<figure><img src="image_1.png" /></figure>

# Mercury concentrations, habitat and trophic position of *Antimora rostrata* and *Macrourus holotrachys* from South Georgia (Southern Ocean)

Diana Vaz<sup>a,b,*</sup>, José P. Queirós<sup>b,c,1</sup>, José C. Xavier<sup>b,c,2</sup>, Paco Bustamante<sup>d</sup>, José Abreu<sup>b,c</sup>, Eduarda Pereira<sup>e</sup>, Philip R. Hollyman<sup>c,f</sup>, João P. Coelho<sup>g</sup>, José Seco<sup>b,h</sup>

## ARTICLE INFO

**Keywords:**

Antarctic

Moridae

Macrouridae

Mercury

Bioaccumulation

Biomagnification

Stable isotope analyses

## ABSTRACT

Mercury (Hg) is a neurotoxic element that can harm marine wildlife. Hg can reach the Southern Ocean through atmospheric and oceanic currents. However, data on Hg in Southern Ocean deep-sea fishes remain scarce. Our study assessed the influence of biological and ecological factors on Hg bioaccumulation in two deep-sea species, blue antimora (*Antimora rostrata*) and bigeye grenadier (*Macrourus holotrachys*), inhabiting the South Georgia region. Specifically, we aimed to: 1) analyse the habitat and trophic position of both species; 2) understand how Hg concentrations vary between tissues (muscle, brain, liver and gills); 3) evaluate how biological (length and weight) and ecological characteristics (trophic position (δ¹⁵N) and habitat (δ¹³C)) influence Hg concentrations. Muscle tissue had the highest Hg concentrations in both species, while the liver in *A. rostrata* and gills in *M. holotrachys* had the lowest. Overall, *A. rostrata* exhibited lower Hg concentrations (51.0 ± 9.0) than *M. holotrachys* (62.0 ± 11.0). No significant relationships were found between Hg concentrations and length, weight nor trophic position of *A. rostrata*. In contrast, *M. holotrachys* showed a positive relationship between Hg concentrations and habitat in all tissues, whereas for length and weight this positive relationship was observed in most tissues except the brain. *A. rostrata* is a pelagic feeder, whereas *M. holotrachys* mostly feeds near the sea bottom, highlighting how feeding strategy and habitat influence Hg bioaccumulation. It also reveals unexpected patterns of Hg distribution among tissues, particularly in the brain, where *M. holotrachys* exhibited one of the highest Hg concentrations.

## 1. Introduction

The Southern Ocean ecosystem is under several global threats, including the continuous influx of anthropogenic contaminants (*Cossa et al., 2011; Constable et al., 2014; Szopińska et al., 2017*). From these, mercury (Hg) is considered one of the most dangerous neurotoxic

elements to wildlife (Miranda et al., 2007; Mieiro et al., 2009; Chu et al., 2019; Teixeira et al., 2020). Hg has a great dispersion capacity, enabling it to reach all regions of the globe, including the Southern Ocean (Lamborg et al., 2014). Among the different forms of Hg, methylmercury (MeHg), its main organic form in biota, is considered the most toxic to living organisms, due to its high liposolubility and high affinity with

<footnote><sup>a</sup> Department of Physics, University of Aveiro, 3810-193, Aveiro, Portugal</footnote>

<footnote><sup>b</sup> University of Coimbra, MARE - Marine and Environmental Sciences Centre/ARNET - Aquatic Research Network, Department of Life Sciences, 3000-456, Coimbra, Portugal</footnote>

<footnote><sup>c</sup> British Antarctic Survey, Natural Environment Research Council, High Cross, Madingley Road, CB3 0ET, Cambridge, United Kingdom</footnote>

<footnote><sup>d</sup> Littoral Environnement et Sociétés (LIENSs), UMR 7266 CNRS - La Rochelle Université, 2 rue Olympe de Gouges, 17000, La Rochelle, France</footnote>

<footnote><sup>e</sup> CESAM/REQUIMTE & Department of Chemistry, University of Aveiro, 3810-193, Aveiro, Portugal</footnote>

<footnote><sup>f</sup> School of Ocean Sciences, Bangor University, Askew Street, Menai Bridge, LL59 5AB, United Kingdom</footnote>

<footnote><sup>g</sup> CESAM & Department of Biology, University of Aveiro, 3810-193, Aveiro, Portugal</footnote>

<footnote><sup>h</sup> CIVG - Vasco da Gama Research Center, University School Vasco da Gama - EUVG, Coimbra, 3020-210, Portugal</footnote>

<footnote>* Corresponding author at: Department of Physics, University of Aveiro, 3810-193, Aveiro, Portugal.
E-mail address: dianabpvvaz@gmail.com (D. Vaz).</footnote>

<footnote n="1">¹ Current address: CIIMAR/CIMAR LA, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, 4450-208 Matosinhos, Portugal.</footnote>

<footnote n="2">² Current address: Centre for Functional Ecology - Science for People & the Planet (CFE), Associate Laboratory TERRA, Department of Life Sciences, Coimbra, Portugal.</footnote>
proteins (Miranda et al., 2007), bioaccumulating throughout the life of organisms (Polak-Juszczak, 2012), and biomagnifying along marine food webs including in the Southern Ocean (Gray, 2002; Polak-Juszczak, 2012; Seco et al., 2021). Therefore, long-lived top predators usually contain high concentrations of Hg (McIntyre and Beauchamp, 2007; Cherel et al., 2018; Seco et al., 2021). Hg accumulation in organisms depends on several biological and ecological factors such as length, age, sex, trophic position and feeding habitat (Havelková et al., 2008). Furthermore, tissue composition also influences the accumulation pattern of marine organisms, with previous studies showing significant differences in Hg concentrations across tissues of the same individual (Łuczyńska et al., 2016; Lopes-Santos et al., 2025). Therefore, a thorough understanding of the biology and ecology of marine species, including in the Southern Ocean, is important to understand Hg concentrations in marine food webs.

Studying inter-tissue Hg concentrations helps to understand bio-
accumulation patterns and detoxification processes, as tissues accumu-
late Hg at varying rates (Polak-Juszczak, 2018). It can also serve as an
environmental and ecological indicator, as variations between tissues
may reflect different exposure pathways, feeding behaviours, and
environmental contamination levels (Mieiro et al., 2012). Additionally,
it helps reveal species-specific metabolism, since different species pro-
cess and store Hg differently (Mieiro et al., 2012; Polak-Juszczak, 2018).

Within the Southern Ocean, South Georgia is considered one of the most productive regions due to its cold and nutrient-rich waters (Atkinson et al., 2001; Murphy et al., 2013). These waters support the development of large zooplankton biomass and provide habitat for vast colonies of top predators, such as seals and seabirds (Murphy et al., 2007). Additionally, it is considered a highly important region for the commercial fishing of Antarctic krill (Euphausia superba) and the long-line fishery for Patagonian toothfish (Dissostichus eleginoides) (Atkinson et al., 2001; Collins et al., 2010).

The blue antimora (*Antimora rostrata*) and the bigeye grenadier (*Macrourus holotrachys*) are two common species in the Southern Ocean deep-sea (Gon et al., 2021), commonly caught as bycatch in South Georgia toothfish fisheries (Collins et al., 2010; Hollyman et al., 2022). They are present in the diet of several top predators, such as the Patagonian toothfish, seals and albatrosses (Morley et al., 2004; Xavier et al., 2004; Stevens et al., 2014; Queirós et al., 2024). Despite some similarities, these fish species present distinct biological and ecological characteristics: *A. rostrata* (Family: Moridae) is a cosmopolitan species that is found across all oceans, from shallow to deep waters (Cohen et al., 1990). In the Southern Ocean, *A. rostrata* can be found in the Scotia Sea, Ross Sea, Amundsen Sea and Weddell Seas (Orlov et al., 2020). It is a bathypelagic species, inhabiting between 350 and 3000 m (Cohen et al., 1990), slow-growing and long-lived species and can reach more than 60 cm in total length (Cohen et al., 1990). Additionally, *A. rostrata* presents a sexual dimorphism with females being larger than males (Horn and Sutton, 2015).

*Macrourus holotrachys* (Family: Macrouridae) is the most abundant macrourid species in many sub-Antarctic island's bycatches (McMillan et al., 2012; Romanelli, 2017). In contrast to *A. rostrata*, its distribution is limited to the Patagonian shelf and to the Southern Ocean, with its abundance decreasing further south (Hanchet et al., 2008; McMillan et al., 2012). It is a bathy-demersal fish species, being found between 200 and > 2000 m depth (Cohen et al., 1990; Laptikhovsky, 2005; Pinkerton et al., 2012). As adults, individuals of *M. holotrachys* can reach over 80 cm in total length and can be found living close to the seafloor, and present life-history traits of a deep-sea species (e.g. long lifespan and late maturation) (Morley et al., 2004; Laptikhovsky, 2005).

