Statistical analyses of regional surface water quality in southeastern Ontario

Historical records from Ontario's Provincial Water Quality Monitoring Network (PWQMN) for rivers and streams were analyzed to assess the feasibility of mapping regional water quality patterns in southeastern Ontario which spans two major geologic zones, the Precambrian Shield and the St. Lawrence Lowlands, thus serving as a paradigm for much of Ontario. Despite biases toward the populated Lowlands and associated pollution problems, general spatial trends are evident via the analysis of intervariable relations, individual parameter maps and multivariate analysis. Using a robust algorithm designed to identify outliers and abstract underlying bivariate relations, a linear regional hardness-alkalinity relation was derived, where most anomalies proved to be mine drainage impacted sites. Regionally, multivariate ordination reveals that central tendencies of common indicators of ionic strength and nutrient richness correlate positively and that site clusters broadly reflect the transition from oligotrophic Shield waters to eutrophic conditions of heavily agricultural Lowland streams. Results suggest that on the Shield more precisely delineated aquatic regions may be realized by applying GIS to integrate river and synoptic lake survey data. Further synthesis with bedrock and surficial geology, physiography, pedology and other temporally invariant spatial attributes should yield regional patterns of background quality from which locally attainable water objectives might be defined. Additional synoptic surveys of sedimentary Lowland streams may be required as most available sites relflect anthropogenic pollution.     

Robust graphical methods for diagnosing trend in irregularly spaced water quality time series

Graphical methods can play an important role in the reliable assessment of trends in typically ill behaved river quality data series both as diagnostic tools and as visual corroborative evidence when assumptions required for formal statistical tests are not met. Robust, graphically-oriented trend diagnosis procedures are presented for data series characterized by nonnormal populations, uneven time spacing, nonmonotonic trend and other factors which can create serious problems for standard parametric time series methods. Cleveland's robust locally weighted regression (RLWR) developed for investigating nonlinearity in x-y scatterplots is adapted as a robust/resistant smoothing filter for the analysis of irregular time series comprising quantitative observations. Low powered RLWR trend lines reveal temporally local phenomena, e.g. abrupt jumps (often associated with point source impacts) and periodicities, while higher powered RLWR yields smooth lines characterizing medium and longer term trends. Simple variants of Tukey smoothing concepts are developed for series with censored observations. Applications to Ontario river quality series reveal that graphical evidence is frequently sufficient to obviate the need for formal trend testing. The methods are generally applicable to most time series.     

Application of PCR and probe hybridization techniques in detection of airborne fungal spores in environmental samples

Specific PCR amplification and probe hybridization techniques were applied to examine the compositions of airborne fungi in samples from three different environments. The results from microscopic and CFU counting were compared to those of the molecular-based detections. The detection sensitivity for PCR amplifications was 9 to 73 spores and 1.3 to 19.3 CFUs per PCR reaction. The hybridization detection limit was 2 to 4 spores and 0.2 to 1.2 CFU. The hybridization method was more sensitive than PCR amplification and showed less variation among samples. Using specific PCR primers and probes we identified the presence of several fungal groups and species in the air samples. Specific detections through probe hybridization to PCR products amplified with universal or group-specific fungal primers have promising applications in the examination of air samples for environmental monitoring.     

Adaptive Management: From More Talk to Real Action

The challenges currently facing resource managers are large-scale and complex, and demand new approaches to balance development and conservation goals. One approach that shows considerable promise for addressing these challenges is adaptive management, which by now is broadly seen as a natural, intuitive, and potentially effective way to address decision-making in the face of uncertainties. Yet the concept of adaptive management continues to evolve, and its record of success remains limited. In this article, we present an operational framework for adaptive decision-making, and describe the challenges and opportunities in applying it to real-world problems. We discuss the key elements required for adaptive decision-making, and their integration into an iterative process that highlights and distinguishes technical and social learning. We illustrate the elements and processes of the framework with some successful on-the-ground examples of natural resource management. Finally, we address some of the difficulties in applying learning-based management, and finish with a discussion of future directions and strategic challenges.     

