DEMOGRAPHIC ANALYSIS ON ATLANTIC BLUE AND SHORTFIN MAKO SHARKS

SCRS/24/122 Col. Vol. Sci. Pap. ICCAT, 58(3): 1157-1165 (25) DEMOGRAPHIC ANALYSIS ON ATLANTIC BLUE AND SHORTFIN MAKO SHARKS Yukio Takeuchi 1, Yasuko Senba 2 and Hideki Nakano 1 SUMMARY Demographic analysis on Atlantic blue shark and shortfin mako shark was done. Uncertainties of input parameters were accounted by Monte Carlo simulation. Current total mortality of those sharks was estimated by catch curve analysis applied to catch at age data of Japanese longline observer data. The results suggested that current fishing mortality on blue shark is sustainable. As for the short fin mako shark current fishing mortality appeared to result in negative intrinsic rate of increase indicating population decline. RÉSUMÉ Une analyse démographique du requin peau bleue et du requin taupe bleue de l Atlantique a été effectuée. Les incertitudes concernant les paramètres d entrée ont été prises en considération par l utilisation de la simulation Monte Carlo. La mortalité totale actuelle de ces requins a été estimée par l analyse de la courbe de capture appliquée au données de prise par âge obtenues par les observateurs palangriers japonais. Les résultats donnaient à penser que la mortalité par pêche actuelle des requins peau bleue est soutenable. En ce qui concerne le requin taupe bleue, la mortalité par pêche actuelle semblait produire un tau intrinsèque négatif de croissance, indiquant ainsi le déclin de la population. RESUMEN Se llevó a cabo un análisis demográfico de la tintorera y el marrajo dientuso atlánticos. Las incertidumbres de los parámetros de los valores de entrada se consideraron mediante la simulación Monte Carlo. La mortalidad total actual de estos tiburones se estimó mediante un análisis de curva de captura aplicado a los datos de captura por edad de los datos de observadores de la pesquería de palangre japonesa. Los resultados sugerían que la mortalidad por pesca actual de tintorera es sostenible. En lo que se refiere al marrajo dientuso, la mortalidad por pesca actual parecía producir una tasa intrínseca negativa de crecimiento, lo que apunta hacia un descenso en la población. KEYWORDS Blue shark, Shortfin mako shark, Demographic analysis 1 National Research Institute of Far Seas Fisheries, 5-7-1 Shimizu-Orido, Shizuoka 4248633, Japan. 2 Department of Aquatic Bioscience, College of Agriculture, University of Tokyo. 1157

1. Introduction Demographic analysis has been widely used to assess the vulnerability of elasmobranches species to eploitation (Caillet 1992). They only require some biological information such as survival rate, age at maturity, longevity and other reproductive parameters. However such information is often unavailable or unreliable for elasmobranches. In recent years it became very common practice to conduct Monte Carlo simulation to incorporate uncertainty of parameters of demographic analysis (Cortes 22). In the case of Atlantic blue shark age and growth and other reproductive parameters recently became available (Skomal and Nattanson 22, Castro and Mejuto 1995). In the case of Atlantic shortfin mako shark, however, validated age and growth information has not been yet available. Even reproductive parameters are not yet known. In this paper following the work of Cortes (22) we calculated intrinsic rate of increase and other demographic parameters with their uncertainties via Monte Carlo simulation. 2. Demographic analysis Intrinsic rate of increase and other demographic parameters are calculated by iteratively solving Euler-Lotka equation (Ebert 1999, Hastings 1996) Ama = lme r Where l is survival rate until age, m is fecundity at age, r is intrinsic rate of increase and A ma is longevity. Other demographic parameters included in the outputs were net reproductive = 1 rate R Ama = = lm, generation time Ama T = l m l m = Selectivity curve of fishing mortality was chosen to have flat top property. To fulfill this requirement we used logistic curve ' 1 Select = 1 + ep( δ( a )) In application they were normalized to have maimum as 1. Select = Select /ma( Select ) ' ' 5 Where a5 is age at selectivity is.5. δ determines the steepness of the curve. We assumed fishing mortality can be decomposed the product of selectivity defined above and fishing mortality at fully selected age. F = Select f s To estimate natural mortality we applied seven life-history methods of (1) Pauly (198), (2) Hoenig (198), (3) and (4) Jensen (1996), (5) Campana (21), (6) Chen and Watanabe (1989) and (7) Petersen and Wroblewski (1984). A ma : maimum age. We follow the definition of longevity (Taylor 1958) as the age at which fish reach 95% of L ln(1.95) Ama = t k where t and k are parameters of von Bertalanffy growth curve. In reality ma reproductive age of female so that it may lower than longevity. A should be maimum 1158

