CATCH RATE INDICES OF YELLOWFIN (THUNNUS ALBACARES AND SKIPJACK (KATSUWONUS PELAMIS ... · 2018-06-12 · tuna. Of these, only 1,413 (1.7%) reported landing, discarding or releasing

SCRS/2008/122

CATCH RATE INDICES OF YELLOWFIN (THUNNUS ALBACARES)

AND SKIPJACK (KATSUWONUS PELAMIS) TUNAS FROM THE

UNITED STATES RECREATIONAL FISHERY IN THE

WESTERN NORTH ATLANTIC OCEAN, 1986-2007

Shannon L. Cass-Calay1

SUMMARY

Catch and effort data from the US Marine Recreational Fisheries Statistical Survey (MFRSS) of

the Atlantic coast and Gulf of Mexico (excluding Texas) were used to construct indices of

abundance for yellowfin and skipjack tunas. Standardized catch rates were estimated using a

Generalized Linear Mixed modeling approach assuming a delta-lognormal error distribution.

The explanatory variables considered for standardization included: geographic area, season,

and fishing mode (a factor that classifies recreational fishing as charter or private/rental boat).

The indices suggest that the catch rates of yellowfin and skipjack vary annually, largely without

trend.

KEY WORDS

Catch/effort, abundance, MRFSS, recreational statistics, multivariate analyses

1 U.S. Department of Commerce, NOAA Fisheries, Southeast Fisheries Science Center, Miami Laboratory, 75

Virginia Beach Drive, Miami, Florida 33149 U.S.A. Email: [email protected]

1. INTRODUCTION

Data collected and estimated by the Marine Recreational Fisheries Statistical Survey (MRFSS) were used to

develop standardized catch per unit effort (CPUE) indices for yellowfin and skipjack tunas in the Gulf of Mexico

and western North Atlantic. The MRFSS survey started in 1979, and its purpose was to establish a reliable

database for estimating the impact of marine recreational fishing on marine resources. More detailed information on the methods and protocols of the survey can be found at http://www.st.nmfs.gov/st1/recreational/overview/

overview.html.

2. MATERIALS AND METHODS

The Marine Recreational Fisheries Statistical Survey (MRFSS) program provides estimates of catch and

effort for the U.S. recreational fishery. Data were collected by scientific samplers during dockside interviews.

Each record includes the following information: catch (by species) in numbers, and whether the catch was

retained, released alive or discarded dead, the number of participating anglers, the number of fishing hours,

information on gear used, target species, mode (shore, headboat, charter, or private/rental), area (inshore, ocean < 3 miles, 3 < ocean < 10 miles, ocean > 10 miles), county/state, and date.

One potential problem with indices derived from the MRFSS database is the selection of trips/interviews that

are relevant to the analysis. The MRFSS program includes information from recreational trips by shore anglers,

inshore fishing trips, as well as large charter vessels fishing offshore. The task is then to identify trips that had a

significant probability of catching yellowfin or skipjack tuna. During the interview, anglers are asked which

species were targeted during the fishing trip (primary and secondary target), and in general the catch composition

reflects the species found in the habitat associated with the targeted species. The fishing trips were classified into

“guilds” based on the intended target. The “guilds” identified were: sharks, pelagic species, inshore species, reef

species, and non-reef species. When no primary or secondary target was specified, the record was assigned an

unclassified status. For the yellowfin index, trips were included in the analysis if the primary or secondary target

was a member of the pelagic guild, or if at least one yellowfin tuna was caught on the trip. For the skipjack index, trips were included in the analysis if the primary or secondary target was a member of the pelagic guild, or

if at least one skipjack tuna was caught on the trip. Pelagic guild members are listed in Table 1.

The MRFSS data includes estimates of catch and effort from 1981 through 2007 from the U.S. States of

Louisiana through Maine. Because very few trips reported catching yellowfin tuna or skipjack before 1986, the

indices were constructed for the period 1986-2007. Nearly all yellowfin and skipjack (>99%) were landed using

hook and line. Therefore, the indices were constructed using only hook and line trips. Additionally, shore and

shelf effort were excluded from the analyses because it is unlikely to land a yellowfin or skipjack tuna from a

dock or shoreline, or within the shallow continental shelf. Finally, because headboat sampling is not reported

consistently in the dataset in time and space, that fishing mode was also excluded from the analysis.

