Regulatory Impact Analysis Framework for Hawaii Pelagic Fishery Management: Progress and Future Plan Keiichi Nemoto (JIMAR) Minling Pan (NMFS-PIFSC) Sam Pooley (NMFS- PIAO)
Past for MMPM Previous work: A Multi-level Multiobjective Programming Model for the Hawaii Fisheries (MMPM1) (Pan et al. 2001) Tradeoff: recreational trips vs. profit of commercial fisheries Near-shore closure of longline fishing to mitigate gear conflicts Include small fleets & recreational Use 1993 data
Focus of regulations and closed areas have been changed EEZ 2, 000 nmi 900 200 MMPM1 Tradeoff between fleets Based on Distance from MHI & NWHI Area 4 Area 5 MMPM2 Turtle Conservation Area A: 44 North 28 North, 168 West - 150 West Area B: 44 North - 28 North, 173 East - 168 West and 150 West - 137 West Area C: 28 North - Equator, 173 East - 137 West
Research Objectives of MMPM2 Enhance MMPM1 to incorporate more flexibly-defined fishing areas Evaluate impacts of regulatory policies, particularly time/area closure for sea turtle conservation. Focus on the Hawaii longline fishery (HILLF).
Progress in the previous year Develop data processors to flexibly adjust to a new area / time definition A definition with 5 areas/5 periods to analyze the recent regulations. Renovate the previous model Make the program/data structure more visible (maintainability up)
Major components of MMPM2 (Mathematical Programming) Objective function Max revenue (or profit) Decision variables # of trips of fleet i target j in area k during period t # of boats for each fleet Constraints: Entry conditions vessel, owner, crew, trip Stock constraints Other equations Catch function (FN) Revenue, cost, etc. Parameters Cost-earnings survey fixed & op. costs fishing & traveling days / trip Auction Data Price ($/lb.) Weight (lb./fish) Logbook Catch, effort, CPUE Stock estimated using catch FN, catch, effort
Data Processing i: fleet (length) j: target k: area t: period s: species Catch & Effort (from Logbook) Summarized by 1 x1 square, month, fleet size (L/M/S), and target (B/M/T) Auction: Price ($/lb.) Weight(lb./fish) Cost-Earnings Survey Fixed costs (annual base) Operating costs ($/FD, $/TD) Expected wage ($/vessel-day) Σ i Σ j Σ k Σ t Σ s Σ j Σ t Σ s Σ i Σ j MMPM2 Parameters
Five Areas to Analyze the Impacts of Turtle Conservation Policies 173 E 180W 170W 137 W 160W 150W 140W 44 N 40 N North Ctr. 35 N North West 158W North East 30 N 25 N MHI 20 N 15 N South 10 N 5 N MHI: 153W - 163W & 15 N 20 N 0 E
1,400 1,200 Fishing Sets by Area and Month (5 periods) T1 T2 T3 T4 T5 1,000 800 600 400 North East North Ctr. North West MHI South 200 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Recent Progress Model Improvement Use a catch FN that is widely used for stock assessment (e.g., SCTB16, Boggs et al., 2000). Estimate prices of the major species using monthly data during 1993-2001. Preliminary Results Model Validation: 1993 and 1998 Analyze the Impacts of Regulatory Policies
Catch FN w/ Decreasing Catch Rate (DCR2) where C F skt = Z ( 1 e ) -Z s,k,t sk,, t X skt,, skt Z s,k,t = M s + F s,k,t = M s + q s E k,t E k,t is standardized fishing effort per month, q s,k is Catchability coefficient, C s,k,t, X s,k,t is the catch and stock of species s at area k, at period t. CPUE s,k,t = CPUE 0 s,k,t α(e k,t ) - CPUE monotonically declines from CPUE 0 as E k,t increases.
