Promoting selective fisheries through certification? An analysis of the PNA unassociated-sets purse seine fishery

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Promoting selective fisheries through certification? An analysis of the PNA unassociated-sets purse seine fishery Rolf A. Groeneveld a,∗ , Martin F. Quaas b,c a b c

Wageningen University, Environmental Economics and Natural Resources Group, Hollandseweg 1, 6706 KN Wageningen, The Netherlands Department of Economics, University of Kiel, Wilhelm-Seelig-Platz 1, 24118 Kiel, Germany Kiel Institute for the World Economy, Kiellinie 66, 24105 Kiel, Germany

a r t i c l e

i n f o

Article history: Received 30 April 2015 Received in revised form 9 October 2015 Accepted 10 October 2015 Available online xxx Keywords: Tuna Certification Pacific Ocean Purse seine Economics

a b s t r a c t The certification by the Marine Stewardship Council of the unassociated-sets purse seine fishery of the Parties to the Nauru Agreement (PNA) has the potential to improve stocks of the fishery’s main three tuna species, as well as to allow the PNA to extract more resource rents from the fishery. In this paper we analyze the economic and biological effects of this certification with a tractable bioeconomic model. We find that under plausible assumptions certification of tuna from the PNA unassociated-sets purse seine fishery can enhance stock size of skipjack tuna and bigeye tuna, but is likely to reduce stocks of yellowfin tuna due to the unassociated-sets fishery’s high catch rate for this stock. The PNA’s access fee declines in most scenarios considered. © 2015 Elsevier B.V. All rights reserved.

1. Introduction The 2011 certification under the Marine Stewardship Council (MSC) of the unassociated-sets purse seine fishery of the Parties to the Nauru Agreement (PNA) (Banks et al., 2011) has the potential to enhance management of tuna resources in the Western and Central Pacific Ocean (WCPO), as well as the resource rents accruing to PNA signatories. Harvesting almost 1.9 million metric tons of tuna, at a value of US$3.9 billion, in 2013 (about 72% and 64% of the total tuna harvest weight and value in the WCPO, respectively) (Williams and Terawasi, 2014), the purse seine fishery is a major player in the WCPO fishery and ecosystem. The widespread use of Fish Aggregation Devices (FADs) in the purse seine fishery, however, has a considerable impact on tuna stocks as well as the wider marine ecosystem (Dagorn et al., 2013; Leroy et al., 2013). Certification of tuna caught by purse seine sets on free-swimming schools (also referred to as unassociated sets), in order to distinguish it from tuna caught by means of FADs, can contribute to sustainable fisheries management in the WCPO. Moreover, MSC certification may enhance resource rents accruing to PNA signatories through the price premium of certified tuna.

∗ Corresponding author. E-mail addresses: [email protected] (R.A. Groeneveld), [email protected] (M.F. Quaas).

Whether eco-certification will have the desired economic and ecological effects, however, is subject to considerable debate in the scientific literature. As the most prolific seafood label, the MSC has been criticized for perceived leniency of requirements and poor representation of developing countries (Gulbrandsen, 2009; Jacquet et al., 2010). Moreover, Froese and Proelss (2012) argue that a number of fisheries certified under MSC or Friend of the Sea (FOS) are nevertheless being overfished, although these results are subject to considerable debate (Agnew et al., 2013; Froese and Proelss, 2013). On the other hand, Gutiérrez et al. (2012) demonstrate that MSC certified seafood is more likely to be sustainably managed, implying that the label does indeed facilitate consumers in choosing more sustainable seafood products. It has also been argued that certification of fisheries that are not yet sustainably managed, but on their way to becoming so, can also stimulate sustainable fisheries management. Many such fisheries, however, appear to remain in the early stage of Fisheries Improvement Projects (FIPs), rather than move on to fully well-managed fisheries as envisaged by the MSC (Sampson et al., 2015). A more theoretical concern is that eco-certification may fail to achieve sustainability goals, even if some consumers are willing to pay a price premium for the certified product (Mattoo and Singh, 1994; Gudmundsson and Wessells, 2000; Sedjo and Swallow, 2002). The rationale behind these concerns is that promotion of the sustainable product can enhance overall demand for both the sustainable and the unsustainable product, with the

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possible perverse effect of increasing, rather than reducing, environmental pollution or overexploitation. This paper investigates the possibility of such perverse outcomes for the specific case of the MSC certification of the PNA unassociated-sets fishery. More specifically, we investigate the effects of an increased preference for canned tuna from unassociated sets on biomass of the three tuna species, tuna consumption, and the access fee of the PNA. We develop a tractable model with three fish stocks with two age classes each, five fisheries, and a representative consumer who chooses a bundle of four different goods, namely a numeraire good, fresh tuna, certified canned tuna, and non-certified canned tuna. The paper proceeds as follows. Section 2 provides a more detailed background of the economic literature on the effects of eco-certification, and of the specific case of the WCPO tuna fishery. Section 3 explains the model structure and data used. Section 4 presents the results of the analysis, and Section 5 concludes.

