Native Perennial Forb Variation Between Mountain Big Sagebrush and Wyoming Big Sagebrush Plant Communities

Big sagebrush (Artemisia tridentata Nutt.) occupies large portions of the western United States and provides valuable wildlife habitat. However, information is lacking quantifying differences in native perennial forb characteristics between mountain big sagebrush [A. tridentata spp. vaseyana (Rydb.) Beetle] and Wyoming big sagebrush [A. tridentata spp. wyomingensis (Beetle & A. Young) S.L. Welsh] plant communities. This information is critical to accurately evaluate the quality of habitat and forage that these communities can produce because many wildlife species consume large quantities of native perennial forbs and depend on them for hiding cover. To compare native perennial forb characteristics on sites dominated by these two subspecies of big sagebrush, we sampled 106 intact big sagebrush plant communities. Mountain big sagebrush plant communities produced almost 4.5-fold more native perennial forb biomass and had greater native perennial forb species richness and diversity compared to Wyoming big sagebrush plant communities (P < 0.001). Nonmetric multidimensional scaling (NMS) and the multiple-response permutation procedure (MRPP) demonstrated that native perennial forb composition varied between these plant communities (P < 0.001). Native perennial forb composition was more similar within plant communities grouped by big sagebrush subspecies than expected by chance (A = 0.112) and composition varied between community groups (P < 0.001). Indicator analysis did not identify any perennial forbs that were completely exclusive and faithful, but did identify several perennial forbs that were relatively good indicators of either mountain big sagebrush or Wyoming big sagebrush plant communities. Our results suggest that management plans and habitat guidelines should recognize differences in native perennial forb characteristics between mountain and Wyoming big sagebrush plant communities.     

Effects of Modern Forest Management on Winter Grazing Resources for Reindeer in Sweden

Boreal forests in Sweden are exploited in a number of ways, including forestry and reindeer husbandry. In the winter, reindeer feed mainly on lichens, and lichen-rich forests are a key resource in the herding system. Commercial forestry has mainly negative effects on reindeer husbandry, and conflicts between these two industries have escalated over the last century. This article reviews the effects of modern forest management practices on the winter resources available for reindeer husbandry. Forestry affects reindeer husbandry at both the stand level and the landscape level and over various time scales. Clear-cutting, site preparation, fertilization, short rotation times, and forest fragmentation have largely resulted in a reduced amount of ground growing and arboreal lichens and restricted access to resource. This article also discusses alternative forestry practices and approaches that could reduce the impacts of forestry on reindeer husbandry, both in the short and long term.     

Measurements of Rural Sulfur Dioxide and Particle Sulfate: Analysis of CASTNet Data, 1987 through 1996

ABSTRACT

The Clean Air Status and Trends Network (CASTNet) was implemented by the U.S. Environmental Protection Agency (EPA) in 1991 in response to Title IX of the Clean Air Amendments of 1990, which mandated the deployment of a national ambient air monitoring network to track progress of the implementation of emission reduction programs in terms of deposition, air quality, and changes to affected ecosystems. CASTNet evolved from the National Dry Deposition Network (NDDN). CASTNet currently consists of 45 sites in the eastern United States and 28 sites in the West. Each site measures sulfur dioxide (SO₂), nitric acid (HNO₃), particle sulfate (SO₄ ⁼), particle nitrate (NO₃ ^- ), and ozone. Nineteen sites collect precipitation samples. NDDN/CASTNet uses a uniform set of site-selection criteria which provides the data user with consistent measures to compare each site. These criteria also ensure that, to the extent possible, CASTNet sites are located away from local emission sources.

This paper presents an analysis of SO₂ and SO₄ ⁼ concentration data collected from 1987 through 1996 at rural NDDN/CASTNet sites. Annual and seasonal variability is examined. Gradients of SO₂ and SO₄ ⁼ are discussed. The variability of the atmospheric mix of SO₂ and SO₄ ⁼ is explored spatially and seasonally. Data from CASTNet are also compared to SO₂ and SO₄ ⁼ data from concurrent monitoring studies in rural areas.     

Two error components model for measurement error: application to radon in homes

In this paper, a simple model for analysing variability in radon concentrations in homes is tested. The approach used here involves two error components, representing additive and multiplicative errors, together with variation between-houses. We use a Bayesian approach for our analysis and apply this model to two datasets of repeat radon measurements in homes; one based on 3-month long measurements for which the original measurements were close to the current UK Radon Action Level (200 Bq m⁻³), and the other based on 6-month measurement data (from regional and national surveys), for which the original measurements cover a wide range of radon concentrations, down to very low levels. The model with two error components provides a better fit to these datasets than does a model based on solely multiplicative errors.     

Heat map visualization of complex environmental and biomarker measurements

Over the past decade, the assessment of human systems interactions with the environment has permeated all phases of environmental and public health research. We are invoking lessons learned from the broad discipline of Systems Biology research that focuses primarily on molecular and cellular networks and adapting these concepts to Systems Exposure Science which focuses on interpreting the linkage from environmental measurements and biomonitoring to the expression of biological parameters. A primary tool of systems biology is the visualization of complex genomic and proteomic data using “heat maps” which are rectangular color coded arrays indicating the intensity (or amount) of the dependent variable. Heat maps are flexible in that both the x-axis and y-axis can be arranged to explore a particular hypothesis and allow a fast overview of data with a third quantitative dimension captured as different colors. We are now adapting these tools for interpreting cumulative and aggregate environmental exposure measurements as well as the results from human biomonitoring of biological media including blood, breath and urine. This article uses existing EPA measurements of environmental and biomarker concentrations of polycyclic aromatic hydrocarbons (PAHs) to demonstrate the value of the heat map approach for hypothesis development and to link back to stochastic and mixed effects models that were originally used to assess study results.     

