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Abstract Native American faculty in science, technology, engineering, and mathematics (NAF‐STEM) disciplines are historically underrepresented. Creating inclusive academia for Indigenous people that typically live and thrive in rural communities requires insights into their personal, relational, and collective experiences. This study was guided by the Six Rs: relationship, respect, responsibility, relevance, representation, and reciprocity, and was informed by Indigenous Research Methodologies. Twelve NAF‐STEM from tribal colleges and non‐tribal institutions were asked to share their perspectives and experiences in seven Research Circles. NAF‐STEM joined sequential hybrid workshops over seven weeks on how to conduct qualitative data analysis. Authors conducted analysis on the transcripts of Research Circles for themes associated with the professional satisfaction and success of NAF‐STEM. Results of the study identified the importance of holistic support systems that remain mindful of both the opportunities and challenges facing NAF‐STEM and emphasize the significance of balancing the need for respectful relationships, adequate representation, shared responsibility, relevance of diversity, and reciprocity in STEM. Through implementation of the Six Rs throughout the research process, the study identified successes, support systems, and challenges of NAF‐STEM at both tribal and non‐tribal colleges and universities. These outcomes can inform institutions to create an equitable and inclusive environment for NAF‐STEM. 

Abstract Beef production systems are at the center of ongoing discussion and debate on food systems sustainability. There is a growing interest among beef producers, consumers, and other beef supply chain stakeholders in achieving greater sustainability within the industry, but the relationship of this interest to general sustainability issues such as climate change, biodiversity loss, food security, livelihood risks, and animal welfare concerns is unclear. Specifically, there is very little research documenting how beef producers define and view the concept of sustainability and how to achieve it. Producer perspectives are critical to identifying constraints to sustainability transitions or to help build agreement with other producers about the shared values such transitions may support. Through a secondary analysis of survey data of U.S. beef producers (n = 911) conducted in 2021 by the Trust in Food division of Farm Journal, a corporation that provides content, data, and business insights to the agricultural community (e.g., producers, processors/distributors, and retailers), we investigated what “sustainable beef” means to U.S. beef producers, highlighting the key components and constraints they perceive to achieving desirable sustainability outcomes. Leveraging the three-pillar model of sustainability as a framework for analysis, we identified key themes producers use to define “sustainable beef.” We found that producers collectively viewed sustainability as: (1) multidimensional and interconnected; (2) semi-closed and regenerative; (3) long-lasting; and (4) producer-centered, although an integrated perspective uniting these aspects was rare. We discuss how these perspectives may be the basis for sustainability efforts supported by producers and raise future research considerations toward a shared understanding of what sustainability is and what is needed for enduring sustainability solutions in the U.S. beef industry. 

Abstract Social connections among individuals are essential components of social‐ecological systems (SESs), enabling people to take actions to more effectively adapt or transform in response to widespread social‐ecological change. Although scholars have associated social connections and cognitions with adaptive capacity, measuring actors' social networks may further clarify pathways for bolstering resilience‐enhancing actions.We asked how social networks and socio‐cognitions, as components of adaptive capacity, and SES regime shift severity affect individual landscape management behaviours using a quantitative analysis of ego network survey data from livestock producers and landcover data on regime shift severity (i.e. juniper encroachment) in the North American Great Plains.Producers who experienced severe regime shifts or perceived high risks from such shifts were not more likely to engage in transformative behaviour like prescribed burning. Instead, we found that social network characteristics explained significant variance in transformative behaviours.Policy implications: Our results indicate that social networks enable behaviours that have the potential to transform SESs, suggesting possible leverage points for enabling capacity and coordination toward sustainability. Particularly where private lands dominate and cultural practices condition regime shifts, clarifying how social connections promote resilience may provide much needed insight to bolster adaptive capacities in the face of global change. Read the freePlain Language Summaryfor this article on the Journal blog. 

Abstract We present measurements of volatile organic compounds (VOCs) and other trace gases taken in Salt Lake City, Utah in August and September 2022. As part of the Salt Lake regional Smoke, Ozone and Aerosol Study (SAMOZA), 35 VOCs were measured with two methods: a proton‐transfer‐reaction time‐of‐flight mass spectrometer (PTR‐ToF‐MS) and 2,4‐dinitrophenylhydrazine (DNPH) cartridges analyzed by high‐performance liquid chromatography (HPLC). Over two months, the total measured VOCs averaged 32 ± 24 ppb (mean ± standard deviation) with the hourly maximum at 141 ppb, and the total calculated OH reactivity averaged 3.7 ± 3.0 s⁻¹(maximum at 20.7 s⁻¹). Among them, methanol and ethanol were the most abundant VOCs, making up 42% of the ambient mixing ratio. Isoprene and monoterpenes contributed 25% of the OH reactivity from VOCs, while formaldehyde and acetaldehyde made up another 30%. The positive matrix factorization analysis showed 5 major sources of VOCs, with 32% of abundance being attributed to secondary production/biogenic sources, 44% from the combination of traffic and personal care products, 15% from industrial solvent use, and the rest from biomass burning (10%). Moderate smoke‐impacted days elevated various hazardous air pollutants (HAPs) on average by 45%–217% compared to smoke‐free days. The ratio of OH reactivity from NO_xto that from VOCs showed that ozone production was mostly VOC‐limited throughout the campaign, consistent with our modeling study. VOCs and NO_xboth showed increased OH reactivity due to smoke influence. NO_xfeatured increased reactivity on weekdays compared to weekends, an effect not shown for VOC reactivity during SAMOZA. 

