Trumpeter Swan Abundance and Growth Rates in Yellowstone National Park

ABSTRACT Decreasing abundance of resident, nonmigratory trumpeter swans (Cygnus buccinator) in Yellowstone National Park (YNP), USA, raised concern that this population, which helped facilitate the restoration of the species across North America, may disappear. We quantified trends in abundance of resident and migratory trumpeter swans in YNP from 1967 to 2007 and investigated the potential mechanisms for declining population trends, including cessation of the supplemental feeding program and relocation programs outside of YNP, density dependence, and annual variations in environmental conditions. Estimated abundance of resident trumpeter swans in YNP ranged from 59 individuals in 1968 to 10 individuals in 2007. Using log-linear modeling, the best approximating model chosen from an a priori set of competing models estimated the annual growth rate (r) of resident swans from 1967 to 2007 was −0.036 (95% CI =−0.042 to −0.030, Akaike wt [w_i] = 0.44). A competing model provided evidence that decreases in abundance became more dramatic after supplemental feeding of grain outside of YNP was terminated in winter 1992–1993 (r̂_1967–1992 = −0.027, 95% CI = −0.039 to −0.015; r̂_1993–2007 = −0.053, 95% CI = −0.029 to −0.080; w_i = 0.42). There was little evidence of density-dependent effects on the resident population growth rates (βYNP_pop = 0.006, 95% CI = −0.017 to 0.007), but rates were lower following severe winters, wetter springs, and warmer summers. Our results indicate that the YNP population of trumpeter swans is decreasing and may act as a sink to surrounding populations. Thus, population levels of YNP trumpeter swans may depend on management outside the Park and we recommend the National Park Service collaborate with surrounding agencies in managing trumpeter swans throughout the Tri-state region where more productive habitats may exist.     

Bayesian analysis of climate change impacts in phenology

The identification of changes in observational data relating to the climate change hypothesis remains a topic of paramount importance. In particular, scientifically sound and rigorous methods for detecting changes are urgently needed. In this paper, we develop a Bayesian approach to nonparametric function estimation. The method is applied to blossom time series of Prunus avium L., Galanthus nivalis L. and Tilia platyphyllos SCOP. The functional behavior of these series is represented by three different models: the constant model, the linear model and the one change point model. The one change point model turns out to be the preferred one in all three data sets with considerable discrimination of the other alternatives. In addition to the functional behavior, rates of change in terms of days per year were also calculated. We obtain also uncertainty margins for both function estimates and rates of change. Our results provide a quantitative representation of what was previously inferred from the same data by less involved methods.     

Random Partition Models and Exchangeability for Bayesian Identification of Population Structure

We introduce a Bayesian theoretical formulation of the statistical learning problem concerning the genetic structure of populations. The two key concepts in our derivation are exchangeability in its various forms and random allocation models. Implications of our results to empirical investigation of the population structure are discussed.     

近年来乌梁素海疣鼻天鹅种群数量变化及原因分析

为查清内蒙古乌梁素海湖泊湿地疣鼻天鹅(Cygnus olor)繁殖期和秋季迁徙前期种群数量,2014至2017年采用路线统计法和样点统计法对其进行精确计数统计,结合近十余年来的文献数据和监测记录,探讨了种群数量的变化及原因。结果显示,2015至2017年春季繁殖成鸟数量依次为84只、92只、80只,基本稳定;2014至2017年,秋季种群数量依次为411只、302只、281只、153只,逐年减少;近几年适宜繁殖地和觅食地面积不断缩小、天敌偷袭、捡蛋和投毒等因素影响亚成鸟和幼鸟的生存。根据文献和保护区监测数据,1996至2004年种群数量逐年增多,与自然保护区的建立、严禁捡蛋和没收猎枪有关,而2005至2013年因干旱缺水、水域被开发利用、芦苇(Phragmites australis)和宽叶香蒲(Typhalatifolia)面积扩增、水质恶化、富营养化加重等原因种群数量下降。研究表明,近几年乌梁素海被过度开发利用,人为干扰频繁,影响疣鼻天鹅正常繁殖栖息;栖息地的科学管理和严禁捡蛋及投毒行为,对该种群的生存及增长至关重要。     

