Philippa Kaur

1.
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).
Google Scholar 
2.
Hampe, A. Plants on the move: the role of seed dispersal and initial population establishment for climate-driven range expansions. Acta Oecol. 37, 666–673 (2011).
Google Scholar 

3.
Kremer, A. et al. Long‐distance gene flow and adaptation of forest trees to rapid climate change. Ecol. Lett. 15, 378–392 (2012).
Google Scholar 

4.
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
Google Scholar 

5.
Felicísimo, Á. M., Muñoz, J. & González-Solis, J. Ocean surface winds drive dynamics of transoceanic aerial movements. PLoS ONE 3, e2928 (2008).
Google Scholar 

6.
Gillespie, R. G. et al. Long-distance dispersal: a framework for hypothesis testing. Trends Ecol. Evol. 27, 47–56 (2012).
Google Scholar 

7.
Muñoz, J., Felicísimo, Á. M., Cabezas, F., Burgaz, A. R. & Martínez, I. Wind as a long-distance dispersal vehicle in the Southern Hemisphere. Science 304, 1144–1147 (2004).
Google Scholar 

8.
Smith, D. J. et al. Intercontinental dispersal of bacteria and archaea by transpacific winds. Appl. Environ. Microbiol. 79, 1134–1139 (2013).
CAS  Google Scholar 

9.
Austerlitz, F., Dutech, C., Smouse, P. E., Davis, F. & Sork, V. L. Estimating anisotropic pollen dispersal: a case study in Quercus lobata. Heredity 99, 193–204 (2007).
CAS  Google Scholar 

10.
Bullock, J. M. & Clarke, R. T. Long distance seed dispersal by wind: measuring and modelling the tail of the curve. Oecologia 124, 506–521 (2000).
CAS  Google Scholar 

11.
Gassmann, M. I. & Pérez, C. F. Trajectories associated to regional and extra-regional pollen transport in the southeast of Buenos Aires province, Mar del Plata (Argentina). Int. J. Biometeorol. 50, 280–291 (2006).
Google Scholar 

12.
Skarpaas, O. & Shea, K. Dispersal patterns, dispersal mechanisms, and invasion wave speeds for invasive thistles. Am. Naturalist 170, 421–430 (2007).
Google Scholar 

13.
Wang, Z. F. et al. Pollen and seed flow under different predominant winds in wind-pollinated and wind-dispersed species Engelhardia roxburghiana. Tree Genet. Genomes 12, 19 (2016).
CAS  Google Scholar 

14.
Soubeyrand, S., Enjalbert, J., Sanchez, A. & Sache, I. Anisotropy, in density and in distance, of the dispersal of yellow rust of wheat: experiments in large field plots and estimation. Phytopathology 97, 1315–1324 (2007).
CAS  Google Scholar 

15.
Born, C., le Roux, P. C., Spohr, C., McGeoch, M. A. & van Vuuren, B. J. Plant dispersal in the sub‐Antarctic inferred from anisotropic genetic structure. Mol. Ecol. 21, 184–194 (2012).
Google Scholar 

16.
Geremew, A., Woldemariam, M. G., Kefalew, A., Stiers, I. & Triest, L. Isotropic and anisotropic processes influence fine-scale spatial genetic structure of a keystone tropical plant. AoB Plants 10, plx076 (2018).
Google Scholar 

17.
Brown, J. K. & Hovmøller, M. S. Aerial dispersal of pathogens on the global and continental scales and its impact on plant disease. Science 297, 537–541 (2002).
CAS  Google Scholar 

18.
Vanschoenwinkel, B., Gielen, S., Seaman, M. & Brendonck, L. Any way the wind blows—frequent wind dispersal drives species sorting in ephemeral aquatic communities. Oikos 117, 125–134 (2008).
Google Scholar 

19.
Ahmed, S., Compton, S. G., Butlin, R. K. & Gilmartin, P. M. Wind-borne insects mediate directional pollen transfer between desert fig trees 160 kilometers apart. Proc. Natl Acad. Sci. USA 106, 20342–20347 (2009).
CAS  Google Scholar 

20.
Larson-Johnson, K. Field observations of Carpinus (Betulaceae) demonstrate high dispersal asymmetry and inform migration simulations with implications for times of rapid climate change. Int. J. Plant Sci. 177, 389–399 (2016).
Google Scholar 

21.
Nathan, R. et al. Spread of North American wind‐dispersed trees in future environments. Ecol. Lett. 14, 211–219 (2011).
Google Scholar 

22.
Sorte, C. J. Predicting persistence in a changing climate: flow direction and limitations to redistribution. Oikos 122, 161–170 (2013).
Google Scholar 

23.
Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).
CAS  Google Scholar 

24.
Molinos, J. G., Burrows, M. T. & Poloczanska, E. S. Ocean currents modify the coupling between climate change and biogeographical shifts. Sci. Rep. 7, 1332 (2017).
Google Scholar 

25.
Higgins, S. I. et al. Forecasting plant migration rates: managing uncertainty for risk assessment. J. Ecol. 91, 341–347 (2003).
Google Scholar 

26.
Bullock, J. M. et al. Modelling spread of British wind‐dispersed plants under future wind speeds in a changing climate. J. Ecol. 100, 104–115 (2012).
Google Scholar 

27.
Kuparinen, A., Katul, G., Nathan, R. & Schurr, F. M. Increases in air temperature can promote wind-driven dispersal and spread of plants. Proc. R. Soc. B 276, 3081–3087 (2009).
Google Scholar 

28.
Davis, H. G., Taylor, C. M., Lambrinos, J. G. & Strong, D. R. Pollen limitation causes an Allee effect in a wind-pollinated invasive grass (Spartina alterniflora). Proc. Natl Acad. Sci. USA 101, 13804–13807 (2004).
CAS  Google Scholar 

29.
Dullinger, S., Dirnböck, T. & Grabherr, G. Patterns of shrub invasion into high mountain grasslands of the Northern Calcareous Alps, Austria. Arct. Antarct. Alp. Res. 35, 434–441 (2003).
Google Scholar 

30.
Payette, S. The range limit of boreal tree species in Québec-Labrador: an ecological and palaeoecological interpretation. Rev. Palaeobot. Palynol. 79, 7–30 (1993).
Google Scholar 

31.
Sandel, B., Monnet, A. C., Govaerts, R. & Vorontsova, M. Late Quaternary climate stability and the origins and future of global grass endemism. Ann. Bot. 119, 279–288 (2016).
Google Scholar 

32.
Svenning, J. C. & Skov, F. Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? Ecol. Lett. 10, 453–460 (2007).
Google Scholar 

33.
Schurr, F. M. et al. Colonization and persistence ability explain the extent to which plant species fill their potential range. Glob. Ecol. Biogeogr. 16, 449–459 (2007).
Google Scholar 

