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Fire Weather Index - ERA-Interim
<p>The Fire Weather Index (FWI) is a numeric rating of fire intensity, dependent on weather conditions. This is a good indicator of fire danger because it contains both a component of fuel availability (drought conditions) and a measure of ease of spread.</p> <p>This is part of a larger dataset providing gridded field calculations from the Canadian Fire Weather Index System using weather forcings from the European Centre for Medium-range Weather Forecast (ECMWF) ERA-Interim reanalysis dataset (Vitolo et al., 2019; Di Giuseppe et al., 2016). The dataset has been developed through a collaboration between the Joint Research Centre and ECMWF under the umbrella of the Global Wildfires Information System (GWIS), a joint initiative of the GEO and the Copernicus Work Programs. The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. The indices are called: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC), Initial Spread Index (ISI), Build Up Index (BUI), Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. </p> <p>Data are generated using the open source software GEFF v3.0 (https://git.ecmwf.int/projects/CEMSF/repos/geff), which now uses settings and parameters provided by the JRC (more info here https://git.ecmwf.int/projects/CEMSF/repos/geff/browse/NEWS.md). </p> <p>This dataset can be manipulated using the caliver R package (Vitolo et al. 2017, 2018). </p> <p>Details: </p> <ul> <li> <p>File format: netcdf4 </p> </li> <li> <p>Coordinate system: World Geodetic System 1984 (also known as WGS 1984, EPSG:4326). </p> </li> <li> <p>Longitude range: [-180, +180] </p> </li> <li> <p>Latitude range: [-90, +90] </p> </li> <li> <p>Temporal resolution: 1 day </p> </li> </ul> <ul> <li> <p>Spatial resolution: 0.7 degrees (~80 Km) </p> </li> <li> <p>Spatial coverage: Global </p> </li> <li> <p>Time span: from 1980-01-01 to 2018-12-31 </p> </li> </ul>
Supplementary Data: Global rise in forest fire emissions linked to climate change in the extratropics
<p>Supplementary Data for the paper "Global rise in forest fire emissions linked to climate change in the extratropics" by Jones et al. (2024, <em>Science</em>).</p> <p>The records include mapped pyromes and data and code used to delineate the pyromes.</p> <h3><strong>Mapped Pyromes</strong></h3> <p>The data records include mapped pyromes in three forms:</p> <ol> <li><strong>Shapefile</strong> (Jones_etal_2024_Global_Forest_Pyromes.shp.zip). Vector features in shapefile format containing data fields <em>pyrome ID</em> and <em>pyrome name</em>. The zipped file contains .shp, .dbf, .prj, .shx files.</li> <li><strong>Lower-resolution NetCDF </strong>(Jones_etal_2024_Global_Forest_Pyromes_Qdeg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at quarter-degree resolution.</li> <li><strong>Higher-resolution NetCDF</strong> (Jones_etal_2024_Global_Forest_Pyromes_005deg.nc). NetCDF version 4 file containing gridded values of <em>pyrome ID</em> at 0.05 degree resolution.</li> </ol> <p>Shapefiles are accessible via GIS programmes such as QGIS or ArcGIS. All files .shp, .dbf, .prj, .shx files must be stored in a single directory</p> <p>NetCDF files can be access by a variety of programming languages such as Python and R. For quick visualisations and access to the data structure, we suggest using the Panoply tool https://www.giss.nasa.gov/tools/panoply/.</p> <h3><strong>Correlation Data</strong></h3> <p>The data records (Correlation_Qdeg.zip) include gridded quarter-degree correlations between forest burned area (BA) and each of the following variables:</p> <ul> <li><em><strong>Fire weather index</strong></em></li> <li><em><strong>Atmospheric instability (continuous Haines index)</strong></em></li> <li><em><strong>Lightning flash density</strong></em></li> <li><em><strong>Soil moisture</strong></em></li> <li><em><strong>Vegetation productivity (Normalised Difference Vegetation Index)</strong></em></li> <li><em><strong>Population density</strong></em></li> <li><em><strong>Cropland cover</strong></em></li> <li><em><strong>Pasture cover</strong></em></li> <li><em><strong>Road density</strong></em></li> <li><em><strong>Potential fuel loads - surface fuels</strong></em></li> <li><em><strong>Potential fuel loads - shrub fuels</strong></em></li> <li><em><strong>Potential fuel loads - canopy and ladder fuels</strong></em></li> <li><em><strong>Terrain ruggedness index</strong></em></li> <li><em><strong>Forest area density</strong></em></li> </ul> <p>The BA data derive from MODIS MCD64A1 collection 6.1 (Giglio et al., 2018). BA data for forests is masked using the MODIS MOD44B product (DiMiceli et al., 2021) with a 30% tree cover threshold. The predictor data derive from multiple sources as desribed by Jones et al. (2024). See Supplementary Methods and Materials.