2. Background of the study 2.1. Economic effects of eco-certification The economic literature on eco-certification has largely focused on estimating the price premium for the certified product, and on the potential for perverse outcomes of certification. Sedjo and Swallow (2002) analyzes the price premium for an ecolabel that signals eco-friendly forestry practices, and demonstrates that the price premium in such schemes depends heavily on the cost difference between the eco-friendly and non-eco-friendly means of production. If that cost difference is small the certified and the noncertified products may still have the same price. Empirical research on the price premium regards stated preference surveys (see e.g. Erwann, 2009) and hedonic analysis, either of supermarket scanner data (see e.g. Roheim et al., 2011) or field observations (see e.g. Sogn-Grundvåg et al., 2013, 2014; Asche et al., 2015). These studies suggest that the price premium consumers are willing to pay for eco-certified fish is between 10% and 15% of the price of the uncertified product. Most of these studies have been conducted in the UK, and it is yet unclear to what extent these results can be extended to other countries. Moreover, the evidence is mixed as to whether a share of the price premium actually accrues to the fishermen. Blomquist et al. (2015) finds in an analysis of Swedish landings and logbook data that MSC certified cod does fetch a higher price than non-certified cod, but this difference is mainly due to the buyers involved as the certified buyers also offered a higher price to fishers before being certified. Stemle et al. (2015) analyses landings and logbook data of several fisheries in Alaska and Japan, and finds a significant increase for chum and pink salmon in Alaska and flathead flounder in Japan, but no significant increase for chinook and coho salmon and halibut in Alaska, and even a significant decrease in price for sockeye salmon. Eco-certification could, in theory, perversely worsen the environmental problems which it is designed to alleviate (Mattoo and Singh, 1994; Gudmundsson and Wessells, 2000). Mattoo and Singh (1994) demonstrate for ‘dolphin safe’ tuna that ecolabeling can increase aggregate demand for tuna, which includes both the certified and the non-certified product, thereby possibly increasing dolphin catches. Gudmundsson and Wessells (2000) identify a similar mechanism where certification can increase fishing effort, exacerbating overfishing. What is driving the results of Mattoo and Singh (1994) and Gudmundsson and Wessells (2000) is that the certification does not address one of the underlying mechanisms of overfishing, namely the absence of strong institutions to limit fishing effort. Gudmundsson and Wessells (2000) conclude that

certification will not improve poorly managed fisheries if certification increases demand. These results, however, have been subject to the assumption that the certified good and the non-certified good interact only through consumers’ choices. As far as externalities are present, such as dolphin bycatch in Mattoo and Singh (1994) or unspecified environmental damage in Sedjo and Swallow (2002), they represent a general social cost that is not reflected in the marginal costs of any of the goods considered. Examples abound, however, where the certified and the non-certified goods interact through physical relations as well as consumer preferences. The most relevant in this respect is the certification of selective fishing practices in a mixed fishery. Many fisheries all over the world catch a mix of different species in different age classes. In this situation promoting selective fishing practices over unselective ones has two potential effects. The first effect is a substitution effect: as consumers value the certified product more than the non-certified product, the selective fishery grows at the expense of the unselective fishery, and, ceteris paribus, bycatch of juveniles and vulnerable species is reduced. The second effect is a scale effect: as certification stimulates demand for fish in the manner described by Mattoo and Singh (1994) and Gudmundsson and Wessells (2000), fishing effort by both the certified and the non-certified fisheries increases. In a fishery where limits are in effect on overall fishing effort, but not on specific fishing methods, the substitution effect likely prevails; in a fishery where no effective limits on fishing effort exist, it depends on such parameters as the elasticity of overall demand which of the two effects prevails. Therefore, it is unclear whether certification of selective fishing practices can reduce overfishing in a mixed fishery where limits on fishing effort are poorly managed or enforced, or absent. So far, however, technical interactions between a certified fishery and a non-certified fishery have not been considered in the resource-economic literature on certification. 2.2. The WCPO tuna fishery and certification of the PNA unassociated-sets purse seine fishery The WCPO purse seine fishery has been around since the 1950s, although it was not before the 1980s that the fishery grew to become the dominant player it is today (Barclay, 2014). The fishery’s dominant species in terms of revenue and catch volume is skipjack tuna (Katsuwonus pelamis). Skipjack tuna is usually sold on the world market as canned tuna and generated an estimated $2.9 billion, or 75% of total purse seine tuna revenues, in 2013 (Williams and Terawasi, 2014). The second dominant tuna species in the purse seine fishery is yellowfin tuna (Thunnus albacares), which generated an estimated $829 million, or 21% of total purse seine tuna revenues, in 2013. The purse seine fishery usually sells adult yellowfin tuna as canned tuna at a price roughly twice that of canned skipjack tuna due to its higher conversion rate (Miyake et al., 2010). Juvenile yellowfin, however, are usually mixed with skipjack tuna, and the mix has a conversion rate similar to that of skipjack tuna. The remainder of purse seine tuna revenues (less than 2%) come from catch of bigeye tuna (Thunnus obesus). Adult yellowfin tuna and bigeye tuna are also targeted by other fisheries, mainly longline fisheries, which sell their catch as fresh or frozen tuna at prices up to 5–6 times that of canned tuna (Miyake et al., 2010). Purse seine fishing is generally done in three different ways. The first is to set a purse seine on free-swimming schools of tuna, which are usually found through visual clues or sonar (Gaertner et al., 1999; Bez et al., 2011). These purse seine sets are generally referred to as unassociated sets (Williams and Terawasi, 2014). The other two types, usually referred to as associated sets, set purse seines on drifting objects, as these tend to attract many different types of fish, including tuna. If the drifting object is not man-made, such as a drifting log, debris, or a dead animal, the set is referred