2.1 Monte Carlo simulation In this simulation we assumed fecundity at age, a5 of logistic selectivity curve and fully selected fishing mortality follows normal distribution with CV=.3 by default. We also assumed that each natural mortality vector has equal probability to happen. As for the maturity of female we assumed knife edge maturity schedule and age at maturity have uniformly deviates from the range of plus and minus age 1 from mean age at maturity. 3. Results 3.1 Determination of biological information of Atlantic blue shark and shortfin mako shark relevant to demographic analysis 3.1.1 Blue shark We used the se combined growth curve of Skomal and Nattanson (24) Lt =.17( t+ 1.43) 286.8(1 e )( FL, cm) longevity of blue shark was calculated as 16.2 years. Then we set A ma ; maimum age in the calculation as 17. Fecundity ( m ), number of the number of female pups produced per one year was calculated from the number of pups in one litter (38) and 1 : 1 embryonic se ratio by Castro and Mejuto (1995) and from reproductive cycle of two years suggested by Nakano (National Research Institute of Far Seas Fisheries, Shizuoka, Japan, personal communication) as 18.5 pups per litter. Maturity at age was also taken from Castro and Mejuto as age 5. The parameters of logistic selectivity curve was chosen so that by age 5 blue shark is almost fully selected( a5 : 3, δ :.922913). This is based on the observation of catch at age data converted from size frequency data of shortfin mako shark catch by Japanese longline observer data from 1995 to 23 (Senba and Nakano 24, Figure 1) by applying se combined growth curve of Senba(23) shown above. Total mortality was also calculated by applying catch curve analysis to catch at age data (Figure 1 and 2). Estimated total mortality was.563. Natural mortality vectors calculated from seven methods were shown in Table 1 and 2. Fishing mortality was the determined by subtracting average of natural mortality (.244) from estimated total mortality. For the methods which give age dependent natural mortality (Chen and Watanabe 1989) we used lowest age dependent M (=.191, age 13 and 14). The result was.319. 3.1.2 Shortfin mako shark There are two major hypotheses of growth of shortfin mako shark (Cailliet et al. 1983; Pratt and Casey 1993). Recent studies on radiocarbon signature of vertebral growth band of one shortfin mako sample (Campana et al. 22) supported the hypothesis that one band pair is produced per year. Recent age and growth study of Shortfin mako shark in North Pacific Ocean caught by Japanese longline (Senba 23) supports one growth band pair per year hypothesis but resulting growth curve was intermediate of eisting hypothesis. Consequently we used the se combined growth curve of Senba (personal communication) as base case. L = e PCL cm t.84( t+ 3.83) 31.(1 )(, ) If we apply Taylor s method as above we obtain 32.58 as the longevity estimate of shortfin mako shark consequently we used 33 as A ma. There is very little literature available on the reproductive biology of female shortfin mako shark, in particular studies specific to Atlantic shortfin mako shark on that issue. Therefore we rely on the review on this topic by Mollet et al (2). Fecundity ( m ), number of the number of female pups produced per one year was calculated from the mean number of pups in one litter (12.5) and 1 : 1 embryonic se ratio and from reproductive cycle of three years suggested by Mollet et al. (2) resulted in 2.8 pups per litter. Senba (personal communication) reported that the length at maturity in north Pacific was more than 2 cm (PCL) from the sample taken by Japanese training vessels operated in north Pacific and other literatures. 2cm (PCL) is about age 1 from her growth curve. The parameters of logistic selectivity curve were chosen so that 1159

