# An Ecosystem model of the West Florida Shelf for use in Fisheries Management and Ecological Research:

Volume II. Model Construction

Florida Marine Research Institute, St. Petersburg, Florida

# An Ecosystem Model of the West Florida Shelf for use in Fisheries Management and Ecological Research:

Volume II. Model Construction

Edited by



## Thomas A. Okey and Behzad Mahmoudi

Authored contributions by

William S. Arnold Scott E. Burghart Roy L. Caldwell William (Monty) Graham
Paula F. Houhoulis
Steven Mackinson
Behzad Mahmoudi
Dan Marelli Cynthia A. Meyer
Robert Muller
James M. Nance Stephen Nesbitt Thomas A. Okey Tracey T. Sutton
Marcelo Vasconcellos
Laura Vidal-Hernandez



## 2002



# Florida Marine Research Institute, 100 Eighth Avenue SE St. Petersburg, Florida 33701-5095Ecopath model of the West Florida Shelf: Volume II. Model construction



### iii 

## Preface

In 1999, the Florida State Legislature pr ovided funds to the Florida Fish and
Wildlife Conservation Commission / Florida Marine Research Institute (FWC/FMRI) to
conduct research on the role of forage fish populations in Florida’s coastal marine ecosystem, and to assess the health of these populations. Sardine and herring, conspicuous forage fishes of the West Fl orida Shelf, support a number of Florida’s
commercial and recreational fisheries either direct ly or indirectly. Such ‘bait fish’ play a critical role in the coastal ecosystem by linking apex predators wit h plankton production. For example, changes in th e abundance of the sardine/he rring complex should cause subsequent changes throughout the biological community across the West Forida Shelf. The trophic modelling approaches described in this volume, and the companion volume, were employed to provide a qu antifyable method to investi gate the roles and health of these types of forage fishes, as well as to investigate and address other pressing ecological dilemmas. The meth odologies described herein repr esent the first real steps towards an ecosystem-based approach to managi ng and protecting Florida’s marine resources. Such an ecosystem-based approach is necessary to enab le the most broad and risk-averse uses of Florida’s living marine resources. Scientists and managers from the FWC/FM RI Division of Marine Fisheries, and scientists from other institutions, review ed several modelling techniques during a twoday workshop held in Novemb er 1999 in St. Petersburg. Ecopath/Ecosim/Ecospace modelling techniques emerged as the most useful approach given the available data and questions. Researchers from the University of British Co lumbia’s Fisheries Centre
worked with scientists from the Florida Fi sh and Wildlife Conservation Commission /
Florida Marine Research Instit ute (FWC/FMRI), the Universi ty of South Florida, the National Marine Fisheries Service (NMFS, SEFC) , and other institutions to construct the present trophic model of the We st Florida Shelf. The electroni c version of the model is available from the FMRI to any interest ed parties. The EwE software can be freely
downloaded from www. Ecopath .org

.

Behzad Mahmoudi
Florida Marine Research In stitute, St. Petersburg May 2002Ecopath model of the West Florida Shelf: Volume II. Model construction



### iv 

## Summary

This volume documents the derivation of input parameter s for the construction of
a mass-balanced food web model of the West Florida Shelf. A broad collaboration of experts on the marine biota of the region us ed a broad literature and information base to derive input parameters and construct this model using trophic modelling approach Ecopath with Ecosim (EwE). The resulting model is an integrated, whole-system view of
the West Florida Shelf biol ogical community during the la te 1990s. This model was made exceptionally transparent by the easy- to-use ‘windows-based’ EwE software, and the present document. The model was construc ted to complement current research in the region with cutting-edge fo od web analysis and dynamic simulation techniques to address pressing ecological and fisheries dilemma s. It holds consi derable potential for education, in addition to it s uses in scientific research , management, and policymaking. The companion volume (Mahmoudi et al. 2002) presents examples of dynamic simulation analyses supported by the model documented here.

Literature Cited (Summary)

Mahmoudi, B., Mackinson, S., Vasconcellos, M., Vi dal-Hernandez, L., Okey, T.A., 2002. An ecosystem model of the West Florida Shelf for use in fisheries management and ecological research: Volume I: summary and analyses. Florida Marine Research Institute-Fish and Wildlife Conservation Commission, St. Petersburg, 39p.Ecopath model of the West Florida Shelf: Volume II. Model construction



### v 

## Table of contents

PREFACE ........................................................................................................................ ............................III

SUMMARY........................................................................................................................ ...........................IV

TABLE OF CONTENTS .............................................................................................................. .................V

LIST OF FIGURES ................................................................................................................ .....................VII

LIST OF TABLES ................................................................................................................. .....................VIII

1. THE ECOPATH APPROACH...................................................................................................................1

1.1 W HAT IS E COPATH ? .............................................................................................................................. 1
1.2 T HE E COPATH MASTER EQUATION ..........................................................................................................2

2. MODEL CO NSTRUC TION .......................................................................................................... .............4

2.1 P ROCEDURE FOR MODEL CONSTRUCTION ...............................................................................................4 General pr otocol............................................................................................................... ....................4 Additional pa rameters .......................................................................................................... ................5
2.2 E STIMATING AREAS OF WFS DEPTH ZONES ............................................................................................6
2.3 B ASIC INPUT PARAMETERS OF THE WFS MODEL .....................................................................................8

3. “BALANCING ” THE MODEL....................................................................................................... ..........10

3.1 T HE MEANING OF “ BALANCING ”.............................................................................................................10
3.2 M ODEL BALANCING STRATEGIES ...........................................................................................................10
3.3 P ARAMETER PEDIGREE ASSESSMENT ...................................................................................................11
3.4 B ALANCING THE MODEL .......................................................................................................................13
3.5 P ARAMETERS OF THE BALANCED MODEL ...............................................................................................14

MODEL INPUTS (SEC TIONS 4 – 13) ................................................................................................. .......17

4. P RIMARY P RODUCERS ...........................................................................................................................18 4.1 Phytoplankton.............................................................................................................. .................18 4.2 Microph ytobentho s .......................................................................................................... .............22
4.3 Macr oalgae................................................................................................................. ..................25 4.4 Seagr asses................................................................................................................. ..................28

5. D ETRITUS

............................................................................................................................... ..............32
5.1 Water colu mn detritus ...................................................................................................... ............32
5.2 Benthic detritus........................................................................................................... ..................33
5.3 Drift ma crophytes .......................................................................................................... ...............34
5.4 Dead carcasses............................................................................................................. ...............34
6. M ICROBIAL HETEROTROPHS ...................................................................................................................36

7. Z OOPLANKTON

............................................................................................................................... .......38 7.1 Zoopl ankton................................................................................................................ ..................38 7.2 Ichthy oplankton ............................................................................................................ ................42
7.3 Carnivorou s jellyfish ...................................................................................................... ...............45
8. B ENTHIC INVERTEBRATES ......................................................................................................................47
8.1 Mei obenthos................................................................................................................ .................47
8.2 Small infauna.............................................................................................................. ..................49
8.3 Small mob ile epif auna...................................................................................................... ............54
8.4 Bivalves ................................................................................................................... .....................56
8.5 Sessile epibenthos ......................................................................................................... ..............59
8.6 Echinoderms and large gastropods ........................................................................................... ..61 8.7 Adult shrimp............................................................................................................... ...................66 8.8 Large crabs................................................................................................................ ...................74Ecopath model of the West Florida Shelf: Volume II. Model construction



### vi 8.9 Lobsters................................................................................................................... .....................79
8.10 Stom atopods ............................................................................................................... ...............81 8.11 Octopods .................................................................................................................. ..................86 8.12 Squids.................................................................................................................... .....................88
9. F ISHES

............................................................................................................................... ..................90 9.1 Systematic categoriz ation of fi sh groups................................................................................... ...90 9.2 Biomasses of fish groups ................................................................................................... ..........90 9.3 Production (P/B ) and Consumption (Q /B) estima tes....................................................................98
9.4 Fish diet compositions..................................................................................................... ...........101 9.5 Initial fish par ameter estimates........................................................................................... ........102
10. S EABIRDS

............................................................................................................................... ..........108
11. S EA T URTLES

............................................................................................................................... ....110
12. M ARINE MAMMALS .............................................................................................................................11 4
12.1 Odonte cetes ............................................................................................................... ..............114
12.2 Manatees.................................................................................................................. ................116
13. F ISHERIES

............................................................................................................................... .........118
13.1 Landi ngs.................................................................................................................. .................118 13.2 Bycatch and disc ards ...................................................................................................... .........123

14. LIMITATIONS, CAUTIONS, AND GUIDELINES FOR US E ..............................................................126

14.1 S CALE AND INFERENCE : T HE NATURE OF E COPATH MODELS ..............................................................126
14.2 P ARAMETER UNCERTAINTIES ............................................................................................................128
14.3 P ROBLEMS WITH FUNCTIONAL GROUP AGGREGATION .......................................................................129
14.6 G UIDELINES FOR APPROPRIATE USES OF THE MODEL .........................................................................131

APPENDI CES..................................................................................................................... ......................133

A PPENDIX 1. L IST OF C ONTRIBUTORS ......................................................................................................133
A PPENDIX 2. D IET MATRIX OF THE W EST F LORIDA S HELF E COPATH MODEL ...............................................135

A PPENDIX 3. D ETRITUS FATE OF EACH FUNCTIONAL GROUP .......................................................................139

A PPENDIX 4. M ARKET VALUES OF UTILIZED GROUPS .................................................................................141
A PPENDIX 5. G UIDE TO ‘ DATA PEDIGREE ’ VALUES ......................................................................................142

A PPENDIX 6. E MPIRICALLY BASED CONVERSION FACTORS .........................................................................143

A PPENDIX 7. F UNCTIONAL GROUPINGS AND PARAMETERS FOR FISHES .......................................................144Ecopath model of the West Florida Shelf: Volume II. Model construction



### vii 

## List of Figures

Figure 2.1. Depth strata for the West Florida Shelf (Meters). (SeaWiFS natural color satellite image provided by the SeaWiFS Project, NASA/Godda rd Space Flight Center, and ORBIMAGE.)..............7 Figure 7.1. Changes in the abundance and spatial dist ributions of ichthyoplankt on on the West Florida Shelf relative to zooplankton captured concurrently . Error bars are standard error; large error bars represent uneven spatial distributions. Sample size s (number of stations) ranged from 45 to 71. ...43 Figure 8.1. Seasonal change of polychaete abundance and biomass on the West Florida Shelf in 1975 and 1976 (data from SUSFIO 1977). Polychaetes ma ke up approximately 64% of ‘small infauna’ individuals on the SouthWest Florida Shelf (ESE et al. 1987). Error bars are standard error...........50 Figure 8.2. Distribution of pink shrimp catches in t he Gulf of Mexico. Areas of higher catches are darker (from Klim a 1989).............................................................................................................. .................66 Figure 9.1. Relative abundance of top-eleven species (contributing >2% of total biomass) caught in coastal demersal trawl surveys on the West Florid a Shelf (Darcy and Guntherz 1984; Darnell et al. 1987). ......................................................................................................................... ........................92Ecopath model of the West Florida Shelf: Volume II. Model construction



### viii 

## List of Tables

Table 2.1. Surface areas of depth st rata for the West Florida Shelf............................................................ .6
Table 2.2. Best estimates used as initia l inputs for the West Florida Shelf model .......................................8 Table 3.1. Assigned pe digree index values ...................................................................................... ..........11 Table 3.2. Basic parameters of the balanced West Flori da Shelf mo del....................................................14 Table 4.1. Estimates of phytoplankton bi omass over the West Florida Shelf.............................................19 Table 4.2. Estimates of phytoplankton produ ction over the West Florida Shelf. ........................................19
Table 4.3. Derivation of Production/Biomass va lues from assimilati on value estimates............................20 Table 4.4. Estimates of microphy tobenthos biomass in marine sand s of the West Floirda Shelf..............24
Table 4.5. Estimates of mi crophytobenthic produ ction on the West Floirda S helf. ....................................24 Table 4.6. Numbers of species in the 5 divi sions of plants on the We st Florida Shelf. ..............................25 Table 4.7. Estimates of macroalgae biomass on the West Florida S helf. ..................................................25 Table 4.8. Estimates of seagrass production relevant to the west Florida Shelf........................................30
Table 5.1. Derivation of wa ter column detritus for t he West Florid a Shelf..................................................32 Table 5.2. Percent organic carbon in sediment relevant to the We st Florida Shelf....................................33 Table 6.1. Estimated diet composition of micr obial heterotrophs on the West Florida Shelf. ....................36
Table 7.1. Warm season biomass estimate s for WFS zooplankt on by depth zone. ..................................40
Table 7.2. Annualized Ecopath parameters for zooplankton of the West Flor ida Shelf. ............................40
Table 8.1. Biomass estimates of small inf auna biomass on the West Florida Shelf. .................................50 Table 8.2. Representat ive abundances and biomasses from Rook ery Bay and Charlotte Harbor............51 Table 8.3. Estimated diet composition of ‘s mall infauna’ on the We st Florida Shelf ..................................51 Table 8.4. Estimates of the diet composition of small mobile epifauna on the West Florida Shelf ............54
Table 8.5. Parameter estimates for bivalves on t he WFS including infaunal bivalves and scallops. .........56 Table 8.6. Estimated diet composition of bivalves on the West Florida Shelf. ...........................................57 Table 8.7. Estimated diet compositions for sessile epibenthos on the We st Florida Shelf. .......................60
Table 8.8. Biomass estimates of echinoderms and large gastropods on the We st Florida Shelf. .............62 Table 8.9. Estimated diet composition of echinode rms and large gastropods on the West Florida Shelf. 64 Table 8.10. Estimates of adult shrimp biomass on the West Florida Shelf. ...............................................68 Table 8.11. Number of WFS shrimp caught in 433 trawl samples in a swept area of about 28 km 2 ..........70 Table 8.12. Rough estimates of adult shrimp diet composition on the We st Florida Shelf. .......................73 Table 8.13. Summary of Ecopath parameters for large crabs on the West Flor ida Shelf. .........................74 Table 8.14. Estimated diet composition for large crabs on the West Florida Shelf. ...................................75 Table 8.15. Diet composition of horseshoe cr abs on the Atlantic continental shelf. ..................................78 Table 8.16. Rough estimate of lobster diet composition on the West Florida Shelf. ..................................79 Table 8.17. Estimated biomass of neogonodactylids in hard bottom habitats of the east Panama shelf. .82 Table 8.18. Estimated diet composition of stomatopods on the We st Florid a Shelf...................................84 Table 8.19. Estimated diet composition of octopods on the West Florida Shelf.........................................86 Table 8.20. Estimated diet composition of squid on the West Florida Shelf...............................................88
Table 9.1. Some common shelf region species.................................................................................... ......92
Table 9.2. Stock size estimates for adult and juvenile king and Sp anish mackerel. ..................................93 Table 9.3. Spotted seatrout numbers and weight at age for No rthwest Florida. ........................................94
Table 9.4. Fish biomass es timates based on Catc h/F............................................................................. ...94
Table 9.5. Biomass estimates for small pelagics on the West Florida S helf. .............................................96
Table 9.6. Relative abundance of fish species on West Florida S helf........................................................97 Table 9.7. Comparison of P/B (Z) and natural mortality (M) estimates for small pe lagics .........................99 Table 9.8. Temperature data for West Flor ida Shelf............................................................................. ....100
Table 9.9. Diet information source s specific to t he study r egion. .............................................................1 02 Table 9.10. Best weighted esti mates and ranges of paramete rs for WFS fish groups.............................103
Table 10.1. Abundance of bait fish consuming seabirds in t he Gulf coast of Flori da...............................108 Table 10.2. Seabird parameters for the West Florida Shelf m odel...........................................................109 Table 11.1. Sea turtle strandings reported in West Florida counties between 1990 and 1999................111 Table 11.2. CPUE and density of sea turtles estimated from shrimp bycatch data in the Gulf shelf. ......112
Table 11.3. Sea turtle parameter estimates..................................................................................... .........113 Table 12.1. Mean body weight, density and biomass of ce tacean spec ies..............................................115 Table 13.1. State and Federal fisheries landings (tonnes) from West Florida Shelf, 1998-1999.............121 Table 14.1. Landings and discards of large oceani c piscivo res ...............................................................123Ecopath model of the West Florida Shelf: Volume II. Model construction



### ix Table 14.2. Discards by shrimp tr awlers........................................................................................ ...........124
Table A2.1. Diet Matrix of the West Florida Shel f model (%) ...................................................................1 35
Table A3.1. Fate of detritu s for each func tional gr oup......................................................................... .....139
Table A4.1. Market values of functional groups................................................................................. .......141
Table A5.1. Confidence intervals associated with p edigree ra nks ...........................................................142
Table A6.1. Conversion factors used in the construction of t he West Florida Shelf model......................143 Table A7.1. Summary table of all fi sh group parameter s and refere nces ................................................144Ecopath model of the West Florida Shelf: Volume II. Model construction



### 1 

## 1. The Ecopath approach

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### 1.1 W HAT IS 

### E COPATH 

### ?

An Ecopath model is a quantitative description of biomass flow s in a food web (i.e., energy flows). These computer models are c onstructed by defining a model area and time, organizing species (and detritus) in to convienient functional groupings, and estimating the biological (i.e., energy ) characteristics of each grouping. Ecopath models and their defined components are then ‘balanced’ in terms of mass or energy to gain insights into ecosystem and its biotic comp onents, and to obtain a whole-system view of the biological community. The Ecopath mass-balance approach was initially developed by Polovina (1984). Since that initial application, over 100 Ecopath models have been constructed, mostly in marine eco systems, and the approach has been refined considerably (see http://www. Ecopath .org ).

Ecopath models can be analyzed in their static form (Christensen and Pauly 1992), but the the dynamic simulation routines Ecosim and Ecospace (Walters et al. 1997, Walters et al. 1999) expanded the utility of the approach considerab ly. These dynamic routines
use the information in Ecopath models to simulate the potent ial responses of a system’s biota to changes in fisheries harvest strategies or disturbance regimes ( Ecosim ), and such analyses can be conducted in a habitat-based context ( Ecospace ). Ecosim also enables exploration of social, economic, and ecol ogical trade-offs in harvest strategies.

These complimentary approaches, Ecopath and Ecosim , provide a rigorous and relatively simple framework to provide test able insights into the causes of ecosystem changes. Most importantly, they can be used to implement ecosystem-based management by aiding in the design of policie s that account for indirect impacts of human activities. The relative importance of factors that shape communities can be explored by comparing (temporal and spatial) simulations to empirical information about such changes. Simulation results are oft en consistent with ecological theory (Christensen 1995, Vasconcellos et al. 1997), but they can be even more useful when
simulation results are counter intuitive. Either way, Ecopath with Ecosim analyses can provide useful insights into marine ecosystem organization and functioning.

Physical forces are not explicitly included in the parameterization of Ecopath models, though they can be included in the Ecosim routine to distinguish the relative roles of trophic and physical forces. Approaches such as physical forcing, trophic mediation, and time-series fitting are available to com pare and combine simula ted biological and physical forces. These approaches are discu ssed by Christensen et al. (2000).

Ecopath models are never final because ecosyst em knowledge is never complete. The usefulness of such models can improve c onsiderably, however, through iterative combinations of simulation and empirical res earch in a whole eco system context (Pauly et al. 2000).Ecopath model of the West Florida Shelf: Volume II. Model construction



### 2 

### 1.2 T HE 

### E COPATH MASTER EQUATION



### The parameters necessary for the construction of an Ecopath model are found in the Ecopath master equation (Equation 1.1):

B i 

### ⋅ (P/B) i 

### ⋅ EE i 

### = Y i 

### + Σ B j 

### ⋅ (Q/B) j 

### ⋅ DC ji 

### + BA i 

### + NM i 

### Equation 1.1

where,
B i and B j = biomasses of prey ( i ) and predators ( j ) respectively;
P/B i = production / biomass; equivalent to total mo rtality (Z) in most circumstances (Allen 1971);

EE i = ecotrophic efficiency; the fraction of the total production of a group that is utilized in the system;

Y i = fisheries catch per unit area and time (i.e., Y = F*B);
Q/B j = food consumption per unit biomass of j; and
DC ji = contribution of i to the diet of j;

BA i = biomass accumulation of i (positive or negative);

NM i = net migration of i (emigration less immigration).



### This equation expresses a balance between a group’s net production (terms to the left of the equal sign) with all sources of its morta lity (terms to the right). It states that the net production of a functional group equals the sum of (1) the total mass (or energy) removed by predators and fisheries, (2) the group’s total natural se nescence (i.e., flow to detritus), (3) the net biomass accumulation of the group, and (4) the net migration of the group’s biomass.

The thermodynamic constraints implied by Equation 1.1 underscore the power of Ecopath models as a focal point for refinement of ecosystem information. The need to reconcile energy producti on and demand among components of the food web narrows the possible ranges of parameter estimates for particular groups.

The law of conservation of mass or energy is expressed in this master equation, but the biomass accumulation and migration terms distinguishes this ‘energy continuity’ approach from a strictly ‘stead y state’ approach. This basic ‘continuity’ constraint enables representation of c hanges in populations (i.e ., functional groups) when expressed in dynamic form (not discussed here).

Because the Ecopath model of the entire system is a set of these linear (master) equations solved simultaneously, the Ecopath routine can solve for any of the four basic input parameters; B, P/B, Q/B, and EE (Christensen and Pa uly 1992). These along with diet compositions, are the ma in parameters derived in Sections 4 – 13 of this report. Other information such as spatial and tem poral distributions, habitat preferences, assimilation efficiencies, detritus fate, and other pertinent information are also covered in these parameter estimation sections.

Literature cited (The Ecopath approach)

Allen, R.R. 1971. Relation between production and bi omass. J. Fish. Res. Board Can. 28:1573-1581. Christensen, V. 1995. Ecosystem maturity - towards quantification. Ecol. Modelling 77:3-32.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 3 Christensen, V. and D. Pauly. 1992. ECOPATH II - a software for balancing steady-state models and calculating network characterist ics. Ecol. Modelling 61:169-185. Christensen, V., C.J. Wa lters and D. Pauly. 2000. Ecopath with Ecosim – A User’s Guide. Univ. of British Columbia, Fisheries Centre, Vancouver, Ca nada and ICLARM, Penang, Malaysia, 131 p. Pauly, D., V. Christensen, and C. Walters. 2000. Ecopath

, Ecosim , and Ecospace as tools for evaluating ecosystem impact of fisheries. ICES Journal of Marine Science 57: 697-706. Polovina, J. J. 1984. Model of a coral reef ecosystem I. The ECOPATH model and its applications to French Frigate Shoals. Coral Reefs 3:1-11. Vasconcellos, M., S. Mackinson, K. Sloman and T.J. Pitcher. 1997. The stability of trophic mass-balance models of marine ecosystems: a comparative analysis. Ecological Modelling 100: 125-134. Walters, C.J., Christensen, V. and D. Pauly. 1997. Structuring dynami c models of exploited ecosystems from trophic mass-balance assessments. Review s in Fish Biology and Fisheries 7(2):139-172. Walters, C. J., Pauly, D ., and Christensen, V., 1999. Ecospace : prediction of mesoscale spatial patterns in trophic relationships of exploited ecosystems, with emphasis on the impacts of marine protected areas. Ecosystems 2:539-554.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 4 

## 2. Model construction

2.1 P ROCEDURE FOR MODEL CONSTRUCTION



### General protocol

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Procedures for deriving model i nput parameters and constructing Ecopath models include literature reviews by individuals or a small groups, empirical studies by individuals or groups, or coordinated approaches by broad collaborations of experts (e.g., Okey and Pauly 1999). The West Flori da Shelf model was was constructed by a core group of researchers based on contri butions by expert collaborators. These regional and topical experts c ontributed written sections to this compendium describing basic parameter derivations for each functio nal group. Inputs to th is model were based
on the latest available information on the entire suite of biotic co mponents of the West Florida Shelf. Appendix 1 lists these contri butors with their contact information, and
authors of each sectio n are specified.

Eight steps can be taken to construct an Ecopath model:

1. Define the ecosystem in space and time – the spatial extent of the system and the represented time period must be clearl y defined. Parameter estimates are expressed in annual units, but any time period can be represented.
2. Define functional groups – Myriad species comprise interaction webs, but these species must be aggregated in to related groupings that make sense in terms of ecological function, and the types of questions of interest. Fifty-nine functional groups comprise the West Florida Shelf model.
3. Estimate basic parameters for each functional group. These parameters are listed in Section 1.2, and documenti ng these derivations makes up the bulk of this volume.

4. Estimate fisheries information – Landings, discards, discard fates, and economic information can be entered for each fisheries gear type.

5. Estimate additional Ecopath parameters – detritus fates, assimilation rates, multiyear trends, spatial and temporal di stributions, and habitat associations.
6. Enter parameters into the windows-based input interfaces (see www. Ecopath .org ).

7. Characterize model pedigree by ranking parameter qu ality (i.e., confidence).
8. Balance the model according to thermodynamic constraints.

The biological components of the eco system are generally represented in Ecopath using average values, or other meaningful measures of cent ral tendency that take into account both annual (seasonal) changes and ontogenetic ch anges. Production rates, consumption rates, and diet compositions vary among seasons and life histroy stages for most species in aquatic systems. However, explicit inclusion of seasonal information
into Ecopath with Ecosim modelling merely makes answers messy rather than changing the basic results of analyses (based on experience with a large number of Ecopath models; C. Walters, pers. comm.).Ecopath model of the West Florida Shelf: Volume II. Model construction



### 5 Ontogenetic changes can be incorporated using Ecopath with Ecosim using two approaches. First, groups can be split into adul t and juvenile ‘pools’ that are linked through and age structured gr owth and recruitment param eters; Second, numerous ontogenetic ‘stanzas’ can be specified for an integrated calculation of a given Ecopath parameter. This latter approach in particular enables real-time incorporation of variable growth, production, or consumption models into the representation of Ecopath parameters (C. Walters, UBC Fisheries Centre, pers. comm.).

Still, the assumption of ‘aver age’ representation of parameters is a useful convenience for modelling at the scale of entire systems because these values describe the basic interaction and energy structure of a food web. ‘Energy c ontinuity’ offers a powerful mass-balance-type constraint to model param eterization and construction. Section 3 describes the balancing methodology employ ed for the West Florida Shelf model.

Additional parameters

Steven Mackinson CEFAS Fisheries Lab, Lowestoft, UK



### (i) Unassimilated / Consumption

Only a fraction of the food eaten by organi sms is assimilated to the body; nonassimilated food is expelled. Proportions of unassimilated food mu st be specified in Ecopath , and this fraction flows to specified det ritus pools. A default value of 0.2 was used for carnivorous fish groups (Wi nberg 1960) since assimilation efficiency information for particular fish species was sc arce. This means that 80% of the food was
considered assimilated. Values of 0. 4 and 0.3 were applied to herbivores and planktivores since these groups preying on harder-to-digest food.

(ii) Detritus Fate

The fate of detritus is the defined pool of detritus that unasimialted food and dead organisms are specified to flow in to. A portion of the dead and decaying animals falling through the water column (including fishery di scards) is directed to ‘dead carcasses.‘ The specific proportions are assumed based on a subjective judgment relating to the habitat and niche of the various organisms (A pendix 2). The majority of detritus from
non-assimilated food is directed to water co lumn detritus and sediment detritus, but these ratios vary depending on the types of organisms. Approx imately 50% of the detritus from birds is considered to be expor ted from the system (i .e., corpses and feces end up on land). All dead and decaying macr oalgae and seagrasses contribute to the drift macrophytes detritus pool. Ultimately, detritus from the 4 detritus groups flow to the sediment detritus pool which is then exported from the system as sediment detritus is
buried and rendered unavail able to the system.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 6 

### 2.2 E STIMATING AREAS OF 

### WFS DEPTH ZONES



### Paula F. Houhoulis Florida Marine Research Institute, St. Petersburg



### Estimating biomass of species occuring on the West Florida Shelf often requires calculating the areal ex tent over which organi sms are distributed. To derive these areal estimates, bathymetry data, digitized from NO AA Nautical Charts r anging in scale from 1:450,000 to 1:1,200,000, were compiled along wi th detailed shoreline data provided by FMRI. Using De Soto Canyon as the northernmost limit, and the northern boundary of the Florida Keys as the southernmost limit, the study area was stratified into depth zones based on the contours defined by NOAA (Figure 2.1). Estimates of area covered by different depth strata are given in Table 2.1.

Table 2.1. Surface areas of depth strata for the West Florida Shelf.

Depth Stratum (m) Area (km 2 ) Percent
of area Cumulative area (km 2 ) 0-5* 5,629 2.6 5,629 5-9* 2,900 1.4 8,529 9-18 36,151 16.9 44,680 18-37 44,084 20.6 88,764 37-91 50,915 23.8 139,679 91-183 22,848 10.7 162,527 183-366 20,417 9.6 182,944 366-1,829 30,845 14.4 213,789 Total 213,789 100.0

* Depth strata 0-5 m and 5-9 m were calculated for the Big Bend area only, which extends from Lighthouse Point in the north to North Anclote Key in the south (see Figure 2.1).



### The limit of the West Florida Shelf was def ined as the 200m isobath. Based on the areal measurements given in Table 2.1, the to tal area was taken as being 170,000 km 2 

### . This value was used in biomass calculations where appropriate.

Literature cited (Model construction)

Allen, R.R. 1971. Relation between production and bi omass. J. Fish. Res. Board Can. 28:1573-1581. Okey, T. A. and D. Pauly. 1999. A mass-balanced model of trophic flows in Prince William Sound: Decompartmentalizing ecosystem knowledge. pp. 621- 635 In: Ecosystem Approaches for Fisheries Management. University of Alaska Sea Grant, AK-SG-99-01, Fairbanks. Winberg, G.G. 1960. Rate of metabolism and food require ments of fishes. Fish. Res. Bd. Can. Translation
series No. 194.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 7 Figure 2.1. Depth strata for the West Florida Shelf (Meters). (SeaWiFS natural color satellite image provided by the SeaWiFS Project, NASA/Godda rd Space Flight Center, and ORBIMAGE.)Ecopath model of the West Florida Shelf: Volume II. Model construction



### 8 

### 2.3 B ASIC INPUT PARAMETERS OF THE 

### WFS MODEL



### Basic input parameters estimated for each of the functional groups in the West Florida Shelf model are shown in Table 2.2 with ranges of estimates, or reasonable confidence ranges, when available. These values represent independently-derived estimates contributed by experts prior to the constr uction and balancing of the model. Examples of diet compositions and other input parameters are provi ded in Section 3, and in parameter estimation sections (Sections 4 – 13). Fisheries catch and discard information is provided in Section 13; the fi nal input parameters of the balanced model are presented in Section 3.5; the diet matrix for the We st Florida Shelf model is provided in Appendix 2; detritus fate info rmation is provided in Appendix 3; and the market value of harvested groups is shown in Appendix 4.

The relative confidence in the parameter estimates for each functional group depends
on several factors. For exam ple, the behavior and habitat of some species reduces their availability to trawl sampli ng, and thus their abundance is underrepresented. For other groups, the general paucity of data insists t hat for the time be ing, parameters are borrowed from similar species and settings.

Table 2.2. Best estimates used as initia l inputs for the West Florida Shelf model

.
Biomass (t

⋅ km -2 ) P/B (year -1 ) Q/B (year -1 ) Functional group Best Max Min Best Max Min Best Max Min Whales and dolphins 40.86

Sea birds 3.00 80.00

Turtles 0.15 3.50

Manatees 0.001 0.10 36.50
Large oceanic piscivores 0.079 0. 077 0.079 0.68 1.64 0.66 10.56 12.76 8.54
Large ocean planktivores 0.11 1.80

Coastal sharks 0.038 0.41 0.42 0.31 3.29 4.10 3.61
Rays and skates 0.239 0.568 0. 075 0.38 0.38 7.43 7.72 7.72 0.37
Pelagic oceanic piscivores 0.150 0.845 0.150 1.06 25.54
Pelagic coastal piscivores 0.097 0.131 0.036 0.64 0.67 0.61 11.53 14.02 10.23

Mackerels adult 0.183 0.267 0. 023 0.38 0.89 0.53 9.49 25.70 8.95

Juvenile mackerels 0.124 0.77 1.78 1.06 18.97 51.40 17.00
Sardine/Herring 0.457 6.076 0.033 1.18 1.18 1.05 12.11 12.58 10.49
PelOceJelly/eaters 2.693 2.693 0. 185 1.59 1.59 1.56 8.07 23.78 8.08

PelOcePlanktivores 3.139 4.559 0.076 0.87 0.87 0.83 11.78 12.97 11.71

DemOceInvert/eaters 0.041 0.084 0.041 2.17 15.76

DemCoasPisc 0.067 0.126 0. 030 0.64 6.33 6.75 6.24

DemCoasInvert/eaters 2.713 5.300 0.244 0.65 0.65 0.57 8.06 9.15 7.92

DemCoasOmniv 1.429 2.036 0.271 1.60 1.60 1.34 15.13 15.04 10.71

BentOcePisc 0.109 0.217 0.055 0.30 0. 75 0.30 7.94 8.86 7.94

BentOceInvert/eat ers 0.121 0.148 0.055 2.24 2.24 2.44 15.78 15.78 15.79

BentCoasPisc 0.262 0.368 0. 028 0.30 8.39 8.38 8.34

BentCoasInvert/eaters 1.669 2.329 0.436 1.16 1.23 0.86 12.21 12.20 10.11Ecopath model of the West Florida Shelf: Volume II. Model construction



### 9 SurfacePelagics 0.0003 2.60 11.70 96.73 11.70

StrucAssCoasPisc 0.722 1.038 0. 224 0.63 0.63 0.55 7.81 8.60 5.40
LgGroupers 0.119 0.149 0.031 0. 46 0.47 0.40 4.10 4.79 2.59

StrucAssCoasInvert/eaters 1.345 1. 969 0.192 0.75 0.77 0.54 8.34 8.36 7.33

StrucAssCoasOmniv 0.060 0.066 0. 035 1.33 1.33 1.32 29.15 29.25 24.37

StrucAssCoasPlank 0.003 2.60 10.00

NearshAssPisc 0.005 1.06 7.67 17.25 7.67
Mullets (all) 0.329 0.329 0.0006 0.70 0.90 0.79 11.03 11.03 10.37

NearshPlanktivores 0.181 0.186 0.010 0.60 1.32 0.60 15.91 16.42 14.54

Other fishes 3.877 5.806 0.271 0.70 7.04
Squid 0.267 2.67 3.16 1.70 36.50
Adult shrimps 0.086 0.627 0. 020 4.08 5.38 4.08 19.20

Lobsters 0.007 0.035 0.007 0.90 8.20
Large crabs 0.088 0.445 0.089 1.38 2.80 1.38 8.50
Octopods 3.10 3.10 1.12 7.30 7.30 3.56
Stomatopods 0.994 1.890 0.099 1.34 74.32 74.32 0.9944
Echinoderms/large gastro. rggstgastropods 19.246 28.869 19.246 1.20 1.50 1.20 3.70 4.00 3.70

Bivalves 48.598 145.790 16.190 1.21 1.20 1.00 23.00
Sessile epibenthos 219.00 472.500 219.000 0.80 9.00

Small infauna 9.923 20.000 6.960 4.60 6.49 2.75 15.90 25.00 7.00
Small mobile epifauna 7.01 7.38 5.62 27.14 27.14 18.73

Meiofauna 2.051 12.50 12.50 5.33 25.00 42.15 25.00
Small Copepods 8.300 8.200 16. 800 17.30 22.12 17.30 57.67 116.48 57.67
Other mesozooplankton 6.700 7.100 12.800 17.30 22.12 17.30 57.67 116.48 57.67
Carnivorous zooplankton 21.600 41. 700 22.800 8.70 15.00 8.70 29.00 110.00 29.00
Ichthyoplankton 0.047 0.095 0.024 50.45 75.67 33.63 132.13 198.20 88.09
Carnivorous jellyfish 0.221 0.402 0.040 40.15 80.00
Microbial heterotrophs 60.000 120.000 30.000 100.00 215.00
Macroalgae 36.050 108.200 12.000 4.00 N/A N/A N/A
Microphytobenthos 29.780 47.000 17. 000 23.73 55.60 12.68 N/A N/A N/A
Phytoplankton 25.000 73.000 11.000 182.13 516.00 108.00 N/A N/A N/A
Sea grasses 175.620 263.430 117.08 0 9.01 13.52 6.01 N/A N/A N/A

Dead carcasses 0.001 N/A N/A N/A N/A N/A N/A

Sediment detritus 390.000 585.000 260.000 N/A N/A N/A N/A N/A N/A

Watercolumn detritus 125.000 187.50 0 83.330 N/A N/A N/A N/A N/A N/A
Drift macrophytes 2.659 7.9770 0.886 N/A N/A N/A N/A N/A N/A

Note: Values within given ranges were prefered during balancing. Bolded values are very uncertain.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 10 

## 3. “Balancing” the model

Steven Mackinson CEFAS Fisheries Lab, Lowestoft, UK

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### 3.1 T HE MEANING OF 

### “ BALANCING 

### ”

Ecopath models must be ‘balanced’ in the sens e of achieving continuity among energy fluxes in the defined ecosystem , not in the sense of ‘static equilibrium.’ Continuity of energy fluxes must likewise be achieved fo r each particular group within the overall system, as discussed in Section 1.2.

Because an attempt is made to account for all fluxes, Ecopath models do not inherently assume ‘steady state.’ If the total combin ed demand of energy on a particular group exceeds the production of that group (plus the energy needed for respiration), the group is commonly said to be out of ‘balance, ’ in the sense of energy discontinuity. Ecopath models constructed with good information for mo st or all components in a system tend to require minimal ‘balancing.’ This is becaus e energy continuity is a true property of real world ecosystems.

The degree of discontinuity, or “imbalance,” in each functional group is revealed by the calculated ‘ecotrophic efficiency’ values. Ecotr ophic efficiency (EE) is the proportion of the net production of a group t hat is consumed by predators or fisheries (or directly exported). These EE terms ar e calculated after initial input parameters have been derived and entered. An ecotroph ic efficiency value of greater than one is impossible, as it indicates that total energy demand on a functional group exceeds total production and maintenance of that group. EE values greater than one are thus used as diagnostic indicators of model discontinuity or “imbal ance.” This is the handle for balancing, and changes in these values are monito red while adjusting model inputs.



### 3.2 M ODEL BALANCING STRATEGIES



### Strategic approaches are im plemented when balancing Ecopath models to optimize the representation of the system , and to avoid erosion of contributed information. For example, adjustments to input parameters are best made after prioritization according to ‘degree of imbalance,’ ‘quality of estimates,’ or other criteria applicable to the system at hand. The quality of estimates can be characte rized by specified confidence bounds for each parameter (Table 2.2) or by ranking the data ‘pedigr ee’ of parameters (Section 3.3).

Some experienced Ecopath modelers suggest that model balancing should focus on diet composition adjustments because diet composition dat a tends to be very high relative to other parameters (V. Christens en, UBC Fisheries Centre, pers. com., 7 November 2000). However, this relative uncertainty among parameters should beEcopath model of the West Florida Shelf: Volume II. Model construction



### 11 assessed on a case-specific basis. Indeed, fo r some functional groups, the uncertainty of input parameter estimates such as biom ass might rival or surpass uncertainties associated with diet composit ions. Finally, model users can introduce bias into the model through a one sided approach to bal ancing. For example, a model can be erroneously inflated by increasing prey bioma sses, or production rates, or both, rather than taking a balanced approac h by including the reducti on of predator consumption rates, or by re-allocating diet compositions.

Commonly, ‘top-down’ balancing strategi es have been applied to balancing Ecopath models, in that the production and/or biomass estimates of lower trophic levels (where uncertainty can be more common) is incr eased to meet the demands of upper trophic
levels. The result of such a method is that the biomass or production rates at the lower trophic levels can be inflated unrealisticall y to achieve a balanced model (T. Dean in Okey and Pauly 1999). Clearly, such a result is unrealistic and this potential interjection of bias points to the need to make a conscious effort to apply a more evenhanded approach during balancing. Not only should the accounts tally , but more importantly they should stay within the s pecified bounds of confidence a nd make intuitive sense in terms of ecological interactions. The fundamental importance of the balancing procedure as a crucial bridge to the ecology of a system must be emphasized to users who might otherwise view the balancing step as merely a necessary technical modeling procedure.



### 3.3 P ARAMETER PEDIG REE ASSESSMENT



### Parameter ‘pedigree’ index values can be assigned to each input parameter of an Ecopath model. Ecopath ’s parameter pedigree routine is an approach to convert qualitative rankings of param eter quality to quantitative confidence intervals (See Appendix 5). The output of this routine can be us ed during manual balancing, automated balancing and analysis routines, such as the Monte Carlo routine

‘ Ecoranger ,’ or in meta-analyses that compare va rious models in terms of relationships
between model attributes and overall data pedigree. Assigning pedigree values to functional groups whose paramet ers are derived from combi ned estimates from many data sources of varying quality is a subjective task, but nevertheless instructive. In a more general sense, it is informative to future users of the model to be as explicit as possible about the level of confidence in input parameters. T he parameter pedigree routine thus inhances model transparenc y beyond a description of of parameter
derivation.

The pedigree index value repres ents the quality or relative confidence assigned to each parameter estimate. Pedigree index values assigned to West Florida Shelf model parameters are shown in Table 3.1.

Table 3.1. Assigned pedigree index values

Functional Group B P/B Q/B Diets Catches 1 Whales and dolphins 0.7 0.2 0.2 0.7

2 Sea birds 0.4 0.2 0.2 0.7Ecopath model of the West Florida Shelf: Volume II. Model construction



### 12 3 Turtles 0.7 0.2 0.2 0.5

4 Manatees 1 1 1 1 5 Large oceanic piscivor es 0.4 0.6 0.6 0.7 1 6 Large ocean planktivores 0 0.2 0.2 0.2

7 Coastal sharks 0.4 0.6 0.6 0.7 1 8 Rays and skates 0.7 0.6 0.6 0.7 1 9 Pelagic oceanic piscivores 0.7 0.6 0.6 0.2 1 10 Pelagic coastal piscivores 0.4 0.6 0.6 0.7 1

11 Mackerels adult 1 1 0.7 1

12 Juvenile mackerels 1 1 0.7 1 13 Sardine/Herring 0.4 0.8 0.7 1 1 14 PelOceJelly/eaters 0.4 0.5 0.5 0.2 1

15 PelOcePlanktivores 0.4 0.7 0.7 0.7 1

16 DemOceInvert/eaters 0.4 0.6 0.6 0.7 1

17 DemCoasPisc 0.7 0.6 0.6 1 1

18 DemCoasInvert/eater s 0.7 0.6 0.6 0.7 1

19 DemCoasOmniv 0.7 0.6 0.6 0.7 1

20 BentOcePisc 0.4 0.1 0.1 0.2

21 BentOceInvert/eater s 0.4 0.1 0.6 0.7 1

22 BentCoasPisc 0.7 0.6 0.6 0.7 1

23 BentCoasInvert/eater s 0.7 0.6 0.6 0.7 1 24 SurfacePelagics 0 0.5 0.5 0.7

25 StrucAssCoasPisc 0.7 0.6 0.6 0.7 1 26 LgGroupers 0.7 0.6 0.6 0.7 1

27 StrucAssCoasInvert/eaters 0.7 0.6 0.6 0.7 1

28 StrucAssCoasOmniv 0 0.6 0.6 0.7 1

29 StrucAssCoasPlank 0 0.1 0.1 0.7 1

30 NearshAssPisc 0 0.6 0.6 0.7 1

31 Mullets 0.7 0.6 0.6 0.7 1

32 NearshPlanktivores 0 0.5 0.5 0.7

33 Other fishes 0.4 0.1 0.1 0.2 1 34 Squid 0.4 0.8 0.8 0.7 35 Adult shrimps 0.4 0.6 0.6 0.5

36 Lobsters 0 0.6 0.2 0.5 37 Large crabs 0 0.6 0.6 0.5 38 Octopods 0 0.6 0.6 0.7 39 Stomatopods 0.7 0.8 0.8 0.7 40 Echinoderms/large gastro. 0.7 0.6 0.6 0.5

41 Bivalves 0.7 0.5 0.5 0.7 42 Sessile epibenthos 0.7 0.6 0.6 0.7

43 Small infauna 0.7 0.6 0.6 0.7 44 Small mobile epifauna 0 0.6 0.8 0.5

45 Meiofauna 0.7 0.7 0.7 0.7 46 Small Copepods 1 1 1 1 47 Other mesozooplankton 1 1 1 1 48 Carnivorous zooplankton 1 1 1 1 49 Ichthyoplankton 0.7 0.6 0.6 0.7 50 Carnivorous jellyfish 0.7 0.8 0.8 0.7 51 Microbial heterotrophs 0.4 0.6 0.6 0.5 52 Macroalgae 0.4 0.8 53 Microphytobenthos 0.4 0.8 54 Phytoplankton 1 1Ecopath model of the West Florida Shelf: Volume II. Model construction



### 13 55 Sea grasses 0.7 1

56 Dead carcasses 0

57 Sediment detritus 0.7

58 Watercolumn detritus 0.7 59 Drift macrophytes 0

Notes: Pedigree index values repres ent relative data quality of parameter estimates. Appendix 5 presents confidence intervals associated with pedigree index values.



### 3.4 B ALANCING THE MODEL



### We took an iterative and manual approach to balancing the the We st Florida Shelf model. Our approach was largely based on t he pedigree rankings assigned to each parameter, as well as more detailed and parameter-specif ic considerations. This ensured incorporation of ‘b est judgement’ and common sense during thermodynamic balancing. It also provided maximum insights into ecosystem function.

A number of groups were unbalanced when out put parameters were first estimated (Ecotrophic Efficiency’ values were greater than 1). This indicated the impossible
situation that the biomass ‘ demand’ from several functional groups was greater than their ‘supply.’ Such unbalance is co mmon (and expected) when whole food web models (e.g., Ecopath ) are constructed due to the error inherent in estimating biological parameters. This unbalance is a tangible impet us for refinement of estimates, and the
model thus serves as a vehicle for refinement of ecosystem knowledge.

In light of this unbalance, in itial parameter estimates were re-evaluated and refined after dialogue and negotiation am ong coordinators regardi ng the limits of possibilities for their parameters. The process of balancing the model involved two iterations. The first
iteration strived to achieve a preliminary ‘mass-balance’ usin g our initial input parameter estimates leaving some parameters to be estimated by Ecopath . The balancing procedure integrated an assessment of unc ertainty and the relative degree on ‘imbalance’ for all groups. Assignments of pedigree values provided one basis for this prioritization. Another key aspect of the procedure was determining the most sensitive parameters. Balancing was then conducted by making adjustments from both ‘top down’ and ‘bottom up,’ such that predator demands were met by realistic prey productivity. For example, we opted to reduc e the biomass and or consumpt ion of predators in cases where predator demands on prey groups were very high and inflation of prey groups
would have exceeded confidenc e bounds for those groups.

To guide the model balancin g procedure we established ecological ‘anchor’ groups, each of which was positioned at different trophic levels. T hese groups were considered to have strong connections throughout the food web, and their parameters were thought to be of good quality. Mackere l and sardine-herring were our main anchor groups. We held constant the parameter estimates fo r these anchor groups while adjusting less robust values for other groups. The sardin e-herring component was considered to be a particularly good anchor, since it is a cruc ial channel of energy fr om primary producers to top predators. This group occurs at a mid trophic level, a nd has a substantialEcopath model of the West Florida Shelf: Volume II. Model construction



### 14 estimated biomass. The collab orative team effort to ‘bal ance’ the model resulted in negotiation among specialists on different trophic realms, and this resulted in a toggling between ‘top down’ and ‘botto m up’ adjustments during more than 30 minor iterations as parameters were adjust ed around anchor points based on these ‘taxo-trophically’ different perspectives.

Two common problems encounte red during this preliminar y balancing process were Cycles , where two groups are the main consumers of each other, and Cannibalism

, when aggregation of interacting species into a single group increase s the proportion of
the diet that comes from within the group. These problem s were corrected by, for example, reducing cannibalism to 1-2% for most groups, and spreading the diet proportionally among other prey.

Gross food conversion efficiency (GE), or t he ratio of total production to total food consumption, was used as a biological ‘cross check.’ This ratio ranges from 0.05 to 0.25 for most vertebrate groups, but can be higher for some gr oups of small invertebrates and fish larvae (e.g. for coral reefs, bacteria, nauplii, fish larvae and other small, fastgrowing organisms). Empirically-derived GE (P /Q) estimates were available for some groups, and values were maintained at taxonom ically reasonable levels for each group.

The preliminary balanced model was reviewed by the architect of Ecosim , Dr. Carl Walters. His analysis indicated unrealistica lly high production rates for several fish groups (making them overly resilient to dist urbance). It also indica ted that the fishing mortality rate estimate for shrimp was cons iderably lower than typical, implying that shrimp biomasses were overestimated (fishi ng mortality = catch / biomass). The same fishing mortality rate problem also applied to some fish groups, a consequence again of aggregation into functional gr oups (see Section 14.3).

A second iteration of model balancing then commenced in re sponse the inconsistencies pointed out during this review, and additional information. Parameter estimates were then revised using these additional data. The model was refined and ‘balanced’ according to four criteria:

1. Parameter estimates are based on the best and most recent available data; 2. Estimates are within acceptable ranges based on the present available data; 3. The model generally represents t he West Florida Shelf ecosystem;
4. The model enables ‘useful’ simulations to explore policy alternatives;



### 3.5 P ARAMETERS OF THE BALANCED MODEL



### Table 3.2 reveals that departures of final (balanced) input parameters from the best estimates are reasonably small. Appendix 2 presents the diet matrix of the balanced
model.

Table 3.2. Basic parameters of the balanced West Florida Shelf model.

