# Modelling the impact of hypoxia on critical essential fish habitats throughout the life cycle of exploited marine species

Juliette Champagnat, Elliot John Brown, Etienne Rivot, Olivier Le Pape

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Juliette Champagnat, Elliot John Brown, Etienne Rivot, Olivier Le Pape. Modelling the impact of hypoxia on critical essential fish habitats throughout the life cycle of exploited marine species. ICES Journal of Marine Science, 2025, 82, (5), pp. fsae178. 10.1093/icesjms/fsae178. hal-05086177

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# Modelling the impact of hypoxia on critical essential fish habitats throughout the life cycle of exploited marine species

Juliette Champagnat ID<fnref n="1" />,<fnref n="2" />,*, Elliot John Brown ID<fnref n="3" />, Etienne Rivot ID<fnref n="1" />, Olivier Le Pape ID<fnref n="1" />

## Abstract

Spawning grounds and juvenile habitats are critical essential fish habitats (CEFH) facing multiple anthropogenic pressures. Yet the impacts of these pressures on fish population dynamics are poorly quantified. In this study, we address the impact of hypoxia on CEFH and its consequences on the productivity of exploited marine fish populations. Our approach relies on an age- and stage-structured simulation model, including habitat effects on egg, juvenile, or spawner mortalities to integrate the impact of hypoxia on spawning and juvenile habitats. We applied this framework in the Baltic Sea (BS) to explore population responses of cod, plaice, and herring, which face hypoxic conditions in their CEFH. All scenarios lead to modifications in fishing mortality, population size, and yield at maximum sustainable yield, but with different magnitudes depending on the impacted life stage. Perturbations of spawning habitats that result in higher mortality of eggs had the least impact, while the more direct effect on spawners themselves had the largest impact. Within each scenario, the responses also depended upon species and life history traits, with contrast in steepness and lifespan that explain most of the differences between species. Cumulative impacts of pressures in both CEFH were also investigated and revealed mainly nonadditive responses. Our framework is a generic tool, transferable to a large panel of case studies, to quantify the relative impact of CEFH degradation/restoration for the dynamics of marine exploited populations.

**Keywords:** anthropogenic pressures; juvenile habitat; spawning habitat; population model; cumulative effects

## Introduction

Coastal and estuarine ecosystems are highly productive areas (Costanza et al. 1997) serving as spawning grounds, juvenile habitats, foraging areas, and migration corridors for numerous fish and invertebrates (Beck et al. 2001). Coastal and estuarine areas are used in at least one life-history stage by 44% of the commercially important species for which advice is provided by the International Council for the Exploration of the Sea (ICES) in the Northeast Atlantic (Seitz et al. 2014). A similar level of dependence is acknowledged worldwide (Fodrie and Mendoza 2006, Lellis-Dibble et al. 2008, Brown et al. 2019a). They are hence often considered as essential fish habitats (Magnuson-Stevens Fishery Act 2007). In this paper, we will use the more precise terminology of critical essential fish habitat (CEFH) to designate spatially restricted areas where fish aggregate to complete a specific life stage.

CEFH located in coastal and estuarine areas face pressures from multiple human activities (Halpern et al. 2008). This high level of anthropogenic pressure induced a widespread degradation and destruction of native nearshore ecosystems worldwide (Halpern et al. 2008, Bulleri and Chapman 2010) and related loss in their function as CEFH (Worm et al. 2006, Barbier et al. 2011).

The most current pressures impacting coastal fish species are contamination by xenobiotics, nutrient excess (and its consequences for oxygen levels), biological invasion, and surface area losses (Brown et al. 2018). In addition to their marginal

effects, those pressures may interact and have cumulative impacts on coastal ecosystems (Halpern et al. 2009) and related CEFH (Brown et al. 2018). The interactions between these various pressures can be simply additive, synergistic (magnitude is greater than the sum of independent pressures), or antagonistic (magnitude of combined impact is less than the sum of independent pressures) (Crain et al. 2008).

The impact of local anthropogenic pressures at the scale of individual fish has been widely documented (Brown et al. 2018), but quantitative upscaling at the population scale is less addressed (Sundblad and Bergström 2014, Vasconcelos et al. 2014). Understanding the effects of pressures on CEFH on fish population still remains a grand challenge (Thorson et al. 2021), essential for guiding effective management actions in a context where trade-offs must be made between various human activities (Sundblad and Bergström 2014, Rose et al. 2018).

Some studies have used quantitative methods to link habitat characteristics to fish population dynamics (Levin and Stunz 2005, Fodrie et al. 2009); others have investigated how anthropogenic pressures on CEFH impact fish population dynamics and fisheries yield (Scheuerell et al. 2006, Mizerek et al. 2011, Archambault et al. 2018, Rose et al. 2018, Camp et al. 2020, Champagnat et al. 2021). However, previous work often remains strongly case-specific, hence being highly data-demanding, which prevents their broader application. A comprehensive framework has recently been developed for

<footnote n="1">¹DECOD (Ecosystem Dynamics and Sustainability), L'Institut Agro, IFREMER, INRAE, 35042 Rennes, France</footnote>

<footnote n="2">²School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, United States</footnote>

<footnote n="3">³National Institute of Aquatic Resources, The Technical University of Denmark, 2800 Kongens Lyngby, Denmark</footnote>

<footnote>*Corresponding author. School of Aquatic and Fishery Sciences, University of Washington, Seattle, WA 98195, United States. E-mail: jchampag@uw.edu</footnote>
analysing the impacts of anthropogenic pressures on juvenile habitat (Champagnat et al. 2024). Still, the combined effect of anthropogenic pressures affecting juveniles and spawning habitats, from effects of one pressure on overlapping juveniles and spawners to effects of different pressures on nonoverlapping CEFH along the life stage, has received poor attention so far.

Among important human-induced pressures impacting fish in their coastal and estuarine CEFH, hypoxia and anoxia (hereafter we used hypoxia to describe all levels of oxygen that place physiological constraints/risks on fish) potentially affect both juveniles and spawning habitats. Hypoxic areas can have multiple direct and indirect effects on aquatic organisms, modifying their metabolism, ecology, and life-history traits (Chabot and Dutil 1999, Herbert and Steffensen 2005, Domenici et al. 2007). They can also affect the entire ecosystem functioning (Breitburg 2002, Wu 2002, Diaz and Rosenberg 2008). At an individual level, the response to hypoxia (or to other similar detrimental environmental conditions) could be either to avoid the impacted habitat or to persist and tolerate the stressor (McKenzie 2011). In order to avoid, fish can increase locomotion (speed or turning frequency), at a cost of increasing oxygen demand, which is limiting in both hypoxic and high temperatures (Herbert et al. 2011). The downregulation of energy-demanding activities such as locomotion and digestion (Cook et al. 2014) is a common persistence strategy to try to tolerate the stressor. The strategy depends on the species' tolerances, the magnitude of the stressor, and the individuals' past exposure (Cook and Herbert 2012, Ferrari et al. 2016). Understanding the potential impact of those different strategies, and the way they affect different life stages along the life cycle, will help investigating the impacts of those stressors on populations.

