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42 results for “Fire Seasonality”
Soil nutrient availability from the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016 growing season
This file contains plant-available nitrate (NO3), ammonium (NH4), and phosphate (PO4) in the upper 5-10 cm of organic matter from a burned and unburned site in the southern section of the 2007 Anaktuvuk River fire in northern Alaska. Soil nutrients were assessed using ion-exchange resin membranes incubated in the soil during the growing season of 2016.
Point-frame measurments from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains point-frame measurements from a nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016 at a severely burned site and an unburned site. Pin-vegetation contact was recorded using a 0.75 m2 frame with 41 evenly spaced pin-drop points. Data was collected once during the height of the growing season in 2016 (when fertilization began) 2017, 2018 and 2019. This data was used to measure the impact of fertilization and fire on community composition.
Soil nutrient availability from the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2019 growing season
This file contains plant-available nitrate (NO3), ammonium (NH4), phosphate (PO4), and total free primary amines (TFPA )in the upper 5-10cm of organic matter from a burned and unburned site in the southern section of the 2007 Anaktuvuk River fire in northern Alaska. Soil nutrients were assessed using buried resin bags which incubated for 1 month during the peak of the growing season in 2019.
Leaf area index (LAI) recorded from a nitrogen (N), phosphorus (P) and N+P fertilization experiment at the 2007 Anaktuvuk River, Alaska, USA fire scar during the 2016-2019 growing seasons
This file contains leaf area index (LAI) measurements from an nitrogen (N), phosphorus (P) and N+P fertilization experiment established in the southern section of the 2007 Anaktuvuk River fire in 2016. LAI was recorded using a handheld plant canopy analyzer (LI-COR 2200C; LI-COR, Lincoln, NE, USA) Data spans 4 years from 2016 (when fertilization began) until 2019. Data was recorded once a year at the peak of each growing season.
Effects of Fire Seasonality on Chihuahuan Desert Grasslands at the Sevilleta National Wildlife Refuge, New Mexico (2007-2020)
Desert grassland vegetation is a key resource upon which rangelands in the southwestern US are built, and managing these ecosystems remains a critical challenge today. This experimental fire seasonality research project, in collaboration with the USFWS, USFS Rocky Mountain Research Station, and the Sevilleta LTER, is intended to provide land management agencies with information about vegetation recovery following fire under different seasonal conditions and burning treatments. This experimental research will enable the FWS to more effectively set project objectives for prescribed burning on the Sevilleta NWR to benefit not only wildlife habitat, but to better align the timing and intensity of fire to benefit the reestablishment of the dominant native grama grasses Bouteloua eriopoda and B. gracilis. Since its creation in 1973, management has been devoted to restoring the Sevilleta NWR to the natural conditions that might have been seen around the turn of the century. The Sevilleta NWR is an ideal place for research because climatic conditions, plant species composition and net primary production following wildfire have been well documented by the Sevilleta LTER. Additional experimental research is needed, however, to better inform managers about the timing and use of fire as an ecosystem restoration and management tool. This is an on-going, long-term experiment under the auspices of the Sevilleta LTER program.
Anaktuvuk River fire scar canopy reflectance spectra from the 2008-2014 growing seasons, North Slope Alaska.
The Anaktuvuk River Fire occurred in 2007 on the North Slope of Alaska. In 2008, three eddy covariance towers were established at sites represent ing unburned tundra, moderately burned tundra, and severely burned tundra. During the 2008-2014 growing seasons, canopy vegetation within the footprint of each of these towers was scanned with a handheld spectrophotometer several times throughout the growing season. Average reflectance spectra per site and collection day are presented here.
CSM05 Seasonal summary of numbers of small mammals on the six LTER traplines in prairie habitats on which fire regime has been reversed at Konza Prairie
Data set contains seasonal summaries (spring and autumn) of the number of individuals of each species of small mammal captured (relative abundance) on each grassland trapline. Each record contains year, season, trapline and number of individuals captured of each species. These live trap records are based on daily captures during a single 4-day trapping period in spring (mid-March to early April) and autumn (late October to early December) for each of six permanent traplines established on two fire treatments (three traplines per treatment). These two fire treatments include one treatment that was changed from a 20-year burn to an annual burn and one that was changed from an annual burn to 20 years between fires. Bison do not graze these two habitat types.