Group name Biomass P/B Q/B EE P/Q Unass TrophicEcopath model of the West Florida Shelf: Volume II. Model construction



### 15 (t/km2) (year -1 ) (year -1 ) / cons. level 1 Dolphins 0.038 0.10 40.44 [0.082] [0.002] 0.2 [4.35]
2 Seabirds 0.001 0.10 80.00 [0.000] [0.001] 0.2 [4.23]
3 Turtles 0.007 0.19 3.50 [0.417] [0.055] 0.2 [3.30]
4 Manatees 0.001 0.10 36.50 [0.000] [0.003] 0.4 [2.00] 5 LgOcePisc 0.07 (0.078c,a) 0.68 7.4 (8.54c) [0.845] [0.092] 0.2 [4.72] 6 LgOcePlank [0.043] 0.11 1.80 0.500 [0.061] 0.3 [3.74]
7 Coastalsharks 0.09 (0.038b,a) 0.41 3.29 [0.909] [0.125] 0.2 [4.30] 8 Rays/sharks 0.238 0.38 7.72 [0.651] [0.049] 0.2 [3.65]
9 PelOcePisc 0.150 1.06 8.5 (25.5a) [0.829] [0.124] 0.2 [4.55]
10 PelCoasPisc 0.23 (0.13b) 0.64 10.23 [0.972] [0.063] 0.2 [4.26]
11 MackerelAdul 0.183 0.38 8 (8.95c) [0.938] [0.048] 0.2 [4.25]
12 MackerelJuv 0.126 0.77 9.00 [0.970] [0.085] 0.2 [4.33]
13 Sardine/Herring 2.40 1.05 12.11 [1.000] [0.087] 0.3 [3.11] 14 PelOceJelly/eaters 2.20 1.56 8.07 [0.674] [0.193] 0.2 [4.13]
15 PelOcePlanktivores 1.50 0.87 11.71 [0.949] [0.074] 0.3 [3.43]
16 DemOceInvert/eaters 0.045 (0.041b,a) 1.2 (2.17c,a) 15.76 [0.971] [0.076] 0.2 [3.42]
17 DemCoasPisc 0.120 0.64 6.33 [0.977] [0.101] 0.2 [3.99]
18 DemCoasInvert/eat ers 1.40 0.65 7.92 [0.999] [0.083] 0.2 [3.52]
19 DemCoasOmniv 0.70 1.34 15.13 [0.784] [0.089] 0.2 [2.92]
20 BentOcePisc 0.045 (0.055c) 0.45 7.94 [0.961] [0.057] 0.2 [4.24]
21 BentOceInver t/eaters 0.19 (0.148b) 1.2 (2.24c,a) 15.78 [0.988] [0.076] 0.2 [3.51]
22 BentCoasPisc 0.245 0.55 (0.3 b,a) 8.39 [0.938] [0.066] 0.2 [4.03]
23 BentCoasInvert/ eaters 0.860 0.86 10.11 [0.991] [0.085] 0.2 [3.50] 24 SurfacePelagics [0.099] 2.60 11.70 0.950 [0.222] 0.2 [2.92]
25 StrucAssCoasPisc 0.22 (0.224c) 0.63 5.40 [0.736] [0.117] 0.2 [4.09] 26 LgGroupers 0.119 0.46 4.10 [0.880] [0.112] 0.2 [4.28]

27 StrucAssCoasInvert/eaters 1.20 0.75 7.33 [1.000] [0.102] 0.2 [3.55]
28 StrucAssCoasOmniv [0.312] 1.33 24.37 0.980 [0.055] 0.2 [2.48]
29 StrucAssCoasPlank 0.05 (0.0026c,a) 2 (2.6c,a) 10.00 [0.851] [0.200] 0.3 [3.50]
30 NearshAssPisc [0.013] 1.06 7.67 0.900 [0.138] 0.2 [4.16]
31 Mullets 0.329 0.70 11.03 [0.512] [0.064] 0.2 [2.07]
32 NearshPlanktivores [2.215] 2 (1.32b) 15.92 0.990 [0.126] 0.3 [3.25]
33 Other fishes 3.87 1.3 (0.7b) 7.04 [0.950] [0.185] 0.2 [3.37] 34 Squid 1.1 (0.267a) 3.00 35 (36.5a) [0.987] [0.086] 0.35 [3.78] 35 Adult Shrimps 0.55 5.38 19.20 [0.987] [0.280] 0.4 [2.89]
36 Lobsters 0.028 0.90 8.20 [0.858] [0.110] 0.35 [3.36] 37 Large Crabs [0.705] 2.80 8.50 0.990 [0.329] 0.35 [3.07] 38 Octopods [0.074] 3.10 11.70 0.950 [0.265] 0.13 [3.58] 39 Stomatopods 0.994 1.34 7.43 [0.414] [0.180] 0.3 [3.27]
40 Echinoderms/Lg gastropods 19.246 1.20 3.70 [0.277] [0.324] 0.56 [2.40]
41 Bivalves 48.596 1.21 23.00 [0.168] [0.053] 0.5 [2.11] 42 Sessile epibenthos 219 0.80 9.00 [0.236] [0.089] 0.6 [2.39]
43 Small infauna 19.032 4.60 15.90 [0.401] [0.289] 0.5 [2.31] 44 Small mobile epifauna [12.614] 7.01 27.14 0.950 [0.258] 0.45 [2.35]
45 Meiofauna 13 (2.05 a) 12.50 25.00 [0.822] [0.500] 0.45 [2.36] 46 Small Copepods 8.3 17.30 50 (57.7c,a) [0.939] [0.346] 0.5 [2.15]

47 Other Mesozooplankton 6.7 17.30 50 (57.7c,a) [0.851] [0.346] 0.5 [2.55] 48 CarnivZooplank 21.6 8.70 20 (29c,a) [0.250] [0.435] 0.4 [2.96] 49 Ichthyoplankton 0.048 50.45 132.13 [0.748] [0.382] 0.4 [2.94] 50 CarnivJellyfish 0.265 40.00 80.00 [0.928] [0.500] 0.1 [3.39] 51 Microbial Heterotrophs 60 100.00 215.00 [0.235] [0.465] 0.2 [2.00] 52 Macroalgae 36.05 4.00 - [0.396] - - [1.00] 53 Microphytobenthos 29.78 23.73 - [0.623] - - [1.00]Ecopath model of the West Florida Shelf: Volume II. Model construction



### 16 54 Phytoplankton 25.0 182.13 - [0.304] - - [1.00] 55 Sea grasses 175.62 9.01 - [0.017] - - [1.00]
56 Dead carcasses 1.0 - - [0.906] - - [1.00]
57 Sediment Detritus 390 - - [0.884] - - [1.00]
58 Watercolumn Detritus 125 - - [0.910] - - [1.00] 59 Drift Macrophytes 2.66 - - [0.324] - - [1.00]

Note: Values that fall outside of the range of derived es timates (Table 2.1) have nearest estimate adjacent in (brackets). The derived best estimates are marked with a, maximum estimates are marked with b, and
minimum estimates are mark ed with c. Values in [square brackets and red] are estimated by Ecopath

.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 17 

## Model inputs (Sections 4 – 13)

The following sections document the derivation of input par ameters for the 59 functional groups in the West Florida Shelf model. Func tional groups are organi zed into nine ‘taxotrophic’ realms to organize this documen t: primary producers, detritus, microbial heterotrophs, zooplankton, benthi c invertebrates, fishes, seabirds, sea turtles, and
marine mammals.

The format of most of the parameter deriv ation sections is standard. Each section begins with a definition of the functional group and a brie f description of their taxonomy, distributions, and life history. After the group introduction, detailed approaches to estimating biomass, P/B, Q/B, and diet com positions are presented. Other information such as food assimilation efficiency, gross e fficiency, ecotrophic efficiency, multi-year biomass accumulation trends, and migration information are included in these sections when available and appropriate. Section 9 (Fishes), the exception to this format, features an integrated approa ch to parameter estimation. Section 13 provides information on fisheries landings and discards.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 18 

### 4. P RIMARY 

### P RODUCERS



### 4.1 Phytoplankton

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### The phytoplankton of the We st Florida Shelf consists mainly of diatoms and dinoflagellates. Diatoms are phot osynthetic, single-celled prot ists (division Chrysophyta) with silicified cell walls, which take the form of lid-like valves of a protective ‘frustule’ (Wetzel 1983). Dinoflagellates are protoz oans (subphylum Mastigophora) that are autotrophic (photosynthetic). T hese proto-animals propel t hemselves with flagella, but they are treated as algae by phycologists because they contain chlorophyll and to not feed hererotrophically (Barnes 1987). Phytoplankton is an important component of primary production on the We st Florida Shelf, and they mediate the abundance and production of benthic primary producers by interfering wit h light penetration. Ultramicroscopic bacterioplankton (e.g., cyanobacteri a) are thought to pr oduce 10 – 60 times the as much as the larger phytoplankton constituents. The estimates included herein, however, incorporate this bacterioplankto n component as biomass measurements are based on chlorophyll a concentrations.

Although the global bi omass of marine phytoplankton is relatively low (about three orders of magnitude lowe r than the global biomass of terre strial plants), their production rates are so high that the global primary pr oduction of marine plankton is almost equal to that of terrestrial plants (Smith 1981). This fast production rate allows a relatively low biomass of marine primary producers to suppor t relatively high biomasses of organisms in upper trophic levels. Marine food webs mainta in their dynamic stabil ity by switching to alternate food sources as eph emeral primary producers fl uctuate in response to physical forces in the system . The ephemeral nature of thes e fast producers, however, presents a challenge when attempting to represent these highly productive marine ecosystems, underscoring the importance of understanding the dynamics of these producers and the processes of their utilization.

The planktonic flora of the We st Florida Shelf was very poorly known prior to 1964, despite the central importance of phytoplankt on to Gulf of Mexico food webs (El-Sayed 1972). A number of studies hav e been conducted since that time (Tables 4.1 and 4.2), but most are basic descriptions rather than focused analyses of the ecological implications of phytoplankton dynamics.

The seasonal fluctuations of phytoplankton biomass and production on the West Florida Shelf necessitate careful scrutiny of estimate s when characterizing the annual state of the system with measures of central tendency. Tables 4.1 and 4.2 provide a spectrum of estimates within which grand c entral measures can be chosen.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 19 Table 4.1. Estimates of phytoplankton bi omass over the West Florida Shelf.

Value Provided unit Source Region Biomass (t

⋅ km -2 )

0.392 a ug chl a / litre Vargo 1995 West Florida Shelf, spring -fall 73

13.25 b mg chl a /m2 Steidinger 1973 d Inshore gulf waters 25

0.15 c mg chl a /m3 El-Sayed and Turner 1977 d Stations in Gulf of Mexico 28 5.92 mg chl a /m2 Cahoon et al. 1990 Onslow Bay, North Carolina 11 8.2 mg chl a /m2 Cahoon and Cooke 1992 Onslow Bay, North Carolina 15

Notes: Values in bold are those chosen to represent the West Florida Shelf; a grand mean of spring, summer, and fall data; b median value of provided range, 10 – 16.5; c median value of provided range, 0.1 – 0.2; d Vargo and Hopkins 1990.

Table 4.2. Estimates of phytoplankton pr oduction over the West Florida Shelf.

Value Provided unit Source Region Production (t

⋅ km -2

⋅ y -1 )

Coastal phytoplankton

0.3 f kg C

⋅ m -2

⋅ year 1 Valiela 1984 a General coastal phytoplankton 12474

200 g g C

⋅ m -2

⋅ year -1 B. Bendis b Florida Bay, 1994-1996 8316 27.4 mg C

⋅ m -2

⋅ hr -1 Cahoon and Cooke 1992 Onslow Bay, North Carolina 4990

0.3 f g C

⋅ m -2

⋅ day -1 Thomas 1995 West Florida shelf and loop current 4553 90 g C

⋅ m -2

⋅ year -1 Yoder & Mahood 1983 c West Florida shelf and loop current 3742 0.1 g C

⋅ m -2

⋅ day -1 Steidinger 1973 d Inshore Gulf of Mexico waters 1518

28 g C

⋅ m -2

⋅ year -1 Kondratyeva & Sosa 1966

d NW Cuban coastal waters 1164

27 g C

⋅ m -2

⋅ year -1 El-Sayed 1972 e Gulf of Mexico coastal 1123

0.09 h g C

⋅ m -2

⋅ day -1 Bunt et al. 1972 Florida waters and Caribbeaan 1366

Oceanic phytoplankton

0.2 kg C

⋅ m -2

⋅ year 1 Valiela 1984 in Dawes
1998 General 8316

160 g C

⋅ m -2

⋅ year -1 Kondratyeva & Sosa 1966

c Oceanic waters off Cuba 6653

318 i mg C

⋅ m -2

⋅ day 1 El-Sayed and Turner
1977 d Stations in Gulf of Mexico 4826

Seasonal patterns 0.8397 j g C

⋅ m -2

⋅ day -1 Vargo 1995 W. Fla shelf; 92-93 spring to fall 12744

15 k mg C

⋅ m -2

⋅ hr -1 Ortner et al. 1984 d Oceanic Jan. & Feb. 2732 0.5 g C

⋅ m -2

⋅ day -1 Yoder & Mahood 1983 d West Florida Shelf April 7588

0.625 l g C

⋅ m -2

⋅ day -1 Kondratyeva & Sosa 1966

c Oceanic off Cuba September 9485

0.19 g C

⋅ m -2

⋅ day -1 Kondratyeva & Sosa 1966

c Oceanic off Cuba November 2884

Spatial patterns 0.8 g C

⋅ m -2

⋅ day -1 Yoder & Mahood 1983 d W. Florida shelf; 100 to 200 m Summer 12141 0.4 g C

⋅ m -2

⋅ day -1 Yoder & Mahood 1983 d W. Florida shelf; inshore 100 m Summer 6071

Notes: Values in bold are those chosen to represent the West Florida Shelf; a in Dawes 1998; b Florida Fish and Wildlife Conservation Commission, unpublished data; c in Vargo and Hopkins 1991; d in Vargo and Hopkins 1990; e in El Sayed et al. 1972; f median of given range 0.1 – 0.5; g grand mean of data from three years and four stations; h median of given range 0.04 – 0.14; i median of given range 336 - 300; j grand mean of provided data; k median of given range 14 – 16; l median of given range 0.5 – 0.75.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 20 The values of phytoplankton biomass and pr oduction chosen to represent the West

Florida Shelf are Thomas ’s (1995) production value equivalent to 4,553 t

⋅ km -2



### ⋅ year -1 

### for
the West Florida Shelf and loop current and Steidinger’s (1973) biomass estimate equivalent to 25 t

⋅ km -2 

### for inshore Gulf of Mexico waters. The P/B value corresponding

to these estimates is 182.13

⋅ year -1 

### , which is close to the P/B values corresponding to SUSFIO’s (1977) assimilation value estimates fo r the West Florida Shelf shown in Table 4.3. Representative biomass and pr oduction values were chosen a priori to comparisons with the P/B val ues in Table 4.3. A com parison of Vargo’s (1995) production and biomass values (see Tables 4. 1 and 4.2) renders a P/B estimate of 175

⋅ year -1 

### , also comparable to these values.

Table 4.3. Derivation of Production/Biomass va lues from assimilation value estimates.

Assimilation value (mg C

⋅ mg chl a -1

⋅ hr -1 ) Source Region P/B (

⋅ year -1 )

6.35 Platt and Subba Rao 1975 Caribbean, <22 um fractions 516

3.88 Platt and Subba Rao 1975 Caribbean, >22 um fractions 315
5.20 Cahoon and Cooke 1992 Onslow Bay, North Carolina 423

1.60 SUSFIO 1977 Florida shelf, summer 130

1.33 SUSFIO 1977 Florida shelf, fall 108

1.65 SUSFIO 1977 Florida shelf, winter 134



### The phytoplankton production rate per given ar ea is very high in the region’s estuaries
relative to the West Florida Shelf as a wh ole. Livingston (1984) found that phytoplankton productivity of the Apalachicola Bay est uary system during the 1970s and 1980s ranged from 63-1,694 mgC

⋅ m -2



### ⋅ day -1 

### . Eastbrook (1973 in Livingston 1984) estimated the annual phytoplankton production of the Apalachicola estuary to be 371 gC

⋅ m -2 

### , which is equivalent to 15,426 t

⋅ km -2



### ⋅ year -1 

### . This is over three times the production value chosen
here for the West Florida Shelf. Furthermo re, this comparison includes phytoplankton only. Comparison of benthic pr oduction would undoubtedly sh ow estuaries to have far greater production relative to the shelf as a whole.

In the mid 1970s, SUSFIO (1977) observed the biomass of phytoplankton on the West Florida Shelf to increase considerably near the sediment-water interface during summer (Figure 4.3), repeating the pattern reveal ed by Saunders and Glenn (1969) a decade previously (Figure 4.1). Sutton et al. (2001) has observed similar near-bottom phytoplankton concentrations using higher reso lution sampling near the sea floor. Light penetration is greatest in the summer, but the phenomenon could also be caused by impingement of nutrient rich waters or re-s uspension at the sediment-water interface during that time. This seasonal shift in the vertical distribution of phytoplankton production likely reflects similar shifts in t he ratio of benthic to planktonic production and cycles of secondary production. Shallow micr ophytobenthos has been sh own to triple in abundance during the fall (Figure 5.1) when phytoplankton is least abundant.

Such seasonal and spatial patterns of pr oduction might have changed since the early 1970s as surface plankton blooms increasingly blocked light to deeper primary producers. Estimates of coastal phytoplankt on production in the region surrounding theEcopath model of the West Florida Shelf: Volume II. Model construction



### 21 West Florida Shelf have indeed consistent ly increased since the early 1970s. It is possible that this trend simply reflects an evolution of detectability, or a chance sequence of differences in study areas. Al ternatively, it c ould portend widespread ecological changes on the West Fl orida Shelf that goes un-noticed.

Literature cited (Phytoplankton)

Barnes, R.D. 1987. Invertebrate Zoology, 5 th Edition. Saunders College Publishing, Philadelphia, 893 p. Biggs, D. C., R. A. Zimmerman, R. Gasca, E. Suar ez-Morales, I. Castellanos, R. R. Leben. Note on plankton and cold-core rings in the Gulf of Mexico. Fishery Bulletin 95:369-375. Eastbrook, R. H. 1973. Phytoplankton ecology and hy drography of Apalachicola Bay. M.S. Thesis. Department of Oceanography, Flori da State University, Tallahassee. El-Sayed, S. Z. 1972. Introduction. In: El-Sayed, S. Z., W . M. Sackett, L. M. Jeffrey, A. D. Fredericks, R. P. Saunders, P. S. Conger, G. A. Fryxell, K. A. Stei dinger, and S. A. Earle. 1972. Serial atlas of the marine environment: chemistry, primary productivity, and benthic algae of the Gulf of Mexico. American Geographical Society, New York. Livingston, R. J. 1984. The ecology of the Apalachico la Bay system: an estuarine profile. U.S. Fish and Wildlife Service, FWS/OBS-82/05, 145 pp. Saunders, R. P. and D. A. Glenn. 1969. Diatoms. Me moirs of the Hourglass Cruises, Marine Research Laboratory, Department of Natural Resources, St. Petersburg, Florida. Smith, S. V. 1981. Marine macrophytes as a global carbon sink. Science 211:838-840 Steidinger, K.A., 1973. Phytoplankton ecology: A conc eptual review based on eastern Gulf of Mexico
research. CRC Crit. Rev. Microbiol 3:49-68. SUSFIO. 1977. Baseline monitoring studies, Mississippi, Alabama, Florida, outer continental shelf, 1975- 1976. Volume III. Results. State University System of Florida Instituti on of Oceanography, St. Petersburg. Prepared for the Bureau of Land Management, Washington, DC, 28 June 1977. Sutton, T.T., T.L. Hopkins, A. W. Remsen, and S.E. Burghart. 2001. Multisensor sampling of pelagic ecosystem variables in a coastal environment to estimate zooplankton grazing impact. Cont. Shelf
Res. 21: 69-87. Tomas, T. R. 1995. Dynamics of the early life history of recreational fishes on the Florida shelf. In: Coastal production and sportfish plankton dynamics on the Florida shelf. Prepared by the Florida
Marine Research Institute for the U.S. Fi sh and Wildlife Service, Project F-65. Vargo, G. A. 1995. CP92-2 Primary production data. In : Dynamics of the early life history of recreational fishes on the Florida shelf. In: Coastal production and sportfish plankton dynamics on the Florida shelf. Prepared by the Florida Marine Research In stitute for the U.S. Fi sh and Wildlife Service, Project F-65. Vargo, G.A. and T.L. Hopkins. 1990. Plankton. In: Synt hesis of available biological, geological, chemical, socioeconomic, and cultural resource information for the South Florida area (N.W. Phillips and K.S. Larson, eds.). OCS Study, MMS 90-0019. Pp. 195-230. Wetzel, R. G. 1983. Limnology, 2 nd Edition. Saunders College Publishing, New York, 765 p.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 22 4.2 Microphytobenthos

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Microphytobenthos are microscopic primary producers that live within the top few
millimeters of sea floor sediment and hard substr ate. It has recently come to light that these microscopic photosynthesizers can cont ribute a significant por tion of the primary production in nearshore mari ne ecosystems (Colijn and de Jonge 1984, Cahoon and Cooke 1992, MacIntyre et al. 1996), though high rates of benthic primary production has been recognized for more than 30 years (e.g., Bunt et al. 1972, S ournia 1976, Hartwig 1978). The benthic diatoms, dynoflagellates, eugl enoid flagellates, unicellular eukaryotic algae, and cyanobacteria that make up these microphytobent his are adapted to very low light conditions, and they are present throughout the West Florida Shelf, and probably beyond the 200 m depth contour.

The production of benthic microalgae incr eases when and where the biomass of overshading plankton decreases (Cahoon and Clarke 1992), thus probably evening out primary production across horizontal space on t he shelf as a whole. Nevertheless, both
horizontal and vertical distributions of micro-photosynthesizer s within sub-systems undoubtedly mediate the character of secondar y and higher level production in the West Florida Shelf food web. Light penetration into marine syst ems like the West Florida Shelf is a critical determinant of microphy tobenthic production, biom ass, and distribution (MacIntyre et al. 1996, Miller et al. 1996), t hough it can persist at low light levels (Cahoon and Cooke 1992). The photo synthetic dinoflagellates t hat live symbiotically in the tissues of corals and anemones, zooz anthellae, are also included in the microphytobenthos category.

The realization of high produc tion of microphytobenthos begs for the reorganization of quantitative, and conceptual, energy flow models of most nearshore systems. For example, many species traditionally categori zed as detritivores can now be thought of as more like herbivores. A large proportion of the energy and nutrient requirements of these organisms can be met by microphytobent hos, a high quality and fast growing food, rather than by dead organic detritus, a lower quality an d less biologically available food (Miller et al. 1996). Most benthic detritus feeders and deposit feeders eat microphytobenthos, from microf auna (ciliated protozoans), to the suite of meiofaunal and macrofaunal species (and phyla) like am phipods and polychaetes (fish food), to megafaunal invertebrates like ho lothurians, large, gastro pods, asteroids, crabs, and shrimps. But furthermore, suspens ion feeders and zooplankton consume microphytobenthos re-suspended by wave surge or by bioturbation. Biot ic facilitation of this production also occurs through localized nutrient enrichment, such as around worm tubes (Miller et al. 1996).

The rate of primary production by microphytobent hos, as well as its biomass, is strongly influenced, and limited, by t he amount of light r eaching the sediment (Hartwig 1978, MacIntyre et al. 1996, Miller et al. 1996). On the West Flor ida Shelf, microphytobenthic production on the West Florida Shelf declines with depth, but it also varies amongEcopath model of the West Florida Shelf: Volume II. Model construction



### 23 seasons. Higher microphytobenthic production in October appears counterintuitive since more light should penetrate the water colu mn during summer when solar radiation is more direct. However, this pattern can be explained by summertime phytoplankton blooms, which may inhibit microphytobenthic production, as obs erved by Hartwig (1978). Declining microphytoben thic production with increasing depth has also been observed off Madagascar, though various studies indicate intrinsic minimum values for biomass and production of microphytoben thos (Hartwig 1978, Cahoon and Cooke 1992).

Representative values of biomass (29.778 t

⋅ km -2 

### ) and productivity (706.496 t

⋅ km -2



### ⋅ year 1 

### ) for the West Florida Shelf were chosen fr om a variety of estimates resulting from
studies of marine sands on continental shel ves. Considering the depth attenuation of microphytobenthic production on the West Fl orida Shelf and on other continental shelves, normalized estimates were corrected wit h a coverage factor of 0.25 (except for estimate from Cahoon et al. 1990, whic h integrated deeper depths). The biomass

estimate of 29.778 t

⋅ km -2 

### and the productivity estimate of 706.496 t

⋅ km -2



### ⋅ year -1 

### are the respective means of the normalized and corrected values shown in bold in Table 4.4 and Table 4.5. The resulting P/B value for microphytobenthos is 23.725. However,
Cahoon and Cooke 1992 derived an assimilation value for benthic microalgae in the shallow sands of Onslow Bay, North Carolina of 0.8 mg C

⋅ mg chl a -1



### ⋅ hr -1 

### , which corresponds to a P/B value of 65 using conv ersions specified in Appendix 6. Although these are different locations, this discr epancy begs the questi on of whether the representative production value was biased downward by exclusion of higher end values (see Table 4.5).

Literature cited (Microphytobenthos)

Bunt, J. S., C. C. Lee, and E. Lee. 1972. Prim ary production and related data from tropical and subtropical marine sediments. Marine Biology 16:28-36. Cahoon, L. B. and J. E. Cooke. 1992. Benthic microalgal production in Onslow Bay, North Carolina, USA. Marine Ecology Progress Series 84:185-196. Cahoon, L. B., R. S. Redman, C. R. Tronzo. 1990. Benthic Macroalgal biomass in sediments of Onslow Bay, North Carolina. Estuarine, Coastal and Shelf Science 31:805-816. Colijn, F., and V. N. de Jonge. 1984. Primary pro duction of microphytobent hos in the Ems-Dollard Estuary. Marine Ecology Progress Series 14:185-196. Hartwig, E. O. 1978. Factors affe cting respiration and photosynthesis by the benthic community of a subtidal siliceous sediment. Marine Biology 46:283-293. Miller, D. C., R. J. Geider, and H. L. MacIntyre. 1996. Microphytobenthos: The ecological role of the “secret garden” of unvegetated, shallow-water marine habitats. II. Ro le in sediment stability and shallow-water food webs. Estuaries 19(2A):202-212. MacIntyre, H. L., R. J. Geider, and D. C. Miller. 1996. Microphytobenthos: T he ecological role of the “secret garden” of unvegetated, shallow-water mari ne habitats. I. Distribution, abundance and primary production. Estuaries 19(2A):186-201. Sournia, A. 1976. Primary production of sands in t he lagoon of an atoll, and the role of foraminiferan symbionts. Marine Biology 37:29-32.Ecopath model of the West Florida Shelf: Volume II. Model construction24Table 4.4. Estimates of microphytobenthos biomass in marine sands of the West Floirda Shelf.Chlorophyllaconcentration (mg⋅m-2)Source Location Depths (m) SedimentBiomass withinhabitat(t⋅km-2)Biomasson shelf(t⋅km-2)293.5 Sournia 1976 Takapoto Atolllagoon 0 - 17 calcereous sands 548 137101aBunt et al. 1972 Key Biscayne, Florida 3 – 60 calcereous sands 1894770.8 Colijn & de Jonge 1984 EMS Dollard estuary, Netherlands intertidal sandy / muddy 132 3358bPlante-Cuny 1978eMadagascar 5 – 60 marine sands 1082744cColocoloff 1972eMediterranean coast, France, to 12 marine sands 822136.4 Cahoon and Cooke 1992 Onslow Bay, North Carolina 14.6 – 41 marine sands 681719.9 Cahoon et al. 1990 Onslow Bay, North Carolina 11 – 285 marine sands 373714.4dHartwig 1978 La Jolla bight, California 9 - 24 marine sands 27 7Notes:Values in bold are those chosen for averaging to derive a representative value.amean of June and July transects;bmean of provided values 38and 78;cmedian of provided range 24 – 64;dmedian of range values 5.3 – 23.5 estimated from afigure by MacIntyre et al. 1996 (high uncertainty);einColijn and de Jonge 1984.Table 4.5. Estimates of microphytobenthic production on the West Floirda Shelf.Value Provided unit Source Location Depths (m) SedimentProduction inhabitat(t⋅km-2⋅year-1)Production onshelf(t⋅km-2⋅year-1)146.5amg C⋅m-2⋅hr-1Sournia 1976 Takapoto Atoll lagoon 0 - 17 calcereous sands 26681 66701.1bkg C⋅m-2⋅year-1Valiela 1984 general 21175 529497.5mg C⋅m-2⋅hr-1Sournia 1976 Takapoto Atoll lagoon 0 - 17 calcereous sands 17757 443937mg C⋅m-2⋅hr-1Colijn & de Jonge 1984EMS Dollard estuary, Netherlandsintertidal sandy / muddy 6738 168524.9mg C⋅m-2⋅hr-1Cahoon and Cooke 1992 Onslow Bay, North Carolina 14.6 – 41 marine sands 377994566g C⋅m-2⋅year-1Plante-Cuny 1978fMadagascar 5 – 60 marine sands 2744686158cmg C⋅m-2⋅day-1Hartwig 1978 La Jolla bight, California 9 - 24 marine sands 2398599157dmg C⋅m-2⋅day-1Colocoloff 1972fMediterranean coast, France, to 12 marine sands 23835968.1emg C⋅m-2⋅hr-1Bunt et al. 1972 Key Biscayne, Florida 3 – 60 calcereous sands 1475 369Notes:Values in bold are those chosen for averaging to derive a representative value.amedian of provided range 72 – 221 converted from oxygen databy Cahoon and Cooke 1992;bmedian of provided range 0.2 – 2 in Dawes 1998;cmean of provided data in Table 1 of Hartwig 1978;dmedian of providedrange 120 – 194;eprobably an underestimate (see Bunt et al. 1972);fin Colijn and de Jonge 1984Ecopath model of the West Florida Shelf: Volume II. Model construction



### 25 4.3 Macroalgae

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### The contribution of macroalgae to marine organi c production is known to be significant along the West Florida Shelf, and in other continental shelf ecosystems (El-Sayed 1972). Earle (1972) described 357 species of marine plants on the West Florida Shelf, 90% of which were macroalgae (Table 4.6 also see El-Sayed et al. 1972; Plate 6). Few studies of benthic macroalgae h ad been conducted on this shelf prior to Earle’s (1972) investigation, leading to her conclusion t hat macroalgal assemblages beyond 10 miles on the western Florida shelf “remains unexpl ored” (Earle 1972). Since that time, oil development studies have treated the West Fl orida Shelf macroalgae descriptively (see review by Phillips and Thomps on 1990). Explicit quantitative or detailed studies of this diverse flora are still lacking.

Table 4.6. Numbers of species in the 5 divisions of plants on
the West Florida Shelf.

Division Species Rhotophyta (red algal macrophytes) 171 Chlorophyta (green algal macrophytes) 97 Phaeophyta (brown algal macrophytes) 52 Cyanophyta (blue-green algae, or cyanobacteria) 30 Tracheophyta (seagrasses) 6 Xanthophyta 1

Total 357

Note: From Earle (1972).



### The biomass estimate for attached macroalgae over the whole of the West Florida Shelf (36 t

⋅ km -2 

### ) was derived by combining estimates of live macroalgae occurring on ‘live bottoms,’ in seagrass habitats, and in algal nodule habitats. These estimates are shown in Table 4.7. The approaches to deriving a ll of these estimates are described in the following sections

Table 4.7. Estimates of macroalgae biomass on the
West Florida Shelf.

Macroalgae category Biomass (t

⋅ km -2 ) Macroalgae on ‘live bottom’ reefs 25.7 Macroalgae in seagrass habitats 6.00 Algal nodules 6.00 Floating Sargassum 0.35

Total 36.05



### The biomass of macroalgae associated with ‘live bottom’ reefs (25.7 t

⋅ km -2 

### ) was roughly estimated using the following information. Phil lips and Thompson ( 1990) found that forty percent of live-bottom organisms on the We st Florida Shelf was allocated among macroalgae, hard corals, octocorals, and bivalves. Assuming that macroalgaeEcopath model of the West Florida Shelf: Volume II. Model construction



### 26 comprises 10% of the total li ve bottom biomass (i.e., 700 t

⋅ km -2 

### ; see ‘sessile epibenthos’ section), macroalgae would equal approximately 73.5 t

⋅ km -2 

### . The estimate

of 25.7 t

⋅ km -2 

### was derived by correcting this value wit h an areal correction factor of 0.35 (hard bottom is ~35% of the total shelf area ; Parker et al. 1983, Phillips et al. 1990 in Phillips and Thompson 1990). This estimate represents the lower end of the range for the estimate of ‘live-bottom organisms,’ t he average value of which was 1,500, which would likewise lead to a macroalgae estimate of 52 t

⋅ km -2 

### . The arbitrary assumption that macroalgae makes up 10% of the biomass of ‘live-bottom organisms’ indicates that reasonable confidence bounds for macroalgae biomass would surround these estimates by at least a factor of three.

The biomass estimate for live macr oalgae in seagrass habitats (6 t

⋅ km -2 

### ) was derived using the following approach. Ziem an et al. (1989) found 15.3 g dwt

⋅ m -2 

### in the seagrass habitats of Florida Bay, wh ich is equivalent to 117.81 t

⋅ km -2 

### when adjusted with a dry weight to wet weight conversion factor of 7.7. This value was fu rther adjusted with an
areal conversion factor of 0. 05, representing the proportion of the West Florida Shelf
covered with Thalassia seagrass beds (see Section 4.4). However, using the macroalgae estimate by Zieman et al. (1989) for Florida Bay may underestimate the seagrass-associated macroalgae biomass fo r the West Florida Shelf as a whole because their estimate represents 18% of t he total macrophyte standing crop, whereas Dawes et al. (1979, 1985 in Da wes 1998) estimated that macroalgae comprised up to 40% of the total standing crop of Florida’s seagrass beds.

A large band of alga l nodules can be found bet ween 70 and 100 m depths on the outer
reaches of the West Florida Shelf. Estimates of the bioma ss of algal nodules in these habitats are not currently available. In or der to include this macrophyte component in the model, however, place-holder values equivalent to the values used for macroalgae in seagrass beds are used for algal nodules. An areal conversion factor of five percent is approximately correct for algal nodul es after Phillips and Thompson (1990).

Drifting mats of Sargassum , which are entirely pelagic, ar e present across large areas
of the Gulf of Mexico. Parr (1939 in Earle 1972) es timated the density of Sargassum in

the Gulf of Mexico to be one short ton per square mile. This is equivalent to 0.35 t

⋅ km -2



### . Howard and Menzes (1969) estimated thei r production at to be approximately 0.32 gC

⋅ m -2



### ⋅ day -1 

### , which is equivalent to 2,248 t

⋅ km -2



### ⋅ year -1 

### , but these values are not compatible for calculating a P/B ratio, as t hey are from different places and times.

The P/B ratio used for West Florida Shelf macroalgae is 4 year -1 

### , from Luning (1990).

Literature cited (Macroalgae)

Dawes, C. J. 1998. Marine Botany, 2 nd Edition. John Wiley & Sons, Inc., New York. El-Sayed, S. Z. 1972. Introduction. In: El-Sayed, S. Z., W . M. Sackett, L. M. Jeffrey, A. D. Fredericks, R. P. Saunders, P. S. Conger, G. A. Fryxell, K. A. Stei dinger, and S. A. Earle. 1972. Serial atlas of the marine environment: chemistry, primary producti vity, and benthic algae of the Gulf of Mexico. American Geographical Society, New York.hEcopath model of the West Florida Shelf: Volume II. Model construction



### 27 Earle, S. A. 1972. Benthic algae and seagrasses In: El-Sayed, S. Z., W . M. Sackett, L. M. Jeffrey, A. D. Fredericks, R. P. Saunders, P. S. Conger, G. A. Fryxell, K. A. Steidinger, and S. A. Earle. 1972. Serial atlas of the marine environment: chemistry, primary productivity, and benthic algae of the Gulf of Mexico. American Geographical Society, New York. Howard, K. L., and R. J. Menzes. 1969. Distribution and production of Sargassum in the waters off the
Carolina coast. Botanica Marina 12:244-254. Luning, K. 1990. Pages 355-370. Seaweeds: Their Environment, Biogeography, and Ecophysiology. John Wiley and Sons, Inc., New York. Phillips, N. W., and M. J. Thompson. 1990. Offshore benthic communities. pp. 155-193 in N. W. Phillips, K. S. Larson (eds.) Synthesis of available bi ological, geological, chemical, socioeconomic, and
cultural resources information for the south Flori da area. Prepared by Continental Shelf Associates for Minerals Management Service, OCS Study - MMS 90-0019, May 1990. Zieman, J. C., J. W. Fourqurean, and R. L. Iverson. 1989. Distribut ion, abundance, and productivity of seagrasses and macroalgae in Florida Bay. Bulletin of Marine Science 44(1):292-311.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 28 4.4 Seagrasses

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Production of seagrasses makes up a consi derable portion of the total primary production on the West Florida Shelf. The sea grasses in this unique region are comprised mainly of Thalassia testudinum , which occurs between 1 and 10 m depths,
and Halophila spp., occurring between 8 and 40 m depths. Syringodium spp. is found at the outer edge of Thalassia beds, or intermixed with Thalassia

. Halodule spp. is found at the shoreward margins of these seagr ass beds (Iverson and Bittaker 1986, Josselyn et al. 1986, Phillips and Thompson 1990). A va riety of types of species use seagrasses
as food or habitat on the West Florida Shelf.

The overall biomass estimate of 175.617 t

⋅ km -2 

### and the overall production estimate of

1583 t

⋅ km -2



### ⋅ year -1 

### were made based on the estimates shown in Tables 4.8 and 4.9.

These estimates lead to a P/B ratio of 9.014

⋅ year -1 

### , which is close to the P/B estimate

of 7.3

⋅ year -1 

### that corresponds to 2%

⋅ day -1 

### , independently suggest ed by P. Carlson for Thallassia seagrasses on the West Florida Sh elf (Florida Marine Resources Institute, personal communication, 3 March 2000).

The P/B for Thallassia is approximately 2% per day in the productive parts of the seagrass beds, though production rates might be lower in the deeper parts of seagrass beds. This is equivalent to a P/B estimate of 7.3

⋅ year -1 

### for such grasses on the West
Florida Shelf.

The gross primary production value of a bed of seagrasses and algae was measured at 4,650 gC

⋅ m -2



### ⋅ year -1 

### (dry weight) at long key in the Florida keys (Odum 1957 in Earle 1972). However, Livingston (1984) esti mated net annual seagr ass production in Apalachicola Bay to be 500 gC

⋅ m -2



### ⋅ year -1 

### for Thalassia and from 320-350 gC

⋅ m -2



### ⋅ year -1



### for Vallisneria . Even with these lower figures; Livi ngston estimated the total production of seagrasses in the 210 km -2 

### Apalachicola Bay system as 27,213 tC

⋅ year -1



### .

Hallophillia seagrass beds are far more extensive than the narrow bands of Thallassia along shorelines because of their adaptation to low light, and because of their opportunistic nature. They are far less dense than Thallassia , but their P/B ratio is also subsequently much higher. Josselyn et al. ( 1986) found that the biomass of Halophila beds ranged from 5 to 12 g

⋅ m -2 

### , and their production ranged from 100 to 500 mgC

⋅ m 2



### ⋅ day -1 

### , for a median P/B ratio of 31

⋅ year -1



### .

Burkholder et al. (1959) found that Thal assia sp. beds made up between 2.5 and 33 (short) tons per acre in Puerto Rico (in Humm 1973).

Literature cited (Seagrasses)

Burkholder, P. R., L. M. Burkholder, and J.A. Rivero . 1959. Some chemical consituents of turtle grass,
Thalassia testudonum . Bull. Torrey Bot. Club 86:88-93.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 29 Dawes, C. J. 1998. Marine Botany, 2 nd Edition. John Wiley & Sons, Inc., New York. Earle, S. A. 1972. Benthic algae and seagrasses In: El-Sayed, S. Z., W . M. Sackett, L. M. Jeffrey, A. D. Fredericks, R. P. Saunders, P. S. Conger, G. A. Fryxell, K. A. Steidinger, and S. A. Earle. 1972. Serial atlas of the marine environment: chemistry, primary productivity, and benthic algae of the Gulf of Mexico. American Geographical Society, New York. Hall, M. O., M. J. Durako, J. W. Fourqurean, J. C. Zeiman. 1999. Decadal changes in seagrass distribution and abundance in Florida Bay. Estuaries 22(2B):445-449. Humm, H. J. 1973. Seagrasses. in Jones, J. I., R. E. Ring, M. O. Ri nkel, R. E. Smith (eds.) A summary of knowledge of the eastern Gulf of Mexico. The Stat e University System of Florida, Institute of Oceanography, St. Petersberg, Florida. Iverson, R. L., and H. F. Bittaker. 1986. Seagrass distribution and abundan ce in eastern Gulf of Mesxico waters. Estuarine, Coastal and Shelf Science 22:577-602. Jones, J. A. 1968. Primary productivity by the tropical marine turtle grass, Thalassia testudonum Konig, and its epiphytes. Doctoral dissertation, Inst. of Mar. Sci., University of Miami, University of Miami, 196 pp. Josselyn, M., M. Fonseca, T. Neisen, and R. Lars on. 1986. Biomass, production and decomposition of a deep water seagrass, Halophila decipiens ostenf. Aquatic Botany 25:47-61. Kohn, A. J. and P. Helfrich (1957) Primary organic pr oductivity of a Hawiian coral reef. Limnology and Oceanography 2:241-251. Odum, H. T. 1957. Primary productiv ity measurements in eleven Florida springs and a Marine turtle grass community. Limnology and Oceanography 2:85-97. Phillips, R. C. 1960. Observ ations on the ecology and di stribution of the Florida seagrasses. Professional Paper Series, Number 2. Florida State Board Conserv. Mar. Lab., 72 pp. Phillips, N. W., and M. J. Thompson. 1990. Offshore benthic communities. pp. 155-193 in N. W. Phillips, K. S. Larson (eds.) Synthesis of available bi ological, geological, chemical, socioeconomic, and
cultural resources information for the south Flori da area. Prepared by Continental Shelf Associates for Minerals Management Service, OCS Study - MMS 90-0019, May 1990. Pomeroy, L. R. 1960. Primary productivity of Boca Ciega Bay, Florida. Bull. Mar. Sci. Gulf Carib. 10:1-10. Zieman, J. C. 1987. A review of ce rtain aspects of the life, death, and di stribution of the seagrasses of the
southeastern United St ates 1960-1985. p. 53-76 In: M. J. Durako, R. C. Phillips, R. R. Lewis, III (eds.) Proceedings of the symposium on s ubtropical-tropical seagrasses of the southeastern United States, 12 August 1985, Florida Department of Natural Resources, Bureau of Marine Science, St. Petersburg, Florida. Zieman, J. C., J. W. Fourqurean, and R. L. Iverson. 1989. Distribut ion, abundance, and productivity of seagrasses and macroalgae in Florida Bay. Bulletin of Marine Science 44(1):292-311.Ecopath model of the West Florida Shelf: Volume II. Model construction30Table 4.8. Estimates of seagrass production relevant to the west Florida Shelf.SpeciesProvidedvalueCentralmeasureProvided unit Source RegionProduction inhabitat(t⋅km-2⋅year-1)Production onshelfe(t⋅km-2⋅year-1)Seagrasses 0.5 - 16 8.25g C⋅m-2⋅day-1Zieman 1987 South Florida 57967 2898Seagrasses 0.4 - 1.5 0.95 kg C⋅m-2⋅year-1Valiela 1984in Dawes 1998 general 18288 914Seagrasses 900 900 g C⋅m-2⋅year-1Jones 1968 Miami area 17325 866Thalassiabeda0.9 - 16 8.45g C⋅m-2⋅day-1Odum 1953aTexas 59372 2969Thalassia testudinum0.6 - 7.2 3.9 g C⋅m-2⋅day-1Buesa 1972, 1974 Cuba 27402 1370Thalassia testudinum0.051c0.051 g dwt⋅g dw-1⋅day-1Zieman et al. 1989 Florida Bay 9589479Thalassia testudinum330 330 g C⋅m-2⋅year-1Dawes 1998 Florida area 6353 318Thalassia testudinum0.97d0.97 g dwt⋅m-2⋅day-1Zieman et al. 1989 Florida Bay 2726 136Syringodium filiforme0.8 - 3.0 1.9g C⋅m-2⋅day-1Zieman 1987 South Florida 13350 667Syringodium filiforme0.052c0.052 g dwt⋅g dw-1⋅day-1Zieman et al. 1989 Florida Bay 5364268Halophila decipiens100 – 500 300mg C⋅m-2⋅day-1Josselyn et al. 1986 Salt River canyon 2108827Halodule wrightii0.072c0.072g dwt⋅g dw-1⋅day-1Zieman et al. 1989 Florida Bay 15998Total production 1583Notes:Values in bold are those chosen to represent each species on the West Florida Shelf.aTotal of seagrass, epiphytes, and benthic algae in aThalassiabed;band Odum 1963; Jones 1968; Zieman 1975;bBuesa 1972 in Zieman 1987;cDetermined using C14 uptake technique;dDeterminedusing leaf-mark technique;eCalculated by multiplying habitat-specific production by species-specific areal coverage factors on the West Florida Shelf;0.05 was applied to general seagrasses,Thalassia testudinium,andSyringodium filiformecorresponding to the ratio of Iverson and Bittaker’s (1986)areal estimates and the overall West Florida Shelf areal estimate of 170,000 km-2(Houhoulis, this volume); 0.005 was applied toHalodule wrightiicorresponding to 1/10 of the area covered byThalassiaandSyringodiumbeds; and 0.392 was applied toHalophilia decipienscorresponding to half ofthe proportion of area between 9 and 37 m (Houhoulis, this volume).Ecopath model of the West Florida Shelf: Volume II. Model construction31Table 4.9. Estimates of seagrass biomass relevant to the west Florida Shelf.SpeciesProvidedvalueCentralmeasureProvided unit Source RegionBiomass inhabitat(t⋅km-2)Biomass onshelfd(t⋅km-2)Seagrass Meadow 100 - 1200 650g dwt⋅m-2Dawes 1998 general (Florida area) 5005 250Thalassia testudinumbed500-3100 1800g dwt⋅m-2Bauersffeld et al 1959aFlorida, West Coast 13860 693Thalassia testudinumbed66.9b66.9 g dwt sc⋅m-2Zieman et al. 1989 Florida Bay 2944 147Thalassia testudinumbed126 - 215 170.5 g dwt⋅m-2Hall et al. 1999 Florida Bay, pre-decline 131366Thalassia testudinum123.26 123.26 g dwt⋅m-2Hall et al. 1999 Florida Bay, post decline 949 47Thalassia testudinumbed63 - 137 100 g leaf afdwt⋅m-2Iverson and Bittaker 1986 NW and S. Florida 770 39Syringodium filiforme100-300c200g dwt⋅m-2Zieman 1987 Florida 154077Syringodium filiforme36.7 36.7 g dwt sc⋅m-2Zieman et al. 1989 Florida Bay 514 26Halodule wrightii50-250 150g dwt⋅m-2Zieman 1987 Florida 11556Halodule wrightii7.9c7.9 g dwt sc⋅m-2Zieman et al. 1989 Florida Bay 111 1Halophila decipiens5 to 12 9g dwt⋅m-2Josselyn et al. 1986 Salt River canyon 6927Seagrass drift 0.309 0.309g⋅m-2Zieman et al. 1989 Florida Bay 2.4 2Total biomass176Notes:Values in bold are those chosen to represent each species on the West Florida Shelf.aand Phillips 1960, Taylor et al. 1973inZieman 1987;bEstimate is for ‘standing crop'; value was divide by 0.175 for biomass estimate corresponding to the specified ratio of standing crop to biomass forThalassia testudinium;cEstimate is for ‘standing crop'; value was divide by 0.55 for biomass estimate corresponding to the specified ratio of standingcrop to biomass forSyringodium filiformeandHalodule wrightii;dCalculated by multiplying habitat-specific production by species-specific areal coveragefactors on the West Florida Shelf; seeTable 4.1 for these coverage factors.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 32 

### 5. D ETRITUS

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Four detritus groups are included in the West Florida Shelf model: water column detritus, benthic detritus, drifting macr ophytes, and dead carcasses. Approaches for estimating biomass for each of these groups are included in the following sections.

5.1 Water column detritus

Water column detritus exists as particulate organic carb on (POC) and dissolved organic carbon (DOC), the latter being approximatel y an order of magnitude greater in mass
than the former on the West Florida Shelf. These two components exhibit converse seasonal fluctuations when pl otted on comparable scales.

Values chosen to represent POC and DOC ma sses are the means of values converted those given by Fredericks and Sackett (1970 in Fredricks 1972) and SUSFIO (1977).

The sum of these values 313 t

⋅ km -2 

### , which is multiplied by the assumed biologically useable proportion of water column organic carbon (0.4) leading to a water column

detritus estimate of 125 t

⋅ km -2 

### . Conversion factors for carbon mass to dry mass was 2.5 and dry to wet weight was 5 (Parsons et al. 1977 in Browder 1993). Volume data were integrated to 70 m depth, and an areal adjustment factor of 0.25 was applied to account for the fact that OC decreases rapidly be low the surface (Fredricks 1972), but probably increases again near the benthic boundary layer. A summary of the biomass estimates
is shown in Table 5.1.

Table 5.1. Derivation of water column detritus for the West Florida Shelf.

Carbon mass (mg C

⋅ l -1 ) Source Detritus wet mass (t

⋅ km -2 ) Particulate organic carbon

0.214 Fredericks and Sackett 1970 b 47

0.128 a SUSFIO 1977 28

Mean 37
Dissolved organic carbon

1.080 Fredericks and Sackett 1970 b 236

1.440 a SUSFIO 1977 315

Mean 276 Total organic carbon in water column 313

Estimated water column detritus 125

a presented values are the grand means from two transects and three seasons over the West Florida Shelf (Tables 59 and 60 in SUSFIO 1977);

b in Fredericks 1972.



### The rate of import of detritus into the pool of water column detritus is another important aspect of water column detritus, and this flow was calculated as follows. Based on information from Moody (1967) and Brooks et al. (1971) in Fredericks (1972), the runoffEcopath model of the West Florida Shelf: Volume II. Model construction



### 33 of DOM to the West Fl orida Shelf equals 15,938 t

⋅ year -1 

### , assuming that the shelf receives 5.313% of the total DOM input to the Gulf of Mexico, which is half of the shelf’s areal proportion (to account for a disproportionately higher contribution of DOM to the area adjacent to the mouth of the Mississippi River). Mor eover, the net export (annual output minus input) of the Apalachicola-Cha ttahoochee-Flint drainage basin is 213,800 tonnes of Carbon, 21,480 tonne s of Nitrogen, and 1,652 tonnes of Phosphorus (Mattraw
and Elder 1982 in Livingston 1984). The latter figures represent all forms of carbon imports into the West Florida Shelf system . Several other (smaller) drainage basins boarder the West Florida Shelf system, so the sum of these two figures (229,738 t

⋅ year 1 

### ) expressed on a shelf-wide basis equals (1.351 t

⋅ km -2



### ⋅ year -1 

### ) should be considered an underestimate of the organic carbon inputs to the system.

5.2 Benthic detritus

The wet mass of sediment detritus on the We st Florida Shelf is estimated to be 390

t

⋅ km -2 

### . Degradable organic carbon on continental margins is estimated to range from

450-760 tC

⋅ km -2 

### , while on abyssal plains it may range from 53-103 tC

⋅ km -2 

### (Emerson et al. 1987). Bunt (1972) found the same percentage organic carbon in Florida and
Caribbean calcerious sands as Emerson et al. (1987) found to be the average on the world’s abyssal plains (Table 5.2). Therefore, the median of the range given above for mass of OC on abyssal plains (78 tC

⋅ km -2 

### ) is a reasonable value for the mass of carbon
in the sediment of the West Florida Sh elf. Cushing (1984) might have used similar reasoning, as he reported the sa me value for the detritus poo l on the West Florida Shelf (78 gC

⋅ m -2 

### ), though this was prior to the work of Emerson et al. (1987). A wet mass estimate for sediment organic carbon (975 t

⋅ km -2 

### ) was then derived using a factor of 2.5 to convert carbon mass to dry mass and a fact or of 5 to convert dry to wet weight (Parsons et al. 1977 in Browder 1993). Fina lly, the assumption that only 40% of this organic carbon is biological ly useable leads to390 t

⋅ km -2 

### as a sediment detritus estimate (useable organic carbon) on the West Florida Shelf.

Table 5.2. Percent organic carbon in sedim ent relevant to the West Florida Shelf.

Percent Source Location

2.29 a ESE et al. 1987 Southwest Florida 0.159 Cahoon et al. 1990 Onslow Bay, North Carolina

4 Josselyn et al. 1986 Salt River canyon

0.34 Bunt et al. 1972 Florida & Caribbean calcareous sand 0.15 Hartwig 1978 La Jolla Bight, California 1.02 Emerson et al. 1987 Earth's continental margins 0.34 Emerson et al. 1987 Earth's abyssal plains

a mean of provided data from sampled stations



### Rowe and Menzel (1971) foun d the equivalent of 0.409 t

⋅ km -2 

### of sediment organic carbon on the upper slope (180 and 275 m depth) in the Gulf of Mexico. The mass of useable organic carbon (i.e., detritus) in the sediment declines logarithmically with
distance from shore in the Gulf of Mexico, and this could expl ain such low values on the
shelf break. These authors al so found patches of terrestri al and shallow water plantEcopath model of the West Florida Shelf: Volume II. Model construction



### 34 detritus beyond the shelf break in which organi c carbon was considerably higher. These elevated patches were not incl uded in the average va lue above, but they are implicitly
included in Section 5.3.

