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PurposeThere are notable geographic variations in incidence rates of suicide both in Japan and globally. Previous studies have found that rurality/urbanity shapes intra-regional differences in suicide mortality, and suicide risk associated with rurality can vary significantly by gender and age. This study aimed to examine spatial patterning of and rural–urban differences in suicide mortality by gender and age group across 1887 municipalities in Japan between 2009 and 2017.MethodsSuicide data were obtained from suicide statistics of the Ministry of Health, Labour and Welfare in Japan. We estimated smoothed standardized mortality ratios for suicide for each of the municipalities and investigated associations with level of rurality/urbanity using Bayesian hierarchical models before and after adjusting for socioeconomic characteristics.ResultsThe results of the multivariate analyses showed that, for males aged 0–39 and 40–59 years, rural residents tended to have a higher suicide risk compared to urban ones. For males aged 60+ years, a distinct rural–urban gradient in suicide risk was not observed. For females aged 0–39 years, a significant association between suicide risk and rurality was not observed, while for females aged 40–59 years and females aged 60 years or above, the association was a U-shaped curve.ConclusionOur results showed that geographical distribution of and rural–urban differences in suicide mortality in Japan differed substantially by gender and age. These findings suggest that it is important to take demographic factors into consideration when municipalities allocate resources for suicide prevention.

Evidence of climate-change-drivenshifts in plant and animal phenology haveraised concerns that certain trophic interactions may be increasingly mismatched in time,resultingin declines in reproductive success. Given the constraints imposed by extreme seasonalityat high latitudes and the rapid shifts in phenology seen in the Arctic, we would also expectAntarctic species to be highly vulnerable to climate-change-drivenphenological mismatcheswith their environment. However, few studies have assessed the impacts of phenological changein Antarctica. Using the largest database of phytoplankton phenology, sea-icephenology, andAdélie Penguin breeding phenology and breeding success assembled to date, we find that, whilea temporal match between Penguin breeding phenology and optimal environmental conditionssets an upper limit on breeding success, only a weak relationship to the mean exists. Despiteprevious work suggesting that divergent trends in Adélie Penguin breeding phenology areapparentacross the Antarctic continent, we find no such trends. Furthermore, we find no trendin the magnitude of phenological mismatch, suggesting that mismatch is driven by interannualvariability in environmental conditions rather than climate-change-driventrends, as observed inother systems. We propose several criteria necessary for a species to experience a strong climate-change-drivenphenological mismatch, of which several may be violated by this system

Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a computational method, BASS, that enables multi-scale and multi-sample analysis for single-cell resolution spatial transcriptomics. BASS performs cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a Bayesian hierarchical modeling framework. We illustrate the benefits of BASS through comprehensive simulations and applications to three datasets. The substantial power gain brought by BASS allows us to reveal accurate transcriptomic and cellular landscape in both cortex and hypothalamus.

The sex ratio at birth (SRB; ratio of male to female live births) imbalance in parts of the world over the past few decades is a direct consequence of sex-selective abortion, driven by the coexistence of son preference, readily available technology of prenatal sex determination, and fertility decline. Estimation of the degree of SRB imbalance is complicated because of unknown SRB reference levels and because of the uncertainty associated with SRB observations. There are needs for reproducible methods to construct SRB estimates with uncertainty, and to assess SRB inflation due to sex-selective abortion. We compile an extensive database from vital registration systems, censuses and surveys with 10,835 observations, and 16,602 country-years of information from 202 countries. We develop Bayesian methods for SRB estimation for all countries from 1950 to 2017. We model the SRB regional and national reference levels, the fluctuation around national reference levels, and the inflation. The estimated regional reference levels range from 1.031 (95% uncertainty interval [1.027; 1.036]) in sub-Saharan Africa to 1.063 [1.055; 1.072] in southeastern Asia, 1.063 [1.054; 1.072] in eastern Asia, and 1.067 [1.058; 1.077] in Oceania. We identify 12 countries with strong statistical evidence of SRB imbalance during 1970–2017, resulting in 23.1 [19.0; 28.3] million missing female births globally. The majority of those missing female births are in China, with 11.9 [8.5; 15.8] million, and in India, with 10.6 [8.0; 13.6] million.

Overdispersion is a common feature of models of biological data, but researchers often fail to model the excess variation driving the overdispersion, resulting in biased parameter estimates and standard errors. Quantifying and modeling overdispersion when it is present is therefore critical for robust biological inference. One means to account for overdispersion is to add an observation-level random effect (OLRE) to a model, where each data point receives a unique level of a random effect that can absorb the extra-parametric variation in the data. Although some studies have investigated the utility of OLRE to model overdispersion in Poisson count data, studies doing so for Binomial proportion data are scarce. Here I use a simulation approach to investigate the ability of both OLRE models and Beta-Binomial models to recover unbiased parameter estimates in mixed effects models of Binomial data under various degrees of overdispersion. In addition, as ecologists often fit random intercept terms to models when the random effect sample size is low (<5 levels), I investigate the performance of both model types under a range of random effect sample sizes when overdispersion is present. Simulation results revealed that the efficacy of OLRE depends on the process that generated the overdispersion; OLRE failed to cope with overdispersion generated from a Beta-Binomial mixture model, leading to biased slope and intercept estimates, but performed well for overdispersion generated by adding random noise to the linear predictor. Comparison of parameter estimates from an OLRE model with those from its corresponding Beta-Binomial model readily identified when OLRE were performing poorly due to disagreement between effect sizes, and this strategy should be employed whenever OLRE are used for Binomial data to assess their reliability. Beta-Binomial models performed well across all contexts, but showed a tendency to underestimate effect sizes when modelling non-Beta-Binomial data. Finally, both OLRE and Beta-Binomial models performed poorly when models contained <5 levels of the random intercept term, especially for estimating variance components, and this effect appeared independent of total sample size. These results suggest that OLRE are a useful tool for modelling overdispersion in Binomial data, but that they do not perform well in all circumstances and researchers should take care to verify the robustness of parameter estimates of OLRE models.

