Closing the high seas to fisheries: Possible impacts on aquaculture

Marine Policy xxx (xxxx) xxx

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Closing the high seas to fisheries: Possible impacts on aquaculture ~ alosa Martinell a, *, Tim Cashion b, Robert Parker b, U. Rashid Sumaila b Daniel Pen a

Instituto Polit�ecnico Nacional, Centro Interdisciplinario de Ciencias Marinas, Avenida Instituto Politecnico Nacional SN, Playa Palo de Santa Rita, 23096, La Paz, B.C. S., Mexico b The University of British Columbia, Fisheries Economics Research Unit, Vancouver, British Columbia, Canada

A R T I C L E I N F O

A B S T R A C T

Keywords: Aquafeed Feed price Fishmeal Model Reduction fisheries Governance

Consumption of seafood has increased steadily over the past several decades and this trend is expected to continue with projected increases in global population and affluence. Wild capture fisheries catches have likely reached their peak, and therefore any significant increase in future fish supply is expected to come primarily from aquaculture. However, aquaculture continues to rely on wild stocks by using fishmeal to support culture of fed species. Recently, concerns regarding wild fish populations have led to calls for the closure of the high seas (i. e., international waters) to fishing. Such a policy would decrease marine fish catch in the short term while potentially increasing future catch. Here, we assess the potential impacts of closing the high seas to fishing on marine fish catch that goes to reduction into fishmeal. We quantify the potential effects of these changes on the price of fishmeal and profitability of the global aquaculture industry. Not surprisingly, we find a stronger effect of closing the high seas to fishing for high-value carnivorous species such as shrimp and salmonids. Overall, however, our study suggests that the impact of closing the high seas to fishing on aquaculture is likely to be insignificant.

1. Introduction Fish and other aquatic products are critical sources of protein to a growing global population and contribute heavily to food security, in­ come, and trade [1]. They also play an important role in the alleviation of poverty, representing a substantial part of the income of nearly 10% of total world population [2–4]. World population growth and rising affluence are expected to in­ crease fish demand by 19% by 2026 compared to 2014 (OECD- [5]. Capture fisheries production has not increased in two decades, and aquaculture will be required to meet most of this increased demand. Aquaculture already accounts for over half of fish and shellfish products destined for human consumption [6] of which 69% are fed species [1]. In order to satisfy future seafood demand, aquaculture would have to supply two thirds of edible production by 2030 [7]; Kobayashi et al., 2015). Aquaculture production is closely linked to wild stocks through the sourcing of fishmeal (FM) and fish oil (FO) for aquafeeds [8,9]. FM and FO are important components of compound feeds, particularly for high-value carnivorous species. These feed inputs are sourced primarily from reduction fisheries targeting small pelagic species, although

roughly 25–35% of FM is sourced from processing by-products from both wild and farmed species [1,10]. 16 million tonnes of small pelagic and other fish species were harvested for reduction to FM and FO in 2014 [1]. Inclusion rates in aquafeeds vary by species, ranging from 5 to 55% [11]. Fish inputs account for up to half of the cost of global aquafeed production [12] and feeds account for up to 60% of total culture production costs depending on the species, production system, and location [13–15]. Importantly, wild harvests for FMFO production have not increased as aquaculture production has expanded, but have in fact declined, and FM prices have concurrently increased since the early 1990s alongside rising demand from aquaculture (Fig. 1). Aquaculture’s estimated growth for 2015 was calculated at 4%, 2% points below the 6% mean growth estimated for the period 2001–2015 and far below the double digits observed throughout the 1980s and 1990s [5]. However, this value is still the largest of all food production industries and production is expected to keep growing, increasing FM prices alongside. Limited supplies of wild stocks and increasing FM prices have pro­ vided motivation to reduce aquaculture’s reliance on FM and FO. Much of the divergence has until now been facilitated by shifting consumption of FM and FO away from other animals like pigs and chickens, to be

* Corresponding author. E-mail addresses: [email protected], [email protected] (D. Pe~ nalosa Martinell). https://doi.org/10.1016/j.marpol.2020.103854 Received 14 September 2019; Received in revised form 30 January 2020; Accepted 2 February 2020 0308-597X/© 2020 Elsevier Ltd. All rights reserved.

