Coastal And Marine Ecological Classification Standard Research Articles

Classification of Lakebed Geologic Substrate in Autonomously Collected Benthic Imagery Using Machine Learning

Mapping benthic habitats with bathymetric, acoustic, and spectral data requires georeferenced ground-truth information about habitat types and characteristics. New technologies like autonomous underwater vehicles (AUVs) collect tens of thousands of images per mission making image-based ground truthing particularly attractive. Two types of machine learning (ML) models, random forest (RF) and deep neural network (DNN), were tested to determine whether ML models could serve as an accurate substitute for manual classification of AUV images for substrate type interpretation. RF models were trained to predict substrate class as a function of texture, edge, and intensity metrics (i.e., features) calculated for each image. Models were tested using a manually classified image dataset with 9-, 6-, and 2-class schemes based on the Coastal and Marine Ecological Classification Standard (CMECS). Results suggest that both RF and DNN models achieve comparable accuracies, with the 9-class models being least accurate (~73–78%) and the 2-class models being the most accurate (~95–96%). However, the DNN models were more efficient to train and apply because they did not require feature estimation before training or classification. Integrating ML models into benthic habitat mapping process can improve our ability to efficiently and accurately ground-truth large areas of benthic habitat using AUV or similar images.

Deep octopod habitat in the western North Atlantic characterized by Standard Ecological Classification from videos

AbstractHabitat characterization is important to assess fully the niches of different organisms. There is a large knowledge gap regarding habitat use by deep‐sea benthic incirrate octopods, partly due to their assumed preference for hard‐to‐sample rocky substrata. This study uses observations from in situ videos recorded by remotely operated vehicles (ROVs) deployed from the NOAA Ship Okeanos Explorer and implements the Coastal and Marine Ecological Classification Standard (CMECS) to describe the habitat of three common species of bathyal incirrate octopods living in the western North Atlantic Ocean: Bathypolypus bairdii (Verrill 1873), Graneledone verrucosa (Verrill 1881), and Muusoctopus johnsonianus (Allcock, Strugnell, Ruggiero, & Collins 2006). Significant differences in species' preferences for geoform setting, depth, and substrate type were found. All three species are most likely to be observed by ROV in a submarine canyon and least likely to be seen on a seamount. B. bairdii was found shallower than G. verrucosa and M. johnsonianus. This is the first study of its kind using CMECS to classify the habitat of specific organisms as opposed to the habitat types in a specific area.

Post-Hurricane Sandy Benthic Habitat Mapping at Fire Island National Seashore, New York, USA, Utilizing the Coastal and Marine Ecological Classification Standard (CMECS)

In 2012, Hurricane Sandy created a new tidal inlet at Fire Island National Seashore (FIIS) in New York, USA, consequently altering environmental conditions within Great South Bay. This event presented a unique opportunity to establish new ecological baselines, assess resulting ecological change, and explore management implications. This study focuses on benthic mapping within the bayside of FIIS using acoustic, grab sample, and imagery data. Biotope (habitat) maps were developed describing relationships between macrofaunal communities and their environment. Additionally, biotopes were prioritized by “ecological value” based on user-defined criteria (presence of seagrass and potential for higher trophic level interactions) to guide management. While there are limited pre-Sandy data for comparison, findings from this study suggest the inlet has been a positive ecological influence on the nearby area. Dense concentrations of mature blue mussels (Mytilus edulis) documented near the inlet are considered ecologically beneficial and represent a post-Sandy distinction in ecosystem structure; M. edulis was last common when the inlet was previously open (early 1800s). The inlet is also likely responsible for seagrass expansion near the inlet but decline in other areas. This study advances the utility of the Coastal and Marine Ecological Classification Standard (CMECS) by including CMECS-defined data in analyses and expanding the definition of “dominance.” CMECS played a key role in developing map units, interpreting biotopes, and establishing statistically significant and ecologically meaningful biotic–abiotic relationships. This study also highlights the value and management applications of benthic mapping specific to FIIS and more broadly and advocates for similar studies elsewhere.

