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1985

A bioeconomic model of the middle Atlantic surf clam (Spisula solidissima) fishery

Thomas M. Armitage College of William and Mary - Virginia Institute of Marine Science

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Recommended Citation Armitage, Thomas M., "A bioeconomic model of the middle Atlantic surf clam (Spisula solidissima) fishery" (1985). Dissertations, Theses, and Masters Projects. Paper 1539616551. https://dx.doi.org/doi:10.25773/v5-a3d5-we79

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"U nharafy Mkroflhm _ ififrrmdiiwuil

6604205

Aimllagi, Thom ti Marshall

A BIOECONOMIC MODEL OF THE MIDDLE ATLANTIC SURF CUM (SPISULA SOLIDISSIMA) FISHERY

The of William and Mary in Virginia Ph.O. 1985

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Copyright 1986 by Armitage, Thomas Marshall All Rights Reserved

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University Microfilms International i 3IOECOHOHIC MODEL OF THE MIDDLE ATLANTIC SURF CLAM

<3PISULA SOLI PI SSI HA) FISHERY

A Dissertation

Presented to

The Faculty of the School of Marine Science

The College of William and Mary in Virginia

In Partial Fulfillment

of the Requirements for the Degree of

Doctor of Philosophy

by

Thornes Marshall Armitage

1 985 APPROVAL SHEET

This dissertation L> submitted in partial fulfillment of

the requirement* for tha degree oT

Doctor of Philosophy

Thomas N. Armitsge

Approved, December 1965

Herbert M. Austi n. Ph. D. ( Chai rman)

m/B ar tie tt Thebarga, LL. H.

Jr. - Ph. D.

Hi 111am D. DuPaul, Ph. D.

Robert J. Byrhe, Ph. D. J2ja Ivar B, Strand, Ph. D. University of Maryland 1S60

THOMAS MARSHALL ARMITAGE

All Mights Rawrved TABLB OF C0MT6HT3

Page fable of Contents...... , r . . , ...... i i i icknowl edgements...... , . , ...... v List of Tables...... vi List of Figures...... vi i A bstract ...... v iii

Chapter I. Introduction and Hypothesis - Objective of the Study ...... 2

Chapter II. Methods - The Surf Clam Fishery as a Bioeconomic System ...... 8

Concept of the Fishery Simulation Model.... 8 Harvesting and Processing Sectors ...... 13 Life History and Population Dynamics ...... 16

Chapter III. Methods - Demand Analysis...... 39

Variables in the Price Equation...... 39 Mechanism of Price Formation...... 47 Market Area...... 49 Statistical Estimation Procedure...... 51 Results of Demand Analysis...... - - 54 Discussion...... 61 Problems and Future Research Heeds...... 64

Chapter IV. Methods - Cost Analysis...... 68

Sources of Fishing Sector Data...... 69 Identification and Quantification of Pishing Costs ...... 70 Sources of Processing Sector Data...... 90 Identification and Quantification or Processing Costs ...... 91 Processing Plant Charactariat 1csr r . r ...... 95 Production Capacity and drouth Potential. . . 102

Chapter V. Methods - Model Construction ...... 107

A Unique Modeling Approach ...... 107 Model s tr u c tu r e ...... 110

Chapter 1/1. Results - Ceee Studies ...... 119

Chapter VII. Discussion and Conclusions ...... 154

Abilities or Potential of This Approach. . . . 154 Discussion oT Case S tu d ie s ...... 155

iii Literature Cited ...... , ...... , ...... r ...... 1 60

Glossary, , . . . , ...... 167

Appendix A - Modal Subroutines, Variables, and Program C o d a , ...... 170

Appendix B - Survey Questionnaires. , , . r , „ . , , , . 253

V ita...... 263

i v ACKtiONLBDOBHfcHTS

I Wish to tx p ra ss my a p p re c ia tio n to Drs. Herbert H. Austin and William J. Hargis Tor their guidance and encouragement during the course of thia study. I am particularly indebted to Or. Hargis for financial support received through a research asaiatantship in hia 1abora tor y.

I would also like to thank Professor N. B Theberge and the other members of my dissertation committee Tor their critical revise of the manuscript.

Finally, 1 wish to thank my wife Louise for her patience, support and encouragement during my graduate s t ud ies. LIST or TABLES

Page Table 1. van Bertalanffy Growth of Surf Cl im l...... 26

Table 2. Length-Drained Neat Height Relationship in SurT Clams ...... 28

Table 3. Estimated Recruitment Size in Height and Numbers of Middle A tlantic Surf Clams ( 1965-1 975) ...... 31

Table 4. Population Structure of Mid-Atlantic Surf Clams...... 33

Table 5, Fishing Power C oefficients in the SurT Clam In d u stry ...... 36

Table 6, Regression Coefficients Describing the Ex-vessel Pries Modal for Middle Atlantic Surf Clams...... 59

Table 7. Mean Characteristics of Vessels Sampled in the Surf Clam Fishing Industry Survey ...... 73

Table S. Mean Vessel Charscteristice Obtained From Vessel Licenses ...... 74

Table 9, Summary of Average Reported Fixed Fishing Costs...... 77

Table 10. Electronic Equipment Required for SurT Fishing Vessel Operation ...... 78

Table 11. Average Annual Fixed Costs of Vessel Operation ...... 79

Table 12. Summary of Average Variable Pishing C osts ...... 87

Tabl e 13. Surf Clam Processing Costs ...... 93

Table 14. Program Subroutines and functions...... 111

vi LIST OF FIGURES

Page Figure 1 Conceptual Nodal of M id-A tlan tic Surf Clam Fishery...... 10

Figure 2 Area Designations Used Tor Surf Clam Assessments...... 1 2

Figure 3. Stages in the Development of Surf Clam L arvae ...... 1 6

Figure 4. General Age and Growth Relationship for Surf Clama...... 27

Figure 5. Surf Clam Landings ...... 55

Fi gura e. Plot or Caas One Results...... 1 27

Fi gura 7. Plot or Case Two Results ...... 131

Fi gura S. Plot of Case Three Results ...... 1 35

Fi gura 9. Plot of Case Four Results ...... 1 39

Fi gure i a. Plot of Case Five Results ...... 142

Fi gure 11 . Plot of Case Six Results ...... 143

Pi gure 12. Plot of Can Seven Results...... 145

Fi gure 1 3. Plot or Case Eight Results...... 147

Fi gure 14. Plot or Case Nina Results...... 14B

Fi gure 1 5. Plot of Case Ten Rea u l t s ...... 1 49

F i gure 1 6. Plot of Case Eleven Results ...... 1 52

vi i ABSTRACT

A bioeconomie simulation modal of the middle Atlantic s u rf clam i flpisula solldlssimai fishery has been developed from a aurvty of biological and econometric relationships. While identifying the biological input parameters available in the literature, the economic submodel of the fishery has been developed with price and landings time series data, and with data obtained through the use of survey questionnaires and interviews with surT clam fisherman and processors. Alternative management scenarios in the fishery have been evaluated from industry costs in both the harvesting and processing sectors and analysie or the demand for raw product confronting surf clam fishermen.

Multiple regression analysis of time series data indicates that surf clam ex-vessel prices may be negatively related to surT clam landings whereaa hard clam prices are positively related to ex-vessel ocean quahog prices and ex-vessel oyster prices. Tha strength of thia relationship confirms the status or ocean quahogs as very close substitutes for surf clams.

Tha results of case studies using the model suggest that tha Kid-Atlantic Fishery Management Council has followed a prudent course of action in managing the surf clam fishery. The model also projects that, 1) larger yield quotas may be possible in the immediate future without jeopardising surf clam population stability, 2) overcapitalisation in the fishery appears to remain a problem, and 3) tha economic outlook for the operators or small vessels remains relatively bleak.

vi i i BIOECONOHlC MODEL OF THE MIDDLE ATLAHT1C SURF CLAM I BFtflllLA SOLI PI SSI MA> FISHERY INTRODUCTION

The concept of sovereignty over territorial waters adjacent to coastal etatee has been a subject of international debate for more than 300 yeara, During the past century, the discovery of natural gas, oil, minerala, and abundant fishery reeourcea on and above the continental ahelf oT the United States has provided motivation to extend jurisdiction over virtually the entire ahelf region. In 1976, the Fishery Conservation and Management

Act < FCHAl established a 197 mile wide Fishery Conservation

Zone extending from the baseline or the United states territorial sea. In 1963, a 200 mile wide Exclusive

Economic Zone was established by Presidential Proclamation,

Turther asserting sovereign rights ovar oaean resources within 200 miles of the baseline from which the breadth or the territorial sea is measured (Federal Register, 1933).

The PC HA directed that management plana be drafted Tor all non-migratory species harvested within tha Fishery

Conservation Zone. Certain highly migratory species, such as tuna, were excluded from management, Management plane were bo be draTted upon tha basis of maximum sustainable yield (N5T> , modified by any relevant economic and aocial factors, to develop an optimum sustainable yield ( OSTi,

The requisite data for optimum management or these apeclas, however, were not immediately available to policy makera.

2 3 in many cases, policy la still formuLatsd upon the basis of inadequate data. Early management plans wars, o r necessity, band upon aducattd gutasta and limited, but best Information available.

The objective of this study is to demonstrate how effective management of an offshore fishery can be achieved through the development of a bioeconomic simulation model, and the provision of improved economic data.

The first Atlantic coast fishery to be regulated under the fCHi was the offshore clam fishery comprised of surT clams (Sniaula soli dlaai ms) and to a leaser extent, ocean quahogs < irctlca i ca^flpdlca). The surf clam fishery is the focua of this investigation. Management attention waa initially focused upon the offshore clam fishery because overcspitali mation threatened the resource with economic extinction. In 1977, regional management or the surf clam fishery was provided when federal regulations were promulgated under an approved management plan (Federal

Register, 1977).

Prior to implementation of management efforts, it became evident that the surf clam industry was suffering from an economic ill typical or many industries exploiting common property resources. Individual firms were overharvesting in communal fishing areas. The open access market resulted in a level or fishing effort much greater than required for optimum economic and biological return. 4

Optimum output In an economic system oocupi at tha point where marginal costs ir* equal to nurglnal rtvtnui, an

Increase in output ibovt thia point Hill rtsult in a net iconomic loss to industry. Tha divergence bttvaan private marginal coata incurrad by individual Tiaharman and aocial marginal coata incurrad by aociaty at a whola haa batn documantad in a numbar of opan acotaa fisheries

< Damaata, 19671. Market f a ilu r a s have baan atudlad by uaing fish e ry modala (Gordon, 1954; C hriaty and Scott,

1965; Smith, 1969; Carlaon, 1970; Cop**, 1970). In tha caia of tha aurf clam fiahing induatry, tha Kid-Atlantic

Piahary Management Council baliavid that a raatrlctiva managamant program would ba tha baat c o u n t of action to follow in ordar to ravaraa tha downward tren d s in aconomic and biological yiald in tha fiahary.

Thua, managamant oT tha aurf clam fiahary rapraaanta a caaa atudy in ragional or multi-atata allocation of raaourcaa. A principal objective Of tha fiahary managamant plan initially draftad by tha Mid-Atlantic Piahary

Kanagamant Council waa to a t a b i l i i a tha abundance of declining middle Atlantic aurf clam population*, and then to rebuild thaaa populations to lavala that would austain total annual harvaata of 50 million pounda of meata. To achieve thia objective, tha plan aatahlished a variety of regulation*, including an annual total landings quota oT

1.6 million buahala (30 million pounds of meats! „ As a result of thase stringent management regulations, total 5 middle Atlantic surT clam landings rrom tha fiahary conservation aona declined from 43 million pounds of manta in 1977 to 31.4 million pounds of meata in 1978 ( Huratrski and Surchuk, 1979>. National Marina Fisheries Service biological aaaasamanta have indicated that aurf clam biomass oTf Haw Jaraay Increased in 1982 dua to strong 1976 and 1977 year claasas (Hid-Atlantie fiahary Managamant

Council, 1982). Data from ths logbook racordi of surf clam fishermen fu rth e r suggest that I n i t i a l managamant e ff o r ts have succeeded in stabilising tha abundance of the resource. This in evidenced by the raot that quarterly mean catch par effort indices did not vary greatly during the period of 1978 through 1981,

Although tha current fishery management regime for surf clams haa addressed the important problem of stock replenishment upon the basis of maximum suntainable yield, controversy surrounds many of tha management plan regulations. Economic d islo c a tio n of the labor force in the Tishlng sector of the industry, curtailed supplies of raw product for clam processors, and the expense, difficulty, and possible geographic disparity of enforcing and adm inistering cumbersome reg u la tio n s have a ll been cited by various sources, including industry representativea, as undesirable by-products of the current surf clam management plan.

The complexity of biological and economic interactions in many fisheries makes assessment of a large 6 number of management option* an extremely formidable tank.

On* mat hod by which alternative fiahary management

•tratagiig may be evaluated ia through tha development oT bioeconomic model* (Anderson, 1977; Grant and Griffin,

1979>. Hodel developm ent ia a lo g ic a l and system atic method for representing the response of a natural resource to proponed alternative management plans. hodel development, however, is extremely time-consuming and data- intenaive for fishery management.

By definition, an eoonometrlc model attempts to quantify an obaerved relationship between two or more economic variables. For example, tha price of a commodity in a market may b* regarded as a function of the quantity of that commodity eupplied, the pricae of substitute commodities, disposable income, and other factors.

Economic theory describee hypothetical and actual functional relationships comprising the whole of an economic system. Econometric researchers estimate these relationships, measure them, then it atiatically test them.

The purpose of this atudy ia to survey and refine tha biological input parameter! and econometric relationships required for the development of a realistic bioeconomic

■ im ulation model or tha middle Atlantic aurf clam fishery.

Thase relationships hove bean used to construct a simulation model of tha fishery with data from this survay. The model can be used for assessing tha impact of alternative management strategies upon clam induatry 1 profit* and levels of clam population abundance. While

Identifying tha biological input parameters available in the literature, thia study has emphasised the development of an economic submodel of the fishery. In d u stry coats in both the harvesting and processing sectors have been id e n tifie d and determined, analy sis or the demand Tor raw product confronting aurf clam fishermen has bean undertaken, and the potential economic impact of the e x istin g fishery management plan has baan investigated and deecri bed. Chapter II

The Surf Clam Fiahary ■■ a Btovconomic System

Concept of a Fishery Simulation Modal

Simulation models have long been used to eolve problems in the physical sciences, engineering, and bualneaa. However, only in recent years have they been used to analyse and understand the behavior of dynamic biological and socioeconomic systems. Fisheries, as dynamic ayateme, may realistically ha described through the use of sim ulation models. Indeed, a number oT g eneralised single epeciee fishery simulators have been constructed

{Halters, 1969} Devanney et al, , 1977; Anderson et *1. ,

19B2). The most re lia b le models, however, use species specific characteristics of a stock and fishery. Halters

(1969) has noted that the fishery simulation modal is simply a bookkeeping procedure employed to solve commonly used equations Tor density, growth, and yield by age group, and to Join age groups over time. Thus, sim ulation models can represent the structure of a Tish population as a series of specific compartments representing an age or siie portion of the total Tish population. Changes in the structure and sise of the population can be depicted by moving individuals from one compartment to another with a

e 9

i«riia of rata equations.

Siseenwine <>977> has Identified the important

activities that remove the fish from s given compartment

and advance them to a new compartment. Piehlng results in

a continuous transfer of rish from an aga or sise

compartment to a yield compartment. Natural mortality

reeulte in a continuous decay of each age-sise compartment

and a loss of fiah from the system. Aging results in

discrete advancement of fish to tha neat higher age group

at the beginning of each year or designated time interval.

Recruitment ia represented aa the discrete addition of

individuals to the youngest age group or the model at the

beginning of each year or other time interval. Growth

results in continuous increase in aise. and can therefore

move individuals from one sise compartment to another. If

age, growth, mortality, and recruitment to a fishery can be

described and incorporated within a net of input variables,

a realistic model of the fishery may be developed. Once a

biological simulator is constructed, estimated yields may

be provided as input to an economic submodel which quantifies the relationship between harvest and annual economic rent in the fishery.

A conceptual model of the major biological and economic aspects oT the middle Atlantia aurf clam fishery is presented in Figure 1. The biological rates and

variables influencing the movement of clams through the

system, and the economic inputs and parameters required to 1 o

Pi gu n 1

Conceptual Modal of Mid-Atlantic Surf Clam Fiahary Stock or Fishery Submodel

G Wwmit Q Fqn fundifrni ^ C «ttt A Srafh*

Harvesting Processing Submodel Submodel Key to Compartments in Figure 1

Factors Influencing Flux Compartment Contenta I nto and Out of Compartment

I* Biomesa of Environmental factors, siae newly recruited of parent stock. cl a ms

Xi , la , X* Biomass of clams Growth rates, processes of available for natural mortality, harvest in each Hid-Atlantic sub- region,

X* , I t > IT Clams removed due Processes of natu ral to natural mortality mortality, within each aub- region (biomass, number! ,

X* , X* , Xi * Biomass and number of Processes of fishing clams harvested in mortality, each sub-region.

Xi 1 Entire mid-Atlantic Processes of fishing harvest ( biomata) , mortal i ty.

X. * Unit costs of Economic con d itio n s. f iah i ng.

Xi i Fishing effort by Effort restrictions, vessel class, sub- yield quotas. region, season.

Xi * Humber of v e s se ls BfTort restrictions, in each size class. profitability.

Xi 4 Effective fishing Fishing power. e ffo rt.

II * Variable and fixed Economic con d itio n s. fishing costs.

11 r Revenue from Function of surf clam fish in g . price and supply.

Ii • Vessel Profits. Total fix e d and v a ria b le costs and harvesst i ng re venues. Factors influencing flux C o m P a r t m e n t Contents into and out of compartment

X i f Supply or aurf Harvest, determined by cl sms. profitability and management ragulati obb .

It 9 Prices of a h sll- Economic conditions, fish species ussd supply of substitutes, as substitutes for surf clams.

Xi i Processor demand. Processor demand function, economic conditions

I » 2 Ex-vessal surf Function of supply and clam price. prices of substitute commodi ties.

X j i Total processing Supply processed, economic costs. conditions.

Xi * Unit costs of Economic conditions, processing. x3 1 Hholesale prices Total processing ousts, associated with supply of clams available levels of return. for processing. 1 1 fittnttf economic health of the fiibiry are displayed in this figure. As Figure 1 illustrates, the surf clam fishery cen be compartmentalieed, and the factors controlling the flow of biomass and economic resources between compartments can bs identified.

Flaw through the system, depicted in Figure 1, begins in compartment It, which contains the biomass of clams newly recruited each year to the middle Atlantic stock.

The middle Atlantic stock is defined to include those clams in the Fishery Conservation tone (FCi), an area reaching from three to 200 miles offshore from Cape H attaras to Cape

Cod - or the area offshore of eastern Long Island (Figure

2.) This la the region under management ju r is d ic tio n of the Mid-Atlantic Fishery Management Council. Although there is a small inshore fishery for surf clame, this analysis is confined to the offshore fishery of the FCI.

There are inadequate data available to include inshore clams under state Jurisdiction in this analysis. Moreover, recent statistics indicate that surf clam harvesting activity is concentrated in the FCI {during the past seven years only 7-13 percent of the reported surf clam harvest has been identified as having been taken from the territorial sea). Environmental factors, and the else or the parent stock in each subregion influence recruitment success and the movement of clams in to subregional compartments Xi, Xj, and I* (Ropes et si., 1976;

Kurawski and Surchuk, 1981). As noted below, the re la tiv e 1 2

Pigur* 2

Araa Daaignatlona Ufl«d for Surf Clam Asaaaamanta Am UaalptttlGU* QmA for dart C l« AiivM mtr

tUttP CLAM AtlCMMENT ANEAt -MttAvnfAL m trt

■» c i T i n 1 - N NJ

i - a n j

3 - DMV

\

»m rtR t\p«i MiTtn* ^ l i i w w t a i m t£ »

If ram IrHm tt aL. IPT) 1 3

importance or th in factor* in determining tha racruitmtnt success oT clams la presently unknown. Compartments I3,

Ii, and I* contain tha bionaaa of surf climi in thraa distinct lubrtgiont, northern Haw Jaraty, aoutharn Haw

Jaraay, and Dtlmarva. Tha thraa subregiona ara grouping* of

Hational Marina Fisherian Service surf clam nurvay strata

idantifiad to be hOmogenioUB in depth and aediment type

( Brown at a l. t 1977>.

Hithin tha thraa subregional compartments, seasonal growth rataa determine the availability of surf clam bionaaa to riahermen. Prociaaaa of natural mortality move clams from each of theae compartmenta into compartments

Xs, Xi, and I t whsra thay ara loat to tha syatsin.

Fishing mortality, which ia dependant upon fishing effort, moves clama from each of tha subregional compartments into com partm ents X«, 1«, and XiThaae compartments contain the biomass of tha middle Atlantic surf clam harvest within each subregion. Compartment It* contains the biomaas of the entire mid-Atlantic harvest.

Conceptualisation s £ Harvesting pjuL FrPCtOClng StPtora

The level of iurf clam harvest is determined by input from a harvesting sector submodel depicted in Figure 1. flours of fishing by each vessel class in each subregion are input to the system through compartment I i j , and a re controlled by erfort restrictions yield quotas, and waather. Compartment In contains effective fishing 1 A

cfTort described ■■ effort days of fishing. BIT active

fishing effort Is determined by converting effort hours to

efrort deys end multiplying by the number of vessels

present in the fishery, end by a cetchebility coefficient

derived eeperetely Tor each vessel clsss (Anderson et el.,

196-2; Mid-Atlantic Fishery Management Council, 1961). One

effort day may be taken to equal 12 hours of fishing

activity. Vessel surveys and interview data described in

Chapter IV have Indicated that a fishing trip involves

approximately 12 or more hours or steaming time to travel

to and from the clam beds, and 12 hours or actual fishing

activity. Elements within compartment X, % function as

input to the biological submodel used to calculate clam

harvests. Compartment Xt« contains the number of vessels

in each of three separate else categories within each subregion. This compartment determines the effective

fishing effort contained in compartment In . The number of vessels within each fishery subregion is determined by

vessel profits and returns to vessel owners and operators.

It is reasonable to expect fishing activity to be concentrated in subregions where the highest return on

investment could be achieved. In the absence or limited entry, it is also reasonable to aspect the entry of new

vessels into the fishery if raturns exceed those available

in other sectors oT the economy. Similarly, vessels could

be expected to exit the Tlshery in times of low available

returns on Investment. Vessel profits, contained in * 5 compartment In , ara determined by total fixed and v a r ia b le riihing coats contained In compartment lit. and by harvesting rtvanuta input from tha processing sector submodel, Total coats ara a function of fishing effort contained in compartment Iij and unit fishing costa contained in compartment Xu. Those coata were derived by vessel surveys and interviews described in Chapter IV.

Revenue from fishing operations, contained in compartment

X i t , is a function of surf olam price and supply. Both price and supply are inputs from the processing sector submodel.

The processing sector submodel of the fishery ia also depicted in figure 1. Within the processing sector of the surf clam fishery the ex-vessel price of surf clams la determined by input variables in s processor demand function contained in compartment Ij i . This relationship describee surf clam ex-vessel price, compartment h i, as a function of supply, compartment I m , and the prices of apaciaa used ae substitutes for aurf clama, In ,

Wholesale prices that ore associated with various levels of processor profits are contained in compartment Xu.

Theaa prices are a function of the unit costa of processing, *, t o t a l p ro c e s s in g c o s ta , X>i, and the supply of clams processed, Xt*.

The remainder of this chapter is devoted to identification or the biological rectors influencing movement of clam biomass through the system. The processor 1 6 demand runction ia derived in Chapter III, and riahing and processing coat inputs ara quantified in chapter IT.

Li fa lild. Population Dynamic

Spawning or aurf clam haa baan atudiad along tha Haw

Jersey coaat (Ropaa, 1966), Paproduct!va behavior of aurf damn off Now Jaraay ia not atypical of tha spawning activity occurring throughout tha middle Atlantic region.

Ropaa determined that major spawning occurs in mid-July to early August with a second minor spawning in mid-October to early November. Equal proportions of each saw occur, with hermaphroditism being observed ae an anomaly (Ropes, 19801 .

Studies of age at sexual maturity have indicated that sexual maturity la attained within two years after larval

■ etblament (Ropes, 1976). Balding (1910) reported that surf clams in Hassachusetta waters reach sexual maturity at a length of 45 mm (1.9 inches} a short time after reaching one year of age.

Although temperature has not clearly baan observed to be a stimulus for spawning, it is believed that a minimum temperature threshold can elicit a spawning response, and is the driving mechanism dictating when spawning will occur

(Ropes, 1966). In the laboratory, abrupt temperature change can induce spawning activity. However, it is believed that gradual gonadal development prior to spawning, accompanied by the production of neurosea ret ory products, leads to the induction of spawning activity 1 7

ILoosanoff and Da via, 1963), Although Ropes' 1960 publication cited above ie the most authoritative study of surf clam spawning behavior, several earlier investigations have reported temperature-dependent spawning activity in thin species. Meatmen and Bldwell <1946) indicated that populations of aurf clame occurring in the ocean off Long

Island, Haw York were observed to begin spawning when water temperatures reached 16 degrees C, Stickney (1965) reported that the optimum temperature range for spawning and development of larval aurf clams ia 14 to 20 degrees C. A review of the available scientific data appears to indicate that, in the middle Atlantic region, progressive development of the gonads to a turgid or ripe atate during the period of warming temperatures to between B and 12 degrees C precedes the first annual spawning of surf clams, Ripeness and sensitivity to spawning stimuli praoead the natural spawning period of surf clams by many months, Thus, seasonal temperature change appears to be a significant environmental determinant of spawning patterna in aurf clams.

Larval Development

After fertilisation, larval aurf clams pass through developmental stages typical of bivalvea (trochophore, veliger, and pedlveliger) , Figure 3 depicts stages in the development of surT clam larvae, Loosanoff and Davis

(1963) have studied surf clam larval development 1 s

Figure 3

Stagea in the Development of Surf Clem Larvae in tha dmlopunt of mrf d«i U m i! (i) adult aili and fwili clwa apawning, (b) apan, (c) i||i, (d) unfirtlltitd eit. (t) two-c«ll iti|t of dlvlalon iltir fartilliatian, (f) four-call itifi, (|) tight-call atagt, (h) trochophora larva, (1) vallgar larva with valua aittndad, and (j) padIvaUgtr -larva with foot and v i lt t nutandad.

(From Ropos, 19801 19 extensively In tha laboratory. Larva* have baan ralaad to tha pediveliger atagt in 19 days at 22 degrees C. Tha aarly valigar ataga can davtlop 2B hour* aftar fartl1isation at 22 degrees C, but forma more slowly at lower temperatures. Stiokney (1965) noted that larval growth appears to ba optimum at 20 dtgraaa C, but ia inhibited at higher and lower temperature*. When hald at

20 degree* C, larvae metamorphoned 18 to 30 daya aftar fertilisation. Recant raaearch h>i indicated that Bkman tranaport and upwelling, can influence year claaa aucceaa in many spades or marine organisms by transporting larvae to and from areas conducive to survival (Vorcron and 3haw,

1983). Because of their relatively long period of larval development, 20 to 30 days, recruitment success of surf clams may ho dependent upon factors such as: 1) occurrence or optimum environmental temperatures for larval development, 2) favorable wind driven circulatory patterns

[live surf clams have never been recovered from depths greater than 12B m, and olame living in the beach sons are frequently washed onto land by severe storms (Ropes,

1980)1, 3) abundance of larval food resources ranging from nanno-and ultraplankton to larger phytoplankters, and 4) abundance and distribution of predators. The importance of thee* factors in influencing year class success in many fisheries has been well documented (Applegate, 19B3;

Austin, 1972; Cushing, 1972). 20

£l*m P«v*loDwtnt tAfl Environmental ElStOfi ifftflU ng QrawtJi

ATter reaching tha pediveligar stags, juvenile clams akim tha substrate searching Tor a aultabla araa within which to iaiun« an infaunal existence. Factors influencing tha location of settlement ara presently unknown. At tha tima of settlement, tha pediveligara ara about 250 microns long. Ropaa (197A) haa suggested that tha anvironmantal factors moat important to year class strength aftar metamorphosis ara: 1) adequacy of food abundance

(principally diatoms), 2) oxygenation of bottom water, and

3) abundance of predators. Thera are, however, few data to support thasa hypotheses.

Few diseases of economic significance have baan identified in surf clams. A protoxoan hyparparasite, the haploaporidean Uroenorldlum soisull. has been observed to infect an aniaskid nematode round in surf damn (Perkins et al. , 1975). This infection, while apparently causing few problems for surf clems themselves, turns tha meat of th a adductor muscle and foot brown whan the haploaporidean sporulates. This type of brown mast, while probably not harmful to consumers, haa baan withheld from tha market.

Although disease has not baan documented in aurf clams, there are undoubtedly some parasitic organisms that contribute to surf clam natural mortality.

The effect oT abundance of food upon aurf clam growth has been in v e s t ig a t e d . Ambrose et s i. <19B0), assum ing that reduction in surf clam food abundance occurred with 21 increased diotancs offahon, compand surT clam growth rates at naar ahora and offshore atatlona. Hatir temperature, dapth, and popu^tion danaity were aleo correlated with growth rataa to determine their ralativa importance. Tha results of thin etudy indicatad that more rapid rataa of growth can be observed at offshore atatlona rather than inahore atatlona. Ambrose at al. thus hypothesised that variables such aa temperature, water depth, and population danaity may be more limiting to growth and subsequent year class strength than food abundance, although food densities were not measured In this study. Correlation of these environmental parameters to growth rate wan undertaken. Intercorrelat1 one between the independent environmental variables, however, obscured the importance of each to the growth of clams. Ambrose at el. stated a belief that lower water temperature, greater water depth, and decreased population danaity ofrahore were responsible for faster growth rates. Thia wae not, however, quantitatively demonstrated in their etudy.

Jones (1900) found an inverse correlation betwaan tha growth rate of aurf clama and sea surface temperature. He noted that it ia not clear why cool years are mare conducive to growth and recruitment than warm years.

Temperature may influence growth rate directly, or do ao indirectly through other factors such as availability of food, nutrients, or oxygen.

On at least one occasion low oxygen levels in the 22

Middle Atlantic Bight have given rise to mass mortalities or aurf clams (National Marina Fisheries Service, 1976),

KoHivar, in tha absence of anomaloui climatologioal conditions and excessive nutriant loading, low oxygen livils do not ganarally present a problem for aurf dams.

According to Ropaa (197B), predation ia probably greatest upon amall juvenile surf clams bacauaa o l t h a i r aiae and inability to burrow deeply into bottom aubstratA.

Pi ah, craba ( C a n c e r i rroratua and Cancer borealis)■ and moon anaila (Lunatla heroa and Poli nlcea duplicate) have all bean identified as predators of juvanile and adult aurf clama. Studies in Chincotaagua Ini at have documented that holen were bored in 50 percent of tha sheila of amall surf clama (Ropes and Merrill, 1970). Frans ( 1977) reported that off Long Island, New Tork, moon snails preyed mostly upon clams lass than five yearn old and 60 mm long.

It should be quite evident from tha research results discussed above that aurf clam recruitment success is dependent upon complex interaction among a number of anvironmental factors. As such, difficulties are ancountared in attempting to develop a biological predictive model of recruitment for surf clams. Halters

(1969) has indicated that recruitment into many fish populations, at the start of any year, may be some function or the spawning stock present and a set or age specific reproductive potentials. Spawner-recruit curvea have baan proposed to estimate the relationship between stock Bias 23 and recruitment In fish stocks I Ricktr, 1994; Deverton md

Holt, 1 957; L ark in at al. , 19641. Surf clam r t c r u i t m i n t ,

howtvtr, ia highly variable. Hancock ( 1973) haa noted

that, "cocklti and other burrowing bivalves m n to be able

to make a spectacular recovery rrom low stock levels. With clama and scallops, extremely irregular recruitment seems

to be a function of variable environmental factors, which mask any dapendance on spawning stock. ffith clama,

recruitment is so irregular that any relationship between

parent stock and recruits Is not apparent*. Because there

is no demonstrated or quantified relationship between parent etock and recruits in the surf clam fishery, year to year variability cannot be predicted beyond that period of time during which a specified population of pre-recruit cohorts gradually enters the fishery. If population surveys can provide reasonably ecourata estimates of the

initial surf clam population siae end age structure, then

individual year clan population aiae and weight may be reliably predicted within a period of time termed the pla nning horison.

Population Dynamics, Aae and flfPHth

Once an incoming year class has been recruited to the m iddle A tla n tic reg io n , i t becomes p o s s ib le to d e r iv e difference equations describing the fluctuations in population slse that occur with time.

Growth of clama within discrete time intervals can 24

but ba dneribid through the development or species

■pacific growth currti, Surf clam growth haa baan axtanaivaly studied by a number of invastigatora using several different mat hoda t J o n a a at al. . 1976). Thaaa etudiea Indicate that growth may ba vary accurately described through tha uaa of tha von Bertalanffy growth aquation ( von Bertalanffy, 1936), Balding <1910) atudiad aurf clam growth ofr Capa Cod, Haaaachuaatta by keaping clama in aand Filled raft bona and periodically [manuring their growth. Annular shall ring formation in surf clams has baan verified and atudiad by: Hestman and Sidwall

C 1946) off Long Inland, Kaw fork, Xerswell (1944), off

Prince Edward Island, Canada, Welch (1963) off Point

Pleasant, Haw Jaraay, Caddy end Blllard (1976) off

Buctouche, Canada, Loesch and Ropes (1977> oFf Virginia,

Murawaki and Serchuk (1979) ofr Virginia, Kurawaki (1977) off Maryland, end Hurawski and Surchuk (1979) throughout the middle Atlantic region. Ropes et el* (1970) studied shell siaea of discrete Battlements of surf clams at yaarly intervals at Chincoteagua Inlet, Virginia, and Yancy and

Welch (1966) reported the growth of a discrete settlement of surf clama during tha summer at Boothbay Harbor, Main*.

Growth studies have recently baan performed by cutting the sheila or aurf clams and measuring ring distances directly on the Sectioned shall and upon an impression created in plastic ( Jonea et el., 197B), the von Bertalanffy growth equations derived from some of thane studies are displayed 25

In Table 1, Chang a t a l ( 1 976) examined tha relationship between ahell length and clam weight to determine whether iaometria growth Le evident in aurf clama. A statistical t e e t of iso m e try wee i nsi gni f i cant for shell length versus drained meat weight, There fore, applying the von

Bertalanffy growth e quation baaed upon length and weight is v a lid , Tha maximum age attained by surf clams seldom exceeds 25 yeare ofT shore and 16 years inshore ( Ambrose et

»1,, i960), F i gura 4 displays a general growth curve for eurr clama using the equa tio n of Chang et al. (1970, It illustrates a pattern of growth during which 50 percent of maximum le n g th ia ach iev e d by year three, 75 percent of maximum le n g th ia a c h iev ed by year tii, and 95 percent of maximum le n g th i s ach iev e d by year twelve. Kean length at sexual maturity ia 70 mm (two years of age, and the age at which clama reach ninety percent of t h e i r maximum len g th

(157,3 mm) ia eight and o ne h a lf years.

Lenqth-Weiqht Relationships

The length-drained meat weight relationship in surf clams, where wt*( a) < length) * * 1 , has been reported (Chang et a l., 1976; Mid-Atlantic Fishery Management Council,

1979; Mid-Atlantia Fishery Management Council, 1961).

