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The expansion and impact of the Wari polity across the Andes has been heavily debated by scholars over the past two decades. We use radiocarbon dates, Bayesian statistics, and contextual data from several Andean regions to review the chronology for Wari expansion, political reorganization, and impact on local settlements. We recognize that Wari political and economic influence will vary in space and time and present a model that addresses how influential moments (expansion, reorganization, collapse) in Wari's trajectory may or may not have had broader impacts across the landscapes of its peer polities. Our model, while not completely comprehensive, draws on both Wari's presence in the north highlands and in the south, where scholars have argued for distinct trajectories and character of Wari influence. It also examines data from local communities in these same regions contemporary with identified Wari settlements, but perhaps without outward indicators of Wari hegemony, to evaluate broader patterns in Middle Horizon settlement through the latter half of the first millennium CE. [ABSTRACT FROM AUTHOR]

Recent articles provide a captivating set of radiocarbon dates from Machu Picchu and environs, with important ramifications for the site and the Inca Empire more broadly. Here, we present Bayesian models of these dates that incorporate life history details of each dated individual, including age at death, age of tissue formation for dated samples, family relationships, and associated grave goods. We calibrate dates with a bespoke calibration curve mixture specific to Machu Picchu. These model boundaries suggest the site was first occupied ∼ AD 1435 (1410–1455, 95%) and abandoned ∼ AD 1495 (1480–1515, 95%). This is consistent with dates from the region and builds on the consensus that ethnohistoric dates are unreliable. Anchoring Inca history to this radiocarbon chronology allows us to explore the ethnohistoric sources in a new light. We can more plausibly trace the life history of the ruler Pachacutec, including his founding of the Machu Picchu royal estate. Regionally, this approach leads us to reassess the conquest of the earliest and largest Inca conquest, the southern quarter. This area was subject to waves of intense interaction for nearly two centuries by multiple royal houses, possibly starting under Yahuar Huacac, Pachacutec's grandfather, events that were almost completely scrubbed from Inca praise narratives. [ABSTRACT FROM AUTHOR]

Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when updating a particular model. Despite this, prior and posterior beliefs are either inferred from sequential participant actions or elicited through impoverished means. We had participants to play a version of the game ‘Plinko’, to first elicit individual participant priors in a theoretically agnostic manner. Subsequent learning and updating of participant beliefs was then directly measured. We show that participants hold various priors that cluster around prototypical probability distributions that in turn influence learning. In follow‐up studies, we show that participant priors are stable over time and that the ability to update beliefs is influenced by a simple environmental manipulation (i.e., a short break). These data reveal the importance of directly measuring participant beliefs rather than assuming or inferring them as has been widely done in the literature to date. The Plinko game provides a flexible and fecund means for examining statistical learning and mental model updating. [ABSTRACT FROM AUTHOR]

Recent studies highlight non-relay functions of the thalamus. The mediodorsal thalamus regulates recurrent dynamics in the prefrontal cortex. Coding properties of the thalamus and cortex appear distinct and complementary. Trans-thalamic computations may support executive function via Bayesian principles. Frontal thalamic function across scales is consistent with a regularization perspective. The brain exhibits a remarkable ability to learn and execute context-appropriate behaviors. How it achieves such flexibility, without sacrificing learning efficiency, is an important open question. Neuroscience, psychology, and engineering suggest that reusing and repurposing computations are part of the answer. Here, we review evidence that thalamocortical architectures may have evolved to facilitate these objectives of flexibility and efficiency by coordinating distributed computations. Recent work suggests that distributed prefrontal cortical networks compute with flexible codes, and that the mediodorsal thalamus provides regularization to promote efficient reuse. Thalamocortical interactions resemble hierarchical Bayesian computations, and their network implementation can be related to existing gating, synchronization, and hub theories of thalamic function. By reviewing recent findings and providing a novel synthesis, we highlight key research horizons integrating computation, cognition, and systems neuroscience. The brain exhibits a remarkable ability to learn and execute context-appropriate behaviors. How it achieves such flexibility, without sacrificing learning efficiency, is an important open question. Neuroscience, psychology, and engineering suggest that reusing and repurposing computations are part of the answer. Here, we review evidence that thalamocortical architectures may have evolved to facilitate these objectives of flexibility and efficiency by coordinating distributed computations. Recent work suggests that distributed prefrontal cortical networks compute with flexible codes, whereas thalamus regularizes them to promote efficient reuse. Thalamocortical interactions resemble hierarchical Bayesian computations, and both observations can be related to existing gating, synchronization, and hub theories of thalamic function. Reviewing and synthesizing these points highlights key research horizons integrating computation, cognition, and systems neuroscience. [ABSTRACT FROM AUTHOR]

Childhood is a period of life unique to humans. Childhood may have evolved through the need to acquire knowledge and subsistence skills. In an effort to understand the functional significance of childhood, previous research examined increases with age in returns to foraging across food resources. Such increases could be due to changes in knowledge, or other factors such as body size or strength. Here, we attempt to unpack these age-related changes. First, we estimate age-specific foraging returns for two resources. We then develop nonlinear structural equation models to evaluate the relative importance of ecological knowledge, grip strength and height in a population of part-time children foragers on Pemba island, Tanzania. We use anthropometric measures (height, strength, n = 250), estimates of ecological knowledge (n = 93) and behavioural observations for 63 individuals across 370 foraging trips. We find slower increases in foraging returns with age for trap hunting than for shellfish collection. We do not detect any effect of individual knowledge on foraging returns, potentially linked to information sharing within foraging parties. Producing accurate estimates of the distinct contribution of specific traits to an individuals foraging performance constitutes a key step in evaluating different hypotheses for the emergence of childhood.

