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Graduate students across disciplines are eager for experiential training that enables them to address real-world environmental challenges. Simultaneously, communities across the world face numerous environmental challenges, including increased frequency of extreme heat in summer and poor air quality, and could benefit from the expertise and engagement of graduate students with the requisite skills and interests to address these challenges. In this paper we bring together lessons learned from three interdisciplinary graduate training programs focused on preparing graduate students to contribute to urban environmental solutions by working in partnerships with non-academic organizations. We discuss the multiple elements required for partnerships to be mutually beneficial, including using a T-shaped approach to training that incorporates bothdepthandbreadthwhile making strong efforts to broaden participation. We share lessons with the goal of enhancing graduate programs to improve training of students to address urban environmental challenges globally. This training aligns with the United Nations Sustainable Development Goal 17, “Partnership for the Goals,” which aims to achieve sustainable development goals through partnerships among entities.

 

The assembly of species into communities and ecoregions is the result of interacting factors that affect plant and animal distribution and abundance at biogeographic scales. Here, we empirically derive ecoregions for mammals to test whether human disturbance has become more important than climate and habitat resources in structuring communities.

Conterminous United States.

2010–2021.

Twenty‐five species of mammals.

We analysed data from 25 mammal species recorded by camera traps at 6645 locations across the conterminous United States in a joint modelling framework to estimate relative abundance of each species. We then used a clustering analysis to describe 8 broad and 16 narrow mammal communities.

Climate was the most important predictor of mammal abundance overall, while human population density and agriculture were less important, with mixed effects across species. Seed production by forests also predicted mammal abundance, especially hard‐mast tree species. The mammal community maps are similar to those of plants, with an east–west split driven by different dominant species of deer and squirrels. Communities vary along gradients of temperature in the east and precipitation in the west. Most fine‐scale mammal community boundaries aligned with established plant ecoregions and were distinguished by the presence of regional specialists or shifts in relative abundance of widespread species. Maps of potential ecosystem services provided by these communities suggest high herbivory in the Rocky Mountains and eastern forests, high invertebrate predation in the subtropical south and greater predation pressure on large vertebrates in the west.

Our results highlight the importance of climate to modern mammals and suggest that climate change will have strong impacts on these communities. Our new empirical approach to recognizing ecoregions has potential to be applied to expanded communities of mammals or other taxa.

 

Trees in proximity to power lines can cause significant damage to utility infrastructure during storms, leading to substantial economic and societal costs. This study investigated the effectiveness of non-parametric machine learning algorithms in modeling tree-related outage risks to distribution power lines at a finer spatial scale. We used a vegetation risk model (VRM) comprising 15 predictor variables derived from roadside tree data, landscape information, vegetation management records, and utility infrastructure data. We evaluated the VRM’s performance using decision tree (DT), random forest (RF), k-Nearest Neighbor (k-NN), extreme gradient boosting (XGBoost), and support vector machine (SVM) techniques. The RF algorithm demonstrated the highest performance with an accuracy of 0.753, an AUC-ROC of 0.746, precision of 0.671, and an F1-score of 0.693. The SVM achieved the highest recall value of 0.727. Based on the overall performance, the RF emerged as the best machine learning algorithm, whereas the DT was the least suitable. The DT reported the lowest run times for both hyperparameter optimization (3.93 s) and model evaluation (0.41 s). XGBoost and the SVM exhibited the highest run times for hyperparameter tuning (9438.54 s) and model evaluation (112 s), respectively. The findings of this study are valuable for enhancing the resilience and reliability of the electric grid.

 

Interactions between plants and herbivores are central in most ecosystems, but their strength is highly variable. The amount of variability within a system is thought to influence most aspects of plant-herbivore biology, from ecological stability to plant defense evolution. Our understanding of what influences variability, however, is limited by sparse data. We collected standardized surveys of herbivory for 503 plant species at 790 sites across 116° of latitude. With these data, we show that within-population variability in herbivory increases with latitude, decreases with plant size, and is phylogenetically structured. Differences in the magnitude of variability are thus central to how plant-herbivore biology varies across macroscale gradients. We argue that increased focus on interaction variability will advance understanding of patterns of life on Earth.

 

Group incentives can and have been used to address a range of environmental and resource problems. These schemes base individual penalties and/or rewards on the performance of a group of individuals or firms who contribute to the environmental or resource problem. The economics literature on team incentives and public goods, as well as the literature specifically on environmental and natural resource management, provides insights into the design of group incentives. This article reviews the literature on group incentives in the context of environmental protection and natural resource policy. This literature suggests that group incentives can be effective and even efficient as environmental policy tools. However, the outcomes under group incentives will likely depend on a combination of the policy design and the nature of the internal group interactions. Within-group interactions are likely to be particularly important when policies involve thresholds so that coordination is needed to reach a cooperative equilibrium. Expected final online publication date for the Annual Review of Resource Economics, Volume 14 is October 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

Abstract Transformation toward a sustainable future requires an earth stewardship approach to shift society from its current goal of increasing material wealth to a vision of sustaining built, natural, human, and social capital—equitably distributed across society, within and among nations. Widespread concern about earth’s current trajectory and support for actions that would foster more sustainable pathways suggests potential social tipping points in public demand for an earth stewardship vision. Here, we draw on empirical studies and theory to show that movement toward a stewardship vision can be facilitated by changes in either policy incentives or social norms. Our novel contribution is to point out that both norms and incentives must change and can do so interactively. This can be facilitated through leverage points and complementarities across policy areas, based on values, system design, and agency. Potential catalysts include novel democratic institutions and engagement of non-governmental actors, such as businesses, civic leaders, and social movements as agents for redistribution of power. Because no single intervention will transform the world, a key challenge is to align actions to be synergistic, persistent, and scalable. 

There is widespread agreement about the need to assess the success of programs training scientists to communicate more effectively with non-professional audiences. However, there is little agreement about how that should be done. What do we mean when we talk about “effective communication”? What should we measure? How should we measure it? Evaluation of communication training programs often incorporates the views of students or trainers themselves, although this is widely understood to bias the assessment. We recently completed a 3-year experiment to use audiences of non-scientists to evaluate the effect of training on STEM (Science, Technology, Engineering and Math) graduate students’ communication ability. Overall, audiences rated STEM grad students’ communication performance no better after training than before, as we reported in Rubega et al. 2018. However, audience ratings do not reveal whether training changed specific trainee communication behaviors (e.g., jargon use, narrative techniques) even if too little to affect trainees’ overall success. Here we measure trainee communication behavior directly, using multiple textual analysis tools and analysis of trainees’ body language during videotaped talks. We found that student use of jargon declined after training but that use of narrative techniques did not increase. Flesch Reading Ease and Flesch-Kincaid Grade Level scores, used as indicators of complexity of sentences and word choice, were no different after instruction. Trainees’ movement of hands and hesitancy during talks was correlated negatively with audience ratings of credibility and clarity; smiling, on the other hand, was correlated with improvement in credibility, clarity and engagement scores given by audience members. We show that objective tools can be used to measure the success of communication training programs, that non-verbal cues are associated with audience judgments, and that an intensive communication course does change some, if not all, communication behaviors.