Please cite this article as: Daniel Peñalosa Martinell, Marine Policy, https://doi.org/10.1016/j.marpol.2020.103854

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replaced with soymeal or other inputs [16,17]. Feed conversion ratios have also improved across cultured species as genetics and culture conditions have allowed for increased efficiencies [9,11]. Alternative protein sources have also been increasingly studied and included in aquafeed compositions, including vegetable sources such as soymeal [18,19] as well as animal products like poultry feather meal, poultry oil, mammalian meal, or blood meal [20,21]. Future compositions are ex­ pected to further reduce reliance on wild stocks, reserving FM and FO for targeted feeds with special nutrient requirements [22]. While these developments have helped avoid the economic impact of limited wild inputs, provision of FM and FO remain important drivers of feed costs and overall costs of aquaculture production. Over 30% of the world’s fisheries are overexploited [1]. Closing the high seas (HS) to fishing has been proposed to help alleviate pressure on fish stocks and allow their recovery, therefore supporting more sus­ tainable future production [23]. Fisheries in the HS are currently under-regulated and almost in an open access scheme [24]. Closure of the HS would create a “fish bank” which would allow fish to reproduce, eventually returning a portion back to the exclusive economic zone (EEZ) [23–25]. The relationships between HS fisheries, EEZ fisheries, and aquaculture and other industries are illustrated in Fig. 2. Potential economic effects of a HS closure policy have been evalu­ ated previously [20,23]. However, potential effects on aquaculture have not been estimated. If the HS were to be closed, landings may decrease in the short term, including those destined for FMFO production, leading to higher prices [26]. Nonetheless, in the long run, the effect of using the HS as a fish bank [23] should increase yields obtained in the EEZ due to the migration from the HS. The effect on landings would vary pending on the percentage of fishes that moved from the HS into the EEZ [23,24], referred hereafter as migration rate (MR) (Fig. 2). Different MRs have been proposed; according to White & Costello [24] MR are expected to be between 30% and 42%. On the other hand, Sumaila et al. [23]. conclude that with an 18% MR, economic performance of wild fisheries results in no loss of aggregate global catch and landed value with respect to status quo. Given the important role that aquaculture—including fed aqua­ culture—is expected to play in future seafood supply, assessment of potential impacts of this policy on aquaculture is needed to identify any economic and food security impacts. To this end, this paper explores the potential impact of closing the high seas on aquaculture producers via changes in the availability of fishmeal. Specifically, we aim to: (i) describe historical production of reduction fisheries for FM and FO in the HS; (ii) estimate the effects that closing the HS to fisheries would have on FM prices due to changes in reduction fisheries’ landings and con­ current increases in aquaculture production; and (iii) analyze the effect

of modeled FM price changes on profits for fed aquaculture producers. 2. Methods To investigate the effect that closing the HS would have on aqua­ culture, the relationship between aquaculture and reduction fisheries was modeled for three different groups of cultured species. Models were informed by historic data to gauge the expected effect of HS closure would have on past trends, considering observed changes in reduction fisheries landings, aquaculture production, and FM prices. 2.1. Reduction fisheries’ characterization and aquaculture groups analyzed First, reduction species of relevance to HS production were identified and their corresponding historic landings were extracted from the Sea Around Us Project database [27,28]. Annual data series from 1988 to 2014 were obtained from several databases to complete the information needed to populate the model. FM prices were obtained from the World Bank webpage [7], inflation rates for the U.S.A were obtained from the International Monetary Fund (imf.org, 2018), and aquaculture produc­ tion and value for the different groups were obtained from the FAO aquaculture product database [5]. All values were adjusted for 2010 real US dollars. Three groups were created according to their FM inclusion rates catalogued in low (G1), medium (G2), and high (G3) with average FM inclusion rates of 5, 20 and 30% respectively. Using values reported by Tacon & Metian [29]; two species were chosen to represent each group, except for G1 where three representatives were chosen (Table 1). Rev­ enue, total costs and feed costs were obtained from the literature (Table 1) and averaged for both species used in each group regardless of the production method. Those values were then used to obtain benefit margins and percentage of total costs attributed to feed. When no data were available on the percentage of FM costs (G1 and G2), values were calculated depending on the amount of estimated FM used by the species [29] and FM price using the relationship that exists between the amount of FM used for production and the price of FM. . Results were then compared with the proportions published for salmon [26]; Table 1). 3. The model In line with the objectives set for this paper, our model can be divided in two interlinked sub-models. One studies the impact that closing the HS to fishing would have on aquaculture profits, the second one evaluates the impact on fishmeal prices.

