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Rehder 1949.pdf
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Reiter, Mark
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Expertise Search
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Relationship between fire, climate oscillations, and drought in British Columbia, Canada, 1920–2000
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Climate oscillations such as El Nin ̃o–Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) are known to affect temperature and precipitation regimes and fire in different regions of the world. Understanding the relationships between climate oscillations, drought, and area burned in the past is required for anticipating potential impacts of regional climate change and for effective wildfire-hazard management. These relationships have been investigated for British Columbia (BC), Canada, either as part of national studies with coarse spatial resolution or for single ecosystems. Because of BC’s complex terrain and strong climatic gradients, an investigation with higher spatial resolution may allow for a spatially complete but differentiated picture. In this study, we analyzed the annual proportion burned– climate oscillation–drought relationships for the province’s 16 Biogeoclimatic Ecosystem Classification (BEC) zones. Analyses are based on a digital, spatially explicit fire database, climate oscillation indices, and monthly precipitation and temperature data with a spatial resolution of 400 m for the period 1920–2000. Results show that (1) fire variability is better related to summer drought than to climate oscillations, and that (2) fire variability is most strongly related to both, climate oscillations and summer drought in southeastern BC. The relationship of area burned and summer drought is strong for lower elevations in western BC as well. The influence of climate oscillations on drought is strongest and most extensive in winter and spring, with higher indices being related to drier conditions. Winter and spring PDO and additive winter and spring PDO ENSO indices show BC’s most extensive significant relationship to fire variability. Western BC is too wet to show a moisture deficit in summer that would increase annual area burned due to teleconnections.
Keywords: area burned, aridity index, Canada, ENSO, PDO, wildfire
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Relationships of Fire and Precipitation Regimes in Temperate Forests of the Eastern United States
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Fire affects virtually all terrestrial ecosystems but occurs more commonly in some than in others. This paper investigates how climate, specifically the moisture regime, influences the flammability of different landscapes in the eastern United States. A previous study of spatial differ- ences in fire regimes across the central Appalachian Mountains suggested that intra-annual precipitation variability influences fire occurrence more strongly than does total annual precipitation. The results presented here support that conclusion. The relationship of fire occurrence to moisture regime is also considered for the entire eastern United States. To do so, mean annual wildfire density and mean annual area burned were calculated for 34 national forests and parks representing the major vegetation and climatic conditions throughout the eastern forests. The relationship between fire activity and two climate variables was analyzed: mean annual moisture balance [precipitation P 2 potential evapotranspiration (PET)] and daily precipitation variability (coefficient of variability for daily precipitation). Fire activity is related to both climate variables but displays a stronger relationship with precipitation vari- ability. The southeastern United States is particularly noteworthy for its high wildfire activity, which is associated with a warm, humid climate and a variable precipitation regime, which promote heavy fuel production and rapid drying of fuels.
KEYWORDS: Wildfire; Fire climatology; Precipitation variability; Climatic variability
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Reliability of Indicators of Decline in Abundance
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Although there are many indicators of endangerment (i.e., whether populations or species meet criteria that justify conservation action), their reliability has rarely been tested. Such indicators may fail to identify that a population or species meets criteria for conservation action (false negative) or may incorrectly show that such criteria have been met (false positive). To quantify the rate of both types of error for 20 com- monly used indicators of declining abundance (threat indicators), we used receiver operating characteristic curves derived from historical (1938–2007) data for 18 sockeye salmon (Oncorhynchus nerka) populations in the Fraser River, British Columbia, Canada. We retrospectively determined each population’s yearly status (reflected by change in abundance over time) on the basis of each indicator. We then compared that popu- lation’s status in a given year with the status in subsequent years (determined by the magnitude of decline in abundance across those years). For each sockeye population, we calculated how often each indicator of past status matched subsequent status. No single threat indicator provided error-free estimates of status, but indicators that reflected the extent (i.e., magnitude) of past decline in abundance (through comparison of current abundance with some historical baseline abundance) tended to better reflect status in subsequent years than the rate of decline over the previous 3 generations (a widely used indicator). We recommend that when possible, the reliability of various threat indicators be evaluated with empirical analyses before such indicators are used to determine the need for conservation action. These indicators should include estimates from the entire data set to take into account a historical baseline.
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Reminder! TRBN Warrior Award Nominations
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Deadline extended: June 30, 2024.
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News & Events
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Remington Clench 1925.pdf
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Renewable Energy and Energy Efficiency Incentives: A Summary of Federal Programs
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Summary
Energy is crucial to the operation of a modern industrial and services economy. Recently, there have been growing concerns about the availability and cost of energy and about environmental impacts of fossil energy use. Those concerns have rekindled interest in energy efficiency, energy conservation, and the development and commercialization of renewable energy technologies.
Many of the existing energy efficiency and renewable energy programs have authorizations tracing back to the 1970s. Many of the programs have been reauthorized and redesigned repeatedly to meet changing economic factors. The programs apply broadly to sectors ranging from industry to academia, and from state and local governments to rural communities.
Since 2005, Congress has enacted several major energy laws: the Energy Policy Act of 2005 (EPACT 2005; P.L. 109-58); the Energy Independence and Security Act of 2007 (EISA; P.L. 110- 140); the Energy Improvement and Extension Act (EIEA), enacted as Division B of the Emergency Economic Stabilization Act (EESA; P.L. 110-343); and the American Reinvestment and Recovery Act (ARRA; P.L. 111-5). Each of those laws established, expanded, or modified energy efficiency and renewable energy research, development, demonstration, and deployment (RDD&D) programs. The Department of Energy (DOE) operates the greatest number of efficiency and renewable energy incentive programs. The Department of the Treasury and the Department of Agriculture (USDA) operate several programs. A few programs can also be found among the Departments of Interior (DOI), Labor (DOL), Housing and Urban Development (HUD), Veterans Affairs (VA), and the Small Business Administration (SBA).
This report describes federal programs that provide grants, loans, loan guarantees, and other direct or indirect incentives for energy efficiency, energy conservation, and renewable energy. For each program, the report provides the administering agency, authorizing statute(s), annual funding, and the program expiration date. The appendixes provide summary information in a tabular format and also list recently expired programs.
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Reparian Restoration Image Aug 2014
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Image RPCCR
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Tools & Resources
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Resampling Methods for Evaluating Classification Accuracy of Wildlife Habitat Models
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Predictive models of wildlife-habitat relationships often have been developed without being tested The
apparent classification accuracy of such models can be optimistically biased and misleading. Data resampling methods exist that yield a more realistic estimate of model classification accuracy. These methods are simple and require no new sample data. We illustrate these methods (cross-validation.
jackknife resampling, and bootstrap resampling) with computer simulation to demonstrate the increase in precision of the estimate. The bootstrap method is then applied to field data as a technique for model comparison We recommend that biologists use some resampling procedure to evaluatewildlife habitat models prior to field evaluation.
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