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SCTB16 Working Paper

BBRG–9

Factors affecting (Xiphias gladius) catch rate in the New Zealand longline

Draft

Talbot Murray & Lynda Griggs

NIWA, Wellington. New Zealand

July 2003

1

Factors affecting swordfish (Xiphias gladius) catch rate in the New Zealand tuna longline fishery

Talbot Murray Lynda Griggs National Institute of Water & Atmospheric Research Ltd P.O. Box 14 901, Kilbirnie Wellington, New Zealand

ABSTRACT

Swordfish catches by longline have dramatically increased in the southwestern Pacific since the mid-1990s. This increase is due to the development of a target fishery off Australia and the rapid expansion of domestic longlining in the New Zealand EEZ. Combined catches are over 3500 t, 1000 t of which comes from New Zealand waters. The New Zealand fishery responsible for most of the swordfish catch primarily targets bigeye tuna and to a lesser extent . Catches are mostly in the 1st and 2nd quarters of the year and primarily in waters north of 41º S.

In the EEZ there are differences in swordfish CPUE between fleets, season, area, environmental factors and differences in practices. We used discriminant function analysis and commercial logsheet data to demonstrate that sets catching swordfish could be distinguished from those that did not with moderate success ( 64.6% of sets correctly classified). Modest improvement in the ability to correctly classify sets (72.9%) that had high vs. low swordfish CPUE was also demonstrated. The factors that contributed most to distinguishing CPUE categories were the number of hooks set, month and start of set time, latitude, moon phase and catch rates of and bigeye tuna were also significant but contributed little to the discriminant functions. ANOVA results of observer data demonstrated that month, latitude, number of hooks set, start of set time, albacore catch rate and the number of hooks set were significant factors explaining the variance in CPUE of both swordfish and bigeye tuna. In addition moon phase was significant in explaining the variance in swordfish CPUE but not for bigeye tuna while length of time fished was important for bigeye but not swordfish.

Of all the factors examined, the number of light sticks used had the most profound effect on swordfish CPUE with low use nearly doubling CPUE relative to not using them and moderate to high use increasing swordfish CPUE by about four times relative to not using them. Light stick use also affects bigeye tuna CPUE but the effect is only for low to moderate levels and the increase is only about 40% relative to not using them or deploying high levels of light sticks. The percent of bait used (anecdotally reported to enhance bigeye catch rates) was not a significant factor in explaining the variance in either swordfish or bigeye tuna CPUE. Our results point to the importance of light and shallow sets at night as major determinants of high swordfish CPUE in the EEZ. Evidence is also presented that indicates that the increase in swordfish CPUE and catches is likely to be due to the widespread use of light sticks and that some level of swordfish targeting has been occurring since the mid-1990s and has likely been increasing despite a ban on swordfish targeting.

Despite increased catches, there has been no evidence of a trend in swordfish CPUE, average size or sex ratio that would suggest changes in the fishery as have been seen in other areas where sustainability has been a concern.

Keywords swordfish; Xiphias gladius; southwest Pacific Ocean catches, longline CPUE; targeting

INTRODUCTION

Swordfish (Xiphias gladius) are caught throughout the Pacific Ocean from 50° N to 50° S (Carocci & Majkowski, 1996), usually as bycatch in longline and purse seine targeting various tuna species. Swordfish are also targeted in a few Pacific Ocean fisheries. Ward and Elscot (2000) review the swordfish target fisheries in Chile (longline, driftnet, and formerly harpoon), Japan (longline, driftnet, and harpoon), Hawaii (longline), and off the east coast of Australia (longline) noting declines in catch per unit effort (CPUE) and changes in size composition where this species is regularly targeted. In New Zealand, swordfish are primarily a bycatch of tuna longlining, mainly north of 43°S (Murray et al. 1999) although some targeting by longline is suspected.

Swordfish have been a regular component of tuna longline sets since longlining began in the southwestern Pacific Ocean by the distant water Japanese fleet in the 1950s. Prior to the 1990s vessels from Japan and to a lesser extent Korea and Taiwan were the only vessels longlining in New Zealand waters and adjacent high seas areas (defined here as the area south of 25º S, from 160º E to 160º W). These vessels targeted albacore, bigeye and southern bluefin , however, during the 1990s domestic longlining for bigeye and southern bluefin tunas rapidly increased and by 1995 had totally replaced foreign licensed longlining within the EEZ. Coincident with the domestic expansion of , catches of swordfish dramatically increased from 41 t in 1990 to about 1000 t since 1999. This rapid rise in catches in the EEZ has resulted in concerns over sustainability of the resource and the potential for localized depletion.

Swordfish catches in the southwestern Pacific Ocean south of 25º S

It is not just New Zealand fishers that are increasingly catching swordfish but also distant water and South Pacific coastal States longline fleets. Ward and Elscot (2000) report on the rapid expansion of the Australian longline fishery targeting swordfish in eastern Australian waters and adjacent high seas areas. We have used longline catch and effort data from 1962 to 2000 provided by the Secretariat of the Pacific Community (www.spc.int/oceanfish/html/sctb/data) to place New Zealand swordfish catches in a broader Pacific Ocean stock context. We have divided the southwestern Pacific Ocean into two areas, an Australian province west of 160º E and a New Zealand province extending from the mid-Tasman Sea at 160º E eastwards to 160º W south of 25º S. In the context of analysing swordfish CPUE, the combined areas are defined here as the southwestern Pacific because over 76% of the catch south of the equator has come from this area on average and because swordfish CPUE in this area by 5º latitudinal band is typically a factor of four or more higher than in tropical waters fro 0º to 25º S.

Swordfish catches in the southwestern Pacific by longline have gradually increased since the mid- 1960s off Australia and since the mid-1970s in the New Zealand region. However, the most dramatic increases (see Figure 1) have corresponded to the increased fishing effort by domestic longline fleets in Australia starting in about 1994 and in New Zealand from about 1995. The catches by all fleets in the two areas are of similar magnitude with the most recent catches about 2000 t off Australia and about 1600 t in the New Zealand region. Domestic longliners caught about 1000 t and the remainder was taken primarily by Japanese longliners on the high seas.

Differences between the fleets operating in the two regions are also evident when comparing the longline fishing effort. In the Australian area fishing effort has been roughly stable at 10 to 20 million hooks set per year, while in the New Zealand region effort, declining from the 1970s dramatically increased since 1995. The fact that swordfish catch in the New Zealand region is lower than that in the Australian region despite twice the longline effort reflects the fact that the Australian fishery is a swordfish target fishery while that in the New Zealand region generally targets tuna. This difference in targeting is also evident in Figure 2 showing swordfish catch rates for the Australian and New Zealand regions. Swordfish CPUE (number per 1000 hooks) is typically higher, usually 2–3 times higher, in the Australian compared to the New Zealand region of the southwestern Pacific. Correlation analyses of catch, fishing effort and CPUE also indicates that there is a positive correlation between effort and CPUE in the Australian (r = 0.36, significant at α = 0.05, 39 d.f.) but not in the New Zealand region further demonstrating a difference in target strategies in the two regions.

2 The New Zealand tuna longline fishery

While there is little information to determine the current status of the swordfish stock in the southwestern Pacific Ocean, there have been concerns that increased catches in the EEZ are related to changes in targeting practices by the domestic tuna longline fleet. The only management measure in place in New Zealand for swordfish is a prohibition on targeting them. Despite this targeting ban, over the last decade the use of light sticks and shallow longline sets, characteristics of swordfish targeting in Hawaii (Ito et al 1998), is reported to have increased. The use of light sticks, bait type, moon phase, time of day are all regarded by New Zealand fishers as important factors influencing catch rate of both swordfish and tuna target species. To date there has been no analysis describing the importance of these factors in the catch rate of swordfish relative to the intended target species in the southwestern Pacific Ocean.

Two longline fleets operate in the New Zealand EEZ. A large and diverse fleet of domestic owned and operated longliners uses monofilament mainline and a small fleet of chartered Japanese longliners that are increasingly using 8-strand multi-filament rather than the traditional kuralon mainline.

