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67 results for “Fire management”
Data from: Irregular forest structures originating after fire: an opportunity to promote alternatives to even-aged management in boreal forests
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Data from: Severe fire weather and intensive forest management increase fire severity in a multi-ownership landscape
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Data from: Fire management in the Brazilian Savanna: first steps and the way forward
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Data from: Phylogenetic measures of plant communities show long-term change and impacts of fire management in tallgrass prairie remnants
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Data from: Age‐dependent habitat relationships of a burned forest specialist emphasise the role of pyrodiversity in fire management
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Data from: Fire and mechanical forest management treatments support different portions of the bird community in fire-suppressed forests
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Data from: Chaparral bird community responses to prescribed fire and shrub removal in three management seasons
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Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa’s rangelands and fill protected area funding gaps
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Estimating social-ecological resilience: fire management futures in the Sonoran Desert
Resilience quantifies the ability of a system to remain in or return to its current state following disturbance. Due to inconsistent terminology and usage of resilience frameworks, quantitative resilience studies are challenging, and resilience is often treated as an abstract concept rather than a measurable system characteristic. We used a novel, spatially-explicit stakeholder engagement process to quantify social-ecological resilience to fire, in light of modeled social-ecological fire risk, across the non-fire-adapted Sonoran Desert Ecosystem in Arizona, USA. Depending on its severity and the characteristics of the ecosystem, fire as a disturbance has the potential to drive ecological state change. As a result, fire regime change is of increasing concern as global change and management legacies alter the distribution and flammability of fuels. Because management and use decisions impact resources and ecological processes, social and ecological factors must be evaluated together to predict resilience to fire. We found highest fire risk in the central and eastern portions of the study area, where flammable fuels occur with greater density and frequency and managers reported fewer management resources than in other locations. We found lowest fire resilience in the southeastern portion of the study area, where combined ecological and social factors, including abundant fuels, few management resources, and little evidence of past institutional adaptability, indicated that sites were least likely to retain their current characteristics and permit achievement of current management objectives. Analyzing ecological and social characteristics together permits regional managers to predict the effects of changing fire regimes across large, multi-jurisdictional landscapes and to consider where to direct resources. This study brought social and ecological factors together into a common spatial framework to produce vulnerability maps; our methods may inform researchers and managers in other systems facing novel disturbance and spatially-variable resilience.
Data from: Interactive effects of pasture management intensity, release from grazing and prescribed fire on forty subtropical wetland plant assemblages
Pasture management intensity, livestock grazing and prescribed fire are three widespread agricultural practices that affect small, isolated wetlands, but few studies have investigated their individual and interactive effects. Pasture management intensity refers to the degree of human alteration of grassland, ranging from intensively managed pastures planted with introduced forage, fertilizer/lime additions and artificial drainage to semi-natural pastures with mixed native and non-native vegetation, no fertilizer/lime additions and little or no artificial drainage. We examined individual and interactive effects of these three agricultural practices on individual, isolated wetlands using a replicated, full-factorial experiment on 40 entire wetlands in south Florida, USA. Wetlands were embedded in two pasture management intensities: intensively managed and semi-natural. After three years of treatment initiation, vegetation of wetlands released from grazing and unburned embedded in semi-natural pastures had significantly lower evenness and coefficient of conservatism scores compared to wetlands released from grazing and burned, grazed unburned wetlands and grazed burned wetlands in the same pasture management intensity. For wetlands embedded in intensively managed pastures, evenness and coefficient of conservatism scores did not differ among treatments. Release from grazing increased abundance of the native, weedy herb, Eupatorium capillifolium. Grazing interacted with prescribed fire to affect shrub abundance and non-native richness; relative abundance of shrubs and non-native richness were greater in wetlands released from grazing and burned and did not differ among burn treatments in grazed wetlands. Interactive effects, especially three-way interactions, were uncommon and not as important as differences between the two pasture management intensities. Synthesis and applications. Vegetation diversity and floristic quality of wetlands embedded in intensively managed pastures resisted common restoration management techniques such as release from grazing and prescribed fire, at least in the short term. In contrast, removing all top-down disturbances from wetlands embedded in semi-natural grasslands can negatively affect vegetation species diversity and floristic quality. Future studies should examine how intensity and seasonality of grazing and prescribed fire affect wetland vegetation, and track long-term responses to evaluate lag effects.
