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The significant variables for the fatal injury model were fire shelter use, slope steepness and flame height. The separation distances needed to ensure no more than a 1 or 5% probability of fatal injury, without the use of a fire shelter, for slopes less than 25% were 20 to 50 m for flame heights less than 10 m, and 1 to 4 times the flame height for flames taller than 10 m. The non-fatal injury model significant variables were fire shelter use, vehicle use and fuel type. At the 1 and 5% probability thresholds for a non-fatal injury, without the use of a fire shelter, the separation distances were 1 to 2, 6 to 7, and 12 to 16 times greater than the current safety zone guideline (i.e. 4 times the flame height) for timber, brush and grass fuel types respectively.
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In North America, decisions about how and when to apply prescribed fire are typically based on the historical-fire-regime concept (HFRC), which holds that replicating the pattern of fires ignited by lightning or preindustrial humans best promotes native species in fire-prone regions. This study found that the practice of inferring historical fire regimes for entire regions or ecosystems often entails substantial uncertainty and can yield equivocal results; ecological outcomes of fire suppression are complex and may not equate to degradation, depending on the ecosystem and context; and habitat fragmentation, invasive species, and other modern factors can interact with fire to produce novel and in some cases negative ecological outcomes. Although the HFRC is a valuable starting point, it should not be viewed as the sole basis for developing prescribed fire programs. Rather, fire prescriptions should also account for other specific, measurable ecological parameters on a case-by-case basis.
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In this paper, optimization models successfully identified areas with low conifer canopy cover, high resilience and resistance to wildfire and annual grass invasion, and high bird abundance to enhance sage-grouse habitat. The inclusion of mesic resources resulted in further prioritization of areas that were closer to such resources, but also identified potential pathways that connected breeding habitats to the late brood-rearing habitats associated with mesic areas. Areas identified by optimization models were largely consistent with and overlapped ongoing conifer removal efforts in the Warner Mountains of south-central Oregon. Land ownership of preferential areas selected by models varied with priority goals and followed general ownership patterns of the region, with public lands managed by the Bureau of Land Management and private lands being selected the most. The increased availability of landscape-level datasets and assessment tools in sagebrush ecosystems can reduce the time and cost of both planning and implementation of habitat projects involving conifer removal. Most importantly, incorporating these new datasets and tools can supplement expert-based knowledge to maximize benefits to sagebrush and sage-grouse conservation.
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Stephen Pyne is Regents Professor in the School of Life Sciences at Arizona State University. This is an abridged version of a piece that appeared on The Conversation; to read the entire piece, go to theconversation.com
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This news story provides some about the history and future of fire behavior research.
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Researchers developed landscape-directed dispersal simulations and tested a series of replicates that emulate independent empirical datasets for greater sage-grouse and eastern fox snake. The study helps establish methods for using liner mixed models to identify the features underlying patterns of dispersal across a variety of landscapes.
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Our results suggest that bird communities in piñon-juniper woodlands can be highly stable when management treatments are conducted in areas with more advanced woodland development and at the level of disturbance measured in our study.
This chapter reviews some of the conceptual and technological advancements and provide examples of how they have influenced rangeland monitoring. It then discuss implications of these developments for rangeland management and highlight what are seen as challenges and opportunities for implementing effective rangeland monitoring. It concludes with a vision for how monitoring can contribute to rangeland information needs in the future.
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In this study, we present a framework for forecasting large fire occurrence – an extreme value event – and evaluating measures of uncertainties that do not rely on distributional assumptions. The statistical model presented here incorporates qualitative fire danger indices along with other location and seasonal specific explanatory variables to produce maps of forecasted probability of an ignition becoming a large fire, as well as numbers of large fires with measures of uncertainties. As an example, 6 years of fire occurrence data from the Western US were used to study the utility of two fire danger indices: the 7-Day Significant Fire Potential Outlook issued by Predictive Services in the US and the National Fire Danger Rating’s Energy Release Component. This exercise highlights the potential utility of the quantitative risk index as a real-time decision support tool that can enhance managers’ abilities to discriminate among planning areas in terms of the likelihood and range of expected significant fire events.
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Resource managers often need scientific information to match their decisions (typically short-term and local) to complex, long-term, large-scale challenges such as adaptation to climate change. In such situations, the most reliable route to actionable science is coproduction, whereby managers, policy makers, scientists, and other stakeholders first identify specific decisions to be informed by science, and then jointly define the scope and context of the problem, research questions, methods, and outputs, make scientific inferences, and develop strategies for the appropriate use of science. This study presents seven recommended practices intended to help scientists, managers, funders and other stakeholders carry out a coproduction project, one recommended practice to ensure that partners learn from attempts at coproduction, and two practices to promote coproduction at a programmatic level.