These relationships are provided in Table 2. Using the relationships in Tables 1 and 2, growth and resultant meat weight of post larval clama can be simulated. T a b lft « o l c- tn C-l >1 X L* o >» e v o tt a L o s 0 £ 3 I h > X Ml ■H *>. jJ o li - HK 0 A L, 01 V o C > o U 0 L H ^ - e ■ V jJ- ■0 to ■M ■t) A 10 1- -o W O - V i-f o in u*> 01 0 * o 10 - ! . I-' « O g C 0 0 * 0 b ■ "I c O c 0 & a c _ ^ X

■•H X n ► c*. r- m xi *> ■»- 0 r H ■r4 r- o t" A r*J o a 10 M 9 A Cl A 9 X O A A ■r* n 3 o P. a e R 0 01 U J . u - r 10 14 -rl m +J ■U r o tj * 9 X ■A d '- (n t o A 0 A A a < G V 1 - X w X S' ts d ■m 0 X ■H A H L -A □ a o 10 u r co 01 so w- w- 3 I* a ■ X A C A >> C-. Li L O A . r> 0 A X X M x X (0 □-3 *□ O - t 01 'I- A in o T_ 10 10 a 10 1 3 a a r L> In _, Li A O X co X ■ <7i T" a O #) o F—1 I"' X 01 X X a A1 01 10 o f\l A 3 A a A p > A s L. 3 l A t V 3 a 3 h H 4 „ X b4 A D Li 0 26 27

F i g u t t 4

G en eral k g * and Growth Relationship for Surf Clama L Length at EGKual matJCity

Central it* *nd trovth ralitlonahlp for turf jplm li loHdi ninn. 28

X n 3 x X H - X X A C~ X A *- A P . 9 B A 9 3 P . a i 3 P h r+ 3 3 A t » >■ P 1 x 4 IQ 4 » 4 a * 4 > c IT A t f • i-* A IT •t X A i * ■ H i * 1 ^ f t A 9 f t 3 A P 0 3 3 3 i t 3 3 3 A r t - (t i t r t - i f A (-* ■M- O **■ ■■ r > f i A fi n 3 o O * 9 a A C ‘'J e m - c *« P. —* 3 3 - 3 « 3 *-■ I d n a O 3 " f i ■ V X 'O *-■ a* **■ A *-■ tr A A O i ^ • >-■ A !-■ A ^ ■< ►1 4 " *< ~ (< X —h f t S ia U) A - g # a> "■. H - 4 —* 4 4 H Hp* —■ Sp* w X m ■ IT tT l-i A A V A O X m n X A a A — M K> *-■ u l - i * A H A D- Q. a A IT i 1 a C l w 3 A- ► n A A 4 O i t rt < T 9 IT 3 H M 9 a 0 ET A l> 9 A M X 3 A a *-■ ( f p t P- E * M- H' n n - ♦*■ «3 - F Ui ■ S o c o □ o S IA 3 o a o o "t e o —* —* *+. N> —*■ Q o to If —*- a H

U t i l LftU fill! i t ProoMUna

Birktr and Merrill (1967) atudiad tha seasonal variation in mist weight avidant In aurf clana taken off

Capa may and Point Pleasant, Haw Jaraay. Thay concludad that mist weight ia ganarally constant throughout tha year, with a slight dicriiii avidant during or aftar tha spawning period. Thia meat weight dicrisai during the spawning period is reflected in higher clam proceseing coats par pound or meat produced. They also evaluated the weight lose due to processing ee drained clam mast weights ware measured before and after processing. Percent weight loss due to removal of stomach and gonad tiaauae from the meats o f s u rf clama was betw een 11 and 20 p e r c e n t of t o t a l wet tissue weight. Heat weight loss resulting Trom processing activity can be simulated using thia relationship.

natural Mortality

Clams are removed from the system through processes

Of natural mortality and fishing mortality. Several authors (Ropes, 1 980; Chang et al. , 1976; Murawski and

Serchuk, 1979) have reported a surf clam population instantaneous natural mortality rate of between 0.20 - 0.28 calculated as an annual unit for all age classes . Ho age specific estieiatea or natural mortality have bean provided by any investigators. All of the estimates cited above were derived through the use of age composition and cohort analysis to determine survival rates and subsequent 30

instantaneoua natural mortality rates.

Population Structure

Population ftructure may be defined 11 th« tilt, iti composition and numerical abundance or year clsssee or cohorts in a population. Development of an analytical

biological planning modal requires the acquisition of

standing stock data, Specifying levels of population abundance, while requiring some approximation, ia not *■ arbitrary as attempting to specify unknown stock-recruitmant relationships. Estimates of surf clam

population structure have been derived by Huraweki and

Surchuk (1961) and indtrion at al. (1962). Chang at al.

(1976), used catch and effort data to aatimata the sixe by waight of the middle Atlantia surf clam population for the y e a rs 1965 t o 1975. These e s tim a te s , d isp la y e d in Table 3,

provide an approximation of year class strength for three

year old clams during a ten year period. Murawski and

Serchuk (1980) have provided relative abundance indices for

aurf clam populations off the coasts of Delmarva, southern

hew Jersey, and northern New Jersey. Abundance estimates

have been taken rrom sample towe of a standard sixe in each

locale. Abundance indices have been provided for clams

leea than 5.5 inches and greater than 5.5 inches. Using

these abundance indices, Anderson et al. (1982) estimated

the population structure of pre-recruit cohorts one through

five (clams less than 5.6 inches long) and six through 31

Table 3

Estimated Recruitment Siae in Haight and Numbera of Middle Atlantic aurf Clamft 11965-1975)*

Tfaqr HJi qht < tona) Kmnbera M Q* )

1965 51,98 2 1,321 1966 164,563 1,931 1967 99,427 1,036 1968 99,437 1,306 1969 99,296 1,499 1970 191,597 2,077 1 971 220,364 2, 291 1972 173,617 1,036 1973 71,756 905 1974 53,932 675 1975 53,615 600

* Chang at al. (1976> 32 twenty-five ( clams greater than 5.5 inches long) in 1961.

land upon tha data provided by Nuravski and Sarchuk

(1961), indaraon at al. ( 1962) estimate tha numerical abundance of surf clams graatar than 5,5 inches long to ba approximately 2.2 X 1011 off Delmarva. This estimate was derivad by dividing tha number o r clams takan par experimental toa by tha numbar oT aquara matara covtrad by a toa, and multiplying timaa tha numbar or aquara matara covarad by known Dalmarva clam bada. Using thia aatimata and ralativa abundanoa indices, population antimataa war* derivad for clama laaa than 5.5 inchaa long and greater than 5. 5 inchaa long off northern Nat* Jersey, southern Maw

Jersey, and Dalmarva. Uaing thaaa data, Anderson at al. estimated tha population structure displayed in Table 4.

Although this population structure raflaota the limited knowledge of tha distribution and abundance of surf clams, it is in accord with reports in the literature that southern Hew Jersey does not have a strong year class which will recruit to the fishery over the next five years.

Reasonable abundance estimates are essential to tha development of a realistic analytical fishery model.

Limited information concerning abundance and distribution is perhaps the weakest link in the chain of information required for tha development of a surf clam fishery model.

While confidence can be placed in tha orders of magnitude of these estimates, it is more difficult to assess their precision within one order of magnitude. He var tha leas, 33

Tabla 4

Population Structure of Hid-Atlantic Surf Clams

Cohort Delmarva Southern Haw Jeraev northern Haw Jeraev

6-25 2. 2 X 1 o' a 2. 797 X i a ‘ & 6.903 X 1 O'

5 1. 2 X 1 0 ‘ a 2 L 365 X 1 Q* 1.92 X 1Q‘ s

4 1 . 6 X 10‘ t 2. 365 X 1 o’ 0. 348 X 101*

3 6. 7 X 1 o' a 2. 365 X 1 o’ 1. 92 X 10ie

2 1 . 6 X 1 Q* 2. 365 X 1 o’ 1.467 X 10‘ 0

1 1. 2 X 1 0l a 2. 365 X 10* 2. 365 I 1 0 ‘ 0

*Anderaon et al. 11982) 34 they represent an adequate data baa* Tor comparing tha relative marl to of alternative regulatory atratagiaa.

Effort* FAjJllm Power. and Catchability £a.g£llfii mtfl

After accounting for aurf clam growth t Xi, Xj, and X*) and natural mortality (It, X *, and X?>, i t la possible to calculate fiahing mortality

Xt a) and subsequent level of harveat to be uaad as input

Into a processing sector auto-modal. fishing mortality will vary as a function of fiahing effort (Xii) and vessel/gear fishing power lit*). Host frequently, rishing mortality (Pi is expressed as a function of fiahing effort

F'lqMf). In this equation, r represents effective fiahing effort and q, termed the catchability coefficient, ia an operator relating fiahing effort to fiahing mortality.

Fishing mortality rates are most frequently expressed aa instantaneous annual units IRounsefell and Everhart, 1953;

Ricker. 1968; Royca, 1972).

Z --I In S)

Z * M+ P

Z ■ Instantaneous total mortality rate.

X"Instantaneous natural mortality rate.

F-Inatantaneous fishing mortality rate.

S-Survival rate. 35

Several iavastigators have estimated the relative fiahing power of viitala in the m rf clam fishery. Chang et al. (1976)* uaing catch and effort data from the middle

Atlantic region, estimated a catchability coefficient Tor the entire surT clam fishery. Thia estimate did not account for differences in gear efficiency and fishing power between fiahing vessels. Effective effort wee measured in hourn, and an estimate of the catchability coefficient (q) was 6. 0 K 10 * effort hour *l, This aame ntudy also determined fishing potfer coefficients using vessel horsepower as a criterion of evaluation. Pishing power coefficients determined uaing catch per unit effort data and vessel horsepower data are displayed in Table 5.

Recently, estimates of catchability coefficients have been calculated separately for each of three vessel classes defined on the basis of gross registered tonnage (Anderson et al., 1962). Effort was measured on the basis of fishing trips. Using the equation, Catch/Vessel**Catchability

Coef Ti c i ent>( Effort Trips) (Surf Clam Population Siae), catchability coefficients for each else category of vessel were calculated. Date on catch per unit oT efTort and effort were available Trom National Karine Fisheries

Service publications and the Kid-Atlantic Fishery

Management Council. These cstchability coefficients were calculated for clams of length greater than 5. 5 inches.

Depending upon gear efficiencies, catchability coefficients may be lower for clams of length lass than 5.5 inches. 36

Table 5

Fiahing Power Coefficients in the surf Clam Fishery

Vessel Horsepower Pishing Power Coefficient

0-100 i . ooo

101-200 1.11266

201-300 1 . 4641 4

301-400 1 l 67947

401-600 1 . 21196

601-900 1 . 06349

901 - 1 . 02339

*Chang at al. ( 1976) 37

Probably this la because email clerne ere not captured

between the dredge elate. However, Inadequate data exist

to teat thia hypothesis, Estimates or catchability

c o e f f i c i e n t s d e riv e d by Anderson at *1. were:

Claaa I Vessels * 9,6054 x 10’’ effort trips-1. ( 0-50 Gross Registered Tone)

Class n Vessels • 1.1B9B x 10‘* effort trips'1. (50-100 d ro ss R e g is te re d Tons}

C lass I II V e sse ls - 1.BBB4 X 10'* e f f o r t t r i p s ' 1. I i 100 Gross R e g is te r e d Tone)

Effort trips or eTfort days are a very convenient unit of effort to use in the surf clam fishery because Tithing activities do not occur over s period of time longer than

12 to 24 houre. Veanela return to port with their catches after a single day-long fiahing trip. The determination of catchability coefficient ia important because it permits calculation or harvest levels from eTfort and population data.

After a thorough review of the literature, it becomee apparent that, while development of a complex biological simulator will require more information about the effects of environmental factors and stock alae upon recruitment success, adequate data do exist to develop a model capable of accounting for age, growth, natural mortmlityi and fishing mortality. Cartainly then, adequate data do exist to develop a biological simulator capable of providing yield data to an economic sub-model of the fiahing 36

indiiatry. Thin typ# of modal, whila 1 » b uaaful n a

pradictor of long rang* ibiolut* fluctuationa in population livils, flnda It* graataat valua aa a managamant tool capabla of aaaaaaing tha marlta of altarnativa managamant atrataglaa in tha abort run. CHAPTER III

JBHAHD ANALT3I5

Thia chapter outlines tha procadurt followed to

derive a abort run prica modal Tor aurf clams In tha middle

Atlantic landings market. The modal is postulated to

eiplain the demand for alama confronting middle Atlantic

aurf clam riaharmen. Thia relationship will provide price

levele to be used in tha datermination of total revenua

accruing to aurf clam vassal owners. Fishing revenue is a

function of ex-vessel price and level of harvest. The

primary Function of thia price relationship la, therefore,

to predict certain ex-vessel price levele associated with

various levels of harvest snd prices of substitute commodities. Eetimataa of total vessel revenue can then be calculated and ranked according to management option, thereby providing resource managers with a tool for evaluating tha merita of each proposed set of regulations.

Variables La the Price Rq.mfcian

In an industry-wide survey conducted by the author

(Appendix B), processing firms have identified factors that are influential in determining ex-vessel prices for surf clams. The alii of clams offered for aale is a price determinant. According to current management regulations, aurf clama harvested in tha Fishery Conservation Tone must be or a length greater than or equal to five and one half

39 40 inches in order to be ti«rv«it*d,. Three or the ton major firm* purchasing surf cl sms indicated that a prica differential Is paid Tor different sis* categories of surf clams. Small clams, usually taken inshore, are more costly to process than large clams taken oTfehore, Although size categories are not clearly defined by processors, e premium prica may be paid for a shipment of clams i t the processor determines that they are sufficiently large, Clam else may therefore be viewed as influential in determining price, with large clams commending higher prices,

A review of the fishery price and demand literature indicates that several additional economic variables have also been considered in price models for clams, Visgilio

<1973), in a review of fishery demand literature, specified several variables that may determine ex-vessel prices for surr clams. Visgilio' a study indicated that quantity of fiah product landed, inventory of fish product, fish siae, consumer income, and the prices of substitute products, msy all be important in specification of a price model for aurf clams. Economists at the Virginia Polytechnic Institute and State University have developed a preliminary econom etric model of V i r g in i a 's hard clam fish e ry ,

Preliminary studies have indicated that ex-vessel hard clam price may be determined by landings of hard clams, consumer demand fo r hard clams r e f l e c t e d in r e t a i l p ric e s, and the prices of substitute products available (Earns, 1981).

Such a price model may be applied to the surf clam 41 fishery. Using only summary landings data from Fishery

Statistics of ths Unltsd Statss, ths Mid-Atlantic Fishery

Management Council has developed predictive annual and quarterly price equations Tor surf clams i Kid-Atlantic

Fishery Management Council, 1981). The annual model described aurf clam price as a function of United States per capita aurT clam supply, United States per capita ocean quahog supply, and deflated United States per capita income. The q u arterly model Incorporated data Tor a five year period from 1976 through 1960. Surf clam price was determined to be a function of quarterly United States aurf clam landings, quarterly United States ocean quahog prices, and quarterly United States per capita disposable income.

Dummy variables were used to control Tor perceived structural differences in the fishery. Caseins and Strand

<1976) postulated that surf clams may be viewed as an exhaustible resource. They hypothesised that the price of surf clams may be a function of tima, aurf olam supply, and the price of one su b stitu te commodity, hard clams.

In summary, it would appear that middle Atlantic ex-vessel surT olam price can be r e a l i a t ! c a l l y described am a function or the following variables; production, ex-vassel prices of substitute commodities, consumer demand reflacted in r e t a il clam product prices, * iia oT clams, quality of clam meats, processor inventory of fresh and frossn product, consumer income, and time. Some of these variables are not included in the price model developed 42 below bfldUM their dlitint rtlitlon and >lonirictnc« to

■ urf olam prica doaa not warrant a reduction in tha modal'a degrees of frtadom. Othar variablea are not included in tha price modal bacauae adequate time seriit of data do not exist, and satisfactory proxy data are not available for bhasa variables.

frariablss Included in Um jUulyaitf

In aiimination of the variables expected to influence enrT clam price in d icates that there are a number of significant variables for which time series data are available. These variables have been included in the middle Atlantic price model.

quantity of surf clams landed in the middle Atlantic region ware expected to be negatively related to price.

Tima aeries data of aionthly landings of surf clams are available and were included in the price equation,

Tha q u a n tity demanded of a commodity has tra d itio n a lly been expressed as a function of its price, the prices of a ll other commodities, and consumer Income, Q*f(pi,p>i p i , ,.. p . , t ), where p*.,.p, represent the prices of substitute commodities and y rep resents consumer income

( Shumpetar, 195*), The results of a processor survey

(Appendix B) and a review of the literature have identified substitute commodities for surf clams. Caseins and Strand

< 19TB) have noted that lsrgs "chowder41 hard clams < Venua marcsnaria) taken inshore may serve as substitutes for surf 43 clams. Surf clam processing industry executives note that,

«■ a m u lt of improved procataing techniques, ocean quahogs have become com pletely eubatitutable for canned aurf clam products, "In the canning industry it doss not matter bow small clams ere, you can atill grind them up.

In the breaded clam strip buiintas, a large foot ia required. Companies that are not selling branded product can uae ths smaller ocean quahog." (Personal Communication,

Andrew Drawer, *982), A survey of clam processors has indicated that consumers may view the following items an substitutes for aurf olam products: all clam products, including whole and processed hard and soft clams ( Venup msr.Cinarla and Hva a r s n a r ia > , oysters, searood stew and chowder products, breaded shrimp, and breaded Fish producta. Visgilio ( 1973) indicates that soft clams and ocean quahogs may be viewed as close substitutes Tor surf clams. H ille r and Nash ( 1970) have indicated th a t there may be a seasonal pattern of clam consumption that complements a seasonal change in oyster consumption.

Monthly ei*vaastl landings and value data are available Tor ocean quahogs, hard class, soft clams, end oystara Trom the middle A tlan tic market area. The ex-venae! pricaa of thane species were selected to be used as su b stitu te commodity v aria b le s in ths price model.

Monthly landings data, in pounds of meats and d o lla r value of landings, were av a ilab le Tor a ll of the middle A tlantic states except Delaware. Delaware state landings ware not 44 recorded, and war* thfrafort ticludsd from tha prica modal. 4a in d ic a te d bilott, tha small p o rtio n of to ta l landing! contributad by thia atata justifies it* exclusion. Monthly middla Atlantic landing* war* obtainad by aggregating tha landings of each individual atata.

Dollar valuas of landings in each month ware similarly summed ov*r all four states. Ex-vessel prica* war* calculat«d on tha basis oT masts lsnd*d, and than daflatad by tha producar prica indax to constant 1967 dollars. This yaar ia ua«d in U. S. Dapartmant of Comm*rc* raporta as a standard for calculation of a daflator indax. Whan adequate data ar* available, othsr variabla* may b* conaidarad in tha davalopmant of an ax-vasaal prica aquation. Kotnvar, adequate monthly tint aarias data war* not availabla to include tha following ralavsnt variablas in tha ax-vaisal prica aquation; consumar damand raflactad in retail surf clam product prices, clam xiit, quality of clam masts, and processor inventory of clama. Hholesale fish prices from Fulton Fish Karkat in Haw fork, and

National Marina Fisheries Sarvic* records of annual cold storage holdings might b* used to massurs tha influence of consumar demand and processor inventory. These variables, however, war* not included In this analysis.

Disposable income, often included in demand analysis, has not bean included in the surf clam price aquation becauia, as V isg ilio <1973) notai, "D isposable income, i s regarded ** insignificant with respect to the damand for 45 aurf clama at the processor level. Thia judgement nets upon tha f a c t th a t th a para antage oT diepoeable per capita incoiH devoted to the purch aia of all olame la approvi Ria t al y 0, OD01 parcan t." Although soma have advocated t ha use of par capita supply data to avoi d confusing t ha time trend Tor population with one that ml ght reflect oth ar affects

Ocean quahoga, because of their status as n e a rly perTe ct substitute commodities for surf clams, d eserve apeci al consideration. Historical ex-vessel prices for ocean quahogs should certainly bo included in a iu r f clam price modal. Kiddle Atlantic clam processors have shipped

1 arga numbers of ocean quahogs to their plants to be used in ca nned products. Since 1976, moat of th is product, ao to sa percent, has come from ports in New Jersey and

Haryls nd. Prior to 1976, however, the center oT the oc aan quahog f is h in g in d u s tr y was the a ta t e of Rhode Island,

Pri or to 1976, there were virtually no landings of ocean quahog a in any eastern seaboard atate except Rhode 1 a 1 and.

The pr imary landings market for thia product, howe v tr, has alwayi consisted of clam processors in the middle A tla n tic atataa Ocean quahogs were shipped to middle Atla nt 1 c plants in refrigerated trucks. Because of the i mportance 46 of th is clam *s a s u b s titu te commodity for su rf clans, and because a middle Atlantic fishery Tor this species has only developed in recant years, monthly Rhode Island landings and value data for ocean quahoga covering the entire history of the fishery have been included in the data set used to develop a middle Atlantic price model for surf e 1 a me,

An es-vessel price model for aurf clams in the middle

Atlantic landings market is thus represented by the following equation:

Pi"f4 Q, ■ Pi i , Pi c i Pt < i Pi i > I i Si 11 S4 ] i H< » ■ >

Where,

Pi - Deflated ex-vessel surf clam price.

0, - SurT clam landings in pounds of meats*

P*, " Deflated ex-vea»al ocean quahog price.

Pi< " Deflated ex-vassal sort clam price.

Pm - deflated ex-veaaal hard clam price.

£<1,3*2,- A dummy variables used

to explain seasonal price fluctuations.

M<«. - A dummy variable accounting for the

e ffe c ts of the su rf clam management plan.

Tha results oT the processing and harvesting sector surveys indicate that surf clam demand may slacken during tha aummer months creating a eoft seasonal market.

Processors attribute this slackening of demand to fluctuations in consumer preferences for soups and 47

specialty products, Tha affect of this shift upon

• ■-vassal prica ia taatad through tha uaa of dummy

vari ablas.

Hdum ia a dummy variable inserted to dascriba tha

effects of tha aurf clam management plan estab lish ed in

1977. A numbar of structural changaa in tha fishery

occurred with tha implementation of fishery management

regulations. In addition to aupply constraints, minimum

siae limit restrictions resulted in tha substitution of ocean quahogs for small surf clama, and tha harvesting of

larger eurT clams. The net affect of these changes was to

raise ex-vessel prices in the years after regulations were

placed in force. The effect of structural change in the

fishery ia reflected in slightly higher ex-vssaal prices.

Mechanism &£ Price format!ftn

A survey of surT clam processing firms and fishermen

has indicated that, while it is generally accepted that

induatry-wida prices exist, short run ax-vess*l prices do

fluctuate s ig n ific a n tly depending upon a numbar of

determining rectors. Results of the processor survey

(Appendix B> indicate that thara era in fact no formal

contractual or written agreements between clam processors

and boat owners to deliver quantities or clama at specific

prices. Moat prooaesing firms hava long standing

agreements or relationships with aurf olam fishermen. Most

of these informal verbal agreements have existed for 12 to 46

1 5 ye are. To pure base c 1 ame, processors must contact n ■ he rman, request a ape ei f i c supply of clems, and quot th a a x- vea eel pric a th at t hey would be willing to pay.

Cl am fishe rman are t hue gi van the option of agreeing to a uppl y clams at a quoted price, or contacting different processors to seek higher prices. During period* when clams are abundant, fishermen often contact processing firms directly to offer damn for sal*. Thus, Tor varying periods of tima there is a relatively standard industry price which fluctuates depending upon, among other factors, the supply of dome offered for sale. The standard industry price fluctuates as processing firms rsiae or loser their prices. One industry executive (Andrew Drawer,

Personal Communication, 1962) has provided the following account of ex-vessel aurf clam pricing:

If a company in creases its price i t te lls the catchers that it needs additional supplies, other catchers then come in and sell at a higher price. Other processors must then raise their prices in order to obtain product* Processors will follow tha price leader. Since there are generally less than ten companies that buy clama, in d iv id u al companies can*t afford to pay higher or lower prices for any length or time, they are more or lose fmrced into lina. When prices are rising, price information filters back to individual processors through the catchers. When prices are declining, processors see reflections of thin in the marketplace.

Thus, quantity of clam meats harvested, tempered by less than perfect competition among processing firms, appears to be e significant short run determinant of ex-vessel clam 49 pri ce.

Market AClt

th* Tirst task to be undtrtskan in an analysis of commodity prica formation ia dttarmination or a rtlavant market ar*a for prica. Thia modal confinaa itaalf to a conoidaration of surT clam prica in tha middle Atlantic region, Tha antir* middle Atlantic region conatltutea a relatively homogeneous ex-vessel market area for aurf clama and related ahellfiah product!, Thia argument ia supported by information obtained from a survey of eurT clam risharmen and processors, and alao by tha results of several recent analyses of clam price formation.

During tha period of tima spanning the years of 1950 through 1980, New England p o rta c o n trib u te d only 0,5 percent of tha surf clam meats landed in the United

States. The remainder of the landings have came from the middle Atlantic states of New York, New Jersey, Delaware,

Maryland, and Virginia. Surf clam landings in Delawara have historically contributed only 3.B percent of the clam meats landed in the middle Atlantic region. Furthermore, landings in Delaware have been inconsistent. Few surf clams were landed at Delaware ports during the years or 1970 through 1975. These data would indicate thst 96 percent of the ex-veeael landings market for aurf clams exists at ports in ths four middle Atlantic states of New fork, Haw Jersey,

Maryland, and Virginia. Landings from these states comprise 50 virtually tha entire aurf clam catch taken in tha United

States, It was determined, therefore, that these four middle Atlantic etatee should comprise tha market area to be used in the development of an ex-vessel price modal.

The homogeneity of this market area may be inferred

Trom the processor survey (Appendix B) , Five of the ten middle Atlantic ahucking plants surveyed do not operate landing facilities at their processing plants, Virtually sll processing operations, even the dockeide plants, purchase a large portion of their raw product directly from catchers landing clams at middle Atlantic porta. Clams are then shipped to plants in refrigerated trucks. Although processing plants prefer to obtain clams from the nearest available port, clama are routinely Shipped from all of the middle Atlantic porta to processing plants throughout the middle Atlantic region. Homogeneity of the middle Atlantic shellfish market has been further subetantiat ad by resource economists at the Virginia Polytechnic Institute and State

University. An analysis of variance of hard clam prices in eastern seaboard states has indicated that prices in the middle Atlantic atates era not significantly different. It waa determined that the middle Atlantic region constitutes a large homogeneous market area for hard clams ( Kerns,

1961). Interstate transport of shellfish has, in Tact, created a large relatively homogeneous market in ths middle

Atlantic region. Tha mobility of the surT clam fleet, and the willingness of processing firms to purchase product 51 wherever it ia available, clearly indicates that tha middle

Atlantic states of Virginia, Maryland, Nan Jersey, and Haw

York comprise a wall dafinad and ralativaly stir containad markat area.

Statistical Estimation Procedure

Ordinary laaat squares rtgrtiiion was utad to aatimata tha prica modal described above. Us* of tha single aquation laaat aquaras approach appaara to be justified in thia case because, as indicated balow, all of tha independent variables in the price aquation may ba viewed as exogenous variables.

Arguments hav* been developed to support tha use of both single aquation and simultaneous equation methods in demand analysis. Fox (1953) has indicated that tha decision regarding tha use of single or multiple equation approach** is dependant upon tha purpose of tha investigation, and the possible joint dependence between dependant and independent variables. Fox notes:

“If the purpose of the investigation ia to estimate the expected price associated with given values for such variables as all* of crop and consumer income, tha best anawar can be obtained by lea st squares regression with price dependant and other variables independent. If the purpose is to estim ate ths e l a s t i c i t y of demand and other structural coefficients, this equation may not give an unbiased estimate. It will do so if, and only if, current supply and other independent variables are not measurably affeated by price during the marketing period. These conditions are approximately mat for Term products. If they are not mat, a system 52

of simultaneous equations io needed if valid estimates of ttvcrtl coefficients of interest to tcononiists and commodity analysts irt to fa* obtained."

Support for tha ut* of tha single aquation approach ia evidant In tha work of Visgilio (1973) who identifies tha proponents oT this approach.

"Foots (1957) supported tha position that tha singla aquation approach under certain conditions provides valid estimates of damand e l a s t i c i t y . Ha proclaim ed, * In soma analyses tre can assume that tha quantity supplied is essentially unarrected by currant price...Under these circumstances we may be able to obtain valid estimates of tha elasticity or damand by use or a least squares regression analysis Tor which price ia the dependant variable and aupply and aome demand s h i f t e r s are used as independent variables. ' "

Other resource economists have addreeaad the problem of determining whether to use single or simultaneous equation systems. Working (1927) has noted that:

"Even though shifts of the supply and demand curves are c o r r e la te d , a curve which is fitted to ths points of intersection will be useful for purposes of price fo re c a stin g , prov id ed no new factors are Introduced which affect price during the period of study,"

The purpose of this study is, in fact, evaluation of the efficacy of existing management efforts through forecasting. Hildreth (1960) and Klein (1960) both have argued that the use of single aquation ordinary least squares analysis may be appropriate in situations where the investigator has adequate & priori knowledge of the economic sector being studied. Hildreth has stated: 53

"It is tha rtsponeibility oT tsch empirical researcher to make tha bast choice based on available knovladga of statistical properties of dirr

Visgilio (I 973) furthar notes:

“Tha bast pradictlon of aconomic variablaa is oftan provided by tha singla aquation laaat aquaras approach; while tha unbiaaad estim ation of parameters such as damand alaatlcity may raqulra a simultansoua aquation approach. ■*

Tha choica oT tha singla aquation mathod, therefore, appaars to ba justiriad if aithar or two conditions ara satisfiad. First, tha primary objective of tha atudy should ha prica pradictlon. Secondly, simultaneity should not exist between currant price and currant quantity during tha market period.

Development or a price modal ia undertaken in this study in order to evaluate the aconomic impact of alternative surf clam management strategies. A primary objective of the study is the prediction of prices under d iffe re n t management regimes. This stu d y th e re fo re satisfies the first condition listed above. The second condition is also satisfied because, during the monthly market period surf clam supply is primarily determined by population stock else, level of fishing effort, weather conditions, and the operating schedules of surf clam processors. Tha importance of these factors in determining 54

aurf clam supply has been substantiated by tha results of

ths survey of surf olam fishermen undertaken in this

invastigation, Under the assumption that monthly surf clam

landings are an exogenous variable, singla equation

ordinary least squares regression provides the best linear

unbiased estimator of price. This estimation technique can

produce an accurate unconditional forecast of ax-vaasel

price. The most practical alternative to tha use of

ordinary least squares regression would be to run a two

ataga least squares procedure (Johnston, 1963). Given the

complex nature of the supply function described above, the

use of ordinary least squares regression is probably the

moat realistic, and certainly the most straightforward

technique available to predict ex-vessel prices in a comparative management model.

Results

The ex-vessel landings and value data used to derive

the demand model were both located and id e n tifie d by atate

and aggregated Tor the entire middle Atlantic region.

Monthly deflated ex-vassal surf clam prices were calculated

using the producer price index. The time series (Figure 6>

illustrates a general increase in landings, peaking in

1995. This peak is followed by a substantial decline in

landings during ths yeara following 1975. Seasonal

fluctuations in landings, while not clearly discernible, 55

Ftgura 5

Surf Clam Landings SURF CLAM LANDINGS I960 - 1982

1960 1965 1970 1975 1980 1985 MOVING flVERflGt BY YEAR BY flVERflGt MOVING 56 nay be Indicated. Deflated ex-vessel surf clam prices exhibit ralatlva stability until 1976, ranging from $0.13 par pound to $0.08 par pound. 1 ti 1976, thara »** a marked incraaaa in daflatad prica to $0.26 par pound. This prica incraaaa ia probably dua to raatrictad surf clam aupply raaulting Trom raaourca daplation in 1976, and landings quotaa established in 1977. Six* limit restrictions may have alao in part baen responsible for ex-vessel prica incraaa*a. Dummy variables have been introduced into the price modal to maasurs the effects of seasonal demand shifts and externalities imposed by th* establishmant of a new management regime.

Bivariate linear regressions of deflated ei-vaisal

■urf clam price on surf clam landings, on deflated ocean quahog price, on deflated hard Clam price, on deflated soft clam price, and on deflated oyster price war* derived.

These variables were ahosen upon the basis of their relationship as substitutes Tor surf clam meats. i demand aquation eae estimated using ordinary least squares multiple regression.

Various combinations and transformations of the data were applied, including log transformations and generalised first difference equations. In determining the type oT transformation to be used, on* seeks to decrease th* heterogeneity of variance, bring the data closer to normality, and produce an additive model of the independent variables. As Snedecor and Cochran (198Q> note, the 57 transrormation that restores additivity will unfortunately usually diTfer fro* tha tran*formation that improvta homogenei ty of variance or induces naar normality. Divan thia conflict, thara la not likely to ba an ideal transformation for a aingla body of data. It has baan suggested that, in view of tha simplicity and efficiency of an additive modal, much can ba said for giving primary nttantion to ranoval of non-additivlty whlla remaining attentive to haterogenaity of variance ( Snadacor and

Cochran, 1 960) <

The criterion uaad to choose the appropriate relationship was tha squared correlation Tor a significant regression coefficient, and a double logarithmic relationship was chosen as tha appropriate transformation.

Thera wars soma missing values of ocean quahog prices in the time series of data. Ocean quahog prices were reported on * monthly basis only from 1969 through 19B2.

SPSS (Statistical Package for the Social Sciences, release

7-9, 1961) was used to analyse the data, This package provided several options for dealing with missing values.

Each correlation coefficient is thus oomputed using records with complete data on the pair of variables correlated, regardless of whether th* records have missing values on any other variable. Options included: 1) listwiec deletion from the computation of all records with missing values,

21 pairwise deletion of records with missing valuee, 3) mean substitution

Purbin-Watson atatiatic was computed correctly. The number of workable observations was reduced from 276 to 145.

However, this methodology ensured that partial correlations were a l l computed From the same populations. Tha r e e u lts of the regression analysis are displayed in Table fi. As

Table G indicates, the equation demonstrates reasonably solid statistical properties, and appears to perform wall.

The squared correlation, R\ is high, explaining 94 percent of tha variation in middle Atlantic ex-veesal price. All of the regression coefficients ere significantly different from aero with at least 97 percent assuredness. An overall P teat for goodness of fit of the regression equation indicates that the multiple correlation is significantly different from sere with greater than 99 percent assuredness. This result, however, should be interpreted with reservation due to the indication of autocorrelation described below. A sequential plot of the residuals was examined end provided no evidence of positive or negative heteroscedasticity. A frequency distribution of the standardised reaiduals superimposed upon a standard normal curve indicated that the rasiduals are approximately T*bl« H U"l o Ld Ld > ■u a ■rH f O ■H *» x A M at ■H X ■3 rH Ld X ■H ■a TJ V A A •i « « id u a d u U V E H > a « O’ Li * n a 0 0 V □ Ld V c 0 A H u f A c V w L- c i H H h rH 4 Hi +1 •H wl CH rH c d 3 Id « a. ai in o CD X o. nTJ in o P O <£) •+ ■*r t- Ifi S 5 X o o rs qo tfl o i- O < 1 to u _> U m a o C d— tr iD o * r + 7 C + *■ * 0 1 1 l i 4 3 i i o W « rsi (N IN a M * LA o (N r- ca a 10 (M V 0 LO NLP la CN o 10 *J L id Ld , P} <33 m CJl . £ f 'H ■fi o A A O i L A TJ II di e u u ■ *- c o A J J h - <7i c a a ' £1 O' 3 AIF ii at 'rH - - L i i L 4 o 0> c 3 ->

r i* " rA r "1 * -i -rl f H H H HHT- rH rH rH 3 H CP B O OL w t" H J3 u rH r H0 -H B ■rt —■ y V W L> u X X ■U rH rH rH rH rH rH rH Pt tn fl (H -a iH 4J TJ id L, M C o o C c E & o A 3 rH A id e s. -o s. L o A tl A d«A A A A « id 0 « « A W A H A A « > lH A A A n • A A •» tn 0 V 3 L U 3 3 3 3 3 L, N 1 H ki ■o HAAAp A A pH A A A rH HH F—t (» ■H 0oxo x o (0 +> i-l L4 £ ■ri TJ A A A rH ■U P- m o A E ^ V V < A c A A A A o A £ « c C 3 Cl C B 3 t> « e C Li A A A U pt O O’ O’ tn tp o td A A A A L. 1 HE H U V ^ J II A H TJ H L l r A B rH Hi rH A k A H Ok r ^ ^L y IO Li w ^ ■rl ^ A A c n «£ « V C x X H H H _ J 0 "A E" O A *0TJ > > A A ■ if' f Li A A A 0* A >* A A O L A A -U -AA L E A O L L L L O O 0 l hc h h ll a A O 0 0 1 1 1 1 l , e. J V U « TJ a H Li rH OrH -g O yj ' f tn JH j L L LL 0 L* L O C tn t> E a CAA A .C L ■u Lr> Hi X A £ Hi O a o TJ X 4J rH ■rH n rH £ A E O' 0 e E ti Ld a y O A 0 A nA06 0 e A tn Ld Ld II □ U t> 0 dOr Ld £ A B 3 E E >> &■ h 59 H A 9 or rH w- ■rH n. A w - 4 3- 0 Id O Li A A N - L rH ■rH ■H Jj ■rH H-> HH rH L rH ■ L. rH p ■rH rH +dl JA TJ CO U A C CO A L. 3 r- 3 d L C a a 3 H A y L, dQ C 0 > 3 E E , > A A V A o Li IS II h h b. -- rH r- Li Hi a JH Ld A « “ A h 3 A Li >1 II A O O A E 0 £ a 3 £ i. A H - _ - 60

normally distributed. Examination of th* correlation

matrix indicated that, while thara is soaa degree of

mul t icol 11 naari ty, this ia not a problem of importance. tn

fact, one could infer that tha sig n ific a n ce oT th*

regression coefficients may ba slightly underestimated as a

result oT variance inflation associated with

mul t i col 11 naari ty.

Certainly, th* most significant problem with this

price equation is th* evidence of some positive aerial

correlation. The D value of the Durbin-Watson atatiitic is

rather Low, indicating some positive autocorrelation of th*

error terms in the equation. This result is not unexpected

since the data baee used to davalop the price equation is monthly time series date. Serial correlation has been

noted in several seasonal and monthly seafood demand models

developed from similar data (Waugh and Horton, 1969;

Visglllo, >973; Cessine and Strand, 1976; Kerne, 1991).

A utocorrelation of th* error component would v io late the

Gauss-Merkov assumption that th* covariana* of the error

terms must equal aero. Tha estimated regression coefficient is thus underestimated, resulting in inflated T

ratios, and th* appearance of greater levels of

significance than in fact exist. Some econometricians have

argued that, when sarious autocorrelation is evident, th*

abaolut* value of th* T ratios should b* quit* large to

ensure a highly significant relationship between tha

dependant and independent variables (Granger and Hawbold, 61

1974} H The T ratio* of th* r>gr«e«ion coefficients in thia

•quation lia within an acceptable rang* < 2. 1 4-31. 041 .

