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Bird Densities (bird + density)
Selected AbstractsBird densities are associated with household densitiesGLOBAL CHANGE BIOLOGY, Issue 8 2007JAMIE TRATALOS Abstract Increasing housing density is an important component of global land transformation, with major impacts on patterns of biodiversity. However, while there have been many studies of the changes in biodiversity across rural,urban gradients, which are influenced in large part by housing densities, how biodiversity changes across the full range of regional variation in housing density remains poorly understood. Here, we explore these relationships for the richness and abundance of breeding birds across Britain. Total richness, and that of 27 urban indicator species, increased from low to moderate household densities and then declined at greater household densities. The richness of all species increased initially faster with household density than did that of the urban indicator species, but nonurban indicator species richness declined consistently after peaking at a very low housing density. Avian abundance showed a rather different pattern. Total abundance and that summed across all urban indicator species increased over a wide range of household densities, and declined only at the highest household densities. The abundance of individual urban indicator species generally exhibited a hump-shaped relationship with housing density. While there was marked intraspecific variation in the form of such relationships, almost invariably avian abundance declined at housing densities below that at which the UK government requires new developments to be built. Our data highlight the difficulties of maintaining biodiversity while minimising land take for new development. High-density housing developments are associated with declines in many of those species otherwise best able to exploit urban environments, and those components of native biodiversity with which human populations are often most familiar. [source] Mapping sea bird densities over the North Sea: spatially aggregated estimates and temporal changesENVIRONMETRICS, Issue 6 2005Edzer J. Pebesma Abstract In the Dutch sector of the North Sea, sea bird densities are recorded bi-monthly by using airborne strip-transect monitoring. From these data we try to estimate: (i) high-resolution spatial patterns of sea bird densities; (ii) low-resolution spatial-average bird densities for large areas; and (iii) temporal changes in (i) and (ii), using data on Fulmaris glacialis as an example. For spatial estimation, we combined Poisson regression for modelling the trend as a function of water depth and distance to coast with kriging interpolation of the residual variability, assuming spatial (co)variances to be proportional to the trend value. Spatial averages were estimated by block kriging. For estimating temporal differences we used residual cokriging for two consecutive years, and show how this can be extended to analyse trends over multiple years. Approximate standard errors are obtained for all estimates. A comparison with a residual simple kriging approach reveals that ignoring temporal cross-correlations leads to a severe loss of statistical accuracy when assessing the significance of temporal changes. This article shows results for Fulmaris glacialis monitored during August/September in 1998 and 1999. Copyright © 2005 John Wiley & Sons, Ltd. [source] Information needs to support environmental impact assessment of the effects of European marine offshore wind farms on birdsIBIS, Issue 2006A.D. FOX European legislation requires Strategic Environmental Assessments (SEAs) of national offshore wind farm (OWF) programmes and Environmental Impact Assessments (EIAs) for individual projects likely to affect birds. SEAs require extensive mapping of waterbird densities to define breeding and feeding areas of importance and sensitivity. Use of extensive large scale weather, military, and air traffic control surveillance radar is recommended, to define areas, routes and behaviour of migrating birds, and to determine avian migration corridors in three dimensions. EIAs for individual OWFs should define the key avian species present; as well as assess the hazards presented to birds in terms of avoidance behaviour, habitat change and collision risk. Such measures, however, are less helpful in assessing cumulative impacts. Using aerial survey, physical habitat loss, modification, or gain and effective habitat loss through avoidance behaviour can be measured using bird densities as a proxy measure of habitat availability. The energetic consequences of avoidance responses and habitat change should be modelled to estimate fitness costs and predict impacts at the population level. Our present ability to model collision risk remains poor due to lack of data on species-specific avoidance responses. There is therefore an urgent need to gather data on avoidance responses; energetic consequences of habitat modification and avoidance flights and demographic sensitivity of key species, most affected by OWFs. This analysis stresses the importance of common data collection protocols, sharing of information and experience, and accessibility of results at the international level to better improve our predictive abilities. [source] An operational model predicting autumn bird migration intensities for flight safetyJOURNAL OF APPLIED ECOLOGY, Issue 4 2007J. VAN BELLE Summary 1Forecasting migration intensity can improve flight safety and reduce the operational costs of collisions between aircraft and migrating birds. This is particularly true for military training flights, which can be rescheduled if necessary and often take place at low altitudes and during the night. Migration intensity depends strongly on weather conditions but reported effects of weather differ among studies. It is therefore unclear to what extent existing predictive models can be extrapolated to new situations. 