Mercury levels in commercial mid-trophic level fishes along the Portuguese coast – Relationships with trophic niche and oxidative damage

Silva, Joana M. da; Alves, Luís M.F.; Laranjeiro, Maria I.; Silva, Andreia; Angélico, Maria Manuel; Norte, Ana Cláudia; Lemos, Marco F.L.; Ramos, Jaime A.; Novais, Sara C.; Ceia, Filipe R.

doi:10.1016/j.ecolind.2020.106500

Cited by 11 publications

(5 citation statements)

References 57 publications

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“…In the aquatic environment, fish have been widely utilized as environmental bioindicators of microplastic pollution [ 3 , 4 , 5 , 6 ] metal pollution [ 1 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ], sewage sludge pollution [ 17 ], suspended solid pollution [ 18 ], polychlorinated biphenyl (PCBs) pollution, and polycyclic aromatic hydrocarbon (PAH) pollution [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…The feeding ecology of the turbot is benthivorous, which is, it feeds on organic detritus and small preys inhabiting the sediment superficial layers [ 14 ]. In general, toxic metals accumulate more in benthic fish species when compared to pelagic ones [ 9 , 11 ]. Moreover, the turbot does not undertake long and transboundary migrations; therefore it is an important instrument in assessing water pollution [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues

Petrea

Costache²,

Moga

et al. 2020

Molecules

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Metals are considered to be one of the most hazardous substances due to their potential for accumulation, magnification, persistence, and wide distribution in water, sediments, and aquatic organisms. Demersal fish species, such as turbot (Psetta maxima maeotica), are accepted by the scientific communities as suitable bioindicators of heavy metal pollution in the aquatic environment. The present study uses a machine learning approach, which is based on multiple linear and non-linear models, in order to effectively estimate the concentrations of heavy metals in both turbot muscle and liver tissues. For multiple linear regression (MLR) models, the stepwise method was used, while non-linear models were developed by applying random forest (RF) algorithm. The models were based on data that were provided from scientific literature, attributed to 11 heavy metals (As, Ca, Cd, Cu, Fe, K, Mg, Mn, Na, Ni, Zn) from both muscle and liver tissues of turbot exemplars. Significant MLR models were recorded for Ca, Fe, Mg, and Na in muscle tissue and K, Cu, Zn, and Na in turbot liver tissue. The non-linear tree-based RF prediction models (over 70% prediction accuracy) were identified for As, Cd, Cu, K, Mg, and Zn in muscle tissue and As, Ca, Cd, Mg, and Fe in turbot liver tissue. Both machine learning MLR and non-linear tree-based RF prediction models were identified to be suitable for predicting the heavy metal concentration from both turbot muscle and liver tissues. The models can be used for improving the knowledge and economic efficiency of linked heavy metals food safety and environment pollution studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues

Petrea

Costache²,

Moga

et al. 2020

Molecules

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“…Variable T-Hg concentrations in fish were reported in the literature. These variations may be related to the sampled fish (species, age, and size), sampling sites, and season (Da Silva et al, 2020;Elsayed et al, 2020, Wei et al, 2018. Nevertheless, previous studies conducted in the Gulf region reported similar results (Elsayed et al, 2020;Al-Ansari, 2017;Laird et al, 2017;Ahmad et al, 2016;Burger et al, 2014).…”

Section: Fish Contamination Levelsmentioning

confidence: 76%

Health risk assessment of methyl mercury from fish consumption in a sample of adult Qatari residents

Al-Sulaiti¹,

Ramadan²,

Soubra³

2023

Environ Monit Assess

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Fish constitutes an essential source of high-quality protein and is, at the same time, the source of exposure to many hazardous contaminants, namely mercury and methyl mercury (MeHg). This study aims at assessing the risk that MeHg poses to the health of adult Qatari residents through fish consumption. Data on fish consumption were collected using a self-administered online survey composed of three sections that collected information about the fish-eating patterns of the participants. The fish species that were reported to be consumed by ≥ 3% of the respondents were sampled and analyzed for their total mercury (T-Hg) content levels. MeHg concentrations were derived from T-Hg content levels using a scenario-based approach. Disaggregated fish consumption and contamination data were combined using the deterministic approach to estimate MeHg intakes. The average, 75th, and 95th percentiles of the MeHg intake estimates were determined and compared to the tolerable weekly intake (TWI) set by the European Food Safety Agency (EFSA) (1.3 μg·kg−1·w−1). All fish samples contained T-Hg at levels ˂ 0.3–0.5 µg/g with a mean value of 0.077 µg/g. The study population had an average fish consumption of 736.0 g/week. The average estimated weekly intakes of MeHg exceeded TWI for some fish consumers including females of childbearing age and those following a high-protein diet. Our study highlights the need to establish regulatory guidelines and dietary advice based on risk/benefit ratio.

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“…All the most captured species in this study, i.e. Trachurus spp., Scomber spp., S. pilchardus and E. encrasicolus are pelagic/semi-pelagic and opportunistic planktivorous species that show specific feeding traits (da Silva et al, 2020(da Silva et al, , 2022Garrido et al, 2015). Species-specific feeding characteristics should be relevant for survival when sharing the same environment.…”

Section: Species Divergence In the Use Of Resourcesmentioning

confidence: 99%

“…There is a relatively good knowledge of these species from a commercial use perspective (Leitão et al, 2014), and in terms of diet (Garrido et al, 2008(Garrido et al, , 2015Garrido & Murta, 2011;Costalago et al, 2012). However, their trophic relationships within the communities and niche occupancy from an ecological point of view are less understood (Bode et al, 2007;da Silva et al, 2020da Silva et al, , 2022. In this context, recent biochemical methods, such as analyses of stable isotopes, are relevant tools to investigate the trophic relationships of co-occurring species with a high degree of ecological similarity (da Silva et al, 2020(da Silva et al, , 2022Golikov et al, 2020), thus presenting great potential for deciphering food web structure and function in marine systems (Ceia et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Drivers of niche partitioning in a community of mid-trophic level epipelagic species in the North Atlantic

et al. 2023

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According to the principles of community ecology, sympatric species may suffer a selective pressure to decrease their niche overlap through mechanisms of niche partitioning. However, there is still a gap in knowledge of the main drivers influencing niche differentiation, particularly in communities composed by small-sized and inconspicuous species. The coastal epipelagic community structure of mid-trophic level species in the Northeast Atlantic was examined using stable isotope analysis to (1) decipher intrinsic and extrinsic factors promoting niche partitioning and (2) examine species divergence in the use of resources. A total of 474 individuals from a guild of 11 species were sampled during three years, and the influence of potential drivers of isotopic divergence was assessed. The niche partitioning was mainly determined by the taxonomic group in accordance with the fundamental principles of niche theory, which was consistent for both δ13C (i.e. habitat) and δ15N (i.e. prey). Body size was highly important in driving niche partitioning within species. Our results show that the predominant species in the guild were the most generalist, when compared with more specialised species with which they coexist. This study reveals that the intrinsic factors are determinant to decrease inter- and intra-specific niche overlap at the community level.

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Mercury levels in commercial mid-trophic level fishes along the Portuguese coast – Relationships with trophic niche and oxidative damage

Cited by 11 publications

References 57 publications

A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues

A Machine Learning Approach in Analyzing Bioaccumulation of Heavy Metals in Turbot Tissues

Health risk assessment of methyl mercury from fish consumption in a sample of adult Qatari residents

Drivers of niche partitioning in a community of mid-trophic level epipelagic species in the North Atlantic

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