How do fishery policies affect Hawaii's longline fishing industry? Calibrating a positive mathematical programming model
Jonathan R. Sweeney, Richard E. Howitt, Hing Ling Chan, Minling Pan, PingSun Leung
HHow do fishery policies affect Hawaii’s longlinefishing industry? Calibrating a positivemathematical programming model ∗ Jonathan R. Sweeney † Richard E. Howitt Hing Ling ChanMinling Pan PingSun LeungJuly 14, 2017
Abstract
We present a vessel and target-specific positive mathematical programmingmodel (PMP) for Hawaii’s longline fishing fleet. Although common in agricul-tural economics, PMP modeling is rarely attempted in fisheries. To demon-strate the flexibility of the PMP framework, we separate tuna and swordfishproduction technologies into three policy relevant fishing targets. We findthe model most accurately predicts vessel-specific annual bigeye catch in theWCPO, with an accuracy of 12% to 35%, and a correlation between 0.30 and0.53. To demonstrate the model’s usefulness to policy makers, we simulatethe economic impact to individual vessels from increasing and decreasing thebigeye catch limit in the WCPO by 10%. Our results suggest that such pol-icy changes will have moderate impacts on most vessels, but large impactson a few generating a fat tailed distribution. These results offer insights intothe range of winners and losers resulting from changes in fishery policies, andtherefore, which policies are more likely to gain widespread industry support.As a tool for fishery management, the calibrated PMP model offers a flexibleand easy-to-use framework, capable of capturing the heterogeneous responseof fishing vessels to evaluate policy changes. ∗ This manuscript is published as
Natural Resource Modeling. 2017;e12127. † Corresponding Author. Address: 2424 Maile Way; 542 Saunders Hall; Honolulu, HI 96822. a r X i v : . [ q -f i n . E C ] J u l Introduction
Understanding the economic impact of a proposed policy is crucial for ensuring policyobjectives are met without being excessively burdensome on the regulated industry.In fisheries, managers are often responsible for preventing over-fishing of common-pool fish stocks. This involves developing policies that balance biological sustain-ability with economic impacts to the fishing industry. To date, many tools availableto managers measure economic impacts at the aggregate industry-level. These toolsconceal important information on differences between the impacts felt by individualfirms or by types of vessels. Sorting firms that benefit and those that are harmedcan help managers understand the economic implications from the policy and whichpolicies are expected to be equitable.We investigate individual vessel response to fishery policy changes using a vesseland target-specific positive mathematical programming (PMP) model. This researchis important for several reasons. To the best of our knowledge, there have only beenthree previous attempts to apply PMP modeling to fisheries, although none havebeen published in a peer-reviewed journal.
This provides an opportunity to for-malize the PMP model structure for fisheries, which will serve as reference point inthe literature and encourage further model development. Given the panel data struc-ture available for Hawaii’s longline fishery, we are able to evaluate the performanceof the fishery PMP model by comparing out-of-sample predictions to observationsfrom reference years. By calibrating a vessel and target-specific PMP model, thispaper provides insights into the range of individual vessel responses to realistic policychanges. Finally, this paper develops a flexible tool for fishery managers to evaluateheterogeneous policy impacts with relatively few data requirements.Recent research suggests that fisher heterogeneity is particularly important inthe Hawaii longline fleet. Fishers have differing attitudes toward risk ([19]), makeentry/exit decisions depending on individual fisher characteristics ([23]), and chooseremuneration schemes based on owner/operator status (Nguyen and Leung 2009).The network position of individual fishers in the industry has also been shown toplay an important role in determining outcomes ([2]). These studies taken together
Tele: NA. E-mail: [email protected] . Niels Vestagaard [1998] Policy Model for a Regulated Industry: From Command and Controlto Property Rights in a Danish Multispecies Fishery, Dissertation Chapter. John Walden [2006] Applying Positive Math Programming to a Fisheries Problem: Formulat-ing the Closed Area Model Structure, Social Sciences Branch, NEFSC, Wood Hole, MA, 02543,Unpublished Manuscript. Kathereen Bisack and Gisele Magnusson [2009] Modifications to the Harbor Porpoise TakeReduction Plan. Final Environmental Assessment, NOAA-NMFS Northeast Region. The first model by [9], later modified by [12, 11],applied a linear programming (LP) framework to optimally allocate fishing timeacross fishing regions and target species to maximize fleet-wide profits. The results,however, did not accurately reproduce observed fishing behavior. [17] evaluated theLP model and concluded that this shortcoming resulted from the omission of micro-level decision-making by vessel owners and operators. To address this problem,[22] developed a two-level two-objective mathematical programming model whichincorporated the behavior of fishers as well as fishery managers, including separateobjectives of recreational and commercial fisheries. Their approach produced moreplausible optimal solutions, but it remained unclear whether the approximated profit Curtis and Hicks (2000) investigated the impacts of fishery closure due to turtle interactioncaps using a random utility model to account for spatial choice behavior of fishers.