The existence and justification to the home advantage – the benefit a sports team receives when playing at home – has been studied across sport. The majority of research on this topic is limited to individual leagues in short time frames, which hinders extrapolation and a deeper understanding of possible causes. Using nearly two decades of data from the National Football League (NFL), the National Collegiate Athletic Association (NCAA), and high schools from across the United States, we provide a uniform approach to understanding the home advantage in American football. Our findings suggest home advantage is declining in the NFL and the highest levels of collegiate football, but not in amateur football. This increases the possibility that characteristics of the NCAA and NFL, such as travel improvements and instant replay, have helped level the playing field. [ABSTRACT FROM AUTHOR]

Interactions between density and environmental conditions have important effects on vital rates and consequently on population dynamics and can take complex pathways in species whose demography is strongly influenced by social context, such as the African lion, Panthera leo. In populations of such species, the response of vital rates to density can vary depending on the social structure (e.g. effects of group size or composition). However, studies assessing density dependence in populations of lions and other social species have seldom considered the effects of multiple socially explicit measures of density, and—more particularly for lions—of nomadic males. Additionally, vital‐rate responses to interactions between the environment and various measures of density remain largely uninvestigated. To fill these knowledge gaps, we aimed to understand how a socially and spatially explicit consideration of density (i.e. at the local scale) and its interaction with environmental seasonality affect vital rates of lions in the Serengeti National Park, Tanzania. We used a Bayesian multistate capture–recapture model and Bayesian generalized linear mixed models to estimate lion stage‐specific survival and between‐stage transition rates, as well as reproduction probability and recruitment, while testing for season‐specific effects of density measures at the group and home‐range levels. We found evidence for several such effects. For example, resident‐male survival increased more strongly with coalition size in the dry season compared with the wet season, and adult‐female abundance affected subadult survival negatively in the wet season, but positively in the dry season. Additionally, while our models showed no effect of nomadic males on adult‐female survival, they revealed strong effects of nomads on key processes such as reproduction and takeover dynamics. Therefore, our results highlight the importance of accounting for seasonality and social context when assessing the effects of density on vital rates of Serengeti lions and of social species more generally. [ABSTRACT FROM AUTHOR]

Introduction: The advent of high throughput spatial transcriptomics (HST) has allowed for unprecedented characterization of spatially distinct cell communities within a tissue sample. While a wide range of computational tools exist for detecting cell communities in HST data, none allow for the characterization of community connectivity, i.e., the relative similarity of cells within and between found communities--an analysis task that can elucidate cellular dynamics in important settings such as the tumor microenvironment. Methods: To address this gap, we introduce the analysis of community connectivity (ACC), which facilitates understanding of the relative similarity of cells within and between communities. We develop a Bayesian multi-layer network model called BANYAN for the integration of spatial and gene expression information to achieve ACC. Results: We demonstrate BANYAN's ability to recover community connectivity structure via a simulation study based on real sagittal mouse brain HST data. Next, we use BANYAN to implement ACC across a wide range of real data scenarios, including 10x Visium data of melanoma brain metastases and invasive ductal carcinoma, and NanoString CosMx data of human-small-cell lung cancer, each of which reveals distinct cliques of interacting cell sub-populations. An R package banyan is available at https://github.com/dongjunchung/banyan. [ABSTRACT FROM AUTHOR]

Causal reasoning—the ability to reason about causal relations between events—is fundamental to understanding how the world works. This paper reviews two prominent theories on early causal learning and offers possibilities for theory bridging. Both theories grow out of computational modeling and have significant areas of overlap while differing in several respects. Explanation‐Based Learning (EBL) focuses on young infants' learning about causal concepts of physical objects and events, whereas Bayesian models have been used to describe causal reasoning beyond infancy across various concept domains. Connecting the two models offers a more integrated approach to clarifying the developmental processes in causal reasoning from early infancy through later childhood. We further suggest that everyday language practices offer a promising space for theory bridging. We provide a review of selective work on caregiver–child conversations, in particular, on the use of scaffolding language including causal talk and pedagogical questions. Linking the research on language practices to the two cognitive theories, we point out directions for further research to integrate EBL and Bayesian models and clarify how causal learning unfolds in real life. This article is categorized under:Psychology > LearningCognitive Biology > Cognitive Development [ABSTRACT FROM AUTHOR]

Background Distributed lag non-linear models (DLNMs) are the reference framework for modelling lagged non-linear associations. They are usually used in large-scale multi-location studies. Attempts to study these associations in small areas either did not include the lagged non-linear effects, did not allow for geographically-varying risks or downscaled risks from larger spatial units through socioeconomic and physical meta-predictors when the estimation of the risks was not feasible due to low statistical power. Methods Here we proposed spatial Bayesian DLNMs (SB-DLNMs) as a new framework for the estimation of reliable small-area lagged non-linear associations, and demonstrated the methodology for the case study of the temperature-mortality relationship in the 73 neighbourhoods of the city of Barcelona. We generalized location-independent DLNMs to the Bayesian framework (B-DLNMs), and extended them to SB-DLNMs by incorporating spatial models in a single-stage approach that accounts for the spatial dependence between risks. Results The results of the case study highlighted the benefits of incorporating the spatial component for small-area analysis. Estimates obtained from independent B-DLNMs were unstable and unreliable, particularly in neighbourhoods with very low numbers of deaths. SB-DLNMs addressed these instabilities by incorporating spatial dependencies, resulting in more plausible and coherent estimates and revealing hidden spatial patterns. In addition, the Bayesian framework enriches the range of estimates and tests that can be used in both large- and small-area studies. Conclusions SB-DLNMs account for spatial structures in the risk associations across small areas. By modelling spatial differences, SB-DLNMs facilitate the direct estimation of non-linear exposure-response lagged associations at the small-area level, even in areas with as few as 19 deaths. The manuscript includes an illustrative code to reproduce the results, and to facilitate the implementation of other case studies by other researchers. [ABSTRACT FROM AUTHOR]

