STATUS OF THE RED SNAPPER IN U.S. WATERS OF THE GULF OF MEXICO:

UPDATED THROUGH 1998

Michael J. Schirripa

and

Christopher M. Legault

September 17, 1999

(Dedicated to Floyd)

Southeast Fisheries Science CenterSustainable Fisheries Division

75 Virginia Beach DriveMiami, FL 33149-1099

Sustainable Fisheries Division Contribution: SFD-99/00-75

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BIOLOGICAL CHARACTERISTICS

MORPHOMETRICS

Data sources. Morphometric, growth and otherbiological characteristics of red snapper were evaluatedusing a composite of length and other measurements ofGulf of Mexico red snapper that have been collectedduring research and monitoring programs through theyears. The present evaluation combined the data fromprior analyses with more recent observations from avariety of sources. A description of the earlier data andsources are given in Parrack (1986a and 1986b) andParrack and McClellan (1986), who obtained the dataand prepared computer files of the various data sets. Inaddition, data collected during the trip interceptportions of the National Marine Recreational FisheriesStatistics Survey (MRFSS); the NMFS Headboatsurvey; and samples of commercial and recreationalcatches collected as part of the Trip Interview Program(TIP) of the State/Federal Cooperative StatisticsProgram provided additional data sources. A biologicalprofiles sampling program by the NMFS Panama City(Florida) Laboratory provided additional observationsof growth and fecundity as well as morphometrics (A.Collins and A. Johnson, personal communication).Additional data were provided from research programsat the University of South Alabama (R. Shipp and S.Szedlmayer, personal communication); Louisiana StateUniversity (C. Wilson and J. Render, personalcommunication); the University of West Florida (S.Bortone, personal communication); and the LouisianaUniversity Marine Consortium (E. Chesney, personalcommunication).

Weights of landings in this document are reportedas pounds, whole weight, and lengths are reported asinches, total length. Many of the original length andweight measurements of individual fish were recordedin different units. Conversions among units were donewith sufficient precision to maintain the precision of theoriginal measurement. In addition, the various researchprograms that provide data for the analyses make theiroriginal observations in different physical units (eg.fork or total length) which require conversion to acommon unit when the data are pooled.

Length to weight conversions. Many analysesrequired estimates of individual fish weights in order tocompute mean weights and other population statistics. Since most of the observations of fish sizes wererecorded as length rather than weight, it was often

necessary to estimate weights from lengths. Thepropensity for samples to be measured in a particularunit varied among the fisheries sampling programs. Forexample, headboat length samples were most oftenrecorded as mm total lengths while MRFSS sampleswere in mm fork length. Other sampling programs oftenrecorded both types of length observations. Whererequired, total lengths (TL) were converted to poundswhole weight (WW) using the fitted model ofEquation1. Fork lengths (FL) were converted topounds whole weight using the model of Equation 2.

WW (lbs) = 4.40E-04 * TL (in) ^ 3.056 (1)

WW (lbs) = 6.62E-04 * FL (in) ^ 2.997 (2) Commercial landings are often in gutted condition

and conversions for total and fork lengths to guttedweight (GW) were also needed for several analyses.Data from the TIP samples were used to establish therelation between total length and gutted weight(Equation 3) and fork length and gutted weight(Equation 4). The resulting two equations were used toassign weights from lengths for the commercialsamples, as appropriate.

GW (lbs) = 3.51E-04 * TL (in) ^ 3.114 (3)

GW (lbs) = 6.83E-04 * FL (in) ^ 2.973 (4)

Length conversions. Lengths were commonly recordedas either whole length or fork length. Most of thelength observations from the commercial fishery werein units of fork length, as were most of the observationsfrom the NMRFSS intercept sampling. However, othersurveys took total length measurements. Conversionsamong length units used the regression equation ofEquation 5. The few measurements of standard lengthin the available data that required conversion to totallength used the regression of total length on standardlength (SL) presented in Equation 6.

TL (in) = 0.1729 + FL (in) * 1.059 (5)

TL(in) = 0.0291 + SL (in) * 1.278 (6)

Weight conversions. In 1964 the then Bureau ofCommercial Fisheries established a policy of recordingfinfish landings in units of whole weight (Udall 1964).Since most red snapper are landed in gutted condition,a conversion factor was required to convert the landed

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weight to its equivalent value in whole weight. Aconversion factor of 1.11 was adopted for this purpose.The basis for this value is unknown. However, theFlorida red snapper landings from 1986 to the presentand those of all other states have been adjusted upwardby this factor before entry into the computer files whichconstitute the historical data base for the red snapperfishery. Florida landings prior to 1986 were neverconverted from landed to whole weight (E. Snell,SEFSC, personal communication).

The same problem exists with other species in thedata base. An evaluation of the correction factor beingapplied to grouper landings revealed that theconversion factor being used to convert red grouperfrom gutted to whole weight was in substantial error(Goodyear and Schirripa 1993). Consequently, theaccuracy of the conversion factor for red snapper wasevaluated by regressing total weight on gutted weight(Equation 7).

GW (lbs) = 1.106 * WW(lbs) - 0.02 (7)

The slope of the resulting model, 1.106 is anestimate of the conversion factor and is very close tothe value currently being used. As a consequence, theconversion between whole and gutted weights used foranalyses presented in this report retain the historicallyapplied value of 1.11. This value is somewhat smallerthan the value of 1.15 derived by Camber (1955).Anecdotal information suggests variability in the extentof evisceration among fishermen and variability in theratio of gutted weight to whole weight across seasons.Consequently, additional data may lead to a bettercharacterization the conversion rate for the commercialfishery for this stock.

MORTALITY

Natural mortality. As with most exploited fish stocks,the level of natural mortality in the Gulf of Mexico redsnapper stock is not well defined. It is an importantvariable for the assessment of the status of the stockand its management. Among other things, the level ofnatural mortality determines the unfished lifespan.Amendment 3 to the management plan for this stock(GMFM C 1991) sets the allowable duration of therecovery period for the stock to 1.5 times the unfishedgeneration time, which is controlled in part by longevityof the species. Consequently, the actual level ofnatural mortality in the stock has a direct influence on

the timing and severity of conservation measures torebuild the stock, as well as the estimation of its currentstatus.

Most analyses in past assessments andmanagement advice have assumed a natural mortalityrate of 0.20 based on estimates in the Literature.Goodyear (1994) argued that the true level of naturalmortality was probably less than 0.2 based onobservations by Szedlmayer and Shipp (1992) whichincluded several fish from age 30 to a maximumestimated age of 42 years, indicating a long lifespan.More recent data include fish to age 53 (Wilson et. al.1994). It is also clear from historical records (Camber1955) that large (and presumably old) red snapper wereonce relatively common, which also provides evidenceof a low natural mortality rate.

Pauly (1980) used multiple regression to developa model to predict natural mortality from annual meanwater temperature (C) and the von Bertalanffy valuesfor L4 (cm) and K. Nelson and Manooch (1982) appliedhis model to red snapper from Louisiana, West Florida,East Florida and the Carolinas and estimated M to be inthe range of 0.18-0.20. However, they inadvertentlyused the wrong units for L4 (mm instead of cm, R.Nelson, personal communication). The revisedestimates from Pauly's model and their approximate 95%confidence intervals are 0.37 (0.15-0.94) for Louisiana,0.38 (0.15-0.96) for West Florida, 0.34 (0.13-0.86) for EastFlorida, and 0.35 (0.14-0.88) for the Carolinas. The samemethod, with the parameters of the pooled growthmodel and the mean average temperature off Louisianaof 22 C (Nelson and Manooch 1982) predicts a naturalmortality of 0.357 with a confidence interval of 0.14 to0.91. The other growth models in provide estimates ofM from .12 to .38 with 95% confidence bounds thatrange from a low of 0.02 to high values over 1.0.

As mentioned earlier, the age frequencies of redsnapper assigned ages from examination of their hardparts contain individuals with age assignments up to 53years (Figure 1). Alverson and Carney (1975) presenta method for estimating natural mortality from the valueof K in the fitted von Bertalanffy growth model, theoldest observed fish in the (unfished) population, andtheir empirical estimate of the ratio of Tmb (age at whicha cohort has its maximum biomass in the absence offishing) to the maximum observed age. Applying their

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Figure 1. Age frequencies of red snapper assigned ages from hardparts.

method with the estimate of K for the pooled growthmodel and a maximum age of 53 produces an estimate ofM=0.02. Maximum ages of 30 and 40 years giveestimates of M=0.04 and M= 0.08. Given that the redsnapper data comes from a fished stock, these estimatesof natural mortality chould be significantlyoverestimated, i.e. M should be less than 0.02, if theirmethod holds for this species.

Hoenig (1983) developed models to predict totalmortality from the maximum observed age for fourtaxonomic groupings using linear regression. Hoenig'sregression model for fish stocks with a maximum age of53 years produced an estimate of total mortality of 0.078with an approximate 95% confidence interval of 0.02 to0.30. With his model for all taxa, the prediction was0.086 with an approximate 95% confidence interval of0.035 to 0.21. Even with only a 30 year maximum, thetotal mortality predictions are 0.14 (0.045 to 0.436) and0.15 (0.065 to 0.35) for the models for fish only and forall taxa combined. Since the method predicts totalmortality, the predicted values are upper bounds on thevalue of M.

Hoenig (1983) also presented equations relatingmaximum observed age to total mortality and samples ize. For the red snapper sample size of 6803 and theobserved maximum age of 53, the total mortality rate (Z)is predicted to be 0.18. Again, given that fishingmortality is included, this result implies that naturalmortality is less than 0.18. This method presumesrandom sampling from a stock with constant recruitmentand total mortality. None of these assumptions arevalid for the Gulf of Mexico red snapper data. Many ofthe older fish in the samples were obtained by samplingfishing tournaments to obtain large specimens, andtherefore older fish would be expected to be more

strongly represented in the collections than in thepopulation at large. However, the oldest fish in thedata was collected as a part of a sample of thecommercial catch, and these samples are intended to berandom with respect to the catch.

The apparent longevity of this species arguesstrongly that natural mortality must relatively low.However, the imprecision of the estimates derivedherein, and uncertainty about their robustness whenderived from fished stocks with variable recruitment,result in little guidance about the actual level of naturalmortality for the Gulf of Mexico stock of red snapper.

Release mortality. Gulf of Mexico red snapper lessthan 15 inches total length are now protected fromharvest by a size limit for the commercial andrecreational fishery. The minimum size for recreationalfisherman was increased to 18 inches for the last monthof the recreational season in 1999. Anecdotalcomments from fishermen attest to the consequence ofthis regulation i.e., significant numbers of undersize redsnapper are caught and must be released. The mortalityof released fish is an important consideration inevaluating the conservation effects of regulations thatset minimum sizes and total allowable catch.

Data from some recent studies of the mortality ofreef fishes after being caught and released weresummarized by Parker (1991) who in an earlier reportobserved no immediate mortality of 30 red snapper (<16in TL) caught from 30 m off the Texas coast andreleased at the surface (Parker 1985). That report alsodescribed experiments which found a mortality of 21%for red snapper that were caught from 22 m, returned tothe capture depth and held in wire cages. A similarstudy at 30 m resulted in 11% mortality. Gitschlag andRenaud (1994) found that mortality of small (<32 cm) redsnapper caught by hook and line off Texas and releasedat the surface was 1% at 21-24 m (n=138), 10% at 27-30m (n=27), and 44% at 37-40 m (n=47). These authorsalso observed a mortality of 36% for red snapper thatwere caught from 50 m, returned to the capture depthand held in wire cages. No significant survival benefitwas observed for venting the air bladder with a syringe.Render and Wilson (1993) also found no benefit frommechanical bladder deflation. The latter study foundmean mortality to be 20% for red snapper caught at 21m and released at the surface into a 9-m-deep cage after48 hours. Release mortality was higher in the fall thanspring. Also, there was a nonsignificant increase inmortality with depth of capture. Data from an ongoingmark-recapture study also suggests that mortality

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Figure 2. Lengths of red snappercaught by handlines and power assistedhandlines as a function of depth ofcapture.

increased when capture depth increased from 20 to 30m. About 14% of the fish at 30 m showed signs ofs t r e s s upon r e l ea se (R . Sh ipp , pe r sona lcommunication).

In addition to the hooking and handling mortality,predation of released fish may be important in areaswith significant concentrations of large predators.Parker (1985) noted 19.5% mortality of reef fish caughtand released in 20-30m depths off Daytona, Florida dueto predation. In contrast, Gitschlag and Renaud (1994)noted that predation was not apparent in their study.

Patterson (1999) reported having tagged 2,932 redsnapper from 1995 to 1998. Through October 31, 1998,463 total recaptures were made of 427 fish for arecapture rate of 14.6%. Of the recaptured fish, thirty-five were recaptured twice, and one fish was recapturedthree times. These data further suggest that redsnapper can survive the catch-and-release experience.

It is clear from these studies that significantmortalities of fish caught and released because of sizeor creel limits can be expected and the mortality rate willincrease with increasing depth of capture. The preciseextent of such mortality is not clear. Many of theanalyses in this report assume release mortality of 20%for recreational and 33% for commercial fisheries basedon the depth distribution of their respective effort.

DISTRIBUTION AND MOVEMENTS

Older red snapper appear to favor areas of hardlimestone bottoms or irregular bottom formations butjuveniles are often found over sandy or muddy bottoms(Moseley 1966), but even there they tend to associatedwith structure (Workman and Foster, 1994). The TripInterview Program (TIP) sampling of catches inquiresabout depth of capture for fish that are measured. Thisinformation proved useful for the characterization of thesize distribution of red grouper with depth (Goodyearand Schirripa, 1993). A similar analysis for red snapperis presented in Figure 2 based on red snapper takenwith handlines during the period 1984-1991. The lineevident in the figure is a three point moving average ofthe lengths of red snapper by depth. There is asuggestion of a possible trend of increasing size withdepth in the shallowest depths; however, closerinspection of the data reveals that the "trend" is theresult of a comparative paucity of larger snapper at theshallower depths. Small snapper are found throughoutthe depth range examined.

Mark-recapture studies conducted offshore ofAlabama in 1990 and 1991 (data courtesy S. Lazauski,Alabama Department of Natural Resources; S.Szedlmayer, Auburn University; and R. Shipp,University of South Alabama) suggest that red snapperare relatively sedentary. However, the rapid decay inthe returns of tags with time (D) in these studiessuggests either an extremely high mortality of thetagged fish or a high rate of tag shedding. Either ofthese factors would inhibit identification of possiblelonger term dispersal of fish with age. Past mark-recapture studies also support the notion that adultsare sedentary. Beaumariage and Bullock (1976)commented that red snapper definitely show specificreef residency based on their seasonal returns tosummer forage areas and distinct congregation at reefsin deeper water. Red snapper tagged and recovered indeeper (45-35 m) water also displayed little movement(Fable 1980). Recaptures of fish tagged in shallowerwater (27 m) suggest that the fish moved off the reefafter being tagged but returned to the same reef a yearlater. Inshore-offshore movements are widely reportedand are apparently related to seasonal weather patterns(Beaumariage and Bullock 1976, Bradley and Bryan1975, Moseley 1966, Topp 1964). Occasionalindividuals were observed to move considerabledistances with a maximum of about 350 km in about 3years (Beaumariage 1969).

A more recent study conducted by Patterson (1999)concluded that red snapper did not demonstrate strongsite fidelity to artificial reefs off Alabama. In this study2,932 red snapper over nine artificial reefs off Alabama

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were tagged, with 427 recaptures reported. Of these 427recaptures, 195 were recaptured at their site of releaseand 232 were recaptured at another site. Estimated sitefidelity from a joint encounter model was 48% per year,while estimated site fidelity from declines in recapturesat tagging sites was 21% per year. Hurricanes Georgesand Opal were found to be responsible for are least partof the west-to-east movement of tagged fish. Estimatesof velocity of movement ranged from 0.025 km per dayfor fish transported prior to release to 0.136 km per dayfor fish at liberty during Opal and Georges.

Red snapper tagged and recaptured by Patterson(1999) were reported to have moved greater distancesthan those reported in any previous study. Of the 432fish that were recaptured at sites other than taggingsites, the largest distance moved was 344 km for a fishthat was at large for 400 days. More recent datacontained a red snapper that moved 351 km while atlarge for 598 days. Patterson (1999) suggests that fishin his study were shown to move greater distances inpart because of his larger sample size when comparedto other studies. A larger sample size increases theprobability of recapturing a fish that is at large forlonger periods of time. The conclusion of Goodyear(1995) that the available data indicate that red snapperdo not participate in along-shore seasonal migrationsmay have been due to the lack of fish at large forextended periods of time. Goodyear’s (1995) conclusionthat red snapper do exhibit short range movements andthe possibility of gradual dispersal of individualsthrough emigration from centers of abundance seemsconsistent with the findings of Patterson (1999). Suchmovements could explain the observed rapidcolonization of many artificial reef structures by severalage classes of red snapper. Nonetheless it is possiblethat the main mode of dispersal in the population mayrely primarily on the hydrodynamic transport of theeggs or early larvae, as well as by regular large scaleevents such as hurricanes.

STOCK STRUCTURE

The notion that red snapper larvae are dispersed byoceanic currents promotes the view that the US fisheryis supported by a single genetic stock. This view issupported by a traditional analysis of genetic variationin mitochondrial DNA (Camper et al. 1993). In contrast,Chapman et al. (1995) found differences inmitochondrial DNA in samples from Texas, Alabamaand Florida that suggest that the fish came from

genetically distinct sources. However, the authors ofthe later study noted that their results should beviewed with some reservation because the sample sizeswere somewhat restricted. As a result, although thereis some evidence to the contrary, the best availableinformation suggests that management of the US Gulfof Mexico red snapper as single stock is appropriate.

Besides the extended west-to-east movement of redsnapper reported by Patterson (1999), this study furtherdemonstrated that growth rate and asymptotic length ofred snapper caught off Alabama were are similar tothose fish caught of Louisiana. These findingsstrongly suggest that the red snapper associated withthe artificial reefs off of Alabama do not constitute anindividual population, but rather are part of one Gulf-wide unit stock.

