study and divided into subsystems according to the six river sections as from source to Lanzhou (S1-L1), Lanzhou to Toudaoguai (L1-T), Toudaoguai to Longmen (T-L2), Longmen to Sanmenxia (L2-S2), Sanmenxia to Huayuankou (S2-H) and Huayuankou to the mouth of Bo Sea (H-B). The results show that (i) the organization levels of L1-T and H-B are better than those of S1-L1 and T-L2, with those of L2-S2 and S2-H the worst; (ii) the synthesized water use intensity has been improving, of which T-L2, L2-S2 and S2-H are at the highest levels while H-B the lowest. In addition, the comparison between TSTI and other metrics and the relationship between ascendency and TSTI are discussed, from which the importance of TSTI is reflected and the optimization criterions for sustainable development of six subsystems are derived. It can be concluded that the application of ENA in water use systems can provide new angles for water resource management to address the challenges of assessing and optimizing options to obtain more sustainable water use.     

Throughflow analysis: A stochastic approach

Ecological network analysis (ENA), predicated on systems theory and Leontiev input–output analysis, is a method widely used in ecology to reveal ecosystem properties. An important ecosystem property computed in ENA is throughflows, the amount of matter/energy leaving each compartment of the ecosystem. Throughflows are analyzed via a matrix representing their relationships to the driving input at the boundary. Network particle tracking (NPT) builds on ENA to offer a Lagrangian particle method that describes the activity of the ecosystem at the microscopic level. This paper introduces a Lagrangian throughflow analysis methodology using NPT and shows that the NPT throughflow matrix, , agrees with the conventional ENA throughflow matrix, , for ecosystems at steady-state with donor-controlled flows. The matrix is computed solely from the pathways (particles’ histories) generated by NPT simulations and its average over multiple runs of the algorithm with longer simulation time agrees with the Eulerian matrix (Law of Large Numbers). While the traditional NEA throughflow analysis is mostly used with steady-state ecosystem models, the Lagrangian throughflow analysis that we propose can be used with non-steady-state models and paves the way for the development of dynamic throughflow analysis.     

Comparative network analysis toward characterization of systemic organization for human–environmental sustainability

A preliminary study in comparative ecological network analysis was conducted to identify key assumptions and methodological challenges, test initial hypotheses and explore systemic and network structural characteristics for environmentally sustainable ecosystems. A nitrogen network for the U.S. beef supply chain – a small sub-network of the industrial food system analyzed as a pilot study – was constructed and compared to four non-human carbon and nitrogen trophic networks for the Chesapeake Bay and the Florida Everglades. These non-human food webs served as sustainable reference systems. Contrary to the main original hypothesis, the “window of vitality” and the number of network roles did not clearly differentiate between a human sub-network and the more complete non-human networks. The effective trophic level of humans (a partial estimate of trophic level based on the single food source of beef) was much higher (8.1) than any non-human species (maximum of 4.88). Network connectance, entropy, total dependency coefficients, trophic efficiencies and the ascendency to capacity ratio also indicated differences that serve as hypotheses for future tests on more comprehensive human food webs. The study elucidated important issues related to (1) the steady state assumption, which is more problematic for industrial human systems, (2) the absence or dearth of data on contributions of dead humans and human wastes to feed other species in an integrated food web, (3) the ambiguity of defining some industrial compartments as living versus non-living, and (4) challenges with constructing compartments and trophic transfers in industrial versus non-human food webs. The two main novel results are (1) the progress made toward adapting ecological network analysis (ENA) methodology for analysis of human food networks in industrial cultures and (2) characterizing the critical aspects of comparative ENA for understanding potential causes of the problems, and providing avenues for solutions, for environmental sustainability. Based on this work, construction and comparative network analysis of a more comprehensive industrial human food network seems warranted and likely to provide valuable insights for modifying structures of industrial food networks to be more like natural networks and more sustainable.     

