Amino Acid δ¹⁵N Can Detect Diet Effects on Pollution Risks for Yellow-Legged Gulls Overlooked by Trophic Position

Abstract

The use of top-consumers as bioindicators of the health of food webs is hampered by uncertainties in their effective use of resources. In this study, the abundance of stable nitrogen isotopes in amino acids from homogenised eggs of the Yellow-legged Gull (Larus michahellis) allowed to identify variations in trophic resource exploitation between geographically adjacent nesting colonies in the Ria de Vigo (NW Spain) that exhibited marked differences in pollutants. Eggs from nests in the Cíes Islands (located in a National Park) showed a large variability in stable carbon and nitrogen isotopes in bulk egg content encompassing that of eggs from Vigo city (a major fishing harbour). However, both colonies differed in the relative concentration and abundance of nitrogen isotopes of lysine, an essential amino acid present in marine prey, but also extensively used in feed stocks for poultry and swine. Notwithstanding the similarity in trophic position for both colonies, gulls from Cíes Islands may have acquired a substantial fraction of lysine from garbage dump sites, while those of the urban colony relied on fish discards. This unexpected conclusion is partly supported by the large variability reported for gull’s diet in this region and calls for detailed estimations of diet when assessing the conservation status and pollution risks of marine ecosystems.

Introduction

Top consumers from oceanic food webs are good indicators of the biogeochemistry and conservation status of marine ecosystems. They integrate processes operating at lower food web levels and thus represent the result of ecosystem functioning at large spatial and temporal scales (Hebert et al., 2016; Ruiz-Cooley et al., 2017). Estimations of their trophic position (TP) are key to determine ecosystem properties as food-chain length (Vander Zanden and Fetzer, 2007; Reum et al., 2015) or the biomagnification of pollutants (Storr-Hansen et al., 1995; Dietz et al., 2000; Kelly et al., 2008). For instance, changes in TP are indicative of alterations in food web structure (Jenkins and Davoren, 2020) or in nutrient inputs (Ruiz-Cooley et al., 2017). Particularly, seabirds have been extensively used as biomonitors of persistent organic and trace metal contaminants (e.g., OSPAR, 1999), with biomagnification effects critically dependent on their TP (Ramos et al., 2013; Gatt et al., 2020). Methodological uncertainties, however, difficult the accurate determination of TP in these organisms.

Direct quantification of diets cannot be achieved for most field populations, as they may be inaccessible for long periods (e.g., Matias and Catry, 2010; Gatt et al., 2020) and the analysis of stomach contents, pellets or regurgitates cannot identify all the consumed prey (González-Solís, 2003; Abdennadher et al., 2010; Moreno et al., 2010; Calado et al., 2018; Méndez et al., 2020). Alternative determinations based on the progressive enrichment in heavy nitrogen isotopes (expressed as δ¹⁵N) through the food web allow for estimations of TP and diet composition integrated in space and time, but are highly dependent on factors as the appropriate selection of reference baselines (Post, 2002), the assumptions of the enrichment model (Hussey et al., 2014; Jennings and van der Molen, 2015) and the tissue analysed (Caut et al., 2009; Hebert et al., 2016). Nevertheless, these limitations have not impeded the extensive use of δ¹⁵N in bulk tissues to infer TP and biomagnification of pollutants in many studies (e.g., Abdennadher et al., 2010; Ramos et al., 2013). The application of compound-specific isotopic analyses (e.g., δ¹⁵N in amino acids) have reduced some of the uncertainties in TP estimations, because the changes in the nutrient sources at the base of the food web can be accounted for Chikaraishi et al. (2009). Even when there are still uncertainties in the variability of the isotopic enrichment along the food web (McMahon and McCarthy, 2016; Ohkouchi et al., 2017; Whiteman et al., 2019) the new estimations based on amino acids are being increasingly applied to marine birds (McMahon et al., 2015; Wu et al., 2018; Gatt et al., 2020).

