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34 results for “Prescribed Burn”
Data from: Prescribed burning protects endangered tropical heathlands of the Arnhem Plateau, northern Australia
1. There are concerns that frequent intense fires are reducing biodiversity on the Arnhem Plateau within Kakadu National Park, northern Australia. Since the 1980s, prescribed burning in the early dry season has aimed to reduce the extent of late dry season wildfires. A programme of more strategic prescribed burning has been undertaken since 2007, aiming to increase intervals between fires affecting heathland and rain forest communities. 2. We assess the effectiveness of prescribed burning in Kakadu's Stone Country using a Landsat satellite-derived fire history (1980–2011), in terms of achieving 'tolerable fire intervals' for dominant plant communities. 3. Our analysis indicates that fire regimes have become substantially more favourable for biodiversity since the early 1980s. Although annual extent of burning has remained unchanged, two significant changes in fire regimes have occurred over the long term: (i) a switch from late dry season dominance to early dry season dominance and (ii) an increase in the abundance of long-unburnt vegetation, both of which are likely to benefit biodiversity. Demonstrating the statistical significance of changes associated with recent, more strategic fire management (2007–2011) is limited by the short duration of this management approach, although there is evidence of increasing abundance of long-unburnt vegetation during this time. 4. The view that the Arnhem Plateau's fire regimes are increasingly driving biodiversity loss (due to frequent late dry season wildfires) is erroneous; they are in a more benign state now than at any time over the last three decades, most likely due to extensive use of prescribed burning. 5. Synthesis and applications. In highly fire-prone landscapes, such as savannas, prescribed burning can be an effective means of: (i) bringing forward peak fire activity to the time of year when fire conditions are relatively mild and (ii) increasing abundance of long-unburnt vegetation. These changes are likely to favour persistence of a range of fire-sensitive communities. Our case study supports the strategic use of prescribed burning to protect fire-sensitive biota within highly fire-prone landscapes throughout the world.
Data from: Trade-offs in berry production and biodiversity under prescribed burning and retention regimes in Boreal forests
1. Green tree retention and prescribed burning are practices used to mitigate negative effects of forestry. Beside their effects on biodiversity, these practices should also promote non-timber forest products (NTFPs). We assessed: (1) how prescribed burning and tree retention influence NTFPs by examining production of bilberry Vaccinium myrtillus and cowberry; Vaccinium vitis-idaea (2) if there are synergies or trade-offs in the delivery of these NTFPs in relation to delivery of species richness, focusing on five groups of forest dwelling species. 2. We used a long-term experiment located in eastern Finland with three different harvesting treatments: clearcut-logging, logging with retention patches and unlogged, which were combined with or without prescribed burning. Eleven years after the treatment application, we scored plant cover and berry production in different microhabitats within these treatments, while species richness data for five species groups (ground-layer lichens and bryophytes, vascular plants, saproxylic beetles, pollinators – here bees and hoverflies) were collected at the stand level. 3. Logging favoured cowberry production, particularly for plants growing in the vicinity of stumps. Logging was detrimental for cover and berry production of bilberry. Retention mitigated these negative effects slightly, but cover and berry production were still substantially lower compared to unlogged forests. Prescribed burning increased cowberry production in retention patches and in unlogged forest. Bilberry production decreased with burning, except in unlogged forest where the effect was neutral. 4. No single management treatment simultaneously favoured all values - NTFPs and richness - and trade-offs among values were common. Only bilberry production and beetle diversity were higher under retention forestry, or in unlogged stands, compared to logged stands. Prescribed burning favoured many values when performed in combination with retention forestry, or in unlogged stands, but different treatment combinations favoured different species groups. 5. Synthesis and applications. Our results demonstrate that widely-applied conservation practices in managed forests are unlikely to benefit all ecosystem values everywhere. If high multi-functionality is desired, managing at a landscape scale, countering the local trade-offs among values, may be more appropriate than the stand scale conservation practices commonly practiced today.
