Ecology

1.
Hezel, M. P. & Weitzberg, E. The oral microbiome and nitric oxide homoeostasis. Oral Dis. 21, 7–16 (2015).
CAS  PubMed  Google Scholar 
2.
Lundberg, J. O. & Govoni, M. Inorganic nitrate is a possible source for systemic generation of nitric oxide. Free Radic. Biol. Med. 37, 395–400 (2004).
CAS  PubMed  Google Scholar 

3.
Pannala, A. S. et al. The effect of dietary nitrate on salivary, plasma, and urinary nitrate metabolism in humans. Free Radic. Biol. Med. 34, 576–584 (2003).
CAS  PubMed  Google Scholar 

4.
Lundberg, J. O., Carlström, M. & Weitzberg, E. Metabolic effects of dietary nitrate in health and disease. Cell Metab. 28, 9–22. https://doi.org/10.1016/j.cmet.2018.06.007 (2018).
CAS  Article  PubMed  Google Scholar 

5.
Wang, X. et al. Fruit and vegetable consumption and mortality from all causes, cardiovascular disease, and cancer: systematic review and dose-response meta-analysis of prospective cohort studies. BMJ https://doi.org/10.1136/bmj.g4490 (2014).
Article  PubMed  PubMed Central  Google Scholar 

6.
Schreiber, F. et al. Denitrification in human dental plaque. BMC Biol. 8, 24 (2010).
PubMed  PubMed Central  Google Scholar 

7.
Kapil, V. et al. Physiological role for nitrate-reducing oral bacteria in blood pressure control. Free Radic. Biol. Med. 55, 93–100 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

8.
Cutler, C. et al. Post-exercise hypotension and skeletal muscle oxygenation is regulated by nitrate-reducing activity of oral bacteria. Free Radic. Biol. Med. 143, 252 (2019).
CAS  PubMed  Google Scholar 

9.
Joshipura, K. J., Muñoz-Torres, F. J., Morou-Bermudez, E. & Patel, R. P. Over-the-counter mouthwash use and risk of pre-diabetes/diabetes. Nitric Oxide 71, 14–20 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

10.
Senthil Eagappan, A. R. et al. Evaluation of salivary nitric oxide level in children with early childhood caries. Dent. Res. J. (Isfahan) 13, 338–341 (2016).
CAS  Google Scholar 

11.
Doel, J. J. et al. Protective effect of salivary nitrate and microbial nitrate reductase activity against caries. Eur. J. Oral Sci. 112, 424–428 (2004).
CAS  PubMed  Google Scholar 

12.
Li, H. et al. Salivary nitrate—an ecological factor in reducing oral acidity. Oral Microbiol. Immunol. 22, 67–71 (2007).
PubMed  Google Scholar 

13.
Jockel-Schneider, Y. et al. Stimulation of the nitrate-nitrite-NO-metabolism by repeated lettuce juice consumption decreases gingival inflammation in periodontal recall patients: a randomized, double-blinded, placebo-controlled clinical trial. J. Clin. Periodontol. 43, 603–608 (2016).
CAS  PubMed  Google Scholar 

14.
Velmurugan, S. et al. Dietary nitrate improves vascular function in patients with hypercholesterolemia: a randomized, double-blind, placebo-controlled study. Am. J. Clin. Nutr. 103, 25–38 (2016).
CAS  PubMed  Google Scholar 

15.
Vanhatalo, A. et al. Nitrate-responsive oral microbiome modulates nitric oxide homeostasis and blood pressure in humans. Free Radic. Biol. Med. 124, 21–30 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

16.
Rosier, B. T., Marsh, P. D. & Mira, A. Resilience of the oral microbiota in health: mechanisms that prevent dysbiosis. J. Dent. Res. 97, 371–380 (2018).
CAS  PubMed  Google Scholar 

17.
Govoni, M., Jansson, E. A., Weitzberg, E. & Lundberg, J. O. The increase in plasma nitrite after a dietary nitrate load is markedly attenuated by an antibacterial mouthwash. Nitric Oxide 19, 333–337 (2008).
CAS  PubMed  Google Scholar 

18.
Kilian, M. et al. The oral microbiome—an update for oral healthcare professionals. Br. Dent. J. 221, 657–666 (2016).
CAS  PubMed  Google Scholar 

19.
Backlund, C. J., Sergesketter, A. R., Offenbacher, S. & Schoenfisch, M. H. Antibacterial efficacy of exogenous nitric oxide on periodontal pathogens. J. Dent. Res. 93, 1089–1094 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

20.
Koopman, J. E. et al. Nitrate and the origin of saliva influence composition and short chain fatty acid production of oral microcosms. Microb. Ecol. 72, 479–492 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

21.
Ferrer, M. D. et al. Effect of antibiotics on biofilm inhibition and induction measured by real-time cell analysis. J. Appl. Microbiol. https://doi.org/10.1111/jam.13368 (2016).
Article  Google Scholar 

22.
Mira, A. et al. Development of an in vitro system to study oral biofilms in real time through impedance technology: validation and potential applications. J. Oral Microbiol. 11, 1609838. https://doi.org/10.1080/20002297.2019.1609838.eCollection2019 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Rosier, B. T. et al. Caries incidence in a healthy young adult population in relation to diet. JDR Clin. Trans. Res. 2, 142–150 (2017).
CAS  PubMed  Google Scholar 

24.
Camelo-Castillo, A. J. et al. Subgingival microbiota in health compared to periodontitis and the influence of smoking. Front. Microbiol. 6, 119 (2015).
PubMed  PubMed Central  Google Scholar 

25.
Navazesh, M. & Christensen, C. M. A comparison of whole mouth resting and stimulated salivary measurement procedures. J. Dent. Res. 61, 1158–1162 (1982).
CAS  PubMed  Google Scholar 

26.
Junka, A. F. et al. Use of the real time xCelligence system for purposes of medical microbiology. Pol. J. Microbiol. 61, 191–197 (2012).
PubMed  Google Scholar 

27.
Ferrer, M. D., Lamarche, B. & Mira, M. Studying Bacterial Biofilms Using Cellular Impedance. xCELLigence® Real-Time Cell Analyzers (2017).

28.
Gutiérrez, D., Hidalgo-Cantabrana, C., Rodríguez, A., García, P. & Ruas-Madiedo, P. Monitoring in real time the formation and removal of biofilms from clinical related pathogens using an impedance-based technology. PLoS ONE 11(10), e0163966 (2016).
PubMed  PubMed Central  Google Scholar 

29.
Holden, N. M. & Scholefield, D. Paper test-strips for rapid determination of nitrate tracer. Commun. Soil Sci. Plant Anal. 26, 1885–1894 (1995).
CAS  Google Scholar 

30.
Ferrer, M. D. et al. A pilot study to assess oral colonization and pH buffering by the probiotic Streptococcus dentisani under different dosing regimes. Odontology 108, 180–187 (2019).
PubMed  Google Scholar 

31.
Helmke, A. et al. The acidification of lipid film surfaces by non-thermal DBD at atmospheric pressure in air. New J. Phys. https://doi.org/10.1088/1367-2630/11/11/115025 (2009).
Article  Google Scholar 

32.
Dzidic, M. et al. Oral microbiota maturation during the first 7 years of life in relation to allergy development. Allergy 73, 2000–2011 (2018).
CAS  PubMed  Google Scholar 

33.
Boix-Amorós, A., Collado, M. C. & Mira, A. Relationship between milk microbiota, bacterial load, macronutrients and human cells during lactation. Front. Microbiol. 7, 492 (2016).
PubMed  PubMed Central  Google Scholar 

34.
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Claesson, M. J. et al. Comparison of two next-generation sequencing technologies for resolving highly complex microbiota composition using tandem variable 16S rRNA gene regions. Nucleic Acids Res. 38, e200 (2010).
PubMed  PubMed Central  Google Scholar 

36.
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

37.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
PubMed  PubMed Central  Google Scholar 

38.
Camelo-Castillo, A. et al. Nasopharyngeal microbiota in children with invasive pneumococcal disease: identification of bacteria with potential disease-promoting and protective effects. Front. Microbiol. 10, 11 (2019).
PubMed  PubMed Central  Google Scholar 

39.
Dzidic, M. et al. Oral microbiome development during childhood: an ecological succession influenced by postnatal factors and associated with tooth decay. ISME J. 12, 2292–2306 (2018).
PubMed  PubMed Central  Google Scholar 

40.
R Core Team. R: a language and environment for statistical computing. https://www.R-project.org/, https://www.R-project.org/ (2014).

41.
41Oksanen, 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., Szoecs, E., Wagner, H. vegan: Community Ecology Package. R package version 2.4-2 https://CRAN.R-project.org/package=vegan, https://CRAN.R-project.org/package=vegan (2017).

42.
Kaul, A., Mandal, S., Davidov, O. & Peddada, S. D. Analysis of microbiome data in the presence of excess zeros. Front. Microbiol. 8, 2114 (2017).
PubMed  PubMed Central  Google Scholar 

43.
Qu, X. M. et al. From nitrate to nitric oxide: the role of salivary glands and oral bacteria. J. Dent. Res. 95, 1452–1456 (2016).
CAS  PubMed  Google Scholar 

44.
Tiedje, J. M. Ecology of denitrification and dissimilatory nitrate reduction to ammonium. Biol. Anaerob. Microorg. 717, 179–244 (1988).
Google Scholar 

45.
Ten Cate, J. M. Novel anticaries and remineralizing agents: prospects for the future. J. Dent. Res. 91, 813–815 (2012).
PubMed  Google Scholar 

46.
Burleigh, M. et al. Dietary nitrate supplementation alters the oral microbiome but does not improve the vascular responses to an acute nitrate dose. Nitric Oxide 89, 54–63 (2019).
CAS  PubMed  Google Scholar 

47.
Doel, J. J., Benjamin, N., Hector, M. P., Rogers, M. & Allaker, R. P. Evaluation of bacterial nitrate reduction in the human oral cavity. Eur. J. Oral Sci. 113, 14–19 (2005).
CAS  PubMed  Google Scholar 

48.
Hyde, E. R. et al. Metagenomic analysis of nitrate-reducing bacteria in the oral cavity: implications for nitric oxide homeostasis. PLoS ONE 26, 3 (2014).
Google Scholar 

49.
Abusleme, L. et al. The subgingival microbiome in health and periodontitis and its relationship with community biomass and inflammation. ISME J. 7, 1016–1025 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

50.
Griffen, A. L. et al. Distinct and complex bacterial profiles in human periodontitis and health revealed by 16S pyrosequencing. ISME J. 6, 1176–1185 (2012).
CAS  PubMed  Google Scholar 

51.
Kistler, J. O., Booth, V., Bradshaw, D. J. & Wade, W. G. Bacterial community development in experimental gingivitis. PLoS ONE 8, e71227 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

52.
Ikeda, E. et al. Japanese subgingival microbiota in health vs disease and their roles in predicted functions associated with periodontitis. Odontology https://doi.org/10.1007/s10266-019-00452-4 (2019).
Article  PubMed  Google Scholar 

53.
Meuric, V. et al. Signature of microbial dysbiosis in periodontitis. Appl. Environ. Microbiol. 83, e00462-e517 (2017).
PubMed  PubMed Central  Google Scholar 

54.
Joshi, V., Matthews, C., Aspiras, M., de Jager, M., Ward, M. & Kumar, P. Smoking decreases structural and functional resilience in the subgingival ecosystem. CONFIDENTIAL DATA (2014).

55.
Corrêa, J. D. et al. Oral microbial dysbiosis linked to worsened periodontal condition in rheumatoid arthritis patients. Sci. Rep. 9(1), 8379. https://doi.org/10.1038/s41598-019-44674-6 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

56.
Kataoka, H. et al. Rothia dentocariosa induces TNF-alpha production in a TLR2-dependent manner. Pathog. Dis. 71, 65–68 (2014).
CAS  PubMed  Google Scholar 

57.
Binder, D., Zbinden, R., Widmer, U., Opravil, M. & Krause, M. Native and prosthetic valve endocarditis caused by Rothia dentocariosa: diagnostic and therapeutic considerations. Infection 25, 22–26 (1997).
CAS  PubMed  Google Scholar 

58.
Alcaraz, L. D. et al. Identifying a healthy oral microbiome through metagenomics. Clin. Microbiol. Infect. 18, 54–57 (2012).
CAS  PubMed  Google Scholar 

59.
Belda-Ferre, P. et al. The oral metagenome in health and disease. ISME J. 6, 46–56 (2012).
CAS  PubMed  Google Scholar 

60.
Crielaard, W. et al. Exploring the oral microbiota of children at various developmental stages of their dentition in the relation to their oral health. BMC Med. Genomics 4, 22. https://doi.org/10.1186/1755-8794 (2011).
CAS  Article  PubMed  PubMed Central  Google Scholar 

61.
Seerangaiyan, K., van Winkelhoff, A. J., Harmsen, H. J. M., Rossen, J. W. A. & Winkel, E. G. The tongue microbiome in healthy subjects and patients with intra-oral halitosis. J Breath Res. 6, 3 (2017).
Google Scholar 

62.
Jakubovics, N. S. & Burgess, J. G. Extracellular DNA in oral microbial biofilms. Microbes Infect. 17, 531–537 (2015).
CAS  PubMed  Google Scholar 

63.
Aas, J. A. et al. Bacteria of dental caries in primary and permanent teeth in children and young adults. J. Clin. Microbiol. 46, 1407–1417 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

64.
Rosier, B. T., de Jager, M., Zaura, E. & Krom, B. P. Historical and contemporary hypotheses on the development of oral diseases: are we there yet?. Front. Cell Infect. Microbiol. 4, 92 (2014).
PubMed  PubMed Central  Google Scholar 

65.
Zhou, J. et al. Influences of pH and iron concentration on the salivary microbiome in individual humans with and without caries. Appl. Environ. Microbiol. 83, e02412-02416 (2017).
PubMed  PubMed Central  Google Scholar 

