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 33701-5095 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

iii 

Preface 

In 1999, the Florida State Legislature provided 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 Florida Shelf, support a number of Florida’s 

commercial and recreational fisheries either directly or indirectly. Such ‘bait fish’ play a 

critical role in the coastal ecosystem by linking apex predators with plankton production. 

For example, changes in the abundance of the sardine/herring 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 quantifyable method to investigate the roles and health of 

these types of forage fishes, as well as to investigate and address other pressing 

ecological dilemmas. The methodologies described herein represent the first real steps 

towards an ecosystem-based approach to managing and protecting Florida’s marine 

resources. Such an ecosystem-based approach is necessary to enable the most broad 

and risk-averse uses of Florida’s living marine resources. 

Scientists and managers from the FWC/FMRI Division of Marine Fisheries, and 

scientists from other institutions, reviewed several modelling techniques during a twoday workshop held in November 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 Columbia’s Fisheries Centre 

worked with scientists from the Florida Fish and Wildlife Conservation Commission / 

Florida Marine Research Institute (FWC/FMRI), the University of South Florida, the 

National Marine Fisheries Service (NMFS, SEFC), and other institutions to construct the 

present trophic model of the West Florida Shelf. The electronic version of the model is 

available from the FMRI to any interested parties. The EwE software can be freely 

downloaded from www.Ecopath.org. 

Behzad Mahmoudi 

Florida Marine Research Institute, St. Petersburg 

May 2002 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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Summary 

This volume documents the derivation of input parameters 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 used 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 biological community during the late 1990s. This model was 

made exceptionally transparent by the easy-to-use ‘windows-based’ EwE software, and 

the present document. The model was constructed to complement current research in 

the region with cutting-edge food web analysis and dynamic simulation techniques to 

address pressing ecological and fisheries dilemmas. It holds considerable potential for 

education, in addition to its 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., Vidal-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 

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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 WHAT IS ECOPATH? ..............................................................................................................................1

1.2 THE ECOPATH MASTER EQUATION..........................................................................................................2

2. MODEL CONSTRUCTION .......................................................................................................................4

2.1 PROCEDURE FOR MODEL CONSTRUCTION ...............................................................................................4

General protocol...................................................................................................................................4

Additional parameters ..........................................................................................................................5

2.2 ESTIMATING AREAS OF WFS DEPTH ZONES ............................................................................................6

2.3 BASIC INPUT PARAMETERS OF THE WFS MODEL .....................................................................................8

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

3.1 THE MEANING OF “BALANCING”.............................................................................................................10

3.2 MODEL BALANCING STRATEGIES...........................................................................................................10

3.3 PARAMETER PEDIGREE ASSESSMENT ...................................................................................................11

3.4 BALANCING THE MODEL .......................................................................................................................13

3.5 PARAMETERS OF THE BALANCED MODEL...............................................................................................14

MODEL INPUTS (SECTIONS 4 – 13) ........................................................................................................17

4. PRIMARY PRODUCERS...........................................................................................................................18

4.1 Phytoplankton...............................................................................................................................18

4.2 Microphytobenthos .......................................................................................................................22

4.3 Macroalgae...................................................................................................................................25

4.4 Seagrasses...................................................................................................................................28

5. DETRITUS .............................................................................................................................................32

5.1 Water column detritus ..................................................................................................................32

5.2 Benthic detritus.............................................................................................................................33

5.3 Drift macrophytes .........................................................................................................................34

5.4 Dead carcasses............................................................................................................................34

6. MICROBIAL HETEROTROPHS...................................................................................................................36

7. ZOOPLANKTON......................................................................................................................................38

7.1 Zooplankton..................................................................................................................................38

7.2 Ichthyoplankton ............................................................................................................................42

7.3 Carnivorous jellyfish .....................................................................................................................45

8. BENTHIC INVERTEBRATES ......................................................................................................................47

8.1 Meiobenthos.................................................................................................................................47

8.2 Small infauna................................................................................................................................49

8.3 Small mobile epifauna..................................................................................................................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...................................................................................................................................74
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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8.9 Lobsters........................................................................................................................................79

8.10 Stomatopods ..............................................................................................................................81

8.11 Octopods ....................................................................................................................................86

8.12 Squids.........................................................................................................................................88

9. FISHES .................................................................................................................................................90

9.1 Systematic categorization of fish groups......................................................................................90

9.2 Biomasses of fish groups .............................................................................................................90

9.3 Production (P/B) and Consumption (Q/B) estimates....................................................................98

9.4 Fish diet compositions................................................................................................................101

9.5 Initial fish parameter estimates...................................................................................................102

10. SEABIRDS .........................................................................................................................................108

11. SEA TURTLES ...................................................................................................................................110

12. MARINE MAMMALS.............................................................................................................................114

12.1 Odontecetes .............................................................................................................................114

12.2 Manatees..................................................................................................................................116

13. FISHERIES ........................................................................................................................................118

13.1 Landings...................................................................................................................................118

13.2 Bycatch and discards ...............................................................................................................123

14. LIMITATIONS, CAUTIONS, AND GUIDELINES FOR USE ..............................................................126

14.1 SCALE AND INFERENCE: THE NATURE OF ECOPATH MODELS..............................................................126

14.2 PARAMETER UNCERTAINTIES............................................................................................................128

14.3 PROBLEMS WITH FUNCTIONAL GROUP AGGREGATION .......................................................................129

14.6 GUIDELINES FOR APPROPRIATE USES OF THE MODEL.........................................................................131

APPENDICES...........................................................................................................................................133

APPENDIX 1. LIST OF CONTRIBUTORS ......................................................................................................133

APPENDIX 2. DIET MATRIX OF THE WEST FLORIDA SHELF ECOPATH MODEL ...............................................135

APPENDIX 3. DETRITUS FATE OF EACH FUNCTIONAL GROUP.......................................................................139

APPENDIX 4. MARKET VALUES OF UTILIZED GROUPS .................................................................................141

APPENDIX 5. GUIDE TO ‘DATA PEDIGREE’ VALUES......................................................................................142

APPENDIX 6. EMPIRICALLY BASED CONVERSION FACTORS .........................................................................143

APPENDIX 7. FUNCTIONAL GROUPINGS AND PARAMETERS FOR FISHES.......................................................144
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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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/Goddard Space Flight Center, and ORBIMAGE.)..............7

Figure 7.1. Changes in the abundance and spatial distributions of ichthyoplankton on the West Florida 

Shelf relative to zooplankton captured concurrently. Error bars are standard error; large error bars 

represent uneven spatial distributions. Sample sizes (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 make 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 the Gulf of Mexico. Areas of higher catches are darker 

(from Klima 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 Florida Shelf (Darcy and Guntherz 1984; Darnell et al. 

1987). .................................................................................................................................................92
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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List of Tables 

Table 2.1. Surface areas of depth strata for the West Florida Shelf.............................................................6

Table 2.2. Best estimates used as initial inputs for the West Florida Shelf model .......................................8

Table 3.1. Assigned pedigree index values ................................................................................................11

Table 3.2. Basic parameters of the balanced West Florida Shelf model....................................................14

Table 4.1. Estimates of phytoplankton biomass over the West Florida Shelf.............................................19

Table 4.2. Estimates of phytoplankton production over the West Florida Shelf. ........................................19

Table 4.3. Derivation of Production/Biomass values from assimilation value estimates............................20

Table 4.4. Estimates of microphytobenthos biomass in marine sands of the West Floirda Shelf..............24

Table 4.5. Estimates of microphytobenthic production on the West Floirda Shelf. ....................................24

Table 4.6. Numbers of species in the 5 divisions of plants on the West Florida Shelf. ..............................25

Table 4.7. Estimates of macroalgae biomass on the West Florida Shelf. ..................................................25

Table 4.8. Estimates of seagrass production relevant to the west Florida Shelf........................................30

Table 5.1. Derivation of water column detritus for the West Florida Shelf..................................................32

Table 5.2. Percent organic carbon in sediment relevant to the West Florida Shelf....................................33

Table 6.1. Estimated diet composition of microbial heterotrophs on the West Florida Shelf. ....................36

Table 7.1. Warm season biomass estimates for WFS zooplankton by depth zone. ..................................40

Table 7.2. Annualized Ecopath parameters for zooplankton of the West Florida Shelf. ............................40

Table 8.1. Biomass estimates of small infauna biomass on the West Florida Shelf. .................................50

Table 8.2. Representative abundances and biomasses from Rookery Bay and Charlotte Harbor............51

Table 8.3. Estimated diet composition of ‘small infauna’ on the West 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 the 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 West Florida Shelf. .......................60

Table 8.8. Biomass estimates of echinoderms and large gastropods on the West Florida Shelf. .............62

Table 8.9. Estimated diet composition of echinoderms 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 km2..........70

Table 8.12. Rough estimates of adult shrimp diet composition on the West Florida Shelf. .......................73

Table 8.13. Summary of Ecopath parameters for large crabs on the West Florida Shelf. .........................74

Table 8.14. Estimated diet composition for large crabs on the West Florida Shelf. ...................................75

Table 8.15. Diet composition of horseshoe crabs 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 West Florida 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 Spanish mackerel. ..................................93

Table 9.3. Spotted seatrout numbers and weight at age for Northwest Florida. ........................................94

Table 9.4. Fish biomass estimates based on Catch/F................................................................................94

Table 9.5. Biomass estimates for small pelagics on the West Florida Shelf. .............................................96

Table 9.6. Relative abundance of fish species on West Florida Shelf........................................................97

Table 9.7. Comparison of P/B (Z) and natural mortality (M) estimates for small pelagics .........................99

Table 9.8. Temperature data for West Florida Shelf.................................................................................100

Table 9.9. Diet information sources specific to the study region. .............................................................102

Table 9.10. Best weighted estimates and ranges of parameters for WFS fish groups.............................103

Table 10.1. Abundance of bait fish consuming seabirds in the Gulf coast of Florida...............................108

Table 10.2. Seabird parameters for the West Florida Shelf model...........................................................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 cetacean species..............................................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 oceanic piscivores ...............................................................123
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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Table 14.2. Discards by shrimp trawlers...................................................................................................124

Table A2.1. Diet Matrix of the West Florida Shelf model (%) ...................................................................135

Table A3.1. Fate of detritus for each functional group..............................................................................139

Table A4.1. Market values of functional groups........................................................................................141

Table A5.1. Confidence intervals associated with pedigree ranks ...........................................................142

Table A6.1. Conversion factors used in the construction of the West Florida Shelf model......................143

Table A7.1. Summary table of all fish group parameters and references ................................................144
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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1. The Ecopath approach 

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

1.1 WHAT IS ECOPATH? 

An Ecopath model is a quantitative description of biomass flows in a food web (i.e., 

energy flows). These computer models are constructed by defining a model area and 

time, organizing species (and detritus) into 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 components, 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 ecosystems, 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 considerably. These dynamic routines 

use the information in Ecopath models to simulate the potential 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 ecological trade-offs in harvest strategies. 

These complimentary approaches, Ecopath and Ecosim, provide a rigorous and 

relatively simple framework to provide testable insights into the causes of ecosystem 

changes. Most importantly, they can be used to implement ecosystem-based 

management by aiding in the design of policies 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 often consistent with ecological theory 

(Christensen 1995, Vasconcellos et al. 1997), but they can be even more useful when 

simulation results are counterintuitive. 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 compare and combine simulated biological and 

physical forces. These approaches are discussed by Christensen et al. (2000). 

Ecopath models are never final because ecosystem knowledge is never complete. The 

usefulness of such models can improve considerably, however, through iterative 

combinations of simulation and empirical research in a whole ecosystem context (Pauly 

et al. 2000). 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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1.2 THE ECOPATH MASTER EQUATION

The parameters necessary for the construction of an Ecopath model are found in the 

Ecopath master equation (Equation 1.1): 

Bi ⋅ (P/B)i ⋅ EEi = Yi + Σ Bj ⋅ (Q/B)j ⋅ DCji + BAi + NMi

 Equation 1.1 

where, 

Bi

 and Bj = biomasses of prey (i) and predators (j) respectively; 

P/Bi

 = production / biomass; equivalent to total mortality (Z) in most circumstances (Allen 1971); 

EEi

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

Yi = fisheries catch per unit area and time (i.e., Y = F*B); 

Q/Bj

 = food consumption per unit biomass of j; and 

DCji = contribution of i to the diet of j; 

BAi

 = biomass accumulation of i (positive or negative); 

NMi

 = 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 mortality (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 senescence (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 production 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 ‘steady state’ approach. This basic ‘continuity’ constraint 

enables representation of changes 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 Pauly 1992). These along with 

diet compositions, are the main parameters derived in Sections 4 – 13 of this report. 

Other information such as spatial and temporal 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 biomass. 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 characteristics. Ecol. Modelling 61:169-185. 

Christensen, V., C.J. Walters and D. Pauly. 2000. Ecopath with Ecosim – A User’s Guide. Univ. of British 

Columbia, Fisheries Centre, Vancouver, Canada 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 dynamic models of exploited ecosystems 

from trophic mass-balance assessments. Reviews 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 

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2. Model construction 

2.1 PROCEDURE FOR MODEL CONSTRUCTION

General protocol 

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

Procedures for deriving model input 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 Florida Shelf model was was constructed by a 

core group of researchers based on contributions by expert collaborators. These 

regional and topical experts contributed written sections to this compendium describing 

basic parameter derivations for each functional group. Inputs to this model were based 

on the latest available information on the entire suite of biotic components of the West 

Florida Shelf. Appendix 1 lists these contributors with their contact information, and 

authors of each section 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 clearly 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 into 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 documenting 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 distributions, and habitat associations. 

6. Enter parameters into the windows-based input interfaces (see www.Ecopath.org). 

7. Characterize model pedigree by ranking parameter quality (i.e., confidence). 

8. Balance the model according to thermodynamic constraints. 

The biological components of the ecosystem are generally represented in Ecopath

using average values, or other meaningful measures of central tendency that take into 

account both annual (seasonal) changes and ontogenetic changes. 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 

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Ontogenetic changes can be incorporated using Ecopath with Ecosim using two 

approaches. First, groups can be split into adult and juvenile ‘pools’ that are linked 

through and age structured growth and recruitment parameters; 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 ‘average’ 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 continuity’ offers a powerful 

mass-balance-type constraint to model parameterization and construction. Section 3 

describes the balancing methodology employed 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 organisms is assimilated to the body; nonassimilated food is expelled. Proportions of unassimilated food must be specified in 

Ecopath, and this fraction flows to specified detritus pools. A default value of 0.2 was 

used for carnivorous fish groups (Winberg 1960) since assimilation efficiency 

information for particular fish species was scarce. 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 discards) 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 (Apendix 2). The majority of detritus from 

non-assimilated food is directed to water column detritus and sediment detritus, but 

these ratios vary depending on the types of organisms. Approximately 50% of the 

detritus from birds is considered to be exported from the system (i.e., corpses and feces 

end up on land). All dead and decaying macroalgae 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 unavailable to the system. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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2.2 ESTIMATING 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 extent over which organisms are distributed. To derive these areal 

estimates, bathymetry data, digitized from NOAA Nautical Charts ranging in scale from 

1:450,000 to 1:1,200,000, were compiled along with 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 (km2

) Percent 

of area 

Cumulative area 

(km2

) 

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 defined as the 200m isobath. Based on the areal 

measurements given in Table 2.1, the total area was taken as being 170,000 km2. This 

value was used in biomass calculations where appropriate. 

Literature cited (Model construction) 

Allen, R.R. 1971. Relation between production and biomass. 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 requirements of fishes. Fish. Res. Bd. Can. Translation 

series No. 194. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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Figure 2.1. Depth strata for the West Florida Shelf (Meters). (SeaWiFS natural color satellite image 

provided by the SeaWiFS Project, NASA/Goddard Space Flight Center, and ORBIMAGE.) 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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2.3 BASIC 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 construction and balancing of the model. Examples of 

diet compositions and other input parameters are provided in Section 3, and in 

parameter estimation sections (Sections 4 – 13). Fisheries catch and discard 

information is provided in Section 13; the final input parameters of the balanced model 

are presented in Section 3.5; the diet matrix for the West Florida Shelf model is 

provided in Appendix 2; detritus fate information 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 example, the behavior and habitat of some species reduces their 

availability to trawl sampling, and thus their abundance is underrepresented. For other 

groups, the general paucity of data insists that for the time being, parameters are 

borrowed from similar species and settings. 

Table 2.2. Best estimates used as initial 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/eaters 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 10.11 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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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 175.620 263.430 117.080 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 125.000 187.500 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 

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3. “Balancing” the model 

Steven Mackinson 

CEFAS Fisheries Lab, Lowestoft, UK 

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

3.1 THE MEANING OF “BALANCING” 

Ecopath models must be ‘balanced’ in the sense 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 for 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 combined 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 most or all components in a system tend 

to require minimal ‘balancing.’ This is because 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. Ecotrophic efficiency (EE) is the proportion of 

the net production of a group that is consumed by predators or fisheries (or directly 

exported). These EE terms are calculated after initial input parameters have been 

derived and entered. An ecotrophic 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 “imbalance.” This is the handle for balancing, and 

changes in these values are monitored while adjusting model inputs. 

3.2 MODEL BALANCING STRATEGIES

Strategic approaches are implemented 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 characterized by specified confidence bounds for 

each parameter (Table 2.2) or by ranking the data ‘pedigree’ of parameters (Section 

3.3). 

Some experienced Ecopath modelers suggest that model balancing should focus on 

diet composition adjustments because diet composition data tends to be very high 

relative to other parameters (V. Christensen, UBC Fisheries Centre, pers. com., 7 

November 2000). However, this relative uncertainty among parameters should be 
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assessed on a case-specific basis. Indeed, for some functional groups, the uncertainty 

of input parameter estimates such as biomass might rival or surpass uncertainties 

associated with diet compositions. Finally, model users can introduce bias into the 

model through a one sided approach to balancing. For example, a model can be 

erroneously inflated by increasing prey biomasses, or production rates, or both, rather 

than taking a balanced approach by including the reduction of predator consumption 

rates, or by re-allocating diet compositions. 

Commonly, ‘top-down’ balancing strategies 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 increased 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 unrealistically 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 specified bounds of confidence and 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 PARAMETER PEDIGREE 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 parameter quality to quantitative confidence intervals (See 

Appendix 5). The output of this routine can be used during manual balancing, 

automated balancing and analysis routines, such as the Monte Carlo routine 

‘Ecoranger,’ or in meta-analyses that compare various models in terms of relationships 

between model attributes and overall data pedigree. Assigning pedigree values to 

functional groups whose parameters are derived from combined 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. The parameter pedigree 

routine thus inhances model transparency beyond a description of of parameter 

derivation. 

The pedigree index value represents 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 0.7 
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3 Turtles 0.7 0.2 0.2 0.5 

4 Manatees 1 1 1 1 

5 Large oceanic piscivores 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/eaters 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/eaters 0.4 0.1 0.6 0.7 1 

22 BentCoasPisc 0.7 0.6 0.6 0.7 1 

23 BentCoasInvert/eaters 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 1 
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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 represent relative data quality of 

parameter estimates. Appendix 5 presents confidence intervals 

associated with pedigree index values. 

3.4 BALANCING THE MODEL

We took an iterative and manual approach to balancing the the West Florida Shelf 

model. Our approach was largely based on the pedigree rankings assigned to each 

parameter, as well as more detailed and parameter-specific considerations. This 

ensured incorporation of ‘best judgement’ and common sense during thermodynamic 

balancing. It also provided maximum insights into ecosystem function. 

A number of groups were unbalanced when output 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 common (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 impetus for refinement of estimates, and the 

model thus serves as a vehicle for refinement of ecosystem knowledge. 

In light of this unbalance, initial parameter estimates were re-evaluated and refined after 

dialogue and negotiation among coordinators regarding 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’ using our initial input parameter 

estimates leaving some parameters to be estimated by Ecopath. The balancing 

procedure integrated an assessment of uncertainty 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 reduce the biomass and or consumption of predators in cases 

where predator demands on prey groups were very high and inflation of prey groups 

would have exceeded confidence bounds for those groups. 

