Avery Selberg

Convergent evolution offers a natural test for adaptive predictability, yet pinpointing its molecular basis remains difficult. Current methods often grapple with distinguishing adaptive convergence from background noise, either by demanding overly stringent identical substitutions or relying on low-resolution evolutionary rate shifts. Here we introduce BUSTED-PH (Branch-site Unrestricted Statistical Test for Episodic Diversification – Phenotype), a branch-site codon model that detects phenotype-associated episodic diversifying selection. Unlike standard approaches, BUSTED-PH explicitly contrasts selective regimes between phenotype-positive (foreground) and phenotype-negative (background) lineages, effectively winnowing out spurious associations driven by pervasive background adaptation. BUSTED-PH has already been applied in numerous independent studies, and here we rigorously validate it using canonical positive controls and simulations to confirm high power and strict false positive control. A genome-wide scan of 120 mammalian species using strict statistical criteria ( FDR ≤ 0.01) identified 72 genes associated with echolocation; while recovering paradigmatic auditory drivers (e.g., Prestin, TMC1 ), we also uncover novel candidates in physiological support systems ranging from lipid homeostasis to neural development. A parallel analysis of mammalian gigantism identifies 91 genes linked to musculoskeletal reinforcement, organ size governance, and genomic integrity, characterizing the molecular adaptations required to support massive body size. As projects like Zoonomia and the Vertebrate Genomes Project expand comparative datasets, BUSTED-PH provides a robust framework for dissecting the genetic architecture of complex convergent traits.

Endothermy has independently evolved in several vertebrate lineages but remains rare among fishes. Using an integrated approach combining phylogenomic and ecomorphological data for 1051 ray-finned fishes, a time-dependent evolutionary model, and comparative genomic analyses of 205 marine vertebrates, we show that ecological interactions with modern cetaceans coincided with the evolution of endothermy in ray-finned fishes during the Eocene-Miocene. This result is supported by evidence of temporal and geographical overlap between cetaceans and endothermic fish lineages in the fossil record, as well as correlations between cetacean diversification and the origin of endothermy in fishes. Phylogenetic comparative analyses identified correlations between endothermy, large body sizes, and specialized swimming modes while challenging diet specialization and depth range expansion hypotheses. Comparative genomic analyses identified several genes under selection in endothermic lineages, including carnmt1 (involved in fatty acid metabolism) and dcaf6 (associated with development). Our findings advance the understanding of how ecological interactions and genomic factors shape key adaptations.

Positive selection is an evolutionary process which increases the frequency of advantageous mutations because they confer a fitness benefit. Inferring the past action of positive selection on protein-coding sequences is fundamental for deciphering phenotypic diversity and the emergence of novel traits. With the advent of genome-wide comparative genomic datasets, researchers can analyze selection not only at the level of individual genes but also globally, delivering systems-level insights into evolutionary dynamics. However, genome-scale datasets are generated with automated pipelines and imperfect curation that does not eliminate all sequencing, annotation, and alignment errors. Positive selection inference methods are highly sensitive to such errors. We present BUSTED-E: a method designed to detect positive selection for amino acid diversification while concurrently identifying some alignment errors. This method builds on the flexible branch-site random effects model (BUSTED) for fitting distributions of dN/dS, with a critical modification: it incorporates an “error-sink” component to represent an abiological evolutionary regime. Using several genome-scale biological datasets that were extensively filtered using state-of-the art automated alignment tools, we show that BUSTED-E identifies pervasive residual alignment errors, produces more realistic estimates of positive selection, reduces bias, and improves biological interpretation. The BUSTED-E model promises to be a more stringent filter to identify positive selection in genome-wide contexts, thus enabling further characterization and validation of the most biologically relevant cases.

Understanding factors that determine a species' geographical range is crucial for predicting climate‐induced range shifts. Two milkweed species, Asclepias syriaca and Asclepias speciosa , have overlapping ranges along a moisture gradient in North America and are primary food sources for endangered monarch caterpillars. With decreasing moisture, long‐lived species often exhibit slower growth and greater drought tolerance, while many annual species exhibit faster growth strategies. Using this fast‐slow framework, we assessed whether traits of these two sister species differ along a fast‐slow growth continuum and could explain their distributions. We measured leaf and root functional traits in common gardens and greenhouse experiments. In key measures indicative of drought tolerance (e.g., growth, transpiration, and water potentials), the species were nearly identical. Contrary to expectations, A. speciosa did not exhibit greater drought tolerance, raising the question of how it survives in the more arid west. A reciprocal transplant study showed selection against A. syriaca in the western garden and that A. speciosa was better able to avoid seedling mortality. Focusing on seedling establishment, we found that A. speciosa exhibited faster deep‐root development and a narrow leaf phenotype associated with slower wilting and delayed drought‐induced mortality. Rather than differences on the fast‐slow growth spectrum, our results indicate that A. speciosa avoids drought through faster deep‐root growth and a slower wilting phenotype. Our study suggests that A. syriaca's range is limited by its drought tolerance, whil e A. speciosa employs a number of drought avoidance strategies to survive in more arid environments.

Positive selection is an evolutionary process which increases the frequency of advantageous mutations because they confer a fitness benefit. Inferring the past action of positive selection on protein-coding sequences is fundamental for deciphering phenotypic diversity and the emergence of novel traits. With the advent of genome-wide comparative genomic datasets, researchers can analyze selection not only at the level of individual genes but also globally, delivering systems-level insights into evolutionary dynamics. However, genome-scale datasets are generated with automated pipelines and imperfect curation that does not eliminate all sequencing, annotation, and alignment errors. Positive selection inference methods are highly sensitive to such errors. We present BUSTED-E: a method designed to detect positive selection for amino acid diversification while concurrently identifying some alignment errors. This method builds on the flexible branch-site random effects model (BUSTED) for fitting distributions of dN/dS, with a critical modification: it incorporates an “error-sink” component to represent an abiological evolutionary regime. Using several genome-scale biological datasets that were extensively filtered using state-of-the art automated alignment tools, we show that BUSTED-E identifies pervasive residual alignment errors, produces more realistic estimates of positive selection, reduces bias, and improves biological interpretation. The BUSTED-E model promises to be a more stringent filter to identify positive selection in genome-wide contexts, thus enabling further characterization and validation of the most biologically relevant cases.