SCTB16 Working Paper
RG–3
Comparison of deterministic and statistical habitat-based models to estimate effective longline effort and standardized cpue for bigeye and yellowfin tuna
Keith A. Bigelow1, Mark Maunder2, Michael Hinton2
July 2003
1
NOAA Fisheries, Pacific Islands Fisheries Science Center, 2570 Dole Street, Honolulu, HI 96822, USA Email:
[email protected] 2 Inter-American Tropical Tuna Commission, 8604 La Jolla Shores Drive, La Jolla, CA 92037-1508, USA
COMPARISON OF DETERMINISTIC AND STATISTICAL HABITAT-BASED MODELS TO ESTIMATE EFFECTIVE LONGLINE EFFORT AND STANDARDIZED CPUE FOR BIGEYE AND YELLOWFIN TUNA Keith A. Bigelow1, Mark Maunder2, Michael Hinton2
SUMMARY Habitat-based standardization (HBS) models have been applied to estimate effective longline effort and standardized CPUE for yellowfin and bigeye tuna in the Pacific. A statistical HBS (statHBS) was developed (Hinton et al. 2001) that allows parameter (e.g. habitat preferences, factors modifying the behavior of the gear or species) estimation based on fitting the model to observed catch and effort data. In this paper we compare estimates of three effort series 1) nominal, 2) HBS and 3) statHBS to ascertain which series better explains variation in Japanese longline catch for yellowfin tuna in the WCPO and bigeye tuna in the Pacific Ocean. The order of best to poorest fit of the variation in WCPO yellowfin catch was: 1) statHBS based on ambient temperature, 2) statHBS based on isotherms, 3) nominal effort and 4) HBS. The order of best to poorest fit of the variation in bigeye catch was: 1) statHBS based on ambient temperature, 2) HBS based on the Hawaii hypothesis, 3) nominal effort and 4) HBS based on the Tahiti hypothesis.
1. INTRODUCTION The Japanese distant-water longline fleet has exploited bigeye and yellowfin tuna in the Pacific Ocean since the early 1950s. Annual longline catches of the bigeye and yellowfin stock occurring in the western and central Pacific Ocean (WCPO) ranged from 110,000 to 140,000 mt during the 1990s with the largest portion of the catch captured by the Japanese distant-water and offshore fleets (annual range 26,000–64,000 mt, Lawson 2002). Recent assessments of bigeye and yellowfin tuna in the WCPO have been conducted with MULTIFAN-CL (Hampton 2002a, 2002b), an age-structured model that requires data components such as: total catch and effort data, length and/or weight frequency data and tagging data. Longline catch and effort data are a critical input to the assessment model as either yellowfin or bigeye tuna are actively targeted by most longline fleets in the Pacific; however trends in nominal longline effort and catch rates 1
NOAA Fisheries, Pacific Islands Fisheries Science Center, 2570 Dole Street, Honolulu, HI 96822, USA Email:
[email protected] 2 Inter-American Tropical Tuna Commission, 8604 La Jolla Shores Drive, La Jolla, CA 920371508, USA
1
may differ from trends in actual effective longline effort and relative abundance due to gear modifications and spatial expansion of the fishery within the time-series. There are two generally applicable standardization approaches utilized in pelagic fisheries. Traditionally, catch and effort data have been fit by generalized linear models (GLMs), a statistical approach which directly attempts to account for the variation in CPUE based on nominal effort by fitting a predicted CPUE to observed CPUE. While GLMs are general in scope, there is an inability to include information on the scientific understanding among explanatory variables. A method of habitat based standardization (HBS) was initially developed by Hinton and Nakano (1996) and represents a deterministic modeling approach whereby effective longline effort is modeled as the joint probability of the vertical distribution of hooks in the water column and the distribution of the species. A species vertical distribution is based on habitat preferences (e.g. temperature, oxygen) in combination with environmental data. HBS applications of fishing effort have become the preferred method to estimate effective longline effort for various tuna (Bigelow et al. 1999, 2002) and billfish (Hinton and Nakano 1996, Hinton and Deriso 1998) captured by longline in the Pacific, though the method is not routinely applied to species in other oceans. While HBS methods may have a greater intuitive sense compared to GLMs, several scientific concerns have been raised regarding various assumptions inherent in HBS methods, such as: 1) inaccurate assumptions about a species habitat preferences and associated vertical distribution, 2) applicability of transferring habitat preferences of particular species to another species or to other oceans, 3) predictability of actual longline depth and gear behavior, 4) differential catchability aspects of sinking, settled and rising gear for some species and 5) the assumption that the average environmental conditions in a 5°-month strata are the same as the environmental conditions where the fish is located in that strata. Unlike a GLM, a deterministic HBS does not estimate parameters by fitting to the catch data. Therefore, effective effort estimates may not predict catch better than nominal effort if habitat preferences are structured incorrectly. A statistical HBS (statHBS) was developed for striped marlin (Hinton et al. 2001) that allows parameter (e.g. habitat preferences, factors modifying the behavior of the gear or species) estimation based on fitting the model to observed catch and effort data. In this paper we compare estimates of three effort series 1) nominal, 2) HBS and 3) statHBS to ascertain which series better explains variation in Japanese longline catch for yellowfin tuna in the WCPO and bigeye tuna in the Pacific Ocean.