CPUE Profile (DCR2) (compared with those used in MMPM1, DCR1 & CCR) 9.00 8.00 CPUE 0 CPUE index (CPUE =1.000 for zero effort level) 7.00 6.00 5.00 4.00 3.00 2.00 1.00 DCR1 DCR2 CCR actual (observed) catch & effort 0.00 0 200 400 600 800 1,000 1,200 1,400 1,600 Effort Intensity (the units of 1,000 hooks per month )
Price Estimation Model: Examine the effects of weight /fish and local supply on price P = AW Q b b s, t s, t s, t 1 2 b b b b b s, t s, t s, t s, t s, t ( ) 2 W s, t = AW W N = AW N = W / n: weight per fish Q s,t : total landings of species s 1 1 2 2 s: 9 species (BET, YFT, ALB, SWF, BM, SM, Mahimahi, Opah, Ono) ln P t = a 0 + a 1 ln P t-1 + b 3 ln (W/n) t b 2 ln N t + + d 1 D1 t + d 2 D2 t + d 3 D3 t + d 4 D4 t + e t Jan-Mar: D1 = 1, other dummy vars = 0 Apr-May: D2 = 1, other dummy vars = 0 Jun-Jul: D3 = 1, other dummy vars = 0 Aug-Sep: D4 = 1, other dummy vars = 0 Oct-Dec: all dummy vars = 0
Estimation results BET YFT ALB SWF Intercept 0.4386-0.4204-1.3561 0.6466 Lag 0.3601 0.1601 ln(w/n) 0.2193 0.5677 0.7484 ln N -0.0777-0.0494-0.1739-0.0364 D1 0.0522 0.0461 0.2295 0.3170 D2-0.0277-0.0228-0.0259 0.2110 D3-0.2814-0.3280-0.1484 0.3490 D4-0.0105-0.1633 0.0250 0.2196 ln(be) -0.1230 D98-0.5690 R2 adj 0.4447 0.4499 0.4741 0.4190 F-stat 13.13 13.50 14.65 13.86 MAPE 0.1106 0.1462 0.1675 0.1912
Estimation Results (Cont d) BM SM Mahimahi Opah Ono Intercept 2.7789 2.1290 0.6104-1.7543-1.0681 Lag 0.3230 0.3318 0.2377 0.3847 0.2545 ln(w/n) -0.0278 0.1957 0.2340 0.7127 0.8783 ln N -0.3868-0.2659-0.1166-0.2438-0.1607 D1 0.0535 0.1247 0.1637 0.2655 0.2020 D2 0.0407-0.0178 0.0429 0.0265-0.1332 D3 0.0851-0.1407 0.0788 0.0770-0.0273 D4 0.1302 0.0827 0.1340 0.1863 0.0738 ln(substitute) -0.0503-0.2265-0.0629 Mah. SM BM -0.0650 Opah R2 adj 0.6888 0.7959 0.3863 0.5151 0.7007 F-stat 30.32 52.68 10.53 17.09 28.57 MAPE 0.1576 0.1418 0.1678 0.1826 0.1481
5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 Bigeye Tuna Monthly Price: Observed vs. Predicted Bigeye tuna price: observed vs. predicted 1993.1 1993.4 1993.7 1993.10 1994.1 1994.4 1994.7 1994.10 1995.1 1995.4 1995.7 1995.10 1996.1 1996.4 1996.7 1996.10 1997.1 1997.4 1997.7 1997.10 1998.1 1998.4 1998.7 1998.10 1999.1 1999.4 1999.7 1999.10 2000.1 2000.4 2000.7 2000.10 2001.1 2001.4 2001.7 2001.10 Predict 3 Observed Year/Month Price ($/lb.)
