NOAA Technical Memorandum NMFS-SEFSC-468

 

 

Site Characterization for Biscayne National Park:

Assessment of Fisheries Resources and Habitats

Jerald S. Ault, Steven G. Smith, Geoffrey A. Meester, Jiangang Luo, and James A. Bohnsack

U.S. Department of Commerce

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Southeast Fisheries Science Center

75 Virginia Beach Drive

Miami, Florida 33149

October 2001

 NOAA Technical Memorandum NMFS-SEFSC-468

 

 

 

Site Characterization for Biscayne National Park:

Assessment of Fisheries Resources and Habitats

Jerald S. Ault 1, Steven G. Smith 1, Geoffrey A. Meester 1, Jiangang Luo 1, and James A. Bohnsack 2

U.S. DEPARTMENT OF COMMERCE

Donald L. Evans, Secretary

National Oceanic and Atmospheric Administration

Scott B. Gudes, Acting Under Secretary for Oceans and Atmosphere

National Marine Fisheries Service

William T. Hogarth, Assistant Administrator for Fisheries

October 2001

This technical memorandum series is used for documentation and timely communication of preliminary results, interim

reports, or special purpose information. Although the memoranda are not subject to complete formal review, editorial

control, or detailed editing, they are expected to reflect sound professional work.

1

 University of Miami, Rosenstiel School of Marine and Atmospheric Sciences, Miami, FL

2

 NOAA/NMFS Southeast Fisheries Science Center, Miami, FL
NOTICE

The National Marine Fisheries Service (NMFS) does not approve, recommend, or endorse any proprietary product or

material mentioned in this publication. No reference shall be made to the NMFS, or to this publication furnished by

NMFS, in any advertising or sales promotion which would indicate or imply that NMFS approves, recommends, or

endorses any proprietary product or proprietary material mentioned herein or which has as its purpose any intent to cause

directly or indirectly the advertised product to be used or purchased because of NMFS publication.

This report should be cited as follows:

 Ault, J.S., S.G. Smith, G.A. Meester, J Juo, and J.A. Bohnsack. 2001. Site Characterization for Biscayne National Park: Assessment of

Fisheries Resources and Habitats. NOAA Technical Memorandum NMFS-SEFSC-468. 156 pp.

This report will be posted on the NMFS Southeast Fisheries Science Center website at URL – http://www.sefsc.noaa.gov/

Copies may be obtained by writing:

National Marine Fisheries Service

Southeast Fisheries Science Center

75 Virginia Beach Drive

Miami, Florida 33149

or

National Technical Information Service

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Springfield, Virginia 22161

(800) 553-6847 or (703) 605-6000

<http://www.ntis.gov/numbers.htm>

 

ii
iii

Executive Summary

Biscayne National Park (BNP) in southeastern Florida is a unique tropical marine environment

of national significance, renown for its productive coral reef ecosystem, diverse natural

resources, important fishing opportunities, and spectacular scenic beauty. BNP’s coastal bay and

coral reef habitats play a critical role in the function and dynamics of the larger Florida Keys

coral reef ecosystem and contribute substantially to the multibillion dollar tourism and fishing

industry. Park waters provide critical nursery habitats and food web links for many important

commercial and recreational fishery resources including: bonefish, snook, tarpon, permit, pink

shrimp, spotted seatrout, oysters, clams, blue crabs and stone crabs, baitfishes; and, numerous

coral reef fishes that include snappers, groupers, grunts, barracuda, spadefish, spiny lobster,

parrotfish, surgeonfish and triggerfish. The production dynamics of these valuable natural

resources are inextricably linked not only to fishing and rates of exploitation but also to the

spatial distribution and quality of bay waters and habitats, themselves greatly affected by

growing human uses and management practices. Since BNP’s natural resources are intimately

related to the broader regional ecosystem through water movements and animal migrations, any

Park degradation will have consequences well beyond its boundaries.

Fishery management strategies now being proposed for BNP require precise spatial data and

estimates of resource distribution and status prior to implementation of any spatial management

policy. These data will facilitate concise and unambiguous baseline estimation of the Park

resource status, provide critical inputs for models used to forecast potential impacts of policy

changes, and enable design of an efficient sampling program to monitor future performance of a

given management policy.

In a proactive movement towards development of strategic planning, this report takes the first

steps in establishing the quantitative characterization and assessment of fisheries resources and

habitats in BNP. The site characterization was based on an extensive overview and analysis of

pertinent scientific and technical literature and biological, fishery, and physical databases

relevant to the status and dynamics of fishery resources in Biscayne National Park, Florida. The

estimation and simulation modeling “process” involved database assimilation, visualization and

mapping of fish-habitat linkages, and multispecies assessments. In the process, we developed a

new approach to stock assessment of multispecies coral reef fisheries that compare status of

stocks relative to Federal fishery management guidelines. Our assessment research focused on 3

data sets containing more than 25 years of fishery-dependent (1976-1999 via BNP recreational

creel survey) and fishery-independent (1979-2000 via SCUBA diver visual census and

rollerframe trawls) survey sampling in BNP. Digital maps were created for a variety of spatial

“habitats” including bathymetry, benthic substrates, salinity and other water quality parameters

that relate to fishery resource distributions and abundances. Biological data were separated into

4 life-stage phases in terms of length for analysis: juveniles; pre-exploited; mature; and,

exploited. Habitat data were co-plotted with species life-stage abundance and community

diversity distributions to assess the spatial distribution and dynamics of fisheries and their

dependence on habitats found in BNP. We noted:

C Biscayne Bay plays an important role as primary nursery area for many coastal bay and

coral reef fishes and macroinvertebrates (i.e., gray, mutton, lane, schoolmaster and

yellowtail snappers; bonefish, tarpon, permit, sharks, molluscs, sponges, snook, seatrout,

and pink shrimp, blue crabs, and spiny lobsters). Therefore, we conducted our analyses

with respect to the relevant life stages of each species partitioned into four phases:
iv

juvenile (from length of recruitment to size of sexual maturity); pre-exploited

(recruitment length to minimum legal harvest size); mature (length of sexual maturity to

maximum size); and, exploited (minimum legal to maximum size). We found that thirtyfive (35) species of fish (about 11% of total BNP fishes) were present in all three survey

types (TRAWL, CREEL, RVC), suggesting that there is use of habitats amongst lifestage

phases. Fish diversity was lowest on the western fringe of Biscayne Bay (particularly at

areas proximal to SFWMD canal outfalls and attendant freshwater influxes), increased

moving seaward, and was highest in eastern BNP (areas on the barrier coral reef system

open towards the Straits of Florida). The diverse spatial patterning of many species

distributions reflects specialized ontogenetic use of certain habitats across the seascape.

This suggests that a critical mosaic of interconnected habitats may be essential for

sustaining fishery resources and ecosystem productivity.

We used a systems science approach to conduct a new multispecies assessment on exploited

fishery resources found in BNP. The analyses used both fishery-independent and fisherydependent data sources. We also acquired extensive biological and fishery information on

population dynamics including age-growth, age (size) class distribution, and natural mortality

rates. We estimated new fishery indicator variables developed from fishery-independent diver

visual census data and fishery-dependent creel survey for 91 exploited species to assess the

current status of the stocks. However, the reliability of population dynamic parameter datasets

for a number of these species were suspect, or unavailable. As a result, this study focused on 35

important reef fish species that reflected the predominance of species and reliable population

dynamics data. Commercial fisheries data were also acquired, but were deemed generally

unreliable. Combining population-dynamic parameters and estimates of fisheries indices with

the LBAR and REEFS assessment models facilitated evaluation of a series of fishery

management benchmarks and estimated exploitation levels. We compared these estimates of

current stock biomass and fishing mortality levels to the Federal standards. The following points

summarize our findings:

C For all of the harvested species analyzed, the average sized fish within the exploited

phase of these populations for the last 25 years has remained relatively constant and is

very close to minimum harvest sizes, not natural historical unfished population size.

Many species with extremely low average length in the past have had very little change

in average length even though new minimum size limits were imposed. The average size

of black grouper is now 40% of what it was in 1940 and the spawning stock is now less

than 5% of its historical unfished maximum.

C Overall, 77% of the 35 individual stocks that could be analyzed are overfished. An

analysis of the Spawning Potential Ratios ( SPR) of exploited reef fish shows that 13 of

16 grouper species, 11 of 13 snapper species, barracuda, and 2 of 5 grunt species for

which there are reliable population dynamics data are below the SPR that constitutes

overfishing by Federal definitions (Magnuson-Stevens Fishery Management

Conservation Act).

C Stock biomass is critically low for most of the key targeted species within the recreational

fishery. For example, the current level of fishing mortality for grouper stocks range from

3 to 10 times the exploitation level that would achieve Maximum Sustainable Yield

(MSY). Some stocks appear to have been chronically overfished since at least the late

1970's.
v

C Exceptionally high and sustained exploitation pressures have precipitated “serial

overfishing” of key fishery resources. Smaller and less desirable species have

consistently increased in the catch as larger more vulnerable species are eliminated.

C The recreational fishing fleet in South Florida has grown at a near exponential rate since

1964 (a 444% increase in recreational boats from 1964 to 1998) with no limits on the

number of boats allowed to fish. The relative effective vessel “fishing power” of the

recreational fleet has quadrupled due to depth indicators and fish finders, global

positioning systems, improved vessel designs, larger motors, and two-way radio

communications.

C A proportion of the fish in catches observed at the Biscayne NP boat ramps during creel

surveys are smaller than the minimum size limit for legal harvest. This is as high as 70%

for yellowtail and mutton snapper! Inadequate enforcement and knowledge of fishery

regulations (reflected in preponderance of undersized “illegal” fish seen in creel catch

size frequency distributions) and the extremely poor status of reef fish resources (BNP is

the worst situation in the entire Keys) signals eminent resource collapses. Enforcement

needs to be stronger to discourage poachers.

C In fact, we also found that 13 of 35 species we analyzed have their minimum size of legal

harvest set lower than the minimum size of sexual maturity, that is, these fishes are being

captured before they have ever had a chance to spawn.

C Little is known about a number of highly-prized species like bonefish, tarpon and permit,

despite the fact that they are the focus of extremely important recreational fisheries. For

example, bonefish have undoubtedly been under sampled, as only seven (7) fish have

been reported in 23 years of BNP creel sampling, despite the fact that bonefish support an

enormous regional recreational fishery. There are no monitoring programs for pink

shrimp despite the large inshore biomass of juvenile shrimp that fed a broad range of

juvenile and adult fishes in the BNP ecosystem, but is also under heavy exploitation

pressures from an intensive commercial bait-shrimp and recreational wing-net fishery. In

addition, economically-important spiny lobster, blue crabs and stone crabs lack

monitoring in BNP.

The history of fishery management actions by the State and Federal Fishery Management

Council for Biscayne NP waters clearly reflects the characteristics of trying to manage fisheries

under increasing stress with conventional approaches. Actions have been taken only after

declines have already occurred and were finally acknowledged. Actions are then usually only

minimal and not at a level that ensures recovery will take place. (The history of increasingly

more restrictive size limits placed on several grouper species until the fishery for some was

eventually closed is a prime example of failure in conventional management approaches).

Fisheries are not sustainable in BNP under the present levels of exploitation affecting stock

status and habitat quality. We make the following recommendations:

C We believe that a broader, more integrated strategy of monitoring, assessment and

modeling is needed for effective fishery management in BNP. This comprehensive

strategy must consider sampling of Bay, mangrove, coral reef, and pelagic fishery

environments, and some consideration of new or enhanced use of data and technologies
vi

(e.g., recreational creel and commercial catches, visual census, trawls and other nets,

acoustics, LIDAR, etc.). There are severe limitations to the use of a single survey geartype to conduct a meaningful synoptic study of fish and shellfish. The current, somewhat

piecemeal, monitoring program must be reconfigured to meet assessment data demands.

Monitoring efforts need to be integrated into a overarching design to facilitate short term

fishery and “habitat” surveys. The analysis of a cost-effective composite design for n key

target species is needed. In the course of the design, some special attention must be given

to the barrier islands, mangrove fringe, and patch reef environments of the Park which

have been sparsely sampled to date.

C Envisaged ecosystem modeling and management endeavors must rely on a precise and

cost-effective mechanism to understand and manage these valuable and essential fishery

resources. Advanced monitoring efforts will also require refined resolution and precision

of existing base habitat maps, including directed efforts to fill in data gaps. An

experimental design to assess water quality effects of Everglades restoration on fishes in

the “tide” will be important to develop. Monitoring activities should be designed at a

time frequency dependent on key process rates, and such these integrated data will be

critical for monitoring the efficacy of proposed management alternatives, ultimately

leading to design of a longer-term program of optimal sampling, monitoring and

assessment of the coral reef fish community and associated habitats in BNP.

C Many of BNP’s most important fishery resources have received little attention (e.g.,

shellfish, bonefish, tarpon, snook, etc.). There is great need to develop additional

population dynamics models and conduct fishery-independent surveys for these

resources. This will require an improved capacity at BNP to monitor fishery resources

across the breadth of environments. Little to no information was available for bonefishtarpon, two mainstays of the highly valuable inshore recreational fishery, and broad

knowledge gaps exist. There is an obvious need to protect “flats” environments from

excessive use and degradation, and to develop a quantitative reef and coastal fish

community index which may help in determining how close the “real” situation

approximates “ideal” community structure.

The qualities that make BNP and the Florida Keys such venerable tourism and fishing

destinations are eroding. Clearly, without some type of immediate proactive fishery

management in BNP, collapse of many important fisheries resources is imminent. A multistock

recovery strategy to sustainable target levels is needed. This strategy must address the great fleet

fishing capacity and develop strategies to buffer exploitation in productive regions and reduce

stress on key habitats The obvious system connectivity that links BNP with the rest of the

Florida Keys coral reef ecosystem means that long-term planning efforts for BNP should also

include close integration of efforts with FKNMS, DTNP and the State of Florida. Marine

protected areas and research natural areas are being implemented regionally; these may be a

necessary step to build sustainable fisheries and to conserve marine biodiversity. To facilitate

recovery of the region’s precious natural resources, it is clearly in the best interests of BNP to

strongly support marine reserve development and implementation in the Dry Tortugas and other

suitable upstream candidate sites.
vii

TABLE OF CONTENTS

BNP Site Characterization: Fisheries Resources and Habitats

J.S. Ault, S.G. Smith, G.A. Meester, J. Luo, and J.A. Bohnsack

Executive Summary............................................................................................ iii

Table of Contents................................................................................................ vii

1.0 Introduction........................................................................................................ 1

2.0 Databases for Resource Assessment and Analysis.......................................... 6

2.1 Reef fish visual census (RVC)...................................................................... 6

2.2 Roller-frame trawl survey (TRAWL)............................................................ 7

2.3 Recreational fishery creel census (CREEL).................................................. 8

2.4 Supplemental fishery-dependent databases.................................................. 8

2.5 Biological and physical “habitat” databases................................................ 8

2.5.1 Benthic habitats and bathymetry.................................................... 9

2.5.2 Water currents and salinity dynamics........................................... 9

3.0 Overview of Species and Habitats.................................................................... 10

3.1 BNP fishery ecosystem................................................................................. 10

3.2 Species inventory and fishes at risk.............................................................. 10

3.3 Fish habitats and community diversity......................................................... 13

3.4 Water currents and juvenile recruitment pathways....................................... 19

3.5 Size distributions and spatial ontogeny for exploited species...................... 21

3.6 Quantitative assessment of habitat use......................................................... 37

4.0 Fishery Overview and Estimation of Stock Status Indices........................... 42

4.1 Fishing effort and regional human population growth................................. 42

4.2 Commercial landings.................................................................................... 42

4.3 Size at first capture restrictions.................................................................... 47

4.4 Indicator variables of population status........................................................ 49

4.4.1 Catch-per-unit-effort (CPUE)........................................................ 49

4.4.2 Average size in exploited phase.................................................... 53

4.5 Non-compliance with fishery minimum size regulations............................. 57

5.0 Multispecies Fishery Stock Assessments........................................................ 60

5.1 Synthesis of population dynamics parameters............................................. 60

5.2 Estimation of total mortality rate from ‘average size’ statistics.................. 60

5.3 Management benchmarks analyses.............................................................. 66

5.3.1 Fishery yields and YPR................................................................ 68

5.3.2 Spawning potential ratios SPR..................................................... 68

5.3.3 Limit control rules........................................................................ 70

5.4 Baseline status of BNP exploited fish stocks.............................................. 73

6.0 Conclusions and Recommendations............................................................... 78

7.0 Literature Cited................................................................................................ 83

8.0 Appendices........................................................................................................ 87
1

1.0 Introduction

Biscayne National Park (BNP), encompassing approximately 728 km2 in southeastern Florida

(Figure 1), is a unique tropical marine environment of national significance, renown for its

productive coral reef ecosystem, diverse natural resources, broad fishing opportunities, and

spectacular scenic beauty. Explosive regional population growth and documented resource declines

have raised concerns about the future of these resources. The NPS requires general management

plans (GMP) for each national park unit. The GMP framework sets forth the management

philosophy for decision making and problem solving in National Parks for the next 15 to 20 years

to meet current and future conservation objectives and resource uses, while enhancing visitor

experiences. In an effort consistent with the GMP framework and as an initial step leading towards

development of a comprehensive fishery management plan for the Park, this report characterizes the

current status of BNP’s fisheries resources and associated habitats.

We view fisheries assessment and management from a systems science perspective (Ault

1996, Bohnsack and Ault 1996, Rothschild et al. 1996, Ault et al. 1998, Ault and Luo 1998,

Bohnsack et al. 1999, Lindeman et al. 2000, Ault et al. 2000b), illustrated in Figure 2. In this

approach, the fisheries assessment and management “system” is an organized set of scientific

protocols and methods designed to achieve three main goals: (1) to understand fisheries resources

and habitats within the context of the aquatic ecosystem; (2) to assess the impacts of human

activities and economic drivers on these resources; and (3) to analyze and evaluate the degree of

success of proposed and implemented management policies in mitigating human impacts on fisheries

resources. A systems science overview of BNP fisheries resources is as follows. Located just south

of Miami at the northernmost end of the Florida Keys, BNP’s coastal bay and coral reef habitats play

a critical role in the function and dynamics of the larger Florida Keys coral reef ecosystem. The

inshore waters of Biscayne Bay serve as a nursery area for larvae and juveniles of a wide variety of

fish and shellfish. Many of these live and reproduce in barrier coral reef and other offshore habitats

as adults. The coastal bay environment also provides habitats for a number of highly prized

gamefishes, such as bonefish, tarpon and snook. Fish and shellfish populations in the Florida Keys

region currently support a multibillion dollar tourist and fishing economy. Over the past several

decades, public use of and conflicts over fishery resources have increased sharply, while some

fishery catches from historically productive snapper and grouper stocks have declined (Bohnsack

et al. 1994). A recent quantitative retrospective assessment of the Florida Keys multispecies reef

fish community showed that fishing mortality levels are very intense, that many stocks are

"overfished", and that signs of overfishing have been evident since the late 1970's (Ault et al. 1997a,

1998). A series of management actions, begun in the early- to mid-1980s, included establishing size,

season, and bag limits on a number of species. Several species have been closed to harvest

altogether.

Habitats of fish and shellfish in the Florida Keys ecosystem, particularly those located in

nearshore areas, have also been impacted and compromised by human activities. Over the last eight

decades, the Florida Keys, Florida Bay and Biscayne Bay have undergone dramatic changes in

environmental conditions due to human alteration of the natural hydrology in southern Florida.

These changes are now the focus of an intensive effort to restore the ecosystem by returning the

hydrology to more natural conditions (Harwell et al. 1998, RESTUDY www.acoe.jax.mil). Coastal

bay nursery grounds will likely bear the brunt of the proposed changes in freshwater outflows to

estuarine and marine environments. The combination of rapidly growing human populations,

overfishing, habitat degradation, pollution and changes in regional water quality from Everglades

“restoration” make the Keys region an "ecosystem-at-risk" as one of the nation's most significant,
2
3

Fishery Reserve

Non-Fishery Reserve

LARVAL DISPERSAL

Application

Fisheries and

Human Socioeconomic

System

Aquatic Ecosystem

Fishery Resources

& Habitats

Resource Management

& Managers

Fisheries Risk Assessment &

Information Management System

Survey Design & Implementation

Data Assimilation & Statistical Analysis

Population Dynamics Modeling

Stock Assessment

Bioeconomic Analysis & Modeling

Management Policy Scenario Testing

 & Performance Analysis

Resource

Management

Policies

Impacts

Sampling

Surveys Data

Sampling

Surveys

Data

Policy

Alternatives

Policy Analysis

Results

Figure 2: Conceptual overview of a systems science approach to fisheries assessment and management.
4

yet most stressed, marine resource regions under management of NOAA, the State of Florida, and

the National Park Service (Ault et al. 1997a, 1998, 2000ab; Schmidt et al. 1999).

The goal of this report is to formally employ the systems science approach to assess the

baseline status of multispecies fishery resources in BNP circa 2000 (Figure 3). The approach links

the acquisition and assimilation of physical, biological and fishery databases to advanced statistical

and modeling procedures to conduct multispecies stock assessments. Therefore, the objectives of

this report are:

• To assimilate BNP regional resource databases and overview both species and habitats.

• To estimate biological indicators of stock status from several sources of fishery-independent

and fishery-dependent survey data (i.e., creel census, coral reef visual census, and

rollerframe trawl survey).

• To synthesize and estimate a suite of population dynamics parameters for assessment model

building.

• To conduct length-based multispecies stock assessments for key exploited fishery resources

in BNP waters using our new methods, to compare current estimates of exploitation to stateof-the-art fishery management benchmarks for fisheries sustainability, and to conduct a

preliminary risk assessment of fishery management strategies for overfished stocks

consistent with the Magnuson-Stevens Fishery Management Conservation Act.

The results of the analyses presented in this report provide guidance and identify priorities

for developing comprehensive General Management and Fishery Management Plans for Biscayne

National Park. In addition, these results will facilitate strategic formulation of policy alternatives

that optimize conservation and use of Park fishery resources, and the design of high precision

information systems that enable evaluation of the likelihood of success of a given policy in

achieving Park natural resource management goals.
5

Management Policy Formulation

& Survey Design Recommendations

Resource Databases for

Assessment and Analysis

Habitat/Water Quality:

 Bathymetry

 Benthic Habitats

 Water Quality Sampling

 Hydrodynamics Model

Animals:

 Reef Fish Diver Visual Survey

 Roller Frame Trawl Survey

 Recreational Creel Census

 Recreational Headboat Landings

 Commercial Landings

Population Dynamics Parameter

Synthesis and Estimation

Length-Age Growth Model

Weight-Length Relationship

Max Size-Age

Size-Age at Maturity

Size-Age at First Capture

Natural Mortality Rate

Fishing Mortality Rate (F)

Multispecies Production Analysis

Yield in Weight at F

Spawning Stock Biomass at F

Management Benchmark Analysis

Spawning Potential Ratio

Limit Control Rule Analysis

Overview of Species, Habitats, & Fisheries

and Estimation of Biological Indicators

Habitat Spatial Distribution L

Species Inventory & Diversity Catch, effort, CPUE

Animal Spatial Distribution Animal Use of Habitats

Figure 3.- Flow diagram of the systems science approach employed to assess Dry Tortugas region coral reef fishery resources.

Solid lines depict analyses flow of this report, while dashed lines indicate future steps in fishery management plan development.
6

2.0 Databases for Resource Assessment and Analysis

We assimilated, analyzed and visualized a number of fishery-independent and fisherydependent survey databases on fishes and macroinvertebrates in Biscayne National Park. Databases

of physical and biological “habitat” features of BNP were also integrated into these analyses. In

addition to the following descriptions, a brief summary of each database is provided in Appendix

A. 

2.1 Reef Fish Visual Census (RVC)

Biological data from Biscayne National Park were collected as part of a Florida Keys-wide

fishery-independent sampling survey for reef fish conducted by NOAA Fisheries and University of

Miami RSMAS scientists. The RVC (reef-fish visual census) survey database for BNP, extracted

from the Keys-wide survey database, covered the years 1983 to 1992 and 1995 to 1999 for a total

of 13 years. Data were collected by standard, non-destructive, in situ visual monitoring methods by

highly trained and experienced divers using open circuit SCUBA (Bohnsack et al. 1999). Visual

methods are ideal for assessing reef fishes in the Florida Keys because of prevailing good visibility

and management concerns requiring the use of low impact assessment methods. The method

provides reliable quantitative estimates of species composition, abundance (density per plot),

frequency-of-occurrence, and individual size composition for the reef fish community. Data are

collected by a stationary diver centered in a randomly selected circular plot (Bohnsack and Bannerot

1986, Ault et al. 1998). Divers sample 7.5 m radius circular plots for 5 minutes attempting to count

all fish observed within each imaginary cylinder extending from the bottom to the limits of vertical

visibility (usually the surface). Divers begin each sample by facing in one direction and listing all

species within the field of view. When no new species are noted, new sectors are scanned

sequentially by rotating in one direction for the 5 min period. Several complete rotations were

usually made for each plot. After the initial 5 min, data are then recorded on abundance and

minimum, mean, and maximum lengths for each species listed. Depth, bottom composition,

estimated percentage cover, and maximum relief are recorded for each plot from the polar

perspective of the centrally located observer. A calibration ruler held out perpendicularly at the end

of a meter stick was used to reduce apparent magnification errors in size estimates. Divers

periodically calibrate their sample radius estimates with either a meter stick or fiberglass tape.

Species with few individuals (e.g. angelfish, barracuda, hogfish) are counted and their size estimated

immediately. Highly mobile species that are unlikely to remain in the area (e.g. sharks, carangids)

are tabulated when first observed and then ignored. For common species (e.g. damselfish, wrasses,

etc.) one 360o

 rotation is made for each species by working back up the list in reverse order of

recording to reduce potential bias by avoiding counting a species when they were particularly

abundant or obvious. The time required to record each sample averages 15-20 min (range 5-30),

depending on the habitat.

We used a two stage stratified random survey design to optimize sampling effort and choose

sampling locations (Cochran 1977). Bottom features are first classified into habitat strata and

mapped based on bottom structure and location. The Florida Keys sampling domain was partitioned

into strata based on geographical location and habitat characteristics. The Florida Keys domain is

overlain in a Geographical Information System (GIS) with a grid of 1 km2 cells. Each cell contains

25 blocks of 200 x 200 m which are the primary sample units. Block size was chosen such that

divers can easily swim from a moored vessel to any location within the boundaries. Second stage

sample stations are randomly positioned in each block. There are 226 non-overlapping possible 7.5
7

m radius fish sampling stations within each block. Because of concerns about autocorrelation and

safe diving practices, each fish sampling station consists of the average of combined stationary point

estimates from two individual divers (i.e., a “buddy pair”). From 1983 through 1999, a total of 863

diver stations were sampled in BNP. Annual and total sample sizes are given in Table 1.

 

Table 1.- Sample size summary of fish sampling for Biscayne National Park for fishery-independent

stratified random trawl (TRAWL) surveys, two-stage stratified random visual census (RVC), and

fishery-dependent BNP dock-intercept recreational creel (CREEL) surveys from 1976 to 1999.

 

Biscayne Bay Biscayne National Park

Stratified Random Trawl Survey Reef Fish Visual Census & Recreational Creel Census

 

 Month-Year TRAWL Year RVC CREEL

 

 1976 1388

 1977 2373

1978 2845

1979 2699

1980 2326

1981 2098

1982 3161

1983 18 2686

 1984 2 535

 1985 3 1191

 1986 4 1146

1987 19 927

1988 114 609

1989 159 635

1990 141 737

1991 41 697

1992 63 703

April - May 1996 118 1993 620

August 1996 93 1994 844

 Nov-Dec 1996 150 1995 68 366

 March 1997 122 1996 36 541

 September 1997 151 1997 79 417

 November 1997 120 1998 79 396

 Oct-Nov 1999 119 1999 37 

 Feb-March 2000 110 

 

Totals 983 863 29,940

 

2.2 Roller-Frame Trawl Survey (TRAWL)

Trawl sampling was conducted in Biscayne Bay from Rickenbacker Causeway to Long

Arsenicker Key in Biscayne Bay, FL, at depths greater than 1 m, using a habitat-based stratified

random survey design (see Ault et al. 1999a for details). Sampling was carried out exclusively at

night to maximize the capture probability of shrimps, but captured about 120 finfish species. The

survey employed a standard roller frame trawl used in the South Florida commercial live bait shrimp
8

fishery (cf., Serafy et al. 1997a). The trawl consists of a 10 mm square mesh net attached to a 3 m

wide by 0.5 m high metal frame outfitted with a slotted roller along the lower portion. At a given

sampling station, two nets were towed simultaneously on either side of the sampling vessel. A

station tow distance of 200 m yielded a tow area of 600 m2

 for each trawl net. Upon completion of

a tow, captured fish and shrimp were processed separately for each trawl net. Pink shrimp were

sorted from the total catch of fishes and other macroinvertebrates and brought back to the laboratory,

where each individual was sexed and carapace length (CL) recorded as the distance from the

postorbital margin to the posterior margin of the carapace (Diaz et al. 2001). Individual fish were

immediately identified and sized (to nearest mm total length TL) onboard, and then released back

into the water. Water temperature, salinity, dissolved oxygen, and depth were measured at each

sampling station. Sampling strata were delineated into distinct “habitats” based on a combination

of depth, bottom substrate, and salinity zone. Eight “snapshot” surveys (2-3 weeks in duration) were

performed in different seasons over the period from spring 1996 to winter 2000. Trawl station

locations were randomly selected within each strata for each survey. The TRAWL survey database

contains a total of 983 randomized trawl stations. Sample sizes for each seasonal survey are listed

in Table 1. 

2.3 Recreational Fishery Creel Census (CREEL)

Creel census interviews of recreational fishers at public launch ramps have been conducted

by BNP personnel and volunteers using standardized data collection procedures since 1976 (Davis

and Thue 1979, Harper et al. 2000). Data collection included a fishing party interview and

biological sampling of landings conducted at the conclusion of each recreational fishing trip.

Information on angler effort and catches were distributed among areal partitions of BNP and detailed

data collected included: date of trip, trip duration, number of anglers per vessel, hours fished,

species, numbers and sizes of fish captured, numbers and species of fish released, targeted species,

angler residence, trip origin, and party composition. Biological sampling consisted of recording fish

lengths and numbers by species. The BNP CREEL census database from 1976 to 1998 covers 23

years of information obtained from a total of 29,940 interviews of sportfishers (Table 1) who fished

either inside the Park boundaries or in the pelagic zone adjacent to the eastern Park boundary.

Interviews of sportfishers who primarily fished in other areas outside the Park were not considered

in our analysis.

2.4 Supplemental Fishery-Dependent Databases

Other fishery dependent databases were used to supplement the three primary biological

databases described above. The 1981-1995 NMFS headboat landings database (Bohnsack et al.,

1994; Dixon and Huntsman, 1992) provided information on multispecies stock status in the greater

Florida Keys-Dry Tortugas coral reef ecosystem, including but not limited to the BNP region. We

also obtained commercial fishery landings reported for the statistical area covering Biscayne Bay,

Barnes Sound, and Card Sound for 1986-1998. These were extracted from the NMFS general

canvas landings database for the south Florida region (P. Eyo, NOAA SEFSC).

2.5 Biological and Physical “Habitat” Databases

The principal physical and biological “habitat” data used in our analyses involved benthic

substrate types, bathymetry, water quality, and hydrodynamics.
9

2.5.1 Benthic Habitats and Bathymetry

Geographical information system (GIS) layers of bottom substrate classifications interpreted

from aerial photographic surveys were provided by the Florida Marine Research Institute and

Miami-Dade County Department of Environmental Resources Management. Data for the Biscayne

National Park region were extracted from the Florida Keys-wide database. Bottom types included

multiple categories of coral reef, seagrass, hardbottom, and sand/rock substrates. Data layer

coverages pertaining to land and shoreline delineations were also included.

We acquired the National Ocean Service Hydrographic Survey database of depth soundings

for the Florida Coast (NOAA, Silver Spring, MD; NOS, Boulder, CO). Water depths at latitudelongitude point locations were extracted for the BNP region and gridded into GIS layers.

2.5.2 Water Currents and Salinity Dynamics

Water current and salinity dynamics were described for the BNP region using a 2D finite

element numerical hydrodynamic model (Wang et al. 1988). Model inputs include empirical

information on winds, tides, water currents, canal discharges, salinities, rainfall, evaporation,

overland water flow, and groundwater flow collected at various times and locations within the region

for approximately 25 years of observations (Wang et al. 2001). The model was developed to

describe the currents, residence times, salinity patterns, and larval transport in the Biscayne Bay

lagoonal system. The principal model outputs are water current vectors and salinity values along

a horizontal spatial grid of resolution between 150 and 1000 m depending on the specific location

in the model domain. The model was validated using point samples of surface, mid-water and

bottom water quality parameters extracted from the DBHYDRO database provided by the South

Florida Water Quality Management District. Data were utilized from 54 stations sampled within

southern Biscayne Bay at 2-8 week time intervals during the period 1995-1998.
10

3.0 Overview of Species and Habitats

The databases described in Section 2 were assimilated and analyzed to characterize the

species and habitats of fish and shellfish (macroinvertebrate) community in Biscayne National Park.

In our assessments, we begin by documenting the community structure in BNP, and then discuss and

quantify the spatial and temporal relationships between fishery resources and habitats.

3.1 BNP Fishery Ecosystem

Coastal fishes, particularly reef fishes and shellfish, are ideal indicators of environmental

stress (e.g., fishing, habitat changes, etc.) because they are tightly linked to the southern Florida

coastal ecosystem. Many important reef species directly use Biscayne Bay and inshore habitats as

nursery and juvenile forage areas before migrating offshore to the coral reefs (Figure 4A). They

are likely to be directly and indirectly impacted by the Everglades restoration efforts. Mature

animals spawn at the deep edge of the bank reefs, and then their larvae are advected coastward

where juveniles settle in lagoons and seagrass beds on barrier islands. Obvious examples include

pink shrimp (Farfantepenaeus duorarum) spiny lobster (Panulirus argus), barracuda (Sphyraena

barracuda), hogfish (Lachnolaimus maximus), Goliath grouper (Epinephelus itajara), gray snapper

(Lutjanus griseus), as well as several other snappers (Lutjanidae), groupers (Serranidae), grunts

(Haemulidae), spadefish (Ephippidae), surgeonfish (Acanthuridae), triggerfish (Balistidae),

parrotfish (Scaridae) and jacks (Carangidae). Even tarpon and snook are closely tied to Biscayne

Bay as they extensively utilize reef habitats as adults. In most cases, however, the relative

importance of different habitats in Biscayne Bay and offshore of the Keys has rarely been quantified

for different reef or coastal species.

Key aspects of the south Florida fishery ecosystem pertaining to BNP are diagramed in

Figure 4B. Progressing from west to east, there are a wide variety of aquatic habitats spread over

a relatively short distance. The western edge of BNP is lined with marsh and mangrove habitats

which serve as the entry point for freshwater inflow to the Biscayne Bay environment via an

extensive network of drainage canals. The Park’s eastern boundary is characterized by an oceanic

pelagic zone dominated by the Gulf Stream ocean current. A subtropical coastal bay environment,

leeward barrier islands, and an offshore coral reef system are situated in between these boundaries

along a west-to-east gradient of increasing salinity. Animal surveys, described above, targeted three

principal habitats, the coastal bay (TRAWL and CREEL), coral reef tract (RVC and CREEL), and

near-shore pelagic (CREEL) zones.

3.2 Species Inventory and Fishes at Risk

An overall species inventory for BNP was compiled by combining the following information

from animal survey databases: (i) animals observed in the RVC survey that were identified to genus

and species by NOAA and RSMAS divers; (ii) animals captured in the TRAWL survey that were

identified to genus and species by RSMAS scientists; and (iii) animals captured and kept by

sportfishers that were identified to genus and species by BNP scientists conducting the CREEL

census. For the CREEL database, animals captured and released by sportfishers, and thus not

positively identified by BNP scientists, were not included in the compilation because of the

questionable reliability of these data. The overall compiled species inventory is provided in

Appendix B. The three BNP animal survey databases contained 325 species of fishes and

macroinvertebrates representing 77 different families (Table 2).
11

Figure 4a. - Conceptual organization of the south Florida ecosystem and key attributes of “bay to

reef” ontogeny for many important coastal fishes and shellfishes. (A) Linkages between animal

lifestages and coastal habitat environments extending from freshwater and mangrove marshes on

the western fringe to deep reef - pelagic environments in the east. (B) Actual distribution of habitats

in Biscayne National Park.
12

Freshwater Everglades Restoration

Figure 4b.- Biscayne National Park Fishery Ecosystem
13

Table 2.- Number of species, families, and species unique to survey type within each dataset from

three BNP fishery-independent (RVC, TRAWL) and fishery-dependent (CREEL) surveys.

RVC TRAWL CREEL Total

Species 182 155 143 325

Families 47 45 45 77

Unique Species 67 78 57

In addition, each survey database contained a substantial number of species not seen or captured in

other surveys (Table 2), reflecting differing survey targets with respect to habitats and species life

stages. The TRAWL survey targets juvenile and subadult pink shrimp, as well as smaller preexploited life stages of fishes, in the coastal bay environment. The RVC survey targets all life stages

of fishes in coral reef habitats within the offshore reef system. The CREEL census targets exploited

life stages of animals in the coastal bay, offshore reef, and pelagic environments.

At present, none of the fishes that inhabit BNP are on federal or Florida lists of threatened

or endangered species. However, it is only very recently that marine fish species have been

proposed for inclusion on national and international rare or endangered animal lists (Musick 1998,

1999; Musick et al. 2000; Hudson and Mason 1996). This situation exists in part because: (1) there

is societal and scientific doubt that marine fish species can become extinct, (2) the out of sight – out

of mind concept; and, (3) they have traditionally been of lower conservation concern than their

terrestrial counterparts. In 1996, the World Conservation Union (IUCN) and the World Wildlife

Fund (WWF) published rare/threatened marine species criteria and revised animal lists to include

marine fish species. The IUCN list includes 18 species found in Biscayne National Park (Table 3).

Concurrently, the American Fisheries Society (AFS) has evaluated the risk of extinction for marine

fish species using new quantitative criteria adopted by IUCN (Hudson and Mace 1996), and

published a list of 82 marine fish species at risk in North America (Musick et al. 2000). Of these 82

marine finfish species, 22 are found in BNP.

3.3 Fish Habitats and Community Diversity

Habitats of BNP were described in terms of bottom substrates (Figure 5), bathymetry

(Figure 6) and seasonal salinity patterns (Figures 7 and 8). The coastal bay-lagoonal environment

of BNP exhibits a basin topography, with shallower (0-2 m) areas along the mainland (western) and

leeward island (eastern) shorelines and deeper (2-4 m) areas in the central, mid-basin region.

Bottom substrates of the coastal bay zone were grouped into three basic types: seagrass, hard

bottom, and bare bottom (Figure 5). Seagrass consisted of sandy or silt-clay sediments vegetated

by the seagrasses Thallasia spp., Halodule spp., or Syringodium spp. Seagrasses in the coral reef

tract were not identified to species. Hard bottom was characterized by a foundation of oolitic

limestone covered by a thin sediment layer populated with a variety of soft coral and sponge species.

Bare bottom was substrate generally devoid of large benthic organisms. Seasonal salinity patterns

in the coastal bay zone (Figures 7 and 8) highlight three broad regions with respect to magnitude

and variability of salinity. The first region is located in the eastern bay adjacent to the Atlantic

Ocean
14

Table 3. - Draft list of marine and estuarine fish stocks at risk in Biscayne National Park and

surrounding waters. Source: Tom Schmidt, NPS. Superscript 1

by common name indicates

species were not observed in RVC surveys.

Family Common Name Scientific Name BNP Habitat Protection Criteria

Acanthuridae Gulf surgeonfish1 Acanthurus randalli Coral, seagrass AFS vulnerable

Balistidae Queen triggerfish Balistes vetula Coral reefs IUCN vulnerable

Carcharhinidae Blacktip shark Carcharhinus limbatus Coastal bays and reefs IUCN vulnerable

Carcharhinidae Dusky shark1 Carcharhinus obscurus Coral, grassbeds IUCN endangered

Centropomidae Swordspine snook Centropomus ensiferus Mangrove, estuarine AFS vulnerable

Centropomidae Fat snook1 Centropomus parallelus Mangrove, estuarine AFS vulnerable

Centropomidae Tarpon snook1 Centropomus pectinatus Mangrove, estuarine AFS vulnerable

Gobiidae Spot-tail goby Gobionellus stigmaturus Seagrass, tidal flats AFS vulnerable

Gobiidae Orangespotted goby Nes longus Mud and sand bottoms AFS vulnerable

Labridae Hogfish Lachnolaimus maximus Coral reefs, seagrass IUCN vulnerable

Lutjanidae Mutton snapper Lutjanus analis Coral reefs, seagrass IUCN vulnerable

Lutjanidae Cubera snapper Lutjanus cyanopterus Coral reefs, seagrass IUCN vulnerable

Myliobatidae Spotted eagle ray Aetobatus narinari Coastal bays and reefs State of Florida protected

Pristidae Smalltooth sawfish1 Pristis pectinata Coastal bays IUCN/AFS endangered, FL protected

Scaridae Scarus guacamia Rainbow parrotfish Coral reefs, seagrass IUCN vulnerable

Sciaenidae Blue croaker1 Bairdella batabana hardbottoms, seagrass AFS vulnerable

Scombridae Bluefin tuna1 Thunnus thynnus Oceanic IUCN endangered

Serranidae Speckled hind Epinephelus drummondhayi Coral reefs IUCN/AFS critically endangered

Serranidae Yellowedge grouper Epinephelus flavolimbatus Coral reefs AFS endangered

Serranidae Jewfish Epinephelus itajara Coral reefs, mangroves AFS endangered, U.S./FL protected

Serranidae Marbled grouper Epinephelus inermis Coral reefs IUCN vulnerable

Serranidae Warsaw grouper1 Epinephelus nigritus Coral reefs, hardbottoms IUCN/AFS critically endangered

Serranidae Snowy grouper1 Epinephelus niveatus offshore hardbottoms IUCN/AFS vulnerable

Serranidae Nassau grouper Epinephelus striatus Coral reefs, seagrass AFS/IUCN threatened, U.S./FL protected

Serranidae Blue hamlet Hypoplectus gemma Coral reefs AFS vulnerable

Serranidae Black grouper Mycteroperca bonaci Coral reefs, hardbottoms AFS vulnerable

Serranidae Yellowmouth grouper Mycteroperca interstitialis Coral reefs, hardbottoms AFS vulnerable

Serranidae Gag grouper Mycteroperca microlepis Coral reefs, seagrass AFS/IUCN vulnerable

Serranidae Scamp Mycteroperca phenax Coral reefs, hardbottoms AFS vulnerable

Syngnathidae Fringed pipefish1 Anarchopterus cringer Seagrass AFS vulnerable

Syngnathidae Lined seahorse Hippocampus erectus Seagrass IUCN vulnerable

Syngnathidae Longsnout seahorse Hippocampus reidi Seagrass AFS/IUCN vulnerable

Syngnathidae Dwarf seahorse Hippocampus zosterae Seagrass AFS/IUCN vulnerable

Syngnathidae Oppossum pipefish1 Microphis brachyurus Seagrass AFS vulnerable
15

Figure 5. - Diversity of BNP fish community (number of species per surveyed site as determined

by TRAWL and RVC sampling methods) overlain on digital map of benthic substrate types (green

is seagrass, red is coral reef reefs, yellow is hardbottom mixed with seagrass, orange is hardbottom,

light blue is mud or sand substrate.
16

Figure 6.- Biscayne Bay and BNP regional 3-dimensional bathymetery from Miami to North Key

Largo. Depth scale (m) is shown in upper right. Shallowest is red while deepest is blue. White line

indicates boundary of BNP.
17
18
19

and is characterized by near-oceanic salinities that vary little throughout the year. The second midbasin region is characterized by somewhat lower average salinities during the peak of the wet season

(Jul-Sep) that also vary to a moderate degree. The third is a lower-salinity region with high

variability located in the western bay, an area influenced by freshwater discharges along the

coastline from drainage canals.