Effort was excluded in certain time/area combinations because fishing did not generally occur there, or was

not directed at tropical tunas. In the northeastern and mid Atlantic U.S. (CT, RI, MA, NH, ME, DE, NJ, NY, VA,

MD) fishing effort that occurred during the winter and spring (Dec-May) were excluded from the analysis.

The following factors were considered as possible influences on the proportion of trips that observed

yellowfin /skipjack tuna (proportion positive), and the catch rates on trips that caught yellowfin/skipjack tuna.

Because of the small number of records for some states, regional areas were defined and used as a spatial factor.

Months were aggregated into seasons to account for seasonal fishery distribution through the year.

FACTOR LEVELS VALUES

YEAR 22 1986-2007

SEASON 4 WIN = (Dec-Feb) SPR = (Mar-May)

SUM = (Jun-Aug) AUT = (Sep-Nov)

MODE 2 Charter (CB) and Private (PB)

REGION 4

NE U.S. (CT, RI, MA, NH, ME, DE, NJ, NY)

Mid Atlantic U.S. (VA, MD, NC)

Southeast U.S. (FL East Coast, GA, SC)

Gulf of Mexico (FL West Coast, AL, MS, LA)

Catch per unit effort (CPUE) was defined as the total kept, discarded or released (AB1B2, in number of fish)

per 1000 angler hours.

CPUE = (Number Landed + Discarded Dead + Released Alive) / 1000 Angler Hours

A delta-lognormal approach (Lo et al. 1992) was used to develop the standardized catch rate indices. This method combines separate generalized linear modeling (GLM) analyses of the proportion positive sets (sets that

caught bluefin tuna) and the catch rates of successful sets to construct a single standardized index of abundance.

Parameterization of each model was accomplished using a GLM procedure (GENMOD; Version 8.02 of the SAS

System for Windows © 2000. SAS Institute Inc. Cary, NC, USA).

A forward stepwise regression procedure was used to determine the set of fixed factors and interaction terms

that explained a significant portion of the observed variability. Each potential factor was added to the null model

individually, and the resulting reduction (%RED) in deviance per degree of freedom (DEV/DF) was examined.

The factor that caused the greatest reduction in deviance per degree of freedom was added to the model if the

factor was significant based upon a Chi-Square test (PROB > CHISQ), and the reduction in deviance per degree

of freedom was ≥1%. This model then became the base model, and the process was repeated, adding factors and two-way interaction terms individually until no factor or interaction met the criteria for incorporation into the

final model. Higher order interaction terms were not examined.

Once a set of fixed factors was identified, the influence of the YEAR*FACTOR interactions were examined.

As per the recommendation of the statistics and methods working group of the SCRS (1999), YEAR*FACTOR

interaction terms were included in the model as random effects. Selection of the final mixed model was based on

the Akaike’s Information Criterion (AIC), Schwarz’s Bayesian Criterion (BIC), and a chi-square test of the

difference between the –2 log likelihood statistics between successive model formulations (Littell et al. 1996).

The final delta-lognormal model was fit using the SAS macro GLIMMIX and the SAS procedure PROC MIXED

(SAS Institute Inc. 1997) following the procedures described by Lo et al. (1992).

3. RESULTS AND DISCUSSION

3.1 Yellowfin Tuna

The GLM model results and statistics are summarized in Tables 2-3 (binomial component) and Tables 4-5

(lognormal component). The final models selected were as follows.

PPT = REGION+MODE+SEASON+YEAR+MODE*REGION+YEAR*SEASON+YEAR*REGION

LOG(CPUE) = SEASON+YEAR+REGION+YEAR*SEASON+YEAR*REGION

The analysis dataset included 90,026 trips that either targeted pelagic species, or caught at least one

yellowfin tuna. Of these, only 10,200 (11.3%) reported landing, discarding or releasing yellowing tuna. The

annual proportion of positive trips (PPT: trips that caught yellowfin) was low, ranging from 5% to 17% (Fig. 1;

Table 6). PPT was generally less than 9% before 1992, and then increased to 10 to 17% thereafter. Nominal

CPUE follows a similar pattern (Fig 2; Table 6). The lowest levels were observed during 1986 to 1992. Nominal

CPUE increased to a higher level after 1992.