66.
López-López, A., Camelo-Castillo, A. J., Ferrer García, M. D., Simon-Soro, A. & Mira, A. Health-associated niche inhabitants as oral probiotics: the case of Streptococcus dentisani. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.00379 (2017).
Article  PubMed  PubMed Central  Google Scholar 

67.
Chen, C. et al. Oral microbiota of periodontal health and disease and their changes after nonsurgical periodontal therapy. ISME J. https://doi.org/10.1038/s41396-017-0037-1 (2018).
Article  PubMed  PubMed Central  Google Scholar 

68.
Socransky, S. S., Haffajee, A. D., Cugini, M. A., Smith, C. & Kent, R. L. Jr. Microbial complexes in subgingival plaque. J. Clin. Periodontol. 25, 134–144 (1998).
CAS  PubMed  Google Scholar 

69.
Costalonga, M. & Herzberg, M. C. The oral microbiome and the immunobiology of periodontal disease and caries. Immunol. Lett. 162, 22–38 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

70.
Vargas-Reus, M. A., Memarzadeh, K., Huang, J., Ren, G. G. & Allaker, R. P. Antimicrobial activity of nanoparticulate metal oxides against peri-implantitis pathogens. Int. J. Antimicrob. Agents 40, 135–139 (2012).
CAS  PubMed  Google Scholar 

71.
Seerangaiyan, K., Jüch, F. & Winkel, E. G. Tongue coating: its characteristics and role in intra-oral halitosis and general health-a review. J. Breath Res. 12, 034001 (2018).
ADS  CAS  PubMed  Google Scholar 

72.
Mathioudakis, V. L., Vaiopoulou, E. & Aivasidis, A. Addition of nitrate for odor control in sewer networks: laboratory and field experiments. Glob. NEST 8, 37–42 (2006).
Google Scholar 

73.
Ren, W. et al. Tongue coating and the salivary microbial communities vary in children with halitosis. Sci. Rep. https://doi.org/10.1038/srep24481 (2016).
Article  PubMed  PubMed Central  Google Scholar 

74.
Huizenga, J. R., Vissink, A., Kuipers, E. J. & Gips, C. H. Helicobacter pylori and ammonia concentrations of whole, parotid and submandibular/sublingual saliva. Clin. Oral Investig. 3, 84–87 (1999).
CAS  PubMed  Google Scholar 

75.
Kraft, B. et al. Nitrogen cycling. The environmental controls that govern the end product of bacterial nitrate respiration. Science 345, 676–679 (2014).
ADS  CAS  PubMed  Google Scholar 

76.
Wolff, M. et al. In vivo effects of a new dentifrice containing 1.5% arginine and 1450 ppm fluoride on plaque metabolism. J. Clin. Dent. 24, A23–A31 (2013).
Google Scholar 

77.
Koopman, J. E. et al. Changes in the oral ecosystem induced by the use of 8% arginine toothpaste. Arch Oral Biol. 73, 79–87 (2016).
PubMed  Google Scholar 

78.
Moncada, G. et al. Salivary urease and ADS enzymatic activity as endogenous protection against dental caries in children. J. Clin. Pediatr. Dent. 39, 358–363 (2015).
CAS  PubMed  Google Scholar 

79.
Nascimento, M. M., Gordan, V. V., Garvan, C. W., Browngardt, C. M. & Burne, R. A. Correlations of oral bacterial arginine and urea catabolism with caries experience. Oral Microbiol. Immunol. 24, 89–95 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

80.
Skibsted, L. H. Nitric oxide and quality and safety of muscle based foods. Nitric Oxide 24, 176–183 (2011).
CAS  PubMed  Google Scholar 

81.
Sindelar, J. J. & Milkowski, A. L. Human safety controversies surrounding nitrate and nitrite in the diet. Nitric Oxide 26, 259–266 (2012).
CAS  PubMed  Google Scholar 

82.
Link, L. B. & Potter, J. D. Raw versus cooked vegetables and cancer risk. Cancer Epidemiol. Biomark. Prev. 13, 1422–1435 (2004).
Google Scholar 

83.
Ward, M. H. Too much of a good thing? Nitrate from nitrogen fertilizers and cancer. Rev. Environ. Health 24, 357–363 (2009).
CAS  PubMed  PubMed Central  Google Scholar 

84.
Kobayashi, J., Ohtake, K. & Uchida, H. NO-rich diet for lifestyle-related diseases. Nutrients 7, 4911–4937 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

85.
Scientific Opinion of the Panel on Contaminants in the Food chain. Opinion of the Scientific Panel on Contaminants in the Food chain on a request from the European Commission to perform a scientific risk assessment on nitrate in vegetables. EFSA J. 698, 1–79 (2008).
Google Scholar  More

1.
Coon, K. L., Brown, M. R. & Strand, M. R. Gut bacteria differentially affect egg production in the anautogenous mosquito Aedes aegypti and facultatively autogenous mosquito Aedes atropalpus (Diptera: Culicidae). Parasite Vector 9, 375 (2016).
Google Scholar 
2.
Gaio, A. D. O. et al. Contribution of midgut bacteria to blood digestion and egg production in Aedes aegypti (Diptera: Culicidae) (L.). Parasite Vector 4, 105 (2011).
Google Scholar 

3.
Bascuñán, P. et al. Factors shaping the gut bacterial community assembly in two main Colombian malaria vectors. Microbiome 6, 148 (2018).
PubMed  PubMed Central  Google Scholar 

4.
Chandler, J. A., Liu, R. M. & Bennett, S. N. RNA shotgun metagenomic sequencing of Northern California (USA) mosquitoes uncovers viruses, bacteria, and fungi. Front. Microbiol. 6, 1–16 (2015).
Google Scholar 

5.
Coon, K. L., Vogel, K. J., Brown, M. R. & Strand, M. R. Mosquitoes rely on their gut microbiota for development. Mol. Ecol. 23, 2727–2739 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

6.
Gimonneau, G. et al. Composition of Anopheles coluzzii and Anopheles gambiae microbiota from larval to adult stages. Infect. Genet. Evol. 28, 715–724 (2014).
PubMed  Google Scholar 

7.
Muturi, E. J. et al. Culex pipiens and Culex restuans mosquitoes harbor distinct microbiota dominated by few bacterial taxa. Parasite Vector 9, 18 (2016).
Google Scholar 

8.
Muturi, E. J., Ramirez, J. L., Rooney, A. P. & Kim, C. H. Comparative analysis of gut microbiota of mosquito communities in central Illinois. PLoS Negl. Trop. Dis. 11, e0005377 (2017).
PubMed  PubMed Central  Google Scholar 

9.
Muturi, E. J. et al. Mosquito microbiota cluster by host sampling location. Parasite Vector 11, 468 (2018).
Google Scholar 

10.
Osei-Poku, J., Mbogo, C. M., Palmer, W. J. & Jiggins, F. M. Deep sequencing reveals extensive variation in the gut microbiota of wild mosquitoes from Kenya. Mol. Ecol. 21, 5138–5150 (2012).
CAS  PubMed  Google Scholar 

11.
Wang, Y. et al. Dynamic gut microbiome across life history of the malaria mosquito Anopheles gambiae in Kenya. PLoS ONE 6, 1–9 (2011).
Google Scholar 

12.
Carissimo, G. et al. Antiviral immunity of Anopheles gambiae is highly compartmentalized, with distinct roles for RNA interference and gut microbiota. Proc. Natl. Acad. Sci. 112, E176–E185 (2015).
CAS  PubMed  Google Scholar 

13.
Dennison, N. J., Jupatanakul, N. & Dimopoulos, G. The mosquito microbiota influences vector competence for human pathogens. Curr. Opin. Insect Sci. 3, 6–13 (2014).
PubMed  PubMed Central  Google Scholar 

14.
Hegde, S., Rasgon, J. L. & Hughes, G. L. The microbiome modulates arbovirus transmission in mosquitoes. Curr. Opin. Virol. 15, 97–102 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

15.
Jupatanakul, N., Sim, S. & Dimopoulos, G. The insect microbiome modulates vector competence for arboviruses. Viruses 6, 4294–4313 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

16.
Ramirez, J. L. et al. Reciprocal tripartite interactions between the Aedes aegypti midgut microbiota, innate immune system and dengue virus influences vector competence. PLoS Negl. Trop. Dis. 6, e1561 (2012).
PubMed  PubMed Central  Google Scholar 

17.
Charan, S. S. et al. Comparative analysis of midgut bacterial communities of Aedes aegypti mosquito strains varying in vector competence to dengue virus. Parasitol. Res. 112, 2627–2637 (2013).
PubMed  Google Scholar 

18.
Dickson, L. B. et al. Carryover effects of larval exposure to different environmental bacteria drive adult trait variation in a mosquito vector. Sci. Adv. 3, e1700585 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

19.
Gonçalves, C. M. et al. Distinct variation in vector competence among nine field populations of Aedes aegypti from a Brazilian dengue-endemic risk city. Parasite Vector 7, 320 (2014).
Google Scholar 

20.
Campos, S. S. et al. Zika virus can be venereally transmitted between Aedes aegypti mosquitoes. Parasite Vector 10, 605 (2017).
Google Scholar 

21.
Sánchez-Vargas, I. et al. Demonstration of efficient vertical and venereal transmission of dengue virus type-2 in a genetically diverse laboratory strain of Aedes aegypti. PLOS Negl. Trop. D 12, e0006754 (2018).
Google Scholar 

22.
Valiente Moro, C. V. et al. Diversity of culturable bacteria including Pantoea in wild mosquito Aedes albopictus. BMC Microbiol. 13, 70 (2013).
PubMed  PubMed Central  Google Scholar 

23.
Zouache, K. et al. Bacterial diversity of field-caught mosquitoes, Aedes albopictus and Aedes aegypti, from different geographic regions of Madagascar. FEMS Microbiol. Ecol. 75, 377–389 (2011).
CAS  PubMed  Google Scholar 

24.
Foster, W. A. Mosquito sugar feeding and reproductive energetics. Annu. Rev. Entomol. 40, 443–474 (1995).
CAS  PubMed  Google Scholar 

25.
Minard, G., Mavingui, P. & Moro, C. V. Diversity and function of bacterial microbiota in the mosquito holobiont. Parasite Vector 6, 146–158 (2013).
Google Scholar 

26.
Coon, K. L., Brown, M. R. & Strand, M. R. Mosquitoes host communities of bacteria that are essential for development but vary greatly between local habitats. Mol. Ecol. 25, 5806–5826 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

27.
Ponnusamy, L. et al. Diversity of bacterial communities in container habitats of mosquitoes. Microb. Ecol. 56, 593–603 (2008).
PubMed  PubMed Central  Google Scholar 

28.
Yee, D. A., Allgood, D., Kneitel, J. M. & Kuehn, K. A. Constitutive differences between natural and artificial container mosquito habitats: vector communities, resources, microorganisms, and habitat parameters. J. Med. Entomol. 49, 482–491 (2012).
CAS  PubMed  Google Scholar 

29.
Rani, A. et al. Bacterial diversity analysis of larvae and adult midgut microflora using culture-dependent and culture-independent methods in lab-reared and field-collected Anopheles stephensi-an Asian malarial vector. BMC Microbiol. 9, 96 (2009).
PubMed  PubMed Central  Google Scholar 

30.
Guégan, M. et al. The mosquito holobiont: fresh insight into mosquito-microbiota interactions. Microbiome 6, 49 (2018).
PubMed  PubMed Central  Google Scholar 

31.
Mancini, M. V. et al. Estimating bacteria diversity in different organs of nine species of mosquito by next generation sequencing. BMC Microbiol. 18, 126 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

32.
Segata, N. et al. The reproductive tracts of two malaria vectors are populated by a core microbiome and by gender-and swarm-enriched microbial biomarkers. Sci. Rep. 6, 24207 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

33.
Rossi, P. et al. Mutual exclusion of Asaia and Wolbachia in the reproductive organs of mosquito vectors. Parasite Vector 8, 278 (2015).
Google Scholar 

34.
Tchioffo, M. T. et al. Dynamics of bacterial community composition in the malaria mosquito’s epithelia. Front. Microbiol. 6, 1500 (2016).
PubMed  PubMed Central  Google Scholar 

35.
Moll, R. M. et al. Meconial peritrophic membranes and the fate of midgut bacteria during mosquito (Diptera: Culicidae) metamorphosis. J. Med. Entomol. 38, 29–32 (2001).
CAS  PubMed  Google Scholar 

36.
Moncayo, A. C. et al. Meconial peritrophic matrix structure, formation, and meconial degeneration in mosquito pupae/pharate adults: histological and ultrastructural aspects. J. Med. Entomol. 42, 939–944 (2005).
PubMed  Google Scholar 

37.
Chavshin, A. R. et al. Escherichia coli expressing a green fluorescent protein (GFP) in Anopheles stephensi: a preliminary model for paratransgenesis. Symbiosis 60, 17–24 (2013).
CAS  Google Scholar 

38.
Chavshin, A. R. et al. Malpighian tubules are important determinants of Pseudomonas transstadial transmission and longtime persistence in Anopheles stephensi. Parasite Vector 8, 36 (2015).
Google Scholar 

39.
Deveau, A. et al. Bacterial-fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol. Rev. 42, 335–352 (2018).
CAS  PubMed  Google Scholar 

40.
Alencar, Y. B., Ríos-Velásquez, C. M., Lichtwardt, R. W. & Hamada, N. Trichomycetes (Zygomycota) in the digestive tract of arthropods in Amazonas Brazil. Mem. Inst. Oswaldo Cruz. 98, 799–810 (2003).
PubMed  Google Scholar 

41.
Angleró-Rodríguez, Y. I. et al. An Aedes aegypti-associated fungus increases susceptibility to dengue virus by modulating gut trypsin activity. Elife 6, e28844 (2017).
PubMed  PubMed Central  Google Scholar 