The reef environment extends eastward from the barrier island shallow (0-2 m) shoreline area

to the deeper (>20-25 m) outer edge of the coral reef tract (Figure 5). Bottom substrates include

large areas of seagrass, hard bottom, and bare sand categories; however, coral reefs are the most

prominent substrate feature. Two types of coral reef communities are present in the BNP reef

system, inshore patch reef and offshore platform margin reef. The platform margin reef or ‘reef

tract’ community is characterized by coral reefs that form a quasi-continuous offshore platform or

shelf edge parallel to the axis of the Florida Current. Inshore patch reefs are comprised of small and

moderate size discrete coral communities that occur between the barrier island shoreline and the

western edge of the platform margin reef. Salinities are oceanic with very little variability yearround in the reef and adjacent deeper pelagic environments (Figures 7 and 8).

Spatial point values of fish community species richness, a measure of diversity, were

overlain on maps of bottom substrate (Figure 5) and bathymetry (Figure 6). Values in the coastal

bay environment represent the number of fish species captured at an individual TRAWL station

location (sampling area 1,200 m2

). Point values in the reef system represent the number of fish

species observed by divers within a 200m by 200m primary sampling unit (area 40,000 m2). For

primary units sampled repeatedly over multiple years, point values represent the annual average

species richness for the latest 3-year period.

Although a comparable number of species were observed in the RVC and TRAWL surveys

(Table 2), diversity was substantially higher at reef sampling locations compared to bay sampling

locations (Figures 5 and 6). These differences are perhaps partly due to the smaller station

sampling area and restricted gear size-selectivity of the TRAWL survey compared to the RVC

survey. Within the bay, diversity was generally higher in mid-basin seagrass habitats. Low

diversity was apparent in the southern bay hard bottom habitat and also in the highly variable

salinity zone proximal to the mainland (Figures 5 and 8). In the reef system, diversity was higher

along the platform margin reef and somewhat lower in inshore patch reefs.

3.4 Water Currents and Juvenile Recruitment Pathways

The hydrodynamics of Biscayne Bay have been well studied (Wang et al. 1988, Wang et al.

2001). Principal flows enter and exit the Bay through the Safety Valve, and Angelfish-BroadCaesar’s creeks (Figure 9). Residence time in the Bay varies widely from several months in the

more enclosed Barnes Sound, to on the order of a month in western parts of south Biscayne Bay, and

to near zero in the vicinity of ocean inlets (e.g., Safety Valve). 

The hydrodynamic model was coupled to a biological dynamics model for the Lagrangian

drift dynamics and recruitment of pink shrimp (Ault et al. 1999b, Wang et al. 2001). In model

simulated larval drift and transport experiments, we placed particles (i.e., cohorts of shrimp and

fishes) in the physical circulation model near the Safety Valve entrance. Both passive (driven only

by advection by currents) and behavioral (swimming vertically) cohorts were used in transport

simulations. Passive particles cycled in drift trajectories corresponding to tidal cycles (Figure 9).

Behavioral particles moved directly into preferred low salinity (~20 ppt) areas. Passive particles

took twice as long to reach the Bay’s western shoreline. Simulations of drift trajectories of pink
20
21

shrimp larvae and recruited juvenile shrimp spatial abundance distributions were in good agreement

with settlement patterns of live shrimp from synoptic sampling surveys (Ault et al. 1999a).

3.5 Size Distributions and Spatial Ontogeny for Exploited Species

The three animal survey databases were synthesized to describe spatial distributions of

different life stages for species captured by sportfishers in BNP. Life stage was viewed from both

biological (juvenile, adult) and fishery (pre-exploited, exploited) perspectives. For a given species,

biological life stages were defined according to length at sexual maturity (Lm), where

juveniles<Lmadults. Fishery life stages were demarcated using length at first capture (Lc), where

pre-exploited<Lcexploited. Presence-absence maps were constructed from fishery-independent

survey data (RVC and TRAWL) in the following manner. For the reef system, RVC primary unit

point locations where a given species life stage was observed were indicated with a solid dot,

whereas an open circle denoted sampling locations where the species life stage was not observed.

A similar procedure was used for TRAWL station locations in the coastal bay system, with the

exception that only stations where a species life stage was observed were plotted to avoid potential

seasonal biases. To aid in the interpretation of the presence-absence maps, we developed lengthfrequency histograms comparing the size structure for the TRAWL (coastal bay system) and RVC

(reef system) surveys. Presence-absence maps and accompanying length-frequency histograms are

shown for representative species in the exploited snapper-grouper-grunt complex: black grouper

(Figures 10 and 12B), red grouper (Figures 11 and 12A), mutton snapper (Figures 13 and 16A),

gray snapper (Figures 14 and 16B), yellowtail snapper (Figures 15 and 16C), white grunt (Figures

17 and 19A), and bluestriped grunt (Figures 18 and 19B). CREEL survey data provided very coarse

spatial information on distributions of exploited life stages among the general coastal bay, reef, and

pelagic environments of BNP functionally limiting its usefulness for spatially-explicit analysis. A

list of gear-specific capture regions for species in the CREEL database is provided in Table 4. For

a given species, gears comprising >5% of database records and capture regions accounting for >10%

of gear-specific records are listed.

At least three distinct ontogenetic patterns of species’ habitat use in BNP are evident in the

snapper-grouper-grunt fisheries complex (Figures 10-19, Table 4). Mutton snapper, gray snapper,

and bluestriped grunt individuals appear to primarily utilize habitats in the coastal bay environment

as early juvenile nursery grounds, and then extend their spatial domain out to the reef system as late

juveniles and adults. Yellowtail snapper and white grunt individuals seem to utilize both coastal bay

and reef system habitats as early juvenile nursery areas, and remain in these general environments

as late juveniles and adults. Early juvenile nursery areas in BNP for red and black grouper have not

been identified. Sampling surveys conducted in other areas of the Florida Keys indicate that

nearshore shallow hard bottom habitats in the reef system may serve this function (personal

communication, James Colvocoresses, Florida Marine Research Institute). This particular habitat

has not been targeted to date by fishery independent surveys conducted in BNP. Late juvenile and

adult groupers primarily utilize the reef system (red grouper) or a mixture of bay and reef habitats

(black grouper). Great barracuda, a top-level predator in BNP, occurs in all environments (bay, reef,

pelagic) at both juvenile and adult life stages (Figure 16D, map not shown). A summary list of

species captured or seen in all three animal surveys (TRAWL, RVC, CREEL) is provided in Table

5. We can infer the following characteristics for these species: they are subject to exploitation by

sportfishers, they occur in both bay and reef systems, and they utilize coastal bay habitats as juvenile

nursery grounds to some extent.
22
23
24

0

0.05

0.10

0.15

0.20

0.25

2 8 14 20 26 32 38 44 50 56 62 68

Length (cm)

Relative Frequency

(proportion of n)

Red Grouper

nRVC=38 Lc

Lm

(A)

0

0.02

0.04

0.06

0.08

0.10

0.12

2 8 14 20 26 32 38 44 50 56 62 68

Length (cm)

Relative Frequency

(proportion of n)

Black Grouper

nRVC=19 L Lc

m

(B)

Figure 12.- Length frequency distributions for TRAWL (hatched bar) and RVC (solid bar) surveys

in Biscayne National Park for: (A) red grouper; and (B) black grouper. Total number of individuals

(n) observed in each survey, length at maturity (Lm), and length at first capture (Lc) are denoted for

each species.
25
26
27
28

0

0.05

0.10

0.15

0.20

0.25

1 6 11 16 21 26 31 36 41 46 51 56

Length (cm)

Relative Frequency

(proportion of n)

Mutton Snapper

nRVC=21

Lc

Lm

nTrawl=108

0

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1 6 11 16 21 26 31 36 41 46

Length (cm)

Relative Frequency

(proportion of n)

Gray Snapper

nRVC=1895

Lc

Lm

nTrawl=1925

(A)

(B)

Figure 16.- Length frequency distributions for TRAWL (hatched bar) and RVC (solid bar) surveys

in Biscayne National Park for: (A) mutton snapper; (B) gray snapper; (C) yellowtail snapper; and

(D) great barracuda. Total number of individuals (n) observed in each survey, length at maturity

(Lm), and length at first capture (Lc) are denoted for each species
29

0

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

0.20

1 6 11 16 21 26 31 36 41 46

Length (cm)

0

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

5 30 55 80 105 130 155 180

Length (cm)

Relative Frequency

(proportion of n)

Yellowtail Snapper

nRVC=3745

Lc

Lm

nTrawl=151

Relative Frequency

(proportion of n)

Barracuda

nRVC=412

L’c Lm nTrawl=1

Figure 16.- (continued)

(C)

(D)
30
31
32

0

0.02

0.04

0.06

0.08

0.10

0.12

0.14

0.16

0.18

1 6 11 16 21 26 31 36

Length (cm)

0

0.05

0.10

0.15

0.20

0.25

1 6 11 16 21 26 31

Length (cm)

Relative Frequency

(proportion of n)

White Grunt

RVC

nRVC=4056

L’c Lm

nTrawl=9643

Relative Frequency

(proportion of n)

Bluestriped Grunt

nRVC=3240

L’c Lm

nTrawl=3312

(A)

(B)

Figure 19.- Length frequency distributions for TRAWL (hatched bar) and RVC (solid bar) surveys

in Biscayne National Park for: (A) white grunt; and (B) bluestriped grunt. Total number of

individuals (n) observed in each survey, length at maturity (Lm), and length at first capture (Lc) are

denoted for each species.
33

Table 4 - Summary of CREEL survey catches by Family by gear types and capture regions where species were captured,

as well as the principal gear responsible for the majority of captures in the recreational fishery survey.

Fishing Gears & Principal 

FAMILY Scientific Name Common Name Capture Regions Gear

Groupers

Serranidae Epinephelus adscensionis rock hind Hook & Line--Reef Hook & Line

Spearfishing--Reef

Serranidae Epinephelus cruentatus graysby Hook & Line--Reef Hook & Line

Spearfishing--Reef

Serranidae Epinephelus drummondhayi speckled hind Hook & Line--Reef Hook & Line

Serranidae Epinephelus flavolimbatus yellowedge grouper Hook & Line--Bay, Reef Spearfishing

Spearfishing--Reef

Serranidae Epinephelus fulvus coney Hook & Line--Reef Hook & Line

Spearfishing--Reef

Serranidae Epinephelus guttatus red hind Hook & Line--Reef Hook & Line

Spearfishing--Reef

Serranidae Epinephelus itajara Goliath grouper Hook & Line--Bay, Reef Spearfishing

Spearfishing--Reef

Serranidae Epinephelus morio red grouper Hook & Line--Reef Hook & Line

Spearfishing--Reef

Serranidae Epinephelus striatus nassau grouper Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Serranidae Mycteroperca bonaci black grouper Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Serranidae Mycteroperca interstitialis yellowmouth grouper Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Serranidae Mycteroperca microlepis gag Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Serranidae Mycteroperca phenax scamp Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Serranidae Mycteroperca venenosa yellowfin grouper Hook & Line--Bay, Reef Spearfishing

Spearfishing--Reef

Snappers

Lutjanidae Lutjanus analis mutton snapper Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Lutjanidae Lutjanus apodus schoolmaster Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Lutjanidae Lutjanus cyanopterus cubera snapper Hook & Line--Reef Spearfishing

Spearfishing--Reef

Lutjanidae Lutjanus griseus gray snapper Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Lutjanidae Lutjanus jocu dog snapper Hook & Line--Bay, Reef Spearfishing

Spearfishing--Reef

Lutjanidae Lutjanus mahogoni mahogony snapper Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Lutjanidae Lutjanus synagris lane snapper Hook & Line--Bay, Reef Hook & Line
34

Table 4.- (Continued)

Fishing Gears & Principal 

FAMILY Scientific Name Common Name Capture Regions Gear

Lutjanidae Ocyurus chrysurus yellowtail snapper Hook & Line--Bay, Reef Hook & Line

Lutjanidae Rhomboplites aurorubens vermilion snapper Hook & Line--Reef Hook & Line

Grunts

Haemulidae Anisotremus surinamensis black margate Hook & Line--Bay, Reef Spearfishing

Spearfishing--Reef

Haemulidae Anisotremus virginicus porkfish Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Haemulidae Haemulon album margate Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Haemulidae Haemulon aurolineatum tomtate Hook & Line--Bay, Reef Hook & Line

Haemulidae Haemulon carbonarium ceasar grunt Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Haemulidae Haemulon flavolineatum french grunt Hook & Line--Bay, Reef Hook & Line

Haemulidae Haemulon macrostomium spanish grunt Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Haemulidae Haemulon parra sailors choice Hook & Line--Bay, Reef Hook & Line

Haemulidae Haemulon plumieri white grunt Hook & Line--Bay, Reef Hook & Line

Haemulidae Haemulon sciurus bluestriped grunt Hook & Line--Bay, Reef Hook & Line

Haemulidae Haemulon striatum striped grunt Hook & Line--Bay, Reef Hook & Line

Haemulidae Orthopristis chrysoptera pigfish Hook & Line--Bay, Reef Hook & Line

 Other Fish and Shellfish

Labridae Lachnolaimus maximus hogfish Hook & Line--Bay, Reef Spearfishing

Spearfishing--Reef

Sphyraenidae Sphyraena barracuda great barracuda Hook & Line--Bay, Reef, Pelagic Hook & Line

Sparidae Calamus bajonado jolthead porgy Hook & Line--Bay, Reef Hook & Line

Sparidae Calamus calamus saucereye porgy Hook & Line--Bay, Reef Hook & Line

Carangidae Caranx bartholomaei yellow jack Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Carangidae Caranx crysos blue runner Hook & Line--Bay, Reef Hook & Line

Carangidae Caranx hippos crevalle jack Hook & Line--Pelagic Hook & Line

Carangidae Caranx latus horse-eye jack Hook & Line--Pelagic Hook & Line

Carangidae Caranx ruber bar jack Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Kyphosidae Kyphosus sectatrix bermuda chub Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Balistidae Balistes capriscus gray triggerfish Hook & Line--Bay, Reef Hook & Line

Balistidae Balistes vetula queen triggerfish Hook & Line--Reef Hook & Line

Balistidae Cantherhines sufflamen ocean triggerfish Hook & Line--Reef, Pelagic Hook & Line

Spearfishing--Reef

Sciaenidae Cynoscion nebulosus spotted seatrout Hook & Line--Bay Hook & Line

Carangidae Trachinotus falcatus permit Hook & Line--Bay, Reef Hook & Line

Spearfishing--Reef

Carangidae Alectis ciliaris african pompano Hook & Line--Pelagic Hook & Line

Coryphaenidae Coryphaena hippurus dolphin Hook & Line--Pelagic Hook & Line
35

Table 4.- (Continued)

Fishing Gears & Principal 

FAMILY Scientific Name Common Name Capture Regions Gear

Scombridae Sarda sarda atlantic bonito Hook & Line--Pelagic Hook & Line

Scombridae Euthynnus alletteratus little tunny Hook & Line--Reef, Pelagic Hook & Line

Istiophoridae Istiophorus platypterus sailfish Hook & Line--Pelagic Hook & Line

Scombridae Katsuwonus pelamis skipjack tuna Hook & Line--Pelagic Hook & Line

Scombridae Thunnus atlanticus blackfin tuna Hook & Line--Pelagic Hook & Line

Scombridae Scomberomorus cavalla king mackerel Hook & Line--Pelagic Hook & Line

Scombridae Scomberomorus maculatus spanish mackerel Hook & Line--Pelagic Hook & Line

Spearfishing--Reef

Scombridae Scomberomorus regalis cero Hook & Line--Bay, Reef, Pelagic Hook & Line

Spearfishing--Reef

Carangidae Seriola dumerili greater amberjack Hook & Line--Reef, Pelagic Hook & Line

Spearfishing--Reef

Lobotidae Lobotes surinamensis tripletail Hook & Line--Pelagic Hook & Line

Palinuridae Panulirus argus Caribbean spiny lobster Lobster Diving--Bay, Reef Lobster Diving

Scombridae Acanthocybium solandri wahoo Hook & Line--Pelagic Hook & Line

Acanthuridae Acanthurus chirurgus doctorfish Hook & Line--Bay, Reef Hook & Line
36

Table 5.- Species captured or seen in all three fishery surveys (RVC, CREEL, TRAWL) during 1976-1999.

Strong bay-to-reef ontogenetic linkages suggest candidate species list for "restoration" indicator species.

Common name Family Scientific Name

Groupers

Black grouper Serranidae Mycteroperca bonaci

Snappers

Mutton snapper Lutjanidae Lutjanus analis

Schoolmaster Lutjanidae Lutjanus apodus

Yellowtail snapper Lutjanidae Ocyurus chrysurus

Gray snapper Lutjanidae Lutjanus griseus

Dog snapper Lutjanidae Lutjanus jocu

Lane snapper Lutjanidae Lutjanus synagris

Hogfish Labridae Lachnolaimus maximus

Grunts

Porkfish Haemulidae Anisotremus virginicus

Tomtate Haemulidae Haemulon aurolineatum

French Grunt Haemulidae Haemulon flavolineatum

Sailors choice Haemulidae Haemulon parrai

White grunt Haemulidae Haemulon plumieri

Bluestriped grunt Haemulidae Haemulon sciurus

Other Reef Fishes

Great barracuda Sphyraenidae Sphyraena barracuda

Atlantic spadefish Ephippidae Chaetodipterus faber

Ocean surgeonfish Acanthuridae Acanthurus bahianus

Doctorfish Acanthuridae Acanthurus chirurgus

Rainbow parrotfish Scaridae Scarus guacamaia

Gray triggerfish Balistidae Balistes capriscus

Ocean triggerfish Balistidae Cantherhines sufflamen

Scrawled filefish Balistidae Aluterus scriptus

Trunkfish Ostraciidae Lactophrys trigonus

Sea bream Sparidae Archosargus rhomboidalis

Littlehead porgy Sparidae Calamus proridens

Sand perch Serranidae Diplectrum formosum

Spotted moray eel Muraenidae Gymnothorax moringa

Gray angelfish Pomacanthidae Pomacanthus arcuatus

Coastal Inshore baitfishes

Ballyhoo Exocoetidae Hemiramphus brasiliensis

Yellowfin mojarra Gerreidae Gerres cinereus

Blue runner Carangidae Caranx crysos

Macroinvertebrates

Caribbean spiny lobster Palinuridae Panulirus argus
37

3.6 Quantitative Assessment of Habitat Use

Although the primary focus of this report is to conduct a multispecies stock assessment for

exploited species in BNP, assessment and management of important habitats that provide critical

support to animals is also a concern. Here we present a case study for the pink shrimp population

in Biscayne Bay to demonstrate an analysis approach that provides a more detailed, quantitative

understanding of habitat use by animals compared to the more qualitative overview presented above.

Fishery-independent TRAWL survey data (Ault et al. 1999a) were utilized to assess pink

shrimp (Farfantepenaeus duorarum) habitat use in Biscayne Bay. Since a shift in spatial

distribution occurs at the onset of sexual maturation (Ault et al. 1999a), juvenile (6-17 mm carapace

length) and subadult (>17 mm CL) life stages were analyzed separately. Nine distinct “habitats”

were delineated (Figure 20, Table 6) by combining three bottom substrates (seagrass, hard bottom,

bare bottom) with three east-west depth gradients (mainland subtidal, 1-2 m; basin axis, >2 m;

leeward subtidal, 1-2 m). We designated a basic habitat unit as a 600 m2

 trawl station sampling unit.

Drawing on resource selection theory (Manly et al. 1993), three measures of habitat use by pink

shrimp were evaluated for each habitat type: (1) probability of use p(use), i.e., the proportion of

habitat units occupied by at least one animal; (2) per unit amount of use, i.e., animal density

(numbers per m2

); and (3) population amount of use, i.e., animal abundance (number of animals).

A design-based framework (Cochran 1977) was used to estimate the mean and variance of each

measure. The three measures in concert provide a fairly comprehensive understanding of

differential use of habitats.

 

Table 6.- Description of habitat codes of Figure 20, and the probability p of use of each habitat for

juvenile and subadult pink shrimp in August and November 1996.

August 1996 November 1996

Habitat Substrate Depth

(m)

Cross-shelf

Position

Juvenile

p(use)

Subadult

p(use)

Juvenile

p(use)

Subadult

p(use)

1 Seagrass 1-2 Mainland 1.0 0.750 1.0 1.0

2 Seagrass >2 Basin Axis 1.0 0.636 1.0 1.0

3 Seagrass 1-2 Leeward 1.0 0.813 1.0 1.0

4 Hardbottom 1-2 Mainland 1.0 0.545 1.0 1.0

5 Hardbottom >2 Basin Axis 1.0 0.400 1.0 1.0

6 Hardbottom 1-2 Leeward 1.0 0.0 1.0 1.0

7 Barebottom 1-2 Mainland 1.0 1.0 1.0 1.0

8 Barebottom >2 Basin Axis 1.0 0.500 1.0 1.0

9 Barebottom 1-2 Leeward 1.0 0.500 1.0 0.500

Habitat-specific estimates of p(use) for two seasons are given in Table 6. In late summer,

just after the major period of postlarval immigration and settlement, p(use)=1.0 for juvenile pink
38

Figure 20. - Map of Biscayne Bay, Florida, showing nine habitat types. Habitat codes are as

follows: SMS, seagrass mainland subtidal; SBA, seagrass basin axis; SLS, seagrass leeward subtidal;

HMS, hard bottom mainland subtidal; HBA, hard bottom basin axis; HLS, hard bottom leeward

subtidal; BMS, bare bottom mainland subtidal; BBA, bare bottom basin axis; bare bottom leeward

subtidal.Habitat codes are further defined in Table 6. Habitat areas in km2

 are given in parentheses.
39

shrimp in all habitats. Even though subadult abundance is quite low during this season, from 60%

to 80% of seagrass habitat units contained at least one subadult shrimp, with lower p(use) in other

habitats. In late fall, the period between recruitment into (summer) and emigration from (late

winter) Biscayne Bay, p(use)=1.0 for both juveniles and subadults in nearly all habitats. These

results indicate that virtually every habitat unit in the Bay is occupied by at least one juvenile and

one subadult pink shrimp at this time.

Habitat-specific mean density and corresponding 95% confidence intervals for the late fall

season are plotted in Figure 21 for juveniles (Figure 21A) and subadults (Figure 21B). The

horizontal dotted line is the stratified random estimate of overall mean density in Biscayne Bay.

Mean density of juveniles was 3-4 times higher in mainland shallow seagrass (habitat 1) compared

to the average Bay value. Juvenile density in other habitats was at or below the average Bay value.

Subadult shrimp were found in highest densities in mainland and basin axis seagrass (habitats 1 and

2). Density was also above the Bay average in leeward seagrass (habitat 3) and mainland and basin

axis hard bottom (habitats 4 and 5). Lowest densities of subadults occurred in leeward hard bottom

(habitat 6) and bare bottom habitats (7-9).

Results for population amount of use are expressed in terms of the proportion of the total

population (abundance) that resides in a particular habitat (Figure 22). Approximately 80-90% of

both juvenile and subadult pink shrimp in Biscayne Bay are found in mainland and basin axis

seagrass (habitats 1 and 2), with 10-20% of the population distributed among the remaining habitats

(Figure 22A). A formal test for habitat selection is made by comparing the population proportion

p(P) against the proportion of total area p(A) for a given habitat. Statistical differences between p(P)

and p(A) were evaluated with a standard t-test (significance level "=0.05), with outcomes

interpreted as follows:

p(P) > p(A): positive habitat selection;

p(P) = p(A): no selection;

p(P) < p(A): negative selection.

While 85% of juveniles are in habitats 1 and 2, they are clearly selecting habitat 1 over habitat 2

(Figure 22A). Subadult shrimp also exhibit positive selection of habitat 1, but occupy habitat 2 in

equal proportion p(P) to area p(A). Both life stages exhibit negative selection of bare bottom

environments (habitats 7-9).

Broad-scale factors underlying pink shrimp habitat selection are illustrated in Figures 22B

and 22C, which group habitats in terms of bottom substrate (Figure 22B) and salinity zone-depth

(Figure 22C). While juveniles exhibited positive selection for seagrass over other bottom types

(Figure 22B), the selection of mainland shallow water environments without regard to bottom

substrate is much more striking (Figure 22C). In contrast, subadult pink shrimp selected seagrass

habitats irrespective of salinity zone and depth. Recruit shrimp (post-larvae) seem to settle on the

western shore of Biscayne Bay in waters less than 1 m deep, then move seaward as they age, grow

and mature (Ault et al. 1999a, G.A. Diaz, personal communication).
40

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

0.45

0.50

123456789

Habitat

Density

0.0

0.4

0.8

1.2

1.6

2.0

123456789

Habitat

Density

Figure 21.- Mean density and corresponding 95% confidence intervals by habitat type for pink

shrimp (A) juveniles and (B) subadults. The dashed horizontal line represents the stratified random

estimateof mean density for the entire sampling domain (Biscayne Bay).
41

0.0

0.2

0.4

0.6

1234 567 89

Habitat

Relative Frequency

*

*

*

* * * *

*

* * *

0.0

0.2

0.4

0.6

0.8

1.0

sg hb bb

Substrate

Relative Frequency

*

*

*

*

*

0

0.2

0.4

0.6

0.8

ms la ls

Cross Shelf

Relative Frequency

*

*

* *

Figure 22.- Population proportion p(P) of pink shrimp juveniles (hatched bars) and subadults 

(stippled bars) and corresponding proportion of habitat area p(A) (shaded bars) for (A) nine

 habitat types specified in Figure 20, (B) bottom substrate habitats, and (C) cross-shelf salinity 

zone-depth habitats. Asterisks denote significant differences (p<0.05) between p(P) and p(A).

Seagrass is sg, hardbottom is hb, and barebottom is bb. Mainland is ls, basin axis is la, and 

leeward is ls.
42

4.0 Fishery Overview and Estimation of Stock Status Indices

In this section we review and analyze a host of fishery-dependent and fishery-independent

databases and scientific and technical literature on fishery resources in BNP and the broader Florida

Keys region. These results were then used to facilitate evaluation of the current and historical levels

of fishery catch and effort. We also use these data to estimate CPUE (catch-per-unit-effort) and L

(average length of exploited phase individuals), two principal statistics or indicator variables

essential to stock assessments.

4.1 Fishing Effort and Regional Human Population Growth

South Florida reef fish stocks in the Florida Keys and BNP are exploited by large and diverse

commercial and recreational fleets. These fleets have experienced dramatic growth over the last

several decades (1964-1998) in both absolute numbers of registered vessels and in the relative

“fishing power” of each of those vessels. The recreational fleet has dramatically grown since 1964

(Figure 23). In Dade County, the number of registered vessels increased 2.83 times between 1964

(17,966 vessels) and 1998 (50,811). The five county area of south Florida including Dade, Monroe,

Collier, Broward and Palm Beach counties, experienced a 444% increase in the nominal number of

vessels over the same period of time (37,435 in 1994 to 166,343 in 1998). The commercial fleets

have also grown over that same period, with a 1.65 times increase in the number of vessels

registered in Dade County (1,242 to 2,046) and 1.97 times increase in the five county area (5,316

to 10,465). The growth of the fishing fleets is directly correlated with the growth of human

populations in Florida (Figure 24). The Sunshine State has experienced explosive growth over the

past 160 years. During 1960 to 2000 Florida’s human population grew an astounding 223% (from

4.95 to 15.98 million persons)! During this same period, in addition to the sheer increase in both

recreational and commercial fleet sizes, the relative effective vessel “fishing power” of these fleets

has quadrupled due to better hydroacoustics (fish finders and depth gauges), global positioning

systems (GPS), improved vessel designs and propulsion systems, air conditioning, and more

advanced and effective communication networks utilized by both recreational and commercial

fishermen (Bohnsack and Ault 1996, Mace 1997). These increases in fleets sizes and effective

fishing power have not only directly impacted multispecies fishery stocks through exploitation in

BNP, but have negative indirect impacts through habitat degradation and destruction (Rothschild

et al. 1994, Ault et al. 1997b). Excessive boat traffic from a growing fleet and the presence of jet

skis on and near flats are disturbing “habitats” (e.g., flats environments) and premier sportfishes like

bonefish, tarpon, permit and sharks, and causing a significant increase in manatees and turtles hit

and killed by boaters. These conditions have fueled widespread user conflicts between recreational

and commercial fishermen in south Florida waters through interactions precipitated by intensive use

of gillnets, excessive bycatch from shrimp trawls for food and “bait”, and over-use of baitfish

resources compromising the sensitive ecological balance of predator-prey dynamics. Some

mitigation has occurred (e.g., banning of jet skis in BNP circa 1998), but serious concern has arisen

because of documented “serial overfishing” of reef fishery resources in the Florida Keys (Bohnsack

et al. 1994, Ault et al. 1997, 1998). 

4.2 Commercial Landings

Commercial landings in weight by species reported for the area encompassing Biscayne Bay,

Card Sound, and Barnes Sound for 1986-1999 are presented in Tables 7 and 8. These data provide

a good indication of the magnitude of commercial extractive activities in the coastal bay portion of
43
44
45

Table 7. - Commercial landings (lbs) by species reported for the area encompassing Biscayne Bay, Card

 Sound, and Barnes Sound for 1986-1996. Source: FFWCC and NOAA.

YEAR

SPECIES 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996

Groupers, Snappers, Grunts

Red Grouper 5

Nassau Grouper 14

Goliath 708

Black Grouper 1003 1048 42 32 15 30

Gag Grouper 618 36 9 17

Grouper, Other 7 510

Gray Snapper 105 4 2197 1186 22 131 3 30 1123

Mutton Snapper 91 22 4 255 407

Yellowtail Snapper 2698 3269 1147 1433 1859 1824 2507 2214 571 479 2190

Snapper, Other 31 17548 10 405 167

Hogfish 18 16 152

Grunt Spp. 67 3757 866 59 77 100 11 7 176 327

Other Fishes

Blue Runner 18 37 1364 530

Crevalle Jack 5 200 1195 6

Jack, Other 32 10 16 205

Pompano spp. 2 71 84 270 129

King Mackerel 55

Spanish Mackerel 8 128 95 602 100

Cero 521 2 16

Flyingfish spp. 72 924 155 22

Cobia 87 10

Great Barracuda 6.25 322

Shark spp. 99

White Marlin 1094

Triggerfish spp. 21

Porgy spp. 6 108

Spotted Seatrout 50 2301 395 58

Seatrout, Other 25 15

Spot 1687

Mullet spp. 21544 20965 26961 11555 11765 16625 11375 15445 1419 5460 19081

Mullet Roe 530 23 1683 923 886 1193

Sardine spp. 1642 4563

Baitfish (herrings, ballyhoo,, etc) 1895 4548 1739 194 460 3648 312 5002

Misc. Fishes 7193 10609 12906 11273 7358 592 3443 344 2430 186

Macroinvertebrates

Blue Crabs 278 2.5 72 1051 9725 36041 23777 41352 73836

Stone Crabs 6 3 326 895 108 167 419 1889 1778 5881

Spiny Lobster 1536 3645 2068 2029 4572 8615 7379 3716 1946 20192 18150

Pink Shrimp 1474 1257 2286.1 8499 17773 29458 37045 33655 58525 72849 135028

Misc. Invertebrates 150

Sponges

Grass Sponge 11950 6190 2435 59

Sheepswool Sponge 51813 42531 20241 279

Yellow Sponge 12866 4295 1802 47
46

Table 8.- Commercial landings (lbs.) and dockside value (dollars) by species reported for the area encompassing

Biscayne Bay, Card Sound, and Barnes Sound for 1997-1999. Source: FFWCC and NOAA.

1997 1998 1999

Species Landings Value Landings Value Landings Value

Groupers, Snappers, Grunts

Red Grouper 31 $65

Black Grouper 118 $200

Gag Grouper 45 $95

Rock Hind 83 $25

Gray Snapper 101 $209 4 $9 65 $135

Mutton Snapper 306 $558

Yellowtail Snapper 116 $250 895 $1,681 28 $63

Grunt spp. 2,169 $1,951 501 $422 217 $251

Other Fishes

Blue Runner 1,435 $1,132 2,598 $2,838 33 $28

Crevalle Jack 18 $18 70 $70

King and Cero Mackerel 437 $672

Spanish Mackerel 491 $636

Flyingfish spp. 1,036 $134

Great Barracuda 208 $416

Shark spp. 104 $75

Parrotfish spp. 30 $45

Pinfish 113 $776 212 $1,647 145 $1,155

Sheepshead 36 $45

Spotted Seatrout 30 $60

Seatrout, Other 200 $140

Flounder spp. 15 $9

Spot 821 $698

Mullet spp. 24,830 $20,754 20,120 $19,643 26,118 $27,241

Sardine spp. 3,673 $2,401 220 $165

Misc. Fishes 410 $185 3,156 $1,988 4,674 $3,734

Macroinvertebrates

Blue Crabs 89,135 $123,414 77,451 $105,690 117,858 $147,393

Stone Crabs 28,382 $45,245 19,244 $75,499 12,628 $55,422

Spiny Lobster 14,953 $69,031 4,107 $18,329 4,465 $19,307

Pink Shrimp 412,775 $1,225,735 412,265 $1,135,457 612,489 $1,343,564

TOTALS 579,542 1,492,876 $ 542,965 1,364,864 $ 779,157 1,598,965 $ 
47

Biscayne National Park. Unfortunately, commercial landings data specific to the offshore coral reef

habitat of BNP are not available. The magnitude of commercial catches of groupers, snappers, and

grunts in the coastal bay environment of BNP were rather small throughout the reporting period, but

nonetheless showed a declining trend. In contrast, commercial landings of macroinvertebrates

generally showed an increasing trend. For pink shrimp in particular, this trend may partly be due

to a fundamental change in data collection procedures used by the State of Florida and reflect more

complete reporting of catches by commercial fishers in recent years (personal communication,

Florida Fish & Wildlife Conservation Commission) which were functionally omitted early on.

However, this increasing trend in recent years may also reflect increased fishing power of the fleet

and a redirection of fishing effort from declining finfish species resources to the much more

lucrative macroinvertebrate species. Sponges were landed in significant quantities in the late 1980s

and early 1990s prior to the ban on harvest imposed in BNP waters. The aggregate dockside value

of commercial catches for 1997-1999 was approximately 4.7 million dollars . Macroinvertebrates

(pink shrimp, blue crab, stone crab, and spiny lobster) accounted for 93% of the total dollar value.

4.3 Size at First Capture Restrictions

To stem the tide of decreasing fisheries catches and resource productivity, the principal

methods used by fishery resource managers for regulating and controlling fisheries impacts has

traditionally been fish minimum size and fishing effort restrictions. Implementation of minimum

size restrictions sets lower bounds on the sizes of fish allowed to be captured and in catches of the

commercial and recreational fleets. The history of implementation and use of minimum size

regulations in south Florida and the Florida Keys is shown in Table 9. The fishery management

system for south Florida and BNP was laissez faire prior to management measures implemented by

the South Atlantic Fishery Management Council in 1983. In 1985 the Florida Marine Fisheries

Commission was formed and began implementation of a series of size, bag limit and gear restrictions

(note: the history of Florida regulations is listed at http://www.dep.state.fl.us/mfc/MFC-rulehist.htm). Some notable changes in size regulations pertaining to the reef fish complex are as

follows. In 1985 (due to observed reductions in catches) 18 inch minimum size limits were set for

several groupers (Goliath, red, nassau, black, gag, yellowfin), and 12 inch limits were set for several

snappers (mutton, red, yellowtail) (Table 9). By 1990, non-response of the Goliath grouper stock

to recovery by any conventional management efforts prompted a complete catch moratorium, which

is still in effect today. In 1990, the red snapper size limit was increased to 13" (20" in 1992), and

the FMC added schoolmaster (10"), blackfin, gray, dog, lane, silk, vermillion and queen snappers.

Also in 1990, most groupers (and additionally yellowmouth and scamp) had minimum size limits

increased to 20 inches. Hogfish size limits were set at 12" in 1994.

Normally, size restrictions are implemented to prevent “growth overfishing” and to prevent

capture of individual species of fish before they have reached their individual potential to produce

yields (in weight) to the fishery. Variations in setting minimum size limits relates to the fact that

different species (and taxa) grow at different rates and reach different maximum sizes (e.g., groupers

grow to much larger sizes than grunts and are substantially older). But probably a more important

aspect of the setting of minimum sizes relates to the potential production of future generations of

fish by the mature parent stock. That is, the minimum size of first capture by the fishery should

ideally be set higher than the first size of sexual maturity to ensure that each fish has a chance to

produce offspring at least once in its lifetime.
48

Table 9.- Fishing regulations promulgated by SAFMC and FL FMC from 1985-present. Regulations shown are the minimum size

 of capture (inches) and the year the size limit is implemented. The last column shows the current size restrictions.

 Ranges indicate size slot limits, and 'moratorium' means that the fishery for the species is completely closed.

1983 1984 1985 1987 1989 1990 1992 1994 1996 1998 1999 2000 Current

Groupers

Goliath 18 moratorium closed

Red 12" 18 20 20

Nassau 12" 18 20 moratorium closed

Black 18 20 24 24

Yellowmouth 20 20

Gag 18 20 24 24

Scamp 20 20

Yellowfin 18 20 20

Black Sea Bass 8 10 10

Southern Sea Bass 8 8

Snappers

Mutton Snapper 12 16 16

Schoolmaster 10 10

Blackfin 12 12

Red Snapper 12" 12 13 20 20

Yellowtail Snapper 12 12

Cubera 12 12

Gray 10 10

Dog 12 12

Mahogany 12 12

Lane 8 8

Silk 12 12

Vermilion 8 10-20 10 11" 11

Hogfish 12 12

Queen 12 12

Pelagics and Others

Gray Triggerfish 12 12

Permit 10-20 10-20

Pompano 10 10-20 10-20

African Pompano 24 24

King Mackerel 20 24 24

Spanish Mackerel 12 12

Amberjack 28 28

Lesser Amberjack 14-20 14-22 14-22

Red Porgy 12 12

Snook 24 26-34 26-34

Lobster 3 3

Spotted Sea Trout 14-24 15-20 15-20

Tripletail 15 15
49

4.4 Indicator Variables of Population Status

To understand the effects of fishing and environmental changes on fishery resources requires

identification of a quantitative measure that reflects the status of a population subjected to fishing

or other environmental changes, that is, a stock assessment indicator variable. Because reef fishes

integrate aspects of the coastal ocean environment over their lifetime, a robust measure of population

"health” or status can provide a sensitive indicator of direct and indirect stress on the stock, and

perhaps the regional marine ecosystem (Fausch et al. 1990). The RVC and CREEL survey databases

were utilized to produce annual estimates of two biological indicators, average catch-per-unit-effort

(CPUE) and average length of exploited phase individuals ( ) for 90 exploited and/or ecologically L

important species in BNP (Appendix C). A flow diagram illustrating the data processing and

analysis procedures employed for each survey is shown (Figure 25).

4.4.1 Catch-per-Unit Effort (CPUE)

The average population size (in average numbers of fish during the time interval t  t+1)

during a given year t is written as

where N0(t) is the initial population size in year t, F(t) is fishing mortality rate, M is the natural

mortality rate, and E is the exploitation rate (Ricker 1975). Catches (or yields in number YN(t) or

weight Yw(t)) are the product of fishing mortality and average population size. As a result, by

allowing F=qf(t), catch per unit nominal fishing effort f(t) provides a quantitative index of fish

population size

where q is the fraction of the stock removed per unit of nominal fishing effort (Beverton and Holt

1956, 1957). The index provides an average measure of relative population abundance when

independent surveys of populations are made with gears or techniques where the individual effort

units can be intercalibrated. For the RVC survey, CPUE is defined as the number of animals

observed by a diver (YN) per sampling station (f=1 station). Since f covers a fixed area of 176 m2 in

this survey, and q (a measure of capture or ‘observation’ efficiency) is presumed near 1.0 for reef

fish divers, CPUE reflects actual stock density (number per 176 m2

). For the CREEL survey, CPUE

is the number of fish captured and kept (YN) per person-hour of fishing (f). Since q is unknown in

this case, CREEL CPUE is a relative index of stock abundance.

The RVC survey sampled platform margin reef habitats consistently from year to year;

however, this was not the case for inshore reefs, and data from these habitats were excluded from

the analysis. The annual numbers of diver stations used for CPUE estimation are listed in Table 1.

For the RVC survey, annual mean CPUE was estimated using a two-stage simple random sampling

(SRS) design-based methodology following Cochran (1977) and shown in Appendix D1.

Estimation was carried out for three life stages of each species, juvenile (<Lm), adult (Lm), and
50

fkdat3.sas

fkdat3a_bnp.sas fkdat3b_bnp.sas

Creates basic numbers-at-length

dataset from raw diver sampling 

data, all species in BNP region

Reef Fish Visual Survey

Raw Data

Creates L analysis dataset

for BNP region 

Creates CPUE analysis

dataset for BNP region

platform margin reefs 

rvc_lbar.sas Computes L by year, 

single species rfds1.sas

Computes 2-stage SRS 

CPUE estimates by year,

single species life stage 

BNP Recreational Creel Census

Raw Data

Excel spreadsheets Initial error checking and 

data cleanup 

creelcpdat.sas

Creates catch (number landed)

and effort analysis datasets 

from raw interview catch-effort

data, all species; includes 

extensive error checking and 

data cleanup procedures 

Computes L by year from

raw interview length data,

 single species 

creellbar.sas

creelcpue.sas

Computes SRS estimates of 

CPUE by year, single species 

and gear

Figure 25.- Data processing, analysis and synthesis flow diagram for estimation ( ) and CPUE L

from the reef fish diver visual survey (upper panel) and BNP recreational CREEL survey (lower

panel). SAS source code for reef fish visual census RVC is listed in Appendix D1. SAS source code

for BNP recreational CREEL survey is listed in Appendix D2.
51

CPUE

n

CPUEi

i

= ∑

1

exploited (Lc), as defined by the following length intervals:

Life History Phase Interval Description 

Juvenile Phase Lr –> Lm Immature juveniles from the size of first recruitment

to the size of first sexual maturity.

Mature Adult Phase Lm –> L Size of first sexual maturity to the maximum size in

the stock.

Exploited Phase Lc –> L Size of first capture to the maximum size in the stock.

As shown above (sections 3.5 and 3.6), these intervals reflect logical ontogenetic groupings that

pattern animals in space and time over the Biscayne National Park seascape. In many cases,

exploited phase CPUE estimates were computed using two different length at first capture Lc values:

‘legal’ denotes the Lc value regulated by law in a particular year, whereas ‘actual’ denotes a

modified Lc value (here referred to as L’c) that sportfishers adhered to as determined from inspection

of CREEL survey species length data (see section 4.5 for further discussion). The ‘actual’ exploited

phase CPUE estimated from the RVC survey thus corresponds to the life stage comprising the

CREEL survey CPUE estimates.

The CREEL database required extensive processing and error checking before estimation

of CPUE could be carried out in a reliable fashion (see Appendix D2). Interview records with

missing or mis-coded key information—e.g., gear type, area fished, number of persons

fishing—were necessarily excluded from analysis. CREEL data indicated three principal gear types

(hook and line, spearfishing, and lobster diving), and three fishing regions (coastal bay, reef, and

pelagic). The annual number of CREEL fishing trip interviews by gear and region considered in

CPUE estimation are listed in Table 10. A number of interviews recorded catch information for a

general family category, e.g., unidentified snappers or groupers, rather than for a particular genus

and species. Records containing combined catches for a given species group (e.g., snappers) were

excluded from CPUE estimation for a particular species (e.g., gray snapper) in that group.

