Distribution modelling of oceanic megafauna towards dynamic ocean management

Abstract
Place-based management is a common strategy for managing ocean activities and safeguarding vulnerable species and their habitats. Generally implemented as a static approach, such strategies aim to separate apparently incompatible activities from vulnerable marine species. However, static conservation strategies cannot account for the dynamic nature of pelagic ecosystems in which marine megafauna reside, nor can they deal with the dynamism of human activities threatening megafauna taxa. An alternative to this approach is Dynamic Ocean Management, which uses near real-time data on the shifting physical, biological and ocean resource uses to rapidly update ocean zoning in space and time.
This PhD project will develop and test a modelling framework to predict distribution and density hotspots for key megafauna species and probability of risk from human activities in near real-time. First, multiple datasets and different modelling approaches will be used to develop species distribution and density models for key air-breathing megafauna species (cetaceans and turtles) using the commonly explored environmental variables, and to assess their predictive performance. Then, the models will be applied to near real-time remotely sensed oceanographic data to predict dynamic species hotspots. Finally, these outcomes will be combined with data on marine traffic to map areas of acute collision risk and estimate the proportion of the population that is exposed. Temporal scale of the final outputs will be determined, according to the temporal resolution of remotely-sensed oceanographic data and marine traffic, and how the model fits with management objectives. This PhD project is a first step towards the development of a tool that can assist both resource users and decision makers to assess and manage risk from human activities on megafauna species.

Candidate’s profile
Preference will be given to candidates with advanced science degree (Msc. Degree or equivalent), with a strong background in statistical analyses and/or modelling techniques and good computational skills with knowledge in programming languages. Relevant experience in a related field such as marine biology or physical oceanography and scientific publications on the PhD topic are a plus.

Name(s) of supervisor(s)
Mónica A. Silva, Ocean Sciences Institute – Okeanos / University of the Azores, Portugal
Manuela Juliano, Ocean Sciences Institute – Okeanos / University of the Azores, Portugal
Elliott Hazen, National Oceanic and Atmospheric Administration, United States of America

Identification of PhD Program

PhD Programme in Sciences of the Sea, University of the Azores, Portugal, Universidade dos Açores | (uac.pt)

Notice of the call (english version)

Notice of the call ( portuguese version)

We are no longer accepting applications for this scholarship. Thank you.