3.00 2.50 2.00 1.50 1.00 0.50 0.00 Swordfish Monthly Price: Observed vs. Predicted Swordfish price 1993.1 1993.4 1993.7 1993.10 1994.1 1994.4 1994.7 1994.10 1995.1 1995.4 1995.7 1995.10 1996.1 1996.4 1996.7 1996.10 1997.1 1997.4 1997.7 1997.10 1998.1 1998.4 1998.7 1998.10 1999.1 1999.4 1999.7 1999.10 2000.1 2000.4 2000.7 2000.10 2001.1 2001.4 2001.7 2001.10 Predict Observed Year / Month price ($/lb)
5.00 4.50 4.00 3.50 3.00 2.50 2.00 1.50 1.00 0.50 0.00 Yellowfin Tuna Monthly Price: Observed vs. Predicted 1993.1 2001.4 1993.4 1993.7 1993.10 1994.1 1994.4 1994.7 1994.10 1995.1 1995.4 1995.7 1995.10 1996.1 1996.4 1996.7 1996.10 1997.1 1997.4 1997.7 1997.10 1998.1 1998.4 1998.7 1998.10 1999.1 1999.4 1999.7 1999.10 2000.1 2000.4 2000.7 2000.10 2001.1 2001.7 2001.10 Predict Observed Year/Month Price ($/lb.)
Fish Price Analysis: Summary Larger pelagic species worth more per pound, except for SWF, BM, Opah Local supply decreases its own price (inelastic) Effect is greater for local species (10-40%) Effect is small (7.8%) for BET and YFT No effect for SW price (link to the mainland US market) Substitution effect on certain species: BET on YFT price, BM on SM price Mahimahi, Opah on Ono price Effect of previous month price is generally significant (20-30%), except for YF, SW 0.39 < R 2 adj < 0.70, 0.11 < MAPE < 0.19
Simulation: Max. revenue until all profits are dissipated (open access) $ Ex-vessel Revenue B Actual eqlm. A Open Access Max Rent MMPM1 O MMPM1 Effort
Simulation Results Profile Actual Simulated 1993 1998 1993 1998 Revenue 53.36 46.64 68.65 56.77 ($ Million) Total hooks 13.03 17.37 15.57 20.76 (Million hooks) Minimum # of boats Small 24 17 15 38 Medium 54 56 56 79 Large 44 41 71 28 Total 122 114 142 145
Numbers of Active LL Vessels by Size, 1993-2002 Number of Hawaii-based longline vessels 140 120 Number of active vessels 100 80 60 40 Large Medium Small 20 0 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Large 44 44 37 36 35 41 46 48 32 31 Medium 54 58 52 49 51 56 58 61 53 54 Small 24 23 21 18 19 17 16 16 16 15 Year
Results suggest that the number of large vessels would decrease? Tuna sets by large vessel actually increased (as well as medium size) Vessels might go further away from MHI to catch large bigeye tuna, which are expected to yield higher price. Larger vessels (faster & more advanced?) may be preferred to yield higher returns.
Number of fishing sets by target, 1993-2001 1,600 1,400 1,200 1,000 800 600 Swordfish Mixed Tuna 400 200 0 1993.1 1993.4 1993.7 1993.10 1994.1 1994.4 1994.7 1994.10 1995.1 1995.4 1995.7 1995.10 1996.1 1996.4 1996.7 1996.10 1997.1 1997.4 1997.7 1997.10 1998.1 1998.4 1998.7 1998.10 1999.1 1999.4 1999.7 1999.10 2000.1 2000.4 2000.7 2000.10 2001.1 2001.4 2001.7 2001.10 Monthly number of sets Year/Month
Number of Fishing Sets by Area, 1993-2001 1,400 1,300 1,200 1,100 1,000 900 800 700 600 North East North Middle North West MHI South 500 400 300 200 100 0 1993.L1 1994.L1 1995.L1 1996.L1 1997.L1 1998.L1 1999.L1 2000.L1 2001.L1 Period Number of fishing sets / month
Number of Sets by Large LL by Area 3,500 3,000 2,500 # of sets 2,000 1,500 North East North A North West Central South 1,000 500 0 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year