Nearshore Benthic Mapping in the Great Lakes: A Multi-Agency Data Integration Approach in Southwest Lake Michigan

The Laurentian Great Lakes comprise the largest assemblage of inland waterbodies in North America, with vast geographic, environmentally complex nearshore benthic substrate and associated habitat. The Great Lakes Water Quality Agreement, originally signed in 1972, aims to help restore and protect the basin, and ecosystem monitoring is a primary objective to support adaptive management, environmental policy, and decision making. Yet, monitoring ecosystem trends remains challenging, potentially hindering progress in lake management and restoration. Consistent, high-resolution maps of nearshore substrate and associated habitat are fundamental to support management needs, and the nexus of high-quality remotely sensed data with improvements to analytical methods are increasing opportunities for large-scale nearshore benthic mapping at project-relevant spatial resolutions. This study attempts to advance the integration of high-fidelity data (airborne imagery and lidar, satellite imagery, in situ observations, etc.) and machine learning to identify and classify nearshore benthic substrate and associated habitat using a case study in southwest Lake Michigan along Illinois Beach State Park, Illinois, USA. Data inputs and analytical methods were evaluated to better understand their implications with respect to the Coastal and Marine Ecological Classification Standard (CMECS) classification hierarchy, resulting in an approach that could be easily applied to other shallow coastal environments. Classification of substrate and biotic components were iteratively classified in two Tiers in which classes with increasing specificity were identified using different combinations of airborne and satellite data inputs. Classification accuracy assessments revealed that for the Tier 1 substrate component (3 classes), average overall accuracy was 90.10 ± 0.60% for 24 airborne data combinations and 89.77 ± 1.02% for 12 satellite data combinations, whereas the Tier 1 biotic component (2 classes) average overall accuracy was 93.58 ± 0.91% for 24 airborne data combinations and 92.67 ± 0.71% for 11 satellite data combinations. The Tier 2 result for the substrate component (2 classes) was 93.28% for 2 airborne data combinations and 95.25% for the biotic component (2 classes). The study builds on foundational efforts to move towards a more integrated data approach, whereby data strengths and limitations for mapping nearshore benthic substrate and associated habitat, expressed through classification accuracy, were evaluated within the context of the CMECS classification hierarchy, and has direct applicability to critical monitoring needs in the Great Lakes.

A Global Ecological Classification of Coastal Segment Units to Complement Marine Biodiversity Observation Network Assessments

A new data layer provides Coastal and Marine Ecological Classification Standard (CMECS) labels for global coastal segments at 1 km or shorter resolution. These characteristics are summarized for six US Marine Biodiversity Observation Network (MBON) sites and one MBON Pole to Pole of the Americas site in Argentina. The global coastlines CMECS classifications were produced from a partitioning of a 30 m Landsat-derived shoreline vector that was segmented into 4 million 1 km or shorter segments. Each segment was attributed with values from 10 variables that represent the ecological settings in which the coastline occurs, including properties of the adjacent water, adjacent land, and coastline itself. The 4 million segments were classified into 81,000 coastal segment units (CSUs) as unique combinations of variable classes. We summarize the process to develop the CSUs and derive summary descriptions for the seven MBON case study sites. We discuss the intended application of the new CSU data for research and management in coastal areas.

Using CMECS to Create Benthic Habitat Maps for Pleasant Bay, Cape Cod, Massachusetts

Benthic habitat maps are an important part of ecosystem-based management as they document biotic and abiotic resources. Integrating multiple ecosystem and environmental components is challenging, and the ability to do this is highly dependent on the methodology employed. The US Coastal and Marine Ecological Classification Standard (CMECS) uses a standardized set of classifications in a hierarchal structure to create benthic habitat maps. This study used acoustic surveys and benthic samples from Pleasant Bay, Cape Cod, MA, to develop benthic habitat maps and test relationships between biotic and abiotic variables. The collected data comprise a critical baseline record of biological and physical characteristics and will inform future management decisions as well as guide future studies of coastal resources in Pleasant Bay.

Applications of the Coastal and Marine Ecological Classification Standard (CMECS) in a Partially Restored New England Salt Marsh Lagoon

The Coastal and Marine Ecological Classification Standard (CMECS) was used to create standardized maps of habitats and biotopes by combining CMECS’s four stand-alone components: Geoform, Substrate, Biotic, and Water, based on data collected in a shallow New England salt marsh lagoon. East Harbor on Cape Cod, MA, USA, was artificially isolated from tidal flow in the late 1860s. Consequently, estuarine fish and invertebrate populations declined until partial tidal flow was restored in 2002. In early September 2017, we conducted an acoustic survey covering 1.29 km2 (mean depth = 1.27 m) of East Harbor using a vessel mounted phase measuring sidescan sonar. From the acoustic survey data, submerged aquatic vegetation (SAV) was mapped and used for a stratified random sampling scheme. On 2 days in late September 2017, East Harbor was sampled for benthic invertebrates, sediment, and water properties (pH, dissolved oxygen, salinity, and temperature) at sixteen stations (eight bare substrates and eight SAV). As the restoration project develops over time from a newly restored (2002) to a more fully restored (2017) system, links between abiotic and biotic components strengthen. Consequently, we were able to explain 89.39% of species distribution in East Harbor using measured abiotic variables (e.g., sediment characteristics) and show the diminishing influence of “distance to culvert” in explaining species distribution in East Harbor.