Prediction of Growth: A Hierarchical Bayesian Approach

A nonparametric hierarchical growth curve model is proposed. Different levels in the model hierarchy are intended to correspond to different sources of variation in an individual's growth. The nonparametric character of the model offers considerable flexibility in fitting the growth curves to empirical data. Here the emphasis is on prediction, and for this purpose the adopted Bayesian inferential approach seems particularly natural and efficient. A Markov chain Carlo method is used to perform the numerical computations. As an illustration of the techniques, we consider the growth of children, during their first two years.     

缺失数据和贝叶斯系统发育分析精确度之探索

The effect of missing data on phylogenetic methods is a potentially important issue in our attempts to reconstruct the Tree of Life. If missing data are truly problematic, then it may be unwise to include species in an analysis that lack data for some characters (incomplete taxa) or to include characters that lack data for some species. Given the difficulty of obtaining data from all characters for all taxa (e.g., fossils), missing data might seriously impede efforts to reconstruct a comprehensive phylogeny that includes all species. Fortunately, recent simulations and empirical analyses suggest that missing data cells are not themselves problematic, and that incomplete taxa can be accurately placed as long as the overall number of characters in the analysis is large. However, these studies have so far only been conducted on parsimony, likelihood, and neighbor-joining methods. Although Bayesian phylogenetic methods have become widely used in recent years, the effects of missing data on Bayesian analysis have not been adequately studied. Here, we conduct simulations to test whether Bayesian analyses can accurately place incomplete taxa despite extensive missing data. In agreement with previous studies of other methods, we find that Bayesian analyses can accurately reconstruct the position of highly incomplete taxa (i.e., 95% missing data), as long as the overall number of characters in the analysis is large. These results suggest that highly incomplete taxa can be safely included in many Bayesian phylogenetic analyses.     

Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

Aim

Our aim involved developing a method to analyse spatiotemporal distributions of Arctic marine mammals (AMMs) using heterogeneous open source data, such as scientific papers and open repositories. Another aim was to quantitatively estimate the effects of environmental covariates on AMMs’ distributions and to analyse whether their distributions have shifted along with environmental changes.

Location

Arctic shelf area. The Kara Sea.

Methods

Our literature search focused on survey data regarding polar bears (Ursus maritimus), Atlantic walruses (Odobenus rosmarus rosmarus) and ringed seals (Phoca hispida). We mapped the data on a grid and built a hierarchical Poisson point process model to analyse species’ densities. The heterogeneous data lacked information on survey intensity and we could model only the relative density of each species. We explained relative densities with environmental covariates and random effects reflecting excess spatiotemporal variation and the unknown, varying sampling effort. The relative density of polar bears was explained also by the relative density of seals.

Results

The most important covariates explaining AMMs’ relative densities were ice concentration and distance to the coast, and regarding polar bears, also the relative density of seals. The results suggest that due to the decrease in the average ice concentration, the relative densities of polar bears and walruses slightly decreased or stayed constant during the 17‐year‐long study period, whereas seals shifted their distribution from the Eastern to the Western Kara Sea.

Main conclusions

Point process modelling is a robust methodology to estimate distributions from heterogeneous observations, providing spatially explicit information about ecosystems and thus serves advances for conservation efforts in the Arctic. In a simple trophic system, a distribution model of a top predator benefits from utilizing prey species’ distributions compared to a solely environmental model. The decreasing ice cover seems to have led to changes in AMMs’ distributions in the marginal Arctic region.     