According to FishBase (Froese and Pauly, 2025), *A. rostrata* occupies a mid-trophic position (δ¹³N: 3.6 ± 0.47) and feed mainly on mesopelagic and benthopelagic invertebrates, such as crustaceans and cephalopods, although small fishes are also part of its diet. In contrast, *M. holotrachys* is positioned slightly higher in the food web (δ¹⁵N: 3.7 ± 0.64), reflecting its benthic feed habits, and preys predominantly on

benthic invertebrates and small demersal fishes.

While information on the ecology of *M. holotrachys* in the Southern Ocean is limited, less is known about the ecology of *A. rostrata*. Filling this gap of scientific knowledge is crucial due to its importance as a prey species for top predators and as an important bycatch in toothfish fisheries (Horn and Sutton, 2015; Nacari et al., 2022).

Despite their ecological importance and potential role as a Hg pathway to top predators in the Southern Ocean, only one study reported Hg concentrations in *A. rostrata* in the Southern Ocean (Hanchet et al., 2012), while no studies exist for *M. holotrachys*. Hg concentrations in *A. rostrata* muscle caught on the Ross Sea continental slope averaged 0.19 µg g⁻¹ dw, ranging from 0.04 to 0.68 µg g⁻¹ dw (Hanchet et al., 2012). Furthermore, this study found that Hg concentrations increased with fish length, and were influenced by the location, with fish found further south exhibited lower Hg concentrations (Hanchet et al., 2012). Despite this study, major gaps remain regarding how Hg concentrations in *A. rostrata* are influenced by its biology and ecology, including in other Antarctic regions (other than the Ross Sea), as well as how these are distributed across the different tissues. Carbon stable isotopes (δ¹³C) helps identify feeding habitats and primary productivity sources, distinguishing between coastal and offshore environments or pelagic and benthic regimes (Cherel and Hobson, 2007; Stowasser et al., 2012). Furthermore, stable nitrogen isotope analysis (δ¹⁵N) provides a good insight into the biomagnification pattern of contaminants in a food web by determining the trophic position of species (Cabana and Rasmussen, 1994; Stowasser et al., 2012; Chouvelon et al., 2014). Within this context, this study aims to study the habitat, trophic position and Hg concentrations in *A. rostrata* and *M. holotrachys* in South Georgia (Southern Ocean). In detail, we i) investigate the feeding habitat and trophic ecology of both species; ii) examine Hg accumulation across different tissues (muscle, brain, liver and gills) in both species; iii) investigate which biological (i.e. length and weight) and ecological (i.e. habitat and trophic position) factors influence Hg bioaccumulation in these species. To evaluate which ecological factors influence the Hg concentrations in both species, stable isotopes of δ¹³C and δ¹⁵N were analysed in the muscle to determine the habitat and trophic ecology.

## 2. Materials and methods

2.1. *Data collection*

Individuals of *A. rostrata* (n = 23) and *M. holotrachys* (n = 22) were collected in June 2020 at South Georgia ((Commission for the Conservation of Antarctic Marine Living Resources) (CCAMLR) sub-Area 48.3) (Fig. 1) aboard the *FV Nordic Prince* during the longline Patagonian toothfish fishing season. It is important to note that samples were not randomly collected but to create a length gradient to ensure a range of individuals were represented in the analysis.

Total length (± 1 cm), standard length (SL) (± 1 cm), weight (± 0.05 Kg), sex and maturity stage (following Kock and Kellermann, 1991) were recorded for each individual of both species. After measurements, samples of muscle, brain, liver and gills were collected and preserved in individual plastic bags at -30 °C. In the laboratory, samples were freeze dried for 72 h at -50 °C and cut in to small pieces using stainless steel scissors.

2.2. *Stable isotope analyses*

Stable isotope analysis of δ¹³C and δ¹⁵N were performed on the muscle to allow comparisons with previous studies. Furthermore, the muscle provides a stable and representative isotopic signature of the fish's diet as it has a relatively low turnover rate, reflecting the diet over a longer time period than metabolically active tissues such as the liver.

Prior to analyses, between 22 and 28 mg of muscle samples were weighed in an identified glass tube and delipidated using three successive rinses with cyclohexane following Chouvelon et al. (2011). Briefly,
<figure><img src="image_2.png" /><figcaption>Fig. 1. Map of the study area. White dots represent the sampling locations. Solid line represent the Polar Front (PF) and the dotted line represent the Southern Boundary of the Antarctic Circumpolar Current (SBACC).</figcaption></figure>

glass tubes were shaken for 10 min at approximately 1000 rpm with 4 ml of cyclohexane and then centrifuged for 5 min at 4500 rpm. The supernatant containing the lipids was discarded. After delipidation, all samples were dried overnight in an oven at 45 °C. Delipidation was performed because the presence of lipids influences δ¹³C values and different species have different lipid contents. Therefore, delipidation allows the comparison between individuals and species with different lipid content. After drying, between 0.2 and 0.4 mg of muscle was weighed in a tin capsule using a Mettler Toledo microbalance. Carbon and nitrogen stable isotope values were measured using a Continuous Flow Isotope Ratio Mass Spectrometer (CF-IRMS) coupled to an elemental analyzer (Thermo Scientific EA Flash®). Isotope data were calculated using the following equation:

$$ \delta^{13}\text{C} \text{ or } \delta^{15}\text{N} (\text{‰}) = ((R_{\text{sample}}/R_{\text{standard}}) - 1) \times 1000 $$

where R is ¹³C/¹²C or ¹⁵N/¹⁴N. Isotopic results are presented in delta notation (δ in per mil, ‰) relative to Vienna PeeDee Belemnite and atmospheric nitrogen (N₂) for δ¹³C and δ¹⁵N, respectively. Quality control was assessed using two certified reference materials: USGS-61 (caffeine) and USGS-63 (caffeine). The analytical precision was <0.04 ‰ for δ¹³C and < 0.06 ‰ δ¹⁵N.

## 2.3. Mercury analyses

Hg analyses were performed on all tissues (muscle, brain, liver and gills). Concentrations of total Hg (hereafter Hg) were determined by atomic absorption spectrophotometry with thermal decomposition and gold amalgamation using an Advanced Mercury Analyzer (AMA) LECO® 254 (Costley et al., 2000) with a limit detection of 0.01 ng. Analytical

quality control was performed using the certified reference materials DORM-4 (n = 10; fish protein Hg = 0.41 µg g⁻¹) with a recovery of 96.0 ± 7.8 % and TORT-3 (n = 11; lobster hepatopancreas Hg = 0.29 µg g⁻¹) with a recovery of 95.9 ± 9.3 %. Hg concentrations were further expressed in µg g⁻¹ relative to the dry weight.

## 2.4. Data analyses

All statistical analyses were performed using GraphPad Prism 8 and considering alpha at 0.05. The normality of all study variables was first tested using the Shapiro-Wilk test. δ¹³C and δ¹⁵N values for both species followed a normal distribution (p > 0.05) and were therefore treated as parametric variables. For Hg concentrations, muscle and brain tissues of A. rostrata followed a normal distribution (p = 0.49 and p = 0.08, respectively), while gills and liver (p = 0.05 and p = 0.04, respectively), and all tissues of M. holotrachys (p < 0.0001) did not follow a normal distribution. Accordingly, parametric tests were applied to δ¹³C and δ¹⁵N values, whereas non-parametric tests were used for most Hg data.

For stable isotope analyses, a t-test was performed to compare the δ¹³C and δ¹⁵N values between the two species. Furthermore, a relationship between the biological variables (standard length and weight) and δ¹⁵N values and also between the biological variables and δ¹³C values, were conducted to get some information about the bio-accumulation patterns and habitat use, respectively, for both species.

For comparisons of Hg concentrations across tissues within each species, a Friedman test was applied because the four tissues were measured in the same individuals, thus representing repeated and paired measurements. When significant results were obtained, a Nemenyi post hoc test was used to identify differences between tissues. To compare Hg
concentrations between the two species for each tissue, a Mann-Whitney tests were performed. To test Hg bioaccumulation in these species, we modeled the length/weight-Hg relationship using a Lognormal approach, fitting a Gaussian linear model on the log scale: $log(Hg) = \alpha + \beta log (SL or Weight) + \varepsilon$, $\varepsilon \sim N(0, \sigma^2)$, where $\alpha\alpha$ is the intercept and $\beta$ is the allometric exponent. Assumptions were checked on the log scale. These statistical analyses were performed in R (version 4.5.1; R Core Team, 2025). Finally, a linear regression was performed for each species to test relationships between Hg concentration and the $\delta^{15}N$ values in the muscle.

