SCRS/2007/069
Collect. Vol. Sci. Pap. ICCAT, 62(2): 445-468 (2008)
STANDARDIZED CATCH RATES FOR BIGEYE TUNA (THUNNUS OBESUS)
FROM THE PELAGIC LONGLINE FISHERY IN THE
NORTHWEST ATLANTIC AND THE GULF OF MEXICO
John Walter1, Mauricio Ortiz and Craig Brown
SUMMARY
Two indices of abundance of bigeye tuna from the United States pelagic longline fishery in the Atlantic
are presented; an index of number of fish per thousand hooks estimated from numbers of bigeye tuna
caught and reported from Pelagic longline logbook data from 1986-2006 and a biomass index (dressed
weight) per thousand hooks estimated from the dealer weight-out data for the period 1982-2006 .The
standardization analysis procedure included the following variables; year, area, quarter of the year,
gear characteristics (light sticks, etc) and fishing characteristics (operations procedure, and target
species). Standardized indices of abundance by year and quarter of the year were also constructed from
the dealer weight-out data for 1984-2006. Standardized indices were estimated using Generalized
Linear Mixed Models under a delta lognormal model. Both indices indicate an overall decline since the
mid-1980s with slight increases since 2004.
RÉSUMÉ
Ce document présente deux indices d’abondance du thon obèse de la pêcherie palangrière pélagique
des Etats-Unis dans l’Atlantique : un indice du nombre de poissons par mille hameçons, estimé d’après
le nombre de thons obèses capturés et déclarés dans les livres de bord des palangriers pélagiques de
1986 à 2006 et un indice de biomasse (poids manipulé) par mille hameçons, estimé d’après les données
des pesées des négociants pour la période 1982-2006. La procédure d’analyse de standardisation
incluait les variables suivantes : année, zone, trimestre, caractéristiques des engins (baguettes
lumineuses, etc.) et caractéristiques de pêche (procédure des opérations et espèces cibles). Les indices
standardisés de l’abondance par année et trimestre ont également été élaborés d’après les données des
pesées des négociants pour 1984-2006. Les indices standardisés ont été estimés à l’aide de Modèles
Linéaires Généralisés Mixtes (GLMM) dans le cadre d’un modèle delta lognormal. Les deux indices
indiquent un déclin global depuis le milieu des années 1980, avec de légères augmentations depuis
2004.
RESUMEN
Se presentan dos índices de abundancia de patudo de la pesquería de palangre pelágico de Estados
Unidos en el Atlántico; un índice en número de ejemplares por mil anzuelos, estimado a partir del
número de patudos capturados y comunicados en los datos de los cuadernos de pesca de la pesquería
de palangre pelágico de 1986 a 2006, y un índice de biomasa (peso eviscerado) por mil anzuelos
estimado a partir de los datos de venta al peso de los comerciantes para el periodo 1982-2006. El
procedimiento de análisis de estandarización incluía las siguientes variables: año, zona, trimestre del
año, características del arte (bastones luminosos, etc.) y características de la pesca (procedimiento de
las operaciones y especies objetivo). También se obtuvieron los índices estandarizados de abundancia
por año y trimestre del año utilizando los datos de venta al peso de los comerciantes para el periodo
1984-2006. Los índices estandarizados se estimaron utilizando modelos lineales generalizados mixtos
con un enfoque de modelo delta-lognormal. Ambos índices indican un descenso global desde mediados
de los ochenta con ligeros incrementos desde 2004.
KEYWORDS
Bigeye tuna, abundance indices, catch/effort, catch rate standardization,
Generalized Linear Model, pelagic longline fisheries
1
NOAA Fisheries, Southeast Fisheries Center, Sustainable Fisheries Division, 75 Virginia Beach Drive, Miami, FL, 33149-1099, USA. Email: john.f.walter@noaa.gov
445
1. Introduction
Relative abundance indices are critical indicators of stock status as well as essential inputs into stock
assessments. Fishery catch per unit effort (CPUE) indices operate under the basic assumption that catch per unit
effort is proportional to stock size. Numerous factors including changes in vessel characteristics, fishing
locations and methods, stock size and distribution and targeting of alternative species erode this basic
relationship and complicate the interpretation of raw CPUE. Provided that data on covariates that influence the
relationship between CPUE and abundance exist, these can be incorporated into standardized measures that
presumably more accurately reflect population relative abundance (Maunder and Punt 2004).
The report presents updated standardized CPUE estimates in numbers of bigeye tuna obtained from pelagic
longline logbook data (PLL) for years 1986 to 2006 and in weight from dealer weigh-out data (DLS) for the
years 1982-2006. We use a delta lognormal approach implemented as a generalized linear mixed model in SAS
using methodology similar to Ortiz and Diaz (2003) and Ortiz (2004). We also present nominal catch rates by
geographic area and as a function of various explanatory variables and length-frequency histograms and plots of
mean length by year and area.
2. Materials and methods
Data for this analysis comes from the United States Atlantic and Gulf of Mexico Pelagic longline fishery
described in detail by Hoey and Bertolino (1988). Swordfish, yellowfin and bigeye tuna are the predominant
target species. The pelagic longline fishing grounds of the US fleet extends from the Grand Banks in the North
Atlantic to 5-10° south of the Equator, mainly in the Western Atlantic including the Caribbean Sea and the Gulf
of Mexico (Fig. 1).The fishery has operated under several time-area restrictions since 2000 due to management
regulations related to swordfish and other species (Federal Register 2000, Fig. 1). These restrictions included
two permanent closures to pelagic longline fishing, one in the Gulf of Mexico known as the Desoto Canyon,
effective since 1st November 2000, and the second permanent closure on the Florida East Coast effective since
1st March 2001. In addition, three time-area restrictions were also imposed for the pelagic longline gear in the US
Atlantic coast: the Charleston Bump, an area off the South Carolina coast closed from 1st February to 30th April
starting in year 2001, The Bluefin tuna protection area off the South New England coast closed from 1st June to
30th June starting in 1999, and the Grand Banks area that was closed from 17 July 2001 to 9 January 2002 as a
result of an emergency rule implementation (Cramer 2002).
Catch and effort data is available from three sources: pelagic logbooks reported daily by vessel captains (Scott et
al 1993, Cramer and Bertolino 1998, Ortiz et al 2000), pelagic observer program data collected beginning in
1992 by onboard fishery observers on approximately 4-5% of the Pelagic Longline trips (Lee and Brown 1999)
and dealer weigh-out data sheets submitted at the completion of each trip. Dealer weight-out data consists of
summarized catch per total effort for an entire trip so that spatial and temporal information on fishing locations
and catch rates represent averages for an entire trip. In contrast, logbook and observer daily reports generally
have spatially and temporally explicit information on individual longline sets. U.S. Pelagic logbook data are
available from 1986. However, from 1986 to 1991, submission of logbooks was voluntary, and became
mandatory in 1992. Weigh-out data collection began in 1981 and observer data collection began in 1992. In this
paper we use both vessel logbook reports and dealer weigh-out data. This data and further descriptions of the
data collection and processing are available from the National Marine Fisheries Service Southeast Fisheries
Science Center (http://www.sefsc.noaa.gov/commercialprograms.jsp).
