# Management Implications of Mesopelagic Forage Fisheries for Bigeye Tuna Stocks

Ciara Willis, Di Jin, Melina Kourantidou, Simon R Thorrold

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Ciara Willis, Di Jin, Melina Kourantidou, Simon R Thorrold. Management Implications of Mesopelagic Forage Fisheries for Bigeye Tuna Stocks. Fisheries Management and Ecology, In press, 10.1111/fme.70054. hal-05497972

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ARTICLE OPEN ACCESS

# Management Implications of Mesopelagic Forage Fisheries for Bigeye Tuna Stocks

Ciara Willis<fnref n="1" />,² | Di Jin³ | Melina Kourantidou⁴,⁵ | Simon R. Thorrold<fnref n="1" />

**Correspondence:** Ciara Willis (cwillis@mbari.org)

**Received:** 18 May 2025 | **Revised:** 6 January 2026 | **Accepted:** 8 January 2026

**Keywords:** Biodiversity Beyond National Jurisdiction | cost-benefit analysis | economics | multi-stock model | open ocean | trophic ecology

## ABSTRACT

Many large marine predators forage on mesopelagic fish stocks, including commercially valuable tunas. The mesopelagic is under increasing interest for commercial exploitation, given its large biomass with potential to supply fishmeal for aquaculture feed or fish oil. However, the implications of large-scale mesopelagic fisheries on tunas and other predators may be substantial. Here, we adapt a bioeconomic model with a new predator-prey dynamic to evaluate the effects of potential commercial-scale mesopelagic fisheries on bigeye tuna (*Thunnus obesus*). Specifically, we use a novel diet share model term and the latest stable isotope food web data to operationalize predator-prey interactions. Model results highlight the importance of accounting for the predator-prey interactions in management of mesopelagic fisheries and demonstrate the sensitivity of equilibrium economic and ecological conditions for the tuna stock under different price and cost scenarios. Overall, this work suggests that a new mesopelagic fishery could be economically viable in and of itself, but may have significant negative impacts on existing tuna fisheries by reducing forage availability.

## 1 | Introduction

Mesopelagic fishes are attracting increased interest for commercial exploitation, driven by a growing human population (United Nations 2024) and increased global demand for aquaculture feed due to limited potential for growth of capture fisheries (FAO 2024). With an estimated biomass of 10 billion tonnes (Irigoien et al. 2014; Proud et al. 2019), small mesopelagic fishes such as lanternfish and bristlemouths could serve as a major source of raw materials for aquaculture fishmeal, fish oil or nutraceuticals more broadly, and bioprospecting (Alvheim et al. 2020; Grimaldo et al. 2020). Interest in, and preliminary explorations of, mesopelagic fisheries commenced in the 1970s (Gjøsaeter and Kawaguchi 1980) by nations including the Soviet Union (1980s; Kock 2000; Payne and Hoagland 2022) and Iceland (2000s; Lamhauge et al. 2008). These efforts ended due to low

profits and low catches, respectively. Major challenges for fisheries include the depth of the mesopelagic zone (200–1000 m), the small mesh size required to catch target species (leading to high fuel consumption), and the need for preservation of rapidly deteriorating catch (requiring adequate refrigeration technology or short trip duration) (Standal and Grimaldo 2020; Andrés and Álvarez 2021; Paoletti et al. 2021). Additionally, mesopelagic fishing results in high bycatch (Standal and Grimaldo 2020), including catch-regulated species, that complicates both sustainability efforts and economic feasibility of the fisheries in question. Processing difficulties further challenge their use as aquaculture feed, necessitating technological advancements for efficient utilization (DG MARE 2018; Olsen et al. 2020). Despite these unsuccessful early attempts, investigation of economically viable mesopelagic fisheries continues, particularly in the north-east Atlantic (e.g., Prellezo 2019; Grimaldo et al. 2020; Standal

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2026 The Author(s). *Fisheries Management and Ecology* published by John Wiley & Sons Ltd.

<footnote n="1">¹Biology Department, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA | ²MIT-WHOI Joint Program in Oceanography/Applied Ocean Science and Engineering, Cambridge and Woods Hole, Massachusetts, USA | ³Woods Hole Oceanographic Institution, Marine Policy Center, Woods Hole, Massachusetts, USA | ⁴AMURE, Institut Universitaire Européen de la Mer, Université de Bretagne Occidentale, Plouzané, France | ⁵Department of Business and Sustainability, University of Southern Denmark Business School, Esbjerg, Denmark</footnote>
and Grimaldo 2020; Vastenhoud 2023). Advances in trawl technology and anticipated increases in demand (and price) for mesopelagic fishes suggest that commercial deep pelagic fisheries could become viable in the near future. Additionally, recent model simulations indicate that mesopelagic fisheries could be economically profitable for harvesters, with their inclusion in global fishmeal production reducing fishmeal prices and increasing affordability for aquaculture farmers to use it as a feed source (Quang et al. 2024).

