Bayesian Reconstruction of Two-Sex Populations by Age: Estimating Sex Ratios at Birth and Sex Ratios of Mortality.
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ABSTRACT:
SUBMITTER: Wheldon MC
PROVIDER: S-EPMC4657758 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Adult sex ratio (ASR, the proportion of males in the adult population) is a central concept in population and evolutionary biology, and is also emerging as a major factor influencing mate choice, pair bonding and parental cooperation in both human and non-human societies. However, estimating ASR is fraught with difficulties stemming from the effects of spatial and temporal variation in the numbers of males and females, and detection/capture probabilities that differ between the sexes. Here, we critically evaluate methods for estimating ASR in wild animal populations, reviewing how recent statistical advances can be applied to handle some of these challenges. We review methods that directly account for detection differences between the sexes using counts of unmarked individuals (observed, trapped or killed) and counts of marked individuals using mark-recapture models. We review a third class of methods that do not directly sample the number of males and females, but instead estimate the sex ratio indirectly using relationships that emerge from demographic measures, such as survival, age structure, reproduction and assumed dynamics. We recommend that detection-based methods be used for estimating ASR in most situations, and point out that studies are needed that compare different ASR estimation methods and control for sex differences in dispersal.This article is part of the themed issue 'Adult sex ratios and reproductive decisions: a critical re-examination of sex differences in human and animal societies'.
| S-EPMC5540855 | biostudies-literature
Project description:AimsTo develop methods to disaggregate World Health Orgagnization estimates of tuberculosis (TB) incidence and mortality for each country by sex and age.MethodsFor countries where incidence estimates derived from a factor adjustment of notifications and case detection ratio over 0.85, or with <1000 reported TB cases, we disaggregated incidence proportional to notifications. For each other country, a prior was constructed using a hierarchical model of age-stratified prevalence survey data, meta-analysis of sex ratios, and mathematical modelling for children under 15 years. Samples from this prior were used to disaggregate incidence and accepted if incidence exceeded notifications in each age/sex category. Results were inspected and, if implausible, incidence was disaggregated proportional to notifications. Mortality was disaggregated proportional to patterns in vital registration (VR) data in countries with VR data. Where VR data were lacking, a case-fatality ratio (CFR) approach was applied to estimated incidence, with separate CFRs by HIV/ART status, child/adult age groups, and anti-TB treatment status. Uncertainty in all disaggregated country estimates was constructed to be consistent with corresponding overall uncertainty.ResultsWe generated disaggregated results for 216 countries. For 125 countries, incidence disaggregation was based on notifications. Of the rest, accepted samples from the prior were considered implausible in 4 countries. For 72 countries, mortality disaggregation was based on VR data; the rest were based on the CFR approach.ConclusionsWhile multi-stage, this approach is comparatively simple in overall logic. Disaggregated estimates have relatively larger uncertainty and should be used with caution.
| S-EPMC8128472 | biostudies-literature
Project description:BackgroundNitrogen isotope analysis of bone collagen has been used to reconstruct the breastfeeding practices of archaeological human populations. However, weaning ages have been estimated subjectively because of a lack of both information on subadult bone collagen turnover rates and appropriate analytical models.MethodologyTemporal changes in human subadult bone collagen turnover rates were estimated from data on tissue-level bone metabolism reported in previous studies. A model for reconstructing precise weaning ages was then developed using a framework of approximate Bayesian computation and incorporating the estimated turnover rates. The model is presented as a new open source R package, WARN (Weaning Age Reconstruction with Nitrogen isotope analysis), which computes the age at the start and end of weaning, (15)N-enrichment through maternal to infant tissue, and [Formula: see text] value of collagen synthesized entirely from weaning foods with their posterior probabilities. The model was applied to 39 previously reported Holocene skeletal populations from around the world, and the results were compared with weaning ages observed in ethnographic studies.ConclusionsThere were no significant differences in the age at the end of weaning between the archaeological (2.80±1.32 years) and ethnographic populations. By comparing archaeological populations, it appears that weaning ages did not differ with the type of subsistence practiced (i.e., hunting-gathering or not). Most of [Formula: see text]-enrichment (2.44±0.90‰) was consistent with biologically valid values. The nitrogen isotope ratios of subadults after the weaning process were lower than those of adults in most of the archaeological populations (-0.48±0.61‰), and this depletion was greater in non-hunter-gatherer populations. Our results suggest that the breastfeeding period in humans had already been shortened by the early Holocene compared with those in extant great apes.