Number of Sets by Medium/Small LL by Area 7,000 6,000 5,000 Medium LL # of fish 4,000 3,000 North East North Ctr. North West Central South 2,000 1,000 0 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year 3,000 2,500 2,000 North East Small LL # of sets 1,500 North Ctr. North West Central South 1,000 500 0 1993 1994 1995 1996 1997 1998 1999 2000 2001 Year
Policy Sim. Results Simulation Actual Base Policy 1 Policy 2 No policy Policy 1 Policy 2 (1998) 1998 2000 2001 2002 Revenue 56.77 50.51 35.92 46,640 50,150 ($ Million) Hooks 20.76 19.69 17.00 17.37 20.24 22.34 26.89 (Million hooks) No. of vessels Small 38 39 30 17 16 16 15 Medium 79 71 53 56 61 53 54 Large 28 23 17 41 48 32 31 Total 145 133 100 114 125 101 100 No. of fishing sets by target Tuna 10,063 10,075 9,947 7,865 9,035 11,724 13,765 SW 2,169 1,805 0 1,210 542 27 (193) Mixed 2,135 1,230 0 3,408 3,322 426 21 Total 14,367 13,110 9,947 12,483 12,899 12,177 13,786 Policy 1 Policy 2 Close "North Ctr." year round No shallow sets & close "South"
Spatial distribution of sets: comparison across actual and 3 sim. Cases 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 Actual 1998 Base Policy 1 Policy 2 North East North Ctr North West MHI South
Conclusion Catch function allow this model to link the recent results by stock assessment studies (e.g., SCTB16) Time-series price analysis indicates larger fish are worth more, while more local supply decrease prices slightly. The model predicts the effort distribution across areas and periods under the turtle conservation policies reasonably, except for that (i) actually more tuna sets are conducted than predicted, (ii) more large vessels enter to HILLF for BET.
Future Plan More analyses are needed to update costearnings of LL and the relationship between LL s profitability and its characteristics / fishing strategy. Proposed Project w/ M. Parke: Spatial Modeling of the Tradeoff between Sea Turtle Take Reduction and Economic Returns to the HILLF Two objectives programming Utilize GIS, Observers data to estimate turtle takes and catch of pelagic species more precisely.
Acknowledgement PFRP, JIMAR NMFS-HL personnel F. Dowdell, R. Ito K. Kawamoto, B. Walsh
References Hampton, J., P. Kleiber, Y. Takeuchi, H., Kurota, and M. Maunder. 2003. Stock assessment of bigeye tuna in the western and central Pacific Ocean, with comparison to the entire Pacific Ocean. SCTB16, Working Paper. Boggs, C., P. Dalzell, T. Essington, M. Labelle, D. Mason, R. Skillman, and J. Witherall (2000) Recommended overfishing definitions and control rules for the WPRFMC s pelagic fishery management plan. Ward, P., J.M. Porter, and S. Elscot (2000) Broadbill swordfish: status of established fisheries and lessons from developing fisheries, Fish and Fisheries 1: 317-336
Price Estimation w/ Set Type Model 1 Model 2 Model 3 Intercept 0.4386-0.6821-0.7348 (0.4821) Lag (P3) 0.3601 ** 0.2750 ** 0.2731 ** (0.1000) ln(w/n) 0.2193 ** 0.5788 ** 0.5922 ** (0.0691) ln N -0.0777 ** -0.1070 ** -0.1074 ** (0.0379) D1 0.0522 0.0499 0.0507 (0.0462) D2-0.0277-0.0783-0.0770 (0.0556) D3-0.2814 ** -0.3785 ** -0.3789 ** (0.0656) D4-0.0105-0.1305 * -0.1323 * (0.0731) D-SW/MX -0.1375 ** (0.0360) D-SW -0.1553 ** D-MX -0.1309 ** R2 adj 0.4447 0.3257 0.3239 F -stat 13.13 17.54 15.59 (P -value) 7.4E-12 5.9E-21 2.2E-20 n 107 275 275 n Weight Price Tuna 107 73.5 3.02 Mixed 102 84.1 2.87 Swordfish 66 103.0 3.15