Bayesian Analysis of Growth Curves Using Mixed Models Defined by Stochastic Differential Equations

Summary Growth curve data consist of repeated measurements of a continuous growth process over time in a population of individuals. These data are classically analyzed by nonlinear mixed models. However, the standard growth functions used in this context prescribe monotone increasing growth and can fail to model unexpected changes in growth rates. We propose to model these variations using stochastic differential equations (SDEs) that are deduced from the standard deterministic growth function by adding random variations to the growth dynamics. A Bayesian inference of the parameters of these SDE mixed models is developed. In the case when the SDE has an explicit solution, we describe an easily implemented Gibbs algorithm. When the conditional distribution of the diffusion process has no explicit form, we propose to approximate it using the Euler–Maruyama scheme. Finally, we suggest validating the SDE approach via criteria based on the predictive posterior distribution. We illustrate the efficiency of our method using the Gompertz function to model data on chicken growth, the modeling being improved by the SDE approach.     

A Bayesian Latent Variable Mixture Model for Longitudinal Fetal Growth

Summary Fetal growth restriction is a leading cause of perinatal morbidity and mortality that could be reduced if high‐risk infants are identified early in pregnancy. We propose a Bayesian model for aggregating 18 longitudinal ultrasound measurements of fetal size and blood flow into three underlying, continuous latent factors. Our procedure is more flexible than typical latent variable methods in that we relax the normality assumptions by allowing the latent factors to follow finite mixture distributions. Using mixture distributions also permits us to cluster individuals with similar observed characteristics and identify latent classes of subjects who are more likely to be growth or blood flow restricted during pregnancy. We also use our latent variable mixture distribution model to identify a clinically meaningful latent class of subjects with low birth weight and early gestational age. We then examine the association of latent classes of intrauterine growth restriction with latent classes of birth outcomes as well as observed maternal covariates including fetal gender and maternal race, parity, body mass index, and height. Our methods identified a latent class of subjects who have increased blood flow restriction and below average intrauterine size during pregnancy. These subjects were more likely to be growth restricted at birth than a class of individuals with typical size and blood flow.     

Estimation of Parameters for Macroparasite Population Evolution Using Approximate Bayesian Computation

Summary We estimate the parameters of a stochastic process model for a macroparasite population within a host using approximate Bayesian computation (ABC). The immunity of the host is an unobserved model variable and only mature macroparasites at sacrifice of the host are counted. With very limited data, process rates are inferred reasonably precisely. Modeling involves a three variable Markov process for which the observed data likelihood is computationally intractable. ABC methods are particularly useful when the likelihood is analytically or computationally intractable. The ABC algorithm we present is based on sequential Monte Carlo, is adaptive in nature, and overcomes some drawbacks of previous approaches to ABC. The algorithm is validated on a test example involving simulated data from an autologistic model before being used to infer parameters of the Markov process model for experimental data. The fitted model explains the observed extra‐binomial variation in terms of a zero‐one immunity variable, which has a short‐lived presence in the host.     

A Bayesian Hierarchical Model for Classification with Selection of Functional Predictors

Summary In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample‐related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis–Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.     

气候变化对大兴安岭北部蒙古栎种群动态的影响

通过对气象因子和样地数据分析表明,20余年来大兴安岭北部气候趋于变暖,低海拔地带蒙古栎呈现明显的进展趋势,演替趋于以蒙古栎为优势种的阔叶林,海拔较高地带蒙古栎更新不良,演替趋于兴安落叶松和几种阔叶树的混生林,蒙古栎种群发展与干暖化具有一致性,在各类气象因子中,5月均低温是影响蒙古栎更新的决定性因子,由海拔升高引起的区域干燥度降低也是影响更新的重要因子,这也说明蒙古栎对冷湿生境的不适应性。     

Utilizing Gaussian Markov Random Field Properties of Bayesian Animal Models

Summary In this article, we demonstrate how Gaussian Markov random field properties give large computational benefits and new opportunities for the Bayesian animal model. We make inference by computing the posteriors for important quantitative genetic variables. For the single‐trait animal model, a nonsampling‐based approximation is presented. For the multitrait model, we set up a robust and fast Markov chain Monte Carlo algorithm. The proposed methodology was used to analyze quantitative genetic properties of morphological traits of a wild house sparrow population. Results for single‐ and multitrait models were compared.     