It has been suggested that there exists a pool ofolder and larger red snapper that is currentlyunavailable to the fishery. Lengths from thecommercial bottom longline fishery were used asevidence of the existence of these fish. Indeed, there isevidence to suggest that red snapper caught on bottomlonglines are larger and older than those fish caught byhandlines. In an effort to verify the existence of thispool of fish, the NOAA Ship FERREL conducted aresearch cruise (Cruise FE-99-10-SEF, 5/06-19/99) offAlabama. The objectives of the cruise where todetermine the presence of absence of red snapper indeep waters (35-80 fathoms) in the northeastern Gulf ofMexico via longline or hook and line, as well as tocollect environmental data at each station. A total of 72stations were sampled with 100 hooks per stationsoaked for one hour. A total of seven red snapper werecaught with a total weight of 20.4 kgs. (44.9 lbs.) withthe largest one 8.5 kgs. (18.7 lbs.). Consequently, theremaining 6 fish totaled 11.9 kgs.(26.2 lbs.), for anaverage of 1.98 kgs. (4.4 lbs.) each.

It should be kept in mind that the age structure offishery dependent samples rarely if ever represent theage structure of the population as a whole. Analyticmethods applied in stock assessments allow forvariability in selectivity for the gears underconsideration and explicitly do not assume that thecatch represents the population.

REPRODUCTION

Red snapper spawn offshore beginning in spring orearly summer and appear to move away from reefstructure to spawn (Bradley and Bryan 1975). Collins

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Figure 3. Maturity as a function of totallength for female red snapper. Data fromWilson et al (1994).

Figure 4. Estimated total annual eggdeposition of mature female red snapperas a function of length. Data fromCollins et al (1994). The open circleswere excluded from the regression.

et. al. (1994) noted maxima in the gonosomatic indicesin June, July and August. Juveniles begin to appear intrawl samples in June at about 5 cm total length andcontinue to be seen at this size until about the end ofOctober. These data suggest a protracted summerspawning period which begins in May or early Juneand extends through August or September.

Recently, several sets of data have been collectedthat provide a better characterization of fecundity forred snapper than was available for past assessments.These include estimates of fraction mature as a functionof length, and batch and total fecundity as a function oflength. The maturity-length data were collected byWilson et.al., (1994) from red snapper taken incommercial and recreational gears from the north-centralGulf of Mexico (Figure 3). These data indicate a rapidincrease in fraction maturity from about 10 to 14 inchestotal length.

Complementing these data are estimates of batchfecundity by length and spawning frequency by year

that permit estimation of total annual egg deposition ofmature females by length (Collins et.al. 1994). Visualinspection of the data suggested several possibleoutliers. In particular, estimates of egg deposition ofseveral fish appeared much lower than others of thesame length. These samples were excluded from theanalysis and the affected fish are denoted by opencircles in the scattergram of Figure 4. The fittedfunction of Figure 4 is used in this analysis to estimateannual egg deposition of mature females. Analternative view of these data is that the fecundity-length relationship has a very steep initial slope for thefirst maturing females which declines to lower value forthe older fish. Under this paradigm the fitted functionwould characterize the latter relationship.

This view is supported by the results of Chesneyand Filippo (1994) who injected young female redsnapper with human chorionic gonadotropin to induceovulation. This study focused on the spawningpotential of the smallest (youngest) females that mightcontribute to reproduction. This study concluded thatage 2 females are unlikely to contribute significantly tothe spawning stock biomass, a result consistent withthe maturity-length relation of Figure 3. The batchfecundity at length for those individuals that weresuccessfully induced to spawn is presented in Figure 5with the fitted relationship. The batch fecundity-lengthrelation from these data is much steeper than wasobtained from the prior analysis (Figure 4).

This result supports the notion that the overallfecundity-length relationship is a two-tier function witha very steep initial slope for the first maturing females

and a less steep slope for the older fish. If so, then the

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Figure 5. Hormone induced batchfecundity of young red snapper as afunction of length. Data from Chesneyand Filippo (1994).

Figure 6. Average annual eggdeposition of individual female redsnapper at age.

Figure 7. Relative reproductive importanceof female red snapper by age in the unfishedcondition.

fecundity of the first-maturing individuals would beoverstated by the fitted model of Figure 4. The estimateof average female fecundity at length is the product ofthe maturity schedule (Figure 3) and the averagefecundity of mature females at length (Figure 4). Theaverage fecundity of females below about 10 inchestotal length should be sharply curtailed by the maturityschedule. Consequently, the length-fecundity functionused here and shown in Figure 4 is truncated to zero for

sizes below 10.5 inches (the X-axis intercept from Figure3) when applied to estimate population fecundity.Nonetheless, this model may overestimate thereproductive value of females in the range of about 10to 20 inches total length.

Age-specific estimates of female fecundity fromthese length-fecundity relationships require estimatesof length at age. Because the length-fecundity relationis a power function, the fecundity at mean size is lessthan the average fecundity over all sizes at age.Consequently the mean fecundity at age was evaluatedfrom the length distribution at age at the beginning ofthe spawning season. This was accomplished by firstdividing the age class into 33 different lengthgroupings representing equal numbers of fish based onthe normal distribution (CV=0.10) and the predictedmean size at age from the pooled growth model (Figure6). The mean fecundity of each length group wasestimated from the truncated length-fecundity equationfrom Figure 4, and the average fecundity over all lengthgroups within an age was computed. The resultingestimates of mean fecundity at age are presented inFigure 7 and Table 1. The relative importance of eachage group in the absence of fishing was evaluated bydiscounting the age specific fecundities by theexpected survivorship to age for natural mortality ofM=0.10 (Figure 7, Table 1). These data indicate that theage of maximum egg production in the absence offishing mortality would be 14 for M=0.1.

GENERATION TIME

Generation time (G) for this stock is importantbecause the management plan for this species specifiesthat the recovery schedule for overfished stocks is tobe no greater than 1.5 times the unfished generationtime. It is estimated as

n n

G = [ E a Ea Na ] / [ E Ea Na ] (8)

a=1 a=1

where, a = age, n = number of ages in the unfishedpopulation, Ea = mean fecundity of females at age a, Na

is the average number of females alive at age a in theabsence of fishing, i.e.,

a-1

Na = N1 exp( - E M j ), (9)

j=1

and M j = Natural mortality of females of age a whilethey were age j.

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Figure 8. Distribution of summer SEAMAPtrawl samples.

Expression 8 provides the same result for any constantvalue of N1, so the values of N in expressions 8 and 9are evaluated here on a per recruit basis (N1 = 1).Inspection of the equations reveals that the otherimportant parameters are fecundity and naturalmortality. Fecundity per recruit and estimatedgeneration times for M=0.10 are presented in Table 1.The generation time estimates were 19.6 years.

TRENDS IN RECRUITMENT

Fall Groundfish Survey. The NMFS Pascagoula, MS,Laboratory have conducted a bottom trawl survey inthe northern Gulf of Mexico in the fall of each yearsince 1972. This program has become known as the“Fall Groundfish Survey” and is described in somedetail by Nichols and Pellegrin (1992). This survey isconducted primarily with 40-foot bottom trawls inOctober and November. The early years of the surveywere primarily conducted in the north central Gulf butthe survey was later expanded, primarily westward, toinclude samples off the Texas coast. In order tomaintain a consistent time series, the samples selectedfor inclusion in indexing red snapper recruitmentstrength were from the primary survey area thatincludes depths of 5 to 50 fathoms between 88/ and91/30’W. For each set, the number of snapper caught,their total weight and trawl duration are known. Thisinformation provides an index of numerical abundance(i.e. , the number captured per tow-hour). It alsoprovides information on the mean size of the redsnapper that are caught (total weight/total number).Summaries of the Fall Groundfish Survey werecalculated in the same manner as described in Goodyear(1995).

Weighted and unweighted mean catches per tow-hour were estimated for 40 foot trawl samples based ontow duration and period (Table 2). Weightings werebased on tow duration. Mean size of red snappercaught ranged from 0.04 to 0.19 kg during the period,indicating that most of the fish caught are small, pre-recruits (at leas t to the directed fishery). These datathen provide a time series for indexing juvenileabundance beginning in 1972. They indicate thatrecruitment declined during the mid 1980s and reachedit’s lowest level in 1987 (Table 2). These data alsoindicate recruitment increased in 1990 to the highestlevel in about 10 years. The 1995, 1996, and 1997estimates were also high compared to most years after

1982, but still lower than the 1970's.Since 1985, length frequencies of several species

including red snapper have also been taken during thesurvey. These data demonstrate that both age-0 andage-1 red snapper are common in the catch and themean weight of the catch reflects the ratio of the twoage classes in a given year (Goodyear 1995). Therelatively low mean weight of the sampled red snapperin 1997 indicates that these samples were dominated byage-0 fish. Coupled with this were observations ofrelatively high numerical abundance for 1997. Theseobservations could indicate a relatively strong yearclass in 1997.

Summer SEAMAP Survey. The SEAMAP program hasbeen coordinating trawl samples in the Gulf of Mexicoduring June and July since 1982. The data aremaintained at the NMFS Pascagoula, MS, Laboratoryand were provided by Pascagoula Laboratory staff forthe analyses in this report. These samples are takenwith the same gear used in the NMFS Fall GroundfishSurvey, but even when restricted to the 5 to 50 fathomdepth interval, they cover a much larger area (Figure 8).Summaries of the Summer SEAMAP Survey werecalculated in the same manner as described in Goodyear(1995) are given in Tables 3-5.

Exploration of the trawl catch data in Goodyear(1995) revealed that several different vessels hadcaught red snapper during the survey period. Theseanalyses were updated with resource cruise datathrough 1998 (Table 3). Individual catches werestandardized by dividing the catch by hours trawled

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Figure 9. Red snapper catch-per-hourtowed in the Fall Groundfish and SummerSEAMAP surveys.

71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97

YEAR CLASS

0

1

2

3

4

RE

LA

TIV

E

ST

RE

NG

TH

Figure 10. Trend in red snapper year-class strengths based on the resourcesurvey data (bars) and GLM estimate (linewith 80% CI). Each time series has beenscaled to it’s mean to provide comparativeresults.

and multiplying the result by the ratio of trawling speedto 2.5 knots. The NMFS vessel Oregon II provided thebulk of the samples and had the broadest geographiccoverage. As noted in Goodyear (1995), the dataappeared too sparse to attempt a meaningful calibrationof catch rates among the vessels; however, catch ratesdid not appear grossly different for the different

vessels. Inspection of the mean weights indicated thatsome vessels may have selected more strongly for age-0 fish than did the Oregon II. Based on similarity ofmean size and sampling consistency, I restricted furtheranalysis to trawl samples collected by the Oregon II,Tommy Monroe, and LUMCON Pelican. Only datafrom the Oregon II were available for 1995, 1996, 1997,and 1998. The annual mean catch per tow-hour, andmean weighted by tow duration (total catch/total hourstowed) are given in Table 4. The unweighted meancatch per tow during the 1997 survey was down relativeto 1994-1996, but still relatively high when consideringthe entire time series. Comparison of the fall- andsummer- survey trends in catch per tow are presentedin Figure 9.

Functional regression of the resulting unweightedcatch per tow-hour for the age-1 component of the FallSurvey on the summer catch rates for comparable yearswas found to be significant with a near zero intercept inGoodyear (1995). Therefore, the fall age-1 values wereconverted to their summer equivalents by dividing bythe mean ratio of the 1985-1994, age-1 catch per hour forthe Fall Groundfish Survey to the 1985-1994 catch perhour in the summer SEAMAP samples (0.474). Thisprocedure provided estimates of the abundance of age-

1 red snapper back to 1972 (year class strengths back to1971) in units equivalent to the summer SEAMAPsurvey. A GLM procedure using the “delta” method(Lo et al. 1992) was also applied to this data. First, amodel was built only around tows with positive catchesof red snapper to arrive at a (balanced design) estimateof the standardized mean catch per positive tow foreach year. A second model was then built consideringthe presence/absence or red snapper within each cell(year, grid/depth) to model the probability of successfulcatch of red snapper. The final index was thencalculated by multiplying the GLM index of CPUE fromthe first model by the GLM index of percent-positive-tows from the second model. Significant effects werefound for area (statistical grid) and depth. The resultsare presented in Table 5 and Figure 10. Future effortmay directed at examining more closely the distributionof the presence/absence model and perhaps assuminga binomial distribution to logit of complimentary log-logtransformed data rather than the approach used by Loet al (1992) (log transform of proportion positive + 1).

CATCH TRENDS

COMMERCIAL CATCH

This section updates the commercial catches forthe U.S. Gulf of Mexico red snapper though 1998. Weuse the same methods and data sources used in the1995 (Goodyear 1995) , 1996 (Schirripa and Legault

10

1996), and 1997 (Schirripa 1998) assessments, as well asmaking use of more historical records. Some recordspertaining to the red snapper fishery extent as far backas the 1800's. During these early years (and eventoday) several species of Lutjanus were marketed as“red” snapper, so landings may represent a less thanpure species composition. Nonetheless, Lutjanuscampechanus was the most sought after and marketablesnapper species and clearly dominated the catches.This aside, we felt that this information was worthy ofa closer examination and would contribute valuablehistoric insight into the long-term productivity of thestock.

11

Figure 11. Gulf of Mexico showing statistical grids used in this assessment.

12

HAND AND POWERLINE VESSELS

FLA

0

200

400

600

800

1000

A L

0

10

20

30

MISS

0

5

1 0

1 5

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0

5 0

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T O T A L

0

200

400

600

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4 9 5 2 5 5 5 8 6 1 6 4 6 7 7 0 7 3 7 6 7 9 8 2 8 5 8 8 9 1 9 4 9 7

Figure 12. Number of documented vesselsusing handlines landing in each state by year.

Trends in the Commercial Fleet. The fishery for reeffish in the Gulf of Mexico uses several types of gearincluding hand lines, power assisted lines (bandit rigs),bottom longlines, buoys, and fish traps. Red snapperare caught by each of these gears although, as

discussed later, the total landings of red snapper fromfish traps have been small. Red snapper are primarilyharvested with handlines that are operated eithermanually or with the assistance of electric or hydraulicreels. For the most part, the landings from all of these"handline" gears have been reported under a singlegear code. Consequently, they cannot be partitionedinto more discrete categories and are referenced hereinas "power and hand lines" or simply "handlines."Similarly, bottom longlines and buoys have beencombined into a single category termed "bottomlongline."

The data from the operating units files on thecomposition of the fishing vessels categorize them as"documented vessels" or "boats". Documentedvessels are those which meet the criteria that requirethem to have Coast Guard documentation numbers.Boats include all other vessels. They are generallysmaller, state registered vessels. The structure of thehistorical files related to the number of boats in thefishery prohibits separation of those which are usedinshore and those that might venture offshore to fishfor reef fish. Analysis of the logbook data filesrevealed that documented vessels accounted for about86% of the total red snapper landings reported since1990 (89.8% in 1990, 88.1% in 1991, and 79.6% in 1992).Consequently only documented vessels will beconsidered here.

The numbers of unique documented handlinevessels landing from 1949 to 1998 by state are given inTable 6. An individual vessel may be included in thetotal for more than one state if it visited ports of morethan one state in a year. Similarly, it might be includedin more than one of the gear types if it fished more thanone gear during the year. The total number ofdocumented vessels fishing with handlines hasincreased greatly over the years (Figure 12). However,most of this increase was accounted for by the increasein Florida. Also, there was a notable increase in thenumber of such vessels landing in Louisiana beginningin 1983.

Camber (1954) reported that the size of the redsnapper fleet operating from Florida ports for variousyears 1875 to 1940 ranged from a low of 52 vessels in1923 to a high of 76 vessels in 1927. Pensacola was themain port for the red snapper fleet. Camber (1954)

further reported the size of the red snapper fleetoperating out of Pensacola (Table 7). The number ofred snapper vessels operating out Pensacola forvarious years 1875 to 1951 ranged from a low of 10vessels in 1878 to a high of 42 vessels in 1895. Thenumber of vessels originating from ports in the UnitedStates and operating regularly on the Campeche Banksin 1951 was about 39. These were distributed asfollows: Pensacola, Florida, 22 vessels; Mobile,Alabama, 9 vessels; Galveston, Texas, 3 vessels; Tampa, Florida, 2 vessels; Freeport, Texas, 2 vessels, andNiceville, Florida, 1 vessels (Camber 1954). While someminor discrepancies were found in some of the data, it

13

B - DIRECTED COM RED SNAPPER LANDINGS - TX

0.0E+002.0E+064.0E+066.0E+068.0E+061.0E+071.2E+071.4E+071.6E+07

80 859095 0 5 1015 202530 3540 4550 556065 7075 8085 9095

YEAR

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A - DIRECTED COM RED SNAPPER LANDINGS - FL

0.0E+00

2.0E+06

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80 859095 0 5 10 152025 303540 45505560 6570 75808590 95

YEAR

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DS

C - DIRECTED COM RED SNAPPER LADNINGS - GOM

0.0E+002.0E+064.0E+066.0E+068.0E+061.0E+071.2E+071.4E+071.6E+07

80 859095 0 5 101520 2530 354045 5055 6065 707580 8590 95

YEAR

PO

UN

DS

D - TOTAL COM RED SNAPPER LANDINGS - US GULF

0.0E+002.0E+064.0E+066.0E+068.0E+061.0E+071.2E+071.4E+071.6E+071.8E+072.0E+07

48 5052 54 5658 6062 64 6668 70 7274 7678 80 8284 86 8890 92 9496

YEAR

PO

UN

DS

Shrimp Bycatch

Foreign Waters

US Waters

Figure 13. U.S. directed commercial landings of red snapper for Florida (A), Texas(B), and the entire U.S. (C), 1880-1998(some years missing); U.S. directed and non-directed commercial landings from domestic and foreign waters (D), 1948-1998.

remains clear that during the early to mid parts of thenineteen-hundreds the red snapper fleet wasapproximately one quarter the size of the 132 Class A(2000 pound/day) endorsements that are active today.

