Appendices 1.0 Functional group biomass 1.1 Phytoplankton Phytoplankton biomass was estimated from satellite‐derived, 9‐km resolution, 8‐day composite images of chlorophyll‐a concentration (mg chl‐a m‐3; Table A.1). Each composite image was clipped to the model domain and individual pixels were spatially joined with the closest bathymetric sounding (Table A.1) in GIS software (ESRI ArcMap v10.1). Depth‐integrated chl‐a concentration for each pixel (mg m‐2) was estimated by multiplying mg chl‐a m‐3 by the sounding (m), assuming the mixed layer depth extended to the sea floor throughout the entire spatiotemporal domain. The annual mean, depth‐integrated phytoplankton wet weight (WWT) biomass was calculated by converting the annual mean mg chl‐a m‐2 to tonnes (t) WWT km‐2 using the following conversion factors: carbon:chl‐a = 50 (Mortazavi et al., 2000; Lehrter and Pennock, 1999), C:DWT = 0.5, and DWT:WWT = 0.2 (Strickland, 1960).

1.2 Microzooplankton Microzooplankton biomass was derived from samples collected at stations near the Mississippi River outflow (M. Sutor, unpubl.). Whole water samples were collected from discrete depths with a pump sampling system and preserved in 5% acid Lugols solution. Sample aliquots (10‐100mL) were analyzed using an inverted microscope and individuals were quantified, identified, and measured. The community assemblage consisted of dinoflagellates (Ceratium sp. Dinophysis sp., Gyrodinium sp., Perdinium sp. Prorocentrum sp., Strombidium sp.), tintinnids (Favella sp., Codonella sp., Helicostomella sp.), and other cyclotrich and oligotrich ciliates. Biovolume (µm3) was calculated by applying the individual measurements to volume equations for the shape of the cell (spherical, conical, or cylindrical). Cell biovolume (µm3) was converted to carbon biomass (µg carbon) using equations given in (Verity and Lagdon, 1984; Putt and Stoecker, 1989; Ota and Taniguchi, 2003). Carbon biomass was converted to mg WWT per liter using conversion factors (C:DWT of 0.5 and DWT:WWT of 0.2) for phytoplankton (S. Strom WWU and D. Gifford URI, pers. comm.). The depth‐integrated biomass (t WWT km‐2) at each station was then found using the depth interval (m) at which the cells were collected. 1.3 Mesozooplankton Biomass of the detritus group ‘fish eggs’, and all other zooplankton functional groups (i.e. ‘large copepods’, ‘euphausiids’, ‘other mesozooplankton’, ‘fish larvae’, ‘gelatinous filter feeders’, ‘small gelatinous carnivores’, and ‘small squids’) was derived using a subset of plankton data from the Fisheries Oceanography Program of Coastal Alabama (FOCAL, Carassou et al. 2012) and from cruise data collected by M. Roman and J. Pierson (University of Maryland Center for Environmental Science) on the eastern Louisiana shelf and the central Mississippi Bight (Kimmel et al. 2010; Elliott et al., 2012; Roman et al., 2012).

FOCAL performed monthly cross‐shelf transects from 30.43°N, 88.01°W to 29.79°N, 88.21°W (Carassou et al., 2012). FOCAL estimates of fish eggs and larvae were derived from Bedford Institute of Oceanography Net Environmental Sampling System (BIONESS), plankton ring net, and neuston net samples; all other groups were derived using only BIONESS and plankton ring net samples. The BIONESS system had a 0.25‐m2 mouth opening, and was fitted with 333‐µm mesh plankton nets. BIONESS nets were towed horizontally to sample a discrete depth bin. A neuston net (1.0 x 2.0‐m or 0.5 x 1.0‐m) was towed at the surface, targeting a depth bin of 0 to 0.5 m. The 1‐m diameter, 333‐µm mesh plankton ring net was towed obliquely from 2.0 m above the sea floor to the surface.

Roman and J. Pierson provided mean estimates (individuals m‐3) of zooplankton identified down the lowest taxonomic resolution possible (often genus). For the years 2003, 2004, 2006‐2008, zooplankton samples were collected during CTD casts using an Ingersoll‐Rand diaphragm pump by filtering water through a 64 µm mesh sieve (Kimmel et al., 2010; Elliott et al., 2012). Zooplankton samples in 2010‐2011 were collected from 20 Liter Niskin bottles that were filtered through a 64 µm mesh sieve.

Densities of taxonomic groups (e.g. calanoid copepods) were converted to depth‐ integrated biomass using either the sampling depth bin (m), or water depth (m) if a plankton ring net was used, and taxon‐specific WWTs (Table A.13). Wet weights were derived from published length‐to‐weight and mass‐to‐mass relationships (Table A.14). Annual biomass (t WWT km‐2) for each functional group was found by finding the total biomass of all taxonomic groups assigned to that group in a given year. The long‐term annual mean, variance, and standard deviation were then calculated and scaling factors applied as needed (Table A.24) to achieve a balanced model.

1.4 Small gelatinous carnivores Small gelatinous carnivore biomass was derived using the FOCAL plankton survey count data set as described above and a separate data set of total gelatinous zooplankton biovolume from the same survey. Estimates from the biovolume data set were included to better represent the biomass of adult ctenophores (Mnemiopsis sp. and Beroe sp.) that are typically removed from the plankton samples aboard ship prior to preservation. Total biovolume (mL) from a given BIONESS plankton tow sample was converted to WWT (g) using published biovolume‐to‐WWT relationships from (Lucas et al., 2011) given in Table A.15. Biomass (g WWT) was divided by the volume filtered by the net (m3) to yield (g WWT m‐3). This value was multiplied by the net depth bin (m) to yield depth‐integrated biomass (g WWT m‐2), which was then scaled to t WWT km‐2. Small gelatinous carnivore biomass was then scaled to equivalent fish weight weights by multiplying by 0.167, where the DWT:WWT of fish = 0.3 and DWT:WWT of chaetognaths is 0.05 (Postel et al., 2000).