</p> <p>The gridded correlations data are provided in Hierarchical Data Format version 5 (.hdf5) files, zipped to Correlation_Qdeg.zip. File names describe the variables used.<em> Cropland_Pasture_Qdeg.hdf5 </em>contains data for both cropland and pasture. Each file contains layers describing the Spearman's rho (ρ) correlation coefficient and the related p-value.</p> <p>As explained and justified by Jones et al. (2024), the correlation structure used depends on the variable (see Supplementary Methods and Materials) as per the following categories:</p> <ul> <li><strong><em>Fire Weather Index, Atmospheric Instability, and Lightning Flash Density:</em></strong> Monthly correlation between forest BA and each variable across all fire season months in the period 2001-2021 at the quarter-degree resolution.</li> <li><strong><em>Soil Moisture:</em></strong> Inter-annual correlation between (i) mean soil moisture during the fire season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li><strong><em>Vegetation Productivity (NDVI):</em></strong> Inter-annual correlation between (i) mean NDVI during the prior growing season and (ii) accumulated forest BA during the fire season at quarter-degree resolution across years 2001-2021. </li> <li><strong><em>Population Density, Cropland Cover, Pasture Cover, Road Density, T</em></strong><strong><em>errain Ruggedness Index, Forest Area Density: </em></strong>Spatial correlation between mean annual forest BA and each variable across the 0.05° cells within each quarter-degree cell during 2001-2021.</li> </ul> <p>Note that these grids are provided for insights into spatial variation in the input correlation data. Pyromes are defined based on correlations fitted on the spatial scale of Olson ecoregions, not quarter-degree grid cells (see further details below).</p> <h3><strong>Clustering Code</strong></h3> <p>DEMO_Clustering.zip contains R Statistics code for clustering forest ecoregions into pyromes based on correlations observed between forest BA and 14 predictors at regional level. The <em>Input</em> directory contains a .RData data frame with correlations between forest BA and each predictor for ecoregions. For demonstrative purposes the code is applied to cluster forest ecorgions of North America into pyromes. The <em>Regions</em> directory contains ecoregions of North America in shapefile format. The <em>Output</em> directory contains output generated by M. Jones, which can be used for validation purposes once other users have trialled the code.</p>
Observational constraints of fire, environmental and anthropogenic on pantropical tree cover - Data
<p>Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes.</p> <p>Land surface properties:</p> <ul> <li><strong>TreeCover </strong>- Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from <sup>1</sup>, regridded as per <sup>2</sup>.</li> <li><strong>urban </strong>cover from the History Database of the Global Environment, Version 3.1 (HYDE) <sup>3,4</sup></li> <li><strong>crop </strong>cover (from HYDE)</li> </ul> <ul> <li><strong>pas - Pasture </strong>Cover (from HYDE)<strong>PopDen </strong>(population density from HYDE)</li> <li><strong>BurntArea_xxxxx </strong>- Burnt area with xxxx denoting different products, provided by fireMIP <sup>5–7</sup>: <ul> <li>GFED_four: Global Fire Emissions Database, Version 4 (GFED4) <sup>8</sup></li> <li>GFED_four_s: Global Fire Emissions Database, Version 4.1, including small fires (GFEDv4.1) <sup>9</sup></li> <li>MCD_forty_five: MCD45 <sup>10</sup></li> <li>Meris: Fire_CCI4.0 <sup>11</sup></li> <li>MODIS: Fire_CCI5.1 <sup>12</sup></li> </ul> </li> </ul> <p>Climate:</p> <ul> <li><strong>MAP_xxx </strong>- Mean annual precipitation where xxx denotes data source: <ul> <li><strong>CMORPH </strong><sup>13,14</sup></li> <li><strong>CRU </strong>from version 4.03 of the Climatic Research Unit Time Series high-resolution gridded dataset (CRU TS v4.01) <sup>15</sup></li> <li><strong>GPCC: </strong><sup>16</sup></li> <li><strong>MSWEP: </strong><sup>17</sup></li> </ul> </li> <li><strong>MAT </strong>- Mean annual temperature from CRU)</li> <li><strong>MConc_xxx </strong>– Mean annual concentration of rainfall as defined by <sup>18</sup>, where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MADD_xxx</strong>- Mean annual fractional dry days from CRU - i.e. seasonality of rainfall), where xxx denotes precip data source (see “MAP_xxx”)</li> <li><strong>MDDM_xxx </strong>– Mean fractional dry days of the driest month.