All Positions

Research
Computer sciences and mathematics

Understanding and predicting seagrass decline in lagoonal environment with a modelling approach

DC-62
IFREMER and Queensland University of Technology
Brest (FR) and Brisbane (AU)

Position Description

Soon available

Proposed Projects

Option 1

Understanding and predicting the causes and the consequences of seagrass fragmentation in the lagoonal environment of Reunion Island

Seagrasses form coastal habitats of high ecological value as they are ecosystem engineers, supporting high levels of biodiversity, improving water quality, protecting coastlines from erosion, storms and floods, and trapping carbon. They have been declining for nearly one century with an estimated annual global loss of 7% (United Nations Environment Programme 2020). In tropical regions, anthropogenic pressures are very high, seagrass decline is high and biodiversity is greatly threatened.

On Reunion Island, seagrass beds are distributed patchily throughout the lagoon. Seagrass coverage was maintained between 1951 and 2016: although phases of decline and regeneration occurred, the meadow was highly resilient. But in 2017, seagrasses began to decline and have almost disappeared today, suggesting that beyond a certain threshold of fragmentation, the meadow can no longer regenerate.

The project aims to analyse the seagrass fragmentation dynamics over decades in a tropical environment and explore the causes of meadow fragmentation using an existing time and space seagrass dynamics model coupling a process-based hydrodynamic-sediment transport model at 10-m resolution with a probabilistic seagrass growth model. This modelling approach will enable interrogation of the effect of the environmental forcings on the seagrass growth, and also to model the effect of seagrass obstruction on the hydrodynamics and sediment dynamics. Fragmentation thresholds beyond which the meadow is no longer resilient could be identified in this highly anthropogenic tropical system experiencing climate change. Both an applied and methodological process will be used to better understand fragmentation processes and their uncertainty in real world systems, make predictions to provide risk-informed decision support, and form the toolsets to address many other social, biological, ecological and complex systems fragmentation processes.

Option 2

Understanding and predicting the natural and anthropogenic causes of seagrass decline in the lagoonal environment of Reunion Island

As lagoonal systems are relatively protected from strong currents and waves, they are suitable areas for seagrass development, however growing anthropogenic pressures and climate change are causing seagrass decline. This is the case on Reunion Island where seagrasses have been disappearing since 2017, well below the cover documented between 1951 and 2016.

The aim of the project is to identify and evaluate the processes impacting seagrass decline on Reunion Island. The study will be based on satellite and hyperspectral data and photographic analysis of the seagrass cover since 1951 combined with identification and quantification of environmental and anthropogenic processes that could be linked to the seagrass decline. This dataset will enable different hypotheses explaining the seagrass dynamics on Reunion Island to be tested using a modelling approach. The research will involve improving and modifying an existing time and space seagrass dynamics model which is a probabilistic seagrass growth model coupled with a process-based hydrodynamic-sediment transport model.

The work will focus on the probabilistic seagrass growth model including local processes acting on the seagrass dynamics such as mechanical destruction from austral and cyclonic waves, overgrazing by megaherbivores, chemical contaminants, protection from the coral reef, freshwater resurgences, etc. For this probabilistic modelling, particular attention will be paid to the evaluation of thresholds on the different pressures acting on the seagrass dynamics. Field campaigns might be organized to assess some of these thresholds and model the identified processes in the probabilistic seagrass growth model. For example, thresholds associated with grazing pressure could be deduced from field experiments to assess the impact of grazing on seagrass dynamics.

The output of the study will be a conceptual model describing the seagrass dynamics on Reunion Island highlighting the processes initiating and maintaining the ongoing decline of seagrass. This will help to inform the rehabilitation of seagrass beds in the lagoon.

Option 3

Understanding and predicting the seagrass dynamics in the lagoonal environment of Reunion Island to inform seagrass restoration

Seagrasses are classified as sentinel species because they clearly indicate marine environmental changes at local, regional and global scales. They are considered as an indicator of water quality in the Water Framework Directive and are a bioengineer species monitored in the Marine Strategy Framework Directive. Moreover, they are the subject of several conservation initiatives (Flora Fauna Habitat Directive, Nairobi Convention,…) and research projects whose purpose is to design tools that will help to prevent their degradation.

Seagrass dynamics are complex, responding to a range of forcings over different time scales. Therefore, an integrated ecosystem approach is required to understand the drivers of seagrass decline and is there is a motivation to develop efficient models. Our teams have developed a time and space seagrass dynamics model coupling a process-based hydrodynamic-sediment transport model and a probabilistic seagrass growth model to simulate the evolution of seagrasses at regional scale over decades. Such a tool is useful for local authorities to set up the best management practices to protect seagrasses.

On Reunion Island, seagrasses are declining at an alarming rate since 2017 with no clear causes identified. The project aims to model the effect of management scenarios to prevent the Reunion Island seagrass decline and to rehabilitate the seagrass in the lagoon in partnership with local authorities. It also aims to improve existing indicators for monitoring seagrass habitats and to make proposals for adapting current monitoring programs in the framework of the various European directives. This implies identifying and overcoming the limits of our model: taking into account anthropic pressures (different from temperature changes and light modifications), integrating a socio-economic dimension, defining management scenarios, etc. In this context, field monitoring could be considered to optimize the current monitoring networks. The developed model, which will feed the monitoring indicators, will help to discriminate the natural variability from the variability induced by humans in the seagrass dynamics. This scientific project will be in partnership with local authorities from the nature reserve whose mission is to monitor of the state of coastal ecosystems and which coordinates the various associated monitoring programs.

Research Areas

Modelling; Data science (machine learning, bayesian networks); Coastal physical oceanography; Coastal hydro-sediment and ecological processes; Programming