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Within the perspective of siting wind farms offshore of Rhode Island, USA, the State and National Environmental Agencies had requested a local marine ecological assessment, which led to an ecological zoning of the area. In view of expanding this zoning outside its limit of the test area and filling gaps in ecological zones, an effort to model those ecological zones using marine landscape or abiotic features was carried out. This study tests the accuracy of selected machine learning algorithmic models, decision tree, and random forest, for relating marine landscapes features to ecological sub-regions. Both models show to be good predictive tools with accuracy after cross validation of the order of 5-3%. Key abiotic variables to provide an accurate model were investigated. The study demonstrates the importance of the distance to coast, the sediment characteristics (fraction of clay, median size of the sediments), the hydrodynamic features, in particular not only tidal current/drag force, but also wave drag force, and finally the oceanographic characteristics such as stratification and sea surface temperature to built a good predictive model. Those findings provide some insight on the pre-monitoring effort optimization. [ABSTRACT FROM AUTHOR]

Efforts are in motion globally to address coastal and marine management needs through spatial planning and concomitant seabed habitat mapping. Contrasting strategies are often evident in these processes among local, regional, national and international scientific approaches and policy needs. In answer to such contrasts among its member states, the United States Northeast Regional Ocean Council formed a Habitat Working Group to conduct a regional inventory and comparative evaluation of seabed characterization, classification, and modeling activities in New England. The goals of this effort were to advance regional understanding of ocean habitats and identify opportunities for collaboration. Working closely with the Habitat Working Group, we organized and led the inventory and comparative analysis with a focus on providing processes and tools that can be used by scientists and managers, updated and adapted for future use, and applied in other ocean management regions throughout the world. Visual schematics were a critical component of the comparative analysis and aided discussion among scientists and managers. Regional consensus was reached on a common habitat classification scheme (U.S. Coastal and Marine Ecological Classification Standard) for regional seabed maps. Results and schematics were presented at a region-wide workshop where further steps were taken to initiate collaboration among projects. The workshop culminated in an agreement on a set of future seabed mapping goals for the region. The work presented here may serve as an example to other ocean planning regions in the U.S., Europe or elsewhere seeking to integrate a variety of seabed characterization, classification and modeling activities. [ABSTRACT FROM AUTHOR]

Abstract: The Benthic Habitat Quality (BHQ) index was used to assess habitat visible in sediment profile images (SPI) following hypoxia disturbance in a shallow (<10m) estuarine embayment in Rhode Island, USA. We tested for associations between the BHQ, SPI features and water quality over several assessment windows (1, 3, 7, 14, and 28 days prior to imaging) and at multiple dissolved oxygen (DO) thresholds (2.0mgl−1, 2.9mgl−1, and 4.8mgl−1). Using categorical data analysis, we established empirical relationships between hypoxia prevalence and presence/absence of biogenic features visible in SPI. Fecal pellets, tubes, feeding pits, voids, mounds, and BHQ score were good affirmative features, meaning that their presence (or score greater than 5) indicated a high probability of good water quality. However, low sensitivity to hypoxia precluded their usefulness as indicators, and was attributed to rarity in images and to factors acting on time intervals longer than those examined, e.g. long-term organic enrichment or hypoxia. Burrow structures and the apparent redox potential discontinuity (aRPD), or oxidized layer of surface sediment, were good discriminatory features, with high sensitivity and specificity for both hypoxia and normoxia. Both were strong surrogates for water quality over multiple assessment windows and DO thresholds, and had the highest overall predictive values. We conclude that SPI images can be used to widen the spatial extent of water quality monitoring efforts by utilizing the relationships between aRPD, burrows and hypoxia prevalence. [Copyright &y& Elsevier]

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 (eographic Data Committee (FGDC I-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. [ABSTRACT FROM AUTHOR]