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368 results for “wildfires”
2017 December California Wildfires Evacuation Survey Data
<p>Following the December Southern California Wildfires in 2017, an online survey was distributed by researchers from the University of California, Berkeley to collect information on the individual choices of those impacted by the fires in California. Collected from March to July 2018, the data includes questions regarding risk perceptions, communications, evacuation decisions, potential usage of the sharing economy in disasters, opinions of evacuation management, and demographic information. The survey was distributed with the assistance of local partners (i.e., transportation agencies, emergency management agencies, local city and county governments, CBOs, and news outlets). Partners were allowed to post the survey using electronic communication methods including but not limited to: Facebook, Twitter, Nextdoor, agency websites, news websites, email listservs, and alert subscription services. The survey received 552 valid responses, of which 303 were completed. Subsequent papers using this data retained 226 cleaned survey responses for discrete choice modeling, based on the respondents' completion of key choice and demographic questions. The survey was incentivized with the chance to win one of five $200 gift cards. The survey questions are included in a separate PDF document.</p> <p>We request that those who download the data send a courtesy email to the lead author, Dr. Stephen Wong (swong1392@gmail.com). To ensure that any new research makes unique contributions to knowledge and does not duplicate past analyses, users are requested to read and cite publications using this data including:</p> <p>Wong, S., Broader, J., Walker, J. & Shaheen, S. (2021). Understanding California Wildfire Evacuee Behavior and Joint Choice-Making. Retrieved from <a href="https://escholarship.org/uc/item/4fm7d34j">https://escholarship.org/uc/item/4fm7d34j</a></p> <p>Wong, S., Walker, J., & Shaheen, S. (2020). Role of Trust and Compassion in Resource Sharing in Evacuations: A Case Study of the 2017 and 2018 California Wildfire. <em>International Journal of Disaster Risk Reduction. </em><a href="https://escholarship.org/content/qt1zm0q2qc/qt1zm0q2qc.pdf">https://www.sciencedirect.com/science/article/abs/pii/S2212420920314023</a></p> <p>Wong, S., Chorus, C., Shaheen, S. & Walker, J. (2020). A Revealed Preference Methodology to Evaluate Regret Minimization with Challenging Choice Sets: A Wildfire Evacuation Case Study. <em>Travel Behaviour and Society.</em> Retrieved from <a href="https://www.sciencedirect.com/science/article/pii/S2214367X19303291"> https://www.sciencedirect.com/science/article/pii/S2214367X19303291</a></p> <p>Wong, S., Broader, J., Shaheen, S. (2020). Review of California Wildfire Evacuations from 2017 to 2019. Retrieved from <a href="https://escholarship.org/uc/item/5w85z07g">https://escholarship.org/uc/item/5w85z07g</a></p> <p>Wong, S. & Shaheen, S. (2019). Current State of the Sharing Economy and Evacuations: Lessons from California. SB 1 Report. Retrieved from <a href="https://escholarship.org/uc/item/16s8d37x">https://escholarship.org/uc/item/16s8d37x</a></p> <p> </p> <p>Additional framing work on evacuations can be found here:</p> <p>Wong, S. (2020). Compliance, Congestion, and Social Equity: Tackling Critical Evacuation Challenges through the Sharing Economy, Joint Choice Modeling, and Regret Minimization. University of California, Berkeley. Dissertation. <a href="https://escholarship.org/uc/item/9b51w7h6">https://escholarship.org/uc/item/9b51w7h6</a></p>
Wildfires drive multi-year water quality degradation over the western U.S.