Longlines are played out as the vessel is steaming while the crew place buoy lines and dropper lines (snoods or gangions) at regular intervals. The buoy line allows the mainline to sink to a predetermined depth and the rate at which the mainline is set allows it to sag so that the hooks fish a broad depth range. The sag in each catenary is determined by the line shooting speed relative to boat speed, fast line setting relative to boat speed sets the mainline deep, setting the line at the same speed as the boat ensures the line will fish relatively shallow depths. Regularly spaced between each buoy are a variable number of snoods, each with a single baited hook or artificial lure. Baits used include squid, and a number of baitfish especially pilchards , and jack mackerel. The depth each hook fishes depends on the sag in the mainline, its position along the catenary, the length of the float lines and snoods, as well as by wind and current in the area. To the extent possible, fishers vary these factors to fish different depth ranges for each target species. In tropical waters hook depth can exceed 400 m (Boggs, 1992). Observers report that fishers in the EEZ usually try to set hooks shallower than 155 m whether targeting bigeye or southern bluefin tunas but try to set some lines as deep as 290 m and as shallow as 50 m. Yano and Abe (1998), using monofilament and multifilament mainlines in tropical waters, demonstrated that multifilament longlines fish deeper (about 50 m) and sink faster (about 2 m/min) than monofilament longlines. The maximum depth they found hooks to reach averaged 211.4 m for multifilament longlines with 10 hooks per basket and 119.8 m for the same material with 5 hooks per basket. Table 1 summarises data on the longline characteristics of observed vessels in each fleet in the New Zealand EEZ since 1989. The average number of hooks per basket varies from 7 (charter vessels) to 10 (domestic vessels). Although we have no direct measurements of hook depth, Yano and Abe’s (1998) results suggest that longlines set in the EEZ are probably fishing at maximum depths of 100–211 m.

The number of hooks each vessel sets in a single fishing operation depends largely on vessel size. Charter vessels average 3028 hooks in each fishing operation while domestic average 1456 hooks per set. Domestic vessels either blast freeze or use an ice slurry to hold their catch and most trips are less than a week long. There is a tendency for larger vessels with blast freezers to make longer trips. Domestic longline vessels primarily target bigeye and southern bluefin tunas throughout the year and average 35–72 days fishing per season. Charter vessels tend to stay at sea for the length of time they fish in the EEZ and blast freeze their catch. Charter vessels typically target southern bluefin tuna primarily during April to July averaging 43–108 days fishing per vessel each season.

Longlining is separated into three stages: setting, soaking and hauling (see Table 1). Setting typically takes 3.6–5.4 hours depending on longline length, charter vessels longlines average 128.2 km while domestic longlines average 59.4 km. The longline is then left to fish or “soak” for 4.5 hours (charter vessels) or 8.7 hours (domestic vessels). After the soak period the vessel retrieves the line by steaming slowly along the line recovering and storing the mainline, removing buoy lines and snoods and processing fish in preparation for the next longline set. Hauling the longline is the longest part of the operation and on average takes 8.4 hours (domestic vessels) to 12.1 hours (charter vessels).

3 METHODS

Tuna longline catch and effort logsheet data, observer data and Licensed Fish Receiver reports, all from the Ministry of Fisheries, were used to determine trends in CPUE, size, sex ratio and total catches of swordfish. CPUE analyses also used fishing operational data from catch and effort logsheets while environmental data was taken from observer data and public domain databases. New Zealand fishers are required as a condition of licensing to report the catch of all species landed which enabled us to include estimates of other bycatch taxa. In cases where species were poorly represented in the logsheet data (eg and species other than swordfish) these taxa were aggregated into “Shark” and “Other Billfish” CPUE categories to minimize the number of zero values in the commercial catch and effort logsheet data.

Environmental variables have frequently been shown to be important in predicting catch rates or explaining changes in distributions of species. While such data are generally not recorded during fishing operations, they can often be derived from ancillary sources. We have incorporated data on bathymetry, moon phase, el niño–la niña events, and sea surface temperature. The spatial and temporal resolution of these data was limited by the data provided on the logsheets to the start of set position and date when fishing was done. Bathymetry was incorporated by co-locating data on bottom depth at the start of each longline set based on the position provided on the catch and effort logsheet linked to data from bathymetric database maintained at NIWA. Since most longline fishing in the EEZ is done at night, the proportion of the illuminated lunar disk (hereafter called moon phase) was used as a proxy for relative light levels during the fishing operation. Moon phase was calculated using the method of Danby (1988) based on Julian date calculated from the start of each longline set date provided on the catch and effort logsheet using the method of Sinnott (1991). The Southern Oscillation Index (SOI) was used as a proxy for ENSO events in these analyses and monthly SOI data were obtained from the US National Weather Service Climate Prediction Center website (www.cpc.ncep.noaa.gov). Sea surface temperatures recorded by fishers on logsheets or when present, by observers were also used.

Commercial tuna longline and observer data were groomed and outliers removed using the catch and effort constraints described by Wei (2003). Analyses were done using commercially available statistical and data manipulation software (Excel and Statistica). Because the datasets used were large (> 26 000 cases for the commercial data; > 3000 cases for observer data) data were deleted on a case- wise basis. The smallest dataset used was that comparing swordfish CPUE when light sticks and bait type were known, although this information has only been collected in sufficient detail over the past few years there were still 375 longline sets available for the analysis of variance.

In cases where the scale of measurement for a variable could influence results, variables were normalized (mean = 0 and standard deviation = 1.0). Variables that were normalised include hooks and depth (units in thousands), sea surface temperature (units in tens) and CPUE (units in 0-50 or so). This approach preserves the data structure and information content of the data for each analysis and was primarily done for the discriminant function analysis.

Initial exploratory data analysis was done over the entire period for which data were available for both the chartered and the domestic owned and operated longline fleets. Foreign licensed longline fleets which have not operated in the Exclusive Economic Zone since 1995 were excluded. Discriminant function analysis was done on commercial catch and effort data for the domestic fleet since 1995, the period when changes in swordfish catches are most evident. Data were further limited to sets reported as targeting either bigeye or southern bluefin tunas. The analysis of variance was limited to observer data from domestic longline sets targeting bigeye tuna since 1997.

In a number of analyses continuous variables were grouped into categories. This was done either for presentational reasons (eg in describing general trends) or because it was required by a particular analysis eg, to generate factors to be used as treatment effects in the analysis of variance or to create grouping variables in the discriminant function analysis. In the case of CPUE, four categories (high, moderate, low, and zero CPUE) were created from cumulative frequency distributions of CPUE for a species with high and low CPUE categories defined by the upper and lower 20% (+/- 1%) of non-zero CPUE values in a given year. In the analysis of variance, factors related to the fishing operation were

4 generated in a similar manner but “high” and “low” categories were defined by the upper and lower 33.3% of the cumulative frequency distribution over the entire period used.

The observer data on use of chemiluminescent light sticks and bait type on domestic longliners was restricted to sets where the specified target was bigeye tuna. This data set included all of the operational and environmental data used in the analysis of commercial logsheet data. Because the number of observations in the different categories was variable we used an unbalanced analysis of variance (ANOVA) on the main effects and did not consider potential interaction effects.

Generalised discriminant function analyses (DFA) allowed the incorporation of both categorical (month and target species) and continuous variables (all others). Examination of descriptive statistics indicated that data while approximately normal exhibited both skewness and kurtosis. However, DFA is generally robust to violations of this assumption. The more significant problems of heterogeneity of variance/covariance matrix across groups and correlations between means and variances across groups were not evident either because these assumptions were not violated in the original data or because standardising the data compensated for any such tendencies.

The general form of the discriminant functions estimated were:

DFAi = b0 + bj*Xj + e where the predictors (Xj ‘s) were:

1. Factors • month – January to December • target species – bigeye tuna, southern bluefin tuna, other species 2. Covariates • proportion of illuminated lunar disk • Southern Oscillation Index • latitude – standardised • fishing effort – standardised • start of fishing (hour) – standardised • sea surface temperature (ºC) – standardised • bottom depth (m) – standardised • albacore CPUE (no. fish/1000 hooks) – standardised • bigeye CPUE (no. fish/1000 hooks) – standardised and the response variable was the grouping variable corresponding to the different categories of swordfish CPUE (no. fish/1000 hooks). The number of discriminant functions estimated to separate groups was one less than the number of groups and in the case where four groups were being discriminated amongst, the significance of each was tested. At each step of the DFA, that variable which had the highest F-to-enter value and had a P-to-enter not less than 0.05 was added to the model. Variables were thus sequentially added to the model based on which contributed the most to the overall variance and these were retained in the model provided they didn’t fall below the F-to-remove or P-to-remove criteria. After running models with and without target species as a factor, comparisons of the mis-classification tables indicated that inclusion of this predictor contributed ≤ 0.5% while substantially adding to the complexity of the models (in the models target species accounted for 2 degrees of freedom and target/month interactions 22 degrees of freedom). Consequently in the DFAs target species was excluded as a factor from further consideration.