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to as a log set (Banks et al., 2011). The floating object can also be artificial, in which case it is referred to as a Fish Aggregation Device (FAD). FADs are typically bamboo rafts, sometimes equipped with GPS systems and echosounders (Dagorn et al., 2013), or guarded by an employee of the fishing company. Logs and especially FADs enable fishing companies to make substantial cuts in search costs, but only at the expense of catching a larger share of juvenile tuna (as well as other species) than sets on free-swimming schools do. Use of FADs has been criticized by scientists and environmental NGOs (see e.g. WWF, 2011). A major issue with FADs is the bycatch of juvenile yellowfin and bigeye tuna (Dagorn et al., 2013), as well as other species such as mahi-mahi (Coryphaena hippurus), rainbow runner (Elagatis bipinnulata), and several shark species (Leroy et al., 2013). Research also suggests that FADs could act as an ecological trap as tuna near FADs are more likely to have empty stomachs (Leroy et al., 2013). Tuna resources are of utmost importance to many Pacific island economies: the GDP of one of the most fishery-dependent PNA country, Kiribati, depends for 20% on fisheries (Gagern and van den Bergh, 2013). As most tuna is usually caught by distant water fishing vessels (DFWVs) (more than 60% in the case of Kiribati), the ability to extract access fees from DFWVs is an important source of income, long amounting to 5–6% of tuna revenue for years, but after the introduction of the VDS increasing to 8–13% (Havice, 2013). It is therefore not surprising that the PNA has been ahead of its fellow WCPFC members in sustainably managing its tuna resources (Aqorau, 2009; Barclay, 2014; Miller et al., 2014). One of its most important moves towards sustainable fisheries management was the introduction of a Vessel Day Scheme (VDS) by 1 December 2007. Given the dominant position of the PNA in the WCPO - over 60% of total volume of tuna catch in the WCPO was caught inside the EEZs of PNA countries in 2011 – the introduction of the VDS has generated considerable economic benefits for the PNA, but it has not yet succeeded in achieving its main goal of limiting fishing pressure on PNA tuna resources (Havice, 2013). Other measures include seasonal closures of FADs and a requirement that PNA-licensed vessels refrain from fishing in the high seas pockets near the PNA EEZs (Aqorau, 2009; Barclay, 2014). A more recent step in the PNA’s fisheries management concerns the certification of the PNA unassociated-sets fishery under the Marine Stewardship Council (MSC). The MSC certification procedure assesses a fishery along three principles (Banks et al., 2011): (1) the fishery must be conducted in a manner that does not lead to overfishing or depletion of the exploited populations and, for populations that are depleted, the fishery must be conducted in a manner that leads to their recovery; (2) fishing operations should allow for the maintenance of the structure, productivity, function and diversity of the ecosystem on which the fishery depends; and (3) the fishery must be subject to an effective management system that respects local, national, and international laws and standards. Of the three main purse seine fisheries in PNA waters, the log set fishery failed the second principle due to its catch of bigeye tuna (Thunnus obesus), whereas the FAD fishery was not considered at all. This leaves the PNA unassociated-sets fishery as the first WCPO purse seine fishery to be certified by the MSC.

3. Material and methods 3.1. Model structure 3.1.1. The market for tuna We represent the global tuna market by a representative consumer who derives utility from overall tuna consumption (T) and a quantity of a numeraire commodity (X) as in Quaas and Requate, 2013:

U(X, T ) = X +

3

 T (−1)/ −1

(1)

where  denotes the overall elasticity of demand for tuna. Tuna consumption is composed of canned tuna (CC ) and fresh tuna (CF ) as a CES1 aggregate: T (CC , CF ) =



(−1)/

CC

+ (ϕ CF )(−1)/

/(−1) (2)

where  is the elasticity of substitution between canned and fresh tuna; and ϕ is a coefficient reflecting the different utility derived from fresh tuna as compared to canned tuna. Canned tuna is further divided in tuna from certified fisheries (CM ) and tuna from other, non-certified fisheries (CN ): CC = CN +  CM

(3)

where  ≥ 1 is a coefficient reflecting a possible preference for canned tuna from certified fisheries over tuna from non-certified fisheries. We assume that all fresh tuna on the market is adult yellowfin tuna or adult bigeye tuna from the non-purse seine fisheries, which largely consists of the longline fisheries in the WCPO and the longline and handline fisheries in the archipelagic waters of Indonesia and The Philippines. Canned tuna consists of a mix of juvenile and adult skipjack tuna, yellowfin tuna, and bigeye tuna from the purse seine fishery. We apply the following convention for the notation of catch by the different fisheries and stocks considered in the model. Let Hfzs denote the catch of species s in size class z by fishery f. Species included are skipjack (S), yellowfin (Y), and bigeye (B), which can be caught as juveniles (J) or adults (A). Fisheries included in the model are the PNA associated-sets fisheries (P), the PNA unassociated-sets fishery (M), the associated-sets and unassociated-sets fisheries in other WCPFC waters (V and W, respectively); and non-purse seine fisheries and fisheries in the Indonesian and Philippine archipelagic waters (O). Under this notation the relation between tuna consumption and tuna harvests is assumed to be as follows: CN =

 

vfzs Hfzs +

f∈ /{M,O} z

CM =

 z

CF =





vOJs HOJs + vOAS HOAS

(4a)

s

vMzs HMzs

(4b)

s

vOAs HOAs

(4c)

s= / S

where vfzs denotes the conversion rate of tuna caught by fishery f in size class z of species s. The conversion rate reflects the fraction of harvest that is present in the final product (Miyake et al., 2010). For purse seine harvests we assume Schaefer harvest functions as we aim to model their fishing effort endogenously, whereas for other fisheries we assume a fixed fishing mortality: Hfzs = qfzs Ef Bzs ∀f = / O

(5a)

HOzs = F Ozs Bzs

(5b)

where qfzs denotes catchability of the stock of size class z of species s by purse seine fleet f; Ef denotes fishing effort of purse seine fishery f (f ∈ {M, P, V, W}); Bzs denotes the biomass of size class z of species s; and F Ozs denotes fishing mortality induced by the other fisheries. The assumption of fixed catchability coefficients in Eq. (5a) means that, for a given effort level, bycatch is determined by the relative abundances of the different species and age groups, which

1

Constant Elasticity of Substitution.