34.
Saha, S. et al. The NCEP Climate Forecast System Reanalysis. Bull. Am. Meteorol. Soc. 91, 1015–1058 (2010).
Google Scholar 

35.
Hamann, A., Roberts, D. R., Barber, Q. E., Carroll, C. & Nielsen, S. E. Velocity of climate change algorithms for guiding conservation and management. Glob. Change Biol. 21, 997–1004 (2015).
Google Scholar 

36.
Kling, M. M., Auer, S. L., Comer, P. J., Ackerly, D. D. & Hamilton, H. Multiple axes of ecological vulnerability to climate change. Glob. Change Biol. 26, 2798–2813 (2020).
Google Scholar 

37.
Keeley, A. T. et al. New concepts, models, and assessments of climate-wise connectivity. Environ. Res. Lett. 13, 073002 (2018).
Google Scholar 

38.
Savage, D., Barbetti, M. J., MacLeod, W. J., Salam, M. U. & Renton, M. Timing of propagule release significantly alters the deposition area of resulting aerial dispersal. Diversity Distrib. 16, 288–299 (2010).
Google Scholar 

39.
Nathan, R. et al. Long‐distance biological transport processes through the air: can nature’s complexity be unfolded in silico? Divers. Distrib. 11, 131–137 (2005).
Google Scholar 

40.
Zeller, K. A., McGarigal, K. & Whiteley, A. R. Estimating landscape resistance to movement: a review. Landsc. Ecol. 27, 777–797 (2012).
Google Scholar 

41.
Treml, E. A., Halpin, P. N., Urban, D. L. & Pratson, L. F. Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation. Landsc. Ecol. 23, 19–36 (2008).
Google Scholar 

42.
Fernández‐López, J. & Schliep, K. rWind: download, edit and include wind data in ecological and evolutionary analysis. Ecography 42, 804–810 (2019).
Google Scholar 

43.
Thompson, S. & Katul, G. Plant propagation fronts and wind dispersal: an analytical model to upscale from seconds to decades using superstatistics. Am. Naturalist 171, 468–479 (2008).
Google Scholar 

44.
Savage, D., Barbetti, M. J., MacLeod, W. J., Salam, M. U. & Renton, M. Can mechanistically parameterised, anisotropic dispersal kernels provide a reliable estimate of wind-assisted dispersal? Ecol. Model. 222, 1673–1682 (2011).
Google Scholar 

45.
Regal, P. J. Pollination by wind and animals: ecology of geographic patterns. Annu. Rev. Ecol. Syst. 13, 497–524 (1982).
Google Scholar 

46.
Carroll, C., Lawler, J. J., Roberts, D. R. & Hamann, A. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10, e0140486 (2015).
Google Scholar 

47.
Jackson, S. T. & Sax, D. F. Balancing biodiversity in a changing environment: extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25, 153–160 (2010).
Google Scholar 

48.
Ackerly, D. D. et al. The geography of climate change: implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).
Google Scholar 

49.
Owens, J. N. The Reproductive Biology of Lodgepole Pine Extension Note 07 (Forest Genetics Council of British Columbia, 2006).

50.
Bontrager, M. & Angert, A. L. Gene flow improves fitness at a range edge under climate change. Evol. Lett. 3, 55–68 (2019).
Google Scholar 

51.
Sexton, J. P., Strauss, S. Y. & Rice, K. J. Gene flow increases fitness at the warm edge of a species’ range. Proc. Natl Acad. Sci. USA 108, 11704–11709 (2011).
CAS  Google Scholar 

52.
Rehfeldt, G. E., Ying, C. C., Spittlehouse, D. L. & Hamilton, D. A. Jr Genetic responses to climate in Pinus contorta: niche breadth, climate change, and reforestation. Ecol. Monogr. 69, 375–407 (1999).
Google Scholar 

53.
Wang, T., O’Neill, G. A. & Aitken, S. N. Integrating environmental and genetic effects to predict responses of tree populations to climate. Ecol. Appl. 20, 153–163 (2010).
CAS  Google Scholar 

54.
Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).
Google Scholar 

55.
Dobrowski, S. Z. et al. The climate velocity of the contiguous United States during the 20th century. Glob. Change Biol. 19, 241–251 (2013).
Google Scholar 

56.
van Etten, J. R Package gdistance: distances and routes on geographical grids. J. Stat. Softw. 76, 1–21 (2017).
Google Scholar 

57.
IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).

58.
Schleussner, C. F. et al. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C. Earth Syst. Dyn. 7, 327–351 (2016).
Google Scholar 

59.
Little, E. L. Jr Atlas of United States Trees. Volume 1, Conifers and Important Hardwoods Miscellaneous Publication 1146 (US Department of Agriculture, 1971).

60.
Wang, T., Hamann, A., Yanchuk, A., O’Neill, G. A. & Aitken, S. N. Use of response functions in selecting lodgepole pine populations for future climates. Glob. Change Biol. 12, 2404–2416 (2006).
Google Scholar 

61.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Google Scholar 

62.
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Google Scholar 

63.
R Core Team (2017). R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017); https://www.R-project.org/

64.
Kling, M. M. & Ackerly, D. D. Scripts and Data used in ‘Global Wind Patterns and the Vulnerability of Wind-Dispersed Species to Climate Change (Zenodo Repository, 2020); https://doi.org/10.5281/zenodo.3860687

65.
Kling, M. M. Windscape R Package v.1.0.0 (Zenodo Repository, 2020); https://doi.org/10.5281/zenodo.3857730 More

1.
The Global Burden of Disease: 2004 Update (WHO, 2004).
2.
Sheldon, B. C. & Verhulst, S. Ecological immunology: costly parasite defences and trade-offs in evolutionary ecology. Trends Ecol. Evol. 11, 317–321 (1996).
CAS  PubMed  Google Scholar 

3.
Schmid-Hempel, P. Variation in immune defence as a question of evolutionary ecology. Proc. R. Soc. B. 270, 357–366 (2003).
PubMed  Google Scholar 

4.
Schmid-Hempel, P. Evolutionary Parasitology (Oxford Univ. Press, 2013).

5.
Rook, G. A. Regulation of the immune system by biodiversity from the natural environment: an ecosystem service essential to health. Proc. Natl Acad. Sci. USA 110, 18360–18367 (2013).
CAS  PubMed  Google Scholar 

6.
von Hertzen, L., Hanski, I. & Haahtela, T. Natural immunity. Biodiversity loss and inflammatory diseases are two global megatrends that might be related. EMBO Rep. 12, 1089–1093 (2011).
Google Scholar 

7.
Belkaid, Y. & Hand, T. W. Role of the microbiota in immunity and inflammation. Cell 157, 121–141 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

8.
Lambrecht, B. N. & Hammad, H. The immunology of the allergy epidemic and the hygiene hypothesis. Nat. Immunol. 18, 1076–1083 (2017).
CAS  PubMed  Google Scholar 

