Ecology

Ilker, E. & Hinczewski, M. Modeling the growth of organisms validates a general relation between metabolic costs and natural selection. Phys. Rev. Lett. 122, 238101 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Boltaña, S. et al. Influences of thermal environment on fish growth. Ecol. Evol. 7, 6814–6825 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Rosenfeld, J., Richards, J., Allen, D., Van Leeuwen, T. & Monnet, G. Adaptive trade-offs in fish energetics and physiology: Insights from adaptive differentiation among juvenile salmonids. Can. J. Fish. Aquat. Sci. 77, 1243–1255 (2020).Article 

Google Scholar 
Robertson, D. R. & Collin, R. Inter- and intra-specific variation in egg size among reef fishes across the isthmus of Panama. Front. Ecol. Evol. 2, 84 (2015).Article 

Google Scholar 
Zueva, K. J., Lumme, J., Veselov, A. E., Kent, M. P. & Primmer, C. R. Genomic signatures of parasite-driven natural selection in north European Atlantic salmon (Salmo salar). Mar. Genom. 39, 26–38 (2018).Article 

Google Scholar 
Rajkov, J., El Taher, A., Böhne, A., Salzburger, W. & Egger, B. Gene expression remodelling and immune response during adaptive divergence in an African cichlid fish. Mol. Ecol. 30, 274–296 (2021).Article 
CAS 
PubMed 

Google Scholar 
Verhille, C. E. et al. Inter-population differences in salinity tolerance and osmoregulation of juvenile wild and hatchery-born Sacramento splittail. Conserv. Physiol. 4, 1–12 (2016).Article 

Google Scholar 
Froese, R. & Pauly, D. FishBase (version Feb 2018). In: Species 2000 & ITIS Catalogue of Life, 2019 Annual Checklist (Roskov Y. et al.). (2018). www.catalogueoflife.org/annual-checklist/2019. ISSN 2405–884X.Karås, P. & Klingsheim, V. Effects of temperature and salinity on embryonic development of turbot (Scophthalmus maximus L.) from the North Sea, and comparisons with Baltic populations. Helgolander Meeresuntersuchungen 51, 241–247 (1997).Article 
ADS 

Google Scholar 
Barbut, L. et al. How larval traits of six flatfish species impact connectivity. Limnol. Oceanogr. 64, 1150–1171 (2019).Article 
ADS 

Google Scholar 
Bouza, C., Presa, P., Castro, J., Sánchez, L. & Martínez, P. Allozyme and microsatellite diversity in natural and domestic populations of turbot (Scophthalmus maximus) in comparison with other Pleuronectiformes. Can. J. Fish. Aquat. Sci. 59, 1460–1473 (2002).Article 
CAS 

Google Scholar 
Nielsen, E. E., Nielsen, P. H., Meldrup, D. & Hansen, M. M. Genetic population structure of turbot (Scophthalmus maximus L.) supports the presence of multiple hybrid zones for marine fishes in the transition zone between the Baltic Sea and the North Sea. Mol. Ecol. 13, 585–595 (2004).Article 
PubMed 

Google Scholar 
Vandamme, S. G. et al. Regional environmental pressure influences population differentiation in turbot (Scophthalmus maximus). Mol. Ecol. 23, 618–636 (2014).Article 
CAS 
PubMed 

Google Scholar 
Vilas, R. et al. A genome scan for candidate genes involved in the adaptation of turbot (Scophthalmus maximus). Mar. Genom. 23, 77–86 (2015).Article 

Google Scholar 
Turan, C. et al. Genetics structure analysis of turbot (Scophthalmus maximus, Linnaeus, 1758) in the Black and Mediterranean Seas for application of innovative Management Strategies. Front. Mar. Sci. 6, 740 (2019).Article 

Google Scholar 
Ivanova, P. et al. Genetic diversity and morphological characterisation of three turbot (Scophthalmus maximus L., 1758) populations along the Bulgarian Black Sea coast. Nat. Conserv. 43, 123–146 (2021).Article 

Google Scholar 
do Prado, F. D. et al. Parallel evolution and adaptation to environmental factors in a marine flatfish: Implications for fisheries and aquaculture management of the turbot (Scophthalmus maximus). Evol. Appl. 11, 1322–1341 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
do Prado, F. D. et al. Tracing the genetic impact of farmed turbot Scophthalmus maximus on wild populations. Aquac. Environ. Interact. 10, 447–463 (2018).Article 

Google Scholar 
Robledo, D. et al. Integrating genomic resources of flatfish (Pleuronectiformes) to boost aquaculture production. Comp. Biochem. Physiol. Part D Genom. Proteom. 21, 41–55 (2017).CAS 

Google Scholar 
Sánchez-Molano, E. et al. Detection of growth-related QTL in turbot (Scophthalmus maximus). BMC Genomics 12, 473 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Rodríguez-Ramilo, S. T. et al. QTL detection for Aeromonas salmonicida resistance related traits in turbot (Scophthalmus maximus). BMC Genom. 12, 541 (2011).Article 

Google Scholar 
Robledo, D. et al. Integrative transcriptome, genome and quantitative trait loci resources identify single nucleotide polymorphisms in candidate genes for growth traits in turbot. Int. J. Mol. Sci. 17, 243 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Sciara, A. A. et al. Validation of growth-related quantitative trait loci markers in turbot (Scophthalmus maximus) families as a step toward marker assisted selection. Aquaculture 495, 602–610 (2018).Article 

Google Scholar 
Ma, A., Huang, Z., Wang, X. & Xu, Y. & Guo, X.,. Identification of quantitative trait loci associated with upper temperature tolerance in turbot, Scophthalmus maximus. Sci. Rep. 11, 1–12 (2021).Article 

Google Scholar 
Cui, W. et al. Comparative transcriptomic analysis reveals mechanisms of divergence in osmotic regulation of the turbot Scophthalmus maximus. Fish Physiol. Biochem. 46, 1519–1536 (2020).Article 
CAS 
PubMed 

Google Scholar 
Martínez, P. et al. Identification of the major sex-determining region of turbot (Scophthalmus maximus). Genetics 183, 1443–1452 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Martínez, P. et al. A genome-wide association study, supported by a new chromosome-level genome assembly, suggests sox2 as a main driver of the undifferentiatiated ZZ/ZW sex determination of turbot (Scophthalmus maximus). Genomics 113, 1705–1718 (2021).Article 
PubMed 

Google Scholar 
Martínez, P. et al. Turbot (Scophthalmus maximus) genomic resources:application for boosting aquaculture production. Genomics in Aquaculture (Elsevier Inc., 2016). https://doi.org/10.1016/B978-0-12-801418-9.00006-8.Saura, M. et al. Disentangling genetic variation for resistance and endurance to scuticociliatosis in turbot using pedigree and genomic information. Front. Genet. 10, 539 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Aramburu, O. et al. Genomic signatures after five generations of intensive selective breeding: Runs of homozygosity and genetic diversity in representative domestic and wild populations of turbot (Scophthalmus maximus). Front. Genet. 11, 1–14 (2020).Article 

Google Scholar 
Aramburu, O., Blanco, A., Bouza, C. & Martínez, P. Integration of host-pathogen functional genomics data into the chromosome-level genome assembly of turbot (Scophthalmus maximus). Aquaculture 564, 739067 (2023).Article 
CAS 

Google Scholar 
Saul, M. C., Philip, V. M., Reinholdt, L. G. & Chesler, E. J. High-diversity mouse populations for complex traits. Trends Genet. 35, 501–514 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moen, T. et al. Epithelial cadherin determines resistance to infectious pancreatic necrosis virus in Atlantic salmon. Genetics 200, 1313–1326 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Pavelin, J. et al. The nedd-8 activating enzyme gene underlies genetic resistance to infectious pancreatic necrosis virus in Atlantic salmon. Genomics 113, 3842–3850 (2021).Article 
CAS 
PubMed 

Google Scholar 
Barson, N. J. et al. Sex-dependent dominance at a single locus maintains variation in age at maturity in salmon. Nature 528, 405–408 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Chen, J. et al. Functional differences between TSHR alleles associate with variation in spawning season in Atlantic herring. Commun. Biol. 4, 795 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Imsland, A. K., Brix, O., Nævdal, G. & Samuelsen, E. N. Hemoglobin genotypes in turbot (Scophthalmus maximus Rafinesque), their oxygen affinity properties and relation with growth. Comp. Biochem. Physiol. A Physiol. 116, 157–165 (1997).Article 

Google Scholar 
Imsland, A. K., Foss, A., Stefansson, S. O. & Nævdal, G. Hemoglobin genotypes of turbot (Scophthalmus maximus): Consequences for growth and variations in optimal temperature for growth. Fish Physiol. Biochem. 23, 75–81 (2000).Article 
CAS 

Google Scholar 
Andersen, Ø., Rubiolo, J. A., De Rosa, M. C. & Martinez, P. The hemoglobin Gly16β1Asp polymorphism in turbot (Scophthalmus maximus) is differentially distributed across European populations. Fish Physiol. Biochem. 46, 2367–2376 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Torrisi, M., Pollastri, G. & Le, Q. Deep learning methods in protein structure prediction. Comput. Struct. Biotechnol. J. 18, 1301–1310 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
AlQuraishi, M. Machine learning in protein structure prediction. Curr. Opin. Chem. Biol. 65, 1–8 (2021).Article 
CAS 
PubMed 

Google Scholar 
Powder, K. E., Cousin, H., McLinden, G. P. & Craig Albertson, R. A nonsynonymous mutation in the transcriptional regulator lbh is associated with cichlid craniofacial adaptation and neural crest cell development. Mol. Biol. Evol. 31, 3113–3124 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lamichhaney, S. et al. Evolution of Darwin’s finches and their beaks revealed by genome sequencing. Nature 518, 371–375 (2015).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Gupta, A. M., Chakrabarti, J. & Mandal, S. Non-synonymous mutations of SARS-CoV-2 leads epitope loss and segregates its variants. Microbes Infect. 22, 598–607 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Verde, C. et al. Structure, function and molecular adaptations of haemoglobins of the polar cartilaginous fish Bathyraja eatonii and Raja hyperborea. Biochem. J. 389, 297–306 (2005).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pearce, R. & Zhang, Y. Toward the solution of the protein structure prediction problem. J. Biol. Chem. 297, 100870 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, Y. I-TASSER server for protein 3D structure prediction. BMC Bioinf. 9, 40 (2008).Article 

Google Scholar 
Pirolli, D. et al. Insights from molecular dynamics simulations: Structural basis for the V567D mutation-induced instability of zebrafish alpha-dystroglycan and comparison with the murine model. PLoS ONE 9, e103866 (2014).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Lee, J., Freddolino, P. L. & Zhang, Y. From Protein Structure to Function with Bioinformatics. In From Protein Structure to Function with Bioinformatics: Second Edition (ed. Rigden, D. J.) (2017). https://doi.org/10.1007/978-94-024-1069-3Baek, M. et al. Accurate prediction of protein structures and interactions using a 3-track neural network. Science 373, 871–876 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Castro, J. et al. Potential sources of error in parentage assessment of turbot (Scophthalmus maximus) using microsatellite loci. Aquaculture 242, 119–135 (2004).Article 
CAS 

Google Scholar 
Chen, S., Zhou, Y., Chen, Y. & Gu, J. Fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv ID 1303.3997v2 00, 1–3 (2013).Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 80–92 (2012).Article 
CAS 
PubMed 

Google Scholar 
Vera, M. et al. Development and validation of single nucleotide polymorphisms (SNPs) markers from two transcriptome 454-runs of turbot (Scophthalmus maximus) using high-throughput genotyping. Int. J. Mol. Sci. 14, 5694–5711 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ellis, J. A. & Ong, B. The MassARRAY® system for targeted SNP genotyping. Methods in molecular biology vol. 1492 (2017).Choi, Y. & Chan, A. P. PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31, 2745–2747 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Costello, M. J. Ecology of sea lice parasitic on farmed and wild fish. Trends Parasitol. 22, 475–483 (2006).Article 
PubMed 

Google Scholar 
Blanchet, S., Rey, O. & Loot, G. Evidence for host variation in parasite tolerance in a wild fish population. Evol. Ecol. 24, 1129–1139 (2010).Article 

Google Scholar 
Rousset, F. GENEPOP’007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).Article 
PubMed 

Google Scholar 
Foll, M. & Gaggiotti, O. A Genome-scan method to identify selected loci appropriate for both dominant and codominant markers: A bayesian perspective. Genetics 993, 977–993 (2008).Article 

Google Scholar 
Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).Article 
PubMed 

Google Scholar 
Narum, S. R. & Hess, J. E. Comparison of FST outlier tests for SNP loci under selection. Mol. Ecol. Resour. 11, 184–194 (2011).Article 
PubMed 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucl. Acids Res. 25, 3389–3402 (1997).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Romero, P. et al. Sequence complexity of disordered protein. Prot. Struct. Funct. Genet. 42, 38–48 (2001).Article 
CAS 

Google Scholar 
Jones, D. T. & Cozzetto, D. DISOPRED3: Precise disordered region predictions with annotated protein-binding activity. Bioinformatics 31, 857–863 (2015).Article 
CAS 
PubMed 