Corrtotion for autocorrelation a* described below was determined to b* impractical and, daapit* lom* aerial correlation indicated by th* Durbin tiatson Coefficient, th*

•quation r*pr*a*nta a useful tool for tha projection of monthly •a-veaaal aurf clam prices.

Discussion

With tha axe eption of the seasonal dummy v ariable, tha aigna of all ragr* selon coefficient* in th* demand aquation are aonaisten t with theoretical and & Priori knowledge of tha Tiaha ry, Surf clam *x- vessel pricea were negatively related to aurf clam landings, hard clam prices, and a winter fiahing season during th* months of December,

January, and February, The price of a commodlty i a normally aspectad to b e negatively r*la tad to th* pricea of aubatitute commodities, Th* regression coefficients of the equation are displayed in Table 6

Th* negative regression coefficient or the • i - v e s s a l aurf clem quantity variable conforms to economic t heory.

Th* price flenlblli ty estimate for surT clams of -. 0596 would indicate that surf clam price elasticity of demand i a relatively elastic.

The negative regreaaion coefficient associated with winter aeaaon is contrary to tha expected relationship,

Decreased winter landings due to bad weather and in creased 62 winter demand for soup and chowder product* were expected to reault in higher seasonal pricea. Several dummy variables were initially introduced into the regression equation. Spring* summer, and f a ll dummy v ariables, and various combinations of these variables, were dropped from the aquation becauaa they were statistically insignificant. The negative regression coefficient on the dummy variable can be attributed to several factors. This equation did not account for processor or retailer inventories. The confounding effects of inventory shifte may actually produce a lower winter price despite higher consumer demand during these months. An additional explanation of this negative price-season relationship may be inherent variation in seasonal demand for several different surf clam products. Although soups and chowders may be in greater demand during the winter months* it is certainly poasible that demand for other aurf clam products, clam strips* minced clams* clam Juice, and atuffed clams, may demonstrate peaks during the spring, summer, or fall seasons.

The negative regression coefficient of the ex-vessel hard clam ( Venus m ercenaries price v ariable would appear to contradict economic theory. The complexity oT the relationship between hard clam* and aurf clams, however, explains the observed result. Herd clams displace surf damn in the aoup market. Rising ex-vessel pricea of hard damn are often Indicative of clam harvests containing a 63 large percentage of chowder grada clams. This result occurs because, if taken in sufficient quantity, lower priced, lees desirable, chowder grade hard clams are culled before a sale ia made. The ex-vesnel price of hard clams thus rises since the harvest is considered to be more desirable by processors and shuckers. If taken in less abundance, chowder grade hard clams are not culled before a sale is made and eK-veeael hard clam prices remain constant. Therefore, ex-vessel aurf clam prices are negatively affected by rising hard clam prices, because rising hard clam pricea are often associated with the availability of more chowder grade hard clams in the soup market. The increased availability of chowder grade clams functions to drive down tha ex-vsanel price oT surr clama.

Surf clam ax-vassal prices were positively related to ex-vessel ocean quahog prices, ex-vessel oyster prices, and a dummy variable accounting Tor recent structural changes cauaed by new management re g u la tio n s . B i-v a s s a l ocean quahog prices demonstrate a highly significant positive relationship with ex-veaael surT clam price. The strength of thia relationship confirms the atatua of ocean quahoga as vary clone substitutes for surf clama. The positive relationship between ex-vessel surf clam and oyatar prices indicates a degree of aubatitutabi1ity oT oystara for surf clams. This result is in accord with the views expressed by many surf clam processors, who believed that oyatere and surf clama are competitors in the soup and seafood 64 specialty markets. The dummy variable, used to account Tor a structural change In the fishery induced by the surf clam management plan, indicates that management has functioned to increase the ex-vessel price of surf clama,

Ex-vessel s o ft clam prices were not s ig n i f i c a n t l y related to ex-vessel aurT clam prices. This variable was dropped from the price equation. Such a result is not unexpected. Although small numbers of soft clama are processed to provida clam strips, soft clams are not used to produce soup, chowders, or other aurf clam food products. Ocean quahogs, chowder grade hard clams and oyatera were identified as the closest substitutes for aurf clama. These speciea are all used in soupsi chowders, etawa, and specialty products. Thus, the price equation accurately reflaots both theoretical economic precepts and ft. Priori beliefa regarding tha demand for surf clams in the middle Atlantic landings market.

Problems aa A Future Research Hdtfld

Construction of an improved ex-vessel price modal for aurf clama would require the development of a realistic aupply relationship, and experimentation with additional variables in the price equation. Development oT a surf clam aupply curve will be very difficult, IT not impossible, because binding quotas are uaed to manage the fishery. Therefore, the equation developed above must suffice for this analysis. As indicated above, an 65 attempt was mad* to correct this prica aquation for autocorrelation. (Jain? tha method or Theil and Magar

(1971), the first order autocorrelation coerricient was calculated, and all variables ware tramrorimd into generalised f ir s t d ifferancaa as Indicated below.

x - n3 ( 1 -1 /2d) + k*n* - kJ

where:

a - Estimated autocorrelation coefficient

d ■■ Durbin - Watson coefficient

k - Humber oT variables in the equation

n ■ Humber of observations in the time

sari ea

y* ■ yi - yi-t

r* • *11- ijiH

where:

y* * Transformed dependent variable

y« - Dependent variable in time period t

yt - i * Dependent variable in period t-1

x* * Transformed independent variable

*j i* independent variable in period t

*j t-i • Independent variable in period

t-1 66

Th* generalised first difference aquation failed to

produo* an improved model- Th«r« are two probabl* reasons

why this correction did not lmprova th* model, Th*

Durbi n-Vition coefficient teats Tor any non-randomn*e* in

tha error term. Th* problem might, in fact, ba csus*d by omittad variables, not aubocorrelation.

It i* hard to form a judgement about th* amount of

biaa that could ba caused by omitted variables, A variable such a* aurf clan landing* could b* positively correlated with omitted variable* iuch a* aurf clam quality or alia.

Omission of th*a« variables could result in ovareetimation

of th* rigrsiaion coefficient on aurf clam landing*.

Similarly, high levels of processor inventories may be correlated with lower surf clam prices as well as lower ocean quahog prices. Omission of this variable would

result in und*rastim*tion of tha regression coefficient on

ocean quahog price. Future research might attempt to

include some of th* variables identified above in e surf

clam price model. Adequate ti m* series data are not

currently available to develop a more comprehensive price modal. It ia also possible that higher order autocorrelation is a cause of serial correlation. It has

bean demonstrated that, if higher order autocorrelation

does present a problem, it ia much batter to apply ordinary

least squares regression analysis than to correct for first order autocorrelation (personal communication, Moody,

1962). Future res*srah might, therefore, focus upon 67

identiTication of the higher order autocorrelation

problem. Daepite i t e lim lta tio n a , the price model developed above appeara to be quite satisfactory for evaluating the afracte oT alternative aurf clam management strategies. Chapter IV

Coat Analysis

tn order to develop a realistic bioeconomic modal or

aurf clam fishing and processing optrationi, it ia

imperative that information about the coat structure or tha

fishing and processing industry be gathered. Thaae data are oTten very dirricult to collect due to the reluctance of entrepranaura and businessmen to disclose information

perceived to ba oT value to competitors. Fortunately, aomi data describing the cost structure of the surf clam rishing industry have bean gathered by the National Narine

F ish eries Service. These data are reported in the

Regulatory Impact Review of Amendment #3 to the Surf Clam and Ocean Quahog Fishery Hanagement Plan 1 H id-A tlantic

Fishery Hanagement Council, 1961). The cost inputs generated Tor use in this model have been derived from the

Regulatory Impact Review, as well as from a survey of all aurf clam rishing and procaaaing firms in the United

States, The surveys employed are displayed in Appendix B.

The following chapter identifies the variable and fined costs incurred during business operations in tha surf clam industry, describes the capital and raw material inputs required by processors and rinhermen, and characterises tha surf clam processing industry. The chapter also explains the procedure followed to gather these data.

SPUJ-S-TB PX Fishi no Sector Data

68 69

Survey questionnaires provided in Appendix B were mailed to the owners or operators of 320 fishing vssaela licensed to harvest aurf clama or ocean quahogs in the

United States, Personal interviews based upon the results of questionnaire raturne were conducted with ten vessel owners. Thus, virtually all vessels licensed to fish for surf clams or ocean quahogs in the United States ware contacted. Responses were obtained from 36 active surf clam vessels, or approximately ten percent of all licensed surf clam fishing vessels. Although this represents a relatively amall percentage return, it should be noted that many vessels licensed to harvest surf clama are not presently operating in the fishery. Current regulations require that, in the middle Atlantic region, vessels must harvest at least B, 000 bushels of clams from the Fishery

Conservation tone Isurf clams or ocean quahogs) annually in order to receive a permit in a subsequent year. tn New

England, permits nT vessels that do not meat this criterion can be renewed indefinitely without requiring that the permit holder actually harvest any surf clams or ocaart quahogs. Thus, many Kew England owners r e t a i n aurT clam and ocean quahog harvesting permits while fishing for groundfish or lobsters. A number of Haw England clam vessel owners responding to the questionnaire survey indicated that they plan to initiate fishing operations for ocean quahoge in the future. Bi-weekly and monthly logbook reports on Fishery Conservation Zone surf clam fishing 70 activities were obtained from th* National Marine Fisheries

Service for tha y**r* 1978. 1979, 1980, and 1981 (National

Marina Fisheries Service, 1981). Than* reports indicate that, as or July 1981, the laat month examined, there were a total of 145 vaaaals licensed to harvest surf clams and ocean quahogs in New England, and only 63 vessels licensed to fish for ocean quahogs only, According to National

Marine Fisheries Service logbook records examined, an average of only 70 vessels ware actually engaged in clam harvesting activities during each monthly reporting period from 1978 through 1981. The total number oC lic e n s e d vessels wan 331 in 1982. The low percentage return of survey questionnaires can thus be attributed both to the reluctance of fisherman to disclose any financial information concerning their operations, and to the large number of license holders who do not operate their vessels in tha Tiahary. According to National Marine Fisheries

Service statistics, only 44 vessels harvest approximately

70 percent of Fishery Conservation Zone surf clams

(National Harin* Fisheries Service, 19811. It would appear, therefore, that even a small sample of rishing vessels would be quite representative of harvesting a c t i vi t i is ,

tdsnti Tied Costs sit Fishing

Survey returns and a review of the literature have indicated that the following cost categories define the 71 surf clam fishing operation. Vassal costs have baan aagragatad according to thraa vassal sine classes. Thia division ia in keeping with tha convention amployad by tha

Hid-Atlantic Fishery Hanagaaiant Council for various analyses of the aurf clam fishery. Tha thraa vassal classes hava baan determined upon tha basis of gross ragiatared tonnage. Class 1 vassals are lass than or equal to 50 gross regiatarad tons, Class IE vassals sra between

51 and 100 gross registered tons inclusive, and Class III vassals are greater than 100 gross registered tons. Fixed costs of fishing include: 11 port fees (wharfage), 21 fixed boat and equipment repair and maintenance coats (this includes services for welding, electricians, mechanics, shipbuilders, and the marine railway), 3) vessel hull insurance, 4) personal liability and Indemnity insurance,

5) loan interest payments, 6) license fees and taxes, 7) legal-accounting feea, and 8) depreciation on vessels and equipment. Variable fishing costs include: 1) labor, 2) fuel and oil, 3) food, 4) variable maintenance, and 5) variable supplies.

Quantification al IfllntlfHfl LLuA Costs

Because of the diversity of vessels engaged in the surf clam fishery, and the variety of operations management strategies in the fishery, it is difficult to identify average costa of rishing for surf clama. Costs within specific categories may be widely divergent from one 72

fishing operation to another. Table 7 displays the mean

valuta or vtaitl characteriatic* for vessels sampled In the fishing industry survey. This table reflects the great variation in physical characteristics of vessels comprising the aurf clam fleet. Table 8 displays data obtained from surf clam fishing license applications by the Hid-Atlantic

Fishery Hanagement Council, and further demonstrates the diversity of surf clam rieet characteristics. These data describe tha surf clam neat in 1979. According to tha

Council, "Tonnage per vessel ranged from one to 308 tons, with an average of 110 tens. Vassal length ranged from 198 to 148 feet, with an average or 81 feet. Craw siie ranged from two to aeven men, with an average of three men. The aixe of the dredges used on eurT clam vessels ranged Trom

22 to 240 inches in width with an average length or 8B inches" (Hid-Atlantic Fishery Hanagement Council, 19811.

Development of a bioeconomic management model requires estimates or average operating expenses to be used as input parameters. Average costs are therefore described and defined below. Costs are defined in 1981 dollars because this was the last year for which complete data wart available at the time or the vessel survey. Where deemed necessary, costs that have been obtained from the literature have been inflated or deflated to 1981 dollars using the consumer price index.

Port Fees 73

Table 7

Mian Characteristic! of Vassals Sampled in tha Surf Clam Fishing Industry Survey

Characteristic Class 1 Class I_L class J 11

Lb ngt h 37 r t 7 4.9 f t 64. 3 ft

D ra ft 4. 25 f t 7. 4 ft 1 1 ft

Fuel Capacity 530 gal 2, 693. 7 gal 10,000 gal

Cruising Range 1 00 mi 691.3 mi 5, 060 mi

Age 21 yr 22. 4 yr 1 0 .3 yr

No of Dradgea 1. 0 1 , 1 1 . 0

Blade Width 46 in 63.5 in 96.6 in

Crew 5 is e 1. 6 2. D 2. 3

Load Capacity 200 bu 606 bu 1> 400 bu

Market Value 153.333 $225, 000 $530.000 74

Table 8

Hisn Vessel Ch&racteriatlcs Obtained From Vessel Licenses

Length Gross Tonnage Crew Siae

Hi ni mum 18 ft 1 2

Hasi mum 146 r t 306 7

Average 60 rt 1 00 3 75

Port fa* expenses war* not uni Tor ml y Incur rad by all vaitala in tha surf els* rishing fleet. Many vaaaal oparatora onnad their docking facilitlaa and tharafora raportad no port r««<> Movement bttw *tn p o rta, however, oftan necessitated payment of port rees. Average port fa* costs, whsn incur rad, nan $300.00 par month or $2,400.00 par year.

6sjl1 BdPairi. hull Insurance. PePreclillon^ and. Intaraat

Tha fixed coats of boat rapair and maintenance, hull insurance, depraciation, and intaraat payments vara datarminad to ba vary cloialy ralatad and unique to aach vaaaal. It ia virtually impossible to separata thaaa

Regulatory Impact Review or Amendment #3 to tha Surf Clam

Fishery Management Plan, "Charges for hull inauranca, maintenance, depreciation, and interest are probably unique to aach venaal, and are determined by age, vessel construction, vesaal activity, type of ownership, and length of current vessel ownership. These four coat items remain tha iamt, given th* else of th* vassal. For example, a new stael hulled vessel would have low hull insurance and maintenance coats and vary high depreciation and interest costa, An old wooden hulled vessel would hava very high insurance and maintenance coats and very low 76 depreciation and intaraat coBts" ( Hid-Atlentic Fishery

Management Council, 1981). For iaa* titramaly old vaaaala, hull insurance was identified as an unaccaptahla operating axpansi. Tabla 9 provides the average annual costa for these items as identified by tha survey of aurf clam fishermen conducted Tor this study. Table 10 displays a liet of tha electronic equipment required to conduct aurf clam riahing operations and associated average annual depreciation tipansea. Tabla 11 displays average depreciation expenses associated with other durable equipment, average annual coats Tor hull insurance, fixed equipment repair and maintenance costs, and other fixed costs. Average annual values Tor interest costs and vessel depreciation expenses are not indicated because of the great variation in thane expenses among vessels within the same siae claaits. These values provide interesting comparative statistics. As noted above, however, it is impossible to assign meaningful absolute values to each of these coats when they are considered independently. The

Individual magnitude of each of these expanses is determined by the operating strategy of aach vessel owner.

Distribution or expanses among the cost categories is dependant upon tha operator' s decision to use old or new equipment, A more realistic, albeit lees precise, approach to defining these coats has been outlined in the Regulatory

Im p act Review o f Amandsiant *3 to th e Surf Clam Ocean Quahog

Fishery Hanagement Plan ( Hid-Atlantic Fishery Hanagement 77

Table 9

Summary of 1981 Average Reported Fixed Pishing Costa

Class I Claaa II Class III Fixed Coats t H-22J L H ^ I L

Port Fees 12.400 $2,400 $2,400

Boat Repair, Hull Insurance, Vessel Depreciation, Interest Coats $34,000 $49,000 $78,000

Pil Insurance $5,000 $5,000 $10,000

License Fees, Texes $1,000 $2,000 $4,000

G eneral Admin. Expenses $900 $1 , 000 $1,000

Total Fixed Cos te $43, 200 $60,400 $94, 500 76

Table 10

Electronic Equipment Required For eurT Clam Fiahing Yaaeel Operation

i% of vessel* operating with equipment) ClftBB I Claaa ti E lm IIL *11 Vaasela Bauipment < N-14) I N-22) ( H-9)

Radar 64* 1 00* 1 00* 92*

Fat homater 1 00$ 100* 1 00* 1 00*

YHF Radio 1 00* 100* 1 00* 1 00*

CB Radio 35* 50* 351 43*

Scanner 33* 25* 0* 21 *

L0RAN 1 00* 1 DG* 1 00* 1 00*

P lo t te r 0* 75* 100* 79*

A u to p ilo t 0* 50* 65* 43*

Knotm eter 35* 38* 0* 29*

Ave rage Yearn or Equi pment L ife 5 5 5 5

Average Repiacement Coat oT E l e c tr o n ic Equipment $15,333 $35,265 $42,500 $27,000

Average Annual D epraci a t 1 on Expense $3,066 $7,057 $6,500 $5,400 79

Table 11

Average Annual Fixed Costs of Vessel Operation

Claes I Class II Class III Costs { H-1 4? LH±.21> < W-9>

Equipment Repair and Maintenance $5,000 $20,000 $40,000

Insurance $3,100 $10,000 $14,000

Vasa a 1 Depreciation Highly Variable Depending Upon Sise/Age

Interest Highly Variable Depending Upon 5iae/Age

Main Engine Depreciation $1,400 $2,283 $2,166

Pump Engine Depreciation $1,400 $2,283 $2,166

Time Between Engine Overhauls 5 yr 5. 4 yr 6 yr

Main Engine Life 27.5 yr 20. G yr M/A

Replacement Cost of Engine $10,000 $25,166 $50,000

Coat per Overhaul of Hain Engine $7,000 $12,333 $13,000

Legs I * Account!ng Pees $1,300 $1,397 $2,000

Hydraulic Dredge Depreciation Expense $1,600 $1,572 $1,625

Replacement Cost of Dredge $8, 000 $8,333 $8,125

Dredge L-fe 5 yr 5. 3 yr 5. 25 yr

Depreciation on Other Gear $5,000 $5,000 $5,000

Pert Fees $2,400 $2,400 $2,400

Gen Admin Costs $900 $2,000 $4,000 80

Table 11 Continued

Coats Clam I ClflEff II Class III

License Fees and Tattea $1, 000 $2,000 $4,000

Note; Engine depreciation has been calculated as overhaul coat/average time between overhauls.

Overhaul costa far main and pump engines are approximately equal. Pump engine life ia estimated to be 20V longer than main engine.

Depreciation costs oT other gear includes hoses, cables, etc. 61

C o u n cil, 1961). (Jn4*r th is approach, th e fix e d c o a ts or

boat repair and maintenance, hull insurance, depreciation,

and interest payments are estimated to equal aeven percent

Of the value of the vaaail, This value estimate ia dependent upon the tonnage of the vessel and the annual

landings of the vessel. Using this approach, ths average annual fixed costs oT hull insurance, maintenance, interest, and depreciation for each vessel class wars estimated to be: Class L Clase U. DLL

634, ODD (49,000 676,000

FtflPnal Liability and Indemnity Insurance

Vessel insurance costs have escalated dramatically during the past decade. Because many vessel owners are covered under an "umbrella* insurance policy, the fishing industry survey conducted Tor this study has not permitted separation of personal liability and indemnity or "PlI" insurance from vessel hull insurance expenses. Average annual total insurance expanses for a Class I vessel were reported to be 63, 100.00. Average insurance expenses for a

Class II vessel were 66,(100.00, and average insurance expenses for a Class 111 vessel were 610,000.00.

License Fees end Taxes

Although not taxed uniformly in every state, vesael owners Indicated that personal property taxes are paid on surf clam fishing vessels. State licenses are required far 82 surf clam fiahing within the state territorial water* o t

Dalmirt, Maryland, Haw Jirtty, and Maw York. ill vnatla must ba licensed by tha United States Coast guard and obtain a federal fiahing permit Tor iurf clams and ocean quahogs, ocean quahogs only, or Hew England surT clams only. Average annual license Tees and tales were reported to be $1,000.00 for a Class 1 vessel, $2,000.00 for a Class n vessel, and $A,000, 00 Tor a Clase 111 vessel. In

Virginia personal property tax liability is highly variable, depending upon home port. Cities and counties use different valuation formulas and tan rates. In the

City of Hampton, vessels are valued at ten percent of purchase price, and are assessed at $4.75 for every tan dollars of valuation.

Leoel-Account1 no Fees

Host vassal owners surveyed retained an accountant to handle tax reporting and corporate record keeping.

Legal assistance was also occasionally required. Average annual expense for these items were reported to be

$1,300.00 for Class 1 vessels, $1,397.00 for Class II vessels, end $2,000.00 for Class III vessels.

general Adiwi nlatrati ve Expenses

An additional fixed coat of conducting fishing operations was identified categorically as general administrative expense. This cost category includes 83 travel, offie* expanses, and Land based equipment dapraciation expenses. Most independent fishermen operated offices in their own homes with no additional employees raquirad. Vertically integrated fiahing operations t minimised fixed costa of fishing by conducting operations from large consolidated central ofricea. Estimated additional fixed administrative cotta were reported to equal $400.00 per year Tor Clasa 1 vessels, and $500.00 per year for Class U and III vessels.

Quantification Identified Variable Coats a£ Ffhinq

Labor

Labor repreaents the largest variable expense incurred by aurf clam vessel operators. Mages are paid to crew members and vessel captains as percent shares of gross stock. The average boat share for both Claes I and Class

II vessels waa reported to be thirty percent of gross stock. In each case, twenty percent of the groan atook was reserved at compensation for the vessel captain. Class III vessels reported an average crew and captain boat share totaling thirty-five percent of groan stock. Twenty percent of thin total was paid as crew compensation, and fiTteen percent was paid as compensation to vesael captains. Thase wage estimates are in agreement with labor coat estimates or the Mid-Atlantic Fishery Management

Council, which total one third of gross stock for the a« entire surf clam fishing fleet ( Hid-At lent1c Fishery

Management C ouncil, 1981). Owners Mho are a ls o c a p ta in s of their vessels collect the captain's share plus any additional income accruing ae vessel earnings.

It should be noted that aoms Individual vessel owners may pay larger boat shares, taking their own compensation as a greater portion of ore* share instead of higher income through vessel aarninga. The Mid-Atlantic Fishery

Management Council has noted that, "Some corporately o*nad vessels, which pay crew espanaes such as personal gear, rood, and taxes, may pay a considerably lower crew share."

However, if total compensation and benefits paid by the owner to the ere* are included. Labor costs are generally quite uniform.

Fuel Expenses

The second largeat variable cost incurred by vessel owners ia the cost of Tuei. Survey results indicate that

Tuel and oil coats for surf clam fishermen increased 1Ofl percent between 1977 and 1981. Fuel prices have, however, remained stable during the psst four years. The average price of a gallon of fuel available to surf clam vessel owners in 1981 was <1.10. In 19B2, this price declined for the Tirst time in a decade to (1,OS per gallon. Fuel costs are dependent upon vessel Tuel efficiency, which is determined by vessel age, engine specification* and tune-up, and vessel design. Differences in required travel 85

distances to the surf clam bad* and weather condition* also

determine fuel coat*, Despite the** difference*, it it

possible to identify variable Tuel expenses associated with

each vessel class. Vassal owner* provided data describing

fuel consumption during a fishing trip. Class I vea**la

required an average of 300 gallons of fuel per trip, Class

II vessel* required an average of 500 gallons of fuel per

trip, and Class III vessels required an average of COO

gallons of fuel per trip. These values can be expressed a*

gallons of fu*l consumed per bushel or clam* harvested.

National Karine Fisheries Service logbook data for 1978,

1979, 1980, and 1981 may be used to derive average catch

per trip estimates for each vessel class. Calculations

indicate that during these years, Class 1 veaaela consumed

an average of 1.75 gallons of fuel par bushel of surf clams

harvested, Class II vessels consumed an average or 2.14

gallon* of Tuel per bushel of clams harvested, and Clasa

III vessels consumed an average of 1.47 gallon* of fuel per

bushel of clams harvested. Class II vessel* would thus

appear to be lea* fuel efficient than Class I or Class III

vessel* with respect to harvesting capacity. If the

population density of surf clams were to increase significantly, fuel costs per buahel harvested could be expected to decline. However, given the relative stability of National Harine Fisheries Service catch par unit of effort statistics, thsa* fuel consumption statistics appear to accurately reflect present variable fuel coiti in the ae r 1 she ry.

Other V.ttrldfrlv Costs

The remaining variable costa of conducting rishing operations include the coat of food and miscellaneous supplies consumed during a day or riahing. Such supplies include replacement bladea for dredges, rope, and gear replacement parte. Average food coat per trip wee reported to be ISO. 00 fo r C laaa I v i a a i l e , end $30. 00 fo r both Class

11 and C lase 111 vaaaels. An average variable coet estimate Tor both food and miscellaneoua supplies equaled

$372.00 per trip for Claaa I vtisalt, #371.00 per trip for

Class II vessels, and #577.00 per trip for Clase 111 vessels. These values can be expressed as approximate costs per bushel of clems harvested by using National Marine

Fisheries Service logbook statistics of buehela of clans harvested per trip in 1973, 1979, 1960, and 1931. Once this calculation is performed, it ia evident that food and miscellaneous supply ooata have amounted to $1.99 per bushel harveetad for Class I vessels, $1.74 per bushel for

Class 13 vessels, and $1.56 per bushel for Claaa til vessels. These cost estimates reflact the economies of scale associated with larger veaael siae.

Table 11 summarisea the average annual raported fixed costs or surf clam fishing operations, and Table 12 summsriaes the variable coats of conducting aurf clam fishing operations. Variable costa are reported ai both & 7

Table 1 2

Summary of 19B1 Avertgi Variable Fishing Costs

Variable Costs C la s s I Claaa II C la ss 111

Crew Nages 20ft Gross 20ft G ross 20ft Gross

Capt ai n' a Wage* 10ft Gross 1 Oft G ross 15ft Gross

Fuel 300 gal/day 500 gal/day 600 gal/day

Mi seal1snaoue 1372/day $371/d a y $577/d ay Supplies and Pood ( *1 . 99/ bu) ( $1. 74/bu> ( f t . 5 6 / bu)

Costa of Switching to Ocean quahoga $25, 000 $25, 000 $25, 000

Coats of Switching to Rad Crabs or Lobsters $1 50,000 $150,000 $1 50, 000 BA costs per one day fiahing trip and aa costa par bushel of aurf clams harvested. Also reported in Table 12 ara estimated costs of switching from tha aurf clam fishery to ocean quahog or lohater/red crab fishing operations. The results of the fiahing industry survey conducted Tor this investigation indicate that, if veeaal owners were no longer able to operate at a profit in the surf clam fishery, &5 percent of the vessels currently equipped to fish only for aurf clams would attempt a permanent a witch to a directed Tiahery for ocean quahogs. Tha average estim ated sw itc h in g c o s t to go Trom s u rf clam to ocean quahog fishing operations was estimated by fishermen to equal #17,500.00. As noted in previous chapters, ocean quahogs are generally smeller than surf clema. Thus, switching to s directed fishery Tor this apecias would require welding additional slats on the hydraulic dredges used for surf clam harvesting. Start up costs associated with the initiation of new and unfamiliar fiahing operations also comprise an undefined portion of switching coats. Switching to lobster and red crab fishing operations was identified as the next most attractive option after ocean quahog fishing. The average coats aeeocisted with this switch ware estimated to be

|1 50, 000. 00. Vessel owners surveyed also indicated that they would switch their fiahing operations to target upon bottomfish should surf clams become unavailable, but no switching costs were reported. Approximately five percent 89 or tha veaaels >urv

InflltlanTJf Changes in Costs

In addition to raquaating data defining the cost of fiahing operations, the fishing industry survey conducted for this study solicited estimates of operating cost changes that have occurred during the past ten years,

Responses to theee questions indicated that: during the five year period between 197E and 1961, boat repair and maintenance costa increased 75 percent, port fees remained relatively constant, interest rates on loan payments increased 100 percent from an annual percentage rate of eight percent to an annual rate or 16 percent, legal-accounting Tees increased 50 percent, and during the three year period between 1977 nnd 1981, fuel expenses increased 100 percent. These cost increases have inflicted hardships upon clam harvesters because, during the period of general coat increases, the ex-vessel price of surr clams declined from more than 112.00 per bushel to lets than $9,00 per bushel. Management quotas also reduced the allowable catch during this same period of time.

The results of the Tishing industry survey and a review of the literature have provided realistic estimates of aurf clam fishing coats to be used as input parameters for the economic submodel of the harvesting sector. All of 90 the aurf clam rishing vessel owners surveyed provided these data reluctantly, and aome war* unwilling to provide data at all. The large vertically integrated firms were the laaat cooperative providers of economic data. To develop modal input parametere in even greater detail, collection of data will require the force of law.

Sources a£ Processing Sector C a l p

There are two market levels in the eurf clam processing sector. Clam shucking operations purchasing raw product e*-vaeeel constitute the first market level.

Canners and Clam specialty product producers comprise the second market level. On the second level, proceeeore purchase shucked clam meats that are produced by clam shuckers on the first level. Producers may also produce their own shucked output in a vertically integrated operation. Thus, it is not always possible to distinguish the linss of demarcation between these levels, "In the case where large processing companies are vertically integrated from the wholesaling of packed surf clam products to engaging in rishing operations, distinct lines of separation among market levels becomes extremely vague or non-existent" (Viegilio, 1973). The processing plants of interest in this Btudy, however, are those that purchase or obtain their clams directly from fishing vessels. The processing sector in thla modal is therefore defined ae that portion of the industry obtaining ex-vessel clams and 91

processing those c U dib i n t o raw or Troign meats to be further processed.

Survey quntionnsires were mailed to all 30 plants processing surf clams and ocean quahogs in the United

States, and personal interviews were conducted with eight of the nine owners or managers of surf clam and ocean quahog processing plants in Virginia. Data were obtained from 12 different processing plants representing 40 percent of the identified surf clam processing operations in tha

United States. Host or the clams harvested in the middle

Atlantic region are purchased by ten companies. Five of these major purchasers participated in thia study. Several large vertically integrated firma declined to participate in the study. Those firms indicated that they feared leakage oT confidential information and economic data to compati to n .

Identified Costs a£ FrffCfBBlng

Based upon tha results of the processor survey, the following cost categories have heen identified for clam shucking operations. Host of these costs are either variable or semi-variable: raw product coata, cost of transporting clams into plants, royalty coat paid for the use of eviscerating machinery, labor costa, cost of salt or other chemicals used in the washing atage of processing, coat of dleael Tuel used to heat plant and water, coat or propane used in shucking machinery and forklifta, cost of 92 packaging material, electricity coats, depreciation of machinery and equipment, repair and maintenance costs, overhead coata (includes office and admlnletrative expenses), and insurance costs. Clam-shucking operations sell clams in tha fresh state to companies that slice tham, bread tham, freaxe them, can them, or incorporate them into other spacialty products. As the price of clams rises or falls, tha selling price of shucked meats rises or falls.

Shucking operations thus define ail of of their processing costs in terms of coats per unit of shucked output, end determine the coat of shucked meets by adding these costs plus a profit margin to the ex-vessel price of clams. In ao doing, processors thus attempt to maintain s stable

profit margin. In moat plants, processing coats ara evaluated semi-annually and adjusted for inflation IT deemed necusary.

Ouanti f icati on a£ Processing £_aftlA

Table 13 displays the mean percentage of total

processing costs that processors allocated to each cost category identified above. It is somewhat difficult to characterise an average clam shucking operation because economies of scale, geographic location, and management efrectivenesa all contribute to operating efficiency.

However, as Baumol <1961) notes, * In competitive equilibrium, every firm in the industry must hsvs the same coats, for the product price will be the same for all such 93

Table 13

Surf Clam Proeosing Costs 1931-1982

Coat Category Hesn Percentage of : per Pound of

Raw Product *

Transportation oT Raw Product to Plant 6 . 71

Payment for Use of Patented Mac hi nary 2 . 31

Labor Costa 15. OS

Fuel Oil 3 . 41

P ropane 4 . 3 *

Electricity 7. 7%

Equipment Depreciation 5. 21

Repair and Maintenance 9 . 61

Overhead [Includes All Other Administrative and Miscellaneous Costa) 1 4 . 7*

In su ra n c e 2 . 61

Legal-Account!ng 1. 2%

Storage and Freezing 1. 3%

Salt and Other Chemicals 1 . a*

* As noted in Chapter 3, the cost oT raw product fluctuates depending upon its availability and the prices of substitute commodities. It la therefore considered separately from other processing costa. 94 companies. and both marginal and average coata will equal

price for all firms. " Moreover, as illustrated below in

the discussion of processing plant oharaoterletics, many of

the c1 am-ahucking operations surveyed are similar in scale,

annual volume processed, plant capacity, and other

operating characteristics. Labor costs, averaging 15 percent of processing costs, constitute the largest single percentage of total processing costs exclusive of ran

product costs. Many plant operators, however, in currently reducing labor costs through increased automation of shucking, separating, and washing activities. Overhead costs, equipment repair and maintenance costs, fuel costs, transportation costa, and packaging costs contribute decreasing percentages of total processing coats r e s p e c t i vely .

The total cost of processing a pound of clam meats did not display great variation among processing operations surveyed. Processing costs do, however, demonstrate marked seasonal fluctuation. Processors have indicated that, during the spring and fall months prior to clam spawning, the yield of ifioati par bushel of clams is higher than during the summer and winter months after spawning has occurrad. Thus, processing coats per pound of clam meats are reduced ten to fifteen percent during the higher yielding months. During the months of low yield, less meat is produced, although the sama number of ahalls must be handled. During the summer and winter months of 1961-1962, 95

the nun ooit of processing clam maita has 10. 34 per

pound. During the spring and fall month* the man coat of

p r o c e s s in g clam m eats was (0 .3 1 per pound. Than*

processing cost* ere slightly isss than the identified

costs of processing ocean quahogs because ocean quahogs are

smeller and more shells must he handled. Mean coat of

processing ocean quahog meats was reported to be (0.40 per

pound during the summer and winter fiahing seasons and

(0.34 per pound during the spring and fall fishing seasons.

LadMstrg frofi t W grain

A a noted above, the price at which shucked meats are

sold is determined by adding raw product costs and

processing costs to a profit margin. The profit margins

(return on investment! of the processing firms

participating in this study ranged from ten percent to 15

p e r c e n t or p r o c e s s in g c o s ts . The mean p r o f i t margin

identified for surf clam shucking operations was 11.7 percent of processing costs. Some limited data were obtained identifying profit margins associated with higher

levels of the claei processing industry (i.e. secondary processing of raw product). These date indicated that profit margins were considerably lower, approximately rive percent of operating costa, for secondary processing operations farther removed from shucking operations.

Procese1ng FL l n t Characteristics 95

In addition to deriving aurf clam processing coats, on* objective of this research was to investigate tha raw materials and capital inputs required for clam shucking operations, and to characterise elemental facets of processing operations. The remainder of this chapter describes plant operations identified by the processor a urvay.

Supply aourqti. Products, and F a clllt i e s

While most of the businesses participating in the study survey processed primarily surT clams, some plants also processed other species of shellfish, including oysters, hard clems, and ocean quahogs. Some secondary processing operations alao processed canned vegetables in addition to clam products, Many processing plants that initially began operations as suppliers of hard clama and oysters later began processing surT clams, hors recently they have begun processing ocean quahogs because tha supply oT surr clems has been limited by yield quotas. Tha mean length of time that plants have been processing surf clams is 11, B years. This is in contrast to an average length of time of 3. 37 years for ocean quahogs. Surf clams comprised approximately 75. 57 percent of the clams processed by the plants surveyed, uhlle ocean quahogs constituted approximately 21 percent or the clams processed. The remaining 3.79 percent of processed product use identified as other shellfish. Thus, surf clame remain the moat 97

important product for moat of tha processing plants

survtytd. A small number of plants have converted their

operations to ocean quahog meat production. ill of tha

plants surveyed, however, process some surf clams.

All of the plants surveyed have owned surf clam

fishing boats in the paet. In recent years, escalating

fishing coats and restrictive regulations have caused many

owners to sell vessels and buy at least part of their raw

product from independent fishermen. Vessel ownership

averaged 1.71 vessels per processing operation. In 1982,

the processing plants surveyed obtained an average oT 80

percent oT their raw product from independent fishermen.