AIM: Functional traits of organisms are key to understanding and predicting biodiversity and ecological change, which motivates continuous collection of traits and their integration into global databases. Such trait matrices are inherently sparse, severely limiting their usefulness for further analyses. On the other hand, traits are characterized by the phylogenetic trait signal, trait–trait correlations and environmental constraints, all of which provide information that could be used to statistically fill gaps. We propose the application of probabilistic models which, for the first time, utilize all three characteristics to fill gaps in trait databases and predict trait values at larger spatial scales. INNOVATION: For this purpose we introduce BHPMF, a hierarchical Bayesian extension of probabilistic matrix factorization (PMF). PMF is a machine learning technique which exploits the correlation structure of sparse matrices to impute missing entries. BHPMF additionally utilizes the taxonomic hierarchy for trait prediction and provides uncertainty estimates for each imputation. In combination with multiple regression against environmental information, BHPMF allows for extrapolation from point measurements to larger spatial scales. We demonstrate the applicability of BHPMF in ecological contexts, using different plant functional trait datasets, also comparing results to taking the species mean and PMF. MAIN CONCLUSIONS: Sensitivity analyses validate the robustness and accuracy of BHPMF: our method captures the correlation structure of the trait matrix as well as the phylogenetic trait signal – also for extremely sparse trait matrices – and provides a robust measure of confidence in prediction accuracy for each missing entry. The combination of BHPMF with environmental constraints provides a promising concept to extrapolate traits beyond sampled regions, accounting for intraspecific trait variability. We conclude that BHPMF and its derivatives have a high potential to support future trait‐based research in macroecology and functional biogeography.

Extreme sea levels are a significant threat to life, property, and the environment. These threats are managed by coastal planers through the implementation of risk mitigation strategies. Central to such strategies is knowledge of extreme event probabilities. Typically, these probabilities are estimated by fitting a suitable distribution to the observed extreme data. Estimates, however, are often uncertain due to the small number of extreme events in the tide gauge record and are only available at gauged locations. This restricts our ability to implement cost-effective mitigation. A remarkable fact about sea-level extremes is the existence of spatial dependences, yet the vast majority of studies to date have analyzed extremes on a site-by-site basis. Here we demonstrate that spatial dependences can be exploited to address the limitations posed by the spatiotemporal sparseness of the observational record. We achieve this by pooling all of the tide gauge data together through a Bayesian hierarchical model that describes how the distribution of surge extremes varies in time and space. Our approach has two highly desirable advantages: 1) it enables sharing of information across data sites, with a consequent drastic reduction in estimation uncertainty; 2) it permits interpolation of both the extreme values and the extreme distribution parameters at any arbitrary ungauged location. Using our model, we produce an observation-based probabilistic reanalysis of surge extremes covering the entire Atlantic and North Sea coasts of Europe for the period 1960–2013.

In this paper, we explore how keyword ambiguity can affect search advertising performance. Consumers arrive at search engines with diverse interests, which are often unobserved and nontrivial to predict. The search interests of different consumers may vary even when they are searching using the same keyword. In our study, we propose an automatic way of examining keyword ambiguity based on probabilistic topic models from machine learning and computational linguistics. We examine the effect of keyword ambiguity on keyword performance using a hierarchical Bayesian approach that allows for topic-specific effects and nonlinear position effects, and jointly models click-through rate (CTR) and ad position (rank). We validate our study using a novel data set from a major search engine that contains information on consumer click activities for 2,625 distinct keywords across multiple product categories from 10,000 impressions. We find that consumer click behavior varies significantly across keywords, and such variation can be partially explained by keyword ambiguity. Specifically, higher keyword ambiguity is associated with higher CTR on top-positioned ads, but also a faster decay in CTR with screen position. Therefore, the overall effect of keyword ambiguity on CTR varies across positions. Our study provides implications for advertisers to improve the prediction of keyword performance by taking into account keyword ambiguity and other semantic characteristics of keywords. It can also help search engines design keyword planning tools to aid advertisers when choosing potential keywords.

The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood protection. This article reviews recent progress in the statistical modeling of spatial extremes, starting with sketches of the necessary elements of extreme value statistics and geostatistics. The main types of statistical models thus far proposed, based on latent variables, on copulas and on spatial max-stable processes, are described and then are compared by application to a data set on rainfall in Switzerland. Whereas latent variable modeling allows a better fit to marginal distributions, it fits the joint distributions of extremes poorly, so appropriately-chosen copula or max-stable models seem essential for successful spatial modeling of extremes.