Google Scholar 
Mészáros, B., Erdös, G. & Dosztányi, Z. IUPred2A: Context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucl. Acids Res. 46, W329–W337 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Ishida, T. & Kinoshita, K. PrDOS: Prediction of disordered protein regions from amino acid sequence. Nucl. Acids Res. 35, W460-464 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Ito, N., Komiyama, N. H. & Fermi, G. Structure of deoxyhaemoglobin of the Anctartic fish Pagothenia bernacchi and structural basis of the root effect. J. Mol. Biol. https://doi.org/10.2210/pdb1hbh/pdb (1995).Article 
PubMed 

Google Scholar 
Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).Article 
PubMed 

Google Scholar 
Gou, X. et al. Whole-genome sequencing of six dog breeds from continuous altitudes reveals adaptation to high-altitude hypoxia. Genome Res. 24, 1308–1315 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Grossman, S. R. et al. Identifying recent adaptations in large-scale genomic data. Cell 152, 703–713 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Macpherson, J. M., Sella, G., Davis, J. C. & Petrov, D. A. Genomewide spatial correspondence between nonsynonymous divergence and neutral polymorphism reveals extensive adaptation in Drosophila. Genetics 177, 2083–2099 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Howe, D. G. et al. ZFIN, the Zebrafish model organism database: Increased support for mutants and transgenics. Nucl. Acids Res. 41, 854–860 (2013).Article 

Google Scholar 
Huber, C. D., Kim, B. Y., Marsden, C. D. & Lohmueller, K. E. Determining the factors driving selective effects of new nonsynonymous mutations. Proc. Natl. Acad. Sci. USA 114, 4465–4470 (2017).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stenson, P. D. et al. The Human Gene Mutation Database (HGMD®): Optimizing its use in a clinical diagnostic or research setting. Hum. Genet. 139, 1197–1207 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Naruse, K., Hori, H., Shimizu, N., Kohara, Y. & Takeda, H. Medaka genomics: A bridge between mutant phenotype and gene function. Mech. Dev. 121, 619–628 (2004).Article 
CAS 
PubMed 

Google Scholar 
Chintalapati, M. & Moorjani, P. Evolution of the mutation rate across primates. Curr. Opin. Genet. Dev. 62, 58–64 (2020).Article 
CAS 
PubMed 

Google Scholar 
Rodin, R. E. et al. The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing. Nat. Neurosci. 24, 176–185 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cayuela, H. et al. Thermal adaptation rather than demographic history drives genetic structure inferred by copy number variants in a marine fish. Mol. Ecol. 30, 1624–1641 (2021).Article 
CAS 
PubMed 

Google Scholar 
Kess, T. et al. A putative structural variant and environmental variation associated with genomic divergence across the Northwest Atlantic in Atlantic Halibut. ICES J. Mar. Sci. 78, 2371–2384 (2021).Article 

Google Scholar 
Le Moan, A., Bekkevold, D. & Hemmer-Hansen, J. Evolution at two time frames: ancient structural variants involved in post-glacial divergence of the European plaice (Pleuronectes platessa). Heredity (Edinb). 126, 668–683 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ruigrok, M. et al. The relative power of structural genomic variation versus SNPs in explaining the quantitative trait growth in the marine teleost Chrysophrys auratus. Genes (Basel). 13, 1129 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
De la Herran, R. et al. A chromosome-level genome assembly enables the identification of the follicle stimulating hormone receptor as the master sex determining gene in Solea senegalensis. Mol. Ecol. Resour. 00, 1–19 (2023).
Google Scholar 
Harrison, P. W. et al. The FAANG data portal: Global, open-access, “FAIR”, and richly validated genotype to phenotype data for high-quality functional annotation of animal genomes. Front. Genet. 12, 639238 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Figueras, A. et al. Whole genome sequencing of turbot (Scophthalmus maximus; Pleuronectiformes): A fish adapted to demersal life. DNA Res. 23, 181–192 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moore, J. S. et al. Conservation genomics of anadromous Atlantic salmon across its North American range: Outlier loci identify the same patterns of population structure as neutral loci. Mol. Ecol. 23, 5680–5697 (2014).Article 
CAS 
PubMed 

Google Scholar 
Barrio, A. M. et al. The genetic basis for ecological adaptation of the Atlantic herring revealed by genome sequencing. Elife 5, e12081 (2016).Article 

Google Scholar 
Pettersson, M. E. et al. A chromosome-level assembly of the Atlantic herring genome-detection of a supergene and other signals of selection. Genome Res. 29, 1919–1928 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bo, J. et al. Opah (Lampris megalopsis) genome sheds light on the evolution of aquatic endothermy. Zool. Res. 43, 26–29 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, S. et al. Resequencing and SNP discovery of Amur ide (Leuciscus waleckii) provides insights into local adaptations to extreme environments. Sci. Rep. 11, 5064 (2021).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Meng, Z., Hu, P., Lei, J. & Jia, Y. Expression of insulin-like growth factors at mRNA levels during the metamorphic development of turbot (Scophthalmus maximus). Gen. Comp. Endocrinol. 235, 11–17 (2016).Article 
CAS 
PubMed 

Google Scholar 
Duan, C., Ren, H. & Gao, S. Insulin-like growth factors (IGFs), IGF receptors, and IGF-binding proteins: Roles in skeletal muscle growth and differentiation. Gen. Comp. Endocrinol. 167, 344–351 (2010).Article 
CAS 
PubMed 

Google Scholar 
Duan, C., Ding, J., Li, Q., Tsai, W. & Pozios, K. Insulin-like growth factor binding protein 2 is a growth inhibitory protein conserved in zebrafish. Proc. Natl. Acad. Sci. USA 96, 15274–15279 (1999).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Furqon, A., Gunawan, A., Ulupi, N., Suryati, T. & Sumantri, C. A Polymorphism of Insulin-like growth factor binding protein 2 gene associated with growth and body composition traits in Kampong Chickens. J. Vet. 19, 183 (2018).
Google Scholar 
Kibbey, M. M., Jameson, M. J., Eaton, E. M. & Rosenzweig, S. A. Insulin-like growth factor binding protein-2: Contributions of the C-terminal domain to insulin-like growth factor-1 binding. Mol. Pharmacol. 69, 833–845 (2006).Article 
CAS 
PubMed 

Google Scholar 
Coughlan, J. P. et al. Microsatellite DNA variation in wild populations and farmed strains of turbot from Ireland and Norway: A preliminary study. J. Fish Biol. 52, 916–922 (1998).Article 
CAS 

Google Scholar 
Zhang, H. et al. Characterization and Identification of Single Nucleotide Polymorphism within the IGF-1R gene associated with growth traits of Odontobutis potamophila. J. World Aquac. Soc. 49, 366–379 (2018).Article 
CAS 

Google Scholar 
Guo, L., Yang, S., Li, M. M., Meng, Z. N. & Lin, H. R. 2016) Divergence and polymorphism analysis of IGF1Ra and IGF1Rb from orange-spotted grouper, Epinephelus coioides (Hamilton). Genet. Mol. Res. 15, 1. https://doi.org/10.4238/gmr15048768 (2016).Article 
CAS 

Google Scholar 
Yu, X. et al. Genome-wide association analysis of adaptation to oxygen stress in Nile tilapia (Oreochromis niloticus). BMC Genomics 22, 426 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Harano, T. et al. Hemoglobin Kawachi [α44 (CE2) Pro → Arg]: A new hemoglobin variant of high oxygen affinity with amino acid substitution at α1β2 contact. Hemoglobin 6, 43–49 (1982).Article 
CAS 
PubMed 

Google Scholar 
Alharby, E. et al. A homozygous potentially pathogenic variant in the PAXBP1 gene in a large family with global developmental delay and myopathic hypotonia. Clin. Genet. 92, 579–586 (2017).Article 
CAS 
PubMed 

Google Scholar 
Ceinos, R. M. et al. Differential circadian and light-driven rhythmicity of clock gene expression and behaviour in the turbot, Scophthalmus maximus. PLoS ONE 14, e0219153 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nishiwaki-Ohkawa, T. & Yoshimura, T. Molecular basis for regulating seasonal reproduction in vertebrates. J. Endocrinol. 229, R117–R127 (2016).Article 
CAS 
PubMed 

Google Scholar 
Wood, S. H. et al. Circadian clock mechanism driving mammalian photoperiodism. Nat. Commun. 11, 4291 (2020).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Piovesan, D. et al. DisProt 7.0: A major update of the database of disordered proteins. Nucl. Acids Res. 45, 219–227 (2017).Article 

Google Scholar 
Pajkos, M. & Dosztányi, Z. Chapter Two – Functions of intrinsically disordered proteins through evolutionary lenses. in Dancing Protein Clouds: Intrinsically Disordered Proteins in the Norm and Pathology, Part C (ed. Uversky, V. N. B. T.-P. in M. B. and T. S.) vol. 183 45–74 (Academic Press, 2021).Malagrinò, F. et al. Understanding the binding induced folding of intrinsically disordered proteins by protein engineering: Caveats and pitfalls. Int. J. Mol. Sci. 21, 3484 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Doyle, A., Cowan, M. E., Migaud, H., Wright, P. J. & Davie, A. Neuroendocrine regulation of reproduction in Atlantic cod (Gadus morhua): Evidence of Eya3 as an integrator of photoperiodic cues and nutritional regulation to initiate sexual maturation. Comput. Biochem. Physiol. -Part A Mol. Integr. Physiol. 260, 111000 (2021).Silver, S. J., Davies, E. L., Doyon, L. & Rebay, I. Functional dissection of eyes absent reveals new modes of regulation within the retinal determination gene network. Mol. Cell. Biol. 23, 5989–5999 (2003).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jin, M. & Mardon, G. Distinct biochemical activities of eyes absent during drosophila eye development. Sci. Rep. 6, 23228 (2016).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McGowan, K. L., Passow, C. N., Arias-Rodriguez, L., Tobler, M. & Kelley, J. L. Expression analyses of cave mollies (Poecilia mexicana) reveal key genes involved in the early evolution of eye regression. Biol. Lett. 15, 20190554 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cui, W. et al. Transcriptomic analysis reveals putative osmoregulation mechanisms in the kidney of euryhaline turbot Scophthalmus maximus responded to hypo-saline seawater. J. Oceanol. Limnol. 38, 467–479 (2020).Article 
CAS 

Google Scholar 
Mármol-Sánchez, E., Quintanilla, R., Cardoso, T. F., Jordana Vidal, J. & Amills, M. Polymorphisms of the cryptochrome 2 and mitoguardin 2 genes are associated with the variation of lipid-related traits in Duroc pigs. Sci. Rep. 9, 9025 (2019).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Takvam, M., Wood, C. M., Kryvi, H. & Nilsen, T. O. Ion transporters and osmoregulation in the didney of teleost fishes as a function of salinity. Front. Physiol. 12, 664588 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Engelund, M. B. & Madsen, S. S. The role of aquaporins in the kidney of euryhaline teleosts. Front. Physiol. 2, 51 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nam, B. H. et al. Identification and characterization of the prepro-vasoactive intestinal peptide gene from the teleost Paralichthys olivaceus. Vet. Immunol. Immunopathol. 127, 249–258 (2009).Article 
CAS 
PubMed 

Google Scholar 
Paladini, F. et al. Age-dependent association of idiopathic achalasia with vasoactive intestinal peptide receptor 1 gene. Neurogastroenterol. Motil. 21, 597–602 (2009).Article 
CAS 
PubMed 

Google Scholar 
Hosseinpour, L., Nikbin, S., Hedayat-Evrigh, N. & Elyasi-Zarringhabaie, G. Association of polymorphisms of vasoactive intestinal peptide and its receptor with reproductive traits of turkey hens. South Afr. J. Anim. Sci. 50, 345–352 (2020).Article 
CAS 

Google Scholar 
Pereiro, P., Figueras, A. & Novoa, B. A novel hepcidin-like in turbot (Scophthalmus maximus L.) highly expressed after pathogen challenge but not after iron overload. Fish Shellfish Immunol. 32, 879–889 (2012).Article 
CAS 
PubMed 

Google Scholar 
Zhang, J., Yu, L., Ping, L., Fei, M. & Sun, L. Turbot (Scophthalmus maximus) hepcidin-1 and hepcidin-2 possess antimicrobial activity and promote resistance against bacterial and viral infection. Fish Shellfish Immunol. 38, 127–134 (2014).Article 
PubMed 

Google Scholar  More

Plankton are microscopic organisms at the base of aquatic food webs and therefore essential to all life on Earth. In our view, international legal protection of plankton is urgently needed because of their high susceptibility to the effects of climate change, including ocean warming and acidification.
Competing Interests
The authors declare no competing interests. More