The remainder was harvested with processor owned and operated vessels. This percentage has changed in recent years. Although inadequate data were obtained to determine

the magnitude of this change, several processors indicated that prior to implementation or the aurf clam management plan, the industry-wide percentage of raw product purchased from in d e p e n d e n t f is h e rm e n was 40 to 50 p e rc e n t.

None of the plants surveyed had written contractual agreements with aurf clam fishermen, but all processors entered into informal agreements with clam suppliers Tor exclusive rights to vessel harvests. The mean number or

Suppliers ps r processing plant survtytd was 5. 4. The most

important factors influencing a decision to request clama from a specific independent vessel operator were quality of product (i.e. if the host provides a full 32 bushels per cage measure), reliability, and willingness to sell at the

prtviiling market price.

All or the plsnte surveyed indicated that they

wished to take daily deliveries! but in most cases were

only able to obtain clams three to four times per week. In

order to obtain adequate deliveries of class, processors

were required to seek delivery fro* many differant ports.

This situation is a result of similarities in allocated

fiahing time at geographically prosimet* ports. For

example, vessels operating out of the major clem producing

p o r t s or Virginia and Maryland, Chincotesgus end Ocean

City, are Currently limited to the seme two days Of fishing

every week. Processors in Virginia wishing to operate more

than two days per week must ship clams to their plants

rrom ports farther north.

Independent sources of surf clam supply have not changed significantly during the past ten yaara. The

number of suppliers has decreased, primarily as a result of curtailed harvesting activities among processors.

Other Innuts and Waste By-Products Processing

The labor requirements of processing plants surveyed

varied depending upon the capacity of the plant, Total employment ranged from 68 to 150 workers. The mean number of employees per plant wan 92.6. surveyed plants averaged

8.6 salaried workers, including managers and supervisors, and 76. B hourly workers. Some of plants surveyed had not 99

instituted automated shucking operations at the time of the

survey. These plants employed a piecework labor force oT

35,3 in d iv id u als per plant to shuck clame and remove

bellies. Planta utilising piecework employees used 50

percent fewer salaried worker* than plants without

pieceworkers. Average wage statistics were unavailable for

salaried employees. Hourly and piecework wages fluctuated

depending upon the geographic locations of tha plants.

Wages paid in rural areas were lower than wages paid in and

near heavily populated areas. The average hourly wage paid

to processing plant employees was t4.35 per hour, and the average piecework wage waa 11.42 per five gallon pot oT clam meats processed.

The volume of meats processed by the plants surveyed

in 19B1 ranged Trom 1,200,000 pounds per year to 12,775,000 pounds per year. Production was clustered about the mean of 4,250,000 pounds of meats. Pounds of meats processed is presently dependent upon regulated eupply and availability of clame.

Haste products produced by processing plants

include: shell, clam visceral mass, and wastewater. All or the plants surveyed treated end disposed of waste products similarly. Clam shell is either shipped directly to landfill disposal sites, provided free of charge to processors requiring landfill, stored and sold as oyster cultch or road fill, or stored and planted on oyster leasee held by the processing firms. Hone of the firms surveyed 100

indicated that they encountered or anticipated any

difficulty disposing of shell. Although all of the firm*

Indicated that there is presently a market for shell

bringing $0. 30 to $0. 45 per bushel, selling this by-product

requires a relatively large storage area. Lack of adequate

storage space was cited as an impediment to selling shell.

Clem belly material is pumped to holding tanks for later

disposal in municipal sewage systems, trucked to landfill

disposal sites, or provided free to persons using it ae hog

feed, fertiliser, and bait. Ho problema are encountered in

disposing of this material. Processing firms indicated

that wastewater disposal, although not problematical at

this time, will become prohibitively expensive for many

firms if effluent standards become more reatrictive.

Because moat plants dispose of wastewater into natural embaymente or rivers, wastewater must meet standards set by

tha Clean Water Act Amendments for pH, suspended solids,

BOP, and oil and greasa. Quarterly effluent reports are

Bent by firms to their respective state water control authorities, and tffluant is tasted annually to verify the accuracy of these reports. Eighty-five percent of tha firms surveyed were capable of meeting effluent standards by simply screening their wastewater, s relatively inexpensive procedure. Host of the solids are thereby captured when the screen ia emptied several times per day.

The remaining firms were required to screen their wastewater and pass it through grit chambers, air 1 01

floatation tanks, and settling tanks before discharging

effluent. Tha additional estimated coat of treating tha

wastewater with thee* systems was between 40 end 50

thousand dollars per year.

Because fresh water is used in repeated washing

steps, it is an important resource. Hater uai calculations

across all shucking plants indicate that an average of fi, 13

gallons of fresh water are required for every pound of clam

meats processed. This figure ranged Trom 2. 5 gallons of

water per pound of meats processed to 9.0 gallons per pound

of meats processed. Only one plant surveyed indicated that

obtaining an adequate fresh water supply was a problem.

Geographic location appears to determine whether fraah

water supply limits production. In heavily populated areas, where water is obtained from municipal reservoirs,

water may become a lim itin g resource Tor in d u stria l growth. Where wells are used as sources of fresh water, state regulatory agencies apply annual supply ceilings.

Individual processing plants on Virginia’s Eastern Shore may presently pump up to 1 50 million gallons of watar per year Trom their wella. Thia total is much higher than any

■ingle plant's annual water requirement. Several processors, however, expressed fears that as the demand for watar rises with increased population density, industry will be allocated a smaller supply. Hhather water will become a limiting resource remains to be demonstrated. 1 02

Production Inventory, Sales, and Distribution Network

During warm weather, rapid processing (within 24 hours of landing} of clame ia required to avoid spoilage.

Clams may he held as long as five days without spoilage loss at temperatures near or below freasing. All of the processing plants surveyed, however, processed clama the same day they were delivered, and tharafore held little or no raw product inventory. One third of the plants surveyed held inventories of processed neats in either frosen or raw form. Inventories ranged from 25,000 to 300,000 pounds of me a t a.

Nona oT the firms surveyed aold nhucked product through their own retail outlets, although several vertically integrated firma engaged in asoondary processing operations to produce products which ware sold by the firm. This survey would indicate, however, that a majority of clam shucking operation* sail product direatly and through brokers to other Tirma Tor further processing Into wholesale products. Advertising was not demonstrated to be important at the primary level of production. Only 16 percent of the firms surveyed engaged in any kind of advertising activity.

Induatrlal Production Capacity and Growth Fgfc.mUH

Fluctuations in the supply and prices of surf clams have affected the growth of the aurf clam processing industry. Although several of the firms surveyad have been 1 03

operating successfully nines the early 1900'a, many companies have recently been required to curtail harvesting activities, cut surf clam meat production, and shirt processing operations to ocean quahoga. None or the firms surveyed have introduced new surf clam products into their markets, however some firms currently engaged in shucking activities only are planning to integrate breading and freezing operations into their businesses. Some firma have introduced new frozen oyster end hard clam products to replace lo s t volume of surf clame. Frozen oy sters on tha half shell were identified by shucking plant managers as an# product with good future market potential.

Hone of the plants surveyed are currently producing at full plant capacity. Processors estimated that they could increase thair production from 10 to 100 percent without any expansion or need for additional c a p ita l except increased personnel. The calculated mean possible production increase over all plants surveyed was 72,5 percent. Production capacity per sight hour shift was estimated by ell plants surveyed, and demonstrated remarkably little variation among plants. Production capacity ranged from 10,000 pounds of meats per shift to

40,000 pounds of meets per shift. Mean production capacity per ahift over all plants surveyed was 22,500 pounds of meats. Most plants surveyed indicated that only two production shifts could be utilized because of the necessity for a third six to eight hour cleaning shift. 104

Thera do#*, ho«iv*rt appear to be significant underutilisation of processing capacity in the industry.

Factors imp*ding surf clam procanning planta from operating at full capacity vara investigated. Tha problem moat frequently cited van tha scarcity of Trash raw product. Shortages may exist because of management quotas

(low resource abundance) and poor weather. Processors indicated that, with increased availability of raw product, it would be possible to boost production approximately ten percent per year within market constraints.

Scarcity of labor was not identified as a problem by any oT the processors surveyed, although worker shortages were temporarily encountered. Tha labor force employed by processing plants was identified aa unskilled or sami-nkillad with most activities requiring no education or only one to two days of training. A large percentage of transient workers are employed by processing plants.

Quality of available personnel and work force turnover were identified aa problems by moat processing firms surveyed.

On tha Eastern Shore of Maryland and Virginia, processing plants employ workers on an evening shift. Those workers also work a daytime shift at chicken or vegetable processing plants. Processors indicated that work force turnover averaged 20 percent annually.

Unionisation is not popular among workers in surf clam plants. This may be attributable to the transient nature of employment in surf clam processing plants. None 1 05 of the plants surveyed were unionised. In recent years, unions have tried to enter two plants, but ware defeated in both cases.

Capital and equipment required to initiate processing o perations were id e n tifie d aa land, b u ild in g s, trucks, forklifts, shucking machinery, conveyor systems, a reel washer, floatation tanka, extracting equipment, washing equipment, pumps, a fresh water well system, refrigeration and freezing equipment, and boats. The minimum estimate of venture capital required to establish such an operation was $500,000.00. No exclusive patents would prevent acquisition of the necessary equipment.

Payment for the rights to use eviscerating machinery, however, would be required.

Although adequate sources of capital, labor, and market potential exist for business growth, entry of new firms into the processing industry appears to be unlikely while clam supply is restricted by short natural supplies re q u irin g r e s t r i c t i v e management regulations. Limited growth potential exists for processing firms presently in the industry. One third of the processing firms surveyed indicated that they will not expand buaineas operations unless clam supply limitations are eliminated. Two thirds of tha Tirma surveyed planned to expand, "w ithin the Limits or the management plan", investing in more sophisticated automation and expanding into new markets for breaded and sliced clama. A pervasive business strategy under the 1 06 current regulatory regime sppe are to bet 1) gain access to more expensive limited aupplla i or clams by cutting costs through investment in new affi cient equipment and curtailment of expensive rietii ng operations, 2) increase production at the expense of c ompetitors, 3) vertically in te g rate and expand into new markets for breaded or sliced product, 4) expand production □T chowder meats by processing more ocean quahogs and bard clams and using salvage more efficiently, and 5) where possible expand operations to process and eell oy sters and hard clams. Chapter 1

Hods! Construction

Using the parameters derived in the foregoing

chapters, s nurf clem management model has been constructed

to measure the response of middle Atlantic surf clam

populations and the economic environment to fishing

a c tiv itie s . As noted above, a number of generalised

fishery models have been previously constructed. However,

there have been no previous efforts to develop an

analytical single species model Tor the middle Atlantic

surf clam fishery. Anderson et si. £ 1 982> developed a

generalised multispecias, multicohort fishery model

originally designed to investigate management options for

the New England groundfiah fishery. After its construction,

the model was applied to in v estig a te nurf clam and ocean quahog harvesting activities. The Anderson model, while clearly influential in the direction taken by the present

investigation, differs substantially from this work as

illustrated in the following section of this report.

4 Unique Modeling Approach

The foundation of the present analysis lies in the work or many investigators, but the management model described below is unique in the sev eral respects. First, this research quantifies industry costa in both the

1 07 1 OB processing and harvesting sectors through a survey of all s u rf clam fishermen and processors in the United S ta te s, and interviews with processors and fishermen in Virginia and Maryland. The Anderson model cited above, on the other hand, developed empirical estimates of harvesting coats using running time, fuel use, and other factors reported in tha literature. Certainly, these estimates have been useful as a starting point Tor a more comprehensive investigation. Host of tha cost equations employed by

Anderson at «1. were generalised for use in a “typical" multiepeciea fishery, and were initially derived from information on the New England groundfish fishery. The processing sector was not included in the Anderson model, and no survey work specific to the surf clam rishery was performed. Second, this research examines the relationship between ex-vessel price, quantity landed, and tha prices of substitute species in the surf clam fishery, Bx-vessel clam prices can thus be expressed as a function of the quantities listed above. The Anderson approach was to use a constant price modal, the g re a t weakness of which is that i t does not Include demand e l a s t i c i t i e s . At high le v e l s of clam harvest, this is a serious omission. Failure to account Tor cross elasticity of demand between substitute species and surf clams can also lend to the development of an unrealistic model. Third, in contrast to previous efforts, this research employs a biologically based method of calculating biomass in pre-recruit cohorts. The von 1 09

Bertalsnfry growth aquations and length-wsight

relationships are used in this modal to calculate biomaas.

Fourth, while this research seeks to develop a epeciss

specific fish ery model, i t is fle x ib le enough to

incorporate new biological and economic information as it

becomes available, and to evaluate the perm utations of

combined management stra te g ie s . The model is capable or

incorporating cohort specific natural mortalities aa well

as total natural mortalities specific to any one of the

three middle Atlantic subregions. The Brody growth

coefficient can be altered by subregion or season to

account for temporal or regional fluctuations in growth

rate. In addition to evaluating the harvest of mature

clams greater than 5, 5 inches in langth as permitted by

current management reg u latio n s, the model can determine the economic and biological effects or harvesting clams of

length smaller than 5,5 inches. Various fixed and variable

costs of vassal operations can be changed according to

vessel class or region to examine tha impact of changing specific costs. Variable processing costs can be altered,

the effects of an sx-vessal price floor or changing fixed

processing costs can be examined, and vessels can be permitted to leave or enter the fishery depending upon

profits available inside the fishery relative to those outside the fishery. Effort hours can be altered by subregion and vessel class, landings taxes can be imposad by subregion and by vesael class, quotas may be imposed by 1 1 0

season, and the movement of v i i i t l t from araai of low

productivity to araaa of high productivity may ba

invtitigated. Few of theae management options have been

included in earlier fishery models.

While ttlie research builds upon the work of earlier

investigators, the model described below has not been drawn

from any previous investigative work. This model is an

improvement over earlier ones, and ia useful in improving

the management Of the surf clam/ocean quahog resource.

Model S tru ctu re

The model has been written as a set of iquationa, and

has been programmed in FORTRAN for use on a d i g i t a l

computer. All or this work has been performed using

thaPrime 850 computer at the Virginia Institute of Narine

Science. The model has been c o n stru c te d In the framework

of 11 subroutines containing equations representing the

flow of resources through the fishery. Each of these

subroutines ia called by a main program designed is a

bookkeeping function to coordinate and track activities occurring in the fishery. These subroutines and their

functions are displayed in table 14. The equations and

input parameters used in each subroutine are detailed in model program included in Appendix A.

Main Program

At its outsat, the main program calls subroutine

INPUT to initialise all modal variables, and then 111

Table 14

Program Subroutines and Functions

Subroutine Functi on

Main Program i) Drives tha model. 2> Calls subroutines as required on a monthly or yearly basis. 31 Haintains and updates moot hi and yearly statistics. 4} Increments model on a mont hi and yearly basis. 5) Requests and implement a user initiated changes in management variables.

INPUT 1 ) Initialises all input parameters and passes them to subroutines. GROWTH 2 ) Calculates biomass change in each subregion by cohort based upon von B a rta la n ffy growth.

EFFORT 1) Calculates the number or eTfort days of fishing per month of three classes of vessels in three subregions. This is based upon allow able effort hours.

HARV5T 1} Calculates, by three classes of vessels, harvest in three subregions in biomass. cqUota 1) checks to see whether q u a rte rly h arvest quotas have been exceeded. 2) If they have, it closes the Tishery until the end of the current quarter. 1 1 2

Subroutine Functi on

TESRBT 1) Calculates svarsgi and total vessel revenues and profits by vessel c la ss and aubregjon based upon defined rish in g costs, ex-vessel prices, and harvest.

PROftET 11 Calculates processing coats and Wholesale prices associated with various levels of pro fit.

MONDAT 1) If requested by user, writes monthly statistics to an output file and to tha user's termi nal.

SWITCH 1) Calculates prorit potential in all subregions according to vessel class and moves vessels from subregions of low p ro fit to subregions of high profit.

VENEX 1) If requested, calculates potantisl return on invest­ ment available outsida the fishery and moves vassals either into or out of the fishery according to profit potential. Moratorium on vessel entry-exit may also be imposed.

YRPRHT 1> Writes annual statistics to an output f i l e and to the user's terminal. 1 1 3 interactively requests input concerning tha number of years to be analysed. It also requests instructions regarding the generation of monthly or annual output.

I n i t i a l management option* are entered aa v aria b le s in subroutine INPUT, and may be alterad after the completion of each year of analysis or at the end of an entire simulation run, This option permits considerable analytical flexibility. The main program next enters a loop responsible for yearly increments. If ex-veaael prices are not fixed, the price model nested within subroutine YESRET is automatically invoked. The program next enters a loop accounting for monthly time step

Increments. Hithin this loop subroutines are called to deTine monthly groMth and natural mortlity, fishing effort, and harvest according to vassal class, subregion, and cohort. A check is performed to determine whether quarterly harvest quotas have been exceeded. If they have, the fishery is closed until the beginning of the next quarter. All of tha calculations performed in these subroutines are displayed in detail in Appendix A.

After calculating monthly harvests, tha main program must again recalculate cohort sis* accounting Tor fishing mortality. To accomplish this, harvests for each or the three vessel c la sse s are added to g eth er and s u b tra c te d Trom cohort weight in each subregion. The total weight of each cohort group is calculated as the product of cohort sise and the weight of clama in a cohort (darivad form 1 1 4 subroutine GROWTH) . At this point, the main program

separately calculates the siae and weight of the surf clam

population in each subregion and the aggregate middle

Atlantic population.

The next task addressed by the main program is the calling of subroutines raquirad to invoke an economic

submodel of the harvesting and processing sectors.

Subroutine VBSRET ia called to calculate harvesting costa,

revenues, and returns. The ex-vessel price model located within subroutine VBSRET is called to calculate harvesting coats, revenues, and returns, Subroutine PRORET is next called to calculate processing coats and to identify the wholesale prices that would be required to break even, and to achieve various levels of return against costs. All of these subroutines must communicate with each other, and with the main program, and must therefore pasa input and output parameters through common variables. After calling subroutines comprising the economic submodel, the main program chacks to see if the user requires monthly output and if subroutine HOHDAT should be called. This subroutine creates monthly output files, writes data to those files, and writes monthly output data to the user's terminal screen.

The main program must also determine how potential p r o f its w ill a ff e c t the movement of f is h in g v e sse ls between subregions of the middle Atlantic. Historically, the middle Atlantic surf clam fishing fleet has moved an masse 1 1 5

between euhregione, exploiting the available resource in a

subregion until new clam beds with higher profiot potential

ware located. Subroutine SWITCH surveys the profit

potential of each middle Atlantic aubreglon on a monthly

banis, moving tha fleet to aubregiona where the highest

profits may be realised. After accounting for monthly

fleet movement, the main program newt calls subroutine

VENEX, which is responsible for moving vessela into and out

of the fishery. This subroutine calculates vessel profits on an annual basis and compares them to return on

investment capital available outside the fishery. IT potential profits are greater within the fishery, new fishing vessels enter the fishery at the end of each year.

IT a veeael entry moratorium ia indicated, vessela are prevented from entering the fishery. If potential profits are higher in another fishery, vessels leava tha clam f i shery.

ATter accounting for changes in fiahing fleet distribution and else, monthly iterations are completed, and the main program leaves tha monthly loop. At this time, tha remaining subroutine, TRPRNf, is called to create an output file to receive annual data. YRPRNT writes annual data to thin file and to the user* e terminal screen.

Two proceduras remain to be completed by the main program before moving to a new year within the annual loop. The user is given the opportunity to change management parameters at the end of each year. The main 1 1 6 program request* information regarding the following variablea: tha p rio n of competing apaolaa ean be changed, landings taxes can be implemented or changed, monthly effort hour limit* can be changed in any subregion, quarterly quotas can be changed Tor any specific quarter, and natural mortality rates can be changed according to subregion and cohort. The last task to be completed by the main program is tha advancement of clams in each cohort group, after having aged one year, to the next cohort group. This occurs at the and oT each year.

it the conclusion of eaah year, monthly procedures are reinitiated to calculate activity within the fishery

Tor a new year. Once the end of the yearly loop is reached, output files are closed, and the user ia notified that tha simulation run has endad. Tha calculations performed by each subroutine are displayed in detail in

Appendix A.

The Model construct described above has bean designed specifically to evaluate alternative management strategies in the surf clam fishery. Gaps in our knowledge of density independent and dependant influences upon year class strength have precluded the development of a stock or environmental predictor or recruitmant. Aa Ricker (1975) notes, the ebundance of mature spawnera in a fish population is often of sufficient importance to make it of real value for analysis and prediction. Beverton and Holt

<19571, Ricker (1950), and Cushing (1972) have proposed 1 1 7

■ took-reoruitment relationahipa. Tha Rioker model permit! the partitioning or mortality during the prerscrult stage

□r the lire cycle into density dependent and density independent mortality:

R-ASa’ et

where A is the coefficient or deneity-1ndependent mortality and D is the coefficient of density independant mortality.

5 (the siae of the spawning stock) and ft (recruited stock) are measured in eggs spawned and lifetime egg production or recruits suffering only natural mortality. In some cases, variability in recruitment unexplained by the stock-racrultment function can be explained by abiotic factors. Nelson, Ingham, and Sehaaf <1977) correlated deviations in recruitment from s Ricker stoek-recruitment function with anomalies in aonal Bkman transport. However, uncertainties regarding the stock-recruitment relationship in tha surf clam fishery, and the response of the aurf clam fleet to increasing or decreasing profits have necessitated the use of several assumptions described in Appendix A.

Despite its lack or aophlatication in these areas, the model remains an extremely flexible and useful tool with which to compare and contrast the possible advantages and dieadvent ages of management options, and to make decisions that optimise economic and biological yields. The following chapter describes the results of several 1 1 6 different approaches to regulating harvests In the surf clam fishery, and demonstrates how this model may be used to evaluate these management strategies. Chapter VI

ftsaultB. fl£ Mmulatlon atnditg

The data and relationshlpa bttvnn •conoaic and biological paramatara daacrlbad in pravioua chapters were fitted into tha modal framework outlined in chapter V, and an evaluation of a lte rn a tiv e management s tr a te g ie s was conducted. Before describing the results of varying management variables, however, it is important that a brief discussion of the details associated with verification oT this model be presented.

Caswell (1976) has defined two general purposes for which ecosystem models can be daveloped. This research project may be viewed in the contest of these purposes.

Models may be developed for: 1) prediction oT the behavior of state variables in response to specific possible perturbations and 3) tha organisation of thought concerning the internal dynamics of an ecosystem. In the Termer case the model ia used primarily as a tool for ecosyatam management decisions. This research ia concerned with evaluating fish ery management a lte rn a tiv e s and is therefore concerned with the f i r s t general purpose l i s t e d above. Ho modal can, however, produce a f a ith f u l one-to-one reflection of reality. Data gaps and an incomplete understanding of various rate expressions transferring resources between model compartments have necessitated the

1 19 1 20 use of simplifications described in previous chapters.

Nevertheless, as Levins ( 1966) has noted, "all models leave out a lot and are in that sense Talaa, incomplete and inadequate. The value of a model ia not that it is true but that it generate good te s ta b le hypotheses relevant to important problems". In that regard, this model is quite useful. Development of thia model has in fa c t been a process through which gaps in the continuum of knowledge have been id en tified , the impact or a lte rn a tiv e management strategies has been evaluated, and future research needs have bean id en tified .

Model verification tests tha relationship between a modal and the results of a simulation, answering the questions, how good is the program and, does the program perform to its design specifications. Developmental methods have been used to verify thia model. Structured programming, including top down design, has v e rifie d that there ia no circularity of flow and no backward branching in the model. Code walk throughs were performed during model development, and tables were w ritten deriving values that all variables in the program must assume at each step. The source level debugger available on the PRIME system was invaluable in this regard. Documentation development also was used to v e rify the model. Running commentary was inserted into the program to facilitate analyeis and verify results. Because of the relatively uncomplicated feedback structure of the model, post 1 21

developmental statistical mthoda were not required to

verify the model,

Cbm Studies

A bioflconomlc evaluation of the middle Atlantic surT clam fishery vat performed under conditions describing different management scenarios or cases. These scenarios

include a range of management options, each case representing a different approach to managing the fishery,

The flexibility built into this model's analytical framework would permit many more different esses or permutations of management options to be evaluated. The caaes presented in thia chapter, however, were selected because each uniquely represents a basic policy decision or perturbation having significant impact. Kore specific questions and problems could be addressed by industry planners and managers using thia analytical tool as the need arises. In each aasa, all dollar values of costs and prices are based upon data obtained in late 1981 and 19B2.

These values may, therefore he considered as 1982 dollars.

Case number one is a baseline simulation of what could be expected during a six year planning horiaon if the st at us a uo is maintained in the fishery. Conditions extant at ths end of 1902 ware mad to simulate the baseline situation. Annual landinge quotas of 45 million pounds of meats, drained meat weight, were imposed by quarter, and vessels wars limited to 24 hours of fishing time per week. 1 22

The fishing fleet of 103 vessela was distributed according

to vessel class primarily in the southern New Jarsey end

Delmerva subregions, a re fle c tio n of the minimal fish in g efTort concentrated in northern New Jersey. A vessel moratorium wae imposed, preventing entry of any new vessels

into the fishery. Vessela did not leave the fishery. This

is a reflection of the current situation. Vessel owners are extremely reluctant to leave the fishery, even when faced with loasea, because they are threatened with the possibility of ruture veeeel catch rights regulations based upon h is to r ic a l lendings data. A minimum else lim it of 5.5 inches was imposed upon all surr clams harvested in the middle Atlantic region, a constant rate of natural mortality was assumed, and no landings taxes were imposed.

Case number two m aintains the same conditions in the fishery, but lifts the moratorium imposed upon both vessel entry into and exit Trom the fishery. This would reflect a management decision to ab o lish both the current moratorium and any future consideration of a catch rights system of fishery management.

Caae number th re e sim ulates a management sc en a rio in which fishery managers might choose to ragulate the fishery through the imposition oT a landings tax. Under the conditions of case number three, all effort restrictions, quotas, and the vessel entry-axit moratorium are removed.

The 5.5 inch s is e lim it Is reta in e d , however, an ex-vessel lendings tax Of 00.40 per pound of clam meats is 123

imposed upon su rf clam fishermen in e ll subregions. This

tax may asaily be converted to dollars per buahel by using

a conversion factor of 17 pounds of clam meats per buahel.

Case number Tour examines the impact of elim inating the minimum sixe limit restriction. In this case, the 5.5

inch six* limit restriction is dropped, permitting

fishermen to harvest clama as small as four inches in length. Clams of length shorter than four inches are prerecruita, considered too small to be useful to processors. This case thua eliminates all aise limit restrictions while keeping the vessel moratorium, quotas, and effort hour restrictions in force.

Cases number five, six, and seven demonstrate the affacta of changing ex-vassal prices of the closest market competitor to tha su rf clam, the ocean quahog. In each of

thane cases, baseline fishery conditions are maintained.

In case five, the ex-vessel price of ocean quahogs is increased by 00.10 per pound, In case six, the ex-vessel price of ocean quahogs is increased by 00. 20 per pound, and

in case number seven, the ex-vessel price of ocean quahogs

ii decreased by 00.20 per pound.

The situation of restrictive quotas is represented by case one. Case eight depicts a s itu a tio n in which an annual yield quota oT 50 million pounds of clam meats, the estimated maximum sustainable yield in the fishery, is

1 mposed.

Cases number nine end ten demonstrate the e ffe c ts 1 24 or high and loir eTfort hour restrictions while maintaining all othtr basilina fishery conditions. Cait nina incrcasta tha p erm itted monthly ria h in g hours by 100 p trc a n t and c a n tan drcrtaaaa the permitted monthly fishing hours by SO p e rc e n t„

The f in a l case considered, c u t number eleven, examinee the impact of elevated lavala of natural mortality. This cane assumes a natural mortality rate of

0.00 in the northern Nee Jersey subregion during the second year of model projections. Such a situation in fact occurred in the summer of 1976 during an anoxic event with associated massive fish kills. Case number alavtn, therefore, projects what might be expected in the event oT a similar occurrence.

MaultP 2X the Analyses

Of the many output variables generated by the model for each can, seven were selected on the basis of their biological and economic importance as indicators oT r e f l e c t i n g the e f f e c ts of fis h e ry management. Changes in these parameters were graphically examined by generating annual plota of the data with a graphics subroutine. The p lo tte d v a ria b le s include; number of fis h in g vasaels in each vessel class, fleet landings by vessel class, fleet profits by vessel class, average monthly profits per vessel by vessel class, average monthly profits per vessel per year by vessel elan, surf clam stock size, and 1 25 wholesale clam meat prices. The results of a selected series of plots are displayed and described below. These

Plata provide approximate values of each variable examined.

Case Humber Qa*

Under the baseline conditions imposed by case number one, the rieet six* would remain conatant because of the vessel entry moratorium and the assumption that vessels are very re lu c ta n t to leave the fishery.

As indicated in Figure 6. fleet landings in case one range from 27 million pounds of clam meats in year one to

47 m illion pounds of clam meats in year four. The trend in landings demonstrates a sharp Increase between years two and three, a gradual increase to peak landings at the end or year four, and somewhat lower but conatant level of landings between years five and six. These landings reflect the papulation age structure described in Chapter

111. Landings by class III vessels constitute more than 75 percent of the total clam meata landed. Landings by class

II and class I vessels show little fluctuation in level with c la sa II veaaels landing between six and eight m illion pounds of meats, and clasa I vessels landing one to two million pounda of meats annually,

A plot oT case one fleet profits indicates that, under the baseline conditions total fleet profits would range from minus two m illion d o lla rs during periods of resource scarcity to 5. 5 million dollars annually 1 26

Figure 6), Projections indicate that iurf clam fishing fleet profits would increase during the six year planning horlson rrom levels below tha break even mark, to a peak of

5. 5 million dollars at the end of year four, and then begin a decline, falling to the 3. 75 million dollar level by the end of year six. Nearly all of this profit would be taken by the claas HI fishing vessel fleet. Under baseline conditions, the clans I vessel fleet will not reach the break even level at all during tha six yaar projection.

The clasa II fleet is projected to reach peak profit levels of approximately 0, 225 million dollars in year four and then decline to pront levels of approximately 0. 12 million dollars by year six. In all three vessel classes, fleet profits exhibit a trend of decline after peaking in year four. This is a reflection Of resource abundance.

Average p ro fits per vessel p lo tte d by month are displayed In Figure 6. While providing a detailed picture or monthly and seasonal fluctuations in vessel profits, the detail of these plots makes them leas useful than plots of average annual data for the identification of trends.

Monthly plots of vessel pronto indicate that as quarterly landings in year three begin to exceed quotas, the fishery will be closed under baseline restrictions and fixed costs will result in negative profits. Figure 6 shows that monthly profits per vessel will range between $16,00D and

48. 000 Tor class III vessels, between *6,000 and *5, D00 Tor class II vessels, and between *2, OOD and $4,000 for class I 1 27

Figure 6

Case 1 FLEET LANDNGS BY VFSSEl CLASS

I

A/ERACE WlOrJHY PROflS PER VESSEL A'ERAGT MONH.y PR OFT PER VESSEL

■ I '■a* Arit -MU!------

.■ H I frU I

SURF ClAM 3 0 CK '3/V. M RT.UiON WHOLESALE CLAM MEAT PRICES 1H

/ . V Kt'jrjN -k u . .< 4 d V #; : f/ ’ s?y :*■ ■■'. ** „ fffis w {iTLhirf;* ■

r j H - I 1 29

vessels. This plot also shows a trend of profit* par

v(#eel piakitif in year four and declining through yaar six,

superimposed upon periodic monthly loaaea during oloiure of

the Tishery and regular declines in vesael profits during

the winter season.

Figure 5 displays monthly profita per vessel

calculated on an average annual basin. Monthly profits

among class II veesels fluctuate from -$1,500 in year one

to nearly $7,000 in year four, and decline to $5,000 in

year six, Monthly class II vessel prorite are considerably

lower, dropping to -$1,800 in year one, increasing to

$1,000 in year two, and falling once again to approximately

$500 in year six, Monthly p ro fits among class 1 vessels remain helow the break even level, dropping to nearly

-$2,D0D in year two, and remaining at levels near -$500 during other projected years. It should he emphasised that these p ro jectio n s are for net p ro fits a fte r payment or veasal captain and crew vagea. Vessel owners have stayed in the surT clam fishery for five years or longer below the break even level because limited entry has mads a fishing permit a valuable if unmarketable commodity.

Figure 6 displays estimates of stock siae, biomasa of clams greater than 5.5 Inches long in each subregion.

Middle Atlantic stock else estimates in case one range from approximately 12 billion pounds of meats to nearly 50 billion pounds oT meats, reflecting variable recruitment to the fishery and management quotas. Middle A tlantic stock 1 29 biomass displays an Increase to its maximum value in yeir four, and than undirgott a constant decline through year

■In, Biomass in southarn Haw Jersey continues to decline at a constant rata from tha year one level or approximately six billion pounds of maata to levels of between two and three billion pounds of meats by year six. Northern New

Jersey biomass increases to a maximum eise of approximately

14 billion pounds of meats in year three, and than exhibits a nearly constant rate of decline through year sis. Stock in the Delmarva subregion shows a gradual increase to 6.5 billion pounds or meats through year three, increases to 10 b i l l i o n pounda in year four, and shows a gradual decline through year aix to levels equal to thoae seen in northern

New Jersey.

Average wholesale clam meat prices in case one at the ten percent return level for ahuckere show constancy at

$1.03 per pound of meats for the firat two projected years. Prices then drop to $0.99 per pound through year five, and finally increase to $1.02 during year six. At the break even level, wholesale prices are considerably lower, dropping from $0.94 per pound in year one to $0.90 per pound in years three, four, end five, and increasing to

$0.92 per pound in year six.

Caa« Two

Case two, depicting the short run impact of removing the vessel moratorium with baseline quotas, shows 1 30

interesting changes from the baseline study. It should be

noted that this simulation does not reflect the long term

impact of overcapitalisation that could occur if the vessel

moratoriurn were removed. Figure 7 in d icates that without

the vessel moratorium, the class 111 vessel fleet would

demons tra te a short run decrease in size. The fleet would

decrease in eise from 60 vessels in year one to 3D vessels

a fte r two years, and recover to 50 vessels by the end of

year four, This change in rieet size is a reflection of

profits available under conditions oT fluctuating resource

abundance, management quotas , and the absence of limited en try assumed by case two. The class 111 fle e t would fluctuate in size between 35 and 40 vessels during years

five and six. The class II vessel fleet size demonstrates a decrease from its year one size of 33 vessels to approximately 20 vessels arter two years, and i ncreases nearly to yaar one levels by the end of year four, During years five and size, the size of the class II vessel f le e t declines significantly to approxim ately one th ird of it B size in year one. The class I vessel fleet shows a sma 11 decline in siia after one year from 13 vessels to 9 vessels, it increases to its year one size by year Tour, and shows s lig h t d eclines d u rin g years five and six.

This case indicates that under baseline effort restrictions and quotas, removal of the moratorium and the possibility of future vessel quota assignments based upon historical catch would result in a decrease in fleet size P i gure

Case FLEE11 ANDMCS BY VFSSQ a ASS

WEFAGf MOr+TlY PROHS PER VESSEL AVERAGE MOM-LY PROFThtR VESSEL

MOLEbAiL : lav r ^ A r ^ a s

----- R-^

tuI & 1 L w . 1-E^.RS ilH !■* NUM3ER OF VESSELS BY CLASS FLEET PROfE 0Y VESSEL CLASS

M- s C ■ I

EIBL III 1 32

among a ll vassal c I b b b i s a fte r b I i years of fishing. This dtcriBBi in flaat siai would ba concentrated in tha class

111 and c I b i b II fishing fleets.

Caaa two flo at landings are illu a tr a ta d in fig u re 7.

As thin figure indicates, In the absence of a vaaaei moratorium, landings could be expected to decline to 21 million pounds of neats In year two, increase to 40 million pounds in year four, and again decline to 22 million pounds of meats by year all. Caaa two fleet profits are plotted by vassal claaa in figure 7, As indicated, if the vessel moratorium were lifted under baseline conditions, changes in Fleat profits would follow the same pattarn exhibited in case one, Ovarall fleet profits would improve considerably, increasing by appr ok imat aly two m illion dollars during yaar four due to the elimination of unprofitable claas I and class II risking operations.

Class 111 f le a t p r o fits would peak in year four and show a steep decline during years five and six. Class II operations would climb to a paak oT nearly one m illio n dollars during year four and stabilise at .5 million dollars. The class I vessel fleet would become marginally profitable, with profits fluctuating above and below the break even level.

Case two monthly profits par vessel plotted on a monthly basis are displayed in figure 7. This figure shows that removal of the vessel moratorium under baseline restrictions would cause monthly vessel profits to peak 1 33 at $13,000 among class III vassals, $5,000 among class II vssasla, and $1,000 among class 1 vessels during year thraa, thereby increasing average profit per v e s s e l by

$3,000 to $3,000 through the elimination oT unprofitable operations and the reduction of the number of neceasary closures of the rishery. Declines In profits during years four, rive, and six within the planning horiion follow these maxima. It should be noted that these declines in profits, may be attributable to fluctuations in recruitment to the fish ery . Looking beyond the planning horison, temporary stock recovery and increased fishing efTort in the absence of an entry moratorium could exacerbate the problem of declining profits and overcapitall sation,

Within the planning horiaon, seasonal dips in profits per vessel of IQ-15 thousand dollars are evident during the winter months. Average monthly profits par vessel plotted on an annual basis in Figure 7 provide, with a smoothing effect, a further demonstration that profits per vassal could be expected to decline through year six after peaking in year three in case two.