Fig. 1. Trends in aquaculture production, reduction fisheries landings, and fishmeal prices (in real USD), 1988–2014. 2

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Fig. 2. Relationship between fisheries and fed aquaculture production. Table 1 Calculated mean values obtained from the literature for profit margin, share of feed to total costs and fishmeal inclusion rates. Ranges and average () values for model parameters. Group

Fishmeal Inclusion rate (%)

Group’s Representative species

Feed costs (% of total costs)

Profit Margin (%)

Fishmeal costs (% of feed costs)

Sources

Low Group 1 (G1)

<10

63-69(66)

21-27 (24)

25-35 (30)

Medium Group 2 (G2) High Group 3 (G3)

11–30

Catfish (Ictalurus punctatus, Ictaluridae), Tilapia (Oreochromis niloticus, Cichlidae) and Carp (Cyprinus carpio, Cyprinidae) Whiteleg Shrimp (Penaeus vannamei, Penaeidae) and Giant tiger prawn (Penaeus monodon, Penaeidae) Salmon (Salmo salar, Salmonidae) and rainbow trout (Oncorhynchus mykiss, Salmonidae)

35-53 (43)

11-40 (27)

45-55 (50)

Martinovska-Stoycheska et al. [30]; Yuan et al. [13]; Tacon & Metian [29] Engel et al. [15]; Braga et al. [31]. Tacon & Metian [29]

42-75 (60)

10-154* (37)

38-48 (43)

>30

3.1. Aquaculture profits

Rt ¼ Qt ⋅Pt

Following Sumaila et al. [33]. who estimated changes in profits due to the existence of subsidies in fisheries using the Gordon-Schaefer profit function, this paper analyzes changes in aquaculture profits using a common profit function (equation (1)). Since the impact of closing the HS to aquaculture will most likely be observed in changes in production costs, a series of relationships were established in order to assess the impact of rising FM prices on aqua­ culture profits. First, let profit (π) be defined as the difference of revenue and total P costs ( C), or the product of the profit margin (γ) and revenue. X π t ¼ Rt Ct ¼ γ⋅Rt (1)

FAO [26]; Asche & Olegard [32]; Lasner et al. [14]; Tacon & Metian [29]

(2)

Profit, revenue, total costs, and feed cost (Cf ) data from the literature (Table 1) were used to estimate γ using equation (3), and the percentage of total costs that belong to feed (τÞ using equation (4) for each group. Once γ and τ values were established for each group, global values of production, price and revenue (value) were used. γ¼

π Rt

(3)

P

τ¼

Ct Cft

(4)

Using equation (2), we can now estimate the total costs as a function of value and profit margin. X Ct ¼ Rt γ⋅Rt (5)

Then, let us define revenue (R) as a product of quantity produced (Q) and price (P).

3

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calculate the probability of obtaining a positive effect on profits (that is, that profits calculated for the new scenario are higher than those observed in status quo) for each scenario. Two kinds of sensitivity analysis were undertaken using the one factor at a time (OFAT) method. First, a 1% change in the values of the parameters γ, ω and τ was developed; for γ and τ the effect on profits was observed; the parameter ω was changed in the same way, but this time the effect on production costs attributed to FM were studied. For the second evaluation, the closure of the HS was assumed, and the rate of aquaculture growth needed to obtain a positive impact on profits was found for each MR scenario proposed in S1.

With total cost estimation, we can use calculated τ to estimate feed costs for the complete FAO database. X Cf;t ¼ τ Ct (6) Protein can account for up to 60% of feed costs [12]. Of said feed costs, the percentage of the total costs that are attributed to FM (ω) permits the estimation of total costs attributed to FM (CFM ). In the case of salmon, FM represented 43% of raw material costs in 2007 [26]. CFM can also be obtained by multiplying the amount of FM used for production (QFM ) times the price of FM (FP ). CFM;t ¼ ωCf ;t ¼ QFM;t ⋅FP;t

(7)

4. Results

3.2. Fishmeal price

4.1. Reduction fisheries’ characterization

The effect that closing the HS to fishing will have on aquaculture will most likely be through changes in Fp. In order to study this change, Fp was modeled using a linear autoregressive distributed lag (ARDL) model as done previously for other agricultural commodities like soybean and wheat [34]. Fisheries’ catches (RfC) and fed aquaculture production (AQ) were used as independent variables, and the coefficients were estimated using ordinary least squares.