Objective: In 2000, the Laguna Atascosa National Wildlife Refuge acquired the Bahia Grande (Texas) management unit, a space that had lain barren and arid for 70 years. A large cooperative partnership launched a restoration project to replenish the basin and recover its original tidal hydrology. In 2005, the construction of a pilot channel successfully restored water throughout the basin, and plans to eventually widen the channel were developed. Our study aims to evaluate an estuarine habitat restoration by assessing ecological drivers and the impacts on species diversity. Methods: We evaluated species richness, detection/occupancy rates, and species–habitat relationships, and we estimated the sampling effort required to achieve a given level of relative precision if relative abundance was used instead of occupancy to inform future sampling. Sampling gear included bag seines for juvenile life stages and gill nets for capturing subadult and adult life stages. For analysis, we used a Bayesian negative binomial linear mixed‐effects model to evaluate richness–habitat relationships and a hierarchical Bayesian multispecies model to evaluate individual species–habitat relationships, and we calculated the total number of fish captured and relative standard error by gear and sample year to produce a precise estimate of relative abundance. Result: Overall, 29 species were caught between 2018 and 2021. Salinity emerged as a clear driver in the Bahia Grande, as both species richness and individual‐level responses were negatively associated with high salinity values. We found that catch estimated as relative abundance had much variability, as is typical of most survey programs assuming constant detectability, and the number of net sets or seine hauls required to achieve a given level of relative precision varied considerably depending on the species, season, year, and gear type. The most collected species were found in the upper extremes of their salinity tolerances—potentially a unique adaptation to this hypersaline system. Conclusion: Baseline data suggest that for the channel widening to be successful, there must be a noticeable increase in suitable habitat characteristics throughout the basin. Impact statementHabitat restoration is a unique opportunity to assess changes in aquatic communities. We used hierarchical Bayesian models to describe prerestoration community relationships for one of the largest coastal restorations in Texas. This flexible framework can be applied to other complex habitat restorations. [ABSTRACT FROM AUTHOR]

Ischemic stroke is responsible for significant morbidity and mortality in the United States and worldwide. Stroke treatment optimization requires emergency medical personnel to make rapid triage decisions concerning destination hospitals that may differ in their ability to provide highly time-sensitive pharmaceutical and surgical interventions. These decisions are particularly crucial in rural areas, where transport decisions can have a large impact on treatment times – often involving a trade-off between delay in pharmaceutical therapy or a delay in endovascular thrombectomy. In this work, we explore a Bayesian modeling framework to address this decision-making process, showing how these techniques may be used to fully account for diagnostic and therapeutic uncertainty. We demonstrate how these techniques can contextualize triage decision at a fine-grained spatial scale. We further show the application of this modeling approach in the US State of Iowa, using data from the Virtual International Stroke Trials Archive (VISTA), and describe potential next steps for improved triage.ABBREVIATIONLVO: large vessel occlusion; non-LVO, non-large vessel occlusion; IVT: intravenous tissue plasminogen activator; EVT: endovascular thrombectomy; CSC: comprehensive stroke centers; PSC: primary stroke centers; DS: drip and ship; MS, mothership; EMS: Emergency Medical Service; BGLM: Bayesian Generalized Linear Model; BGAM: Bayesian Generalized Additive Model; BART: Bayesian Additive Regression Trees; VISTA: Virtual International Stroke Trials Archive; NIHSS: National Institute of Health Stroke Severity Scale; ASPECTS: Alberta Stroke Programme Early CT Score; mRS, modified Rankin score; ROCAUC: Area under the receiver operating characteristic curve; ELPD: Expected Log pointwise Predictive Density; SE: Standard Error; ICA: Internal Carotid Artery; M1: Middle Cerebral Artery segment 1; M2: Middle Cerebral Artery segment 2; TIA: Transient Ischemic Attack; Cr-I: Credible Intervals; LKW: Last Known Well [ABSTRACT FROM AUTHOR]

This work reviews paleodietary reconstructions of hunter‐gatherers from the northeast province of Chubut, Argentina. The isotopic record of human bone can complement information from zooarchaeological and technological studies with its particular resolution on the long‐term diet of individuals. Previous δ13C and δ15N studies found dietary differences between the coastal area and the lower basin of the Chubut River, both in the proportion of marine resources in the diet and the trophic positions of prey. However, it is challenging to test hypotheses concerning human diets in northeastern Patagonia, where C3 and CAM plants grow and terrestrial, riverine, and marine prey are available. Here, we focus on the local isotopic ecology to assess the scopes and limitations of δ13C and δ15N stable isotopes analyses. We provide new data on native faunas spanning different trophic levels of terrestrial, riverine, and marine environments. After establishing the basic patterns in the local isotope record, we reanalyze the available δ13Ccol, δ15N, and δ13Capa measurements from 23 individuals studied 15 years ago. That analysis reveals a relatively continuous distribution of several overlapping resources on the coast of northeastern Patagonia. Bayesian mixing models identified a combination of terrestrial and riverine resources aggregated as the main component of long‐term diets. However, it generated diffuse estimations. The Bayesian ellipses, describing isotopic niches, emphasized more the spatial variations in diet between coastal and valley groups. [ABSTRACT FROM AUTHOR]

Integrated distribution models (IDMs), in which datasets with different properties are analysed together, are becoming widely used to model species distributions and abundance in space and time. To date, the IDM literature has focused on technical and statistical issues, such as the precision of parameter estimates and mitigation of biases arising from unstructured data sources. However, IDMs have an unrealised potential to estimate ecological properties that could not be properly derived from the source datasets if analysed separately. We present a model that estimates community alpha diversity metrics by integrating one species‐level dataset of presence–absence records with a co‐located dataset of group‐level counts (i.e. lacking information about species identity). We illustrate the ability of community IDMs to capture the true alpha diversity through simulation studies and apply the model to data from the UK Pollinator Monitoring Scheme, to describe spatial variation in the diversity of solitary bees, bumblebees and hoverflies. The simulation and case studies showed that the proposed IDM produced more precise estimates of the community diversity than the single models, and the analysis of the real dataset further showed that the alpha diversity estimates from the IDM were averages of the single models. Our findings also revealed that IDMs had a higher prediction accuracy for all the insect groups in most cases, with this performance linked to the information provided by a data source into the IDM. [ABSTRACT FROM AUTHOR]