Removal of the vessel moratorium would not appear to have a great impact upon stock sise in the entire middle

Atlantic region or any of its subregions if catch quotas and hourly effort restrictions are maintained {Figure 7).

With removal of the vessel moratorium and maintenance oT all other baseline conditions, wholesale clam meat prices are slightly higher at both the break even and 134 ten percent return levels (Figure 7). This result is probably s function of the decreased short run supply of raw product available to processors under this management opt ion,

Case Thrfta

Case three removes all effort restrictions, quotas, and the vessel moratorium, regulating the fishery with a landings tax imposed upon clam fishermen st the end of year one. With a landings tax sufficient to raise fishing coats to, 40 per pound of clams harvested, all three vessel class fleets demonstrate large decreases in side from year one levels to year six levels. Clearly, landings taxes s u ffic ie n t to raise fishing costs $0,40 per pound of meats harvested out fishing prorite so severely that fleat size is dramatically reduced.

The effect of a landings tax increasing fishing costa toy $0. 40 per pound of harvest upon clam landings is illustrated in Figure a. Landings peak in year two, after which reductions in fleet sise would lead to concomitantly reduced landings through year aix when less than 50 million pounds oT meats are landed. Clearly, such a tax would reduce landings to leas than the maximum sustainable yield by year six through rather drastic reductions in the siae of the s u rf clam fle e t. This management option, however, could be expected to produce long run stock reductions because of overharvesting during the years immediately 1 35

Figure A

C ase 3 FLEET L A N D N G S HV V R i l O . Q 1

! h3 f

2 — ¥■■ YlWh “ ■ 1 HI

A'FRAH MONTHV ^-VH^ Vf ^ S B >VL I J I v : ^i. :l 1 I t K' VI ' 1i. I s " & ■ \

A l£iL Ll iL=>j haLirtJ VF.4R3 t j h i i u i PU#Bt tf VOSEL3MK) 4JVK ■M NUMBEF OF VESSELS BV CLASS V :as= ^ -HI vntoida ------FLDIT Ph'OiT. > H i *— a II■ Uh •■"EAWT ' \ 4 j f i I j' jfi € 1 36 following implementation of the tax.

The landings tax would produce hard economic times in tha surf clam fishing industry, After imposition of the tax, profits for the entire fleat drop to or below the break even mark, with relatively large losses incurred by tha class III fleet in years four, five, and six. A plot

□f average monthly proTit per vessel with a landings tax sufficient to raisa fishing costa by $0. 40 per pound of meats harvested (Figure 8) reveals that the tax would depress profits among all three vessel classes. The tax would lead to monthly profit levels of -910,000 to -935,000 among all classes of vasaels after year one, with class III vessels displaying the pooraat performance. Once again, seasonal fluctuations in vessel profits are evident.

Monthly profits per vessel plotted on an average annual basis with a landings tax raising fishing costs by $0. 40 per pound of harvest reveals similar results without seasonel fluctuations. The tax leads to consistent losses among all classes of vessels, with class III vessel losses being the greatest.

The impact of a landings tax upon surf clam stock size is illustrated in Figure 6. The tax permits an overall reduction of middle Atlantic stock by 160 million pounds below baseline values at the end of the six year planning horizon. Thie reduction is primarily a result of increased fiehing effort in the northern New Jersey eubregion. Although the fleet size is reduced, the absence 1 3? or fish in g hour re s tric tio n s and q u arterly catch quotas permits fishing effort to increase in this subregion. Both the Delmarva end southern Hew Jersey stocks remain re la tiv e ly unchanged in eise from beeeline values. Middle

Atlantic fishing effort is concentrated in northern Hew

Jersey because resource abundance, a function of the population structure input variables, and profit potential are greatest there. Further reduction in stock siSe could be expected beyond the planning horixon due to poor recruitment resulting from recruitment ovsrharvea t i ng.

Wholesale clam meat price fluctuations under the landings tax are illustrated in Figure B, These prices represent only increase* in excess of th at p o rtion of the tax that would be pasied through to processors. The management option of a landings tax initially depresses wholesale prices by approximately $0. 10 per pound because, in the absence of quotas, the supply of meats landed initially increases. However, as profitability of fishing operations decreases, fishing effort and landings decline, and wholesale clam meat prices begin to climb.

C aw Foyf

Case Four maintains a ll baseline management restrictions in the fishery, but describes the impact of harvesting clams smaller than 5. 5 inches in length. Minimum sise limits are set at 4.0 inches for this case.

The number of vessels in the fishery in this case 1 3B

remains constant because the vessel entry moratorium is

imposed. As Figure 9 illustrates, the harvest of smaller clams has a smoothing impact upon fluctuations in fleet

landings during the six year planning horison. Total middle Atlantic landings during years one and two increase by more than eight million pounds, peaking in year three, and leveling off in years four through six. The model suggests that early harvest of strong year classes could spread landings over a longer portion or the six year planning horiaon. It should be recognised that harvesting smaller clame would decrease the mean age of clama recruiting to the fishery, and thus decrease the theoretical yield per recruit at equilibrium. Regulations permitting a minimum siae limit below tha maximum yield per recruit can quickly lead to overharvesting. Strict enforcement of quarterly harvest quotas can control this problem. However, changing the minimum siae limit in a fishery should be approached with extreme caution.

Tha model suggests that available fleet profits could also be spread over a greater portion of the planning period ir siae limits were lowered (Figure 9). Slightly larger catches bring substantial maximum monthly profits for class II and 111 vessels, on the order of $5,000 per month for class III vessels and $3,000 per month for class

11 vessels. However, the necessity for a greater number of fishery closures in the absence of higher quarterly quotas would keep average monthly profits during the planning FIgur*

Case 4 H E-HLArO-J^ HY VP.iSLL UAS‘J H Li I , ^ -.11 ■

«39LOja ftH U_ PL. j ’V^ARS

AVERAGE MOM-LY PROflS PFR VE.bSEL MRfltiL MONMY mOFTP£R VFcJSLl

1 / * M»-| \ f"

,1 b1* . vM± I I i ri 1 40 horizon at the name Levels obtained under baseline condi t 1 one.

By permittins m o re intensive harvesting activity of smell clams in northern New Jersey and Delmarva, lowering the size limit to 4.0" would be reduce stock size 400 million pounds by year six. This would not eliminate the stock, but it undoubtedly represents a severe reduction.

Given the uncertainty of absolute biomass estimates, case four results would indicate that size limit reductions should he approached with extreme caution. IT smaller clams could be harvested, wholesale clam meat p ric e s would be lowered $0.03 to $0.04 per pound during years one and two, but would then recover to levels obssrved under the cu rren t management regime for years th re e through six.

Cnees Six, and Seven

The effects of increasing ocean quahog ex-vessel price by $0.10 and $0.20 per pound are examined in cases five and six respectively, end the effect of a $0.10 per pound decrease in ocean quahog ex-vessel price is axamined in case seven. These cases project the economic impact of a decline in the abundancs of a substitute commodity for surf clams. This analysis ie restricted to examining the impact of these price changes upon fleet p ro fits and wholesale surf clam prices. Although ocean quahog price changes might be expected to affect surf clam rieet size, landings, and stock size, these casss were simulated under 1 41 tha assumption that tha vessel moratorium, and tha reluctance or surf clam fisherman to laave tha rishery, would causa only minimal changes in flaat siia and surf clam landings. Further investigation of Tlaet six* changes and landings fluctuations would be possible if tha assumption of a vessel moratorium were lifted. As Figure

10 indicates, a $0.10 per pound increase in ocean quahog price would lead to higher ex-vessel surf clam prices during the peak harvest period of years three and Tour, and increase total neat proTita more than one million dollars above profit projections under the current management regime. Class I vessel fleat profits would be liTted to the brisk even level through year six. A plot of ease six fleet profits (Figure 11> shows that an increase of $0.20 par pound in ocean quahog ax-vessel price would raise class tit vessel profits even more, while having only a relatively small impact upon class I and class II vessel fleet profits. A plot of case seven fleet profits (Figure

12) demonstrates the impact of a $0. 10 per pound decrease in ex-vassal ocean quahog price. Total fleet proTits are

50 percent lower than those ohsarved in case one baseline conditions. Levels of pront in both tha class 1 and class

11 vessel fleets remain below the break even level through year six. With an ex-vessel ocean quahog price decline of

$0. 10 par pound, class I and class II vessels are able to return a profit to their owners during months when tha 1 42

Figure 10

Css* 5 r WLRAGI Mi.MHY PRO! PLP VESSEL

U

UHI y#-" W O O K*M HCH> r n in i T * O j

FLEET PROFT, HY VESSi.l ."I ASS WHOt K'jALE CLAM i*lF,T PRICES

/ i ‘ / S' / i — 1 p»1f e y /

v t t o L ■J..*Eri >- t*'uiH* ■ I d * A. 4 K-4- Y 1 43

Fi gure 11

Case 6 Jl '. 'J . IX IW .

■I Ml i '"vrT >■: ■ I . i Wf KA!’ Sal f C.. Af PPO. S

D* J

* K jU lfr 1 44 fishery is open. However, closures necessitated by the quarterly quota system push these vessels below the break even level when monthly p ro fits per vessel are plotted on an average annuel basis

Wholesale surf clam meet prices could be expected to increase between $0.11 and tO. 1 5 per pound during years two through six with ocean quahog ex-vessel price increases of

$0.10 per pound {Figure 10). A surf clam price increase of

$0. 25 to $0. 30 per pound could be expected with a $0, 20 per pound ex-vessel ocean quahog price increase (Figure 11> - A

$0.10 per pound drop in ex-vessel ocean quahog prices would produce wholesale Surf clam meat prices only $0. 20 to $0. 03 lower during years two through six (Figure 12)

Cases Eight, Wine, and Ten

The efrect of a quota imposed by the management plan has been examined in case one. Case eight examines the impact of an intermediate yield quota of 50 million pounds cf meats per year. This is the current estim ate of maximum sustainable yield. Case nine examines the affect of increasing the effort hour restriction ceiling by 1(30 percent above the baaeline simulation, and case tan examines the effect of imposing an effort hour restriction ceiling 50 percent lower than the baseline simulation.

because conditions of the vessel moratorium remain in

Torce under all of these cases, no change in fle e t aixe will occur. As illustrated in Figure 13, an annual yield 1 45

Pi gura 12

Casa 7 W1.HWjI WONHir Vr!W:. WLKKa MPN> 1.T M<| Hr M ^ V ■ I

14 J t v \

■r—

•M-

J- T L

0 rn ^ t s 0 i vtsa.. c: ass w h x c '.'^a l e ■j l a m ml>:i I^ I lIS

K \ \ \ \■.^ V - - _ i ^ . r

. i •L-ril M-i. j ■ ■ W1S “• MiW * MMHW | ‘f c f i t . 1 46 quota or 50 m illion pounds oT meats would permit fle e t landings to increase by approximately ten million pounds per year during years three, four, and five. Landings would begin to show a decline during year six due to fluctuations in year dais strength. A plot or the effects of increasing allowable fishing effort hours by 100 percent above the baseline (Figure 14) shows that the e ffe c t upon landings would be minimal. However, a 50 percent cut in allowable fishing effort hours would decrease landings by almost 30 million pounds during years three, four, and five

( Pigure 15).

Fleet profits would be affected by all of these management options. Increasing the annual yield quota to

50 m illion pounds of meats would increase fle e t p ro fits by more than five million dollars per year during years three, four, and five. Fleet profits among class II vessels would be modestly affected, and profits among clsss II vessels would show little or no change (Figure 13). Increasing the monthly fishing effort hour ceiling by 100 percent would cause widely fluctuating fleet profits during years three, four, and rive due to rishery closures (Figure 14).

This is because p ro fits plummet during periods when the fishery is closed. Decreasing effort hours by 50 percent , however, would have dev astatin g e ffe c ts upon fle e t p ro fits

(Figure 15). An overall fleet operating loss of four million dollars would be incurred during year two, peak p ro fits during years three, four, and five would be cut an 1 47

Fi gure 13

Case B R £flLJVCM G S BY VESSE1 CLASS

TtARs

ft/E R A a WOfJHY H R Vl.S'jt W[.r«iTj *a)ni it fWM :,i R vi- ■ .SI i

\ i \ i , ' ■r /

■A Mi . u 'iir'i “.it i

I *i 4 h 4 qjiii: ■in:i.. •.i.u 'i uslm 1-1

[LrCEKOEF BY V'C;:| [ ' WHOLt.SAL. O ^ M G A l P t t G B

J

FtDU.'i

TLtfiS 1 4B

Fi gure 14

Caafl 9 FLEHLA1S0MGS BY VESSEL a ASS

<1

■ ■ii -- h

ft/FRAfjf. MCJNIiy PROFl'PER VESSEL

kjJCO OHIh'

i ■ w I is h vt sSt.L class WHOLESALE CLAM MEAT PRICES

i i . i i i f nvs - UE

s t YOrtS ■ H PIPI 1 49

Pi gu n 15

Case 10 i F A £ i PpDOL lMK /WLHAGE MC.AI II '"*1 i .T -‘ rT Hi.TTi i-i‘ 3 v tEjkP^i R>r, } l V-Jf I vl r 1} nRt>ir-, FLEET CLASS VESSO. BYLANDNGS - irffti ■ UAL.. • - ■ H k l i t f W -

VI ^j-l .lAii'j r w 1 T i t i < T j WHOLESALE" CLAMMEAT FffiCES V

(Figure 13). Average profits per vassal, however, drop below tha break even mark for class I vessels by the end of year six. Cutting allowable effort hours by 50 percent (Figure 15) would eliminate tha necessity for quarterly closures of the fishery because reduced harvest would not exceed q u a r te r ly quotas. However, re s u lta n t levels or harvest would be too low to prevent any claaa of vessel from staying above tha break even mark by year six.

Plots of surf clam stock tin by region indicate that none of these management options would seriously afreet stock sisee in any subregion within the six year planning hor i son.

Wholesale clam meat prices could be expected to increase $0.01 to $0.02 per pound during yaara Tour, five, and six if annual yield quotas wars raised to 50 million pounds or meats (Figure 13). Wholesale clam meat prices are $0. 05 to $0. 10 per pound lower when allowable fishing effort hours are increased by 100 percent. This decrease in price is due to the somewhat larger supply of clams harvested under this management option (Figure 14). 1 51

Wholesale clam meat price increases of $D. 02 to $0.03 are

demonstrated in case tan (Figure 15). Under this

management option, restrictions in fishing effort hours end a subsequent drop in surT clams available to processors result in higher wholesale surr clam meat prices.

Case Eleven

The last case considered in this analysis examines the impact of an anoxic event and asso ciated mass mortalities similar to those observed in the northern New

Jersey subregion during the summer of 1976. To simulate such an occurrence, natural mortality estimates in northern

New Jersey were elevated to 0. BO during the entire second year of tha simulation.

Once again, due to the restrictive vessel moratorium and tha reluctance of fisherman to leave the fishery, fleet aiae would not change in any vaaael class. The impact of such an occurrence upon year classes recruiting into the northern New Jersey fishery during years three, four, and five ia evident in Figure 16. During these years, landings declined by rive to six million pounds oT meats. In case eleven, fleet profits are one to two million dollars lower during years three to six, and tha class 1 fleet ia unable to even approach the break even level. The distribution of fishing effort and biomass among tha three subregions of tha middle Atlantic region would be altered dramatically by 1 52

Fi gura 16

Casa 1 1 FLEET LANDINGS GY VESSEL Q ASS

A/ERACE MQNRy PROH F-^R V i-S S U

si • H . =j ■ lijl • •■rf-Ll' IJWJ

S L M a AM STOCK StfF HY Rl LJUh YWV3 F5AEF. LlAJwi MEAT TWITES

1- i - ^ CrfON 3 .... A 41(1 AI h 4 1..4 - V 11 ijH.' «■

J >LaP'.y ■ caps 1 53 an anemic want similar to that described in case eleven.

A plot of middle Atlantic clam biomass expected during tha sis year planning horiaon of case eleven illustrates that by year nix, overall biomass is more than five billion pounds lower than under fishery conditions in case one.

Hhile southern New Jersey stock sixe would not be greatly affected, during years four, five, and six, fishing effort would shift from northern New Jersey to Pelmarva, Delmarva stocks would be reduced by one to two b illio n pounds more than under case one baseline conditions. The northern New

Jersey subregion shows stock sixes between three and four billion pounds lower than baseline estimates during years three, four, five, and six. Decreased clam landings following the anoxic event result in an increase in wholesale surf clam prices of £0.01 to $0.02 par pound

< Fi gura 16 >. C hapter VII

A b i l it ie s an pgtejyttEl q £_ Thlfi Approach

This model has been developed to provide a method by which fishery managers could consider alternatives for the complex issues confronting them in the development oT public policy. A review or surf clam literature has been conducted, and the structure of the surf clam fishing and processing industries have bean researched.

Model predictions are baaed upon population estimates from the National Marins F is h e r ie s Service. The model provides a short term (6 year) outlook for managers evaluating alternative decision choices. This constraint is necessary because of uncertainties regarding the a toek-recruitment relationship and tha unknown effect oF environmental variables on year class recruitment success.

This research does, however, provide a technique by which quantitative analysis oT decision alternatives may be accomplished. The impact of management alternatives on stock siae may be assessed, and the impact of management decisions on the consumer through wholesale price fluctuations evaluated. Perhaps most significantly, this model provides e s tim a te s of fu tu re p r o f i t s or cash Tlows in the fishery generated by each alternative policy. Nith these data in hand, managers can proceed to apply evaluative capital budgeting techniques. Three of the most

1 54 1 55

broadly accepted techniques, calculation or internal rates

of return, calculation of net present values, and the use

of profitability indices are readily adaptable to the

output of this model, and could be employed to rank decision alternatives.

Discussion oil Ease Studies

The results of model analyses similar to those

presented in the foregoing chapters could be used to make

recommendations for management in the middle Atlantic surf clam fishery. The main objectives of surf clam management as identified by fishery managers are to protect surf clam stocks to permit eventual sustained harvests approaching 60 million pounds of meats (Maximum Sustainable field), to minimise economic dislocations while encouraging efficiency in the fishery, and to provide the greatest degrees of freedom and flexibility to all harvesters of this stock consistent with attainment of other management objectives.

As the results presented above suggest, regulations simulated in case one (an approach similar to the current management framework) can effectively prevent overhervasting. This type of management framework can, however, give rise bo economic inefficiencies, unnecessarily depressing industry profits and inflating the wholesale price oT surf clam meats. Some have noted that this regulatory approach is also expansive to enforce.

Model sim u latio n s suggest th at we have currently 1 56 entered a period within which greater yield quotes may be permissible. If this ie in Fact true, economic yield in the fishery could be improved without jeopardising or sacrificing future eurT clem population stability. Model simulations project that, if yield quotas are not raised in the near future, even a 50 percent decrease in allowable

rishing efTort hours will not be enough to avoid fishery closures. The model in d ic a te s that yield quotas somewhat closer to the current estimate or maximum sustainable yield, 50 million pounds of meats, would probably not have detrimental effects upon clam stock a m within a six year planning horiion. Such an increase would improve tha profitability of the claas II and class III vessel fishing fleets significantly, and minimise economic dislocations in the fishery by assuring processors of an adequate and constant supply of raw product. The need for fishery closures would also be virtually eliminated. In conjunction with increased yield quotas, model case studies indicate that total allowable fishing effort hours could also be increased without causing disruptive closures in the fishery. The choice of such a management option might also result In a small reduction in the wholesale price of clam meats.

Overcapitalisation in the fishery appears to remain a problem. Uncertainty regarding the future direction of surf clam management has aggravated the problem of overcapitalisation by keeping many marginal operators in 1 57

the fishery. As the results of case two assumptions

indicate, removal of a vessel moratorium coupled with restrictive yield quotas could result in an overall decrease in the surf clam fishing fleet. This change is baaed upon potential economic return in the fishery. This result would be dependant upon tha elimination of u ncertainty regarding future management regulations. The elimination of unprofitable risking operations would appear to boost average f le e t p r o fits and bring even c la ss I operations to marginal productivity.

Model case studies suggest that regulation of the surf clam fishery through implementation of a landings tax would impose undue economic hardship upon the su rf clam fishing industry, and could jeopardise long run stock siae by permitting overharvesting. An adequate level oT capitalisation with only modest short run declines in stock aiae could be maintained with a landings tax Sufficient to increase fishing costs by $0.40 per pound of meets harvested, but long run reductions in stock else would be likely to occur. Moreover, vessel profits would be reduced below levels achieved under current management regulations.

The results of this analysis indicate that sixe limit restrictions in the surf clam fishery may deserve further study. A lower sixe limit restriction could result in slightly greater vessel earnings among class II and III vessels while producing minimal stock siie reductions 15ft during the six year planning hcriaon. However, uncertainties regarding the atook-recruitment relatione hip in the surf clam fishery indicate that reduction in slit limitn should be approached with extreme caution.

This a n a ly s is examines major management d irectio n s that might be followed to improve economic efficiency and protect stock size in the surf clam fishery during a six year planning horizon. It suggests that the Hid-Atlsntic fishery Management Council has bean following a prudent course of action. Results of this analysis suggest that the council should continue to manage the fishery using quarterly yield quotas and efTort hour restrictions.

However, consideration should be given to lowering the minimum siae limit on surf clams. Lowering the size limit to 4.0”, essentially an unregulated harvest, would appear to threaten stock integrity. Consideration should also be given to raising the yield quota to a level closer to theoretical MSY, In the absence of higher yield quotas, the f is h e ry w ill experience a large number of closures in the sh o rt run. Model re s u lts also suggest that consideration should be given to elimination of the vessel entry moratorium. Additional data describing propensity of surf clam vessel operators to enter and leave the fishery are required to determine whether lifting the moratorium is a feasible option.

Using this model fishery managers and planners may test and refine the results of even more specific ideas 1 59

that could e v e n tu a lly lead to optimum management strategies. This model accomplishes its main objectives.

It permits the idantincation of segments of the surf clam fishery that deserve further study, and elucidates general trends in Tishery hehavior in response to changes that might be imposed upon tha system by management decisions.

Continued research into the biology and population dynamics or the turT clam is vital to the construction of models with a greater degree of realism. This research has used what is known of su rf clam biology and in v estig a ted recant changes that have occurred in the economy of the fishery to further our knowledge of the interaction between the biology and economics of the fishery. 1 GO

LITERATURE CITED

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Anderson, L, G. and A. Ben-Israel. 1982, A Modal Description and User's Manual Tor the University of Delaware Fishery Simulation Project. A Report to the University of Delaware Sea Grant Program Under Contract No. NA79 AA-D-00118,

Applegate, A. J. 1983. An Environmental Model Predicting the Relative Recruitment Success of the Blue Crab, ^pllinectes sanldue (Rethbun), in tha Chesapeake Bay, Virginia. M.S. Thesis, V irginia I n s t i tu t e of Marine Science.

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Barker, A.M. and A. S. Merrill. 1967. Total Solids and Length-Height Relation of the Surf Clam, Soisula solidisaima. Proceedings of the National Shell­ fish Association, 57: 90-94.

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Caddy, J. F. and A. ft. B illard. 1976. A F irst Estimate of Production from an Unexploitsd Population oT the Bar Clam, Boiaula solidisalma. Technical Report No. GAB, F ish eries Research Board of Canada. 1 61

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Chang, C. W, , J, H, Ropes, and A, S. Merrill. 1976. An Evaluation of tha Surf Clam Population and Fishery in the Middle A tlantic Bight. Sandy Hook Lab Reference Number 76-1.

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Federal R egister. 1977. Surf Clam Fishery Management Plan. Federal Register, Vol. 42, No. 227, Friday November 25, 1977.

Foote, R. 1957. Analytical Toole for Studying Demand and Price Structures. Agriculture Handbook Ho. 146, United States Department of Agriculture, Waahi ngton, D. C. 1 6 2

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Franz, D. R. 1977. Size and Age-Specific Predation by Lunatia heroa ( Say, 1 622) on the Surf Clam SPltula aolldlaslma (Dillwyn, 16171 off Western Long Island, Hew York. Veliger 20:1 44-1 50.

Gordon, H, 1954. The Economic Theory of a Common Property Resource: The Fishery. Journal of Political Economy, 62:1 24-1 42.

Grant, W. E. and W. L. G riffin. 1 979. A Bioaconomic Model or the Gulf of Mexico Shrimp Fishery. T ransactions of tha American Fisheries Society, 106: 1-13.

Granger, T. and ft. Newhold. 1974. Spurious Regressions in Econometrics. Journal of Econometrics, 2:111-120.

Hancock, D, A. 1973. The R elationship Between Stock and Recruitment in Exploited Invertebrates. In: Fish Stocks and ftacruitment, Proceedings of a Symposium Held in Garhys, 7-10 July 1970. B. B. Parrish, ed. , ftapporta et Frocaa Verbaux dee Reunion, Conseil International Pour 1' Bxploration de la Her, Vol. 1 64.

Hildreth, C. i960. Simultaneous Equations: Any Verdict Yet7 Economatrica, 29:5 5-58.

Johnston, J. 1963. Econometric Methods. McGraw-Hill Book Company, Inc. , Hew York, Mew York.

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Jones, D. 5. , I. Thompson, and H. Ambrose. 1978. Age and Growth Rate Determinations for tha Atlantic Surf Clam, SPlsula solldiesima (Bivalvla: Mactraceal, Based on In tern al Growth Lines in Shell Cross-Sections. Marine Biology, 47:63-70.

Kerswill, C. J. 1 944. The Growth Rata of Bar Clams, Fish eries Research Board of Canada, Progress Report, Atlantic Coast Section, Ho. 35. p. 18-20.

Kerns, W. 1981. An Econometric Model of V ir g in ia 's Hard Clem Industry. Technical Paper Humber 2, Virginia Joint Legislative Audit and Review Committee.

Klein, L. R. 1 960. Single Equation vs. Equation System Methods of Estimation in Econometrics. Econometrics, 26:866-871. 1 63

Larkin, P. A. , P. P. Raleigh, and N.J. Hi 11 monsky. 1964. Some Alternative Premise* for Constructing Theoretical Reproduction Curves. Journal of the Fisheries Research Board of Canada, 21:477-484.

Levins, R, 1966, The Strategy of Model Building in Population Biology. American S c ie n tist 54(4) 421-431.

Loesch, J. G. and J. H. Ropes 1977. Assessment of Surf Clam Stocks in Nearshore Waters Along the Delmarva Peninsula and in the Fishery South of Capa Henry. Proceedings or the National Shellfish Association, 67:29-34.

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M id-Atlantic Fishery Management Council, 1979. Amendment Number 2 to the Surf Clam and Ocean Quahog Fishery management Plan.

M id-Atlantic Fishery Management Council. 1981. Amendment Humber 3 to the Surf Clam and Ocaan Quahog Fishery management Plan.

Mid-Atlantic Fishery Management Council. 19B2. proposed Amendment Number 4 to the SurT Clam and Ocean Quahog Fishery Management Plan.

H iller, M X. and D A, Nash, 1970. Regional and Other Related Aspects of Shellfish Consumption -- Some Prelim inary Findings Trom the 1969 Consumer Panel Survey. Division of Economic Research, United S tates Bureau or Commercial fish e rie s, Working Paper No. 74,

Moody, C, 1982. Personal Communication

Hurawski, S. A. 1977. Yield per Recruit Analysis of Surf Clams in the Middle A tlantic Bight, N. M. F. 3 . , Woods Hole Lab Reference Number 77-12, 10 p.

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National Marina Fisheries Service, 1978, Status Report, Anoxia in tha Haw York Bight, 1976. NOAA Professional Paper Ho. 3, C. Sindermann and R. L. Swanson, ads.

National Karine Fisheries Service, 1978-1981, Logbook Records of SurT Clam Fishery Statistics, Northeast Regional Information System Status of FCMA - Regulated Shell Fish Fisheries, NEREIS Reports 197B-19 B1.

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Glossary

Ago Composition Analysis - A general method of estimating

survival by comparing tha number of animals alive at

successi vs ages.

A utocorrelation - Measurement of the degree of correlation

among elements of a given time series through the

use of lagged coefficients.

Brody Growth Coefficient - A growth rata coefficient

expressing the difference between the asymptotic

eiaa and the actual siae of e fish- A measure

of growth rate.

Catchabi1ity Coeffecient - Tha fraction of a fish stock

which is caught by a defined unit of fishing effort.

Cohort Analysis - Computation oT the fishing mortality rate

experienced by a year class a successive ages using

i t s catch at each age as obtained from catch

statistics and yearly age composition data.

Durbin-Watson Statistic - A statistic used to test for

serial correlation among the error terms in

regression analysis. 1 66

Econometrics - The application of mathematical form And

statistical techniques to the testing of economic

t heori aa.

Economic D islo catio n - D isruption of e s ta b lia h s d economic

order because Of failure in normal market mechanisms.

Economic Rent - Income or gain th a t is a d i f f e r e n t i a l

return or Surplus above costs.

Effective Fishing Effort - Rate of fishing effort which

accounts for effort and gear restrictions.

Exogenous Variable - Originating or determined entirely by

factors external to the equation system. Not

influenced by other variables in the equation.

Grose Stock - Total revenue taken in by the fishing vessel.

Meteroseedasticity - A situation which occurs when the

variance of a aeries of observations around a

regression line is not constant. In a time series, the

variance could become larger or smaller with the

passage of time. This is an example of

heteroscedaaticity, and the Gauaa-Harkov assumption of 1 69

constant variance is violated.

Marginal Coats - Incremental costa, or differential costs.

Marginal Revenue - D iffe re n tia l revenue.

Mill t i col 1 i naari t y - Several independent variables having

the same line or plane in common. This would be

indicative of i nt er co r n 1 at i on

Overcapitalization - An aggregate level of investment in a

fishery which prevents individual firms from obtaining

the optimum economic return.

Private Costa - Economic co sts incurred by individual

f i rms.

Social Costs - Macroeconomic costs incurred by the industry

aa a whole or so ciety as a whole. 1 70

Appendix A

The calculations pirTorflied by each model subroutine are described in detail below. Each subroutine is presented below in the order in which it appears after the main program. This order is determined prim arily by the frequency with which each subroutine must be used by the model. The program code follows the d escrip tio n of each subroutine.

Bubroutine GROWTH

Subroutine GROWTH calculates the change in size of each cohort resulting from natural mortality, and the weight of each cohort re s u ltin g from von Bertalanffy growth. The following equations ere used to calculate cohort size and weights

(1) COH. , , * COHx . , - Zm.t * COH,

where:

COH,,! = Population of clama in subregion x and cohort y l*.r = Natural mortality rate of population in

subregion x and cohort y.

(21 Ln» , f * < 1 -a ' * x * H[I"° * l ” ' r0J , l whe re 171

Ln. H , * Shell length of clams (mm) in subregion x and

cohort y,

Li inf* - Asymptotic shall length of surf clams (mm).

B * Brody growth coefficient for clams in region x < mm) .

I MO * Month.

T* * Hypothetical time at which shell length is equal to

zero ( yrs) .

(3) H t , , t - (A * ( I n , , / ) ) * ( B)

who re;

WtNl) ■- Drained meat weight of clams in subregion x and

cohort y (1bs),

A * Experimentally derived conatantt .000111 for Delmarva,

.000100 for New Jersey (see chapter II).

B - Conversion factor expressing grama as pounds (.002046).

This subroutine returns the following values to the main

program: shall length of clams in each region and cohort weight of clams in each cohort and subregion. A weighted average of clam meat weight Is used in cohorts 6-25 to calculate the weight of clams greater than 5. 5 inches long.

Subroutine FRQRET

Subroutine PRORET determines total and categorical costs of processing clam meats harvested in the rishery.

Ab indicated in Chapter IV, total processing costs may 17 2 fluctuate depending upon the season of the year. During the months of January, February, March, July, August, and

September, total processing costs are equal to the winter-surnmer rate described in Chapter IV. During the months of April, May, June, October, November, and December the total processing costs are set equal to the spring-fall rate described in chapter IV. Variations in processing costs a rise from d iffe re n c e s in meat weight re s u ltin g from weight changes before and after spawning.

The subroutine calculates the number of bushels of clams av ailab le to processors and the pounds or meats from this harvest that are actually used by processing plants.

Two conversion factors are used to derive these values.

One bushel oT middle Atlantic surf clams is considered to yield an average of 17 pounds of maats (Mid-Atlantic

Fishery Management Council, 1982) and, aa indicated in

Chapter II, one bushel of clams yields an average of 11 pounds of clam meats a f te r removal of the v isc e ral m aterial and cleaning. Individual processing coats are derived using tha following relationships.

(4) C5T, ■ < PRCST) * ( PCST* )

where:

CST, - Coat contributed by category x toward processing a

pound of clam meats ( %) .

PRCST - Total cost to produce a pound of meats ( $) . 173

PCT * Percentage of total pfoctitlng cost contributed by

cost category x ( .

(5) TC3T. - ( C5T„) *

where:

TCSTy ■ Total monthly processing coats contributed by

Cost category x.

PLBS - Pounds of clam meats produced during the month

< lbs) ,

Total monthly processing costs are determined by summing the costs derived Tor each processing coat category

identified in Chapter IV. Because processing costs are

primarily semi-variable, a lower limit on coats is identified in subroutine PRORET. Processing costs that fall below the identified floor are discarded. Wholesale prices are calculated in this subroutine using the following relationships.

(6) TRE V„ * (TPROC) * (TPRGF.)

where:

TREV. * Total industry revenue required to obtain profits

x in excess of costs (A).

TPROC - Total p ro c e s s in g c o s ts ($) .

TPROF- ■ Rate of profit x in excess of costs. 1 74

! 7) WPR„ - < TREV. ) /( PLBSl

wbe ra:

WPR, = Wholesale price of clam meats associated with

profit x in excess of costs <$),

PLUS * Humber of pounds of clam meats produced (lbs),

Subroutine CQUOTA

The function of subroutine C4U0TA is to determine

whether quarterly harvest quotas have been exceeded, and to

make it possible to stop all fishing activities if quotas

have been exceeded. To accomplish this task, the

subroutine identifies each quarter of the year by keying

upon the month, keeps a running q u a rte rly to ta l of clam

harvest levels during each quarter, and compares this

running totel with quarterly quotas applied to the

fishery. If quarterly quotas are in excess of seesonal

harvest totals, the fishery remains open. If, however, quarterly harvest totals ever exceed quarterly quotas, control is transferred to a step at the end of the subroutine that seta effort hours equal to aero until the end of the quarter. At the end of each quarter, control is once again transferred to tha beginning of the subroutine where effort houra are reinstated and running quarterly harvest totals are once again aet equal to aero. 1 75

Subrout i ne HODAT

Subroutine HODAT la responsible for writing monthly

output to a user's terminal and to an output nil. This

Hubroutlns opens an output Tile called HODAT into which the

following data are entered at the termination of each

month: current year and month of the simulation, quarterly

harvest quota, and harvest during the current month. The following data are entered separately for northern New

Jersey, Delmarva, and the e n tire middle A tlantic Region.

These data are segregated according to vessel class, number of vessels actively fishing, number of effort days utilised, average profit realised per vessel, total fleet profits for each region, customary captain’s share paid per vessel, and average vessel profits lass customary captain’s share. The following data are entered separately for each subregion and for the en tire middle Atlantic: number of remaining clams in the natural population greater than or equal to 5.5 inches in length. This entry is made in numbers, pounds of meat weight, and in bushels of clams. A small amount of information about the processing sector is also entered each month. The following data are entered: bushels of surf clams processed, pounds of meats produced, total middle Atlantic processing costa, and the wholesale prices of aurf clam meats that would be associated with breaking even, earning a 5* return over costs, earning a

IDS return over costa, earning a 15* return over costs, and earning a 201 return over coats. Output is visible on a 1 76 terminal screen during A simulation run, or may be printed from the output file.

Subroutine HARYST

Subroutine HARYST is responsible for calculating clam harvests in all regions. Harvest in each region is calculated by region and cohort using the following re 1 ationshi pa:

( 8) HARV.hT,, -V,,, * ED, , , * C A r1 ■ * COH,, T

where:

HARV,irit ■* Harvest in subregion of * of cohort y by

vessel class a.

V,,, - Humber of vessels in subregion x of class a.

ED,,, - Humber of fish in g effort daya worked by v essels

in subregion x and class a.

CA,,, » Catchability coefficient Tor cohort y by vessel

c lass a.

COH, , r *■ Population s u e of clams in subregion x of

cohort y.

THARV,,. - Sum of harvests in suhregion x by vessels of

c la ss 3.

( 10) LBTn ,. - HARV, . T t ± * WT,

where: 1 77

LBT... - Total weight of clams harvested In subregion x

by venae la of olaaa a.

HT, « Representative meat weight of a clam in cohort yr

After the total weight of the harvest has been determined

for each vessel class and subregion, these values are

summed to calculate total harvest for the entire middle

Atlantic region and for each vessel class.