Data
To calibrate the PMP model, evaluate its performance, and simulate policy outcomes,we used data from four sources. We obtained data on individual vessel input costsfor 2005 from the 2005 cost and earnings survey ([20]), and for 2012 from the 2012cost and earnings survey ([21]). We obtained data on annual vessel catch from 2005-2013 using the dealer data from the State of Hawaii ([24]). We obtained data onannual hooks deployed from 2005-2013 from Federal logbook data ([18]). To evaluateout-of-sample prediction accuracy we adjusted all input and output prices to 2012dollars using the Consumer Price Index for all urban consumers nationally. Inputlevels for the variable costs were then scaled relative to the number of fishing hooksdeployed to enable efficient optimization during model calibration and simulations.Prices of inputs were adjusted using the inverse scaling ratio to preserve the observedexpenditure for each input. We were able to match vessels across data sources usingvessel name, permit number, and commercial license.In 2012, there were 129 vessels operating in Hawaii’s longline fishery. Of the 129vessels operating, 114 were represented in the cost and earnings survey ([21]). Weimputed input cost for missing vessels using random regression imputation consid-ering gear usage, vessel catch profile, and time spent on each target as regressionvariables. Variable costs were then grouped into six categories: fuel, captain pay,crew pay, bait, other, and gear. We grouped fuel and oil costs under fuel, fixedcaptain pay and shares paid to the captain under captain pay, combined crew fixedpay and crew shares paid under crew pay, total bait costs under bait, and gear re-placement cost under gear. Table 3 shows the degree of fleet heterogeneity based onthese inputs. According to the survey data, total variable costs exceeded total grossrevenue for six vessels. Rather than dropping these vessels because they violated theprofit maximizing assumption, we scaled their input costs such that annual profitswere 0.We then disaggregated individual vessel expenditure, catch, and revenue by threepolicy relevant targets: bigeye EPO, bigeye WCPO, and swordfish. The EPO andWCPO management regions are separated at 150 W longitude. Bigeye and swordfishfishing sets differ by depth, with swordfish lines set shallower than deep set bigeyelines. We used set-type and location from 2012 logbook data to calculate the pro-portion of total trip time spent each trip on each target. Trip target time was thenaggregated by vessel over the entire year indicating how much time each vessel spenton each target for 2012. Using the dealer data from 2005-2013, we matched vesseltrips to observed landings to calculate annual catch and revenue by vessel and target.Observations in the dealer data recorded daily sales. Fish sales were either recorded6y individual fish or groups of fish sold together. Daily vessel revenue was calculatedby multiplying pounds sold per fish, or group of fish by recorded ex vessel price perpound. The data were then aggregated on vessel and year to calculate the annualpounds of swordfish and bigeye caught, and the total value of vessel catch. Thesedata were then used to calculate fleet-wide average price of swordfish and bigeye,vessel-specific price premium for swordfish and bigeye, and price of non-target catchrepresenting its added value. Input expenditures for each vessel were disaggregatedby target according to the proportion of time spent on each target in 2012. Table1 summarizes the total active fleet size, and model sample size for each target overthe years 2005-2013.
