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Purpose: The lack of critical thinking in new graduates has been a concern to the nursing profession. The purpose of this study was to investigate the effects of an innovative, evidence-based skills fair intervention on nursing students' achievements and perceptions of critical thinking skills development. Design/methodology/approach: The explanatory sequential mixed-methods design was employed for this study. Findings: The findings indicated participants perceived the intervention as a strategy for developing critical thinking. Originality/value: The study provides educators helpful information in planning their own teaching practice in educating students.

Muskrat (Ondatra zibethicus) populations have been in apparent decline across their native range in North America for decades. Several hypotheses exist for the causes of these declines, including loss of wetlands. We used time‐to‐detection data from 925 surveys from 276 sites across Rhode Island, USA, between 2021–2023 to fit an occupancy model that related the probability of muskrat occupancy at a site to land cover classification. We found that muskrat occupancy was higher in areas with more open water, urban land cover, or a second‐order or larger stream, and lower in areas with salt water. We estimated changes in wetland area throughout Rhode Island using the National Land Cover Database classifications from 2001 and 2019 and found a net loss in wetland cover of 219 ha. We calculated the distance between wetland patches in each of these periods and found that patches were closer together than the dispersal distance of muskrats, suggesting isolation is unlikely to be driving muskrat declines. Additionally, when we used our model to predict changes in muskrat occupancy between 2001 and 2019, both mean and median predicted occupancy changed by <0.005. These results indicate that muskrat declines are not driven by habitat loss, and suggest future research is needed that focuses on other hypothesized mechanisms of muskrat declines such as disease, declining habitat quality, predation, and competition. [ABSTRACT FROM AUTHOR]

Animal diel activity patterns can aid understanding of (a) how species behaviourally adapt to anthropogenic and natural disturbances, (b) mechanisms of species co‐existence through temporal partitioning, and (c) community or ecosystem effects of diel activity shifts. Activity patterns often vary spatially, a feature ignored by the kernel density estimators (KDEs) currently used for estimating diel activity. Ignoring this source of heterogeneity may lead to biased estimates of uncertainty and misleading conclusions regarding the drivers of diel activity. Thus, there is a need for more flexible statistical approaches for estimating activity patterns and testing hypotheses regarding their biotic and abiotic drivers. We illustrate how trigonometric terms and cyclic cubic splines combined with hierarchical models can provide a valuable alternative to KDEs. Like KDEs, these models accommodate circular data, but they can also account for site‐to‐site and other sources of variability, correlation amongst repeated measures, and variable sampling effort. They can also more readily quantify and test hypotheses related to the effects of covariates on activity patterns. Through empirical case studies, we illustrate how hierarchical models can quantify changes in activity levels due to seasonality and in response to biotic and abiotic factors (e.g. anthropogenic stressors and co‐occurrence). We also describe frequentist and Bayesian approaches for quantifying site‐specific (conditional) and population‐averaged (marginal) activity patterns. We provide guidelines and tutorials with detailed step‐by‐step instructions for fitting and interpreting hierarchical models applied to time‐stamped data, such as those recorded by camera traps and audio recorders. We conclude that this approach offers a viable, flexible, and effective alternative to KDEs when modelling animal activity patterns. [ABSTRACT FROM AUTHOR]

Shellfish aquaculture is a globally expanding industry, including in urban estuaries that support non‐breeding waterfowl. The effects of shellfish aquaculture on the spatial distribution of non‐breeding waterfowl, however, are poorly understood and depend on the ecology of waterfowl and cultivated shellfish species. We investigated how proximity to shellfish aquaculture influenced habitat selection and movement patterns of American black ducks (Anas rubripes) during the non‐breeding season (~Oct–May) in 2020–2023 in Rhode Island, USA. The extent to which proximity to aquaculture influenced habitat selection of black ducks depended on factors specific to individuals' primary non‐breeding sites, although proximity to aquaculture did not have biologically meaningful influences on black duck movement rates across all sites. Black ducks across sites consistently selected for areas better suited for aquaculture (i.e., areas of potential future development) relative to areas poorly suited for aquaculture (i.e., areas less likely to be developed). The continued expansion of aquaculture into preferred black duck habitats will increase black duck interactions with aquaculture and therefore should be considered in the decision‐making process for siting future aquaculture leases. Future studies should quantify the extent to which continued expansion of aquaculture in those preferred coastal habitats directly influences black ducks. [ABSTRACT FROM AUTHOR]

