Estimate of catch for silky shark (Carcharhinus falciformis) caught by Japanese longline vessel in the EPO from 2000 and 2010.

Yasuko Semba (National Research Institute of Far Seas Fisheries)

This is the first report of catch estimated for silky shark (Carcharhinus falciformis) caught by Japanese longline fishery operating in the EPO. Silky shark is distributed in the tropical waters and abundant in offshore, pelagic and littoral areas (Compagno 1984). This species has been treated as bycatch in Japanese longline vessel operating in the EPO and the catch number (or weight) of this species has been combined with other sharks and recorded as “sharks” until 1993. Japan developed a new log-book reporting system in the longline fishery to record the catch of sharks in 6 categories (blue shark, shortfin mako, porbeagle/salmon shark, oceanic whitetip shark, thresher sharks, and other sharks) in 1997. In logbook system, this species is included in the “other sharks”, which combines the species with some commercial value. Silky shark is utilized for human consumption in various areas (Compagno 1984) and the record of fishery training vessels in Japan indicates that silky shark has been utilized to some extent, which suggests that this species has some value to commercial longline vessels, too. To estimate the catch of silky shark from aggregated catch of “other sharks”, we checked the species composition of sharks reported by training vessels conducting tuna longline operation in the EPO. By applying the ratio of silky shark in the catch of “other shark with some commercial value” (i.e. other than 5 categories indicated above), the catch number of silky sharks by Japanese commercial longline vessels operating in the EPO was estimated from 2000 and 2010.

Materials and Methods For the estimation, we used the catch and effort data for tuna longline in the EPO between 2000 and 2010. As there was time-lag between the promulgation of new logbook system (6 categories of sharks) and the commencement of this reporting rule by all commercial vessels, which is mainly due to long trip (one-two years) away from Japan in these vessels, we regarded it is after around 2000 that this new reporting style was actually implemented.

Data screening Catch of sharks in Japanese logbook data contains varying degree of precision (Nakano and Honma 1996), mainly because sharks are often treated as bycatch because of lower commercial values than tunas and billfishes, and the decision to land sharks (i.e. record of catch) is different depending on species and vessels. Matsunaga (2007) assumed the data of cruise with reporting rate (i.e. the number of operations with positive shark catch per total number of operations) higher than 80% contains information on catch of all commercially important sharks. We followed this criteria and extracted catch and effort data with reporting rate higher than 80% with additional filtering for this data.

Area stratification We arbitrarily divided the EPO area into 4 subareas based on the spatial pattern of nominal CPUE of “other sharks” in the filtered dataset, after aggregating year (Figure 1).

Calculation of species composition Using the data of tuna longline training operations conducted by fishery high schools and research vessels from 2000 to 2010, the species composition per operation was checked. Here, we focused on the proportion of silky shark to the sharks with some commercial value other than sharks of 5 categories indicated above (i.e. other than blue shark, porbeagle/salmon shark, shortfin mako, oceanic whitetip shark and thresher sharks). For these sharks, we checked whether each species was retained or not and selected the species which tends to be retained in the vessels. As a result, the proportion of silky shark in the sharks with less commercial value was; 91% for Area1, 92% for Area2, 92% for Area3, and 75% for Area4.

Results Table 1 indicates the total number of hooks used for estimation and the estimated catch (in number) of silky sharks caught by commercial Japanese longline fishery in the EPO. In total, the effort in the filtered dataset occupied 16.7% of total effort (i.e. before filtering) recorded in this area for 11 years. The trends of catch for silky shark by areas were shown in Figure 2. As indicated in this figure and table 1, the total catch from 2004 and 2006 was quite low. With regard to this, the shift of effort to the Indian Ocean targeting for yellowfin and the closure of tuna longline fishery due to economic reason may partly explain the decline of effort and estimated catch. After 2007, both the filtered effort and estimated catch increased, which is mainly due to the temporal change of the reporting rate (i.e. the increase of vessels which retain the shark and report the catch of shark properly). In the middle of 2008, the domestic rule to encourage full utilization of sharks caught by longline was issued and fishermen followed this because the catch of tunas and billfish was apparently decreasing in recent years.