While 97% of the reduction fisheries’ landings are source from within the EEZ [27,28], the HS yielded an average of 552 thousand tonnes year 1 between 2010 and 2014, with catches mainly coming from the Pacific Ocean (Fig. 3). Major reduction fisheries’ species in the HS, with annual HS landings over one thousand tonnes are shown in Table 2.

Fp;t ¼ β1 þ β2 ⋅ Fpðt

1Þ

þ β3 ⋅ RfCt þ β4 ⋅ AQt þ ε

4.2. Impact in fishmeal price and aquaculture profits

(8)

Once FM prices and its relation to fisheries were established, the values of RfCt corresponding to the catches obtained from the HS were removed and new FM prices were predicted. In order to assess the effect of this price changes on aquaculture costs, we used equation (7) assuming QFM remains constant.

Parameters obtained for the Fp model show a negative relation be­ tween fisheries and a positive relation with aquaculture growth as ex­ pected for the proposed supply and demand relationship described for this commodity (Table 3). All variables were significantly related to FM price in the study period, with an adjusted R2 value of 0.94 (p < 0.001). All scenarios’ results are presented in terms of average annual rate of change with respect to status quo, which is the current situation with the HS open to fisheries in an open access scheme and reported aquaculture production of the three groups.

3.3. Scenarios Three different scenarios were explored to determine the relative influence of HS closure, migration rates, and aquaculture growth on results. The scenarios were all modeled over the period of 1990–2014 assuming past catches would have been the same except for the differ­ ences caused by the HS closure. Scenario 1 (S1) assumed full closure of the HS and varying MRs of 0, 18, 20, 30 and 42%. MRs selected are supposed to cover a wide range of possible scenarios: The 0% migration rate was selected to assess the risk of having no migration after the HS closure, the 18% scenario was selected for being the increase in landings needed to preserve current fisheries profits [23], 20% was selected as a high MR scenario consid­ ered by White & Costello [24]; and 42% was selected as a very high MR as control to assess the sensibility of the model to MR. Scenario 2 (S2) assumed no HS closure but assessed variable rates of aquaculture growth from 2 to 10%. Aquaculture growth scenarios were selected according to observed and expected aquaculture annual production growth, with average values of 10% for the 1980’s and 90’s, and current values ranging 4% [35]. Scenario 3 (S3) assumed closure of the HS with varying MRs as in S1, as well as a constant 6% aquaculture production increase (equal to the annual growth for the period 2000–2016 rounded up [1]). In all cases, farm-gate prices and other production costs were assumed constant and profits varied only with changes in production costs and amounts of fish produced. Due to the high uncertainty of the parameters γ, ω andτ, a stochastic analysis was performed using the Monte Carlo method with 2000 iter­ ations using Oracle Crystal Ball software, and the parameters mentioned above as supposition variables, that is, assuming variability with a specific probabilistic distribution. Since there is scarce information about each parameter, a triangular distribution was assumed, with the minimum and maximum values (minus negative values for profits) used as range and the average value used as the more probable case for each parameter (Table 1). Average rate of change in profits with respect to status quo was set as the prevision variable. Results were then used to

4.2.1. Scenario 1 Effect of HS closure on aquaculture profits was estimated between cero and five percent depending on cultured species and fish MR (Table 4). The highest potential profit loss was found for shrimps, while there was no significant effect on tilapias and carps or salmonids. As for FM prices, mean price increases range between three and four percent depending on the MR. FM prices were found to be particularly sensitive to HS closure from 1989 to 2000 (see supplemental material). From the year 2000 onwards, the impact of closing the HS is reduced and it stabilizes from 2010 to 2014 with FM price increases of less than 2% derived from the closure of the HS. This is consistent with the relationship observed between FM prices and reduction fisheries, with higher dependence on reduction fisheries’ landings between 1989 and 1999 and a higher correlation to aquaculture growth after the year 2000 (Fig. 1). A similar effect was observed in predicted aquaculture profits when a 0% MR is expected (Supplementary Fig. 2). For G1 there is a slight profit loss when the impact on FM prices would have been higher due to the closure of the HS in the year 1994; nonetheless, profit losses for this group due to the closure of the HS are not significant and below 1% in most cases. For G2 and G3, which have a higher dependence on FM, a higher profit loss would have come from FM price increases. The largest loss would be for G2 in 1994 with values of 11%, whereas G3 would have experienced its highest loss of 2.5%. As FM prices stabilized due to the closure of the HS, profit losses stabilize as well and from the year 2010 onwards, profit losses would be almost insignificant for G1 and G3, with rate changes inferior to 0.5%, and significantly smaller than the average for G2, with expected change in profits of 1.4%. If a 42% MR occurs [24], the expected average loss for all groups between 2010 and 2014 was below 0.5% (Supplementary Fig. 3). The relative likelihood of achieving positive profits despite HS 4

