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Marine and Freshwater Research, 2003, 54, 409–417

Spatio-temporal distribution of longline catch per unit effort, sea surface temperature and Atlantic marlin C. Phillip Goodyear 415 Ridgewood Road, Key Biscayne, FL 33149, USA. Email: [email protected] Abstract. Atlantic blue and white marlin are currently overfished, primarily as a result of bycatch in pelagic longlines directed at other species. One possible management measure to reduce fishing mortality on these species would be to restrict fishing effort in times and places with exceptionally high marlin catch per unit effort (CPUE). The International Commission for the Conservation of Atlantic Tunas maintains a database of catch and catch-effort statistics of participating nations. These data were analysed to determine whether the distribution of CPUE is sufficiently heterogeneous in time and space that such measures might provide meaningful management alternatives. The resulting distributions of catch rates were also contrasted with monthly average sea surface temperatures to examine the possible association between temperature and CPUE. The results show spatio-temporal heterogeneity in catch rates that may be partly explained by seasonal changes in sea surface temperatures. The time–area concentrations of high CPUE differ between the species. This observed heterogeneity might be exploited to develop alternatives for reducing fishing mortality for future management of the fisheries, but additional research is needed to refine the spatial scale of the analysis and to more fully understand the factors contributing to the observed distribution. Extra keywords: blue marlin, bycatch, catch effort, seasonal variations, white marlin.

Introduction The most recent stock assessment for Atlantic blue and white marlin (Makaira nigricans and Tetrapturus albidus respectively) found them to be overfished, primarily as a result of bycatch on pelagic longlines targeting tunas and swordfish (Anonymous 2000). Bycatch fishing mortality poses a special problem because management institutions are generally more focused on the target species, and tend to ignore bycatch species even to the point of near extinction (Casey and Myers 1998). This problem is especially severe when the population of the bycatch species is more susceptible to overfishing than the population of one or more of the target species, which is the likely situation for Atlantic marlins (Goodyear 2001). Because of the current condition of these stocks, the International Commission for the Conservation of Atlantic Tunas (ICCAT) Scientific Committee on Research and Statistics recommended that management take steps to reduce the catch of both species as much as possible. These recommendations, among other possible actions, included the establishment of time–area closures to restrict fishing effort in times and places with exceptionally high marlin catch rates. This notion is based on the premise that species preferences for physical attributes of their habitat may be exploited to reduce catchability in the population as a whole. These features might include factors such as water depth that are strictly a function of geography, or be a function © CSIRO 2003

of variables such as water current or temperature that may vary seasonally. For example, de Sylva (1990) argued that temperature is one of the chief determinants of the distribution in abundance of blue marlin, and Hinton and Nakano (1996) model the depth distribution of Pacific blue marlin as a function of the difference between temperature at depth and the sea surface temperature (SST). This paper presents a preliminary analysis of ICCAT catch-effort (task ii) data to determine if the monthly average catch per unit effort (CPUE) distributions are sufficiently heterogeneous that time–area closures might provide meaningful management alternatives, and examines their relationship to monthly average SST. Methods The catch-effort records for blue and white marlin were extracted from the ICCAT catch-effort longline database (ICCAT task ii data through revision updr40 of the database). Each record provided latitude and longitude, country of origin for the vessel, year, month, number of hooks deployed and the number of each species caught. Inspection of the resulting data indicated a variety of metrics used to quantify the effort and the record set was further limited to those records where the effort was measured in numbers of hooks. The CPUE was then calculated as the ratio of the number of fish caught to the number of hooks deployed for each species. The population of both blue and white marlin in the Atlantic is known to have declined during the time interval represented by the data. In addition, the fleets of different nations target different species, and there are temporal trends in species targeted within various nations. 10.1071/MF01255

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Catch-effort data for 1982–1997 were selected for analysis. A general linear model (GLM) was employed with year and country as fixed factors to standardize the CPUE series by removing the year, country, and the year × country effects from the data series. This was achieved by substituting the ‘standardized’ GLM residuals for the raw catch per hook values. The resulting data were placed into bins of months and latitude–longitude geographic cells. Monthly means were derived for each latitude–longitude cell. These values were sorted to obtain the cumulative frequency distribution of the mean standardized CPUE for each species by latitude, longitude and month. The data for each time– area cell was then plotted with a pattern identifying its location within the cumulative frequency distribution. The annual monthly average SST by 1◦ of latitude and longitude was obtained for 1982–1997 from the International Research Institute for Climate Prediction at Columbia University (Reynolds and Smith 1994). These data were averaged by 1◦ of latitude and longitude for each month over the years available, and the resulting data were plotted using a colour code to represent temperature. Also, the standardized CPUE by year, month and 5◦ latitude–longitude cell were paired with the corresponding SST to evaluate the relationship between CPUE and temperature for those time–area strata with positive catches of each species.

