Development of a Novel BP Oil Spill / Food Web Interaction Model
for Coastal Waters in the Northern Gulf of Mexico (NGI BP-GRI Phase II):
A New Task in Support of Task 3 (Phase I/II)
Investigator:
Dr. Scott P. Milroy (community ecology; coupled biophysical models) – PI
Task 3 (Phase I/II) Synergy:
1) Time series data for a multitude of critical physical and chemical oceanographic measures have been
acquired from stations within the Mississippi Sound (MsS), Mississippi Bight (MsB), and Chandeleur
Sound (ChS) as part of NGI BP-GRI Task 3. These, coupled with the CODAR stations (NGI BPGRI Task 2) within the Central Gulf of Mexico Ocean Observing System (CenGOOS) will provide
critical initialization/validation information for oil spill trajectory forecasting.
2) Task 3 collections/analyses of polycyclic aromatic hydrocarbon (PAH) contamination within
surface/bottom waters, surficial sediments, suspended particulates (including phytoplankton and
zooplankton assemblages within the 20-53 m and 53+ m size-fractions), and the commerciallyand ecologically-valuable macrobiota (incl. Menhaden, Pink Shrimp, Oyster) will provide key
boundary conditions and calibration/validation data for food web model parameters of toxicant
bioaccumulation, transfer efficiencies, and other insights into oil impacts on ecosystem function.
Oil Spill / Food Web Interaction Model – Action Items:
1) Utilize Task 3 (Phase I/II) PAH in situ and bioaccumulation data to inform the development of a
novel oil spill / food web interaction model.
2) Synthesize the pertinent elements of the Thomann et al. (1992), Chapra (1997), and Gin et al. (2001)
food web models for a best-fit amalgam which most faithfully represents the coastal food web of the
MsS, MsB, and ChS.
3) Engage in original Fortran 95 code development, testing, calibration, and hindcast/forecast model
validation on the USM High Performance Computing Cluster (HPCC).
4) Test the forecast capabilities of the new oil spill / food web interaction model, using Milroy’s Task 3
(Phase I) PAH bioaccumulation data from 2010, to predict Milroy’s “current” Task 3 (Phase II) PAH
bioaccumulation and/or depuration data from 2011.
Oil Spill / Food Web Interaction Model – Background:
The lethal and sub-lethal effects of PAH exposure on marine organisms (particularly the more
sensitive juvenile forms) are expected to drive significant shifts in primary and secondary production,
larval recruitment, and community structure within the lower tiers of the marine food web (Gin et al.
2001). While the acute toxic effects of spilled oil can be as devastating to marine ecosystems as they are
immediate, it is also critical to understand the more subtle, long-term ecological perturbations which arise
from chronic, sub-lethal perfusions of oil and oil products into the various compartments within complex
marine/coastal food webs.
Indeed, the chronic exposure to petroleum hydrocarbons, in concentrations as low as 0.05 ppm, has
been shown to progressively depress photosynthetic activity in algal cultures (Clark 1997). Beyond 0.25
ppm, feeding efficiencies in the copepod Acartia spp. are decreased and food selection is altered (Gin et
al. 2001). While the robust nekton are generally able to avoid areas most grievously affected by oil, the
phytoplankton, holoplankton, and meroplankton are incapable of avoiding oiled water masses and are
typically more sensitive to exposures than are the larger and/or adult forms (Reed and Spaulding 1978).
Hatching success of fertilized capelin (smelt) eggs is significantly reduced in crude oil concentrations as
low as 0.01 ppm, while development abnormalities within the surviving ichthyoplankton were observed
where crude oil concentrations exceeded 250 ppm (Clark 1997).
Of course, the ecological repercussions of these chronic effects would not be constrained to a single
compartment within the coastal/marine food webs of the northern Gulf of Mexico; rather, the intimate
connectivity between these compartments would necessarily transfer oil-related toxicants (e.g. PAHs) to
several key groups of marine organisms within the affected food web. The potential incorporation of
PAH-related carcinogens into local fisheries (Baker 1983) and shellfish (e.g. oysters, shrimp, crab) is also
a major cause of concern. Hence, the ability to forecast the impacts of PAH-contamination within
ecologically- and commercially-important species would not only serve to define the extent of ecosystem
impairment as a result of the Deepwater Horizon (DwH) spill, but such information could also be used by
resource managers to forecast production, mortality, and the bioaccumulation of PAH-toxins among endmembers within the larger marine food web.
Oil Spill / Food Web Interaction Model – Model Development:
Using measures of PAH chemical species and relative concentrations within bulk seawater, surficial
sediments, suspended particulates (of both the 20-53 m and 53+ m size fractions), as well as the standard
biometrics, lipid fraction, and PAH concentrations from the macrobiota from Milroy’s NGI BP-GRI Task 3
(Phase I/II) complimentary research, a coastal food web interaction model (Fig. 1) shall be developed to assess
probable impacts of the DwH spill on several key marine organisms within northern Gulf of Mexico coastal
waters. In the proposed model, the organism-oil toxicant (PAH) interaction may take place either through the
passive uptake of dissolved PAHs in the water column or from the sediments, or through the consumption of
oil-contaminated prey (similar to the food web interaction model utilized by Gin et al. 2001).
