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263 results for “forest fire”
FireCaster Wildland Fire Fuels Database for Corsican - Mediterranean Forest stand types
<p>This database includes wildland fuels data for Mediterranean basin vegetation stands types and in particular for those in Corsica, for fire/forest management, risk assessment and decision-support purposes. It gathers together some of the most common input parameters needed by wildfire’s models at several vegetation scales (i.e., stand, elements, particles). It has been conceived by using a layering approach, this is, assuming that a vegetation stand type is constituted by one or more structurally distinct pseudo-homogenous layers of vegetation. Vegetation stand types considered are based on the fuel classification and mapping of the BDForêt® <em>2.0 – Corsica.</em> Fuel attributes have been defined to be meaningful at regional/landscape scales and are representative of stand-level characteristics. National Forest Inventory (NFI) data have been mainly used for determining the fuel attributes for forest stand types. The building methods and the different data sources have been detailed in a paper which is under review.</p> <p> The attached dataset consists of two tables and one text document:</p> <p> - The first table (<em>FuelLayersData.csv</em>) contains fuel layers and fuel elements attributes for each vegetation stand type. The table has 15 columns. The first one (<em>CODE_TFV</em>) corresponds to the code assigned to each vegetation stand type following the BDForêt®<em> </em>nomenclature. Next columns, refer to the layer numbering, the stratum of the layer and the species scientific name. After that, next six columns correspond to the layer attributes and four columns correspond to the fuel element attributes. The last column is the diameter at breast height (DBH) for canopy layers. The empty cells in the table indicate that the corresponding attribute is not applicable for this particular layer.</p> <p> - The second table (<em>FuelParticlesData.csv</em>) contains the particle attributes, this is, the surface-to-volume ratio, particles density and low heat content.</p> <p> - The text document (<em>StandTypesDescription.docx</em>) is derived from BDForêt®<em> version 2.0 – Corsica</em> (https://geo.isula.corsica/wp-content/uploads/2021/01/descriptif-contenu-bd_foret-IGN.pdf) and contains a short description of the different stand types considered according to the CODE_TFV.</p> <p>This work was supported by the Agence Nationale de la Recherche, France (grant number ANR-16-CE04-0006 FIRECASTER) and by H2020-EU.3.5. Programme (FIRE-RES, Grant agreement ID: 101037419).</p> <p> </p> <p>Pérez-Ramirez Y, Ferrat L, Filippi JB. (2024) Wildland Fire Fuels Database for Corsican – Mediterranean Forest stand types. Forest Ecology and Management, 565, 122002.</p>
Fig.1 in R Ec O V E Re D S Ui Ta Bi Li T Y O F D E Er H A B Itat In Hem Ibo Rea L W O Odl And 23 Ye Ars Afte R Va St Forest Fire In Slītere National Park, Latvia
Fig.1. Location of pellet group counts and habitat characteristics summarized at a hexagon level (10ha) regarding the borders of fire-affected area.
Figure 2 in Fire effects on Atlantic Forest sites from a composition, structure and functional perspective
Figure 2. Average of species richness (A), basal area (B), Shannon index (C), tree density (D), CWM Height (E), CWM Leaf length (F), CWM wood density (G), CWM Leaf deciduousness (H), CWM dispersal mode (I), CWM shade tolerance (J) for tree species inventoried in burned and unburned sites in Paraíba do Sul river basin, Southeast Atlantic Forest biome, Brazil.Same letters represent no statistical difference.
