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• We investigated the variation in tree fine root traits and their functional diversity along a local topographic gradient in a Neotropical montane forest to test if fine root trait variation along the gradient is consistent with the predictions of the root economics spectrum on a shift from acquisitive to conservative traits with decreasing resource supply. • We measured five fine root functional traits in 179 randomly selected tree individuals of 100 species and analysed the variation of single traits (using Bayesian phylogenetic multilevel models) and of functional trait diversity with small-scale topography. • Fine roots exhibited more conservative traits (thicker diameters, lower specific root length and nitrogen concentration) at upper slope compared with lower slope positions, but the largest proportion of variation (40–80%) was explained by species identity and phylogeny. Fine root functional diversity decreased towards the upper slopes. • Our results suggest that local topography and the related soil fertility and moisture gradients cause considerable small-scale variation in fine root traits and functional diversity along tropical mountain slopes, with conservative root traits and greater trait convergence being associated with less favourable soil conditions due to environmental filtering. We provide evidence of a high degree of phylogenetic conservation in fine root traits.

Background In medical, social, and behavioral research we often encounter datasets with a multilevel structure and multiple correlated dependent variables. These data are frequently collected from a study population that distinguishes several subpopulations with different (i.e., heterogeneous) effects of an intervention. Despite the frequent occurrence of such data, methods to analyze them are less common and researchers often resort to either ignoring the multilevel and/or heterogeneous structure, analyzing only a single dependent variable, or a combination of these. These analysis strategies are suboptimal: Ignoring multilevel structures inflates Type I error rates, while neglecting the multivariate or heterogeneous structure masks detailed insights. Methods To analyze such data comprehensively, the current paper presents a novel Bayesian multilevel multivariate logistic regression model. The clustered structure of multilevel data is taken into account, such that posterior inferences can be made with accurate error rates. Further, the model shares information between different subpopulations in the estimation of average and conditional average multivariate treatment effects. To facilitate interpretation, multivariate logistic regression parameters are transformed to posterior success probabilities and differences between them. Results A numerical evaluation compared our framework to less comprehensive alternatives and highlighted the need to model the multilevel structure: Treatment comparisons based on the multilevel model had targeted Type I error rates, while single-level alternatives resulted in inflated Type I errors. Further, the multilevel model was more powerful than a single-level model when the number of clusters was higher. A re-analysis of the Third International Stroke Trial data illustrated how incorporating a multilevel structure, assessing treatment heterogeneity, and combining dependent variables contributed to an in-depth understanding of treatment effects. Further, we demonstrated how Bayes factors can aid in the selection of a suitable model. Conclusion The method is useful in prediction of treatment effects and decision-making within subpopulations from multiple clusters, while taking advantage of the size of the entire study sample and while properly incorporating the uncertainty in a principled probabilistic manner using the full posterior distribution.

Background This paper focuses on the period from 2019 to 2021 and investigates the factors associated with the high prevalence of C-section deliveries in South India. We also examine the nuanced patterns, socio-demographic associations, and spatial dynamics underlying C-section choices in this region. A cross-sectional study was conducted using large nationally representative survey data. Methods National Family Health Survey data (NFHS) from 2019 to 2021 have been used for the analysis. Bayesian Multilevel and Geospatial Analysis have been used as statistical methods. Results Our analysis reveals significant regional disparities in C-section utilization, indicating potential gaps in healthcare access and socio-economic influences. Maternal age at childbirth, educational attainment, healthcare facility type size of child at birth and ever pregnancy termination are identified as key determinants of method of C-section decisions. Wealth index and urban residence also play pivotal roles, reflecting financial considerations and access to healthcare resources. Bayesian multilevel analysis highlights the need for tailored interventions that consider individual household, primary sampling unit (PSU) and district-level factors. Additionally, spatial analysis identifies regions with varying C-section rates, allowing policymakers to develop targeted strategies to optimize maternal and neonatal health outcomes and address healthcare disparities. Spatial autocorrelation and hotspot analysis further elucidate localized influences and clustering patterns. Conclusion In conclusion, this research underscores the complexity of C-section choices and calls for evidence-based policies and interventions that promote equitable access to quality maternal care in South India. Stakeholders must recognize the multifaceted nature of healthcare decisions and work collaboratively to ensure more balanced and effective healthcare practices in the region.

