Effects of grazing management on beetle and plant assemblages during the re-creation of a flood-plain meadow

Flood-plain meadows (Alopecurus-Sanguisorba grassland) are a floristically rich community of conservation importance throughout Europe. Declines in their distribution due in part to modern farming practices mean they now cover less than 1500 ha in the UK. To investigate the effect of grazing regime during the re-creation of this grassland type, target plant species were sown onto ex-arable land during 1985. Traditional management, based on a July hay cut followed by aftermath grazing was subsequently instigated, and the site was divided into replicated grazing regimes of cattle, sheep and an un-grazed control. Plant and beetle assemblages were sampled and compared to those of target flood-plain meadows and improved grassland communities. Within the re-creation treatments the absence of aftermath grazing reduced beetle abundances and species richness. Assemblages of plants were closest to that of the target flood-plain meadow under sheep grazing, although this differed little from cattle grazing. Beetle species assemblages and functional group structure were, however, closest to the target grassland under cattle grazing. For all taxa the greatest resilience to succession to the target flood-plain meadow occurred when grazing was not part of the management prescription. Although successful re-creation had not been achieved for either the plants or beetles, cutting followed by aftermath cattle grazing has provided the best management to date. (c) 2006 Elsevier B.V. All rights reserved.

Diversification of a large genus in a continental biodiversity hotspot: temporal and spatial origin of Muraltia (Polygalaceae) in the Cape of South Africa

Phylogenetic relationships in the largely South African genus Muraltia (Polygalaceae) are assessed based on DNA sequence data (nuclear ribosomal ITS, plastid atpB-rbcL spacer, trnL intron, and trnL-F spacer) for 73 of the 117 currently recognized species in the genus. The previously recognised subgenus Muraltia is monophyletic, but the South African endemic genus Nylandtia is embedded in Muraltia subgenus Psiloclada. Subgenus Muraltia is found to be sister to subgenus Psiloclada. Estimates show the beginning of diversification of the two subgenera in the early Miocene (Psiloclada, 19.3+/-3.4 Ma; Muraltia, 21.0+/-3.5 Ma) pre-dating the establishment of the Benguela current (intermittent in the middle to late Oligocene and markedly intensifying in the late Miocene), and summer-dry climate in the Cape region. However, the later increase in species numbers is contemporaneous with these climatic phenomena. Results of dispersal-vicariance analyses indicate that major clades in Muraltia diversified from the southwestern and northwestern Cape, where most of the species are found today.

Plumatella geimermassardi, a newly recognized freshwater bryozoan from Britain, Ireland, and continental Europe (Bryozoa : Phylactolaemata)

Plumatella geimermassardi is a newly recognized species of phylactolaemate bryozoan. Its known range extends from Ireland east through southern Norway and south into Italy. Colonies grow close to the substrate with little free branching; the body wall is mostly transparent and without an obvious raphe. Floatoblasts are broadly oval and relatively small, with distinctively large dorsal fenestra and uniformly narrow ventral annulus. The sessoblast basal valve is low and dish-shaped; the annulus bears tubercles which vary in their prominence. This species brings to 14 the number of phylactolaemate bryozoans known in the region.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy. The accuracy was reduced in urban areas partly because of TerraSAR-X’s restricted visibility of the ground surface due to radar shadow and layover.

A near real-time algorithm for flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management and flood forecasting. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, and in urban areas with reasonable accuracy.

Hyperresolution global land surface modeling: meeting a grand challenge for monitoring Earth's terrestrial water