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Fig. 3. Reduction fisheries’ distribution and global fishing intensity in 2014. a) All reduction fisheries’ species b) Top 10 reduction fisherieries’ species caught in the HS.

closure was greater for G1 and G3 species groups than for G2 (Fig. 4). The Monte Carlo analysis shows that, if the full time series average is used, and assuming a 100% closure of the HS with an 18% exchange rate, G1 and G3 have a 41% and 45% chance of increasing profits, respectively, G2 has only a 14% chance of having higher profits after the closure policy. If the results are observed using the average between 2010 and 2014, when values stabilize, G1 and G3 present similar results, but G2’s probability of increased profits respect to status quo increases to 37% (Supplementary Figs. 4, 5, 6). If a 42% MR is assumed, all groups have above 40% probability of having higher profits, even without extra production. Although median values are still slightly below 0% change, average values are greater than 0% or higher for all groups (Fig. 4). The groups distribute more compactly, with impact below the 40%, while the distribution observed above the 0% rate of change is more dispersed with impacts of up to 120% positive change in average profits. These differences are assumed to be due to the impact that different production techniques have on profits.

that would gain more from increased production is G1, and it is the only group whose profits would increase at a higher rate than production increase. In the case of G3 a production increase of 10% year 1 would come with a profit increase of 2.4%, while for G2 would only increase profits by 1.4%. As opposed to what was observed in S1, the dynamic variation of profit increases due to increased production is not as high for G1 and G3 (Supplementary Fig. 7), G2 does have higher variation through time, nonetheless, said variations stabilize after 2006 with average profit in­ crease of 2% as a result of increased production. All three group’s profits would increase as they increased production from 2006 to 2014. Group 2 would have negative values between 1994 and 1996, which means a higher sensitivity to changes in FM prices, even with current fishing situation. In a stochastic analysis, a 6% increase in production shows high stability in results for G2 and more variability for parameters used in G1 and G3; still all groups have positive average values of rate of change in profits due to increased production (Fig. 4). All groups have over 50% probability of having a positive effect on profits compared with current levels of production (supplementary Figures 8, 9 and 10).

4.2.2. Scenario 2 Excluding the effect of HS closure and only considering growth in aquaculture, changes in profits for the three modeled species groups ranged from 0 to 14% (Table 5). FM average price increases due to an increased aquaculture pro­ duction range between 1.7 and 8.0%. An increase of 6% in aquaculture production would have had a higher impact than closing the HS with a 0% expected MR. Just as in S1, FM price variations are different when observed from a dynamic perspective (Supplementary Fig. 1). If aqua­ culture production had been 6% year 1 higher, FM prices would have increased an average of 5%; as opposed to the stabilization towards no change with the closure of the HS, FM price increases stabilized in 2006 and a 5% year 1 increase respect the original prices is observed. In the case of aquaculture, all groups’ profits increase by increasing production, suggesting increased production has a higher impact than increased FM price (or estimated increased production costs). The group

4.2.3. Scenario 3 Combined, HS closure and steady aquaculture growth were modeled to have an effect on aquaculture profits ranging from 3 to 9% (Table 6). Cumulative effects of closure of the HS and aquaculture growth have a significate impact in FM prices, with average increases between 7.3 and 8.8% year 1 assuming a constant 6% increase in production. When observed dynamically (Supplementary Fig. 1), we can see the adding effect that S1 and S2 would have on FM prices, with up to a 16% increase in 1994 and a steady increase of 6% year 1 from 2010 to 2014. Even though FM prices are expected to increase substantially, G1 and G3 may expect a positive impact in profits with the combination of growth an closure of the HS if an 18% MR is assumed, although, on 5