42.
Ramirez, J. L. et al. Entomopathogenic fungal infection leads to temporospatial modulation of the mosquito immune system. PLoS Negl. Trop. Dis. 12, e0006433 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

43.
Wei, G. et al. Insect pathogenic fungus interacts with the gut microbiota to accelerate mosquito mortality. Proc. Natl. Acad. Sci. 114, 5994–5999 (2017).
CAS  PubMed  Google Scholar 

44.
Frankel-Bricker, J., Buerki, S., Feris, K. P. & White, M. M. Influences of a prolific gut fungus (Zancudomyces culisetae) on larval and adult mosquito (Aedes aegypti)-associated microbiota. Appl. Environ. Microbiol. 86, e02334-19 (2020).
PubMed  PubMed Central  Google Scholar 

45.
Lichtwardt, R. W. Species of Harpellales living within the guts of aquatic Diptera larvae. Mycotaxon 19, 529–550 (1984).
Google Scholar 

46.
Williams, M. C. & Lichtwardt, R. W. Infection of Aedes aegypti larvae by axenic cultures of the fungal genus Smittium (trichomycetes). Am. J. Bot. 59, 189–193 (1972).
Google Scholar 

47.
Horn, B. W. Ultrastructural changes in trichospores of Smittium culisetae and S. culicis during in vitro sporangiospore extrusion and holdfast formation. Mycologia 81, 742–753 (1989).
Google Scholar 

48.
Horn, B. W. Physiological changes associated with sporangiospore extrusion from trichospores of Smittium culisetae. Exp. Mycol. 14, 113–123 (1990).
Google Scholar 

49.
Karl, P. J. et al. Effects of psychological, environmental and physical stressors on the gut microbiota. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.02013 (2018).
Article  PubMed  PubMed Central  Google Scholar 

50.
McCreadie, J. W. & Beard, C. E. The microdistribution of the trichomycete Smittium culisetae in the hindgut of the black fly host Simulium vittatum. Mycologia 95, 998–1003 (2003).
PubMed  Google Scholar 

51.
Hibbing, M. E., Fuqua, C., Parsek, M. R. & Peterson, S. B. Bacterial competition: Surviving and thriving in the microbial jungle. Nat. Rev. Microbiol. 8, 15 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Schloss, P. D. Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. mBio 9, e00525-18 (2018).
PubMed  PubMed Central  Google Scholar 

53.
Frankel-Bricker, J., Song, M. J., Benner, M. J. & Schaack, S. Variation in the microbiota associated with Daphnia magna across genotypes, populations, and temperature. Microb Ecol. https://doi.org/10.1007/s00248-019-01412-9 (2019).
Article  PubMed  PubMed Central  Google Scholar 

54.
Sinha, R. et al. The microbiome quality control project: Baseline study design and future directions. Genome Biol. 16, 276 (2015).
PubMed  PubMed Central  Google Scholar 

55.
Sinha, R. et al. Assessment of variation in microbial community amplicon sequencing by the Microbiome Quality Control (MBQC) project consortium. Nat. Biotechnol. 35, 1077–1086 (2017).
CAS  PubMed  PubMed Central  Google Scholar 

56.
Bahl, M. I., Bergström, A. & Licht, T. R. Freezing fecal samples prior to DNA extraction affects the Firmicutes to Bacteroidetes ratio determined by downstream quantitative PCR analysis. FEMS Microbiol. Lett. 329, 193–197 (2012).
CAS  PubMed  Google Scholar 

57.
Cardona, S. et al. Storage conditions of intestinal microbiota matter in metagenomic analysis. BMC Microbiol. 12, 158 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

58.
Foggie, T. & Achee, N. Standard operating procedures: rearing Aedes aegypti for the HITSS and box laboratory assays training manual (2009).

59.
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).
CAS  PubMed  Google Scholar 

60.
Takahashi, S. et al. Development of a prokaryotic universal primer for simultaneous analysis of bacteria and archaea using next-generation sequencing. PLoS ONE 9, e105592 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

61.
R Core Team. R: A Language Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).

62.
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods. 13, 581–583 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

63.
Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

64.
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
CAS  PubMed  Google Scholar 

65.
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).
CAS  PubMed  Google Scholar 

66.
McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

67.
Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).
PubMed  PubMed Central  Google Scholar 

68.
Davis, N. M. et al. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226 (2018).
PubMed  PubMed Central  Google Scholar 

69.
Wickham, H. Ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).
Google Scholar 

70.
Lüdecke, D. sjstats: statistical functions for regression models. https://cran.r-project.org/web/packages/sjstats/in (2017).

71.
Marwick, B. & Krishnamoorthy, K. cvequality: tests for the equality of coefficients of variation from multiple groups. https://github.com/benmarwick/cvequality (2016).

72.
Feltz, C. J. & Miller, G. E. An asymptotic test for the equality of coefficients of variation from k populations. Stat. Med. 15, 647–658 (1996).
Google Scholar 

73.
Krishnamoorthy, K. & Lee, M. Improved tests for the equality of normal coefficients of variation. Comput. Stat. 29, 215–232 (2014).
MathSciNet  MATH  Google Scholar 

74.
Kandlikar, G. S. et al. ranacapa: an R package and Shiny web app to explore environmental DNA data with exploratory statistics and interactive visualizations. F1000 Res. 7, 1734 (2018).
Google Scholar 

75.
Anderson, M. J. Permutational multivariate analysis of variance (PERMANOVA). Wiley StatsRef Stat. Ref. Online https://doi.org/10.1002/9781118445112.stat07841 (2017).
Article  Google Scholar 

76.
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).
Google Scholar 

77.
Kindt, R. Package ‘ BiodiversityR .’ R Proj. https://CRAN.R-project.org/package=BiodiversityR (2016).

78.
Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693 (2006).
PubMed  Google Scholar 

79.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253 (2006).
MathSciNet  PubMed  MATH  Google Scholar 

80.
Bates, D. et al. Package “lme4”: linear mixed-effects models using “Eigen” and S4. J. Stat. Softw. https://CRAN.R-project.org/package=lme4 (2015).

81.
Fox, J. et al. Package “car.” R Doc. https://CRAN.R-project.org/package=car (2018). More

1.
Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).
ADS  CAS  Google Scholar 
2.
Denny, M. W., Hunt, L. J. H., Miller, L. P. & Harley, C. D. G. On the prediction of extreme ecological events. Ecol. Monogr. 79, 397–421 (2009).
Google Scholar 

3.
Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).
ADS  CAS  PubMed  Google Scholar 

4.
McCain, C. M. & King, S. R. B. Body size and activity times mediate mammalian responses to climate change. Glob. Change Biol. 20, 1760–1769 (2014).
ADS  Google Scholar 

5.
Morley, S. A., Barnes, D. K. A. & Dunn, M. J. Predicting which species succeed in climate-force polar seas. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00507 (2019).
Article  Google Scholar 

6.
Jenni, L. & Kéry, M. Timing of autumn bird migration under climate change: advances in long-distance migrants, delays in short-distance migrants. Proc. R. Soc. Lond. B. 270, 1467–1471 (2003).
Google Scholar 

7.
Barbraud, C. et al. Contrasted demographic responses to facing future climate change in Southern Ocean seabirds. J. Anim. Ecol. 80, 89–100 (2010).
PubMed  Google Scholar 

8.
Chambers, L. E. et al. Determining trends and environmental drivers from long-term marine mammal and seabird data: examples from Southern Australia. Reg. Environ. Change 15, 197–209 (2015).
Google Scholar 

9.
Soldatini, C., Albores-Barajas, Y. V., Massa, B. & Gimenez, O. Forecasting ocean warming impacts on seabird demography: a case study on the European storm petrel. Mar. Ecol. Prog. Ser. 552, 255–269 (2016).
ADS  Google Scholar 

10.
Hays, G. C., Broderick, A. C., Glen, F. & Godley, B. J. Climate change and sea turtles: a 150-year reconstruction of incubation temperatures at a major marine turtle rookery. Glob. Change Biol. 9, 642–646 (2003).
ADS  Google Scholar 

11.
Barange, M. et al. Impacts of climate change on fisheries and aquaculture: synthesis of current knowledge, adaptation and mitigation options. FAO Fisheries and Aquaculture Technical Paper No. 627: Rome. https://www.fao.org/3/i9705en/i9705en.pdf (2018).

12.
Hoegh-Guldberg, O. & Bruno, J. F. The impact of climate change on the world’s marine ecosystems. Science 328, 1523–1528 (2010).
ADS  CAS  PubMed  Google Scholar 

13.
Martínez, C. et al. Coastal erosion in central Chile: A new hazard?. Ocean Coast. Manag. 156, 141–155 (2018).
Google Scholar 

14.
Neumann, J. E. et al. Climate change risk to US infrastructure: impacts on roads, bridges, coastal development, and urban drainage. Clim. Change 131, 97–109 (2015).
ADS  Google Scholar 

15.
Frederiksen, M., Daunt, F., Harris, M. P. & Wanless, S. The demographic impact of extreme events: stochastic weather drives survival and population dynamics in a long-lived seabird. J. Anim. Ecol. 77, 1020–1029 (2008).
CAS  PubMed  Google Scholar 

16.
Schumann, N., Gales, N. J., Harcourt, R. G. & Arnould, J. P. Impacts of climate change on Australian marine mammals. Aust. J. Zool. 61, 146–159 (2013).
Google Scholar 

17.
Galbraith, H., DesRochers, D. W., Brown, S. & Reed, J. M. Predicting vulnerabilities of North American shorebirds to climate change. PLoS ONE 9, e108899 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

18.
Bartholomew, G. A. A model for the evolution of pinniped phylogeny. Evolution 24, 546–559 (1970).
PubMed  Google Scholar 

19.
Antonelis, G. A. Rookeries. In Encyclopedia of marine mammals (eds. Perrin, W. F., Würsig, B. & Thewissen, J. G. M) 1051–1052 (San Diego, CA: Academic Press, 2002).

20.
Ban, S. & Trites, A. W. Quantification of terrestrial haul-out and rookery characteristics of Steller sea lions. Mar. Mamm. Sci. 23, 496–507 (2007).
Google Scholar 

21.
Arnould, J. P. & Littnan, C. L. Pup production and breeding areas of Australian fur seals. Aust. Mammal. 22, 51–55 (2000).
Google Scholar 

22.
Pemberton, D. & Gales, R. Australian fur seals (Arctocephalus pusillus doriferus) breeding in Tasmania: population size and status. Wildlife Res. 31, 301–309 (2004).
Google Scholar 

23.
Crespo, E. A., Oliva, D., Dans, S. L. & Sepúlveda, M. Estado de situación del lobo marino común en su área de distribución (Editorial Universidad de Valparaíso, Valparaíso, Chile, 2012).
Google Scholar 

24.
Venegas, C. et al. Distribución y abundancia de lobos marinos (Pinnipedia: Otariidae) en la Región de Magallanes. Chile. An. Inst. Pat. Ser. Cienc. 30, 67–82 (2002).
Google Scholar 

25.
Oliva, D. et al. Estimación poblacional de lobos marinos e impacto de la captura incidental. Informe Final Proyecto FIPA 2018–54, 1–150 (2020).
Google Scholar 

26.
Bailys, A. M. M. et al. Diving deeper into individual foraging specializations of a large marine predator, the Southern sea lion. Oecologia 179, 1053–1065 (2015).
ADS  Google Scholar 

27.
Sepúlveda, M. et al. Rol ecológico del lobo marino común en el territorio y aguas jurisdiccionales chilenas. Infome Final Proyecto FIPA 2014–28, 1–160 (2016).
Google Scholar 

28.
Acevedo, J., Aguayo-Lobo, A. & Sielfeld, W. Eventos reproductivos del león marino común Otaria flavescens (Shaw 1800), en el norte de Chile (Pacífico suroriental). Rev. Biol. Mar. Oceanog. 38, 69–75 (2013).
Google Scholar 

29.
Rivas, M. & Trimble, M. Aggregation behaviour in South American sea lion (Otaria flavescens) pups at Isla de Lobos. Uruguay. Aquat. Mamm 35, 55–71 (2009).
Google Scholar 

30.
McLean, L. J., George, S., Lerodiaconou, D., Kirkwood, R. J. & Arnould, J. P. Y. Impact of rising sea levels on Australian fur seals. PeerJ 6, e5786 (2018).
PubMed  PubMed Central  Google Scholar 

31.
Reeves, R. R. Speculations on the impact of global warming on aquatic mammals. Proceedings of the American Cetacean Society, Monterrey, CA. American Cetacean Society, San Pedro (1990).

32.
Boyd, I. L., Lunn, N. J. & Barton, T. Time budgets and foraging characteristics of lactating Antarctic fur seals. J. Anim. Ecol. 60, 577–592 (1991).
Google Scholar 

33.
Muñoz, L., Pavez, G., Inostroza, P. & Sepúlveda, M. Foraging trips of female South American sea lions (Otaria flavescens) from isla Chañaral. Chile. Lat. Am. J. Aquat. Mamm. 9, 140–144 (2011).
Google Scholar 

34.
Milette, L. L. & Trites, A. W. Maternal attendance patterns of Steller sea lions (Eumetopias jubatus) from stable and declining populations in Alaska. Can. J. Zool. 81, 340–348 (2003).
Google Scholar 

35.
Jiménez, J., Armaroli, C. & Bosom, E. Preparing for the Impact of Coastal Storms, A Coastal Manager-oriented Approach. In Coastal Storms, Processes and Impacts (eds. Ciavola, P. & Coco, G) 217–239 (Wiley Blackwell, 2017).