Background: Studying how the bull sharks aggregate and how they can be driven by life history traits such as reproduction, prey availability, predator avoidance and social interaction in a National Park such as Cabo Pulmo, is key to understand and protect the species. Methods: The occurrence variability of 32 bull sharks tracked with passive acoustic telemetry were investigated via a hierarchical logistic regression model, with inference conducted in a Bayesian framework, comparing sex, and their response to temperature and chlorophyll. Results: Based on the fitted model, occurrence probability varied by sex and length. Juvenile females had the highest values, whereas adult males the lowest. A strong seasonality or day of the year was recorded, where sharks were generally absent during September–November. However, some sharks did not show the common pattern, being detected just for a short period. This is one of the first studies where the Bayesian framework is used to study passive acoustic telemetry proving the potential to be used in further studies. [ABSTRACT FROM AUTHOR]

Abstract Yellowfin tuna, Thunnus albacares, represents an important component of commercial and recreational fisheries in the Gulf of Mexico (GoM). We investigated the influence of environmental conditions on the spatiotemporal distribution of yellowfin tuna using fisheries’ catch data spanning 2012–2019 within Mexican waters. We implemented hierarchical Bayesian regression models with spatial and temporal random effects and fixed effects of several environmental covariates to predict habitat suitability (HS) for the species. The best model included spatial and interannual anomalies of the absolute dynamic topography of the ocean surface (ADTSA and ADTIA, respectively), bottom depth, and a seasonal cyclical random effect. High catches occurred mainly towards anticyclonic features at bottom depths > 1000 m. The spatial extent of HS was higher in years with positive ADTIA, which implies more anticyclonic activity. The highest values of HS (> 0.7) generally occurred at positive ADTSA in oceanic waters of the central and northern GoM. However, high HS values (> 0.6) were observed in the southern GoM, in waters with cyclonic activity during summer. Our results highlight the importance of mesoscale features for the spatiotemporal distribution of yellowfin tunas and could help to develop dynamic fisheries management strategies in Mexico and the U.S. for this valuable resource.

The representation of complex phenomena via combinations of simple discrete features is a hallmark of human cognition. But it is not clear exactly how (or whether) discrete features can effectively represent the complex probabilistic fabric of the environment. This article introduces information-theoretic tools for quantifying the fidelity and efficiency of a featural representation with respect to a probability model. In this framework, a feature or combination of features is "faithful" to the extent that knowing the value of the features reduces uncertainty about the true state of the world. In a single dimension, a discrete feature is faithful if the values of the feature correspond isomorphically to distinct classes in the probability model. But in multiple dimensions, the situation is more complicated: The fidelity of each feature depends on the direction in multidimensional feature space in which the feature is projected from the underlying distribution. More interestingly, distributions may be more effectively represented by combinations of projected features—that is, compositionality. For any given distribution, a variety of compositional forms (features and combination rules) are possible, which can be quite different from one another, entailing different degrees of fidelity, different numbers of features, and even different induced regularities. This article proposes three specific criteria for a compositional representation: fidelity, simplicity, and robustness. The information-theoretic framework introduces a new and potentially useful way to look at the problem of compositionality in human mental representation. [ABSTRACT FROM AUTHOR]

In this manuscript, we discuss the substantial importance of Bayesian reasoning in Social Science research. Particularly, we focus on foundational elements to fit models under the Bayesian paradigm. We aim to offer a frame of reference for a broad audience, not necessarily with specialized knowledge in Bayesian statistics, yet having interest in incorporating this kind of methods in studying social phenomena. We illustrate Bayesian methods through case studies regarding political surveys, population dynamics, and standardized educational testing. Specifically, we provide technical details on specific topics such as conjugate and non-conjugate modeling, hierarchical modeling, Bayesian computation, goodness of fit, and model testing. [ABSTRACT FROM AUTHOR]

Species distribution models and maps from large‐scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi‐species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi‐species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold‐out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples. [ABSTRACT FROM AUTHOR]

Population pharmacokinetic (pop-PK) models constructed for model-informed precision dosing often have limited utility due to the low number of patients recruited. To augment such models, an approach is presented for generating fully artificial quasi-models which can be employed to make individual estimates of pharmacokinetic parameters. Based on 72 concentrations obtained in 12 patients, one- and two-compartment pop-PK models with or without creatinine clearance as a covariate were generated for piperacillin using the nonparametric adaptive grid algorithm. Thirty quasi-models were subsequently generated for each model type, and nonparametric maximum a posteriori probability Bayesian estimates were established for each patient. A significant difference in performance was found between one- and two-compartment models. Acceptable agreement was found between predicted and observed piperacillin concentrations, and between the estimates of the random-effect pharmacokinetic variables obtained using the so-called support points of the pop-PK models or the quasi-models as priors. The mean squared errors of the predictions made using the quasi-models were similar to, or even considerably lower than those obtained when employing the pop-PK models. Conclusion: fully artificial nonparametric quasi-models can efficiently augment pop-PK models containing few support points, to make individual pharmacokinetic estimates in the clinical setting. [ABSTRACT FROM AUTHOR]

