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Marine Ecology Progress Series

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MEPS 447:15-30 (2012)  -  DOI:

Improving habitat models by incorporating pelagic measurements from coastal ocean observatories

Laura Palamara1,*, John Manderson2, Josh Kohut1, Matthew J. Oliver3, Steven Gray4, John Goff5

1Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, New Jersey 08901, USA
2NOAA, National Marine Fisheries Service, Northeast Fisheries Science Center, Ecosystems Processes Division,  James J. Howard Marine Sciences Laboratory, Highlands, New Jersey 07732, USA
3College of Earth, Ocean and Environment, University of Delaware, Lewes, Delaware 19958, USA
4Department of Natural Resources and Environmental Management, University of Hawaii, Honolulu, Hawaii 96822, USA
5Institute for Geophysics, Jackson School of Geosciences, University of Texas at Austin, Austin, Texas 78758, USA

ABSTRACT: As in all temperate coastal seas, habitats in the Mid-Atlantic Bight are spatially and temporally dynamic. Understanding how species respond to the dynamics of their environment is important for developing effective management strategies. In this study, we used canonical correspondence analysis (CCA) to determine habitat variables most important in explaining variation in fish and invertebrate communities sampled with bottom trawls. We also quantified the relative explanatory power of seabed habitat features, pelagic features measured in situ and pelagic features measured remotely, all of which can be used to explain  species variability. Pelagic habitat features, most notably surface and bottom temperature and stratification, explained 76% of the community variation observed, compared with 40.9% explained by seabed features, mainly depth. Remotely sensed pelagic characteristics explained 46.9% of the variation that was accounted for and were redundant for features measured in situ; this suggests that remotely sensed features are representative of features measured in situ including certain subsurface features. Cross-shelf and seasonal variation in environmental variables were the major predictors of species distributions and accounted for 71.3% of the total explained community variation. We described the seasonal dynamics of important habitat gradients and the responses of species with different habitat requirements and geographic range distributions to those gradients. We argue that consideration of dynamic pelagic features in addition to slowly changing features is important. Dynamic approaches are necessary for effective management and ocean observing systems can be used to develop dynamic space-based management strategies.

KEY WORDS: Habitat characteristics · Pelagic · Remote sensing · Spatial fisheries management · Canonical correspondence analysis · CCA · Mid-Atlantic Bight

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Cite this article as: Palamara L, Manderson J, Kohut J, Oliver MJ, Gray S, Goff J (2012) Improving habitat models by incorporating pelagic measurements from coastal ocean observatories. Mar Ecol Prog Ser 447:15-30.

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