SCRS/2008/044 Estimated sailfish catch-per-unit-effort for the U.S. Recreational Billfish Tournaments and U.S. recreational fishery (1973-2007) John P. Hoolihan, Mauricio Ortiz, Guillermo A. Diaz, and Eric D. Prince 1 SUMMARY An index of abundance for sailfish from the United States recreational billfish tournament fishery is presented for the period 1973-2006 and for non-tournament recreational fisheries for the period 1981-2007. Tournament catch-per-unit-effort (number of fish caught per 1000 hours fishing) was estimated from catch and effort data submitted by recreational tournament coordinators and U.S. National Marine Fisheries observers under the Recreational Billfish Survey program. A selection process was applied to restrict the data to tournaments that primarily target sailfish, using live bait only, along the Florida East coast. Non-tournament recreational data was compiled from the Marine Recreational Fisheries Statistical Survey (MRFSS) interviews. The standardization procedure included the variables year, area, and season. Standardized indices were estimated using Generalized Linear Mixed Models under a Delta lognormal model approach. KEYWORDS Catch/effort, abundance, sport fishing, pelagic fisheries, multivariate analysis 1 U.S. Dept of Commerce NOAA-NMFS Southeast Fisheries Science Center, Sustainable Fisheries Division. 75 Virginia Beach Dr Miami, FL 33149 US. Sustainable Fisheries Division Contribution No. SFD-2008-###.
1. Introduction Information on the relative abundance of sailfish (Istiophorus platypterus) is necessary to tune stock assessment models. Two sources of this information were investigated in this document: 1) the Recreational Billfish Tournament Survey (RBS), and 2) the non-tournament Marine Recreational Fishery Statistics Survey (MRFSS). Tournament catch and effort data from U.S. recreational tournaments along the Atlantic East coast (including the Bahamas), Gulf of Mexico, and Caribbean (U.S. Virgin Islands and Puerto Rico) have been collected by the RBS program since 1972. Beardsley and Conser (1981), and Prince et al (1990) have described the survey. From this data, indices of abundance for sailfish have been previously estimated (Farber 1994, Ortiz and Brown 2002). Catch (in numbers) and effort data were obtained from tournament data documented by the RBS, which were voluntarily submitted to the National Marine Fisheries Service (NMFS) from 1972-1990 and became mandatory in 1991. In addition, scientific observers that monitored selected billfish tournaments in the Gulf of Mexico are also part of the RBS data base. This report documents the analytical methods applied to the available RBS data for the period 1973-2006 and presents correspondent standardized CPUE indices for sailfish. Recreational catch and effort are collected during the intercept survey component of the MRFSS, in which anglers are intercepted, screened, and interviewed at assigned access sites upon completion of their fishing trips (U.S. Department of Commerce 1990). Intercepts are not conducted at tournament sites. Data from oceanic trips off the U.S. East Coast, south of North Carolina, from 1981-2007 were used to develop standardized CPUE indices. 2. Materials and Methods 2.1 RBS Browder and Prince (1990) describe the main features of the recreational tournaments that take place in the West Atlantic and Caribbean, and review the available catch and effort data from RBS data. Standardized catch rate indices of sailfish were previously estimated for the 1994 stock assessment using Generalized Linear Models (GLM) (Farber 1994) and, more recently, in 2001 using GLM with a Delta lognormal approach (Ortiz and Brown 2002). The present report updates the catch and effort information through 2007 and includes analyses of variability associated with random factor interactions, particularly for the Year effect, following the suggestion of the billfish working group of the ICCAT Scientific Committee of Research and Statistics (SCRS). Logbook records from the recreational tournaments have been collected since 1972 either by NMFS personnel or through voluntary submission by tournament organizers. Changes in U.S. regulations during the mid-1990 s require mandatory recreational billfish tournaments to register and submit catch and effort data to the NMFS (Anonymous 1999). To reduce the confounding effects of multiple gears and bait configurations on catch rates, we limited our analyses to tournaments using live-baiting techniques only. This reduced the geographical range of tournament sailfish catch to an area along the FEC (Figure 1), roughly extending from the city of Stuart (North), to Key West (South) Florida. This RBS data subsample comprised a total of 1,519 records dating from 1973 through 2006. Each record represents information on hooked and caught fish by tournament-day. Fishing effort was estimated from the number of boats participating in the tournament multiplied by the average-fishing hours per boatday. Records also include total number of fish hooked, their fate (i.e. lost, release, tagged and released, or boated) by species, and morphometric information (size and weight) for boated fish.