Recently, a number of predictive coding models have been proposed to account for post-traumatic stress disorder (PTSD)'s symptomatology, including intrusions, flashbacks and hallucinations. These models were usually developed to account for traditional/type-1 PTSD. We here discuss whether these models also apply or can be translated to the case of complex/type-2 PTSD and childhood trauma (cPTSD). The distinction between PTSD and cPTSD is important because the disorders differ in terms of symptomatology and potential mechanisms, how they relate to developmental stages, but also in terms of illness trajectory and treatment. Models of complex trauma could give us insights on hallucinations in physiological/pathological conditions or more generally on the development of intrusive experiences across diagnostic classes. [ABSTRACT FROM AUTHOR]

Detection error can bias observations of ecological processes, especially when some species are never detected during sampling. In many communities, the probable identity of these missing species is known from previous research and natural history collections, but this information is rarely incorporated into subsequent models. Here, I present prior aggregation as a method for including information from external sources in Bayesian hierarchical detection models. Prior aggregation combines information from multiple prior distributions, in this case, an ecologically informative, species-level prior, and an uninformative community-level prior. This approach incorporates external information into the model without sacrificing the advantages of modeling species in the context of the community. Using simulated data supplied to a multispecies occupancy model, I demonstrated that prior aggregation improves estimates of (1) metacommunity richness and (2) environmental covariates were associated with species-specific occupancy probabilities. When applied to a dataset of small mammals in Vermont, prior aggregation allowed the model to estimate occupancy correlates of the Eastern cottontail Sylvilagus floridanus, a species observed at several sites in the region but never captured. Prior aggregation can be used to improve the analysis of several important metrics in population and community ecology, including abundance, survivorship, and diversity. [ABSTRACT FROM AUTHOR]

Genomic selection (GS) has great potential to increase genetic gain in poultry breeding. However, the performance of genomic prediction in duck growth and breast morphological (BM) traits remains largely unknown. The objective of this study was to evaluate the benefits of genomic prediction for duck growth and BM traits using methods such as GBLUP, single‐step GBLUP, Bayesian models, and different marker densities. This study collected phenotypic data for 14 growth and BM traits in a crossbreed population of 1893 Pekin duck × mallard, which included 941 genotyped ducks. The estimation of genetic parameters indicated high heritabilities for body weight (0.54–0.72), whereas moderate‐to‐high heritabilities for average daily gain (0.21–0.57) traits. The heritabilities of BM traits ranged from low to moderate (0.18–0.39). The prediction ability of GS on growth and BM traits increased by 7.6% on average compared to the pedigree‐based BLUP method. The single‐step GBLUP outperformed GBLUP in most traits with an average of 0.3% higher reliability in our study. Most of the Bayesian models had better performance on predictive reliability, except for BayesR. BayesN emerged as the top‐performing model for genomic prediction of both growth and BM traits, exhibiting an average increase in reliability of 3.0% compared to GBLUP. The permutation studies revealed that 50 K markers had achieved ideal prediction reliability, while 3 K markers still achieved 90.8% predictive capability would further reduce the cost for duck growth and BM traits. This study provides promising evidence for the application of GS in improving duck growth and BM traits. Our findings offer some useful strategies for optimizing the predictive ability of GS in growth and BM traits and provide theoretical foundations for designing a low‐density panel in ducks. [ABSTRACT FROM AUTHOR]

Smooth pursuit eye movements respond on the basis of both immediate and anticipated target motion, where anticipations may be derived from either memory or perceptual cues. To study the combined influence of both immediate sensory motion and anticipation, subjects pursued clear or noisy random dot kinematograms (RDKs) whose mean directions were chosen from Gaussian distributions with SDs = 10- (narrow prior) or 45- (wide prior). Pursuit directions were consistent with Bayesian theory in that transitions over time from dependence on the prior to near total dependence on immediate sensory motion (likelihood) took longer with the noisier RDKs and with the narrower, more reliable, prior. Results were fit to Bayesian models in which parameters representing the variability of the likelihood either were or were not constrained to be the same for both priors. The unconstrained model provided a statistically better fit, with the influence of the prior in the constrained model smaller than predicted from strict reliability-based weighting of prior and likelihood. Factors that may have contributed to this outcome include prior variability different from nominal values, low-level sensorimotor learning with the narrow prior, or departures of pursuit from strict adherence to reliability-based weighting. Although modifications of, or alternatives to, the normative Bayesian model will be required, these results, along with previous studies, suggest that Bayesian approaches are a promising framework to understand how pursuit combines immediate sensory motion, past history, and informative perceptual cues to accurately track the target motion that is most likely to occur in the immediate future. [ABSTRACT FROM AUTHOR]

Multi-view datasets are becoming increasingly prevalent. These datasets consist of different modalities that provide complementary characterizations of the same underlying system. They can include heterogeneous types of information with complex relationships, varying degrees of missingness, and assorted sample sizes, as is often the case in multi-omic biological studies. Clustering multi-view data allows us to leverage different modalities to infer underlying systematic structure, but most existing approaches are limited to contexts in which entities are the same across views or have clear one-to-one relationships across data types with a common sample size. Many methods also make strong assumptions about the similarities of clusterings across views. We propose a Bayesian multi-view clustering approach (BMVC) which can handle the realities of multi-view datasets that often have complex relationships and diverse structure. BMVC incorporates known and complex many-to-many relationships between entities via a probabilistic graphical model that enables the joint inference of clusterings specific to each view, but where each view informs the others. Additionally, BMVC estimates the strength of the relationships between each pair of views, thus moderating the degree to which it imposes dependence constraints. We benchmarked BMVC on simulated data to show that it accurately estimates varying degrees of inter-view dependence when inter-view relationships are not limited to one-to-one correspondence. Next, we demonstrated its ability to capture visually interpretable inter-view structure in a public health survey of individuals and households in Puerto Rico following Hurricane Maria. Finally, we showed that BMVC clusters integrate the complex relationships between multi-omic profiles of breast cancer patient data, improving the biological homogeneity of clusters and elucidating hypotheses for functional biological mechanisms. We found that BMVC leverages complex inter-view structure to produce higher quality clusters than those generated by standard approaches. We also showed that BMVC is a valuable tool for real-world discovery and hypothesis generation.