<p>Information on the 245 burned basins, 293 unburned basins, and 356 associated fires from across the U.S. West which were used in statistical analyses of post-wildfire water quality response. Included are physiographic characteristics, as well as ESRI Shapefile polygons representing delineations for each basin and fire. Additionally, daily carbon, nitrogen, phosphorus, sediment, and turbidity data sampled from the basins' outlets are provided from 1974-2022. R coding scripts used in data processing and modeling also included, as well as data directly used in generating manuscript and "Supplementary Information" plots.</p> <p>Water quality data used to create this dataset are from the Water Quality Portal and wildfire burn perimeters are from the Monitoring Trends in Burn Severity database.</p>
Scaling landscape fire history in sagebrush: Wildfires not historically frequent in the main population of threatened Gunnison Sage-grouse
<p>The main population of ∼5,000 Threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90-143 years in GUSG ranges, which is not frequent fire (≤25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, Thiessen polygons around sites, and sagebrush. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90-135 years, a land-survey estimate 82-131 years, and three spatial scar-based estimates 93-107 years, showing agreement. However, comparing land-survey and fire-scar results showed that using fire scars spatially only 43% matched land surveys. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. An adequate historical fire reconstruction could require ∼45-60 fire-scar sites, making only ∼30,000 ha of sagebrush feasible. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82-135 years. </p>
Data from: Solar energy resource availability under extreme and historical wildfire smoke conditions
<p>The data in this repository are used to generate the figures in the article "Solar energy resource availability under extreme and historical wildfire smoke conditions" by Corwin et al. (accepted 2024) in <em>Nature Communications</em>. Data are the final processessed and merged datasets sourced from the following publicly available data products:</p> <ul> <li>National Renewable Energy Laboratory’s (NREL) National Solar Radiation Database (NSRDB) (<a href="https://nsrdb.nrel.gov/)">https://nsrdb.nrel.gov/)</a>. <ul> <li>Bulk download in July 2023 via AWS: <a href="https://registry.opendata.aws/nrel-pds-nsrdb/">https://registry.opendata.aws/nrel-pds-nsrdb/</a></li> <li>Variables: modeled irradiance (clear-sky and all-sky direct normal (DNI) and global horizontal (GHI) irradiance, aerosol optical depth, and cloud optical depth</li> </ul> </li> <li>National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Hazard Mapping System (HMS) smoke product. <ul> <li>Access: <a href="https://www.ospo.noaa.gov/Products/land/hms.html#maps">https://www.ospo.noaa.gov/Products/land/hms.html#maps</a></li> <li>Variables: smoke plume locations</li> </ul> </li> <li>National Aeronautics and Space Administration's (NASA) Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product (MCD19A2 MODIS/Terra + Aqua land aerosol optical depth daily L2G Global 1km SIN Grid V006). <ul> <li>Access: <a href="https://lpdaac.usgs.gov/products/mcd19a2v006/">https://lpdaac.usgs.gov/products/mcd19a2v006/</a></li> <li>Variables: aerosol optical depth and cloud mask</li> </ul> </li> <li>NASA's Clouds and the Earth’s Radiant Energy System (CERES) cloud data product (SYN1deg-1Hour Edition 4.1) <ul> <li>Access: <a href="https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp">https://ceres-tool.larc.nasa.gov/ord-tool/jsp/SYN1degEd41Selection.jsp</a></li> <li>Variables: cloud optical depth</li> </ul> </li> </ul> <p>A detailed description of the data processing methods used to produce the final merged data are available in the article by Corwin et al. </p> <p>Associated code scripts are located in the linked code repository.</p>
Distribution and Characteristics of Lightning-Ignited Wildfires in Boreal Forests - the BoLtFire database
<p>This repository holds a dataset of lightning-ignited wildfires across the boreal biome. The BoLtFire dataset covers the period 2012 to 2022 and encompasses 6,902 fires - 4,201 in Eurasia and 2,701 in North America.</p> <p>The layers included in this dataset are: FireID, StartDate, EndDate, FireYdear, AreaHa (burned area), ClassSize, BiomeName, EcoBiome, EcoName, EcoID, Realm, LCDN (Land cover number), LCName (land cover name), Country, Continent, HoldoverD (days), HoldoverRD (holdover rounded), IgnLat (Ignition location Latitude), IgnLong (Ignition Location Longitude), DisPol (Distance of the ignition location to the fire perimeter if it is located outside the polygon), and PerCheck (designates if the ignition location is within the fire perimeter or oustide the perimeter).</p> <p> </p> <p>The datasets are available per continent (North America, Europe, and Asia) as shapefiles. The spatial reference system is Global LANd Cover mapping and Estimation (GLANCE) Grids - Version 01 CRS.</p> <p> </p> <p>*Please note: Versions 1 and 2 are missing LIW from Canada between 2021-2022. </p>
