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1,989 results for “Fires”
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>
Effect of Long-Range Transported Fire Aerosols on Cloud Condensation Nuclei Concentrations and Cloud Properties at High Latitudes
<p>The tab-delimited files contain Aerosol chemical composition data, size distribution data measured by AMS, ACSM, MAAP, and SMPS, and DMPS at the SMEAR IV station (Puijo) and the Zepplein observatory (Zep) . Please read the file named 'Information.txt' for more details about the files.</p>
Small-scale fires interact with herbivore feedbacks to create persistent grazing lawn environments
<p>Fire-herbivory feedbacks strongly influence the formation of grazing lawns in savanna ecosystems. Preliminary findings suggest that small-scale (< 25 ha) fires can engineer grazing lawns by concentrating herbivores on the post-burn green flush; however, the persistence of such grazing lawns over the longer term and without repeated fire is unknown.</p> <p>We used high-resolution Light Detection and Ranging (LiDAR) to investigate the long-term effects of fire manipulation on short grass structure (height, cover, volume, and spatial continuity) and grazing lawn establishment in Kruger National Park, South Africa. We analysed the effects of fire exclusion and experimental burns applied over a 7-year period (2013-2019) followed by a one-year cessation of burning at varying spatial scales during the early and late dry seasons.</p> <p>Fires contributed a fourfold increase in short grass cover, regardless of fire season or size. The distribution of grass height differed significantly between fire-induced grazing lawns and recently unburnt parts of the landscape where controlled fires were excluded over the experimental period. The volume (corresponding to bulk density) of short grass on the landscape responded strongly to fires, with grass volume <20 cm in height increasing with both early and late dry season fires.</p> <p>Early dry season fires caused larger and more homogeneous short grass patches. Furthermore, early dry season fires were more influential in increasing the cover of the shortest grass height class (1-5 cm).</p> <p><em>Synthesis and applications</em>. Our results demonstrate that fire-induced grazing lawns can persist over the longer-term, even when fires are no longer applied, leading to the creation of vertical and horizontal heterogeneity in the grass layer. Small-scale fires, therefore, represent a feasible management approach to expanding grazing lawn extent, potentially benefiting grazer coexistence and diversity.</p>
Rare but not lost: Endemic mountain lizard occupancy following mega-fire and grazing disturbances
<p>Wildfires and grazing by invasive herbivores can influence habitat suitability for ground‐dwelling fauna, such as reptiles. Australia has a large and diverse reptile fauna, with the Australian Alps bioregion in the southeast of the continent supporting a disproportionately high number of threatened species. In this bioregion, many species are threatened by fire, habitat loss or modification, and invasive species. The range of one such threatened endemic lizard, <em>Cyclodomorphus praealtus</em> (family Scincidae), was impacted by the 2019–20 mega-fires and is also subject to widespread grazing by invasive species. We investigated the relationship between <em>C. praealtus</em> site occupancy and fire and grazing. We completed 2045 surveys across 120 sites over 4 years, detecting the species at 43% of sites and increasing the species' known geographic range. Using single-season detection occupancy models, we found <em>C. praealtus</em> occupancy was not associated with elevation, vegetation height, or whether the site was burnt, but was positively associated with grazing activity. Our results indicate that <em>C. praealtus</em> can persist following a single fire in some cases and that habitats with high occupancy probabilities are subject to high grazing pressure. However, our results do not rule out more nuanced impacts associated with these disturbances, which affect a large proportion of <em>C. praealtus</em>' habitat. Our cumulative detection probability calculations revealed that considerable survey effort is often required to determine <em>C. praealtus</em> site occupancy. We therefore recommend that impact assessments assume species presence within areas of suitable habitat within the species' range. Our study improves our understanding of disturbance impacts on <em>C. praealtus</em>' occupancy while demonstrating the need for sufficiently resourced impact assessments for cryptic and threatened species.</p>
