A Knowledge-Based Approach to Environmental Biomonitoring

This paper presents the design, development and implementation of an integrated GIS-controlled knowledge-based system for environmental monitoring applications, utilizing indigenous flora for assessing quality. The system gathers and combines geographical, ecological, and physicochemical data of organisms' response to pollution within an intelligent computer program that (a) recognises groups of indigenous species suitable for long-term monitoring of a specific pollutant or a combination of pollutants, (b) estimates the ambient concentration of pollutant(s) from the population of the species comprising the bioindicator group and (c) provides biomonitoring capacity indices at national and international/transboundary levels. Significantly, a novel system in the form of a rational framework at the conceptual design level has been developed, that actually contributes towards achieving a cost-effective long-term biomonitoring program, with the flexibility to counter on-course any (anticipated or not) variations/modifications of the surveillance environment: the scheme assumes a robust dynamic cooperation between instrumental and biomonitoring systems, with a view to minimise uncertainty and monitoring costs and increase reliability of pollution control and abatement, aiming eventually at the shifting, partially or totally, from instrumental to natural monitoring. The proposed approach is presently implemented at pilot-scale for establishing a biomonitoring network at a large industrial area in Greece. The results obtained indicate that a cost-effective program can be only attained and maintained under a suitable financial/organizational scheme at the macro level, whereas the micro level viability strongly depends upon careful management of human resources and fixed assets.     

Management of invasive populations of the freshwater crayfish <Emphasis Type="Italic">Procambarus clarkii</Emphasis> (Decapoda,Cambaridae): test of a population-control method and proposal of a standard monitoring approach

Invasive species are one of the main threats to biodiversity. When an alien species is introduced into a new environment, fast identification and definition of management strategies may avoid or minimize impacts. When an invasive species is already established, the most adopted approaches are population control and monitoring. In order to perform such strategies, assessment of characteristics of the invasive population is imperative. This study tested a new method of population size estimation and monitoring in an invasive population of crayfish Procambarus clarkii in a conservation area in the Atlantic Rain Forest (Southeastern Brazil). The population dynamics was studied for 1 year to examine the efficacy of the selected method and to evaluate if the population is stable. Later, the effect of periodical removal of animals on the population size was tested. The method of population estimation used in this study proved to be very effective. We recommend using it to monitor invasive populations of P. clarkii. The population size varied discretely over the year with variable but low growth rate, indicating that the population is already established which introduce a notable threat to native species. The continuous removal of specimens proved to be inefficient since the growth rate was higher after the removal. One intensive removal event might be more effective than a continuous moderate removal as the one applied in this study.     

An Interval-Parameter Fuzzy-Stochastic Programming Approach for Municipal Solid Waste Management and Planning

In this study, an integrated fuzzy-stochastic linear programming model is developed and applied to municipal solid waste management. Methods of chance-constrained programming and fuzzy linear programming are incorporated within a general interval-parameter mixed-integer linear programming framework. It improves upon the existing optimization methods with advantages in uncertainty reflection, data availability, and computational requirement. The model can be used for answering questions related to types, times and sites of solid waste management practices, with the objective of minimizing system costs over the planning horizon. The model can effectively reflect dynamic, interactive, and uncertain characteristics of municipal waste management systems. In its solution process, the model is transformed into two deterministic submodels, corresponding to upper and lower bounds of the desired objective function values under a given significance level, based on an interactive algorithm. Results of the method's application to a hypothetical case indicate that reasonable outputs have been obtained. It demonstrates the practical applicability of the proposed methodology.     

Application of a Life Cycle Simulation Model to Evaluate Impacts of Water Management and Conservation Actions on an Endangered Population of Chinook Salmon