Sustainability Assessment for Agriculture Scenarios in Europe’s Mountain Areas: Lessons from Six Study Areas

Sustainability assessment (SA) is a holistic and long-range strategic instrument capable of assisting policy-making in electing, and deciding upon, future development priorities. The outcomes of an SA process become more relevant and strengthened when conducted with multi-stakeholder engagement, which provides for multiple dialogues and perspectives. This was the object of research of the SA team in the context of BioScene (Scenarios for Reconciling Biodiversity Conservation with Declining Agriculture Use in Mountain Areas in Europe), a three-year project (2002–2005) funded by the European Union 5th Framework Program, which aimed to investigate the implications of agricultural restructuring and decline for biodiversity conservation in the mountain areas of Europe, using three distinct methodological streams: the ecological, the socio-economic, and the SA approaches. The SA approach drew on the previous two to assess the importance for biodiversity management of different scenarios of agri-environmental change and rural policy in six countries (France, Greece, Norway, Slovakia, Switzerland, and the United Kingdom), develop causal chains, include stakeholder views, and identify potential contributions for, or conflicts with, sustainability. This article tells how SA was used, what sustainability meant in each study area through different objectives of sustainability considered, discusses the methods used in SA, and the benefits arising. The SA was conducted by a team independent of any study area, who developed and oversaw the application of the SA methodology, assisting national teams, and developing a cross-country understanding of the sustainability of proposed scenarios in the different geographical and social contexts, and their implications for policy-making. Finally, it reflects on the persistent challenges of interdisciplinary research, compounded by multi-cultural teams, and concludes on the BioScene’s lessons for the further development and application of SA.

Identification of highly brominated analogues of Q1 in marine mammals

Three novel halogenated organic compounds (HOCs) have been identified in the blubber of marine mammals from coastal New England with the molecular formulae C(9)H(3)N(2)Br(6)Cl, C(9)H(3)N(2)Br(7), and C(9)H(4)N(2)Br(5)Cl. They were identified using high and low resolution gas chromatography mass spectrometry (GCMS) and appear to be highly brominated analogues of Q1, a heptachlorinated HOC suspected to be naturally produced. These compounds were found in Atlantic white sided dolphin (Lagenorhynchus acutus), bottlenose dolphin (Tursiops truncatus), common dolphin (Delphinus delphis), Risso's dolphin (Grampus griseus), harbor porpoise (Phocoena phocoena), beluga whale (Delphinapterus leucas), fin whale (Balaenoptera physalus), grey seal (Halichoerus grypus), harp seal (Phoca groenlandica) and a potential food source (Loligo pealei) with concentrations as high as 2.7 microg/g (lipid weight). The regiospecificity of C(9)H(3)N(2)Br(6)Cl is suggestive of a biogenic origin. Debromination of C(9)H(3)N(2)Br(6)Cl may be significant in the formation of C(9)H(4)N(2)Br(5)Cl.     

Uncertainty, learning, and the optimal management of wildlife

Wildlife management is limited by uncontrolled and often unrecognized environmental variation, by limited capabilities to observe and control animal populations, and by a lack of understanding about the biological processes driving population dynamics. In this paper I describe a comprehensive framework for management that includes multiple models and likelihood values to account for structural uncertainty, along with stochastic factors to account for environmental variation, random sampling, and partial controllability. Adaptive optimization is developed in terms of the optimal control of incompletely understood populations, with the expected value of perfect information measuring the potential for improving control through learning. The framework for optimal adaptive control is generalized by including partial observability and non-adaptive, sample-based updating of model likelihoods. Passive adaptive management is derived as a special case of constrained adaptive optimization, representing a potentially efficient suboptimal alternative that nonetheless accounts for structural uncertainty.