Hierarchical Bayesian Approach Research Articles

Are accelerators akin to breweries or wineries? A Bayesian variance decomposition of accelerator and cohort effects

AbstractResearch SummaryThe literature on startup accelerators uncovers multiple factors associated with accelerators’ advantages. Yet, we have a limited understanding of the relative magnitude of these factors. We ask: Are accelerators akin to breweries, where quality is mainly a function of the institution of origin (i.e., brewery for beer, accelerator for startups); or are they similar to wineries, where quality varies across cohorts (i.e., for a given winery, some vintages are of higher quality)? We explore this question using data from 1,350 tech‐startups graduating from dozens of accelerators in a global technology hub. A Bayesian hierarchical variance decomposition approach is introduced to account for the highly‐skewed zero‐inflated distribution in startups’ performance. We find that a notable fraction of startup performance is due to vintage; within‐accelerator, cross‐cohort variation.Managerial SummaryStartup accelerators (i.e., short‐term programs designed to help startups grow) are highly popular, with dozens of accelerators operating around the globe. Our focus is on accelerator programs aimed at catapulting technology ventures towards high growth. We ask: Are accelerators akin to breweries, where quality is mainly a function of the institution of origin (i.e., brewery for beer, accelerator for startups); or are they similar to wineries, where quality also varies across cohorts (i.e., for a given winery, some vintages are of higher quality)? A Bayesian hierarchical variance decomposition approach isused to study data from a global technology hub, detailing the performance of hundreds of startups that graduated across multiple accelerators. We find that a significant portion of startup success is linked to cohort‐specific factors within accelerators, highlighting the role of timing and dynamics of each accelerator cohort.

Evaluating the direct effect of vaccination and non-pharmaceutical interventions during the COVID-19 pandemic in Europe

BackgroundAcross Europe, countries have responded to the COVID-19 pandemic with a combination of non-pharmaceutical interventions and vaccination. Evaluating the effectiveness of such interventions is of particular relevance to policy-makers.MethodsWe leverage almost three years of available data across 38 European countries to evaluate the effectiveness of governmental responses in controlling the pandemic. We developed a Bayesian hierarchical model that flexibly relates daily COVID-19 incidence to past levels of vaccination and non-pharmaceutical interventions as summarised in the Stringency Index. Specifically, we use a distributed lag approach to temporally weight past intervention values, a tensor-product smooth to capture non-linearities and interactions between both types of interventions, and a hierarchical approach to parsimoniously address heterogeneity across countries.ResultsWe identify a pronounced negative association between daily incidence and the strength of non-pharmaceutical interventions, along with substantial heterogeneity in effectiveness among European countries. Similarly, we observe a strong but more consistent negative association with vaccination levels. Our results show that non-linear interactions shape the effectiveness of interventions, with non-pharmaceutical interventions becoming less effective under high vaccination levels. Finally, our results indicate that the effects of interventions on daily incidence are most pronounced at a lag of 14 days after being in place.ConclusionsOur Bayesian hierarchical modelling approach reveals clear negative and lagged effects of non-pharmaceutical interventions and vaccination on confirmed COVID-19 cases across European countries.

Progress and scenario-based projections of health service availability and coverage towards UHC in the post-conflict and post-pandemic Iraq: a Bayesian hierarchical regression approach

BackgroundTwo decades have passed since the beginning of the Iraq War in 2003. Iraq has long suffered from conflicts and instability, where the people have limited access to healthcare. The...

Hierarchical Bayesian modeling for calibration and uncertainty quantification of constitutive material models: Application to a uniaxial steel material model