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regardless of the MR. Average values can be misleading, since the effect of the closure of the HS is more pronounced in the last decade of the 20th century. When observed dynamically, G1 and G3 behave in a stable manner through the complete time series (Supplementary Fig. 11). G2 on the other hand, would have had experienced negative profits between 1990 and 2009, but the stabilization noted from 2010 to 2014 would translate to a 0% change in profits respect to status quo. Stochastic analysis shows that, with a 6% production increase and a 30% MR, and analyzing the average where profit changes stabilize, that is between 2010 and 2014, all groups have over a 50% probability of having a positive impact on profits respect to status quo (supplementary figures 12, 13 and 14). All groups present average profit means above the 0% change respect to status quo. For G1, median is around an 8% increase, while G2 and G3 present medians close to 0% (Fig. 4). Dispersion of negative impacts on profits is very low with minimum values for G1 of 29% and maximum value for the same group of 128%.

Table 2 Major species, or groups of species, harvested in the high seas for reduction to FM and FO and associated average annual landings from 2010 to 2014. Main taxa

Major production region

Average HS Landings designated for FM and FO (mt)

Share of total landings corresponding to the HS (%)

Chilean jack mackerel (Trachurus murphyi, Carangidae) Japanese anchovy (Engraulis japonicus, Engraulidae) Antarctic krill (Euphausia superba, Euphausiidae) Blue whiting (Micromesistius poutassou, Gadidae) Jumbo flying squid (Dosidicus gigas, Ommastrephidae) Pacific saury (Cololabis saira, Scomberesocidae) Other jacks and pompanos (Carangidae) Chub mackerel (Scomber japonicus, Scombridae) Herrings, sardines, menhadens (Clupeidae) Marine fishes not identified Japanese horse mackerel (Trachurus japonicus, Carangidae) Squids, cuttlefishes, octopus (Cephalopoda)

Southeast Pacific

149,787

21

Northwest Pacific

108,536

13

Southern Ocean

68,818

45

Northeast Atlantic

16,107

5

Southeast Pacific

15,407

3

North Pacific

10,783

21

Global

69,203

16

Northeast Pacific

16,884

3

Global

13,254

9

NEI

72,516

10

Northwest Pacific

3402

11

Global

4476

25

4.3. Sensitivity analysis We tested the sensitivity of our results to our estimates of profit margin (γ), percentage of total costs corresponding to feed (τ) and per­ centage of feed costs corresponding to fishmeal (ω) by altering each variable by 1% and evaluating the effect on profits. The model present similar responses in all parameters for all groups except for the effect of the share of FM costs regarding feed costs for G1, where the model appears to be more sensitive (Table 7). Production increase needed to maintain profits equal for G1 is virtually equal to zero in all cases. For G3, increases would have to be between 3.8 and 2.3%. G2 would be the most affected of all, with an increase between 25 and 15% in production needed in order to maintain profits equal if the complete time series average is used (Table 8). Effects observed in G2 are significantly reduced if only the average from 2010 to 2014 is considered, with a maximum production increase needed of 6.8% when a 0% migration rate is observed and 3.8% increased pro­ duction with a 42% expected migration rate. This difference is less marked in G1 and G3, where values are similar with both averages. 5. Discussion

Table 3 Results of the FM price (Fp Þ linear autoregressive distributed lag (ARDL) model parametrization. Parameter

Variable

Coefficient

tvalue

pvalue

β1 β2 β3

Intercept FM Price (t-1) (USD/mt) Reduction fisheries’ landings (mt) Fed aquaculture production (mt)