Eggs of the Yellow-legged Gull (Larus michahellis) have been used to monitor organic and trace metal pollutants (Abdennadher et al., 2010; Ramos et al., 2013; Otero et al., 2018; Viñas et al., 2020). The large feeding plasticity reported for this species, however, difficult the identification of the sources of pollutants. For instance, gull colonies near urban areas have been shown to consume more terrestrial and anthropogenic resources than marine prey (Arizaga et al., 2013; Méndez et al., 2020; Zorrozua et al., 2020). A previous study of two colonies only 10 km apart in the Ria de Vigo (NW Spain) revealed significant differences in the load of pollutants in their eggs, pointing to a rapid adaptation to local feeding resources (Viñas et al., 2020). The colony nesting on the Cíes Islands (located in a marine protected area) showed lower concentrations of pollutants and a wider trophic niche than the colony nesting in the industrialised Vigo city. Furthermore, the estimated diet composition in the region, including colonies in the Cíes Islands, showed marked year to year variations related to the availability of marine vs. anthropogenic resources, the latter including mainly beef, pork, and chicken remains (Moreno et al., 2010; Calado et al., 2020). These findings suggest that major changes in the diet of the Yellow-legged Gull may affect TP and therefore TP can be used as an indicator of changes in the trophic structure of the food web. Alternatively, pollution loads could be caused by the differential exploitation of trophic resources without a substantial change in TP.

The objective of this study is to determine the trophic position of Yellow-legged Gull in two colonies differing in the accumulation of pollutants (Viñas et al., 2020). We applied for the first time estimations derived from the amino acid stable isotope composition of eggs, allowing to infer differences in trophic position and diet between the gulls in the two colonies. These estimations aimed to assist in the interpretation of the differential impact of contaminants in these colonies by revealing the sensitivity of TP estimations to diet variability.

Materials and Methods

Sampling

Yellow-legged Gull eggs were collected from nests in the Cíes Islands (11 eggs) and in the city of Vigo (7 eggs) during the breeding season of 2011 (April to early August). First laid eggs in each nest were selected for analysis, weighed and measured (length and width) before opening (Supplementary Table 1). Embryonated eggs were discarded and the remaining were individually homogenised, stored frozen (−20°C), and freeze-dried before analysis. Further details on sampling were provided in Viñas et al. (2020).

Analysis

Two types of isotopic determinations were made. First, C:N ratios and natural abundances of carbon (δ¹³C) and nitrogen (δ¹⁵N) stable isotopes were determined in bulk egg samples using an isotope ratio mass spectrometer coupled to an elemental analyzer. Egg samples were analysed in triplicate with precision < 0.05‰ for both isotopes. Due to the high lipid content of egg samples, δ¹³C values were corrected using the C:N ratio of each sample (Elliott and Elliott, 2016). These analyses were described in more detail in Viñas et al. (2020).

Second, 10 mg aliquots of the homogenised eggs were also used for determinations of δ¹⁵N in individual amino acids (Chikaraishi et al., 2009). Samples were hydrolysed with 6N HCl, esterified with acetyl chloride:2-propanol, and treated with a mixture of 3:1 diclomethane:trifluoracetic anhydride (McCarthy et al., 2013; Mompeán et al., 2016). Derivatised amino acids were purified by solvent extraction in 1:2 chloroform:phosphate buffer and centrifugation (Loick et al., 2019), evaporated at room temperature under N₂, and stored at −20°C in 3:1 diclomethane:trifluoracetic anhydride for up to 6 months until isotope analysis. The individual amino acids were separated in a chromatography column (TraceGOLD TG-5MS, 60 m, 0.32 mm ID, 1.0 μm film) using a gas chromatograph (Trace1310GC, Thermo Fisher Scientific), and subsequently injected into a mass spectrometer (DeltaV Advantage, Thermo Fisher Scientific) via a continuous flow interface and combustion module (GC Isolink, Thermo Fisher Scientific). Amino acid δ¹⁵N values were calibrated with the values obtained for isolated standards (Shoko Science) analysed by combustion as described for bulk analysis, and further corrected using an internal L-norleucine standard (SIGMA) of known isotopic composition added to each sample. The molar fraction of each amino acid (% molar) was also determined in the same analytical run by calibration of the spectrometric signals with amino acid standards (McCarthy et al., 2013). Precision (±SE) of triplicate samples (two injections per sample) was <0.6‰ per individual amino acid.

Values of δ¹⁵N and %molar were obtained for trophic and source amino acids (McClelland and Montoya, 2002; McCarthy et al., 2013; McMahon and McCarthy, 2016). Trophic amino acids included alanine (Ala), leucine (Leu), isoleucine (Ile), proline (Pro), valine (Val), and the mixtures of glutamine (Gln) and glutamic acid (Glu), and of aspartamine (Asn) and aspartic acid (Asp). The latter mixtures were caused by the acid hydrolysis and were termed as Glx and Asx, respectively. Source amino acids included glycine (Gly), threonine (Thr), serine (Ser), methionine (Met), phenylalanine (Phe), and lysine (Lys). The representation of these amino acids in the bulk protein was assessed by correlation of their molar-weighted average δ¹⁵N (δ¹⁵N_THAA) with δ¹⁵N values for bulk samples (δ¹⁵N_bulk). Similarly, the correlations between molar-weighted averages of trophic (δ¹⁵N_trp) or source amino acids (δ¹⁵N_src) with δ¹⁵N_bulk were used to determine the relative importance of variations in trophic vs. nitrogen source factors for the interpretation of δ¹⁵N_bulk.