A general increase of recreational sailfish tournament fishing effort has been observed throughout the time series (Prince et al. 2007). To account for seasonal characteristics, three seasons were defined: (1) January through April, (2) May through August, and (3) September through December. Tournament logbooks primarily record numbers of fish caught and, sometimes, size and weight. As per the suggestion of the SCRS billfish working group, indices of abundance are reported in weight rather than numbers of fish. To convert numbers of fish to weight, size data on sailfish boated by recreational tournaments were retrieved from the RBS database. Mean size by each year/area/season stratum was estimated if there were 20 or more records per cell. For a cell with less than 20 fish, the annual mean size of the area was used, if for a given area-year, the number were still less than 20, the mean size by year across all areas was applied. Mean size was converted to weight (kg) using the current size-weight relationships for sailfish-combined sex (Prager et al. 1995). Analysis of catch rates involved the total number of caught sailfish (including caught and released fish, tagged fish, and boated fish). This was because of the implementation of minimum size regulations (first implemented in 1988); and the prevalence of catch and releasing sailfish in U.S. tournaments throughout the time series (Prince et al. 2007). For the RBS tournament data, a relative index of abundance for sailfish was estimated using a GLM approach. Because of the restriction of data to live bait fishing effort tournament, targeting primarily sailfish, the proportion of sailfish catch was over 95% in each year. Therefore, it was decided to restrict the analysis to positive sailfish catch observations. The mean catch rate of trip/day assumed a lognormal error distribution. The log-transformed frequency distributions of catch rates in numbers for sailfish are shown in Figure 2. Estimated catch rates were estimated as a linear function of fixed factors and random effect interactions, particularly when the Year effect was within the interaction. Year and season, and their interaction were the factors included in the analysis. A step-wise regression procedure was used to determine the set of systematic factors and interactions that significantly explained the observed variability. Final selection of explanatory factors was conditional on: a) the relative percent of explained by adding the factor in evaluation, normally factors that explained more than 5% were selected, b) the χ 2 test significance, and c) the type III test significance within the final specified model. Once a set of fixed factors was specified, statistically significant interactions were evaluated as random factors of the model, in particular interactions between the Year effect and other factors. Selection of the final mixed model was based on the Akaike s Information Criterion (AIC), the Schwarz s Bayesian Criterion (SBC), and a chi-square test of the difference between the 2* loglikelihood statistic of a successive nested model formulations (Littell et al. 1996). Analyses were done using the MIXED procedures from the SAS statistical computer software (SAS Institute Inc. 1997). 2.2 MRFSS The survey methodology of the MRFSS is described by the U.S. Department of Commerce (1990). The intercept data available for this study was restricted to the region off the eastern coast of Florida, North Carolina, the Caribbean (Puerto Rico and U.S. Virgin Islands), and the U.S. Gulf of Mexico. Only oceanic fishing in the charter or private/rental modes was included. Since the MRFSS is a generalized survey of all saltwater recreational fishing and sailfish targeted effort represents only a small component of oceanic fishing, the data was further restricted to fishing trips that target typical oceanic species such as tunas, king mackerel, dolphin fish, sailfish and
marlins. Thus, the MFRSS survey did not distinguish between live bait effort and other fishing methods. Catch rate was defined as the number of sailfish caught (both kept and released alive fish) divided by the number of angler hours. The log-transformed frequency distributions of catch rates in numbers for sailfish are shown in Figure 3. The available variables included year, season, area, trip type (charter or private/rental), and zone (inshore, offshore). Parameterization of the model used a Generalized Linear Model (GLM) with the delta lognormal model approach. The proportion of successful (i.e. positive observations) trips per strata was assumed to follow a binomial distribution where the estimated probability was a linearized function of fixed factors and interactions. The logit function linked the linear component and the assumed binomial distribution. Similarly, the estimated catch per angler hour observed on positive trips was modeled as function of similar fixed factors with the log function as a link. A stepwise approach was used to quantify the relative importance of the main factors explaining the variance in catch rates. Factors and interactions which resulted in the greatest reduction in per degree of freedom were incorporated into the model. The product of the standardized proportion positives and the standardized positive catch rates was used to calculate overall standardized catch rates. For comparative purposes, each relative index of abundance was obtained dividing the standardized catch rates by the mean value in each series. 