Database of clustered vOTUs recovered from a viromics prescribed burn study of forest soil
<p>Database of dereplicated viral operational taxonomic units (vOTUs) recovered from a viromics (viral-size fraction metagenomics) prescribed burn study of forest soil</p>
Data from: Combined effects of retention forestry and prescribed burning on polypore fungi
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Data from: Prescribed burning protects endangered tropical heathlands of the Arnhem Plateau, northern Australia
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Data from: Trade-offs in berry production and biodiversity under prescribed burning and retention regimes in Boreal forests
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Data from: Genetic restoration in the eastern collared lizard under prescribed woodland burning
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Data from: Prescribed burning impacts on ecosystem services in the British uplands: a methodological critique of the EMBER project
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Data from: The impact of prescribed burning on native bee communities (Hymenoptera: Apoidea: Anthophila) in longleaf pine savannas in the North Carolina sandhills
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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: Community-level responses of African carnivores to prescribed burning
<p>Fires are common in many ecosystems worldwide, and are frequently used as a management tool. Although the responses of herbivores to fire have been well-studied, the responses of carnivores to fire remain unclear. In particular, post-fire habitat changes, and the associated changes in prey availability, might affect the coexistence or competition of carnivore species within the larger carnivore community, but few studies have focused on how fires influence multiple carnivore species simultaneously.</p> <p>Using South African carnivores as our focal community, we explored relative changes in carnivore intensity of use in post-fire landscapes associated with hypothesized changes in prey availability and top-down suppression.</p> <p>We monitored carnivore intensity of use in relation to prescribed burning using camera traps, with a Before-After-Control-Impact study design. We analyzed the camera trap data using community N-mixture models to understand how individual species, as well as the carnivore guild as a whole, respond to burning.</p> <p>Changes in carnivore intensity of use in response to prescribed burns were not uniform; however, no species decreased intensity of use of post-fire landscapes. The apex predator, the lion (<i>Panthera leo</i>), increased use of prey-rich burnt areas, but other large carnivore species exhibited neutral responses to fire despite the associated prey increase. Responses of medium- and small-sized carnivores were species-specific, and included both neutral and positive responses. Positive responses to fire by lions and herbivores were short-lived, and did not persist a year after burning occurred.</p> <p><i>Synthesis and Applications: </i>Our results indicate that fire does not promote carnivore coexistence by creating conditions for all carnivores to increase use of burned areas, but that it also likely does not result in spatial avoidance of subordinate predators. Instead, fires might cause a suppression of opportunities for subordinate large carnivores because they need to avoid the apex predator, rather than take advantage of short-term increased hunting opportunities in recently burned areas. Our results highlight the complexity of understanding species-specific and community-level responses of carnivores to fire, and overlooked ecological effects of its use as a management tool.</p>
Data from: Community-level responses of African carnivores to prescribed burning
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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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Data set of resilience indices to drought and prescribed burning of Pinus nigra ssp. salzmannii and P. sylvestris L. trees
<p>Data on resilience and resistance indices to drought and burning inferred from tree ring width and carbon 13 isotopes for burned and unburned <em>P. nigra</em> spp. <em>salzmannii</em> and <em>P. sylvestris </em>trees<em>.</em>The dataset contains two files:</p> <p>TreeGrowth.txt: Data on resilience and resistance indices to drought and prescribed burning as well as the ratio of latewood to earlywood. Indices are inferred from total tree-ring, earlywood, and latewood widths. Included variables:</p> <ul> <li> Idsite (factor): code to identify uniquely each locality. Two levels: Miravé (1) and Lloreda (2).</li> <li> Idplot (factor): code to identify uniquely burned plots. Four levels: Miravé-Fall (1), Mirave-Spring (2), Lloreda-Fall (3) and Lloreda-Spring (4).</li> <li>Treatment (factor): whether the plot was burned or not. Two levels: control or left unburned (C) or burned (B)</li> <li>BurningSeason (factor): season of the burn. Three levels: control or left unburned (C), fall burn (F) or spring burn (S)</li> <li>Sp (factor): species. Two levels: <em>Pinus sylvestris</em> (ps) or<em> Pinus nigra </em>(pn)</li> <li>TreeCode (numeric): code to identify trees in the field</li> <li>dbh (numeric): diameter at breast height (cm)</li> <li>c12 (numeric): competition index before burning</li> <li>rci15 (numeric): release from tree competition 2 years post-burning calculated as the difference between pre (CI12) and post-burning competition (CI15) indices relative to pre-burning levels </li> <li>bchmin (numeric): minimum bole scorch height (cm)</li> <li>bchmax (numeric): maximum bole scorch height (cm)</li> <li>whiteAshes (numeric): white ashes after burning (%, in 1m radius from tree center)</li> <li>Resistance (numeric): Average basal area increment (BAI) during the stress period (drought 2012 and prescribed burning 2013) divided by the average BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR (numeric): Average total tree ring BAI of the two years after the stress period (2014 and 2015) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR14 (numeric): Total tree ring BAI of the first year after the stress period (2014) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilTTR15 (numeric): Total tree ring BAI of the second year after the stress period (2015) divided by average total tree ring BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW (numeric): Average earlywood BAI of the two years after the stress period (2014 and 2015) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW14 (numeric): Earlywood BAI of the first year after the stress period (2014) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilEW15 (numeric): Earlywood BAI of the second year after the stress period (2015) divided by average earlywood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW (numeric): Average latewood BAI of the two years after the stress period (2014 and 2015) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW14 (numeric): Latewood BAI of the first year after the stress period (2014) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>resilLW15 (numeric): Latewood BAI of the second year after the stress period (2015) divided by average latewood BAI of the three years preceding the stress period (2009 to 2011)</li> <li>preLWEW (numeric): mean pre-stress ratio of latewood to earlywood calculated for the period 2006-2011</li> <li>difLWEW (numeric): mean post-stress latewood:earlywood (2014-2015) minus mean pre-stress latewood:earlywood (2006-2011)</li> </ul> <p>d13c.txt: Early and late-wood carbon 13 isotope from 2011 to 2015 for <em>P. nigra</em> spp. <em>salzmannii</em> and <em>P. sylvestris</em> burned in spring and fall in year 2013 at two sites. </p> <ul> <li>Idsite (factor): code to identify uniquely each locality. Two levels: Miravé (1) and Lloreda (2).</li> <li>Sp (factor): species. Two levels: Pinus sylvestris (ps) or Pinus nigra (pn)</li> <li>BurningSeason (factor): season of the burn. Three levels: control or left unburned (C), fall (F) or spring (S)</li> <li>TreeCode (factor): code to identify trees in the field."Pool" means that a pool of 5 individuals was used to determine carbon 13 isotope.</li> <li>SeasonalGrowth (factor): seasonal wood growth. Two levels: earlywood (E) and latewood (L).</li> <li>year (numeric): calendar year.</li> <li>d13c (numeric): carbon 13 isotope<br> </li> </ul>
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OpenNeuro
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