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Background:Minimum exam averages are an essential component to ensuring academic rigor and subsequent licensure in nursing education, yet there is scant evidence to support such practices. Method:Using a descriptive correlational design, nursing faculty at a medium-sized program in the Northeast explored the relationship between establishing a 77% (C+) minimum exam average requirement for the program and licensure exam passage rates between the 2023 cohort intervention group and the 2022 cohort control group. Results:The implementation of an exam average threshold per course produced a statistically significant effect on the National Council Licensure Examination (NCLEX-RN) pass rate (z = −3.481,p< .001) and provided support for the 77% (C+) examination threshold. Conclusion:A minimum exam average policy may relieve faculty of the moral distress associated with course failures, while also safeguarding academic rigor within the undergraduate program and promoting NCLEX-RN readiness and success. 

ABSTRACT The Importance of the Regional Species PoolThe regional species pool—the set of species capable of entering a local community—is a foundational concept for understanding ecological processes that occur between local and extensive (biogeographic) spatial scales. However, the lack of precise definitions for the regional species pool, coupled with limited research into the dynamics of regional biodiversity, has impeded the development of a comprehensive framework to explain the mechanisms shaping these pools. Processes Governing Regional Species PoolsAlthough ecological processes at local and extensive scales are relatively well understood, the mechanisms shaping regional biota remain less clear. Regional species pools are likely shaped by a unique set of processes that often overlap minimally with those operating at local or extensive scales. Despite their significance, our understanding of the specific mechanisms driving the dynamics of regional species pools remains incomplete. The Need for a Theory of Regional Species PoolsWe argue that it is essential to prioritise the study of the regional species pool for two reasons. First, the regional species pool bridges spatial and temporal scales from ecological dynamics in landscapes to the long‐term processes shaping the biotas of entire biogeographic provinces. As such, understanding the dynamics of species pools addresses fundamental questions about the origin, maintenance, and dynamics of biodiversity. Second, effective biodiversity conservation in the Anthropocene hinges on understanding the processes that operate at regional scales. 

ABSTRACT MotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Formatcsv and. SQL. 

Abstract There is considerable interest in understanding patterns of β‐diversity that measure the amount of change in species composition through space or time. Most hypotheses for β‐diversity evoke nonrandom processes that generate spatial and temporal within‐species aggregation; however, β‐diversity can also be driven by random sampling processes. Here, we describe a framework based on rarefaction curves that quantifies the nonrandom contribution of species compositional differences across samples to β‐diversity. We isolate the effect of within‐species spatial or temporal aggregation on beta‐diversity using a coverage standardized metric of β‐diversity (β_C). We demonstrate the utility of our framework using simulations and an empirical case study examining variation in avian species composition through space and time in engineered versus natural riparian areas. The primary strengths of our approach are that it provides an intuitive visual null model for expected patterns of biodiversity under random sampling that allows integrating analyses across α‐, γ‐, and β‐scales. Importantly, the method can accommodate comparisons between communities with different species pool sizes, and it can be used to examine species turnover both within and between meta‐communities. 

Summary Leaf economic spectrum (LES) relationships have been studied across many different plant lineages and at different organizational scales. However, the temporal stability of the LES relationships is largely unknown. We used the wild blueberry system with high genotypic diversity to test whether trait–trait relationships across genotypes demonstrate the same LES relationships found in the global database (GLOPNET) and whether they are stable across years.We studied leaf structure, photosynthesis, and leaf nutrients for 16 genotypes of two wild blueberry species semi‐naturally grown in a common farm in Maine, USA, across 4 yr.We found substantial variation in leaf structure, physiology, and nutrient traits within and among genotypes, as well as across years in wild blueberries. The LES trait–trait relationships (covariance structure) across genotypes were not always found in all years. The trait syndrome of wild blueberries was shifted by changing environmental conditions over the years. Additionally, traits in 1 yr cannot be used to predict those of another year.Our findings show that LES generally holds among genotypes but is temporally unstable, stressing the significant influence of trait plasticity in response to fluctuating environmental conditions across years, and the importance of temporal dimensions in shaping functional traits and species coexistence. 

Social scientists have highlighted jail incarceration as an important social problem in the United States. However, few national-level generalizable studies have investigated how the characteristics of communities and their local governments influence jail incarceration, despite jails being an intrinsically community-level institution largely run by county governments. In this paper, we ask how place-based community characteristics, particularly county government characteristics, are associated with jail incarceration. To answer this question, we draw primarily from two literatures, place-based punishment vulnerability and research on the local state, to develop a conceptual framework for analyzing local jail incarceration. We utilize a unique data set that includes primary data collected from county governments across the nation. We examine the extent to which socioeconomic, sociodemographic, and county government characteristics are associated with jail incarceration rates using multivariate regression analysis with state-fixed effects for 1400 counties. We find that elevated jail incarceration rates are found in high-poverty, evangelical counties of mid-level economic health with county governments that have introduced service cuts and have local leaders that engage in carceral entrepreneurship. These findings have important implications for the study of jails across the United States. 