Neotropical mammal responses to forest fires in Serra do Amolar, Brazil
<p>The increasing frequency and severity of human-caused fires likely have deleterious effects on species distribution and persistence. In 2020, megafires in the Brazilian Pantanal burned 43% of the biome's unburned area and resulted in mass mortality of wildlife. We investigated changes in habitat use or occupancy for an assemblage of eight mammal species in Serra do Amolar, Brazil, following the 2020 fires using a pre- and post-fire camera trap dataset. Additionally, we estimated density for two naturally marked species, jaguars <em>Panthera onca</em> and ocelots <em>Leopardus pardalis</em>. Of the eight species, six (ocelots, collared peccaries <em>Dicotyles tajacu</em>, giant armadillos <em>Priodontes maximus</em>, Azara's agouti <em>Dasyprocta azarae</em>, red brocket deer <em>Mazama americana, </em>and tapirs <em>Tapirus terrestris</em>) had declining occupancy following fires, and one had stable habitat use (pumas <em>Puma concolor</em>). Giant armadillo experienced the most precipitous decline in occupancy from 0.431 ± 0.171 to 0.077 ± 0.044 after the fires. Jaguars were the only species with increasing habitat use, from 0.393 ± 0.127 to 0.753 ± 0.085. Jaguar density remained stable across years (2.8 ± 1.3, 3.7 ± 1.3, 2.6 ± 0.85 / 100km<sup>2</sup>), while ocelot density increased from 13.9 ± 3.2 to 16.1 ± 5.2 / 100km<sup>2</sup>. However, the low number of both jaguars and ocelots recaptured after the fire period suggests that immigration may have sustained the population. Our results indicate that the megafires will have significant consequences for species occupancy and fitness in fire affected areas. The scale of megafires may inhibit successful recolonization, thus wider studies are needed to investigate population trends.</p>
Real-Time Dataset of Fire Sensor Measurements Collected During the Resisto Project
<p>This dataset contains real-time environmental measurements from fire detection sensors across multiple locations. These sensors has been deployed on diferent locations, principally on the Doñana National Park. </p>
Assessment of Vegetation Indices for Mapping Burned Areas Using a Deep Learning Method and a Comprehensive Forest Fire Dataset from Landsat Collection.
<p>This repository contains a dataset focused on the delineation of burned areas (BA) in forests, created from Landsat satellite images covering the period from 1985 to 2021. The study also explores the integration of vegetation spectral indices (VIs) within a Convolutional Neural Network (CNN) detector, utilizing U-Net architecture. Along with the dataset of historical BA in Galicia from 1985, we provide the necessary images and code to facilitate the analysis and application of these methods. This repository aims to serve as a valuable resource for researchers and professionals in the field of forest fire management and remote sensing, highlighting the potential advantages of using VIs for improved burned area detection and analysis.</p> <p>DOI for published article: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.asr.2024.12.001" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.asr.2024.12.001</span></span></a></p>
Forest Fire Clustering: A Novel Tool for Identifying Star Members of Clusters
<p>In Tables 4 and 5, the <strong>Cluster</strong> column represents the name of the cluster. </p> <p><strong>Table 4</strong>: The columns <strong>ra</strong>, <strong>dec</strong>, <strong>pmra</strong>, <strong>pmdec</strong>, and <strong>parallax</strong> correspond to the median values for the cluster's position, parallax, and proper motions, respectively. The <strong>[Fe/H]</strong> and <strong>[Fe/H]_err</strong> columns indicate the cluster's [Fe/H] and its associated error. The <strong>logt</strong> and <strong>log_t_err</strong> columns represent the logarithmic age and its error, while the <strong>m-M</strong> and <strong>m-M_err</strong> columns denote the distance modulus and its error. Additionally, the <strong>E(BP-RP)</strong> and <strong>E(BP-RP)_err</strong> columns specify the cluster's reddening and its error, and the <strong>A_V</strong> and <strong>A_V_err</strong> columns represent the extinction and its error.