Bayesian Network Reconstruction Using Systems Genetics Data: Comparison of MCMC Methods

Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis–Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data.     

Assessing Population Viability of Black Bears using Spatial Capture-Recapture Models

The Central Georgia Bear Population (CGP) is the least abundant and most isolated of Georgia's 3 American black bear (Ursus americanus) populations. Beginning in 2011, changes to regulations governing harvest of the CGP resulted in an increase in female bear harvest, creating concern that future harvest could be an important influence on population viability. Hence, our objective was to assess viability of the CGP under various levels of female mortality. During 2012–2016, we used barbed-wire hair snares to collect bear hair samples from within the range of the CGP in Georgia, USA. We used microsatellite genotyping to identify individual bears and created robust-design, spatial detection histories for all female bears detected. We fit open population spatial capture-recapture (SCR) models to the detection histories in a Bayesian framework. We used the Widely Applicable Information Criterion (WAIC) to rank models that varied with respect to sources of variation in detection probability, survival, and per capita recruitment, and used the model with the lowest WAIC to forecast dynamics of the CGP 50 years into the future under various levels of female mortality. We assessed the 50-year extinction probability under a continuation of mortality levels documented during 2012–2016, and under incremental increases in female mortality above this baseline. The top model included density-dependent per capita recruitment, annual variation in detection probability, and a trap-level behavioral response. Abundance increased from 106 (95% CI = 86–132) females in 2012 to 136 (95% CI = 113–161) females in 2013 and remained relatively stable thereafter. Annual female survival was 0.75 (95% CI = 0.69–0.82) and did not vary among years. The per capita recruitment rate decreased over time as density increased, and was 0.49 (95% CI = 0.33–0.66) during the first time interval and 0.29 (95% CI = 0.20–0.38) during the final time interval. Annual growth rate () was 1.28 (95% CI = 1.07–1.52) between 2012 and 2013 but decreased throughout the study, ending at 1.04 (95% CI = 0.93–1.17). Forecasts indicated continuation of the female mortality levels experienced from 2012–2016 were sustainable over 50 years, with the estimated extinction risk being <0.001%. Increasing annual harvest by 5 females introduced a negligible increase in the 50-year probability of extinction, but harvesting an additional 10 females/year caused extinction risk to rise to 1.15%. We recommend that harvest regulations are structured such that mortality rates remain at current levels or do not increase by more than an annual average of 5 females above levels observed during our study. Furthermore, we recommend that managers continue to monitor the population so that harvest regulations and population models can be refined over time. © 2020 The Wildlife Society.     

Comparison of Hierarchical Bayesian Models for Overdispersed Count Data using DIC and Bayes' Factors

Summary .  When replicate count data are overdispersed, it is common practice to incorporate this extra-Poisson variability by including latent parameters at the observation level. For example, the negative binomial and Poisson-lognormal (PLN) models are obtained by using gamma and lognormal latent parameters, respectively. Several recent publications have employed the deviance information criterion (DIC) to choose between these two models, with the deviance defined using the Poisson likelihood that is obtained from conditioning on these latent parameters. The results herein show that this use of DIC is inappropriate. Instead, DIC was seen to perform well if calculated using likelihood that was marginalized at the group level by integrating out the observation-level latent parameters. This group-level marginalization is explicit in the case of the negative binomial, but requires numerical integration for the PLN model. Similarly, DIC performed well to judge whether zero inflation was required when calculated using the group-marginalized form of the zero-inflated likelihood. In the context of comparing multilevel hierarchical models, the top-level DIC was obtained using likelihood that was further marginalized by additional integration over the group-level latent parameters, and the marginal densities of the models were calculated for the purpose of providing Bayes' factors. The computational viability and interpretability of these different measures is considered.     

