How do species emerge?

Abstract

Establishing how phenotypic traits evolve and interact during speciation remains a central challenge in evolutionary biology. Trait variance-covariance matrices have long been used to quantify covariation among traits, with well-accepted recognition of these matrices as dynamic products of development and selection. However, their role in key evolutionary transitions often remains obscured by methodological limitations in phenotypic data acquisition. In this thesis, I address these limitations by developing, validating, and applying scalable, automated phenotypic methods for high-resolution morphological trait quantification, with the overarching aim of enhancing their utility for broad evolutionary questions, including those surrounding the dynamics of speciation. To this end, I establish an integrative framework that bridges methodological innovation with evolutionary application. In Chapter Two, I evaluate the ability of a landmark-free approach, Deterministic Atlas Analysis (DAA), in capturing biological shape variation across a taxonomically diverse dataset of mammalian crania. I demonstrate that DAA reliably captures biologically meaningful variation while improving scalability, both critical requirements for macroevolutionary research. In Chapter Three, I broaden my methodological toolkit by developing a deep learning segmentation pipeline capable of rapidly and accurately extracting internal and external phenotypic traits from computed tomography (CT) scans. These methodological advances are then applied in Chapter Four to a novel dataset of 1,687 fossil specimens from the planktonic foraminifera genus Menardella, encompassing four species and two speciation events over a 2.5-million-year interval. Using automated segmentation, I generate high-resolution measurements of the functionally relevant volumetric traits: total volume and percentage calcite. Analyses of the variance–covariance matrices generated for these traits reveal that trait covariation is stronger and more conserved in ancestral species, whereas younger species exhibit weakened covariance structures and more variable phenotypic trajectories. This weakening of covariance between traits facilitates morphospace expansion and phenotypic differentiation, thereby promoting species divergence. Integration with geochemical proxies further indicates that local environmental conditions exert a dominant influence on phenotypic evolution, particularly in older lineages. Overall, I demonstrate that rigorously validated and thoughtfully applied automated phenotyping pipelines can transform our capacity to test classical evolutionary hypotheses at unprecedented scales and resolutions. By bridging technological development with evolutionary application, this work contributes both a scalable methodological framework and new insights into the evolutionary processes shaping biodiversity.

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Identifiers

ORCID for James Michael Mulqueeney: orcid.org/0000-0003-3502-745X

ORCID for Tom Ezard: orcid.org/0000-0001-8305-6605

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