Sensitivity of fire weather indices, fuel sticks and satellite observations to fuel moisture content in Central European forests - Data
<p>This data repository contains datasets for destructively measured fuels of different types (FMC_insitu), meteorological data including 10-hour fuel stick measurements (FWS_30min) and calculated fire weather index components (FWS_FWI_24h) for four different sites in the Tharandt forest and Saxon Switzerland National Park in the Free State of Saxony (Germany) during the years 2022 (only DE-Tha) and 2023 (all four sites).</p> <p>The provided folders contain .csv files for each study site. Meteorological data in 30min for DE-Tha can be derived from the ICOS data portal (https://data.icos-cp.eu/portal/). For the remaining three sites (DE-BLB, DE-BWB, DE-SHW), past and recent data can be viewed via EMS Brno (e.g., http://www.emsbrno.cz/p.axd/en/Beech__Landberg.TU__DRESDEN.html). Upon request, the authors can share the data. </p> <p><strong>FMC_insitu</strong>: Destructively sampled fuel moisture content of different fuel types.</p> <p><strong>FWS_30min</strong>: Original measurements from the fire weather stations in 30 min time steps</p> <p><strong>FWS_FWI_24h</strong>: Measurements from fire weather stations in 24h time steps and the calculated fire weather index and its components. As requested for calculation of the FWI, meteorological variables are used at 13:00 (UTC), while PREC and PBC are the 24h sum prior to 13:00. </p> <p><strong>readme.txt</strong>: Description of repository content and the variables provided within the .csv files.</p> <p><strong>stations.csv</strong>: Contains the coordinates and a short description of the study sites. </p>
Рис. 1. Δинамика чисΛенности меΛких мΛекопитающих в Цасучейском бору: 1 — суммарная чисΛенность (особей / 100 циΛинΑро-суток); Αоминирующие виΑы: 2 — забайкаΛьский хомячок, 3 — бурозубка тунΑряная, 4 — бурозубка крошечная, 5 — поΛёвка монгоΛьская, 6 — поΛёвка РаΑΑе, 7 — красная поΛёвка; A — остепнённый сосняк, B — первичная гарь, С — старая гарь, D — повторная гарь; стреΛка указывает время прохожΑения пожара. Ось X — гг., ось Y — чисΛенность Fig. 1. Population dynamics of small mammals in the Tsasucheysky Pine Forest: 1 — total abundance (individuals / 100 cylinder-days); dominant species: 2 — Cricetulus pseudogriseus, 3 — Sorex tundrensis, 4 — S. minutissimus, 5 — Alexandromys mongolicus, 6 — Lasiopodomys raddei, 7 — Myodes rutilus; A — steppe pine forest, B — primary burns site, С — old burns site; D — repeated burns site; the arrow indicates the time of the fire. The X-axis shows years; the Y-axis shows population density in Population dynamics of small mammals after spring fires in steppe pine forest
Рис. 1. Δинамика чисΛенности меΛких мΛекопитающих в Цасучейском бору: 1 — суммарная чисΛенность (особей / 100 циΛинΑро-суток); Αоминирующие виΑы: 2 — забайкаΛьский хомячок, 3 — бурозубка тунΑряная, 4 — бурозубка крошечная, 5 — поΛёвка монгоΛьская, 6 — поΛёвка РаΑΑе, 7 — красная поΛёвка; A — остепнённый сосняк, B — первичная гарь, С — старая гарь, D — повторная гарь; стреΛка указывает время прохожΑения пожара. Ось X — гг., ось Y — чисΛенность Fig. 1. Population dynamics of small mammals in the Tsasucheysky Pine Forest: 1 — total abundance (individuals / 100 cylinder-days); dominant species: 2 — Cricetulus pseudogriseus, 3 — Sorex tundrensis, 4 — S. minutissimus, 5 — Alexandromys mongolicus, 6 — Lasiopodomys raddei, 7 — Myodes rutilus; A — steppe pine forest, B — primary burns site, С — old burns site; D — repeated burns site; the arrow indicates the time of the fire. The X-axis shows years; the Y-axis shows population density
Fig. 23. Forest Fires. The scene after a in The herpetofauna of Coahuila, Mexico: composition, distribution, and conservation status
Fig. 23. Forest Fires. The scene after a forest fire in the vicinity of Arteaga, in the municipality of Arteaga. Photo by Manuel Nevárez de los Reyes.
Fig. 7. Forest fires. A in The herpetofauna of Hidalgo, Mexico: composition, distribution, and conservation status
Fig. 7. Forest fires. A forest fire for land use conversion in the vicinity of El Naranjal, in the municipality of Pisaflores. Photo by Christian Berriozabal-Islas.
Fig. 4 in Animal use of rehabilitated formerly fire damaged peat-swamp forest in western Sabah, Malaysia
Fig. 4. Temporal activity patters for the nine most frequently photographed animal species in the northern part of Klias Forest Reserve, Sabah, Malaysia. Rehabilitated forest (L); Intact forest (R). Black bars indicate nocturnal activity; dotted bars indicate diurnal activity. Number in parenthesis represents number of independent camera trap records.