Diet Estimations

The potential contribution of marine and anthropogenic food sources to the diet of the gulls of each colony was estimated from the δ¹⁵N signature of source amino acids. Reference values for marine sources were provided by sardines (Sardina pilchardus) and anchovies (Engraulis encrasicholus) collected in spring (April 2017 and April 2019) near the Ria de Vigo as part of a regular fish survey (see Bode et al., 2018). Anthropogenic sources were provided by samples of beef, pork and chicken meat obtained from local markets. Four individuals of each fish species and three replicates of each meat type were analysed as described for the gull eggs. The potential contribution of these sources to the gulls diet were estimated using the Mix SIAR Bayesian mixing model (SIAR v4.1.3) of Stock and Semmens (2013) by using only the δ¹⁵N values of individual amino acids that varied significantly between colonies and assuming no isotopic fractionation between sources and eggs. This simplified approach was intended only to highlight the potential implications of the marked differences in δ¹⁵N among the sources.

Trophic Position

Estimations of the trophic position of Yellow-legged Gull individuals were made using the δ¹⁵N values of representative trophic (Glx) and source (Phe) amino acids (TP_AA) and a model considering two trophic discrimination factors (McMahon and McCarthy, 2016):

Where, β is the difference between Glx and Phe in primary producers, and TDFp and TDFb are the trophic discrimination factors for plankton and marine birds, respectively. Mean (±SE) values were 3.4 ± 0.9‰ for β (Chikaraishi et al., 2009), 7.6 ± 1.2‰ for TDFp and 3.5 ± 0.4‰ for TDFb (McMahon et al., 2015; Wu et al., 2018; Gatt et al., 2020). As there was isotopic fractionation between the different tissues, increments of 2.5‰ and 0.9‰ were applied to the measured values of δ¹⁵N_Glx and δ¹⁵N_Phe, respectively, to produce TP estimations equivalent to those obtained for the muscle of adult gulls (Hebert et al., 2016).

For comparative purposes, additional TP estimations were made using the measured δ¹⁵N_bulk in eggs using the classical model (Post, 2002):

where, δ¹⁵N_zoo was the value for zooplankton samples collected in the nearby shelf during spring 2011 (mean ± se = 5.97 ± 0.92‰, n = 14) as described in Bode et al. (2018), and TDF_z = 3.4‰ (Post, 2002). The error for all TP estimations was computed by error propagation by taking into account the analytical errors in δ¹⁵N measurements in bulk or trophic and source amino acids, as well as the errors reported for TDF and β values employed (Post, 2002; Ohkouchi et al., 2017).

Further comparisons of TP were made with those reported in the literature for Yellow-legged Gull and similar species (Table 1). In the case of data reported in Lopezosa et al. (2019), TP was estimated from the reported δ¹⁵N_bulk in body feathers and diet data using the model described in Hebert et al. (2016) by considering the consumption of pelagic fish, demersal fish, and garbage.

Statistics

Differences in isotopic measurements between eggs from Cíes and Vigo colonies were analysed by means of non-parametric ANOVA (Kruskal-Wallis). Regressions between variables were computed using reduced major axis regression, as there were large differences in the measurement error of the variables. Statistical analyses were performed using SPSS 17.0 (SPSS Inc.) and Past 4.0 (Hammer et al., 2001).

Results

There were no significant differences in median bulk δ¹⁵N or δ¹³C between both colonies, nor in averaged δ¹⁵N values for trophic or source amino acids (Kruskal-Wallis test, P > 0.05, Figure 1). However, the weighted means of δ¹⁵N for total amino acids (δ¹⁵N_THAA) and trophic amino acids (δ¹⁵N_trp) were linearly correlated with bulk δ¹⁵N (P < 0.001, n = 16), while the means for source amino acids showed no correlation (P > 0.05, Supplementary Figure 1).