The Pelagic Longline Logbook data comprises a total of 292,507 recorded sets from 1986 through 2006. Each
record contains information of catch by set, including: date-time, geographical location, catch in numbers of
targeted and bycatch species, number of hooks, light stick and various other gear parameters for each set as well
as environmental conditions such as temperature. Various data restrictions were necessary to eliminate
incomplete or erroneous records or records that were non-standard such as very short sets of fewer than 100
hooks, weight of fish incorrectly recorded as number or set with zero fish of any species captured. Restricting the
logbook data only to sets from 1986 onward, those with greater than 100 hooks per set, and with complete catch,
effort, location and date information, resulted in a total of 266,296 sets, of which 74,933 (28.1%) reported
positive catches of bigeye tuna. The pelagic dealer weigh-out data comprises a total of 40,025 records. Similarly
we applied a set of data restrictions that eliminated incomplete or clearly erroneous records resulting in a total of
37,040 reports of which 14,764 (39.8%) reported positive catches of bigeye tuna.
446
2.1 Dependent variables
Two dependent variables were used for the analysis, catch per unit effort (1000 hooks) in number (CPUEN) and
in weight (CPUEW). CPUEN was obtained from the logbook data as the number of bigeye tuna caught per 1000
hooks for each longline set. This number included kept as well as live and dead discarded bigeye tuna, however
discarded bigeye tuna represented less than 5% of the total number of caught tuna. CPUEW was obtained from
the DLS weigh-out database as the total dressed carcass weight of bigeye tuna landed for an entire trip divided
by the total number of hooks set for the trip. For comparison nominal catch rates of bigeye tuna caught per 1000
hooks was also obtained from the DLS database. Note that the DLS recorded tuna did not include discarded fish.
2.2 Factors
Five fixed factors and a random effect of year and interactions between the factors were evaluated in the
analysis. Eight geographical areas (area) of longline fishing have been traditionally used for classification; these
include: the Caribbean, Gulf of Mexico, Florida East coast, South Atlantic Bight, Mid-Atlantic Bight, New
England coastal, northeast distant waters, the Sargasso Sea, and the offshore area (Figure 1). Calendar quarters
(qtr) were used to account for seasonal fishery distribution through the year (Jan-Mar, Apr-Jun, Jul-Sep, and OctDec). Other factors included in the analyses of catch rates included; the use and number of light-sticks (lightc)
expressed as the ratio of light-sticks per hook, and a variable named operations procedures (OP), which is a
categorical classification of US longline vessels based on their fishing configuration, type and size of vessel,
main target species, and area of operation(s).
Fishing effort is reported as number of hooks per set, and nominal catch rates were calculated as number of
bigeye tuna caught per 1000 hooks for each observation. The U.S. Atlantic longline fleet targets mainly
swordfish and yellowfin tuna, but other tuna species are also targeted including bigeye tuna and albacore (to a
lesser extent, some of the trips-sets target other pelagic species including sharks, dolphin and small tunas). We
define targeting (targ) as a categorical variable with four levels based on the proportion of the number of
swordfish caught to the total number of fish per set, with four discrete target categories corresponding to the
ranges 0-25%, 25-50%, 50-75%, and 75-100%. As fishing practices targeting swordfish generally diverge from
those targeting bigeye this provides an empirical means to determine whether vessels are targeting particular
species. Note that there is a record for target species within the PLL database, however it is deemed to be
uninformative (L. Beerkircher, SEFSC pers comm.)
Factors related to bigeye-specific fishing strategy such as longer gangions, more hooks between floats and longer
floatlines were examined. Due to changes in the units with which gangion length were recorded this variable
proved impossible to use at this time and no clear relationship between floatline length or number of hooks
between floats were observed.
Indices of abundance of bigeye were estimated by a generalized linear modeling approach assuming a delta
lognormal model (Lo et al. 2002). The standardization procedure splits the dependent data into two parts, one of
which models the proportion of positive sets which is assumed to have a binomial error distribution. The second
part models the mean catch rate (CPUEN or CPUEW) of successful sets and assumes a lognormal error
distribution. The standardized index is the product of the two. Parameterization of the model used the GLM
structure where the proportion of successful sets is a linear function of the fixed factors the random year effect
and random year-interaction effects when the year term was within the interaction. The logit function was
selected as link between the linear factor component and the binomial error.
Analyses were done using Glimmix and Mixed procedures from the SAS® statistical computer software (SAS
Institute Inc., 1997, Littell et al. 1996). A step-wise regression procedure was used to determine the set of factors
and interactions that significantly explained the observed variability. We used criterion which assumes that the
difference in deviance between two consecutive models follows a Chi-square distribution. The deviance is
essentially a measure of the residuals explained by the model. Using this statistic, with degrees of freedom equal
to the number of additional parameters estimated minus one, a Chi-square test was constructed which indicates if
the additional factor is or is not statistically significant (McCullagh and Nelder, 1989). Deviance analysis tables
were constructed for both components of the delta model: Proportion of successful trips/sets, and mean catch rate
in both numbers and weights of positive trips/sets. Each deviance table includes the deviance explained by the
additional factor or interaction, the overall percent explained by each factor, and the Chi-square probability test.
Final selection of explanatory factors was conditional to: a) the relative percent of deviance explained by the
added factor, normally factors that explained more than 5-10% of deviance were included and b) the Chi-square
significant test and c) if an interaction was deemed significant, the main effects of each component of the
447
interaction were included. Once a set of fixed factors was specified, all possible 1 st level interactions were
evaluated, in particular random interactions between the year effect and other factors.
Standard indices of abundance were calculated as the product of the year effect least square means (LSmeans)
from the binomial and the lognormal components of the delta lognormal model. LSmeans estimates included a
weight proportional to the observed margins of the input data, to account for the unbalanced distribution of the
data. Lognormal estimates also included a bias back-transformation correction factor as describe by Lo et al.
(1992). Variances were obtained by the delta method for the approximate variance of the product of two random
variables (Zhou 2002). Quarterly indices of abundance were obtained as the least square mean estimates
obtained by defining a new variable as year*qtr however the quarterly index was only estimated for years 19842006 as years 1982-83 had several quarters with zero catch. Note that indices in both weight and number are not
age or size-specific.
