Not just corals and fishes: distribution modelling and management of dark diversity on coral reefs.
Biodiversity loss is one of the greatest threats to ecosystems worldwide (Butchart et al., 2010; McCauley et al., 2015). The loss of biological species is intimately linked to a reduction of ecosystem services, which can be detrimental to the functioning of natural ecosystems and reduce the availability of resources, such as food and natural products (e.g. new pharmaceuticals) for humankind. As such, the accurate quantification of biodiversity and biodiversity loss is of utmost importance for ecosystem management in our changing world.
Coral reefs harbor the greatest diversity of marine species on the planet, and their rapid, global decline threatens the vast diversity of cryptic invertebrates (e.g. sponges, crustaceans), and microbial communities found within their complex structural framework (Pandolfi et al. 2003, Jackson et al. 2014, Hughes et al. 2017). While efforts are being made to establish conservation priorities for halting the decline of reefs (Wear et al., 2016), we are still limited by inadequate knowledge of local and regional reef biodiversity, species distributions and basic ecology of cryptic species across the world’s reefs. For successful management strategies to mitigate human impacts on reefs, we need more precise baselines that target not only the conspicuous majority (e.g. corals and fishes).
A powerful technique to estimate the diversity of otherwise hard to measure species is DNA metabarcoding (i.e. amplicon sequencing), which consists of the bulk extraction of DNA from environmental samples, followed by the mass amplification and identification of a multitude of taxa based on universal markers. This molecular technique has been widely and successfully employed to quantify biodiversity (Leray & Knowlton, 2015; Ransome et al., 2017). This project will use metabarcoding data from bulk samples of cryptic reef taxa to develop cutting-edge distribution models across global reefs in collaboration with the Smithsonian Institute Global ARMS Program (https://naturalhistory.si.edu/research/global-arms-program).
To achieve accurate species counts for distribution models, the student will first develop tools to better quantify diversity from the cytochrome c oxidase subunit I (COI) gene region, which plays a pivotal role in the global effort to document biodiversity. The student will quantify the inflation of diversity using available methods and tackle current limitations, such as the influence of pseudogenes on species delimitation (e.g. Tay et al., 2017; Brandt et al., 2019). The student will then develop distribution models of cryptic reef taxa using an existing dataset that spans more than 100 reef sites across the Pacific. They will adopt a range of species distribution modelling techniques, including single species and recently developed joint distribution models, to elucidate key species interactions. These models will be used to explore the composition of cryptic reef communities, and to model the role of the possible drivers of cryptic species distributions using benthic community data and satellite-derived datasets. Data outputs will provide baselines of coral reef species distributions, which can then be used to inform policy recommendations for the monitoring, management and conservation of these understudied groups of organisms.
The project requires the development of bioinformatics techniques, quantitative data analysis, and knowledge of spatial distribution models. We are looking for an enthusiastic student with experience in ecological data collection and quantitative data analysis, preferably with evidence of a strong maths or computing background in their degree. Good programming skills in R and Python are essential.
PI1: Emma Ransome, Imperial College London ([Email Address Removed])
PI2: Nathalie Pettorelli, Zoological Society London (ZSL)
PI3: Chris Meyer, National Museum of Natural History (NMNH), Smithsonian Institute
Application Deadline: February 10th, 2020 (please email [Email Address Removed] with CV and cover letter)
Start Date: June 1st 2020
Location: Imperial College London, Silwood Park Campus, Ascot, SL5 7PY, UK