3. Results and Discussion 3.1 RBS Table 1 shows the analysis for sailfish from the U.S. RBS tournament data. For sailfish, the factors Year, Season, and Year*Season were the main explanatory variables for the proportion of positive trip-days; and, all factors were significant for sailfish mean catch rate. Once a set of fixed factors was selected, we evaluated first level random interaction between the Year and other effects. Table 2 shows the results from the random test analyses for sailfish and the two criteria statistics used for final model selection. Diagnostic plots of the fit to the proportion of positive model component (Figure 4), and fit of the positive observations model (Figure 5) corroborate the final model selection. Standardized CPUE series for the U.S. RBS sailfish tournament data (1973-2006) are shown in Table 3 and Figure 6. Catch rates of sailfish decreased to their lowest value in 1983, followed by a gradual increase. Coefficient of variation ranged from 30.3% to 55.2%. 3.2 MRFSS The analysis for sailfish from the U.S. MRFSS recreational survey data is shown in Table 4. In turn, the random test analyses for the proportion of positives and positive catch rate is depicted in Table 5. The diagnostic plots of the fit to the proportion of positive model component (Figure 7), and fit of the positive observations model (Figure 8) corroborate the final model selection. Standardized CPUE series for the U.S. MRFSS recreational survey sailfish data (1981-2007) are shown in Table 6 and Figure 9. Catch rates of sailfish decreased to their lowest value in 1983, followed by a gradual increase. Coefficient of variation ranged from 30.6% to 79.7%.
References ANONYMOUS. 1999. Amendment 1 to the Atlantic Billfish Fishery Management Plan. U.S. Dept. of Comm., NOAA-NMFS. April 1999. BEARDSLEY, G.L. and R.J. Conser. 1981. An analysis of catch and effort data from the U.S. recreational fishery for billfishes (Istiophoridae) in the western North Atlantic Ocean and Gulf of Mexico, 1971-78. Fish. Bull. 79:49-68. BROWDER, J.A. and E.D. Prince. 1990. Standardized estimates of recreational fishing success for blue marlin and white marlin in the western North Atlantic Ocean, 1972-1986. In: R.H. Stroud (ed.), Planning the Future of Billfishes, Research and Management in the 90 s and Beyond. Proceedings of the Second International Billfish Symposium, Kailua-Kona, HI. August 1-5, 1988. National Coalition for Marine Conservation, Inc. Savannah, GA. pp. 215-229. FARBER, M.I. 1994. Standardization of U.S. recreational fishing success for sailfish (Istiophorus platypterus) 1973-92, using general linear model techniques. Col. Vol. Sci. Pap. ICCAT, 42:346-352. 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. MCCULLAGH, P. and J.A. Nelder. 1989. Generalized Linear Models 2nd edition. Chapman & Hall. ORTIZ M. and C.A. Brown 2002. Standardized catch rates for sailfish (Istiophorus platypterus) from United States recreational fishery surveys in the northwest Atlantic and the Gulf of Mexico. Col. Vol. Sci. Pap. ICCAT, 54:772-790. PRAGER, M.H., E.D. Prince and D.W. Lee. 1995. Empirical length and weight conversion equation: for blue marlin, white marlin, and sailfish from the North Atlantic Ocean. Bull. Mar. Sci. 56(1):201-210. PRINCE, E.D., A.R. Bertolino and A.M. Lopez. 1990. A comparison of fishing success and average weights of blue marlin and white marlin landed by the recreational fishery in the western Atlantic Ocean, Gulf of Mexico, and Caribbean Sea, 1972-1986. In: R.H. Stroud (ed.), Planning the Future of Billfishes, Research and Management in the 90 s and Beyond. Proceedings of the Second International Billfish Symposium, Kailua-Kona, HI. August 1-5, 1988. National Coalition for Marine Conservation, Inc. Savannah, GA. pp. 159-178. SAS Institute Inc. 1997, SAS/STAT Software: Changes and Enhancements through Release 6.12. Cary, NC, USA:Sas Institute Inc., 1997. 1167 pp. U.S. Department of Commerce. 1990. Marine Recreational Fishery Statistics Survey, Atlantic and Gulf coasts, 1987-1989. Current Fishery Statistics No. 8904. Washington, DC. 363 pp.
