Estimating multiple years, tributary-specific, and overall Atlantic salmon smolt abundance in a large Canadian catchment using capture-mark-recapture experiments

Population monitoring of Atlantic salmon (Salmo salar L.) abundance is an essential element to understand annual stock variability and inform fisheries management processes. Smolts are the life stage marking the transition from the freshwater to the marine phase of anadromous Atlantic salmon. Estimating smolt abundance allows for subsequent inferences on freshwater and marine survival rates. Annual abundances of out-migrating Atlantic salmon smolts were estimated using Bayesian models and an 18-year capture-mark-recapture time series from two to five trapping locations within the Restigouche River (Canada) catchment. Some of the trapping locations were at the outlet of large upstream tributaries, and these sampled a portion of the total out-migrating population of smolts for the watershed, whereas others were located just above the head of tide of the Restigouche River and sampled the entire run of salmon smolts. Due to logistic and environmental conditions, not all trapping locations were operational each year. Additionally, recapture rates were relatively low (<5%), and the absolute number of recaptures was relatively few (most often a few dozen), leading to incoherent and highly uncertain estimates of tributary-specific and whole catchment abundance estimates when the data were modeled independently among trapping locations and years. Several models of increasing complexity were tested using simulated data, and the best-performing model in terms of bias and precision incorporated a hierarchical structure among years on the catchability parameters and included an explicit spatial structure to account for the annual variations in the number of sampled locations within the watershed. When the best model was applied to the Restigouche River catchment dataset, the annual smolt abundance estimates varied from 250,000 to 1 million smolts, and the subbasin estimates of abundance were consistent with the spatial structure of the monitoring programme. Ultimately, increasing the probabilities of capture and the absolute number of recaptures at the different traps will be required to improve the precision and reduce the bias of the estimates of smolt abundance for the entire basin and within subbasins of the watershed. The model and approach provide a significant improvement in the models used to date based on independent estimates of abundance by trapping location and year. Total abundance and relative production in discrete spawning, nesting, or rearing areas provide critical information to appropriately understand and manage the threats to species that can occur at subpopulation spatial scales.

Hierarchical Bayesian modeling of heterogeneous outcome variance in cluster randomized trials

BACKGROUND

Heterogeneous outcome correlations across treatment arms and clusters have been increasingly acknowledged in cluster randomized trials with binary endpoints, where analytical methods have been developed to study such heterogeneity. However, cluster-specific outcome variances and correlations have yet to be studied for cluster randomized trials with continuous outcomes.

METHODS

This article proposes models fitted in the Bayesian setting with hierarchical variance structure to quantify heterogeneous variances across clusters and explain it with cluster-level covariates when the outcome is continuous. The models can also be extended to analyzing heterogeneous variances in individually randomized group treatment trials, with arm-specific cluster-level covariates, or in partially nested designs. Simulation studies are carried out to validate the performance of the newly introduced models across different settings.

RESULTS

Simulations showed that overall the newly introduced models have good performance, reporting low bias and approximately 95% coverage for the intraclass correlation coefficients and regression parameters in the variance model. When variances are heterogeneous, our proposed models had improved model fit over models with homogeneous variances. When used to analyze data from the Kerala Diabetes Prevention Program study, our models identified heterogeneous variances and intraclass correlation coefficients across clusters and examined cluster-level characteristics associated with such heterogeneity.

CONCLUSION

We proposed new hierarchical Bayesian variance models to accommodate cluster-specific variances in cluster randomized trials. The newly developed methods inform the understanding of how an intervention strategy is implemented and disseminated differently across clusters and can help improve future trial design.

Recalibrating single-study effect sizes using hierarchical Bayesian models

Introduction

There are growing concerns about commonly inflated effect sizes in small neuroimaging studies, yet no study has addressed recalibrating effect size estimates for small samples. To tackle this issue, we propose a hierarchical Bayesian model to adjust the magnitude of single-study effect sizes while incorporating a tailored estimation of sampling variance.

Methods

We estimated the effect sizes of case-control differences on brain structural features between individuals who were dependent on alcohol, nicotine, cocaine, methamphetamine, or cannabis and non-dependent participants for 21 individual studies (Total cases: 903; Total controls: 996). Then, the study-specific effect sizes were modeled using a hierarchical Bayesian approach in which the parameters of the study-specific effect size distributions were sampled from a higher-order overarching distribution. The posterior distribution of the overarching and study-specific parameters was approximated using the Gibbs sampling method.

Results

The results showed shrinkage of the posterior distribution of the study-specific estimates toward the overarching estimates given the original effect sizes observed in individual studies. Differences between the original effect sizes (i.e., Cohen's d) and the point estimate of the posterior distribution ranged from 0 to 0.97. The magnitude of adjustment was negatively correlated with the sample size (r = -0.27, p < 0.001) and positively correlated with empirically estimated sampling variance (r = 0.40, p < 0.001), suggesting studies with smaller samples and larger sampling variance tended to have greater adjustments.

Discussion

Our findings demonstrate the utility of the hierarchical Bayesian model in recalibrating single-study effect sizes using information from similar studies. This suggests that Bayesian utilization of existing knowledge can be an effective alternative approach to improve the effect size estimation in individual studies, particularly for those with smaller samples.

A hierarchical model for external electrical control of an insect, accounting for inter-individual variation of muscle force properties

Cyborg control of insect movement is promising for developing miniature, high-mobility, and efficient biohybrid robots. However, considering the inter-individual variation of the insect neuromuscular apparatus and its neural control is challenging. We propose a hierarchical model including inter-individual variation of muscle properties of three leg muscles involved in propulsion (retractor coxae), joint stiffness (pro- and retractor coxae), and stance-swing transition (protractor coxae and levator trochanteris) in the stick insect Carausius morosus. To estimate mechanical effects induced by external muscle stimulation, the model is based on the systematic evaluation of joint torques as functions of electrical stimulation parameters. A nearly linear relationship between the stimulus burst duration and generated torque was observed. This stimulus-torque characteristic holds for burst durations of up to 500ms, corresponding to the stance and swing phase durations of medium to fast walking stick insects. Hierarchical Bayesian modeling revealed that linearity of the stimulus-torque characteristic was invariant, with individually varying slopes. Individual prediction of joint torques provides significant benefits for precise cyborg control.