Capital assets framework for analysing household vulnerability during disaster

This paper examines the vulnerability of households to disasters, using an asset vulnerability framework to represent livelihoods. Such frameworks are widely employed to analyse household poverty and focus on living conditions and well-being rather than money-metric measures of consumption and income. The conceptualisation of household vulnerability is a challenge in current studies on coping with disasters. The paper considers whether a capital assets framework is useful in identifying and assessing household vulnerability in the context of the Wenchuan earthquake in China in 2008. The framework has five categories of assets (financial, human, natural, physical, and social capital) and attempts to measure the resilience and vulnerability of households. When applied to a major disaster, asset-based methods face the problem of heterogeneity of the population, such as with regard to livelihood type or residence. Moreover, the effect of external interventions, such as the provision of relief assistance, must be taken into account.     

Spatial Prediction of Stream Temperatures Using Top-Kriging with an External Drift

Top-kriging is a method for estimating stream flow and stream flow-related variables on a river network. Top-kriging treats these variables as emerging from a two-dimensional spatially continuous process in the landscape. The top-kriging weights are estimated by a family of variogram models (regularisations) for different catchment areas (kriging support), which accounts for the different scales and the nested nature of the catchments. This assures that kriging weights are distributed to both hydrologically connected and unconnected sites of the stream network according to the data situation: top-kriging gives most weight to close-by sites at the same river system, but when the next hydrologically connected site is far away, more weight is given to a close-by site at an adjacent river system. The distribution of weights is in contrast to ordinary kriging and stream distance-based kriging which does not account for both spatial proximity and network connectivity. We extend the top-kriging method by incorporating an external drift function to account for the deterministic patterns of the spatial variable. We test the method for a comprehensive Austrian stream temperature dataset. The drift is modelled by exponential regression with catchment altitude. Top-kriging is then applied to the regression residuals. The variogram used in top-kriging is fitted by a semiautomatic optimisation procedure. A leave-one-out cross-validation analysis shows that the model performs well for the study domain. The residual mean squared error (cross-validation) decreases by 20 % when using top-kriging in addition to the regression model. For regions where the observed stream temperatures deviate from the expected value of the drift model, top-kriging corrects these regional biases. By exploiting the topological information of the stream network, top-kriging is able to improve the local adjustment of the drift model for the main streams and the tributaries.     

Application of the probability-based Maryland Biological Stream Survey to the state’s assessment of water quality standards

The Clean Water Act presents a daunting task for states by requiring them to assess and restore all their waters. Traditional monitoring has led to two beliefs: (1) ad hoc sampling (i.e., non-random) is adequate if enough sites are sampled and (2) more intensive sampling (e.g., collecting more organisms) at each site is always better. We analyzed the 1,500 Maryland Biological Stream Survey (MBSS) random sites sampled in 2000–2004 to describe the variability of Index of Biotic Integrity (IBI) scores at the site, reach, and watershed scales. Average variability for fish and benthic IBI scores increased with increasing spatial scale, demonstrating that single site IBI scores are not representative at watershed scales and therefore at best 25% of a state’s stream length can be representatively sampled with non-random designs. We evaluated the effects on total taxa captured and IBI precision of sampling for twice as many benthic macroinvertebrates at 73 MBSS sites with replicate samples. When sampling costs were fixed, the precision of the IBI decreased as the number of sites had to be reduced by 15%. Only 1% more taxa were found overall when the 73 sites where combined. We concluded that (1) comprehensive assessment of a state’s waters should be done using probability-based sampling that allows the condition across all reaches to be inferred statistically and (2) additional site sampling effort should not be incorporated into state biomonitoring when it will reduce the number of sites sampled to the point where overall assessment precision is lower.     

Equivalency of a personal dust monitor to the current United States coal mine respirable dust sampler

The United States National Institute for Occupational Safety and Health, through an informal partnership with industry, labor, and the United States Mine Safety and Health Administration, has developed and tested a new instrument known as the Personal Dust Monitor (PDM). The new dust monitor is an integral part of the cap lamp that coal miners normally carry to work and provides continuous information about the concentration of respirable coal mine dust within the breathing zone of that individual. Previous laboratory testing demonstrated that there is a 95% confidence that greater than 95% of individual PDM measurements fall within +/-25% of reference measurements. The work presented in this paper focuses on the relationship between the PDM and respirable dust concentrations currently measured by a coal mine dust personal sampler unit utilizing a 10 mm Dorr-Oliver nylon cyclone. The United Kingdom Mining Research Establishment instrument, used as the basis for coal mine respirable dust standards, had been designed specifically to match the United Kingdom British Medical Research Council (BMRC) criterion. The personal sampler is used with a 1.38 multiplier to convert readings to the BMRC criterion. A stratified random sampling design incorporating a proportionate allocation strategy was used to select a sample of mechanized mining units representative of all US underground coal mines. A sample of 180 mechanized mining units was chosen, representing approximately 20% of the mechanized mining units in production at the time the sample was selected. A total of 129 valid PDM/personal sampler dust sample sets were obtained. A weighted linear regression analysis of this data base shows that, in comparison with the personal sampler, the PDM requires a mass equivalency conversion multiplier of 1.05 [95% C.I.=(1.03, 1.08)] when the small intercept term is removed from the analysis. Removal of the intercept term results in a personal sampler-equivalent concentration increase of 2.9% at a PDM measurement of 2.0 mg m(-3).