2.3 Time-area restrictions
Because of the time-area restrictions mentioned above, it was important to evaluate their possible effect on catch
rates of bigeye tuna. We did so in a manner similar to Ortiz (2004) by constructing a separate standardized catch
indices for only the areas that are outside of any of the closed areas and comparing these indices to the all-area
indices. For the Pelagic Logbook data, it was possible to assign most of the longline sets to specific latitudelongitude positions and evaluate historic catch trends in and out of the time-area restrictions. In contrast, the DLS
data represents total catch for a trip with only a general geographic zone that is larger than the time-area
closures. Therefore it was not possible to precisely allocate the bigeye tuna catch to closure or non-closure
locations. An approximate solution was to assign an average latitude-longitude by linking the weight-out records
to the logbook set by set information using the an unique trip identifier number. However, prior to 1996 the trip
identifier did not exist so it was not possible to assign these trips to closed or open areas.
3. Results and discussion
3.1 Differences from previous standardization
This analysis differs from Ortiz (2004) in several substantive ways. First sample sizes are larger in this study
because Ortiz (2004) removed all observations if they lacked an operations code and if they were of OP code 1.
Since 2002 and increasing percentage of the total logbook records do not have an OP code, potentially resulting
in the removal of 20-30% of the total logbook records. For this reason I kept all unknown OP code records
except OP code 1. In addition several logbook records appeared to have anomalous numbers of bigeye tuna and
inspection of the datasheets indicated that weight of fish was recorded as number. In this paper the presented
standardized catch rates are for all areas, both inside and outside of the area closures. In general, however, the
catch rates trends differ little between indices including or excluding the closed areas (Figure 13). Particularly
the DLS index is essentially the same because DLS records prior to 1996 cannot be assigned a latitude and
longitude and likely due to the loss of spatial resolution when the location of all trips within a set was averaged.
Lastly the models selected differ from Ortiz (2004) in that no interaction terms were deemed significant.
For the time period 1986-2006, an average of 11,369 longline sets were recorded in the pelagic logbook
database. These comprise the most complete records of US pelagic longline CPUE available for the Atlantic and
Gulf of Mexico. During this time period, there has been a reduction in the total numbers of vessels in the fishery,
with a concomitant increase in the number of hooks set per vessel. This has kept overall fishing effort relatively
constant. However, effort has been displaced from a series of time area closure areas (Figure 1 and 2). Figure 2
presents the geographic distribution of fishing effort and nominal catch rates for bigeye tuna from the pelagic
logbooks for eight selected years from 1987 through 2005. The plotted values are the sum of annual log(hooks)
deployed and the mean catches in number per 1000 hooks calculated on grids of approximately 40x40 nautical
miles. The plots show the substantial reduction of fishing effort, particularly after the implementation of
time/area closures.
3.2 Nominal catch rates
Nominal catch rates by area and year indicate higher catch rates in the Mid-Atlantic Bight, Northeast Coast and
Northeast distant waters (Figures 3-6). Catch rates are very low in the Gulf of Mexico throughout the entire time
period. In recent years, however, catch rates on the Florida East coast have increased primarily to vessels
displaced from closed areas in the Florida Straits to the waters north of the Bahamas where bigeye tuna appear to
be more abundant (Figure 2). Comparison of the DLS and the logbook standardized catch rates in number of
448
bigeye tuna per 1000 hooks shows fairly close agreement as would be expected since the logbook data comes
from individual set reports and the DLS data are trip summaries (Figure 7). Deviations may be due to released
fish which were included in the logbook CPUEN.
3.3 Model selection
The fixed factors in the analysis were chosen through deviance table analysis (Tables 1 and 2) and no fixed
factor interactions were deemed significant. Random effects interactions were chosen on the basis of likelihood
ratio tests and reduction in AIC values (Tables 3 and 4).
The final models selected for the binomial and lognormal components for the CPUEN were as follows (note that
factor OP was included as a fixed factor in the proportion positive model because it was included in the
interactions):
Proportion Positive= Year Area qtr targ OP Year*area Year* OP Year*qtr
log(CPUEN) = Year Area qtr targ OP Year*area Year* OP Year*qtr
The final model selected for the binomial and lognormal components for the CPUEW in biomass were:
Proportion Positive = Year Area targ OP Year*area Year*op Year*targ
log(CPUEW) = Year Area targ qtr OP Year*area Year*OP Year*targ Year*qtr
The final model selected for the binomial and lognormal components of the quarterly CPUEW index in biomass
were:
Proportion Positive = YearQtr Area targ OP YearQtr*area YearQtr*targ
log(CPUEW) = YearQtr Area targ OP YearQtr*area YearQtr*targ
3.4 Standardized catch rates
Histograms of nominal CPUEN from the logbooks and CPUEW indicate no severe departures from the assumed
lognormality (Figure 8). Diagnostic plots for the delta lognormal model fit to the Pelagic Logbook data of
residuals for positive CPUE, chi-square residuals of proportion positive by year, Q-Q plot of cumulative
normalized for positive CPUE and a histogram of residuals for positive CPUE indicate no severe violations
(Figure 9). The same plots for CPUE from the DLS weigh-out data also indicate fairly well-behaved data with
no clear patterns or trends in the residuals (Figure 10).
Standardized catch rates of bigeye tuna numbers and biomass per 1000 hooks (Figures 11, Tables 3 and 4)
indicate a decline since 1987, a stable trend from 1992-2002 and slight increases since 2003. Ortiz (2004) noted
a similar decreasing trend for the period 1987-2003 with both logbook and weigh-out indices. Nominal catch
rates show less of a trend and diverge from standardized indices in the early part of the time series being higher
in the logbook number index (Figure 11A) and lower than the DLS weight index (Figure 11B). Standardized
catch rates in weight from the DLS data by quarter or season of the year were also created which indicated
seasonal variation in catch rates (Figure 12, Table 5) with catch rates highest in the fall. Both standardized catch
in number and weight show very similar patterns (Figure 13) reflective of the absence of a trend in mean size
over the time period for the entire fishery (Figures 14-16). Mean sizes, do, however differ between regions with
the larger bigeye tuna in the Mid-Atlantic and South Atlantic Bight (Figure 14).
3.5 Closed Area analysis
Standardized indices were constructed for all areas and for just areas the areas that have been open to fishing
throughout the entire time period (Figure 17A, B). Removing all observations that occurred in a area that was
closed as some time reduced the number of observations in the Logbook database by almost ½ for the time
period 1987-1997 but the indices show similar trends. With the reduction in overall effort and in particular in the
closed areas, the total number of observations has decreased since 1998 but, again the trends between the open
areas and all the areas appear similar. The DLS indices for all areas and for just the open areas do not differ. This
449
is due to the inability to assign DLS data to locations prior to 1996 so that the data is the same and to the lack of
spatial precision in assigning locations after 1996.
4. Literature cited
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pelagic longline observer program data, 1992-2006. Collect. Vol. Sci. Pap. ICCAT, 62, in press (SCRS/
2007/056).