RESULTS

Target species

Data are available since 1989 for the chartered longline fleet and since 1991 for the domestic owned and operated fleet. It is unclear how fishers determine the intended target species for a longline set but it is likely to reflect either the predominant species they are likely to catch in a given season or area or the species they primarily land. In the case of the chartered fleet, Japanese vessels and fishing masters reportedly accumulate extensive records of their catches by time and area that can span decades. In

5 this case it seems that target information is linked to particular fishing strategies (Suzuki 1977) and knowledge of the species being sought. Analysis of logbook records supplied to the Ministry of Fisheries since 1989 indicate that 97% of hooks set by the charter fleet are reported as targeting southern bluefin tuna, 2% as bigeye tuna and the remainder are unidentified. Anecdotal reports from observers and fishers on domestic owned and operated longline vessels suggest that this fleet is more likely to fill in target species information based on what they catch rather than what they intend to catch. For the domestic fleet 75% of hooks set are reported as targeting bigeye, 16% as southern bluefin tuna, 7% as albacore and the remaining 2% as other tunas and billfish. Table 2 indicates the probability of a domestic longline set with a reported target species catching that species. Target information only appears reliable when the reported target is either bigeye or southern bluefin tuna, however, it is not clear whether target information is based on what is caught or what is being fished for. We are inclined to accept the target information provided for these two species and to disregard other reported targets. Our reason for doing so is because if target species information was based on what was caught rather than what was intended then we would expect the probability of catching albacore when it was the stated target to be very high. This is clearly not the case in Table 2.

Swordfish CPUE by fleet

The trend in swordfish catches was estimated using a local regression scatter plot smoother, loess (Chambers & Hastie 1993). Figure 3 shows a dramatic increase in swordfish landings since 1995 that coincides with the increasing number of domestic longline vessels fishing primarily for bigeye tuna. In fact for the 13 year period shown in Figure 3 swordfish landings and longline vessels numbers are highly correlated (r = 0.80). While catch is expected to increase with increased fishing effort, Murray et al. (2001) noted a positive linear relationship between swordfish CPUE and effort which could be interpreted as increased targeting of swordfish over time. Figure 4 shows the difference in swordfish CPUE between the chartered and domestic fleets. It is clear from this figure that the increasing trend in CPUE reported by these authors has continued for the domestic fleet while the chartered fleet shows no trend. Swordfish CPUE in this latter fleet is substantially lower (< 30% of domestic CPUE) in the most recent four years. While CPUE is lower in the charter fleet, the size of swordfish caught is substantially larger with fish averaging 40-60% heavier than those caught by the domestic fleet.

We estimated the binomial probability of a set catching at least one swordfish for a longline vessel in the domestic fleet. Figure 5 shows the probability of catching a swordfish (+/- 95% Confidence Interval) each year since 1995. It is clear that over this the period while longline effort has effectively tripled (from 2564 sets in 1995 to 7531 sets in 2001) the probability of a set catching a swordfish has done the same. The increased probability of a set catching swordfish, however, is not linear which might be expected if swordfish catch and effort were independent. Instead the trend in Figure 5 shows a significant exponential increase (r = 0.925) that may be due to increased targeting of swordfish.

Swordfish catch rate by area and season

To determine if there was a seasonal or area component to swordfish CPUE we examined the number of domestic longline sets, regardless of target species, by Fisheries Management Area (FMA) and quarter (Quarter 1 = January to March). Table 3 indicates that swordfish are caught throughout the year but are caught more frequently from January through June (34.2% c.f. 15.3%) when most longline fishing (61%) occurs. Table 4 summarises the frequency of longline sets catching swordfish by FMA. From this table it is clear that most fishing (92.7%) and most of the swordfish catch (47.2%) is within the EEZ in waters north of 41º S, chiefly in FMA1, FMA2 and FMA9. In both cases the percentage of sets fished in an area or quarter and the percentage of sets catching swordfish was highly correlated (r = 0.98 in both cases).

The spatial distribution of sets by swordfish catch rate category (ie zero, low, moderate, and high CPUE) indicated that in each area where longline sets were made, sets could occur in each of the four CPUE categories. However, fewer high CPUE and more low or zero CPUE sets occur in southern waters that are traditionally part of the southern bluefin tuna fishery, in particular FMA3, FMA5 and FMA7. The spatial distribution of low swordfish CPUE is shown in Figure 6 and high swordfish CPUE in Figure 7 to demonstrate this pattern.

6 Factors discriminating between swordfish catch rate categories

Initial exploratory data analysis indicated some differences between high and low swordfish CPUE groups that while showing considerable overlap in a bivariate sense might contribute to a model that explained the difference between swordfish CPUE groups. To determine what combination of variables were most important in distinguishing between swordfish CPUE groups we used discriminant function analysis (DFA) to predict swordfish CPUE group membership (positive vs. zero CPUE sets; high vs. low CPUE sets; and between zero, low, moderate and high CPUE sets).

Discriminant function analysis is a multivariate statistical procedure, related to linear regression, which solves for a linear combination of variables maximizing the separation between groups of known identity. It assumes variables are independent and come from a multivariate normal rather than multi-modal distribution. In practice some variables may be correlated, in which case models incorporating them will have redundant information. Model development in a DFA usually uses a step-wise model building approach to eliminate redundancy, either adding variables according to how much variance they explain (forward stepping) or deleting them from a model with all variables included until a significant loss of variance occurs (backwards stepping).

In this analysis we used a forward stepping approach in the DFA and transformed continuous variables to eliminate measurement scale as a factor so all such variables have a mean = 0.0 and a standard deviation = 1.0. Data from the charter fleet were excluded because swordfish CPUE for that fleet has been stable and considerably lower than domestic owned and operated vessels over time. The domestic data included 26 273 sets after removal of missing and extreme values. Initial analysis used 16 variables related to fishing operation (target species, number of hooks, set start time, latitude, month and bottom depth), environmental factors (sea surface temperature, southern oscillation index, and moon phase), and biological factors that may relate to competition for hooks, predation and/or pelagic community complexity using CPUE of common bycatch taxa (albacore, bigeye, northern + Pacific bluefin, southern bluefin and yellowfin tunas, other billfish species, and ). The original 16 variables were reduced to 11 largely because of the large number of zero values for CPUE for taxa other than albacore and bigeye tuna. Although inclusion of information on target species marginally improved the probability of correctly classifying “Low” CPUE longline sets it was dropped from the final model because it increased the misclassification rate of the “High” CPUE group.

Reasonable separation between groups was accomplished when we discriminated between the “zero” and “non-zero” swordfish CPUE groups (64.6% of 26 273 cases correctly classified) and between “high” and “low” swordfish CPUE groups (72.9% of 5547 cases correctly classified). However, the discrimination between “zero”, “low”, “moderate”, and “high” CPUE groups was very poor at distinguishing groups and most of the “low”, “moderate”, and “high” CPUE groups were mis- classified as belonging to the “zero” CPUE group. This DFA will not be considered further because of its general poor performance. Table 5 gives the classification/mis-classification table for the three DFAs.