1.5 Invertebrates, elasmobranchs, and bony fishes Biomasses of ‘benthic epifauna' (e.g. crabs), elasmobranchs, and bony fishes were estimated using data from the Southeast Area Monitoring and Assessment Program bottom‐ trawl surveys conducted by the National Marine Fisheries Service (NMFS) and Gulf States Marine Fishery Commission (SEAMAP; http://seamap.gsmfc.org/). Extrapolated taxon counts from SEAMAP bottom trawls were converted to a wet weight biomass using either a published family, genus, or ‐specific length‐to‐weight relationship or mean individual WWT (Table A.16). A cruise mean WWT was employed when trawl‐specific biometric data was unavailable. Taxon count was multiplied by an individual mean WWT (g) and then divided by 1000 to yield taxon‐specific biomass (kg) from each trawl. To find the depth‐integrated biomass (t km‐2), biomass was first standardized to volume filtered (kg m‐3). The volume filtered (m3) by the trawl net was found by multiplying the area of the net opening (m2) by the distance (m) the trawl net was towed. The distance the trawl net was towed was estimated differently for ‘pelagic’ and ‘demersal’ taxa because it was assumed taxa inhabiting the water column (i.e. pelagic taxa) were primarily collected on the deployment and recovery of the net while demersal taxa could have been collected at any time during the tow. Trawl distance for pelagic taxa was calculated following Robinson and Graham (2013). For demersal taxa, the distance the trawl net was towed was estimated using the Spherical Law of Cosines (Eq. 1),

m acos ∆ 1000 Eq. 1

where is the start latitude of the trawl, is the end latitude of the trawl, ∆ is the difference between the end longitude λ2 and start longitude λ1, and R is the radius of the Earth (6371 km). The depth‐integrated biomass (kg m‐2) was then calculated by multiplying the standardized biomass (kg m‐3) by the by the deepest water depth (m) recorded for the station.

Depth‐integrated biomass (kg m‐2) for each functional group was calculated by finding the sum of the depth‐integrated biomasses for all taxonomic groups assigned to a functional group (Table A.2) at each station and then dividing by the area filtered. The annual mean and variance for each functional group (t WWT km‐2) was then estimated assuming a delta‐lognormal distribution in MATLab (MathWorks vR2013a). A delta‐lognormal distribution was used given marine taxonomic count data sets are often zero‐inflated (Lo et al., 1992).

1.6 Large jellyfish Large jellyfish (Aurelia spp. and Chrysaora sp.) biomass was estimated using SEAMAP and FOCAL data. Biomass estimates of large jellyfish were derived using the same methods outlined above for SEAMAP invertebrates. Large jellyfish were considered pelagic taxa and a static mean WWT of 342 g and 82.9 g was used for Aurelia spp. and Chrysaora sp. (i.e. large jellyfish), respectively, to covert SEAMAP densities (individuals m‐3) to WWT m‐3. Taxon‐specific, total biovolume (mL) from the FOCAL BIONESS plankton tows was converted to WWT (g) using biovolume‐to‐WWT relationships (Table A.15; Lucas et al., 2011). Biomass was divided by the volume filtered by the net (m3) to yield (g WWT m‐3). This value was multiplied by the net depth bin (m) to yield depth‐integrated biomass (g WWT m‐2), which was then scaled to t WWT km‐2. The mean biomass from the FOCAL and SEAMAP data sets was then scaled to equivalent fish weight weights by multiplying it by 0.094, where the DWT:WWT of fish is 0.3 and the average DWT:WWT of Aurelia spp. and Chrysaora sp. is 0.028 (Lucas et al., 2011).

1.7 Bivalves Bivalve biomass included estimates of oysters from an Alabama oyster reef survey (K. Heck, Dauphin Island Sea Lab, Alabama, USA) and paper scallop data from the SEAMAP bottom trawl survey (Table A.1). Paper scallop biomass was estimated using the methods outlined above for demersal invertebrates.

1.8 Dolphins, other toothed whales, and baleen whales Marine mammal biomasses were estimated using Gulf‐wide stock assessment reports (Waring et al., 2012), and mean body mass estimates from Trites and Pauly (1998) (Tables A.16, A.17). Marine mammal densities from stock assessments were divided by the survey area thought to be occupied by the population as indicated in (Waring et al., 2011; Tables A.16). Survey areas (km2) included inland bays and estuaries, waters between 0‐20 m and 20‐200‐m isobaths and between the 200‐m isobath and the U.S. Exclusive Economic Zone. Areas were calculated in using the Spatial Statistics tools in ArcMap v10.1. These areas demarcated coastal, shelf, slope, and oceanic areas respectively. Taxon‐specific densities were then multiplied by the fraction of the population in the model domain along its east‐to‐west axis and the fraction of the domain’s depth range (i.e. 0‐20m) covered by the survey (Table A.17). These scaled densities were then converted to a wet weight biomass (t km‐2) using values from Trites and Pauly (1998; Table A.18).

1.9 Marine birds Marine birds were represented by three species present during the model period: brown pelicans, royal and least terns, and black skimmers. Biomass was generated from published nest counts as well as unpublished surveys of adult abundance (Table A.1). Least tern, royal tern, and black skimmer biomass (Table A.19) was derived using records of annual maximum nest count data synthesized by Love et al. (2013) and individual WWTs (Table A.18). Brown pelican biomass was estimated using nest count data sets synthesized by (Love et al., 2013) and published and unpublished estimates of the number of adult and juvenile birds (Table A.1). Nests counts were multiplied by two to estimate the number of adult birds and then by the species‐specific mean individual WWT. Counts were standardized to area (km2) using taxon‐specific foraging ranges: 20 km for brown pelicans (Fritts et al., 1983; Visser et al., 2005), 8 km for black skimmers (Burger and Gochfeld, 1990), 32 km for least terns (Fritts et al. 1983), and 48 km for royal terns (McGinnis and Emslie, 2001). These distances were used to create species‐specific polygon buffers around all nest sites (i.e. foraging areas; Table A.19). Foraging areas that intersected with the model domain were then extracted, projected into the ‘USA Contiguous Albers Equal Area Conic’ coordinate system, and their areas estimated. The long‐term annual mean biomass for each group was calculated and divided by the foraging area to yield t WWT km‐2 yr‐1 (Table A.19).

1.10 Marine turtles Green sea turtle and loggerhead biomasses were estimated using annual nest counts (5055 and 910, respectively) done in Florida (NMFS, 2007a, 2007b). Nest counts were scaled to the number of adult turtles by dividing by the number of nests laid per female per season (three nests for green turtles, five nests for loggerheads) and then multiplying by two to account for males. Kemp’s Ridley and leatherback sea turtle biomasses were estimated by multiplying numeric densities given in Okey and Mahmoudi (2002) by mean individual wet weights (Table A.18). Biomass was then converted to t WWT km‐2 using the area (3.35 x 105 km2) falling within the 0‐200 m isobaths.