CRAMER, J. 2002. Large Pelagic Logbook Newsletter 2000. NOAA Tech. Mem. NMFS SEFSC 471, 26 pages.
CRAMER, J. and A. Bertolino. 1998. Standardized catch rates for swordfish (Xiphias gladius) from the U.S.
longline fleet through 1997. Collect Vol. Sci. Pap. ICCAT, 49(1): 449-456.
CRAMER, J. and M. Ortiz. 1999. Standardized catch rates for bigeye (Thunnus obesus) and yellowfin (T.
albacares) from the U.S. longline fleet through 1997. Collect. Vol. Sci. Pap. ICCAT, 49(2): 333-356.
FEDERAL REGISTER. 2000. Atlantic Highly Migratory Species; Pelagic Longline Management; Final rule. 50
CFR part 635. Vol. 65, No. 148 August 1, 2000.
HOEY, J.J. and A. Bertolino. 1988. Review of the U.S. fishery for swordfish, 1978 to 1986. Collect. Vol. Sci.
Pap. ICCAT, 27: 256-266.
LEE, D.W. and C.J. Brown. 1999. Overview of the SEFSC Pelagic Observer Program in the northwest Atlantic
from 1992-1996. Collect. Vol. Sci. Pap. ICCAT, 49(4): 398-409.
LITTELL, R.C., G.A. Milliken, W.W. Stroup and R.D. Wolfinger. 1996. SAS® System for Mixed Models, Cary
NC: SAS Institute Inc., 1996. 663 pp.
LO, N.C., L.D. Jacobson, and J.L. Squire. 1992. Indices of relative abundance from fish spotter data based on
delta-lognormal models. Can. J. Fish. Aquat. Sci. 49:2515-2526.
MAUNDER, M. and A. Punt. 2004. Standardization of catch and effort data: a review of recent approaches.
Fisheries Research 70:141-159.
MCCULLAGH, P. and J.A. Nelder. 1989. Generalized Linear Models 2nd edition, Chapman & Hall.
ORTIZ, M. 2004. Standardized catch rates for bigeye tuna (Thunnus obesus) from the Pelagic longline fishery in
the Northwest Atlantic and the Gulf of Mexico. ICCAT SCRS/04/133.
ORTIZ, M. and G. A. Diaz. 2004. Standardized catch rates for yellowfin tuna (Thunnus albacares) from the U.S.
pelagic longline fleet. Collect. Vol. Sci. Pap. ICCAT, 56(2): 660-675.
ORTIZ, M., J. Cramer, A. Bertolino and G.P. Scott. 2000. Standardized catch rates by sex and age for swordfish
(Xiphias gladius) from the U.S. longline fleet 1981-1998. Collect. Vol. Sci. Pap. ICCAT, 51(5): 15591620.
PINHEIRO, J.C. and D.M. Bates. 2000. Mixed-effect models in S and S-Plus. Statistics and Computing.
Springer-Verlag New York, Inc.
SAS INSTITUTE INC. 1997. SAS/STAT® Software: Changes and Enhancements through release 6.12. Cary
NC: SAS Institute Inc., 1997. 1167 pp.
SCOTT, G.P., V.R. Restrepo and A. R. Bertolino. 1993. Standardized catch rates for swordfish (Xiphias gladius)
from the US longline fleet through 1991. Collect. Vol. Sci. Pap. ICCAT, 40(1): 458-467.
ZHOU, S. 2002. Estimating Parameters of Derived Random Variables: Comparison of the Delta and Parametric
Bootstrap Methods. Transctions of the American Fisheries Society. 131: 667-675.
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Table 1. Deviance analysis table of BET CPUE in number of tuna/1000 hooks from the pelagic logbooks.
Percent of total deviance refers to the deviance explained by the full model; p value refers to the Chi-square
probability test between two consecutive models.
Bigeye Tuna Logbook Catch (Numbers of fish)
Model factors positive catch rates values
Residual
deviance
d.f.
Change in % of total
deviance deviance
0
69340.2
20
67194.8
2145.37
11.7%
year area
8
58344.6
8850.14
48.1%
year area qtr
3
57194.3
1150.31
6.2%
year area qtr targ
3
54531.5
2662.83
14.5%
year area qtr targ op
9
53363.1
1168.36
6.3%
year area qtr targ op lghtc
3
52850.9
512.22
2.8%
year area qtr targ op lghtc targ*lghtc
9
52781.8
69.12
0.4%
year area qtr targ op lghtc qtr*lghtc
9
52623.4
158.39
0.9%
year area qtr targ op lghtc qtr*op
27
52581.9
41.56
0.2%
year area qtr targ op lghtc year*targ
60
52550.7
31.14
0.2%
year area qtr targ op lghtc year*lghtc
9
52505.2
45.47
0.2%
year area qtr targ op lghtc qtr*targ
60
52495.6
9.60
0.1%
year area qtr targ op lghtc op*lghtc
27
52434.4
61.25
0.3%
year area qtr targ op lghtc area*op
27
52356.5
77.87
0.4%
year area qtr targ op lghtc targ*op
57
52327.3
29.23
0.2%
year area qtr targ op lghtc year*qtr
58
52257.6
69.71
0.4%
year area qtr targ op lghtc area*qtr
24
52231.0
26.55
0.1%
year area qtr targ op lghtc year*op
151
52015.7
215.37
1.2%
year area qtr targ op lghtc area*lghtc
24
51911.0
104.63
0.6%
year area qtr targ op lghtc area*targ
24
51559.6
351.38
1.9%
year area qtr targ op lghtc year*area
159
50926.8
632.86
3.4%
year
Model factors proportion positives
Residual
deviance
d.f.
Change in % of total
deviance deviance
0
137330.131
20
135454.104
1876.03
2%
year area
8
71050.992
64403.11
70%
year area qtr
3
67159.379
3891.61
4%
year area qtr targ
3
54303.111
12856.27
14%
year area qtr targ op
9
51978.602
2324.51
3%
year area qtr targ op lghtc
3
50686.828
1291.77
1%
year area qtr targ op lghtc targ*lghtc
9
50577.044
109.78
0%
year area qtr targ op lghtc qtr*targ
9
50040.013
537.03
1%
year area qtr targ op lghtc op*lghtc
27
49716.544
323.47
0%
year area qtr targ op lghtc qtr*lghtc
9
49590.296
126.25
0%
year area qtr targ op lghtc year*targ
60
49395.605
194.69
0%
year area qtr targ op lghtc area*op
62
49167.071
228.53
0%
year area qtr targ op lghtc year*lghtc
60
49118.119
48.95
0%
year area qtr targ op lghtc qtr*op
27
49025.101
93.02
0%
year area qtr targ op lghtc year*qtr
59
48469.926
555.17
1%
year area qtr targ op lghtc targ*op
27
48312.352
157.57
0%
year area qtr targ op lghtc area*lghtc
24
48094.742
217.61
0%
year area qtr targ op lghtc year*op
157
47157.511
937.23
1%
year area qtr targ op lghtc area*qtr
24
46959.534
197.98
0%
year area qtr targ op lghtc area*targ
24
45547.839
1411.70
2%
year area qtr targ op lghtc year*area
159
42396.875
3150.96
3%
year
451
Table 2. Deviance analysis table of BET CPUE in kg of tuna/1000 hooks from the dealer weight out data.
