Recent interest in offshore renewable energy within the United States has amplified the need for marine spatial planning to direct management strategies and address competing user demands. To assist this effort in Rhode Island, benthic habitat classification maps were developed for two sites in offshore waters being considered for wind turbine installation. Maps characterizing and representing the distribution and extent of benthic habitats are valuable tools for improving understanding of ecosystem patterns and processes, and promoting scientifically-sound management decisions. This project presented the opportunity to conduct a comparison of the methodologies and resulting map outputs of two classification approaches, “top-down” and “bottom-up” in the two study areas. This comparison was undertaken to improve understanding of mapping methodologies and their applicability, including the bottom-up approach in offshore environments where data density tends to be lower, as well as to provide case studies for scientists and managers to consider for their own areas of interest. Such case studies can offer guidance for future work for assessing methodologies and translating them to other areas.
The traditional top-down mapping approach identifies biological community patterns based on communities occurring within geologically defined habitat map units, under the concept that geologic environments contain distinct biological assemblages. Alternatively, the bottom-up approach aims to establish habitat map units centered on biological similarity and then uses statistics to identify relationships with associated environmental parameters and determine habitat boundaries. When applied to the two study areas, both mapping approaches produced habitat classes with distinct macrofaunal assemblages and each established statistically strong and significant biotic–abiotic relationships with geologic features, sediment characteristics, water depth, and/or habitat heterogeneity over various spatial scales. The approaches were also able to integrate various data at differing spatial resolutions. The classification outputs exhibited similar results, including the number of habitat classes generated, the number of species defining the classes, the level of distinction of the biological communities, and dominance by tube-building amphipods. These results indicate that both approaches are able to discern a comparable degree of habitat variability and produce cohesive macrofaunal assemblages. The mapping approaches identify broadly similar benthic habitats at the two study sites and methods were able to distinguish the differing levels of heterogeneity between them.
The top-down approach to habitat classification was faster and simpler to accomplish with the data available in this study when compared to the bottom-up approach. Additionally, the top-down approach generated full-coverage habitat classes that are clearly delineated and can easily be interpreted by the map user, which is desirable from a management perspective for providing a more complete assessment of the areas of interest. However, a higher level of biological variability was noted in some of the habitat classes created, indicating that the biological communities present in this area are influenced by factors not captured in the broad-scale geological habitat units used in this approach.
The bottom-up approach was valuable in its ability to more clearly define macrofaunal assemblages among habitats, discern finer-scale habitat characteristics, and directly assess the degree of macrofaunal assemblage variability captured by the environmental parameters. From a user perspective, the map is more complex, which may be perceived as a limitation, though likely reflects natural gradations in habitat structure and likely presents a more ecologically realistic portrayal of the study areas. Though more comprehensive, the bottom-up approach in this study was limited by the reliance on full-coverage data to create full-coverage habitat classes. Such classes could only be developed when sediment data was excluded, since this point-sample dataset could not be interpolated due to high spatial heterogeneity of the study areas. Given a higher density of bottom samples, this issue could be rectified.
While the top-down approach was more appropriate for this study, both approaches were found to be suitable for mapping and classifying benthic habitats. In the United States, objectives for mapping and classification for renewable energy development have not been well established. Therefore, at this time, the best-suited approach primarily depends on mapping objectives, resource availability, data quality and coverage, and geographical location, as these factors impact the types of data included, the analyses and modeling that can be performed, and the biotic–abiotic relationships identified.