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SUMMARY The strength of the subduction interface plays a major role in controlling subduction dynamics on both local and global scales. While previous studies have primarily examined interface strength in 2-D models, natural subduction zones are inherently 3-D, with interface strength varying along-strike due to spatial differences in factors such as sediment input. Here, we use the geodynamic code ASPECT to conduct fully dynamic 3-D subduction models in which interface strength varies along-strike. We find that the interaction between strong and weak segments of the interface leads to a narrower range of convergence velocities while broadening the range of viable interface stresses compared to 2-D or homogeneous 3-D models. Stronger segments, when adjacent to weaker ones, exhibit increased convergence velocities. This promotes higher interface stresses and facilitates the subduction of otherwise stagnant strong segments. We find that the interface viscosity of the strong segment controls the baseline stress, whereas the viscosity contrast along-strike controls the magnitude of amplification of the stress due to velocity increases. The elevated interface stresses at strong segments also generate greater compressional forces in the overriding plate than expected from 2-D models. Combined with along-strike variations in convergence velocity, this results in trench migration, with stronger segments displaying more advanced trench positions relative to weaker segments. Possible natural analogues include the Bolivian Orocline in the central Andes and the Lesser Antilles, both of which show enhanced overriding plate compression and trench advance in areas of reduced sediment supply. 

Metal and nutrient loads were calculated from 2019-2024 from the inflow stream to Falling Creek Reservoir (FCR), a drinking water reservoir located in Vinton, Virginia, USA. The reservoir is owned and operated by the Western Virginia Water Authority and is managed as a secondary drinking-water source for the city of Roanoke, VA. Only Fe, Mn, and nutrients (TN and TP) were analyzed and calculated in 2019. The full suite of metals (Li, Na, Mg, Al, K, Ca, Fe, Mn, Cu, Sr, Ba) and nutrients were analyzed from 2020-2024. The loads that were collected using an ISCO automated sampler located at the main inflow tributary to FCR. Sampling frequency was approximately fortnightly from spring to fall (March - November). Load calculations were performed using the calculated cumulative flow over the sampling period from the ISCO and the analyzed total metal and nutrient concentrations. Please note we are publishing this data package before the nutrient samples have been analyzed, but will be included in later versions. 

Abstract Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates. 

Abstract Zooplankton play an integral role as indicators of water quality in freshwater ecosystems, but exhibit substantial variability in their density and community composition over space and time. This variability in zooplankton community structure may be driven by multiple factors, including taxon-specific migration behavior in response to environmental conditions. Many studies have highlighted substantial variability in zooplankton communities across spatial and temporal scales, but the relative importance of space vs. time in structuring zooplankton community dynamics is less understood. In this study, we quantified spatial (a littoral vs. a pelagic site) and temporal (hours to years) variability in zooplankton community structure in a eutrophic reservoir in southwestern Virginia, USA. We found that zooplankton community structure was more variable among sampling dates over 3 years than among sites or hours of the day, which was associated with differences in water temperature, chlorophyll a, and nutrient concentrations. Additionally, we observed high variability in zooplankton migration behavior, though a slightly greater magnitude of DHM vs. DVM during each sampling date, likely due to changing environmental conditions. Ultimately, our work underscores the need to continually integrate spatial and temporal monitoring to understand patterns of zooplankton community structure and behavior in freshwater ecosystems. 

Abstract Models have become a key component of scientific hypothesis testing and climate and sustainability planning, as enabled by increased data availability and computing power. As a result, understanding how the perceived ‘complexity’ of a model corresponds to its accuracy and predictive power has become a prevalent research topic. However, a wide variety of definitions of model complexity have been proposed and used, leading to an imprecise understanding of what model complexity is and its consequences across research studies, study systems, and disciplines. Here, we propose a more explicit definition of model complexity, incorporating four facets—model class, model inputs, model parameters, and computational complexity—which are modulated by the complexity of the real‐world process being modelled. We illustrate these facets with several examples drawn from ecological literature. Overall, we argue that precise terminology and metrics of model complexity (e.g., number of parameters, number of inputs) may be necessary to characterize the emergent outcomes of complexity, including model comparison, model performance, model transferability and decision support. 

