1. Combining Ocean Observing Systems with Statistical Analysis to Account for a Dynamic Habitat
Collin Dobson1,John Manderson2,Josh Kohut1,Laura Palamara1,Oscar Schofield1,Amelia Snow1
1Rutgers, the State University of New Jersey
2James J. Howard Marine Laboratory, National Oceanic Atmospheric Administration
Significance
Results
Potential Applications to Management
Once the model was created it was applied to the survey to determine the amount of trawls considered habitat for each year. This was done by using the 50th percentile of predicted CPUE (catch per unit effort), which was approximately 1.5kg/tow as a cutoff between habitat and non-habitat; tows predicted over 1.5kg/tow were defined as habitat and tows predicted under 1.5kg/tow were defined as non-habitat.
Butterfish (Peprilus triacanthus) live in the extremely dynamic habitat of the Mid-Atlantic Bight(MAB). Species that live in the MAB are constrained to make habitat selection decisions that are based on the conditions of the water column. As pelagic species, butterfish habitat selection is affected by many conditions. Certain environmental factors, especially temperature, affect their migratory habits. As the climate of the ocean varies from year to year, so do the tendencies of butterfish. Their susceptibility to be influenced by the conditions of the water column makes them a great indicator species.
The application of the GAM model can be used to weight butterfish catch, but alone it cannot estimate the total butterfish population size. In order to estimate the total population size, a few additional habitat variables must be taken into consideration.
Butterfish (Peprilus triacanthus)
The number of fall tows for each year that were predicted in habitat (predicted >1.5, black) and in non-habitat(predicted <1.5,red).
The mean observed CPUE for each year, of those trawls that were either predicted habitat(blue), and predicted non-habitat(green). The red line is the indices of the NOAA survey.
Observing the habitat and migration habits of butterfish will be a significant contribution to understanding the MAB. Bottom trawl surveys are a useful indicator of butterfish abundance, but they alone do not tell the whole story.
Ocean Observing Systems sample the water column and can provide very useful information on habitat availability.
Boltzmann–Arrhenius response curve parametrized to approximately match the GAM model
Below is a comparison of actual CPUE, based on the NMFS survey, versus predicted CPUE, based on the GAM model, for Fall Circled are areas in which the predicted habitat suitability was high, but the observed CPUE was low.
Purpose
Test and verify the combination of ocean observing systems and butterfish bottom trawl surveys with statistical analysis
Create a stable habitat model that will contribute essential information for long-term ecosystem management
Provide insight as to how the model could improve our interpretation of the survey data
Materials and Methods
In order to create a statistically-informed habitat model, environmental data was combined with fishery abundance data from National Marine Fisheries Service (NMFS) bottom trawl surveys. Accompanying each bottom trawl that occurred in the survey was a CTD (conductivity temperature depth) cast. The bottom temperature variable from each cast was used to create a GAM (generalized additive model). The GAM represents a statistical relationship between the environment and biomass of butterfish.
Habitat prediction for the Fall 2002 survey shows relatively narrow habitat suitability.
Habitat prediction for the Fall 1985 survey shows a better habitat suitability.
These models are based on bottom temperature from the Regional Ocean Modeling System and the Boltzmann–Arrhenius relationship
Provides a mechanistic relationship between temperature and biomass, rather than a purely statistic relationship provided by the GAM.
Using bottom temperature from physical models allows us to measure how much habitat is available and how much is actually sampled by the survey.
3D physical models can also help model habitat that cannot to be sampled by the survey, such as inshore and offshore areas, and the upper water column.
Check to see if population density is higher in areas that are narrow habitat, and check to see if population density is lower in broad habitats.
Log transformed CPUE for Fall Blue colors indicate a lower CPUE, while red colors indicate a higher CPUE.
Predicted habitat for Fall Blue colors are relatively low predicted habitat, while red colors predict a relatively good habitat. +
Below is a comparison of actual CPUE, based on the NEFSC survey, versus predicted habitat, based on the GAM model, for Fall 2006, which shows a few distinct areas of mismatch between the GAM predictions and observed CPUE. In the circles are particular areas of interest because they were predicted to have a low habitat suitability but yielded a higher observed CPUE.
Conclusion
Each year, the number of tows in predicted habitat, and the number of tows in predicted non-habitat were relatively close.
The mean CPUE of tows that were predicted to be habitat was significantly higher than the mean CPUE of tows that were predicted to be non-habitat.
The comparison between the GAM's predictions and observed CPUE varied from year to year. Further work needs to be done to determine the reasons for the poor predictions in certain years.
Habitat predictions can be used to weight butterfish catch and provide a step toward habitat-based assessment, but an estimate of total habitat is needed to estimate a population size. =
CTD measurements taken with every trawl
Locations of NMFS bottom trawls
Acknowledgments
This entire project was funded by, and would not have been possible without, CINAR. Additional thanks to NOAA, Enrique Curchitser and ROMS, and The Rutgers Coastal Ocean Observation Lab.
Log transformed CPUE for Fall Blue colors indicate a lower CPUE, while red colors indicate a higher CPUE.
Predicted habitat for Fall Blue colors are relatively low predicted habitat, while red colors predict a relatively good habitat.
Butterfish response to bottom temperature and solar elevation (time of day)