Trends in Landings. The commercial landings arepresented in Table 8 by state of landing. Red snapperlandings into the U.S. have traditionally come intoFlorida (Figure 13A), and to a less extent Texas (Figure13B). Interesting to note is the decline in landings inthe 1920's and 1930's (Figure13C). Camber (1954)attributes this decline to the Great Depression andsubsequent drop in the price given to fisherman fortheir catch. Starting in approximately 1950, however,landings again start to increase. Nineteen-fifty was alsothe year in which electric hand reels were invented andintroduced into the fishery. These reels made itpossible for fishers to fish deeper waters, presumablya previously unexploited part of the stock.

The total poundage of red snapper caught from theentire Gulf of Mexico and landed in the U.S. peakedtwice: once at approximately 14 million pounds ataround the turn of the century, and again at

approximately 14 million pounds in 1965 (Figure 13C). At the time of the 1900 peak, little or no recreationalfishery was operating in the U.S. Furthermore, thecommercial fleets of the U.S. had not yet made a majormove to the Campeche Banks of Mexico (Camber 1954).This then suggests that the 1900 peak of 14 millionpounds represents the entire harvest of red snapperfrom U.S. waters. In these early days of the fishery,before ice was used, the sailing vessels of the fleetrelied on live wells to keep their catch alive until theywere ready to be sold. It is likely that many of the fishdid not survive the duration of the trip and werejettisoned before reaching port. Consequently, the 14million pounds mentioned above most probably underestimates the actual catch from the stock. Unlike the1900 peak, the 1965 peak was made up of approximatelyhalf red snapper from domestic waters and half fromforeign waters. However, in 1965 there was asubstantial recreational fishery operating in U.S. waterswhose catch is not accounted for in these estimates.Ellis et al. (1958) estimated that 81 headboats made over11 thousand trips and 241 charter boats made over 11thousand trips off the Florida west coast in 1955. At

14

the time of this assessment there is no reliable estimateof recreational harvest for the 1960's, so we are not ableto determine the total harvest that was taking place atthis time. Moe (1963) also notes the importance of redsnapper to the commercial and early recreational fisheryas far south as Naples, Florida.

After 1965 commercial landings of red snapperstarted a long decline. This decline is attributablemostly to the decreased landing coming from theCampeche banks. Because this decline pre-dates theMagnuson-Stevenson Act of 1976, which essentiallyprohibited U.S. vessels from fishing in foreign waters,it is possible that this decline was due to a decline inproductivity caused by overfishing of the CampecheBanks stock. After 1976 the Campeche Fleet of the U.S.relocated and fished only the U.S. waters of the Gulf ofMexico. This sudden and large increase in effort on thealready exploited red snapper stocks likely lead to thecontinued decline in landings and the eventualoverfishing of the stocks in U.S. waters.

An attempt was made to fill in an entire red snappercommercial catch matrix for all gears from 1948 to 1998using as little estimation as possible. Estimates ofTexas red snapper landings by shrimp trawls wereavailable for 1950 to 1971 (Bradley and Brian 1974).Estimates of total U.S. red snapper landings by shrimptrawls were available for 1962-1988. This resulted in 10overlapping years (1962-1971). These years were usedto produce a ratio of total U.S. trawl landings to Texastrawl landings (Total:Texas = 3.94). This ratio was thenused to estimate the total U.S. trawl landings of redsnapper from 1950-1961 by multiplying it by the Texastrawl landings. To complete this part of the matrix, U.S.trawl landings for 1948 and 1949 were set equal to the1950 value. Estimates of discarded red snapper viashrimp bycatch were available from 1972 to 1998.However, estimates of sold red snapper via shrimpbycatch were available from 1950 to 1988 (using theabove estimates). This resulted in 17 years ofoverlapping estimates (1972-1988). These years wereused to regress discarded bycatch against soldbycatch:

discarded bycatch = (sold bycatch * 2.53) + 260

This aproach assumes that the average discarded tolanded bycatch during the period 1950-1972 was equalto that from 1972-1988. Non-stationary age classdistributions could result in different rates.

To estimate the proportion of landings thatoriginated from U.S. waters as opposed to Mexican

waters, similar relations were established on a statespecific basis. For Florida, ratios of U.S. to total catchwere available for 1948-1951 (Camber 1954) and 1964-1966. This mean ratio of these years (0.6261) was usedto estimate foreign catch for 1952 to 1963. For Alabama,Mississippi, and Texas an increasing trend was evident,showing that an increasing number of landings werecoming from foreign waters. Consequently, only themost recent years were used in the ratio: 1964-1965 forAlabama, 0.0344; 1964-1966 for Mississippi, 0.2684;1964-1966 for Texas). For all available records 100% ofred snapper landings from Louisiana came from U.S.waters. Consequently, all landings from 1948 to 1964were considered domestic. For all states combined theratio of U.S. to total landings used to estimate domesticlandings was 1964-1966 mean (0.5032). Although notused for any type of formal analysis in this assessmentthe above estimation procedures, coupled withlandings data, resulted in a more complete catch matrixof commercial red snapper from U.S. and foreign watersfor 1948 to 1998 (Figure 13D) and offer a more historicalperspective than presented in previous red snapperassessments.

Reflection upon the history of the red snapperfishery lead to the conclusion that these catch statisticswere not appropriate to use for estimating maximumsustainable yield for three reasons. First, due to theinefficient manner in which red snapper were handledand the resulting unknown number of discarded fish,the actual production of the stock (i.e. catch) wasprobably considerably higher than the landings datasuggests. Second, the history of the geography of thefishery shows that the red snapper stock wassequentially overfished, starting in Pensacola andworking down to southern Florida (and eventuallyleading to the Campeche Banks). This type ofsequential overfishing means that the productivity ofthe fishery was maintained not by the stock respondingwith increased recruitment, but rather by moving to newareas that were previously not exploited. Because theoriginal areas of exploitation were presumably fished onthe way to and back from the newly exploited areas (i.e.the waters off Pensacola were most probably fished onthe way to and back from fishing the “MiddleGrounds”), the original areas were not given a chanceto rebuild. Further consideration of this catch timeseries, taking into account changes in the fisheries andin view of the above mentioned effects, could provideadditional information on stock productivity.

Spatial Distribution. The bulk of the recent

15

Figure 14. Spatial distribution of the commercialcatch of red snapper in the U.S. Gulf of Mexicofor four time periods, regardless of the locationswhere they were landed.

commercial catch of red snapper is from the northernGulf of Mexico between Panama City, Florida, andGalveston, Texas. Tables 9-13 give the spatialdistribution by state of landing and Tables 14-16 givethe spatial distribution by gear. Landings in the earlieryears of the dataset peaked in grids 13-14 (Table 9).This pattern was true at least since 1965-72 but becamemore pronounced with the relatively greater decline inlandings to east and west of this area by 1975-1992(Figure 14). In the most recent years the importance ofthis area to the landings has declined and the focus ofthe fishery has continued to move westward even aftersignificant conservation measures were imposed.

A significant proportion of the domestic snapperlanded in Florida has been derived from areas west ofthe Mississippi River since at least 1972 (Table 10). Thesame observation is true for Alabama and Mississippi(Table 11). Louisiana landings of red snapper aredominated by fish caught off Louisiana in grids 13 - 14(Table 12). Texas commercial landings are primarilycomposed of fish from grids off Texas, but some arealso taken off Louisiana (Table 13). In earlier years , aconsiderable proportion of the landings in Texas werefrom Mexican waters (the other category of Table 13).The general trend is for red snapper sold in a state tohave been captured in adjacent waters or to the west.It is clear from these data that the commercial harvest ofred snapper has moved steadily westward in the Gulf ofM exico since at least the late 1960s, and the species isnow essentially commercially extinct in the eastern halfof their historical range in the U.S. Gulf of Mexico.Also, landings attributed to foreign waters began todecline after 1967, well before the fleet was excluded

from Mexican waters.

Catch per unit effort. Data for this analysis wereavailable from the Reeffish Logbook Program. TheReeffish Logbook Program was initiated in 1990, and atthis time required that all vessels holding reeffishpermits in the states of Alabama, Mississippi,Louisiana, and Texas, and all trap fisherman in the stateof Florida, to report on each fishing trip made. ForFlorida permitted vessels, only those fishermanrandomly selected each year (constituting a 20 percentsub-sample of all permitted vessels in Florida) wererequired to report (note that this 20% sub-sample couldbe reporting on fishing done anywhere in the Gulf ofMexico, not just Florida). Mandatory reporting for allFlorida permitted vessels began in 1993. Because thiscreates a difference in the sampling universe, anytrends discovered in the data should be viewed withthis possible bias in mind. Because releases are notreported in the Reeffish Logbook Program, theseestimates reflect only fish kept. Only those tripsreported to have landed red snapper were considered.Due to the different trip limits of the class ‘A’ and class‘B’ endorsements and the set amount of endorsements,it is possible that biases exist in the logbooks thatcould influence estimates of CPUE. Consequently, wechose not to estimate CPUE in a formal manner butrather to examine the data for trends that might indicatechanges in the fishery and/or stock size.

The number of logbooks reporting landings of redsnapper in 1998 was 3885 (Table 17A). This is thehighest number since the program began, giving thepossible indication that the proportion of positive tripsis increasing. The mean catch per trip has stayedrelatively constant since 1995, however, this could bebiased due to the trip limits as discussed above. Themean number of days per trip has shown a steadilydecreasing trend, from a high of 5.83 days in 1990 to alow of 1.94 days in 1998 (Table 17B). Coupled with thishowever is steady increase in the number of hooks perline, from a low of 7.63 in 1991 to a high of 14.51 in 1998(Table 17D). Although there is a noticeable change inthe trends between 1992 and 1993 (when 100% vesselcoverage was initiated) the trends continue from 1993to 1998, suggesting the trend is real. Increasedefficiency is also indicated by the decreasing number ofhours that the gear is fished (Table 17E).

T he above mentioned trends indicate that thecommercial handline fishery is increasing in efficiency.Commercial handline vessels appear to be making

16

Figure 18. Length frequencies anddispositions of red snapper collected byobservers aboard handline vessels during the1995 fishing season.

0 500 1000 1500 2000 2500

POUNDS PER TRIP

0.0

0.2

0.4

0.6

0.8

1.0

CU

MU

LAT

IVE

FR

EQ

93-94

95-96

97-98

COMMERCIAL HANDLINES

Figure 15. Cumulative frequency distribution ofpounds-per-trip for commercial handline trips byyears 93-94, 95-96, and 97-98.

Figure 16. Length frequencies of red snapper bygear 1979-1998

Figure 17. Length frequencies of red snappercaught by handlines by area, 1979-1998.

shorter trips, using more hooks per line and catchingthe same poundage of red snapper. This change infishing strategy could be the fishery responding to triplimits and/or changes in stock abundance. Figure 15shows the cumulative frequency distribution of thepounds per trip for commercial handline vessels. Thisfunction clearly shows the bimodal distribution of thecatch per trip frequency distribution: one mode atapproximately 200 pounds per trip (class ‘B’endorsements) and another mode at approximately 2000p ounds (class ‘A’ endorsements). Vessels with class‘B’ endorsements made up less of the total reported

catch in 1993-94 than they did in 1997-98 (Figure 15).There appears to be an increasing trend towards the

vessels with class ‘A’ endorsements making up agreater portion of the total red snapper catch with time.

Size Distribution of the Commercial Harvest. Thecommercial handline fishery was found to harvest adifferent size distribution of red snapper than does thecommercial bottom longline fishery (Figure 16). For thisreason, the harvest from these two gears were stratifiedinto two different groups and treated individually:harvest from longlines, and harvest from every othergear (nearly all of which is handline gear).

Examination of the length distributions from thecommercial handline gear over geographic area revealedthat there was also a difference between the east andwest areas of the Gulf of M exico (Figure 17). For thisreason, the landings from the handline gear wherefurther partitioned into those coming from grids 1-10(east Gulf of Mexico), and those coming from grids 11-21 (west Gulf of Mexico). As with past assessments,this resulted in a total of three partitions for thecommercial landings.

Discards . It would appear from the data summarized inFigures 16 and 17 that red snapper have beencommercially harvested from about 10 inches to over 36inches since 1978. The initial effect of the minimum sizein 1984 was rather modest. However, given the cleardecline in the proportion of larger fish in the landings,the minimum size probably had a significant positiveconservation impact by limiting the downward shift inthe mean size of the landings. The smallest fish in theharvest before the minimum size were from offshore

17

Texas and Louisiana, and the spatial distribution of theharvest has shifted westward with time (Figure 18).Consequently, the proportion of the catch that iscurrently being discarded is probably greater thanindicated by the change in the length frequencies at thetime the minimum size was implemented.

An observer program to collect data about the reeffish fisheries in the Gulf of Mexico was initiated in 1995,and included some red snapper trips aboard handlinevessels (data provided by E. Scott-Denton and S.Russell). Among other information, the observersrecord the lengths and fates of the catch, including thediscards. Data from these trips during the 1995 seasonindicated that 40.7% of the red snapper caught werediscarded (Figure 18). By weight these fish constitutedabout 18.6% of the red snapper catch. Theobservations were of fish caught at an average depth of40 m (range 33 to 62 m) generally offshore Louisianaand east Texas. Only about 1.6 percent of the catchwas discarded dead, but most of the discarded fish hadeither stomachs or eyes protruding and many of theseprobably suffered delayed mortality.

The reef fish logbooks also contain fields forreporting the number of discarded fish since 1993.Most captains have left that field blank. However, forthose captains who reported discards, the fraction ofthe catch discarded by number was about 31% in 1993,28% in 1994, and 30% in 1995. These rates are less thanthe observer data indicated. The differences may be theresult of small sample sizes or different geographicalcoverage between the two sources. It is apparent,however that large numbers of fish are currently beingdiscarded because of the minimum size.

The number of fish discarded at each age wasestimated by dividing the estimated harvest at age bythe average fractions of the age classes above age 2that were below the minimum size during the fishingseason each year. This procedure ignores the bycatchof ages younger than 3, and assumes that vulnerabilityto the gear is a function of age for ages older than 3.This method was applied to the harvested componentof the observer data with a minimum size of 15 inchesbased on the transition from discarded to kept fish inFigure 18. The predicted number of discards is veryclose to the number observed, but have a slightly olderpredicted mean age. Subsequent applications of thistechnique to estimate the discards at age for thecommercial catch employ a transition size 0.5 in largerthan the minimum size limit and predict total discards of25 to 30 percent of the total catch by number. Thenumber of discarded fish at age that died and

contributed to fishing mortality each year was estimatedas the product of the estimated number of discards andthe release mortality for the commercial fishery.

RECREATIONAL FISHERY

Data sources. The recreational harvest estimates forred snapper are derived from a combination of threesources. The primary data source for the recreationalharvest of red snapper is the National M arine FisheriesService (NMFS), Marine Recreational Fishery StatisticsSurvey (MRFSS), which covers the period 1981-1998.This survey provides estimates of the numbers of redsnapper harvested during bimonthly periods (waves)by state and mode (shorebound, private/rental boatsand party/charter boats) with several exceptions. Therewere no estimates of harvest for wave 1 (January-February) in 1981. Texas boat mode was not sampledfrom 1982-1984. Texas was not included in the surveyafter 1986. Party boat (headboat) sampling wasdiscontinued after 1985 for all waves and states. Thischange resulted in discrete estimates of the charter boatcatches for 1986 and later years. MRFSS adopted a newalgorithm in 1994 and reestimated past catches wherethe required input data were available. The currentanalyses use these "new" estimates, except for Texaswave 4 in 1981-1985 and 1979-1980, which were notupdated.

The suspension of the party boat sampling by theMRFSS coincided with an expansion of NMFSHeadboat Survey conducted by the NMFS Beaufort

18

Figure 19. Estimated numbers of red snapperharvested by recreational fishers by area, 1981-1998.

Figure 20. Estimated numbers of red snapperharvested by recreational fishers by mode, 1981-1998.

Laboratory (data courtesy B. Dixon, SEFC BeaufortLaboratory) to include U.S. Gulf of Mexico ports. Thislatter data provide estimates of landings by partyboatsfor all states after 1985 and constitutes the secondsource of recreational harvest estimates.

The third source of recreational harvest estimateswas provided by the Texas Parks and WildlifeDepartment (TPWD) coastal sport fishing survey (datacourtesy Texas Parks and Wildlife). This surveyprovides estimates for numbers harvested by boatmodes exclusive of party boats for Texas for 1986 andlater years. Harvest by shorebound fishermen has notbeen included in the Texas estimates since 1985. Datafrom Texas Parks and Wildlife Coastal Sport FishingSurvey was not available for 1998 at the time of thisassessment. This survey covers the private/rental andcharter boat mode for this state. However, the majorityof recreational landings of red snapper from Texas comefrom the headboat fishery, which was included in theNMFS Headboat Survey. the 1997 estimates for theprivate/rental and charter boat mode of Texas were usedfor the missing 1998 data

Each of these surveys also collected data throughintercept programs to obtain information on effort andthe size and species composition of the catch. Thesedata were used to estimate mean weights and otherrequired statistics. Additional data relevant to therecreational harvest were obtained from the TIP filesand research programs at the NMFS Panama City, FL,Laboratory. The latter include data from the bioprofilessampling program and the Charterboat Logbookprogram which provides catch and effort data for thatsector (data courtesy J. Lacey). The Alabama MarineResources Division initiated a program in 1991 tosupplement the charterboat logbook data with sizeinformation and provided extensive data on the sizecomposition of red snapper harvested by the Alabamacharter fleet (data courtesy H. Lazauski).

Trends in recreational catch and harvest. Theestimated mean weight of recreationally landed redsnapper by state, year, and mode are given in Table 20-21. The total estimated recreational harvest (in pounds)by the various areas and modes shown in Figures 19and 20, respectively.

The MRFSS also estimates the number of fish thatare caught and released (Table 22). The mode coveredby this survey have varied over the years, butinspection of the estimates show a consistent trend ofan increased fraction of the catch released with time foreach mode until the most recent years (Table 22).

