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368 results for “wildfires”
Data from: Wildfire smoke impacts the body condition and capture rates of birds in California
<p>Despite the increased frequency with which wildfire smoke now blankets portions of world, the effects of smoke on wildlife, and birds in particular, are largely unknown. We used two decades of banding data from the San Francisco Bay Bird Observatory to investigate how fine particulate matter (PM<sub>2.5</sub>) – a major component and indicator of wildfire smoke – influenced capture rates and body condition of 21 passerine or near-passerine bird species. Across all study species, we found a negative effect of acute PM<sub>2.5</sub> exposure and a positive effect of chronic PM<sub>2.5</sub> exposure on avian capture rates. Together, these findings are indicative of decreased bird activity or local site removal during acute periods of wildfire smoke, but increased activity or site colonization under chronic smoke conditions. Importantly, we also observed a negative relationship between chronic PM<sub>2.5</sub> exposure and body mass change in individuals with multiple captures per season. Our results indicate that wildfire smoke likely influences the health and behavior of birds, ultimately contributing to a shift in activity and body condition, with differential short-term versus long-term impacts. Although more research is needed on the mechanisms driving these observed changes in bird health and behavior, as well as validation of these relationships in more areas, our results suggest that wildfire smoke is a potentially frequent large-scale environmental stressor to birds that deserves increasing attention and recognition.</p>
Data and Code from: Wildfire influences species assemblage and habitat utilisation of boreal wildlife after more than a decade in northern Sweden
<p>Data and Code supporting the analyses presented in: Fredriksson, Cromsigt & Hofmeester - Wildfire influences species assemblage and habitat utilisation of boreal wildlife after more than a decade in northern Sweden as published in Wildlife Biology</p> <p><strong>Abstract</strong></p> <p><span>Fires can strongly change the vegetation structure and the availability of resources for wildlife, but fire suppression has long affected the natural role of fire in shaping boreal ecosystems in northern Europe. Recently, wildfires have increased in frequency, possibly due to global warming. In contrast to the boreal systems in North America, there have been few studies on responses of wildlife to wildfires in northern Europe. Based on the findings from North America, we predict that responses of wildlife to wildfire vary among wildlife species: where mammalian herbivores, such as moose (<em>Alces alces</em>) and mountain hare (<em>Lepus timidus</em>), will be attracted to burnt areas following an increase in food availability, other species, such as reindeer (<em>Rangifer tarandus</em>), are negatively impacted due to fire reducing their preferred food. We then tested our predictions by contrasting wildlife utilization of sites that burnt by wildfire in 2006 with nearby unburnt control sites in three areas in northern Sweden. To measure wildlife utilization, we used 72 camera traps, equally divided between the burnt and control sites, with two placement strategies: random and on wildlife trails. The cameras recorded 27 mammal and bird species during summer 2018. Species assemblage differed between burnt and control sites. Fieldfare (<em>Turdus pilaris</em>) used burnt sites more than control sites, while pine marten (<em>Martes martes</em>) and western capercaillie (<em>Tetrao urogallus</em>) used control sites more than burnt sites. We however did not find support for a positive effect of past forest fires on any of the observed wild mammals. We discuss how, due to the impact of forestry, forage-rich habitat may not be as limiting in Scandinavia as in the North-American context, potentially leading to recently burnt sites being less attractive to herbivores such as moose.</span></p>
Emission factors of trace gases and aerosols from wildfire events in central Portugal
<p>The aim of this data set is to provide a comprehensive overview of the chemical composition (trace elements, water-soluble ions and organic compounds) in smoke aerosol particles from some representative wildfires for use in emission inventorying and source apportionment modelling. </p> <p> </p>
Figure 9 in The impact of wildfires on the Lake Skadar/Shkodra environment
Figure 9. Flooded burnt areas near the island of Vranjina in the northern part of Lake Skadar/Shkodra (photographed on October 3, 2020). Photo by V. Pešić.
Figure 8 in The impact of wildfires on the Lake Skadar/Shkodra environment
Figure 8. Burnt areas (brown patches) resulted from wildfires southeastward of Lake Šas are shown for (a) 11 September, (b) 13 September and (c) 21 September 2020 (MSI Sentinel-2). Yellow arrow shows zoom on the selected area marked by yellow square.
Figure 5 in The impact of wildfires on the Lake Skadar/Shkodra environment
Figure 5. The suspended particulate matter oncentration (SPM, g/m3) in Lake Šas on (a) 11 September 2020, 09:40 GMT, MSI Sentinel-2B; (b) 13 September 2020, 09:30 GMT, MSI Sentinel-2A; (c) 21 September 2020, 09:40 GMT, MSI Sentinel-2B; (d) 8 October 2020, 09:30 GMT, MSI Sentinel-2B. The River Bojana/Buna passes in the southeastern corner of the frame.