Diagnostic plots were constructed to examine the fit of the components of the delta-lognormal model. The

chi-square residuals, by factor, are shown in Fig. 3. The strong effect of a few positive outliers is noted. The

frequency distribution of the proportion of positive trips by strata (year, region, fishing mode and season) is

shown in Fig. 4. It is evident that most strata have low numbers of positive trips. To use the binomial model, it is

generally recommended that at least 10-20% of trips observe the species of interest. Therefore, it is possible that the low proportion of positive trips violates the assumptions of this model component.

The residuals of the lognormal model, by factor, are shown in Fig. 5. In this case, the residuals are more

evenly distributed above and below zero, indicating a proper fit of the lognormal component. The frequency

distribution of nominal catch rates is shown in Fig. 6. Ideally, the frequency distribution of log(cpue) should

resemble the normal distribution overlaid in red (Fig. 6). In this case, some departure from the expectation is

noted, but the model fit appears adequate. The QQ-Plot (Fig. 7) also indicates the degree of departure from the

assumption of a normal distribution (red line). In this case, the QQ-Plot indicates an appropriate fit to the

lognormal component of the delta-model.

The standardized index and the nominal CPUE are shown in Fig 8 and Table 6. To facilitate comparison,

both series were scaled by dividing the annual estimates by the series mean. Although annual catch rates are

quite variable, the index suggests that yellowfin tuna catch rates were lowest during 1987-1992, and have increased since.

3.1 Skipjack Tuna

The GLM model results and statistics are summarized in Table 7 (binomial component) and Table 8

(lognormal component). Unlike the yellowfin models, YEAR*FACTOR interaction terms were not examined

during the construction of the skipjack index because the number of positive trips was insufficient. The final

models selected were as follows.

PPT = MODE + REGION + YEAR + SEASON

LOG(CPUE) = REGION + YEAR + MODE + SEASON

The analysis dataset included 83,566 trips that either targeted pelagic species, or caught at least one skipjack

tuna. Of these, only 1,413 (1.7%) reported landing, discarding or releasing skipjack tuna. The annual proportion

of positive trips (PPT: trips that caught skipjack) was very low, ranging from 0.8% to 3.7% (Fig. 9; Table 9).

During 2005-2007, the proportion of positive trips was substantially lower than the series average. During 1986-

2007, nominal CPUE varied without obvious trend (Fig 10; Table 9). Although like PPT, during 2005-2007

CPUE was substantially lower than average.

Diagnostic plots were constructed to examine the fit of the components of the delta-lognormal model. The

chi-square residuals, by factor, are shown in Fig. 11. The residuals are distributed fairly evenly above and below

zero, but some large positive outliers are noted. The frequency distribution of the proportion of positive trips by strata (year, region, fishing mode and season) is shown in Fig. 12. It is evident that most strata have very low

numbers of positive trips. To use the binomial model, it is generally recommended that at least 10-20% of trips

observe the species of interest. Therefore, it is evident that the low proportion of positive trips violates the

assumptions of this model component.

The residuals of the lognormal model, by factor, are shown in Fig. 13. In this case, the residuals are more

evenly distributed above and below zero, indicating a proper fit of the lognormal component. The frequency

distribution of nominal catch rates is shown in Fig. 14. Ideally, the frequency distribution of log(cpue) should

resemble the normal distribution overlaid in red (Fig. 14). In this case, considerable departure from the

expectation is noted. The QQ-Plot (Fig. 15) also indicates the degree of departure from the assumption of a

normal distribution (red line). For the skipjack model, the QQ-Plot suggests an adequate fit to the lognormal component of the delta-model, although some departure from the expected distribution is noted at the extremes.

The standardized index and the nominal CPUE are shown in Fig 16 and Table 9. To facilitate comparison,

both series were scaled by dividing the annual estimates by the series mean. Although annual catch rates are

quite variable and lacking obvious trend, the index suggests that skipjack tuna catch rates were lowest during

2005-2007.

The quality of this index is quite uncertain due to the low number of trips that observe skipjack each year (31

to 149; Table 9) As discussed above, this causes a violation of the assumptions of the binomial component of the

delta-lognormal model. Therefore, the tropical tuna species group should carefully consider whether this index is

appropriate for use in formal stock assessment proceedings.

4. ACKNOWLEDGMENTS

I would like to acknowledge the assistance of Patty Phares, Mauricio Ortiz and Craig Brown of NOAA

Fisheries (SEFSC) who helped to extract relevant data, and provided advice regarding analytical techniques.