Standardized Geomorphic Classification of Seafloor Within the United States Atlantic Canyons and Continental Margin

Accurate seafloor maps serve as a critical component for understanding marine ecosystems and guiding informed ocean management decisions. From 2004-2015, a vast region of the Atlantic Ocean continental margin offshore of the United States has been systematically mapped using multibeam sonars. This work was done in support of the U.S. Extended Continental Shelf (ECS) Project and for baseline characterization of the Atlantic canyons, but the question remains as to the relevance of these margin-wide data sets for conservation and management decisions pertaining to these areas. This study utilized an automatic segmentation approach to initially identify landform features from the bathymetry of the region, then translated these results into complete coverage geomorphology maps of the region utilizing the Coastal and Marine Ecological Classification Standard (CMECS) to define geoforms. Abyssal flats make up more than half of the area (53%), with the continental slope flat class making up another 30% of the total area. Flats of any geoform class (including continental shelf flats and guyot flats) make up 83.06% of the study area. Slope classes make up a cumulative total of 13.26% of the study region (8.27% abyssal slopes, 3.73% continental slopes, 1.25% seamount slopes). While ridge features comprise only 1.82% of the total study area (1.03% abyssal ridges, 0.63 continental slope ridge, and 0.16% seamount ridges). Key benefits of the study’s semi-automated approach include computational efficiency for large datasets, and the ability to apply the same methods to large regions with consistent results.

Applying the Coastal and Marine Ecological Classification Standard (CMECS) to Nearshore Habitats in the Northeastern Gulf of Mexico

Many countries have classification standards for their environmental resources including criteria for classifying coastal and marine ecosystems. Until 2012, the United States just had a nationwide protocol for classifying terrestrial and aquatic habitats with no national standard for marine and most coastal habitats. In 2012 the Coastal and Marine Ecological Classification Standard (CMECS) was implemented to address this need. In the past, coastal and marine classifications were developed at the regional or local level. Since its inception, the CMECS has not been applied in many geographic areas. My study was one of the first to apply the CMECS to the benthic habitats in the nearshore Northeastern Gulf of Mexico. Sidescan sonar mapping and dive surveys were completed at 33 sites at depths 10–23 m. Hardbottom and sand habitats characterized the study area, and the underwater surveys revealed hard corals, sponges, and macroalgae as the dominant taxa on the hardbottom. The CMECS was applied to the overall study area rather than each individual site or groups of similar sites because habitat and environmental characteristics, primarily outside the context of the CMECS, appeared to influence the distribution of taxa across sites more than the CMECS geoform, substrate, and water column components. The CMECS worked well for classifying the entire study area, but was not adequate for classifying the complex fine-scale habitats and temporal variations observed; modifications to the CMECS could help resolve these issues.

Application of Coastal and Marine Ecological Classification Standard to organize island ecosystem: the Abu Musa Island, Persian Gulf case study

Abstract. Zare R, SinaeiM, ShakouriA, KourandehMB, GeramiMH. 2017. Application of Coastal and Marine Ecological Classification Standard to organize island ecosystem: the Abu Musa Island, Persian Gulf case study. Biodiversitas 18: 153-159. A first step in marine conservation plans is to identify and classify habitat types. These classifications should be scientificallyrigorous with high application. The Coastal and Marine Ecological Classification Standard (CMECS) is a nested, hierarchical framework that applies a uniform set of rules that have a high capacity in marine habitats classifications. In this study, this capacity was tested for the Abu Musa Island ecosystem, Persian Gulf. The Surface Geology Component and the Biotic CoverComponent were analyzed and encoded with the given instructions. In total, 21 codes were determined for benthic communities and 19 codes for geoform component, which showed high diversity of habitats and variety of biotopes in this island.Finally, results indicated that the CMECS approach was successful in Abu Musa habitat classification. In addition, results were very useful for sustainable development especially for ecotourism plans.
 Keywords: Abu Musa Island, CMECS, ecotourism, habitat classification

Combining biological and geomorphological data to introduce biotopes of Bushehr Province, the Persian Gulf.

