Data from: Abiotic proxies for predictive mapping of near-shore benthic assemblages: implications for marine spatial planning
McHenry, Jennifer, Florida State University
Steneck, Robert S., University of Maine
Brady, Damian C., University of Maine
Published Nov 12, 2019 on Dryad.
Cite this dataset
McHenry, Jennifer; Steneck, Robert S.; Brady, Damian C. (2019). Data from: Abiotic proxies for predictive mapping of near-shore benthic assemblages: implications for marine spatial planning [Dataset]. Dryad. https://doi.org/10.5061/dryad.cs17q
Marine spatial planning (MSP) should assist managers in guiding human activities towards sustainable practices and in minimizing user-conflicts in our oceans. A necessary first step is to quantify spatial patterns of marine assemblages in order to understand the ecosystem’s structure, function, and services. However, the large spatial scale, high economic value, and density of human activities in near-shore habitats often makes quantifying this component of marine ecosystems especially daunting. To address this challenge, we developed an assessment method that employs abiotic proxies to rapidly characterize marine assemblages in near-shore benthic environments with relatively high resolution. We evaluated this assessment method along 300 km of the State of Maine’s coastal shelf (< 100m depth)—a zone where high densities of buoyed lobster traps typically preclude extensive surveys by towed sampling gear (i.e., otter trawls). During the summer months of 2010-2013, we implemented a stratified-random survey using a small remotely operated vehicle that allowed us to work around lobster buoys and to quantify all benthic megafauna to species. Stratifying by substrate, depth, and coastal water masses, we found that abiotic variables explained a significant portion of variance (37- 59%) in benthic species composition, diversity, biomass and economic value. Generally, the density, diversity, and biomass of assemblages significantly increased with the substrate complexity (i.e., from sand-mud to ledge). The diversity, biomass and economic value of assemblages also decreased significantly with increasing depth. Lastly demersal fish densities, sessile invertebrate densities, species diversity, and assemblage biomass increased from east to west, while the abundance of mobile invertebrates and economic value decreased, corresponding mainly to the contrasting water-mass characteristics of the Maine Coastal Current system (i.e., summertime current direction, speed, and temperature). Integrating modeled predictions with existing GIS layers for abiotic conditions allowed us to scale up important assemblage attributes to define key foundational ecological principles of MSP and to find priority regions where some bottom-disturbing activities would have minimal impact to benthic assemblages. We conclude that abiotic proxies can be strong forcing functions for the assembly of marine communities and therefore useful tools for spatial extrapolations of marine assemblages in congested (heavily used) near-shore habitats.
The data comes from a remotely operated vehicle (ROV) survey conducted during the summer months of 2010-2013 along the coastal Gulf of Maine shelf. The survey aimed 1) to characterize the distribution and abundance of benthic megafauna with respect to near-shore abiotic conditions, 2) to develop spatially-explicit maps of ecological attributes of near-shore benthic assemblages, and 3) use such maps to inform the application of ecological principles when engaging in marine spatial planning. This file contains a data matrix of species abundances for the most dominantly observed species (i.e., greater than 5 observations), assemblage biomass, economic value, and shannon-weiner species diversity by site, along with values of associated summertime abiotic conditions (i.e.,mean transect depth, substrate type, water-mass position, bottom temperature, bottom current speed/direction, and bottom salinity). Depth and substrate type values were recorded from the ROV surveys, while the water-mass values were acquired from HYCOM GLBu0.08 4D model output via the Marine Geo-spatial Ecology Toolbox in ArcGIS 10.3. See McHenry et al. 2016 for a full description the survey design, data processing methods, and analytical approaches used by this study.