We develop our work using the Baltic Sea (BS) as a motivating example. In the BS, hypoxia in the deeper basin areas is a naturally occurring phenomenon that has been exacerbated in both severity and scale by human activities, such that the volumes of hypoxic water are larger, cover broader areas, and persist for longer across the seasons (Rahikainen et al. 2017, Piehl et al. 2022). In particular, basin-scale hypoxia has become a routine feature in various known spawning habitats. Atlantic cod (Gadus morhua), and European plaice (Pleuronectes platessa) both spawn in these basins (Eero et al. 2011, Petereit et al. 2014, Rahikainen et al. 2017, Brander 2022, Receveur et al. 2022, Svedäng et al. 2022), where autumn hypoxia is extending into winter and early springtime, during spawning. With the expansion of hypoxia, the risk of coastal hypoxia increases in the BS (Støttrup et al. 2018), in concert with coastal marine heatwaves (Rolff et al. 2022). Persistent warming in coastal marine habitats has already excluded adult cod from these areas (Dinesen et al. 2019). In the western BS, Atlantic herring (Clupea harengus) spawn in these shallower waters on macroalgae and gravels. These habitats are becoming more exposed to pulses of hypoxia and thermal stress (Moyano et al. 2022), which may overlap with different stocks' spawning times in late summer/autumn. Moreover, juvenile cod, plaice, and herring are also located in coastal areas (Seitz et al. 2014), where they face hypoxic conditions, especially intense during their summer growth period.

In this paper, we developed a population model to evaluate the impact of hypoxia on both juvenile habitats and spawning grounds, with important commercial species in the Southwest BS as examples. The approach is based on a generic

age- and stage-structured population model parameterized to assess the impact of scenarios modifying juvenile and spawning habitats on population dynamics and productivity and designed to be easily adapted to different species with contrasted life histories. The model was applied to assess the effect of hypoxia on three different species with contrasting life histories: cod, plaice, and herring. We use the model to assess the responses of population productivity and fisheries yield to different scenarios of increase/mitigation of hypoxia (i.e. degradation/restoration) in both juvenile and spawning habitats. Following Champagnat et al. (2024), our expectation is that the response depends upon the species life history. We also compared the population responses under different hypotheses regarding the response of fish to hypoxia on the spawning grounds, i.e. avoidance or persistence. Our expectation is that the two individual strategies should lead to different population response, but that the differences will depend on fish life histories. Last, we evaluated the existence of nonadditive effects of habitat degradation on juvenile and spawning habitats, and we explored how these responses vary across the different life histories.

## Materials and methods

### Population dynamic model

Building on the framework developed by Champagnat et al. (2024), the impact of CEFH degradation on population dynamics and productivity was assessed with a deterministic life cycle model, structured by stage and age, which includes habitat effects on multiple transitions to integrate the impact of degradation on spawning and juvenile habitats. This model considers a single closed population with unique but distinct spawning and juvenile habitat areas.

Here, we define recruitment as the age of transition from the juvenile to the preadult stage when fish leave nursery grounds. Fish are assumed to be recruited at age $a_{rec}$. For all age classes $a_{rec} < a \le a_{max}$, the population abundance ($N$) is described by equation (1):

$$N_{a+1,y+1} = N_{a,y} \times e^{-(M_a+F_{a,y})} \quad (1)$$

with $M$ and $F$ are the natural and fishing annual mortality rates, respectively.

The number of eggs produced in a year, $\omega_y$ (equation 2) is calculated as

$$\omega_y = \sum_{a=a_{rec}}^{a_{max}} N_{a,y} \times e^{-ts \times (M_a+F_{a,y})} \times \text{mat}_a \times p f_a \times \text{fec}_a \quad (2)$$

with $\text{mat}_a$ the probability of being mature at age $a$, $p f_a$ the female proportion at age $a$, and $\text{fec}_a$ the fecundity at age $a$, all considered constant in time. $ts$ is a time-scaling factor applied to consider the mortality happening before spawning, set for each species according to the literature (see Table S2.1).

Recruitment, defined as the number of juveniles produced in juvenile habitats, accounts for the density-dependent and density-independent mortalities during the early-life stages. Following Archambault et al. (2014) and Champagnat et al. (2024), we assumed that the mortality from eggs to recruited juveniles follows a two-stage process:

(i) A density-independent mortality during the eggs and larval drift with a resulting survival $\phi$.

(ii) A combination of density-independent and compensatory density-dependent mortality processes with
mortality rates $M_{DI}$ and $M_{DD}$, respectively, for a duration $a_{rec}$. The compensatory mortality is supposed to result from intracohort competition for trophic resources and/or space in the spatially restricted juvenile habitats (Beverton and Holt 1957).

The resulting recruitment ($N_{a_{rec},y}$) can be expressed as (equation 3; Brooks et al. 2019, Champagnat et al. 2024)

$$N_{a_{rec},y} = \frac{e^{-M_{DI} \times a_{rec}} \times \phi \times \omega_{y-a_{rec}}}{1 + \frac{1}{S} \times \frac{M_{DD}}{M_{DI}} \times (e^{M_{DI} \times a_{rec}} - 1) \times e^{-M_{DI} \times a_{rec}} \times \phi \times \omega_{y-a_{rec}}}. \quad (3)$$

Equation (3) is better known as a Beverton-Holt relationship (Quinn and Deriso 1999). It can be easily reparametrized in terms of its asymptote, K, interpreted as the carrying capacity of the juvenile habitat, and steepness h [equation 4; see Champagnat et al. (2024) for more details on the derivations]:

$$N_{a_{rec},y} = \frac{4 \times h \times \omega_{y-a_{rec}}}{\bar{W} \times (1-h) + \frac{4 \times h \times \omega_{y-a_{rec}}}{K}}. \quad (4)$$

The carrying capacity K is defined as the maximum number of juveniles that a juvenile habitat area can produce (Del Monte-Luna et al. 2004). Under the hypothesis that habitat quality is homogeneous in space, K can be written as the product of K*, the carrying capacity per unit of surface, which denotes the quality of habitat, and S, the surface of the juvenile habitat (equation 5):

$$K = K^* \times S. \quad (5)$$

The carrying capacity per unit of surface can be derived from equation (3) and fundamentally depends upon the balance between $M_{DD}$ and $M_{DI}$ as (equation 6):

$$K^* = \frac{M_{DI}}{M_{DD}} \times \frac{1}{(e^{M_{DI} \times a_{rec}} - 1)}. \quad (6)$$

