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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. 

Abstract AimAddressing global environmental challenges requires access to biodiversity data across wide spatial, temporal and taxonomic scales. Availability of such data has increased exponentially recently with the proliferation of biodiversity databases. However, heterogeneous coverage, protocols, and standards have hampered integration among these databases. To stimulate the next stage of data integration, here we present a synthesis of major databases, and investigate (a) how the coverage of databases varies across taxonomy, space, and record type; (b) what degree of integration is present among databases; (c) how integration of databases can increase biodiversity knowledge; and (d) the barriers to database integration. LocationGlobal. Time periodContemporary. Major taxa studiedPlants and vertebrates. MethodsWe reviewed 12 established biodiversity databases that mainly focus on geographic distributions and functional traits at global scale. We synthesized information from these databases to assess the status of their integration and major knowledge gaps and barriers to full integration. We estimated how improved integration can increase the data coverage for terrestrial plants and vertebrates. ResultsEvery database reviewed had a unique focus of data coverage. Exchanges of biodiversity information were common among databases, although not always clearly documented. Functional trait databases were more isolated than those pertaining to species distributions. Variation and potential incompatibility of taxonomic systems used by different databases posed a major barrier to data integration. We found that integration of distribution databases could lead to increased taxonomic coverage that corresponds to 23 years’ advancement in data accumulation, and improvement in taxonomic coverage could be as high as 22.4% for trait databases. Main conclusionsRapid increases in biodiversity knowledge can be achieved through the integration of databases, providing the data necessary to address critical environmental challenges. Full integration across databases will require tackling the major impediments to data integration: taxonomic incompatibility, lags in data exchange, barriers to effective data synchronization, and isolation of individual initiatives. 

Abstract The species composition of plant and animal assemblages across the globe has changed substantially over the past century. How do the dynamics of individual species cause this change? We classified species into seven unique categories of temporal dynamics based on the ordered sequence of presences and absences that each species contributes to an assemblage time series. We applied this framework to 14,434 species trajectories comprising 280 assemblages of temperate marine fishes surveyed annually for 20 or more years. Although 90% of the assemblages diverged in species composition from the baseline year, this compositional change was largely driven by only 8% of the species' trajectories. Quantifying the reorganization of assemblages based on species shared temporal dynamics should facilitate the task of monitoring and restoring biodiversity. We suggest ways in which our framework could provide informative measures of compositional change, as well as leverage future research on pattern and process in ecological systems. 

Abstract Dated, geo‐referenced museum specimens are a rich data source for reconstructing species' distribution and abundance patterns. However, museum records are potentially biased towards over‐representation of rare species, and it is unclear whether museum records can be used to estimate relative abundance in the field.We assembled 17 coupled field and museum datasets to quantitatively compare relative abundance estimates with the Dirichlet distribution. Collectively, these datasets comprise 73,039 museum records and 1,405,316 field observations of 2,240 species.Although museum records of rare species overestimated relative abundance by 1‐fold to over 100‐fold (median study = 9.0), the relative abundance of species estimated from museum occurrence records was strongly correlated with relative abundance estimated from standardized field surveys (r²range of 0.10–0.91, median study = 0.43).These analyses provide a justification for estimating species relative abundance with carefully curated museum occurrence records, which may allow for the detection of temporal or spatial shifts in the rank ordering of common and rare species. 

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.