2.0 Diet composition Functional group diet composition in the fully resolved model (Table A.4) was determined using taxon‐specific (species, genus, or family) data extracted from either the published literature or www.fishbase.org (Table A.5). The percent contribution of prey items identified in the literature for green, loggerhead, and leatherback sea turtles, benthic infauna, and small gelatinous filter‐feeders were estimated using author expertise. The diet of microzooplankton was estimated using author expertise. In some instances, diet information from taxa representative of the functional group was used (Table A.5). Diets often included aggregated information for recruits, juvenile, and adult life‐history stages. To generate the model diet matrix, taxon‐specific prey and consumers were first assigned to model functional groups. Consumer diet was then adjusted as needed so that the total diet composition summed to one. The mean proportion each taxon‐specific prey item contributed to each consumer functional group diet was then calculated. The diet composition of each consumer functional group was then expressed as the sum of those means.

3.0 Fishery landings

3.1 Commercial landings Annual landings for each functional group was estimated by first finding the sum of all taxonomic groups assigned to that functional group in a given year (Table A.20) and then scaling that value by the fraction of the functional group’s biomass in the model domain (Table A.21). Scaled landings were divided by the model domain area. The long‐term annual mean, variance, and standard deviation were calculated to estimate each functional group’s commercial fishery landings (t WWT yr‐1 km‐2).

3.2 Recreational landings Recreational blue crab (Callinectes spp.) landings were expressed as the fraction of the annual total commercial catch (tonnes) in each state following (VanderKooy, 2013): 4.1% for Louisiana (Guillory, 1998), 4% for Mississippi (Herring and Christmas Jr., 1974), and 20% for Alabama (Tatum, 1982). Species in the MRFSS and MRIP data sets were assigned to model functional groups (Table A.22) using criteria described previously. Catches were reported as one of three types: A (observed harvest), B1 (unobserved harvest), and B2 (caught and released alive); only A and B1 catches were used and only ‘Final’ MRIP records were included. A subset of records including all unique taxonomic groups (species, genus, or family), all years, and all fishing modes was extracted for fishing areas in the Gulf of Mexico sub‐region occurring within the model domain and fishing waves (i.e., 2‐month sampling periods) occurring between March and December. The MRFSS fishing areas ‘Inland’ and ‘Ocean (<= 3 mile)’ were considered to fall within the model domain.

Both the total catch weight in pounds (‘LBS_AB1’) and the total number of individual fish ‘Landings’ (A + B1) were used to estimate total harvest by weight of each taxonomic group. Total catch weight (lbs) was converted to kilograms by multiplying by 0.45. If the value for total catch weight (lbs) was null, then biomass was estimated by multiplying ‘Landings’ (number of individual fish) by either the species‐specific mean individual wet weight of A fish (kg) measured in that sample or by the species‐specific individual mean wet weight (kg) in the sub‐region, with priority given the former. Landing records for 2004‐2009 included ‘Missed Fish’ (i.e., A + B1 + ‘Missed Fish’). The total annual landings (tonnes) for each functional group was calculated by first multiplying total catch weight (kg) by 0.001 and the finding the yearly sum of the landings for all taxonomic groups assigned to the functional group. Total annual landings (tonnes) were then scaled by the fraction of the functional group’s biomass in the model domain (Table A.21) and normalized to the model domain area. The long‐term annual mean, variance, and standard deviation were then calculated to estimate the recreational fishery landings (t WWT km‐2 yr‐1) for each functional group. Recreational landings of small squids and penaeid shrimp were set to zero since landings information could not be found.

4.0 Fishery discards 4.1 Commercial discards Commercial fishery discards were estimated using data from pelagic troll, reef fish bottom longline, reef fish handline gears (NMFS, 2011), the menhaden purse seine fishery (SEDAR, 2013), and species‐specific by‐catch characterization samples from the shrimp fishery (Scott‐Denton et al., 2012). Discard (t yr‐1) of each functional group was scaled by multiplying it by the fraction of the functional group’s biomass in the model domain (Table A.21) and dividing it by the model area to yield t WWT yr‐1 km‐2.

4.1.1 Marine mammals, turtles, and birds Commercial by‐catch of marine mammal, turtle, and birds from Scott‐Denton et al. (2012) shrimp trawl discard data were specific to the model domain; however, data from the NMFS bycatch report provided only combined discard data for the Atlantic and Gulf of Mexico for fisheries utilizing pelagic troll, reef fish bottom longline, reef fish handline, drift‐strike‐and‐ bottom gillnet, pelagic longline, and shark bottom longline gears. It was assumed 50% of the discarded marine mammals and leatherback sea turtles were from Gulf‐based fisheries. The percent of reported strandings in the Gulf of Mexico by (Thompson, 1988) was used to allocate discards for loggerhead (11%), green (31%), and Kemp’s Ridley (49%) sea turtles.

Because not all bycatch results in mortality, a 20% mortality rate was applied to all marine mammals and turtle groups (Scott‐Denton et al. 2012). The fraction of moribund individuals was then multiplied by a species‐specific mean individual mean wet weight (g) (Tables A.17) to derive a total annual discarded biomass (t WWT). For marine mammals, discarded biomass (t WWT yr‐1) was scaled by the proportion of the functional group’s biomass in the model domain. Marine turtles discarded biomass was scaled by the percentage (6.1%) the model domain area represents for the entire NMFS jurisdiction in the Gulf of Mexico (U.S. coastline to the Exclusive Economic Zone boundary). Scaled values were divided by the model area to yield (t WWT yr‐1 km‐2). No scaling factors were applied to the Scott‐Denton et al. (2012) shrimp trawl discard data as those were all within the model domain.

4.2 Recreational discards Recreational fishery discards (t WWT yr‐1 km‐2) of finfish were estimated using B2 fish (number caught and released alive) in the MRFSS and MRIP data sets (Table A.1) and applying mortality estimates (Table A.23) as fish released alive often suffer some post‐release mortality. The number of B2 fish were first converted to WWT biomass by multiplying B2 by (in order of priority) the species‐specific average weight of a type A or B1 fish (kg) in the sample, the average weight of A fish (kg), the average weight of an individual fish in the Gulf of Mexico sub‐ region, the long‐term average weight of A fish (kg), or the long‐term average weight of an individual fish in the sub‐region (kg). The total annual B2 biomass for each functional group was then calculated by first multiplying the species‐specific, B2 biomass (kg) by 0.001 and the finding the annual sum of all taxonomic groups assigned to the functional group. Annual functional group B2 biomass (tonnes WWT) was scaled by the fraction of the functional group was represented in the model domain (Table A.21) and normalized to the model area. This value was then multiplied by the estimated fraction of the B2 fish lost to mortality (Table A.23), following Geers (2012). A 20% mortality was assumed if an estimate could not be identified in the literature or stock assessments as this was the published mode (see Table A.23). Recreational discards of blue crab (Callinectes sapidus) and bivalves were assumed to be 20% of recreational landings for this reason. The long‐term annual mean, variance, and standard deviation were then calculated. Recreational discard rates were set to zero for penaeid shrimp, euphausiids, small squids, and large jellyfish as no information was available in the NFMS data set and no recreational fishery exists to the best of our knowledge for these taxa in the northern Gulf of Mexico.