Fisheries and water resource managers are challenged to maintain stable or increasing populations of Chinook salmon in the face of increasing demand on the water resources and habitats that salmon depend on to complete their life cycle. Alternative management plans are often selected using professional opinion or piecemeal observations in place of integrated quantitative information that could reduce uncertainty in the effects of management plans on population dynamics. We developed a stochastic life cycle simulation model for an endangered population of winter-run Chinook salmon in the Sacramento River, California, USA with the goal of providing managers a tool for more effective decision making and demonstrating the utility of life cycle models for resource management. Sensitivity analysis revealed that the input parameters that influenced variation in salmon escapement were dependent on which age class was examined and their interactions with other inputs (egg mortality, Delta survival, ocean survival). Certain parameters (river migration survival, harvest) that were hypothesized to be important drivers of population dynamics were not identified in sensitivity analysis; however, there was a large amount of uncertainty in the value of these inputs and their error distributions. Thus, the model also was useful in identifying future research directions. Simulation of variation in environmental inputs indicated that escapement was significantly influenced by a 10% change in temperature whereas larger changes in other inputs would be required to influence escapement. The model presented provides an effective demonstration of the utility of life cycle simulation models for decision making and provides fisheries and water managers in the Sacramento system with a quantitative tool to compare the impact of different resource use scenarios.     

Confronting Management Challenges in Highly Uncertain Natural Resource Systems: a Robustness–Vulnerability Trade-off Approach

This paper presents a framework for the study of policy implementation in highly uncertain natural resource systems in which uncertainty cannot be characterized by probability distributions. We apply the framework to parametric uncertainty in the traditional Gordon–Schaefer model of a fishery to illustrate how performance can be sacrificed (traded-off) for reduced sensitivity and hence increased robustness, with respect to model parameter uncertainty. With sufficient data, our robustness–vulnerability analysis provides tools to discuss policy options. When less data are available, it can be used to inform the early stages of a learning process. Several key insights emerge from this analysis: (1) the classic optimal control policy can be very sensitive to parametric uncertainty, (2) even mild robustness properties are difficult to achieve for the simple Gordon–Schaefer model, and (3) achieving increased robustness with respect to some parameters (e.g., biological parameters) necessarily results in increased sensitivity (decreased robustness) with respect to other parameters (e.g., economic parameters). We thus illustrate fundamental robustness–vulnerability trade-offs and the limits to robust natural resource management. Finally, we use the framework to explore the effects of infrequent sampling and delays on policy performance.     

Marginal Abatement Cost Curves: Combining Energy System Modelling and Decomposition Analysis

Marginal abatement cost (MAC) curves are a useful policy tool to communicate findings on the technological structure and the economics of CO₂ emissions reduction. However, existing ways of generating MAC curves do not display consistent technological detail and do not consider system-wide interactions and uncertainty in a structured manner. This paper details a new approach to overcome the present shortcomings by using an energy system model, UK MARKAL, in combination with index decomposition analysis. In addition, this approach allows different forms of uncertainty analysis to be used in order to test the robustness of the MAC curve. For illustration purposes, a sensitivity analysis concerning fossil fuel prices is applied to the transport sector of the UK. The resulting MAC curves are found to be relatively robust to different fuel costs at higher CO₂ tax levels. The new systems-based approach improves MAC curves through the avoidance of an inconsistent emissions baseline, the incorporation of system-wide interactions and the price responsiveness of demand.     

A dynamic model of the spatial spread of an infectious disease: the case of fox rabies in Illinois

A spatially explicit computer model is developed to examine the dynamic spread of fox rabies across the state of Illinois and to evaluate possible disease control strategies. The ultimate concern is that the disease will spread from foxes to humans through the pet population. Modeling the population dynamics of rabies in foxes requires comprehensive ecological and biological knowledge of the fox and pathogenesis of the rabies virus. Variables considered including population densities, fox biology, home ranges, dispersal rates, contact rates, and incubation periods, can greatly effect the spread of disease. Accurate reporting of these variables is paramount for realistic construction of a spatial model. The spatial modeling technique utilized is a grid-based approach that combines the relevant geographic condition of the Illinois landscape (typically described in a georeferenced database system) with a nonlinear dynamic model of the phenomena of interest in each cell, interactively connected to the other appropriate cells (usually adjacent ones). The resulting spatial model graphically links data obtained from previous models, fox biology, rabies information and landscape parameters using various hierarchical scales and makes it possible to follow the emergent patterns and facilitates experimental stimulus/result data collection techniques. Results of the model indicate that the disease would likely spread among the native healthy fox population from East to West and would occur in epidemiological waves radiating from the point of introduction; becoming endemic across the State in about 15 years. Findings also include the realization that while current hunting pressures can potentially wipe out the fox in the State, some level of hunting pressure can be effectively utilized to help control the disease. This revised version was published online in July 2006 with corrections to the Cover Date.     