Results The ICCAT longline data set provided 11 430 catch per hook records in time-area strata with positive catches of blue marlin, and 9143 catch per hook records in time-area strata with

positive catches of white marlin, for the time interval 1982– 1997 (the most recent compilation available at the time of the analysis). As expected, the GLM results showed year, country and year × country effects to be significant for both species (Tables 1 and 2) and the CPUE series was standardized to remove these effects. Because the geographic resolution for most of the data was to 5◦ latitude and longitude, this scale was selected for analysis. The averages of the standardized CPUE in each latitude–longitude-month cell were sorted by value, and the magnitudes corresponding to the 95th, 80th, and 50th percentiles of the distribution were determined. The resulting information was then plotted for each month with the relative magnitude of the catch rate in each time-area cell indicated by its associated pattern along with the mean SST (Figs 1 and 2). Areas on Figs 1 and 2 that are not enclosed by a 5◦ box did not have positive catches for the particular species within the ICCAT longline catch-effort data selected for analysis. Fishing effort exists in many of these cells and some marlins are probable caught as a result. However, it seems likely that such areas would tend to be toward the lower end of the actual cumulative frequency distribution of catch rates and would not distort the time–area pattern of higher catch rates observed in these figures.

Table 1. Results of general linear model analysis of the International Commission for the Conservation of Atlantic Tunas (task ii) Atlantic blue marlin longline catch per unit effort (catch per hook) with country and year as main effects Tests of between-subjects effects Source

Type III sums of squares

df

Mean square

F

Significance

Model Country Year Country × year Error Total

3.253E – 03a 9.718E – 05 9.417E – 04 1.978E – 03 1.300E – 02 1.625E – 02

82 11 15 55 11 348 11 430

3.967E – 05 8.835E – 06 6.278E – 05 3.596E – 05 1.146E – 06

34.630 7.712 54.801 31.386

0.000 0.000 0.000 0.000

a R2

= 0.200 (adjusted R2 = 0.194).

Table 2. Results of general linear model analysis of the International Commission for the Conservation ofAtlantic Tunas (task ii)Atlantic white marlin longline catch per unit effort (catch per hook) with country and year as main effects Tests of between-subjects effects Source

Type III sums of squares

df

Mean square

F

Significance

Model Country Year Country × year Error Total

7.537E – 05a 8.558E – 06 1.697E – 06 5.920E – 06 4.422E – 04 5.176E – 04

82 11 15 55 9061 9143

9.191E – 07 7.780E – 07 1.131E – 07 1.076E – 07 4.881E – 08

18.832 15.941 2.318 2.205

0.000 0.000 0.003 0.000

a R2

= 0.146 (adjusted R2 = 0.138).