</p> <p><strong>Table 5</strong>: The <strong>rc_pc</strong> and <strong>e_rc_pc</strong> columns indicate the core radius and its error, while the <strong>rt_pc</strong> and <strong>e_rt_pc</strong> columns represent the tidal radius and its error. The <strong>rh_pc</strong> column provides the radius containing half of the total number of stars in the cluster, and the <strong>rhm_pc</strong> column gives the half-mass radius. The <strong>R_J</strong> and <strong>R_J_err</strong> columns represent the Jacobi radius and its error. The <strong>mass</strong> and <strong>mass_err</strong> columns show the total mass of the cluster and its error, and the <strong>fb</strong> column denotes the binary fraction of the cluster. Finally, the <strong>trlx</strong> and <strong>trlx_err</strong> columns represent the relaxation time and its error. The units of<strong> trlx</strong> and <strong>trlx_err</strong> are Myr</p> <p>Note: NULL values for <strong>rc_pc, e_rc_pc, rt_pc, </strong>and <strong>e_rt_pc </strong>indicate the inapplicability of the RDP method. For <strong>Bootes I, NGC 104, NGC 3201, NGC 6121, NGC 6544, </strong>and <strong>NGC 6656</strong>, the parameters listed as “N/A”—including <strong>rhm, rJ, rJ_err, mass, mass_err, fb, trlx_Myr,</strong> and<strong> trlx_err</strong>—cannot be determined using our methods due to their faint magnitudes. This limitation arises because Gaia’s observational capacity extends only to 21 mag.</p> <p> </p>
Tracking and classifying Amazon fire events in near-real time
<p><strong>Summary</strong></p> <p>Time-series (2018-2024) of the Amazon dashboard, including minor updates to the methods.</p> <p>The Amazon dashboard data product tracks individual fire events across most of South America (10N - 25S, 85W - 30W) in near-real time. The model classifies fires into four key fire types (deforestation, forest, small clearing and agricultural, and savanna and grassland fires) and provides estimates of individual fire carbon emissions. Methods are described in Andela et al. (2022). Near-real time estimates are provided at https://amzfire.servirglobal.net/ and here we archive historic time-series.</p> <p><strong>Methods</strong></p> <p>The data archived here (v1.1) include several small updates.</p> <p>Two updates relate to the use of VIIRS active fire detections. First, VIIRS active fire detections have been updated from collection 1 to collection 2. Second, any full day of missing data from either the VIIRS instrument onboard NOAA-20 or Suomi NPP is now replaced by data of the other instrument. This "gap" filling helps reduce the impact of periods with instrument outage, like those of Suomi NPP VIIRS during the 2024 burning season. </p> <p>The other two updates relate to the emissions calculations. First, to convert dry matter burned to carbon emissions, we have introduced fire type specific emissions factors instead of the earlier assumption of 50% carbon content for all fire types. Second, as part of the Sense4Fire project (https://sense4fire.eu/), we provide daily gridded emissions estimates of Dry Matter (DM), C, CO2, CO, and NOx at 0.1 degree resolution. We used emissions factors provided by Andrea (2019) for savanna and grassland fires as well as small clearing and agricultural fires while for forest and deforestation fires we reviewed the literature to select the most relevant emissions factors (Table 1). </p> <p>Table 1: Emissions factors (gram species per kg dry matter burned) used to calculate C, CO2, CO, and NOx emissions. </p> <table> <tbody> <tr> <td>Fire type / trace gas emissions</td> <td>C</td> <td>CO2</td> <td>CO</td> <td>NOx</td> </tr> <tr> <td>Savanna and grassland</td> <td>480</td> <td>1656</td> <td>69.2</td> <td>2.5</td> </tr> <tr> <td>Small clearing and agricultural</td> <td>430</td> <td>1431</td> <td>76.2</td> <td>2.4</td> </tr> <tr> <td>Forest</td> <td>480</td> <td>1561</td> <td>104.0</td> <td>2.0</td> </tr> <tr> <td>Deforestation</td> <td>490</td> <td>1641</td> <td>95.5</td> <td>1.7</td> </tr> </tbody> </table> <p> </p> <p><strong>Dataset description<br></strong></p> <p>For full detail, please see Andela et al. (2022). The tables below (Tables 2 - 4) describe the content of the fire event (polygon) and active fire detections (point) shapefiles as well as the gridded emissions product. The active fire detections and associated estimates of dry matter burned can be combined with emissions factors (Table 1) to derive daily trace gas emissions time series for species and areas of interest.</p> <p>Table 2: Explanation of fire event shapefile attribute table.