Table 1. Deviance analysis table for the U.S. Recreational Billfish Survey (RBS) sailfish data (1973-2006), using the GLM model. The dependent variable is the total hooked fish per hour (HPUE) in weight units, while p value refers to the 5% Chi-square probability between consecutive models. Model factors positive catch rates values d.f. Residual Change in % of total p 1 1 1564.70 Year 33 1413.04 151.7 26.7% < 0.001 Year Season 2 1149.95 263.1 46.3% < 0.001 Year Season Year*Season 48 996.20 153.8 27.0% < 0.001 Table 2. Analyses of delta lognormal mixed model formulations for sailfish catch rates from the U.S. Recreational Billfish Survey (RBS) data (1973-2006). Likelihood ratio tests the difference of -2 REM log likelihood between two nested models. An * indicates the selected model for each component of the final delta mixed model, while p represents the 5% Chi-square probability for the likelihood ratio test. Sailfish Recreational Billfish Survey (RBS) -2 REM Log likelihood Akaike's Information Criterion Schwartz's Bayesian Criterion Likelihood Ratio Test p Positive Catch Year Season 3644.9 3646.9 3652.0 Year Season Year*Season 3579.9 3583.9 3588.7 65 0.0000
Table 3. Sailfish nominal and standardized CPUE (fish / 1000 hours), coefficient of variation, index, and 95% confidence interval (CI) limits for the standardized index from the U.S. Recreational Billfish Survey (RBS) data (1973-2006). Year Number observations Nominal CPUE Standardized CPUE Coefficient Variation Index Upper 95% CI Lower 95% CI 1973 14 9.719 14.004 43.0% 1.065 0.467 2.428 1974 25 9.767 10.582 34.0% 0.805 0.415 1.560 1975 28 18.475 14.126 33.6% 1.075 0.558 2.068 1976 21 10.299 10.725 37.6% 0.816 0.395 1.687 1977 28 14.467 16.421 37.3% 1.249 0.607 2.571 1978 29 13.848 15.170 37.0% 1.154 0.564 2.361 1979 28 13.318 14.377 37.3% 1.094 0.531 2.251 1980 40 24.496 16.589 35.9% 1.262 0.629 2.534 1981 12 15.235 16.005 41.1% 1.218 0.553 2.682 1982 6 5.002 3.575 55.2% 0.272 0.097 0.763 1983 15 3.421 3.471 49.7% 0.264 0.103 0.676 1984 49 11.687 7.654 33.3% 0.582 0.305 1.113 1985 43 8.303 5.792 35.4% 0.441 0.222 0.875 1986 60 13.157 8.407 34.6% 0.640 0.326 1.254 1987 78 10.979 7.263 34.3% 0.553 0.284 1.076 1988 68 11.801 10.790 31.8% 0.821 0.441 1.528 1989 80 8.335 7.510 31.7% 0.571 0.308 1.060 1990 74 10.895 11.834 30.8% 0.900 0.493 1.644 1991 90 8.337 14.128 30.3% 1.075 0.594 1.945 1992 72 10.851 18.540 30.8% 1.410 0.772 2.577 1993 28 10.970 12.182 36.6% 0.927 0.456 1.883 1994 32 12.525 19.471 32.2% 1.481 0.790 2.777 1995 54 16.001 17.182 32.1% 1.307 0.698 2.447 1996 42 19.969 18.480 35.6% 1.406 0.704 2.808 1997 45 22.040 20.268 35.8% 1.542 0.770 3.087 1998 15 19.900 23.685 35.8% 1.802 0.899 3.609 1999 21 14.005 13.522 37.7% 1.029 0.496 2.134 2000 20 12.118 10.298 37.6% 0.783 0.379 1.620 2001 26 14.088 11.273 35.2% 0.858 0.433 1.698 2002 31 12.958 14.665 32.3% 1.116 0.594 2.097 2003 32 13.411 17.408 31.8% 1.324 0.711 2.465 2004 37 17.511 12.625 31.5% 0.960 0.519 1.776 2005 41 15.634 13.652 31.6% 1.039 0.560 1.926 2006 44 27.459 15.281 31.1% 1.162 0.634 2.132