Surface and subsurface oceanographic features drive forage fish distributions and aggregations: Implications for prey availability to top predators in the US Northeast Shelf ecosystem

Forage fishes are a critical food web link in marine ecosystems, aggregating in a hierarchical patch structure over multiple spatial and temporal scales. Surface-level forage fish aggregations (FFAs) represent a concentrated source of prey available to surface- and shallow-foraging marine predators. Existing survey and analysis methods are often imperfect for studying forage fishes at scales appropriate to foraging predators, making it difficult to quantify predator-prey interactions. In many cases, general distributions of forage fish species are known; however, these may not represent surface-level prey availability to predators. Likewise, we lack an understanding of the oceanographic drivers of spatial patterns of prey aggregation and availability or forage fish community patterns. Specifically, we applied Bayesian joint species distribution models to bottom trawl survey data to assess species- and community-level forage fish distribution patterns across the US Northeast Continental Shelf (NES) ecosystem. Aerial digital surveys gathered data on surface FFAs at two project sites within the NES, which we used in a spatially explicit hierarchical Bayesian model to estimate the abundance and size of surface FFAs. We used these models to examine the oceanographic drivers of forage fish distributions and aggregations. Our results suggest that, in the NES, regions of high community species richness are spatially consistent with regions of high surface FFA abundance. Bathymetric depth drove both patterns, while subsurface features, such as mixed layer depth, primarily influenced aggregation behavior and surface features, such as sea surface temperature, sub-mesoscale eddies, and fronts influenced forage fish diversity. In combination, these models help quantify the availability of forage fishes to marine predators and represent a novel application of spatial models to aerial digital survey data.

Distributed Compressive Sensing for Wireless Signal Transmission in Structural Health Monitoring: An Adaptive Hierarchical Bayesian Model-Based Approach

Signal transmission plays an important role in the daily operation of structural health monitoring (SHM) systems. In wireless sensor networks, transmission loss often occurs and threatens reliable data delivery. The massive amount of data monitoring also leads to a high signal transmission and storage cost throughout the system's service life. Compressive Sensing (CS) provides a novel perspective on alleviating these problems. Based on the sparsity of vibration signals in the frequency domain, CS can reconstruct a nearly complete signal from just a few measurements. This can improve the robustness of data loss while facilitating data compression to reduce transmission demands. Extended from CS methods, distributed compressive sensing (DCS) can exploit the correlation across multiple measurement vectors (MMV) to jointly recover the multi-channel signals with similar sparse patterns, which can effectively enhance the reconstruction quality. In this paper, a comprehensive DCS framework for wireless signal transmission in SHM is constructed, incorporating the process of data compression and transmission loss together. Unlike the basic DCS formulation, the proposed framework not only activates the inter-correlation among channels but also provides flexibility and independence to single-channel transmission. To promote signal sparsity, a hierarchical Bayesian model using Laplace priors is built and further improved as the fast iterative DCS-Laplace algorithm for large-scale reconstruction tasks. Vibration signals (e.g., dynamic displacement and accelerations) acquired from real-life SHM systems are used to simulate the whole process of wireless transmission and test the algorithm's performance. The results demonstrate that (1) DCS-Laplace is an adaptative algorithm that can actively adapt to signals with various sparsity by adjusting the penalty term to achieve optimal performance; (2) compared with CS methods, DCS methods can effectively improve the reconstruction quality of multi-channel signals; (3) the Laplace method has advantages over the OMP method in terms of reconstruction performance and applicability, which is a better choice in SHM wireless signal transmission.

Territory and population attributes affect Florida scrub-jay fecundity in fire-adapted ecosystems

Fecundity, the number of young produced by a breeding pair during a breeding season, is a primary component in evolutionary and ecological theory and applications. Fecundity can be influenced by many environmental factors and requires long-term study due to the range of variation in ecosystem dynamics. Fecundity data often include a large proportion of zeros when many pairs fail to produce any young during a breeding season due to nest failure or when all young die independently after fledging. We conducted color banding and monthly censuses of Florida scrub-jays (Aphelocoma coerulescens) across 31 years, 15 populations, and 761 territories along central Florida's Atlantic coast. We quantified how fecundity (juveniles/pair-year) was influenced by habitat quality, presence/absence of nonbreeders, population density, breeder experience, and rainfall, with a zero-inflated Bayesian hierarchical model including both a Bernoulli (e.g., brood success) and a Poisson (counts of young) submodel, and random effects for year, population, and territory. The results identified the importance of increasing "strong" quality habitat, which was a mid-successional state related to fire frequency and extent, because strong territories, and the proportion of strong territories in the overall population, influenced fecundity of breeding pairs. Populations subject to supplementary feeding also had greater fecundity. Territory size, population density, breeder experience, and rainfall surprisingly had no or small effects. Different mechanisms appeared to cause annual variation in fecundity, as estimates of random effects were not correlated between the success and count submodels. The increased fecundity for pairs with nonbreeders, compared to pairs without, identified empirical research needed to understand how the proportion of low-quality habitats influences population recovery and sustainability, because dispersal into low-quality habitats can drain nonbreeders from strong territories and decrease overall fecundity. We also describe how long-term study resulted in reversals in our understanding because of complications involving habitat quality, sociobiology, and population density.

Present and future bright and dark spots for coral reefs through climate change

Marine heatwaves can cause coral bleaching and reduce coral cover on reefs, yet few studies have identified "bright spots," where corals have recently shown a capacity to survive such pressures. We analyzed 7714 worldwide surveys from 1997 to 2018 along with 14 environmental and temperature metrics in a hierarchical Bayesian model to identify conditions that contribute to present-day coral cover. We also identified locations with significantly higher (i.e., "bright spots") and lower coral cover (i.e., "dark spots") than regionally expected. In addition, using 4-km downscaled data of Representative Concentration Pathways (RCPs) 4.5 and 8.5, we projected coral cover on reefs for the years 2050 and 2100. Coral cover on modern reefs was positively associated with historically high maximum sea-surface temperatures (SSTs), and negatively associated with high contemporary SSTs, tropical-cyclone frequencies, and human-population densities. By 2100, under RCP8.5, we projected relative decreases in coral cover of >40% on most reefs globally but projected less decline on reefs in Indonesia, Malaysia, the central Philippines, New Caledonia, Fiji, and French Polynesia, which should be focal localities for multinational networks of protected areas.

Sources of Interference in Memory Across Development

Episodic memory involves remembering not only what happened but also where and when the event happened. This multicomponent nature introduces different sources of interference that stem from previous experience. However, it is unclear how the contributions of these sources change across development and what might cause the changes. To address these questions, we tested 4- to 5-year-olds (n = 103), 7- to 8-year-olds (n = 82), and adults (n = 70) using item- and source-recognition memory tasks with various manipulations (i.e., list length, list strength, word frequency), and we decomposed sources of interference using a computational model. We found that interference stemming from other items on the study list rapidly decreased with development, whereas interference from preexperimental contexts gradually decreased but remained the major source of interference. The model further quantified these changes, indicating that the ability to discriminate items undergoes rapid development, whereas the ability to discriminate contexts undergoes protracted development. These results elucidate fundamental aspects of memory development.