Longline effort and Atlantic marlin

Seasonal changes in the distribution of SST are evident in Figs 1 and 2, as are seasonal shifts in the areas of highest standardized CPUE. There is a northward expansion of the time–area cells with high CPUE values during the summer in the northern hemisphere and a southward movement in the summer in the southern hemisphere for both species. Most of the cells with the highest catch rates for blue marlin tend to be in the areas where mean SST is about 26–27◦ C. White marlin seem to have a somewhat broader thermal preference, with high catch rates in waters with average SST from about 22–29◦ C. However, it is also clear that SST does not completely explain the catch rate distributions for either species. Some areas with mean temperatures lower than what appears to be optimum have catch rates in the upper 50th percentile of the cumulative frequency distribution. The process of averaging both CPUE and temperature may have obscured the underlying relationship between temperature and abundance shown in Figs 1 and 2. However, most time–area cells within what appears to be the optimum temperature ranges for each species and near the centre of the geographical distributions, have catch rates in the lower 50th percentile of the cumulative frequency distribution. The frequency distributions of positive catches and mean CPUE by mean monthly SST in the 5◦ square at the time of capture also indicate the presence of both species over a broad range of temperatures (Figs 3 and 4). The frequency distributions are influenced by different total effort in each temperature stratum, and are unlikely to reflect the actual distribution of either species with respect to temperature. The mean CPUE for blue marlin shows a bimodal distribution, with one peak at 15◦ C and another at 30◦ C, the highest temperature at which blue marlin were caught. Mean CPUE for white marlin was similarly distributed, with a peak at 29– 30◦ C and another at about 23◦ C, but the effect was not as pronounced as with blue marlin. Although CPUE for white marlin was somewhat higher at intermediate temperatures, it was fairly similar from 15◦ C to 28◦ C. Discussion Hinton and Nakano (1996) model the depth distribution of blue marlin as a function of water temperature at the surface. The results here cannot test their hypothesis, but since CPUE declines as SST falls below about 15◦ C, they do support the notion that temperature is an important determinant of the distribution of both blue and white marlin in the Atlantic. However, the effect does not appear to be nearly so strong as hypothesized by de Sylva (1990). The relatively high CPUE observed in areas with relatively low mean SST may be partly explained by the fact that fishermen often selectively set along oceanographic features such as current edges that have temperatures higher than the average for the surrounding water. Some of this variability might also reflect the expression of sexually dimorphic and/or size–age differences in thermal


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preferences. However, it seems clear that both species occur over a relatively wide range of environmental temperatures. It is also clear that other factors contribute to the distribution, as reflected in the catch rate pattern. Local depletions in areas of high effort may cause downward bias in CPUE where it might otherwise be high. Also, there may be some residual gear effects not completely removed by the standardization. Nonetheless, it is likely that other factors, perhaps related to prey abundance or current patterns, also play important roles in marlin habitat. The distributions of standardized catch rates derived from the ICCAT catch-effort data do not presuppose which environmental factors contribute to the observed distributions, but rather reflect the outcome of all factors involved. As a result, the actual time–area distributions of high catch rates provide the most reliable indicators of preferred habitats, and best identify potential times and areas for closure or other restrictions on fishing to reduce marlin bycatch. Closed areas have been used in many parts of the world to control bycatch mortality (Alverson et al. 1994). Marine reserves (also called marine protected areas) have also been promoted as management tools to enhance conservation of fishery resources (Bohnsack 1994; Shackell and Wilson 1995; Hutchings 1995, 1996; Allison et al. 1998; Lauck et al. 1998). Hutchings (1996) noted that marine reserves might have considerable merit in reducing bycatch when assessed against the effectiveness of other forms of regulatory control such as bycatch limitations and catch quotas. Often, however, the concept of marine reserves is restricted to closures of particular habitats or portions of habitats. Kenchington (1990) noted that such marine reserves would be of little use for species such as billfish that have both pelagic larvae and highly pelagic adults. Among other options for reducing bycatch, Alverson et al. (1994) observed that time–area control of fishing activity offers an opportunity to reduce unwanted bycatch. The main objective of employing time-area strategies is to take advantage of variation in the catchability of target and bycatch species (Murawski 1992). The current results indicate the presence of spatial heterogeneity in the distribution of catch rates of marlins in the Atlantic that may provide the opportunity for time–area closures to selectively protect these species from overfishing. These findings are similar to previous results that only considered data from the US longline fishery (Goodyear 1999). However, as with the previous study, considerable additional research will be required before it can be concluded that time–area restrictions on the fishery would provide meaningful conservation measures. Murawski (1992) pointed out that bycatch reduction plans involving time-area manipulation of the fishery should be economically viable and the proposed programme must be effectively implemented and enforced. The effectiveness of such controls obviously depends on the degree of overlap between the bycatch and the targeted species (Adlerstein and Trumble 1992). The identification of realistic management

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C. P. Goodyear

Fig. 1. Spatio-temporal distribution of Atlantic blue marlin longline catch per unit area and mean sea surface temperatures. Patterns indicate the position of monthly 5◦ latitude–longitude cells in the cumulative frequency distribution of standardized longline catch per unit area. The colour scheme depicts monthly mean sea surface temperatures.



