Development of a rapid method for direct detection of tet(M) genes in soil from Danish farmland

A method for direct detection of antibiotic resistance genes in soil samples has been developed. The tetracycline resistance gene, tet(M), was used as a model. The method was validated on Danish farmland soil that had repeatedly been treated with pig manure slurry containing resistant bacteria. The tet(M) gene was directly detected in 10-80% of the samples from the various farmland soils and could be detected in all samples tested after selective enrichment. To validate the obtained results, the method was applied to garden soil samples where lower prevalence of resistance was found. RESULT: A detection limit of 10(2)-10(3) copies of the tet(M) gene per gram of soil (in a Bacillus cereus group bacterium) was achieved. tet(M) gene was detected in soil samples with the highest prevalence on farmland treated with pig manure slurry.     

Presence of erm genes among macrolide-resistant Gram-positive bacteria isolated from Danish farm soil

The presence of erm genes was investigated among macrolide-resistant Gram-positive bacteria isolated from soil samples collected from four Danish farms that had been treated with animal waste. Soil samples were collected before, a few days after spread and 1 1/2 months and 5 months later. In 33% (9/27) of these isolates, an erm gene was detected using PCR. Eight isolates were positive for erm(B) and one isolate was positive for erm(C). No isolates contained erm(A), erm(D) or erm(F). The positive isolates were identified to genus level. Two erm(B) positive isolates were identified as Enterococcus spp., and the erm(C)-positive isolate as a Streptococcus spp., probably indicating survival from animal waste. The remaining six erm(B) positive isolates all belonged to the Bacillus cereus group. The erm(B) gene has not previously been identified in B. cereus but is frequently found among enterococci. This result could indicate horizontal transfer from bacteria of animal origin to indigenous soil bacteria.     

Public Participation and Local Environmental Planning: the collective action problem and the potential of social capital

Expanding the opportunities for public participation in environmental planning is not always the best option. Starting from an institutional public choice analysis of public participation in terms of the collective action problem, this paper emphasises the roots of participatory activities in the incentive structures facing potential participants. It then goes on to consider the strategies that may be adopted for encouraging greater public involvement and looks particularly to the social capital literature for suggestions of how institutional redesign may alter these incentive structures. The paper concludes by distinguishing three different modes of environmental planning, in terms of the rationale for participation, the severity of the collective action problem and the associated participatory strategy that can be adopted.     

Land cover composition determinants in the Uckermark (NE Germany) over a 220-year period

There is broad consensus that land use/cover has changed in central Europe over millennia. However, few studies have addressed the roles of the anthropogenic (socio-economic) and natural (biophysical) factors driving these changes over shorter periods (e.g., 100 years). In this study, we analyse the determinants of land cover composition at three discrete time periods (c. 1780, 1890, and 2000). We hypothesise that different anthropogenic and natural factors are the determinants of the main land cover types (arable fields, grasslands, and forests) and that the effects of natural and anthropogenic factors on the main land cover types differ among landscapes with highly dynamic and more stable forest-open land distributions. The study was carried out in the Uckermark region located in northeastern Germany. We compiled data on natural and anthropogenic factors (e.g. forest cover and number of inhabitants) of 65 municipalities in four landscape sections of equal size (10 × 10 km). Landscape sections were selected to reflect different dynamics (high/low) in conversion from forest to arable fields or grasslands and vice versa from 1780 to 2000. Averaged linear mixed-effect models explained between 7.5 and 81.2 % of the variance. The unique effect of anthropogenic factors varied from 2.1 to 18.7 % and that of natural factors varied from 0.2 to 43.4 %. In four of six models that included both types of factors, the natural factors were more influential than the anthropogenic factors. Except in three cases, anthropogenic and natural factors showed opposite effects on land cover types in more dynamic and more stable windows. Though the Uckermark region has been influenced by human activity for thousands of years, natural factors were a major determinant of land cover composition during all time periods, whereas anthropogenic factors became more important only during the latest time period under investigation.     