## 3. Results

### 3.1. Habitat use and trophic position of *A. rostrata* and *M. holotrachys*

Standard length of *A. rostrata* ranged from 32 to 62 cm and weight from 0.34 to 1.34 kg, while *M. holotrachys* had a standard length ranging from 40 to 82 cm and a weight ranging between 0.40 and 2.80 kg (Table 1).

Significant differences were found in $\delta^{13}C$ and $\delta^{15}N$ values between the two species ($\delta^{13}C: t = 9.36, p < 0.0001$; $\delta^{15}N: t = 13.90, p < 0.0001$), with *A. rostrata* presenting lower $\delta^{13}C$ (1.74 ‰) and $\delta^{15}N$ (2.52 ‰) values than *M. holotrachys* (Fig. 2; Table 1). No significant relationships were found between standard length and both $\delta^{13}C$ ($p = 0.072$) or $\delta^{15}N$ values ($p = 0.676$) in *A. rostrata*, as well as no significant relation with weight ($\delta^{13}C: p = 0.073$; $\delta^{15}N: p = 0.544$). Regarding *M. holotrachys*, a significant relationship was found between standard length and $\delta^{13}C$ values ($p = 0.021$; $r^2 = 0.239$; $\delta^{13}C = 0.03205SL - 22.93$) and between weight and $\delta^{13}C$ values ($p = 0.026$; $r^2 = 0.223$; $\delta^{13}C = 0.4867 W - 21.61$). No relationship was found between standard length or weight and $\delta^{15}N$ values ($p = 0.159$ and $p = 0.137$, respectively) in *M. holotrachys*.

### 3.2. Inter-tissue specific Hg accumulation in *A. rostrata* and *M. holotrachys*

Hg concentrations in the tissues of *A. rostrata* decreased in the following order: muscle > brain > gills > liver (Table 1). Significant differences were found for Hg concentrations in *A. rostrata* between tissues ($p < 0.0001$), with multiple comparison test showing significant differences between all tissues (muscle/gills; muscle/liver; brain/liver: $p < 0.0001$; brain/gills: $p < 0.05$), except muscle/brain ($p = 0.1520$) and gills/liver ($p = 0.090$) (Fig. 3).

Regarding *M. holotrachys*, Hg concentrations in the tissues decreased in the following order: muscle > brain > liver > gills (Table 1). Significant differences were also found for Hg concentrations in *M. holotrachys* tissues ($p < 0.0001$), with multiple comparison test showing significant differences between all tissues (muscle/gills and muscle/liver: $p < 0.0001$; brain/gills: $p = 0.0008$; brain/liver: $p = 0.0003$), except muscle/brain ($p = 0.7743$) and gills/liver ($p > 0.9952$) (Fig. 3).

Significant differences were found for Hg concentrations between all tissues of the two studied species (muscle: $U = 64$; $p < 0.0001$; brain: $U = 80$; $p < 0.0001$; liver: $U = 27$; $p < 0.0001$; gills: $U = 75$; $p < 0.0001$). Hg concentrations in *A. rostrata* tissues were lower than concentrations in *M. holotrachys* tissues, i.e. 2-fold in muscle, 3-fold lower in the brain,

9-fold in liver and 4-fold in gills (Table 1).

### 3.3. Hg bioaccumulation in relation to biological factors in *A. rostrata* and *M. holotrachys*

No significant relationships were found between Hg concentration and either standard length or weight in any tissue of *A. rostrata*. In contrast, *M. holotrachys* exhibited significant positive relationships between Hg concentrations and both standard length and weight in most tissues, except for the brain, which showed no significant relationships with either variable (Table 2).

No significant relationship was found between Hg concentrations in the different tissues and the $\delta^{15}N$ values in both species (*A. rostrata*: muscle: $p = 0.259$; brain: $p = 0.962$; liver: $p = 0.958$; gills: $p = 0.063$; *M. holotrachys*: muscle: $p = 0.132$; brain: $p = 0.056$; liver: $p = 0.219$; gills: $p = 0.070$).

## 4. Discussion

To the best of our knowledge, this is the first study assessing Hg concentrations in *M. holotrachys* in the South Georgia. Using stable isotopes and Hg analysis, we found that *A. rostrata* occupies a lower trophic position and forages more in the pelagic environment than *M. holotrachys* which is a demersal feeder, resulting in consistently lower Hg concentrations. Only *M. holotrachys* showed length-related Hg accumulation, likely reflecting age-related bioaccumulation and potential dietary shifts with growth. These finding highlight the role of species-specific ecology in shaping contaminant exposure in deep-sea environments.

*A. rostrata* and *M. holotrachys* presented a limited range of lengths, with all individuals exceeding 32 cm and 40 cm, respectively, that suggest the sampled specimens were predominantly adults. This limited length variation may influence their ecological characteristics (habitat use, feeding behaviour, trophic position) and Hg bioaccumulation patterns observed in our study.

### 4.1. Habitat use and trophic position of *A. rostrata* and *M. holotrachys*

Our results, suggest that, despite captured in the same region, *A. rostrata* and *M. holotrachys* use different habitats. Lower $\delta^{13}C$ values of *A. rostrata* suggest that this species forage more in pelagic environments, whereas *M. holotrachys* is more associated with benthic habitats (Newsome et al., 2007). This result is supported by previous studies showing that *A. rostrata* is a bathypelagic species strongly connected to the copepod-based pelagic food web (Cohen et al., 1990; Stowasser et al., 2009), contrary to *M. holotrachys* which is a bathypelagic species living closer to the seafloor and feeds essentially in ostracods, isopods, amphipods, decapods and polychaetes (Cohen et al., 1990; Laptikhovsky, 2005; Nacari et al., 2022).

However, our results contrast with a previous study by Nacari et al. (2023) in the Southeastern Pacific Ocean with *A. rostrata* exhibiting a more demersal foraging strategy ($\delta^{13}C = -18.9 \pm 0.2$ ‰) compared to *M. holotrachys* ($\delta^{13}C = -19.7 \pm 1.6$ ‰). These differences may be correlated to both geographic variation and/or body length differences between the two studies. Additionally, *A. rostrata* and *M. holotrachys*

**Table 1**

Number (overall and per sex), standard length, weight, mercury (Hg) concentrations in the analysed tissues and stable isotopic values of $\delta^{13}C$ and $\delta^{15}N$ in the muscle of *Antimora rostrata* and *Macrourus holotrachys* from South Georgia. Values are mean $\pm$ standard deviation (minimum - maximum). ♀ Females; ♂ Males.

<table><thead><tr><th rowspan="2">Species</th><th rowspan="2">n<br>Total</th><th rowspan="2">♀</th><th rowspan="2">♂</th><th rowspan="2">Standard length<br>(cm)</th><th rowspan="2">Weight (kg)</th><th colspan="4">Hg (µg g<sup>-1</sup> dw)</th><th rowspan="2">δ<sup>13</sup>C (‰)</th><th rowspan="2">δ<sup>15</sup>N (‰)</th></tr><tr><th>Muscle</th><th>Brain</th><th>Liver</th><th>Gills</th></tr></thead><tbody><tr><td>Antimora<br>rostrata</td><td>23</td><td>20</td><td>3</td><td>51.0 ± 9.0<br>(32.0-62.0)</td><td>1.4 ± 0.7<br>(0.2-3.1)</td><td>0.7 ± 0.2<br>(0.3-1.3)</td><td>0.6 ± 0.2<br>(0.2-1.2)</td><td>0.1 ± 0.1<br>(0.1-0.3)</td><td>0.2 ± 0.1<br>(0.1-0.6)</td><td>-22.6 ± 0.5<br>(-23.4 - -21.5)</td><td>10.5 ± 0.5<br>(9.5-11.6)</td></tr><tr><td>Macrourus<br>holotrachys</td><td>22</td><td>17</td><td>5</td><td>62.0 ± 11.0<br>(40.0-82.0)</td><td>1.4 ± 0.7<br>(0.4-2.8)</td><td>1.8 ± 1.8<br>(0.7-9.2)</td><td>1.8 ± 2.0<br>(0.3-9.5)</td><td>1.5 ± 3.9<br>(0.2-18.7)</td><td>1.0 ± 1.4<br>(0.1-6.6)</td><td>-20.9 ± 0.7<br>(-22.4 - -19.4)</td><td>13.0 ± 0.7<br>(12.0-11.4)</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Fig. 2. δ¹³C and δ¹⁵N values in the muscle of *Antimora rostrata* (n = 23) and *Macrourus holotrachys* (n = 22) from South Georgia. Mean ± standard deviation.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3. Mercury concentrations (Mean ± 1 SD, µg g⁻¹ dw) in different tissues (Muscle, Brain, Liver and Gills) of *Antimora rostrata* and *Macrourus holotrachys*. Different lowercase (for *Antimora rostrata*) and uppercase (for *Macrourus holotrachys*) letters above bars indicate significant differences among tissues in each species (Friedman test with Nemenyi post hoc test, *p* < 0.05). \* between bars indicate differences between the same tissue in both species (Mann-Whitney test, *p* < 0.0001).</figcaption></figure>

analysed in our study were, on average, larger (51.0 ± 9.0 and 62.0 ± 11.0 cm, respectively) than those reported by Nacari et al. (2023) (48.9 ± 7.2 and 56.5 ± 6.3 cm, respectively). Environmental factors such as depth distribution, prey availability, and carbon sources differ across locations, potentially influencing the δ¹³C signature of these species (Rodríguez-Malagón et al., 2021).

