Habitat suitability modelling to improve understanding of seagrass loss and recovery and to guide decisions in relation to coastal discharge. (January 2023)

• Record Type: Journal Article
• Authors: Erftemeijer, Paul L.A.; van Gils, Jos; Fernandes, Milena B.; Daly, Rob; van der Heijden, Luuk; Herman, Peter M.J.
• Is Part Of: Marine pollution bulletin. Volume 186(2023)
• Publication Date: 2023-01
• Subjects: Decision support -- Goodness-of-fit -- Habitat suitability -- Load reduction -- Seagrass recovery -- Water quality; Marine pollution -- Periodicals; Marine Biology -- Periodicals; Water Pollution -- Periodicals; Mer -- Pollution -- Périodiques; Publications périodiques; Pollution des mers; Lutte antipollution; Electronic journals; 363.7394
• DOI: 10.1016/j.marpolbul.2022.114370
• Languages: English
• ISSNs: 0025-326X

Abstract

Abstract: Habitat suitability modelling was used to test the relationship between coastal discharges and seagrass occurrence based on data from Adelaide (South Australia). Seven variables (benthic light including epiphyte shading, temperature, salinity, substrate, wave exposure, currents and tidal exposure) were simulated using a coupled hydrodynamic-biogeochemical model and interrogated against literature-derived thresholds for nine local seagrass species. Light availability was the most critical driver across the study area but wave exposure played a key role in shallow nearshore areas. Model validation against seagrass mapping data showed 86 % goodness-of-fit. Comparison against later mapping data suggested that modelling could predict ~745 ha of seagrass recovery in areas previously classified as 'false positives'. These results suggest that habitat suitability modelling is reliable to test scenarios and predict seagrass response to reduction of land-based loads, providing a useful tool to guide (investment) decisions to prevent loss and promote recovery of seagrasses. Highlights: Habitat suitability modelling reliably predicted seagrass response to land-based load reductions. Light availability and nearshore wave exposure were main drivers of seagrass dynamics. Model validation against seagrass mapping data showed 86 % goodness-of-fit. Modelling correctly predicted ~745 ha of seagrass recovery confirmed by later mapping. Model proven useful as tool to guide decisions to prevent loss and promote recovery of seagrass.