Overall, they indicate that red snapper were rarelyreleased in the early years of the survey but that morethan half of those caught were being released by 1990,but the proportion released declined thereafter (Figure21). The sharp decrease seen in 1990 is due to anincrease in the minimum size put into place that year (13

inches) as well as the relatively strong 1989 year classrecruiting to the fishery (but not yet legal size).Releases then start to decline after 1990 but increaseagain in 1994 when the minimum legal size was raised to14 inches. In 1995 and 1996 the estimated numberreleased continued to increase as the minimum legalsize was raised to 15 inches and year class strengths

19

M R F S S R E L E A S E S 1 9 9 8

0

1 0 0

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TH

OU

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ISH

M R F S S R E L E A S E S 1 9 9 3 - 1 9 9 7

0

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5 0 0

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1 2 3 4 5 6W A V E

TH

OU

SA

ND

S O

F F

ISH

R E L E A S E D

K E P T

Figure 22. Mean estimated number of releasedred snapper from charter and private/rental boatsfor 1993-1997, and for 1998 by wave.

Figure 21. Estimated fractions of red snappercaught and released by recreational fishers, 1981-1998.

generally increased. This pattern is consistent withchanges in the length frequency of red snapperharvested (see below) and is likely a result of theminimum size and the growth of the 1989 year class aswell as the observed increase in recruitment for 1993-1995.

In 1998 the red snapper recreational fishery quotawas met and the fishery closed from September toDecember thirty-first. This lead to concerns beingraised regarding the increased numbers of red snapperthat would be caught but subsequently released in partof wave 5 (September & October) and all of wave 6(November & December). Concern that a large number

of these fish would die due to release mortality any wayresulted in questions regarding the usefulness of aclosed season as a management tool to acheiveconservation objectives. Examination of the number ofreleased red snapper by wave shows that while 100percent of the red snapper caught in wave 6 were in factreleased, the estimated number of released fish was lessthan half the 1993-1997 average (Table 23).Consequently, although a high percentage of redsnapper were released, the closed season appears tohave resulted in fewer fish being caught. Thissuggests that recreational fishers are able to direct theireffort away from red snapper at the close of the season.Although the recreational fishery apparently got off toslow start in wave one, the fishery nonetheless madeup this for this with a higher than average catch inwaves 2, 3, 4, and part of wave 5 in 1998 (Figure 22).

Catch and harvest per unit effort. Trends in catch-per-unit effort (CPUE) were used as indices of abundancefor the red snapper stock. Data supplied by the MarineRecreational Fishery Statistical Survey (MRFSS) werecombined with survey data from Texas Parks andWildlife to estimate recreational CPUE. These twosources of data were chosen because they enabled usto consider those fish that were released as well asthose harvested. While the Texas Parks and Wildlifesurvey does not record releases directly, we chose toconsider the data for years previous to 1990, the yearwhen meaningful regulations could have resulted insignificant releases. CPUE was estimated by thefollowing equation:

CPUE = (ncaught / (nhours * nanglers))

where ncaught is equal to the total number of redsnapper caught, nhours is equal to the number ofhours in the trip, nanglers is the number of anglerscontributing to ncaught. A GLM procedure using the“delta” method (Lo et al. 1992) was applied to this data.First, a model was built only around trips with positivecatches of red snapper to arrive at a (balanced design)estimate of the standardized mean catch per positivetrip for each year. A second model was then builtconsidering the presence/absence or red snapper withineach cell to model the probability of successful catchof red snapper. The final index was then calculated bymultiplying the GLM index of CPUE from the first modelby the GLM index of percent-positive-trips from thesecond model. Significant effects were found for state,month, and mode. Recent evidence of the red snapper

20

A - PROPORTION POSITIVE

0.00

0.50

1.00

1.50

2.00

2.50

79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98

YEAR

RE

LA

TIV

E I

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Figure 23. Proportion positive (A), catch per unit effort (B), and GLM index (C) for seven variousmodels of recreational CPUE by year. Panel D depicted the final GLM index.

population expanding into areas where, at least inrecent years , red snapper were found, we felt includingthe proport ion of positive trips into an index wouldbetter describe the relative abundance of the part of thestock captured by the recreational fishery.

Several explanatory factors were considered in theGLM procedure, including state, month, and mode offishing. To examine the effects of each of these factorswe first ran the model using a total of seven differentconfigurations: (1) no factors (none), (2,3, & 4) each ofthe factors individually (state, month, and mode), (5) allthree factors combined (smm), (6) all three combinedwith state month interaction (smm-st*mth), and (7) allthree combined with month mode interaction (smm-mth*md). The results of these seven models used toestimate proportion of positive trips, all scaled to theirrespective means, are shown in Figure 23A, to estimateCPUE in Figure 23B, and the GLM Index in Figure 23C.Although each of the factors considered resulted in asignificant (p < 0.0001) effect, the overall trendestimated by each of the models are very similar. Fromthis we concluded that each of the models describedthe index of abundance in a similar manner the

choosing one model over another would not alter theoutcome of the assessment in any meaningful way.

The proportion of trips reporting positive catch forred snapper has been increasing fairly steadily sinceabout 1990 (Figure 23A). However, the rate at whichfishers caught red snapper (i.e. CPUE) for that sametime period has not shown such a high rate of increase(Figure 23B). When these two are multiplied togetherto arrive at an overall GLM index of abundance (Figure23C) the red snapper population is seen as increasingstarting some time at about 1990. In an effort to use thesimplest model necessary to adequately describe thetrend in stock abundance without over parameterizingthe model, we choose to use the model that consideredthe factors state, month, and mode without anyinteractions. While the interaction terms wherestatistically significant, they added very little to the fitof the model. The results of this model are shown inFigure 23D.

Size Distribution of the Recreational Harvest. The sizedistributions of the recreational landings were examinedto determine if stratification of the harvest was

21

Figure 24. Length frequencies of recreationallycaught red snapper, 1979-1998.

Figure 25. Estimates of annual total numbers ofred snapper discarded and shrimp bycatch andweighted shrimping effort by year.

necessary. As with all other previous red snapperassessments in the Gulf of Mexico, examination of thelength frequencies of the recreational landings by mode

did not reveal any significant differences betweenmodes. It was apparent, however, that the minimumsize regulations were having an effect on the sizes ofred snapper that were being harvested. Furtherexamination of length frequencies by state also lead tothe conclusion that stratification of the recreationallandings by geographic area for the purpose of analysiswas also not necessary (Figure 24).

Discards. The number of fish caught and released byrecreational fishermen each year was estimated from thetotal numbers harvested based on the release fractionsestimated for the MRFSS survey. This was done bydividing the total recreational catch by the fraction ofthe MRFSS catch that were retained by fishermen to getthe total catch over all modes and areas. This total wasthen multiplied by the MRFSS fraction released toestimate the total recreational releases. The releasedfish were partitioned into ages by multiplying the totalnumber of released fish by a fraction at age. Thefraction at age was estimated by first taking theproducts of the 1984-1989 selectivities at age, theproportions of each age that are smaller than the sizelimit, and the relative strengths of each year classpresent in a given year discounted for fishing mortalityof 0.2. The fraction at age was then determined as theratio of the resulting product for the age to the sumover all ages. The number of discarded fish at age thatdied and contributed to fishing mortality each year wasestimated as the product of the estimated number of

discards and the release mortality for the recreationalfishery.

SHRIMP TRAWL DISCARDS

Although the discard mortality associated withshrimp trawls is not a part of the harvest, it is a part ofthe overall fishing mortality. The possible role of thissource of mortality as an agent leading to declines inred snapper abundance was raised by Moe (1963) andBradley and Bryan (1975). Both studies noted that redsnapper fishermen believed shrimp bycatch of juvenilesnapper lead to declines in red snapper abundance.

Annual estimates of the bycatch of red snapper inshrimp trawls are shown in Figure 25 and Table 24.These estimates were provided by S. Nichols, NMFSPascagoula Laboratory and were derived using a GLMto predict catch rates from resource survey data basedon the relation between resource survey catch rates anddirect measurements of catch rates by observers aboardshrimp vessels (personal communication). Themethods and assumptions are presented in Nichols,Shah, Pellegrin and Mullin (1987), Nichols (1990), andNichols and Pellegrin (1992). The most recent estimatesincorporate recent observer data taken as part of acooperative research program addressing finfishbycatch in the shrimp fisheries of the Southeast UScoast (Anom. 1995) by observer data set. Nichols(personal communication) recommended using thepooled data set and we adopt these estimates as thebest available for subsequent analyses.

Current management of red snapper in the US Gulf

22

Figure 26. Annual mean monthlypercentages of the shrimp bycatch and theirmonthly age distributions.

Figure 27. Estimated biomass of the combinedcommercial and recreational harvest of U.S. Gulfof Mexico red snapper, 1981-1998.

of Mexico involves a reduction in shrimp bycatchmortality from historical levels. The baseline adoptedfor measuring the change is the average bycatch fishingmortality on the 1983-1988 year classes which weremost vulnerable to the shrimp trawl fishery from 1984-1989. The annual effort in the areas with significantbycatch relative to that in the baseline period increasedafter the baseline period and has been relativelyconstant throughout 1990 to 1998. Inspection of thesedata indicate that the absolute magnitude of thebycatch estimates is more variable than the effort. Thissuggests that variations in year class strength influencethe number of red snapper killed in shrimp trawls morethan recent variations in shrimp effort.

Nichols and Pellegrin (1992) estimated the monthlymean percentages of the annual bycat ch (Figure 26.The age composition of the bycatch by month wasestimated from the length frequencies in the resourcesurvey and bycatch characterization trawl samples. Theresulting estimates indicate that young of the yearbegin to recruit to the bycatch in June and July, andbecome the dominant part of the bycatch by September(Figure 26). Age-1 red snapper constitute an importantpart of the bycatch each month.

The estimated proportion of age 0 and 1 fish in thebycatch can vary depending on the time and area oftrawling, the type of BRD used (if any) in shrimp trawlsemployed and the relative abundance of age 0 and age1 fish to the trawls. Goodyear (1995) developedprocedures to take these effects into account bydeveloping regressions relating fishery independentmeasures of year-class strength to the size frequencydistribution of the bycatch. For the most part, these

relationships were established for the fishery operatingwithout BRDs. Implementation of BRD regulations in1998 appear to have resulted in reduced bycatch rates(Nichols, pers comm), which is at least partiallyevidenced by relatively lower estimates of bycatch for1998 compared to immediately prior years. To furtherevaluate the potential effect of BRD regulations, theproportion of age 0 fish in samples of bycatch takenfrom the fleet in 1998 were compared. Thesep roportions were estimated based on length as per thealgorithm developed by Goodyear (1995). Overallproportions estimated from approximately 38,000 fishlengths are represented in the figure (about 22,000measured from gear without BRDs and about 16,000measured from gear with BRDs). On average, the sampleproportions of model age 0 fish in the catch for gearwith BRDs are about 19% higher than gear withoutBRDs (conversely, samples from gear with BRDs hadapproximately 19% lower proportions of model age 1fish in the catch). For the purposes of stock assessmentevaluations, the season/area specific sampleproportions were applied to the season/area estimatesof bycatch. This resulted in a proportional distributionof the 1998 bycatch of approximately 64% model age 0and 36% model age 1.

COMBINED HARVEST

The combined recreational and commercial harvestfor 1981 through 1998 is presented in Figure 27. Thecommercial harvest has been constrained by size limitsand quotas since 1991 while the recreational harvesthas been constrained by creel and size limits. In themost recent years the total commercial harvest has beennear the commercial allocation but the recreational

23

harvest has been substantially larger than it’sallocation. Because recreational harvest estimates are availableonly for the period 1981-1998, it is possible only toestimate the combined harvest of red snapper for thatperiod (Figure 27). In 1983 the estimated landingsstarted a steady declined to a low of 3.7 million poundsin 1990. The total harvest in 1990 and thereafter isaffected by the ban on possession of red snapperbelow the minimum size which is primarily a factor forthe recreational harvest. The 1991 and later catcheswere also constrained by commercial quotas. It is clearfrom Figure 27 that both the commercial and recreationallandings suffered significant declines beforemeaningful conservation actions were imposed in 1990.However, the decline in the commercial segment wasless than in the recreational fishery, possibly as a resultof greater efficiency at low stock sizes.

ASSESSMENT OF CURRENT CONDITION OFTHE STOCK

FISHING MORTALITY

Benchmarks. Evaluation of recovery potential for redsnapper under a range of management actions andnatural mortality rate assumptions has been conducted(Goodyear 1995). The projected rate of recovery to aSpawning Potential Ratio (SPR) standard within a timeframe linked to generation time, is a function ofmanagement constraints imposed on the fisheries (bothdirected and bycatch) and natural mortality rate. Basedon the above cons idera t ions and on therecommendations of Mace et. al. (1996; Mace, P.M., D.Gregory, N. Ehrhardt, M. Fisher, C.P. Goodyear, R.Muller, J. Powers, A. Rosenberg, J. Shepard, D.Vaughan and S. Atran. 1996. An evaluation of the useof SPR levels as the basis for overfishing definitions inGulf of Mexico finfish fishery management plans. Gulfof Mexico Fishery Management Council, Tampa,Florida, 46p), the FMP, in 19xx, established recovery toa 20% transitional SPR level by year 2019 (a 1.5generation time frame) as the objective of managementof the fisheries affecting Gulf red snapper. Thesestandards implied a natural mortality rate (M)assignment of 0.1 for evaluation of fishery managementperformance, since generation time is related to M.Higher M implies shorter generation time and thereforea shorter period in which to achieve the statedmanagement goal (Goodyear 1995).

The Fishery Management guidelines under whichthe recovery period of 1.5 generations to a recoverystandard of 20% transitional SPR were establishedrelated to the expected level at which recruitmentoverfishing would occur for red snapper. As recentlyoutlined in a report to the SAFMC1 and repeated in thefollowing text, revisions to the Magnuson-StevensFishery Conservation and Management Act(MSFCMA) contain a set of National Standards forfishery conservation and management, the first ofwhich s tates “Conservation and managementmeasures shall prevent overfishing while achieving,on a continuing basis, the optimum yield from eachfishery for the United States fishing industry.” Therevised MSFCMA also required the Secretary ofCommerce to “establish advisory guidelines (whichshall not have the force and effect of law), based onthe national standards, to assist in the development offishery management plans.” These Guidelines(hereinafter termed the National Standard Guidelines,NSG’s) were published as a final rule on May 1, 1998.Following the NSG’s, Technical Guidelines weredeveloped (Restrepo et al. 1998) to translate the NSG’sinto usable scientific criteria so that scientific advicecould be offered to the Councils to assist inimplementing the MSFCMA. Key points arising fromthe MSFCMA, the NSG’s which reflect the nationalfisheries management policies, and the TechnicalGuidelines were: 1) that Maximum Sustainable Yield(MSY) is to be viewed as a limit (i.e. a threshold not tobe exceeded); 2) that two measures were to be used todetermine a fish stock’s management status, the fishingmortality rate relative to the fishing mortality rate thatwould produce MSY (this criterion is denoted asF/FMSY); and the amount of spawning biomass relativeto the spawning biomass at MSY (denoted B/BMSY); 3)that there should be maximum standards of fishingmortality rates which should not be exceeded, called theMaximum Fishing Mortality Rate Threshold (MFMT);4) that there should be a Minimum Stock Size Threshold(MSST) under which a stock’s spawning biomass

1Control Parameters and Alternatives for ControlRules for Selected Stocks under the Jurisdiction of theSouth Atlantic Fishery Management Council, a technicalreport prepared in September 1999, by the SoutheastFisheries Science Center for the SAFMC (available from theSEFSC).

24

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Figure 28. Generic default control rule.

would be considered depleted; and 5) that these criteriaand measures should be linked together throughControl Rules which specify actions to be taken (i.e.changes in management measures to alter fishingmortality rates) depending upon the status of thespawning biomass relative to BMSY and MSST and thestatus of the fishing mortality rate relative to FMSY andMFMT.

Two types of control rules are specified in theTechnical Guidelines: Limit Control Rules and TargetControl Rules. Limit control rules are a simplestatement linking the threshold criteria of F/FMSY andB/BMSY, i.e. establishing the limits which should not besurpassed in order to achieve the National FisheriesManagement Policy defined in the MSFMCA. Theselimits are established based upon the availablescientific data, taking into account uncertainty aboutthat data. Recognizing that information is oftenincomplete or too uncertain to be of utility, theTechnical Guidelines suggest a default limit control ruleand a number of proxies for the establishing the criteriaused in determining resource status relative to theselimits. Target control rules link the managementobjectives (management targets) of fishing mortalityrate and stock biomass and are, thus, under thebailiwick of the Councils. In a sense, the target controlrules are a statement of the Optimum Yield objectivestraditionally used in the FMP’s; however, theMSFCMA places further restrictions on theirdefinitions. Nevertheless, the Councils haveconsiderable flexibility in determining the target controlrules.

For purposes of defining the Limit Control Rule,the following parameter estimates (or proxyapproximations) need to be established:

MFMT (Maximum Fishing MortalityThreshold). The status determination criterionfor determining if overfishing is occurring. Forthese purposes this threshold limit will beequivalent to the F corresponding to the MSY(FMSY) or a proxy, thereof;

MSST (Minimum Stock Size Threshold). Thegreater of (a) 0.5BMSY, or (b) the minimum stocksize at which rebuilding to BMSY will occurwithin 10 years of fishing at the MFMT.MSST should be measured in terms ofspawning biomass or other appropriatemeasures of productive capacity or proxies,thereof.

