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The use of habitat suitability modelling for seagrass: A review

Chiara Bertelli Orcid Logo, Holly Stokes, James Bull Orcid Logo, Richard Unsworth Orcid Logo

Frontiers in Marine Science, Volume: 9

Swansea University Authors: Chiara Bertelli Orcid Logo, Holly Stokes, James Bull Orcid Logo, Richard Unsworth Orcid Logo

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DOI (Published version): 10.3389/fmars.2022.997831

Abstract

Coastal ecosystems, including coral reefs, mangroves, and seagrass, are in global decline. Mitigation approaches include restoration and other managed recovery interventions. To maximise success, these should be guided by an understanding of the environmental niche and geographic limits of foundatio...

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Published in: Frontiers in Marine Science
ISSN: 2296-7745
Published: Frontiers Media SA 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa61575
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The same predictor variables were also commonly used in the 35 seagrass Habitat Suitability Models (HSM) but in the following order: bathymetry (74%), salinity (57%), light availability (51%), and temperature (51%). The most popular method used in marine macrophyte HSMs was an ensemble of models (29%) followed by MaxEnt (17%). Cross-validation was the most commonly used selection procedure (24%), and threshold probability was the favoured model validation (33%). Most studies (87%) did not calculate or report uncertainty measures. The approach used to create an HSM was found to vary by location and scale of the study. 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spelling 2022-10-27T13:23:37.1196683 v2 61575 2022-10-17 The use of habitat suitability modelling for seagrass: A review ef2a5aa98cae33d09caf7b77f6f16e71 0000-0002-9799-2522 Chiara Bertelli Chiara Bertelli true false 6ee6932996059ed9e4d581641acce2f7 Holly Stokes Holly Stokes true false 20742518482c020c80b81b88e5313356 0000-0002-4373-6830 James Bull James Bull true false b0f33acd13a3ab541cf2aaea27f4fc2f 0000-0003-0036-9724 Richard Unsworth Richard Unsworth true false 2022-10-17 SBI Coastal ecosystems, including coral reefs, mangroves, and seagrass, are in global decline. Mitigation approaches include restoration and other managed recovery interventions. To maximise success, these should be guided by an understanding of the environmental niche and geographic limits of foundational species. However, the choices of data, variables, and modelling approaches can be bewildering when embarking on such an exercise, and the biases associated with such choices are often unknown. We reviewed the current available knowledge on methodological approaches and environmental variables used to model and map habitat suitability for coastal ecosystems. While our focus is on seagrass, we draw on information from all marine macrophyte studies for greater coverage of approaches at different scales around the world. We collated 75 publications, of which 35 included seagrasses. Out of all the publications, we found the most commonly used predictor variables were temperature (64%), bathymetry (61%), light availability (49%), and salinity (49%), respectively. The same predictor variables were also commonly used in the 35 seagrass Habitat Suitability Models (HSM) but in the following order: bathymetry (74%), salinity (57%), light availability (51%), and temperature (51%). The most popular method used in marine macrophyte HSMs was an ensemble of models (29%) followed by MaxEnt (17%). Cross-validation was the most commonly used selection procedure (24%), and threshold probability was the favoured model validation (33%). Most studies (87%) did not calculate or report uncertainty measures. The approach used to create an HSM was found to vary by location and scale of the study. Based upon previous studies, it can be suggested that the best approach for seagrass HSM would be to use an ensemble of models, including MaxEnt along with a selection procedure (Cross-validation) and threshold probability to validate the model with the use of uncertainty measures in the model process. Journal Article Frontiers in Marine Science 9 Frontiers Media SA 2296-7745 habitat suitability modelling (HSM), seagrass, macrophyte, restoration, ensemble, Maxent (maximum entropy) 26 10 2022 2022-10-26 10.3389/fmars.2022.997831 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University External research funder(s) paid the OA fee (includes OA grants disbursed by the Library) This study was part of the ReSOW (Restoring Seagrass for Ocean Wealth) project funded by NERC (Natural Environment Research Council) NE/V01711X/1. 2022-10-27T13:23:37.1196683 2022-10-17T13:45:14.7306313 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Chiara Bertelli 0000-0002-9799-2522 1 Holly Stokes 2 James Bull 0000-0002-4373-6830 3 Richard Unsworth 0000-0003-0036-9724 4 61575__25598__9d66253e468c42ff9a292c7b5521b083.pdf 61575_VoR.pdf 2022-10-27T13:22:36.2757489 Output 1590949 application/pdf Version of Record true © 2022 Bertelli, Stokes, Bull and Unsworth. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). true eng http://creativecommons.org/licenses/by/4.0/
title The use of habitat suitability modelling for seagrass: A review
spellingShingle The use of habitat suitability modelling for seagrass: A review
Chiara Bertelli
Holly Stokes
James Bull
Richard Unsworth
title_short The use of habitat suitability modelling for seagrass: A review
title_full The use of habitat suitability modelling for seagrass: A review
title_fullStr The use of habitat suitability modelling for seagrass: A review
title_full_unstemmed The use of habitat suitability modelling for seagrass: A review
title_sort The use of habitat suitability modelling for seagrass: A review
author_id_str_mv ef2a5aa98cae33d09caf7b77f6f16e71
6ee6932996059ed9e4d581641acce2f7
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b0f33acd13a3ab541cf2aaea27f4fc2f
author_id_fullname_str_mv ef2a5aa98cae33d09caf7b77f6f16e71_***_Chiara Bertelli
6ee6932996059ed9e4d581641acce2f7_***_Holly Stokes
20742518482c020c80b81b88e5313356_***_James Bull
b0f33acd13a3ab541cf2aaea27f4fc2f_***_Richard Unsworth
author Chiara Bertelli
Holly Stokes
James Bull
Richard Unsworth
author2 Chiara Bertelli
Holly Stokes
James Bull
Richard Unsworth
format Journal article
container_title Frontiers in Marine Science
container_volume 9
publishDate 2022
institution Swansea University
issn 2296-7745
doi_str_mv 10.3389/fmars.2022.997831
publisher Frontiers Media SA
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
document_store_str 1
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description Coastal ecosystems, including coral reefs, mangroves, and seagrass, are in global decline. Mitigation approaches include restoration and other managed recovery interventions. To maximise success, these should be guided by an understanding of the environmental niche and geographic limits of foundational species. However, the choices of data, variables, and modelling approaches can be bewildering when embarking on such an exercise, and the biases associated with such choices are often unknown. We reviewed the current available knowledge on methodological approaches and environmental variables used to model and map habitat suitability for coastal ecosystems. While our focus is on seagrass, we draw on information from all marine macrophyte studies for greater coverage of approaches at different scales around the world. We collated 75 publications, of which 35 included seagrasses. Out of all the publications, we found the most commonly used predictor variables were temperature (64%), bathymetry (61%), light availability (49%), and salinity (49%), respectively. The same predictor variables were also commonly used in the 35 seagrass Habitat Suitability Models (HSM) but in the following order: bathymetry (74%), salinity (57%), light availability (51%), and temperature (51%). The most popular method used in marine macrophyte HSMs was an ensemble of models (29%) followed by MaxEnt (17%). Cross-validation was the most commonly used selection procedure (24%), and threshold probability was the favoured model validation (33%). Most studies (87%) did not calculate or report uncertainty measures. The approach used to create an HSM was found to vary by location and scale of the study. Based upon previous studies, it can be suggested that the best approach for seagrass HSM would be to use an ensemble of models, including MaxEnt along with a selection procedure (Cross-validation) and threshold probability to validate the model with the use of uncertainty measures in the model process.
published_date 2022-10-26T04:20:30Z
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