Josef C. Uyeda

Summary: Phylogenetic comparative methods (PCMs) help researchers understand and predict trait evolutionary relationships. While improvements to PCMs have focused on increasing model complexity, understanding processes remains difficult due to persistent challenges in grounding complex models in biological reality and synthesizing findings across multiple analyses. We examined the evolution of growth habit in eudicots (75% of all angiosperms) and tested how variables such as vessel diameter, leaf phenology, and minimum temperature influence macroevolutionary inference. We used a series of PCMs to synthesize our understanding of trait interrelationships, explored plausible causal relationships using phylogenetic path analysis, and employed phylogenetic cross‐validation to assess predictive performance among taxa. We found that discrete coding of growth form was linked to other measured and unmeasured traits, and that these interrelationships can help overcome limitations arising from incomplete data and simplistic coding of complex traits. Analysis of growth form using phylogenetic path analysis helps reconcile competing views of trait interrelationships from previous studies. Furthermore, including identified covariates improves prediction of growth habit and other traits. Our study shows that incorporating causal structure improves macroevolutionary inference, identifies when analyses that omit key causal traits become unreliable, and underscores the importance of integrating phylogenetic models with natural‐history knowledge

Species must adapt to persist in a changing world. As global temperatures rise, how species adapt and respond to thermal shifts is crucial for anticipating global patterns of biodiversity change. Land vertebrates can be divided into two major thermoregulatory strategies, endothermy and ectothermy. One might hypothesize that, given their reputation as being “cold blooded,” ectotherms are thermal generalists, capable of operating across a greater range of body temperatures than endotherms and exhibit greater plasticity and evolvability in body temperature. However, a wide variety of traits and ecologies could modulate responses of thermal physiology to environmental change. Here, we employ macroevolutionary models to estimate the rate of adaptation of thermal physiology across squamates, mammals, and birds in the context of their ecology, physiology, and changing climatic conditions and whether there are fundamental differences in how the three clades respond to their environments. We find stronger relationships between squamates’ body temperature and their environment than in birds and mammals, significant effects of diel activity (nocturnal and diurnal) on body temperature evolution in all clades, and no effect of aquatic/terrestrial habits and rumination on the evolution of body temperature in mammals. Most surprisingly, our findings suggest shared limits on the evolution of thermal physiology across ectothermic and endothermic groups that argue for universal constraints on the rate of evolution in thermal physiology while explaining disparate patterns of body temperature and niche evolution across groups.

A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution. (Model and code can be found at imageomics.github.io/phylo-diffusion)

Aim: The relationship between body size and trophic position (BS–TP) typically exhibits a positive correlation in aquatic foodwebs, but the strength of this relationship is contingent on ecosystem type and climate. Different hypotheses have been proposed to elucidate climate and ecosystem type effects on the BS–TP relationship for ray‐finned fish. However, our understanding of whether such a relationship evolved in a correlated fashion, spanning various climates and ecosystem types, remains limited.Location: Temperate and tropical marine and freshwater ecosystems.Time Period: Present to millions of years ago.Major Taxa Studied: Ray‐finned fish.Methods: We used a phylogenetic tree and TP and BS data of more than a thousand freshwater and marine ray‐finned fishes, from distinct climates and ecosystems, to investigate patterns on macroevolutionary time scales of the evolutionary correlation of BS and TP. As part of our investigation, we also ran analyses excluding herbivores and detritivores from the dataset, then further focusing solely on carnivores.Results: We found distinct patterns of the BS–TP evolutionary correlation for different climates and ecosystems. The evolutionary correlation between BS and TP was significant for all ecosystem type–climate combinations, except for tropical freshwater ecosystems. The results remained consistent even after accounting for phylogenetic uncertainty and when excluding herbivores and detritivores from the analysis.Main Conclusions: We found a weaker evolutionary correlation between BS and TP in tropical freshwater ecosystems. These findings are consistent with the stronger BS–TP relationship between extant taxa in temperate climates compared to the tropics, illustrating how evolutionary dynamics might have influenced the trophic structure of fish and contributed to shaping macroecological patterns of the BS–TP relationship. Our findings suggest that limitations that hinder evolutionary integration between BS and TP might be primarily attributed to energetic constraints imposed by temperature and the availability of C‐rich food resources at the base of the foodweb.

Reconstructing ancestral states for discrete characters is essential for understanding trait evolution in organisms. However, most existing methods are limited to individual characters and often overlook the hierarchical and interactive nature of traits. Recent advances in phylogenetics now offer the possibility of integrating knowledge from anatomy ontologies to reconstruct multiple discrete character histories. Nonetheless, practical applications that fully harness the potential of these new approaches are still lacking. This paper introduces ontophylo, an R package that extends the PARAMO pipeline to address these limitations. Ontophylo enables the reconstruction of phenotypic entities composed of amalgamated characters, such as anatomical regions or entire phenomes. It offers three new applications: (1) reconstruction of evolutionary rates of amalgamated characters using phylogenetic non‐homogeneous Poisson process (pNHPP) that allows modelling rate variation across tree branches and time; (2) reconstruction of morphospace dynamics; and (3) visualization of evolutionary rates on vector images of organisms. Ontophylo incorporates ontological knowledge to facilitate these applications. Benchmarking confirms the accuracy of pNHPP in estimating character rates under different evolutionary scenarios, and example applications demonstrate the utility of ontophylo in studying morphological evolution in Hymenoptera using simulated data. Ontophylo can be easily integrated with other ontology‐oriented and general‐purpose R packages and offers new opportunities to examine morphological evolution on a phenomic scale using new and legacy data.

