Anjali Goswami, Julien Clavel

Organismal morphology has been at the core of study of biodiversity for millennia before the formalization of the concept of evolution. In the early to mid-twentieth century, a strong theoretical framework was developed for understanding both pattern and process of morphological evolution on a macroevolutionary scale. The past half century has been a transformational period for the study of evolutionary morphology, in both the quantification of morphology and novel analytical tools for estimating how and why morphological diversity changes through time, with a marked increase in studies apparent in the early 1990s. We are now at another inflection point in the study of morphological evolution, with the availability of vast amounts of high-resolution data sampling extant and extinct diversity allowing ‘omics’-scale analysis. Artificial intelligence is already increasing the pace of phenomic data collection even further. This new reality, where the ability to obtain data is quickly outpacing the ability to analyse it with robust, realistic evolutionary models, brings a new set of challenges, and we here present analyses demonstrating these challenges and discussing solutions. Fully transitioning the study of morphological evolution into the “Omics” era will involve the development of tools to automate the extraction of meaningful, comparable morphometric data from images, integrate fossil data into large phylogenetic trees and downstream evolutionary analyses, and generate robust models that both accurately reflect the complexity of evolutionary processes and are well-suited for high-dimensional data. Combined, these advancements will solidify the emerging field of evolutionary phenomics and appropriately center it around the analysis of unambiguously critical deep-time data.