Climate warming can reduce biocontrol efficacy and promote plant invasion due to both genetic and transient metabolomic changes

Climate change may affect plant-herbivore interactions and their associated ecosystem functions. In an experimental evolution approach, we subjected replicated populations of the invasive Ambrosia artemisiifolia to a combination of simulated warming and herbivory by a potential biocontrol beetle. We tracked genomic and metabolomic changes across generations in field populations and assessed plant offspring phenotypes in a common environment. Using an integrated Bayesian model, we show that increased offspring biomass in response to warming arose through changes in the genetic composition of populations. In contrast, increased resistance to herbivory arose through a shift in plant metabolomic profiles without genetic changes, most likely by transgenerational induction of defences. Importantly, while increased resistance was costly at ambient temperatures, warming removed this constraint and favoured both vigorous and better defended plants under biocontrol. Climate warming may thus decrease biocontrol efficiency and promote Ambrosia invasion, with potentially serious economic and health consequences.

Quantification of the pluriannual dynamics of grapevine growth responses to nitrogen supply using a Bayesian approach

The effect of nitrogen (N) nutrition on grapevine carbon (C) dynamics has been well studied at the annual scale, but poorly addressed at a pluriannual timescale. The aim of this study was to quantify, in an integrated conceptual framework, the effect of N nutrition on potted grapevine growth and storage over 2 consecutive years. The consequences of using destructive measurements were investigated using a hierarchical Bayesian model. The rate and duration of leaf growth were both positively impacted by the chlorophyll content of the leaves, but they were negatively impacted by the initial carbohydrate measurements, raising a distortion in the estimation of initial reserves. The C production per unit of global radiation depended on the leaf area dynamics. The allocation of dry matter mainly relied on the phenological stage. The present study highlights the importance of using appropriate statistical methods to overcome uncertainties due to destructive measurements. The genericity of the statistical approach presented may encourage its implementation in other agronomy studies. Based on our results, a simple conceptual framework of grapevine pluriannual growth under various N supplies was built. This provides a relevant basis for a future model of C and N balance and responses to N fertilization in grapevine.

Using machine learning to model nontraditional spatial dependence in occupancy data

Spatial models for occupancy data are used to estimate and map the true presence of a species, which may depend on biotic and abiotic factors as well as spatial autocorrelation. Traditionally researchers have accounted for spatial autocorrelation in occupancy data by using a correlated normally distributed site-level random effect, which might be incapable of modeling nontraditional spatial dependence such as discontinuities and abrupt transitions. Machine learning approaches have the potential to model nontraditional spatial dependence, but these approaches do not account for observer errors such as false absences. By combining the flexibility of Bayesian hierarchal modeling and machine learning approaches, we present a general framework to model occupancy data that accounts for both traditional and nontraditional spatial dependence as well as false absences. We demonstrate our framework using six synthetic occupancy data sets and two real data sets. Our results demonstrate how to model both traditional and nontraditional spatial dependence in occupancy data, which enables a broader class of spatial occupancy models that can be used to improve predictive accuracy and model adequacy.

Predicting Depressive Symptom Severity Through Individuals' Nearby Bluetooth Device Count Data Collected by Mobile Phones: Preliminary Longitudinal Study

BACKGROUND

Research in mental health has found associations between depression and individuals' behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones.

OBJECTIVE

This study aimed to explore the value of the NBDC data in predicting depressive symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8).

METHODS

The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features.

RESULTS

A number of significant associations were found between Bluetooth features and depressive symptom severity. Generally speaking, along with depressive symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547).

CONCLUSIONS

Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals' behaviors and statuses concurrent with the changes in the depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting depressive symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.

A new robust Bayesian small area estimation via α -stable model for estimating the proportion of athletic students in California

In the last few years, diabetes mellitus and obesity revealed to be one of the fastest‐growing chronic diseases in youth in the United States. The number of new diabetes cases is dramatically increasing, and, for the moment, effective therapy does not exist. Experts believe that one of the causes of this increase is the decline in exercise behavior. The California Education Code requires local educational agencies (LEAs) to administer the FITNESSGRAM, the Physical Fitness Test (PFT), to Californian students of public schools. This test evaluates six fitness areas, and experts defined that a passing result on all six areas of the test represents a fitness level that offers some protection against the diseases associated with physical inactivity. We consider 2015–2016 data provided by the California Department of Education (CDE): for each Californian county (m=57), we aim at estimating the county‐level proportion of students with a score equal to six. To account for the heterogeneity of the phenomenon and the presence of outlying counties, we extend the standard area‐level model by specifying the random effects as a symmetric α‐stable (SαS) distribution that can accommodate different types of outlying observations. The model can accurately estimate the county‐level proportion of students with a score equal to six. Results highlight some interesting relationships with social and economic situations in each county. The performance of the proposed model is also investigated through an extensive simulation study.

Hierarchical Bayesian clustering design of multiple biomarker subgroups (HCOMBS)

Given the Food and Drug Administration's (FDA's) acceptance of master protocol designs in recent guidance documents, the oncology field is rapidly moving to address the paradigm shift to molecular subtype focused studies. Identifying new "marker-based" treatments requires new methodologies to address the growing demand to conduct clinical trials in smaller molecular subpopulations, identify effective treatment and marker interactions, and control for false positives. We introduce our methodology, Hierarchical Bayesian Clustering Design of Multiple Biomarker Subgroups (HCOMBS), a two-stage umbrella Phase II design with effect size clustering and information borrowing across multiple biomarker-treatment pairs. HCOMBS was designed to reduce required sample size, differentiate between varying effect sizes, and control for operating characteristics in the multi-arm setting. When compared to independently applied Simon's Optimal two-stage design, we showed through simulations that HCOMBS required less participants per treatment arm with a well-controlled family-wise error rate and desirable marginal power. Additionally, HCOMBS features a statistical approach that simultaneously conducts clustering and hypothesis testing in one step. We also applied the proposed design on the alliance brain metastases umbrella trial.