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is a common assumption in the literature (e.g. Skonhoft et al., 2012). This is, however, a rather conservative assumption with respect to selectivity, as in reality fishermen have several options to target specific age groups, including the choice of mesh sizes and types of fishing gear (Diekert et al., 2010; Quaas et al., 2013), as well as the time and location of harvest (Squires, 1987; Asche et al., 2007; Branch and Hilborn, 2008; Abbott et al., 2015). Note, however, that the catchability coefficients qfzs in Eq. (5a) will be different for the unassociated and associated fishery. This reflects the fact that the schools of different age groups or different species of tuna are available (and catchable) to a different extent to the different fisheries. 3.1.2. Biological growth For both species we assume that the stock of juveniles depends on recruitment, harvest, and maturation of juveniles. Recruitment is assumed to take place according to a Beverton and Holt (1957) stock-recruitment function: GJs

 as BAs = − ˛s BJs − Hf Js 1 + bs BAs



Hf As

(7)

where GAs denotes growth in adult biomass; ˇs denotes the amount of biomass added to adult biomass through maturation of juveniles; and ms denotes natural mortality of adults. Note that the original Beverton–Holt model would have ˛s = ˇs as it defines biomass in numbers of individuals; in our model, however, we must allow ˛s < ˇs to reflect the larger size of adult tuna. 3.2. Equilibrium conditions 3.2.1. Economic equilibrium conditions The economic equilibrium is characterized by two sets of conditions: (1) the entry–exit conditions of the fishery; and (2) the market-clearing conditions of the tuna market, taking into account the representative consumer’s demand function. The entry–exit conditions reflect the assumption that in a fully open-access fishery, fishing effort increases until all rents in the fishery have dissipated. This implies that under open access, the costs of fishing equal its benefits for each fishery. In this context we can interpret the PNA’s access fee as the shadow price of its restriction on fishing effort. Hence, in general form the entry–exit conditions can be specified as follows:

pN

 z

s

z

s

z

s



pM



vfzs qfzs Bzs = wf

∀ f ∈ {V, W}

(−1)/

[CN +  CM ]

(8a)

vPzs qPzs Bzs = wP + 

(8b)

vMzs qMzs Bzs = wM + 

(8c)

+ (ϕ CF )(−1)/

(/(−1))((−1)/)−1

−1/

[CN +  CM ]

= pN

(9a)



(−1)/

 [CN +  CM ]

+ (ϕ CF )(−1)/

(/(−1))((−1)/)−1

−1/

[CN +  CM ]

= pM

(9b) −1/

f

pN



ϕ(−1)/ CF

where GJs denotes growth in juvenile biomass, as and bs are coefficients of the Beverton–Holt recruitment function for species s, and ˛s denotes the fraction of juvenile biomass that becomes an adult, corrected for biomass growth of juveniles. Because our model assumes only two size classes for each species we cannot interpret the variables in Eq. (6) in numbers of individuals, as is common in the Beverton–Holt stock-recruitment model. Nevertheless, defining biomass in tons in this model allows us to capture the most relevant mechanisms in the fishery. Adult biomass grows through the maturation of juvenile biomass, minus natural mortality and harvesting:

(8d)

where pN and pM denote prices of non-certified canned tuna and certified canned tuna, respectively; wf denotes the costs per unit of effort of fishery f;  denotes the access fee required by the PNA from foreign vessels; and E VDS denotes the limit on total fishing effort in the PNA imposed by the Vessel Day Scheme (VDS). Lastly, the market equilibrium conditions imply that the representative consumer’s inverse demand for each tuna product equals its price:

(6)

f

GAs = ˇs BJs − ms BAs −

EM + EP = E VDS



(−1)/

[CN +  CM ]

+ (ϕCF )(−1)/

(/(−1))((−1)/)−1

= pF

(9c) 3.2.2. Biological equilibrium conditions In the steady state biomass of all species and size classes are constant:

 as BAs − ˛s BJs − Hf Js = 0 1 + bs BAs

ˇs BJs − ms BAs −



(10a)

f

Hf As = 0

(10b)

f

3.2.3. Solving the model Eqs. (4), (5) and (8)–(10) form a system of 47 conditions, which together determine the value of 47 endogenous variables: the three tuna products’ consumption (CN , CM , and CF ) and prices (p0 , pM , and pF ); harvest of all combinations of two size classes (z), three species s, and five fisheries f (Hfzs ); biomass of six combinations of size class and species (Bzs ); fishing effort of four purse seine fisheries (Ef ), and the PNA’s access fee (). This system of equations is solved numerically in a GAMS model, the code of which is available upon request. 3.3. Parameterisation 3.3.1. Input data Input data for model calibration include (1) biomass of the three tuna species; (2) cath of the three tuna species per fishery and region; (3) the age composition of catch; and (4) whole fish to end product conversion rates. For biomass the WCPFC stock assessments for the three tuna species of 2014 were used (Rice et al., 2014; Davies et al., 2014; Harley et al., 2014) (Table 1). Catch data include the distribution of purse seine catch over set types and geographical 5◦ by 5◦ quadrants in the WCPFC statistical area (WCPFC, 2014a), and tuna catch per fishery (WCPFC, 2014b) in the years 2010–2013 (Table 2). High-resolution catch data per EEZ were not available, so an overlay of an EEZ map on a 5◦ by 5◦ spatial grid was used to assign catch and effort in WCPFC (2014a) to countries proportionally to the fraction of EEZs in the quadrants. The age distribution of tuna catch in the main tuna fisheries was estimated by visual inspection of graphs in Williams and Terawasi (2014), assuming length at first maturity of skipjack, yellowfin, and bigeye tuna of 40 cm, 104 cm, and 110 cm respectively (Fishbase, 2015a,b,c). Whole fish to end product conversion rates were taken from Miyake et al. (2010)

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Table 1 Unexploited biomass, biomass under maximum sustainable yield, and current biomass estimated for skipjack tuna (SKJ) (Rice et al., 2014), yellowfin tuna (YFT) (Davies et al., 2014), and bigeye tuna (BET) (Harley et al., 2014).

