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Performance-based conservation has long been recognized as crucial to improving program effectiveness, particularly when environmental conditions are dynamic. Yet few studies have investigated the use of environmental performance information by staff of conservation organizations. This article identifies attitudinal, policy and organizational factors influencing the use of a type of performance information-water quality information-by Soil and Water Conservation District staff in the Upper Mississippi River Basin region. An online survey (n = 277) revealed a number of important variables associated with greater information use. Variables included employees' prosocial motivation, or the belief that they helped people and natural resources through their job, the perceived trustworthiness of data, the presence of a U.S. Clean Water Act Total Maximum Daily Load standard designation, and staff discretion to prioritize programs locally. Conservation programs that retain motivated staff and provide them the resources and flexibility to plan and evaluate their work with environmental data may increase conservation effectiveness under changing conditions.

The global Aquatic Warbler (Acrocephalus paludicola, Vieillot, 1817) population has suffered a major decline due to the large-scale destruction of its natural habitat (fen mires). The species is at risk of extinction, especially in NE Germany/NW Poland. In this study, we developed habitat suitability models based on satellite and environmental data to identify potential areas for habitat restoration on which further surveys and planning should be focused. To create a reliable model, we used all Aquatic Warbler presences in the study area since 1990 as well as additional potentially suitable habitats identified in the field. We combined the presence/absence regression tree algorithm Cubist with the presence-only algorithm Maxent since both commonly outperform other algorithms. To integrate the separate model results, we present a new way to create a metamodel using the initial model results as variables. Additionally, a histogram approach was applied to further reduce the final search area to the most promising sites. Accuracy increased when using both remote sensing and environmental data. It was highest for the integrated metamodel (Cohen’s Kappa of 0.4, P

A new method has been developed to perform environmental assessment at regional scale. This involves a combination of a self-organizing map (SOM) neural network and principal component analysis (PCA). The method is capable of clustering ecosystems in terms of environmental conditions and suggesting relative cumulative environmental impacts of multiple factors across a large region. Using data on land-cover, population, roads, streams, air pollution, and topography of the Mid-Atlantic region, the method was able to indicate areas that are in relatively poor environmental condition or vulnerable to future deterioration. Combining the strengths of SOM with those of PCA, the method offers an easy and useful way to perform a regional environmental assessment. Compared with traditional clustering and ranking approaches, the described method has considerable advantages, such as providing a valuable means for visualizing complex multidimensional environmental data at multiple scales and offering a single assessment or ranking needed for a regional environmental assessment while still facilitating the opportunity for more detailed analyses.

The objective of this research was to determine the spatial distributions of childhood lead poisoning and soil lead contamination in urban Tijuana. The Bocco-Sanchez model of point-source emissions was evaluated in terms of validity and reliability. We compared the model's predicted vulnerable populations with observed cases of childhood lead poisoning in Tijuana, identified fixed point sources in the field, and analyzed 76 soil samples from 14 sites. The soil lead results were compared to the blood lead analyses performed on Tijuana children whose blood lead levels were > or =10 microg/dL, who reported that they did not use lead-glazed ceramics for cooking or storing food (n = 63). Using GIS, predicted vs observed risk areas were assessed by examining spatial patterns, including the distribution of cases per designated risk area. Chi-square analysis of expected vs observed values did not differ significantly at the p = 0.02 level, showing that the model was strikingly accurate in predicting the distribution of subjects with elevated blood lead. Results reveal that while point sources are significant, other sources of lead exposure are also important. The relative public health risk from exposure to lead in an urban setting may be assessed by distinguishing among sources of exposure and associating concentrations to blood lead levels. The results represent an iterative approach in environmental health research by linking environmental and human biomarker lead concentrations and using these results to validate an environmental model of risk to lead exposure.