Copernicus EMS fire activations delimitations (2012 - 2020) rasterised at 30m and aggregated per year and season
<p>This dataset was created as part of the <a href="https://opendatascience.eu/">Geo-harmonizer project</a>, with the scope of making open data easier to access. It contains all the fire activations (forest fire, wild fire, wildfire) mapped by the<a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid"> Copernicus Emergency Rapid Mapping Service</a> between 2012 and 2020. To obtain these GeoTIFFs, the vector data packages from CEMS were individually downloaded, rasterized and mosaicked per year and season, resampled at 30-m and reprojected to <a href="https://epsg.io/3035">EPSG 3035: ETRS89-extended / LAEA Europe</a>. If no CEMS fire activation was identified in a specific year and season, the raster was not created. The rasters are provided as COG files, type=16Int, nodata value is 255.</p> <p>To allow an easier and faster search through all 2012 - 2020 CEMS fire activations, we have prepared a point vector layer (geojson) containing one point for each fire activation area of interest with the following attributes attached: CEMS identification number <ems_id>, area of interest defined by CEMS <ems_aoi>, URL link to the CEMS activation <ems_link>, year of the event <year_start>, <year_end> , <season> and the name of the <geo_harmonizer_raster> where the 30m rasterised delimitations of the burned areas of the corresponding fire activation can be found. </p> <p>For any additional questions regarding the data please contact the author at codrina.ilie[at]terrasigna.com.</p> <p>The Copernicus Emergency Rapid Mapping Service data access policy is available <a href="https://emergency.copernicus.eu/mapping/sites/default/files/files/CopernicusEMS-Data_and_Dissemination_Policy.pdf">here</a>.</p>
Winter Season Spectral Snowpack Albedo Data For the Caldor and Creek Fires
<p>* Description: The file "Caldor_Creek_Fires_Winter_Snow_Albedo_Spectrometer_Dataset.csv" is a comma-delimited file containing the spectral snowpack albedo measurements from the Caldor and Creek Fires in California and the associated burn severities at the location of each measurement.</p> <p> </p> <p>Data and File Overview</p> <p>======================</p> <p>Summary Metrics</p> <p>---------------</p> <p>* File count: 1</p> <p>* Total file size: 909 KB </p> <p>* Range of individual file sizes: 909 KB </p> <p>* File formats: .csv</p> <p> </p> <p>Naming Conventions</p> <p>------------------</p> <p>* File naming scheme: One file that includes all dates, all burn severities, all wavelengths.</p> <p>* Format(s): Comma-separated value (.csv) file</p> <p>* Size(s): 909 KB</p> <p>* Dimensions: 17,209 rows x 6 columns</p> <p>* Variables:</p> <p> * Measurement_Number: An index of the measurement number, unitless</p> <p> * wavelength: The wavelength of light (units in nanometers) for which albedo sample ranging from 350-2500 nm</p> <p> * Type: Measurement type is albedo in various burn severity environments (_hb is high burn severity, _mb is moderate burn severity, _ub is unburned, with the last letter corresponding to the month (J is January, F is February, A is April). NA corresponds to estimated January unburned data for the Caldor Fire where unburned albedo for April in the Creek Fire was adjusted downwards by 0.04 to account for less grain-size growth (Colbeck 1982, Rev. Geophys., https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/RG020i001p00045). </p> <p> * Albedo: The albedo (unitless), is a measurement of the solar radiation reflected by the snow surface divided by the radiation incident on its surface. In other words, albedo is the fraction of the incident sunlight reflected by the snow.</p> <p> * month: The month when the measurement was taken</p> <p> * burn: Burn severity at measurement location based upon classifications (High, Medium,, Unburned) from the Monitoring Trends in Burn Severity Dataset (https://mtbs.gov/). NA corresponds to the estimated unburned data for the Caldor Fire using April unburned data in the Creek Fire.</p> <p> * Missing data codes: No missing data is presented.</p> <p> </p> <p>Dates and Locations</p> <p>-------------------</p> <p>* Dates of data collection: Surface albedo collected in 27-28 February 2021, 1 April 2021, and 21 January 2022</p> <p>* Geographic locations of data collection: Data collected within the Caldor Fire perimeter in the central Sierra Nevada, California-Nevada (January 2022) and the Creek Fire perimeter, California (February and April 2021).</p> <p> </p> <p>Setup</p> <p>-----</p> <p>* Recommended software/tools to open file: parentage- R Studio; file can be opened using any text editor or programming language (e.g., R Studio, MATLAB, Python, TextMate, Microsoft Excel, etc)</p> <p> </p>
Fire Season Multi-Resolution Database
