Research on regime shifts has focused primarily on how changes in the intensity and duration of press disturbances precipitate natural systems into undesirable, alternative states. By contrast, the role of recurrent pulse perturbations, such as extreme climatic events, has been largely neglected, hindering our understanding of how historical processes regulate the onset of a regime shift. We performed field manipulations to evaluate whether combinations of extreme events of temperature and sediment deposition that differed in their degree of temporal clustering generated alternative states in rocky intertidal epilithic microphytobenthos (biofilms) on rocky shores. The likelihood of biofilms to shift from a vegetated to a bare state depended on the degree of temporal clustering of events, with biofilm biomass showing both states under a regime of non-clustered (60 d apart) perturbations while collapsing in the clustered (15 d apart) scenario. Our results indicate that time since the last perturbation can be an important predictor of collapse in systems exhibiting alternative states and that consideration of historical effects in studies of regime shifts may largely improve our understanding of ecosystem dynamics under climate change.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/ecy.2578 | DOI Listing |
Adv Wound Care (New Rochelle)
January 2025
Translational Medicine Center, Baotou Central Hospital (Baotou Clinical Medical College, Affiliated to Inner Mongolia Medical University), Baotou, China.
Wound healing is a dynamic process involving multiple cell types and signaling pathways. Dermal sheath cells (DSCs), residing surrounding hair follicles, play a critical role in tissue repair, yet their regulatory mechanisms remain unclear. This study used single-cell proteomics with the mouse model to explore DSC function across different healing stages.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Great Ormond Street Institute of Child Health, University College London, London, UK.
Introduction: Unsupervised feature learning methods inspired by natural language processing (NLP) models are capable of constructing patient-specific features from longitudinal Electronic Health Records (EHR).
Design: We applied document embedding algorithms to real-world paediatric intensive care (PICU) EHR data to extract patient-specific features from 1853 patients' PICU journeys using 647 unique lab tests and medication events. We evaluated the clinical utility of the patient features via a K-means clustering analysis.
Glob Chang Biol
January 2025
Scripps Institution of Oceanography, UC San Diego, La Jolla, California, USA.
High spatial or temporal variability in community composition makes it challenging for natural resource managers to predict ecosystem trajectories at scales relevant to management. This is commonly the case in nearshore marine environments, where the frequency and intensity of disturbance events vary at the sub-kilometer to meter scale, creating a patchwork of successional stages within a single ecosystem. The successional stage of a community impacts its stability, recovery potential, and trajectory over time in predictable ways.
View Article and Find Full Text PDFFront Public Health
January 2025
Department of Environmental and Occupational Health and Safety, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Background: Malaria is a major global health hazard, particularly in developing countries such as Ethiopia, where it contributes to high morbidity and mortality rates. According to reports from the South Omo Zone Health Bureau, despite various interventions such as insecticide-treated bed nets and indoor residual spraying, the incidence of malaria has increased in recent years. Therefore, this study aimed to assess the spatial, temporal, and spatiotemporal variation in malaria incidence in the South Omo Zone, Southwest Ethiopia.
View Article and Find Full Text PDFSci Rep
January 2025
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454003, China.
Owing to China's massive area and vastly differing regional variations in the types and efficiency of energy, the spatiotemporal distributions of regional carbon emissions (CE) vary widely. Regional CE study is becoming more crucial for determining the future course of sustainable development worldwide. In this work, two types of nighttime light data were integrated to expand the study's temporal coverage.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!