The PMP framework consists of an objective function defining profit maximizationand resource and policy constraints that restrict input allocation decisions. To allowfor non-linearity in production and limited substitution between inputs we chose touse a generalized constant elasticity of substitution (CES) production function, andfor simplicity a linear expenditure function. When paired with a CES productionfunction, the linear expenditure function allows for smooth responses to changes inpolicy and resource constraints without adding more parameters to calibrate. Wedefine subscript to index the set of 128 vessels in our sample, indexes targets EPO,WCPO, and SF, and indexes inputs for fuel, captain pay, crew pay, bait, other andgear. Given a CES specification the production function for vessel targeting is givenbelow. y i,r = α i,r ( (cid:88) j β i,j,r x ρi,j,r ) δ/ρ . (1)We define the scale parameter for vessel technology as α i,r , input share as β i,j,r , elas-ticity of substitution as ρ , and the returns to scale coefficient as δ . By relating effortto catch, the scale parameter is analogous to a vessel-specific catchability parameterin traditional fishery production models. The returns to scale coefficient is definedusing a myopic definition ([4]) relating returns to scale to supply elasticity ( η ) δ myo = η η . (2)Because there have been no direct estimates of supply elasticity of catch in Hawaii’slongline fleet, we assume η = 0 .
5, which lies in the range of published supply elas-7 a b l e : T i m e s e r i e s d a t a s u mm a r y o f t o t a l a c t i v e a nd m o d e l e d v e ss e l s f r o m t h e t o2013 d e a l e r d a t a . B ec a u s e s o m e v e ss e l s fi s h m o r e t h a n o n e t a r g e t , t o t a l v e ss e l s m o d e l e d c a nb e l e ss t h a n t h e s u m o f e a c h t a r g e t . Y e a r T o t a l V e ss e l s O p e r a t i n g T o t a l V e ss e l s M o d e l e d V e ss e l s m o d e l e d ( W C P O ) V e ss e l s m o d e l e d ( EP O ) V e ss e l s m o d e l e d ( S F ) ρ = σ − σ , (3)where the untransformed elasticity of substitution ( σ ) is assumed to be 0.17 for allinputs. At present, we are unable to estimate an elasticity of substitution from thedata available, and the value of 0.17 allows for limited substitution between inputs,which we borrow from the agriculture literature and feel is reasonable in a fisherysetting ([8]). Model sensitivity analyses for these assumptions are provided in Figure3 and indicate our results are robust to changes in assumed parameter values.Although our production function only models targeted catch, fisher’s revenuewill depend on their ability to land quality fish, and on the value of non-target butcommercially valuable bycatch. To fully capture these components of revenue wemodel the price of swordfish and bigeye separately for each vessel. The fleet-wideaverage prices for swordfish and bigeye are given by p i,sf , and p i,be , vessel-specificprice premiums for swordfish and bigeye accounting for variation in quality are givenby p i,sfpr , and p i,bepr , and the additional values from non-targeted bycatch are givenby p i,nsf , and p i,nbe . By adding these three components together, we specify a vessel-specific price for bigeye (BE), and swordfish (SF). p i,SF = p i,sf + p i,sfpr + p i,nsf (4) p I,EP O = p i,W CP O = p i,be + p i,bepr + p i,nbe (5)This specification allows us to exactly reproduce observed vessel revenue, while onlymodeling the production of the policy relevant targets. Implicit in this price speci-fication we assume the price of bigeye from the EPO is the same as bigeye from theWCPO, which we feel is reasonable given they belong to the same species and areboth caught throughout the year.For simplicity, we specify a linear expenditure function. The input cost data onlyprovides total annual costs per input, therefore we assume input prices ( c i,j,r ) are 1,which implies input levels ( x i,j,r ) are in dollar units. The choice set x i,j,r is the vectorof individual vessel input levels for each target. Profit maximization is constrained bythree policies. We model annual catch limits for bigeye tuna in the EPO ( ACL
EP O )and WCPO (
ACL
W CP O ), and a total annual catch limit for swordfish (
ACL SF ).Vessel heterogeneity implies that the unobserved value of catch for each constraintwill vary by vessel. We therefore define the unobserved value of catch as µ i,r overvessels and targets. The maximization problem is given below.9ax x i,j,r (cid:80) i (cid:80) r (cid:104) ( p i,r + µ i,r ) y i,r − (cid:80) j c i,j,r x i,j,r (cid:105) s.t. (cid:80) i y i,EP O ≤ ACL
EP O (cid:80) i y i,W CP O ≤ ACL
