Collisions with windows on buildings are a major source of bird mortality. The current understanding of daytime collisions is limited by a lack of empirical data on how collisions occur in the real world because most data are collected by recording evidence of mortality rather than pre-collision behaviour. Based on published literature suggesting a causal relationship between bird collision risk and the appearance of reflections on glass, the fact that reflections vary in appearance depending on viewing angle, and general principles of object collision kinematics, we hypothesized that the risk and lethality of window collisions may be related to the angle and velocity of birds' flight. We deployed a home security camera system to passively record interactions between common North American bird species and residential windows in a backyard setting over spring, summer and fall seasons over 2 years. We captured 38 events including 29 collisions and nine near-misses in which birds approached the glass but avoided impact. Only two of the collisions resulted in immediate fatality, while 23 birds flew away immediately following impact. Birds approached the glass at variable flight speeds and from a wide range of angles, suggesting that the dynamic appearance of reflections on glass at different times of day may play a causal role in collision risk. Birds that approached the window at higher velocity were more likely to be immediately killed or stunned. Most collisions were not detected by the building occupants and, given that most birds flew away immediately, carcass surveys would only document a small fraction of window collisions. We discuss the implications of characterizing pre-collision behaviour for designing effective collision prevention methods.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9784330 | PMC |
http://dx.doi.org/10.7717/peerj.14604 | DOI Listing |
Viruses
December 2024
Southeast Poultry Research Laboratory, U.S. National Poultry Research Center, United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Athens, GA 30605, USA.
Avian reoviruses (ARVs) represent a significant economic burden on the poultry industry due to their widespread prevalence and potential pathogenicity. These viruses, capable of infecting a diverse range of avian species, can lead to a variety of clinical manifestations, most notably tenosynovitis/arthritis. While many ARV strains are asymptomatic, pathogenic variants can cause severe inflammation and tissue damage in organs such as the tendons, heart, and liver.
View Article and Find Full Text PDFViruses
November 2024
College of Animal Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Duck Tembusu virus (DTMUV), a novel positive-sense RNA virus, has caused significant economic losses in the poultry industry of Eastern and Southeast Asia since its outbreak in 2010. Furthermore, the rapid transmission and potential zoonotic nature of DTMUV pose a threat to public health safety. In this study, a 4D-DIA quantitative proteomics approach was employed to identify differentially expressed cellular proteins in DTMUV-infected DF-1 cells, which are routinely used for virus isolation and identification for DTMUV, as well as the development of vaccines against other poultry viruses.
View Article and Find Full Text PDFViruses
November 2024
U.S. Geological Survey, National Wildlife Health Center Madison, Madison, WI 53711, USA.
The introduction of HPAI H5N1 clade 2.3.4.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Biological and Environmental Sciences, Liverpool John Moores University, James Parsons Building, Byrom Street, Liverpool L3 3AF, UK.
Camera traps offer enormous new opportunities in ecological studies, but current automated image analysis methods often lack the contextual richness needed to support impactful conservation outcomes. Integrating vision-language models into these workflows could address this gap by providing enhanced contextual understanding and enabling advanced queries across temporal and spatial dimensions. Here, we present an integrated approach that combines deep learning-based vision and language models to improve ecological reporting using data from camera traps.
View Article and Find Full Text PDFSensors (Basel)
December 2024
School of Aerospace and Mechanical Engineering, The University of Oklahoma, Norman, OK 73019, USA.
Autonomous vehicles (AVs) offer significant potential to improve safety, reduce emissions, and increase comfort, drawing substantial attention from both research and industry. A critical challenge in achieving SAE Level 5 autonomy, full automation, is path planning. Ongoing efforts in academia and industry are focused on optimizing AV path planning, reducing computational complexity, and enhancing safety.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!