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58 results for “natural risk”
The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf
<p>This dataset contains the measured hourly mean microclimate and surface energy flux data from a field experiment. The experiment consisted of three treatments: unirrigated artificial turf, unirrigated natural turf, and irrigated natural turf (4 mm/day, 13:00-13:23 local time). The experiment was conducted from 2024-01-28 to 2024-03-18 in Burnley, Melbourne, Australia.<br><br>For each treatment, the measured hourly mean data included albedo, soil moisture content, air temperature, vapour pressure of water, wind speed, black globe temperature, mean radiant temperature, universal theraml climate index, wet-bulb globe temperature, soil temperature, turf surface temperature, incoming and outgoing longwave and shortwave radiant fluxes, sensible heat flux, latent heat flux, and ground heat flux. <br><br>Turf surface temperature, and incoming and outgoing longwave and shortwave radiant fluxes were measured at 1.5 m above ground surface.<br>Air temperature and vapour pressure of water were measured at 0.6 and 1.1 m above ground surface.<br>Wind speed, black globe temperature, mean radiant temperature, universal thermal climate index, and wet-bulb globe temperature were measured at 1.1 m above ground surface.<br>Soil moisture content, soil temperature and ground heat flux were measured at 0.1 m below ground surface.<br>Sensible heat flux and latent heat flux were calculated using the Bowen ratio-energy balance method.<br><br>Additionally, the hourly mean background weather conditions (air temperature and cloud amount) from the nearest public climate station in the study period were included in 'ReferenceClimateStation.csv'. Hourly total rainfall data measured at the study site was also included. <br><br>The aims of this study was to:<br>1. Compare the microclimate and human heat stress among the three treatments.<br>2. Assess and compare the human skin burn risks of the three treaments from their turf surface temperatures.<br>3. Analyse the surface energy fluxes of the three treatments to identify the mechanisms by which artificial turf develops any microclimate, human heat stress and turf surface temperature differences.<br><br>This study was published in:</p> <p><span>Cheung, P. K., & Livesley, S. J. (2025). The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf. <em>Building and Environment</em>, 112679. https://doi.org/10.1016/j.buildenv.2025.112679<br></span><br>Contact person: Dr Paul Cheung (cheung.p@unimelb.edu.au)</p>
Fig. 1 in Evaluation of reduced-risk insecticides to control chilli thrips (Thysanoptera: Thripidae) and conserve natural enemies on ornamental plants
Fig. 1. Mean percentage (± SEM) of Rhaphiolepsis indica foliage with Scirtothrips dorsalis feeding damage 42 days afer insecticide treatment. Different letters indicate significant differences between treatments using Tukey-Kramer HSD means comparison (P <0.05). Cyantraniliprole low (59.1 mL per 378.5 L) and cyantraniliprole high (236.6 mL per378.5 L).
Quantifying the effects of species traits on predation risk in nature: a comparative study of butterfly wing damage
<p>1) Evading predators is a fundamental aspect of the ecology and evolution of all prey animals. In studying the influence of prey traits on predation risk, previous researchers have shown that crypsis reduces attack rates on resting prey, predation risk increases with increased prey activity, and rapid locomotion reduces attack rates and increases chances of surviving predator attacks. However, evidence for these conclusions is nearly always based on observations of selected species under artificial conditions. In nature, it remains unclear how defensive traits such as crypsis, activity levels, and speed influence realized predation risk across species in a community. Whereas direct observations of predator-prey interactions in nature are rare, insight can be gained by quantifying bodily damage caused by failed predator attacks. 2) We quantified how butterfly species traits affect predation risk in nature by determining how defensive traits correlate with wing damage caused by failed predation attempts, thereby providing the first robust multi-species comparative analysis of predator-induced bodily damage in wild animals. 3) For 34 species of fruit-feeding butterflies in an African forest, we recorded wing damage and quantified crypsis, activity levels, and flight speed. We then tested for correlations between damage parameters and species traits using comparative methods that account for measurement error. 4) We detected considerable differences in the extent, location, and symmetry of wing surface loss among species, with smaller differences between sexes. We found that males (but not females) of species that flew faster had substantially less wing surface loss. However, we found no correlation between cryptic colouration and symmetrical wing surface loss across species. In species in which males appeared to be more active than females, males had a lower proportion of symmetrical wing surface loss than females. 5) Our results provide evidence that activity greatly influences the probability of attacks and that flying rapidly is effective for escaping pursuing predators in the wild, but we did not find evidence that cryptic species are less likely to be attacked while at rest. 15-Oct-2019</p>
Predation risk shapes the degree of placentation in natural populations of live-bearing fish
<p class="manuscriptABSATZ"><span>The placenta is a complex life-history trait that is ubiquitous across the tree of life. Theory proposes that the placenta evolves in response to high performance-demanding conditions by shifting maternal investment from pre- to post-fertilization, thereby reducing a female's reproductive burden during pregnancy. We test this hypothesis by studying populations of the fish species <i>Poeciliopsis retropinna</i> in Costa Rica. We found substantial variation in the degree of placentation among natural populations associated with predation risk: females from high predation populations had significantly higher degrees of placentation compared to low predation females, while number, size and quality of offspring at birth remained unaffected. Moreover, a higher degree of placentation correlated with a lower reproductive burden and hence likely an improved swimming performance during pregnancy. Our study advances an adaptive explanation for why the placenta evolves by arguing that an increased degree of placentation offers a selective advantage in high predation environments. </span></p>
Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta: supporting data - high water levels
<p>This dataset contains modelled high water levels in the Guayas delta supporting the paper 'Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta' published in HESS 2024.</p> <p>The dataset is organised through two folders:</p> <ul> <li>mangroves: all data from the scenarios with mangroves included in the domain</li> <li>no_mangroves: all data from the scenarios without mangroves included in the domain</li> </ul> <p>Each folder is further divided along 6 subfolders:</p> <ul> <li>I: El Niño Ocean & river</li> <li>II: El Niño Ocean</li> <li>III: El Niño river</li> <li>IV: Neutral</li> <li>I_50per: 50 % increase in seaward El Niño anomalies</li> <li>I_150per: 150 % increase in seaward El Niño anomalies</li> </ul> <p>Each folder contains two files:</p> <ul> <li>vars.csv - each row represents a mesh node and there are three columns: <ul> <li>X: x value of the model mesh node [m]</li> <li>Y: y value of the model mesh node [m]</li> <li>HIGH_WATER: high water level [m]</li> </ul> </li> </ul>
A global map of species at risk of extinction due to natural hazards
<p>An often-overlooked question of the biodiversity crisis is how natural hazards contribute to species extinction risk. To address this issue, we explored how four natural hazards: earthquakes, hurricanes, tsunamis, and volcanoes, overlapped with the distribution ranges of amphibians, birds, mammals, and reptiles that have either narrow distributions or populations with few mature individuals. To assess which species are at risk from these natural hazards, we combined the frequency and magnitude of each natural hazard to estimate a probability of impact. We considered species at risk if they overlapped with regions where any of the four natural hazards historically occurred (n = 3,722). Those species with at least a quarter of their range subjected to a high probability of impact were considered at high risk (n = 2,001) of extinction due to natural hazards. In total, 834 reptiles, 617 amphibians, 302 birds, and 248 mammals were at high risk and they were mainly distributed on islands and in the tropics. Hurricanes (n = 983) and earthquakes (n = 868) affected the most species, while tsunamis (n = 272), and volcanoes (n = 171) affected considerably fewer. The region with the highest number of species at high risk was the Pacific Ring of Fire, especially due to volcanoes, earthquakes and tsunamis, while hurricane-related high-risk species were concentrated in the Caribbean Sea, Gulf of Mexico, and northwestern Pacific Ocean. Our study provides important information regarding the species at risk due to natural hazards and can help guide conservation attention and efforts to safeguard their survival.</p>
Natural History With Focus on Oncological Risk Evaluation in Pediatric Patients With PTEN Pathogenic Variants - Observational Study
ClinicalTrials.gov study NCT06805734. IPD Sharing: YES. Countries: 1. Publications: 3.
Predation risk shapes the degree of placentation in natural populations of live-bearing fish
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Data for: Spatial-temporal analysis of natural hazards and disasters in the Greater Horn of Africa between 2010 and 2024 to inform disaster risk reduction, and surveillance and control strategies for climate and environmentally sensitive diseases
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A global map of species at risk of extinction due to natural hazards
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Quantifying the effects of species traits on predation risk in nature: a comparative study of butterfly wing damage
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"Natural" solutions could speed up climate change mitigation, with risks, compared to emissions reductions alone. Additional options are needed.
<p>Mitigation of climate change by intentionally storing carbon in tropical forests, soils and wetlands, and by reducing greenhouse gas fluxes from these settings has been promoted as rapidly deployable and cost-effective. This approach, sometimes referred to as "natural" mitigation, could keep post-industrialization warming below 1.5°C, when coupled with reductions in fossil fuel emissions, as confirmed here with a simple numerical model of future emissions. However, such mitigation could cease in response to changes in future climate, land use or natural resource policies, or there could be CO<sub>2</sub> released from reservoirs of stored carbon. Model simulations suggest cumulative emissions could be similar, under scenarios where carbon storage ceases, or stored carbon is released, to emissions expected in the absence of any natural mitigation. If climate change is to be minimized, low-risk natural mitigation (e.g. by reducing deforestation) should be considered, as emissions targets that could limit warming to 1.5°C cannot be met without mitigation of this magnitude. However, additional mitigation options should also be considered that can reduce CO<sub>2</sub> emissions and remove CO<sub>2</sub> from the air (and store it permanently) and/or reduce the temperature of the atmosphere or the ocean.</p>
Data from: Predator-driven natural selection on risk-taking behavior in anole lizards
Biologists have long debated the role of behavior in evolution, yet understanding of its role as a driver of adaptation is hampered by the scarcity of experimental studies of natural selection on behavior in nature. After showing that individual Anolis sagrei lizards vary consistently in risk-taking behaviors, we experimentally established populations on eight small islands either with or without Leiocephalus carinatus, a major ground predator. We found that selection predictably favors different risk-taking behaviors under different treatments: Exploratory behavior is favored in the absence of predators, whereas avoidance of the ground is favored in their presence. On predator islands, selection on behavior is stronger than selection on morphology, whereas the opposite holds on islands without predators. Our field experiment demonstrates that selection can shape behavioral traits, paving the way toward adaptation to varying environmental contexts.