Modeling the contribution of speeding and impaired driving to insurance claim counts and costs when contributing factors are unknown

PROBLEM: There are no specific indicators for distinguishing insurance claims related to speeding and impaired driving in the information warehouse at the Insurance Corporation of British Columbia. Contributing factors are only recorded for that part of the claim data that is also reported by the police. Most published statistics on crashes that are related to alcohol or speeding are based on police-reported data, but this represents only a fraction of all incidents. METHOD: This paper proposes surrogate models to estimate the counts and the average costs associated with speeding and impaired driving to insurance claims when contributing factors are unknown. Using police-reported data, classification rules and logistic regression models are developed to form such estimates. One approach applies classification rules to categorize insurance claims into those related to speeding, impaired driving, and other factors. The counts and the costs of insurance claims for each of these strata and overall are then estimated. A second method models the probability that an insurance claim is related to speeding or impaired driving using logistic regression and uses this to estimate the overall counts and the average costs of the claims. The two methods are compared and evaluated using simulation studies. RESULTS: The logistic regression model was found to be superior to the classification model for predicting insurance claim counts by category, but less efficient at predicting average claim costs. IMPACT: Having estimates of counts and costs of insurance claims related to impaired driving or speeding for all reported crash events provides a more accurate basis for policy-makers to plan changes and benefits of road safety programs.     

Estimating the contributions of speeding and impaired driving to insurance claim cost

INTRODUCTION: Logistic regression modelling of crash counts likely associated with speeding and impaired driving was earlier found [Zheng, Y., Cooper, P. J., & Dean, C. B. (2007). Modeling the contribution of speeding and impaired driving to insurance claim counts and costs when contributing factors are unknown. Journal of Safety Research, 38(1)] to successfully predict known outcomes in a series of simulations, but the same types of models did not accurately predict average crash costs. The aim of the study reported here was to develop a means to adjust classification model results that would improve their cost-predicting efficiency. METHOD: A classification modelling process was adjusted at the back-end using non-linear optimization to rationalize the classified proportions with the true proportions when the model was applied to representative subsets of the training data. Corrections were developed to account for cost (severity) differences arising from the classification process that were not due to true variations. The process was then applied to insurance claim test data where crash contributors were unknown. RESULTS: The optimization and severity correction procedure resulted in substantial improvement in average crash cost prediction for both impaired and unsafe speed collision involvements. The error measured against true values in 20 simulations was about half for the adjusted classification model of what it was for either unadjusted classification or logistic regression models. CONCLUSIONS: Non-linear optimization of classification matrices appears to be a workable tool for improving the predictive efficiency of models where desired outcomes represent average characteristics of records as compared to simple counts or proportions. IMPACT ON INDUSTRY: Using the methodology on a full-year of insurance claim data indicated that reliance on police-reported records alone would have underestimated the total cost of unsafe speed and impaired crashes by about 40%. Since most jurisdictions use police data to base policy decisions and set program spending around such safety issues, this finding has important implications.     

Altered ecohydrologic response drives native shrub loss under conditions of elevated nitrogen deposition

Many regions of southern California's coastal sage scrub (CSS) are rapidly declining as exotic annual plants replace native shrubs. During this conversion, the subsurface hydrology of the semiarid hillslopes that support CSS may be altered. This could chronically suppress the ability of native shrubland to revegetate the landscape since ecosystem processes of nutrient availability and of seedling establishment rely on spatial patterns of available soil water. In this work, soil water and nutrient N regimes were compared over a 2-yr period between a southern California site where CSS has declined (approximately 5% shrub cover) with high additions of anthropogenic N, and one where CSS remains dominant (over 50% shrub cover) with predominantly background atmospheric additions of N. These two sites have similar climate, bedrock lithology, soils, and topography, and had the same vegetation type (Riversidean CSS) 30 years ago. We found that the depth and rate of rainwater percolation into wildland hillslope soils in response to early-season storm events has been greatly reduced after loss of CSS shrubs and vegetation type conversion to invasive grassland. With decreased rainwater redistribution to soil depths of 100 to 150 cm, the predominant zone of soil water has become the upper 25 cm. This shift exacerbates vegetation type conversion by (i) concentrating smog-produced nitrogenous (N) chemicals in the uppermost soil, where they become readily available, along with high soil water, to shallow-rooted exotic grasses early in the growing season and (ii) depriving adult and juvenile shrubs of deeper regolith water.     

Solid wastes from the enzyme production as a potential biosorbent to treat colored effluents containing crystal violet dye

Environmental Science and Pollution Research - Sugarcane bagasse, a largely available waste worldwide, was submitted to solid-state fermentation (SSF) using the fungus Metarhizium anisopliae,...