9.
Rook, G. A., Martinelli, R. & Brunet, L. R. Innate immune responses to mycobacteria and the downregulation of atopic responses. Curr. Opin. Allergy Clin. Immunol. 3, 337–342 (2003).
CAS  PubMed  Google Scholar 

10.
Rosenblum, M. D., Remedios, K. A. & Abbas, A. K. Mechanisms of human autoimmunity. J. Clin. Invest. 125, 2228–2233 (2015).
PubMed  PubMed Central  Google Scholar 

11.
Lafferty, K. D. Biodiversity loss decreases parasite diversity: theory and patterns. Philos. Trans. R. Soc. Lond. B 367, 2814–2827 (2012).
Google Scholar 

12.
Kamiya, T., O’Dwyer, K., Nakagawa, S. & Poulin, R. Host diversity drives parasite diversity: meta-analytical insights into patterns and causal mechanisms. Ecography 37, 689–697 (2014).
Google Scholar 

13.
McDade, T. W., Georgiev, A. V. & Kuzawa, C. W. Trade-offs between acquired and innate immune defenses in humans. Evol. Med. Public Health 2016, 1–16 (2016).
PubMed  PubMed Central  Google Scholar 

14.
Lindstrom, K. M., Foufopoulos, J., Parn, H. & Wikelski, M. Immunological investments reflect parasite abundance in island populations of Darwin’s finches. Proc. R. Soc. B 271, 1513–1519 (2004).
PubMed  Google Scholar 

15.
Mayer, A., Mora, T., Rivoire, O. & Walczak, A. M. Diversity of immune strategies explained by adaptation to pathogen statistics. Proc. Natl Acad. Sci. USA 113, 8630–8635 (2016).
CAS  PubMed  Google Scholar 

16.
Scharsack, J. P., Kalbe, M., Harrod, C. & Rauch, G. Habitat-specific adaptation of immune responses of stickleback (Gasterosteus aculeatus) lake and river ecotypes. Proc. R. Soc. B 274, 1523–1532 (2007).
PubMed  Google Scholar 

17.
Kaczorowski, K. J. et al. Continuous immunotypes describe human immune variation and predict diverse responses. Proc. Natl Acad. Sci. USA 114, E6097–E6106 (2017).
CAS  PubMed  Google Scholar 

18.
Herman, A. et al. The role of gene flow in rapid and repeated evolution of cave-related traits in Mexican tetra, Astyanax mexicanus. Mol. Ecol. 27, 4397–4416 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

19.
Fumey, J. et al. Evidence for late Pleistocene origin of Astyanax mexicanus cavefish. BMC Evol. Biol. 18, 43 (2018).
PubMed  PubMed Central  Google Scholar 

20.
Gibert, J. & Deharveng, L. Subterranean ecosystems: a truncated functional biodiversity. BioScience 52, 473–481 (2002).

21.
Tabin, J. A. et al. Temperature preference of cave and surface populations of Astyanax mexicanus. Dev. Biol. 441, 338–344 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

22.
Abolins, S. et al. The comparative immunology of wild and laboratory mice, Mus musculus domesticus. Nat. Commun. 8, 14811 (2017).
PubMed  PubMed Central  Google Scholar 

23.
Trama, A. M. et al. Lymphocyte phenotypes in wild-caught rats suggest potential mechanisms underlying increased immune sensitivity in post-industrial environments. Cell Mol. Immunol. 9, 163–174 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

24.
Aspiras, A. C., Rohner, N., Martineau, B., Borowsky, R. L. & Tabin, C. J. Melanocortin 4 receptor mutations contribute to the adaptation of cavefish to nutrient-poor conditions. Proc. Natl Acad. Sci. USA 112, 9668–9673 (2015).
CAS  PubMed  Google Scholar 

25.
Xiong, S., Krishnan, J., Peuss, R. & Rohner, N. Early adipogenesis contributes to excess fat accumulation in cave populations of Astyanax mexicanus. Dev. Biol. 441, 297–304 (2018).
CAS  PubMed  Google Scholar 

26.
Wiens, G. D. & Vallejo, R. L. Temporal and pathogen-load dependent changes in rainbow trout (Oncorhynchus mykiss) immune response traits following challenge with biotype 2 Yersinia ruckeri. Fish Shellfish Immunol. 29, 639–647 (2010).
CAS  PubMed  Google Scholar 

27.
Krishnan, J. et al. Comparative transcriptome analysis of wild and lab populations of Astyanax mexicanus uncovers differential effects of environment and morphotype on gene expression. J. Exp. Zool. B https://doi.org/10.1002/jez.b.22933 (2020).

28.
Moller, A. M., Korytar, T., Kollner, B., Schmidt-Posthaus, H. & Segner, H. The teleostean liver as an immunological organ: intrahepatic immune cells (IHICs) in healthy and benzo[a]pyrene challenged rainbow trout (Oncorhynchus mykiss). Dev. Comp. Immunol. 46, 518–529 (2014).
CAS  PubMed  Google Scholar 

29.
Traver, D. et al. Transplantation and in vivo imaging of multilineage engraftment in zebrafish bloodless mutants. Nat. Immunol. 4, 1238–1246 (2003).
CAS  PubMed  Google Scholar 

30.
Stockdale, W. T. et al. Heart regeneration in the Mexican cavefish. Cell Rep. 25, 1997–2007 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

31.
Ramsey, S. et al. Transcriptional noise and cellular heterogeneity in mammalian macrophages. Philos. Trans. R. Soc. Lond. B. 361, 495–506 (2006).
CAS  Google Scholar 

32.
Ogryzko, N. V., Renshaw, S. A. & Wilson, H. L. The IL-1 family in fish: swimming through the muddy waters of inflammasome evolution. Dev. Comp. Immunol. 46, 53–62 (2014).

33.
Wittamer, V., Bertrand, J. Y., Gutschow, P. W. & Traver, D. Characterization of the mononuclear phagocyte system in zebrafish. Blood 117, 7126–7135 (2011).
CAS  PubMed  Google Scholar 

34.
Sunyer, J. O. Evolutionary and functional relationships of B cells from fish and mammals: Insights into their novel roles in phagocytosis and presentation of particulate antigen. Infect. Disord. Drug Targets 12, 200–212 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

35.
Lugo-Villarino, G. et al. Identification of dendritic antigen-presenting cells in the zebrafish. Proc. Natl Acad. Sci. USA 107, 15850–15855 (2010).
CAS  PubMed  Google Scholar 

36.
Haugland, G. T. et al. Phagocytosis and respiratory burst activity in lumpsucker (Cyclopterus lumpus L.) leucocytes analysed by flow cytometry. PLoS ONE 7, e47909 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

37.
Lieschke, G. J. & Trede, N. S. Fish immunology. Curr. Biol. 19, R678–R682 (2009).
CAS  PubMed  Google Scholar 

38.
Balla, K. M. et al. Eosinophils in the zebrafish: prospective isolation, characterization, and eosinophilia induction by helminth determinants. Blood 116, 3944–3954 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

39.
Bolnick, D. I., Shim, K. C., Schmerer, M. & Brock, C. D. Population-specific covariation between immune function and color of nesting male threespine stickleback. PLoS ONE 10, e0126000 (2015).
PubMed  PubMed Central  Google Scholar 

40.
Peuß, R. et al. Label-independent flow cytometry and unsupervised neural network method for de novo clustering of cell populations. Preprint at bioRxiv https://doi.org/10.1101/603035 (2020).