Measuring the additive effects of predation on prey survival across spatial scales

The degree to which predation is an additive vs. compensatory source of mortality is fundamental to understanding the effects of predation on prey populations and evaluating the efficacy of predator management actions. In the Columbia River basin, USA, predation by Caspian Terns (Hydroprogne caspia) on U.S. Endangered Species Act (ESA)-listed juvenile salmonids (smolts; Oncorhynchus spp.) has led to predator management actions to reduce predation; however, the assumption that reduced predation translates into greater salmonid survival, either within the life stage where predation occurs or across their lifetime, has remained untested. To address this critical uncertainty, we analyzed a long-term (2008-2018) mark-recapture-recovery data set of ESA-listed steelhead trout (O. mykiss) that were tagged (n = 78,409) and subsequently exposed to predation during smolt out-migration through multiple river reaches (spatial scales), jointly estimating weekly probabilities of steelhead survival, mortality due to bird predation, and mortality due to other causes. This concurrent estimation across time-stratified cohorts allowed for the direct measurement of the strength, magnitude, and direction of relationships between survival and Caspian Tern predation. Estimates of Tern predation on steelhead were substantial in most years, with cumulative annual estimates ranging from 0.075 (95% creditable interval = 0.058-0.099) to 0.375 (0.290-0.461). Increases in Tern predation probabilities were associated with statistically significant decreases in steelhead survival probabilities in all evaluated years and salmonid life stages (smolt out-migration and smolt-to-adult returns). Results provide novel evidence that predation by Caspian Terns may have been a super-additive source of mortality during the smolt life stage and a partially additive source of mortality to the adult life stage. Annual estimates of the difference between observed survival and baseline survival (i.e., in the absence of Tern predation) ranged from 0.052 (0.017-0.103) to 0.314 (0.172-0.459) during the steelhead smolt life stage and from 0.011 (0.001-0.029) to 0.049 (0.025-0.078) to the adult life stage. The estimated levels of compensation have important implications for predator management actions aimed at increasing the survival of endangered salmonids, and the modeling approach developed herein provides a framework to directly quantify the impacts of source-specific mortality factors on prey populations.

Life-History Modeling Reveals the Ecological and Evolutionary Significance of Autotomy

AbstractAutotomy, the self-amputation of body parts, serves as an antipredator defense in many taxonomic groups of animals. However, its adaptive value has seldom been quantified. Here, we propose a novel modeling approach for measuring the fitness advantage conferred by the capability for autotomy in the wild. Using a predator-prey system where a land snail autotomizes and regenerates its foot specifically in response to snake bites, we conducted a laboratory behavioral experiment and a 3-year multievent capture-mark-recapture study. Combining these empirical data, we developed a hierarchical model and estimated the basic life-history parameters of the snail. Using samples from the posterior distribution, we constructed the snail's life table as well as that of a snail variant incapable of foot autotomy. As a result of our analyses, we estimated the monthly encounter rate with snake predators at 3.3% (95% credible interval: 1.6%-4.9%), the contribution of snake predation to total mortality until maturity at 43.3% (15.0%-95.3%), and the fitness advantage conferred by foot autotomy at 6.5% (2.7%-11.5%). This study demonstrated the utility of the multimethod hierarchical-modeling approach for the quantitative understanding of the ecological and evolutionary processes of antipredator defenses in the wild.

Spatial, Temporal, and Genetic Invasion Dynamics of Aedes albopictus (Diptera: Culicidae) in Illinois

The spread of the Asian tiger mosquito, Aedes albopictus Skuse, throughout the United States has implications for the transmission potential of vector-borne diseases. We used a 30-yr data set of occurrence records in Illinois and developed a hierarchical Bayesian model to shed light on the patterns and processes involved in the introduction and expansion along the northern edge of the geographic range of this species. We also collected specimens from 10 locations and sequenced a segment of their mitochondrial COI genes to assess possible introduction sources and geographic patterns in genetic variation present within contemporary populations. We documented an increase in the number of observations throughout the southern and central parts of Illinois over the study period. The process through which this spread occurred is likely only partially due to local dispersal. The probability of successfully overwintering was likewise low, but both these parameters increased over the study period. This suggests that the presence of Ae. albopictus has been largely due to repeated introductions, but that in recent years populations may have become established and are leading to an increase in locally driven dispersal. There was considerable genetic diversity among populations in Illinois, with 13 distinct haplotypes present in 10 sampling locations, several of which matched haplotypes previously found to be present in locations such as Texas or Japan. Further research is needed to understand how the combination of continued propagule pressure and establishment of populations are driving the increase and expansion of this invasive mosquito along its northern distribution limit.

Colonization of Marginal Host Plants by Seed Beetles (Coleoptera: Chrysomelidae): Effects of Geographic Source and Genetic Admixture

The ability to adapt to a novel host plant may vary among insect populations with different genetic histories, and colonization of a marginal host may be facilitated by genetic admixture of disparate populations. We assembled populations of the seed beetle, Callosobruchus maculatus (F.), from four continents, and compared their ability to infest two hosts, lentil and pea. We also formed two cross-continent hybrids (Africa × N.A. and Africa × S.A.). In pre-selection assays, survival was only ~3% in lentil and ~40% in pea. For three replicate populations per line, colonization success on lentil was measured as cumulative exit holes after 75-175 d. On pea, we estimated the change in larval survival after five generations of selection. Females in all lines laid few eggs on lentil, and survival of F1 larvae was uniformly <5%. Subsequently, however, the lines diverged considerably in population growth. Performance on lentil was highest in the Africa × N.A. hybrid, which produced far more adults (mean > 11,000) than either parental line. At the other extreme, Asian populations on lentil appeared to have gone extinct. The Africa × N.A. line also exhibited the highest survival on pea, and again performed better than either parent line. However, no line displayed a rapid increase in survival on pea, as is sometimes observed on lentil. Our results demonstrate that geographic populations can vary substantially in their responses to the same novel resource. In addition, genetic admixtures (potentially caused by long-distance transport of infested seeds) may facilitate colonization of an initially poor host.

Pharmacokinetic/pharmacodynamic data extrapolation models for improved pediatric efficacy and toxicity estimation, with application to secondary hyperparathyroidism

In most drug development settings, the regulatory approval process is accompanied by extensive studies performed to understand the drug's pharmacokinetic (PK) and pharmacodynamic (PD) properties. In this article, we attempt to utilize the rich PK/PD data to inform the borrowing of information from adults during pediatric drug development. In pediatric settings, it is especially crucial that we are parsimonious with the patients recruited for experimentation. We illustrate our approaches in the context of clinical trials of cinacalcet for treating secondary hyperparathyroidism in pediatric and adult patients with chronic kidney disease, where we model both parathyroid hormone (efficacy endpoint) and corrected calcium levels (safety endpoint). We use population PK/PD modeling of the cinacalcet data to quantitatively assess the similarity between adults and children, and use this information in various hierarchical Bayesian adult borrowing rules whose statistical properties can then be evaluated. In particular, we simulate the bias and mean square error performance of our approaches in settings where borrowing is and is not warranted to inform guidelines for the future use of our methods.