Geomatics, Natural Hazards and Risk
<p>This repository was set up accompanying the paper 'Proposal of A Flood Damage Road Detection Method Based on Deep Learning and Elevation Data' in the Geomatics, Natural Hazards and Risk.</p>
Risk Classification of contaminates sites in Anderstorp using the German Einzelfallbewertung Altlastenstandorte (EB) method from the Hessian Agency for Nature Conservation, Environment and Geology (HLNUG)
<p>This data set includes the documents for risk classifying contaminated sites in Anderstorp, Sweden using the German Einzelfallbewertung Altlastenstandorte (EB) method from the Hessian Agency for Nature Conservation, Environment, and Geology (HLNUG).</p>
Code and data for "Mapping the natural disturbance risk to protective forests across the European Alps"
<p>This repository holds the code and output data for the publication entitled "Mapping the natural disturbance risk to protective forests across the European Alps",<br>accepted in the Journal of Environmental Management, see <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jenvman.2024.121659" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.jenvman.2024.121659</span></a></p> <p>The output maps can be viewed online here: <a href="https://risiko-gebirgswald.shinyapps.io/maps/">https://risiko-gebirgswald.shinyapps.io/maps/ </a></p> <ul> <li><strong>lib</strong>: scripts used for data processing and analysis</li> <li><strong>Alps</strong>: output data (hazard, vulnerability and risk maps) for the European Alps</li> <li><strong>Bayern</strong>: output data (hazard, vulnerability and risk maps) for the Bavarian Alps</li> </ul> <p>All of the data used in this study are publicly available:</p> <ul> <li>GEDI Level 2A and 2B products can be downloaded at<a href="https://lpdaac.usgs.gov/products/gedi02_av002/"> https://lpdaac.usgs.gov/products/gedi02_av002/ </a>and <a href="https://lpdaac.usgs.gov/products/gedi02_bv002/">https://lpdaac.usgs.gov/products/gedi02_bv002/</a>, respectively. </li> <li>The European disturbance map (Senf & Seidl, 2021) is available at <a href="https://doi.org/10.5281/zenodo.3924381">https://doi.org/10.5281/zenodo.3924381 </a></li> <li>The attribution of disturbance agents for the disturbance map is available at <a href="../records/4607230">https://zenodo.org/records/4607230</a></li> <li>CHELSA climate data is available at <a href="https://www.chelsa-climate.org">www.chelsa-climate.org</a></li> <li>The Copernicus DEM can be downloaded from <a href="https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1.">https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1.</a></li> <li>Wind data were obtained from <a href="https://map.neweuropeanwindatlas.eu/">https://map.neweuropeanwindatlas.eu/</a></li> <li>Soil data were obtained from <a href="https://esdac.jrc.ec.europa.eu/content/european-soil-database-v20-vector-and-attribute-data.">https://esdac.jrc.ec.europa.eu/content/european-soil-database-v20-vector-and-attribute-data. </a></li> <li>The boundary of the Alpine convention is available at <a href="https://www.atlas.alpconv.org/layers/geonode_data:geonode:Alpine_Convention_Perimeter_2018_v2">https://www.atlas.alpconv.org/layers/geonode_data:geonode:Alpine_Convention_Perimeter_2018_v2 </a></li> </ul>
Coda and data for "A natural disaster exacerbates and redistributes disease risk across free-ranging macaques by altering social structure"
<p>This Zenodo repository contains the data and code for the article entitled "<strong>A natural disaster exacerbates and redistributes disease risk across free-ranging macaques by altering social structure</strong>". </p>
Liquidity Risk and Long-Term Finance: Evidence from a Natural Experiment
<p>This package contains all the code necessary to reproduce figures and tables in Choudhary and Limodio (forthcoming). "Liquidity Risk and Long-Term Finance: Evidence from a Natural Experiment" in the Review of Economic Studies. Instructions are also given about accessing the data.</p>
Phase I Study of Umbilical Cord Blood Natural Killer (NK) Cell Therapy for Children With High-risk, R/R Neuroblastoma.
ClinicalTrials.gov study NCT06631391. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Adoptive Immunotherapy of High Risk Acute Myeloblastic Leukemia Patients Using Haploidentical Kir Ligand-mismatched Natural Killer Cells
ClinicalTrials.gov study NCT00799799. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.