Seagrass habitat suitability model for Redang Marine Park using multibeam echosounder data: Testing different spatial resolutions and analysis window sizes

Muhamad, M. A. H.; Hasan, Rozaimi Che; Said, Najhan Md.; Ooi, Jillian Lean Sim

doi:10.1371/journal.pone.0257761

Cited by 4 publications

(1 citation statement)

References 130 publications

(151 reference statements)

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“…In Malaysian coastal waters, an acoustic dataset from a multibeam echosounder and species occurrence data were used to model suitable seagrass habitats. This study found that the bathymetry data was the most influential predictor (Muhamad et al, 2021) when simply focusing on coastal areas and only evaluating reduced current and wave action as factors. This study, however, evaluated a more comprehensive set of factors to obtain more pertinent and helpful data to map habitat suitability and inform effective transplantation efforts.…”

Section: Applicability To Decision-makingmentioning

confidence: 85%

Assessing Environmental Factors for Seagrass Transplantation Site Suitability in Thailand

2024

JFE

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Section: Applicability To Decision-makingmentioning

confidence: 85%

Assessing Environmental Factors for Seagrass Transplantation Site Suitability in Thailand

2024

JFE

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High‐Resolution Ensemble Modelling of Coral Distributions in the Northern Gulf of Mexico Based on Geomorphometry: Coral Diversity and Benthic Habitat Fragmentation From Oil and Gas Infrastructure to Inform Spatial Planning

Lecours,

Oxton,

Khor

et al. 2024

Aquatic Conservation

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The northern Gulf of Mexico is home to several species of corals that provide a wide range of ecosystem services to other organisms. Oil and gas infrastructure, such as platforms and pipelines, form an extensive network throughout the northern Gulf of Mexico. Detrimental impacts associated with oil and gas exploration and extraction have been recorded in this area at depths where corals are found. Due to these ecosystems' vulnerability to long‐term impacts, it is necessary to determine areas of interest that would benefit from further exploration and informed spatial planning. This study aimed to identify potential areas of interest for coral studies in the northern Gulf of Mexico. Ensemble species distribution models for 13 species of corals including scleractinians, black corals, and octocorals were produced based on seafloor characteristics and combined to identify areas with relatively higher coral diversity potential than others. The ensemble modelling approach produced robust outputs, as evaluated by the area under the curve, Cohen's kappa coefficient, sensitivity, specificity and the proportion of correct predictions. The proximity of suitable habitat to active and proposed oil and gas infrastructure was evaluated; this spatial analysis showed that oil and gas infrastructures potentially impact 23.5% of all predicted suitable coral habitat in the study area and contribute to benthic habitat fragmentation. Twelve areas of interest greater than 100 km2 and located outside a 4‐km zone of potential influence from oil and gas infrastructure were delineated and deemed of interest for further exploration and spatial planning, and hypothetical prioritization scenarios for spatial planning are presented. The maps produced can inform discussions among stakeholders to reach the best spatial planning outcomes while considering other ecological, social, economic and governance factors.

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Seagrass Habitat Suitability Models using Multibeam Echosounder Data and Multiple Machine Learning Techniques

Muhamad

Hasan

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Seagrass beds are important habitats in the marine environment by providing food and shelter to dugongs and sea turtles. Protection and conservation plans require detail spatial distribution of these habitats such as habitat suitability maps. In this study, machine learning techniques were tested by using Multibeam Echo Sounder System (MBES) and ground truth datasets to produce seagrass habitat suitability models at Redang Marine Park. Five bathymetric predictors and seven backscatter predictors from MBES data were used to representing topography features and sediment types in the study area. Three machine learning algorithms; Maximum Entropy (MaxEnt), Random Forests (RF), and Support Vector Machine (SVM) were tested. The results revealed that MaxEnt and RF models achieved the highest accuracy (93% and 91%, respectively) with SVM produced the lowest (67%). Depth was identified as the most significant predictor for all three models. The contributions of backscatter predictors were more central for SVM model. High accuracy models showed that suitable habitat for seagrass is distributed around shallow water areas (<20 m) and between fringing reef habitats. The findings highlight that acoustic data and machine learning are capable to predict how seagrass beds are spatially distributed which provide important information for managing marine resources.

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Seagrass habitat suitability model for Redang Marine Park using multibeam echosounder data: Testing different spatial resolutions and analysis window sizes

Cited by 4 publications

References 130 publications

Assessing Environmental Factors for Seagrass Transplantation Site Suitability in Thailand

Assessing Environmental Factors for Seagrass Transplantation Site Suitability in Thailand

High‐Resolution Ensemble Modelling of Coral Distributions in the Northern Gulf of Mexico Based on Geomorphometry: Coral Diversity and Benthic Habitat Fragmentation From Oil and Gas Infrastructure to Inform Spatial Planning

Seagrass Habitat Suitability Models using Multibeam Echosounder Data and Multiple Machine Learning Techniques

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