Application of multiple-population viability analysis to evaluate species recovery alternatives

Population viability analysis (PVA) is a powerful conservation tool, but it remains impractical for many species, particularly species with multiple, broadly distributed populations for which collecting suitable data can be challenging. A recently developed method of multiple-population viability analysis (MPVA), however, addresses many limitations of traditional PVA. We built on previous development of MPVA for Lahontan cutthroat trout (LCT) (Oncorhynchus clarkii henshawi), a species listed under the U.S. Endangered Species Act, that is distributed broadly across habitat fragments in the Great Basin (U.S.A.). We simulated potential management scenarios and assessed their effects on population sizes and extinction risks in 211 streams, where LCT exist or may be reintroduced. Conservation populations (those managed for recovery) tended to have lower extinction risks than nonconservation populations (mean = 19.8% vs. 52.7%), but not always. Active management or reprioritization may be warranted in some cases. Eliminating non-native trout had a strong positive effect on overall carrying capacities for LCT populations but often did not translate into lower extinction risks unless simulations also reduced associated stochasticity (to the mean for populations without non-native trout). Sixty fish or 5-10 fish/km was the minimum reintroduction number and density, respectively, that provided near-maximum reintroduction success. This modeling framework provided crucial insights and empirical justification for conservation planning and specific adaptive management actions for this threatened species. More broadly, MPVA is applicable to a wide range of species exhibiting geographic rarity and limited availability of abundance data and greatly extends the potential use of empirical PVA for conservation assessment and planning.

Spatial synchrony in the response of a long range migratory species (Salmo salar) to climate change in the North Atlantic Ocean

A major challenge in understanding the response of populations to climate change is to separate the effects of local drivers acting independently on specific populations, from the effects of global drivers that impact multiple populations simultaneously and thereby synchronize their dynamics. We investigated the environmental drivers and the demographic mechanisms of the widespread decline in marine survival rates of Atlantic salmon (Salmo salar) over the last four decades. We developed a hierarchical Bayesian life cycle model to quantify the spatial synchrony in the marine survival of 13 large groups of populations (called stock units, SU) from two continental stock groups (CSG) in North America (NA) and Southern Europe (SE) over the period 1971-2014. We found strong coherence in the temporal variation in postsmolt marine survival among the 13 SU of NA and SE. A common North Atlantic trend explains 37% of the temporal variability of the survivals for the 13 SU and declines by a factor of 1.8 over the 1971-2014 time series. Synchrony in survival trends is stronger between SU within each CSG. The common trends at the scale of NA and SE capture 60% and 42% of the total variance of temporal variations, respectively. Temporal variations of the postsmolt survival are best explained by the temporal variations of sea surface temperature (SST, negative correlation) and net primary production indices (PP, positive correlation) encountered by salmon in common domains during their marine migration. Specifically, in the Labrador Sea/Grand Banks for populations from NA, 26% and 24% of variance is captured by SST and PP, respectively and in the Norwegian Sea for populations from SE, 21% and 12% of variance is captured by SST and PP, respectively. The findings support the hypothesis of a response of salmon populations to large climate-induced changes in the North Atlantic simultaneously impacting populations from distant continental habitats.

Detection error influences both temporal seroprevalence predictions and risk factors associations in wildlife disease models

Understanding the prevalence of pathogens in invasive species is essential to guide efforts to prevent transmission to agricultural animals, wildlife, and humans. Pathogen prevalence can be difficult to estimate for wild species due to imperfect sampling and testing (pathogens may not be detected in infected individuals and erroneously detected in individuals that are not infected). The invasive wild pig (Sus scrofa, also referred to as wild boar and feral swine) is one of the most widespread hosts of domestic animal and human pathogens in North America.We developed hierarchical Bayesian models that account for imperfect detection to estimate the seroprevalence of five pathogens (porcine reproductive and respiratory syndrome virus, pseudorabies virus, Influenza A virus in swine, Hepatitis E virus, and Brucella spp.) in wild pigs in the United States using a dataset of over 50,000 samples across nine years. To assess the effect of incorporating detection error in models, we also evaluated models that ignored detection error. Both sets of models included effects of demographic parameters on seroprevalence. We compared our predictions of seroprevalence to 40 published studies, only one of which accounted for imperfect detection.We found a range of seroprevalence among the pathogens with a high seroprevalence of pseudorabies virus, indicating significant risk to livestock and wildlife. Demographics had mostly weak effects, indicating that other variables may have greater effects in predicting seroprevalence.Models that ignored detection error led to different predictions of seroprevalence as well as different inferences on the effects of demographic parameters.Our results highlight the importance of incorporating detection error in models of seroprevalence and demonstrate that ignoring such error may lead to erroneous conclusions about the risk associated with pathogen transmission. When using opportunistic sampling data to model seroprevalence and evaluate risk factors, detection error should be included.

Nest use dynamics of an undisturbed population of bald eagles

Management or conservation targets based on demographic rates should be evaluated within the context of expected population dynamics of the species of interest. Wild populations can experience stable, cyclical, or complex dynamics, therefore undisturbed populations can provide background needed to evaluate programmatic success. Many raptor species have recovered from large declines caused by environmental contaminants, making them strong candidates for ongoing efforts to understand population dynamics and ecosystem processes in response to human-caused stressors. Dynamic multistate occupancy models are a useful tool for analyzing species dynamics because they leverage the autocorrelation inherent in long-term monitoring datasets to obtain useful information about the dynamic properties of population or reproductive states. We analyzed a 23-year bald eagle monitoring dataset in a dynamic multistate occupancy modeling framework to assess long-term nest occupancy and reproduction in Lake Clark National Park and Preserve, Alaska. We also used a hierarchical generalized linear model to understand changes in nest productivity in relation to environmental factors. Nests were most likely to remain in the same nesting state between years. Most notably, successful nests were likely to remain in use (either occupied or successful) and had a very low probability of transitioning to an unoccupied state in the following year. There was no apparent trend in the proportion of nests used by eagles through time, and the probability that nests transitioned into or out of the successful state was not influenced by temperature or salmon availability. Productivity was constant over the course of the study, although warm April minimum temperatures were associated with increased chick production. Overall our results demonstrate the expected nesting dynamics of a healthy bald eagle population that is largely free of human disturbance and can be used as a baseline for the expected dynamics for recovering bald eagle populations in the contiguous 48 states.

Cross-scale effects of spruce budworm outbreaks on boreal warblers in eastern Canada

Insect outbreaks are major natural disturbance events that affect communities of forest birds, either directly by affecting the food supply or indirectly by changing the vegetation composition of forest canopies. An examination of correlations between measures of bird and insect abundance across different spatial scales and over varying time lag effects may provide insight into underlying mechanisms. We developed a hierarchical Bayesian model to assess correlations between counts of eight warbler species from the Breeding Bird Survey in eastern Canada, 1966 to 2009, with the presence of spruce budworm (Choristoneura fumiferana Clem.) at immediate local scales and time-lagged regional scales, as measured by extent of defoliation on host tree species. Budworm-associated species Cape May warbler (Setophaga tigrina), bay-breasted warbler (Setophaga castanea), and Tennessee warbler (Oreothlypis peregrina) responded strongly and positively to both local and regional effects. In contrast, non-budworm-associated species, Blackburnian warbler (Setophaga fusca), magnolia warbler (Setophaga magnolia), Canada warbler (Cardellina canadensis), black-throated blue warbler (Setophaga caerulescens), and black-throated green warbler (Setophaga virens), only responded to regional effects in a manner that varied across eastern Canada. The complex responses by forest birds to insect outbreaks involve both increased numerical responses to food supply and to longer term responses to changes in forest structure and composition. These effects can vary across spatial scales and be captured in hierarchical population models, which can serve to disentangle common trends from data when examining drivers of population dynamics like forest management or climate change.