Any assessment of the economic benefits from industrial-scale fishing in the mesopelagic will need to consider trade-offs between commercial profits and reductions in the value of ecosystem services provided by deep pelagic habitats. For instance, the daily (diel) vertical migration of the mesopelagic community, moving between mesopelagic depths during the day to escape visual predators and surface waters at night to feed (Haddock and Choy 2024), is a substantial component of the global biological carbon pump (Robinson et al. 2010; Aksnes et al. 2023; McMonagle et al. 2023). The biological carbon pump facilitates carbon sequestration through the transport of organic material and has been valued at over USD $300 billion annually (Hoagland et al. 2019). Exploitation of mesopelagic fish stocks is likely to impact the flux of carbon from surface waters (Dişa et al. 2024), and may lead to losses in carbon export that exceed the profits of mesopelagic harvest (Groeneveld et al. 2024). Mesopelagic fishes are also a key component of pelagic food webs (e.g., Iglesias et al. 2023) and are prey to a wide range of large marine predators, including commercially valuable fish and mammals and birds of conservation concern (e.g., Duffy et al. 2017; Goetsch et al. 2018; Logan et al. 2021; Watanuki and Thiebot 2018). Previous work examined the implications of mesopelagic fisheries for higher trophic level stocks (e.g., Dowd et al. 2022; Kourantidou and Jin 2022), but used a relatively low reliance of predators on prey in their modeling efforts [e.g., <20% diet share in Dowd et al. (2022)]. However, reliance of predators on mesopelagic forage may be substantially greater than previously assumed. More recent work by Willis et al. (2025) estimated that Atlantic bigeye tuna, yellowfin tuna, and swordfish may source 50%-60% of their diet from mesopelagic food webs. Consistent, significant reliance on mesopelagic forage will increase the likelihood that predator species will be impacted by mesopelagic exploitation.

The purpose of this study is to assess the economic trade-offs between commercial harvest of mesopelagic fishes and of their predators to inform future management strategies. Most existing assessments of mesopelagic stocks ignore predator-prey interactions, and previous studies (e.g., Kourantidou and Jin 2022) do not fully capture these dynamics. Here, we extend the existing mesopelagic predator-prey fishery model of Kourantidou and Jin (2022), by adopting a new function specification describing predator-prey dynamics and with recent estimates of the reliance of bigeye tuna on mesopelagic prey from the north Atlantic Ocean. Bigeye tuna are managed at the international level via Regional Fisheries Management Organisations (RFMOs) through the mandate of the United Nations Fish Stocks Agreement (United Nations 1995). International management bodies for mesopelagic fish stocks have yet to be developed but will presumably be shaped by the recent United Nations agreement on Biodiversity Beyond National Jurisdiction (Gjerde et al. 2021). While some domestic policies on

mesopelagic fisheries exist, such as the moratorium on the harvest of certain mesopelagic fishes by the Pacific Fisheries Management Council in the US Pacific exclusive economic zone (PFMC 2019), they have yet to be instituted on larger scales and in international waters. Simplified bioeconomic models offer a conceptually powerful framework for evaluating trade-offs among economic, ecological, and social goals without requiring complex computational infrastructure. This makes them particularly useful for exploratory analysis and decision-making in data-limited contexts. Our model considers fisheries management of both tuna and mesopelagic stocks that aims to maximize economic profitability while maintaining ecological sustainability. This work serves as a form of management strategy evaluation, identified as a useful tool for data-poor fisheries to clarify trade-offs (Smith 1994; Butterworth et al. 2010) and increasingly used for tuna (e.g., WCPFC 2014).

## 2 | Methods

We adapt the two species, two fisheries model of Kourantidou & Jin (K&J) (2022) for a migratory predator and mesopelagic prey. Our predator is modeled on bigeye tuna (Thunnus obesus) and our prey as a generic mesopelagic fish stock, both in the context of the Atlantic Ocean. We combine mesopelagic fishes (e.g., Maurolicus muelleri, Benthosema glaciale) into one generic stock for mathematical simplicity without loss of generality [as in Groeneveld et al. (2024)] as most mesopelagic fishes have similar life histories (e.g., small bodied, short generation times, and low fecundity; Salvanes and Kristoffersen 2001). Here, we adapt the model of K&J by updating biological and economic parameter estimates to match advancements in the literature and refining the process of modeling the response of predator biomass to a change in prey biomass. We accomplish the latter using carbon stable isotope data from predators and prey (Willis et al. 2025). To demonstrate the importance of including predator-prey linkages in this type of fisheries assessment, we compare model runs with empirically informed estimates of predator-prey interactions and with no predator-prey interactions. We then explore a range of scenarios to determine the optimal distribution of efforts between a fishery on the predator (tuna) and a fishery on the prey (mesopelagic fishes) under different price and cost conditions. This work is intended to demonstrate trade-offs in the data-limited context of an emerging fishery and to highlight primary interactions among biological and economic factors. Model scenario results should primarily be interpreted relative to one another, not as absolute values.