</p> <table> <tbody> <tr> <td>Attribute class</td> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>Fire type classification</td> <td>Fire type</td> <td>(1) savanna and grassland, (2) small clearing and<br>agriculture, (3) forest, and (4) deforestation fires</td> </tr> <tr> <td> </td> <td>Confidence</td> <td>(1) low, (2) moderate, and (3) high</td> </tr> <tr> <td>Fire Atlas</td> <td>Size</td> <td>Fire size in km2</td> </tr> <tr> <td> </td> <td>Start day</td> <td>Day of new fire start as day of year (1-366)</td> </tr> <tr> <td> </td> <td>Duration</td> <td>Fire duration in days</td> </tr> <tr> <td> </td> <td>C Emissions</td> <td>Fire carbon emissions (ton C)</td> </tr> <tr> <td>Fire characterization</td> <td>Tree cover</td> <td>Average tree cover fraction within perimeter (%)</td> </tr> <tr> <td> </td> <td>Biomass</td> <td>Average biomass within fire perimeter (ton ha-1)</td> </tr> <tr> <td> </td> <td>Deforestation </td> <td>Fraction of 550 m grid cells with historic<br>deforestation (five years prior to fire) within fire perimeter (%)</td> </tr> <tr> <td> </td> <td>FRP</td> <td>Average fire radiative power (FRP) for all fire<br>detections within fire perimeter (MW)</td> </tr> <tr> <td> </td> <td>Persistence</td> <td>Average fire persistence across 550 m grid cells<br>within fire perimeter (days)</td> </tr> <tr> <td> </td> <td>Progression</td> <td>Average fire progression fraction across 550 m<br>grid cells within perimeter (%)</td> </tr> <tr> <td> </td> <td>Daytime</td> <td>Fraction of 1:30 pm detections (%) for all fire<br>detections within fire perimeter</td> </tr> <tr> <td> </td> <td>Detections</td> <td>Total active fire detections within fire perimeter</td> </tr> </tbody> </table> <p> </p> <p>Table 3: Explanation of active fire detection shapefile attribute table.</p> <table> <tbody> <tr> <td>Attribute class</td> <td>Attribute</td> <td>Explanation / units</td> </tr> <tr> <td>VIIRS active fire detections</td> <td>FRP</td> <td>Fire radiative power (MW)</td> </tr> <tr> <td> </td> <td>DOY</td> <td>Day of year (1-366)</td> </tr> <tr> <td>Fire type classification</td> <td>Fire type</td> <td>(1) savanna and grassland, (2) small clearing and agriculture, (3) forest, and (4) deforestation fires</td> </tr> <tr> <td> </td> <td>Confidence</td> <td>(1) low, (2) moderate, and (3) high</td> </tr> <tr> <td>Emissions</td> <td>C Emissions</td> <td>Fire carbon emissions (ton C) associated with each active fire detection</td> </tr> <tr> <td> </td> <td>DM Emissions</td> <td>Dry matter burned (ton) associated with each active fire detection</td> </tr> </tbody> </table> <p> </p> <p>Table 4: Content of daily gridded (0.1 degree resolution) emissions netcdf files. The daily emissions product provides emissions estimates of dry matter, C, CO2, CO, and NOx. For DM and CO partitioned emissions are also provided by fire type, for other species these can be derived by multiplying the dry matter burned (DM) estimates with trace gas specific emissions factors (Table 1). Values of each grid cell can be multiplied by the number of seconds per day and grid cell area to calculate total emissions (convert "kg species m-2 s-1" to "kg species day-1 per grid cell").</p> <table> <tbody> <tr> <td>/ancill</td> <td>grid_cell_area</td> </tr> <tr> <td>/partitioned_DM_emissions</td> <td>Deforestation emissions</td> </tr> <tr> <td> </td> <td>Forest emissions</td> </tr> <tr> <td> </td> <td>Savanna and grassland emissions</td> </tr> <tr> <td> </td> <td>Small clearing and agricultural emissions</td> </tr> <tr> <td>/partitioned_CO_emissions</td> <td>Deforestation emissions</td> </tr> <tr> <td> </td> <td>Forest emissions</td> </tr> <tr> <td> </td> <td>Savanna and grassland emissions</td> </tr> <tr> <td> </td> <td>Small clearing and agricultural emissions</td> </tr> <tr> <td>/total_emissions</td> <td>DM emissions</td> </tr> <tr> <td> </td> <td>C emissions</td> </tr> <tr> <td> </td> <td>CO2 emissions</td> </tr> <tr> <td> </td> <td>CO emissions</td> </tr> <tr> <td> </td> <td>NOx emissions</td> </tr> </tbody> </table> <p> </p> <p><strong>Results</strong></p> <p>Despite the various small improvements to the dataset, the data are largely consistent with the original dataset published for 2019-2020 (Table 5). </p> <p>Table 5: Comparison of model versions (original from Andela et al., 2022 and v1.1 published here) for April-December 2019 (equator-25S, 85W - 30W). Note that the current version (v1.1) is complete for 2019, but the original dataset had missing data due to incomplete active fire detections from NOAA-20 VIIRS at that time.