Abstract Wildlife conservation depends on supportive social as well as biophysical conditions. Social identities such as hunter and nonhunter are often associated with different attitudes toward wildlife. However, it is unknown whether dynamics within and among these identity groups explain how attitudes form and why they differ. To investigate how social identities help shape wildlife‐related attitudes and the implications for wildlife policy and conservation, we built a structural equation model with survey data from Montana (USA) residents (n = 1758) that tested how social identities affect the relationship between experiences with grizzly bears (Ursus arctos horribilis) and attitudes toward the species. Model results (r² = 0.51) demonstrated that the hunter identity magnified the negative effect of vicarious property damage on attitudes toward grizzly bears (β = −0.381, 95% confidence interval [CI]: −0.584 to −0.178,p < 0.001), which in turn strongly influenced acceptance (β = −0.571, 95% CI: −0.611 to −0.531,p < 0.001). Our findings suggested that hunters’ attitudes toward grizzly bears likely become more negative primarily because of in‐group social interactions about negative experiences, and similar group dynamics may lead nonhunters to disregard the negative experiences that out‐group members have with grizzly bears. Given the profound influence of social identity on human cognitions and behaviors in myriad contexts, the patterns we observed are likely important in a variety of wildlife conservation situations. To foster positive conservation outcomes and minimize polarization, management strategies should account for these identity‐driven perceptions while prioritizing conflict prevention and promoting positive wildlife narratives within and among identity groups. This study illustrates the utility of social identity theory for explaining and influencing human–wildlife interactions. 

Abstract Wildfires have become larger and more frequent because of climate change, increasing their impact on air pollution. Air quality forecasts and climate models do not currently account for changes in the composition of wildfire emissions during the commonly observed progression from more flaming to smoldering combustion. Laboratory measurements have consistently shown decreased nitrogen dioxide (NO₂) relative to carbon monoxide (CO) over time, as they transitioned from more flaming to smoldering combustion, while formaldehyde (HCHO) relative to CO remained constant. Here, we show how daily ratios between column densities of NO₂versus those of CO and HCHO versus CO from the Tropospheric Monitoring Instrument (TROPOMI) changed for large wildfires in the Western United States. TROPOMI‐derived emission ratios were lower than those from the laboratory. We discuss reasons for the discrepancies, including how representative laboratory burns are of wildfires, the effect of aerosols on trace gas retrievals, and atmospheric chemistry in smoke plumes. 

Abstract Agricultural and prescribed burning activities emit large amounts of trace gases and aerosols on regional to global scales. We present a compilation of emission factors (EFs) and emission ratios from the eastern portion of the Fire Influence on Regional to Global Environments and Air Quality (FIREX‐AQ) campaign in 2019 in the United States, which sampled burning of crop residues and other prescribed fire fuels. FIREX‐AQ provided comprehensive chemical characterization of 53 crop residue and 22 prescribed fires. Crop residues burned at different modified combustion efficiencies (MCE), with corn residue burning at higher MCE than other fuel types. Prescribed fires burned at lower MCE (<0.90) which is typical, while grasslands burned at lower MCE (0.90) than normally observed due to moist, green, growing season fuels. Most non‐methane volatile organic compounds (NMVOCs) were significantly anticorrelated with MCE except for ethanol and NMVOCs that were measured with less certainty. We identified 23 species where crop residue fires differed by more than 50% from prescribed fires at the same MCE. Crop residue EFs were greater for species related to agricultural chemical use and fuel composition as well as oxygenated NMVOCs possibly due to the presence of metals such as potassium. Prescribed EFs were greater for monoterpenes (5×). FIREX‐AQ crop residue average EFs generally agreed with the previous agricultural fire study in the US but had large disagreements with global compilations. FIREX‐AQ observations show the importance of regionally‐specific and fuel‐specific EFs as first steps to reduce uncertainty in modeling the air quality impacts of fire emissions. 

Obtaining high certainty in predictive models is crucial for making informed and trustworthy decisions in many scientific and engineering domains. However, extensive experimentation required for model accuracy can be both costly and time-consuming. This paper presents an adaptive sampling approach designed to reduce epistemic uncertainty in predictive models. Our primary contribution is the development of a metric that estimates potential epistemic uncertainty leveraging prediction interval-generation neural networks.This estimation relies on the distance between the predicted upper and lower bounds and the observed data at the tested positions and their neighboring points. Our second contribution is the proposal of a batch sampling strategy based on Gaussian processes (GPs). A GP is used as a surrogate model of the networks trained at each iteration of the adaptive sampling process. Using this GP, we design an acquisition function that selects a combination of sampling locations to maximize the reduction of epistemic uncertainty across the domain.We test our approach on three unidimensional synthetic problems and a multi-dimensional dataset based on an agricultural field for selecting experimental fertilizer rates.The results demonstrate that our method consistently converges faster to minimum epistemic uncertainty levels compared to Normalizing Flows Ensembles, MC-Dropout, and simple GPs.