W CP O (cid:80) i y i,SF ≤ ACL SF (6) We adapted the calibration procedure developed by [4]. Their calibration procedureis the most recent methodological advance in the PMP literature, comprehensivelyaddressing the criticism by [7] regarding the calibration of shadow values. Ratherthan estimated using an LP or ad hoc measures as was done previously, all unknownparameters and the shadow values are calibrated simultaneously using the samestructural forms as used in model simulations, in this case a CES production functionwith a linear expenditure function. [4] calibrated a PMP model for agriculture. Inagriculture, the constrained input is typically land, however, in fisheries, productioninputs can be purchased at any desired level on a common market and the constrainedresource is catch. We adapted the calibration procedure to account for this difference.For each target we specified a shadow value ( λ r ). We then calibrated the modelby minimizing the sum of squared error between observed expenditures and modelexpenditures resulting from the choice variable λ r as specified below.min λ (cid:88) i (cid:88) r (cid:34) ( p i,r + λ )¯ q i,r δ − (cid:88) j c i,j,r (cid:35) (7)The objective function is subject to four sets of constraints that determine thecalibration of unknown parameters. The first set of constraints requires productionparameters reproduce observed output (¯ q i,r ) for each vessel and target.¯ q i,r = α (cid:32)(cid:88) j β i,j,r x ρi,j,r (cid:33) δ/ρ , ∀ i, r (8)The second set of constraints requires the first order conditions of profit maximizationhold. The first order condition will be specified for each input, vessel, and target asbelow. 10 i,r α i,r δ (cid:32)(cid:88) j β i,j,r x ρi,j,r (cid:33) ( δ/ρ ) − β i,j,r x ρ − i,j,r = c i,j,r − ( λ + µ i,r ) αδ (cid:32)(cid:88) j β i,j,r x ρi,j,r (cid:33) ( δ/ρ ) − β i,j,r x ρ − i,j,r , ∀ i, j, r (9)The third set of constraints allows us to recover the vessel and target-specific unob-served value of catch ( µ i,r ). p i,r (cid:88) j αδ (cid:32)(cid:88) j β i,j,r x ρi,j,r (cid:33) ( δ/ρ ) − β i,j,r x ρ − i,j,r = (cid:88) j c i,j,r − ( λ + µ i,r ) αδ (cid:88) j (cid:32)(cid:88) j β i,j,r x ρi,j,r (cid:33) ( δ/ρ ) − β i,j,r x ρ − i,j,r , ∀ i, j. (10)Finally, our calibration procedure requires that for each vessel-target combinationthe sum of the input share parameters is one. (cid:88) j β i,j,r = 1 , ∀ i, r. (11) The PMP model calibration procedure is designed to calibrate unknown parametersand constraint shadow values such that profit maximizing vessels, subject to the baseyear resource constraints, will optimally allocate the observed base year levels of in-put, generating the observed outputs and revenues, and the observed expenditures.To evaluate whether the calibration was successful, we examine the range of cali-brated parameter values and the differences between the observed and the modeledinput levels using the base year catch constraints in 2012.In Table 2, we present the range of calibrated model parameters. The largestmagnitude of variation is found in unobserved shadow prices of catch and the scaleparameters. These parameters carry the most weight for modeling the heterogeneousresponses of the fleet. The share parameters also show significant variation indicatingthe model captured a large amount of vessel heterogeneity in input expenditures.11able 2: Summary of calibrated parameters for vessels modeled vessel and target-specific PMP model. The mean and standard deviation for each target-specific pa-rameter are given.
Description Symbol WCPO EPO SFScale parameter α λ -7.70 (NA) -7.57 (NA) -4.45 (NA)Unobserved price of catch µ β fuel β cap β crew β bait β other β gear Across targets, the share parameter for fuel are consistently larger than the otherinputs, which is expected given fuel is the largest single input cost. To verify thecalibration procedure, we examine the differences between observed and modeledinput levels for each input and each vessel’s output using the base year constraints.The largest difference in input is 2.02x10-14% and the largest difference in output is9.53x10-6%. Such small differences indicate that we achieve an accurate calibrationof all unknown parameters, and that our model can very closely replicate the observedbase year economic behavior of each vessel.To further verify the calibration procedure, we compare the shadow values tothe observed average price per pound of fish. The shadow value on each resourceconstraint can be interpreted as the value of relaxing the resource constraint by onepound of either bigeye or swordfish. Taken in absolute value terms, the calibratedshadow values of -7.70, -7.57, and -4.45, representing bigeye catch in the WCPO,EPO, and swordfish catch respectively, appear to be accurately calibrated. Whencompared to the average observed price per pound of bigeye, and swordfish ($7.99,$4.30 respectively), our calibrated shadow values are within a few cents of the averageobserved fish prices. Although average prices and shadow values do not share thesame interpretation, comparing the two does provide a useful validation of the overallcalibration procedure.