To guide the model balancing procedure we established ecological ‘anchor’ groups, 

each of which was positioned at different trophic levels. These groups were considered 

to have strong connections throughout the food web, and their parameters were thought 

to be of good quality. Mackerel and sardine-herring were our main anchor groups. We 

held constant the parameter estimates for these anchor groups while adjusting less 

robust values for other groups. The sardine-herring component was considered to be a 

particularly good anchor, since it is a crucial channel of energy from primary producers 

to top predators. This group occurs at a mid trophic level, and has a substantial 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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estimated biomass. The collaborative team effort to ‘balance’ the model resulted in 

negotiation among specialists on different trophic realms, and this resulted in a toggling 

between ‘top down’ and ‘bottom up’ adjustments during more than 30 minor iterations 

as parameters were adjusted around anchor points based on these ‘taxo-trophically’ 

different perspectives. 

Two common problems encountered during this preliminary 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 increases the proportion of 

the diet that comes from within the group. These problems 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 the 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 groups 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 taxonomically reasonable levels for each group. 

The preliminary balanced model was reviewed by the architect of Ecosim, Dr. Carl 

Walters. His analysis indicated unrealistically high production rates for several fish 

groups (making them overly resilient to disturbance). It also indicated that the fishing 

mortality rate estimate for shrimp was considerably lower than typical, implying that 

shrimp biomasses were overestimated (fishing mortality = catch / biomass). The same 

fishing mortality rate problem also applied to some fish groups, a consequence again of 

aggregation into functional groups (see Section 14.3). 

A second iteration of model balancing then commenced in response 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 the West Florida Shelf ecosystem; 

4. The model enables ‘useful’ simulations to explore policy alternatives; 

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

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(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/eaters 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 BentOceInvert/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 

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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 estimates (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 marked with c. Values in [square brackets and red] are estimated by Ecopath. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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Model inputs (Sections 4 – 13) 

The following sections document the derivation of input parameters for the 59 functional 

groups in the West Florida Shelf model. Functional groups are organized into nine ‘taxotrophic’ realms to organize this document: primary producers, detritus, microbial 

heterotrophs, zooplankton, benthic invertebrates, fishes, seabirds, sea turtles, and 

marine mammals. 

The format of most of the parameter derivation sections is standard. Each section 

begins with a definition of the functional group and a brief description of their taxonomy, 

distributions, and life history. After the group introduction, detailed approaches to 

estimating biomass, P/B, Q/B, and diet compositions are presented. Other information 

such as food assimilation efficiency, gross efficiency, 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 approach to parameter estimation. Section 13 provides 

information on fisheries landings and discards. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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4. PRIMARY PRODUCERS

4.1 Phytoplankton 

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

The phytoplankton of the West Florida Shelf consists mainly of diatoms and 

dinoflagellates. Diatoms are photosynthetic, single-celled protists (division Chrysophyta) 

with silicified cell walls, which take the form of lid-like valves of a protective ‘frustule’ 

(Wetzel 1983). Dinoflagellates are protozoans (subphylum Mastigophora) that are 

autotrophic (photosynthetic). These proto-animals propel themselves 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 West Florida Shelf, and they mediate the abundance and 

production of benthic primary producers by interfering with light penetration. Ultramicroscopic bacterioplankton (e.g., cyanobacteria) are thought to produce 10 – 60 times 

the as much as the larger phytoplankton constituents. The estimates included herein, 

however, incorporate this bacterioplankton component as biomass measurements are 

based on chlorophyll a concentrations. 

Although the global biomass of marine phytoplankton is relatively low (about three 

orders of magnitude lower than the global biomass of terrestrial plants), their production 

rates are so high that the global primary production 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 support relatively high biomasses of organisms 

in upper trophic levels. Marine food webs maintain their dynamic stability by switching to 

alternate food sources as ephemeral primary producers fluctuate in response to 

physical forces in the system. The ephemeral nature of these 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 West Florida Shelf was very poorly known prior to 1964, 

despite the central importance of phytoplankton to Gulf of Mexico food webs (El-Sayed 

1972). A number of studies have 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 estimates 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 central measures can be chosen. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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Table 4.1. Estimates of phytoplankton biomass 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; cmedian value of provided range, 

0.1 – 0.2; d

Vargo and Hopkins 1990. 

Table 4.2. Estimates of phytoplankton production 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⋅year1

 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⋅year1Valiela 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⋅day1El-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; bFlorida Fish 

and Wildlife Conservation Commission, unpublished data; cin Vargo and Hopkins 1991; din Vargo and 

Hopkins 1990; ein El Sayed et al. 1972; fmedian of given range 0.1 – 0.5; ggrand mean of data from three 

years and four stations; h

median of given range 0.04 – 0.14; imedian of given range 336 - 300; jgrand 

mean of provided data; kmedian of given range 14 – 16; lmedian of given range 0.5 – 0.75. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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The values of phytoplankton biomass and production 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 for the West Florida Shelf shown in Table 

4.3. Representative biomass and production values were chosen a priori to 

comparisons with the P/B values in Table 4.3. A comparison 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 values 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 area is very high in the region’s estuaries 

relative to the West Florida Shelf as a whole. Livingston (1984) found that phytoplankton 

productivity of the Apalachicola Bay estuary 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. Furthermore, this comparison includes phytoplankton 

only. Comparison of benthic production would undoubtedly show 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 revealed by Saunders and Glenn (1969) a decade 

previously (Figure 4.1). Sutton et al. (2001) has observed similar near-bottom 

phytoplankton concentrations using higher resolution 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-suspension at the sediment-water interface 

during that time. This seasonal shift in the vertical distribution of phytoplankton 

production likely reflects similar shifts in the ratio of benthic to planktonic production and 

cycles of secondary production. Shallow microphytobenthos has been shown to triple in 

abundance during the fall (Figure 5.1) when phytoplankton is least abundant. 

Such seasonal and spatial patterns of production might have changed since the early 

1970s as surface plankton blooms increasingly blocked light to deeper primary 

producers. Estimates of coastal phytoplankton production in the region surrounding the 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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West Florida Shelf have indeed consistently 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. Alternatively, it could portend widespread 

ecological changes on the West Florida Shelf that goes un-noticed. 

Literature cited (Phytoplankton) 

Barnes, R.D. 1987. Invertebrate Zoology, 5th Edition. Saunders College Publishing, Philadelphia, 893 p. 

Biggs, D. C., R. A. Zimmerman, R. Gasca, E. Suarez-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 hydrography of Apalachicola Bay. M.S. Thesis. 

Department of Oceanography, Florida 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. 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. 

Livingston, R. J. 1984. The ecology of the Apalachicola 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. Memoirs 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 conceptual 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 Institution 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. Fish 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 Institute for the U.S. Fish and Wildlife Service, 

Project F-65. 

Vargo, G.A. and T.L. Hopkins. 1990. Plankton. In: Synthesis 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, 2nd 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 Columbia, Fisheries Centre 

Microphytobenthos are microscopic primary producers that live within the top few 

millimeters of sea floor sediment and hard substrate. It has recently come to light that 

these microscopic photosynthesizers can contribute a significant portion of the primary 

production in nearshore marine 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, Sournia 1976, Hartwig 

1978). The benthic diatoms, dynoflagellates, euglenoid flagellates, unicellular eukaryotic 

algae, and cyanobacteria that make up these microphytobenthis 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 increases when and where the biomass of 

overshading plankton decreases (Cahoon and Clarke 1992), thus probably evening out 

primary production across horizontal space on the shelf as a whole. Nevertheless, both 

horizontal and vertical distributions of micro-photosynthesizers within sub-systems 

undoubtedly mediate the character of secondary and higher level production in the West 

Florida Shelf food web. Light penetration into marine systems like the West Florida 

Shelf is a critical determinant of microphytobenthic production, biomass, and distribution 

(MacIntyre et al. 1996, Miller et al. 1996), though it can persist at low light levels 

(Cahoon and Cooke 1992). The photosynthetic dinoflagellates that live symbiotically in 

the tissues of corals and anemones, zoozanthellae, are also included in the 

microphytobenthos category. 

The realization of high production of microphytobenthos begs for the reorganization of 

quantitative, and conceptual, energy flow models of most nearshore systems. For 

example, many species traditionally categorized 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 microphytobenthos, a high quality and fast growing 

food, rather than by dead organic detritus, a lower quality and less biologically available 

food (Miller et al. 1996). Most benthic detritus feeders and deposit feeders eat 

microphytobenthos, from microfauna (ciliated protozoans), to the suite of meiofaunal 

and macrofaunal species (and phyla) like amphipods and polychaetes (fish food), to 

megafaunal invertebrates like holothurians, large, gastropods, asteroids, crabs, and 

shrimps. But furthermore, suspension feeders and zooplankton consume 

microphytobenthos re-suspended by wave surge or by bioturbation. Biotic 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 microphytobenthos, as well as its biomass, is strongly 

influenced, and limited, by the amount of light reaching the sediment (Hartwig 1978, 

MacIntyre et al. 1996, Miller et al. 1996). On the West Florida Shelf, microphytobenthic 

production on the West Florida Shelf declines with depth, but it also varies among 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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seasons. Higher microphytobenthic production in October appears counterintuitive since 

more light should penetrate the water column during summer when solar radiation is 

more direct. However, this pattern can be explained by summertime phytoplankton 

blooms, which may inhibit microphytobenthic production, as observed by Hartwig 

(1978). Declining microphytobenthic production with increasing depth has also been 

observed off Madagascar, though various studies indicate intrinsic minimum values for 

biomass and production of microphytobenthos (Hartwig 1978, Cahoon and Cooke 

1992). 

Representative values of biomass (29.778 t⋅km-2) and productivity (706.496 t⋅km-2⋅year1

) for the West Florida Shelf were chosen from a variety of estimates resulting from 

studies of marine sands on continental shelves. Considering the depth attenuation of 

microphytobenthic production on the West Florida Shelf and on other continental 

shelves, normalized estimates were corrected with a coverage factor of 0.25 (except for 

estimate from Cahoon et al. 1990, which 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 conversions specified in Appendix 6. Although 

these are different locations, this discrepancy begs the question 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. Primary 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 production of microphytobenthos in the Ems-Dollard 

Estuary. Marine Ecology Progress Series 14:185-196. 

Hartwig, E. O. 1978. Factors affecting 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. Role 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: The ecological role of the 

“secret garden” of unvegetated, shallow-water marine habitats. I. Distribution, abundance and primary 

production. Estuaries 19(2A):186-201. 

Sournia, A. 1976. Primary production of sands in the 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 construction 

Table 4.4. Estimates of microphytobenthos biomass in marine sands of the West Floirda Shelf. 

Chlorophyll a

concentration (mg⋅m-2) Source Location Depths (m) Sediment 

293.5 Sournia 1976 Takapoto Atoll lagoon 0 - 17 calcereous sands 5101 a Bunt et al. 1972 Key Biscayne, Florida 3 – 60 calcereous sands 1

70.8 Colijn & de Jonge 1984 EMS Dollard estuary, Netherlands intertidal sandy / muddy 158 b Plante-Cuny 1978 e Madagascar 5 – 60 marine sands 1

44 c Colocoloff 1972 e Mediterranean coast, France, to 12 marine sands 8

36.4 Cahoon and Cooke 1992 Onslow Bay, North Carolina 14.6 – 41 marine sands 619.9 Cahoon et al. 1990 Onslow Bay, North Carolina 11 – 285 marine sands 314.4 d Hartwig 1978 La Jolla bight, California 9 - 24 marine sands 2

Notes: Values in bold are those chosen for averaging to derive a representative value. amean of June and July transects; b

and 78; cmedian of provided range 24 – 64; dmedian of range values 5.3 – 23.5 estimated from a figure by MacIntyre et al. 

Colijn and de Jonge 1984. 

Table 4.5. Estimates of microphytobenthic production on the West Floirda Shelf. 

Value Provided unit Source Location Depths (m) Sediment 

Prod

h

(t⋅km

146.5 a mg C⋅m-2⋅hr-1 Sournia 1976 Takapoto Atoll lagoon 0 - 17 calcereous sands 

1.1 b kg C⋅m-2⋅year-1Valiela 1984 general 

97.5 mg C⋅m-2⋅hr-1 Sournia 1976 Takapoto Atoll lagoon 0 - 17 calcereous sands 

37 mg C⋅m-2⋅hr-1 Colijn & de Jonge 1984 EMS Dollard estuary, Netherlands intertidal sandy / muddy 

24.9 mg C⋅m-2⋅hr-1 Cahoon and Cooke 1992 Onslow Bay, North Carolina 14.6 – 41 marine sands 

66 g C⋅m-2⋅year-1 Plante-Cuny 1978 f Madagascar 5 – 60 marine sands 

158 c mg C⋅m-2⋅day-1 Hartwig 1978 La Jolla bight, California 9 - 24 marine sands 

157 d mg C⋅m-2⋅day-1 Colocoloff 1972 f Mediterranean coast, France, to 12 marine sands 

8.1 e mg C⋅m-2⋅hr-1 Bunt et al. 1972 Key Biscayne, Florida 3 – 60 calcereous sands 

Notes: Values in bold are those chosen for averaging to derive a representative value. amedian of provided range 72 – 221 

by Cahoon and Cooke 1992; bmedian of provided range 0.2 – 2 in Dawes 1998; cmean of provided data in Table 1 of Hartw

range 120 – 194; eprobably an underestimate (see Bunt et al. 1972); fin Colijn and de Jonge 1984 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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4.3 Macroalgae 

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

The contribution of macroalgae to marine organic 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 had been conducted on this shelf prior to Earle’s (1972) 

investigation, leading to her conclusion that macroalgal assemblages beyond 10 miles 

on the western Florida shelf “remains unexplored” (Earle 1972). Since that time, oil 

development studies have treated the West Florida Shelf macroalgae descriptively (see 

review by Phillips and Thompson 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 all 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. Phillips and Thompson (1990) found that forty 

percent of live-bottom organisms on the West Florida Shelf was allocated among 

macroalgae, hard corals, octocorals, and bivalves. Assuming that macroalgae 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

26 

comprises 10% of the total live 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 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). This estimate represents the lower end of the range for 

the estimate of ‘live-bottom organisms,’ the 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 macroalgae in seagrass habitats (6 t⋅km-2) was derived 

using the following approach. Zieman et al. (1989) found 15.3 g dwt⋅m-2 in the seagrass 

habitats of Florida Bay, which 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 further 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 for the West Florida Shelf as a whole 

because their estimate represents 18% of the total macrophyte standing crop, whereas 

Dawes et al. (1979, 1985 in Dawes 1998) estimated that macroalgae comprised up to 

40% of the total standing crop of Florida’s seagrass beds. 

A large band of algal nodules can be found between 70 and 100 m depths on the outer 

reaches of the West Florida Shelf. Estimates of the biomass of algal nodules in these 

habitats are not currently available. In order 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 nodules after Phillips and Thompson (1990). 

Drifting mats of Sargassum, which are entirely pelagic, are present across large areas 

of the Gulf of Mexico. Parr (1939 in Earle 1972) estimated 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 their 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 they 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, 2nd 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. 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.h 
Ecopath 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 biological, geological, chemical, socioeconomic, and 

cultural resources information for the south Florida 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. Distribution, 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 Columbia, Fisheries Centre 

Production of seagrasses makes up a considerable 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 seagrass beds (Iverson and Bittaker 1986, Josselyn 

et al. 1986, Phillips and Thompson 1990). A variety 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 suggested by P. Carlson for 

Thallassia seagrasses on the West Florida Shelf (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) estimated net annual seagrass 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; Livingston 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⋅m2

⋅day-1, for a median P/B ratio of 31⋅year-1. 

Burkholder et al. (1959) found that Thalassia 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, 2nd 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. Rinkel, R. E. Smith (eds.) A summary of 

knowledge of the eastern Gulf of Mexico. The State University System of Florida, Institute of 

Oceanography, St. Petersberg, Florida. 

Iverson, R. L., and H. F. Bittaker. 1986. Seagrass distribution and abundance 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. Larson. 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 productivity of a Hawiian coral reef. Limnology and 

Oceanography 2:241-251. 

Odum, H. T. 1957. Primary productivity measurements in eleven Florida springs and a Marine turtle grass 

community. Limnology and Oceanography 2:85-97. 

Phillips, R. C. 1960. Observations on the ecology and distribution 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 biological, geological, chemical, socioeconomic, and 

cultural resources information for the south Florida 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 certain aspects of the life, death, and distribution of the seagrasses of the 

southeastern United States 1960-1985. p. 53-76 In: M. J. Durako, R. C. Phillips, R. R. Lewis, III (eds.) 

Proceedings of the symposium on subtropical-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. Distribution, 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 

Table 4.8. Estimates of seagrass production relevant to the west Florida Shelf. 

Species Provided value Central measure Provided unit Source Region Produc

hab

(t⋅km-2

Seagrasses 0.5 - 16 8.25 g C⋅m-2⋅day-1 Zieman 1987 South Florida 579Seagrasses 0.4 - 1.5 0.95 kg C⋅m-2⋅year-1

Valiela 1984  in Dawes 1998 general 182

Seagrasses 900 900 g C⋅m-2⋅year-1 Jones 1968 Miami area 173

Thalassia beda 0.9 - 16 8.45 g C⋅m-2⋅day-1 Odum 1953a Texas 593

Thalassia testudinum 0.6 - 7.2 3.9 g C⋅m-2⋅day-1 Buesa 1972, 1974 Cuba 274

Thalassia testudinum 0.051c 0.051 g dwt⋅g dw-1⋅day-1 Zieman et al. 1989 Florida Bay 95

Thalassia testudinum 330 330 g C⋅m-2⋅year-1 Dawes 1998 Florida area 63

Thalassia testudinum 0.97d 0.97 g dwt⋅m-2⋅day-1 Zieman et al. 1989 Florida Bay 27

Syringodium filiforme 0.8 - 3.0 1.9 g C⋅m-2⋅day-1 Zieman 1987 South Florida 133

Syringodium filiforme 0.052c 0.052 g dwt⋅g dw-1⋅day-1 Zieman et al. 1989 Florida Bay 53

Halophila decipiens 100 – 500 300 mg C⋅m-2⋅day-1 Josselyn et al. 1986 Salt River canyon 21

Halodule wrightii 0.072c 0.072 g dwt⋅g dw-1⋅day-1 Zieman et al. 1989 Florida Bay 15Total production Notes: Values in bold are those chosen to represent each species on the West Florida Shelf. aTotal of seagrass, epiphytes

Thalassia bed; band Odum 1963; Jones 1968; Zieman 1975; bBuesa 1972 in Zieman 1987; cDetermined using C14 uptake 

using leaf-mark technique; eCalculated by multiplying habitat-specific production by species-specific areal coverage factors 

0.05 was applied to general seagrasses, Thalassia testudinium, and Syringodium filiforme corresponding to the ratio of Ive

areal estimates and the overall West Florida Shelf areal estimate of 170,000 km-2 (Houhoulis, this volume); 0.005 was appl

corresponding to 1/10 of the area covered by Thalassia and Syringodium beds; and 0.392 was applied to Halophilia decip

the proportion of area between 9 and 37 m (Houhoulis, this volume). 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

Table 4.9. Estimates of seagrass biomass relevant to the west Florida Shelf. 

