Impact of Industrial Tuna Fisheries on Fish Stocks and the Ecosystem of the Pacific Ocean www.soest.hawaii.edu/PFRP/large_pelagics/large_pelagic_predators John Sibert Pelagic Fisheries Research Program University Hawaii Thanks to the folks who actually do the work: John Hampton Oceanic Fisheries Programme, SPC, Noumea Adam Langley Pierre Kleiber NOAA Pacific Island Fisheries Science Center, Honolulu Mark Maunder Inter-American Tropical Tuna Commission, La Jolla Shelton Harley Yukio Takeuchi National Research Institute of Far Seas Fisheries, Shimizu Momoko Ichinokawa Tom Polacheck CSIRO, Hobart Alain Fonteneau IRD, Sete Outline • History of industrial tuna fishing • How do you estimate biomass? • Data available • Model components • Results – analysis & synthesis – Biomass trends – Changes in size structure – Changes in trophic structure – Regime shifts? • Fishery management options • (Compare with CPUE analysis) Expansion of Fishing Grounds Increase in Catch By Species By Gear By Area Estimating Biomass: Stock Assessment Models • Infer status of stock from analysis of fisheries data • Process – Demographic model of fish population – Model of fishing process • Stochastic components – Process error – Observation error • Likelihood • No explicit environmental forcing Fisheries Data • Spatially resolved time series starting in 1952 • Catch and effort by fishing gear and national flag • Size: catch by length or weight • Tag release and recapture • Non-existent experimental design – Inconsistent spatial resolution – Changes in biomass confounded with changes in fishery – Not all time-area strata sampled Simple Example – The Schaefer Model dB = rB 1 − B − f B Demographic Model dt K f = qE Fishing Mortality Cb = f B Predicted Catch `(C,E|r,K,q) = ∑(C −Cb)2 Likelihood • Stock assessment models reconstruct biomass trajectories • q can be set to 0 to explore potential biomass trajectories in absence of fishing • Extremely simple example useful only heuristically and in restricted situations (e.g. EPO surface fishery in the 1950s). Don’t try this at home! dB • Assumption of equillibrium dt = 0 leads directly to MSY concept C C • C = qEB implies E = qB i.e. E (or CPUE) is an index of abundance if q is constant. More Complex example – MULTIFAN-CL http://www.multifan-cl.org Rlog(ϕt )αrγtr a = 1; 1 ≤ t ≤ T 0 0 Na,1,r 1 < a < A; t = 1 Natr = Demographic Model −Z e a−1,t−1,r Na−1,t−1,r 1 < a < A; 1 < t ≤ T −Z −Z e a−1,t−1,r Na−1,t−1,r + e a,t−1,r Na,t−1,r a = A; 1 < t ≤ T ∑ Zatr = f ∈ fr Fat f + Ma Total Mortality ε Fat f = sa f qt f Et f e t f Fishing Mortalty F C = at f 1 − e−Zatr N Predicted Catch bat f Zatr atr h i2 ΘC C A A = p ∑t ∑ f log(1 + ∑a Cat f ) − log(1 + ∑a Cbat f ) Likelihood Spatial Structure 120˚ 150˚ 180˚ 210˚ 240˚ 270˚ 40˚ 40˚ 1 2 7 20˚ 20˚ 0˚ 3 4 0˚ −20˚ 8 −20˚ 5 6 −40˚ −40˚ 120˚ 150˚ 180˚ 210˚ 240˚ 270˚ Size-frequency Information Region 4 Purse Seine, Associated Region 1 Longline n=2524 Tagging Information Movement Mortality More Diagnostics Growth “Effort Deviations” εt f Availability of stock assessments Current Assessments Needed Assessments “Stock” Status “Stock” Status WCPO Yellowfin∗ 3 Ono 0 EPO Yellowfin∗ 3 Mahi mahi 0 Southern Albacore∗ 3 Oceanic Whitetip Shark 0 Northern Albacore∗ 2 Pacific Bigeye∗ 3 WCPO Bigeye 3 WCPO Skipjack∗ 2 Pacific Swordfish 1 Pacific Blue Marlin 1 Pacific Blue Shark 1 3: current & defintive; 2: current, but needs work; 1: in progress or needs updating; 0: probably insufficient data ∗ Used in this presentation Biomass Trends Impact of Fishery on Total Biomass Changes in Size Spectra Impact of Fishery on Size Impact of Fishery on Spawning Biomass Ecopath Trophic Level Calculations Central Pacific Eastern Tropical Pacific Cox et al., 2002 Olson & Watters, 2003 Species Small Weight (kg) Length (cm) Large Small Length (cm) Large Bigeye 3.82 39.0 123 4.06 4.53 80 5.17 Yellowfin 3.91 14.8 94 4.12 4.57 90 4.66 Albacore 3.96 13.3 87 4.10 4.60 Blue Shark 3.99 4.05 Blue Marlin 44.0 4.61 Swordfish 10.0 4.32 4.42 150 4.96 Skipjack 3.85 13.4 91 4.57 “Marlins” 5.22 150 5.32 “Sharks” 5.23 150 4.93 Bluefin 4.37 Sailfish 4.63 150 4.89 Average 3.91 4.21 4.77 4.83 Cox, S., S. Martell, C. Walters, T. Essington, J. Kitchell, C. Boggs, and I. Kaplan. 