2. METHODS Catch and effort data Catch and effort data were compiled from 1952 to 2000 at a 5°-month resolution at the National Research Institute of Far Seas Fisheries. Gear configuration data as hooks between floats (HBF) were available for 1966 and after 1974. Gear configuration in the Japanese longline fishery ranged from 3 to 22 HBF. Data corresponding to 3–4 HBFs were deleted prior to analysis because these shallow gear types were used to target swordfish. Data from 1975 to 2000 containing HBF information were used in the modeling of the effort series.
2
Modeling of catch and effort series A likelihood function was used as a measure of how well the predicted catch from the various effort series fit the observed catch. The likelihood function has the form:
(ln(C exp −
L=∏ i
1
σ j 2π
i, y
+ δ ) − ln(Cˆ i , j , y + δ ) 2σ j
)
2
2
where Ci , y is the observed catch for observation i, and year y, Cˆ i , j , y is the predicted catch for observation i, effort series j and year y and σ 2 is the lognormal variance weighted by the 1/sqrt(number of observations). A constant ( δ =0.1) was added to the observed and predicted catch to avoid computational problems when observed catch or standardized effort was zero. For individual observations (i) from an effort (E) series j, an estimate of catch (C) in year y is obtained as Cˆ i , j , y = Ei , j , y q j B y , where q is overall catchability and B is abundance. Year effects ( θ y = qBy ) are estimated because both q and B are unknown. The negative log-likelihood is minimized by simultaneously estimating various parameters with the function minimizer in AD Model Builder. We fit models to yellowfin in the WCPO (n=91,175 5°-month-HBF strata) and bigeye in the Pacific Ocean (n=163,092 5°-month-HBF strata) as three different effort series: nominal, HBS and statHBS. Models for nominal and HBS effort estimated a year effect, whereas the statHBS model estimated a year effect and habitat (temperature) preferences within the water column. The structure of the HBS and statHBS was similar as values for individual hook depths were modeled as a catenary function for each of the 18 gear categories (5–22 HBF). Vertical distribution of bigeye and yellowfin in the model followed previous habitat-based longline effort standardizations. Bigeye tuna were vertically distributed based on ambient temperature. Available time-at-temperature distributions include studies from Tahiti (Dagorn et al. 2000) and Hawaii (Musyl et al. 2003). An HBS application was conducted for each of the Tahiti and Hawaii temperature hypotheses. For each 5°-month stratum, bigeye tuna were vertically distributed given temperature preferences and corresponding vertical temperature profiles from an Ocean Global Circulation Model (SODA analysis, Carton et al. 2000a, 2000b, http://apdrc.soest.hawaii.edu).
Two yellowfin HBS were conducted by 1) distributing the fish at temperatures in relation to the surface layer as tracking studies in the Pacific (Holland et al. 1990, Block et al. 1997, Brill et al. 1999), have all noted that vertical distribution is not limited by ambient temperature, but rather by the change in temperature occurring between the surface layer and below the thermocline and 2) that yellowfin were vertically distributed at ambient temperatures, similar to the bigeye tuna statHBS. Isotherms and ambient temperature were calculated from the SODA OGCM.