The daily association between 24-hour physical behavior compositions (moderate-to-vigorous physical activity (MVPA), light physical activity (LPA), standing, sedentary, and sleep) and psychological outcomes—such as momentary affective state assessments and working memory—remains understudied. We investigated whether the daily 24-hour compositions, particularly MVPA and SB considering the remaining behaviors, are associated with affective states and working memory. We conducted an ambulatory assessment study with 199 university employees. Physical behaviors were measured continuously via thigh-worn accelerometers throughout the day. Affective states (i.e., valence, energetic arousal, and calmness) and working memory performance (i.e., numeric updating task) were captured up to six times a day via electronic diaries and tasks on a smartphone. We conducted Bayesian multilevel compositional data analysis to analyze within-person, and between-person associations of 24-hour physical behavior composition with affective states, and working memory. Aggregated same-day outcomes were used for main analyses to capture concurrent associations, and next-day outcomes were used for exploratory analyses to capture prospective associations. Concurrent analyses showed that higher moderate-to-vigorous physical activity relative to the remaining physical behaviors was associated with 2.49 [95%CI 1.00, 4.06] higher valence and 3.65 [95%CI 2.11, 5.28] higher energetic arousal (but not calmness) ratings at the within-person, but not at the between-person level. Sedentary behavior relative to the remaining physical behaviors was not associated with any affective states. Spending more time in moderate-to-vigorous physical activity, followed by light physical activity, and standing, each at the expense of the other behaviors was associated with higher affective state ratings on the same day ( between-person : ≥1.29 [0.19, 2.51] higher valence, 1.23 [0.04, 2.40] higher calmness; within-person : ≥0.62 [0.04, 1.22] higher valence, ≥ 1.10 [0.63, 1.58] higher energetic arousal, ≥ 0.95 [0.18, 1.74] higher calmness). The 24-hour physical behavior composition was not associated with working memory. Findings underline the importance of the 24-hour composition of physical behavior for mental health, by demonstrating significant concurrent associations with affective states. Even small reallocations of behaviors may positively influence affective states, providing valuable insights for the development of future interventions.

Partnerships in marine monitoring combining Traditional Ecological Knowledge and western science are developing globally to improve our understanding of temporal changes in ecological communities that better inform coastal management practices. A fuller communication between scientists and Indigenous partners about the limitations of monitoring results to identify change is essential to the impact of monitoring datasets on decision-making. Here we present a 5-year co-developed case study from a fish monitoring partnership in northwest Australia showing how uncertainty estimated by Bayesian models can be incorporated into monitoring management indicators. Our simulation approach revealed there was high uncertainty in detecting immediate change over the following monitoring year when translated to health performance indicators. Incorporating credibility estimates into health assessments added substantial information to monitoring trends, provided a deeper understanding of monitoring limitations and highlighted the importance of carefully selecting the way we evaluate management performance indicators.

Magnetic resonance imaging (MRI) diffusion studies have shown chronic microstructural tissue abnormalities in athletes with history of concussion, but with inconsistent findings. Concussions with post‐traumatic amnesia (PTA) and/or loss of consciousness (LOC) have been connected to greater physiological injury. The novel mean apparent propagator (MAP) MRI is expected to be more sensitive to such tissue injury than the conventional diffusion tensor imaging. This study examined effects of prior concussion severity on microstructure with MAP‐MRI. Collegiate‐aged athletes (N = 111, 38 females; ≥6 months since most recent concussion, if present) completed semistructured interviews to determine the presence of prior concussion and associated injury characteristics, including PTA and LOC. MAP‐MRI metrics (mean non‐Gaussian diffusion [NG Mean], return‐to‐origin probability [RTOP], and mean square displacement [MSD]) were calculated from multi‐shell diffusion data, then evaluated for associations with concussion severity through group comparisons in a primary model (athletes with/without prior concussion) and two secondary models (athletes with/without prior concussion with PTA and/or LOC, and athletes with/without prior concussion with LOC only). Bayesian multilevel modeling estimated models in regions of interest (ROI) in white matter and subcortical gray matter, separately. In gray matter, the primary model showed decreased NG Mean and RTOP in the bilateral pallidum and decreased NG Mean in the left putamen with prior concussion. In white matter, lower NG Mean with prior concussion was present in all ROI across all models and was further decreased with LOC. However, only prior concussion with LOC was associated with decreased RTOP and increased MSD across ROI. Exploratory analyses conducted separately in male and female athletes indicate associations in the primary model may differ by sex. Results suggest microstructural measures in gray matter are associated with a general history of concussion, while a severity‐dependent association of prior concussion may exist in white matter. Mean apparent propagator‐magnetic resonance imaging (MAP‐MRI) is sensitive to diffusion differences associated with history of prior concussion. A severity‐dependent association of prior concussion with widespread differences in MAP‐MRI metrics was observed in white matter. A general history of prior concussion is associated with localized differences in MAP‐MRI metrics in gray matter.