The crucial impact of the microbiome on human health and disease has gained significant scientific attention. Researchers seek to connect microbiome features with health conditions, aiming to predict diseases and develop personalized medicine strategies. However, the practicality of conventional models is restricted due to important aspects of microbiome data. Specifically, the data observed is compositional, as the counts within each sample are bound by a fixed‐sum constraint. Moreover, microbiome data often exhibits high dimensionality, wherein the number of variables surpasses the available samples. In addition, microbiome features exhibiting phenotypical similarity usually have similar influence on the response variable. To address the challenges posed by these aspects of the data structure, we proposed Bayesian compositional generalized linear models for analyzing microbiome data (BCGLM) with a structured regularized horseshoe prior for the compositional coefficients and a soft sum‐to‐zero restriction on coefficients through the prior distribution. We fitted the proposed models using Markov Chain Monte Carlo (MCMC) algorithms with R package rstan. The performance of the proposed method was assessed by extensive simulation studies. The simulation results show that our approach outperforms existing methods with higher accuracy of coefficient estimates and lower prediction error. We also applied the proposed method to microbiome study to find microorganisms linked to inflammatory bowel disease (IBD). To make this work reproducible, the code and data used in this article are available at https://github.com/Li‐Zhang28/BCGLM. [ABSTRACT FROM AUTHOR]

Archaeological research on traditional Hawaiian agriculture has generally focused on two primary strategies: irrigated pondfield and intensive dryland cultivation. However, other cultivation strategies, such as colluvial slope agriculture, were practiced but have been less intensively studied and remain poorly understood. To begin to remedy this paucity of information, a joint education and research project between International Archaeological Research Institute, Inc., the University of Hawai'i at Mānoa (UHM) Department of Anthropology, and Kamehameha Schools (KS) was conducted in Punalu'u Ahupua'a to examine an integrated slope agricultural, religious, and residential complex. Subsurface investigations and radiometric dating indicate that initial vegetation clearing of the area may have occurred as early as the late thirteenth to early fifteenth centuries AD. As early as the fifteenth century, some stands of economic trees were growing, which may have coincided with initial placement of boundary walls along some portions of the slopes. Major expansion of the surface agricultural infrastructure along the landscape occurred through the sixteenth and eighteenth centuries AD. Religious structures and associated features were built as early as the seventeenth century and later. These results situate colluvial slope agriculture within the broader context of traditional Hawaiian agronomic strategies and political and social processes across the Hawaiian archipelago. [ABSTRACT FROM AUTHOR]

Context: The abandonment of traditional practices has transformed agro-pastoral systems, leading to a more frequent occurrence of passive rewilding of Mediterranean landscapes. Reconstructing ecosystem responses to climate under different grazing conditions (i.e., wild, and domestic ungulates) is important to understand the future of these ecosystems. Objectives: Here we study the different roles of domestic and wild herbivory in defining the climate-vegetation interaction. Specifically, we evaluated (1) the effect of climate on primary productivity at the landscape scale and (2) the long-term trends of vegetation biomass in response to passive rewilding or maintenance of traditional grazing systems. Methods: This study was carried out in South-eastern Spain. We used satellite images to generate NDVI time series that proxy primary productivity and vegetation biomass. We combined the NDVI and climate data from two key landscapes: one with wild ungulates and another predominantly with domestic ungulates. Results: We detected a secondary succession process in areas with only wild ungulates. In domestic herbivory areas, vegetation biomass remained constant throughout time (30 years). In domestic herbivory areas temperature and seasonal precipitation affected primary productivity. In areas with only wild herbivory, primary productivity was mainly driven by annual precipitation, and it was less dependent on seasonal precipitation. Conclusion: These results highlight the distinctive roles of herbivores in defining Mediterranean landscapes' adaptability to climate, through passive rewilding or traditional livestock use. Maintaining both ecosystems can enhance landscape heterogeneity and ecological sustainability in a context of climatic changes. [ABSTRACT FROM AUTHOR]

The diffusion of Environmental, Social and Governance (ESG) metrics is increasingly affecting corporates behaviour and their ability to attract investors. Corporate ESG practices are nowadays considered as a key element in evaluating creditworthiness and the cost of capital, to direct funds to the best‐performing companies that limit the harmful impact on the planet and the societies. Due to this increased interest in ESG by companies, investors and policymakers, a high number of ESG scores and metrics have been developed, each with different methodologies and scopes. Because of this variety, it could be therefore challenging for investors to understand and compare ESG measures. In this paper, we address this issue by proposing a method to combine the information provided by different ESG scores into a single aggregate measure of company sustainability and link this combined score to the credit rating of companies. The proposed methodology can help investors to improve their investment decisions by combining more diverse information on company sustainability as a driver of companies' credit rating, thereby reducing information asymmetries. [ABSTRACT FROM AUTHOR]

Combining public opinion data from different sources enables new cross-national and longitudinal research, but is accompanied by unique challenges. The analytic strategy we propose relies on Bayesian explanatory item response theory models to address differences in the measurement of attitudes, and poststratification that uses administrative population data to improve the quality of estimates and correct for differences in sample representativeness. Partially pooled models with data from all countries would be prohibitively slow, so we estimate separate by-country models in a way that maintains comparability of estimates across countries. We apply this strategy to data from 13 cross-national research projects from 27 European countries to estimate trajectories of political trust between 1989-2019.