3
A statHBS similar in structure to Hinton et al. (2001) was applied to bigeye and yellowfin tuna. Habitat preferences are structured as parameters in the statHBS and estimated simultaneously with the year effect ( θ y = qBy ). In the statHBS application to bigeye, 30 ambient temperature preferences at 1°C intervals from 4-34°C of the Hawaii hypothesis were used as priors. Distributions of mean zero were assigned and priors were relatively informative (normal distribution, CV=0.2) or noninformative (uniform distribution). The contribution of this prior to the objective function is:
Θh =
ε2 2 2σ
where ε are residuals of the habitat preferences and is the standard deviation of the prior distribution which has median . Approximate confidence intervals of the model parameters were computed from the Hessian and covariance matrix. Two statHBS analyses were conducted for yellowfin. A statHBS analysis was conducted with the hypothesis that yellowfin were distributed at temperatures in relation to the surface layer. Nine priors were used to represent the proportion of yellowfin in the surface layer and in each of the eight isotherms below the surface layer. Additionally, a statHBS analysis was conducted with the hypothesis that yellowfin were vertically distributed at ambient temperatures, similar to the bigeye tuna statHBS. Noninformative priors (uniform distribution) were used in each yellowfin statHBS.
3. RESULTS AND DISCUSSION Yellowfin Details of the yellowfin model results are given in Table 1. Although no statistical test was conducted for model comparison, the order of best to poorest fit of the variation in catch was: 1) statHBS based on ambient temperature, 2) statHBS based on isotherms, 3) nominal effort and 4) HBS. Residuals were normally distributed for both the HBS and statHBS, but non-normal for nominal effort (Figure 1). Fitted ambient temperature preferences from the statHBS are illustrated in Figure 2 for the WCPO and EPO. Preferences were typically from 23–27°C in the WCPO with slightly cooler preference values in the EPO due to catches in the equatorial cold tongue. These temperatures are indicative of yellowfin preferences within 10° of the equator where most of the catch occurred (Figure 3). Previous yellowfin tracking studies in the Pacific have all noted that vertical distribution is not limited by ambient temperature, but rather by the change in temperature occurring between the surface layer and below the thermocline (Holland et al. 1990, Block et al. 1997, Brill et al. 1999). The results of all adult fish (n=8) indicate that adult yellowfin spent approximately 60% of their time in the surface layer, with decreasing proportions at depths below the surface layer. Observed yellowfin proportions in relation to the surface layer were incorporated into the HBS; however, the fitted isotherm preferences in the statHBS distributed the majority of yellowfin at depths where temperatures were 4–6°C cooler than the surface layer. These deeper depths are unrealistic given our current understanding of the yellowfin vertical distribution behavior ascertained from acoustic and archival tagging, thus further investigation is required for the statHBS
4
based on structuring vertical distribution by isotherms. Alternatively, the estimation of a single distribution of yellowfin proportions at temperatures in relation to the surface layer may be inappropriate over the large zonal scale of the WCPO. Comparison of year effects between models show moderate differences in standardized CPUE trends (Figure 4). The year effects are relatively flat for nominal effort, but year effects for HBS and statHBS (ambient) increase from 1975 to the early 1990’s, reach a peak in 1995 and decline thereafter. Year effects were relatively similar for the HBS and statHBS from 1975 to 1990, but statHBS had lower CPUE in the 1990s. Confidence intervals of the year effects were relatively small ( ± 0.001). Results of nominal and statHBS (ambient) models in a spatial context are illustrated in Figure 5. A median residual estimate was calculated for each 5°-month stratum from 1975 to 2000. Residuals from nominal effort are negative (predicted catch>catch) at high latitudes (>25°) and positive (catch>predicted catch) near the equator from 150°E–180°. In contrast to nominal effort, effective effort from the statHBS (ambient) predicted catch well at high latitudes. The statHBS (ambient) also predicted catch well within 10° of the equator were the longline fishery occurs; however there were eight 5° strata to the east of Papua New Guinea where predicted catch was substantially lower than actual catch. Bigeye