Prey capture and structure of the visual space of an insect that hunts by sight on the litter layer (Notiophilus biguttatus F., Carabidae,Coleoptera)

Summary The carabid beetle Notiophilus biguttatus hunts springtails and mites by visual cues. The preycapture behaviour of the beetle and the escape behaviour of the springtails were analysed by means of highspeed films. N. biguttatus has between 900 and 1250 ommatidia in each compound eye. The visual space covers ca. 200° in the horizontal plane, with a binocular overlap of no more than 74°. The fovea, the part of the eye where the pseudopupil is largest, points straight ahead of a beetle in its normal posture.The structure of the visual space was determined from measurements of the optical axes in the horizontal plane (plane of fixation) over the middle of the eye. Because of the slanted position of the ommatidia under the cornea, the optical axes point more towards the front or the back of the animal than do the anatomical axes.The optical axes were used to construct the binocular visual space in the horizontal plane. The point E , to which an estimation of distance is possible, lies on the midline 42.6 mm away from the front edges of the eyes. Resolution rapidly decreases with increasing distance, particularly depth resolution.At a distance corresponding to that from which the beetle attacks its prey, depth and width resolution correspond roughly to the dimensions of the smallest prey animals. The smallest measured directional corrections made by the beetle prior to attack (2°–3°) correspond approximately to the divergence angles in the fovea ( _h=2.2°), and the smallest measured distance correction prior to attack (0.2 mm) corresponds approximately to the depth resolution at attack distance.Supported by the Deutsche Forschungsgemeinschaft (SFB 4)     

Environmental modelling of use of treated organic waste on agricultural land: a comparison of existing models for life cycle assessment of waste systems.

Modelling of environmental impacts from the application of treated organic municipal solid waste (MSW) in agriculture differs widely between different models for environmental assessment of waste systems. In this comparative study five models were examined concerning quantification and impact assessment of environmental effects from land application of treated organic MSW: DST (Decision Support Tool, USA), IWM (Integrated Waste Management, U.K.), THE IFEU PROJECT (Germany), ORWARE (ORganic WAste REsearch, Sweden) and EASEWASTE (Environmental Assessment of Solid Waste Systems and Technologies, Denmark). DST and IWM are life cycle inventory (LCI) models, thus not performing actual impact assessment. The DST model includes only one water emission (biological oxygen demand) from compost leaching in the results and IWM considers only air emissions from avoided production of commercial fertilizers. THE IFEU PROJECT, ORWARE and EASEWASTE are life cycle assessment (LCA) models containing more detailed land application modules. A case study estimating the environmental impacts from land application of 1 ton of composted source sorted organic household waste was performed to compare the results from the different models and investigate the origin of any difference in type or magnitude of the results. The contributions from the LCI models were limited and did not depend on waste composition or local agricultural conditions. The three LCA models use the same overall approach for quantifying the impacts of the system. However, due to slightly different assumptions, quantification methods and environmental impact assessment, the obtained results varied clearly between the models. Furthermore, local conditions (e.g. soil type, farm type, climate and legal regulation) and waste composition strongly influenced the results of the environmental assessment.     

Using Wildlife Communities to Improve Vegetation Classification for Conserving Biodiversity

Determining which vegetation types organisms perceive similarly and classifying these types into groups that function as similar habitats are necessary steps toward expanding the focus of conservation strategies from single species to ecosystems. Therefore, the methods used to determine these habitat classifications are crucial to the successful design and implementation of these conservation strategies. Typically, this process has been accomplished through best professional judgement. We used quantitative techniques to group vegetation types into habitats based on the occurrence of breeding wildlife species ( n = 420) in Oregon. After calculating faunal similarities among all regional vegetation types ( n = 130), we used cluster analysis to group vegetation types into wildlife habitats. We classified the original 130 vegetation types into 30 wildlife habitat types that we believe function similarly. We tested this classification to assess whether vegetation types could be correctly classified into habitat types based on wildlife species composition. Discriminant analysis correctly classified 95% of the vegetation types into their wildlife habitat types, strengthening our confidence in this approach. This approach for classifying habitat types allows consistent development of conservation strategies at coarse resolutions and aids in identifying vegetation types where additional biodiversity surveys are needed. Finally, this approach can be refined continuously as the precision of vegetation mapping and our understanding of organism-habitat associations improve.     

Associations between grab sample and integrated radon measurements in dwellings in Maine and Texas

Radon concentrations were measured in several locations in each of approximately 100 dwellings in central Maine and in Houston, TX. Integrated samples were taken during the heating (or cooling) seasons with commercially available passive alpha track devices, while grab samples were taken at the time of integrated sampler deployment. It was found that both indoor and outdoor measurements in both areas were distributed log normally, and that the geometric mean of indoor measurements in Maine was three times higher than that of corresponding measurements in the Houston area. It was also noted that the mean of the indoor grab sample measurements was not significantly different from the mean of the indoor integrated measurements, and that the degree of correlation between the grab samples and a given indoor integrated sample was nearly as good as between integrated samples taken at different living area locations.