Extracting Essential Ocean Variables for benthic habitats and fishes from existing, mature protocols
Institut Français pour l’Exploitation de la Mer (IFREMER)
Regionally scalable analyses of benthic habitat- and fish-related Essential Ocean Variables
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). It will confront the modelled 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.
The modelling framework enabling the joint analysis of these data will account for the specifics of each protocol and the complementarity of the observations (taxa, scales and replication) and resulting EOV metrics. Applications to data from temperate and coral reef ecosystems will provide regionally consistent assessment of these EOVs 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 repositories.
Assessing data fitness for benthic habitat- and fish-related Essential Ocean Variables
There is global recognition that our oceans are currently facing a multitude of human-induced threats ranging from climate change to direct and indirect impacts of fishing, oil and gas, and transportation industries, increased pollution from land, and ecosystem-altering effects of species introductions. Adding to this are the effects of the emerging deep sea mining, and offshore mariculture and energy sectors. The Global Ocean Observing System has developed Essential Ocean Variables (EOVs) to be able to deliver standardised assessments of management effectiveness against threats across habitats and jurisdictions. This PhD will investigate the fitness of existing, established imagery data collection protocols in shallow coral reef and temperate ecosystems in Australia, France and New Caledonia to derive these existing fish- and habitat-related EOVs. The project will undertake spatial and temporal analyses to highlight limitations and consistency of current EOVs derived from underwater imagery. The project will also assess the sensitivity of imagery-derived EOVs to detecting the impacts of threats and management on marine fishes and habitats.
Developing suitable indicator metrics of change from underwater imagery datasets
Government agencies on marine values often suffer from a lack of spatially comprehensive, long term quantitative datasets on which to base objective assessments. Likewise, most existing datasets are local to regional in scale, and there is little opportunity to aggregate these more widely to report at greater scales, much less at national scales. The Global Ocean Observing System (GOOS) has developed Essential Ocean Variables (EOVs) to standardise metrics reporting on Ocean Health. In parallel, there is an increasing recognition for the need in establishing national, integrated monitoring programs, able to report into adaptive management frameworks. Currently, very few programs monitoring biodiversity are sufficiently mature to enable engaging in this process, particularly ones based on quantitatively comparable standard operating protocols. Stuart-Smith et al. (2017; 10.1093/biosci/biw180) made significant progress for reporting on marine values by assessing a number of biodiversity indicators in the case of diver-based Underwater Visual Censuses in shallow waters (i.e. < 15 m water depth). However, the EOVs proposed under GOOS remain largely untested on imagery datasets, while image-based protocols are able to provide an unparalleled wealth of observation on marine biodiversity. This PhD will investigate a range of new imagery-based biodiversity metrics from extensive existing underwater marine imagery datasets collected in shallow coral reef and temperate ecosystems in Australia, France and New Caledonia. Based on these large datasets, the PhD will assess the robustness and generality of the metrics, and how they may be implemented to document the EOV required for national and international reporting on biodiversity. This work is expected to contribute to development of operational EOV that will be used for periodic monitoring of coastal marine biodiversity, in the view of national and international conservation policies.
Quantitative ecology, marine ecology, fisheries science, community ecology, data science, applied statistics, analysis of large multivariate data sets, advanced programming level in R