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1. Extending the social cohesion hypothesis: is group social structure associated with dispersal in yellow-bellied marmots ( Marmota flaviventer )?

   https://doi.org/10.1093/cz/zoae002  

   Schneidman, Sara A. ; Philson, Conner S. ; Blumstein, Daniel T. ; Jia, ed., Zhi-Yun ( January 2024 , Current Zoology)

   Dispersal is an important individual decision which may influence individual fitness as well as population viability. The social cohesion hypothesis posits more social individuals remain at home, which is supported by prior work across taxa. However, how the sociality and connectivity of the group an individual resides in—their group social structure—relates to dispersal decisions has not been explored. We extend the social cohesion hypothesis to predict individuals residing in more social groups would remain at home, and we quantified the affiliative and agonistic social network structure of female yellow-bellied marmots (Marmota flaviventer), a facultatively social ground-dwelling squirrel, where about half of all females disperse. Using mixed-effects models, we found no support for the hypothesis that affiliative group structure explained any variation in a marmot’s decision to disperse. We did find marmots in groups with less agonistic centralization (around one or few individuals) were less likely to disperse. The former finding may result from limited ability to perceive group structure whereas the latter may reflect individuals in less agonistically centralized groups are less likely to be reproductively suppressed. These results suggest individual dispersal decisions are more impacted by individual sociality and not that of their social group. Thus, the social cohesion hypothesis may not scale to the level of the group. Further work is required to determine whether dispersal decisions in obligately social species are influenced by group social structure.

    

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2. Phylogeography of Solomon Islands blossom bats reflects oceanic divides and Pleistocene connections

   https://doi.org/10.1111/jbi.14585  

   Lavery, Tyrone H. ; DeCicco, Lucas H. ; Olson, Karen V. ; Holland, Piokera S. ; Moyle, Robert G. ( March 2023 , Journal of Biogeography)

   Periodic lowering of sea levels and formation of land bridges can reshape phylogeographic patterns of insular biotas. Using archipelago‐wide sampling, we aimed to test if phylogeography of an old‐endemic bat lineage reflected Pleistocene land bridges.

   Solomon Islands and Papua New Guinea.

   MelonycterisandNesonycterisbats (Pteropodidae).

   We sequenced genome‐wide RADseq data for 49 specimens from 15 islands. We assessed phylogenetic relationships using maximum likelihood inRAxMLand quartet‐based methods inSVDquartets, population structure usingStructure, and admixture using maximum likelihood methods inTreeMix. We tested for genetic and geographic distance correlations using distance‐based redundancy analyses (dbRDA), identifying best‐fit models using stepwise model selection.

   Phylogenetic analyses identified fiveNesonycterisclades corresponding to Greater Bukida, Guadalcanal, Makira, Malaita and New Georgia group. Makira samples were sister to remainingNesonycteris.Structureidentified four populations: New IrelandMelonycteris melanops; andNesonycterisfrom Greater Bukida (including Guadalcanal); Malaita and Makira; and New Georgia group. Genetic backgrounds of Mono, Ngella and Guadalcanal separated from remaining Greater Bukida islands. Makira and Malaita separated into two populations. New Georgia group lacked structure, and genetic and geographic distances were not correlated. The best‐fit geographic distance models forNesonycterisand a Greater Bukida subset were least shore‐to‐shore distance; and Euclidean and least‐cost distances respectively.

   Influences of modern and Pleistocene island isolation and connectivity were evident in the overall Phylogeography ofNesonycteris. The lack of structure or geographic distance correlations within the New Georgia group indicated all islands were interconnected during the Last Glacial Maximum or contemporary oceanic divides are ineffective barriers. Conversely, genetic divergence across Greater Bukida islands reflected land‐bridge constrained dispersal. A Makira clade sister to allNesonycterispossibly indicates an origin on Makira. Alternately it reflects Makira's long‐isolated geographic status, as similar results exist for a range of taxa.

    

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3. Multilevel selection on social network traits differs between sexes in experimental populations of forked fungus beetles

   https://doi.org/10.1093/evolut/qpac012  

   Costello, Robin A. ; Cook, Phoebe A. ; Brodie, III, Edmund D. ; Formica, Vincent A. ( December 2022 , Evolution)

   Both individual and group behavior can influence individual fitness, but multilevel selection is rarely quantified on social behaviors. Social networks provide a unique opportunity to study multilevel selection on social behaviors, as they describe complex social traits and patterns of interaction at both the individual and group levels. In this study, we used contextual analysis to measure the consequences of both individual network position and group network structure on individual fitness in experimental populations of forked fungus beetles (Bolitotherus cornutus) with two different resource distributions. We found that males with high individual connectivity (strength) and centrality (betweenness) had higher mating success. However, group network structure did not influence their mating success. Conversely, we found that individual network position had no effect on female reproductive success but that females in populations with many social interactions experienced lower reproductive success. The strength of individual-level selection in males and group-level selection in females intensified when resources were clumped together, showing that habitat structure influences multilevel selection. Individual and emergent group social behavior both influence variation in components of individual fitness, but impact the male mating success and female reproductive success differently, setting up intersexual conflicts over patterns of social interactions at multiple levels.

    

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4. Stagewise Generalized Estimating Equations with Grouped Variables

   https://doi.org/10.1111/biom.12669  

   Vaughan, Gregory ; Aseltine, Robert ; Chen, Kun ; Yan, Jun ( February 2017 , Biometrics)

   Forward stagewise estimation is a revived slow-brewing approach for model building that is particularly attractive in dealing with complex data structures for both its computational efficiency and its intrinsic connections with penalized estimation. Under the framework of generalized estimating equations, we study general stagewise estimation approaches that can handle clustered data and non-Gaussian/non-linear models in the presence of prior variable grouping structure. As the grouping structure is often not ideal in that even the important groups may contain irrelevant variables, the key is to simultaneously conduct group selection and within-group variable selection, that is, bi-level selection. We propose two approaches to address the challenge. The first is a bi-level stagewise estimating equations (BiSEE) approach, which is shown to correspond to the sparse group lasso penalized regression. The second is a hierarchical stagewise estimating equations (HiSEE) approach to handle more general hierarchical grouping structure, in which each stagewise estimation step itself is executed as a hierarchical selection process based on the grouping structure. Simulation studies show that BiSEE and HiSEE yield competitive model selection and predictive performance compared to existing approaches. We apply the proposed approaches to study the association between the suicide-related hospitalization rates of the 15–19 age group and the characteristics of the school districts in the State of Connecticut.

    

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5. Incorporating Grouping Information into Bayesian Decision Tree Ensembles

   Du, Junliang ; Linero, Antonio Ricardo ( July 2019 , Proceedings of the 36th International Conference on Machine Learning)

   We consider the problem of nonparametric regression in the high-dimensional setting in which P≫N. We study the use of overlapping group structures to improve prediction and variable selection. These structures arise commonly when analyzing DNA microarray data, where genes can naturally be grouped according to genetic pathways. We incorporate overlapping group structure into a Bayesian additive regression trees model using a prior constructed so that, if a variable from some group is used to construct a split, this increases the probability that subsequent splits will use predictors from the same group. We refer to our model as an overlapping group Bayesian additive regression trees (OG-BART) model, and our prior on the splits an overlapping group Dirichlet (OG-Dirichlet) prior. Like the sparse group lasso, our prior encourages sparsity both within and between groups. We study the correlation structure of the prior, illustrate the proposed methodology on simulated data, and apply the methodology to gene expression data to learn which genetic pathways are predictive of breast cancer tumor metastasis. 

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