Commensal rodents serve as important reservoirs of rodent-borne pathogens. Efforts to reduce the risk of pathogen transmission include decimating rodent populations, altering access pathways, upholding good waste management practices and denying easy access to food sources. In our study, we examined the incidence of rodent activity in waste collection premises in public residential estates in Singapore and examined the factors associated with rodent activity to inform the priority of rodent control measures of resource limited municipal estate managers.Of the three types of waste collection premises, rodent activity had the highest incidence in refuse bin centres followed by CRCs and IRC bin chambers. Refuse bin centres are prone to refuse spillage because refuse is manually transferred from refuse collection carts into bulk bins and refuse compactors located within the centres. Bin centres tend to be larger than CRCs and IRC bin chambers and the storage of bulky waste that provide additional areas of rodent harbourage are a common sight in Singapore. IRC chambers and refuse bin centres in combination far outnumber CRCs, and the former two are a distinct characteristic of older public housing estates in Singapore. This suggests that older public housing estates have a higher propensity for rodent infestation compared to newer ones. Aging infrastructure can also provide a greater number of harbourage areas and alternate access pathways for rodent travel that increase their ability to obtain food sources. Our study findings were in support of previous studies which found that older infrastructure was associated with a greater likelihood of rodent activity22,23.We also found that the number of IRC bin chutes was positively associated with rodent infestation. Fluids from food waste in IRC bin chambers are drained directly into a sanitary line that is common to all other bin chambers within the same building. A possible explanation therefore is that rodents which find their way into the sanitary line can easily access all bin chambers in the same building. This suggests that preventing individual bin chamber access may reduce food availability to rodents which traverse the sanitary line in search of food sources.In the present study, we observed that rodent sightings were relatively higher in some months in the first half of the calendar year compared to the second half. Even though our estimates were positive, those for some months were not statistically significant. In Singapore, end-December, January to February are usually associated with increased food production due to the year-end (Christmas and New Year celebrations) and early-year (Chinese New Year) festivities. A proportionate increase in food waste over that period could improve survivability of rodents that leads to increased mating and reproduction. We therefore postulate that the higher seasonal rodent activity is plausible but recommend that future studies be conducted with sufficient longitude to examine the differences in the seasonal pattern across the three categories of premises more closely. A previous study in Harbin, China27 reported a seasonal pattern in the age composition of R. norvegicus while an ecological study on R. norvegicus in Salvador Brazil did not find any difference in the number of rats trapped between the dry and rainy seasons28. The inconsistent seasonal findings between studies could be due to the differences in the climate, degree of urbanization and environmental conditions of study locations.The relative rise in rodent activity in the first half of the year coupled with older estates being at greater risk of rodent activity suggest that municipal town councils which prioritize regular infrastructural repairs and improvements in older estates and complete them in the second half of each calendar year would help mitigate the anticipated rise in the first half of the new calendar year.In our study, we examined the relations between visual cues and rodent activity to help estate managers prioritise their control efforts. We found that rodent droppings were a common positive predictor of rodent activity across all three categories of waste collection premises. In particular, the odds of droppings in IRC bin chambers were the highest among the three categories of premises. We hypothesize that the probability of rodent dropping sightings was in part related to the accessibility of food waste and thus time spent by rodents within the respective waste collection premises. Each IRC chamber contains an open top bin that receives waste that is disposed down the IRC chamber chute. Food waste in IRC bin chambers are thus more easily accessed by rodents compared to in CRCs where waste is stored in a compactor and in bin centres where bulk bins are covered until the waste is compacted or collected.In Salvador, Brazil, the presence of Rattus norvegicus droppings were independently associated with an increased risk of Leptospira infection in humans29. Further research on site-specific Leptospira infection risks in Singapore are required to affirm the utility of droppings as an indicator for Leptospira infection risk. In addition, rub marks and gnaw marks were also positive predictors of rodent activity in CRCs and IRC bin chambers. A study in Chile reported that gnaw marks and holes, as well as grease or rub marks left behind by rodent travel were indicators of rodent activity30. A previous study carried out in an urban city in Taiwan reported that rodent droppings and rub marks were well correlated with rodent infestation31. Our findings, which were in support of these previous studies, suggest that estate managers can maximise the cost effectiveness of their resources by focusing their control efforts based on visual cues without relying solely on trapping activities for surveillance.We found a positive relationship between the number of rodent burrows and rodent activity in all three waste collection premises, though this was only significant for refuse bin centres. That the direction of effect for burrows was consistent these three premises, was a reassuring observation. It is possible that we did not have enough study power to establish the observed positive relations in CRCs and IRC bin chambers. Therefore future studies should seek to confirm our findings. R. norvegicus excavate extensive burrow systems that are able to house a large number of rats32. They exhibit a strong preference for creating burrows in loose soil and on sloping terrain33 and construct shallow burrows in close proximity to water bodies and food sources34. As rodent burrows are primarily used for nesting, food storage and harbourage purposes35, burrows can provide important information about the extent of rodent activity in an area and may be used as an indicator for estate managers to focus their investigations.A previous study in New York, United States found that the presence of numerous restaurants, or having older infrastructure were associated with increased levels of R. norvegicus22. Unexpectedly, we did not find any evidence that the number of dining establishments was associated with rodent activity. However, instances of rodent activity have been reported in food establishments in Singapore36,37,38. We hypothesize that rodent movement is restricted to the surrounding area of the food establishments due to the plethora of food available, with little reason for rodents to venture into waste collection premises. Future studies examining the relationship between rodent activity in food establishments and waste collection premises are required to confirm this.In our study, the presence of gnaw marks (aOR: 5.61), rub marks (aOR: 5.04) in CRCs and rodent droppings in CRCs (aOR: 6.20), IRC bin chambers (aOR: 90.84) and bin centres (aOR: 3.61) had the largest strengths of association with rodent activity. Comparatively, in a study in Johannesburg, South Africa, predictors such as dampness (aOR: 2.54) and cracks (aOR: 1.92) in homes had relatively smaller effects on rodent activity20, while a study in Salvador, Brazil found relatively larger effects of homes with dilapidated fences and walls (aOR: 8.95) and those built on earthen slopes (aOR: 4.95)21. This suggests that rodent activity can be strongly influenced by site- and setting-specific factors, and supports the body of evidence on the strong adaptability of rodents in our urban environment”.Urban environments have the capacity to alter the biology of the pathogens, hosts and vectors, which can influence disease transmission39. The proximate setting of dense urban environments allows for close contact between humans and synanthropic rodents, thereby increasing the transmission risk of zoonotic diseases4. In addition to causing diseases in human populations, urban rats are also known to compromise food safety, damage infrastructure and cause mental health distress25,40. The responsibility of rodent control in residential estates is important but may be one among many other competing public health and estate management responsibilities that municipal town councils have to undertake. Consequently, estate managers have to prioritize their limited resources in order to maximise the cost effectiveness of their resource allocation choices. Based on our study findings, we recommend that estate managers adopt a risk-based approach in vector control resource allocation in waste collection premises according to infrastructural age and visual cues for rodent activity.IRC bin chambers which are a distinct feature of the oldest residential buildings, were observed with a substantially higher odds of rodent activity compared to the other categories of waste collection premises. This suggests that rodent control resource allocation should be prioritized in older residential estates. The clear seasonal pattern of rodent activity in CRCs suggests that estate managers can increase their rodent control activities thereat in the first half of the year. Finally, easy access to food waste directly increases the probability of survival and consequently the rodent population size. Future research should examine the quality of municipal solid waste management and the waste processing flow in residential estates to determine how rodent access to food waste can be further minimized to reduce the population of rodents.Study strengths and limitationsWe analysed data from all public residential estates in Singapore; our findings are thus generalizable at the national level. The use of outcomes and independent measures from individual waste collection premises over multiple cycles of inspection provided stronger evidence for causal inferences. We analysed data over 12 months to account for within-year variations that could influence the outcome measure. Rodents were visually identified without molecular speciation because no trapping was carried out. Though the majority of rodents were observed to be Rattus norvegicus, which is the most common species of rodents in public housing estates in Singapore, we could not rule out misclassification of rodents. However, our findings remain relevant for municipal authorities seeking to prioritize resources for vector control in waste collection premises under their care. More

Falkowski, P. The power of plankton. Nature 483, 17–20 (2012).Article 

Google Scholar 
Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: Integrating terrestrial and oceanic components. Science 281, 237 (1998).Article 
CAS 
PubMed 

Google Scholar 
Grossart, H. P. & Simon, M. Significance of limnetic organic aggregates (lake snow) for the sinking flux of particulate organic matter in a large lake. Aquat. Microb. Ecol. 15, 115–125 (1998).Article 

Google Scholar 
Weyhenmeyer, G. A. & Bloesch, J. The pattern of particle flux variability in Swedish and Swiss lakes. Sci. Total Environ. 266, 69–78 (2001).Article 
CAS 
PubMed 

Google Scholar 
Fender, C. K. et al. Investigating particle size-flux relationships and the biological pump across a range of plankton ecosystem states from coastal to oligotrophic. Front. Marine Sci. 6, https://doi.org/10.3389/fmars.2019.00603 (2019).Iversen, M. H., Nowald, N., Ploug, H., Jackson, G. A. & Fischer, G. High resolution profiles of vertical particulate organic matter export off Cape Blanc, Mauritania: Degradation processes and ballasting effects. Deep-Sea Res. Pt I Oceanogr. Res. Papers. 57, 771–784 (2010).Article 
CAS 

Google Scholar 
Griffiths, J. R. et al. The importance of benthic–pelagic coupling for marine ecosystem functioning in a changing world. Glob. Change Biol. 23, 2179–2196 (2017).Article 

Google Scholar 
Diaz, R. J. & Rosenberg, R. Spreading dead zones and consequences for marine ecosystems. Science 321, 926–929 (2008).Article 
CAS 
PubMed 

Google Scholar 
Jenny, J. P. et al. Global spread of hypoxia in freshwater ecosystems during the last three centuries is caused by rising local human pressure. Glob. Chang Biol. 22, 1481–1489 (2016).Article 
PubMed 

Google Scholar 
Carstensen, J., Andersen, J. H., Gustafsson, B. G. & Conley, D. J. Deoxygenation of the Baltic Sea during the last century. Proc. Natl Acad. Sci. 111, 5628–5633 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Simon, M., Grossart, H. P., Schweitzer, B. & Ploug, H. Microbial ecology of organic aggregates in aquatic ecosystems. Aquat. Microb. Ecol. 28, 175–211 (2002).Article 

Google Scholar 
Burd, A. B. & Jackson, G. A. Particle aggregation. Ann. Rev. Mar. Sci. 1, 65–90 (2009).Article 
PubMed 

Google Scholar 
Kiørboe, T., Lundsgaard, C., Olesen, M. & Hansen, J. L. S. Aggregation and sedimentation processes during a spring phytoplankton bloom: A field experiment to test coagulation theory. J. Mar. Res. 52, 297–323 (1994).Article 

Google Scholar 
Boyd, P. W. & Trull, T. W. Understanding the export of biogenic particles in oceanic waters: Is there consensus? Prog. Oceanogr. 72, 276–312 (2007).Article 

Google Scholar 
Legendre, L. & Rivkin, R. B. Fluxes of carbon in the upper ocean: regulation by food-web control nodes. Mar. Ecol. Prog. Ser. 242, 95–109 (2002).Article 

Google Scholar 
Kaneko, H. et al. Eukaryotic virus composition can predict the efficiency of carbon export in the global ocean. iScience 24, 102002 (2021).Article 
CAS 
PubMed 

Google Scholar 
Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 532, 465 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Grossart, H.-P. et al. Fungi in aquatic ecosystems. Nat. Rev. Microbiol. 17, 339–354 (2019).Article 
CAS 
PubMed 

Google Scholar 
Amend, A. et al. Fungi in the marine environment: Open questions and unsolved problems. mBio 10, e01189–01118 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ortiz-Álvarez, R., Triadó-Margarit, X., Camarero, L., Casamayor, E. O. & Catalan, J. High planktonic diversity in mountain lakes contains similar contributions of autotrophic, heterotrophic and parasitic eukaryotic life forms. Sci. Rep. 8, 4457 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Gutiérrez, M. H., Pantoja, S., Tejos, E. & Quiñones, R. A. The role of fungi in processing marine organic matter in the upwelling ecosystem off Chile. Mar. Biol. 158, 205–219 (2011).Article 

Google Scholar 
Edgcomb, V. P., Beaudoin, D., Gast, R., Biddle, J. F. & Teske, A. Marine subsurface eukaryotes: The fungal majority. Environ. Microbiol. 13, 172–183 (2011).Article 
CAS 
PubMed 

Google Scholar 
Frenken, T. et al. Integrating chytrid fungal parasites into plankton ecology: research gaps and needs. Environ. Microbiol. 19, 3802–3822 (2017).Article 
PubMed 

Google Scholar 
Van den Wyngaert, S. et al. Seasonality of parasitic and saprotrophic zoosporic fungi: linking sequence data to ecological traits. ISME J. 16, 2242–2254 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Gsell, A. S. et al. Long-term trends and seasonal variation in host density, temperature, and nutrients differentially affect chytrid fungi parasitising lake phytoplankton. Freshwat. Biol. https://doi.org/10.1111/fwb.13958 (2022).Gutiérrez, M. H., Jara, A. M. & Pantoja, S. Fungal parasites infect marine diatoms in the upwelling ecosystem of the Humboldt current system off central Chile. Environ. Microbiol. 18, 1646–1653 (2016).Article 
PubMed 