5.3 Drift macrophytes

Zeiman (1989) estimated the biomass of seagrass drift to be 2.379 t

⋅ km -2 

### in Florida Bay, he also estimated the biomass of drift algae to be 0.28 t

⋅ km -2 

### . The sum of these estimates (2.659 t

⋅ km -2 

### ) is taken to represent the biom ass of drift macrophytes, though
the latter estimate of dri ft macroalgae is probably an underes timate for the shelf as a whole. Drifting mats of sargassum on the surface are not count ed here because drift macrophytes is a category of detritus, not a category of living algae, even though drift macroalgae is often still alive. The main distri bution of living Sargasso mats in the Gulf of Mexico rarely intersects with the We st Florida Shelf (El Sayed et al. 1972).

5.4 Dead carcasses

There are two components of the ‘dead carcasses’ group: one is the standing mass of dead carcasses, and the second is the flow of dead carcasses. Ecopath models are more sensitive to the second component, as it is the energy (biomass) flows upon which
mass balanced models are focused.

We use a standing estimate of 0.001 t

⋅ km -2 

### for a standing mass of dead carcasses. Assuming that the average residence time of a dead carcass is 10 days (the assumed average time for a dead nektonic organism to be consumed on the sea floor), the standing biomass of ‘dead ca rcasses’ is the flow of nekton input (0.020 t

⋅ km -2



### ⋅ year -1 

### ) multiplied by the ratio of average residenc e duration to annual duration (10 days / 365 days), resulting in a standing biomass of 0.0005 t

⋅ km -2 

### . Doubling this value to account for natural inputs of dead carcasses (other than fishery inputs) results in a standing

dead carcass estimate of 0.001 t

⋅ km -2



### .

The flow of dead discards to the dead ca rcass group is estimated to be 0.058 t

⋅ km 2



### ⋅ year -1 

### , based on the following formulation. Cushing (1984) provided estimates for annual flow of shrimp discards on the West Florida Shelf betw een 0 and 44 m depths.
This zone was taken to be ro ughly 60% of the 170,000 km 2 

### area of the presently defined West Florida Shelf, and his estimates were adjusted accordingly. Using this approach, the flow of shrimp discards to the ‘dead carcass’ group would be 0.0115 t

⋅ km 2



### ⋅ year -1 

### . Cushing (1984) uses a factor of 10 to then estimate fish discards (e.g., fish

discards are 0.1152 t

⋅ km -2



### ⋅ year -1 

### ), but J. Nance (NMFS, per s. com., 18 October 2000) suggests that a factor of four (4) is mo re realistic (i.e., fish discards are 0.046 t

⋅ km 2



### ⋅ year -1 

### ). This leads to a summed flow of 0.058 t

⋅ km -2



### ⋅ year -1



### .

In the West Florida Shelf model, dead discar ds for each group were estimated based on fisheries-dependent information (see Secti on 13.1), and these dead discards were specified to flow to the ‘dead carcass’ gr oup using the ‘discard fate’ interface. The estimate of fishery discards based on Cushi ng’s (1984) shrimp discard estimate and J.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 35 Nance’s four-fold shrimp to non-sh rimp conversion factor (0.058 t

⋅ km -2



### ⋅ year -1 

### ) was 25% lower than the fisheries-dependent es timate for dead discards (0.077 t

⋅ km -2



### ⋅ year -1 

### ).

Natural deaths are specified to go to the ‘d ead carcass’ group thro ugh the ‘detritus fate’ settings. Because the flow of carcasses from natural death are unknown, a value of 1% was assigned to the ‘dead carcass’ group from all groups except for primary producer and detritus groups (squid was set at 5% becaus e of high mortality after spawning). The resulting total flow of carcasses to the ‘dead carcass’ group is 143 t

⋅ km -2



### ⋅ year -1 

### , and this value might be high.

Literature Cited (Detritus)

Browder, J. A. 1993. A pilot model of the Gulf of Mexi co continental shelf. p. 279-284. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Bunt, J. S., C. C. Lee, and E. Lee. 1972. Prim ary production and related data from tropical and subtropical marine sediments. Marine Biology 16:28-36. Cahoon, L. B., R. S. Redman, C. R. Tronzo. 1990. Benthic Macroalgal biomass in sediments of Onslow Bay, North Carolina. Estuarine, Coastal and Shelf Science 31:805-816. Cushing, D. H. 1984. Do discards affect the production of shrimps in the Gulf of Mexico? pp. 254-257 In J. A. Gulland and B. J. Rothschild (eds.) Penaeid shrimps—their biology and management. Fishing News Books, Farnham, Surrey England, 308 pp. Dames and Moore. 1979. The Mississippi, Alabama, Florida, outer continental shelf baseline environmental survey MAFLA 1977/1978. Volume 1-B. Executive Summary Report. Dames and Moore, Los Angeles, CA. Prepared for Bureau of Land Management, Washington, DC, 26 January
1979. El-Sayed, S. Z., W. M. Sackett, L. M. Jeffrey, A. D. Fredericks, R. P. Saunders, P. S. Conger, G. A. Fryxell, K. A. Steidinger, and S. A. Earle. 1972. Se rial atlas of the marine environment: chemistry, primary productivity, and benthic algae of the Gulf of Mexico. American Geographical Society, New
York. Emerson, S., C. Stump, P. M. Grootes, M. Stuiver, G. W. Farwell, F. H. Schmidt. 1987. Estimates of degradable organic carbon in deep-sea surface sedi ments from 14C concentrations. Nature 329 (6134):51-53. ESE et al. 1987. SouthWest Florida Shelf ecosystems study, Volume II – Data synthesis report. Environmental Science and Engineering, Inc. Gainsville, Florida, LGL Ecological Research Associates, Inc., Bryan, Texas, and Continental Sh elf Associates, Inc. Tequesta, Florida. Prepared for Golf of Mexico OCS Region, New Orleans, Loui siana, US Department of the Interior/Minerals Management Service, April 1987. Fredricks, A. D. 1972. Dist ribution of organic carbon. In: El-Sayed, S. Z., W . M. Sackett, L. M. Jeffrey, A. D. Fredericks, R. P. Saunders, P. S. Conger, G. A. Fryxell, K. A. St eidinger, and S. A. Earle. 1972. Serial atlas of the marine environment: chemistry, primary productivity, and benthic algae of the Gulf of Mexico. American Geographical Society, New York. Hartwig, E. O. 1978. Factors affe cting respiration and photosynthesis by the benthic community of a subtidal siliceous sediment. Marine Biology 46:283-293. Josselyn, M., M. Fonseca, T. Neisen, and R. Lars on. 1986. Biomass, production and decomposition of a deep water seagrass, Halophila decipiens ostenf. Aquatic Botany 25:47-61. Livingston, R. J. 1984. The ecology of the Apalachico la Bay system: an estuarine profile. U.S. Fish and Wildlife Service, FWS/OBS-82/05, 145 pp. Mattraw, H. C. and J. F. Elder. 1982. Nutrient and detri tus transport in the Apalachicola River, Florida. U.S. Geological Survey, Water S upply Paper 2196-C. U.S. Government Printing Office, Washington
D.C. Rowe, G. T. and D. W. Menzel. 197 1. Quantitative benthic samples from the deep Gulf of Mexico with some comments on the measurement of deep sea biom ass. Bulletin of Marine Science 21(2):556-
566.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 36 SUSFIO. 1977. Baseline monitoring studies, Mississippi, Alabama, Florida, outer continental shelf, 1975- 1976. Volume III. Results. State University System of Florida Instituti on of Oceanography, St. Petersburg. Prepared for the Bureau of Land Management, Washington, DC, 28 June 1977. Walsh, J. J., E. T. Premuzic, and T. E. Whitledg e. 1981. Fate of nutrient enrichment on continental shelves as indicated by the C/ N content of bottom sediments. In J. C. Nihoul (ed.) Ecohydrodynamicized, 13—50 Elsevier, New York.



### 6. M ICROBIAL HETEROTROPHS

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### The microbial community is centrally important in the utilization and flows of energy in marine ecosystems. It is, how ever, the least known ecosyst em component due to its inconspicuous nature. Organisms in this ca tegory include bacteria, fungi, and ciliated protozoans. Many of the larger biological components of marine ecosystems rely directly on the energy flow fr om the microbial community, and they influence all of these components directly or indirectly. In turn , larger components of the ecosystem shape
the structure and interactions of the microbial community thr ough foraging activities and patterns defecation, excretion, and mortality.

The biomass estimate for microbial heterot rophs on the West Florida Shelf (60 t

⋅ km -2 

### ) is the sum of bacteria in substrate, bacteri a in water column, and forams (Odum and
Odum 1955 and Sorokin 1987 in Opitz 1993). This value will be used until estimates more specific to the West Florida Sh elf can be made. Dames and Moore (1979) did estimate that living carbon (microbes) account ed for roughly 0.25% of the total organic carbon in the sediment. Livingston (1984) es timated that 0.005% of the sediment dry weight in the Apalachicola is composed of bacterial biomass. This proportion is derived from the sediment relationships only, and sh ould not be applied to the water column, the pool with the bulk of the organic carbon (see Section 5).

The P/B value for microbial heterotrophs of the West Florida Shelf (100 year -1 

### ) was taken from Sorokin (1987 in Opitz 1993), and the Q/B value (215 year -1 

### ) is from Opitz (1993). The assimilation efficiency of microbi al heterotrophs is low (e.g., 0.60 at the most). Microbial heterotrophs probably consume all four of the identified detritus categories, microphytoplankton, and microphy tobenthos, roughly in proportion to their respective abundance. An estimated diet co mposition is shown in Table 6.1 based on this assumption.

Table 6.1. Estimated diet composition of microbial heterotrophs on the West Florida Shelf.

Prey categories Proportion of diet

Water column detritus 0.87

Sediment detritus 0.05 Phytoplankton 0.03 Microphytobenthos 0.03 Drift macrophytes 0.01Ecopath model of the West Florida Shelf: Volume II. Model construction



### 37 Dead carcasses 0.01

Literature cited (Microbial heterotrophs)

Dames and Moore. 1979. The Mississippi, Alabama, Florida, outer continental shelf baseline environmental survey MAFLA 1977/1978. Volume 1-B. Executive Summary Report. Prepared for Bureau of Land Management, Washington, DC. NTIS PB-294 228. Livingston, R. J. 1984. The ecology of the Apalachico la Bay system: an estuarine profile. U.S. Fish and Wildlife Service, FWS/OBS-82/05, 145 pp. Opitz, S. 1993. A quantitative model of the trophic in teractions in a Carribean coral reef ecosystem, p.
259-267. In V. Christensen and D. Pauly (eds.) Tr ophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 38 

### 7. Z OOPLANKTON



### 7.1 Zooplankton

Tracey T. Sutton and Scott E. Burghart University of South Florida College of Marine Science



### The West Florida Shelf (WFS) zooplankton community is comprised of oceanic and neritic forms. Hydrographic conditions exis t that can transport oceanic and nearshore fauna onto the shelf (Stepien 1980). Upwel ling associated with the Loop Current significantly affects plankton production and standing crop on the WFS (Khromov, 1965; Bogdanov et al ., 1969; Austin and Jones, 1971). These effects are seasonal, with greatest impact in summer. The nearshore pl ankton communities are also impacted by
seasonal variations in r unoff, with greatest runoff occurring in summer. Annual variations in temperature al so affect plankton abundance to some extent, mainly along the north Gulf coast (Hopkins, 1966). Overall, evidence for distinct seasonal patterns in WFS zooplankton is weak (Vargo and Hopkin s, 1990), with peaks of biomass only 2-3 times the minima. Kelly and Dragovitch (1967) found larger macrozooplankton biovolumes differed by a factor of 10 from summer to winter in a study of Tampa Bay and the adjacent Gulf. Fall and spring values were one-hal f and one-fourth those of summer, respectively. As this estuary under goes a dramatic seasonal freshwater input cycle (wet in summer, dry in winter), it is reasonable to assume that WFS zooplankton abundances vary less than this amount.

There are few detailed, quantit ative published reports on th e zooplankton of the WFS. Most previous studies of zooplankton fr om the eastern Gulf describe estuarine ecosystems (Hopkins, 1966; Kelly and Dragov itch, 1967; McIlwain, 1968; Gillespie, 1971; Hopkins, 1977). The few studies deal ing with the shelf fauna were primarily
taxonomic and results were not quantitative (King, 1949; Davis, 1950; Grice, 1953; Fleminger, 1965). Arnold (1958) reported plankton volumes for shelf to be 0.171 ml m -3 

### (0.031 mg WW m -3 

### using the conversion regression of Wiebe et al ., 1975). The most extensive Gulf shelf zoopl ankton data are those of the Soviet-Cuban fishery investigations (Bogdanov et al., 1969). They found highest plankton productivity in the northern Gulf, mediated by runo ff from the Mississippi River and winter destratification. Highest zooplankton biomass occurs in fa ll and winter in this region. Loop Currentgenerated upwelling in summer enhances the southWest Florida Shelf production, which results in a biomass peak during this season. Annual ranges of biovolumes for
the northern Florida shelf are 0.3-1 ml m -3 

### (0.06-0.2 mg WW m -3 

### ), while the southWest
Florida Shelf values are 0.3-0.6 ml m -3 

### (~0.06-0.12 mg WW m -3 

### ). Standing crops within these areas can be locally high (e.g. Middle Grounds; Austin and Jones, 1971).

More recently, Sutton et al . (2001) conducted a high-resolu tion survey of zooplankton abundance across the central WFS using a mult isensor towed array. These data were collected during the warm season (September 1998) and will be the focus of this chapter. Sampling was conducted using 163 μm mesh nets, chosen to include the smaller copepod forms that dominate this f auna. Most previous studies used larger meshes, excluding the smaller mesozooplank ton. Zooplankton samples were processedEcopath model of the West Florida Shelf: Volume II. Model construction



### 39 at the University of South Florida Co llege of Marine Science using standard subsampling techniques. Biomass was determined by applying length/dry weight regressions to size frequency distributions of the subsamples. Dry weights were then converted to wet weight biomass for this model using the empirical relationships of
Wiebe et al . (1975). Biomass was estimated as mg m -3 

### and then converted to areal estimates (t WW km -2 

### ) by integrating depth of wate r column and distance traveled during each deployment.

The overall zooplankton distributional pattern determined by optical sensors (Optical Plankton Counter, Dual Light Sheet; see Sutton et al ., 2001) revealed a close correlation between hydr ography and zooplankton a bundance. Abundance maxima were seen nearshore, associated with a salinity gradient, and al ong the pycnocline offshore. Increased suspended particulate ma tter characterized both of these zones. These distribution patterns mirror those foun d on the northeastern Florida shelf (NEFS). Paffenhöfer (1983) found t hat the dominant copepods of the NEFS region ( Oncaea, Temora, Eucalanus) showed a significant positive correlation with the abundance of particulate matter. He also found that on a subtropical vertically stratified shelf multicellular zooplankton is most abundant in cooler upwelled water than warmer surface water (Paffenhöfer,1980). Thus, the patterns seen during our warm season transect are reasonably characteristic of the low latitude shelf regimes of the region.

For this model our data were divided in to three groups. Group 1 includes carnivorous zooplankton (mainly chaetognaths). The primary prey of this group is copepods, while this group is preyed on primaril y by planktivorous fishes (e.g. clupeids). This group dominates the zooplankton biomass in many areas of the WFS, and so is given separate treatment. Group 2 includes small (< 1.5 mm TL) copepods (Paracalanidae

, Oncaea, Oithona ). This group is primarily herbivorous , but much of its diet intake may consist of detritus, as the distribution of this group’s members is highly correlated with suspended particulate matter at hydrographic discontinuities. This group is preyed on largely by larger zooplankton and larval fi shes. This group is the numerically dominant component of the WFS zooplankton. Sutton et al . (2001) found that the genus Oncaea alone contributes 50% of the zooplankton nu mbers across the WFS and is the dominant grazing component (low specific grazing ra tes are more than offset by high abundance). Group 3 includes other mesozooplankton (lar ger copepods, meroplankton, ostracods). These groups are the most herbivorous of the three groups, feeding mostly on phytoplankton (e.g. chain-forming diatoms). This group is also the principal prey of the planktivorous baitfishes of the West Florida Shelf (e.g. clupeids, engraulids, carangids), and is thus given separate treatment. In summary, these groupings represent the biomass dominants (Group 1), the numerical and grazing dominants (Group 2), and the principal diet component of plank tivorous fishes (Group 3).

Standing stock values across the WFS as a function of depth zone showed variable contributions by each group (Table 7.1). Chaetognaths alone accounted for threefourths of zooplankton standing stock in t he inshore zone (shore to 20 m isobath), while accounting for ~ 40% offshore (20-100 m). Over all biomass estimates of the two zones were quite similar. It should be noted that these estimates should be consideredEcopath model of the West Florida Shelf: Volume II. Model construction



### 40 minimal, as younger life stages of the dominant small copepods are undersampled by traditional net-based methodologies.

Table 7.1. Warm season biomass estimates for WFS zooplankton by depth zone.

Inshore biomass a Offshore biomass b Zooplankton component (t WW km -2 ) (t WW km -2 ) Carnivorous zooplankton 41.7 22.8 Small copepods 8.2 16.8 Other mesozooplankton 7.1 12.8
Total 57 52.4

a shore to 20 m isobath

b 20 m to 100 m isobath



### Seasonal changes in zooplankt on biomass are presently being investigated. As a first order approximation, t he values presented in Table 7.1 can be considered the summer peak for the southWest Florida Shelf and the winter peak for the northern Gulf (Bogdanov et al ., 1969). Minimum values could be estimated as one-third of these values (Vargo and Hopkins, 1990). Annualized va lues were then calc ulated for the three WFS zooplankton components based on these parameters (Table 7.2). Offshore vs. inshore biomass estimates (Table 7.1) were prorated by the area of each zone on the WFS to generate the annualized values. T he copepod assemblage of the WFS is relatively short-lived due to high temperat ure, resulting in a high P/B ratio (P=B × no. generations year -1 

### ). Generation times of two and four weeks were taken as summer and winter values, respectively, for small c opepods and other mesozooplankton (Raymont, 1983). Generation times of one and two m onths were taken as summer and winter values, respectively, for carnivorous zooplankton (Reeve et al ., 1970). Consumption values (Q) were calculated by applying a 30% gross growth efficiency to the yearly production values. Thus, Q/B = P/B × 3.33. Lacking a way to measure ecotrophic efficiency, we assigned a value of >0.90.

Assigning diet composition for various zoopl ankton groups is problematic, especially in low latitude coastal regimes. The concept of herbivory is rarely applicable. Most “herbivorous” forms are omnivorous, taking phytoplankton, detritus, and microzooplankton in varying amounts. The best understood group is the chaetognaths, who show a marked selectivity for copepods (Reeve et al ., 1970). The small copepods are known to take phytoplankton and detritus, with the latter comp onent unquantified. A first order approximation would be to assign 50% herbivory and 50% detritivory to this group. Larger mesozooplankton take phyt oplankton, other crustacean zooplankton (mainly nauplii and early copepodites), a nd protozoan microplank ton. A first order approximation would be to assign 75% herbiv ory and 25% carnivory to this group. Knowledge of the feeding of some potentia lly important zooplankton components (e.g. ostracods) is totally lacking.

Table 7.2. Annualized Ecopath parameters for zooplankton of the West Florida Shelf.

Zooplankton component Biomass (t km -2 ) P/B (year -1 ) Q/B (year -1 ) EEEcopath model of the West Florida Shelf: Volume II. Model construction



### 41 Carnivorous zooplankt on 21.6 8.7 29.0 > 0.90 Small copepods 8.3 17.3 57.7 > 0.90 Other Mesozooplankton 6.7 17.3 57.7 > 0.90
Total 36.5 13 43.3 > 0.90

Literature Cited (Zooplankton)

Arnold, E.L. 1958. Gulf of Mexico plankton investigati ons, 1951-53. Spec. Sci. Rep ., U.S. Fish Wildl. Ser.
Fisheries No. 269. Austin, H.M. and J.I. Jones. 1971. Se asonal variation in bul k plankton on the Florida middle ground, and
its relation to water masses on the West Florida Shelf. Fla. Sci. 37: 16-32. Bogdanov, D.V., V.A. Sokolov, and N.S. Khromov. 1969. Regions of high biological and commercial productivity in the Gulf of Mexico and Caribbean Sea. Oceanology 8: 371-381. Davis, C.C. 1950. Observations on plankton taken in marine waters off Florida in 1947 and 1948. Quart.
J. Fla. Acad. Sci. 12: 67-103. Fleminger, A. 1956. Taxonomic and distributional studies on the epiplanktonic calanoid copepods (Crustacea) of the Gulf of Mexico . Ph.D. dissertation, Harvard University Library, Cambridge, MA. Gillespie, M.C. 1971. Analysis and treatment of zo oplankton of estuarine waters of Louisiana. In: Cooperative Gulf of Mexico Estuarine Invent ory and Study, Louisiana. Louisiana Wildlife and
Fisheries Commission. Grice, G.D. 1953. A qualitative and quantitative seasonal study of the Copepoda and Cladocera of Alligator Harbor. MS Thesis. Florida State University. Hopkins, T.L. 1966. Plankton of the S t. Andrews Bay system of Florida. Publ. Inst. Mar. Sci. Univ. Tex.
11: 12-64. Hopkins, T.L. 1977. Zooplankton distribution in surface waters of Tampa Bay, Florida. Bull. Mar. Sci. 27:
467-478. Kelly, J.A. and A. Dragovitch. 1967. Occurrence of macrozooplankton in Tampa Bay, Florida, and the adjacent Gulf of Mexico. Fish. Bull., US 66: 209-221. Khromov, N.S. 1965. Distribution of plankton in the Gulf of Mexico and some aspects of its seasonal dynamics. In: Soviet-Cuban Fishery Research, A.S. Bogdanov, editor, p. 36-56 (IPST Translation).
U.S. Document TT 69-59016. King, J.E. 1949. A preliminary report on the plankton of the west coast of Florida. Quart. J. Fla. Acad. Sci.
12: 109-137. McIlwain, T.D. 1968. Seasonal occurrence of the pelagic Copepoda in Mississippi Sound. Gulf Res. Rep.
2: 257-270. Paffenhöfer, G.-A. 1980. Zooplankton distribution as related to summer hydrographic conditions in Onslow Bay, North Carolina. Bull. Mar. Sci. 30: 819-832. Paffenhöfer, G.-A. 1983. Vertical zoopl ankton distribution on the northeast ern Florida shelf and its relation to temperature and food abundance. J. Plankton Res. 5: 15-33. Raymont, J.E.G. 1983. Plankton and Productivity in t he Oceans. Vol. 2. Zooplankton. Pergamon Press. Reeve, M.R., J.E.G. Raymont, and J.K.B. Raymon t. 1970. Seasonal biochemical composition and energy sources of Sagitta hispida . Mar. Biol. 6: 357-364. Stepien, J.C. 1980. The occurrence of chaetognaths, pteropods and euphausiids in relation to deep flow reversals in the Straits of Florida. Deep-Sea Res. 27: 987-1011. Sutton, T.T., T.L. Hopkins, A. W. Remsen, and S.E. Burghart. 2001. Multisensor sampling of pelagic ecosystem variables in a coastal environment to estimate zooplankton grazing impact. Cont. Shelf
Res. 21: 69-87. Turner, J.T. and S.B. Collard. 1980. Winter distribut ion of pontellid co pepods in the neus ton of the eastern
Gulf of Mexico continental shelf. Vargo, G.A. and T.L. Hopkins. 1990. Plankton. In: Synt hesis of available biological, geological, chemical, socioeconomic, and cultural resource information for the South Florida area (N.W. Phillips and K.S. Larson, eds.). OCS Study, MMS 90-0019. Pp. 195-230. Wiebe, P.H., S. Boyd, and J.L. Cox. 1975. Relation ships between zooplankton displacement volume, wet weight, dry weight, and carbon. Fish. Bull. US 73: 777-786.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 42 7.2 Ichthyoplankton

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### The early life stages of many fishes are planktonic. Ichthyoplankton are the planktonic eggs and larvae of these fishes. The importanc e of ichthyoplankton is self-evident by their potential fate as fish (those that escape predation), but t heir potentially profound ecological importance in their own right on the West Flori da Shelf is indicated by the high estimates for consum ption rates (P/B = 50.448

⋅ year -1 

### ) and production rates (Q/B =

132.13

⋅ year -1 

### ). Ichthyoplankton influence the st ructure of the plankton community through their voracious feeding, and they ma y be important food for a spectrum of
animals on the West Florida Shelf.

Thomas (1995) found dramatic spatial concentrations and patterning of ichthyoplankton
on the West Florida Shelf that varied acco rding to season. He suggested that high ichthyoplankton concentrations were asso ciated with nutrient in puts and chlorophyll plumes, and his results also show the Fl orida middle grounds to support elevated biomass. This could be due to nutrient impi ngement (upwelling) over this area, or high occurrences of fish spawning related to the pinnacles there. Distinc t concentrations of fish larvae in particular areas of the shelf may also be rela ted to elevated nutrients in cold core cells associated with the loop curr ent, or physical entrai nment in them, or
both. He also found that low salinity stress associated with rivers could have a detrimental effect on ichthyoplankton dur ing years of high precipitation.

In general, ichthyoplankton were most abundant in the northern and eastern areas of the West Florida Shelf, as well as nears hore between Tampa and Sanibel during the
late summer. Larval densities in some areas north of Tampa Bay and in the Big Bend

area reached 200 larvae

⋅ 10m -3 

### . These highly concentrated cells at the middle coast area during summer moved offshore during fall. See Thomas (1995) for figures of spatial distributions.

Although both fish eggs and fish larvae were always more abundant than zooplankton in these samples, fish larvae increased fr om a relatively low abundance and even distribution during February to an increas ingly higher abundance and patchy distribution during April and then August (F igure 7.1). Higher standard error bars depict less even spatial distributions, since samples were di stributed throughout the West Florida Shelf. During August, highly concentra ted cells of ichthy oplankton accounted for most of the fish larvae over the whole shelf. Fish eggs, however, were more even in both time and

space.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 43 0 10 20 30 40

February April August

Month (1992)Abundance (#/10m3) Larvae

Eggs

Zooplankton

Figure 7.1. Changes in the abundance and spatial dist ributions of ichthyoplankt on on the West Florida Shelf relative to zooplankton captured concurrently . Error bars are standard error; large error bars represent uneven spatial distributions. Sample si zes (number of stations) ranged from 45 to 71.



### The estimated biomass of ichthyoplankton on the West Florida Shelf (0.04754 t

⋅ km -2 

### ) was derived by multiplying the grand m ean densities for larvae and eggs (2.092

individuals

⋅ m -3 

### and 1.084 individuals

⋅ m -3 

### ) by 70 m, and by the wet we ight of a larval fish individual (0.0002858 g ww; conv erted using 7.5 from mean dr y weight from Theilacker 1987) and the assumed wet weight of a fi sh egg (0.000075 g ww) respectively. The above grand mean estimates of density were calculated from unpublished data from
Carmelo Tomas and the Florida Marine Res earch Institute (also see Tomas 1995)

The P/B (50.448

⋅ year -1 

### ) and Q/B (132.13

⋅ year -1 

### ) estimates used for ichthyoplankton on
the West Florida Shelf were derived from consumption and growth data in Theilacker (1987) from a laboratory experiment in which la rval northern anchovies ( Engraulis

mordax ) were placed in a solution of 2 rotifers

⋅ ml -1 

### (2 million

⋅ m -3 

### ). These values might overestimate rates of growth and consumpti on in the real ocean even though the data used were from the lower experimental food c oncentration. This concentration of rotifers is, however, not comparable to C. Tomas’ z ooplankton data since his sampling was not designed to assess rotifers. De-La Cruz-Ag uero (1993) used P/B and Q/B values of 15

⋅ year -1 

### and 110

⋅ year -1 

### respectively and these lower val ues should be considered if the Theilacker (1987) values cause the rmodynamic problems in the model.

The assimilation efficiency of ichthyoplankt on used in the West Florida Shelf model (0.40) is slightly higher than the mean of values derived by Theilacker (1987) for anchovy larvae in the laboratory (0.374). Erri ng on the high side of this value assumes that the assimilation efficiency of wild fish larvae would be at least slightly higher than those fed relatively high densitie s of food in t he laboratory.

Literature Cited (Ichthyoplankton)Ecopath model of the West Florida Shelf: Volume II. Model construction



### 44 De-La Cruz-Aguero, G. 1993. A pr eliminary model of Mandinga Lagoon, Veracruz, Mexico, p. 193-196. In V. Christensen and D. Pauly (eds.) Trophic Models of Aquatic Ecosystems. ICLARM Conf. Proc. 26, 390 p. Theilacker, G. H. 1987. Feeding ecology and gr owth energetics of larv al northern anchovy, Engraulis
mordax . Fishery Bulletin 85(2):213-228. Tomas, T. R. 1995. Dynamics of the early life history of recreational fishes on the Florida shelf. In: Coastal production and sportfish plankton dynamics on the Florida shelf. Prepared by the Florida
Marine Research Institute for the U.S. Fi sh and Wildlife Service, Project F-65.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 45 7.3 Carnivorous jellyfish

William (Monty) Graham Dauphin Island Sea Lab. (and U. of S. Alabama)



### The jellyfish or ‘jellies’ group is co mprised of gelatinous zooplankters such scyphomedusae, hydromedusae, siphonophor es and ctenophores. These organisms are all omivorous or carnivorous, consum ing other zooplankton including copepods and ostrocods, and ichthyoplankton including fish eggs and larvae. Some direct consumption of phytoplankton occurs, but not in quantity. Characterizing the gelatinous zooplankton of the West Florida Shelf is a c hallenging task, as the distinctly different regimes in its northern and s outhern zones further complic ate the problems presented by limited information. Neverthel ess, there is some basis for making estimations of parameters for a model of t he West Florida Shelf.

The biomass estimate for jellies on the West Florida Shelf (0.221 t ww

⋅ km -2 

### ) was derived by multiplying the volu metric estimate (0.00067 g dw

⋅ m -3 

### ) by the average depth of the West Florida Shelf (60 m), and then by a factor of 10 to convert from dry to wet weight. The volumetric estimate is the aver age of two estimates from the north (my own data), collected during August 1999 in the vici nity of the DeSoto Canyon. These are 0.52 and 0.82 mg dw

⋅ m -3 

### from a 20 m representation of the middle water column (of a 80 m total water column). The resulting value of 0.402 t ww

⋅ km -2 

### is then corrected for seasonal and areal variability as ex plained in the following two paragraphs.

The region is divided into two halves. The northern repr esents a temperate fauna and the southern, a sub-tropical f auna (Phillips 1971). The norther n region likely supports maximum biomass and production during the spring and summer, while the southern sub-tropical region likely s upports highest production and biomass during the 'wet' season (Winter and Spring).

My best estimate from numerical comparisons primarily is that th e southern half's peak biomass likely approximates the northern half's peak biomass even if these maxima are offset temporally (Larson 1982) . Therefore a good start would be to use simply the peak

areal biomass of 0.402 t ww

⋅ km -2 

### for 6 months and cut this number to 10% or 0.0402 tonnes for the remaining low pr oductivity period. Thus the average volumetric biomass estimate throughout the year and thr oughout the region would be 0.221 t ww

⋅ km -3



### .

No data for daily P/B of jellies exists fo r the eastern Gulf. However, a reasonable
number to use woul d probably be 0.20 day -1 

### . I chose this number as the maximum daily turnover since it reflects maximum daily gr owth rates of young jellies. Assuming that bioenergetic efficiencies remain constant as animals grow, production would go into
maintenance. This value is higher than the 10% day -1 

### value used in the model of the Prince William Sound ecosystem (Okey et al. 1999), but the warmer waters warrant
increased turnover rates.

P/B estimate of 40.15 year -1 

### was derived using a similar approach to that discussed
above. The mean of the estimated ma ximum daily growth rate (0.2 day -1 

### ) and 10% ofEcopath model of the West Florida Shelf: Volume II. Model construction



### 46 that value (0.02 day -1 

### ), which represents the minimum da ily growth rate during the lessproductive season, results in the esti mated daily growth rate (0.11 day -1 

### ), which was multiplied by 365 days.

The ratio of consumption to biomass (Q/B) for jellies of the West Florida Shelf is estimated to be approximately 80 year -1 

### assuming a maximum turnover of once every 5 days and an assimilation efficiency of 0.9 (see Purcell 1983) This is probably a reasonable number given the warmer wa ter and higher metabolic costs.

The diet composition used for the jellies of the West Florida Sh elf based on our own composition informati on from the scyphomedusa Aurelia aurita (Graham and Kroutil, submitted): 67% omnivorous meso-zooplan kton, 23% small herbi vorous copepods, and 10% fish eggs and larvae is mo dified from the diet used fo r jellies in Prince William Sound (Okey et al. 1999). Die tary proportions for omnivorous meso-zooplankton and small herbivorous copepods were switched based on my per sonal observations of cut contents from the region. As a final note, I think eggs and larvae are important in the diets of jellies; but 10% used here may ul timately be high and could accordingly be adjusted in the future.

Literature Cited (Carnivorous jellyfish)

Graham, W. M. & R. M. Kroutil, Subm itted, Sixe-based prey selectivity and dietary shifts in the jellyfish,
Aurelia aurita . Journal of Plankton Research . Larson, R. J., 1982, Medusae (Cnidaria) from Carrie Bo w Cay, Belize. In Rutzler, K. &I. G. MacIntyre (ed.), The Atlantic Barrier Reef ecosystem at Ca rrie Bow Cay, Belize. 1. Structure and Communities:
253-258. Okey, T. A., R. J. Foy, J. Pu rcell. Carnivorou s Jellies. P. 19 In T.A. Okey and D. Pauly (eds.) A trophic
mass balance model of Alaska’s Prince William Sound ecosystem, for the post-spill period 1994- 1996, 2nd edition. Fisheries Centre Research Report 7 (4), University of Brit ish Columbia, Vancouver. Phillips, P. J., 1971, The Pelagic Cnidaria of the Gu lf of Mexico. In Biology, pp. 212. College Station: Texas A & M University. Purcell, J. E., 1983, Digestion rates and assimilation efficiencies of siphonophores fed zooplankton prey. Marine Biology 73: 257-261.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 47 

### 8. B ENTHIC INVERTEBRATES



### 8.1 Meiobenthos

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Meiobenthos are benthic invertebrates smaller t han 0.5 mm in length, most of which live interstitially (between sediment grains) in soft sediment. The lower size limit of meiobenthos is thought of as 65 microns (0 .065 mm) as most organisms smaller than
this threshold are microbes. Meiobenthos have close trophic relationships with microbes (as predators) and macrofauna (as prey). M any meiobenthos live on the sediment surface as ‘micro-epifauna’.

Nematodes (round worms) are the most abundant meiobenthic group on the West Florida Shelf followed by harpac ticoid copepods (a type of benthic copepod). These are, in turn, followed by turbellarians and gastrot richs, and others includ ing priapulid larvae, crustacean larvae, polychaete larvae, kinory nchs, tartigrades, coelenterates, and halacarid mites (SUSFIO 1977). Meiobenthos undergo extreme seasonal fluctuations and they are extremely patchy on the West Florida Shelf, relative to macroinfauna and macroepifauna which are far mo re stable in time and spac e (Dames and Moore 1979). Neverthesess, the sampling design used by SUSFIO (1977) integrates this heterogeneity in space and time, resulting in representative density estimates for these organisms.

The biomass estimate of meiobenthos on the West Flor ida Shelf (2.051 t

⋅ km -2 

### ) was derived from MAFLA surveys (SUSFIO 1977) and information on individual sizes of

meiobenthos. Nematodes av eraged 330,775 individuals

⋅ m -2 

### , and copepods had an overall average of 58,333 individuals

⋅ m -2 

### in the MAFLA study area (Mississippi, Alabama, Florida, outer continental s helf baseline environmental survey), which overlaps considerably with t he West Florida Shelf area def ined herein. These density

estimates lead to biomass estimates of 1.45 t

⋅ km -2 

### and 0.415 t

⋅ km -2 

### respectively after
mean individual nematode and meiofaunal c opepod weights were applied (0.00000438

g ww

⋅ individual -1 

### and 0.00000712, the respective averages of a suite of estimates (see Donovario et al. 1999, Warwick et al. 1977 , and Schwinghammer et al. 1986) These average individual sizes also compares fa vorably with meiofaunal estimates adapted from Drgas et al. (1998) by converting from carbon to wet weight. The sum of these values (1.865 t

⋅ km -2 

### ) is increased by an additional 10% to account for meiobenthos
other than nematodes and harpactictoid copepods.

The P/B for meiobenthos was set at 12.5 year -1 

### based on estimates by Schwinghamer et al. (1986). It is worth noti ng here that Elmgren (1984 in Pauly and Christensen 1993) used 5.33 year -1 

### as P/B for meiobenthos, but War wick et al. (1979 in Pauly and Christensen 1993) used a P/B of 8.38 year -1 

### for nematodes.

The Q/B was set at the reasonable minimum value of 25 year -1 

### —twice the P/B value. This is very close to the Q/B value 22.5 year -1 

### provided by Tom Shirley for an EcopathEcopath model of the West Florida Shelf: Volume II. Model construction



### 48 model of Prince William Sound, Alaska (per s. com., UAF Institute of Marine Sciences; see Okey 1999), but Shirley’s value for Prince William Sound is likely to underestimate the Q/B of meiobenthos for a sub-tropical se tting like the West Florida Shelf where the higher temperature would m ean higher metabolic costs. Vidal (2000) has uses a
meiobenthic Q/B of 42.15 for her overall Gulf of Mexico model, and this may be closer
to the actual value.

The diet used for meiobenthos on the West Florida Shelf is 10 % other meiobenthos, 25% microbial heterotrophs, 25% microphy tobenthos, and 40% sediment detritus.

Literature cited (Meiobenthos)

Pauly, D. and V. Christensen. 1993. Graphical representation of steady -state trophic ecosystem models, p. 20-28. In V. Christensen and D. Pauly (eds.) Trophi c models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Dames and Moore. 1979. The Mississippi, Alabama, Florida, outer continental shelf baseline environmental survey MAFLA 1977/1978. Volume 1-B. Executive Summary Report. Prepared for Bureau of Land Management, Washington, DC. NTIS PB-294 228. Donovario, R., A. Dell’Anno, D. Martorano, P. Par odi, N. D. Marrale, and M. Fabiano. 1999. Seasonal variation in the biochemical composition of deep-sea nematodes: Bioenergetic and methodological considerations. Marine Ecology Progress Series 170:273-283. Drgas, A., T. Radziejewska, J. Warzocha. 1998. Biomass size spectra of near-shore shallow-water benthic communities in the Gulf of Gdansk (Sout hern Baltic Sea). Marine Ecology 19(3):209-228. Okey, T. A. 1999. Deep benthic groups and meiobenthos, p. 23-25. In T.A. Okey and D. Pauly (eds.) A trophic mass balance model of Alaska’s Prince Will iam Sound ecosystem, for the post-spill period 1994-1996, 2nd edition. Fisheries Centre Research Report 7 (4), University of British Columbia,
Vancouver. Schwinghamer, P., B. Hargrave, D. Peer, and C. M. Hawkins. 1986. Partitioning of production and respiration among size groups of organisms in an intertidal benthic community. Marine Ecology Progress Series 31:131-142. SUSFIO. 1977. Baseline monitoring studies, Mississippi, Alabama, Florida, outer continental shelf, 1975- 1976. Volume III. Results. State University System of Florida Instituti on of Oceanography, St. Petersburg. Prepared for the Bureau of Land Management, Washington, DC, 28 June 1977. Vidal, L. 2000. Exploring the Gulf of Mexico as a la rge marine ecosystem through stratified spatial model. MSc Thesis, University of Brit ish Columbia, Vancouver, 152 p.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 49 8.2 Small infauna

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### The ‘small infauna’ group consists of sea fl oor invertebrates that do not wash through a 500 micron screen (0.5 mm), but are generally smaller than 20 mm. The term ‘small infauna’ is being used here in place of, and sometimes interchangeably with the more conventional term ‘macrofauna.’ This group does not include the interstitial ‘meiofauna,’ which are smaller than (0.5 mm). Small inf auna live in the top la yers of sediment (infauna) or on the sediment surface (epifauna), and they include polychaetes, small crustaceans, small molluscs, and small echi noderms. The species in this aggregated group exhibit a variety of life habits and niches, but in general, they consume microphytobenthos, microbial heterotrophs, me iobenthos, each other, mobile epifauna, and detritus, whether suspende d or deposited. Small infaun a are important prey for larger fish and invertebrate predators on the West Flori da Shelf (Dames and Moore 1979). For the purposes of this model, small infauna is distinguished from swarming macro-epifauna, which is a separate group (s ee Section 8.3). This distinction is made because the general goal of this modeling effort is to examine the trophic forces relating to the production of forage fi shes on the West Florida Shel f. This distinction is also based on recent work showing the functional differences between these two groups to be profound (Okey, in press).

The amount of carbon present as living biomass in the be nthos of the slopes and the deep basins of the Gulf of Mexico is rela tively small because of the low surface productivity (Rowe and Menzel 1971 in Collard and D’Asaro 1973), but also probably
because of efficient utilization of available carbon within sh elf ecosystems, effectively minimizing carbon transport across shelves. The logarithmic decline in the amount of carbon in live organisms with increasing dist ance from coasts (Rowe and Menzel 1971) probably also holds for useable organic carbon (i.e., detritus) in the sediment (see Section 5).

In general, the small infaunal community of the shelf has been characterized as considerably more even in time and spac e than the more ephemeral and patchy shelf meiobenthos (Dames and Moore 1979). Nevert heless, macrofaunal assemblages do
exhibit some seasonal fluc tuations (Figure 8.1).Ecopath model of the West Florida Shelf: Volume II. Model construction



### 50 0 500 1000 1500

Summer Fall Winter

Seasons (1975 - 1976)Density (individuals /m2)

0 2 4 6 8 10Biomass (t /km2)

Density

Biomass

Figure 8.1. Seasonal change of polychaete abundance and biomass on the West Florida Shelf in 1975 and 1976 (data from SUSFIO 1977). Polychaetes ma ke up approximately 64% of ‘small infauna’ individuals on the SouthWest Florida Shelf ( ESE et al. 1987). Error bar s are standard error.



### The Mississippi, Alabama, Flor ida, Outer Continental Shelf Baseline Environmental Survey (MAFLA) included the only samp ling programs that appeared capable of characterizing the small infau nal assemblage for the whole West Florida Shelf (e.g., SUSFIO 1977). The estimate of small inf aunal biomass on the West Florida Shelf (9.923 t

⋅ km -2 

### ) chosen from this program neverthel ess contains uncertainty because the value was extrapolated from polychaete biomass to overa ll small infauna biomass with a ratio developed from abundance information from the southWest Florida Shelf (ESE et al. 1987). Estimates from other studies were used for verification, but the SUSFIO (1977) estimates contained the least uncertain ty of the studies identified (see Table 8.1), for the purposes of this modeling exercise.

Table 8.1. Biomass estimates of small in fauna biomass on the West Florida Shelf.

Value Provided
unit Source Region Biomass in
habitat (t ww /km^2) Coverage
ratio Biomass on
shelf (t ww /km^2)

<20 g

⋅ m -2 ESE et al. 1987 Southwest Florida Shelf <20.000 g 1 <20.000
1,970 #

⋅ 0.59 m -2 Dames and Moore 1979 West Florida Shelf 19.032 h 1 19.032
7,062 a mg

⋅ m -2 Rowe and Menzel 1971 Compeche shelf break, GoA 7.062 1.5 i 10.593

6.349 b g

⋅ m -2 SUSFIO 1977 West Florida Shelf (MAFLA) 6.349 1.563 j 9.923

9.05 c g

⋅ m -2 Continental Shelf
Associates 1986 f 20-25 m off Tampa Bay 9.050 1 9.050

25.533 d g

⋅ m -2 Sheridan 1997 Rookery Bay 25.533 0.333 k 8.502

20.9 e g

⋅ m -2 Estevez 1986 Charlotte Harbor 20.900 0.333 k 6.960

Notes: Bolded value is the one chosen to represent the defined shelf area. a mean of values 6800 and 7320 taken from 185 and 282 m depth respectively; b grand mean of polychaete biomas ses from three seasons and six transects in the MAFLA area; c median of range 2 – 16.1 in 20 to 25 m off Tampa Bay; d mean of medianEcopath model of the West Florida Shelf: Volume II. Model construction



### 51 biomasses from different habitats; e 25 stations and two different seasons in Charlotte Harbor; f in ESE et al. 1987; g but Vittor (1979 in ESE 1987) might contain data to calculate accurate values; h the product of #

⋅ m -2
and the mean weight of a sandflat polychaete (0.0057 g) from Okey (1997); i Assuming that infaunal biomass is 50% greater on the shelf as a whole than at the shelf break; j Conversion from polychaetes, which make up approximately 64% of macrofaunal individuals on the southWest Florida Shelf (ESE et al. 1987); k the present author’s best judgment for converting from estuaries to the West Florida Shelf, assuming that small infauna are less abundant on the more distant portions the We st Florida Shelf, as observed by SUSFIO (1977), Dames and Moore (1979), and ESE et al. (1987 ) (also see Rowe and Menzel 1971).



### Average sizes of polychaetes were derived from density and biomass data from three Rookery Bay, Florida habitats (Sheridan 1997) in order to estimate biomass from
macrofaunal abundance information on ot her studies (see Table 8.2).

Table 8.2. Representative abundances and bioma sses from Rookery Bay and Charlotte Harbor.

Habitat Abundance (individuals

⋅ m -2 ) Overall
abundance Biomass (g

⋅ m -2 ) Overall
biomass Individual biomass (g) Red mangrove a 22,591 – 52,914 37,752 3.6 – 8.2 5.9 0.000156 Mixed seagrasses a 6,347 – 23,545 14,946 15.7 – 87.4 51.6 0.003452 Non-vegetated mud a 3,611 – 22,465 13,038 11.9 – 26.2 19.1 0.001464 Overall Rookery Bay 21,912 25.5 0.001163 Charlotte (May-June) b 17,165 19.0 Charlotte (September) b 19,584 22.8
Overall Charlotte Har. b 18,375 20.9
West Florida Shelf c 3,339 3,339 19.0

a Abundance and biomass ranges from Sheridan (1997) ; average individual biomasses were calculated using median values of abundance and overall biomass; b Mean abundance values calculated from 25 stations monitored by Estevez (1986). Overall Char lotte Harbor biomasses were calculated by applying
the overall individual biomass from the c Rookery Bay habitats (0.001163 g; Sheridan 1997); From Dames and Moore (1979); 1,970 individuals

⋅ 0.59 m -2 converted to m -2 and multiplied with the overall individual biomass for a sandflat polychaete (0.0057 g) from Okey (1997).



### Rowe and Menzel (1971) f ound a high correlation betw een wet weight and carbon (0.96), numbers and carbon (0.86), and wet weight and dry weight (0.85), and a
moderate correlation between dr y weight and carbon (0.73). They also found the wet weight of these infauna to be 5 to 10 times the dry weight, and dry weight was 5 to 15 times organic carbon weight.

The P/B and Q/B estimates for small in fauna on the West Florida Shelf (4.6

⋅ year -1 

### and 15.9 year -1 

### respectively) were developed for Yucatan shelf annelids by ArreguínSánchez et al. (1993). These values were developed as independently estimated inputs, not outputs of their model.

The diet composition estimation for the We st Florida Shelf is shown in Table 8.3. However, these diet compositions are based on studies by Feder and Jewitt 1988 and Feder et al. 1989 in subpolar environments. The assimilation efficiency of small infauna is probably low since they consume low qualit y, or difficult to digest, food items. Assimilation efficiency should be set at 0.06 at best.

Table 8.3. Estimated diet composition ofEcopath model of the West Florida Shelf: Volume II. Model construction



### 52 ‘small infauna’ on the West Florida Shelf

Prey categories Proportion of diet a
Phytoplankton 0.22 Microphytobenthos 0.22

Sediment detritus 0.15 Microbial heterotrophs 0.15

Meiobenthos 0.10

Water column detritus 0.05 Drift macrophytes 0.05

Macrofauna 0.05 Sm. mobile epifauna 0.01

Notes: Diets expanded and modified from Feder and Jewitt (1988) and Feder et al. (1989 in Jewett 1999). a Jewett’s (1999) 61% value for detritus was equally apportioned among detritus, two microbe categories, and
meiobenthos.

Literature Cited (Small infauna)

Arreguín-Sánchez, F., J. C. Seijo and E. Valero-Pacheco. 1993. An application of ECOPATH II to the north continental shelf ecosystem of Yucatan, Mexico, p. 269-278. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Dames and Moore. 1979. The Mississippi, Alabama, Florida, outer continental shelf baseline environmental survey MAFLA 1977/1978. Volume 1-B. Executive Summary Report. Prepared for Bureau of Land Management, Washington, DC. NTIS PB-294 228. ESE et al. 1987. SouthWest Florida Shelf ecosys tems study, Volume I – Executive Summary. Environmental Science and Engineer ing, Inc. Gainsville, Florida, LG L Ecological Research Associates, Inc., Bryan, Texas, and Continental Shelf Associates , Inc. Tequesta, Florida. Prepared for Golf of Mexico OCS Region, New Orleans, Louisiana, US Department of the Interior/Minerals Management Service, April 1987. OCS Study MMS 87-0022
ESE et al. 1987. SouthWest Florida Shelf ecosystems study, Volume II – Data Synthesis Report. Environmental Science and Engineer ing, Inc. Gainsville, Florida, LG L Ecological Research Associates, Inc., Bryan, Texas, and Continental Shelf Associates , Inc. Tequesta, Florida. Prepared for Golf of Mexico OCS Region, New Orleans, Louisiana, US Department of the Interior/Minerals Management Service, April 1987. OCS Study MMS 87-0023 Estevez, E.D. 1986. Infanal macroinvertebrates of t he Charlotte Harbor estuarine system and surrounding inshore waters, Florida. United States Geological Survey Water Resources Investigations Report 85- 4260. 116 pp. Feder, H.M. and S.C. Jewett. 1988. The subtidal benthos, p. 165-202. In : D.G. Shaw and M.J. Hameedi (eds.) Environmental Studies of Port Valdez, Al aska. Lecture Notes on Coastal and Estuarine Studies

, Vol. 24

. Springer-Verlag, Berlin. Feder, H.M., A.S. Naidu, J.M. Hameedi, S.C. Jewett, and W.R. Johnson. 1989. The Chukchi Sea
continental shelf: benthos-env ironmental interactions. OCSEAP Final Report (RU 687). Hall, J.R., and C. H. Saloman. 1975. Distribution and abundance of macroinvertebrate species of six phyla in Tampa Bay, Florida, 1963-64 and 1969. Nati onal Marine Fisheries Service Data Report 100:1-
505. Jewett, S. 1999. Macr ofauna. p. 22-23. In T.A. Okey and D. Pauly (eds.) A trophic mass balance model of Alaska’s Prince William Sound ecosystem, for the post-spill period 1994-1996, 2nd edition. Fisheries Centre Research Report 7 (4), University of Brit ish Columbia, Vancouver. Okey, T. A. 1997. Sediment flushing observatio ns, earthquake flushing, and benthic community changes in Monterey Canyon head. Continental Shelf Research 17 (8):877-897.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 53 Okey, T. A. ( in press ). Macrobenthic colonist guilds and reneg ades in Monterey Canyon drift algae: Partitioning multidimensions. Targ et journal: Ecological Monographs. Parker, R. O., Jr., D. R. Colby, and T. D. Willis. 1983. Estimated amount of reef habitat on a portion of the
South Atlantic and the Gulf of Mexico continent al shelf. Bulletin of Marine Science 33:935-940. Rowe, G. T. and D. W. Menzel. 197 1. Quantitative benthic samples from the deep Gulf of Mexico with some comments on the measurement of deep sea bi omass. Bulletin of Marine Science 21(2):556-566. Sheridan, P. 1997. Benthos of adjac ent mangrove, seagrass and non-vegetated habitats in Rookery Bay, Florida, USA. Estuarine, Coas tal and Shelf Science 44:455-469. SUSFIO. 1977. Baseline monitoring studies, Mississippi, Alabama, Florida, outer continental shelf, 1975- 1976. Volume III. Results. State University System of Florida Instituti on of Oceanog raphy, St. Petersburg. Prepared for the Bureau of Land Management, Washington, DC, 28 June 1977. Vittor, B. A. 1979. Macroinfaunal polychaetes. In The Mississippi, Alabama, Florida outer continental shelf baseline environmental survey 1977/1978, Ch. 15. Washi ngton, D.C., U. S. Department of the Interior, Bureau of Land Management.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 54 8.3 Small mobile epifauna

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Small mobile epifauna are highly mobile, epifaunal macro-invert ebrates (0.5 mm to approximately 20 mm in size) that live asso ciated with the sea floor, but which swim
above it in search of food or refuge. Smal l mobile epifauna are fo und over hard and soft bottoms, often in association with drift macr ophytes. They mainly consist of swarming amphipod crustaceans and mysids. Ostrocods , some other crustaceans, and a few polychaetes can emerge demersally from the se diment to become m obile epifauna. In this model, juvenile shrimp and small cr abs are included in ‘small mobile epifauna.’ Swarming amphipods live mainly by cropping mi crobial flora from the surfaces of decomposing macrophytes, by directly gr azing micro and macroalgae, by scavenging animal carcasses, and by eating zooplan kton and small infauna. Mysids mainly consume phytoplankton. Small mobile epifauna are an important, though underemphasized and understudied, link of secondary production in marine food webs of continental shelves and the deep sea. They are also probably an underemphasized component of the diet of fishes and other larger organisms.