607.32 0.37 2.53 Exp05 1.66 Exp-05

2.41 2.77 4.06

0.03 0.01 0.00

2.58

0.02

β4

Most of reduction fisheries’ activities take place within the EEZ; nonetheless, some activity is still undertaken in the HS, mainly in South America, Mexico and in the northwest Pacific Ocean. Since more than 90% of the catches come from the EEZ, it can be expected that the closure of the HS to fisheries would have a very small impact in the overall production of FM. Even though the impact in the amount pro­ duced can be small, there is still an effect observed on prices due to reduced access to raw materials. This effect is somewhat similar to what is observed with an increase in aquaculture production. A special case of HS fishing that affects FMFO production is the Antarctic krill (Euphausia superba, Euphausiidae) fishery, which takes place near the Antarctic peninsula (Fig. 3b) and represents 1% of total HS catches. It is one of reduction fisheries’ most important species caught in the HS, representing up to 10% of total HS catches destined for FMFO. Landings have increased substantially over the last decade [36], due to its commercialization as a nutraceutical supplement for its high content of Omega-3 and 6 fatty acids. As this fishery occurs almost exclusively in the HS, their closure would substantially limit its exploi­ tation. On the other hand, the fishing of Krill in the Southern Ocean is already controversial with some arguing that krill fishing vessels active near sensitive Southern Ocean habitats are putting pressure on an ecosystem that is already threatened by climate change. More specif­ ically, it has been argued that Antarctica’s Krill fisheries threaten pen­ guins and whales [37]. The modeled effects of the closure of HS to fishing on aquaculture profits are highly variable and depend on the species produced and the

Table 4 1989–2014 average expected rates of change (%)in each group’s profits and fishmeal prices when closing the high seas with different migration rates. Migration rate (%) 0 10 18 30 42

FM Price rate of change (%)

Profits’ rate of change (%)

Fp

G1

4.3 3.9 3.6 3.1 2.6

0.5 0.4 0.4 0.4 0.3

G2 4.5 4.0 3.7 3.2 2.6

G3 1.0 0.9 0.9 0.7 0.6

average, G3’s variations are not significant for any MR observed with variations between 0.6 and 1.0%, G2 would see a profit loss between 3.1 and 1.5%, and G1’s profits would increase at a steady 8% 6

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Fig. 4. 2010–2014 average expected rates of change in profits respect to status quo, due to changes in fishmeal prices for 3 different scenarios: S1 assumes closure of the high seas and a 42% migration rate with no aquaculture growth. S2 assumes open HS and a 6% aquaculture growth, and S3 assumes closure of the HS with a combination of 42% migration rate and a 6% increase in aquaculture production. Legend displays group name (FM inclusion rate category). Median, 25th and 75th percentiles are presented. Outliers were left out to improve visualization.

Table 5 1989–2014 average expected rates of changes (%) respect to status quo in each group’s profits and fishmeal prices due to increase in aquaculture production assuming the HS remain open. Aquaculture production increase (%) 2 4 6 8 10

FM Price rate of change (%)

Profits’ rate of change (%)

Fp

G1

G2

G3

1.7 3.4 5.0 6.5 8.0

3.0 6.0 8.8 11.5 14.1

0.3 0.6 0.9 1.2 1.4

0.5 1.0 1.5 1.9 2.4

Table 8 Aquacultures average production increase (%) needed to increase profits respect to status quo after the closure of the high seas for different migration rates. Migration Rates (%) G1 G2 G3

0 10 18 30 42

FM price rate of change (%)

Profits’ rate of change (%)

Fp

G1

8.8 8.5 8.2 7.7 7.3

8.4 8.4 8.5 8.5 8.6

G2 3.1 2.8 2.5 2.0 1.5

G3 0.6 0.7 0.7 0.8 1.0

Table 7 OFAT sensitivity analysis for profit margin (γ), percentage of total costs corre­ sponding to feed (τ ) and percentage of feed costs corresponding to fishmeal (ω). Presented rates of change (%) in profit for γ and τ and in fishmeal costs for ω. G1 G2 G3