</p> <table> <tbody> <tr> <td>Dataset</td> <td>Fire type</td> <td>Fire detections (x1,000)</td> <td>Mean fire radiative power (MW)</td> <td>Number of events (x1,000)</td> <td>Emissions (Tg C)</td> </tr> <tr> <td>Original</td> <td>Deforestation</td> <td>756.65</td> <td>15.15</td> <td>24.24</td> <td>99.18</td> </tr> <tr> <td>Original</td> <td>Forest</td> <td>637.58</td> <td>12.73</td> <td>5.28</td> <td>85.46</td> </tr> <tr> <td>Original</td> <td>Small clearing and agricultural</td> <td>348.49</td> <td>10.91</td> <td>154.68</td> <td>10.55</td> </tr> <tr> <td>Original</td> <td>Savanna and grassland</td> <td>1935.06</td> <td>12.11</td> <td>296.42</td> <td>71.75</td> </tr> <tr> <td>v1.1</td> <td>Deforestation</td> <td>742.64</td> <td>14.81</td> <td>24.02</td> <td>97.18</td> </tr> <tr> <td>v1.1</td> <td>Forest</td> <td>626.92</td> <td>12.06</td> <td>5.16</td> <td>77.84</td> </tr> <tr> <td>v1.1</td> <td>Small clearing and agricultural</td> <td>350.7</td> <td>10.89</td> <td>155.56</td> <td>9.27</td> </tr> <tr> <td>v1.1</td> <td>Savanna and grassland</td> <td>1877.16</td> <td>11.89</td> <td>299.17</td> <td>70.55</td> </tr> </tbody> </table> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The Sense4Fire project is funded by ESA under ESA Contract Number: 4000134840/21/I-NB. </p> <p><strong>References</strong></p> <p>Andela, N., Morton, D.C., Schroeder, W., Chen, Y., Brando, P.M. and Randerson, J.T., 2022. Tracking and classifying Amazon fire events in near real time. Science advances, 8, eabd2713. https://doi.org/10.1126/sciadv.abd2713.</p> <p>Andreae, M.O., 2019. Emission of trace gases and aerosols from biomass burning–an updated assessment. Atmospheric Chemistry and Physics, 19, 8523-8546. https://doi.org/10.5194/acp-19-8523-2019.</p>
Repeated measure plant community data after fire in boreal forest, Taiga Shield, Northwest Territories, Canada, 1998-2018
<p>10 transects were established in the years following fire in boreal forest stands on the Taiga Shield, Northwest Territories, Canada in 1998-1999. These were remeasured annually. Six transects were returned to on 2018 for another measurement. At each measurement, we recorded ground covers, species identities, and counted tree stems (seedlings).</p>
Fire danger indices historical data from the Copernicus Emergency Management Service
<p>June, July August consolidated data.</p> <p>Generated using Copernicus Climate Change Service information 2021.</p>
Supporting data for: Post-fire early successional vegetation buffers surface microclimate and increases survival of planted conifer seedlings in the southwestern United States
<p>Climate change and fire-exclusion have increased the flammability of western US forests, leading to forest cover loss when wildfires occur under severe weather conditions. Increasingly large high-severity burn patches are a limitation to natural regeneration because of dispersal distance, increasing the chance that these areas are converted to non-forest. Post-fire planting can overcome dispersal limitations, yet warmer and drier post-fire conditions can still limit survival. Early successional vegetation can alter surface microclimate; however, it is unclear whether this is enough to increase planted seedling survival in southwestern US forests. Here we examined how two shrub species of different canopy density would affect survival rates of planted tree seedlings following a high-severity fire in northern New Mexico. We expected that shrubs with a higher density canopy (Gambel oak) would have a greater effect on buffering below-shrub climate than shrubs with a lower density canopy (New Mexico locust) and seedlings planted under Gambel oak would have higher survival rates. We found that seedlings planted under Gambel oak had survival rates approximately 10% to 35% greater than those planted under New Mexico locust. The higher light availability beneath New Mexico locust corresponded to higher temperatures, lower humidity, and higher VPD, which impacted the mortality of planted tree seedlings. These results suggest that by waiting for post-fire shrub establishment, shrubs can be leveraged to buffer microclimate and increase post-fire planting success in the southwestern US.</p>
Limited increases in savanna carbon stocks over decades of fire suppression
<p>Savannas cover a fifth of the land surface and contribute a third of terrestrial net primary production, accounting for three quarters of global area burned and over half of global fire-driven carbon emissions. Fire suppression and afforestation have been proposed as tools to increase carbon sequestration in these ecosystems. A robust quantification of whole-ecosystem carbon storage in savannas is lacking, however, especially under altered fire regimes. Here, we provide the first direct estimates of whole-ecosystem carbon response to over 60 years of fire exclusion in a mesic African savanna. We found that fire suppression increased whole-ecosystem carbon storage by only 35.4 ± 12% (mean ± standard error), even though tree cover increased by 78.9 ± 29.3%, corresponding to total gains of 23.0 ± 6.1 Mg C ha<sup>-1</sup> at an average ~0.35 ± 0.09 Mg C ha<sup>-1 </sup>yr<sup>-1</sup>, more than an order of magnitude lower than previously assumed. Frequently burned savannas had substantial belowground carbon, especially in biomass and deep soils. These belowground reservoirs are not fully considered in afforestation or fire suppression schemes but may mean that the decadal sequestration potential of savannas is negligible, especially weighed against concomitant losses of biodiversity and function.</p>