Percent of total deviance refers to the deviance explained by the full model; p value refers to the Chi-square
probability test between two consecutive models.
Model factors positive catch rates values
Residual
deviance
d.f.
1
Change in
deviance
% of total
deviance
p
0
24136.64
24
22861.28
1275.35
12.4%
< 0.001
Year Area
7
18402.18
4459.10
43.2%
< 0.001
Year Area Targ
3
16155.13
2247.05
21.8%
< 0.001
Year Area Targ Qtr
3
15457.91
697.23
6.8%
< 0.001
Year Area Targ Qtr Op
7
14924.28
533.63
5.2%
< 0.001
Year Area Targ Qtr Op Area*Op
34
14767.33
156.95
1.5%
< 0.001
Year Area Targ Qtr Op Targ*Qtr
9
14751.15
16.18
0.2%
0.063
Year Area Targ Qtr Op Qtr*Op
20
14719.94
31.21
0.3%
0.053
Year Area Targ Qtr Op Year*Targ
72
14690.04
29.90
0.3%
1.000
Year Area Targ Qtr Op Targ*Op
20
14661.15
28.89
0.3%
0.090
Year Area Targ Qtr Op Area*Qtr
21
14659.08
2.07
0.0%
Year Area Targ Qtr Op Year*Qtr
70
14563.22
95.86
0.9%
0.022
Year Area Targ Qtr Op Year*Op
140
14321.53
241.69
2.3%
< 0.001
Year Area Targ Qtr Op Area*Targ
21
14249.66
71.87
0.7%
< 0.001
Year Area Targ Qtr Op Year*Area
144
13819.21
430.44
4.2%
< 0.001
Year
Model factors proportion positives
Residual
deviance
d.f.
1
Year
0
21198.214
Change in
deviance
% of total
deviance
1.000
p
23
20785.378
412.84
3%
< 0.001
Year Area
6
12120.961
8664.42
62.0%
< 0.001
Year Area Targ
3
9207.745
2913.22
20.8%
< 0.001
Year Area Targ Qtr
3
8685.060
522.69
3.7%
< 0.001
Year Area Targ Qtr Op
6
8407.089
277.97
2.0%
< 0.001
Year Area Targ Qtr Op Targ*Op
18
8260.900
146.19
1.0%
< 0.001
Year Area Targ Qtr Op Area*Op
29
8234.770
26.13
0.2%
0.619
Year Area Targ Qtr Op Targ*Qtr
9
8133.526
101.24
0.7%
< 0.001
Year Area Targ Qtr Op Qtr*Op
18
8062.019
71.51
0.5%
< 0.001
Year Area Targ Qtr Op Year*Targ
69
8002.598
59.42
0.4%
0.788
Year Area Targ Qtr Op Year*Qtr
69
7994.912
7.69
0.1%
1.000
Year Area Targ Qtr Op Area*Targ
18
7948.536
46.38
0.3%
< 0.001
Year Area Targ Qtr Op Area*Qtr
18
7936.258
12.28
0.1%
0.833
Year Area Targ Qtr Op Year*Op
130
7564.328
371.93
2.7%
< 0.001
Year Area Targ Qtr Op Year*Area
134
7222.092
342.24
2.4%
< 0.001
452
Table 3. Random factor interaction table for BET CPUE in # of tuna/1000 hooks from the logbook data. Percent
of total deviance refers to the deviance explained by the full model; p value refers to the likelihood ratio test
between two consecutive models.
Bigeye tuna GLM Mixed Models
Deviance
-2 REM Log
likelihood
Akaike's
Information
Criterion
Schwartz's
Bayesian
Criterion
Likelihood Ratio Test
Proportion positives
*
Year Area Qtr Target OP
Year Area Qtr Target OP Year*area
Year Area Qtr Target OP Year*area Year*Target
Year Area Qtr Target OP Year*area Year*Target Year*op
Year Area Qtr Target OP Year*area Year*op Year*targ Year*qtr
*
Year Area Qtr Target OP
Year Area Qtr Target OP Year*area
Year Area Qtr Target OP Year*area Year*Target
Year Area Qtr Target OP Year*area Year*Target Year*op
Year Area Qtr Target OP Year*area Year*op Year*Target Year*qtr
53782.012
45341.741
44586.8808
41373.1841
40099.2724
80517.9
79419.5
79480.6
79283.1
79272.9
80519.9
79423.5
79486.6
79291.1
79282.9
80527.7
79429.9
79496.3
79304.1
79299.1
1098.4
-61.1
197.5
10.2
0.0000000
#NUM!
0.0000000
0.0014044
53129.1338
51195.4094
50924.3512
50522.6726
50128.9348
186722
184588
184349
184081
183672
186724
184592
184355
184089
183682
186733
184599
184365
184102
183698
2134
239
268
409
0.0000000000
0.0000000000
0.0000000000
0.0000000000
Positive catch rates
453
Table 4. Random factor interaction table for BET CPUE in kg of tuna/1000 hooks from the dealer weight out
data. Percent of total deviance refers to the deviance explained by the full model; p value refers to the likelihood
ratio test between two consecutive models.
Bigeye tuna GLM Mixed Models
Deviance
-2 REM Log
likelihood
Akaike's
Information
Criterion
Schwartz's
Bayesian
Criterion
Likelihood Ratio Test
Proportion positives
*
Year Area Qtr Target OP
Year Area Qtr Target OP Year*area
Year Area Qtr Target OP Year*area Year*op
Year Area Qtr Target OP Year*area Year*op Year*targ
Year Area Qtr Target OP Year*area Year*op Year*targ Year*qtr
8849.0607
7663.0668
7460.8084
7343.9668
6999.5101
21948.9
21731.5
21609.7
21548.3
21608.5
21950.9
21735.5
21615.7
21556.3
21618.5
21957.3
21741.9
21625.3
21569.1
21634.5
217.4
121.8
61.4
-60.2
0.0000000
0.0000000
0.0000000
#NUM!
14924.2811
13871.9758
13717.4501
13615.5177
13349.4332
39156.3
38587.8
38560.6
38535.6
38427
39158.3
38591.8
38566.6
38543.6
38437
39165.8
38598.2
38576.1
38556.3
38452.9
568.5
27.2
25
108.6
0.0000000000
0.0000001835
0.0000005733
0.0000000000
Positive catch rates
*
Year Area Qtr Target OP
Year Area Qtr Target OP Year*area
Year Area Qtr Target OP Year*area Year*op
Year Area Qtr Target OP Year*area Year*op Year*targ
Year Area Qtr Target OP Year*area Year*op Year*targ Year*qtr
454
Table 5. Nominal and standardized catch rates of bigeye tuna in #/1000 hooks (CPUEN) from the pelagic
logbook data. Note that the nominal CPUE includes zero catches.