While medical audit in infection control today is one important element in the quality assurance of health care, environmental auditing, approved in 1993 by the Council of the European Communities for the industrial sector, so far has not been used as a tool to control and reduce environmental pollution caused by medical care. The aim of this study was to investigate whether environmental auditing according to the European Eco-Management and Audit Scheme (EMAS) can be implemented in hospitals as a process of improvement in protection of the environment. In a prior publication the methodological issues and the organizational steps that had to be taken were described. An environmental review of the activities of the Freiburg University Hospital and an ecoanalysis of the input and output were performed. The results of this analysis, published in an environmental report, provide a fundamental data set for the consumption of energy, water, materials, and the burdens of major pollutants and waste. Regarding the organizational structure of the hospital, the first steps towards an integrating environmental management system as demanded by EMAS could be taken. Beside supporting advantages,e.g., improvement of environmental safety, public image and staff contentment, and potential economic benefits such as less cost to be paid for energy and water consumption, there are important restrictions of environmental auditing in hospitals. Examples are the lack of basic environmental data, staff motivation (especially of physicians), cooperation of the organizational substructures, and funds for prefinancing urgently needed improvements in ecology. Based on the study findings, a textbook on environmental auditing in hospitals, including checklists covering all important environmental objectives, has been published to support hospitals in their efforts to achieve an optimized and sustainable practice of providing health care. While medical audit in infection control today is one important element in the quality assurance of health care, environmental auditing, approved in 1993 by the Council of the European Communities for the industrial sector, so far has not been used as a tool to control and reduce environmental pollution caused by medical care. The aim of this study was to investigate whether environmental auditing according to the European Eco-Management and Audit Scheme (EMAS) can be implemented in hospitals as a process of improvement in protection of the environment. In a prior publication the methodological issues and the organizational steps that had to be taken were described. An environmental review of the activities of the Freiburg University Hospital and an ecoanalysis of the input and output were performed. The results of this analysis, published in an environmental report, provide a fundamental data set for the consumption of energy, water, materials, and the burdens of major pollutants and waste. Regarding the organizational structure of the hospital, the first steps towards an integrating environmental management system as demanded by EMAS could be taken. Beside supporting advantages,e.g., improvement of environmental safety, public image and staff contentment, and potential economic benefits such as less cost to be paid for energy and water consumption, there are important restrictions of environmental auditing in hospitals. Examples are the lack of basic environmental data, staff motivation (especially of physicians), cooperation of the organizational substructures, and funds for prefinancing urgently needed improvements in ecology. Based on the study findings, a textbook on environmental auditing in hospitals, including checklists covering all important environmental objectives, has been published to support hospitals in their efforts to achieve an optimized and sustainable practice of providing health care.

The majority of environmental effects monitoring (EEM) frameworks that have been proposed compare selected indicator variables as a means of assessing whether significant changes in stressed ecosystems have occurred. Most are deterministic in nature and do not appropriately account for the natural variability and dynamics within the systems being comapred. This suggests that the comparative procedures should be statistically based and immediately raises the issue of whether the selected comparative procedures are to be used as decision-making tools or conclusive procedures. Conclusive procedures require a significant body of evidence before rejecting the postulated null hypothesis. The costs and time involved in environmental data collection accordingly bias action toward the maintenance of a status quo approach to environmental management. if, however, EEM is treated as a decision-making procedure, risk functions that include consideration of type I and II statistical error may be developed and combined with costs to select a minimum expected loss strategy for environemental management. Examples of the interpretative difficulties and conclusion reversal phenomena caused when EEM is used as a conclusive procedure are presented. In addition, risk functions appropriate for environmental management within an EEM context are constructed and applied. Only when such tools are fully developed and applied can EEM expect to have significant impacts on minimizing environmental degradation.

The ability to access information for use in decision making is a well-recognized need within the context of management sciences. A similar need exists in order to make effective technical decisions pertaining to environmental resource management. Data bases are the principle vehicle by which scientists, engineers, and resource managers store and access environmental information. An integrated data-base mechanism is essential in order for federal agencies to manage programs such as enforcement of the Canadian Environmental Protection Act (CEPA), state of the environment (SOE) reporting, and the environmental assessment and review process (EARP). A data-base structure and data dissemination mechanism under current development within Environment Canada, Conservation and Protection, Atlantic Region, is presented along with some of its operational benefits and constraints.