Human dimensions research is valuable to managing human‐wildlife interactions, especially in urban environments where such interactions are common. Survey data, which commonly contain Likert scales and questions, are useful in this field; however, these data can be difficult to analyze with formal modeling approaches. We demonstrate one approach, based on hierarchical Bayesian ordinal regression, to evaluate human‐coyote relationships in Rhode Island, USA. We implemented a survey to collect demographic and sociocultural characteristics of Rhode Island residents and information related to their knowledge of and experiences with coyotes. Our objectives were to assess how these characteristics affected respondents' valuation of and interactions (sightings and incidents) with coyotes. We analyzed 980 surveys from October to December 2020. We found that respondents who had fear of coyotes or experienced an incident between an owned animal and coyote, had the lowest valuation of coyotes. The same demographic of respondents also reported the highest sightings of and incidents with coyote. These results indicate that fearful residents, in addition to pet and livestock owners, are priority targets for disseminating information or programming about coyotes. Our analyses and findings demonstrate how Bayesian ordinal regression can provide clear and appropriate inference from survey data on how groups of people vary in their relationship with wildlife. These results are important in effectively and efficiently allocating resources towards mitigation, education, and management of human‐wildlife interactions. [ABSTRACT FROM AUTHOR]

Understanding mesocarnivore responses to both natural and anthropogenic disturbance is crucial for understanding species' potential to maintain landscape persistence into the future. We examined the response of five mesocarnivore species (bobcat, coyote, fisher, gray fox, and red fox) to both types of disturbances and climatic conditions. The Northeastern U.S. has experienced multiple large‐scale disturbances, such as a mass defoliation event following larval spongy moth outbreak and high densities of infrastructure that divide the natural landcover into roadless zones where these species inhabit. Using dynamic occupancy models in a Bayesian framework, we aimed to (1) examine variation in species' responses over a 4‐year study by estimating variation in site‐level occupancy, colonization and extirpation of each species in the state of Rhode Island relative to natural disturbance (i.e., defoliation event), anthropogenic disturbance (i.e., parceling of natural landcover bounded by roads, distance to roads), and climate (i.e., seasonal precipitation) and (2) compare current occurrence trends to predicted asymptotic occupancy to identify key variables contributing to distribution instability. Our findings indicated declines in the occurrence of both fox species, and fisher. There was variation in mesocarnivore response to disturbance among the species. We found gray fox and fisher occupancy dynamics to be sensitive to all forms of disturbance and coyote occurrence was positively associated with anthropogenic disturbance. Although bobcat and red fox were predicted to respond positively to future climate scenarios, fisher and gray fox were not, and persistence of fisher and gray fox in a landscape of disturbance relies on large areas with high forest and shrubland cover. With the wide‐spread spongy moth outbreak across much of southern New England, our findings indicate that efforts to conserve forested lands may be crucial in maintaining the persistence of several mesocarnivore species in this region experiencing large‐scale disturbance. [ABSTRACT FROM AUTHOR]