Stochastic diffusion is the noisy process through which dynamics like epidemics, or agents like animal species, disperse over a larger area. These processes are increasingly important to better prepare for pandemics and as species ranges shift in response to climate change. Unfortunately, modelling is mostly done with expensive computational simulations or inaccurate deterministic tools that ignore the randomness of dispersal. We introduce ‘mean-FLAME’ models, tracking stochastic dispersion using approximate master equations to follow the probability distribution over all possible states of an area of interest, up to states active enough to be approximated using a mean-field model. In the limit where we track all states, this approach is locally exact, and in the other limit collapses to traditional deterministic models. In predator–prey systems, we show that tracking a handful of states around key absorbing states is sufficient to accurately model extinction. In disease models, we show that classic mean-field approaches underestimate the heterogeneity of epidemics. And in nonlinear dispersal models, we show that deterministic tools fail to capture the speed of spatial diffusion. These effects are all important for marginal areas that are close to unsuitable for diffusion, like the edge of a species range or epidemics in small populations. 

There is voluminous literature concerning the scope of topological relations that span various embedding spaces from R1 to R2, Z2 , S1 and S2 , and T2. In the case of the *1 spaces, those relations have been considered as conceptualizations of both spatial relations and temporal relations. Missing from that list are the set of digital relations that exist within Z1 , representing discretized time, discretized ordered line segments, or discretized linear features as embedding spaces. Discretized time plays an essential role in timeseries data, spatio-temporal information systems, and geo-foundation models where time is represented in layers of consecutive spatial rasters and/or spatial vector objects colloquially referred to as space–time cubes or spatio-temporal stacks. This paper explores the digital relations that exist in Z1 interpreted as a regular topological space under the digital Jordan curve model as well as a folded-over temporal interpretation of that space for use in spatio-temporal information systems and geo-foundation models. The digital Jordan curve model represents the maximum expressive power between discretized objects, making it the ideal paradigm for a decision support system model. It identifies 34 9-intersection relations in Z1 , 42 9-intersection + margin relations in Z1 , and 74 temporal relations in Z1 , utilizing the 9+-intersection, the commercial standard for spatial information systems for querying topological relations. This work creates opportunities for better spatio-temporal reasoning capacity within spatio-temporal stacks and a more direct interface with intuitive language concepts, instrumental for effective utilization of spatial tools. Three use cases are demonstrated in the discussion, representing each of the utilities of Z1 within the spatial data science community. 

Pests and pathogens are a primary threat to honey bee(Apis mellifera)colonies worldwide. Selective breeding for honey bees resistant to these stressors represents a promising approach for mitigating their impacts on honey bee health. UBeeO is a novel hygiene-eliciting selection tool that has been used to identify honey bee colonies that are resistant to the parasitic miteVarroa destructor, and that are more likely to survive winter without beekeeper intervention. Here, we used three separate case studies to evaluate the effectiveness of the UBeeO assay in identifying colonies resist to disease. In three distinct geographic regions, we measured UBeeO scores along with the prevalence and load of key fungal and viral honey bee pathogens. We show that UBeeO can be used to identify colonies resistant to several other diseases, including the two fungal pathogens chalkbrood (Ascosphaera apis) andVairimorphaspp. (previouslyNosema), and multiple viruses, all critically important to honey bee health and survival. Furthermore, we identify potential UBeeO resistance thresholds for each pathogen, demonstrating an inverse relationship between pathogen virulence and the minimum UBeeO score associated with resistance to that pathogen. These findings suggest that UBeeO-guided selection strategies have the potential to significantly improve honey bee breeding programs by facilitating identification of resilient and pathogen-resistant colonies. The broad geographic range of our study sites underscores the robustness and applicability of UBeeO across varying environmental contexts. Since honey bees provide essential pollination services in both natural and agricultural ecosystems, this work has major implications for environmental health, crop productivity, and food security on a global scale. 

Topological relations form the backbone of qualitative spatial reasoning and, as such, play a paramount role in geographic information systems. Three decades of research have provided a proliferation of sets of qualitative topological relations in both continuous and discretized spaces, but only in continuous spaces has the concept of organizing these relations into a larger framework (called a conceptual neighborhood graph) been considered. Previous work leveraged matrix differences to derive the anisotropic scaling neighborhood for these relations. In this paper, a simulation protocol is used to derive conceptual neighborhood graphs of qualitative topological relations in Z2 for the operations of translation and isotropic scaling. It is further shown that, when aggregating raster relations into their continuous counterparts and collapsing neighborhood connections within these groups, the familiar conceptual neighborhood structures for continuous regions appear.