Background: Performance of migrating birds can be affected by a number of intrinsic and extrinsic factors like morphology, meteorological conditions and migration strategies. We compared travel speeds of four raptor species during their crossing of the Sahara desert. Focusing the analyses on this region allows us to compare different species under equivalent conditions in order to disentangle which factors affect migratory performance.

Methodology/principal Finding: We tracked raptors using GPS satellite transmitters from Sweden, Spain and Italy, and evaluated their migratory performance at both an hourly and a daily scale. Hourly data (flight speed and altitude for intervals of two hours) were analyzed in relation to time of day, species and season, and daily data (distance between roosting sites) in relation to species, season, day length and tailwind support.

Conclusions/significance: Despite a clear variation in morphology, interspecific differences were generally very small, and did only arise in spring, with long-distance migrants (>5000 km: osprey and Western marsh-harrier) being faster than species that migrate shorter distances (Egyptian vulture and short-toed eagle). Our results suggest that the most important factor explaining hourly variation in flight speed is time of day, while at a daily scale, tailwind support is the most important factor explaining variation in daily distance, raising new questions about the consequences of possible future changes in worldwide wind patterns.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3388085PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0039833PLOS

Publication Analysis

Top Keywords

morphology meteorological
8
meteorological conditions
8
conditions migration
8
migration strategies
8
daily scale
8
flight speed
8
time day
8
species season
8
factor explaining
8
species
5

Similar Publications

Introduction: Environmental factors appear to play an important role in the development and course of Multiple Sclerosis (MS). Seasonal variability in disease activity has been described and it is postulated that it may vary according to geographical area. The aim of this study is to analyse the monthly distribution of activity observed on Magnetic Resonance Imaging (MRI) and to look for a possible relationship with climate in patients with relapsing remitting MS.

View Article and Find Full Text PDF

The growing desire to live experiences in naturalistic environments that are also opportunities for psycho-physical well-being has meant that the issue of accessibility is now involving environmental contexts that by their nature are often almost inaccessible due to both the morphology of the places and the meteorological-geographical conditions. It is evident that in such contexts the degree of accessibility cannot be fully satised by acting on the environment, it is, therefore, necessary to refer to notions such as reasonable accommodation or equivalent accessibility. In this sense, the degree of accessibility achievable involves more organizational aspects and the provision of special aids, reducing the number and scope of interventions in the physical environment.

View Article and Find Full Text PDF

Coarse aerosol particles containing chloride in tropical forests are significant for understanding biogeochemical cycles and atmospheric processes, with implications for environmental health and climate change mitigation. This study explored the sources of super-coarse carbonaceous aerosol particles containing chloride in a tropical savanna climate. Aerosol samples were collected from an agro-forest site in Thailand during the dry season and analyzed using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), and Fourier-transform infrared (FTIR) spectroscopy.

View Article and Find Full Text PDF
Article Synopsis
  • This study investigated the effectiveness of various machine learning prediction models in estimating the growth and physical characteristics of different goat hybrids at yearling age using early body measurements and weather data from extensive pasture systems.
  • Seven machine learning algorithms, including linear regression and neural networks, were used to analyze data from 1,530 goat offspring, focusing on traits like weight and body dimensions at different ages within the first nine months.
  • The findings indicated that combining meteorological data with early measurements could significantly enhance predictive accuracy, with the ExtraTree model achieving the best results, highlighting its potential as a valuable tool for goat breeding decisions.
View Article and Find Full Text PDF
Article Synopsis
  • Extreme meteorological events and urbanization have caused significant urban flooding issues, prompting the need for better assessment methods of flooding susceptibility in densely populated areas.
  • This study introduces a novel framework combining machine learning techniques (XGBoost, SHAP, and PDP) with K-means clustering to accurately evaluate urban flood risks related to city design and rainfall conditions.
  • Results show that urban morphology significantly influences flooding, with specific conditions—like building density and floor area ratios—potentially reducing flood risks, highlighting the importance of strategic urban planning.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!