### 2.1 | The Predator-Prey Model

As in K&J, we begin with the standard two-species model described in Clark (2010):

$$ \frac{dx_1}{dt} = F(x_1, x_2) - h_1(t) \quad (1) $$

$$ \frac{dx_2}{dt} = G(x_1, x_2) - h_2(t) \quad (2) $$

where $x_1$ denotes predator biomass and $x_2$ prey biomass, $F$ and $G$ their respective growth functions, and $h_1$ their harvest ($i=1, 2$). The K&J model's growth functions were logistic with interaction
terms following Clark (2010) to describe changes in predator biomass to prey biomass and vice versa:

$$F(x_1, x_2) = rx_1\left(1 - \frac{x_1}{K}\right) + \alpha x_1 x_2 \quad (3)$$

$$G(x_1, x_2) = sx_2\left(1 - \frac{x_2}{L}\right) + \beta x_1 x_2 \quad (4)$$

where r and s are intrinsic growth rates and K and L are carrying capacities of the predator and prey, respectively. The α and β terms describe the interactions among predators and prey, with α indicating the functional response of the predator biomass to changes in prey biomass, and β the response of prey biomass to changes in predator biomass (Kar and Chaudhuri 2003). Here, α is a positive value and β is negative to reflect benefits to the predator of increased prey availability, but harm to the prey (i.e., increased consumption) as predator biomass increases. Note that for $F(x_1, x_2) = 0$ we have the range of $x_1$:

$$0 \le x_1 \le K\left(1 + \frac{\alpha}{r}x_2\right) \quad (5)$$

A major disadvantage of using a positive interaction term (α) to describe biomass changes in this analysis is that the high-end biomass of $x_1$ (the effective carrying capacity of $x_1$ with a nonzero $x_2$) is augmented beyond its carrying capacity K in the logistic growth component of function F. Firstly, this can lead to ecologically unrealistic results, with vastly augmented biomasses. Secondly, this complicates comparison of scenarios with different α values, as the maximum potential population size of $x_1$ is not consistent.

Here, we modify the predator growth function F (Equation 3) so that the effective carrying capacity of $x_1$ is based on the predator's reliance on the prey ($0 \le D \le 1$) but the upper bound of the carrying capacity of $x_1$ is kept at K when the predator is independent of the prey or D = 0:

$$0 \le x_1 \le K(1-D) + \left(KD \frac{x_2}{L}\right) \quad (6)$$

where D is exogenous. The above modification allows us to investigate the bioeconomic effects of predator-prey interactions using the stable carbon isotope results of Willis et al. (2025) which estimated that ~60% of the carbon contained in North Atlantic bigeye tuna tissues originated from mesopelagic food webs. These stable isotope results are of greater relevance to modeling predator biomass than results from stomach content studies, as they reveal the long-term (~5-8 months; Madigan et al. 2012) average carbon sourcing of a consumer rather than a snapshot of prey in the stomach that is biased toward identification of species with distinctive hard structures (e.g., bones).

We conceptualize the overall biomass of the predator ($x_1$) as proportional to its carbon biomass. Thus, the carrying capacity of the predator is directly proportional to its “$K_{carbon}$”. We assume that the Willis et al. estimate of bigeye tuna reliance on mesopelagic food webs represent our simulated tuna's evolved reliance on the mesopelagic prey stock ($x_2$) and use D as the diet share of the prey to the predator. This allows us to split K into

two components: $K \times (1-D)$ for the mesopelagic-independent portion of predator biomass, and $K \times D$ for the mesopelagic-dependent portion. The latter must be scaled by the carrying capacity of the prey, so we add the term $x_2/L$. We assume that the prey populations (e.g., in the epipelagic) that contribute to the mesopelagic-independent portion of predator biomass are stable over time. Overall, this results in the updated predator growth function:

$$F = rx_1\left(1 - \frac{x_1}{K(1-D) + KD \frac{x_2}{L}}\right) \quad (7)$$

This new predator growth function (Equation 7) behaves more consistently and logically than the original (Equation 3). At D = 0 or D > 0 and $x_2 = L$, it returns to the first term of Equation (3), that is, basic logistic growth. At D > 0 and $x_2 < L$, the predator's effective carrying capacity declines as a function of prey biomass scaled by the diet share. We keep the prey growth function of K&J (Equation 4), as β (a negative value) does not have the same implications for biomass augmentation beyond the logistic carrying capacity as did α (a positive value). Figure 1 depicts the growth of both stocks following Equation (7) with D > 0 and β < 0. Panel A clearly illustrates the decreases in tuna growth and carrying capacity as the mesopelagic stock (i.e., the food for tuna) declines, while B shows that mesopelagic stock growth peaks at moderate mesopelagic biomass levels before declining slightly when tuna abundance increases.