Mapping Invasive Aquatic Vegetation in the Sacramento-San Joaquin Delta using Hyperspectral Imagery

The ecological and economic impacts associated with invasive species are of critical concern to land managers. The ability to map the extent and severity of invasions would be a valuable contribution to management decisions relating to control and monitoring efforts. We investigated the use of hyperspectral imagery for mapping invasive aquatic plant species in the Sacramento-San Joaquin Delta in the Central Valley of California, at two spatial scales. Sixty-four flightlines of HyMap hyperspectral imagery were acquired over the study region covering an area of 2,139 km² and field work was conducted to acquire GPS locations of target invasive species. We used spectral mixture analysis to classify two target invasive species; Brazilian waterweed (Egeria densa), a submerged invasive, and water hyacinth (Eichhornia crassipes), a floating emergent invasive. At the relatively fine spatial scale for five sites within the Delta (average size 51 ha) average classification accuracies were 93% for Brazilian waterweed and 73% for water hyacinth. However, at the coarser, Delta-wide scale (177,000 ha) these accuracy results were 29% for Brazilian waterweed and 65% for water hyacinth. The difference in accuracy is likely accounted for by the broad range in water turbidity and tide heights encountered across the Delta. These findings illustrate that hyperspectral imagery is a promising tool for discriminating target invasive species within the Sacramento-San Joaquin Delta waterways although more work is needed to develop classification tools that function under changing environmental conditions.     

Design elements of monitoring programs: The necessary ingredients for success

Natural resource managers must know the condition of resources entrusted to their steward-ship so that they can maintain unimpaired resources and know when to restore impaired ecosystems. Resource monitoring programs should be designed to provide indications of ecosystem health, define limits of normal variation, identify abnormal conditions, and suggest potential agents of abnormal changes. Development of a conceptual model that identifies all ecosystem components and their relationships is the first step in the design of such a diagnostic monitoring program. Design studies, with field testing on each selected system component, are required to determine the parameters to be measured and to establish monitoring protocols. The best approach to diagnostic monitoring appears to be based on the population dynamics of selected species relative to physical and chemical environmental factors. Both management and monitoring of natural ecosystems need to be recognized as experimental endeavors, and thus approached in an iterative fashion with the scientific method to reduce uncertainty and cost.     

The Precautionary Principle as a Robust Cost-Effectiveness Problem

This paper offers an interpretation of the precautionary principle in terms of a safety target that a decision-maker has to reach at a minimal cost in a robust way. A two-period model is used. The precautionary principle corresponds to a situation in which the decision-maker, facing an ex ante indecision, is not able to reach a safe target from the initial condition in a worst-case framework. However, he can efficiently succeed whenever the uncertainty at the second period is revealed to him. An example coping with the management of a renewable resource illustrates the general results of the paper.

Design to monitor trend in abundance and presence of American beaver (Castor canadensis) at the national forest scale

Wildlife conservationists design monitoring programs to assess population dynamics, project future population states, and evaluate the impacts of management actions on populations. Because agency mandates and conservation laws call for monitoring data to elicit management responses, it is imperative to design programs that match the administrative scale for which management decisions are made. We describe a program to monitor population trends in American beaver (Castor canadensis) on the US Department of Agriculture, Black Hills National Forest (BHNF) in southwestern South Dakota and northeastern Wyoming, USA. Beaver have been designated as a management indicator species on the BHNF because of their association with riparian and aquatic habitats and its status as a keystone species. We designed our program to monitor the density of beaver food caches (abundance) within sampling units with beaver and the proportion of sampling units with beavers present at the scale of a national forest. We designated watersheds as sampling units in a stratified random sampling design that we developed based on habitat modeling results. Habitat modeling indicated that the most suitable beaver habitat was near perennial water, near aspen (Populus tremuloides) and willow (Salix spp.), and in low gradient streams at lower elevations. Results from the initial monitoring period in October 2007 allowed us to assess costs and logistical considerations, validate our habitat model, and conduct power analyses to assess whether our sampling design could detect the level of declines in beaver stated in the monitoring objectives. Beaver food caches were located in 20 of 52 sampled watersheds. Monitoring 20 to 25 watersheds with beaver should provide sufficient power to detect 15–40% declines in the beaver food cache index as well as a twofold decline in the odds of beaver being present in watersheds. Indices of abundance, such as the beaver food cache index, provide a practical measure of population status to conduct long-term monitoring across broad landscapes such as national forests.     