Regarding δ¹⁵N values, our results showed that *A. rostrata* occupies a lower trophic position than *M. holotrachys*. Considering a trophic enrichment factor of 3.4 ‰ per trophic position, our results suggest that *A. rostrata* is positioned ≈0.75 trophic position lower than *M. holotrachys* in the South Georgia food web (Minagawa and Wada, 1984; Post, 2002). This pattern is consistent with Queirós et al. (2025), where they identified *A. rostrata* occupying the 4th trophic level and *M. holotrachys* the 5th trophic level reinforcing the distinct δ¹⁵N values observed in our study. Studies of the diet of adult *A. rostrata* showed that they are opportunistic scavengers and mainly pelagic feeders, presenting a varied diet based on several taxonomic groups, although fish and squid make up the majority of their prey (Wenner and Musick, 1977; Mauchline and Gordon, 1986; Deng et al., 2011; Reid et al., 2013; Stowasser et al., 2009). In contrast, studies of the diet of adult *M. holotrachys* showed that they are mainly demersal feeders, feeding near the bottom, presenting a wide variety of vertebrates and invertebrates such as benthic and benthopelagic crustaceans, e.g. *Thymops birsteini*, *Notocrangon* sp., and benthopelagic fish, e.g. *Muraenolepis microps* and *Psilodraco breviceps* (McLellan, 1977; Morley et al., 2004; Laptikhovsky, 2005). Aside from the different diet and feeding strategies, the differences found in δ¹⁵N

values between the two species may also be attributed to the fact that benthic food webs are typically longer than pelagic food webs (Queirós et al., 2025). In longer food webs, energy assimilation losses are greater, requiring higher predators to consume more prey to get the same energy input. This not only reduces population stability but also increases the biomagnification of contaminants through the food web (Post, 2002b; Queirós et al., 2025).

According to FishBase (Froese and Pauly, 2025), the trophic position of *A. rostrata* (δ¹⁵N: 3.6 ± 0.47) and *M. holotrachys* (δ¹⁵N: 3.7 ± 0.64), estimated from food items, are very similar. These values are consistent with our stable isotope results, which revealed close but distinct δ¹⁵N values. The similarity in trophic positions reported by FishBase also suggests that length-related differences, particularly in *M. holotrachys*, may play an important role in explaining the stronger length-Hg relationships observed in this species. Larger individuals may consume prey of higher trophic positions within the benthic food web, enhancing bioaccumulation patterns. In contrast, the more pelagic feeding behaviour of *A. rostrata* may result in a less pronounced relationship between size and Hg concentration.

Our results suggest that for *M. holotrachys*, length may influence δ¹³C isotopic signature, possibly due to an ontogenetic shift in habitat use, where larger individuals may forage in different and/or deeper habitats or consume different prey. In contrast, the absence of a relation between length and δ¹³C values may suggest that *A. rostrata* does not undergo a similar ontogenetic shift in habitat as it grows. Nonetheless, we need to be cautious as an ontogenetic shift in habitat may occur
<caption>**Table 2**</caption>

Summary of statistical relationships between mercury (Hg) concentrations and biological variables (standard length and weight) in different tissues (muscle, brain, liver, gills) of *Antimora rostrata* and *Macrourus holotrachys*.

<table><thead><tr><th>Species</th><th>Tissue</th><th>Biological variable</th><th>p-value</th><th>r<sup>2</sup></th><th>Equation</th></tr></thead><tbody><tr><td rowspan="6">Antimora rostrata</td><td rowspan="2">Muscle</td><td>SL</td><td>0.253</td><td>0.062</td><td>Hg = 0.11840 SL<sup>0.466</sup></td></tr><tr><td>Weight</td><td>0.311</td><td>0.049</td><td>Hg = 0.72805 W<sup>0.108</sup></td></tr><tr><td rowspan="2">Brain</td><td>SL</td><td>0.165</td><td>0.094</td><td>Hg = 0.03474 SL<sup>0.713</sup></td></tr><tr><td>Weight</td><td>0.218</td><td>0.075</td><td>Hg = 0.55817 W<sup>0.165</sup></td></tr><tr><td rowspan="2">Liver</td><td>SL</td><td>0.093</td><td>0.128</td><td>Hg = 0.00443 SL<sup>0.915</sup></td></tr><tr><td>Weight</td><td>0.15</td><td>0.096</td><td>Hg = 0.15652 W<sup>0.206</sup></td></tr><tr><td rowspan="6">Macrourus holotrachys</td><td rowspan="2">Muscle</td><td>SL</td><td>0.397</td><td>0.034</td><td>Hg = 0.04439 SL<sup>0.453</sup></td></tr><tr><td>Weight</td><td>0.466</td><td>0.026</td><td>Hg = 0.25918 W<sup>0.101</sup></td></tr><tr><td rowspan="2">Brain</td><td>SL</td><td>0.029</td><td>0.216</td><td>Hg = 0.00288 SL<sup>1.522</sup></td></tr><tr><td>Weight</td><td>0.041</td><td>0.193</td><td>Hg = 1.370 W<sup>0.467</sup></td></tr><tr><td rowspan="2">Liver</td><td>SL</td><td>0.105</td><td>0.132</td><td>Hg = 0.00156 SL<sup>1.638</sup></td></tr><tr><td>Weight</td><td>0.155</td><td>0.103</td><td>Hg = 1.191 W<sup>0.466</sup></td></tr><tr><td rowspan="6">Macrourus holotrachys</td><td rowspan="2">Muscle</td><td>SL</td><td>0.014</td><td>0.265</td><td>Hg = 3.85e-06 SL<sup>2.943</sup></td></tr><tr><td>Weight</td><td>0.03</td><td>0.214</td><td>Hg = 0.58664 W<sup>0.860</sup></td></tr><tr><td rowspan="2">Brain</td><td>SL</td><td>0.021</td><td>0.238</td><td>Hg = 4.19e-05 SL<sup>2.359</sup></td></tr><tr><td>Weight</td><td>0.028</td><td>0.22</td><td>Hg = 0.59399 W<sup>0.737</sup></td></tr></tbody></table>

before the fish reach the 32 cm in length. The lack of a relationship between length and δ¹³C values in both species, suggests that these species may not change their trophic position throughout the studied life period. Similar to *A. rostrata* for δ¹³C values, these results should be carefully analysed as ontogenetic shifts in diet, and consequently in trophic position, may occur earlier in the life cycle. This is supported by previous diet studies in *M. holotrachys* that showed that smaller individuals typically prey on smaller and different organisms, with dietary shifts life cyclebs they grow and move to deeper habitats (Morley et al., 2004; Laptikhovsky, 2005; Pinkerton et al., 2012, 2013; Moore et al., 2022). Further studies are needed to fully understand the trophic ecol- ogy of *A. rostrata* in South Georgia.

## 4.2. Inter-tissue specific Hg accumulation in *A. rostrata* and *M. holotrachys*

The muscle was the tissue with the highest Hg concentrations in both species. Higher Hg concentrations in the muscle are linked to the affinity of Hg for thiols of the cysteine of proteins composing the muscle, leading to its preferential accumulation in this tissue in fish (Havelková et al., 2008). The muscle as the main tissue accumulating Hg was also observed for other fish species worldwide such as bonnethead (*Sphyrna tiburo*) and silky shark (*Carcharhinus falciformis*) (O'Bryhim et al., 2017), bird beak dogfish (*Deania calcea*) and smooth lanternshark (*Etomopterus pusillus*) (Teixeira et al., 2020) in the Atlantic Ocean, and the Atlantic cod (*Gadus morhua* L.) (Amlund et al., 2007). From an ecological perspective, the higher Hg accumulation in the muscle plays a significant role in food webs, as it highlights the potential for biomagnification through trophic position. As muscle is a key tissue for energy storage and is normally the largest tissue by mass of the animal, it has a big impact on the Hg burden that is transferred along the food web (from prey to

predator). This underscores the importance of understanding Hg dynamics in tissues to assess the ecological risks posed by Hg exposure across food webs.