Species Provided value Central measure Provided unit Source Region 

Seagrass Meadow 100 - 1200 650 g dwt⋅m-2 Dawes 1998 general (Florida area) Thalassia testudinum bed 500-3100 1800 g dwt⋅m-2 Bauersffeld et al 1959a Florida, West Coast Thalassia testudinum bed 66.9b 66.9 g dwt sc⋅m-2 Zieman et al. 1989 Florida Bay 

Thalassia testudinum bed 126 - 215 170.5 g dwt⋅m-2 Hall et al. 1999 Florida Bay, pre-decline 

Thalassia testudinum 123.26 123.26 g dwt⋅m-2 Hall et al. 1999 Florida Bay, post declin

Thalassia testudinum bed 63 - 137 100 g leaf afdwt⋅m-2 Iverson and Bittaker 1986 NW and S. Florida 

Syringodium filiforme 100-300c 200 g dwt⋅m-2 Zieman 1987 Florida 

Syringodium filiforme 36.7 36.7 g dwt sc⋅m-2 Zieman et al. 1989 Florida Bay 

Halodule wrightii 50-250 150 g dwt⋅m-2 Zieman 1987 Florida 

Halodule wrightii 7.9c 7.9 g dwt sc⋅m-2 Zieman et al. 1989 Florida Bay 

Halophila decipiens 5 to 12 9 g dwt⋅m-2 Josselyn et al. 1986 Salt River canyon 

Seagrass drift 0.309 0.309 

g⋅m-2 Zieman et al. 1989 Florida Bay 

Total biomass Notes: Values in bold are those chosen to represent each species on the West Florida Shelf. aand Phillips 1960, Taylor et abEstimate is for ‘standing crop'; value was divide by 0.175 for biomass estimate corresponding to the specified ratio of stan

Thalassia testudinium; cEstimate is for ‘standing crop'; value was divide by 0.55 for biomass estimate corresponding to the 

crop to biomass for Syringodium filiforme and Halodule wrightii; dCalculated by multiplying habitat-specific production by sp

factors on the West Florida Shelf; see Table 4.1 for these coverage factors.
Ecopath model of the West Florida Shelf: Volume II. Model construction 

32 

5. DETRITUS

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

Four detritus groups are included in the West Florida Shelf model: water column 

detritus, benthic detritus, drifting macrophytes, 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 carbon (POC) and dissolved organic 

carbon (DOC), the latter being approximately an order of magnitude greater in mass 

than the former on the West Florida Shelf. These two components exhibit converse 

seasonal fluctuations when plotted on comparable scales. 

 Values chosen to represent POC and DOC masses 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 below 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 runoff 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

33 

of DOM to the West Florida 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). Moreover, the net export (annual 

output minus input) of the Apalachicola-Chattahoochee-Flint drainage basin is 213,800 

tonnes of Carbon, 21,480 tonnes 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⋅year1

) 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 West 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 Shelf. Cushing (1984) might have used similar 

reasoning, as he reported the same value for the detritus pool 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 factor of 5 to convert dry to wet weight 

(Parsons et al. 1977 in Browder 1993). Finally, the assumption that only 40% of this 

organic carbon is biologically 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 sediment 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) found 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 explain such low values on the 

shelf break. These authors also found patches of terrestrial and shallow water plant 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

34 

detritus beyond the shelf break in which organic carbon was considerably higher. These 

elevated patches were not included in the average value 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 biomass of drift macrophytes, though 

the latter estimate of drift macroalgae is probably an underestimate for the shelf as a 

whole. Drifting mats of sargassum on the surface are not counted 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 distribution of living Sargasso mats in the Gulf 

of Mexico rarely intersects with the West 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 carcasses’ is the flow of nekton input (0.020 t⋅km-2⋅year-1) 

multiplied by the ratio of average residence 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 carcass group is estimated to be 0.058 t⋅km2

⋅year-1, based on the following formulation. Cushing (1984) provided estimates for 

annual flow of shrimp discards on the West Florida Shelf between 0 and 44 m depths. 

This zone was taken to be roughly 60% of the 170,000 km2 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⋅km2

⋅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, pers. com., 18 October 2000) 

suggests that a factor of four (4) is more realistic (i.e., fish discards are 0.046 t⋅km2

⋅year-1). This leads to a summed flow of 0.058 t⋅km-2⋅year-1. 

In the West Florida Shelf model, dead discards for each group were estimated based on 

fisheries-dependent information (see Section 13.1), and these dead discards were 

specified to flow to the ‘dead carcass’ group using the ‘discard fate’ interface. The 

estimate of fishery discards based on Cushing’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-shrimp conversion factor (0.058 t⋅km-2⋅year-1) was 25% 

lower than the fisheries-dependent estimate for dead discards (0.077 t⋅km-2⋅year-1). 

Natural deaths are specified to go to the ‘dead carcass’ group through 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% because 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 Mexico 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. Primary 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. Serial 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 sediments 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 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. 

Fredricks, A. D. 1972. Distribution 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. 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. 

Hartwig, E. O. 1978. Factors affecting 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. Larson. 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 Apalachicola 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 detritus transport in the Apalachicola River, Florida. 

U.S. Geological Survey, Water Supply Paper 2196-C. U.S. Government Printing Office, Washington 

D.C. 

Rowe, G. T. and D. W. Menzel. 1971. Quantitative benthic 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 construction 

36 

SUSFIO. 1977. Baseline monitoring studies, Mississippi, Alabama, Florida, outer continental shelf, 1975-

1976. Volume III. Results. State University System of Florida Institution 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. Whitledge. 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. MICROBIAL HETEROTROPHS

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

The microbial community is centrally important in the utilization and flows of energy in 

marine ecosystems. It is, however, the least known ecosystem component due to its 

inconspicuous nature. Organisms in this category include bacteria, fungi, and ciliated 

protozoans. Many of the larger biological components of marine ecosystems rely 

directly on the energy flow from 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 through foraging activities and 

patterns defecation, excretion, and mortality. 

The biomass estimate for microbial heterotrophs on the West Florida Shelf (60 t⋅km-2) is 

the sum of bacteria in substrate, bacteria 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 Shelf can be made. Dames and Moore (1979) did 

estimate that living carbon (microbes) accounted for roughly 0.25% of the total organic 

carbon in the sediment. Livingston (1984) estimated 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 should 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 microbial heterotrophs is low (e.g., 0.60 at the 

most). Microbial heterotrophs probably consume all four of the identified detritus 

categories, microphytoplankton, and microphytobenthos, roughly in proportion to their 

respective abundance. An estimated diet composition 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 0.01 
Ecopath 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 Apalachicola 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 interactions in a Carribean coral reef ecosystem, p. 

259-267. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM 

Conf. Proc. 26, 390 p. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

38 

7. ZOOPLANKTON 

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 exist that can transport oceanic and nearshore 

fauna onto the shelf (Stepien 1980). Upwelling 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 plankton communities are also impacted by 

seasonal variations in runoff, with greatest runoff occurring in summer. Annual 

variations in temperature also 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 Hopkins, 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-half and one-fourth those of 

summer, respectively. As this estuary undergoes 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, quantitative published reports on the zooplankton of the WFS. 

Most previous studies of zooplankton from the eastern Gulf describe estuarine 

ecosystems (Hopkins, 1966; Kelly and Dragovitch, 1967; McIlwain, 1968; Gillespie, 

1971; Hopkins, 1977). The few studies dealing 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 zooplankton data are those of the Soviet-Cuban fishery 

investigations (Bogdanov et al., 1969). They found highest plankton productivity in the 

northern Gulf, mediated by runoff from the Mississippi River and winter destratification. 

Highest zooplankton biomass occurs in fall 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-resolution survey of zooplankton 

abundance across the central WFS using a multisensor 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 fauna. Most previous studies used larger 

meshes, excluding the smaller mesozooplankton. Zooplankton samples were processed 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

39 

at the University of South Florida College 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 water 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 hydrography and zooplankton abundance. Abundance maxima 

were seen nearshore, associated with a salinity gradient, and along the pycnocline 

offshore. Increased suspended particulate matter characterized both of these zones. 

These distribution patterns mirror those found on the northeastern Florida shelf (NEFS). 

Paffenhöfer (1983) found that 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 into three groups. Group 1 includes carnivorous 

zooplankton (mainly chaetognaths). The primary prey of this group is copepods, while 

this group is preyed on primarily 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 fishes. 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 numbers across the WFS and is the dominant 

grazing component (low specific grazing rates are more than offset by high abundance). 

Group 3 includes other mesozooplankton (larger 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 planktivorous 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 the inshore zone (shore to 20 m isobath), while 

accounting for ~ 40% offshore (20-100 m). Overall biomass estimates of the two zones 

were quite similar. It should be noted that these estimates should be considered 
Ecopath 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 biomassa

 Offshore biomassb

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 zooplankton biomass are presently being investigated. As a first 

order approximation, the 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 values were then calculated 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. The copepod assemblage of the WFS is 

relatively short-lived due to high temperature, 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 copepods and other mesozooplankton (Raymont, 

1983). Generation times of one and two months 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 zooplankton 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 component unquantified. A 

first order approximation would be to assign 50% herbivory and 50% detritivory to this 

group. Larger mesozooplankton take phytoplankton, other crustacean zooplankton 

(mainly nauplii and early copepodites), and protozoan microplankton. A first order 

approximation would be to assign 75% herbivory and 25% carnivory to this group. 

Knowledge of the feeding of some potentially 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) EE 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

41 

Carnivorous zooplankton 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 investigations, 1951-53. Spec. Sci. Rep., U.S. Fish Wildl. Ser. 

Fisheries No. 269. 

Austin, H.M. and J.I. Jones. 1971. Seasonal variation in bulk 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 zooplankton of estuarine waters of Louisiana. In: 

Cooperative Gulf of Mexico Estuarine Inventory 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 St. 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 zooplankton distribution on the northeastern 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 the Oceans. Vol. 2. Zooplankton. Pergamon Press. 

Reeve, M.R., J.E.G. Raymont, and J.K.B. Raymont. 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 distribution of pontellid copepods in the neuston of the eastern 

Gulf of Mexico continental shelf. 

Vargo, G.A. and T.L. Hopkins. 1990. Plankton. In: Synthesis 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. Relationships 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 Columbia, Fisheries Centre 

The early life stages of many fishes are planktonic. Ichthyoplankton are the planktonic 

eggs and larvae of these fishes. The importance of ichthyoplankton is self-evident by 

their potential fate as fish (those that escape predation), but their potentially profound 

ecological importance in their own right on the West Florida Shelf is indicated by the 

high estimates for consumption rates (P/B = 50.448 ⋅year-1) and production rates (Q/B = 

132.13 ⋅year-1). Ichthyoplankton influence the structure of the plankton community 

through their voracious feeding, and they may 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 according to season. He suggested that high 

ichthyoplankton concentrations were associated with nutrient inputs and chlorophyll 

plumes, and his results also show the Florida middle grounds to support elevated 

biomass. This could be due to nutrient impingement (upwelling) over this area, or high 

occurrences of fish spawning related to the pinnacles there. Distinct concentrations of 

fish larvae in particular areas of the shelf may also be related to elevated nutrients in 

cold core cells associated with the loop current, or physical entrainment in them, or 

both. He also found that low salinity stress associated with rivers could have a 

detrimental effect on ichthyoplankton during years of high precipitation. 

In general, ichthyoplankton were most abundant in the northern and eastern areas of 

the West Florida Shelf, as well as nearshore 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 from a relatively low abundance and even 

distribution during February to an increasingly higher abundance and patchy distribution 

during April and then August (Figure 7.1). Higher standard error bars depict less even 

spatial distributions, since samples were distributed throughout the West Florida Shelf. 

During August, highly concentrated cells of ichthyoplankton 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 distributions of ichthyoplankton on the West Florida 

Shelf relative to zooplankton captured concurrently. Error bars are standard error; large error bars 

represent uneven spatial distributions. Sample sizes (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 mean densities for larvae and eggs (2.092 

individuals⋅m-3 and 1.084 individuals⋅m-3) by 70 m, and by the wet weight of a larval fish 

individual (0.0002858 g ww; converted using 7.5 from mean dry weight from Theilacker 

1987) and the assumed wet weight of a fish 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 Research 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 larval 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 consumption in the real ocean even though the data 

used were from the lower experimental food concentration. This concentration of rotifers 

is, however, not comparable to C. Tomas’ zooplankton data since his sampling was not 

designed to assess rotifers. De-La Cruz-Aguero (1993) used P/B and Q/B values of 15 

⋅year-1 and 110 ⋅year-1 respectively and these lower values should be considered if the 

Theilacker (1987) values cause thermodynamic problems in the model. 

The assimilation efficiency of ichthyoplankton 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). Erring 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 densities of food in the laboratory. 

 

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

44 

De-La Cruz-Aguero, G. 1993. A preliminary 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 growth energetics of larval 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. Fish 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 comprised of gelatinous zooplankters such 

scyphomedusae, hydromedusae, siphonophores and ctenophores. These organisms 

are all omivorous or carnivorous, consuming 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 challenging task, as the distinctly different 

regimes in its northern and southern zones further complicate the problems presented 

by limited information. Nevertheless, there is some basis for making estimations of 

parameters for a model of the West Florida Shelf. 

The biomass estimate for jellies on the West Florida Shelf (0.221 t ww⋅km-2) was 

derived by multiplying the volumetric 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 average of two estimates from the north (my own 

data), collected during August 1999 in the vicinity 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 explained in the following two paragraphs. 

The region is divided into two halves. The northern represents a temperate fauna and 

the southern, a sub-tropical fauna (Phillips 1971). The northern region likely supports 

maximum biomass and production during the spring and summer, while the southern 

sub-tropical region likely supports highest production and biomass during the 'wet' 

season (Winter and Spring). 

My best estimate from numerical comparisons primarily is that the 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 productivity period. Thus the average volumetric biomass 

estimate throughout the year and throughout the region would be 0.221 t ww⋅km-3. 

No data for daily P/B of jellies exists for the eastern Gulf. However, a reasonable 

number to use would probably be 0.20 day-1. I chose this number as the maximum daily 

turnover since it reflects maximum daily growth 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 maximum daily growth rate (0.2 day-1) and 10% of 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

46 

that value (0.02 day-1), which represents the minimum daily growth rate during the lessproductive season, results in the estimated 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 water and higher metabolic costs. 

The diet composition used for the jellies of the West Florida Shelf based on our own 

composition information from the scyphomedusa Aurelia aurita (Graham and Kroutil, 

submitted): 67% omnivorous meso-zooplankton, 23% small herbivorous copepods, and 

10% fish eggs and larvae is modified from the diet used for jellies in Prince William 

Sound (Okey et al. 1999). Dietary proportions for omnivorous meso-zooplankton and 

small herbivorous copepods were switched based on my personal 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 ultimately be high and could accordingly be 

adjusted in the future. 

Literature Cited (Carnivorous jellyfish)

Graham, W. M. & R. M. Kroutil, Submitted, Sixe-based prey selectivity and dietary shifts in the jellyfish, 

Aurelia aurita. Journal of Plankton Research . 

Larson, R. J., 1982, Medusae (Cnidaria) from Carrie Bow Cay, Belize. In Rutzler, K. &I. G. MacIntyre 

(ed.), The Atlantic Barrier Reef ecosystem at Carrie Bow Cay, Belize. 1. Structure and Communities: 

253-258. 

Okey, T. A., R. J. Foy, J. Purcell. Carnivorous 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 British Columbia, Vancouver. 

Phillips, P. J., 1971, The Pelagic Cnidaria of the Gulf 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 

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8. BENTHIC INVERTEBRATES

8.1 Meiobenthos 

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

Meiobenthos are benthic invertebrates smaller than 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). Many 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 harpacticoid copepods (a type of benthic copepod). These are, 

in turn, followed by turbellarians and gastrotrichs, and others including priapulid larvae, 

crustacean larvae, polychaete larvae, kinorynchs, 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 more stable in time and space (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 Florida Shelf (2.051 t⋅km-2) was 

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

meiobenthos. Nematodes averaged 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 shelf baseline environmental survey), which 

overlaps considerably with the West Florida Shelf area defined 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 copepod 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 favorably 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 noting here that Elmgren (1984 in Pauly and Christensen 1993) 

used 5.33 year-1 as P/B for meiobenthos, but Warwick 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 Ecopath
Ecopath model of the West Florida Shelf: Volume II. Model construction 

48 

model of Prince William Sound, Alaska (pers. 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 setting like the West Florida Shelf where the 

higher temperature would mean 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% microphytobenthos, 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.) 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. 

Donovario, R., A. Dell’Anno, D. Martorano, P. Parodi, 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 (Southern 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 William 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 Institution 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 large marine ecosystem through stratified spatial model. 

MSc Thesis, University of British 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 Columbia, Fisheries Centre 

The ‘small infauna’ group consists of sea floor 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 infauna live in the top layers of sediment 

(infauna) or on the sediment surface (epifauna), and they include polychaetes, small 

crustaceans, small molluscs, and small echinoderms. The species in this aggregated 

group exhibit a variety of life habits and niches, but in general, they consume 

microphytobenthos, microbial heterotrophs, meiobenthos, each other, mobile epifauna, 

and detritus, whether suspended or deposited. Small infauna are important prey for 

larger fish and invertebrate predators on the West Florida Shelf (Dames and Moore 

1979). For the purposes of this model, small infauna is distinguished from swarming 

macro-epifauna, which is a separate group (see 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 fishes on the West Florida Shelf. 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 benthos of the slopes and the 

deep basins of the Gulf of Mexico is relatively 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 shelf ecosystems, effectively 

minimizing carbon transport across shelves. The logarithmic decline in the amount of 

carbon in live organisms with increasing distance 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 space than the more ephemeral and patchy shelf 

meiobenthos (Dames and Moore 1979). Nevertheless, macrofaunal assemblages do 

exhibit some seasonal fluctuations (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

10

Density

Biomass (t /km2)

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 make up approximately 64% of ‘small infauna’ 

individuals on the SouthWest Florida Shelf (ESE et al. 1987). Error bars are standard error. 

The Mississippi, Alabama, Florida, Outer Continental Shelf Baseline Environmental 

Survey (MAFLA) included the only sampling programs that appeared capable of 

characterizing the small infaunal assemblage for the whole West Florida Shelf (e.g., 

SUSFIO 1977). The estimate of small infaunal biomass on the West Florida Shelf 

(9.923 t⋅km-2) chosen from this program nevertheless contains uncertainty because the 

value was extrapolated from polychaete biomass to overall 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 uncertainty of the studies identified (see Table 

8.1), for the purposes of this modeling exercise. 

Table 8.1. Biomass estimates of small infauna 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; bgrand mean of polychaete biomasses from three seasons and 

six transects in the MAFLA area; cmedian of range 2 – 16.1 in 20 to 25 m off Tampa Bay; dmean of median 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

51 

biomasses from different habitats; e25 stations and two different seasons in Charlotte Harbor; fin ESE et al. 

1987; g

but Vittor (1979 in ESE 1987) might contain data to calculate accurate values; hthe 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; jConversion 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 West 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 other studies (see Table 8.2). 

Table 8.2. Representative abundances and biomasses from Rookery Bay and Charlotte Harbor. 

Habitat Abundance 

(individuals⋅m-2) 

Overall 

abundance 

Biomass 

(g⋅m-2) 

Overall 

biomass 

Individual 

biomass (g) 

Red mangrovea

 22,591 – 52,914 37,752 3.6 – 8.2 5.9 0.000156 

Mixed seagrassesa 6,347 – 23,545 14,946 15.7 – 87.4 51.6 0.003452 

Non-vegetated muda 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 Charlotte Harbor biomasses were calculated by applying 

the overall individual biomass from the cRookery 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) found a high correlation between wet weight and carbon 

(0.96), numbers and carbon (0.86), and wet weight and dry weight (0.85), and a 

moderate correlation between dry 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 infauna 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 West 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 quality, or difficult to digest, food items. 

Assimilation efficiency should be set at 0.06 at best. 

Table 8.3. Estimated diet composition of 
Ecopath 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 ecosystems study, Volume I – Executive Summary. 

Environmental Science and Engineering, Inc. Gainsville, Florida, LGL 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 Engineering, Inc. Gainsville, Florida, LGL 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 the 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, Alaska. 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-environmental 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. National Marine Fisheries Service Data Report 100:1-

505. 

Jewett, S. 1999. Macrofauna. 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 British Columbia, Vancouver. 

Okey, T. A. 1997. Sediment flushing observations, 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 renegades in Monterey Canyon drift algae: 

Partitioning multidimensions. Target 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 continental shelf. Bulletin of Marine Science 33:935-940. 

Rowe, G. T. and D. W. Menzel. 1971. Quantitative benthic 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. 