Google Scholar 
Kilias, E. S. et al. Chytrid fungi distribution and co-occurrence with diatoms correlate with sea ice melt in the Arctic Ocean. Commun. Biol. 3, 183 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hassett, B. T., Ducluzeau, A. L. L., Collins, R. E. & Gradinger, R. Spatial distribution of aquatic marine fungi across the western Arctic and sub-arctic. Environ. Microbiol. 19, 475–484 (2017).Article 
CAS 
PubMed 

Google Scholar 
Lepelletier, F. et al. Dinomyces arenysensis gen. et sp. nov. (Rhizophydiales, Dinomycetaceae fam. nov.), a chytrid infecting marine dinoflagellates. Protist 165, 230–244 (2014).Article 
PubMed 

Google Scholar 
Hassett, B. T. & Gradinger, R. Chytrids dominate arctic marine fungal communities. Environ. Microbiol. 18, 2001–2009 (2016).Article 
CAS 
PubMed 

Google Scholar 
Le Calvez, T., Burgaud, G., Mahé, S., Barbier, G. & Vandenkoornhuyse, P. Fungal diversity in deep-sea hydrothermal ecosystems. Appl. Environ. Microbiol. 75, 6415–6421 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Richards, T. A. et al. Molecular diversity and distribution of marine fungi across 130 european environmental samples. Proc. R. Soc. B Biol. Sci. 282, 20152243 (2015).Article 

Google Scholar 
Taylor, J. D. & Cunliffe, M. Multi-year assessment of coastal planktonic fungi reveals environmental drivers of diversity and abundance. ISME J. 10, 2118–2128 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Comeau, A. M., Vincent, W. F., Bernier, L. & Lovejoy, C. Novel chytrid lineages dominate fungal sequences in diverse marine and freshwater habitats. Sci. Rep. 6, 30120 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, Y., Sen, B., He, Y., Xie, N. & Wang, G. Spatiotemporal distribution and assemblages of planktonic fungi in the coastal waters of the Bohai Sea. Front. Microbiol. 9, 584 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Gao, Z., Johnson, Z. I. & Wang, G. Molecular characterization of the spatial diversity and novel lineages of mycoplankton in Hawaiian coastal waters. ISME J. 4, 111–120 (2009).Article 
PubMed 

Google Scholar 
Duan, Y. et al. A high-resolution time series reveals distinct seasonal patterns of planktonic fungi at a temperate coastal ocean site (Beaufort, North Carolina, USA). Appl. Environ. Microbiol. 84, e00967–00918 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cleary, A. C., Søreide, J. E., Freese, D., Niehoff, B. & Gabrielsen, T. M. Feeding by Calanus glacialis in a high arctic fjord: Potential seasonal importance of alternative prey. ICES J. Mar. Sci. 74, 1937–1946 (2017).Article 

Google Scholar 
Renaud, P. E., Morata, N., Carroll, M. L., Denisenko, S. G. & Reigstad, M. Pelagic–benthic coupling in the western Barents Sea: Processes and time scales. Deep Sea Res. Part II: Topical Stud. Oceanogr. 55, 2372–2380 (2008).Article 
CAS 

Google Scholar 
Lepère, C., Ostrowski, M., Hartmann, M., Zubkov, M. V. & Scanlan, D. J. In situ associations between marine photosynthetic picoeukaryotes and potential parasites – a role for fungi? Environ. Microbiol. Rep. 8, 445–451 (2016).Article 
PubMed 

Google Scholar 
Kagami, M., Gurung, T. B., Yoshida, T. & Urabe, J. To sink or to be lysed? Contrasting fate of two large phytoplankton species in Lake Biwa. Limnol. Oceanogr. 51, 2775–2786 (2006).Article 

Google Scholar 
Gerphagnon, M., Colombet, J., Latour, D. & Sime-Ngando, T. Spatial and temporal changes of parasitic chytrids of cyanobacteria. Sci. Rep. 7, 6056 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Ibelings, B. W. et al. Chytrid infections and diatom spring blooms: Paradoxical effects of climate warming on fungal epidemics in lakes. Freshwat. Biol. 56, 754–766 (2011).Article 

Google Scholar 
Gsell, A. S. et al. Spatiotemporal variation in the distribution of chytrid parasites in diatom host populations. Freshwat. Biol. 58, 523–537 (2013).Article 

Google Scholar 
Grami, B. et al. Functional effects of parasites on food web properties during the spring diatom bloom in Lake Pavin: A linear inverse modeling analysis. PLOS ONE. 6, e23273 (2011).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Klawonn, I. et al. Characterizing the “fungal shunt”: Parasitic fungi on diatoms affect carbon flow and bacterial communities in aquatic microbial food webs. Proc. Natl Acad. Sci. 118, e2102225118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kagami, M., Miki, T. & Takimoto, G. Mycoloop: Chytrids in aquatic food webs. Front. Microbiol. 5, 166 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Laundon, D. & Cunliffe, M. A call for a better understanding of aquatic chytrid biology. Front. Fungal Biol. 2, https://doi.org/10.3389/ffunb.2021.708813 (2021).Ploug, H., Iversen, M. H. & Fischer, G. Ballast, sinking velocity, and apparent diffusivity within marine snow and zooplankton fecal pellets: Implications for substrate turnover by attached bacteria. Limnol. Oceanogr. 53, 1878–1886 (2008).Article 

Google Scholar 
Laurenceau-Cornec, E. C., Trull, T. W., Davies, D. M., De La Rocha, C. L. & Blain, S. Phytoplankton morphology controls on marine snow sinking velocity. Mar. Ecol. Prog. Ser. 520, 35–56 (2015).Article 

Google Scholar 
Tréguer, P. et al. Influence of diatom diversity on the ocean biological carbon pump. Nat. Geosci. 11, 27–37 (2018).Article 

Google Scholar 
Alldredge, A. L., Gotschalk, C., Passow, U. & Riebesell, U. Mass aggregation of diatom blooms: Insights from a mesocosm study. Deep Sea Res. Part II: Topical Stud. Oceanogr. 42, 9–27 (1995).Article 
CAS 

Google Scholar 
Seto, K., Van den Wyngaert, S., Degawa, Y. & Kagami, M. Taxonomic revision of the genus Zygorhizidium: Zygorhizidiales and Zygophlyctidales ord. nov. (Chytridiomycetes, Chytridiomycota). Fungal Syst. Evol. 5, 17–38 (2020).Article 
CAS 
PubMed 

Google Scholar 
Engel, A. in Practical Guidelines for the Analysis of Seawater (eds Wurl O & Raton B) (CRC Press, 2009).Cisternas-Novoa, C., Lee, C. & Engel, A. A semi-quantitative spectrophotometric, dye-binding assay for determination of Coomassie Blue stainable particles. Limnol. Oceanogr. Methods. 12, 604–616 (2014).Article 

Google Scholar 
Passow, U. & Alldredge, A. L. A dye-binding assay for the spectrophotometric measurement of transparent exopolymer particles (TEP). Limnol. Oceanogr. 40, 1326–1335 (1995).Article 
CAS 

Google Scholar 
Iversen, M. H. & Ploug, H. Temperature effects on carbon-specific respiration rate and sinking velocity of diatom aggregates – potential implications for deep ocean export processes. Biogeosciences 10, 4073–4085 (2013).Article 

Google Scholar 
van der Jagt, H., Friese, C., Stuut, J.-B. W., Fischer, G. & Iversen, M. H. The ballasting effect of Saharan dust deposition on aggregate dynamics and carbon export: Aggregation, settling, and scavenging potential of marine snow. Limnol. Oceanogr. 63, 1386–1394 (2018).Article 

Google Scholar 
Grossart, H. P. & Ploug, H. Microbial degradation of organic carbon and nitrogen on diatom aggregates. Limnol. Oceanogr. 46, 267–277 (2001).Article 
CAS 

Google Scholar 
Iversen, M. H. & Ploug, H. Ballast minerals and the sinking carbon flux in the ocean: Carbon-specific respiration rates and sinking velocity of marine snow aggregates. Biogeosciences 7, 2613–2624 (2010).Article 
CAS 

Google Scholar 
Ploug, H. & Grossart, H. P. Bacterial growth and grazing on diatom aggregates: Respiratory carbon turnover as a function of aggregate size and sinking velocity. Limnol. Oceanogr. 45, 1467–1475 (2000).Article 
CAS 

Google Scholar 
Belcher, A. et al. Depth-resolved particle-associated microbial respiration in the northeast Atlantic. Biogeosciences 13, 4927–4943 (2016).Article 

Google Scholar 
Ploug, H., Grossart, H. P., Azam, F. & Jørgensen, B. B. Photosynthesis, respiration, and carbon turnover in sinking marine snow from surface waters of Southern California Bight: Implications for the carbon cycle in the ocean. Mar. Ecol. Prog. Ser. 179, 1–11 (1999).Article 
CAS 

Google Scholar 
Turner, J. T. Zooplankton fecal pellets, marine snow, phytodetritus and the ocean’s biological pump. Prog. Oceanogr. 130, 205–248 (2015).Article 

Google Scholar 
Nguyen, T. T. H. et al. Microbes contribute to setting the ocean carbon flux by altering the fate of sinking particulates. Nat. Commun. 13, 1657 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zakem, E. J., Cael, B. B. & Levine, N. M. A unified theory for organic matter accumulation. Proc. Natl Acad. Sci. 118, e2016896118 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Alcolombri, U. et al. Sinking enhances the degradation of organic particles by marine bacteria. Nat. Geosci. 14, 775–780 (2021).Article 
CAS 

Google Scholar 
Buesseler, K. O. & Boyd, P. W. Shedding light on processes that control particle export and flux attenuation in the twilight zone of the open ocean. Limnol. Oceanogr. 54, 1210–1232 (2009).Article 
CAS 

Google Scholar 
Henson, S., Le Moigne, F. & Giering, S. Drivers of carbon export efficiency in the global ocean. Glob. Biogeochem. Cycles. 33, 891–903 (2019).Article 
CAS 

Google Scholar 
Gsell, A. S., De Senerpont Domis, L. N., Verhoeven, K. J. F., Van Donk, E. & Ibelings, B. W. Chytrid epidemics may increase genetic diversity of a diatom spring-bloom. ISME J. 7, 2057–2059 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Agha, R., Saebelfeld, M., Manthey, C., Rohrlack, T. & Wolinska, J. Chytrid parasitism facilitates trophic transfer between bloom-forming cyanobacteria and zooplankton (Daphnia). Sci. Rep. 6, 35039 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rasconi, S. et al. Parasitic chytrids upgrade and convey primary produced carbon during inedible algae proliferation. Protist 171, 125768 (2020).Article 
CAS 
PubMed 

Google Scholar 
Guidi, L. et al. Effects of phytoplankton community on production, size, and export of large aggregates: A world-ocean analysis. Limnol. Oceanogr. 54, 1951–1963 (2009).Article 

Google Scholar 
Boyd, P. W. & Newton, P. P. Does planktonic community structure determine downward particulate organic carbon flux in different oceanic provinces?. Deep-Sea Res. Pt I Oceanogr. Res. Papers. 46, 63–91 (1999).Article 
CAS 

Google Scholar 
van der Jagt, H., Wiedmann, I., Hildebrandt, N., Niehoff, B. & Iversen, M. H. Aggregate feeding by the copepods Calanus and Pseudocalanus controls carbon flux attenuation in the arctic shelf sea during the productive period. Front. Mar. Sci. 7, 543124 (2020).Article 

Google Scholar 
Steinberg, D. K. et al. Bacterial vs. zooplankton control of sinking particle flux in the ocean’s twilight zone. Limnol. Oceanogr. 53, 1327–1338 (2008).Article 

Google Scholar 
Cavan, E. L., Henson, S. A., Belcher, A. & Sanders, R. Role of zooplankton in determining the efficiency of the biological carbon pump. Biogeosciences 14, 177–186 (2017).Article 
CAS 

Google Scholar 
Gachon, C. M. M., Küpper, H., Küpper, F. C. & Šetlík, I. Single-cell chlorophyll fluorescence kinetic microscopy of Pylaiella littoralis (Phaeophyceae) infected by Chytridium polysiphoniae (Chytridiomycota). Eur. J. Phycol. 41, 395–403 (2006).Article 

Google Scholar 
Senga, Y., Yabe, S., Nakamura, T. & Kagami, M. Influence of parasitic chytrids on the quantity and quality of algal dissolved organic matter (AOM). Water Res. 145, 346––353 (2018).Article 
PubMed 

Google Scholar 
Roberts, C., Allen, R., Bird, K. E. & Cunliffe, M. Chytrid fungi shape bacterial communities on model particulate organic matter. Biol. Lett. 16, 20200368 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blackburn, N., Fenchel, T. & Mitchell, J. Microscale nutrient patches in planktonic habitats shown by chemotactic bacteria. Science 282, 2254–2256 (1998).Article 
CAS 
PubMed 

Google Scholar 
Smriga, S., Fernandez, V. I., Mitchell, J. G. & Stocker, R. Chemotaxis toward phytoplankton drives organic matter partitioning among marine bacteria. Proc. Natl Acad. Sci. 113, 1576–1581 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).Article 
CAS 
PubMed 