Abstract Species distribution models and maps from large‐scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi‐species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi‐species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold‐out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

Multi-view datasets are becoming increasingly prevalent. These datasets consist of different modalities that provide complementary characterizations of the same underlying system. They can include heterogeneous types of information with complex relationships, varying degrees of missingness, and assorted sample sizes, as is often the case in multi-omic biological studies. Clustering multi-view data allows us to leverage different modalities to infer underlying systematic structure, but most existing approaches are limited to contexts in which entities are the same across views or have clear one-to-one relationships across data types with a common sample size. Many methods also make strong assumptions about the similarities of clusterings across views. We propose a Bayesian multi-view clustering approach (BMVC) which can handle the realities of multi-view datasets that often have complex relationships and diverse structure. BMVC incorporates known and complex many-to-many relationships between entities via a probabilistic graphical model that enables the joint inference of clusterings specific to each view, but where each view informs the others. Additionally, BMVC estimates the strength of the relationships between each pair of views, thus moderating the degree to which it imposes dependence constraints. We benchmarked BMVC on simulated data to show that it accurately estimates varying degrees of inter-view dependence when inter-view relationships are not limited to one-to-one correspondence. Next, we demonstrated its ability to capture visually interpretable inter-view structure in a public health survey of individuals and households in Puerto Rico following Hurricane Maria. Finally, we showed that BMVC clusters integrate the complex relationships between multi-omic profiles of breast cancer patient data, improving the biological homogeneity of clusters and elucidating hypotheses for functional biological mechanisms. We found that BMVC leverages complex inter-view structure to produce higher quality clusters than those generated by standard approaches. We also showed that BMVC is a valuable tool for real-world discovery and hypothesis generation.

Abstract Genomic selection (GS) has great potential to increase genetic gain in poultry breeding. However, the performance of genomic prediction in duck growth and breast morphological (BM) traits remains largely unknown. The objective of this study was to evaluate the benefits of genomic prediction for duck growth and BM traits using methods such as GBLUP, single‐step GBLUP, Bayesian models, and different marker densities. This study collected phenotypic data for 14 growth and BM traits in a crossbreed population of 1893 Pekin duck × mallard, which included 941 genotyped ducks. The estimation of genetic parameters indicated high heritabilities for body weight (0.54–0.72), whereas moderate‐to‐high heritabilities for average daily gain (0.21–0.57) traits. The heritabilities of BM traits ranged from low to moderate (0.18–0.39). The prediction ability of GS on growth and BM traits increased by 7.6% on average compared to the pedigree‐based BLUP method. The single‐step GBLUP outperformed GBLUP in most traits with an average of 0.3% higher reliability in our study. Most of the Bayesian models had better performance on predictive reliability, except for BayesR. BayesN emerged as the top‐performing model for genomic prediction of both growth and BM traits, exhibiting an average increase in reliability of 3.0% compared to GBLUP. The permutation studies revealed that 50 K markers had achieved ideal prediction reliability, while 3 K markers still achieved 90.8% predictive capability would further reduce the cost for duck growth and BM traits. This study provides promising evidence for the application of GS in improving duck growth and BM traits. Our findings offer some useful strategies for optimizing the predictive ability of GS in growth and BM traits and provide theoretical foundations for designing a low‐density panel in ducks.

Research on cancer in dogs and cats, among other diseases, finds an important source of information in registry data collected from hospitals. These sources have proved to be decisive in establishing incidences and identifying temporal patterns and risk factors. However, the attendance of patients is not random, so the correct delimitation of the hospital catchment area (CA) as well as the identification of the factors influencing its shape is relevant to prevent possible biases in posterior inferences. Despite this, there is a lack of data-driven approaches in veterinary epidemiology to establish CA. Therefore, our aim here was to apply a Bayesian method to estimate the CA of a hospital. We obtained cancer (n = 27,390) and visit (n = 232,014) registries of dogs and cats attending the Veterinary Medical Teaching Hospital of the University of California, Davis from 2000 to 2019 with 2,707 census tracts (CTs) of 40 neighboring counties. We ran hierarchical Bayesian models with different likelihood distributions to define CA for cancer cases and visits based on the exceedance probabilities for CT random effects, adjusting for species and period (2000-2004, 2005-2009, 2010-2014, and 2015-2019). The identified CAs of cancer cases and visits represented 75.4 and 83.1% of the records, respectively, including only 34.6 and 39.3% of the CT in the study area. The models detected variation by species (higher number of records in dogs) and period. We also found that distance to hospital and average household income were important predictors of the inclusion of a CT in the CA. Our results show that the application of this methodology is useful for obtaining data-driven CA and evaluating the factors that influence and predict data collection. Therefore, this could be useful to improve the accuracy of analysis and inferences based on registry data.

Managing disturbed forests for climate mitigation and biodiversity requires monitoring the carbon (C) cycle consequences of replacing established exotic vegetation with native seedlings. Standard approaches rely on allometric growth equations with unexplored limitations for measuring C changes during restoration. Most plants lack species-specific allometric growth equations. Additionally, these equations may perform poorly for different growth forms, especially when applied to both mature trees and seedlings. To address these limitations, we generated and compared allometric growth equations for four woody species with different biogeographic origins and growth forms, including two high impact invasive species, Cupaniopisis anacardioides and Schinus terebinthifolia. By borrowing strength from sampling across species to reduce estimation error within species, Bayesian multilevel models generated more accurate estimates than either independent species-level models or generic equations, although improvements over independent species-level models were modest. Because errors systematically changed with plant size, especially for species with unusual growth forms, allometric growth equations from custom multilevel models generated higher baseline aboveground biomass estimates and lower post-restoration estimates, which has important implications for monitoring C consequences of invasive tree management.

