Comparative Ecosystem Macroecology and Ecosystem Physics
Project Details
Program
Marine Science
Field of Study
Environmental Science, Ecology, Data and Computer Science
Division
Biological and Environmental Sciences and Engineering
Faculty Lab Link
Center Affiliation
Red Sea Research Platform
Project Description
Why are coral reefs among the most productive ecosystems on Earth, despite dwelling in nutrient-poor tropical waters? And what fundamental principles rooted in physics, evolution, and ecology explain the vast differences in biomass and productivity observed across ecosystems? This visiting studentship invites the student to explore these deep questions through the lens of comparative macroecology and ecosystem physics. Grounded in emerging theoretical frameworks that link energy-thermal regimes, elemental stoichiometry, habitat dimensionality, spatial connectivity, and evolutionary legacy, the studentship engages with some of the most fundamental and unresolved questions in ecology. Particular attention will be given to understanding how coral reefs, shaped by the unique biology of reef-building corals and the physical structure they create, emerge as global hotspots of animal biomass and secondary production. The student will engage with primary literature spanning metabolic theory, ecological stoichiometry, and spatially subsidised ecosystem frameworks, and will assist with tasks related to literature synthesis or data compilation supporting ongoing comparative analyses in the ROCKNHub. This studentship suits students driven by intellectual curiosity about how life organises itself across scales, from individual organisms to entire ecosystems.
About the Researcher
Renato Morais
Desired Project Deliverables
By the end of the internship, the student will have developed a working understanding of the theoretical foundations of comparative ecosystem functioning and the exceptional ecological status of coral reefs. Deliverables include an annotated literature summary on one or more of the five axes of ecosystem productivity discussed in the literature, and a contribution to an ongoing synthesis task assigned by the supervisor.
Recommended Student Background
Background in Physics
Mathematics
Theoretical Biology
Computer Science or Data Science