Year
Nominal
CPUE
Stand.
CPUE
Coeff
Var
Std
Error
Numb
obs
Index
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
4.72
3.00
2.41
2.87
2.54
2.55
1.96
2.37
2.16
2.01
1.69
2.17
2.40
2.74
1.77
2.33
1.76
1.09
1.23
1.72
2.26
2.39
3.86
2.96
3.25
2.01
1.58
1.10
1.21
1.06
1.02
1.29
1.38
1.56
2.04
1.66
2.01
2.01
0.93
0.62
0.89
0.97
0.357
0.231
0.245
0.234
0.262
0.276
0.301
0.294
0.302
0.301
0.287
0.280
0.272
0.267
0.273
0.260
0.252
0.316
0.353
0.318
0.316
0.853
0.891
0.725
0.760
0.528
0.436
0.330
0.357
0.320
0.308
0.370
0.386
0.424
0.546
0.453
0.522
0.507
0.295
0.217
0.281
0.307
2026
14844
16033
17751
16210
15061
15046
14675
15787
16881
16236
15343
11975
12319
11715
10489
9832
9702
9867
8018
6460
1.40
2.26
1.74
1.90
1.18
0.93
0.64
0.71
0.62
0.60
0.76
0.81
0.91
1.20
0.97
1.18
1.18
0.55
0.36
0.52
0.57
Upp CI Low CI
95%
95%
2.80
3.57
2.82
3.02
1.98
1.59
1.16
1.27
1.12
1.08
1.33
1.40
1.56
2.03
1.66
1.97
1.94
1.01
0.72
0.97
1.06
0.70
1.44
1.07
1.20
0.71
0.54
0.36
0.40
0.34
0.33
0.43
0.47
0.54
0.71
0.57
0.71
0.72
0.29
0.18
0.28
0.31
Obs
prop
pos
0.32
0.29
0.29
0.33
0.30
0.32
0.27
0.31
0.30
0.31
0.25
0.28
0.30
0.29
0.26
0.28
0.28
0.19
0.16
0.23
0.28
Table 6. Nominal and standardized catch rates of bigeye tuna in kg/1000 hooks (CPUEW) from the dealer
weighout system (DLS).
Year
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
Nominal
CPUE
312.96
384.64
350.43
232.70
294.83
222.46
141.14
137.64
138.93
143.67
110.05
137.69
121.84
115.41
70.66
84.52
84.82
153.46
85.86
136.93
101.31
64.12
89.06
122.64
201.21
Standar
d CPUE
746.47
442.54
364.28
311.52
410.68
333.92
321.80
276.03
215.40
233.78
144.08
146.64
130.13
129.08
119.07
106.17
131.79
169.04
132.39
119.99
134.39
84.42
60.58
92.47
126.32
Coeff
Var
0.460
0.338
0.304
0.302
0.256
0.239
0.221
0.221
0.218
0.227
0.226
0.223
0.224
0.227
0.217
0.224
0.215
0.216
0.212
0.216
0.208
0.219
0.236
0.223
0.230
Std
Error
343.2
149.8
110.6
94.2
105.0
79.9
71.1
61.0
47.0
53.1
32.6
32.7
29.1
29.3
25.8
23.7
28.4
36.5
28.1
25.9
28.0
18.5
14.3
20.7
29.1
Numb
obs
Index
98
156
167
186
349
838
1139
886
916
1372
1894
2194
2316
2479
2677
2874
2435
2346
2354
1856
1676
1628
1696
1373
1052
3.40
2.02
1.66
1.42
1.87
1.52
1.47
1.26
0.98
1.07
0.66
0.67
0.59
0.59
0.54
0.48
0.60
0.77
0.60
0.55
0.61
0.38
0.28
0.42
0.58
455
Upp CI
95%
8.17
3.90
3.01
2.57
3.10
2.44
2.27
1.95
1.51
1.67
1.03
1.04
0.92
0.92
0.83
0.75
0.92
1.18
0.92
0.84
0.92
0.59
0.44
0.66
0.91
Low CI
95%
1.42
1.04
0.92
0.79
1.13
0.95
0.95
0.81
0.64
0.68
0.42
0.43
0.38
0.38
0.35
0.31
0.39
0.50
0.40
0.36
0.41
0.25
0.17
0.27
0.37
Obs
prop
pos
0.26
0.47
0.44
0.37
0.45
0.45
0.47
0.47
0.43
0.40
0.39
0.42
0.40
0.42
0.33
0.34
0.33
0.39
0.38
0.44
0.47
0.39
0.33
0.45
0.49
Table 7. Nominal and standardized catch rates of bigeye tuna in kg/1000 hooks (CPUEW) from the dealer
weighout system (DLS) by year and quarter.
Year
1984
1984
1984
1984
1985
1985
1985
1985
1986
1986
1986
1986
1987
1987
1987
1987
1988
1988
1988
1988
1989
1989
1989
1989
1990
1990
1990
1990
1991
1991
1991
1991
1992
1992
1992
1992
1993
1993
1993
1993
1994
1994
1994
1994
1995
1995
1995
1995
1996
1996
1996
1996
quarter
Nom- inal
CPUE
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
6.787
210.338
319.755
709.396
80.081
125.989
283.348
336.859
176.870
63.357
496.951
331.998
138.709
124.396
357.163
272.831
149.502
93.150
101.417
238.392
178.300
75.321
111.787
196.066
109.359
34.225
132.058
246.739
91.018
31.482
212.396
271.356
84.670
32.322
144.242
176.145
62.662
27.072
210.503
204.322
71.367
46.812
169.163
182.099
42.608
46.594
147.929
202.413
42.238
43.861
94.877
97.810
Stand.