An unexpected result was the mercury concentrations in the brain, which were comparable to those in the muscle. The brain was the second most contaminated tissue in both species, which was surprising, as it typically considered one of the tissue with the lowest mercury accumulation. The higher Hg bioaccumulation in the brain may indicate that these species may experience significant neurological challenges, among other potential toxic effect of MeHg. These species could face impaired cognitive function, affecting their foraging capacities, predator avoidance, and overall survival (Weis and Candelmo, 2012). Additionally, Hg exposure may disrupt immunity, reproductive processes and potentially affecting population dynamics in the region, with long-term ecological consequences (Crump and Trudeau, 2009; Bera et al., 2022). Due to its critical role in organisms, marine organisms have evolved mechanisms to protect and prevent high concentrations of contaminants in the brain (Rouleau et al., 1999; Manceau et al., 2021). However, previous studies also found high concentrations of Hg in the brain of zebrafish (*Danio rerio*) raised in aquaria and in the golden mullet (*Liza aurata*) captured in Ria de Aveiro estuaries (Gonzalez et al., 2005; Mieiro et al., 2009). These studies found that MeHg is capable of crossing the blood-brain barrier by passive diffusion and by actively neutral amino acid transport systems due to its structural mimicry of methionine and the MeHg-cysteine complex (Gonzalez et al., 2005; Mieiro et al., 2009). It is possible that this mimicry process is present in our studied species, however, future studies will be necessary to understand the Hg bioaccumulation in the brain of *A. rostrata* and *M. holotrachys*.

Another possible explanation is based on a previous study that suggests that higher concentrations may still occur because of detoxification processes, particularly the sequestration of Hg as mercury-selenium complexes (HgSe) or Hg-selenocysteine complexes (Manceau et al., 2021).

Surprisingly, the liver, which is a tissue responsible for storage, redistribution, detoxification of Hg in vertebrates (Marsálek et al., 2007; Seco et al., 2020; Yamashita et al., 2005), presents the lowest Hg concentrations in *A. rostrata* and the second lowest in *M. holotrachys*. Rather than indicating rapid excretion, this pattern may reflect effective detoxification mechanisms that convert MeHg into less toxic forms, such as HgSe which are then sequestered and retained in other tissues or excreted at a later stage.

Studies in other vertebrates, including birds and mammals, have shown that detoxification often results in long-term storage rather than elimination, particularly in the form of insoluble compounds like tiemannite (mercury selenide granules) (Kojadinovic et al., 2007; Lail-son-Brito et al., 2012). Although fewer studies exist for fish, emerging evidence suggests that similar processes may occur, with detoxification leading to internal sequestration rather than direct elimination (Siscar et al., 2014).

In *M. holotrachys*, the gills presented the lowest Hg concentrations among all studied tissues. The gills are usually responsible for the intake of some waterborne contaminants in fish (Marrone et al., 2021). While the gills are a primary site for the uptake of waterborne contaminants in fish (Marrone et al., 2021), their relatively low Hg content suggests that they do not serve as a major site of mercury accumulation. Instead, as observed in other species, most Hg is likely incorporated through dietary exposure and subsequently stored in internal tissues such as muscle and liver (Gonzalez et al., 2005; Hall et al., 1997).

Our results showed that *A. rostrata* presented lower Hg concentrations in all tissues compared to *M. holotrachys*. This difference was expected and can be explained either by the lower trophic position or the more pelagic distribution of *A. rostrata* in relation to *M. holotrachys*. However, the absence of relationship between Hg concentrations and δ¹³C values suggests that biomagnification may not be the primary factor driving these differences, but the habitat use, as benthic food webs are generally enriched in Hg (Li et al., 2022).
### 4.3. Hg bioaccumulation and inferences on Hg biomagnification

Surprisingly, no relationships were found between either length or weight and Hg concentrations in *A. rostrata*. This may be explained by its feeding behaviour, i.e. an opportunistic scavenger (Reid et al., 2013). Therefore, *A. rostrata* may not present an ontogenetic shift in the diet which could lead to the absence of visible bioaccumulation if Hg incorporation decreases while growth continues, due to a tissue dilution effect (Sánchez-Hernández et al., 2019). It may also be related to habitat use and detoxification mechanisms. In contrast, Hg concentrations were positively related to the length and weight in most tissues of *M. holotrachys*, with the exception of the brain, where no significant relationships were observed. The largest and heaviest individual exhibiting the highest Hg concentration. This positive relationship could be explained by changes to higher trophic-level prey as it grows, leading to increased Hg bioaccumulation, with this pattern being reinforced by its benthic habitat. The absence of length- or weight-related patterns in the brain may reflect physiological regulation mechanisms, such as detoxification or protection of neural tissues.

The absence of a significant relationship between Hg concentration and δ¹⁵N in both species could be explained by various ecological and physiological factors. In *A. rostrata*'s case, its opportunistic scavenging behaviour and association with the pelagic food web, could result in highly variable Hg exposure. Since scavenged prey may originate from different trophic positions and habitats, Hg accumulation may be influenced more by prey availability than by trophic position. In *M. holotrachys*, despite its association with demersal food web, where Hg concentrations are usually higher, the lack of correlation with δ¹⁵N suggests that other factors, such as habitat use and physiological processes, i.e. Hg detoxification or excretion rates, may play a major role in determining Hg concentrations.

In terms of biomagnification, considering the Trophic Magnification Slope (TMS) equation proposed by Seco et al. (2021) for the pelagic food web in the Scotia Sea ($Hg = 0.2782 \times \delta^{15}\text{N} - 3.0960$), we expected an average Hg concentration of approximately 0.85 µg g⁻¹ dw for *A. rostrata*. Indeed, this value is similar to those measured here (0.79 ± 0.26 µg g⁻¹ dw), indicating that the biomagnification factor of this species fits in the pelagic food web of the Scotia Sea. In contrast, the expected Hg concentration for *M. holotrachys* would be of 1.72 µg g⁻¹ dw, which is slightly lower than our results (1.89 ± 1.83 µg g⁻¹ dw) and roughly twice as high as the of *A. rostrata*, suggesting that the Hg biomagnification factor of this species differs from those in the pelagic food web of this ecosystem. This was expected considering that this species is associated to the benthic food web (discussed above; Morley et al., 2004; Ñacari et al., 2022). However, we must be cautious as the standard deviation is relatively high and includes the expected value of Hg following this TMS (Seco et al., 2021). Further studies are needed to confirm this hypothesis.

## 5. Conclusion

In this study, we analysed stable isotopes of δ¹³C and δ¹⁵N in the muscle and Hg concentrations in 4 tissues (muscle, brain, liver and gills) of two Southern Ocean deep-sea fish species that, despite their important ecological role, remain poorly studied. Our results indicated that *A. rostrata* forage in the pelagic zone while *M. holotrachys* has a stronger association to the benthic habitat. Unexpectedly, high Hg concentrations were found in the brain in both species, challenging conventional assumptions about contamination patterns among tissues and raising the question of the neurotoxic effects and the detoxification processes involved to cope with potential damages due to MeHg. This suggests a potential vulnerability to Hg accumulation in this tissue in both species, possibly due to complex interactions with the blood-brain barrier and metabolic processes. Additionally, the contrasting Hg concentrations between tissues and species hint at differential dietary habits and

possible detoxification mechanisms. However, it is worth highlighting that only adult individuals were analysed in this study. Future studies should also consider juvenile individuals to have a greater understanding of their patterns of accumulation along their life cycle. Overall, our study underscores the importance of further research into the ecological dynamics and contamination risks associated with these species.

### CRediT authorship contribution statement

**Diana Vaz:** Writing – original draft, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **José P. Queirós:** Writing – review & editing, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **José C. Xavier:** Writing – review & editing, Visualization, Validation, Supervision, Project administration, Formal analysis, Conceptualization. **Paco Bustamante:** Writing – review & editing, Visualization, Validation, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Conceptualization. **José Abreu:** Writing – review & editing, Visualization, Validation, Funding acquisition, Formal analysis, Conceptualization. **Eduarda Pereira:** Writing – review & editing, Methodology. **Philip R. Hollyman:** Writing – review & editing, Visualization, Validation, Supervision, Formal analysis, Conceptualization. **João P. Coelho:** Writing – review & editing, Visualization, Validation, Supervision, Formal analysis, Conceptualization. **José Seco:** Writing – review & editing, Visualization, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Queiros J. P. reports financial support was provided by Foundation for Science and Technology. Seco J. reports financial support was provided by Foundation for Science and Technology. Abreu J. reports financial support was provided by Foundation for Science and Technology. Coelho J. P. reports financial support was provided by Foundation for Science and Technology. Bustamante P. reports financial support was provided by Contrat de Projet Etat-Région. Bustamante P. reports financial support was provided by Fonds Européen de Développement Régional. Queiros J. P. reports a relationship with Foundation for Science and Technology that includes: funding grants. Seco J. reports a relationship with Foundation for Science and Technology that includes: funding grants. Coelho J. P. reports a relationship with Foundation for Science and Technology that includes: funding grants. Abreu J. reports a relationship with Foundation for Science and Technology that includes: funding grants. Bustamante P. reports a relationship with Contrat de Projet Etat-Région that includes: funding grants. Bustamante P. reports a relationship with Fonds Européen de Développement Régional that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