FIGURE 1

Values of δ¹⁵N values for most amino acids were similar for the two colonies (Figure 2A), except in the case of lysine that showed lower values for the Vigo colony (Kruskal-Wallis test, P < 0.05). Mean ± SD δ¹⁵N values for lysine in eggs from the Cíes Islands were 1.50 ± 1.18‰ (n = 9) while in those from Vigo were 4.68 ± 0.77‰ (n = 7). Even when adjusted for variations in the δ¹⁵N of the canonical source amino acid phenylalanine, the differences in isotopic composition of Lys between colonies persisted, and were indicative of differences in the use of marine resources (Supplementary Table 2). The eggs from the Cíes Islands had also lower mean relative molar concentration of lysine and higher concentration of alanine than those from Vigo (Kruskal-Wallis tests, P < 0.05, Figure 2B).

FIGURE 2

As for gull eggs, there were also significant differences in Lys δ¹⁵N among food sources (Mann-Whitney test, P < 0.05, Supplementary Figure 2A). Using these values, the estimations of diet suggested that, notwithstanding marine sources had a major contribution potential for both colonies, the gulls from Cies had a higher contribution of anthropogenic sources than those from Vigo (Supplementary Figure 2B).

The estimations of TP (Table 1) indicated that the studied Yellow-legged Gulls were almost tertiary consumers (i.e., TP ca. 3) and revealed no significant differences using either bulk or amino acid δ¹⁵N (Kruskal-Wallis test, P > 0.05). Besides, there were no significant differences in TP between colonies (Figure 3, Kruskal-Wallis test, P > 0.05).

FIGURE 3

Discussion

The results of this study represent the first estimation of Yellow-legged Gulls TP obtained with amino acid δ¹⁵N. These estimations are comparable to those obtained using bulk δ¹⁵N in this and previous studies in the same and related gull species (Table 1), and in other marine birds (Gatt et al., 2020). Furthermore, the significant correlation between δ¹⁵N of trophic amino acids and bulk δ¹⁵N (and the lack of correlation for δ¹⁵N in source amino acids) supports previous assumptions using bulk δ¹⁵N as an index of TP (Abdennadher et al., 2010; Muñoz-Arnanz et al., 2012; Pedro et al., 2013; Calado et al., 2018). However, the determination of δ¹⁵N in individual amino acids provided additional information on the variability of feeding among individuals that complements TP estimations. For instance, the similarity in TP between the studied colonies supports that the differences in pollution loads reported in Viñas et al. (2020) were caused by changes in diet unrelated to TP, even when detailed diet composition was not available for the individuals studied. Similar conclusions were reached in other studies where, diet and TP could be determined (Muñoz-Arnanz et al., 2012; Ramos et al., 2013).

The low δ¹⁵N in lysine found in eggs from the Cíes Island colony suggest the consumption of non-marine prey. First, because the mean difference between δ¹⁵N of Lys and Phe for the Cíes eggs was significantly lower than values typical of phytoplankton (Nielsen et al., 2015). Second, because the estimated contributions of anthropogenic sources to the diet in Cíes were generally higher than those in Vigo (Supplementary Figure 2). This is an unexpected result, as the gulls from the islands would have, in principle, more access to marine prey than those nesting in the city. Lysine is an essential amino acid in the diet of animals and is one of the major additives to feed stocks, notably for poultry and swine raising (Toride, 2002). It is produced industrially through fermentation with the addition of sugars and inorganic nitrogen (Ikeda, 2017). Industrial lysine is likely to have low δ¹⁵N, since ammonium derived from atmospheric nitrogen represents the most economic source for fertilizers (Smil, 2001). For instance, the values of Lys δ¹⁵N measured in beef, pork and chicken meat in this study were lower than those measured in fish. These results agree with those reported for bulk δ¹⁵N from chick gull regurgitates in the Bay of Biscay (Arizaga et al., 2013). Therefore, a substantial consumption of rearing animals remains by gulls from the Cíes Island colony cannot be discarded, as noted by previous studies (Moreno et al., 2010). Given the large variability reported in the diet of the Yellow-legged Gull, it would be not surprising that the Cíes Islands colony partly relied on garbage, at least during the time frame considered in this study. This is supported by the year-to-year reduction in the consumption of fish by this species along the NW Spanish coast and attributed to the decline in sardine populations (Calado et al., 2020). Furthermore, the reported mean composition of the gull diet in this region indicated the dominance of anthropogenic over marine food items during 2010, an exceptional contribution compared to the series for the whole recording period (Supplementary Table 3). In this regard, it must be noted that the isotopic composition of eggs is considered representative of both recent (ca. 1 month) and past (ca. 1 year) diet of the breeding adult females (Hebert et al., 2016). The high consumption of marine resources in Vigo agrees with the general pattern reported for the region and could be favoured by the proximity of the fishing harbour, one of the largest in Europe with 84,000 metric tons of fresh marine products landed in 2011 (Port Authority of Vigo, 2012). Fish offal would be a favoured resource for gulls nesting in Vigo, as reported for similar species elsewhere (Hebert et al., 1999; González-Solís, 2003; Calado et al., 2018).