Fire, grazers and browsers interact with grass competition to determine tree establishment in an African savanna
<p>In savanna ecosystems, fire and herbivory alter the competitive relationship between trees and grasses. Mechanistically, grazing herbivores favor trees by removing grass, which reduces tree-grass competition and limits fire. Conversely, browsing herbivores consume trees and limit their recovery from fire. Herbivore feeding decisions are in turn shaped by risk-resource trade-offs that potentially determine the spatial patterns of herbivory. Identifying the dominant mechanistic pathways by which fire and herbivores control tree cover remains challenging, but is essential for understanding savanna dynamics. We used an experiment in the Serengeti ecosystem and a simple simulation driven by experimental results to address two main aims: (1) determine the importance of direct and indirect effects of grass, fire and herbivory on seedling establishment; and (2) establish whether predators determine the spatial pattern of successful seedling establishment via effects on mesoherbivore distribution. We transplanted tree seedlings into plots with a factorial combination of grass and herbivores (present/absent) across a lion kill-risk gradient in the Serengeti, burning half of the plots near the end of the experiment. Ungrazed grass limited tree seedling survival directly via competition, indirectly via fire, and by slowing seedling growth, which drove higher seedling mortality during fires. These effects restricted seedling establishment to below 18% and, in conjunction with browsing, resulted in seedling establishment dropping below 5%. In the absence of browsing and fire, grazing drove a 7.5-fold increase in seedling establishment. Lion predation risk had no observable impact on herbivore effects on seedling establishment. The severe negative effects of grass on seedling mortality suggests that regional patterns of tree cover and fire may overestimate the role of fire in limiting tree cover, with regular fires representing a proxy for the competitive effects of grass.</p>
Short-interval fires increasing in the Alaskan boreal forest as fire self-regulation decays across forest types: Code and data
<p>Code and data to reproduce results in the associated paper. Additional downloads of climate data and various R packages will be required for some analyses.</p> <p>Abstract: Climate drivers are increasingly creating conditions conducive to higher frequency fires. In the coniferous boreal forest, the world’s largest terrestrial biome, fires are historically common but relatively infrequent. Post-fire, regenerating forests are generally resistant to burning (strong fire self-regulation), favoring millennial coniferous resilience. However, short intervals between fires are associated with rapid, threshold-like losses of resilience and changes to broadleaf or shrub communities, impacting carbon content, habitat, and other ecosystem services.</p> <p>Fires burning the same location 2+ times comprise approximately 4% of all Alaskan boreal fire events since 1984, and the fraction of short-interval events (<20 years between fires) is increasing with time. While there is strong resistance to burning for the first decade after a fire, from 10-20 years post-fire resistance appears to decline. Reburning is biased towards coniferous forests and in areas with seasonally variable precipitation, and that proportion appears to be increasing with time, suggesting continued forest shifts as changing climatic drivers overwhelm the resistance of early postfire landscapes to reburning. As area burned in large fire years of ~15 years ago begin to mature, there is potential for more widespread shifts, which should be evaluated closely to understand finer grained patterns within this regional trend.</p>
Data from: Tree growth responses to extreme drought after mechanical thinning and prescribed fire in a Sierra Nevada mixed-conifer forest, USA