Quantitative density or biomass information on small mobile epif auna was not found for the West Florida Shelf ecosystem, alt hough good evidence exists of a diverse and abundant small epifauna (e.g., Myers 1981, Menzies and Kru czynski 1983, Oritz 1991). A large proportion of this group is comprised of organisms that are highly mobile, and which actively seek out food resources an d refuges that can have extremely patchy distributions in time and space (e.g., sw arming benthic amphipods and mysids; Okey 1997; Okey, in press.). These characteristi cs make reliable estimates of density and biomass difficult to develop wit hout sampling programs that expl icitly account for relative distributions in the context of their patchy re sources (e.g., drift algae, carcass falls). The biomass for this group was left to be estimated by the Ecopath model, as P/B, Q/B, and EE values were entered as inputs.

The P/B and Q/B estimates for small mobile epifauna on the West Florida Shelf (7.01

⋅ year -1 

### and 27.14 year -1 

### respectively) were developed for Yucitan shelf microcrustaceans by Arreguín-Sánchez et al . (1993). These values were developed as independently estimated inputs, not outputs of their model.

The estimated diet composition shown in Table 8.4 is based roughly on estimates by Dean (1999), considering that his small epibenthos group was defined differently than the current ‘small mobile ep ifauna’ group, and that the pr esent model contains more possible prey groups.

Table 8.4. Estimates of the diet composition of small mobile epifauna on the West Florida Shelf

Prey categories Proportion of diet
Microbial heterotrophs 0.15 Drift macrophytes 0.15Ecopath model of the West Florida Shelf: Volume II. Model construction



### 55 Macroalgae 0.12 Microphytobenthos 0.11 Phytoplankton 0.10

Sediment detritus 0.07

Water column detritus 0.07

Meiobenthos 0.07

Dead carcasses 0.05

Small infauna 0.05 Seagasses 0.05 Sm. mobile epifauna 0.01

Note: Based on estimates by Dean (1999).

Literature Cited (Small mobile epifauna)

Arreguín-Sánchez, F., J. C. Seijo and E. Valero-Pacheco. 1993. An application of ECOPATH II to the north continental shelf ecosystem of Yucatan, Mexico, p. 269-278. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Dean, T. A. 1999. Shallow small epifauna. pp. 20-21 In: Okey, T. A. and D. Pauly (eds.). A trophic mass balance model of Alaska’s Prince William Sound ecosystem, for the post-spill period 1994-1996, 2nd edition. Fisheries Centre Research Report 7 (4), University of British Columbia, Vancouver. Feder, H. M. and S. C. Jewett. 1986. The subtidal benthos. p. 347-396 In : D. W. Hood and S. T. Zimmerman (eds.). The Gulf of Alaska: physica l environment and biological resources. U.S. Department of Commerce, NOAA, Washi ngton, D.C. OCS study, MMS 86-0095. Guénette, S. 1996. Macrobenthos. p. 65-67 In : D. Pauly and V. Christensen (eds.). Mass-balance models of North-eastern Pacific Ecosystems. Fisheries Cent re Research Report 4(1) , University of British Columbia, Vancouver. Menzies, R. J. and W. L. Kruczynski. 1983. Isopod crustacea (exclu sive of epicaridea). Memoirs of the Hourglass Cruises. Marine Research Laboratory, Florida Department of Natural Resources, St. Petersberg, Florida. Myers, A. A. 1981. Amphipod Crustacea. I. Family Aori dae. Memoirs of the Hour glass Cruises. Marine Research Laboratory, Florida Department of Natural Resources, St. Petersberg, Florida. Okey, T. A. 1997. Sediment flushing observatio ns, earthquake flushing, and benthic community changes in Monterey Canyon head. Continent al Shelf Research 17(8):877-897. Okey, T.A. In press . Macrobenthic colonist guilds and renegad es in Monterey Canyon drift algae: Partitioning multidimensions. Ecological Monographs Oritz, M. 1991. Amphipod Crustacea. II. Family Ba teidae. Memoirs of the Hourglass Cruises. Marine Research Laboratory, Florida Department of Natural Resources, St. Petersberg, Florida.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 56 8.4 Bivalves

William S. Arnold and Dan Marelli
Florida Marine Research In stitute, St. Petersburg

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Marine bivalves are a diverse and energetica lly important compone nt of Florida’s
nearshore and shallow shelf co mmunities. Unfortunately, basic population parameters for non-commercial species do not exist in these habitats. Understandably, the only
research of this nature has been conduc ted in estuarine or very near shore environments and mainly focusing on species t hat are targets of fi sheries. The other 250 plus species of bivalves that occur along the Florida Gulf coast, which are not fisheries species, are often small, abundant, and important in marine food webs. Obtaining more than qualitative information on these bivalves is difficult, and we have had to generalize and make estimates based on experience and in formation gleaned
from the literatur e. Clearly for these numbers to be meaningful, an extensive survey of the habitats in question is re quired. Both deposit-feedi ng and suspension-feeding modes are represented within the Bivalvia. Suspension-feeding bivalves generally dominate in hard-bottom and sandy substrates. These are usually replaced by depositfeeding species in muddy substrates. Alt hough they are treated as one group in this model of the West Florida Shelf, we have separated the approaches of estimating the parameters for scallops and in faunal clams, as source data are separate for these groups. For similar reasons, oysters and a ttached sessile bivalves are included in another group of this model (i .e., sessile epibenthos).

Table 8.5. Parameter estimates for bivalves on the WFS including infaunal bivalves and scallops.

Bivalve group Biomass (t

⋅ km -2 ) P/B (year -1 ) Q/B (year -1 )

Infaunal bivalves 16.75 a 1.35 c

-- Scallops 31.848 b 1.15 d

-- Overall 48.598 1.219 e 23 f

a This rough approximation of infaunal bivalve biomass is simply the mean of the two median values from the estimated ranges of small and large infaunal bivalves, assuming that small and large bivalves have equal biomasses. Ranges are provided in the text;

b Sum of the estimates of Calico and Bay scallops (see text);

c Mean of given estimates for small and large infaunal bivalves;

d Sum of 1.0 for replacement and Barber and Blake’s (1980) estimate for cost of reproduction (0.15);

e Average of P/B estimates for infaunal bivalves and scallops weighted by biomass of those two groups;

f From Guénette (1996).



### Infaunal bivalves The vast majority of bivalves in soft-botto m communities are sma ll (D. Marelli, personal knowledge) and have high production to biomass ratios (Dame 1996). Biomass
estimates for these bivalves do not exist for the West Florida Shelf, but for the purposesEcopath model of the West Florida Shelf: Volume II. Model construction



### 57 of this modeling exercise, I (D. Marelli) es timate that the biomass of small infaunal

bivalves in the defined West Flor ida Shelf area ranges from 5 to 50 t

⋅ km -2 

### , and the biomass of larger infaunal biva lves ranges from 2 to 10 t

⋅ km -2 

### . Dame (1996) suggests that bivalve production to biomass ratios av erage 1.2. Thus, an es timate between 1 and 1.2 is probably a good value for larger infa unal bivalves. Smaller bivalves have greater ratios of P/B, with values pr obably exceeding 1.5. Many of these smaller bivalves have short generation times and multiple generations annually. Considerable uncertainty still exists for infaunal bivalves, and more pr ecise estimates would require an increased
investment of scientific resources in the region.

Scallops Scallops are a specialized group of mob ile epifaunal bivalves found on soft bottoms. Their visual abilities and their ability to swim to escape predation and otherwise move about sets them apart from all other bivalv ed molluscs. Scallops are suspension feeders like many other clams and sessile epibent hos. On the West Florida Shelf, scallops include calico scallops ( Argopecten gibbus ) and bay scallops ( Argopecten irridians ).

The biomass of scallops on the West Florida Shelf (31.848 t

⋅ km -2 

### ) is equal to the sum of the biomasses of Calico and Bay scall ops. Cummins (1971) found Calico scallop densities equal to 43 scallops

⋅ m -2 

### in commercially fished areas. The average wet weight
of the soft tissue of a calic o scallop is 7.4 grams (B. Arnol d, unpublished data), which is equal to 318.2 t

⋅ km -2 

### . If we assume that 10% of the total area of the shelf supports scallops, the biomass of scallops on the West Florida Shelf would be 31.82 t

⋅ km -2



### . Given the paucity of quant itative information for scallops outside of the fishery zones, we will take this as a working biomass estimate for Calico scallops on the West Florida Shelf. The biomass estimate for Bay scallo ps in their preferred habitat (0.555 t

⋅ km -2 

### ) is based on FMRI surveys (B. Arnold; publi shed and unpublished dat a) and the same individual weight estimates as for Calic o scallops above. Th e bay scallop biomass

estimate for the whole shelf area would be 0.028 t

⋅ km -2 

### assuming that Bay scallop
habitat covers 5% of the West Florida Shelf.

We take the ratio of production to biomass (P/B ) for bay scallops to be 1.0 plus the cost of reproduction, which Barber and Blake (1980) estimated to be approximately 0.15 for this species. This leads to a P/B of 1.15 year -1 

### for bay scallops. This is very close to the P/B ratios suggested by Dame ( 1996), discussed above (1.2 year -1 

### ). We use 1.2 as the P/B for the bivalve group in the m odel of the West Florida Shelf.

The value used for bivalve Q/B in this model (23 year -1 

### ) is from estimates given by Guénette (1996) for an Ecopath model of the North Paci fic. The estimated diet composition of bivalves on the West Flor ida Shelf is shown in Table 8.6. The assimilation efficiency of bivalves is probably low because of the low quality of their food (e.g., 0.50).

Table 8.6. Estimated diet composition of bivalves on the
West Florida Shelf.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 58 Prey categories Proportion
of diet

Sediment detritus 0.25 Phytoplankton 0.23 Microphytobenthos 0.22

Water column detritus 0.19 Microbial heterotrophs 0.10

Meiobenthos 0.01

Literature cited (Bivalves)

Barber, and N. J. Blake. 1986. Reproductive effort and cost in the Bay Scallop, Argopectin irridians
concentricus . Institute of invertebrate repr oduction and development 10:51-57. Cummins, R., Jr. 1971. Calico scallops of Southeas tern United States, 1959-1969. USDOC/NOAA/NMFS. Special Scientific Report – Fisheries, No 627. 22 pp. Dame, R.F. (1996). Ecology of Marine Bivalves: An Ecosystem Approach. CRC Press, Boca Raton, 254

pages. Guénette, S. 1996. Macrobenthos. p. 65-67 In : D. Pauly and V. Christensen (eds.). Mass-balance models of North-eastern Pacific Ecosystems. Fi sheries Centre Research Report 4(1).Ecopath model of the West Florida Shelf: Volume II. Model construction



### 59 8.5 Sessile epibenthos

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Sessile epibenthos are non-mob ile, suspension or filter f eeding invertebrates, usually
attached to hard substrate. The West Florida Shelf contains extensiv e areas of low-lying carbonate reef platforms that provide subs trate for most sessile epibenthos. Organisms include sponges, corals, tunicates, gorgonian s, hydroids, bryozoans, attached bivalves, crinoids, and ophioroids. These assemblages co mprise what local fisheries scientists refer to as “live bottoms,” with which mean communities that develop on exposed hard bottoms. The organisms in these low reef habitats are adapted to varying degrees of sand scour and inundation; exposed hard botto ms are characterized by a dynamic veneer of carbonate sand (Phillips and Thomps on 1990). The algal nodule community is another type of live bottom that is relatively extensive on the West Florida Shelf. These beds of rhotoliths (branching, non-attached, non-geniculate, calcifi ed, red algae) harbor a unique suite of epifauna and interstitial macroinfauna.

The biomass estimate for sessile benthic epif auna on the West Flor ida Shelf is 472.5

t

⋅ km -2 

### , based on the following derivation. The biomass of sessile epibenthos (and macroalgae) on ‘live bottom’ in the shallo w portion of the West Florida Shelf was estimated to be 2,000 t

⋅ km -2 

### by Continental Shelf Asso ciates, Inc. (1987), though deeper portions of the shelf had less cover and biomass. Assuming the deeper biomass is 1,000 t

⋅ km -2 

### , the mean biomass on hard bottom for the overall shelf would be 1,500

t

⋅ km -2 

### . Sponges comprised fully 58% of the bioma ss of sessile epibenthic fauna (Phillips and Thompson 1990; and a 1984 estimate by Da rcey and Gutherez was 54%), with the bulk of the remaining porti on split between hard corals, octocorals, bivalves, and macroalgae. Thus, subtracting 10% of th is ‘live bottom’ (the rough percentage of macroalgae) leads to a sessile epibenthos estimate of 1,350 t

⋅ km -2 

### . Correcting this with
an areal correction factor of 0. 35 (hard bottom is ~35% of the total shelf area; Parker et al. 1983, Phillips et al. 1990 in Phillips and Thompson 1990) leads to an estimate of

472.5 t

⋅ km -2 

### for epifauna over the West Florida Shelf.

This estimate of very high for any group in any Ecopath model, and this aggregated group would have the potential to dominate energetic processes in the model system. The lower limit of the confidence range presented by Phillips and Thompson 1990 results in a sessile epifauna biomass estimate of 219 t

⋅ km -2 

### . Use of this lower estimate might be preferable for this m odel, especially if problems ar e encountered related to its
size.

The P/B value of sessile epibenthos on the West Florida Shelf (0.8

⋅ year -1 

### ) is from Odum and Odum (1955) and Sorokin (1987) in Opitz (1993). The Q/B value (9

⋅ year -1 

### ) is the mean of sponges (based on Wilkinson 1987) and corals (based on Sorokin 1987) also in Opitz (1993). The assimilation ef ficiency of sessile epibenthos should be relatively low (e.g., 0.60). Table 8.7 shows t he estimated diet compositions of sessile epibenthos on the West Florida Shelf.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 60 Table 8.7. Estimated diet compositions for sessile epibenthos on the West Florida
Shelf.

Prey categories Proportion of
diet

Water column detritus 0.27 Microbial heterotrophs 0.25 Phytoplankton 0.20 Sm. mobile epifauna 0.07 Zooplankton 0.05

Sediment detritus 0.05 Microphytobenthos 0.05

Meiobenthos 0.02 Sessile epibenthos 0.01

Dead carcasses 0.01

Demersal reef fishes 0.01

Small infauna 0.01

Literature cited (Sessile epibenthos)

Darcey, G. H. and E. J. Gutherez. 1984. Abundance and density of demersal fishes on the West Florida Shelf, January 1978. Bulletin of Marine Science 34(1):81-105. Phillips, N. W., and M. J. Thompson. 1990. Offshore benthic communities. pp. 155-193 in N. W. Phillips, K. S. Larson (eds.) Synthesis of available bi ological, geological, chemical, socioeconomic, and
cultural resources information for the south Flori da area. Prepared by Continental Shelf Associates for Minerals Management Service, OCS Study - MMS 90-0019, May 1990. Parker, R. O., Jr., D. R. Colby, and T. D. Willis. 1983. Estimated amount of reef habitat on a portion of the
South Atlantic and the Gulf of Mexico continent al shelf. Bulletin of Marine Science 33:935-940.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 61 8.6 Echinoderms and large gastropods

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### The Echinoderms and large gastropods group for the West Florida S helf model consists
of a suite of mobile invert ebrates larger than 20 mm (except for crabs, shrimps, and lobsters, which have their own groups). Echinoderms and large gastropods are comprised of asteroids (sea stars), holothur ians (sea cucumbers), echinoids (urchins and sand dollars), and large gastropods, both wit h and without shells. This aggregated group includes predators, sca vengers, detritus feeders, and filter feeders. They are grouped together because the focus of the present model is fo rage fishes and directly associated sub-webs, and because information on some of these groups is limited for
the West Florida Shelf.

Information from the Hourgl ass Cruises (e.g., Serafy 1979, Miller and Pawson 1984) was used to estimate composite biomass and di et compositions for th is group. Thirteen stations were sampled at five depth interv als across the West Flor ida Shelf during this exploratory monitoring progr am. Distinct depth partitioning among species was revealed within several of the classes of organi sms aggregated into Echinoderms and large gastropods. Even after aggregation, Echinoderms and large gastropods were generally
concentrated near shore becau se of the effect of highl y concentrated and abundant
sand dollars.

The biomass estimate of Echinoderms and la rge gastropods on the West Florida Shelf

is 19.246 t

⋅ km -2 

### —the sum of biomass estimates for ec hinoids, holothurians, asteroids, and gastropods. The derivation of this bioma ss estimate is described in the following
subsections and in Table 8.8. However, the estimates herein are likely to underestimate actual biomass because of the cryptic nat ure of urchins and the expectation of undersampling these organisms on reefs.

Opitz (1993) estimated echinoderms to be two orders of magnitude higher in a Caribbean coral reef ecosystem than our current estimates for the West Florida Shelf. However, echinoderms would be expected to be considerably more abundant on coral
reefs than on the West Florida Shelf. Browder’ s (1993) estimate for total benthos on the

West Florida Shelf was 5 t

⋅ km -2 

### , which is considerably less than the total benthos biomass estimates in the present model.

The P/B value of ‘echinoderms and large gastropods’ on the West Florida Shelf (1.2

year -1 

### ) is a mean of values from Lewis (1981) and Schwinghamer et al. (1986) in Opitz (1993). The Q/B value (3.7 year -1 

### ) is the mean of the echi noids and holothurians from Pauly et al. (1993). The assimilation effi ciency of echinoderms and large gastropods should be low (i.e., 0.60 maximum). See Table 8.9 for diet compositions.

Echinoids Echinoids exhibit depth partitioning, but overall they are profoundly mo re abundant at the most shallow depths because of dense sand dollar ( Mellita quinquiesperforata ) bedsEcopath model of the West Florida Shelf: Volume II. Model construction



### 62 in a narrow band beyond the surf zone. The various urchin species have preferred depth ranges, but they are rela tively evenly distributed ac ross the West Florida Shelf when considered overall (Serafy 1979).

The biomass of echinoids was estimated to be 18.789 t

⋅ km -2 

### on the West Florida Shelf based on samples collected during the hourgla ss cruises (Table 8.8). During these 1965 to 1967 cruises, a 0.91 m - wide box dredge was towed along the sea floor for 30 minutes at a speed of 2 knots, covering 1,685 m 2 

### area (0.001685 km 2 

### ) at each of 10
stations distributed across the West Florida Shelf (two east-west transects, each with five sampling stations, were located approx imately adjacent to Tampa Bay and Fort Meyers, and were sampled monthly for 28 months).

Densities of echinoids (individuals

⋅ km -2 

### ) at each of five southern stations were calculated by dividing r eported abundances by 0.04718 km 2 

### (28 months x 0.001685

km 2 

### )(Table 1). Biomass estimates were then calculated by multiplying estimated abundances by mean individual weig hts for sand dollars (0.0000995 t

⋅ individual -1 

### ; Lane and Lawrence 1980; converted from dry weight s of the sand dollar) in the shallowest depth zone (6 m depth) and for urchins (0.000125 t

⋅ individual -1 

### ; adapted from Lawrence and Byrne 1994) in the rest of the zones, where more spherical echinoids (i.e., urchins) predominated. The overall echinoid value should be considered a minimum biomass estimates since box dredge samples were not independent in time, and since sampling efficiency for this species was likely less than 1. However, it should be noted that these values include the tests and spines.

Table 8.8. Biomass estimates of ec hinoderms and large gastropods on
the West Florida Shelf.

Depth (m) Total
individuals Individuals (

⋅ km -2 ) Biomass (t

⋅ km -2 )

Echinoidea a

6 39,870 845,061 84.084 18 1,201 25,456 3.182 37 536 11,361 1.420 55 1,094 23,188 2.898 73 892 18,906 2.363 Overall 43,593 184,794 18.789
Holothuriodea b

6 34 360 0.118

18 69 731 0.240
37 92 975 0.320

55 5 53 0.017

73 13 138 0.045
Overall 213 226 0.148

Asteroidea c 0.258 Gastropoda c 0.050

Total 19.246Ecopath model of the West Florida Shelf: Volume II. Model construction



### 63 a To calculate densities, total holothurian individuals were divided by 0.04718, corresponding to the area samp led at each station. Overall echinoid densities were divided by five since abundance data came from one station at each of five depths (Serafy 1979);

b Total holothurian individuals were divided by 0.09436, rather than 0.04718, to calculate densities, and ov erall holothurian densities were divided by ten, rather than five, since abundance c data came from two stations for each of five depths (Miller and Pawson 1984); See appropriate subsection for estimation method used.



### Bluhm et al. (1998) found the urchin Strongylocentrotus pallidus to have an average biomass of about 1 g afdw

⋅ m -2 

### in the northern Barents S ea, which is approximately equivalent to a wet weight biomass 5 to 7.5 t

⋅ km -2 

### (using conversions listed in Appendix 6). This range represents a relatively high bi omass (see Bluhm et al. 1998), yet it is only a small portion of the estimate derived above for the West Florida Shelf. Ebert (1982) estimated Z (total mortality) to be as low as 0.58 for Echinometra mathaei . In equilibrium conditions, Z is equal to P/B. The P/B rati o suggested by Bluhm et al. (1998) for the North Barents Sea populat ion of urchins was estimated to be 0.07 year -1 

### , a value that is far lower than the P/B estimate used herein. This very low P/B value in the northern Barents Sea may reflect a relatively slow-growt h population structure in very cold waters of high latitudes, but these lower values shou ld be kept in mind during model balancing.

Sea urchins are famous for eating macrophy tes. For example, the consumption of seagrasses by urchin populations can rival or surpass the seagrass production (Moore and McPherson 1965, Camp et al. 1973). Howeve r, urchins consume a variety of other prey, such as bryozoans, forams, gas tropods, worms, sponges, octocorals, coral polyps, mussels, sand dollars, crustaceans, and strips of fish (see Serafy 1979). Sand dollars eat detritus, diatom s, dynoflagellates, sponges, fine sand, and forams. Heart
urchins consume detritus and other items. In one individual heart urchin (Meoma ventricosa ventricosa), 3,000 mollusks from 167 species were found (see Serafy 1979).

Holothurians Taxonomically related to asteriods (sea stars) and echinoids (urchins), the 16 species of holothurians (sea cucumbers) on the West Fl orida Shelf exhibit dep th partitioning, but overall they are more abundant in shallow to mid depths. These are deposit feeders, which ingest large quantities of sediment relative to their body size in order to absorb nutrition from small animals, micr obes, and other organic material.

The estimate of holothurian biomass on the West Florida Shelf (0.148 t

⋅ km -2 

### ) was made using abundance information from Hourglass Su rveys (Miller and Pawson 1984), as in the echinoid example above. Density es timates were multiplied by 0.000328 t, assuming that the average weight of a West Florida Shelf ho lothurian is 328 g, twice the
size of the holothurians that reside on Phillippines coral reefs (Pinto 1982 in Pauly et al. 1993). Table 8.8 also shows this derivation.

The dietary habits of holothurians are divers e. Many species are strictly deposit feeders, consuming sediment to extract microflora , meiobenthos, and some macrofauna (i.e.,Ecopath model of the West Florida Shelf: Volume II. Model construction



### 64 ‘small infauna’), but others are suspension feeders and plankton feeders. Still others can apparently be more selective with live prey (see Miller and Pawson 1984).

Asteriods Little is known of the population dynamics of sea stars on the West Florida Shelf. The biomass estimate of asteroids (0.258 t

⋅ km -2 

### ) was estimated by extrapolating from commercial bycatch (437.7 t

⋅ year -1 

### ) based on the assumption of a 1% year -1 

### exploitation rate. This leads to a standing biomass estima te of 43775 t, which is equivalent to 0.258

t

⋅ km -2 

### . Catch data were provided by Bob Muller (Florida Fish and Wildlife Commission, April 2000). Although the FMRI landings data are reliable, the assumed exploitation rate used here is little more than a guess. The re sulting biomass estimate for asteroids should be considered a placeholder.

Large gastropods Biomass estimates for large gastropods on t he West Florida Shelf are also unavailable to my knowledge. The biomass estimate of large gastropods (0.050 t

⋅ km -2 

### ) was estimated by extrapolating from commercial bycatch (84.6 t

⋅ year -1 

### ) based on the assumption of a 1%

⋅ year -1 

### exploitation rate. This gives a standing biomass estimate of 8460 t, which is equi valent to 0.050 t

⋅ km -2 

### . Catch data were provided by Bob Muller (Florida Fish and Wildlife Commission, Apr il 2000). As for asteroids, the assumed exploitation rate used here is little more than a guess. As such, the resulting biomass estimate for large gastropods is a place holder.

The estimated diet composition for echinoderms and large gastropods (Table 8.9) is based on the qualitative diet composition information available for echinoids and holothurians, discussed in the previous sect ions and estimations for asteroids and gastropods.

Table 8.9. Estimated diet composition of echi noderms and large gastropods on the West Florida
Shelf.

Proportion of diet Prey categories Echinoids a Holothurians a Asteroids b Gastropods b Overall c
Drift macrophytes 0.19 0.05 0.02 0.30 0.181 Macroalgae 0.13 0.02 0.00 0.10 0.122 Sessile epibenthos 0.10 0.03 0.18 0.10 0.102 Microbial heterotrophs 0.10 0.19 0.10 0.05 0.101

Sediment detritus 0.10 0.20 0.05 0.10 0.100 Microphytobenthos 0. 10 0.18 0.05 0.10 0.099

Water column detritus 0.07 0.01 0.03 0.00 0.067

Small infauna 0.04 0.10 0.20 0.10 0.049 Seagasses 0.05 0.02 0.00 0.05 0.047

Meiobenthos 0.03 0.10 0.03 0.03 0.031 Phytoplankton 0.03 0.03 0.00 0.00 0.028 Echino. / lg. gastro. 0.02 0.00 0.10 0.02 0.023 Sm. mobile epifauna 0.02 0.05 0.02 0.01 0.020

Dead carcasses 0.02 0.02 0.02 0.01 0.020

Bivalves 0.00 0.00 0.20 0.03 0.009Ecopath model of the West Florida Shelf: Volume II. Model construction



### 65 a Roughly adapted from Serafy 1979 and Miller and Pawson 1984;

b These are currently placeholder guesses;

c Overall echinoderms and large gastropods diets ar e averages weighted by the relative biomass estimates for each predator group.

Literature Cited (Echinoderms and large gastropods)

Bluhm, B. A., D. Piepenburg, K. Juterzenka. 1998. Di stribution, standing stock, growth, mortality and production of Strongylocentrotus pallidus (Echinodermata: Echinoidea) in the northern Barents Sea. Polar biology 20(5):325-334. Browder, J. A. 1993. A pilot model of the Gulf of Mexi co continental shelf. p. 279-284. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Camp, D. K., S. P. Cobb, and J. Van Breedveld. 1973. Overgrazing of seagrasses by a regular urchin, Lytechinus varegatus . Bioscience 23(1):37-38. Lane, J. E. M., and J. M. Lawrence. 1980. Seasonal vari ation in body growth, density and distribution of a population of sand dollars, Mellita quinquiesperforata (Leske). Bulletin of Marine Science 30(4):871-
882. Lawrence, J. M., and M. Byrne. 1994. Allocation of resources to body components in Heliocidaris erythrogramma and Heliocidaris tuberculata (Echinodermata:Echinoidea). Zoological Science 11(1):133-137. Lewis, J. B. 1981. Estimates of secondary producti on of reef corals. Proceedings of the Fourth International Coral Reef Symposium. 2:369-374. Lyons, W. G. 1979. Molluscan communities of the We st Florida Shelf. Transactions A. Malacol. U.
1979:37-40. Miller, J. E., and D. L. Pawson. 1984. Holothurians . Memoirs of the Hourglas s Cruises 5(3), Florida Department of Natural Resources, Marine Resear ch Laboratory, St. Petersberg, Florida, 119 pp. Moore, H. B. and B. F. McPherson. 1965. A contribution to the study of productivity of the urchins and Lytechinus variegates. Bull. Mar. Sci. 15(4):855-871. Opitz, S. 1993. A quantitative model of the trophic in teractions in a Carribean coral reef ecosystem, p.
259-267. In V. Christensen and D. Pauly (eds.) Tr ophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Pauly, D., V. Sambilay, Jr., and S. Opitz. 1993. Es timates of relative food consumption by fish and invertebrate populations, required for modeling the Bolinao Reef ecosystem, Phillippines. P. 236-251

In V. Christensen and D. Pauly (eds.) Trophic mo dels of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Serafy, D. K. 1979. Echinoids (Echinodermata: Ec hinoidea). Memoirs of the Hourglass Cruises 5(3), Florida Department of Natural Resources, Marine Research Laboratory, St. Petersberg, Florida, 119

pp.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 66 8.7 Adult shrimp

Thomas A. Okey University of British Co lumbia, Fisheries Centre

James M. Nance National Marine Fisheries Service, Galveston Laboratory



### The West Florida Shelf contains at least 17 known species of shrimp and 5 other shrimp genera (Darnell et al. 1987). Pink shrimp, Farfantepenaeus duorarum , are the most abundant of the three commercially important penaeid shrimps on the shelf (i.e., white, pink, and brown shrimp). Most of the pink sh rimp catch is concentrated in the southern region of the West Florida Shelf, though an impor tant portion of the fishery occurs in the big bend area (Figure 8.2). This group is ca lled ‘adult shrimp’ because most of the information available for shrimp pertain to commercial sizes, or to individuals near
commercial sizes. Smaller shri mp are included in the ‘small mobile epibenthos’ group, and this younger portion of the shrimp popul ations probably comprise most of the overall shrimp biomass on the West Florida Shelf.

Figure 8.2. Distribution of pink shrimp catches in t he Gulf of Mexico. Areas of higher catches are darker (from Klima 1989).



### Adult shrimp are a very important component of the Gulf of Mexico from the standpoint of commercial fisheries. Virtually all the individuals caught are either newly recruited into the fishery (through growth) or t hey are smaller than legal size. Effort studies show that average fishing days per trip do ubled in central and southern regions (Fort Myers, St. Petersburg, and Key west ports)(Nance 1993), yet relatively consistent annual catches over long time periods (Nance 1997, Nance and Harper 1999) indicates that shrimp as a group have withstood these intensive recrui tment fisheries. This enigma has been the subject of some debate in the past (e.g. Cu shing 1984), and the answer may lie in the competitive advantages afforded to shrimp given the exotic stresses imposed on the system as a whole (e.g., trawling, coastal eut rophcation)—explanations that are beyond simple population-expl oitation analyses. 26 ° 83 °

30 °

28 ° 85 ° 84 °Ecopath model of the West Florida Shelf: Volume II. Model construction



### 67 The considerable uncertainty of adult shrimp biomass estimates for the West Florida Shelf does little to alleviate this conundrum. Several approaches to estimate the biomass of shrimp on the West Florida Shel f are presented in t he following paragraphs to provide a range of choice of biomass esti mates, as well as confidence bounds. Most of these methods, including the one ultimately chosen to estimate shrimp biomass, are based on two sources of landings data: the National Marine Fisheries Service (NMFS) and the Florida Marine Research Institute (FMRI).

The 1998 NMFS landings estima te for the West Florida Shel f including the Florida Keys (i.e., statistical zones 1-9) was 8,324 t onnes (1,8346,160 pounds) of pink, brown, and white shrimp (Nance, unpublished data). These data covered data collection from all the port agents in the Gulf of Mexico. The FMRI to tal shrimp landings estimate for the West Florida Shelf was 5,339 tonnes for all the areas except the Florida Keys (except Monroe County landings) (R. Muller, FMRI, unpublished dat a). These landings data are from the Florida state trip ticket information program. The estimates from these two independent programs are only 8% different when FMRI’s 1998 Florida Keys landings data (2,330 tonnes) are added to their estimate for the We st Florida Shelf area (the resulting inclusive landings value is 7,669 tonnes). This verification of accuracy of the two approaches is apparent despite different delin eations of the annual shrimping season (NMFS data corresponds to the calender year , while FMRI data corre sponds to the July 97-June 98 season). The FMRI value was chos en for the purposes of the present model
because the defined area does not include the Florida keys, and to keep the model consistent with respect to other biotic compartments w hose parameters are based on the same 1998 FMRI landings database.

The total catch of all shrimps on the West Flor ida Shelf in 1998 is es timated to be 7,297
tonnes. This catch estimate is the sum of the total shrimp landings reported for that year (5,339 tonnes; R. Muller, FMRI, unpublished data) and the estimated the annual shrimp discards on the West Florida Shelf (1 ,958 tonnes; adapted from Cushing 1984). The discards of organisms other than shrimp from the shrimp trawl fishery is taken as four times the amount of shrimp discard ( see Section 5 for explanation of shrimp discard derivation). To put these estimates in r egional perspective, the total penaeid shrimp
catch for the western central Atlantic region (the Caribbean, the Central American states, and the Gulf of Mexico) has fluct uated around 170,000 tonnes (373,850,000 lbs.)
for the last two decades. Most of this catch comes from the Gulf of Mexico (FAO 1997). Vidal (2000) estimat ed the annual GoM shrimp catch to be 144,000 t

⋅ year -1 

### (317,520,000 lbs.) by adding Mexican and US reported landings (SEMARNAP 1987, NMFS 1987). If these estimates are accurate, the catch of shrimp on the West Florida Shelf comprises only 4.4% of the total shrimp catch in the Gulf of Mexico.

An estimate of the overall adult shrimp biomass on the West Florida Shelf, 0.0859 t

⋅ km 2 

### , or 14,595 tonnes overall was calculated by dividing the 1998 West Florida Shelf shrimp catch (~7,297 tonnes or 16,090,767 lbs.) by a 50% exploitation rate corresponding to the findings of a recent VPA assessment of Gulf of Mexico shrimp (J. Nance, unpublished estimate). The estimate derived above is taken as the biomass of adult shrimp on the West Florida Shelf for the purposes of initial parameterization of theEcopath model of the West Florida Shelf: Volume II. Model construction



### 68 Ecopath model. This estimate falls in the center of the range of other estimates derived during this exercise (Table 8.10). The next higher estimate in that range (0.2635 t

⋅ km -2 

### ) is a back-calculation from Walsh’s (in Cushi ng 1984) estimate of total shrimp production on the West Florida Shelf (1.0752 t

⋅ km -2



### ⋅ year -1 

### ; 70% area-adjusted). This production estimate was divided by the adult shrimp P/B estimate (4.08 year -1 

### ).

Population parameter estimates for Flor ida adult shrimp do vary considerably, sometimes leading to profoundly divergent estimates of abundance and biomass (Table 8.10). For example, a biom ass estimate of 0.4293 t

⋅ km -2 

### , or 72,974 tonnes can be
derived in the same manner usi ng an exploitation rate of 10%, which is based on the shrimp catchability coefficient from another st udy (Steele 1997). App lication of Ehrhardt and Legault’s (1999) assumed fishi ng mortality rate of 1.4 for pink shrimp in Florida Bay
to the same west Florida ca tch data leads to a West Fl orida Shelf st anding biomass

estimate of 0.0307 t

⋅ km -2 

### , or 5,212 tonnes. Note that this example is more than an order of magnitude smaller than the first.

Table 8.10. Estimates of adult shrimp biomass on the West Florida Shelf.

Biomass Approach a Sources t

⋅ km -2 Tonnes Pounds

F MSY SAFMC 1999 0.0201 3,421 7,542,431 F = 1.4 Ehrhardt & Legault 1999 0.0307 5,212 11,493,405 50% exploitation J. Nance (unpub. estimate) 0.0859 14,595 32,181,534 Production estimate Walsh (in Cushing 1984) 0.2635 44,800 98,784,000 10% exploitation Steel e 1997 0.4293 72,974 160,907,670Combined fisheryindependent estimates Coleman & Koenig 1998,
Darnell et al. 1987 0.6273 107,307 236,613,412

Unknown method, minus
30% Browder 1993 8.708 1,480,360 3,264,193,800

a Fishery-dependent approaches combined the 19 98 reported shrimp landings (5,339 tonnes, or 11,772,495 lbs.) and estimated discards (1,958 tonnes) with different scenarios of exploitation rate to estimate standing biomass on the 170,000 km 2 West Florida Shelf. See text for a detailed explanation of each approach; fishery-independent approaches are explained in the accompanying

text.



### An even smaller biomass estimate for the West Florida Shelf, 0.0201 t

⋅ km -2 

### or 3,421 tonnes can be derived by back-calculating from MSY and the annual catch estimate used above. Considering that the mean total landings of shrimps in the south Atlantic region of US is currently used as a pr oxy for MSY (South Atlantic Fisheries Management Council, 1999), the rationale is that at B MSY 

### (biomass that produces the maximum sustainable yield) the exploitation rate (F/Z) shoul d be in the order of 0.4. With a natural mortality rate of 3.6 year -1 

### , F MSY 

### is estimated at 2.13 year -1 

### , and B MSY 

### at 1,806.25 tonnes. There are, how ever, many sources of uncertainty in such a
calculation.

Such an extreme variability in estimates ca lls for a comparison to other estimates and approaches. Both of these estimates appear very low when compared to other estimates for shrimp biomass in other Gulf of Mexico habitats, as summarized by VidalEcopath model of the West Florida Shelf: Volume II. Model construction



### 69 (2000). These habitats include GoM soft bottoms 12.44 t

⋅ km -2 

### (Browder 1993), estuaries

13.11 t

⋅ km -2 

### , and other non-estuarine areas 11.59 t

⋅ km -2 

### . Applying Browder’s softbottom estimate to the West Florida Shelf area (and subtracting 30% for hard bottoms) leads to an estimate of (1,480,360 tonnes, or 3,264,193, 800 lbs.)—an enormous biomass that seems very unlikely given more recent information.

An alternate, fishery independent, approach fo r estimating adult shrimp biomass on the West Florida Shelf resulting in an estimate of 0.6273 t

⋅ km -2 

### . This was derived by combining a recent estimate of pink sh rimp abundance in big bend area seagrass beds with an older estimate of shrimp abundanc e on the broader West Florida Shelf (documented in the following paragraphs). The overall estimate used here was the area-weighted average of these two estimates. The resulting estimate is equivalent to a biomass of 107,307 tonnes (236,613, 412 lbs.) over the whole of the West Florida Shelf. Dividing the 1998 total Florida shrimp ca tch (7,297 tonnes) by the total biomass estimate leads to an annual shrimp catch rate of 0.07, which seems low considering that the area swept for shrimp on the West Flori da Shelf is quite high (B. Mahmoudi, FMRI, pers. com., 17 July 2000). This low back-calculated estimate for shrimp catch rate indicates that the biomass value resulting fr om this composite appr oach, detailed in the following paragraphs, is an overestimate.

The rough biomass estimate for pink sh rimp in big bend area sea grass beds (used above) was calculated from a reported of 500 and 20,000 individuals per hectare (Coleman and Koenig 1998). These values lead to a medi an estimate of 6.15 t

⋅ km -2


### based on a mean adult shrimp weight of 6g (Pauly and Christensen 1993). My assumption that seagrass beds containing such densities cover 10% of the West Florida

Shelf leads to a biomass estimate of 0.615 t

⋅ km -2 

### , if spread over the 170,000 km 2 

### area of the West Florida Shelf.

A rough estimate of the biomass of adult shri mp on the other areas of the West Florida Shelf, 0.0137 t

⋅ km -2 

### , was calculated using abundance estimates from Table 3 in Darnell et al. (1987), which shows abundance of sh rimp by species captured in 433 trawl samples that were distributed throughout the West Florida Shelf area (Table 8.11). The sampled area corresponding to the adult shrimp abundance data (28.148 km 2 

### ) was calculated by multiplying the distanc e of each trawl tow (5.556 km) with the
standardized effective width of the trawl ne t opening (0.0117 km) resulting in a single tow swept area of (0.0644 km 2 

### ; corresponding to a net headrope length of 0.0137 km (45 feet) based on the empiri cal equations presented by Da rnell et al. (1987)). The swept area of one tow was then multiplied by the number of tows in the sampling regime (433 tows) resulting in an overall swept area of 28.148 km 2 

### . The density of adult shrimp on the West Florida Shelf (2,282 individual shrimp

⋅ km -2 

### ) was then calculated by dividing the number of captured shrimp ( 64,235 individuals) by the sampled area (28.148 km 2 

### ). Adult shrimp biomass (0.0137 t

⋅ km -2 

### ) was then calculated by applying an average biomass for an individual shrimp (0.000006 t) from Pauly and Christensen (1993). The density estimates from each of the studies described in this and the previous paragraph were multip lied by the proportion of ar ea that they inhabit on the shelf. The two adjusted values were subsequently added.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 70 Table 8.11. Number of WFS shrimp caught in 433 trawl samples in a swept area of about 28

km 2

.

Scientific name Common name Number Percent

Solenocera atlantidis 18,169 28.29 Farfantepenaeus duorarum Pink shrimp 10,408 16.20 Trachypeneus sp. 6,624 10.31 Sicyonia brevirostris Rock shrimp 6,484 10.09 Trachypeneus similis 4,489 6.99 Metapenaeopsis goodei 4,452 6.93 Sicyonia dorsalis Rock shrimp 2,862 4.46 Farfantepenaeus aztecus Brown shrimp 2,417 3.76 Mesopenaeus tropicalis 2,310 3.60 Solenocera sp. 1,608 2.50 Parapenaeus sp. 1,416 2.20 Sicyonia sp. 1,119 1.74 Trachypeneus constrictus 665 1.04 Sicyonia typica Rock shrimp 444 0.69 Litopenaeus setiferus White shrimp 368 0.57 Parapenaeus longirostris 187 0.29 Penaeus sp. 66 0.10

Solenocera vioscai 54 0.08 Sicyonia stimpsoni Rock shrimp 33 0.05 Sicyonia burkenroadi Rock shrimp 28 0.04 Sicyonia laevigata Rock shrimp 27 0.04 Solenocera necopina 5 0.01
Total 64,235 100.00

Notes: Sampling is reasonably representative of the West Florida Shelf as a whole, and it
indicates the relative abundance distribution of both commercial and non-commercial shrimp species (values directly from Darnell et al . 1987, but scientific names are updated here).



### The ratio of production to biomass (P/B = 4.08 year -1 

### ) for West Florida Shelf adult shrimp was calculated using the followin g approach. Ehrhardt and Legault (1999) assumed the natural mortality (M) of pink shrimp to be 3.6; the fishing mortality (F) is 1.4. Likewise, Parrack (1981) found M to equal 2.8 and F to be 0.36 for brown shrimp, which was within 5% of the average annual ca tch of pink shrimp in 1998. Since P/B, under equilibrium, equals total mortality (All en 1971), and Z = M + F, the P/B ratios can be roughly estimated as 3.6 + 1.4 = 5 year -1 

### for pink shrimp and 2.8 + 0.36 = 3.16 year -1 

### for brown shrimp, resulting in a mean sh rimp equation of 3.2 + 0.88 = 4.08 year -1 

### . The P/B estimate by Arreguín-Sánchez et al . (1993) for the north continental shelf ecosystem of the Yucatan provides some veri fication for this rough estimate, as it is close (5.38 year -1 

### ).

The ratio of consumption to biomass for adul t shrimp of the West Florida Shelf (19.2

year -1 

### ) was taken from an independent estimate for the adult shrimp of the Yucatan continental shelf (Arreguí n-Sánchez et al. 1993).Ecopath model of the West Florida Shelf: Volume II. Model construction



### 71 The diet of adult penaeid shrimp includes sediment, detritus, algae, and benthic organisms nematodes, annelids, molluscs, crustaceans, higher plants, algae, and
diatoms. These have been found in the stomachs of whit e shrimp, which are considered selective particulate feeders (Steele 1997) . Brown shrimp ingest detritus and algae throughout their lives, but they active ly seek out polychaetes, nematodes, and chironomid larvae as adults (Broad 1965 in Jones 1973). Eldred et al. (1961) found the following groups in the stomachs of pink shrimp: sand, debris, diatoms, seagrass particles, dinoflagellates, foraminifer ans, nematodes, polychaetes, ostracods, copepods, mysids, isopods, carridean shrimp, ca ridean eggs, molluscs, and fish scales. The ontogenetic shift in the diet of shrimp is important to note. Juvenile shrimp consume detritus and a small amount of fishery di scards, while adults primarily consume meiobenthos, macrobenthos, and fishery di scards (Cushing 1984). Shrimp are important prey for other species; they are eaten by virtually any animal big enough and inclined to catch them (Steele 1997; also see Costello and Allen 1970). Huff and Cobb (1979) presented quantitat ive diet information for seven s pecies of shrimp on the West
Florida Shelf and this was ada pted here (Table 8.12). The assimilation efficiencies of adult shrimp are low when the algae mat is the ma in source of food or when the diet is largely carbonate (Cushing 1984 ). But, the AE can be surpri singly high when the food consists of detritus (Qasim and Easterson 1974).

Literature cited (Adult shrimp)

Allen, R.R. 1971. Relation between production and bi omass. J. Fish. Res. Board Can. 28:1573-1581. Arreguín-Sánchez, F., J. C. Seijo and E. Valero-Pacheco. 1993. An application of ECOPATH II to the north continental shelf ecosystem of Yucatan, Mexico, p. 269-278. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Broad, A. C. 1965. Environmental requirements of shrimp. Publ. Hlth. Serv. Publs. Wash. (999-WP25):86-91. Browder, J. A. 1993. A pilot model of the Gulf of Mexi co continental shelf. p. 279-284. In V. Christensen and D. Pauly (eds.) Trophic models of aquat ic ecosystems. ICLARM Conf. Proc. 26. Cobb, J. S. and J. F. Caddy. 1989. The population biol ogy of decapods. P. 327-374 in J. F. Caddy (ed.) Marine invertebrate fisheries: their assessment and management, John Wiley and sons, New York. Coleman & Koenig 1998; [Phil Steele has this citation] Costello, T. J., and D. M. Allen. 1970. Synopsis of biological data on the pink shrimp Penaeus duorarum Burkenroad, 1939. FAO Fish . Rep. 57:1499-1537. Cushing, D. H. 1984. Do discards affect the production of shrimps in the Gulf of Mexico? pp. 254-257 In J. A. Gulland and B. J. Rothschild (eds.) Penaeid shrimps—their biology and management. Fishing News Books, Farnham, Surrey England, 308 pp. Darnell, R. M., J. A. Kleypas and R. E. Defenbough. 1987. Eastern gulf shelf bio-atlas: A study of demersal fishes and penaeid shrimp of soft bottoms of the continental shelf from the Mississippi River Delta to the Florida Keys. U.S. Department of the Interior/Minerals Management Service, New Orleans Louisiana. 548 pp. Eldred, B., R. M. Ingle, K. D. Woodburn, R.F. Hutton and H. Jones. 19 61. Biological observations of the commercial shrimp, Panaeus duorarum, Burkenroad, in Florida waters. Florida State Board of Conservation, Prof. Paper Ser. 3:1-139, 52 Figs. Enhardt, N. M. and C. M. Legault. 1999. Pink shri mp, Farfantepenaus duorarum: Recruitment variability as an indicator of Florida bay dynamics. Estuaries 22:471-483.
FAO. 1997. Review of the state of wo rld fishery resources: marine fis heries. FAO Fisheries Circular 920 FIRM/C920, Rome.
Flint R. W. and N. N. Rabelais. 1981. Gulf of Mexi co shrimp production: a food web hypothesis. Fish.
Bull. 79:737-748.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 72 Huff, J. A. and S. P. Cobb. 1979. Penaeoid and surgestoid shrimps (C rustacea: Decapoda ). Memoirs of the Hourglass Cruises. Marine Research Laboratory , Florida Department of Natural Resources, St. Petersburg, Florida, 102 p. Jones, R. R. 1973. Utilization of Louisiana estuarine sediments as a s ource of nutrition for the brown shrimp Penaeus aztecus. Ph.D. Dissertation. Loui siana State University, Ba ton Rouge, Louisiana, 125 pp. Klima, E. F. 1989. Approaches to research and managem ent of U.S. fisheries for Penaeid shrimp in the Gulf of Mexico. P. 87-113 in J. F. Caddy (ed.) Marine invertebrate fisheries: their assessment and management, John Wiley and sons, New York. Nance, J.M. 1993. Effort trends fo r the Gulf of Mexico Shrimp fishery. NOAA Technical Memorandum, NMFS-SEFSC-337, 37 pp. Nance, J.M. 1997. Stock asse ssment for brown, white, and pink shrimp in the U.S. Gulf of Mexico, 1960- 1996. Report to the Gulf of Mexico Fishery Management Council, 13 p. Nance, J. M., and D. Harper. 1999. Southeast and Ca rribean invertebrate fisheries. In: Our living oceans: Report on the status of U.S. living marine reso urces, 1999. U.S. Dep. Commer., NOAA Tech.Memo. NMFS-F/SPO-41, on-line version, http://spo.nwr.noaa.gov/unit11.pdf. NMFS. 1997. Annual landings from fisheries of Unit ed States. http:/www/st.gov/webplcomm/plsq/webst1 Parrack, M. L. 1981. Some aspects of brown shrimp exploitation in the northern Gulf of Mexico. Paper presented at the workshop on the scientific basis for the management of penaeid shrimp. Key West, Florida, 1981. Pauly, D. and V. Christensen. 1993. Graphical representation of steady -state trophic ecosystem models, p. 20-28. In V. Christensen an d D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Qasm S. Z. and D. C. V. Easterson. 1974. Energy conversion in the shrimp Metapenaeus monoceros (Fabricius). Ind. J. Mar. Sci. 3:131-134. SAFMC. 1999. Stock assessment and fishery evaluation repo rt for the shrimp fisher y of the south Atlantic region, Volume 1. South Atlantic Fishery Ma nagement Council, Charleston, South Carolina, September 1999. SEMARNAP, S. d. r. n. y. p. 1997. Anuar io Estadistico de Produccion Pesquera. htt:/www.semarnap,gob.mx/sspesca Steele, P. 1997. Stock assessment pr ofile for the penaeid shrimp fisheries of the southeastern United
States and the Gulf of Mexico. Florida Marine Fisheries Commission. Steele, P. 2000. Stock assessment pr ofile for the penaeid shrimp fisheries of the southeastern United
States and the Gulf of Mexico. Gulf of Mexico Fishery Conservation Commission. Darnell, R. M., J. A. Kleypas and R. E. Defenbough. 1987. Eastern gulf shelf bio-atlas: A study of Demersal fishes and penaeid shrimp of soft bottoms of the continental shelf from the Mississippi River Delta to the Florida Keys. U.S. Department of the Interior / Minerals Management Service, New Orleans Louisiana. 548 pp. Vidal, L. 2000. Exploring the Gulf of Mexico as a la rge marine ecosystem through stratified spatial model. MSc Thesis, University of Brit ish Columbia, Vancouver, 152 p.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 74 8.8 Large crabs

Thomas A. Okey University of British Co lumbia, Fisheries Centre

Cynthia A. Meyer
Florida Marine Research In stitute, St. Petersburg



### The three most conspicuous crabs on t he West Florida Shelf are the blue crab ( Callinectes sapidus ), the stone crab ( Menippe mercenaria ), and the wester n Gulf stone crab ( M. adina ). Others include various s pecies of hermit crabs (e.g., Pylopagurus operculatus and Clibanaris vittatus ), arrow crabs (e.g., Stenorynchus seticornis and Metoporhaphis calcarata ), and spider crabs (e.g., Stenocionops furcatus ). Each crab species exploits a unique niche, but for t he purposes of the present trophic model, all crab species are aggregated into one functi onal group. In addit ion, horseshoe crabs ( Limulus polyphemus ) are included in the crab gr oup, even though they are phylogenetically distant from tr ue crabs. This crab group includ es only individuals larger than 5 cm; crabs smaller than 5 cm are included in the ‘small mobile epifauna’ group. The derivation of generalized parameters for West Florida Shelf large crabs are shown
in Table 8.13.