Before 2003, silky shark was suggested to be caught in not only in Area 3 and Area4 but also Area1 and Area2, while exclusively recorded in Area3 and Area4 after 2007. The continuous decrease of effort in Area2 corresponds to this change (Figure 3), while further investigation is necessary to interpret the temporal change of effort and catch in Area1. In this document, we roughly estimated the catch of silky shark from the record by Japanese longline fishermen who were supposed to retain and record the catch of this species. As the amount of dead discard and underreporting was not considered in this report, some attention and/or correction (substitution) would be needed to this preliminary estimation when using this data for stock assessment. Further refinement of methodology for estimation, the investigation on the mortality at haul-on

deck and post-release, and the collect ion of research and/or observer data for basic information on the distributional pattern (including species composition in other shark species) would improve this estimation.

References Compagno LJV 1984. FAO species catalogue, Vol. 4: Sharks of the world; Part 2 Carcharhiniformes. Food and Agricultural Organization of the United Nations. Rome, Italy. 655p. Matsunaga H. 2007. Standardized CPUE for blue sharks caught by the Japanese longline fishery in the Indian Ocean, 1971-2005. IOTC-2007-WPEB-17 Nakano H and Honma M. 1996. Historical CPUE of pelagic sharks caught by the Japanese longline fishery in the Atlantic Ocean. ICCAT CVSP 46 (4):393-398.

Table 1. Total number of hook used in the estimation (filtered) and the estimated catch for silky sharks by year (above) and by year and area (below).

yr hook No. (filtered) catch (estimated) 2000 14,111,177 3,415 2001 16,854,739 2,137 2002 14,623,469 1,035 2003 12,620,800 1,608 2004 8,606,575 77 2005 7,332,208 172 2006 4,578,173 67 2007 4,753,136 1,633 2008 7,424,846 2,394 2009 10,730,726 4,118 2010 12,929,747 3,452

total 114,565,596 20,107

Hooks Area Catch No. Area yr 1 2 3 4 yr 1 2 3 4 2000 2,208,467 745,053 7,024,192 4,133,465 2000 822 398 1,728 467 2001 1,810,182 2,328,857 6,554,363 6,161,337 2001 477 500 1,032 128 2002 2,437,205 556,945 6,499,596 5,129,723 2002 507 51 412 65 2003 2,057,738 607,917 5,835,715 4,119,430 2003 151 156 642 659 2004 1,785,350 263,330 3,876,730 2,681,165 2004 20 1 42 14 2005 2,085,657 324,892 2,563,541 2,358,118 2005 5 84 74 9 2006 2,027,116 120,420 1,547,202 883,435 2006 30 0 28 9 2007 614,823 42,042 1,846,331 2,249,940 2007 1 15 720 897 2008 382,106 15,240 3,812,600 3,214,900 2008 23 15 1,724 632 2009 470,433 6,604 5,084,881 5,168,808 2009 57 7 2,855 1,199 2010 734,770 45,817 7,430,043 4,719,117 2010 90 34 2,778 549

Area1 Area2

Area3 Area4

Figure 1. Area stratification used for check of species composition of sharks.

4500 4000 3500 3000 Area 4 2500 Area 3 2000 Area 2

Catch NumberCatch 1500 Area 1 1000 500 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year

Figure 2. Estimated catch number of silky shark from filtered logbook data of Japanese tuna longline fishery by area.

60,000,000 Area1 50,000,000 Area2 Area3 40,000,000 Area4 30,000,000

Hook NumberHook 20,000,000

10,000,000

0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Year

Figure 3. Temporal change of hook number in the total dataset (before filtered) by 4 subareas in the EPO.