Tables 6 and 7 summarise the standardised coefficients contributing to the discrimination between “zero” and “non-zero” swordfish CPUE sets and between “high” and “low” swordfish CPUE sets respectively. Coefficients with larger absolute values have a greater effect on the ability of the DFA to distinguish between groups. The significance of a coefficient is determined from the partial Wilks lambda statistic, this statistic is approximately distributed as an F-distribution. Values for Wilks lambda range from 0.0 (total contribution to perfect discrimination) to 1.0 (no contribution to the discrimination). In both DFAs the coefficient explaining most of the separation between groups is the intercept. In the DFA separating “zero” and “non-zero” swordfish CPUE sets all 10 variables were statistically significant and examining the partial Wilks lambdas in ascending order (Table 6) we can see that the coefficients explaining most of the variance in the DFA are in order of importance: month, set start time, number of hooks set, moon phase, latitude, sea surface temperature, SOI, bigeye CPUE, albacore CPUE, and bottom depth. Similarly for the DFA separating “high” and “low” swordfish CPUE sets (Table 7) we find that only 7 of the 10 variables were statistically significant and in this DFA the coefficients explaining most of the variance are in order of importance: number of hooks set, month, set start time, albacore CPUE, moon phase, bigeye CPUE, and latitude. Sea surface temperature, bottom depth and SOI did not contribute to explaining the difference between “high” and

7 “low” swordfish CPUE sets and were not included in the discriminant function. The untransformed means for these CPUE groups are given in Table 8. Examination of the means of the CPUE groups gives an indication as to why there is a high degree of mis-classification using these functions. Looking at the means in Table 8 for the three most influential variables in separating the “zero” vs. “non-zero” and “high” vs. low groups we note that for both DFAs that the variables are month, number of hooks set and start of set time. Although the differences in these means are relatively small there appears to be: • an inverse relationship between number of hooks and swordfish CPUE suggesting vessels setting fewer hooks (= shorter longlines) have higher catch rates than sets with more hooks; • a positive relationship between start of set time and CPUE such that later sets have higher CPUE; and • a positive relationship between CPUE and month such that higher CPUEs are realised in the March–May period. It should be noted that these differences are small and combined with the overlap in their distributions reflected in the standard deviations, the interpretations are intended as a qualitative description of the factors contributing to the discrimination between groups.

The effect of bait type and light sticks on swordfish catch rate

Data collected by observers, while more limited in extent, provided an opportunity to examine additional factors not available in the commercial logsheet data that are considered important by fishers to maximize catch rates of bigeye tuna and of swordfish. We used a main effects ANOVA to determine which factors were significant in explaining the variance in the CPUE of both swordfish and bigeye tuna. The analysis of variance incorporated a range of factors considered to be important in determining catch rates that relate to seasonality, area, environment, and fishing practices. The factors included were: • month, • latitude, • number of hooks set, • start of set time, • number of hooks per catenary; • soak time, • total time fished; • moon phase, • SOI; • Average hourly SST during fishing; • Albacore CPUE; • Bigeye CPUE (swordfish analysis) or swordfish CPUE (bigeye analysis), • % of bait used that was squid; and the • number of light sticks used per 1000 hooks.

The use of bait types other than squid was not included because nearly all of the sets analysed used either squid or fish or a mixture of the two and these bait types (as a percent of bait types used) were 2 inversely related and highly correlated (R = 0.996; n = 376; fish = –1.01 * squid + 100.01). To avoid confounding light stick use with amount of longline set, the number of light sticks per 1000 hooks set was used to standardise for different lengths of longlines used.

The results of the ANOVA are summarised in Table 9 for swordfish and in Table 10 for bigeye tuna. Of the 14 factors included in the analysis, 7 explained a significant amount of the variance in swordfish and bigeye tuna CPUE. Month, latitude, number of hooks set, set start time, albacore catch rate and number of light sticks were significant factors in explaining the variance in both swordfish and bigeye tuna CPUE. In additional moon phase was a significant factor for swordfish but not bigeye while the length of the fishing operation was significant for bigeye but not swordfish.

The behaviour of the weighted means and their 95% confidence intervals of each factor was examined graphically to determine what accounted for the significant differences. Swordfish CPUE was appreciably higher during March to May when it was more than twice that during other months.

8 Bigeye tuna in contrast was highest (again more than double) in September and October. CPUE of swordfish and bigeye as a function of latitude was variable and while there were clearly some latitudinal bands where CPUE was particularly high, there was no clear trend beyond a tendency for bigeye CPUE to be higher north of 35º S and swordfish CPUE to be high south of 38º S.

There appears to be an inverse relationship between the number of hooks set and CPUE for swordfish and bigeye tuna with high swordfish CPUE on small sets (< 955 hooks) and high bigeye CPUE on large sets (>1263 hooks). The time when longlines are set has a more pronounced and less variable effect on swordfish CPUE than on bigeye CPUE with swordfish CPUE markedly lower on sets starting after midnight and before mid-afternoon. Sets starting during this period have CPUEs that are less than half those starting after 1600 hours. The length of a fishing operation was significant for bigeye but not swordfish with CPUE about double for longline sets fishing longer than 20 hours. Moon light had a marked effect on swordfish but not bigeye with CPUE increasing linearly from the low group (<33.3% illuminated) through the middle to the high group (>66.7% illuminated), the high group being double that of the low group. Catch rate of albacore (the most common commercial bycatch species) was also a significant factor explaining the variance in swordfish and bigeye tuna CPUE but the difference amongst groups is related to low bigeye CPUE when albacore are not caught and apparent high swordfish CPUE when albacore catch rates are low.

The most dramatic effect on CPUE is the use of light sticks. Light stick use is significant for both swordfish and bigeye tuna, although the magnitude of the effect is more marked for swordfish. In the use of light sticks equates with an increase in swordfish CPUE of about four times while for bigeye, light stick use equates with about a 40% increase in CPUE for low to moderate numbers of light sticks per 1000 hooks. For bigeye tuna high numbers of light sticks had the same effect on bigeye CPUE as not using them. Examination of Figures 5 and 6 indicate that in swordfish there is an increasing trend in CPUE with increasing numbers of light sticks per 1000 hooks while in bigeye, although CPUE is slightly higher there does not appear to be such a relationship.

Trends in swordfish size and sex ratio

Swordfish caught in the EEZ by longline since 1987 have been predominantly females. This departure from a 1:1 sex ratio is well known from temperate waters. Figure 10 shows a typical size frequency distribution for male and female swordfish. This size frequency distribution from 2001 observer data demonstrates that males are skewed to smaller sizes (mean lower jaw to fork length = 154 cm) while females are significantly larger (mean = 177 cm). The sex ratio for this sample of 613 fish is 0.39 or about 2.5 times as many female swordfish as males.

To determine if either swordfish size or sex ratio had changed over time we estimated the mean lower jaw to fork length +/- 95% confidence interval by year for the chartered and domestic longline fleets separately again using observer data. Figure 11 shows the trends for the two fleets with the trend line fitted to the mean lengths. In each case a third order polynomial gave the best fit to these data. While neither the charter fleet nor domestic fleet swordfish length time series shows evidence of a change in swordfish size, swordfish caught by the charter fleet are significantly larger, 195.3 cm cf 162.2 cm on average (standard deviations of 41.9 and 41.0; sample sizes of 1294 and 1266 respectively). This is likely to mean that the charter fleet catches proportionately more females than does the domestic fleet. The difference in size between fleets is likely to be attributable to area fished since there is a separation in areas where the two fleets operate. The charter fleet largely operating south of 40º S targeting southern bluefin tuna while the domestic fleet largely fishes north of 40º S, targeting bigeye and to a lesser extent southern bluefin tuna. Figure 12 shows that for the domestic fleet (charter fleet data excluded) there is a trend in swordfish size with latitude with larger swordfish caught south of 40º S where the charter fleet operates. There was no trend in swordfish size with month fished.

The variation in sex ratio for swordfish caught by the charter and domestic fleets is shown in Figure 13. There does not appear to be any trend in the data from these two fleets and overall the sex ratio for swordfish has varied around the long term mean of 0.311 (males:females).

9 METHODS FOR MONITORING SWORDFISH STOCK STATUS

Throughout the Pacific Ocean fishing is generally controlled by national limited entry regimes and national permitting schemes and responsibility for management generally rests with national fisheries administrations. Of the regulations applying to swordfish, New Zealand has in regulation a prohibition on targeting swordfish although its retention as longline bycatch is not limited. The most detailed regulations are those governing longlining in Hawaiian waters (Anon 2002). These regulations are designed to limit turtle bycatch in the swordfish target fishery and detail a range of measures including time and area closures, prohibitions on the number of swordfish to be landed per trip, requirements for specific training of operators in the release and resuscitation of turtles, specifications of snood length, buoy line length, depth hooks must fish, number of hooks per basket and a prohibition on the use of light sticks. In the eastern Pacific Ocean the I-ATTC monitors catches of swordfish but no management measures apply. In the Indian Ocean no management measures for swordfish apply. In the Atlantic Ocean and Mediterranean and Caribbean Seas swordfish are managed by the International Commission for the Conservation of Atlantic Tunas as separate North and South Atlantic stocks with national quotas based on an annual TACs. In addition size limits apply throughout the Atlantic and discards must be estimated (ICCAT 2003). The management objective is to restore swordfish stocks to levels capable of producing MSY within 10 years (2009).