Google Scholar 
Shibl, A. A. et al. Diatom modulation of select bacteria through use of two unique secondary metabolites. Proc. Natl Acad. Sci. 117, 27445–27455 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Guidi, L. et al. Relationship between particle size distribution and flux in the mesopelagic zone. Deep-Sea Res. Part I Oceanogr. Res. Papers. 55, 1364–1374 (2008).Article 
CAS 

Google Scholar 
Jackson, G. A. et al. Particle size spectra between 1 μm and 1 cm at Monterey Bay determined using multiple instruments. Deep-Sea Res. Pt I Oceanogr. Res. Papers. 44, 1739–1767 (1997).Article 

Google Scholar 
Frenken, T. et al. Warming accelerates termination of a phytoplankton spring bloom by fungal parasites. Glob. Change Biol. 22, 299–309 (2016).Article 

Google Scholar 
Mari, X., Passow, U., Migon, C., Burd, A. B. & Legendre, L. Transparent exopolymer particles: Effects on carbon cycling in the ocean. Prog. Oceanogr. 151, 13–37 (2017).Article 

Google Scholar 
Passow, U. Transparent exopolymer particles (TEP) in aquatic environments. Prog. Oceanogr. 55, 287–333 (2002).Article 

Google Scholar 
Prieto, L. et al. Scales and processes in the aggregation of diatom blooms: high time resolution and wide size range records in a mesocosm study. Deep-Sea Res. Pt I Oceanogr. Res. Papers. 49, 1233–1253 (2002).Article 

Google Scholar 
Kiørboe, T., Andersen, K. P. & Dam, H. G. Coagulation efficiency and aggregate formation in marine phytoplankton. Mar. Biol. 107, 235–245 (1990).Article 

Google Scholar 
Vidal-Melgosa, S. et al. Diatom fucan polysaccharide precipitates carbon during algal blooms. Nat. Commun. 12, 1150 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gärdes, A., Iversen, M. H., Grossart, H. P., Passow, U. & Ullrich, M. S. Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii. ISME J. 5, 436–445 (2011).Article 
PubMed 

Google Scholar 
Grossart, H. P. & Simon, M. Interactions of planktonic algae and bacteria: Effects on algal growth and organic matter dynamics. Aquat. Microb. Ecol. 47, 163–176 (2007).Article 

Google Scholar 
Short, S. M. The ecology of viruses that infect eukaryotic algae. Environ. Microbiol. 14, 2253–2271 (2012).Article 
PubMed 

Google Scholar 
Carlström, D. The crystal structure of α-chitin (Poly-N-acetyl-d-glucosamine). J. Biophysical Biochemical Cytol. 3, 669–683 (1957).Article 

Google Scholar 
Miklasz, K. A. & Denny, M. W. Diatom sinkings speeds: Improved predictions and insight from a modified Stokes’ law. Limnol. Oceanogr. 55, 2513–2525 (2010).Article 

Google Scholar 
Bidle, K. D. & Azam, F. Accelerated dissolution of diatom silica by marine bacterial assemblages. Nature 397, 508 (1999).Article 
CAS 

Google Scholar 
Gerphagnon, M. et al. Comparison of sterol and fatty acid profiles of chytrids and their hosts reveals trophic upgrading of nutritionally inadequate phytoplankton by fungal parasites. Environ. Microbiol. 21, 949–958 (2019).Article 
CAS 
PubMed 

Google Scholar 
Kagami, M., Von Elert, E., Ibelings, B. W., De Bruin, A. & Van Donk, E. The parasitic chytrid, Zygorhizidium, facilitates the growth of the cladoceran zooplankter, Daphnia, in cultures of the inedible alga, Asterionella. Proc. R. Soc. B Biol. Sci. 274, 1561–1566 (2007).Article 

Google Scholar 
Carney, L. T. & Lane, T. W. Parasites in algae mass culture. Front. Microbiol. 5, 1–8 (2014).Article 

Google Scholar 
Williams, D. M. Synedra, Ulnaria: definitions and descriptions – a partial resolution. Diatom Res. 26, 149–153 (2011).Article 

Google Scholar 
Arar, E. J. & Collins, G. B. Method 445.0: In vitro determination of chlorophyll and phaeophytin a in marine and freshwater algae by fluorescence. U.S. Environemental Protection Agency, Cinncinnati, Ohio Revision 1.2, 1–22 (1997).Klawonn, I., Dunker, S., Kagami, M., Grossart, H.-P., Van den Wyngaert, S. Intercomparison of two fluorescent dyes to visualize parasitic fungi (Chytridiomycota) on phytoplankton. Microb. Ecol. 85, 9–23 (2023).Article 
CAS 
PubMed 

Google Scholar 
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Alldredge, A. L. & Gotschalk, C. In situ settling behavior of marine snow. Limnol. Oceanogr. 33, 351 (1988).Article 

Google Scholar 
Jackson, G. A. Coagulation in a rotating cylinder. Limnol. Oceanogr. Methods. 13, e10018 (2015).Article 

Google Scholar 
Shanks, A. L. & Edmondson, E. W. Laboratory-made artificial marine snow: a biological model of the real thing. Mar. Biol. 101, 463–470 (1989).Article 

Google Scholar 
Cowen, R. K. & Guigand, C. M. In situ ichthyoplankton imaging system (ISIIS): system design and preliminary results. Limnol. Oceanogr. Methods. 6, 126–132 (2008).Article 

Google Scholar 
Jackson, G. A. & Burd, A. B. Simulating aggregate dynamics in ocean biogeochemical models. Prog. Oceanogr. 133, 55–65 (2015).Article 

Google Scholar 
Petrik, C. M., Jackson, G. A. & Checkley, D. M. Aggregates and their distributions determined from LOPC observations made using an autonomous profiling float. Deep-Sea Res. Pt I Oceanogr. Res. Papers. 74, 64–81 (2013).Article 

Google Scholar 
Johnson, C. P., Li, X. & Logan, B. E. Settling velocities of fractal aggregates. Environ. Sci. Technol. 30, 1911–1918 (1996).Article 
CAS 

Google Scholar 
Laurenceau-Cornec, E. C. et al. New guidelines for the application of Stokes’ models to the sinking velocity of marine aggregates. Limnol. Oceanogr. 65, 1264–1285 (2020).Article 
CAS 

Google Scholar 
Ploug, H. & Grossart, H. P. Bacterial production and respiration in suspended aggregates – A matter of the incubation method. Aquat. Microb. Ecol. 20, 21–29 (1999).Article 

Google Scholar 
Berggren, M., Lapierre, J.-F. & del Giorgio, P. A. Magnitude and regulation of bacterioplankton respiratory quotient across freshwater environmental gradients. ISME J. 6, 984–993 (2012).Article 
CAS 
PubMed 

Google Scholar 
R.CoreTeam. R: A language and environment for statistical computing. Vienna, Austria. Retrieved from https://www.R-project.org/ (2016). More

Canfield, D. E.; Kristensen, E.; Thamdrup, B. The Sulfur Cycle. In Advances in Marine Biology; Aquatic Geomicrobiology; Academic Press, 2005; Vol. 48, pp 313–381. https://doi.org/10.1016/S0065-2881(05)48009-8.Jørgensen, B. B., Findlay, A. J. & Pellerin, A. The biogeochemical sulfur cycle of marine sediments. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.00849 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Thamdrup, B., Fossing, H. & Jørgensen, B. B. Manganese, iron and sulfur cycling in a coastal marine sediment, Aarhus Bay. Denmark. Geochim. Cosmochim. Acta 58(23), 5115–5129. https://doi.org/10.1016/0016-7037(94)90298-4 (1994).Article 
ADS 
CAS 

Google Scholar 
Holmer, M. & Storkholm, P. Sulphate reduction and sulphur cycling in lake sediments: A review. Freshw. Biol. 46(4), 431–451. https://doi.org/10.1046/j.1365-2427.2001.00687.x (2001).Article 
CAS 

Google Scholar 
Koschorreck, M. Microbial sulphate reduction at a low PH. FEMS Microbiol. Ecol. 64(3), 329–342. https://doi.org/10.1111/j.1574-6941.2008.00482.x (2008).Article 
CAS 
PubMed 

Google Scholar 
Kwon, M. J. et al. Impact of organic carbon electron donors on microbial community development under iron- and sulfate-reducing conditions. PLoS ONE 11(1), e0146689. https://doi.org/10.1371/journal.pone.0146689 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fründ, C. & Cohen, Y. Diurnal cycles of sulfate reduction under oxic conditions in cyanobacterial mats. Appl. Environ. Microbiol. 58(1), 70–77. https://doi.org/10.1128/aem.58.1.70-77.1992 (1992).
Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Marschall, C., Frenzel, P. & Cypionka, H. Influence of oxygen on sulfate reduction and growth of sulfate-reducing bacteria. Arch. Microbiol. 159(2), 168–173. https://doi.org/10.1007/BF00250278 (1993).Article 
CAS 

Google Scholar 
Borzenko, S. V., Kolpakova, M. N., Shvartsev, S. L. & Isupov, V. P. Biogeochemical conversion of sulfur species in saline lakes of steppe Altai. J. Oceanol. Limnol. 36(3), 676–686. https://doi.org/10.1007/s00343-018-6293-8 (2018).Article 
ADS 
CAS 

Google Scholar 
Häusler, S. et al. Sulfate reduction and sulfide oxidation in extremely steep salinity gradients formed by freshwater springs emerging into the dead sea. FEMS Microbiol Ecol 90(3), 956–969. https://doi.org/10.1111/1574-6941.12449 (2014).Article 
CAS 
PubMed 

Google Scholar 
Komor, S. C. Bidirectional sulfate diffusion in saline-lake sediments: Evidence from Devils Lake, Northeast North Dakota. Geology 20(4), 319–322. https://doi.org/10.1130/0091-7613(1992)020%3c0319:BSDISL%3e2.3.CO;2 (1992).Article 
ADS 
CAS 

Google Scholar 
Valiente, N. et al. Tracing sulfate recycling in the hypersaline Pétrola Lake (SE Spain): A combined isotopic and microbiological approach. Chem. Geol. 473, 74–89. https://doi.org/10.1016/j.chemgeo.2017.10.024 (2017).Article 
ADS 
CAS 

Google Scholar 
Moreira, N., Walter, L., Vasconcelos, C., McKenzie, J. & McCall, P. Role of sulfide oxidation in dolomitization: Sediment and pore-water geochemistry of a modern hypersaline lagoon system. Geology 32(8), 701–704. https://doi.org/10.1130/G20353.1 (2004).Article 
ADS 
CAS 

Google Scholar 
Jolly, I. D., McEwan, K. L. & Holland, K. L. A review of groundwater-surface water interactions in arid/semi-arid wetlands and the consequences of salinity for wetland ecology. Ecohydrology https://doi.org/10.1002/eco.6 (2008).Article 

Google Scholar 
Williams, W. D. Environmental threats to salt lakes and the likely status of inland saline ecosystems in 2025. Environ. Conserv. 29(2), 154–167. https://doi.org/10.1017/S0376892902000103 (2002).Article 

Google Scholar 
CHE. Confederación Hidrográfica del Ebro. https://www.chebro.es/ (Accessed 1 June 2022).Comín, F. A., Rodó, X. & Comín, P. Lake Gallocanta (Aragon, NE Spain), a paradigm of fluctuations at different scales of time. Limnetica 8(1), 79–86 (1992).Article 

Google Scholar 
Luna, E.; Latorre, B.; Castañeda, C. Rainfall and the Presence of Water in Gallocanta Lake. http://digital.csic.es/handle/10261/117417. (2014).San Roman Saldaña, J.; García Vera, M. Á.; Blasco Herguedas, Ó.; Coloma López, P. Toma de Datos, Modelación y Gestión Del Agua Subterránea En La Cuenca Endorréica de La Laguna de Gallocanta (España); Alicante, Spain, 2005; pp 551–557.Orellana-Macías, J. M., Merchán, D. & Causapé, J. Evolution and assessment of a nitrate vulnerable zone over 20 years: Gallocanta groundwater body (Spain). Hydrogeol. J. https://doi.org/10.1007/s10040-020-02184-0 (2020).Article 

Google Scholar 
Gracia, F. J., Gutierrez, F. & Gutierrez, M. Origin and evolution of the Gallocanta Polije (Iberian range, NE Spain). Z. Geomorph. N. F. 46(2), 245–262 (2002).Article 

Google Scholar 
García-Vera, M.A.; San Román Saldaña, J.; Blasco Herguedas, O.; Coloma López, P. Hidrogeología de La Laguna de GalIocanta e Implicaciones Ambientales. In M.A. Casterad and C. Castañeda (Eds.). La Laguna de Gallocanta: Medio Natural, Conservación y Teledetección. Memorias de la Real Sociedad Española de Historia Natural. 2009, 7, 79–104.Comín, F. A., Juli, R., Comín, P. & Plana, F. Hydrogeochemistry of Lake Gallocanta (Aragón, NE Spain). Hydrobiologia 197, 51–66. https://doi.org/10.1007/bf00026938 (1990).Article 