STEM technician education programs face a world in which cutting-edge technologies are transforming existing industries and creating new ones at an unprecedented pace. In light of this, the NSF ATE project Preparing Technicians for the Future of Work conducted industry site interviews and regional convenings of academic partners and industry leaders representing a wide range of technical fields to learn how technology impacts technician job tasks and roles. Through these activities, the project identified three skill areas common across multiple technologies and deemed essential for future STEM technicians: data knowledge/analysis, advanced digital literacy, and business knowledge/processes. These “cross-disciplinary STEM core” skill sets and recommendations for integrating them into technical programs are described in A Framework for a Cross-Disciplinary STEM Core. To facilitate adoption of the Framework at a systemic level, the project is sharing an adoption toolkit with concrete steps a college can take, tools it can use with employers to prioritize STEM Core skill sets and faculty activities for identifying where prioritized skills are taught within existing program curriculum and instructional gaps where new cross-disciplinary skill sets could be easily integrated. 

Because of increased variability in populations, communities, and ecosystems due to land use and climate change, there is a pressing need to know the future state of ecological systems across space and time. Ecological forecasting is an emerging approach which provides an estimate of the future state of an ecological system with uncertainty, allowing society to preemptively prepare for fluctuations in important ecosystem services. However, forecasts must be effectively designed and communicated to those who need them to make decisions in order to realize their potential for protecting natural resources. In this module, students will explore real ecological forecast visualizations, identify ways to represent uncertainty, make management decisions using forecast visualizations, and learn decision support techniques. Lastly, students customize a forecast visualization for a specific stakeholder's decision needs. The overarching goal of this module is for students to understand how forecasts are connected to decision-making of stakeholders, or the managers, policy-makers, and other members of society who use forecasts to inform decision-making. The A-B-C structure of this module makes it flexible and adaptable to a range of student levels and course structures. This EDI data package contains instructional materials and the files necessary to teach the module. Readers are referred to the Zenodo data package (Woelmer et al. 2022; DOI: 10.5281/zenodo.7074674) for the R Shiny application code needed to run the module locally. 

Abstract Communicating and interpreting uncertainty in ecological model predictions is notoriously challenging, motivating the need for new educational tools, which introduce ecology students to core concepts in uncertainty communication. Ecological forecasting, an emerging approach to estimate future states of ecological systems with uncertainty, provides a relevant and engaging framework for introducing uncertainty communication to undergraduate students, as forecasts can be used as decision support tools for addressing real‐world ecological problems and are inherently uncertain. To provide critical training on uncertainty communication and introduce undergraduate students to the use of ecological forecasts for guiding decision‐making, we developed a hands‐on teaching module within the Macrosystems Environmental Data‐Driven Inquiry and Exploration (EDDIE;MacrosystemsEDDIE.org) educational program. Our module used an active learning approach by embedding forecasting activities in an R Shiny application to engage ecology students in introductory data science, ecological modeling, and forecasting concepts without needing advanced computational or programming skills. Pre‐ and post‐module assessment data from more than 250 undergraduate students enrolled in ecology, freshwater ecology, and zoology courses indicate that the module significantly increased students' ability to interpret forecast visualizations with uncertainty, identify different ways to communicate forecast uncertainty for diverse users, and correctly define ecological forecasting terms. Specifically, students were more likely to describe visual, numeric, and probabilistic methods of uncertainty communication following module completion. Students were also able to identify more benefits of ecological forecasting following module completion, with the key benefits of using forecasts for prediction and decision‐making most commonly described. These results show promise for introducing ecological model uncertainty, data visualizations, and forecasting into undergraduate ecology curricula via software‐based learning, which can increase students' ability to engage and understand complex ecological concepts. 

Depth profiles of water temperature on 1m intervals from 0.1 to 9 m depth; dissolved oxygen at 5 and 9 m depth; pressure at 9 m depth; and temperature, dissolved oxygen, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, fluorescent dissolved organic matter, and pressure at ~1.6 m depth were collected with a suite of high-frequency sensors at Falling Creek Reservoir (Vinton, Virginia, USA) on the 10-minute scale in 2018-2022. Falling Creek Reservoir is owned and managed by the Western Virginia Water Authority as a primary drinking water source for Roanoke, Virginia. This data product consists of one dataset compiled from water temperature data measured at multiple depths by thermistors, two dissolved oxygen sensors at multiple depths, pressure measured at one depth, and a YSI EXO2 sonde that measures temperature, dissolved oxygen, pressure, conductivity, specific conductance, chlorophyll a, phycocyanin, total dissolved solids, and fluorescent dissolved organic matter, at one depth, all measured at the deepest site of the reservoir adjacent to the dam.