The marginal growth rates for $x_1$ and $x_2$ from (7) are:

$$\frac{\partial F}{\partial x_1} = r\left(1 - \frac{2x_1}{K(1-D) + KD \frac{x_2}{L}}\right) \quad (8)$$

$$\frac{\partial F}{\partial x_2} = \frac{rx_1^2 KD}{L\left(K(1-D) + KD \frac{x_2}{L}\right)^2} \quad (9)$$

We otherwise follow the methods of K&J. Briefly, harvests follow the Gordon-Schaefer model:

$$h_i = q_i E_i x_i \quad (10)$$

where $q_i$ is catchability and $E_i$ is effort for each stock's fishery ($i=1, 2$). Assuming constant costs per unit effort ($c_i$), the cost function for i is:

$$C_i(x_i) = \frac{c_i}{q_i x_i} \quad (11)$$

In this study, we investigate the hypothetical case in which the fishery manager maximizes the joint profit from both fisheries. This work is thus a normative analysis to see how best to manage both fisheries together for maximizing total shared profit (i.e., the socially optimal solution), rather than a positive analysis, which would describe how fisheries actually compete in practice. The total profit at equilibrium (i.e., steady state) is given by:

$$\pi(x_1^*, x_2^*) = [p_1 - C_1(x_1^*)]F(x_1^*, x_2^*) + [p_2 - C_2(x_2^*)]G(x_1^*, x_2^*) \quad (12)$$
<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Logistic growth for a predator–prey relationship between the (A) predator (tuna, *x*₁) and (B) prey (mesopelagic, *x*₂) stocks, where *D* > 0 and *β* < 0.</figcaption></figure>

See K&J (2022) or Data S1 for a full description of solving the optimal control problem. Essentially, we optimize harvests of the two stocks to maximize total profit across the two fisheries while keeping harvest rates equal to growth rates for stock persistence at steady state. All modeling work was conducted in MATLAB (version 24.1 (R2024a) Update 3; The MathWorks Inc 2024). All monetary values are in 2024 USD.

## 2.2 | Model Parameterization

We inform our ecological and economic parameters using empirical data where possible and following the assumptions of Kourantidou and Jin (2022) as needed. Baseline model parameters are summarized in Table 1. To allow for correct scaling of predator (simulated bigeye tuna) and prey (small mesopelagic fishes) population sizes, we use the Atlantic-wide estimates of bigeye (ICCAT 2021) vs. mesopelagic fishes (Pauly et al. 2021). Critically, we assume prices for the bigeye tuna fishery reflect midpoints from the Atlantic purse seine and longline fleets (McKinney et al. 2020), prices and costs of the mesopelagic fishery reflect those of similar Atlantic fisheries (following Groeneveld et al. 2024; Standal and Grimaldo 2020), and use Willis et al. (2025) to inform the choice of diet share *D*.

## 2.3 | Scenarios

All scenarios use *D* and *β* as described above, except S0. We first describe the implications of having trophic interactions

vs. independent species by comparing S0 and S1. We then focus on exploring changes to prices and costs (S2–S5) to determine what conditions are required for economic viability of a mesopelagic fishery and if this economic viability is more sensitive to changes in cost or price.

**S0:** *No trophic interaction.* We set $D=β=0$ to explore optimal harvests if the two fished stocks are independent.

**S1:** *Base model.* All parameters following Table 1.

**S2:** *Price of tuna.* We alter the ex-vessel price of tuna to 10%, 50%, 150%, or 200% of its base value ($300, $1500, $4500, or $6000).

**S3:** *Cost of fishing tuna.* We alter the cost of fishing tuna to 10%, 50%, 150%, or 200% of its base value ($1100, $5500, $16,500, or $22,000).

**S4:** *Price of mesopelagic fishes.* We alter the ex-vessel price of mesopelagic fishes to 10%, 50%, 150%, or 200% of its base value ($50, $250, $750, or $1000).

**S5:** *Cost of fishing mesopelagic fishes.* We alter the cost of fishing mesopelagic fishes to 10%, 50%, 150%, or 200% of its base value ($2500, $12,500, $37,500, or $50,000).

We refer to each scenario's adjusted values as “lowest,” “low,” “high,” and “highest,” respectively. The base model is used as a “mid” point for comparison.
<table><caption>**TABLE 1** | Baseline model parameters. Monetary values are in 2024 USD.</caption><thead><tr><th></th><th></th><th>Value</th><th>Source</th></tr></thead><tbody><tr><td colspan="4"><strong>Biological</strong></td></tr><tr><td>D</td><td>Diet share of prey to predator</td><td>0.6</td><td>Willis et al. (2025)</td></tr><tr><td>β</td><td>Functional response of prey to predator</td><td>-2×10<sup>-8</sup></td><td>Kourantidou and Jin (2022)</td></tr><tr><td>r</td><td>Predator intrinsic growth rate</td><td>0.3</td><td>ICCAT (2021)</td></tr><tr><td>s</td><td>Prey intrinsic growth rate</td><td>1.5</td><td>Groeneveld et al. (2024)</td></tr><tr><td>K</td><td>Predator carrying capacity</td><td>1.5×10<sup>6</sup></td><td>ICCAT (2021)</td></tr><tr><td>L</td><td>Prey carrying capacity</td><td>1.6×10<sup>8</sup></td><td>Pauly et al. (2021)</td></tr><tr><td colspan="4"><strong>Economic</strong></td></tr><tr><td>p<sub>1</sub></td><td>Ex-vessel price of predator ($/t)</td><td>3000</td><td>Williams and Reid (2018), McKinney et al. (2020)</td></tr><tr><td>p<sub>2</sub></td><td>Ex-vessel price of prey ($/t)</td><td>1200</td><td>Standal and Grimaldo (2020)</td></tr><tr><td>c<sub>1</sub></td><td>Cost of fishing predator ($/day)</td><td>11,000</td><td>Kourantidou and Jin (2022)</td></tr><tr><td>c<sub>2</sub></td><td>Cost of fishing prey ($/day)</td><td>25,000</td><td>Groeneveld et al. (2024)</td></tr></tbody></table>