Constitutive material models play an integral role in finite element (FE) modeling of structural components and systems and are governed by a set of parameters. The values of the material model parameters are important for the FE model to be able to predict the real structural behavior as closely as possible. Realistic cyclic material constitutive models well calibrated to experimental data are critical when predicting the nonlinear dynamic response of structural systems to extreme loads (e.g., earthquakes, high winds, blast). Hence, this study focuses on the inference and uncertainty quantification of material model parameters from an experimental dataset consisting of data from experiments on multiple specimens of the same kind, which is a topic of great interest to the structural modeling and simulation community. Traditionally, Bayesian inference is performed by pooling the data from all the specimens, or by considering the data from a single specimen, disregarding the inherent variability (aleatory uncertainty) among specimens of the same kind. Consequently, traditional approaches fail to capture the population distribution of material model parameters, which represents the aleatory specimen-to-specimen variability that is essential for conducting reliability analysis and uncertainty propagation studies. Moreover, these approaches cannot accurately simulate the response of new specimens. To overcome these limitations, this study introduces a novel application of hierarchical Bayesian inference for calibrating material model parameters. By simultaneously considering experimental data from multiple specimens of the same kind, hierarchical Bayesian modeling quantifies both epistemic uncertainty and aleatory specimen-to-specimen variability. In this study, the parameters of the well-known Giuffré-Menegotto-Pinto (GMP) uniaxial material model for reinforcing steel are calibrated and validated based on a dataset of experimental cyclic tests conducted on thirty-six steel specimens, or coupons, originating from three different manufacturing mills, complying to two different manufacturing standards, and subjected to different strain histories. These strain histories are similar to those experienced during seismic events by reinforcing steel bars within reinforced concrete structures. The material model parameters are calibrated using both the traditional and the hierarchical Bayesian approach and the results are compared. Based on the experimental dataset employed in this study, a joint probability distribution of the GMP material model parameters that quantifies both epistemic (due to finite sample size) and aleatory (coupon-to-coupon) variability or uncertainty is provided which can be used for uncertainty propagation in reliability and risk analyses of nonlinear dynamic systems.

Statistical considerations for the platform trial in COVID-19 vaccine priming and boosting

The Platform trial In COVID-19 priming and BOOsting (PICOBOO) is a multi-site, adaptive platform trial designed to generate evidence of the immunogenicity, reactogenicity, and cross-protection of different booster vaccination strategies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants, specific for the Australian context. The PICOBOO trial randomises participants to receive one of three COVID-19 booster vaccine brands (Pfizer, Moderna, Novavax) available for use in Australia, where the vaccine brand subtypes vary over time according to the national vaccine roll out strategy, and employs a Bayesian hierarchical modelling approach to efficiently borrow information across consecutive booster doses, age groups and vaccine brand subtypes. Here, we briefly describe the PICOBOO trial structure and report the statistical considerations for the estimands, statistical models and decision making for trial adaptations. This paper should be read in conjunction with the PICOBOO Core Protocol and PICOBOO Sub-Study Protocol 1: Booster Vaccination. PICOBOO was registered on 10 February 2022 with the Australian and New Zealand Clinical Trials Registry ACTRN12622000238774.

Probabilistic Modelling of the Food Matrix Effects on Curcuminoid's In Vitro Oral Bioaccessibility.

The bioaccessibility of bioactive compounds plays a major role in the nutritional value of foods, but there is a lack of systematic studies assessing the effect of the food matrix on bioaccessibility. Curcuminoids are phytochemicals extracted from Curcuma longa that have captured public attention due to claimed health benefits. The aim of this study is to develop a mathematical model to predict curcuminoid's bioaccessibility in biscuits and custard based on different fibre type formulations. Bioaccessibilities for curcumin-enriched custards and biscuits were obtained through in vitro digestion, and physicochemical food properties were characterised. A strong correlation between macronutrient concentration and bioaccessibility was observed (p = 0.89) and chosen as a main explanatory variable in a Bayesian hierarchical linear regression model. Additionally, the patterns of food matrix effects on bioaccessibility were not the same in custards as in biscuits; for example, the hemicellulose content had a moderately strong positive correlation to bioaccessibility in biscuits (p = 0.66) which was non-significant in custards (p = 0.12). Using a Bayesian hierarchical approach to model these interactions resulted in an optimisation performance of r2 = 0.97 and a leave-one-out cross-validation score (LOOCV) of r2 = 0.93. This decision-support system could assist the food industry in optimising the formulation of novel food products and enable consumers to make more informed choices.

