Spatial and temporal modelling of seagrass distribution in Moreton Bay, Australia, based on long-term citizen science data
Year: 2025
Type: article
Abstract: Seagrasses are essential for coastal carbon sequestration and the survival of marine fauna worldwide. Regular seagrass monitoring is essential for coastal conservation as such areas are susceptible to rapid decline usually due to anthropogenic influence. We used long-term (2015-2021) supervised citizen science data collected in Moreton Bay, Australia, to investigate temporal change over a seven-year period and developed a random forest seagrass distribution model, using depth, water quality, and other predictor variables. We used hierarchical clustering to define seagrass groupings, which resulted in five clusters, named after the dominant seagrass species: ‘OS’ ( Oceana serrulata ), ‘ZM’ ( Zostera muelleri ), ‘HS’ ( Halophila spinulosa ), ‘HO’ ( Halophila ovalis ), and ‘Sparse’, the cluster with little or no seagrass present. To investigate temporal change in seagrass meadows during our study period, we conducted a longitudinal analysis of overall seagrass cover and seagrass cluster dominance using areas that had a sufficient number of samples in at least three consecutive time periods. The predictive model produced a probability map of seagrass occurrence in Moreton Bay, explaining 86.7% of the variability. It highlighted areas of the bay that would suffer from continuing water quality decline. Longitudinal analysis showed a decrease in seagrass presence in shallow water areas, of several areas of the bay across the study period (2015 – 2021), and changes in seagrass community composition across the bay. Our results demonstrate the effective use of citizen science-collected data and statistical modelling techniques to understand the spatial and temporal variability of seagrass communities.
Authors: E. Ovsyanikova, W.N. Venables, J.W. Udy
Institution: The University of Queensland
Language: English
Subfield: Oceanography
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,410
OpenAlex ID: W4416543481