The steepness h is defined as the proportion of unfished equilibrium recruitment produced when the spawning stock biomass is reduced to 20% of its unfished level (Mace and Doonan 1988). It characterizes the drop-off in recruitment when the reproductive potential is reduced. Steepness varies in theory from 0.2, for a linear stock-recruitment relationship (no compensation), to 1 when the compensation is very strong, leading to a constant recruitment when to stock size is above 20% of pristine biomass (Myers et al. 1999, Mangel et al. 2013, Rindorf et al. 2022). Steepness can be expressed as a function of egg survival (φ) and juvenile density-independent mortality rate ($M_{DI}$) and $\bar{W}$ (equation 7):

$$h = \frac{e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}}{4 + e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}}. \quad (7)$$

$\bar{W}$ is the mean number of eggs produced by a recruit during its lifetime in the absence of fishing (Mangel et al. 2010), expressed as the sum over all age class of the product of survival, maturity, proportion of female, and fecundity (equation 8):

$$\bar{W} = \sum_{a=a_{rec}}^{a_{max}} (S_a \times mat_a \times pf_a \times fec_a) \quad (8)$$

with $S_a$ the cumulative survival until age a in the absence of fishing, defined as (equation 9)

$$S_a = \prod_{i=a_{rec}}^{a-1} e^{-M_i}. \quad (9)$$

It is important to note that in our parameterization, the steepness is not only a characteristic of the juvenile nursery habitat ($M_{DD}$ and $M_{DI}$), but that it integrates $\bar{W}$, hence the adult phase of the life cycle.

## Modelling the effect of hypoxia on CEFH

Using the population model, we simulate scenarios that consider the modification of habitat quality induced by hypoxia on two types of CEFH: juvenile habitat and spawning ground (marginal or combined), and different hypotheses regarding the type of response of fish to these impacts, avoid or persist.

### Impact on juvenile habitats (called ‘Juvenile scenario’ hereafter)

Regarding the low capacity of migration of juvenile fish (Le Pape and Cognez 2016), we considered that juvenile cannot avoid anoxic areas, but can only persist and experienced the degraded conditions. Therefore, the *Juvenile scenario* assumed that the surface area of available juvenile habitat is not affected by change in hypoxia conditions, but that the quality of habitat is affected. As the direct consequence of changes in the habitat quality, the juvenile mortality rates $M_{DI}$ and $M_{DD}$ are affected by hypoxia conditions and, in turn, affect the steepness h and the carrying capacity per unit of surface K* (Champagnat et al. 2024). Because no information is available to define how hypoxia would impact differently the density-independent ($M_{DI}$) and dependent mortality rates ($M_{DD}$), we assumed a common multiplier for $M_{DI}$ and $M_{DD}$ as a default hypothesis (see Champagnat et al. (2024) for more details and discussion on this hypothesis).

Let $\alpha_J$ be the multiplier used to model habitat change in the *Juvenile scenario*. To avoid scaling issues and make easier the comparison of different scenarios, habitat scenarios were modelled through multipliers in the scale of the resulting survival rather than in the scale of survival rate. We applied $\alpha_J$ to the resulting density-independent survival of juveniles (equation 10):

$$\alpha_J \times e^{-M_{DI} \times a_{rec}}. \quad (10)$$

Note that $\alpha_J < 1$ in case of habitat degradation, and $\alpha_J > 1$ in case of habitat restoration. Under the hypothesis that hypoxia affect equally $M_{DI}$ and $M_{DD}$, the steepness and carrying capacity per unit of surface given the juvenile habitat multiplier $\alpha_J$ are (equations 11 and 12):

$$h(\alpha_J) = \frac{\alpha_J \times e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}}{4 + \alpha_J \times e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}}, \quad (11)$$

$$K^*(\alpha_J) = \frac{M_{DI}}{M_{DD}} \times \frac{1}{e^{M_{DI} \times a_{rec} - \ln(\alpha_J)}} - 1. \quad (12)$$

### Impact on spawning grounds

To model the impacts of hypoxia on spawning grounds, we conceptualized two different hypotheses based on the expected individual fish response (Fig. 1):

* (i) Avoidance: fish avoid the hypoxic area and spawn in a less suitable area, which leads to an increase in egg
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Diagram of the hypothetical consequences of impacts of hypoxia on spawning grounds. Black and solid lines emphasize the two hypothesized mechanisms of action. Bold text indicates the model parameters modified by the scenarios. Dark grey shaded shapes highlight the consequences at the population scale. Light grey contouring and italic text indicate the name of the scenario. Shapes outlined in grey dashed lines display less likely mechanisms of action, with unclear consequences, not investigated in this study.</figcaption></figure>

mortality, without affecting the future life of spawners (called ‘Egg scenario’ hereafter).

(ii) Persistence: spawners do not avoid hypoxic areas and are directly affected by the hypoxia conditions through an increase of their mortality (called ‘Spawner scenario’ hereafter).

Note that for both hypotheses, scenarios of restoration (i.e. mitigation of hypoxia) lead to a decrease in the mortality of eggs and spawners, while degradation induces an increase in mortality.

For the *Egg scenario* (Fig. 1), we follow the same logic as for the *Juvenile scenario*. The density-independent survival of eggs in equation (3) is $\phi$. We hence applied a multiplier $\alpha_{\omega}$ to this resulting survival. The carrying capacity $K$ is not affected. The new steepness value given the multiplier $\alpha_{\omega}$ is expressed by equation (13) and has a similar effect to $\alpha_j$ on the steepness:

$$h(\alpha_{\omega}) = \frac{\alpha_{\omega} \times e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}}{4 + \alpha_{\omega} \times e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}}. \quad (13)$$

For the *Spawner scenario* (Fig. 1), we modify the population equation with a spawner survival multiplier ($\alpha_S$). Because we suppose fish are affected all years after the first year of reproduction, we set $\alpha_S = 1$ (no effect) for all ages $\le a_{mat}$ (with $a_{mat} \ge a_{rec}$) (equation 14):

$$N_{a+1,y+1} = N_{a,y} \times \alpha_S \times e^{-(M_a+F_{a,y})}. \quad (14)$$

The survival (with no fishing) until age a is modified as follows (equation 15):

$$S_a(\alpha_S) = \prod_{i=a_{rec}}^{a-1} \alpha_S \times e^{-M_i} = \alpha_S^{(a-1)} \times \prod_{i=a_{rec}}^{a-1} e^{-M_i} = \alpha_S^{(a-1)} \times S_a. \quad (15)$$