by age 3 shortfin mako shark is almost fully selected (a5: 1, δ : 1.4722). This is based on the observation of catch at age data converted from size frequency data of blue shark catch by Japanese longline observer data from 1995 to 23 (Senba and Nakano 24, Figure 1) by application of se combined growth curve of Senba shown above. Total mortality was also calculated by applying catch curve analysis to the catch at age data (Figure 3 and 4). Estimated total mortality was.535. Natural mortality vectors calculated from seven methods were shown in Table 1 and 2. Fishing mortality was the determined by subtracting average of natural mortality (.1266) from estimated total mortality. For the methods which give age dependent natural mortality (Chen and Watanabe 1989) we used lowest age dependent M (=.95, age 28). The result was.319. 3.2 Demographic analysis 3.2.1 Blue shark The results of simulation with 1, run were average of r:.23 and SD of r:.247 (lower and upper end points of 9% percentile are -.193 and.433). If we conducted the simulation in the absence of fishing mortality they became as average of r:.341 and SD of r:.354 (lower and upper end points of 9% percentile are.227 and.487). Other demographic parameters are summarized in Table 5. 3.2.2 Shortfin mako shark The results of simulation with 1, run based on the growth curve of north Pacific (Senba 23) were average of r : -.352 and SD of r :.368 (lower and upper end points of 9% percentile are -.53 and -.17). If we conducted the simulation in the absence of fishing mortality they became as average of r:.141 and SD of r:.186 (lower and upper end points of 9% percentile are -.396 and.344). Other demographic parameters are summarized in Table 5. 4. Discussion Choice of reproductive cycle for both sharks was the most difficult task. As for the blue shark we were unable to find any literature on it. Two year reproductive cycle might be the most likely value but one year or some intermediate between one and two, on average, reproductive cycle is also likely. If we replace two year female reproductive cycle with one. The results would shift upward dramatically. In the case of shortfin mako shark Mollet et al recommended three year reproductive cycle although a 2-year cycle could not be ruled out. If three years reproductive cycle is replaced by two the results shift upward (mean of r:.442, SD of r:.449). Another difficulty to conduct demographic analyses of these sharks was insufficient information to estimate both natural and fishing mortalities. In particular our estimation of fishing mortality is based on the size frequency data from Japanese longline observer and assumption of flat top selectivity of longline gear to those sharks. If actual selectivity is dome shaped as suggested in the case of US longline to silky shark by Beetkircher et al (22). Our estimates of fishing mortality may be overestimated. Information to conduct demographic analysis of Atlantic blue shark was almost sufficient ecept for reproductive cycle and mortality. On the other hand information for demographic analysis of Atlantic shortfin mako shark was insufficient. We do not have reliable information of age and growth or reproduction of this shark. Improvement of knowledge about Atlantic shortfin mako shark would be essential to have more reliable results or to conduct more elegant model like age structured population dynamics model. References BEETKIRCHER L., M. Shivji, and E. Cortes. 22. A Monte Carlo demographic analysis of the silky shark (Carcharhinus falciformis): implications of gear selectivity, Fisher Bulletin 11(1): 168-174. CAILLIET, G.M., L.K. Martin, D. Kusher, P. Wolf, and B.A. Welden. 1983. Preliminary studies on the age and growth of the blue, Prionace glauca, common thresher, Alopias vulpinus, and shortfin mako, Isurus oyrinchus, sharks from California waters. NOAA Tech. Rep. NMFS, 8: 179 188. CAMPANA S, Marks L, W. Joyce, S. Harley. 21. Analytical assessment of the porbeagle shark (Lamna nasus) population in the northwest Atlantic, with estimates of long-term sustainable yield. CSAS Res. Doc. 21/67. 116