Uncertainty Quantification in Radial Anisotropy Models Based on Transdimensional Bayesian Inversion of Receiver Functions and Surface-Wave Dispersion: Case Study Sri Lanka

ABSTRACT In geophysical inference problems, quantification of data uncertainties is required to balance the data-fitting ability of the model and its complexity. The transdimensional hierarchical Bayesian approach is a powerful tool to evaluate the level of uncertainty and determine the complexity of the model by treating data errors and model dimensions as unknown. In this article, we take account of the uncertainty through the whole procedure, thus developing a two-step fully Bayesian approach with coupled uncertainty propagation to estimate the crustal isotropic and radial anisotropy (RA) model based on Rayleigh and Love dispersion as well as receiver functions (RFs). First, 2D surface-wave tomography is applied to determine period-wise ambient noise phase velocity maps and their uncertainty for Rayleigh and Love waves. Probabilistic profiles of the isotropic average VS and RA as a function of depth are then derived at station sites by inverting the local surface-wave dispersion and model errors and RFs jointly. The workflow is applied to a temporary seismic broadband array covering all of Sri Lanka. The probabilistic results enable us to effectively quantify the uncertainty of the final RA model and provide robust inferences. The shear-wave velocity results show that the range of Moho depths is between 30 and 40 km, with the thickest crust (38–40 km) beneath the central Highland Complex. Positive RA (VSH>VSV) observed in the upper crust is attributed to subhorizontal alignment of metamorphic foliation and stretched layers resulting from deformation. Negative RA (VSV>VSH) in the midcrust of central Sri Lanka may indicate the existence of melt inclusions and could result from the uplift and folding process. The positive RA in the lower crust could be caused by crustal channel flow in a collision orogeny.

Syrian refugee young adults as community mental health workers implementing problem management plus: Protocol for a pilot randomized controlled trial to measure the mechanisms of effect on their own wellbeing, stress and coping

This pilot randomized controlled trial protocol aims to (1) assess the impact on the wellbeing of Syrian refugee young adults (18–24 years) of being a community mental health worker (CMHW) implementing WHO's evidence-based psychosocial intervention - Problem Management Plus (PM+) - with adults in their community, and (2) identify the mechanisms associated with the outcomes of enhanced wellbeing and coping, and reduced stress among these CMHWs. Over 108 million people have been forcibly displaced as of the end of 2022. Mental health consequences of these displacements are significant, yet human resources for health are not sufficient to meet the needs. A large proportion of refugee populations are youth and young adults (YA). Evidence indicates their engagement in supporting their communities leads to their own enhanced wellbeing and that of their community. This trial trains Syrian refugees to serve their communities as CMHW (n=19) or tutors (n=19) and compare wellbeing, stress and coping outcomes between these two groups and a control group (n = 40). We will also assess 7 mechanisms as potential pathways for the interventions to influence outcomes. Surveys will assess outcomes and mechanisms, hair samples will measure stress cortisol. The primary analysis will use a Bayesian Hierarchical Model approach to model the trajectories of the mechanisms and primary study endpoints over time for individuals in each of the arms. Our results will elucidate critical mechanisms in which engagement of young adults to support their community enhances their own wellbeing. Trial registrationNational Institutes of Mental Health, NCT05265611, Registered prospectively in 2021. Lebanon clinical trials registry #LBCTR2023015206, Registered in 2023.

Predicting Abiotic TCE Transformation Rate Constants—A Bayesian Hierarchical Approach

AbstractFe(II) minerals can mediate the abiotic reduction of trichloroethylene (TCE), a widespread groundwater contaminant. If reaction rates are sufficiently fast for natural attenuation, the process holds potential for mitigating TCE pollution in groundwater. To assess the variability of abiotic TCE reduction rate constants, we collected pseudo‐first‐order rate constants for natural sediments and rocks from the literature, as well as intrinsic (surface‐area‐normalized) rate constants of individual minerals. Using a Bayesian hierarchical modeling approach, we were able to differentiate the contributions of natural variability and experimental error to the total variance. Applying the model, we also predicted rate constants at new sites, revealing a considerable uncertainty of several orders of magnitude. We investigated whether incorporating additional information about sediment composition could reduce this uncertainty. We tested two sets of predictors: reactive mineral content (measured by X‐ray diffraction) combined with surface areas and intrinsic rate constants, or the extractable Fe(II) content. Knowledge of the mineral composition only marginally reduced the uncertainty of predicted rate constants. We attribute the low information gain to the inability to measure the (reactive) surface areas of individual minerals in sediments or rocks, which are subject to environmental factors like aqueous geochemistry and redox potential. In contrast, knowing the Fe(II) content reduced the uncertainty about the first‐order rate constant by nearly two orders of magnitude, because the relationship between Fe(II) content and rate constants is approximately log–log‐linear. We demonstrate how our approach provides estimates for the range of cleanup times for a simple example of diffusion‐controlled transport in a contaminated aquitard.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.