In turn, the *Spawner scenario* has an impact on the mean number of eggs produced by a recruit during its lifetime in the

absence of fishing (equation 16), and hence on the steepness (equation 17).

$$\bar{W}(\alpha_S) = \sum_{a=a_{rec}}^{a_{max}} \alpha_S^{(a-1)} \times S_a \times \mathrm{mat}_a \times p f_a \times \mathrm{fec}_a, \quad (16)$$

$$h(\alpha_S) = \frac{e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}(\alpha_S)}{4 + e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}(\alpha_S)}. \quad (17)$$

## Cumulated impact of scenarios on both CEFH

We also assessed the impacts of cumulative pressure on both juvenile and spawning habitats. Based on our hypothesis (Fig. 1), we build two combined scenarios depending on the type of response to hypoxia on spawning grounds:

(i) impact of hypoxia on both juveniles and spawning habitat, with hypoxia impacting egg survival (Juvenile + Egg scenarios);

(ii) impact of hypoxia on both juvenile and spawning habitat, with hypoxia impacting spawner survival (Juvenile + Spawner scenarios).

The Juvenile + Egg scenarios imply the combinations of multipliers $\alpha_j$ and $\alpha_{\omega}$ in the steepness expression as follows (equation 18):

$$h(\alpha_j, \alpha_{\omega}) = \frac{\alpha_j \times \alpha_{\omega} \times e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}}{4 + \alpha_j \times \alpha_{\omega} \times e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}}. \quad (18)$$

The carrying capacity per unit of surface is only impacted by $\alpha_j$ so its expression remains as defined in equation (12).

In the Juvenile + Spawner scenarios, $\alpha_j$ will modify the value of the carrying capacity per unit of surface as defined in equation (12). $\alpha_S$ will modify the survival of adult through equation (14) and $\bar{W}$ through equation (16). Note that the steepness will be affected by both $\alpha_j$ and $\alpha_S$
<table><caption>**Table 1.** Life history parameters data for the three example species in the baseline configuration (all habitat multipliers = 1).</caption><thead><tr><th>Species</th><th>a<sub>mat</sub></th><th>a<sub>max</sub></th><th>M</th><th>h</th><th>φ</th><th>W&#773;</th><th>M<sub>DI</sub></th><th>M<sub>DD</sub></th></tr></thead><tbody><tr><td>Atlantic cod</td><td>3</td><td>15</td><td>0.27</td><td>0.79</td><td>10<sup>-5</sup></td><td>2.6 &times; 10<sup>6</sup></td><td>0.54</td><td>0.75</td></tr><tr><td>Atlantic herring</td><td>5</td><td>12</td><td>0.24</td><td>0.58</td><td>10<sup>-3</sup></td><td>3.7 &times; 10<sup>4</sup></td><td>1.9</td><td>0.33</td></tr><tr><td>European plaice</td><td>4</td><td>28</td><td>0.14</td><td>0.82</td><td>10<sup>-3</sup></td><td>4.5 &times; 10<sup>5</sup></td><td>3.2</td><td>0.13</td></tr></tbody></table>

$a_{mat}$ and $a_{max}$ refers to maturity and maximum age, M is the natural mortality, and h is the steepness (all extracted from FishLife, Thorson 2020). φ is the survival from eggs to the larvae just before their arrival on juvenile habitat (Le Pape and Bonhommeau 2015). W&#773; is the mean number of egg produced by a recruit during its lifetime in the absence of fishing, computed based on species LHT. Density-independent mortality M<sub>DI</sub> is derived from species data (equation 7) and density-dependent mortality M<sub>DD</sub> is deduced from M<sub>DI</sub> and K* = 1 (equation 6).

<table><caption>**Table 2.** Summary table of scenarios, the habitat they target, the modified parameters, and corresponding multipliers.</caption><thead><tr><th>Scenario</th><th>Target habitat</th><th>Modified parameters</th><th>Multiplier</th></tr></thead><tbody><tr><td>Egg</td><td>Spawning grounds</td><td>Egg mortality</td><td>α<sub>ω</sub></td></tr><tr><td>Juvenile</td><td>Juvenile habitats</td><td>Juvenile mortalities</td><td>α<sub>j</sub></td></tr><tr><td>Spawner</td><td>Spawning grounds</td><td>Adult mortality</td><td>α<sub>S</sub></td></tr><tr><td>Juvenile + Egg</td><td>Nurseries and spawning grounds</td><td>Juvenile and egg mortalities</td><td>α<sub>j</sub> and α<sub>ω</sub></td></tr><tr><td>Juvenile + Spawner</td><td>Nurseries and spawning grounds</td><td>Juvenile and adult mortalities</td><td>α<sub>j</sub> and α<sub>S</sub></td></tr></tbody></table>

(equation 19):

$$h(\alpha_j, \alpha_S) = \frac{\alpha_j \times e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}(\alpha_S)}{4 + \alpha_j \times e^{-M_{DI} \times a_{rec}} \times \phi \times \bar{W}(\alpha_S)}. \quad (19)$$

## Literature-based parametrization

Model parametrization requires to set values for life history traits (LHT: natural mortality, maximum age, age at maturity, weight at age, and fecundity) and stock-recruit parameters (steepness, carrying capacity). Most of them can be extracted from the package FishLife (Thorson et al. 2017, Thorson 2020), which estimates population parameters by applying a multivariate hierarchical model with a taxonomic structure to fish records from FishBase (Froese and Pauly 2022) and stock-recruitment series from the RAM database (Myers et al. 1995). The multivariate framework ensures that correlations among LHTs are considered, while the hierarchical structure strengthens estimates for taxa with insufficient data. In addition to FishLife parameters (Table 1 and Table S2.1), other parameters (weight-length relationship, fecundity, etc.) needed to complete the model were drawn from the literature (Table S2.1). The age at recruitment, a<sub>rec</sub>, was set to 1 for all species, which implies a hypothesis of density-dependent processes impacting only the survival from larval settlement to age 1. The carrying capacity of the juvenile habitat is a scaling factor of population size, which writes as the product of carrying capacity per unit of surface and the nursery habitat surface area (Champagnat et al. 2024); for each of the three species/populations, K was arbitrarily set to 10<sup>6</sup> (number of individuals), with K* = 1 and the surface used as the scaling factor for the population size fixed to S = 10<sup>6</sup> for all species (note that the unit of surface is arbitrary as only the product K* × S matters). The survival to eggs and larval drift (φ) was set to 10<sup>-3</sup> for plaice and anchovy and 10<sup>-5</sup> for cod (Le Pape and Bonhommeau 2015). Baseline values of M<sub>DI</sub> and M<sub>DD</sub> were derived from the LHT, based on equations 6 and 7. M<sub>DI</sub> is derived from steepness, larval survival, and mean number of eggs produced by a recruit during its lifetime (W&#773;) while M<sub>DD</sub>

is totally determined by the values of M<sub>DI</sub> and K* = 1 (Table 1, see Champagnat et al. (2024) for more details on derivation).