γ

τ

ω

2.1 1.6 1.5

2.1 2.8 1.8

6.6 2.0 2.3

10

18

30

42

0.3 2.2 3.9

0.3 2.3 3.5

0.3 2.1 3.2

0.3 1.8 2.8

0.2 1.5 2.3

in reduction fisheries landings and aquaculture production. Actual amounts of FM obtained from each species caught is not included in the model; FM production varies by species and could have an effect if included. Further, the effect of FO was not included in the model; FO production is much lower than FM in terms of relative mass but varies much more between species as well as seasonally. According to Tacon & Metian [29]; aquaculture has been reducing its dependence on fisheries, and reducing the amount of fish inputs required to produce the same amount of farmed fish. The recent efficiency gains may reduce effects of closing the HS to fisheries on aquaculture. Furthermore, fisheries and aquaculture by-products are not included in the estimations even though they account for up to 25–35% of the raw materials used to produce FM [1,38]. Only the effects on fed aquaculture were studied, which now ac­ counts for 69.2% of the whole aquaculture industry [1]. The remaining 30.8% consists of filter feeding fish, bivalves and algae which are not fed, and wouldn’t be directly affected by changing supplies of FM and FO. Therefore, impacts of closing the HS to fisheries on the aquaculture industry as a whole might be smaller than those described here for fed aquaculture. As observed in the first sensitivity analysis, slight changes in profit margins and feed costs has important impacts on profits, sometimes higher than the effect of closing the HS. Therefore, improving produc­ tion practices is more important than the increase of FM prices if indi­ vidual firms wish to remain profitable. The dynamic rates of change in FM prices confirm that the effects were more significantly attributed to availability of raw materials in the last decade of the twentieth century, where a reduction of catches had a higher impact on prices. After the year 2000, prices are much more dependent on aquaculture growth [10], and aquaculture production increases and decreases have a higher impact on FM prices than closure of the HS. This corresponds with aquaculture increased share of FM’s total production that went from 5% in 1988 to 74.5% in 2014 [29,39, 40].

Table 6 1989–2014 average expected rates of change (%) respect to status quo in fish­ meal prices and different aquaculture productions with different expected migration rates and a 6% year 1 increase in production is assumed. Migration rate (%)

0

production methods. In general, high value species like salmonids and marine shrimps are expected to be more affected, which is unsurprising given their consumption of FM. Species like tilapia and carp, which are less valuable but more relevant to global seafood supply, would see no important changes in profits since they have less dependence on FM and can substitute this ingredient more easily. Our model only considered changes in FM prices relative to changes 7

D. Pe~ nalosa Martinell et al.

Marine Policy xxx (xxxx) xxx

The second sensitivity analysis shows that, for G1 any increase in production over a 1% year 1 will result in positive profits in any MR scenario. For G2 and G3, a 2.2 and 3.9% growth respectively is needed to maintain positive profits in a 0% MR scenario. With the 18% proposed by Sumaila et al. [23]. as enough increase in catch to increase fisheries benefits as opposed to status quo, only a 2.1% increase in production is needed for G2 to be profitable and a 3.2% increase would be necessary for G3. If there is a 42% MR, G2 would need to increase production by 1.5% and G3 by 2.3% to maintain positive profits. Since aquaculture growth for 2015 was of 4% and with a mean of 6% per year from 2001 to 2015, closure of the HS would likely not affect the profitability of fed aquaculture. Furthermore, throughout the estimations, no changes in farm-gate prices were assumed. When production costs increase, farm-gate pri­ ces tend to increase as well, reducing the estimated impacts. Since ef­ fects in G1 are almost non-existent and G1 representative species are the ones that contribute the most to food security, prices would only impact on high end aquatic products like shrimp or salmon, possibly differen­ tiating two kinds of aquaculture production, one intended to increase world food availability and another one to supply the markets with higher income. In any case, the closure of the HS will not prevent any of these two kinds of aquaculture from remaining profitable. Closure of the HS may have potential impacts on sectors of the aquaculture industry beyond FM inputs to feeds. Notably, species like tuna which rely on wild capture of juveniles will be affected by wild populations in the HS. According to Metian et al. [41]. between 17 and 37% of Bluefin tuna catches go to aquaculture. The closure of the HS may increase signifi­ cantly the availability of tuna juveniles (seed) in the EEZ [20,24], increasing production capacity, as seed availability is one of the pro­ duction bottle necks. Organisms captured as seed are mostly obtained in the EEZ, nonetheless, due to migration; closure of the HS is expected to increase its availability in the long run. Once the tuna juveniles are caught, they’re fattened using compound feed or directly with whole small pelagic fish [42]. Since most small pelagic fishes are caught within the EEZ, availability of feed would only be slightly reduced. A new estimation would be needed to assess the effect of the closure of the HS on this kind of aquaculture. Overall, effects of closing the HS to fishing on aquaculture can be expected to be minimal. This added to the expected positive effects in the fisheries industry [20,23,24] and on the environment [43] suggest that the closure of the HS to fisheries would globally have a positive effect, with more sustainable profits for fisheries without an important impact on aquaculture production, especially for those species that are relevant to achieve food security.

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Author statement All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the Journal Marine Policy. Acknowledgments DPM would like to acknowledge the Global Affairs Canada Interna­ tional Scholarship Programs for granting him the Emerging Leaders of the Americas award (IRCC special program code 509) which made this work possible. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. 8

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