Details of the bigeye model results are given in Table 1. The order of best to poorest fit of the variation in catch was: 1) statHBS based on ambient temperature, 2) HBS based on the Hawaii hypothesis, 3) nominal effort and 4) HBS based on the Tahiti hypothesis. Residuals were normally distributed for HBS and statHBS, but skewed for nominal effort (Figure 6). Fitted ambient temperature preferences from the statHBS for the entire Pacific Ocean are compared with observed bigeye tuna time-at-temperature data from Hawaii and Tahiti in Figure 7. Each distribution was bimodal which reflects time spent in cooler, deeper waters during the day and warmer, shallower water at night. Fitted preferences were mainly from 9–16°C and 23–27°C. The modes of fitted preferences were typically broader than modes of the individual hypotheses, which probably result from estimating the preferences over a large spatial area (Figure 8). The year effects were similar for the four effort series from 1975 to 1981, but there were substantial differences thereafter (Figure 9). The standardized CPUE time-series trend based on nominal effort was most optimistic with similar values at the beginning and end of the time-series. There were moderate declines in the HBS (Hawaii) and statHBS timeseries and a precipitous decline in the HBS (Tahiti) time-series. Results of nominal and statHBS (ambient) models in a spatial context are illustrated in Figure 10. Residuals from nominal effort are negative over the entire western Pacific and near central America and positive at mid-latitudes in the EPO. The statHBS predicted catch well in the western Pacific except for a zonal area of 5 degrees from 140°E–180°. Positive residuals remain in the mid-latitudes of the EPO. Similarly, substantial negative residuals remain near the coast of central America, though this is an area of minimal catch (Figure 8).
5
Effective longline effort data for MULTIFAN-CL Effective longline effort were produced for the yellowfin and bigeye SCTB16 MULTIFANCL assessments (Hampton and Kleiber 2003, Hampton et al. 2003). A summary of effective effort data from various models is: 1. Yellowfin – deterministic HBS based on isotherms, analogous to the effective effort data used in the SCTB15 assessment 2. Yellowfin – statHBS based on ambient temperature. Parameter estimates from the model were incorporated into a HBS with improvements conducted since SCTB15 (see below) 3. Bigeye – deterministic HBS based on ambient temperature from the Tahiti hypothesis, analogous to the effective effort data used in the SCTB15 assessment 4. Bigeye – statHBS based on ambient temperature. Parameter estimates from the model were incorporated into a HBS There were two improvements to the HBS model (Bigelow et al. 2002) since SCTB15. Japanese longline data from 1952 to 1961 were obtained and incorporated into the analysis, therefore the current analysis provides data for a time period from 1952 to 2001. Oceanographic temperature and current data were obtained (SODA analysis, Carton et al. 2000a, 2000b, http://apdrc.soest.hawaii.edu) for the period from 1950 to 2001 and replaced climatologies previously used to vertically distribute the species in the water column and to shoal longline gear. For each species, statHBS models based on ambient temperature distribution provided the best fit to variation in catch data. From the statHBS models (datasets 2 and 4), estimates of nominal and standardized CPUE are aggregated into five and eight areas based on strata used in the MULTIFAN-CL assessment, respectively for yellowfin (Figure 11) and bigeye tuna (Figure 12). Standardized CPUE for each area was effort weighted ( yellowfin/ Effective longline effort). Recommendations for future research If the statistical HBS approach is used for future assessment purposes, the model could be improved by: Structure additional habitat preferences as parameters. In particular, appropriate preferences for bigeye tuna could include dissolved oxygen and/or ambient light.
Additional factors could be added such as categorical variables (e.g. month, area).
Conduct the statHBS on smaller spatial scales such as the MFCL stratification instead of the entire stock area which may predict catch better in some geographical areas.
6
Incorporate parameters associated with gear configuration (e.g. current shear, retrieval) that have been used in other statHBS applications (Hinton et al. 2001).
Apply the statHBS model to other major fleets in the WCPO or Pacific Ocean as the Japanese component of total yellowfin and bigeye longline catch in the WCPO has declined from 63% in the 1970s to 34% in the 1990s.