5.0 Model balancing The model was balanced such that predation and fishery demands did not exceed the net production for any defined group. Model balancing was preceded by the use of ‘PREBAL’ diagnostic procedures (Link, 2010). Diagnostics revealed several issues: First, biomasses of functional groups spanned 9 orders of magnitude and was mid‐trophic level heavy rather than declining with increasing trophic level. This issue was addressed by aggregating a few of the low‐to‐mid trophic groups. Second, mesozooplankton functional groups’ grazing on phytoplankton was too high (i.e. biomass ratio was >1) and so zooplankton diets were modified so that they consumed more microzooplankton. Third, total human removals of four groups (penaeid shrimp, menhaden, mullet, and red drum) were found to be greater than the total, modeled production (t km‐2) of each taxa in the ecosystem. Following this diagnostic, scaling factors were applied to most biomass estimates to account for gear capture efficiencies and differences between species and sample depth ranges (Table A.24).

6.0 Model generation for Monte Carlo analyses So that the production of mass‐balanced models from randomly generated parameter sets was reasonably efficient, we found it necessary to constrain the sampling distribution of 10 functional groups (euphausiids, other mesozooplankton, fish eggs, fish larvae, large jellyfish, large demersal fish, small squids, brown pelicans, terns, black skimmers) such that the coefficient of variation about the mean did not exceed 5. Our rule‐of‐thumb for a minimum sampling efficiency was 1 mass‐balanced model produced per 50,000 randomly generated parameter sets. Scenario analyses were conducted on all 1000 models simultaneously to obtain indices of uncertainty about each model‐derived metric. Appendix References Abdurahiman, K. P., Harishnayak, T., Zacharia, P. U., and Mohamed, K. S. 2004. Length‐weight relationship of commercially important marine fishes and shellfi shes of the southern coast of Karnataka, India. NAGA, WorldFish Center Quarterly, 27: 9‐14.

AMRD. 2007. 2007 Spotted Seatrout Assessment. 11 pp.

Ara, K. 2001. Length‐weight relationships and chemical content of the planktonic copepods in the Cananeia Lagoon estuarine system, Sao Paulo, Brazil. Plankton Biology and Ecology, 48: 121‐127.

Arreguín‐Sánchez, F., and Manickchand‐Heilman, S. 1993. The trophic role of lutjanid fish and impacts of their fisheries in two ecosytems in the Gulf of Mexico. Journal of Fish Biology, 53: 143‐153.

Arthur, K.E., Boyle, M.C., and Limpus, C.J. 2008. Ontogenetic changes in diet and habitat use in green sea turtle (Chelonia mydas) life history. Marine Ecology Progress Series, 362: 303‐ 311.

Aydin, K., McFarlane, G. A., King, J. R., and Megrey, B. A. 2003. The BASS/MODEL report on trophic models of the subarctic Pacific Basin ecosystems. Report 25. 93 pp.

Aydin, K., and Mueter, F. 2007. The Bering Sea‐‐A dynamic food web perspective. Deep Sea Research Part II: Topical Studies in Oceanography, 54: 2501‐2525.

Baeta, M., and Ramón, M. 2013. Feeding ecology of three species of Astropecten (Asteroidea) coexisting on shallow sandy bottoms of the northwestern Mediterranean Sea. Marine Biology, 160: 2781‐2795.

Barros, N. l. B., and Wells, R. S. 1998. Prey and feeding patterns of resident bottlenose dolphins (Tursiops truncatus) in Sarasota Bay, Florida. Journal of Mammalogy, 79: 1045‐1059.

Barry, K. P., Condrey, R. E., Driggers, W. B., and Jones, C. M. 2008. Feeding ecology and growth of neonate and juvenile blacktip sharks Carcharhinus limbatus in the Timbalier– Terrebone Bay complex, LA, U.S.A. Journal of Fish Biology, 73: 650‐662.

Bethea, D. M., A., B. J., and Carlson, J. K. 2004. Foraging ecology of the early life stages of four sympatric shark species. Marine Ecology Progress Series, 268: 245‐264.

Bielsa, L. M., Murdich, W. H., and Labisky, R. F. 1983. Species profiles: life histories and environmental requirements of coastal fishes and invertebrates (south Florida) ‐‐ pink shrimp. Report FWS/OBS‐82/11.17 and TR EL‐82‐4. 21 pp.

Bjorndal, K. A. 1996. Foraging ecology of sea turtles. In The Biology of Sea Turtles, pp. 199‐227. Ed. by P. L. Lutz, and J. A. Musick. CRC Press. Böer, M., Gannefors, C., Kattner, G., Graeve, M., Hop, H., and Falk‐Petersen, S. 2005. The Arctic pteropod Clione limacina: seasonal lipid dynamics and life‐strategy. Marine Biology, 147: 707‐717.

Boltovskoy, D. 1999. South Atlantic Zooplankton. p. 1705. Backhuys, Leiden.

Boer, M., Gannefors, C., Kattner, G., Graeve, M., Hop, H., Falk‐Petersen, S. 2005. The Arctic pteropod Clione limacina: seasonal lipid dynamics and life‐strategy. Marine Biology, 147:707‐717.

Bowman, R., Stillwell, C., E., Michaels, W. L., and Grosslein, M. D. 2000. Food of Northwest Atlantic fishes and two common species of squid. NMFS‐NE‐155. 149 pp.

Brito, R., Chimal, M. E., Gaxiola, G., and Rosas, C. 2000. Growth, metabolic rate, and digestive enzyme activity in the white shrimp Litopenaeus setiferus early postlarvae fed different diets. Journal of Experimental Marine Biology and Ecology, 255: 21‐36.

Broadhurst, M. K., Butcher, P. A., and Cullis, B. R. 2011. Post‐release mortality of angled sand mullet (Myxus elongatus: Mugilidae). Fisheries Research, 107: 272‐275.

Browder, J. A. 1990. Trophic flows and organic matter budget Gulf of Mexico continental shelf. In International Council for the Exploration of the Sea, p. 5.

Brown, B. E., Browder, J. A., Powers, J., and Goodyear, C. D. 1991. Biomass, yield models, and management strategies for the Gulf of Mexico ecosystem. In Food chains, yields, models, and management of large marine ecosystems, pp. 125‐163. Ed. by K. Sherman, L. M. Alexander, and B. D. Gold. Westview Press, Boulder, CO.

