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   (µm 3 )   was   calculated   by   applying   the   individual   measurements   to   volume   equations   for   the   shape   of   the   cell   (spherical,   conical,   or   cylindrical).
Cell   biovolume   (µm 3 )   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
‐ m 2   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   (m 3 )   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   species ‐ 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 ),   b iomass   was   first   standardized   to   volume   filtered   (kg   m ‐ 3 ).
The   volume   filtered   (m 3 )   by   the   trawl   net   was   found   by   multiplying   the   area   of   the   net   opening   (m 2 )   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   λ
2
and   start   longitude   λ
1
,   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   s tatic   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 .
T axon ‐ 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   (m 3 )   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   (km 2 )   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   (km 2 )   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   10 5   km 2 )   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.

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