36.
Oliveira, L. R. et al. Ancient female philopatry, asymmetric male gene flow, and synchronous population expansion support the influence of climatic oscillations on the evolution of South American sea lion (Otaria flavescens). PLoS ONE 12(6), e0179442 (2017).
PubMed  PubMed Central  Google Scholar 

37.
Grandi, M. F., Dans, S. L. & Crespo, E. A. Social composition and spatial distribution of colonies in an expanding population of South America sea lion. J. Mamm. 89, 1218–1228 (2008).
Google Scholar 

38.
Hoffman, J. I. & Forcada, J. Extreme natal philopatry in female Antarctic fur seals (Arctocephalus gazella). Mamm. Biol. 77, 71–73 (2012).
Google Scholar 

39.
Hofmeyr, G. J. G., Bester, M. N., Makhado, A. B. & Pistorius, P. A. Population changes in subantarctic and antarctic fur seals at Marion Island. S. Afr. J. Wildl. Res. 36, 55–68 (2006).
Google Scholar 

40.
Raum-Suryan, K. L., Pitcher, K. W., Calkins, D. G., Sease, J. L. & Loughlin, T. R. Dispersal, rookery fidelity, and metapopulation structure of Steller sea lions (Eumetopias jubatus) in an increasing and a decreasing population in Alaska. Mar. Mamm. Sci. 18, 746–764 (2002).
Google Scholar 

41.
Harcourt, R. Factors affecting early mortality in the South American fur seal (Arctocephalus australis) in Peru: Density-related effects and predation. J. Zool. 226, 259–270 (1992).
Google Scholar 

42.
Sillmann, J., Kharin, V. V., Zwiers, F. W., Zhang, X. & Bronaugh, D. Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. J. Geophys. Res. Atmos. 118, 2473–2493 (2013).
ADS  Google Scholar 

43.
Fuentes, M. et al. Adaptive management of marine mega-fauna in a changing climate. Mitig. Adapt. Strat. Glob. Chang. 21, 209–224 (2016).
Google Scholar 

44.
Hofmeyr, G. J. G., du Toit, M. & Kirkman, S. P. Early post-release survival of stranded Cape fur seal pups at Black Rocks, Algoa Bay. S. Afr. Afr. J. Mar. Sci. 33, 463–468 (2011).
Google Scholar 

45.
Fink, S. Loss of habitat: impacts on pinnipeds and their welfare. In Marine Mammal Welfare (ed. Butterworth, A.) 241–252 (Springer, Berlin, 2017).
Google Scholar 

46.
Adame, K., Pardo, M. A., Salvadeo, C., Beier, E. & Elorriaga-Ver-Plancken, F. Detectability and categorization of California sea lions using an unmanned aerial vehicle. Mar. Mamm. Sci. 33, 913–925 (2017).
Google Scholar 

47.
McIntosh, R., Holmberg, R. & Dann, P. Looking without landing—using remote piloted aircraft to monitor fur seal populations without disturbance. Front. Mar. Sci. 5, 1–13 (2018).
Google Scholar 

48.
Tolman, H. User manual and system documentation of WAVEWATCH III version 4.18. Environmental Modeling Center Marine Modeling and Analysis Branch (2014).

49.
Ardhuin, F. et al. Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation. J. Phys. Oceanogr 40, 1918–1941 (2010).
ADS  Google Scholar 

50.
Stopa, J. & Cheung, K. Intercomparison of wind and wave data from the ECMWF Reanalysis Interim and the NCEP Climate Forecast System Reanalysis. Ocean Model. 75, 65–83 (2014).
ADS  Google Scholar 

51.
Saha, S. et al. The NCEP climate forecast system reanalysis. B. Am. Meteorol. Soc. 19, 1015–1057 (2010).
Google Scholar 

52.
Smith, W. & Sandwell, D. Global seafloor topography from satellite altimetry and ship depth soundings. Science 277, 1957–1962 (1997).
Google Scholar 

53.
Wessel, P. & Smith, W. A global, self-consistent, hierarchical, high-resolution shoreline database. J. Geophys. Res. 101, 8741–8743 (1996).
ADS  Google Scholar 

54.
Beyá, J., Hidalgo, H., Winckler, P., Gallardo, A. & Alvarez, M. Generation and validation of the Chilean Wave Atlas database. Ocean Model. 116, 16–32 (2017).
ADS  Google Scholar 

55.
Massey, T., Anderson, M., Smith, J. M., Gomez, J. & Jones, R. ERDC/CHL SR-11-1: STWAVE: Steady-State Spectral Wave Model User’s Manual for STWAVE, Version 6.0.Washington DC: USACE: Coastal and Hydraulics Laboratory. Flood and Coastal Storm Damage Reduction Research and Development Program (2011).

56.
SHOA. Atlas Hidrográfico de Chile. Valparaíso: SHOA (2017).

57.
Hasselmann, K. et al. Measurements of wind-wave growth and swell decay during the Joint North Sea Wave Project (JONSWAP). Deutschen Hydrographischen Zeitschrift 12, 1–95 (1973).
Google Scholar 

58.
Pawlowicz, R., Beardsley, B. & Lentz, S. (2002) Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE. Comput. Geosci. 28, 929–937 (2002).
ADS  Google Scholar 

59.
Herbich, J. B. Handbook of coastal engineering (McGraw-Hill, New York, 2000).
Google Scholar 

60.
Goda, Y. Random seas and design of maritime structures, 2nd Ed. Advanced Series on Ocean Engineering – Volume 15. World Scientific Publishing Co (2000).

61.
R Core Team. R: A language and environment for statistical computing. R Found Stat Comput 3 (2013). More

1.
Ernande, B., Dieckmann, U. & Heino, M. Adaptive changes in harvested populations: plasticity and evolution of age and size at maturation. Proc. R. Soc. Lond. B 271, 415–523 (2004).
Google Scholar 
2.
Miner, B. G., Sultan, S. E., Morgan, S. G., Padilla, D. K. & Relyea, R. A. Ecological consequences of phenotypic plasticity. Trends Ecol. Evol. 20, 685–692 (2005).
PubMed  Google Scholar 

3.
Schmalhausen, I. I. Factors of Evolution: The Theory of Stabilizing Selection (Blakiston, Lymington, 1949).
Google Scholar 

4.
Oomen, R. A. & Hutchings, J. A. Genetic variability in reaction norms in fishes. Environ. Rev. 23, 353–366 (2015).
Google Scholar 

5.
Gordon, S. P., Hendry, A. P. & Reznick, D. N. Predator-induced contemporary evolution, phenotypic plasticity, and the evolution of reaction norms in guppies. Copeia 105, 514–522 (2017).
Google Scholar 

6.
Olsen, E. M. et al. Small-scale biocomplexity in coastal Atlantic cod supporting a Darwinian perspective on fisheries management. Evol. Appl. 1, 524–533 (2008).
PubMed  PubMed Central  Google Scholar 

7.
Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).
PubMed  PubMed Central  Google Scholar 

8.
Reznick, D. N. Norms of reaction in fishes. In The Exploitation of Evolving Resources (eds Stokes, K. et al.) 72–90 (Springer, Berlin Heidelberg, 1993).
Google Scholar 

9.
Trippel, E. A. Age at maturity as a stress indicator in fisheries. Bioscience 45, 759–771 (1995).
Google Scholar 

10.
Mims, M. C. & Olden, J. D. Fish assemblages respond to altered flow regimes via ecological filtering of life history strategies. Freshw. Biol. 58, 50–62 (2013).
Google Scholar 

11.
Bennett, M. G., Whiles, M. R. & Whitledge, G. W. Population-level responses of life history traits to flow regime in three common stream fish species. Ecohydrology 9, 1388–1399 (2016).
Google Scholar 

12.
Congdon, J. D., Dunham, A. E., Hopkins, W. A., Rowe, C. L. & Hinton, T. G. Resource allocation-based life histories: A conceptual basis for studies of ecological toxicology. Environ. Toxicol. Chem. 20, 1698–1703 (2001).
CAS  PubMed  Google Scholar 

13.
Walsh, M. R. & Reznick, D. N. Interactions between the direct and indirect effects of predators determine life history evolution in a killifish. PNAS 105, 594–599 (2008).
ADS  CAS  PubMed  Google Scholar 

14.
Sokolova, I. M. Energy-Limited tolerance to stress as a conceptual framework to integrate the effects of multiple stressors. Integr Comp Biol 53, 597–608 (2013).
PubMed  Google Scholar 

15.
Roff, D. A. The Evolution of Life History Parameters in Teleosts. Can. J. Fish. Aquat. Sci. 41, 989–1000 (1984).
Google Scholar 

16.
Ward, H. G. M., Post, J. R., Lester, N. P., Askey, J. P. & Godin, T. Empirical evidence of plasticity in life-history characteristics across climatic and fish density gradients. Can. J. Fish. Aquat. Sci. 74, 464–474 (2016).
Google Scholar 

17.
Thorson, J. T., Munch, S. B., Cope, J. M. & Gao, J. Predicting life history parameters for all fishes worldwide. Ecol. Appl. 27, 2262–2276 (2017).
PubMed  Google Scholar 

18.
Alberto, F. et al. Adaptive responses for seed and leaf phenology in natural populations of sessile oak along an altitudinal gradient. J. Evol. Biol. 24, 1442–1454 (2011).
CAS  PubMed  Google Scholar 

19.
Toräng, P. et al. Large-scale adaptive differentiation in the alpine perennial herb Arabis alpina. New Phytol. 206, 459–470 (2015).
PubMed  Google Scholar 

20.
Sparks, M. M., Westley, P. A. H., Falke, J. A. & Quinn, T. P. Thermal adaptation and phenotypic plasticity in a warming world: Insights from common garden experiments on Alaskan sockeye salmon. Glob. Change Biol. 23, 5203–5217 (2017).
ADS  Google Scholar 

21.
Wright, P. J. Methodological challenges to examining the causes of variation in stock reproductive potential. Fish. Res. 138, 14–22 (2013).
Google Scholar 

22.
Conover, D. O. & Baumann, H. PERSPECTIVE: The role of experiments in understanding fishery-induced evolution. Evol. Appl. 2, 276–290 (2009).
PubMed  PubMed Central  Google Scholar 

23.
Nagrodski, A., Murchie, K. J., Stamplecoskie, K. M., Suski, C. D. & Cooke, S. J. Effects of an experimental short-term cortisol challenge on the behaviour of wild creek chub Semotilus atromaculatus in mesocosm and stream environments. J. Fish Biol. 82, 1138–1158 (2013).
CAS  PubMed  Google Scholar 

24.
Winemiller, K. O. Life history strategies, population regulation, and implications for fisheries management. Can. J. Fish. Aquat. Sci. 62, 872–885 (2005).
Google Scholar 

25.
Weeks, S. C. Phenotypic plasticity of life-history traits in clonal and sexual fish (Poeciliopsis) at high and low densities. Oecologia 93, 307–314 (1993).
ADS  PubMed  Google Scholar 

26.
Ziegler, P. E., Lyle, J. M., Haddon, M. & Ewing, G. P. Rapid changes in life-history characteristics of a long-lived temperate reef fish. Mar. Freshwater Res. 58, 1096–1107 (2008).
Google Scholar 

27.
Genner, M. J. et al. Body size-dependent responses of a marine fish assemblage to climate change and fishing over a century-long scale. Glob. Change Biol. 16, 517–527 (2010).
ADS  Google Scholar 

28.
Wanner, G. A., Shuman, D. A. & Willis, D. W. Food habits of juvenile Pallid Sturgeon and adult Shovelnose Sturgeon in the Missouri River downstream of Fort Randall Dam, South Dakota. J. Freshw. Ecol. 22, 81–92 (2007).
Google Scholar 

29.
Grohs, K. L., Klumb, R. A., Chipps, S. R. & Wanner, G. A. Ontogenetic patterns in prey use by Pallid Sturgeon in the Missouri River, South Dakota and Nebraska. J. Appl. Ichthyol. 25, 48–53 (2009).
Google Scholar 

30.
Gerrity, P. C., Guy, C. S. & Gardner, W. M. Juvenile Pallid Sturgeon are piscivorous: a call for conserving native cyprinids. Trans. Am. Fish. Soc. 135, 604–609 (2006).
Google Scholar 

31.
Keenlyne, K. D., Grossman, E. M. & Jenkins, L. G. Fecundity of the Pallid Sturgeon. Trans. Am. Fish. Soc. 121, 139–140 (1992).
Google Scholar 

32.
Koch, J. D., Steffensen, K. D. & Pegg, M. A. Validation of age estimates obtained from juvenile Pallid Sturgeon Scaphirhynchus albus pectoral fin spines. J. Appl. Ichthyol. 27, 209–212 (2011).
Google Scholar 

33.
Hamel, M. J. et al. Using mark–recapture information to validate and assess age and growth of long-lived fish species. Can. J. Fish. Aquat. Sci. 71, 559–566 (2014).
Google Scholar 

34.
Braaten, P. J. et al. Age estimations of wild Pallid Sturgeon (Scaphirhynchus albus, Forbes & Richardson 1905) based on pectoral fin spines, otoliths and bomb radiocarbon: inferences on recruitment in the dam-fragmented Missouri River. J. Appl. Ichthyol. 31, 821–829 (2015).
Google Scholar 

35.
Keenlyne, K. D. & Jenkins, L. G. Age at sexual maturity of the Pallid Sturgeon. Trans. Am. Fish. Soc. 122, 393–396 (1993).
Google Scholar 

36.
Dryer, M. P. & Sandvol, A. J. Pallid Sturgeon recovery plan (United States Fish and Wildlife Service, Bismarck, North Dakota, 1990).
Google Scholar 

37.
Bramblett, R. G. & White, R. G. Habitat use and movements of Pallid and Shovelnose Sturgeon in the Yellowstone and Missouri Rivers in Montana and North Dakota. Trans. Am. Fish. Soc. 130, 1006–1025 (2001).
Google Scholar 

38.
Jordan, G. R. et al. Status of knowledge of the Pallid Sturgeon (Scaphirhynchus albus Forbes and Richardson, 1905). J. Appl. Ichthyol. 32, 191–207 (2016).
Google Scholar 

39.
Hesse, L. W. Taming the wild Missouri River: what has it cost?. Fisheries 12, 2–9 (1987).
Google Scholar 

40.
Latka, D. C., Nestler, J. & Hesse, L. W. Restoring physical habitat in the Missouri River: a historical perspective. In Restoration Planning for the River of the Missouri River Ecosystem (eds Hesse, L. W. et al.) 350–359 (Biological Report 19, National Biological Survey, Washington, D.C., 1993).
Google Scholar 

41.
Yager, L. A., Dixon, M. D., Cowman, T. C. & Soluk, D. A. Historic changes (1941–2008) in side channel and backwater habitats on an unchannelized reach of the Missouri River. River Res. Appl. 29, 493–501 (2013).
Google Scholar 

42.
Galat, D. L. & Lipkin, R. Restoring ecological integrity of great rivers: historical hydrographs aid in defining reference conditions for the Missouri River. In Assessing the Ecological Integrity of Running Waters. Developments in Hydrobiology (eds Jungwirth, M. et al.) 29–48 (Springer, Dordrecht, 2000).
Google Scholar 

43.
Welker, T. L. & Drobish, M. R. Pallid Sturgeon population assessment project. United States Army Corps of Engineers, Omaha District, Volume 1.6 Yankton (SD) (2016).