Table 8.13. Summary of Ecopath parameters for large crabs on the West Florida Shelf.

Type of crab 1998 landings a (t) Biomass (t

⋅ km -2 ) Biomass (t) Biomass (1000s of lbs. ) P/B (year -1 )
Blue crab 4,012.45 0.0381 b 6,482 14,293 1.12 f
Stone crab 1,127.00 0.0323 c 5,497 12,122 1.69 g

Hermit crab 3.46 0.0020 d 346 762
Arrow crab 0.82 0.0005 d 82 181 Spider crab 0.33 0.0002 d 33 73
Horseshoe crab 131.25 0.0154 e 2,618 5,773 Generalized crab 5,275.31 0.0886 15,058 33,203 1.3816 h

a Landings from FMRI landings database combining federal and state fishery landings data;

b Derived by applying a mean fishing mortality to mean catches; c Derived from landings and exploitation assumptions (see text); d Derived by applying assumed exploitation rates of 0.01 to 1998 catches; e Derived by applying assumed exploita tion rates of 0.05 to 1998 catches; f P/B value equivalent to total mortality from GSFMC, in press; g P/B value equivalent to total mortality from Ehrhardt and Restrepo 1989; h Biomass-weighted average of the two crab groups with P/B
estimates.



### The biomass estimate used for large crabs of the West Florida Shelf (0.0886 t

⋅ m -2 

### ) is the sum of the independently-e stimated biomasses of blue, stone, hermit, arrow, spider, and horseshoe crabs. The ratio of production to biomass (P/B) used for large crabs in this model (1.3816 year -1 

### ) is a biomass weighted aver age of empirically-based P/B estimates for blue and stone crabs (Table 8.13) . The derivation of these parameters can be found in the following sub-se ctions on individual crab spec ies. The estimate of Q/B (consumption/biomass) used for this composite group (8.5 year -1 

### ) is an independentlyestimated parameter used in an Ecopath model of the nearby Yucatan continental shelf (Arreguín-Sánchez et al. 1993). The estimated diet composition for large crabs on the
West Florida Shelf is a biom ass weighted average of the es timated diets of the sevenEcopath model of the West Florida Shelf: Volume II. Model construction



### 75 crab groups in this composite crab group (Table 8.14). Specific information on diets can
be found in the sub-sections th at follow. There is consi derable uncertainty in the parameters for this group, and continuing research will l ead to more representative
estimates.

Table 8.14. Estimated diet composition fo r large crabs on the West Florida Shelf.

Proportion of diet in six categories of large crabs Prey category Blue a Stone b Hermit c Arrow c Spider c Horseshoe d Overall
diet

Bivalves 0.25 0.25 0.930 0.364
Sessile epibenthos 0.14 0.25 0.16 0.20 0.153

Small infauna 0.03 0.03 0.09 0.30 0.30 0.020 0.032
Large crabs 0.20 0.10 0.02 0.120

Sediment detritus 0.05 0.11 0.30 0.15 0.11 0.005 0.070
Microbial heterotrophs 0.05 0.05 0.20 0.15 0.11 0.005 0.045

Fishes 0.14 0.01 0.02 0.061
Microphytobenthos 0. 05 0.04 0.10 0.15 0.05 0.005 0.038

Meiofauna 0.02 0.04 0.20 0.03 0.02 0.005 0.029

Echinoderms & lg. gastropods 0.10 0.10 0.015 0.041

Adult shrimp 0.06 0.01 0.02 0.005 0.029

Dead carcasses 0.01 0.01 0.01 0.01 0.01 0.005 0.009
Drift macrophytes 0.01 0.10 0.05 0.04 0.005 0.008

Notes: Diets in P 

### roportion wet weight;

a Adapted from quantitative diet compos ition provided by Laughlin (1982);

b Adapted from D. Scholnick, Eckerd College, St. Petersburg, pers comm. July 2000;

c Rough estimates of diet compositions;

d Adapted from (Botton 1984 and Botton and Ropes 1989).



### Blue crab Blue crabs ( Callinectes sapidus ) are estuarine animals, and so occur in highest densities along west Florida’s coastal embaym ents. The estimate of blue crab biomass on the West Florida Shelf (0.0381 t

⋅ km -2 

### ) was derived by dividi ng the average landings in the region from 1989 to 1997 (4,012 tonnes ; from FMRI landings database combining
federal and state fis hery landings data) by the mean fish ing mortality (0.619; provided by GSMFC, in press.) to roughly estimate the biomass of the whole population (6,482 tonnes, or 14,293,128 lbs.). Th is overall biomass was then divided by the estimated area of the West Florida Shelf to 200 m depth (170,000 km -2 

### ), resulting in a blue crab

biomass estimate of 0.0381 t

⋅ km -2 

### on the West Florida Shelf. This biomass of blue crabs is overwhelmingly concentrated inshore.

Total mortality of blue crabs has been estimated to be 1.12 ± 0.02 (GSFMC, in press.). This value (1.12) is used as the P/B esti mate for blue crabs since P/B equals total mortality when populations are at equilibrium (Allen 1971).

Blue crabs are opportunistic benthic omnivore s, feeding on fish, aquatic vegetation, mollusks, crustaceans, and annelid worms (Dar nell 1961, Muller 1999). In Apalachicola Bay, Laughlin (1982) found that blue crabs f ed on fishes, xanthid crabs, smaller blueEcopath model of the West Florida Shelf: Volume II. Model construction



### 76 crabs, and bivalves such as American oyster s, Mercenaria hard cl ams, coot clams, mussels, rangia, and periwinkles (Millikin and Williams 1984 in Williams et al. 1990). Smaller blue crabs (3 – 7.4 cm) contain e qual volumes of crustaceans and molluscs, but larger blue crabs (7.4 – 12. 5 cm) contain larger proporti ons of molluscs (Jaworski 1972 in Steele 2000).

Stone crab The two stone crab species on the West Florida Shelf ( M. mercenaria and M. adina ) feed primarily on bivalves until their claw s are pulled off by fishers. The small percentage of stone crabs that survive the initial dismem berment and the associated increases in predation risk (~ 30%) undergo the dietary stress of switching to detritus feeding while their claws regenerat e. The number of stone crab traps in the region attest to the importance of the fishery in Florid a: Muller and Bert (1997) estimated that 600,000 traps existed in Florida, but the consens us of participants of the fishery is that the number of traps is closer to 1,000,000 (T. Bert, Florida Marine Research Institute, pers. comm., 11 July 2000).

Florida boasts the only extensiv e stone crab fishery, and most of it occurs along the gulf coast. Fully 75-80% of the catch is landed in southwest Florida; 10- 15% is landed in NW Florida; and 10-15% is landed elsewhere. Ov erall, approximately 3 - 3.5 million pounds
of stone crab claws are landed annua lly in Florida, but only a portion of these claws come from the West Florida Shelf (when t he Florida Keys area ar e treated separately). Like lobsters, the density of stone crabs is undoubtedly lower in the areas north of the Keys due to physical habitat differences, but also because of thermal stress ( M.
mercenaria prefers a temperature range of 23-26°C, while the water temperature reaches 30°C on some parts of the West Fl orida Shelf north of the Keys) (T. Bert, Florida Marine Research Institute, Pers. Comm., 11 July 2000).

The estimate of stone crab biomass on the West Florida Shelf, 0.0323 t

⋅ km -2 

### , or 5,497 Tonnes (12,122,000 lbs.) is based on the average annual landings between 1988 and 1999 (provided by J. O’hop, M. Tupper, and S. Brown, Florida Marine Research Institute, 13 July 2000; Florida Fish and Wildlife C onservation Commission Marne Fisheries Information System) and the fo llowing assumptions: (1) half of Monroe County’s stone crab landings come from within the defined West Florida Shelf (excluding the Keys), (2) an average stone crab’ s claw makes up 41% of its total weight (D. Scholnick, Eckerd College, St. Petersburg, pers. comm., July 2000), (3) the fishing industry lands claws from cr abs that represent half of the standing biomass of stone crabs each season (when all age classes lar ger than 5 cm are considered). Based on these assumptions, half of the Monroe Count y landings were subtracted from the average landings over the last ten years to exclude th e Keys, claw weight was converted to crab weight by dividing by 0. 41, and the resulting biomass was doubled to roughly account for the biomass of the population in individual s that are not represented in landings.

The resulting estimate is close to half of the median of the r ange of standing biomass suggested by T. Bert (20-30 million pounds; Florida Marine Research Institute, pers.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 77 comm., 11 July 2000). Recent field observati ons of lower-than-expected occurrences of regenerated claws (C. Meyer, personal observati ons, July 2000; lower than the 10% of total landings suggested by Savage et al . 1975) indicates eit her a higher population than estimated above (e.g., closer to T. Bert’s suggestion) or a lower de-clawing survival rate. However, landings of stone crabs have reached a plateau, and the managers currently tracking the fishery believe that virtually every adult crab is captured each year, and that de-clawing su rvival rates that are lower than sometimes believed (8- 30% rather than 25-97%)(J. O’hop, M. T upper, and S. Brown, FMRI, pers. comm.,13 July 2000). This implies that the Florida stone crab fishery has become a recruitment fishery rather than a reusable resource in the sense of Erhardt and Restrepo (1989). The uncertainty of the stone crab biomass esti mate for the West Fl orida Shelf is high irrespective of these considerations, as our third assumption in the previous paragraph is a place-holder estimate.

The estimated total mortalit y of stone crabs (1.69 year -1 

### ) is used as the P/B estimate for the purposes of this model bas ed on the notion that P/B equals the rate of total mortality (Z) as the product of the inst antaneous rate of natural mort ality (M) and fishing mortality (F) in a population in equilibrium (Allen 1971) . This estimate of total mortality was
derived from estimates of instantaneous natural mortality rates (1.61 year -1 

### ) and fishing mortality (0.08 year -1 

### ) the latter of which was based on the specified claw si ze limit of 70 mm propodus length and estimated declawing survival rates ( see Ehrhardt and Restrepo 1989).

As mentioned above, the diet of stone crabs consists of bivalves and some large gastropods. More specifically, their diet includes Mercenaria mercenaria , Florida fighting conchs, mussels, Atlantic surf clams, tulip s hells, and Atlantic bay scallops. The diet of
crabs whose claws have been removed shifts to detritus, microbial heterotrophs, and microphytobenthos.

Horseshoe crab The horseshoe crab, Limulus polyphemus , is not a crustacean, but a living fossil related to spiders (arachnids). This species resides in estuarine and cont inental shelf habitats (Botton and Loveland 1989), and their prolific spawning aggregations can be observed at and above the waterline on low energy beaches during spring tides. They are scavengers and they prey on benthic infauna. Al though taxonomically di stinct from true crabs, horseshoe crabs have enough conver gent morphological and ecological similarities to be grouped with the crab functional group for t he purposes of this model.

The biomass estimate of Limulus on the West Florida Shelf (0.0154 t

⋅ km -2 

### , or 2,625 tonnes, or 5,788,125 lbs. on the overall shelf) is a rough appr oximation, based only on reported 1998 landings (131.249 tonnes) divided by an assumed catch rate (0.05).

Horseshoe crabs are dietary generalists and opportunistic foragers (Botton 1984, Botton and Haskin 1984), but they appear to spec ialize on bivalves, as Botton found Limulus guts to contain primarily bivalves and othe r mollusks. Interestingly, Botton and Ropes (1989) found that gut contents in Limulus comprised only 0.23 % of the total body massEcopath model of the West Florida Shelf: Volume II. Model construction



### 78 of males and a mere 0.097% of the body ma ss of females (the average weight of a male was 0.947 kg compared with 2.257 kg for a female). The estimated diet composition for Horshoe crabs on the West Florida Shelf is shown in Table 8.15.

Table 8.15. Diet composition of horseshoe crabs
on the Atlantic continental shelf.

Prey categories Proportion of diet

Bivalves 0.932 Gastropods 0.052 Polycheates 0.003
Crustaceans 0.002
Misc. 0.011

Notes: These diets were adapted from Botton and Roper 1989 to West Florida Shelf model groups for integration into the overall ‘large crab’ diet (Table 8.14).

Literature cited (large crabs)

Allen, K. R. 1971. Relation between production and biomass. J. Fish. Res. Board. Can. 28:1573-1581. Botton, M. L. 1984. Diet and food preferences of the adult horseshoe crab Limulus polyphemus in Delaware Bay, New Jersey, USA. Marine Biology 81: 199-207. Botton, M. L., and H. H. Haskin. Distributionand feeding of the horseshoe crab, Limulus polyphemus , on the continental shelf off New Jersey. Fishery Bulletin 82(2):383-389. Botton, M. L. and J. W. Ropes. 1989. Feeding ecolog y of horseshoe crabs on the continental shelf, New Jersey to North Carolina. Bulletin of Marine Science 45(3):637-647. Darnell, R. M. 1961. Trophic sp ectrum of an estuarine communi ty, based on studies of Lake Pontchartrain. Ecology 42:553-558. Ehrhardt, N. M., and V. R. Restrepo. 1989. The Flori da stone crab fishery: a reusable resource? pp. 225-
240 in J. F. Caddy (ed.) Marine invertebrate fish eries: their assessment and management, John Wiley and Sons, New York. Guillory V, Perry H, and VanderKooy S. (editors). In prep.The blue crab fishery of the Gulf of Mexico, United States: a regionalmanagement plan. Gu lf States Marine Fisheries Commission, PublicationNumber ?, ? pp. Jaworski, E. 1972. The blue crab fishery, Baratara estuary. Center for Wetland Resources, Louisiana State University, LSU-SG-72- 01, Baton Rouge, Louisiana. Millikin, M. R., and A. B. Williams. 1984. Sy nopsis of biological data on the blue crab, Callinectes sapidus

. FAO Fish. Synop. 138. Muller, R. G. 1999. Florida's inshore and nearshore species: 1999 status and trends report. Florida Marine Research Institute, Florida Fi sh and Wildlife conservation Commission. Williams, C. D., D. M. Nelson, M. E. Monaco, S. L. St one, C. Iancu, L. Coston-Cle ments, L. R. Settle, and
E. A. Irlandi. 1990. Distribution and abundance of fishes and invertebrates in Eastern Gulf of Mexico Estuaries. ELMR Rept. No. 6. Strategi c Assessment Branch, NOS/NOAA. Rockville, MD. 105 p.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 79 8.9 Lobsters

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Two general types of lobsters occur on the West Florida Shelf—spiny lobster ( Panulirus

arqus ) and slipper lobsters (Scyllaridae). Spin y lobster range from North Carolina, USA to Rio de Janeiro, Brazil, and the US fishery for spiny lobster is concentrated in South Florida, as the region offers good habi tat for juvenile and adult life stages (GoM&SAFMC 1982). Spiny lobsters are trul y a transboundary stock as the planktonic larvae of the South Florida population likely orig inate in the Caribbean (Ingle et al. 1963, Little 1977). Postlarvae and juvenile spiny lobsters recruit abundantly into Florida Bay because the local seagrass, macrophyte, and mangrove habitats are ideal for rearing and growth. Spiny lobsters move out of these nursery areas to deeper water reef habitats as they approach commercial sizes (Ly ons et al. 1981). Much of this habitat is in the Florida Keys—outside our defined area—but many other adult lobsters reside on the West Florida Shelf. Spiny lobsters emigra te from this ‘source’ region as adults by walking along the sea floor perhaps to the east around the Florida peninsula, but the details of their migration ar e not well known (R. Muller, FMR, pers. comm., July 2000;
also see Kanciruk and Herrnkind 1978).

Approximately 90% of the US spiny lobster catch occurs in the Florida keys, and some evidence suggests that fishing effort has been higher then optimal in the recent past (Muller et al. 1997). There is considerable unc ertainty in any attempts to estimate overall biomass of spiny l obsters on the West Florida Shelf because of the limited nature of the available information—especia lly information about the relationship of catch to stock size (R. Muller and J. O’hop, FMRI, pers. comm., July 2000).

The estimate of lobster biomass on the We st Florida Shelf used in this modeling exercise (0.007 t

⋅ km -2 

### , or 1,186 tonnes, or 2,614,367 lb s.) is based on the average annual landings between 1988 and 1999 (provided by J. O’ hop, M. Tupper, and S. Brown, Florida Marine Research Institute, 13 July 2000; Florida Fish and Wildlife
Conservation Commission Marne Fisheries In formation System). I summed the mean landings from all west Florida counties and subtracted half of Monroe County’s landings to estimate the total landings from the defi ned West Florida Shelf area (excluding the keys). This value was doubled to estimate sp iny lobster biomass on the wFs, assuming
that half of the overall biomass is taken annually. This spiny lobster biomass estimate
was then doubled to account fo r scyllarid lobsters, assuming scyllarid lobster biomass is equal to that of spiny lobsters.

The P/B and Q/B estimates of 0.9 year -1 

### and 8.2 year -1 

### respectively are taken from independent estimates for lobsters from another Ecopath model of the nearby Yucatan continental shelf (Arreguín-Sánchez et al . 1993). The estimates for lobster diet composition specified in Ta ble 8.16 are roughly estimated by the present author, and should be refined based on empirical information from the region.

Table 8.16. Rough estimate of lobster dietEcopath model of the West Florida Shelf: Volume II. Model construction



### 80 composition on the West Florida Shelf.

Group Proportion

Small infauna 0.20 Adult shrimp 0.10 Sessile epibenthos 0.10

Bivalves 0.10

Sediment detritus 0.10

Lobsters 0.05 Large crabs 0.05 Microbial heterotrophs 0.05 Microphytobenthos 0.05

Dead carcasses 0.05

Benthic fishes 0.05

Demersal fishes 0.05 Ichthyoplankton 0.02

Meiobenthos 0.01 Other mesozooplankton 0.01

Water column detritus 0.01

Literature cited (Lobsters)

Arreguín-Sánchez, F., J. C. Seijo, and E. Valero-Pacheco. 1993. An application of ECOPATH II to the north continental shelf ecosystem of Yucatan, Mexico, p. 269-278. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. GoM&SAFMC. 1982. Fishery management plan, envir onmental impact statement, and regulatory impact review for spiny lobster in the Gulf of Mexico a nd South Atlantic. Gulf of Mexico and South Atlantic Fishery Management Councils, March 1982. Ingle, R. M., B. Eldred, H. W. Si ms, Jr., and E. Eldred. 1963. On the possible Caribbean origin of Florida’s spiny lobster populations. Fla. State. Board Conserv. Mar. Lab. Tech. Ser. No. 40. 12 pp. Kanciruk P., and W. F. Herrnkind. 197 8. Mass migration of spiny lobster, Panulirus argus (Crustacea: Palinuridae): behavior and environmental correl ates. Bull. Mar. Sci. 2(4):601-623. Little, E. J., Jr. 1977. Observations on recruitment of postlarval spiny lobsters, Panulirus argus , to the south Florida coast. Florida Mar. Res. Publ. No. 29. 35 pp. Lyons, W. G., D. G. Barber, S. M. Foster, F. S. Ke nnedy, Jr., and G. R. Milano. 1981. The spiny lobster,
Panulirus argus , in the middle and upper Florida keys: population structure seasonal dynamics and reproduction. Florida Marne Research Publications 38. 38 pp. Muller, R. G., J. H. Hunt, T. R. Matthews, and W. C. Sharp. 1997. Ev aluation of effort reduction in the Florida keys spiny lobster, Panulirus argus , fishery using an age-structured population analysis.
Mar. Freshwater Res. 4:1045-1058.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 81 8.10 Stomatopods

Cynthia A. Meyer
Florida Marine Research In stitute, St. Petersburg

Roy L. Caldwell Department of Integrative Biology, Un iversity of California at Berkeley



### Stomatopods, or mantis shrimp, are crustaceans that inhabit burrows and crevices on the shallow sea floor of tropical and sub-tropi cal regions (Caldwell 1987). Stomatopods are carnivorous predators that use their ma ssive raptorial second thoracopods (Caldwell and Childress 1989) and their excellent vision to surprise shrimps, crabs, mollusks, fishes, and other animals with their powerful st rikes. They are divided into two functional groups—smashers (i.e., neogonodatylids) and spear ers (i.e., squillids)—based on the function and morphology of the raptorial thoracopod (Caldwell and Childress 1989 in Caldwell and Dingle 1976). The Lysquillin a and other small borrowing squillids and lysiosquillids are li e-in-wait predators. Squilla empusa and other large Squilla will forage actively at night away from their burrow. Neogonodactylus do most of their foraging during frequent trips away from their cavity, often going up to several meters to find a
snail or crab and then return with it to thei r cavity for processing (Caldwell et al. 1989). At least twenty-eight specie s of stomatopods occur in th e Gulf of Mexico. Thirteen species identified on the West Florida Shelf are known to inhabit bathymetric ranges from the littoral zone to dept hs exceeding 200 m (Camp 1973).

The cryptic nature of stomat opods makes estimation of thei r biomass challenging, but their behaviorally-dramatic lifestyles and thei r role as predators in structuring benthic communities makes their study particularly des irable, especially in the context of wholesystem trophic models capab le of dynamic simulati on. Stomatopods might have keystone effects (proportionally la rge effects) related to t heir predation on herbivores and detritivores. For this reason, it is important to derive reasonably accurate estimations of their population parameters in order to captur e the structure and dynamics of the lower trophic levels in the West Florida Shelf model.

The rough estimate of stomat opod biomass used here for the West Florida Shelf model (0.9944 t

⋅ km -2 

### ) was derived taking the average of estimates from two sources: Wenner (1989), and a West Florida Shelf estima te based on the Panamanian studies of Caldwell et. al. (1989, 1991, unpublished da ta). Wenner (1989) used trawl surveys to examine the compositi on and abundance of decapods and stomatopods between 8 and 20 m depths on the east coast of Florida. We estimated the biomass of his collected stomatopods to be 0.0987 t

⋅ km -2 

### by dividing the weight of his collected specimens by the total area covered by his surveys (1.61 km 2 

### ; i.e., 2.2 hectares x 18 stations x 4 cruises). This estimate only includes the Squilla empusa

, S. neglecta , and Lysiosquilla
scabricauda found in the coastal area between 8-20 meters depth. The limited comparability of this study to the West Fl orida Shelf as a whole (to 200 m depth) is underscored by the presence of at least thirt een different species endemic to the latter region (Camp 1973). Despite iss ues of limited comparability, Wenner’s (1989) study isEcopath model of the West Florida Shelf: Volume II. Model construction



### 82 useful to us, as it provides some guideline for stomatopod density in the vicinity of the
area delineated for the construction of the West Florida Shelf model.

Density estimates of the P anamanian stomatopods (Caldwell et al. 1989, R. Caldwell, unpublished data) were also us ed to derive a biomass estimate of the West Florida Shelf stomatopods (1.89 t

⋅ km -2 

### ). This estimate is derived by applying Panamanian density estimates for Squilla and Neogonodoctyla groups to the West Florida Shelf stomatopod fauna based on relative areal di stributions of similar habitats. The Neogonodactyla reside primarily in har d bottom habitats, and the Squilla live in soft
bottoms. Hard bottoms are estimated to make up ~35% of the total area of the West Florida Shelf (Parker et al . 1983, Phillips et al. 1990 in Phillips and Thompson 1990), leaving ~65% of the area covered by soft sedi ments. In addition, St omatopods are likely
to be most abundant in areas shallower than 60 m, which takes up approximately 67%
of the area of the West Florida Shelf, or 114,222 km -2 

### of the ~170,000 km -2 

### shelf (see Houhoulis, this volume). The estimate was derived by taking a habitat-weighted estimate of the two groups (2.5 t

⋅ km -2



### ⋅ 0.35) + (3 t

⋅ km -2



### ⋅ 0.65) and then multiplying the result by 0.67 to adjust the estimates by a simple assumed depth attenuation gradient with the pivot at 60 m depth. Derivations of group-specific biomass values are shown in
below.

The estimate for all neogonodactylids over 16 mm on hard bottom habitats on the east

Panama shelf is 5 individuals

⋅ m -2 

### , and the areal biomass estimate is 2-3 t

⋅ km -2 

### (2-3 g

⋅ m 2 

### ). Densities of N. bredini between 36-45 mm in length have been documented to be 1.5

individuals

⋅ 10 m -2 

### (Caldwell et al. 1989), but this was only one third the density of similar sized neogonodactylids on other back reef ar eas. Indeed, a large proportion of the population is smaller and larger than this 36-45 mm size class, and much higher densities of neogonodactylids have been revealed in this region ( see Table 8.17). The estimate for all neogonodactylids over 16 mm is 5 individuals

⋅ m -2 

### , equaling approximately 2-3 t

⋅ km -2 

### (Caldwell et al. 1991).

The biomass estimate for all squillids and lysiosqui llids in the Bay of Panama is

~3 t

⋅ km -2 

### , based on 200 (1 m 2 

### ) quadrats (Caldwell, unpublished data). Four species of squillid inhabited the soft bot toms of the bay, with the lar gest getting up to 18 cm. The
median size was about 40 mm and densities were around 5-6 m -2 

### . The 40 mm animals weighed about 0.7 g, but squillids in the 10 cm range weighed approximately 10 g and the largest 16 cm animals woul d have weighed over 20 g.

Table 8.17. Estimated biomass of neogonod actylids in hard bottom habitats of the
east Panama shelf.

Neogonodactylid size class (mm) Density (individuals

⋅ m -2 ) Individual weight (g) Biomass (t

⋅ km -2 )

16 0.075

35 4 0.5 0.288

40 1.0
50 2.0
60 1

3.0 2.0

Overall 5 ~2-3Ecopath model of the West Florida Shelf: Volume II. Model construction



### 83 Note: Adapted from Caldwell (1991)



### The lack of quantitative density information about stomatopods on the West Florida Shelf compelled us to rely on density in formation from these similar settings. The
studies used for the biomass estimate focu sed on relatively shallow areas rarely exceeding 20 m (Wenner 1989). No substantial information is available for stomatopods living in deeper waters, although Camp (19 73) noted the occurrence of squillids to a depth of 346m in the Gulf of Mexico. T he biomass estimate we chose for initial parameterization of the Ecopath model of the West Florida Shelf (0.9944 t

⋅ km -2 

### ) was the average of the two methods presented here. In the survey by Wenner (1989) the average weight for the Squilla sp. was 14.5 g. This was considerably higher than the average weight of 2.5 g estimated by the st udies in Panama (Caldw ell et al 1989), and weight differences may be one source of th e discrepancy observed here. The trawl nets utilized by Wenner (1989) may have skewed hi s samples towards larger specimens, but the trawling approach may have underestima ted stomatopod biomass overall.

The production/biomass ratio of stomatopods on the West Florida S helf was calculated based on the estimated life spans of Squilla sp (3-4 years) and Neogonodatyla sp (5-6 years) (R. Caldwell, unpublished data). As in Alagajara ( 1984), the species life span was considered to be the age at which 99% of a cohort had di ed of natural mortality. That means, under the assumption of constant exponential rate of decline of numbers in a cohort, that

M = - ln (0.01)/T

where M is the natural mortality rate (year -1 

### ) and T is the life span (or longevity) of the species (years). The average of the resulti ng mortality estimates for the two genera (0.89 year -1 

### ) is taken as the mean nat ural mortality rate for the group, and this would equal P/B if no fishing mortality exists. Assumi ng, however, that fishing mortality (F) of West Florida Shelf stomatopods equals half of the natural mo rtality, the P/B would then equal the sum of M and F (i.e. 1.335 year -1 

### ).

The consumption rate (Q/B) of stomatopods (7.432 year -1 

### ) was estimated based on studies of neogonodactylids, which make fr equent foraging trips in search of prey (Caldwell et al. 1989, Ca ldwell and Childr ess 1989). It was observed that each stomatopod took an average of 12.1 small gastropods, 1.5 medium gastropods and 5.3 small crustaceans per day. The wet weight of the prey items was 1 mg for the small gastropods and small crustaceans, and 5-8 mg for medium gastropods (6.5 mg was used as an average for medium gastropods). Th is estimated 27.15 mg of food for each stomatopod each day was divided by 1.2 g—t he average weight of a stomatopod in the study—yielding a daily consumption rate of 22.623 (mg

⋅ g -1



### ⋅ day -1 

### ). This was multiplied by 365 days and 1000 mg/g to der ive the annual consumption rate (Q/B) 8.258 year -1



### . Stomatopods do not appear to feed during molting, which is approximately 10% of the year (R. Caldwell, personal observations). T he consumption rate estimate was weighted for this correction; we thus estimate a Q/B of 7.432 year -1



### .Ecopath model of the West Florida Shelf: Volume II. Model construction



### 84 Stomatopods are carnivorous predators. During the Hourglass surveys (Camp 1973) the gut contents of 105 specim ens of stomatopods of 8 diffe rent species were analyzed. Stomatopods selected for gut content analysis were the largest adults of the species. These species include Neogonodactylus bredini along with several Squillids and Meiosquillids. Occurrence information in t he gut content analysis were converted to biomass proportions qualitativ ely—based on my best judgment. Unidentified animal tissue was apportioned to squid and bivalv e groups. Camp (1973) found large crustaceans as the predominant food item; this is apportioned to the adult shrimp, and large crab prey categories, and his is the primary source used in the development of Table 8.18. However, gut cont ent analyses by R. Caldwell (unpublished data) show the
unidentified material in the Neogonodactylus gut to be mollu sc, particularly gastropod. Although it mainly represent s the squillids, these latter findings may be general for stomatopods. Specific diets of the Neogonodactylids and L ysiosquillids are not well represented anywhere. Diets of adult Neogonodactylus consisti ng mainly of crabs and gastropods (Caldwell, unpublished data) is so mewhat reflected in the following
estimates. The diet of the L ysioquillids, which is composed mainly of fish (Caldwell, unpublished data), is not accounted for here.

Table 8.18. Estimated diet composition of stomatopods on the West Florida Shelf.

Prey category Proportion of diet Shrimp 0.20 Microbial heterotrophs 0.20 Microphytobenthos 0.12
Bivalves 0.11 Squid 0.10
Crabs 0.09
Small infauna 0.09 Small mobile epibenth 0.06
Echinoderms 0.01
Small demersals 0.01 Macroalgae 0.01

Note: Diet extrapolated and modified from occurrence information in Camp (1973).

Literature cited (Stomatopods)

Alagaraja, K. 1984. Simple methods for estimation of parameters for assessing exploited fish stocks.
Indian J. Fish. 31:177-208. Allen, K. R. 1971. Relation between production and biomass. J. Fish. Res. Board. Can. 28:1573-1581. Caldwell, R. L. 1987. Assessment strategies in stomatopods. Bull. Mar. Sci. 41:135-50. Caldwell, R. L. and M. J. Childre ss. 1989. Prey selection and proce ssing in a stomatopod crustacean. In: Behavioral Mechanisms of Food Selection, R.N. Hughes, ed. NATO ASI Series, Series G: Ecological Sciences, Vol. 20:143-64, Springer-Verlag, Berlin. Caldwell, R. L., G. Roderick, and S. Shuster. 1989. Studies of predation by Gonodactylus bredini . In: E. A. Ferrero, (ed.) R. B. Manning , M. L. Reaka, W. Wales (co-eds.) "Biology of Stomatopods", Collana UZI: Selected Symposia and Monographs, Mucchi Editore, Modena (Italy), pp. 117-131. Camp, D. 1973. Stomatopods. Memoirs of the Hourglas s Cruises. Marine Resear ch Laboratory, Florida Department of Natural Resources, St. Petersburg, Florida.. Vol 3:2. May 1973. Griffiths, C. L. and M. J. Blaine. 1988. Distribution, population and bi ology of stomatopod Crustacea off
the west coast of South Afri ca. South African Journal of Marine Science, Vol 7 pp 45-50.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 85 Hughs R. N. 1989. Behavioral mechanisms of f ood selection. NATO Series G vol 20 1989. Darnell, R. M., J. A. Kleypas and R. E. Defenbough. 1987. Eastern gulf shelf bio-atlas: A study of Demersal fishes and penaeid shrimp of soft bottoms of the continental shelf from the Mississippi River Delta to the Florida Keys. U.S. Department of the Interior / Minerals Management Service, New Orleans Louisiana. 548 pp. Parker, R. O., Jr., D. R. Colby, and T. D. Willis. 1983. Estimated amount of reef habitat on a portion of the
South Atlantic and the Gulf of Mexico continent al shelf. Bulletin of Marine Science 33:935-940. Phillips, N. W., and M. J. Thompson. 1990. Offshore benthic communities. pp.155-193 in N. W. Phillips, K. S. Larson (eds.) Synthesis of available bi ological, geological, chemical, socioeconomic, and
cultural resources information for the south Florid a area. Prepared by Continental Shelf Associates for Minerals Management Service, OCS Study - MMS 90-0019, May 1990. Steger, R. and R. L. Caldwell 1991. Reef flat st omatopods. In: Long-term assessment of the oil spill at Bahia Las Minas, Panama, interim report, volume II: technical report. Keller, B. D. and J.B.C. Jackson, eds. OCS Study MMS 90-0031. U.S. Department of Interior, Minerals Management Service, Gulf of Mexico OCS Regional Office, New Orleans, La., pp. 97-119. Wenner, E. L. and C. A. Wenner. 1989. Seasonal composition and abundance of decapod and stomatopod crustaceans from coastal habitats, sout heastern United States. Fishery Bulletin 87(1)
:155-176.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 86 8.11 Octopods

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Octopods are intelligent and voracious pr edators, which share the molluscan class cephalopoda with squids, cuttlefishes, and argonauts. Most octopoods of the West Florida Shelf live and hunt on the sea floor, while squids mostly live and feed above the sea floor. The most important octopod on the West Florida Shelf is probably Octopus vulgaris , which is common throughout the Atlant ic and the Mediterranean (Grubert et al. 1999), and Octopus maya , which is common along the east coast of Mexico (ArreguínSánchez et al. 1993). Squids are treated Section 8.12 as an invertebrate segue to forage fishes, because of ecological similariti es of squids and forage fishes (e.g., Pauly 1999).

The biomass of octopods on the West Fl orida Shelf is unkno wn from independent studies. However, the biomass value of 0.074 t

⋅ km -2 

### was calculated by the Ecopath software by setting the ecotrophic efficiency at 0.95 in the context of the empirically derived P/B and Q/B estimates, the input diet compositi on, and the energy flows among the surrounding compartments. This estimate fo r octopods on the West Florida Shelf is equal to less than 1% of the estima te for the Compeche Bank (17.62 t

⋅ km -2 

### ), which was also estimated by the Ecopath software. In the case of octopods, the estimated biomass parameters for both of these systems are cons idered equally uncertain. As a potentially important predator in this syst em, investments in octopod research should be seriously
considered.

The P/B value of octopods on t he West Florida Shelf (3.1 year -1 

### ) is the mean of empirically derived P/Bs from fema les and males of South African Octopus vulgaris from Buchan and Smale (1981; in Opitz 1993).

The Q/B value (7.3 year -1 

### ) is from Guerra (1979 in Paul y 1993) from studies in the Mediterranean Sea (near Barcelona) and off No rthwest Africa—systems similar to the West Florida Shelf. The estimate for octopod assimilation efficiency (0.97) is the gross conversion efficiency (K; from Pauly et al. 1993) plus 0.15 for respiration.

It should be noted that Arreguín-Sánchez et al. (1993) used a P/ B value of 1.10 year -1 

### and a Q/B value of 3.5 year -1 

### for octopods of the nearby Compeche bank off the
northern Yucatan. These valu es appear to be conservative, and can be considered a lower range during mass balancing.

The diet composition used for octopods on the West Florida Shelf are adapted from Whitaker et al. (1991; present ed in Grubert et al. 1999), and is shown in Table 8.19.

Table 8.19. Estimated diet compositio n of octopods on the West Florida
Shelf.

Proportion of diet Prey categories a Yucatan b S. Carolina c Florida dEcopath model of the West Florida Shelf: Volume II. Model construction



### 87 Octopods 0.033 Large crabs 0.60 0.200

Benthic fishes 0.200

Lobsters 0.15 0.099 Adult shrimp 0.05 0.099

Demersal fishes 0.081

Mobile macrofauna 0.036

Dead carcasses 0.026 Echino. / lg. gastro

. 0.016

Bivalves 0.20 0.099 0.100 Sessile epibenthos 0.100

Meiobenthos 0.010

Crustaceans 0.377

Fishes 0.325

Other 0.199

a Prey categories in italics are general categories from source documents that were adapted to the more specific categories of this model; b Octoopus maya diet from Solís (1962, 1967; in ArreguínSánchez et al. 1993); c Octopus vulgaris diet roughly adapted from
%occurrence results from Whitaker et al. (1991; presented in Grubert et al. 1999); d Adapted mostly from Whittaker et al. (1999) for the West
Florida Shelf model.

Literature Cited (Octopods)

Arreguín-Sánchez, F., J. C. Seijo and E. Valero-Pacheco. 1993. An application of ECOPATH II to the north continental shelf ecosystem of Yucatan, Mexico, p. 269-278. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Buchan, P. R., and M. J. Small. 1981. Estimates of biomass, consumption and production of Octopus vulgaris Couvier off the east coast of South Af rica. Oceanographic Research Institute, Investigational Report 50, 9 p. Grubert, M. A., V. A. Wadley, and R. W. G. White. 1999. Diet and feeding strategy of Octopus maorum in
Southeast Tasmanea. Bulletin of Marine Science 65(2):441-451. Guerra, A. 1979. Fitting a v on Bertalanffy expression to Octopus vulgaris growth. Invest. Pesq. 43(1):319-
326. Jarre, A., M. L. Palomares, M. Soriano, V. Sambila y, Jr. and D. Pauly. 1990. MAXIMS computer program for estimating food consumption of fishes. ICLARM Software 4, 19 p. Opitz, S. 1993. A quantitative model of the trophic interactions in a Ca ribbean coral reef ecosystem, p.
259-267. In V. Christensen and D. Pauly (eds.) Tr ophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Pauly, D. 1998. Why squid, though not fish, may be better understood by pretending they are. South
African Journal of Marine Science 20:47-58. Pauly, D., V. Simbilay, Jr., and S. Opitz. 1993. Es timates of relative food consumption by fish and invertebrate populations, required for modeling the Bolinao reef ecosystem, Philippines, p. 236-251.

In V. Christensen and D. Pauly (eds.) Trophic m odels of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Solís, M. J. 1962. Contribut ión al estudio del pulpo ( Octopus vulogaris , Lamark) de la sonda de Campeche. Trabajos de Divulgación. Inst. Nat. Pesca Mexico 3(24):1-30. Solís, M. J. 1967. Aspectos biologícos del pulpo Octopus maya Voss y Solís. Inst Nac. Invest. Biol. Pesq.
Mex. 18:1-90. Whittaker, J. D., L. B. Delancey and J. E. Jenkins, 1991. Aspects of the biology and fisheries potential for Octopus vulgaris off the coast of South Carolina. Bull. Mar. Sci. 49:482-493.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 88 8.12 Squids

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Squid are cephalopod molluscs that are recognized as an important food for a variety of medium and large predators throughout the world’s oceans. Less emphasized, but just as important, is their role as voracious predators of highly productive benthos and zooplankton. A trophically-unbiased view of s quid reveals their role in linking the “furnaces” of primary and secon dary production to the organism s at upper levels of the trophic pyramid—a mediating role also pl ayed by forage fishes. It has been suggested that squid might be “better under stood by pretending they are [fish]”, based on criteria relating to their size and growth (Pauly 1998) and their life hi stories—the parallels mostly apply to ‘forage fishes.’ Common types of squid of the West Florida Shelf include two loliginids, Loligo pealei and L. plei, and one ommastrephid, Illex spp. (Vecchione 1988).

Like on the West Florida Shelf, L. pealei and L. plei are the main species of squid on the nearby and ecologically si milar northeastern Venezuel a shelf. For that system, Mendoza (1993) derived estimates for squid biomass (0.267 t

⋅ km -2 

### ), P/B (1.70), and Q/B (36.5), using growth and mortality estimate s from Pauly (1985) and consumption and
diet information from Amaratunga (1983).

We will use Mendoza’s biomass and Q/B esti mates shown above, but his P/B estimate
matches estimates for Sepioteuthis lessoniana and Illex illecebrosus in Pauly et al. (1993), though they present a higher estimate for two Loligo species (3.16 year -1 

### ). Since the common species on the West Florida Shelf consist of two Loligo species and one
Illex species, the two P/B estimates are aver aged by proportion of representation, yielding a P/B estimate of 2.673 year -1 

### for squid on the West Florida Shelf.

To my knowledge, Mendoza’s estimates are the best available for the similar West Florida Shelf; I am aware of no evidence other than that st ated above indicating that the
Venezuela estimates would either overes timate or underestimate corresponding parameters for West Florida Shelf squid. Mendosa’s (1993) diets were adapted to the groups used in this model, as shown in Table 8.20.

Table 8.20. Estimated diet composition of squid on the West Florida Shelf.

Proportion of diet Prey a Venezuela b Monterey c Florida d
Sm. mobile epifauna 0.008 0.06 Carnivorous zooplank. 0.483 0.22 Mesozooplankton 0.483 0.23 Small copepods 0.015 0.02 Stomatopods 0 0.05 e Ichthyoplankton 0.001 0.01
Small infauna 0.003 0.02 Carnivorous jellyfish 0 0.02 Squid 0.02 0.003 0.01Ecopath model of the West Florida Shelf: Volume II. Model construction



### 89 Small pelagics 0.41 0.004 0.36 Zooplankton 0.46 Heterotrophic benthos 0.11

a Prey categories in italics are general categories from source documents that were adapted to the more specific categories of this model;

b Squid diet presented in Mendoza (1993) from Amaratunga (1983);

c Loligo opalescens diet from Karpov and Cailliet (1978);

d Adapted squid diet used for the West Florida Shelf model;

e See Meyer and Caldwell (this volume).



### Exactly 38 tonnes of squid were landed in Florida in 19 99. Mendoza’s (1993) biomass value of 0.267 is equivalent to a standing stock of 45,390 tonnes of squid on the 170,000 km 2 

### West Florida Shelf area. Application of Mendoza’s biomass estimate to the
West Florida Shelf means th at the instantaneous rate of fishing mortality (F; year -1 

### ) would be very low 0.00084, a ssuming that all squid caught on the West Florida Shelf
are landed in Florida.

Literature cited (Squids)

Amaratunga, T. 1983. The role of cephalopods in the ma rine ecosystem, p. 379-412. In J. F. Caddy (ed.) Advances in assessment of world cephalopo d resources. FAO Fish. Tech. Pap. 231. Karpov, K.A. and G.M. Cailliet. 1978. Feeding dynami cs of Loligo opalescens. p. 45-66 In: Reckseik, C.W. and Frey, H.W. Biological, Oceanographic, an d Acoustic aspects of the market squid, Loligo opalescens Berry. California Department of Fish and Game, Fish Bulletin 169. Mendoza, J. J. 1993. A preliminary biomass budget fo r the northeastern Venezuel a shelf ecosystem, p. 285-297. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Pauly, D. 1995. Population dynamics of short-lived species, with emphasis on squids. NAFO Sci. Couns.
Stud. 9:143-154. Pauly, D. 1998. Why squid, though not fish, may be better understood by pretending they are. South
African Journal of Marine Science 20:47-58. 

### Vecchione, M. 1988. In-situ observations on a large squid-spawning bed in the eastern Gulf of Mexico. Malacologia 29(1):135-141.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 90 

### 9. F ISHES

Steven Mackinson CEFAS Fisheries Lab, Lowestoft, UK



### Input parameters for the majority of West Florida Shelf fish groups were estimated using an integrated approach. Parameters for a few fi sh groups (i.e., large oceanic piscivores, coastal sharks, and rays and skates) were however estimated separately, as
information sources differed from those available for most fish groups.

9.1 Systematic categorization of fish groups

The general goal of our systematic categorization of fish groups was to define functional groups by determining ecological similari ty among fish species. This systematic approach consisted of three steps:

(1) Build a catalogue of West Florida Shelf fish species

Reported catches and expert opinion of FMRI scientists were integrated to determine which fish species were present on the We st Florida Shelf in at least moderate abundance. A preliminary compr ehensive list of over 600 spec ies, taken from the NOAA website, was reduced to about 260 specie s (or genus or family in some cases).

(2) Sort each species in a database based on 5 criteria:

a. Horizontal distribution (oceanic migr ant, coastal resident, estuary migrant) b. Vertical distribution (surface , mid-water, demersal, benthic) c. Habitat type (pelagic, coral reef, other structured bottom, soft bottom, sea grass) d. Trophic habit (planktivore, piscivore, omnivore, herbivore, invertebrate eater, detritivore, coral eater) e. Approximate size (large, medium, small)

(3) Group species functionally

Species were organized into ecologically func tional groups by FMRI scientists using the database classifications. Particular emphasis was given to trophic similarities during grouping because species with dissimilar diets can erode t he representativeness of the model. Appendix 7 shows the final lis t of all functional fish groups.

9.2 Biomasses of fish groups

Four approaches were used to estimate t he biomass of fish groups; (i) Fishery independent survey data, (ii) Stock-asse ssment VPA models, (iii) A basic catch equation, and (iv) Estimates fr om existing literature.

(i) Fishery independent survey dataEcopath model of the West Florida Shelf: Volume II. Model construction



### 91 Four sets of fishery independent survey data were used to derive abundance estimates
for coastal demersal/ structur e related species, coastal pelagic species, and oceanic species, though this varied among species.

Average abundance estimates derived from two reports detailing We st Florida Shelf surveys in coastal waters (Darcy and Gunther z 1984, Darnell et al. 1987) were used for coastal species for which detailed stock a ssessment estimates were not available. Average estimates for each species were der ived from surveys during which a catch
was made. Maximum and minimum estimates fr om the 2 surveys were used to define confidence limits for model balancing. Deme rsal trawl-based estimates of non-demersal species were taken as minimum estimates if no other estimates were available for such species. Similarly, estimates using non-tr awl methods were emphasized for structureassociated fish species, since trawl esti mates in structured habitats were considered unrealiable (Darnell et al. 1987).

A 1978 trawl survey covering the whole West Florida Shelf region (Darcy and Guntherz 1984) was used to derive biomass estimates for various fish groups. These estimates were made by dividing the number of tonnes of each species captured by the defined area of 170,000 km 2 

### and also applying a trawl efficien cy factor of 0.25%, estimated by Gutherz in Klima (1976, also see Darcy and Gutherz 1984). Relative biomasses of each species were calculated based on the to tal trawl catch of 433,000 tonnes and the proportion of the total catch comprised by each species.

Fish biomass estimates were also calcul ated based on 12 surveys (433 trawl stations) conducted from late 1960s to early 1980s, covering Lousiana, Alabama and Florida
from the shore or barrier Is lands to the 120m isobath (Darne ll et al. 1987). All data from this study were standardized to a trawl size of 45 feet, a towing speed of 3 knots, and a towing time to 60 minutes. Numbers of fish of each species / family were converted to densities per km 2 

### using the swept area formulae prov ided by Darnell et al. (1987). Estimates of relative biomass per km 2 

### were derived by applying average weights of each species derived from st ate and Federal catches (Section 13) and from published sources. A trawl efficiency factor of 0. 25% (Darcy and Gutherz 1984) was applied here as well. Relative abundance of the top elev en species caught in trawls is shown in Figure 9.1Ecopath model of the West Florida Shelf: Volume II. Model construction



### 92 Atlantic

cutlassfish

Tomtate

Dusky flounder

Longspine

porgy Moray family Pigfish Spot Pinfish Scrawled

cowfish

Sand perch

Spottail pinfish

Figure 9.1. Relative abundance of top-eleven species (contributing >2% of total biomass) caught in coastal demersal trawl surveys on the West Florida Shelf (Darcy and Guntherz 1984; Darnell et al. 1987).



### Pelagic trawl surveys conducted during 1994 to 1999 by FMRI / FWC’s Fishery Independent Monitoring program were used to estimate biomasses of small coastal pelagic species. These surveys cover insh ore coastal waters and use a high opening
bottom trawl. Estimates of total bioma ss for each species up to 91m isobath was adjusted to account for a 50% trawl effici ency (Mahmoudi, pers. comm.) and multiplied by the area bounded by 91m isobath (50,915 km 2 

### ). These total abundance estimates were then divided by the to tal area defined for the West Florida Shelf (170,000 km 2 

### ) to estimate the average biomass dens ity on the West Florida Shelf.

Offshore survey data collected by the NMFS during 1988, 1990, 1992, 1995 and 1996
were used to biomass estimates of th ose species with predominantly offshore distributions (oceanic functional groups). NM FS trawl surveys were conducted in shelfbreak/offshore waters (around 200m isobath) using high opening bottom trawls directed to sample small pelagics in daytime aggregations near the s eabed. This NMFS
database included direct estimates of ca tch rate (relative biomass) per km 2 

### for each species in each year (for each area covered by the surv ey). Table 9.1 shows the most common species captured by the high opening bottom trawl. Catch rates were converted to estimates of total biomass per km 2 

### by applying a 50% trawl efficiency rate (Mahmoudi, pers. comm) and by using species di stribution maps (Darne ll et al. 1987) to determine the proportion of the defined ar ea occupied by each species.

Table 9.1. Some common shelf region species.