41.
van der Meer, W., Scott, C. S. & de Keijzer, M. H. Automated flagging influences the inconsistency and bias of band cell and atypical lymphocyte morphological differentials. Clin. Chem. Lab. Med. 42, 371–377 (2004).
PubMed  Google Scholar 

42.
Getz, G. S. Thematic review series: the immune system and atherogenesis. Bridging the innate and adaptive immune systems. J. Lipid Res. 46, 619–622 (2005).
CAS  PubMed  Google Scholar 

43.
Wan, F. et al. Characterization of gammadelta T cells from zebrafish provides insights into their important role in adaptive humoral immunity. Front. Immunol. 7, 675 (2016).
PubMed  Google Scholar 

44.
Shilpi, Paul,S. & Lal, G. Role of gamma-delta (gammadelta) T cells in autoimmunity. J. Leukoc. Biol. 97, 259–271 (2015).
PubMed  Google Scholar 

45.
Fan, X. & Rudensky, A. Y. Hallmarks of tissue-resident lymphocytes. Cell 164, 1198–1211 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

46.
Papotto, P. H., Reinhardt, A., Prinz, I. & Silva-Santos, B. Innately versatile: gammadelta17 T cells in inflammatory and autoimmune diseases. J. Autoimmun. 87, 26–37 (2018).
CAS  PubMed  Google Scholar 

47.
Fay, N. S., Larson, E. C. & Jameson, J. M. Chronic Inflammation and gammadelta T. Cells Front. Immunol. 7, 210 (2016).
PubMed  Google Scholar 

48.
Rossi, D. J. et al. Cell intrinsic alterations underlie hematopoietic stem cell aging. Proc. Natl Acad. Sci. USA 102, 9194–9199 (2005).
CAS  PubMed  Google Scholar 

49.
Bolli, N. et al. Expression of the cytoplasmic NPM1 mutant (NPMc+) causes the expansion of hematopoietic cells in zebrafish. Blood 115, 3329–3340 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

50.
Stachura, D. L. et al. Clonal analysis of hematopoietic progenitor cells in the zebrafish. Blood 118, 1274–1282 (2011).
CAS  PubMed  PubMed Central  Google Scholar 

51.
Reavie, L. et al. Regulation of hematopoietic stem cell differentiation by a single ubiquitin ligase-substrate complex. Nat. Immunol. 11, 207–215 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

52.
Cabezas-Wallscheid, N. et al. Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. Cell Stem Cell 15, 507–522 (2014).
CAS  PubMed  Google Scholar 

53.
Cheng, J. et al. Hematopoietic defects in mice lacking the sialomucin CD34. Blood 87, 479–490 (1996).
CAS  PubMed  Google Scholar 

54.
Anjos-Afonso, F. et al. CD34(–) cells at the apex of the human hematopoietic stem cell hierarchy have distinctive cellular and molecular signatures. Cell Stem Cell 13, 161–174 (2013).
CAS  PubMed  Google Scholar 

55.
Amin, R. H. & Schlissel, M. S. Foxo1 directly regulates the transcription of recombination-activating genes during B cell development. Nat. Immunol. 9, 613–622 (2008).
CAS  PubMed  PubMed Central  Google Scholar 

56.
Han, S., Zheng, B., Schatz, D. G., Spanopoulou, E. & Kelsoe, G. Neoteny in lymphocytes: Rag1 and Rag2 expression in germinal center B cells. Science 274, 2094–2097 (1996).
CAS  PubMed  Google Scholar 

57.
Naito, Y. et al. Germinal center marker GL7 probes activation-dependent repression of N-glycolylneuraminic acid, a sialic acid species involved in the negative modulation of B-cell activation. Mol. Cell Biol. 27, 3008–3022 (2007).
CAS  PubMed  PubMed Central  Google Scholar 

58.
Laszlo, G., Hathcock, K. S., Dickler, H. B. & Hodes, R. J. Characterization of a novel cell-surface molecule expressed on subpopulations of activated T and B cells. J. Immunol. 150, 5252–5262 (1993).
CAS  PubMed  Google Scholar 

59.
Fänge, R. & Nilsson, S. The fish spleen: structure and function. Experientia 41, 152–158 (1985).
PubMed  Google Scholar 

60.
Steinel, N. C. & Bolnick, D. I. Melanomacrophage centers as a histological indicator of immune function in fish and other poikilotherms. Front. Immunol. 8, 827 (2017).
PubMed  PubMed Central  Google Scholar 

61.
Cervenak, L., Magyar, A., Boja, R. & Laszlo, G. Differential expression of GL7 activation antigen on bone marrow B cell subpopulations and peripheral B cells. Immunol. Lett. 78, 89–96 (2001).
CAS  PubMed  Google Scholar 

62.
Secombes, C. J., Wang, T. & Bird, S. The interleukins of fish. Dev. Comp. Immunol. 35, 1336–1345 (2011).
CAS  PubMed  Google Scholar 

63.
Weisberg, S. P. et al. Obesity is associated with macrophage accumulation in adipose tissue. J. Clin. Invest. 112, 1796–1808 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

64.
Christ, A. et al. Western diet triggers NLRP3-dependent innate immune reprogramming. Cell 172, 162–175 e114 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

65.
McAlpine, C. S. et al. Sleep modulates haematopoiesis and protects against atherosclerosis. Nature 566, 383–387 (2019).

66.
Heidt, T. et al. Chronic variable stress activates hematopoietic stem cells. Nat. Med. 20, 754–758 (2014).
CAS  PubMed  PubMed Central  Google Scholar 

67.
Mitchell, R. G., Russell, W. H. & Elliott, W. R. Mexican Eyeless Characin Fishes, Genus Astyanax: Environment, Distribution, and Evolution (Texas Tech Press, 1977).