2We used radar measurements of bird densities in the Netherlands to analyse the relationship between weather and nocturnal migration. Using our data, we tested the performance of three regression models that have been developed for other locations in Europe. We developed and validated new models for different combinations of years to test whether regression models can be used to predict migration intensity in independent years. Model performance was assessed by comparing model predictions against benchmark predictions based on measured migration intensity of the previous night and predictions based on a 6-year average trend. We also investigated the effect of the size of the calibration data set on model robustness. 3All models performed better than the benchmarks, but the mismatch between measurements and predictions was large for existing models. Model performance was best for newly developed regression models. The performance of all models was best at intermediate migration intensities. The performance of our models clearly increased with sample size, up to about 90 nocturnal migration measurements. Significant input variables included seasonal migration trend, wind profit, 24-h trend in barometric pressure and rain. 4Synthesis and applications. Migration intensities can be forecast with a regression model based on meteorological data. This and other existing models are only valid locally and cannot be extrapolated to new locations. Model development for new locations requires data sets with representative inter- and intraseasonal variability so that cross-validation can be applied effectively. The Royal Netherlands Air Force currently uses the regression model developed in this study to predict migration intensities 3 days ahead. This improves the reliability of migration intensity warnings and allows rescheduling of training flights if needed. [source] Assessing the impacts of grazing levels on bird density in woodland habitat: a Bayesian approach using expert opinionENVIRONMETRICS, Issue 7 2005Petra M. Kuhnert Abstract Many studies on birds focus on the collection of data through an experimental design, suitable for investigation in a classical analysis of variance (ANOVA) framework. Although many findings are confirmed by one or more experts, expert information is rarely used in conjunction with the survey data to enhance the explanatory and predictive power of the model. We explore this neglected aspect of ecological modelling through a study on Australian woodland birds, focusing on the potential impact of different intensities of commercial cattle grazing on bird density in woodland habitat. We examine a number of Bayesian hierarchical random effects models, which cater for overdispersion and a high frequency of zeros in the data using WinBUGS and explore the variation between and within different grazing regimes and species. The impact and value of expert information is investigated through the inclusion of priors that reflect the experience of 20 experts in the field of bird responses to disturbance. Results indicate that expert information moderates the survey data, especially in situations where there are little or no data. When experts agreed, credible intervals for predictions were tightened considerably. When experts failed to agree, results were similar to those evaluated in the absence of expert information. Overall, we found that without expert opinion our knowledge was quite weak. The fact that the survey data is quite consistent, in general, with expert opinion shows that we do know something about birds and grazing and we could learn a lot faster if we used this approach more in ecology, where data are scarce. Copyright © 2005 John Wiley & Sons, Ltd. [source] A comparison of nocturnal call counts of migrating birds and reflectivity measurements on Doppler radarJOURNAL OF AVIAN BIOLOGY, Issue 4 2004Andrew Farnsworth Several studies have found that the peak in bird density in the atmosphere during nocturnal migration occurs before midnight, while the peak in vocalizations from migrating birds occurs after midnight, in the hours just before dawn. In a recent study, the patterns of calling from a single species of migrating birds correlated well with the patterns of density estimates of migrating birds. We test the null hypothesis that the patterns of reflectivity measurements and number of vocalizations during nocturnal migration are not related. We sampled radar data and nocturnal flight calls during spring and fall 2000 in northwestern South Carolina and southeastern New York. We analyzed changes in the hour-to-hour patterns of bird density and vocalizations for 556 hours on 58 nights. We also analyzed the night-to-night changes in the patterns of peak hour bird density and peak hour of vocalizations on 32 nights. We found that most of the hour-to-hour and night-to-night patterns of density and vocalization counts are significantly related and reject the null hypothesis. However, despite significant relationships between reflectivity measurements and vocalization counts, a great deal of variation in vocalization counts remains unexplained. These results suggest that factors other than bird density are responsible for the variation in vocalizing by migrating birds. [source] | ||||||||||