</li> <li><strong>MADM_xxx – </strong>Mean annual precipitation of the driest month<strong>.</strong></li> <li><strong>MTWM </strong>- Mean Maximum Temperature of the warmest month from CRU</li> <li><strong>MTCM </strong>- Mean minimum temperature of the coldest month from CRU</li> <li><strong>SW1 </strong>- direct downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>SW2 </strong>- diffuse downwards SW simulated using the SLASH model using CRU cloud cover</li> <li><strong>MaxWind </strong>(Mean Max Windspeed from CRU-(National Centers for Environmental Prediction <sup>15</sup></li> </ul> <p>‘output_summary’ contains framework output. There are several directories for different experiments, each containing a netcdf file. Along with standard latitude and longitude,each file contains ‘model_level_number’ dimension, with each layer representing the 1, 5, 10, 25, 50, 75, 90, 95 and 99% quantiles of the model posterior. The folder represents the experiment:</p> <ul> <li>Control – standard full model reconstruction</li> <li>noHumans – without human influence (from crop, pasture, population density or urban influence)</li> <li>noMortality – without disturbance stress (burnt area, wind, heat stress, rainfall seasonality</li> <li>noMAP – without mean annual precip influence.</li> <li>noNoneMAT – without mean annual temperature influence.</li> <li>noFire – tree cover without the influence of fire</li> <li>noDrought – without the influence of rainfall distribution</li> <li>noTasMort – without mortality from heat stress</li> <li>noWind – without influence from max. windspeed</li> <li>noPas – without exclusion from pasture</li> <li>noCrop – without exclusion from crop</li> <li>noPop – without reduction from population density</li> <li>noUrban – without exclusion from urban</li> <li>firePlus1pc – tree cover with burnt area was 1% higher.</li> </ul> <p> </p> <p><strong>References</strong></p> <p> </p> <p>1. Dimiceli, C. & Others. MOD44B MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, 2015). Preprint at (2015).</p> <p>2. Kelley, D. I. <em>et al.</em> How contemporary bioclimatic and human controls change global fire regimes. <em>Nat. Clim. Chang.</em> <strong>9</strong>, 690–696 (2019).</p> <p>3. Klein Goldewijk, K., Goldewijk, K. K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. <em>Glob. Ecol. Biogeogr.</em> <strong>20</strong>, 73–86 (2010).</p> <p>4. Hurtt, G. C. <em>et al.</em> Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. <em>Climatic Change</em> vol. 109 117–161 Preprint at https://doi.org/10.1007/s10584-011-0153-2 (2011).</p> <p>5. Hantson, S., Arneth, A., Harrison, S. P. & Kelley, D. I. The status and challenge of global fire modelling. (2016).</p> <p>6. Hantson, S. <em>et al.</em> Quantitative assessment of fire and vegetation properties in simulations with fire-enabled vegetation models from the Fire Model Intercomparison Project. <em>Geoscientific Model Development</em> vol. 13 3299–3318 Preprint at https://doi.org/10.5194/gmd-13-3299-2020 (2020).</p> <p>7. Rabin, S. S., Melton, J. R. & Lasslop, G. The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols with detailed model descriptions. <em>Geoscientific Model</em> (2017).</p> <p>8. Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4). <em>J. Geophys. Res. Biogeosci.</em> <strong>118</strong>, 317–328 (2013).</p> <p>9. van der Werf, G. R. <em>et al.</em> Global fire emissions estimates during 1997–2016. <em>Earth Syst. Sci. Data</em> <strong>9</strong>, 697–720 (2017).</p> <p>10. Roy, D. P., Boschetti, L., Justice, C. O. & Ju, J. The collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product. <em>Remote Sensing of Environment</em> vol. 112 3690–3707 Preprint at https://doi.org/10.1016/j.rse.2008.05.013 (2008).</p> <p>11. Alonso-Canas, I. & Chuvieco, E. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. <em>Remote Sens. Environ.</em> <strong>163</strong>, 140–152 (2015).</p> <p>12. Chuvieco, E. <em>et al.