Depending on the management measures, available information on stock status, and the extent of concern over sustainability there are likely to be different requirements for monitoring. Swordfish stock structure in the Pacific Ocean remains uncertain with some evidence (Reeb et al 2000) suggesting a cline in genetic difference around the Pacific basin from Japan to the southwestern Pacific with swordfish there different from those in Japan. However, combining western and eastern Australian samples may be the source of the significant difference seen in these. At present no international body has responsibility for conservation and management measures west of 150 E while the area east of this meridian of longitude corresponds to the area of competence of the Inter- American Tropical Tuna Commission and national fisheries authorities. In the western half of the Pacific Ocean responsibility for conservation and management is solely the responsibility of national fisheries authorities. The regulatory measures that currently apply in the southwestern Pacific are limited for swordfish and include limited entry through licensing for all coastal States in the region and monitoring is primarily through catch and effort logbooks. Additional data for monitoring is collected by scientific observer programmes in New Zealand, New Caledonia (although swordfish are seldom caught in this fishery), and Fiji. The Australian east coast longline fishery, the largest swordfish fishery in the southwest Pacific has no at-sea observer programme or independent verification of catch against landings (Caton 2001).

Currently it is only in ICCAT that monitoring includes more than changes in CPUE derived from commercial logsheet data. In ICCAT size composition of some catches is also undertaken but this is directed at deriving an index of recruitment. At present New Zealand is the only place in the southwestern Pacific Ocean that both catches appreciable numbers of swordfish and has an observer programme. Observer data was used to derive the trends in average swordfish size (lower jaw to fork length) in Figure 11 and in sex ratio in Figure 13. While these figures do not suggest that swordfish stock condition in the EEZ is currently cause for concern they provide examples of how these data provide a baseline that could be used to monitor swordfish stock condition. In addition, there is clear evidence that swordfish size increases with latitude suggesting that deriving a recruitment index from the EEZ is unlikely to be possible. However, the size composition data could be re-analysed to estimate a parental biomass index which could provide evidence of fishing down the older age classes as happened with the southern bluefin tuna stock during the 1960s and 1970s. Monitoring older age classes could be particularly useful if done on a small enough scale to provide an indication of localized depletion. Similarly since females tend to be much larger and more common than males in the EEZ, continued monitoring of swordfish sex ratio should also be continued.

DISCUSSION

In New Zealand waters there are differences in the swordfish catch rates between fleets, season and area that help explain variability in swordfish catch rate. In the EEZ swordfish catch is largely associated with the domestic longline fleet targeting bigeye tuna north of 41º S, especially during the

10 March–May period. Swordfish catch is proportional to longline effort with the proportion of sets fished in an area or in a quarter highly correlated (r = 0.98) with the sets catching swordfish. CPUE is typically low in the charter fleet (average = 0.2 swordfish per 1000 hooks), while in the domestic fleet CPUE has steadily increased since 1995 from 0.4 to 1.6 swordfish per 1000 hooks (Figure 3). Since 1995 domestic longline sets have become increasingly likely to catch swordfish, probability increasing from about 20% to over 50% (Figure 5). The domestic and charter vessels differ in size, fishing gear and practices, primary target species, fishing areas, season and other factors. Domestic vessels tend to be smaller, fish shorter longlines with half the number of hooks, set hooks shallower (longline set with little slack, as well as shorter buoy lines and snoods) and different mainline material relative to charter vessels (Table 1). While the amount of gear set is about half that of the charter fleet, the total time fished on average is equivalent (20 cf 22 hours per set). The charter fleet primarily fishes in the second quarter while the domestic fleet fishes throughout the year. While low and high swordfish CPUE sets occur in the same areas (Figures 6 and 7) most of the sets catching swordfish occur in Fisheries Management Areas FMA1 and FMA2 (Table 4) where bigeye tuna are targeted by the domestic fleet, southern bluefin tuna are also targeted in FMA2 but to a lesser extent. In contrast, the least swordfish are caught in the areas where southern bluefin tuna are targeted by the charter fleet (FMA3, FMA5 and FMA7).

While swordfish CPUE is substantially lower than in the Australian area, the increasing trend in the domestic longline fishery since the start of the fishery suggests that domestic fishers may increasingly be targeting swordfish. It is clear that the domestic fleet is more effective catching swordfish, and continues to increase its effectiveness relative to the charter fleet. To identify the factors contributing to the increasing trend in swordfish CPUE we have used discriminant function analysis to determine if longline sets with differing levels of swordfish catch rate could be distinguished on the basis of area, season, environmental effects and/or operational aspects of the fishing operation. If sets catching swordfish are distinguishable then the factors contributing to the discrimination should give insights about the degree to which fishers are able to target swordfish as opposed to catch rates being determined by seasonal, area or environmental effects.

Discriminant function analyses of swordfish CPUE was done to determine if there was sufficient contrast in the environmental, area, seasonal and operational aspects of longlining between sets catching and those not catching swordfish to have predictive value. A similar analysis was conducted to determine our ability to correctly predict which longline sets would have high as opposed to low CPUE. As noted previously we were only moderately successful in correctly classifying domestic sets catching vs. those not catching swordfish (64.6% of sets correctly classified). A higher proportion of sets were correctly classified when discriminating between those sets with high or low CPUE (72.9% correct out of 5547 sets catching swordfish). This result is probably not surprising since we are likely selecting a more homogeneous group of longline sets than in the zero vs. non-zero CPUE discrimination. The factors contributing most to separating high and low CPUE sets were the number of hooks set, month and set start time. Latitude, moon phase, and the catch rates of albacore and bigeye tuna were statistically significant but contributed little to the discriminant function based on the Wilks lamda statistic while sea surface temperature, bottom depth and southern oscillation index were not statistically significant. Certainly there is a separation between these groups in a multivariate sense that is not evident in bivariate comparisons. Our results suggest that operational factors relating to when fishing is done (seasonal effects and diurnal behaviour of swordfish) together with either a vessel size effect or a specific setting strategy (number of hooks set) are important factors affecting swordfish catch rate in the domestic longline fleet.

Since commercial logsheet data are limited in respect to the variables that are routinely reported we performed an analysis of variance using a smaller data set collected by observers that also included information on bait type and use of light sticks. These latter two factors are reported by fishers to be important in maximizing catch rates of both bigeye tuna and swordfish. For both species the ANOVA results indicate that month, latitude, number of hooks set, set start time, albacore catch rate and the number of light sticks used per 1000 hooks were significant in explaining the variance of CPUE of swordfish and bigeye tuna. In addition moon phase was important in explaining the variance in swordfish CPUE but not for bigeye, while the length of time fished was important for bigeye but not swordfish. The proportion of squid used as bait, widely reported anecdotally as an important factor, was not found to be significant.

11

Our results indicate that operational aspects of fishing, area, and season play an important role in maximizing catch rates of swordfish and of the main target of the domestic longline fleet, bigeye tuna. Of the environmental factors considered only moon phase was significant and then only for swordfish. Light stick use was important for both swordfish and for bigeye but the magnitude of the effect differed with low to moderate light stick use (0 < moderate < 200 per 1000 hooks) increasing bigeye CPUE by nearly 40% relative to not using them or using them more extensively. In contrast light stick use had a much greater effect on swordfish CPUE with a continuous increase over the different CPUE categories. In swordfish low levels of light stick deployment (0 < low < 133 per 1000 hooks) nearly doubling CPUE relative to sets not using light sticks. Higher levels of light stick deployment resulted in swordfish CPUE increases of about four times those of sets not using light sticks. These results together with those showing elevated CPUE with moon phase levels corresponding to increasing light levels, and set times that have the longline fishing throughout the night point to the importance of light levels in determining the CPUE of this visual predator. Ambient light and the diurnal behaviour swordfish must be major determinants of the vulnerability of swordfish to longlines set in the surface layer at night.