<p>In the realm of wildfire research and analysis, the need for comprehensive and adaptable datasets has never been more critical. To address this imperative, we introduce the "Fire Season Multi-Resolution Database." This meticulously curated dataset represents a significant advancement in the realm of wildfire data, offering a versatile, multi-resolution approach to understanding the intricate dynamics of fire seasons.</p> <p>The Fire Season Multi-Resolution Database is a set of geospatial earth grids, thoughtfully designed to accommodate diverse research needs by providing information at varying levels of detail. Within each grid, a wealth of data is embedded, concerning fire seasons and their comprehensive characterizations at every individual pixel.</p> <p>Within each pixel of these multi-resolution grids, a set of nine variables about their respective fire seasons can be found.<br> </p> <ul> <li> FsOrNot (int): a binary variable (0,1) that contains information whether a pixel has a fire season or not </li> <li> MainStart (int): Starting month of the fire season </li> <li> MainEnd (int) : Ending month of the fire season </li> <li> SecondStart (int): Start month of a possible secondary fire seasson (if it is bimodal) </li> <li> SecondEnd (int) : End month of the secondary fire seasson </li> <li> Length (int): Months that lasts the fire season </li> <li> C (float): Seasonal concentration of the fire seasson </li> <li> P (float): Seasonal phase or timming of the fire season </li> <li> FBA (float): Fraction of Burneable Area </li> </ul> <p> </p> <p>Github used to build these files: </p> <p>https://github.com/delatorre96/Development-of-a-multi-resolution-fire-season-database-on-a-global-scale</p> <p>Finally, the sources of information used to build this dataset is the Copernicus project, specifically the web page: https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-fire-burned-area?tab=overview. This dataset has been constructed with the aim of enhancing wildfire research and aiding in the development of effective wildfire management and mitigation strategies. </p>
Anatuvuk River fire scar thaw depth measurements during the 2008 to 2014 growing season
The Anaktuvuk River Fire occurred in 2007 on the North Slope of Alaska. In 2008, three eddy covariance towers were established at sites represent ing unburned tundra, moderately burned tundra, and severely burned tundra. Several times during the 2008-2014 growing seasons, thaw depth was measured at approximately 70 points near each of these towers . Data presented here are the individual measurements for each site and date.
Data supporting: Success of post-fire plant recovery strategies varies with shifting fire seasonality
<p><span>Wildfires are increasing in size and severity and fire seasons are lengthening, largely driven by climate and land use change.</span> <span>Many plant species from fire prone ecosystems are adapted to specific fire regimes corresponding to historical conditions and shifts beyond these bounds may have severe impacts on vegetation recovery and long-term species persistence</span><span>. Here, we conduct a meta-analysis of field-based studies across different vegetation types and climate regions to investigate how post-fire plant recruitment, reproduction and survival are affected by fires that occur outside of the historical fire season. We find that fires outside of the historical fire season may lead to decreased post-fire recruitment for many species, particularly obligate seeding species. Conversely, we find a general increase of post-fire survival in resprouting species. </span><span>Our results highlight the trade-offs that exist when considering the effects of changes in the seasonal timing of fire, an already present aspect of climate-related global fire regime change. </span></p>
SeasFire Cube: A Global Dataset for Seasonal Fire Modeling in the Earth System
<p>The <strong>SeasFire Cube</strong> is a scientific datacube for seasonal fire forecasting around the <strong>globe</strong>. Apart from seasonal fire forecasting, which is the aim of the SeasFire project, the datacube can be used for several other tasks. For example, it can be used to model teleconnections and memory effects in the earth system. Additionally, it can be used to model emissions from wildfires and the evolution of wildfire regimes.<br> <br> It has been created in the context of the <a href="https://seasfire.hua.gr/">SeasFire project</a>, which deals with "<em>Earth System Deep Learning for Seasonal Fire Forecasting</em>" and <strong>is funded by the European Space Agency (ESA) </strong> in the context of ESA Future EO-1 Science for Society Call.<br> <br> It contains <strong>21 years</strong> of data (2001-2021) in an <strong>8-days</strong> time resolution and <strong>0.25 degrees grid</strong> resolution. It has a diverse range of seasonal fire drivers. It expands from atmospheric and climatological ones to vegetation variables, socioeconomic and the target variables related to wildfires such as burned areas, fire radiative power, and wildfire-related CO2 emissions.