Model scenarios that result in negative profits to one fishery but positive profits to the other are possible given that we maximize the joint profit. Results with profit losses to the tuna fishery and gains to the mesopelagic fishery indicate economic conditions under which it is more profitable to fish the tuna stocks down and thus minimize their reduction (via $\beta$) of the mesopelagic stock.

We additionally conduct a sensitivity analysis on the trophic interaction terms ($D$, $\beta$). Full description and results are available in the Supplemental.

## 3 | Results

### 3.1 | With or Without Trophic Interaction

The presence or absence of trophic interactions among the two fished stocks greatly altered the model results (Table 2). Under scenario S0, in which $D=\beta=0$, both fisheries are profitable. With the introduction of realistic trophic interactions (base model S1), the mesopelagic fishery is largely unchanged, but the tuna fishery is unprofitable. The mesopelagic fish stock is much larger than the tuna fish stock and is less sensitive to changes in the tuna stock than is the tuna stock to mesopelagic stock changes

<table><caption>**TABLE 2** | Model output at equilibrium of fisheries on bigeye tuna & mesopelagic fishes with either no trophic interactions or a predator & prey relationship.</caption><thead><tr><th></th><th>No trophic<br>interaction (S0)</th><th>Trophic<br>interaction (S1)</th></tr></thead><tbody><tr><td colspan="3"><strong>Tuna</strong></td></tr><tr><td>Stock size (t)</td><td>2.06E+05</td><td>1.80E+05</td></tr><tr><td>Fishing effort<br>(day/year)</td><td>1.33E+04</td><td>1.28E+04</td></tr><tr><td>Harvest (t/year)</td><td>5.34E+04</td><td>4.48E+04</td></tr><tr><td>Landed value<br>($/year)</td><td>1.60E+08</td><td>1.34E+08</td></tr><tr><td>Fishing costs<br>($/year)</td><td>1.46E+08</td><td>1.40E+08</td></tr><tr><td>Profit ($/year)</td><td>1.42E+07</td><td>-6.05E+06</td></tr><tr><td colspan="3"><strong>Mesopelagics</strong></td></tr><tr><td>Stock size (t)</td><td>8.08E+07</td><td>8.09E+07</td></tr><tr><td>Fishing effort<br>(day/year)</td><td>7.57E+04</td><td>7.57E+04</td></tr><tr><td>Harvest (t/year)</td><td>5.97E+07</td><td>5.97E+07</td></tr><tr><td>Landed value<br>($/year)</td><td>2.98E+10</td><td>2.99E+10</td></tr><tr><td>Fishing costs<br>($/year)</td><td>1.89E+09</td><td>1.89E+09</td></tr><tr><td>Profit ($/year)</td><td>2.79E+10</td><td>2.80E+10</td></tr><tr><td>Summed two-<br>fishery profit ($/<br>year)</td><td>2.80E+10</td><td>2.80E+10</td></tr></tbody></table>

(Figure 1). Following the specification in Equation (7), the existence of a mesopelagic fishery prevents the tuna biomass from reaching its maximum carrying capacity (by causing $x_2/L < 1$). The tuna fishery profit is highest when the stock's carrying capacity is at its maximum $K$ (i.e., no mesopelagic fishery such that $x_2=L$, or $D=0$). A range of trophic interaction values and their impact on summed two-fishery profit is depicted in Figure 2, see Supplemental for further sensitivity analyses. Specifically, Figure 2 illustrates how equilibrium profits for the tuna fishery (panel A) and the mesopelagic fishery (panel B) change with variations in diet share and predator–prey interaction.

Equilibrium profit for tuna decreases as the diet share increases (Figure 2A), meaning that as tuna relies more on mesopelagic prey, its profitability is negatively impacted. Similarly, stronger trophic coupling ($\beta$ more negative) leads to lower predator equilibrium profit. The highest predator profit occurs at low $D$ and weak trophic interactions (upper-left, yellow region), meaning that the tuna fishery benefits when it is less dependent on mesopelagics or when mesopelagic depletion has a weaker impact on its growth. In Panel B, we see that profit is highest when $\beta$ is weakly negative (yellow region at the top), suggesting that as long as the predation pressure remains moderate,
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Gradient of equilibrium profit for the fisheries on (A) tuna (predator) (B) mesopelagics (prey) under different predator–prey dynamics (*D*, *β*) at base model prices and costs.</figcaption></figure>

the mesopelagic fishery remains highly profitable. However, as $\beta$ becomes more negative, mesopelagic profits decline, meaning that stronger predator–prey interactions reduce prey fishery profitability.

