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63 results for “Prescribed fires”
Data from: Chaparral bird community responses to prescribed fire and shrub removal in three management seasons
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Data from: Past tree influence and prescribed fire mediate biotic interactions and community reassembly in a grassland-restoration experiment
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Data from: Longleaf pine proximity effects on air temperatures and hardwood top-kill from prescribed fire
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How does prescribed fire shape bird and plant communities in a temperate dry forest ecosystem?
<p>To mitigate the impact of severe wildfire on human society and the environment, prescribed fire is widely used in forest ecosystems to reduce fuel loads and limit fire spread. To avoid detrimental effects on conservation values, it is imperative to understand how prescribed fire affects taxa having a range of different adaptations to disturbance. Such studies will have greatest benefit if they extend beyond short-term impacts of burning. We used a field study to examine the effects of prescribed fire on birds and plants across a 36-year post-fire chronosequence in a temperate dry forest ecosystem in south-eastern Australia, and by making comparison with long-unburnt reference sites (79 years since wildfire). We modelled changes in the relative abundance of 22 bird species and the cover of 39 plant species, and examined how individual species, functional groups, species richness and community composition differed between sites with different fire history. For most individual bird and plant species modelled, relative abundance or cover at sites subject to prescribed fire did not change significantly with time since fire or differ from that of long-unburnt vegetation. When bird species were pooled into functional groups, time since prescribed fire had strong effects on birds that forage in the lower-midstorey, facultative-resprouting shrubs and obligate-seeding shrubs. Species richness for both taxa did not differ between sites subject to prescribed fire and those in long-unburnt vegetation. Bird communities varied significantly between the youngest (0-3 years) and oldest (79 years) post-fire age-classes, driven by species associated with understorey vegetation. Plant community composition showed little evidence of a post-fire successional trajectory. The prevalence of bird species with broad habitat and dietary niches and plant regeneration through resprouting, make bird and plant communities in these forests relatively resilient to small and patchy prescribed fires they have experienced to date. Application of prescribed fire will be most compatible with maintaining biodiversity by taking a landscape approach that: 1) plans for a geographic spread of stands with a range of between-prescribed-fire intervals to ensure provision of suitable habitat for all taxa, and 2) avoids burning in moist gullies to maintain their value as fire refuges.</p>
Data from: Extreme prescribed fire during drought reduces survival and density of woody resprouters
Management intervention in ecosystems with degraded environmental services requires innovative resource management strategies that go beyond conventional restoration and conservation practices. We established a unique study that experimentally targeted extreme fire conditions during drought in humid subtropical and semiarid ecoregions. In the southern Great Plains of North America, conventional restoration and conservation practices have been either historically ineffective or economically cost prohibitive at restoring grass-dominated ecosystems following conversion to resprouting shrublands. Our aim was to assess the potential for extreme fire during drought to force the system along an alternate ecological trajectory from its current progression toward closed-canopy resprouting shrubland, something conventional fire prescriptions have been unable to accomplish. We first tested the potential for high intensity fires exhibiting extreme behaviour to disrupt the progression from grassland to shrubland. In both ecoregions, significant levels of mortality were observed for mature woody resprouters. As a result, densities were either maintained or reduced three years following extreme fire treatments, whereas resprouter densities continued to increase in areas that were not burned. A second interventionist approach involving extreme fire and herbicide treatment combinations was not supported. Interactions between prescribed extreme fire and herbicide did not significantly reduce resprouter densities more than using herbicide alone at either site. Synthesis and applications: Extreme fires during drought resulted in exceptionally high levels of mortality across all sizes of woody resprouters and limited recruitment, resulting in 35–55% lower densities of resprouters than in areas not burned. These findings counter prevailing scientific and management expectations, which are based largely on studies that impose tight controls over prescribed fire conditions and avoid extreme fire behaviour. Future interventions for controlling woody resprouters with fire may require rethinking the present ideology that extreme fire behaviour has no place in modern social-ecological landscapes.
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: Optimizing the spatial planning of prescribed burns to achieve multiple objectives in a fire-dependent ecosystem.
1. There is potential for negative consequences for the ecological integrity of fire-dependent ecosystems as a result of inappropriate fire regimes. This can occur when asset (property) protection is prioritised over conservation objectives in burn programs. 2. Optimisation of fire management for multiple objectives is rarely undertaken. Here, we use integer linear programming to identify burn scheduling solutions that will cost-effectively achieve asset protection and conservation objectives. 3. An approach to burn scheduling that favours a risk-averse asset protection strategy results in poor conservation outcomes. Conversely, a conservation-focused approach achieves only modest asset protection benefits. However, when formulated as a multi-objective problem, good conservation outcomes can be realised with only a small reduction in potential benefits for asset protection. 4. A conservation-focused approach resulted in substantially more heterogeneity in burns at multiple spatial scales and a marked reduction in mean time since fire among all forest patches relative to an asset protection scenario. This increase in heterogeneity improves ecological integrity, while the resulting reduction in fuel load is beneficial for asset protection. 5. Synthesis and applications. Mathematical optimisation is a powerful framework for informing fire management that improves the prioritisation and scheduling of controlled burns to efficiently achieve management objectives. By quantifying the trade-offs that exist between the two competing objectives of conservation and asset protection we demonstrate that compromise solutions can be identified that achieve good outcomes for both objectives. In a transparent and equitable manner, we show that conservation value may be improved within a fire-dependent ecosystem with only modest concession to asset protection performance. Explicitly evaluating trade-offs among competing objectives enables managers to identify potentially undesirable outcomes, and facilitate development of preferred solutions. Heterogeneous burning under the auspices of conservation also has the potential to reduce overall fuel loads within the ecosystem and thus its value for asset protection is likely underappreciated.