Burger, J., and Gochfeld, M. 1990. The Black Skimmer: social dynamics of a colonial species, Columbia University Press, New York. 355 pp.

Carassou, L., Hernandez, F. J., Powers, S. P., and Graham, W. M. 2012. Cross‐Shore, seasonal, and depth‐related structure of ichthyoplankton assemblages in coastal Alabama. Transactions of the American Fisheries Society, 141: 1137.

Christensen, V., and Pauly, D. 1993. Trophic models of aquatic ecosystems. p. 390. International Center for Living Aquatic Resources Management.

Cowan, J. H., and Houde, E. D. 1990. Growth and survival of bay anchovy Anchoa mitchilli larvae in mescosm enclosures. Marine Ecology Progress Series, 68: 47‐57.

D'Ambra, I. 2012. Application of stable isotopes in the analysis of trophic interactions between gelatinous zooplankton and fish. In Marine Sciences, p. 105. University of South Alabama, Mobile. De La Cruz‐Aguero, G. 1993. A preliminary model of Mandinga Lagoon, Veracruz, Mexico. In Trophic models of aquatic ecosystems, pp. 193‐196. Ed. by V. Christensen, and D. Pauly. ICLARM Conference Proceedings. deVries, M. S. 2012. The feeding morphology and ecology of stomatopod crustaceans. In Integrative Biology, p. 101. University of California, Berkeley, Berkeley, CA.

Diagle, S. T. 2011. What is the importance of oil and gas platforms in the community structure and diet of benthic an demersal communities in the Gulf of Mexico? In Biological Sciences, p. 110. Louisiana State University, Baton Rouge, LA.

Duffy, J. 1999. Catch‐and‐release mortality studies of spotted seatrout and red drum in coastal Alabama. In National Symposium on Catch and Release in Marine Recreational Fisheries, pp. 110‐113. Virginia Sea Grant.

DuPaul, W. D., and Rudders, D. B. 2004. Seasonal, interannual, and geographic variations in sea scallop shell height and meat weight. ICES Document Virginia Marine Resources Report 2004‐3. 16 pp.

Elliott, D. T., Pierson, J. J., and Roman, M. R. 2012. Relationship between environmental conditions and zooplankton community structure during summer hypoxia in the northern Gulf of Mexico. Journal of Plankton Research, 34: 602‐613.

Espinoza, P., and Bertrand, A. 2008. Revisiting Peruvian anchovy (Engraulis ringens) trophodynamics provides a new vision of the Humboldt Current system. Progress in Oceanography, 79: 215‐227.

Fauchald, K., and Jumars, P. A. 1979. The diet of worms: a study of polychaete feeding guilds. Oceanography and Marine Biology Annual Review, 17: 193‐284.

Fogarty, M. J., Nesbitt, S. A., and Gilbert, C. R. 1981. Diet of nestling Brown Pelicans in Florida. Florida Field Naturalist, 9: 38‐40.

Forys, E. A., Arya, P.‐B., Krajcik, K., and szelistowski, W. A. 2013. Roof‐nesting least terns travel to forage in brackish/marine waters. Southeastern Naturalist, 12: 238‐242.

Fritts, T. H., Irivine, B., Jennings, R. D., Collum, R. D., Hoffman, W., and McGehee, M. A. 1983. Turtles, birds, and mammals in the northern Gulf of Mexico and nearby Atlantic waters. NTIS No. PB84‐144138. FWS/OBS‐82/65. 455 pp.

Froese, R., and Pauly, D. 2013. FishBase.

Geers, T. M. 2012. Developing an ecosystem‐based approach to management of the Gulf menhaden fishery using Ecopath with Ecosim. In ProQuest Dissertations and Theses, p. 116. State University of New York at Stony Brook, Ann Arbor. Geers, T. M., Pikitch, E. K., and Frisk, M. G. 2014. An original model of the Northern Gulf of Mexico using Ecopath with Ecosim and its implications for the effects of fishing on ecosystem structure and maturity. Deep Sea Research Part II: Topical Studies in Oceanography.

George, S. B. 1995. Enchinoderm egg and larval quality as a function of adult nutritional state. Oceaologica Acta, 19: 297‐308.

Gibson, D. M., and Paffenhöfer, G. A. 2000. Feeding and growth rates of the doliolid, Dolioletta gegenbauri Uljanin (Tunicata, Thaliacea). Journal of Plankton Reserach, 22: 1485‐1500.

Gifford, D. J., and Dagg, M. J. 1988. Feeding of the estuarine cpepod Acartia tonsa Dana: carnivory vs. herbivory in natural microplankton assemblages. Bulletin of Marine Science, 43: 458‐468.

Gleason, D. F., and Wellington, G. M. 1988. Food resources of postlarval brown shrimp Penaeus aztecus in a Texas salt marsh. Marine Biology, 97: 329‐337.

González, J. A., Quiles, J. A., and Santana, J. I. 2000. The Family Calappidae (Decapoda, Brachyura) around the Canary Islands. Crustaceana, 73: 1007‐1014.

Gorsky, G., Palazzoli, I., and Fenaux, R. 1987. Influence of temperature changes on oxygen uptake and ammonia and phosphate excretion, in relation to body size and weight, in Oikopleura dioica (Appendicularia). Marine Biology, 94: 191‐201.

Guillory, V. 1998. A survey of the recreational blue crab fishery in Terrebonne Parish, Louisiana. Journal of Shellfish Research, 17: 543‐549.

Haefner, P. A., Jr. 1964. Morphometry of the common Atlantic squid, Loligo pealei, and the brief squid, Lolliguncula brevis in Delaware Bay. Chesapeake Science, 5: 138.

Hansson, L. J. 1997. Effect of temperature on growth rate of Aurelia aurtia (, Scyphozoa) from Gullmarsfjorden, Sweden. Marine Ecology Progress Series, 161: 145‐ 153.

Herring, R., and Christmas Jr., J. Y. 1974. Blue crabs for fun...and food. Mississippi Game and Fish, 1974: 12‐14.

Hirst, A. G., Roff, J. C., and Lampitt, R. S. 2003. A synthesis of growth rates in marine epipelagic invertebrate zooplankton. In Advances in Marine Biology, pp. 3‐119. Ed. by A. J. Southward, P. A. Tyler, C. M. Young, and L. A. Fuiman. Academic Press.

Hoffmayer, E. R., and Parsons, G. R. 2003. Food Habits of Three Shark Species from the Mississippi Sound in the Northern Gulf of Mexico. Southeastern Naturalist, 2: 271‐280. Holland, D. L., and Walker, G. 1975. The biochemical composition of the cypris larva of the barnacle Balanus balanoides L. Journal du Conseil, 36: 162‐165.

