Regulatory Impact Analysis Framework for Hawaii Pelagic Fishery Management:

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1 Regulatory Impact Analysis Framework for Hawaii Pelagic Fishery Management: Progress and Future Plan Keiichi Nemoto (JIMAR) Minling Pan (NMFS-PIFSC) Sam Pooley (NMFS- PIAO)

2 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

3 Focus of regulations and closed areas have been changed EEZ 2, 000 nmi 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 West Area B: 44 North - 28 North, 173 East West and 150 West West Area C: 28 North - Equator, 173 East West

4 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).

5 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)

6 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

7 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

8 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

9 1,400 1,200 Fishing Sets by Area and Month (5 periods) T1 T2 T3 T4 T5 1, North East North Ctr. North West MHI South Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

10 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 Preliminary Results Model Validation: 1993 and 1998 Analyze the Impacts of Regulatory Policies

11 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.

12 CPUE Profile (DCR2) (compared with those used in MMPM1, DCR1 & CCR) CPUE 0 CPUE index (CPUE =1.000 for zero effort level) DCR1 DCR2 CCR actual (observed) catch & effort ,000 1,200 1,400 1,600 Effort Intensity (the units of 1,000 hooks per month )

13 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 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

14 Estimation results BET YFT ALB SWF Intercept Lag ln(w/n) ln N D D D D ln(be) D R2 adj F-stat MAPE

15 Estimation Results (Cont d) BM SM Mahimahi Opah Ono Intercept Lag ln(w/n) ln N D D D D ln(substitute) Mah. SM BM Opah R2 adj F-stat MAPE

16 Bigeye Tuna Monthly Price: Observed vs. Predicted Bigeye tuna price: observed vs. predicted Predict 3 Observed Year/Month Price ($/lb.)

17 Swordfish Monthly Price: Observed vs. Predicted Swordfish price Predict Observed Year / Month price ($/lb)

18 Yellowfin Tuna Monthly Price: Observed vs. Predicted Predict Observed Year/Month Price ($/lb.)

19 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

20 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

21 Simulation Results Profile Actual Simulated Revenue ($ Million) Total hooks (Million hooks) Minimum # of boats Small Medium Large Total

22 Numbers of Active LL Vessels by Size, Number of Hawaii-based longline vessels Number of active vessels Large Medium Small Large Medium Small Year

23 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.

24 Number of fishing sets by target, ,600 1,400 1,200 1, Swordfish Mixed Tuna Monthly number of sets Year/Month

25 Number of Fishing Sets by Area, ,400 1,300 1,200 1,100 1, North East North Middle North West MHI South L L L L L L L L L1 Period Number of fishing sets / month

26 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, Year

27 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, Year 3,000 2,500 2,000 North East Small LL # of sets 1,500 North Ctr. North West Central South 1, Year

28 Policy Sim. Results Simulation Actual Base Policy 1 Policy 2 No policy Policy 1 Policy 2 (1998) Revenue ,640 50,150 ($ Million) Hooks (Million hooks) No. of vessels Small Medium Large Total 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, , (193) Mixed 2,135 1, ,408 3, 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"

29 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

30 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.

31 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.

32 Acknowledgement PFRP, JIMAR NMFS-HL personnel F. Dowdell, R. Ito K. Kawamoto, B. Walsh

33 References Hampton, J., P. Kleiber, Y. Takeuchi, H., Kurota, and M. Maunder 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:

34 Price Estimation w/ Set Type Model 1 Model 2 Model 3 Intercept (0.4821) Lag (P3) ** ** ** (0.1000) ln(w/n) ** ** ** (0.0691) ln N ** ** ** (0.0379) D (0.0462) D (0.0556) D ** ** ** (0.0656) D * * (0.0731) D-SW/MX ** (0.0360) D-SW ** D-MX ** R2 adj F -stat (P -value) 7.4E E E-20 n n Weight Price Tuna Mixed Swordfish

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