Subroutine YEHBX

Subroutine VENfiX calculates the number or vessels to

enter or leave the fishery at the end of each year, depending upon potential profits available. At the

beginning of thin subroutine, nine profit arrays are established, one for each vessel class and subregion. Each month, fleet profits are entered into a unique location of the profit array crested for each specific vessel clans and subregion. At the end or the year, all of the monthly array locations are summed to derive annual fleet profits for each vessel class and subregion. Vessel proTits are compared to profits that could be obtained by investing a sum equal to the market value of the fishing vessel at a prevailing market interest rate. If the rate of return available for a class of vessel fishing within any subregion is greater than the rata or return outside of tha fishery, then the fleet sixe is automatically increased by a user defined percentage termed VINCi, where z 1 78 represents vessel class. IT the rats of return available within the fishery is lower than that available outside the fishery, fleet siae is decreased by a user defined percentage termed VDEC,, where a represents vessel class.

To simulate the effects of a moratorium on vessel entry and exit from the fishery, bath VlHCa and VDECi may be set equal to aero. As noted in Chapter IV, it is very difficult to derive an average market value for vessels in any particular siae category; however, the procedure that was used to produce estimates of veaael market value according to alia classification was described there.

Survey data ware inadequate to measure the percentage of vessels that would leave or enter the fishery in search of greater profits during any single year. There is no single absolute expression that may be used to describe vessel movement into and out of the fishery. In order to simulate the effects of removing the vessel moratorium, VINC and

VDEC were both initially set equal to 0. 33. It is not unrealistic to assume that if greater profits were available in another fishery or occupation, and no disincentives to leave the Tishery existed (i.e tha threat of future vessel quota plans baaed upon levels of past harvest), one th ird of the Tleet would exit the fishery.

The difficulty associated with predicting such behavior is evident in the statistics describing vessel distribution in the middle Atlantic surf clam fishery. During the years between 1976 and 1902 inclusive, a period of time generally 1 79 associated with depressed and declining profits for class I vessels in the fishery, the following percent changes in fle e t siae occurred annually: -6 percent, -33 percent, -5 percent, 0 3 percent, -50 percent, *1A percent, -6 percent

( Hid- At 1 an tie Fishery Management Council, 1 982). Of course, during this same period of time, fishermen did not leave the fish e ry in great numbers because they were not certain whether they would he able to reenter the fishery at a later date. Thus, the relationship describing vessel entry and exit from the fishery reflects a limited knowledge of behavior among surf clam vessel owners.

Subroutine I HPVT

Subroutine INPUT defines all variables initially required for model calculations. In tabular form, this subroutine defines the variables listed below.

Vi , , x Number of vessels fishing in subregion i and

vessel class j.

CAi , j * Catchability coefficient for cohort i in

subregion j (equal to zero when fishery closes).

KNCOi - Size or cohort i in northern New Jersey.

SCOi - Size of cohort i in southern New Jersey.

DCOi ■ Size oT cohort i in Dalmarva.

Z>,j - Natural mortality (instantaneous as monthly units)

for cohort i in subregion J.

ZQQP - Ocean quahog ex-vessel price. 1 BO

2HCP ■ Hard clam < Hercensri a roercgnPT1 ft1 ex-vessel price.

Pishing costa:

20YP “ Oyster ex-vessel price.

CSTi , j * Fixed cast of fishing where i is a cost category

(1 " port fees, Z " interest payments, 3 *

personal liability and indemnity insurance,

4 * license feea and taxes, 5 *■ legal accounting

fees, 6 - general administrative expanse)

and j ia a vessel class.

FU1,FU2, FU3 * Fuel consumption of class I, II, and III

vassals respectively.

FUPR - Fuel price,

ZNISx - Miscellaneous variable rishing costs identified

in Chspter IV where a re p re s e n ts vessel c lass.

CAS. * Percent of gross vessel stock paid as crew wages.

CPSi * Percent of gross vessel stock paid as captain' s

wages.

Processing costs:

WC5T - Total winter - summer processing cost.

3FC5T - Total spring - fall processing cost.

TRN3 - Transportation cost.

PAT ■ Payment of patent fees.

VLAB - Labor cost.

ELEC - Electricity cost.

FULQ * Puel oil cost.

DEPFt ■ Depreciation cost.

REAR - Repair cost. 1 SI

ZOVH ■ Overhand co»t,

ZJNS - Insurance coat.

ZLEGL - Legal - accounting fees.

STRG - Storage coat.

CHEM - Coat oT chemicals.

PFLR * A Cost floor defined for processing operations.

Other variables:

ARATE. ■ A discount rate representing the prevailing rate

of interest in each subregion x,

XMVALr - Vessel market value estimates for three classes

of fish in g vessel a.

Management variables:

EHRS. - Permitted monthly fishing effort hours in

subregion x

TXLi.i ■ Landings taxes in subregion x applicable to

vessel class s.

QUOTAi * Quarterly landings permitted in quarter i.

WUTILi - Winter utilization rate of permitted fishing

hours among vessels in class i.

SUTILi = Summer utilisation rate of parmitted fishing

hours among vessels in class i.

SWTCHt =* Fishing costs incurred by vassals in class x

when moving fish in g oparations from one

subregion to another.

RCVTHi * Time required to recover costs of moving between

subregions Tor vessels of class 3.

BNJ = Brody growth coefficient for clams in waters off 1 62

New Jersey.

BDEL ■ Brody growth coefficient for clams in waters off

Delmar vs.

AVHTi , m - Weighted average of clam meat weight in cohorts

G through 25, where i ■ cohort and x ■

subregion.

EXFLft =* Ex-vessel price floor for su rf clams.

Subroutine SWf TCH

This subroutine calculates the number of vessels in each c la ss moving from one subregion to another aa a function of catch potential. Potential profits in each subregion are initially calculated using the following equat i ons.

<111 P H ..,,. * 1.0 * ED*,. * C li.i * CO. . ,

where:

PHi,, , t - Potential harvest in subregion x of cohort y

by vessels in class a.

ED.,, * Fishing e ffo rt days expended by vessels of class

3.

CiT . i r Catchabi1ity coefficient for cohort y by vessels

of c lass s.

CO. „ * =■ Siae of cohort y in subregion x.

<121 PVR* t t - LBT, , , - ( SCP - TXL. . ,) 1 83

PVR. . ¥ - Potential rtvtnui of fleet in subregion x and

vessel class 3.

LET.., - Pounds of clam meats harvested by vessels in

subregion x and vessels class a.

SCP - Ex-vessel surT clam price.

TXL.,, - Landings tax per pound or clam meats harvested

in subregion X by vessels of class 3.

(131 POP, . i - PCSTi * (( FU, * FUPR * ZMIS,)

* ED,,. + ( CRS, * PVR ,,*

(CPS. * PVR,.,))

where:

POP., I - Potential operating cost of vessels in subregion

x and in class 3.

FCSTi * Monthly fixed costs of Tishing by vessels in

class s.

FUi = Fuel consumption per trip of vessels in class a.

FUPR * Fuel price per gallon.

Z Ml Si * Miscellaneous variable costa of fishing by

vessels in class a.

ED« ,i * Effort days of fishing expanded by vessels of

class a in subregion x.

CRS, » Crew share of gross vessel stock.

PVR,,■ * Potential gross stock (total revenue) for a

vessel of class a fishing in subregion x. 1 04

CPS. - Captain's share of gross stock for a vessel of

class a.

(14) PR.„, ■ PVR.,. - POP,,,

where:

PR.,. ■ Potential earnings of vessels in class a

fishing in subregion x, The other variables are

as defined above.

(15) XPOT. . , - PR.,. - ( SHTCH,/RCVTK. 1

whe re:

XPOT.,! ■ Profit potential of vessels in class 7 fishing

in subregion x.

PR,., - Vessel earnings as defined above.

StfTCH, - Costs required for vessels or class 3 to move

between subregions.

RCYTMi * Period of tima within which a class 3 vessel

would expect to recover his costs to move to

a new subregion.

L ittle is known or the w illingness of su rf clam vessel owners to move their fishing operations along the mid-Atlantic coast line. Clearly, a wi11ingness-to-pay study would be useful in determining this type oT behavior.

Such a study, however, is beyond the scope of this work. 1 85

Data obtained Trom surveys and personal interviews shed l i t t l e lig h t upon tha movement p a tte rn of the fis h in g f le e t between subregions in search of higher profits.

Historically, however, the surf clam fleet has demonstrated great mobility and a readiness to exploit newly discovered clam beds. It is, therefore, not unreasonable to assume that one half of the vessels fish in g in any p a rtic u la r subregion would move to a new subregion if profits were potentially greater there. Subroutine SWITCH incorporates this behavior pattern. If, in fact, a greater percentage of the fleet shifts operations to newly discovered clam beds in any subregion, subroutine SWITCH could underestimate the rate or exploitation in that subregion for a period of several months. Estimates or annual harvest would, however, probably not be affected to any s ig n ific a n t extent. If h is to r ic a l s u r f clam f le e t movement is any indicator of future behavior, it appears unlikely that more than half of the fleet would be willing to move within a month to clam beds in other subregions for the sake of greater catches.

Subroutine EFFORT

Subroutine EFFORT calculates the number of allowable fishing effort days utilised by vassele of each class within each subregion. The subroutine first converts effort hours into affort days. Subroutine EFFORT assumes that one e ff o r t day is equal to 12 hours of fis h in g 1 B6

activity. Vessel surveys and interviews have indicated

that a fishing trip involves approximately 12 or more hours

of steaming time to trav el to and from the clam beds, and

12 or mors hours of actual fishing a c tiv ity , Under

restrictions of 24 hours of allowable fishing activity per

week, most su rf clam vessels can make a maximum of only two

trips per week. Subroutine EFFORT also accounts for a

percentage of allowable fishing hours that are not used by

the clam fishing fleet. Full Tleet utilization oT

allowable fishing hours is often prevented by bad weather

conditions. Moreover, a sig n ific a n t number of su rf clam

fishing vessels operate marginally, harvesting only a

sufficient quantity of clams to keep their harvesting

permits. These vessel owners are staying in the fishery

because they expect that changes in management, increased

surf clam population siae, and improved market conditions

will bring them greater Tuture profit potential. The

Mid-Atlantic Fishery Management Council has identified the average percent utiltixation of allowable fishing time

according to vessel class in the surf clam fishery

( Mid- At 1 antic Fishery Management Council, 19S1>, These estimates provide statistics to be used in subroutine

EFFORT.

Subroutine VE5RET

Subroutine VE3RET calculates economic returns to vessel owners and operators. The f i r s t step undertaken by 1 B 7 this subroutine ie the 1 mplii««ntat 1 on of the tx-vtsail price inodsl. Seasonal dummy variables are initialized depending upon the value of I MO, and the ex-vessel price equation described in Chapter III is applied. If ex-vessel prices are to be fixed, output from the price model is ignored end surf clam prices are set each year according to instructions from the user.

After defining the variable SCP, ex-vessel surf clam price, subroutine VE5RET proceeds to calculate vessel revenues and costs according to vessel class and subregion using the following equations:

(16) ¥R„ . , ■ XLBT. , * * < SCP - TXL. , , )

where:

VJti . i * Revenues in subregion x taken by vessels of class

a.

XLBT,,, * Pounds of clam meats h arvested by vessels of

class i in subregion x.

SCP = Ex-vessel surf clam price.

TXL,,t 1 Landings tax imposed upon meats landed by

vessels of class z in subregion x.

( 1 7) VftA.* t - VR, „ r tV, . ,

where:

VBA,,, * Average vessel revenue in subregion x by vessels 1 8B

of class z. VR, ,i - As defined above.

V.,i - Humber of vessels of clase s in subregion x.

Total mid-Atlantic vessel revenue is calculated

according to vessel class by summing the revenues from each subregion. Vessel coats are calculated in a fashion similar to the algorithm described above in subroutine

SWITCH. The following equations are employed.

(18) FC3T, - IW12) * ( CST1 . +■ CST2, + CST31 +

CST*. + CST5, + CSTfi,)

where:

FCSTj * Total monthly fixed costs of fishing for a vessel

of class z.

CST1t ■ Annual cost of port fees for a vessel of class a.

CST2* ■ Aggregate annual cost of boat repair, hull

inaurancet and interest for a vessel of class s.

CST3r * Annual cost of personal liability and indemnity

insurance for a vessel of class z.

C5T4. * Annual cost of License fees and taxes for a

vessel of class z.

CST5. ■ Annual coat of 1egal-accounting Tees for a vessel

of class z.

CST6. “ Annual cost of general administrative expenses

for a vessel of class z. 1 89

M 9 > x o p . , , - PcsT, + (( ru, * f u p r +■ z h i s .) *

ED. , . + ( CRS. * VRA. , ,)1

where:

XOP,,i * Total monthly operating costs < fixed and

variable) for a vessel of class z operating

in subregion k .

PCST, - Monthly Tixed costs as described above incurred

by a vessel pr cl asa a,

FU, * Monthly fuel use for vessels of class z.

PUPR - Fuel price per gallon,

THIS, =■ Monthly mi seel 1 nneous variable costs of fishing

for vessels of class z.

ED,,t J Monthly effort days for vessels of class z in

subregion x.

VHi..i * Average vessel revenue for vessels of class z

fishing in subregion x,

< 20) TCST■ . . - XOP, , * * V. . ,

where:

TCST. „t ■ Total fleet costs of class z vessels operating

in subregion x.

I0P,,, » Number of vessels of class z fishing in

s ubregi on x.

< 21 > AERN, „ , - VRA, , . - XOP. , , 1 90

where:

AERNt, i “ Average monthly earnings or a vessal of class ?

fiahing in subregion x.

VRA..■ = Average v t s s i l revenue as defined above.

XOP ■ Operating costa aa defined above.

Total fleet earnings for the entire middle Atlantic region and average vessel earnings for the entire middle

Atlantic region are computed separately for each vessel class by summing subregional totals,

Subroutine TttPRNT

The last subroutine to be called in the bioeconomic model is subroutine YRPRHT, The function of this subroutine is to write yearly output to the user's term inal, and to an annual output file , YRPRHT, opened by the main program. The f i r s t task performed by YRPRNT is to produce a fishing sector summary or "snapshot” describing conditions in the industry at the end of each year. The following information is provided and segregated according to vessel class, subregion, and region: number of vessels fishing, number of fiahing effort days, total harvest in pounds, total harvest in bushels, and total fleet profits.

After providing a fishing sector summary, YRPRHT next produces a biological summary. The biomass of clams and an estim ate of the number of individual clams in each cohort 191 and subregion and rtgion are displayed. Finally, subroutine TRPftHT produces an end of the year processing sector summary. Total annual processing industry costs are displayed and identified according to the following coat categories: raw product costa, transportation costs, cost of patents, cost of labor, cost of energy (electricity,

Tuel oil, propane), packaging costa, depreciation coats, overhead costs, insurance coats, 1ega1-accounting coats, storage costs, and the coat of chemicals used in processing. Alac displayed are the number of bushels of clams produced during the preceding year, the number of pounds of clam meats produced by the processing industry, and the wholesale prices that clam shucking plant owners would be required to command for breaking even, and receiving 5, 10, 15, and 20 percent returns over costa. Sue* e:s*i?pv **an a sement «toel

initial brc j d A*** i ng *-30-e3

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INVOKE HOMELY LGQP FOR ALL MONTHLY INCREMENTS AND CALCULATIONS

DO £0 J * 1112 IMQ* J

CALL MONTHLY SUBROUTINES INVCKlNS GROWTH* NATURAL MORTALITY » AND FlShlNG EFFORT

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RECALCULATE cohort size accounting for FISHING MORTALITY

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S*3) D53) h h of of A 1 :+ s ' D2Z + 1"ID23) + D2Z D32+HO33) :

h w w 3 3

+ i CQ n WEIGHT WEIGHT S61+HS6Z+ 031* O*1 + HO*2 + HOA3 > HOA3 + HO*2 + O*1 C5 h h i ch total TN5*)(NCD5 TN6*XNCC6 TS2*5CP2 : 5 - h h h oc 5» TN3«X*.TN3*X TNS«X TSZ*X h h w SC0**SCC4-(HS fcl+HSA2+HS*3) SC0**SCC4-(HS MS53) + SC35-SC05-MS51HS52 + SC36*5C36-( 0C31-0CC1*CMD11 + HDU + H013) + HDU + + 0C31-0CC1*CMD11 DC32»0C02-CN021 OC33-0CC3-C DC£K-DCC*-£ dcd 3CD6*uCC4-tHCil + H[J62*H0 63 3 63 H[J62*H0 3CD6*uCC4-tHCil+ TrfTN2«XWTN2e*NeC2 TrfTN2«XWTN2e*NeC2 T THTSl-XWTSl*SCOt THTSl-XWTSl*SCOt T«TN6»X T T T«TS3"XWTS3*SC03 THTS4*XHTS*#SC04 T*HNl«XWTNlftXNC2l T*HNl«XWTNlftXNC2l TNP3P«XNCDl+RNC02+XNCQ3+XNC0A+XtiCQS*KNCQ4 TNP3P«XNCDl+RNC02+XNCQ3+XNC0A+XtiCQS*KNCQ4 TNUT-TWTN1+TWTN2+TUTN3+TWTN4*THTN5*THTN6 THTS3-XHTSS*SCOS THTS6»*WTSA*5C06 TwTN*■X*TN**XNCC* TWTD2»XWT02*DCQ2 TWTD2»XWT02*DCQ2 TSP0P*$Cf]l+SCD2 + SC03+SCQ**$C05+SC06 SC03+SCQ**$C05+SC06 + TSP0P*$Cf]l+SCD2 TWTD*«KWTD4*DC34 TWTD*«KWTD4*DC34 TSwT-JWTSl+TkTS2+TWTS3+TWT5A*TWTS3+TWTSA TWT01*XWT01*DC01 TWT01*XWT01*DC01 TwTD6«XwTQ6*OC36 TOPGP»DCQl+OCG2+DC03+DCQ*+OCO5+OCO6 TOPGP»DCQl+OCG2+DC03+DCQ*+OCO5+OCO6 TWT03«XUT01#DC03 TWT03«XUT01#DC03 TWTD5*XwTQS#DC25 TWTD5*XwTQS#DC25 T0WT»THT01+THT02*TWTD3+TWTD*+TWT03+TWT04 CALCULATE TOTAL NORTHERN NEW JERSEY POPULATION SIZE AND SIZE AND JERSEY POPULATION NEW NORTHERN TOTAL CALCULATE CALCULATE TOTAL WEIGHT WEIGHT TOTAL CALCULATE CALCULATE TOTAL SOUTHERN NEW JERSEY POPULATION SIZE AND WEIGHT SIZE AND JERSEY POPULATION NEW SOUTHERN TOTAL CALCULATE CALCULATE TOTAL WEIGHT OF EACH DELHARVA COHORT GROUP COHORT OF DELHARVA EACH WEIGHT TOTAL CALCULATE CALCULATE TOTAL DELMARVA POPULATION SIZE A NO WEIGHT NO SIZE A POPULATION DELMARVA TOTAL CALCULATE C C C CALCULATE C C GROUP COHORT C

a u u o u u ij ij u uu u u o u C HEIGHT SIZE AND POPULATION MID-ATLANTIC TOTAL CALCULATE C 19V

c

TnrfT»TMWT+TSwT+TD*T C C CALL. SCCN2MC SUBROUTINES (VESSEL R ETURN S . PROCESSOR RETURNS) AND C CAUL PRINTOUT 3* MONTHLY DATA IF REQUIRED C CALL YE5»ET ■JRITECL.*) SCR CALL PRESET ifcans 6 . e:.* y ')C all mopat

C UPDATE ANNUAL TCT6LS 0* ALL VARIABLES INCORPORATING MONTHLY CHANGES

C ANNUAL EPPJRT DAYS

A£D L1 *AE D L1 + E 011 AED1Z«AED12+!012 REDl3BAE313*ED13 AED21-AEG21+ED21 AED2Z»A£Q2£+ECZ2 AEDZ3-AED23+E0Z3 AED31*AED31+ED31 AE03Z-AED3Z+ED32 AED33*AED33+E033 AEDMAl*AEDU + AS02l + AED31 AEDMA2-AED1Z+AE0Z2+AEDJZ A£0MA3*AEO13+AEDZJ+AED33 C C annual harvest C ANLBT1»ANL5T1+KNL5T1 ANLBT2-ANL3T:*XNLST2. ANLBT3*AHLBT3 + )tNLBT3 ASLBT1*ASLBTI+5LBT1 ASL&TZ»ASL|TZ+5L6T2 ASLBT3-ASLBT3+SL6TJ AOLBTlvADLETl+DLBTl ADL&T2*A0LBT2+SLBT2 AQLBT3-ADLBT3+DLBT3 AHAL61-AHALSI+XIBTOT AMALB2-AHALB2+SLBTDT AHALB3>AHALI3+DLSTDT C C VESSEL EARNINGS Z YE*SNl»rERNNl+EftNNL T6RNN2»VEflNN2+ERHNZ VERNS3aVERNN3+ERNN3 rE»NSl«TERNSl+ERNSl YERN52-TERNS2+PRNS2 VERN53-VERNS3+ERNS3 YEftNO1*TEftNDl+EflN01 1 98

Y ERND2-YERN02+ERNC2 Y=RNQJ*YERN03+EAN03

SHE OF PCPULATI3N GREATER THAN 5 1/2 INCHES IK LENGTH

AThTNS»TWTN4+ATWTN4 ATdTSt-TWTSi+ATwTSi ATJT0a*TMTCi+AT^TD4 AXNCD4-XNCC6+AXNCD6 AxSCCi*SCQS+AxSCGG AX0C34-DC3J*AX DC06 ASUN»ATmTN4/17.0 AaUS*ATWTSS/l 7*0 A&UCI-ATHTD4/J 7.0

ANNUAL PROCESSING SECTOR SUMMARY

APL6S*APLSS+PLSS aprdc - aprcc +tppcc ASh CST-ASNCST+Sh CST ATRN5*ATRNS+TTRNS APAT*APAT+TPAT axlab - axlab +txlas AELEC-AELEC*T£LEC AFULC"APULO*TFULO APRCP-APROP+TPRCP APK&«APKG+TPRG AOEPR»AOfPP+TDEPR AOVH*A0VH+T20VH AINS*AINS+TZIN5 alegl - alegl +tilegl ASTRG^ASTfiG+TSTRS ACMEM«ACHEM+TC^EN

CALL SUBROUTINE MOVING* VE SS ELS PROM ONE REGION TO ANOTHER

CALL SWITCH

CHANGE NUMBER OF VESSELS IN FISHERY IF END DP YEAR HAS OCCURRED

CALL VENEX END OF MONTHLY LOOP INCREMENTS

CONTINUE

CALCULATE AVERAGE ANNUAL wholesale prices required FOR PROCESSOR RETURN OF Sit 10*» I5*t 20?» AND BREAK EVEN

AREV1-APPOC AWPR1-AREV1/APLBS AREV£«APROC*l* 05 AWPfl£*RREV2/APLBS AREV3-APRQCP1.10 AWPR3«AREV3/APLBS MONTH' NORTHER n n o o n r> A L SUSRCJTI CALL SCREEN CN' RTC,) ETR EFFORT HOURS 'ENTER SOUTHERN FOR NEW JERSEY' WRITEC1,*) R H R S1 “E H P Si YOU CHANGE TO WISH 'DO EFFORT HOUR PER LIMITS WRITECIt*) EDL* EHRS3 READCLt*) EHRS1 EFFORT HDURS NORTHERN 'ENTER FDR REAOCli*) NEW JERSEY' WRITECl**) T x L 3 3 » T X L 3 1 RTU* 'NE EFFORT HOURS DELHARVA' 'ENTER FDR WRITEU»*0 RhR52«EHPS2 EHRS2 REAOCUF) ANS3 READCliO NEW 'ENTER LANDINGS FQR TAX DELHARVA'WRIT E JC11* TXL23-TXL21 N IF END TXL22*TXL2I FAS.O Y' 'JTHENIFCANS3.EO. T X L 3 2 - TR X E L A 3 D 1 C 1T , * ) X L 3 1 T X L 1 3 - T X L 1 I RTCA 'NE Nu OYSTER PRICE' NEu 'ENTER WRITECWAO ZMCP NEW 'ENTER HARO CLAM PRICE' REAOCli*) URIT£ClfP> EO1* TXL21 REAOC1,*) 'ENTER„l^£W LANDINGS SOUTHERN TAX FOR HRITEC1 NEW i*} JERSEY' NEW OQLLARS LANDINGS 'INTER IN TAX ) POUND PER * , PDR URITEC1 EOl* ZCCP REAOClt*) NEW 'ENTER OCEAN QUAHQG WAITEC1.*} PRICE' T X L 1 2 * T X L 1 1 RTCi) D YU IH O CHANCE TO YOU ANS NISH LANDINGS THE 'DC 2 TAX* REAOCt,*) WRITECli*) YOU CHANCE TO WISH COMPETING 'DC OP PRICES READC ANSI SPECIES' 1*4) WflITS(l*<0 RTC*) NW JERSEY' 'NEWR E A D CTX I iWRITECl**) * ) L 1 1 N IP END IOTP R6ADC1(*J IYRS t ' TEAR DP 'END WRITEtl,*} AN ( F I S3 * 5 C 'Y '* 3 TC A L R L P R N T I*<*Y ANSl.EC* '1THEN AWPRS*AREV*/APLsS A REv 5 A«PB4*AREVt/'APLBS AREVL-APPOCFl.15 AREVL-APPOCFl.15 FIR.GI»SG T 30 TO IFCIYRS.EG.IY»SS)G: «APRCC

n p TOE 1.30 1.30

T hen print ANNUAL 3 AT OUTPUT AND TCA 5CHG TC FILE 1 99

200

RHRS3-EHftS3 End I* WA1TEU.*) 'DO YOU WISH TO CHANGE QUARTERLY $001*5' REACCl.O ANS4 1FCANS4.ED . 'Y ')T+i£N WRITEC1**) 'ENTER QUOTA FDR FIRST QUARTER' R£AD(1.*) 3UCTA1 WRITEd .*) ' ENTER SUCTA FCfl SECOND QUARTER REA0C1,*) 0U5T42 WftlTEU,*) 'ENTER QUCTA F0R TmIRO QUARTER' REA0C1*«) CUCTA3 WRITEd**) 'ENTER QUOTA FOR FOURTH QUARTER READCl,*) DUCT AA END IF w frlTEd,*) 'CD YOU wISN TO CHANGE THE NATURAL MORTALITY RATE IN AN c r' WRZTEClf*) 'REGION' REA0(1,4) ANS5 IFCANSS.EO,'T'JTMEN WRITEC1**) 'DO YOU WISH TO CHANGE RATES IN NORTHERN’ NEW JERSEY ' RE*0(1*4) ANST IPCANS7.E0* 'Y')TH£N WfllTEd**) 'ENTER NORTHERN JERSEY NATURAL HQRTALlTY RATES IN COHD CRTS ONE THROUGH SIX, SEPARATED BY COMMAS' REAOt1,*) 111,112,113,114,115,116 END IP NRlTEClt*) ' DO TDU WISH TO CHANGE RATES IN SOUTHERN N JERSEY ' READClftA) ANS6 IPCANSa.EQ.'Y'} THEN WRITECl,*) 'ENTER-, SOUTHERN JERSEY MORTALITY RATES BY COHORT' RE A 0(1,*) 121*122,123*124,12 5.126 ENO IF WRITEC1,*) '00 TOU WISH T0 CHANGE RATES IN DELNAftVA ' REA0(1,4) AN59 I F{ AN S 9« E 2 • 'Y 'JTHEN WRITECl**) 'ENTER DELHARVA RATES BY COHORT' READtl,*> 7 31,132,13 3,134,135,136 END If END IF

ADVANCE CLAMS IN EACH COHORT TQ THE NEXT COHORT SINCE THEY HAVE AGED DNE V EAft *

NORTHERN NEW JERSEY

XNCQ6*XNCD6+XNC05 XNC05»CXNC05-XNCDS>+XNCD+ XNC04«(XNC04-XNC04)+XNC03 XNCC3*CXNC03-XNCD3)+XNCD2 201

j(ncz:*c jcmc32 -xncc£ a + *5CC2 SC0i»CSCC£-SCC:5*SCCl SCD1-SCC1-SCC1 c C DELMARvA C DCD6*DC£B+DCDS 0C05-CDC05-DCCE3+DCD* OCDW-CDC3L-OCQO + DCD3 0CD3-CDC03-DCC3)+DCa: 0CD2-CDC0Z-3C02)+0CC1 OCD1■DCCl-DC01 C C ENO YEARLY LOOP AND INCREMENT TO NEXT TEAR C 10 CONTINUE C 30 WRITEC1»*3 'END 0* SIMULATION' CLOSE CP) CLOSE CT) STOP END C Subroutine growth c C THIS SUBROUTINE CALCULATES THE CHANGE IN SIZE QF EACH COHORT C RESULTING PROM NATURAL MORTALITY AND THE HEIGHT OP EACH COHORT C RESULTING PROM von BERTALLANFFY GROWTH. THE WEIGHT OF COHORT NUMBER 6 C IS BASED UPON A WEIGHTED AVERAGE 0? TEAR CLASSES b THROUGH 20. C CCHH0N/HARV/HN11t HN21 ,«N311*N* 1*HN5l,HN61tHN1Z , WN2Z,HN32 ,HN*2, CHN52iHN62 »HN13,HN23t HN33t MNL3fHNS3tHN63 ^HSll*HS21t HS31»HSA 1* CHS 31 »h S61#HS12,HS22»HS32*HS4Z.HS52,HS6Z»HSl3*HS23.HS33fHSL3, CHSS3.HS6 3,HD11.hD2l|HDJ1*HDM»NOS 1 *H0611HO 12 * HD 2 Z*h 03Z »rtD*2, CH0S2*H062,HDl3,H0Z3,H3 33»HD^3»H0B3,HD63f Vll,V12»V13,V2ltV22, CVZ3,V31,V32,V33tEail#ED12 *ED13fE021t E022,E023t E03l.E03£fED33. CCAlliCA2ltCA3l»CA«l,CA51t CA6LfCAlZfCAZZiCA32tCAAZ,CA5ZTCA^2t CCAl3iCAZ3t CA3 3,CAL3*CA53t CA63tVNCOl,XNC02»KHC03»XNCQ«* XNCOSt CXNC06,SC01iSC0Z»SC03t SCOA,SC0Sf SCO«iDC01fOCfi2tOCQ3»OCOLf DC05f C0CO6f XNLBTl( XNLBTZt XNLBT3iSLBTl,SLBT2fSLBT3t0LBTl( DLBT2,0LET3f CO TOTi HATOT tXLBTOT(SLBTOTt OL6TOT»XMAL&S C0HH0N/GRW/Z11,ZI2,Zi3tZl*.Zl5iZl6.£21,ZZ2iZ23«Z24*Z25,226,Z3l» Cl 32*Z 33 Z 35 «£36t XLNN1, XLNN2, XLNN31 *LNN4, XLNNSf *LNN**XLNSlt

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CALCULATE THE SIZE An O HEIGHT OF NORTHERN NEW JERSEY COHORTS

CALCULATE THE LOSS TO EACH NORTHERN MEW JERSEY COHORT DUE TO AGE SPECIFIC NATURAL MORTALITY 2 0 j

*NCCl-*NC01-IUtt)CNCDl Z 12-1** ^CC.£ XNCC2-*NCD2-Z13*XNCCJ XNC04«XNC34-Z14*XNCG4 XNCOS- xn COS-HE s XNCCS *NCi:6*XNC::6-Z16*XNCC6 C C CALCULATE LENGTH C f XNCIVIOUA lS IN EACH NORTHERN JERSEY COHORT GROUP c £•2.719251? XLNNl*166. 64* (l-C E**C-5NJ*< 1*0/12.O-,0ZS53)3) KLNN2>16i.6t*C1-C E**C-SNJ*C(IMD +123/12.0-.025S 33 3 3 XLNN3*166.6fc*(l-tE**C-BNj*(Cin3+2*)/l2.0-.0255 33 3} XLNN4«l66.64*(l-3 3 C C CALCULATE THE AVERAGE WEIGHT OR INDIVIDUALS IN EACH NORTHERN JERSEY C COHORT GROUP C XHTNl«t.0001 DO*C XLNN1**2.0251 3 3*, 00 20 46 XJTN2*t. DOOlOO*tILNN2**2* B2S1J 3*.00204 6 XHTN3*C. 000100MXLNN3**2.8251))*.002Q4i XWTN4-C,000100*(XLNNA**2.9251>3**002046 X h TNS* C.000l00*tXLNN5**2.82 Si >>*.002046 XWTN6*AVWT61

C CALCULATE THE SIZE AND WEIGHT OR SOUTHERN NEW JERSEY CO h CRTS

C CALCULATE LOSS DUE TO NATURAL MORTALITY .