Houde, E. D., and Zastrow, C. E. 1993. Ecosystem‐ and taxon‐specific dynamic and energeticsproperties of larval fish assemblages. Bulletin of Marine Science, 53: 290‐335.

Hubareva, E., Svetlichny, L., Kideys, A., and Isinibilir, M. 2008. Fate of the Black Sea Acartia clausi and Acartia tonsa (Copepoda) penetrating into the Marmara Sea through the Bosphorus. Estuarine, Coastal and Shelf Science, 76: 131.

Hueter, R.E., and Manire, C.A. 1994. Bycatch and catch‐release mortality of small sharks in the Gulf coast nursery grounds of Tampa Bay and Charlotte Harbor. Report No. 368. 186 pp.

Iguchi, N., and Ikeda, T. 1998. Elemental composition (C, H, N) of the euphausiid Euphausia pacifica in Toyama Bay, southern Japan Sea. Plankton Biology and Ecology, 45: 79‐84.

Ikeda, T. 1970. Relationship between respiration rate and body size in marine plankton as a function of temperature. Bulletin of the Faculty of Fisheries Hokkaido University, 21: 2‐ 112.

James, M. C., Scott, A. S.‐M., and Ransom, A. M. 2007. Population characteristics and seasonal migrations of leatherback sea turtles at high latitudes. Marine Ecology Progress Series, 337: 245‐254.

Johnson, W. S., and Allen, D. M. 2005. Zooplankton of the Atlantic and Gulf coasts: a guide to their identification and ecology, John Hopkins University Press, Baltimore, MD. 379 pp.

Jones, T. T., Bostrom, B. L., Hastings, M. D., Van Houtan, K. S., Pauly, D., and Jones, D. R. 2012. Resource requirements of the Pacific leatherback turtle population. PLoS ONE, 7: e45447.

Kaeriyama, H., and Ikeda, T. 2002. Vertical distribution and population structure of the three dominant planktonic ostracods (Discoconcoecia pseudodiscophora, Orthoconchoecia haddoni and Metaconchoecia skogsbergi) in the Oyashio region, western North Pacific. Plankton Biology and Ecology, 49: 66‐74.

Kaeriyama, H., and Ikeda, T. 2004. Metabolism and chemical composition of mesopelagic ostracods in the western North Pacific Ocean. ICES Journal of Marine Science: Journal du Conseil, 61: 535‐541.

Kimmel, D. G., Boicourt, W. C., Pierson, J. J. et al. 2010. The vertical distribution and diel variability of mesozooplankton biomass, abundance and size in response to hypoxia in the northern Gulf of Mexico USA. Journal of Plankton Research, 32: 1185–1202.

King, K. A. 1989. Food habits and oganochlorine contaminants in the diet of Black Skimmers, Galveston Bay, Texas, USA. Colonial Waterbirds, 12: 109‐112. Kiorboe, T., Mohlenberg, F., and Hamburger, K. 1985. Bioenergetics of the planktonic copepod Acartia tonsa: relation between feeding, egg production and respiration, and composition of specific dynamic action. Marine Ecology Progress Series, 26: 85‐97.

Kleppel, G. S. 1993. On the diets of calanoid copepods. Marine Ecology Progress Series, 99: 183‐ 195.

Krylov, V. V. 1968. Relation between wet formalin weight of copepods and copepod body length. Oceanology, 8: 723‐727.

Lassuy, D. R. 1983. Species profiles: life histories and environmental requirements (Gulf of Mexico) brown shrimp. FWS/OBS 82/11.1 and TR EL‐82‐4. 15 pp.

Lebour, M. V. 1931. Clione limacina in Plymouth Waters. Journal of the Marine Biological

Lehrter, J. C., and Pennock, J. R. 1999. Microzooplankton grazing and nitrogen excretion across a surface estuarine‐coastal interface. Estuaries, 22: 113‐125.

Link, J. S. 2010. Adding rigor to ecological network models by evaluating a set of pre‐balance diagnostics: A plea for PREBAL. Ecological Modelling, 221: 1580.

Lo, N. C. H., Jacobson, L. D., and Squire, J. L. 1992. Indices of relative abundance from fish spotter data based on delta‐lognormal models. Canadian Journal of Fisheries and Aquatic Sciences, 49: 1515‐1526.

Love, M., Baldera, A., Yeung, C., and Robbins, C. 2013. The Gulf of Mexico ecosystem: a coastal and marine atlas. Ocean Conservancy, Gulf Restoration Center, New Orleans.

Lucas, C. H., Pitt, K. A., Purcell, J. E., Lebrato, M., and Condon, R. H. 2011. What's in a jellyfish? Proximate and elemental composition and biometric relationships for use in biogeochemical studies Ecology, 92: 1704.

Martinussen, M. B., and Båmstedt, U. 1999. Nutritional ecology of gelatinous planktonic predators. Digestion rate in relation to type and amount of prey. Journal of Experimental Marine Biology and Ecology, 232: 61‐84.

McGinnis, T. W., and Emslie, S. D. 2001. The foraging ecology of Royal and Sandwich terns in North Carolina, USA. Waterbirds: The International Journal of Waterbird Biology, 24: 361‐370.

McManus, G. B., and Foster, C. A. 1998. Seasonal and fine‐scale variations in egg production and triacylglycerol content of the copepod Acartia tonsa in a river‐dominated estuary and its coastal plume. Journal of Plankton Research, 20: 767‐785. Mortazavi, B., Iverson, R. L., Landing, W. M., Lewis, F. G., and Huang, W. R. 2000. Control of phytoplankton production and biomass in a river‐dominated estuary: Apalachicola Bay, Florida, USA. Marine Ecology‐Progress Series, 198: 19‐31.

Muncy, R. J. 1984. Species profiles: life histories and environmental requirements of coastal fishes and invertebrates (Gulf of Mexico) ‐‐ white shrimp. FWS/OBS‐82/11.20 and TR EL‐ 82‐4. 19 pp.

Mutlu, E. 1999. Distribution and abundance of ctenophores and their zooplankton food in the Black Sea. II. Mnemiopsis leidyi. Marine Biology, 135: 603‐613.

Mutlu, E. 2013. Morphometry and areal growth cohorts of common epifaunal species on a sand bottom of the Cilician shelf (Turkey), Mediterranean Sea. Journal of Applied Biological Sciences, 7: 42‐53.

Nakamura, Y., and Turner, J. T. 1997. Predation and respiration by the small cyclopoid copepod Oithona similis: How important is feeding on ciliates and heterotrophic flagellates? Journal of Plankton Research, 19: 1275‐1288.