BACKGROUND AND OBJECTIVES: This research aims to develop a new approach to modeling systematic and unsystematic risks as well as geopolitical risks, in financial solvency within the insurance industry in Iran. The objective is to improve the accuracy of prediction models used in the industry. METHODS: The research follows developmental-practical approach and unilizes a descriptivesurvey method. Data from 2011 to 2021, covering an 11-year period, were collected and analyzed. A total of 33 risk indicators affecting the financial solvency of insurance companies were examined using BMA, TVP-DMA, TVP-DMS, and BVAR models. FINDINGS: The BMA model demonstrated the highest accuracy based on error rate. Through the analysis, 11 main variables were identified as significant factors influencing financial solvency including economic growth, inflation uncertainty, exchange rate, sanctions, KOF index, return on working capital, cash adequacy ratio, total debt-to-equity ratio, loss factor, Herfindahl-Hirschman index, and geopolitical risk. The results The results highlight the complex nature of financial solvency prediction in the insurance industry, emphasizing the need for a comprehensive and systematic approach. CONCLUSION: This study emphasizes the limitations of relying on a single conceptual model in financial solvency modeling and decision-making. The multiplicity of factors influencing financial solvency requires a systemic perspective in managing insurance companies. Additionally, it is important to consider a wide range of variables rather than relying on a specific model or set of variables to ensure a comprehensive understanding of financial solvency in the industry. [ABSTRACT FROM AUTHOR]

As global climate continues to change, so too will phenology of a wide range of insects. Changes in flight season usually are characterised as shifts to earlier dates or means, with attention less often paid to flight season breadth or whether seasons are now skewed. We amassed flight season data for the insect order Odonata, the dragonflies and damselflies, for Norway over the past century‐and‐a‐half to examine the form of flight season change. By means of Bayesian analyses that incorporated uncertainty relative to annual variability in survey effort, we estimated shifts in flight season mean, breadth, and skew. We focussed on flight season breadth, positing that it will track documented growing season expansion. A specific mechanism explored was shifts in voltinism, the number of generations per year, which tends to increase with warming. We found strong evidence for an increase in flight season breadth but much less for a shift in mean, with any shift of the latter tending toward a later mean. Skew has become rightward for suborder Zygoptera, the damselflies, but not for Anisoptera, the dragonflies, or for the Odonata as a whole. We found weak support for voltinism as a predictor of broader flight season; instead, voltinism acted interactively with use of human‐modified habitats, including decrease in shading (e.g. from timber extraction). Other potential mechanisms that link warming with broadening of flight season include protracted emergence and cohort splitting, both of which have been documented in the Odonata. It is likely that warming‐induced broadening of flight seasons of these widespread insect predators will have wide‐ranging consequences for freshwater ecosystems. [ABSTRACT FROM AUTHOR]

High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single‐cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub‐populations of cells within a tissue sample that may inform biological phenomena. Existing computational methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic‐based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew‐normal distributions, which is capable of identifying distinct cellular sub‐populations in HST data. We further implement a novel combination of Pólya–Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment. An R package spruce for fitting the proposed models is available through The Comprehensive R Archive Network. [ABSTRACT FROM AUTHOR]

The article explores the problem of predicting the emotional burnout syndrome of employees, which is relevant due to the high level of stress in the modern world. The study uses the publicly available dataset "Are your employees burning out" from the competition on the HackerEarth platform. A comparative analysis of three traditional machine learning models based on classical machine learning approaches (linear regression, Random Forest, XGBoost) and three Bayesian models (Bayesian linear regression, varying intercept model, varying intercept and slope model) was carried out in the study. The change in the quality of the models is studied for different sizes of data sets, ranging from 13,000 (i.e., the full training set, which accounted for 70% of all data) to 25 observations, including testing on the full data set. It is demonstrated that XGBoost is the best model for large data sets. However, when the training sample size is reduced to less than 5000 observations, the validation performance of the XGBoost model becomes significantly less accurate and becomes lower than the corresponding metrics for Bayesian models. After optimizing such hyperparameters as tree depth, number of trees, learning rate, and others, the quality of XGBoost improved significantly, but did not make it stable enough to demonstrate better results than Bayesian models on samples of less than 600 observations. Bayesian models, on the other hand, in addition to being better on small samples, also allow estimating the "confidence" in the predicted values, which is an important feature for a specific tasks. However, they also have a significant disadvantage in the form of much greater computational complexity, which leads to an increase in training time. In conclusion, results of this study emphasize the importance of careful selection of a model that considers the peculiarities of the amount and quality of available data. Bayesian models have proven to be highly effective with a small amount of data, due to their ability to consider uncertainty and insufficient information. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.), the third most widely cultivated cereal crop in the world, plays a critical role in global food security. To improve the efficiency of selecting superior genotypes in breeding programs, researchers have aimed to identify key genomic regions that impact agronomic traits. In this study, the performance of multi-trait, multi-environment deep learning models was compared to that of Bayesian models (Markov Chain Monte Carlo generalized linear mixed models (MCMCglmm), Bayesian Genomic Genotype-Environment Interaction (BGGE), and Bayesian Multi-Trait and Multi-Environment (BMTME)) in terms of the prediction accuracy of flowering-related traits (Anthesis-Silking Interval: ASI, Female Flowering: FF, and Male Flowering: MF). A tropical maize panel of 258 inbred lines from Brazil was evaluated in three sites (Cambira-2018, Sabaudia-2018, and Iguatemi-2020 and 2021) using approximately 290,000 single nucleotide polymorphisms (SNPs). The results demonstrated a 14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach. The accuracy of predictions also improved by 6.4% when using a single trait in a multi-environment scheme compared to using multi-trait analysis. Additionally, deep learning models consistently outperformed Bayesian models in both single and multiple trait and environment approaches. A complementary genome-wide association study identified associations with 26 candidate genes related to flowering time traits, and 31 marker-trait associations were identified, accounting for 37%, 37%, and 22% of the phenotypic variation of ASI, FF and MF, respectively. In conclusion, our findings suggest that deep learning models have the potential to significantly improve the accuracy of predictions, regardless of the approach used and provide support for the efficacy of this method in genomic selection for flowering-related traits in tropical maize. [ABSTRACT FROM AUTHOR]