Habitat suitability modelling to improve understanding of seagrass loss and recovery and to guide decisions in relation to coastal discharge

Paul L A Erftemeijer; Jos van Gils; Milena B Fernandes; Rob Daly; Luuk van der Heijden; Peter M J Herman

doi:10.1016/j.marpolbul.2022.114370

Habitat suitability modelling to improve understanding of seagrass loss and recovery and to guide decisions in relation to coastal discharge

Mar Pollut Bull. 2023 Jan:186:114370. doi: 10.1016/j.marpolbul.2022.114370. Epub 2022 Nov 29.

Authors

Paul L A Erftemeijer¹, Jos van Gils², Milena B Fernandes³, Rob Daly⁴, Luuk van der Heijden², Peter M J Herman²

Affiliations

¹ School of Biological Sciences and Oceans Institute, University of Western Australia, Crawley, WA 6009, Australia. Electronic address: paul.erftemeijer@uwa.edu.au.
² Deltares, Department of Marine and Coastal Systems, PO Box 170, 2600 MH Delft, the Netherlands.
³ SA Water, GPO Box 1751, Adelaide, SA 5001, Australia; College of Science and Engineering, Flinders University, GPO Box 2100, Adelaide, SA 5001, Australia.
⁴ SA Water, GPO Box 1751, Adelaide, SA 5001, Australia.

PMID: 36459773
DOI: 10.1016/j.marpolbul.2022.114370

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.

Keywords: Decision support; Goodness-of-fit; Habitat suitability; Load reduction; Seagrass recovery; Water quality.

MeSH terms

Ecosystem*
South Australia