Our survey design was structured by the model output (Habitat model and movie of the model) and the logistical constraints of trying to sample the entire mid-Atlantic Bight Continental Shelf break over 7 days in December using a single fishing boat. Our model indicated there were hotspots at Atlantis, Wilmington, and Norfolk canyons. We confirmed these hotspots over the telephone with fishermen before the survey to see whether the model was in the ballpark. We used these canyons as focal points for evaluation sampling. Since the habitat model also indicated that butterfish concentrations varied by daylight and nighttime hours, we sampled a pixel of ocean predicted to be “good habitat” and a pixel of “bad habitat” from the model at each canyon during the day and again at night. In addition, Chris Roebuck the fishermen picked a 3rd station in each set where he believed butterfish would be located based upon his experience.
Using this design we learned a huge amount about what we know about butterfish based upon analysis of data at broad scales, and what fishermen like Chris know based on their time on the water fishing very precisely in areas where they think fish probably live. The work we are doing now is based on three of the most important lessons we learned:
1) Our models accurately captured butterfish responses to ocean features like temperature fronts and upwelling zones at spatial scales of 10s to 100s of kilometers. However, there were habitat features at finer spatial scales of centimeters to kilometers important to butterfish that we couldn’t capture using the data we had available to us. We made the model using data from the NOAA fisheries surveys of the Northwest Atlantic Continental shelf. The average distance between the stations in those surveys is about 20 kilometers (11 nautical miles). As a result, we are unable to detect with certainty species responses to ocean features at scales smaller than about twice that distance or about 40 kilometers. So our models and approach are useful, but for problems at relatively coarse spatial scales where the size of the pixel is about 40 kilometers. The models have similar limits in temporal resolution. Each year the NOAA shelf wide surveys are conducted during the spring and fall. Our model captures dramatic dynamic features of butterfish habitat but at relatively coarse temporal scales. So as ecosystem scientists, habitat ecologists and oceanographers, we can use our data to make models of the ways dynamic ocean features define habitats and drive changes in patterns of species distribution over relatively broad scales of space and time.
2) In contrast, fishermen like Chris Roebuck have an extremely fine scale knowledge of associations of animals with ocean features we can’t capture using traditional fishery independent surveys. Their livelihoods depend on close observations of fine scale ocean features associated with variations in the oceans plumbing and other properties that control phytoplankton production, the concentration of plants and animals in the food web and the feeding interactions resulting in the mortality of some organisms and growth of others. Just those sorts of features associated with places and times they catch fish. These are scales where mechanisms operate that create the broad scale patterns and relationships we ecosystem scientists detect in our data sets. By working together with the fishermen we can try to connect the broad scale patterns we see in our data and models over seasons and decades and the mechanisms the fishermen observe at fine spatial scales on a daily basis. This integration should benefit all of us in our search for cause and effect and in a science more likely to reflect the true realities of the ocean. All of us agree that ocean management needs to be based on just that kind of science.
3) We also found during our experiment with Chris showed us some habitats inshore in shallow water and offshore in deep water that are ecologically important to the animals but not accessible to the big ships used for regional fish surveys.