Fig. 1. (continued)

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C. P. Goodyear

Fig. 2. Spatio-temporal distribution of Atlantic white marlin longline catch per unit area and mean sea surface temperatures. Patterns indicate the position of monthly 5◦ latitude–longitude cells in the cumulative frequency distribution of standardized longline catch per unit area. The colour scheme depicts monthly mean sea surface temperatures.



Fig. 2. (continued)

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2400 Positive cells Frequency

1800

1200

600

0

N/10 000 hooks

Standardized CPUE 3.6

2.4

1.2

0.0 10

12

14

16 18 20 22 24 26 Sea surface temperature (ºC)

28

30

Fig. 3. Frequency of monthly 5◦ cells with positive blue marlin catch and mean standardized catch per unit effort (CPUE) by mean sea surface temperature at the time of capture, 1983–1996.

measures must specify reasonable, contiguous geographic areas where pelagic longline closures would be both practical and of the greatest benefit to increased billfish survival, hopefully with minimum effect on other fisheries. Further evaluations of possible time-area closures for the protection ofAtlantic marlins should consider the distribution of the ratios of marlin to target species in order to identify those cells where closures would have the least impact on directed fisheries. The potential gains of increased marlin survival and any concomitant reductions in catches of target species must also be quantified, including the evaluation of the impacts of the effort displaced by time–area closures. The identification of viable management options will also require additional research to refine the spatial scale of the analysis. Because most of the existing data is pooled by 5◦ latitude and longitude, such an effort will require new analyses of the raw data from each of the nations contributing to the ICCAT database. It might also prove valuable to evaluate the potential to simultaneously optimize time-area closures for all ICCAT species that might benefit from reduced fishing mortality; however, such efforts pose daunting challenges. Acknowledgments I thank K. Davy and R. Nelson for helpful comments on the draft manuscript. The Billfish Foundation supported this work. References

2000 Positive cells Frequency

1500

1000

500

0

N/10 000 hooks

Standardized CPUE 2.4

1.6

0.8

0.0 10

12

14

16 18 20 22 24 26 Sea surface temperature (ºC)

28

30

Fig. 4. Frequency of monthly 5◦ cells with positive white marlin catch and mean standardized catch per unit effort (CPUE) by mean sea surface temperature at the time of capture, 1983–1996.

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Goodyear, C. P. (2001). Atlantic blue marlin and yellowfin tuna: comparative population vulnerability to fishing mortality. American Fisheries Society Symposium 25, 219–24. Hinton, M. G., and Nakano, H. (1996). Standardizing catch and effort statistics using physiological, ecological, or behavioral constraints and environmental data, with an application to blue marlin (Makaira nigricans) catch and effort data from the Japanese longline fisheries in the Pacific. Bulletin of the International Commission for Conservation of Atlantic Tunas 21, 171–200. Hutchings, J. A. (1995). Seasonal marine protected areas within the context of spatial–temporal variation in the northern cod fishery. In ‘Marine Protected Areas and Sustainable Fisheries’. (Eds N. L. Shackell and J. H. M. Willison.) pp. 39–53. (Science and Management of Protected Areas Association: Wolfville, Nova Scotia.) Hutchings, J. A. (1996). Spatial and temporal variation in the density of northern cod and a review of hypotheses for the stock’s collapse. Canadian Journal Fisheries and Aquatic Science 53, 943–62.

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Kenchington, R. A. (1990). ‘Managing Marine Environments.’ (Taylor and Francis: New York.) Lauck, T, Clark, C. W., Mangel, M and Munro, G. R. (1998). Implementing the precautionary principle in fisheries management through marine reserves. Ecological Applications 8 (Suppl.), S72–8. Murawski, S. A. (1992). The challenges of finding solutions in multispecies fisheries. In ‘Proceedings of the National Industry Bycatch Workshop, 4–6 February 1992, Newport, Oregon.’ (Eds R. W. Schoning, R. W. Jacobson, D. L. Alverson, T. G. Gentle and Jan Auyong.) pp. 35–45 (Natural Resources Consultants: Seattle, WA.) Reynolds, R. W., and Smith, T. M. (1994). Improved global sea surface temperature analyses. Journal of Climate 7, 929–48. Shackell, N. L., and Willison, J. H. M. (Eds) (1995). ‘Marine Protected Areas and Sustainable Fisheries.’ (Science and Management of Protected Areas Association: Wolfville, Nova Scotia.) Manuscript received 5 September 2001; revised and accepted 21 March 2002.

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