</em> Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. <em>Earth System Science Data</em> vol. 10 2015–2031 Preprint at https://doi.org/10.5194/essd-10-2015-2018 (2018).</p> <p>13. Joyce, R. J., Janowiak, J. E., Arkin, P. A. & Xie, P. CMORPH: A Method that Produces Global Precipitation Estimates from Passive Microwave and Infrared Data at High Spatial and Temporal Resolution. <em>J. Hydrometeorol.</em> <strong>5</strong>, 487–503 (2004).</p> <p>14. Marthews, T. R., Blyth, E. M., Martínez-de la Torre, A. & Veldkamp, T. I. E. A global-scale evaluation of extreme event uncertainty in the eartH2Observe project. <em>Hydrol. Earth Syst. Sci.</em> <strong>24</strong>, 75–92 (2020).</p> <p>15. Harris, I. C. & Jones, P. D. CRU TS4.03: Climatic Research Unit (CRU) Time-Series (TS) version 4.03 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2018). (2019) doi:10.5285/10D3E3640F004C578403419AAC167D82.</p> <p>16. Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A. & Ziese, M. GPCC Full Data Monthly Product Version 2018 at 0.5◦: Monthly Land-Surface Precipitation from Rain-Gauges Built on GTS-Based and Historical Data. <em>Deutscher Wetterdienst: Offenbach am Main, Germany</em> (2018).</p> <p>17. Beck, H. E., Van Dijk, A. & Levizzani, V. MSWEP: 3-hourly 0.25 global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data. <em>Hydrol. Earth Syst. Sci.</em> (2017).</p> <p>18. Kelley, D. I., Harrison, S. P., Wang, H. & Simard, M. A comprehensive benchmarking system for evaluating global vegetation models. (2013).</p>
Arbuscular mycorrhizal fungal response to fire and urbanization in the Great Smoky Mountains National Park
Wildfires are increasing in frequency and intensity as drier and warmer climates increase plant detrital fuel loads. At the same time, increases in urbanization position 9% of fire-prone land within the US at the wildland-urban interface. While rarely studied, the compounded effects of urbanization and wildfires may have unknown synergistically negative effects on ecosystems. Previous studies at the wildland-urban interface often focus on aboveground plant communities, but belowground ecosystems may also be affected by this double disturbance. In particular, it is unclear how much fire and urbanization independently or interactively affect nutritional symbioses such as those between arbuscular mycorrhizal (AM) fungi and the majority of terrestrial plants. In November 2016, extreme drought conditions and long-term fire suppression combined to create a wildfire within the Great Smoky Mountains National Park (GSMNP) and the neighboring exurban city of Gatlinburg, TN. To understand how the double disturbance of urbanization and fire affected AM fungal communities, we collected fine roots from the five dominant understory species in September 2018 at each of 18 sites spanning three burn severities in both exurban and natural sites. Despite large variation in burn severity, plant species identity had the largest influence on AM fungi. AM fungal colonization, richness, and composition all varied most among plant species. Fire and urbanization did influence some AM fungal metrics; colonization was lower in burned sites and composition was more variable among exurban locations. There were no interactions among burn severity and urbanization on AM fungi. Our results point to the large influence of plant species identity structuring this obligate nutritional symbiosis regardless of disturbance regime. Therefore, the majority of AM fungal taxa may be buffered from fire-induced ecosystem changes if plant community composition largely remains intact, plant species life history tr
Datasets for: A global review of pyrosomes: Shedding light on the ocean’s elusive gelatinous ‘fire-bodies’
These are the datasets used to create all figures included in: "Lilly, L.E., Suthers, I.M., Everett, J.D., Richardson, A.J. (2023). A Global Review of Pyrosomes: Shedding light on the ocean’s elusive gelatinous ‘fire-bodies’. Limnology & Oceanography Letters." The review presents a comprehensive global description of the body of current knowledge on pyrosomes, a zooplanktonic tunicate taxon closely related to salps, doliolids, and appendicularians. For review analyses, we used pyrosome observations and associated information from literature-published studies and four databases: NOAA COPEPOD Urochordates database (NOAA, 2022; https://www.st.nmfs.noaa.gov/copepod/atlas/html/taxatlas_4350000.html), BCO-DMO Jellyfish Database Initiative (JeDI; Condon et al., 