Common name Species % of total catch

Gulf butterfish Peprilus burti 20.710Ecopath model of the West Florida Shelf: Volume II. Model construction



### 93 Round scad Decapterus punctatus 11.297 Rough scad Trachurus lathami 11.186 Spanish sardine Sardinella aurita 10.764 Round herring Etrumeus teres 8.328 Longspine porgy Stenotomus caprinus 5.924

Chub mackerel Scomber japonicus 5.818 Spot Leiostomus xanthurus 5.651 Stingray genus Dasyatis spp. 1.790

Pinfish Lagodon rhomboides 1.580 Silver-rag Ariomma bondi 1.575

Sandbar shark Carcharhinus plumbeus 1.287

Atlantic cutlassfish Trichiurus lepturus 1.240

Tomtate Haemulon aurolineatum 1.234 Bigeye scad Selar crumenophthalmus 0.121

Tilefish Malacanthidae 0.006

Offshore hake Merluccius albidus 0.004 Striated argentine Argentina striata 0.002

Note: From NMFS trawl survey data.



### (ii) Stock Assessment models – Vi rtual Population Analysis (VPA)

Virtual Population Analysis (VPA ) estimates were available for king mackerel, Spanish mackerel, sheepshead, spotted seatr out, gag, menhaden, red snapper, red drum, snook, and mullet. VPA abundance estimates for red snapper and menhaden cover the whole Gulf of Mexico, and so only the fish ing mortality estimates used in the VPA’s
were used here to determine abundance on t he West Florida Sh elf using simple relationship Biomass=Catch/F (see next se ction). The same procedure was applied to
red drum and snook to derive biomass estima tes based on 1998 catches from the West
Florida Shelf. I include some ex amples of this approach here.

Biomass of adult Spanish mackerel (age 2+ ), king mackerel ( age 4+) and juvenile
mackerel were calculated based on biomass at age estimated from VPA (Legult 1998) (Table 9.2). Summing fishing mortality and natural mortality rates from the VPA provided estimates of total mortality, whic h also equals P/B (All en 1971). The mean value (weighted by biomass) for adults was 0.384; for juveniles, 0.434. These values probably underestimate production of juveni les because their mortality rates are probably much higher than those of adults (M=0.2 king mackerel, M=0.3 Spanish mackerel). We opted to assume a juvenile P/B two times that of the adult. The VPA stock assessments produce reasonably reliab le estimates of mackerel stocks.

Table 9.2. Stock size estimates for adult and juvenile king and Spanish mackerel.

Stock size (t) Species Adults Juvenile Spanish mackerel 14,195 3,206 King mackerel 16,861 17,939

Note: Based on VPA (Legult 1998).Ecopath model of the West Florida Shelf: Volume II. Model construction



### 94 The West Florida Shelf sheepshead popul ation was estimated to be 14,854,835 individuals using VPA (Murphy and McDonald 2000). Its biomass was estimated as 13,904 tonnes, or 0.082 t/km 2 

### , by dividing by the average weight from west Florida catches (0.936 kg). The current stock size of spotted seatrout in the northwest region (big bend sea grasses) is approximately 15 ,000,923 individuals (Murphy et al 1999).
The biomass is estimated to be 1,818 tonnes (Table 9.3). Biomass of gag was taken as 4,498 tonnes based on a recent NMFS stock assessment (Steve Atran memo to Wayne Swingle).

Table 9.3. Spotted seatrout numbers and weight at age for Northwest
Florida.

Numbers at age Weight at age (kgs) Age Male Female Male Female Biomass (t)

0 979665 2010975 0.236364 0.107438 447.6123

1 871312 2482720 0.363636 0.165289 727.2077

2 515085 588145 0.454545 0.206612 355.6471

3 254210 154265 0.531818 0.241736 172.4848

4 80796 72662 0.563636 0.256198 64.15545

5 32750 12354 0.718182 0.326446 27.55337

6 13995 5393 0.954545 0.433884 15.6988

7 6406 1.209091 0.549587 7.745436

Sum 1818.105

Biomass t/km 2 0.010695

Note: From 1998 VPA assessment (Murphy et al 1999).



### (iii) Estimation usi ng Biomass = Catch/ Fishing mortality

The basic catch equation (Baranov 1918) wa s used to derive rough estimates of
biomass on the West Florida Shelf for seve ral species. This equation assumes that catches are proportional to t he biomass present in the area (C=F/B). Our estimates are based on the simplistic assumption that st ocks are under the same fishing pressure throughout their WFS distribution. Our calc ulations may either overestimate or underestimate the biomas of highly migratory fish in the area, considering that fishing effort, stock biomass and catchability ar e not spatially homogeneous, and fishing mortality ought to vary between fishing areas . Fishing mortality values were derived
from various sources. The more reliable ones were taken from species-specific stock assessments covering the whole Gulf of Me xico. Table 9.4 presents derivations and

sources.

Table 9.4. Fish biomass estimates based on Catch/F.

Common name Species name F Reference Catch (t) Biomass (t)

Swordfish Xiphias gladius 0.3 ICAAT 141.37 471.23

Blue marlin Makaira nigricans 0.2 Brown et al. 1991 6.6 a 33 bEcopath model of the West Florida Shelf: Volume II. Model construction



### 95 White marlin and spearfish Tetrapturus spp. 0.2 Brown et al. 1991 2.4 a 12.2 b

Sailfish Istiophorus platypterus 0.2 Brown et al. 1991 13.39 66.97 Dolphin fish Coryphaena hippurus 0.4 Brown et al. 1991 4074.38 10185.95

Thresher shark Alopias spp. 0.4 Brown et al. 1991 6.94 17.35 Longfin mako Isurus paucus 0.4 Brown et al. 1991 9.13 22.82 Sixgill shark Hexanchus griseus 0.4 Brown et al. 1991 0.032 0.08

Yellowfin tuna Thunnus albacares 0.5 ICAAT 300.96 601.92

Albacore Thunnus alalunga 0.5 ICAAT 0.10 0.19

Bluefin tuna Thunnus thynnus 0.23 ICAAT 0.38 1.65

Blackfin tuna Thunnus atlanticus 0.3 Brown et al.1991 504.90 1683.00 Bigeye tuna Thunnus obesus 0.35 ICAAT 2.66 7.61 Skipjack tuna Katsuwonus pelamis 0.2 Brown et al. 1991 54.87 274.36 Scalloped
hammerhead Sphyrna lewini 0.13 NMFS 1998 9.11 70.11

Great
hammerhead Sphyrna mokarran 0.13 NMFS 1998 12.71 97.80

Sandbar shark Carcharhinus plumbeus 0.093 NMFS 1998 473.31 5089.41 Dusky shark Carcharhinus obscurus 0.13 NMFS 1998 19.07 146.73

Blacknose shark 1 Carcharhinus acronotus 0.21 NMFS 1998 19.61 93.38 Spinner shark Carcharhinus brevipinna 0.13 NMFS 1998 0.30 2.29 Silky shark Carcharhinus falciformis 0.13 NMFS 1998 5.03 38.68 Blacktip shark Carcharhinus limbatus 0.166 NMFS 1998 91.30 550.01

Bull shark Carcharhinus leucas 0.13 NMFS 1998 48.49 372.97

Finetooth shark 1 Carcharhinus isodon 0.13 NMFS 1998 3.52 27.06 Atlantic sharpnose
shark 1 Rhizoprionodon
terraenovae 0.35 NMFS 1998 20.26 57.88

Tiger shark Galeocerdo cuvier 0.13 NMFS 1998 1.11 8.56

Lemon shark Negaprion brevirostris 0.13 NMFS 1998 0.59 4.55 Dogfish 1 Squalidae 0.06 NMFS 1998 0.01 0.12 Sand tiger 1 Odontaspis taurus 0.16 NMFS 1998 0.00 0.00

Wahoo Acanthocybium solandri 0.3 Brown et al. 1991 161.72 539.06 Little tunny Euthynnus alletterus 0.3 Brown et al. 1991 435.99 1453.29

Cobia Rachycentron canadum 0.3 Brown et al. 1991 493.18 1643.95 Crevalle jack Caranx hippos 0.4 ? 497.65 1244.13 Greater amberjack Seriola dumerili 0.2 Potts et al. 1998 699.73 3498.66

Red drum Sciaenops ocellatus 0.69 Goodyear 1996 (from VPA) 1623.98 2353.60

Black drum Pogonias cromis 0.4 Brown et al. 1991 133.89 334.72 Red snapper Lutjanus campechanus 0.402 Schirripa and Legult 1999 (from VPA) 891.38 2217.37

Snowy grouper Epinephelus niveatus 0.48 Potts et al. 1998 77.38 161.21

Great barracuda Sphyraena barracuda 0.2 Brown et al. 1991 248.90 1244.49

Jewfish Epinephelus itajara 0.7 Eklund 1994 0.00 0.00 Red grouper Epinephelus morio 0.26 Potts et al. 1998 3522.37 13547.56 Speckled hind Epinephelus drummondhayi 0.43 Potts et al. 1998 32.56 75.73

Scamp Mycteroperca phenax 0.18 Manooch et al. 1997 200.15 1111.97 Yellowtail snapper Ocyurus chrysurus 0.16 Brown et al. 1991 179.56 1122.28 Ladyfish Elops saurus 0.3 Brown et al. 1991 97.19 323.96

Common snook 2 Centropomus undecimalis 0.31 Muller 2000 (from VPA) 150.51 485.51

* Estimates using Catch (Landings+Discards) rather than landings alone.

1 Fishing mortality rate for small coastal sharks in the period 1986-1991 was estimated to be 89% ofEcopath model of the West Florida Shelf: Volume II. Model construction



### 96 Fmsy (NMFS, 1998b). Assuming that Fmsy ~ 0.6 M, the current fishing mortality rate of the individual species was considered to be approximately 0.5 M. Natural mortality rates were estimated using Pauly’s (1980) empirical equation.

2 F from 1998, recently F has declined

a Using catch data from NMFS (1998), and b assuming 1/5 th of total biomass is present in study area (see species descriptions section)



### (iv) Other estimates

For some species, multiple approaches were used to estimate biomass. For example, an acoustic assessment (Erhardt 1993) led to a Spanish mackerel biomass estimate (28,585 t) that was twic e the 1998 VPA estimate.

Another example is that, egg and larvae surv eys from plankton colle ctions made in the
eastern Gulf of Mexico from 1971-1974 led to alternative abundance estimates of round herring ( Etrumeus teres ) (Houde 1977). Later, Houde and Be rkley (1982), provided egg and larvae based estimates for several small pe lagic species; Atlantic thread herring ( Opisthonema oglinum ), Spanish sardine ( Sardinella aurita ), round scad ( Decapterus punctatus ), round herring ( Etrumeus teres ), rough scad ( Trachurus lathami ), and Atlantic bumper ( Chloroscombrus chrysurus ). The abundance estimates derived by these methods likely overestimated abundance, as the estimates did not account for batch spawning (Mahmoudi, pers. comm). Thes e estimates serve only as a reference
for other abundance estimates.

Menhaden ( Brevoortia sp. ) biomass on the West Florida Shelf (8,003 tonnes) was derived by assuming that the proportion of the over all population occurring in the West Florida Shelf region was equival ent to the proportion of the ov erall fisheries catch taken there (1.37% of the overall catch). The tota l menhaden catch in the Gulf of Mexico was 569,600 t in 1989, and the tota l catch in the WFS area wa s (7,800 t). The total 1997 estimate of spawning stock biomass (femal es only) in the Gulf States Alabama, Mississippi, Texas and Florida was 292,100 t (VPA; Vaughan et al. 1999). Assuming a 50:50 male:female ratio (Vaughan pers. comm), the total Gulf stock size of menhaden was estimated to be 584,200 t. This might great ly underestimate the West Florida Shelf population since it is based on ly on catches taken in t he Northeast Panhandle region (Mahmoudi, pers. comm.).

Brown et al (1991) provided Gu lf wide biomass estimates for a number of species in the Gulf of Mexico (Table 9.5). These estimate s of small pelagics are used here only to provide reference points and alternative estimates.

Table 9.5. Biomass estimates for small pelagics on the West Florida
Shelf.

Biomass (tonnes)

Common name Southeast Northeast Total t/km 2

Thread herring 240,000 50,000(a) 290,000 1.706

Round herring 380,000 10,000 390,000 2.294Ecopath model of the West Florida Shelf: Volume II. Model construction



### 97 Spanish sardines 250,000 100,000 350,000 2.059

Scaled sardine 185,000 20,000 205,000 1.206

Atlantic bumper 20,000(a) 20,000 0.118

Round scad 100,000 100,000(a) 200,000 1.176

Rough scad 20,000 20,000 0.118

Silver driftfish 6,000 6,000 0.035

Anchovies 10,000(a) 20,000(a) 30,000 0.176

Notes: From Brown et al. (1991): “ Coastal pelagic estimates for he

Eastern Gulf (south of 29 ° N) were rounded from Houde (1976), which were estimated from larval surveys. Coastal pelagic biomass

estimates for the Northeast Gulf (north of 29 ° N and east of 88 ° W) were obtained from unpublished data by C. Gledhill, NOAA/NMFS/SEFC, Pascagoula, MS. We assumed a trawl catchability coefficient of 0.25 and multiplied Gledhill’s cruise data by 4 to use in our estimates. Items indicated with an (a) have been adjusted upward from the original estimates. The upward adjustment was based on (1) landings statistics for the area, (2) stomach content analyses of predators in the area, (3) comments of cruise-data experts. ”



### For species with no reliable biomass estima tes, approximate biomass estimates were derived using catch data and average fishing mo rtality estimate from other species in the same functional group. If no substitute va lues of F were available, the catch was used as a minimum proxy estimate for the biomass. Approximate biomass estimates were preferred over no estimates because groups with no biomass would strongly bias parameters (e.g., production, consumption, and fi shing mortality) that were derived with biomass weighting.

Table 9.6 provides a summary of the relati ve biomass of West Florida Shelf species ranked by abundance estimates derived herein. To tal biomass of fish was estimated to be 3.5 million tonnes, with range 0.5 to 6.1 million tonnes. De tails of the best estimates for each species are presented with their sources in Appendix 7.

Table 9.6. Relative abundance of fish species on West Florida Shelf.

% Total
biomass % Total
biomass Common name
>1% Common name
0.5 to 1% Gulf butterfish 12.12 Littlehead porgy 0.98 Rough scad 6.55 Silver-rag 0.92 Round herring 5.16 Gray flounder 0.92 Dusky flounder 3.69 Bank sea bass 0.90

Chub mackerel 3.40 Round scad 0.89 Longspine porgy 2.40 Anchovies 0.87 Moray family 2.40 Atlantic croaker 0.81 Pigfish 2.36 Cownose ray 0.76

Pinfish 2.28 Hardhead catfish 0.76 Spot 2.12 Atlantic cutlassfish 0.73 Tomtate 1.97 Spanish sardine 0.67Ecopath model of the West Florida Shelf: Volume II. Model construction



### 98 Scrawled cowfish 1.93 Inshore lizardfish 0.67

Other mullets 1.55 Barbfish 0.62 Sand perch 1.49 White grunt 0.62 Spottail pinfish 1.43 Fringed filefish 0.60

Rock sea bass 1.07 Black sea bass 0.54 Other fish 18.77 Mojarras 0.54 King mackerel 0.51

Notes: Based on combination of biomass estimation methods. Only those whose relative abundance is greater than 0.5% are listed



### 9.3 Production (P/B) and Consumption (Q/B) estimates

For each functional group, weighted aver age production and consum ption parameter
estimates were calculated from estimate s of P/B and Q/B for each group member (species) weighted by their relative bioma ss in the group. From 264 species listed within the functional groups, individual estimate s of P/B and Q/B were made for 229 and 213 of them respectively. Where several alternat ive estimates existed, local (West Florida Shelf) and species specific values were chosen in preference ov er more generalized (less robust) estimates for initial model parameterization (see Section 3.3). Minimum
and maximum estimates were recorded in all cases for use as balancing limits (Table 9.10). The following sections detail the me thods and sources used to derive P/B and
Q/B estimates.

Production/ Biomass (P/B)

(i) Determined from total mortality (Z ) and natural mortality estimates (M)

Total mortality rate (Z) is equal to product ion rate (P/B), under the assumption of steady-state (Allen 1971). Estimates of tota l mortality were derived for a number of species by adding fishing mortality and natural mortality (Z=F+M) from VPA models or other sources (Appendix 6). Independent estimates of Z were gleaned from several species synopses; blue runner, 0.47 (Goodw in & Johnson 1986), round scad, 0.92 (Naughton et al. 1986), lane snapper, 0.68 (Manooch and Mason 1984), vermilion snapper, 0.48 (Manooch & Johnson 1998).

The P/B of small pelagic fish was re-che cked by calculating Z from mean length (Beverton and Holt 1956). This calculation relies on t he assumption of steady recruitment into the population:

Z=K(L

∞ 

### -L avg 

### )/( L

∞ 

### -L ′ )

L

∞ = asymptotic length (cm);

L avg = average length;

L ′ = cut-off length; K of the VonBertallanfy growth equationEcopath model of the West Florida Shelf: Volume II. Model construction



### 99 Estimates of natural mortalit y (M) for small pelagics were derived from an empirical formula described by Brey (1999), a modificati on of Pauly’s M formula, as an additional
cross-check.

Log(M) = 4.355-0.083log W

∞ 

### +6.390(W

∞ 

### /L

∞ 3 

### )+0.627log(K)+1190.43(1/T+273)

K of the VonBertallanfy growth equation; T= temperature in centigrade;

W

∞ = asymptotic weight (g);

L

∞ = asymptotic length (cm).



### Total mortality estimates were generally lowe r than original model estimates, supporting
a reduction in P/B to value closer to t he published value for round scad on the West Florida Shelf (Saloman and Naughton 1986). However, many P/B estimates were left questionable since empirical estimates of natur al mortality were al most double the total mortality estimates (Table 9.7).

Table 9.7. Comparison of P/B (Z) and natural mortality (M) estimates for small pelagics

Group Initial estimate (P/B) P/B (Z) from av. Length 1 M est. (Pauly’s M modified by Brey 1999) Atlantic thread herring 1.60 a 0.58 1.02
Scaled sardine 1.57 a 1.04 1.76 Spanish sardine 0.95 a 1.15 1.58
Menhadens 0.95 a 0.56 0.93
Round scad 0.92 b 0.66 1.12

1 based on assumption that the average length in the population is 2/3rds the asymptotic length and the length of 1st c apture (cut-off length) is 2/3r ds the average length in the population (assumes knife-edge selection).

a average values derived from other GoM models

b estimated total mortaility (Naughton & Saloman 1986)



### For unfished species (e.g. large oceanic pl anktivores), however, total mortality is equivalent to natural mortalit y (M). Values of M taken from Fishbase, were used as minimum proxy value of P/B for 20 specie s (generally unfished) for which no other
estimate could be derived.

(ii) Borrowed from other Gulf of Mexico models

In lieu of local direct estimate s, P/B values used in published Ecopath models from the Gulf of Mexico were compiled by specie s and their average values applied as rough
estimates for use in the West Florida Sh elf model. Where no specific values were available for the species included in the West Florida Shelf model, values from similar functional groups were taken. Thes e cases are shown in Appendix 7.

(iii) Intrinsic rate of increase, r

Under equilibrium assumptions, the intrinsic rate of increase, r, the maximum possible rate of growth given no cons traints, may correspond to the total mortality rate (=P/B).Ecopath model of the West Florida Shelf: Volume II. Model construction



### 100 The value of r, was used as an es timate of P/B for sandbar shark Carcharhinus plumbeus and blacktip shark C. limbatus.

Consumption/ Biomass (Q/B)

Three approaches were used to estimate c onsumption rates (Q/B) for West Florida Shelf fishes: (1) empirically-based re lationships between morphology, physical variables, and consumption rates, (2) specie s-specific measurements, and (3) estimates from other models. These appr oaches are described below.

(1) Estimates derived by empirically-based relationships

Palomares et al. (1998) presented the follo wing empirically-based equation describing how consumption rate can be predicted by a combination of size, temperature, tail aspect ratio (i.e., lifestyle / feeding ty pe), and qualitative diet information:

Log(Q/B) = 7.964-0.204logW

∞ 

### -1.965T ′ + 0.083A + 0.532h + 0.398d

W

∞ = asymptotic weight; from literature or calculated using maximum length and species specific lengthweight relationship;

T ′ = 1000/Kelvin (Kelvin= ° C +273.15); A = aspect ratio of caudal fin, h 2 /s , where h is the height and s is the surface area of the caudal fin, extending to the narrowest part of the caudal peduncle. Calculated for each species from photographs and drawings;
h = Herbivorous feeding ( h=1, d=0 ); d = Detritivorous feeding ( h=0, d=1 ); Carnivorous feeding ( h=0, d=0 ).



### Morphometric information and mean water temper ature of fish habi tat were compiled from FMRI (1998), NOAA (1997), FishBase (www.fishbase.org), and other individual species reports. Tail aspect ratios were sometimes calculated using the FishBase graphic caudal fin selection tool and applying a applying a water temperature of 22.2 ° C to the empirical equation calculator. WFS temperatures are shown in Table 9.8.

Table 9.8. Temperature data for West Florida Shelf

Average water temperature ( ° C) Depth zones Winter Summer Year round.

Surface 20.0 29.8 24.9
Bottom 17.0 22.0 19.5 Annual average temperature through the water column 22.2 ° C

Notes: From Gulf of Mexico dat a atlas (Berryhill 1977).



### (2) Species-specific measurements

Independent estimates of Q/B used in deriv ing empirical equations are given in Palomares and Pauly (1989, 1998). These values were used as alternative estimates, since many of them were not spec ific to the Gulf of Mexico.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 101 Other estimates were taken directly from Brown et al. (1991) who published a list of consumption per year as a propor tion of body weight (Q/B year -1 

### ) from other published sources. They made tw o general adjustments to original estimates: (1) they multiplied by 2 as an approximate conversi on from routine to field (Wi nberg 1960); (2) they divided by 0.65 to account for losses due to egestion, excretion, and specif ic dynamic action consumption was estimated were based on oxygen consumption (Kitchell et al. 1978).

Other independent estimates include those fo r sandbar and lemon sharks. Stillwell and Kohler (1992) estimated the daily ration of sandbar sharks, C. plumbeus, to be between 1.43 and 0.86% of mean body weight, leadi ng to annual food consumption (Q/B) estimates of between 5.1 and 3.1 year -1 

### . Daily ration of lemon sharks was estimated to range from 1.5 to 2.1% of body weight (Cortés and Grubber 1990), giving a Q/B of 5.5 to 7.7 year -1



### .

(3) Estimates from other Gulf of Mexico models

Q/B values used in pub lished Gulf of Mexico Ecopath models were compiled by species and their average values used as rough estimates, in cases were Q/B values were not
otherwise available. Wher e no specific values were available for the species included in the west shelf model, values from simila r functional groups were taken when speciesspecific values were not available (Appendix 7).

9.4 Fish diet compositions

Quantitative information on the di et of 171 of the 260 fish species was compiled from
detailed stomach content st udies and from general feeding habit studies. Composite diets for each functional group were derived by employing 3 steps:

(1) Weighting each species’ diet compos ition by their relative consumption;

For those species with diet information, but no consumption estimate (Q) due to lack of biomass or Q/B estimates, relative consumption =1. For all other species, relative consumption = [1 + Indi vidual consumption (Q i 

### = B i 

### *Q/B i 

### ) divided by total consumption of species within the group ( Σ i=1 to n 

### Q)].

(2) Summing the relative proportions of t he prey species within their respective functional group;

(3) Normalizing the diet proportions of each pr edator group to 1 by dividing each relative proportion of prey, by the total proportion of prey consumed.

No quantitative diet information was availabl e for large oceanic planktivores, pelagic oceanic piscivores, Pelagic oceanic jelly eater s, or benthic oceanic piscivores. The diets for these groups were constructed based on qualitative information, expert knowledge, and best judgment.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 102 Diet information from the West Florida Shelf and the Gulf of Mexico before were given priority before extending the search to other similar regions (Tabl e 9.9). Comprehensive local reports included the Hour glass memoirs, diet studies in Tampa bay (Motta et al. 1995, Peebles and Hopkins 1983) and life hi story summaries for Gulf of Mexico estuarine species (NOAA 1997 ). When several published diet s were available for a single species, the most local diet was us ed, or diet compositions were averaged.

More generalized diets were derived for sw ordfish (Nakamura 1985; NMFS 1998), tunas (Dragovich 1969; NMFS 1998), sharks (Cortés 1999; Smale 1991; Dudley and Cliff 1993; Cortés and Grubber 1990; Lowe et al. 1996; Co rtés et al. 1996) and rays (Stehmann et al. 1978) due to lack of specific local information. Randall’s (1967) work
on stomach contents of West I ndies reef fishes (% weight ) was the largest source of
diet data for the West Florida Shelf model.

Ninety percent of the striped mullet diet was specified as imported food, based on evidence that striped mullet do not generally feed during their residence on the West Florida Shelf (winter months during spawni ng season). Similarly, demersal coastal piscivores (seatrout) that are restrict ed to nearshore and seagrass habitat were
assumed to obtain 35% of food from outside the system.

Table 9.9. Diet information source s specific to the study region.

Species Reference

Marlin Davies and Bortone 1976 Sailfish Jolley Jr 1977; NMFS 1998 Dolphinfish Rose and Hassler 1974 King Mackerel (NW Florida) Saloman and Naughton 1983 Spanish mackerel (NW Florida) Saloman and Naughton 1983 Juvenile King and Spanish mackerel Finucane et al. 1990 Crevalle jack Saloman and Naughton 1984 Gag Naughton and Saloman 1985 Vermilion snapper Grimes 1979; Sedberry and Cueller 1993 Silver jenny Motta et al. 1995 Spotted seatrout Peebles and Hopkins 1983 Thread herring Vega-Cendejas et al. 1994
Saddle bass Bullock and Smith 1991
Black drum Overstreet and Heard 1982
Red drum Overstreet and Heard 1978 Lane snapper Bortone and Williams 1986 Fringed flounder Topp and Hoff 1972 Blue spotted searobin Ross 1983
Common snook NOAA 1997 Dolphin fish Rose and Hassler 1974
Marlin Davies and Bortone 1976 Sailfish Jolley Jr. 1977 and NMFS 1998
Sandbar shark Stillwell and Kohler 1992 Spanish sardine Wang and Qui 1986



### 9.5 Initial fish parameter estimatesEcopath model of the West Florida Shelf: Volume II. Model construction



### 103 Basic input parameter estimates for West Fl orida Shelf fish groups are shown in Table 9.10 with ranges. Appendix 7 lists sources of parameter estimates for each fish species.

Table 9.10. Best weighted estimates and rang es of parameters for WFS fish groups.

Biomass (t/km2) Q/B per year P/B per year Group Best Max Min Best Max Min Best Max Min

1 Whales and dolphins 40.86 40.86 40.86

2 Sea birds 80.00 80.00 80.00 3. 00 3.00 3.00

3 Turtles 3.50 3.50 3.50 0.15 0.15 0.15

4 Manatees 0.0008 36. 50 36.50 36.50 0. 10 0.10 0.10

5 Large oceanic piscivores 0.0788 0. 0774 0.0788 10.56 12.76 8.54 0.68 1.64 0.66

6 Large ocean planktivores 0.0000 0. 0000 0.0000 1.80 1.80 1.80 0.11 0.11 0.11

7 Coastal sharks 0.0381 0.0381 0.0381 3.29 4.10 3. 61 0.41 0.42 0.31

8 Rays and skates 0.2387 0.5683 0.0754 7.72 7.72 0. 37 0.38 0.38 7.43

9 Pelagic oceanic piscivores 0.1504 0. 8448 0.1505 25.54 25.54 25.54 1.06 1.06 1.06

10 Pelagic coastal piscivores 0.0974 0.1313 0.0361 11.53 14. 02 10.23 0.64 0.67 0.61

11 Mackerels adult 0.1832 0.2673 0.0234 9.49 25.70 8. 95 0.38 0.89 0.53

12 Juvenile mackerels 0.1244 0.124 4 0.1244 18.97 51.40 17.00 0.77 1.78 1.06

13 Sardine/Herring 0.4570 6.0765 0.0328 12.11 12.58 10. 49 1.18 1.18 1.05

14 PelOceJelly/eaters 2.6929 2.692 9 0.1855 8.07 23.78 8.08 1.59 1.59 1.56

15 PelOcePlanktivores 3.1387 4.5586 0.0762 11.78 12.97 11. 71 0.87 0.87 0.83

16 DemOceInvert/eaters 0.041 0 0.0840 0.0409 15.76 15.76 15.76 2.17 2.17 2.17

17 DemCoasPisc 0.0671 0.1262 0. 0303 6.33 6.75 6. 24 0.64 0.64 0.64

18 DemCoasInvert/eaters 2.7134 5.2996 0.2444 8.06 9.15 7.92 0.65 0.65 0.57

19 DemCoasOmniv 1.4289 2.0358 0. 2708 15.13 15.04 10. 71 1.60 1.60 1.34

20 BentOcePisc 0.1089 0.2175 0.0550 7.94 8.86 7.94 0.30 0.75 0.30

21 BentOceInvert/eaters 0.121 0 0.1480 0.0550 15.78 15.78 15.79 2.24 2.24 2.44

22 BentCoasPisc 0.2621 0.3684 0. 0280 8.39 8.38 8. 34 0.30 0.30 0.30

23 BentCoasInvert/eater s 1.6686 2.3294 0.4364 12. 21 12.20 10.11 1. 16 1.23 0.86

24 SurfacePelagics 0.0003 0.0003 0.0003 11.70 96.73 11.70 2.60 2.60 2.6

25 StrucAssCoasPisc 0.7221 1.0376 0. 2243 7.81 8.60 5. 40 0.63 0.63 0.55

26 LgGroupers 0.1193 0.1493 0.030 6 4.10 4.79 2.59 0.458 0.47 0.40

27 StrucAssCoasInvert/eaters 1.3453 1. 9691 0.1925 8.34 8.36 7.33 0.75 0.77 0.54

28 StrucAssCoasOmniv 0.0605 0.0657 0.0350 29.15 29.25 24. 37 1.33 1.33 1.32

29 StrucAssCoasPlank 0.0026 0.0026 0.0026 10.00 10.00 10.00 2.60 2.60 2.6

30 NearshAssPisc 0.0048 0.0048 0. 0048 7.67 17.25 7. 67 1.06 1.06 1.06

31 Mullets 0.3292 0.3292 0.000 6 11.03 11.03 10.37 0.70 0.90 0.79

32 NearshPlanktivores 0.1811 0.1857 0.0097 15.91 16. 42 14.54 0.60 1.32 0.60

33 Other fishes 3.8768 5.8063 0.2709 7.04 7.04 7.04 0.70 0.70 0.70

Notes: Best estimates were used as initial input to the model. Values between minimum and maximum were used in the model during balancing (Table 3.2). Values in bold are highly uncertain.



### Input parameter confidence is influenced by several fact ors. Trawl sampling, for example, under-samples certain species due to inter-specific variability in behavior andEcopath model of the West Florida Shelf: Volume II. Model construction



### 104 preferred habitat. Place-holder values are used for other species due to limited information. The highly uncertain estimates are highlighted in bold in Table 9.10, and
these uncertain cases are discussed below.

10. Pelagic coastal piscivores : Large uncertainty in biomass for this group could have a large effect on prey groups in the model. It was assumed during m odel balancing that this group’s biomass must be hi gher the original estimate because this group comprises many species whose combined biomass is likely to be great er than the biomass of adult mackerel, upon which this estimate was based.
16. Demersal oceanic invert eaters : This group contains only 2 species, goatfish and blackmouth bass, for which there are no good parameter estimates. The initial (borrowed) P/B estimate was likely too hi gh. In the future, these species could be included in another group. 20. Benthic oceanic piscivores (largescale lizardfish, shor tjaw lizardfish, offshore lizardfish, duckbill eels): T hese species might be consi derably more abundant on the
West Florida Shelf than the tr awl estimates suggest. This group has a strong effect on
the demersal invertebrate eaters and invert ebrate functional groups upon which they feed. Incorrect parameterizat ion of this group thus indirectly influences the thermodynamic ‘balance’ of thes e prey groups in the model.
21. Benthic oceanic invert eaters (pancake batfish, spinycheek scorpionfish, slender searobin, shortwing searobin, saddle bass, t ilefish family, luminous hake): Very little information is available on any species in th is functional group. The best estimate P/B was considered too to high and was c onsiderably reduced during balancing. 24. Surafce pelagics (Halfbeaks and flying fish): Thes e species were not well sampled by any of the gear used to in abundance esti mation. However, these species are found in the gut contents of many West Floric a shelf fishes, suggesting much higher abundances than estimated. This group’s biomass was estimated by Ecopath during balancing. 29. Structure associated coastal planktivores (twospot cardinalfish, sponge cardinal fish, purple reeffish, yellowtail reeffish, blue chromi s, jawfish): All basic parameters in this group are poorly estimated due to limited information. 32. Nearshore associated planktivores : (bay anchovy, striped anchovy, silverside family, alewife). Anchovies are not well sampled by the methods used in this system, but gut contents of other predators indicate a high bi omass for this group. This group’s biomass was estimated by Ecopath during balancing.
33. Other fishes (all varieties including, flounders, soles, toadfish, spikefishes, boxfishes, cowfish, goosefish, dragonets, st argazers, temperate basses, brotulas, pearlfish, clingfish, codlets, argentines, sculpins, pipefish and seahorse): Biomass of the ‘other fishes’ group is simply the remainder of the total biomass estimated from trawl surveys after all other species were accoun ted for. Placeholder P/B and Q/B estimates fall mid-way between piscivore s and invertebrate eaters.

Literature cited (Fishes)

Allen, R.R. 1971. Relation between production and bi omass. J. Fish. Res. Board Can. 28:1573-1581. Baranov, F.I. 1918. On the question of the biological basis of fisheries. Nauch. issledov. ikhtiol. Inst. Isv.
1:81-128. Berryhill, H.L., 1977. US DOC, NOAA, National Sa tellite, Data and Information Service, 1982.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 105 Beverton, R.J.H and S.J. Holt. 1956. A review of met hods for estimating mortality rates in exploited fish populations, with special reference to sources of bias in catch sampling. Rapp. P.-v. Réun. CIEM,
140:67-83. Bortone, S.A., and J.L. Williams. 1986. Species profil es: life histories and envir onmental requirements of coastal fishes and invertebrates (South Florida) - gray, lane, mutton, and yellowtail snappers. U.S. Fish and Wildl. Serv. Biol. Rep. 82(11.52). U. S. Army Corps of Engineers, TR EL-82-4. 18 pp. Brey, T. 1999. A collection of empirical relations for use in Ecological modelling. Naga, The ICLARM Fishbyte quarterly. Vol. 22, (3), July-September 1999. Brown, B.E., J.A. Browder, J. Powers and C.D. Goodyear. 1991. Biomass, yield models, and management strategies for the Gulf of Mexico ecosystem. P125-163 In: Sherman, K., L.M. Alexander and B.D. Gold (eds.). 1991. Food chai ns, yields, models and management of large marine ecosystems. Westview Press, San Francisco. Bullock, L.H., and G.B. Smith. 1991. Seabasses (Pisce s: Serranidae). Memoirs of the Hourglass Cruises Vol. VIII, Part II. Cortés, E. 1999. Standardized diet compositions and trophic levels of sharks. ICES J. Mar Sci. 56:707-

717. Cortés, E. and S. H. Gruber. 1990. Diet, feeding habits and estimates of daily ration of young lemon sharks, Negaprion brevirostris (Poey). Copeia 1:204-218. Cortés, E.; Manire, C. A. and R. E. Hueter. 1996. Diet, feeding habits, and diel feeding chronology of the bonnethead shark, Sphyrna tiburo , in Southwest Florida. Bulletin of Marine Science, 58(2):353-367. Darcy, G. H. and E. J. Guntherz. 1984. Abundance and density of demersal fishes on the West Florida Shelf, January 1978. Bull. Mar. Sci. 34(1):81-105. Darnell, R. M., J. A. Kleypas and R. E. Defenbough. 1987. Eastern gulf shelf bio-atlas: A study of demersal fishes and penaeid shrimp of soft bottoms of the continental shel f from the Mississippi River Delta to the Florida Keys. U.S. Department of the Interior/Minerals Management Service, New Orleans Louisiana. 548 pp. Davies and Bortone (1976). Partial food list of 3 spec ies of Istiophoridae Pisces from the northeastern Gulf of Mexico. Fla. Sci. 39 (4) 249-253. Dragovich, A. 1969. A review of studies of tuna food in the Atlantic Ocean. Unites States Department of the Interior – special scientific repo rt-fisheries No. 593. Pages 1-21. Dudley, S. F. J. and G. Cliff. 1993. S harks caught in the protective gill nets off natal, South Africa. 7. The blacktip shark, Carcharhinus limbatus , (Valneciennes). S. Afr. J. Mar. Sci., 13:237-254. Eklund, A.M. 1994. Status of the stock of Nassau grouper ( Epinephelus striatus ) and jewfish ( Epinephelus itajara ). Ahdoc finfish stock assessment panel briefing book. 1998.
Erhardt 1993. Acoustic stock asse ssment of Spanish mackerel Finucane, J.H., Grimes, C.B. and S.P. Naughton. 1990. Diets of young king and spanish mackerel off the
southwst Unites States. Northea st Gulf Science 11(2):145-153.
FMRI 1998. Status and Trends on Florida Fisheries 1998. Goodyear, C.P. 1996. Status of t he red drum stocks of the Gulf of Mexico. NMFS-SEFSC, Miami Lab.
MIA 95/96-47. Grimes, C.B. 1979. Diet and feeding Ecology of t he vermilion snapper, Rhomboplites aurorubens (Cuvier) from North Carolina and South Carolina Waters. Bull. Mar. Sci. 29(1) 53-61. Houde, E. 1976. Abundance and potential for fisheries development of some sardine-like fishes in the
eastern Gulf of Mexico. Proc. Gulf Carib. Fish. Inst. 28:73-82. Houde, E. 1977. Abundance and potential yield of t he round herring, Etrumeus teres, and aspects of its early life history in the eastern Gulf of Mexico. Fishery Bulletin 75 (1): 61-89. Houde, E. and S.A. Berkeley. 1982. Gulf of Mexico “c oastal herrings”. South east fisheries centre, stock assessment group. Jolley Jr., J. W. 1977. The biology and fishery of Atlantic sailfish Istiophorus platypterus , from Southeast Florida. Florida Marine Research Publications, 28, 31 p. Kitchell, J.F., W.H. Neill, A.E. Di zon, and J.J. Magnuson. 1978. Bioene rgetic spectra of skipjack and yellowfin tunas. Pp. 357-368 In G.D Sharp and A. E. Dizon (Eds.). The physiological ecology of tunas. Academic press, New York. Legult,C.M. 1998. Stock assessment analysis on Atlant ic migratory group King mackerel, Gulf of Mexico migratory group King mackerel, Atlantic migrator y group Spanish mackerel, and Gulf of MexicoEcopath model of the West Florida Shelf: Volume II. Model construction



### 106 migratory group Spanish mackerel. Prepared for the mackerel stock assessment panel meeting,
March 23-27th 1998. MASP/98/09. Lowe, C. G.; Wetherbee, B. M.; Crow, G. L. and A. L. Tester. 1996. Ontogenetic dietary shifts and feeding behavior of the tiger shark, Galeocerdo cuvier , in Hawaian waters. Environmental Biology of Fishes
47:203-211. Manooch, C.S. III, and D.L. Mason. 1984. Age, grow th, and mortality of lane snapper from southern
Florida. Northeast Gulf Science 7:109-115. Manooch,C.S., Potts, J.C., Burton, M.L., Harris, P.J. 1997. Popultaion assessment of the scamp, Myctoperca phenax , from the south-eastern US. South Atl antic fishery management council 1997. Motta, P.J., K.B. Clifton, P.Hernandez, B.T. E ggold, S.D. Giordano and R. Wilcox. 1995. Feeding relationships among nine species of seagrass fish es of Tampa Bay, Florida. Bull. Mar. Sci. 56(1):185-200. Muller, R. 2000. Stock assessment of common snook. FMRI report. Murphy, M and T. McDonald, 2000. A stock assessment of sheepshead. FMRI report Murphy, M., G. Nelson, R.Muller. 1999. An update on the stock assessment of spotted seatrout, Cynoscion nebulosus . Florida fish and Wildlife conservation commision. Florida Marine Research Institute. St. Petersburg, Florida. Nakamura, I., 1985. FAO species catal ogue. Vol. 5. Billfishes of the world. An annotat ed and illustrated catalogue of marlins, sailfishes, spearfishes an d swordfishes known to date. FAO Fish. Synop. 5(125):65 p. Naughton, S.P. and C.H. Salo man. 1985. Food of gag ( Myctoperca microplepis ) from north Carolina and three areas of Florida. NOAA technical memo NMFS-SEFC-160. 37 p. Naughtonm S.P., C.H. Saloman and Vaught, R. N. 1986. Species profile of round scad, Decapterus punctatus . NOAA tech memo NMFS-SEFC-181. 39 p. NMFS, 1998 (sharks) Draft amendment 1 to ... October 1998. NMFS, 1998b (sharks, paper with modelling numbers)
NOAA. 1997. Distribution and abundance of fishes and inve rtebrates in Gulf of Mexico Estuaries. Vol II: species life history summaries. NOAA’s Estuarine Living Marine Resources Program. 377p. Overstreet, R.M. and Heard, R.W. 1978. Food of the red drum, Sciaenops ocellata , from Mississippi sound. Gulf Res. Rep. 6(2):131-135. Overstreet, R.M. and Heard, R.W. 19 82. Food contents of 6 commercial fishes from Mississippi sound. Gulf Res. Rep., 7(2) 137-149. Palomares, M.L. and D. Pauly. 1989. A multiple regr ession model for predicting the food consumption of marine fish populations. Aust. J. Mar. Freshwat. Res. 40:259-273. Palomares, M.L. and D. Pauly. 1998. Predicting food consumption of fish populations as functions of mortality, food type, morphometrics, temperature and salinity. Mar. Freshwater Res. 49: 447-453. Parker, R. O., Jr., D. R. Colby, and T. D. Willis. 1983. Estimated amount of reef habitat on a portion of the
South Atlantic and the Gulf of Mexico continent al shelf. Bulletin of Marine Science 33:935-940. Pauly, D. 1980. On the interrelationships betw een natural mortality, growth parameters, and mean environmental temperature in 175 fish stoc ks. J. Cons. int. Explor. Mer 39: 175-192. Peebles, E.B. and Hopkins, T. 1993. Feeding habits of eight fish species from Tampa Bay with observations on opportunistic predation. Final repo rt for the Florida inshore habitat trophic analysis program submitted to the Florid Research Institute. Potts, J.C., Burton, M.L. and Manooch, C.S. 1998. Trends in catch data and estimated static SPR vlaues for 15 species of reef fish landed along the South- eastern US. South Atlant ic Fisheries Management
Council Feb 1998. Randall, J. E. 1967. Food habits of reef fishes in the West Indies. Studies in Tropical Oceanography (Miami) 5:665-847. Rose and Hassler. 1974. Don’t have full reference – diet of dolphin fish, Cryphaena hypurus . Trans. Amer. Fish. Soc. 1, pages ? Ross, S.T. 1983. Memoirs of the hourglass cruise. S earobins (Pisces: Triglidae). Florida department of natural resources, marine research laboratory. Saloman, C.H. and S.P. Naughton. 1983. Food of Spanish mackerel, Scomberomorus maculatus , from
the Gulf of Mexico and southeastern seaboard ofthe United States. NOAA Technical Memo NMFSSEFC-128.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 107 Saloman, C.H. and Naughton. S.P. 1984. Food of the crevalle jack ( Caranx hippos ) from Florida, Louisiana, and Texas. NOAA Technical Memo NMFS-SEFC-134. Schirripa, M.J., and C.M. Legault. 1999. Status of the red snapper stocks of the Gulf of Mexico: updated through 1998. Report from the NMFS-SEFSC Sust ainable Fisheries Division, Miami, Contibution:
SFD-99/00-75. Sedberry, G.R. and Cueller, N. 1993. Planktonic and benthic feeding by the reef-associated vermilion

snapper, Rhomboplites aurorubens (Teleostei, Lutjanidae). Fishery Bulletin 91:699-709. Smale, M. J. 1991. Occurrence and feeding of three sharks species , Carcharhinus brachyurus, C.
obscurus and Sphyrna zygaena , on the Eastern Cape coast of South Africa. S. Afr. J. Mar. Sci.: 31-

42. South Atlantic Fisheries Management Council. 1999. Stock assessment and fishery evaluation report for the shrimp fishery in the south Atlantic region. Volume 1, September, 1999, 92 pp. Stehmann, M., J.D. McEachran and R. Vergara R., 1 978. Dasyatidae. In W. Fi scher (ed.) FAO species identification sheets for fishery purposes. Western Centra l Atlantic (Fishing Area 31). Vol. 1. [pag. var.]. FAO, Rome. Stillwell, C. E. and Kohler, N. E. 1992. Food habits of the sandbar shark Carcharhinus plumbeus off the US northwest coast, with estimates of daily ration. Fisheries Bulletin, US.91: 138-150. Topp, R.W. and Hoff, F.H. 19 72. Memoires of the hour glass cruises. Flatfishes (Pleuronectiformes). Marine Research Laboratory, Florida Department of Natural Resources. St. Petersburg Florida. Vol IV (II):1-135. Vaughan, D.S. Smith,J.W. and M. H. Prager. 1999. Population char actersitics of Gulf menhaden (Brevoortia patronus). NMFS Beuafort Lab, North Carolina. Technical report. Vega-Cendejas, M.E, M. Hernández a nd F. Arreguín-Sánchez. 1994. Trophic interrelations in a beach seine fishery from the northwestern coast of the Yacutan peninsula, Mexico. J. Fish. Biol. 44: 647-
659. Wang, J and S.-Y. Qui. 1986. On the feeding habit of Sardinella aurita in south Fujian-east Guangdong coastal waters. Journal of oceanography in Taiwan Strait/ Taiwan Haixia. Xiamen 1986 vol 5 (1):86-
93. Winberg, G.G. 1960. Rate of metabolism and food require ments of fishes. Fish. Res. Bd. Can. Translation
series No. 194.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 108 

### 10. S EABIRDS

Laura Vidal-Hernandez Departamento de Recursos del Mar CINVESTAV- Mérida, Mexico

Stephen Nesbitt
Florida Fish and Wildlife Conservation Commission Wildlife Research Laboratory



### One hundred and three seabird s pecies were reported for the Southwest Florida region by Owre (1990). Seabirds of this region fe ed on a variety of biot a ranging from small invertebrates (e.g. Phalaropes) to primarily fishes (e.g. Pelicans, Cormorants, etc.),
while others combine fishes and invertebrates in thei r diets (e.g. Storm-Petrels, Tropicbirds, etc). Here, we have included only the species that primary consume bait
fish because detailed abundance and diet in formation is rare. Birds that consume baitfish include Pelicans, Terns, Gulls, Fr igatebirds, Loons, Gannet s, and Cormorants. Of these, Brown Pelicans, Northern G annets, and Double-crested Cormorants are notably abundant (Table 10.1)

Table 10.1. Abundance of bait fish consumi ng seabirds in the Gulf coast of Florida.

Species Occurrence Numbers Body weight (kg) Common Loon Oct- April 4,000 4.00 Brown Pelicans year round 17,000 3.4 White Pelicans September - April 4000 7.0 Magnificent Frigatebird April- October 10,000 1.50 Northern Gannet November-May 15,000 3.00 Double-crested Cormorant year round 20,000 1.70 Misc. Terns and gulls year round 20,000 0.30 Red-breasted merganser, ducks and grebes October-April 10,000 1.00

Note: From Dunning 1993.



### These abundance estimates were used to derive an annual seabir d biomass of 0.001

t

⋅ km -2 

### on the West Florida Shelf. The P/B estimate (0.1 year -1 

### ) was taken from an Ecopath model in the Florida Bay (Acosta et al. 1998); these authors based their P/B estimate on an empirically derived value for seabird mortality in Florida waters. A weighted Q/B was calculated using the empiri cal formula to calculate seabird food consumption per day from Nilsson et al . (1976) and the biomass of each species in the group. These parameters ar e shown in Table 10.2.

Food consumption was estimated with the following formula:

log R = -0.293 + 0.85 * log W

where: R = food consumption per day, in grams W = body weight, also in gramsEcopath model of the West Florida Shelf: Volume II. Model construction



### 109 Table 10.2. Seabird parameters for the West Florida Shelf model.

Species Daily ration (kg) Annual biomass (t) Q/B

w

Common Loon 0.147 0.00005 3.0

Brown Pelicans 0.150 0.00034 19.2

White Pelicans 0.135 0.00010 5.1 Magnificent Frigatebird 0.170 0.00005 3.3

Northern Gannet 0.153 0.00015 8.9

Double-crested Cormorant 0.167 0.00020 12.6 Misc. Terns and gulls 0.216 0.00004 2.9 Red-breasted Merganser, ducks and grebes 0.181 0.00003 2.3

Total Seabirds 0.001 57.3



### Brown Pelicans were estimated to hav e the highest biomass (38%) and total consumption among the bird spec ies scrutinized; and their diet was used as a starting point for estimating the diet composition of birds in t he West Florida Shelf model

. This
diet was refined with the diet information of coastal birds collected by Browder (NMFS, pers. com.) in the South Florida region. Foga rty et al. (1981) characterized the diet of nesting brown pelicans in Florida. They found that the major prey item, by frequency of occurrence were clupeids (30%), sciaenids (21% ), Atlantic threadfins (13%), and mullet (12%). Barret et al . (1993) showed that seabirds also consumed crustaceans, cephalopods from the upper and mid water co lumn, juvenile demersals, inshore benthic fishes, fish carcasses, and discards. The West Florida Shelf seabird diets were modified
based on this additional information.

Literature cited (Seabirds)

Acosta, A., T.Dunmire, and J. Venier. 1998. A prelimin ary trophic model of the fish communities of Florida Bay. Proc. 1998 Florida Bay Science Conference . University of Miami, Miami, Fl USA. Barrett, R. T., Becker, P. H., Furness, R. W., Hunt, G. L., Latrouite, D., Montevecchi, W. A., Olsen, B., Skov, H., Tasker, M. L., and Wright, P. J., 1993. Report of the study group on seabirds/fish
interactions. International Council for the Exploration of the Sea, Copenhagen. Dunning, J. B. 1993. CRC Handbook of Avian Body Masses. CRC Pre ss, Inc. Boca Raton, FL. 371 pp. Fogarty, M. J., S. A. Nesbitt, and C. R. Gilbert. 1981. Di et of nesting brown pelican s in Florida. Fla. Field
Nat. 9:38-40. Nilsson, S.G. and I.N. Nilsson. 1976. Number, food and consumption, and fish predation by birds in Lake Mockeln, Southern Sweden. Ornis. Scand. 7:61-70.
Owre O.T. 1990. Coastal and Marine Birds. In: Cont inental Shelf Associates Inc., ed. Synthesis of available biological, geological, chemical, socioecono mic, and cultural resource information for the South Florida area, MMS U.S. Department of In terior Minerals Management Service Atlantic OCS Region, Chapter 9, p. 303-351.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 110 

### 11. S EA 

### T URTLES

Marcelo Vasconcellos Departamento de Oceanografia, Universidade do Rio Grande



### Three sea turtle species nest regularly on Florida’s beac hes: the loggerhead, Caretta caretta; the green, Chelonia mydas; and the leatherback, Dermochelys coriacea . Two other species, the hawksbill, Eretmochelys imbricate; and Kemp’s ridley, Lepidochelys kempii , occur throughout Gulf of Mexico coas tal waters, bays, and lagoons. These habitats are used as nursery and developm ent areas by juveniles and subadults. Sea turtles come to Florida’s nesting and rear ing areas from feeding grounds scattered throughout the Atlantic. Along Florida’s Gulf c oast, most nesting occurs from Pinellas to
Monroe Counties. Sea turtles hav e not been intentionally har vested in Florida since the 1970s, but they are often caught incidentaly by haul seines, gill nets, shrimp trawls, and pound nets. Most information about west Florida sea turtles in this chapter was taken from Van Meter (1992).