68.
Espinasa, L. et al. A new cave locality for Astyanax cavefish in Sierra de El Abra, Mexico. Subterr. Biol. 26, 39–53 (2018).
Google Scholar 

69.
Embryo Surface Sanitation (Egg Bleaching) Protocol https://zebrafish.org/wiki/protocols/ess (ZIRC, 2019).

70.
Peuß, R., Eggert, H., Armitage, S. A. & Kurtz, J. Downregulation of the evolutionary capacitor Hsp90 is mediated by social cues. Proc. R. Soc. B 282, 20152041 (2015).
PubMed  Google Scholar 

71.
Pfaffl, M. W., Horgan, G. W. & Dempfle, L. Relative expression software tool (REST(C)) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 30, 36e (2002).
Google Scholar 

72.
Zhang, Y. A. et al. IgT, a primitive immunoglobulin class specialized in mucosal immunity. Nat. Immunol. 11, 827–835 (2010).
CAS  PubMed  PubMed Central  Google Scholar 

73.
Rowe, R. G., Mandelbaum, J., Zon, L. I. & Daley, G. Q. Engineering hematopoietic stem cells: lessons from development. Cell Stem Cell 18, 707–720 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

74.
Stachura, D. L. et al. The zebrafish granulocyte colony-stimulating factors (Gcsfs): 2 paralogous cytokines and their roles in hematopoietic development and maintenance. Blood 122, 3918–3928 (2013).
CAS  PubMed  PubMed Central  Google Scholar 

75.
de Jong, J. L. & Zon, L. I. Use of the zebrafish system to study primitive and definitive hematopoiesis. Ann. Rev. Genet. 39, 481–501 (2005).
PubMed  Google Scholar 

76.
Athanasiadis, E. I. et al. Single-cell RNA-sequencing uncovers transcriptional states and fate decisions in haematopoiesis. Nat. Commun. 8, 2045 (2017).
PubMed  PubMed Central  Google Scholar 

77.
Zeng, A. et al. Prospectively isolated tetraspanin(+) neoblasts are adult pluripotent stem cells underlying planaria regeneration. Cell 173, 1593–1608 (2018).
CAS  PubMed  Google Scholar 

78.
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
CAS  Google Scholar 

79.
Sun, K. et al. Endotrophin triggers adipose tissue fibrosis and metabolic dysfunction. Nat. Commun. 5, 3485 (2014).
PubMed  PubMed Central  Google Scholar 

80.
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014).

81.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar  More

1.
Farquhar, J., Zerkle, A. L. & Bekker, A. in The Atmosphere – History 2nd edn, Vol. 6 (ed. Farquhar, J.) 91–138 (Elsevier, 2014).
2.
Lyons, T. W., Reinhard, C. T. & Planesky, N. J. The rise of oxygen in Earth’s early ocean and atmosphere. Nature 506, 307–315 (2014).
Google Scholar 

3.
Holland, H. D. Volcanic gases, black smokers and the great oxidation event. Geochim. Cosmochim. Acta 66, 3811–3826 (2002).
Google Scholar 

4.
Kump, L. R. & Barley, M. E. Increased subaerial volcanism and the rise of atmospheric oxygen 2.5 billion years ago. Nature 448, 1033–1036 (2007).
Google Scholar 

5.
Korenaga, J. Crustal evolution and mantle dynamics through Earth history. Philos. Trans. R. Soc. A 376, 2017048 (2018).
Google Scholar 

6.
Canfield, D. E. The early history of atmospheric oxygen: homage to Robert M. Garrels. Annu. Rev. Earth Planet. Sci. 33, 1–36 (2005).
Google Scholar 

7.
Holland, H. D. The oxygenation of the atmosphere and oceans. Philos. Trans. R. Soc. B. 363, 903–915 (2006).
Google Scholar 

8.
Gaillard, F., Scaillet, B. & Arndt, N. T. Atmospheric oxygenation caused by a change in volcanic degassing pressure. Nature 478, 228–232 (2011).
Google Scholar 

9.
Farquhar, J., Bao, H. M. & Thiemens, M. Atmospheric influence of Earth’s earliest sulfur cycle. Science 289, 756–758 (2000).
Google Scholar 

10.
Pavlov, A. A. & Kasting, J. F. Mass-independent fractionation of sulfur isotopes in Archean sediments: strong evidence for an anoxic Archean atmosphere. Astrobiology 2, 27–41 (2002).
Google Scholar 

11.
Symonds, R. B., Rose, W. I., Bluth, G. J. S. & Gerlach, T. M. in Volatiles in Magmas Vol. 30 (eds Caroll, M. R. & Halloway, J. R.) 1–66 (Mineralogical Society of America, 1994).

12.
Oppenheimer, C., Fischer, T. P. & Scaillet, B. in The Crust 2nd edn, Vol. 4 (ed. Rudnick, R. L.) 111–179 (Elsevier, 2014).

13.
National Academies of Sciences, Engineering and Medicine. Volcanic Eruptions and their Repose, Unrest, Precursors, and Timing (The National Academy Press, 2017).

14.
Drummond, S. E. Jr Boiling and Mixing of Hydrothermal Fluids: Chemical Effects on Mineral Precipitation. PhD thesis, Pennsylvania State Univ. (1981).

15.
German, C. R. & Von Damm, K. L. in The Oceans and Marine Geochemistry Vol. 6 (ed. Elderfield, H.) 181–222 (Elsevier, 2006).

16.
Giggenbach, W. F. Redox processes governing the chemistry of fumarolic gas discharges from White Island, New Zealand. Appl. Geochem. 2, 143–161 (1987).
Google Scholar 

17.
Lasaga, A. C. & Ohmoto, H. The oxygen geochemical cycle: dynamics and stability. Geochim. Cosmochim. Acta 66, 361–381 (2002).
Google Scholar 

18.
Ohmoto, H. in The Precambrian Earth: Tempos and Events Vol. 12 (eds Erickson, P. G. et. al.) 361–387 (Elsevier, 2004).

19.
Ohmoto, H. et al. Oxygen, iron and sulfur geochemical cycles on early Earth: paradigms and contradictions. Geol. Soc. Am. Spec. Pap. 504, 55–95 (2014).
Google Scholar 

20.
Burnham, C. W. & Ohmoto, H. in Granitic Magmatism and Related Mineralization Vol. 8 (eds. Ishihara, S. & Takenouchi, S.) 1–11 (1980).

21.
Berry, A. J. et al. A re-assessment of the oxidation state of iron in MORB glasses. Earth Planet. Sci. Lett. 483, 114–123 (2018).
Google Scholar 

22.
Carroll, M. R. & Webster, J. D. in Volatiles in Magmas Vol. 30 (eds Caroll, M. R. & Halloway, J. R.) 231–280 (Mineralogical Society of America, 1994).