5. REFERENCES

LITTELL, R.C., G.A. Milliken, W.W. Stroup, and R.D Wolfinger. 1996. SAS® System for Mixed Models, Cary

NC, USA:SAS Institute Inc., 1996. 663 pp.

LITTELL, R.C., P.R. Henry and C.B. Ammerman. 1998. Statistical analysis of repeated measures data using

SAS procedures. J. Anim. Sci. 76: 1216-1231.

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.

SAS Institute Inc. 1997, SAS/STAT® Software: Changes and Enhancements through Release 6.12. Cary, NC,

USA: SAS Institute Inc., 1997. 1167 pp.

Table 1. Members of the “guild” of pelagic species.

GENUS - SPECIES COMMON NAME GENUS - SPECIES COMMON NAME

Acanthocybium

solandri

wahoo Scomber japonicus chub mackerel

Auxis rochei bullet mackerel Scomber scombrus atlantic mackerel

Auxis thazard frigate mackerel Scomberomorus

cavalla

king mackerel

Coryphaena equisetis pompano dolphin Scomberomorus

maculatus

spanish mackerel

Coryphaena hippurus dolphin Scomberomorus

regalis

cero

Coryphaena spp. dolphin genus Scomberomorus spp. mackerel genus

Coryphaenidae dolphin family Scombridae mackerel family

Echeneidae remora family Sphyrna lewini scalloped

hammerhead

Echeneis naucrates sharksucker Sphyrna mokarran great hammerhead

Echeneis

neucratoides

whitefin sharksucker Sphyrna tiburo bonnethead

Elegatis bipinnulatus rainbow runner Sphyrna tudes smalleye hammerhead

Euthynnus alletteratus

little tunny Sphyrna zygaena smooth hammerhead

Euthynnus spp. Euthynnus genus Tetrapturus albidus white marlin

Gempylus serpens snake mackerel Tetrapturus pfleugeri longbill spearfish

Istiophoridae billfish family Thunnus alalunga albacore

Istiophorus

platypterus

sailfish Thunnus albacares yellowfin tuna

Katsuwonus pelamis skipjack tuna Thunnus atlanticus blackfin tuna

Lepidocybium

flavobrunneum

escolar Thunnus obesus bigeye tuna

Lobotes surinamensis tripletail Thunnus spp. tuna genus

Makaira nigricans blue marlin Thunnus thynnus bluefin tuna

Phtheirichthys lineatus

slender suckerfish Thunnus thynnus young school bluefin

Pomatomus saltatrix bluefish (juveniles) Thunnus thynnus school bluefin

Pomatomus saltatrix bluefish Thunnus thynnus large school bluefin

Rachycentron

canadum

cobia Thunnus thynnus small medium bluefin

Remora australis whalesucker Thunnus thynnus large medium bluefin

Remora brachyptera spearfish remora Thunnus thynnus giant bluefin

Remora osteochir marlinsucker Trichiuridae snake mackerel

family

Remora remora remora Trichiuridae cutlassfish,

unidentified

Ruvettus pretiosus oilfish Trichiurus lepturus atlantic cutlassfish

Sarda chilensis pacific bonito Xiphias gladius swordfish

Sarda orientalis striped bonito

Sarda sarda atlantic bonito

Sarda spp. Sarda genus

Table 2. YFT: Results of the binomial model on the proportion of positive trips.

LR Statistics For Type 3 Analysis Chi- Source DF Square Pr > ChiSq REGION 3 5231.45 <.0001 MODE 1 1233.50 <.0001 SEASON 3 1955.99 <.0001 YEAR 21 1060.08 <.0001 MODE*REGION 3 542.92 <.0001

Table 3. YFT: Analysis of the mixed model formulations for the binomial component of the delta-model. The

likelihood ratio was used to test the difference of –2 REM log likelihood between two nested models. The final

model is indicated with gray shading.

Table 4. YFT: Results of the lognormal model on catch rate on positive trips.

LR Statistics For Type 3 Analysis Chi- Source DF Square Pr > ChiSq SEASON 3 448.33 <.0001 YEAR 21 420.97 <.0001 REGION 3 164.24 <.0001

Table 5. YFT: Analysis of the mixed model formulations for the lognormal component of the delta-model. The

likelihood ratio was used to test the difference of –2 REM log likelihood between two nested models. The final

model is indicated with gray shading.

