Do the Golden-winged Warbler and Blue-winged Warbler Exhibit Species-specific Differences in their Breeding Habitat Use?

We compared habitat features of Golden-winged Warbler (Vermivora chrysoptera) territories in the presence and absence of the Blue-winged Warbler (V. cyanoptera) on reclaimed coal mines in southeastern Kentucky, USA. Our objective was to determine whether there are species specific differences in habitat that can be manipulated to encourage population persistence of the Golden-winged Warbler. When compared with Blue-winged Warblers, Golden-winged Warblers established territories at higher elevations and with greater percentages of grass and canopy cover. Mean territory size (minimum convex polygon) was 1.3 ha (se = 0.1) for Golden-winged Warbler in absence of Blue-winged Warbler, 1.7 ha (se = 0.3) for Golden-winged Warbler coexisting with Blue-winged Warbler, and 2.1 ha (se = 0.3) for Blue-winged Warbler. Territory overlap occurred within and between species (18 of n = 73 territories, 24.7%). All Golden-winged and Blue-winged Warblers established territories that included an edge between reclaimed mine land and mature forest, as opposed to establishing territories in open grassland/shrubland habitat. The mean distance territories extended from a forest edge was 28.0 m (se = 3.8) for Golden-winged Warbler in absence of Blue-winged Warbler, 44.7 m (se = 5.7) for Golden-winged Warbler coexisting with Blue-winged Warbler, and 33.1 m (se = 6.1) for Blue-winged Warbler. Neither territory size nor distances to forest edges differed significantly between Golden-winged Warbler in presence or absence of Blue-winged Warbler. According to Monte Carlo analyses, orchardgrass (Dactylis glomerata), green ash (Fraxinus pennsylvanica) seedlings and saplings, and black locust (Robinia pseudoacacia) saplings were indicative of sites with only Golden-winged Warblers. Sericea lespedeza, goldenrod (Solidago spp.), clematis vine (Clematis spp.), and blackberry (Rubus spp.) were indicative of sites where both species occurred. Our findings complement recent genetic studies and add another factor for examining Golden-winged Warbler population decline. Further, information from our study will aid land managers in manipulating habitat for the Golden-winged Warbler.

Regional Differences in the Prediction of Extratropical Cyclones by the ECMWF Ensemble Prediction System

A regional study of the prediction of extratropical cyclones by the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System (EPS) has been performed. An objective feature-tracking method has been used to identify and track the cyclones along the forecast trajectories. Forecast error statistics have then been produced for the position, intensity, and propagation speed of the storms. In previous work, data limitations meant it was only possible to present the diagnostics for the entire Northern Hemisphere (NH) or Southern Hemisphere. A larger data sample has allowed the diagnostics to be computed separately for smaller regions around the globe and has made it possible to explore the regional differences in the prediction of storms by the EPS. Results show that in the NH there is a larger ensemble mean error in the position of storms over the Atlantic Ocean. Further analysis revealed that this is mainly due to errors in the prediction of storm propagation speed rather than in direction. Forecast storms propagate too slowly in all regions, but the bias is about 2 times as large in the NH Atlantic region. The results show that storm intensity is generally overpredicted over the ocean and underpredicted over the land and that the absolute error in intensity is larger over the ocean than over the land. In the NH, large errors occur in the prediction of the intensity of storms that originate as tropical cyclones but then move into the extratropics. The ensemble is underdispersive for the intensity of cyclones (i.e., the spread is smaller than the mean error) in all regions. The spatial patterns of the ensemble mean error and ensemble spread are very different for the intensity of cyclones. Spatial distributions of the ensemble mean error suggest that large errors occur during the growth phase of storm development, but this is not indicated by the spatial distributions of the ensemble spread. In the NH there are further differences. First, the large errors in the prediction of the intensity of cyclones that originate in the tropics are not indicated by the spread. Second, the ensemble mean error is larger over the Pacific Ocean than over the Atlantic, whereas the opposite is true for the spread. The use of a storm-tracking approach, to both weather forecasters and developers of forecast systems, is also discussed.

Human exposure modelling for chemical risk assessment: a review of current approaches and research and policy implications

A wide variety of exposure models are currently employed for health risk assessments. Individual models have been developed to meet the chemical exposure assessment needs of Government, industry and academia. These existing exposure models can be broadly categorised according to the following types of exposure source: environmental, dietary, consumer product, occupational, and aggregate and cumulative. Aggregate exposure models consider multiple exposure pathways, while cumulative models consider multiple chemicals. In this paper each of these basic types of exposure model are briefly described, along with any inherent strengths or weaknesses, with the UK as a case study. Examples are given of specific exposure models that are currently used, or that have the potential for future use, and key differences in modelling approaches adopted are discussed. The use of exposure models is currently fragmentary in nature. Specific organisations with exposure assessment responsibilities tend to use a limited range of models. The modelling techniques adopted in current exposure models have evolved along distinct lines for the various types of source. In fact different organisations may be using different models for very similar exposure assessment situations. This lack of consistency between exposure modelling practices can make understanding the exposure assessment process more complex, can lead to inconsistency between organisations in how critical modelling issues are addressed (e.g. variability and uncertainty), and has the potential to communicate mixed messages to the general public. Further work should be conducted to integrate the various approaches and models, where possible and regulatory remits allow, to get a coherent and consistent exposure modelling process. We recommend the development of an overall framework for exposure and risk assessment with common approaches and methodology, a screening tool for exposure assessment, collection of better input data, probabilistic modelling, validation of model input and output and a closer working relationship between scientists and policy makers and staff from different Government departments. A much increased effort is required is required in the UK to address these issues. The result will be a more robust, transparent, valid and more comparable exposure and risk assessment process. (C) 2006 Elsevier Ltd. All rights reserved.