Authors would like to thank to the crew and scientific observers onboard the vessel by their support during the collection of the samples. DV, JPQ, JS and JX acknowledge funding from Fundação para a Ciência e Tecnologia, I. P (FCT), under the projects granted to MARE (doi:10.54499/UIDB/04292/2020; doi:10.54499/UIDP/04292/2020), and granted to the Associate Laboratory ARNET (doi:10.54499/LA/P/0069/2020). Thanks are also due to FCT/MCTES for the financial support to CESAM (UIDB/50017/2020, UIDP/50017/2020 and LA/P/0094/2020). JPQ,
JS and JPC were supported by FCT through a PhD Scholarship co-financed by FSE (SFRH/BD/144320/2019) and a CEEC contract (2021/00624/CEECIND and 2020.01778.CEECIND/CP1589/CT0011) and DOI 10.54499/2020.01778.CEECIND/CP1589/CT0011). JA was supported by national funds through FCT - PhD scholarship (2020.07291.BD). The authors are grateful to Carine Churlaud and Maud Brault-Favrou from the platform «Analyses Élémentaires» of LIENSs for their support during sample preparation and to Gaël Guillou from the platform «Analyses Isotopiques» platform of LIENSs for running the stable isotope analyses. Thanks are due to the CPER (Contrat de Projet Etat-Région) and the FEDER (Fonds Européen de Développement Régional) for funding the IRMS of LIENSs laboratory. PB is an honorary member of the IUF (Institut Universitaire de France). JPQ would like to thank Mark Belchier (GSGSSI) for the supervision during the collection of the samples.

## Data availability

Data will be made available on request.

## References

Amlund, L., Lundebye, A., Berntsen, H.G., 2007. Accumulation and elimination of methylmercury in Atlantic cod (*Gadus morhua* L.) following dietary exposure. *Aquat. Toxicol.* 83, 323–330. https://doi.org/10.1016/j.aquatox.2007.05.008

Atkinson, A., Whitehouse, M.J., Priddle, J., Cripps, G.C., Ward, P., Brandon, M.A., 2001. South Georgia, Antarctica: a productive, cold water, pelagic ecosystem. Mar. Ecol. Prog. Ser. 216, 279–308. https://doi.org/10.3354/meps216279

Bera, T., Kumar, S.V., Devi, M.S., Kumar, V., Behera, B.K., Das, B.K., 2022. Effect of heavy metals in fish reproduction: a review. J. Environ. Biol. 43, 631–642. https://doi.org/10.22438/jeb/43/5/MRN-4042

Cabana, G., Rasmussen, J.B., 1994. Modelling food chain structure and contaminant bioaccumulation using stable nitrogen isotopes. Nature 372 (6503), 255–257. https://doi.org/10.1038/372255a0

Cherel, Y., Hobson, K.A., 2007. Geographical variation in carbon stable isotope signatures of marine predators: a tool to investigate their foraging areas in the Southern Ocean. Mar. Ecol. Prog. Ser. 329, 281–287.

Cherel, Y., Barbraud, C., Lahournat, M., Jaeger, A., Jaquemet, S., Wanless, R.M., Phillips, R.A., Thompson, D.R., Bustamante, P., 2018. Accumulate or eliminate? Seasonal mercury dynamics in albatrosses, the most contaminated family of birds. Environ. Pollut. 241, 124–135. https://doi.org/10.1016/j.envpol.2018.05.048

Chouvelon, T., Spitz, J., Cherel, Y., Caurant, F., Sirmel, R., Méndez-Fernandez, P., Bustamante, P., 2011. Interspecific and ontogenic-related differences in d13C and d15N values and Hg and Cd concentrations of cephalopods. Mar. Ecol. Prog. Ser. 433, 107–120.

Chouvelon, T., Caurant, F., Cherel, Y., Simon-Bouhet, B., Spitz, J., Bustamante, P., 2014. Species-and size-related patterns in stable isotopes and mercury concentrations in fish help refine marine ecosystem indicators and provide evidence for distinct management units for hake in the Northeast Atlantic. ICES J. Mar. Sci. 71 (5), 1073–1087. https://doi.org/10.1093/icesjms/fst199

Chu, W., Dang, N., Kok, Y., Yap, K.I., Phang, S., Convey, P., 2019. Heavy metal pollution in Antarctica and its potential impacts on algae. Polar Sci. 20, 75–83. https://doi.org/10.1016/j.polar.2018.10.004

Cohen, D.M., Inada, T., Iwamoto, T., Scialabba, N., 1990. FAO SPECIES CATALOGUE: Gadiform Fishes of the World (Order Gadiformes): An Annotated and Illustrated Catalogue of Cods, Hakes, Grenadiers and Other Gadiform Fishes Known to Date, 125th ed. 10. FAO, Rome, Italy.

Collins, M.A., Brickle, P., Brown, J., Belchier, M., 2010. The Patagonian toothfish: biology, ecology and fishery. Adv. Mar. Biol. 58, 227–300.

Constable, A.J., Melbourne-Thomas, J., Corney, S.P., Arrigo, K.R., Barbraud, C., Barnes, D.K.A., Bindoff, N.L., Boyd, P.W., Brandt, A., Costa, D.P., Davidson, A.T., Ducklow, H.W., Emmerson, L., Fukuchi, M., Gutt, J., Hindell, M.A., Hofmann, E.E., Hosie, G.W., Iida, T., Ziegler, P., 2014. Climate change and Southern Ocean ecosystems i: how changes in physical habitats directly affect marine biota. Glob. Chang. Biol. 20 (10), 3004–3025. Blackwell Publishing Ltd. https://doi.org/10.1111/gcb.12623

Cossa, D., Heimbürger, L.E., Lannuzel, D., Rintoul, S.R., Butler, E.C.V., Bowie, A.R., Averty, B., Watson, R.J., Remenyi, T., 2011. Mercury in the Southern Ocean. Geochim. Cosmochim. Acta 75 (14), 4037–4052. https://doi.org/10.1016/j.gca.2011.05.001

Costley, C.T., Mossop, K.F., Dean, J.R., Garden, L.M., Marshall, J., Carroll, J., 2000. Determination of mercury in environmental and biological samples using pyrolysis atomic absorption spectrometry with gold amalgamation. Anal. Chim. Acta 405.

Crump, K.L., Trudeau, V.L., 2009. Mercury-induced reproductive impairment in fish. Environ. Toxicol. Chem. 28 (5), 895–907. https://doi.org/10.1897/08-151.1

Deng, X., Wagner, H.J., Popper, A.N., 2011. The inner ear and its coupling to the swim bladder in the deep-sea fish Antimora rostrata (Teleostei: Moridae). Deep Sea Res. Part 1 Oceanogr. Res. Pap. 58 (1), 27–37. https://doi.org/10.1016/j.dsr.2010.11.001

Froese, R., Pauly, D., 2025. FishBase. World Wide Web electronic publication. Accessed on September 6.

Gon, O., Miya, T., McMillan, P., Leslie, R., 2021. The distribution of four species of the genus *Macrourus* (Gadiformes: Macrouridae) from the Southern Ocean based on samples from the toothfish longline fishery. Zootaxa 4903 (1), 105–116. https://doi.org/10.11646/zootaxa.4903.1.6

Gonzalez, P., Dominique, Y., Massabuau, J.C., Boudou, A., Bourليaud, J.P., 2005. Comparative effects of dietary methylmercury on gene expression in liver, skeletal muscle, and brain of the zebrafish (*Danio rerio*). Environ. Sci. Technol. 39 (11), 3972–3980. https://doi.org/10.1021/es0483490

Gray, J.S., 2002. Biomagnification in marine systems: the perspective of an ecologist. Mar. Pollut. Bull. 45, 46–52. www.elsevier.com/locate/marpolbul.

Hall, B.D., Bodaly, R.A., Fudge, R.J.P., Rudd, J.W.M., Rosenberg, D.M., 1997. Food as the dominant pathway of methylmercury uptake by fish. Water Air Soil Pollut. 100, 13–24. https://doi.org/10.1023/A:1018071406537

Hanchet, S.M., O'Driscoll, R., Ballara, S., Dunn, A., 2008. Grenadier bycatch in the toothfish longline fishery in the Ross Sea, Antarctica. In: Orlov, A.M., Iwamoto, T. (Eds.), Grenadiers of the World Oceans: Biology, Stock Assessment, and Fisheries. American Fisheries Society Symposium 63, Bethesda, pp. 451–462.