| S-EPMC3754991 | biostudies-literature
Project description: Not available
| S-EPMC4361714 | biostudies-literature
Project description:The elemental composition of seston provide insights into the functioning of lake food webs and how nutrients cycle through the environment. Here, we describe a Bayesian network model that simultaneously estimates relationships between dissolved and particulate nutrients, suspended volatile and non-volatile sediments, and algal chlorophyll. The model provides direct estimates of the phosphorus and nitrogen content of phytoplankton, suspended non-living organic matter, and suspended inorganic sediment. We apply this model to data collected from reservoirs in Missouri, USA to test the validity of our assumed relationships. The results indicate that, on average among all samples, the ratio of nitrogen to phosphorus (N:P) in phytoplankton and non-living organic matter in these reservoirs were similar, although under nutrient replete conditions, N:P in phytoplankton decreased. Phosphorus content of inorganic sediment was lower than in phytoplankton and non-living organic matter. The analysis also provided a means of tracking changes in the composition of whole seston over time. In addition to informing questions regarding seston stoichiometry, this modeling approach may inform efforts to manage lake eutrophication because it can improve traditional models of relationships between nutrients and chlorophyll in lakes.
| S-EPMC7147758 | biostudies-literature
Project description:Prevalence of disease in wildlife populations, which is necessary for developing disease models and conducting epidemiologic analyses, is often understudied. Laboratory tests used to screen for diseases in wildlife populations often are validated only for domestic animals. Consequently, the use of these tests for wildlife populations may lead to inaccurate estimates of disease prevalence. We demonstrate the use of Bayesian latent class analysis (LCA) in determining the specificity and sensitivity of a competitive enzyme-linked immunosorbent assay (cELISA; VMRD (®), Inc.) serologic test used to identify exposure to Neospora caninum (hereafter N. caninum) in three wildlife populations in southeastern Ohio, USA. True prevalence of N. caninum exposure in these populations was estimated to range from 0.1% to 3.1% in American bison (Bison bison), 51.0% to 53.8% in Père David's deer (Elaphurus davidianus), and 40.0% to 45.9% in white-tailed deer (Odocoileus virginianus). The accuracy of the cELISA in American bison and Père David's deer was estimated to be close to the 96% sensitivity and 99% specificity reported by the manufacturer. Sensitivity in white-tailed deer, however, ranged from 78.9% to 99.9%. Apparent prevalence of N. caninum from the test results is not equal to the true prevalence in white-tailed deer and Père David's deer populations. Even when these species inhabit the same community, the true prevalence in the two deer populations differed from the true prevalence in the American bison population. Variances in prevalence for some species suggest differences in the epidemiology of N. caninum for these colocated populations. Bayesian LCA methods could be used as in this example to overcome some of the constraints on validating tests in wildlife species. The ability to accurately evaluate disease status and prevalence in a population improves our understanding of the epidemiology of multihost pathogen systems at the community level.
| S-EPMC4831453 | biostudies-other
Project description:Ancestral state reconstruction models use genetic data to characterize a group of organisms' common ancestor. These models have been applied to salmonellosis outbreaks to estimate the number of transmissions between different animal species that share similar geographical locations, with animal host as the state. However, as far as we are aware, no studies have validated these models for outbreak analysis. In this study, salmonellosis outbreaks were simulated using a stochastic Susceptible-Infected-Recovered model, and the host population and transmission parameters of these simulated outbreaks were estimated using Bayesian ancestral state reconstruction models (discrete trait analysis (DTA) and structured coalescent (SC)). These models were unable to accurately estimate the number of transmissions between the host populations or the amount of time spent in each host population. The DTA model was inaccurate because it assumed the number of isolates sampled from each host population was proportional to the number of individuals infected within each host population. The SC model was inaccurate possibly because it assumed that each host population's effective population size was constant over the course of the simulated outbreaks. This study highlights the need for phylodynamic models that can take into consideration factors that influence the characteristics and behavior of outbreaks, e.g. changing effective population sizes, variation in infectious periods, intra-population transmissions, and disproportionate sampling of infected individuals.