About 14,000 female loggerheads nest in the Southeastern US annually, and this species accounts for ca. 97.9% of the total s ea turtle nesting activity in Florida state (Plotkin, 1995). Florida’s west coast is also a major feeding area for non-nesting loggerheads. Habitat on the shelf is limited by the turtle diving ability to feed on bottomdwelling organisms (logger heads can dive as deep as 50 to 70 m); in southeast Florida, loggerheads are more abundant inside the 50 m isobath (Fritts et al., 1983). Loggerheads forage along the inshore and coasta l waters of the Gulf of Mexico, the Florida Keys and north all the way to New England. Nesting females remain in shallow areas near beaches during the nesting season. They then disperse to feeding grounds throughout the Bahamas, Cuba, Dominican Repl ubic, north along the eastern US coast and south through the Florida Keys and Gulf of Mexico (late Ap ril to September). Florida bay is a developmental habita t for loggerheads originating from Southeast US beaches (Schroeder et al. 1998).

Green turtles nest in Florida from June to late September. Between 60 and 800 green turtles nests are reported each year on Flor ida’s east coast (Plotkin, 1995). Immature green turtles have been reported along the west coast, in Fl orida Bay and in the Cedar Key / Crystal river area, indicating the im portance of these areas as developmental habitat (Schroeder et al., 1998). Green turtles prefer shallow, sandy flats covered with seagrasses or seaweeds.

The leatherback is the largest sea turtle species, weighing 700 to 2,000 pounds and measuring 4 to 8 feet in length as adults. This species inhabitats shallow waters along the northern Gulf of Mexico, the east and west coasts of Florida, and north through coastal New England. Global abundance estima tes of breeding female leatherbacks range from 70,000 to 115,000, and the number of nests repor ted in Florida ranges from 39 to 188 since 1979 (Plotkin, 1995). Leatherbacks nest in Florida from April through July, but they are observed along Florida’ s west coast during all months except February and April (Fritts et al., 1983). Sight ings in southwest Florida have been concentrated between 27 and 166 km from shore (Fritts et al., 1983).Ecopath model of the West Florida Shelf: Volume II. Model construction



### 111 The hawksbill is a small to me dium sized turtle observed re gularly in the Florida Keys. Posthatchlings occupy the pelagic environm ent, taking shelter in weedlines that accumulate at convergence points. They re- enter coastal waters when they reach 20-25 cm carapace length. Coral r eefs are the main foraging habitat of juveniles and adults, which feed primarily on sponges on these reef s. Hawksbills are also known to inhabit mangrove-fringed bays and estuaries where coral reefs are absent.

The Kemp’s Ridley is the rarest and most endangered sea turtle. Adults are found in productive coastal and estuarine waters of the Gulf of Mexico, particularly near the Mississippi River mouth. Juvenile and su badults are widely distributed through US coastal waters from Texas to Maine, but the west Florida coast is the area of ‘maximum abundance’ of Kemp’s Ridley in the U.S. St randing and capture records indicate that Kemp Ridley’s were most often encountered in west Florida in the late spring and summer. This temporal pattern is likel y due to migration or winter dormancy.

Biomass and Production / Biomass

Sea turtles are monitored by their nesting activity along the beaches of Florida. However, the number of nesting females is not an appropriate index of turtle numbers feeding on the shelf because females probabl y do not eat during the reproductive season, and because feedi ng areas are usually distinct fr om nesting areas (A. Meylan,

pers. comm .). I used three sources of informati on to estimate number of foraging
individuals on the West Flori da Shelf: 1) NMFS stranding dat a; 2) turtle counts made by aerial surveys by the Mineral Management Survey; 3) turtle bycatch by shrimp trawlers.

Stranding reports of dead or in jured turtles represent a minimum mortality estimate. Strandings provide an indication of the characteristics of populations in the area, such as species composition and relative ab undance, size and seasonal changes (most stranded turtles have boat related injuries). Table 11.1 shows the number of strandings by species along the west Florida coastline. Loggerheads are most commonly stranded (ca. 54% of total), followed by Kemp’s Ridley (ca. 22%) and Green (16%). Leatherback
and Hawksbill turtles account for less t han 2% of strandings. Henwood and Stuntz (1987) also found a low frequency of occurrenc e of leatherbacks and hawksbill turtles in shrimp trawlers bycatch. They calculated the turtle catch per unit of effort (CPUE) for the three dominant species fr om 1973 to 1984 (Table 11.2) (# of turtles per 30.5 m net headrope length per hour fi shing). I used CPUE to roughly estimate turtle densities by calculating number of turtle s per given swept area. Th e swept area of a trawl net hour was calculated assuming a tra wling speed of ca. 2.5 knots. Based on the information available, I estimated t he density of loggerheads, the mo st abundant species over the Gulf shelf, to be 0.0326 turtles·km 2



### .

Table 11.1. Sea turtle strandings reported in West Florida counties between 1990 and 1999.

Species Caretta

caretta Chelonia mydas Lepidochelys kempii Eretmochelys
imbricata Dermochelys
coriacea Unindentified to species Total

West coast 1094 337 446 28 33 102 2040 Percentage 53.65 16.51 21.86 1.37 1.61 5.00 100Ecopath model of the West Florida Shelf: Volume II. Model construction



### 112 Note: Data from Florida Fish and Wildlife Conservation Commission, Florida Marine Research Institute, Sea Turtle Stranding and Salvage Network Database.

Table 11.2. CPUE and density of sea turtles estimated from shrimp bycatch data in
the Gulf shelf.

Species/area CPUE (turtles/net hour) Density a (turtles/km 2 ) Loggerhead , Eastern Gulf 0.0046 0.0326 Loggerhead , overall Gulf 0.0025 0.0177 Kemp’s ridley , overall Gulf 0.0004 0.0028
Green , overall Gulf 0.0001 0.0007 All turtles,
Gulf shelf from Florida to Texas 0.0031 0.0220

Notes: From Henwood and Stuntz, 1987; a swept area estimates: one trawl net hour is equivalent to 0.1412 km 2

.



### Fritts et al. (1983) reported t hat densities of logger heads in this area ranged from 0.031
to 0.33 turtles/km 2 

### . Their data were obtained duri ng bimonthly surveys over the
southwestern Florida s helf (total area 24,642 km 2 

### ) between May 1980 and April 1981. Density estimates were possible for loggeheads only, since the other two species most frequently sighted, green and Kemp’s, were observe d only a few times. Similarly, Mullin and Hoggard (2000) estimated loggerhea d densities to be 0.0407 turtles/km 2 

### in the Florida panhandle region (waters <100m) and in eastern Gulf s helf slope waters (100 – 2000 m) based on aerial surveys from 1996 to 1998. Estimates of densities of leatherback and Kemp’s ridley turtle s were 0.00194 an d 0.00097 turtles/km 2



### , respectively. Leatherbacks and loggerheads were t he only turtle species sighted in the shelf slope area. The overall density of leatherbacks and logger heads in slope waters
was 0.00238 and 0.002 turtles/km 2 

### , respectivelly.

Given the three independent estimates above, I estimate the density of loggerheads on
the West Florida Shelf to be about 0.03 to 0.04 turtles/km 2 

### . The total density of sea turtles in the study area was calculated by extrapolating the log gerhead density to the other species using the stranding data as a measure of the rela tive abundance (Table 11.1). The sea turtle biomass estimate (0.0069 tonnes/km2) was then calculated by multiplying species densitie s by mean body weight. A logger head production to biomass ratio was estimated as 0.22 year -1 

### , assuming an adult annua l survivorship of 80% (Henry et al. 1998). P/B rati os for the other species were assumed to be 0.15 year -1 

### (Polovina, 1984). The P/B ratio for the gr oup was calculated as the average P/B of individual species, weight ed by species biomass.

Diet and Consumption / Biomass

Loggerheads are omnivorous, but prefer a carn ivorous diet of she llfish and mollusks, especially clams, oysters, and crabs, as well as encrusting animals attached to rocks and reefs. Green turtle hatchlings are carnivor ous, but juvenile and subadults are more omnivorous, eating jellies as well as marine plants. When green turtles are 20 to 25 cm long they begin feeding on algae and seagrasse s on shallow flats (Mendonça, 1983).Ecopath model of the West Florida Shelf: Volume II. Model construction



### 113 Jellies ( Physalia spp. and Scyphozoans) are the pr imary food of leatherbacks. Hawksbills consume a variety of invertebrates, but their predominant prey is sponges. Kemp’s ridley is carnivorous, feeding on s wimming crabs and other crustaceans, clams, mussels, fish and jellies. Blue crabs are thei r preferred food in many places (Creech and Allman, 1998). Diet for the group was calcul ated by the weighted average diet of the individual species, using species total co nsumption as weighting factor. A sea turtle Consumption / Biomass (Q/B) of 3.5 year -1 

### was taken from Polovina (1984).

Table 11.3. Sea turtle parameter estimates.

Species Density
turtles/km 2 Adult mean weight (Kg) Biomass
tons/km 2 P/B

year -1 Q/B

year -1
Loggerhead, Caretta caretta 0.0350 120 0.0042 0.22 3.5 Kemp’s Ridley, Lepidochelys kempii 0.0140 42 0.0006 0.15 3.5 Green, Chelonia mydas 0.0110 136 0.0015 0.15 3.5 Hawksbill, Eretmochelys imbr icata 0.0009 80 0.000072 0.15 3.5 Leatherback, Dermochelys co riacea 0.0011 500 0.0006 0.15 3.5

Total 0.0620

Literature cited (Sea turtles)

Creech, L. and P.E. Allman. 1998. Stomach and gastrointestinal conten ts of stranded Kemp’s Ridley ( Lepidochelys kempii ) sea turtle in Georgia, p. 156. Fritts, T. H., Irvine, A. B., Jennings, R. D., Collum, L. A., Hoffman, W. and M. A. McGehee. 1983. Turtles, birds, and mammals in the northern Gulf of Mexico and nearby Atlantic waters: an overview based on aerial surveys of OCS areas, with emphasis on o il and gas effects. Mineral Mamanagement Service
FWS/OBS-82/65. Henry, J. D., Phaneuf, P. J. and T.H. Sprayberry. 1998 . Survivorship estimates for female loggerhead sea turtles, Caretta caretta nesting on Wassaw and Piine Islands, p. 192. Henwood, T.A. and W.E. Stuntz, 1987. Analysis of s ea turtle captures and mortalities during commercial shrimp trawling. Fish. Bull. 85(4): 813-817. Mendonça, M. T. 1983. Movements and feeding ecology of Immature Green Turtle, Cj=helonia mydas, in a Florida Lagoon. COPEIA 4:1013-1023. Mullin, K. D. and W. Hoggard. 2000. Visual surveys of cetaceans and sea turtles from aircraft and ships. Page 111-172 in R. W. Davis, W. E. Evans, and D. Wu rsig (eds.), Cetaceans, sea turtles and
seabirds in the northern Gulf of Mexico: distribution, abundance a nd habitat association. Volume II: Technical Report. Prepared by Texas A&M at Galv eston and the National Marine Fisheries service, Gulf of Mexico OCS Region, New Orleans, Louisiana. OCS Study MMS 2000-003.346 pp. Plotkin, P. T. (Editor). 1995. National Marine Fisheries Service and U. S. Fish and Wildlife Service status reviews for sea turtles listed under the endangered species act of 1973. National Marine Fisheries Service, Silver Spring, Maryland. Polovina, J. J. 1984. Model of a coral reef ecos ystem I. The ECOPATH model and its applications to French Frigate Shoals. Coral Reefs 3:1-11. Provancha, J. A.; Mota, M.J.; Lower s, R. .; Scheidt, D. M. and Corselho, M. A. 1998. Relative abundance and distribution of marine turtles inhabiting Mosquito Lagoon, Florida, USA, p. 78. Schroeder, B. A., Foleyy, A. M., Witherington, B. E. and A. Mosier. 1998. Ecology of Marine turtles in Florida Bay: population structure, distribution, and occurrence of Fibropapilloma. NOAA Technical Memorandum NMFS-SEFSC-415., 265-267. Van Meter, V. B. 1992. Florida’s Sea Turtle s. Florida Power & Light Company, 60 p.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 114 

### 12. M ARINE MAMMALS

Marcelo Vasconcellos Departamento de Oceanografia, Universidade do Rio Grande



### 12.1 Odontecetes

A variety of marine mammals inhabit the wa ters over the West Florida Shelf and continental slope. The two dominant species are the bottlenose dolphin, Tursiops truncates, and the Atlantic spotted dolphin, Stenella frontalis (Mills and Rademacher, 1996). Several other species are commonly observed in t he study area, including the Risso’s dolphin, Grampus griseus , the short-finned pilot whale, Globicephala macrorhynchus , the false killer whale, Pseudorca crassidens , spinner dolphins, S.clymene and S. longiristris , the striped dolphin, S. coeruleoalba , Gervai’s beaked whale, Mesoplodon europaeus , and Cuvier’s beaked whale, Ziphius cavirostris (Odell, 1990).

Biomass and Production / Biomass

Results from Gulf shelf shipboard surve ys (Mills and Rademacher, 1996) indicated that about 86% of spotted dolphin sightings occurr ed in waters shallower than 100 m, and particulalrly between 15 and 10 0 m isobaths. More sighti ngs recorded east of the Mississippi River mouth (0.4752 sightings per hour) than west of it (0.2489 sights per hour). Survey data also indica ted that spotted dolphins mi grate inshore and offshore seasonally, and possibly alongshore to the sout h during winter. Swartz et al. (1999) estimated that at least 12,896 bottlenose dolp hins and at least 33 individual stocks inhabit the bays, sounds, and est uaries of the eastern Gulf of Mexico coastal area.

Bottlenose dolphins of the Gulf can also be organized onto different stocks region (Leatherwood and Reeves, 1990): 1) one inhabiting outer shelf waters from the U.S./
Mexican border to the Flori da Keys (from about 9 km seawar d of the 18 m isobath); 2)
an eastern Gulf coastal stock from shore to about 9 km seaward of the 18 m isobath and from 84oW longitude to Key West, Flori da; and 3) several small stocks inhabiting bays, sound, and estuaries. T he coastal stock (completely in side the study area) has at least 8,963 individuals; the the outer shelf stock (only part of it in the study area) contains at least 43,233 individuals, and the estuarine, sound, and bay stocks contain at least 1,911 individuals (Leat herwood and Reeves, 1990). I estimate there to be 23,844 bottlenose dolphins in the study area, assuming that 30% of t he outer shelf stock occurs within the study area. The es timated dolphin density is thus 0.146 individuals per km 2



### . This latter estimate is consistent with results of independent aerial surveys between 1996 and 1998 (Mullin and Hoggard 2000).

Mullin and Hoggard (2000) repor t the results of visual surveys of cetaceans in the
continental shelf in t he northeastern Gulf of Mexico ( both ship and aerial). Surveyed
areas were south of the west ern Florida Panhandle in continental shelf waters from 12.5 km offshore to 100 m deep (12,326 km 2 

### ), and also in the contin ental slope waters (100- 2000 m) within the US EEZ (70,470 km 2 

### ). Table 12.1 shows the estimated abundanceEcopath model of the West Florida Shelf: Volume II. Model construction



### 115 of cetaceans from aerial surveys (aerial su rvey estimates were generally higher than ship survey estimates). The continental shelf region is inhabited al most exclusively by bottlenose dolphins and Atlantic spotted dol phins (one dwarf/pygmy whale was sighted in the shelf area). In contra st, sixteen cetacean species were sighted in continental slope waters (Table 12.1). Pantropical spot ted dolphins and spinner dolphins were the most abundant species there in the slope area.

Biomasses of individual species were esti mated by multiplying densities and mean body weights (Trites and Pauly, 1998). The mean bi omass of cetaceans in the study area was calculated by weighting species biomasse s by the approximate areas of shelf (0 – 100 m = 139,679 km 2 

### ) and slope regions (100 – 183 m = 22,848 km 2 

### ) of the study area inhabited by each species. I estimated the tota l biomass of marine mammals in the area
to be 0.038 tonnes·km -2 

### . The P/B ratio West Florida Sh elf odontoceates was taken to be 0.1 year -1 

### (Browder 1993, Matkin and Hobbs 1999).

Table 12.1. Mean body weight, density and biomass of cetacean species.

Species Mean body weight, Kg Shelf
indiv./km 2 Slope
indiv./km 2 Biomass
t/km 2
Bottlenose dolphin, Tursiops truncatus 187.5 0.14798 0.05617 0.025326 Atlantic spotted dolphin, S. frontalis 66.4 0.08890 0.02555 0.005312 Dwarf/pygmy sperm whale 139.0 0.00081 0.00267 0.000149 Bryde’s whale 16,143.0

─ 0.00035 0.000794 Sperm whale 18,518.5

─ 0.00052 0.001354
Cuvier’s beaked whale 828.5

─ 0.00031 3.61E-05 Beaked whale, Mesoplodon spp 444.5 b

─ 0.00084 5.25E-05 Pygmy killer whale 97.5

─ 0.00309 4.24E-05
False killer whale 578.0

─ 0.00213 0.000173 Short-finned pilot whale 643.0

─ 0.00227 0.000205 Rough-toothed dolphin 92.0

─ 0.00234 3.03E-05 Risso’s dolphin 447.2

─ 0.01869 0.001175 Pantropical spotted dolphin 66.4 c

─ 0.19369 0.001808 Striped dolphin 116.0

─ 0.03253 0.00053 Spinner dolphin 41.3

─ 0.12302 0.000714 Clymene dolphin 46.8

─ 0.03253 0.000214

Notes: Density data from aerial surveys in the continen tal shelf of western Florida Panhandle and slope areas during 1996-1998 (Mullin and Hoggard, 2000); a mean weight of males and females (Trites and Pauly, 1998). b assuming the same mean weight as the True’s beaked whale; c assuming the same weight as Atlantic spotted dolphin.



### Diets and Consumption: Biomass ratio

The diet of whales and dolphins was based on the Paul y et al. (1998) standardized citation diet. The proportion of fish groups in the diet of dolphins was calculated based on Barros and Odell (1990), Barros and Wells (1998), and Perrin et al (1989). I estimated Q/B ratios for piscivorous and pl anktivorous marine mammals as 41.07 year -1 

### (Browder, 1993) and 10.95 year -1 

### (Matkin and Hobbs, 1999) re spectively. Q/B for the group was calculated as the average Q/B of individual species, weighted by species biomass. Diet for the group was calculated as the average diet of individual species, weighted by the species total consumption.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 116 12.2 Manatees

Manatees, Trichechus manatus latirostris , occurr in nearshore coastal areas, and they are rarely observed beyond 1 m ile from shore. Manatees mi grate seasonally to warmer areas during winter; these include southern coastal waters, natural springs, and industrial warm water effluents (counts at thes e wintering locations are used to estimate population abundance). This species inhabi ts estuaries and coastal bays during summer (O’Shea et al. 1995). During winter, t hey are known to make foraging trips to Salt River and Crystal Ba y estuaries to feed on Rupia maritima beds (O’Shea et al., 1995).

There are about 350 manatees in Florida’s western coastal areas during summer and about 200 during winter (B. Ackerman, pers. comm .). I estimate that the biomass of manatees in the study area is 0.0008 tonnes/km 2 

### , given a mean individ ual weight of ca. 500 kg. The annual death and birth rates are approximately equal at 10% (B. Ackerman,

pers. comm .). The P/B ratio was estimated to be 0.1 year -1 

### . At least 30% of deaths are
from human causes. Manatees feed exclusiv ely on seagrasses. The daily ration is about 10% of their body weight (B. Ackerman, pers. comm .). The Q/B estimate is thus 36.5 year -1



### .

Literature cited (marine mammals)

Barros, N. B. and R. S. Wells. 1998. Prey and feedin g patterns of resident bottlenose dolphins (Tursiops truncatus) in Sarasota Bay, Florida. Journal of Mammalogy, 79(3):1045-1059. Barros, N. B.and D. K. Odell. 1990. Food habitats of bottlenose dolphins in the southeastern United States p. 309 – 328, In Leatherwood S. and R. R. Reeves. The Bottlenose Dolphin, Academic Press. Browder, J. A. 1993. A pilot model of the Gulf of Mexi co continental shelf. p. 279-284. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Leatherwood, S. and R. R. Reeves. 1990. The bottl enose dolphin. Academic Press, San Diego. Matkin, C. and R. Hobbs. 1999. pp. 60-61 In: T. A. Okey, and D. Pauly (eds.) A trophic mass balance model of Alaska’s Prince William Sound ecosystem, for the post-spill period 1994-1996, 2nd edition. Fisheries Centre Research Report 7(4), University of Brit ish Columbia, Vancouver. Mills, L. R. and K. R. Rademacher. 1996. Atlantic spotted dolphins, Stenella frontalis , in the Gulf of Mexico. Gulf of Mexico Science, 14(2): 114-120. Mullin, K. D. and W. Hoggard. 2000. Visual surveys of cetaceans and sea turtles from aircraft and ships. Page 111-172 in R. W. Davis, W. E. Evans, and D. Wu rsig (eds.), Cetaceans, sea turtles and
seabirds in the northern Gulf of Mexico: distribution, abundance a nd habitat association. Volume II: Technical Report. Prepared by Texas A&M at Galv eston and the National Marine Fisheries service, Gulf of Mexico OCS Region, New Orleans, Louisiana. OCS Study MMS 2000-003.346 pp. Mullin, K. D.; Lohoefener, R. R.; Hoggard, W.; Roden, C. C. and C. M. Rogers. 1990. Abundance of bottlenose dolphins, Tursiops truncatus, in the coas tal Gulf of Mexico. Northeast Gulf Science, 11(2):
113-122. Odell, D. K. 1990. Marine mammals and sea turtle s.p. 267 - 302 in Minerals Management Service.. Synthesis of available biological, geological, chemical, socioeconomic, and cultural resource infomration for the south florida area. 657 p. O’Shea, T. J., Ackerman, B. B. and H. F. Perciv al. 1995. Population biology of the Florida manatee. National Biological Service. Information and Technology Report 1. August 1995. 289p. Pauly, D., Trites, A. W., Capuli, E and V. Christense n. 1998. Diet compostion and trophic level of marine mammals. ICES Journal of Marine Science, 55:467-481.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 117 Perrin, W. F., Caldwell, D. K. and M. C. Caldwell. 1989. Atlantic spotted dolphi n, Stenella frontalis (G. Cuvier, 1829) p. 173 – 187, In Ridgway S. H. and Harrison, R. (Eds). Handbook of Marine Mammals, Volume 5 The First Book of Dolphins.Academic Press. Swartz, S., Palka, D. L., Waring, G. T. and P. J. Clapham. 1999, Marine mammals of the Atlantic region and the Gulf of Mexico. p. 247 - 261. Our Living Oc eans. Report on the status of US living marine
resources. NOAA Technical Memorandum NMFS-F/SPO-41. Trites, A. W. and D. Pauly. 1998. Estimating mean body masses of marine mammals from maximum body lengths. Canadian Journal of Zool ogy, vol. 76, no. 5, pp. 886-896.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 118 

### 13. F ISHERIES



### 13.1 Landings

Robert Muller
Florida Marine Research In stitute, St. Petersburg



### The variety of species available to any gear makes fisheries on the West Florida Shelf complex. Fishers landed 48 000 tonnes from the shelf dur ing the 1997-98 fishing year. The major fisheries on the shelf are: hook-and- line fishing for the reef fish species such as snappers and groupers, trolling and net fisher ies for coastal pelagic species such as king or Spanish mackerel or dolphin fish, long-line fisheries for deeper water species such as tilefish or sharks, traps for invert ebrate species such as stone crabs, and trawls for Penaeid shrimp and a variety of ornamental species. To illustrate this complexity, when a hook-and-line boat drops fishing lines over hard bottom habitat, fishers can catch snappers, groupers, grunt s, amberjacks, or other spec ies with the same setup. It is necessary to capture this diversity when analyzing catch records.

Harvest information on the West Florida Shel f comes from a variety of sources. The
Florida Fish and Wildlife Conservation Commission (FWC) tracks commercial harvests through trip tickets. The information collected includes the fisher’s identification, the
wholesale dealer’s identificat ion, the date landed, county landed, gear fished, depth fished, area fished, and, for each specie s landed on a trip, species code, quantity landed, size category, and price paid. In addi tion to commercial fisheries operating on these stocks, recreational angl ers also pursue these species. The National Marine Fisheries Service (NMFS) has a program, Marine Recreational Fisheries Statistical Survey (MRFSS), that estimates recreational catches, landings, and releases using angler interviews to identify species and sizes harvested and a telephone survey to estimate the number of recreational fishing trips by two-month time period, geographic and fishing mode (shore, private/rental boat, or charterboat). Average weights by were used to convert landings estimates in number to kg. The National Marine Fisheries Service has another program to monitor angl er activity on head boats by month and location. In Florida, FWC personnel conduc t the interviews for both of NMFS’s recreational programs.

Landings were summarized into 15 gears ( only 11 of which had landing data) with recreational landings and headboat landings were inclu ded as separate gear codes. For
the ECOPATH analysis, we extracted landings from the three sources for the latest, complete fishing year, July 1997 through June 1998. We used a fishing year because many of the shelf species ar e seasonal and, for example, it is necessary to include December and January into the same season.

Landings from the shelf were broken down in to geographic regions and also by distance from shore. The three regions were: northern – Escambia through Gulf counties, central region – Franklin through Pasco counties, and southern – Pinellas through Collier counties. Commercial landings were assign ed to geographical regions using the county where the species were landed, recreational and headboat la ndings were estimated forEcopath model of the West Florida Shelf: Volume II. Model construction



### 119 the three geographic regions using MRFSS’s post-stratification program. Data were available to identify inland and bay waters, state territorial waters (beach to nine miles out) and federal waters (beyond nine miles). Bay and inland waters were not included in this analysis because the focu s on the West Florida Shelf and the number of species which only occur inshore. Sim ilarly the Florida Keys were omitted because of the large number of sub-tropical species t hat are only found in the Keys.

A conversion table was created to link comme rcial species codes with their equivalent
National Ocean Data Center (NODC) species codes. Commercial landings were
combined with MRFSS and hea dboat landings by NODC species code s and then were
linked to the functional groups defined in Ecopath west Florida Shelf model. Thus, landings in kg were tallied by functional group, gear, geographic region, and area based on distance from shore. Table 13.1 displays fishery catches on the West Florida Shelf during the 1998-1999 season.Ecopath model of the West Florida Shelf: Volume II. Model construction121Table 13.1. State and Federal fisheries landings(tonnes) from West Florida Shelf, 1998-1999.Group Unknown Trawl Recreation Headboats Gill net Trammel net Spear / gig Hook and line Purse seine Haul seine Long line TrapsTOTALLarge oceanic piscivores 0.47 0.00 4520.68 1.41 0.00 0.00 0.00 79.62 0.00 0.00 507.00 0.00 5109.17Coastal sharks 4.27 0.06 171.26 0.66 0.02 0.00 0.00 21.81 0.00 0.00 508.04 0.00 706.12Rays and skates 0.00 0.14 44.50 4.93 0.00 0.00 0.00 0.09 0.00 0.03 0.00 0.00 49.69Pelagic oceanic piscivores 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.00 0.00 15.62 0.00 15.92Pelagic coastal piscivores 8.07 147.60 2044.79 37.31 11.28 0.00 0.24 293.77 61.10 58.23 41.79 2.32 2706.50Mackerels adult 0.17 0.71 2289.10 7.03 0.63 0.00 0.00 99.48 0.11 0.02 1.01 0.00 2398.27Sardine-herring-scad complex 155.40 200.29 316.51 0.00 0.00 0.00 0.00 0.00 2830.56 358.44 0.00 0.00 3861.20Pelagic oceanic jelly eaters 0.01 434.31 0.00 0.00 0.00 0.00 0.00 0.18 183.06 0.07 0.00 0.00 617.63Pelagic oceanic planktivores 0.00 136.51 0.00 0.00 0.00 0.00 0.00 0.00 332.28 0.00 0.00 0.00 468.79Demersal oceanic invertebratefeeders0.00 67.49 0.00 0.00 0.00 0.00 0.00 0.00 0.75 0.00 0.00 0.00 68.24Demersal coastal piscivores 0.40 19.63 974.540.00 0.24 0.00 0.00 47.03 0.34 0.44 0.00 0.18 1042.79Demersal coastal invertebratefeeders4.63 212.17 2343.35 68.38 214.28 0.17 1.97 78.09 5.07 14.46 1.64 51.16 2995.38Demersal coastal omnivore 0.15 8.96 325.73 0.47 0.00 0.00 0.00 7.34 0.00 0.31 0.00 5.15 348.11Benthic oceanic piscivores 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Benthic oceanic invertebrate feeders 0.00 4.67 2.80 0.01 0.00 0.00 0.00 13.77 0.00 0.00 274.86 0.00 296.12Benthic coastal piscivores 1.02 14.46 76.42 0.47 0.03 0.00 49.38 4.45 0.00 0.69 9.68 1.17 157.79Benthic coastal invertebrate feeders 0.00 2.88 22.93 1.80 0.00 0.00 0.00 1.58 1.50 0.00 8.37 0.00 39.06Surface pelagics 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Structure associated coastalpiscivore1.32 2.90 1535.93 100.17 0.43 0.00 0.66 109.63 0.02 0.02 127.11 2.83 1881.01Large groupers 47.26 30.21 1779.13 104.71 0.69 0.00 8.94 1563.47 0.00 0.00 2062.34 821.07 6417.82Structure associated coastalinvertebrate feeders7.94 38.33 1696.47 191.53 0.37 0.00 3.09 980.73 1.44 3.73 15.88 84.49 3024.00Structure associated coastalomnivores0.00 0.65 2.04 0.01 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 2.75Structure associated coastalplanktivores0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.21Nearshore associated piscivores 35.31 32.18 154.62 0.00 0.00 0.00 0.00 6.58 11.38 7.63 0.00 0.00 247.70Mullets 14.89 2110.47 618.43 0.00 18.23 0.00 0.87 7.98 71.57 369.32 0.00 0.01 3211.77Nearshore planktivores 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Other fishes 0.00 58.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.16 0.00 0.00 58.40Squid (Loligo opalescence) andcuttlefish0.06 34.28 0.00 0.00 0.00 0.00 0.00 0.04 3.62 0.00 0.00 0.00 38.00Shrimps 18.12 5304.36 0.00 0.00 0.00 0.00 0.00 4.90 0.00 0.41 0.00 11.48 5339.27Lobsters (Spiny - Panulirus argus) 0.27 23.69 0.00 0.00 0.00 0.00 0.00 0.86 0.00 0.00 0.00 25.75 50.57Crabs (Blue, stone, fiddler & others) 18.01 193.63 0.00 0.00 0.00 0.00 1.94 6.57 14.60 7.09 0.00 4000.88 4242.71Ecopath model of the West Florida Shelf: Volume II. Model construction122Octopods 0.00 0.70 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.00 0.00 2.12 2.85Stomatopods 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00(other) large mobile epifauna 0.00 874.13 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.00 0.00 874.23Sand dollars 0.00 765.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 765.77Large infauna 0.00 191.97 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 191.98Sessile epifauna 6.34 863.42 0.00 0.00 0.00 0.00 0.00 0.38 0.00 0.00 0.00 0.25 870.39Small infauna 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04Small mobile epifauna 0.00 84.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.61 85.08Jellies 0.00 0.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.55Macroalgae 0.00 1.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.46 0.00 0.00 1.50Ecopath model of the West Florida Shelf: Volume II. Model construction



### 123 13.2 Bycatch and discards

Marcelo Vasconcellos Departamento de Oceanografia, Universidade do Rio Grande

Steven Mackinson CEFAS Fisheries Lab, Lowestoft, UK



### Two sources of discards in West Florida Shelf were included in the model, from longline and shrimp trawl fisheries.

Longline discards are mainly comprised of sharks and billfishes . Cramer and Scott (1998) estimated that about 37% of sharks caught in longline fisheries in the Gulf of
Mexico are discarded. To calculate discard s of coastal and large oceanic sharks, the reported landings of shark spec ies from longline fisheries were assumed to represent only 63% of the total catches. There are no reported catches of billfishes (white and blue marlin, and sailfish) from longline fisher ies in west Florida (State and Federal Landings Database). Farber (1992) estimated that approximat ely 85 tons of billfishes were discarded in longline fisheries in the Gulf of Mexico. The total discards of billfishes from longline fisheries operatin g in the Gulf of Mexico in 1996 are estimated by NMFS (1998) as 24.7 tons of blue marlin, 11.6 tons of white marlin, and 42. 1 tons of sailfish. To calculate the total catches (d iscarded) of billfishes by longl ines in west Florida it was
assumed that ca. 1/5 of Gulf catches occur o ff west Florida, bas ed on the fact that billfishes spend at least 1/5 of a year, dur ing peak spawning months, in west Florida. Therefore total catches and discards (tonnes) of billfishes in west Florida were estimated as (Table 14.1):

Table 14.1. Landings and discards of large oceanic piscivores

Species Landings Long line discards Catches

Blue marlin 6.60 6.60
White marlin 2.40 2.40
Sail fish 13.4 8.42 21.82

Total 13.4 17.42 30.82



### Shrimp trawlers have a significant bycatch of non-target finfish, invertebrates, and other endangered or threatened species such as turtles and manat ees. The average finfish: shrimp ratio in shrimp trawling in the Gulf of Me xico is 10 to 1, but varies significantly by area, from 2:1 in the Dry Tort ugas to 14:1 off the Mississippi river. On average, the total discards from offshore shrimp trawling in the Gulf of Mexico repr esent ca. 97% of the total bycatch (Alverson et al., 1994), although the bycatch discarded in inshore shrimp trawl fisheries is gerenerally lower (Maharaj and Recksiek 1991). In this section we
consider that all finfish and invertebrates bycatch in shrimp trawlers are discarded.

To calculate bycatch (discards) from sh rimp trawlers we followed three steps:

Firstly, we compiled avail able information on species com position in the bycatch of shrimp trawlers in west Florida. For ins hore trawl fisheries (<ar eas within 9 nauticalEcopath model of the West Florida Shelf: Volume II. Model construction



### 124 miles from the shore) we used data from Steele et al ., (unpublished, a a nd b). Two gear types are commonly used in inshore shrimp fi sheries of west Florida: ottertrawls and
rollerframe trawl. Otter trawls are used in coastal areas and bays (i ncluding Tampa Bay) on nonvegetated, sandy bottom ar eas. Rollerframe trawls are used in seagrass beds to harvest both food shrimp and shrimp used for bait. Bycatch data for offshore trawlers
were obtained from the Gulf of Mexico Fishery Managem ent Council (NOAA). In the latter we assumed that the depth of 10 me ters approximately corresponds to the 9
nautical miles distance used as limit between in shore and offshore areas in the state of
Florida’s catch database.

Secondly, we used the total landings of sh rimp by area (insho re, offshore) and the average bycatch: shrimp catch ratio to calc ulate the total expected amount of bycatch originated from trawlers in west Florida. Finfish: shrimp rati os for inshore fisheries were estimated to be 3.31:1 (Steele et al ., 2002; Steele et al., in review). On average, the catch of inshore trawlers is comprised of 30-70% finfish, 15% shrimps, 15-58% horseshoe crabs and blue crabs, and 25% of miscellaneous invert ebrates (Steele, et al., in review). We therefore estimated the crab and invertebrate: shrimp ratios in inshore fisheries to be ca. 2.4 and 1.7, respectively. For offshore fisheries the estimated ratio is 3.16:1 (Gulf of Mexico Fis hery Management Council, NOAA).

Finally, the total bycatch estimated above was then splited among species and functional groups according to species proporti on in trawlers bycatch. Table 14.2 shows the bycatch discarded by functional group es timated by accounting for differences in landings, bycatch:shrimp ratios and species co mposition in the catch of shrimp tralwers
in inshore and offshore areas. For mackerel we used discards estimates provided by Legault (1998).

Table 14.2. Discards by shrimp trawlers.

Functional group Discards (tonnes) Discards (t·km 2 ) Mackerel juveniles 571.390 0.0034

Mackerel adults 194.166 0.0011 Sardine-herring complex 17.101 0.0001 Demersal coastal piscivores 83.453 0.0005

Demersal coastal invertebrate feeders 1873.639 0.0110

Demersal coastal omnivore 1463.698 0.0086

Benthic oceanic inverteb rate feeders 169.573 0.0010 Benthic coastal piscivores 13.199 0.0001

Benthic coastal invertebrate feeders 2331.251 0.0137 Structured associated coastal piscivore 0.908 <0.00001

Structured associated coastal invertebrate f eeders 288.089 0.0017 Nearshore planktivores 20.863 0.0001

Other fish 154.179 0.0009

Crabs 2479.909 0.0146 Large mobile epifauna 814.518 0.0048 Sessile epifauna 814.518 0.0048Ecopath model of the West Florida Shelf: Volume II. Model construction



### 125 Literature Cited (Bycatch and discards)

Alverson, D. L., M. H. Freeberg, S.A. Murawski, J. G. Pope. 1994. A global assessment of fisheries bycatch and discards. FAO Fisheries Technical Paper 339, 233 pp. Cramer, J and G. P. Scott. 1998. Summarization of ca tch and effort in the pelagic longline fishery and analysis of the effect of two degree square on swordfish and discards landings. Sustainable
Fisheries Division Contribution MIA-97/98-17. Farber, M. I. 1992. Report of the bycatch of billf ishes based on swordfish l ogbook data from 1987 through 1990. ICCAT CICAA 39: 659-667. Meeting of t he ICCAT Standing Committee on Research and Statistics, Madrid (Spain), Nov 1991. Legult, C. M. 1998. Stock asse ssment analysis on Atlantic migratory group King mackerel, Gulf of Mexico migratory group King mackerel, Atlantic migrator y group Spanish mackerel, and Gulf of Mexico migratory group Spanish mackerel. Prepared for the mackerel stock assessment panel meeting,
March 23-27th 1998. MASP/98/09. Maharaj, V. and C. Recksiek. 1991. The by-catch from the artisanal shrimp trawl fishery, Gulf of Paria,
Trinidad. Mar. Fish. Rev. 53: 10-15. NMFS, 1998. Draft amendment 1 to the Atlantic Billfish Fishery Management Plan. National Marine Fisheries Service, October 1998. Steele, P., T. M. Bert, S. Levett and K. Johnston. In review. Effectiveness of byca tch reduction devices in rollerframe trawls used in the Florida shrimp fishery. (e-mail: phil.steele@noaa.gov) Steele, P., T. M. Bert, K. Johnston and S. Levett. 2002. Efficiency of bycatch reduction devices in small otter trawls used in the Florida shrimp fishery. Fishery Bulletin 100: 338-350.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 126 

## 14. Limitations, cautions, and guidelines for use

14.1 S CALE AND INFERENCE 

### : T HE NATURE OF 

### E COPATH MODELS

Thomas A. Okey University of British Co lumbia, Fisheries Centre



### Ecosystems are complex. Moreover, the comple xity of ecosystems is fractal; increasing complexity occurring at increasingly na rrow scales of examination. Ecosystem processes that occur on micro-scales hav e fundamental importanc e to broader-scale ecosystem structure and properties.

To undertake an integrated and quantitative anal ysis of a whole ecosystem, ones building blocks must represent relatively broad components or processes. The input parameters of an Ecopath model (e.g., B, P/B, Q/B, and DC of aggregat ed functional groups) are such building blocks, and thei r broad and generalized nature, or the inherent de-emphasis of micro-scale proc esses, can lead to confusion about the usefulness of the approach. The lack of explicit articulati on of micro-scale processes does not, however, imply that these processe s are excluded from cons ideration, or even from parameterization. Ecopath parameters, and model s, implicitly int egrate micro-scale

processes.

Indeed, each of the Ecopath parameters represents the sum, or the result, of all smaller scale processes (that are considered), wh ich lead to the emergence of measurable ecosystem properties. Ecopath parameters thus ideally in tegrate all smaller scale processes into a description of the food w eb, vis-a-vis all biotic components of the system (throughout all trophic levels). This is accomplished through measurement and empirical information from appropriate sca les of examination. In essence, an Ecopath model represents a system that results fr om micro-processes. This approach is, therefore, not subject to the additivity and multiplicativ ity of errors that are typically
encountered when such models are construc ted from the ‘botto m up.’ The inherent complexity of ecosystems virtually prevent s analytical endeavors in which attempts are made to reconstruct all the micro-scale pr ocesses to support each consecutive level.

The Ecopath approach begins by describing ecosyst em components at all levels, with a focus on the ‘data richness’ of each ‘integrat ive’ input parameter (Section 2), and then learning and refining from examination of the imbalance resulting from the broader integration of these paramet ers into a system-wide picture (Section 3). These initial parameters are subse quently adjusted until t hermodynamic constraints are met (‘model balancing’). The resulting ‘balanced’ mode l then represents one po ssible average state
of the flows in the food web. The model balancing process provides an opportunity for learning about ecosystem structure as th e model builders attempt to minimize the
introduction of bias.

In contrast, ‘bottom up’ mechanistic models are attempts to reconstruct nature through representations of deterministic processes on a much finer detail. Although the scale of
examination of such an approach effectively prevents representation of whole foodEcopath model of the West Florida Shelf: Volume II. Model construction



### 127 webs, the information emerging from these det ailed efforts can be used to parameterize components of Ecopath models.

Notwithstanding this qualitat ively important contrast between these two approaches, a
clear view of the nature of Ecopath models is essential for understanding its limitations. An understanding of its limitations, in turn, leads to an understanding of its usefulness.

Aspects of the Ecopath approach have been described in al l of the previous sections. The salient point about Ecopath with Ecosim in relation to its limitations relates to the scale at which the system is being ex amined. Biotic components of a food web analyzed with this approach ar e generally aggregated into 50 aggregated groups or less. This approach gives us a broad vi ew of the system; the premise of Ecopath with
Ecosim is that the interaction of these broad ecosystem components in dynamic simulations will represent the interact ion of real ecosystem components because enough population and energetic information exists on th is broad scale of examination
to characterize the mechanisms of interest. Most of the uncertainties, simplifications, and assumptions of the approach relate to th is aggregation. This issue has bearing on analyses using Ecopath

, Ecosim , and Ecospace , and this issue is therefore an organizing focus of the discussions in this section.

Detailed discussion of the capabilities, lim itations, and major pitfalls of the Ecopath with
Ecosim approach are discussed by Christensen and Walters ( 2000). Their discussion of how the following major pitfalls can be avoided, or accounted for, is particularly useful:

• Incorrect assessment of predat ion impacts on rare prey;

• Trophic mediation effects (e.g ., biogenic habitat effect);

• Underestimates of predation vulnerabilities;

• Non-additivity in predation rate s due to shared foraging arenas;

• Temporal variation in species-specific habitat factors.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 128 

### 14.2 P ARAMETER UNCERTAINTIES

Thomas A. Okey University of British Co lumbia, Fisheries Centre

Steven Mackinson CEFAS Fisheries Lab, Lowestoft, UK



### Information about ecosystems is generally li mited and uncertain, and these constraints limit the usefulness of ecosyst em models. However, explicit recognition of the nature of uncertainties can guide users toward appropria te interpretation of model results. In addition to documenting derived ranges for i nput parameters, data uncertainties were characterized during model construction usi ng the ‘data pedigree’ approach (Section 3).

Constructing an Ecopath model can serve to reduce uncertainty, not only through synthesis, integration, and consilience of in formation, but because such efforts provoke new empirical programmatic efforts to fill gaps of information needed for whole ecosystem analyses.

A key element of our analysis is the iterative t oggling between Ecopath

, Ecosim , and Ecospace routines. Ecopath forms the foundation upon which Ecosim and Ecospace analyses are constructed, yet results from these analyses can in turn highlight weaknesses in the foundation; thus serving for refinement of the Ecopath model. Users must always bear in mind the intimate link between Ecopath-Ecosim-Ecospace , and not consider results of dynamic simulations as solely dependent Ecosim / Ecospace parameterization. Co mparison of the analyses to in dependently-derived information about the system is an im portant effective way to refi ne the model and continue to reduce uncertainty.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 129 

### 14.3 P ROBLEMS WITH FUNCTIONAL GROUP AGGREGATION

Steven Mackinson CEFAS Fisheries Lab, Lowestoft, UK



### Several pathological problem s arise as a consequence of aggregating groups in an Ecopath model. It is particularly difficult to a lleviate such problems when the number of species in the model system is very large. Th is is true of the West Florida Shelf model, where in many cases more than 10+ spec ies are aggregated into one functional group. It is necessary to take a pragmatic approach to alleviate such problems and thus minimize crazy dynamics during simulations with Ecosim . The problems and solutions relating to the West Florida Shelf model are:

(i) Fishing mortality is overestimated in Ecopath when functional groups are composed of several species, for which catch data exists for all, but biomass
estimates do not. Since Ecopath determines F as F=C/ B, an underestimated
biomass will result in an ov erestimated F. The solution is to derive a biomass estimate for all the species for which we have catches. To do this we assumed the average F of the other species in t he groups and used B=C/F to estimate the
biomass. (ii) Fishing mortality tends to be under estimated for particular species within functional groups that compri se of many species, of which only several might be fished. Essentially, important species t hat may have a high fi shing mortality are lost in the aggregat ed group. Their fishing mortality is effectively ‘diluted’ within the group. The consequence in Ecosim is that the groups will look very as if they can sustain a much higher fishing mortalit y than they really can. In reality the gear may only catch one or two species, and these may be heavily exploited. The match between the gear types select ivity is not represented well when groups are aggregated. Little sense ca n be made from examining harvest scenarios on such groups, and an anylitical focus on these species should be
avoided. In the West Florida Shelf m odel, such groups include (18) Demersal coastal invertebrate eaters, (23) Benthic coastal invertebrate eaters, (25) Structure associated piscivores, (27) St ructure associated invertebrate eaters. The solution is to pay close attenti on to model structure during planning. Functional groups should be aggregated so as to capture the ecological linkages and also emphasize particular groups that attention will be di rected during model
simulation of harvest scenarios. In the model here, mackerel adults, juvenile, small pelagics and la rge groupers are important gr oups that have been ‘broken out’ in to separat e box. Ideally Ecopath models should examine various levels of aggregation and test if the general results of simulations are robust at the different representations of model structure. Similar warnings are provided by Pauly et al. 2000. (iii) Cannibalism in Ecopath models can lead to misl eading results of dynamic simulations. The aggregation process resu lts in apparent cannibalism emerging within the group because species within a functional group may prey on each other. It is best to separate predator and prey groups wherever possible when constructing models. Cannibalism was redu ced to a small fraction (1-2%) of theEcopath model of the West Florida Shelf: Volume II. Model construction



### 130 diet composition in most groups to avoid misleading dynamics. This was recommended by D. Pauly.

(iv) In Ecospace , spatial distributions for each f unctional group tend to become widely distributed when many species ar e aggregated in to a single functional group. This has implications for interpre ting the spatial dynamics of particular species. In some cases, the apparent wider distributions, makes the groups available to predation that particular specie s in reality would not be subjected to. The same is true for distribution of pr edators, and thus overall the spatial overlaps of highly aggr egated groups tend to be quite large and mask real, small scale spatial separations.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 131 

### 14.6 G UIDELINES FOR APPROPRIATE USES OF THE MODEL

Thomas A. Okey University of British Co lumbia, Fisheries Centre

Steven Mackinson CEFAS Fisheries Lab, Lowestoft, UK

Marcelo Vasconcellos Departamento de Oceanografia, Universidade do Rio Grande

Laura Vidal-Hernandez Departamento de Recursos del Mar CINVESTAV- Mérida, Mexico



### The most important guideline for use of the West Florida Shelf model is to carefully consider how the research question compar es to the spatio-temporal scales of the model and the degree of aggregation of functi onal groups. The model is best designed to address questions regarding processes occuring shelf wide and on time scales greater than one year. For example, the model is not well designed to address questions about processes that occur in a very small portion of the West Florida Shelf
as a whole.

The West Florida Shelf model is structured specifically for application to fisheriesrelated questions, such as sardine/herring and mackerel interactions. For an in-depth analysis of the consequences of levels of m odel aggregation, ideally, multiple models of the West Florida Shelf should be constructed; the aim being to search for generalized ‘robust’ results from various levels of aggregation.

The structure of the m odel is also considered suitable to explore the effects of other disturbances, whether natural or anthropogenic. These could include any disturbance or source of stress with a known, or presumed, effect on some biotic component(s) of the system. One example is the simulation of nutrient enrichment and subsequent plankton blooms in the waters overlying the shelf in or der to explore the inte rference effects of such blooms on benthic primary producers. Such analyses can point to implications of such changes on the organizati on of the broader biotic community, and this example is being pursued as a continuing re search question. Another res earch group is interested in using the model to explore questions rela ted to the interactions of grouper and their benthic habitat. Many such ‘ecosystem organi zation’ can be addressed, although the users should always be aware of how their questions relate to the functional groups defined in the model. In this respect, some inve stigators might find it helpful to modify the structure (and or content) of the model, to address particu lar questions of interest. Radical modifications might include usin g the current model and accompanying documentation as a template and a guide for constructing a model that covers a smaller region within the West Florida Shel f such as the Big Bend area.

In general, we emphasize that the model should be used by the broadest possible array of researchers at least for exploratory gaming. Furthermore, the model should be a ‘living model’ in the sense that investigators f eel free to refine the model or modify it forEcopath model of the West Florida Shelf: Volume II. Model construction



### 132 their uses. We emphasize just as strongly, however, that refinements or improvements
to the model should be document ed and published in some form.

One relatively new line of research emerging from the new capabilities of Ecosim is the effort to distinguish biotic forces from phy sical forces in ecosystems. Traditionally, the capabilities of food web models were limited to explorations of the relative importance of trophic forces, or simulations of the indirect trophic forces assuming constant physical conditions, or assuming no physical forci ng. Now, several tools are available in Ecosim to begin teasing apart all the variables and fa ctors that may influence particular biotic components of concern. These tools incl ude forcing functions, trophic mediation functions, and the ability to fit simulations and traj ectories to real time series data. As an example, the approach can now be used to address seemingly intractable mysteries
such as the reasons for declines of Stellar Sea Lions in the Aleutian Islands, or the relative importance of fisheries on mari ne mammal populations on the West Florida
Shelf.

A very recent development soon to be included within the framework of the Ecopath approach is a routine for qua ntifying biomaccumulation of pollutants. The routine requires input of the decay rates of poll utants to trace their fate throughout the ecosytem (contact Villy Christensen for further details).