Annual average CPUE for CREEL survey species was estimated as follows:

where CPUEi

 is catch per person-hour for trip i and n is sample size. For a given species, CPUE was

estimated from fishing trips for the principal gear type and associated capture regions as listed in

Table 4. Sample size n (Table 10) was thus the sum of fishing trips for the principal gear over the

capture regions inhabited by a given species. These numbers reflect BNP personnel sampling efforts

and are not necessarily proportional to true fishing effort. Regions not inhabited by a species were

not included in the estimation.

The complete time series of annual CPUE estimates derived from both the RVC and CREEL

surveys for 90 species are provided in Appendix C (Tables C1-C90, one table per species). A

synthesis of these results for selected species is presented in Table 11. For ease of interpretation,

CREEL CPUE values are expressed as the expected time a party of four persons would need to fish

(or dive) to land (capture and keep) one animal of a given species. Shorter fishing times thus reflect
52

higher stock abundance and longer fishing times reflect lower stock abundance. The fisherydependent CREEL and fishery-independent RVC abundance indices corresponded quite well for the

recent 1995-1998 period, showing the same rank order of reef fish species abundance with the

exception of bluestriped grunt. The restricted sampling domain of the RVC survey compared to the

more expansive capture regions of the CREEL census may somewhat account for this discrepancy.

Comparing CREEL estimates between the periods 1995-1998 and 1985-1988 sheds some light on

stock abundance trends over the past 10-15 years in BNP (Table 11). Lobster diving in BNP

appears to have improved from the late 1980s to the late 1990s, as measured by the decrease in

capture time and thus increase in CPUE. It is interesting to note that establishment of the spiny

lobster sanctuary, which eliminated lobster fishing in the coastal bay portion of BNP, occurred in

1984. Hook-and-line fishing for dolphin in the pelagic zone, and for grunts (white and bluestriped)

in bay-reef environments, has remained steady or improved slightly in the past 10-15 years. In

contrast, reef fishing for snappers and groupers seems to have worsened to a lesser (gray and

yellowtail snapper) or greater (mutton snapper and red grouper) degree over this time frame.

Exploited phase black groupers have apparently been quite rare in BNP for some time.

 

Table 10.- CREEL survey sampling (number of recreational fishing trip interviews) by year, gear

type (hook and line, spearfishing, and lobster diving) and region (bay, reef tract, and pelagic zone)

from 1976 to 1998. The Bay portion of BNP was declared a lobster sanctuary in 1983.

 

 Hook & Line Spearfishing Lobster Diving

Year Bay Reef Pelagic Reef Bay Reef

 

1976 411 424 314 112 56 71

1977 574 714 446 324 139 176

1978 767 793 546 391 158 190

1979 861 713 511 340 106 168

1980 798 627 262 278 167 194

1981 505 653 340 335 53 212

1982 874 975 497 395 67 353

1983 999 795 387 304 51 150

1984 196 148 37 40 1 113

1985 339 455 236 100 1 60

1986 346 328 249 76 3 144

1987 282 282 167 60 4 132

1988 116 225 113 56 0 99

1989 119 217 130 46 0 123

1990 100 236 143 52 3 203

1991 79 257 195 45 2 119

1992 180 279 187 36 0 21

1993 173 231 134 45 0 37

1994 244 277 190 89 0 44

1995 103 122 60 34 2 45

1996 174 167 107 58 1 34

1997 118 137 98 37 1 26

1998 98 150 82 37 0 29
53

Table 11.- Comparison of CREEL and RVC exploited phase (actual) CPUE estimates for selected

species. Units for the CREEL census are expressed as the expected time a party of four persons

would need to fish (or dive) to land one animal. RVC survey CPUE units are number observed per

diver station. Values are 4-year averages for the time period indicated.

Species

CREEL 90's

1995-1998

CREEL 80's

1985-1988

RVC 90's

1995-1998

spiny lobster 10 min 20 min —

dolphin 1 h 1 h —

white grunt 1 h 1 h 0.689

gray snapper 2.5 h 1.5 h 0.227

bluestriped grunt 3.5 h 4.5 h 0.605

yellowtail snapper 7 h 5.5 h 0.157

red grouper 23 h 11 h 0.016

mutton snapper 26 h 15 h 0.010

black grouper 71 h 100 h 0.000

4.4.2 Average Size in Exploited Phase L

To describe baseline status for a fish population in multispecies community settings, a robust

population dynamic variable is required to relate current trends in human and environmental

stressors to expected future condition of the stocks over relatively broad spatial and temporal scales.

A powerful choice is the metabolic-based pool variable average size of animals (in either length or

weight) in the exploited phase of the stock (Beverton and Holt 1957, Gulland 1983, Ault 1988, Ault

and Ehrhardt 1991, Ehrhardt and Ault 1992). ‘Average size’, denoted as , is a physiologicallybased variable that is a very sensitive indicator of direct and indirect stress on marine ecosystems

(Ault et al. 1997a, 1998, Quinn and Deriso 1999). The of a reef fish stock (or population if closed

intra-breeding unit) is strongly correlated with population size in both numbers and biomass, and

thus can be used as an indicator variable of population health. The formal definition of is
54

where tc is minimum age at first capture, t is oldest age in the stock, N(a,t) is abundance for age

class a, L(a,t) is length, and F(t) is the instantaneous fishing mortality rate at time t. 

The use of in stock assessment has deep roots in traditional fisheries management

(Beverton and Holt 1956, 1957, Ricker 1975). In general, it is well-known that is highly

correlated with average population size, and so reflects the rate of fishing mortality operating in the

fishery. As fishing mortality rate increases, decreases at a rate proportional to the populationdynamic tolerance of a stock. Minimally, average size is greatest when fishing mortality is lowest

(or near zero), and decreases as the rate increases (Figure 26). Assuming that mortality occurs

proportionally to stock age-size spatial distributions, will continue to decrease until at high

exploitation rates it will be nearly equal to the minimum size of first capture regulated by fishery

management. Secondly, there exists a value of corresponding to a population size that produces

maximum sustainable yields on a continuing basis. 

We computed ‘average length’ for each of the principal data sources available for BNP.

Estimates of the mean, variance, and 95% confidence interval followed Sokal and Rohlf (1969).

The complete time series of annual estimates of average length for the RVC and CREEL surveys

for the period 1976 to 1999 are provided in Appendix C for 90 species. To understand the status

of stocks in BNP relative to the greater Florida Keys ecosystem, we also computed from several

region-wide survey databases, both fishery-independent (RVC surveys in the Florida Keys and Dry

Tortugas) and fishery-dependent (headboat surveys in the Keys, Tortugas). The time series of 

estimates for six different types of survey data are plotted in Figure 27 for three primary and

representative exploited species. We first note that species’ estimates of average size (e.g., black

grouper, gray snapper, and yellowtail snapper) have been relatively constant for the past 25 years.

This constant “average size” has been very close to the minimum size of first capture. Secondly,

the estimates of average size have been lowest in BNP relative to the Keys and Dry Tortugas. The

third item to note that reflects the effectiveness of traditional fishery management methods is that

many species have had extremely low sizes of average length in the past, and that very little

recovery of average length has occurred, even as minimum size limits were imposed.

Estimates of average length for fishery-dependent CREEL surveys and fishery-independent

RVC surveys are extremely close for each of the three example species of Figure 27, which is

characteristic of the other 90+ species analyzed (Appendix C). This would indicate that the two

data sources are producing similar estimates of the effects of mortality on these stocks. An

important factor in the use of the statistic to measure population mortality rates and to assess the

effects of exploitation is that it can be reliably computed from both fishery-dependent and fisheryindependent data sources. Theoretically, the average size of fish in the exploited phase landed for

any given exploited species should be equal to the average size in the exploited phase of the

remaining population in the sea just after fishing. The greater the correlation between the two

independent estimates of , the more robust ‘average length’ should be as an indicator of stock

status subject to exploitation. This is a powerful conclusion in that it allows several independent

observations of ‘average length’ to be computed and compared for consistency and reliability. Our

results for BNP corroborate previous research which demonstrated close agreement between fisheryindependent RVC estimates of average length and fishery-dependent headboat survey estimates

for Florida 
55

Figure 26. - Theory of reduction of average length in the exploited phase with increasing fishing

mortality for black grouper.
56
57

Keys reef fishes (Ault et al. 1997a, 1998). 

4.5 Non-Compliance with Fishery Minimum Size Regulations

In computation of ‘average size’ statistics for further assessment of fishery management

benchmarks, an alarming observation we noted in analysis of the CREEL database was that a fairly

high proportion of the catches returned to the BNP ramp by recreational fishermen contained fishes

that were smaller than the minimum size of first capture regulated by regional fishery management.

This was particularly evident for several snapper species, e.g., mutton snapper, in which more than

70% of the fish landed were below minimum size (Figure 28a,b), but was also evident for hogfish

(Figure 28c), gray triggerfish (Figure 28d), and a number of groupers and grunts.
58

Mutton Snapper

300

350

400

450

500

550

600

1970 1975 1980 1985 1990 1995 2000

Year

Average Length (mm)

Legal

Actual

Yellowtail Snapper

200.0

220.0

240.0

260.0

280.0

300.0

320.0

340.0

360.0

380.0

1970 1975 1980 1985 1990 1995 2000

Year

Average Length (mm)

Legal

Actual

LC

LC

(A)

(B)

Figure 28. - Impacts of non-compliance with regulated minimum size Lc (dashed line) restrictions.

The ‘average length’ in the exploitable phase estimated from the CREEL data is shown for: (A)

yellowtail snapper; (B) mutton snapper; (C) gray triggerfish; and, (D) hogfish. The actual statistic

L’c includes many individuals that are below Lc, resulting in much lower estimates of average

size(and higher fishing mortality rates) than if the law were enforced or received full compliance

from recreational fishermen.
59

Hogfish

300

310

320

330

340

350

360

370

380

390

1970 1975 1980 1985 1990 1995 2000

Year

Average Length (mm)

Legal

Actual

Gray Triggerfish

200

250

300

350

400

450

500

1975 1980 1985 1990 1995 2000

Year

Average Length (mm)

Legal

Actual

LC

LC

(C)

(D)

Figure 28.- (continued)
60

5.0 Multispecies Fishery Stock Assessments

In this section, we conduct a formal quantitative assessment of exploited fish stocks in BNP

and evaluate their current status with respect to established fishery management benchmarks. An

overview of the general assessment procedure, comprised of 8 separate steps, is summarized in the

flow diagram of Figure 29. Steps 1 to 3 of the procedure were described in previous sections. This

section details Steps 4 through 8. 

5.1 Synthesis of Population Dynamics Parameters

To facilitate assessment of the multispecies fisheries resources in BNP, we conducted a

thorough search of the scientific and technical literature (c.f., Claro 1994; FISHBASE, Froese and

Pauly 2000) and our own databases to assemble a comprehensive suite of biological and population

dynamic information that contains key rate parameters necessary for computing the relevant fishery

management benchmarks for sustainability of resources. These parameters included age-growth,

weight-length and weight-fecundity relationships, size-age at first recruitment, minimum size-age

at first sexual maturity, maximum size-age, sex ratios and age (size) class distributions, natural

mortality rates, and other key fisheries indices (Tables 12 and 13). These data were required to run

our suite of multispecies fishery stock assessment computer models (Ehrhardt and Ault 1992; FAO

1997, Ault et al. 1996, 1997a, 1998). Of the 91 exploited and/or ecologically-important species we

identified within the BNP databases, we found that the available population dynamics parameters

varied widely in breadth and statistical quality. Therefore, we classified each species’ parameter set

according to a ‘parameter confidence’ rating that ranged from no data available (scored 0) to high

confidence (scored 3). Given the disparity in quality between data sets available for various stocks,

and in some cases the complete lack of critical population dynamic data, we feel some additional

refinement of these critical data will be necessary in the longer run, as these data are required to

assess exploitation effects and to determine the appropriate management strategies to achieve Park

goals. Nonetheless, these data were essential to producing a baseline assessment of BNP

multispecies fisheries resources.

5.2 Estimation of Total Mortality Rate from ‘Average Size’ Statistics

While persistent heavy fishing reduces the average fishable population size over time, it also

leaves a distinguishing size-age structure signature on the exploited population, which provides a

robust basis for mortality estimation (Figure 30). We capitalized on this aspect of demographic

theory by using the statistic to assess the current levels of exploitation of the multispecies fishery

community in BNP. To estimate the total instantaneous mortality rate Z(t) given an estimate of ,

we used the following equation (Ault and Ehrhardt 1991; Ehrhardt and Ault 1992):

where Lc is size at first capture, L is maximum size in the stock, K and L are parameters of the von

Bertalanffy equation, and t is year. While no explicit formula exists for analytical estimation of Z(t),
61

Figure 29.- Flow chart showing the steps used in the multispecies reef fish assessment. See Ault

et al. (1998) for additional details.

Begin Multispecies Assessment

?

Step 1: Conduct fishery surveys (RVC, CREEL, TRAWL, etc.) for fish community in year

t and intercalibrate sampling efficiency by species, site, and year.

?

Begin Management Analyses for species s

?

Step 2: Using intercalibrated survey data, compute annual estimates of and associated

95% confidence intervals from size and abundance data integrated over the range of

exploitable sizes.

?

Step 3: Compute CPUE by species by lifestage by year t for each data type.

?

Step 4: Use population dynamics parameters (Table 10) to parameterize the LBAR (Ault et

al., 1996) and REEFS (Ault et al. 1998) computer algorithms.

?

Step 5: Use estimate in LBAR computer algorithm to estimate fishing mortality rates

as for each species by year for the several data sources, i.e., time

series of RVC, CREEL and headboat data.

?

Step 6: Use the REEFS numerical model to: (1) compute expected given the reported

population dynamics and parameter values; (2) compute yield per recruit (YPR)

and assess growth overfishing; and, (3) compute spawning stock biomass (SSB) for

the fishery in unexploited and (for maximum sustainable yield and current) exploited

states (i.e., F=0, F=Fmsy, and F= , respectively) and evaluate spawning potential

ratio (SPR) to assess recruitment overfishing.

?

Step 7: Use REEFS to compute and assess limit control rules.

?

Step 8: From these results make specific fishery management recommendations on control

strategies of F and Lc consistent with eumetric fishing principles and the

precautionary approach of the MSFMCA that minimize the potential for overfishing.

?

Conduct next species analyses?

 STOP
62

Table 12.- Parameters, definitions and units for life table variables common to the LBAR and REEFS

numerical models used in simulation analysis of Florida Keys reef fish population dynamics. See

Tables 13 for parameter values.

 

 Parameter Definition Units

 

s Reef fish species (s=1,á,n)

a Cohort age class (a=1,...,t)

tr Age of recruitment months

Lr Size at recruitment mm

tm Minimum age of maturity months

Lm Minimum size of maturity mm

tc Minimum age of first capture months

Lc Minimum size of first capture mm

t Oldest (largest) age years

L Largest (oldest) size mm

W Ultimate weight kg

L Ultimate length mm

K Brody growth coefficient year-1

t0 Age at which size equals 0 years

"WL Scalar coefficient of weight-length function dimensionless

ßWL Power coefficient of weight-length function dimensionless

W(a,t) Weight at age a at time t g

L(a,t) Length at age a at time t mm

N(a,t) Numbers at age a at time t number of fish

M(a,t) Natural mortality rate at age a at time t year-1

F(a,t) Fishing mortality rate at age a at time t year-1

S(a) Survivorship to age a dimensionless

Z(t) Total mortality rate in year t dimensionless

(a) Sex ratio at age a dimensionless

B(a,t) Biomass at age a in year t kg

Yw(t) Yield in weight in year t mt

SSB(t) Spawning stock biomass in year t mt

SPR(t) Spawning potential ratio in year t mt
63
64
65
66

this can be achieved fairly easily using an iterative numerical algorithm called LBAR (Appendix F)

developed by Ault et al. (1996) (also found in the FAO stock assessment library, FAO 1997). The

algorithm provides a means to an unbiased estimator of total instantaneous population mortality rate

Z(t) (Quinn and Deriso 1999). Estimation of instantaneous fishing mortality rate F(t) is accomplished

by subtracting the rate of natural mortality M from the estimate. The statistic is robust to any

population survey measure (i.e., visual census, creel, or headboat survey data). Iterative application of

the mortality estimation method using annual estimates of provided time-series information on

fishing mortality rates, and thus abundance, for all key species included in the analysis (see Appendix

C). This estimation procedure is explained in detail in Ault et al. (1998). Current estimates of are

given in Table 14.

5.3 Management Benchmarks Analyses

To assess the status of the multispecies fishery based on the estimated rates of fishing mortality,

we employed a population computer simulation model called REEFS (reef-fish equilibrium exploitation

fishery simulator) (Appendix F). The model was applied to 35 reef fish species in 5 families: groupers,

Serranidae; snappers, Lutjanidae; grunts, Haemulidae; the hogfish, Lachnolaimus maximus, Labridae;

and, the great barracuda, Sphyraena barracuda, Sphyraenidae. These are among the primary fishery

targets of the recreational and commercial fishing fleets in the south Florida coral reef ecosystem. Other

species (e.g., bonefish, tarpon, spiny lobster, etc.) lacked sufficient indices of population status

(Appendix C) and/or population dynamics data (Table 13) necessary for comprehensive quantitative

stock assessments in BNP. The hogfish (or hog snapper) was grouped with snappers for analytical

purposes.

The REEFS model is a size-based algorithm that embodies a stochastic age-independent model

for ensemble number at a given length ( ) for the entire population (Ault and Rothschild 1991,

Ault and Olson 1996, Ault 2001)

where R(--a) is recruitment, S(a) is survivorship at age a, (a) is sex ratio at age, and P(L
a) is the

probability of being length L given the fish is age a (Ault 1988, Ault and Rothschild 1992, Ault et al.

1997, 1998). The modeled fishing mortality rate of recreational and commercial fishers (or ‘viewing

power’ of divers) was assumed to remove (sight) fish with a ‘knife-edged selectivity pattern’ (see

Gulland, 1983) over the range of exploitable sizes

where the size of first capture Lc is that regulated by regional fishery management. Species-specific

population dynamics parameters (Table 13) were used as model inputs. 

The REEFS model was configured to assess several fishery management decision making

reference points, or benchmarks, including yield-per-recruit (YPR) and spawning potential ratio (SPR).

These two reference points are relatively robust measures of potential yields and recruitment, 
67

Table 14.- Full fishery management benchmark analysis for 35 reef fish stocks in Biscayne National Park showing current estimates.

Species with dashed boxes indicate current minimum size regulations are at or set lower than the minimum sexual maturity.

Taxa Common name Ave. L F M K M/K Lm Lc Lλ SPR F/Fmsy B/Bmsy B0 Bmsy Bnow

Groupers 1 Rock Hind 260.00 0.510 0.250 0.191 1.31 336.0 203.2 453.3 5.78 2.04 0.26 18367 4053 1061

2 Graysby 250.00 0.275 0.200 0.350 0.57 108.0 203.2 362.7 40.14 1.37 0.83 8824 4286 3542

3 Speckled Hind 442.95 0.269 0.200 0.130 1.54 460.1 290.0 845.7 18.45 1.34 0.68 120668 32620 222654 Yellowedge 509.58 2.000 0.200 0.170 1.17 524.8 240.0 792.4 3.19 10.02 0.07 126653 58387 4037

5 Coney 280.00 0.645 0.180 0.145 1.24 185.1 203.2 647.8 8.41 3.58 0.25 22919 7859 1927

6 Red Hind 279.14 0.146 0.180 0.207 0.87 247.0 203.2 382.8 44.25 0.81 1.18 19520 7350 86387 Goliath 691.26 0.430 0.130 0.130 1.00 978.0 508.0 2178.3 1.27 3.31 0.05 5827829 1548036 739238 Red 560.00 1.065 0.180 0.153 1.18 433.8 508.0 869.0 14.71 5.91 0.30 125310 60588 184349 Warsaw 691.26 0.431 0.080 0.054 1.48 940.5 508.0 2178.3 1.17 5.39 0.05 6322926 1609331 73970

10 Snowy 493.07 2.000 0.130 0.113 1.15 462.1 508.0 909.8 6.97 15.38 0.13 228770 125243 15937

11 Nassau 551.86 0.313 0.180 0.145 1.24 480.0 508.0 648.2 46.49 1.74 0.77 48595 29247 2259012 Black 628.78 0.625 0.150 0.160 0.94 597.0 508.0 1153.4 5.91 4.17 0.16 532994 191373 3147613 Yellowmouth 545.40 0.423 0.180 0.063 2.86 468.0 508.0 710.7 21.32 2.35 0.48 170878 76345 3642714 Gag 569.84 1.258 0.230 0.149 1.54 657.0 508.0 1034.4 0.92 5.47 0.03 133456 41421 123415 Scamp 470.25 2.000 0.143 0.126 1.13 491.0 508.0 932.2 4.09 13.99 0.09 319185 138084 13065

16 Yellowfin 520.00 2.000 0.180 0.170 1.06 524.8 508.0 792.4 3.99 11.11 0.08 151123 74204 6024

Snappers 17 Mutton 450.00 1.249 0.214 0.129 1.66 275.8 406.4 797.8 14.59 5.84 0.32 91558 42117 13355

18 Schoolmaster 280.00 2.037 0.250 0.180 1.39 144.6 254.0 503.8 12.44 8.15 0.27 20614 9563 2565

19 Blackfin 350.00 0.447 0.230 0.084 2.75 233.4 304.8 459.2 50.65 1.94 0.77 7317 4837 370620 Red 356.00 1.898 0.190 0.162 1.17 303.5 508.0 901.6 1.78 9.99 0.05 146407 49553 260921 Yellowtail 330.00 0.795 0.210 0.209 1.00 194.3 304.8 433.4 35.99 3.78 0.59 17173 10438 618122 Cubera 488.65 0.495 0.150 0.140 1.07 407.3 304.8 1153.1 5.83 3.30 0.19 639420 196466 3728423 Gray 300.00 0.966 0.300 0.136 2.21 230.2 254.0 556.5 14.70 3.22 0.36 17396 7077 255824 Dog 358.71 0.588 0.330 0.100 3.30 298.0 304.8 568.5 27.91 1.78 0.64 19922 8627 556025 Lane 245.00 0.573 0.300 0.097 3.09 202.3 203.2 418.3 23.26 1.91 0.57 7396 2998 172026 Silk 260.00 2.000 0.230 0.092 2.50 303.7 304.8 512.0 7.10 8.70 0.13 23033 12217 1636

27 Black 250.00 0.478 0.300 0.650 0.46 203.0 203.4 650.0 27.61 1.59 0.68 7356 2984 203128 Vermillion 279.21 2.000 0.300 0.206 1.45 310.9 254.0 532.9 0.50 6.68 0.01 11330 4081 57

29 Hogfish 330.00 1.639 0.250 0.190 1.32 195.5 304.8 515.4 19.86 6.56 0.38 32515 16857 6456

Grunts 30 Margate 270.00 0.995 0.374 0.174 2.15 324.4 203.2 578.4 4.72 2.66 0.18 17222 4550 813

31 Tomtate 206.39 2.000 0.330 0.220 1.50 136.1 203.2 250.0 47.10 6.06 0.65 2533 1845 1193

32 Sailors choice 235.00 0.760 0.430 0.220 1.95 100.4 203.2 320.1 48.81 1.77 0.80 3139 1926 153233 Bluestriped 226.90 0.762 0.500 0.484 1.03 108.4 203.2 273.5 56.54 1.52 0.87 2269 1481 128334 White 216.50 2.000 0.370 0.186 1.99 174.0 203.2 410.9 11.64 5.41 0.27 8676 3695 1010

Others 35 Great barracuda 590.00 1.291 0.200 0.172 1.16 625.1 508.0 1151.0 1.81 6.46 0.05 140803 48491 2543
68

respectively. As such, they help to focus on biological (size) and fishing (intensity) controls for

managing current and future fishery production. We also used the REEFS stochastic simulation model

to assess population risks relative to ‘limit control rules’ (e.g., NMFS 1999). Taken together, these

management benchmarks characterize the status of stocks under exploitation relative to Federal fishery

management standards. These analyses thus provide the basis for the assessment of the entire reef fish

community, and indicate the efficacy of current fishery management practices and their sufficiency to

provide sustainable fisheries now and into the future.

5.3.1 Fishery Yields and YPR

Since biomass B(a,t) is the product of numbers-at-age times weight-at-age, yield in weight Yw

from a given species s was calculated as

Yield-per-recruit (YPR), or the lifetime yield expected from a single recruited individual, can then

calculated by scaling yield to average recruitment.

5.3.2 Spawning Potential Ratios (SPR)

Mature or spawning stock biomass SSB(t) is a measure of the stock’s reproductive potential or

capacity to produce newborn, ultimately realized at the population level as successful cohorts or year

classes. Spawning stock biomass is obtained by integrating over individuals between the minimum size

of first maturity (Lm) and maximum reproductive size (here assumed to be the maximum size L) in the

stock

Spawning potential ratio (SPR) is a contemporaneous management reference point that measures the

stock’s potential capacity to produce optimum yields on a sustainable basis. SPR is a fraction expressed

as the ratio of exploited spawning stock biomass relative to the equilibrium unexploited SSB

Resultant estimated SPRs are then compared to the U.S. Federal standards which define 30% SPR as

the “overfishing” threshold (Rosenberg et al., 1996). 

The relationship between YPR and SPR with respect to fishing mortality rate is shown for black

grouper in Figure 31. Note that the current estimates of F for black grouper place SPR and YPR well

below optimal levels for sustainability of the fishery resource. An important attribute of the progression

of average size as fishing mortality increases is shown in Figure 32 for black grouper. Increasing

exploitation successively eliminates older, more fecund size classes through a process known as

“juvenescence”, which ultimately produces an overall younger stock (Ricker 1963, Ault 1988). This

fact is extremely important in the context of stock and recruitment, since the fecundity potential of

individuals increases exponentially with size. Such a phenomenon will be reflected by reductions of
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the stock’s spawning capacity, which itself is related to the expectation of new recruits to sustain the

population over the longer run. For black grouper, fishing at the rate of mortality that produces

“maximum sustainable yield” reduces the spawning potential ratio (the proportion of the virgin

spawning biomass available) to about 36% (Figure 31). Remarkably, the current estimated rate of

fishing mortality has reduced the spawning potential ratio to less than 5% of its historical maximum.

From the perspective of ecological theory, this is an ominous result in terms of black grouper population

stability and resilience. From the perspective of fishers, a current 5% SPR implies that the average size

of black grouper (Figure 32) is 60% of what it once was (circa 1940). 

The summary of the SPRs for the Biscayne National Park exploited reef fish complex in Figure

33 shows that 13 of 16 groupers, 11 of 13 snappers, barracuda, and 2 of 5 grunts for which there are

population dynamics data are below the SPR that constitutes overfishing by Federal definitions.

Overall, 77% of the 35 stocks that could be analyzed were overfished.

 

5.3.3 Limit Control Rules

The Magnuson-Stevens Fishery Conservation and Management Act (MSFCMA) contains a set

of National Standards for fishery conservation and management, the first of which states:

“Conservation and management measures shall prevent overfishing while achieving, on a

continuing basis, the optimum yield from each fishery for the United States fishing industry.”

The MSFCMA also required the Secretary of Commerce to “establish advisory guidelines (which shall

not have the force and effect of law), based on the national standards, to assist in the development of

fishery management plans”. These Guidelines (or national standard guidelines or NSGs) were

published as a final rule in May 1998. Following the NSGs, Technical Guidelines were developed

(NMFS 1999, Restrepo and Powers 1999) to translate the NSGs into usable scientific criteria so that

scientific advice could be offered to the regional Fishery Management Councils to assist in

implementing the MSFMCA. Key points arising were: (1) that maximum sustainable yield (MSY) is

to be viewed as a limit (i.e., a threshold NOT to be exceeded); (2) that two measures were to be used

to determine a fish stock’s management status, (a) the current fishing mortality rate relative to the

fishing mortality rate that would produce MSY (denoted as F/Fmsy), and (b) the current amount of

spawning biomass relative to the spawning biomass at MSY (denoted as B/Bmsy); (3) that there should

be maximum standards of fishing mortality rates which should not be exceeded, called Maximum

Fishing Mortality Threshold (MFMT); (4) that there should be a Minimum Stock Size Threshold

(MSST) under which a stock’s spawning biomass would be considered as depleted; and (5) that these

criteria and measures should be linked together through “control rules” which specify actions to be

taken (i.e., changes in management measures to alter fishing mortality rates) depending upon the status

of current spawning biomass relative to Bmsy and MSST and the status of the fishing mortality rate

relative to Fmsy and MFMT.

To address these emerging fishery management benchmark criteria for BNP multispecies

resources, we conducted a new cutting-edge analysis that established fishery limit control rules

consistent with the “precautionary approach” (NMFS 1999, Restrepo and Powers 1999, Darcy and

Matlock 1999, Butterworth and Punt 1999). Criteria used to set target catch levels as explained above

are explicitly risk averse. A risk averse Precautionary Approach would set OY (optimum yield) below

MSY as a function of uncertainty. Thus, the greater the uncertainty, the greater the distance between

the two. The precautionary approach to fisheries management requires avoidance of overfishing,

restoration of already overfished stocks, explicit specification of management objectives including
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operational targets and constraints (e.g., target and limit reference points), taking account of uncertainty

by being more conservative, and avoidance of excess harvest capacity. In addition, this approach

requires formulation of decision rules that stipulate in advance what actions will be taken to prevent

overfishing and promote stock rebuilding.

Limit reference points are designed to constrain exploitation within safe biological limits so that

stocks retain the ability to produce maximum sustainable yield. Overfishing is a level or rate of fishing

mortality that jeopardizes the long-term capacity of a stock or stock complex to produce MSY on a

continuing basis. In this arrangement, the fishing mortality rate which generates MSY should be

regarded as the minimum standard for limit reference points. The limit MSST, minimum stock size

threshold, is used to decide what level of fishing mortality indicates “overfishing”, and when the stock

is in an “overfished” condition. If spawning biomass drops below MSST, then the regional fishery

management councils MUST take remedial actions to end overfishing and rebuild overfished stocks to

MSY levels very rapidly (generally in 10 years or less).

A graphical application of limit control rule theory is shown in Figure 34 for black grouper in

BNP. The region defined by (A) represents a developing fishery where fishing mortality rates are below

the level required to achieve MSY, and the stock biomass is greater than the biomass at MSY. The (B)

region defines an area where overfishing is occurring and passes up to the threshold rate that will lead

to an overfished stock. The (C) region formally defines an overfished stock that violates principles of

sustainability and that requires strong intervention by fishery management. The (D) region defines a

stock under recovery where the current fishing mortality rate has been reduced to a level that promotes

rebuilding of the resource over a 10 year time horizon. We used the natural mortality rate as a proxy

for Fmsy (e.g., Gulland 1983). These estimates of Fmsy were then input to REEFS to estimate .

We note that all the yearly estimates of the black grouper fishery show that substantial overfishing has

occurred in BNP, and that most recent estimates place the level of overfishing at 3-5 times the level of

fishing required to produce maximum sustainable yield under Federal definitions (note that the 1981

estimate is dubious). Minimally, this means that fleet effort would need to be reduced by some 80%

to achieve the longer-term sustainability goals under these standards. However, it should be noted that

the National Park Service standards may be even more conservative than those established under the

MSFMCA, and thus effort reductions would have to be even more severe to achieve NPS goals for

BNP.

The limit control rule technique is demonstrated for black grouper (Figure 34); but the process

is reflective of the analyses for every fish stock or community member (groupers, snappers, grunts, etc.).

Fishing mortality rates were estimated to be from 2 to 10 times higher than Fmsy for 71% (i.e., 25/35)

of the exploited species analyzed (Figure 35). Moreover, the current levels of stock biomass are

critically low for more than 70% of the key targeted species. The most current estimates and results are

summarized in Table 14. 

5.4 Baseline Status of BNP Exploited Fish Stocks

Our results indicate that BNP reef fish populations are currently heavily fished (Figures 33 and

35, Table 14). Despite using conservative assumptions, the estimated fishery exploitation rates we

estimated suggest that many BNP and Florida Keys reef fish stocks are overfished according to

definitions for U.S. fisheries (Rosenberg et al., 1996). Many desirable grouper and snapper stocks have

extremely low spawning potential ratios (SPRs). Moreover, our analysis indicates that these stocks

have experienced high rates of exploitation over at least the last two decades (see also Ault et al. 1998).

The estimated average lengths in the exploitable phase from statistically independent data sources were
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highly comparable for groupers, snappers, and grunts, which supports their use in the multispecies

assessment. The trends in average size for grouper, snapper and grunt stocks was relatively flat over

the last 25 yr and close to the minimum exploitable length. The flatness is explained by considering

expected from a modeled range of F in an analytical model, given knowledge about current values

of . The slope of on F is very shallow in the range of the analytical model, corroborating empirical

estimates. Some stocks appear to have been chronically overfished since at least the late 1970's.

There is also substantial corroborating evidence of these trends from our fishing effort and

CPUE analyses. Total fishing effort has increased substantially because of greater average fishing

power per vessel and a much larger recreational fishery fleet The arithmetic increase of recreational

fishing vessels is an extremely important factor for any future assessments. However, absolute

magnitude of the recreational fishing effect on reef fish stocks is poorly known because the recreational

fleet is heterogeneously distributed across south Florida and the Keys seascape, and has been poorly

sampled or studied to date. Inverse relationships between increased fishing effort (particularly by the

recreational sector) and the long-term decreased average size and stock biomass of the most desirable

species (e.g., groupers and snappers) are of particular concern. Declining CPUE trends observed in

CREEL data also support our overfishing conclusions.

We also noted similarities in key relationships within various taxa that separate out into

somewhat discrete clusters when plotting maximum size versus maximum age by species. This pattern

suggests that species within the various taxa groupings will likely respond to exploitation in a similar

manner. The sensitivity to exploitation is highest for groupers, followed by snappers and then grunts.

The Florida Keys reef fishery shows the classic pattern of serial overfishing, in which the more

vulnerable species are progressively depleted (Munro and Williams, 1985; Russ and Alcala, 1989). The

longest lived, latest maturing, and lowest mortality (M) stocks (i.e., groupers) are those first to

experience significant declines in population biomass, followed in sequence by intermediate-lived

(snappers), and finally by short-lived stocks (grunts). Within families, the inverse relationships between

the spawning potential ratio and ex-vessel market price is consistent with serial overfishing (Ault et al.

1998). As expected, the most valuable snappers and groupers also tend to have the lowest spawning

potentials. 

The process of serial overfishing has decimated the grouper stocks and current levels of fishing

mortality range from 3 to more than 10 times the exploitation level that would achieve MSY. The only

stocks at about the Federal target are rock hind, graysby, coney and red hind. This is because these

species rarely reach a maximum size greater than about 16 inches in total length, and thus are not

generally targeted by fishermen. However, given the current extremely POOR status of the snappergrouper fishery in the Florida Keys and BNP, these species too are now becoming targets. The serial

overfishing phenomenon is clearly reflected in the fact that snappers, and now grunts, stock sizes are

falling below federal target levels under the precautionary approach of the MSFMCA. Now there are

hardly any fish big enough or mature enough to support the waning resources, or to affect any hopes

of system recovery given the pervasive increases in fishers and fleet fishing power. This is particularly

distressing scenario when coupled with pervasive trends in increased coastal development, habitat

degradation, and coastal pollution in south Florida.

Our data suggest that there may have been substantial changes in the composition of the biomass

and abundance of the reef fish community over the past several decades. While many groupers and

snappers have apparently declined in response to growing fishing effort, some grunts have increased

in relative abundance. Claro (1991) noted a similar process in the Golfo de Batabano, Cuba, and
77

hypothesized that chronic over-harvesting of snappers resulted in community shifts in favor of grunts.

Another indication of significant change shown by Ault et al. (1998) is the explosive growth of

barracuda which may be explained by several factors. First, there is little directed commercial or

recreational fishing for barracuda as food Keys-wide due to health concerns. Second, growth of catchand-release fishing by sport anglers and reduced emphasis on spearfishing may have substantially

lowered barracuda mortality. Third, other top predators such as groupers, snappers and sharks have

been intensively fished which appears to have lowered competition while barracuda still retain a large

and possibly increasing prey base of grunts and other small fishes. Increased abundance and biomass

of a top predator like barracuda could be a management concern if barracuda substantially impact reef

fish community dynamics. For example, excessive predation on popular sport fishes like snappers could

counteract potential reductions in fishing mortality sought by traditional management.

During the time frame of this study, numerous measures have been taken to reduce fishing

mortality in state and federal waters. Fish traps were progressively eliminated between 1980 and 1992

and numerous bag limits and minimum size limits were imposed. Fisheries were closed for queen conch

(Strombus gigas), Goliath grouper (Epinephelus itajara), and Nassau grouper (E. striatus). These

actions are evidence of trends reported in this study. These management measures have been largely

ineffectual to reduce the observed declining trends in stock sizes and productivity. The patterns of

fishery size regulations in south Florida have followed those characteristic of fisheries under stress, and

reflect too little action too late. Ault et al. (1998) have shown the Florida Keys multispecies reef

fisheries to have been seriously overfished since at least the late 1970s.

 Adjusting minimum sizes of first capture (Lc) and fishing mortality rates (F) may mitigate the

apparent growth and recruitment overfishing conditions in the fishery. A striking result we discovered,

however, was that 13 of 35 species we closely analyzed have the minimum size of first capture by the

fishery set lower than the minimum size of first sexual maturity (Table 14)! However, traditional

management actions alone are unlikely to be sufficient because they can be circumvented and habitually

fail to effectively control fishing effort, particularly in an open access fishery (Bohnsack and Ault 1996,

Ault et al. 1998). For example, bycatch mortality and high fishing effort from the expanding fleets can

make size limits ineffective. In theory, every fish can be caught once it reaches minimum legal size

resulting in insufficient mature adult survival. The problems we have identified have been compounded

by a clear lack of compliance of fishery regulations by sportfishers, and the apparent lack of

enforcement of existing regulations. Surprisingly, there has been little to no follow-up plan to evaluate

whether regulations and policies invoked are achieving their intended results. What is needed is clear

plan of action to ameliorate these trends in declining and to build sustainable fisheries and conserve

marine biodiversity in the face of ecosystem changes and regional human population growth.
78

6.0 Conclusions and Recommendations

In the report we conducted a baseline characterization and fishery management analyses using

a new systems methodology comprised of advanced methods in fish population dynamics,

mathematical, and statistical modeling to provide a quantitative baseline assessment of the distribution

and status of fishery resources and their essential habitats in BNP. We focused on explicit modeling

of linkages between fish community distribution, abundance and size structure in relation to key

“habitat” characteristics. As a result, precise estimates of the essential parameters are required to

provide critical guidance for future cost-effective sampling and resource assessment efforts. A principal

key to our ability to assess the multispecies reef fish stocks in BNP was the strategic use of ‘average

size’ (in length) of fish in the exploitable phase of the population ( ) as a quantitative indicator of

stock status and response to exploitation. The average length statistic is extremely robust to the data

source from which the population estimation measure is made (i.e., RVC, CREEL or head boat survey

data). In addition, by providing an enhanced and rigorous baseline reference point of reef fish stock

status and spatial abundance for the multispecies community, this research will greatly facilitate the

assessment of natural system variability versus regional changes that may result from proposed fishery

and spatial ecological management strategies. Our principal findings with respect to problems with

overfishing, habitat degradation, and fishery management were:

C An analysis of the Spawning Potential Ratios ( SPR) of exploited reef fish shows that 13 of 16

grouper species,11 of 13 snapper species, barracuda, and 2 of 5 grunt species for which there

are reliable population dynamics data are below the SPR that constitutes overfishing by Federal

definitions (Magnusson-Stevens Fishery Management Conservation Act).

C For example, the average size of black grouper is now 60% of what it was in 1940 and the

spawning stock is now less than 5% of its historical unfished maximum.

C Overall, 77% of the 35 individual stocks that could be analyzed are overfished.

C The current level of fishing mortality for grouper stocks range from 3 to 10 times the

exploitation level that would achieve Maximum Sustainable Yield (MSY).

C Stock biomass is critically low for more than 70% of the key targeted species within the

recreational fishery.

C Some stocks appear to have been chronically overfished since at least the late 1970's.

C For all of the harvested species analyzed, the average sized fish within these exploited

populations for the last 25 years has remained relatively constant and is very close to minimum

harvest sizes (for 77% of those analyzed), not natural historical unfished population size.

C Many species with extremely low average length in the past have had very little change in

average length even though new minimum size limits were imposed.

C The recreational fishing fleet in South Florida has grown at a near exponential rate since 1964

(a 444% increase in recreational boats from 1964 to 1998) with no limits on the number of boats
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allowed to fish.

C The relative effective vessel “fishing power” of the recreational fleet has quadrupled due to

depth indicators and fish finders, global positioning systems, improved vessel designs, larger

motors, and two-way radio communications.

C Exceptionally high and sustained exploitation pressures have precipitated “serial overfishing”

of key fishery resources. Smaller and less desirable species have consistently increased in the

catch as larger more vulnerable species are eliminated.

The following outlines critical habitat-animal relationships, fishery management, policy alternatives and

planning, survey design analysis, and fishery statistics that underlie the portent of future problems:

Bay-to-Reef Dynamics.- Biscayne Bay and BNP is a very important nursery environment. There are

more than 325 fish and macroinvertebrate species in BNP, of these more than 150 of these species are

under direct assault or subject to some form of fishing pressure from recreational commercial fishing

activities that can have long-term deleterious social, economic and ecological consequences. More than

30 of these species are seen in all three existing survey mechanisms (RVC, creel and trawl surveys), but

substantial size differences exist between species and areas of BNP. The species captured by all three

gears use the entire seascape on their ontogenetic migrations from the bay-to-reef, making them

excellent candidates for “key” indicator species that can assist evaluation of the impacts and successes

of Everglades restoration on the sensitive and dynamic coastal environments of BNP. However, many

species that are known to occur in the Bay are rarely, if ever, found in the samples. This fact requires

innovations in gear deployments, new technologies, and directed efforts to sample fringe environments

like mangroves, channels, canals, tidal creeks, etc., to detect species such as pink shrimp (we know they

use ALL habitats, as do bonefish, tarpon, snook, permit, black and silver mullet, jack crevalle and

yellow jacks, anchovies and sardines, black- and white-tipped sharks, nurse sharks, bonnetheads,

sheepshead, red and black drum, etc.). Florida waters have produced 13 of 16 standing world records

for bonefish, and they may be one of the most lucrative fisheries in BNP. However, there is little or no

information system available to document the status or sustainability of many of these economic and

ecologically important resources (i.e., bonefish-tarpon-permit). 

The Everglades restoration includes a comprehensive effort to understand and model the

physical and biological processes of Florida Bay and it’s connectivity to the coral reef tract. As the

restoration enters the implementation phase, it is essential to have effective monitoring programs to

assess system changes and to run predictive models that guide decision making. Ensuring the sustained

function and productivity of this unique environment through prudent use and strategic management

decision making will result in substantial biological, ecological and economic benefits to the scientific,

commercial fishing and public communities. Concern about overfishing, habitat degradation and

progressively escalating resource use resulted in the establishment of the FKNMS in 1990. It is

imperative that BNP management link up with the larger regional efforts of NOAA, NPS and the State

of Florida decision-making entities on the resource management and sustainability issues critical to the

Florida Keys. Fishes are extremely important to monitor with high precision, because in terms of the

species composition, size/age structure, and fishery catch and economic productivity, they are a direct

public concern and an obvious measure of management success (Bohnsack and Ault 1996, Ault et al.

1998, Meester et al. 1999).
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Fishery Management Problems.- The history of fishery management actions by the State and Federal

Fishery Management Council for BNP waters clearly reflects the characteristics of trying to manage

fisheries under increasing stress with conventional approaches. Actions have been taken only after

declines have already occurred and were finally acknowledged. Actions are then usually only minimal

and not at a level that ensures recovery will take place. (Note: The history of increasingly more

restrictive size limits placed on several grouper species until the fishery for some was eventually closed

is a prime example of failure in conventional management approaches).