Uptake shall depend upon the organism’s respiration rate and the PAH transfer efficiency across the
(idealized) organism membrane. Disparate PAH assimilation efficiencies for a specific oil compound, such as
naphthalene or benzo(a)pyrene shall be estimated from available ecotoxicology resources and calibrated for the
proposed model using the kinetics suggested by Chapra (1997) and adjusted for the representative species
chosen for each food web compartment, as:
Phytoplankton:
C phy
t
Zooplankton:
Planktivorous Fishes:
C pf
C ff
t
Benthic Invertebrates:
(1)
C zoo
 K zoo BCFzooCd  C zoo    zoo phy I L ( zoo) C phy
t
t
Foraging/Predatory Fishes:
 K phy BCFphyCd  C phy 
 K pf BCF pf C d  C pf 
(2)
(3)
 Ppf  phy pf  phy I L ( pf ) C phy  Ppf  zoo pf  zoo I L ( pf ) C zoo
 K ff BCF ff Cd  C ff 
(4)
 Pff inv ff inv I L ( ff ) Cinv  Pff  pf  ff  pf I L ( ff ) C pf

Cinv
 Kinv BCFinv Esed Cd ( sed )  Ed Cd  Cinv
t
 Pinv phyinv phy I L ( inv)C phy

 Pinv zooinv zoo I L ( inv)Czoo  Pinvsedinvsed I L ( oc)Csed
(5)
Water
Phytoplankton
and 20-53 m
Particulate PAHs
Zooplankton
and 53+ m
Particulate PAHs
Planktivorous
Fishes
(PAHs)
Foraging and
Predatory Fishes
(PAHs)
Suspensionfeeding Benthic
Inverts (PAHs)
Depositfeeding Benthic
Inverts (PAHs)
Dissolved
PAHs
Dissolved
PAHs
Detritus/
Particulate
PAHs
Sediment
Figure 1. Schematic of the proposed oil spill / food web interaction model, adapted from Gin et al. 2001.
where Ci is the PAH concentration in the ith group on a lipid basis (g PAH kg-1 lipid), Cd is the dissolved
concentration of a specific PAH in the water column (g m-3), BCFi is the bioconcentration factor for the ith
group (m3 kg-1), i→j is the PAH assimilation efficiency (g PAH absorbed g-1 PAH ingested) for the ith group
feeding on the jth prey, ILi is the lipid-specific consumption rate (kg lipid in prey kg-1 lipid in predator s-1) of the
ith group, ILoc is the rate of organic carbon ingestion relative to lipid ingestion (kg organic carbon ingested kg-1
lipid ingested s-1), Pi→j is the proportional preference factor (unitless) for the ith group feeding on the jth prey,
and Ei is the fractional PAH uptake (unitless) from either the water column or from the surficial sediments.
Oil Spill / Food Web Interaction Model – Timeline/Milestones:
Jan – Apr 2011:
Core Fortran 95 code development
Apr – Jun 2011:
Test individual subroutines; benchmark processing speeds on the USM HPCC
Jun – Aug 2011:
Consolidate Fortran 95 code; test integrity/fidelity of the full model
Aug – Sep 2011:
Adjust core Fortran 95 code (bug-testing and code revision)
Sep – Dec 2011:
Calibrate/Validate model with Milroy’s Task 3 (Phase I) data; compare model
performance (forecast fidelity) relative to Milroy’s Task 3 (Phase II) data
Jan – Feb 2012:
Draft final project report
15 Feb 2012:
Final project report due
Oil Spill / Food Web Interaction Model – Summary Budget:
Total salaries & benefits
$ 51,827
Total Rents/Travel
$ 3,609
Total Commodities/Software Licenses
$ 8,080
Equipment (Dell Workstation)
$ 7,098
Total Indirect Costs (46.5% of TDC)
$ 27,857
Total Costs
$ 94,861
References:
Baker, JM (1983) Impact of oil pollution on living resources. Commission on Ecology Papers 4: 218-230.
Clark, RB (1997) Marine Pollution. Gookcraft Ltd., Great Britain, UK, pp. 23-59.
Chapra, SC (1997) Surface Water Quality Modeling. McGraw-Hill, New York, USA, pp. 784-794.
Collins, JF et al. (1998) Potency equivalency factors for some polycyclic aromatic hydrocarbons
and polycyclic aromatic derivatives. Regul. Toxicol. Pharmacol. 28: 45-54
Gin, KYH, et al. (2001) An oil spill-food chain interaction model. Marine Pollution Bulletin
42(7): 590-597.
Reed, M & ML Spaulding (1978) An oil spill-fishery interaction model: comparison of treated and
untreated spill impacts. Environmental Assessment of Treated versus Untreated Oil Spills: Second
Interim Progress Report, No. E(11-1)4047. U.S. Department of Energy, Washington DC, USA.
Thomann, RV, JP Connolly, and TF Parkerton (1992) An equilibrium model of organic chemical
accumulation in aquatic food webs with sediment interaction. Environmental Toxicology and
Chemistry 11: 615-629.