Network mutualism: Positive community-level relations in ecosystems

Empirically observable energy and matter transfers in ecosystems create network structures commonly called food webs. The relation or interaction type associated with each link between pair-wise objects can be classified as (+, −) or (−, +) depending on the net gain or loss experienced by each object. If objects are not adjacent in the food web, then their observed direct interaction is neutralism (0, 0). From this perspective, a zero-sum balance exists between the number of positive and negative relations in the ecosystem. However, community-level relations arise from observable direct and unobservable indirect pathways within a food web, giving rise to indirectly mediated relations, mutualism (+, +) and competition (−, −). Determination of community-level relations requires a systemic or holistic approach. Utility measures from environ analysis in the broader frame of ecological network analysis (ENA) provide such a methodology to investigate the relations resulting from all observed and indirect transfers. This research demonstrates the methodology and shows three important results from the analysis. First, all objects in ecological networks are related either through their input and output environs, and therefore all objects interact with and influence the others in the web: there are no null community-level relations. Second, the community-level relations can and do differ from direct relations: what you see is not always what you get. Third, due to the web of trophic and non-trophic interactions, community-level relations usually have a greater occurrence of mutualism than competition making them more positive than the direct relations that produced them: this is the property called network mutualism.     

Ecological network analysis: network construction

Ecological network analysis (ENA) is a systems-oriented methodology to analyze within system interactions used to identify holistic properties that are otherwise not evident from the direct observations. Like any analysis technique, the accuracy of the results is as good as the data available, but the additional challenge is that the data need to characterize an entire ecosystem's flows and storages. Thus, data requirements are substantial. As a result, there have, in fact, not been a significant number of network models constructed and development of the network analysis methodology has progressed largely within the purview of a few established models. In this paper, we outline the steps for one approach to construct network models. Lastly, we also provide a brief overview of the algorithmic methods used to construct food web typologies when empirical data are not available. It is our aim that such an effort aids other researchers to consider the construction of such models as well as encourages further refinement of this procedure.     

Artificial neural network modeling of the river water quality—A case study

The paper describes the training, validation and application of artificial neural network (ANN) models for computing the dissolved oxygen (DO) and biochemical oxygen demand (BOD) levels in the Gomti river (India). Two ANN models were identified, validated and tested for the computation of DO and BOD concentrations in the Gomti river water. Both the models employed eleven input water quality variables measured in river water over a period of 10 years each month at eight different sites. The performance of the ANN models was assessed through the coefficient of determination (R²) (square of the correlation coefficient), root mean square error (RMSE) and bias computed from the measured and model computed values of the dependent variables. Goodness of the model fit to the data was also evaluated through the relationship between the residuals and model computed values of DO and BOD. The model computed values of DO and BOD by both the ANN models were in close agreement with their respective measured values in the river water. Relative importance and contribution of the input variables to the model output was evaluated through the partitioning approach. The identified ANN models can be used as tools for the computation of water quality parameters.     

Ecosystem network analysis indicators are generally robust to parameter uncertainty in a phosphorus model of Lake Sidney Lanier, USA

Understanding how data uncertainty influences ecosystem analysis is critical as we move toward ecosystem-based management. Here, we investigate how 18 Ecological Network Analysis (ENA) indicators that characterize ecosystem growth, development, and condition are affected by uncertainty in an ecosystem model of Lake Sidney Lanier (USA). We applied ENA to 122 plausible parameterizations of the ecosystem developed by Borrett and Osidele (2007, Ecological Modelling 200, 371-387), and then used the coefficient of variation (CV) to compare system indicator variability. We considered Total System Throughput (TST) as a measure of the underlying model uncertainty and tested three hypotheses. First, we hypothesized that non-ratio indicators whose calculation includes the TST would be at least as variable as TST if not more variable. Second, we postulated that indicators calculated as ratios, with TST in the numerator and denominator would tend to be less variable than TST because its influence will cancel. Last, we expected the Average Mutual Information (AMI) to be less variable than TST because it is a bounded function. Our work shows that the 18 indicators grouped into four categories. The first group has significantly larger CVs than the CV for TST. In this group, model uncertainty is amplified rendering these three indicators less useful. The second group of four indicators shows no significant difference in variability with respect to TST. Finally, there are two groups whose CV values are significantly lower than that for TST. The least variable group includes the ratio-based indicators and Average Mutual Information. Due to their low variability, we conclude that these indicators are the most robust to the parameter uncertainty and most useful for ecosystem assessment and comparative ecosystem analysis. In summary, this work suggests that we can be as certain, or more certain, in most of the selected ENA indicators as we are in the parameters of the model analyzed.     