## 3.2 | Price and Cost Scenarios

Investigation of differing prices and costs for the two fisheries under joint management revealed that the modeled tuna fishery profits are sensitive to different economic conditions, while the mesopelagic fishery profits are relatively resilient (Figure 3). Tuna fishery profits varied widely, from losses of over $100 million to profits exceeding $250 million. Profitable tuna fisheries occurred under high tuna prices, low tuna harvesting costs, and low mesopelagic prices. On the contrary, higher mesopelagic prices led to sharp declines in profitability. Mesopelagic profits were always positive and generally stable around $28 billion, except when mesopelagic prices were altered. Generally, changes to equilibrium profits were more sensitive to changes in prices than in costs, suggesting that market dynamics (pricing) play a stronger role than operating costs in determining equilibrium profits. This relates to the cost function (C, Equation (11)) in which overall costs are affected by the unit cost (c, the altered parameter), stock size ($x$), and catchability ($q$) for each fishery.

To achieve the optimal joint profit under most modeled economic conditions (including the base model), tuna is harvested at negative profit such that the tuna stock ($x_1$) is lowered and the negative impact on the prey ($x_2$) minimized. The greatest tuna yields (total harvest) occurred with lowest mesopelagic price, highest tuna price, and highest tuna fishing cost (Figure 4; the latter is due to the relationship among increased costs, decreased efforts, and thus increased stock size increasing the harvests of the remaining fishers even as profits decline). Thus, high tuna harvests in the model can result from either increased gains to the tuna fishery (S2) or to the mesopelagic fishery (S3). Additionally, higher mesopelagic prices lead to lower tuna harvest, suggesting

that when mesopelagic products (e.g., fishmeal) become more valuable, harvesting pressure shifts toward the mesopelagic fishery, reducing tuna extraction. However, changes in mesopelagic harvesting costs have little impact on tuna harvest, suggesting that mesopelagic economic conditions do not significantly dictate tuna harvesting decisions. The mesopelagic yield is almost unchanged across all scenarios, suggesting the equilibrium availability of mesopelagic products (e.g., fishmeal) is steady across economic conditions. Changes in mesopelagic harvesting costs have little impact on tuna harvest, suggesting that mesopelagic economic conditions do not significantly dictate tuna harvesting decisions. Tuna price and cost have virtually no effect on mesopelagic harvest, suggesting that tuna stock dynamics do not strongly constrain mesopelagic yield at equilibrium. Across scenarios, tuna stock size varied widely from ~160 to 600 thousand tonnes, with a stock size of 180 thousand tonnes in the base model (Figure S1). Higher tuna stock sizes were associated with scenarios with higher tuna fishery profits. Mesopelagic stock size (~8×10^7 t) was almost unchanged across scenarios, except for an increase under the lowest mesopelagic price scenario (Figure S1) which also had the lowest mesopelagic harvest (Figure 4).

## 4 | Discussion

The interaction between mesopelagic fishes and their migratory predators is of critical importance to management of any future mesopelagic fishery. Here, we introduce a new predator–prey dynamic to capture the effect of diet share of a prey on predator carrying capacity. Given the importance of carrying capacity in determining maximum sustainable yield, this analytical framework presents a useful approach to assist with the implementation of ecosystem-based fisheries management. Specifically, we use a generic mesopelagic fish stock as the prey and bigeye tuna as the predator in an Atlantic Ocean context (see Box 1 for comparison of our model results to real-world values). We model simultaneous fisheries on these two stocks to determine the conditions that maximize joint profit. If the
<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Profit at optimal equilibrium for (A) the tuna (predator) fishery and (B) the mesopelagic (prey) fishery. Each “mid” value represents the base model.</figcaption></figure>

predator–prey interaction is ignored, the joint profit is overestimeted. By including empirical estimates of predator reliance on the prey (Willis et al. 2025), we see dramatically different model results for the tuna fishery. Overall, this work suggests that profitable fisheries on both predator and prey are possible, but highly uncertain. Most of the modeled scenarios resulted in significant negative impacts of large-scale mesopelagic fishing on the existing, economically and culturally valuable tuna fishery.

Our study followed the classic two-species bioeconomic model approach (Clark 2010) as in Kourantidou and Jin (2022) with three major extensions. First, as noted above, a new function specification describing predator–prey dynamics (Equation 7) is introduced in the study. Next, the predator–prey model parameters are calibrated using new results from biological studies (Willis et al. 2025). Finally, other model parameters are also significantly modified so that the model is focused on a specific predator fishery, the bigeye tuna fishery in the north Atlantic Ocean. These extensions enable the direct comparison of model results from this study to real-world values (Box 1), which strengthens the management policy relevance of this work.