The default limit control rule we define (as in theTechnical Guidelines) by expressing the limit fishingmortality rate for a specific level of spawning biomass,B; this is denoted F(B)

F(B) = FMSY proxy if B/BMSY proxy $ (1-c)

F(B) = [(FMSY proxy) (B/BMSY proxy )] /(1-c) if B/BMSY proxy # (1-c)

where the value of c is either the natural mortality rateM or ½, whichever is smaller. For the large majority ofstocks c will be equal to M. Additionally, for a largemajority of stocks, we will presume that MSST is equalto the larger of ½BMSY and (1-M) BMSY, unless the

stock’s productivity potential is too low to allow thestock to recover to BMSY from that threshold within 10years. A diagram of a generic default limit control rulewith M=0.2 is in Figure 28. This diagram is anexpression of several major features: 1) if a stockexhibits fishing mortality rates greater than F/FMSY = 1,then this is considered overfishing; 2) if a stock’sbiomass falls below MSST, then this stock isconsidered overfished; 3) as the stock’s biomass fallsbelow 1-M, the upper limit on the allowable fishingmortality rate decreases (the sloped line on the left),thus, smaller fishing mortality rates are required asspawning biomass deteriorates. In sum, any

25

combination of spawning biomass and fishing mortalityrates that are above or to the left of the limit control ruleline are considered to be “bad”, i.e. situations to beavoided. Combinations to the right and below the limitcontrol rule line are acceptable.

It remains to specifically express the abovediagram for each stock. In the case of red snapper, theGulf Council has adopted an FMSY proxy of F26% SPR,based on evaluations of available stock-recruitmentobservations for red snapper (RFSAP 1998). Thisproxy is in agreement with suggestions by Mace et al.2

for reef species and others. Current fishing mortalityrates are estimated from the current assessment.

Proxies for BMSY are constructed based onexpected recruitment at MSY. In the case of redsnapper, this has been linked to available informationon year class strength for a period prior to the availablecatch at age information set used in assessments.

Current condition of the Stock. The approach toevaluate the current condition of the red snapper stockutilized the flexible forward computations assessmentmodel ASAP (Age-Structured Assessment Program;Legault and Restrepo 1998). This model is based onseparating fishing effects by different gears into yearand age components, as in a separable virtualpopulation analysis. However, the model allows forchanges in selectivity and catchability over time anddoes not require gear specific catch at age for all years.This flexibility requires minimization of the objectivefunction with many, hundreds or thousands, ofparameters. The software package AD Model Builderuses automatic differentiation to compute thederivatives used in the minimization algorithm tomachine precision and thus allow for these largenumber of parameters to be estimated. Constraints mustbe place on how much parameters can vary over timeand the relative importance of different parts of theobjective function must be input.

Conversion of Gear Specific Catch Distributions fromLength to Age. The Goodyear (1997) probabilisticmethod was used to convert annual directed catch at

length to catch at age. The SEAMAP time series wasused as the basis for cohort strength over time andthree iterations starting with F=M were utilized toproduce the catch at age. Four gears were chosen forthe conversion: commercial handline east, commercialhandline west, commercial longline, and recreational.The recreational fishery converted length to ageseparately for four modes (shore, charterboat,headboat, and private/rental), with the yearly overallvalues used when sample sizes were not sufficient(n<50) for any particular mode in a given year. Therecreational catch and discards at age were thensummed over all modes to produce the single gear“Recreational” for use in the assessment. The date ofcapture was available for all fish and a single birth datewas assumed for all fish such that the fraction of a yearfor each fish could be computed when assigning theprobabilities of each age for a given fish. Ages 1-30were assigned and afterwards the catch for ages 15-30was summed to produce a 15+ group.

One advantage of this approach over straight age-slicing is the ability to estimate total discards for thecommercial fisheries. This is done by assumingselectivity is age based and the number of fish at agelanded reflects the total number at age caught. As wasdone in previous assessments, it was assumed that fishare not vulnerable to commercial gear for discardinguntil they are three years old. The fraction of fish ateach age below the minimum size for that year can thenbe used to estimate the total releases and a releasemortality rate used to determine how many of those die.

This probabilistic length to age method alsogenerates the age distribution of the recreationaldiscards. The total recreat ional discards come directlyfrom MRFSS estimates. The program only partitions theannual totals by age according to an input selectivityfunction.

Release mortality rates used to derive the discardsfor the four gear strata are: commercial handline east,commercial handline west, and commercial longline allset to 0.33, and recreational set to 0.20 (see discussionin previous sections of this document).

A large ageing program was conducted this year atthe NMFS Panama City Laboratory for red snapper.Over 13,000 otoliths were collected from the fishery ina random sampling scheme from the fall season of 1998.Time constraints forced a subsampling of this total forreading. The sub samples were collected proportionalto the amount of catch by each gear/location followingthe stratification outlined above: commercial gears byhandline east, handline west, longline, and recreational

2Mace, P.M., D. Gregory, N. Ehrhardt, M. Fisher,C. \P. Goodyear, R. Muller, J. Powers, A. Rosenberg, J.Shepard, D. Vaughan and S. Atran. 1996. An evaluation ofthe use of SPR levels as the basis for overfishingdefinitions in Gulf of Mexico finfish fishery managementplans. Gulf of Mexico Fishery Management Council, Tampa,Florida, 46.

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gears by headboat, charter boat, and private/rental. Asecond set of ageing data was available from LouisianaState University (Dave Nieland, pers. comm.) for thecommercial handline west sector for years 1995-1998.The samples for 1995-1997 were from both the springand fall seasons, while the samples for 1998 were onlyfrom the spring season. The age distributions by gearand year from the otolith readings were compared withthose produced by the length to age algorithm. Thereappeared to be a difference between the modes fromaged and length converted distributions, with the ageddistributions containing younger fish in general. Onepossible way to more closely match the two sets ofdistributions would be to change the t-zero of the vonBertalanffy growth equation used in the length to agealgorithm. The value used previously was 0.04666,which gives a birthdate of June 1 and assigns fish ages1.5-2.5 to age 2. Making t-zero smaller (more negative)would reduce the ages of the given catch at lengthdistribution, while increasing t-zero would increase theages of the catch produced by the algorithm. A rangeof t-zero from -2.0 to +0.5 by 0.1 was examined and theresidual sum of squares between otolith ageddistributions and algorithm age distributions for each ofthe gears and years was computed (Table 25). The bestoverall match was found using a t-zero of -0.5, althoughindividual gears and years were matched better bydifferent values of t-zero (Figures 29-31). Preliminarycalculations applying t-zero of -0.5 to all ages and allyears produced a separate catch at age by gear andyear table for use in ASAP. The conclusions reachedusing this other set of catch at age information did notdiffer from those using the previous t-zero of 0.04666.Since there is no way to know if a t-zero of -0.5 isappropriate for years prior to 1995, and it was seen thatdifferent t-zero values match different components ofthe total fishery differently in different years, it wasdecided to maintain the previous value for t-zero of0.0466 for this assessment. As more otoliths are readover time, there will be a better opportunity to do thissort of matching to determine if the t-zero, or othergrowth parameters, should be changed in the length toage algorithm.

Catches and discards at age by gear used in theassessment are given in Table 26.

Stock Assessment and Projections

Methods. A complete description of the program ASAPis not repeated here (see Appendix II and Legault andRestrepo 1998), rather the modifications to the program

for use in the red snapper assessment are described.Modifications were made to both allow for more realismand to improve the estimation properties. Discards arenow included in the program, fishery-specificselectivity is estimated for only some ages while theremainder are fixed, and the process of estimatingrecruits is modified. The inclusion of discards requiresa matrix of the fraction of fish caught that are releasedby year and age. This matrix is then used to determinewhich fish caught by the total selectivity pattern will belanded and which released, with some of the releasedfish suffering release mortality.

The selectivity at age for each gear is estimatedonly for a range of ages, but these values can be eitherbelow or above 1.0. Thus, the selectivity over all agescan become dome shaped even if older ages are fixed at1.0. The ages which are fixed can be set at any values,but the pattern will remain the same. Deviations inselectivity over time only occur for the estimated ages,but since the estimated ages can be either below orabove 1.0, the total selectivity pattern can change fromflat-topped to dome shaped or vice versa.

The method used to estimate annual recruitmentsnow is based on deviations from an estimated stock-recruitment relationship (SRR). Previously, therecruitment values were estimated and then a SRR wasfit to the observations. This forced a good matchbetween the estimated SRR and the annual stock andrecruitment values, but caused problems for theminimization routine because given stock-recruitmentdata could be fit equally well by quite different SRRs.The new approach has better estimation properties butforces dependence upon the estimated SRR.

Some additional penalties were added to theprogram to prevent unreasonable solutions. If a gearspecific F multiplier was greater than 3.0 for any year, apenalty was added to the objective function of 8(F-3.0)2, where lambda is a weight for the penalty. Similarly,if an estimated selectivity value was greater than 100,8(sel-100.0)2 was added to the objective function.

Projections are done by combining the F at age inthe three years prior to the last year from all gears intothree F vectors which are averaged to form directed anddiscard selectivity patterns. The last year was notincluded in this average because bycatch reductiondevices were already being implemented. Thesepatterns are used to project different F multipliers, suchas Fmsy or Fcurrent, or to solve for the F multiplierneeded to generate a given amount of landings inweight, for example a 6, 9, or 12 million pound directedfishery catch. Thus, if allocations amongst the directed

27

gears change in the future, the projections will not becorrect. The relationship between the directed and non-directed gears can be either linked through a singleselectivity function, as was done for maximumsustainable yield calculations, or kept separate, as wasdone to create future transitional spawning potentialratio isopleths.

The AD Model Builder software package onlyallows a single function to be minimized during one run.The estimates of Fmsy or the F to achieve a given catchwere solved through a bisection algorithm carried out30 times which gives precision in F to approximately2.0E-07. The Fmsy estimate was computed bycalculating the spawning stock per recruit (SPR) andyield per recruit (YPR) under a given F. The stockrecruitment relationship was rearranged such thatspawning stock is a function of SPR to derive thespawning stock for that F value. Plugging thisspawning stock back into the stock recruitmentrelationship generates an estimate of the expectedrecruitment in equilibrium at that F value. Multiplyingthis equilibrium recruitment by the yield per recruitgives an estimate of the yield in equilibrium for that Fvalue. The F value is then changed until the equilibriumyield is maximized.

Red Snapper Application. The time series of dataavailable for analysis was 1984-1998. A longer timeseries could not be produced due to limitations in thehistorical catch databases (see previous sections fordiscussion) which have so far not been overcome.

Five separate gears were employed in both timeseries: commercial handline east, commercial handlinewest, commercial longline, recreational, and shrimptrawl fleet (capture of juveniles only). These gears hadtotal catch in weight and catch at age in numbers forevery year. Two tuning indices were available for theanalyses, the SEAMAP time series and the recreationaltime series, and were given equal weight. Naturalmortality was constant over all years and set to 0.5 forage 0, 0.3 for age 1, and 0.1 for ages 2-15+. Spawningstock was measured in millions of eggs.

Selectivities for each directed fishery gear wereestimated for ages 1-9 and allowed to change each year.The selectivities for ages 10-15+ were set to 1.0 forthese gears, but because the selectivity for ages 1-9could be greater than 1.0 the older age classes could berescaled to lower values. The non-directed fishery (theshrimp trawl fleet) had selectivity for ages 0 and 1estimated each year.

Preliminary analyses determined that the stock-

recruitment relationship could not be well estimatedgiven the short time series, most likely due to lack ofregression range. To prevent this from occurring, thesteepness parameter of the reparameterized Bevertonand Holt stock-recruitment relationship was fixed at sixdifferent levels (0.8, 0.9, 0.925, 0.95, 0.975 and 0.99) andthe virgin spawning stock size calculated to producemaximum recruitment of either 245 or 163 million age 0recruits. The 245 million recruits comes from previousassessments where the SEAMAP time series was usedto estimate the maximum recruitment based on therelationship between the number of recruits estimatedin years 1984-1994 and the index. The 163 value is 2/3 ofthis maximum, under the assumption that the maximumindex value was not representative of the maximumrecruitment, but rather the average value from the earlypart of the index time series is representative of themaximum recruitment. These fixed values for steepnesswere selected to most closely match the assumed stock-recruitment relationship used in previous assessments,as well as the resulting objective function values. Thesteepness parameter is bounded by 0.2, a straight linerelationship between stock and recruitment, and 1.0,essentially constant recruitment.

Results and Discussion Components of the likelihoodfunctions for the six assumed stock recruitmentrelationships are presented in Table 27. The lambdasgiven in the table were derived based on considerationsof the uncertainty associated with each component inthe objective function. For log normally distributederrors about a given observation, the lambda (8) can bederived from the coefficient of variation (CV) as8=1/ln(CV2+1). The CV’s were chosen based on pastexperience and knowledge of the data associated wi ththe particular component of the objective function. Forexample, total catch in weight by any gear is knownmuch better than the total discards while selectivity isassumed to vary over time more than catchability.

The tuning indices were both fit relatively well in allcases as seen in Table 28 and Figure 32. Only oneexample is presented in most figures due to thecloseness of the estimates from the twelve assumedstock-recruitment relationships. When results from onlyone example is presented, the high recruitment (245million) and steepness of 0.95 is shown, because this isthe maximum recruitment level used in the previousassessments and the objective function is minimized atthis steepness. It should be noted that the model haslittle choice but to fit the SEAMAP index well becauseit was used to produce the catch at age matrix. The

28

recreational index can also be fit well because it coversa wide range of ages (2-15+), although the use of theestimated selectivity function mitigates this impactsomewhat.

The changes over time in selectivity were mostlydue to changes in minimum size regulation, as expected,and show that only the commercial longline fishery istargeting older (and larger) red snapper (Figure 33). Asthe total landings from the commercial longline fisherydecline over time, the total directed selectivity patterndecreases for older ages. Some of the younger fishselected by each directed gear are discarded as thefigure shows the full selectivity patterns. The largepercentage of releases by the recreational fisherycauses the selectivity to be highest at young ages, butthe low release mortality rate for this gear (20%) meansthat the fishing mortality rate will not be as high atthese young ages as would be assumed justconsidering the selectivity pattern. The commercialselectivity patterns are low for ages 0-1 due to minimumsize regulations. The selectivity pattern for the non-directed fishery (the shrimp trawl fleet) is higher for age1 than age 0 for most years due to the much larger stocksize at age 0 than age 1.

The ASAP analyses show an increasing trend infishing mortality multipliers for the recreational sector,a flat trend for the commercial handline west and shrimpbycatch sectors, and a decreasing trend for thecommercial handline east and commercial longlinesectors (Figure 34). These patterns were repeated overall twelve stock recruitment assumptions examined, butthe magnitude of the estimates changed, as seen inTable 29 under Fcurrent. Lower steepness valuescorrespond with lower directed fishery F values as didlower maximum recruitment in the stock recruitmentrelationships. Note, however, that the biologicalreference points also change under different stockrecruitment assumptions. This table is discussed morecompletely later in this document.

The differences in fishing mortality rates amongstthe different stock recruitment relationships are due todifferent population abundance estimates. All twelveASAP analyses produced similar recruitment trends,both in trend and magnitude (Figure 35, see also Table28). However, the population abundance in the plusgroup (ages 15 and greater) differ in both trend andmagnitude (Figure 36) amongst the models fit. Thisdifference in plus group abundance is accounted for bythe stock recruitment relationship being more resilientwith a higher steepness, and thus requiring fewer olderfish to produce the same level of recruitment. The two

levels of assumed maximum recruitment did not impactthe estimates of recruitment, but do change theestimates of maximum sustainable yield (see Table 29).The assumed stock recruitment relationship alsodetermines the time required for recovery, and theability to recover, under given constant catchprojections, as discussed later. All twelve analysesestimated strong recruitment in 1989 and 1984 and lowrecruitment in 1985 and 1988. The consistency inrecruitment trends is not surprising given the method toconvert catch at length to catch at age, while theconsistency of magnitude shows the relative stabilityof these estimates. In contrast, the plus groupabundance has highly different magnitudes, with lowersteepness values requiring more adult fish to producethe given recruitment levels. The plus group abundancetrends are somewhat similar showing a decline from1984 to 1992 followed by an increase to 1996 and thena slight decline to 1998. The amount of increase from1992 to 1996 depends upon the steepness, with lowersteepness values having a much larger relative increaset han lower steepness values. This change in relativeincrease from 1992 to 1996 is large enough to changethe overall trend from an increasing trend for steepnessof 0.8 to an overall decreasing trend for steepness of0.99. As the strong 1984 and 1989 year classes enter theplus group, it is expected that the plus group willincrease again. Whether or not it makes a large enoughincrease to increase recruitment remains to be seen.

The twelve assumed stock recruitment relationshipsare shown in Figure 37. All analyses estimate currentconditions to be near the origin relative to the virginconditions and to the left of the bend in the assumedBeverton and Holt curve. None of the plots s h o w areasonable regression range for estimating the stockrecruitment relationship. It should be noted that themodel is estimating deviations from the Beverton andHolt form and thus a reasonable fit is guaranteedbecause the stock recruitment points are notindependent of the fit curve. Other stock recruitmentrelationships could be fit to these points with similarresidual sum of squares and would create differences inthe maximum sustainable yield estimates. This featureshould be examined in future assessments.

The catchability coefficients did not change muchfor any of the analyses (the changes cannot be seengraphically if the y-axis minimum is set to zero). Therecreational index did have a monotonically increasingtrend in catchability, as would be expected, but itseffect in terms of magnitude was small. The SEAMAPindex had a decreasing then increasing trend in

29

catchability, but the magnitude of change was so smallit had virtually no effect on the results.

For the projections, a single selectivty pattern wasformed for the total directed fishery and another singlepattern for the total discarded dead fish. These patternswere formed by summing the directed and discard F atage from 1995-1997 and rescaling by the maximumdirected F at age. Thus, the discarded dead selectivitypattern is a function of the directed fishing mortalityrate. The resulting selectivities were similar for alltwelve ASAP analyses (e.g. Figure 38). The nondirected fishery (shrimp trawl fleet) was either linked tothe directed fishery in projections for maximumsustainable yield and SPR at Bmsy calculations, or elsetreated separately with a range of bycatch reductionassumed for the constant catch projections. When theshrimp bycatch selectivity pattern was linked to thedirected fishery it had a selectivity greater than one, asseen in Figure 38. When the shrimp bycatch F was notlinked to the directed fishery, both age 0 and age 1fishing mortality rates, 0.38 and 1.68 respectively, werereduced equally over a range of possible reductionsstarting in 1999.