SC01-SCD1-I21*SC01* - SC02*SCC2-Z2Z*SC0Z $C03*$C03-Z23*SC33 sco4*sco4-:24*sco4 SC05*SCC5-Z25*SCQ5 St06*SC36-Z26*SC06 C C CALCULATE the LENGTH op INOIVIOUALS IN EACH SOUTHERN JERSEY COHORT c XLN$1"166.64*C1-CE»*C-SNJ* XLNS3-166.64*C1-CE**t-BNJ*CCIMD+243/12.0-.0255 33 33 XLN54*l66.64*Cl-(E**<-BNj*taNQO6>/l2.0-.e2S5>)>3 XLN$S-166.6**U->3 C C CALCULATE THE WEIGHT OP INDIVIDUALS IN EACH SOUTHERN JERSEY COHORT C XWT51*t.Q0Dl00*

C CALCULATE THE SHE AND WEI&h T c* DELHARVA COHORTS C C CALCULATE THE LCSS DUE TC NATURAL MORTALITY C 0Cai»0C0l-I31*0CGl DC02»2CC2-Z3 Z*DCQ 2 0C03*DCQ3-Z33*DCC3 0C0**0CC*-Z3**DCC* DC05*0CG5-Z35*DCCS DCa6»0C06-Z36*DC06 C C CALCULATE THE length of INDIVIDUALS IN EACH DELHARVA CCftDRT C XLN01»166.*3*C1-CE**C-B0EL*>) XLN02-166.*3*Cl-< E**C-BDEL*(CIMD +123 / 12,0-*OT9*)))) ' XLND3*16 6.*3*C1-3*,0020*6 XWTD2•(*000111«

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TZINS-CITNS*PL*S TZLEGL*CZL5GLPpLSS TSTRG*C5TRG*p L5S TC4E«-CCH£M*Pt.S£

CALCULATE total •»! D-ATL A NTI C PROCESSING COSTS FOR MONTHLY PERI22

TPRDC*TTRN3+TPiT+TxLA5‘*TELEC+TFUL0+TPRQP+TPKG+T0EPA+TREDR+ CT iOVH + TiiNS*T2LEGL*TSTRG+TCHEMtSHCST

SET PROCESSING COST FLOC R EOUAL TO SE FI- V ARIA BLE COST FDR PERIOD AND C“ ECK TO SEE IP THIS FLOOR S hOULQ £E INVOKED

IF{TPROC.LT,PFLR)TPRDC»PFLR C C CALCULATE WHOLESALE PRICE OP SHUCKED OUTPUT ASSOCIATED wITH C 01, l%t 10¥t 1ST, AMO 20 T RETURN TO PROCESSOR BEFORE TAX PA Y HE NT S C IFCPLBS.NE.O^th EN TREV1■TPRDC WPR1*TREV1/PLBS TREV2*TPRDCPI.05 UPft2*TREV2/PLBS TRfV3-TPR0CAI.10 UPR3«TREV3/PLBS TREV**TPflDC*l.15 HPRA*TREV4/PLB5 TREV3*TPRQC*1*20 WPR5-TREV5/PLSS C ELSE TREVIpO T R EV 2*0 TREV3-0 TRE¥4*0 TRfVS-0 WPR1*0 WPft2*0 WPR3*0 WpR4*0 WPR 5*0 ENO IF C RETURN END C subroutine ccucta c C THIS SUBROUTINE CHECKS TO SEE WHETHER QUARTERLY QUOTAS HAVE BEEN C EXCEEDED AND SETS EFFORT HOURS TO ZERO IF THfT HAVE C CCNH0M/HARV/MN11 ,HN21t HN3I, HN41 iHN5J,HN61tHN12iHN2 2, HN32,HN4 2, CHNS2,HN62,HNl3t HN23tHN33»HN4St HN53tHN63tHSIl, HS21»H$31#HS41f CHS51,HS61*HS12,HS22,H532 ,HSL2, HS52 ,HSt2tH513,HS23,HS33 ,HSi3, CMS53,M563^Dll,h:2lrH:3ifH04l,H05i,KOil,HDi:tM022,MD;2,Hj^;( C*05 2 ,15*2 tnD13*MDi3 ,h0 3 3,HD43,HD53» *04 3 iV U »V12*Vl3*V2l,v22f CV23»V3l ,V32, V33tE0U«ED12iEDl3tE32lfE0 22 .ED23*ED3l'EQ3:tE[>3 3( CCAI1 ■CaZllCA3liC*«ltC451t£i6l *CA1 £«CA22tC*32aCA*2»tA 52 ,CA*2t CC At3,CA23iCA3 3,CA*3.CA53,CAA3,XNC01,XNC02.XNCa3»XNC:4*XNC::5t CXNCD6,SCCl,SC:2tSCC3f SC:*tSC = 5tSC0 6*DCDl,OCaiiOCD3»OCa*tOC:5t cocae, XNLfT;,>4LST2»XNL3T3tSLJTl, SL0T2f5L&T3tDl,aTl, DlST2t3L4T 3 t CDTZT,MAT0T,«LfTaT.SLBT3TiDLSTaT,)CWii,BS CQMMaN/GH*/ZlliIL2tI13*il**Z15,il6T Z21tZ2 2 112 3,12 4 , £ 25 , 226,1 3 l, CZ32,i33iZ34.Z35*I34tXLNNl»XLNN2,XtNN3tXLNN*tXLNNSt*LNN6.XLNSlt

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CKMT5«fXMT35t XWTS*iXWT01)xwT02»XUT03iXMTD4fXyTD5iXMTD6fTwTNltTWT^2»

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CVflS3»VRASl,VRA£2iVflA$3»va01»VRDr,VR03,VftA01tVRAD2,VRAQ3,VRTNtVRTS,

CVRT DiV R1A,FCST1 , PCST2*FC5T3iCSTl11CST21,tST31 1 C S T ,CST5111STl2f

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COMKON/E*RT/EMliSl,ENRS2 , (1R S3 *MUTI LI i W UTIL2 , WUTIL3* SUTIL 1 »SUTlL2t CSUTIL3

COMMON/5U0T/RHRSI,RHRS2 , RMRS3tQUQTl11 QUOTA2, QUQT*3, SEHRT1 ,SEHRT2, CSEMRT3,SEHRTA,QUQTA+, QUOTA COHMDN/SwTCri/SUTChl, $ WTCM2, SMTCH 3 , RC VTN1, ItC VTM21 RCYT*3

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3 3 , 21 U

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CALN5£,XLNS3tXLN$4,XLNS5i X LN5 4 , XLND L , XLN021 XLND3 , X1.ND4, *LND5 , XLNDs , CIM[]»lYRS,XUTNl,XWTNZ,XWTN3,XWTN4r XWTN5,XWTN6,XUTSl,XwT52,X*T53*

CXWTS4,XwTSS,XUTS6,XWTD1,XWTC2,XWTD3,XHTO*,XWT05 ,XWTD6,T¥TNI , TWTN2,

CTHTN3,TwTN4,TWTNS,TWTN6,TwT51,TwTS2,TWT5 3»THTS4«THTS 5 * T«TS6,TUTD1 , CTJTD2,T*TD3,T*T04tTnTDStTHTD6*TNP:jP,TSPOP1TOPO?,TNHT,TS*TfTDkT, CTMPDP,TMWT,&NJ,BDELiAVWT6WAVHT62, AV*T63

COHHQN/VBET/ZLSCP# ZQQP, Z*CP,ZOYP'URhll * V&N2, VRN3 ,VRANl»VRAN2»VRAN3,

CTXLll,TXLl2,TXLl3,TXL21,TXL22i TX L23 , TXL31, TXt.3 2 , TXL33,VRS1, VftS2,

CVRS3,VRAS1|VRAS2,V(!A5 3, VftDl , V« D 2, V R Z> 3 , V Ft AD 1, V* 402, V RAD3 , V R T N, V RT S »

CvRT0*V«l4A*FCSTl,PCST2.PCST3t C5TXl,CST21 ,CST31f tSTAWCSTSl t CSTl2,

CCST22tC5T32»CST*2t CST521CST13#CST23*CST33*C5T+3,CST53,FUl( FU2,FU3, CFUPR,ZHIS1*Z«IS2*ZMIS 3 ,CRSI,C#S2,CRS3.CP SI,CPS2,CPS3.TCSTNl, CTCSTN2,TCSTN3,TCSTS1»TCSTS2,TCSTS3,TCST01,TCSTD2,TC5TD3tERNN1 ,

CERNN2, ERNN3,ERN51»ERNS2 ,E*NS3 , ERNQ1,ERND2, EflN03. ERNNT, ERNST, ERNDT, CT«AERN,FGMT,SCP,CST6lt CST62,CST63,EXFLfl, ClEftNNl,1ERNN2.AERNM3, AERN51, AERNS2, AERAIS3t * ERND1, AEHMD 2,AE*N03

COMMON/PRET/riSCST*SPCST ,P*CST ,9 UP, PLB 5 ,SHtST,TRN$,PAT,XLAB,ELEC, CFULD,PRDP,PKG,D£PR,«EPR,Z0VM,I1NS,ZLE&L,STRG*CMEM,TTRN3,TPATt

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COHMDN/JUQT/flMRSl,PHPS2,RMRS3.QUDTAl, QU0TA2,QUDTA3*SEM«T1, 5EHRT2, CSENRT3.SEMRT*.QUOTA*,QUOTA CDMMDN/SWTCH/SWTCrti,SUTCMa,SMTCH3*RCVTMl,*CVTMl,*CVTM3

COMNON/TffLT/AEDll, AED12,AE013,AE021, AED22,A!Di3,AE031tAED32, AED33. CAEDMA1» AEDHA2 «REDMA3 »ANLBT1, ANL6T2, ANL6T3, ASLBT1, ASLBT2, A$1BT3 t CADLBU , ADLBT2 ,ADLBT3 , AK1LS1, AMALB2, ANALB3, YEPNN1, YEftNf42 , YEGNIN3 , CTERNSl,TFRNS2*T€RNS3*TERNDl»rERND2iYERND3, ATWTN6,AtwT5 A. ATWTO4, CAXNC06,AXSC06,AXDCD6,ABUN,A&US,ASUD,4PLBS,APRQC , ^11

CASHCST,4TR*JSt d»AT,4XL4S , A EL EC, AF'JlOi A PRQP , APKG , AD E PR , A CV+i , A I M S , CALEGL,ASTRGtAtH&H,AWPRl,A*PR2,AWPR3f AWPR*f AwPR5

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COMMON/EFRT/EHRSI,EHRS2.EMRS3fgUTILlt«UTlL2iWUTlL3i*OTlLl«SUTIL2* CSUTIL3

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COMMON/EPftT/EHRS 1. EHRS2 ,EMR53 , VUTIL1, WUTIL2»WUTIL3, SUTIH , SLFtlLl, CSUT1L3

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CanN0N/TffLT/AEDLltAESl£«AED13fAED2l|AED22tAED23,AED3WAE032,AED33, CAEDMA1,AEQMAZ , IED*A3,ANLBT1 *ANLST2, ANLBT3, ASLBT1, ASLBT2, A$LBT3, CA3LBTl,ADLBT2,ADLaT3t AnALBlt 4MALB2*AMALB3*TERNNl,TERNN2,TERNN3, CTERNS1, TERNS2 , TERNS3. TERND1, TERN02,VERND3, ATNTN6 , ATHtSA, ATWTD6 , CAXNC06, AXSCOi,l*CCC4iABUN,ABUS,ABUD,APLBS,APRDC( CASHCST, ATRNS, A"AT, AXLAB,AELEC,AFULO,APRDP,AP*G,ADEPR ,ACVM ,AlNS, CALEGL,ASTRGt*CHEN,AUPRl,NWPR2*AVPR3, AW*R4, AWPR5 C C C CALCULATE ANNUAL PROFIT AND NUMBER OF VESSELS C EITHER ENTERING DR LEAVING THE -FISHERY C DIMENSION KNPR0P1CI2)i XNPR0F2<12)| XNPRDF3C12) DIMENSION 5PRDF1(12> * SPROP2C12), S PRDF3C12 3 DIMENSION O P R O F U m , DPROF2U2), DPRQF3C12) C DO 5 1*1*12 XNPR0FK1 >*0 XNPROFZCD-O XNPRDF 3 t I )*0 SPR0F1CI)*0 SPRDF Z(E)*0 SPRQF3( I)*0 QPRQF1(I >*0 QPRIJP2(I)*0 DPR0F3CO-0 5 CONTTNUE C C NORTHERN JERSEY CLASS I d Z \

X-1H0 XNPRDF1CK)*CB« n I IP(K.Ea-l2)TMeN XNAPRC1*Q OC LO 1*1t 12 XNAPR31*XNAP*0 10 CONTINUE ALRET1*ARATE1*XMVAL1 XNOIFi-XNAPROl-ALRETl !FCXN0XP1.GT.Q)THEN DVEL1*VINC1*V11 vu*iFix+V12 ELSE IFCXNOIF2.LT.OJTHEN 0V E12*VDfC2*V12 Y12*V12-IFIXC0V(12> END IF ENOIF C C NORTHERN JERSEY CLASS I I I C XNPRQF3C 0-ERNN3 IFCK.EQ.12JTHEN NNAPRDS-0 00 30 1*1,12 XNAPRQ3*XNAPRQ34XNPRQF3 Cl) XMPR0F3(I)*0 30 CONTINUE 4t. 4LRET3-ARATE3«XHVAL3 XNDIF3-XNAFR03-ALRET IFtXNDIF3.GT-0>TH|N DVE13*VINC3PV13 ¥13*IFIXCDVE131+V13 ELSE I*CXNDIF3.LT.0)THEN 0 VE13*VDEC3«V13 V13»V13-IFIX(DV£13> END IF END IF southern jersey cuss i-

SPR3FlCK)*tRN£l i f c k . e: . 12 jt* = n S4PRD1*0 DO *0 1*1•I 2 SAPRDl*SiPSCl+SPRCFici) S PffQFl<1>-0 CONTINUE SDIFl*SAPRQi-*LRSTl IFCSDIFl.GT.OJThEN OVE2l*VlNCl*V2l V21*IFIXCDVE^1)+V21 ELSE IFCSDIFI.LT^OITMEN DVE2l*VDECl*V2l VZ1*V21-I*1X END IF END IF

SOUTHERN NEM JERSEY CLASS II VESSELS

SPRDF2CO -ERNS2 IFCR*ED-12)TH£N SAPRC2-0 DO 50 1-1,12 SAPfTD2*SAPR02+SPR£JF2Cl) SFROe 2 t I ) - 0 CONTINUE * ■* SDIP2-SAPR02-ALRET2 IF(SDI*2.GT.0)THEN VDE22*VI

SOUTHERN NEU JERSEY CLASS H I VESSELS

SPRQF3CK)*ERNS3 iFCK.ea.i2) then SAPR03-0 00 60 1 -1,12 SAPR03-SAPRD3+SPRDF3CI) SPRCF3CI)*0 CONTINUE S0IF3*SAPRC3-ALRET3 IF

DELMASVA CLASS I VESSELS

CPR£FICK)*S&ND1 IFCH.E0,12)tMEN DAPRD1-0 DD 70 1* 1,12 DAPR£1«04PR01 + DPR:f1CD QPftOFI Cl)*0 continue D31Fl*0APfl?l-4Lflen IFCDOIF-GT.O)THEN 0v E31*VINC1*V31 V3WFIX(DV£31) + Y3l ELSt IFC001F1,LT,03TMEN Dv E31-VDEC1*V31 ¥3l*V31-IFlXfOVf31) END IF END IF

DELNARVA CLASS n VESSELS

DPR3F2CK)"EflND2 IFCK.EQ,12>TMEN DA PftQ2*0 DO BO I *1«12 * ‘ □ APRC2-DAPR0I+0PA0F2CII DFRCF2CI3-0 CONTINUE D0IF2*DAPft02-lLR£T2 IFC0DI*2.GT.0 )Tn EN 0VE32-VINC2*V32 V32"IFIXCDV632 > + V32 ELSE IFC0D1F2.LT.03TMEN DVE32*VDSC2*V32 V32*Y32-1FIXCDVE32) END IF END IF

OELNARVA CLASS I I I VESSELS

DPR0F3C* >■FRN D3 TFCX, ED.12)TMEN □APRC3-0 DD 90 1*1*12 DAPRQ3*DiPP03+DPflQF3CI) 22l j. DPflC*3(I)-C 90 CONTINUE 0DlF3OAeR03-ALRET3 I F(SC : F3 * GT. 0 )Th EN Ov £J3« v I n C3*y 33 V33*IFIx(DV£?3)+V33 ELSE IPCDCIF’ ,LT.O)TMiN DV£33-vDEC5*v33 V33»V33-IFIXCDVE33) END IF END IF c RETURN END C SUBROUTINE INPUT C C THIS SUBROUTINE INITIALIZES ALU INPUT PARAMETERS FOR Th E H3DEL AND C PASSES THE« TO THE MAIN PROGRAM AND SUBROUTINES WHERE JHET ARE C REQUIRED FOR CALCULATIONS C C0HMaN/HARV/HNllf HN2l»HN3lt HN4l,HN51tHN*l,HNlJt HNZ2,HN3Z,HN4 2t CHN52 t HN6Z fHN13iHN23|HN33,HN4 3 »HN5 3|HN63tMSl1 *HSZlfH$31 ,MS41, CHSSIfHS61fHSlZ1HS2ZiHS321HS4£,HSSZfHS62tHS13tHS23>HS33tHS43t CHSS3tHS63tH0IIfHDZltHD31tH04l,HDSl,HD6ItMDlZt HD22tHD32iHD42f CH052»H0E2|H013fHD23fHD33t MD4?,HD53t HD63»Vll ,V i2 ,Y 1 3 ,V 2 l,¥ 2 2 , CV23,V3I,V32fV33,(Dll,E012*E013tED21tED2 2,EC23tBD31, ED32,ED33t CCAI1,CA21,CA31*CA4l,CA3I,CAS1, CA12,CA22, CA32, CA42, CA 52,CA 62 ♦ CCAl3,CA23»CA33fCAA3»CA53tCA63|RNCCI*KNC02tXNC03.KNC0A,XNCDSt CXNCOAiSCOltSCOZ r SC03»SCOA,JCCSt SCOA»OCOll DCO2tOCO3,OC0L»DCOS, COCOA,XNL0Tlt XNLBT2t XNLBT3, SLBTItSLBT2, SLBT3, DLBTl, DLBT2 , DLBT3, COTOT, MATDT »XL3TQT«SLBT0T,DL6T0TtXMALBS CDNMON/GRM/2J1,112,'113,114,215,116,Z21,122,123,224,125,Z2*,Z3i, CZJ2,233f234'Z35,Z36tXLNNl,XLKM2'XLNN3,XLNN4,XLNN5tXLNN6iXLNSl,

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CCST22,CST3 2(CST*2tCST52tCST13*CST2 3,CST3 3,CST43rCST53,FUlt F u 2 ,F u 3 f CFUPf,I«IS1^ 2 mI s £«INl53,C*SltCRS2,C R S3 , CPS 1, CPS 2 , C P S 3 , TC $T NI, CTC5TN2jTCSTN3|TC5T51»7tSTS2tTCST53»TCSTDl»TCSTD2pTC5TD3t ESNNlt

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COMMQN/TRLY/AEDU,*£312tAEOl3 .AED21, 1ED22.AEQ23. AED31, 1ED32.AED33* CA i * AEO-*A2 , A£CMiJt AML9T1, ANLBT2 ,AMLBT3 , ASLITU ASLBT2 .4 SLBT3, CAOLBTI, A0L8T2*AQLBT3(AMALB1,AHA LB 21ANALB3( T£RNNl, TERNM2♦VEPMM3 , CYERH51, YERM52t YERNS3, tERNOl, TERND2, TESMD3. *TWTM6,ATwT36tATHTD6f C*XMCD6»AXSCQ6, iXOCQi,ABUV,*BUS,*BUD**PLiS, *PRDCt CA SMC ST, ATRNS, A&AT f AXLAfl .AELEC * AFULD*APRQP* APKG t A0£PA tACYH( AINS, CALEGL t ASTRG,ACM EM »AVPR1,ArfPR2 * Art PR 3 , AWPR*, IHBRS C C C INITIALIZE NUMBER DF VESSELS IN EACM REGION C ¥11*0 V12*0 V 13*0 V21-4..0 V22*?0*C ¥23*20,0 V3l*».0 v 3 ; ■ i( 3. o ¥33*41,0 c C INITIALIZE CATCNABILITY CO£Fe ICIfNTS FOR EACH COHORT IN THREE REGIONS c C A11*0 CA21-0 C A31■0 CA41*0 CASI*0 CA41*.OOOOOOSAQ54 CA12-0 c a ::-q CA32*Q CA*2*0 CA52-0 CA42*.0000011698 C A 13*0 CA23-0 C A 33*0 CA43*Q CAS 3*0 CA43*. 0000018864 C c initialize population structure (a.*. Nuness o* individuals in EACH C COHORT in EACH REGION) c XNC31-236500QQQQ.00 XHt02*1*670000000.00 XNCC3M920QOOOOOO.QO XNC04*83460000000.00 XNC05*19ZOOOOOOQO*00 XNCQ6*6960QOOOOO.OO SCQL-2365000000.00 5t02*2365000000.00 5C03-23*EOOOOOO.00 SC04-2365000000.00 SC05-2365000000.00 SCO6*2T9TOOOO0OO.OQ DC31*lli5OOOOO0O.OQ OCD2*155QQOOQODQ.OO DCQ3*feT*OQOQOOOO.00 DC04-1S500000000.00 Dcni-iiasooooooo.oo 0006*22000000000*ao c c initialize age specific natural mortality for each region c 111*.02 112*.02 113*.02 21 A*.02 215*.02 116*.02 221*.02 222*.02 123*.02 Z24-.02 225*.02 226*.02 131**02 232*.02 233*.02 234*.02 235*.02 2ZJ

Z 31* - 02 C C INITIALIZE LENGTH CF =40 C 3HjST GROUP IN EACH □F THREE REGIONS C UNNl *30 * 94 »lNA.2*69,*6 xlnns *?;.*? XLNN4-11?.? 5 XLMN5*1Z3.EG XLNNi-134,40 XLNS1*30 * 94 XLNS 2*69.46 XLNS 3*92-a■ XLNS4-1IC*35 XLNS5*12’ .F0 XLNS6*l34-40 XLN0l*39,97 XLNG2*T2.60 XLN03*96*90 XLNO^. i 1**T7 XLNOS*L2P.09 XLND6»13T* 98 C C INITIALIZE PRICES DF C3NP £T1NG SPECIES C Z0QP-.3519 Zh CP*3.0500 2 3Y P■2 * 5Q00 C C INITIALIZE FIXEC COSTS QF VESSEL operations C CST11-24C0.00 CST21-34000.00 CST3t*3lC0*00 * 1 CST41*1000.00 CST51-1300.00 CST61-400 .00 CST12-2400.00 tST22*49000.00 C ST3 2 *6000 « 00 CST42*2000.00 CST 5 2 *1397,00 CS T62*500 4 00 C ST 13 * 2*00 4 00 CST23-T8000.00 CST3 3 *10000.00 C ST 43* 4000.00 CST53-2000-00 CST63*SOO.OO C c initialize FUEL CONSUMPTION curing vessel operations C (GALLONS USEC PER TRIP) c FU1*300.00 FtJ2-5O(KO0 FU3-600.00 C C INITIALIZE FUEL PRICE C fupr - i . id C INITIALIZE VARIABLE MI SC ELL AN E CUS COSTS □* VESSEL OPERATIONS cCR C THREE CLASSES C ZMJS1072.00 ZNIS2*371.Q0 ZHIS3-577.00 C C INITIALIZE CRSm Share AND CAPTAIN'S SHARE FDR THREE VESSEL CLASSES t CRS1*.2Q CR5Z-.2Q CRS3-.Z0 C P Si ■ * 10 CP52*.10 C P S3 * • 15 C C INITIALIZE WINTER - SUMMER AND SPRING - FALL PROCESSING COSTS C MSCST-,3* SFC5T*.3l C C INITIALIZE VARIABLE processing c o sts c TANS',06T PAT*.023 XL1I-.1S ELIC**077 . .. F ULO*•03A PRDP*.0A3 PKG'.OOl DEPR-.052 R EPS*.099 ZJVH*,l*7 1INS-.02B ZlEGL'.OlZ STRG'.Ql? C HEM*.0 1 B C C INITIALIZE COST FLOOR POR PROCESSING C PFLR-0

C INITIALIZE RATE OF RETURN AVAILABLE DUTSIOE THE PIS HE R 7 FQR EACH C REGION C ARATE1«,10 ARATE2-.10 ABATE 3*.10 C c initialize market value oc t-sss vessel classes c XMvalI-53333.00 XNVALZ-Z2500Q.00 * MYAL3*5?Ot?O0 .00 C C INITIALIZE MUM!£R of EFFORT “ OU*S permitted »ER MQNTm c EHRSI-96.0D EhRS2*96*00 EHRS3«?6»00 RnftSl*96.00 ft MR S Z■96.00 RHRS3-96.00 C C INITIALIZE LANDINGS TAXES c TAL11*Q Tal IZ fO T XL 13■0 TXLZ1-0 TXL2 2*0 TXL Z 3 *0 TXL31*0 T XL3Z *0 TXL33-0 C C INITIALIZE 3UARTERLT QUOTAS C QUQTA1*6900QOO qucta:*s500ooo OuaT63-0500000 - ■ * quCT*4>6iaoooo c C INITIALIZE WINTER UTILIZATION RATE OP PERMITTED HOURS C HUTIL1**J3 NUTIL2».ZZ WUTIL3-.30 C c initialize summer utilization rate dp permitted hours c SUT1L1*#13 SUTILZ-.2Z SUTIL3-.30 C C INITIALIZE SWITCHING COSTS TO MOVE TO ANDTNERh REGION C SWTCrtl*0 SWTCH2«0 SWTC h 3*0 C C INITIALIZE EKOEC+iP TTME RECIPE: TC SECCVEft SWITCHING CCSTS C SC^Tmj-i.O BCVT**:»l .0 RC VT*3*1 .0 c C INITIALIZE 5RZDV GRCwTh CDEFICIENT FOR EACH REGION ftNJ*. ZT21 BDEL-.290* C C INITIALIZE % INCREASE AND DECREASE OF VESSELS IN FISHERY c as a function o = profit and l : ss c VINC1-C V1NC2-Q VINC3-0 VQEC1*0 VDEC2-0 VDEC3* 0 C C C INITIALIZE AVERAGE WEIGHT FOR COHORT 6 CMATURE CLANS) AVWT61*,3 AVHT6Z*.3 AVWT63*.3 C C C INITIALIZE EX-VESSEL PRICE FLDUfi FOR SURF CLANS C EXFLK*0*i0 MGNT*L C RETURN END - ^ c Subroutine switch c C THIS SUBROUTINE CALCULATES THE NJHBER OF VESSELS MOVING FROM DNS C REGION TC ANOTHER C COMKQN/hAflV/HNll»HNZliHN31 .HNA1»MNJ1tHN6 1 ,HN12 i HHZ2, HN32 ,HN*Z t CHNSZ|HN6i,HNl3,hN2 3tHN3 3,HN*3tWN53 *HN63* MS 11«H521,H$31*HS* 1* CHS5ItHSAltHSl2,H5IZ,HS32tHS«2fHS5ItMS62tH513(HS23tHS33fHS43f CHS53*HS6 3iH011,MDZ1,HC31*HD*1, MDSl, hD611HOt2 •HD2 1 1HO 32 .HO A1, CHDS2fH062iH013tMD23tH033fHD43»H053fHD63»VIIiVI2fVI3*V2l*V22r CV23,V3l*V32*V33*F0ll#£Dl2*EDl3*E021»ED22tE023»E031»£032»E0 33, CCA11t CAZLtCA31tCA*,lfCASltCAAl,CAIZ,CAZitCA32,CA*2,CA52 ,C*62t CCAl3.CA23fCA33,CAA3tCA53tCA63(XNC0ttXNCD2«NNCO3lINC04t XNCDSr CXNCQ6,SCC1tSCCZtSCa3*SCC*tSCOS,SC06,0CQI,OC02,DCQ3,0CO4 ,DCC5, CDC06!XNL6T1, (NLBT2,XNLBT3, SL1T1, SLST2, SLBTJ.DLBTl, OLiTZ,DLET3 t COTQTtMATOT,XLBTOTt SLBTOTf OLBTOI,XMALBS C0MMDN/GRW/IU,IL£tI13*Il*f 115* U6'ZZ1 »122,223t 224iZ25»I 26t131 i C132 iZ33>Z3*tZ35»Z36tXLNN1, ALNNZ, XLNN3, KLNN*, XLNWS. XLNN6 , XLNS1, CXLN5£»XLNS3fXLUS4t *LNS 5p *LN 5 61 XLN D11 X L NO1 1 XLND 3 t XLNQ* p XLN05 * XLND4 t Cl HOflYRSp XyTNl■XwTN2tXWT^31XUTN4l XUTht»t XUTN6(XUTSltXMTS2f XMTS3

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CALCULATE POTENTIAL PROFITS FRON EACH REGION - CLASS I

NORTHERN NEW JERSEY non n o n C CALCULATE SHITCHING VESSELS BEHAVIOR I CLASS FOR C + PHD51*XWTM+PHD61*XWTD6 CXWTS9+PH$614XUTS6 CXrfTNS+BHN6l*XrfTNi DP0T1*PRND1-C5WTCH1/RCVTHI) SPQT1»PRNS1-(SWTCH1/RCVTNL> i i v IPC-PCST1*LT.$P0T1)THEN IP(OPQT1.LT.SPOT1JThEN IP(OPQT1.LT.SPOT1JThEN IFCKNPQT1.LT.SPQT13THEK ELSE ENDIF V3l«IFlN

^ 2

SOUTHERN NEW JERSEY C C PNLST2*PHN12#* n o II CLASS CALCULATE PROMn EACH REGION - PROFITS POTENTIAL C NORTHERN NEW JERSEY C+PNN62*XWTN6 PRNN2-PVRN2-POPN2 PHNE PHN52»1*0*E012*CA52«XNC05 PHN42*l«0*EDl2*CA*ZFXNC04 PHN32*1.0«ED12*CA32*XNC03 PHN32*1.0«ED12*CA32*XNC03 ■* 'PHN22-l.Q*EDl2*CA12 ENDIF ENOIF ENDIF ¥21 ) /2 V21«IFIX( PHN12-1.0*ED1 2*CA12*XNCD1PHN12-1.0*ED1 ENDIF Vll*lFXX V21*IFtX POPN2«FCST2+(CFU2PFUPR+IMlS2)AE012+CC«S2*PVRN2)+tCPS2«PvRN2)> POPN2«FCST2+(CFU2PFUPR+IMlS2)AE012+CC«S2*PVRN2)+tCPS2«PvRN2)> PVRN2"PNLST2*CSCP-TXL12 ) ELSE ?l/2> tV ¥31«IFlX lFt XNPOTUEa-Ot»aTl)THEN lFt > V21»I*lX{V21/2 ELSE 1F<-^C3T1.LT*DP0TI>TMEN 1F<-^C3T1.LT*DP0TI>TMEN ELSE ENDIF V31*tFTK(V3l/2) E£*XNPDTlJTHEWIFCS^OTl. EN01 ■ I*C-FCST1,LT-KNP3T1JTmEN IF0TnTrtEN eC5Tl.LT,Dt>0TnTrtEN - ( F I

no 3«V1I IX W31«CVI1*I“ V ll* C V 2 1 -IF H (v 2 l/2 )) + CV 3l-IFI<{V 31/2)) + 31/2)) Vll + )) 3l-IFI<{V CV l/2 2 (v H -IF 1 2 V C ll* V ¥2l*{¥21-1*1*(¥31/2)5+V 21 ¥2l*{¥21-1*1*(¥31/2)5+V v 3 i * c v i i - i » i < c v i i / 2 j i + c v : i - i e i)(c v :i/:> )* v 3 i i 3 v )* :i/:> v i)(c e i - i : v c + i j 2 / i i v c < i » i - i i v c * i 3 v V21«CV11-I*JV(V11/£))*V21 i V31-CV31-IFIXCV21/21J+V31 V31-CV31-IFIXCV21/21J+V31 i 2«1.0**D12*CA62*)( f w TN1*PNN22*XWTN2*PHN3 2*XWTN3*PNN*I*KWTN**PHN5 2*XWT V11-IFKCV11/2) CM /1 1 1 *X k S n n >(Vl-*X I2) 1 3 V + )) C lI/2 ll-I*IX ¥ V )>*( n COA CD 2 n S PriSl2*l .Q*ED2 2*CA12*SCQ1 PrtS22-l.C*ED22*CA22*5C02 PHS3 2-1*0v*D22*CA32*SCO3 PrtSL2*l. 0*f&2 2*Cfl*2*SC3 4 PHS52*1.0*EC2 2*CA5 2*SCQS Ph S62«1*0*E022ttCAfcZ*SCC6 PSLST;«**Sl2*X*TSl+PHS2£ttXwTS2*PHS32**UT53+PHS*2WAJTSfc+PhS52ti CX^TSS+sHSi2wKWTS6 PVS52*pSL9T2»CSCP-T*L22J PQPi2*KC5T2+CC*y2»PUPe+:«IS2 5*E022+CCRS2wPVRS2)+CCpS2ttPVQS2ii P3NS2-PVPS2-PQP52

DELIA*VA

PHD 12*1.0*ED32*CA12*0C01 PM022-1,0PFC32*CA2 2*DCO2 PHD32M.0*fO32*CA32*OCO3 Ph D*2*1 ,0«SD32*CAH2*DCtH PND52-1*0»FD3 2*CA5 2*OCOS PHD62*1.0*E032*CA62*DCCI6 PDLBT2»PhD12*XWTDt+P«D2 2**UTD2+PHD32*lWTD3+PMD4 2*XWTD*+ CPmDS2**n TD5 + ph D62*JCSCP-TXL32) PDPD2*FCST2 + (CPU2*PUPft + 2^I52 >*E032 + tCRS2*PV«02) ■+ CCPS2*PVRD2)> PRND2*PV RD2-P0P02 XNPQT2»PRNNZ*(SwTCH2/RCVTH2> SP3T2-PRNS2-CSWTCM2/RCVTM25 DPDT2"PRN32-(SgTCM2/RCVTM23

CALCULATE switching SEHAVIOB FOR CLASS II VESSELS

IFC XHPEIT2.lt.S*0T2>THEN IF(0P0T2.LT,SPDT2)THfN IFC*FCST2.LT-SPDT2)THEN V2 2-CV12-IFIXCV12/25 3 + CV3 2-I*IX CV32/2)J+V22 Vl2»IPlX+V22 Vl2*IFlXCV12/2) ELSE V32-CV12-IFIXCV12/23 3+CV22-IFIXCV22/2))*VJ2 VI2-IFIXCVI2/2) V22-IFIXCV22/2) ENDIF ENDIF ENDIF ELSE IFC0F0T2.LT.XNP0T2)TNEN IFC-FCST2.LT.XNPDT2)THEN DELMARVA C C n r> rv SOUTHERN NEH JERSEY CALCULATE III POTENTIAL CLASS FROM PROFITS EACH REGION - C+PM55S*XWTS5+P C+PMN5 3*XMTN5+PHN63*XHTN6 PN543*1.0*fD23*CA*3*SCD4 PN543*1.0*fD23*CA*3*SCD4 P HS33 * 0*E . 1 D23*CA 33*SCC 3 P0PS3*FCST3 + fCFU3*PUPRPVR53*P5LBT3*CSCP-TXL23) + IMIS3)*ED2 3+CCRS3*PVRS3)*CCPS*PVRS35 ) 3*CA23*SCn2PHS23-1.0*fD2 PH513«1.0*!DZ3*CA13*SCD1 PSLBT3*PH513»XU1S1+PH$2 6 3*XUTS2+PHS33*XWTS3*PH5A3*XWT54PH563*1.0*ED2J*tA63*SC0 PRNN3*PVRN3-PQPN3 PDPN3«FCST3 + CCPL)i9FLtPR+ZPlS3)*EDl3 + + v32»I*:xcv32/2) IF£XNPDT2.*;.DD3T2lTHEN IF(— eCS T 2 * L T . DELSE B D T 2 ) T H E N ENDIF EfiOI* /22* lF :x {v 22/23 22/23 {v :x lF /22* E L 5 V32*IFIX( e I F ( $ P 0 T 2 . * C . I ( N P 2 T 2 ) T H E N w N43*1*0*FD13*CA4 3*XNC04 V3Z»CV2 2-I*I*CV22/2)>*V32 V3Z»CV2 2-I*I*CV22/2)>*V32 V22* +V32 V32*CV22-IFlX£ V2 2-IFIXCV12/23> )3+(VI /2 2 V12*(V£2-IFlx:V3 2/2))+CV32-IFHC VJ2/2 VJ2/2 5J+V12 2/2))+CV32-IFHC V12*(V£2-IFlx:V3

ne Z/U iZ V * jersey h S63*XUT56

J! ■

n o n CALCULATE VESSELS SWITCHING BEHAVIOR I I I CLASS FQR C V23-IFIXCV23/2) V23-IFIX(V23/23 ELSE V33-IFIX IFCDPOT3,LT,XNPQT3)THEN ■•* V23-IFIXCV23/2J EN3IF ENOIF IFC-FCST3.LT, XNPOTDT ELSE ENDIF IF(XNPQT3.EQ«DPOT3)THEN V33*IFIXTHEN IFC-FCST3.LT.DPaT3>THEN ELSE V13-IFIXCV13/2J IFCSPDT3.E0.XNP0T3JTHEN ELSE ELSE ENDIF V33*IFIX(V3J/2) VI3-IFIXCVI3/2) lFTHE* PRN03-PVP03-PQP03 PVR03*b0L6T3o(SCP-TXL3 3 > PVR03*b0L6T3o(SCP-TXL3 + R 4 D S 3 i P X W T D 5 + P H O i 3 f t X t t f T 0 □ P0T3-PfiND3-CSWTC4SP0T3*PfltJS3-CSyTCM3/RCVT**3) 3/PCVT**3)XHPCT3«PRNH3-(S*(TC43/RCVTM3) POLST3*P«Ol3*KWT01 + PN02 P3»KtaTaZ*PH033wUTD3 + PM043ttXhTO*. 4063* I. 0**0 3 3*0*6 3*000 6 PCP03*FCST3 + eU3#F(jPR £ f 3*P.VR0+ 3 lwi$3}*5D33*{CRS 3)+(CFS3*PVRD3) PHD53»1.0ttE033«Ci53ft3C:5 P^D^S-l.OttSOSS^Ci^BOOCCt PhD33*1.0*E0 3PhD33*1.0*E0 3*Ca33*0CC3 PHaZ3*L.0tt!O3 3*CA23*DCDZ prroi3*i.o*E033aCAi3PDC::i prroi3*i.o*E033aCAi3PDC::i VJ3*CV V2 3-CV33-IFIXCV 33/2)>*V23 VZ3*CVl3-IFIXCV13/233+V23 v33*tV23-IFlXCV23/2)J+ v33*tV23-IFlXCV23/2)J+ V3 3 Y33»CVl?-IFTX£VI3/2)3+£V23-IFI Y33»CVl?-IFTX£VI3/2)3+£V23-IFI XCV23*2> V23*CV13*IFIXCV13/Z))+CV33-IFI 23-1 Fix £V23/2J3 + £ K 1■ I - 3 3 V C V33/23) + 3 I V h h EN EN

6

j CV33/2))+V23 i* V33 6 ^ 2 7 j 7

ELSE V3J-CV2 3-1FIXC V2 3V2) 3+tVl3-IFlX(Vl3/2) > + V3 3 V23*Ia I*CV23/2> V13-IElXCV13/2> ENOI* ENOI * ENOIF SN=XF C RETURN En O c SUBROUTINE EFFORT C C THIS SUBROUTINE CALCULATES THE NJHBER OF EFFORT OATS C PER MONTH e 3ft EACH VESSEL CLASS C CaHNCNyHAaV/HNU»HN21»MN3L|HNtl,HN51(HN6lt HNl2tHN22fHN3£fHIV4£, ChN52,mN62iHK1S »MN23 t NN33 , H*43 »M*B3 , hNA 3 ,MS 11 , H521«HS3L *H$M , CHSS1, HS6l*HS12tHS2 2*HS32t HS42 f MS52 »HSi2,HS13,MS23»MS33 ,HS*3, CMS53tMS63,H01I»H021.HD31|HD41lHDSl,H061tHDl2iWD22iMD3ZtMD*2t CM052»MO6 2#H013*HD23»MD3 3rrt043.H0S3,HO63,Vll*V12,V13,V21,V22, CV23.V31 , V3Jf V 3 3, E O U , £01 2 , 6 01 3 , E 021 f ff 02 21 «D2 3 f S 0 3 1 1 E 0 3 2 * E 033 * CCU1 .CA21 »CA31rCA4l,CA51,CA61 , C A12 , C12 2 * C *3 2 , C A*2 , C A 5 2 , C162 , CCA13,CA23»CA33,CA43tCAS3iCA 43 .XNC0 1 »XWC02. XNC03tKNCQA.XNCOS, CXNCQ6tSCQl.SC02iSC03,SC DA, SCO 5 , SC0 6 tOCO1»DC02t 0C03, DC 0A *DC05* CDC36»XNLET1, 1NLBT2 • XNLBT3* SLBT1 , SLBT2 * SL9T3, DL6T1, 0L9T2 * DL3T3 , CDTOT»MAT5TTKL6T0T,SLBT0T,0LST0T,>fMALBS C0HM0N/GRU/Ztl.ZL2t2l3tII A,Z 15,II6,121 *Z22 , 123»22<•11251126113L» C232*I33»Z3*iZ3S*I36,XLNM fXLNN2tXLNN3tXLNNA»XLNN5>XLNN6,XL«S 1 ,

CALNSZt RLh53.JtLNS4,XLN55*XLNS6tXLNDl»XLND2,XLND3*XLND4,XLND5,XLND6t CI MOtIT RS« X WTN1t XUTN2 * X WTN3*XWTN4t XUTN5 « XWTN6i X WT S 1* X WT S2iX HT S3 i - d * CXWTS4,XWTSS»XWTS6t XUT01tXWT02* XUTG3,XWTD4»XtfT05,XMT06»TmT N L ,TWTNZ,

CTWTN3,TMTN4,TWTN3,TWTN*,TyXSl* TUTS2,TrfTS3,TWTSA*TWTSS,TWTS6 i TwTQl, CTWT02.TyT03 *TWT04.TWTDS|TWTD6fTNPOPiTSPOP »TDPOR,TNWTfTSMT »TDwT , CTMPgpt TMyT( 3NJt B0EL*AVUT6lt«VHT62»AVWT63

COMMQN/VRET/£LSCPtIOOPt £HCPtZOTF,VRNl*VRN2tVRN3,VRANIt VRAN2t VRAN3t

CTXLllt TXLl2fTXL13»TXL21»TXL2 2* TXL2 3* TXL31,TxL32 .TXL33 tVBS 1 *V*S2t CVRSJfVRASliVRAS2.VRAS3,VR01tVR02»vR03»VRA01|V*A02*VRAO3tVRTN,VRTS,

CVRTDfVftMA, PCST1, FCSTZiFC5T3 « CSTH , CST21 ,CST311CJTAliCST5 11CSTIZt

CCST22l CST32|CST*2,CST5 2*CSTl3,CST2 3,CST33(CST*3tCST53*FUl»FU2,FU3* CFUPR*2MIS1iIMlS2»2MIS3«CRSltCRS2tCRS3tCPSl*CPS2iCPS3fTCSTNU CTCSTN2*TCSTN3,TCSTSl,TCSTS2tTCSTS3,TCST01t TCST02,TCST03fERNNl ,

CERNN2,ERNN3 tERNSl« ERNSZ*ERNS3» EHNO 1 * E R ND2 *E RND3 »ERNNTt ERNST f ERNDT t CTMAERNt MGHTf SCP,CST61iC5Tfr2fCST63,EXfLRf 218

Ca*H:3N/PR = T/w&CSTl SFtST,fr*CST.BUB,PLSS»SHCST,TftNSt#AT*)(LAB,ELEC, CcuLCtPflOB.PKGfOEPft,aEPR,Z-VNtiIHS*lLtSLtSTRC»CMe»tTTRN5,TPAT,

CTXLA3r TcLEC,TFULCtTeftOP,TP*G,TDEPP,T2CVMtTZINJ PT2LEGL*TSTR&,TC*fMt CTPRCC,PFLfi,TPEVL,TREV;,TREV3tTREV4|TSEV5*WPflliMPff2,«°»3tt«P**, CWPflS tTREPP COrtMON/ENEX/X*VAL L, XMV1LZ ,XPVAL3, A«ATEl|ARAT6Z,ARATE3,VlNCl, CVINC2,V IN: 3 ,V DEC I,VO EC 2»VDEC 3

COMMDN/E*RT/*hRSl,EH*S2,EHRS3,WUTlLltWUTlL2,VUTlL3,SUTlLl,SUTlL2i C5UTIL3

CCMNOW/auar/RHBSl,RMRS2,RHRS3tQUCTAlt3UDTA2,OUCT*3f SEHfiTl* SEHRT2 , CSEHRT3,SEHPr*fauCTAfcrauOTA COIMON/S^TCH/SWTCWI,SMTCHZjSMTCH3,RCVTMJ tRCVTM2,RCVTM3

COMMQN/YRLT/AEOll|AE012 , AE013,AED21,A£D22,AE023*AED31»4E03Z,AEQ33, CAED^Al,AE0M42,AE0HA3»iNLBTi,*^LBT2fANLBT3f4SLBTliA3LBT2tASLBT3l CADL3Tl,ADLST2,ADLBT3,AMALfll,AMALa2*AHALB3,YfRNNl,TERNN2,YERNN3, CYERNSliTERNSZ^tRNSS.YERNDl, YERND2*YERND3 *ATh TN6,ATwTSA,ATWTDA, CAXNC06,ATSC04*AXDCQ6#ABUNrABUS,ABUD,APL6S,APPOe, CASHCST*ATRVStARAT, AXLABiAELECiAPULOfAPRDPf APKGtAOEPR«ACVH»AINSi CALEGLiASTRGfACHEHfAUPRlfAriPRZtAMPRBtAUPRAtA^PRS

CALCULATE Ef*QRT OATS PER MQNTh FOR EACH VESSEL CLASS. EFFORT OATS * 12 HOURS PER EFFORT DAY X AVERAGE PERCENT UTILIZATION OF ALLOWABLE FISHING HOURS BY A VESSEL OF THAT CLASS.