Statistical Simulation to Estimate Uncertain Behavioral Parameters of Hybrid Energy-Economy Models

In energy-economy modeling, new hybrid models attempt to combine the technological explicitness of bottom-up models with the macroeconomic feedbacks and statistically estimated behavioral parameters of top-down models. However, statistical estimation of behavioral parameters (portraying firm and household technology choices) with such models is challenged by the number of uncertain variables and the lack of historical data on technologies in terms of capital costs, operating costs, and market shares. Multiple combinations of parameter values might equally explain past technology choices. This paper reports on the application of a Bayesian statistical simulation approach for estimating the most likely values for these key behavioral parameters in order to best explain past technology choices and then simulate policies to influence future technology choices. The method included (1) data collection of key technology market shares, capital costs, and operating costs over the past; (2) backcasting a hybrid energy-economy model over a historical time period; and (3) the application of Markov chain Monte Carlo statistical simulation using the Metropolis–Hastings algorithm as a tool for estimating distributions for key parameters in the model. The results provide a means of indicating the uncertainty bounds around key behavioral parameters when generating forecasts of the effect of certain policies. However, the results also indicate that this approach may have limited applicability, given that future available technologies may differ substantially from past technologies and that it is difficult to separate the effects of parameter uncertainty from model structure uncertainty.     

Mitigation Portfolio and Policy Instruments When Hedging Against Climate Policy and Technology Uncertainty

In this paper, we use a stochastic integrated assessment model to evaluate the effects of uncertainty about future carbon taxes and the costs of low-carbon power technologies. We assess the implications of such ambiguity on the mitigation portfolio under a variety of assumptions and evaluate the role of emission performance standards and renewable portfolios in accompanying a market-based climate policy. Results suggest that climate policy and technology uncertainties are important with varying effects on all abatement options. The effect varies with the technology, the type of uncertainty, and the level of risk. We show that carbon price uncertainty does not substantially change the level of abatement, but it does have an influence on the mitigation portfolio, reducing in particular energy R&D investments in advanced technologies. When investment costs are uncertain, investments are discouraged, especially during the early stages, but the effect is mitigated for the technologies with technological learning prospects. Overall, these insights support some level of regulation to encourage investments in coal equipped with carbon capture and storage and clean energy R&D.     

Optimized contaminated land investigation at minimum overall cost to achieve fitness-for-purpose

A methodology for optimized contaminated land investigation (OCLI) is described that balances the uncertainty of measurements against the cost of taking the measurements and the financial losses that may arise from misclassification of the land. Uncertainty from the sources of both field sampling and chemical analysis is estimated using existing techniques, based on the taking of duplicated samples. The actual costs of sampling and analysis and the expected costs that could arise from either 'false positive' or 'false negative' classification of areas of land were estimated. A loss function was constructed that calculates the expectation of financial loss that will arise for a given uncertainty of measurement. The function shows a clear minimum value of cost at an optimal value of uncertainty. Application of this OCLI technique to two case studies demonstrated this minimum value. Below the optimum value of uncertainty, the costs increased due to higher measurement costs. Above the optimum, the costs increased due to increasing risk of factors such as unnecessary remediation or potential litigation over undetected contamination. Many areas for further development of OCLI are identified, but the technique is demonstrated as a useful new approach to judging fitness-for-purpose of such measurements.     

Using species spectra to evaluate plant community conservation value along a gradient of anthropogenic disturbance