Data from: Prioritizing land management efforts at a landscape scale: a case study using prescribed fire in Wisconsin
One challenge in the effort to conserve biodiversity is identifying where to prioritize resources for active land management. Cost-benefit analyses have been used successfully as a conservation tool to identify sites that provide the greatest conservation benefit per unit cost. Our goal was to apply cost-benefit analysis to the question of how to prioritize land management efforts, in our case the application of prescribed fire to natural landscapes in Wisconsin, USA. We quantified and mapped frequently burned communities, and prioritized management units based on a suite of indices that captured ecological benefits, management effort, and the feasibility of successful long-term management actions. Data for these indices came from LANDFIRE, Wisconsin's Wildlife Action Plan, and a nationwide Wildland Urban Interface assessment. We found that the majority of frequently burned vegetation types occurred in the southern portion of the state. However, the highest-priority areas for applying prescribed fire occurred in the central, northwest, and northeast portion of the state where frequently burned vegetation patches were larger and where identified areas of high biological importance area occurred. Although our focus was on the use of prescribed fire in Wisconsin, our methods can be adapted to prioritize other land management activities. Such prioritization is necessary to achieve the greatest possible benefits from limited funding for land management actions, and our results show that it is feasible at scales that are relevant for land management decisions.
Data - Prescribed fire in longleaf pine ecosystems: fire managers' perspectives on priorities, constraints, and future prospects
<p><strong>The following information describes the data coded in the corresponding database as it relates to survey question responses. This dataset includes all data used to produce graphs in:</strong></p><p>Kupfer, J.A., Lackstrom, K., Grego, J.M., Dow, K., Terando, A.J., and Hiers, J.K. 2022. Perspectives on prescribed fire management in longleaf pine ecosystems: Current constraints and future prospects. <i>Fire Ecology</i> 18, 27. <a href="https://doi.org/10.1186/s42408-022-00151-6">https://doi.org/10.1186/s42408-022-00151-6</a>..</p><p><strong>Part I: Criteria for Prioritizing Burn Sites (Columns A-S)</strong></p><p><strong>Question #1: "For the characteristics listed below, please rank your top 3 criteria for determining whether a site has a high priority for burning." Scale: 1 = highest, 2 = next; 3 = next; NULL = not in top 3. Columns K-S indicate simply whether a criterion was chosen (1) or not (0), regardless of rank.</strong></p><p>A. Participant ID</p><p>B. TimeSinceBurnRk = Time since the last fire </p><p>C. FuelReductionRk = Burn to reduce fuels</p><p>D. EcoHealthRk = Burn to improve ecosystem health</p><p>E. TimberRk = Improving timber</p><p>F. FirebreaksRk = Presence of firebreaks</p><p>G. TandERk = Burn to assist T&E species</p><p>H. WUIrk = Proximity to Wildland Urban Interface</p><p>I. ExoticInvasiveRk = Prioritizing due exotics / invasives</p><p>J. OtherCritRk = Other criteria not included above</p><p>K. TimeSinceBurn = Time since the last fire (1 = cited in any order; NULL = not in top 3)</p><p>L. FuelReduction = Burn to reduce fuels (same ranking as previous)</p><p>M. EcoHealth = Burn to improve ecosystem health (same ranking as previous)</p><p>N. Timber = Improving timber (same ranking as previous)</p><p>O. Firebreaks = Presence of firebreaks (same ranking as previous)</p><p>P. TandE = Burn to assist T&E species (same ranking as previous)</p><p>Q. WUI = Proximity to Wildland Urban Interface (same ranking as previous)</p><p>R. ExoticInvasive = Prioritizing due exotics / invasives (same ranking as previous)</p><p>S. OtherCrit = Other criteria not included above (same ranking as previous)</p><p> </p><p><strong>Part II: Longleaf Pine Burning Frequency (T-U)</strong></p><p><strong>Question #3: "Recommendations for the frequency of prescribed burns depend on the local conditions, including: 1) the diversity of sites with longleaf ecosystems, 2) variation in the suite of understory species that define the habitat, and 3) the 'historic' fire return interval. How often, on average, should longleaf pine stands in your unit(s) be burned?</strong></p><p>T. RecentBurnFreq: 1 = < every 2 yrs, 2 = every 2-4 yrs, 3 = every 4-5 yrs, 4 = > every 5 yrs </p><p><strong>Question #4. In practice, how often, on average, have longleaf pine units in your area been burned over the past 10 years?