Declines of Aerial Insectivores in North America Follow a Geographic Gradient

North American birds that feed on aerial insects are experiencing widespread population declines. An analysis of the North American Breeding Bird Survey trend estimates for 1966 to 2006 suggests that declines in this guild are significantly stronger than in passerines in general. The pattern of decline also shows a striking geographical gradient, with aerial insectivore declines becoming more prevalent towards the northeast of North America. Declines are also more acute in species that migrate long distances compared to those that migrate short distances. The declines become manifest, almost without exception, in the mid 1980s. The taxonomic breadth of these downward trends suggests that declines in aerial insectivore populations are linked to changes in populations of flying insects, and these changes might be indicative of underlying ecosystem changes.

Modeling Habitat Associations for the Common Loon (Gavia immer) at Multiple Scales in Northeastern North America

Common Loon (Gavia immer) is considered an emblematic and ecologically important example of aquatic-dependent wildlife in North America. The northern breeding range of Common Loon has contracted over the last century as a result of habitat degradation from human disturbance and lakeshore development. We focused on the state of New Hampshire, USA, where a long-term monitoring program conducted by the Loon Preservation Committee has been collecting biological data on Common Loon since 1976. The Common Loon population in New Hampshire is distributed throughout the state across a wide range of lake-specific habitats, water quality conditions, and levels of human disturbance. We used a multiscale approach to evaluate the association of Common Loon and breeding habitat within three natural physiographic ecoregions of New Hampshire. These multiple scales reflect Common Loon-specific extents such as territories, home ranges, and lake-landscape influences. We developed ecoregional multiscale models and compared them to single-scale models to evaluate model performance in distinguishing Common Loon breeding habitat. Based on information-theoretic criteria, there is empirical support for both multiscale and single-scale models across all three ecoregions, warranting a model-averaging approach. Our results suggest that the Common Loon responds to both ecological and anthropogenic factors at multiple scales when selecting breeding sites. These multiscale models can be used to identify and prioritize the conservation of preferred nesting habitat for Common Loon populations.

Alkyl nitrates in outflow from North America over the North Atlantic during Intercontinental Transport of Ozone and Precursors 2004

This paper is based on alkyl nitrate measurements made over the North Atlantic as part of the International Consortium for Research on Atmospheric Transport and Transformation (ICARTT). The focus is on the analysis of air samples collected on the UK BAe-146 aircraft during the Intercontinental Transport of Ozone and Precursors (ITOP) project, but air samples collected on board the NASA DC-8 and NOAA WP-3D aircraft as part of a Lagrangian experiment are also used. The ratios between the alkyl nitrates and their parent hydrocarbons are compared with those expected from chemical theory. Further, a box model is run to investigate the temporal evolution of the alkyl nitrates in three Lagrangian case studies and compared to observations. The air samples collected during ITOP do not appear to be strongly influenced by oceanic sources, but rather are influenced by emissions from the N.E. United States and from Alaskan fires. There also appears to be a widespread common source of ethyl nitrate and 1-propyl nitrate other than from their parent hydrocarbons. The general agreement between the alkyl nitrate data and photochemical theory suggests that during the first few days of transport from the source region, photochemical production of alkyl nitrates, and thus ozone, had taken place. The observations in the more photochemically processed air masses are consistent with the alkyl nitrate production reactions no longer dominating the peroxy radical self/cross reactions. Further, the results also suggest that the rates of photochemical processing in the Alaskan smoke plumes were small.

Toward an integrated seasonal forecasting system for South America

This study proposes an objective integrated seasonal forecasting system for producing well-calibrated probabilistic rainfall forecasts for South America. The proposed system has two components: ( i) an empirical model that uses Pacific and Atlantic sea surface temperature anomalies as predictors for rainfall and ( ii) a multimodel system composed of three European coupled ocean - atmosphere models. Three-month lead austral summer rainfall predictions produced by the components of the system are integrated ( i. e., combined and calibrated) using a Bayesian forecast assimilation procedure. The skill of empirical, coupled multimodel, and integrated forecasts obtained with forecast assimilation is assessed and compared. The simple coupled multimodel ensemble has a comparable level of skill to that obtained using a simplified empirical approach. As for most regions of the globe, seasonal forecast skill for South America is low. However, when empirical and coupled multimodel predictions are combined and calibrated using forecast assimilation, more skillful integrated forecasts are obtained than with either empirical or coupled multimodel predictions alone. Both the reliability and resolution of the forecasts have been improved by forecast assimilation in several regions of South America. The Tropics and the area of southern Brazil, Uruguay, Paraguay, and northern Argentina have been found to be the two most predictable regions of South America during the austral summer. Skillful rainfall forecasts are generally only possible during El Nino or La Nina years rather than in neutral years.

A Bayesian approach for multi-model downscaling: Seasonal forecasting of regional rainfall and river flows in South America

This study addresses three issues: spatial downscaling, calibration, and combination of seasonal predictions produced by different coupled ocean-atmosphere climate models. It examines the feasibility Of using a Bayesian procedure for producing combined, well-calibrated downscaled seasonal rainfall forecasts for two regions in South America and river flow forecasts for the Parana river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.