</p> Datacube properties <table><tbody><tr> <th> <p><strong>Feature</strong></p> </th> <th> <p><strong>Value</strong></p> </th> </tr> </tbody><tbody> <tr> <td> <p>Spatial Coverage</p> </td> <td> <p>Global</p> </td> </tr> <tr> <td> <p>Temporal Coverage</p> </td> <td> <p>2001 to 2021</p> </td> </tr> <tr> <td> <p>Spatial Resolution</p> </td> <td> <p>0.25 deg x 0.25 deg</p> </td> </tr> <tr> <td> <p>Temporal Resolution</p> </td> <td> <p>8 days</p> </td> </tr> <tr> <td> <p>Number of Variables</p> </td> <td> <p>54</p> </td> </tr> <tr> <td> <p>Tutorial Link </p> </td> <td> <p><a href="https://github.com/SeasFire/seasfire-datacube">https://github.com/SeasFire/seasfire-datacube</a></p> </td> </tr> </tbody> </table> <table> <tbody><tr> <th>Full name</th> <th>DataArray name</th> <th>Unit</th> <th>Contact *</th> </tr> </tbody><tbody> <tr> <th>Dataset: <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=overview">ERA5 Meteo Reanalysis Data</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Mean sea level pressure</th> <td>mslp</td> <td>Pa</td> <td>NOA</td> </tr> <tr> <th>Total precipitation</th> <td>tp</td> <td>m</td> <td>MPI</td> </tr> <tr> <th>Relative humidity</th> <td>rel_hum</td> <td>%</td> <td>MPI</td> </tr> <tr> <th>Vapor Pressure Deficit</th> <td>vpd</td> <td>hPa</td> <td>MPI</td> </tr> <tr> <th>Sea Surface Temperature</th> <td>sst</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Skin temperature</th> <td>skt</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Wind speed at 10 meters</th> <td>ws10</td> <td>m*s-2</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Mean</th> <td>t2m_mean</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Min</th> <td>t2m_min</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Temperature at 2 meters - Max</th> <td>t2m_max</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Surface net solar radiation</th> <td>ssr</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Surface solar radiation downwards</th> <td>ssrd</td> <td>MJ m-2</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 1</th> <td>swvl1</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th> <table> <tbody> <tr> <th>Volumetric soil water level 2</th> </tr> </tbody> </table> </th> <td>swvl2</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 3</th> <td>swvl3</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Volumetric soil water level 4</th> <td>swvl4</td> <td>m3/m3</td> <td>MPI</td> </tr> <tr> <th>Land-Sea mask</th> <td>lsm</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: Copernicus <p><a href="http://cds.climate.copernicus.eu/cdsapp#!/dataset/cems-fire-historical?tab=overview">CEMS</a></p> </th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Drought Code Maximum</th> <td>drought_code_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Drought Code Average</th> <td>drought_code_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Maximum</th> <td>fwi_max</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Fire Weather Index Average</th> <td>fwi_mean</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://confluence.ecmwf.int/display/CKB/CAMS%3A+Global+Fire+Assimilation+System+%28GFAS%29+data+documentation">CAMS: Global Fire Assimilation System (GFAS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Carbon dioxide emissions from wildfires</th> <td>cams_co2fire</td> <td>kg/m²</td> <td>NOA</td> </tr> <tr> <th>Fire radiative power</th> <td>cams_frpfire</td> <td>W/m²</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://climate.esa.int/en/projects/fire/data/">FireCCI - European Space Agency’s Climate Change Initiative</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from Fire Climate Change Initiative (FCCI)</th> <td>fcci_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of FCCI burned areas</th> <td>fcci_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th><br> Fraction of burnable area</th> <td>fcci_fraction_of_burnable_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Number of patches</th> <td>fcci_number_of_patches</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Fraction of observed area</th> <td>fcci_fraction_of_observed_area</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: Nasa MODIS <a href="https://lpdaac.usgs.gov/products/mod11c1v006/">MOD11C1</a>, <a href="https://lpdaac.usgs.gov/products/mod13c1v006/">MOD13C1</a>, <a href="https://lpdaac.usgs.gov/products/mcd15a2hv006/">MCD15A2</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Surface temperature at day</th> <td>lst_day</td> <td>K</td> <td>MPI</td> </tr> <tr> <th>Leaf Area Index</th> <td>lai</td> <td>m²/m²</td> <td>MPI</td> </tr> <tr> <th>Normalized Difference Vegetation Index</th> <td>ndvi</td> <td>unitless</td> <td>MPI</td> </tr> <tr> <th>Dataset: Nasa SEDAC <a href="https://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density-adjusted-to-2015-unwpp-country-totals-rev11">Gridded Population of the World (GPW), v4</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Population density</th> <td>pop_dens</td> <td>persons per square kilometers</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://www.globalfiredata.org/data.html">Global Fire Emissions Database (GFED)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GFED (large fires only)</th> <td>gfed_ba</td> <td>hectares (ha)</td> <td>MPI</td> </tr> <tr> <th>Valid mask of GFED burned areas</th> <td>gfed_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>GFED basis regions</th> <td>gfed_region</td> <td>N</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="http://gwis.jrc.ec.europa.eu/apps/country.profile/downloads">Global Wildfire Information System (GWIS)</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Burned Areas from GWIS</th> <td>gwis_ba</td> <td>ha</td> <td>NOA</td> </tr> <tr> <th>Valid mask of GWIS burned areas</th> <td>gwis_ba_valid_mask</td> <td>0-1</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://psl.noaa.gov/data/climateindices/list/">NOAA Climate Indices</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Arctic Oscillation Index</th> <td>oci_ao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Western Pacific Index</th> <td>oci_wp</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific North American Index</th> <td>oci_pna</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>North Atlantic Oscillation</th> <td>oci_nao</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Southern Oscillation Index</th> <td>oci_soi</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Global Mean Land/Ocean Temperature</th> <td>oci_gmsst</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Pacific Decadal Oscillation</th> <td>oci_pdo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Eastern Asia/Western Russia</th> <td>oci_ea</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>East Pacific/North Pacific Oscillation</th> <td>oci_epo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Nino 3.4 Anomaly</th> <td>oci_nino_34_anom</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Bivariate ENSO Timeseries</th> <td>oci_censo</td> <td>unitless</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://www.esa-landcover-cci.org/">ESA CCI</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Land Cover Class 0 - No data</th> <td>lccs_class_0</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 1 - Agriculture</th> <td>lccs_class_1</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 2 - Forest</th> <td>lccs_class_2</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 3 - Grassland</th> <td>lccs_class_3</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 4 - Wetlands</th> <td>lccs_class_4</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 5 - Settlement</th> <td>lccs_class_5</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 6 - Shrubland</th> <td>lccs_class_6</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 7 - Sparse vegetation, bare areas, permanent snow and ice</th> <td>lccs_class_7</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Land Cover Class 8 - Water Bodies</th> <td>lccs_class_8</td> <td>%</td> <td>NOA</td> </tr> <tr> <th>Dataset: <a href="https://ecoregions.appspot.com/">Biomes</a></th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Dataset: Calculated</th> <td> </td> <td> </td> <td> </td> </tr> <tr> <th>Grid Area in square meters</th> <td>area</td> <td>m²</td> <td>NOA</td> </tr> </tbody> </table> <p>*The datacube specifications (temporal, spatial resolution, chunk size) have been set up by the Max Planck Institut (MPI) team. For the variables that the contact is MPI, Lazaro Alonso (lalonso <at> bgc-jena.mpg.de) has led the efforts to collect and process them. For the variables that the contact is NOA, Ilektra Karasante (ile.karasante <at> noa.gr) has led the efforts to collect and process them.</p>
Data supporting: Success of post-fire plant recovery strategies varies with shifting fire seasonality
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Flint Hills, KS PurpleAir PM2.5 data from the 2022 prescribed fire season
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PurpleAir PM2.5 from the 2022-23 Florida agricultural-fire season
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Anaktuvuk River fire scar eriophorum vaginatum flowering during the 2008-2014 growing seasons
The Anaktuvuk River Fire occurred in 2007 on the North Slope of Alaska. In 2008, three eddy covariance towers were established at sites representing unburned tundra, moderately burned tundra, and severely burned tundra. Eriophorum vaginatum flowers were counted from annual photographs of each site during peak flowering season (6/17-7/20).
