Improving habitat models by incorporating pelagic measurements from coastal ocean observatories

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 corre- spondence 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 fea- tures 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 fea- tures. 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 impor- tant. Dynamic approaches are necessary for effective management and ocean observing systems can be used to develop dynamic space-based management strategies.