Fig. 3 in Animal use of rehabilitated formerly fire damaged peat-swamp forest in western Sabah, Malaysia
Fig. 3. The observed species accumulation curves in rehabilitated forest (solid lines) versus intact forest (dashed lines) and their 95% Confidence Intervals for all animals detected (L) and for mammal species only (R) in Klias Forest Reserve. The curves were constructed using an abundance-based rarefaction approach with 100 randomisation runs in EstimateS (Colwell, 2013).
Fig. 1 in Animal use of rehabilitated formerly fire damaged peat-swamp forest in western Sabah, Malaysia
Fig. 1. Klias Forest Reserve and Binsulok Forest Reserve located in the Klias Peninsula in south-western Sabah, northern part of Malaysian Borneo (inset).
Fig. 2 in Animal use of rehabilitated formerly fire damaged peat-swamp forest in western Sabah, Malaysia
Fig. 2. Maps of the Normalised Difference Vegetation Index (NDVI) of the northern part of Klias Forest Reserve indicating the burnt forest areas (white) in 1998 (L) and the same study area in 2016 including oil palm plantations (white) in the north and eastern part of the reserve (R). Red circles indicate locations of camera trapping points in rehabilitated/burnt areas; Yellow circles indicate locations of camera trapping points in intact/unburnt areas.
Data and code for: Roost selection by male northern long-eared bats (Myotis septentrionalis) in a managed fire-adapted forest
<p>Data and code for: Roost selection by male northern long-eared bats (<em>Myotis septentrionalis</em>) in a managed fire-adapted forest</p>
Data and code for "increasing aridity causes larger and more severe forest fires across Europe"
<p>### Data and code for "increasing aridity causes larger and more severe forest fires across Europe"</p> <p>This repository holds code and data for the publication in GCB entitled: Increasing aridity causes larger and more severe forest fires across Europe (manuscript accepted)</p> <p> </p> <p># scripts</p> <p>all scripts are in the folder "lib". In order to perform the full analysis, please follow through all scripts.<br> we provide the results of some steps in order to reduce runtime for the user. we indicate this in the headings of the scripts.</p> <p>1. in the first script we prepare the ERA5-Land data. The code for the download is provided.<br> The user needs a CDS API for downloading.</p> <p>2. the data from the ERA5-Land summer VPD is extracted for the fire complexes</p> <p>3. calculation of the maximum fire size to total burned area relationship</p> <p>4. the models are calibrated and compared. In this script the figure 3 and 4 are created </p> <p>5. preparation of the future climate dataset. Again, the script for the download is provided but the user needs an API.</p> <p>6. Extraction of the CMIP6 VPD.</p> <p>7. Future climate analysis</p> <p>8. Plotting of all figures that were not done in the previous scripts</p> <p><br> # data</p> <p>climate: we provide the climate grid. all other climate data can be downloaded following the instructions within the scripts.</p> <p>complexes: we provide the fire complexes of each country. This data contains all information needed for the analysis including year, size, severity and polygon information. The complexes are based on the data from Senf & Seidl, 2021 (https://doi.org/10.1038/s41893-020-00609-y) which can be downloaded here: https://doi.org/10.5281/zenodo.7080016</p> <p>countries: we provide the shapefiles of each country and Europe that are needed for the analysis in this folder.</p> <p>ecoregions: Olson et al. terrestrial ecosystems should be downloaded from: https://www.arcgis.com/home/item.html?id=be0f9e21de7a4a61856dad78d1c79eae</p> <p>models: we provide all final models used for the analysis.</p> <p>results: we provide the results of the individual steps. This should help to reduce the runtime for the user.</p> <p> </p> <p># additional information</p> <p>R version 3.6.3 (2020-02-29)<br> Platform: x86_64-pc-linux-gnu (64-bit)<br> Running under: Ubuntu 18.04.5 LTS</p>
Herbaceous vegetation responses to experimental fire in savannas and forests depend on biome and climate
<p>Fire-vegetation feedbacks potentially maintain global savanna and forest distributions. Accordingly, vegetation in savanna and forest ecosystems should have differential responses to fire, but fire response data for herbaceous vegetation has yet to be synthesized across biomes. Here, we examined herbaceous vegetation responses to experimental fire at 30 sites spanning four continents. Across a variety of metrics, herbaceous vegetation increased in abundance where fire was applied, with larger responses to fire in wetter and in cooler and/or less seasonal systems. Compared to forests, savannas were associated with a 4.8 (±0.4) times larger difference in burned versus unburned herbaceous vegetation abundance. In particular, grass cover decreased with fire exclusion in savannas, largely via decreases in C<sub>4</sub> grass cover, whereas changes in fire frequency had a relatively weak effect on grass cover in forests. These differential responses underscore the importance of fire for maintaining the vegetation structure of savannas and forests.</p>
