Augmenting Seafloor Characterization via Grain Size Analysis with Low-Cost Imagery: Minimizing Sediment Sampler Biases and Increasing Habitat Classification Accuracy
Bottom-grab samplers have long been the standard to describe nearshore marine habitats both qualitatively and quantitively. However, sediment samplers are designed to collect specific grain sizes and therefore have biases toward those sediments. Here, we discuss seafloor characterizations based on g...
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2024-11-01
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| author | Sean Terrill Agnes Mittermayr Bryan Legare Mark Borrelli |
| author_facet | Sean Terrill Agnes Mittermayr Bryan Legare Mark Borrelli |
| author_sort | Sean Terrill |
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| description | Bottom-grab samplers have long been the standard to describe nearshore marine habitats both qualitatively and quantitively. However, sediment samplers are designed to collect specific grain sizes and therefore have biases toward those sediments. Here, we discuss seafloor characterizations based on grain size analysis alone vs. grain size analysis augmented with quantitative benthic imagery. We also use both datasets to inform a prevalent benthic habitat classification system. The Coastal and Marine Ecological Classification Standard (CMECS) was used to test this hypothesis. CMECS was adopted by the federal government to standardize habitat classification in coastal U.S. waters. CMECS provides a hierarchal framework to define and interpret benthic habitats but does not prescribe specific sampling methods. Photography has been utilized for many decades in benthic ecology but has rarely been employed in habitat classification using CMECS. No study to date has quantitatively examined the benefit of incorporating benthic imagery into the classification of biotopes using CMECS. The objective of this study is to classify a roughly 1 km<sup>2</sup> subtidal area within Herring Cove in Provincetown, MA with CMECS and quantify the benefit of augmenting classification with low-cost imagery. A benthic habitat survey of the study area included grab sampling for grain-size analysis and invertebrate taxonomy, benthic imagery, water quality sampling at 24 sampling stations, and acoustic mapping of the study area. Multivariate statistical analyses were employed to classify biotic communities and link environmental and biological data to classify biotopes. The results showed that benthic imagery improved the classification and mapping of CMECS components. Furthermore, the classification of habitats and biotopes was improved using benthic imagery data. These findings imply that the incorporation of low-cost benthic imagery is warranted in coastal benthic biotope classification and mapping studies and should be regularly adopted. This study has implications for coastal benthic ecologists classifying benthic habitats within the CMECS framework. |
| format | Article |
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| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Geosciences |
| spelling | doaj-art-394cbee747bd412ca1bed6f14bf715e42025-08-20T01:53:53ZengMDPI AGGeosciences2076-32632024-11-01141131310.3390/geosciences14110313Augmenting Seafloor Characterization via Grain Size Analysis with Low-Cost Imagery: Minimizing Sediment Sampler Biases and Increasing Habitat Classification AccuracySean Terrill0Agnes Mittermayr1Bryan Legare2Mark Borrelli3School for the Environment, University of Massachusetts Boston, Boston, MA 02125, USACenter for Coastal Studies, 5 Holway Ave, Provincetown, MA 02657, USACenter for Coastal Studies, 5 Holway Ave, Provincetown, MA 02657, USASchool for the Environment, University of Massachusetts Boston, Boston, MA 02125, USABottom-grab samplers have long been the standard to describe nearshore marine habitats both qualitatively and quantitively. However, sediment samplers are designed to collect specific grain sizes and therefore have biases toward those sediments. Here, we discuss seafloor characterizations based on grain size analysis alone vs. grain size analysis augmented with quantitative benthic imagery. We also use both datasets to inform a prevalent benthic habitat classification system. The Coastal and Marine Ecological Classification Standard (CMECS) was used to test this hypothesis. CMECS was adopted by the federal government to standardize habitat classification in coastal U.S. waters. CMECS provides a hierarchal framework to define and interpret benthic habitats but does not prescribe specific sampling methods. Photography has been utilized for many decades in benthic ecology but has rarely been employed in habitat classification using CMECS. No study to date has quantitatively examined the benefit of incorporating benthic imagery into the classification of biotopes using CMECS. The objective of this study is to classify a roughly 1 km<sup>2</sup> subtidal area within Herring Cove in Provincetown, MA with CMECS and quantify the benefit of augmenting classification with low-cost imagery. A benthic habitat survey of the study area included grab sampling for grain-size analysis and invertebrate taxonomy, benthic imagery, water quality sampling at 24 sampling stations, and acoustic mapping of the study area. Multivariate statistical analyses were employed to classify biotic communities and link environmental and biological data to classify biotopes. The results showed that benthic imagery improved the classification and mapping of CMECS components. Furthermore, the classification of habitats and biotopes was improved using benthic imagery data. These findings imply that the incorporation of low-cost benthic imagery is warranted in coastal benthic biotope classification and mapping studies and should be regularly adopted. This study has implications for coastal benthic ecologists classifying benthic habitats within the CMECS framework.https://www.mdpi.com/2076-3263/14/11/313low-costbenthicimagerymarine habitatmapping |
| spellingShingle | Sean Terrill Agnes Mittermayr Bryan Legare Mark Borrelli Augmenting Seafloor Characterization via Grain Size Analysis with Low-Cost Imagery: Minimizing Sediment Sampler Biases and Increasing Habitat Classification Accuracy Geosciences low-cost benthic imagery marine habitat mapping |
| title | Augmenting Seafloor Characterization via Grain Size Analysis with Low-Cost Imagery: Minimizing Sediment Sampler Biases and Increasing Habitat Classification Accuracy |
| title_full | Augmenting Seafloor Characterization via Grain Size Analysis with Low-Cost Imagery: Minimizing Sediment Sampler Biases and Increasing Habitat Classification Accuracy |
| title_fullStr | Augmenting Seafloor Characterization via Grain Size Analysis with Low-Cost Imagery: Minimizing Sediment Sampler Biases and Increasing Habitat Classification Accuracy |
| title_full_unstemmed | Augmenting Seafloor Characterization via Grain Size Analysis with Low-Cost Imagery: Minimizing Sediment Sampler Biases and Increasing Habitat Classification Accuracy |
| title_short | Augmenting Seafloor Characterization via Grain Size Analysis with Low-Cost Imagery: Minimizing Sediment Sampler Biases and Increasing Habitat Classification Accuracy |
| title_sort | augmenting seafloor characterization via grain size analysis with low cost imagery minimizing sediment sampler biases and increasing habitat classification accuracy |
| topic | low-cost benthic imagery marine habitat mapping |
| url | https://www.mdpi.com/2076-3263/14/11/313 |
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