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67 results for “Fire management”

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dryad24/100

Data from: Modelling variability in the fire-response of an endangered bird to improve fire-management

Conservation managers regularly burn vegetation to regenerate habitat for fire-dependent species. When determining the time-since-fire at which to burn, managers model change in a species' occurrence over time, post-fire (fire-response curve) and identify the time-since-fire associated with decline in occurrence. However, where species exhibit variability in their fire-response across space, using a single fire-response curve to determine the timing of burns may lead to burning habitat at an inappropriate time-since-fire. We tested if elevation, local topography, soil properties, vegetation type or evapotranspiration affect the fire-response of the endangered mallee emu-wren Stipiturus mallee and its hummock-grass habitat Triodia scariosa in south-eastern Australia (n= 217). Previous work on the mallee emu-wren found a unimodal fire-response with decline in occurrence at ~30-50 years-since-fire and a time-window of occurrence of ~30 years. We found that time-since-fire and elevation interact to affect the mallee emu-wren fire-response. At high elevations (55-98 m), mallee emu-wrens declined in occurrence at ~50 years-since-fire, with a time-window of occurrence of 20-40 years. However, at low elevations (28-55 m), mallee emu-wrens showed no decline in occurrence with increasing time-since-fire with a time-window of occurrence of up to 107 years. Extent cover of Tall T. scariosa showed similar patterns to the mallee emu-wren, indicating that vegetation structure is a likely driver of variability in the mallee emu-wren fire-response. We speculate that the effect of low elevation is mediated by increased soil nutrient and water availability for key plants. We used our findings to map the appropriate time-since-fire at which to burn to regenerate habitat for the mallee emu-wren across the study-region. We recommend no burning for regeneration across one-third of potential habitat, because the mallee emu-wren showed no decline in occurrence in these areas. We recommend managers model variability in species' fire-responses across space to improve the timing of burns for regeneration.

opencc-zeroJul 2020View details →
dryad24/100

Data from: Survey design for precise fire management conservation targets

Common goals of ecological fire management are to sustain biodiversity and minimize extinction risk. A novel approach to achieving these goals determines the relative proportions of vegetation growth stages (equivalent to successional stages, which are categorical representations of time since fire) that maximize a biodiversity index. The method combines data describing species abundances in each growth stage with numerical optimization to define an optimal growth-stage structure which provides a conservation-based operational target for managers. However, conservation targets derived from growth-stage optimization are likely to depend critically on choices regarding input data. There is growing interest in use of growth-stage optimization as a basis for fire management, thus understanding of how input data influence the outputs is crucial. Simulated datasets provide a flexible platform for systematically varying aspects of survey design and species inclusions. We used artificial data with known properties, and a case-study dataset from southeastern Australia, to examine the influence of (i) survey design (total number of sites, and their distribution among growth stages) and (ii) species inclusions (total number of species and their level of specialization) on the precision of conservation targets. Based on our findings, we recommend that survey designs for precise estimates would ideally involve at least 80 sites, and include at least 80 species. Greater numbers of sites and species will yield increasingly reliable results, but fewer might be sufficient in some circumstances. An even distribution of sites among growth stages was less important than the total number of sites, and omission of species is unlikely to have a major influence on results as long as several species specialize on each growth stage. We highlight the importance of examining the responses of individual species to growth stage before feeding survey data into the growth-stage optimization black box, and advocate use of a resampling procedure to determine the precision of results. Collectively, our findings form a reproducible guide to designing ecological surveys that yield precise conservation targets through growth-stage optimization, and ultimately help sustain biodiversity in fire-prone systems.

opencc-zeroDec 2016View details →
ClinicalTrials.gov24/100

Evaluation of the Effectiveness of the Training Given to Mothers With 0-5 Age Group Children for Fire Management

ClinicalTrials.gov study NCT06043479. IPD Sharing: NO. Countries: 1. Publications: 0.

closedIPD-NOFeb 2026View details →
ClinicalTrials.gov24/100

Stress Management Programs in Fire-fighters

ClinicalTrials.gov study NCT02137941. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →
ClinicalTrials.gov24/100

Fire Fighter Fatigue Management Program: Operation Fight Fatigue

ClinicalTrials.gov study NCT01672502. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →
dryad24/100

Data from: Survey design for precise fire management conservation targets

Open the record for dataset details and reuse information.

publicAug 2017View details →
dryad24/100

Data from: Modelling variability in the fire-response of an endangered bird to improve fire-management

Open the record for dataset details and reuse information.

publicJul 2020View details →

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record