Current conditions relative to common biologicalreference points and maximum sustainable yieldparameters under these projected selectivity patternsare given in Table 29 for the twelve ASAP analyses. Allanalyses show the red snapper stock is currentlyundergoing overfishing, whether the overfishingdefinition is set as Fmsy or F20%SPR. The current F isalso above the common reference points F(0.1) andFmax for all twelve analyses. The current staticspawning potential ratio is also always below 20% andbelow the SPR which corresponds with maximumsustainable yield. The MSY values increase withincreases in either steepness or maximum recruitment,while the spawning stock at MSY decreases withincreasing steepness. This is because a lower spawningstock can produce the same recruitment as a higherspawning stock that has a lower steepness. Defaultcontrol rule plots using maximum sustainable yieldrelated parameters to define overfishing and overfishedare presented in Figure 39. In all twelve analyses, thered snapper stock is both overfished and undergoingoverfishing based on B/Bmsy<(1-M = 0.9) andF/Fmsy>1.0, respectively. The value (1-M)Bmsy isrecommended in the National Technical Guidelinesdocument (Restrepo et al. 1998) as a default minimumstock size threshold (MSST). These plots show thatincreasing steepness leads to larger F/Fmsy ratios and

smaller B/Bmsy ratios meaning the stock is further awayfrom the goal of MSY.

Since the Sustainable Fisheries Act Amendment hasnot taken effect yet, the MSY related definitions ofoverfishing and overfished are not yet underconsideration. This information is presented to showwhat to expect if this amendment is implemented. Thecurrent recovery schedule is for a 20% transitionalspawning potential ratio in 2019, which could bereplaced by a threshold of 26% transitional spawningpotential ratio in 2034 or a biomass proxy in the nearfuture. The ability to reach either of these goals undera range of constant catches and bycatch reductionswas considered for all twelve ASAP analyses (Figure40-51). These isopleths can be used to determine whichcombinations of bycatch reduction and directed fisherycatch can be used to achieve transitional SPRs above20% in 2019 and/or 26% in 2034. In those same plots,the SPR equivalent to expected biomass in equilibriumunder Fmsy for the assumed stock recruitmentrelationship is also shown.

For the baseline situation (steepness = 0.95 andhigh maximum recruitment) a sensitivity analysis wasconducted under the assumption that bycatchreduction occurred only for age-1 and did not effectage-0. Results of this analysis are shown in Figure 52for 2019 and Figure 53 for 2034. In these scenarios age-1 shrimp bycatch mortality needed to be increased inorder to achieve SPR values equivalent to those whenreduction in mortality occurred at both age-0 and age-1.

Some stock recruitment relationships can achieveone of these goals but not the other under a givenconstant catch and bycatch reduction, but are usuallysimilar in result. The reason for this is the nearly linearincrease in transitional SPR that occurs after a shortperiod of building the stock that occurs when either thedirected or bycatch F, or both, is reduced (Figure 54).All of these recovery projections are deterministic. Itshould be noted that a constant catch that allowsrecovery for one ASAP analysis could cause the stockto crash for a different ASAP analysis.

Uncertainty in the estimates of spawning stock size,recruitment, and plus group abundance from theHessian matrix are shown in Figure 55 for one ASAPanalysis. The uncertainty of the current stock statuscan be more precisely estimated through likelihoodprofiling in ASAP (Figure 56).

30

1995 HL-W

01020304050

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1995 HL-W

-15

-10

-5

0

5

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

1996 HL-W

01020304050

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1997 HL-W

01020304050

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1998 Jan-June HL-W

0

10

20

30

40

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1996 HL-W

-15

-10

-5

0

5

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

1997 HL-W

-30

-20

-10

0

10

20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

1998 Jan-June HL-W

-30

-20

-10

0

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

Figure 29. Comparison of Louisiana State University otolith based age distributions (labeled Sampled)and those from the length to age algorithm under three different t-zero values: the original (0.04666), thebest overall fit (-0.5), and the best fit for that year (see Tabled values).

31

1998B Headboat

0

20

40

60

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1998B Headboat

-20

-10

0

10

20

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

1998B Charterboat

0102030405060

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1998B Charterboat

-40

-20

0

20

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

1998B Private

01020304050

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1998B Private

-20

-10

0

10

20

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

Figure 30. Comparison of NMFS Panama City Laboratory otolith based age distributions (labeledSampled) and those from the length to age algorithm under three different t-zero values: the original(0.04666), the best overall fit (-0.5), and the best fit for that recreational gear (see Tabled values).

32

1998B Handline East

0

20

40

60

80

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1998B Handline East

-40-30-20-10

01020

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

1998B Handline West

01020304050

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1998B Handline West

-20-15-10-505

1015

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

1998B Bottom Longline

0

10

20

30

40

1 3 5 7 9 11 13 15+

Age

Fre

q

Sampled Orig t0 t0=-0.5 best t0

1998B Bottom Longline

-30-20-10

0102030

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Est-

Sam

ple

d F

req

Orig t0 t0=-0.5 best t0

Figure 31. Comparison of NMFS Panama City Laboratory otolith based age distributions (labeledSampled) and those from the length to age algorithm under three different t-zero values: the original(0.04666), the best overall fit (-0.5), and the best fit for that commercial gear (see Tabled values).

33

Recreational

0

0.20.4

0.60.8

11.21.4

1.61.8

1980 1985 1990 1995 2000

Sca

led In

dex

SEAMAP

0

0.5

1

1.5

2

2.5

1980 1985 1990 1995 2000

Sca

led In

dex

Figure 32. Tuning index fits for high recruitment (245 million) and steepness of 0.95 fromASAP. Filled diamonds denote observed values, empty squares denote predicted values.

Handline East

0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Sel

ectiv

ity

Handline West

0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Sel

ectiv

ity

Bottom Longline

0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Sel

ectiv

ity

Recreational

0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Sel

ectiv

ity

Shrimp Bycatch

0.00

0.25

0.50

0.75

1.00

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Sel

ectiv

ity

Total Directed Selectivity

0.00

0.25

0.50

0.75

1.00

2 3 4 5 6 7 8 9 10 11 12 13 14 15

Age

Sel

ectiv

ity

1984

1985

Figure 33. Selectivity at age for each gear including discard selectivity estimated byASAP for high recruitment (245 million) and steepness of 0.95. Each line denotes aseparate year.

34

F multiplier

00.050.1

0.150.2

0.250.3

0.350.4

0.45

1980 1985 1990 1995 2000

Dir

ecte

d F

ish

eri

es

00.20.40.60.811.21.41.61.82

Sh

rim

p B

ycatc

h

HL East HL West BLLRec Shrimp Bycatch

Figure 34. Fishing mortality rate on fully selected agefor each gear estimated by ASAP for highrecruitment (245 million) and steepness of 0.95.

0

20

40

60

80

100

120

140

1980 1985 1990 1995 2000

Ag

e 0

Recru

its (

mill

ion

s)

s08los08his09los09his0925los0925his095los095his0975los0975his099los099hi

Figure 35. Twelve estimated recruitment trends (innumbers) for different assumptions about the stockrecruitment relationship from ASAP.

0

2

4

6

8

10

12

1980 1985 1990 1995 2000

Plu

s G

rou

p N

um

bers

(m

illio

ns)

s08los08his09los09his0925los0925his095los095his0975los0975his099los099hi

Figure 36. Twelve estimated plus group trends (innumbers) from different assumptions about the stockrecruitment relationship from ASAP.

35

steepness 0.8

0.0E+00

5.0E+07

1.0E+08

1.5E+08

2.0E+08

2.5E+08

0.0E+00 5.0E+09 1.0E+10 1.5E+10 2.0E+10 2.5E+10

Eggs (millions)

Rec

ruits

(num

ber

s)

steepness 0.9

0.0E+00

5.0E+07

1.0E+08

1.5E+08

2.0E+08

2.5E+08

0.0E+00 5.0E+09 1.0E+10 1.5E+10 2.0E+10 2.5E+10

Eggs (millions)

Rec

ruits

(num

ber

s)

steepness 0.925

0.0E+00

5.0E+07

1.0E+08

1.5E+08

2.0E+08

2.5E+08

0.0E+00 5.0E+09 1.0E+10 1.5E+10 2.0E+10 2.5E+10

Eggs (millions)

Rec

ruits

(num

ber

s)

steepness 0.95

0.0E+00

5.0E+07

1.0E+08

1.5E+08

2.0E+08

2.5E+08

0.0E+00 5.0E+09 1.0E+10 1.5E+10 2.0E+10 2.5E+10

Eggs (millions)

Rec

ruits

(num

ber

s)

steepness 0.975

0.0E+00

5.0E+07

1.0E+08

1.5E+08

2.0E+08

2.5E+08

0.0E+00 5.0E+09 1.0E+10 1.5E+10 2.0E+10 2.5E+10

Eggs (millions)

Rec

ruits

(num

ber

s)

steepness 0.99

0.0E+00

5.0E+07

1.0E+08

1.5E+08

2.0E+08

2.5E+08

0.0E+00 5.0E+09 1.0E+10 1.5E+10 2.0E+10 2.5E+10

Eggs (millions)

Rec

ruits

(num

ber

s)

Figure 37. Stock recruitment relationships for the twelve ASAP analyses. Filleddiamonds denote the estimated spawning stock size (measured in eggs) and the estimatedrecruitment, note that estimated recruitment deviates from the assumed stock recruitmentrelationship, for the high (245 million) maximum recruits cases. The low maximumrecruitment cases have estimated spawning stock and recruitment points in such a similarlocation as the high recruitment case that the symbols would overlap, and thus are notshown on the plots. The two lines show the assumed deterministic stock recruitmentrelationship under high (open circle) and low (asterisk) maximum recruitment. Thesymbols at the end of the deterministic stock recruitment lines denote the virgin conditions.

36

0

1

2

3

4

0 5 10 15

Age

Sele

cti

vit

y

Dir Sel

Disc Sel

Linked Sel

Figure 38. Selectivity at age used in ASAPprojections.

Steepness 0.8

0

1

2

3

4

5

6

0 0.2 0.4 0.6 0.8 1

B/Bmsy

F/F

msy

Steepness 0.9

0

1

2

3

4

5

6

0 0.2 0.4 0.6 0.8 1

B/Bmsy

F/F

msy

Steepness 0.95

0

1

2

3

4

5

6

0 0.2 0.4 0.6 0.8 1

B/Bmsy

F/F

msy

Steepness 0.99

0

1

2

3

4

5

6

0 0.2 0.4 0.6 0.8 1

B/Bmsy

F/F

msy

Figure 39. Default control rule plots for four levels of steepness under thetwo levels of maximum recruitment, high (open diamonds) and low(crosses). Each symbol denotes a year from the historical time series 1984to 1998. Note, a few points in the steepness 0.99 have F/Fmsy greaterthan 6.0 and are not shown.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(DIR

EC

TED

FIS

HE

RY

)ST8- 2019-H

0.2

0

0.3

0 0.4

0 0.5

0

0.6

0

Figure 40. SPR as a function of TAC andreduction in shrimp bycatch mortality, steepness=0.8, in 2019.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(DIR

EC

TED

FIS

HE

RY

)

ST9- 2019-H

0.1

0

0.2

0

0.3

0

0.4

0 0.5

0

Figure 41. SPR as a function of TAC andreduction in shrimp bycatch mortality, steepness=0.9, in 2019.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (

DIR

EC

TE

D F

ISH

ER

Y)

ST925- 2019-H

0.1

0

0.2

0

0. 3

0

0

. 40

Figure 42 . SPR as a function of TAC andreduction in shrimp bycatch mortality, steepness=0.925, in 2019.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(DIR

EC

TED

FIS

HE

RY

)

0.01

ST95- 2019-H

0.10

0.2

0

0.3

0

0.4

0 0.5

0 0

.60

Figure 43. SPR as a function of TAC andreduction in shrimp bycatch mortality, steepness=0.95, in 2019.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(DIR

EC

TED

FIS

HE

RY

)

0.01

ST99- 2019-H

0.10

0.20

0.3

0 0.4

0

0.5

0

0

.60

Figure 45 . SPR as a function of TAC and reductionin shrimp bycatch mortality, steepness =0.99, in 2019.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(D

IRE

CTE

D F

ISH

ER

Y)

0.01

ST975- 2019-H

0.1

0

0.2

0

0. 3

0

0. 4

0 0

. 50

0

. 60

Figure 44 SPR as a function of TAC and reductionin shrimp bycatch mortality, steepness =0.975, in2019.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (D

IRE

CT

ED

FIS

HE

RY

)

0.2

6

0.4

0

ST8- 2034-H

0.2

0

0. 3

0

0. 4

0

0

. 50

0

. 60

0

. 70

0.

80

Figure 46. SPR as a function of TAC and reduction inshrimp bycatch mortality, steepness =0.8, in 2034.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (

DIR

EC

TE

D F

ISH

ER

Y)

0.2

6

0.3

6

ST9- 2034-H

0. 2

0

0. 3

0

0

. 40

0

. 50

0

.6

0

0

.7

0

0

.8

0

Figure 47. SPR as a function of TAC and reductionin shrimp bycatch mortality, steepness =0.8, in 2034.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (D

IRE

CT

ED

FIS

HE

RY

)

0.2

6

0.3

4

ST925- 2034-H

0.10

0.20

0.3

0

0.4

0

0.50

0.60

0.70

Figure 48. SPR as a function of TAC and reduction inshrimp bycatch mortality, steepness =0.925, in 2034.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (

DIR

EC

TE

D F

ISH

ER

Y)

0.2

6

0.3

2

ST95- 2034-H

0.1

0

0. 2

0

0. 3

0

0

.5

0 0

.6

0

0

.7

0

0

.8

0

Figure 49. SPR as a function of TAC and reductionin shrimp bycatch mortality, steepness =0.95, in2034.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (

DIR

EC

TE

D F

ISH

ER

Y)

0.2

6 0

.29

ST975- 2034-H

0.1

0

0.2

0

0.3

0

0.5

0

0.6

0

0.7

0

0.80

Figure 50. SPR as a function of TAC and reductionin shrimp bycatch mortality, steepness =0.975, in2034.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(DIR

EC

TED

FIS

HE

RY

)

0.2

6

0.2

6

ST99- 2034-H

0.10

0. 2

0 0

. 30

0

.4

0

0

.5

0

0

. 60

0

.7

0

0

.8

0Figure 51. SPR as a function of TAC and reductionin shrimp bycatch mortality, steepness =0.99, in2034.

39

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (D

IRE

CT

ED

FIS

HE

RY

)

0.01

ST95AGE1- 2019-H

0.10

0.2

0

0.3

0 0

.40

Figure 52. SPR as a function of TAC and reduction in shrimpbycatch morality only on age-1 in 2019.

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (D

IRE

CT

ED

FIS

HE

RY

)

0. 2

6

ST95AGE1- 2034-H

0.10

0.2

0

0.

30

0.

40

0.5

0

Figure 53. SPR as a function of TAC and reduction inshrimp bycatch morality only on age-1 in 2034.

40

Steepness 0.95 High Recruits

0.00E+002.00E+074.00E+076.00E+078.00E+071.00E+081.20E+081.40E+08

84 86 88 90 92 94 96 98

Year

Eggs

(mill

ions)

Steepness 0.95 High Recruits

0

20

40

60

80

100

120

84 85 86 87 88 89 90 91 92 93 94 95 96 97 98

YearR

ecru

its (m

illio

ns)

Steepness 0.95 High Recruits

0.00.20.40.60.81.0

1.21.4

84 85 86 87 88 89 90 91 92 93 94 95 96 97 98

Year

Plu

s G

roup (m

illio

ns)

Figure 55. Uncertainty in spawning stock size(millions of eggs), age 0 recruits (millions offish), and plus group abundance (millions offish) for ASAP analysis with steepness of0.95 and high (246 million) maximumrecruitment. Bar denotes mean values andvertical lines denote two standard deviations.

Steepness 0.95 High Recruits

5.0E+07 7.0E+07 9.0E+07 1.1E+08 1.3E+08 1.5E+08

Eggs (millions) in 1998

Lik

elih

ood

Figure 56. Likelihood profile for spawningstock size (millions of eggs) in 1998 from ASAPanalysis with steepness of 0.95 and high (245million) maximum recruits.

Steepness 0.95 50% Bycatch Red

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

1985 1995 2005 2015 2025 2035

Tra

nsi

tional

SP

R

Figure 54. Transitional spawning potentialratio predicted under a constant catch of 9million pounds with a 50% reduction inbycatch for the ASAP analyses withsteepness set to 0.95. The top and bottomsolid lines denote the low and high maximumrecruitment scenarios, respectively. Thedashed lines denote the two recovery goals,20% in 2019 and 26% in 2034. The long-dash line denotes the SPR at Fmsy.

41

1150

1200

1250

1300

1350

1400

1450

0.080 0.900 0.925 0.950 0.975 0.990

S/R STEEPNESS

OB

JE

CT

IVE

F

UN

CT

ION

High Recruit

Low Recruit

Figure 57. Objective function values for the range of steepness and recruitmentlevels modeled.

42

LITERATURE CITED

Alverson, D.L. and M.J. Carney. 1975. A graphicreview of the growth and decay of populationcohorts. Cons. Int. Explor. Mer, 36(2): 133-43.

Anonomous. 1995. Cooperative research programaddressing finfish bycatch in the Gulf ofMexico and South Atlantic shrimp fisheries: Areport to Congress. National Marine FisheriesService, Southeast Regional Office, St.Petersburg, Fl. 1-67.

Beaumariage, D. S. 1969. Returns from the 1964 Schlitztagging program including a cumulativeanalysis of previous results. FloridaDepartment of Natural Resources, MarineResearch Laboratory, Technical Series no. 59,38pp.

Beaumariage, D. S., and L. H. Bullock. 1976. Biologicalresearch on snappers and groupers as relatedto fishery management requirements. Pages86-94 in H.R. Bullis, Jr., and A.C. Jones,editors, Proceedings: colloquium on snapper-grouper fishery resources of the westerncentral Atlantic Ocean. Florida Sea GrantColloquium Report 17. 333 pp.

