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Aims Invasion by annual grasses (IAGs) and concomitant increases in wildfire are impacting many drylands globally, and an understanding of factors that contribute to or detract from community resistance to IAGs is needed to inform postfire restoration interventions. Prefire vegetation condition is often unknown in rangelands but it likely affects variation in postfire invasion resistance across large burned scars. Whether satellite‐derived products like the Rangeland Analysis Platform (RAP) can fulfill prefire information needs and be used to parametrize models of fire recovery to inform postfire management of IAGs is a key question. Methods We used random forests to ask how IAG abundances in 669 field plots measured in the 2‐3 years following megafires in sagebrush steppe rangelands of western USA responded to RAP estimates of annual:perennial prefire vegetation cover, the effects of elevation, heat load, postfire treatments, soil moisture–temperature regimes, and land‐agency ratings of ecosystem resistance to invasion and resilience to disturbance. Results Postfire IAG cover measured in the field was % and RAP‐estimated prefire annual herbaceous cover was %. The random forest model had an R² of 0.36 and a root‐mean‐squared error (RMSE) of 4.41. Elevation, postfire herbicide treatment, and prefire estimates from RAP for the ratio of annual:perennial and shrub cover were the most important predictors of postfire IAG cover. Threshold‐like relationships between postfire IAG cover and the predictors indicate that maintaining annual:perennial cover below 0.4 and shrub cover below <10% prior to wildfire would decrease invasion, at low elevations below 1400 m above sea level. Conclusion Despite known differences between RAP and field‐based estimates of vegetation cover, RAP was still a useful predictor of variation in IAG abundances after fire. IAG management is oftentimes reactive, but our findings indicate impactful roles for more inclusively addressing the exotic annual community, and focusing on prefire maintenance of annual:perennial herbaceous and shrub cover at low elevations.
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During the 2011-to-2021 study period, increases in the number of days of heavy smoke were observed in communities representing 87.3% of the US population, with notably large increases in communities characterized by racial or ethnic minority status, limited English proficiency, lower educational attainment, and crowded housing conditions.
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While increased length and intensity of wildfire seasons in many places have led to more concern about wildland firefighter safety, we believe ethnography has been underutilized as a method within this domain. In response, we begin building a shared idiom for ethnographic engagement with wildland firefighter safety and similar occupations. We draw on ethnographic approaches to late industrialism to develop a method called discursive risk analysis (DRA) as an initial stage in a broader collaborative and generative research practice. By collaborative, we mean cooperation among stakeholder, disciplinary, professional, and other groups. We use DRA to analyze ethnographic data and documentary sources relevant to discussions of ‘the Big Lie’ among firefighters and agency leadership. The Big Lie is a term that both firefighters and agency leaders used to suggest that wildland firefighters are being harmed by agency discourse that says firefighters will be kept safe despite the unavoidable danger of the job. It is important to the Big Lie discussion that this harm is conceptualized by firefighters as discursively driven, necessitating a research method attentive to discourse. Discursive Risk Analysis of the Big Lie discussion suggests two discursive gaps that may result in two discursive risks. The first gap, found in agency discourse, is that ‘everyone knows the job is dangerous’ but ‘zero fatalities is a reasonable goal.’ This gap is associated with a discursive risk, a possible decrease in trust among wildland firefighters in agency leadership. The second gap, observed in firefighter discourse, is that ‘the job is dangerous’ but ‘no one will get hurt today.’ This gap is associated with another discursive risk, the possibility of decreased situational awareness. Finally, we clarify each of these gaps and risks through two anthropological concepts (the Sapir–Whorf hypothesis and the public secret) that can bring new perspectives to discussions about institutional cultures of health and safety.
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Effective wildlife management requires robust information regarding population status, habitat requirements, and likely responses to changing resource conditions. Single-species management may inadequately conserve communities and result in undesired effects to non-target species. Thus, management can benefit from understanding habitat relationships for multiple species. Pinyon pine and juniper are expanding into sagebrush-dominated ecosystems within North America and mechanical removal of these trees is frequently conducted to restore sagebrush ecosystems and recover greater sage-grouse. However, pinyon-juniper removal effects on non-target species are poorly understood, and changing pinyon-juniper woodland dynamics, climate, and anthropogenic development may obscure conservation priorities. To better predict responses to changing resource conditions, evaluate non-target effects of pinyon-juniper removal, prioritize species for conservation, and inform species recovery within pinyon-juniper and sagebrush ecosystems, we modeled population trends and density-habitat relationships for four sagebrush-associated, four pinyon-juniper-associated, and three generalist songbird species with respect to these ecosystems. We fit hierarchical population models to point count data collected throughout the western United States from 2008 to 2020. We found regional population changes for 10 of 11 species investigated; 6 of which increased in the highest elevation region of our study. Our models indicate pinyon-juniper removal will benefit Brewer’s sparrow, green-tailed towhee, and sage thrasher densities. Conversely, we predict largest negative effects of pinyon-juniper removal for species occupying early successional pinyon-juniper woodlands: Bewick’s wren, black-throated gray warblers, gray flycatcher, and juniper titmouse. Our results highlight the importance of considering effects to non-target species before implementing large-scale habitat manipulations. Our modeling framework can help prioritize species and regions for conservation action, infer effects of management interventions and a changing environment on wildlife, and help land managers balance habitat requirements across ecosystems.