</strong></p><p>U. CompBurnFreq: -1 = less frequently than previous answer; 0 = as frequently as previous answer; 1 = more frequently than previous answer (the response was compared to that from Question 3 and scored comparatively).</p><p> </p><p><strong>Part III: Current legal, institutional, and managerial constraints (V-AB)</strong></p><p><strong>Question #6. In this section we would appreciate your help in better understanding how specific factors constrain or limit your ability to conduct prescribed burns. Do the following factors pose constraints to prescribed burning at your units? Scale: 1 = not a constraint; 2 = sometimes a constraint; 3 = commonly a constraint; -9999 = Not applicable. (note: #NULL! = no response)</strong></p><p>V. ConstrPublic: Burning constrained by public concerns </p><p>W. ConstrWUI: Burning constrained by nearby development </p><p>X. ConstrRisk: Burning constrained by concerns about liability, career, etc.</p><p>Y. ConstrPartners: Burning constrained by partnerships and agreements</p><p>Z. ConstrIncent: Limited incentives or organizational pushback</p><p>AA. ConstrLegal: Legal constraints (e.g. NEPA, etc.)</p><p>AB. ConstrOther: Other constraints</p><p><strong> </strong></p><p><strong>Part IV: Environmental and resource constraints (AC-AJ)</strong></p><p><strong>Question 7: Please indicate how often these factors constrain prescribed burning in your management unit during the longleaf pine dormant season and growing season. Scale: 1 = not a constraint; 2 = sometimes a constraint; 3 = commonly a constraint; -9999 = Not applicable. (</strong><i><strong>note: #NULL! = no response</strong></i><strong>)</strong></p><p>AC. ConstrDormWx: Inappropriate weather conditions during dormant season</p><p>AD. ConstrDormFuel: Concerns about fuel loads during dormant season</p><p>AE. ConstrDormAQ: Air quality / smoke management issues during dormant season</p><p>AF. ConstrDormRes: Shortage of resources (personnel, money, equip) during dormant season</p><p>AG. ConstrGrowWx: Inappropriate weather conditions during growing season</p><p>AH. ConstrGrowFuel: Concerns about fuel loads during growing season</p><p>AI. ConstrGrowAQ: Air quality / smoke management issues during growing season</p><p>AJ. ConstrGrowRes: Shortage of resources (personnel, money, equip) during growing season</p><p> </p><p><strong>Part V: Seasonal Weather Constraints (AK-BD)</strong></p><p><strong>Question 8: For weather conditions during the dormant season and growing season, please check the boxes for all factors which are common constraints on prescribed burning. (scale: 1 = factor selected, #NULL! = factor not selected).</strong></p><p>AK. ConstrHiTDorm: High temperature is a common constraint during the dormant season</p><p>AL. ConstrHiTGrow: High temperature is a common constraint during the growing season</p><p>AM. ConstrLowTDorm: Low temperature is a common constraint during the dormant season</p><p>AN. ConstrLowTGrow: Low temperature is a common constraint during the growing season</p><p>AO. ConstrHiRHDorm: High relative humidity is a common constraint during the dormant season</p><p>AP. ConstrHiRHGrow: High relative humidity is a common constraint during the growing season</p><p>AQ. ConstrLowRHDorm: Low relative humidity is a common constraint during the dormant season</p><p>AR. ConstrLowRHGrow: Low relative humidity is a common constraint during the growing season</p><p>AS. ConstrHiWindDorm: High winds are a common constraint during the dormant season</p><p>AT. ConstrHiWindGrow: High winds are a common constraint during the growing season</p><p>AU. ConstrLowWindDorm: Low winds are a common constraint during the dormant season</p><p>AV. ConstrLowWindGrow: Low winds are a common constraint during the growing season</p><p>AW. ConstrDaysRainDorm: # of days since last rain is a common constraint during the dormant season</p><p>AX. ConstrDaysRainGrow: # of days since last rain is a common constraint during the growing season</p><p>AY. ConstrDroughtDorm: Drought conditions are a common constraint during the dormant season</p><p>AZ. ConstrDroughtGrow: Drought conditions are a common constraint during the growing season</p><p>BA. ConstrAtmoDispDorm: Low atmospheric dispersion is a common constraint during the dormant season</p><p>BB. ConstrAtmoDispGrow: Low atmospheric dispersion is a common constraint during the growing season</p><p>BC. ConstrTransWindDorm: Adequate transport winds are a common constraint during the dormant season</p><p>BD. ConstrTransWindGrow: Adequate transport winds are a common constraint during the growing season</p><p><strong> </strong></p><p><strong>Part VI: Expectations for future changes in burning constraints due to climate change and urban growth (BE-BW)</strong></p><p><strong>Question 9: To what extent do you think climate change currently affects your management decisions? </strong></p><p>BE. CurrClimChange. Scale: 0 = not at all; 1 = a little; 2 = moderately; 4 = a great deal; -9999 = don't know. #NULL! = no response.