2002. Reconstructing ecosystem dynammics in the central Pacific Ocean, 1952-1998. I. Estimating population biomass and recritment of tunas and billfishes. Can. J. Fish, Aquat. Sci. 59:1724-1735. Cox, S., T. Essington, J. Kitchell, S. Martell, C. Walters, C. Boggs, and I. Kaplan. 2002. Reconstructing ecosystem dynammics in the central Pacific Ocean, 1952-1998. II. A preliminary assessment of the trophic impacts of fishing and effects on tuna dynamics. Can. J. Fish, Aquat. Sci. 59:1736-1747. Olson, R. and G. Watters. 2003. A model of the pelagic ecosystem in the estaern tropical Pacific Ocean. IATTC Bulletin 22:135-218. Eastern and Western Food Webs Hinke, J, I. Kaplan, K. Aydin, G. Watters, R. Olson and J. Kitchell. 2004. Visualizing the food-web effects of fishing for tunas in the Pacific Ocean. Ecology and Society 9:1-10. http://www.ecologyandsociety.org/vol9/iss1/art10 Eating your way to the top http://www.flmnh.ufl.edu/fish/Gallery/Descript/YellowfinTuna/YellowfinTuna.html Trophic Transisitions Ecopath Switch Ontogenetic Central Pacific Eastern Tropical Pacific Impact of Fishery on Trophic Level (1) WCPO Ontogentic Impact of Fishery on Trophic Level (2) EPO Switch Impact of Ecosystem on Fishery Conclusions • Impact of fisheries on biomass is variable – Expansion of the purse seine fishery had extended fishing mortality to all age classes of some species – Some stocks have declined to a point where management intervention is required – Some stocks appear to have increased in abundance • Fish larger than 150cm have declined to about 20% of their predicted abundance in the absence of fishing • Impact on trophic stucture within the guild of “top predators” is not detectable • Fishery-independent trends in recruitment and biomass • Estimated increase in skipjack biomass consistent with predictions from food web models – Further work on skipjack stock assessment should be given priority – Possiblities for assessments of mahi mahi, ono, and small tunas should be evaluated Fishery Management Options • United States Domestic: F > MMSY (overfishng), but B > BMSY (not overfished) – US catch comprises approximately 0.5% longline and 5% purse seine yellowfin catch – US catch comprises approximately 1% longline bigeye catch • International – IATTC – WCPFC The Claims “... large predatory fish biomass today is only about 10% of pre- industrial levels.” Ransom A. Myers and Boris Worm. 2002. Rapid worldwide depletion of predatory fish communities Nature 423:280-283. “I know that the human being and the fish can coexist.” George W. Bush • Misinterpretation of CPUE – CPUE is not a reliable index of abundance – “Community” CPUE a bogus concept • Omits of most of data Interpretation of Catch per Unit Effort (1) Albacore South of the Equator 4 Taiwan 3 2 1 CPUE (Fish/100 Hooks) Japan 0 1950 1960 1970 1980 1990 2000 Year Hampton, J, J. Sibert, P.Kleiber, M. Maunder, S. Harley. 2005. Decline of Pacific tuna populations exaggerated? Nature 434:E1-E2. Interpretation of Catch per Unit Effort (2) Yellowfin South of 10 South _ 6 35% Decline by 2900 tonne removal 5 _ _ 4 10000 3 60% Decline by 8900 tonne removal 2 _ 5000 Total Catch (mt) 1 CPUE (Fish/100 Hooks) 0 0 1950 1960 1970 1980 1990 2000 Year Yellowfin Between 10 South and 10 North 4e+05 2.0 1.0 2e+05 Total Catch (mt) CPUE (Fish/100 Hooks) 0.0 0e+00 1950 1960 1970 1980 1990 2000 Year Selective use of data (1) Selective use of data (2).