Bs0 Bscurr SB0s SBcurr s SBMSY s MSY Hs

Unexploited total biomass (mln mt) Recent average total biomass (mln mt) Unexploited spawning biomass (mln mt) Recent average spawning biomass (mln mt) Spawning biomass under MSY (mln mt) MSY harvest (mln mt per year)

SKJ

YFT

BET

6.281 3.615 5.940 3.261 1.683 1.532

4.319 1.995 2.467 0.999 0.728 0.586

2.286 0.743 1.207 0.325 0.345 0.109

5

Table 3 Economic parameter values in reference simulation. Symbol

Description

Value

 ϕ pF /pC

Demand elasticity of tuna Utility weight fresh tuna Price ratio of fresh and canned tuna

1.5 3 5

Table 4 Estimated catchability and fishing mortality parameters. Skipjack

for canned tuna products and from Langley et al. (2006) for fresh yellowfin and bigeye tuna. As regards tuna demand (Table 3) we take into account the observation that the price of fresh tuna is usually around five times that of canned tuna (Williams and Terawasi, 2014). The model has no explicit currency, so the magnitude of costs and prices gives no information as such other than its sign and the ratio between costs and prices. Therefore we assume pF /pC = 5 in the baseline scenario where  = 1. Moreover, it is reasonable to assume that all else equal, consumers have a higher appreciation of fresh tuna than of canned tuna; hence we should have ϕ > 1. It can also be shown that we should have ϕ < pF /pC for  to be positive. In our reference simulation we assume ϕ = 3, but in the sensitivity analysis we will explore the wider parameter space. Miyake et al. (2010) argues that the price elasticity of tuna demand is about 1.55 for canned tuna and 2.53 for sashimi. Because  reflects overall tuna demand, which consists largely of canned tuna, we assume  = 1.5 in the reference scenario but we will explore higher values of  in the sensitivity analysis. Lastly, PNA access fees have recently increased from 5–6% of catch value to 8–13% (Havice, 2013); we therefore assume a baseline share of 10% which will be allowed to change under alternative values of . 3.3.2. Parameter estimation For total tuna harvest by purse seines we use the numbers provided by WCPFC (2014b), assuming a distribution of catch and effort over associated and unassociated sets as suggested by WCPFC (2014a). This gives estimates of qfzs and F Os , as given in Table 4. The biological model is parameterized such that it reproduces the current situation as described by Rice et al. (2014), Davies et al.

PNA associated PNA unassociated Non-PNA associated Non-PNA unassociated Other fisheries

Yellowfin

Bigeye

Juvenile

Adult

Juvenile

Adult

Juvenile

Adult

.178 .005 .168 .004 .415

.554 .460 .522 .321 .096

.271 .111 .288 .057 .123

.155 .319 .165 .165 .104

.414 .028 .298 .014 .050

.064 .034 .046 .016 .205

Table 5 Parameter values in the biological model and biomass benchmarks as predicted by the model. Symbol Description

Skipjack Yellowfin Bigeye

ms ˛s ˇs as bs Bˆ s0 Bˆ scurr

0.900 0.900 12.130 0.332 0.379 11.253 3.615

0.900 0.710 1.178 1.813 1.908 4.447 1.995

0.535 0.241 0.599 1.115 3.465 2.286 0.743

Unexploited spawning biomass (mln mt) 10.475

2.511

1.207

Recent average total biomass (mln mt)

3.261

0.999

0.325

Spawning biomass under MSY (mln mt) MSY harvest (mln mt per year)

3.244 1.532

0.890 0.586

0.368 0.109

SBs

Mortality rate adult biomass Fraction juvenile tuna becoming adult Biomass entering adult cohort Coefficient a recruitment function Coefficient b recruitment function Unexploited total biomass (mln mt) Recent average total biomass (mln mt)

0

SBs

curr

SBs ˆ sMSY H

MSY

(2014), and Harley et al. (2014), while minimizing the relative difference from the estimates of unexploited biomass and spawning biomass, and spawning biomass under MSY, with the additional restriction that ˛j , ˛y < 0.9. More details on the parameterization are given in Appendix A. Table 5 shows the estimated parameter values and the current, unexploited, and MSY biomass and spawning biomass as estimated by the biological model.

Table 2 Input data in the model: (1) average share per region of associated (A) and unassociated (U) purse seine sets in total catch (mln mt); (2) average catch (mln mt) per tuna species per fishery; (3) share of juvenile tuna in catch weight per species per fishery; and (4) conversion rates (whole weight to processed weight) of tuna products. PS

Share in total purse seine catch (PDD)

PNA

Non-PNA

SKJ YFT BET SKJ YFT BET

A

U

.358 .327 .705 .054 .055 .082

.428 .485 .129 .048 .040 .001

Catch (YB)

SKJ YFT BET

Share of juveniles in total catch (WT)

SKJ YFT BET

.034 .636 .893

SKJ YFT BET YFT BET

.4 .4 .4 .55 .55

Conversion rate (L/M)

All sizes Juveniles Adults

LL

1.344 .335 .067

PL

IP

Not applicable

.001 .078 .072

.191 .028 .005

.111 .033 .006

.001 .257 .517

0.0 .027 .079

.079 0.0 0.0

.370 .776 .934

.4 .4 .4 .55 .55

.4 .4 .4 .87 .91

.4 .4 .4 .87 .91

.4 .4 .4 .87 .91

PDD: WCPFC (2014a); YB: WCPFC (2014b); WT: Williams and Terawasi (2014); L: Langley et al. (2006); M: Miyake et al. (2010).