Multi-model approach and evaluation of the uncertainty of model results. Rationale and applications to predict the behaviour of contaminants in the abiotic components of the fresh water environment

This work aims at discussing some concepts pertaining to the theory and practice of environmental modelling in view of the results of several model validation exercises performed by the group “Model validation for radionuclide transport in the system watershed-river and in estuaries” of project EMRAS (Environmental Modelling for Radiation Safety) supported by the IAEA (International Atomic Energy Agency). The analyses here performed concern models applied to real scenarios of environmental contamination. In particular, the reasons for the uncertainty of the models and the EBUA (empirically based uncertainty analysis) methodology are discussed. The foundations of multi-model approach in environmental modelling are presented and motivated. An application of EBUA to the results of a multi-model exercise concerning three models aimed at predicting the wash-off of radionuclide deposits from the Pripyat floodplain (Ukraine) was described. Multi-model approach is, definitely, a tool for uncertainty analysis. EBUA offers the opportunity of an evaluation of the uncertainty levels of predictions in multi-model applications.     

Information indices as a tool for quantifying development of below-ground terrestrial ecosystems

Information indices from ecosystem network analysis (ENA) describe the size and organization of an ecosystem and are claimed to quantify ecosystem development [Ulanowicz, R.E., 1986, Growth and Development, Springler-Verslag, New York, 203 pp.]. To date, these indices were not used to describe a gradient of ecosystem development for a field situation. Here we used information indices to quantify soil succession with soils of different age on the island Schiermonnikoog, The Netherlands. We evaluated whether information indices describe ecosystem development as predicted by ENA.For the Island of Schiermonnikoog the biomasses of soil organisms and roots were measured on four stages of succession (0, 10, 25 and 100 years old soils). Organisms were grouped based on their feeding characteristics. With these data consumption, respiration, excretion, external input and output flows to, from and between groups were calculated. These flows, in turn, were used to calculate the information indices. Relative information indices describe the organization of an ecosystem; i.e. level of organisation (specialization of flows), diversity and evenness of flows, and disorganisation. Absolute indices describe both size (in terms of energy flow) and organisation of the system. System size is used to scale the absolute indices and will be analysed separately as well.We found that the absolute indices increased when succession processed, as predicted by theory. This pattern could have been due to the build-up of biomass, which apparently did not level off. Because the succession gradient deals mostly with young soils (0, 10 and 25 years old) and only one older field (100 years old), the gradient should include more soils of around 100 years old and older to exclude this possibility. Relative indices, on the other hand, increased initially, but then levelled off. We think that this was due to the strong aggregation of functional groups, especially at lower trophic levels, because information in some functional groups showed (expected) trends.Our results suggest that the absolute indices are able to describe ecological succession of terrestrial below-ground ecosystems. The relative indices, in contrast, appeared to be insensitive to subtle succesional changes.     

Implementation of ecological modeling as an effective management and investigation tool: Lake Kinneret as a case study

The need for scientifically based management of lakes, as key water resources, requires the establishment of quantitative relationships between in-lake processes responsible for water quality (WQ) and the intensity of major management measures (MM, e.g. nutrient loading). In this paper, we estimate the impact of potential changes in nutrient loading on the Lake Kinneret ecosystem. Following validation of the model against a comprehensive dataset, we applied an approach that goes beyond scenario testing by linking the lake ecosystem model DYRESM–CAEDYM with a set of ecosystem variables included in a pre-assessed system of water quality indices. The emergent properties of the ecosystem predicted from the model simulations were also compared with lake data as a form of indirect validation of the model. Model output, in good agreement with lake data, indicated differential effects of nitrogen and phosphorus nutrient loading on concentrations, and major in-lake fluxes, of TN and TP, and dynamics and algal community structure. Both model output and lake data indicated a strong relationship between nitrogen loading and in-lake TN values. This relationship is not apparent for phosphorus and only a weak relationship exists between phosphorus loading and in-lake TP. The modeling results, expressed in terms of water quality, allowed establishment of critical/threshold values for the nutrient loads. Implementation of the ecological modeling supplemented with the quantified set of WQ indices allowed us to take a step towards establishment of the association between permissible ranges for water quality and major management measures, i.e. towards sustainable management.     

A fast Bayesian method for updating and forecasting hourly ozone levels

A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows that the model predicted maps are more accurate than the maps based solely on the Eta-CMAQ forecast data for a 2 week test period. These out-of sample spatial predictions and temporal forecasts also outperform those from regression models with independent Gaussian errors. The method is fully Bayesian and is able to instantly update the map for the current hour (upon receiving monitor data for the current hour) and forecast the map for several hours ahead. In particular, the 8 h average map which is the average of the past 4 h, current hour and 3 h ahead is instantly obtained at the current hour. Based on our validation, the exact Bayesian method is preferable to more complex models in a real-time updating and forecasting environment.     