Our results differ slightly from those of Bigelow et al (1999) for the Hawaiian longline fishery where swordfish were targeted. The main difference relates to the larger spatial scale of this fishery and the fact that it spans a range of oceanographic features not present in the New Zealand EEZ where swordfish are caught (subarctic and subtropical frontal zones). These authors found that sea surface temperature and mesoscale oceanographic variables related to ocean fronts (not included in our analysis) were significant predictors of swordfish CPUE. In addition they found, as we did, that swordfish CPUE linearly increased over a similar range of light stick use (0–200 per 1000 hooks) to that used in the EEZ but at higher levels they found CPUE was variable and did not increase appreciably. Also similar to our findings they found that latitude and time were significant predictors of swordfish CPUE. The differences in our results appear to be largely due to the different spatial scale over which the Hawaiian fishery operated (inclusion of longitude) and additional environmental variables (inclusion of wind speed and mesoscale oceanographic indices) which we did not consider in this analysis.

In summary, season, area, environment and operational practices all affect swordfish and bigeye CPUE. The main features of these relationships in the New Zealand EEZ are: • Swordfish CPUE was found to be highest during the March to May period while bigeye CPUE is highest in September and October. • Swordfish CPUE tends to be higher south of 38º S while bigeye CPUE tends to be higher north of 35º S. • Swordfish CPUE is higher on small sets (< 955 hooks) while bigeye CPUE is high on longer sets (> 1263 hooks). • Swordfish CPUE is markedly lower (about half) on sets starting after midnight and before noon, the effect on bigeye CPUE is variable. • Swordfish CPUE increases with increasing moonlight and is at its highest around full moon, bigeye CPUE is not related to moon phase. • Swordfish CPUE increases with increasing use of light sticks moderate to high use is associated with CPUE levels about four times those sets not using light sticks; low to moderate use increases bigeye CPUE about 40%, high levels light stick use reduces bigeye CPUE.

While it seems likely that swordfish targeting, either intentionally or not, has been increasing in the domestic longline fishery. Several aspects of longline operations contribute to the increased catch rate of swordfish, including time of setting, setting on or near full moon, number of hooks set and the use of light sticks. Of all the factors affecting swordfish CPUE, the use of light sticks has the most dramatic effect on CPUE. While light sticks also affect bigeye CPUE the effect is much smaller than for swordfish. It also seems that while the increase in swordfish CPUE is in large part driven by light stick use, the average number of light sticks used per set is sufficiently variable that we were unable to detect a significant increasing trend in light stick use over the past five years. This would suggest that only part of the domestic fleet targets swordfish. Bait type, generally regarded by fishers as important

12 in targeting bigeye tuna does not appear to have a significant effect on either swordfish or bigeye CPUE.

Despite increasing catches and apparent targeting of swordfish in the EEZ by domestic longline fishers there is no evidence of declining swordfish catch rate, or change in average size or change in sex ratio that would suggest that swordfish are currently being over fished. In terms of ongoing monitoring of swordfish in the EEZ it is suggested that observers continue collecting length, weight and sex data so that the trend in these biological parameters can be tracked. In addition analysis of discard practices should be closely monitored to insure that size and sex data are comparable over the history of the fishery.

ACKNOWLEDGEMENTS

We thank the Ministry of Fisheries for their support through project SWO2001/01 and for granting access to catch effort logsheet data and observer data. We also thank Rebecca Perrott (FishServe) for providing the Licensed Fish Receiver data on swordfish landings.

REFERENCES

Anon. 2002: Fisheries off West Coast states and in the western Pacific; western Pacific pelagic fisheries; pelagic longline gear restrictions, seasonal area closure, and other sea turtle take mitigation measures. United States Federal Register vol. 67 no 113, Rules and Regulations, pages 40232–40238

Bigelow, K.A.; Boggs, C.H.; He, X. 1999: Environmental effects on swordfish and blue shark catch rates in the US North Pacific longline fishery. Fisheries Oceanography 8: 178–198.

Boggs, C.H. 1992: Depth, capture, and hooked longevity of longline–caught pelagic fish: Timing bites of fish with chips. Fishery Bulletin 90: 642–658.

Caton, A. (editor) 2001: Fishery status reports 2000–2001. Agriculture, Forest and Fisheries – Australia, Bureau of Rural Sciences. vii + 252 p.

Carocci, F.; Majkowski, J. 1996: Pacific tuna and . Atlas of commercial catches. FAO, Rome. Iii + 9p, 28 maps.

Chambers, J.M.; Hastie, T.J. 1993: Statistical models in S. Chapman and Hall, London. xiv + 608p.

ICCAT 2003: Report for the biennial period, 2002–03. Part I (2002) – vol. 2. International Commission for the Conservation of Atlantic Tunas, Madrid, Spain. 216 p.

Ito, R.Y.; Dollar, R.A.; Kawamoto, K.E. 1998: The Hawaii-based longline fishery for swordfish, Xiphius gladius. US Department of Commerce, NOAA Technical Report 142: 77–88.

Murray, T.; Richardson, K.; H. Dean, H.; L. Griggs, L. 1999: New Zealand tuna fisheries with reference to stock status and swordfish bycatch. NIWA and Ministry of Fisheries Unnumbered Report June 1999. v + 126 p.

Reeb, C.A.; Arcangeli, L.; Block, B.A. 2000: Structure and migration corridors in Pacific populations of the swordfish Xiphius gladius, as inferred through analyses of mitochondrial DNA. Marine Biology 136: 1123–1131.

Richardson, K.M.; Murray, T.; Dean, H. 2001: Models for southern bluefin tuna in the New Zealand EEZ, 1998–99. N.Z. Fisheries Assessment Report 2001/18, 21 p. NZ Ministry of Fisheries, Wellington.

13 Suzuki, Z.; Warashina, Y.;Kishida, M. 1977: The comparison of catches by regular and deep tuna longline gears in the western and central Pacific. Bulletin of the Far Seas Fisheries Research Laboratory 15: 51–89.

Ward, P.; Elscot, S. 2000: Broadbill swordfish: Status of world fisheries. Bureau of Rural Sciences, Canberra. xiv + 208 p.

Wei, F. 2003: Database documentation: tuna. NIWA Internal Report. 23 p.

Williams, P. 2002. Estimates of annual catches of billfish species taken in commercial fisheries of the western and central Pacific Ocean. Secretariat of the Pacific Community Standing Committee on Tuna and Billfish Working Paper SCTB15/SWG-3.

Yano, K. and O. Abe. 1998: Depth measurement of tuna longline by using time-depth recorder. Nippon Suisan Gakkaishi 64:178–188.

14 120 NZ SWO (t)(L) AUS SWO (t)(L) NZ Hooks (millions)(R) AUS Hooks (millions)(R) 2000 100

80 1500

60

1000 (t) catch SWO No. hooks (millions) hooks No. 40

500 20

0 0 1960 1965 1970 1975 1980 1985 1990 1995 2000 Year

Figure 1. Loess trend lines of swordfish catch (t) and longline effort (millions of hooks) by all fleets fishing in the New Zealand and Australian regions since 1962.

2.2 NZ SWO CPUE 2.0 AUS SWO CPUE

1.8

1.6

1.4

1.2

1.0

0.8

CPUE (no. per 1000 hooks) SWO 0.6

0.4

0.2

0.0 1960 1965 1970 1975 1980 1985 1990 1995 2000

Year

Figure 2. Loess trend lines for swordfish CPUE (number per 1000 hooks) in the Australian and New Zealand regions since 1962.

15

1200 140

120 1000

100 800

80 600

60 400 40

No. longline vessels fishing

SWO landings (green weight, t) 200 20

0 0

-200 -20 Var2(L) 1988 1990 1992 1994 1996 1998 2000 2002 Var3(R) Year

Figure 3. New Zealand swordfish landings (green weight, t) and number of domestic longline vessels fishing since 1989.

2.00

1.50

1.00 Domestic Charter 0.50

hooks) (no/1000 CPUE

0.00 1988 1990 1992 1994 1996 1998 2000 2002

Year

Figure 4. Swordfish CPUE trends for domestic and chartered longline fleets within the EEZ since 1989.