Differences in diet, thus, were the likely cause of the high levels of pollutants in eggs from the Vigo colony (Viñas et al., 2020). Marine fishes of high TP were reported to bioaccumulate organic and inorganic pollutants. For instance, Hg compounds in tuna (e.g., Méndez et al., 2001) can reach concentrations equivalent to those found in the gull eggs from Vigo (Viñas et al., 2020). These results imply that biomagnification does not only depend on TP but also on the diet of the consumer species. Therefore, future assessments of pollution risks in food webs would require coupled studies of diet and TP. Gull diet can be reconstructed from observations of feeding remains or stomach contents, but also from instrumental techniques as the analysis of marker fatty acids (Hebert et al., 2016). The latter can be very sensitive when coupled to stable isotope determinations (Twining et al., 2020). The application of these techniques will help to determine the appropriate biomagnification factors when the monitored species, as the Yellow-legged Gull, feeds on different habitats or food webs.

Conclusion

Our study shows that reliable TP estimations for the yellow-legged gull can be obtained using δ¹⁵N either in bulk homogenised eggs or in individual source and trophic amino acids. However, the latter technique would be preferred because data or assumptions about the baseline reference values from other species are not required, and because it can provide valuable information on the variability in diet among individuals. Indeed, diet, rather than TP, appears as the dominant factor affecting the bioaccumulation of pollutants in the Yellow-legged Gull.

References

• 1

  AbdennadherA.RamirezF.RomdhaneM. S.RuizX.JoverL.SanperaC. (2010). Biomonitoring of coastal areas in Tunisia: Stable isotope and trace element analysis in the yellow-legged gull.Mar. Pollut. Bull.60440–447. 10.1016/j.marpolbul.2009.10.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 2

  ArizagaJ.JoverL.AldalurA.CuadradoJ. F.HerreroA.SanperaC. (2013). Trophic ecology of a resident yellow-legged gull (Larus michahellis) population in the Bay of Biscay.Mar. Environ. Res.87-8819–25. 10.1016/j.marenvres.2013.02.016

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3

  BodeA.CarreraP.González-NuevoG.NogueiraE.RiveiroI.SantosM. B. (2018). A trophic index for sardine (Sardina pilchardus) and its relationship to population abundance in the southern Bay of Biscay and adjacent waters of the NE Atlantic.Prog. Oceanogr.166139–147. 10.1016/j.pocean.2017.08.005

  • CrossRef
  • Google Scholar

• 4

  CaladoJ. G.MatosD. M.RamosJ. A.MonizF.CeiaF. R.GranadeiroJ. P.et al (2018). Seasonal and annual differences in the foraging ecology of two gull species breeding in sympatry and their use of fishery discards.J. Avian Biol.49UNSe01463. 10.1111/jav.01463

  • CrossRef
  • Google Scholar

• 5

  CaladoJ. G.PaivaV. H.RamosJ. A.VelandoA.MunillaI. (2020). Anthropogenic food resources, sardine decline and environmental conditions have triggered a dietary shift of an opportunistic seabird over the last 30 years on the northwest coast of Spain.Reg. Environ. Change2010. 10.1007/s10113-10020-01609-10116

  • CrossRef
  • Google Scholar

• 6

  CautS.AnguloE.CourchampF. (2009). Variation in discrimination factors (Δ¹⁵N and Δ¹³C): the effect of diet isotopic values and applications for diet reconstruction.J. Appl. Ecol.46443–453. 10.1111/j.1365-2664.2009.01620.x

  • CrossRef
  • Google Scholar

• 7

  ChikaraishiY.OgawaN. O.KashiyamaY.TakanoY.SugaH.TomitaniA.et al (2009). Determination of aquatic food-web structure based on compound-specific nitrogen isotopic composition of amino acids.Limnol. Oceanogr. Methods7740–750. 10.4319/lom.2009.7.740