A model of radiocesium cycling in a sand hills—Turkey Oak (Quercus laevis) ecosystem

Digital simulation models of radiocesium cycling in Turkey Oaks were developed from in situ ¹³⁴Cs tagging studies. Predictions of ¹³⁴Cs steady-state distribution for 3-, 4- and 5-compartment, donor-controlled models were compared with the estimated fallout ¹³⁷Cs distribution as a measure of model validation; output from the 5-compartment model compared best. Sensitivity analysis demonstrated that Turkey Oak burden of ¹³⁴Cs was equally sensitive to the output rate from the tree compartment and the availability of ¹³⁴Cs for uptake (i.e., presence in the root zone) but not the rate of uptake by Turkey Oaks. Observed distribution and model predictions indicate that radiocesium is readily bioaccumulated by Turkey Oaks (～13% of the ecosystem burden) from the soil and is cycled within the sand hills—Turkey Oak ecosystem.     

湖泊营养物参照状态建立方法研究

建立生态分区内各类型湖泊营养物的参照状态是营养物基准制定过程中最为核心的内容之一。在系统分析和评价国外确定湖泊营养物参照状态的若干种方法,包括参照湖泊法、湖泊群体分布法、三分法、回归分析等几种统计学方法以及模型推断和古湖沼学重建方法后,文章根据总磷（total phosphorus,TP）、总氮（total nitrogen,TN）、叶绿素a（chlorophyll a,Chl-a）和塞氏透明度（Secci depth）等四项指标的历史监测数据,应用若干统计学方法建立了巢湖的营养物基准参照状态。通过互相之间的比较分析以及长江中下游湖区古湖沼学重建数据的验证,推荐采用湖泊群体分布法5％点位对应的值作为巢湖营养物基准的参照状态。因此,巢湖营养物参照状态阈值范围为总磷0．023～0．27mg·L^-1,总氮O．62～0．63mg·L^-1,叶绿素aO．65～0．67mg·m^-1,塞氏透明度0．65-0．72m。     

Simulation experiments comparing alternative process formulations using a factorial design

This paper reviews methods for exploring the differences between alternative equations in complex ecosystem models. A factorial design is proposed as a method for exposing possible interactions between equation forms in their effect on model output as well as to clarify differences between the main candidate equations. A number of display methods arising from statistical analysis are used including normal Q-Q plots, linear rank plots and interaction diagrams. The methods were illustrated using a complex ecosystem model of Lake Ontario. We found the methods effective at illustrating major differences between equations although several difficulties arose due to the complexity of the models and the diffuse nature of the data supporting model validation. Questions of the method for standardization of equation forms so that the compared equations are in some way analogous, are important. These methods are probably most useful in cases where the data are of sufficient quality to indicate not only how different equation forms affect model output but also which are to be preferred.     

An integrated optimization and simulation approach for air pollution control under uncertainty in open-pit metal mine

Air Pollution Control model is developed for open-pit metal mines. Model will aid decision makers to select a cost-effective solution. Open-pit metal mines contribute toward air pollution and without effective control techniques manifests the risk of violation of environmental guidelines. This paper establishes a stochastic approach to conceptualize the air pollution control model to attain a sustainable solution. The model is formulated for decision makers to select the least costly treatment method using linear programming with a defined objective function and multi-constraints. Furthermore, an integrated fuzzy based risk assessment approach is applied to examine uncertainties and evaluate an ambient air quality systematically. The applicability of the optimized model is explored through an open-pit metal mine case study, in North America. This method also incorporates the meteorological data as input to accommodate the local conditions. The uncertainties in the inputs, and predicted concentration are accomplished by probabilistic analysis using Monte Carlo simulation method. The output results are obtained to select the cost-effective pollution control technologies for PM_2.5, PM₁₀, NO_x, SO₂ and greenhouse gases. The risk level is divided into three types (loose, medium and strict) using a triangular fuzzy membership approach based on different environmental guidelines. Fuzzy logic is then used to identify environmental risk through stochastic simulated cumulative distribution functions of pollutant concentration. Thus, an integrated modeling approach can be used as a decision tool for decision makers to select the cost-effective technology to control air pollution.