Safety Evaluation and Influential Factor Analysis of Urban Expressway Diversion Areas Based on Non-Stationary Conflict Extremes

To evaluate the safety conditions of expressway exit areas with different exit forms, a safety analysis method based on the conflict extreme model is proposed. Based on video data from four typical urban expressway diversion areas, longitudinal conflicts and lateral conflicts were identified using time to collision and cross time to collision methods, respectively, and traffic parameters such as traffic flow were extracted. A Bayesian hierarchical conflict extreme model for joint conflict types was constructed by integrating the peak over threshold and Bayesian hierarchical approach. The threshold for conflicts was determined using the mean residual life plot and the threshold stability plot. Subsequently, a logarithmic link function for the scale parameter was established by combining different covariates. Then, the model parameters were estimated using the Markov chain Monte Carlo simulation method. Finally, based on the fitted distribution, a comprehensive safety evaluation and influential factor analysis were conducted for each diversion area. The results show that an increase in traffic flow and the ratio of large vehicles leads to a higher probability of traffic accidents. However, an increase in the average speed reduces the probability of traffic accidents. The construction form of the diversion area and the number of off-ramp lanes do not demonstrate an explicit relationship with the probability of traffic accidents. The research conclusion indicates that the Bayesian hierarchical extreme model can effectively utilize short-term observed conflicts for long-term crash estimation, and it has the potential to be applied in the field of safety evaluation.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.

Although transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g., healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, i.e., reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, versus state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.

Proposing a Bayesian hierarchical growth curve model (BHGCM) for tourism and hospitality research

Curvilinear growth trajectories of products/services are common in the tourism and hospitality industry. To fit nonlinear growth patterns with multilevel structures, this study proposed Bayesian hierarchical growth curve models (BHGCMs) in line with the increasing adoption of Bayesian analysis in tourism and hospitality academia. We provided the basic form of BHGCM along with its unique advantages. For an empirical test, this study applied several growth curves to approximate online reviews of U.S. hotels from August 2020 to January 2021. After selecting a Gompertz curve as a mean function of the GCM, a Bayesian hierarchical approach was employed to estimate growth parameters—namely, base and maximum volume of hotel reviews, inflection week, and relative growth rate—and identify their determinants. Our findings demonstrate the superiority of the proposed BHGCM in fitting the growth patterns of hotel reviews while revealing the effect of price and accumulated reviews on the parameters.

Untangling the complex food webs of tropical rainforest streams.

Food webs depict the tangled web of trophic interactions associated with the functioning of an ecosystem. Understanding the mechanisms providing stability to these food webs is therefore vital for conservation efforts and the management of natural systems. Here, we first characterised a tropical stream meta-food web and five individual food webs using a Bayesian Hierarchical approach unifying three sources of information (gut content analysis, literature compilation and stable isotope data). With data on population-level biomass and individually measured body mass, we applied a bioenergetic model and assessed food web stability using a Lotka-Volterra system of equations. We then assessed the resilience of the system to individual species extinctions using simulations and investigated the network patterns associated with systems with higher stability. The model resulted in a stable meta-food web with 307 links among the 61 components. At the regional scale, 70% of the total energy flow occurred through a set of 10 taxa with large variation in body masses. The remaining 30% of total energy flow relied on 48 different taxa, supporting a significant dependency on a diverse community. The meta-food web was stable against individual species extinctions, with a higher resilience in food webs harbouring omnivorous fish species able to connect multiple food web compartments via weak, non-specialised interactions. Moreover, these fish species contributed largely to the spatial variation among individual food webs, suggesting that these species could operate as mobile predators connecting different streams and stabilising variability at the regional scale. Our results outline two key mechanisms of food web stability operating in tropical streams: (i) the diversity of species and body masses buffering against random and size-dependent disturbances and (ii) high regional diversity and weak omnivorous interactions of predators buffering against local stochastic variation in species composition. These mechanisms rely on high local and regional biodiversity in tropical streams, which is known to be strongly affected by human impacts. Therefore, an urgent challenge is to understand how the ongoing systematic loss of diversity jeopardises the stability of stream food webs in human-impacted landscapes.