Hanchet, S.M., Tracey, D., Dunn, A., Horn, P., Smith, N., 2012. Mercury concentrations of two toothfish and three of its prey species from the Pacific sector of the Antarctic. Antarct. Sci. 24 (1), 34–42. https://doi.org/10.1017/S0954102011000654

Havelková, M., Dušek, L., Némethová, D., Poleszczuk, G., Svobodová, z., 2008. Comparison of mercury distribution between liver and muscle - a biomonitoring of fish from lightly and heavily contaminated localities. Sensors 8 (7), 4095–4109. https://doi.org/10.3390/s8074095

Hollyman, P.R., Soeffker, M., Roberts, J., Hogg, O.T., Laptikhovsky, V.V., Queirós, J.P., Collins, M.A., 2022. Bioregionalization of the South Sandwich Islands through community analysis of bathyal fish and invertedbrate assemblages using fishery-derived data. Deep Sea Res. Part II Top. Stud. Oceanogr. 198, 105054.

Horn, P.L., Sutton, C.P., 2015. An assessment of age and growth of violet cod (Antimora rostrata) in the Ross Sea, Antarctica. Polar Biol. 38 (9), 1553–1558. https://doi.org/10.1007/s00300-015-1702-3

Kock, K.H., Kellermann, A., 1991. Reproduction in Antarctic notothenioid fish. Antarct. Sci. 3 (2), 125–150. https://doi.org/10.1017/S0954102091000172

Kojadinovic, J., Bustamante, P., Le Corre, M., Cosson, R.P., 2007. Trace elements in three marine birds breeding on Reunion Island (Western Indian Ocean): part 2-factors influencing their detoxification. Arch. Environ. Contam. Toxicol. 52, 431–440. https://doi.org/10.1007/s00244-005-8225-9

Lailson-Brito, J., Cruz, R., Dorneles, P.R., Andrade, L., Azevedo, A.F., Fragoso, A.B., Vidal, L.G., Costa, M.B., Bisi, T.L., Almeida, R., Carvalho, D.P., Bastos, W.R., Malm, O., 2012. Mercury-selenium relationships in liver of Guiana dolphin: the possible role of Kupffer cells in the detoxification process by tiemannite formation. PLoS One 7. https://doi.org/10.1371/journal.pone.0042162

Lamborg, C.H., Hammerschmidt, C.R., Bowman, K.L., Swarr, G.J., Munson, K.M., Ohnemus, D.C., Lam, P.J., Heimbürger, L.E., Rijkenberg, M.J.A., Saito, M.A., 2014. A global ocean inventory of anthropogenic mercury based on water column measurements. Nature 512 (1), 65–68. https://doi.org/10.1038/nature13563

Laptikhovsky, V.V., 2005. A trophic ecology of two grenadier species (Macrouridae, Pisces) in deep waters of the Southwest Atlantic. Deep-Sea Research Part I-Oceanographic Research Papers 52, 1502–1514.

Li, M.L., Gillies, E.M., Briner, R., Hoover, C.A., Sora, K.J., Loseto, L.L., Walters, W.J., Cheung, W.W.L., Giang, A., 2022. Investigating the dynamics of methylmercury bioaccumulation in the Beaufort Sea shelf food web: a modeling perspective. Environ. Sci Process Impacts 24, 1010–1025. https://doi.org/10.1039/D2EM00108J

Lopes-Santos, S., Xavier, J.C., Seco, J., Coelho, J.P., Hollyman, P.R., Pereira, E., Phillips, R.A., Queirós, J.P., 2025. Squid beaks as a proxy for mercury concentrations in muscle of the giant warty squid *Moroteuthopsis longimana*. Mar. Environ. Res. 204, 1–7. https://doi.org/10.1016/j.marenres.2024.106841

Lucyriska, J., Lucyriski, M., Paszczyk, B., 2016. Assessment of mercury in muscles, liver and gills of marine and freshwater fish. J. Elem. 21 (1), 113–129. https://doi.org/10.5601/jelem.2015.20.2.879

Manceau, A., Gaillot, A.C., Glatzel, P., Cherel, Y., Bustamante, P., 2021. In vivo formation of HgSe nanoparticles and Hg-tetraselenide complex from methylmercury in seabird - implications for the Hg-Se antagonism. Environ. Sci. Technol. 55 (3), 1515–1526.

Marrone, A., La Russa, D., Brunelli, E., Santovito, G., La Russa, M.F., Barca, D., Pellegrino, D., 2021. Antarctic fish as a global pollution sensor: metals biomonitoring in a twelve-year period. Front. Mol. Biosci. 8. https://doi.org/10.3389/fmolb.2021.794946

Marsálek, P., Svobodová, Z., Randák, T., 2007. The content of total mercury and methylmercury in common carp from selected Czech ponds. Aquac. Int. 15 (3–4), 299–304. https://doi.org/10.1007/s10499-007-9076-3

Mauchline, J., Gordon, J.D.M., 1986. Foraging strategies of deep-sea fish. Mar. Ecol. Prog. Ser. 27, 227–238.

McIntyre, J.K., Beauchamp, D.A., 2007. Age and trophic position dominate bioaccumulation of mercury and organochlorines in the food web of Lake Washington. Sci. Total Environ. 372, 571–584. https://doi.org/10.1016/j.scitenv.2006.10.035

McLellan, T., 1977. Feeding strategies of the macrourids. Deep-Sea Res. 24, 1019–1036.

McMillan, P., Iwamoto, T., Stewart, A., Smith, P.J., 2012. A new species of grenadier, genus Macrourus (Teleostei, Gadiformes, Macrouridae) from the southern hemisphere and a revision of the genus. Zootaxa 3165, 1–24.

Mieiro, C.L., Pacheco, M., Pereira, M.E., Duarte, A.C., 2009. Mercury distribution in key tissues of fish (Liza aurata) inhabiting a contaminated estuary - implications for
human and ecosystem health risk assessment. J. Environ. Monit. 11 (5), 1004–1012.
https://doi.org/10.1039/b821253h.

Mieiro, C.L., Coelho, J.P., Pacheco, M., Duarte, A.C., Pereira, M.E., 2012. Evaluation of species-specific dissimilarities in two marine fish species: mercury accumulation as a function of metal levels in consumed prey. Arch. Environ. Contam. Toxicol. 63, 125–136. https://doi.org/10.1007/s00244-011-9740-5.

Minagawa, M., Wada, E., 1984. Stepwise enrichment of 15N along food chains: Further evidence and the relation between δ15N and animal age. Geochim. Cosmochim. Acta 48, 1135–1140. https://doi.org/10.1016/0016-7037(84)90204-7.

Miranda, M., Souza, S., Remy, J., Guimarães, D., Correia, R., Oliveira, D., 2007. Mercúrio em Sistemas Aquáticos: Fatores ambientais que afetam a Metilação. Oecologia Brasiliensis 11 (2), 240–251.

Moore, B.R., Parker, S.T., Marriott, P.M., Sutton, C., Pinkerton, M.H., 2022. Comparative biology of the grenadiers *Macrourus caml* and *M. whitsoni* in the Ross Sea region, Antarctica. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.968848.

Morley, S.A., Mulvey, T., Dickson, J., Belchier, M., 2004. The biology of the bigeye grenadier at South Georgia. J. Fish Biol. 64, 1514–1529. https://doi.org/10.1111/j.1095-8649.2004.00405.x.

Murphy, E.J., Watkins, P.N.T., Reid, K., Meredith, M.P., Thorpe, S.E., Johnston, N.M., Clarke, A., Tarling, G.A., Collins, J.F., Shreeve, R.S., Atkinson, A., Korb, R., Whitehouse, M.J., Ward, P., Rodhouse, P.G., Enderlein, P., Hirst, A.G., Martin, A.R., Hill, S.L., Staniland, I.J., Pond, D.W., Briggs, D.R., Cunningham, N.J., Fleming, A.H., 2007. Spatial and temporal operation of the Scotia Sea ecosystem: a review of large-scale links in a krill centred food web. Philos. Trans. R. Soc. B 362, 113–148. https://doi.org/10.1098/rstb.2006.1957.

Murphy, E.J., Hofmann, E.E., Watkins, J.L., Johnston, N.M., Piñones, A., Ballerini, T., Hill, S.L., Trathan, P.N., Tarling, G.A., Cavanagh, R.A., Young, E.F., Thorpe, S.E., Fretwell, P., 2013. Comparison of the structure and function of Southern Ocean regional ecosystems: the Antarctic Peninsula and South Georgia. J. Mar. Syst. 109–110, 22–42. https://doi.org/10.1016/j.jmarsys.2012.03.011.