Sheridan, P. 1997. Benthos of adjacent mangrove, seagrass and non-vegetated habitats in Rookery Bay, 

Florida, USA. Estuarine, Coastal 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 Institution of Oceanography, 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. Washington, 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 Columbia, Fisheries Centre 

Small mobile epifauna are highly mobile, epifaunal macro-invertebrates (0.5 mm to 

approximately 20 mm in size) that live associated with the sea floor, but which swim 

above it in search of food or refuge. Small mobile epifauna are found over hard and soft 

bottoms, often in association with drift macrophytes. They mainly consist of swarming 

amphipod crustaceans and mysids. Ostrocods, some other crustaceans, and a few 

polychaetes can emerge demersally from the sediment to become mobile epifauna. In 

this model, juvenile shrimp and small crabs are included in ‘small mobile epifauna.’ 

Swarming amphipods live mainly by cropping microbial flora from the surfaces of 

decomposing macrophytes, by directly grazing micro and macroalgae, by scavenging 

animal carcasses, and by eating zooplankton 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 epifauna was not found for 

the West Florida Shelf ecosystem, although good evidence exists of a diverse and 

abundant small epifauna (e.g., Myers 1981, Menzies and Kruczynski 1983, Oritz 1991). 

A large proportion of this group is comprised of organisms that are highly mobile, and 

which actively seek out food resources and refuges that can have extremely patchy 

distributions in time and space (e.g., swarming benthic amphipods and mysids; Okey 

1997; Okey, in press.). These characteristics make reliable estimates of density and 

biomass difficult to develop without sampling programs that explicitly account for relative 

distributions in the context of their patchy resources (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 epifauna’ group, and that the present 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 0.15 
Ecopath 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: physical environment and biological resources. U.S. 

Department of Commerce, NOAA, Washington, 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 Centre Research Report 4(1), University of British 

Columbia, Vancouver. 

Menzies, R. J. and W. L. Kruczynski. 1983. Isopod crustacea (exclusive 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 Aoridae. Memoirs of the Hourglass Cruises. Marine 

Research Laboratory, Florida Department of Natural Resources, St. Petersberg, Florida. 

Okey, T. A. 1997. Sediment flushing observations, earthquake flushing, and benthic community changes 

in Monterey Canyon head. Continental Shelf Research 17(8):877-897. 

Okey, T.A. In press. Macrobenthic colonist guilds and renegades in Monterey Canyon drift algae: 

Partitioning multidimensions. Ecological Monographs 

Oritz, M. 1991. Amphipod Crustacea. II. Family Bateidae. 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 Institute, St. Petersburg 

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

Marine bivalves are a diverse and energetically important component of Florida’s 

nearshore and shallow shelf communities. Unfortunately, basic population parameters 

for non-commercial species do not exist in these habitats. Understandably, the only 

research of this nature has been conducted in estuarine or very near shore 

environments and mainly focusing on species that are targets of fisheries. 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 information gleaned 

from the literature. Clearly for these numbers to be meaningful, an extensive survey of 

the habitats in question is required. Both deposit-feeding 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. Although 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 infaunal clams, as source data are separate for these 

groups. For similar reasons, oysters and attached 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-bottom communities are small (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 purposes 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

57 

of this modeling exercise, I (D. Marelli) estimate that the biomass of small infaunal 

bivalves in the defined West Florida Shelf area ranges from 5 to 50 t⋅km-2, and the 

biomass of larger infaunal bivalves ranges from 2 to 10 t⋅km-2. Dame (1996) suggests 

that bivalve production to biomass ratios average 1.2. Thus, an estimate between 1 and 

1.2 is probably a good value for larger infaunal bivalves. Smaller bivalves have greater 

ratios of P/B, with values probably 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 precise estimates would require an increased 

investment of scientific resources in the region. 

Scallops 

Scallops are a specialized group of mobile 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 bivalved molluscs. Scallops are suspension feeders 

like many other clams and sessile epibenthos. 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 scallops. 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 calico scallop is 7.4 grams (B. Arnold, 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 quantitative 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 scallops in their preferred habitat (0.555 t⋅km-2) is 

based on FMRI surveys (B. Arnold; published and unpublished data) and the same 

individual weight estimates as for Calico scallops above. The 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 model 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 Pacific. The estimated diet 

composition of bivalves on the West Florida 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 reproduction and development 10:51-57. 

Cummins, R., Jr. 1971. Calico scallops of Southeastern 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. Fisheries 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 Columbia, Fisheries Centre 

Sessile epibenthos are non-mobile, suspension or filter feeding invertebrates, usually 

attached to hard substrate. The West Florida Shelf contains extensive areas of low-lying 

carbonate reef platforms that provide substrate for most sessile epibenthos. Organisms 

include sponges, corals, tunicates, gorgonians, hydroids, bryozoans, attached bivalves, 

crinoids, and ophioroids. These assemblages comprise 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 bottoms are characterized by a dynamic 

veneer of carbonate sand (Phillips and Thompson 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, calcified, red algae) harbor 

a unique suite of epifauna and interstitial macroinfauna. 

The biomass estimate for sessile benthic epifauna on the West Florida Shelf is 472.5 

t⋅km-2, based on the following derivation. The biomass of sessile epibenthos (and 

macroalgae) on ‘live bottom’ in the shallow portion of the West Florida Shelf was 

estimated to be 2,000 t⋅km-2 by Continental Shelf Associates, 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 biomass of sessile epibenthic fauna (Phillips 

and Thompson 1990; and a 1984 estimate by Darcey and Gutherez was 54%), with the 

bulk of the remaining portion split between hard corals, octocorals, bivalves, and 

macroalgae. Thus, subtracting 10% of this ‘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 model, especially if problems are 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 efficiency of sessile epibenthos should be 

relatively low (e.g., 0.60). Table 8.7 shows the 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 biological, geological, chemical, socioeconomic, and 

cultural resources information for the south Florida 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 continental 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 Columbia, Fisheries Centre 

The Echinoderms and large gastropods group for the West Florida Shelf model consists 

of a suite of mobile invertebrates 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), holothurians (sea cucumbers), echinoids (urchins 

and sand dollars), and large gastropods, both with and without shells. This aggregated 

group includes predators, scavengers, detritus feeders, and filter feeders. They are 

grouped together because the focus of the present model is forage fishes and directly 

associated sub-webs, and because information on some of these groups is limited for 

the West Florida Shelf. 

Information from the Hourglass Cruises (e.g., Serafy 1979, Miller and Pawson 1984) 

was used to estimate composite biomass and diet compositions for this group. Thirteen 

stations were sampled at five depth intervals across the West Florida Shelf during this 

exploratory monitoring program. Distinct depth partitioning among species was revealed 

within several of the classes of organisms aggregated into Echinoderms and large 

gastropods. Even after aggregation, Echinoderms and large gastropods were generally 

concentrated near shore because of the effect of highly concentrated and abundant 

sand dollars. 

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

is 19.246 t⋅km-2—the sum of biomass estimates for echinoids, holothurians, asteroids, 

and gastropods. The derivation of this biomass 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 nature 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 echinoids and holothurians from 

Pauly et al. (1993). The assimilation efficiency 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 more abundant at 

the most shallow depths because of dense sand dollar (Mellita quinquiesperforata) beds 
Ecopath 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 relatively evenly distributed across 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 hourglass 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 m2

 area (0.001685 km2) at each of 10 

stations distributed across the West Florida Shelf (two east-west transects, each with 

five sampling stations, were located approximately 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 reported abundances by 0.04718 km2 (28 months x 0.001685 

km2)(Table 1). Biomass estimates were then calculated by multiplying estimated 

abundances by mean individual weights for sand dollars (0.0000995 t⋅individual-1; Lane 

and Lawrence 1980; converted from dry weights 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 echinoderms 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 19.246 
Ecopath 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 sampled 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 overall 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 Sea, 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 biomass (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 ratio suggested by Bluhm et al. (1998) for the 

North Barents Sea population 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-growth population structure in very cold waters 

of high latitudes, but these lower values should be kept in mind during model balancing. 

Sea urchins are famous for eating macrophytes. For example, the consumption of 

seagrasses by urchin populations can rival or surpass the seagrass production (Moore 

and McPherson 1965, Camp et al. 1973). However, urchins consume a variety of other 

prey, such as bryozoans, forams, gastropods, worms, sponges, octocorals, coral 

polyps, mussels, sand dollars, crustaceans, and strips of fish (see Serafy 1979). Sand 

dollars eat detritus, diatoms, 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 Florida Shelf exhibit depth 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, microbes, 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 Surveys (Miller and Pawson 1984), as in 

the echinoid example above. Density estimates were multiplied by 0.000328 t, 

assuming that the average weight of a West Florida Shelf holothurian 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 diverse. 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 estimate 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 resulting biomass estimate for asteroids 

should be considered a placeholder. 

Large gastropods 

Biomass estimates for large gastropods on the 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 equivalent to 0.050 t⋅km-2. Catch data were provided by Bob Muller 

(Florida Fish and Wildlife Commission, April 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 sections and estimations for asteroids and 

gastropods. 

Table 8.9. Estimated diet composition of echinoderms 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 0.009 
Ecopath 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 are 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. Distribution, 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 Mexico 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 variation 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 production of reef corals. Proceedings of the Fourth 

International Coral Reef Symposium. 2:369-374. 

Lyons, W. G. 1979. Molluscan communities of the West Florida Shelf. Transactions A. Malacol. U. 

1979:37-40. 

Miller, J. E., and D. L. Pawson. 1984. Holothurians. Memoirs of the Hourglass Cruises 5(3), Florida 

Department of Natural Resources, Marine Research 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 interactions in a Carribean coral reef ecosystem, p. 

259-267. In V. Christensen and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM 

Conf. Proc. 26, 390 p. 

Pauly, D., V. Sambilay, Jr., and S. Opitz. 1993. Estimates 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 models of aquatic ecosystems. ICLARM Conf. Proc. 

26, 390 p. 

Serafy, D. K. 1979. Echinoids (Echinodermata: Echinoidea). 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 Columbia, 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 shrimp catch is concentrated in the southern 

region of the West Florida Shelf, though an important portion of the fishery occurs in the 

big bend area (Figure 8.2). This group is called ‘adult shrimp’ because most of the 

information available for shrimp pertain to commercial sizes, or to individuals near 

commercial sizes. Smaller shrimp are included in the ‘small mobile epibenthos’ group, 

and this younger portion of the shrimp populations probably comprise most of the 

overall shrimp biomass on the West Florida Shelf. 

Figure 8.2. Distribution of pink shrimp catches in the 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 they are smaller than legal size. Effort studies show that 

average fishing days per trip doubled 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 recruitment fisheries. This enigma has been the 

subject of some debate in the past (e.g. Cushing 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 eutrophcation)—explanations that are beyond 

simple population-exploitation 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 Shelf are presented in the following paragraphs 

to provide a range of choice of biomass estimates, 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 estimate for the West Florida Shelf including the Florida Keys 

(i.e., statistical zones 1-9) was 8,324 tonnes (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 total 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 data). 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 West Florida Shelf area (the resulting 

inclusive landings value is 7,669 tonnes). This verification of accuracy of the two 

approaches is apparent despite different delineations of the annual shrimping season 

(NMFS data corresponds to the calender year, while FMRI data corresponds to the July 

97-June 98 season). The FMRI value was chosen 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 whose parameters are based on 

the same 1998 FMRI landings database. 

The total catch of all shrimps on the West Florida Shelf in 1998 is estimated 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 regional perspective, the total penaeid shrimp 

catch for the western central Atlantic region (the Caribbean, the Central American 

states, and the Gulf of Mexico) has fluctuated 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) estimated 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⋅km2

, 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 the 
Ecopath 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 Cushing 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 Florida adult shrimp do vary considerably, 

sometimes leading to profoundly divergent estimates of abundance and biomass (Table 

8.10). For example, a biomass estimate of 0.4293 t⋅km-2, or 72,974 tonnes can be 

derived in the same manner using an exploitation rate of 10%, which is based on the 

shrimp catchability coefficient from another study (Steele 1997). Application of Ehrhardt 

and Legault’s (1999) assumed fishing mortality rate of 1.4 for pink shrimp in Florida Bay 

to the same west Florida catch data leads to a West Florida Shelf standing 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 

FMSY 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 Steele 1997 0.4293 72,974 160,907,670 

Combined 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 1998 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 km2

 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 proxy 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) should be in the order of 0.4. 

With a natural mortality rate of 3.6 year-1, FMSY is estimated at 2.13 year-1, and BMSY at 

1,806.25 tonnes. There are, however, many sources of uncertainty in such a 

calculation. 

Such an extreme variability in estimates calls 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 Vidal 
Ecopath 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 for 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 shrimp abundance in big bend area seagrass beds 

with an older estimate of shrimp abundance 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 catch (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 Florida 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 from this composite approach, detailed in the 

following paragraphs, is an overestimate. 

The rough biomass estimate for pink shrimp 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 median 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 km2

 area 

of the West Florida Shelf. 

A rough estimate of the biomass of adult shrimp 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 shrimp 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 km2) was 

calculated by multiplying the distance of each trawl tow (5.556 km) with the 

standardized effective width of the trawl net opening (0.0117 km) resulting in a single 

tow swept area of (0.0644 km2; corresponding to a net headrope length of 0.0137 km 

(45 feet) based on the empirical equations presented by Darnell 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 km2. 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 km2). 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 multiplied by the proportion of area 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 

km2. 

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 following 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 catch of pink shrimp in 1998. Since P/B, 

under equilibrium, equals total mortality (Allen 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 shrimp 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 verification for this rough estimate, as it is 

close (5.38 year-1). 

The ratio of consumption to biomass for adult 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 (Arreguín-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 white shrimp, which are considered 

selective particulate feeders (Steele 1997). Brown shrimp ingest detritus and algae 

throughout their lives, but they actively 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, foraminiferans, nematodes, polychaetes, ostracods, 

copepods, mysids, isopods, carridean shrimp, caridean 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 discards, while adults primarily consume 

meiobenthos, macrobenthos, and fishery discards (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 quantitative diet information for seven species of shrimp on the West 

Florida Shelf and this was adapted here (Table 8.12). The assimilation efficiencies of 

adult shrimp are low when the algae mat is the main source of food or when the diet is 

largely carbonate (Cushing 1984). But, the AE can be surprisingly high when the food 

consists of detritus (Qasim and Easterson 1974). 

Literature cited (Adult shrimp) 

Allen, R.R. 1971. Relation between production and biomass. 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 Mexico continental shelf. p. 279-284. In V. Christensen 

and D. Pauly (eds.) Trophic models of aquatic ecosystems. ICLARM Conf. Proc. 26. 

Cobb, J. S. and J. F. Caddy. 1989. The population biology 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. 1961. 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 shrimp, Farfantepenaus duorarum: Recruitment variability 

as an indicator of Florida bay dynamics. Estuaries 22:471-483. 

FAO. 1997. Review of the state of world fishery resources: marine fisheries. FAO Fisheries Circular 920 

FIRM/C920, Rome. 

Flint R. W. and N. N. Rabelais. 1981. Gulf of Mexico 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 (Crustacea: 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 source of nutrition for the brown 

shrimp Penaeus aztecus. Ph.D. Dissertation. Louisiana State University, Baton Rouge, Louisiana, 

125 pp. 

Klima, E. F. 1989. Approaches to research and management 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 for the Gulf of Mexico Shrimp fishery. NOAA Technical Memorandum, 

NMFS-SEFSC-337, 37 pp. 

Nance, J.M. 1997. Stock assessment 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 Carribean invertebrate fisheries. In: Our living oceans: 

Report on the status of U.S. living marine resources, 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 United 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 and 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 report for the shrimp fishery of the south Atlantic 

region, Volume 1. South Atlantic Fishery Management Council, Charleston, South Carolina, 

September 1999. 

SEMARNAP, S. d. r. n. y. p. 1997. Anuario Estadistico de Produccion Pesquera. 

htt:/www.semarnap,gob.mx/sspesca 

Steele, P. 1997. Stock assessment profile for the penaeid shrimp fisheries of the southeastern United 

States and the Gulf of Mexico. Florida Marine Fisheries Commission. 

Steele, P. 2000. Stock assessment profile 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 large marine ecosystem through stratified spatial model. 

MSc Thesis, University of British 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 Columbia, Fisheries Centre 

Cynthia A. Meyer 

Florida Marine Research Institute, St. Petersburg 

The three most conspicuous crabs on the West Florida Shelf are the blue crab 

(Callinectes sapidus), the stone crab (Menippe mercenaria), and the western Gulf stone 

crab (M. adina). Others include various species 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 the purposes of the present trophic model, all 

crab species are aggregated into one functional group. In addition, horseshoe crabs 

(Limulus polyphemus) are included in the crab group, even though they are 

phylogenetically distant from true crabs. This crab group includes 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; cDerived 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 exploitation rates of 0.05 to 1998 catches; fP/B 

value equivalent to total mortality from GSFMC, in press; gP/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-estimated 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 average 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-sections on individual crab species. 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 biomass weighted average of the estimated diets of the seven 
Ecopath 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 that follow. There is considerable uncertainty in the 

parameters for this group, and continuing research will lead to more representative 

estimates. 

Table 8.14. Estimated diet composition for 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 Proportion wet weight; a

Adapted from quantitative diet composition 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 embayments. The estimate of blue crab biomass 

on the West Florida Shelf (0.0381 t⋅km-2) was derived by dividing the average landings 

in the region from 1989 to 1997 (4,012 tonnes; from FMRI landings database combining 

federal and state fishery landings data) by the mean fishing 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.). This 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 estimate for blue crabs since P/B equals total 

mortality when populations are at equilibrium (Allen 1971). 

Blue crabs are opportunistic benthic omnivores, feeding on fish, aquatic vegetation, 

mollusks, crustaceans, and annelid worms (Darnell 1961, Muller 1999). In Apalachicola 

Bay, Laughlin (1982) found that blue crabs fed on fishes, xanthid crabs, smaller blue 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

76 

crabs, and bivalves such as American oysters, Mercenaria hard clams, coot clams, 

mussels, rangia, and periwinkles (Millikin and Williams 1984 in Williams et al. 1990). 

Smaller blue crabs (3 – 7.4 cm) contain equal volumes of crustaceans and molluscs, but 

larger blue crabs (7.4 – 12.5 cm) contain larger proportions 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 claws are pulled off by fishers. The small 

percentage of stone crabs that survive the initial dismemberment and the associated 

increases in predation risk (~30%) undergo the dietary stress of switching to detritus 

feeding while their claws regenerate. The number of stone crab traps in the region attest 

to the importance of the fishery in Florida: Muller and Bert (1997) estimated that 

600,000 traps existed in Florida, but the consensus 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 extensive 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. Overall, approximately 3 - 3.5 million pounds 

of stone crab claws are landed annually in Florida, but only a portion of these claws 

come from the West Florida Shelf (when the Florida Keys area are 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 Florida 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 Conservation Commission Marne 

Fisheries Information System) and the following 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 crabs that represent half of the standing biomass of stone 

crabs each season (when all age classes larger than 5 cm are considered). Based on 

these assumptions, half of the Monroe County landings were subtracted from the 

average landings over the last ten years to exclude the 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 individuals that are not represented 

in landings. 

The resulting estimate is close to half of the median of the range 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 observations of lower-than-expected occurrences of 

regenerated claws (C. Meyer, personal observations, July 2000; lower than the 10% of 

total landings suggested by Savage et al. 1975) indicates either 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 survival rates that are lower than sometimes believed (8-

30% rather than 25-97%)(J. O’hop, M. Tupper, 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 estimate for the West Florida Shelf is high 

irrespective of these considerations, as our third assumption in the previous paragraph 

is a place-holder estimate. 

The estimated total mortality of stone crabs (1.69 year-1) is used as the P/B estimate for 

the purposes of this model based on the notion that P/B equals the rate of total mortality 

(Z) as the product of the instantaneous rate of natural mortality (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 size 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 shells, 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 continental 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. Although taxonomically distinct from true 

crabs, horseshoe crabs have enough convergent morphological and ecological 

similarities to be grouped with the crab functional group for the 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 approximation, 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 specialize on bivalves, as Botton found Limulus

guts to contain primarily bivalves and other mollusks. Interestingly, Botton and Ropes 

(1989) found that gut contents in Limulus comprised only 0.23 % of the total body mass 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

78 

of males and a mere 0.097% of the body mass 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 ecology 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 spectrum of an estuarine community, based on studies of Lake 

Pontchartrain. Ecology 42:553-558. 