C A proportion of the fish in catches observed at the Biscayne NP boat ramps during creel surveys

are smaller than the minimum size limit for legal harvest. This is as high as 70% for yellowtail

and mutton snapper!

C 13 of 35 species closely analyzed have their minimum legal harvest size set lower than the

minimum size of first sexual maturity.

In this way, our baseline computations allow BNP resource managers to determine and

understand the current status of the fishery resources and habitats prior to consideration and

implementation of fishery management alternatives (e.g., spatial protection zones (RNAs), catch and

release only zones, etc.) that build sustainable fisheries. These baseline computations will also allow

efficacy assessment of any fishery management alternatives applied in the short- or longer-term. As

a result, this study forms the precursor to more intensive analyses of the multi species fish community

in Biscayne National Park (BNP), and their connections with the broader south Florida coastal ocean

ecosystem. Some particular recommendations for research and fishery management are:

Formulation of Policy Alternatives.- The tradition of open-access management systems coupled with

risk-prone management decisions remains a principal obstacle to achieving renewable resource

sustainability (Rosenberg et al., 1993). Reversing adverse trends in the reef fishery are likely to require

other innovative approaches to controlling exploitation rates. Rothschild et al. (1996) recommended

that fishery management maintain a systems view of the resources, emphasizing strategy over tactics.

With this in mind, we recommend consideration of management alternatives that couple traditional

management measures with a spatial network of “no take” marine reserves (Fogarty et al. 2001).

Marine reserves provide an ecosystem management strategy for achieving long-term goals of protecting

biodiversity while maintaining sustainable fisheries. The establishment of a network of small no-take

reserves may be a first step. A key to the success of this effort is a conscientious, continuous

assessment program using integrated fishery-independent and fishery-dependent data to evaluate their

effectiveness (Bohnsack and Ault, 1996, Ault et al. 1998). With adaptive management (e.g., Walters,

1986), improvements can be implemented over time.

Fishery Management Planning.- Fisheries in Florida were loosely managed prior to the formation

of the Florida Marine Fisheries Commission in 1983. The Magnuson-Stevens Conservation and Fishery

Management Act explicitly defines a process for managing sustainable resources in the U.S. EEZ. The

planning must be based in a ecosystem perspective. However, NPS preferred alternatives for Park

status may likely be more conservative that the MSFMCA standards. In addition, management changes

have been recently made as part of the Florida Keys National Marine Sanctuary (FKNMS) and Dry

Tortugas National Park (DTNP) management plans. The most important change is the use of spatial
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protection including the establishment of 'no-take' marine protected areas or research natural areas

initiatives (NPS 2000; www.coralreef.gov), and these strategies may be appropriate for Biscayne

National Park resource management. 

Survey Design Analysis.- This report also presents the quantitative bases required for future

preparation of an optimal sampling design analysis that produces precise statistics required for future

implementation of a robust fishery management plan for BNP. This research will lead to subsequent

development of an efficient survey and monitoring design that optimizes in a cost-effective way the

precision of estimates for key management variables to assess resource status and to evaluate

management system efficacy. We recommend:

• Increasing the resolution and precision of base “habitat” maps for key environmental variables

(i.e., salinity, benthic substrates, bathymetry, etc.).

• Developing “statistical” fish-habitat suitability models for individual populations, taxa, and

community of inshore and reef fishes and macroinvertebrates. Use these models to optimize

sampling survey designs for monitoring of trends with implementation of spatial management

measures (Ault et al. 1999a, Ault and Luo 1998, Rubec et al. 1999, 2001).

• Conducting and analyzing broad sampling design issues such as key habitats presently not

monitored (e.g., baywide, mangroves, reef fish (RVC and CREEL), pelagic environment) but

which may provide critical signals for management.

An additional important attribute of this technology is that fishery-independent measures of stock status

become even more important when spatial management strategies (i.e., such as marine protected areas

or “no-take” marine reserves) are implemented, because these methods are capable of addressing

estimation of population mortality rates from both within the “closed” reserve areas and areas outside

under exploitation without reliance on estimation of fishery catch data, and does so in a non-intrusive

and non-destructive way.

Fishery-dependent versus Fishery-independent Statistics.- Over time there will be an increasing

need to rely on fishery-independent data because fishery-dependent data will become less available and

less useful as larger size limits, closed seasons, closed fishing areas, and prohibitions on species are

imposed. Also, the shifting emphasis from commercial to recreational fishing in the Florida Keys

makes collecting fishery-dependent data much more difficult and expensive. Fishery-dependent data

has the potential to be biased by under-reporting and lack of cooperation on the part of the fishery itself.

Although fishery-independent assessments can provide reliable measures of reef fish abundance,

population dynamics and community composition (Gunderson, 1993), there have been few applications

of the approach for optimizing the performance of multispecies fisheries in tropical coral reefs. We

have therefore developed techniques and methods for the extraction of a useful indicator of reef fish

stock health from fishery-independent and fishery-dependent monitoring and assessment surveys.

As a result, the quantitative multispecies stock assessment methods presented here should help

provide a backbone for the future use of fishery-independent and fishery-dependent assessment surveys

in management decisions regarding fish stocks at broader levels. This could lead to development of

spatial models of multicohort-multistock dynamics for BNP and the Florida Keys coral reef ecosystem
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following Ault and Olson (1996), Ault et al. (1999b), and Cosner et al. (1999). These estimates and

models are then the precursors to more exacting management analyses like multispecies stock

assessments and modeling of spatial management alternatives. Furthermore, once determined these

fundamental relationships could be embedded in a biophysical spatial simulation model (e.g., REEFS

model has been generalized to incorporate space as well as time Ault et al. 1999b, 2000; Meester 2000)

to assess the consequences of preferred management alternatives for the Park, or to provide quantitative

insights into the longer-term goals of maintaining ecosystem integrity, building sustainable fisheries,

and conserving marine biodiversity that meet management targets and goals.

Acknowledgments

This research was conducted for BNP support under NPS grant No. CA-525000024. RVC data

were available under support provided from NOAA Fisheries, Florida Keys National Marine Sanctuary

program (Grant No. NA67RJ0149), South Florida Ecosystem Restoration Prediction and Modeling

Program (Grant No. NA67RJ0149), National Undersea Research Center (Grant No.

37NURPUNCW9926), and the University of Miami RSMAS. NOAA/FMRI provided digital benthic

substrate habitat maps. Doug Harper of NMFS/SEFSC provided commercial and recreational vessel

registered vessel digital database. Joe O’Hop of FMRI and Pat Eyo of NMFS provided commercial

fishing data. Gene Huntsman at NMFS Beaufort, NC, provided headboat survey data. John D. Wang

of RSMAS provided salinity data from his 2-D finite element Biscayne Bay-northern Florida Keys coral

reef tract hydrodynamic model. Guillermo A. Diaz of RSMAS provided technical assistance on the

habitat use analyses. We also thank the Army Corps of Engineers (Grant No. DACW 39-94-K0032)

and NOAA Coastal Ocean Program (Grant No. NA-85-WCH0631) for support to conduct rollerframe

trawl surveys; FISHBASE (www.fishbase.org) for access to population dynamic parameters; and,

National Sea Grant College Program (Grant No. R-L/R-47B) for development support on fish-habitat

models. Last, by not least, we thank a host of folks for technical assistance and reviews required to

complete this exciting project including: Rick Clarke, Monica Mayr, Brian Lockwood, Jim Tilmant,

Sherri Fields, Tom Schmidt, Pat Kenney, John Hoesterey, Richard Curry, Brenda Lazendorf, Sarah

Bellmund and Matt Patterson of NPS; Gary Hickman of TVA; Ken Lindeman of Environmental

Defense; Billy Causey, Ben Haskell and Brian Keller of FKNMS; and David McClellan, Anne Marie

Eklund, and Joe Serafy of NMFS.
83

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List of Appendices

Appendix A.- Biscayne National Park regional databases used in the site characterization.

Appendix B.- Species list comprised of 325 species for Biscayne National Park, ordered by family and

Latin name. The number of each species within the three surveys (reef fish visual surveys (RVC),

CREEL survey, and TRAWL surveys) used to compute average length estimates is given under the

appropriate column, a number greater than 0 indicates that the species is present in the survey. A 1*

indicates that the species was present in the survey, but no lengths were available. Highlighted rows

indicate species for which further fisheries assessment is conducted.

Appendix C.- Indicators of stock status for BNP exploited fisheries resources (about 90 species) for

the period 1976 to 1999 as determined by the BNP Creel survey and the RVC survey databases: n is the

number of survey samples; legal is computed using the minimum size of first capture regulated by

fishery management. SE is the standard error of the mean estimate of ; Actual is computed from

the minimum size of first capture determined from Creel survey catches; CPUE is catch (number of

fish) per person-hours fishing. CPUE for the RVC survey is: IM computed between the integral limits

of Lr  Lm; MAT is between Lm  L; and, Legal is Lc  L.

Appendix D(1).- SAS programs used for processing reef fish visual census (RVC) data and estimating

average length in the exploited phase and catch per unit effort.

Appendix D(2).- SAS programs used for processing recreational creel survey (CREEL) data and

estimating average length in the exploited phase and catch per unit effort.

Appendix E.- LBAR FORTRAN computer programs, users guide, and data input files.

Appendix F.- REEFS FORTRAN computer programs, users guide, and data input files.
88

Appendix A

Biscayne National Park Region Databases

(A) Benthic Habitat Databases

[1] NOAA/NOS Hydrographic Surveys

Source: NOAA Silver Spring, MD; NOS, Boulder, Colorado.

Description: Depth soundings, NOS Hydrographic Survey Data Vol. 1, Ver. 4;

Spatial Coverage: Depth soundings, Florida coast.

Variables: Latitude, Longitude, Depth

[2] FMRI Florida Keys Benthic Habitats Survey

Source: Chris Friel and Frank Sargeant, FMRI St. Petersburg

Description: Benthic habitat characterization interpreted from aerial photographic

surveys.

Spatial Coverage: Entire Florida Keys region, including Biscayne National Park. 

Overview: GIS layers of bottom substrate classifications, generally limited to depths

shallower than 30 feet.

Variables: Multiple categories of coral reef, seagrass, hardbottom, and sand/rock benthic

substrates; land and shoreline coverages. 

(B) Water Quality Databases

[3] SFWMD Water Quality Sampling

Source: South Florida Water Quality Management District, DBHYDRO Database

Description: Point samples of surface, mid-water and bottom water quality parameters.

Time Frame: 1995-1998

Spatial Coverage: 54 sampling stations within southern Biscayne Bay.

Variables: Station ID, Date, Latitude, Longitude, Depth, Temperature, Salinity,

Nutrients.

[4] RSMAS / Wang Biscayne Bay Hydrodynamic Model

Source: John Wang, RSMAS

Description: 2D finite element numerical model of water currents and salinity.

Time Frame: 1995-1998

Spatial Coverage: South Biscayne Bay, Card Sound, Barnes Sound.

Input Data: Information on winds, tides, water currents, canal discharges, salinities,

rainfall, evaporation, overland water flow, groundwater flow.

Model Outputs: Water current vectors and salinity, horizontal spatial grid resolution 150-

1000 m.
89

(C) Animals—Fishery Independent Databases

[5] NMFS / Bohnsack Reef Fishes Visual Census

Source: James Bohnsack, SEFSC, and Jerald Ault, RSMAS

Description: Fishery-independent diver survey of reef fish population abundance and size

structure using the Bohnsack and Bannerot (1986) stationary visual census

technique.

Time Frame: 1983-1992, 1995-1999

Spatial Coverage: 17 reef locations in the Biscayne National Park region.

Sampling Overview: 863 total diver samples; 182 species; 47 families.

Variables: Sample ID, Date, Reef ID, Latitude, Longitude, Depth, Bottom type, Reef

habitat, Species, Abundance (number observed), Length (cm).

[6] RSMAS/ Ault Roller Frame Trawl Survey

Source: Jerald Ault, RSMAS

Description: Stratified random fishery-independent roller frame trawl survey of

macroinvertebrate and fish population abundance and size structure (Ault et al.

1999).

Time Frame: 1996-1997, 1999-2000

Spatial Coverage: 983 station locations in southern Biscayne Bay.

Sampling Overview: 983 total trawl samples; 155 species; 49 families.

Variables: Sample ID, Date, Latitude, Longitude, Tow area, Depth, Temperature,

Salinity, Dissolved Oxygen, Habitat Type, Species, Catch, Length (mm).

(D) Animals—Fishery Dependent Databases

[7] BNP Recreational Creel Census

Source: Doug Harper, NOAA Fisheries SEFSC; Brian Lockwood, NPS BNP

Description: Fishery-dependent dockside sampling survey conducted by BNP personnel

of recreational fishing effort, species landings and size composition.

Time Frame: 1976-1998

Spatial Coverage: BNP and surrounding area.

Sampling Overview: 29,940 total interviews; 143 species; 45 families.

Variables: Record ID, Date, Area, Gear, Effort, Species, Number landed, Length,

Number released.

[8] NMFS Recreational Headboat Landings

Source: NMFS, Beaufort

Description: Fishery-dependent sampling survey conducted by NMFS personnel of the

species and size composition of the recreational headboat catch.
90

Time Frame: 1981-1995

Spatial Coverage: Florida Keys, including BNP region.

Sampling Overview: 853 total trip samples; 275 species categories.

Variables: Trip ID, Date, Area, Number of Anglers, Species, Catch in Numbers, Catch in

Weight, Sex, Length, Weight. 

[9] NMFS General Canvas Commercial Landings

Source: Pamela Eyo, NOAA SEFSC

Description: Commercial sector landings data collected by NMFS Port Agents and the

State of Florida trip-ticket system for harvested species.

Time Frame: 1986-1999

Spatial Coverage: Water body code #944.2, encompassing Biscayne Bay, Card Sound,

and Barnes Sound.

Overview: 57 species categories.

Variables: Year, Species, Total Catch (lbs.), Dockside Value (1997-99 only).
91

Appendix B. - Species list comprised of 325 species for Biscayne National Park, ordered by family and Latin name. The number

of each species within the three surveys (reef fish visual surveys (RVC), CREEL survey, and TRAWL surveys) used to compute 

average length estimates is given under the appropriate column, a number greater than 0 indicates that the species is present in 

the survey. A 1* indicates that the species was present in the survey, but no lengths were available. Highlighted rows indicate

species for which further fisheries assessment is conducted.

Survey

FAMILY LATIN COMMON RVC CREEL TRAWL

Acanthuridae Acanthurus bahianus ocean surgeon 5897 2 1

Acanthuridae Acanthurus chirurgus doctorfish 1553 34 20

Acanthuridae Acanthurus coeruleus blue tang 4595 2 0

Albulidae Albula vulpes bonefish 0 7 0

Antennariidae Histrio histrio sargassumfish 0 0 1

Apogonidae Apogon planifrons pale cardinalfish 0 0 1

Apogonidae Astrapogon alutus bronze cardinalfish 0 0 1637

Apogonidae Astrapogon puncticulatus blackfin cardinalfish 0 0 220

Apogonidae Astrapogon stellatus conchfish 0 0 2

Apogonidae Phaeoptyx pigmentaria dusky cardinalfish 0 0 1

Atherinidae Atherinomorus stipes hardhead silverside 0 0 8

Atherinidae Hypoatherina harringtonensis reef silverside 17500 0 0

Atherinidae Membras martinica rough silverside 0 0 11

Aulostomidae Aulostomus maculatus trumpetfish 285 0 0

Balistidae Aluterus monocerus unicorn filefish 0 0 1

Balistidae Aluterus schoepfi orange filefish 14 2 0

Balistidae Aluterus scriptus scrawled filefish 114 11 18

Balistidae Balistes capriscus gray triggerfish 24 208 2

Balistidae Balistes vetula queen triggerfish 40 73 0

Balistidae Cantherhines macrocerus whitespotted filefish 15 0 0

Balistidae Cantherhines pullus orangespotted filefish 37 0 0

Balistidae Cantherhines sufflamen ocean triggerfish 25 321 2

Balistidae Melicthys niger black durgon 2 0 0

Balistidae Monacanthus ciliatus fringed filefish 0 0 2665

Balistidae Monacanthus hispidus planehead filefish 15 0 739

Balistidae Monacanthus tuckeri slender filefish 6 0 12

Batrachoididae Opsanus beta gulf toadfish 0 0 4480

Belonidae Tylosurus acus agujon (needlefish) 0 0 1

Belonidae Tylosurus crocodilus houndfish 0 42 0

Blenniidae Hypsobennius ionthas freckled blenny 0 0 1

Blenniidae Ophioblennius atlanticus redlip blenny 5 0 0

Blenniidae Scartella cristata molly miller 1 0 1

Bothidae Bothus lunatus peacock flounder 1 0 46

Bothidae Bothus ocellatus eyed flounder 2 0 47

Bothidae Bothus robinsi twospot flounder 0 0 8

Bothidae Citharichthys arenaceus sand whiff 0 0 2

Bothidae Citharichthys macrops spotted whiff 0 0 20

Bothidae Citharichthys spilopterus bay whiff 0 0 1

Bothidae Paralichthys albigutta gulf flounder 0 1 4

Bythitidae Ogilbia cayorum key brotula 0 0 1

Bythitidae Petrotyx sanguineus redfin brotula 0 0 1

Callionymidae Diplogrammus pauciradiatus spotted dragonet 0 0 44

Carangidae Alectis ciliaris african pompano 0 24 0

Carangidae Caranx bartholomaei yellow jack 478 524 0

Carangidae Caranx crysos blue runner 474 1954 2

Carangidae Caranx hippos crevalle jack 0 80 0

Carangidae Caranx latus horse-eye jack 0 19 0

Carangidae Caranx ruber bar jack 3637 97 0

Carangidae Decapterus macarellus mackerel scad 1 32 0

Carangidae Elagatis bipinnulata rainbow runner 0 15 0

Carangidae Oligoplites saurus leatherjack 0 24 0

Carangidae Selar crumenophthalmus bigeye scad 0 15 0

Carangidae Selene vomer lookdown 0 44 0

Carangidae Seriola dumerili greater amberjack 2 107 0

Carangidae Seriola fasciata lesser amberjack 0 4 0

Carangidae Seriola rivoliana almaco jack 0 10 0

Carangidae Trachinotus carolinus Florida pompano 0 2 0

Carangidae Trachinotus falcatus permit 2 24 0
92

Appendix B.- (Continued)

Survey

FAMILY LATIN COMMON RVC CREEL TRAWL

Carapidae Carapus bermudensis pearfish 0 0 1

Carcharhinidae Carcharhinus brevipinna spinner shark 0 1 0

Carcharhinidae Carcharhinus falciformis silky shark 0 1 0

Carcharhinidae Carcharhinus limbatus blacktip shark 0 7 0

Carcharhinidae Negaprion brevirostris lemon shark 0 3 0

Centropomidae Centropomus ensiferus swordspine snook 0 1 0

Centropomidae Centropomus undecimalis common snook 0 1* 0

Chaetodontidae Chaetodon capistratus foureye butterflyfish 809 0 3

Chaetodontidae Chaetodon ocellatus spotfin butterflyfish 495 0 0

Chaetodontidae Chaetodon sedentarius reef butterflyfish 353 0 0

Chaetodontidae Chaetodon striatus banded butterflyfish 154 0 0

Cirrhitidae Amblycirrhitus pinos redspotted hawkfish 4 0 0

Clinidae Malacoctenus macropus rosy blenny 1 0 0

Clinidae Malacoctenus triangulatus saddled blenny 1 0 0

Clinidae Paraclinus marmoratus marbled blenny 0 0 44

Clupeidae Harengula jaguana scaled sardine 10600 0 0

Clupeidae Jenkinsia majua little-eye herring 0 0 2

Coryphaenidae Coryphaena equisetis pompano dolphin 0 2 0

Coryphaenidae Coryphaena hippurus dolphin 0 6993 0

Cyprinodontidae Cyprinodon variegatus sheepshead minnow 0 0 2

Cyprinodontidae Floridichthys carpio goldspotted killifish 0 0 3

Cyprinodontidae Lucania parva rainwater killifish 0 0 61

Dasyatidae Dasyatis americana southern stingray 11 0 0

Echeneidae Echeneis naucrates sharksucker 13 1 0

Echeneidae Remora remora remora 0 5 0

Elopidae Elops saurus ladyfish 0 27 0

Elopidae Megalops atlanticus tarpon 0 1* 0

Ephippidae Chaetodipterus faber atlantic spadefish 276 36 3

Exocoetidae Chriodorus atherinoides hardhead halfbeak 0 0 4

Exocoetidae Hemiramphus balao balao 0 0 1

Exocoetidae Hemiramphus brasiliensis ballyhoo 547 38 1

Exocoetidae Hyporhamphus unifasciatus silverstripe halfbeak 0 0 10

Fistularidae Fistularia petimba red cornetfish 0 0 2

Fistularidae Fistularia tabacaria bluespotted cornetfish 1 0 0

Gadidae Urophycis regia spotted hake 0 0 2

Gasterosteidae Pungitius pungitius ninespine stickleback 0 0 1

Gerreidae Eucinostomus argenteus spotfin mojarra 0 0 232

Gerreidae Eucinostomus gula silver jenny 0 0 3160

Gerreidae Eucinostomus jonesi slender mojarra 0 0 50

Gerreidae Gerres cinereus yellowfin mojarra 32 18 5

Gobiidae Bathigobius curacao notchtongue goby 0 0 1

Gobiidae Coryphopterus dicrus colon goby 32 0 2

Gobiidae Coryphopterus glaucofraenum bridled goby 1294 0 24

Gobiidae Coryphopterus personatus masked goby 653 0 0

Gobiidae Coryphopterus punctipectophorus spotted goby 0 0 12

Gobiidae Gnatholepis thompsoni goldspot goby 92 0 0

Gobiidae Gobionellus saepepallens dash goby 0 0 1

Gobiidae Gobionellus shufeldti freshwater goby 0 0 1

Gobiidae Gobionellus stigmalophius spotfin goby 0 0 32

Gobiidae Gobionellus stigmaturus spottail goby 0 0 5

Gobiidae Gobiosoma bosc naked goby 0 0 1

Gobiidae Gobiosoma evelynae sharknose goby 1 0 0

Gobiidae Gobiosoma oceanops neon goby 96 0 0

Gobiidae Gobiosoma randalli yellownose goby 0 1 0

Gobiidae Gobiosoma robustum code goby 0 0 1

Gobiidae Ioglossus calliurus blue goby 12 0 0

Gobiidae Ioglossus helenae hovering goby 1 0 0

Gobiidae Microgobius microlepis banner goby 0 0 87

Gobiidae Nes longus orangespotted goby 0 0 2

Haemulidae Anisotremus surinamensis black margate 75 207 0

Haemulidae Anisotremus virginicus porkfish 1113 200 9
93

Appendix B.- (Continued)

Survey

FAMILY LATIN COMMON RVC CREEL TRAWL

Haemulidae Haemulon album margate 19 269 0

Haemulidae Haemulon aurolineatum tomtate 26483 97 73

Haemulidae Haemulon carbonarium ceasar grunt 354 81 0

Haemulidae Haemulon chrysargyreum smallmouth grunt 2104 4 0

Haemulidae Haemulon flavolineatum french grunt 5975 113 76

Haemulidae Haemulon macrostomium spanish grunt 113 35 0

Haemulidae Haemulon melanurum cottonwick 106 0 0

Haemulidae Haemulon parra sailors choice 187 594 61

Haemulidae Haemulon plumieri white grunt 10179 14499 3855

Haemulidae Haemulon sciurus bluestriped grunt 3628 4569 2350

Haemulidae Haemulon striatum striped grunt 5 78 0

Haemulidae Orthopristis chrysoptera pigfish 0 33 57

Holocentridae Holocentrus adscensionis squirrelfish 53 57 0

Holocentridae Holocentrus rufus longspine squirrelfish 58 0 0

Holocentridae Holocentrus vexillarius dusky squirrelfish 8 0 0

Inermiidae Inermia vittata boga 180 0 0

Istiophoridae Istiophorus platypterus sailfish 0 38 0

Istiophoridae Makaira nigricans blue marlin 0 1 0

Istiophoridae Tetrapturus albidus white marlin 0 2 0

Kyphosidae Kyphosus incisor yellow chub 0 44 0

Kyphosidae Kyphosus sectatrix bermuda chub 1472 176 0

Labridae Bodianus pulchellus spotfin hogfish 5 0 0

Labridae Bodianus rufus spanish hogfish 188 39 0

Labridae Clepticus parrae creole wrasse 1148 0 0

Labridae Halichoeres bivittatus slippery dick 3200 14 0

Labridae Halichoeres garnoti yellowhead wrasse 4382 0 0

Labridae Halichoeres maculipinna clown wrasse 3150 0 0

Labridae Halichoeres pictus rainbow wrasse 6 0 0

Labridae Halichoeres poeyi blackear wrasse 6 0 0

Labridae Halichoeres radiatus puddingwife 318 66 0

Labridae Hemipteronotus martinicensis rosy razorfish 226 0 0

Labridae Hemipteronotus novacula pearly razorfish 13 0 0

Labridae Hemipteronotus splendins green razorfish 28 0 0

Labridae Lachnolaimus maximus hogfish 333 6510 163

Labridae Thalassoma bifasciatum bluehead 41819 0 0

Lobotidae Lobotes surinamensis tripletail 0 50 0

Lutjanidae Etelis oculatus queen snapper 0 9 0

Lutjanidae Lutjanus analis mutton snapper 22 1966 65

Lutjanidae Lutjanus apodus schoolmaster 1940 597 8

Lutjanidae Lutjanus buccanella blackfin snapper 0 16 0

Lutjanidae Lutjanus campechanus red snapper 0 25 0

Lutjanidae Lutjanus cyanopterus cubera snapper 0 11 0

Lutjanidae Lutjanus griseus gray snapper 2314 10688 594

Lutjanidae Lutjanus jocu dog snapper 11 58 2

Lutjanidae Lutjanus mahogoni mahogony snapper 16 32 0

Lutjanidae Lutjanus synagris lane snapper 82 409 242

Lutjanidae Lutjanus vivanus silk snapper 0 7 0

Lutjanidae Ocyurus chrysurus yellowtail snapper 3861 5243 142

Lutjanidae Rhomboplites aurorubens vermilion snapper 0 23 0

Malacanthidae Malacanthus plumieri sand tilefish 46 77 0

Mullidae Mulloidichthys martinicus yellow goatfish 580 5 0

Mullidae Pseudupeneus maculatus spotted goatfish 435 4 0

Muraenidae Gymnothorax funebris green moray 5 1 0

Muraenidae Gymnothorax miliaris goldentail moray 2 0 0

Muraenidae Gymnothorax moringa spotted moray 8 0 2

Muraenidae Gymnothorax nigromarginatus blackedge moray 0 0 10

Muraenidae Gymnothorax saxicola honeycomb moray 1 0 7

Muraenidae Gymnothorax vicinus purplemouth moray 1 0 0

Myliobatidae Aetobatus narinari spotted eagle ray 1 0 0

Ogcocephalidae Ogcocephalus corniger longnose batfish 0 0 2

Opistognathidae Opistognathus aurifrons yellowhead jawfish 26 0 0
94

Appendix B.- (Continued)

Survey

FAMILY LATIN COMMON RVC CREEL TRAWL

Ostraciidae Lactophrys bicaudalis spotted trunkfish 13 0 4

Ostraciidae Lactophrys polygonia honeycomb cowfish 3 0 5

Ostraciidae Lactophrys quadricornis scrawled cowfish 15 0 784

Ostraciidae Lactophrys trigonus trunkfish 6 8 85

Ostraciidae Lactophrys triqueter smooth trunkfish 132 10 0

Palinuridae Panulirus argus Caribbean spiny lobster 1* 16764 1*

Palinuridae Panulirus guttatus spotted spiny lobster 0 28 0

Pempheridae Pempheris schomburgki glassy sweeper 686 0 0

Penaeidae Penaeus duorarum pink shrimp 0 0 103896

Pomacanthidae Centropyge argi cherubfish 1 0 0

Pomacanthidae Holacanthus bermudensis blue angelfish 172 0 0

Pomacanthidae Holacanthus ciliaris queen angelfish 174 2 0

Pomacanthidae Holacanthus tricolor rock beauty 697 0 0

Pomacanthidae Pomacanthus arcuatus gray angelfish 899 2 11

Pomacanthidae Pomacanthus paru french angelfish 194 0 0

Pomacentridae Abudefduf saxatilis sergeant major 7819 0 0

Pomacentridae Chromis cyanea blue chromis 1844 0 0

Pomacentridae Chromis insolata sunshinefish 6 0 0

Pomacentridae Chromis multilineata brown chromis 797 0 0

Pomacentridae Chromis scotti purple reeffish 18 0 0

Pomacentridae Microspathodon chrysurus yellowtail damselfish 1057 0 0

Pomacentridae Pomacentrus diencaeus longfin damselfish 89 0 0

Pomacentridae Pomacentrus fuscus dusky damselfish 720 0 0

Pomacentridae Pomacentrus leucostictus beaugregory 296 0 1

Pomacentridae Pomacentrus partitus bicolor damselfish 30183 0 1

Pomacentridae Pomacentrus planifrons threespot damselfish 1702 0 1

Pomacentridae Pomacentrus variabilis cocoa damselfish 700 0 1

Pomatomidae Pomatomus saltatrix bluefish 0 72 0

Portunidae Callinectes sapidus blue crab 0 4 0

Priacanthidae Priacanthus arenatus bigeye 4 84 0

Prosobranchia Strombus gigas queen conch 0 1* 0

Rachycentridae Rachycentron canadum cobia 0 12 0

Rhincodontidae Ginglymostoma cirratum nurse shark 8 15 0

Scaridae Cryptotomus roseus bluelip parrotfish 145 0 8

Scaridae Nicholsina usta emerald parrotfish 0 0 593

Scaridae Scarus coelestinus midnight parrotfish 108 0 3

Scaridae Scarus coeruleus blue parrotfish 214 13 0

Scaridae Scarus croicensis striped parrotfish 8476 0 24

Scaridae Scarus guacamaia rainbow parrotfish 148 0 3

Scaridae Scarus taeniopterus princess parrotfish 3329 0 3

Scaridae Scarus vetula queen parrotfish 414 0 0

Scaridae Sparisoma atomarium greenblotch parrotfish 223 0 12

Scaridae Sparisoma aurofrenatum redband parrotfish 8321 0 1

Scaridae Sparisoma chrysopterum redtail parrotfish 317 0 629

Scaridae Sparisoma radians bucktooth parrotfish 31 0 128

Scaridae Sparisoma rubripinne redfin parrotfish 396 0 20

Scaridae Sparisoma viride stoplight parrotfish 3361 0 6

Sciaenidae Bairdiella chrysoura silver perch 0 0 2

Sciaenidae Cynoscion nebulosus spotted seatrout 0 565 34

Sciaenidae Equetus acuminatus high-hat 55 0 26

Sciaenidae Equetus lanceolatus jackknife-fish 0 0 4

Sciaenidae Equetus punctatus spotted drum 13 0 0

Sciaenidae Equetus umbrosus cubbyu 3 0 0

Sciaenidae Odontoscion dentex reef croaker 18 0 0

Scombridae Acanthocybium solandri wahoo 0 111 0

Scombridae Auxis rochei bullet mackerel 0 2 0

Scombridae Euthynnus alletteratus little tunny 0 140 0

Scombridae Katsuwonus pelamis skipjack tuna 0 41 0

Scombridae Sarda sarda atlantic bonito 0 87 0

Scombridae Scomberomorus cavalla king mackerel 0 198 0

Scombridae Scomberomorus maculatus spanish mackerel 1 132 0
95

Appendix B.- (Continued)

Survey

FAMILY LATIN COMMON RVC CREEL TRAWL

Scombridae Scomberomorus regalis cero 60 586 0

Scombridae Thunnus albacares yellowfin tuna 0 1 0

Scombridae Thunnus atlanticus blackfin tuna 0 27 0

Scorpaenidae Neomerinthe hemingwayi spinycheek scorpionfish 0 0 1

Scorpaenidae Scorpaena brasiliensis barbfish 0 0 296

Scorpaenidae Scorpaena dispar hunchback scorpionfish 0 0 1

Scorpaenidae Scorpaena grandicornus plumed scorpionfish 0 0 16

Scorpaenidae Scorpaena inermis mushroom scorpionfish 0 0 3

Scorpaenidae Scorpaena plumieri spotted scorpionfish 7 0 6

Scyllaridae Scyllarides aequinoctialis spanish (shovelnose) lobster 0 33 0

Serranidae Diplectrum bivittatum dward sand perch 0 0 12

Serranidae Diplectrum formosum sand perch 2 160 94

Serranidae Epinephelus adscensionis rock hind 7 84 0

Serranidae Epinephelus cruentatus graysby 567 522 0

Serranidae Epinephelus drummondhayi speckled hind 0 5 0

Serranidae Epinephelus flavolimbatus yellowedge grouper 0 12 0

Serranidae Epinephelus fulvus coney 13 28 0

Serranidae Epinephelus guttatus red hind 15 234 0

Serranidae Epinephelus inermis marbled grouper 0 1 0

Serranidae Epinephelus itajara Goliath grouper 0 4 0

Serranidae Epinephelus morio red grouper 42 2093 0

Serranidae Epinephelus striatus nassau grouper 10 739 0

Serranidae Hypoplectrus chlorurus yellowtail hamlet 1 0 0

Serranidae Hypoplectrus gemma blue hamlet 100 0 0

Serranidae Hypoplectrus guttavarius shy hamlet 3 0 0

Serranidae Hypoplectrus indigo indigo hamlet 11 0 0

Serranidae Hypoplectrus nigricans black hamlet 15 0 0

Serranidae Hypoplectrus puella barred hamlet 50 0 0

Serranidae Hypoplectrus unicolor butter hamlet 320 0 0

Serranidae Liopropoma eukrines wrasse bass 1 0 0

Serranidae Mycteroperca bonaci black grouper 22 618 1

Serranidae Mycteroperca interstitialis yellowmouth grouper 2 48 0

Serranidae Mycteroperca microlepis gag 1 327 0

Serranidae Mycteroperca phenax scamp 2 13 0

Serranidae Mycteroperca venenosa yellowfin grouper 1 26 0

Serranidae Paranthias furcifer creole-fish 0 2 0

Serranidae Rypticus saponaceus greater soapfish 4 3 0

Serranidae Serranus baldwini lantern bass 5 0 1

Serranidae Serranus tabacarius tobaccofish 123 0 0

Serranidae Serranus tigrinus harlequin bass 464 0 0

Soleidae Achirus lineatus lined sole 0 0 39

Soleidae Gymnachirus melas naked sole 0 0 1

Soleidae Symphurus diomedianus spottedfin tonguefish 0 0 1

Soleidae Symphurus plagiusa blackcheek tonguefish 0 0 21

Soleidae Symphurus urospilus spottail tonguefish 0 0 1

Soleidae Trinectes insciptus scrawled sole 0 0 2

Sparidae Archosargus rhomboidalis sea bream 3 113 42

Sparidae Calamus arctifrons grass porgy 0 64 65

Sparidae Calamus bajonado jolthead porgy 37 1642 0

Sparidae Calamus calamus saucereye porgy 159 725 0

Sparidae Calamus nodosus knobbed porgy 0 18 0

Sparidae Calamus penna sheepshead porgy 0 9 3

Sparidae Calamus proridens littlehead porgy 5 66 2

Sparidae Lagodon rhomboides pinfish 0 346 8163

Sphyraenidae Sphyraena barracuda great barracuda 418 1841 2

Sphyraenidae Sphyraena picudilla southern sennet 0 0 1*

Sphyrnidae Sphyrna lewini scalloped hammerhead 2 0 0

Sphyrnidae Sphyrna mokarran great hammerhead 0 4 0

Sphyrnidae Sphyrna tiburo bonnethead shark 0 28 0

Stromateidae Psenes maculatus silver driftfish 0 0 5

Syngnathidae Cosmocampus albirostris whitenose pipefish 0 0 180
96

Appendix B.- (Continued)

Survey

FAMILY LATIN COMMON RVC CREEL TRAWL

Syngnathidae Hippocampus erectus lined seahorse 0 0 253

Syngnathidae Hippocampus reidi longsnout seahorse 0 0 37

Syngnathidae Hippocampus zosterae dwarf seahorse 0 0 12

Syngnathidae Syngnathus floridae dusky pipefish 0 0 145

Syngnathidae Syngnathus louisianae chain pipefish 0 0 3

Syngnathidae Syngnathus pelagicus sargassum pipefish 0 0 1

Syngnathidae Syngnathus scovelli gulf pipefish 0 0 27

Synodontidae Synodus foetens inshore lizardfish 1 0 69

Synodontidae Synodus intermedius sand diver 10 0 0

Synodontidae Trachinocephalus myops snakefish 0 0 1

Tetraodontidae Canthigaster rostrata sharpnose puffer 618 0 3

Tetraodontidae Chilomycterus antennatus bridled burrfish 1 0 1

Tetraodontidae Chilomycterus schoepfi striped burrfish 1 0 71

Tetraodontidae Diodon holocanthus balloonfish 31 0 6

Tetraodontidae Diodon hystrix porcupinefish 12 0 1

Tetraodontidae Sphoeroides maculatus northern puffer 0 0 7

Tetraodontidae Sphoeroides nephelus southern puffer 0 0 1

Tetraodontidae Sphoeroides spengleri bandtail puffer 12 0 325

Tetraodontidae Sphoeroides testudineus checkered puffer 0 0 2

Triglidae Prionotus scitulus leopard searobin 0 0 27

Uranoscopidae Dactyloscopus tridigitatus sand stargazer 0 0 2

Urolophidae Urolophus jamaicensis yellow stingray 69 0 4

Xanthidae Menippe mercenaria Florida stone crab 0 2 0
97

Appendix C1.- rock hind

Epinephelus adscensionis

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.001

1979 1 300.0 0.0 0.004 NO RVC SURVEY CONDUCTED

1980 3 400.0 57.7 0.001

1981 5 342.0 42.1 0.002

1982 11 303.6 23.2 0.005

1983 12 330.8 31.4 0.001 0 0.000 0.000 0.000

1984 0 0.000 0 0.000 0.000 0.000

1985 10 312.0 24.1 0.002 0 0.000 0.000 0.000

1986 5 394.0 44.5 0.001 FIXED- 0 0.000 0.000 0.000

1987 1 300.0 0.0 0.000 SITE 0 0.000 0.000 0.000

1988 3 346.7 14.5 0.001 DESIGN 0 0.003 0.000 0.000

1989 4 342.5 48.7 0.001 0 0.000 0.000 0.000

1990 2 295.0 25.0 0.001 1 320.0 0.0 0.005 0.000 0.000

1991 5 276.0 44.5 0.001 0 0.000 0.000 0.000

1992 11 260.0 15.4 0.005 0 0.000 0.000 0.000

1993 3 350.0 26.5 0.001 . . . .

1994 2 325.0 25.0 0.001 . . . .

1995 0 0.000 KEYS-WIDE 0 0.000 0.000 0.000

1996 1 310.0 0.0 0.001 HABITAT 0 0.000 0.000 0.000

1997 0 0.000 STRATIFICATION 0 0.000 0.000 0.000

1998 4 242.5 19.3 0.001 DESIGN 0 0.000 0.000 0.000

1999 1 210.0 0.0 0.064 0.000 0.050

Appendix C2.- graysby

Epinephelus cruentatus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 5 242.0 11.1 0.002

1979 10 277.0 14.6 0.007

1980 19 252.6 8.3 0.009

1981 48 256.7 5.8 0.011

1982 8 242.5 10.8 0.007

1983 52 251.2 5.2 0.010 7.0 221.4 0.9 0.194 0.194 0.194

1984 6 253.3 16.3 0.002 0.0 0.000 0.000 0.000

1985 35 260.6 6.1 0.006 0.0 0.000 0.000 0.000

1986 15 268.0 6.3 0.005 0.0 0.000 0.000 0.000

1987 26 248.5 5.0 0.005 6.0 251.7 1.5 0.351 0.162 0.162

1988 59 256.6 4.1 0.017 13.0 226.2 0.9 0.206 0.070 0.070

1989 20 264.0 6.0 0.008 45.8 226.7 0.4 0.284 0.161 0.161

1990 50 248.6 4.5 0.019 32.0 227.2 0.8 0.376 0.133 0.133

1991 9 255.6 9.6 0.002 9.0 210.0 0.5 0.507 0.108 0.108

1992 7 267.1 14.1 0.002 6.0 231.7 2.8 0.387 0.062 0.062

1993 19 250.0 8.3 0.010 . . . .

1994 24 248.3 4.7 0.010 . . . .

1995 9 242.2 12.3 0.008 5.0 216.0 1.1 0.245 0.042 0.042

1996 15 250.0 7.7 0.012 4.0 250.0 3.5 0.208 0.056 0.056

1997 50 236.8 3.9 0.023 4.0 211.3 1.1 0.321 0.056 0.056

1998 6 236.7 9.2 0.002 3.5 248.6 2.9 0.140 0.053 0.053

1999 0.5 250.0 0.0 0.356 0.017 0.017
98

Appendix C3.- speckled hind

Epinephelus drummondhayi

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.000

1980 0 0.000

1981 0 0.000

1982 0 0.000

1983 0 0.000

1984 0 0.000

1985 1 300.0 0.0 0.000

1986 1 550.0 0.0 0.000

1987 0 0.000

1988 1 410.0 0.0 0.000

1989 0 0.000

1990 0 0.000

1991 0 0.000

1992 0 0.000

1993 0 0.000

1994 0 0.000

1995 0 0.000

1996 1 500.0 0.0 0.002

1997 0 0.000

1998 1 290.0 0.0 0.001

1999

Appendix C4.- yellowedge grouper

Epinephelus flavolimbatus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.000

1980 0 0.000

1981 0 0.000

1982 3 256.7 6.7 0.000

1983 7 497.1 23.8 0.003

1984 0 0.000

1985 0 0.000

1986 0 0.000

1987 0 0.000

1988 0 0.000

1989 0 0.000

1990 0 0.000

1991 0 0.000

1992 1 240.0 0.0 0.000

1993 1 630.0 0.0 0.000

1994 0 0.000

1995 0 0.000

1996 0 0.000

1997 0 0.000

1998 0 0.000

1999
99

Appendix C5.- coney

Epinephelus fulvus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.000

1980 1 250.0 0.0 0.000

1981 0 0.000

1982 3 253.3 14.5 0.001

1983 1 220.0 0.0 0.001 0 0.000 0.000 0.000

1984 0 0.000 0 0.000 0.000 0.000

1985 0 0.000 0 0.000 0.000 0.000

1986 0 0.000 0 0.000 0.000 0.000

1987 4 312.5 34.2 0.001 2 250.0 2.0 0.000 0.081 0.054

1988 3 246.7 3.3 0.001 0 0.003 0.000 0.000

1989 0 0.000 1 350.0 0.0 0.000 0.004 0.004

1990 0 0.000 0 0.009 0.000 0.000

1991 0 0.000 0 0.040 0.000 0.000

1992 8 250.0 9.6 0.002 2 250.0 0.0 0.000 0.018 0.018

1993 2 255.0 15.0 0.001 . . . .

1994 1 280.0 0.0 0.000 . . . .