Nates, S. F., and McKenney Jr., C. L. 2000. Ontogenetic changes in biochemical composition during larval and early postlarval development of Lepidophthalmus louisianensis, a ghost shrimp with abbreviated development. Comparative Biochemistry and Physiology Part B, 127: 459‐468.

NEFSC. 2010. Summer Flounder Asssessment Summary for 2010. 20 pp.

NMFS. 2011. U.S. National Bycatch Report. NOAA Technical Memorandum NMFS‐F/SPO‐117E. 509 pp.

NMFS, and Service, U. S. F. a. W. 2007a. Green sea turtle (Chelonia mydas) 5‐year review: summary and evaluation. 102 pp.

NMFS, and Service, U. S. F. a. W. 2007b. Loggerhead sea turtle (Caretta caretta) 5‐year review: summary and evaluation. 65 pp.

Odum, W. E., and Heald, E. J. 1972. Trophic Analyses of an Estuarine Mangrove Community. Bulletin of Marine Science, 22: 671‐738.

Okey, T. A., and Mahmoudi, B. 2002. An ecosystem model of the West Florida shelf for use in fisheries management and ecological research p. 151. Florida Marine Research Institute, St Petersburg, FL.

Olsen, Z., Fulford, R., Dillon, K., and Graham, W. 2014. Trophic role of gulf menhaden Brevoortia patronus examined with carbon and nitrogen stable isotope analysis. Marine Ecology Progress Series, 497: 215‐227. Optiz, S. 1993. A quantitative model of the trophic interactions in a Carribean coral reef ecosystem. In Trophic models of aquatic ecosystems, pp. 259‐267. Ed. by V. Christensen, and D. Pauly. ICLARM Conference Proceedings.

Ota, T., and Taniguchi, A. 2003. Standing crop of planktonic ciliates in the East China Sea and their potential grazing impact and contribution to nutrient regeneration. Deep Sea Research Part II: Topical Studies in Oceanography, 50: 423‐442.

Overstreet, R. M., and Heard, R. W. 1978. Food of the red drum, Sciaenops ocellata, from Mississippi Sound. Gulf Research Reports, 6: 131‐135.

Overstreet, R. M., and Heard, R. W. 1982. Food content of six commercial fishes from Mississippi Sound. Gulf Research Reports, 7: 137‐149.

Palomares, M. L., and Pauly, D. 1989. A multiple regression model for predicting the food consumption of marine fish populations. Australian Journal of Marine and Freshwater Research, 40: 259‐273.

Parsons, T. R., Takahashi, M., and Hargrave, B. 1984. Biological Oceanographic Processes. Third edn, p. 330. Pergamon Press, New York, NY.

Pauly, D., Trites, A. W., Capuli, E., and Christensen, V. 1998. Diet composition and trophic levels of marine mammals. ICES Journal of Marine Science: Journal du Conseil, 55: 467‐481.

Pomeroy, L., R. 2001. Caught in the food web: complexity made simple? Scientia Marina, 65: 31‐40.

Pond, D. W., and Ward, P. 2011. Importance of diatoms for Oithona in Antarctic waters. Journal of Plankton Research, 33: 105‐118.

Postel, L., Fock, H., and Hagen, W. 2000. Biomass and abundance. In ICES Zooplankton methodology manual, pp. 83‐192. Academic Press, San Diego, CA.

Purcell, J. E. 1981. Dietary composition and diel feeding patterns of epipelagic siphonophores. Marine Biology, 65: 83‐90.

Purcell, J. E. 1982. Feeding and growth of the siphonophore (Cunningham 1893). Journal of Experimental Marine Biology and Ecology, 62: 39‐54.

Purcell, J. E. 1985. Predation on fish eggs and larvae by pelagic cnidarians and ctenophores. Bulletin of Marine Science, 37: 739‐755.

Purcell, J. E. 1992. Effects of predation by the scyphomedusa Chrysaora quinquecirrha on zooplankton populations in Chesapeake Bay, USA. Marine Ecology Progress Series, 87: 65‐76. Purcell, J. E., and Decker, M. B. 2005. Effects of climate on relative predation by scyphomedusae and ctenophores on copepods in Chesapeake Bay during 1987‐2000. Limnology and Oceanography, 50: 376‐387.

Purcell, J. E., and Kremer, P. 1983. Feeding and metabolism of the siphonophore Sphaeronectes gracilis. Journal of Plankton Research, 5: 95‐106.

Purcell, J. E., Nemazie, D. A., Dorsey, S. E., Houde, E. D., and Gamble, J. C. 1994. Predation mortality of bay anchovy Anchoa mitchilli eggs and larvae due to scyphomedusae and ctenophores in Chesapeake Bay. Marine Ecology Progress Series, 114: 47‐58.

Purcell, J. E., and Sturdevant, M. V. 2001. Prey selection and dietary overlap among zooplanktivorous jellyfish and juvenile fishes in Prince William Sound, Alaska. Marine Ecology Progress Series, 210: 67‐83.

Putt, M., and Stoecker, D. K. 1989. An experimentally determined carbon: volume ratio for marine “oligotrichous” ciliates from estuarine and coastal waters Limnology and Oceanography, 34: 1097‐1103.

Rapoza, R., Novak, D., and Costello, J. H. 2005. Life‐stage dependent, in situ dietary patterns of the lobate ctenophore Mnemiopsis leidyi Aggassiz 1865. Journal of Plankton Research, 27: 951‐956.

Revelles, M., Cardona, L., Aguilar, A., and Fernández, G. 2007. The diet of pelagic loggerhead sea turtles (Caretta caretta) off the Balearic archipelago (western Mediterranean): relevance of long‐line baits. Journal of the Marine Biological Association of the United Kingdom, 87: 805‐813.

Robinson, K. L., and Graham, W. M. 2013. Long‐term change in the abundances of northern Gulf of Mexico scyphomedusae Chrysaora sp. and Aurelia spp. with links to climate variability. Limnology and Oceanography, 58: 235‐253.

Robinson, O. J., and Dindo, J. J. 2011. Egg success, hatching success, and nest‐site selection of brown pelicans, Gaillard Island, Alabama, USA. The Wilson Journal of Ornithology, 123: 386‐390.

Roman, M. R., Pierson, J. J., Kimmel, D. G., Boicourt, W. C., and Zhang, X. 2012. Impacts of hypoxia on zooplankton spatial distributions in the northern Gulf of Mexico. Estuaries and Coasts, 35: 1261‐1269.

Ross, R. M. 1982. Energetics of Euphausia pacifica. I. Effects of body carbon and nitrogen and temperature on measured and predicted production. Marine Biology, 68: 1‐13.