Data and outputs for chapter 'The impacts of the 2019-20 wildfires on Australian fungi ' in 'Australia's Megafires: Biodiversity Impacts and Lessons from 2019-2020
<p><strong>Dataset includes raw data downloaded from the following sources: fungi_data.csv</strong></p> <ul> <li>Atlas of Living Australia occurrence download: https://doi.org/10.26197/ala.9e0ca388-9da2-4096-b1a3-26e2aaa51d8a. Accessed 2021-09-16. GBIF.org (16 September 2021)</li> <li>GBIF Occurrence Download https://doi.org/10.15468/dl.secenk</li> <li>Fungimap (https://fungimap.org.au/ (data obtained directly from Fungimap Inc.)</li> <li>MycoPortal (https://mycoportal.org/portal/index.php)</li> <li>iNaturalist (https://www.inaturalist.org/home)</li> </ul> <p><strong>Output files from point and polygon overlap with fire layer:</strong></p> <ul> <li>Fungi and fire analysis point overlap.xlsx</li> <li>Fungi and fire analysis polygon overlap.xlsx</li> </ul> <p> </p> <p> </p>
Data: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management
<p>2018 Boreal forest fires in Sweden: Measurements of soil CO2 and CH4 fluxes, soil microclimate and nutrient content during the first growing season after a wildfire, from forest sites impacted by different fire severity (tree mortality) and post-fire management.</p> <p> </p> <p>Data used in: Boreal forest soil carbon fluxes one year after a wildfire: Effects of burn severity and management; Julia Kelly, Theresa S. Ibáñez, Cristina Santín, Stefan H. Doerr, Marie-Charlotte Nilsson, Thomas Holst, Anders Lindroth, Natascha Kljun; Global Change Biology, 27, 4181-4195, https://doi.org/10.1111/gcb.15721</p> <p> </p> <p> </p> <p> </p>
Data supporting "Burn Period: A use-inspired metric to track wildfire risk across the southwest U.S."
<p>Comma delimited data file of derived daily meteorological metrics from hourly, gap filled and quality controlled Remote Automated Weather Station (RAWS) data for Arizona and New Mexico (southwest U.S.) provided by the Climate, Ecosystems, and Fire Applications (CEFA) program at the Desert Research Institute (Brown, 2022, unpublished data). Data file contains daily average dewpoint temperature, air temperature, maximum Hot-Dry-Windy Index, maximum Fosberg Fire Weather Index, maximum vapor pressure deficit, and total number of hours/day with relative humidity below 20% for 124 RAWS from 2000-2022.</p>
Wildfire and climate teleconnection data in the Western Mediterranean Basin
<p>This dataset contains the data used to conduct all analyses of the manuscript entitled "<strong>Spatio-temporal domains of wildfire-prone teleconnection patterns in the Western Mediterranean Basin</strong>". Submitted to Geophysical Research Letters.</p> <p>File <em>fire_data.csv</em> contains monthly gridded data of total burned area (BA), number of fire ignitions (N) and the 95th percentile of fire size (S) at 0.5 degree spatial resolution. The spatial extent covers Portugal, Spain, Southern France, Corsica and Sardina. Original data sources have been acknowledged in the manuscript file.</p> <p>File <em>teleconnections.csv</em> contains monthly data of the the North Atlantic Oscillation (NAO), the East Atlantic (EA), the Atlantic Multidecadal Oscillation (AMO), the El Niño Southern Oscillation (ENSO), the Mediterranean Oscillation (MOI), the Pacific Decadal Oscillation (PDO), the Scandinavian pattern (SCAND) and the Western Mediterranean oscillation (WeMOi). Original data sources have been acknowledged in the manuscript file.</p>
Dataset of traffic dynamics during the 2019 Kincade Wildfire Evacuation