Bradley, E. and C. E. Bryan. 1975. Life history andfishery of the red snapper (Lutjanuscampechanus) in the northwestern Gulf ofMexico): 1970-1974. Proceedings Gulf andCaribbean Fisheries Institute 27:77-106.

Camber, C.I. 1954. A survey of the red snapper fisheryin the Gulf of Mexico with special reference tothe Camp eche Banks. M.S. Thesis, Universityof Miami, Coral Gables, Florida, USA. 170p.

Camber, C. I. 1955. A survey of the red snapper fisheryof the Gulf of Mexico, with special reference tothe Campeche Banks. Technical Series ofFlorida State Board Conservation No 12:1-64.

Camper, J. D., R. C. Barber, L. R. Richardson, and J. R.Gold. 1993. Mitochondrial DNA variationamong red snapper (Lutjanus campechanus)from the Gulf of Mexico. Molecular MarineBiology and Biotechnology 2:154-161.

Chapman, R. W., S. A. Bortone and C.M. Woodley.1995. A molecular approach to stockidentification and recruitment patterns in red

snapper, Lutjanus campechanus. Final reportfor Cooperative Agreement #NA17FF0379-03Marine Fisheries Initiative (MARFIN)Program. Institute for Coastal and EstuarineResearch, University of West Florida,Pensacola, Florida 32514, and MarineResources Research Institute, South CarolinaWildlife and Marine Resources Department,Charleston, South Carolina 29412.

Collins, L. A., A. G. Johnson, and C. P. Keim. 1994.Spawning and annual fecundity of the redsnapper, Lutjanus campechanus, from thenortheastern Gulf of Mexico. manuscript.

Ellis, R.W., A. Rosen, and A.W. Moffett. 1958. Asurvey of the number of anglers and of theirfishing effort and expenditures in the coastalrecreational fishery of Florida. State of FloridaBoard of Coservation Technical Series No. 24.

Fable, W. A., Jr. 1980. Tagging studies of red snapper(Lutjanus campechanus) and vermilionsnapper (Rhomboplites aurorubens) off thesouth Texas coast. Contributions to MarineScience 23:115-121.

Fielder, R.H. 1941. Fishery industries of the UnitedStates, 1938. Rep. U.S. Comm. Fish., 1939,App. III: 418-474.

Gitschlag, G. R. and M. L. Renaud. 1994. Fieldexperiments oh survival rates of released redsnapper. North American Journal of FisheriesManagement 14:131-136.

Goodyear, C.P. 1995. Red snapper in U.S. waters of theGulf of Mexico, National Marine FisheriesService, Southeast Fisheries Science Center,Miami Laboratory, Miami MIA—95/96-05.

Goodyear, C.P. 1997. An evaluation of the minimumreduction in the 1997 red snapper shrimpbycatch mortality rate consistent with the 2019recovery target. 16 pages.

Goodyear, C. P., and M. J. Schirripa. 1993. The redgrouper fishery of the Gulf of Mexico. NationalMarine Fisheries Service, Southeast FisheriesScience Center, Miami Laboratory, Miami MIA92/93-75.

Hoenig, J.M. 1983. Empirical use of longevity data toestimate mortality rates. Fish. Bull., 82(1):898-903.

Lo, N.C., L.D. Jackson, J.L. Squire. 1992. Indices of

43

relative abundance from fish spotter databased on delta-lognormal models. Can. J.Fish. Aquat. Sci. 49: 2515-2526.

Moe, M. A. 1963. A survey of offshore fishing inFlorida. Professional Paper Series MarineLaboratory Florida No. 4:1-117. St. Petersburg,Florida.

M oseley, F. N. 1966. Biology of the red snapper,Lutjanus aya Bloch, of the northwestern Gulfof Mexico. Publications Institute of MarineScience, Texas 11:90-101.

Nelson, R. S., and C. S. Manooch III. 1982. Growth andmortality of red snappers in the west-cent ralAtlantic ocean and northern Gulf of Mexico.T ransactions of the American FisheriesSociety 111:465-475.

Nichols, S. 1990. The spatial and temporal distributionof the bycatch of red snapper by the shrimpfishery in the offshore waters of the US Gulf ofMexico. National Marine Fisheries Service,Southeast Fisher ies Science Center ,Mississippi Laboratories, Pascagoula Facility,Pascagoula, MS. 1-66.

Nichols, S., A. Shah, G. Pellegrin, Jr., K. Mullin. 1987.Estimates of annual shrimp fleet bycatch forthirteen finfish species in the offshore watersof the Gulf of Mexico. National MarineFisheries Service, Southeast Fisheries ScienceCenter, Mississippi Laboratories, PascagoulaFacility, Pascagoula, MS.

Nichols, S., and G. J. Pellegrin. 1992. Revision andupdate of estimates of shrimp fleet bycatch1972-1991. National Marine Fisheries Service,Southeast Fisher ies Science Center ,Mississippi Laboratories, Pascagoula MS.

Parker, R. O. 1985. Survival of released red snapper.Progress report to South Atlantic and Gulf ofMexico Fisheries Management Councils,Charleston, South Carolina, and Tampa,Florida.

Parker, R. O. 1991. Survival of released fish -- Asummary of available data. Report to SouthAtlantic and Gulf of Mexico FisheriesManagement Councils, Charleston, SouthCarolina, and Tampa, Florida.

Parrack, N. C. 1986a. A review of Gulf of Mexico redsnapper age and growth. National MarineFisheries Service, Southeast Fisheries ScienceCenter, Miami Laboratory, Miami CRD-86/87-2.

Parrack, N. C. 1986b. Review and update of Gulf ofMexico red snapper biometrics:1. length-weight relations, 2. length-length conversions.National Marine Fisheries Service, SoutheastFisheries Science Center, Miami Laboratory,Miami CRD-86/87-3.

Parrack, N. C. and D. B. McClellan. 1986. Trends in Gulfof Mexico Red Snapper Population Dynamics,1979-85. National Marine Fisheries Service,Southeast Fisheries Science Center, MiamiLaboratory, Miami CRD-86/87-4.

Patterson, K. R., and G. P. Kirkwood. 1995. Comparativeperformance of ADAPT and Laurec-Shepherdmethods for estimating fish populationparameters and in stock management. ICESJournal Marine Science. 52:183-196.

Patterson, W.F. 1999. Aspects of the poplulatione c o l o g y o f r e d s n a p p e r , L u t j a n u scampechanus, in an artificial reef area offAlabama. Ph.D. dissertation, University ofSouth Alabama, Mobile, Al.

Pauly, D. 1980. On the interrelationships betweennatural mortality, growth parameters, andmean environmental temperature in 175 fishstocks. Journal du Conceil, CounseilInternational pour L'Exploration de la Mer39:175-192.

Render J. H., and C. A. Wilson. 1993. Mortality rate andmovement of hook-and-line caught andreleased red snapper. Final report forCooperative Agreement #NA90AAHMF762Marine Fisheries Initiative (MARFIN)Program. Coastal Fisheries Institute, Center forCoastal , Energy, and EnvironmentalResources, Louisiana State University, BatonRouge, LA. LSU-CFI-93-8.

Schirripa, M.J. and C.M. Legault. 1997. Status of thered snapper in U.S. waters of the Gulf ofM exico: updated through 1996. SoutheastFisheries Science Center, Miami Laboratory,Miami MIAC97/98-05.

Schirripa, M.J. 1998. Status of the red snapper in U.S.waters of the Gulf of Mexico: updated through1997. NOAA/NMFS Sustainable FisheriesDivivion Contribution, SFD-97/98-30.

Szedlmayer, S. T. and R. L. Shipp. 1992. Production of alarge artificial reef area in the northeastern Gulfof Mexico. Final report for Cooperative

44

Agreement #NA90AAHMF733 MarineFisheries Initiative (MARFIN) Program.Coastal Research and Development Institute,University of South Alabama, Mobile, AL.

Topp, R. 1964. Residence habits of the red snapper.Underwater Naturalist 2(3):15-17.

Udall, S. L. 1964. Manual for conducting statisticalsurveys of the fisheries of the United States.U.S. Department of the Interior. Bureau ofCommercial Fisheries. Washington, D.C. 58pp.

Wilson, C. A., J. H. Render, and D. L. Nieland. 1994.Life history gaps in red snapper (Lutjanuscampechanus), swordfish (Xiphias gladius),and red drum (Sciaenops ocellatus) in thenorthern Gulf of Mexico; age distribution,growth, and some reproductive biology. Finalreport to U. S. Dept. Comm., Nat. Mar. Fish.Ser., Mar. Fish. Init. (MARFIN) Coop.Agreement NA17FF0383-02. 79 pp.

Workman, I. K., and D. G. Foster. 1994. Occurrence andbehavior of juvenile red snapper, Lutjanuscampechanus, on commercial shrimp fishinggrounds in the Northeastern Gulf of Mexico.Marine Fisheries Review 56 (2):9-11.

APPX 1 - 1

Appendix I

Does it Matter When Compensation Occurs in the Red Snapper Stock Recruitment Relationship?

One concern that has been raised relative to red snapper recovery is if density dependence in the stock-recruitment relationship occurs at age 2 instead of age 0, would reductions in bycatch not impact the spawning stockand thus be less or not important in terms of the recovery of red snapper? Under this hypothesis it is sometimessuggested that a relatively small decrease in numbers of red snapper at age 2 due to bycatch would occur whencompensation occurs at age 2 causes in comparison to the pre- age 2 compensation scenario. This document examinesthis hypothesis by first deriving the equations for post-juvenile compensation in the stock-recruitment relationship andthen examines the full implications of this scenario in terms of the impact of catch of age 0 and 1 fish upon the redsnapper stock and fishery as a whole. The biological causes of density dependence in the stock-recruitment relationshipare not explored here.

Start with the standard Beverton and Holt stock-recruitment relationship measured in eggs and numbers ofrecruits at age 0, respectively,

RE

E=

+α β

where " and $ are parameters fit to data.The recruits are reduced in number by a total mortality (Z) over two years (ages 0 and 1)

.N Re Z2 = −

If instead of compensation occurring before red snapper are caught by shrimp trawls, compensation occurs atthe start of age 2, then the following equations can be used. From the original stock recruitment relationship, the densityindependent component is determined by the slope at the origin

. NE

0 ' =β

(Note the use of primes for post-juvenile compensation variables.) These numbers are reduced by the total mortality overtwo years as before

N N e Z2 0' '= −

and then a density dependent compensation occurs to produce the “recruitment” to age 2 which for the sake of simplicityis assumed to follow a Beverton and Holt parameterization

RN

cN d'

'

'=

+2

2

where c and d are parameters to be determined.

APPX 1 - 2

If the numbers of “recruits” at age 2 are assumed to be equal when there is no fishing mortality

.N R2 = '

Thus,

ReN

cN dM− =

+2

2

'

'

and then by further substitution,

EE

e

E e

c E e d

M

M

Mββ

β+

=+

−

−

−

which can be simplified to

.cE

e daEM

ββ

β− + =

+

When eggs are zero, then d=1, which means c=aeM. Thus, the post-juvenile compensation stock-recruitment relationshipis given by

R

E e

e E e

Ee E e

Z

M Z M Z'=+

=+

−

−

β

αβ

α β1

where " and $ have the same values as above. A graphical comparison of the effects of these hypotheses can be made using these equations. The parameter

values used to create this figure are as follows:cumulative M ages 0 and 1 = 0.666667cumulative F ages 0 and 1 = 2.09 for full bycatch or 1.045 for 50% reduction in bycatch Falpha in the SRR = 4.076E-9beta in the SRR = 1.736E+5.

APPX 1 - 3

0.00E+00

2.50E+07

5.00E+07

7.50E+07

1.00E+08

1.25E+08

0.0E+00 5.0E+14 1.0E+15 1.5E+15 2.0E+15

Eggs

Re

cru

its

at

Ag

e 2

M Only50% Bycatch F Reduc, Comp Age 2Bycatch with Compensation at Age 250% Bycatch F Reduc, Comp Age 0Bycatch with Compensation at Age 0

The slope at the origin (1/[$eZ]) for the stock-“recruit” curve thus depends upon the total mortality rate, bothnatural and fishing, during the period before compensation occurs, while the maximum recruitment (1/["eM]) dependsonly upon the natural mortality rate during this period. Bycatch would not reduce maximum recruitment under theseconditions, but would change the slope of the stock “recruit” relationship. (Note, “recruit” refers to the number of redsnapper at age 2 after compensation has occurred.) Changes in the slope of the stock “recruit” relationship haveimplications for sustainable fishing, the lower the slope (caused by larger bycatch) the lower the fishing rate on adultfish that will be sustainable. It follows that maximum sustainable yield would also change depending upon the bycatch,even though the maximum number of fish at the start of age 2 is the same for any level of bycatch. Therefore, theargument that compensation at age 2 would limit the importance of bycatch is incorrect. Reductions in catches of age0 and 1 red snapper would increase the maximum sustainable yield and speed recovery under a given fixed level ofdirected catch regardless of when compensation occurs.

This point is demonstrated, in the example pictured below. The yield under a range of F values for an assumedM vector based on red snapper, was computed for both the compensation at age 0 (denoted pre juvenile compensation)and compensation at age 2 (denoted post juvenile compensation) scenarios (see graph below). There is in fact anincrease in yield when compensation occurs as the fish turn age 2 relative to compensation occurring at age 0. However,the curves cross the x-axis at the same value of F, meaning that post juvenile compensation does not allow moreexploitation by the combined fleet than the pre juvenile compensation scenario. The maximum sustainable yields occurat similar levels of F, both of which require a major reduction in both shrimp trawl bycatch and directed catch in thisexample. From the point of view of yield foregone, the increased yield under the post juvenile compensation scenarioargues more strongly for F reduction measures to be implemented on age 0 and 1 fish than under the pre juvenilecompensation scenario, because there is more to gain.

APPX 1 - 4

020406080

100120140160180

0.0 0.2 0.4 0.6 0.8 1.0

F

Yie

ld (m

illio

n p

ou

nd

s)

Current F

Pre JuvenileCompensation

Post JuvenileCompensation

Returning to the title of this document, the timing of compensation in red snapper is not a critical question

based on this particular analysis. It has been shown that although the absolute magnitude of change in number of age-2red snapper caused by bycatch reduction depends upon when compensation occurs, the implications for managementare not radically changed. The catch of age-0 and age-1 red snapper must be reduced significantly to achieve maximumsustainable yield. Given that the potential gains are larger when compensation occurs post-juvenile than pre-juvenile,post-juvenile compensation argues even stronger for reductions in age 0 and 1 catches.

These conclusions are based on the particular method to derive the stock-“recruit” relationship, but theimplications for management should hold for most other methods. A number of assumptions are made during thederivation of this post-juvenile compensation stock recruitment relationship that are difficult to justify. Thecompensation occurs exactly and instantly as the red snapper become age 2. It is difficult to imagine such a biologicalphenomenon occurring throughout the entire range of red snapper in the Gulf of Mexico. This approach also assumesthat M and F are additive during ages 0 and 1 when compensation occurs at age 2. The validity of this assumption woulddepend upon the proposed mechanism that causes compensation to occur. If M and F are not additive during the pre-compensation period, then the reduction of catch at ages pre-compensation will be less important.

APPX 2 - 1

APPENDIX II

(As submitted to the GMFMC, “Red Snapper/Shrimp Research Program, Summer 1998, Final Report, April 1999)

Preliminary Application of an Age-Structured Assessment Program (ASAP) to Red Snapper

Data from the 1997 stock assessment of red snapper (Schirripa and Legault 1997) was used as inputin an age-structured assessment program (ASAP) (Legault and Restrepo 1998) and the results compared to the 1997assessment. Details of the program ASAP are not reported here (see Legault and Restrepo 1998), although modificationsare described. Basically, ASAP is based on forward computations assuming separability of fishing mortality into yearand age components, which can change over time. Other parameters often considered fixed in stock assessments areallowed to vary as well, such as the catchability coefficients for tuning indices. The solution of this highly flexible modelis constrained by user supplied penalties for how much parameters are allowed to vary relative to each other. The numberof parameters in such a system can be quite large, hundreds or thousands, and an automatic differentiation softwarepackage (AD Model Builder, Otter Research Ltd.) is used to calculate to machine precision the partial derivatives of eachparameter with respect to each data point such that the minimization routine can find the solution.

Red snapper catch at age from the 1997 stock assessment was separated into five fleets: threecommerical fleets, eastern Gulf of Mexico, western Gulf of Mexico, and bottom longline; a recreational fleet; and bycatchfrom the shrimp trawl fishery (Figure 1). Catch at age for each fleet was available for the years 1984 through 1996 andconsisted of ages 0 through 15+, meaning all catch for ages 15 and older was grouped together. All ages were not caughtby all fleets. Two tuning indices were used: the summer SEAMAP fishery independent index was tuned to age 1; andthe MRFSS private/rental mode catch per angler trip was tuned to ages 1 through 10 and linked to the estimatedselectivity pattern of the recreational fleet (see Schirripa and Legault 1997 for further details about the indices).

Modifications to ASAP for this red snapper example were the inclusion of a penalty for steepness ofthe stock recruitment relationship deviating from a prescribed level, the inclusion of uncertainty in the natural mortalityrate (M) for adult fish, and the ability to designate fleets as directed or nondirected for management benchmarkcalculations. The uncertainty in M was included by estimating a single parameter that was then applied to ages 2 andolder for all years. The M for juvenile red snapper was held fixed at 0.5 for age 0 and 0.3 for age 1 for all years. The adultM was constrained by a penalty for deviations from 0.1, the rate set in the 1997 stock assessment. The spawning stocksize was calculated as the spawning stock biomass of fish age 7 and older within the model instead of using a fecundityfunction, as was done in the 1997 stock assessment. Nondirected fleets can have their fishing mortality rate multiplierincreased or decreased in projections, but are held constant during solution of directed fishing mortality rate benchmarkssuch as F0.1 and FMSY.