</p><p><strong>Question 10: To what extent do you think urban growth currently affects your management decisions? </strong></p><p>BF. CurrUrbanGrowth: Scale: 0 = not at all; 1 = a little; 2 = moderately; 4 = a great deal; -9999 = don't know. #NULL! = no response.</p><p><strong>Question 12:</strong> <strong>To what extent do you think future climate change (e.g., increasing temperature, more intense rainstorms, and/or extreme weather events) will affect your prescribed burning decisions? </strong>. <strong>Scale for all variables: 0 = not at all; 1 = a little; 2 = moderately; 3 =a great deal; -9999 = don't know. #NULL! = no response.</strong></p><p>BG. ClimChangeShort: Effects over the next 5-10 years</p><p>BH. ClimChangeMed: Effects over the next 10-30 years</p><p>BI. ClimChangeLong: Effects over the next 30-50 years</p><p><strong>Question 13:</strong> <strong>To what extent do you think future urbanization patterns (e.g., changes in the wildland urban interface, loss of habitat to restore longleaf pine ecosystems) will affect your prescribed burning decisions? Scale for all variables: 0 = not at all; 1 = a little; 2 = moderately; 3 =a great deal; -9999 = don't know. #NULL! = no response.</strong></p><p>BJ. UrbChangeShort: Effects over the next 5-10 years</p><p>BK. UrbChangeMed: Effects over the next 10-30 years</p><p>BL. UrbChangeLong: Effects over the next 30-50 years</p><p><strong>Question 15: Thirty years from now, which of these constraints do you think will be the most significant constraints to your use of prescribed burning? Please select up to 5 constraints. Scale: 1 = selected; #NULL! = selected</strong></p><p>BM. FutConstrWx: Inappropriate weather conditions (1 = cited in any order; NULL = not in top 5)</p><p>BN. FutConstrFuel: High fuel loads (same scale as previous)</p><p>BO. FutConstrAQ: Air quality issues, including smoke management (same scale as previous)</p><p>BP FutConstrRes: Shortage of resources (personnel, money, equipment) (same scale as previous)</p><p>BQ. FutConstrPublic: Avoiding public objections or concerns over burning (same scale as previous)</p><p>BR. FutConstrWUI: Residential or other development in or near burn areas (same scale as previous)</p><p>BS. FutConstrRisk: Risk aversion (liability, career, political repurcussions) (same scale as previous)</p><p>BT. FutConstrAgree: Challenges posed by agreement and partnerships (same scale as previous)</p><p>BU FutConstrIncent: Limited incentives, institutional history (same scale as previous)</p><p>BV. FutConstrLegal: Legal constraints (same scale as previous)</p><p>BW. FutConstrOther: Other constraints</p><p> </p><p><strong>Part VII: Responder Demographics (BX)</strong></p><p>Respondent provided state and management unit information. Scale 1 = yes; 0 = no.</p>
And it burns, burns, burns, the ring-of-fire: Reviewing and harmonizing terminology on wildfire management and policy
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Fire Fighter Fatigue Management Program: Operation Healthy Sleep
ClinicalTrials.gov study NCT01988129. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Post-fire Debris Flows: Leveraging Science for Environmental Management and Community Resiliency
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Data from: Assessing the sensitivity of biodiversity indices used to inform fire management
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Data from: Interactive effects of pasture management intensity, release from grazing and prescribed fire on forty subtropical wetland plant assemblages
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Fire and functional traits: using functional groups of birds and plants to guide management in fire-prone, heathy woodland ecosystem
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Data from: Prioritizing land management efforts at a landscape scale: a case study using prescribed fire in Wisconsin
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Estimating social-ecological resilience: fire management futures in the Sonoran Desert
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ScienceDex guides
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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.