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Sagebrush shrublands in the Great Basin, USA, are experiencing widespread increases in wildfire size and area burned resulting in new policies and funding to implement fuel treatments. However, we lack the spatial data needed to optimize the types and locations of fuel treatments across large landscapes and mitigate fire risk. To address this, we developed treatment response groups (TRGs)—sagebrush and pinyon-juniper vegetation associations that differ in resilience to fire and resistance to annual grass invasion (R&R) and thus responses to fuel treatments.
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Many nations administer national forest inventory programs for unbiased estimation of forest attributes over broad spatial and temporal extents. However, management and conservation decisions often demand reliable estimates for finer spatiotemporal domains. In the western US, wildfire activity is expanding and postfire regeneration must contend with a warmer, drier climate. We evaluate the potential of K nearest neighbor (KNN) strategies for estimation of stocking across postfire measurements of Forest Inventory & Analysis plots in 11 western US states, and subsequently for model-assisted (MA) estimation of stocking over domains defined by aggregations of burned areas within individual states and 4-year periods. In particular, we develop and evaluate a form of constrained KNN that allows for unbiased MA domain estimation under simple random sampling by drawing only on measurements external to a domain of interest. KNN strategies based on geographically, radiometrically, and climatically proximate measurements are found to provide more accurate estimates of stocking at the plot level than domain means. Applying the selected external KNN strategy also reduced standard errors of MA domain estimates by 16% over direct domain estimators, but bias correction introduces substantial variability over synthetic estimates. Further applications of the external constraint imposed on KNN are discussed.
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Like many communities across the western United States, the greater Flagstaff area in northern Arizona has experienced multiple wildfires in recent years that have resulted in postfire flooding. The 2019 Museum Fire provides a case study for better understanding how the cascading disturbances of wildfire and postfire flooding, which can be further compounded by adjacent disturbances like monsoon-related flooding, impacted Flagstaff residents, and how they were informed of, perceive, and respond to these risks. In 2022, we conducted a survey in Flagstaff after 2021 flooding associated with the Museum Fire burn scar and monsoonal events to better understand attitudes “before” and “after” flooding. This resulted in findings in eight thematic areas: 1) respondent demographics; 2) geographic distribution of respondents in 2022; 3) experiences with recent flooding events; 4) communication during flood events; 4) flood risk perceptions; 6) flood insurance coverage; 7) mitigating flood risk; and 8) managing flood risk, wildfires, and forest management. This work builds upon a survey we completed in 2019 immediately following the Museum Fire that evaluated respondents’ experience with the fire and evacuation, communication of fire emergency information, and opinions regarding forest management.
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Wildfire regimes are changing dramatically across North American deserts with the spread of invasive grasses. Invasive grass fire cycles in historically fire-resistant deserts are resulting in larger and more frequent wildfire. This study experimentally compared how single and repeat fires influence invasive grass-dominated plant fuels in the Great Basin, a semi-arid, cold desert, and the Mojave, a hyper-arid desert. Both study sites had identical study designs. In the summer of 2011, we experimentally burned half of each experimental block, the other half remaining as an unburned control. Half of the burned plots were reburned 5 years later to simulate increasing burn frequency. We estimated non-woody plant biomass, cover, and density in plots from 2017 to 2020.
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Pyrodiversity may affect biodiversity by diversifying available ecological niches, stabilizing community networks and/or supporting diverse species pools available for post-fire colonization. Further, pyrodiversity’s effects on biodiversity vary across different spatial, temporal and organismal scales depending on the mobility and other life history traits of the organisms in question and
may be mediated by regional eco-evolutionary factors such as historical fire regimes. Developing a generalizable understanding of pyrodiversity effects on biodiversity has been challenging, in part because pyrodiversity can be quantified in various ways.
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Mapped representations of species−habitat relationships often underlie approaches to prioritize area-based conservation strategies to meet conservation goals for biodiversity. Generally a single surrogate species is used to inform conservation design, with the assumption that conservation actions for an appropriately selected species will confer benefits to a broader community of organisms. Emerging conservation frameworks across western North America are now relying on derived measures of intactness from remotely sensed vegetation data, wholly independent from species data. Understanding the efficacy of species-agnostic planning approaches is a critical step to ensuring the robustness of emerging conservation designs. We developed an approach to quantify ‘strength of surrogacy’, by applying prioritization algorithms to previously developed species models, and measuring their coverage provided to a broader wildlife community. We used this inference to test the relative surrogacy among a suite of species models used for conservation targeting in the endangered grasslands of the Northern Sagebrush Steppe, where careful planning can help stem the loss of private grazing lands to cultivation. In this test, we also derived a simpler surrogate of intact rangelands without species data for conservation targeting, along with a measure of combined migration representative of key areas for connectivity. Our measure of intactness vastly outperformed any species model as a surrogate for conservation, followed by that of combined migration, highlighting the efficacy of strategies that target large and intact rangeland cores for wildlife conservation and restoration efforts.