Although the Ecopath with Ecosim approach is designed for fi sheries applications, we suggest that the approach be viewed as a complement to current single-species fisheries models, or currently implemented fisheries strat egies. The approach is not a replacement of these other time-tested approaches. The Ecopath approach is a powerful new tool in the sense that it is a si ngle analytical framewor k that integrates all biotic components of the ecosystem, and has the capability of integrating physical forces. The main purpose of the approach is the help managers and policy makers prevent themselves from making profoundly bad decisions by giving them a view of the possible surprising and counter-intuitive e ffects of particular management and policy options.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 133 

## APPENDICES

A PPENDIX 

### 1. L IST OF 

### C ONTRIBUTORS

William S. Arnold
Fish and Wildlife Conservation Commission Florida Marine Research Institute, St. Petersburg 100 Eighth Avenue SE St. Petersburg, FL 33701-5095 Phone: (727) 896-8626
Fax: 727-893-1374 Email: Bill.Arnold@fwc.state.fl.us

Scott E. Burghart University of South Florida College of Marine
Science
140 7th Avenue South St. Petersburg, FL 33701 Phone: (727) 553-1130 Fax: (727) 553-1189 Email: scott@marine.usf.edu

Roy L. Caldwell Department of Integrative Biology University of Calif ornia at Berkeley Berkeley, CA 94720-3140 Phone (office): (510) 642-1391 Phone (lab): (510) 643-5448 Fax: (510) 643-6264 Email: 4roy@socrates.berkeley.edu http://ib.berkeley.edu/faculty/Caldwell,RL.html

William (Monty) Graham Dauphin Island Sea Lab. (and U. of S. Alabama)
101 Bienville Blvd Dauphin Island, AL 36528 Phone: (334) 861- 2141 Fax: (334) 861-4646 Email: mgraham@jaguar1.usouthal.edu

Paula F. Houhoulis
Fish and Wildlife Conservation Commission Florida Marine Research Institute, St. Petersburg 100 Eighth Avenue SE St. Petersburg, FL 33701-5095 Phone: (727) 896-8626 Email: Paula.Houhoulis@fwc.state.fl.us Anne Jackson
Fish and Wildlife Conservation Commission Florida Marine Research Institute, St. Petersburg 100 Eighth Avenue SE St. Petersburg, FL 33701-5095 Phone: (727) 896-8626 Email: Anne.Jackson@fwc.state.fl.us

Steven Mackinson
11 The Street Gillingham, Beccles
Suffolk
NR34 0LH
UK Phone: +44 (0) 1502 715280 Email: S.Mackinson@cefas.co.uk

Behzad Mahmoudi
Fish and Wildlife Conservation Commission Florida Marine Research Institute, St. Petersburg 100 Eighth Avenue SE St. Petersburg, FL 33701-5095 Phone: (727) 893-9860 x4120 Email: Behzad.Mahmoudi@fwc.state.fl.us

Dan Marelli
Fish and Wildlife Conservation Commission Florida Marine Research Institute, St. Petersburg 100 Eighth Avenue SE St. Petersburg, FL 33701-5095 Phone: (727) 896-8626 Email: Dan.Marelli@fwc.state.fl.us

Cynthia A. Meyer
Fish and Wildlife Conservation Commission Florida Marine Research Institute, St. Petersburg 100 Eighth Avenue SE St. Petersburg, FL 33701-5095 Phone: (727) 896-8626 X4132 Email: Cynthia.Meyer@fwc.state.fl.usEcopath model of the West Florida Shelf: Volume II. Model construction



### 134 Robert Muller
Fish and Wildlife Conservation Commission Florida Marine Research Institute, St. Petersburg 100 Eighth Avenue SE St. Petersburg, FL 33701-5095 Phone: (727) 896-8626 Email: Robert.Muller@fwc.state.fl.us

James M. Nance
National Marine Fisheries Service SEFSC Galveston Laboratory
4700 Avenue U Galveston, TX 77551 Phone: (409) 766-3507 Fax: (409) 766-3508 Email: James.M.Nance@noaa.gov

Stephen Nesbitt
Florida Fish and Wildlife Conservation
Commission Wildlife Research Laboratory
4005 South Main Street Gainsville, Fl. 3601-9099 Phone: (352) 955-2230 Fax: (352) 376-5359 Email: Stephen.Nesbitt@fwc.state.fl.us

Thomas A. Okey University of British Co lumbia, Fisheries Centre
2204 Main Mall Vancouver, B.C. V6T 1Z4
Canada Email: t.okey@fisheries.ubc.ca

Tracey T. Sutton University of South Florida College of Marine
Science
140 7th Avenue South St. Petersburg, FL 33701 Phone: (727) 553-1187 Fax: (727) 553-1189 Email: tsutton@marine.usf.edu

Marcelo Vasconcellos Departamento de Oceanografia
Universidade do Rio Grande Caixa Postal 474, Rio Grande, RS, Brasil
96200-900
Phone/Fax: 0055 532 36-2735/99758991 Email: m.vasconcellos@fisheries.ubc.ca Laura Vidal-Hernandez Departamento de Recursos del Mar
CINVESTAV- Mérida
Mexico Phone: 52(5).549-40-52 Fax: 52(5).549-40-52 Email: levidal@mda.cinvestav.mxEcopath model of the West Florida Shelf: Volume II. Model construction135APPENDIX2.DIET MATRIX OF THEWESTFLORIDASHELFECOPATH MODELTable A2. Diet Matrix of the West Florida Shelf model (%)PredatorsPrey1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 241 Dolphins.022 Seabirds3 Turtles.024 Manatees5 LgOcePisc1 0.26 LgOcePlank0.17 Coastalsharks1 2 0.78 Rays/skates1.2 159 PelOcePisc7 710 PelCoasPisc1.7 0.2 8.5 2.5 1 111 MackerelAdul5.7 0.3 1 0.312 MackerelJuv1.5 0.2 5.7 0.3 2 0.513 Sardine/Herring5.3 24 0.7 7.3 3.7 10 15 45 41 0.1 1.6 1.7 0.614 PelOceJelly/eaters0.2 9.2 3 10 7.6 0.115 PelOcePlanktivores1.6 5 9.6 12 3 11 6.2 11 22 0.1 116 DemOceInvert/eaters0.6 1017 DemCoasPisc9.9 1.2 1 1018 DemCoasInvert/eaters14 1.5 3 8.1 2.9 15 4 0.4 8.819 DemCoasOmniv21 9.9 5.2 5.6 2 0.1 5.420 BentOcePisc0.1 0.121 BentOceInvert/eaters0.1 0.3 0.2 20 4.722 BentCoasPisc1.5 4 2.1 0.1 5 123 BentCoasInvert/eaters0.1 13 1.6 2.2 4.5 0.3 2.5 0.6 15 2.124 SurfacePelagics1.5 0.2 9.8 0.9 9.425 StrucAssCoasPisc1.4 3.1 2.3 0.126 LgGroupers0.5 2.427StrucAssCoasInvert/eaters 1.1 1.1 3.7 6.2 3.4 4.5 2.3 0.1 5.8 1028 StrucAssCoasOmniv1.1 1.3 0.7 2.3 5.4 2 229 StrucAssCoasPlank30 NearshAssPisc0.2 3.2 0.4 1.131 Mullets2.3 12 0.4 1.332 NearshPlanktivores13 6.2 0.7 2.1 2.3 19 12 10 1 20 5.2 13 1233 Other fishes7.7 3.3 9 1.5 10 1 3.4 5.8 3.6 2.2 5.2 1.1 10 7.2 7.1 9.1Ecopath model of the West Florida Shelf: Volume II. Model construction13634 Squid40 12 6.5 9.1 10 29 7.4 5.6 5.8 1.4 6 0.1 735 Adult Shrimps0.5 1 1 0.5 1 1.6 0.1 0.1 1 1 4 1 1 4 1 836 Lobsters0.1 0.2 0.237 Large Crabs0.1 23 1.5 3 10 1 0.1 4 3 1 3 3 3 238 Octopods0.1 1.6 4.9 2 139 Stomatopods0.5 0.4 2 1.3 0.4 4 1 140Echinod / Lggastropods0.1 4.9 1.8 16 5.5 8.6 4.7 10 6.4 841 Bivalves0.1 22 4 12 0.3 2 2 8.442 Sessile epibenthos23 4.2 1.7 4 8.4 30 0.6 743 Small infauna1.2 15 0.3 1.7 44 0.6 14 7.8 9 27 14 17 5.844 Small mobile epifauna1.5 17 4.3 0.1 5 6.8 35 15 32 15 17 30 13 35 2645 Meiofauna3.2 0.3 046 Small Copepods24 32 12 2.6 047Other Mesozooplankton 24 5 5 21 27 5 4.6 1.9 2.8 0.848 CarnivZooplank1.1 0.1 23 5 1.7 6 59 30 0.2 4.5 1.1 8.3 0.1 5.749 Ichthyoplankton0.1 10 1 0.550 CarnivJellyfish10 4.9 4051MicrobialHeterotrophs52 Macroalgae11 100 2.9 0.1 0.6 13 2.853 Microphytobenthos11 1.3 1.6 0.2 0.2 0.1 5 0.454 Phytoplankton55 Sea grasses1.8 9 16 4756 Dead carcasses1.5 6.657 Sediment Detritus2.6 0.1 2.9 1858 Watercolumn Detritus8.559 Drift Macrophytes60 Import4 35Ecopath model of the West Florida Shelf: Volume II. Model construction137PredatorsPrey25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 5112345678 0 1.69 0.3 0.510 0.5 0.7 2.511 0.112 0.113 5.8 5.8 0.9 3.8 0.514 515 3.8 1.5 0.516 2.51718 2 4.2 9.5 8.1 119 0.4 6.6 0.7 1.820 0.1 2.3 0.121 0.9 3.1 0.222 0.8 2.3 0.123 3.8 2.2 0.5 1.5 20 224 125 1 2.4 126 0 0.127 13 8.6 0.128 7.3 4.7 0.7 129 4.4 6.2 13031 3.6 5.5 132 2.6 2.5 1.3 9.5 533 1.7 0.4 3.6 0.6 2 26 0.134 6 6.3 3.1 0 10 1 0.1 0.8 635 3 2 5 1 0.1 0.4 1 0.1 1036 0.1 0.2 0.1 0.137 15 15 3 9 0.1 19 0.5 0.5 3Ecopath model of the West Florida Shelf: Volume II. Model construction13838 3.3 10 0.7 0.139 1 2 0.940 2.1 1.5 10 0.1 5 0.6 23 4.1 5.6 1 141 5 0 16 0 18 36 24 11 142 1.5 16 21 1.6 14 8.2 5.7 15 12 10 143 5 17 3 8.2 2 20 3.7 15 16 15 4.9 0.1 1 444 11 2 22 5.6 17 13 0.5 9 30 15 23 2 1.9 10 18 2 2 1 145 0.1 1 2 12 2.9 1 3.1 1 3 10 7 146 1.8 1.5 20 34 4 2 0.1 2 12 12 35 2047 2.2 2.6 0.7 36 36 4 33 0.1 1 10 25 4748 4 1.9 16 3.9 8.4 19 33 1 4 1049 0.2 1 0.1 0.2 150 2 0.151 22 11 20 10 10 30 15 19 35 13 40 64 5 2352 54 0.3 5 2 0.3 3.7 1 12 1253 1 10 3.8 6 9.9 12 4 9 8 20 4 5 5 0.554 0.5 3.1 0 2.8 23 23 22 10 80 37 10 15 0.555 0.3 0.7 1 0 4.7 556 1 1.5 3.1 0.9 3.3 2 1 5 0.757 12 3 2 21 7 6 11 41 17 32 7 44 1 5 4558 5 6.7 13 18 5 7 0 1 4 2.8 5 5059 2.5 0.8 18 0 0 5 15 3.560 90Ecopath model of the West Florida Shelf: Volume II. Model construction



### 139 

### A PPENDIX 

### 3. D ETRITUS FATE OF EACH FUNCTIONAL GROUP

Table A3. Fate of detritus for each functional group.

Source \ Fate Dead

carcasses Sediment
Detritus Watercolumn
Detritus Drift Macrophytes Export Sum

Dolphins 0.01 0.29 0.7 0 0 1

Seabirds 0.01 0 0.5 0 0.49 1

Turtles 0.01 0.29 0.7 0 0 1

Manatees 0.01 0.29 0.7 0 0 1 LgOcePisc 0.01 0.29 0.7 0 0 1 LgOcePlank 0.01 0.29 0.7 0 0 1

Coastalsharks 0.01 0.29 0.7 0 0 1 Rays/skates 0.01 0.7 0.29 0 0 1

PelOcePisc 0.01 0.29 0.7 0 0 1

PelCoasPisc 0.01 0.29 0.7 0 0 1

MackerelAdul 0.01 0.29 0.7 0 0 1

MackerelJuv 0.01 0.29 0.7 0 0 1 Sardine/Herring 0.01 0.29 0.7 0 0 1 PelOceJelly/eaters 0.01 0.29 0.7 0 0 1

PelOcePlanktivores 0.01 0.29 0.7 0 0 1

DemOceInvert/eaters 0.01 0.8 0.19 0 0 1

DemCoasPisc 0.01 0.8 0.19 0 0 1

DemCoasInvert/eaters 0.01 0.8 0.19 0 0 1

DemCoasOmniv 0.01 0.8 0.19 0 0 1

BentOcePisc 0.01 0.9 0.09 0 0 1

BentOceInvert/eaters 0.01 0.9 0.09 0 0 1

BentCoasPisc 0.01 0.9 0.09 0 0 1

BentCoasInvert/eater s 0.01 0.9 0.09 0 0 1 SurfacePelagics 0.01 0.29 0.7 0 0 1

StrucAssCoasPisc 0.01 0.8 0.19 0 0 1 LgGroupers 0.01 0.8 0.19 0 0 1

StrucAssCoasInvert/eaters 0.01 0.8 0.19 0 0 1

StrucAssCoasOmniv 0.01 0.8 0.19 0 0 1

StrucAssCoasPlank 0.01 0.8 0.19 0 0 1

NearshAssPisc 0.01 0.29 0.7 0 0 1

Mullets 0.01 0.29 0.7 0 0 1

NearshPlanktivores 0.01 0.29 0.7 0 0 1

Other fishes 0.01 0.29 0.7 0 0 1 Squid 0.5 0.25 0.25 0 0 1 Shrimps 0.01 0.89 0.1 0 0 1

Lobsters 0.01 0.89 0.1 0 0 1

Crabs 0.01 0.89 0.1 0 0 1 Octopods 0.01 0.94 0.05 0 0 1 Stomatopods 0.01 0.99 0 0 0 1 Echinoderms/Lg. gastropods 0.01 0.99 0 0 0 1

Bivalves 0.01 0.79 0.2 0 0 1 Sessile epibenthos 0.01 0.89 0.1 0 0 1

Small infauna 0.01 0.99 0 0 0 1 Small mobile epifauna 0.01 0.79 0.2 0 0 1

Meiofauna 0.01 0.89 0.1 0 0 1Ecopath model of the West Florida Shelf: Volume II. Model construction



### 140 Small Copepods 0.01 0.09 0.9 0 0 1 Other Mesozooplankton 0.01 0.09 0.9 0 0 1 CarnivZooplank 0.01 0.09 0.9 0 0 1 Ichthyoplankton 0.01 0.09 0.9 0 0 1 CarnivJellyfish 0.01 0.09 0.9 0 0 1 Microbial Heterotrophs 0.01 0.49 0.5 0 0 1 Macroalgae 0 0 0 1 0 1 Microphytobenthos 0 0.9 0.1 0 0 1 Phytoplankton 0.01 0.04 0.95 0 0 1 Sea grasses 0 0 0 1 0 1

Dead carcasses 0 0.9 0.1 0 0 1

Sediment Detritus 0 0 0 0 1 1

Watercolumn Detritus 0 1 0 0 0 1 Drift Macrophytes 0 0.9 0.1 0 0 1

Note: The specific proportions are assumed based on a subjective judgment relating to the habitat and niche of the various organisms.Ecopath model of the West Florida Shelf: Volume II. Model construction



### 141 

### A PPENDIX 

### 4. M ARKET VALUES OF UTILIZED GROUPS



### The table below lists the price per unit of landed weight (pounds ) of the harvested groups in West Florida shelf. The market value of catches was calculated as the weighted average price of landin gs of individual species in the group. Price information is from the 1999 Annual Landings Summary of the Marine Fisheries Information System, Florida Fish and Wild life Conservation Commission.

Table A4. Market values of functional groups

Group Name $/pound LgOcePisc 1.55

Coastalsharks 0.40 Rays/skates 0.19

PelOcePisc 0.89

PelCoasPisc 1.01

MackerelAdul 1.12 Sardine/Herring 0.23 PelOceJelly/eaters 0.89

PelOcePlanktivores 1.12

DemOceInvert/eaters 0.50

DemCoasPisc 1.66

DemCoasInvert/eaters 1.25

DemCoasOmniv 3.60

BentOceInver t/eaters 1.39

BentCoasPisc 1.96

BentCoasInvert/eaters 1.13

StrucAssCoasPisc 1.87 LgGroupers 2.04

StrucAssCoasInvert/eaters 1.12

StrucAssCoasOmniv 0.89

StrucAssCoasPlank 0.89

NearshAssPisc 0.69

Mullets 0.74

Other fishes 0.89 Squid 0.40 Adult Shrimps 2.47

Lobsters 4.31 Large Crabs 0.71 Octopods 0.99 Echinoderms/Large gastropods 0.95

Bivalves 6.33 Sessile epibenthos 0.94

Small infauna 0.94 Small mobile epifauna 0.94 CarnivJellyfish 0.94Ecopath model of the West Florida Shelf: Volume II. Model construction



### 142 

### A PPENDIX 

### 5. G UIDE TO 

### ‘ DATA PEDIGREE 

### ’ VALUES

Table A5. Confidence intervals associated with pedigree ranks

Pedigree categories Index value Conf. int. (+/- %)

Biomass Estimated by Ecopath 0 80

From other model 0 80

Guesstimate 0 80 Consumer: Approximate or indirect method Producer: Indirect method (remote sensing) 0.4 50

Consumer: Sampling based, low precision Producer: Locally based, low precision 0.7 30

Consumer: Sampling based, high precision Producer: Locally based, high precision 1 10

P/B and Q/B Estimated by Ecopath 0 80

Guesstimate 0.1 70

From other model 0.2 60 Empirical relationship 0.5 50 Consumer: Similar group/species, similar system Producer: Similar system, low precision 0.6 40

Similar group/species, same system Producer: Same system, low precision 0.7 30

Consumer: Same group/species, similar system Producer: Similar system, high precision 0.8 20

Consumer: Same group/species, same system Producer: Same system, high precision 1 10

Diets General knowledge of related group/species 0 80

From other model 0 80 General knowledge for same group/species 0.2 60 Qualitative diet composition study 0.5 50
Quantitative but limited diet composition study 0.7 30 Quantitative, detailed, di et composition study 1 10

Catches

Guesstimate 0.1 70

From other model 0.1 70

FAO statistics 0.2 80

National statistics 0.5 50 Local study, low precision/incomplete 0.7 30 Local study, high precision/complete 1 10Ecopath model of the West Florida Shelf: Volume II. Model construction



### 143 A PPENDIX 

### 6. E MPIRICALLY BASED CONVERSION FACTORS

Table A6. Conversion factors used in the cons truction of the West Florida Shelf model

Conversion Ratio Source

Chlorophyll a to Carbon 1 to 25 Parsons et al. 1977 in Browder 1993

Chlorophyll a to Carbon (Phytoplankton) 1 to 44.9 Durbin & Durbin 1998

Carbon to dry organic matter 1 to 2.5 Parsons et al. 1977 in Browder 1993 Carbon to dry organic matter (Phytoplankton) 1 to 5.4 Durbin & Durbin 1998 Carbon to dry organic matter (Meiobenthos) 1 to 2.5 Warwick (Plymouth marine laboratory; pers. comm. 12 June 2000) Carbon to dry organic matter (Benthic macrofauna) 1 to 10 Rowe and Menzel (1971) ("5 to 15 times")

Dry to wet organic matter 1 to 5 Parsons et al. 1977 in Browder 1993 Dry to wet weight (Benthic primary producers) 1 to 7.7 Arreguín-Sánchez et al. 1993

Dry to wet weight (Benthic macrofauna) 1 to 7.5 Rowe and Menzel (1971) ("5 to 10 times")

Dry to wet weight (Shrimp) 1 to 7.7 Bougis 1979 in Cushing 1984 Carbon to dry organic matter (Crustaceans) 1 to 2.5 Bougis 1979 in Cushing 1984

Carbon to wet weight (Shrimp) 1 to 19.2 Cushing 1984

Literature cited (Conversions)

Arreguín-Sánchez, F., J. C. Seijo and E. Valero-Pacheco. 1993. An application of ECOPATH II to the north continental shelf ecosystem of Yucatan, Mexico, p. 269-278. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Browder, J. A. 1993. A pilot model of the Gu lf of Mexico continental shelf. p. 279-284. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26, 390 p. Cushing, D. H. 1984. Do discards affect the produc tion of shrimps in the Gulf of Mexico? pp. 254-257 In J. A. Gulland and B. J. Rothschild (eds.) P enaeid shrimps—their biology and management. Fishing News Books, Farnham, Surrey England, 308 pp. Durbin, A.G., and E.G. Durbin. 1998. Effects of menhaden predation on plankton populations in Narragansett Bay, Rhode Island. Estauries 21(3):449-465. Parsons, T. R., M. Takahashi, and B. Hargrave. 1977. Biological oceanographic processes. Permagon Press, New York, 332 p. Rowe, G. T. and D. W. Menzel. 1971. Quantitative be nthic samples from the deep Gulf of Mexico with some comments on the measurement of deep-sea biomass. Bulletin of Marine Science 21(2):556-
566.Ecopath model of the West Florida Shelf: Volume II. Model construction144APPENDIX7.FUNCTIONAL GROUPINGS AND PARAMETERS FOR FISHESTable A7. Summary table of all fish group parameters and referencesBiomassQ/B P/Bt/km2 Reference per yr Reference peryrReference1Whales and dolphins Whales and dolphins40.86 Brown et al. 19912Sea birds Sea birds80.00 GoM models 5.40 Other GoM model3Turtles Turtles3.50 GoM models 0.15 Other GoM model4Manatees Manatees0.001 ? 36.50 ?0.10 Indep.5Large oceanic piscivoresXiphias gladiusswordfish 0.00277 B=C/F 2.9184Brown et al. 1991 0.50 Z=P/BMakaira nigricansblue marlin 0.00019 B=C/F 4.56Brown et al. 1991 0.40 Z=P/BTetrapturus spp.white marlin andspearfish0.00007 B=C/F 6.384 Brownet al. 1991 0.40 Z=P/BIstiophorus platypterussailfish 0.00039 B=C/F 7.296 Brown et al. 1991 0.45 Z=P/BCoryphaena hippurusdolphin fish 0.05992 B=C/F 4.73 Pred.Palomares and Pauly 1987 0.8 Z=P/BAlopias spp.thresher shark 0.00010 B=C/F 8.97 Other GoM model 0.54 Z=P/BIsurus paucuslongfin mako 0.00013 B=C/F 8.97 Other GoM model 0.54 Z=P/BThunnus albacaresyellowfin tuna 0.00354 B=C/F 12.221 Brown et al. 1991 1.2 Z=P/BThunnus alalungaalbacore 0.00000 B=C/F 9.4 Pred.Palomares and Pauly 1987 0.78 Z=P/BThunnus thynnusbluefin tuna 0.00001 B=C/F 12.16Brown et al. 1991 0.43 Z=P/BThunnus atlanticusblackfin tuna 0.00990 B=C/F 15.455 Browder model 0.87 Z=P/BThunnus obesusbigeye tuna 0.00004 B=C/F 7.9 Pred.Palomares and Pauly 1987 0.64 Z=P/BKatsuwonus pelamisskipjack tuna 0.00161 B=C/F 23.8944Brown et al. 1991 1.14 Z=P/BHexanchus griseuesSixgill shark 0.00000 B=C/F 8.975 Pred.Palomares and Pauly 1987 0.49 Z=P/B6Large ocean planktivores10.00 GoM models 1.4 Other GoM modelMobulidaeManta ray 1.30 Pred. Palomares andPauly 1989 0.11 M pred.(Pauly 1980)Rhincodon typuswhale shark 0.80 Pred. Palomaresand Pauly 1989 0.07 M pred.(Pauly 1980)Cetorhinus maximusbasking shark 3.70 Pred. Palomaresand Pauly 1989 0.09 M pred.(Pauly 1980)Mola molaocean sunfish 1.40 Pred. Palomaresand Pauly 1989 0.18 M pred.(Pauly 1980)7Coastal sharks7.84 GoM models 0.55 Other GoM modelSphyrna lewiniscalloped hammerhead 0.00041 B=C/F 2.48 Palomares eqn. 0.43 Z=P/BSphyrna mokarrangreat hammerhead shark 0.00058 B=C/F 1.90 Pred. Palomares and Pauly 1989 0.25 Z=P/BCarcharhinus plumbeussandbar shark 0.02994 B=C/F 4.10 Stillwell and Kohler 1992 0.263 Z=P/BCarcharhinus obscurusdusky shark 0.00086 B=C/F 2.20 Pred.Palomares and Pauly 1989 0.21 Z=P/BCarcharhinus acronotusblacknose shark 0.00055 B=C/F 3.20 Pred.Palomares and Pauly 1989 0.62 Z=P/BCarcharhinus brevipinnaspinner shark 0.00001 B=C/F 3.00 Pred.Palomares and Pauly 1989 0.44 Z=P/BCarcharhinus falciformissilky shark 0.00023 B=C/F 2.00 Pred.Palomares and Pauly 1989 0.28 Z=P/BEcopath model of the West Florida Shelf: Volume II. Model construction145Carcharhinus limbatusblacktip shark 0.00324 B=C/F 3.60 Pred.Palomares and Pauly 1989 0.466 Z=P/BCarcharhinus leucasbull shark 0.00219 B=C/F 2.37Palomares eqn. 0.21 Z=P/BCarcharhinus isodonfinetooth shark 0.00016 B=C/F 3.30 Pred. Palomares and Pauly 1989 0.39 Z=P/BRhizoprionodonterraenovaeAtlantic sharpnose shark 0.00034B=C/F 6.40 Vidal model 1.04 Z=P/BGaleocerdo cuviertiger shark 0.00005 B=C/F 1.70 Pred.Palomares and Pauly 1989 0.21 Z=P/BNegaprion brevirostrislemon shark 0.00003 B=C/F 6.00 Cortes and Grubber 1990 0.75 Z=P/BSqualidaeDogfish 0.00000 B=C/F 4.77 Pred. Palomares and Pauly 1989 0.17 Z=P/BOdontaspis taurussand tiger 0.00000 1.70 Pred. Palomares and Pauly 1989 0.62 Z=P/B"all sharks"8Rays and skates7.84 GoM models 0.20 Other GoM modelRaja texanaroundel skate 0.018 Trawl surveys 19.4 Pred. Palomares and Pauly 1989 0.9 pred. M (Pauly 1980)Raja eglanteriaclearnose skate 0.050 Trawl surveys 11.8 Pred. Palomares and Pauly 1989 0.39 pred. M (Pauly 1980)Dasyatis spp.stingray genus 0.009 Trawl surveys 2.5 Pred. Palomares and Pauly 1989 0.26 pred. M (Pauly 1980)Aetobatus narinarispotted eagle ray 2.3 Pred. Palomares and Pauly 1989 0.18 pred. M (Pauly 1980)Rhinoptera bonasuscownose ray 0.158 Trawl surveys 6.4 Pred. Palomares and Pauly 1989 0.24 pred. M (Pauly 1980)Rhinobatos lentiginosusAtlantic guitarfish 0.003 Trawlsurveys 11.9 Pred. Palomares and Pauly 1989 0.51 pred. M (Pauly 1980)Sphyrna tiburobonnethead 0.001 Trawl surveys 5 Pred. Palomares and Pauly 1989 0.45 pred. M (Pauly 1980)Ginglymostoma cirratumnurse shark 1.8 Pred. Palomaresand Pauly 1989 0.15 pred. M (Pauly 1980)9Pelagic oceanicpiscivoresTrichiurus lepturus(?distribution?)Atlantic cutlassfish 0.150 NMFS trawl 25.54 Brown et al. 1991 1.06 Browder model (pelagicpredators)Ruvettus pretiosusoilfishMerluccius albidusoffshore hake 0.000 NMFS trawl1.06 Browder model (pelagicpredators)Brama spspomfrets 0.000 NMFS trawl 1.06 Browder model (pelagicpredators)Lepidocybiumflavobrunneumescolar 0.000 NMFS trawl10Pelagic coastalpiscivoresAcanthocybium solandriwahoo 0.003 B=C/F 25.54 Brown et al. 1991 0.90 ZSarda sardaAtlantic bonitoEuthynnus alletteruslittle tunny 0.009 B=C/F 15.11 Palomares equation 0.90 ZAuxis thazardfrigate mackerelPomatomus saltatrixbluefish 0.001 Trawl surveys 9.36 GoM models 0.70 ZRachycentron canadumcobia 0.001 B=C/F 7.30 Brown et al. 1991 0.70 ZCaranx crysosblue runner 0.038 FMRI pelagicsurveys10.00 Independent? 0.47 Goodwin & Johnson1986 (Z est)Caranx hipposcrevalle jack 0.007 B=C/F 10.00 Independent? 0.60 Other GoM modelEcopath model of the West Florida Shelf: Volume II. Model construction146Caranx bartholomaeiyellow jack 10.00 GoM models 0.60 Other GoM modelCaranx latushorse-eye jack 10.00 GoM models 0.60 Other GoM modelCaranx ruberbar jack 6.15 GoM modelsSeriola zonatabanded rudderfish 0.000 Trawl surveys10.00 GoM models 0.60 Other GoM modelSeriola dumeriligreater amberjack 0.021 B=C/F 5.90 Other Q/B 0.40 ZSeriola fasciatalesser amberjack 10.00 GoM models 0.60 Other GoM modelSeriola rivolianaalmaco jack 10.00 GoM models 0.60 Other GoM modelElagatis bipinnulatarainbow runner 10.00 GoM models 0.60 Other GoM modelEcheneidaeremora family 0.016 Trawl surveys 32.13 Browder model(pelagic predators) 1.057 Browder model (pelagicpredators)Belonidaeneedlefish family 8.80 GoM models 0.3 Other GoM modelLobotes surinamensistripletail 3.51 Palomares equation"carangids"11Mackerels adultScomberomorusmaculatusSpanish mackerel 0.083 NMFS assessment(Chris Legult 1998)10.02 GoM models 0.41 Legult (1998) VPA mortScomberomorus cavallaking mackerel 0.099 NMFS assessment(Chris Legult 1998)9.29 GoM models 0.36 Legult (1998) VPA mortScomberomorus regaliscero mackerel 9.00 Other Q/B 0.99 Other GoM model12Juvenile mackerelsScomberomorusmaculatusSpanish mackerel 0.019 NMFS assessment (Legult1998)Scomberomorus cavallaking mackerel 0.106 NMFS assessment (Legult1998)Scomberomorus regaliscero mackerel13Sardine-herring-scad complexOpisthonema oglinumAtlantic thread herring 0.039 FMRI pelagicsurveys13.26 GoM models 1.60 Other GoM modelHarengula jaguanascaled sardine 0.048 FMRI pelagicsurveys12.89 GoM models 1.57 Other GoM modelSardinella auritaSpanish sardine 0.139 FMRI pelagicsurveys11.45 GoM models 0.95 Other GoM modelBrevoortia spp.menhadens 0.047 Vaughan et al. 199913.64 GoM models 0.95 Other GoM modelDecapterus punctatusround scad 0.184 FMRI pelagicsurveys10.00 GoM models 0.92 Naughton & Saloman1986 (Z)14Pelagic oceanic jellyeatersAriomma bondisilver-rag 0.190 NMFS trawl8.2 Fishbase 1.12 M fishbasePeprilus burtigulf butterfish 2.503 NMFS trawl 8 Fishbase 1.85 M fishbaseHyperoglyphe perciformisbarrelfish15Pelagic oceanic planktivoresMaurolicus weitzmanihatchet fishEcopath model of the West Florida Shelf: Volume II. Model construction147Etrumeus teresround herring 1.065 Houde 1977 11.45GoM models 0.95 Other GoM modelJenkinsia familydwarf round herring 0.004 Trawl surveys11.45 GoM models 0.95 Other GoM modelTrachurus lathamirough scad 1.352 NMFS trawl 10.00GoM models 0.60 Other GoM modelSelar crumenophthalmusbigeye scad 0.015 NMFS trawl 10.00GoM models 0.60 Other GoM modelScomber japonicuschub mackerel 0.703 NMFS trawl 11.45 GoM models 1.70 Other GoM modelDiaphus spLanternfish 14.89 GoM models 0.61 Other GoM modelBregmaceros atlanticusantenna codletArgentina striatastraited argentine 0.000 NMFS trawlEngraulis eurystolesilver anchovy 14.24 GoM models 0.60 Other GoM model16Demersal oceanic invertebrate feedersMullus auratusred goatfish 0.041 Trawl surveys 15.76 Browder model (demersal fish) 2.17 Browder model(demersal fish)Synagrops bellusblackmouth bass 0.000 NMFS trawl 15.76 Browdermodel (demersal fish) 2.17 Browder model(demersal fish)17Demersal coastalpiscivoresCynoscion nothussilver seatrout 0.015 Trawl surveys 6.80 Independ. Est 0.64 Other GoM modelCynoscion nebulosusspotted seatrout 0.011VPA estimate5.29 Palomares equation 0.64 Other GoM modelCynoscion arenariussand seatrout 0.042 Trawl surveys 6.73 GoM models 0.64 Other GoM model18Demersal coastal invertebrate feedersBairdiella chrysourasilver perch 0.072 Trawl surveys 9.17 GoM models 0.74 Other GoM modelEquetus umbrosusCubbyu 0.028 Trawl surveys 10.00 GoM models 0.35 Other GoM modelLeiostomus xanthurusspot 0.439 Trawl surveys 9.09 Palomares equation 0.35 Other GoM modelMenticirrhus sp.kingfish 0.033 Trawl surveys 7.32 Palomares equation 0.35 Other GoM modelMicropogonias undulatusAtlantic croaker 0.168 Trawl surveys 5.41Palomares equation 0.35 Other GoM modelSciaenops ocellatusred drum 0.014 B=C/F 3.67 Palomares equation 0.35 Other GoM modelPogonias cromisblack drum 0.002 B=C/F 3.67 Palomares equation 0.35 Other GoM modelChloroscombruschrysurusAtlantic bumper 0.042 FMRI pelagicsurveys11.70 Independ 0.60 Other GoM modelTrachinotus carolinusFlorida pompano 11.60 Vidal 0.60 Other GoM modelTrachinotus falcatuspermit 7.62 Palomares equation 0.60 Other GoM modelOligoplites saurusleatherjacket 0.000 Trawl surveys 3.00 GoM models 0.30 Other GoM modelAlectis ciliarisAfrican pompano 10.00 GoMmodels 0.60 Other GoM modelArius felishardhead catfish 0.157 Trawl surveys 9.70 GoM models 0.55 Other GoM modelBagre marinusgafftopsail catfish 0.014 Trawl surveys10.14 GoM models 0.55 Other GoM modelCalamus arctifronsgrass porgy 0.043 FMRI surveys 5.20 GoM models 0.65 Other GoM modelPagrus pagrusred porgy 0.004 Trawl surveys 5.20GoM models 0.65 Other GoM modelStenotomus caprinuslongspine porgy 0.497 Trawl surveys5.20 GoM models 0.65 Other GoM modelHaemulon aurolineatumtomtate 0.407 FMRI pelagicsurveys9.18 GoM models 0.38 Other GoM modelOrthopristis chrysopterapigfish 0.487 Trawl surveys 10.65GoM models 1.25 Other GoM modelEcopath model of the West Florida Shelf: Volume II. Model construction148Lutjanus synagrislane snapper 0.095 Trawl surveys 4.95 GoM models 0.68 Manooch & Mason(1984) (Z)Eucinostomus sp.mojarras 0.111 FMRI pelagicsurveys15.34 GoM models 1.25 Other GoM modelBalistes capriscusgray triggerfish 0.095 Trawl surveys 9.07 Palomares equation 2.17 Browder model(demersal fish)19Demersal coastalomnivoreAluterus schoepfiorange filefish 0.024 Trawl surveys 17.42Palomares equation 2.17 Browder model(demersal fish)Monacanthus ciliatusfringed filefish 0.123 Trawl surveys 15.10 Fishbase 1.50 M fishbaseMonacanthus hispidusplanehead filefish 0.002 NMFS trawl 8.20 Fishbase 1.26 M fishbaseCantherhines pullusorangespotted filefishCantherhines sufflamenocean triggerfishLactophrys polygoniahoneycomb filefishDiplodus holbrookispottail pinfish 0.295 Trawl surveys 6.30 Fishbase predicted 1.50 M fishbaseLagodon rhomboidespinfish 0.470 FMRI pelagicsurveys15.21 13.4 (Bioenergetic model); 14.6(Stoner and Livingstone 1978); 17.8(Peters and Kjelson)1.25 Other GoM modelChaetodipterus faberAtlantic spadefish 0.044 Trawlsurveys 15.76 Browder model (demersal fish) 2.17 Browder model(demersal fish)Lactophrys quadricornisscrawled cowfish 0.399 Trawl surveys 8.17 GoM modelss 1.26 M fishbaseTetraodontidaepuffer family 0.072 Trawl surveys 37.45 Looe key (small reef herbivores) 1.90 Looe key (small reefherbivores)20Benthic oceanicpiscivoresSaurida brasiliensislargescale lizardfish 0.067 Trawl surveys 7.94 GoM modelss 0.30 Other GoM modelSaurida normanishortjaw lizardfish 0.023 Trawl surveys 7.94 GoM modelss 0.30 Other GoM modelSynodus poeyioffshore lizardfish 0.005 Trawl surveys7.94 GoM modelss 0.30 Other GoM modelHoplunnis spp.duckbill eels 0.014 Trawl surveys21Benthic oceanic invertebrate feedersHalieutichthys aculeatuspancake batfish 0.004 Trawl surveys 15.80 Fishabse 2.44 M fishbaseNeomerinthe hemingwayispinycheek scorpionfish 0.005 Trawlsurveys 2.17 Browder model(demersal fish)Peristedion gracileslender searobin 0.009 NMFS trawl 2.17 Browder model(demersal fish)Prionotus stearnsishortwing searobin 0.008 NMFStrawl 2.17 Browder model(demersal fish)Serranus notospilussaddle bass 0.006 Trawl surveys 15.80 Fishbase 2.44 M fishbaseMalacanthidaetilefish family 0.066 Trawl surveys 15.76 Browder model (demersal fish) 2.17 Browder model(demersal fish)Steindachneria argentaluminous hake 0.023 Trawl surveys 2.17 Browder model(demersal fish)Ecopath model of the West Florida Shelf: Volume II. Model construction14922Benthic coastalpiscivoresSynodus foetensinshore lizardfish 0.138 Trawl surveys7.94 GoM models 0.30 Other GoM modelSynodus intermediussand diver 0.076 Trawl surveys 7.94GoM models 0.30 Other GoM modelParalichthys spp.lefteye flounder genus 0.045 Trawl surveys 9.46 GoM models 0.30 Other GoM modelOphichthidaesnake eel family 0.003 Trawl surveys23Benthic coastal invertebrate feedersDiplectrum bivittatumdwarf sand perch 0.041 Trawl surveys 15.76 Browder model (demersal fish) 2.17 Browder model(demersal fish)Diplectrum formosumsand perch 0.307 Trawl surveys 9.67 Palomares equation 2.45 M fishbaseEtropus crossotusfringed flounder 0.013 Trawl surveys9.46 GoM models 0.30 Other GoM modelEtropus rimosusgray flounder 0.190 Trawl surveys9.46 GoM models 0.30 Other GoM modelSyacium papillosumdusky flounder 0.763 Trawl surveys9.46 GoM models 0.30 Other GoM modelSymphurus plagiusablackcheek tonguefish 0.006 Trawl surveys 9.46 GoM models 0.30 Other GoM modelPrionotus roseusblue spotted searobin 0.019 Trawl surveys 15.76 Browder model (demersal fish) 2.17 Browder model(demersal fish)Prionotus scitulusleopard searobin 0.031 Trawl surveys 15.76 Browder model (demersal fish) 2.17 Browder model(demersal fish)Scorpaena brasiliensisbarbfish 0.128 Trawl surveys8.6 Fishbase 1.01 M fishbaseScorpaena calcaratasmoothhead scorpionfish 0.014 Trawl surveys 13.5 Fishbase 2.03 M fishbasePolydactylus octonemusAtlantic threadfin 0.000 Trawl surveys 15.76 Browder model (demersal fish) 2.17 Browder model(demersal fish)Urophycis regiaspotted hake 0.020 Trawl surveys 12.92 Browder model (demersal predators) 0.855 Browder model(demersal preds.)Urophycis floridanasouthern hake 0.005 Trawl surveys 12.92 Browder model (demersal predators) 0.855 Browder model(demersal preds.)Ariosoma balearicumbandtooth conger 0.016 Trawl surveys 12.92 Browder model (demersalpredators) 0.855 Browder model(demersal preds.)GobiidaeGobies 12.30 GoM models 0.30 Other GoM modelOphidiidaecusk-eels (gadiforms) 0.079 Trawl surveys 12.92 Browder model (demersal predators) 0.855 Browder model(demersal preds.)Ogcocephalidaebatfish family 0.041 Trawl surveys 12.92 Browdermodel (demersal predators) 0.855 Browder model(demersal preds.)24Surface pelagicsHemirhamphidaehalfbeaks 0.000 Trawl surveys 11.70 Independ 2.60 Looe key (smallplanktivores)Exocoetidaeflyingfish family25Structure associated coastal piscivoreLutjanus campechanusred snapper 0.013 B=C/F 16.78Brown et al. 1991 0.50 ZLutjanus griseusgray snapper 0.002 Trawl surveys 7.24 Palomares equation 0.57 Other GoM modelLutjanus cyanopteruscubera snapper 4.95 GoM models 0.57 Other GoM modelLutjauns analismutton snapper 4.95 GoM models 0.57 Other GoM modelEcopath model of the West Florida Shelf: Volume II. Model construction150Lutjauns apodusschoolmaster 4.95 GoM models 0.57 Other GoM modelLutjanus buccanellablackfin snapper 4.95 GoM models 0.57 Other GoM modelLutjanus jocudog snapper 4.95 GoM models 0.57 Other GoM modelLutjanus mahogonimahogany snapper 4.95 GoMmodels 0.57 Other GoM modelLutjanus purpureuscaribbean red snapper 4.95GoM models 0.57 Other GoM modelLutjanus vivanussilk snapper 4.95 GoM models 0.57 Other GoM modelPristipomoidesaquilonariswenchman 0.020 Trawl surveys 4.95GoM models 0.57 Other GoM modelEtelis oculatusqueen snapper 4.95 GoM models 0.57 Other GoM modelLutjanidae snapper family0.57 Other GoM model"snappers"0.57 Other GoM modelEpinephelus cruentatusgraysby 0.001 Trawl surveys 2.30GoM models 0.40 Other GoM modelEpinephelus niveatussnowy grouper 0.001 B=C/F 10.21 Brown et al. 1991 0.61 ZAntennariidaeAnglerfish 0.040 Trawl surveys 12.92 Browder model (demersal predators) 0.855 Browder model(demersal preds.)Holocentridaesquirrelfish family 0.030 Trawl surveys 12.92 Browder model (demersal predators) 0.855 Browder model(demersal preds.)Sphyraena barracudagreat barracuda 0.007 B=C/F 10.21 Brown et al. 1991 0.40 ZMuraenidaemoray family 0.495 Trawl surveys 7.30 GoM models 0.95 Other GoM modelCongridae (conger eels)conger eel family 0.048 Trawl surveys 12.92 Browder model (demersal predators) 0.855 Browder model(demersal preds.)Grammistidaesoapfish 0.064 Trawl surveys 12.92 Browder model (demersal predators) 0.855 Browder model(demersal preds.)26Large groupersEpinephelus itajarajewfish 2.52 Palomares equation 0.85 ZEpinephelus moriored grouper 0.080 B=C/F 4.47 GoM models 0.44 ZEpinephelus flavolimbatusyellowedge grouper 0.006 Trawl surveys 4.32Palomares equation 0.40 Other GoM modelEpinephelus adscensionisrock hind 5.58 Palomares equation 0.40 Other GoM modelEpinephelusdrummondhayispeckled hind 0.000 B=C/F 3.6 Palomares equation 0.63 ZEpinephelus guttatusred hind 2.30 GoM models 0.40 Other GoM modelEpinephelus nigrituswarsaw grouper 2.69 Palomares equation 0.40 Other GoM modelMycteroperca interstitialisyellowmouth grouper 7.63 Palomares equation 0.40 Other GoM modelMycteroperca venenosayellowfin grouper 2.30 GoM models 0.40 Other GoM modelMycteroperca phenaxscamp 0.007 B=C/F 5.25 Palomares equation 0.32 ZMycteroperca microlepisgag 0.026 NMFS assessment(Steve Atran memoto Wayne Swingle)4.15 Palomares equation 0.42 Z27Structure associated coastal invertebrate feedersCentropristis ocyurusbank sea bass 0.187 Trawl surveys 9.20 Fishbase 1.12 M fishbaseCentropristis striatablack sea bass 0.112 Trawl surveys 9.20 Fishbase 1.12 M fishbaseEcopath model of the West Florida Shelf: Volume II. Model construction151Centropristisphiladelphicarock sea bass 0.221 Trawl surveys 9.20 Fishbase 1.12 M fishbaseSerranus subligariusbelted sandfish 0.000 Trawl surveys 9.20 Fishbase 1.12 M fishbaseHemanthias leptuslongtail bass 0.000 Trawl surveys 9.20 Fishbase 1.12 M fishbaseHypoplectrus unicolorbutter hamlet 0.000 Trawl surveys 9.20 Fishbase 1.12 M fishbaseParanthias furcifercreole fish 0.000 NMFS trawl9.20 Fishbase 1.12 M fishbaseHalichoeres bivitattusslippery dick 0.000 Trawl surveys 9.20 Fishbase 1.12 M fishbaseHalichoeres caudalispainted wrasse 0.000 Trawl surveysHalichoeres garnotiyellowhead wrasseLanchnolaimus maximushogfish 0.039 Trawl surveys 4.66 Palomares equation 0.60 guessBodianus pulchellusspotfin hogfishBodianus rufusspanish hogfishDecodon puellarisred hogfish 0.006 Trawl surveys 0.60 guessThalassoma bifasciatumblueheadOdontoscion dentexreef croaker 10.00 GoMmodels 0.35 Other GoM modelEquetus lanceolatusjackknife-fish 0.036 Trawl surveys 10.21 Brown et al. 1991 0.60 guessOcyurus chrysurusyellowtail snapper 0.007 B=C/F 4.95 GoM models 0.37 ZRhomboplites aurorubensvermilion snapper 0.045 Trawl surveys4.95 GoM models 0.48 Manooch &Johnson1998 (Z)Opsanus pardusleopard toadfish 0.000 Trawl surveys 0.60 guessScorpaenidaescorpian fish 0.010 Trawl surveys 13.50 Fishbase 2.03 M fishbaseChaetodon capistratusfoureye butterflyfishPriacanthidaebigeyes 0.031 Trawl surveys 12.80 Looe key (smallreef carnivores) 1.60 Looe key (small reefcarn.)Archosargusprobatocephalussheepshead 0.082 Murphy andMcDonald 200011.11 GoM modelsCalamus proridenslittlehead porgy 0.203 Trawl surveys 5.20 GoM models 0.65 Other GoM modelCalamus bajonadojolthead porgy 0.100 Trawl surveys 5.20 GoM models 0.65 Other GoM modelCalamus calamussaucereye porgy 0.007 Trawl surveys5.20 GoM models 0.65 Other GoM modelCalamus leucosteuswhitebone porgy 0.101 Trawl surveys5.20 GoM models 0.65 Other GoM modelCalamus nodosusknobbed porgy 0.003 Trawl surveys 5.20 GoM models 0.65 Other GoM modelHaemulon plumieriwhite grunt 0.127 Trawl surveys 9.38 GoM models 0.66 ZHaemulon flavolineatumFrench grunt 0.005 Trawl surveys 8.14 GoM models 0.38 Other GoM modelHaemulon macrostomumSpanish grunt 0.005 Trawl surveys 8.14 GoM models 0.38 Other GoM modelHaemulon albummargate 0.005 Trawl surveys 8.14GoM models 0.38 Other GoM modelHaemulon sciurusbluestriped grunt 0.005 Trawl surveys8.14 GoM models 0.38 Other GoM modelHaemulon striatumstriped grunt 0.003 Trawl surveys 8.14 GoM models 0.38 Other GoM modelHaemulon parrasailor's grunt 0.005 Trawl surveys 8.14 GoM models 0.38 Other GoM modelAnisotremus virginicusporkfish 0.001 Trawl surveys 8.14GoM models 0.38 Other GoM modelGobiosoma oceanopsneon goby 12.30 GoM models 0.30 Other GoM modelEcopath model of the West Florida Shelf: Volume II. Model construction15228Structure associated coastal omnivoresHolacanthus bermudensisblue angelfish 0.039 Trawl surveysPomacanthus arcuatusgray angelfish 0.001 Trawl surveys 37.45 Looe key (small reef herbivores) 1.90 Looe key (small reefherb.)Centropyge argicherubfishHolacanthus tricolorrock beautyPomacentrus variabiliscocoa damselfish 0.000 Trawl surveys 37.45 Looe key (small reef herbivores) 1.90 Looe key (small reefherb.)Pomacentrus partitusbicolor damselfish 0.000 Trawl surveys 37.45 Looe key (small reef herbivores) 1.10 Looe key (small reefherb.)Pomacentrus leocostictusbeau gregory 0.000 Trawl surveys 37.45 Looe key (smallreef herbivores) 1.10 Looe key (small reefherb.)Microspathodonchrysurusyellowtail dameselfish 0.000 Trawl surveys 37.45 Looe key(small reef herbivores) 1.10 Looe key (small reefherb.)Parablennius marmoreusseaweed blenny 0.000 Trawl surveys12.80 GoM models 1.60 Other GoM modelAcanthuridaeocean surgeonsScarus croicensisstriped parrotfish 0.020 Trawl surveys22.80 GoM models 1.55 Other GoM modelCoryphopterusglaucofraenumbridled goby 12.30 GoM models 0.30 Other GoM modelKyphossus sectarixbermuda chub29Structure associated coastal planktivoresApogon pseudomaculatustwospot cardinalfish 0.002 Trawl surveysPhaeoptyx xenussponge cardinal fish 0.000 Trawl surveys 10.00 Looekey (small planktivores) 2.60 Looe key (smallplanktivores)Chromis scottipurple reeffish 0.000 Trawl surveys 10.00 Looe key (small planktivores) 2.60 Looe key (smallplanktivores)Chromis enchrysurusyellowtail reeffish 0.000 Trawl surveys 10.00 Looekey (small planktivores) 2.60 Looe key (smallplanktivores)Chromis cyaneablue chromis 0.000 Trawl surveys 10.00 Looe key(small planktivores) 2.60 Looe key (smallplanktivores)Opisthognathus aurifronsjawfish 0.000 Trawl surveys 10.00 Looe key (small planktivores) 2.60 Looe key (smallplanktivores)30Nearshore associated piscivoresTarpon atlanticaAtlantic tarpon 9.20 Palomaresequation 1.06 Browder model (pelagicpreds.)Elops saurusladyfish 0.002 B=C/F 9.13 Palomares equationCentropomus undecimaliscommon snook 0.003 B=C/F 5.44 Palomares equationAlbula vulpesbonefish31MulletsMugil cephalusstriped mullet 0.010 B=C/F 12.30 Vidal 0.60 ZMugil spp.other mullets 0.319 B=C/F 10.37 GoM models 0.90 Other GoM model32Nearshore planktivoresEcopath model of the West Florida Shelf: Volume II. Model construction153Anchoa mitchillibay anchovy 0.177 Brown et al. 1991 14.24 GoM models 0.60 Other GoM modelAnchoa hepsetusstriped anchovy 14.24 GoM models 0.60 Other GoM modelAtherinidaesilverside family (e.g.Menidia sp.)0.000 Trawl surveys 26.02 Palomares equation 0.60 Other GoM modelAlosa spp.alewife 0.005 NMFS trawl 10.91 Palomares equation 0.60 Other GoM model33Other fishesOther fish 3.877 Trawl surveys 7.04 GoM models 0.70 Other GoM modelEcopath model of the West Florida Shelf: Volume II. Model construction



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