| S-EPMC6645465 | biostudies-literature
Project description:BackgroundThe infant mortality rate (IMR) is a critical indicator of population health, but its measurement is subject to response bias in countries without complete vital registration systems who rely instead on birth histories collected via sample surveys. One of the most salient bias is the fact that child deaths in these birth histories tend to be reported with a large amount of heaping at age 12 months. Because of this issue, analysts and international agencies do not directly use IMR estimates based on surveys such as Demographic and Health Surveys (DHS); they rely instead on mortality models such as model life tables. The use of model life tables in this context, however, is arbitrary, and the extent to which this approach appropriately addresses bias in DHS-based IMR estimates remains unclear. This hinders our ability to monitor IMR levels and trends in low-and middle-income countries. The objective of this study is to evaluate age heaping bias in DHS-based IMR estimates and propose an improved method for adjusting this bias.Methods and findingsOur method relies on a recently-developed log-quadratic model that can predict age-specific mortality by detailed age between 0 and 5. The model's coefficients were derived from a newly constituted database, the Under-5 Mortality Database (U5MD), that represents the mortality experience of countries with high-quality vital registration data. We applied this model to 204 DHS surveys, and compared unadjusted IMR values to IMR values adjusted with the log-quadratic model as well as with the classic model life table approach. Results show that contrary to existing knowledge, age heaping at age 12 months rarely generates a large amount of bias in IMR estimates. In most cases, the unadjusted IMR values were not deviating by more than +/- 5% from the adjusted values. The model life table approach, by contrast, introduced an unwarranted, downward bias in adjusted IMR values. We also found that two regions, Sub-Saharan Africa and South Asia, present age patterns of under-5 mortality that strongly depart from the experience represented in the U5MD. For these countries, neither the existing model life tables nor the log-quadratic model can produce empirically-supported IMR adjustments.ConclusionsAge heaping at age 12 months produces a smaller amount of bias in DHS-based IMR estimates than previously thought. If a large amount of age heaping is present in a survey, the log-quadratic model allows users to evaluate, and whenever necessary, adjust IMR estimates in a way that is more informed by the local mortality pattern than existing approaches. Future research should be devoted to understanding why Sub-Saharan African and South Asian countries have such distinct age patterns of under-five mortality.
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Project description:Population sex ratios are of high ecological relevance, but are challenging to determine in species lacking conspicuous external cues indicating their sex. Acoustic sexing is an option if vocalizations differ between sexes, but is precluded by overlapping distributions of the values of male and female vocalizations in many species. A method allowing the inference of sex ratios despite such an overlap will therefore greatly increase the information extractable from acoustic data. To meet this demand, we developed a novel approach using Approximate Bayesian Computation (ABC) to infer the sex ratio of populations from acoustic data. Additionally, parameters characterizing the male and female distribution of acoustic values (mean and standard deviation) are inferred. This information is then used to probabilistically assign a sex to a single acoustic signal. We furthermore develop a simpler means of sex ratio estimation based on the exclusion of calls from the overlap zone. Applying our methods to simulated data demonstrates that sex ratio and acoustic parameter characteristics of males and females are reliably inferred by the ABC approach. Applying both the ABC and the exclusion method to empirical datasets (echolocation calls recorded in colonies of lesser horseshoe bats, Rhinolophus hipposideros) provides similar sex ratios as molecular sexing. Our methods aim to facilitate evidence-based conservation, and to benefit scientists investigating ecological or conservation questions related to sex- or group specific behaviour across a wide range of organisms emitting acoustic signals. The developed methodology is non-invasive, low-cost and time-efficient, thus allowing the study of many sites and individuals. We provide an R-script for the easy application of the method and discuss potential future extensions and fields of applications. The script can be easily adapted to account for numerous biological systems by adjusting the type and number of groups to be distinguished (e.g. age, social rank, cryptic species) and the acoustic parameters investigated.
| S-EPMC6013104 | biostudies-literature
Project description:Spatial correlation raises challenges in estimating confidence intervals for region specific event rates and rate ratios between geographic units that are nested. Methods have been proposed to incorporate spatial correlation by assuming various distributions for the structure of autocorrelation patterns. However, the derivation of these statistics based on approximation may have to condition on the distributional assumption underlying the data generating process, which may not hold for certain situations. This paper explores the feasibility of utilizing a Bayesian convolution model (BCM), which includes an uncorrelated heterogeneity (UH) and a conditional autoregression (CAR) component to accommodate both uncorrelated and correlated spatial heterogeneity, to estimate the 95% confidence intervals for age-adjusted rate ratios among geographic regions with existing spatial correlations. A simulation study is conducted and a BCM method is applied to two cancer incidence datasets to calculate age-adjusted rate/ratio for the counties in the State of Kentucky relative to the entire state. In comparison to three existing methods, without and with spatial correlation, the Bayesian convolution model-based estimation provides moderate shrinkage effect for the point estimates based on the neighbor structure across regions and produces a wider interval due to the inclusion of uncertainty in the spatial autocorrelation parameters. The overall spatial pattern of region incidence rate from BCM approach appears to be like the direct estimates and other methods for both datasets, even though "smoothing" occurs in some local regions. The Bayesian Convolution Model allows flexibility in the specification of risk components and can improve the accuracy of interval estimates of age-adjusted rate ratios among geographical regions as it considers spatial correlation.
| S-EPMC6560155 | biostudies-literature