Ehrhardt, N. M., and V. R. Restrepo. 1989. The Florida stone crab fishery: a reusable resource? pp. 225-

240 in J. F. Caddy (ed.) Marine invertebrate fisheries: 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. Gulf 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. Synopsis 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 Fish and Wildlife conservation Commission. 

Williams, C. D., D. M. Nelson, M. E. Monaco, S. L. Stone, C. Iancu, L. Coston-Clements, 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. Strategic 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 Columbia, Fisheries Centre 

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

arqus) and slipper lobsters (Scyllaridae). Spiny 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 habitat for juvenile and adult life stages 

(GoM&SAFMC 1982). Spiny lobsters are truly a transboundary stock as the planktonic 

larvae of the South Florida population likely originate 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 (Lyons 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 emigrate 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 are 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 uncertainty in any attempts to estimate 

overall biomass of spiny lobsters on the West Florida Shelf because of the limited 

nature of the available information—especially 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 West Florida Shelf used in this modeling 

exercise (0.007 t⋅km-2, or 1,186 tonnes, or 2,614,367 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 

Conservation Commission Marne Fisheries Information 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 defined West Florida Shelf area (excluding the 

keys). This value was doubled to estimate spiny 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 for 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 Table 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 diet 
Ecopath 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, environmental impact statement, and regulatory impact 

review for spiny lobster in the Gulf of Mexico and South Atlantic. Gulf of Mexico and South Atlantic 

Fishery Management Councils, March 1982. 

Ingle, R. M., B. Eldred, H. W. Sims, 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. 1978. Mass migration of spiny lobster, Panulirus argus (Crustacea: 

Palinuridae): behavior and environmental correlates. 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. Kennedy, 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. Evaluation 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 Institute, St. Petersburg 

Roy L. Caldwell 

Department of Integrative Biology, University of California at Berkeley 

Stomatopods, or mantis shrimp, are crustaceans that inhabit burrows and crevices on 

the shallow sea floor of tropical and sub-tropical regions (Caldwell 1987). Stomatopods 

are carnivorous predators that use their massive raptorial second thoracopods (Caldwell 

and Childress 1989) and their excellent vision to surprise shrimps, crabs, mollusks, 

fishes, and other animals with their powerful strikes. They are divided into two functional 

groups—smashers (i.e., neogonodatylids) and spearers (i.e., squillids)—based on the 

function and morphology of the raptorial thoracopod (Caldwell and Childress 1989 in 

Caldwell and Dingle 1976). The Lysquillina and other small borrowing squillids and 

lysiosquillids are lie-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 their cavity for processing (Caldwell et al. 1989). 

At least twenty-eight species of stomatopods occur in the Gulf of Mexico. Thirteen 

species identified on the West Florida Shelf are known to inhabit bathymetric ranges 

from the littoral zone to depths exceeding 200 m (Camp 1973). 

The cryptic nature of stomatopods makes estimation of their biomass challenging, but 

their behaviorally-dramatic lifestyles and their role as predators in structuring benthic 

communities makes their study particularly desirable, especially in the context of wholesystem trophic models capable of dynamic simulation. Stomatopods might have 

keystone effects (proportionally large effects) related to their predation on herbivores 

and detritivores. For this reason, it is important to derive reasonably accurate 

estimations of their population parameters in order to capture the structure and 

dynamics of the lower trophic levels in the West Florida Shelf model. 

The rough estimate of stomatopod 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 estimate based on the Panamanian studies of 

Caldwell et. al. (1989, 1991, unpublished data). Wenner (1989) used trawl surveys to 

examine the composition 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 km2; 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 Florida Shelf as a whole (to 200 m depth) is 

underscored by the presence of at least thirteen different species endemic to the latter 

region (Camp 1973). Despite issues of limited comparability, Wenner’s (1989) study is 
Ecopath 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 Panamanian stomatopods (Caldwell et al. 1989, R. Caldwell, 

unpublished data) were also used 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 distributions of similar habitats. The 

Neogonodactyla reside primarily in hard 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 sediments. In addition, Stomatopods 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⋅m2

). 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 areas. 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 lysiosquillids in the Bay of Panama is 

~3 t⋅km-2, based on 200 (1 m2) quadrats (Caldwell, unpublished data). Four species of 

squillid inhabited the soft bottoms of the bay, with the largest 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 would have weighed over 20 g. 

Table 8.17. Estimated biomass of neogonodactylids 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 ~2-3 
Ecopath 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 information from these similar settings. The 

studies used for the biomass estimate focused on relatively shallow areas rarely 

exceeding 20 m (Wenner 1989). No substantial information is available for stomatopods 

living in deeper waters, although Camp (1973) noted the occurrence of squillids to a 

depth of 346m in the Gulf of Mexico. The 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 studies in Panama (Caldwell et al 1989), and 

weight differences may be one source of the discrepancy observed here. The trawl nets 

utilized by Wenner (1989) may have skewed his samples towards larger specimens, but 

the trawling approach may have underestimated stomatopod biomass overall. 

The production/biomass ratio of stomatopods on the West Florida Shelf 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 died 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 resulting mortality estimates for the two genera 

(0.89 year-1) is taken as the mean natural mortality rate for the group, and this would 

equal P/B if no fishing mortality exists. Assuming, however, that fishing mortality (F) of 

West Florida Shelf stomatopods equals half of the natural mortality, 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 frequent foraging trips in search of prey 

(Caldwell et al. 1989, Caldwell and Childress 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). This estimated 27.15 mg of food for each 

stomatopod each day was divided by 1.2 g—the 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 derive 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). The 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 specimens of stomatopods of 8 different 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 the gut content analysis were converted to 

biomass proportions qualitatively—based on my best judgment. Unidentified animal 

tissue was apportioned to squid and bivalve 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 content analyses by R. Caldwell (unpublished data) show the 

unidentified material in the Neogonodactylus gut to be mollusc, particularly gastropod. 

Although it mainly represents the squillids, these latter findings may be general for 

stomatopods. Specific diets of the Neogonodactylids and Lysiosquillids are not well 

represented anywhere. Diets of adult Neogonodactylus consisting mainly of crabs and 

gastropods (Caldwell, unpublished data) is somewhat reflected in the following 

estimates. The diet of the Lysioquillids, 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. Childress. 1989. Prey selection and processing 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 Hourglass Cruises. Marine Research 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 biology of stomatopod Crustacea off 

the west coast of South Africa. 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 food 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 continental 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 biological, geological, chemical, socioeconomic, and 

cultural resources information for the south Florida 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 stomatopods. 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, southeastern 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 Columbia, Fisheries Centre 

Octopods are intelligent and voracious predators, 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 Atlantic 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 similarities of squids and forage fishes (e.g., Pauly 

1999). 

The biomass of octopods on the West Florida Shelf is unknown 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 composition, and the energy flows among 

the surrounding compartments. This estimate for octopods on the West Florida Shelf is 

equal to less than 1% of the estimate 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 considered equally uncertain. As a potentially 

important predator in this system, investments in octopod research should be seriously 

considered. 

The P/B value of octopods on the West Florida Shelf (3.1 year-1) is the mean of 

empirically derived P/Bs from females 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 Pauly 1993) from studies in the 

Mediterranean Sea (near Barcelona) and off Northwest 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 values 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; presented in Grubert et al. 1999), and is shown in Table 8.19. 

Table 8.19. Estimated diet composition of octopods on the West Florida 

Shelf. 

Proportion of diet Prey categories a

Yucatan b S. Carolina c Florida d
Ecopath 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 Africa. 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 von Bertalanffy expression to Octopus vulgaris growth. Invest. Pesq. 43(1):319-

326. 

Jarre, A., M. L. Palomares, M. Soriano, V. Sambilay, 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 Caribbean coral reef ecosystem, p. 

259-267. In V. Christensen and D. Pauly (eds.) Trophic 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. Estimates 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 models of aquatic ecosystems. ICLARM Conf. Proc. 

26, 390 p. 

Solís, M. J. 1962. Contributión 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 Columbia, 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 squid reveals their role in linking the 

“furnaces” of primary and secondary production to the organisms at upper levels of the 

trophic pyramid—a mediating role also played by forage fishes. It has been suggested 

that squid might be “better understood by pretending they are [fish]”, based on criteria 

relating to their size and growth (Pauly 1998) and their life histories—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 similar northeastern Venezuela 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 estimates from Pauly (1985) and consumption and 

diet information from Amaratunga (1983). 

We will use Mendoza’s biomass and Q/B estimates 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 averaged 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 stated above indicating that the 

Venezuela estimates would either overestimate 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 0.01 
Ecopath 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 1999. Mendoza’s (1993) biomass 

value of 0.267 is equivalent to a standing stock of 45,390 tonnes of squid on the 

170,000 km2 West Florida Shelf area. Application of Mendoza’s biomass estimate to the 

West Florida Shelf means that the instantaneous rate of fishing mortality (F; year-1) 

would be very low 0.00084, assuming 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 marine ecosystem, p. 379-412. In J. F. Caddy (ed.) 

Advances in assessment of world cephalopod resources. FAO Fish. Tech. Pap. 231. 

Karpov, K.A. and G.M. Cailliet. 1978. Feeding dynamics of Loligo opalescens. p. 45-66 In: Reckseik, 

C.W. and Frey, H.W. Biological, Oceanographic, and 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 for the northeastern Venezuela 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. FISHES

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 fish 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 similarity 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 West Florida Shelf in at least moderate 

abundance. A preliminary comprehensive list of over 600 species, taken from the NOAA 

website, was reduced to about 260 species (or genus or family in some cases). 

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

a. Horizontal distribution (oceanic migrant, 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 functional groups by FMRI scientists using the 

database classifications. Particular emphasis was given to trophic similarities during 

grouping because species with dissimilar diets can erode the representativeness of the 

model. Appendix 7 shows the final list of all functional fish groups. 

9.2 Biomasses of fish groups 

Four approaches were used to estimate the biomass of fish groups; (i) Fishery 

independent survey data, (ii) Stock-assessment VPA models, (iii) A basic catch 

equation, and (iv) Estimates from existing literature. 

(i) Fishery independent survey data 
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Four sets of fishery independent survey data were used to derive abundance estimates 

for coastal demersal/ structure related species, coastal pelagic species, and oceanic 

species, though this varied among species. 

Average abundance estimates derived from two reports detailing West Florida Shelf 

surveys in coastal waters (Darcy and Guntherz 1984, Darnell et al. 1987) were used for 

coastal species for which detailed stock assessment estimates were not available. 

Average estimates for each species were derived from surveys during which a catch 

was made. Maximum and minimum estimates from the 2 surveys were used to define 

confidence limits for model balancing. Demersal 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-trawl methods were emphasized for structureassociated fish species, since trawl estimates 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 km2

 and also applying a trawl efficiency 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 total trawl catch of 433,000 tonnes and the 

proportion of the total catch comprised by each species. 

Fish biomass estimates were also calculated based on 12 surveys (433 trawl stations) 

conducted from late 1960s to early 1980s, covering Lousiana, Alabama and Florida 

from the shore or barrier Islands to the 120m isobath (Darnell 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 km2 using the swept area formulae provided by Darnell et al. (1987). 

Estimates of relative biomass per km2 were derived by applying average weights of 

each species derived from state 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 eleven species caught in trawls is shown in 

Figure 9.1 
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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 inshore coastal waters and use a high opening 

bottom trawl. Estimates of total biomass for each species up to 91m isobath was 

adjusted to account for a 50% trawl efficiency (Mahmoudi, pers. comm.) and multiplied 

by the area bounded by 91m isobath (50,915 km2

). These total abundance estimates 

were then divided by the total area defined for the West Florida Shelf (170,000 km2) to 

estimate the average biomass density 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 those species with predominantly offshore 

distributions (oceanic functional groups). NMFS 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 seabed. This NMFS 

database included direct estimates of catch rate (relative biomass) per km2

 for each 

species in each year (for each area covered by the survey). 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 km2

 by applying a 50% trawl efficiency rate 

(Mahmoudi, pers. comm) and by using species distribution maps (Darnell et al. 1987) to 

determine the proportion of the defined area occupied by each species. 

Table 9.1. Some common shelf region species. 

Common name Species % of total catch 

Gulf butterfish Peprilus burti 20.710 
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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 – Virtual Population Analysis (VPA) 

Virtual Population Analysis (VPA) estimates were available for king mackerel, Spanish 

mackerel, sheepshead, spotted seatrout, 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 fishing mortality estimates used in the VPA’s 

were used here to determine abundance on the West Florida Shelf using simple 

relationship Biomass=Catch/F (see next section). The same procedure was applied to 

red drum and snook to derive biomass estimates based on 1998 catches from the West 

Florida Shelf. I include some examples 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, which also equals P/B (Allen 1971). The mean 

value (weighted by biomass) for adults was 0.384; for juveniles, 0.434. These values 

probably underestimate production of juveniles 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 reliable 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). 
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The West Florida Shelf sheepshead population 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/km2

, 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/km2

 0.010695 

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

(iii) Estimation using Biomass = Catch/ Fishing mortality 

The basic catch equation (Baranov 1918) was used to derive rough estimates of 

biomass on the West Florida Shelf for several species. This equation assumes that 

catches are proportional to the biomass present in the area (C=F/B). Our estimates are 

based on the simplistic assumption that stocks are under the same fishing pressure 

throughout their WFS distribution. Our calculations may either overestimate or 

underestimate the biomas of highly migratory fish in the area, considering that fishing 

effort, stock biomass and catchability are 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 Mexico. 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 6.6a 33 b 
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White marlin and 

spearfish 

Tetrapturus spp. 0.2 Brown et al. 1991 2.4 a 12.2b

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 shark1 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 shark1 Carcharhinus isodon 0.13 NMFS 1998 3.52 27.06 

Atlantic sharpnose 

shark1 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 

Dogfish1 Squalidae 0.06 NMFS 1998 0.01 0.12 

Sand tiger1 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 snook2 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% of 
Ecopath 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 1/5th 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 twice the 1998 VPA estimate. 

Another example is that, egg and larvae surveys from plankton collections 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 Berkley (1982), provided egg 

and larvae based estimates for several small pelagic 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). These 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 overall population occurring in the West 

Florida Shelf region was equivalent to the proportion of the overall fisheries catch taken 

there (1.37% of the overall catch). The total menhaden catch in the Gulf of Mexico was 

569,600 t in 1989, and the total catch in the WFS area was (7,800 t). The total 1997 

estimate of spawning stock biomass (females 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 greatly underestimate the West Florida Shelf 

population since it is based only on catches taken in the Northeast Panhandle region 

(Mahmoudi, pers. comm.). 

Brown et al (1991) provided Gulf wide biomass estimates for a number of species in the 

Gulf of Mexico (Table 9.5). These estimates 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/km2

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

Round herring 380,000 10,000 390,000 2.294 
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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 estimates, approximate biomass estimates were 

derived using catch data and average fishing mortality estimate from other species in 

the same functional group. If no substitute values 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 fishing mortality) that were derived with 

biomass weighting. 

Table 9.6 provides a summary of the relative biomass of West Florida Shelf species 

ranked by abundance estimates derived herein. Total biomass of fish was estimated to 

be 3.5 million tonnes, with range 0.5 to 6.1 million tonnes. Details 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 

Common name biomass 

>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 0.67 
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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 average production and consumption parameter 

estimates were calculated from estimates of P/B and Q/B for each group member 

(species) weighted by their relative biomass in the group. From 264 species listed within 

the functional groups, individual estimates of P/B and Q/B were made for 229 and 213 

of them respectively. Where several alternative estimates existed, local (West Florida 

Shelf) and species specific values were chosen in preference over 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 methods 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 production rate (P/B), under the assumption of 

steady-state (Allen 1971). Estimates of total 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 (Goodwin & 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-checked by calculating Z from mean length 

(Beverton and Holt 1956). This calculation relies on the assumption of steady 

recruitment into the population: 

Z=K(L∞-Lavg)/( L∞-L′) 

L∞ = asymptotic length (cm); 

Lavg = average length; 

L′ = cut-off length; 

K of the VonBertallanfy growth equation 
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Estimates of natural mortality (M) for small pelagics were derived from an empirical 

formula described by Brey (1999), a modification 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 lower than original model estimates, supporting 

a reduction in P/B to value closer to the 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 natural mortality were almost 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. 

Length1

M est. (Pauly’s M 

modified by Brey 1999) 

Atlantic thread herring 1.60a 0.58 1.02 

Scaled sardine 1.57a 1.04 1.76 

Spanish sardine 0.95a

 1.15 1.58 

Menhadens 0.95a

 0.56 0.93 

Round scad 0.92b 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 capture (cut-off length) is 2/3rds 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 planktivores), however, total mortality is 

equivalent to natural mortality (M). Values of M taken from Fishbase, were used as 

minimum proxy value of P/B for 20 species (generally unfished) for which no other 

estimate could be derived. 

(ii) Borrowed from other Gulf of Mexico models 

In lieu of local direct estimates, P/B values used in published Ecopath models from the 

Gulf of Mexico were compiled by species and their average values applied as rough 

estimates for use in the West Florida Shelf model. Where no specific values were 

available for the species included in the West Florida Shelf model, values from similar 

functional groups were taken. These 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 constraints, 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 estimate of P/B for sandbar shark Carcharhinus 

plumbeus and blacktip shark C. limbatus. 

Consumption/ Biomass (Q/B) 

Three approaches were used to estimate consumption rates (Q/B) for West Florida 

Shelf fishes: (1) empirically-based relationships between morphology, physical 

variables, and consumption rates, (2) species-specific measurements, and (3) estimates 

from other models. These approaches are described below. 

(1) Estimates derived by empirically-based relationships 

Palomares et al. (1998) presented the following empirically-based equation describing 

how consumption rate can be predicted by a combination of size, temperature, tail 

aspect ratio (i.e., lifestyle / feeding type), 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, h2

/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 temperature of fish habitat 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 data atlas (Berryhill 1977). 

(2) Species-specific measurements 

Independent estimates of Q/B used in deriving empirical equations are given in 

Palomares and Pauly (1989, 1998). These values were used as alternative estimates, 

since many of them were not specific 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 proportion of body weight (Q/B year-1) from other published 

sources. They made two general adjustments to original estimates: (1) they multiplied 

by 2 as an approximate conversion from routine to field (Winberg 1960); (2) they divided 

by 0.65 to account for losses due to egestion, excretion, and specific dynamic action 

consumption was estimated were based on oxygen consumption (Kitchell et al. 1978). 

Other independent estimates include those for 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, leading 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 published 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. Where no specific values were available for the species included in 

the west shelf model, values from similar functional groups were taken when speciesspecific values were not available (Appendix 7). 

9.4 Fish diet compositions 

Quantitative information on the diet of 171 of the 260 fish species was compiled from 

detailed stomach content studies and from general feeding habit studies. Composite 

diets for each functional group were derived by employing 3 steps: 

(1) Weighting each species’ diet composition 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 + Individual consumption (Qi = Bi*Q/Bi) divided by total consumption 

of species within the group (Σi=1 to nQ)]. 

(2) Summing the relative proportions of the prey species within their respective 

functional group; 

(3) Normalizing the diet proportions of each predator group to 1 by dividing each relative 

proportion of prey, by the total proportion of prey consumed. 

No quantitative diet information was available for large oceanic planktivores, pelagic 

oceanic piscivores, Pelagic oceanic jelly eaters, 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 (Table 9.9). Comprehensive 

local reports included the Hourglass memoirs, diet studies in Tampa bay (Motta et al. 

1995, Peebles and Hopkins 1983) and life history summaries for Gulf of Mexico 

estuarine species (NOAA 1997). When several published diets were available for a 

single species, the most local diet was used, or diet compositions were averaged. 