The diversity and abundance of information available for vulnerability assessments can present a challenge to decision-makers. Here we propose a framework to aggregate and present socioeconomic and environmental data in a visual vulnerability assessment that will help prioritize management options for communities vulnerable to environmental change. Socioeconomic and environmental data are aggregated into distinct categorical indices across three dimensions and arranged in a cube, so that individual communities can be plotted in a three-dimensional space to assess the type and relative magnitude of the communities’ vulnerabilities based on their position in the cube. We present an example assessment using a subset of the USEPA National Estuary Program (NEP) estuaries: coastal communities vulnerable to the effects of environmental change on ecosystem health and water quality. Using three categorical indices created from a pool of publicly available data (socioeconomic index, land use index, estuary condition index), the estuaries were ranked based on their normalized averaged scores and then plotted along the three axes to form a vulnerability cube. The position of each community within the three-dimensional space communicates both the types of vulnerability endemic to each estuary and allows for the clustering of estuaries with like-vulnerabilities to be classified into typologies. The typologies highlight specific vulnerability descriptions that may be helpful in creating specific management strategies. The data used to create the categorical indices are flexible depending on the goals of the decision makers, as different data should be chosen based on availability or importance to the system. Therefore, the analysis can be tailored to specific types of communities, allowing a data rich process to inform decision-making.

The integration of Landsat TM and environmental GIS data sets using artificial intelligence rule-induction and decision-tree analysis is shown to facilitate the production of vegetation maps with both floristic and structural information. This technique is particularly suited to vegetation mapping in disturbed or hilly environments that are unsuited to either conventional remote sensing methods or GIS modeling using environmental data bases.

Decision tree analysis was used to predict the distribution of forest communities in an area on the south coast of New South Wales, Australia. The analysis was carried out using a geographical information system environmental data base of those topographic and geological variables thought to influence the distribution of vegetation and derived from cartographic sources. The resulting maps of forest communities are of a resolution sufficient to delimit individual forest stands and contain much ecological information.

Probability plots of trace element concentrations may be used to partition, or segregate, sample data into its constituent populations. Two populations are typically present in data from sites requiring remedial action, one representing clean, uncontaminated soil or sediment (background) and the other representing contaminated ground. The use of such plots in the analysis and evaluation of environmental data permits a statistical characterization of the background populations, from which defensible cleanup criteria may be developed. These criteria will be environmentally conservative, yet will minimize the amount of soil removed in a remedial action. An example is given from a successful cleanup of a surface impoundment.

The desire to capture natural regions in the landscape has been a goal of geographic and environmental classification and ecological land classification (ELC) for decades. Since the increased adoption of data-centric, multivariate, computational methods, the search for natural regions has become the search for the best classification that optimally trades off classification complexity for class homogeneity. In this study, three techniques are investigated for their ability to find the best classification of the physical environments of the Mt. Lofty Ranges in South Australia: AutoClass-C (a Bayesian classifier), a Kohonen Self-Organising Map neural network, and a k-means classifier with homogeneity analysis. AutoClass-C is specifically designed to find the classification that optimally trades off classification complexity for class homogeneity. However, AutoClass analysis was not found to be assumption-free because it was very sensitive to the user-specified level of relative error of input data. The AutoClass results suggest that there may be no way of finding the best classification without making critical assumptions as to the level of class heterogeneity acceptable in the classification when using continuous environmental data. Therefore, rather than relying on adjusting abstract parameters to arrive at a classification of suitable complexity, it is better to quantify and visualize the data structure and the relationship between classification complexity and class homogeneity. Individually and when integrated, the Self-Organizing Map and k-means classification with homogeneity analysis techniques also used in this study facilitate this and provide information upon which the decision of the scale of classification can be made. It is argued that instead of searching for the elusive classification of natural regions in the landscape, it is much better to understand and visualize the environmental structure of the landscape and to use this knowledge to select the best ELC at the required scale of analysis.

Knowledge of the forces driving and modifying ecosystems can be employed in concert with signal analysis to target the data most likely to yield sensitivity and resilience information. One can optimize return of information per investment of resources by targeting segments of signals that are dominated by the force of interest, coupled with scientific understanding of the system of interest. This force analysis approach is an effective means both to design efficient new monitoring programs and to target relevant information in large data files. We present five example applications of force analysis. Three examples illustrate this approach for an evaluation of whether Canadian rivers might be sensitive to changes in climate. It was concluded that Canadian rivers appear to be sensitive to changing climate. A fourth example illustrates how automated snow pillow data may be evaluated to ascertain the sensitivity of snow accumulation to change in climate. It was concluded that snow accumulation at the site evaluated did appear to be sensitive to changing climate. The fifth example illustrates the assessment of whether a river recovers with the elimination of inputs of iron from an abandoned mine. It was concluded that resilience remained unproven since the river had not as yet restabilized. The force analysis approach focuses data collection or data evaluation on those data required to answer specific resource management questions, greatly reducing collection or consideration of data that are not relevant to that question. This approach is potentially very cost-efficient and therefore is likely to be of interest to hydrologists, climatologists, and environmental data managers.