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a. Tuna consumption (mln mt)

80

0.6

EW

70

CM

0.5

b. Fishing effort (1000 days) EV

60

0.4

40 EM

30

0.2

20

CF

0.1 0

EP

50

CN

0.3

10

0

1%

2%

3%

4%

5%

0

Increase in preference for certified canned tuna (λ) μ

1.05 1

1%

2%

B AB

1.3

B JB

pN

1.1

0.95

0.85

1%

2%

3%

4%

5%

Increase in preference for certified canned tuna (λ)

B JS

B AS

B JY

B AY

1 0.9

0

5%

d. Biomass (relative to baseline biomass)

1.2

pF

4%

1.4

pM

0.9

3%

Increase in preference for certified canned tuna (λ)

c. Prices and PNA access fee (relative to baseline) 1.1

0

0.8

0

1%

2%

3%

4%

5%

Increase in preference for certified canned tuna (λ)

Fig. 1. Key variables in the reference simulation as a function of consumers’ relative preference for certified over non-certified canned tuna (): a. Consumption (mln mt) of certified canned tuna (CM ), fresh tuna (CF ), and non-certified canned tuna (CN ); b. Purse seine fishing effort (1000 days) of the PNA unassociated-sets fishery (EM ), the PNA associated-sets fishery (EP ), the non-PNA unassociated-sets fishery (EW ), and the non-PNA associated-sets fishery (EV ); c. Prices of certified canned tuna (pM ), non-certified canned tuna (pN ), and fresh tuna (pF ), and the PNA access fee, relative to their value at  = 1; d. Biomass of juvenile skipjack (BJS ), adult skipjack (BAS ), juvenile yellowfin (BJY ), adult yellowfin (BAY ), juvenile bigeye (BJB ), and adult bigeye (BAB ), relative to their values at  = 1.

The economic parameters remaining to be estimated are the substitution elasticity between canned tuna and fresh tuna (), and the costs of fishing effort (wf ). The value of  is estimated such that the model reproduces current consumption patterns under the assumed values of ϕ and pF /p0 . wf and  are estimated by solving the model for the current values of fishing effort, biomass, and harvest, assuming that in the business-as-usual scenario revenues from access fees constitute about 10% of fishing revenues (Havice, 2013) and  = 1. 4. Results 4.1. Introduction The reference parameter set presented in Tables 3–5 was analyzed for  ∈ [1.00, 1.05]; the results of this simulation are discussed in Section 4.2. The results of a sensitivity analysis using values  ∈ {1.5, 2, 2.5} and ϕ ∈ {2.5, 3, 4, 4.9} are presented in Section 4.3. The following sections discuss the effects on the variables biomass (Bzs ), fishing effort (Ef ), and consumption (CN , CM , and CF ). 4.2. Reference simulation An increase in  from its baseline value  = 1 has the predictable effect that non-certified canned tuna is replaced by certified canned tuna (Fig. 1a). When the PNA associated-sets fishery has disappeared altogether, this replacement becomes much more slowly as it is now solely driven by a slow decline in the non-PNA associatedsets fishery. The increase in fresh tuna consumption masks a decline in the catch of adult yellowfin tuna, which is more than compensated by an increase in the catch of bigeye tuna. Both changes are driven by changes in stock, which declines for yellowfin tuna but increases for bigeye tuna (Fig. 1d). The main driver behind this difference is the PNA unassociated-sets fishery’s high catch rate for

yellowfin tuna compared to the catch rate of the PNA associatedsets fishery (see Table 4). As regards fishing effort, the first effect is that unassociated sets become unprofitable outside the PNA already at a very small increase in  (less than 0.05%) (Fig. 1b). The main reason to use FADs is the higher success rates of purse seine sets, so as the price of non-certified canned tuna (pN ) declines, it becomes less attractive to avoid using FADs in the absence of a reward from certification. If the extra willingness to pay for certified tuna exceeds about 2.5% the next fishery to disappear altogether is the associated-sets fishery within the PNA (Fig. 1b). This is probably due to the PNA’s access fee, which is assumed in the model to keep total fishing effort in the PNA at E VDS . In the remainder of this paper we will refer to this value * ≈ 1.025 as the choke lambda. We denote the variable val∗ , C ∗ , and so on, and their values at the upper bound ues at * by BJS 0  = 1.05 considered in our analysis by BJS , C N , etc. Prices of canned tuna change no more than about 4%, which is not surprising given the small values of  considered.2 Strikingly, the access fee is about 12% lower at * than at  = 1.05, but it increases again when the PNA associated-sets fishery has disappeared (Fig. 1c). The main driver of the decline in the access fee is the decline in the stock of yellowfin tuna, which, under the catch functions assumed in the model, leads to a proportionate decline in CPUE for yellowfin tuna. 4.3. Sensitivity analysis In the sensitivity analysis we ran the model for 11 different combinations of values of  (1.5, 2, or 2.5) and ϕ (2.5, 3, 4, 4.9).3 Fig. 2 presents the range of values for the choke lambda (* ), the relative

2 It straightforward to verify that the price ratio of certified canned tuna and noncertified canned tuna (pU /p0 ) equals . 3 The case where  = 1.5 and ϕ = 3 is the reference parameter set.

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a. λ* and biomass at λ*

B AB

Relative change with respect to baseline

0.4

0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1 -1.2

0.8

B AB

Relative change with respect to baseline

Relative change with respect to baseline

Relative change with respect to baseline

R.A. Groeneveld, M.F. Quaas / Fisheries Research xxx (2015) xxx–xxx

0.3 0.2 0.1 0 -0.1 -0.2

0.5

λ*

B JS

B AS

B AY

B JY

B JB

0.3 0.2 0.1 0 -0.1 -0.2

B JS

B AS

B JY

B AY

B JB

b. Consumption, prices, and access fee at λ*

CN

CM

CF

pM

pN

pF

μ

d. Consumption, prices, and access fee at λ = 1.05

c. Biomass at λ = 1.05

0.4

7

0.6 0.4 0.2

0 -0.2

-0.4 -0.6 -0.8

CN

CM

CF

pM

pN

pF

μ

Fig. 2. Range (grey bars) and reference values (black bars) of relative changes in key variables in the reference simulation and the sensitivity analysis, which considered different values for overall tuna demand elasticity ( ∈ {1.5, 2, 2.5}) and preferences for fresh tuna (ϕ ∈ {2.5, 3, 3.5, 4, 4.9}).