44.
Wildhaber, M. L., Albers, J. L., Green, N. S. & Moran, E. H. A fully-stochasticized, age-structured population model for population viability analysis of fish: Lower Missouri River endangered Pallid Sturgeon example. Ecol. Model. 359, 434–448 (2017).
Google Scholar 

45.
Schrey, A. W. & Heist, E. J. Stock structure of Pallid Sturgeon analyzed with microsatellite loci. J. Appl. Ichthyol. 23, 297–303 (2007).
Google Scholar 

46.
Eichelberger, J. S., Braaten, P. J., Fuller, D. B., Krampe, M. S. & Heist, E. J. Novel single-nucleotide polymorphism markers confirm successful spawning of endangered Pallid Sturgeon in the upper Missouri River basin. Trans. Am. Fish. Soc. 143, 1373–1385 (2014).
CAS  Google Scholar 

47.
Hamel, M. J. et al. Range-wide age and growth characteristics of shovelnose sturgeon from mark–recapture data: implications for conservation and management. Can. J. Fish. Aquat. Sci. 72, 71–82 (2014).
Google Scholar 

48.
Fabens, A. J. Properties and fitting of the von Bertalanffy growth curve. Growth 29, 265–289 (1965).
CAS  PubMed  Google Scholar 

49.
Isely, J. J. & Grabowski, T. B. Age and growth. In Analysis and Interpretation of Freshwater Fisheries Data (eds Guy, C. S. & Brown, M. L.) 187–228 (American Fisheries Society, Bethesda, 2007).
Google Scholar 

50.
Pauly, D. Gill size and temperature as governing factors in fish growth: a generalization of von Bertalanffy’s growth formula. Ber. Inst. F. Meereskunde Univ. Kiel., No. 63 (1979)

51.
Kirkwood, G. P. Estimation of von Bertalanffy growth curve parameters using both length increment and age–length data. Can. J. Fish. Aquat. Sci. 40, 1405–1411 (1983).
Google Scholar 

52.
Hewitt, D. A. & Hoenig, J. M. Comparison of two approaches for estimating natural mortality based on longevity. Fish. Bull. 6, 433 (2005).
Google Scholar 

53.
Seber, G. A. F. The Estimation of Animal Abundance and Related Parameters 2nd edn. (MacMillian, New York, 1982).
Google Scholar 

54.
Steffensen, K. D., Pegg, M. A. & Mestl, G. E. Population characteristics of Pallid Sturgeon (Scaphirhynchus albus (Forbes & Richardson)) in the Lower Missouri River. J. Appl. Ichthyol. 29, 687–695 (2013).
Google Scholar 

55.
Holmquist, L. M., Guy, C. S., Tews, A. & Webb, M. A. H. First maturity and spawning periodicity of hatchery-origin Pallid Sturgeon in the upper Missouri River above Fort Peck Reservoir, Montanna. J. Appl. Ichthyol. 35, 138–148 (2019).
Google Scholar 

56.
Pegg, M. A., Pierce, C. L. & Roy, A. Hydrological alteration along the Missouri River Basin: a time series approach. Aquat. Sci. 65, 63–72 (2003).
Google Scholar 

57.
Jacobson, R. B. & Galat, D. L. Flow and form in rehabilitation of large-river ecosystems: an example from the Lower Missouri River. Geomorphology 77, 249–269 (2006).
ADS  Google Scholar 

58.
McEwen, B. S. & Wingfield, J. C. The concept of allostasis in biology and biomedicine. Horm. Behav. 43, 2–15 (2003).
PubMed  Google Scholar 

59.
Olsen, E. M. et al. Maturation trends indicative of rapid evolution preceded the collapse of northern cod. Nature 428, 932–935 (2004).
ADS  CAS  PubMed  Google Scholar 

60.
Kopp, M. & Matuszewski, S. Rapid evolution of quantitative traits: theoretical perspectives. Evol. Appl. 7, 169–191 (2014).
PubMed  Google Scholar 

61.
Phillis, C. C. et al. Shifting thresholds: rapid evolution of migratory life histories in Steelhead/Rainbow Trout Oncorhynchus mykiss. J. Hered. 107, 51–60 (2016).
PubMed  Google Scholar 

62.
Bell, G. The costs of reproduction and their consequences. Am. Nat. 116, 45–76 (1980).
Google Scholar 

63.
Wright, P. J. & Trippel, E. A. Fishery-induced demographic changes in the timing of spawning: consequences for reproductive success*. Fish Fish. 10, 283–304 (2009).
Google Scholar 

64.
Steffensen, K. D., Hamel, M. J. & Spurgeon, J. J. Post-stocking Pallid Sturgeon Scaphirhynchus albus growth, dispersal, and survival in the lower Missouri River. J. Appl. Ichthyol. 35, 117–127 (2019).
Google Scholar  More

Typhlatya species are found throughout marine and fresh groundwater habitats and are some of the most abundant and widespread stygofauna component in the anchialine ecosystems of the Yucatan Peninsula3,15,18,19,20. In contrast with previous authors suggesting that anchialine fauna has no distribution patterns within underwater caves21, results herein show differently. Shrimp abundance varied markedly in space and the resulting patterns differed from one system to another depending on topographic features, solar influence and geographical position (Fig. 2B). In addition, we found both diel and seasonal variations in the vertical distribution and abundance of T. mitchelli, one of the most common species in the study area3,15. Furthermore, carbon source analysis show a distinct feeding pattern across Typhlatya species. Overall, our observations on three Typhlatya species in four groundwater systems of Yucatan portray a niche partitioning where salinity appears to play a fundamental role in separating the realized niche of T. dzilamensis from that of T. mitchelli and T. pearsei, whilst solar influence, food selection, space, and diel behavior would partitions the later species´ ecological niche. The upcoming discussion focuses on the biological processes that could explain the patterns identified in the present study.
One of the most outstanding characteristics of the karstic anchialine ecosystems in Yucatan is the vertical stratification of water masses, where the marine intrusion from the coast infiltrates under the meteoric water that infiltrates underground, resulting in clearly defined layers with marked salinity changes. Salinity gradients in aquatic habitats are considered one of the most important limiting factors of species distributions22. Changes in environmental salinity may impose severe physiological stress23. Therefore, salinity commonly governs geographical distributions, adaptive radiations, speciation and physiology22,24. Whilst crustaceans are known to have originated in the ocean, atyid shrimps have a long history since the colonization of freshwater environments and their systematics have revealed frequent cave invasions4,25,26. Studies on Halocaridina rubra from anchialine ponds in Hawaii, which are subject to marked daily fluctuations in environmental salinity, have shown these shrimp maintain constitutively activated mechanisms of ion regulation and high cellular osmoregulation in the gills regardless of salinity24. Typhlatya, as an anchialine cave restricted genus25, must have developed a series of adaptations enabling their survival in coastal caves. Furthermore, speciation in Yucatan’s Typhlatya must have resulted in physiological, biochemical and genetic adaptations that derived in a different tolerance to salinity between closely related species and the colonization of subterranean habitats which are heterogeneous in depth and distance from the coast. Our results show that these shrimps are distributed according to salinity: T. mitchelli was found in all sites exclusively in the freshwater layer, T. pearsei was only observed in the fresh water at Nohmozon, and T. dzilamensis was only found in saline water at Ponderosa. The distribution patterns of Typhlatya species observed in this study constitute initial evidence in support of a physiological differentiation among species. Future research in adaptive physiology in response to salinity is key to reveal the osmoregulatory mechanisms and bioenergetics that will further explain the habitat selection (or limits) of these anchialine species (Chávez-Solís et al. in prep.).
It is noteworthy that T. pearsei was most abundant in the system with the highest dissolved oxygen in our study (Fig. 1). Perhaps, T. pearsei is more sensitive to hypoxia than the rest of its congeners. If this hypothesis is true, future studies should demonstrate a reduced metabolic capacity of T. pearsei under hypoxia, whereas T. mitchelli and T. dzilamensis should be comparatively less affected. Moreover, T. mitchelli should present a higher physiological performance, enabling to outcompete T. pearsei in hypoxic freshwater environments. Correspondingly T. pearsei should outcompete T. mitchelli under normoxia. Research on tolerance to hypoxia in stygobionts could test these predictions and provide a deeper understanding of the distribution patterns and adaptation mechanisms to dissolved oxygen variations in caves.
Despite the importance of oxygen and salinity determining the distribution patterns of many crustacean populations22,27, it does not explain the vertical and horizontal distribution of Typhlatya species in systems without a halocline. Abundance differences of T. mitchelli with depth in both Tza Itza and Kankirixche (Fig. 5) suggest that other explanatory variables are involved. Whilst light would appear to be a poor candidate explaining the distribution of blind stygobionts, negative phototaxis, as suggested by the results in the present study, has also been observed in anophthalmic stygobiont beetles Paroster macrosturtensis from Australian calcrete aquifers28. Evidence of this behavior is supported by observations of T. pearsei in the cenote pool at Nohmozon occurring only at night. In addition, transects nearest to the surface in Tza Itza and Kankirixche (i.e. those where sunlight had its greatest influence) consistently had low occurrence of T. mitchelli, particularly during day observations (Fig. 5). Furthermore, day/night differences in the abundance of T. mitchelli in Tza Itza and Nohmozon were limited to the shallow transects, were light influence was strongest. Differences in the way direct sunlight enters and reaches the water surface at Kankirixche compared to Tza Itza, together with the negative phototaxis, could account for the variations in the daily patterns of T. mitchelli observed between these two systems. Measuring traces of light in a cavern with commercial instruments can be challenging. Mejía-Ortiz et al.29 implemented an elegant solution by using long exposure photographs to show trace light at different depths in a dry cavern. Automated light quantification, however, is needed to determine whether light intensity triggers diel behavior in Typhlatya.
Diel migrations and nocturnal activity as that observed in this study have been previously reported in other blind stygobitic crustacean species, such as Creaseria morleyi30,31, Halocaridina rubra32, and Hadenoecus subterraneous33. Species restricted to caves are generally characterized by the reduction of visual structures and are part of the common troglomorphic features observed in stygobionts. Although some vestigial eye structures are observable in Typhlatya, no sign of visual function or pigments are evident, suggesting these species could be grouped as microphthalmic, or even anophthalmic (sensu Friedrich33). Whilst the assumption of anophthalmy—defined as “the lack of eyes at any stage of the life cycle and across populations”—is based on the absence of peripherally observable eyes, it may overlook vestiges of internalized visual organs and does not exclude the existence of other extra-retinal photoreceptors33. The negative phototactic behavior as a probable cause for the diel migrations described in the present study must find support in a mechanism of light detection amongst Typhlatya or closely related species. If these shrimps are still capable of perceiving light despite their visual reduction, then the way light reaches the water column will have a relevant role in keeping the circadian clock tuned, hence activity confined exclusively to dark hours night. Our observations also suggest that populations inhabiting the aphotic cave hydro-regions are present regardless of the time of day or night, as in T. dzilamensis. The constancy of biotic and abiotic parameters in this region may prevent the synchronization of the biological clocks of stygobionts33. The lack of a synchronizer in a cave population could result in a shift of their circadian rhythm producing an unsynchronized circadian rhythm among the population, an arrhythmic biological clock33, or a reduction of sleep duration34. Our recurrent observations of T. dzilamensis in caves at any given time of day or night is consistent with any of these scenarios. Research on the anatomy of the eye, the nervous system, photoreceptors, biological clocks and genetic expression in Typhlatya is needed to further explain the differences in behavior and activity patterns observed both among and within these species.
A possible contributing factor to Typhlatya diel behavior in the cenote pools and light influenced caverns could be related to the presence of epigean and stygobitic predators that may also influence the distribution and size of prey populations. Predation has been shown to modify prey behavior by inducing vertical migration patterns or forcing prey to retrieve to refuges during light periods32,35. Predators in cenote pools include a diverse array of freshwater fish36 and other stygobionts, such as Ophisternon infernale, Typhlias pearsei, Creaseria morleyi and the stygofile Rhamdia guatemalensis, all of which were recorded during night observations in the present study.
Habitat preference in the underground ecosystems is certainly linked to a number of ecological tradeoffs. A balancing component for blind prey living in the sun influenced hydro regions (thus an easy prey) could be the access to recently deposited plant debris rich in nutrients, or algae which are high in nitrogen content and easier to assimilate than plants10,37. This could be selecting T. mitchelli and T. pearsei to remain close to the cenote pools.
Advantages of shallow waters could also be a greater amount of dissolved oxygen and other organic inputs that are unlikely to reach the cave passages. If cenote pools are the only place in anchialine systems where photosynthesis takes place and constitute sinkholes for allochthonous input, then these hydro regions represent a nutrient attraction for cave primary consumers. Stygobionts in this trophic level would increase in density at cenote pools, further attracting epigean and hypogean predators. Results in this direction would suggest that cenote pools are “feeding hotspots” for all species in the heterotrophic anchialine ecosystem. If, on the other hand, photosynthetic and allochthonous nutrient input is scarce or absent in the cenote pool or represents a decimating risk due to visual predators, then Typhlatya must find food in the oligotrophic caves. If the aphotic dwelling T. dzilamensis deep inside caves has developed a strategy to incorporate in situ production sources (such as chemosynthesis or methane derived biomass) to their diet, then most individuals would keep away from the busy photosynthetic hot-spots.
Caves are considered oligotrophic because of a severe and almost constant scarcity of food. Additionally, bacterial mats have been suggested to yield lower energy transfer than that of photosynthesis38. Even so, a trade-off in energy transfer versus the risk of predation can be recognized. In either scenario, the anchialine ecosystems would appear to have a bottom-up control trophic structure, where the availability of autotrophs governs the abundance and distribution of the community. Our results show a greater abundance in hydro-regions linked to the surface (namely cenote pools and caverns), while cave populations were the least abundant throughout this study. Stable isotopes and radiocarbon analysis would link the distribution patterns herein observed with the available feeding sources and the importance of these sources to each of the species.
Metabolic pathways in autotrophs have different isotopic fractionation rates—a differential uptake of isotopes—which create specific carbon-isotope fingerprints11. δ13C values of consumers reflect those of their feeding sources and will be passed on to higher trophic levels, enabling the reconstruction of food webs9,10,11. The wide range of δ13C and Δ14C values observed in Typhlatya species collected from fresh groundwater (FGW) and saline groundwater (SGW) environments suggests a mixed contribution of photosynthetic and chemosynthetic derived matter, as well as modern and ancient carbon contributing to their biomass. Nevertheless, our results show each species has a specific carbon composition indicating a differential food proportion from each of the available sources.
The range of C/N ratios of  More