E 01■C E HR S1/12 J E02-CEHRS2/12) E03*tEHRS3/121 ' * C C SET PUTXL ACCORDING TO SEASON C IP(IHO*GE.10,OP.IHO.L£.3)THEN PUTIL1-UUTIL1 PUTJL2*WUTIL2 PUTIL3*WUTIL3 else PUTIL1-SUTIL1 PUTlL2wSUTlL2 PUTIL3*SUTIL3 END IF C C CALCULATE EFFORT DATS c ED1I-ED1PPUTIL1 E012-ED1PPUTIL2 E013*ED1*PUTIL3 E021*ED2»PUTIL1 E022*E02*PUTIL2 £;2J*cD2*?UTlU3 ED31*£03*^UTIL1 ED32-E03*PUTTl: ED33*£33nPUT:L3 e RETURN E NO C SUBROUTINE VE5*ET c C THIS SUBROUTINE CALCULATE vessel retu rn s c C3HP*aN/t*ARV/HNll,WN2li*iP*31tMN + ltHN51rMN6LtHNli,MN22,HM3r»HN4.2l CrtNS2,MNSitHNl3,MPi23rHN33#HN^3 ,HN53,HN63,H511,HS21 »WS31 »HS41, CMS5L,KS41,MSI 2,NSi2,MS32,MSt2 tiS52,HS62.MS13,MS23rHS33fH$43, CHS53,r+S6?tMDIi tH021 »HC31,MD41TMa51 , HD61,4012,HD2 2tHD32,M0-.2t ChOS2,WD62,HD|3*ND23»H03 3,HD43tHD3 3,HD63*VllfVI2, VI3 , V21»V22, CV23*V31 tV12tV33 »eDU,EDi2«E013tf02lt EO221ED23,ED 31,ED32,E033 , CCA11,CA21,CA31,CA41,CASI,CA61,CA12,CA22.CA32,CA42(CA52,CA42, CCAU»CA23|CA3 3iCA*3,CAS3» CA63.XNC01 , XNCQ2,XNCQ3,XNCO*,XNCD5. CXNC06iSCQ1«SC02»SCD3*SC04.SCOS,SC 06»DCC1,DCC2,DCQ3,OCO4,DCD5, CDC06,XNL-T: , XNL6T2.XNLST3,SLtTl, 5LBT2,SLST3,DLBTW0LST2,DLBT3, CCTOTiHATQTiXLBTQT tSLSTGTtOLBTOT ,X MA LB 5 C0MMQN/GRW/Z11tl12,113,:i*,II5,Z16,221,122,1 23 ,124,125,126 , :21, Cl 32,133,234, 2 3 5,23t,XLHNl,XLNN2,XLNN3•XLNN4tXLNN5,XLNN6tXLNS1t

CXLNS2,XLNS3,XLNS* , XLNS5.XLNSt,XLNDI, XLND2,XLND3,XLN^4,XLN05,XLN06, C1H2,IXRS,XWTN1, XWTN2*XWTN3,XUTN4,XWtNS,XWTN6, XyTSl, XrfTS2 tXWTS3 *

CXUTS*,X4TS3,XWT5t,XWTDI,XWTD2,XWTD3,XrfTDA,XWTD5,X*T06,TWTN1,TWTN2,

CTWTN3,TWTN4,TWTNS,TWTN6,T«TSl,TyTS2,THTS3*T^TS4(TWTSitTUTS6,THT01t CTWTD2,TWTD3»Tb*TD4»TyT0 3,Tt(TD6,TNPQP,TSPOP.TOPOP,ThWT,TSHTITOMT, CTMPCP ,TMWT ,BN J t Br)£Lt*VWT6 I, AVUT62 ,AVUTt3

COMMON/VRET/ZLSCP,ZOQP , IHCP, 10Tf>, VPN1 TVRN2 iVRN3, VflAMI, VRAN2 , VRAN3,

CTXLll,TXLl2,TXLl3,TXL21,TXL2 2iTXL23,TKL3lf T*L32 ,TXL33,VR$1 ,VRS2,

CVRS3.VRA51,VRAS2,VSAS3,VR01,VRD2,VR03,VR401.VRJDZ,VRA03,VPTN,VSTS,

CVRT0tVRMAt FCSTl,PCST2I PCST3.CSTllf CST21t CST3l»CST*J fCST31,CST12 ,

CCST22tCST32,CST4 2,CST52,CST13»CST23,C5T33,CST+3rCSTJ3#Mui,PU2,FU3, CFUPR,2MIS1,I^IS2,IM1S3,CRS1,CRS2,CRS3,CPS1,CPS2,CP53,TCSTN1, CTCS1X2»TCSTN3,TCSTS1,TCSTS2,TCSTS3#TCSTD1,TCSTD2*TCSTD3,EfiNfU ,

C£PNN2,ERNN3,fRNS1,ERNS2,£RNS3( £RN01,ERNC>2,EPNOJ*f«WNT,ERNST,ERNOT, CTMAEPN,NGMT,SCP,CST61,CST42.CST4 3,EXfLR, CAERNN1,AERNN2,AESNN3,AERNSI,AERNS2,AERNS3,iERH01,AERN02,AERN0 3

COMMDN/RRET/NSCSTt SPCST,PRCST,BUP,PL3S, SHCST,TRNS,P*T ,XLA8 , ELEC , CFLLO, PR3P.RXGtOEPR,REPR,ZDVM,21NS, ZLEGL,STRG*CHEM,TTfiNS,TP AT, 2 l\\)

CTKLft8,TELE:tTCUL3*TP»2PjTPK&»TDEPBtTI0V^,TIlNStTIL£GL*TSTRG,TChE^p CTPROC « PFL« tTOEVl t TREV2 , TR * V 3 t T R E V t f TP E v 5, WPR1 , WPR2 , *PR 3,1.» R * , CwPRS » TRE^R CDMHQN/ENEX/X.MVALl,XMVAL2*XNVAL3f ARATEltARATE2,APATE3,VlnCl, CVINCi * YINC3 »YDECl»V0EC2,VDEC 3

C3«HUM/EeRT/EKaSl»EhR52»EMRS3

COMMON/QUCT/R^e51 ,R*RSZ»fl*ftS3i3bDTA:,tjgETA2,QUDTA3,S0HBTl,S5nfiT2f CSEWflTJ,SEHPTA tCU2TA*»auDTA COMMON/SWTCn /SHTCHI ^SUTCHi^HTC^RCvTNl.RCVTMZtRCVTNi

COMMQN/VRLT/AEDU t A EDI 2 t A c 013 , A ED21 , At 0 22 .1 ED23 « AS 331, A E D321 AED 33* CA £im iiA C0"A 2, AEDMAl, ANL3T1, A*LeT2,AHL8T3, A5LST1 t A SL3T2»A SL5T 3» CA3LST1, A0L6T2»4DL£T3tR"'AL&liRMALB2*AMALB3fVEftNSl, TFRNN2, TERNN3 , CTERNSltV!RWS2|TERNS3iTERNOl»TERNC2»TERMD3tAT^TN6tATirfTS6|lTi#T06( CAXNCQ6»AX$C36t A XDC06 ,ABUN, A BUS *A8U0tAFLBSiAPROC» CA5tlC£T'ATRN$,APAr,AXLAtt AELECiAFULStAPR3P,AFKG'ADEPRtADVH,AtN5t CALE&L tASTR&, AC*EM, 4WPR1 , A WPR 2 t AUPA 3 , AU PR* , AWP ft 5

SET VALUES FOR DUMMY VARIABLES DEPENDING UPON SEASON AND TEAR

I*<1MO.LT.3.CR*IMO.GT.113 THEN 501-1.0 ELSE S01»0 END IF IFCMGMT,NE,Q>THEN IMDUM-1.0 ELSE ■ ■ 1H0UM-0 END IF C C CALCULATE THE EX-VESSEL SURF CLAM PRICE PER POUND BASED UPON C PRICES 0* COMPETING SPECIES A NO SURF CLAN LANDINGS C WRITECIi *} X«ALfiS IPCXM4LBS.LE.0)THEN SCP*0.0 GO TO 10 ELSE ZLSCP*-.0464-,0J96*ALQGCXNALB$>+,6373*ALDG(ZD&P)- . 196GPAL0GCIHCPJ+ C.lAa5*AL0GCI0T"J+.B7*3*ZM0UN-.0728*SOI END IF C C CONVERT NATURAL LOG QF SURF CLAM PRICE TO SURF CLAN EX-VESSEL PRICE C SCP-EXPCZLSC*} I eCEX*L‘! , 0) SCR-E*BLR C c c a lc u la te vessel revenues for a ll c la s s i vessels wishing in C NORTHERN NEW JERSEY c 10 V*hll»XNLSTl*C SCR-TXLll )

C CALCULATE AVERAGE RETURN RES NORTHERN N£y JERSEY CLASS I VESSELS C I f CVll.N£,OT THEN VRAMlaVRNl/Vll ELSE VRAN1 *0*0 ENCI*

CALCULATE VESSEL REVENUES eQR CLASS II VESSELS FISHING IN 0RTHERN NEW JERSEY

VRN2»XNL?T2*CSCR-TXLI2)

C CALCULATE AVERAGE REVENUE »ER NORTHERN NEW JERSEY CLASS II VESSEL C I*CV12,NE,0) THEN VRAH£*VRN£/Vl2 ELSE VRAN2*0tQ ENO 1B C C CALCULATE VESSEL REVENUE FOR CLASS I I I VESSELS f ; sh ING IN northern C NEW JERSEY f" * ■ * VRN3*XWLST3*CSCP-TXL13) c C CALCULATE AVERAGE REVENUE FOR a CLASS I I I VESSEL fish in g in northern C NEW JERSEY C I*(V13•NE.0) Th e n VRAN3*VRN3/V13 ELSE VRAN3«0.0 END IF C C CALCULATE TOTAL NORTHERN NEW JERSEY VESSEL REVENUES C VRTN»VRN1+VRN2+VRNJ C C CALCULATE VESSEL REVENUE FDR CLASS I VESSELS FISHING IN SOUTHERN C NEW JERSEY C YR51*SLST1*(SCR-TXL21) C CALCULATE AVERAGE REVENUE * j R A CLASS I VESSEL FISHING IN SOUTHERN C N£W JERSEY C Ietv2l.NE.0)THEN VAA51»YR SI/Y2 1 ELSE VRA5I»0.0 Em: if

C calculate vessel revenue for class i i vessels fishing im SOUTHERN C MEri J£B SET

V^Si*SL6T2

C CALCULATE AVERAGE REVENUE e 0 R CLASS II VESSELS FISHING IN SOUTHERN C NEW JERSEY lFtV2Z.ME.05 THEN VRA52-VRS2/V2Z ELSE VRAS2*0.0 END I*

CALCULATE VESSEL REVENUE FOR CLASS I I I VESSELS FISHING I n OUTHER N NEW JERSEY

VRS3*SLBT3#CSCP-TKL323

: CALCULATE AVERAGE VESSEL REVENUE FDR A C^ASS I I I VESSEL FISHING N SOUTHERN NEW JERSEY - ..

!FtvZ3.NE.05Tw£N VRAS3-VRS3/V23 ELSE VRAS3*0*O END I B

CALCULATE TDTAL SOUTHERN NEw JERSEY VESSEL REVENUE

VRTS-VRSI+VRS2+VRS3

CALCULATE VESSEL REVENUE *Dft CLASS Z VESSELS FISHING IN OELMARVA

VR01»0LETlt«(SCP-TAL31)

CALCULATE AVERAGE VESSEL REVENUE FOR A CLASS I VESSEL IN DELHARVA

IFCV31.NE.05 THEN VRA0l*VRDl/V31 ELSE C CLUAE PRTN CSS TRE LSE O VESSELS IN OF CLASSES THREE CALCULATE p 3 OPERATING COSTS C C VSES AVSIG LM I DFEET REGIONS DIFFERENT HARVESTING CLAMS IN AND AND VESSELS VARIABLE, EARNINGS OF FIXED CALCULATEC COSTS TCTAL C C NORTHERN NEW JERSEY C CLUAE VRG VSE RVNE R CAS I VSE FISHING VESSEL CALCULATE AVERAGE REVENUE VESSELOpF III F CLASS A QR C C C C DELHAfiVA CLUAE ESL EEU PR LS II ESL FSIG N ■ IN FISHING VESSELS CALCULATE III REVENUE CLASS PGR VESSEL C DELH4PVAC C CALCULATE VESSEL AVERAGE FISHING CLASS VESSEL a?V=NUE II A FOR C C C nnnnfi o n o non CALCULATE C IE COSTS FIXED AC L T MDALNI VSE REVENUECALCULATE VESSEL MID-ATLANTIC CALCULATE COSTS VESSEL CALCULATE DELMARVA TCTAL REVENUES VESSEL D E L M A R V A FCST2»(1/12,03*(CST12+CST22+CST32*CST42+CST52+CST62) FCST3-Cl/12-0>AtCSTl34CST2i+CST33+CST43+CSTS3+CST63) FCST2»(1/12,03*(CST12+CST22+CST32*CST42+CST52+CST62) vhna FCSTl»(l/12.C)*CCSTll+CSTZl*t5T31+CST41+tST5l+C5T6l> FCSTl»(l/12.C)*CCSTll+CSTZl*t5T31+CST41+tST5l+C5T6l> VRDT»VRD1+VRD2+VRD3 VRA03«VftD3^V?3 VRD3»OLST3*CSCP-TXL3 3) VRAD VftD2»;LETZncSCl>-TXLS2) V R A C 3 *0 ELSE IFCV33*NE-0)TMEN N IF END END IF vRAD2*0.0 vRAD2*0.0 ELSE FV2K-> T IFCV32,KE-C> V* AC1 END IF - 2*VffD2/V 32 vrtn

•0.0 vessel + vrts

+ EEU PR US I ESL PSIG N DEL*ARv: VESSELS IN PISHING II REVENUE CUSS PCR vrtq k = n

* * ■

I n

o o n n o n n non non n n h n n n n n non r*i n TOTAL NORTHERN EARNINGS FLEET JERSEY FLEET EARNINGS CLASS BY VESSEL AVERAGE EARNINGS VESSEL NORTHERN NEW JERSEY SOUTHERN JERSEY AVERAGE EARNINGS VESSEL CALCULATE TOTAL EACH XL REGION FDR COSTS VESSEL IN VESSELS L OPERATING PELHA COSTS IN R VA OPERATING SOUTHERN COSTS IN NEW JERSEY AERNS2-VRAS2-XOR5Z AERN51"VRAS1-XDPJ1 AERNS3-VRA53-XOPS3 TCSTS XOPS3*V2 ■ 3 TCSTS2»X0PS2*V22 3 TCSTSl»XaPSl*V21 ERNN3-VRN3-TCSTN3 ERNN2-VRN2-TCSTN2 TCSTN3»X0PN3#V13 ERNNT-ERNN1+ERNN2+ERNN3 ERNNl-VRNl-TCSTNl TCSTN2*X0PN2*V12 TCSTNl*XQPNl*Vll AERNN3*VR4N3-XCPN3AERNN2*VRAN2-X2PN2 A E R N N 1 * V R 4 N 1 - X D P 1 U X0PD3**CST3+{(*U3aBUPfi+lNlS3)*£333+ CSTl+(<«ul*eUOR+Z«ISl)*ED2l*t:RSl*¥R*Sl)> X3PSX»t XGPN3*BCST3+(CBU3$*UP«-*ZMiS3)*E0l3+tCRS3*VftAN3)) XDPS3-FCST3--C J+CCSS I53>*£02 J*VRAS3>t^uS^FU^R*:* XDPS2-e CETr > + tt*U2tt^iJPR + I' IF CV13.E5.D3AERNN3-0 IF CVI2*EQ.0)AERNN2»0 1*(V1I.EC,03AERNNL-Q £ 4 ^ ^ 3

i«= c v2 i.s;.o jaernsi«o IEtV2I.E:-0)a?ftNS2-0 IFCV23,£3*0>AERN53*0 C C FLEET EARNINGS 3V VESSEL CLASS C ESNS1»VRS1-TCSTS1 ERNS2-VBS2-TC STS2 ERNS3"Vl!S3-TC STs3 C C TCTAL SCUT HE p fo NEh JERSEY ■L E £T EARNINGS C EflMST-EftNSl*5RN52*ERN5? C C DELFARVA C TCSTDl*MaP01*V3t TCSTD2*XDPD2*V3Z TCST03-XDPD3^V33 * C C AVERAGE VESSEL EARNINGS C AERND1*VRAD1-XQPD1 AE4N02TVOA3Z-XQPG2 AERND3»VRA03-XCP03 IFCV3I.EC.0>AERNG1»0 IFCV32.E0.CJAEA»32*0 m v 3 3 ,E C .O JAERN03*0 C C FLEET EARNINGS BY VESSEL CLASS C ERN01*VRDl-TCSTDI EftN02*VRt)2*TCSTDi ERN03-VRC3-TC5T03 . „ C C TOTAL DELNAftVA FLEET EARNINGS ' C ERNDT-ERN01+ERND2+6RNC3 C C CALCULATE EARNINGS FOR THE ENTIRE N1D-ATLANTIC FLEET C thaern - ernnt +eqnst +erndt C RETURN END C SUBROUTINE VPPRNT c c this subroutine writes yearly output to THE USER'S TERMINAL C AND tc a file called vroat c C CHMON/HARV/HN11,HN21*HN3l,HN*l »MNS11HN61•HN12 ,HN2 ZtHN32 ,HNL2 t CHNS2|HN62»HN13

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COMMON/EFRT/EHRS J , EHRS2 t EHR$3t tftlTILl *WUTIL2, HUTIL 3»$UT1LWSUTIL2 , CSUTIL3

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+TE»NS2+TERN02 YERNN2,YERN52 ,YERfiD2,TT16 rE*HI.;, YERNS2 * YSKN&2* TT16 3*v33 V13*V23,V33 ,TT17 V13, ¥23,V33.TT17 CLASS 111 ,11X,F4*Q,11J<,F4.0,11X,F4.0> AEQ13,AED23,AE033 AED13.AED23,AED33 +ASLBT3+AOL9T3 ANIET3,ASltT3,ADLBT3,TT19 ANLaT3,ASLBT3,ADLBT3,TT19 /I 7 * 0 /I 7. 0 ✓ 17,Q 7*0 7720,T721,7722,TT23 7T2Q,TT21,TTZ2,TT23 + Y ERNS 3 +Y 5 RND3

YERNN3rYfR*53,Y£ANO3,7724 YERNN3* TERNS3 *TERN33i7724

21 *7DTAL «I0 ATLAN71C S747ISTICS'> 9+7717 7 T £ 5 7 72 5 23 X,'NUMBER OF VESSELS',7 3 3 ,F5.Q3 +AMALB2+A7ALB3 7727 7T27 25 X,'TOTAL HARVEST < LS S1 ' , 73 3 , G14* 7 ) T15+TTT TT2B TT28 26 X* '707AL HARVEST C6J)',T33*G14*T3 I6+TT24

TT29 249

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6 6 F3R“ATClX»4Xt'5LSCTPICITY',9X»3l4.T> WRITEC?,*?) APULC WRXTEC1»6T) APULC 47 *aRHATClXt4X,'*UEL OIL"*L2*tG14BT1 WRITECT,*8) APP£P W RITEC,49) 1P C 3 P 4fi FCR**ATC* .4Xf 'PROPANE '.13X«G14.T5 W R1T E C T,69 5 AP*G wRETEC1»495 APkG 49 P0RNATCl>*4Xf*RACK6G£NG'>llXiGl4«T3 WRITECT,505 ACfPfl W R I T E O , 5 G 5 AD* PE 50 F2RHATC1X,43(, '3EPRcCIATI0N',8X,G14.n WRITECT,51) A QVM W R I T E C , 51) ACVH 51 F0RKATtlI,4X, 'OVERHEAD ",12X,G14,7) WRITE CT,52) AInS WRITEC,S25 AINS 5 £ F OR WATCX* 61, "INSURANCE",11X»014,T) NRlTECT,535 A LEGL W R IT E C ,535 ALEGL 53 F0RNATClX,4X,"LEGAL-4eC2LJNTlNG’ ,4X,Gl4*T) WRITE(T,S41 a s t r g WRITEC,54) ASTRG 54 F0RMATCX,4X,"STORAGE",13X,G14.7) WRITEtTfS3> A CHEW WRITEC.55) ACHEM 55 FDRNATCX,4X, "CHEMICALS ',11X»!114*7) APP*AMALBl+AMAL62+AMAL!3 a b p -a p p / i t .0 WRITECT,365 ABP WRITEC 56 5 A &p 56 FORNATC X,2X*"BUSHELS PROCESSED",5X,G1<*T) WRITEC ST 5 APL&S WRITECT 575 APLBS 5T FOR MAT C *,2X, 'LBS OF NEAT PROOLfC ED " , 2 X, G14 , T ) WRITECT 5 B 3 A WP R 1 WRITEC 5 0 3 A WP R 1 58 FORNATC G",2X*"BA&AK EVEN WHOLESALE PRICE PER POUND (A) ' 3 X ,FT■2) WRITECT 595 A u PR 2 WRITECl 595 AWPR2 59 FQfiMATC X,2X*"WHOLESALE PRICE AT St RETURN WRITECT 601 AWPR3 WRITEC 60) AWPR3 60 F3RMATC1X,2X,"WHOLESALE PRICE AT 101 RETURN <*>*. 10**F7.2 1 WRITECT 615 AwPRt WRITEC 615 A W P R 6 61 FGRHATCXfZX* "WHOLESALE PRICE AT lS t RETURN ( * 5 " , 10X, F 7.£ ) WRITECT ,62 5 A*PR5 WRITECl 162 5 AwPRS 62 FOR*ATCX,2Xr "WHOLESALE PRICE AT 201 RETURN C*>", 10X,F7,2) 63 FORNATC"1",3X,"COHORT 4',9X |G14,T,3X,G14.7) C RETURN Appendix B

This Appendix contains the survey questionnaires distributed to surf clam fisherman and processors, Longe r questionnaires not included in this appendix were used for selected interviews end distribution. a u m u u l«m COLLEGE OF WILLIAM AND MARY

VIRGINIA INtTlTUTIC* MARINE SClENCl SCHOOL OR MARINI SCI (NCI

gn — MlM SUL Illll h f [«*> fttHLI

Dear S ir : A research project la currently In progress At the Virginia Institute of Karina Science to gather Important Information about the iurf clam/ocean quatioi fishing and procaaalni industry. Tha purpoaa of this study Is to examlna tha economic health of Aa industry, and to understand whether diffarsnt flahary management approachaa could lmprova tha aconomle racum to clam flaharman and ptocaaaora. 1 am conducting tha study through paraonal confidential Interviews and w ritten responses to a confidential survey. I ask chat you taka a small amount of tlaa to template the ancloead questionnaire and return it to ms in tha enclosed envelope. This is your chance to provlda valuable Informscion that w ill give resource managers a batter understanding of your economic problems end priorities. Tha results of tha study w ill be made available to you and to tha Mid-Atlantic Fisheries Management Council. Sunnarlxlng statistics wlLL be used In tha final report, thereby maintaining tha confidentiality of sLL Information collected from individual participants in the study. If you have any questions regarding this survey or the study, please call me at the Virginia Institute of Marina Science (604)-641-2111, extension 196, Full participation In the study Is extremely important, Flaasa assist me by completing and returning the b rltf questionnaire enclose*. Thank you very much.

S in c e re ly

Thomas W. Ansi tags CONFIDENTIAL Virginia Institute of Marin* Saline* s u i v j Y o r sv>F ciam/ociam qua hoc fish m e in d u s try E. OWHCT/OM1ATOR INFORMATION

1. How u d t >urf e lu or ocean quahog fishing vessels do you currently ownf {If vessel* ora company owned lad Lett* how UT 17 )

2. Are you ■ ciptala of any of thee a 1. What ware your hams porta during th* 1981 season?

4, If you have worked out of any other porta during th* p u t 10 y u rt, plaas* ladle*ta th* port and th* y**r you worked tiler* .

II. BUSINESS STRUCTURE 1. I* your business *(o> Individual proprietorship Partnership Corporation If a corporation plea** lndiest* how way “ atockholdara , 2 . Fleasa dascribe eh* eaployaas Involved lo your flatting operation. Number I share or salary- per year or salary/ trip C r a w ______' S k ip p er _ (not* captain'a bonu# it any) Office help ______O th er ( d e s c r ib e )

3. Othar employ** benefits paid. Has 1th Insurance Z Unemployment tar % Othat ____ Z 4. Floss* estimate your total labor coats (wag*a/year or vag**/trip):

c u r r a n t I t 1981 1979 1977 197} 1973 1971 I I I . VESSEL CHARACTERISTICS Foe aaeh of your vassal*, plaasa Identify tha characteristic* listed an tha following tabla. VESSEL # 1 2 3 4 5 6 7 8 9 10 11 II 13 14 15 Langth DisplacsaOflt F o a l Capacity C ru isin g Bangs

**■ D r a f t Hull H a ttria l

K a x , L o a d Capacity Enqlea Batad H.P. Typa Aga Vaar Last K abulIt * . 1 Caar # of Dradgaa ' Blada width* Navigation E q u lp u n t indicate If boat h a s : Radar FathO satar Shin to shara Radio Lomu A or C Flsasa specify other aqulpaanc VESSEL # 1 2 3 A i 6 7 a 9 10 11 L2 13 14 Li Qthar Info.

P rim t HacUt Valua of Bd«e Cost of K m Englua Ehglne L ifa With OvarhauL* How Many O v«rh

r 1. Approxlaataly how aueh hava you lavaatad In navigation aqulpnnt?

3, What would Chia aqulpnant coat now 7

4. Row long do you axpaet co uaa chia aqiApaaot? t v . ECO WO MIC IMTOEMATICT*

1, flaaaa ultimata tha currant coat par trip of tha follwing Ltan*, and If thay hava changad lndioata what thaaa coaCa wura in cha pravlou* yaara Llatad. a) Fual and Oil C u rre n tly 1981 1979 1977 1973 1973 1971 b) ?Laa*a Lite and aatinata any other cotta of fiahlnf trip* for the yaara Indicated balov Coat C u rre n tly 1981 1979 1977 1973 1973 1971

2. Plaaae aatlmata tha currant coat par yaar of tha fallowing ltama, and If thay hav« changed Indicate what thaaa coat* war* In tha pravloui y*ara llatad. a) Boat Repair and Kalntananca C u rra n t ly 1981 L979 1977 1975 1973 1971 b> Port Fee* Currently 1981 1979 1977 1973 1973 1971 c> Loan Payment* c u r r a n t ly 1981 1979 1977 1973 1973 1971 d) Legal-Accountlng Faaa Curran tly 1981 1979 1977 1973 1973 1971

3. How much would It cott to raplaca your flatting gear? Replacement Coat Yaara of Uaafulna** Clam Dredge* Pu&pa Othar Goar (plaas* H at)

i. What wa* your total grot* revenue ftca flahing operation* for tha following yaara? Currant yaar to data 1981 1979 1977 1973 1973 1971

3. What vat your nat Income from flthing operation* for tha following yaara? C u rra n t y a a r to d a ta 1981 19?9 1977 1873 1973 1971

6 . What percentage of your catch la aurf clam*, and vhat percentage la oca an quahoga? Currant ly 1981 1979 1977 1973 1973 1971 S u rf Clam Ocean Quahog CONFIDENT LA L Virginia Inatltut* of.Marin* Sciatic* SUNVTY OF CLAM PROCESSING INDUSTRY 1 . SUPPLY SOURCES AND PROCESSING ACTIVITT A. P ro d u ct* 1. Liac tha aaafood apacta* that you procaa* (aurf clan, ocaan quahog, othar fl*h and ahallflah}

2. PLaaia aatinata th* percentage of raw product that •aeh apeclaa haa contrlbutad to your butIn*)a In aach of th* following yaara; Spaclaa Currantly 19B1 1979 1977 l»75 1973 1971

3. What p*rcanckga of your elan* do you obtain fron Independent fiih*np*a? Sp*ciaa Currantly 1981 1979 1977 1973 1973 1971

4. What percentage of your clam* do you obtain fron othar procaaaora or ■htieking houiai? Specie* Currant ly LMl 1979 1977 1973 1973 1971

i. How many day* par waak ar* das* or othar raw product delivered to your plant? *urf cl*ai ocaan Ouahoaa othar * '* (apeeifY)

B. F a c llltl* < 1. Do you own boat*? If yea, how many did you own In aach of tha following yaara? Currantly 1901 1979 1977 1973 1973 1971 c, Price Paid 1. Do you pay a price differential for different alza a u r f clam a7 If yea, what la tha dlffarantlal and why do you pay It (proceaslng coat, product* produced, * te ,)

2. How do you datemlne what you w ill pay for raw product? What factor* ar* Involved? Who a*t< prlcaa? 3, What era tha average price* that you m at pay for tha different alia* of aurf clam* and ocaan quahaas? Surf elan Currant ly 1981 1979 1977 1973 1973 1971 Htdlua Large Ocaan qoahog p ro cessin g Plaaaa Indieat* tha typaa of processed product* you **11 Ci.a. raw s h u c k e d , raw cooked, frosen breaded, canned, chowder. ate.) and aetlmat* tha quantity and dollar* aold of aach in 1961. ^2S-ES2dlJ££. 1961 Quantity 1961 dollar* *o!d

2. What la tha total number of people employed Ln your clam procaeaing operation? Total eanloyae* Currently L9«l 1979 1977 1975 1973 1971 SaLariad Hourly P iec e Work 3. What 1* tha currant avaraga wage you nuat pay aalaried worker* and place work employ a* a (for place work indicate rata per amount p ro d u c e d )?

A. How do you di*po*a of *hell,b*Lly, or Othar wait* product*?

3. How do you treat your waata water? How do you dlapoaa of it? plaaaa aatimata the added coat that thi* require*.

6 . Plaaaa try to **timet* the amount of fraah water you u*e per day of operation, Fraah water uae par day Buahal* of clam* nroca***d nar day or pound* Pound* of finished product processed per day ______7, Do you obtain water from a wall or purchase it from a municipality? 6 . please describe tha water u*a quota* with which you must comply-

9. Do Cheae quota* evar present a problem for you? Explain. 1 0 . “Hi* following ooata have bean identified a* najor eoata for a claw procceolng operation. Plaaaa try to eatiaeta eh a percentage that aaah contribute* to your total coat of operation, and al*o eetlaate aach coat la cant* par pound of clan neat proceeded. If you hava additional coata, or u*a altaraattv* coat categdrlet, plaaaa add than at tha and of tha lilt. X of Total Coat Coat par pound of aurf Coat par pound clan seat proceaaad o f quahog oaa (If different)

Raw Product TranaportatIon * in Machinery to remove bally, or othar r o y a l t l e * Labor Payroll tax Salt or othar ch em ical*

Fual fdieeal) O th a r f u a l Propane for •huckina nacfainaa or forkllfta Packaging Clactricity * 1,1 Depreciation Repair 4 Maintenance Freight * out O verhead Znauranc* Other (cpeclfy) 262

11, Plena* aetimet* th* total coat {(/lb.) of procaeeing • pound of clam m at. Total coat for quahoge ______Total coat for aurf clan* _ I I , CSOUTH 1, What la tha plant capacity (Lb*/day) of your proceiiing o p e ra tio n ?

I , How o a n y ablfta da you work par day Opacify tha number of cleaning and processing ahifta)

1. What ar* th« Limiting fattora that lnpada your elan proceaalng oparatlon froo running at full capacity?

4, What da you eanaldor to be tha most aarloua problamO) praaently confronting tha elan proeaaalng industry?

5. What ara y o u r feeLinga about tha nanagamaot plan for aurf clan* and ocaan quahoga? (ha* It hurt you, what ara ita weaknaaaes, how would you Improve tha plan)

6 , Which anafood product! do you faal represent tha greatest, competition In tha marketplace for your product!?

7, Ara you a iclt entity, subsidiary, or parent corporation? If aubaldlary, of whom? If parant, of whom? B, Do you attanpt to nalntaln price at a percentage markup or margin over c o a t? ______I o r ______d o lla r* 263

VITA

UiamftJA H- Armltaq*

Born in Cheater, Pennsylvania, January 1 3, 1952.

Graduated Trom Panncreat High S c h o o l in Lima, Pennsylvania,

June 1970. Received B.A. in biology from tha University or

Pennaylvania, Philadelphia, Pa. in 1974; H. B. A. from T em p le

University, Philadelphia, Pa. in 1977; and M.S. in marine biology from th* Plorida Institute of Technology, Melbourne

Florida, in 1979.

Entered th* S c h o o l of Marine Science as a graduate research assistant in 1979. Awarded Sea Grant Fellowship to serve as fisheries advisor on atafr of U.S. Senator Ted

Stevens (Alaska), 1964-1965. Currently employed as a staff scientist, Ecological Effects Branch, U.S. Environmental

Protection Agency, Washington, B.C.