Predicting population declines is a key challenge in the face of global environmental change. Abundance-based early warning signals have been shown to precede population collapses; however, such signals are sensitive to the low reliability of abundance estimates. Here, using historical data on whales harvested during the 20th century, we demonstrate that early warning signals can be present not only in the abundance data, but also in the more reliable body size data of wild populations. We show that during the period of commercial whaling, the mean body size of caught whales declined dramatically (by up to 4 m over a 70-year period), leading to early warning signals being detectable up to 40 years before the global collapse of whale stocks. Combining abundance and body size data can reduce the length of the time series required to predict collapse, and decrease the chances of false positive early warning signals.
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http://dx.doi.org/10.1038/s41559-017-0188 | DOI Listing |
Water Res
January 2025
Department of Chemical Engineering, University of Bath, Claverton Down, Bath BA2 7AY, UK; SWING - Department of Built Environment, Oslo Metropolitan University, St Olavs plass 0130, Oslo, Norway. Electronic address:
Climate resilience in water resource recovery facilities (WRRFs) necessitates improved adaptation to shock-loading conditions and mitigating greenhouse gas emission. Data-driven learning methods are widely utilised in soft-sensors for decision support and process optimization due to their simplicity and high predictive accuracy. However, unlike for mechanistic models, transferring machine-learning-based insights across systems is largely infeasible, which limits communication and knowledge sharing.
View Article and Find Full Text PDFBMC Genomics
January 2025
Department of Virology, Norwegian Institute of Public Health, Oslo, 0456, Norway.
The COVID-19 pandemic has underscored the importance of virus surveillance in public health and wastewater-based epidemiology (WBE) has emerged as a non-invasive, cost-effective method for monitoring SARS-CoV-2 and its variants at the community level. Unfortunately, current variant surveillance methods depend heavily on updated genomic databases with data derived from clinical samples, which can become less sensitive and representative as clinical testing and sequencing efforts decline.In this paper, we introduce HERCULES (High-throughput Epidemiological Reconstruction and Clustering for Uncovering Lineages from Environmental SARS-CoV-2), an unsupervised method that uses long-read sequencing of a single 1 Kb fragment of the Spike gene.
View Article and Find Full Text PDFJ Infect Public Health
January 2025
Ecosystem Change and Population Health Research Group, School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia. Electronic address:
Background: Several studies have examined the effect of non-pharmaceutical interventions (NPIs) on COVID-19 and other infectious diseases in Australia and globally. However, to our knowledge none have sufficiently explored their impact on other infectious diseases with robust time series model. In this study, we aimed to use Bayesian Structural Time Series model (BSTS) to systematically assess the impact of NPIs on 64 National Notifiable Infectious Diseases (NNIDs) by conducting a comprehensive and comparative analysis across eight disease categories within each Australian state and territory, as well as nationally.
View Article and Find Full Text PDFTraffic Inj Prev
January 2025
School of Civil and Hydraulic Engineering, NingXia University, YinChuan, China.
Objective: This study aims to address the issue of driving safety on highways in the desert region of Northwest China during extreme weather conditions such as sandstorms, with the goal of reducing driver risk. It explores driver behavior under extreme conditions of sandstorms and sand accumulation, proposing safety speed recommendations and warning models for different environments to calculate the optimal warning distance in windy and sandy conditions.
Methods: Natural driving simulation experiments were conducted in windy and sandy environments, collecting driving behavior data from 45 drivers under varying visibility and road conditions with or without sand accumulation.
Lancet Reg Health West Pac
January 2025
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China.
Background: Due to global climate change, high temperature and heatwaves have become critical issues that pose a threat to human health. An effective early warning system is essential to mitigate the health risks associated with high temperature and heatwaves. However, most of the current heatwave early warning systems are not adequately developed based on the heat-health risk model, and the health impact of hot weather has not been well managed in most countries.
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