0.6

a. Tuna consumption (mln mt) CM

0.5 0.4

CN

0.3 0.2 CF

0.1 0

0

1%

2%

3%

4%

5%

90 80 70 60 50 40 30 20 10 0

Increase in preference for certified canned tuna (λ)

b. Fishing effort (1000 days) EV

EP EM

0

1%

2%

3%

4%

d. Biomass (relative to baseline biomass)

c. Prices and PNA access fee (relative to baseline)

1.7

1.4 1.3

μ

B AB

1.6 1.5

1.2

5%

Increase in preference for certified canned tuna (λ)

B JB

1.4 1.1 1.0 0.9 0.8

0

1%

2%

3%

4%

pM

1.3

pN

1.2

pF

1.1

5%

Increase in preference for certified canned tuna (λ)

1.0

B JY B AY B JS B AS

0

1%

2%

3%

4%

5%

Increase in preference for certified canned tuna (λ)

Fig. 3. Key variables as a function of consumers’ relative preference for certified over non-certified canned tuna () under qMAY = qPAY = 0.253: a. Consumption (mln mt); b. Purse seine fishing effort (1000 days); c. Tuna prices and PNA access fee, relative to their value at  = 1; d. Tuna biomass relative to its value at  = 1.

changes at the choke lambda, and the relative changes at  = 1.05. The black bars indicate the reference simulation. Fig. 2a and c suggest that in all alternative scenarios considered  has to increase only a few percent to eliminate the non-PNA unassociated-sets fishery (not shown) and the PNA associated-sets fishery. The results for skipjack and yellowfin biomass seem fairly robust to the parameter estimates, but those for juvenile and adult bigeye stocks vary between +17% and +37%. These results vary mostly by the appreciation for fresh tuna (ϕ): the increase in bigeye stocks is highest when ϕ is close to its maximum value of 5.

Nevertheless, the general impression from the results remains that yellowfin stocks stand to lose from a higher demand for canned tuna from the PNA unassociated fleet, whereas bigeye stocks are likely to benefit. Results for consumption and prices (Fig. 2b and d) are fairly robust to the assumed parameter values. The most uncertain appears to be the effect on consumption of certified canned tuna, the relative increase of which varies between 54% and 71%. The low estimates of this change are associated with a relatively low appreciation for fresh tuna (ϕ = 2.5) and a relatively high elasticity

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of overall demand for tuna ( = 2.5). The effect on the PNA access fee (), however, varies wildly, where in the most extreme case the access fee gets close to zero at * , or increases by a fifth at  = 1.05. In general the lowest access fees at the choke lambda are associated with lower appreciation for fresh tuna (ϕ). The effects on the stocks of adult yellowfin tuna, and their implications for the access fee beg the question to what extent the results are driven by the unassociated sets fishery’s higher catch rate for this stock. To investigate this we ran a simulation with qMAY = qPAY = 0.253, so that total catch of adult yellowfin tuna matches observations but both purse seine set types within the PNA have the same catch per unit effort (Fig. 3). In this simulation, increasing  enhances yellowfin tuna stocks, probably because of the lower fishing pressure on juvenile yellowfin tuna; the access fee now increases in . This suggests that the reduced catch of juvenile yellowfin tuna induced by certification has a positive impact on adult yellowfin tuna stocks, but this positive effect is obscured by the unassociated-sets fishery’s higher catch rate for adult yellowfin tuna. This effect is also what drives the decline in the access fee in the reference simulation.

5. Discussion and conclusion Certification of sustainably managed fisheries aims to reward these fisheries with a higher price for their products than noncertified fisheries, although the jury is still out as to what price premium is actually achieved, and what share of it trickles down to fishers. Certification could be considered a valuable addition to the pool of policy measures for fisheries management if it would actually improve fish stocks and reward the certified fishery for doing so. We have studied the effects of the certification of the PNA unassociated-sets fishery on stocks, prices, and consumption, and on the PNA’s access fee. Our analysis gives mixed results: stimulating demand for products with less juvenile bycatch can potentially contribute to conservation of bigeye stocks, but under the catch coefficients estimated in this study the effect on yellowfin stocks is most likely to be negative as the PNA unassociated-sets fishery tends to catch more adult yellowfin tuna than the associated-sets fishery. Likewise, the effect of certification on the PNA’s access fee is highly uncertain as it depends heavily on the assumptions made with respect to the demand and substitution elasticities of canned and fresh tuna. In most of our scenarios, however, the effect appears to be negative. Our analysis rests on a number of assumptions that may influence the results. We have abstracted from transaction costs of certification. In reality these costs may be significant, and could limit the ability of the market to adapt to the price incentive given by the certification. We have futher assumed that certified and non-certified canned tuna are perfect substitutes for the consumer, except for the extra willingness to pay for certified tuna. We regard this to be a realistic assumption, as both products are essentially indistinguishable for consumers, except for the label. We further have assumed that fishing costs are linear in effort. This implies that there are no switching costs between associated and unassociated sets, which is in line with the fact that both fisheries use almost the same technology. Linear effort costs, i.e. constant marginal costs of effort, make the open-access outcome in the zone outside the PNA rather sensitive to changes in demand. Marginal fishing costs might be increasing with effort, for example because of increasing marginal transport costs to and from fishing locations. Increasing marginal effort cost may reduce the susceptibility of the open-access outcome to certification, and a stronger stimulation may be required to yield the changes in the fishery structure we have found in our analysis. Finally, we have assumed that catch coefficients are fixed while in reality fishers have more