1.
Hartmann, N. B. et al. Are we speaking the same language? Recommendations for a definition and categorization framework for plastic debris. Environ. Sci. Technol. 53, 1039–1047 (2019).
ADS  CAS  PubMed  Google Scholar 
2.
Horton, A. A., Walton, A., Spurgeon, D. J., Lahive, E. & Svendsen, C. Microplastics in freshwater and terrestrial environments: evaluating the current understanding to identify the knowledge gaps and future research priorities. Sci. Total Environ. 586, 127–141 (2017).
ADS  CAS  PubMed  Google Scholar 

3.
Cole, M., Lindeque, P., Halsband, C. & Galloway, T. S. Microplastics as contaminants in the marine environment: a review. Mar. Pollut. Bull. 62, 2588–2597 (2011).
CAS  PubMed  Google Scholar 

4.
Ballent, A., Corcoran, P. L., Madden, O., Helm, P. A. & Longstaffe, F. J. Sources and sinks of microplastics in Canadian Lake Ontario nearshore, tributary and beach sediments. Mar. Pollut. Bull. 110, 383–395 (2016).
CAS  PubMed  Google Scholar 

5.
Wagner, M. & Lambert, S. Freshwater Microplastics (Springer International Publishing, Cham, 2018).
Google Scholar 

6.
Lechner, A. et al. The Danube so colourful: a potpourri of plastic litter outnumbers fish larvae in Europe’s second largest river. Environ. Pollut. 188, 177–181 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

7.
Miller, R. Z., Watts, A. J. R., Winslow, B. O., Galloway, T. S. & Barrows, A. P. W. Mountains to the sea: river study of plastic and non-plastic microfiber pollution in the northeast USA. Mar. Pollut. Bull. 124, 245–251 (2017).
CAS  PubMed  Google Scholar 

8.
Mani, T., Hauk, A., Walter, U. & Burkhardt-Holm, P. Microplastics profile along the Rhine River. Sci. Rep. 5, 1–7 (2015).
Google Scholar 

9.
Castañeda, R. A., Avlijas, S., Simard, M. A. & Ricciardi, A. Microplastic pollution in St. Lawrence River sediments. Can. J. Fish. Aquat. Sci. 71, 1767–1771 (2014).
Google Scholar 

10.
Pomeroy, C., Haggart, O., Vermaire, J. C., Herczegh, S. M. & Murphy, M. Microplastic abundance and distribution in the open water and sediment of the Ottawa River, Canada, and its tributaries. Facets 2, 301–314 (2017).
Google Scholar 

11.
Klein, S., Worch, E. & Knepper, T. P. Occurrence and spatial distribution of microplastics in river shore sediments of the rhine-main area in Germany. Environ. Sci. Technol. 49, 6070–6076 (2015).
ADS  CAS  PubMed  Google Scholar 

12.
Hurley, R., Woodward, J. & Rothwell, J. J. Microplastic contamination of river beds significantly reduced by catchment-wide flooding. Nat. Geosci. 11, 251–257 (2018).
ADS  CAS  Google Scholar 

13.
Tan, Z. et al. Microplastics in the surface sediments from the Beijiang River littoral zone: composition, abundance, surface textures and interaction with heavy metals. Chemosphere 171, 248–258 (2016).
PubMed  Google Scholar 

14.
Leslie, H. A., Brandsma, S. H., van Velzen, M. J. M. & Vethaak, A. D. Microplastics en route: field measurements in the Dutch river delta and Amsterdam canals, wastewater treatment plants, North Sea sediments and biota. Environ. Int. 101, 133–142 (2017).
CAS  PubMed  Google Scholar 

15.
Mani, T. et al. Repeated detection of polystyrene microbeads in the lower Rhine River. Environ. Pollut. 245, 634–641 (2019).
CAS  PubMed  Google Scholar 

16.
Wilson, S. et al. Microplastic pollution in the surface waters of the Laurentian Great Lakes. Mar. Pollut. Bull. 77, 177–182 (2013).
PubMed  Google Scholar 

17.
McCormick, A., Hoellein, T. J., Mason, S. A., Schluep, J. & Kelly, J. J. Microplastic is an abundant and distinct microbial habitat in an urban river. Environ. Sci. Technol. 48, 11863–11871 (2014).
ADS  CAS  PubMed  Google Scholar 

18.
Windsor, F. M., Tilley, R. M., Tyler, C. R. & Ormerod, S. J. Microplastic ingestion by riverine macroinvertebrates. Sci. Total Environ. 646, 68–74 (2019).
ADS  CAS  PubMed  Google Scholar 

19.
Sanchez, W., Bender, C. & Porcher, J. M. Wild gudgeons (Gobio gobio) from French rivers are contaminated by microplastics: preliminary study and first evidence. Environ. Res. 128, 98–100 (2014).
CAS  PubMed  Google Scholar 

20.
Kuśmierek, N. & Popiołek, M. Microplastics in freshwater fish from Central European lowland river (Widawa R, SW Poland). Environ. Sci. Pollut. Res. 27, 11438–11442 (2020).
Google Scholar 

21.
Wagner, M. et al. Microplastics in freshwater ecosystems: what we know and what we need to know. Environ. Sci. Eur. 26, 58 (2014).
Google Scholar 

22.
Scherer, C., Brennholt, N., Reifferscheid, G. & Wagner, M. Feeding type and development drive the ingestion of microplastics by freshwater invertebrates. Sci. Rep. 7, 17006 (2017).
ADS  PubMed  PubMed Central  Google Scholar 

23.
Aljaibachi, R. & Callaghan, A. Impact of polystyrene microplastics on Daphnia magna mortality and reproduction in relation to food availability. PeerJ 6, e4601 (2018).
PubMed  PubMed Central  Google Scholar 

24.
Bruck, S. & Ford, A. T. Chronic ingestion of polystyrene microparticles in low doses has no effect on food consumption and growth to the intertidal amphipod Echinogammarus marinus?. Environ. Pollut. 233, 1125–1130 (2018).
CAS  PubMed  Google Scholar 

25.
Mateos-Cárdenas, A. et al. Polyethylene microplastics adhere to Lemna minor (L.), yet have no effects on plant growth or feeding by Gammarus duebeni (Lillj.). Sci. Total Environ. 689, 413–421 (2019).
ADS  PubMed  Google Scholar 

26.
Rehse, S., Kloas, W. & Zar, C. Short-term exposure with high concentrations of pristine microplastic particles leads to immobilisation of Daphnia magna. Chemosphere 153, 91–99 (2016).
ADS  CAS  PubMed  Google Scholar 

27.
Jemec, A., Horvat, P., Kunej, U., Bele, M. & Kr, A. Uptake and effects of microplastic textile fibers on freshwater crustacean Daphnia magna. Environ. Pollut. 219, 201–209 (2016).
CAS  PubMed  Google Scholar 

28.
Weber, A., Scherer, C., Brennholt, N., Reifferscheid, G. & Wagner, M. PET microplastics do not negatively affect the survival, development, metabolism and feeding activity of the freshwater invertebrate Gammarus pulex. Environ. Pollut. 234, 181–189 (2018).
CAS  PubMed  Google Scholar 

29.
Jambeck, J. R. et al. Plastic waste inputs from land into the ocean. Science 347, 768–771 (2015).
ADS  CAS  PubMed  Google Scholar 

30.
Geyer, R., Jambeck, J. & Law, K. Production, use, and fate of all plastics ever made. Sci. Adv. 3, 25–29 (2017).
Google Scholar 

31.
Van Sebille, E. et al. A global inventory of small floating plastic debris. Environ. Res. Lett. 10, 124006 (2015).
ADS  Google Scholar 

32.
Dawson, A. L. et al. Turning microplastics into nanoplastics through digestive fragmentation by Antarctic krill. Nat. Commun. 9, 1–8 (2018).
Google Scholar 

33.
Jang, M., Shim, W. J., Han, G. M., Song, Y. K. & Hong, S. H. Formation of microplastics by polychaetes (Marphysa sanguinea) inhabiting expanded polystyrene marine debris. Mar. Pollut. Bull. 131, 365–369 (2018).
CAS  PubMed  Google Scholar 

34.
Alimi, O. S., Farner Budarz, J., Hernandez, L. M. & Tufenkji, N. Microplastics and nanoplastics in aquatic environments: aggregation, deposition, and enhanced contaminant transport. Environ. Sci. Technol. 52, 1704–1724 (2018).
ADS  CAS  PubMed  Google Scholar 

35.
Consolandi, G., Ford, A. T. & Bloor, M. C. Feeding behavioural studies with freshwater Gammarus spp.: the importance of a standardised methodology. In Reviews of Environmental Contamination and Toxicology 1–41 (2019).

36.
Wright, S. L., Thompson, R. C. & Galloway, T. S. The physical impacts of microplastics on marine organisms: a review. Environ. Pollut. 178, 483–492 (2013).
CAS  PubMed  Google Scholar 

37.
Redondo-Hasselerharm, P. E., Falahudin, D., Peeters, E. T. H. M. & Koelmans, A. A. Microplastic effect thresholds for freshwater benthic macroinvertebrates. Environ. Sci. Technol. 52, 2278–2286 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

38.
Blarer, P. & Burkhardt-holm, P. Microplastics affect assimilation efficiency in the freshwater amphipod Gammarus fossarum. Environ. Sci. Pollut. Res. 23, 23522–23532 (2016).
CAS  Google Scholar 

39.
Straub, S., Hirsch, P. E. & Burkhardt-Holm, P. Biodegradable and petroleum-based microplastics do not differ in their ingestion and excretion but in their biological effects in a freshwater invertebrate Gammarus fossarum. Int. J. Environ. Res. Public Health 14, 774 (2017).
PubMed Central  Google Scholar 

40.
Catarino, A. I., Frutos, A. & Henry, T. B. Use of fluorescent-labelled nanoplastics (NPs) to demonstrate NP absorption is inconclusive without adequate controls. Sci. Total Environ. 670, 915–920 (2019).
ADS  CAS  PubMed  Google Scholar 

41.
Schür, C. et al. When fluorescence is not a particle: the tissue translocation of microplastics in daphnia magna seems an artifact. Environ. Toxicol. Chem. 38, 1495–1503 (2019).
PubMed  Google Scholar 

42.
Luo, H. et al. Leaching behavior of fluorescent additives from microplastics and the toxicity of leachate to Chlorella vulgaris. Sci. Total Environ. 678, 1–9 (2019).
ADS  CAS  PubMed  Google Scholar 

43.
Gewert, B., Plassmann, M. M. & Macleod, M. Pathways for degradation of plastic polymers floating in the marine environment. Environ. Sci. Process. Impacts 17, 1513–1521 (2015).
CAS  PubMed  Google Scholar 

44.
ter Halle, A. et al. Understanding the fragmentation pattern of marine plastic debris. Environ. Sci. Technol. 50, 5668–5675 (2016).
ADS  PubMed  Google Scholar 

45.
Weinstein, J. E., Crocker, B. K. & Gray, A. D. From macroplastic to microplastic: degradation of high-density polyethylene, polypropylene, and polystyrene in a salt marsh habitat. Environ. Toxicol. Chem. 35, 1632–1640 (2016).
CAS  PubMed  Google Scholar 

46.
Zhu, L., Zhao, S., Bittar, T. B., Stubbins, A. & Li, D. Photochemical dissolution of buoyant microplastics to dissolved organic carbon: Rates and microbial impacts. J. Hazard. Mater. 383, 121065 (2020).
CAS  PubMed  Google Scholar 

47.
Song, Y. K. et al. Combined effects of UV exposure duration and mechanical abrasion on microplastic fragmentation by polymer type. Environ. Sci. Technol. 51, 4368–4376 (2017).
ADS  CAS  PubMed  Google Scholar 

48.
Hakkarainen, M. & Albertsson, A. C. Environmental degradation of polyethylene. Adv. Polym. Sci. 169, 177–199 (2004).
CAS  Google Scholar 

