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Through an interdisciplinar of habitat scientists, oceanograp managers, social scientists, and fishermen we developed ecologica models for the specific purpose butterfish (Peprilus triacanthus) by longfin squid (Doryteuthis pealeii) f Mid-Atlantic Bight (MAB). We use additive modeling (GAM) to associ biomass collected during Natio Fisheries Service (NMFS) bottom t with benthic and pelagic habitat ch Pelagic variables were obtained fro survey CTD, satellite and high-freqThe sea surface measures of temp color from the satellites and the su fields from the HF radar were prov the Mid-Atlantic Regional Associa Ocean Observing System (MAR regional component of the U.S. Inte Observing System (IOOS).Index Terms-Habitat Models, Ocean Obse By-catch avoidance.

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 Resale or republication not permitted without written consent of the publisherMar Ecol Prog Ser 447: [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] 2012 frequent and dramatic changes in the environment. In the short term the animals move to optimal habitats in new areas rather than remaining in the same location and adapting to new environmental conditions (Nye et al. 2009, Lucey & Nye 2010. They use a variety of habitats at different times in their life cycles and at different times of year. To develop effective spaceand time-based conservation strategies, it is important to un derstand how animal populations respond to changing habitat components like temperature (Yamashita et al. 2001), circulation (Bakun 2010) and primary pro duction (Yamashita et al. 2001).There is a tendency to concentrate on seabed characteristics when describing habitats selected by marine species. However, characteristics of ocean habitats are strongly defined by the pelagic environment for both bottom-dwelling and pelagic species (Manderson et al. 2011). Many species spend at least part of their life cycle in the water column -oft...