More generalized diets were derived for swordfish (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; Cortés 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 Indies 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 spawning season). Similarly, demersal coastal 

piscivores (seatrout) that are restricted to nearshore and seagrass habitat were 

assumed to obtain 35% of food from outside the system. 

Table 9.9. Diet information sources 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 estimates 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

103 

Basic input parameter estimates for West Florida 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 ranges 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 0.1244 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 2.6929 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 0.0410 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 0.1210 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/eaters 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 0.0306 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 0.0006 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 factors. Trawl sampling, for 

example, under-samples certain species due to inter-specific variability in behavior and 
Ecopath 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 model balancing that 

this group’s biomass must be higher the original estimate because this group comprises 

many species whose combined biomass is likely to be greater 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 high. In the future, these species could be 

included in another group. 

20. Benthic oceanic piscivores (largescale lizardfish, shortjaw lizardfish, offshore 

lizardfish, duckbill eels): These species might be considerably more abundant on the 

West Florida Shelf than the trawl estimates suggest. This group has a strong effect on 

the demersal invertebrate eaters and invertebrate functional groups upon which they 

feed. Incorrect parameterization of this group thus indirectly influences the 

thermodynamic ‘balance’ of these prey groups in the model. 

21. Benthic oceanic invert eaters (pancake batfish, spinycheek scorpionfish, slender 

searobin, shortwing searobin, saddle bass, tilefish family, luminous hake): Very little 

information is available on any species in this functional group. The best estimate P/B 

was considered too to high and was considerably reduced during balancing. 

24. Surafce pelagics (Halfbeaks and flying fish): These species were not well sampled 

by any of the gear used to in abundance estimation. However, these species are found 

in the gut contents of many West Florica 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 chromis, 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 biomass 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, stargazers, 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 accounted for. Placeholder P/B and Q/B estimates 

fall mid-way between piscivores and invertebrate eaters. 

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10. SEABIRDS

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 species were reported for the Southwest Florida region 

by Owre (1990). Seabirds of this region feed on a variety of biota ranging from small 

invertebrates (e.g. Phalaropes) to primarily fishes (e.g. Pelicans, Cormorants, etc.), 

while others combine fishes and invertebrates in their diets (e.g. Storm-Petrels, 

Tropicbirds, etc). Here, we have included only the species that primary consume bait 

fish because detailed abundance and diet information is rare. Birds that consume 

baitfish include Pelicans, Terns, Gulls, Frigatebirds, Loons, Gannets, and Cormorants. 

Of these, Brown Pelicans, Northern Gannets, and Double-crested Cormorants are 

notably abundant (Table 10.1) 

Table 10.1. Abundance of bait fish consuming 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 seabird 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 empirical formula to calculate seabird food 

consumption per day from Nilsson et al. (1976) and the biomass of each species in the 

group. These parameters are 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 grams 
Ecopath 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/Bw 

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 have the highest biomass (38%) and total 

consumption among the bird species scrutinized; and their diet was used as a starting 

point for estimating the diet composition of birds in the 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. Fogarty 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 column, 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 preliminary 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 Press, Inc. Boca Raton, FL. 371 pp. 

Fogarty, M. J., S. A. Nesbitt, and C. R. Gilbert. 1981. Diet of nesting brown pelicans 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: Continental Shelf Associates Inc., ed. Synthesis of 

available biological, geological, chemical, socioeconomic, and cultural resource information for the 

South Florida area, MMS U.S. Department of Interior Minerals Management Service Atlantic OCS 

Region, Chapter 9, p. 303-351. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

110 

11. SEA TURTLES

Marcelo Vasconcellos 

Departamento de Oceanografia, Universidade do Rio Grande 

Three sea turtle species nest regularly on Florida’s beaches: 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 coastal waters, bays, and lagoons. These 

habitats are used as nursery and development areas by juveniles and subadults. Sea 

turtles come to Florida’s nesting and rearing areas from feeding grounds scattered 

throughout the Atlantic. Along Florida’s Gulf coast, most nesting occurs from Pinellas to 

Monroe Counties. Sea turtles have not been intentionally harvested 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 sea 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 (loggerheads 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 coastal 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 Replubic, north along the eastern US coast 

and south through the Florida Keys and Gulf of Mexico (late April to September). Florida 

bay is a developmental habitat 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 Florida’s east coast (Plotkin, 1995). Immature 

green turtles have been reported along the west coast, in Florida Bay and in the Cedar 

Key / Crystal river area, indicating the importance 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 estimates of breeding female leatherbacks 

range from 70,000 to 115,000, and the number of nests reported 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). Sightings 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 medium sized turtle observed regularly in the Florida Keys. 

Posthatchlings occupy the pelagic environment, taking shelter in weedlines that 

accumulate at convergence points. They re-enter coastal waters when they reach 20-25 

cm carapace length. Coral reefs are the main foraging habitat of juveniles and adults, 

which feed primarily on sponges on these reefs. 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 subadults 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. Stranding 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 likely 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 probably do not eat during the reproductive 

season, and because feeding areas are usually distinct from nesting areas (A. Meylan, 

pers. comm.). I used three sources of information to estimate number of foraging 

individuals on the West Florida Shelf: 1) NMFS stranding data; 2) turtle counts made by 

aerial surveys by the Mineral Management Survey; 3) turtle bycatch by shrimp trawlers. 

Stranding reports of dead or injured turtles represent a minimum mortality estimate. 

Strandings provide an indication of the characteristics of populations in the area, such 

as species composition and relative abundance, 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 than 2% of strandings. Henwood and Stuntz 

(1987) also found a low frequency of occurrence of leatherbacks and hawksbill turtles in 

shrimp trawlers bycatch. They calculated the turtle catch per unit of effort (CPUE) for 

the three dominant species from 1973 to 1984 (Table 11.2) (# of turtles per 30.5 m net 

headrope length per hour fishing). I used CPUE to roughly estimate turtle densities by 

calculating number of turtles per given swept area. The swept area of a trawl net hour

was calculated assuming a trawling speed of ca. 2.5 knots. Based on the information 

available, I estimated the density of loggerheads, the most abundant species over the 

Gulf shelf, to be 0.0326 turtles·km2

. 

 

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 5.00 100 
Ecopath 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/km2) 

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 km2

. 

Fritts et al. (1983) reported that densities of loggerheads in this area ranged from 0.031 

to 0.33 turtles/km2. Their data were obtained during bimonthly surveys over the 

southwestern Florida shelf (total area 24,642 km2) 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 observed only a few times. Similarly, Mullin 

and Hoggard (2000) estimated loggerhead densities to be 0.0407 turtles/km2 in the 

Florida panhandle region (waters <100m) and in eastern Gulf shelf slope waters (100 – 

2000 m) based on aerial surveys from 1996 to 1998. Estimates of densities of 

leatherback and Kemp’s ridley turtles were 0.00194 and 0.00097 turtles/km2, 

respectively. Leatherbacks and loggerheads were the only turtle species sighted in the 

shelf slope area. The overall density of leatherbacks and loggerheads in slope waters 

was 0.00238 and 0.002 turtles/km2, 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/km2. The total density of sea 

turtles in the study area was calculated by extrapolating the loggerhead density to the 

other species using the stranding data as a measure of the relative abundance (Table 

11.1). The sea turtle biomass estimate (0.0069 tonnes/km2) was then calculated by 

multiplying species densities by mean body weight. A loggerhead production to biomass 

ratio was estimated as 0.22 year-1, assuming an adult annual survivorship of 80% 

(Henry et al. 1998). P/B ratios for the other species were assumed to be 0.15 year-1

(Polovina, 1984). The P/B ratio for the group was calculated as the average P/B of 

individual species, weighted by species biomass. 

Diet and Consumption / Biomass 

Loggerheads are omnivorous, but prefer a carnivorous diet of shellfish and mollusks, 

especially clams, oysters, and crabs, as well as encrusting animals attached to rocks 

and reefs. Green turtle hatchlings are carnivorous, 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 seagrasses on shallow flats (Mendonça, 1983). 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

113 

Jellies (Physalia spp. and Scyphozoans) are the primary food of leatherbacks. 

Hawksbills consume a variety of invertebrates, but their predominant prey is sponges. 

Kemp’s ridley is carnivorous, feeding on swimming crabs and other crustaceans, clams, 

mussels, fish and jellies. Blue crabs are their preferred food in many places (Creech 

and Allman, 1998). Diet for the group was calculated by the weighted average diet of 

the individual species, using species total consumption 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/km2

Adult mean 

weight (Kg) 

Biomass 

tons/km2

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 imbricata 0.0009 80 0.000072 0.15 3.5 

Leatherback, Dermochelys coriacea 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 contents 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 oil 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 sea 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. Wursig (eds.), Cetaceans, sea turtles and 

seabirds in the northern Gulf of Mexico: distribution, abundance and habitat association. Volume II: 

Technical Report. Prepared by Texas A&M at Galveston 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 ecosystem I. The ECOPATH model and its applications to 

French Frigate Shoals. Coral Reefs 3:1-11. 

Provancha, J. A.; Mota, M.J.; Lowers, 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 Turtles. Florida Power & Light Company, 60 p. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

114 

12. MARINE MAMMALS

Marcelo Vasconcellos 

Departamento de Oceanografia, Universidade do Rio Grande 

12.1 Odontecetes 

A variety of marine mammals inhabit the waters 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 the 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 surveys (Mills and Rademacher, 1996) indicated that 

about 86% of spotted dolphin sightings occurred in waters shallower than 100 m, and 

particulalrly between 15 and 100 m isobaths. More sightings recorded east of the 

Mississippi River mouth (0.4752 sightings per hour) than west of it (0.2489 sights per 

hour). Survey data also indicated that spotted dolphins migrate inshore and offshore 

seasonally, and possibly alongshore to the south during winter. Swartz et al. (1999) 

estimated that at least 12,896 bottlenose dolphins and at least 33 individual stocks 

inhabit the bays, sounds, and estuaries 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 Florida Keys (from about 9 km seaward 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, Florida; and 3) several small stocks inhabiting 

bays, sound, and estuaries. The coastal stock (completely inside 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 (Leatherwood and Reeves, 1990). I estimate there to be 23,844 

bottlenose dolphins in the study area, assuming that 30% of the outer shelf stock occurs 

within the study area. The estimated dolphin density is thus 0.146 individuals per km2

. 

This latter estimate is consistent with results of independent aerial surveys between 

1996 and 1998 (Mullin and Hoggard 2000). 

Mullin and Hoggard (2000) report the results of visual surveys of cetaceans in the 

continental shelf in the northeastern Gulf of Mexico (both ship and aerial). Surveyed 

areas were south of the western Florida Panhandle in continental shelf waters from 12.5 

km offshore to 100 m deep (12,326 km2), and also in the continental slope waters (100-

2000 m) within the US EEZ (70,470 km2

). Table 12.1 shows the estimated abundance 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

115 

of cetaceans from aerial surveys (aerial survey estimates were generally higher than 

ship survey estimates). The continental shelf region is inhabited almost exclusively by 

bottlenose dolphins and Atlantic spotted dolphins (one dwarf/pygmy whale was sighted 

in the shelf area). In contrast, sixteen cetacean species were sighted in continental 

slope waters (Table 12.1). Pantropical spotted dolphins and spinner dolphins were the 

most abundant species there in the slope area. 

Biomasses of individual species were estimated by multiplying densities and mean body 

weights (Trites and Pauly, 1998). The mean biomass of cetaceans in the study area 

was calculated by weighting species biomasses by the approximate areas of shelf (0 – 

100 m = 139,679 km2

) and slope regions (100 – 183 m = 22,848 km2) of the study area 

inhabited by each species. I estimated the total biomass of marine mammals in the area 

to be 0.038 tonnes·km-2. The P/B ratio West Florida Shelf 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./km2

Slope 

indiv./km2

Biomass 

t/km2

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 444.5b ─ 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 66.4c ─ 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 continental 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 Pauly 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 planktivorous marine mammals as 41.07 year-1

(Browder, 1993) and 10.95 year-1 (Matkin and Hobbs, 1999) respectively. 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 mile from shore. Manatees migrate seasonally to warmer 

areas during winter; these include southern coastal waters, natural springs, and 

industrial warm water effluents (counts at these wintering locations are used to estimate 

population abundance). This species inhabits estuaries and coastal bays during 

summer (O’Shea et al. 1995). During winter, they are known to make foraging trips to 

Salt River and Crystal Bay 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/km2

, given a mean individual 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 exclusively 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 feeding 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 Mexico 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 bottlenose 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 British 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. Wursig (eds.), Cetaceans, sea turtles and 

seabirds in the northern Gulf of Mexico: distribution, abundance and habitat association. Volume II: 

Technical Report. Prepared by Texas A&M at Galveston 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 coastal Gulf of Mexico. Northeast Gulf Science, 11(2): 

113-122. 

Odell, D. K. 1990. Marine mammals and sea turtles.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. Percival. 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. Christensen. 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 dolphin, 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 Oceans. 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 Zoology, vol. 76, no. 5, pp. 886-896. 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

118 

13. FISHERIES

13.1 Landings 

Robert Muller 

Florida Marine Research Institute, 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 during 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 fisheries 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 invertebrate 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, grunts, amberjacks, or other species with the same setup. It 

is necessary to capture this diversity when analyzing catch records. 

Harvest information on the West Florida Shelf 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 identification, the date landed, county landed, gear fished, depth 

fished, area fished, and, for each species landed on a trip, species code, quantity 

landed, size category, and price paid. In addition to commercial fisheries operating on 

these stocks, recreational anglers 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 angler activity on headboats by month and 

location. In Florida, FWC personnel conduct 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 included 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 are seasonal and, for example, it is necessary to include 

December and January into the same season. 

 

Landings from the shelf were broken down into 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 assigned to geographical regions using the county 

where the species were landed, recreational and headboat landings were estimated for 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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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 focus on the West Florida Shelf and the number of species 

which only occur inshore. Similarly the Florida Keys were omitted because of the large 

number of sub-tropical species that are only found in the Keys. 

A conversion table was created to link commercial species codes with their equivalent 

National Ocean Data Center (NODC) species codes. Commercial landings were 

combined with MRFSS and headboat landings by NODC species codes 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 construction 

Table 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 s

Large oceanic piscivores 0.47 0.00 4520.68 1.41 0.00 0.00 0.00 79.62 0.00 Coastal sharks 4.27 0.06 171.26 0.66 0.02 0.00 0.00 21.81 0.00 Rays and skates 0.00 0.14 44.50 4.93 0.00 0.00 0.00 0.09 0.00 Pelagic oceanic piscivores 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.30 0.00 Pelagic coastal piscivores 8.07 147.60 2044.79 37.31 11.28 0.00 0.24 293.77 61.10 5Mackerels adult 0.17 0.71 2289.10 7.03 0.63 0.00 0.00 99.48 0.11 Sardine-herring-scad complex 155.40 200.29 316.51 0.00 0.00 0.00 0.00 0.00 2830.56 35Pelagic oceanic jelly eaters 0.01 434.31 0.00 0.00 0.00 0.00 0.00 0.18 183.06 Pelagic oceanic planktivores 0.00 136.51 0.00 0.00 0.00 0.00 0.00 0.00 332.28 Demersal oceanic invertebrate feeders 

0.00 67.49 0.00 0.00 0.00 0.00 0.00 0.00 0.75 

Demersal coastal piscivores 0.40 19.63 974.54 0.00 0.24 0.00 0.00 47.03 0.34 Demersal coastal invertebrate feeders 

4.63 212.17 2343.35 68.38 214.28 0.17 1.97 78.09 5.07 1

Demersal coastal omnivore 0.15 8.96 325.73 0.47 0.00 0.00 0.00 7.34 0.00 Benthic oceanic piscivores 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Benthic oceanic invertebrate feeders 0.00 4.67 2.80 0.01 0.00 0.00 0.00 13.77 0.00 Benthic coastal piscivores 1.02 14.46 76.42 0.47 0.03 0.00 49.38 4.45 0.00 Benthic coastal invertebrate feeders 0.00 2.88 22.93 1.80 0.00 0.00 0.00 1.58 1.50 Surface pelagics 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Structure associated coastal piscivore 

1.32 2.90 1535.93 100.17 0.43 0.00 0.66 109.63 0.02 

Large groupers 47.26 30.21 1779.13 104.71 0.69 0.00 8.94 1563.47 0.00 Structure associated coastal invertebrate feeders 

7.94 38.33 1696.47 191.53 0.37 0.00 3.09 980.73 1.44 

Structure associated coastal omnivores 

0.00 0.65 2.04 0.01 0.00 0.00 0.00 0.00 0.00 

Structure associated coastal planktivores 

0.00 0.21 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Nearshore associated piscivores 35.31 32.18 154.62 0.00 0.00 0.00 0.00 6.58 11.38 Mullets 14.89 2110.47 618.43 0.00 18.23 0.00 0.87 7.98 71.57 36Nearshore planktivores 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Other fishes 0.00 58.24 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Squid (Loligo opalescence) and cuttlefish 

0.06 34.28 0.00 0.00 0.00 0.00 0.00 0.04 3.62 

Shrimps 18.12 5304.36 0.00 0.00 0.00 0.00 0.00 4.90 0.00 Lobsters (Spiny - Panulirus argus) 0.27 23.69 0.00 0.00 0.00 0.00 0.00 0.86 0.00 Crabs (Blue, stone, fiddler & others) 18.01 193.63 0.00 0.00 0.00 0.00 1.94 6.57 14.60 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

Octopods 0.00 0.70 0.00 0.00 0.00 0.00 0.00 0.04 0.00 Stomatopods 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 Sand dollars 0.00 765.77 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Large infauna 0.00 191.97 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Sessile epifauna 6.34 863.42 0.00 0.00 0.00 0.00 0.00 0.38 0.00 Small infauna 0.00 0.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Small mobile epifauna 0.00 84.47 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Jellies 0.00 0.55 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Macroalgae 0.00 1.04 0.00 0.00 0.00 0.00 0.00 0.00 0.00 
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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 discards of coastal and large oceanic sharks, the 

reported landings of shark species 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 fisheries in west Florida (State and Federal 

Landings Database). Farber (1992) estimated that approximately 85 tons of billfishes 

were discarded in longline fisheries in the Gulf of Mexico. The total discards of billfishes 

from longline fisheries operating 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 (discarded) of billfishes by longlines in west Florida it was 

assumed that ca. 1/5 of Gulf catches occur off west Florida, based on the fact that 

billfishes spend at least 1/5 of a year, during 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 manatees. The average finfish: 

shrimp ratio in shrimp trawling in the Gulf of Mexico is 10 to 1, but varies significantly by 

area, from 2:1 in the Dry Tortugas to 14:1 off the Mississippi river. On average, the total 

discards from offshore shrimp trawling in the Gulf of Mexico represent 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 shrimp trawlers we followed three steps: 

Firstly, we compiled available information on species composition in the bycatch of 

shrimp trawlers in west Florida. For inshore trawl fisheries (<areas within 9 nautical 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

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miles from the shore) we used data from Steele et al., (unpublished, a and b). Two gear 

types are commonly used in inshore shrimp fisheries of west Florida: ottertrawls and 

rollerframe trawl. Otter trawls are used in coastal areas and bays (including Tampa Bay) 

on nonvegetated, sandy bottom areas. 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 Management Council (NOAA). In the 

latter we assumed that the depth of 10 meters approximately corresponds to the 9 

nautical miles distance used as limit between inshore and offshore areas in the state of 

Florida’s catch database. 

Secondly, we used the total landings of shrimp by area (inshore, offshore) and the 

average bycatch: shrimp catch ratio to calculate the total expected amount of bycatch 

originated from trawlers in west Florida. Finfish: shrimp ratios 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 invertebrates (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 Fishery Management Council, NOAA). 

Finally, the total bycatch estimated above was then splited among species and 

functional groups according to species proportion in trawlers bycatch. Table 14.2 shows 

the bycatch discarded by functional group estimated by accounting for differences in 

landings, bycatch:shrimp ratios and species composition 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·km2

) 

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 invertebrate 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 feeders 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 0.0048 
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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 catch 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 billfishes based on swordfish logbook data from 1987 through 

1990. ICCAT CICAA 39: 659-667. Meeting of the ICCAT Standing Committee on Research and 

Statistics, Madrid (Spain), Nov 1991. 