  • CrossRef
  • Google Scholar

• 8

  DietzR.RigetF.CleemannM.AarkrogA.JohansenP.HansenJ. C. (2000). Comparison of contaminants from different trophic levels and ecosystems.Sci. Total Environ.245221–231. 10.1016/S0048-9697(99)00447-7

  • CrossRef
  • Google Scholar

• 9

  ElliottK. H.ElliottJ. E. (2016). Lipid extraction techniques for stable isotope analysis of bird eggs: Chloroform–methanol leads to more enriched ¹³C values than extraction via petroleum ether.J. Exp. Mar. Biol. Ecol.47454–57. 10.1016/j.jembe.2015.09.017

  • CrossRef
  • Google Scholar

• 10

  ForeroM. G.BortolottiG. R.HobsonK. A.DonazarJ. A.BertellotiM.BlancoG. (2004). High trophic overlap within the seabird community of Argentinean Patagonia: a multiscale approach.J. Anim. Ecol.73789–801. 10.1111/j.0021-8790.2004.00852.x

  • CrossRef
  • Google Scholar

• 11

  GattM. C.ReisB.GranadeiroJ. P.PereiraE.CatryP. (2020). Generalist seabirds as biomonitors of ocean mercury: The importance of accurate trophic position assignment.Sci. Total Environ.740140159. 10.1016/j.scitotenv.2020.140159

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  González-SolísJ. (2003). Impact of fisheries on activity, diet and predatory interactions between Yellow-legged and Audouin’s gulls breeding at the Chafarinas Islands.Sci. Mar.67(Suppl 2)83–88. 10.3989/scimar.2003.67s283

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 13

  HammerØHarperD. A. T.RyanP. D. (2001). PAST: Paleontological statistics software package for education and data analysis.Palaeontologia Electronica49.

  • Google Scholar

• 14

  HebertC. E.PoppB. N.FernieK. J.Ka’apu-LyonsC.RattnerB. A.WallsgroveN. (2016). Amino acid specific stable nitrogen isotope values in avian tissues: insights from captive american kestrels and wild herring gulls.Environ. Sci. Technol.5012928–12937. 10.1021/acs.est.6b04407

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 15

  HebertC. E.ShuttJ. L.HobsonK. A.WeselohD. V. C. (1999). Spatial and temporal differences in the diet of Great Lakes Herring Gulls (Larus argentatus): evidence from stable isotope analysis.Can. J. Fish. Aquat. Sci.56323–338. 10.1139/f98-189

  • CrossRef
  • Google Scholar

• 16

  HusseyN. E.MacNellM. A.McMeansB. C.OllinJ. A.DudleyS. F. J.CliffG.et al (2014). Rescaling the trophic structure of marine food webs.Ecol. Lett.17239–250. 10.1111/ele.12226

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 17

  IkedaM. (2017). Lysine Fermentation: History and Genome Breeding.Adv. Biochem. Eng. Biotechnol.15973–102. 10.1007/10_2016_27

  • CrossRef
  • Google Scholar

• 18

  JenkinsE. J.DavorenG. K. (2020). Seabird species- and assemblage-level isotopic niche shifts associated with changing prey availability during breeding in coastal Newfoundland.Ibis163183–196. 10.1111/ibi.12873

  • CrossRef
  • Google Scholar

• 19

  JenningsS.van der MolenJ. (2015). Trophic levels of marine consumers from nitrogen stable isotope analysis: estimation and uncertainty.ICES J. Mar. Sci.722289–2300. 10.1093/icesjms/fsv120

  • CrossRef
  • Google Scholar

• 20

  KellyB. C.IkonomouM. G.BlairJ. D.GobasF. A. P. C. (2008). Bioaccumulation behaviour of polybrominated diphenyl ethers (PBDEs) in a Canadian Arctic marine food web.Sci. Total Environ.40160–72. 10.1016/j.scitotenv.2008.03.045

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 21

  LoickN.Fernández-UrruzolaI.EgliteE.LiskowI.NauschM.Schulz-BullD.et al (2019). Stratification, nitrogen fixation, and cyanobacterial bloom stage regulate the planktonic food web structure.Glob. Change Biol.25, 794–810. 10.1111/gcb.14546

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22

  LopezosaP.ForeroM. G.RamirezF.NavarroJ. (2019). Individuals within populations: No evidences of individual specialization in the trophic habits of an opportunistic predator.Estuar. Coast Shelf Sci.229106427. 10.1016/j.ecss.2019.106427