<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., ‘Traffic dynamics during the 2019 Kincade wildfire evacuation’, [Submitted for peer-review to an international journal.], 2022.</em></p> <p><strong>CRediT author statement</strong></p> <p><strong>Arthur Rohaert: </strong>Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing - original draft, Writing - review & editing. <strong>Erica D. Kuligowski: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Adam Ardinge: </strong>Conceptualization, Formal analysis, Investigation, Methodology, Resources, Validation, Writing - review & editing. <strong>Jonathan Wahlqvist:</strong> Conceptualization, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Steven M.V. Gwynne: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Amanda Kimball: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Project Administration, Resources, Supervision, Validation, Writing - review & editing. <strong>Noureddine Bénichou: </strong>Conceptualization, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - review & editing. <strong>Enrico Ronchi: </strong>Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Supervision, Validation, Writing - original draft, Writing - review & editing</p> <p><strong>Acknowledgements</strong></p> <p>This work has been funded under award 60NANB20D191 from the National Institute of Standards and Technology (NIST), U.S. Department of Commerce. The authors would like to thank the WUI-NITY team (Guillermo Rein, Nikolaos Kalogeropoulos, Harry Mitchell, Max Kinateder, Maxime Berthiaume). The authors also acknowledge the technical panel of the project for their support and guidance: Carole Adam, Amy Christianson, Tom Cova, Lauren Folk, Abishek Gaur, Paolo Intini, Justice Jones, Bryan Klein, Chris Lautenberger, Ruggiero Lovreglio, Jerry McAdams, Ruddy Mell, Elise Miller-Hooks, Cathy Stephens, Steve Taylor, Sandra Vaiciulyte, Xilei Zhao, Rita Fahy, Lucian Deaton, and Michele Steinberg. </p> <p> </p>
EO4WildFires: An Earth Observation multi-sensor, time-series machine-learning-ready benchmark dataset for wildfire impact prediction
<p>This paper presents a benchmark dataset called EO4WildFires; a multi-sensor (multi spectral; Sentinel-2, Synthetic-Aperture Radar - SAR; Sentinel-1, meteorological parameters; NASA Power) time-series dataset that spans 45 countries, which can be used for developing machine learning and deep learning methods targeted for the estimation of the area that a forest wildfire might cover.</p> <p>This novel EO4WildFires dataset is annotated using EFFIS (European Forest Fire Information System) as forest fire detection and size estimation data source. A total of 31,742 wildfire events are gathered from 2018 to 2022. For each event, Sentinel-2 (multispectral), Sentinel-1 (SAR) and meteorological data are assembled into a single data cube. The meteorological parameters that are included in the data cube are: ratio of actual partial pressure of water vapor to the partial pressure at saturation, average temperature, bias corrected average total precipitation, average wind speed, fraction of land covered by snowfall, percent of root zone soil wetness, snow depth, snow precipitation, as well as percent of soil moisture.</p> <p>The main problem that this dataset is designed to address, is the severity forecasting before wildfires occur. The dataset is not used to predict wildfire events, but rather to predict the severity (size of area damaged by fire) of a wildfire event, if that happens in a specific place under the current and historical forest status, as recorded from multispectral and SAR images, and meteorological data.</p> <p>Using the data cube for the collected wildfire events, the EO4WildFires dataset is used to realize three (3) different preliminary experiments, in order to evaluate the contributing factors for wildfire severity prediction. The first experiment evaluates wildfire size using only the meteorological parameters, the second one utilizes both the multispectral and SAR parts of the dataset, while the third exploits all dataset parts. In each experiment, machine learning models are developed, and their accuracy is evaluated.</p>
Integrated ground-based data for wildfires occurred in the Western US in September 2020
<p>Data set used in paper Kassianov <em>et al</em>. <strong>Radiative impact of record-breaking wildfires from integrated ground-based data</strong> to be submitted to <em>Sci. Rep.</em></p> <p>For details of data file formats see attached Readme file</p>
Physical, Social, and Biological Attributes for Improved Understanding and Prediction of Wildfires: FPA FOD-Attributes Dataset
<p><strong>Wildfires are increasingly impacting social and environmental systems in the United States. The ability to mitigate the undesirable effects of wildfires increases with the understanding of the social, physical, and biological conditions that co-occurred with or caused the wildfire ignitions and contributed to the wildfire impacts. To this end, we developed the FPA FOD-Attributes dataset, which augments the sixth version of the Fire Program Analysis-Fire Occurrence Database (FPA FOD v6) with nearly 270 attributes that coincide with the date and location of each wildfire ignition in the contiguous United States (CONUS). FPA FOD v6 contains information on the location, jurisdiction, discovery time, cause, and final size of >2.2 million wildfires from 1992-2020 in CONUS. For each wildfire, we added physical (e.g., weather, climate, topography, infrastructure), biological (e.g., land cover, normalized difference vegetation index), social (e.g., population density, social vulnerability index), and administrative (e.g., national and regional preparedness level, jurisdiction) attributes. This publicly available dataset can be used to answer numerous questions about the covariates associated with human- and lightning-caused wildfires. Furthermore, the FPA FOD-Attributes dataset can support descriptive, diagnostic, predictive, and prescriptive wildfire analytics, including the development of machine learning models.</strong></p>