16

0.600 y = 0.178Ln(x) + 0.229

2 R = 0.925 0.500

0.400

0.300 CI) SWO 95% (+/-

catching sets of Proportion 0.200 1995 1996 1997 1998 1999 2000 2001

Ye ar

Figure 5. The proportion of domestic longline sets (+/- 95% confidence interval) catching swordfish by year.

165o E170o E 175o E 180o 175o W

FMA 10

FMA 1 35o S FMA 9 1000 m

FMA 2 FMA 8 40o S

FMA 7 1000 m

o 45 S FMA 4 FMA 3 1000 m FMA 5

50o S FMA 6

1000 m

Figure 6. Distribution of domestic longline sets where swordfish CPUE was low (lower 20% of the cumulative frequency distribution for a given year) for the period 1995-2001.

17

165o E170o E 175o E 180o 175o W

FMA 1 FMA 10

35o S FMA 9 FMA 2 1000 m

FMA 8 40o S

1000 m FMA 7

o 45 S FMA 4 FMA 3

1000 m FMA 5

50o S FMA 6

1000 m

Figure 7. Distribution of domestic longline sets where swordfish CPUE was high (upper 20% of the cumulative frequency distribution for each year) for 1995-2001.

8

7

6

5

4

3

CPUE SWO mean Weighted 2

1

0 0123 Light sticks per 1000 hooks

Figure 8. Relationship between the number of light sticks used per 1000 hooks and swordfish CPUE (+/- 95% confidence intervals) The four levels correspond to the main effects ANOVA where level 0 = no light sticks; 0 < level 1 < 133; and 200 < level 3.

18

2.0

1.8

1.6

1.4

1.2

1.0

0.8

Weighted mean BIG CPUE

0.6

0.4

0.2 0123 Light sticks per 1000 hooks

Figure 9. Relationship between the number of light sticks used per 1000 hooks and bigeye tuna CPUE (+/- 95% confidence intervals) The four levels correspond to the main effects ANOVA where level 0 = no light sticks; 0 < level 1 < 133; and 200 < level 3.

30

males 25 fem ales

20

15

frequency 10

5

0 70 85 100 115 130 145 160 175 190 205 220 235 250 265 280 295 310

Figure 10. Length frequency distribution of male and female swordfish caught in the New Zealand EEZ in 2001.

19 Domestic fleet

y = 0.05x3 - 1.80x2 + 18.56x + 114.46 300.0 R2 = 0.56

250.0

200.0

150.0

100.0

50.0

(cm) CI mean 95% +/- length 0.0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

Year

Charter fleet

y = 0.25x3 - 3.54x 2 + 7.83x + 220.07 350.0 R2 = 0.63 300.0

250.0 200.0

150.0

100.0

50.0

(cm) CI mean 95% +/- length 0.0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year

Figure 11. Mean swordfish length +/- 95% confidence interval by year and fleet with third order polynomial trend lines through the means.

20

320

300

280

260

240

220

200

180 Lower Jaw-Fork length (cm) 160

140

120

100 30 32 34 36 38 40 42 44 46

Latitude south

Figure 12. Swordfish average length (mean +/- 95% confidence interval) as a function of latitude.

1.00 charter domestic avg M:F = 0.311 0.80

0.60

0.40

Sex ratio (M:F) 0.20

0.00

1989 1991 1993 1995 1997 1999 2001

Year

Figure 13. Variation in swordfish sex ratio by year and fleet.

21 Table 1. Characteristics of longlines used in the New Zealand EEZ since 1989 by fleet.

Longline characteristic Fleet Mean Std. dev. N Mainline length, km Domestic 59.4 34.4 914 Charter 128.2 19.3 2063 Number of hooks per set Domestic 1456 803.8 921 Charter 3028 300.4 2064 Number of catenaries per set Domestic 126 96.5 918 Charter 394 92.5 2064 Buoy line length, m Domestic 11.7 3.3 921 Charter 13.7 1.4 1985 Snood length, m Domestic 11.7 3.0 921 Charter 37.6 2.7 1913 Setting time, hr Domestic 3.6 1.5 921 Charter 5.4 0.6 2064 Soak time, hr Domestic 8.7 3.7 921 Charter 4.5 0.9 2064 Haul time, hr Domestic 8.4 3.1 921 Charter 12.1 1.4 2064

Table 2. The probability that a domestic longline set caught its reported target species.

P(ALB) P(BIG) P(TOR) P(STN) P(YFN) P(SWO) 1995 0.097 0.906 0.000 0.912 0.039 0.004 1996 0.087 0.858 0.067 0.815 0.027 0.000 1997 0.068 0.849 0.065 0.505 0.004 0.000 1998 0.140 0.825 0.014 0.246 0.007 0.002 1999 0.104 0.831 0.032 0.395 0.074 0.005 2000 0.073 0.926 0.011 0.516 0.024 0.000 2001 0.072 0.905 0.010 0.701 0.085 0.000 average 0.088 0.877 0.022 0.584 0.040 0.002

Table 3. Number of domestic longline sets (all targets combined) by swordfish CPUE category and by quarter (Quarter 1 = January to March), 1995-2001.

% sets % all catching Quarter Zero Low Moderate High sets SWO 1 3659 955 2806 972 28.9 16.3 2 4096 918 2930 1333 32.0 17.9 3 3130 782 1670 343 20.4 9.6 4 3747 645 897 105 18.6 5.7 % 50.5 11.4 28.6 9.5 49.5

22 Table 4. Number of domestic longline sets (all targets combined) by swordfish CPUE category and by fisheries management area, 1995-2001.

% sets % all catching Area Zero Low Moderate High sets SWO FMA1 9077 1729 4130 987 54.9 23.6 FMA2 2633 744 2566 1259 24.8 15.8 FMA3 84 1 1 0.3 0.0 FMA4 3 0.0 0.0 FMA5 290 44 18 1 1.2 0.2 FMA6 5 0.0 0.0 FMA7 664 86 70 6 2.8 0.6 FMA8 52 25 42 9 0.4 0.3 FMA9 1529 593 1280 374 13.0 7.8 FMA10 79 37 85 72 0.9 0.7 High seas 150 31 74 36 1.0 0.5 Unknown 66 10 37 9 0.4 0.2 % 50.5 11.4 28.6 9.5 49.5

Table 5. Discriminant function analysis classification matrices for three different swordfish CPUE groupings.

Discrimination between “Zero” and “Non-zero” CPUE Groups

Zero Non-zero Class % correct p = 0.504 p = 0.496 Zero 64.6 85574679 Non-zero 65.4 4515 8522 Total 65.0 1307213201

Discrimination between “High” and “Low” CPUE Groups

Low High Class % correct p = 0.557 p = 0.443 Low 73.7 2277 812 High 71.5 701 1757 Total 72.7 29782569

Discrimination between “Zero”, “Low”, “Moderate”, and “High” CPUE Groups

Zero Low Moderate High Class % correct p = 0.504 p = 0.118 p = 0.285 p = 0.094 Zero 83.6 11065267 1835 69 Low 33.7 4740 108 2525 117 Moderate 4.6 2166 143 766 14 High 4.1 1200 12 1146 100 Total 52.7 19171530 6272 300

23 Table 6. Standardised canonical discriminant function coefficients for the DFA distinguishing between zero and non-zero swordfish CPUE longline sets.

Level of Step Step Wilks Effect Effect entering removed Coefficient lambda F d.f. Signif. Intercept 0.0000 0.7177 10 327.390 1, 26 252 < 0.0001 Latitude 6 -0.3987 0.9927 192.620 1, 26 252 < 0.0001 No. hooks 3 -0.3229 0.9897 274.220 1, 26 252 < 0.0001 Set start time 1 -0.4745 0.9739 703.070 1, 26 252 < 0.0001 SST 2 -0.3642 0.9948 137.490 1, 26 252 < 0.0001 Moon phase 4 -0.2489 0.9925 197.410 1, 26 252 < 0.0001 Bottom depth 10 -0.0515 0.9997 8.020 1, 26 252 0.0046 SOI 7 -0.1887 0.9962 99.760 1, 26 252 < 0.0001 ALB CPUE 9 0.0790 0.9994 15.920 1, 26 252 < 0.0001 BIG CPUE 8 -0.1204 0.9985 38.330 1, 26 252 < 0.0001 Month 15 0.2014 Month 2 -0.1859 Month 3 -0.4288 Month 4 -0.3740 Month 5 -0.4632 0.9384 156.740 11, 26 252 < 0.0001 Month 6 -0.3724 Month 7 -0.1828 Month 8 -0.0931 Month 9 0.3567 Month 10 0.4913 Month 11 0.4530 Eigenvalue 0.1384

24 Table 7. Standardised canonical discriminant function coefficients for the DFA distinguishing between high and low swordfish CPUE longline sets.