Google Scholar 
Mayayo, M. J. et al. Sedimentological evolution of the holocene Gallocanta Lake, NE Spain. Limnol. Spain Tribute Kerry Kelts 14, 359–384 (2003).
Google Scholar 
Pérez, A. et al. Sedimentary facies distribution and genesis of a recent carbonate-rich Saline Lake: Gallocanta Lake, Iberian Chain, NE Spain. Sediment. Geol. 148(1–2), 185–202. https://doi.org/10.1016/S0037-0738(01)00217-2 (2002).Article 
ADS 

Google Scholar 
Corzo, A. et al. Carbonate mineralogy along a biogeochemical gradient in recent lacustrine sediments of Gallocanta Lake (Spain). Geomicrobiol. J. 22(6), 283–298. https://doi.org/10.1080/01490450500183654 (2005).Article 
CAS 

Google Scholar 
Castañeda, C., Gracia, F. J., Luna, E. & Rodríguez-Ochoa, R. Edaphic and geomorphic evidences of water level fluctuations in Gallocanta Lake, NE Spain. Geoderma 239–240, 265–279. https://doi.org/10.1016/j.geoderma.2014.11.005 (2015).Article 
ADS 
CAS 

Google Scholar 
Luzón, A. et al. Holocene environmental changes in the Gallocanta lacustrine basin, Iberian range, NE Spain. Holocene 17(5), 649–663. https://doi.org/10.1177/0959683607078994 (2007).Article 
ADS 

Google Scholar 
Schütt, B. Reconstruction of holocene paleoenvironments in the endorheic basin of laguna de Gallocanta, Central Spain by investigation of mineralogical and geochemical characters from lacustrine sediments. J. Paleolimnol. 20, 217. https://doi.org/10.1023/A:1007924000636 (1998).Article 
ADS 

Google Scholar 
Castañeda, C., Luna, E. & Rabenhorst, M. Reducing conditions in soil of Gallocanta Lake. Northeast Spain. Eur. J. Soil Sci. 68(2), 249–258. https://doi.org/10.1111/ejss.12407 (2017).Article 
CAS 

Google Scholar 
Castañeda, C., Gracia, F. J., Conesa, J. A. & Latorre, B. Geomorphological control of habitat distribution in an intermittent shallow Saline Lake, Gallocanta Lake. NE Spain. Sci. Total Environ. 726, 138601. https://doi.org/10.1016/j.scitotenv.2020.138601 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Comín, F. A., Rodó, X. & Menéndez, M. Spatial heterogeneity of macrophytes in lake Gallocanta (Aragón, NE Spain). Hydrobiologia 267(1–3), 169–178. https://doi.org/10.1007/BF00018799 (1993).Article 

Google Scholar 
Castro, O. D. et al. A Contribution to the characterization of ruppia drepanensis (ruppiaceae), a key species of threatened mediterranean Wetlands. Ann. Mo. Bot. Gard. 106, 1–9. https://doi.org/10.3417/2020520 (2021).Article 

Google Scholar 
Alonso López, J. A., Alonso López, J. C., Cantos, F. J. & Bautista, L. M. Spring crane grus grus migration through Gallocanta, Spain. II. Timing and pattern of daily departures. Ardea 78, 379–388 (1990).
Google Scholar 
Alonso López, J. C., Alonso López, J. A., Cantos, F. J. & Bautista, L. M. Spring crane grus grus migration through Gallocanta, Spain. I. Daily Variations in Migration Volume. Ardea 78, 365–378 (1990).
Google Scholar 
Orellana-Macías, J. M., Bautista, L. M., Merchán, D., Causapé, J. & Alonso, J. C. Shifts in crane migration phenology associated with climate change in southwestern Europe. Avian Conserv. Ecol. 15(1), 1–13. https://doi.org/10.5751/ACE-01565-150116 (2020).Article 

Google Scholar 
Luzón, A., Mayayo, M. J. & Pérez, A. Stable isotope characterisation of co-existing carbonates from the holocene Gallocanta Lake (NE Spain): Palaeolimnological implications. Int. J. Earth Sci. 98(5), 1129–1150. https://doi.org/10.1007/s00531-008-0308-1 (2009).Article 
CAS 

Google Scholar 
Accoe, F. et al. Evolution of the Δ13C signature related to total carbon contents and carbon decomposition rate constants in a soil profile under grassland. Rapid Commun. Mass Spectrom. 16(23), 2184–2189. https://doi.org/10.1002/rcm.767 (2002).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Menéndez-Serra, M., Triadó-Margarit, X., Castañeda, C., Herrero, J. & Casamayor, E. O. Microbial composition, potential functional roles and genetic novelty in gypsum-rich and hypersaline soils of Monegros and Gallocanta (Spain). Sci. Total Environ. 650(September), 343–353. https://doi.org/10.1016/j.scitotenv.2018.09.050 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Kendall, C. & McDonnell, J. J. Isotope Tracers in Catchment Hydrology 1st edn. (Elsevier, 1999).

Google Scholar 
Mayer, B., Fritz, P., Prietzel, J. & Krouse, H. R. The use of stable sulfur and oxygen isotope ratios for interpreting the mobility of sulfate in aerobic forest soils. Appl. Geochem. 10(2), 161–173. https://doi.org/10.1016/0883-2927(94)00054-A (1995).Article 
ADS 
CAS 

Google Scholar 
Otero, N., Canals, À. & Soler, A. Using dual-isotope data to trace the origin and processes of dissolved sulphate: A case study in calders stream (Llobregat Basin, Spain). Aquat. Geochem. 13(2), 109–126. https://doi.org/10.1007/s10498-007-9010-3 (2007).Article 
CAS 

Google Scholar 
Canfield, D. E. Isotope fractionation by natural populations of sulfate-reducing bacteria. Geochim. Cosmochim. Acta 65(7), 1117–1124. https://doi.org/10.1016/S0016-7037(00)00584-6 (2001).Article 
ADS 
CAS 

Google Scholar 
Canfield, D. E. Biogeochemistry of sulfur isotopes. Rev. Mineral. Geochem. 43(1), 607–636. https://doi.org/10.2138/gsrmg.43.1.607 (2001).Article 
CAS 

Google Scholar 
Antler, G., Turchyn, A. V., Ono, S., Sivan, O. & Bosak, T. Combined 34S, 33S and 18O isotope fractionations record different intracellular steps of microbial sulfate reduction. Geochim. Cosmochim. Acta 203, 364–380. https://doi.org/10.1016/j.gca.2017.01.015 (2017).Article 
ADS 
CAS 

Google Scholar 
Kaplan, I. R. & Rittenberg, S. C. Microbiological fractionation of sulphur isotopes. J. Gen. Microbiol. 34(2), 195–212. https://doi.org/10.1099/00221287-34-2-195 (1964).Article 
CAS 
PubMed 

Google Scholar 
Mangalo, M., Meckenstock, R. U., Stichler, W. & Einsiedl, F. Stable isotope fractionation during bacterial sulfate reduction is controlled by reoxidation of intermediates. Geochim. Cosmochim. Acta 71(17), 4161–4171. https://doi.org/10.1016/j.gca.2007.06.058 (2007).Article 
ADS 
CAS 

Google Scholar 
Strebel, O., Böttcher, J. & Fritz, P. Use of isotope fractionation of sulfate-sulfur and sulfate-oxygen to assess bacterial desulfurication in a sandy aquifer. J. Hydrol. 121(1–4), 155–172. https://doi.org/10.1016/0022-1694(90)90230-U (1990).Article 
ADS 
CAS 

Google Scholar 
Sim, M. S., Bosak, T. & Ono, S. Large sulfur isotope fractionation does not require disproportionation. Science 333(6038), 74–77. https://doi.org/10.1126/science.1205103 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Leavitt, W. D., Halevy, I., Bradley, A. S. & Johnston, D. T. Influence of sulfate reduction rates on the phanerozoic sulfur isotope record. Proc. Natl. Acad. Sci. 110(28), 11244–11249. https://doi.org/10.1073/pnas.1218874110 (2013).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Utrilla, R., Pierre, C., Orti, F. & Pueyo, J. J. Oxygen and sulphur isotope compositions as indicators of the origin of mesozoic and cenozoic evaporites from Spain. Chem. Geol. 102(1), 229–244. https://doi.org/10.1016/0009-2541(92)90158-2 (1992).Article 
ADS 
CAS 

Google Scholar 
Driessche, A. E. S. V., Canals, A., Ossorio, M., Reyes, R. C. & García-Ruiz, J. M. Unraveling the sulfate sources of (Giant) gypsum crystals using gypsum isotope fractionation factors. J. Geol. https://doi.org/10.1086/684832 (2016).Article 

Google Scholar 
Wardlaw, G. D. & Valentine, D. L. Evidence for salt diffusion from sediments contributing to increasing salinity in the Salton sea, California. Hydrobiologia 533(1), 77–85. https://doi.org/10.1007/s10750-004-2395-8 (2005).Article 
CAS 

Google Scholar 
Bak, F. & Pfennig, N. Microbial sulfate reduction in littoral sediment of lake constance. FEMS Microbiol. Lett. 85(1), 31–42. https://doi.org/10.1111/j.1574-6968.1991.tb04695.x (1991).Article 
CAS 

Google Scholar 
Dogramaci, S. S., Herczeg, A. L., Schiff, S. L. & Bone, Y. Controls on Δ34S and Δ18O of dissolved sulfate in aquifers of the murray basin, Australia and their use as indicators of flow processes. Appl. Geochem. 16(4), 475–488. https://doi.org/10.1016/S0883-2927(00)00052-4 (2001).Article 
ADS 
CAS 

Google Scholar 
Rodier. L’analyse de l’eau, eaux naturelles, eaux résiduaires, eau de mer; Dunod, 1976.Romain, T. Tester Les Isotopes Stables de l’azote Des Matières Organiques Fossiles Terrestres Comme Marqueur Paléoclimatique Sur Des Séries Pré-Quaternaires, Université Pierre et Marie Curie – Paris VI, 2015. https://tel.archives-ouvertes.fr/tel-01408071. More