49.
Andrady, A. L., Pegram, J. E. & Song, Y. Studies on enhanced degradable plastics. II. Weathering of enhanced photodegradable polyethylenes under marine and freshwater floating exposure. J. Environ. Polym. Degrad. 1, 117–126 (1993).
CAS  Google Scholar 

50.
Porter, A., Smith, K. E. & Lewis, C. The sea urchin Paracentrotus lividus as a bioeroder of plastic. Sci. Total Environ. 693, 133621 (2019).
ADS  CAS  PubMed  Google Scholar 

51.
Cau, A. et al. Benthic crustacean digestion can modulate environmental fate of microplastics in the deep sea. Environ. Sci. Technol. https://doi.org/10.1021/acs.est.9b07705 (2020).
Article  PubMed  Google Scholar 

52.
Macneil, C., Dick, J. T. A. & Elwood, R. W. The trophic ecology of freshwater Gammarus spp. (Crustacea: Amphipoda): problems and perspectives concerning the functional feeding group concept. Biol. Rev. 72, 349–364 (1997).
Google Scholar 

53.
Willoughby, L. G. & Sutcliffe, D. W. Experiments on feeding and growth of the amphipod Gammarus pulex (L.) related to its distribution in the River Duddon. Freshw. Biol. 6, 577–586 (1976).
CAS  Google Scholar 

54.
Agrawal, V. P. Feeding appendages and the digestive system of Gammarus pulex. Acta Zool. 46, 67–81 (1965).
CAS  Google Scholar 

55.
Watling, L. Functional morphology of the amphipod mandible. J. Nat. Hist. 27, 837–849 (1993).
Google Scholar 

56.
Steele, D. H. & Steele, V. J. Biting mechanism of the amphipod anonyx (Crustacea: Amphipoda: Lysianassoidea). J. Nat. Hist. 27, 851–860 (1993).
Google Scholar 

57.
Mayer, G., Maier, G., Maas, A. & Waloszek, D. Mouthpart morphology of Gammarus roeselii compared to a successful invader, Dikerogammarus villosus (Amphipoda). J. Crustac. Biol. 29, 161–174 (2009).
Google Scholar 

58.
Mekhanikova, I. V. Morphology of mandible and lateralia in six endemic amphipods (Amphipoda, Gammaridea) from Lake Baikal, in relation to feeding. Crustaceana 83, 865–887 (2010).
Google Scholar 

59.
Cassone, B. J., Grove, H. C., Elebute, O., Villanueva, S. M. P. & LeMoine, C. M. R. Role of the intestinal microbiome in low-density polyethylene degradation by caterpillar larvae of the greater wax moth, Galleria mellonella. Proc. R. Soc. B Biol. Sci. 287, 20200112 (2020).
Google Scholar 

60.
Besseling, E. et al. Nanoplastic affects growth of S. obliquus and reproduction of D. magna. Environ. Sci. Technol. 48, 12336–12343 (2014).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

61.
Bhattacharya, P., Lin, S., Turner, J. P. & Ke, P. C. Physical adsorption of charged plastic nanoparticles affects algal photosynthesis. J. Phys. Chem. 114, 16556–16561 (2010).
CAS  Google Scholar 

62.
Sendra, M., Staf, E., Pilar, M. & Moreno-Garrido, I. Are the primary characteristics of polystyrene nanoplastics responsible for toxicity and ad/absorption in the marine diatom Phaeodactylum tricornutum. Environ. Pollut. 249, 610–619 (2019).
CAS  PubMed  Google Scholar 

63.
van Weert, S., Redondo-Hasselerharm, P. E., Diepens, N. J. & Koelmans, A. A. Effects of nanoplastics and microplastics on the growth of sediment-rooted macrophytes. Sci. Total Environ. 654, 1040–1047 (2019).
ADS  PubMed  Google Scholar 

64.
Lian, J. et al. Impact of polystyrene nanoplastics (PSNPs) on seed germination and seedling growth of wheat (Triticum aestivum L.). J. Hazard. Mater. 385, 121620 (2020).
PubMed  Google Scholar 

65.
Bosker, T., Bouwman, L. J., Brun, N. R., Behrens, P. & Vijver, M. G. Microplastics accumulate on pores in seed capsule and delay germination and root growth of the terrestrial vascular plant Lepidium sativum. Chemosphere 226, 774–781 (2019).
ADS  CAS  PubMed  Google Scholar 

66.
Cui, R., Kim, S. W. & An, Y. J. Polystyrene nanoplastics inhibit reproduction and induce abnormal embryonic development in the freshwater crustacean Daphnia galeata. Sci. Rep. 7, 1–10 (2017).
Google Scholar 

67.
Rist, S., Baun, A., Almeda, R. & Hartmann, N. B. Ingestion and effects of micro- and nanoplastics in blue mussel (Mytilus edulis) larvae. Mar. Pollut. Bull. 140, 423–430 (2019).
CAS  PubMed  Google Scholar 

68.
Hardesty, B. D. et al. Using numerical model simulations to improve the understanding of micro-plastic distribution and pathways in the marine environment. Front. Mar. Sci. 4, 1–9 (2017).
Google Scholar 

69.
Kalčíková, G., Alič, B., Skalar, T., Bundschuh, M. & Gotvajn, A. Ž. Wastewater treatment plant effluents as source of cosmetic polyethylene microbeads to freshwater. Chemosphere 188, 25–31 (2017).
ADS  PubMed  Google Scholar 

70.
Fendall, L. S. & Sewell, M. A. Contributing to marine pollution by washing your face: microplastics in facial cleansers. Mar. Pollut. Bull. 58, 1225–1228 (2009).
CAS  PubMed  Google Scholar 

71.
Schmitz, E. H. & Scherrey, P. M. Digestive anatomy of Hyalella azteca (Crustacea, Amphipoda). J. Morphol. 100, 91–100 (1983).
Google Scholar 

72.
Monk, D. C. The digestion of cellulose and other dietary components, and pH of the gut in the amphipod Gammarus pulex (L.). Freshw. Biol. 7, 431–440 (1977).
CAS  Google Scholar  More

1.
Popper, A. N., Ramcharitar, J. & Campana, S. E. Why otoliths? Insights from inner ear physiology and fisheries biology. Mar. Freshw. Res. 56, 497–504. https://doi.org/10.1071/MF04267 (2005).
Article  Google Scholar 
2.
Starrs, D., Ebner, B. C. & Fulton, C. J. All in the ears: Unlocking the early life history biology and spatial ecology of fishes. Biol. Rev. 91, 86–105. https://doi.org/10.1111/brv.12162 (2016).
Article  PubMed  Google Scholar 

3.
Schulz-Mirbach, T., Ladich, F., Plath, M. & Heß, M. Enigmatic ear stones: what we know about the functional role and evolution of fish otoliths. Biol. Rev. https://doi.org/10.1111/brv.12463 (2018).
Article  PubMed  Google Scholar 

4.
Campana, S. E. Photographic Atlas of Fish Otoliths of the Northwest Atlantic Ocean. Canadian Special Publication of Fisheries and Aquatic Sciences Vol. 133 (NRC Research Press, Ottawa, 2004).
Google Scholar 

5.
Tuset, V. M., Lombarte, A., González, J. A., Pertusa, J. F. & Lorente, M. J. Comparative morphology of the sagittal otolith in Serranus spp. J. Fish Biol. 63, 1491–1504. https://doi.org/10.1111/j.1095-8649.2003.00262.x (2003).
Article  Google Scholar 

6.
Tuset, V. M. et al. Otolith patterns of rockfishes from the northeastern pacific. J. Morphol. 276, 458–469. https://doi.org/10.1002/jmor.20353 (2015).
Article  PubMed  Google Scholar 

7.
Campana, S. E. & Casselman, J. M. Stock discrimination using otolith shape analysis. Can. J. Fish. Aquat. Sci. 50, 1062–1083. https://doi.org/10.1139/f93-123 (1993).
Article  Google Scholar 

8.
Bose, A. P. H., Adragna, J. B. & Balshine, S. Otolith morphology varies between populations, sexes and male alternative reproductive tactics in a vocal toadfish Porichthys notatus. J. Fish Biol. https://doi.org/10.1111/jfb.13187 (2016).
Article  PubMed  Google Scholar 

9.
Mille, T., Mahe, K., Villanueva, M. C., De Pontual, H. & Ernande, B. Sagittal otolith morphogenesis asymmetry in marine fishes. J. Fish Biol. 87, 646–663. https://doi.org/10.1111/jfb.12746 (2015).
CAS  Article  PubMed  Google Scholar 

10.
Bose, A. P. H., Mccallum, E. S., Raymond, K., Marentette, J. R. & Balshine, S. Growth and otolith morphology vary with alternative reproductive tactics and contaminant exposure in the round goby Neogobius melanostomus. J. Fish Biol. 93, 674–684. https://doi.org/10.1111/jfb.13756 (2018).
Article  PubMed  Google Scholar 

11.
Lombarte, A. & Castellón, A. Interspecific and intraspecific otolith variability in the genus Merluccius as determined by image analysis. Can. J. Zool. 69, 2442–2449. https://doi.org/10.1139/z91-343 (1991).
Article  Google Scholar 

12.
Vignon, M. & Morat, F. Environmental and genetic determinant of otolith shape revealed by a non-indigenous tropical fish. Mar. Ecol. Prog. Ser. 411, 231–241. https://doi.org/10.3354/meps08651 (2010).
ADS  Article  Google Scholar 

13.
Gagliano, M. & McCormick, M. I. Feeding history influences otolith shape in tropical fish. Mar. Ecol. Prog. Ser. 278, 291–296. https://doi.org/10.3354/meps278291 (2004).
ADS  Article  Google Scholar 

14.
Hoff, G. R. & Fuiman, L. A. Morphometry and composition of red drum otoliths: Changes associated with temperature, somatic growth rate, and age. Comp. Biochem. Physiol. 106, 209–219. https://doi.org/10.1016/0300-9629(93)90502-U (1993).
Article  Google Scholar 

15.
Tuset, V. M. et al. Otolith shape lends support to the sensory drive hypothesis in rockfishes. J. Evol. Biol. 29, 2083–2097. https://doi.org/10.1111/jeb.12932 (2016).
CAS  Article  PubMed  Google Scholar 

16.
Gauldie, R. W. Function, form and time-keeping properties of fish otoliths. Comp. Biochem. Physiol. 91, 395–402 (1988).
Article  Google Scholar 

17.
Popper, A. N., Fay, R. R., Platt, C. & Sand, O. Sound detection mechanisms and capabilities of teleost fishes. In Sensory Processing in Aquatic Environments (eds Collin, S. P. & Marshall, N. J.) 3–38 (Springer-Verlag, New York, 2003).
Google Scholar 

18.
Krysl, P., Hawkins, A. D., Schilt, C. & Cranford, T. W. Angular oscillation of solid scatterers in response to progressive planar acoustic waves: Do fish otoliths rock?. PLoS ONE https://doi.org/10.1371/journal.pone.0042591 (2012).
Article  PubMed  PubMed Central  Google Scholar 

19.
Duftner, N. et al. Distinct population structure in a phenotypically homogeneous rock-dwelling cichlid fish from Lake Tanganyika. Mol. Ecol. 15, 2381–2395. https://doi.org/10.1111/j.1365-294X.2006.02949.x (2006).
CAS  Article  PubMed  Google Scholar 

20.
Koblmüller, S., Sefc, K. M., Duftner, N., Warum, M. & Sturmbauer, C. Genetic population structure as indirect measure of dispersal ability in a Lake Tanganyika cichlid. Genetica 130, 121–131. https://doi.org/10.1007/s10709-006-0027-0 (2007).
Article  PubMed  Google Scholar 

21.
Sefc, K. M., Baric, S., Salzburger, W. & Sturmbauer, C. Species-specific population structure in rock-specialized sympatric cichlid species in Lake Tanganyika. East Afr. J. Mol. Evol. 64, 33–49. https://doi.org/10.1007/s00239-006-0011-4 (2007).
ADS  CAS  Article  Google Scholar 

22.
Wagner, C. E. & McCune, A. R. Contrasting patterns of spatial genetic structure in sympatric rock-dwelling cichlid fishes. Evolution 63, 1312–1326. https://doi.org/10.1111/j.1558-5646.2009.00612.x (2009).
Article  PubMed  Google Scholar 

23.
Sefc, K. M. et al. Shifting barriers and phenotypic diversification by hybridisation. Ecol. Lett. 20, 651–662. https://doi.org/10.1111/ele.12766 (2017).
Article  PubMed  PubMed Central  Google Scholar 

24.
Koblmüller, S. et al. Separated by sand, fused by dropping water: Habitat barriers and fluctuating water levels steer the evolution of rock-dwelling cichlid populations in Lake Tanganyika. Mol. Ecol. 20, 2272–2290. https://doi.org/10.1111/j.1365-294X.2011.05088.x (2011).
Article  PubMed  Google Scholar 

25.
Kohda, M. et al. Geographical colour variation in cichlid fishes at the southern end of Lake Tanganyika. Environ. Biol. Fishes 45, 237–248. https://doi.org/10.1007/BF00003091 (1996).
Article  Google Scholar 

26.
Widmer, L. et al. Point-Combination Transect (PCT): Incorporation of small underwater cameras to study fish communities. Methods Ecol. Evol. 10, 891–901. https://doi.org/10.1111/2041-210X.13163 (2019).
Article  PubMed  PubMed Central  Google Scholar 

27.
McGlue, M. M. et al. Seismic records of late Pleistocene aridity in Lake Tanganyika, tropical East Africa. J. Paleolimnol. 40, 635–653. https://doi.org/10.1007/s10933-007-9187-x (2008).
ADS  Article  Google Scholar 

