Miller, J. E. D. | bioRxiv Science

bioRxiv2025

Rarity and reproductive biology drive population genomics of obligate symbioses

Modern perspectives on obligate symbioses increasingly recognize their dynamism, complexity, and global importance. Population genomics has been key to this advancement, yet the extension of existing frameworks to reproducibly integrate across multiple genotype-phenotype-environment relationships remains elusive. We generated 962 metagenomes for six lichens, obligate symbioses between fungi and algae, with contrasting traits sampled at a landscape scale. Core neutral population metrics were primarily driven by rarity and reproductive biology, while genes underlying expression regulation were under selection in response to land use and climate across all species. Examined within a comparative framework, these results illustrate that symbiont interactions are a major factor influencing microevolutionary trajectories, especially neutral processes. We present a conceptual framework for population-scale evolution in obligate symbioses that centers on biotic interactions.

evolutionary biology↗

bioRxiv2017

Functional traits and community composition: multilevel models outperform community-weighted means

O_LIPlant functional traits are increasingly being used to infer mechanisms about community assembly and predict global change impacts. Of the several approaches that are used to analyze trait-environment relationships, one of the most popular is community-weighted means (CWM), in which species trait values are averaged at the site level. Other approaches that do not require averaging are being developed, including multilevel models (MLM, also called generalized linear mixed models). However, relative strengths and weaknesses of these methods have not been extensively compared.\nC_LIO_LIWe investigated three statistical models for trait-environment associations: CWM, a MLM in which traits were not included as fixed effects (MLM1), and a MLM with traits as fixed effects (MLM2). We analyzed a real plant community dataset to investigate associations between two traits and one environmental variable. We then analyzed permutations of the dataset to investigate sources of type I errors, and performed a simulation study to compare the statistical power of the methods.\nC_LIO_LIIn the analysis of real data, CWM gave highly significant associations for both traits, while MLM1 and MLM2 did not. Using P-values derived by simulating the data using the fitted MLM2, none of the models gave significant associations, showing that CWM had inflated type I errors (false positives). In the permutation tests, MLM2 performed the best of the three approaches. MLM2 still had inflated type I error rates in some situations, but this could be corrected using bootstrapping. The simulation study showed that MLM2 always had as good or better power than CWM. These simulations also confirmed the causes of type I errors from the permutation study.\nC_LIO_LIThe MLM that includes main effects of traits (MLM2) is the best method for identifying trait-environmental association in community assembly, with better type I error control and greater power. Analyses that regress CWMs on continuous environmental variables are not reliable because they are likely to produce type I errors.\nC_LI

ecology↗