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function.

Native insect herbivory overwhelms context dependence to limit complex invasion dynamics of exotic weeds

Understanding the role of consumers in density-dependent plant population dynamics is a long-standing goal in ecology. However, the generality of herbivory effects across heterogeneous landscapes is poorly understood due to the pervasive influence of context-dependence. We tested effects of native insect herbivory on the population dynamics of an exotic thistle, Cirsium vulgare, in a field experiment replicated across eight sites in eastern Nebraska. Using hierarchical Bayesian analysis and density-dependent population models, we found potential for explosive low-density population growth (λ > 5) and complex density fluctuations under herbivore exclusion. However, herbivore access drove population decline (λ < 1), suppressing complex fluctuations. While plant-herbivore interaction outcomes are famously context-dependent, we demonstrated that herbivores suppress potentially invasive populations throughout our study region, and this qualitative outcome is insensitive to environmental context. Our novel use of Bayesian demographic modelling shows that native insect herbivores consistently prevent hard-to-predict fluctuations of weeds in environments otherwise susceptible to invasion.

Legacy effects and memory loss: how contingencies moderate the response of rocky intertidal biofilms to present and past extreme events

Understanding how historical processes modulate the response of ecosystems to perturbations is becoming increasingly important. In contrast to the growing interest in projecting biodiversity and ecosystem functioning under future climate scenarios, how legacy effects originating from historical conditions drive change in ecosystems remains largely unexplored. Using experiments in combination with stochastic antecedent modelling, we evaluated how extreme warming, sediment deposition and grazing events modulated the ecological memory of rocky intertidal epilithic microphytobenthos (EMPB). We found memory effects in the non-clustered scenario of disturbance (60 days apart), where EMPB biomass fluctuated in time, but not under clustered disturbances (15 days apart), where EMPB biomass was consistently low. A massive grazing event impacted on EMPB biomass in a second run of the experiment, also muting ecological memory. Our results provide empirical support to the theoretical expectation that stochastic fluctuations promote ecological memory, but also show that contingencies may lead to memory loss.

Dynamic anthropogenic edge effects on the distribution and diversity of fungi in fragmented old-growth forests

Diversity patterns and dynamics at forest edges are not well understood. We disentangle the relative importance of edge-effect variables on spatio-temporal patterns in species richness and occupancy of deadwood-dwelling fungi in fragmented old-growth forests. We related richness and log occupancy by 10 old-growth forest indicator fungi and by two common fungi to log conditions in natural and anthropogenic edge habitats of 31 old-growth Picea abies forest stands in central Sweden. We compared edge-to-interior gradients (100 m) to the forest interior (beyond 100 m), and we analyzed stand-level changes after 10 yr. Both richness and occupancy of logs by indicator species was negatively related to adjacent young clear-cut edges, but this effect decreased with increasing clear-cut age. The occupancy of logs by indicator species also increased with increasing distance to the natural edges. In contrast, the occupancy of logs by common species was positively related or unrelated to distance to clear-cut edges regardless of the edge age, and this was partly explained by fungal specificity to substrate quality. Stand-level mean richness and mean occupancy of logs did not change for indicator or common species over a decade. By illustrating the importance of spatial and temporal dimensions of edge effects, we extend the general understanding of the distribution and diversity of substrate-confined fungi in fragmented old-growth forests. Our results highlight the importance of longer forest rotation times adjacent to small protected areas and forest set-asides, where it may take more than 50 yr for indicator species richness levels to recover to occupancy levels observed in the forest interior. Also, non-simultaneous clear-cutting of surrounding productive forests in a way that reduces the edge effect over time (i.e., dynamic buffers) may increase the effective core area of small forest set-asides and improve their performance on protecting species of special concern for conservation.

A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction

Current phylogenomic data sets highlight the need for species tree methods able to deal with several sources of gene tree/species tree incongruence. At the same time, we need to make most use of all available data. Most species tree methods deal with single processes of phylogenetic discordance, namely, gene duplication and loss, incomplete lineage sorting (ILS) or horizontal gene transfer. In this manuscript, we address the problem of species tree inference from multilocus, genome-wide data sets regardless of the presence of gene duplication and loss and ILS therefore without the need to identify orthologs or to use a single individual per species. We do this by extending the idea of Maximum Likelihood (ML) supertrees to a hierarchical Bayesian model where several sources of gene tree/species tree disagreement can be accounted for in a modular manner. We implemented this model in a computer program called guenomu whose inputs are posterior distributions of unrooted gene tree topologies for multiple gene families, and whose output is the posterior distribution of rooted species tree topologies. We conducted extensive simulations to evaluate the performance of our approach in comparison with other species tree approaches able to deal with more than one leaf from the same species. Our method ranked best under simulated data sets, in spite of ignoring branch lengths, and performed well on empirical data, as well as being fast enough to analyze relatively large data sets. Our Bayesian supertree method was also very successful in obtaining better estimates of gene trees, by reducing the uncertainty in their distributions. In addition, our results show that under complex simulation scenarios, gene tree parsimony is also a competitive approach once we consider its speed, in contrast to more sophisticated models.

Bridging Inter- and Intraspecific Trait Evolution with a Hierarchical Bayesian Approach

The evolution of organisms is crucially dependent on the evolution of intraspecific variation. Its interactions with selective agents in the biotic and abiotic environments underlie many processes, such as intraspecific competition, resource partitioning and, eventually, species formation. Nevertheless, comparative models of trait evolution neither allow explicit testing of hypotheses related to the evolution of intraspecific variation nor do they simultaneously estimate rates of trait evolution by accounting for both trait mean and variance. Here, we present a model of phenotypic trait evolution using a hierarchical Bayesian approach that simultaneously incorporates interspecific and intraspecific variation. We assume that species-specific trait means evolve under a simple Brownian motion process, whereas species-specific trait variances are modeled with Brownian or Ornstein-Uhlenbeck processes. After evaluating the power of the method through simulations, we examine whether life-history traits impact evolution of intraspecific variation in the Eriogonoideae (buckwheat family, Polygonaceae). Our model is readily extendible to more complex scenarios of the evolution of inter- and intraspecific variation and presents a step toward more comprehensive comparative models for macroevolutionary studies.