28.
Duftner, N. et al. Parallel evolution of facial stripe patterns in the Neolamprologus brichardi/pulcher species complex endemic to Lake Tanganyika. Mol. Phylogenet. Evol. 45, 706–715. https://doi.org/10.1016/j.ympev.2007.08.001 (2007).
Article  PubMed  Google Scholar 

29.
Sefc, K. M., Mattersdorfer, K., Hermann, C. M. & Koblmüller, S. Past lake shore dynamics explain present pattern of unidirectional introgression across a habitat barrier. Hydrobiologia 791, 69–82. https://doi.org/10.1007/s10750-016-2791-x (2017).
Article  Google Scholar 

30.
Winkelmann, K., Rüber, L. & Genner, M. J. Lake level fluctuations and divergence of cichlid fish ecomorphs in Lake Tanganyika. Hydrobiologia 791, 21–34. https://doi.org/10.1007/s10750-016-2839-y (2017).
Article  Google Scholar 

31.
Koblmüller, S. et al. Phylogeny and phylogeography of Altolamprologus: Ancient introgression and recent divergence in a rock-dwelling Lake Tanganyika cichlid genus. Hydrobiologia 791, 35–50. https://doi.org/10.1007/s10750-016-2896-2 (2017).
CAS  Article  Google Scholar 

32.
Balshine, S. et al. Correlates of group size in a cooperatively breeding cichlid fish (Neolamprologus pulcher). Behav. Ecol. Sociobiol. 50, 134–140. https://doi.org/10.1007/s002650100343 (2001).
Article  Google Scholar 

33.
Heg, D., Bachar, Z. & Taborsky, M. Cooperative breeding and group structure in the Lake Tanganyika cichlid Neolamprologus savoryi. Ethology 111, 1017–1043. https://doi.org/10.1111/j.1439-0310.2005.01135.x (2005).
Article  Google Scholar 

34.
Bose, A. P. H., Zimmermann, H., Henshaw, J. M., Fritzsche, K. & Sefc, K. M. Brood—tending males in a biparental fish suffer high paternity losses but rarely cuckold. Mol. Ecol. 27, 4309–4321. https://doi.org/10.1111/mec.14857 (2018).
Article  PubMed  PubMed Central  Google Scholar 

35.
Schaedelin, F. C., Van Dongen, W. F. D. & Wagner, R. H. Mate choice and genetic monogamy in a biparental, colonial fish. Behav. Ecol. 26, 782–788. https://doi.org/10.1093/beheco/arv011 (2015).
Article  PubMed  PubMed Central  Google Scholar 

36.
Konings, A. Tanganyika Cichlids in Their Natural Habitat 4th edn. (Hollywood Import & Export Inc., Gainesville, 2019).
Google Scholar 

37.
Ota, K., Hori, M. & Kohda, M. Testes investment along a vertical depth gradient in an herbivorous fish. Ethology 118, 683–693. https://doi.org/10.1111/j.1439-0310.2012.02056.x (2012).
Article  Google Scholar 

38.
Sturmbauer, C. et al. Abundance, distribution, and territory areas of rock-dwelling Lake Tanganyika cichlid fish species. Hydrobiologia 615, 57–68. https://doi.org/10.1007/978-1-4020-9582-5_5 (2008).
Article  Google Scholar 

39.
Heg, D., Brouwer, L., Bachar, Z. & Taborsky, M. Large group size yields group stability in the cooperatively breeding cichlid Neolamprologus pulcher. Behaviour 1, 1–27. https://doi.org/10.1163/156853905774831891 (2005).
Article  Google Scholar 

40.
Spinks, R. K., Muschick, M., Salzburger, W. & Gante, H. F. Singing above the chorus: Cooperative Princess cichlid fish (Neolamprologus pulcher) has high pitch. Hydrobiologia 791, 115–125. https://doi.org/10.1007/s10750-016-2921-5 (2016).
Article  Google Scholar 

41.
Bigirimana, C. Neolamprologus pulcher. The IUCN Red List of Threatened Species 2006: e.T60604A12382292. https://doi.org/10.2305/IUCN.UK.2006.RLTS.T60604A12382292.en (2006). Accessed 8 March 2020.

42.
Bigirimana, C. Neolamprologus caudopunctatus. The IUCN Red List of Threatened Species 2006: e.T60591A12373751. https://doi.org/10.2305/IUCN.UK.2006.RLTS.T60591A12373751.en (2006). Accessed 8 March 2020.

43.
Bigirimana, C. Neolamprologus savoryi. The IUCN Red List of Threatened Species 2006: e.T60605A12382585. https://doi.org/10.2305/IUCN.UK.2006.RLTS.T60605A12382585.en (2006). Accessed 8 March 2020.

44.
Bigirimana, C. Neolamprologus moorii. The IUCN Red List of Threatened Species 2006: e.T60613A12384127. https://doi.org/10.2305/IUCN.UK.2006.RLTS.T60613A12384127.en (2006). Accessed 8 March 2020.

45.
Iwata, H. & Ukai, Y. SHAPE: A computer program package for quantitative evaluation of biological shapes based on elliptic Fourier descriptors. J. Hered. 93, 384–385. https://doi.org/10.1093/jhered/93.5.384 (2002).
CAS  Article  PubMed  Google Scholar 

46.
Crampton, J. S. Elliptic Fourier shape analysis of fossil bivalves: Some practical considerations. Lethaia 28, 179–186. https://doi.org/10.1111/j.1502-3931.1995.tb01611.x (1995).
Article  Google Scholar 

47.
Jackson, D. A. Stopping rules in principal component analysis: A comparison of heuristical and statistical approaches. Ecology 74, 2204–2214. https://doi.org/10.2307/1939574 (1993).
Article  Google Scholar 

48.
Bolles, K. L. & Begg, G. A. Distinction between silver hake (Merluccius bilinearis) stocks in US waters of the northwest Atlantic based on whole otolith morphometrics. Fish. Bull. 98, 451–462 (2000).
Google Scholar 

49.
Lenth, R. emmeans: Estimated marginal means, aka least-squares means. R package version 1.4.4. https://CRAN.R-project.org/package=emmeans (2020).

50.
Richlen, M. L. & Barber, P. H. A technique for the rapid extraction of microalgal DNA from single live and preserved cells. Mol. Ecol. Notes 5, 688–691. https://doi.org/10.1111/j.1471-8286.2005.01032.x (2005).
CAS  Article  Google Scholar 

51.
McCusker, M. R. & Bentzen, P. Positive relationships between genetic diversity and abundance in fishes. Mol. Ecol. 19, 4852–4862. https://doi.org/10.1111/j.1365-294X.2010.04822.x (2010).
Article  PubMed  Google Scholar 

52.
Karl, S. A., Toonen, R. J., Grant, W. S. & Bowen, B. W. Common misconceptions in molecular ecology: Echoes of the modern synthesis. Mol. Ecol. 21, 4171–4189. https://doi.org/10.1111/j.1365-294X.2012.05576.x (2012).
CAS  Article  PubMed  Google Scholar 

53.
Excoffier, L., Laval, G. & Schneider, S. Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol. Bioinform. Online 1, 47–50. https://doi.org/10.1177/117693430500100003 (2005).
CAS  Article  Google Scholar 

54.
Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 497–591 (1992).
Google Scholar 

55.
Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116. https://doi.org/10.1111/2041-210X.12410 (2015).
Article  Google Scholar 

56.
Templeton, A. R., Crandall, K. A. & Sing, C. F. A cladistic analysis of phenotypic associations with haplotypes inferred from restriction endonuclease mapping and DNA sequence data. III. Cladogram estimation. Genetics 132, 619–633 (1992).
CAS  PubMed  PubMed Central  Google Scholar 

57.
Sefc, K. M., Payne, R. B. & Sorenson, M. D. Genetic differentiation after founder events: An evaluation of FST estimators with empirical and simulated data. Evol. Ecol. Res. 9, 21–39 (2007).
Google Scholar 

58.
Sturmbauer, C., Salzburger, W., Duftner, N., Schelly, R. & Koblmüller, S. Evolutionary history of the Lake Tanganyika cichlid tribe Lamprologini (Teleostei: Perciformes) derived from mitochondrial and nuclear DNA data. Mol. Phylogenet. Evol. 57, 266–284 (2010).
CAS  Article  Google Scholar 

59.
Chung, Y., Rabe-Hesketh, S., Dorie, V., Gelman, A. & Liu, J. A nondegenerate penalized likelihood estimator for variance parameters in multilevel models. Psychometrika 78, 685–709. https://doi.org/10.1007/s11336-013-9328-2 (2013).
MathSciNet  Article  PubMed  MATH  Google Scholar 

60.
Solt, F. & Hu, Y. dotwhisker: Dot-and-whisker plots of regression results. R package version 0.5.0. https://CRAN.R-project.org/package=dotwhisker (2018).

61.
Wilson, R. R. Jr. Depth-related changes in sagitta morphology in six Macrourid fishes of the Pacific and Atlantic Oceans. Copeia 4, 1011–1017. https://doi.org/10.2307/1445256 (1985).
Article  Google Scholar 

62.
Lombarte, A. & Lleonart, J. Otolith size changes related with body growth, habitat depth and temperature. Environ. Biol. Fishes 37, 297–306. https://doi.org/10.1007/BF00004637 (1993).
Article  Google Scholar 

63.
Mérigot, B., Letourneur, Y. & Lecomte-Finiger, R. Characterization of local populations of the common sole Solea solea (Pisces, Soleidae) in the NW Mediterranean through otolith morphometrics and shape analysis. Mar. Biol. 151, 997–1008. https://doi.org/10.1007/s00227-006-0549-0 (2007).
Article  Google Scholar 

64.
Hüssy, K. Otolith shape in juvenile cod (Gadus morhua): Ontogenetic and environmental effects. J. Exp. Mar. Bio. Ecol. 364, 35–41. https://doi.org/10.1016/j.jembe.2008.06.026 (2008).
Article  Google Scholar 

65.
Volpedo, A. V. & Fuchs, D. V. Ecomorphological patterns of the lapilli of Paranoplatense Siluriforms (South America). Fish. Res. 102, 160–165. https://doi.org/10.1016/j.fishres.2009.11.007 (2010).
Article  Google Scholar 

66.
Vignon, M. Disentangling and quantifying sources of otolith shape variation across multiple scales using a new hierarchical partitioning approach. Mar. Ecol. Prog. Ser. 534, 163–177. https://doi.org/10.3354/meps11376 (2015).
ADS  Article  Google Scholar 

67.
Sand, O. & Michelsen, A. Vibration measurements of the perch saccular otolith. J. Comp. Physiol. A 123, 85–89. https://doi.org/10.1007/BF00657346 (1978).
Article  Google Scholar 

68.
Schulz-Mirbach, T. et al. In-situ visualization of sound-induced otolith motion using hard X-ray phase contrast imaging. Sci. Rep. 8, 1–12. https://doi.org/10.1038/s41598-018-21367-0 (2018).
CAS  Article  Google Scholar 

69.
Castonguay, M., Simard, P. & Gagnon, P. Usefulness of Fourier analysis of otolith shape for Atlantic mackerel (Scomber scombrus) stock discrimination. Can. J. Biochem. Physiol. 48, 296–302. https://doi.org/10.1139/f91-041 (1991).
Article  Google Scholar 

70.
Friedland, K. D. & Reddin, D. G. Use of otolith morphology in stock discriminations of Atlantic salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 51, 91–98. https://doi.org/10.1139/f94-011 (1994).
Article  Google Scholar 

71.
Turan, C. Otolith shape and meristic analysis of herring (Clupea harengus) in the North-East Atlantic. Arch. Fish. Mar. Res. 48, 283–295 (2000).
Google Scholar 

72.
Reichenbacher, B., Feulner, G. R. & Schulz-Mirbach, T. Geographic variation in otolith morphology among freshwater populations of Aphanius dispar (Teleostei, Cyprinodontiformes) from the southeastern Arabian Peninsula. J. Morphol. 484, 469–484. https://doi.org/10.1002/jmor.10702 (2009).
Article  Google Scholar 

73.
Libungan, L. A., Slotte, A., Huseb, Å & Godiksen, J. A. Latitudinal gradient in otolith shape among local populations of Atlantic herring (Clupea harengus L.) in Norway. PLoS ONE 10, e0130847. https://doi.org/10.1371/journal.pone.0130847 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

74.
Hedrick, P. W. Sex: Differences in mutation, recombination, selection, gene flow, and genetic drift. Evolution 61, 2750–2771. https://doi.org/10.1111/j.1558-5646.2007.00250.x (2007).
Article  PubMed  Google Scholar 

75.
Cardinale, M., Doering-Arjes, P., Kastowsky, M. & Mosegaard, H. Effects of sex, stock, and environment on the shape of known-age Atlantic cod (Gadus morhua) otoliths. Can. J. Fish. Aquat. Sci. 61, 158–167. https://doi.org/10.1139/f03-151 (2004).
Article  Google Scholar 

76.
Parmentier, E., Boistel, R., Bahri, M. A., Plenevaux, A. & Schwarzhans, W. Sexual dimorphism in the sonic system and otolith morphology of Neobythites gilli (Ophidiiformes). J. Morphol. 4, 1–7. https://doi.org/10.1111/jzo.12561 (2018).
Article  Google Scholar 

77.
Sopinka, N. M. et al. Liver size reveals social status in the African cichlid Neolamprologus pulcher. J. Fish Biol. 75, 1–16. https://doi.org/10.1111/j.1095-8649.2009.02234.x (2009).
CAS  Article  PubMed  Google Scholar 

78.
Irisarri, I. et al. Phylogenomics uncovers early hybridization and adaptive loci shaping the radiation of Lake Tanganyika cichlid fishes. Nat. Commun. 9, 1–12. https://doi.org/10.1038/s41467-018-05479-9 (2018).
CAS  Article  Google Scholar 

79.
Breheny, P. & Burchett, W. Visualization of regression models using visreg. R. J. 9, 56–71 (2017).
Article  Google Scholar  More