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Marine heatwaves cause widespread environmental, biological, and socio-economic impacts, placing them at the forefront of 21st-century management challenges. However, heatwaves vary in intensity and evolution, and a paucity of information on how this variability impacts marine species limits our ability to proactively manage for these extreme events. Here, we model the effects of four recent heatwaves (2014, 2015, 2019, 2020) in the Northeastern Pacific on the distributions of 14 top predator species of ecological, cultural, and commercial importance. Predicted responses were highly variable across species and heatwaves, ranging from near total loss of habitat to a two-fold increase. Heatwaves rapidly altered political bio-geographies, with up to 10% of predicted habitat across all species shifting jurisdictions during individual heatwaves. The variability in predicted responses across species and heatwaves portends the need for novel management solutions that can rapidly respond to extreme climate events. As proof-of-concept, we developed an operational dynamic ocean management tool that predicts predator distributions and responses to extreme conditions in near real-time. This study examines the effect of four marine heatwaves in the Northeast Pacific on the distributions of 14 top predators, revealing a wide-array of predator responses both among and within heatwaves. Predator responses were highly predictable, demonstrating capacity for early warning systems of heatwave impacts, similar to weather forecasts.

Assessing the current state of and predicting change in the ocean’s biological and ecosystem resources requires observations and research to safeguard these valuable public assets. The Marine Biodiversity Observation Network (MBON) partnered with the Global Ocean Observing System Biology and Ecosystems Panel and the Ocean Biodiversity Information System to address these needs through collaboration, data standardization, and data sharing. Here, we describe the generalized MBON data processing flow, which includes several steps to ensure that data are findable, accessible, interoperable, and reusable. By following this flow, data collected and managed by MBON have contributed to our understanding of the Global Ocean Observing System Essential Ocean Variables and demonstrated the value of web-based, interactive tools to explore and better understand environmental change. Although the MBON’s generalized data processing flow is already in practice, work remains in building ontologies for biological concepts, improving processing scripts for data standardization, and speeding up the data collection-to-sharing timeframe.

The California Current Trophic Database (CCTD) was developed at NOAA Southwest Fisheries Science Center in collaboration with numerous diet data contributors. We compiled the CCTD from twenty-four data sets, representing both systematic collections and directed trophic studies. Diet composition data, including stomach and scat samples, were obtained from 105,694 individual predators among 143 taxa collected throughout the California Current Large Marine Ecosystem (CCLME) from 1967–2019. Predator taxa consist of squids (n = 5), elasmobranchs (n = 13), bony fishes (n = 118), and marine mammals (n = 7). Extensive time series are available for some predators (e.g., California Sea Lion, Pacific Hake, Chinook Salmon). The CCTD represents the largest compilation of raw trophic data within the CCLME, allowing for more refined analyses and modeling studies within this region. Our intention is to further augment and periodically update the dataset as additional historical or contemporary data become available to increase its utility and impact.

Dynamic management (DM) is a novel approach to spatial management that aligns scales of environmental variability, animal movement and human uses. While static approaches to spatial management rely on one‐time assessments of biological, environmental, economic, and/or social conditions, dynamic approaches repeatedly assess conditions to produce regularly updated management recommendations. Owing to this complexity, particularly regarding operational challenges, examples of applied DM are rare. To implement DM, scientific methodologies are operationalized into tools, i.e., self‐contained workflows that run automatically at a prescribed temporal frequency (e.g., daily, weekly, monthly). Here we present a start‐to‐finish framework for operationalizing DM tools, consisting of four stages: Acquisition, Prediction, Dissemination, and Automation. We illustrate this operationalization framework using an applied DM tool as a case study. Our DM tool operates in near real‐time and was designed to maximize target catch and minimize bycatch of non‐target and protected species in a US‐based commercial fishery. It is important to quantify the sensitivity of DM tools to missing data, because dissemination streams for observed (i.e., remotely sensed or directly sampled) data can experience delays or gaps. To address this issue, we perform a detailed example sensitivity analysis using our case study tool. Synthesis and applications. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four‐stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives. Dynamic management (DM) tools are emerging as viable management solutions to accommodate the biological, environmental, economic, and social variability in our fundamentally dynamic world. Our four‐stage operationalization framework and case study can facilitate the implementation of DM tools for a wide array of resource and disturbance management objectives.