  • CrossRef
  • Google Scholar

• 23

  MatiasR.CatryP. (2010). The diet of Atlantic Yellow-legged Gulls (Larus michahellis atlantis) at an oceanic seabird colony: estimating predatory impact upon breeding petrels.Eur. J. Wildl. Res.56861–869. 10.1007/s10344-010-0384-y

  • CrossRef
  • Google Scholar

• 24

  McCarthyM. D.LehmanJ.KudelaR. (2013). Compound-specific amino acid δ¹⁵N patterns in marine algae: Tracer potential for cyanobacterial vs. eukaryotic organic nitrogen sources in the ocean.Geochim. Cosmochim. Acta103104–120. 10.1016/j.gca.2012.10.037

  • CrossRef
  • Google Scholar

• 25

  McClellandJ. W.MontoyaJ. P. (2002). Trophic relationships and the nitrogen isotopic composition of amino acids in plankton.Ecology832173–2180.

  • Google Scholar

• 26

  McMahonK. W.McCarthyM. D. (2016). Embracing variability in amino acid δ¹⁵N fractionation: mechanisms, implications, and applications for trophic ecology.Ecosphere7e01511.

  • Google Scholar

• 27

  McMahonK. W.PolitoM. J.AbelS.McCarthyM. D.ThorroldS. R. (2015). Carbon and nitrogen isotope fractionation of amino acids in an avian marine predator, the gentoo penguin (Pygoscelis papua).Ecol. Evol.51278–1290. 10.1002/ece3.1437

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 28

  MéndezA.MontalvoT.AymiR.CarmonaM.FiguerolaJ.NavarroJ. (2020). Adapting to urban ecosystems: unravelling the foraging ecology of an opportunistic predator living in cities.Urban Ecosyst.231117–1126. 10.1007/s11252-020-00995-3

  • CrossRef
  • Google Scholar

• 29

  MéndezE.GiudiceH.PereiraA.InocenteG.MedinaD. (2001). Total mercury content:fish weight relationship in swordfish (Xiphias gladius) caught in the southwest Atlantic Ocean.J. Food Compos. Anal.14453–460. 10.1006/jfca.2001.1005

  • CrossRef
  • Google Scholar

• 30

  MompeánC.BodeA.GierE.McCarthyM. D. (2016). Bulk vs. aminoacid stable N isotope estimations of metabolic status and contributions of nitrogen fixation to size-fractionated zooplankton biomass in the subtropical N Atlantic.Deep Sea Res.114137–148. 10.1016/j.dsr.2016.05.005

  • CrossRef
  • Google Scholar

• 31

  MorenoR.JoverL.MunillaI.VelandoA.SanperaC. (2010). A three-isotope approach to disentangling the diet of a generalist consumer: the Yellow-legged Gull in northwest Spain.Mar. Biol.157545–553. 10.1007/s00227-009-1340-9

  • CrossRef
  • Google Scholar

• 32

  Muñoz-ArnanzJ.RoscalesJ. L.VicenteA.AguirreJ. I.JiménezB. (2012). Dechlorane Plus in eggs of two gull species (Larus michahellis and Larus audouinii) from the southwestern Mediterranean Sea.Anal. Bioanal. Chem.4042765–2773. 10.1007/s00216-012-6326-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 33

  NielsenJ. M.PoppB. N.WinderM. (2015). Meta-analysis of amino acid stable nitrogen isotope ratios for estimating trophic position in marine organisms.Oecologia178631–642. 10.1007/s00442-015-3305-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  OhkouchiN.ChikaraishiY.CloseH. G.FryB.LarsenT.MadiganD. J.et al (2017). Advances in the application of amino acid nitrogen isotopic analysis in ecological and biogeochemical studies.Org. Geochem.113150–174. 10.1016/j.orggeochem.2017.07.009

  • CrossRef
  • Google Scholar

• 35

  OSPAR. (1999). CEMP guidelines for monitoring contaminants in biota. Agreement 1999-02.London: OSPAR Commission. Coordinated Environmental Monitoring Programme (CEMP).