2014; https://www.bco-dmo.org/dataset/526852), Global Biodiversity Information Facility (GBIF; https://doi.org/10.15468/dl.a8phvp), and Ocean Biodiversity Information System (OBIS; https://obis.org/taxon/137216). We matched pyrosome observations to corresponding satellite-measured sea surface temperature (NOAA Optimum Interpolation Sea Surface Temperature, V2, high-resolution, https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html) and chlorophyll-a (MODIS-AQUA, 4 km^2 resolution, Melin, 2013; http://data.europa.eu/89h/10161412-a76c-42b0-b4e1-5fcccdc412b2). The files included in this metadata record have been subsetted from all original file sources. Our subsetted files are designed to run with the associated MATLAB scripts to recreate all manuscript files. We include seven MATLAB scripts: 1) A four-part script to clean up all pyrosome observations, divide to species level, and remove duplicate records from multiple databases and within each database, and 2) Three standalone scripts to plot Figs. 1, 2, and 3.
Ecological memory effects on plants and soils in early post-fire steppe, Barton Ecological Research Area, Pocatello, Idaho, 2021
In many regions of the world, wildfires are becoming more frequent due to the invasion of exotic grasses that are highly flammable and often replace native plants as burned landscapes regrow. To prevent invasive species from dominating post-burn landscapes, land managers are increasingly applying seeds of native plants to suppress invasive plants and encourage ecosystem recovery. However, there is still much to learn about the ability of seeded species to establish and suppress flammable invaders. It is also unclear how previous human-caused landscape changes, such as nitrogen pollution or the removal of shrubs (a common practice in western USA rangelands), affect the success of native seed additions and plant recovery from fire. This study addresses these issues by building on a long-term experiment investigating the legacy effects of past nitrogen pollution and shrub removal in a highly invaded sagebrush steppe ecosystem at Idaho State University’s Barton Ecological Research Area in Pocatello, ID. This experiment burned in a wildfire in August, 2020, providing a unique opportunity to evaluate how a history of nitrogen pollution and shrub removal influences plant recovery from wildfire. We developed three native seed mixes intended to suppress invasive plants, particularly flammable annual grasses, and in April, 2021, we sowed the experimental mixes into research plots within the original experiment. To measure the initial effects of the experimental seed additions and the legacy effects of previous nitrogen pollution and shrub removal, we collected the data provided here during the summer of 2021, the first growing season following the wildfire. We established 240 monitoring quadrats (1 m²) within the original experiment, dividing the quadrats between areas where shrubs had formerly been (evidenced by stumps) and intershrub areas. At a microhabitat scale, the presence of shrubs alters soil properties and can create legacy effects after shrub death, and we were int
Stomatal Distribution and Post-fire Recovery: Intra- and Interspecific Variation in Plants of the Pyrogenic Florida Scrub, 2023-2024
Premise of the study: Amphistomy is the presence of stomata on both leaf surfaces. This distribution of stomata can increase photosynthesis, but is relatively infrequent, which is often attributed to high costs such as water loss. This study takes place in the Florida scrub- a hot, dry, shrub-dominated habitat that naturally experiences fire. However, decades of anthropogenic suppression and the reintroduction of controlled burns has created varied fire regimes across the region. In this study, we investigated the links between amphistomy and fire by determining (1) how common the trait is in this habitat, and (2) within-species variation before and after experimental fire, and across a time-since-fire gradient (0.25 - 50 years). Methods: We (1) surveyed 116 plant species across scrub habitats for amphistomy presence, and (2) experimentally and observationally investigated intraspecific variation in stomatal traits in response to fire for two post-fire resprouting species of palmetto, Serenoa repens and Sabal etonia (Arecaceae). Key results: Amphistomy was present in 62.9% of all surveyed species and 85.7% of post-fire obligate reseeders, suggesting amphistomy may be beneficial in this group and in the Florida scrub conditions. The stomatal ratio (upper/total stomatal density) was generally stable in response to fire. Stomatal density decreased following fire in S. etonia, with both species experiencing high variation in the post-fire years. Conclusions: Amphistomy is common in this habitat and relatively stable within species in response to fire, while stomatal density responds plastically during postfire regrowth.