Identification and classification of intertidal areas provides the basic knowledge needed for studies of biogeography, macro-ecology, and faunal populations, as well as for conservation planning and managing human activities in coastal areas. In this research, the eastern coast of Bushehr Province was classified using the Coastal and Marine Ecological Classification Standard (CMECS). Seven substrate subgroups, five geoform types in unconsolidated mineral substrate, five micro-habitats in rock substrate, and nine biotic groups were identified in study stations. The non-metric multidimensional scaling (nMDS) ordinations and one-way analysis of similarity (ANOSIM) showed that macroinvertebrate species composition differed significantly among different geoform types (habitat types). Eight biotopes are introduced for the eastern coast of Bushehr Province based on observational and statistical methods. The results presented here show that identifying intertidal biotopes using CMECS is an appropriate method both for classifying the southern coastal areas of Iran and for integrating biotic and abiotic components.

Application of the coastal and marine ecological classification standard using satellite-derived and modeled data products for pelagic habitats in the Northern Gulf of Mexico

The expansive and dynamic nature of the ocean's water column may limit the feasibility of the frequent in situ sampling that would be necessary to monitor these habitats and produce region-wide map products with any regularity. Alternatives to in situ sampling such as remote sensing and classification offer a means of routinely characterizing the environmental forcing functions that shape and determine habitat suitability and distribution. Four products derived from Moderate Resolution Imaging Spectroradiometer (MODIS)-aqua satellite (chlorophyll concentration, salinity, sea-surface temperature, and euphotic depth) and a hydrodynamic modeled product for bottom to surface temperature differences (Δt) were evaluated to assess the utility of these products as proxies for in situ measurements. MODIS images covering the northern Gulf of Mexico were obtained for a 5-year time period (January 2005 – December 2009; 300 total images) and processed through Automated Processing System. The products were used to classify surface waters in three regions of the northern Gulf of Mexico using subcomponents and modifiers from the Coastal and Marine Ecological Classification Standard (CMECS) Water Column Component (WC) to determine if CMECS categories could be affectively used to categorize the products into meaningful management units. Products were assessed for each month over the five year period. Sea-surface temperature and salinity were classified into CMECS WC temperature and salinity subcomponent categories, respectively. Three modifiers from the WC were also used for the pelagic classification: water column stability, productivity, and turbidity. Δt was used to assign classification for water column stability; surface chlorophyll was used to determine phytoplankton productivity; and euphotic depth was used to indicate the level of turbidity. Statistical analyses of the products compared to the Gulf States Marine Fisheries Commission's Southeast Area Monitoring and Assessment Program in situ data indicated that the MODIS and hydrodynamic modeling products were consistently different from the in situ data; however, we believe the potential is strong for use of these standard products to enhance water column information. Use of the CMECS WC with appropriate modifiers captures all the significant pelagic environmental parameters which influence habitat and species distributions. Of the parameters evaluated, the sea-surface salinity and temperature, as expected, were most useful for making comparisons. Further research incorporating different types of data is necessary to explore the full potential of this approach. Specifically, resource managers would like to see the incorporation of sediment and bathymetry data. We believe addition of these data layers would result in more robust habitat maps and provide an innovative tool for resource managers.

Linking the Coastal and Marine Ecological Classification Standard (CMECS) to ecosystem services: an application to the US Gulf of Mexico

At the first Gulf of Mexico Ecosystem Services Workshop (2010), ecosystem services were linked to the Gulf of Mexico habitat types as defined in the Coastal and Marine Ecological Classification Standard (CMECS), recently endorsed as the first US standard for classifying coastal and marine ecosystems. Participants identified and classified the Gulf habitat types based on CMECS, linked ecosystem services to habitat types, and prioritized services using expert opinion. Only three categories of ecosystem services were considered: regulating, provisioning, and cultural. However, supporting services, such as biological interactions, were considered as part of reclassified services. Highly ranked services were distributed across all the three service categories for each Gulf habitat, except for mangroves and dune/beach (only regulating and cultural services). Predictably, the majority of the habitat types were linked to the service of food. The importance of this exercise lies in the utility of the results for resource managers conducting activities within the Gulf of Mexico coastal and marine environments and can be informative to other regions wishing to conduct a similar exercise. This is, as far as the authors are aware, the first inventory of habitat types and their associated ecosystem services within the Gulf of Mexico region.