Legult, C. M. 1998. Stock assessment analysis on Atlantic migratory group King mackerel, Gulf of Mexico 

migratory group King mackerel, Atlantic migratory 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 bycatch 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. 
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14. Limitations, cautions, and guidelines for use 

14.1 SCALE AND INFERENCE: THE NATURE OF ECOPATH MODELS

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

Ecosystems are complex. Moreover, the complexity of ecosystems is fractal; increasing 

complexity occurring at increasingly narrow scales of examination. Ecosystem 

processes that occur on micro-scales have fundamental importance to broader-scale 

ecosystem structure and properties. 

To undertake an integrated and quantitative analysis 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 aggregated functional 

groups) are such building blocks, and their broad and generalized nature, or the 

inherent de-emphasis of micro-scale processes, can lead to confusion about the 

usefulness of the approach. The lack of explicit articulation of micro-scale processes 

does not, however, imply that these processes are excluded from consideration, or even 

from parameterization. Ecopath parameters, and models, implicitly integrate micro-scale 

processes. 

Indeed, each of the Ecopath parameters represents the sum, or the result, of all smaller 

scale processes (that are considered), which lead to the emergence of measurable 

ecosystem properties. Ecopath parameters thus ideally integrate all smaller scale 

processes into a description of the food web, vis-a-vis all biotic components of the 

system (throughout all trophic levels). This is accomplished through measurement and 

empirical information from appropriate scales of examination. In essence, an Ecopath

model represents a system that results from micro-processes. This approach is, 

therefore, not subject to the additivity and multiplicativity of errors that are typically 

encountered when such models are constructed from the ‘bottom up.’ The inherent 

complexity of ecosystems virtually prevents analytical endeavors in which attempts are 

made to reconstruct all the micro-scale processes to support each consecutive level. 

The Ecopath approach begins by describing ecosystem components at all levels, with a 

focus on the ‘data richness’ of each ‘integrative’ input parameter (Section 2), and then 

learning and refining from examination of the imbalance resulting from the broader 

integration of these parameters into a system-wide picture (Section 3). These initial 

parameters are subsequently adjusted until thermodynamic constraints are met (‘model 

balancing’). The resulting ‘balanced’ model then represents one possible average state 

of the flows in the food web. The model balancing process provides an opportunity for 

learning about ecosystem structure as the 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 food 
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webs, the information emerging from these detailed efforts can be used to parameterize 

components of Ecopath models. 

Notwithstanding this qualitatively 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 all 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 examined. Biotic components of a food web 

analyzed with this approach are generally aggregated into 50 aggregated groups or 

less. This approach gives us a broad view of the system; the premise of Ecopath with 

Ecosim is that the interaction of these broad ecosystem components in dynamic 

simulations will represent the interaction of real ecosystem components because 

enough population and energetic information exists on this broad scale of examination 

to characterize the mechanisms of interest. Most of the uncertainties, simplifications, 

and assumptions of the approach relate to this 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, limitations, 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 predation impacts on rare prey; 

• Trophic mediation effects (e.g., biogenic habitat effect); 

• Underestimates of predation vulnerabilities; 

• Non-additivity in predation rates due to shared foraging arenas; 

• Temporal variation in species-specific habitat factors. 
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14.2 PARAMETER UNCERTAINTIES

Thomas A. Okey 

University of British Columbia, Fisheries Centre 

Steven Mackinson 

CEFAS Fisheries Lab, Lowestoft, UK 

Information about ecosystems is generally limited and uncertain, and these constraints 

limit the usefulness of ecosystem models. However, explicit recognition of the nature of 

uncertainties can guide users toward appropriate interpretation of model results. In 

addition to documenting derived ranges for input parameters, data uncertainties were 

characterized during model construction using the ‘data pedigree’ approach (Section 3). 

Constructing an Ecopath model can serve to reduce uncertainty, not only through 

synthesis, integration, and consilience of information, 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 toggling 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. Comparison of the analyses to independently-derived information 

about the system is an important effective way to refine the model and continue to 

reduce uncertainty. 
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14.3 PROBLEMS WITH FUNCTIONAL GROUP AGGREGATION 

Steven Mackinson 

CEFAS Fisheries Lab, Lowestoft, UK 

Several pathological problems arise as a consequence of aggregating groups in an 

Ecopath model. It is particularly difficult to alleviate such problems when the number of 

species in the model system is very large. This is true of the West Florida Shelf model, 

where in many cases more than 10+ species 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 overestimated 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 the groups and used B=C/F to estimate the 

biomass. 

(ii) Fishing mortality tends to be underestimated for particular species within 

functional groups that comprise of many species, of which only several might be 

fished. Essentially, important species that may have a high fishing mortality are 

lost in the aggregated 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 mortality 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 selectivity is not represented well when 

groups are aggregated. Little sense can be made from examining harvest 

scenarios on such groups, and an anylitical focus on these species should be 

avoided. In the West Florida Shelf model, such groups include (18) Demersal 

coastal invertebrate eaters, (23) Benthic coastal invertebrate eaters, (25) 

Structure associated piscivores, (27) Structure associated invertebrate eaters. 

The solution is to pay close attention 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 directed during model 

simulation of harvest scenarios. In the model here, mackerel adults, juvenile, 

small pelagics and large groupers are important groups that have been ‘broken 

out’ in to separate 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 misleading results of dynamic 

simulations. The aggregation process results 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 reduced to a small fraction (1-2%) of the 
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diet composition in most groups to avoid misleading dynamics. This was 

recommended by D. Pauly. 

(iv) In Ecospace, spatial distributions for each functional group tend to become 

widely distributed when many species are aggregated in to a single functional 

group. This has implications for interpreting the spatial dynamics of particular 

species. In some cases, the apparent wider distributions, makes the groups 

available to predation that particular species in reality would not be subjected to. 

The same is true for distribution of predators, and thus overall the spatial 

overlaps of highly aggregated groups tend to be quite large and mask real, small 

scale spatial separations. 
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14.6 GUIDELINES FOR APPROPRIATE USES OF THE MODEL

Thomas A. Okey 

University of British Columbia, 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 compares to the spatio-temporal scales of the 

model and the degree of aggregation of functional 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 model 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 model 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 order to explore the interference effects of 

such blooms on benthic primary producers. Such analyses can point to implications of 

such changes on the organization of the broader biotic community, and this example is 

being pursued as a continuing research question. Another research group is interested 

in using the model to explore questions related to the interactions of grouper and their 

benthic habitat. Many such ‘ecosystem organization’ 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 investigators might find it helpful to modify 

the structure (and or content) of the model, to address particular questions of interest. 

Radical modifications might include using 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 Shelf 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 feel free to refine the model or modify it for 
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their uses. We emphasize just as strongly, however, that refinements or improvements 

to the model should be documented 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 physical 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 forcing. Now, several tools are available in Ecosim

to begin teasing apart all the variables and factors that may influence particular biotic 

components of concern. These tools include forcing functions, trophic mediation 

functions, and the ability to fit simulations and trajectories 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 marine 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 quantifying biomaccumulation of pollutants. The routine 

requires input of the decay rates of pollutants to trace their fate throughout the 

ecosytem (contact Villy Christensen for further details). 

Although the Ecopath with Ecosim approach is designed for fisheries applications, we 

suggest that the approach be viewed as a complement to current single-species 

fisheries models, or currently implemented fisheries strategies. 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 single analytical framework 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 effects of particular management and policy 

options. 
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APPENDICES 

APPENDIX 1. LIST OF CONTRIBUTORS

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 California 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.us 
Ecopath 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 Columbia, 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.mx 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

APPENDIX 2.

DIET MATRIX OF THE 

WEST 

FLORIDA 

SHELF 

ECOPATH MODEL

Table A2. Diet Matrix of the West Florida Shelf model (%) 

Predators Prey 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 2

1 Dolphins .02 2 Seabirds 3 Turtles .02 4 Manatees 5 LgOcePisc 1 0.2 6 LgOcePlank 0.1 7 Coastalsharks 1 2 0.7 8 Rays/skates 1.2 15 9 PelOcePisc 7 7 

10 PelCoasPisc 1.7 0.2 8.5 2.5 1 1 11 MackerelAdul 5.7 0.3 1 0.3 12 MackerelJuv 1.5 0.2 5.7 0.3 2 0.5 13 Sardine/Herring 5.3 24 0.7 7.3 3.7 10 15 45 41 0.1 1.6 1.7 0.6 14 PelOceJelly/eaters 0.2 9.2 3 10 7.6 0.1 15 PelOcePlanktivores 1.6 5 9.6 12 3 11 6.2 11 22 0.1 1 16 DemOceInvert/eaters 0.6 117 DemCoasPisc 9.9 1.2 1 118 DemCoasInvert/eaters 14 1.5 3 8.1 2.9 15 4 0.4 19 DemCoasOmniv 21 9.9 5.2 5.6 2 0.1 20 BentOcePisc 0.21 BentOceInvert/eaters 0.1 0.3 0.2 222 BentCoasPisc 1.5 4 2.1 0.1 5 23 BentCoasInvert/eaters 0.1 13 1.6 2.2 4.5 0.3 2.5 0.6 124 SurfacePelagics 1.5 0.2 9.8 0.9 9.4 25 StrucAssCoasPisc 1.4 3.1 2.3 0.1 26 LgGroupers 0.5 2.4 27 StrucAssCoasInvert/eaters 1.1 1.1 3.7 6.2 3.4 4.5 2.3 0.1 28 StrucAssCoasOmniv 1.1 1.3 0.7 2.3 5.4 2 2 29 StrucAssCoasPlank 30 NearshAssPisc 0.2 3.2 0.4 1.1 31 Mullets 2.3 12 0.4 1.3 32 NearshPlanktivores 13 6.2 0.7 2.1 2.3 19 12 10 1 20 5.2 33 Other fishes 7.7 3.3 9 1.5 10 1 3.4 5.8 3.6 2.2 5.2 1.1 1
Ecopath model of the West Florida Shelf: Volume II. Model construction 

34 Squid 40 12 6.5 9.1 10 29 7.4 5.6 5.8 1.4 6 0.1 35 Adult Shrimps 0.5 1 1 0.5 1 1.6 0.1 0.1 1 1 4 1 1 36 Lobsters 0.1 0.2 0.2 37 Large Crabs 0.1 23 1.5 3 10 1 0.1 4 3 1 3 38 Octopods 0.1 1.6 4.9 2 1 39 Stomatopods 0.5 0.4 2 1.3 0.4 40 

Echinod / Lg gastropods 0.1 4.9 1.8 16 5.5 8.6 4.7 

41 Bivalves 0.1 22 4 12 0.3 42 Sessile epibenthos 23 4.2 1.7 4 8.4 30 43 Small infauna 1.2 15 0.3 1.7 44 0.6 14 7.8 9 44 Small mobile epifauna 1.5 17 4.3 0.1 5 6.8 35 15 32 15 145 Meiofauna 3.2 0.3 0 46 Small Copepods 24 32 12 2.6 0 47 Other Mesozooplankton 24 5 5 21 27 5 4.6 1.9 48 CarnivZooplank 1.1 0.1 23 5 1.7 6 59 30 0.2 4.5 1.1 49 Ichthyoplankton 0.1 10 1 0.5 50 CarnivJellyfish 10 4.9 40 51 

Microbial Heterotrophs 

52 Macroalgae 11 100 2.9 0.1 0.6 13 53 Microphytobenthos 11 1.3 1.6 0.2 0.2 0.1 5 54 Phytoplankton 55 Sea grasses 1.8 9 16 56 Dead carcasses 1.5 6.6 57 Sediment Detritus 2.6 0.1 2.9 18 58 Watercolumn Detritus 8.5 59 Drift Macrophytes 60 Import 4 35 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

Predators Prey 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 

1 2 3 4 5 6 7 8 0 1.6 9 0.3 0.5 

10 0.5 0.7 2.5 11 0.1 12 0.1 13 5.8 5.8 0.9 3.8 0.5 14 5 15 3.8 1.5 0.5 16 2.5 17 18 2 4.2 9.5 8.1 1 19 0.4 6.6 0.7 1.8 20 0.1 2.3 0.1 21 0.9 3.1 0.2 22 0.8 2.3 0.1 23 3.8 2.2 0.5 1.5 20 2 24 1 25 1 2.4 1 26 0 0.1 27 13 8.6 0.1 28 7.3 4.7 0.7 1 29 4.4 6.2 1 30 31 3.6 5.5 1 32 2.6 2.5 1.3 9.5 5 33 1.7 0.4 3.6 0.6 2 26 0.1 34 6 6.3 3.1 0 10 1 0.1 0.8 6 35 3 2 5 1 0.1 0.4 1 0.1 10 36 0.1 0.2 0.1 0.1 37 15 15 3 9 0.1 19 0.5 0.5 3 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

38 3.3 10 0.7 0.1 39 1 2 0.9 40 2.1 1.5 10 0.1 5 0.6 23 4.1 5.6 1 1 41 5 0 16 0 18 36 24 11 1 42 1.5 16 21 1.6 14 8.2 5.7 15 12 10 1 43 5 17 3 8.2 2 20 3.7 15 16 15 4.9 0.1 1 4 44 11 2 22 5.6 17 13 0.5 9 30 15 23 2 1.9 10 18 2 2 1 1 45 0.1 1 2 12 2.9 1 3.1 1 3 10 7 1 46 1.8 1.5 20 34 4 2 0.1 2 12 12 47 2.2 2.6 0.7 36 36 4 33 0.1 1 10 48 4 1.9 16 3.9 8.4 19 33 1 49 0.2 1 0.1 0.2 50 2 0.1 51 22 11 20 10 10 30 15 19 35 13 40 64 52 54 0.3 5 2 0.3 3.7 1 12 12 53 1 10 3.8 6 9.9 12 4 9 8 20 4 5 54 0.5 3.1 0 2.8 23 23 22 10 80 37 10 55 0.3 0.7 1 0 4.7 5 56 1 1.5 3.1 0.9 3.3 2 1 5 57 12 3 2 21 7 6 11 41 17 32 7 44 1 58 5 6.7 13 18 5 7 0 1 4 2.8 59 2.5 0.8 18 0 0 5 15 60 90 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

139 

APPENDIX 3. DETRITUS 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/eaters 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 1 
Ecopath 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 

APPENDIX 4. MARKET 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 landings of individual species in the group. Price information 

is from the 1999 Annual Landings Summary of the Marine Fisheries Information 

System, Florida Fish and Wildlife 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 

BentOceInvert/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 0.94 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

142 

APPENDIX 5. GUIDE 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, diet 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 10 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

143 

APPENDIX 6. EMPIRICALLY BASED CONVERSION FACTORS

Table A6. Conversion factors used in the construction 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 Gulf 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 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. 

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 benthic 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 construction 

APPENDIX 7.

FUNCTIONAL GROUPINGS AND PARAMETERS FOR FISHES 

Table A7. Summary table of all fish group parameters and references 

Biomass Q/B 

t/km2 Reference per yr Reference 

1 Whales and dolphins Whales and dolphins 40.86 Brown et al. 1991 2 Sea birds Sea birds 80.00 GoM models 3 Turtles Turtles 3.50 GoM models 4 Manatees Manatees 0.001 ? 36.50 ? 5 Large oceanic piscivores 

Xiphias gladius swordfish 0.00277 B=C/F 2.9184 Brown et al. 1991 

Makaira nigricans blue marlin 0.00019 B=C/F 4.56 Brown et al. 1991 

Tetrapturus spp. white marlin and 

spearfish 

0.00007 B=C/F 6.384 Brown et al. 1991 

Istiophorus platypterus sailfish 0.00039 B=C/F 7.296 Brown et al. 1991 

Coryphaena hippurus dolphin fish 0.05992 B=C/F 4.73 Pred. Palomares and Pauly 1987 

Alopias spp. thresher shark 0.00010 B=C/F 8.97 Other GoM model 

Isurus paucus longfin mako 0.00013 B=C/F 8.97 Other GoM model 

Thunnus albacares yellowfin tuna 0.00354 B=C/F 12.221 Brown et al. 1991 

Thunnus alalunga albacore 0.00000 B=C/F 9.4 Pred. Palomares and Pauly 1987 

Thunnus thynnus bluefin tuna 0.00001 B=C/F 12.16 Brown et al. 1991 

Thunnus atlanticus blackfin tuna 0.00990 B=C/F 15.455 Browder model 

Thunnus obesus bigeye tuna 0.00004 B=C/F 7.9 Pred. Palomares and Pauly 1987 

Katsuwonus pelamis skipjack tuna 0.00161 B=C/F 23.8944 Brown et al. 1991 

Hexanchus griseues Sixgill shark 0.00000 B=C/F 8.975 Pred. Palomares and Pauly 1987 

6 Large ocean planktivores 10.00 GoM models 

Mobulidae Manta ray 1.30 Pred. Palomares and Pauly 1989 

Rhincodon typus whale shark 0.80 Pred. Palomares and Pauly 1989 

Cetorhinus maximus basking shark 3.70 Pred. Palomares and Pauly 1989 

Mola mola ocean sunfish 1.40 Pred. Palomares and Pauly 1989 

7 Coastal sharks 7.84 GoM models 

Sphyrna lewini scalloped hammerhead 0.00041 B=C/F 2.48 Palomares eqn. 

Sphyrna mokarran great hammerhead shark 0.00058 B=C/F 1.90 Pred. Palomares and Pauly 1989 

Carcharhinus plumbeus sandbar shark 0.02994 B=C/F 4.10 Stillwell and Kohler 1992 

Carcharhinus obscurus dusky shark 0.00086 B=C/F 2.20 Pred. Palomares and Pauly 1989 

Carcharhinus acronotus blacknose shark 0.00055 B=C/F 3.20 Pred. Palomares and Pauly 1989 

Carcharhinus brevipinna spinner shark 0.00001 B=C/F 3.00 Pred. Palomares and Pauly 1989 

Carcharhinus falciformis silky shark 0.00023 B=C/F 2.00 Pred. Palomares and Pauly 1989 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

Carcharhinus limbatus blacktip shark 0.00324 B=C/F 3.60 Pred. Palomares and Pauly 1989 

Carcharhinus leucas bull shark 0.00219 B=C/F 2.37 Palomares eqn. 

Carcharhinus isodon finetooth shark 0.00016 B=C/F 3.30 Pred. Palomares and Pauly 1989 

Rhizoprionodon terraenovae 

Atlantic sharpnose shark 0.00034 B=C/F 6.40 Vidal model 

Galeocerdo cuvier tiger shark 0.00005 B=C/F 1.70 Pred. Palomares and Pauly 1989 

Negaprion brevirostris lemon shark 0.00003 B=C/F 6.00 Cortes and Grubber 1990 

Squalidae Dogfish 0.00000 B=C/F 4.77 Pred. Palomares and Pauly 1989 

Odontaspis taurus sand tiger 0.00000 1.70 Pred. Palomares and Pauly 1989 

 "all sharks" 

8 Rays and skates 7.84 GoM models 

Raja texana roundel skate 0.018 Trawl surveys 19.4 Pred. Palomares and Pauly 1989 

Raja eglanteria clearnose skate 0.050 Trawl surveys 11.8 Pred. Palomares and Pauly 1989 

Dasyatis spp. stingray genus 0.009 Trawl surveys 2.5 Pred. Palomares and Pauly 1989 

Aetobatus narinari spotted eagle ray 2.3 Pred. Palomares and Pauly 1989 

Rhinoptera bonasus cownose ray 0.158 Trawl surveys 6.4 Pred. Palomares and Pauly 1989 

Rhinobatos lentiginosus Atlantic guitarfish 0.003 Trawl surveys 11.9 Pred. Palomares and Pauly 1989 

Sphyrna tiburo bonnethead 0.001 Trawl surveys 5 Pred. Palomares and Pauly 1989 

Ginglymostoma cirratum nurse shark 1.8 Pred. Palomares and Pauly 1989 

9 Pelagic oceanic 

piscivores 

Trichiurus lepturus (?distribution?) 