Concentrations of PTEs in agricultural soil of oasesThe descriptive statistical results of 11 PTE concentrations in agricultural soils of the four oases are shown in Table 1. The concentration order of different PTEs in the four oases was different. In terms of mean values, the concentrations of Co, Cu, Ni, Pb, Sb, V and Zn in Kashgar Oasis agricultural soil were higher than those in the other three oases, and the concentrations of Sn and Tl in Hotan Oasis agricultural soil were the highest. The Cv value of As in the Hotan Oasis was the highest (0.31), indicating that compared with that in other oases and other PTEs, the As in Hotan Oasis soil was more likely to have external inputs other than natural sources59. According to the “Soil Environmental Quality · Agricultural Land Soil Pollution Risk Control Standard (Trial)” (GB15618-2018)60, the corresponding elements of oasis agricultural soil in the source region of the Tarim River did not exceed the pollution risk control standard. Compared with the soil environmental quality provisions in the “Environmental Quality Assessment Standard for Producing Areas of Edible Agricultural Products” (HJ332-2006)61, the corresponding elements in the soil of the four oases were also below the limits.Table 1 Descriptive statistics of PTEs of oasis agricultural soil in the source region of the Tarim River.Full size tableCompared with the PTE concentrations in the agricultural soil of oases in other arid areas, the concentrations of Cd, Cu and V in the soil of the study area were lower than those of the Bortala River Basin and Ili River Basin3 in the northern Tianshan Mountains, while the concentrations of Co, Ni, Pb and Zn were similar to those of corresponding elements in agricultural soil of the Bortala River Basin and Ili River Basin3. Compared with the concentrations of PTEs in agricultural soil of Wuwei and Jiuquan cities in the Hexi Corridor62,63, the concentrations of Cu, Ni, Pb and V elements in agricultural soil of the four oases in the study area were lower. Similarly, the concentrations of Cu, V and Zn in the study area were much lower than the corresponding concentrations in the agricultural soil of the Bahariya Oasis, Egypt64.Geochemical baseline values of PTEs in oasis agricultural soilsThe PTE concentration of the subsoil (60–80 cm) after logarithmic and reciprocal conversion passed the KS normal distribution test (p  > 0.05). The GBVs of PTEs in agricultural soils of the four oases in the source region of the Tarim River were calculated according to the cumulative frequency method, and the results are shown in Supplementary Figs. S1–4 and Table 2. The GBVs of PTEs in agricultural soils were very different among the four oases. For example, except for those of Ni, Sn, Tl and V, the GBVs of PTEs in agricultural soils of the Hotan Oasis were much lower than those of the other three oases (Table 2). There was also a large gap between the GBVs of PTEs in agricultural soil and the soil background values in China65, Xinjiang66,67 and Xinjiang agricultural land66. In particular, the GBV of Zn obtained in this study was 3.6–5 times the soil background value of Xinjiang agricultural land. Under the same land use type, there are differences in the abundance of environmental PTEs due to heterogeneity in the disturbance degree of human activities and the parent material and soil formation processes68. Therefore, there were some differences in the GBVs of each PTE among the four oases in this study when the land use types were all agricultural soils.Table 2 GBVs of PTEs in oasis agricultural soils of the source region of the Tarim River.Full size tablePollution characteristics of PTEs in agricultural soil of oasesAccording to the GBVs of the above elements, the PF, PLI and PN of PTEs in agricultural soil of the four oases were calculated, and the results are shown in Fig. 2. The results of PF showed that (Fig. 2a), except for As and Cd in the Hotan Oasis, the PTEs of agricultural soils in the four oases were at uncontaminated and slightly contaminated levels, indicating that the oasis agricultural soils in the source region of the Tarim River were affected by human activities to a certain extent. From the perspective of individual elements, the PF of Cd in agricultural soils of the four oases was higher than that of other PTEs. Among them, Cd in most soil samples from the Hotan Oasis (81.25%) showed mild contamination, while Cd in the remaining samples (18.75%) showed moderate contamination. Since Cd in soil is generally regarded as an indicator of agricultural activities involving fertilizer use, the results suggest that soil in oases (especially the Hotan Oasis) in the source region of the Tarim River may be seriously affected by agricultural activities.Figure 2Box-whisker plots of PF (a), PLI (b) and PN (c) of PTEs in oasis agricultural soils.Full size imageThe results of the pollution load index of multiple PTEs showed that the agricultural soils in the Yarkant River Oasis, Kashgar Oasis and Aksu Oasis were between uncontaminated and moderate contamination levels, and most soil samples in the Hotan Oasis were moderately contaminated. PN showed similar results. The reason for this result was the high PF values of As and Cd in the agricultural soil of the Hotan Oasis (Fig. 2a), which caused the high comprehensive pollution index in this area. It is worth noting that compared with those in the other three areas, most PTEs (As, Cd, Pb, Sb, V and Zn) in the agricultural soil of the Hotan Oasis had lower mean values, but their PLI and PN values were higher. This was closely related to the lower GBVs of PTEs in the Hotan area.Source apportionment and source specific risk assessment of PTEs in oasis agricultural soilSource apportionment of soil PTEsFactor analysis and correlation analysis were used to identify the main sources of PTEs in the main oasis agricultural soil in the source region of the Tarim River. The results are shown in Table 3 and Supplementary Fig. S5. Table 3 shows that PTEs in the agricultural soil of the Yarkant River Oasis had two principal factors. The first principal component F1, which explained 61.6% of the total variance, mainly described Co, V, Cu and Sb and moderately described Cd, Ni and Tl. The second principal component, F2, accounted for 21.8% of the variance and mainly described As, Pb and Sn. There were three main factors for PTEs in Kashgar Oasis agricultural soil. Factor 1, which explained 58.9% of the total variance, was mainly composed of As, Co, Cu, Ni, Pb, Sb, V and Zn; factor 2, which accounted for 24.8% of the variance, was mainly composed of Sn and Tl; and factor 3, which accounted for 7.9% of the variance, was composed of Cd. There were three main factors for the PTEs of Aksu Oasis agricultural soil. Factor 1 was mainly composed of Cd, Co, Cu, Ni, Pb, V and Zn; factor 2 was mainly composed of Sn and Tl; and factor 3 was composed of As and Sb. PTEs in agricultural soil of the Hotan Oasis had only one main factor, which was composed of all (11) elements. To verify the above results of PTE extraction from oasis agricultural soil by PCA, Spearman correlation analysis was performed on PTEs from the soils of the four oases. The results showed that the correlations between PTEs in agricultural soils of the four oases were consistent with the results of PCA, indicating the reliability of the PCA results.Table 3 Principal component analysis results of PTEs of oasis agricultural soil in the source region of the Tarim River.Full size tableTo further determine the specific sources and contribution rates of PTEs in oasis agricultural soil in the source region of the Tarim River, the PMF model was adopted for analysis, and the results are shown in Fig. 3. Overall, the classification results of PTEs in agricultural soils of the four oases by the PMF model were consistent with the results of PCA. According to the above results, eight environmental factors related to the source of soil PTEs were selected: distance from factory (DF), distance from road (DR), pH, soil type (ST), total nitrogen (TN), soil fine silty particle size percentage (Fine silty), soil silty particle size percentage (Silty), and soil coarse silty particle size percentage (Coarse silty). A Geodetector model of PTEs and environmental factors in soil was constructed, and the results are shown in Supplementary Table S5. Among them, the TN content in soil was considered to be an index related to the intensity of agricultural activities69. Since the particle composition of atmospheric dust in the study area is mainly silt70, the silty particle size content of soil was selected as the atmospheric dust index in this study.Figure 3PMF analysis results of PTEs of agricultural soils in the Yarkant River Oasis (a), Kashgar Oasis (b) and Aksu Oasis (c).Full size imageIn the Yarkant River Oasis, Co, Cu and Zn showed no pollution at most of the sampling points for the first source factor of PTEs in agricultural soil (Fig. 2), indicating that the levels of these elements in soil were less affected by human activities. Meanwhile, the results of the factor detector showed that ST was one of the factors explaining the spatial distribution of Co, Cu, Sb, V and Zn (Supplementary Table S5). The Geodetector results revealed TN as one of the strong drivers of Cd and Tl, the former of which is found in pesticides and fertilizers and the latter of which may also enter agricultural soil through sewage irrigation and fertilizers contaminated by industrial wastewater71,72. Therefore, it is inferred that F1 in the agricultural soil of the Yarkant River Oasis had a mixed source of natural resources and agricultural activities. The three elements in F2 (As, Pb and Sn) were closely related to fuel combustion and traffic factors, among which Pb is usually selected as an identifying element for traffic sources (including leaded exhaust gas, vehicle tires and brake pads)73. In the results of the Geodetector model, the best explanatory factors for Pb and Sn were Silt and DR, while the best explanatory factor for As was Slit. Therefore, F2 was inferred to be the source of road dust/atmospheric dust.The weights of F1 in Kashgar Oasis agricultural soil PTEs were mainly included As, Co, Cu, Ni, Pb, Sb, V and Zn; the weights of F2 were mainly included Sn and Tl; and F3 were mainly included Cd (Fig. 3b). Similar to the results observed for the Yarkant River Oasis, the factor detector results showed that F1 might have a mixed anthropogenic source, including transportation, industrial and other sources. Both Sn and Tl in F2 are widely used in industrial manufacturing, and Sn can be used as an additive to enhance the properties of steel or alloys74. Tl is used in many different industrial manufacturing and medical fields, and metal smelting, sulfuric acid production, coal burning, cement manufacturing and other industrial activities involving the use of Tl minerals are the main pathways by which this element enters the environment71. At the same time, the strongest explanatory factor for Sn and Tl in the results of the geographic detector was DF, so F2 was inferred to be an industrial source. F3 contained only Cd (related to agricultural activities), so F3 was inferred to be the source of agricultural activities (Supplementary Table S5).In the PMF results, the weights of F1 for the PTEs of Aksu Oasis agricultural soil were mainly included Cu, Ni, Cd, Zn, Co, V and Pb; the weights of F2 were mainly included Sn and Tl; and the weights of F3 were mainly included As and Sb (Fig. 3c). Similarly, from the factor detector calculation results, it was inferred that F1 in the Aksu Oasis was a mixed source. According to the results of Geodetector analysis, the best explanatory factors for Sn and Tl in the agricultural soil of the Aksu Oasis were TN and ST. Previous studies have also shown that Sn in soil may also come from agricultural practices (pesticides)75, so F2 was inferred to represent agricultural activities and natural sources (rock mineralization). Similarly, according to Supplementary Table S5, F3 was inferred to be the source of agricultural activities. Previous studies have also concluded that agricultural activities such as the application of phosphate fertilizer are also the main source of As and Sb in soil76,77, which was consistent with the results of the Geodetector model.The explanatory factors for the PTEs of agricultural soil in the Hotan Oasis were the total nitrogen content and the silty particle size percentage, indicating that the PTEs in agricultural soil in the Hotan Oasis mainly came from agricultural activities and atmospheric dust fall. The economy in the Hotan Oasis is dominated by irrigated agriculture, and since most oases border large deserts, wind and sand disasters are extremely serious (annual average dust days exceeding 220 days)78. The sources of PTEs inferred by the Geodetector model in this study were consistent with the reality.Source-specific ecological risk assessmentBased on the results of source analysis, the source-specific ecological risks posed by agricultural soil PTEs were evaluated in this study, and the results are shown in Fig. 4. The total ecological risks caused by PTEs in the agricultural soils in the Yarkant River Oasis, Kashgar Oasis and Aksu Oasis were null, while those at of all sampling sites in the Hotan Oasis were moderate risk. According to the results of the source-specific ecological risk of PTEs, there was no direct relationship between the contribution degree of source-specific risks to the total ecological risk and its contribution to the existence of PTEs in soil. In particular, the agricultural activity source (F3), which accounted for only 7.9% of the PTEs in Kashgar Oasis agricultural soil, contributed the most to the total risk, the mixed source (F1), which contributed the most to the existence of PTEs in soil, contributed the second most to the total risk, and the industrial source (F2) contributed the least. The reason for this result was that the toxicity factor of Cd (30) introduced by agricultural activities was significantly higher than that of PTEs released from other sources, which was consistent with the conclusion of other studies16,19 that the main source of highly toxic elements was more likely to cause ecological risks than that of low-toxicity elements.Figure 4Source-specific ecological risk boxplots of PTEs of agricultural soils in oases in the source region of the Tarim River.Full size imageTo explore the spatial distribution of ecological risks generated by PTEs from different sources, a distribution map of ecological risks posed by specific sources at each sampling point was generated (Fig. 5). The total ecological risk of the sampling sites near Awat County in the Aksu Oasis was higher than 40, indicating moderate risk. In addition, the total ecological risk at the other sampling sites was at a low level. However, the ecological risks caused by mixed sources (F1) and agricultural activities and natural sources (F2) were high in the southwestern Aksu Oasis and the sampling sites near Aksu city, resulting in high total ecological risks. The ecological risks at all the sampling sites in the two oases in Kashgar (Kashgar Oasis and Yarkant River Oasis) were low. Notably, the sampling sites near Kashgar city had high total ecological risks due to the high ecological risks caused by PTEs from mixed sources (F1) and agricultural activity sources (F3). The total ecological risk at all sampling sites in the Hotan Oasis was moderate. In particular, the sampling sites near Hotan city had the highest total ecological risk generated by PTEs from agricultural activities and atmospheric dust sources (73.71).Figure 5Spatial distribution of the ecological risks of PTEs from different sources in the four oases. The graphs were generated by QGIS 3.26.3 (https://www.qgis.org) and the land use data are from the ESA global 30 m resolution land use cover dataset (https://viewer.esa-worldcover.org/worldcover). The combination of graphs (a–d) was accomplished with linkscape 1.2.1 (https://inkscape.org).Full size imageSource-specific human health risk assessmentAccording to the proportions of different sources of each PTE obtained from the PMF model, the source-specific health risks of PTEs in agricultural soil in the four oases to the human body were calculated, and the results are shown in Tables 4 and 5 and Fig. 6. Although the results of noncarcinogenic risk caused by different sources of PTEs in agricultural soil in each oasis showed that adults and children were not at risk, the total THI values of PTEs for children in the four regions were all greater than 1, indicating that soil PTEs in the study area posed significant noncarcinogenic risks for children. In terms of carcinogenic risk, the total TCR values of PTEs for adults and children were on the order of 1E−05, within the range of 1E−06 and 1E−04, indicating that the carcinogenic risk of soil PTEs for the human body is acceptable in the study area. In general, the noncarcinogenic and carcinogenic risks of PTEs from different sources in children were higher than those in adults, which can be explained by children having more opportunities PTE contamination through hand-to-mouth ingestion and dermal contact than adults due to the areas where they play and unhealthy eating habits (e.g., children are more likely to suck their fingers)79. Therefore, different parameters were set when employing the health risk assessment model.Table 4 Specific noncarcinogenic risks of PTEs from different sources.Full size tableTable 5 Specific carcinogenic risks of PTEs from different sources.Full size tableFigure 6Human health risk proportions of PTEs from different sources in the Aksu Oasis (a), Kashgar Oasis (b) and Yarkant River Oasis (c).Full size imageIn terms of the proportion of human health risks caused by PTEs from different sources (Fig. 6), those from mixed sources, which were considered to have the largest contribution to PTEs in the Aksu Oasis, Kashgar Oasis and Yarkant River Oasis, were not the largest. This might be explained by the presence of more toxic elements, such as As and Tl, in other sources of the three oases contributing the most (Fig. 3), while their low RfD may explain their greater noncarcinogenic risk. PTEs from agricultural activities and natural sources accounted for the largest proportion of carcinogenic risk in the Aksu Oasis, while mixed sources accounted for the largest proportion of carcinogenic risk in the Kashgar Oasis and Yarkant River Oasis. This was mainly because the presence of As, Cd, Co, Ni and Pb elements with carcinogenic risk in the above two sources accounted for a large proportion (Fig. 3), thus posing a large carcinogenic risk. The results of the proportion of health risks attributed to different sources of PTEs to the human body showed that the control of human health risks of PTEs cannot be determined based on their concentration alone. Therefore, in view of the obvious noncarcinogenic risk of soil PTEs in the four oases to children, from the results of the contribution of different sources of PTEs to the noncarcinogenic risk to children, agricultural activities and natural sources, industrial sources and atmospheric dust fall were the priority control sources in the Aksu Oasis, Kashgar Oasis and Yarkant River Oasis, respectively. More