Mapping Shallow Coastal Ecosystems: A Case Study of a Rhode Island Lagoon

In order to effectively study, manage, conserve, and sustain shallow-subtidal ecosystems, a spatial inventory of the basic resources and habitats is essential. Because of the complexities of shallow-subtidal substrates, benthic communities, geology, geomorphology, and water column attributes, few standard protocols are fully articulated and tested that describe the mapping and inventory processes and accompanying interpretations. In this paper, we describe a systematic approach to map Rhode Island's shallow-subtidal coastal lagoon ecosystems, by using, integrating, and reconciling multiple data sets to identify the geology, soils, biological communities, and environments that, collectively, define each shallow-subtidal habitat. We constructed maps for these lagoons via a deliberate, step by step approach. Acoustics and geostatistical modeling were used to create a bathymetric map. These data were analyzed to identify submerged landforms and geologic boundaries. Geologic interpretations were verified with video and grab samples. Soils were sampled, characterized, and mapped within the context of the landscape and geologic boundaries. Biological components and distributions were investigated using acoustics, grab samples, video, and sediment profile images. Data sets were cross-referenced and ground-truthed to test for inconsistencies. Maps and geospatial data, with Federal Geographic Data Committee (FGDC)-compliant metadata, were finalized after reconciling data set inconsistencies and made available on the Internet. These data allow for classification in the revised Coastal and Marine Ecological Classification Standard (CMECS). With these maps, we explored potential relationships among and between physical and biological parameters. In some cases, we discovered a clear match between habitat measures; in others, however, relationships were more difficult to distinguish and require further investigation.

Comparison of methods for integrating biological and physical data for marine habitat mapping and classification

An important first step in marine spatial planning and ecosystem-based management efforts is the creation of benthic habitat maps that allow scientists and managers to understand the distribution of living and non-living resources on the seafloor. However, the location of boundaries between and composition of habitats is highly dependent on the approach taken to integrate abiotic and biotic information. The purpose of this study was to test “top-down” and “bottom-up” approaches for integrating physical and biological data derived from commonly used sub-tidal benthic mapping tools to create a habitat map compatible with the US Coastal and Marine Ecological Classification Standard (CMECS). We found that a top-down framework, where we tested for differences in macrofauna assemblages among side scan sonar facies, defined two broad-scale and general habitats. Using the bottom-up approach, where patterns in abiotic and biotic variables were examined with multivariate statistics (BEST, LINKTREE, ANOSIM, SIMPER), we generated seven biotopes based on the macrofauna abundance, percent sand, water depth, and backscatter standard deviation that corresponded well to, but provided more fine-scale detail than the top-down habitats. We were able to use the statistical relationship between abiotic variables and macrofauna assemblages in the LINKTREE to predict the spatial distribution of assemblages over ∼50% of the study area. We created a local catalogue of biotopes specific to our study area that contributes to the CMECS library. In addition, we were able to fully map CMECS Geoform, Surface Geology, and Biotic Cover Components. This mapping effort represented real progress toward reconciling the “data density mismatch” between physical and biological mapping methods, and it provided further evidence that using a bottom-up methodology preserves species–environment relationships.

Evaluating the NOAA Coastal and Marine Ecological Classification Standard in estuarine systems: A Columbia River Estuary case study

A common first step in conservation planning and resource management is to identify and classify habitat types, and this has led to a proliferation of habitat classification systems. Ideally, classifications should be scientifically and conceptually rigorous, with broad applicability across spatial and temporal scales. Successful systems will also be flexible and adaptable, with a framework and supporting lexicon accessible to users from a variety of disciplines and locations. A new, continental-scale classification system for coastal and marine habitats—the Coastal and Marine Ecological Classification Standard (CMECS)—is currently being developed for North America by NatureServe and the National Oceanic and Atmospheric Administration (NOAA). CMECS is a nested, hierarchical framework that applies a uniform set of rules and terminology across multiple habitat scales using a combination of oceanographic (e.g. salinity, temperature), physiographic (e.g. depth, substratum), and biological (e.g. community type) criteria. Estuaries are arguably the most difficult marine environments to classify due to large spatio-temporal variability resulting in rapidly shifting benthic and water column conditions. We simultaneously collected data at eleven subtidal sites in the Columbia River Estuary (CRE) in fall 2004 to evaluate whether the estuarine component of CMECS could adequately classify habitats across several scales for representative sites within the estuary spanning a range of conditions. Using outputs from an acoustic Doppler current profiler (ADCP), CTD (conductivity, temperature, depth) sensor, and PONAR (benthic dredge) we concluded that the CMECS hierarchy provided a spatially explicit framework in which to integrate multiple parameters to define macro-habitats at the 100 m 2 to >1000 m 2 scales, or across several tiers of the CMECS system. The classification's strengths lie in its nested, hierarchical structure and in the development of a standardized, yet flexible classification lexicon. The application of the CMECS to other estuaries in North America should therefore identify similar habitat types at similar scales as we identified in the CRE. We also suggest that the CMECS could be improved by refining classification thresholds to better reflect ecological processes, by direct integration of temporal variability, and by more explicitly linking physical and biological processes with habitat patterns.