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.     

Desert pastoralists’ negative and positive effects on rare wildlife in the Gobi

In arid regions of the developing world, pastoralists and livestock commonly inhabit protected areas, resulting in human–wildlife conflict. Conflict is inextricably linked to the ecological processes shaping relationships between pastoralists and native herbivores and carnivores. To elucidate relationships underpinning human–wildlife conflict, we synthesized 15 years of ecological and ethnographic data from Ikh Nart Nature Reserve in Mongolia's Gobi steppe. The density of argali (Ovis ammon), the world's largest wild sheep, at Ikh Nart was among the highest in Mongolia, yet livestock were >90% of ungulate biomass and dogs >90% of large‐carnivore biomass. For argali, pastoral activities decreased food availability, increased mortality from dog predation, and potentially increased disease risk. Isotope analyses indicated that livestock accounted for >50% of the diet of the majority of gray wolves (Canis lupus) and up to 90% of diet in 25% of sampled wolves (n = 8). Livestock composed at least 96% of ungulate prey in the single wolf pack for which we collected species‐specific prey data. Interviews with pastoralists indicated that wolves annually killed 1–4% of Ikh Nart's livestock, and pastoralists killed wolves in retribution. Pastoralists reduced wolf survival by killing them, but their livestock were an abundant food source for wolves. Consequently, wolf density appeared to be largely decoupled from argali density, and pastoralists had indirect effects on argali that could be negative if pastoralists increased wolf density (apparent competition) or positive if pastoralists decreased wolf predation (apparent facilitation). Ikh Nart's argali population was stable despite these threats, but livestock are increasingly dominant numerically and functionally relative to argali. To support both native wildlife and pastoral livelihoods, we suggest training dogs to not kill argali, community insurance against livestock losses to wolves, reintroducing key native prey species to hotspots of human–wolf conflict, and developing incentives for pastoralists to reduce livestock density.     

Uncertainty characterization for emergy values

While statistical estimation of uncertainty has not typically accompanied published emergy values, as with any other quantitative model, uncertainty is embedded in these values, and lack of uncertainty characterization makes their accuracy not only opaque, it also prevents the use of emergy values in statistical tests of hypotheses. This paper first attempts to describe sources of uncertainty in unit emergy values (UEVs) and presents a framework for estimating this uncertainty with analytical and stochastic models, with model choices dependent upon on how the UEV is calculated and what kind of uncertainties are quantified. The analytical model can incorporate a broader spectrum of uncertainty types than the stochastic model, including model and scenario uncertainty, which may be significant in emergy models, but is only appropriate for the most basic of emergy calculations. Although less comprehensive in its incorporation of uncertainty, the proposed stochastic method is suitable for all types of UEVs. The distributions of unit emergy values approximate the lognormal distribution with variations depending on the types of uncertainty quantified as well as the way the UEVs are calculated. While both methods of estimating uncertainty in UEVs have their limitations in their presented stage of development, this paper provides methods for incorporating uncertainty into emergy, and demonstrates how this can be depicted and propagated so that it can be used in future emergy analyses and permit emergy to be more readily incorporated into other methods of environmental assessment, such as LCA.     

The green dividend of urban biking? Evidence of improved community and sustainable development

As the cost of car ownership has skyrocketed, urban biking has experienced the largest share increase of any transportation mode, rising by 40% between the years 2000 and 2014. Growing attention is being paid to the potential local economic development impacts of urban neighbourhoods becoming more bike-friendly. It is now a green economic development strategy in cities as diverse as Chicago, New York City, Portland, and San Francisco to increase bicycling as a transportation mode. This paper reports the results of a survey of 2032 responses from faculty, staff, and students of a car-dependent, downtown university. We use a mixed methods approach, including data from the American Community Survey, to support our arguments and to inform potential savings and economic benefit calculations that can be achieved from bicycle infrastructure investments and anticipated redistributed spending patterns. We argue that urban biking results in a green dividend that promotes local community development and more importantly results in zero carbon emissions.     

Ecological literacy and beyond: Problem-based learning for future professionals

Ecological science contributes to solving a broad range of environmental problems. However, lack of ecological literacy in practice often limits application of this knowledge. In this paper, we highlight a critical but often overlooked demand on ecological literacy: to enable professionals of various careers to apply scientific knowledge when faced with environmental problems. Current university courses on ecology often fail to persuade students that ecological science provides important tools for environmental problem solving. We propose problem-based learning to improve the understanding of ecological science and its usefulness for real-world environmental issues that professionals in careers as diverse as engineering, public health, architecture, social sciences, or management will address. Courses should set clear learning objectives for cognitive skills they expect students to acquire. Thus, professionals in different fields will be enabled to improve environmental decision-making processes and to participate effectively in multidisciplinary work groups charged with tackling environmental issues.     

Preferential flow estimates to an agricultural tile drain with implications for glyphosate transport

Agricultural subsurface drains, commonly referred to as tile drains, are potentially significant pathways for the movement of fertilizers and pesticides to streams and ditches in much of the Midwest. Preferential flow in the unsaturated zone provides a route for water and solutes to bypass the soil matrix and reach tile drains faster than predicted by traditional displacement theory. This paper uses chloride concentrations to estimate preferential flow contributions to a tile drain during two storms in May 2004. Chloride, a conservative anion, was selected as the tracer because of differences in chloride concentrations between the two sources of water to the tile drain, preferential and matrix flow. A strong correlation between specific conductance and chloride concentration provided a mechanism to estimate chloride concentrations in the tile drain throughout the storm hydrographs. A simple mixing analysis was used to identify the preferential flow component of the storm hydrograph. During two storms, preferential flow contributed 11 and 51% of total storm tile drain flow; the peak contributions, 40 and 81%, coincided with the peak tile drain flow. Positive relations between glyphosate [N-(phosphonomethyl)glycine] concentrations and preferential flow for the two storms suggest that preferential flow is an important transport pathway to the tile drain.