CAMPANA SE, L. J. Natanson, S. Myklevoll. 22. Bomb dating and age determination of large pelagic sharks. Can. J. Fish. Aquat. Sci. 59:45-455. CASTRO, JA and J. Mejuto. 1995. Citation: Castro, JA and J. Mejuto, 1995. Reproductive parameters of blue shark, Prionace glauca, and other sharks in the Gulf of Guinea.. Mar. Freshwat. Res. 46:967-973. CHEN, S. and S. Watanabe. 1989. Age Dependence of Natural Mortality Coefficient in Fish Population Dynamics, Nippon Suisan Gakkaishi, 55(2),25-28. CHEN, S., S. Watanabe and K. Takagi. 1988. Growth Analysis on Fish Population in the Senescence with Special Reference to an Estimation of Age at End of Reproductive Span and Life Span (in Japanese), Nippon Suisan Gakkaishi, 54(9), 1567-1572. CORTES, E. 1998. Demographic analysis as an aid in shark stock assessment and management. Fish. Res. 39, 199-28. CORTES, E. 22. Incorporating Uncertainty into Demographic Modeling: Application to Shark Populations and Their Conservation, Conservation Biology 16,148-162. EBERT, T.A. 1999. Plant and Animal Populations, Methods in Demography, Academic Press, 312 p. HASTINGS, A. 1996. Population Biology, Concepts and Models, Springer, 22 p. HOENIG, J.M. 1983. Empirical use of longevity data to estimate mortality rates. Fish. Bull. 82, 898-93. JENSEN, A.L. 1996. Beverton and Holt life history invariants result from optimal trade-off of reproduction and survival, Can. J. Fish. Aquat. Sci. 53, 82-822. LESSA, R., F.M. Santana and F.H. Hazin. 24. Age and growth of blue shark Prionace glauca (Linnaeus, 1758) off northeastern Brazil, Fish. Res. 66, 19-3. MOLLET, H.F., and G.M. Cailliet. 22. Comparative population demography of elasmobranches using life history tables, Leslie matrices and size-based matri models, Marine and Fresh Water Research, 53, 53-516. MOLLET, H.F., G. Cliff, H.L.Jr. Pratt and J.D. Stevens. 2. Reproductive biology of the female shortfin mako, Isurus oyrinchus Rafinesque, 181, with comments on the embryonic development of lamnoids, Fish. Bull. 98,299-318. NAKANO, H. and M.P. Seki. 23. Synopsis of biological data on the blue shark, Prionace glauca Linnaeus, Bull. Fish. Res. Agen. 6,18-55. PAULY, D. 198. On the interrelationships between natural mortality, growth parameters and mean environmental temperature in 175 fish stocks. J. Cons. Int Eplor. Mer. 39, 175-192. PETERSEN, I and J.S. Wroblewski. 1984. Mortality Rate of Fishes in the Pelagic Ecosystem, Can. J. Fish. Aquat. Sci., 41,1117-112. PRATT, H.,L., Jr. 1979. Reproduction in the blue shark, Prionace glauca. Fish. Bull. 77, 445-47. PRATT, H.L., Jr.; Casey, J.G. 1983. Age and growth of the shortfin mako, Isurus oyrinchus, using four methods. Can. J. Fish. Aquat. Sci. 4:1944-1957. QUINN, II, T.J. and Deriso, R.B. 1999. Quantitative Fish Dynamics, Oford Press,542 pp. SENBA, Y. 23. Studies on shortfin mako stock in the North Pacific Ocean (in Japanese), Master thesis to department of agriculture, University of Tokyo. SENBA, Y., H. Nakano. 24. Summary of species composition and nominal CPUE of pelagic sharks based on observer data from the Japanese longline fishery in the Atlantic Ocean from 1995 to 23., SCRS/24/117. SKOMAL, G.B. and L.J. Nattanson. 23. Age and growth of the blue shark (Prionace glauca) in the North Atlantic Ocean, Fishery Bulletin 11(3), 627-639. SMITH, S.E., D.W. Au and C. Show. 1998. Intrinsic rebound potential of 26 species of Pacific sharks, Mar. Freshwater Res., 49, 663-78. TAYLOR, C.C. 1958. Cod growth and temperature, J. Cons. Int. Eplor. Mer. 23, 366-37. URSIN, E. 1967. A mathematical model of some aspects of fish growth, respiration, and mortality. J. Fish Res. Board Can. 24, 2355-2453. WALKER, T.I. 1998. Can shark resources be harvested sustainably? A question revisited with a review of shark fisheries. Marine and Freshwater Research 49, 553-72. 1161