<p class="MsoNormal">An estimated 128 M trees died during the 2012-2016 California drought, largely in the southern Sierra Nevada Range. Prescribed burning and mechanical thinning are widely used to reduce fuels and restore ecosystem properties, but it is unclear if these treatments improve tree growth and vigor during extreme drought. This study examined tree growth responses after thinning, prescribed burning, and extreme drought at the Teakettle Experimental Forest, a historically frequent fire mixed-conifer forest in the southern Sierra Nevada of California, USA. Mechanical thinning (no thin, understory thin, and overstory thin) and prescribed burning (unburned, fall burning) were implemented in 2000-2001. Using annual growth data from increment cores, over 10,000 mapped and measured trees, and lidar-derived metrics of solar radiation and topographic wetness, we had two primary questions. First, what were the growth responses to thinning and prescribed burning treatments, and did these responses persist during the 2012-2016 drought? Second, what tree-level attributes and environmental conditions influenced growth responses to treatments and drought?</p> <p class="MsoNormal">Thinning increased residual tree growth and that response persisted through extreme drought 10 -15 years after treatments. Growth responses were higher in overstory versus understory thinning, with differences between thinning types more pronounced during drought. Species-specific growth responses were strongest with overstory thinning, with sugar pine (Pinus lambertiana) and incense-cedar (Calocedrus decurrens) having higher growth responses compared to white fir (Abies concolor) and Jeffery pine (Pinus jeffreyi). For individual trees, factors associated with higher growth responses were declining pretreatment growth trend, smaller tree size, and post-treatment low neighborhood basal area. Growth responses were initially not influenced by topography, but topographic wetness became important during extreme drought. Mechanical thinning resulted in durable increases in residual tree growth rates during extreme drought over a decade after thinning occurred, indicating treatment longevity in mitigating drought stress. In contrast, tree growth did not improve after prescribed burning, likely due to fire effects that reduced surface fuels, but had little effect on reducing tree density. Thinning treatments promoted durable growth responses, but focusing on stand-level metrics may ignore important tree-level attributes such as localized competition and topography associated with higher water availability. Mechanical thinning was effective at improving growth in trees that had been experiencing declining growth trends, but was less effective in improving growth responses in large old higher ecological importance.</p>
2021_Fires_weather_station_data
<p>Data from the closest weather station to each fire. The file is an Excel file. The table fields are fire name, weather station name, day, hour, average temperature (°C), maximum temperature(°C), minimum temperature (°C), average relative humidity (%), precipitation (mm), wind speed (10 m, km/h), wind direction (10 m, degrees), wind gusts (10 m, km/h), pressure (hPa), radiation (W/m).</p> <p>Source: Meteo.cat, Servei Meteorològic de Catalunya</p>
2021_Fires_Isochrones
<p>Isochrones for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca and Sierra Bermeja fires, in shapefile format. Each file has associated an attribute table identifying the hour (in UTC), the affected area (ha), the rate of spread (km/h), and direction (degrees). Source: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p>
Fires_Behavior_resume
<p>Fire behavior resume for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca, Llançà, Alfarràs and Sierra Bermeja fires (Spain). The differences in the data shown respond to the possibility of launching sondes, recreating isochrones, and observing the plume column during each fire.</p> <p>In those cases where the information was obtained through these three ways, the variables available are: column type, ABL and LCL height (m), sonde ID, rate of spread (km/h), ROS observed / ROS expected ratio, fireline intensity expected and observed (kW/m), and affected area (ha).</p>
Drivers of extreme wildfire years in the 1965–2019 fire regime of the Tłı̨chǫ First Nation territory, Canada
<p>Datasets, metadata and Rscript used to describe 1965-2019 wildfire regime and extreme wildfire years in central NWT.</p> <p> </p>
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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.