4. REFERENCES BIGELOW, K., J. Hampton and N. Miyabe. 1999. Effective longline effort within the yellowfin habitat and standardized CPUE. Working paper YFT–3, 12th Meeting of the Standing Committee on Tuna and Billfish, 10pp. BIGELOW, K., J. Hampton and N. Miyabe. 2002. Application of a habitat-based model to estimate effective longline fishing effort and relative abundance of Pacific bigeye tuna (Thunnus obesus). Fish. Oceanogr. 11(3): 143–155. BLOCK, B., J. Keen, B. Castillo, H. Dewar, E. Freund, D. Marcinek, R. Brill and C. Farwell. 1997. Environmental preferences of yellowfin tuna (Thunnus albacares) at the northern extent of its range. Mar. Biol. 130:119−132. BRILL, R., B. Block, C. Boggs, K. Bigelow, E. Freund and D. Marcinek. 1999. Horizontal movements and depth distribution of large adult yellowfin tuna (Thunnus albacares) near the Hawaiian Islands, recorded using ultrasonic telemetry: implications for the physiological ecology of pelagic fishes. Mar. Biol. 133:395−408. CARTON, H., G. Chepurin and X. Cao. 2000a. A simple ocean data assimilation analysis of the global upper ocean 1950–95. Part I: Methodology. J. Phys. Ocn. 30:294:309. CARTON, H., G. Chepurin and X. Cao. 2000b. A simple ocean data assimilation analysis of the global upper ocean 1950–95. Part II: Results. J. Phys. Ocn. 30:311:326. DAGORN, L., P. Bach, and E. Josse. 2000. Movement patterns of large bigeye tuna (Thunnus obesus) in the open ocean determined using ultrasonic telemetry. Mar. Biol. 136:361–371. HAMPTON, J. 2002a. Stock assessment of yellowfin tuna in the western and central Pacific Ocean. Working paper YFT–1, 15th Meeting of the Standing Committee on Tuna and Billfish, 38pp. HAMPTON, J. 2002b. Stock assessment of bigeye tuna in the western and central Pacific Ocean. Working paper BET–1, 15th Meeting of the Standing Committee on Tuna and Billfish, 36pp. HAMPTON, J. and P. Kleiber. 2003. Stock assessment of yellowfin tuna in the western and central Pacific Ocean. Working paper YFT–1, 16th Meeting of the Standing Committee on Tuna and Billfish, 64pp. HAMPTON, J, P. Kleiber, M. Maunder, S. Takeuchi and C-L Sun 2003. Stock assessment of bigeye tuna in the western and central Pacific Ocean. Working paper BET–1, 16th Meeting of the Standing Committee on Tuna and Billfish HINTON, M. and H. Nakano. 1996. Standardizing catch and effort statistics using physiological, ecological, or behavioral constraints and environmental data, with
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an application to blue marlin (Makaira nigricans) catch and effort data from Japanese longline fisheries in the Pacific. Bull. Int. Am. Trop. Tuna Comm. 21(4): 171−200. HINTON, M.G. and R. Deriso. 1998. Distribution and stock assessment of swordfish, Xiphias gladius, in the eastern Pacific Ocean from catch and effort data standardized on biological and environmental parameters. In: Biology and Fisheries of Swordfish, Xiphias gladius. Papers from the International Symposium on Pacific Swordfish, Ensenada, Mexico, 11−14 December 1994. Barrett, I., Sosa-Nishizaki, O. and Bartoo, N. (eds). NOAA Tech. Rep. NMFS 142:161−179. HINTON, M., M. Maunder and Y. Uozumi. 2001. Status of Striped Marlin, Tetrapturus audax, Stocks of the Eastern-Central Pacific. Third International Billfish Symposium. HOLLAND, K., R. Brill and R. Chang. 1990. Horizontal and vertical movements of yellowfin and bigeye tuna associated with fish aggregating devices. Fish. Bull. 88:493–507. LAWSON, T. A. (Ed.) 2002. Secretariat of the Pacific Community tuna fishery yearbook 2001. Secretariat of the Pacific Community: Noumea, New Caledonia, 170pp. MUSYL, M., R. Brill, C. Boggs, D. Curran, T. Kazama and M. Seki. 2003. Vertical movements of bigeye tuna (Thunnus obesus) associated with islands, buoys, and seamounts near the main Hawaiian Islands from archival tagging data. Fish. Oceanogr. 12(3): 152–169.
8
Table 1. Comparison of models to estimate standardized CPUE for yellowfin and bigeye tuna.
