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1. Multiple merger coalescent inference of effective population size

   https://doi.org/10.1098/rstb.2023.0306  

   Zhang, Julie; Palacios, Julia A (February 2025, Philosophical Transactions of the Royal Society B: Biological Sciences)

   NA (Ed.)

   Variation in a sample of molecular sequence data informs about the past evolutionary history of the sample’s population. Traditionally, Bayesian modelling coupled with the standard coalescent is used to infer the sample’s bifurcating genealogy and demographic and evolutionary parameters such as effective population size and mutation rates. However, there are many situations where binary coalescent models do not accurately reflect the true underlying ancestral processes. Here, we propose a Bayesian non-parametric method for inferring effective population size trajectories from a multifurcating genealogy under the Lambda-coalescent. In particular, we jointly estimate the effective population size and the model parameter for the Beta-coalescent model, a special type of Lambda-coalescent. Finally, we test our methods on simulations and apply them to study various viral dynamics as well as Japanese sardine population size changes over time. The code and vignettes can be found in the phylodyn package. This article is part of the theme issue ‘“A mathematical theory of evolution”: phylogenetic models dating back 100 years’. 

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2. Bayesian Nonparametric Monotone Regression of Dynamic Latent Traits in Item Response Theory Models

   https://doi.org/10.3102/1076998619887913  

   Liu, Yang; Wang, Xiaojing (June 2020, Journal of Educational and Behavioral Statistics)

   Parametric methods, such as autoregressive models or latent growth modeling, are usually inflexible to model the dependence and nonlinear effects among the changes of latent traits whenever the time gap is irregular and the recorded time points are individually varying. Often in practice, the growth trend of latent traits is subject to certain monotone and smooth conditions. To incorporate such conditions and to alleviate the strong parametric assumption on regressing latent trajectories, a flexible nonparametric prior has been introduced to model the dynamic changes of latent traits for item response theory models over the study period. Suitable Bayesian computation schemes are developed for such analysis of the longitudinal and dichotomous item responses. Simulation studies and a real data example from educational testing have been used to illustrate our proposed methods. 

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3. Microbiome Data Integration via Shared Dictionary Learning

   https://doi.org/10.1101/2024.10.04.616752  

   Yuan, Bo; Wang, Shulei (October 2024, bioRxiv)

   Abstract Data integration is a powerful tool for facilitating a comprehensive and generalizable understanding of microbial communities and their association with outcomes of interest. However, integrating data sets from different studies remains a challenging problem because of severe batch effects, unobserved confounding variables, and high heterogeneity across data sets. We propose a new data integration method called MetaDICT, which initially estimates the batch effects by weighting methods in causal inference literature and then refines the estimation via a novel shared dictionary learning. Compared with existing methods, MetaDICT can better avoid the overcorrection of batch effects and preserve biological variation when there exist unobserved confounding variables or data sets are highly heterogeneous across studies. Furthermore, MetaDICT can generate comparable embedding at both taxa and sample levels that can be used to unravel the hidden structure of the integrated data and improve the integrative analysis. Applications to synthetic and real microbiome data sets demonstrate the robustness and effectiveness of MetaDICT in integrative analysis. Using MetaDICT, we characterize microbial interaction, identify generalizable microbial signatures, and enhance the accuracy of disease prediction in an integrative analysis of colorectal cancer metagenomics studies. 

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4. Continental‐scale tree‐ring‐based projection of Douglas‐fir growth: Testing the limits of space‐for‐time substitution

   https://doi.org/10.1111/gcb.15170  

   Klesse, Stefan; DeRose, Robert_Justin; Babst, Flurin; Black, Bryan_A; Anderegg, Leander_D_L; Axelson, Jodi; Ettinger, Ailene; Griesbauer, Hardy; Guiterman, Christopher_H; Harley, Grant; et al (June 2020, Global Change Biology)

   Abstract A central challenge in global change research is the projection of the future behavior of a system based upon past observations. Tree‐ring data have been used increasingly over the last decade to project tree growth and forest ecosystem vulnerability under future climate conditions. But how can the response of tree growth to past climate variation predict the future, when the future does not look like the past? Space‐for‐time substitution (SFTS) is one way to overcome the problem of extrapolation: the response at a given location in a warmer future is assumed to follow the response at a warmer location today. Here we evaluated an SFTS approach to projecting future growth of Douglas‐fir (Pseudotsuga menziesii), a species that occupies an exceptionally large environmental space in North America. We fit a hierarchical mixed‐effects model to capture ring‐width variability in response to spatial and temporal variation in climate. We found opposing gradients for productivity and climate sensitivity with highest growth rates and weakest response to interannual climate variation in the mesic coastal part of Douglas‐fir's range; narrower rings and stronger climate sensitivity occurred across the semi‐arid interior. Ring‐width response to spatial versus temporal temperature variation was opposite in sign, suggesting that spatial variation in productivity, caused by local adaptation and other slow processes, cannot be used to anticipate changes in productivity caused by rapid climate change. We thus substituted only climate sensitivities when projecting future tree growth. Growth declines were projected across much of Douglas‐fir's distribution, with largest relative decreases in the semiarid U.S. Interior West and smallest in the mesic Pacific Northwest. We further highlight the strengths of mixed‐effects modeling for reviving a conceptual cornerstone of dendroecology, Cook's 1987 aggregate growth model, and the great potential to use tree‐ring networks and results as a calibration target for next‐generation vegetation models. 

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5. How does host social behavior drive parasite non-selective evolution from the within-host to the landscape-scale?

   https://doi.org/10.1007/s00265-021-03089-y  

   Janecka, Mary J.; Rovenolt, Faith; Stephenson, Jessica F. (November 2021, Behavioral Ecology and Sociobiology)

   Social interactions with conspecifics are key to the fitness of most animals and, through the transmission opportunities they provide, are also key to the fitness of their parasites. As a result, research to date has largely focused on the role of host social behavior in imposing selection on parasites, particularly their virulence and transmission phenotypes. However, host social behavior also influences the distribution of parasites among hosts, with implications for their evolution through non-random mating, gene flow, and genetic drift, and thus ability to respond to that selection. Here, we review the paucity of empirical studies on parasites, and draw from empirical studies of free-living organisms and population genetic theory to propose several mechanisms by which host social behavior potentially drives parasite evolution through these less-well studied mechanisms. We focus on the guppy host and Gyrodactylus (Monogenea) ectoparasitic flatworm system and follow a spatially hierarchical outline to highlight that social behavior varies between individuals, and between host populations across the landscape, generating a mosaic of ecological and evolutionary outcomes for their infecting parasites. We argue that the guppy-Gyrodactylus system presents a unique opportunity to address this fundamental knowledge gap in our understanding of the connection between host social behavior and parasite evolution. Individual differences in host social behavior generates fine-scale changes in the spatial distribution of parasite genotypes, shape the size, and diversity of their infecting parasite populations and may generate non-random mating on, and non-random transmission between hosts. While at population and metapopulation level, variation in host social behavior interacts with landscape structure to affect parasite gene flow, effective population size, and genetic drift to alter the coevolutionary potential of local adaptation. 

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