The existence of a profitable tuna fishery at equilibrium was highly sensitive to changes in prices and costs, especially prices, compared to the relatively stable mesopelagic fishery. The relative consistency of modeled mesopelagic fishery features across scenarios suggests that a mesopelagic fishery may be resilient to economic fluctuations, with the possible exception of a dramatically lower ex-vessel price for the mesopelagic catch. Real prices and costs for a projected mesopelagic fishery may vary widely from current estimates. For example, increased prices for mesopelagic fishes could reflect increased demand for fishmeal to facilitate the growing aquaculture sector (FAO 2024; Quang et al. 2024), and decreased costs of fishing the mesopelagic could reflect technological advances or subsidization of mesopelagic fisheries. Subsidization may be most likely as direct government incentives to develop a new fishery that may include a goal of establishing historical baselines in advance of potential negotiations (Sinan et al. 2022) over transboundary mesopelagic stocks. However, indirect subsidization of mesopelagic fleets is also likely (e.g., regulation-induced subsidies; providing incentives for fleets to fish for mesopelagics in the off-season from their primary catch) and may result in non “optimal” mesopelagic fishing behavior. Established tuna fisheries are less likely to have dramatic changes in prices or costs
<figure><img src="image_7.png" /><figcaption>B Mesopelagic fishery harvest (t)</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**FIGURE 4** | Harvest at optimal equilibrium for (A) the tuna (predator) fishery and (B) the mesopelagic (prey) fishery. Each “mid” value represents the base model.</figcaption></figure>

from present estimated values, though tuna prices have generally increased over time (e.g., Williams and Reid 2018). Determining total unit cost of any fishery is a challenging undertaking and will always include some degree of error (Lam et al. 2011). The base parameter estimates for all four cost/price values altered in scenarios S2–S5 appear to be near their respective “turning points” for tuna fishery profitability, indicating high sensitivity of profitability to economic conditions.

Importantly, the apparent resilience of the mesopelagic stock across scenarios does not imply weak ecosystem effects. Instead, it highlights an asymmetry in trophic sensitivity: predator populations can be highly responsive to prey availability even when prey biomass remains relatively stable due to high productivity and large carrying capacity. This distinction is important for ecosystem-based fisheries management, as it implies that economically valuable predator fisheries may experience substantial losses well before changes in prey stocks (if any) become detectable.

A key aspect of this study's modeling approach is that losses in the tuna fishery under the base trophic scenario (S1) and

multiple price-cost scenarios arise from the structure of the joint-profit optimization rather than from strong depletion of the mesopelagic stock itself. Because predator carrying capacity is endogenously linked to prey availability (Equation 7), even modest reductions in mesopelagic biomass—while remaining close to carrying capacity—reduce tuna growth potential when trophic dependence is high. Under the social planner objective, it is therefore optimal to accept negative profits in the tuna fishery in order to reduce predation pressure on the mesopelagic stock, which generates substantially higher economic returns. This outcome reflects an intentional trade-off under optimal management rather than an unintended ecological collapse of either stock. In a real-world case, the two fisheries would likely not be jointly managed under a single decision-maker (i.e., the social planner model; a hypothetical tuna- and mesopelagic-managing RFMO), but would rather be managed independently and prosecuted by fleets from multiple nations. Nonetheless, the trend demonstrated here of greater resilience of mesopelagic fishery outcomes compared to tuna fishery outcomes is likely to hold, for example via bottom-up controls on tuna populations from their prey (e.g., Frederiksen et al. 2006; Hunt and McKinnell 2006). Most
**BOX 1** | Comparison of model results to real-world values.

The model presented here is a simplification of real-world conditions, and thus its outputs should primarily be considered relative to one another, not as absolute harvests, profits, etc. However, to assist with their consideration in a larger context, we compare our base equilibrium model outputs to real fishery statistics, where available. All values are per year, all model values are at equilibrium.

Tuna landed value: Our base model estimates tuna landed value as $0.13 million. The total estimated landed value of bigeye tuna across the Atlantic is approximately $0.28 million (McKinney et al. 2020). Modeled landed values on the order of real values occurred in the “high tuna price” scenario S2 (landed value $0.27 million).

Tuna stock size: Our base model estimates tuna stock size as 180 thousand tonnes. Stock size across modelled scenarios ranges from approx. 160–600 thousand tonnes. The spawning stock biomass of Atlantic bigeye tuna is approx. 400 thousand tonnes (ICCAT 2021).

Tuna fishery effort: Our base model estimates Atlantic tuna fishery effort as 12 thousand days. Comparable Atlantic tuna fishery efforts are not easily accessible, so we compare to efforts in the western and central Pacific Ocean (WCPO), the world's largest tuna fishery. WCPO tuna purse seine efforts are on the order of 50 thousand days per year (SPC-OFP 2018). Atlantic efforts on the order of one quarter of WCPO efforts appear realistic.