Table 4. Deviance analysis table of explanatory variables in the delta lognormal model for sailfish catch rates from the U.S. recreational survey (MRFSS) data (1981-2007). Percent of total refers to the explained by the full model; p value refers to the 5% Chi-square probability between consecutive models. Model factors positive catch rates values d.f. Residual Change in % of total p 1 1 2323.7600 Year 26 2280.7036 43.1 16.7% 0.019 Year Area 1 2243.2606 37.4 14.5% < 0.001 Year Area Season 3 2219.0351 24.2 9.4% < 0.001 Year Area Season Mode 1 2171.4202 47.6 18.4% < 0.001 Year Area Season Mode Region 1 2126.5000 44.9 17.4% < 0.001 Year Area Season Mode Region Guild 2 2120.0902 6.4 2.5% 0.041 Year Area Season Mode Region Guild Area*Season 3 2119.3382 0.8 0.3% 0.861 Year Area Season Mode Region Guild Season*Mode 3 2119.0989 1.0 0.4% 0.803 Year Area Season Mode Region Guild Area*Mode 1 2118.9784 1.1 0.4% 0.292 Year Area Season Mode Region Guild Area*Guild 2 2118.1906 1.9 0.7% 0.387 Year Area Season Mode Region Guild Season*Guild 6 2115.1870 4.9 1.9% 0.556 Year Area Season Mode Region Guild Mode*Guild 2 2114.7471 5.3 2.1% 0.069 Year Area Season Mode Region Guild Year*Mode 26 2105.6390 14.5 5.6% 0.967 Year Area Season Mode Region Guild Year*Area 26 2103.2570 16.8 6.5% 0.914 Year Area Season Mode Region Guild Year*Guild 41 2092.2065 27.9 10.8% 0.941 Year Area Season Mode Region Guild Year*Region 23 2090.0038 30.1 11.7% 0.147 Year Area Season Mode Region Guild Year*Season 75 2065.6823 54.4 21.1% 0.965 Model factors proportion of positive/total obs d.f. Residual Change in % of total p 1 1 11803.6450 Year 26 11246.0860 557.6 6.6% < 0.001 Year Area 1 10685.1283 561.0 6.7% < 0.001 Year Area Season 3 9067.4279 1617.7 19.2% < 0.001 Year Area Season Mode 1 8249.7212 817.7 9.7% < 0.001 Year Area Season Mode Region 1 6785.5201 1464.2 17.4% < 0.001 Year Area Season Mode Region Guild 2 3685.6521 3099.9 36.8% < 0.001 Year Area Season Mode Region Guild Area*Season 3 3659.7813 25.9 0.3% < 0.001 Year Area Season Mode Region Guild Season*Mode 3 3613.6150 72.0 0.9% < 0.001 Year Area Season Mode Region Guild Year*Area 26 3604.4096 81.2 1.0% < 0.001 Year Area Season Mode Region Guild Area*Mode 1 3599.5284 86.1 1.0% < 0.001 Year Area Season Mode Region Guild Year*Mode 26 3591.6397 94.0 1.1% < 0.001 Year Area Season Mode Region Guild Year*Region 26 3556.4837 129.2 1.5% < 0.001 Year Area Season Mode Region Guild Year*Guild 52 3517.9996 167.7 2.0% < 0.001 Year Area Season Mode Region Guild Year*Season 77 3375.7049 309.9 3.7% < 0.001
Table 5. Analyses of delta lognormal mixed model formulations for sailfish catch rates from the U.S. recreational survey (MRFSS) data (1981-2007). Likelihood ration tests the difference of -2 REM log likelihood between two nested models. An * indicates the selected model for each component of the final delta mixed model, while p represents the 5% Chi-square probability for the likelihood ratio test. Sailfish recreational survey (MRFSS) -2 REM Log likelihood Akaike's Information Criterion Schwartz's Bayesian Criterion Likelihood Ratio Test p Proportion Positives Year Area Region Guild Season 13782.0 13784.0 13789.7 Year Area Region Guild Season Year*Region 13706.6 13710.6 13714.6 75.4 0.0000 Year Area Region Guild SeasonYear*Region Year*Season 13665.8 13671.8 13677.7 40.8 0.0000 Positive Catch Year Area Season Mode Region Guild 9786.0 9788.0 9794.4 Year Area Season Mode Region Guild Year*Season 9769.5 9773.5 9778.8 16.5 0.0000 Year Area Season Mode Region Guild Year*Season Year*Mode 9769.4 9775.4 9783.4 0.1 0.7518 Year Area Season Mode Region Guild Year*Season Year*Mode Year*Region 9755.3 9763.3 9773.9 14.1 0.0002 Year Area Season Mode Region Guild Year*Season Year*Mode Year*Region Year*guild 9748.7 9758.7 9772.0 6.6 0.0102