Harvested wildlife populations should ideally be monitored to inform harvest policies and decision‐making to help achieve management objectives. When the age of harvested individuals can be obtained, these data (i.e., age‐at‐harvest data) can be used to estimate trends of abundances, demographic rates, and harvest probabilities by the statistical reconstruction of the living population. This approach was developed primarily within the frequentist framework and requires the inclusion of auxiliary data (e.g., radiotelemetry data). We developed a novel Bayesian hierarchical approach allowing the population reconstruction from the definition of the species' life cycle without auxiliary data. The hierarchical model assumes that individuals are harvested from an open population whose fluctuations result from demographic processes, and the definition of a superpopulation composed of pseudo‐individuals from which the harvested population is drawn. We evaluated the ability of our model to estimate abundances, survival, recruitment, and harvest probabilities based on simulations guided by the demographic processes of a long‐lived mammal population. We considered model performance across scenarios, including varying age and temporal structures, superpopulation size, and prior information. We showed how prior information selected based on life history characteristics affects the accuracy of estimated parameters. We found that the model estimates accurate demographic parameters and abundances when the age‐at‐harvest matrix comprises more than two age classes. Furthermore, an increase in demographic information (number of age groups and years) increased the precision of the estimated parameters. We apply our model to a population of harvested (2012–2021) white‐tailed deer (Odocoileus virginianus) and a mammalian carnivore, the fisher (Pekania pennanti), from Rhode Island, USA. Our model estimated biologically realistic population size and demographic rates for both species. Our approach provides robustness to track the population abundance of harvested species through time and estimate fundamental demographic parameters. Such results can be used to monitor whether population objectives are being met and whether harvest policy changes are required. Furthermore, this information can be critical for evaluating the effect of harvest on population growth and projecting trajectories of age‐structured populations under different harvest scenarios. Therefore, our framework can help to guide management decisions and species conservation. [ABSTRACT FROM AUTHOR]

Research purpose. Smart City technologies offer great promise for a higher quality of life, including improved public services, in an era of rapid and intense global urbanization. The use of intelligent or smart information and communication technologies to produce more efficient systems of services in those urban areas, captured under the broad rubric of “smart cities,” also create new vectors of risk and vulnerability. The aim of this article is to raise consideration of an integrated cross-domain approach for risk reduction based on the risks smart cities are exposed to, on the one hand, from natural disasters and, on the other, from cyber-attacks.

The social system of animals involves a complex interplay between physiology, natural history, and the environment. Long relied upon discrete categorizations of "social" and "solitary" inhibit our capacity to understand species and their interactions with the world around them. Here, we use a globally distributed camera trapping dataset to test the drivers of aggregating into groups in a species complex (martens and relatives, family Mustelidae, Order Carnivora) assumed to be obligately solitary. We use a simple quantification, the probability of being detected in a group, that was applied across our globally derived camera trap dataset. Using a series of binomial generalized mixed-effects models applied to a dataset of 16,483 independent detections across 17 countries on four continents we test explicit hypotheses about potential drivers of group formation. We observe a wide range of probabilities of being detected in groups within the solitary model system, with the probability of aggregating in groups varying by more than an order of magnitude. We demonstrate that a species' context-dependent proclivity toward aggregating in groups is underpinned by a range of resource-related factors, primarily the distribution of resources, with increasing patchiness of resources facilitating group formation, as well as interactions between environmental conditions (resource constancy/winter severity) and physiology (energy storage capabilities). The wide variation in propensities to aggregate with conspecifics observed here highlights how continued failure to recognize complexities in the social behaviors of apparently solitary species limits our understanding not only of the individual species but also the causes and consequences of group formation. [ABSTRACT FROM AUTHOR]

How animals use the diel period (24‐h light–dark cycle) is of fundamental importance to understand their niche. While ecological and evolutionary literature abound with discussion of diel phenotypes (e.g. diurnal, nocturnal, crepuscular, cathemeral), they lack clear and explicit quantitative definitions. As such, inference can be confounded when evaluating hypotheses of animal diel niche switching or plasticity across studies because researchers may be operating under different definitions of diel phenotypes.We propose quantitative definitions of diel phenotypes using four alternative hypothesis sets (maximizing, traditional, general and selection) aimed at achieving different objectives. Each hypothesis set is composed of mutually exclusive hypotheses defined based on the activity probabilities in the three fundamental periods of light availability (twilight, daytime and night‐time).We develop a Bayesian modelling framework that compares diel phenotype hypotheses using Bayes factors and estimates model parameters using a multinomial model with linear inequality constraints. Model comparison, parameter estimation and visualizing results can be done in the Diel.Niche R package. A simplified R Shiny web application is also available.We provide extensive simulation results to guide researchers on the power to discriminate among hypotheses for a range of sample sizes (10–1280). We also work through several examples of using data to make inferences on diel activity, and include online vignettes on how to use the Diel.Niche package. We demonstrate how our modelling framework complements other analyses, such as circular kernel density estimators and animal movement modelling.Our aim is to encourage standardization of the language of diel activity and bridge conceptual frameworks and hypotheses in diel research with data and models. Lastly, we hope more research focuses on the ecological and conservation importance of understanding how animals use diel time. [ABSTRACT FROM AUTHOR]