23.
Carmichael, I. S. E. The redox states of basic and silicic magmas: a reflection of their source region? Contrib. Mineral. Petrol. 106, 129–141 (1991).
Google Scholar 

24.
Frost, D. J. & McCammon, C. A. The redox state of Earth’s mantle. Annu. Rev. Earth Planet. Sci. 36, 389–420 (2008).
Google Scholar 

25.
Evans, K. A. The redox budget of subduction zones. Earth Sci. Rev. 113, 11–32 (2012).
Google Scholar 

26.
Richards, J. P. The oxidation state, and sulfur and Cu contents of arc magmas: implications for metallurgy. Lithos 233, 27–45 (2015).
Google Scholar 

27.
Chappell, B. W. & White, A. J. R. Two contrasting granite types. Pac. Geol. 8, 173–174 (1974).
Google Scholar 

28.
Ishihara, S. The magnetite-series and ilmenite-series granitic rocks. Min. Geol. 27, 291–305 (1977).
Google Scholar 

29.
Savarino, J. et al. UV induced mass-independent sulfur isotope fractionation in stratospheric volcanic sulfate. Geophys. Res. Lett. 30, 2131 (2003).
Google Scholar 

30.
Hattori, S. et al. SO2 photoexcitation mechanism links mass-independent sulfur isotopic fractionation in cryospheric sulfate to climate impacting volcanism. Proc. Natl Acad. Sci. USA 110, 17661–17656 (2019).
Google Scholar 

31.
Whitehill, A. R., Jiang, B., Guo, H. & Ono, S. SO2 photolysis as a source for sulfur mass-independent isotope signatures in stratospheric aerosols. Atmos. Chem. Phys. 15, 1843–1864 (2015).
Google Scholar 

32.
Sasaki, A. & Ishihara, S. Sulfur isotopic composition of the magnetite-series and ilmenite-series granitoids in Japan. Contrib. Mineral. Petrol. 68, 107–115 (1979).
Google Scholar 

33.
Alt, J. C., Shanks, W. C. & Jackson, M. C. Cycling of sulfur in subduction zones: the geochemistry of sulfur in the Mariana Island Arc and back-arc trough. Earth Planet. Sci. Lett. 119, 477–494 (1993).
Google Scholar 

34.
Ohmoto, H. et al. Chemical processes of Kuroko formation. Econ. Geol. Mon. 5, 570–604 (1983).

35.
Ohmoto, H. Formation of volcanogenic massive sulfide deposits: the Kuroko perspective. Ore Geol. Rev. 10, 135–177 (1996).
Google Scholar 

36.
Ohmoto, H. & Goldhaber, M. B. in Geochemistry of Hydrothermal Ore Deposits 3rd edn (ed. Barnes, H. L.) 517–611 (Wiley, 1997).

37.
Kishima, N. A thermodynamic study on the pyrite–pyrrhotite–magnetite–water system at 300–500 °C with relevance to the fugacity/concentration quotient of aqueous H2S. Geochim. Cosmochim. Acta 53, 2143–2155 (1989).
Google Scholar 

38.
Schoonen, M. A. A. & Barnes, H. L. Mechanisms of pyrite and marcasite formation from solutions. III. Hydrothermal processes. Geochim. Cosmochim. Acta 55, 3491–3504 (1991).
Google Scholar 

39.
Graham, U. M. & Ohmoto, H. Experimental study of formation mechanisms of hydrothermal pyrite. Geochim. Cosmochim. Acta 58, 2187–2202 (1994).
Google Scholar 

40.
Kerrich, R. & Said, N. Extreme positive Ce anomalies in a 3.0 Ga submarine volcanic sequence, Murchison Province: oxygenated marine bottom waters. Chem. Geol. 280, 232–241 (2011).
Google Scholar 

41.
Kerrich, R., Said, N., Manikyamba, C. & Wyman, D. Sampling oxygenated Archean hydrosphere: implications from fractionations of Th/U and Ce/Ce* in hydrothermally altered volcanic sequences. Gondwana Res. 23, 506–525 (2013).
Google Scholar 

42.
van Keken, P. E., Kiefer, B. & Peacock, S. M. High-resolution models of subduction zones: implications for mineral dehydration reactions and the transport of water into the deep mantle. Geochem. Geophys. Geosyst. 3, 1056 (2002).
Google Scholar 

43.
Hyndman, R. D. & Peacock, S. M. Serpentinization of the forearc mantle. Earth Planet. Sci. Lett. 212, 417–432 (2003).
Google Scholar 

44.
Tomkins, A. G. & Evans, K. A. Separate zones of sulfate and sulfide release from subducted mafic oceanic crust. Earth Planet. Sci. Lett. 428, 73–83 (2015).
Google Scholar 

45.
Scaillet, B., Clemente, B., Evans, B. & Pichavant, M. Redox control of sulfur degassing in silicic magmas. J. Geophys. Res. 103, 23937–23949 (1998).
Google Scholar 

46.
Wallace, P. J. Volatiles in subduction zone magmas: concentrations and fluxes based on melt inclusions and volcanic gas data. J. Volcanol. 140, 217–240 (2005).
Google Scholar 

47.
Jugo, P. J. Sulfur content at sulfide saturation in oxidized magmas. Geology 37, 415–418 (2009).
Google Scholar 

48.
Ishihara, S. et al. in Evolution of Early Earth’s Atmosphere, Hydrosphere and Biosphere—Constraints from Ore Deposits Vol. 198 (eds Kesler, S. E. & Ohmoto, H.) 67–80 (Geological Society of America, 2006).

49.
Barboni, M. et al. Early formation of the Moon 4.51 billion years ago. Sci. Adv. 2017, 1602365 (2017).
Google Scholar 

50.
Delano, J. W. Redox history of the Earth’s interior since ~3,900 Ma: implications for prebiotic molecules. Orig. Life Evol. Biosphere 31, 311–341 (2001).
Google Scholar 

51.
Nicklas, R. W., Puchtel, I. S. & Ash, R. D. Redox state of the Archean mantle: evidence from V partitioning in 3.5–2.4 Ga komatiites. Geochim. Cosmochim. Acta 222, 447–446 (2018).
Google Scholar 

52.
Li, Z.-X. A. & Lee, C.-T. A. The constancy of upper mantle fO2 through time inferred from V/Sc ratios in basalts. Earth Planet. Sci. Lett. 228, 483–493 (2004).
Google Scholar 

53.
Trail, D., Watson, E. B. & Tailby, N. D. The oxidation state of Hadean magmas and implications for early Earth’s atmosphere. Nature 480, 79–83 (2011).
Google Scholar 

54.
Watanabe, Y., Farquhar, J. & Ohmoto, H. Anomalous fractionations of sulfur isotopes during thermochemical sulfate reduction. Science 324, 370–373 (2008).
Google Scholar 

55.
Oduro, H. et al. Evidence of magnetic isotope effects during thermochemical sulfate reduction. Proc. Natl Acad. Sci. USA 108, 17635–17638 (2011).
Google Scholar 

56.
Ohmoto et al. (Bio)geochemical cycles of S, C, Fe, and O on the hotter Archean Earth. Goldschmidt Abstr. 2018, abstr. 1913 (2018).