We evaluate model predictions in two ways. First, we compare predicted and ob-served catch from 2009 to 2013. Of the 128 vessels modeled, 126 were operating in12013; however, going back to 2009, as few as 119 of the original 128 were previouslyoperating (Table 1). For each year, we simulate the model by setting the fleet-widecatch constraint less than or equal to the total observed catch of the vessels remain-ing from our 2012 sample. This implies that our simulated fleet size decreases asvessels operating in 2012 are no longer observed in more distant years. To accountfor changes in input costs over time, we adjust the cost of fuel using U.S. number 2diesel retail price and the costs for captain pay and crew pay using annual salarydata from Bureau of Labor Statistics occupational profiles for farming, fishing, andforestry occupations. Regressing the predicted revenue on observed revenue for theyears 2009-2013, we examine the correlation coefficient and the amount of variationexplained by our model (Figure 1). We find the model performs best predictingbigeye catch in the WCPO, modestly for bigeye catch in the EPO, and poorly forswordfish catch. The best out-of-sample model predictions are made for the 2011bigeye catch in the WCPO (R-squared=0.35, correlation coefficient=0.53). For alltargets, model predictions become less accurate moving further in time away fromthe calibrated base year. This is expected as biological stock level, individual fishinglocation decisions, and environmental conditions could vary substantially over thistime, while our model assumes conditions remain constant. In the short-term themodel makes reliable predictions of individual vessel catch for the largest target inthe fishery, bigeye in the WCPO.Second, we evaluate the model input level predictions for each target comparingthe observed input levels from the 2005 cost and earnings data to the predicted inputlevels simulated using our PMP model. Results are shown in Table 4. In order tocompare the values, we match vessels that appear in both sets, reducing our sampleto 71, 25, and 1 for the WCPO, EPO, and SF targets respectively. Results froma paired Wilcoxon test comparing the observed and predicted input expendituresshow the model significantly under-predicts all inputs except gear and bait for theWCPO target. The model tends to over-predict input costs for the EPO target, andit over-predicts all inputs except fuel for the one matched vessel targeting SF. Bycomparing observed expenditures in 2012 (Table 3) to 2005 (Table 4), the primarysource of prediction error is the large differences in the observed expenditures between2012 and 2005. For instance, fundamental changes to the remuneration schemes overthese years, including the wide-spread transition from crew shares paid to domesticcrew to fixed pay for foreign crew, could account for the observed differences in crewpay and captain pay. We also observed a reduction in fuel expenditures in 2005 inthe WCPO and EPO, and increase in SF, which could reflect a change in fishing c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . llll ll lll ll lll lll ll ll ll lll lll lll l llll l ll ll ll lllll ll ll l llll ll ll lll l llll lllll ll lll ll l lll llll ll lllll ll lll lllll llll llll lll . . . . .
00 0 . . . . . Predicted Catch (Scaled lbs) c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . llll ll lll ll llll ll ll ll lll lll lll lll l ll ll ll lllll ll ll lll ll ll ll lll l llll ll lll ll lll ll l lll lll ll ll lll lll ll ll lllll lll l llll lll . . . . .
00 0 . . . . . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . llll ll lll ll lll lll ll lll lll lll lll lll lll l ll ll ll lll ll ll ll lll ll ll ll lll l llll ll lll ll lll ll l lll llll ll ll lll lll lllll llll l ll l llll l llll . . . . .
00 0 . . . . . c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = llll ll lll ll lll lll ll lll lll lll lll lll l llll l ll ll ll lll ll ll ll lll ll ll ll lll l llll lllll ll lll ll l lll llll ll ll lll ll l ll lll ll lllll l ll l llll l llll . . . . .