Table 1. Estimated natural mortality of blue shark from the methods which estimate average mortality. Method M(/year) Pauly.272 Hoenig.225 Jensen 1 Jensen 2.255 Jensen 3.272 Campana.284 Table 2. Estimates of natural mortality of blue shark by the methods of Chen and Watanabe and Peterson and Wroblewski. Age Chen and Watanabe Peterson and Wroblewski.788.476 1.52.335 2.385.272 3.321.236 4.282.213 5.256.197 6.237.186 7.223.177 8.213.171 9.25.166 1.199.162 11.195.158 12.192.156 13.191.154 14.191.152 15.192.151 16.195.149 Table 3. Estimated natural mortality of bluer shark from the methods which estimate average mortality. Method M(/year) Pauly.168 Hoenig.122 Jensen 1 Jensen 2.126 Jensen 3.134 Campana.141 1162

Table 4. Estimates of natural mortality of shortfin mako shark by the methods of Chen and Watanabe and Peterson and Wroblewski. Age Chen and Peterson and Wroblewski Watanabe.368.335 1.289.28 2.242.245 3.21.221 4.187.23 5.17.189 6.157.178 7.147.17 8.139.162 9.132.156 1.126.151 11.121.147 12.117.143 13.113.14 14.11.137 15.18.134 16.15.132 17.13.13 18.11.128 19.1.127 2.98.126 21.97.124 22.97.123 23.96.122 24.95.121 25.95.12 26.95.12 27.95.119 28.95.118 29.95.118 3.96.117 31.97.117 32.98.116 Table 5. Intrinsic rate of increase and other demographic parameters with 9% percentiles of blue shark r LCL UCL T LCL UCL R LCL UCL with Fishing.23 -.19.433 6.314 5.14 7.67 3.917.87 8.747 mortality no Fishing mortality.343.227.487 7.725 6.546 8.893 9.894 6.389 14.288 Table 6. Intrinsic rate of increase and other demographic parameters with 9% percentiles of shortfin mako shark r LCL UCL T LCL UCL R LCL UCL with Fishing mortality -.352 -.532 -.17 11.387 9.956 12.875.32.2.119 no Fishing mortality.14 -.4.34 14.997 13.76 16.292 1.236.938 1.587 1163

Blueshark catch at age 14 12 1 Number 8 6 4 2 1 2 3 4 5 6 7 8 9 1 11 12 13 14 15 16 17 18 19 2 21 22 23 24 25 26 27 Figure 1. Catch at age of blue shark converted by Japanese longline observer data in Atlantic from 1995 to 23. 12 log frequency 1 8 6 4 2 y = -.563 + 9.712 R 2 =.9569 5 1 15 2 age Figure 2. Catch curve of blue shark from Japanese longline observer data. When straight line was fitted, only data of age 3 and older were used. 1164

Number 35 3 25 2 15 1 5 1 2 3 4 5 6 7 8 9 1 11 12 13 age Figure 3. Catch at age of shortfin mako shark converted by Japanese longline observer data in Atlantic from 1995 to 23. 7 6 5 y = -.5346 + 5.7365 R 2 =.8999 logfrequency 4 3 2 1-1 1 2 3 4 5 6 7 8 9 1 11 12 13 age Figure 4. Catch curve of blue shark from Japanese longline observer data. When straight line was fited, only data of age 3 and older were used. 1165