Understanding spatiotemporal variation in environmental conditions is important to determine how climate change will impact ecological communities. The spatial and temporal autocorrelation of temperature can have strong impacts on community structure and persistence by increasing the duration and the magnitude of unfavorable conditions in sink populations and disrupting spatial rescue effects by synchronizing spatially segregated populations. Although increases in spatial and temporal autocorrelation of temperature have been documented in historical data, little is known about how climate change will impact these trends. We examined daily air temperature data from 21 General Circulation Models under the business-as-usual carbon emission scenario to quantify patterns of spatial and temporal autocorrelation between 1871 and 2099. Although both spatial and temporal autocorrelation increased over time, there was significant regional variation in the temporal autocorrelation trends. Additionally, we found a consistent breakpoint in the relationship between spatial autocorrelation and time around the year 2030, indicating an acceleration in the rate of increase of the spatial autocorrelation over the second half of the 21 century. Overall, our results suggest that ecological populations might experience elevated extinction risk under climate change because increased spatial and temporal autocorrelation of temperature is expected to erode both spatial and temporal refugia.
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http://dx.doi.org/10.1038/s41598-018-33217-0 | DOI Listing |
Chembiochem
January 2025
Xidian University, School of Life Science and Technology, 266 Xinglong Section of Xifeng Road, 710126, Xi'an, CHINA.
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December 2024
Degenerative and Chronic Diseases of the Faculty of Health Sciences (FGW), University Potsdam, 14469 Potsdam, Germany.
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View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Computer Science, King AbdulAziz University, Jeddah 21589, Saudi Arabia.
Traffic flow prediction is a pivotal element in Intelligent Transportation Systems (ITSs) that provides significant opportunities for real-world applications. Capturing complex and dynamic spatio-temporal patterns within traffic data remains a significant challenge for traffic flow prediction. Different approaches to effectively modeling complex spatio-temporal correlations within traffic data have been proposed.
View Article and Find Full Text PDFSensors (Basel)
January 2025
NUS-ISS, National University of Singapore, Singapore 119615, Singapore.
Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Information Technology, Quaid e Awam University, Nawabshah 67450, Pakistan.
Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed.
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