## Simulations

Once parameterized for each species, the model was used to simulate population dynamics under the five habitat scenarios (the Egg, Juvenile, and Spawner scenarios and the two combination, Juvenile + Egg and Juvenile + Spawner scenarios). Habitat scenarios were run by setting survival multipliers (α<sub>ω</sub> for egg, α<sub>j</sub> for juvenile or α<sub>S</sub> for spawner) to values between 0.8 (hypoxia increase, e.g. lower habitat quality) and 1.2 (hypoxia mitigation, e.g. higher habitat quality). Marginal effects (i.e. the Egg, Juvenile, and Spawner scenarios) were investigated by varying only one multiplier, the others being fixed to one. Cumulative effects (the two combined scenarios) were evaluated by exploring the cumulated impact of habitat scenarios on both nursery and spawning grounds (Table 2). For the sake of clarity, cumulative effects were only evaluated by combining identical values of multipliers, one for degradation (both multipliers fixed at 0.9, to simulate habitat degradation in both juvenile and spawning habitat) and one for restoration (both multipliers fixed at 1.1 to simulate habitat restoration for both juvenile and spawning habitat). The cumulative habitat impact scenarios were evaluated by comparing the sum of the two marginal effects with the effect measured from the scenario resulting from their combination. Based on the comparison of these two values, we can characterize three types of effects (Crain et al. 2008, Côté et al. 2016):

(i) Additive effects: the combination of effects equals the sum of the two marginal effects.

(ii) Synergistic effects (positive or negative depending on the sign of the effect): the combination of effects is greater than the sum of the marginal effects.

(iii) Antagonistic effects (positive or negative): the combination of effects is lower than the sum of the marginal effects.

Equilibrium curve relating catch (C) and spawning stock biomass (SSB) at equilibrium were built by simulating the population dynamics with constant conditions (F and all parameters) for 500 years to ensure an equilibrium state is reached, with F ranging from 0 to 1.5. We used equilibrium production curve to calculate management reference points such as SSB<sub>MSY</sub>, F<sub>MSY</sub>, and C<sub>MSY</sub> [i.e. at the maximum sustainable yield (MSY); widely used key indicators of the stock status and management reference points; Hilborn and Walters 1992].

Consequences of each habitat scenario were investigated through the gain/loss relative to the baseline scenario, calculated as (equation 20)

$$\% gain/loss = \frac{\text{indicator}_{\text{scenario}} - \text{indicator}_{\text{baseline}}}{\text{indicator}_{\text{baseline}}} \times 100 \quad (20)$$
<figure><img src="image_3.png" /><figcaption>**Figure 2.** Marginal effects of modifying habitat with *Egg* (left panel), *Juvenile* (middle panel), or *Spawner scenario* (right panel) on equilibrium productivity curves of the Atlantic Cod. Black line is the baseline scenario, and blue and red lines show scenarios of restoration (multiplier > 1) and degradation (multiplier < 1), respectively. The grey points highlight changes in (*MSY*, *F*<sub>MSY</sub>) according to simulations.</figcaption></figure>

with indicator being $F_{MSY}$, $SSB_{MSY}$, or $C_{MSY}$.

Model simulations were performed with R, and all the scripts used are available at https://github.com/jchampag/EFHimpacts.

## Results

First, a detailed interpretation of the marginal effects of *Egg*, *Juvenile*, and *Spawner scenarios* is presented based on the Atlantic cod example. Then the differences between species are highlighted to illustrate the sensitivity of the responses to life histories. Last, we assess the cumulative effects (i.e. the two combined scenarios) in both juvenile and spawning grounds simultaneously for the three species.

### Atlantic cod

Habitat scenarios dramatically affect population dynamics and productivity. Equilibrium productivity curves and derived management reference points are modified with important differences between the three scenarios (Fig. 2).

Modifying the egg survival on spawning grounds (*Egg scenario*) has the lowest effects on productivity curve and level of fishing mortality at MSY. By contrast, modifying the quality of juvenile habitat (*Juvenile scenario*) has a larger impact, which is the direct consequence of the effects of scenario's impact on the carrying capacity (equation 12), which sets the size of the population. $F_{MSY}$ slightly increases when juvenile and egg survival increase, which was expected as the survival multipliers act in the same way in the steepness equation (equations 11 and 13). The *Spawning scenario* has the strongest impact on the productivity curve: the shape of the equilibrium production curve is deeply modified across multiplier with a contraction when restoring and a spreading when degrading. This strong response results from the changes in spawners survival that accumulate over the whole life duration of spawners. These major modifications of the curve shape result in strong modification of MSY. $F_{MSY}$ is affected too but to a lesser extent and decreases when habitat is restored.

## Comparing the response across species and life histories

As illustrated for cod, for all three species, the *Egg scenario* has the least effect on the three indicators, while the *Spawning scenario* is the most impactful. In *Juvenile* and *Egg scenarios*, effects of restoration and degradation are symmetric, while for *Spawning scenario* restoration has a higher impact than degradation.

*Juvenile* and *Egg scenarios* exhibit identical changes in $F_{MSY}$, but with different impacts on population size and catch (Fig. 3). For those scenarios, the $F_{MSY}$ of herring is the most impacted, which is expected as it has the lowest baseline values of steepness and hence the highest dependency of population dynamics to the egg's deposition. $SSB_{MSY}$ is rather insensitive to *Egg scenario* but is impacted by *Juvenile scenario* with cod exhibiting the strongest response, which is expected because of the low value of $M_{DI}$ and high value of $M_{DD}$ (lowest and highest of the three species, Table 1), which drives the sensitivity of K to the habitat multiplier (equation 12).

For the *Spawning scenario*, plaice is the most impacted, and herring the least. This difference was expected given the differences in the lifespan and age at maturity characteristics. Indeed, plaice live the longest with a moderate age at maturity (28 and 4 years, respectively, Table 1). Hence, any increase in spawners' mortality accumulates over the entire lifespan and necessarily affects this species more than herring, which is shorter-lived and later maturing (12 and 5 years, respectively, Table 1).