1995 1 300.0 0.0 0.001 0 0.000 0.005 0.000

1996 0 0.000 0 0.000 0.000 0.000

1997 1 270.0 0.0 0.001 0 0.000 0.000 0.000

1998 1 280.0 0.0 0.001 0 0.000 0.000 0.000

1999 0 0.000 0.000 0.000

Appendix C6.- red hind

Epinephelus guttatus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 2 280.0 10.0 0.004

1979 6 358.3 26.4 0.012

1980 21 309.5 10.1 0.009

1981 28 328.6 11.8 0.006

1982 25 308.8 14.4 0.015

1983 13 313.8 21.8 0.003 1 200.0 0.0 0.028 0.000 0.028

1984 6 345.0 27.7 0.004 0 0.000 0.000 0.000

1985 43 224.0 11.2 0.005 0 0.000 0.000 0.000

1986 9 280.0 16.2 0.002 0 0.000 0.000 0.000

1987 11 302.7 13.1 0.002 0 0.000 0.000 0.000

1988 20 302.0 8.7 0.009 0 0.007 0.000 0.000

1989 2 360.0 70.0 0.000 2 260.0 6.0 0.004 0.002 0.006

1990 3 320.0 17.3 0.001 2 250.0 0.0 0.010 0.000 0.010

1991 7 264.3 15.1 0.008 0 0.000 0.000 0.000

1992 9 273.3 12.7 0.005 0 0.000 0.000 0.000

1993 3 310.0 11.5 0.001 . . . .

1994 4 295.0 32.8 0.001 . . . .

1995 5 290.0 9.5 0.005 2 290.0 1.0 0.031 0.005 0.005

1996 5 302.0 17.7 0.002 0 0.000 0.000 0.000

1997 2 300.0 20.0 0.001 0 0.000 0.000 0.000

1998 10 347.0 21.8 0.002 1 200.0 0.0 0.008 0.000 0.008

1999 1 340.0 0.0 0.014 0.029 0.029
100

Appendix C7.- marbled grouper

Epinephelus inermis

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0

1977 0

1978 0

1979 0

1980 0

1981 0

1982 0

1983 1 440.0 0.0

1984 0

1985 0

1986 0

1987 0

1988 0

1989 0

1990 0

1991 0

1992 0

1993 0

1994 0

1995 0

1996 0

1997 0

1998 0

1999

Appendix C8.- Goliath grouper

Epinephelus itajara

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 0 0 0.000

1979 0 0 0.001

1980 0 0 0.000

1981 3 1170.0 225.0 3 1170.0 225.0 0.001

1982 0 0 0.000

1983 1 1010.0 0.0 1 1010.0 0.0 0.001

1984 0 0 0.000

1985 0 0 0.000

1986 0 0 0.000

1987 0 0 0.000

1988 0 0 0.000

1989 0 0 0.000

1990 0 0 0.000

1991 0 0 0.005

1992 0 0 0.000

1993 0 0 0.000

1994 0 0 0.000

1995 0 0 0.000

1996 0 0 0.000

1997 0 0 0.000

1998 0 0 0.000

1999
101

Appendix C9.- red grouper

Epinephelus morio

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 307 347.6 6.1 307 347.6 6.1 0.004

1977 91 402.1 13.0 91 402.1 13.0 0.000

1978 115 431.1 8.5 115 431.1 8.5 0.009

1979 87 446.3 11.8 87 446.3 11.8 0.043

1980 133 434.6 9.2 133 434.6 9.2 0.025

1981 227 426.5 6.0 227 426.5 6.0 0.034

1982 80 413.8 12.9 80 413.8 12.9 0.019

1983 232 382.2 6.0 232 382.2 6.0 0.043 0 0 0.000 0.000 0.000 0.000

1984 66 382.7 9.5 66 382.7 9.5 0.031 0 0 0.000 0.000 0.000 0.000

1985 42 511.0 6.8 188 388.0 5.7 0.036 0 0 0.000 0.000 0.000 0.000

1986 25 528.8 15.5 48 450.2 15.1 0.013 0 0 0.000 0.000 0.000 0.000

1987 29 498.6 6.6 69 422.3 9.3 0.024 0 0 0.027 0.000 0.000 0.000

1988 32 503.8 8.0 70 437.4 9.1 0.016 0 0 0.020 0.000 0.000 0.000

1989 22 525.9 11.6 63 428.6 10.8 0.016 0 2 350.0 0.0 0.000 0.000 0.000 0.000

1990 12 556.7 10.8 24 515.0 10.5 0.006 1 700.0 0.0 3 533.3 8.3 0.013 0.000 0.000 0.000

1991 10 559.0 9.9 13 543.1 11.5 0.006 0 0 0.010 0.000 0.000 0.000

1992 30 592.3 14.4 36 576.1 13.4 0.012 0 1 450.0 0.0 0.000 0.000 0.000 0.000

1993 14 555.7 9.1 32 513.1 8.2 0.020 . . . . . .

1994 31 563.9 8.0 39 547.9 8.2 0.011 . . . . . .

1995 9 544.4 12.8 13 525.4 12.1 0.008 0 0 0.030 0.000 0.000 0.000

1996 10 562.0 8.7 12 551.7 10.0 0.017 0 1 450.0 0.0 0.015 0.015 0.000 0.015

1997 13 530.0 7.3 23 508.7 7.0 0.009 0 0 0.063 0.000 0.000 0.000

1998 23 539.1 7.3 29 529.7 6.8 0.009 0 0 0.008 0.000 0.000 0.000

1999 0 1 460.0 0.0 0.048 0.050 0.000 0.050

Appendix C13.- nassau grouper

Epinephelus striatus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 99 439.7 13.2 99 439.7 13.2 0.001

1977 67 462.1 23.7 67 462.1 23.7 0.000

1978 98 444.6 8.8 98 444.6 8.8 0.001

1979 64 450.2 11.4 64 450.2 11.4 0.014

1980 91 427.0 8.9 91 427.0 8.9 0.012

1981 120 468.4 6.3 120 468.4 6.3 0.004

1982 26 450.8 22.5 26 450.8 22.5 0.007

1983 26 418.8 18.8 26 418.8 18.8 0.002 0 0 0.000 0.000 0.000 0.000

1984 5 398.0 10.2 5 398.0 10.2 0.002 0 0 0.000 0.000 0.000 0.000

1985 10 546.0 20.8 34 415.0 16.7 0.005 0 0 0.000 0.000 0.000 0.000

1986 7 601.4 9.6 14 489.3 32.2 0.002 0 0 0.000 0.000 0.000 0.000

1987 7 555.7 54.1 11 491.8 43.4 0.001 0 0 0.000 0.000 0.000 0.000

1988 7 512.9 16.0 14 446.4 22.3 0.002 0 0 0.000 0.000 0.000 0.000

1989 9 528.9 15.1 12 496.7 20.6 0.002 1 460.0 0.0 1 460.0 0.0 0.002 0.000 0.002 0.002

1990 7 565.7 16.3 7 565.7 16.3 0.000 1 600.0 0.0 1 600.0 0.0 0.000 0.004 0.004 0.004

1991 1 650.0 0.0 1 650.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1992 2 515.0 5.0 2 515.0 5.0 0.000 1 600.0 0.0 1 600.0 0.0 0.018 0.009 0.009 0.009

1993 0 0 0.000 . . . . . .

1994 0 0 0.000 . . . . . .

1995 0 0 0.000 0 0 0.036 0.000 0.000 0.000

1996 0 0 0.001 0 0 0.014 0.000 0.000 0.000

1997 0 0 0.000 1 640.0 0.0 1 640.0 0.0 0.000 0.008 0.008 0.008

1998 2 590.0 20.0 2 590.0 20.0 0.001 0 0 0.000 0.000 0.000 0.000

1999 0 0 0.000 0.000 0.000 0.000
102

Appendix C14.- black grouper

Mycteroperca bonaci

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 1 370.0 0.0 1 370.0 0.0 0.000

1977 11 603.6 51.5 11 603.6 51.5 0.000

1978 25 612.0 39.7 25 612.0 39.7 0.002

1979 59 555.9 19.7 59 555.9 19.7 0.007

1980 72 536.8 16.3 72 536.8 16.3 0.006

1981 93 585.2 17.6 93 585.2 17.6 0.005

1982 34 497.6 26.6 34 497.6 26.6 0.007

1983 46 541.5 27.3 46 541.5 27.3 0.002 0 0 0.000 0.000 0.000 0.000

1984 29 602.1 35.2 29 602.1 35.2 0.005 0 0 0.000 0.000 0.000 0.000

1985 20 584.5 23.4 53 450.8 17.5 0.006 0 0 0.000 0.000 0.000 0.000

1986 10 587.0 20.3 14 532.1 28.9 0.001 0 0 0.000 0.000 0.000 0.000

1987 11 613.6 39.0 18 517.2 38.0 0.002 0 0 0.000 0.000 0.000 0.000

1988 9 571.1 50.3 15 501.3 38.5 0.001 0 4 380.0 27.0 0.025 0.000 0.000 0.018

1989 16 607.5 31.7 21 556.7 31.8 0.004 0 0 0.000 0.000 0.000 0.000

1990 12 650.0 33.6 13 638.5 33.0 0.001 1 650.0 0.0 1 650.0 0.0 0.005 0.004 0.004 0.004

1991 4 702.5 79.2 5 662.0 73.5 0.000 0 0 0.024 0.000 0.000 0.000

1992 7 688.6 27.9 7 688.6 27.9 0.000 1 650.0 0.0 1 650.0 0.0 0.010 0.010 0.010 0.010

1993 7 624.3 32.4 7 624.3 32.4 0.001 . . . . . .

1994 11 677.3 20.8 11 677.3 20.8 0.002 . . . . . .

1995 7 708.6 27.9 7 708.6 27.9 0.005 0 0 0.000 0.000 0.000 0.000

1996 10 706.0 20.7 11 687.3 26.5 0.003 0 0 0.015 0.000 0.000 0.000

1997 12 610.8 15.2 13 602.3 16.4 0.000 0 0 0.000 0.000 0.000 0.000

1998 9 663.3 24.2 9 663.3 24.2 0.006 0 0 0.023 0.000 0.000 0.000

1999 0 0 0.035 0.000 0.000 0.000

Appendix C15.- yellowmouth grouper

Mycteroperca interstitialis

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 1 530.0 0.0 1 530.0 0.0 0.000

1979 1 520.0 0.0 1 520.0 0.0 0.000

1980 5 406.0 24.8 5 406.0 24.8 0.001

1981 26 545.4 29.5 26 545.4 29.5 0.003

1982 1 780.0 0.0 1 780.0 0.0 0.000

1983 1 730.0 0.0 1 730.0 0.0 0.000 0 0 0.000 0.000 0.000

1984 0 0 0.000 0 0 0.000 0.000 0.000

1985 0 0 0.000 0 0 0.000 0.000 0.000

1986 0 0 0.000 0 0 0.000 0.000 0.000

1987 0 0 0.000 0 0 0.000 0.000 0.000

1988 8 582.5 35.7 8 582.5 35.7 0.000 0 0 0.000 0.000 0.000

1989 0 0 0.000 0 0 0.000 0.000 0.000

1990 0 0 0.000 0 0 0.008 0.000 0.000

1991 0 0 0.000 0 0 0.000 0.000 0.000

1992 0 0 0.000 0 0 0.000 0.000 0.000

1993 0 0 0.000 . . . . .

1994 0 0 0.000 . . . . .

1995 0 0 0.000 0 0 0.000 0.000 0.000

1996 0 0 0.000 0 0 0.000 0.000 0.000

1997 0 0 0.000 0 0 0.000 0.000 0.000

1998 0 0 0.000 0 0 0.000 0.000 0.000

1999 0 0 0.000 0.000 0.000
103

Appendix C16.- gag

Mycteroperca microlepis

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 5 486.0 110.7 5 486.0 110.7 0.000

1977 9 454.4 45.1 9 454.4 45.1 0.000

1978 37 566.8 25.8 37 566.8 25.8 0.002

1979 14 440.7 16.1 14 440.7 16.1 0.005

1980 34 442.4 13.8 34 442.4 13.8 0.002

1981 27 532.2 30.9 27 532.2 30.9 0.002

1982 34 375.0 14.4 34 375.0 14.4 0.008

1983 46 468.3 20.8 46 468.3 20.8 0.004 0 0 0.000 0.000 0.000 0.000

1984 13 443.8 39.6 13 443.8 39.6 0.004 0 0 0.000 0.000 0.000 0.000

1985 7 644.3 61.7 14 508.6 48.6 0.001 0 0 0.000 0.000 0.000 0.000

1986 4 685.0 33.0 6 580.0 71.9 0.001 0 0 0.000 0.000 0.000 0.000

1987 8 511.3 24.2 19 428.4 20.1 0.003 0 0 0.000 0.000 0.000 0.000

1988 15 560.0 22.1 15 560.0 22.1 0.003 0 0 0.000 0.000 0.000 0.000

1989 1 630.0 0.0 3 423.3 103.5 0.001 0 0 0.000 0.000 0.000 0.000

1990 2 720.0 170.0 3 633.3 130.9 0.000 0 0 0.000 0.000 0.000 0.000

1991 11 603.6 15.4 11 603.6 15.4 0.002 0 0 0.000 0.000 0.000 0.000

1992 1 530.0 0.0 3 510.0 10.0 0.000 0 0 0.000 0.000 0.000 0.000

1993 6 710.0 37.8 6 710.0 37.8 0.001 . . . . . .

1994 5 574.0 30.9 8 541.3 24.6 0.005 . . . . . .

1995 2 585.0 45.0 5 528.0 27.5 0.001 0 0 0.000 0.000 0.000 0.000

1996 3 633.3 37.6 3 633.3 37.6 0.000 0 0 0.000 0.000 0.000 0.000

1997 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1998 1 570.0 0.0 1 570.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1999 0 0 0.000 0.000 0.000 0.000

Appendix C17.- scamp

Mycteroperca phenax

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 0 0 0.000

1979 2 320.0 20.0 2 320.0 20.0 0.000

1980 3 346.7 6.7 3 346.7 6.7 0.000

1981 4 587.5 47.5 4 587.5 47.5 0.000

1982 0 0 0.000

1983 0 0 0.000 0 0 0.000 0.000 0.000

1984 0 0 0.000 0 0 0.000 0.000 0.000

1985 0 0 0.000 0 0 0.000 0.000 0.000

1986 1 350.0 0.0 1 350.0 0.0 0.000 0 0 0.000 0.000 0.000

1987 0 0 0.000 0 0 0.000 0.000 0.000

1988 0 0 0.000 0 0 0.000 0.000 0.000

1989 0 0 0.000 0 0 0.000 0.000 0.000

1990 0 0 0.000 0 0 0.000 0.000 0.000

1991 0 0 0.000 0 0 0.000 0.000 0.000

1992 0 0 0.000 0 0 0.000 0.000 0.000

1993 0 0 0.000 . . . . .

1994 0 0 0.000 . . . . .

1995 0 0 0.000 0 0 0.000 0.000 0.000

1996 0 0 0.000 0 0 0.000 0.000 0.000

1997 0 0 0.000 0 0 0.000 0.000 0.000

1998 1 580.0 0.0 1 580.0 0.0 0.000 0 0 0.000 0.000 0.000

1999 0 0 0.050 0.000 0.000
104

Appendix C18.- yellowfin grouper

Mycteroperca venenosa

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 5 564.0 52.8 5 564.0 52.8 0.006

1979 1 450.0 0.0 1 450.0 0.0 0.009

1980 10 401.0 11.1 10 401.0 11.1 0.002

1981 1 590.0 0.0 1 590.0 0.0 0.000

1982 8 640.0 45.5 8 640.0 45.5 0.012

1983 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1984 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1985 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1986 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1987 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1988 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1989 0 1 300.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1990 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1991 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1992 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1993 0 0 0.000 . . . . . .

1994 0 0 0.000 . . . . . .

1995 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1996 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1997 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1998 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1999 0 0 0.000 0.000 0.000 0.000

Appendix C19.- mutton snapper

Lutjanus analis

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 134 409.6 13.7 134 409.6 13.7 0.000

1977 62 351.5 20.8 62 351.5 20.8 0.000

1978 70 445.3 18.8 70 445.3 18.8 0.004

1979 62 399.8 14.3 62 399.8 14.3 0.021

1980 138 477.8 11.9 138 477.8 11.9 0.022

1981 149 463.9 12.2 149 463.9 12.2 0.019

1982 64 402.5 17.0 64 402.5 17.0 0.015

1983 161 392.4 11.9 161 392.4 11.9 0.016 1 300.0 0.0 1 300.0 0.0 0.000 0.028 0.028 0.028

1984 64 396.9 15.4 64 396.9 15.4 0.017 0 0 0.000 0.000 0.000 0.000

1985 55 408.9 14.3 67 381.0 13.8 0.006 0 0 0.000 0.000 0.000 0.000

1986 51 413.5 14.5 69 371.2 13.8 0.012 0 0 0.000 0.000 0.000 0.000

1987 82 424.1 12.8 198 325.1 8.0 0.029 0 0 0.000 0.000 0.000 0.000

1988 62 397.4 14.2 80 367.3 12.7 0.018 4 382.5 69.3 5 364.0 56.8 0.000 0.048 0.044 0.048

1989 65 389.7 13.2 76 368.9 12.7 0.020 2 355.0 25.0 2 355.0 25.0 0.000 0.007 0.007 0.007

1990 67 404.6 10.4 78 385.6 10.4 0.017 3 360.0 20.8 3 360.0 20.8 0.000 0.009 0.009 0.009

1991 56 419.3 12.4 63 400.8 12.9 0.023 3 433.3 33.3 3 433.3 33.3 0.000 0.040 0.040 0.040

1992 82 391.1 9.6 94 376.6 9.3 0.022 1 400.0 0.0 4 292.5 37.3 0.027 0.018 0.009 0.036

1993 78 413.2 11.6 95 385.2 11.4 0.028 . . . . . .

1994 36 501.7 14.7 99 385.1 10.8 0.023 . . . . . .

1995 15 476.7 16.6 29 408.6 16.8 0.017 0 0 0.000 0.000 0.000 0.000

1996 13 526.9 29.6 26 436.9 23.7 0.007 0 0 0.000 0.000 0.000 0.000

1997 6 523.3 52.8 14 409.3 37.0 0.007 1 400.0 0.0 1 400.0 0.0 0.000 0.013 0.013 0.013

1998 14 517.9 34.5 23 445.2 29.0 0.007 0 0 0.000 0.000 0.000 0.000

1999 1 450.0 0.0 1 450.0 0.0 0.013 0.025 0.025 0.025
105

Appendix C20.- schoolmaster

Lutjanus apodus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 4 305.0 26.0 4 305.0 26.0 0.002

1979 11 286.4 18.4 11 286.4 18.4 0.003

1980 15 264.7 12.5 15 264.7 12.5 0.004

1981 51 262.5 6.5 51 262.5 6.5 0.005

1982 12 267.5 5.2 12 267.5 5.2 0.004

1983 55 306.9 10.8 55 306.9 10.8 0.003 0 0 0.000 0.000 0.000 0.000

1984 4 272.5 23.9 4 272.5 23.9 0.001 0 0 0.000 0.000 0.000 0.000

1985 10 249.0 9.9 10 249.0 9.9 0.002 0 0 0.000 0.000 0.000 0.000

1986 27 270.4 9.4 27 270.4 9.4 0.002 0 0 0.000 0.000 0.000 0.000

1987 37 264.9 10.5 37 264.9 10.5 0.008 0 0 0.000 0.000 0.000 0.000

1988 30 267.7 5.6 30 267.7 5.6 0.005 177 244.8 3.0 177 244.8 3.0 0.021 1.072 0.887 0.887

1989 45 264.0 4.8 45 264.0 4.8 0.013 123 269.7 3.4 123 269.7 3.4 0.225 0.587 0.403 0.403

1990 26 303.8 10.2 30 295.3 9.7 0.005 277 283.4 2.3 285 282.4 2.3 0.211 3.037 1.176 1.225

1991 15 288.0 11.5 18 280.0 10.4 0.003 9 297.8 12.9 9 297.8 12.9 0.021 0.281 0.094 0.094

1992 38 284.7 5.5 42 280.5 5.4 0.008 9 280.4 11.4 10 276.3 11.0 0.280 0.918 0.086 0.086

1993 17 286.5 7.6 19 281.6 7.5 0.006 . . . . . .

1994 57 272.8 3.3 61 270.7 3.2 0.011 . . . . . .

1995 14 281.4 9.9 16 276.3 9.3 0.011 33 285.2 5.0 33 285.2 5.0 0.045 0.842 0.379 0.379

1996 9 271.1 9.2 13 261.5 7.5 0.004 110 305.2 3.1 110 305.2 3.1 0.154 1.972 1.521 1.521

1997 16 270.6 4.0 18 267.2 4.3 0.007 61 301.1 4.9 64 298.2 4.9 0.125 1.308 1.021 1.071

1998 24 290.4 3.8 24 290.4 3.8 0.002 1 280.0 0.0 1 280.0 0.0 0.265 0.409 0.008 0.008

1999 0 0 0.013 0.050 0.000 0.000

Appendix C21.- blackfin snapper

Lutjanus buccanella

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0

1977 0 0

1978 0 0

1979 0 0

1980 4 315.0 9.6 4 315.0 9.6

1981 0 0

1982 0 0

1983 2 385.0 5.0 2 385.0 5.0

1984 0 0

1985 0 0

1986 0 0

1987 0 0

1988 0 0

1989 0 0

1990 0 0

1991 3 360.0 32.1 5 320.0 30.2

1992 0 3 233.3 3.3

1993 0 0

1994 0 0

1995 0 0

1996 0 0

1997 0 0

1998 0 0

1999
106

Appendix C22.- red snapper

Lutjanus campechanus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0

1977 0 0

1978 0 0

1979 0 0

1980 0 0

1981 0 0

1982 0 0

1983 7 345.7 13.1 7 345.7 13.1

1984 0 0

1985 1 740.0 0.0 1 740.0 0.0

1986 5 356.0 6.0 5 356.0 6.0

1987 0 0

1988 0 0

1989 0 10 210.0 3.0

1990 0 0

1991 0 0

1992 0 0

1993 0 0

1994 0 0

1995 0 0

1996 0 0

1997 0 0

1998 0 0

1999

Appendix C23.- yellowtail snapper

Ocyurus chrysurus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 237 231.7 2.4 237 231.7 2.4 0.002

1977 55 300.9 18.1 55 300.9 18.1 0.000

1978 171 245.7 3.9 171 245.7 3.9 0.032

1979 104 266.7 5.2 104 266.7 5.2 0.107

1980 341 268.7 3.7 341 268.7 3.7 0.153

1981 272 246.3 3.3 272 246.3 3.3 0.125

1982 175 245.0 3.8 175 245.0 3.8 0.141

1983 422 241.3 2.4 422 241.3 2.4 0.095 73 230.9 3.4 73 230.9 3.4 2.556 1.750 2.028 2.028

1984 165 251.0 3.1 165 251.0 3.1 0.070 0 0 0.000 0.000 0.000 0.000

1985 137 331.0 3.4 338 290.9 2.3 0.085 0 0 0.000 0.000 0.000 0.000

1986 47 344.5 7.1 98 303.8 5.3 0.021 0 0 0.429 0.143 0.000 0.000

1987 91 345.5 5.4 131 322.9 4.8 0.025 0 3 266.7 8.8 0.324 0.270 0.000 0.081

1988 79 331.6 4.4 179 297.9 3.1 0.047 3 346.7 29.1 10 288.0 15.3 1.837 0.379 0.008 0.040

1989 41 339.5 7.8 118 292.0 4.3 0.043 8 328.8 17.7 153 259.2 1.8 1.059 1.334 0.026 0.419

1990 93 324.6 2.9 174 300.5 2.6 0.040 47 321.0 3.6 171 279.2 2.3 3.085 1.308 0.190 0.683

1991 78 342.4 5.8 137 310.3 4.6 0.043 4 312.5 12.5 12 277.2 8.6 3.845 0.488 0.048 0.136

1992 130 343.3 4.8 250 308.4 3.5 0.046 0 0 1.288 0.109 0.000 0.000

1993 158 323.4 1.8 305 298.9 1.8 0.093 . . . . . .

1994 139 321.5 2.5 386 289.3 1.6 0.078 . . . . . .

1995 23 333.9 7.9 93 282.8 3.8 0.048 0 2 265.0 5.0 2.141 0.120 0.000 0.016

1996 52 320.8 3.1 122 293.7 2.7 0.030 1 300.0 0.0 36 258.2 2.2 1.669 0.861 0.014 0.500

1997 21 317.1 3.9 54 290.4 3.6 0.019 1 310.0 0.0 5 266.0 11.7 1.321 0.221 0.013 0.077

1998 44 339.8 14.3 118 293.8 6.3 0.048 0 3 250.0 0.0 0.323 0.127 0.000 0.038

1999 1 300.0 0.0 1 300.0 0.0 0.292 0.086 0.013 0.013
107

Appendix C25.- gray snapper

Lutjanus griseus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 549 255.9 2.9 549 255.9 2.9 0.008

1977 209 237.0 3.2 209 237.0 3.2 0.000

1978 293 271.8 4.1 293 271.8 4.1 0.026

1979 234 253.3 3.5 234 253.3 3.5 0.285

1980 496 262.8 2.8 496 262.8 2.8 0.281

1981 635 285.4 2.9 635 285.4 2.9 0.241

1982 339 262.9 3.6 339 262.9 3.6 0.197

1983 1108 260.6 1.9 1108 260.6 1.9 0.254 4 250.0 0.0 4 250.0 0.0 0.000 0.111 0.111 0.111

1984 501 251.2 2.7 501 251.2 2.7 0.298 0 0 0.000 0.000 0.000 0.000

1985 1138 254.5 1.8 1138 254.5 1.8 0.201 0 0 0.000 0.000 0.000 0.000

1986 438 265.0 3.1 438 265.0 3.1 0.114 0 0 0.000 0.000 0.000 0.000

1987 555 254.7 2.1 555 254.7 2.1 0.161 0 0 0.000 0.000 0.000 0.000

1988 335 272.3 3.1 335 272.3 3.1 0.111 35 229.7 5.2 35 229.7 5.2 0.091 0.057 0.106 0.106

1989 440 272.5 2.9 440 272.5 2.9 0.157 135 227.7 2.9 135 227.7 2.9 0.135 0.074 0.120 0.120

1990 179 318.2 4.9 193 312.5 4.7 0.041 163 293.7 3.8 232 277.7 3.1 0.058 0.074 0.074 0.074

1991 175 303.0 4.1 185 299.6 4.0 0.063 4 307.5 7.5 4 307.5 7.5 0.688 0.042 0.042 0.042

1992 321 290.6 2.7 328 289.5 2.7 0.074 24 310.4 12.1 27 302.5 11.5 0.979 0.201 0.187 0.198

1993 366 300.6 2.8 390 296.9 2.8 0.154 . . . . . .

1994 539 297.5 2.5 574 294.0 2.4 0.124 . . . . . .

1995 221 299.2 3.2 234 295.9 3.2 0.107 12 276.1 9.8 14 271.0 8.7 1.334 0.076 0.031 0.055

1996 301 281.4 2.1 318 279.2 2.0 0.087 6 286.7 5.6 6 286.7 5.6 0.239 0.083 0.083 0.083

1997 199 303.0 4.4 211 299.4 4.3 0.088 73 277.0 3.0 73 276.7 3.0 0.375 0.356 0.348 0.356

1998 258 302.4 3.8 276 298.3 3.7 0.135 29 281.7 5.5 30 281.0 5.5 0.773 0.417 0.409 0.417

1999 0 1 240.0 0.0 0.438 0.050 0.000 0.050

Appendix C26.- dog snapper

Lutjanus jocu

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 0 0 0.000

1979 2 600.0 20.0 2 600.0 20.0 0.004

1980 4 300.0 33.4 4 300.0 33.4 0.004

1981 16 393.1 46.9 16 393.1 46.9 0.009

1982 3 226.7 3.3 3 226.7 3.3 0.007

1983 3 593.3 31.8 3 593.3 31.8 0.001 0 0 0.000 0.000 0.000

1984 8 272.5 26.7 8 272.5 26.7 0.003 0 0 0.000 0.000 0.000

1985 2 470.0 250.0 2 470.0 250.0 0.004 0 0 0.000 0.000 0.000

1986 0 0 0.000 0 0 0.000 0.000 0.000

1987 0 0 0.000 0 0 0.000 0.000 0.000

1988 8 376.3 35.1 8 376.3 35.1 0.001 0 0 0.000 0.000 0.000

1989 2 415.0 175.0 2 415.0 175.0 0.001 2 350.0 0.0 2 350.0 0.0 0.000 0.008 0.008

1990 2 400.0 70.0 2 400.0 70.0 0.001 1 350.0 0.0 1 350.0 0.0 0.000 0.004 0.004

1991 0 0 0.000 0 0 0.000 0.000 0.000

1992 0 0 0.000 1 300.0 0.0 1 300.0 0.0 0.000 0.010 0.010

1993 0 0 0.000 . . . . .

1994 1 420.0 0.0 1 420.0 0.0 0.002 . . . . .

1995 0 0 0.000 0 0 0.109 0.000 0.000

1996 1 430.0 0.0 1 430.0 0.0 0.004 0 0 0.000 0.000 0.000

1997 0 0 0.000 0 0 0.000 0.000 0.000

1998 0 0 0.000 0 0 0.000 0.000 0.000

1999 0 0 0.000 0.000 0.000
108

Appendix C27.- mahogony snapper

Lutjanus mahogoni

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 0 0 0.000

1979 0 0 0.000

1980 0 0 0.000

1981 0 0 0.000

1982 2 440.0 10.0 2 440.0 10.0 0.000

1983 0 0 0.000 0 0 0.000 0.000 0.000

1984 0 0 0.000 0 0 0.000 0.000 0.000

1985 0 0 0.000 0 0 0.000 0.000 0.000

1986 12 213.3 12.3 12 213.3 12.3 0.002 0 0 0.000 0.000 0.000

1987 3 316.7 24.0 3 316.7 24.0 0.000 0 0 0.000 0.000 0.000

1988 0 0 0.000 2 225.0 25.0 2 225.0 25.0 0.008 0.000 0.008

1989 2 380.0 10.0 2 380.0 10.0 0.000 4 257.5 40.5 4 257.5 40.5 0.011 0.008 0.015

1990 2 320.0 10.0 2 320.0 10.0 0.000 1 300.0 0.0 1 300.0 0.0 0.000 0.008 0.004

1991 2 380.0 50.0 2 380.0 50.0 0.000 0 0 0.000 0.000 0.000

1992 0 0 0.000 0 0 0.000 0.000 0.000

1993 0 0 0.000 . . . . .

1994 4 350.0 24.8 4 350.0 24.8 0.000 . . . . .

1995 0 0 0.000 0 0 0.000 0.000 0.000

1996 0 0 0.000 0 0 0.028 0.000 0.000

1997 1 370.0 0.0 1 370.0 0.0 0.001 0 0 0.000 0.000 0.000

1998 0 0 0.000 0 0 0.015 0.000 0.000

1999 0 0 0.000 0.000 0.000

Appendix C28.- lane snapper

Lutjanus synagris

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 9 241.1 13.6 9 241.1 13.6 0.010

1979 6 300.0 13.4 6 300.0 13.4 0.009

1980 32 253.4 5.8 32 253.4 5.8 0.016

1981 35 239.4 8.5 35 239.4 8.5 0.013

1982 18 225.0 9.4 18 225.0 9.4 0.013

1983 26 235.0 8.7 26 235.0 8.7 0.011 0 0 0.000 0.000 0.000 0.000

1984 5 202.0 8.6 5 202.0 8.6 0.002 0 0 0.000 0.000 0.000 0.000

1985 49 241.4 9.6 49 241.4 9.6 0.007 0 0 0.000 0.000 0.000 0.000

1986 18 242.2 11.6 18 242.2 11.6 0.005 0 0 0.000 0.000 0.000 0.000

1987 43 242.1 7.7 43 242.1 7.7 0.009 3 230.0 0.0 3 230.0 0.0 0.027 0.081 0.081 0.081

1988 46 229.8 6.2 46 229.8 6.2 0.014 0 0 0.000 0.000 0.000 0.000

1989 21 255.7 11.8 21 255.7 11.8 0.008 15 293.6 12.9 15 293.6 12.9 0.015 0.045 0.056 0.056

1990 13 236.2 5.7 15 230.0 6.5 0.002 0 0 0.012 0.000 0.000 0.000

1991 24 236.7 6.4 26 233.1 6.4 0.009 5 216.8 8.9 5 216.8 8.9 0.028 0.056 0.069 0.069

1992 5 240.0 14.1 6 231.7 14.2 0.001 3 300.0 11.5 3 300.0 11.5 0.000 0.009 0.009 0.009

1993 8 251.3 21.6 9 244.4 20.2 0.001 . . . . . .

1994 5 240.0 11.8 5 240.0 11.8 0.002 . . . . . .

1995 1 290.0 0.0 1 290.0 0.0 0.009 0 0 0.000 0.000 0.000 0.000

1996 4 207.5 7.5 5 204.0 6.8 0.003 0 0 0.000 0.000 0.000 0.000

1997 0 0 0.000 1 250.0 0.0 1 250.0 0.0 0.006 0.017 0.017 0.017

1998 0 0 0.000 1 255.0 0.0 1 255.0 0.0 0.000 0.008 0.008 0.008

1999 2 273.3 109.1 2 273.3 109.1 0.050 0.075 0.125 0.125
109

Appendix C29.- silk snapper

Lutjanus campechanus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0

1977 0 0

1978 0 0

1979 0 0

1980 0 0

1981 0 0

1982 0 0

1983 0 0

1984 0 0

1985 5 244.0 2.4 5 244.0 2.4

1986 0 0

1987 0 0

1988 2 260.0 20.0 2 260.0 20.0

1989 0 0

1990 0 0

1991 0 0

1992 0 0

1993 0 0

1994 0 0

1995 0 0

1996 0 0

1997 0 0

1998 0 0

1999

Appendix C31.- vermilion snapper

Rhomboplites aurorubens

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 0 0 0.000

1979 0 0 0.000

1980 0 0 0.000

1981 1 250.0 0.0 1 250.0 0.0 0.001

1982 13 260.0 14.3 13 260.0 14.3 0.003

1983 0 0 0.000

1984 0 0 0.000

1985 6 208.3 8.3 6 208.3 8.3 0.000

1986 1 290.0 0.0 1 290.0 0.0 0.000

1987 1 270.0 0.0 1 270.0 0.0 0.000

1988 0 0 0.000

1989 0 0 0.000

1990 0 0 0.000

1991 0 0 0.000

1992 0 0 0.000

1993 0 0 0.000

1994 0 0 0.000

1995 0 0 0.000

1996 0 0 0.000

1997 0 0 0.000

1998 0 0 0.000

1999
110

Appendix C32.- hogfish

Lachnolaimus maximus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 389 335.8 4.8 389 335.8 4.8 0.515

1977 157 369.2 7.8 157 369.2 7.8 0.558

1978 360 368.7 5.1 360 368.7 5.1 0.663

1979 84 361.3 11.7 84 361.3 11.7 0.554

1980 231 361.3 7.0 231 361.3 7.0 0.579

1981 687 354.8 3.3 687 354.8 3.3 0.719

1982 189 349.9 6.6 189 349.9 6.6 0.567

1983 773 349.9 3.1 773 349.9 3.1 0.706 13 227.1 5.6 13 227.1 5.6 0.201 0.354 0.354 0.354

1984 145 345.9 5.9 145 345.9 5.9 0.690 1 250.0 0.0 1 250.0 0.0 0.000 0.250 0.250 0.250

1985 457 357.7 4.2 457 357.7 4.2 0.543 0 0 0.000 0.000 0.000 0.000

1986 310 378.0 6.0 310 378.0 6.0 0.500 0 0 0.143 0.000 0.000 0.000

1987 215 321.4 5.1 215 321.4 5.1 0.546 4 242.5 14.4 4 242.5 14.4 0.027 0.108 0.108 0.108

1988 264 323.2 4.2 264 323.2 4.2 0.497 25 252.0 7.2 25 252.0 7.2 0.028 0.111 0.111 0.111

1989 212 349.8 9.4 211 341.6 4.7 0.615 27 259.6 8.7 27 259.6 8.7 0.045 0.075 0.075 0.075

1990 342 310.9 3.4 342 310.9 3.4 0.666 31 274.8 7.6 31 274.8 7.6 0.039 0.080 0.080 0.080

1991 197 323.4 5.4 197 323.4 5.4 0.404 7 297.1 37.8 7 297.1 37.8 0.135 0.088 0.088 0.088

1992 263 318.6 4.2 263 318.6 4.2 0.648 18 265.0 18.9 18 265.0 18.9 0.235 0.133 0.133 0.133

1993 184 328.9 6.0 184 328.9 6.0 0.882 . . . . . .

1994 299 351.0 3.2 426 328.2 2.9 0.887 . . . . . .

1995 103 362.7 7.8 121 349.8 7.2 0.579 5 390.0 55.7 8 342.5 40.8 0.073 0.071 0.030 0.050

1996 178 345.1 3.7 205 335.9 3.7 0.792 1 370.0 0.0 3 293.3 39.3 0.000 0.056 0.014 0.042

1997 95 354.6 6.8 109 344.0 6.5 0.510 1 305.0 0.0 7 263.1 8.8 0.198 0.083 0.008 0.054

1998 116 349.1 4.4 138 338.0 4.3 0.389 1 320.0 0.0 3 274.0 19.7 0.043 0.053 0.000 0.023

1999 2 310.0 17.3 4 284.3 20.8 0.231 0.164 0.056 0.102

Appendix C33.- black margate

Anisotremus surinamensis

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.001

1979 4 337.5 37.7 0.006

1980 9 394.4 29.9 0.006

1981 20 332.0 15.1 0.020

1982 3 360.0 50.3 0.013

1983 10 387.0 23.1 0.010 0 0.000 0.000 0.000

1984 3 383.3 46.7 0.010 0 0.000 0.000 0.000

1985 14 343.6 13.5 0.044 0 0.000 0.000 0.000

1986 17 344.7 25.0 0.013 0 0.000 0.000 0.000

1987 13 430.8 25.6 0.024 0 0.000 0.000 0.000

1988 21 335.7 15.0 0.022 11 338.2 19.2 0.049 0.020 0.061

1989 7 327.1 21.9 0.007 11 289.1 14.7 0.043 0.008 0.051

1990 21 339.5 10.5 0.013 22 332.3 10.6 0.058 0.020 0.078

1991 12 338.3 10.6 0.012 13 314.6 11.2 0.188 0.000 0.135

1992 14 340.0 11.4 0.032 2 295.0 5.0 0.019 0.000 0.019

1993 8 380.0 26.4 0.008 . . . .

1994 12 341.7 15.8 0.006 . . . .

1995 7 350.0 14.5 0.019 7 318.2 11.3 0.104 0.000 0.104

1996 7 361.4 12.0 0.011 1 250.0 0.0 0.014 0.000 0.014

1997 2 310.0 20.0 0.000 0 0.000 0.000 0.000

1998 3 283.3 12.0 0.009 1 290.0 0.0 0.008 0.000 0.008

1999 0 0.000 0.000 0.000
111

Appendix C34.- porkfish

Anisotremus virginicus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 31 298.4 9.6 0.000

1979 1 210.0 0.0 0.001

1980 1 250.0 0.0 0.005

1981 14 250.0 16.3 0.002

1982 5 236.0 6.8 0.001

1983 12 206.7 8.9 0.003 5 206.0 16.6 0.222 0.028 0.139

1984 0 0.001 2 230.0 0.0 0.000 0.500 0.500

1985 4 250.0 8.2 0.000 0 0.000 0.000 0.000

1986 8 262.5 8.4 0.001 0 0.000 0.000 0.000

1987 12 256.7 18.4 0.002 9 244.4 7.1 0.027 0.216 0.243

1988 3 250.0 20.0 0.001 103 220.6 4.0 0.398 0.171 0.507

1989 6 265.0 27.9 0.001 117 243.1 5.2 0.218 0.338 0.526

1990 12 252.5 6.4 0.003 96 234.3 5.9 0.195 0.140 0.290

1991 8 238.8 14.9 0.002 55 219.4 3.1 0.539 0.270 0.712

1992 20 219.0 7.4 0.004 35 207.7 6.8 0.443 0.131 0.328

1993 1 240.0 0.0 0.000 . . . .

1994 21 220.5 7.0 0.005 . . . .

1995 7 220.0 18.3 0.002 27 207.6 8.1 0.355 0.044 0.220

1996 15 216.0 8.9 0.004 9 206.7 18.3 0.128 0.042 0.126

1997 6 208.3 11.9 0.002 18 244.2 13.1 0.235 0.183 0.256

1998 4 222.5 14.4 0.002 17 230.3 12.3 0.142 0.142 0.225

1999 5 205.6 19.1 0.228 0.050 0.227

Appendix C35.- margate

Haemulon album

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 21 224.3 5.1 0.001

1979 6 345.0 47.8 0.003

1980 8 475.0 36.2 0.002

1981 12 337.5 26.7 0.002

1982 44 289.3 11.7 0.006

1983 18 365.6 29.0 0.002 0 0.000 0.000 0.000

1984 3 236.7 14.5 0.001 0 0.000 0.000 0.000

1985 20 266.0 16.4 0.003 0 0.000 0.000 0.000

1986 21 323.3 22.8 0.006 0 0.000 0.000 0.000

1987 21 316.7 20.8 0.004 0 0.000 0.000 0.000

1988 5 320.0 40.4 0.002 0 0.000 0.000 0.000

1989 7 282.9 28.5 0.001 0 0.000 0.000 0.000

1990 17 270.0 17.2 0.004 7 323.9 46.4 0.026 0.004 0.030

1991 4 380.0 41.0 0.000 0 0.000 0.000 0.000

1992 19 287.4 29.9 0.003 0 0.000 0.000 0.000

1993 7 392.9 20.1 0.001 . . . .

1994 10 249.0 12.6 0.002 . . . .

1995 2 565.0 15.0 0.000 0 0.000 0.000 0.000

1996 0 0.000 0 0.000 0.000 0.000

1997 6 270.0 34.2 0.002 0 0.000 0.000 0.000

1998 3 416.7 26.0 0.000 1 220.0 0.0 0.091 0.000 0.015

1999 0 0.000 0.000 0.000
112

Appendix C36.- tomtate

Haemulon aurolineatum

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.000

1980 5 176.0 14.4 0.001

1981 1 170.0 0.0 0.000

1982 2 210.0 10.0 0.001

1983 8 168.8 4.4 0.002 1123 175.9 0.6 29.993 37.674 31.181

1984 0 0.001 0 0.000 3.750 0.000

1985 17 161.2 7.7 0.005 0 0.000 0.000 0.000

1986 0 0.000 2 160.0 0.0 6.536 19.607 0.261

1987 7 171.4 4.6 0.001 988 192.3 0.9 18.904 52.204 26.709

1988 23 169.6 6.2 0.004 642 164.3 0.4 8.267 6.629 2.318

1989 6 176.7 2.1 0.003 1004 176.5 0.8 12.867 7.469 5.595

1990 24 180.8 6.8 0.009 296 152.4 0.6 7.514 3.429 1.203

1991 0 0.000 42 153.5 0.8 0.677 0.677 0.432

1992 0 0.000 20 180.0 0.0 5.745 1.190 0.227

1993 0 0.000 . . . .

1994 0 0.000 . . . .

1995 0 0.000 67 157.7 1.1 4.391 0.760 0.667

1996 0 0.007 66 199.2 0.8 6.482 0.944 0.917

1997 0 0.000 102 195.8 0.8 3.150 1.700 1.692

1998 0 0.000 177 174.6 1.9 3.264 2.521 2.266

1999 5 180.6 6.0 0.000 0.500 0.450

Appendix C37.- ceasar grunt

Haemulon carbonarium

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 1 240.0 0.0 0.000

1980 1 210.0 0.0 0.001

1981 0 0.000

1982 0 0.001

1983 1 200.0 0.0 0.000 0 0.000 0.000 0.000

1984 0 0.000 0 0.000 0.000 0.000

1985 3 313.3 41.8 0.000 0 0.000 0.000 0.000

1986 0 0.000 0 0.000 0.000 0.000

1987 9 217.8 12.4 0.002 0 0.000 0.000 0.000

1988 6 206.7 15.6 0.000 8 202.5 5.6 0.052 0.008 0.037

1989 4 235.0 2.9 0.001 17 210.0 10.2 0.117 0.023 0.063

1990 17 187.6 6.4 0.003 33 208.8 4.3 0.112 0.039 0.138

1991 3 230.0 10.0 0.001 2 230.0 20.0 0.024 0.010 0.024

1992 2 295.0 5.0 0.000 9 173.3 6.3 0.040 0.009 0.018

1993 14 228.6 7.6 0.004 . . . .

