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Bats host a high diversity of coronaviruses, including betacoronaviruses that have caused outbreaks and pandemics in humans and other species. Here, we study the spatiotemporal dynamics of co-circulating coronaviruses in Pteropus spp bats (flying foxes) in eastern Australia over a three-year period across five roost sites ( n  = 2537 fecal samples). In total, we identify six betacoronavirus clades, all within the nobecovirus subgenus. Genome sequencing supports overall clade assignments, however, also demonstrates the important role recombination has played in both the long-term and contemporary evolution of these viruses. Using a statistical framework that integrates individual and population level data, we assess the variability in prevalence of viral clades over space and time. Coronavirus infections and co-infections are highest among juveniles and subadults, particularly around the time of weaning. The overlapping shedding dynamics across multiple clades suggest opportunities for recombination, especially in younger bats. Understanding the ecological and host-viral drivers of these seasonally dynamic infections, co-infections, and recombination events will inform future predictive frameworks for coronavirus emergence in humans and other animals. Bats harbor diverse coronaviruses but temporal dynamics are less well studied. Here, the authors analyzed coronaviruses in Australian flying foxes over 3 years showing peak shedding and co-infections in juveniles and subadults and providing evidence of historical and contemporary recombination between viral clades.

Assessment of similarity in species composition or abundance across sampled locations is a common goal in multi‐species monitoring programs. Existing ordination techniques provide a framework for clustering sample locations based on species composition by projecting high‐dimensional community data into a low‐dimensional, latent ecological gradient representing species composition. However, these techniques require specification of the number of distinct ecological communities present in the latent space, which can be difficult to determine in advance. We develop an ordination model capable of simultaneous clustering and ordination that allows for estimation of the number of clusters present in the latent ecological gradient. This model draws latent coordinates for each sample location from a Dirichlet process mixture model, affording researchers with probabilistic statements about the number of clusters present in the latent ecological gradient. The model is compared to existing methods for simultaneous clustering and ordination via simulation and applied to two empirical datasets; JAGS code to fit the proposed model is provided in an appendix. The first dataset concerns presence‐absence records of fish in the Doubs river in eastern France and the second dataset describes presence‐absence records of plant species in Craters of the Moon National Monument and Preserve (CRMO) in Idaho, USA. Results from both analyses align with existing ecological gradients at each location. Development of the Dirichlet process ordination model provides wildlife managers with data‐driven inferences about the number of distinct communities present across monitored locations, allowing for more cost‐effective monitoring and reliable decision‐making for conservation management.

Variability in ecological community composition is often analyzed by recording the presence or abundance of taxa in sample units, calculating a symmetric matrix of pairwise distances or dissimilarities among sample units and then mapping the resulting matrix to a low‐dimensional representation through methods collectively called ordination. Unconstrained ordination only uses taxon composition data, without any environmental or experimental covariates, to infer latent compositional gradients associated with the sampling units. Commonly, such distance‐based methods have been used for ordination, but recently there has been a shift toward model‐based approaches. Model‐based unconstrained ordinations are commonly formulated using a Bayesian latent factor model that permits uncertainty assessment for parameters, including the latent factors that correspond to gradients in community composition. While model‐based methods have the additional benefit of addressing uncertainty in the estimated gradients, typically the current practice is to report point estimates without summarizing uncertainty. To demonstrate the uncertainty present in model‐based unconstrained ordination, the well‐known spider and dune data sets were analyzed and shown to have large uncertainty in the ordination projections. Hence to understand the factors that contribute to the uncertainty, simulation studies were conducted to assess the impact of additional sampling units or species to help inform future ordination studies that seek to minimize variability in the latent factors. Accurate reporting of uncertainty is an important part of transparency in the scientific process; thus, a model‐based approach that accounts for uncertainty is valuable. An R package, UncertainOrd, contains visualization tools that accurately represent estimates of the gradients in community composition in the presence of uncertainty. Variability in ecological community composition is often visualized with a low‐dimensional ordination projection. However, most existing approaches ignore the uncertainty present in this process. This manuscript details methods for understanding uncertainty in unconstrained ordination.

While computing has become an important part of the statistics field, course offerings are still influenced by a legacy of mathematically centric thinking. Due to this legacy, Bayesian ideas are not required for undergraduate degrees and have largely been taught at the graduate level; however, with recent advances in software and emphasis on computational thinking, Bayesian ideas are more accessible. Statistics curricula need to continue to evolve and students at all levels should be taught Bayesian thinking. This article advocates for adding Bayesian ideas for three groups of students: intro-statistics students, undergraduate statistics majors, and graduate student scientists; and furthermore, provides guidance and materials for creating Bayesian-themed courses for these audiences. Supplementary files for this article are available on line.