Rumohr, H., Brey, T., and Ankar, S. 1987. A compilation of biometric conversion factors for benthic invertebrates of the Baltic Sea. Baltic Marine Biologist Publication, 9: 1‐56. Russell‐Hunter, W. D., Apley, M. L., and Hunter, D. R. 1972. Early life‐history of Melampus and the significance of semilunar synchrony. Biological Bulletin, 143: 623‐656.

Ruzicka, J. J., Brodeur, R. D., Emmett, R. L., Steele, J. H., Zamon, J. E., Morgan, C. A., Thomas, A. C., et al. 2012. Interannual variability in the Northern California Current food web structure: Changes in energy flow pathways and the role of forage fish, euphausiids, and jellyfish. Progress in Oceanography, 102: 19‐41.

Sangun, L., Tureli, C., Akamca, E., and Duysak, O. 2009. Width/length and width‐length relationships for 8 crab species from the northeaster Mediterranean coast of Turkey. Journal of and Veterinary Advances, 8: 75‐79.

Schwinghamer, P., Hargrave, B., and Hawkins, C. M. 1986. Partitioning of production and respiration among size groups of organisms in an intertidal benthic community Marine Ecology Progress Series, 31: 131‐142.

Scott‐Denton, E., Cryer, P. F., Duffy, M. R., Gocke, J. P., Harrelson, M. R., Kinsella, D. L., Nance, J. M., et al. 2012. Characterization of the U.S. Gulf of Mexico and South Atlantic penaeid and rock shrimp fisheries based on observer data. Marine Fisheries Review, 74: 1‐26.

SEDAR. 2013. Gulf of Mexico menhaden stock assessment report. ICES Document SEDAR 32A. 422 pp.

SEDAR. 2009a. Atlantic Red Drum. Report No. 18. 56 pp.

SEDAR. 2009b. South Atlantic and Gulf of Mexico King Mackerel. Report No. 16. 484 pp.

SEDAR. 2005. Gulf of Mexico Red Snapper. Report No. 7. 480 pp.

Seney, E. E. 2003. Historical diet analyis of loggerhead (Caretta caretta) and Kemp's Ridley (Lepidochelys kempi) sea turtles in Virginia. In Marine Science, p. 136. College of William and Mary, Williamsburg, VA.

Shiplett, R. M. 2011. Selective feeding and icthyoplankton predation by scyphomedusae in the northern Gulf of Mexico. In Marine Sciences, p. 110. University of South Alabama, Mobile, AL.

Sprung, M. 1993. Estimating macrobenthic secondary production from body weight and biomass: a field test in a non‐boreal intertidal habitat. Marine Ecology Progress Series, 100: 103‐109. Sprung, M. 1984. Physiological energetics of mussel larvae (Mytilus edulis). I. Shell growth and biomass. Marine Ecology Progress Series, 17: 283‐293.

STC 2014. Information about sea turtles: Kemp's Ridley Sea Turtle. Sea Turtle Conservancy. Strickland, J. D. H. 1960. Measuring the production of marine phytoplankton. Bulletin of the Fisheries Research Board of Canada, 122: 172.

Sulkin, S. D., and McKeen, G. L. 1999. The significance of feeding history on the value of heterotrophic microzooplankton as prey for larval crabs. Marine Ecology Progress Series, 186: 219‐225.

Sutton, G., and Wagner, T. 2007. Stock assessment of blue crab (Callinectes sapidus) in Texas coastal waters. No. 249. 53 pp.

Tarjuelo, I., and Turon, X. 2004. Resource allocation in ascidians: reporductive investment vs. other life‐history traits. Invertebrate Biology, 123: 168‐180.

Tatum, W. 1982. The blue crab fishery of Alabama. Number 7. 23‐28 pp.

Thompson, N. B. 1988. The status of Loggerhead, Caretta caretta; Kemp's Ridley, Lepidochelys kempi; and Green, Chelonia mydas, sea turtles in U.S. waters. Marine Fisheries Review, 50: 16‐23.

Trites, A. W., and Pauly, D. 1998. Estimating mean body masses of marine mammals from maximum body lengths. Canadian Journal of Zoology, 76: 886‐896.

Troedsson, C., Ganot, P., Bouquet, J.‐M., Aksnes, D. L., and Thompson, E. M. 2007. Endostyle cell recruitment as a frame of reference for development and growth in the Urochordate Oikopleura dioica. The Biological Bulletin, 213: 325‐334.

U.S. Fish and Wildlife Service. 2012a. Green Sea Turtle (Chelonia mydas). 2 pp.

U.S. Fish and Wildlife Service. 2012b. Loggerhead sea turtle (Caretta caretta). 2 pp.

Uebelacker, J. M., Johnson, P. G., and Barry, A. V. 1984. Taxonomic guide to the polychaetes of the northern Gulf of Mexico, Barry, A. Vittor Associates for the Minerals Management Service, U.S. Dept. of the Interior, Gulf of Mexico Regional Office, Metairie, La.

Uye, S.‐i. 1982. Length‐weight relationships of important zooplankton from the Inland Sea of Japan. Journal of the Oceanographical Society of Japan, 38: 149‐158.

VanderKooy, S. J. 2013. GDAR 01 ‐ Stock assessment report: Gulf of Mexico blue crab. ICES Document GSMFC Number 215. 313 pp.

Verity, P. G., and Lagdon, C. 1984. Relationships between lorica volume, carbon, nitrogen, and ATP content of tintinnids In Narragansett Bay. Journal of Plankton Research, 6: 859‐868.

Visser, J. M., Vermillion, W. G., Evers, D. E., Linscombe, R. G., and Sasser, C. E. 2005. Nesting habitat requirements of the brown pelican and their management implications. Journal of Coastal Research: e27‐e35. Walters, C., Martell, S. J. D., Christensen, V., and Mahmoudi, B. 2008. An Ecosim model for exploring Gulf of Mexico ecosystem management options: implications of including multistanza life‐history models for policy predictions. Bulletin of Marine Science, 83: 251‐271.

Wang, M.‐C., Shao, K.‐T., Huang, S.‐L., and Chou, L.‐S. 2012. Food partitioning among three sympatric odontocetes (Grampus griseus, Lagenodelphis hosei, and Stenella attenuata). Marine Mammal Science, 28: E143‐E157.

Waring, G. T., Josephson, E., Maze‐Foley, K., and Rosel, P. E. 2012. U.S. Atlantic and Gulf of Mexico marine mammal stock assessement ‐ 2011. p. 319. NOAA National Marine Fisheries Service, Woods Hole, MA.

Wilkinson, P. M., Nesbitt, S. A., and Parnell, J. F. 1994. Recent history and status of the eastern brown pelican. Wildlife Society Bulletin, 22: 420‐430.