Organismal anatomy is a hierarchical system of anatomical entities often imposing dependencies among multiple morphological characters. Ontologies provide a formal and computable framework for incorporating prior biological knowledge about anatomical dependencies in models of trait evolution. They also offer new opportunities for working with semantic representations of morphological data. In this work, we present a new R package—rphenoscate—that enables incorporating ontological knowledge in evolutionary analyses and exploring semantic patterns of morphological data. In conjunction with rphenoscape, it allows for assembling synthetic phylogenetic character matrices from semantic phenotypes of morphological data. We showcase the package functionality with data sets from bees and fishes. We demonstrate that ontologies can be employed to automatically set up evolutionary models accounting for trait dependencies in stochastic character mapping. We also demonstrate how ontology annotations can be explored to interrogate patterns of morphological evolution. Finally, we demonstrate that synthetic character matrices assembled from semantic phenotypes retain most of the phylogenetic information from their original data sets. Ontologies will become important tools for integrating anatomical knowledge into phylogenetic methods and making morphological data FAIR compliant—a critical step of the ongoing ‘phenomics’ revolution. Our new package offers key advancements towards this goal.

Discovering evolutionary traits that are heritable across species on the tree of life (also referred to as a phylogenetic tree) is of great interest to biologists to understand how organisms diversify and evolve. However, the measurement of traits is often a subjective and labor-intensive process, making trait discovery a highly label-scarce problem. We present a novel approach for discovering evolutionary traits directly from images without relying on trait labels. Our proposed approach, Phylo-NN, encodes the image of an organism into a sequence of quantized feature vectors -or codes- where different segments of the sequence capture evolutionary signals at varying ancestry levels in the phylogeny. We demonstrate the effectiveness of our approach in producing biologically meaningful results in a number of downstream tasks including species image generation and species-to-species image translation, using fish species as a target example

1. Reconstructing ancestral states for discrete characters is essential for understanding trait evolution in organisms. However, most existing methods are limited to individual characters and often overlook the hierarchical and interactive nature of traits. Recent advances in phylogenetics now offer the possibility of integrating knowledge from anatomy ontologies to reconstruct multiple discrete character histories. However, practical applications that fully harness the potential of these new approaches are still lacking. 2. This paper introduces ontophylo , an R package that extends the PARAMO pipeline to address these limitations. Ontophylo enables the reconstruction of phenotypic entities composed of amalgamated characters, such as entire phenomes or anatomical regions. It offers three new applications: (1) reconstructing evolutionary rates of amalgamated characters using phylogenetic non-homogeneous Poisson process (pNHPP), allowing for rate variation and shifts over time and phylogeny; (2) explicit reconstruction of morphospace dynamics; and (3) direct visualization of evolutionary rates and statistics on vector images of organisms. Ontophylo incorporates ontological knowledge to facilitate these applications. 3. Benchmarking confirms the accuracy of pNHPP in estimating character rates under different evolutionary scenarios, and example applications demonstrate the utility of ontophylo in studying morphological evolution in Hymenoptera using simulated data. 4. Ontophylo is easily integrated with other ontologyoriented and general-purpose R packages and offers new opportunities to examine morphological evolution on a phenomic level using new and legacy data.

Discovering evolutionary traits that are heritable across species on the tree of life (also referred to as a phylogenetic tree) is of great interest to biologists to understand how organisms diversify and evolve. However, the measurement of traits is often a subjective and labor-intensive process, making trait discovery a highly label-scarce problem. We present a novel approach for discovering evolutionary traits directly from images without relying on trait labels. Our proposed approach, Phylo-NN, encodes the image of an organism into a sequence of quantized feature vectors -- or codes -- where different segments of the sequence capture evolutionary signals at varying ancestry levels in the phylogeny. We demonstrate the effectiveness of our approach in producing biologically meaningful results in a number of downstream tasks including species image generation and species-to-species image translation, using fish species as a target example.

Organismal anatomy is a complex hierarchical system of interconnected anatomical entities often producing dependencies among multiple morphological characters. Ontologies provide a formalized and computable framework for representing and incorporating prior biological knowledge about anatomical dependencies in models of trait evolution. Further, ontologies offer new opportunities for assembling and working with semantic representations of morphological data. In this work we present a new R package— rphenoscate —that enables incorporating ontological knowledge in evolutionary analyses and exploring semantic patterns of morphological data. In conjunction with rphenoscape it also allows for assembling synthetic phylogenetic character matrices from semantic phenotypes of morphological data. We showcase the new package functionalities with three data sets from bees and fishes. We demonstrate that ontology knowledge can be employed to automatically set up ontologyinformed evolutionary models that account for trait dependencies in the context of stochastic character mapping. We also demonstrate how ontology annotations can be explored to interrogate patterns of morphological evolution. Finally, we demonstrate that synthetic character matrices assembled from semantic phenotypes retain most of the phylogenetic information of the original data set. Ontologies will become an increasingly important tool not only for enabling prior anatomical knowledge to be integrated into phylogenetic methods but also to make morphological data FAIR compliant—a critical component of the ongoing ‘phenomics’ revolution. Our new package offers key advancements toward this goal.