Two separate examples were conducted using ASAP and the red snapper data. The two examples aredistinguished by the change of two penalties, the deviation from equilibrium in the first year and recruitment deviations(Table 1). These examples do not constitute a formal assessment but rather point out the possibility of many stockstructures generating similar catch and index information, while maintaining reasonable relative magnitudes for thepenalties. For example, the total catch in weight is known much better than the catch at age, and the deviations aboutthe estimated stock recruitment relationship are expected to be larger than the deviations in the catchability coefficientsfor tuning indices over time. The use of these penalties allowed a total of 361 parameters to be estimated in bothexamples. In a formal assessment, it would be critical to examine the value to be assigned to the different penalties in amanner that is consistent with prior beliefs.

Fits to the tuning indices from the two methods, Fadapt and ASAP, were similar with the SEAMAPindex fit well and the late part of the MRFSS index fit well but the early part of the MRFSS index not fit well (Figure 2).Both ASAP fits were better than the Fadapt fit based on residual sum of squares, with ASAP2 fitting the two indices

APPX 2 - 2

slightly better than ASAP1. The trend in number of age 2 red snapper was similar among the three results, with ASAP2estimating higher abundance (Figure 3). The adult average fishing mortality rate was similar between Fadapt and ASAP1,while ASAP2 values were lower but followed a similar trend (Figure 3). The abundance in the plus group changed bothin magnitude and trend between the two ASAP examples (Figure 4). The ASAP1 plus group abundance was similar, butless than, the Fadapt estimates, while the ASAP2 values were two orders of magnitude larger than the Fadapt andASAP1 estimates.

Estimates of adult M were not statistically different based on the overlap of confidence intervals, butt he ASAP2 M was higher than the ASAP1 M, 0.105 and 0.096, respectively. The catchability coefficient for the SEAMAPindex was not allowed to vary much due to its high penalty while the MRFSS q could, and did, due to its lower penaltyin both ASAP examples (Figure 5). The decrease in the ASAP1 MRFSS q over time was unexpected, although perhapsit could be explained as an artifact of changing regulations. In both ASAP examples, fleet specific selectivity patternswere allowed to change every three years, except for bycatch which only changed once due to the restricted number ofages caught, while fleet specific fishing mortality multipliers changed every year. Directed fleet selectivities for the plusgroup were high for ASAP1 and low for ASAP2 (Figure 6). The fishing mortality rate multipliers were also higher forASAP1 than ASAP2, although the trends were similar (Figure 7). These differences in scale and or trend are caused bythe estimates of plus group abundance from the two methods. The population estimated by ASAP1 has a small plusgroup which is heavily fished while the population estimated by ASAP2 has a large plus group which is barely exploited.

These differences in plus group estimates are apparent in the stock recruitment relationship (SRR)estimated by the two ASAP examples (Figure 8). Note the order of magnitude difference in the x-axis scales. In theASAP1 example, the SRR has a high value of steepness (0.987) and has a maximum approximately equal to the largestestimated recruitment. While in the ASAP2 example, the SRR has a moderate value of steepness (0.735) and has amaximum approximately equal to the average of the estimated recruitment. Both of these SRRs are quite different fromthe one assumed in the 1997 stock assessment, which forces the maximum recruitment to correspond to levels muchhigher than observed during the period 1984-1996 based on historical bycatch information. Under the ASAP1 estimatedSRR, the current directed fishing mortality rate (0.53) is three to five times higher than the common benchmarks of F0.1

(0.11), FMAX (0.19), and FMSY (0.14). The ASAP2 example estimates the current directed fishing mortality rate (0.14) to beless than F0.1 (0.43) and FMAX (0.60) but above FMSY (0.05). The relative values of the management benchmarks in ASAP2do not follow the common expectation that FMSY should fall between F0.1 and FM A X due to the large unexploited plus groupbiomass. Projection of a constant catch of 12 million pounds into the future causes the spawning stock biomass toincrease over time for ASAP1 and decrease over time for ASAP2 (Figure 9). Thus, the sustainability of a 12 million poundconstant catch dep ends upon the penalties in ASAP, which in the two cases examined here resulted in similar objectivefunction values, 757.5 and 796.7 for ASAP1 and ASAP2, respectively.

Some advantages of the ASAP model are the relative ease of producing uncertainty estimates fordesired variables and the ability to estimate likelihood profiles. For example, the uncertainty of the historical spawningstock biomass estimates does not require Monte Carlo simulation, but rather is derived automatically by the AD ModelBuilder software from the hessian (Figure 10). The spawning stock biomass estimates from ASAP2 are much moreuncertain than those from ASAP1, average coefficient of variation of 31% and 11%, respectively. Likelihood profilingis a way to refine estimates of uncertainty about variables, but requires more time to examine how the variable changesrelative to all the parameters. The likelihood profile is in general faster to compute than a corresponding Monte Carlosimulation, although this is not true for projected variables that require repeated iterative solution of the catch equation.The likelihood profiles of the spawning stock biomass in 1996 relative to that in 1994 for the two ASAP examples showthat although estimates of SSB are more uncertain in ASAP2 than ASAP1, the ratio of change is less variable in ASAP2than in ASAP1 (Figure 11).

Further modifications to ASAP are needed for application to red snapper and other species. In theexample given, dead discards from the commercial and recreational fleet were considered as catch because there iscurrently not a way to distinguish catch from dead discards. Thus, any changes to regulations of minimum size, maximumsize, bag limits, etc. cannot be accounted for in the estimation process and projections. The procedure to estimate the

APPX 2 - 3

stock recruit relationship should be modified to allow inclusion of data outside the time period under consideration.Further exploration of the process of estimating the stock recruit relationship is needed because there may beconfounding in some of the parameter estimates in the current arrangement. Sudden changes in fleet specific fishingmortality rates, the extreme being the termination of a fleet, are not handled well currently in ASAP due to the Fmultdeviations penalty. This should be fixed. Estimates of directed F to achieve given spawning potential ratios should beadded such that the SPR corresponding to MSY could be calculated. Finally, and most importantly in this particular case,the method to estimate population abundance at age in the first year must be improved, especially for the plus group.

Literature CitedLegault, C.M and V.R. Restrepo. 1998. A flexible forward age-structured assessment program. ICCAT Working

Document SCRS/98/58. 15p.Schirripa, M.J. and C.M. Legault. 1997. Status of the red snapper in U.S. waters of the Gulf of Mexico: Updated

through 1996. NMFS Sustainable Fisheries Division Contribution MIA-97/98-05. 37p.

Table 1. Red snapper penalties for two examples using ASAP.Penalty

Source Example 1 Example 2Total Catch 10000 10000

Catch at Age 50-150 50-150

Tuning Indices Residuals SEAMAP 100 100

MRFSS 25.5 25.5q deviations

SEAMAP 10000 10000

MRFSS 25 25Fmult deviations 11.6 11.6

Recruitment deviations 4.48 0

N in year 1 devaitions 0 4.48Stock recruit deviations 6.74 6.74

Selectivity deviations 4.48 4.48

Selectivity curvature at age 4.48 4.48Selectivity curvature over time 0 0

Steepness devaitions from 0.7 11.6 11.6

Adult M deviations from 0.1 100 100

APPX 2 - 4

0.00E+002.00E+064.00E+066.00E+068.00E+061.00E+071.20E+071.40E+071.60E+071.80E+072.00E+07

84 85 86 87 88 89 90 91 92 93 94 95 96

Year

Catc

h (

po

un

ds)

BycatchRec

Comm LLComm West

Comm East

Figure 1. Red snapper catch in weight by the fivefleets.

SEAMAP

0

0.5

1

1.5

2

2.5

3

84 85 86 87 88 89 90 91 92 93 94

Year

Scale

d In

dex

obs

Fadapt

ASAP1

ASAP2

MRFSS

0

0.5

1

1.5

84 85 86 87 88 89 90 91 92 93 94 95 96

Year

Scale

d In

dex

obs

Fadapt

ASAP1

ASAP2

Figure 2. Fits to tuning indices.

Age 2

0.0E+00

5.0E+06

1.0E+07

1.5E+07

2.0E+07

2.5E+07

3.0E+07

3.5E+07

84 85 86 87 88 89 90 91 92 93 94 95 96Year

Nu

mb

er

of

Fis

h

Fadapt

ASAP1

ASAP2

Ages 2-15+

0

0.1

0.2

0.3

0.4

0.5

84 85 86 87 88 89 90 91 92 93 94 95 96Year

Avera

ge F

Fadapt

ASAP1

ASAP2

Figure 3. Estimated number of age two red snapper (left panel) and average adult fishingmortality (right panel).

APPX 2 - 5

SEAMAP

0.00E+00

2.50E-08

5.00E-08

84 85 86 87 88 89 90 91 92 93 94

Year

Catc

hab

ilit

y

ASAP1

ASAP2

MRFSS

0.00E+00

2.00E-08

4.00E-08

6.00E-08

8.00E-08

1.00E-07

1.20E-07

84 85 86 87 88 89 90 91 92 93 94 95 96

Year

Catc

hab

ilit

y

ASAP1

ASAP2

Figure 5. Index specific catchability coefficients.

Plus Group

0.0E+00

2.0E+04

4.0E+04

6.0E+04

8.0E+04

1.0E+05

1.2E+05

84 85 86 87 88 89 90 91 92 93 94 95 96

Year

Nu

mb

er

of

Fis

hFadapt

ASAP1Plus Group

0.0E+00

5.0E+06

1.0E+07

1.5E+07

2.0E+07

2.5E+07

3.0E+07

3.5E+07

84 85 86 87 88 89 90 91 92 93 94 95 96

Year

Nu

mb

er

of

Fis

h

Fadapt

ASAP2

Figure 4. Estimated number of red snapper in the oldest age group.

APPX 2 - 6

Com. East 1

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15

Age

Sel

ectiv

ity 84-86

87-89

90-9293-96

Com. West 1

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15

Age

Sel

ectiv

ity 84-8687-89

90-92

93-96

Com. East 2

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15

Age

Sel

ectiv

ity 84-86

87-89

90-9293-96

Com. West 2

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15

Age

Sel

ectiv

ity 84-8687-89

90-92

93-96

APPX 2 - 7

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

84 86 88 90 92 94 96

Year

F m

ult

ipli

er

Com East 1

Com West 1

Com LL 1Rec 1

Com East 2

Com West 2Com LL 2

Rec 2

0

0.5

1

1.5

2

2.5

84 86 88 90 92 94 96

Year

F m

ult

ipli

er

Bycatch 1Bycatch 2

Figure 7. Fleet specific fishing mortality rate multipliers from ASAP1 and ASAP2.

Com. Longline 1

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15

Age

Sel

ectiv

ity 84-86

87-89

90-92

93-96

Recreational 1

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15

Age

Sel

ectiv

ity 84-8687-89

90-92

93-96

Com. Longline 2

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15

Age

Sel

ectiv

ity 84-86

87-89

90-92

93-96

Recreational 2

0

0.2

0.4

0.6

0.8

1

1.2

0 5 10 15

Age

Sel

ectiv

ity 84-8687-89

90-92

93-96

Figure 6. Fleet specific selectivity patterns by year intervals for ASAP1 (left) andASAP2 (right).

APPX 2 - 8

0.0E+00

3.0E+07

6.0E+07

9.0E+07

1.2E+08

1.5E+08

1.8E+08

0.0E+00 5.0E+07 1.0E+08 1.5E+08 2.0E+08

Spawning Stock Biomass (pounds)

Re

cru

its

(n

um

be

rs)

Fcurrent Fmax

Fmsy

F0.1

0.0E+00

3.0E+07

6.0E+07

9.0E+07

1.2E+08

1.5E+08

1.8E+08

0.0E+00 5.0E+08 1.0E+09 1.5E+09 2.0E+09

Spawning Stock Biomass (pounds)

Re

cru

its

(n

um

be

rs)

FcurrentFmax FmsyF0.1

Figure 8. Stock recruitment relationship (curved line) with estimated observations (line withsymbols) and replacement lines for specific fishing mortality rates (dotted lines) estimated byASAP1 (left panel) and ASAP2 (right panel). Note the order of magnitude difference in the x-axis scales.

12 million pound quota

0.0E+00

4.0E+08

8.0E+08

1.2E+09

1.6E+09

1980 1990 2000 2010 2020 2030

Year

SS

B (p

ounds)

ASAP1 ASAP2

Figure 9. Spawning stock biomass, symbols denotehistorical estimates, lines denote projected valuesunder an annual quota of 12 million pounds.

APPX 2 - 9

ASAP1

0.0E+00

1.0E+07

2.0E+07

3.0E+07

4.0E+07

84 85 86 87 88 89 90 91 92 93 94 95 96

Year

SS

B (

po

un

ds

)ASAP2

0.0E+00

5.0E+08

1.0E+09

1.5E+09

2.0E+09

2.5E+09

84 85 86 87 88 89 90 91 92 93 94 95 96

Year

SS

B (

po

un

ds

)

Figure 10. Spawning stock biomass estimate with error bars denoting two standard deviations.

00.5

11.5

22.5

33.5

44.5

5

0 1 2 3 4

SSB ratio (1996/1984)

Lik

elih

ood p

rofil

e

ASAP1

ASAP2

Figure 11. Likelihood profiles from the two ASAPexamples for the ratio of spawning stock biomass in1996 to that in 1984.

APPX 3 - 1

APPENDIX III

Date: 09/17/1999Sender: rvminton.amrdgs@gulftel.comTo: Michael SchirripaPriority: NormalSubject: Red Snapper Ovary Weight --------------------------------------- see attachments --

Attached are data gathered this summer by a graduate student working for Dr. Ron Phelps, Auburn University, on Red Snapper ovary weights. I have also attached graphs I worked up from the data. Looks like the weights are very different than those reported by Collins especially in the smaller fish.

APPX 3 - 2

APPENDIX IV

Response to questions raised by the Gulf of Mexico Fisheries Management Council

In an April 16, 1998 letter that was written following the Gulf of Mexico Fisheries Management Council’sMarch 1998 meeting in Duck Key, Florida, the Council requested that NMFS perform a series of analyses of hypotheticalred snapper scenarios requested by the Reef Fish Stock Assessment Panel (RFSAP). This letter stated specifically thatthese scenarios were “ABSOLUTELY NOT TO BE CONSIDERED as a means to evaluate progress towards reachingmanagement goals, but in order to estimate the effect of these changes on the current estimate of stock biomass andSPR”. The three cases requested are as follows:

(1) A 20-30% reduction in the historical shrimp fishery bycatch (already evaluated)(2) A 100% under-reporting of the commercial harvest prior to 1990.(3) A 15% reduction in the selectivities of fish >12 years of age due to a refuge effect(4) A 10-15% reduction in the natural mortality rate of pre-recruit (ages 0-2) snapper due to a refuge effect(5) The following combination of effects: 15% reduction in bycatch; 50% under-reporting of commercial

harvest; 7.5% reduction in selectivites of fish > 12 years old; and, 7.5% reduction of the naturalmortality of pre-recruits.

This Appendix addresses the above mentioned request. Case (1) was addressed prior to the 1999 assessment.Case (3) was addressed in a separate report to the GMFMC and reproduced in Appendix II in this document. Cases (2),(4) and (5) are addressed in this Appendix. Specifically the cases are addressed in the same manner that most otherprojections are addressed: in form of isopleths that estimate the recovery of red snapper in terms of the value of SPR inthe year 2019 and 2034. For comparison. this analysis uses a “base case” scenarios of the 1999 red snapper stockassessment which assumes the red snapper stock-recruitment curve corresponding to a steepness of 0.95 and a “high”maximum recruitment.

Comparison between the base case isopleths (Appendix IV, Figure 1 for 2019 and Figure 5 for 2034) and theoutcome of the three cases are shown in Appendix IV, Figures 2,3, and 4 for 2019 and Figures 6,7, and 8 for 2034.

APPX 3 - 3

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(DIR

EC

TED

FIS

HE

RY

)

0.01

BASE - 2019

0.10

0.2

0

0.30

0.4

0 0.5

0

0.6

0

Figure 13. SPR as a function of bycatch mortalityand TAC in 2019 for the “bae case” scenario”

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(DIR

EC

TED

FIS

HE

RY

)

CASE 1 - 2019

0.10

0.2

0

0.3

0

0.4

0

0.5

0

0.6

0

Figure 14. SPR as a function of bycatchmortality and TAC in 2019 for case (1).

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(DIR

EC

TED

FIS

HE

RY

)

CASE 2 - 2019

0.10

0.2

0

0.30

0.4

0 0.5

0

0.6

0

Figure 15. SPR as a function of bycatchmortality and TAC in 2019 for case (2).

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (D

IRE

CT

ED

FIS

HE

RY

)

0.01

CASE 3 - 2019

0.10 0

.20

0.3

0

0.4

0

0.5

0

0.6

0

Figure 16. SPR as a function of bycatchmortality and TAC in 2019 for case (3).

APPX 3 - 4

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (

DIR

EC

TE

D F

ISH

ER

Y)

0.01

0. 2

6

BASE - 2034 0

.10

0.

20

0.3

0

0.5

0

0.6

0

0.7

0

0.8

0

Figure 17.SPR as a function of bycatch mortalityand TAC in 2034 for the “base case” scenario”

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (

DIR

EC

TE

D F

ISH

ER

Y)

0.0

1

0.2

6

CASE 1 - 2034

0.10

0.2

0

0.3

0

0.4

0

0.5

0 0.6

0

0.7

0

0.80

0.9

0

Figure 18. SPR as a function of bycatchmortality and TAC in 2034 for case (1).

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TA

C (

DIR

EC

TE

D F

ISH

ER

Y)

0.01

0. 2

6

CASE 2 - 2034

0.1

0

0.

20

0.3

0

0.4

0

0.

50

0.60

0.7

0

0.80

0.9

0

Figure 19. SPR as a function of bycatch mortalityand TAC in 2034 for case (2).

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

REDUCTION IN BYCATCH MORTALITY

0

2

4

6

8

10

12

14

TAC

(DIR

EC

TED

FIS

HE

RY

)

0.01

0.26

CASE 3 - 2034

0.10

0. 2

0

0

. 30

0

.4

0

0

. 50

0

.6

0

0

.7

0

0

.8

0

Figure 20. SPR as a function of bycatchmortality and TAC in 2019 for case (3).