A Two‐stage Bayesian Small Area Estimation Approach for Proportions

SummaryWith the rise in popularity of digital Atlases to communicate spatial variation, there is an increasing need for robust small area estimates. However, current small area estimation methods suffer from various modelling problems when data are very sparse or when estimates are required for areas with very small populations. These issues are particularly heightened when modelling proportions. Additionally, recent work has shown significant benefits in modelling at both the individual and area levels. We propose a two‐stage Bayesian hierarchical small area estimation approach for proportions that can account for survey design, reduce direct estimate instability and generate prevalence estimates for small areas with no survey data. Using a simulation study, we show that, compared with existing Bayesian small area estimation methods, our approach can provide optimal predictive performance (Bayesian mean relative root mean squared error, mean absolute relative bias and coverage) of proportions under a variety of data conditions, including very sparse and unstable data. To assess the model in practice, we compare modelled estimates of current smoking prevalence for 1,630 small areas in Australia using the 2017–2018 National Health Survey data combined with 2016 census data.

Municipal-level estimates (2020) of adult obesity in Mexico drawn from a hierarchical Bayesian estimator

Since the beginning of the 21st Century obesity has become a major public health concern in Mexico. Survey data have been key to tracking the evolution of the national and regional prevalence of obesity over time. However, these data are insufficient for policymakers and researchers interested in obesity from a more local and spatial perspective. This paper uses two secondary data sources: the Mexican National Health and Nutrition Survey 2021 and the Mexican National Population Census 2020. This paper implements a Bayesian hierarchical approach to model survey and census data to produce municipal-level estimates for Mexico in 2020. The results indicate that obesity has inter and intra-regional variability. Obesity is more prevalent in the north and in the Yucatan peninsula and tends to be lower in the state of Chiapas. However, within these regions there is some degree of variability in obesity rates. The results provide a more detailed geographical picture of obesity across Mexico and raise the possibility of using the resulting estimates for further statistical and policy-relevant research.

A statistical population reconstruction model for wildlife populations: A case study with white‐tailed deer and fisher

AbstractHarvested wildlife populations should ideally be monitored to inform harvest policies and decision‐making to help achieve management objectives. When the age of harvested individuals can be obtained, these data (i.e., age‐at‐harvest data) can be used to estimate trends of abundances, demographic rates, and harvest probabilities by the statistical reconstruction of the living population. This approach was developed primarily within the frequentist framework and requires the inclusion of auxiliary data (e.g., radiotelemetry data). We developed a novel Bayesian hierarchical approach allowing the population reconstruction from the definition of the species' life cycle without auxiliary data. The hierarchical model assumes that individuals are harvested from an open population whose fluctuations result from demographic processes, and the definition of a superpopulation composed of pseudo‐individuals from which the harvested population is drawn. We evaluated the ability of our model to estimate abundances, survival, recruitment, and harvest probabilities based on simulations guided by the demographic processes of a long‐lived mammal population. We considered model performance across scenarios, including varying age and temporal structures, superpopulation size, and prior information. We showed how prior information selected based on life history characteristics affects the accuracy of estimated parameters. We found that the model estimates accurate demographic parameters and abundances when the age‐at‐harvest matrix comprises more than two age classes. Furthermore, an increase in demographic information (number of age groups and years) increased the precision of the estimated parameters. We apply our model to a population of harvested (2012–2021) white‐tailed deer (Odocoileus virginianus) and a mammalian carnivore, the fisher (Pekania pennanti), from Rhode Island, USA. Our model estimated biologically realistic population size and demographic rates for both species. Our approach provides robustness to track the population abundance of harvested species through time and estimate fundamental demographic parameters. Such results can be used to monitor whether population objectives are being met and whether harvest policy changes are required. Furthermore, this information can be critical for evaluating the effect of harvest on population growth and projecting trajectories of age‐structured populations under different harvest scenarios. Therefore, our framework can help to guide management decisions and species conservation.