The Fire and Fire Surrogate Study: Berkeley Forests, 2001-2020
Dry forests throughout the western United States are fire-dependent ecosystems, and much attention has been given to restoring their ecological function. For this reason, land managers often are tasked with reintroducing the fire process via prescribed fire and fire-surrogate treatments (such as thinning and mastication). During planning, managers are expected to anticipate the effects of management actions on forest structure, ecological function, and future fire behavior. In the case of fire surrogate treatments, managers must understand which components or processes are changed or lost, and with what effects, if treatments such as thinning and mastication are used instead of fire or in combination with fire. As such, a nationwide research effort, The Fire and Fire Surrogate Study, commenced in 2001 to evaluate the impacts of prescribed fire and mechanical fuel reduction treatments. As part of this national effort, a suite of fuel treatments was implemented in 2001 at Blodgett Forest Research Station, which is owned and operated by UC Berkeley Forests, in the northern Sierra Nevada. The Fire and Fire Surrogate Study at Blodgett Forest Research Station is comprised of a network of twelve stands, ranging in size from 35-70 acres, each of which was randomly assigned one of four possible treatments, which represent the basic range of forest restoration and fire hazard reduction options. The treatment options initiated at Blodgett Forest were: 1) Control: no active management. 2) Fire-only: prescribed fire applied to the forest stand. 3) Mechanical-only: Crown thinning followed by commercial thinning from below, which removed mid and larger sized trees, followed by mastication, which chipped/shredded smaller trees in place leaving 10% of them in clumps throughout the forest stand. 4) Mechanical + fire: same mechanical treatment described above, followed by prescribed fire. This dataset contains longitudinal information about forest and fuels composition, as well as und
Plot-level field data and model simulation results, archived to accompany Turner et al. manuscript; reports data from summer 2017 sampling of short-interval fires that burned during summer 2016 in Greater Yellowstone.
Subalpine forests in the northern Rocky Mountains have been resilient to stand-replacing fires that historically burned at 100–300-yr intervals. Fire intervals are projected to decline drastically as climate warms, and forests that reburn before recovering from previous fire may lose their ability to rebound. We studied recent fires in Greater Yellowstone (Wyoming, USA) and asked whether short-interval (less than 30 yrs) stand-replacing fires can erode lodgepole pine (Pinus contorta var. latifolia) forest resilience via increased burn severity, reduced early postfire tree regeneration, reduced carbon stocks, and slower carbon recovery. During 2016, fires reburned young lodgepole pine forests that regenerated after wildfires in 1988 and 2000. During 2017, we sampled 0.25-ha plots in stand-replacing reburns (n=18) and nearby young forests that did not reburn (n=9). We also simulated stand development with and without reburns to assess carbon recovery trajectories. Nearly all prefire biomass was combusted ("crown fire plus") in some reburns in which prefire trees were dense and small (≤ 4 cm basal diameter). Postfire tree seedling density was reduced six-fold relative to the previous (long-interval) fire, and high-density stands (greater than 40,000 stems ha-1) were converted to sparse stands (less than 1,000 stems ha-1). In reburns, coarse wood biomass and aboveground carbon stocks were reduced by 65% and 62%, respectively, relative to areas that did not reburn. Increased carbon loss plus sparse tree regeneration delayed simulated carbon recovery by greater than 150 yrs. Forests did not transition to nonforest, but extreme burn severity and reduced tree recovery foreshadow an erosion of forest resilience.