ANALYSIS OF MIXED MODEL FORMULATIONS

Proportion Positive

-2 REM Log

likelihood

Akaike's

Informatio

n Criterion

Schwartz's

Bayesian

Criterion

Likelihood

Ratio TestP

Scaled

DevianceDispersion

REGION + MODE + SEASON + YEAR + MODE*REGION2603.0 2605.0 2609.3 - - 365.65 9.84

REGION + MODE + SEASON + YEAR + MODE*REGION +

YEAR*SEASON 2545.5 2549.5 2554.5 57.5 <0.0001 337.87 8.49

REGION + MODE + SEASON + YEAR + MODE*REGION +

YEAR*SEASON + YEAR*REGION 2506.2 2512.2 2519.6 39.3 <0.0001 323.21 7.48

Used fixed factors that explained at least 1% of the total variance.

Used the Mixed Model with the lowest log likelihood, AIC and BIC. In every case, the model improvement was significant according to the Likelihood Ratio Test.

The FINAL MODELS are highlighted in gray with bold font.

ANALYSIS OF MIXED MODEL FORMULATIONS

Catch Rates on Positive Trips

-2 REM Log

likelihood

Akaike's

Informatio

n Criterion

Schwartz's

Bayesian

Criterion

Likelihood

Ratio TestP

Scaled

DevianceDispersion

SEASON + YEAR + REGION 27033.9 27035.9 27043.2 - - 10172.00 0.82

SEASON + YEAR + REGION + YEAR*SEASON 26830.6 26834.6 26839.4 203.3 <0.0001 10126.82 0.80

SEASON + YEAR + REGION + YEAR*SEASON + YEAR*REGION 26631.9 26637.9 26645.1 198.7 <0.0001 10086.90 0.77

Used fixed factors that explained at least 1% of the total variance.

Used the Mixed Model with the lowest log likelihood, AIC and BIC. In every case, the model improvement was significant according to the Likelihood Ratio Test.

The FINAL MODELS are highlighted in gray with bold font.

Table 6. YFT: Nominal CPUE, number of trips, number of positive trip, proportion positive trips (PPT),

standardized index of abundance and index statistics.

YEAR Nom

CPUE Trips Pos Trips PPT

Relative

Index CV LCI UCI

1986 28.407 2456 214 0.087 1.939 0.428 0.854 4.402

1987 33.885 3148 282 0.090 0.922 0.449 0.392 2.171

1988 15.888 3584 237 0.066 0.558 0.471 0.228 1.366

1989 17.465 4164 323 0.078 0.569 0.447 0.242 1.335

1990 10.843 3354 168 0.050 0.354 0.485 0.141 0.887

1991 17.964 3666 252 0.069 0.569 0.453 0.240 1.350

1992 10.489 4100 233 0.057 0.388 0.446 0.166 0.909

1993 32.650 3617 409 0.113 0.883 0.419 0.395 1.972

1994 59.268 4204 724 0.172 1.712 0.403 0.788 3.720

1995 44.848 3685 516 0.140 1.438 0.417 0.646 3.201

1996 47.150 5076 659 0.130 0.430 0.436 0.186 0.990

1997 23.872 5088 494 0.097 0.387 0.427 0.171 0.878

1998 53.609 4854 719 0.148 0.780 0.392 0.366 1.662

1999 54.828 4255 574 0.135 1.649 0.403 0.758 3.585

2000 60.485 4814 682 0.142 1.436 0.403 0.662 3.118

2001 46.001 4877 767 0.157 1.536 0.377 0.742 3.182

2002 32.313 5191 492 0.095 1.042 0.385 0.495 2.193

2003 52.058 4815 644 0.134 1.169 0.379 0.562 2.431

2004 40.265 3924 506 0.129 1.029 0.394 0.481 2.201

2005 32.422 3604 405 0.112 0.938 0.396 0.437 2.011

2006 39.705 3935 541 0.137 1.291 0.392 0.606 2.752

2007 23.975 3615 359 0.099 0.981 0.401 0.453 2.123

Table 7. SKJ: Results of the binomial model on the proportion of positive trips.

