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Invasive forb and grass species are likely to expand their ranges and continued increases in temperature, aridity and area burned will increase invasion risk. Monitoring species presence and absence and mapping known and potential ranges with a focus on presence detection, as in our methodology, will aid in identifying new invasions and prioritizing prevention and control.
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Treatments in cooler and moister woodland sites had more positive effects on sagebrush recruitment and perennial grass cover, less negative effects on sagebrush intraspecific interactions, and smaller increases in annual grass cover indicating potential increases in resilience to fire. In warmer and drier invasion sites, reductions in woody fuels resulted in lack of sagebrush recruitment, disruption of sagebrush intraspecific interactions, and progressive increases in annual grass indicating reduced resilience to fire and resistance to invaders.
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Seeded native and introduced bunchgrasses both increased bunchgrass abundance and cover, even though precipitation was below average the first year post-seeding. Seeding introduced wheatgrasses, however, increased bunchgrass cover and abundance more than seeding native bunchgrasses. Seeding introduced wheatgrasses also limited exotic annual grass abundance and cover, but seeding locally sourced native bunchgrasses did not. Native bunchgrasses are slow growing, thus may limit exotic annual grasses in time. Alternatively, additional treatments, such as exotic annual grass control, may be needed to improve their success. The establishment of seeded native bunchgrasses in Wyoming big sagebrush in a below-average precipitation year is a promising result and suggests further research to improve seeded native vegetation success is warranted. The greater establishment of introduced wheatgrasses and their ability to limit exotic annual grasses suggests that successful introduced species may serve as a model for guiding trait selection in native species.
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The encroachment of pinyon-juniper woodlands into sagebrush habitat in the Great Basin Ecoregion of the western USA, represents a potential source of habitat degradation for sagebrush-associated wildlife species. To restore sagebrush habitat, managers are conducting large-scale conifer removal efforts within the Great Basin, particularly within Greater Sage-Grouse (Centrocercus urophasianus) priority areas for conservation. Such largescale habitat modification efforts may result in unintended ecological trade-offs for wildlife. To investigate these trade-offs, we used community science data to develop species distribution models for two sagebrush and three pinyon-juniper associated bird species of conservation concern in the Great Basin. We evaluated the predictive performance of our models with an independent dataset of presence locations derived from systematic monitoring programs. We then simulated conifer removal across the Great Basin and mapped habitat gains and losses for our study species. Despite differing land cover associations, 31%-51% of suitable habitat for our study species coincided with Greater Sage-Grouse priority areas for conservation. Our conifer removal scenario increased suitable habitat by 6%-17% for sagebrush associates and reduced habitat by 11%-41% for pinyon-juniper associates. We identified areas of the Great Basin where conifer removal expanded habitat for sagebrush associates without concurrent habitat loss for pinyon-juniper associates. Our results provide guidance for conducting vegetation management in the Great Basin while addressing the habitat needs for multiple focal species. Our methods, which use freely available community science data and geospatial layers, can easily be transferred to other species and ecoregions.
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This study describes an approach for identifying and monitoring the types of resource benefits and tradeoffs considered in National Forest planning in the United States under the 2012 Planning Rule and demonstrates the use of tools for conceptualizing the production of ecosystem services and benefits from alternative land management strategies. Efforts to apply these tools through workshops and engagement exercises provide opportunities to explore and highlight measures, indicators, and data sources for characterizing benefits and tradeoffs in collaborative environments involving interdisciplinary planning teams. Conceptual modeling tools are applied to a case study examining the social and economic benefits of recreation on the Ashley National Forest.
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This study shows that extreme fire events such as seen in 2020 are not unknown historically, and what stands out as distinctly new is the increased number of large fires (defined here as > 10,000 ha) in the last couple years, most prominently in 2020. Nevertheless, there have been other periods with even greater numbers of large fires, e.g., 1929 had the second greatest number of large fires. In fact, the 1920’s decade stands out as one with many large fires.
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We used a sample of 30 future fire seasons to understand how the plan might be impacted by wildfires and treatment. We found that once fully implemented more than 20% of simulated fires on national forests overlapped fuel treatments, and that roughly 20% of the projects were burned prior to their implementation, suggesting that any plan will undergo significant revision during implementation. Treated areas intersected by wildfire accounted for twice the exposure than non-treated areas that also burned. The study demonstrates the use of scenario planning to design a fuel treatment program that targets wildfire exposure to developed areas, and the methods pave the way for expanded use of scenario planning science to analyze and communicate large scale expansion of current forest and fuel management initiatives.
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Our results show that 57% of structures (homes, schools, hospitals, office buildings, etc.) are located in hazard hotspots, which represent only a third of CONUS area, and ∼1.5 million buildings lie in hotspots for two or more hazards. These critical levels of exposure are the legacy of decades of sustained growth and point to our inability, lack of knowledge, or unwillingness to limit development in hazardous zones. Development in these areas is still growing more rapidly than the baseline rates for the nation, portending larger future losses even if the effects of climate change are not considered.
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We produce a framework needed to compute the livelihood vulnerability index (LVI) for the top 14 American States that are most exposed to wildfires, based on the 2019 Wildfire Risk report of the acreage size burnt in 2018 and 2019: Arizona, California, Florida, Idaho, Montana, Nevada, New Mexico, Oklahoma, Oregon, Utah, Washington, and Wyoming. The LVI is computed for each State by first considering the State’s exposure, sensitivity, and adaptive capacity to wildfire events (known as the three contributing factors). These contributing factors are determined by a set of indictor variables (vulnerability metrics) that are categorized into corresponding major component groups. The framework structure is then justified by performing a principal component analysis (PCA) to ensure that each selected indicator variable corresponds to the correct contributing factor. The LVI for each State is then calculated based on a set of algorithms relating to our framework. LVI values rank between 0 (low LVI) to 1 (high LVI). Our results indicate that Arizona and New Mexico experience the greatest livelihood vulnerability, with an LVI of 0.57 and 0.55, respectively. In contrast, California, Florida, and Texas experience the least livelihood vulnerability to wildfires (0.44, 0.35, 0.33 respectively). LVI is strongly weighted on its contributing factors and is exemplified by the fact that even though California has one of the highest exposures and sensitivity to wildfires, it has very high adaptive capacity measures in place to withstand its livelihood vulnerability. Thus, States with relatively high wildfire exposure can exhibit relatively lower livelihood vulnerability because of adaptive capacity measures in place.
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We first analyzed interannual trends in six phenological measures as a baseline. We then demonstrated how including annual-resolution predictors can provide more nuanced insights into measures of phenology between plant communities and across the ecoregion. Across the study area, higher annual precipitation increased both peak and season-long productivity. In contrast, higher mean annual temperatures tended to increase peak productivity but for the majority of the study area decreased season-long productivity. Annual precipitation and temperature had strong explanatory power for productivity-related phenology measures but predicted date-based measures poorly. We found that relationships between climate and phenology varied across the region and among plant communities and that factors such as recovery from disturbance and anthropogenic management also contributed in certain regions. In sum, phenological measures did not respond ubiquitously nor covary in their responses. Nonclimatic dynamics can decouple phenology from climate; therefore, analyses including only interannual trends should not assume climate alone drives patterns.