Simulated forest dynamics (2016-2100) for six future climate-fire scenarios and five representative landscapes in Greater Yellowstone, USA
We simulated fire (incorporating fuels feedbacks) and forest dynamics on five landscapes spanning the Greater Yellowstone Ecosystem (GYE) to ask: (1) How and where are forest landscapes likely to change with 21st-century warming and fire activity? (2) Are future forest changes gradual or abrupt, and do forest attributes change synchronously or sequentially? (3) Can forest declines be averted by mid-21st-century stabilization of atmospheric greenhouse gas (GHG) concentrations? We used the spatially explicit individual-based forest model iLand to track multiple attributes (forest extent, stand age, tree density, basal area, aboveground carbon stocks, dominant forest types, species occupancy) through 2100 for six climate scenarios. The five study landscapes are representative of dominant forest types and environmental gradients of the Northern Rockies; collectively, they encompass nearly 300,000 ha, of which 279,488 ha are potentially stockable with trees. This data set contains annual landscape-level output data for simulations to 2100 with 6 climate scenarios (3 general circulation models x 2 representative concentration pathways) x 5 landscapes x 20 iterations of simulated fires. We include the data and R scripts used for the analyses of abrupt change in the publication associated with these data; all other analyses used standard functions in R.
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.
Tussock height and diameter in moist acidic tussock tundra at the site of the 2007 Anaktuvuk River fire scar, and nearby unburned tundra measured in 2016
This dataset consists of Eriophorum vaginatum tussock height and width (diameter) measurements, and was used to evaluate differences in physical strucutre of previously burned tundra (2007 Anaktuvuk River fire) and nearby unburned tundra. At each site, all tussocks that intersected four 100 meter transects were measured from soil surface to tussock top in four cardinal directions, and diameter was measured in two directions. These data were used to examine the impact of post-fire changes in plant community composition and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaska moist acidic tundra.
Tussock (Eriophorum vaginatum) density, mortality, and rodent-herbivore activity in moist acidic tussock tundra at the site of the 2007 Anaktuvuk River fire and nearby unburned tundra, measured in 2019
This dataset consists of tussock density, mortality rates and causes, and an assesment of rodent-herbivore activity levels in previously burned (2007 Anaktuvuk River fire) and unburned tussock tundra. Eriophourm vaginatum tussocks were counted every meter within a 1 square meter quadrat along three transects. Cause of tussock mortality, as well as level of rodent herbivory was assessed for each tussock, and rodent herbivore activity was assessed for each quadrat. The goal of the project was to examine the impact of post-fire changes in plant community composition and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaska moist acidic tundra.
Leaf area index (LAI) by plant functional group in moist acidic tussock tundra, at the 2007 Anaktuvuk River fire scar measured in 2017
This file contains leaf area index (LAI) based on biomass measurements from an aboveground pluck in the southern portion of the Anaktuvuk River fire scar, and a nearby unburned site in late July 2017. Vegetation was sampled randomly at 10-m intervals along two 100 meter transects at both the burned and unburned sites. Vegetation was sampled within a 10X40 cm quadrat to the mineral layer, and plant material was sorted into new and old aboveground leaf and woody biomass by species. All samples were dried and weighed, and subsampled leaf material was scanned to determine specific leaf area (centimeterSquaredPerGram biomass) per species, which was then used to transform leaf biomass (gramPerMeterSquared) into the leaf area index for each site.