1994 4 235.0 6.5 0.001 . . . .

1995 2 200.0 10.0 0.002 3 210.0 20.0 0.587 0.010 0.021

1996 6 233.3 9.9 0.002 4 237.5 19.3 0.278 0.042 0.056

1997 6 250.0 12.6 0.001 18 236.4 5.5 0.233 0.225 0.300

1998 0 0.000 8 207.6 9.9 0.341 0.053 0.117

1999 0 0.000 0.000 0.000
113

Appendix C39.- french grunt

Haemulon flavolineatum

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.002

1980 0 0.000

1981 3 240.0 5.8 0.000

1982 4 185.0 6.5 0.003

1983 6 186.7 11.2 0.001 70 183.4 2.4 4.139 1.361 1.944

1984 1 220.0 0.0 0.000 0 0.000 0.000 0.000

1985 42 243.1 5.8 0.009 0 0.000 0.000 0.000

1986 14 226.4 8.4 0.004 34 173.6 0.9 4.821 1.607 4.821

1987 6 191.7 7.9 0.001 49 206.1 3.7 1.216 1.135 1.324

1988 10 172.0 4.4 0.001 78 172.1 2.1 3.322 0.105 0.364

1989 1 280.0 0.0 0.000 269 187.0 1.9 3.239 0.849 1.357

1990 5 184.0 10.3 0.001 165 176.3 1.7 2.363 0.227 0.613

1991 3 246.7 12.0 0.002 20 186.5 5.3 2.865 0.142 0.260

1992 2 185.0 5.0 0.000 15 164.0 2.7 4.280 0.011 0.151

1993 1 170.0 0.0 0.000 . . . .

1994 6 171.7 4.0 0.003 . . . .

1995 3 176.7 3.3 0.008 316 219.6 1.3 3.288 1.573 1.677

1996 0 0.000 40 178.4 0.9 2.121 0.403 0.549

1997 1 160.0 0.0 0.000 34 192.4 2.0 3.508 0.542 0.565

1998 1 160.0 0.0 0.001 13 206.9 7.3 3.000 0.152 0.197

1999 8 173.5 5.6 0.931 0.052 0.209

Appendix C40.- spanish grunt

Haemulon macrostomium

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 2 300.0 90.0 0.000

1980 0 0.000

1981 4 297.5 29.5 0.001

1982 4 292.5 24.3 0.001

1983 1 370.0 0.0 0.001 0 0.000 0.000 0.000

1984 1 240 0 0.000 0 0.000 0.000 0.000

1985 2 320.0 50.0 0.000 0 0.000 0.000 0.000

1986 3 300.0 5.8 0.000 0 0.000 0.000 0.000

1987 2 290.0 10.0 0.000 0 0.000 0.000 0.000

1988 5 286.0 9.3 0.001 2 280.0 50.0 0.008 0.004 0.076

1989 1 270.0 0.0 0.000 19 231.6 10.2 0.113 0.017 0.023

1990 2 345.0 85.0 0.000 11 288.2 17.9 0.024 0.029 0.000

1991 1 290.0 0.0 0.000 2 230.0 0.0 0.056 0.000 0.000

1992 0 0.000 1 270.0 0.0 0.000 0.009 0.188

1993 0 0.000 . . . .

1994 2 240.0 10.0 0.000 . . . .

1995 2 275.0 45.0 0.000 2 235.0 15.0 0.071 0.000 1.016

1996 2 285.0 15.0 0.000 3 293.3 29.6 0.028 0.028 0.014

1997 0 0.000 7 300.7 7.9 0.006 0.108 0.000

1998 1 290.0 0.0 0.000 2 233.3 13.3 0.189 0.000 0.000

1999 0 0.013 0.000 0.022
114

Appendix C42.- sailors choice

Haemulon parra

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 2 205.0 5.0 0.001

1979 4 280.0 16.3 0.004

1980 19 232.6 9.5 0.005

1981 23 268.3 7.5 0.003

1982 0 0.002

1983 14 255.7 6.4 0.004 0 0.000 0.000 0.000

1984 1 300.0 0.0 0.002 0 0.000 0.000 0.000

1985 8 233.8 9.4 0.003 0 0.000 0.000 0.000

1986 22 236.4 5.4 0.002 2 240.0 10.0 0.000 0.429 0.286

1987 48 266.5 4.2 0.007 3 260.0 10.0 0.000 0.081 0.081

1988 89 239.0 2.7 0.025 10 255.0 10.4 0.000 0.063 0.051

1989 37 263.0 6.0 0.014 25 231.0 7.8 0.000 0.109 0.065

1990 25 238.8 4.8 0.006 64 231.7 3.3 0.008 0.332 0.255

1991 18 241.7 5.3 0.007 1 300.0 0.0 0.000 0.035 0.010

1992 23 251.3 7.2 0.005 0 0.021 0.054 0.000

1993 33 241.8 3.9 0.017 . . . .

1994 33 244.5 5.6 0.007 . . . .

1995 50 222.4 2.9 0.019 3 216.7 16.7 0.000 0.047 0.036

1996 55 235.6 3.7 0.018 0 0.000 0.000 0.000

1997 16 238.1 6.5 0.012 4 254.3 8.9 0.000 0.065 0.058

1998 22 237.3 7.2 0.013 1 260.0 0.0 0.000 0.132 0.008

1999 1 260.0 0.0 0.000 0.025 0.013

Appendix C43.- white grunt

Haemulon plumieri

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 1039 205.7 0.8 0.085

1977 154 216.1 2.5 0.008

1978 371 223.7 1.4 0.195

1979 168 237.9 2.8 0.327

1980 433 228.0 1.6 0.345

1981 664 224.8 1.2 0.375

1982 164 221.0 2.1 0.371

1983 694 225.3 1.3 0.189 17 217.1 4.8 0.083 0.472 0.472

1984 320 215.5 1.6 0.173 0 0.000 0.000 0.000

1985 1391 224.6 0.8 0.256 0 0.000 0.000 0.000

1986 848 235.5 1.2 0.187 1 170.0 0.0 0.857 0.000 0.143

1987 706 237.4 1.3 0.184 47 227.0 5.4 0.520 1.155 1.264

1988 730 231.5 1.1 0.240 155 198.9 2.5 3.113 0.651 0.801

1989 584 224.9 1.1 0.202 521 205.0 1.6 2.854 1.065 1.128

1990 821 226.6 1.0 0.255 370 207.5 1.8 3.513 1.032 1.279

1991 414 221.1 1.3 0.117 95 210.9 2.1 0.396 1.210 1.266

1992 703 226.1 1.1 0.178 49 201.9 5.6 2.226 0.299 0.373

1993 953 220.3 0.8 0.308 . . . .

1994 968 221.1 0.8 0.277 . . . .

1995 383 219.6 1.2 0.227 196 189.7 2.0 12.014 0.538 1.613

1996 659 217.9 1.0 0.315 53 199.7 3.1 3.360 0.612 0.737

1997 382 219.9 1.1 0.183 81 222.0 3.8 2.700 1.115 1.242

1998 454 216.5 1.2 0.175 40 210.1 4.6 1.532 0.432 0.462

1999 10 192.6 7.3 1.125 0.215 0.316
115

Appendix C44.- bluestriped grunt

Haemulon sciurus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 394 214.7 1.3 0.050

1977 140 209.9 1.7 0.010

1978 122 245.3 3.4 0.043

1979 55 252.2 3.9 0.082

1980 168 242.5 2.1 0.067

1981 191 235.3 1.9 0.058

1982 112 241.4 2.5 0.131

1983 245 222.8 1.6 0.049 2 235.0 5.0 0.000 0.056 0.056

1984 183 230.8 2.1 0.061 1 230.0 0.0 0.000 0.250 0.250

1985 237 237.2 2.2 0.053 0 0.000 0.000 0.000

1986 202 240.0 1.9 0.041 0 0.286 0.000 0.000

1987 288 235.5 1.6 0.069 17 241.2 4.1 0.081 0.459 0.459

1988 206 230.0 1.8 0.061 56 214.1 5.0 0.151 0.079 0.178

1989 167 234.1 2.0 0.042 304 224.5 1.9 0.640 0.637 0.861

1990 211 226.6 1.7 0.059 247 232.1 3.0 1.580 0.351 0.664

1991 180 228.8 2.2 0.055 146 208.2 1.6 2.628 0.715 1.998

1992 321 231.3 1.5 0.068 64 199.6 3.2 2.083 0.029 0.429

1993 189 225.3 1.6 0.066 . . . .

1994 233 225.3 1.5 0.054 . . . .

1995 130 226.1 1.9 0.064 23 223.0 5.1 1.208 0.135 0.188

1996 194 226.7 1.5 0.076 40 216.6 3.9 1.457 0.225 0.564

1997 156 220.6 1.6 0.073 91 265.8 4.7 2.152 1.206 1.433

1998 81 226.3 2.9 0.056 33 204.9 5.2 0.811 0.106 0.295

1999 6 190.3 8.4 0.496 0.010 0.126

Appendix C45.- striped grunt

Haemulon striatum

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.001

1980 0 0.001

1981 3 216.7 21.9 0.001

1982 0 0.000

1983 0 0.000 0 0.000 0.000 0.000

1984 0 0.000 0 0.000 0.000 0.000

1985 0 0.000 0 0.000 0.000 0.000

1986 0 0.000 0 0.000 0.000 0.000

1987 0 0.000 0 0.000 0.000 0.000

1988 0 0.000 0 0.000 0.000 0.000

1989 0 0.000 0 0.000 0.000 0.000

1990 3 253.3 18.6 0.001 0 0.005 0.000 0.000

1991 1 250.0 0.0 0.000 0 0.042 0.000 0.000

1992 0 0.000 0 0.000 0.000 0.000

1993 0 0.000 . . . .

1994 3 223.3 18.6 0.000 . . . .

1995 0 0.000 0 0.000 0.000 0.000

1996 0 0.000 0 0.000 0.000 0.000

1997 20 221.5 6.3 0.010 0 0.000 0.000 0.000

1998 47 227.4 4.1 0.025 0 0.000 0.000 0.000

1999 0 0.000 0.000 0.000
116

Appendix C46.- pigfish

Orthopristis chrysoptera

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 5 194.0 6.8 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.001

1980 2 265.0 5.0 0.000

1981 0 0.000

1982 1 230.0 0.0 0.000

1983 9 232.2 5.2 0.002

1984 0 0.000

1985 0 0.000

1986 5 184.0 5.1 0.001

1987 2 225.0 55.0 0.001

1988 0 0.000

1989 0 0.000

1990 6 178.3 4.0 0.003

1991 0 0.000

1992 0 0.000

1993 0 0.000

1994 0 0.000

1995 0 0.000

1996 0 0.000

1997 0 0.000

1998 0 0.000

1999

Appendix C47.- great barracuda

Sphyraena barracuda

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 179 553.1 13.9 0.040

1977 38 722.6 37.5 0.028

1978 138 622.8 18.0 0.028

1979 38 554.7 27.2 0.030

1980 88 581.0 23.2 0.053

1981 54 666.5 28.7 0.036

1982 44 583.2 39.4 0.038

1983 66 591.7 28.5 0.023 0 0.000 0.000 0.000

1984 15 640.0 54.6 0.020 0 0.000 0.000 0.000

1985 79 610.5 21.6 0.018 2 1300.0 15.0 0.000 0.333 0.333

1986 123 597.8 20.6 0.026 1 600.0 0.0 0.143 0.000 0.143

1987 116 534.8 13.6 0.038 1 1100.0 0.0 0.000 0.027 0.027

1988 35 636.6 37.0 0.018 22 871.8 8.0 0.027 0.030 0.057

1989 50 539.4 31.6 0.029 97 754.5 2.6 0.086 0.065 0.151

1990 69 646.1 19.1 0.024 124 857.8 2.9 0.086 0.142 0.228

1991 83 554.6 18.2 0.026 92 923.5 3.1 0.448 0.514 0.962

1992 81 638.6 24.9 0.025 6 733.3 16.5 0.000 0.000 0.000

1993 73 650.0 24.1 0.022 . . . .

1994 114 576.3 20.1 0.028 . . . .

1995 47 516.2 26.4 0.027 25 886.0 5.6 0.179 0.097 0.204

1996 49 640.2 32.3 0.026 17 617.7 5.8 0.321 0.042 0.237

1997 53 592.8 28.0 0.024 3 824.0 15.3 0.071 0.017 0.038

1998 26 583.5 33.5 0.038 3 770.0 29.7 0.023 0.013 0.036

1999 0 0.000 0.000 0.000
117

Appendix C48.- jolthead porgy

Calamus bajonado

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.018

1977 0 0.085

1978 27 317.4 12.8 0.145

1979 4 312.5 14.9 0.099

1980 29 285.2 9.9 0.082

1981 132 290.9 4.8 0.073

1982 86 305.8 5.7 0.039

1983 87 290.8 5.5 0.028 2 240.0 0.0 0.056 0.000 0.056

1984 104 286.8 6.3 0.048 0 0.000 0.000 0.000

1985 229 318.7 4.2 0.039 0 0.000 0.000 0.000

1986 43 319.1 10.8 0.009 0 0.286 0.000 0.000

1987 59 318.3 9.3 0.013 0 0.000 0.000 0.000

1988 130 292.1 4.5 0.039 0 0.020 0.000 0.000

1989 80 323.4 6.5 0.023 6 233.3 1.1 0.013 0.000 0.002

1990 137 290.2 4.7 0.026 5 250.0 1.4 0.065 0.000 0.020

1991 77 281.9 4.3 0.023 0 0.000 0.000 0.000

1992 144 304.3 4.6 0.038 0 0.000 0.000 0.000

1993 58 298.4 8.2 0.018 . . . .

1994 69 297.1 5.8 0.016 . . . .

1995 24 282.9 10.9 0.011 0 0.000 0.000 0.000

1996 24 291.3 12.1 0.011 0 0.000 0.000 0.000

1997 23 310.0 10.2 0.009 0 0.000 0.000 0.000

1998 43 280.9 9.0 0.022 0 0.013 0.000 0.000

1999 0 0.000 0.000 0.000

Appendix C49.- saucereye porgy

Calamus calamus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.000

1980 31 303.9 10.1 0.017

1981 125 271.9 5.6 0.038

1982 26 262.7 9.5 0.004

1983 44 265.0 5.9 0.005 0 0.000 0.000 0.000

1984 3 253.3 12.0 0.002 1 280.0 0.0 0.250 0.000 0.250

1985 144 268.9 3.7 0.020 0 0.000 0.000 0.000

1986 46 283.0 6.8 0.006 2 290.0 0.0 0.286 0.000 0.286

1987 17 275.9 11.6 0.002 7 210.7 0.7 0.243 0.000 0.189

1988 6 266.7 29.9 0.002 0 0.081 0.000 0.000

1989 13 233.1 8.3 0.004 8 230.0 1.3 0.050 0.000 0.026

1990 48 254.8 6.0 0.014 9 222.2 0.7 0.138 0.000 0.033

1991 21 282.9 11.4 0.005 2 275.0 2.5 0.028 0.000 0.028

1992 33 287.9 7.7 0.008 0 0.032 0.000 0.000

1993 54 250.9 6.4 0.023 . . . .

1994 41 259.3 5.8 0.010 . . . .

1995 11 257.3 12.8 0.006 5 250.0 2.2 0.179 0.000 0.031

1996 13 242.3 7.7 0.005 1 260.0 0.0 0.028 0.000 0.014

1997 2 240.0 30.0 0.001 3 225.0 1.8 0.110 0.000 0.027

1998 2 270.0 10.0 0.001 0 0.076 0.000 0.000

1999 3 233.7 1.9 0.351 0.000 0.106
118

Appendix C50.- yellow jack

Caranx bartholomaei

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.001

1980 6 440.0 69.3 0.002

1981 46 435.4 18.5 0.007

1982 13 405.4 31.6 0.002

1983 23 452.6 34.6 0.003 65 300.7 6.2 2.056 0.000 1.806

1984 3 360.0 45.8 0.001 0 0.000 0.000 0.000

1985 19 336.3 13.2 0.002 0 0.000 0.000 0.000

1986 16 451.3 29.8 0.001 0 0.000 0.000 0.000

1987 38 391.1 13.9 0.007 29 276.6 7.3 0.838 0.027 0.784

1988 44 389.5 18.6 0.010 19 340.0 15.4 0.558 0.004 0.187

1989 16 385.0 30.8 0.004 37 418.6 19.7 0.079 0.037 0.104

1990 45 399.8 18.5 0.006 61 385.4 11.6 0.063 0.043 0.106

1991 27 388.9 17.4 0.005 3 450.0 28.9 0.010 0.021 0.031

1992 52 382.3 12.2 0.009 71 250.0 0.0 0.060 0.000 0.000

1993 60 317.0 9.7 0.010 . . . .

1994 36 363.1 17.3 0.004 . . . .

1995 18 371.7 21.9 0.001 7 307.1 20.4 0.119 0.000 0.067

1996 15 372.0 21.7 0.003 1 330.0 0.0 0.014 0.000 0.014

1997 10 395.0 19.0 0.006 9 338.9 10.9 0.042 0.000 0.042

1998 14 330.7 9.4 0.006 7 303.2 26.5 0.114 0.015 0.030

1999 0 0.013 0.000 0.000

Appendix C51.- blue runner

Caranx crysos

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 1 300.0 0.0 0.011

1977 1 440.0 0.0 0.007

1978 0 0.004

1979 0 0.015

1980 29 282.1 20.3 0.022

1981 113 260.7 6.3 0.042

1982 86 251.6 4.7 0.041

1983 155 250.7 3.9 0.033 22 303.6 20.6 0.611 0.000 0.611

1984 14 279.3 18.9 0.023 0 0.000 0.000 0.000

1985 91 282.7 7.4 0.033 0 0.667 0.000 0.000

1986 237 260.1 4.5 0.047 0 0.000 0.000 0.000

1987 183 259.5 3.7 0.045 2 210.0 10.0 0.054 0.000 0.054

1988 127 250.7 3.6 0.045 10 300.0 9.1 0.058 0.000 0.058

1989 102 263.0 5.1 0.031 91 223.5 5.0 0.079 0.000 0.079

1990 72 261.1 4.5 0.037 183 302.1 6.4 0.704 0.042 0.738

1991 78 245.4 5.4 0.027 0 0.028 0.000 0.000

1992 99 250.0 3.6 0.021 2 180.0 20.0 0.000 0.000 0.000

1993 70 268.1 5.3 0.030 . . . .

1994 98 245.6 3.3 0.055 . . . .

1995 65 231.4 5.2 0.032 30 250.0 3.9 0.313 0.000 0.313

1996 99 241.6 3.2 0.035 23 300.0 0.0 0.319 0.000 0.319

1997 73 243.6 3.4 0.056 20 183.0 3.2 0.250 0.000 0.250

1998 136 246.1 3.2 0.098 4 257.1 11.1 0.045 0.000 0.045

1999 10 226.0 6.4 0.250 0.000 0.250
119

Appendix C52.- crevalle jack

Caranx hippos

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.004

1980 3 486.7 63.6 0.004

1981 0 0.002

1982 12 285.8 18.6 0.009

1983 13 400.8 26.0 0.009

1984 1 270.0 0.0 0.000

1985 2 245.0 45.0 0.001

1986 4 282.5 17.5 0.001

1987 5 340.0 41.5 0.001

1988 9 236.7 5.8 0.000

1989 3 320.0 110.0 0.001

1990 4 410.0 51.2 0.002

1991 0 0.000

1992 1 250.0 0.0 0.001

1993 3 433.3 83.5 0.002

1994 9 343.3 22.4 0.008

1995 2 260.0 10.0 0.000

1996 2 320.0 40.0 0.002

1997 1 390.0 0.0 0.000

1998 2 255.0 15.0 0.004

1999

Appendix C53.- horse-eye jack

Caranx latus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.000

1980 1 290.0 0.0 0.001

1981 4 430.0 63.4 0.001

1982 1 310.0 0.0 0.000

1983 6 296.7 16.7 0.001

1984 0 0.000

1985 2 630.0 30.0 0.002

1986 2 425.0 85.0 0.001

1987 0 0.000

1988 0 0.000

1989 0 0.000

1990 0 0.000

1991 1 240.0 0.0 0.000

1992 0 0.000

1993 0 0.000

1994 0 0.000

1995 1 690.0 0.0 0.000

1996 0 0.000

1997 0 0.000

1998 0 0.000

1999
120

Appendix C54.- bar jack

Caranx ruber

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 1 320.0 0.0 0.000

1979 0 0.000

1980 2 525.0 125.0 0.000

1981 15 326.0 23.1 0.002

1982 1 320.0 0.0 0.001

1983 2 330.0 40.0 0.001 45 310.0 4.1 2.639 1.250 1.250

1984 0 0.000 0 3.500 0.000 0.000

1985 7 348.6 17.9 0.002 0 0.000 0.000 0.000

1986 7 391.4 17.9 0.001 1 250.0 0.0 0.143 0.143 0.143

1987 8 350.0 21.2 0.001 1 320.0 0.0 3.568 0.027 0.027

1988 5 400.0 20.5 0.000 14 262.0 6.4 1.950 0.051 0.051

1989 1 280.0 0.0 0.000 77 255.2 2.1 1.694 0.297 0.297

1990 5 338.0 41.9 0.001 84 403.7 18.3 1.777 0.361 0.361

1991 4 270.0 0.0 0.001 8 266.3 6.0 1.901 0.092 0.092

1992 4 305.0 42.7 0.000 6 266.4 9.1 1.705 0.047 0.047

1993 9 331.1 26.1 0.006 . . . .

1994 11 367.3 14.9 0.000 . . . .

1995 0 0.000 20 286.9 13.2 1.226 0.190 0.190

1996 2 345.0 15.0 0.000 10 250.0 0.0 4.864 0.139 0.139

1997 5 344.0 21.6 0.001 4 295.0 19.8 0.167 0.021 0.021

1998 3 330.0 17.3 0.002 3 322.0 34.4 1.847 0.038 0.038

1999 2 312.5 108.3 0.061 0.163 0.163

Appendix C55.- bermuda chub

Kyphosus sectatrix

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.004

1980 6 340.0 49.3 0.010

1981 30 280.3 19.5 0.005

1982 21 264.3 11.7 0.006

1983 17 223.5 10.4 0.002 0 0.000 0.000 0.000

1984 7 250.0 20.8 0.002 0 0.000 0.000 0.000

1985 11 252.7 16.2 0.001 0 0.000 0.000 0.000

1986 11 304.5 39.8 0.002 0 0.000 0.000 0.000

1987 16 276.3 17.9 0.003 0 0.000 0.000 0.000

1988 0 0.000 186 285.1 3.8 0.840 0.000 0.833

1989 0 0.000 323 275.0 2.3 1.059 0.000 1.049

1990 1 250.0 0.0 0.002 74 275.8 4.7 0.314 0.000 0.298

1991 14 370.0 13.1 0.002 194 249.3 4.1 2.806 0.000 2.100

1992 6 278.3 23.3 0.001 8 298.8 20.6 0.055 0.000 0.046

1993 12 261.7 13.6 0.004 . . . .

1994 4 290.0 15.8 0.001 . . . .

1995 0 0.000 178 239.2 2.9 2.912 0.000 2.192

1996 0 0.000 132 256.8 4.2 2.602 0.000 1.836

1997 0 0.000 47 261.7 6.0 0.850 0.000 0.694

1998 19 280.0 14.3 0.016 26 223.4 10.5 0.591 0.000 0.392

1999 2 206.7 22.3 0.300 0.000 0.225
121

Appendix C56.- gray angelfish

Pomacanthus arcuatus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 2 350.0 0.0 0.306 0.056

1984 1 380.0 0.0 0.250 0.250

1985 0 0.000 0.000

1986 0 0.286 0.000

1987 5 392.0 15.0 0.162 0.135

1988 10 370.0 5.6 0.552 0.044

1989 18 371.8 6.9 0.358 0.028

1990 33 367.3 4.5 0.513 0.079

1991 0 0.334 0.000

1992 4 397.5 18.9 0.315 0.023

1993 . ..

1994 . ..

1995 0 0.390 0.000

1996 3 360.0 10.0 0.399 0.042

1997 2 357.5 13.0 0.600 0.017

1998 3 365.0 12.7 0.174 0.021

1999 0 0.163 0.000

Appendix C59.- blue parrotfish

Scarus coeruleus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 0 0.028 0.000

1984 0 0.000 0.000

1985 0 0.000 0.000

1986 0 0.000 0.000

1987 0 0.027 0.000

1988 0 0.075 0.000

1989 0 0.066 0.000

1990 0 0.106 0.000

1991 0 0.049 0.000

1992 0 0.048 0.000

1993 . ..

1994 . ..

1995 0 0.070 0.000

1996 0 0.083 0.000

1997 0 0.008 0.000

1998 0 0.114 0.000

1999 0 0.074 0.000
122

Appendix C61.- rainbow parrotfish

Scarus guacamaia

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 0 0.000 0.000

1984 0 0.000 0.000

1985 0 0.000 0.000

1986 0 0.000 0.000

1987 0 0.000 0.000

1988 0 0.039 0.000

1989 5 650.0 0.0 0.060 0.011

1990 0 0.032 0.000

1991 0 0.014 0.000

1992 0 0.028 0.000

1993 . ..

1994 . ..

1995 0 0.071 0.000

1996 0 0.083 0.000

1997 0 0.050 0.000

1998 0 0.087 0.000

1999 0 0.000 0.000

Appendix C62.- princess parrotfish

Scarus taeniopterus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 0 0.278 0.000

1984 0 0.000 0.000

1985 0 0.000 0.000

1986 0 0.000 0.000

1987 18 277.9 11.0 1.662 0.473

1988 14 277.1 13.9 0.689 0.064

1989 19 241.1 6.2 1.264 0.095

1990 6 280.0 15.3 5.579 0.025

1991 6 250.0 12.9 1.868 0.076

1992 30 259.3 5.0 3.395 0.295

1993 . ..

1994 . ..

1995 1 280.0 0.0 1.224 0.005

1996 1 240.0 0.0 2.398 0.014

1997 1 250.0 0.0 1.506 0.015

1998 0 1.174 0.000

1999 2 300.8 33.3 0.218 0.058
123

Appendix C63.- queen parrotfish

Scarus vetula

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 0 0.000 0.000

1984 0 0.000 0.000

1985 0 0.000 0.000

1986 0 0.000 0.000

1987 4 405.0 21.0 0.081 0.108

1988 10 365.0 11.6 0.111 0.050

1989 39 383.1 7.1 0.362 0.135

1990 20 366.5 8.9 0.115 0.083

1991 13 377.7 10.2 0.256 0.141

1992 1 320.0 0.0 0.078 0.011

1993 . ..

1994 . ..

1995 5 350.0 13.8 0.266 0.047

1996 8 337.5 6.2 0.264 0.111

1997 2 353.3 48.1 0.056 0.025

1998 9 340.0 11.7 0.341 0.129

1999 0 0.013 0.000

Appendix C64.- greenblotch parrotfish

Sparisoma atomarium

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 0 0.000 0.000

1984 0 0.000 0.000

1985 0 0.000 0.000

1986 0 0.000 0.000

1987 0 0.000 0.000

1988 0 0.000 0.000

1989 0 0.033 0.000

1990 0 0.090 0.000

1991 0 0.146 0.000

1992 0 0.222 0.000

1993 . ..

1994 . ..

1995 0 0.479 0.000

1996 0 0.139 0.000

1997 0 0.306 0.000

1998 0 0.273 0.000

1999 0 0.075 0.000
124

Appendix C65.- redband parrotfish

Sparisoma aurofrenatum

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 15 186.7 4.9 1.806 0.417

1984 14 223.6 10.8 0.000 3.500

1985 0 0.000 0.000

1986 5 260.6 10.7 1.500 0.643

1987 27 216.8 9.0 1.811 0.730

1988 150 206.9 2.5 5.050 0.722

1989 266 208.5 1.9 4.712 1.072

1990 134 212.1 2.4 5.054 0.541

1991 46 198.9 3.8 3.938 0.569

1992 43 207.8 3.5 3.577 0.403

1993 . ..

1994 . ..

1995 56 208.3 4.3 4.276 0.410

1996 64 219.1 4.1 6.514 0.885

1997 47 208.1 4.5 4.386 0.666

1998 27 218.2 6.7 5.706 0.409

1999 24 201.3 6.0 3.699 0.660

Appendix C65.- redband parrotfish

Sparisoma aurofrenatum

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 15 186.7 4.9 1.806 0.417

1984 14 223.6 10.8 0.000 3.500

1985 0 0.000 0.000

1986 5 260.6 10.7 1.500 0.643

1987 27 216.8 9.0 1.811 0.730

1988 150 206.9 2.5 5.050 0.722

1989 266 208.5 1.9 4.712 1.072

1990 134 212.1 2.4 5.054 0.541

1991 46 198.9 3.8 3.938 0.569

1992 43 207.8 3.5 3.577 0.403

1993 . ..

1994 . ..

1995 56 208.3 4.3 4.276 0.410

1996 64 219.1 4.1 6.514 0.885

1997 47 208.1 4.5 4.386 0.666

1998 27 218.2 6.7 5.706 0.409

1999 24 201.3 6.0 3.699 0.660
125

Appendix C68.- redfin parrotfish

Sparisoma rubripinne

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 0 0.361 0.000

1984 0 0.250 0.000

1985 0 0.000 0.000

1986 0 0.000 0.000

1987 2 360.0 40.0 0.270 0.054

1988 13 382.3 31.0 0.055 0.060

1989 46 347.0 6.7 0.309 0.182

1990 18 341.7 9.4 0.041 0.077

1991 2 425.0 25.0 0.093 0.021

1992 6 330.0 14.4 0.106 0.057

1993 . ..

1994 . ..

1995 9 331.1 16.5 0.291 0.089

1996 6 365.0 15.2 0.328 0.083

1997 8 313.1 8.2 0.173 0.133

1998 6 317.5 8.8 0.265 0.091

1999 3 325.0 16.2 0.089 0.093

Appendix C69.- stoplight parrotfish

Sparisoma viride

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976

1977

1978

1979

1980

1981

1982

1983 0 0.861 0.000

1984 0 0.250 0.000

1985 0 0.000 0.000

1986 0 0.000 0.000

1987 9 405.6 1.1 0.946 0.243

1988 43 410.2 0.4 1.776 0.167

1989 60 406.2 0.4 1.782 0.250

1990 57 414.9 0.4 1.980 0.240

1991 20 407.5 0.5 1.947 0.236

1992 5 400.0 0.0 1.644 0.037

1993 . ..

1994 . ..

1995 4 395.0 0.3 1.805 0.031

1996 2 400.0 0.0 2.987 0.028

1997 2 410.0 1.2 2.773 0.023

1998 2 400.0 0.0 2.314 0.030

1999 2 477.5 15.8 1.264 0.049
126

Appendix C70.- grey triggerfish

Balistes capriscus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 0 0 0.000

1979 0 0 0.000

1980 1 220.0 0.0 1 220.0 0.0 0.002

1981 3 293.3 23.3 3 293.3 23.3 0.001

1982 15 373.3 28.6 15 373.3 28.6 0.006

1983 14 323.6 18.0 14 323.6 18.0 0.002 0 0 0.000 0.000 0.000 0.000

1984 2 365.0 15.0 2 365.0 15.0 0.002 0 0 0.000 0.000 0.000 0.000

1985 7 344.3 22.2 7 344.3 22.2 0.000 0 0 0.000 0.000 0.000 0.000

1986 3 416.7 38.4 3 416.7 38.4 0.001 0 0 0.000 0.000 0.000 0.000

1987 15 322.7 22.9 15 322.7 22.9 0.002 0 0 0.000 0.000 0.000 0.000

1988 15 311.3 17.2 15 311.3 17.2 0.003 0 0 0.000 0.000 0.000 0.000

1989 5 298.0 13.2 5 298.0 13.2 0.002 8 247.5 8.6 8 247.5 8.6 0.058 0.000 0.036 0.036

1990 8 315.0 14.3 8 315.0 14.3 0.001 1 300.0 0.0 1 300.0 0.0 0.004 0.000 0.004 0.004

1991 27 399.3 18.3 27 399.3 18.3 0.013 0 0 0.000 0.000 0.000 0.000

1992 17 405.3 14.6 17 405.3 14.6 0.004 1 240.0 0.0 1 240.0 0.0 0.011 0.000 0.011 0.011

1993 28 352.5 11.3 28 352.5 11.3 0.013 . . . . . .

1994 17 386.5 10.3 25 341.6 15.1 0.008 . . . . . .

1995 3 400.0 30.6 4 367.5 39.0 0.001 0 0 0.000 0.000 0.000 0.000

1996 3 366.7 14.5 4 340.0 28.6 0.001 0 1 250.0 0.0 0.042 0.000 0.000 0.014

1997 1 430.0 0.0 7 272.9 26.7 0.002 0 0 0.000 0.000 0.000 0.000

1998 2 405.0 45.0 3 343.3 66.9 0.001 0 0 0.000 0.000 0.000 0.000

1999 0 0 0.138 0.000 0.000 0.000

Appendix C71.- queen triggerfish

Balistes vetula

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.001

1980 3 326.7 33.8 0.001

1981 7 337.1 20.0 0.001

1982 1 300.0 0.0 0.001

1983 7 337.1 15.8 0.001 0 0.000 0.000 0.000

1984 0 0.000 0 0.000 0.000 0.000

1985 3 360.0 60.0 0.001 0 0.000 0.000 0.000

1986 2 285.0 45.0 0.000 0 0.000 0.000 0.000

1987 6 325.0 16.9 0.002 1 260.0 0.0 0.027 0.000 0.027

1988 1 250.0 0.0 0.000 3 293.3 6.7 0.020 0.000 0.020

1989 7 317.1 6.1 0.004 8 323.8 8.4 0.021 0.000 0.021

1990 10 323.0 10.4 0.002 3 383.3 16.7 0.012 0.000 0.012

1991 4 325.0 17.6 0.001 2 360.0 10.0 0.072 0.000 0.021

1992 1 270.0 0.0 0.000 0 0.000 0.000 0.000

1993 9 292.2 11.2 0.004 . . . .

1994 6 331.7 20.6 0.002 . . . .

1995 0 0.000 0 0.000 0.000 0.000

1996 1 370.0 0.0 0.000 1 250.0 0.0 0.042 0.000 0.014

1997 3 286.7 29.1 0.005 1 305.0 0.0 0.027 0.000 0.015

1998 1 260.0 0.0 0.000 0 0.000 0.000 0.000

1999 1 283.8 89.2 0.148 0.000 0.040
127

Appendix C72.- ocean triggerfish

Cantherhines sufflamen

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.011

1980 16 412.5 14.8 0.014

1981 44 436.8 10.7 0.005

1982 1 290.0 0.0 0.001

1983 23 443.0 12.4 0.008 0 0.000 0.000 0.000

1984 1 420.0 0.0 0.001 0 0.000 0.000 0.000

1985 19 386.8 23.8 0.004 0 0.000 0.000 0.000

1986 28 439.6 9.7 0.005 0 0.000 0.000 0.000

1987 20 364.0 14.2 0.003 1 420.0 0.0 0.000 0.027 0.027

1988 34 399.4 9.8 0.010 8 348.8 24.6 0.025 0.019 0.044

1989 16 426.3 13.4 0.005 5 382.0 15.6 0.010 0.012 0.022

1990 38 408.7 10.0 0.008 2 350.0 0.0 0.008 0.000 0.008

1991 5 346.0 43.4 0.001 1 400.0 0.0 0.000 0.010 0.010

1992 28 399.6 13.4 0.003 2 300.0 0.0 0.023 0.000 0.023

1993 12 421.7 19.3 0.003 . . . .

1994 4 490.0 56.1 0.001 . . . .

1995 12 398.3 13.1 0.008 0 0.000 0.000 0.000

1996 6 430.0 12.4 0.001 1 300.0 0.0 0.014 0.000 0.014

1997 3 523.3 14.5 0.000 2 340.0 50.5 0.033 0.008 0.033

1998 6 428.3 36.6 0.002 0 0.000 0.000 0.000

1999 0 0.000 0.000 0.000

Appendix C74.- doctorfish

Acanthurus chirurgus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.000

1980 0 0.000

1981 0 0.000

1982 0 0.000

1983 1 230.0 0.0 0.001 41 239.8 4.6 2.632 1.146 1.146

1984 0 0.003 5 278.0 17.1 3.500 1.250 1.250

1985 1 270.0 0.0 0.000 0 0.000 0.000 0.000

1986 4 220.0 10.8 0.001 0 0.000 0.000 0.000

1987 6 255.0 8.5 0.001 9 293.3 21.5 0.216 0.243 0.243

1988 1 260.0 0.0 0.000 95 264.9 3.4 0.113 0.407 0.407

1989 4 252.5 36.4 0.001 106 247.6 3.8 0.219 0.234 0.234

1990 6 280.0 9.7 0.001 74 258.5 4.4 0.449 0.092 0.092

1991 0 0.000 8 208.8 6.4 0.774 0.105 0.105

1992 0 0.000 5 264.0 35.3 0.705 0.044 0.044

1993 0 0.000 . . . .

1994 0 0.000 . . . .

1995 3 243.3 3.3 0.001 13 216.2 4.7 0.583 0.097 0.097

1996 1 250.0 0.0 0.003 4 205.0 5.0 2.192 0.056 0.056

1997 3 246.7 14.5 0.001 22 221.9 7.7 1.549 0.282 0.282

1998 3 260.0 11.5 0.002 15 262.4 7.0 0.621 0.200 0.200

1999 7 223.6 12.9 2.027 0.232 0.232
128

Appendix C75.- spotted seatrout

Cynoscion nebulosus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 33 357.3 9.2 0.002

1977 18 443.9 7.8 0.016

1978 43 374.0 8.6 0.034

1979 14 382.1 14.1 0.018

1980 34 412.1 11.6 0.011

1981 19 425.8 18.1 0.007

1982 144 375.3 5.2 0.028

1983 119 349.0 5.1 0.017

1984 31 356.1 9.2 0.024

1985 24 356.3 11.2 0.009

1986 9 372.2 17.7 0.003

1987 10 412.0 17.7 0.002

1988 0 0.000

1989 1 510.0 0.0 0.002

1990 0 0.000

1991 0 0.000

1992 7 381.4 6.7 0.016

1993 0 0.000

1994 2 360.0 10.0 0.008

1995 12 377.5 6.3 0.019

1996 0 0.000

1997 0 0.000

1998 9 407.8 9.2 0.011

1999

Appendix C76.- permit

Trachinotus falcatus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 0 0 0.000

1979 1 1010.0 0.0 1 1010.0 0.0 0.000

1980 0 0 0.000

1981 3 643.3 63.3 3 643.3 63.3 0.001

1982 1 270.0 0.0 1 270.0 0.0 0.000

1983 3 723.3 26.7 3 723.3 26.7 0.000 0 0 0.000 0.000 0.000 0.000

1984 2 585.0 85.0 2 585.0 85.0 0.001 0 0 0.000 0.000 0.000 0.000

1985 1 710.0 0.0 1 710.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1986 1 580.0 0.0 1 580.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1987 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1988 3 476.7 27.3 3 476.7 27.3 0.000 0 0 0.000 0.000 0.000 0.000

1989 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1990 6 573.3 40.1 6 573.3 40.1 0.001 2 500.0 0.0 2 500.0 0.0 0.008 0.000 0.008 0.008

1991 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1992 1 700.0 0.0 1 700.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1993 0 0 0.000 . . . . . .

1994 0 0 0.000 . . . . . .