These authors contributed equally: Arjun Srivathsa, Divya Vasudev, Tanaya Nair, Jagdish Krishnaswamy, Uma Ramakrishnan.National Centre for Biological Science, TIFR, Bengaluru, IndiaArjun Srivathsa, Tanaya Nair, Mahesh Sankaran & Uma RamakrishnanWildlife Conservation Society-India, Bengaluru, IndiaArjun SrivathsaConservation Initiatives, Guwahati, IndiaDivya Vasudev & Varun R. GoswamiDivision of Biosciences, University College London, London, UKTanaya NairDepartments of Biology and Environmental Studies, Macalester College, Saint Paul, MN, USAStotra ChakrabartiWorld Wildlife Fund, Delhi, IndiaPranav Chanchani, Arpit Deomurari, Dipankar Ghose & Prachi ThatteDepartment of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USARuth DeFriesAmity Institute of Forestry and Wildlife, Amity University, Noida, IndiaArpit DeomurariWildlife Institute of India, Dehradun, IndiaSutirtha DuttaFoundation for Ecological Research, Advocacy and Learning, Bengaluru, IndiaRajat Nayak & Srinivas VaidyanathanNetwork for Conserving Central India, Gurgaon, IndiaAmrita NeelakantanWorld Resources Institute, New Delhi, IndiaMadhu VermaSchool of Environment and Sustainability, Indian Institute for Human Settlements, Bengaluru, IndiaJagdish KrishnaswamyAshoka Trust for Research in Ecology and the Environment, Bengaluru, IndiaJagdish KrishnaswamyBiodiversity Collaborative, Bengaluru, IndiaJagdish Krishnaswamy, Mahesh Sankaran & Uma Ramakrishnan More

Graham, J. H., Raz, S., Hel-Or, H. & Nevo, E. Fluctuating asymmetry: Methods, theory, and applications. Symmetry 2(2), 466–540 (2010).ADS 
MathSciNet 

Google Scholar 
Graham, J. H., Emlen, J. M., Freeman, D. C., Leamy, L. J. & Kieser, J. A. Directional asymmetry and the measurement of developmental instability. Biol. J. Lin. Soc. 64(1), 1–16 (1998).
Google Scholar 
Dongen, V., Lensm, L. & Molenberghs, G. Mixture analysis of asymmetry: Modelling directional asymmetry, antisymmetry and heterogeneity in fluctuating asymmetry. Ecol. Lett. 2(6), 387–396 (1999).
Google Scholar 
Palmer, A. R. Symmetry breaking and the evolution of development. Science 306(5697), 828–833 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Møller, A. P. Directional selection on directional asymmetry: Testes size and secondary sexual characters in birds. Proc. R. Soc. Lond. Ser. B Biol. Sci. 258(1352), 147–151 (1994).ADS 

Google Scholar 
Allenbach, D. M. Fluctuating asymmetry and exogenous stress in fishes: A review. Rev. Fish Biol. Fish. 21(3), 355–376 (2011).
Google Scholar 
Werner, Y. L., Rothenstein, D. & Sivan, N. Directional asymmetry in reptiles (Sauria: Gekkonidae: Ptyodactylus) and its possible evolutionary role, with implications for biometrical methodology. J. Zool. 225(4), 647–658 (1991).
Google Scholar 
Loehr, J. et al. Asymmetry in threespine stickleback lateral plates. J. Zool. 289(4), 279–284 (2013).
Google Scholar 
Bell, M. A., Khalef, V. & Travis, M. P. Directional asymmetry of pelvic vestiges in threespine stickleback. J. Exp. Zool. B Mol. Dev. Evol. 308(2), 189–199 (2007).PubMed 

Google Scholar 
Somarakis, S., Kostikas, I. & Tsimenides, N. Fluctuating asymmetry in the otoliths of larval fish as an indicator of condition: Conceptual and methodological aspects. J. Fish Biol. 51, 30–38 (1997).
Google Scholar 
Ratty, F. J., Laurs, R. M. & Kelly, R. M. Gonad morphology, histology, and spermatogenesis in South Pacific albacore tuna Thunnus alalunga (Scombridae). Fish. Bull. 88, 207–216 (1989).
Google Scholar 
Harrod, C. & Griffiths, D. Parasitism, space constraints, and gonad asymmetry in the pollan (Coregonus autumnalis). Can. J. Fish. Aquat. Sci. 62(12), 2796–2801 (2005).
Google Scholar 
Park, I. S., Zhang, C. I., Kim, Y. J. & Bang, I. C. Directional asymmetry of gonadal development in Ayu (Plecoglossus altivelis). Fish. Aquat. Sci. 8(4), 207–212 (2005).
Google Scholar 
Bernet, D., Wahli, T., Kueng, C. & Segner, H. Frequent and unexplained gonadal abnormalities in whitefish (central alpine Coregonus sp.) from an alpine oligotrophic lake in Switzerland. Dis. Aquat. Org. 61(1–2), 137–148 (2004).CAS 

Google Scholar 
Bittner, D. et al. How normal is abnormal? Discrimination between deformations and natural variation in gonad morphology of European whitefish Coregonus lavaretus. J. Fish Biol. 74(7), 1594–1614 (2009).CAS 
PubMed 

Google Scholar 
Fricke, R., Eschmeyer, W. N. & R. van der Laan (eds) 2022. Eschmeyer’s Catalog of Fishes: Genera, Species, References. (http://researcharchive.calacademy.org/research/ichthyology/catalog/fishcatmain.asp). Electronic version Accessed 31 12 2022).Chen, H. M., Shao, K. T. & Chen, C. T. A review of the muraenid eels (Family Muraenidae) from Taiwan with descriptions of twelve new records. Zool. Stud. 33(1), 44–64 (1994).
Google Scholar 
Chen, H. M., Loh, K. H. & Shao, K. T. A new species of moray eel, Gymnothorax taiwanensis (Anguilliformes: Muraenidae) from eastern Taiwan. Raffles Bull. Zool. 19, 131–134 (2008).
Google Scholar 
Loh, K. H., Shao, K. T. & Chen, H. M. Gymnothorax melanosomatus, a new moray eel (Teleostei: Anguilliformes: Muraenidae) from southeastern Taiwan. Zootaxa 3134(1), 43–52 (2011).
Google Scholar 
Loh, K. H., Shao, K. T., Ho, H. C., Lim, P. E. & Chen, H. M. A new species of moray eel (Anguilliformes: Muraenidae) from Taiwan, with comments on related elongate unpatterned species. Zootaxa 4060(1), 30–40 (2015).PubMed 

Google Scholar 
Huang, W. C., Mohapatra, A., Thu, P. T., Chen, H. M. & Liao, T. Y. A review of the genus Strophidon (Anguilliformes: Muraenidae), with description of a new species. J. Fish Biol. 97(5), 1462–1480 (2020).PubMed 

Google Scholar 
Huang, W. C., Smith, D. G., Loh, K. H. & Liao, T. Y. Two New Moray Eels of Genera Diaphenchelys and Gymnothorax (Anguilliformes: Muraenidae) from Taiwan and the Philippines. Zool. Stud. 60, e24 (2021).Matić-Skoko, S. et al. Mediterranean moray eel Muraena helena (Pisces: Muraenidae): biological indices for life history. Aquat. Biol. 13(3), 275–284 (2011).
Google Scholar 
Fishelson, L. Comparative gonad morphology and sexuality of the Muraenidae (Pisces, Teleostei). Copeia 1992, 197–209 (1992).Froese, R. & D. Pauly. Editors. 2022.FishBase. World Wide Web electronic publication. www.fishbase.org. Accessed March 2022.Almany, G. R. Differential effects of habitat complexity, predators and competitors on abundance of juvenile and adult coral reef fishes. Oecologia 141(1), 105–113 (2004).ADS 
PubMed 

Google Scholar 
Hixon, M. A. & Beets, J. P. Predation, prey refuges, and the structure of coral-reef fish assemblages. Ecol. Monogr. 63(1), 77–101 (1993).
Google Scholar 
Muñoz, R. C. Evidence of natural predation on invasive lionfish, Pterois s, by the spotted moray eel, Gymnothorax moringa. Bull. Marine Sci. 93(3), 789–790 (2017).
Google Scholar 
Bos, A. R., Sanad, A. M. & Elsayed, K. Gymnothorax spp. (Muraenidae) as natural predators of the lionfish Pterois miles in its native biogeographical range. Environ. Biol. Fish. 100(6), 745–748 (2017).
Google Scholar 
Bshary, R., Hohner, A., Ait-el-Djoudi, K. & Fricke, H. Interspecific communicative and coordinated hunting between groupers and giant moray eels in the Red Sea. PLoS Biol. 4(12), e431 (2006).PubMed 
PubMed Central 

Google Scholar 
Hedrick, B. P., Antalek-Schrag, P., Conith, A. J., Natanson, L. J. & Brennan, P. L. Variability and asymmetry in the shape of the spiny dogfish vagina revealed by 2D and 3D geometric morphometrics. J. Zool. 308(1), 16–27 (2019).
Google Scholar 
Winters, G. H. Fecundity of the left and right ovaries of Grand Bank capelin (Mallotus villosus). J. Fish. Board Can. 28(7), 1029–1033 (1971).
Google Scholar 
Huang, L.Y. Reproductive biology of Gymnothorax reticularis from the waters off northeastern Taiwan. Master Thesis, Department of Aquaculture, College of Life Sciences, National Taiwan Ocean University, Keelung, Taiwan (2008).Loh, K.H. Molecular phylogeny and reproductive biology of moray eels (Muraenidae) around Taiwan. Ph.D. Thesis, Department of Aquaculture, College of Life Sciences, National Taiwan Ocean University, Keelung, Taiwan (2009).Calhim, S. & Birkhead, T. R. Intraspecific variation in testis asymmetry in birds: evidence for naturally occurring compensation. Proc. R. Soc. B Biol. Sci. 276(1665), 2279–2284 (2009).
Google Scholar 
Palmer, A. R. What determines direction of asymmetry: Genes, environment or chance?. Philos. Trans. R. Soc. B Biol. Sci. 371(1710), 20150417 (2016).
Google Scholar 
Calhim, S. & Montgomerie, R. Testis asymmetry in birds: The influences of sexual and natural selection. J. Avian Biol. 46(2), 175–185 (2015).
Google Scholar 
Johnson, G. D. Revisions of anatomical descriptions of the pharyngeal jaw apparatus in moray eels of the family Muraenidae (Teleostei: Anguilliformes). Copeia 107(2), 341–357 (2019).MathSciNet 

Google Scholar 
Blackburn, D. G. Structure, function, and evolution of the oviducts of squamate reptiles, with special reference to viviparity and placentation. J. Exp. Zool. 282(4–5), 560–617 (1998).CAS 
PubMed 

Google Scholar 
Guioli, S. et al. Gonadal asymmetry and sex determination in birds. Sex. Dev. 8(5), 227–242 (2014).CAS 
PubMed 

Google Scholar 
Witschi, E. Origin of asymmetry in the reproductive system of birds. Am. J. Anat. 56(1), 119–141 (1935).
Google Scholar 
Ramirez-Llodra, E. et al. Deep, diverse and definitely different: Unique attributes of the world’s largest ecosystem. Biogeosciences 7(9), 2851–2899 (2010).ADS 

Google Scholar 
Calhim, S., Pruett-Jones, S., Webster, M. S. & Rowe, M. Asymmetries in reproductive anatomy: insights from promiscuous songbirds. Biol. J. Lin. Soc. 128(3), 569–582 (2019).
Google Scholar 
Quillet, E., Labbe, L. & Queau, I. Asymmetry in sexual development of gonads in intersex rainbow trout. J. Fish Biol. 64(4), 1147–1151 (2004).
Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2022).Lin, Y. J., Qurban, M. A., Shen, K. N. & Chao, N. L. Delimitation of Tiger-tooth croaker Otolithes species (Teleostei: Sciaenidae) from the Western Arabian Gulf using an integrative approach, with a description of Otolithes arabicus sp. nov. Zool. Stud. 58, 10 (2019).
Google Scholar 
Bodenhofer, U., Bonatesta, E., Horejs-Kainrath, C. & Hochreiter, S. msa: An R package for multiple sequence alignment. Bioinformatics 31(24), 3997–9999 (2015).CAS 
PubMed 

Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: An environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 

Google Scholar 
Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H., Szoecs E. & Wagner, H. vegan: Community Ecology Package. R package version 2.5–7. https://CRAN.R-project.org/package=vegan (2020).Clarke, K. R. & Warwick, R. M. A taxonomic distinctness index and its statistical properties. J. Appl. Ecol. 35(4), 523–531 (1998).
Google Scholar 
Murtagh, F. & Legendre, P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion?. J. Classif. 31(3), 274–295 (2014).MathSciNet 
MATH 

Google Scholar 
Legendre, P. & Legendre, L. Numerical Ecology (Elsevier, 2012).MATH 

Google Scholar  More