00 0 . . . . . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . llll ll lll ll lll lll lll ll ll lll lll lll l llll lll ll ll lllll ll lllll ll ll ll lll l llll ll lll ll lll ll l lll llll ll lllll ll ll lll ll lllll lll l llll ll ll . . . . .
00 0 . . . . . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . lllllll lllllll l lll llll ll lllllll lll lll ll lll llllll llll lllllll lll lll lll ll lll . . . . .
00 0 . . . . . Predicted Catch (Scaled lbs) c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . lllllllllllllll l lll lllll lll llllll ll lll llll ll lll lllllll llll llll lll l lll ll ll ll ll llll ll lll . . . . .
00 0 . . . . . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . lllllllll llllllll ll llll lll llllll ll lll llll ll lll lllllll llll lllllllll ll ll lll llll ll llll . . . . .
00 0 . . . . . c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = lllllllll llllllll l lll lllllll lll llllll ll lll llll ll lll lllllll llll lllll lllllllll ll ll ll ll llll ll llll . . . . .
00 0 . . . . . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . llllllll lllllll l ll lllll ll llllll ll ll llll ll lll lllllll llll llll lll lllll ll ll ll llll ll llll . . . . .
00 0 . . . . . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . llll llll l ll ll ll . . . . .
00 0 . . . . . O b se r ve d C a t c h ( sca l e d l b s ) Predicted Catch (Scaled lbs) c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . llll lllll llll ll . . . . .
00 0 . . . . . O b se r ve d C a t c h ( sca l e d l b s ) c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . c oe f. = . , r = . lllll lll ll llll ll . . . . .
00 0 . . . . . O b se r ve d C a t c h ( sca l e d l b s ) c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = c oe f. = , r = lll ll llll ll ll ll ll . . . . .
00 0 . . . . . O b se r ve d C a t c h ( sca l e d l b s ) c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . c oe f. = − . , r = . lllll llll l . . . . .
00 0 . . . . . O b se r ve d C a t c h ( sca l e d l b s ) F i g u r e : E v a l u a t i o n o f m o d e l p r e d i c t i o n s o f i nd i v i du a l v e ss e l c a t c h f o r b i g e y e i n t h e W C P O (r e d ) , b i g e y e i n t h e EP O ( g r ee n ) , a nd s w o r dfi s h ( b l u e ) f r o m - . T h e s o li d li n e i nd i c a t e s t h e - d e g r ee li n e . T h e c o rr e l a t i o n c o e ffi c i e n t a nd R - S q u a r e d f r o m t h e li n e a r m o d e l a r e g i v e n i n t h e t o p - l e f t c o r n e r o f e a c hp l o t . A x e s a r e s c a l e d s o t h e m a x i m u m c a t c h i s t o p r e v e n t d i s c l o s u r e o f c o nfid e n t i a l d a t a . To demonstrate the usefulness of a vessel-specific PMP model for Hawaii’s longlinefishery, we examine vessel responses and impacts on individual vessel catch to changesin the annual catch limit policy. We simulate two policy changes. The first is a policythat increases the annual catch limit of bigeye in the WCPO by 10% from the 2012base year. The second is a policy that decreases the same catch limit by 10% fromthe 2012 base year. A 10% change in the catch limit policy is roughly in line withthe agreed upon changes for bigeye in the WCPO in the next few years which willsee catch limit decrease 11% from 3,763 metric tons in 2014, to 3,345 metric tons in2017.The vessel-specific nature of our PMP model allows us to evaluate the distribu-tional effects of such policy changes. We expect that individual vessels will respondto varying degrees, depending on factors such as technological efficiency and prof-itability, which makes them more or less sensitive to policy changes. In Figure 2, wepresent the distribution of catch responses given an increase and decrease in bigeyecatch limits in the WCPO. The range of responses is large. With a 10% increase incatch limit, we see that vessels respond by increasing catch from less than 5% to 20%.With a 10% decrease in catch limit, the responses are symmetric to the 10% increase15 a b l e : M e a n o b s e r v e d a nd t h e m e d i a nd i ff e r e n ce b e t w ee n o b s e r v e d a ndp r e d i c t e d i npu t e x p e nd i t u r e s . O b s e r v e dd a t a c a m e f r o m t h e C o s t a nd E a r n i n g s Su r v e y . A ll v a l u e s a r e a d j u s t e d t o2012 d o ll a r s . T h e m e d i a nd i ff e r e n ce a ndp - v a l u e s a r e f r o m a t w o - s a m p l e p a i r e d W il c o x o n t e s t . W C P O EP O S F I npu t s M e a n O b s e r v e d ( d o ll a r s ) M e d i a n P r e d i c t e d D i ff e r e n ce ( P - v a l u e ) M e a n O b s e r v e d M e d i a n P r e d i c t e d D i ff e r e n ce ( P - v a l u e ) M e a n O b s e r v e d M e d i a n P r e d i c t e d D i ff e r e n ce ( P - v a l u e ) ( d o ll a r s )( d o ll a r s ) F u e l , - , , , , - , ( < . ) - . ( NA ) C a p t a i n P a y , - , , , , , - . - . ( NA ) C r e w P a y , - , , , , , ( < . ) - . ( NA ) B a i t , , , , , - . - . ( NA ) O t h e r , - , , , , , - . - . ( NA ) G e a r , - , , , , , - . ( < . )( NA ) S a m p l e
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Percent Change in Catch D e n s i t y Figure 2: Distribution of responses for individual vessels measured by the percentchange from 2012 catch levels. Results from 10% increase in annual catch constraintfrom 2012 are given filled black and represent increases in catch. Results from 10%decrease in annual catch constraint from 2012 are filled red and represent decreasesin catch. 17olicy. Vessels reduce catch from less than 5% to 25%. Given the range in policyresponses, individual vessels will clearly be affected differently. Some will be highlysensitive to policy changes; most will experience moderate impacts. Understandingthe distributional implications is clearly important for evaluating economic impactsof fishery policies in Hawaii’s longline fishery.
In this paper, we have shown that the vessel and target specific PMP model ofHawaii’s longline fishery reliably predicts short-term effect of policies on bigeye catchin the WCPO and EPO. Model predictions are more accurate when simulating vesselresponses close to the base year, but lend some insight even at further distances. Bycalibrating at the vessel-specific level, we are able to identify the range of economicresponses to policy changes, capturing the heterogeneous nature of Hawaii’s longlinefleet. This more realistically models vessel responses, as well as provides an eval-uation of the distributional effects of policy changes on catch, which is importantfor evaluating the stability of new policies. For fishery managers, the PMP model ofHawaii’s longline fishery provides a valuable tool for evaluating the economic impactsof current and potential fishery policies.The PMP framework also provides a rich structural model with which we canstudy fisheries in general. Later work will address parameter instability resultingfrom fundamental changes to underlying economic relationships or environmentaland biological conditions, and estimate target switching decisions made by fishers.We will also consider the effects of overlapping policy constraints such as turtleinteraction caps, and explore the individual vessel characteristics that make certainvessels more sensitive to policy changes than others.
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Appendix l l l ll l l l ll l l l ll l l l ll l l l l Year C oe ff i c i en t o f P r ed i c t ed on O b s e r v ed Parameters lllll sup = 0.5, sub = 0.17sup = 0.1, sub = 0.17sup = 0.9, sub = 0.17sup = 0.5, sub = 0.5sup = 0.5, sub = 0.9
WCPO l l l l ll l l l ll l l l ll l l l ll l l l l
Year R − s qua r ed o f P r ed i c t ed on O b s e r v ed Parameters lllll sup = 0.5, sub = 0.17sup = 0.1, sub = 0.17sup = 0.9, sub = 0.17sup = 0.5, sub = 0.5sup = 0.5, sub = 0.9
WCPO l l l l ll l l l ll l l l ll l l l ll l l l l
Year C oe ff i c i en t o f P r ed i c t ed on O b s e r v ed Parameters lllll sup = 0.5, sub = 0.17sup = 0.1, sub = 0.17sup = 0.9, sub = 0.17sup = 0.5, sub = 0.5sup = 0.5, sub = 0.9
EPO l l l l ll l l l ll l l l ll l l l ll l l l l
Year R − s qua r ed o f P r ed i c t ed on O b s e r v ed Parameters lllll sup = 0.5, sub = 0.17sup = 0.1, sub = 0.17sup = 0.9, sub = 0.17sup = 0.5, sub = 0.5sup = 0.5, sub = 0.9
EPO l l l l ll l l ll l l l ll l l l ll l l l l