1995 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1996 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1997 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1998 1 680.0 0.0 1 680.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1999 0 0 0.000 0.000 0.000 0.000
129

Appendix C77.- african pompano

Alectis ciliaris

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 0 0 0.000

1979 1 680.0 0.0 1 680.0 0.0 0.001

1980 1 300.0 0.0 1 300.0 0.0 0.000

1981 0 0 0.000

1982 2 465.0 115.0 2 465.0 115.0 0.001

1983 1 500.0 0.0 1 500.0 0.0 0.000

1984 0 0 0.000

1985 7 384.3 30.9 7 384.3 30.9 0.004

1986 3 526.7 80.9 3 526.7 80.9 0.006

1987 0 0 0.000

1988 1 620.0 0.0 1 620.0 0.0 0.001

1989 0 0 0.000

1990 2 800.0 10.0 2 800.0 10.0 0.001

1991 6 335.0 8.5 6 335.0 8.5 0.001

1992 0 0 0.000

1993 0 0 0.000

1994 0 0 0.000

1995 0 0 0.000

1996 0 0 0.000

1997 0 0 0.000

1998 0 0 0.000

1999

Appendix C78.- dolphin

Coryphaena hippurus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 422 567.6 9.0 0.464

1977 167 612.9 12.9 0.299

1978 297 614.2 10.4 0.315

1979 118 642.9 20.3 0.242

1980 220 614.9 12.7 0.188

1981 493 567.9 8.7 0.386

1982 344 601.7 7.9 0.480

1983 248 661.9 13.3 0.302

1984 5 660.0 93.8 0.107

1985 561 583.3 4.9 0.335

1986 388 724.0 8.5 0.218

1987 148 614.9 14.5 0.151

1988 218 563.8 9.2 0.206

1989 493 532.3 5.8 0.552

1990 365 590.9 8.4 0.213

1991 448 590.4 7.0 0.222

1992 458 576.4 8.3 0.225

1993 443 527.9 6.2 0.304

1994 506 566.0 5.7 0.297

1995 166 657.5 13.8 0.258

1996 222 575.8 11.6 0.253

1997 202 538.2 10.1 0.290

1998 11 596.4 28.2 0.214

1999
130

Appendix C79.- atlantic bonito

Sarda sarda

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.023

1977 0 0.015

1978 0 0.010

1979 0 0.008

1980 7 475.7 16.6 0.012

1981 28 542.1 28.1 0.011

1982 4 422.5 68.0 0.005

1983 6 478.3 101.4 0.003

1984 0 0.000

1985 3 636.7 55.5 0.001

1986 28 425.4 31.8 0.010

1987 0 0.000

1988 0 0.000

1989 3 316.7 16.7 0.001

1990 0 0.000

1991 4 715.0 55.2 0.002

1992 1 670.0 0.0 0.000

1993 1 620.0 0.0 0.000

1994 0 0.001

1995 0 0.000

1996 0 0.000

1997 1 450.0 0.0 0.002

1998 0 0.000

1999

Appendix C80.- little tunny

Euthynnus alletteratus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.002

1980 0 0.000

1981 0 0.000

1982 1 490.0 0.0 0.000

1983 13 578.5 25.1 0.002

1984 0 0.000

1985 4 677.5 70.8 0.001

1986 1 370.0 0.0 0.000

1987 12 503.3 44.5 0.003

1988 7 452.9 73.8 0.003

1989 6 651.7 40.0 0.002

1990 11 446.4 52.5 0.002

1991 11 569.1 50.4 0.002

1992 8 570.0 50.6 0.002

1993 15 444.0 35.5 0.007

1994 18 476.7 33.4 0.004

1995 2 475.0 175.0 0.001

1996 18 620.0 42.1 0.006

1997 2 490.0 180.0 0.001

1998 6 423.3 38.7 0.006

1999
131

Appendix C81.- sailfish

Istiophorus platypterus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 1 1750 0 0.002

1977 0 0.001

1978 0 0.002

1979 1 1750.0 0.0 0.001

1980 2 1740.0 60.0 0.002

1981 1 1650.0 0.0 0.002

1982 2 1870.0 140.0 0.002

1983 4 1875.0 97.2 0.002

1984 0 0.000

1985 5 1708.0 75.6 0.001

1986 1 2450.0 0.0 0.001

1987 1 1870.0 0.0 0.001

1988 1 1590.0 0.0 0.002

1989 1 1670.0 0.0 0.001

1990 1 1590.0 0.0 0.000

1991 1 1750.0 0.0 0.001

1992 0 0.001

1993 1 1780.0 0.0 0.000

1994 2 1615.0 35.0 0.001

1995 1 1600.0 0.0 0.002

1996 1 1700.0 0.0 0.001

1997 0 0.000

1998 0 0.000

1999

Appendix C82.- skipjack tuna

Katsuwonus pelamis

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.002

1980 0 0.000

1981 0 0.000

1982 0 0.000

1983 11 590.9 21.1 0.002

1984 1 500.0 0.0 0.003

1985 0 0.000

1986 0 0.000

1987 3 660.0 47.3 0.002

1988 1 640.0 0.0 0.000

1989 0 0.002

1990 7 625.7 25.4 0.002

1991 0 0.000

1992 5 560.0 10.5 0.003

1993 2 530.0 20.0 0.001

1994 3 606.7 58.1 0.002

1995 0 0.000

1996 2 585.0 55.0 0.003

1997 1 650.0 0.0 0.001

1998 1 610.0 0.0 0.000

1999
132

Appendix C83.- blackfin tuna

Thunnus atlanticus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 0 0.000

1979 0 0.000

1980 1 580.0 0.0 0.005

1981 0 0.001

1982 3 520.0 65.1 0.003

1983 2 460.0 20.0 0.001

1984 0 0.002

1985 1 730.0 0.0 0.001

1986 7 588.6 71.9 0.002

1987 0 0.000

1988 0 0.000

1989 0 0.000

1990 1 800.0 0.0 0.001

1991 0 0.000

1992 1 780.0 0.0 0.000

1993 5 594.0 51.0 0.004

1994 1 320.0 0.0 0.001

1995 0 0.000

1996 0 0.000

1997 2 560.0 0.0 0.002

1998 2 365.0 15.0 0.006

1999

Appendix C84.- king mackerel

Scomberomorus cavalla

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 42 741.7 20.4 42 741.7 20.4 0.000

1977 11 733.6 35.3 11 733.6 35.3 0.000

1978 6 720.0 58.0 6 720.0 58.0 0.001

1979 6 721.7 79.4 6 721.7 79.4 0.005

1980 9 834.4 42.2 9 834.4 42.2 0.010

1981 10 757.0 66.1 10 757.0 66.1 0.007

1982 11 821.8 36.4 11 821.8 36.4 0.007

1983 9 731.1 26.4 9 731.1 26.4 0.006

1984 0 0 0.007

1985 7 931.4 75.3 7 931.4 75.3 0.003

1986 2 910.0 40.0 2 910.0 40.0 0.002

1987 9 747.8 48.9 9 747.8 48.9 0.009

1988 4 800.0 59.6 4 800.0 59.6 0.002

1989 6 668.3 30.2 6 668.3 30.2 0.004

1990 16 848.1 52.9 16 848.1 52.9 0.009

1991 13 773.8 27.1 13 773.8 27.1 0.006

1992 3 740.0 49.3 3 740.0 49.3 0.002

1993 11 788.2 41.9 11 788.2 41.9 0.004

1994 9 860.0 65.0 9 860.0 65.0 0.004

1995 2 875.0 105.0 2 875.0 105.0 0.005

1996 4 845.0 38.6 4 845.0 38.6 0.009

1997 1 1090.0 0.0 1 1090.0 0.0 0.000

1998 3 880.0 90.7 3 880.0 90.7 0.003

1999
133

Appendix C85.- spanish mackerel

Scomberomorus maculatus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 28 571.4 14.2 0.000

1977 11 606.4 38.5 0.000

1978 4 575.0 32.8 0.002

1979 3 410.0 10.0 0.002

1980 4 505.0 42.5 0.011

1981 13 533.8 25.1 0.005

1982 18 567.8 21.8 0.019

1983 2 495.0 65.0 0.003

1984 0 0.002

1985 3 400.0 15.3 0.001

1986 2 660.0 160.0 0.012

1987 8 463.8 30.2 0.007

1988 1 460.0 0.0 0.002

1989 0 0.000

1990 1 450.0 0.0 0.001

1991 11 550.0 38.7 0.006

1992 9 533.3 21.8 0.003

1993 3 606.7 58.1 0.002

1994 2 415.0 25.0 0.001

1995 3 516.7 18.6 0.002

1996 0 0.000

1997 1 470.0 0.0 0.001

1998 1 510.0 0.0 0.000

1999

Appendix C86.- cero

Scomberomorus regalis

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.000

1977 0 0.000

1978 3 783.3 53.6 0.000

1979 22 495.0 18.9 0.007

1980 72 512.2 13.5 0.008

1981 59 508.8 12.2 0.009

1982 21 546.7 24.2 0.006

1983 31 521.3 19.4 0.004 3 566.7 6.0 0.111 0.000

1984 4 487.5 76.0 0.003 0 0.000 0.000

1985 29 491.0 19.7 0.002 0 0.000 0.000

1986 45 612.7 18.5 0.005 0 0.000 0.000

1987 61 482.0 10.6 0.010 1 500.0 0.0 0.027 0.000

1988 27 505.2 17.1 0.004 9 514.4 3.3 0.054 0.000

1989 8 558.8 35.8 0.002 3 463.3 1.3 0.009 0.000

1990 24 521.7 24.9 0.004 16 513.8 3.7 0.068 0.004

1991 12 480.8 33.8 0.002 7 562.9 9.3 0.108 0.000

1992 29 478.6 17.4 0.005 0 0.000 0.000

1993 57 498.1 10.4 0.013 . . .

1994 22 484.1 18.6 0.003 . . .

1995 17 511.2 20.8 0.005 4 525.0 9.7 0.036 0.000

1996 6 476.7 18.2 0.001 6 488.3 5.0 0.097 0.000

1997 11 495.5 34.1 0.002 0 0.006 0.000

1998 9 592.2 31.5 0.003 3 436.7 2.1 0.041 0.000

1999 0 0.000 0.000
134

Appendix C87.- greater amberjack

Seriola dumerili

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 1 1060.0 0.0 1 1060.0 0.0 0.000

1979 4 845.0 119.1 4 845.0 119.1 0.002

1980 4 877.5 76.6 4 877.5 76.6 0.003

1981 25 970.8 62.3 25 970.8 62.3 0.005

1982 11 1106.4 22.5 11 1106.4 22.5 0.002

1983 7 835.7 139.9 7 835.7 139.9 0.001 0 0 0.000 0.000 0.000 0.000

1984 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1985 4 467.5 90.6 4 467.5 90.6 0.000 0 0 0.000 0.000 0.000 0.000

1986 7 702.9 65.1 7 702.9 65.1 0.001 0 0 0.000 0.000 0.000 0.000

1987 15 1058.7 50.9 15 1058.7 50.9 0.003 0 0 0.000 0.000 0.000 0.000

1988 1 1020.0 0.0 1 1020.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1989 6 596.7 164.7 6 596.7 164.7 0.002 0 0 0.000 0.000 0.000 0.000

1990 0 1 400.0 0.0 0.000 2 1675.0 155.0 2 1675.0 155.0 0.000 0.008 0.008 0.008

1991 8 1017.5 24.5 9 953.3 67.7 0.001 0 0 0.000 0.000 0.000 0.000

1992 0 0 0.000 0 0 0.000 0.000 0.000 0.000

1993 1 1000.0 0.0 1 1000.0 0.0 0.000 . . . . . .

1994 2 945.0 75.0 2 945.0 75.0 0.000 . . . . . .

1995 2 1110.0 110.0 2 1110.0 110.0 0.001 0 0 0.000 0.000 0.000 0.000

1996 2 1050.0 20.0 3 900.0 150.4 0.002 0 0 0.000 0.000 0.000 0.000

1997 0 1 670.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1998 1 1010.0 0.0 1 1010.0 0.0 0.000 0 0 0.000 0.000 0.000 0.000

1999 0 0 0.000 0.000 0.000 0.000

Appendix C88.- tripletail

Lobotes surinamensis

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0 0.000

1977 0 0 0.000

1978 0 0 0.000

1979 0 0 0.000

1980 0 0 0.000

1981 4 380.0 44.2 4 380.0 44.2 0.004

1982 5 372.0 32.0 5 372.0 32.0 0.006

1983 0 0 0.000

1984 0 0 0.000

1985 1 370.0 0.0 1 370.0 0.0 0.000

1986 4 402.5 8.5 4 402.5 8.5 0.001

1987 0 0 0.001

1988 0 0 0.000

1989 3 366.7 47.0 3 366.7 47.0 0.001

1990 2 430.0 80.0 2 430.0 80.0 0.002

1991 7 434.3 26.4 7 434.3 26.4 0.002

1992 3 480.0 45.8 3 480.0 45.8 0.001

1993 2 350.0 50.0 2 350.0 50.0 0.002

1994 9 430.0 11.9 9 430.0 11.9 0.003

1995 1 480.0 0.0 1 480.0 0.0 0.003

1996 1 400.0 0.0 2 385.0 15.0 0.005

1997 0 1 340.0 0.0 0.001

1998 1 480.0 0.0 1 480.0 0.0 0.001

1999
135

Appendix C89.- wahoo

Acanthocybium solandri

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 0 0.001

1977 3 940.0 181.5 0.001

1978 3 860.0 113.6 0.002

1979 3 1066.7 23.3 0.006

1980 10 1056.0 67.0 0.004

1981 6 931.7 106.4 0.003

1982 8 1015.0 69.9 0.003

1983 4 1150.0 143.6 0.003

1984 2 1130.0 90.0 0.021

1985 4 1127.5 57.9 0.001

1986 8 1003.8 45.8 0.003

1987 5 1162.0 74.8 0.004

1988 7 985.7 63.8 0.008

1989 3 1150.0 75.1 0.001

1990 2 1015.0 5.0 0.003

1991 9 1134.4 43.5 0.005

1992 4 1055.0 26.3 0.002

1993 8 1183.8 91.3 0.006

1994 7 1002.9 79.8 0.004

1995 2 1095.0 25.0 0.003

1996 6 1045.0 86.1 0.005

1997 1 1200.0 0.0 0.002

1998 2 890.0 210.0 0.003

1999

Appendix C90.- spiny lobster

Panulirus Argus

 CREEL Survey Data RVC Survey Data

 Lbar CPUE Lbar CPUE

Year n Legal S.E. n Actual S.E. Actual n Legal S.E. n Actual S.E. IM Mat Legal Actual

1976 313 838.9 3.5 313 838.9 3.5 1.067

1977 674 855.2 3.0 674 855.2 3.0 0.814

1978 1175 846.4 2.2 1175 846.4 2.2 0.880

1979 658 842.9 2.8 658 842.9 2.8 1.059

1980 1350 844.9 2.0 1350 844.9 2.0 0.944

1981 771 859.4 3.0 771 859.4 3.0 0.672

1982 1695 829.5 1.6 1695 829.5 1.6 0.926

1983 700 829.8 2.5 700 829.8 2.5 0.759

1984 624 836.3 2.5 624 836.3 2.5 0.603

1985 373 845.9 3.4 373 845.9 3.4 0.796

1986 1190 852.2 2.0 1190 852.2 2.0 0.813

1987 879 843.0 2.4 879 843.0 2.4 0.822

1988 732 835.7 2.3 732 835.7 2.3 0.761

1989 873 848.0 2.3 873 848.0 2.3 0.863

1990 1653 857.2 1.8 1653 857.2 1.8 0.780

1991 278 869.7 4.8 278 869.7 4.8 1.359

1992 107 854.4 7.2 107 854.4 7.2 1.523

1993 231 857.9 4.6 231 857.9 4.6 1.079

1994 295 861.6 4.8 295 861.6 4.8 1.443

1995 429 863.2 3.1 429 863.2 3.1 2.184

1996 329 859.4 4.3 329 859.4 4.3 1.416

1997 168 844.9 5.2 168 844.9 5.2 1.222

1998 130 869.5 8.1 130 869.5 8.1 1.375

1999
136

Appendix D1: SAS programs for processing reef fish visual census data and estimating average length in the exploited phase and

catch per unit effort (see Figure 21).

/*program fkdat3.sas*//*processes FK reef fish 99 bio data*//*applies 2-stage numbers-at-length algorithm*//*bnp region, all species*/options nodate nonumber;data r1; infile '/us4/sgsmith/reefsamp/rf99/under5.txt'; input smpcode$ 1-14 species$ 17-24 @33 catch @40 meansize @48 minsize @56 maxsize;run;proc sort; by species;quit;data s1; infile '/us4/sgsmith/reefsamp/species.prn'; input species$ 1-8 latin$ 16-47 common2$ 49-77 family$ 79-99 specnum 101-104 famnum 111-113 bbtrawl 121 creel 129 rvc 137; if rvc=1; keep species specnum;run;proc sort; by species;quit;/*proc print;quit;*/data s2; merge r1 s1; by species; if catch=. then delete;run;/*proc print;quit;*//*proc freq; tables species;quit;*/proc sort; by smpcode;quit;/*proc print;quit;*/data p1; infile '/us4/sgsmith/reefsamp/rf99/rf99_fkphys.txt'; input month 1-2 day 4-5 yr 7-8 rfblk$ 10-16 stn 18 div$ 20 smpcode$ 22-35 observ 37-38 @40 ddlat @50 ddlong @60 depth regkey 67 subreg 69 spa 71-72 reeftype 74; if yr=99 then yr=1999; else yr=yr;run;proc sort; by smpcode;quit;/*proc print;quit;*/data m1; merge p1 s2; by smpcode;

run;data m2; set m1; if stn=. then delete; if regkey=1; drop smpcode observ subreg spa species;run;/*proc print;quit;*/proc sort; by yr month day rfblk stn div specnum;quit;proc transpose out=m3; by yr month day rfblk stn div ddlat ddlong depth regkey reeftype; var catch; id specnum;quit;proc transpose out=m4; by yr month day rfblk stn div ddlat ddlong depth regkey reeftype;quit;data m5; set m4; specode=_name_; if catch=. then catch=0; drop _name_;run;/*proc print;quit;*/data m6; set m5; format ddlat ddlong 9.5; format depth 6.3; file '/us4/sgsmith/reefsamp/tmp1.txt'; put yr 1-4 month 6-7 day 9-10 rfblk$ 12-18 stn 20-21 div$ 23 @25 ddlat @35 ddlong @45 depth regkey 52 reeftype 54 specode$ 56-59 @61 catch;run;data m7; infile '/us4/sgsmith/reefsamp/tmp1.txt'; input yr 1-4 month 6-7 day 9-10 rfblk$ 12-18 stn 20-21 div$ 23 @25 ddlat @35 ddlong @45 depth regkey 52 reeftype 54 specnum 57-59 @61 catch;run;proc sort; by yr month day rfblk stn div specnum;quit;/*proc print;quit;*/data m8; merge m2 m7; by yr month day rfblk stn div specnum catch;run;proc sort; by specnum;quit;/*proc print;
137

quit;*/data s3; set s1;run;proc sort; by specnum;quit;/*proc print;quit;*/data m9; merge m8 s3; by specnum; if catch=. then delete;run;/*proc print;quit;*/data r2; set m9; if meansize=. then meansize=0; else meansize=meansize; if minsize=0 then minsize=.; else minsize=minsize; if maxsize=0 then maxsize=.; else maxsize=maxsize;run;/*proc print;quit;*//*proc univariate; var minsize maxsize;quit;*/data r3a; set r2; if minsize=.; L3=meansize; A3=catch; L1=.; A1=.; L2=.; A2=.; L4=.; A4=.; L5=.; A5=.; drop catch minsize meansize maxsize;run;data r3b; set r2; if minsize ne .; if catch=1; L3=meansize; A3=catch; L1=.; A1=.; L2=.; A2=.; L4=.; A4=.; L5=.; A5=.; drop catch minsize meansize maxsize;run;data r3c;

 set r2; if minsize ne .; if catch=2; L1=minsize; A1=1; L5=maxsize; A5=1; L2=.; A2=.; L4=.; A4=.; L3=.; A3=.; drop catch minsize meansize maxsize;run;data r3d; set r2; if minsize ne .; if catch=3; L1=minsize; A1=1; L3=meansize; A3=1; L5=maxsize; A5=1; L2=.; A2=.; L4=.; A4=.; drop catch minsize meansize maxsize;run;data r3e; set r2; if minsize ne .; if catch=4; L1=minsize; A1=1; L3=meansize; A3=2; L5=maxsize; A5=1; L2=.; A2=.; L4=.; A4=.; drop catch minsize meansize maxsize;run;data r3f; set r2; if minsize ne .; if 5<=catch<=99; L1=minsize; A1=1; L3=meansize; A3=0.5*catch; L5=maxsize; A5=1; L2=(L3+L1)/2; A2=(catch-(A1+A3+A5))/2; L4=(L3+L5)/2; A4=A2; drop catch minsize meansize maxsize;
138

run;data r3g; set r2; if minsize ne .; if catch>99; L1=minsize; A1=0.01*catch; L3=meansize; A3=0.5*catch; L5=maxsize; A5=0.01*catch; L2=(L3+L1)/2; A2=(catch-(A1+A3+A5))/2; L4=(L3+L5)/2; A4=A2; drop catch minsize meansize maxsize;run;/*proc print;quit;*/data r4; set r3a r3b r3c r3d r3e r3f r3g;run;/*proc print;quit;*/data r5a; set r4; if L1=. then delete; len=L1; abund=A1; keep yr month day regkey depth reeftype rfblk stn div ddlat ddlong specnum species len abund;run;data r5b; set r4; if L2=. then delete; len=L2; abund=A2; keep yr month day regkey depth reeftype rfblk stn div ddlat ddlong specnum species len abund;run;data r5c; set r4; if L3=. then delete; len=L3; abund=A3; keep yr month day regkey depth reeftype rfblk stn div ddlat ddlong specnum species len abund;run;data r5d; set r4; if L4=. then delete; len=L4; abund=A4; keep yr month day regkey depth reeftype rfblk stn div ddlat ddlong specnum species len abund;run;data r5e; set r4; if L5=. then delete; len=L5; abund=A5; keep yr month day regkey depth reeftype rfblk stn div

 ddlat ddlong specnum species len abund;run;/*proc print;quit;*/data r6; set r5a r5b r5c r5d r5e;run;proc sort; by specnum yr rfblk stn div len;quit;/*proc print;quit;*/proc means noprint data=r2; class specnum yr rfblk stn; var catch; output out=a1 mean=avnum sum=sumnum;quit;data a2; set a1; if _type_=15; drop _type_ _freq_;run;/*proc print;quit;*/data r7; merge r6 a2; by specnum yr rfblk stn;run;/*proc print;quit;*/data r8; set r7; if abund=0 then num=0; else num=(abund/sumnum)*avnum;run;/*proc univariate; var len num;quit;proc print;quit;*/data r9; set r8; format ddlat ddlong 9.5; format depth 6.3 len 6.1 num 10.3; file '/us4/sgsmith/reefsamp/rf99/fk99/fkdat3_99.txt'; put yr 1-4 month 6-7 day 9-10 rfblk$ 12-18 stn 20-28 @30 ddlat @40 ddlong @50 depth regkey 57 reeftype 59 species$ 61-68 specnum 70-72 @74 len @81 num;run;/*program fkdat3a_bnp.sas*//*processes BNP region reef fish data*//*creates lbar analysis dataset*//*all species*/options nodate nonumber;data b1; infile '/us4/sgsmith/reefsamp/fkdat3_7996.txt'; input yr 1-4 month 6-7 day 9-10 rfblk$ 12-18 specnum 20-22 @24 ddlat @34 ddlong @44 depth regkey 51 reeftype 53 species$ 55-62 stn$ 64-72 @74 len @81 num;
139

 if len>0; keep yr specnum species len num;run;data b2; infile '/us4/sgsmith/reefsamp/rf9798/fkdat3_9798.txt'; input yr 1-4 month 6-7 day 9-10 rfblk$ 12-18 stn$ 20-28 @30 ddlat @40 ddlong @50 depth regkey 57 reeftype 59 species$ 61-68 specnum 70-72 @74 len @81 num; if len>0; keep yr specnum species len num;run;data b3; infile '/us4/sgsmith/reefsamp/rf99/fk99/fkdat3_99.txt'; input yr 1-4 month 6-7 day 9-10 rfblk$ 12-18 stn$ 20-28 @30 ddlat @40 ddlong @50 depth regkey 57 reeftype 59 species$ 61-68 specnum 70-72 @74 len @81 num; if len>0; keep yr specnum species len num;run;data b4; set b1 b2 b3;run;proc sort; by specnum yr;quit;/*proc print;quit;*/data b5; set b4; format len 6.1 num 10.3; file '/us4/sgsmith/bnp/rvc_lbardat.txt'; put yr 1-4 species$ 6-13 specnum 15-17 @19 len @26 num;run;/*program rvc_lbar.sas*/

/*processes BNP region reef fish data*//*computes lbar*//*by species and year*/options nodate nonumber;data b1; infile '/us4/sgsmith/bnp/rvc_lbardat.txt'; input yr 1-4 species$ 6-13 specnum 15-17 @19 len @26 num; if specnum=133;run;data r1; set b1; if yr<1985 and len<19 then delete; if yr>=1985 and len<30 then delete; /*if yr>=1990 and len<51 then delete;*/ /*if len>100 then delete;*/run;/*proc print;quit;*//*proc univariate plot; var len;quit;*/proc means noprint vardef=wdf; class species yr; var len; weight num; id specnum; output out=r2 mean=lbar var=s2 sumwgt=n;

quit;data r3; set r2; if _type_=3; se_lbar=sqrt(s2/n); format lbar 7.2 se_lbar 6.2; drop _type_ _freq_;run;/*proc print;quit;*/data y1; infile '/us4/sgsmith/bnp/rvc_yrts.txt'; input yr id;run;data r4; merge r3 y1; by yr; if (yr<1993 or yr>1994) and (n=.) then n=0; else n=n; if specnum=. then specnum=133; else specnum=specnum; if species=' ' then species='OCY CHRY'; else species=species; drop id;run;proc print noobs; var specnum species yr n lbar se_lbar; title ' ';quit;/*program fkdat3b_bnp.sas*/

/*processes BNP region reef fish data*//*creates cpue analysis dataset*//*all species, reeftypes 4-6*/options nodate nonumber;data b1; infile '/us4/sgsmith/reefsamp/fkdat3_7996.txt'; input yr 1-4 month 6-7 day 9-10 rfblk$ 12-18 specnum 20-22 @24 ddlat @34 ddlong @44 depth regkey 51 reeftype 53 species$ 55-62 stn$ 64-72 @74 len @81 num; if reeftype>=4; keep yr specnum species len num rfblk stn;run;data b2; infile '/us4/sgsmith/reefsamp/rf9798/fkdat3_9798.txt'; input yr 1-4 month 6-7 day 9-10 rfblk$ 12-18 stn$ 20-28 @30 ddlat @40 ddlong @50 depth regkey 57 reeftype 59 species$ 61-68 specnum 70-72 @74 len @81 num; if reeftype>=4; keep yr specnum species len num rfblk stn;run;data b3; infile '/us4/sgsmith/reefsamp/rf99/fk99/fkdat3_99.txt'; input yr 1-4 month 6-7 day 9-10 rfblk$ 12-18 stn$ 20-28 @30 ddlat @40 ddlong @50 depth regkey 57 reeftype 59 species$ 61-68 specnum 70-72 @74 len @81 num; if reeftype>=4; keep yr specnum species len num rfblk stn;run;data b4; set b1 b2 b3;
140

run;proc sort; by specnum yr;quit;/*proc print;quit;*/data b5; set b4; format len 6.1 num 10.3; file '/us4/sgsmith/bnp/rvc_cpuedat.txt'; put yr 1-4 species$ 6-13 specnum 15-17 rfblk$ 19-25 stn$ 27-35 @37 len @44 num;run;/*program rfds1.sas*/

/*2-stage SRS design, FK reef fish data*//*species-lifestage*/options nodate nonumber;data rd1; infile '/us4/sgsmith/bnp/rvc_cpuedat.txt'; input yr 1-4 species$ 6-13 specnum 15-17 rfblk$ 19-25 stn$ 27-35 @37 len @44 num; if specnum=232;run;/*proc print;quit;*/data rd2; set rd1; if len<87.7 then jnum=num; else jnum=0; if len=0 or len>=87.7 then anum=num; else anum=0; if len=0 or len>=35.0 then enum=num; else enum=0; /*if len=0 or (yr<1990 and len>=30) or (yr>=1990 and len>=51) then enum=num; else enum=0;*/run;/*proc print;quit;*/proc means noprint; class yr rfblk stn; var jnum anum enum; id species specnum; output out=r1 sum=j_abund a_abund e_abund;quit;data r2; set r1; if _type_=7; drop _type_ _freq_ ;run;/*proc print;quit;*/proc means noprint; class yr rfblk; var e_abund; id species specnum; output out=r3 mean=cpi var=vari;quit;data r4; set r3; if _type_=3;

 m=_freq_; if vari=. then vari=0; drop _type_ _freq_;run;/*proc print;quit;*//* *//*SRS scheme*//* */proc means data=r4 noprint; class yr; var cpi vari m; id species specnum; output out=r5 mean=avcp mean1 m sum=sum1 varm nm var=s1 var1 var2;quit;data r6; set r5; if _type_=1; n=_freq_; s2=varm/n; keep yr species specnum avcp m n nm s1 s2;run;/*proc print;quit;*/data r7; set r6; mtot=226; ntot=978; fn=n/ntot; fm=m/mtot; vbar_cp=((1-fn)*s1/n)+((fn*(1-fm)*s2)/nm); se_cp=sqrt(vbar_cp); cv_cp=(se_cp/avcp)*100; std2=sqrt(s2); su=s1-(s2/Mtot); stdu=sqrt(su); if stdu ne 0 then mopt=std2/stdu; else mopt=0; format avcp se_cp 9.5; format m mopt 5.2; format cv_cp 6.2;run;data y1; infile '/us4/sgsmith/bnp/rvc_yrts.txt'; input yr id;run;data r8; merge r7 y1; by yr; if (yr<1993 or yr>1994) and n=. then avcp=0; else avcp=avcp; if specnum=. then specnum=232; else specnum=specnum; if species=' ' then species='SPH BARR'; else species=species; drop id;run;proc print noobs; var specnum species yr n m mopt nm avcp se_cp cv_cp; title 'SRS 2-stage';quit;
141

Appendix D2: SAS programs for processing BNP recreational creel survey data and estimating average length in the exploited phaseand catch per unit effort (see Figure 21)./*program creellbar.sas*/

/*processes creel fish length data, 1976-1998*//*computes lbar by species and year*/options nonumber nodate;data f1a; infile '/us4/sgsmith/bnp/creel/creel_len_7687.prn'; input month 7-8 day 14-15 yr 22-23 intnum 26-30 specode 33-36 len 40-43 common$ 51-72 area 75; if area=0 then delete; drop common intnum;run;data f1b; infile '/us4/sgsmith/bnp/creel/creel_len_8891.prn'; input month 6-7 day 13-14 yr 21-22 intnum 25-29 specode 32-35 len 40-43 common$ 52-71 area 74; if area=0 then delete; drop common intnum;run;/*proc print;quit;*/data f1c; infile '/us4/sgsmith/bnp/creel/CreelLen.txt'; input obs 1-6 month 12-13 day 19-20 yr 24-27 area 32-33 specode 40-43 common$ 49-69 len 72-74; if specode=9999 then delete; if area=. then area=37; else area=area; if area=17 or area=36 or area=37 then delete; if yr<1999; drop obs common;run;data f2; set f1a f1b f1c; if len=0 then delete;run;/*proc print;quit;*/proc sort; by specode;quit;data s1; infile '/us4/sgsmith/bnp/creel/creelspecies.prn'; input specode specnum;run;proc sort; by specode;quit;/*proc print;quit;*/data f3; merge s1 f2; by specode; if len=. then delete;run;proc sort; by specnum;quit;

/*proc print;quit;*//*proc freq; tables specode;quit;*/data r1; infile '/us4/sgsmith/reefsamp/species.prn'; input species$ 1-8 latin$ 16-47 common2$ 49-77 family$ 79-99 specnum 101-104 famnum 111-113 bbtrawl 121 creel 129 rvc 137; if creel=1; keep species specnum;run;proc sort; by specnum;quit;/*proc print;quit;*/data r2; merge r1 f3; by specnum; if len=. then delete;run;proc sort; by species;quit;/*proc print;quit;*/data r3; set r2; if yr=76 then yr=1976; else if yr=77 then yr=1977; else if yr=78 then yr=1978; else if yr=79 then yr=1979; else if yr=80 then yr=1980; else if yr=81 then yr=1981; else if yr=82 then yr=1982; else if yr=83 then yr=1983; else if yr=84 then yr=1984; else if yr=85 then yr=1985; else if yr=86 then yr=1986; else if yr=87 then yr=1987; else if yr=88 then yr=1988; else if yr=89 then yr=1989; else if yr=90 then yr=1990; else if yr=91 then yr=1991; else yr=yr; if specnum=16; if yr<1994 and len<22 then delete; if yr>=1994 and len<30 then delete; /*if yr>=1990 and len<51 then delete;*/ /*if len>100 then delete;*/run;/*proc univariate plot; var len;quit;*/proc means noprint; class species yr;
142

 var len; id specnum; output out=r4 n=n mean=lbar stderr=se_lbar;quit;data r5; set r4; if _type_=3; if se_lbar=. then se_lbar=0; format lbar 7.2 se_lbar 6.2; drop _type_ _freq_;run;data y1; infile '/us4/sgsmith/bnp/creel/yr_ts.txt'; input yr id;run;data r6; merge r5 y1; by yr; if n=. then n=0; else n=n; if specnum=. then specnum=16; else specnum=specnum; if species=' ' then species='BAL CAPR'; else species=species; drop id;run;proc print noobs; title ' ';quit;/*program creelcpdat.sas*/

/*processes creel cpue data, 1976-1998*//*creates analysis dataset, all species*/options nonumber nodate;data c1; infile '/us4/sgsmith/bnp/creel/creelint_7687.prn'; input @1 intid persons 17-18 eff_hrs 25-26 trgspec 31-36 area 41-42 party 51-52 month 58-59 day 65-66 yr 73-74 specode 76-80 catch 81-85; if eff_hrs=0 then delete; if area=17 or area>30 then delete; trip=intid*10; yr=yr+1900; type=0; drop intid;run;data c2; infile '/us4/sgsmith/bnp/creel/creelint_8891.prn'; input @1 intid persons 18-19 eff_hrs 26-27 trgspec 32-37 area 42-43 party 53-54 month 60-61 day 66-67 yr 74-75 specode 76-80 catch 81-85; if area=17 then delete; trip=intid*10; yr=yr+1900; type=0; drop intid;run;data c3a; infile '/us4/sgsmith/bnp/creel/Census_91-93.prn'; input trip 3-8 specode 14-18 catch 26-30; if specode=. then specode=0;

 else specode=specode;run;proc sort; by trip;quit;data c3b; infile '/us4/sgsmith/bnp/creel/Trip_91-93.prn'; input trip 2-7 month 9-10 day 12-13 yr 15-18 party 32-33 persons 45-46 eff_hrs 56-57 trgspec 64-69 area 79-80;run;proc sort; by trip;quit;data c3; merge c3a c3b; by trip; if specode=. then specode=0; else specode=specode; if catch=. then catch=0; else catch=catch; if yr=. or yr>1993 then delete; if eff_hrs=. then delete; if area=17 then delete; if party=. then party=0; else party=party; type=0;run;data c4a; infile '/us4/sgsmith/bnp/creel/Census.prn'; input trip 2-7 month 13-14 day 16-17 yr 19-22 specode 28-32 catch 41-45; if specode=. then specode=0; else specode=specode;run;proc sort; by trip yr month day;quit;data c4b; infile '/us4/sgsmith/bnp/creel/Trip.prn'; input trip 3-8 month 13-14 day 16-17 yr 19-22 party 37-38 type 48-49 persons 61-62 eff_hrs 73-74 trgspec 81-86 area 95-96;run;proc sort; by trip yr month day;quit;data c4; merge c4a c4b; by trip yr month day; if specode=. then specode=0; else specode=specode; if catch=. then catch=0; else catch=catch; if eff_hrs=. then delete; if yr<1993 or yr>1998 then delete; if party>7 or party=. then party=0; else party=party; if type=. then type=0; else type=type; if area=17 or area=36 or area=37 then delete;run;data c5; set c1 c2 c3 c4;
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run;proc sort; by yr month day trip;quit;/*proc print;quit;*//*proc freq; tables yr party type area;quit;*//*proc univariate plot; var trip catch;quit;*/proc sort; by specode;quit;data s1; infile '/us4/sgsmith/bnp/creel/creelspecies.prn'; input specode specnum;run;proc sort; by specode;quit;/*proc print;quit;*/data c6; merge s1 c5; by specode; if specnum=. then delete;run;proc sort; by specnum;quit;/*proc print;quit;*//*proc freq; tables specode;quit;*/data r1; infile '/us4/sgsmith/reefsamp/species.prn'; input species$ 1-8 latin$ 16-47 common$ 49-77 family$ 79-99 specnum 101-104 famnum 111-113 bbtrawl 121 creel 129 rvc 137; if creel=1; keep species specnum family;run;proc sort; by specnum;quit;/*proc print;quit;*/data r2; merge r1 c6; by specnum;run;proc sort; by species;quit;/*proc print;quit;*/data r3; set r2; if party=0 then delete;run;

proc sort; by yr trip;quit;data f1; set r3; if specnum=269 and party<6; if trgspec=1211 then lob=0; else lob=1; keep specnum yr trip lob;run;proc sort; by yr trip;quit;data f2; merge f1 r3; by yr trip; if lob=1 then delete; if lob=0 then party=7; else party=party; drop lob;run;data f3; set f2; if (specnum=266 or specnum=270 or specnum=271) and party<6; if trgspec=1211 then lob=0; else lob=1; keep specnum yr trip lob;run;proc sort; by yr trip;quit;data f4; merge f3 f2; by yr trip; if lob=1 then delete; if lob=0 then party=7; else party=party; drop lob;run;/*proc print;quit;*//*proc freq; tables species*party;quit;*/data f5; set f4; if party>5; if specnum=700 then unk=1; else unk=0; if specnum=266 then con=1; else con=0; if 269<=specnum<=271 then lob=1; else lob=0; if unk=0 and con=0 and lob=0 then fsh=1; else fsh=0;run;proc means noprint; class yr trip; var lob fsh con unk; id party; output out=f6 sum=lob fsh con unk;quit;
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data f7; set f6; if _type_=3; if lob>0 and fsh=0 then gear='DL'; else if lob=0 and fsh>0 then gear='DF'; else if lob>0 and fsh>0 then gear='DM'; else if lob=0 and fsh=0 and con>0 then gear='DC'; else gear='DU'; keep yr trip gear;run;data f8; merge f5 f7; by yr trip; if gear='DU' and trgspec=1211 then gear='DL'; else if gear='DU' and (trgspec=0 or trgspec=9999) then gear='DM'; else if gear='DU' and 5300<=trgspec<=8800 then gear='DF'; else if gear='DU' and trgspec=20 then gear='DC'; else gear=gear; if gear='DU' or gear='DC' then delete; drop lob fsh unk con;run;data f9; set f4; if party<=5; if trip=. then delete; gear='HK';run;data f10; set f8 f9; if specnum=700 then species='UNK SPE.';run;proc sort; by yr trip;quit;data u1; set f10; if specnum=700 then unk=1; else unk=0; if specnum ne 700 then oth=1; else oth=0;run;proc means noprint; class yr trip; var unk oth; output out=u2 sum=unk oth;quit;data u3; set u2; if _type_=3; if unk>0 and oth=0 then crct=1; else crct=0; keep yr trip crct;run;data u4; merge f10 u3; by yr trip; if crct=1 and catch>0 then delete; drop crct;run;data a1; set u4; if area=16 or 24<=area<=25 or 34<=area<=35 then delete;

 if 1<=area<=4 or 7<=area<=11 or area=18 or 20<=area<=22 or area=27 or area=31 or area=38 then reg='BAY'; else if area=5 or area=19 or area=23 then reg='REF'; else if area=6 or area=26 then reg='PEL'; else if 12<=area<=14 or area=28 or area=32 then reg='BRF'; else if area=15 then reg='PRF'; else if 29<=area<=30 or area=33 then reg='BPL'; else reg='UNK'; if reg='BPL' then delete;run;proc sort; by yr trip;quit;data a2; set a1; if 87<=specnum<=98 or specnum=100 or specnum=123 or 125<=specnum<=126 or 129<=specnum<=133 or specnum=269 or 198<=specnum<=203 or 214<=specnum<=218 or specnum=227 or specnum=706 or specnum=724 or specnum=731 then reef=1; else reef=0; if specnum=34 or 37<=specnum<=38 or specnum=286 or specnum=290 or specnum=303 or 306<=specnum<=307 or 193<=specnum<=194 or specnum=309 then pela=1; else pela=0; if reef=0 and pela=0 then mixd=1; else mixd=0;run;proc means noprint; class yr trip; var reef pela mixd; output out=a3 sum=reef pela mixd;quit;data a4; set a3; if _type_=3; if reef>0 and pela=0 then spc='R'; else if pela>0 and reef=0 then spc='P'; else if reef>0 and pela>0 then spc='B'; else spc='U'; keep yr trip spc;run;data a5; merge a1 a4; by yr trip; if spc='B' and (reg='BAY' or reg='BRF') then delete; if spc='B' and (reg='PEL' or reg='REF') then reg='PRF'; else reg=reg; if spc='P' then reg='PEL'; else reg=reg; if spc='R' and (reg='PEL' or reg='PRF') then reg='REF'; else reg=reg;run;proc sort; by gear species reg;quit;/*proc print;quit;*//*proc freq; by gear; tables species*reg;quit;*/data a6;
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 set a5; file '/us4/sgsmith/bnp/creel/creel_catch.txt'; put yr 1-4 trip 6-11 specnum 13-15 species$ 17-24 catch 26-30;run;data z1; set a5; if specnum=713 and catch>0 then trig=1; else trig=0; if specnum=702 and catch>0 then jack=1; else jack=0; if specnum=706 and catch>0 then grunt=1; else grunt=0; if specnum=722 and catch>0 then chub=1; else chub=0; if specnum=724 and catch>0 then snap=1; else snap=0; if specnum=729 and catch>0 then tuna=1; else tuna=0; if specnum=731 and catch>0 then group=1; else group=0; if specnum=711 and catch>0 then porgy=1; else porgy=0;run;/*proc print;quit;*//*proc freq; tables specnum*pres;quit;*/proc means noprint; class yr trip; var trig jack grunt chub snap tuna group porgy; id persons eff_hrs gear reg; output out=z2 sum=trig jack grunt chub snap tuna group porgy;quit;data z3; set z2; if _type_=3; drop _type_ _freq_;run;/*proc print;quit;*//*proc freq; tables gear*reg;quit;*/data z4; set z3; file '/us4/sgsmith/bnp/creel/creel_effort.txt'; put yr 1-4 trip 6-11 gear$ 13-14 reg$ 16-18 persons 20-21 eff_hrs 23-24 trig 26 jack 28 grunt 30 chub 32 snap 34 tuna 36 group 38 porgy 40;run;/*program creelcpue.sas*//*processes creel cpue data, 1976-1998*//*computes CPUE by year*//*single species and gear*/options nonumber nodate;data c1; infile '/us4/sgsmith/bnp/creel/creel_catch.txt'; input yr 1-4 trip 6-11 specnum 13-15 species$ 17-24 catch 26-30;

 format species $char8.;run;data c2; set c1; if specnum=232;run;proc sort; by yr trip;quit;data e1; infile '/us4/sgsmith/bnp/creel/creel_effort.txt'; input yr 1-4 trip 6-11 gear$ 13-14 reg$ 16-18 persons 20-21 eff_hrs 23-24 trig 26 jack 28 grunt 30 chub 32 snap 34 tuna 36 group 38 porgy 40; effort=persons*eff_hrs; drop persons eff_hrs;run;proc sort; by yr trip;quit;data e2; merge c2 e1; by yr trip;run;/*proc print;quit;*//* *//*hook and line*//* */data r1h; set e2; /*if porgy=1 then delete;*/ if gear='HK'; if specnum=. and (reg='BRF' or reg='PRF') then delete; if reg='REF' or reg='BRF' or reg='PRF' or reg='BAY' or reg='PEL'; if specnum=. then specnum=232; else specnum=specnum; if species=' ' then species='SPH BARR'; else species=species; if catch=. then catch=0; else catch=catch; cpue=catch/effort; /*if cpue>3 then delete;*/ keep yr trip specnum species gear cpue;run;/* *//*diving gears*//* *//*data r1d; set e2; if group=1 then delete; if gear='DF' or gear='DM'; if specnum=. and gear='DM' then delete; gear='DV'; if specnum=. and (reg='BRF' or reg='PRF') then delete; if reg='REF'; if specnum=. then specnum=218; else specnum=specnum; if species=' ' then species='MYC VENE'; else species=species; if catch=. then catch=0; else catch=catch;
146