Species and habitats are the subjects of legislation that mandates reporting of information on ecosystem conditions. Improvements in sensors, sampling platforms, information systems, and collaborations among experts and information users now enables more effective and up-to-date information to meet regional and national needs. Specifically, advances in environmental DNA (eDNA)-based assessments of biodiversity, community science data, various underwater imaging devices, and environmental, behavioral, and physiology observations from animal telemetry provide new opportunities to address multiple requirements for reporting status and trends, including insights into life in the deep ocean. Passive and active acoustic sensors help monitor marine life, boat traffic, and noise pollution. Satellites provide repeated, frequent, and long-term records of many relevant variables from global to local scales and, when combined with numerical computer simulations, allow planning for future scenarios. Metadata standards facilitate the transfer of data from machine to machine, thus streamlining assessments and forecasting and providing knowledge directly to the public. The Marine Biodiversity Observation Network (MBON) facilitates this exchange of information on life in the sea. The collaborative efforts of the Central and Northern California Ocean Observing System (CeNCOOS) of the US Integrated Ocean Observing System and its partners provide an example of a regional MBON process for information delivery. This includes linking policy and management needs, prioritizing observing data from various platforms and methods, streamlining data handling practices, and delivering information for management such as for the Monterey Bay National Marine Sanctuary and the California Current Large Marine Ecosystem, with iterative process adaptation.

Objectives: Falls are the leading source of accidental injury and hospitalization among adults over the age of 65. Relative to people with intact cognition, individuals with cognitive impairment are at increased risk for falls; however, few falls prevention programs exist to specifically reduce and prevent falls in this population. To address this issue, we developed a novel, multifactorial, cognitively-based falls prevention program, Stepping Out. Based on the popular and effective evidenced-based program, Stepping On, Stepping Out was modified and tailored to the learning needs of individuals with Mild Cognitive Impairment (MCI). We hypothesized that older adults with MCI would find the program understandable, and that program participants would demonstrate reduced falls. Methods: Sixteen veterans, mean age of 77.5, diagnosed with MCI and at increased risk for falls participated in Stepping Out. Falls were collected for the six months prior to intervention and the six months during and after program participation. All participants completed post-program evaluations. Falls incidence was compared using a Wilcoxon paired signed rank test. Results: Stepping Out was found to be feasible and comprehensible by all participants. Program participants exhibited significantly reduced falls, with median reduction of two falls (p = 0.0020), and a range of zero to 12 falls. Discussion: With appropriate modifications, individuals with MCI were able to benefit from a cognitively-based falls prevention program and to reduce accidental falls incidence. Falls are an important and feasible target to address among individuals with cognitive impairment.

The purpose of this qualitative study was to offer an ideal environment, which was defined as a one-semester graduate course, where teachers could be exposed to sound theoretical information about African American learners through research, literature and actual classroom inquiry. The main research questions posed in this study were: (1) What kind of environment is needed to encourage reflective thinking and support teachers in their process of becoming culturally inclusive curriculum developers? and (2) What social or curricular features of the study enabled teachers to become more willing to put their beliefs into practice. Key findings of the study were that teachers needed supportive and collaborative environments to enable them to obtain the insights needed to create curriculum that address the diverse needs of their African American students. Recommendations were made from the research findings which would impact undergraduate teacher education methodology and state policy in regards to curriculum development.

Purpose This study aims to uncover factors related to students’ preference for ebooks with hopes that understanding what drives these preferences will help librarians to figure out how to increase students’ use of ebooks. Design/methodology/approach Based on the unified theory of acceptance and use of technology, researchers developed a model of ebook preference and a survey including constructs related to perceived usefulness and perceived ease of use. Respondents were undergraduate students from a core political science course at a large research university in the USA. Findings Ebook performance expectancy and ebook self-efficacy have indirect effects on ebook preference by way of ebook attitude. Ebook attitude and social influence both have direct effects. Research limitations/implications One of the limitation include respondents being of a similar age and having a similar experience of technology and ebooks. Prior use of ebooks may partially explain the results. Practical implications Librarians should help students develop ebook self-efficacy. Vendors should consider how interfaces may impact ebook self-efficacy. Ebook attitude may be positively influenced by tapping students’ desire to utilize technology. Originality/value This research adds to the understanding about ebook preference while expanding research in libraries by applying a theory and model from another research discipline.

Chester B. Pond, L. Edwin Smart, DeWitt W. Krueger, M. M. Daugherty, Lynn A. Stiles, William G. Herzel, Shelby S. Reid, Jr., Denzel C. Cline, Beulah B. Thull, W. O. Suiter, Ernest M. Black, and John H. Russell discuss the functions of a research section (p. 168), research in California (p. 170), Delaware (p. 173), Illinois (p. 173), Kentucky (p. 175), Louisiana (p. 177), Michigan (p. 180), New York (p. 180, 181), North Carolina (p. 182), Oklahoma (p. 186), and Virginia (p. 186).