Tuna landings: Our base model estimates tuna fishery landings as 44 thousand tonnes. Real Atlantic bigeye tuna landings are approx. 70 thousand tonnes (ICCAT 2021). Note that Atlantic bigeye stocks are considered to be overfished, and until recently (2019) were experiencing overfishing. The ICCAT maximum sustainable yield estimate is 86,833 tonnes (ICCAT 2021).

Mesopelagic landings & landed value: It is not possible to compare our modeled mesopelagic fishery to its “real” counterpart, as such a fishery does not exist at this time. To give a sense of scale for mesopelagic landings and landed value, we compare model results to the total landings and landed values of global fisheries: approximately 90 million tonnes and $141 billion, respectively. Our base model estimates a mesopelagic fishery would harvest 60 million tonnes for a landed value of $30 billion. This modelled harvest may be high but within reason, given that the global biomass of the mesopelagic is estimated to be on the order of 10 billion tonnes, or 100-fold the annual landings of all current capture fisheries (Irigoien et al. 2014; Proud et al. 2019).

importantly, model results of the “social planner problem” can be used as benchmarks for evaluating real-world fisheries and provide useful information for ecosystem-based fisheries management, highlighting the potential trade-offs between predator and prey fisheries under changing economic and ecological conditions.

The model presented here is a simplified version of a complex pelagic food web. We explicitly consider only the bigeye tuna and its generic “mesopelagic fish” prey, assuming that other relevant food web components are stable. We do not explicitly consider a spatial factor, despite bigeye tuna being a highly migratory species, following the assumption that mesopelagic

fisheries would be sufficiently widespread to impact the tuna across its range. Focusing on the predator–prey interaction, we propose that the use of stable isotope data to estimate long-term diet share of a prey to a predator is preferable to the use of stomach contents. Our diet share (D, 60%) is based on assimilation of mesopelagic food web-based carbon into bigeye tuna tissues over several months (Willis et al. 2025). Thus, it represents an overall reliance of bigeye tuna on mesopelagic food webs (including across multiple trophic steps), not their direct consumption of mesopelagic animals by weight or abundance as would be observed in stomach contents.

Regarding the uncertainty in our diet share estimate D, we consider two ecological factors that could indicate our parameter value to be an overestimate or underestimate, respectively, of the “true” value. Firstly, our model does not explicitly consider the possibility of predator diet switching (i.e., to epipelagic resources if the mesopelagic is heavily fished). This could result in an overestimation of D and the impact of mesopelagic population changes on the predator population. However, we suggest that bigeye tuna and other large pelagic predators have limited ability to diet switch, given that many epipelagic resources are already fully or over-exploited by fisheries (Cheung et al. 2024; FAO 2024). Secondly, our model suggests that bigeye tuna feed directly on small mesopelagic fishes like lanternfish. While this does occur, bigeye tuna and other similarly sized fish predators (e.g., yellowfin tuna, swordfish) generally feed about one trophic level up on animals such as barracudina (e.g., Duffy et al. 2017; Logan et al. 2021). Some energy is lost with every trophic transfer (Lindeman 1942; Pauly and Christensen 1995), meaning that the true food web reliance of bigeye tuna on small mesopelagics may be greater than our estimate. Notably, these two factors act in opposite directions on the estimated diet share D.

The conclusions drawn here for bigeye tuna and its fishery are likely to be similar to those for yellowfin tuna or swordfish, given their comparable levels of mesopelagic food web reliance (Willis et al. 2025). Potential impacts on higher trophic levels from loss of mesopelagic forage are not restricted to fish predators, and include marine mammals (Goetsch et al. 2018; Iglesias et al. 2023) and seabirds (Watanuki and Thiebot 2018) of cultural value and conservation concern.

This study joins the growing body of work suggesting that trade-offs among mesopelagic fisheries and existing ecosystem services may lead to substantial societal losses (Buesseler et al. 2022). The primary focus of policy discussions to date has been the implications of mesopelagic harvest for carbon sequestration via the biological carbon pump. The results of several recent studies (Groeneveld et al. 2024; Oostdijk et al. 2024; Prellezo et al. 2024) suggest that a rapid development of mesopelagic fishing could lead to a significantly higher social cost of climate change. Given the large uncertainties and the potential large impacts on oceanic carbon sequestration, a precautionary approach to developing mesopelagic fisheries is warranted. Through this work, we emphasize that mesopelagic fishes are a key component of marine foods webs and demonstrate the potential for large-scale mesopelagic fisheries to disrupt existing ecosystem services and valuable fisheries such as tuna fisheries.
## Acknowledgements

We thank Megan Bailey, Porter Hoagland, Rolf Groeneveld, and Amanda Schadeberg for helpful discussions relating to the development of this work. This work was funded in part by a Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarship—Doctoral (to C.W.) and a Massachusetts Institute of Technology Martin Family Society of Fellows for Sustainability Fellowship (to C.W.). This work was part of the Woods Hole Oceanographic Institution's Ocean Twilight Zone Project, funded as part of the Audacious Project housed at TED.

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

Input data and parameters used in the model are available in the main text.

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section. **Data S1:** fme70054-sup-0001-DataS1.docx.