Study of wildfire smoke effects on ecosystem metabolism in 10 California lakes (2018, 2020, 2021)
This dataset was collected as part of a large-scale study to assess impacts of smoke cover on gross primary production (GPP) and ecosystem respiration (R) in California lakes. The 10 study lakes span large gradients in elevation, size, nutrient concentrations, and water clarity. They include 5 ponds and lakes in Sequoia National Park, Lake Tahoe, Dulzura Lake, Clear Lake, Castle Lake, and a site in the Sacramento-San Joaquin River Delta. Metabolic rates in the upper mixed layer of each lake was estimated from hourly in-situ sensor data during the three smokiest years in California since 2006 (2018, 2020, 2021). The dataset includes daily estimates of GPP and R, mean daily values of variables used in metabolism models (water temperature, dissolved oxygen, mixed layer depth, photosynthetically active radiation, wind speed), and mean daily values of metrics related to smoke cover (shortwave radiation, PM2.5, smoke density derived from remote-sensing).
Assessing change in ecosystem processes twenty four years after the 1988 Yellowstone Wildfires, 2013
The extent of young postfire conifer forests is growing throughout western North America as the frequency and size of high-severity fires increase, making it important to understand ecosystem structure and function in early seral forests. Understanding nitrogen (N) dynamics during postfire stand development is especially important because northern conifers are often N limited. We re-sampled lodgepole pine (Pinus contorta var. latifolia) stands that regenerated naturally after the 1988 fires in Yellowstone National Park (Wyoming, USA) to ask: (1) How have N pools and fluxes changed over a decade (15 to 25 years postfire) of very rapid forest growth? (2) At postfire year 25, how do N pools and fluxes vary with lodgepole pine density and productivity? Lodgepole pine foliage, litter (annual litterfall, forest-floor litter), and mineral soils were sampled in 14 plots (0.25-ha) that varied in postfire lodgepole pine density (1,500 to 344,000 stems ha-1) and aboveground net primary production (ANPP; 1.4 to 16.1 Mg ha-1 yr-1). Previous data collected 15 and 17 years postfire (i.e., 2003 and 2005) provided a reference for assessing change in ecosystem process rates over time. At that time, lodgepole pine foliar nitrogen (N) concentrations had not yet suggested N limitation, and tree density and net primary production strongly influenced ecosystem carbon (C) and N stocks. These data were collected in 2012 and 2013 and are associated with the following publication: Turner, M. G., T. G. Whitby, and W. H. Romme. 2019. Feast not famine: Nitrogen pools recover rapidly in 25-yr old postfire lodgepole pine. Ecology (In press).
First-order vertebrated mortality due the 2020 wildfires in the Pantanal wetland, Brazil
We conducted ground surveys along line transects to estimate the first-order impact of the 2020 wildfires on vertebrates in the Pantanal wetlands, Brazil. We adopted the distance sampling technique (Burnham et al 1980) to estimate the densities and the number of dead vertebrates in the 39,030 square kilometers affected by fire. We covered 123 transects scattered in the floodplain, up to 72 hours after the fires, mostly within 24 or 48 hours. We recorded the perpendicular distance between each carcass found in the field and the line transect. The carcasses were identified at least at Order level, down to species level when possible. The surveys were conducted from August to November 2020.
Impacts of wildfire on stream water chemistry in the Caribou-Poker Creeks Research Watershed during the summers of 2002-2007
The hydrogeochemistry of two streams (C1 and P6) in the Caribou-Poker Creeks Research Watershed (CPCRW) during the summers of 2002-2007. The P6 sub-catchment of the CPCRW was extensively burned during the Boundary Fire of 2004. Samples were analyzed for Ca, Mg, Na, K, NO3, SO4, DOC, TDN, SUVA and conductivity.