 cpue=catch/effort; if cpue>20 then delete; keep yr trip specnum species gear cpue;run;*//*proc print;quit;*//*proc univariate plot; var cpue;quit;*/proc means noprint; class specnum yr; var cpue; id species gear; output out=r2 n=n mean=avcp var=varcp;quit;data r3; set r2; if _type_=3; vbar_cp=varcp/n; se_cp=sqrt(vbar_cp); if avcp>0 then cv_cp=(se_cp/avcp)*100; else cv_cp=0;run;proc print noobs; var specnum species gear yr n avcp se_cp cv_cp;quit;
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Appendix E. - PROGRAM LBARC******************************************************************C COMPUTES Z FROM THE LENTH-BASED FORMULA OF *C EHRHARDT, N.M. and J.S. AULT. 1992. *C Transactions of the American Fisheries Society 121:115-122. *C ~/mab/lbar/lbar.f *C *C****************************************************************** open(unit=4,file='lbar_gr_black.dat',status='old') READ(4,*)XLINF,XLL,XK,XM,ITS,XF READ(4,*)TL,TC,T0 READ(4,*)XLC,XLBAR XZ=XM+XF WRITE(6,*)'CALCULATING MORTALITY ESTIMATE'C*****EHRHARDT & AULT DO 1000 I=1,ITS Z=I*.0001 X1=((XLINF-XLL)/(XLINF-XLC))**(Z/XK) X2=((Z*(XLC-XLBAR))+(XK*(XLINF-XLBAR)))/ $ ((Z*(XLL-XLBAR))+(XK*(XLINF-XLBAR))) XX=X2-X1 IF(XX.LE.0) GOTO 1010 1000 CONTINUE 1010 WRITE(6,*)'COMPLETED MORTALITY ITERATIONS' ZERR=((Z-XZ)/XZ)*100.C*****BEVERTON & HOLT ZBH=(XK*(XLINF-XLBAR))/(XLBAR-XLC) ZERBH=((ZBH-XZ)/XZ)*100.C*****SSENTONGO & LARKIN Y=-ALOG(1.-XLBAR/XLINF) YC=-ALOG(1.-XLC/XLINF) ZSL=XK*(1./(Y-YC)) ZSLERR=((ZSL-XZ)/XZ)*100. XLLO=XLINF*(1.-EXP(-XK*((TC)-T0))) XLHI=XLINF*(1.-EXP(-XK*((TL)-T0))) XF2EA=Z-XM WRITE(6,*)'XLLO = ',XLLO,'XLHI = ',XLHI WRITE(6,*) WRITE(6,*)'INPUT PARAMETERS:' WRITE(6,*)'LINF = ',XLINF,' L-LAMBDA = ',XLL WRITE(6,*)'LC = ',XLC,' K = ',XK WRITE(6,*)'LBAR = ',XLBAR,' Z = ',XZ WRITE(6,*) WRITE(6,*)'E&A Z EST = ',Z WRITE(6,*)'ESTIMATED ERROR E&A = ',ZERR WRITE(6,*)'ESTIMATED F = ',XF2EA WRITE(6,*)c WRITE(6,*)'B&H Z EST = ',ZBHc WRITE(6,*)'ESTIMATED ERROR B&H = ',ZERBHc WRITE(6,*)c WRITE(6,*)'S&L Z EST = ',ZSLc WRITE(6,*)'ESTIMATED ERROR S&L = ',ZSLERR 9999 STOP END
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Appendix F. - PROGRAM REEFSC***********************************************************************CC MAIN PROGRAM MODULE OF REEFS. C Reef fish Equilibrium Exploitation Fishery Simulator C SIMULATES STOCHASTIC CONTINUOUS RECRUITMENT, GROWTH AND C MORTALITY LIFE HISTORIES FOR EXPLOITED MARINE FISH STOCKS. C Version of May 27, 1997. Modified September 1998.C C Dr. Jerald S. Ault C University of Miami C Rosenstiel School of Marine & Atmospheric Science C Division of Marine Biology & Fisheries C 4600 Rickenbacker Causeway C Miami, Florida 33149 C (305)361-4884 ph C (305)361-4791 fax C ault@shark.rsmas.miami.edu C*********************************************************************** INTEGER ZZ,X,SMOS COMMON P(492,492),Z(492,492),A(492,492),XM(492,492),F(492,492), $ R(492),RN(492,492),YN(492,492),YW(492,492),MFR COMMON/A/XAKV,T0,ULINF,WINF,MRM,TR,MFC COMMON/B/FRQ(40),SE(492),SLT(492) COMMON/C/POPLEN(1000),SYLND(1000),XLENPT(1000),TSL(1000), $ TSY,TSLC,SALC,NTOTLI COMMON/D/XDLENPT(1000),DPOP(1000),DYPD(1000),TDL(1000), $ TYD,TPD,TDLC,DALC COMMON/E/PD(492,35),PDY(492,35),XLD(492,35) COMMON/S/XE(492),WT(492),BIOM(492),POPP(492),TLC(492), $TL(492),TWC(492),AL,PBIRTH(492),ALC,AVWT,AFBM,AFPN,FPJ, $CX(492),ACBIR(492),RPJ,YWJ,PPA,BMJ,PJ,SSB, $YNJ,TOTLC,TOTLP,TOTWC,PJCX,CXT,ACBT,FEC(492),FBMJ, $FRPJ,LL,S(492,12),TAC(492),AAC,ALPGTC DIMENSION FM(492),F1MF(492),SXR(492),RA(492),RC(492), *SLT1(492),BIT(492),BIRTHS(492), *RBIOM(492),AP(492),AYW(492),AYN(492),FP(12),AM(12),XMU(5), *ST(492,4) OPEN(UNIT=16,FILE='vb.dat',STATUS='OLD') OPEN(UNIT=15,FILE='fpr.dat',STATUS='OLD') OPEN(UNIT=14,FILE='ypr.dat',STATUS='OLD') OPEN(UNIT=13,FILE='select.dat',STATUS='OLD') OPEN(UNIT=12,FILE='styldn.dat',STATUS='OLD') OPEN(UNIT=11,FILE='lenfreq.dat',STATUS='OLD') OPEN(UNIT=10,FILE='yldpr.dat',STATUS='OLD') OPEN(UNIT=9,FILE='avlength.dat',STATUS='OLD') OPEN(UNIT=8,FILE='probden.dat',STATUS='OLD') OPEN(UNIT=7,FILE='length.dat',STATUS='OLD') OPEN(UNIT=5,FILE='repro.dat',STATUS='OLD') OPEN(UNIT=4,FILE='lendist.dat',STATUS='OLD') OPEN(UNIT=3,FILE='frac.dat',STATUS='OLD')C OPEN(UNIT=2,FILE='fec.dat',STATUS='OLD') OPEN(UNIT=1,FILE='snapper_gray.dat',STATUS='old') WRITE(*,*)'Initializing continuous-time data arrays***' read (1,*) NDT,MOS,NTL,MFR,MFC,MRM,NFOT,NROT read (1,*) NOUT,NAVA,NLL,NOPT,NDIST,NAMP,NPLOT,NSRCH read (1,*) ULINF,WINF,XAKV,T0,XNM,FMORT,ALPHA,BETA read (1,*) EQUIL,TR,PER,PARM1,PARM2,XNMF,DTL read (1,*) ALSLB,BLSB,A4,B4,NYPR,PPAMAX read (1,*) XNTS,NFRANGE,fstep,LLS,LOL,LUP data (fp(i),i=1,12)/1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1./ 

 data (am(i),i=1,12)/1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1./ data (FRQ(I),I=1,7)/.0009,.0016,.0027,.0045,.0072,.0108,.0159/ data (FRQ(I),I=8,14)/.0222,.03,.0389,.0484,.0579,.0665,.0736/ data (FRQ(I),I=15,19)/.0781,.0796,.0781,.0736,.0665/ data (FRQ(I),I=20,24)/.0579,.0484,.0389,.03,.0222/ data (FRQ(I),I=25,31)/.0159,.0108,.0072,.0045,.0027,.0016,.0009/cc 31 FORMAT(8I10)cc 33 FORMAT(3F15.0)cc 29 FORMAT(8F10.0)C***********************************************************************C INITIALIZE CONTINUOUS SIMULATION ARRAYS C*********************************************************************** NS=0 LL=0 Y=0. AYNJ=0. XDT=float(NDT) DT=1/XDT NM=NTL*NDT NMC=NM+1 IF(EQUIL.EQ.0) GOTO 444 NMT=NM+MOS GOTO 445 444 NMT=NM 445 XMU(1)=0.0 XAKV=XAKV/XDT NTS=INT(XNTS) XXNTS=XNTS/12. XXXNTS=1.0/XXNTS NTSS=INT(XXXNTS) T0=T0*XDT J=1 DO 199 I=1,NM XJ=FLOAT(I) XE(J)=1.-EXP(-XAKV*((XJ-0.5)-T0))c XE(J)=1.-EXP(-XAKV*((XJ)-T0)) IF(XE(J))191,191,192 192 SLT(J)=ULINF*XE(J) WT(J)=WINF*(XE(J)**B4)C WRITE(16,*)J,SLT(J),st(j,1)c WT(J)=A4*(SLT(J)**B4) IF(J.LT.MRM) GOTO 193 LL=1 IF(LL.EQ.1) GOTO 11000 IF(LL.EQ.0) GOTO 1100111001 FEC(J)=ALPHA+(BETA*WT(J))11000 FEC(J)=ALPHA*(SLT(J)**BETA) GOTO 198 191 SLT(J)=0.0 WT(J)=0.0 193 FEC(J)=0.0 198 J=J+1 199 CONTINUE DO 55 I=1,NM XI=FLOAT(I) SE(I)=ALSLB*(1.-EXP(-BLSB*(XI-0.5)))ccc SE(I)=ALSLB*((I)**BSLB) 55 CONTINUEC**********************************************************************C NATURAL MORTALITY ARRAY *
149

C********************************************************************** DO 13 I=1,NMT DO 12 J=1,(MFR-1) XM(I,J)=0.0 12 CONTINUE 13 CONTINUEC DO 34 I=1,NMTC XM(I,45)=(XNM/XDT)*0.36C 34 CONTINUE DO 40 I=1,NMT DO 35 J=MFR,NMT XM(I,J)=XNM/XDT 35 CONTINUE 40 CONTINUE GOTO 92C**********************************************************************C AVAILABILITY ARRAY *C**********************************************************************C SELECTIVITY PATTERNS *C VARIABLE NAME = NAVA *C C CONSTANT FOR ALL INTERVALS = 1 *C SEASONALLY AVAILABLE = 2 *C********************************************************************** 92 IF(NAVA.EQ.1) GOTO 41 IF(NAVA.EQ.2) GOTO 91 41 DO 43 I=1,NMT DO 42 J=1,(MFC-1) A(I,J)=0.0 42 CONTINUE 43 CONTINUE DO 50 I=1,NMT DO 45 J=MFC,NM A(I,J)=1.0 45 CONTINUE 50 CONTINUE IF(LLS.EQ.0) GOTO 1566 IF(LLS.EQ.1) GOTO 1560 1560 DO 1564 I=1,NMT DO 1562 J=LOL,LUP A(I,J)=0. 1562 CONTINUE 1564 CONTINUEC*****************************************************C SELECTION ARRAYS *C***************************************************** 1566 READ(13,*)(ST(J,1),ST(J,2),ST(J,3),ST(J,4),J=1,NM)cc 1500 FORMAT(4F10.0) K=0 LL=1 DO 1505 I=1,NDT K=K+1 DO 1504 J=1,NM s(J,I)=ST(J,LL) 1504 CONTINUE IF(K.LT.3) GOTO 1505 K=0 LL=LL+1 1505 CONTINUE GOTO 200C**********************************************************************C SEASONAL AVAILABILITY *C********************************************************************** 91 DO 107 I=1,NLL

 DO 106 M=1,12 K=(I-1)*12+M IF(AM(M).EQ.0) GOTO 95 IF(AM(M).EQ.1) GOTO 97 95 X=K J=1 DO 96 JJ=X,1,-1 IF(J.GT.NMT) GOTO 96 A(JJ,J)=0.0 J=J+1 96 CONTINUE GOTO 106 97 X=K J=1 DO 98 JJ=X,1,-1 IF(J.GT.NMT) GOTO 98 A(JJ,J)=1.0 J=J+1 98 CONTINUE 106 CONTINUE 107 CONTINUE GOTO 200C**********************************************************************C BIRTH RATE FUNCTION ARRAYS *C**********************************************************************C CALCULATION OF AGE(SIZE) SPECIFIC BIRTH RATES *C (NET MATERNITY LxMx) BY MANIPULATION OF THE *C FECUNDITY ON WEIGHT RELATIONSHIP *C********************************************************************** 200 RHO=0.8 XLAMBDA=0.8 FECUN=0.0 SMPFEC=0.0 DO 201 JJ=1,MRM FEC(JJ)=0.0 201 CONTINUE DO 206 JJ=MRM,NM FEC(JJ)=ALPHA*(WT(JJ)**BETA) IF(RHO.NE.XLAMBDA) GOTO 202 IF(RHO.EQ.XLAMBDA) GOTO 203 202 BIT(JJ)=FEC(JJ)*(((XLAMBDA**(JJ+1-MRM))-(RHO** * (JJ+1-MRM)))/(RHO-XLAMBDA)) GOTO 205 203 BIT(JJ)=FEC(JJ)*(JJ+1-MRM)*(RHO**(JJ-MRM)) GOTO 205 205 BIRTHS(JJ)=BIT(JJ)*((EXP(-XM(1,JJ)))**(JJ-1)) FECUN=FECUN+FEC(JJ) 206 CONTINUE DO 207 JJ=1,NM PBIRTH(JJ)=FEC(JJ)/FECUN PBIRTH(JJ)=PBIRTH(JJ)*100. SMPFEC=SMPFEC+PBIRTH(JJ) 207 CONTINUEC**********************************************************************C FISHING MORTALITY ARRAY *C**********************************************************************C FISHING MORTALITY PATTERNS *C VARIABLE NAME = NFOT *C POPE'S (1971) METHOD = 1 *C CONSTANT FOR ALL INTERVALS = 2 *C SEASONAL FISHING STRATEGY = 3 *C********************************************************************** IF(NFOT.EQ.1) GOTO 21 IF(NFOT.EQ.2) GOTO 60
150

 IF(NFOT.EQ.3) GOTO 130C*****POPE'S MID-YEAR APPROXIMATION 21 DO 20 I=1,NMT DO 19 L=1,14 L1=0 L1=(L-1)*12 IF(I.EQ.L1) GOTO 18 IF(I.NE.L1) GOTO 19 18 DO 17 J=1,NMT F(I,J)=FMORT/XDT 17 CONTINUE GOTO 20 19 CONTINUE DO 15 J=1,NMT F(I,J)=0. 15 CONTINUE 20 CONTINUE GOTO 84C*****F CONSTANT EVERY INTERVAL 60 DO 62 I=1,NMT DO 61 J=1,(MFC-1) F(I,J)=0. 61 CONTINUE 62 CONTINUEC DO 63 I=1,NMTC F(I,45)=(FMORT/XDT)*0.36C 63 CONTINUE DO 67 I=1,NMT DO 64 J=MFC,NMT F(I,J)=FMORT/XDT 64 CONTINUE 67 CONTINUE GOTO 84C*****SEASONAL FISHING MORTALITY STRATEGY 130 DO 149 I=1,NLL DO 148 M=1,12 K=(I-1)*12+M IF(FP(M).EQ.0) GOTO 131 IF(FP(M).EQ.1) GOTO 135 131 X=K J=1 DO 141 JJ=X,1,-1 IF(J.GT.NMT) GOTO 141 F(JJ,J)=0. J=J+1 141 CONTINUE GOTO 148 135 X=K J=1 DO 142 JJ=X,1,-1 IF(J.GT.NMT) GOTO 142 F(JJ,J)=FMORT/XNMF J=J+1 142 CONTINUE 148 CONTINUE 149 CONTINUE GOTO 84C**********************************************************************C RECRUITMENT ARRAYS *C**********************************************************************C RECRUITMENT DISTRIBUTION FUNCTIONS *C VARIABLE NAME = NROT *C *C DISCRETE = 1 *

C CONTINUOUS UNIFORM = 2 *C TRIGONOMETRIC DISTRIBUTION = 3 *C BETA DISTRIBUTION = 4 *C *C TR = TOTAL ANNUAL MAGNITUDE OF RECRUITMENT *C********************************************************************** 84 IF(NROT.EQ.1) GOTO 86 IF(NROT.EQ.2) GOTO 87 IF(NROT.EQ.3) GOTO 88 IF(NROT.EQ.4) GOTO 90C*****DISCRETE ANNUAL RECRUITMENT 86 NNTL=NTL+1 DO 71 I=1,NNTL DO 70 J=1,NDT IF(J.NE.1) GOTO 68 IF(J.EQ.1) GOTO 69 69 L=(I-1)*12+J R(L)=TR GOTO 70 68 L=(I-1)*12+J R(L)=0.0 70 CONTINUE 71 CONTINUE GOTO 79C*****CONTINUOUS UNIFORM RECRUITMENT 87 DO 85 I=1,NMT R(I)=TR/XDT 85 CONTINUE GOTO 79C*****TRIGONOMETRIC RECRUITMENT *************************************C VARIABLE NAME = PER *C PER = 12 (BIMODAL ANNUALLY) *C PER = 24 (UNIMODAL ANNUALLY) *C******************************************************************** 88 PI=3.1415927 AMPL=(2*PI)/PER SUM=0. S2=0. S3=0. DO 188 J=1,12 RA(J)=SIN(AMPL*J) RA(J)=ABS(RA(J)) SUM=SUM+RA(J) 188 CONTINUE DO 187 J=1,12 RC(J)=RA(J)/SUM S2=S2+RC(J) 187 CONTINUE LCOUNT=0 DO 189 I=1,NTL S3=0. K=0. DO 186 J=1,12 K=LCOUNT+J R(K)=RC(J)*TR S3=S3+R(K) 186 CONTINUE LCOUNT=LCOUNT+12 189 CONTINUE IF(MOS.EQ.0) GOTO 79 DO 184 J=NMC,NMT KK=J-NM R(J)=RC(KK)*TR 184 CONTINUE
151

 K=0. GOTO 79C***********************************************************************C BETA DISTRIBUTION *C *C PARAMETERS OF THE BETA DISTRIBUTION *C PARM1 = ALPHA 1 *C PARM2 = ALPHA 2 *C*********************************************************************** 90 PI=3.1415927 A1P=PARM1-1. A2P=PARM2-1. A3P=(PARM1+PARM2)-1. XN1F=EXP(-A1P)*(A1P**A1P)*(SQRT(2*PI*A1P)) XN2F=EXP(-A2P)*(A2P**A2P)*(SQRT(2*PI*A2P)) XN3F=EXP(-A3P)*(A3P**A3P)*(SQRT(2*PI*A3P)) BETADS=(XN1F*XN2F)/XN3F GOTO 221c SUM=0.c S2=0.c DO 220 J=1,12c TIME=J/12.c RA(J)=((TIME**A1P)*((1-TIME)**A2P))/BETADSc SUM=SUM+RA(J)c 220 CONTINUEc DO 219 J=1,NDTc RC(J)=RA(J)/SUMc S2=S2+RC(J)c 219 CONTINUEc LCOUNT=0.c NNTL=NTL+1c DO 218 I=1,NNTLc S3=0.c K=0.c DO 217 J=1,12c K=LCOUNT+Jc R(K)=RC(J)*TRc S3=S3+R(K)c 217 CONTINUEc LCOUNT=LCOUNT+12c K=0.c 218 CONTINUEc IF(MOS.EQ.0) GOTO 79c DO 216 J=NMC,NMTc KK=J-NMc R(J)=RC(KK)*TRc 216 CONTINUEc GOTO 79C******************************************************C CONSTRAINED BETA WRT TIME *C****************************************************** 221 XCON=6. SUM=0. S2=0. DO 222 J=1,NDT TIME=J/XCON IF(J.GT.XCON) GOTO 226 RA(J)=((TIME**A1P)*((1-TIME)**A2P))/BETADS WRITE(*,*)TIME,RA(J) SUM=SUM+RA(J) GOTO 222 226 RA(J)=0. 222 CONTINUE DO 223 J=1,NDT

 RC(J)=RA(J)/SUM WRITE(*,*)J,RC(J) S2=S2+RC(J) 223 CONTINUE LCOUNT=0. NNTL=NTL+1 DO 225 I=1,NNTL S3=0. K=0. DO 224 J=1,NDT K=LCOUNT+J IF(J.GT.XCON) GOTO 227 R(K)=RC(J)*TR WRITE(*,*)I,J,R(K) GOTO 224 227 R(K)=0. GOTO 224c S3=S3+R(K) 224 CONTINUE LCOUNT=LCOUNT+12 K=0. 225 CONTINUE IF(MOS.EQ.0) GOTO 79 DO 236 J=NMC,NMT KK=J-NM R(J)=RC(KK)*TR 236 CONTINUEC******************************************************C STATIONARY CONDITIONS *C****************************************************** 79 WRITE(*,*)'CALCULATING STATIONARY VECTORS' IF(NYPR.EQ.0) GOTO 80 WRITE(*,*)'YPR SIMULATION' write(*,*)NTL,NTSS XFM=0. MTL=NTL*NTSS+1 DO 490 NF=1,NFRANGE IF(NF.GT.1) GOTO 491 FMX=0. GOTO 492 491 XFM=XFM+1. FMX=XFM*fstep 492 NTC=0 DO 480 KK=1,MTL YPR=0. FPR=0. IF(KK.GT.1) GOTO 493 MFC=0 XMFC=0. GOTO 494 493 MFC=MFC+NTS XMFC=XMFC+XXNTS 494 WRITE(*,*)'YPR AT',FMX,XMFC,'(',MFC,')' DO 479 I=1,NMT DO 475 J=1,(MFC-1) F(I,J)=0. A(I,J)=0. 475 CONTINUE 479 CONTINUE DO 470 I=1,NMT DO 465 J=MFC,NMT F(I,J)=FMX/XDT A(I,J)=1.0 465 CONTINUE
152

 470 CONTINUE IF(MFC.LT.MFR) GOTO 463 DO 1010 K=1,NMT SMOS=1 CALL DYNSIM(K,SMOS) 1010 CONTINUE GOTO 462 463 YPR=0. FPR=0. GOTO 464 462 YPR=YWJ/R(NM) FPR=SSB/PPAMAXc 464 WRITE(*,*)FMX,SLT(MFC),XMFC,YPR,FPR 464 XKK=0.1358 T00=-0.863 UUL=722.32 SLL=UUL*(1.-EXP(-XKK*(XMFC-T00))) WRITE(14,*)FMX,XMFC,SLL,YPR WRITE(15,*)FMX,XMFC,SLL,FPR 480 CONTINUE 490 CONTINUE GOTO 4001 80 WRITE(*,*)'NORMAL SIMULATION' DO 1000 K=1,NM SMOS=1 CALL DYNSIM(K,SMOS) 1000 CONTINUE K=NM if(NMT.GT.K) GOTO 230 SMOS=1 CALL LAPD(K,SMOS,NAMP,NDIST) CALL SCANDET(K,NM,DTL)C*****Non-equilibrium switch NEQ=1 IF(NSRCH)230,230,229 229 CALL SCANPB(K,NM,NDIST,DTL) WRITE(6,*)'RETURNED SCANPB' 230 GOTO 3000C******************************************************C DYNAMIC CONDITIONS *C****************************************************** 3799 WRITE(6,3800) 3800 FORMAT(7X,'CALCULATING TRANSITIONAL VECTORS'//) DO 4000 K=NMC,NMT SMOS=SMOS+1 CALL DYNSIM(K,SMOS) if(K.LT.NMT) GOTO 250 CALL LAPD(K,SMOS,NAMP,NDIST) CALL SCANDET(K,NM,DTL) WRITE(*,*)'RETURNED FROM SCANDET',NSRCH,K NEQ=1 IF(NSRCH)250,250,249 249 CALL SCANPB(K,NM,NDIST,DTL) WRITE(*,*)'RETURNED FROM SCANPB' 250 GOTO 3000C*********************************************************************C WRITING TO DATA FILES *C*********************************************************************c*****No Path to Statementsc X=Kc J=1c DO 820 I=X,1,-1

c XXXX=Jc IF(J.LT.MFR) GOTO 819c 815 xj=j/12.c WRITE(4,811)xj,BIOM(J)c 811 FORMAT(f7.2,3x,f7.2)C WRITE(2,813)SLT(J),PD(J,K),NPLOTc 813 FORMAT(F15.4,F15.5,I5)c 819 J=J+1c 820 CONTINUEc DO 830 J=1,NTOTLIC WRITE(4,828)XLENPT(J),POPLEN(J),KC WRITE(11,828)XDLENPT(J),DPOP(J),Kc 830 CONTINUEc 828 FORMAT(F15.5,F15.5,I5)c KKK=NM+100c DO 899 J=1,NTOTLIC WRITE(11,828)XDLENPT(J),DYPD(J),KKKc 899 CONTINUEC WRITE(11,848)TDLC,TYD,DALCc 848 FORMAT(7X,'TDLC = ',E12.6,5X,'TYD = ',E12.6,/,c $7X,'DETERMINISTICALLY AGGREGATED c $AVG LENGTH IN CATCH = ',F15.5)c KKKK=NM+200c DO 901 J=1,NTOTLIC WRITE(12,828)XLENPT(J),SYLND(J),KKKKc 901 CONTINUEC WRITE(12,849)TSLC,TSY,SALCc 849 FORMAT(7X,'TSLC = 'E12.6,5X,'TSY = ',E12.6,/,c $7X,'STOCHASTICALLY AGGREGATED c $ AVG LENGTH IN CATCH = ',F15.5)c WRITE(11,836)TPD,TYD,TSY 836 FORMAT(5X,'TOTAL POPN NUMBERS = ',E18.7,/5X, $ 'TOTAL YIELD IN NUMBERS = ',E18.7,/5X, $ 'TOTAL STOCHASTIC YIELD IN NUMBERS = ',E18.7)C**********************************************************************C TRANSITIONAL OUTPUT MODULE *C********************************************************************** 3000 IF(NEQ)3002,3002,3001 3001 MR=K IF(R(MR).EQ.0) GOTO 3902 YPR=YWJ/R(MR) YNPR=YNJ/R(MR) GOTO 3903 3002 MR=NM IF(R(MR).EQ.0) GOTO 3902 YPR=YWJ/R(MR) YNPR=YNJ/R(MR) GOTO 3903 3902 YPR=0. YNPR=0. 3903 WRITE(5,1590)K 1590 FORMAT(7X,'MONTH = ',I5) IF(NOUT.EQ.0) GOTO 3008 IF(NOUT.EQ.1) GOTO 1591 1591 WRITE(5,1592) 1592 FORMAT('AGE',5X,'LENGTH',6X,'WEIGHT',6X,'POPN NO.', $9X,'AVG NO.',6X,'BIOMASS') X=K J=1 DO 1700 I=X,SMOS,-1 WRITE(5,1640)J,SLT(J),WT(J),P(I,J),RN(I,J),BIOM(J) WRITE(16,1640)j,slt(j),wt(j),rn(i,j),yn(i,j),yw(i,j) 1640 FORMAT(I3,3X,F8.3,2X,F8.3,2X,E13.7,2X,E13.7,2X,E13.7)
153

 J=J+1 1700 CONTINUE WRITE(5,1701)PJ,RPJ,BMJ 1701 FORMAT('TOTAL',21X,E13.7,2X,E13.7,2X,E13.7//) 1491 WRITE(5,1492) 1492 FORMAT('AGE',8X,'YIELD NO.',5X,'YIELD WT.',7X,'FMORT',7X,'SELECT') X=K J=1 DO 1400 I=X,SMOS,-1 WRITE(5,1350)J,YN(I,J),YW(I,J),F(I,J)*A(I,J),S(J,1) 1350 FORMAT(I3,3X,E13.7,2X,E13.7,4X,F6.4,4X,F6.4) J=J+1 1400 CONTINUE WRITE(5,1401)YNJ,YWJ 1401 FORMAT('TOTAL',1X,E13.7,2X,E13.7//) WRITE(5,1710)FMORT,XNM,MFC,AL,ALPGTC,ALC,AVWT,AAC, $SLT(MFC),SLT(NM) 1710 FORMAT('ANNUAL F = ',F12.6,5X,'ANNUAL M = ',F12.6/, $'AGE OF FIRST CAPTURE = ',I3/, $'AVG LENGTH IN THE POPN = ',F13.6/, $'AVG LENGTH IN THE POPN >TC = ',F13.6/, $'AVG. LENGTH IN THE CATCH = ',F13.6/, $'AVG. WEIGHT IN THE CATCH = ',F13.6/, $'AVG. AGE IN THE CATCH = ',F13.6/, $'Length at first capture = ',f13.6/ $'Maximum Length class in the catch = ',f13.6/) WRITE(5,1712)YPR,YNPR 1712 FORMAT('YIELD IN WEIGHT PER RECRUIT = ',F15.8/, $'YIELD IN NUMBER PER RECRUIT = ',F15.8//) WRITE(5,3021)FPJ,FRPJ,FBMJ 3021 FORMAT('FISHABLE TOTAL POPN SIZE = ',E13.7/, $'FISHABLE AVG. POPN SIZE = ',E13.7/, $'FISHABLE AVG. POPN BIOMASS = ',E13.7) WRITE(5,3022)AFPN,AFBM,TR,SSB,PPA 3022 FORMAT('AVG. FISHABLE POPN NUMBERS PER RECRUIT = ',F13.7/, $'AVG. FISHABLE POPN BIOMASS PER RECRUIT = ',F13.7/, $'ANNUAL RECRUITMENT = 'F12.0/,'SPAWNING STOCK BIOMASS =',F12.0/ $'STOCK FECUNDITY = ',E15.8) X=K J=1 DO 3090 I=X,1,-1 IF(J.LT.MFR) GOTO 3089 xj=j/12. WRITE(4,3087)xj,SLT(J),BIOM(J) 3087 FORMAT(2(f7.2,3x),f10.2) 3089 J=J+1 3090 CONTINUE GOTO 3999 3008 WRITE(5,3010) 3010 FORMAT(7X,'CONDENSED TRANSITIONAL OUTPUT:',//) WRITE(5,3020)PJ,BMJ,SSB 3020 FORMAT(7X,'POPULATION ABUNDANCE = ',F12.3,/,7X, $'POPULATION BIOMASS = ',E14.7,/,7X,'SPAWNING STOCK BIOMASS = ', $E14.7) WRITE(5,3030)YWJ,YNJ 3030 FORMAT(7X,'YIELD IN WEIGHT = ', $E14.7,/,7X,'YIELD IN NUMBERS = ',E14.7) WRITE(5,3040)AL,ALC 3040 FORMAT('AVG LENGTH IN TOTAL POPN = ',F8.4,/,7X, $'AVG. LENGTH IN CATCH = ',F8.4) WRITE(5,3050)AVWT 3050 FORMAT(7X,'AVG. WT. IN CATCH = ',F8.4,/) WRITE(5,3055)R(K),YPR 3055 FORMAT(7X,'RECRUITMENT = ',F10.5,/,

 $7X,'YIELD PER RECRUIT = ',F9.4,//) 3060 IF(JTRANS.NE.12) GOTO 4000 IF(JTRANS.EQ.12) GOTO 3101 3101 WRITE(5,3100) 3100 FORMAT(7X,'AGE',8X,'POPN. NO.',3X,'Yweight',5X,'Ynumber') X=K J=1 DO 3005 I=X,SMOS,-1 WRITE(5,3004)J,AP(J),AYW(J),AYN(J),SLT(J) 3004 FORMAT(7X,I3,2X,F10.2,5X,F12.3,5X,F12.3,5X,F12.3) J=J+1 3005 CONTINUE WRITE(5,3110)ABMJ,ASSB,APJ 3110 FORMAT(//,7X,'BIOMASS = ',E14.7,/,7X,'SPAWNING BIOMASS = ', $E14.7,/,7X,'POPULATION SIZE = ',E14.7) WRITE(5,3120)ATOTWC,AYNJ 3120 FORMAT(7X,'YIELD IN WEIGHT = ',E14.7,//,7X,'YIELD IN NO. = ', $E14.7) IF(AYNJ.LE.0) GOTO 3128c AALC=ATOTLC/AYNJc AAVWT=ATOTWC/AYNJ GOTO 3129 3128 AALC=0. AAVWT=0. 3129 WRITE(5,3130)AALC,AAVWT 3130 FORMAT(7X,'AVG. LENGTH IN CATCH = ',F8.4,5X,'AVG. WEIGHT IN $CATCH = ',F8.4) WRITE(5,3140)YPR 3140 FORMAT(7X,'ANNUAL YIELD PER RECRUIT = ',F9.6,//) JTRANS=0. 3999 NS=NS+1 IF(MOS.EQ.0) GOTO 4001 IF(NS.GT.MOS) GOTO 4001 IF(NS.EQ.1) GOTO 3799 IF(MOS.GE.1) GOTO 4000 4000 CONTINUE 4001 STOP END

SUBROUTINE DYNSIM(K,SMOS)

C**********************************************************************C CENTERED DYNAMIC SIMULATIONS *C**********************************************************************C *C CALCULATION OF CONTINUOUS RECRUITMENT LOOPS *C *C********************************************************************** DIMENSION XOMEG(4),RM(300) INTEGER K,X,SMOS COMMON P(492,492),Z(492,492),A(492,492),XM(492,492), $ F(492,492), $ R(492),RN(492,492),YN(492,492),YW(492,492),MFR COMMON/A/XAKV,T0,ULINF,WINF,MRM,TR,MFC COMMON/B/FRQ(40),SE(492),SLT(492) COMMON/S/XE(492),WT(492),BIOM(492),POPP(492),TLC(492), $TL(492),TWC(492),AL,PBIRTH(492),ALC,AVWT,AFBM,AFPN,FPJ, $CX(492),ACBIR(492),RPJ,YWJ,PPA,BMJ,PJ,SSB, $YNJ,TOTLC,TOTLP,TOTWC,PJCX,CXT,ACBT,FEC(492),FBMJ, $FRPJ,LL,S(492,12),TAC(492),AAC,ALPGTC data RM1,RM2/-5.5,.171/ XOMEG(1)=1.0
154

 XOMEG(2)=-3.0 XOMEG(3)=3.0 XOMEG(4)=-1.0 TOTLP=0. TOTLC=0. TOTAC=0. TOTWC=0. YWJ=0. YNJ=0. BMJ=0. RPJ=0. FBMJ=0. FRPJ=0. FPJ=0. PJ=0. AVGL=0. EYPR=0. SSB=0. PPA=0. AL=0. ALC=0. AVWT=0. AVWT2=0. AFBM=0. AFPN=0. AAC=0. TLPGTC=0. ALPGTC=0. X=K J=1c write(*,*)'in dynsim',' k = ',K,R(K) IF(LL.GT.1) GOTO 99 LL=1 99 DO 500 I=X,SMOS,-1 BIOM(J)=0. POPP(J)=0. TL(J)=0. TLC(J)=0. TWC(J)=0. IF(I-X)110,120,110 120 P(I,J)=R(K) Z(I,J)=XM(I,J)+(A(I,J)*F(I,J)*S(J,LL)) P(I,J+1)=P(I,J)*(EXP(-Z(I,J))) IF(Z(I,J))83,83,83c RN(I,J)=(P(I,J)/Z(I,J))*(1.0-EXP(-Z(I,J))) GOTO 83 110 Z(I,J)=0. Z(I,J)=XM(I,J)+(A(I,J)*F(I,J)*S(J,LL)) P(I,J)=P(I,J-1)*EXP(-Z(I,J-1)) IF(Z(I,J).EQ.0.) GOTO 111 RN(I,J)=0. RN(I,J)=(P(I,J)/Z(I,J))*(1.0-EXP(-Z(I,J))) RPJ=RPJ+RN(I,J) GOTO 83 111 YN(I,J)=0. RN(I,J)=0. 83 IF(J.LT.MFR) GOTO 150 RM(J)=1./(1.+EXP(-(RM1+RM2*SLT(J)))) RM(J)=1. BIOM(J)=WT(J)*RN(I,J)*RM(J) IF(J.LT.MRM) GOTO 112 SSB=SSB+BIOM(J) POPP(J)=RN(I,J)*FEC(J) PPA=PPA+POPP(J)

 112 BMJ=BMJ+BIOM(J) PJ=PJ+P(I,J) TL(J)=RN(I,J)*SLT(J) TOTLP=TOTLP+TL(J) IF(J.LT.MFC) GOTO 150 FPJ=FPJ+P(I,J) FBMJ=FBMJ+BIOM(J) FRPJ=FRPJ+RN(I,J)c VSUM=0.c DO 109 IV=1,4c VN=IV-1c IF(Z(I,J).EQ.0) GOTO 109c VSUM=VSUM+XOMEG(IV)*EXP(-VN*XAKV*((J-1)-T0))/c $(Z(I,J)+VN*XAKV)*(1-EXP(-(Z(I,J)+VN*XAKV)))c 109 CONTINUE YW(I,J)=0. YN(I,J)=0. YW(I,J)=A(I,J)*F(I,J)*S(J,LL)*RN(I,J)*WT(J)c YW(I,J)=A(I,J)*F(I,J)*S(J,LL)*P(I,J)*WINF*VSUM YWJ=YWJ+YW(I,J) YN(I,J)=A(I,J)*F(I,J)*S(J,LL)*RN(I,J) YNJ=YNJ+YN(I,J) TLC(J)=YN(I,J)*SLT(J) TLPGTC=TLPGTC+RN(I,J)*SLT(J) XX=FLOAT(J) TAC(J)=YN(I,J)*(XX-0.5)c TAC(J)=0. TOTAC=TOTAC+TAC(J) TOTLC=TOTLC+TLC(J) TWC(J)=YN(I,J)*WT(J) TOTWC=TOTWC+TWC(J) 150 J=J+1 500 CONTINUE IF(RPJ) 430,1000,430 430 AL=TOTLP/RPJ IF(YNJ)431,1000,431 431 ALC=TOTLC/YNJ AAC=TOTAC/YNJ AVWT=TOTWC/YNJ AFBM=FBMJ/TR AFPN=FRPJ/TR ALPGTC=TLPGTC/FRPJ LL=LL+1 IF(LL.LT.13) GOTO 1000 LL=0 1000 RETURN END

SUBROUTINE LAPD(K,SMOS,NAMP,NDIST)

C**********************************************************************C LENGTH @ AGE PROBABILITY DISTRIBUTIONS *C********************************************************************** DIMENSION PDM(492),PDYLD(492) COMMON P(492,492),Z(492,492),A(492,492),XM(492,492),F(492,492), $ R(492),RN(492,492),YN(492,492),YW(492,492),MFR COMMON/B/FRQ(40),SE(492),SLT(492) COMMON/E/PD(492,35),PDY(492,35),XLD(492,35) INTEGER SMOS X=K J=1 DO 1 I=1,31 1 CONTINUEc GOTO 991
155

 DO 990 I=K,SMOS,-1 PDM(J)=P(I,J) PDYLD(J)=YN(I,J) DO 970 KK=1,NDIST IF(KK.LE.15) GOTO 968 IF(KK.EQ.16) GOTO 967 IF(KK.GT.16) GOTO 966 968 XTEMP=FLOAT(16-KK)*0.2 XLD(J,KK)=SLT(J)-XTEMP*SE(J)*NAMP PD(J,KK)=PDM(J)*FRQ(KK) PDY(J,KK)=YN(I,J)*FRQ(KK) IF(XLD(J,KK).LE.0.) GOTO 970 IF(PD(J,KK).LE.0.) GOTO 970 XX=FLOAT(J/12) WRITE(7,*)XLD(J,KK),PD(J,KK),XX GOTO 970 967 XLD(J,KK)=SLT(J) PD(J,KK)=PDM(J)*FRQ(KK) PDY(J,KK)=PDYLD(J)*FRQ(KK) IF(XLD(J,KK).LE.0.) GOTO 970 IF(PD(J,KK).LE.0.) GOTO 970 XX=FLOAT(J/12) WRITE(7,*)XLD(J,KK),PD(J,KK),XX GOTO 970 966 XTEMP=FLOAT(16-KK)*0.2 XLD(J,KK)=SLT(J)+XTEMP*SE(J)*NAMP PD(J,KK)=PDM(J)*FRQ(KK) PDY(J,KK)=PDYLD(J)*FRQ(KK) IF(XLD(J,KK).LE.0.) GOTO 970 IF(PD(J,KK).LE.0.) GOTO 970 XX=FLOAT(J/12) WRITE(7,*)XLD(J,KK),PD(J,KK),XX 970 CONTINUE 980 CONTINUE J=J+1 990 CONTINUE 991 X=FLOAT(K) J=1 RETURN END

SUBROUTINE SCANDET(K,NM,DTL)

C**********************************************************************C SEARCHING ROUTINE FOR SCANNING DETERMINISTIC *C POPULATION LENGTH DISTRIBUTION *C********************************************************************** COMMON P(492,492),Z(492,492),A(492,492),XM(492,492),F(492,492), $ R(492),RN(492,492),YN(492,492),YW(492,492),MFR COMMON/B/FRQ(40),SE(492),SLT(492) COMMON/D/XDLENPT(1000),DPOP(1000),DYPD(1000),TDL(1000),TYD, $ TPD,TDLC,DALC WRITE(*,*)'ENTERED SCANDET' XLMAX=0. XLMIN=0. NTOTLI=0. RANGEL=0. XMINLEN=0. XMAXLEN=0. TYP=0. TPD=0. TDLC=0. DALC=0. TYD=0. XLMAX=SLT(NM)+50.0*SE(NM) XLMIN=SLT(MFR)-12.0*SE(MFR)

 XLMIN=INT(XLMIN) XLMAX=INT(XLMAX) IF(XLMIN.GE.0) GOTO 1958 XLMIN=0. 1958 NTOTLI=INT((XLMAX-XLMIN)/DTL) WRITE(*,*) 'CALCULATED # OF LENGTH INCREMENTS' RANGEL=XLMAX-XLMIN DO 1955 ZZ=1,NTOTLI DPOP(ZZ)=0. DYPD(ZZ)=0. TDL(ZZ)=0. IF(ZZ.NE.1) GOTO 1940 XMINLEN=XLMIN XMAXLEN=XLMIN+DTL GOTO 1942 1940 XMINLEN=XMAXLEN XMAXLEN=XMINLEN+DTL 1942 XDLENPT(ZZ)=(XMAXLEN+XMINLEN)/2. X=K J=1 DO 1950 I=X,1,-1 IF(J.LT.MFR) GOTO 1949 IF(SLT(J).LE.XMAXLEN) GOTO 1947 GOTO 1949 1947 IF(SLT(J).GE.XMINLEN) GOTO 1945 GOTO 1949 1945 DPOP(ZZ)=DPOP(ZZ)+P(I,J) DYPD(ZZ)=DYPD(ZZ)+YN(I,J) TDL(ZZ)=TDL(ZZ)+(YN(I,J)*XDLENPT(ZZ)) 1949 J=J+1 1950 CONTINUE TYD=TYD+DYPD(ZZ) TPD=TPD+DPOP(ZZ) TDLC=TDLC+TDL(ZZ) IF(DPOP(ZZ).LE.0) GOTO 1955 WRITE(12,*)XDLENPT(ZZ),DYPD(ZZ) 1955 CONTINUE WRITE(*,*)'COMPLETED SEARCH LOOP' if(TYD.LE.0.) goto 2000 DALC=TDLC/TYD 2000 WRITE(6,*)TDLC,TYD,DALC RETURN END

SUBROUTINE SCANPB(K,NM,NDIST,DTL)

C**********************************************************************C SEARCHING ROUTINE FOR SCANNING *C PROBABILISTIC LENGTH DISTRIBUTIONS *C********************************************************************** COMMON P(492,492),Z(492,492),A(492,492),XM(492,492),F(492,492), $ R(492),RN(492,492),YN(492,492),YW(492,492),MFR COMMON/B/FRQ(40),SE(492),SLT(492) COMMON/C/POPLEN(1000),SYLND(1000),XLENPT(1000),TSL(1000), $ TSY,TSLC,SALC,NTOTLI COMMON/E/PD(492,35),PDY(492,35),XLD(492,35) write(*,*)'Entered SCANPB' TSY=0. TSLC=0. SALC=0.  XLMAX=0. XLMIN=0. NTOTLI=0. RANGEL=0. XMINLEN=0. XMAXLEN=0.
156

 XLMAX=SLT(NM)+50.0*SE(NM) XLMIN=SLT(MFR)-12.0*SE(MFR) XLMIN=INT(XLMIN) XLMAX=INT(XLMAX) IF(XLMIN.GE.0) GOTO 958 XLMIN=0.0 958 NTOTLI=INT((XLMAX-XLMIN)/DTL) WRITE(*,*)XLMAX,XLMIN,NTOTLI,DTL RANGEL=XLMAX-XLMIN DO 955 ZZ=1,NTOTLI POPLEN(ZZ)=0. SYLND(ZZ)=0. TSL(ZZ)=0. IF(ZZ.NE.1) GOTO 940 XMINLEN=XLMIN XMAXLEN=XLMIN+DTL GOTO 942 940 XMINLEN=XMAXLEN XMAXLEN=XMINLEN+DTL 942 XLENPT(ZZ)=(XMAXLEN+XMINLEN)/2. X=K J=1 DO 950 I=X,1,-1 DO 949 KK=1,NDIST IF(J.LT.MFR) GOTO 949 IF(XLD(J,KK).LE.XMAXLEN) GOTO 947 GOTO 949 947 IF(XLD(J,KK).GE.XMINLEN) GOTO 945 GOTO 949 945 SYLND(ZZ)=SYLND(ZZ)+PDY(J,KK) POPLEN(ZZ)=POPLEN(ZZ)+PD(J,KK) TSL(ZZ)=TSL(ZZ)+(PDY(J,KK)*XLENPT(ZZ)) 949 CONTINUE J=J+1 950 CONTINUE TSY=TSY+SYLND(ZZ) TSLC=TSLC+TSL(ZZ) IF(POPLEN(ZZ).LE.0.) GOTO 955 WRITE(11,*)XLENPT(ZZ),SYLND(ZZ)cc WRITE(11,*)XLENPT(ZZ),POPLEN(ZZ) 955 CONTINUE IF(TSY.LE.0.) GOTO 1000 SALC=TSLC/TSY 1000 RETURN END