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>
Data accompanying Jonko et al. "How will future climate change impact prescribed fire across the contiguous United States?"
<p>This CSV file contains prescription information for 83 location across the United States which was analyzed in the publication Jonko, A., J. Oliveto, T. Beaty, A. Atchley, M.A. Battaglia, M.B. Dickinson, M.R. Gallagher, A. Gilbert, D. Godwin, J.A. Kupfer, J.K. Hiers, C. Hoffman, M. North, J. Restaino, C. Sieg, and N. Skowronski: "How will future climate change impact prescribed fire across the contiguous Unites States?", submitted to npj Climate and Atmospheric Science. Please contact the corresponding author at ajonko@lanl.gov if you are interested in using this data in your own research.</p>
Data: Prescribed fire enhances seed removal by ants in a Neotropical savanna
<p>Seed dispersal and predation by animals often drive plant regeneration. In tropical savannas, such as the Cerrado of Brazil, fire is also a key process in ecosystem dynamics, consuming the lower vegetation strata and killing wildlife, but how fire affects seed-animal interactions is virtually unknown. We investigated the effects of prescribed fires on the removal of diaspores from Miconia rubiginosa and sunflower Helianthus annuus in Cerrado from southeast Brazil. Using plots burned one month or one year before sampling and unburned controls, we assessed the effect of prescribed fire on microhabitat structure and diaspore removal by vertebrates and ants. Covered microhabitats experienced higher seed removal by vertebrates than open microhabitats, but microhabitat features did not influence seed removal by ants. Prescribed fire did not change the total amount of seed removal, and ants were responsible for most removals in burned and control plots. However, fire increased the importance of ants as agents of removal compared to vertebrates. These changes were probably mediated by changes in microhabitat cover. It is likely that species whose seeds are often preyed upon by vertebrates benefit from fire to escape predation, while the opposite would be true for those removed by granivorous ants. By changing microhabitats composition and frequency of seed removal by different agents, fire may create pulses of opportunities for certain plant species to increase their populations and enlarge their spatial distribution, while constraining others. However, how different fire intensities and frequencies influence seed fate of different species is still to be investigated.</p>
Can prescribed fires restore C4 grasslands invaded by a C3 woody species and a co-dominant C3 grass species?
<p>Prescribed fire is used to reduce woody plant invasion and restore herbaceous production and diversity in grasslands and savannas worldwide. Here we determined if a concentrated series of repeated-winter, repeated-summer, or alternate-season (winter and summer) fires in a short timeframe ("transition fires") could catalyze the restoration of C<sub>4</sub> perennial grasses in Southern Great Plains, USA grasslands that had become dominated by a fire-tolerant C<sub>3</sub> woody N<sub>2</sub>-fixer (honey mesquite, <i>Prosopis glandulosa</i>) and a C<sub>3</sub> perennial bunchgrass (Texas wintergrass, <i>Nassella leucotricha</i>). We applied transition fires over a 5-year span, and maintenance fires on a portion of each plot 7 or 8 years later. We measured herbaceous standing biomass and cover and soil variables (soil organic C, N, δ<sup>13</sup>C and δ<sup>15</sup>N) in unburned, transition-burned and maintenance-burned treatments. Greater δ<sup>13</sup>C at 10-20 (-17 ‰) than 0-10 (-20 ‰) cm depth increment confirmed that vegetation was historically mostly C<sub>4</sub> grassland that shifted towards C<sub>3</sub> dominance. Transition treatments with summer fire were most effective at top-killing mesquite, but no treatments root-killed >3%. Regrowth of top-killed mesquite was similar in all treatments and reached pre-fire height by 9 to 10 years post-fire. Herbaceous production and cover responses showed that: (1) alternate-season transition fires increased C<sub>4</sub> mid-grass, but did not change Texas wintergrass, (2) repeated-summer fires reduced Texas wintergrass, but did not change C<sub>4</sub> mid-grass, and (3) repeated-winter fires did not change C<sub>4</sub> mid-grass or Texas wintergrass compared to the unburned control. All maintenance fires stimulated Texas wintergrass biomass and cover, thus eliminating the reduction of Texas wintergrass caused by repeated-summer transition fires. There were no long-term effects of transition fires on soil C, N, δ<sup>13</sup>C or δ<sup>15</sup>N. Results advance our understanding of the expectations and limitations of prescribed fire in shifting a woodland alternate state toward what was historically a fire supported C<sub>4</sub> grassland/savanna.</p>
Prescribed fire regimes influence responses of fungal and bacterial communities on new litter substrates in a brackish tidal marsh
<p>Datasets including R code and .csv files of ESV data for fungal and bacterial communities in samples.</p>
Can prescribed fires restore C4 grasslands invaded by a C3 woody species and a co-dominant C3 grass species?
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Data from: Bird functional diversity decreases with time since disturbance: does patchy prescribed fire enhance ecosystem function?
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Data from: Extreme prescribed fire during drought reduces survival and density of woody resprouters
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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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How does prescribed fire shape bird and plant communities in a temperate dry forest 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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Data from: Optimizing the spatial planning of prescribed burns to achieve multiple objectives in a fire-dependent ecosystem.
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