Background: Red needle cast (RNC), caused by Phytophthora pluvialis Reeser, W.L. Sutton & E.M. Hansen, is a significant foliar disease impacting Pinus radiata D.Don in New Zealand. First detected in 2005, the disease has now been observed in all regions of the country. In the most severe cases, defoliation of entire tree crowns can occur at a landscape scale. While some evidence of growth loss and productivity reduction has been reported, quantitative estimates of the effect of RNC on productivity are needed to inform disease management and mitigation decisions. This study aims to assess both short- and long-term losses in radial growth due to RNC. Methods: We used tree cores to quantify yearly basal area increments at two plantations: a 32-year-old stand in Wharerata Forest, with documented history of outbreaks both severe and cyclic in nature, and a 26-year-old stand in Kinleith Forest, where 8 years of continuous disease severity monitoring has been conducted at the tree level. A Bayesian multilevel modelling framework was used to predict growth losses due to RNC at both sites, accounting for yearly weather and outbreak severity. Results: We predicted a 31% to 51.5% radial growth loss in the year following an RNC outbreak, with reduced growth detectable for 3 to 4 years after disease, amounting to up to 30.6% growth loss over the course of a single event. Recurring disease events every three to four years can lead to a 20% reduction in total radial area growth over the period encompassing the presence of the disease, with no evidence that each additional RNC event aggravates growth loss. Conclusions: 

Background In Ethiopia, malaria is one of the public health problems, and it is still among the ten top leading causes of morbidity and mortality among under-five children. However, the studies conducted in the country have been inconclusive and inconsistent. Thus, this study aimed to assess factors associated with malaria among under-five children in Ethiopia. Methods We retrieved secondary data from the malaria indicator survey data collected from September 30 to December 10, 2015, in Ethiopia. A total of 8301 under-five-year-old children who had microscopy test results were included in the study. Bayesian multilevel logistic regression models were fitted and Markov chain Monte Carlo simulation was used to estimate the model parameters using Gibbs sampling. Adjusted Odd Ratio with 95% credible interval in the multivariable model was used to select variables that have a significant association with malaria. Results In this study, sleeping under the insecticide-treated bed nets during bed time (ITN) [AOR 0.58,95% CI, 0.31–0.97)], having 2 and more ITN for the household [AOR 0.43, (95% CI, 0.17–0.88)], have radio [AOR 0.41, (95% CI, 0.19–0.78)], have television [AOR 0.19, (95% CI, 0.01–0.89)] and altitude [AOR 0.05, (95% CI, 0.01–0.13)] were the predictors of malaria among under-five children. Conclusions The study revealed that sleeping under ITN, having two and more ITN for the household, altitude, availability of radio, and television were the predictors of malaria among under-five children in Ethiopia. Thus, the government should strengthen the availability and utilization of ITN to halt under-five mortality due to malaria.

Background Stroke is a chronic cardiovascular disease that puts major stresses on U.S. health and economy. The prevalence of stroke exhibits a strong geographical pattern at the state-level, where a cluster of southern states with a substantially higher prevalence of stroke has been called the stroke belt of the nation. Despite this recognition, the extent to which key neighborhood characteristics affect stroke prevalence remains to be further clarified. Methods We generated a new neighborhood health data set at the census tract level on nearly 27,000 tracts by pooling information from multiple data sources including the CDC’s 500 Cities Project 2017 data release. We employed a two-stage modeling approach to understand how key neighborhood-level risk factors affect the neighborhood-level stroke prevalence in each state of the US. The first stage used a state-of-the-art Bayesian machine learning algorithm to identify key neighborhood-level determinants. The second stage applied a Bayesian multilevel modeling approach to describe how these key determinants explain the variability in stroke prevalence in each state. Results Neighborhoods with a larger proportion of older adults and non-Hispanic blacks were associated with neighborhoods with a higher prevalence of stroke. Higher median household income was linked to lower stroke prevalence. Ozone was found to be positively associated with stroke prevalence in 10 states, while negatively associated with stroke in five states. There was substantial variation in both the direction and magnitude of the associations between these four key factors with stroke prevalence across the states. Conclusions When used in a principled variable selection framework, high-performance machine learning can identify key factors of neighborhood-level prevalence of stroke from wide-ranging information in a data-driven way. The Bayesian multilevel modeling approach provides a detailed view of the impact of key factors across the states. The identified major factors and their effect mechanisms can potentially aid policy makers in developing area-based stroke prevention strategies.

Here we address the current issues of inefficiency and over-penalization in the massively univariate approach followed by the correction for multiple testing, and propose a more efficient model that pools and shares information among brain regions. Using Bayesian multilevel (BML) modeling, we control two types of error that are more relevant than the conventional false positive rate (FPR): incorrect sign (type S) and incorrect magnitude (type M). BML also aims to achieve two goals: 1) improving modeling efficiency by having one integrative model and thereby dissolving the multiple testing issue, and 2) turning the focus of conventional null hypothesis significant testing (NHST) on FPR into quality control by calibrating type S errors while maintaining a reasonable level of inference efficiency. The performance and validity of this approach are demonstrated through an application at the region of interest (ROI) level, with all the regions on an equal footing: unlike the current approaches under NHST, small regions are not disadvantaged simply because of their physical size. In addition, compared to the massively univariate approach, BML may simultaneously achieve increased spatial specificity and inference efficiency, and promote results reporting in totality and transparency. The benefits of BML are illustrated in performance and quality checking using an experimental dataset. The methodology also avoids the current practice of sharp and arbitrary thresholding in the p-value funnel to which the multidimensional data are reduced. The BML approach with its auxiliary tools is available as part of the AFNI suite for general use.