Species
Region
Yellowfin tuna
WCPO
Bigeye tuna
Effort series
Pacific
Log-likelihood
Cross-validation
Nominal
91,013
4,998,741
HBS (isotherms)
94,127
5,389,565
statHBS (isotherms)
89,411
4,791,544
statHBS (ambient)
64,650
2,460,713
101,982
3,672,195
HBS (Hawaii)
99,310
3,536,531
HBS (Tahiti)
107,468
4,039,964
statHBS (cv=0.2)
91,251
3,127,346
statHBS (cv=1.0)
90,698
3,110,631
Nominal
5 8
4
Percent of Total
Percent of Total
6 3
2
4
2 1
0
0 −4
−2
0
2
log(catch residual − nominal effort)
4
−4
−2
0
2
4
log(catch residual − statHBS effort)
Figure 1. Thunnus albacares. Distribution of residuals from a nominal effort model (left) and statHBS (right).
9
0.3 statHBS (WCPO)
Percent of time
statHBS (EPO) 0.2
0.1
0.0 5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 Temperature (°C)
Figure 2. Thunnus albacares. Ambient temperature preference estimated in a statistical HBS (uniform prior) for the western-central Pacific Ocean (WCPO) and eastern Pacific Ocean (EPO).
Figure 3. Distribution of yellowfin catch in the Japanese distant-water longline fishery from 1975 to 2000 and MFCL spatial stratification.
10
7
Nominal HBS (isotherm) statHBS (ambient)
Relative abundance
6 5 4 3 2 1 0 1975
1979
1983
1987
1991
1995
1999
Figure 4. Thunnus albacares. Estimated year effect for nominal CPUE, a habitat-based standardization (HBS) based on distributing fish at temperatures relative to the mixed layer and a statistical HBS based on ambient temperature.
11
1510
40oN -500
-200 -400
-40 -300 -200 0
30oN
-300 -400
172
-100
-300 -200 -100
-100
33
20oN
200 60 0 300 500 0 70
403000
10 00 9008 50 0 60000 700
10oS
-10
0
100
0
-400-30 -200 0
-100
10oN 100
5
-100
0
-18
200 0
100
-35
200 100
0
0
-75
0
-150
20oS
0
-100
30oS
-325 -1000
120oE
140o E
160o E
160o W
180
1510
40oN -100
-200
30oN
172
-100
-100
33
20oN
5 -10
10oN
0
-100
0
0 100
-18
400 300 200 100 780000 0 60 500
10oS
-35
0 0
-75 0
-150
0
20oS
-325 30oS -1000 120oE
140o E
160o E
180
160o
Figure 5. Thunnus albacares. Distribution of residuals from a nominal effort model (top) and statHBS ambient temperature model (bottom).
12
10 8 8
Percent of Total
Percent of Total
6
4
2
6
4
2
0
0 −4
−2
0
2
4
log(catch residual − nominal effort)
−4
−2
0
2
4
log(catch residual − HBS (Tahiti) effort)
14
12
Percent of Total
10
8
6
4
2
0 −4
−2
0
2
4
log(catch residual − statHBS effort)
Figure 6. Thunnus obesus. Distribution of residuals from a nominal effort model (top, left), HBS (top, right) and statHBS (bottom, left).
13
0.3 HBS (Hawaii)
Percent of time
HBS (Tahiti) statHBS
0.2
0.1
0 5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 Temperature (°C)
Figure 7. Thunnus obesus. Ambient temperature preferences from Tahiti (Dagorn et al. 2000) and Hawaii (Musyl et al. 2003) used in a habitat-based standardization (HBS) and preferences estimated in a statistical HBS (uniform prior).
Figure 8. Distribution of bigeye catch in the Japanese distant-water longline fishery from 1975 to 2000 and MFCL spatial stratification.
14
2.00 Nominal HBS (Tahiti)
Relative abundance
1.75
HBS (Hawaii) statHBS
1.50 1.25 1.00 0.75 0.50 0.25 0.00 1975
1979
1983
1987
1991
1995
1999
Figure 9. Thunnus obesus. Estimated year effect for nominal CPUE, habitat-based standardizations (HBS) for Hawaii and Tahiti temperature hypotheses and a statistical HBS.