## Cumulative effects of combined scenarios

*Juvenile + Egg scenarios* mostly lead to additive effects, and few slightly nonadditive effects for cod and plaice only (Fig. 4, upper row). For degradation, those effects are lightly synergistic for $F_{MSY}$, antagonistic for $SSB_{MSY}$, and compensate for $C_{MSY}$. For restoration scenarios, only cod exhibits nonadditive effects that are opposite to degradation effects: antagonistic for $F_{MSY}$, slightly synergistic for $SSB_{MSY}$, and in turn additive effects on $C_{MSY}$.

*Juvenile + Spawning scenarios* lead to stronger nonadditive responses and stronger contrasts between species (Fig. 4,
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Marginal effects of the three habitat scenarios (rows) on the value of *F*<sub>MSY</sub> (left column), *SSB*<sub>MSY</sub> (middle column), and *C*<sub>MSY</sub> (right column) for the three example species (European plaice: blue, Atlantic cod: green, and Atlantic herring: orange). Resto. (restoration) indicates an increase in the survival (multiplier of 1.1) and Degr. (degradation) a decrease (multiplier of 0.9). The '+' indicates out-of-the-graph high values obtained for plaice in *Spawner scenario* (280% for the middle panel, and 166% for the right panel).</figcaption></figure>

bottom row). Herring, which is the less impacted species for the *Spawner scenario*, exhibits mostly additive effects, except for *C*<sub>MSY</sub>, where the nonadditive effects remain light. Cod and plaice responses for *F*<sub>MSY</sub> are antagonistic under the degradation scenario and synergistic for restoration. For *SSB*<sub>MSY</sub>, effects are antagonistic for degradation scenarios and synergistic for restoration (opposite of *F*<sub>MSY</sub> effects here again). Effects on *C*<sub>MSY</sub> are driven by effects on *SSB*<sub>MSY</sub>.

## Discussion

We developed a modelling approach that combines an age- and stage-structured simulation model with a sensible parameterization of habitat quality on both juvenile and spawning habitats to assess the consequences of habitat degradation/restoration on the productivity of exploited marine fish populations. Our framework was applied in the Southwest BS to explore population responses when mitigating/increasing hypoxia within these two CEFH. Scenarios were simulated for three exploited species with contrasted life cycles (plaice, cod, and herring), out of the large number of species impacted by hypoxia in the BS at both juvenile and spawning stages (Jokinen et al. 2016, Kraufvelin et al. 2018). The model was used to simulate marginal effects of habitat scenarios on either egg,

juvenile, and spawner, but also to simulate cumulative effects of pressures affecting populations in both habitats.

## Parameterizing scenarios using multipliers

To allow for comparing and combining the habitat scenarios on different life stages, scenarios were parameterized by multipliers that scale the effect on survival between the different life stages. This is an important modification with regard to Champagnat et al. (2024). Because the different stages have different mortality rates and durations that are raised at the exponential scale to get survival expressed as a proportion of individuals that survive (equations 1 and 3), parameterizing scenarios using multipliers in the scale of mortality rates as in Champagnat et al. (2024) would mechanically have led to higher influence of multipliers applied to transitions with the higher mortality rates and duration. The multipliers applied to mortality rates on adults would then have a lower influence than those applied to eggs and to juveniles (Levin and Stunz 2005). To overcome this scaling issue, we parameterized scenarios by applying multipliers directly on the resulting survival rates (i.e. applied to survival expressed as the proportion of eggs/individuals that survive).
<figure><img src="image_5.png" /><figcaption>**Figure 4.** Effect of the two combined habitat scenarios (rows) as compared with the sum of marginal effects. Effects are shown on the values of *F*<sub>MSY</sub> (left column), *SSB* (middle column), and *C*<sub>MSY</sub> (right column) for the three example species (European plaice: blue, Atlantic cod: green, and Atlantic herring: orange). Rest. (restoration) indicates an increase in the survival (multiplier of 1.1) and Degr. (degradation) a decrease (multiplier of 0.9). The colour filling indicates the effects of the combined scenario, and the black borders show the value of the sum of marginal effects. Dotted borders are used when there is no significative difference (difference between combined and summed values is <5%). The text labels indicate what is the type of cumulative effect observed for out-of-scale high values of effects obtained in spawners scenario ('S+' for positive synergism).</figcaption></figure>

## Life histories shape population responses

The habitat scenarios impacted the level of fishing mortality, population size, and yield at MSY, but with different magnitudes among species depending on their LHT.

The marginal effects of the different scenarios rank similarly between the different species. Perturbations of spawning habitats resulting in increased egg mortality (*Egg scenario*) had the least impact, while the direct effect on spawners survival (*Spawner scenario*) exhibited the largest impact. Those results are consistent with our expectation. Indeed, increasing egg mortality is numerically equivalent to decreasing spawn-ers' fecundity, which has already been shown to induce weak population response (Champagnat et al. 2021). In contrast, increased spawner mortality cumulates over the entire lifespan and logically has a much stronger impact on long-term reproductive potential, and thus stronger impacts on SSB<sub>MSY</sub>, C<sub>MSY</sub>, and F<sub>MSY</sub>.

Our modelling approach also highlighted species-specific responses. As observed in Champagnat et al. (2024), scenarios modifying steepness (i.e. *Egg* and *Juvenile scenarios*) resulted in stronger changes of F<sub>MSY</sub> for species with low steepness value, here herring. Indeed, the steepness (Miller and Brooks 2021, Horbowy and Hommik 2022) combined with other LHT (i.e. life duration and mortality, Thorson 2020) is one of the main driver of the relative productivity of a population. The lower the steepness, the less the recruitment is dampened and the higher the sensitivity to change in spawning biomass.

The *Spawner scenario* was simulated through a change in spawners' mortality. The sensitivity to this scenario was mechanically driven by the number of age class affected, itself determined by lifespan and age at maturity. Therefore, plaice (long-living, medium age at maturity) exhibits very strong responses, while herring (medium living and late maturing) was the least impacted. In our study, herring is the species with both the shorter lifespan and the latest maturation age (Table 1), which further limits the proportion of adults being affected in the spawning phase and thus the strength of the response. Note, however, that the results could be more balanced for other species. Indeed, longevity, maximum size, adult mortality, and age at maturity are generally positively correlated traits among marine fish populations (Kindsvater et al. 2016; Thorson et al. 2017). Consequently, the proportion of impacted year class in a population would likely be more balanced between long- and short-living species because of these trade-offs between LHT.