Identifying functional impacts of heat-resistant fungi on boreal forest recovery after wildfire
<p>Fungi play key roles in carbon (C) dynamics of ecosystems: saprotrophs decompose organic material and return C in the nutrient cycle, and mycorrhizal species support plants that accumulate C through photosynthesis. The identities and functions of extremophile fungi present after fire can influence C dynamics, particularly because plant-fungal relationships are often species-specific. However, little is known about the function and distribution of fungi that survive fires. We aim to assess the distribution of heat-resistant soil fungi across burned stands of boreal forest in the Northwest Territories, Canada, and understand their functions in relation to decomposition and tree seedling growth. We cultured and identified fungi from heat-treated soils and linked sequences from known taxa with high throughput sequencing fungal data (Illumina MiSeq, ITS1) from soils collected in 47 plots. We assessed functions under controlled conditions by inoculating litter and seedlings with heat-resistant fungi to assess decomposition and effects on seedling growth, respectively, for black spruce (Picea mariana), birch (Betula papyrifera), and jack pine (Pinus banksiana). We also measured litter decomposition rates and seedling densities in the field without inoculation. We isolated seven taxa of heat-resistant fungi and found their relative abundances were not associated with environmental or fire characteristics. Under controlled conditions, Fayodia gracilipes and Penicillium arenicola decomposed birch, but no taxa decomposed black spruce litter significantly more than the control treatment. Seedlings showed reduced biomass and/or mortality when inoculated with at least one of the fungal taxa. Penicillium turbatum reduced growth and/or caused mortality of all three species of seedlings. In the field, birch litter decomposed faster in stands with greater pre-fire proportion of black spruce, while black spruce litter decomposed faster in stands experiencing longer fire-free intervals. Densities of seedlings that had germinated since fire were positively associated with ectomycorrhizal richness while there were fewer conifer seedlings with greater heat-resistant fungal abundance. Overall, our study suggests that extremophile fungi present after fires have multiple functions and may have unexpected negative effects on forest functioning and regeneration. In particular, heat-resistant fungi after fires may promote shifts away from conifer dominance that are observed in these boreal forests.</p> <p> </p> <p> </p>
Raman spectroscopic data derived from Calluna vulgaris charcoals, experimentally generated across a range of natural wildfire temperatures
<p>This data has been derived from deconvolved Raman spectra, utilising two first order bands - D (Disordered) and G (Graphitic). Spectra were collected from experimentally pyrolysed charcoals, made from Calluna vulgaris (Ling Heather) separated into three main components; stem, root and flower. For each component at 250, 400, 600 and 800 degrees centigrade respectively, 5 charcoal samples (A, B, C, D, E) were analysed. Following deconvolution, median values for each spectra were produced. These correspond to parameters derived from the Raman data, including D- and G-band width (FWHM), intensity (ID/IG or 'R1') and area (AD/AG) ratios, band separation (G-D or 'RBS'), and band width ratios (D-FWHM/G-FWHM). All parameters have been compiled for each component material, and displayed graphically within this dataset.</p>
Wildfires and climate change push low-elevation forests across a critical climate threshold for tree regeneration
Climate change is increasing fire activity in the western United States, which has the potential to accelerate climate-induced shifts in vegetation communities. Wildfire can catalyze vegetation change by killing adult trees that could otherwise persist in climate conditions no longer suitable for seedling establishment and survival. Recently documented declines in postfire conifer recruitment in the western United States may be an example of this phenomenon. However, the role of annual climate variation and its interaction with long-term climate trends in driving these changes is poorly resolved. Here we examine the relationship between annual climate and postfire tree regeneration of two dominant, low-elevation conifers (ponderosa pine and Douglas-fir) using annually resolved establishment dates from 2,935 destructively sampled trees from 33 wildfires across four regions in the western United States. We show that regeneration had a nonlinear response to annual climate conditions, with distinct thresholds for recruitment based on vapor pressure deficit, soil moisture, and maximum surface temperature. At dry sites across our study region, seasonal to annual climate conditions over the past 20 years have crossed these thresholds, such that conditions have become increasingly unsuitable for regeneration. High fire severity and low seed availability further reduced the probability of postfire regeneration. Together, our results demonstrate that climate change combined with high severity fire is leading to increasingly fewer opportunities for seedlings to establish after wildfires and may lead to ecosystem transitions in low-elevation ponderosa pine and Douglas-fir forests across the western United States.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.