15
40oN
30oN
-300
-100
20oN
200
0
0
0 -20
0
100
-100
-1
10oN
0
00
-1
0 -200
0
0
100
0 20 0
00 -1
10oS
00
-1 0 0
100
100
0
0
0
0
10 0
0
00 -1
20oS
30oS 120oE
-871
140o E
-400
160oE
-70
160 oW
180
-45
-25
-21
140o W
-17
120o W
0
100o W
17
500
976
40oN -300 -200
-100 0
30oN
-500
0
20oN
100
0
-100
-20 0
0
10oN
0
-100 -200
00 0
0
-100 0
-100
-1 0
0
0 0
0
0
0 -100
-100
10oS
0
-200
200
100
0
0
20oS
0
30oS 120oE
-871
140o E
-400
160oE
-70
-45
180
-25
160 oW
-21
140o W
-17
120o W
0
17
100o W
500
976
Figure 10. Thunnus obesus. Distribution of residuals from a nominal effort model (top) and statHBS ambient temperature model (bottom).
16
3.0
24
2.5
20
40N
1 20N
2.0
2
3
0û
4
20S
16
Region 1 1.5
12
1.0
8
0.5
4
5
40S
0.0 120ûE
140ûE
160ûE
180û
160W
0 1956 1962 1968 1974 1980 1986 1992 1998
3.0
24 3.0
24
2.5
20 2.5
20
2.0
16 2.0
16
Region 3 1.5
12 1.5
12
1.0
8 1.0
8
4 0.5
4
0.5
Region 2 0.0
0 0.0 1956 1962 1968 1974 1980 1986 1992 1998
0 1956 1962 1968 1974 1980 1986 1992 1998
3.0
24 3.0
24
2.5
20 2.5
20
2.0
16 2.0
16
Region 4
Region 5
1.5
12 1.5
12
1.0
8 1.0
8
0.5
4 0.5
4
0.0
0 0.0 1956 1962 1968 1974 1980 1986 1992 1998
GMT 2003 Jul 1 08:53:30
0 1956 1962 1968 1974 1980 1986 1992 1998
Yellowfin statHBS (ambient temperature)
Figure 11. Comparison of nominal (line with dots) and standardized CPUE (line) indices for yellowfin in the western and central Pacific from a statistical habitat-based model. Indices are effort weighted ( yellowfin number/ Effective longline effort).
17
40N
1
2
7
20N
3 0û
4
20ûS
5
6
8
40ûS 120E
140ûE
160E
180û
1.5
160W
140ûW
120ûW
100ûW
80ûW
35 1.5
Region 1
1.2
28 1.2
35
Region 2
28
0.9
21 0.9
21
0.6
14 0.6
14
0.3
7 0.3
7
0.0
0 0.0 1956 1962 1968 1974 1980 1986 1992 1998
1.5
0 1956 1962 1968 1974 1980 1986 1992 1998
35 1.5
Region 3
1.2
28 1.2
35
Region 4
28
0.9
21 0.9
21
0.6
14 0.6
14
0.3
7 0.3
7
0.0
0 0.0 1956 1962 1968 1974 1980 1986 1992 1998
1.5 1.2
0 1956 1962 1968 1974 1980 1986 1992 1998
35 1.5
Region 5
28 1.2
35
Region 6
28
0.9
21 0.9
21
0.6
14 0.6
14
0.3
7 0.3
7
0.0
0 0.0 1956 1962 1968 1974 1980 1986 1992 1998
1.5
0 1956 1962 1968 1974 1980 1986 1992 1998
35 1.5
Region 7
1.2
28 1.2
35
Region 8
28
0.9
21 0.9
21
0.6
14 0.6
14
0.3
7 0.3
7
0.0
0 0.0 1956 1962 1968 1974 1980 1986 1992 1998
GMT
2003 Jul 1 09:06:06
0 1956 1962 1968 1974 1980 1986 1992 1998
Bigeye 1952-2001 statHBS analysis
Figure 12. Comparison of nominal (line with dots) and standardized CPUE (line) indices for bigeye in the Pacific Ocean from a statistical habitat-based model. Indices are effort weighted ( bigeye number/ Effective longline effort).
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