## Cumulative pressure on multiple CEFH and life stages

Cumulative effects of combined pressure on juvenile and spawning habitats were more prevalent than simple additive effects in our results (observed in 20 simulations out of 36). Although the prevalence of nonadditive effects is common in ecology (Crain et al. 2008, Darling and Côté 2008, Teichert
et al. 2016), the magnitude of synergy or antagonism was, in our case, generally limited and depends on the scenario, the indicator, and the species. Because our study considers one stressor acting at different life stages through different mechanisms, and hence differs from other multistressor studies assessing different stressors (e.g. Teichert et al. 2016) or assessing couples of stressor-life stage (Archambault et al. 2018), deeper results comparison is limited. Also note that we only simulated combination of degradation or of restoration for both juveniles and spawning habitat. This, of course, hampers the possibility to assess compensatory effects that would result from restoring one habitat while degrading the other. However, such a scenario would have only low credibility in practice for hypoxia. Still, investigating the effect of such combinations for another type of pressure would make a valuable contribution in the future.

## From theoretical simplified simulations to real-world stocks

Several strong assumptions were made to model the population dynamics itself (Champagnat et al. 2024). First, the population model is deterministic, i.e. demographic (Koons et al. 2016, Plard et al. 2019) and environmental (Engen et al. 1998, Pfister and Stevens 2003) stochasticity are not considered in this study. Uncertainty integration is a very important feature for models when aiming to answer realistic management questions and convey results to fisheries and resources practitioners. However, the main contribution of our work is the quantitative investigation of different hypotheses regarding the mechanisms of impact on populations and the parameterization of the cumulative impacts across different life stages. Furthermore, the case species selected provide a qualitative comparison of these same pressures impact upon populations with contrasted life histories. We hence choose not to explicitly include uncertainty in our approach. In a previous study, Champagnat et al. (2024) demonstrate how parameter uncertainty can be introduced in a similar life cycle model by relying on informative prior distribution derived from the Fish-Life package (Thorson et al. 2017, Thorson 2020). Results demonstrate that the main conclusions were robust to the introduction of uncertainty. But they also reveal the difficulty to accurately scale the level of uncertainty on each parameter. Indeed, the level of uncertainty provided by meta-analysis such as FishLife is highly dependent on the amount of information that is integrated in the meta-analysis (which is coupled to species selection). As a result, the amount of uncertainty is highly variable among species and can reveal biologically unreasonable (e.g. steepness values <0.2 or >1) for less informed species. In our view, an accurate quantification of the uncertainty (process error and parameter uncertainty) would require that the model be embedded in a statistically integrated approach where it is fit to multiple sources of data (as done in Archambault et al. (2018)). This would constitute a very valuable contribution in future work.

Second, juvenile and spawning habitat are considered as one homogeneous block, although those areas are marked by high spatial heterogeneities (Fodrie et al. 2009, Nagelkerken et al. 2015). Including explicit spatial dimensions would multiply the complexity of the model and make the parametrization more data demanding (Ono et al. 2019, Champagnat et al. 2021) and scenario design more difficult. Exploring the role of spatial structure and how this interact with habitat scenarios

would, however, constitute rich research avenues and important considerations for management (White 2010, Punt 2019).

We also parametrized the three studied species from the literature and arbitrarily set the baseline population size to $10^6$. We did not embed our model within a full statistical approach to estimate all parameters but rather assess the realism of the simulated population dynamics (i.e. under no habitat modification) by looking at the values of $F_{MSY}$ and $C_{MSY}/S_{BS_{MSY}}$. The baseline scenario led to a $F_{MSY}$ value of 0.14, 0.25, and 0.24 for plaice, cod, and herring, respectively. While cod $F_{MSY}$ value is close to value estimated in stock assessments (0.26, ICES 2022), the $F_{MSY}$ value derived by our model for plaice and herring appears a bit low. Indeed, assessment for the stock that includes plaice in the SW BS and in the North Sea and Skagerrak provides estimates of 0.31 (ICES 2022) and 0.19 (ICES 2021), respectively, while it is 0.31 for herring in Skagerrak, Kattegat, and Western BS (ICES 2022). Embedding our approach with a full integrated statistical model would be critical to apply our model to real case study and produce case-specific assessment (Gernez et al. 2023).

In addition, the way we modelled the impact of habitat scenario also remains theoretical. Because of the multiple potential impacts of hypoxia and the complexity of fish life histories, predicting/quantifying the effect of hypoxia on wild fish populations remains challenging (Rose et al. 2009, Eero et al. 2011). As mentioned above, the two hypothetical mechanisms of impacts of hypoxia on spawning ground (Egg vs. Spawner scenario) led to dramatically different population responses. These hypotheses directly rely on different mechanistic hypotheses of individual fish response (Fig. 1): avoidance, which results in fewer eggs spawned or that survive; or persistence that results in increasing spawners mortality (Petersen and Pihl 1995, Behrens et al. 2018). Another hypothesis could be fish that would skip spawning (Jørgensen et al. 2006, Rideout and Tomkiewicz 2011), which numerically would likely have the same consequences as an increase in egg mortality. These mechanisms are likely in the wild and may even occur simultaneously in a population for different individuals. It would be informative to have estimates on the proportion of individual responses among a population to conduct more precise simulations than two mutually exclusive hypotheses. Also, while we focus on the direct impacts of habitat perturbations, other processes induced by hypoxia probably interact with population dynamics, e.g. massive food deprivation and/or lower assimilation (Brander 2020) all along the lifespan (including larvae, Arevalo et al. 2023), increase in catchability, or reduced fecundity (Rose et al. 2018). In the future, our framework could be adapted to consider these other pressures.

Finally, an important limitation is that no experimental or field-based information was available on the pressure-impact relationship. Hence, it was not possible to rely on observations to set values of the multipliers applied to life-stage-specific survival. Indeed, while many in-laboratory experiments/studies have documented the direct influence of hypoxia on fishes (see Thomas et al. (2019) for a review), quantification of impacts in the wild is still lacking (Rose et al. 2018). Although life stage is an important determinant of the relative sensitivity of an organism to environmental stressors (Thomas et al. 2019, Thorson et al. 2021), the lack of data to get realistic values lead us to test the same multiplier for the three life stages impacted (egg, juvenile, and adult).
## More insights into the effect of hypoxia

Despite these limits in both the model structure and simulated scenarios, the present study brings additional insights to the effects of hypoxia on exploited fish populations in the BS.