Individual FEV1 Trajectories Can Be Identified from a COPD Cohort

OBJECTIVE

We aim to make use of clinical spirometry data in order to identify individual COPD-patients with divergent trajectories of lung function over time.

STUDY DESIGN AND SETTING

Hospital-based COPD cohort (N = 607) was followed on average 4.6 years. Each patient had a mean of 8.4 spirometries available. We used a Hierarchical Bayesian Model (HBM) to identify the individuals presenting constant trends in lung function.

RESULTS

At a probability level of 95%, one third of the patients (180/607) presented rapidly declining FEV1 (mean -78 ml/year, 95% CI -73 to -83 ml) compared to that in the rest of the patients (mean -26 ml/year, 95% CI -23 to -29 ml, p ≤ 2.2 × 10(-16)). Constant improvement of FEV1 was very rare. The rapid decliners more frequently suffered from exacerbations measured by various outcome markers.

CONCLUSION

Clinical data of unique patients can be utilized to identify diverging trajectories of FEV1 with a high probability. Frequent exacerbations were more prevalent in FEV1-decliners than in the rest of the patients. The result confirmed previously reported association between FEV1 decline and exacerbation rate and further suggested that in clinical practice HBM could improve the identification of high-risk individuals at early stages of the disease.

Microsatellite evolutionary rate and pattern in Schistocerca gregaria inferred from direct observation of germline mutations

Unravelling variation among taxonomic orders regarding the rate of evolution in microsatellites is crucial for evolutionary biology and population genetics research. The mean mutation rate of microsatellites tends to be lower in arthropods than in vertebrates, but data are scarce and mostly concern accumulation of mutations in model species. Based on parent-offspring segregations and a hierarchical Bayesian model, the mean rate of mutation in the orthopteran insect Schistocerca gregaria was estimated at 2.1e(-4) per generation per untranscribed dinucleotide locus. This is close to vertebrate estimates and one order of magnitude higher than estimates from species of other arthropod orders, such as Drosophila melanogaster and Daphnia pulex. We also found evidence of a directional bias towards expansions even for long alleles and exceptionally large ranges of allele sizes. Finally, at transcribed microsatellites, the mean rate of mutation was half the rate found at untranscribed loci and the mutational model deviated from that usually considered, with most mutations involving multistep changes that avoid disrupting the reading frame. Our direct estimates of mutation rate were discussed in the light of peculiar biological and genomic features of S. gregaria, including specificities in mismatch repair and the dependence of its activity to allele length. Shedding new light on the mutational dynamics of grasshopper microsatellites is of critical importance for a number of research fields. As an illustration, we showed how our findings improve microsatellite application in population genetics, by obtaining a more precise estimation of S. gregaria effective population size from a published data set based on the same microsatellites.

Genomic Prediction Accounting for Residual Heteroskedasticity

Whole-genome prediction (WGP) models that use single-nucleotide polymorphism marker information to predict genetic merit of animals and plants typically assume homogeneous residual variance. However, variability is often heterogeneous across agricultural production systems and may subsequently bias WGP-based inferences. This study extends classical WGP models based on normality, heavy-tailed specifications and variable selection to explicitly account for environmentally-driven residual heteroskedasticity under a hierarchical Bayesian mixed-models framework. WGP models assuming homogeneous or heterogeneous residual variances were fitted to training data generated under simulation scenarios reflecting a gradient of increasing heteroskedasticity. Model fit was based on pseudo-Bayes factors and also on prediction accuracy of genomic breeding values computed on a validation data subset one generation removed from the simulated training dataset. Homogeneous vs. heterogeneous residual variance WGP models were also fitted to two quantitative traits, namely 45-min postmortem carcass temperature and loin muscle pH, recorded in a swine resource population dataset prescreened for high and mild residual heteroskedasticity, respectively. Fit of competing WGP models was compared using pseudo-Bayes factors. Predictive ability, defined as the correlation between predicted and observed phenotypes in validation sets of a five-fold cross-validation was also computed. Heteroskedastic error WGP models showed improved model fit and enhanced prediction accuracy compared to homoskedastic error WGP models although the magnitude of the improvement was small (less than two percentage points net gain in prediction accuracy). Nevertheless, accounting for residual heteroskedasticity did improve accuracy of selection, especially on individuals of extreme genetic merit.

A state-space modeling approach to estimating canopy conductance and associated uncertainties from sap flux density data

Uncertainties in ecophysiological responses to environment, such as the impact of atmospheric and soil moisture conditions on plant water regulation, limit our ability to estimate key inputs for ecosystem models. Advanced statistical frameworks provide coherent methodologies for relating observed data, such as stem sap flux density, to unobserved processes, such as canopy conductance and transpiration. To address this need, we developed a hierarchical Bayesian State-Space Canopy Conductance (StaCC) model linking canopy conductance and transpiration to tree sap flux density from a 4-year experiment in the North Carolina Piedmont, USA. Our model builds on existing ecophysiological knowledge, but explicitly incorporates uncertainty in canopy conductance, internal tree hydraulics and observation error to improve estimation of canopy conductance responses to atmospheric drought (i.e., vapor pressure deficit), soil drought (i.e., soil moisture) and above canopy light. Our statistical framework not only predicted sap flux observations well, but it also allowed us to simultaneously gap-fill missing data as we made inference on canopy processes, marking a substantial advance over traditional methods. The predicted and observed sap flux data were highly correlated (mean sensor-level Pearson correlation coefficient = 0.88). Variations in canopy conductance and transpiration associated with environmental variation across days to years were many times greater than the variation associated with model uncertainties. Because some variables, such as vapor pressure deficit and soil moisture, were correlated at the scale of days to weeks, canopy conductance responses to individual environmental variables were difficult to interpret in isolation. Still, our results highlight the importance of accounting for uncertainty in models of ecophysiological and ecosystem function where the process of interest, canopy conductance in this case, is not observed directly. The StaCC modeling framework provides a statistically coherent approach to estimating canopy conductance and transpiration and propagating estimation uncertainty into ecosystem models, paving the way for improved prediction of water and carbon uptake responses to environmental change.