  • Google Scholar

• 36

  OteroX. L.De La Pena-LastraS.RomeroD.NobregaG. N.FerreiraT. O.Perez-AlbertiA. (2018). Trace elements in biomaterials and soils from a Yellow-legged Gull (Larus michahellis) colony in the Atlantic Islands of Galicia National Park (NW Spain).Mar. Pollut. Bull.133144–149. 10.1016/j.marpolbul.2018.05.027

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 37

  PedroP.RamosJ.NevesV.PaivaV. (2013). Past and present trophic position and decadal changes in diet of Yellow-legged Gull in the Azores Archipelago, NE Atlantic.Eur. J. Wild. Res.59833–845. 10.1007/s10344-013-0737-4

  • CrossRef
  • Google Scholar

• 38

  Port Authority of Vigo. (2012). Annual report 2011.Vigo: Autoridad Portuaria de Vigo. Puertos del Estado. Ministerio de Fomento.

  • Google Scholar

• 39

  PostD. M. (2002). Using stable isotopes to estimate trophic position: models, methods, and assumptions.Ecology83703–718.

  • Google Scholar

• 40

  RamosR.RamirezF.JoverL. (2013). Trophodynamics of inorganic pollutants in a wide-range feeder: The relevance of dietary inputs and biomagnification in the Yellow-legged Gull (Larus michahellis).Environ. Pollut.172235–242. 10.1016/j.envpol.2012.09.014

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41

  ReumJ. C. P.JenningsS.HunsickerM. E. (2015). Implications of scaled δ¹⁵N fractionation for community predator-prey body mass ratio estimates in size-structured food webs.J. Anim. Ecol.841618–1627. 10.1111/1365-2656.12405

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  Ruiz-CooleyR. I.GerrodetteT.FiedlerP. C.ChiversS. J.DanilK.BallanceL. T. (2017). Temporal variation in pelagic food chain length in response to environmental change.Sci. Adv.3e1701140. 10.1126/sciadv.1701140

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 43

  SmilV. (2001). Enriching the earth. Fritz Haber, Carl Bosch, and the transformation of world food production.Cambridge, MA: The MIT Press.

  • Google Scholar

• 44

  SørmoE. G.LieE.RuusA.GaustadH.SkaareJ. U.JenssenB. M. (2011). Trophic level determines levels of brominated flame-retardants in coastal herring gulls.Ecotoxicol. Environ. Safety742091–2098. 10.1016/j.ecoenv.2011.06.012

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  StockB. C.SemmensB. X. (2013). MixSIAR GUI User Manual.Version 3.1. Available online at: https://github.com/brianstock/MixSIAR accessed date^∗.

  • Google Scholar

• 46

  Storr-HansenE.SpliidH.BoonJ. P. (1995). Patterns of chlorinated biphenyl congeners in harbor seals (Phoca vitulina) and in their food: Statistical analysis.Arch. Environ. Contam. Toxicol.2848–54. 10.1007/BF00213968

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 47

  TorideY. (2002). “Lysine and other amino acids for feed: production and contribution to protein utilization in animal feeding,” in Protein sources for the animal feed industry, ed.FAO (Roma: FAO), 161–168.

  • Google Scholar

• 48

  TwiningC. W.TaipaleS. J.RuessL.BecA.Martin-CreuzburgD.KainzM. J. (2020). Stable isotopes of fatty acids: current and future perspectives for advancing trophic ecology.Philos. Trans. R. Soc. B3751804. 10.1098/rstb.2019.0641

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 49

  Vander ZandenM. J.FetzerW. (2007). Global patterns of aquatic food chain length.Oikos1161378–1388. 10.1111/j.2007.0030-1299.16036.x

  • CrossRef
  • Google Scholar

• 50

  ViñasL.BesadaV.Pérez-FernándezB.BodeA. (2020). Yellow-legged gull eggs (Larus michahellis) as persistent organic pollutants and trace metal bioindicator for two nearby areas with different human impact.Environ. Res.190110026. 10.1016/j.envres.2020.110026

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51

  WhitemanJ. P.Elliott SmithE. A.BesserA. C.NewsomeS. D. (2019). A guide to using compound-specific stable isotope analysis to study the fates of molecules in organisms and ecosystems.Diversity118. 10.3390/d11010008

  • CrossRef
  • Google Scholar

• 52

  WuL.LiuX.XuL.LiL.FuP. (2018). Compound-specific ¹⁵N analysis of amino acids: A tool to estimate the trophic position of tropical seabirds in the South China Sea.Ecol. Evol.88853–8864. 10.1002/ece3.4282

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  ZorrozuaN.EgunezA.AldalurA.GalarzaA.DiazB.HidalgoJ.et al (2020). Evaluating the effect of distance to different food subsidies on the trophic ecology of an opportunistic seabird species.J. Zool.31145–55. 10.1111/jzo.12759

  • CrossRef
  • Google Scholar