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This study was undertaken to determine any differences in the soil-moisture percentage at various soil depths between controlled and uncontrolled sagebrush areas, and to determine the effect of sagebrush control on the snow-holding capacity, of the areas. Published in Weeds, volume 9, issue 1, on pages 27 - 35, in 1961.
Categories: Publication; Types: Citation, Journal Citation; Tags: Weeds
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Mitigation of ecological damage caused by rangeland wildfires has historically been an issue restricted to the western United States. It has focused on conservation of ecosystem function through reducing soil erosion and spread of invasive plants. Effectiveness of mitigation treatments has been debated recently. We searched for literature on postfire seeding of rangelands worldwide. Literature databases searched included SCOPUS, Dissertation Abstracts, Forest Science, Tree search, Web of Science, Google Scholar, and science.gov. Search terms within publications included fire or wildfire in combination with seeding, rehabilitation, restoration, revegetation, stabilization, chaining, disking, drilling, invasives,...
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Post-fire rehabilitation seeding in the U.S. Intermountain West, primarily conducted by the Bureau of Land Management, is designed to reduce the risk of erosion and weed invasion while increasing desirable plant cover. Seeding effectiveness is typically monitored for three years following treatment, after which a closeout report is prepared. We evaluated 220 third-year closeout reports describing 214 aerial and 113 drill seedings implemented after wildfires from 2001 through 2006. Each treatment was assigned a qualitative success rating of good, fair, poor, or failure based on information in the reports. Seeding success varied by both treatment (aerial or drill) and year. Aerial seedings were rated 13.6% good, 18.3%...
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We developed a screening system to identify introduced plant species that are likely to increase wildfire risk, using the Hawaiian Islands to test the system and illustrate how the system can be applied to inform management decisions. Expert-based fire risk scores derived from field experiences with 49 invasive species in Hawai′i were used to train a machine learning model that predicts expert fire risk scores from among 21 plant traits obtained from literature and databases. The model revealed that just four variables can identify species categorized as higher fire risk by experts with 90% accuracy, while low risk species were identified with 79% accuracy. We then used the predictive model to screen 365 naturalized...


    map background search result map search result map Fire Risk Scores from Predictive Model Based on Flammability and Fire Ecology of Non-Native Hawaiian Plants from 2020-2021 Fire Risk Scores from Predictive Model Based on Flammability and Fire Ecology of Non-Native Hawaiian Plants from 2020-2021