Detecting and measuring selection from gene frequency data

The recent advent of high-throughput sequencing and genotyping technologies makes it possible to produce, easily and cost effectively, large amounts of detailed data on the genotype composition of populations. Detecting locus-specific effects may help identify those genes that have been, or are currently, targeted by natural selection. How best to identify these selected regions, loci, or single nucleotides remains a challenging issue. Here, we introduce a new model-based method, called SelEstim, to distinguish putative selected polymorphisms from the background of neutral (or nearly neutral) ones and to estimate the intensity of selection at the former. The underlying population genetic model is a diffusion approximation for the distribution of allele frequency in a population subdivided into a number of demes that exchange migrants. We use a Markov chain Monte Carlo algorithm for sampling from the joint posterior distribution of the model parameters, in a hierarchical Bayesian framework. We present evidence from stochastic simulations, which demonstrates the good power of SelEstim to identify loci targeted by selection and to estimate the strength of selection acting on these loci, within each deme. We also reanalyze a subset of SNP data from the Stanford HGDP-CEPH Human Genome Diversity Cell Line Panel to illustrate the performance of SelEstim on real data. In agreement with previous studies, our analyses point to a very strong signal of positive selection upstream of the LCT gene, which encodes for the enzyme lactase-phlorizin hydrolase and is associated with adult-type hypolactasia. The geographical distribution of the strength of positive selection across the Old World matches the interpolated map of lactase persistence phenotype frequencies, with the strongest selection coefficients in Europe and in the Indus Valley.

Advances in modeling trait-based plant community assembly

In this review, we examine two new trait-based models of community assembly that predict the relative abundance of species from a regional species pool. The models use fundamentally different mathematical approaches and the predictions can differ considerably. Maxent obtains the most even probability distribution subject to community-weighted mean trait constraints. Traitspace predicts low probabilities for any species whose trait distribution does not pass through the environmental filter. Neither model maximizes functional diversity because of the emphasis on environmental filtering over limiting similarity. Traitspace can test for the effects of limiting similarity by explicitly incorporating intraspecific trait variation. The range of solutions in both models could be used to define the range of natural variability of community composition in restoration projects.

Do pathogens become more virulent as they spread? Evidence from the amphibian declines in Central America

The virulence of a pathogen can vary strongly through time. While cyclical variation in virulence is regularly observed, directional shifts in virulence are less commonly observed and are typically associated with decreasing virulence of biological control agents through coevolution. It is increasingly appreciated, however, that spatial effects can lead to evolutionary trajectories that differ from standard expectations. One such possibility is that, as a pathogen spreads through a naive host population, its virulence increases on the invasion front. In Central America, there is compelling evidence for the recent spread of pathogenic Batrachochytrium dendrobatidis (Bd) and for its strong impact on amphibian populations. Here, we re-examine data on Bd prevalence and amphibian population decline across 13 sites from southern Mexico through Central America, and show that, in the initial phases of the Bd invasion, amphibian population decline lagged approximately 9 years behind the arrival of the pathogen, but that this lag diminished markedly over time. In total, our analysis suggests an increase in Bd virulence as it spread southwards, a pattern consistent with rapid evolution of increased virulence on Bd's invading front. The impact of Bd on amphibians might therefore be driven by rapid evolution in addition to more proximate environmental drivers.

Spatial distribution of selection pressure on a protein based on the hierarchical Bayesian model

Proteins adapt to novel environments and/or gain function by substitution in amino acid sequences. Therefore, mutations in protein-coding genes are subject to selection pressure. The strength and character of selection pressure may vary among the regions of the protein. Thus, the spatial distribution of selection pressure provides information on the adaptive evolution of the protein. We developed a hierarchical Bayesian model that detects the spatial distribution of selection pressure on a protein. We expressed selection pressure by the substitution rate ratio of nonsynonymous to synonymous substitutions in the DNA sequence. The Potts model describes the prior distribution of spatial aggregation of selection pressure. The hyperparameters that define the strength and range of spatial clustering are estimated by maximizing the marginal likelihood. Because our prior distribution is un-normalized, we calculated the log marginal likelihood by "thermodynamic integration." We applied the method to historical data on the influenza hemagglutinin protein, comparing the estimated spatial distribution of the substitution rate ratio with that of antigenic sites A-E. The amino acid residues with higher substitution rate ratios, representing diversifying selection pressure, overlapped the antigenic sites.

Characteristics of leaf shapes among two parental Drosera species and a hybrid examined by canonical discriminant analysis and a hierarchical Bayesian model

PREMISE OF THE STUDY

Although nondestructive, convenient species identification is desirable for follow-up research and species conservation, species identification is often confusing, particularly when an interspecific hybrid shows intermediate morphological characteristics between the parental species. •

METHODS

Drosera anglica Hudson (2n = 40) and D. rotundifolia L. (20) bear the hybrid Drosera obovata Mert. et Koch (30). The samples were identified based on seed fertility and a cytological investigation (DNA amount) before examination. Then, 13 measured morphological traits-including leaf size, leaf shape, and flowering-were used in a canonical discriminant analysis (CDA). Leaf length and width were used in a hierarchical Bayesian model (HBM). •

KEY RESULTS

The majority of the traits of D. obovata were intermediate between the two parental species. However, D. obovata developed larger leaves than the parental species. The identification error of the CDA based on the 13 morphological traits was 4.9%. Errors occurred more often with smaller leaves. When the CDA was used for blade length and width only, the error increased to 6.2%. The HBM, based on the relationships between blade length and width, showed the lowest identification error-4.7%-by improving the identification of small leaves. •

CONCLUSIONS

The HBM enabled convenient, nondestructive measurements for species identification by considering nonlinear relationships between morphological traits and measurement error. The HBM is likely to be applicable to various follow-up studies, as well as species conservation.

A Simple Class of Bayesian Nonparametric Autoregression Models

We introduce a model for a time series of continuous outcomes, that can be expressed as fully nonparametric regression or density regression on lagged terms. The model is based on a dependent Dirichlet process prior on a family of random probability measures indexed by the lagged covariates. The approach is also extended to sequences of binary responses. We discuss implementation and applications of the models to a sequence of waiting times between eruptions of the Old Faithful Geyser, and to a dataset consisting of sequences of recurrence indicators for tumors in the bladder of several patients.

Reuse, Recycle, Reweigh: Combating Influenza through Efficient Sequential Bayesian Computation for Massive Data

Massive datasets in the gigabyte and terabyte range combined with the availability of increasingly sophisticated statistical tools yield analyses at the boundary of what is computationally feasible. Compromising in the face of this computational burden by partitioning the dataset into more tractable sizes results in stratified analyses, removed from the context that justified the initial data collection. In a Bayesian framework, these stratified analyses generate intermediate realizations, often compared using point estimates that fail to account for the variability within and correlation between the distributions these realizations approximate. However, although the initial concession to stratify generally precludes the more sensible analysis using a single joint hierarchical model, we can circumvent this outcome and capitalize on the intermediate realizations by extending the dynamic iterative reweighting MCMC algorithm. In doing so, we reuse the available realizations by reweighting them with importance weights, recycling them into a now tractable joint hierarchical model. We apply this technique to intermediate realizations generated from stratified analyses of 687 influenza A genomes spanning 13 years allowing us to revisit hypotheses regarding the evolutionary history of influenza within a hierarchical statistical framework.