The COVID‐19 pandemic has highlighted the importance of efficient sampling strategies and statistical methods for monitoring infection prevalence, both in humans and in reservoir hosts. Pooled testing can be an efficient tool for learning pathogen prevalence in a population. Typically, pooled testing requires a second‐phase retesting procedure to identify infected individuals, but when the goal is solely to learn prevalence in a population, such as a reservoir host, there are more efficient methods for allocating the second‐phase samples. To estimate pathogen prevalence in a population, this manuscript presents an approach for data fusion with two‐phased testing of pooled samples that allows more efficient estimation of prevalence with less samples than traditional methods. The first phase uses pooled samples to estimate the population prevalence and inform efficient strategies for the second phase. To combine information from both phases, we introduce a Bayesian data fusion procedure that combines pooled samples with individual samples for joint inferences about the population prevalence. Data fusion procedures result in more efficient estimation of prevalence than traditional procedures that only use individual samples or a single phase of pooled sampling. The manuscript presents guidance on implementing the first‐phase and second‐phase sampling plans using data fusion. Such methods can be used to assess the risk of pathogen spillover from reservoir hosts to humans, or to track pathogens such as SARS‐CoV‐2 in populations. Pooled testing is a common procedure for efficient sampling to determine population prevalence and identify positive individuals. When the goal is to learn population prevalence, without regard for identifying positive individuals, data integration results in more precise estimates than retesting individuals in positive pools.

Resource managers may be hesitant to make decisions based on environmental (e)DNA results alone since eDNA is an indirect method of species detection. One way to reduce the uncertainty of eDNA is to identify laboratory‐based protocols that ensure repeatable and reproducible results. We conducted a double‐blind round‐robin analysis of probe‐based assays for DNA of dreissenid (Dreissena spp.) mussels, which are prolific aquatic invaders that can cause significant economic and ecological impacts. DNA extract from water samples spiked with known amounts of dreissenid DNA and from water samples collected from waters with and without dreissenids were analyzed by four independent research laboratories. We used results to calculate detection repeatability within laboratories and assays, detection reproducibility among laboratories and assays, and estimated dreissenid DNA copy number precision and accuracy. Laboratory and assay repeatability and reproducibility of detection results were high, 91% and 92%, respectively. The estimated copy numbers were neither precise nor accurate for samples spiked with <773 gene copies. These results suggest that eDNA surveillance of dreissenid mussels, using the protocols evaluated herein, can generate reliable detection data for decision‐making. However, managers should be cautious about using the quantitative information often associated with eDNA detections, especially when DNA is at lower abundance. Our results provide strong support that eDNA has the potential to provide repeatable and reproducible evidence under varying laboratory conditions and for different sample water chemistries. This is reassuring since the demand for eDNA surveillance is widespread and number of laboratories that process eDNA samples is growing steadily. We conducted a double‐blind round‐robin analysis of probe‐based assays for DNA of dreissenid (Dreissena spp.) mussels, which are prolific aquatic invaders that can cause significant economic and ecological impacts. We used results to calculate detection repeatability within laboratories and assays, detection reproducibility among laboratories and assays, and estimated dreissenid DNA copy number precision and accuracy. Laboratory and assay repeatability and reproducibility of detection results were high, 91% and 92%, respectively, but the estimated copy numbers were neither precise nor accurate for samples spiked with <773 gene copies.

During recent decades, pathogens that originated in bats have become an increasing public health concern. A major challenge is to identify how those pathogens spill over into human populations to generate a pandemic threat 1 . Many correlational studies associate spillover with changes in land use or other anthropogenic stressors 2 , 3 , although the mechanisms underlying the observed correlations have not been identified 4 . One limitation is the lack of spatially and temporally explicit data on multiple spillovers, and on the connections among spillovers, reservoir host ecology and behaviour and viral dynamics. We present 25 years of data on land-use change, bat behaviour and spillover of Hendra virus from Pteropodid bats to horses in subtropical Australia. These data show that bats are responding to environmental change by persistently adopting behaviours that were previously transient responses to nutritional stress. Interactions between land-use change and climate now lead to persistent bat residency in agricultural areas, where periodic food shortages drive clusters of spillovers. Pulses of winter flowering of trees in remnant forests appeared to prevent spillover. We developed integrative Bayesian network models based on these phenomena that accurately predicted the presence or absence of clusters of spillovers in each of the 25 years. Our long-term study identifies the mechanistic connections between habitat loss, climate and increased spillover risk. It provides a framework for examining causes of bat virus spillover and for developing ecological countermeasures to prevent pandemics. A study reveals how land-use change and climate interact to drive the spillover of a zoonotic virus, and identifies an ecological mechanism that prevents spillover.

Model selection algorithms are required to efficiently traverse the space of models. In problems with high-dimensional and possibly correlated covariates, efficient exploration of the model space becomes a challenge. To overcome this, a multiset is placed on the model space to enable efficient exploration of multiple model modes with minimal tuning. The multiset model selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. Posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. The effectiveness of MSMS is demonstrated for linear and generalized linear models. Supplementary material for this article is available online.

Model selection algorithms are required to efficiently traverse the space of models. In problems with high-dimensional and possibly correlated covariates, efficient exploration of the model space becomes a challenge. To overcome this, a multiset is placed on the model space to enable efficient exploration of multiple model modes with minimal tuning. The multiset model selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. Posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. The effectiveness of MSMS is demonstrated for linear and generalized linear models. Supplementary material for this article is available online.

Statistics departments play a vital role in educating students on the analysis of data for obtaining information and discovering knowledge. In the last several years, we have witnessed an explosion of data, which was not imaginable in years past. As a result, the methods and techniques used for data analysis have evolved. Beyond this, the technology used for storing, porting, and computing big data has also evolved, and so now must traditionally oriented statistics departments. In this article, we discuss the development of a new computational modeling program that meets these demands, and we detail how to balance the qualitative and quantitative components of modern day data analyses for statistical education. [Received December 2014. Revised August 2015.]