For the Eastern BS Cod, it has been demonstrated that successful egg fertilization is constrained by salinity and oxygen content (Vallin et al. 1999), which is often referred to as ‘reproduction volume’ (Plikshs et al. 1993, McKenzie 2011). The results from the *Egg scenario* used in our study (Fig. 1), whereby the numbers of eggs are affected, would reflect the population-level impacts of changing reproductive volume. This concept has been thoroughly studied in the case of Eastern Baltic cod (MacKenzie et al. 2000, Brander 2022, Svedäng et al. 2022); however, the impacts of temporal variations in reproductive volume have not been examined for the Western BS cod. Our results show that due to the cumulative effects of hypoxic areas across the life-history stages of cod, this environmental stressor could participate in hindering the population’s recovery from overexploitation. Note, however, that our cumulative impacts scenarios focus only on two life-history stages, whereas ontogeny is more complex, and the effect of low oxygen may significantly reduce growth productivity and survival of sub-adults, as well (Brander 2020).

For herring, our results are likely to underestimate the effect of hypoxia on spawning grounds. Indeed, their spawning grounds on gravels, algae, and elgrass largely overlap with the places where their eggs remain fixed. Hence, for this specific spawning strategy, which increases egg exposure to fluctuating coastal environmental conditions, the eggs may be less resilient to periodic hypoxia, increasing the risk to their survival and ultimately recruitment (Polte et al. 2021). Increasing rates of coastal hypoxia (Conley et al. 2011) would impact both spawning and early life-history stages. Also, like the effect of temperature, the impacts of hypoxia can last outside the temporal extent of punctuated events by modifying the community present in any given location (Viitasalo and Bonsdorff 2022). The impact for herring may be that spawners are forced to choose among degraded spawning habitats, even without experiencing hypoxia directly. This problem has been identified and is currently the primary conservation advice for fisheries managers of this species (ICES 2023a).

The conventional dependence of European plaice populations on coastal juvenile habitats indicates that the availability and suitability of these habitats is likely to be the main population bottleneck (Brown et al. 2019b), especially in the enclosed system of the SW BS where eggs and larvae have a high probability of encountering coastal areas (Petereit et al. 2014). Spawning is thought to occur in deeper basin waters (Kijewska et al. 2009) but also in deeper channel areas, which are responsible for delivering oxygenated marine waters to the SW BS (Støttrup et al. 2019). This association with the channels may reduce the interaction of hypoxic waters with spawning plaice and could partially explain the different responses in plaice and cod stock development in the same mixed fishery, where plaice stock continues to grow to record population sizes, while the cod stock collapses (ICES 2023b).

## Management implications

Mitigation of hypoxia, among other pressures, has already been described as a potential lever to enhance fish populations abundance and productivity (Ruiz et al. 2009, Ono et al. 2019). Here, the magnitude of gains in response to a

proportional improvement in theoretical ‘habitat quality’ indicates that there is a great scope for habitat restoration to improve stock productivity. While habitat restoration has theoretical appealing outcomes, the underlying complexity of the natural ecosystems makes the effective return to a natural state more uncertain (Moreno-Mateos et al. 2012). In the particular case of hypoxia, the human lever seems to be an important one because of the importance of watershed inputs. However, because the BS hydrologic system is complex, the relative importance of human and physical drivers remained to be determined. The fact that 40 years of nutrient reduction did not reverse the frequency and magnitude of BS hypoxia, indicates that either other important drivers are at play, the intrinsic loading remains too high, that terrestrial sources of nutrient inputs remain too high, or some combination of these three (Krapf et al. 2022, Naumov et al. 2023). Broad-scale habitat restoration can come from continued efforts to reduce terrestrial loading across the entire catchment (HELCOM 2021), but local-scale efforts may mitigate or reduce hypoxia at small scales as well. For example, efforts to restore vegetation such as seagrasses and macroalgae often depend on local modifications that can reduce sediment hypoxia (e.g. sand-capping or reef building), and once established, may provide microhabitat refugia in coastal areas where the seafloor is in the photic zone (Moore 2004, Steinfurth et al. 2024) and where many exploited species spend some part of their life history (Seitz et al. 2014, Brown et al. 2018). Furthermore, intensive bivalve aquaculture and the restoration of biogenic bivalve reefs, both contribute to capturing nutrients from the water column, which may be meaningful on local scales (Schernewski et al. 2019). There remain, however, processes that are not directly dependent on the management of human activities. For example, the Major Baltic Inflows have a major impact on the water oxygenation and depend upon variables that are determined by regional climatic conditions (Kōuts et al. 2021). Even in this case, though, better management of human-induced climate change would reduce the changes to the patterns of inflow that have been observed in recent history (Schmidt et al. 2021).

Focusing on the management of fisheries, we believe our framework is a valuable contribution on which one could build in the future to assess the effect of hypoxia and to provide advice to managers. Specifically, current assessments of the western Baltic cod stock have identified unexplained mortality as the primary source of uncertainty. Both the rate of this unknown mortality and its variability cannot be quantified, and these unknowns result in distrust of the current assessment model, which downgrades the assessment from a full analytical assessment to one that relies on trends in stock status (ICES 2024). To support the rebuilding of this stock, a better knowledge of these other natural mortality processes is required, and here we have demonstrated mechanisms by which hypoxia may act on the population in our various hypotheses, which can be further tested. This may involve field- or laboratory-based experiments, or further efforts to integrate our model into an empirical framework.

Adapting this framework to provide for more tactical-level evidence-based advice would be require (i) a better knowledge/estimate of the relationships between pressures and demographic rates at various life-history stages and (ii) to account for spatial structure, stochasticity, and uncertainty, all being species-specific. These represent huge challenges before application for management. Investigating the impacts of
hypoxia and other pressures on different life history stages should therefore be a priority to inform both the framework presented here as well as attempts to utilize habitat and spatial management tools to support the exploitation of fish populations (Vasconcelos et al. 2014, Brown et al. 2018).

## Author contributions

J.C., E.J.B., E.R., and O.L.P. conceived the ideas, designed methodology, and analysed the results. J.C. executed the simulations, prepared the figures, and led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

## Acknowledgements

We acknowledge the ICES Working Group on the Values of Coastal Habitats for Exploited Species, where this work was presented, for the constructive feedback and discussions. We also acknowledge the editor and the two anonymous reviewers for their fruitful suggestions to improve the manuscript.

## Supplementary data

Supplementary data is available at *ICES Journal of Marine Science* online.

*Conflict of interest:* None declared.

## Funding

This work was supported by the projects Hapomax (Office Français de la Biovidersité, France) and Seawise (which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101000318). E.J.B. was supported by the European Maritime Fisheries Fund awarded by the Ministry of Food, Agriculture, and Fisheries Denmark and is also part of the SEAwise project.

## Data availability

The authors confirm that all the data underlying this study are fully available. Life history data used to parametrize the simulation model are extracted from the FishLife package (Thorson et al. 2017) and fully presented in Supplementary Material 2. Scripts and data used to perform the analysis are available at https://github.com/jchampag/EFHimpacts.

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