Anthropogenic developments alter the environment and resources available to wildlife communities. In response to these real or perceived threats from this development, species may adjust their spatial occurrence. Additionally, wildlife species may adjust when in diel time (24‐h light–dark cycle) they occupy sites on the landscape to adapt to changing conditions. However, many wildlife studies only focus on where a species does and does not occur, ignoring how species may shift their diel activity at sites to mitigate threats. We used a multi‐state diel occupancy modeling framework to investigate how a community of mammals (mesocarnivores, urban‐adapted omnivores, and herbivore/small mammals) respond to differing levels of anthropogenic development and forest cover across two climatic seasons. We collected camera trap data at 240 survey locations across the summer and winter of 2021–2022. We modeled multi‐state diel occupancy for 14 mammal species with extent of development/forest and season hypothesized to influence diel occupancy and season hypothesized to influence the probability of detection. We found that all species displayed heterogeneity in both diel occupancy and detection either by extent of development/forest and or season. Within the mesocarnivore species group, coyote and red fox were less sensitive to development and had higher occupancy probability at these sites in general but used them more during the night, while more sensitive mesocarnivores including fisher and bobcat occupied the day state only when there was increasing forest cover. Our results highlight the importance of incorporating diel activity in habitat modeling to better understand the relationship between a species and its landscape, particularly in a region that is vulnerable to increased anthropogenic pressure. [ABSTRACT FROM AUTHOR]

Inconsistent performance by gifted students has been a source of frustration for both parents and educators for decades. Several studies on gifted under achievement point to a connection between student learning styles and classroom performance.This study examined the learning styles of gifted middle school students, student perceptions of the classroom environment, and possible connections between learning style, classroom environment, and achievement levels. Eighty gifted students from grades 6, 7 & 8 were administered the Learning Style Inventory (LSI) to identify student learning style preferences. They were also administered the Student Perception Inventory (SPI), developed for the study, in order to determine perceptions of these learning style elements in their classroom environments. Results indicated that the LSI elements of persistence and lighting correlated with achievement in all content areas. Additionally, correlations between higher grade point averages (GPA) and LSI preferences for responsibility and teacher motivation were found in science and math classes. Results of the SPI revealed a correlation between higher grade point averages in social studies and science classrooms and the following items: persistence; motivation; and auditory, tactile, and kinesthetic modalities. All subject areas showed a correlation between higher GPA and the students' perceived level of persistence. Putting the research to use: This study suggests that gifted middle school students should participate in many hands-on activities dealing with real-world problems. Because they have a high preference for tactile and kinesthetic learning activities, these students are more likely to remain motivated and engaged when they are active participants in the discovery process. Gifted students in this study also expressed preference for an informal seating design, which indicates a need for an environment that is flexible and allows students to change their seating based on the nature of the learning activity. These learning style preferences should be taken into consideration when designing programs for gifted students. This is especially true for gifted learners who are performing below their potential. This study also suggests that gifted students will achieve at higher levels when taught by informed teachers who are aware of their learning characteristics and needs. Teachers also need to look at their classrooms and determine how to alter the environment so that students will be more inclined to engage in the learning process. Administering a learning style inventory can provide teachers with valuable information so that learning can be maximized. While most gifted students achieve satisfactorily, many of them will not reach their potential unless the curriculum responds to their needs and stimulates their thinking.