Atlantic cutlassfish 0.150 NMFS trawl 25.54 Brown et al. 1991 

Ruvettus pretiosus oilfish 

Merluccius albidus offshore hake 0.000 NMFS trawl Brama sps pomfrets 0.000 NMFS trawl Lepidocybium flavobrunneum 

escolar 0.000 NMFS trawl 

10 Pelagic coastal 

piscivores 

Acanthocybium solandri wahoo 0.003 B=C/F 25.54 Brown et al. 1991 

Sarda sarda Atlantic bonito 

Euthynnus alletterus little tunny 0.009 B=C/F 15.11 Palomares equation 

Auxis thazard frigate mackerel 

Pomatomus saltatrix bluefish 0.001 Trawl surveys 9.36 GoM models 

Rachycentron canadum cobia 0.001 B=C/F 7.30 Brown et al. 1991 

Caranx crysos blue runner 0.038 FMRI pelagic 

surveys 

10.00 Independent? 

Caranx hippos crevalle jack 0.007 B=C/F 10.00 Independent? 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

Caranx bartholomaei yellow jack 10.00 GoM models 

Caranx latus horse-eye jack 10.00 GoM models 

Caranx ruber bar jack 6.15 GoM models 

Seriola zonata banded rudderfish 0.000 Trawl surveys 10.00 GoM models 

Seriola dumerili greater amberjack 0.021 B=C/F 5.90 Other Q/B 

Seriola fasciata lesser amberjack 10.00 GoM models 

Seriola rivoliana almaco jack 10.00 GoM models 

Elagatis bipinnulata rainbow runner 10.00 GoM models 

Echeneidae remora family 0.016 Trawl surveys 32.13 Browder model (pelagic predatorBelonidae needlefish family 8.80 GoM models 

Lobotes surinamensis tripletail 3.51 Palomares equation 

 "carangids" 

11 Mackerels adult 

Scomberomorus maculatus 

Spanish mackerel 0.083 NMFS assessment 

(Chris Legult 1998) 

10.02 GoM models 

Scomberomorus cavalla king mackerel 0.099 NMFS assessment 

(Chris Legult 1998) 

9.29 GoM models 

Scomberomorus regalis cero mackerel 9.00 Other Q/B 

12 Juvenile mackerels 

Scomberomorus maculatus 

Spanish mackerel 0.019 NMFS assessment (Legult 

1998) 

Scomberomorus cavalla king mackerel 0.106 NMFS assessment (Legult 

1998) 

Scomberomorus regalis cero mackerel 

13 Sardine-herring-scad complex 

Opisthonema oglinum Atlantic thread herring 0.039 FMRI pelagic 

surveys 

13.26 GoM models 

Harengula jaguana scaled sardine 0.048 FMRI pelagic 

surveys 

12.89 GoM models 

Sardinella aurita Spanish sardine 0.139 FMRI pelagic 

surveys 

11.45 GoM models 

Brevoortia spp. menhadens 0.047 Vaughan et al. 1999 13.64 GoM models 

Decapterus punctatus round scad 0.184 FMRI pelagic 

surveys 

10.00 GoM models 

14 Pelagic oceanic jelly 

eaters 

Ariomma bondi silver-rag 0.190 NMFS trawl 8.2 Fishbase 

Peprilus burti gulf butterfish 2.503 NMFS trawl 8 Fishbase 

Hyperoglyphe perciformis barrelfish 

15 Pelagic oceanic planktivores 

Maurolicus weitzmani hatchet fish 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

Etrumeus teres round herring 1.065 Houde 1977 11.45 GoM models 

Jenkinsia family dwarf round herring 0.004 Trawl surveys 11.45 GoM models 

Trachurus lathami rough scad 1.352 NMFS trawl 10.00 GoM models 

Selar crumenophthalmus bigeye scad 0.015 NMFS trawl 10.00 GoM models 

Scomber japonicus chub mackerel 0.703 NMFS trawl 11.45 GoM models 

Diaphus sp Lanternfish 14.89 GoM models 

Bregmaceros atlanticus antenna codlet 

Argentina striata straited argentine 0.000 NMFS trawl 

Engraulis eurystole silver anchovy 14.24 GoM models 

16 Demersal oceanic invertebrate feeders 

Mullus auratus red goatfish 0.041 Trawl surveys 15.76 Browder model (demersal fish) 

Synagrops bellus blackmouth bass 0.000 NMFS trawl 15.76 Browder model (demersal fish) 

17 Demersal coastal 

piscivores 

Cynoscion nothus silver seatrout 0.015 Trawl surveys 6.80 Independ. Est 

Cynoscion nebulosus spotted seatrout 0.011 VPA estimate 5.29 Palomares equation 

Cynoscion arenarius sand seatrout 0.042 Trawl surveys 6.73 GoM models 

18 Demersal coastal invertebrate feeders 

Bairdiella chrysoura silver perch 0.072 Trawl surveys 9.17 GoM models 

Equetus umbrosus Cubbyu 0.028 Trawl surveys 10.00 GoM models 

Leiostomus xanthurus spot 0.439 Trawl surveys 9.09 Palomares equation 

Menticirrhus sp. kingfish 0.033 Trawl surveys 7.32 Palomares equation 

Micropogonias undulatus Atlantic croaker 0.168 Trawl surveys 5.41 Palomares equation 

Sciaenops ocellatus red drum 0.014 B=C/F 3.67 Palomares equation 

Pogonias cromis black drum 0.002 B=C/F 3.67 Palomares equation 

Chloroscombrus chrysurus 

Atlantic bumper 0.042 FMRI pelagic 

surveys 

11.70 Independ 

Trachinotus carolinus Florida pompano 11.60 Vidal 

Trachinotus falcatus permit 7.62 Palomares equation 

Oligoplites saurus leatherjacket 0.000 Trawl surveys 3.00 GoM models 

Alectis ciliaris African pompano 10.00 GoM models 

Arius felis hardhead catfish 0.157 Trawl surveys 9.70 GoM models 

Bagre marinus gafftopsail catfish 0.014 Trawl surveys 10.14 GoM models 

Calamus arctifrons grass porgy 0.043 FMRI surveys 5.20 GoM models 

Pagrus pagrus red porgy 0.004 Trawl surveys 5.20 GoM models 

Stenotomus caprinus longspine porgy 0.497 Trawl surveys 5.20 GoM models 

Haemulon aurolineatum tomtate 0.407 FMRI pelagic 

surveys 

9.18 GoM models 

Orthopristis chrysoptera pigfish 0.487 Trawl surveys 10.65 GoM models 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

Lutjanus synagris lane snapper 0.095 Trawl surveys 4.95 GoM models 

Eucinostomus sp. mojarras 0.111 FMRI pelagic 

surveys 

15.34 GoM models 

Balistes capriscus gray triggerfish 0.095 Trawl surveys 9.07 Palomares equation 

19 Demersal coastal 

omnivore 

Aluterus schoepfi orange filefish 0.024 Trawl surveys 17.42 Palomares equation 

Monacanthus ciliatus fringed filefish 0.123 Trawl surveys 15.10 Fishbase 

Monacanthus hispidus planehead filefish 0.002 NMFS trawl 8.20 Fishbase 

Cantherhines pullus orangespotted filefish 

Cantherhines sufflamen ocean triggerfish 

Lactophrys polygonia honeycomb filefish 

Diplodus holbrooki spottail pinfish 0.295 Trawl surveys 6.30 Fishbase predicted 

Lagodon rhomboides pinfish 0.470 FMRI pelagic 

surveys 

15.21 13.4 (Bioenergetic model); 14.6 

(Stoner and Livingstone 1978); 1(Peters and Kjelson) 

Chaetodipterus faber Atlantic spadefish 0.044 Trawl surveys 15.76 Browder model (demersal fish) 

Lactophrys quadricornis scrawled cowfish 0.399 Trawl surveys 8.17 GoM modelss 

Tetraodontidae puffer family 0.072 Trawl surveys 37.45 Looe key (small reef herbivores) 

20 Benthic oceanic 

piscivores 

Saurida brasiliensis largescale lizardfish 0.067 Trawl surveys 7.94 GoM modelss 

Saurida normani shortjaw lizardfish 0.023 Trawl surveys 7.94 GoM modelss 

Synodus poeyi offshore lizardfish 0.005 Trawl surveys 7.94 GoM modelss Hoplunnis spp. duckbill eels 0.014 Trawl surveys 

21 Benthic oceanic invertebrate feeders 

Halieutichthys aculeatus pancake batfish 0.004 Trawl surveys 15.80 Fishabse 

Neomerinthe hemingwayi spinycheek scorpionfish 0.005 Trawl surveys 

Peristedion gracile slender searobin 0.009 NMFS trawl Prionotus stearnsi shortwing searobin 0.008 NMFS trawl Serranus notospilus saddle bass 0.006 Trawl surveys 15.80 Fishbase 

Malacanthidae tilefish family 0.066 Trawl surveys 15.76 Browder model (demersal fish) Steindachneria argenta luminous hake 0.023 Trawl surveys 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

22 Benthic coastal 

piscivores 

Synodus foetens inshore lizardfish 0.138 Trawl surveys 7.94 GoM models 

Synodus intermedius sand diver 0.076 Trawl surveys 7.94 GoM models 

Paralichthys spp. lefteye flounder genus 0.045 Trawl surveys 9.46 GoM models 

Ophichthidae snake eel family 0.003 Trawl surveys 

23 Benthic coastal invertebrate feeders 

Diplectrum bivittatum dwarf sand perch 0.041 Trawl surveys 15.76 Browder model (demersal fish) 

Diplectrum formosum sand perch 0.307 Trawl surveys 9.67 Palomares equation 

Etropus crossotus fringed flounder 0.013 Trawl surveys 9.46 GoM models 

Etropus rimosus gray flounder 0.190 Trawl surveys 9.46 GoM models Syacium papillosum dusky flounder 0.763 Trawl surveys 9.46 GoM models Symphurus plagiusa blackcheek tonguefish 0.006 Trawl surveys 9.46 GoM models 

Prionotus roseus blue spotted searobin 0.019 Trawl surveys 15.76 Browder model (demersal fish) 

Prionotus scitulus leopard searobin 0.031 Trawl surveys 15.76 Browder model (demersal fish) Scorpaena brasiliensis barbfish 0.128 Trawl surveys 8.6 Fishbase 

Scorpaena calcarata smoothhead scorpionfish 0.014 Trawl surveys 13.5 Fishbase 

Polydactylus octonemus Atlantic threadfin 0.000 Trawl surveys 15.76 Browder model (demersal fish) 

Urophycis regia spotted hake 0.020 Trawl surveys 12.92 Browder model (demersal predatUrophycis floridana southern hake 0.005 Trawl surveys 12.92 Browder model (demersal predatAriosoma balearicum bandtooth conger 0.016 Trawl surveys 12.92 Browder model (demersal predat

Gobiidae Gobies 12.30 GoM models 

Ophidiidae cusk-eels (gadiforms) 0.079 Trawl surveys 12.92 Browder model (demersal predatOgcocephalidae batfish family 0.041 Trawl surveys 12.92 Browder model (demersal predat

24 Surface pelagics 

Hemirhamphidae halfbeaks 0.000 Trawl surveys 11.70 Independ Exocoetidae flyingfish family 

25 Structure associated coastal piscivore 

Lutjanus campechanus red snapper 0.013 B=C/F 16.78 Brown et al. 1991 

Lutjanus griseus gray snapper 0.002 Trawl surveys 7.24 Palomares equation 

Lutjanus cyanopterus cubera snapper 4.95 GoM models 

Lutjauns analis mutton snapper 4.95 GoM models 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

Lutjauns apodus schoolmaster 4.95 GoM models 

Lutjanus buccanella blackfin snapper 4.95 GoM models 

Lutjanus jocu dog snapper 4.95 GoM models 

Lutjanus mahogoni mahogany snapper 4.95 GoM models 

Lutjanus purpureus caribbean red snapper 4.95 GoM models 

Lutjanus vivanus silk snapper 4.95 GoM models 

Pristipomoides aquilonaris 

wenchman 0.020 Trawl surveys 4.95 GoM models 

Etelis oculatus queen snapper 4.95 GoM models 

 Lutjanidae snapper family  "snappers" 

Epinephelus cruentatus graysby 0.001 Trawl surveys 2.30 GoM models 

Epinephelus niveatus snowy grouper 0.001 B=C/F 10.21 Brown et al. 1991 

Antennariidae Anglerfish 0.040 Trawl surveys 12.92 Browder model (demersal predatHolocentridae squirrelfish family 0.030 Trawl surveys 12.92 Browder model (demersal predatSphyraena barracuda great barracuda 0.007 B=C/F 10.21 Brown et al. 1991 

Muraenidae moray family 0.495 Trawl surveys 7.30 GoM models 

Congridae (conger eels) conger eel family 0.048 Trawl surveys 12.92 Browder model (demersal predatGrammistidae soapfish 0.064 Trawl surveys 12.92 Browder model (demersal predat

26 Large groupers 

Epinephelus itajara jewfish 2.52 Palomares equation 

Epinephelus morio red grouper 0.080 B=C/F 4.47 GoM models 

Epinephelus flavolimbatus yellowedge grouper 0.006 Trawl surveys 4.32 Palomares equation 

Epinephelus adscensionis rock hind 5.58 Palomares equation 

Epinephelus drummondhayi 

speckled hind 0.000 B=C/F 3.6 Palomares equation 

Epinephelus guttatus red hind 2.30 GoM models 

Epinephelus nigritus warsaw grouper 2.69 Palomares equation 

Mycteroperca interstitialis yellowmouth grouper 7.63 Palomares equation 

Mycteroperca venenosa yellowfin grouper 2.30 GoM models 

Mycteroperca phenax scamp 0.007 B=C/F 5.25 Palomares equation 

Mycteroperca microlepis gag 0.026 NMFS assessment 

(Steve Atran memo to Wayne Swingle) 

4.15 Palomares equation 

27 Structure associated coastal invertebrate feeders 

Centropristis ocyurus bank sea bass 0.187 Trawl surveys 9.20 Fishbase 

Centropristis striata black sea bass 0.112 Trawl surveys 9.20 Fishbase 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

Centropristis philadelphica 

rock sea bass 0.221 Trawl surveys 9.20 Fishbase 

Serranus subligarius belted sandfish 0.000 Trawl surveys 9.20 Fishbase 

Hemanthias leptus longtail bass 0.000 Trawl surveys 9.20 Fishbase 

Hypoplectrus unicolor butter hamlet 0.000 Trawl surveys 9.20 Fishbase 

Paranthias furcifer creole fish 0.000 NMFS trawl 9.20 Fishbase 

Halichoeres bivitattus slippery dick 0.000 Trawl surveys 9.20 Fishbase 

Halichoeres caudalis painted wrasse 0.000 Trawl surveys 

Halichoeres garnoti yellowhead wrasse 

Lanchnolaimus maximus hogfish 0.039 Trawl surveys 4.66 Palomares equation 

Bodianus pulchellus spotfin hogfish 

Bodianus rufus spanish hogfish 

Decodon puellaris red hogfish 0.006 Trawl surveys 

Thalassoma bifasciatum bluehead 

Odontoscion dentex reef croaker 10.00 GoM models 

Equetus lanceolatus jackknife-fish 0.036 Trawl surveys 10.21 Brown et al. 1991 

Ocyurus chrysurus yellowtail snapper 0.007 B=C/F 4.95 GoM models 

Rhomboplites aurorubens vermilion snapper 0.045 Trawl surveys 4.95 GoM models Opsanus pardus leopard toadfish 0.000 Trawl surveys 

Scorpaenidae scorpian fish 0.010 Trawl surveys 13.50 Fishbase 

Chaetodon capistratus foureye butterflyfish 

Priacanthidae bigeyes 0.031 Trawl surveys 12.80 Looe key (small reef carnivores) 

Archosargus probatocephalus 

sheepshead 0.082 Murphy and 

McDonald 2000 

11.11 GoM models 

Calamus proridens littlehead porgy 0.203 Trawl surveys 5.20 GoM models 

Calamus bajonado jolthead porgy 0.100 Trawl surveys 5.20 GoM models 

Calamus calamus saucereye porgy 0.007 Trawl surveys 5.20 GoM models 

Calamus leucosteus whitebone porgy 0.101 Trawl surveys 5.20 GoM models Calamus nodosus knobbed porgy 0.003 Trawl surveys 5.20 GoM models 

Haemulon plumieri white grunt 0.127 Trawl surveys 9.38 GoM models 

Haemulon flavolineatum French grunt 0.005 Trawl surveys 8.14 GoM models 

Haemulon macrostomum Spanish grunt 0.005 Trawl surveys 8.14 GoM models 

Haemulon album margate 0.005 Trawl surveys 8.14 GoM models 

Haemulon sciurus bluestriped grunt 0.005 Trawl surveys 8.14 GoM models 

Haemulon striatum striped grunt 0.003 Trawl surveys 8.14 GoM models 

Haemulon parra sailor's grunt 0.005 Trawl surveys 8.14 GoM models 

Anisotremus virginicus porkfish 0.001 Trawl surveys 8.14 GoM models 

Gobiosoma oceanops neon goby 12.30 GoM models 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

28 Structure associated coastal omnivores 

Holacanthus bermudensis blue angelfish 0.039 Trawl surveys 

Pomacanthus arcuatus gray angelfish 0.001 Trawl surveys 37.45 Looe key (small reef herbivores) 

Centropyge argi cherubfish 

Holacanthus tricolor rock beauty 

Pomacentrus variabilis cocoa damselfish 0.000 Trawl surveys 37.45 Looe key (small reef herbivores) 

Pomacentrus partitus bicolor damselfish 0.000 Trawl surveys 37.45 Looe key (small reef herbivores) 

Pomacentrus leocostictus beau gregory 0.000 Trawl surveys 37.45 Looe key (small reef herbivores) 

Microspathodon chrysurus 

yellowtail dameselfish 0.000 Trawl surveys 37.45 Looe key (small reef herbivores) 

Parablennius marmoreus seaweed blenny 0.000 Trawl surveys 12.80 GoM models 

Acanthuridae ocean surgeons 

Scarus croicensis striped parrotfish 0.020 Trawl surveys 22.80 GoM models 

Coryphopterus glaucofraenum 

bridled goby 12.30 GoM models 

Kyphossus sectarix bermuda chub 

29 Structure associated coastal planktivores 

Apogon pseudomaculatus twospot cardinalfish 0.002 Trawl surveys 

Phaeoptyx xenus sponge cardinal fish 0.000 Trawl surveys 10.00 Looe key (small planktivores) 

Chromis scotti purple reeffish 0.000 Trawl surveys 10.00 Looe key (small planktivores) 

Chromis enchrysurus yellowtail reeffish 0.000 Trawl surveys 10.00 Looe key (small planktivores) 

Chromis cyanea blue chromis 0.000 Trawl surveys 10.00 Looe key (small planktivores) Opisthognathus aurifrons jawfish 0.000 Trawl surveys 10.00 Looe key (small planktivores) 

30 Nearshore associated piscivores 

Tarpon atlantica Atlantic tarpon 9.20 Palomares equation 

Elops saurus ladyfish 0.002 B=C/F 9.13 Palomares equation 

Centropomus undecimalis common snook 0.003 B=C/F 5.44 Palomares equation 

Albula vulpes bonefish 

31 Mullets 

Mugil cephalus striped mullet 0.010 B=C/F 12.30 Vidal 

Mugil spp. other mullets 0.319 B=C/F 10.37 GoM models 

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

Anchoa mitchilli bay anchovy 0.177 Brown et al. 1991 14.24 GoM models 

Anchoa hepsetus striped anchovy 14.24 GoM models 

Atherinidae silverside family (e.g. 

Menidia sp.) 

0.000 Trawl surveys 26.02 Palomares equation 

Alosa spp. alewife 0.005 NMFS trawl 10.91 Palomares equation 

33 Other fishes Other fish 3.877 Trawl surveys 7.04 GoM models 
Ecopath model of the West Florida Shelf: Volume II. Model construction 

154