Data for: Fire season and time since fire determine AM fungal trait responses to fire management
<p><strong>Rationale:</strong> AM fungi are common mutualists in grassland and savanna systems that are adapted to recurrent fire disturbance. This long-term adaptation to fire means that AM fungi display disturbance associated traits which are useful for understanding environmental and temporal effects on AM fungal community assembly. </p> <p><strong>Methods:</strong> In this work, we evaluated how fire driven ecological selection on AM fungal spore traits varies with fire season (Fall vs. Spring) and time since fire. We tested this by analyzing AM fungal spore traits (e.g., colorimetric, sporulation, and size) from a fire regime experiment. </p> <p><span><span> </span></span><strong>Key results:</strong> Immediately following Fall and Spring fires, spore pigmentation darkened; however, this did not mediate the observed differences between Fall burned and no burn communities. Six months after Fall fires, spores in burned plots were lower in volume, produced less color rich pigment, and had higher sporulation rates, and these differences in spore traits were associated with shifts in AM fungal spore communities.</p> <p><strong>Main conclusion:</strong> This shows that AM fungal responses to fire vary based on season (stronger effects in the Fall) and with time since fire. Variation in AM fungal responses to fire time may reflect greater exposure to fire in Fall, when sporulation is highest.</p>
Season of prescribed fire determines grassland restoration outcomes after fire exclusion and overgrazing
<p>Fire exclusion and mismanaged grazing are globally important drivers of environmental change in mesic C<sub>4</sub> grasslands and savannas. Although interest is growing in prescribed fire for grassland restoration, we have little long-term experimental evidence of the influence of burn season on the recovery of herbaceous plant communities, encroachment by trees and shrubs, and invasion by exotic grasses. We conducted a prescribed fire experiment (seven burns between 2001 and 2019) in historically fire-excluded and overgrazed grasslands of central Texas. Sites were assigned to one of four experimental treatments: summer burns (warm season, lightning season), fall burns (early cool season), winter burns (late cool season), or unburned (fire exclusion). To assess restoration outcomes of the experiment, in 2019, we identified old-growth grasslands to serve as reference sites. Herbaceous-layer plant communities in all experimental sites were compositionally and functionally distinct from old-growth grasslands, with little recovery of perennial C<sub>4</sub> grasses and long-lived forbs. Unburned sites were characterized by several species of tree, shrub, and vine; summer sites were characterized by certain C<sub>3</sub><span> </span>grasses and forbs; and fall and winter sites were intermediate in composition to the unburned and summer sites. Despite compositional differences, all treatments had comparable plot-level plant species richness (range 89–95 species/1000 m<sup>2</sup>). At the local-scale, summer sites (23 species/m<sup>2</sup>) and old-growth grasslands (20 species/m<sup>2</sup>) supported greater richness than unburned sites (15 species/m<sup>2</sup>), but did not differ significantly from fall or winter sites. Among fire treatments, summer and winter burns most consistently produced the vegetation structure of old-growth grasslands (e.g., mean woody canopy cover of 9%). But whereas winter burns promoted the invasive grass<span> </span><i>Bothriochloa ischaemum</i><span> </span>by maintaining areas with low canopy cover, summer burns simultaneously limited woody encroachment and controlled<span> </span><i>B. ischaemum</i><span> </span>invasion. Our results support a growing body of literature that shows that prescribed fire alone, without the introduction of plant propagules, cannot necessarily restore old-growth grassland community composition. Nonetheless, this long-term experiment demonstrates that prescribed burns implemented in the summer can benefit restoration by preventing woody encroachment while also controlling an invasive grass. We suggest that fire season deserves greater attention in grassland restoration planning and ecological research.</p>
Season of prescribed fire determines grassland restoration outcomes after fire exclusion and overgrazing
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