CPUE
35.43
580.08
418.86
675.18
216.21
228.78
299.43
454.59
389.28
223.77
522.51
476.82
312.89
254.44
304.56
384.11
300.69
220.74
201.04
419.98
421.22
195.32
171.02
280.25
250.30
97.10
203.07
356.57
211.51
80.05
278.41
289.40
186.17
44.98
119.40
206.62
166.89
50.45
140.53
249.40
118.12
63.55
101.48
200.88
64.35
61.99
130.09
228.96
94.83
75.25
98.35
134.62
Coeff Var Std Error
0.83
0.32
0.28
0.30
0.46
0.40
0.32
0.31
0.52
0.32
0.24
0.22
0.23
0.22
0.19
0.22
0.20
0.22
0.19
0.17
0.18
0.19
0.22
0.20
0.18
0.24
0.20
0.18
0.21
0.23
0.19
0.18
0.18
0.25
0.20
0.18
0.18
0.25
0.19
0.17
0.20
0.23
0.20
0.17
0.24
0.24
0.18
0.18
0.23
0.21
0.19
0.19
29.53
183.27
118.71
203.86
100.27
91.27
95.52
141.56
201.35
71.45
125.97
106.01
70.54
56.00
58.74
84.34
60.23
47.57
37.52
73.35
75.46
36.38
37.43
57.31
45.87
23.71
41.10
65.80
44.28
18.10
53.14
51.49
33.17
11.45
23.91
37.37
30.32
12.70
26.81
41.18
23.90
14.51
20.79
34.80
15.27
14.98
23.62
40.81
21.43
15.62
18.83
24.91
456
Numb
obs
29
56
48
45
32
43
74
46
29
88
88
146
215
225
222
182
270
309
317
257
273
267
179
178
238
210
183
292
382
363
326
301
417
481
560
436
399
532
749
514
413
626
706
571
433
651
827
568
542
742
865
528
Index
0.19
3.08
2.22
3.58
1.15
1.21
1.59
2.41
2.07
1.19
2.77
2.53
1.66
1.35
1.62
2.04
1.60
1.17
1.07
2.23
2.24
1.04
0.91
1.49
1.33
0.52
1.08
1.89
1.12
0.42
1.48
1.54
0.99
0.24
0.63
1.10
0.89
0.27
0.75
1.32
0.63
0.34
0.54
1.07
0.34
0.33
0.69
1.21
0.50
0.40
0.52
0.71
Upp CI
95%
0.80
5.70
3.88
6.47
2.77
2.62
2.96
4.43
5.47
2.21
4.46
3.93
2.59
2.09
2.37
3.15
2.37
1.79
1.54
3.15
3.19
1.50
1.40
2.23
1.91
0.83
1.61
2.73
1.70
0.66
2.16
2.19
1.41
0.39
0.94
1.57
1.27
0.44
1.09
1.84
0.94
0.53
0.81
1.50
0.55
0.53
0.99
1.73
0.79
0.60
0.76
1.03
Low CI Obs prop
95%
pos
0.04
1.66
1.27
1.98
0.47
0.56
0.85
1.31
0.78
0.64
1.72
1.63
1.06
0.87
1.10
1.32
1.07
0.76
0.74
1.58
1.57
0.72
0.59
0.99
0.92
0.32
0.72
1.31
0.74
0.27
1.01
1.08
0.69
0.14
0.43
0.77
0.62
0.16
0.51
0.95
0.42
0.21
0.36
0.76
0.21
0.20
0.48
0.85
0.32
0.26
0.36
0.49
0.103
0.429
0.604
0.533
0.313
0.326
0.419
0.457
0.379
0.284
0.591
0.486
0.414
0.387
0.527
0.473
0.504
0.369
0.476
0.549
0.520
0.412
0.486
0.494
0.458
0.276
0.492
0.490
0.293
0.262
0.494
0.605
0.393
0.222
0.452
0.491
0.404
0.214
0.491
0.541
0.341
0.243
0.462
0.552
0.263
0.257
0.520
0.581
0.269
0.259
0.410
0.373
Table 7. Continued.
Year
1997
1997
1997
1997
1998
1998
1998
1998
1999
1999
1999
1999
2000
2000
2000
2000
2001
2001
2001
2001
2002
2002
2002
2002
2003
2003
2003
2003
2004
2004
2004
2004
2005
2005
2005
2005
2006
2006
2006
2006
quarter
Nom- inal
CPUE
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
Jan-Mar
Apr-June
Jul-Sept
Oct-Dec
46.727
54.276
121.542
116.498
50.482
38.011
95.008
169.468
60.548
112.797
246.464
183.632
63.546
63.853
94.737
120.340
77.970
83.642
137.777
250.823
90.035
47.747
110.386
164.288
54.308
23.127
39.381
146.375
54.517
8.342
60.414
249.923
45.443
45.300
154.707
305.051
102.079
75.708
270.414
377.349
Stand.
CPUE
91.30
71.26
94.11
130.15
107.30
84.01
114.44
220.98
152.82
152.40
144.89
205.69
142.96
94.79
101.55
162.37
119.79
96.08
90.99
194.42
131.69
83.23
104.55
231.03
102.75
56.48
51.14
199.66
85.35
27.29
36.04
204.22
112.53
72.54
61.62
247.83
175.02
87.68
138.66
264.75
Coeff Var Std Error
0.21
0.24
0.19
0.19
0.21
0.23
0.18
0.17
0.18
0.19
0.19
0.19
0.19
0.19
0.18
0.18
0.19
0.21
0.19
0.18
0.19
0.20
0.18
0.17
0.18
0.23
0.21
0.17
0.22
0.28
0.26
0.19
0.18
0.23
0.21
0.18
0.21
0.23
0.23
0.19
18.88
16.94
18.30
24.68
22.29
19.27
21.10
38.45
27.70
29.08
27.17
38.20
27.88
18.43
18.06
28.66
23.32
19.78
17.07
34.55
25.11
16.34
18.60
38.24
18.94
12.80
10.76
33.63
18.51
7.72
9.26
38.32
19.81
16.96
13.20
45.14
35.92
19.94
31.57
50.19
457
Numb
obs
677
784
814
599
500
745
654
536
543
628
645
530
502
614
702
536
386
463
595
412
340
443
532
361
335
443
453
397
346
509
434
407
355
390
379
249
212
295
355
190
Index
0.48
0.38
0.50
0.69
0.57
0.45
0.61
1.17
0.81
0.81
0.77
1.09
0.76
0.50
0.54
0.86
0.64
0.51
0.48
1.03
0.70
0.44
0.55
1.23
0.55
0.30
0.27
1.06
0.45
0.14
0.19
1.08
0.60
0.38
0.33
1.32
0.93
0.47
0.74
1.40
Upp CI
95%
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
54.00
Low CI Obs prop
95%
pos
0.34
0.47
0.38
0.28
0.42
0.83
0.57
0.55
0.53
0.76
0.52
0.34
0.38
0.61
0.43
0.34
0.33
0.73
0.48
0.30
0.39
0.88
0.38
0.19
0.18
0.76
0.30
0.08
0.12
0.75
0.42
0.24
0.21
0.92
0.62
0.30
0.47
0.96
0.00
0.00
0.292
0.241
0.421
0.412
0.280
0.188
0.425
0.461
0.335
0.325
0.457
0.430
0.303
0.287
0.423
0.506
0.360
0.339
0.452
0.600
0.424
0.339
0.476
0.659
0.484
0.223
0.327
0.552
0.303
0.171
0.288
0.607
0.490
0.269
0.491
0.639
0.458
0.363
0.501
0.726
NED
NEC
MAB
4
SAB
3
GOM 1
5
SAR
SAR
2 FEC
CAR
OFS
OFS
Figure 1. Geographical areas for the US Pelagic Longline fishery: CAR Caribbean, GOM Gulf of Mexico, FEC
Florida east coast, SAB South Atlantic bight, MAB mid Atlantic bight, NEC North east coastal Atlantic, NED
North east distant waters, SNA Sargasso Sea, and OFS Offshore waters. Shaded areas represent the current timearea closures affecting the pelagic longline fisheries. Permanent closures: (1) the DeSoto Canyon in the Gulf of
Mexico and (2) The Florida east coast areas. Non-permanent closures: (3) the Charleston Bump area closed FebApr, (4) the Bluefin tuna protection area closed in June, and (5) the Grand Banks closed since Oct-2000.