Nacari, L.A., Escribano, R., Oliva, M.E., 2022. Endoparasites and diet of the “bigeye grenadier” *Macrourus holotrachys* Günther, 1878 from the deep sea in the Southeastern Pacific Ocean. Deep-Sea Res. I Oceanogr. Res. Pap. 190. https://doi.org/10.1016/j.dsr.2022.103903.

Nacari, L.A., Escribano, R., Harrod, C., Oliva, M.E., 2023. Combined use of carbon, nitrogen and sulfur stable isotopes reveal trophic structure and connections in deep-sea mesopelagic and demersal fish communities from the Southeastern Pacific Ocean. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 197. https://doi.org/10.1016/j.dsr.2023.104069.

Newsome, S.D., del Rios, C.M., Bearhop, S., Phillips, D.L., 2007. A niche for isotopic ecology. Front. Ecol. Environ. 5, 429–436. https://doi.org/10.1890/060150.1.

O'Bryhim, J.R., Adams, D.H., Spaet, J.L.Y., Mills, G., Lance, S.L., 2017. Relationships of mercury concentrations across tissue types, muscle regions and fins for two shark species. Environ. Pollut. 223, 323–333. https://doi.org/10.1016/j.envpol.2017.01.029.

Orlov, A.M., Bannikov, A.F., Orlova, S.Y., 2020. Hypothesis of Antimora spp. (Moridae) dispersion in the world oceans based on data on modern distribution, genetic analysis, and ancient records. J. Ichthyol. 60 (3), 399–410. https://doi.org/10.1134/S0032945220030108.

Pinkerton, M.H., Forman, J., Stevens, D.W., Bury, S.J., Brown, J., 2012. Diet and trophic niche of *Macrourus* spp. (Gadiformes, *Macrouridae*) in the Ross Sea region of the Southern Ocean. J. Ichthyol. 52 (10), 787–799. https://doi.org/10.1134/S0032945212100104.

Pinkerton, M.H., McMillan, P., Forman, J., Marriott, P., Horn, P., Bury, S., Brown, J., 2013. Distribution, morphology and ecology of *Macrourus whitsoni* and *M. calm* (Gadiformes, *Macrouridae*) in the Ross Sea region. CCAMLR Sci. 20, 37–61.

Polak-Juszczak, L., 2012. Bioaccumulation of mercury in the trophic chain of flatfish from the Baltic Sea. Chemosphere 89 (5), 585–591. https://doi.org/10.1016/j.chemosphere.2012.05.057.

Polak-Juszczak, L., 2018. Distribution of organic and inorganic mercury in the tissues and organs of fish from the southern Baltic Sea. Environ. Sci. Pollut. Res. 25, 34181–34189. https://doi.org/10.1007/s11356-018-3336-9.

Post, D.M., 2002. Using stable isotopes to estimate trophic position: models, methods, and assumptions. Ecology 83 (3), 703–718.

Post, D.M., 2002b. The long and short of food-chain length. Trends Ecol. Evol. 17, 269–277.

Queirós, J.P., Xavier, J.C., Abreu, J., Collins, M.A., Belchier, M., Hollyman, P.R., 2024. What inhabits the South Sandwich Islands deep-sea? Biodiversity and biogeography

of bathyal communities using predators as biological samplers. Deep-Sea Res. I Oceanogr. Res. Pap. 205, 104260.

Queirós, J.P., Hollyman, P.R., Bustamante, P., Vaz, D., Belchier, M., Xavier, J.C., 2025. Deep-sea food-web structure at South Sandwich Islands (Southern Ocean): net primary production as a main driver for interannual changes. Ecography. https://doi.org/10.1111/ecog.07263.

R Core Team, 2025. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Reid, W.D.K., Sweeting, C.J., Wigham, B.D., McGill, R.A.R., Polunin, N.V.C., 2013. High variability in spatial and temporal size-based trophodynamics of deep-sea fishes from the Mid-Atlantic Ridge elucidated by stable isotopes. Deep Sea Res 2 Top Stud Oceanogr. 98, 412–420. https://doi.org/10.1016/j.dsr.2013.01.020.

Rodríguez-Malagón, M.A., Speakman, C.N., Sutton, G.J., Angel, L.P., Arnould, J.P.Y., 2021. Temporal and spatial isotopic variability of marine prey species in south-eastern Australia: potential implications for predator diet studies. PLoS One 16 (11). https://doi.org/10.1371/journal.pone.0259961.

Romanelli, J.P., 2017. Ecología trófica de tres especies de granaderos, Coelhorinchus fasciatus, *Macrourus carinatus* y *Macrourus holotrachys*, presentes en el Océano Atlántico Sudoccidental. Universidad Nacional de Mar del Plata, Argentina.

Rouleau, C., Borg-Neczak, K., Gottofrey, J., Tjälve, H., 1999. Accumulation of waterborne mercury (II) in specific areas of fish brain. Environ. Sci. Technol. 33 (19), 3384–3389. https://doi.org/10.1021/es990001v.

Sánchez-Hernández, J., Nunn, A.D., Adams, C., Amundsen, P., 2019. Causes and consequences of ontogenetic dietary shifts: a global synthesis using fish models. Biol. Rev. 94 (2), 539–554. https://doi.org/10.1111/brv.12468.

Seco, J., Xavier, J., Bustamante, P., Coelho, J., Saunders, R., Ferreira, N., Fielding, S., Pardal, M., Stowasser, G., Viana, T., Tarling, G., Pereira, E., Brierley, A., 2020. Main drivers of mercury levels in Southern Ocean lantern fish Myctophidae. Environ. Pollut. 264. https://doi.org/10.1016/j.envpol.2020.114711.

Seco, J., Aparício, S., Brierley, A., Bustamante, P., Ceia, F., Coelho, J., Philips, R., Saunders, R., Fielding, S., Gregory, S., Matias, R., Pardal, M., Pereira, E., Stowasser, G., Tarling, G., Xavier, J., 2021. Mercury biomagnification in a Southern Ocean food web. Environ. Pollut. 275. https://doi.org/10.1016/j.envpol.2021.116620.

Siscar, R., Koenig, S., Torreblanca, A., Solé, M., 2014. The role of metallothionein and selenium in metal detoxification in the liver of deep-sea fish from the NW Mediterranean Sea. Sci. Total Environ. 466–467, 898–905. https://doi.org/10.1016/j.scitotenv.2013.07.081.

Stevens, D.W., Dunn, M.R., Pinkerton, M.H., Forman, J.S., 2014. Diet of Antarctic toothfish (Dissostichus mawsoni) from the continental slope and oceanic features of the Ross Sea region, Antarctica. Antarctic Sci. 26 (5), 502–512. https://doi.org/10.1017/S095410201300093X.

Stowasser, G., McAllen, R., Pierce, G.J., Collins, M.A., Moffat, C.F., Priede, I.G., Pond, D. W., 2009. Trophic position of deep-sea fish-assessment through fatty acid and stable isotope analyses. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 56 (5), 812–826. https://doi.org/10.1016/j.dsr.2008.12.016.

Stowasser, G., Atkinson, A., McGill, R.A.R., Phillips, R.A., Collins, M.A., Pond, D.W., 2012. Food web dynamics in the Scotia Sea in summer: a stable isotope study. Deep Sea Res 2 Top Stud Oceanogr. 59–60, 208–221. https://doi.org/10.1016/j.dsr.2011.08.004.

Szopińska, M., Namieśnik, J., Polkowska, Ż., 2017. How important is research on pollution levels in Antarctica? Historical approach, difficulties and current trends. In: Reviews of Environmental Contamination and Toxicology. 239. Springer New York LLC, pp. 79–156. https://doi.org/10.1007/398_2015_5008.

Teixeira, G., Raimundo, J., Goulart, J., Costa, V., Menezes, G.M., Caetano, M., Pacheco, M., Martins, I., 2020. Hg and Se composition in demersal deep-sea fish from the North-East Atlantic. Environ. Sci. Pollut. Res. 27 (27), 33649–33657. https://doi.org/10.1007/s11356-020-08970-3.

Weis, J.S., Candelmo, A., 2012. Pollutants and fish predator/prey behaviour: a review of laboratory and field approaches. Curr. Zool. 58 (1), 9–20. https://doi.org/10.1093/czoolo/58.1.9.

Wenner, C., Musick, J., 1977. Biology of the Morid Fish Antimora rostrata in the Western North Atlantic. J. Fish. Res. Board Can. 34, 2362–2368.

Xavier, J.C., Trathan, P.N., Croxall, J.P., Wood, A.G., Podestá, G., Podestá, P., Rodhouse, P.G., 2004. Foraging ecology and interactions with fisheries of wandering albatrosses (*Diomedea exulans*) breeding at South Georgia. Fish. Oceanogr. 13 (5), 324–344.

Yamashita, Y., Omura, Y., Okazaki, E., 2005. Total mercury and methylmercury levels in commercially important fishes in Japan. Fish. Sci. 71, 1029–1035.