57.
Ohmoto, H., Watanabe, Y. & Kumazawa, K. Evidence from massive siderite beds for a CO2-rich atmosphere before ~1.8 billion years ago. Nature 429, 395–399 (2004).
Google Scholar 

58.
Finlayson-Pitts, B. J. & Pitts, J. N. Chemistry of the Upper and Lower Atmosphere (Academic Press, 1999).

59.
Seccombe, P. K. Sulphur isotope and trace metal composition of stratiform sulphides as an ore guide in the Canadian Shield. J. Geochem. Explor. 8, 117–137 (1977).
Google Scholar 

60.
Jamieson, J. W., Wing, B. A., Farquhar, J. & Hamington, M. D. Neoarchaean seawater sulphate concentrations from sulphur isotopes in massive sulphide ore. Nat. Geosci. 6, 61–64 (2013).
Google Scholar 

61.
Vaughan, D. J. & Craig, J. R. in Geochemistry of Hydrothermal Ore Deposits 2nd edn (ed. Barnes, H. L.) 367–434 (Wiley, 1979).

62.
Mysen, B. & Boettcher, A. L. Melting of a hydrous mantle. I. Phase relations of natural peridotite at high pressures and temperatures with controlled activities of water, carbon dioxide and hydrogen. J. Petrol. 16, 520–548 (1975).
Google Scholar 

63.
Gaetani, G. & Grove, T. L. The influence of water on melting of mantle peridotite. Contrib. Mineral. Petrol. 131, 323–346 (1998).
Google Scholar 

64.
Henderson, P. & Henderson, G. M. The Cambridge Handbook of Earth Science Data (Cambridge Univ. Press, 2009).

65.
Deines, P. & Harris, J. W. Sulfide inclusion chemistry and carbon isotopes of African diamonds. Geochim. Cosmochim. Acta 59, 3173–3188 (1995).
Google Scholar 

66.
Rudnick, R. L., Eldridge, C. S. & Bulanova, G. P. Diamond growth history from in situ measurement of Pb and S isotopic compositions of sulfide inclusions. Geology 21, 13–16 (1993).
Google Scholar 

67.
Farquhar, J. et al. Mass-independent sulfur of inclusions in diamond and sulfur recycling on early earth. Science 298, 2369–2371 (2002).
Google Scholar 

68.
Hickman, A. H. Review of the Pilbara Craton and Fortescue Basin, Western Australia: crustal evolution providing environments for early life. Isl. Arc 21, 1–31 (2012).
Google Scholar 

69.
van Kranendonk, M. J., Smithies, R. H., Hickman, A. H. & Champion, D. C. in Earth’s Oldest Rocks (eds van Kranendonk, M. J. et al.) 307–337 (Elsevier, 2007). More

1.
McFall-Ngai M, Hadfield MG, Bosch TCG, Carey HV, Domazet-Lošo T, Douglas AE, et al. Animals in a bacterial world, a new imperative for the life sciences. Proc Natl Acad Sci USA. 2013;110:3229–36.
CAS  PubMed  Google Scholar 
2.
Moran NA, Bennett GM. The tiniest tiny genomes. Annu Rev Microbiol. 2014;68:195–215.
CAS  PubMed  Google Scholar 

3.
Douglas AE. Multiorganismal insects: diversity and function of resident microorganisms. Annu Rev Entomol. 2015;60:17–34.
CAS  PubMed  Google Scholar 

4.
Engelstädter J, Hurst GDD. The ecology and evolution of microbes that manipulate host reproduction. Annu Rev Ecol Evol Syst. 2009;40:127–49.
Google Scholar 

5.
Ma WJ, Schwander T. Patterns and mechanisms in instances of endosymbiont-induced parthenogenesis. J Evol Biol. 2017;30:868–88.
PubMed  Google Scholar 

6.
Bondy EC, Hunter MS. Sex ratios in the haplodiploid herbivores, aleyrodidae and thysanoptera: a review and tools for study. Adv Insect Physiol. 2019;56:251–81.
Google Scholar 

7.
Hunter MS, Perlman SJ, Kelly SE. A bacterial symbiont in the Bacteroidetes induces cytoplasmic incompatibility in the parasitoid wasp Encarsia pergandiella. Proc Natl Acad Sci USA. 2003;270:2185–90.
Google Scholar 

8.
Beckmann JF, Ronau JA, Hochstrasser MA. Wolbachia deubiquitylating enzyme induces cytoplasmic incompatibility. Nat Microbiol 2017;2:17007.
PubMed  PubMed Central  Google Scholar 

9.
Harumoto T, Lemaitre B. Male-killing toxin in a bacterial symbiont of Drosophila. Nature 2018;557:252–5.
CAS  PubMed  PubMed Central  Google Scholar 

10.
Hosokawa T, Koga R, Kikuchi Y, Meng XY, Fukatsu T. Wolbachia as a bacteriocyte-associated nutritional mutualist. Proc Natl Acad Sci USA. 2010;107:769–74.
CAS  PubMed  Google Scholar 

11.
Michalkova V, Benoit JB, Weiss BL, Attardo GM, Aksoy S. Vitamin B6 generated by obligate symbionts is critical for maintaining proline homeostasis and fecundity in tsetse flies. Appl Environ Microbiol. 2014;80:5844–53.
PubMed  PubMed Central  Google Scholar 

12.
Moriyama M, Nikoh N, Hosokawa T, Fukatsu T. Riboflavin provisioning underlies Wolbachia’s fitness contribution to its insect host. mBio . 2015;6:e01732–15.
CAS  PubMed  PubMed Central  Google Scholar 

13.
Snyder AK, Rio RVM. ‘Wigglesworthia morsitans’ folate (vitamin B9) biosynthesis contributes to tsetse host fitness. Appl Environ Microbiol. 2015;81:5375–86.
CAS  PubMed  PubMed Central  Google Scholar 

14.
Ju JF, Bing XL, Zhao DS, Guo Y, Xi Z, Hoffmann AA, et al. Wolbachia supplement biotin and riboflavin to enhance reproduction in planthoppers. ISME J. 2019;14:676–87.
PubMed  Google Scholar 

15.
Tsuchida T, Koga R, Shibao H, Matsumoto T, Fukatsu T. Diversity and geographic distribution of secondary endosymbiotic bacteria in natural populations of the pea aphid, Acyrthosiphon pisum. Mol Ecol. 2002;11:2123–35.
CAS  PubMed  Google Scholar 

16.
Baumann P. Biology of bacteriocyte-associated endosymbionts of plant sap-sucking insects. Annu Rev Microbiol. 2005;59:155–89.
CAS  PubMed  PubMed Central  Google Scholar 

17.
Gottlieb Y, Ghanim M, Gueguen G, Kontsedalov S, Vavre F, Fleury F, et al. Inherited intracellular ecosystem: symbiotic bacteria share bacteriocytes in whiteflies. FASEB J. 2008;22:2591–9.
CAS  PubMed  Google Scholar 

