DOCTORAL CANDIDATE
Elizabeth Hasan

SUPERVISORS
Dr. Dominique Pelletier, Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER)
Dr. Jacquomo Monk, University of Tasmania (UTAS)
Dr. Neville Barrett, University of Tasmania (UTAS)
Dr. Ben Scoulding, Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Marine Ecology, Spatial Science, Quantitative Statistics, Underwater Imagery, Ecological Monitoring

In coastal areas, both fishes and habitats are subject to multiple anthropogenic pressures and to global change. Effective management decisions need to be underpinned by data-driven assessments. This PhD will investigate and model the distribution of both fish- and habitat-related Essential Ocean Variables (EOVs) in shallow coral reef and temperate ecosystems (Australia, France and New Caledonia). Essential Ocean Variables were defined by the Global Ocean Observing System (Muller-Karger et al. 2018) as a common framework for describing ecosystem functions and components that are critical for monitoring ocean health and broadly applicable to international management. The EOVs most applicable to this project describe fish distributions and benthic cover in coral reef and temperate ecosystems. In complement, Essential Biodiversity Variables (EBVs) were defined by the Marine Biodiversity Observation Network to describe the complexity of ecosystems (Muller-Karger et al. 2018). The EBVs most applicable to this project describe the complexity of fish communities and benthic habitat composition.

Areas of investigation will fall under three general themes: forecasting change, spatial comparisons, and fishing effects. Forecasting change will entail predictive modelling of fish distributions and habitat composition with environmental stresses as covariates. Spatial comparisons will address the effects of management on habitat composition and the spatial connectivity of critical habitat. Fishing effects will be summarized by habitat use of fish and quantifiable effects of fishing on fish abundance and distribution. This PhD will confront the modelled fish and habitat distributions with maps of stressors (e.g., expected changes in temperature), pressures from human uses (e.g., fishing) and management measures (e.g., marine park zoning). This model will rely on existing comprehensive underwater image-based datasets obtained from operational protocols (e.g., BRUV, STAVIRO, AUV) from Australia, France and New Caledonia. Analyses will be regionally scalable such that assessments are relevant across regional, national, and international management needs. This will be achieved through using common language (i.e., CATAMI protocol; Althaus et al. 2015) and following the methods of Pelletier et al. (2020) for characterization of habitat and fish groupings that allow for scaling. Additionally, this PhD seeks to expand the development of deep-learning methods for automated annotation of benthic imagery (sensu Jackett et al. 2023). Benthic imagery is an invaluable tool for ecosystem assessments; however, manual annotation of imagery is often a limitation of the spatial scale of analysis.

The modelling framework enabling the joint analysis of data sources will account for the specifics of each imaging protocol and the complementarity of the observations (taxa, scales and replication) and resulting EOV/EBV metrics. Applications to data from temperate and coral reef ecosystems will provide regionally consistent assessment of these EOVs/EBVs and support a generic contribution enabling integrating several imaging protocols for increasing knowledge on biodiversity distribution. Outcomes will be disseminated toward global data and EOV/EBV repositories.