Table 6. Sailfish nominal and standardized CPUE (fish / 1000 hours), coefficient of variation, index, and 95% confidence interval (CI) limits for the standardized index from the U.S. recreational survey (MRFSS) data (1981-2007). Year Number observations Nominal CPUE Standardized CPUE Coefficient Variation Index Upper 95% CI Lower 95% CI 1981 1569 0.925 0.223 79.7% 0.501 0.123 2.037 1982 2738 0.681 0.116 75.9% 0.260 0.067 1.001 1983 2033 2.211 0.410 56.3% 0.922 0.323 2.632 1984 2321 1.641 0.269 66.4% 0.604 0.180 2.022 1985 2493 0.976 0.228 68.9% 0.512 0.147 1.782 1986 6771 1.889 0.314 46.1% 0.705 0.293 1.698 1987 11117 0.882 0.189 46.6% 0.425 0.175 1.033 1988 11075 1.174 0.302 41.8% 0.680 0.305 1.516 1989 10378 1.205 0.264 42.2% 0.593 0.264 1.333 1990 9250 1.098 0.296 46.0% 0.665 0.277 1.598 1991 10458 1.851 0.475 36.2% 1.068 0.529 2.157 1992 14680 2.575 0.627 33.7% 1.408 0.731 2.714 1993 12502 1.570 0.482 35.8% 1.083 0.541 2.169 1994 15353 1.470 0.449 34.7% 1.010 0.514 1.983 1995 14128 1.790 0.575 33.0% 1.292 0.679 2.459 1996 17976 1.493 0.443 33.6% 0.995 0.517 1.913 1997 19227 1.827 0.323 35.0% 0.726 0.368 1.433 1998 20661 2.554 0.621 31.0% 1.396 0.761 2.560 1999 24887 4.001 0.750 30.9% 1.685 0.921 3.083 2000 26910 2.547 0.565 31.3% 1.270 0.689 2.343 2001 27451 2.032 0.344 34.0% 0.774 0.399 1.500 2002 27720 3.771 0.627 30.7% 1.409 0.774 2.567 2003 26613 3.153 0.635 31.7% 1.427 0.769 2.648 2004 26039 2.797 0.519 32.8% 1.167 0.615 2.213 2005 22835 3.147 0.927 30.6% 2.084 1.146 3.791 2006 22081 2.801 0.501 33.5% 1.125 0.586 2.162 2007 20969 2.830 0.540 32.6% 1.213 0.642 2.292
70 N 50 N NED MAB NEC 30 N GOM SAB FEC SAR NCA CAR 10 N TUN TUS 10 S 100 W 80 W 60 W 40 W 20 W 0 Figure 1. Geographical classifications of management areas used for the U.S. Recreational Billfish Survey (RBS), and recreational survey (MRFSS) sailfish catch data collection.
Figure 2. Frequency distribution for log transformed CPUE positive catches (number of sailfish) from the U.S. RBS tournament data (1973-2006). Figure 3. Frequency distribution for log transformed CPUE positive catches (number of sailfish) from the U.S. MRFSS recreational sailfish data (1981-2007). Figure 4. Residual distribution, by year, from the proportion of positive/total observations (number of sailfish) model fit for the U.S. RBS sailfish tournament data (1973-2006).
Figure 5. Normalized predicted plot (qq-plot) of residual fit of the positive observations (number of sailfish) GLM model fit for the U.S. RBS sailfish tournament data (1973-2006). 4 Sailfish Scaled CPUE (fish / 1000 hrs) 3 2 1 Standardized Nominal 0 1970 1975 1980 1985 1990 1995 2000 2005 2010 Year Figure 6. Estimated nominal (red diamonds) and standardized (blue line) CPUE for sailfish (fish / 1000 hours) from the U.S. Recreational Billfish Survey (RBS) tournament data (1973-2006). Dotted lines correspond to the 95% confidence interval for the standardized CPUE.
Figure 7. Normalized predicted plot (qq-plot) of residual fit of the positive observations (number of sailfish) GLM model fit for the U.S. MRFSS recreational sailfish data (1981-2007). Figure 8. Residual distribution, by year, from the proportion of positive/total observations (number of sailfish) model fit for the U.S. MRFSS recreational sailfish data (1981-2007).
4 Sailfish Scaled CPUE (fish / 1000 hours) 3 2 1 Standardized Nominal 0 1980 1985 1990 1995 2000 2005 2010 Year Figure 9. Estimated nominal (red diamonds) and standardized (blue line) CPUE for sailfish (fish / 1000 hours) from the U.S. recreational survey (MRFSS) data (1981-2007). Dotted lines correspond to the 95% confidence interval for the standardized CPUE.