Level of Step Step Wilks Effect Effect entering removed Coefficient lambda F d.f. Signif. Intercept 0.0000 0.6564 2893.864 1, 5529 < 0.0001 Latitude 3 & 8 5 -0.0710 0.9992 4.316 1, 5529 < 0.0001 No. hooks 1 -0.7062 0.8875 700.917 1, 5529 0.0378 Set start time 2 0.3518 0.9671 188.235 1, 5529 < 0.0001 SST 0.0000 1.0000 Moon phase 6 0.1657 0.9925 42.035 1, 5529 < 0.0001 Bottom depth 0.0000 1.0000 SOI 0.0000 1.0000 ALB CPUE 5 -0.2436 0.9871 72.228 1, 5529 < 0.0001 BIG CPUE 7 0.1257 0.9963 20.386 1, 5529 < 0.0001 Month 14 -0.1450 Month 24 -0.0147 Month 34 0.4176 Month 44 0.4384 Month 54 0.4723 0.8951 58.900 11, 5529 < 0.0001 Month 64 0.2624 Month 74 0.1305 Month 84 -0.0109 Month 94 -0.3348 Month 10 4 -0.3322 Month 11 4 -0.2027 Eigenvalue 0.3876

25 Table 8. Descriptive statistics for the raw variables used in the DFA by CPUE group.

Variable CPUE Group N Mean Min. Max. Std. dev. Skew. Kurt. Latitude Zero 13236 -36.1 -48 -25 2.80 -1.36 2.93 non-zero 13037 -36.1 -48 -25 2.28 -0.21 0.71 Low 3089 -35.9 -48 -29 2.50 -0.83 1.82 Moderate 7490 -36.0 -46 -28 2.19 -0.15 0.23 High 2458 -36.5 -44 -25 2.21 0.63 0.64 No. hooks set Zero 13236 1037.1 50 3850 409.78 2.51 11.61 non-zero 13037 1097.2 50 4000 361.00 2.45 13.56 Low 3089 1271.6 900 4000 425.06 3.58 14.48 Moderate 7490 1057.2 250 3750 324.03 2.05 11.42 High 2458 999.8 50 2400 302.81 -0.18 0.79 Start of set (hr) Zero 13236 8.8 0 23 9.40 0.54 -1.58 non-zero 13037 12.3 0 23 9.73 -0.22 -1.84 Low 3089 11.1 0 23 9.90 0.03 -1.90 Moderate 7490 12.1 0 23 9.78 -0.16 -1.86 High 2458 14.4 0 23 9.02 -0.74 -1.29 SST Zero 13236 18.4 10.0 29.2 2.13 -0.29 1.08 non-zero 13037 18.9 10.0 28.8 1.87 0.03 0.60 Low 3089 18.6 10.0 24.9 1.90 0.01 0.20 Moderate 7490 18.9 10.0 27.7 1.88 0.03 0.73 High 2458 19.1 10 29 1.78 0.13 0.72 Moon phase Zero 13236 0.5 0.0 1.0 0.29 0.07 -1.12 non-zero 13037 0.5 0.0 1.0 0.29 -0.13 -1.08 Low 3089 0.5 0.0 1.0 0.29 -0.05 -1.11 Moderate 7490 0.5 0.0 1.0 0.29 -0.14 -1.07 High 2458 0.6 0 1 0.28 -0.23 -1.06 Bottom depth 13236 -1844.3 -5875 -201 780.46 -1.09 2.56 (m) Zero non-zero 13037 -1801.3 -7604.5 -200.7 839.82 -0.99 1.57 Low 3089 -1876.5 -5756 -208 814.41 -0.91 1.19 Moderate 7490 -1805.4 -7605 -201 823.78 -1.04 2.07 High 2458 -1694.3 -6405 -204 906.31 -1.03 0.94 SOI Zero 13236 0.2 -3.5 2.0 1.03 -0.76 0.90 non-zero 13037 0.2 -3.5 2.0 1.06 -1.06 1.69 Low 3089 0.2 -3.5 2.0 1.04 -0.92 1.16 Moderate 7490 0.2 -3.5 2.0 1.05 -1.05 1.69 High 2458 0.1 -4 2 1.10 -1.22 2.12 Albacore CPUE Zero 13236 30.1 0.0 483.3 42.47 3.13 14.78 non-zero 13037 28.8 0.0 469.0 38.49 2.93 13.49 Low 3089 28.4 0.0 406.7 38.33 2.74 10.60 Moderate 7490 29.0 0.0 337.5 37.93 2.64 9.52 High 2458 28.6 0 469 40.38 3.86 25.76 Bigeye CPUE Zero 13236 1.3 0.0 30.0 2.35 3.72 22.58 non-zero 13037 1.2 0.0 27.3 2.04 3.30 17.96 Low 3089 1.2 0.0 22.5 1.96 3.15 16.93 Moderate 7490 1.2 0.0 21.2 2.03 2.87 12.08 High 2458 1.1 0 27 2.20 4.45 31.63

26 Table 8. continued

Variable CPUE Group N Mean Min. Max. Std. dev. Skew. Kurt. Swordfish CPUE Zero 13236 0.0 0.0 0.0 0.00 0.00 0.00 non-zero 13037 2.8 0.3 64.3 3.05 6.19 79.49 Low 3089 0.8 0.3 1.1 0.15 -0.75 1.56 Moderate 7490 2.1 0.9 4.4 0.86 0.63 -0.59 High 2458 7.2 3 64 4.66 5.66 49.82 Month Zero 13236 6.4 1 12 3.55 0.05 -1.23 non-zero 13037 5.4 1 12 3.00 0.56 -0.52 Low 3089 6.1 1 12 3.36 0.19 -1.11 Moderate 7490 5.3 1 12 3.00 0.54 -0.53 High 2458 4.6 1 12 2.20 1.06 1.45

Table 9. Main effects ANOVA table for swordfish CPUE, significant factors are in bold.

df SS MS F P Month 9 198.323 22.036 4.378 0.00002 Latitude 9 102.667 11.407 2.266 0.01795 no. hooks set 2 43.299 21.650 4.301 0.01433 Hooks per catenary 2 14.939 7.470 1.484 0.22827 Set start time 3 85.304 28.435 5.649 0.00088 Soak time 2 16.688 8.344 1.658 0.19218 Fishing time 2 11.131 5.565 1.106 0.33223 Moon phase 2 51.692 25.846 5.135 0.00638 SOI 2 28.477 14.238 2.829 0.06055 SST 2 12.709 6.354 1.262 0.28436 BIG CPUE 3 14.685 4.895 0.973 0.40588 ALB CPUE 3 62.223 20.741 4.121 0.00688 % squid bait 3 8.924 2.975 0.591 0.62128 Light sticks per 1000 hooks 3 105.674 35.225 6.998 0.00014 Error 325 1635.881 5.033 Total 372 4756.195

27 Table 10. Main effects ANOVA table for bigeye tuna CPUE, significant factors are in bold.

df SS MS F P Month 9 39.586 4.398 2.793 0.003619 Latitude 9 31.694 3.522 2.236 0.019629 no. hooks set 2 22.664 11.332 7.195 0.000876 Hooks per catenary 2 3.690 1.845 1.172 0.311196 Set start time 3 44.913 14.971 9.506 0.000005 Soak time 2 1.841 0.921 0.585 0.557952 Fishing time 2 42.003 21.001 13.335 0.000003 Moon phase 2 3.489 1.744 1.108 0.331591 SOI 2 8.897 4.449 2.825 0.060786 SST 2 2.799 1.400 0.889 0.412214 BIG CPUE 3 3.414 1.138 0.722 0.539177 ALB CPUE 3 12.956 4.319 2.742 0.043279 % squid bait 3 6.438 2.146 1.363 0.254163 Light sticks per 1000 hooks 3 22.542 7.514 4.771 0.002867 Error 325 511.864 1.575 Total 372 1222.031

28