Search

Populations are being exposed to environmental noise at levels that impact on both mental and physical health, with knock-on effects on productivity and economic performance. Current assessments of exposure are often based on noise levels at building façades, derived from sound propagation models and are usually limited to long-term average noise levels for periods of the day, night or over 24 hours (e.g. Lden). There is a particular lack of information on variations in noise throughout the diurnal cycle and over long time periods. In this study, we deployed 14 low-cost recorders to gather high resolution data on urban noise levels and compared results with those from a conventional noise propagation algorithm. Daily LAeq and diurnal variations in hourly LAeq showed considerable variation in space and time with the middle of the day generally noisiest. Some of these patterns were well captured by the propagation model although it tended to underestimate noise levels from all sources. Although more work is needed, we suggest that well-placed sensors have the potential to enhance exposure assessments e.g. on minor roads and where traffic is not the major noise source

P. 45-56 Little bustard populations have suffered reduction and isolation as a consequence of landscape transformations resulting from changes in traditional agricultural systems. Consequently, the species survives within reduced and fragmentary habitats, like islands isolated in a modified matrix. In this paper, we analyze the spatial variations in male density and habitat quality in a fragmented population located at the limit of the species’ Iberian range, which is affected by agricultural intensification, using a regional modelling approach. Habitat quality (quantified according to the species perception) and bird density decreased along the intensification gradient. However, in the most intensive agricultural zone, the quality of habitats selected by little bustard males increased, while density decreased, against the expected. In possible explanation, we suggest: (1) density is not necessarily a good indicator of habitat quality, (2) population could be under-saturated in this zone, (3) interannual variations in species distribution, or (4) other relevant variables related to the agricultural intensification process not included in this analysis, such as small-scale disturbances. Analysis of population distribution pattern showed a spatial configuration in which the most densely populated squares were located at the core of the biggest population patches, in contact with mid-density squares, and all surrounded by low-density squares. Fragmentation negatively affected habitat quality and male density. Largest population patches, containing higher density values, were located at the beginning of the intensification gradient. Preservation of little bustard densities is related to an adequate management of the farming system. Habitat fragmentation requires an urgent conservation strategy to prevent local and regional scale habitat deterioration, by reducing patch isolation to maintain genetic diversification and functional connectivity.

14 paginas, 11 figuras y 3 tables, 1 Many species are adversely affected by human activities at large spatial scales and their conservation requires detailed information on distributions. Intensive ground surveys cannot keep pace with the rate of land-use change over large areas and new methods are needed for regional-scale mapping. 2 We present predictive models for great bustards in central Spain based on readily available advanced very high resolution radiometer (AVHRR) satellite imagery combined with mapped features in the form of geographic information system (GIS) data layers. As AVHRR imagery is coarse-grained, we used a 12-month time series to improve the definition of habitat types. The GIS data comprised measures of proximity to features likely to cause disturbance and a digital terrain model to allow for preference for certain topographies. 3 We used logistic regression to model the above data, including an autologistic term to account for spatial autocorrelation. The results from models were combined using Bayesian integration, and model performance was assessed using receiver operating characteristics plots. 4 Sites occupied by bustards had significantly lower densities of roads, buildings, railways and rivers than randomly selected survey points. Bustards also occurred within a narrower range of elevations and at locations with significantly less variable terrain. 5 Logistic regression analysis showed that roads, buildings, rivers and terrain all contributed significantly to the difference between occupied and random sites. The Bayesian integrated probability model showed an excellent agreement with the original census data and predicted suitable areas not presently occupied. 6 The great bustard's distribution is highly fragmented and vacant habitat patches may occur for a variety of reasons, including the species’ very strong fidelity to traditional sites through conspecific attraction. This may limit recolonization of previously occupied sites. 7 We conclude that AVHRR satellite imagery and GIS data sets have potential to map distributions at large spatial scales and could be applied to other species. While models based on imagery alone can provide accurate predictions of bustard habitats at some spatial scales, terrain and human influence are also significant predictors and are needed for finer scale modelling, This research was jointly funded by British Council/Ministry of Education and Culture Accion Integrada HB96-82 and HB97-69 awards, DGICYT project PB94-0068, and by the Dirección General del Medio Natural of Madrid Community