Dynamics of phenotypic change: wing length declines in a resident farmland passerine despite survival advantage of longer wings

In many taxa, environmental changes that alter resource availability and energetics, such as climate change and land use change, are associated with changes in body size. We use wing length as a proxy for overall structural body size to examine a paradoxical trend of declining wing length within a Yellowhammer Emberiza citrinella population sampled over 21 years, in which it has been previously shown that longer wings are associated with higher survival rates. Higher temperatures during the previous winter (prior to the moult determining current wing length) explained 23% of wing length decrease within our population, but changes may also be correlated with non‐climatic environmental variation such as changes in farming mechanisms linked to food availability. We found no evidence for within‐individual wing length shrinkage with age, but our data suggested a progressive decline in the sizes of immature birds recruiting to the population. This trend was weaker, although not significantly so, among adults, suggesting that the decline in the sizes of recruits was offset by higher subsequent survival of larger birds post‐recruitment. These data suggest that ecological processes can contribute more than selection to observed phenotypic trends and highlight the importance of long‐term studies for providing longitudinal insights into population processes.     

Berenty 2006: Census of <Emphasis Type="Italic">Propithecus verreauxi</Emphasis> and Possible Evidence of Population Stress

We provide a survey of a folivorous lemur, Propithecus verreauxi (sifaka), in the Berenty Reserve, southern Madagascar. Higher densities of folivores in small patches occur in either high-quality food areas or in less disturbed refugia. The skewness of sex ratio, which in lemurs is often male-biased, can be critically exacerbated in population stress. We predicted that sifaka would show higher densities in areas where protein-rich food is abundant (prediction 1a) and in refugium areas (prediction 1b). Owing to increased competition by brown and ring-tailed lemurs and decreased food production by tamarind trees, we expected an extremely male-biased sex ratio (prediction 2). In November–December 2006, we counted and sexed 206 adult/subadult sifaka (49 groups) during daily walks in different forest zones (Ankoba secondary forest, to the north, Malaza gallery/scrub areas, and spiny forest, to the south). Sifaka may have decreased in the gallery forest to concentrate in Ankoba (in a sort of out-of-Malaza). The area contains protein-rich food (prediction 1a confirmed). Sifaka are proportionally more concentrated in the spiny area than in the degraded scrub forest (prediction 1b confirmed). The sex ratio is extremely male biased, possibly due to either high sifaka density, in Ankoba, or food availability reduction, in Malaza (prediction 2 confirmed). The sifaka population seems to be under stress: researchers need to resume demographic studies, interrupted in Berenty in the mid-1980s, to preserve in situ a species that is difficult to protect ex situ.     

Detecting nonlinear response of spring phenology to climate change by Bayesian analysis

The impact of climate change on the advancement of plant phenological events has been heavily studied in the last decade. Although the majority of spring plant phenological events have been trending earlier, this is not universally true. Recent work has suggested that species that are not advancing in their spring phenological behavior are responding more to lack of winter chill than increased spring heat. One way to test this hypothesis is by evaluating the behavior of a species known to have a moderate to high chilling requirement and examining how it is responding to increased warming. This study used a 60‐year data set for timing of leaf‐out and male flowering of walnut (Juglans regia) cultivar ‘Payne’ to examine this issue. The spring phenological behavior of ‘Payne’ walnut differed depending on bud type. The vegetative buds, which have a higher chilling requirement, trended toward earlier leaf‐out until about 1994, when they shifted to later leaf‐out. The date of male bud pollen shedding advanced over the course of the whole record. Our findings suggest that many species which have exhibited earlier bud break are responding to warmer spring temperatures, but may shift into responding more to winter temperatures (lack of adequate chilling) as warming continues.