458
Figure 2. Spatial distribution of catch and effort in the US Pelagic longline fishery for selected years 1987-2006.
Scale is log(hooks set) per grid cell. Cell size is approximately 40 x 40 nautical miles.
459
Figure 3. Nominal bigeye tuna catch per 1000 hooks for each area from logbook data. Upper and lower limits
are 95% confidence intervals. Numbers are the total number of daily logbook sets reporting positive bigeye
catches.
Figure 4. Nominal percentage of positive sets for bigeye tuna upper and lower limits are approximate 95%
confidence intervals, based on normality. Numbers are the total number of daily logbooks reporting positive
bigeye catches.
460
Figure 5. Nominal bigeye tuna catch per 1000 hooks for each area from dealer weight-out data. Upper and lower
limits are 95% confidence intervals. Numbers are the total number of trips reporting positive bigeye catches.
Figure 6. Nominal percentage of positive sets for bigeye tuna from dealer weight-out data. Upper and lower
limits are approximate 95% confidence intervals, based on normality. Numbers are the total number of trips
reporting positive bigeye catches.
461
2.0
comparison of nominal logbook and DLS
number per 1000 hooks for positive records
1.0
0.0
0.5
scaled mean
1.5
Logbook
DLS number
1990
1995
2000
2005
year
Figure 7. Comparison of nominal logbook and DLS number per 1000 hooks for positive records.
A.
C.
B.
D.
Figure 8. Histograms of nominal catch rates for bigeye tuna from the U.S. Pelagic longline fishery. A. Log(catch
per 1000 hooks) for positive logbook sets. B) Frequency distribution of proportion of positive trips. C)
Log(weight per 1000 hooks) for positive dealer weigh out data, D) Frequency distribution of proportion of
positive trips from dealer weigh out data.
462
Figure 9. Diagnostic plots for the delta lognormal model fit to the Pelagic Logbook data for CPUEN (A)
residuals for positive CPUE and (B) chi-square residuals of proportion positive by year. (C) Q-Q plot of
cumulative normalized for positive CPUE. (D) Histogram of residuals for positive CPUE.
Figure 10. Diagnostic plots for the delta lognormal model fit to the dealer weight-out data for CPUEN (A)
residuals for positive CPUE and (B) chi-square residuals of proportion positive by year. (C) Q-Q plot of
cumulative normalized for positive CPUE. (D) Histogram of residuals for positive CPUE.
463
A.
Scaled CPUE (fish/1000 hooks)
Bigeye Tuna Standardized Logbook CPUE US (95% C)I
3.5
CPUE
obs
3.0
2.5
2.0
1.5
1.0
0.5
0.0
1985
1990
1995
2000
2005
Scaled CPUE (dressed wgt lbs/1000 hooks)
B.
Bigeye Tuna Standardized CPUE in weight
US Longline Dealer weigh-out
6.0
5.0
standardized
observed
4.0
3.0
2.0
1.0
0.0
1981
1986
1991
1996
2001
2006
Figure 11. Nominal and standardized catch rates for bigeye from the U.S. Pelagic longline fishery. A. Top,
logbook data reported as number of fish per thousand hooks. B. Dealer weight out data reported as dress weight
(lbs) per thousand hooks.
464
5
standardized CPUEW from DLS
4.5
4
observed
winter
spring
summer
fall
annual
3.5
3
2.5
2
1.5
1
0.5
0
year*season
Figure 12. Quarterly index obtained from dealer weight out data reported as dressed weight (lbs) per thousand
hooks. Black line is the annual standardized index.
Scaled standardized CPUE to overlapping years
3.5
3.0
Index biomass
Index Number
2.5
2.0
1.5
1.0
0.5
0.0
1985
1990
1995
2000
2005
Figure 13. Comparison of standardized catch rates for bigeye tuna from the U.S. Pelagic longline fishery
logbook #/1000 hooks and dealer weighout kg/1000 hooks. Each index is scaled to the mean value for the
continuous period 1986-2006.
465
Figure 14. Histograms of skipjack tuna straight line fork length (cm) by area. Red lines and numbers indicate
mean fork length.
Figure 15. Histograms of skipjack tuna straight line fork length (cm) by year. Red lines and numbers indicate
mean fork length.
466
Caribbean
Gulf of Mexico
Florida East
3927
2244
Mid-Atl. Bight
648
South Atl. Bight
1936
1469
811
76
479
100
150
329
South offshore
Sargasso Sea
NE Distant
NE Coast
50
FL, mm
200
A.
337
863
524
552
822
999
77
2002
2003
2004
2005
2006
2007
576 1082 366
2001
779
100
150
849 1595 1083 1099 346
2000
1999
1998
1997
1996
1995
1994
1993
1992
50
FL, mm
200
B.
Figure 16. Box and whisker plots (median, 1st, 3rd quartile, minimum and maximum) of bigeye tuna straight line
fork length by (A) area and (B) year. Box widths are proportional to sample size and dots represent lengths that
are outside the 1.5*interquartile range.
467
A.
scaled CPUEN from logbooks
closed areas removed
number closures removed
2
number all areas
1.5
20000
18000
16000
14000
12000
10000
1
8000
6000
0.5
total observations
all areas
2.5
4000
2000
0
8
19 7
88
19
89
19
9
19 0
91
19
92
19
93
19
9
19 4
95
19
96
19
9
19 7
98
19
99
20
00
20
0
20 1
02
20
03
20
0
20 4
05
20
06
19
19
86
0
B.
scaled CPUEN from logbooks
3.5
all areas
closed areas removed
number all areas
number closures removed
3500
3000
3
2500
2.5
2000
2
1500
1.5
total observations
4
1000
1
500
0.5
0
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
0
Figure 17. Comparison of standardized indices constructed using observations from all areas and observations
that occur in closed areas removed. A. logbook CPUEN in #/1000 hooks. B. Dealer weighout CPUEW in
kg/1000 hooks. Note that prior to 1996 it was not possible to assign the DLS data to spatial locations.
468