degrees of freedom to select a particular species or size class. Again, these extra degrees of freedom may make the result less susceptible to changes in demand. Overall, the critical value for the extra willingness to pay for certified tuna may be larger than just a few percent, but apart from the access fee the general results are likely to be unchanged if more complex fishing cost structures are taken into account. Lastly, our parameter estimates depend heavily on publicly available data, especially the 5◦ by 5◦ geographical quadrant catch and effort data (WCPFC, 2014a). These data were attributed to the different EEZs in the WCPO proportionally to the surface area of the EEZs in the quadrants. Higher-resolution data may have led to a different distribution of catch and effort over countries, and hence to different catch coefficients. To conclude, our analysis gives insights for management recommendations and future research. First, we conclude that the certification of the PNA unassociated-sets fishery is likely to enhance the stocks of the most vulnerable tuna species, namely bigeye tuna. Second, certification could also improve yellowfin stocks as well as the access fee, provided that measures are taken that in effect change the catch coefficient of the unassociated-sets fishery for yellowfin tuna. In practice this would mean a change in targeting behaviour by unassociated-sets purse seine fishers, for example by introducing catch quota for yellowfin tuna. Acknowledgements The authors wish to extend their gratitude to Simon Bush, Steven Adolf, the editor, and two anonymous referees for helpful comments on earlier versions of this article. Any remaining errors are the sole responsibility of the authors. Appendix A. Calibration of the biological model Biological parameters as , bs , ˛s , ˇs , and ms are estimated as follows. The WCPFC’s annual stock assessments (Rice et al., 2014; Davies et al., 2014; Harley et al., 2014) give estimates of total biomass and spawning biomass in the absence of fishing (Bs0 and SB0s ); spawning biomass and harvest under maximum sustainable yield (SBMSYs and HsMSY ); and average total biomass and spawning biomass over a recent period (Bcurrs and SBcurrs ). For both species the calibration procedure minimizes the sum of weighted absolute differences between the two estimates:

  Bˆ s0 − Bs0 

Ds =

Bs0

+

+

 0    SBs − SB0s  SB0s

    MSY  Hs − HsMSY  HsMSY

+ ı1

+

 MSY    SBs − SBMSY  s SBMSY s

  Bˆ scurr − Bscurr  Bscurr

+ ı2

 curr    SBs − SBcurr  s SBcurr s

(A.1)

where a hat indicates the WCPFC estimate, and its absence indicates variables estimated by our model; ı1 and ı2 are weights to prioritize the last two arguments, which reflect how the biological model reproduces current total and spawning biomass. D is minimized subject to following constraints. Biomass in the absence of fishing is found by setting all harvests at zero and solving the system formed by Eqs. (6) and (7) for juvenile and adult biomass: Bs0 = SB0s =

ˇs as − ˛s ms ˇs as − ˛s ms + ˛s ms bs ˛s ˇs bs ˇs as − ˛s ms ˛s ms bs

(A.2a)

(A.2b)

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Biomass and harvest of both tuna species under maximum sustainable yield are found as follows. The Lagrangian for maximizing annual steady-state harvest is: L = HAs + HJs + J

 a B s As

1 + bs BAs



− ˛s BJs − HJs

+ A ˇs BJs − ms BAs − HAs





(A.3)

where  J and  A are Lagrange multipliers of the growth function of juvenile and adult biomass, respectively. The conditions for the solution to the optimization problem are −J ˛s + A ˇs = 0 J as (1 + bs BAs )

(A.4a)

− A ms = 0

2

(A.4b)

as BAs − ˛s BJs − HJs = 0 1 + b BAs

(A.4c)

ˇs BJs − ms BAs − HAs = 0

(A.4d)

From (A.4a) and (A.4b) we derive adult biomass under MSY: √ as ˇs − ˛s ms MSY BAs = (A.5) √ bs ˛s ms Proposition 1. No juveniles are caught under maximum sustainable MSY = 0. yield: HJs Proof. From the steady-state conditions for biomass (A.4c) and (A.4d) we find the following expression for total harvest: Hs = HJs + HAs =

as ˇs BAs ˛s − ˇs HJs − ms BAs + ˛s ˛s (1 + bs BAs )

(A.6)

As ˇs > ˛s (to reflect adult tuna’s larger size), total harvest is declining in harvest of juveniles. Therefore HJs should equal 0 to achieve MSY. 䊐 MSY = 0 in (A.6) gives the harvest under MSY. Inserting (A.5) and HJs Hence in the calibration SBMSY and MSY are defined as √ as ˇs − ˛s ms MSY = (A.7a) SBs √ bs ˛s ms



HsMSY

= as ˇs

as ˇs −

√ ˛s ms

bs ˛s

as ˇs





√ as ˇs − ˛s ms √ bs ˛s ms

− ms

(A.7b)

Biomass under current fishing mortality is defined as



SBcurr s

=



Bscurr =



as ˇs ˛s + F Js

1+



ms + F As ˇs

ms + F As

 −1

1 bs

(A.8a)

SBcurr s

(A.8b)

where F Js and F As denote fishing mortality of juveniles and adults, respectively. Extra weights (ı1 = 3, and ı2 = 10) were set to ensure that Bˆ scurr = curr

 s = SBcurr ; moreover, ms and ˛s are restricted to a Bscurr and SB s plausible value (ms , ˛s ≤ 0.9). Table 5 shows the parameter values and estimates. References

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Please cite this article in press as: Groeneveld, R.A., Quaas, M.F., Promoting selective fisheries through certification? An analysis of the PNA unassociated-sets purse seine fishery. Fish. Res. (2015), http://dx.doi.org/10.1016/j.fishres.2015.10.014