This PhD project, hosted at the University of Reading within the AI-INTERVENE department, explores the evolutionary and ecological drivers of flower colour in the Orchidaceae, the largest and most diverse plant family. Flower colour is a key trait mediating interactions with pollinators, dispersers, and herbivores, influencing reproductive isolation and speciation. Despite its ecological and economic importance, the evolution of floral colour across large spatial and evolutionary scales remains poorly understood, with existing data often sparse and subjective.

The project aims to fill these gaps by conducting a comprehensive analysis of orchid flower colour evolution, leveraging the remarkable diversity and specificity of orchids to various pollinators. The student will collect high-quality, standardised image data from botanical garden collections, prioritising approximately 2,500 orchid species with available pollinator, spatial, and genetic data. Unlike previous studies, flower images will be characterised in UV-tetrahedral space using artificial intelligence, accounting for differences in visible spectra as perceived by interacting species.

Key research questions include: systematic variation of floral colour diversity and visual contrast with latitude and climate; associations between pollination syndromes and regions of floral colour space; evolutionary transitions between pollination systems and corresponding shifts in colour; and the extent to which environmental and ecological variables constrain or promote colour diversification across orchid lineages. The project combines empirical data from living collections with macroevolutionary methods, offering powerful insights into the sensory and ecological drivers of floral colour evolution.

Training opportunities include a comprehensive programme in applied AI, biodiversity, and transferable professional and research skills. The student will undertake a placement with an AI-INTERVENE project partner for 3–18 months and present research at national and international conferences, positioning them at the forefront of the discipline and enhancing future employment prospects.

Applicants should have a degree in biology, plant science, ecology, or a closely related environmental or physical science, or be computer science students interested in evolutionary biology. Experience with data handling and analysis (e.g., R) is desirable but not essential, as training will be provided. Enthusiasm and a strong work ethic are required.

Funding is subject to competition, with UKRI funding covering Home fees only. International students may apply but must cover the difference between International and Home fees. The application deadline is January 19, 2026. For more information and to apply, visit the project link.