PhD: Ecological analyses of the oldest deep-sea communities from the Ediacaran

Animals first appear in the fossil record during the Ediacaran time period (631-541 million years ago). It is during the Ediacaran animals evolved some of their most important traits: most obviously large body-size but also tissue-differentiation, mobility, bilateral symmetry and ecosystem engineering (reef-building). The study of Ediacaran organisms is fraught with difficulties because commonly-used morphological approaches have only limited use due to the unique anatomies of Ediacaran organisms. Fortunately, the preservation of Ediacaran fossils is exceptional with thousands of immobile organisms preserved where they lived under volcanic ash. Therefore, the position of the fossil on the rock surface encapsulates their entire life history: how they reproduced and how they interacted with each other and their environment. As such, ecological statistics provides a novel approach for investigating fundamental issues in early animal evolution. It is currently not clear what drives to Ediacaran ecosystem dynamics, since recent work has found that specimen height and local environment do not drive community dynamics, contrary to prior hypotheses (Mitchell and Kenchington 2018, Mitchell et al. 2019). The student will work with existing laser-scan data which includes every deep-water Ediacaran community (those with >50 specimens). Spatial statistics will be used to investigate the environmental and temporal influences on n Ediacaran communities, and so establish what the key drivers are for these ecosystems.

The student will use existing photographic and laser-scan data to reconstruct maps of Ediacaran communities. Spatial point process analyses will be used to determine fine-scale habitat associations and competitive and facilitative interactions between species. Bayesian network inference will be used to create ecological networks. NMDS cluster analyses and multivariate regression analyses will be used to investigate what different variables influence community composition and dynamics. The student will also investigate how different organism traits influence community structure. These analyses will be used to establish how community ecology of Ediacaran systems varies over different temporal and complexity gradients, determining large-scale drivers of Ediacaran communities. The student will be trained in creating 2D and 3D, as well as in Bayesian network inference, spatial point process analyses and multivariate regression analyses. The student will be encouraged to apply for relevant external training courses on statistics and spatial modelling, and to attend national and international conferences to develop presentation and networking skills.