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29 results for “fire followers”
Fig. 1 in Coleoptera Monitoring Following Prescribed Fire Disturbance Yields 43 New State Species Records for Kentucky, USA
Fig. 1. Traps used for specimen collection in Berea College Forest, Madison County, Kentucky, USA. Lindgren flight intercept traps placed in: A) Burned forest, B) Unburned forest. C) Pitfall trap placed in burned forest, D) Burn Unit 1 photographed 48 h post-burn.
Conifer forest plant species composition across nine years following a high severity fire
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Data from: Ants, fire and bark traits affect how African savanna trees recover following damage
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Biodiversity and Forest structure one year following the 2016 Fort McMurray (Alberta) fire event
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Data from: Pyrodiversity promotes avian diversity over the decade following forest fire
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Data from: Multi-trophic occupancy modeling connects temporal dynamics of woodpeckers and beetle sign following fire
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BOREAS Follow-On DSP-09 Saskatchewan Raster Forest Fire Chronology, 1945-1996
This data set contains a pair of raster images and a spreadsheet chronicling the most recent fire history of Saskatchewan from 1945 to 1996. This data set was developed from a series of ARC/INFO export files of annual fire data that were compiled and provided by the Saskatchewan Environment and Resource Management (SERM) Wildlife Branch.
Data from: Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR
There is a public perception that large high severity wildfires decrease biodiversity and increase fire hazard by homogenising vegetation composition and increasing the cover of mid-story vegetation. But a growing literature suggests that vegetation responses are nuanced. LiDAR technology provides a promising remote sensing tool to test hypotheses about post-fire vegetation regrowth because vegetation cover can be quantified within different height strata at fine-scales over large areas. We assess the usefulness of airborne LiDAR data for measuring post-fire mid-story vegetation regrowth over a range of spatial resolutions (10x10m, 30x30m, 50x50m, 100x100m cell size) and investigate the effect of fire severity on regrowth amount and spatial pattern following a mixed severity wildfire in Warrumbungle National Park, Australia. We predicted that recovery would be more vigorous in areas of high fire severity, because park managers observed dense post-fire regrowth in these areas. Moderate to strong positive associations were observed between LiDAR and field surveys of mid-story vegetation cover between 0.5–3m. Thus our LiDAR survey was an apt representation of on-ground vegetation cover. LiDAR-derived mid-story vegetation cover was 22–40% higher in areas of low and moderate than high fire severity. Linear mixed-effects models showed that fire severity was among the strongest biophysical predictors of mid-story vegetation cover irrespective of spatial resolution. However much of the variance associated with these models was unexplained, presumably because soil seedbanks varied at finer-scales than our LiDAR maps. Dense patches of mid-story vegetation regrowth were small (median size 0.01ha) and evenly distributed between areas of low, moderate and high fire severity, demonstrating that high severity fires do not homogenise vegetation cover. Our results are relevant for ecosystem conservation and fire management because they: indicate that native vegetation are responsive and resilient to high severity fire, and show the usefulness of remote sensing tools such as LiDAR to monitor post-fire vegetation recovery over large area in situ.
Data from: Mapping and exploring variation in post-fire vegetation recovery following mixed severity wildfire using airborne LiDAR
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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.