1. Toward Dynamic Ocean Management:
Fisheries assessment and climate projections informed by community developed
habitat models based on dynamic coastal oceanography
OpenOcean
P54A-1740
Josh Kohut1, John Manderson2, Laura Palamara1, Vince Saba2, Grace Saba1, Jon Hare2, Enrique Curchitser1, Peter Moore3, Brad Seibel4, and Greg DiDomenico5
1Rutgers University 2NOAA/NMFS, Northeast Fishery Science Center 3Mid-Atlantic Regional Association Coastal Ocean Observing System (MARACOOS) 4University of South Florida 5Garden State Seafood Association
OVERVIEW
Ocean ecology is often treated similar to landscape ecology, where animals have physiologies that are fairly decoupled from the atmosphere. In contrast, the growth, survival, and reproduction of marine fish are highly dependent on the temperature of the surrounding fluid.
HABITAT BASED AVAILABILTY
HABITAT MODEL
Thermal niche model: nonlinear extension of
Boltzmann-Arrhenius equation
(mechanistic basis in enzyme kinetics)
The time series of availability (black line) with associated uncertainty (dashed lines) was provided to the stock assessment model. The result was a narrower range of values for availability (solid red bounds) based on those used in the prior stock assessment model (red dashed bounds). .
Water temperature hindcast from
oceanographic model
In temperate regions such as the Mid- Atlantic Bight (MAB), USA, ocean temperature is extremely dynamic in both time and space.
Some of the largest seasonal temperature changes in the world
Strong interannual variability and long- term shifts due to climate change
Many migratory species that move with water of optimal temperature
Median ρh = 0.68
( )
Habitat based estimates track changes in availability (ρh) over time.
Thermal habitat based ρh accounts for shifts in species distributions associated with climate impacts on ocean temperatures.
Narrows range of availability and consequently catchability in the stock assessment model.
coupled with
Availability (ρ) to fall NEFSC
survey stations offshore
Daily Temperature
40 vertical layers
~7 km Resolution
Availability (ρ)
Catchability (Q)
gives us a daily estimated bottom habitat suitability index (HSI)
Detectability(δ)
We used butterfish (Peprilus triacanthus), a mobile pelagic forage fish in the MAB, as a test species to map thermal habitat dynamics on the seafloor over a 30-year period.
Economically and ecologically important
Target for a directed fishery
Seasonal migration coincides with a federal fishery independent trawl survey in the fall
Unlike the prior assessment, the 2014 stock assessment model was accepted. From the Mid Atlantic Fishery Management Council (MAFMC) draft 2014 Environmental Assessment for the Butterfish and Longfin Squid fishery specifications:
The butterfish fishery has mostly been an incidental fishery since 2002. 
2014 is the first year of a small directed fishery, with a landings limit of 3,200 mt. 
If that limit is caught at 2013 average prices ($1,481 mt), the resulting revenues would be about $4.7 million.
Under the proposed 2015 specifications, the average landings limit for would be 21,408 mt.  This could potentially translate into $31.7 million additional ex-vessel revenues at 2013 prices. 
October 9, 1987
October 20, 2002
LEVERAGING REGIONAL PARTNERSHIP
We developed a thermal niche model specific to butterfish using a nonlinear extension of the Boltzmann-Arrhenius model of temperature dependence.
Model parameters were estimated using National Marine Fisheries Service (NMFS) bottom trawl collections and temperatures and maximum likelihood estimation. Peak suitability was at 19.2°C.
We coupled the thermal niche model to simulated bottom temperatures from a Regional Ocean Modeling System (ROMS) model to get daily estimated bottom habitat suitability during the fall surveys between 1989 and 2012.
We targeted regional expertise and resources in the form of an expert workgroup (OpenOcean, below) and regional ocean observing systems (IOOS/MARACOOS, right) to develop and implement our approach.
LOOKING AHEAD
A project entitled ‘Indicators of habitat change affecting three key commercial species of the U.S. Northeast Shelf: A design to facilitate proactive management in the face of climate change’, led by Vince Saba (NOAA/NEFSC) in collaboration with the authors on this poster, was recently funded by the NOAA Coastal and Ocean Climate Applications (COCA) program. This project will focus on 3 important Mid-Atlantic Bight Species:
Longfin Squid
Black Sea Bass
Spiny Dogfish
Building on the Butterfish habitat model development we will incorporate:
Laboratory studies of thermal optima based on physiology
Habitat projections under climate change using next generation high resolution climate projections
OpenOcean
A multidisciplinary study group of experts in marine ecology, physical oceanography and stock assessment from the fishing industry, government and academia.
BRINGING ENVIRONMENT INTO STOCK ASSESSMENT MODELS
Industry/Outreach
Chris Roebuck
Dan & Lars Axelsson
Hank Lackner
Geir Monsen (Seafreeze)
Greg DiDomenico
(Garden State Seafood)
Lunds Fisheries
Eleanor A. Bochenek (Rutgers)
John Hoey (NOAA/NEFSC)
Fishery Scientists/Ecologists
John Manderson
(NOAA/NEFSC)
Laura Palamara (Rutgers)
Olaf Jensen (Rutgers)
Tim Miller (NOAA/NEFSC)
Chuck Adams (NOAA/NEFSC)
Howard Townsend (NOAA/NEFSC)
John Quinlan (NOAA/SEFSC)
David Richardson (NOAA/NEFSC)
We decided to focus on the observation process quantified in the stock assessment model. The term Q (catchability) is based on the stock range relative to the survey (availability) and trawl net efficiency (detectability). We used our model of HSI to estimate availability (ρh) to fishery independent surveys.
(ρ)
SUMMARY
HSI = suitability index (0-1) from habitat model
k1...o = samples in a survey
j = locations
i = times (day)
p = number of samples in strata of
sample k
TOTAL Q
Proportion of population in
Survey space-time frame
Ocean observatories and physical models capture the dynamics of coastal systems
Products co-developed with scientists, managers, and the private sector can support greater scientific understanding, management, and assessment.
Through these partnerships, interaction can yield useful and timely products in support of ecosystem based management and assessment.
Physical and Biological Oceanographers
Josh Kohut (Rutgers)
Matt Oliver (U. Delaware)
Andre Schmidt (SMAST)
Nickitas Georgas (Stevens Inst.)
Enrique Curchitser (Rutgers)
Fisheries Management
Jason Didden (MAFMC)
Rick Seagraves (MAFMC)
Human Dimensions
Steven Gray (Mich. St..)
Q = availability * detectability
(δ)
Proportion of fish occupying station caught in net
*The black box indicates the range of values used to estimate Q in the prior stock assessment model.
For more information please contact:
Josh T. Kohut
(848)