LR Statistics For Type 3 Analysis Chi- Source DF Square Pr > ChiSq MODE 1 510.55 <.0001 REGION 3 318.04 <.0001 YEAR 21 225.21 <.0001 SEASON 3 134.56 <.0001

Table 8. SKJ: Results of the lognormal model on catch rate on positive trips. LR Statistics For Type 3 Analysis Chi- Source DF Square Pr > ChiSq REGION 3 59.64 <.0001 YEAR 21 66.89 <.0001 MODE 1 48.13 <.0001 SEASON 3 35.37 <.0001 MODE*REGION 3 60.34 <.0001

Table 9. SKJ: Nominal CPUE, number of trips, number of positive trip, proportion positive trips (PPT),

standardized index of abundance and index statistics.

YEAR Nom

CPUE Trips Pos Trips PPT

Relative

Index CV LCI UCI

1986 6.692 2417 33 0.014 1.546 0.349 0.784 3.047

1987 2.217 3080 31 0.010 0.665 0.367 0.327 1.354

1988 6.035 3526 52 0.015 1.038 0.285 0.594 1.814

1989 3.624 4110 51 0.012 0.976 0.288 0.556 1.715

1990 3.947 3319 44 0.013 0.966 0.308 0.529 1.762

1991 4.146 3611 68 0.019 1.112 0.250 0.679 1.821

1992 1.631 4051 34 0.008 0.443 0.353 0.223 0.879

1993 8.584 3539 77 0.022 1.673 0.235 1.052 2.661

1994 3.208 4088 81 0.020 0.811 0.234 0.511 1.287

1995 1.623 3572 32 0.009 0.448 0.365 0.220 0.908

1996 1.504 4892 47 0.010 0.538 0.302 0.298 0.971

1997 2.701 4965 51 0.010 0.724 0.288 0.412 1.273

1998 3.913 4657 126 0.027 1.407 0.186 0.972 2.036

1999 6.950 4071 149 0.037 2.253 0.174 1.596 3.181

2000 1.924 4547 60 0.013 0.535 0.272 0.313 0.912

2001 5.479 4626 120 0.026 1.634 0.191 1.119 2.386

2002 4.464 5032 98 0.019 1.315 0.208 0.871 1.985

2003 5.154 4602 112 0.024 1.655 0.198 1.118 2.450

2004 3.087 3749 65 0.017 0.843 0.256 0.510 1.394

2005 1.224 3493 39 0.011 0.464 0.333 0.243 0.887

2006 1.101 3794 43 0.011 0.373 0.319 0.200 0.696

2007 2.099 3515 33 0.009 0.582 0.356 0.292 1.162

Figure 1. YFT: Proportion of positive trips, by year.

Figure 2. YFT: Nominal CPUE (fish per 1000 angler hours), by year.

A) B)

C) D)

Figure 3. YFT: Chi-square residuals for the fit to the binomial model, by year (A), region (B), fishing mode (C)

and season (D).

Figure 4. YFT: Frequency distribution of proportion positive trips by the strata year, region and fishing mode.

A) B)

C)

Figure 5. YFT: Residuals of the fit to the lognormal model, by year (A), region (B) and season (C).

Figure 6. YFT: Frequency distribution of nominal catch rates (fish per 1000 angler hours) on positive trips.

Figure 7. YFT: The cumulative normalized residuals (QQ-Plot) from the lognormal model on the catch rates of

positive trips.

Figure 8. YFT: Nominal CPUE (blue line) and the delta-lognormal index (red line) with 95% confidence

intervals (dashed lines). Both series are scaled to a mean of 1.0 to facilitate comparison.

Figure 9. SKJ: Proportion of positive trips, by year.

Figure 10. SKJ: Nominal CPUE (fish per 1000 angler hours), by year.

A) B)

C) D)

Figure 11. SKJ: Chi-square residuals for the fit to the binomial model, by year (A), region (B), fishing mode (C)

and season (D).

Figure 12. SKJ: Frequency distribution of proportion positive trips by the strata year, region and fishing mode.

A) B)

C) D)

Figure 13. SKJ: Residuals of the fit to the lognormal model by year (A), region (B), fishing mode (C) and season

(D).

Figure 14. SKJ: Frequency distribution of nominal catch rates (fish per 1000 angler hours) on positive trips.

Figure 15. SKJ: The cumulative normalized residuals (QQ-Plot) from the lognormal model on the catch rates of

positive trips.

Figure 16. SKJ: Nominal CPUE (blue line) and the delta-lognormal index (red line) with 95% confidence

intervals (dashed lines). Both series are scaled to a mean of 1.0 to facilitate comparison.