Attributing European forest disturbances to storm and fire
<p>This repository contains maps attributing each disturbance patch of the <a href="https://zenodo.org/record/4570157#.YFB27i337OQ">European Forest Disturbance Map</a> (version 1.1.4) to bark beetle/wind, fire or other disturbances (mostly harvest). The dataset is based on methods described in following paper, but have been updated with new reference data covering now also bark beetle disturbances: </p> <p>Senf, C. and Seidl, R. (2021) Storm and fire disturbance in Europe: Distribution and trends. <strong>Global Change Biology</strong>. <a href="https://doi.org/10.1111/gcb.15679">https://doi.org/10.1111/gcb.15679</a></p> <p>To get the year of disturbance, please see the underlaying disturbance maps (version 1.1.4.; link given above).</p> <p><strong>Map classes:</strong></p> <p>NA = no disturbance<br> 1 = bark beetle or wind disturbances (both classes had to be grouped due to technical reasons)<br> 2 = fire disturbances<br> 3 = other disturbances, mostly harvest but might include salvage logging go small-scale natural disturbances and infrequent other natural agents (e.g., defoliation, avalanches, etc.)</p> <p><strong>Reference system:</strong></p> <p>The spatial reference system is EPSG 3035 (ETRS89 / LAEA Europe).</p> <p><strong>Word of caution:</strong></p> <p>Remote sensing-based maps, while fascinating to look at, contain errors. If you intent to use the map for your research, please carefully read the discussion on limitations in the paper accompanying the dataset. There will be many instances where the attribution (or even disturbance detection) is wrong. The maps are intended to give a broad, continental-scale overview on the distribution of disturbance agents.</p>
Data from Widespread exposure to altered fire regimes under 2°C warming is projected to transform conifer forests of the Western United States
<p>This archive includes a minimal dataset needed to reproduce the analysis as well as a table (CSV) and spatial polygons (ESRI shapefile) of the resulting output from the publication:</p> <p>Hoecker, T.J., S. A. Parks, M. Krosby & S. Z. Dobrowski. 2023. Widespread exposure to altered fire regimes under 2°C warming is projected to transform conifer forests of the Western United States. <em>Communications Earth and Environment</em>.</p> <p>Publication abstract:</p> <p>Changes in wildfire frequency and severity are altering conifer forests and pose threats to biodiversity and natural climate solutions. Where and when feedbacks between vegetation and fire could mediate forest transformation are unresolved. Here, for the western U.S., we used climate analogs to measure exposure to fire-regime change; quantified the direction and spatial distribution of changes in burn severity; and intersected exposure with fire-resistance trait data. We measured exposure as multivariate dissimilarities between contemporary distributions of fire frequency, burn severity, and vegetation productivity and distributions supported by a 2 °C-warmer climate. We project exposure to fire-regime change across 65% of western US conifer forests and mean burn severity to ultimately decline across 63% because of feedbacks with forest productivity and fire frequency. We find that forests occupying disparate portions of climate space are vulnerable to projected fire-regime changes. Forests may adapt to future disturbance regimes, but trajectories remain uncertain.</p>
California subalpine forest post-fire diversity and productivity
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Multiple disturbances, multiple legacies: Fire, canopy gaps and deer jointly change the forest seed bank
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Data from: Thinning and prescribed burning increase shade-tolerant conifer regeneration in a fire excluded mixed-conifer forest
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The western United States large forest-fire stochastic simulator (WULFFSS) 1.0: A monthly gridded forest-fire model using interpretable statistics
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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)
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DANDI Archive for NWB datasets
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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.