Monitoring Earth's terrestrial water conditions is critically important to many hydrological applications such as global food production; assessing water resources sustainability; and flood, drought, and climate change prediction. These needs have motivated the development of pilot monitoring and prediction systems for terrestrial hydrologic and vegetative states, but to date only at the rather coarse spatial resolutions (∼10–100 km) over continental to global domains. Adequately addressing critical water cycle science questions and applications requires systems that are implemented globally at much higher resolutions, on the order of 1 km, resolutions referred to as hyperresolution in the context of global land surface models. This opinion paper sets forth the needs and benefits for a system that would monitor and predict the Earth's terrestrial water, energy, and biogeochemical cycles. We discuss six major challenges in developing a system: improved representation of surface‐subsurface interactions due to fine‐scale topography and vegetation; improved representation of land‐atmospheric interactions and resulting spatial information on soil moisture and evapotranspiration; inclusion of water quality as part of the biogeochemical cycle; representation of human impacts from water management; utilizing massively parallel computer systems and recent computational advances in solving hyperresolution models that will have up to 109 unknowns; and developing the required in situ and remote sensing global data sets. We deem the development of a global hyperresolution model for monitoring the terrestrial water, energy, and biogeochemical cycles a “grand challenge” to the community, and we call upon the international hydrologic community and the hydrological science support infrastructure to endorse the effort.

Continental shift? An analysis of convergence trends in European real estate equities

European economic and political integration have been recognised as having implications for patterns of performance in national real estate and capital markets and have generated a wide body of research and commentary. In 1999, progress towards monetary integration within the European Union culminated in the introduction of a common currency and monetary policy. This paper investigates the effects of this ‘event’ on the behaviour of stock returns in European real estate companies. A range of statistical tests is applied to the performance of European property companies to test for changes in segmentation, co-movement and causality. The results suggest that, relative to the wider equity markets, the dispersion of performance is higher, correlations are lower, a common contemporaneous factor has much lower explanatory power whilst lead-lag relationships are stronger. Consequently, the evidence of transmission of monetary integration to real estate securities is less noticeable than to general securities. Less and slower integration is attributed to the relatively small size of the real estate securities market and the local and national nature of the majority of the companies’ portfolios.

Near real-time flood detection in urban and rural areas using high resolution Synthetic Aperture Radar images

A near real-time flood detection algorithm giving a synoptic overview of the extent of flooding in both urban and rural areas, and capable of working during night-time and day-time even if cloud was present, could be a useful tool for operational flood relief management. The paper describes an automatic algorithm using high resolution Synthetic Aperture Radar (SAR) satellite data that builds on existing approaches, including the use of image segmentation techniques prior to object classification to cope with the very large number of pixels in these scenes. Flood detection in urban areas is guided by the flood extent derived in adjacent rural areas. The algorithm assumes that high resolution topographic height data are available for at least the urban areas of the scene, in order that a SAR simulator may be used to estimate areas of radar shadow and layover. The algorithm proved capable of detecting flooding in rural areas using TerraSAR-X with good accuracy, classifying 89% of flooded pixels correctly, with an associated false positive rate of 6%. Of the urban water pixels visible to TerraSAR-X, 75% were correctly detected, with a false positive rate of 24%. If all urban water pixels were considered, including those in shadow and layover regions, these figures fell to 57% and 18% respectively.

The impact of uncertainty in satellite data on the assessment of flood inundation models

The performance of flood inundation models is often assessed using satellite observed data; however these data have inherent uncertainty. In this study we assess the impact of this uncertainty when calibrating a flood inundation model (LISFLOOD-FP) for a flood event in December 2006 on the River Dee, North Wales, UK. The flood extent is delineated from an ERS-2 SAR image of the event using an active contour model (snake), and water levels at the flood margin calculated through intersection of the shoreline vector with LiDAR topographic data. Gauged water levels are used to create a reference water surface slope for comparison with the satellite-derived water levels. Residuals between the satellite observed data points and those from the reference line are spatially clustered into groups of similar values. We show that model calibration achieved using pattern matching of observed and predicted flood extent is negatively influenced by this spatial dependency in the data. By contrast, model calibration using water elevations produces realistic calibrated optimum friction parameters even when spatial dependency is present. To test the impact of removing spatial dependency a new method of evaluating flood inundation model performance is developed by using multiple random subsamples of the water surface elevation data points. By testing for spatial dependency using Moran’s I, multiple subsamples of water elevations that have no significant spatial dependency are selected. The model is then calibrated against these data and the results averaged. This gives a near identical result to calibration using spatially dependent data, but has the advantage of being a statistically robust assessment of model performance in which we can have more confidence. Moreover, by using the variations found in the subsamples of the observed data it is possible to assess the effects of observational uncertainty on the assessment of flooding risk.