Figure 7 in The impact of wildfires on the Lake Skadar/Shkodra environment
Figure 7. Burnt areas (brown patches) resulted from wildfires northwestward of Lake Skadar/Shkodra are shown for (a) 29 August, (b) 13 September and (c) 21 September 2020 (MSI Sentinel-2). Yellow arrows show zoom on three selected areas marked by yellow squares.
Figure 6. The Chlorophyll-a in The impact of wildfires on the Lake Skadar/Shkodra environment
Figure 6. The Chlorophyll-a concentration (Chla, mg/m3) in Lake Šas on (a) 11 September 2020, 09:40 GMT, MSI Sentinel-2B; (b) 13 September 2020, 09:30 GMT, MSI Sentinel-2A; (c) 21 September 2020, 09:40 GMT, MSI Sentinel- 2B; (d) 8 October 2020, 09:30 GMT, MSI Sentinel-2B. The River Bojana/Buna passes in the southeastern corner of the frame.
Figure 1 in The impact of wildfires on the Lake Skadar/Shkodra environment
Figure 1. Satellite image (OLI Landsat-8) of Lake Skadar/Shkodra area on 20 September 2020. Red squares show two areas (the northern and southern) of special interest of the impact of wildfires (white smokes) on the environment. Small red square shows location of Lake Šas.
Figure 2 in The impact of wildfires on the Lake Skadar/Shkodra environment
Figure 2. The true color satellite imagery of Lake Skadar/Shkodra environment on (a) 11 September 2020, 09:40 GMT, MSI Sentinel-2B; (b) 16 September 2020, 09:40 GMT, MSI Sentinel-2A; (c) 21 September 2020, 09:40 GMT, MSI Sentinel-2B; (d) 8 October 2020, 09:30 GMT, MSI Sentinel-2B.
Dataset for "Climatic Variation Drives Loss and Restructuring of Carbon and Nitrogen in Boreal Forest Wildfire"
<p>This dataset is uploaded to support the report 'Climatic Variation Drives Loss and Restructuring of Carbon and<br> Nitrogen in Boreal Forest Wildfire' published in the journal Biogeosciences. See README file for more details.</p>
Data for paper publication 'Important role of stratospheric injection height for the distribution and radiative forcing of smoke aerosol from the 2019/2020 Australian wildfires'
<p>This repository contains version 2.0 data from the aerosol-climate simulations performed with the ECHAM6.3-HAM2.3 model and aerosol lidar profiles, as presented in the paper publication by Heinold et al.: Important role of stratospheric injection height for the distribution and radiative forcing of smoke aerosol from the 2019/2020 Australian wildfires, submitted to Atmos. Chem. Phys. For details, please refer to the enclosed data description (README) file.</p>
Dataset of traffic dynamics during the 2020 Glass Wildfire Evacuation
<p>This dataset contains the </p> <p>This dataset has been sourced from the Performance Measurement System of the California Department of Transportation. The data has been processed, analysed, presented and summarized in the paper: <em>Rohaert et al., ‘T</em>he analysis of traffic data of wildfire evacuation: the case study of the 2020 Glass Fire<em>’, [Submitted for peer-review to an international journal.], 2023.</em></p> <p><strong>Acknowledgements</strong></p> <p>This work has been funded under award 60NANB21D118 from the National Institute of Standards and Technology (NIST), U.S. Department of Commerce.</p>
Data and code from: Shifting social-ecological fire regimes explain increasing structure loss from Western wildfires
<p class="MsoNormal"><span>Higuera, P.E., M.C. Cook, J.K. Balch, E.N. Stavros, A.L. Mahood, and L.A. St. Denis. 2023. Shifting social-ecological fire regimes explain increasing structure loss from Western wildfires. PNAS Nexus 2: In Press.</span></p> <p class="MsoNormal"><span>Structure loss is an acute, costly impact of the wildfire crisis in the western United States ("West"), motivating the need to understand recent trends and causes. We document a 246% rise in West-wide structure loss from wildfires between 1999–2009 and 2010–2020, driven strongly by events in 2017, 2018, and 2020. Increased structure loss was not due to increased area burned alone. Wildfires became significantly more destructive, with a 160% higher structure loss rate (loss/kha burned) over the past decade. Structure loss was driven primarily by wildfires from unplanned human-related ignitions (e.g. backyard burning, power lines, etc.), which accounted for 76% of all structure loss and resulted in 10 times more structures destroyed per unit area burned compared to lightning-ignited fires. Annual structure loss was well explained by area burned from human-related ignitions, while decadal structure loss was explained by state-level structure abundance in flammable vegetation. Both predictors increased over recent decades and likely interacted with increased fuel aridity to drive structure-loss trends. While states are diverse in patterns and trends, nearly all experienced more burning from human-related ignitions and/or higher structure loss rates, particularly California, Washington, and Oregon. Our findings highlight how fire regimes – characteristics of fire over space and time – are fundamentally social-ecological phenomena. By resolving the diversity of Western fire regimes, our work informs regionally appropriate mitigation and adaptation strategies. With millions of structures with high fire risk, reducing human-related ignitions and rethinking how we build are critical for preventing future wildfire disasters.</span></p>