/ Much information is available on changes that occur in natural resources from both spatially-explicit data on environmental conditions and models of the interactions of these conditions and resources with human activities. The strategy for assessing land-use impacts on natural resources developed in this paper provides a framework for using relevant data and models to address questions of how management practices can promote both use and protection of resources. This assessment strategy integrates spatially explicit environmental data using geographic information systems (GIS) with computer models that simulate changes in land cover in response to land-use impacts. The computer models also simulate susceptibility of species to changes in habitat suitability and landscape patterns. The approach is applied to management of limestone barrens on the Oak Ridge Reservation in East Tennessee. Potential limestone barrens habitats are identified by overlaying appropriate soils, geology, slope, and land-use/land-cover conditions. Their validity is tested against known sites containing rare species that occur in these habitats. The location of habitats at risk in the aftermath of human activities is determined by using an available area model that identifies the size and proximity of sites that particular types of species can no longer use as habitat. The resulting risk map can be used in land management planning. The approach uses readily available in situ and remotely sensed data and is applicable to a wide range of locations and land-use scenarios. This approach can be refined based on needs identified by land managers and on the sensitivity of the results to the resolution of available resource information.KEY WORDS: Land management; Assessment; Habitat characterization; Limestone barrens; Ecological modeling; Geographic information systems

/ It has been recognized for a long time that data transformation methods capable of achieving normality of distributions could have a crucial role in statistical analysis, especially towards an efficient application of techniques such as analysis of variance and multiple regression analysis. Normality is a basic assumption in many of the statistical methods used in the environmental sciences and is very often neglected. In this paper several techniques to test normality of distributions are proposed and analyzed. Confidence intervals and nonparametric tests are used and discussed. Basic and Box-Cox transformations are the suggested methods to achieve normal variables. Finally, we develop an application related to environmental data with atmospheric parameters and SO2 and particle concentrations. Results show that the analyzed transformations work well and are very useful to achieve normal distributions.KEY WORDS: Normal distribution; Kurtosis; Skewness; Confidence intervals; Box-Cox transformations; Nonparametric tests

Routine application of significance tests does not extract the maximum information from environmental data and can lead to misleading conclusions. Reasons leading to this are: a significant result can often be reached merely by collecting enough samples; a statistically significant result is not necessarily practically significant; and reports of the presence or absence of statistically significant differences for multiple tests are not comparable unless identical sample sizes are used. These problems are demonstrated by application to pH data for grazed and retired fields, and by discussion of significance tests used in recent US regulations for groundwater quality. The advantages of equivalence tests, where the tester must state the difference of practical difference, are discussed and applied to the field pH problem. We recommend that environmental managers and scientists pay more attention to statistical power and decide on what is a practical difference. Confidence intervals for the size of the differences, accompanied where necessary by equivalence tests, are the preferred means of addressing the question: “is there a difference of practical significance?”

Acid deposition is a muitifaceted environmental phenomenon whose control represents one of the most controversial environmental policy areas. Because of the longrange transport of air pollutants contributing to acid deposition, its control is the responsibility of more than one region. Given the interdependence between the economic and environmental impacts of acid deposition as well as the interregional conflicts over its control, the present article proposes an interregional integrated economic-environmental model for the study of the impacts of acid deposition control policies. This model is solved with the use of a compromise programming procedure which facilitates the explicit treatment of conflicting regional goals. After the theoretical and operational forms and the solution procedure of the proposed model are described, the use of the model is illustrated with a simulation example using the 1963 MRIO accounts and hypothetical environmental data. The compromise programming procedure can form the basis for interactive decision processes when policy makers are exposed to the solutions yielded by the model. The model's usefulness and future research directions are discussed.