The aim of this study was to assess the impact of anthropogenic disturbance on the partitioning of plant communities (species spectra) across a landcover gradient of community types, categorizing species on the basis of their biogeographic, ecological, and conservation status. We tested a multinomial model to generate species spectra and monitor changes in plant assemblages as anthropogenic disturbance rise, as well as the usefulness of this method to assess the conservation value of a given community. Herbaceous and arborescent communities were sampled in five Azorean islands. Margins were also sampled to account for edge effects. Different multinomial models were applied to a data set of 348 plant species accounting for differences in parameter estimates among communities and/or islands. Different levels of anthropogenic disturbance produced measurable changes on species spectra. Introduced species proliferated and indigenous species declined, as anthropogenic disturbance and management intensity increased. Species assemblages of relevance other than economic (i.e., native, endemic, threatened species) were enclosed not only in natural habitats, but also in human managed arborescent habitats, which can positively contribute for the preservation of indigenous species outside remnants of natural areas, depending on management strategies. A significant presence of invasive species in margin transects of most community types will contribute to an increase in edge effect that might facilitate invasion. The multinomial model developed in this study was found to be a novel and expedient tool to characterize the species spectra at a given community and its use could be extrapolated for other assemblages or organisms, in order to evaluate and forecast the conservation value of a site.     

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.     

Rabbit Population Landscape-Scale Simulation to Investigate the Relevance of Using Rabbits in Regulatory Environmental Risk Assessment

This paper describes the development and testing of the ALMaSS rabbit model and its baseline, and subsequently its application to the question of lagomorph population vulnerability in environmental risk assessment (ERA). Development and testing following a pattern-oriented modelling protocol resulted in a model able to replicate local and landscape-level rabbit population patterns. We then tested how robust rabbit populations are to an (imaginary) extreme toxic stressor at a landscape level in a variety of landscapes, and to what extremes key uncertain model parameters must be pushed to cause extinctions. This was contrasted with the same (imaginary) toxic stressor applied to the already existing ALMaSS hare model. For EU risk assessment of plant protection products, these results clearly indicate that if the protection goal is population-level impacts, either in abundance and/or distribution, then the hare is a much more vulnerable species than the rabbit under all the conditions tested. Rabbits would only be more vulnerable than hares if the entire population were to be exposed simultaneously, when lower body mass would then be a critical factor. This did not occur even though the toxicant and exposure scenarios tested here were extreme and, in fragmented landscapes at scales used here, will not occur in reality from the use of plant protection products on crop fields. As well as specifically answering the question on rabbit versus hare vulnerability, this study generally illustrates the potential application of models for setting focal species for risk assessments.     

Application of fuzzy DEMATEL to develop a cause and effect model of municipal solid waste management in Metro Manila

The municipal solid waste management (MSW) evaluation is usually evaluated in subjective and qualitative in nature described in linguistic information; it is very difficult for expert groups to express the preferences using exact numerical values. A MSW expert group is consulted in order to mirror how government officials might reach a cause-effect model regarding MSW management in Metro Manila. A critical issue regarding this is how the expert group can better evaluate the MSW management criteria in uncertainty. A cause and effect model development can help the processing of decision making problem. The Decision Making Trial and Evaluation Laboratory (DEMATEL) not only can convert the relations between cause and effect of criteria into a model but also can be used as a way to handle the inner dependences within a set of criteria. The post survey is further discussed and proved the validity result.     

Probability sampling of stony coral populations in the Florida Keys

Principles of probability survey design were applied to guide large-scale sampling of populations of stony corals and associated benthic taxa in the Florida Keys coral reef ecosystem. The survey employed a two-stage stratified random sampling design that partitioned the 251-km(2) domain by reef habitat types, geographic regions, and management zones. Estimates of the coefficient of variation (ratio of standard error to the mean) for stony coral population density and abundance ranged from 7% to 12% for four of six principal species. These levels of survey precision are among the highest reported for comparable surveys of marine species. Relatively precise estimates were also obtained for octocoral density, sponge frequency of occurrence, and benthic cover of algae and invertebrates. Probabilistic survey design techniques provided a robust framework for estimating population-level metrics and optimizing sampling efficiency.     

Plant invasions along roads: a case study from central highlands, India

Road sides provide suitable conditions for the establishment and growth of non-native species. The phenomenon of non-native species spread through roads has further increased due to rapid anthropogenic developments. Here we intend to investigate the status of native and non-native species and how the species richness and diversity change in a perpendicular road transect across the three different road use types in the central highlands of India. Presence of 55 non-native species was recorded, of the total 71 species along the road sides. Non-native species richness significantly increased with increasing road use type. Although, the species diversity significantly decreased from road verges to the forest interior in all the road use types. Indicating the role of non-native propagule spread through the roads into the interior forest landscapes. The study gives a management implication, to restrict the non-native species spread from the road sides to the forest interior, irrespective of road use types.