Fish & Physics Hit the Press! Research linking ocean observing systems with spatial fish data are published

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Research linking ocean observing systems with spatial fish data published in Marine Ecology Progress Series.  The paper lead by John Manderson highlights the connection between observed spatial data from MARACOOS and distributions of keystone ecosystem species in the Mid Atlantic Bight.

MARACOOS and fish

Ocean observatory data are useful for regional habitat modeling of species with different vertical habitat preferences

John Manderson1, Laura Palamara2, Josh Kohut2, Matthew J. Oliver3
1. Ecosystems Processes Division, NEFSC/NMFS/NOAA, James J. Howard Marine Highlands, New Jersey 07732, USA
2. Institute of Marine and Coastal Science, Rutgers University, New Brunswick, New Jersey 08901, USA
3. College of Earth, Ocean and Environment, University of Delaware, Lewes, Delaware 19958, USA

Paper Abstract: Ocean Observing Systems (OOS) now provide comprehensive descriptions of the physical forcing, circulation, primary productivity and water column properties that subsidize and structure habitats in the coastal ocean. We used generalized additive models (GAM) to evaluate the power of OOS remotely sensed ocean data along with in situ hydrographic and bottom data to explain distributions of 4 species important in the Mid-Atlantic Bight, USA, ecosystem that have different vertical habitat preferences.  Our GAMs explained more abundance variation for pelagic species (longfin inshore squid and butterfish) than demersal species (spiny dogfish and summer flounder). Surface fronts and circulation patterns measured with OOS remote sensing as well as the rugosity and depth of the bottom were important for all species. In situ measurements of water column stability and structure were more useful for modeling pelagic species. Regardless of vertical habitat preference, the species were associated with vertical and horizontal current flows, and/or surface fronts, indicating that pelagic processes affecting movement costs, prey production and aggregation influenced distributions. Habitat-specific trends in abundance of 3 of the 4 species were well described by our OOSinformed GAMs based upon cross validation tests. Our analyses demonstrate that OOS are operationally useful for regional scale habitat modeling. Regional scale OOS-informed statistical habitat models could serve as bases for tactical ecosystem management and for the development of more sophisticated spatially explicit mechanistic models that couple ontogenic habitats and life history processes to simulate recruitment of organisms important to maintaining the resilience of coastal ecosystems.

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