ENVIRONMENTAL INFLUENCES ON LARGE DAILY WILDFIRE GROWTH IN CALIFORNIA
<p>Wildfires have become a major environmental, social, and economic problem in California. The consequences can be especially detrimental when they exhibit behavior like very large daily growth (an individual fire burning >10,000 acres over a 24-hour period). Environmental conditions influencing the risk of large daily growth include weather variables such as temperature, wind, relative humidity, and precipitation; fuel variables such as type, loading, availability, and moisture content; as well as topographic variables such as slope, aspect, elevation, and shape. However, there remains great uncertainty in the importance of these variables relative to each other and the existence of any threshold values in these variables. Our study applied random forest modeling using multivariate and high spatiotemporal data for 16,013 wildfire days in California from 2003 to 2020 to determine feature importance for the task of predicting whether a fire would burn >10,000 acres over a 24-hour period. Shapely Additive Explanations indicate that 100-hour dead fuel moisture, maximum daily air temperature, and soil moisture provide the highest predictive power for large daily growth. Additionally, our study identifies thresholds where the probability of large daily growth significantly increases. These thresholds include a 100-hour dead fuel moisture value of <10%, a maximum air temperature of >75 F, and a 0-10 cm soil moisture of <12%. Finally, we establish the number of days per year that these thresholds are being crossed has increased substantially over the last four decades. </p>
Dataset associated with Senf et al. (2023): "How the extreme 2019-2020 Australian wildfire affected global circulation and adjustments"
<p>This contains data aggregates derived from global ECHAM-HAM simulation for the study for effects due to the extreme Australian wildfire event 2019/2020. This data build the basis for analysis and figures in Senf et al. (2023) submitted to ACP.</p> <p> </p> <p>Simulation Data are</p> <ul> <li>available for freely running ensembles (36 member) and nudged simulations</li> <li>conducted for fire emissions artificially scaled with factors 0, 1, 2, 3, 5.</li> <li>stored for Jan - Mar 2020</li> </ul>
Figure 8 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 8. Examined and verified occurrence records of (A) P. annabelli and (B) P. morti from the Australian Museum dry collection. Main maps showing records in NSW, Queensland and Victoria. Insets showing all of Australia. Source of base map: Hans Braxmeier, maps-for-free.com.
Figure 6 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 6. Boxplots showing comparisons between Paralaoma annabelli and Paralaoma morti in shape metrics: (A) SW/SH, (B) SW/ NOW, (C) SH/NOW, (D) BWH/SH, (E) AW/SW, (F) AH/SH, (G) AH/BWH, (H) AH/AW, and (I) UW/SW. Asterisk above pairwise comparisons of the two species indicate significant difference (p ≤ 0.003) with a Wilcoxon Rank Sum Test with Bonferroni correction.
Figure 5 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 5. Boxplots showing comparisons between Paralaoma annabelli and Paralaoma morti in size and shape metrics: (A) Umbilicus width, (B) Shell width, (C) Shell height, (D) Protoconch width, (E) Body whorl height, (F) Aperture height, (G) Aperture width, (H) Number of whorls, and (I) UW/NOW. Asterisk above pairwise comparisons of the two species indicate significant difference (p ≤ 0.003) with a Wilcoxon Rank Sum Test with Bonferroni correction.
Figure 4 in Delineating Paralaoma annabelli, a Minute Land Snail Impacted by the 2019-2020 Wildfires in Australia
Figure 4. Best Maximum Likelihood tree based on analysis of ELAV-I8 sequences. Numbers on branches indicate nodal support based on 10,000 ultra-fast bootstrap replicates. Scale bar indicating 2% of modelled sequence divergence. Note that the species for the immature specimens were misidentified (L638 = Iotula microcosmos misidentified as P. morti; L645 = Pseudiotula eurysiana misidentified as P. annabelli).
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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.