Coarsened Mixtures of Hierarchical Skew Normal Kernels for Flow and Mass Cytometry Analyses

Cytometry is the standard multi-parameter assay for measuring single cell phenotype and functionality. It is commonly used for quantifying the relative frequencies of cell subsets in blood and disaggregated tissues. A typical analysis of cytometry data involves cell classification—that is, the identification of cell subgroups in the sample—and comparisons of the cell subgroups across samples or conditions. While modern experiments often necessitate the collection and processing of samples in multiple batches, analysis of cytometry data across batches is challenging because differences across samples may occur due to either true biological variation or technical reasons such as antibody lot effects or instrument optics across batches. Thus a critical step in comparative analyses of multi-sample cytometry data—yet missing in existing automated methods for analyzing such data—is cross-sample calibration, whose goal is to align corresponding cell subsets across multiple samples in the presence of technical variations, so that biological variations can be meaningfully compared. We introduce a Bayesian nonparametric hierarchical modeling approach for accomplishing both calibration and cell classification simultaneously in a unified probabilistic manner. Three important features of our method make it particularly effective for analyzing multi-sample cytometry data: a nonparametric mixture avoids prespecifying the number of cell clusters; a hierarchical skew normal kernel that allows flexibility in the shapes of the cell subsets and cross-sample variation in their locations; and finally the “coarsening” strategy makes inference robust to departures from the model not captured by the skew normal kernels. We demonstrate the merits of our approach in simulated examples and carry out a case study in the analysis of a multi-sample cytometry data set. We provide an R package for our method.

On the Initial Spin Period Distribution of Neutron Stars

We derive the initial spin period distribution of neutron stars by studying the population of young pulsars associated with supernova remnants. Our hierarchical Bayesian approach accounts for the measurement uncertainties of individual observations and selection effects. Without correcting for selection effects, as done in previous studies, we find that pulsar initial spin periods follow a Weibull distribution, peaking at 40 ms, which is favored against a lognormal distribution with a Bayes factor of 200. The known selection effects in radio pulsar surveys, including pulse broadening and period-dependent beaming fraction, have been quantitatively investigated. We show that, based on measurements of pulsar luminosity and spin period from the ATNF Pulsar Catalogue, the impact of pulse broadening on the inference of the pulsar period distribution is likely to be insignificant. Correcting for the beaming selection effect, the Weibull distribution remains the preferred model, while its peak slightly shifts to longer periods at 50 ms. Our method will prove useful in constraining the birth properties of neutron stars in the Square Kilometre Array era.

Leveraging climate and remote sensing metrics for predicting forest carbon stock using Bayesian geostatistical modelling under a projected climate warming in Zimbabwe

Climate change, driven by escalating carbon dioxide (CO2) emissions, poses a significant threat to forest ecosystems and the livelihoods of communities reliant on them, especially for the global south countries and regions like the eastern highlands of Zimbabwe. The 2000 land redistribution programme reduced buffer zones between ecologically sensitive forests and land reform beneficiaries near major carbon reservoirs. In light of these challenges, this study aimed to assess the potential effects of climate change on a strategically important plantation forest ecosystem in Zimbabwe's eastern highlands. Using data from the Coupled Model Inter-comparison Project Phase 6 (CMIP6) of the Intergovernmental Panel on Climate Change (IPCC), we modelled and predicted changes in forest carbon (C) stock density under different climate scenarios: current (1970–2000), SSP5–4.5, and SSP5–8.5. Employing a hierarchical Bayesian geostatistical approach, we compared the baseline scenario (1970–2000) with projected scenarios (RCP4.5 and RCP8.5) for 2075 to estimate changes in forest carbon stock distribution. Our results indicated a decline in carbon stock concentration under future climate scenarios, reflecting the adverse impact of greenhouse gas emissions on forest growth. We found that the projected range of forest carbon stock under the RCP8.5 scenario for 2075 is notably lower (2≤MgCha−1≤44.9) than that of the baseline period (1970–2000) (1≤MgCha−1≤97), suggesting a substantial reduction in carbon storage. As the difference in posterior mean C stock (μ̅1−μ̅2), 52.1 MgCha-1 is well above zero, we deduce that the posterior mean C stock distribution of the projected future RCP8.5 2075 climate projection is indeed credibly different from the current (1970–2000) climate scenario. Additionally, there is a high probability (>90%) that forest plantations will be adversely affected by the business-as-usual climate warming projection. Overall, our findings highlight the urgent need for climate change mitigation strategies, such as reforestation programs and careful selection of tree species for plantations, to safeguard forest ecosystems and the communities dependent on them. These insights are crucial for informing effective adaptation measures in the face of future climate uncertainties.