Comparison of vole-grazed and ungrazed Eriophorum vaginatum tussock biomass at the 2007 Anaktuvuk River fire scar in 2019
This file contains biomass measurements from vole-grazed and ungrazed Eriophorum vaginatum tussocks taken from the 2007 Anaktuvuk River Fire scar in 2019. Rodent-grazed and ungrazed tussocks were harvested to assess the impact voles have on biomass. Eighteen grazed tussocks and seven ungrazed tussocks were harvested and taken back to the lab. Ungrazed tussocks were subsampled to make seperation faster. Eight additional ungrzed tussocks were measured in the field and biomass estimates were made using allometry equations based on diameter. The goals of the project were to examine the impact of post-fire changes in plant community composition and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaska moist acidic tundra.
Eriophorum vaginatum rhizome nitrogen content from the 2007 Anaktuvuk River fire scar measured in 2019.
This file contains Eriophorum vaginatum rhizome biomass from a 2017 biomass pluck of previously burned tundra (2007 Anaktuvuk River Fire) and nearby unburned tundra. Rhizome biomass from the pluck was combined with rhizome percent nitrogen estimates (2.47% at the Anaktuvuk River Fire, and 1.05% at the nearby unburned site) to estimate grams of nitrogen per meter squared, to evaluate differences in winter forage quality for the rodent herbivore, Microtus oeconomus. Percent nitrogen estimates were derived from pooled rhizome samples collected from the two sites in late 2018. The goals of the project were to examine the impact of post-fire changes in plant community composition, nutrient quality and structure on habitat suitability and rodent herbivore activity in response to a large, severe, and unprecedented fire in northern Alaska moist acidic tundra.
Tree regeneration after fire: Yukon Lodgepole Pine Surveys, mineral soil analysis
This dataset documents pre- and post-fire tree composition of stands along the current range edge of lodgepole pine (Pinus contorta ssp. latifolia) in the Yukon Territory. The objective of the study was to evaluate whether pine populations at the range edge appear to be expanding in association with fire disturbance. This dataset has been published as: Jill F. Johnstone and F. Stuart Chapin, 2003. Non-equilibrium succession dynamics indicate continued northern migration of lodgepole pine. Global Change Biology, 9(10): 1401-1409.
Tree regeneration after fire: Yukon Lodgepole Pine Surveys, organic soil analysis
This dataset documents pre- and post-fire tree composition of stands along the current range edge of lodgepole pine (Pinus contorta ssp. latifolia) in the Yukon Territory. The objective of the study was to evaluate whether pine populations at the range edge appear to be expanding in association with fire disturbance. This dataset has been published as: Jill F. Johnstone and F. Stuart Chapin, 2003. Non-equilibrium succession dynamics indicate continued northern migration of lodgepole pine. Global Change Biology, 9(10): 1401-1409. Contains measurements of organic soil depth sampled along transects.
Tree regeneration after fire: Yukon Lodgepole Pine Survey, pre fire analysis
This dataset documents pre- and post-fire tree composition of stands along the current range edge of lodgepole pine (Pinus contorta ssp. latifolia) in the Yukon Territory. The objective of the study was to evaluate whether pine populations at the range edge appear to be expanding in association with fire disturbance. This dataset has been published as: Jill F. Johnstone and F. Stuart Chapin, 2003. Non-equilibrium succession dynamics indicate continued northern migration of lodgepole pine. Global Change Biology, 9(10): 1401-1409. Pre-fire diameter and stem counts of trees judged to be alive at the time of burning, based on belt-transect surveys.
Tree regeneration after fire: Yukon Lodgepole Pine Surveys, seedlings analysis
This dataset documents pre- and post-fire tree composition of stands along the current range edge of lodgepole pine (Pinus contorta ssp. latifolia) in the Yukon Territory. The objective of the study was to evaluate whether pine populations at the range edge appear to be expanding in association with fire disturbance. This dataset has been published as: Jill F. Johnstone and F. Stuart Chapin, 2003. Non-equilibrium succession dynamics indicate continued northern migration of lodgepole pine. Global Change Biology, 9(10): 1401-1409. Post-fire seedling count data, made in 2x50m belt transects (all live seedlings/saplings).
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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.