18.
Sloan DB, Moran NA. Genome reduction and co-evolution between the primary and secondary bacterial symbionts of psyllids. Mol Biol Evol. 2012;29:3781–92.
CAS  PubMed  PubMed Central  Google Scholar 

19.
Skaljac M, Zanic K, Ban SG, Kontsedalov S, Ghanim M. Co-infection and localization of secondary symbionts in two whitefly species. BMC Microbiol. 2010;10:142.
PubMed  PubMed Central  Google Scholar 

20.
McCutcheon JP, Von Dohlen CD. An interdependent metabolic patchwork in the nested symbiosis of mealybugs. Curr Biol. 2011;21:1366–72.
CAS  PubMed  PubMed Central  Google Scholar 

21.
Husnik F, Nikoh N, Koga R, Ross L, Duncan RP, Fujie M, et al. Horizontal gene transfer from diverse bacteria to an insect genome enables a tripartite nested mealybug symbiosis. Cell. 2013;153:1567–78.
CAS  PubMed  Google Scholar 

22.
Koga R, Meng XY, Tsuchida T, Fukatsu T. Cellular mechanism for selective vertical transmission of an obligate insect symbiont at the bacteriocyte-embryo interface. Proc Natl Acad Sci USA. 2012;109:E1230–E1237.
CAS  PubMed  Google Scholar 

23.
Fukatsu T, Nikoh N. Two intracellular symbiotic bacteria from the mulberry psyllid Anomoneura mori (Insecta, Homoptera). Appl Environ Microbiol. 1998;64:3599–606.
CAS  PubMed  PubMed Central  Google Scholar 

24.
Degnan PH, Yu Y, Sisneros N, Wing RA, Moran NA. Hamiltonella defensa, genome evolution of protective bacterial endosymbiont from pathogenic ancestors. Proc Natl Acad Sci USA. 2009;106:9063–8.
CAS  PubMed  Google Scholar 

25.
Rao Q, Wang S, Su YL, Bing XL, Liu SS, Wang XW. Draft genome sequence of ‘Candidatus Hamiltonella defensa’ an endosymbiont of the whitefly Bemisia tabaci. J Bacteriol. 2012;194:3558.
CAS  PubMed  PubMed Central  Google Scholar 

26.
Xue J, Zhou X, Zhang CX, Yu LL, Fan HW, Wang Z, et al. Genomes of the rice pest brown planthopper and its endosymbionts reveal complex complementary contributions for host adaptation. Genome Biol. 2014;15:521.
PubMed  PubMed Central  Google Scholar 

27.
Santos-Garcia D, Juravel K, Freilich S, Zchori-Fein E, Latorre A, Moya A, et al. To B or not to B: comparative genomics suggests Arsenophonus as a source of B vitamins in whiteflies. Front Microbiol. 2018;9:2254–70.
PubMed  PubMed Central  Google Scholar 

28.
Ouvrard D, Martin JH. The whiteflies: taxonomic checklist of the world’s whiteflies (Insecta: Hemiptera: Aleyrodidae). 2019. http://www.hemiptera-databases.org/whiteflies/.

29.
Yang P. The greenhouse whiteflies and plant quarantine. Chin Bull Entomol. 1981;18:69–71.
Google Scholar 

30.
Liu SS, De Barro PJ, Xu J, Luan JB, Zang LS, Ruan YM, et al. Asymmetric mating interactions drive widespread invasion and displacement in a whitefly. Science . 2007;318:1769–72.
CAS  PubMed  Google Scholar 

31.
Zchori-Fein E, Lahav T, Freilich S. Variations in the identity and complexity of endosymbiont combinations in whitefly hosts. Front Microbiol. 2014;5:310.
PubMed  PubMed Central  Google Scholar 

32.
Luan JB, Shan HW, Isermann P, Huang JH. Cellular and molecular remodelling of a host cell for vertical transmission of bacterial symbionts. Proc R Soc B. 2016;283:20160580.
PubMed  Google Scholar 

33.
Luan JB, Sun XP, Fei ZJ, Douglas AE. Maternal inheritance of a single somatic animal cell displayed by the bacteriocyte in the whitefly Bemisia tabaci. Curr Biol. 2018;28:459–65.
CAS  PubMed  PubMed Central  Google Scholar 

34.
Shan HW, Luan JB, Liu YQ, Douglas AE, Liu SS. The inherited bacterial symbiont Hamiltonella influences the sex ratio of an insect host. Proc R Soc B. 2019;286:20191677.
CAS  PubMed  Google Scholar 

35.
Rao Q, Rollat-Farnier PA, Zhu DT, Santos-Garcia D, Silva FJ, Moya A, et al. Genome reduction and potential metabolic complementation of the dual endosymbionts in the whitefly Bemisia tabaci. BMC Genom. 2015;16:226.
Google Scholar 

36.
Scott IAW, Workman PJ, Drayton GM, Burnip GM. First record of Bemisia tabaci biotype Q in New Zealand. N Z Plant Prot. 2007;60:264–70.
CAS  Google Scholar 

37.
Qin L, Pan LL, Liu SS. Further insight into reproductive incompatibility between putative cryptic species of the Bemisia tabaci whitefly complex. Insect Sci. 2016;23:215–24.
CAS  PubMed  Google Scholar 

38.
Xu XR, Li NN, Bao XY, Douglas AE, Luan JB. Patterns of host cell inheritance in the bacterial symbiosis of whiteflies. Insect Sci. 2019; https://doi.org/10.1111/1744-7917.12708.

39.
Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative CT method. Nat Protoc. 2008;3:1101–8.
CAS  PubMed  Google Scholar 

40.
Gottlieb Y, Ghanim M, Chiel E, Gerling D, Portnoy V, Steinberg S, et al. Identification and localization of a Rickettsia sp. in Bemisia tabaci (Homoptera: Aleyrodidae). Appl Environ Microbiol. 2006;72:3646–52.
CAS  PubMed  PubMed Central  Google Scholar 

41.
Hadjistylli M, Schwartz SA, Brown JK, Roderick GK. Isolation and characterization of nine microsatellite loci from Bemisia tabaci (Hemiptera: Aleyrodidae) biotype B. J Insect Sci. 2014;14:148.
CAS  PubMed  PubMed Central  Google Scholar 

42.
Bondy EC, Hunter MS. Determining the egg fertilization rate of Bemisia tabaci using a cytogenetic technique. J Vis Exp. 2019;https://doi.org/10.3791/59213.

43.
Ankrah NYD, Luan JB, Douglasa AE. Cooperative metabolism in a three-partner insect-bacterial symbiosis revealed by metabolic modeling. J Bacteriol 2017;199:e00872–16.
CAS  PubMed  PubMed Central  Google Scholar 

44.
Ren FR, Bai B, Hong JS, Huang YZ, Luan JB. A microbiological assay for biotin determination in insects. Insect Sci. 2020; https://doi.org/10.1111/1744-7917.12827.

45.
Salem H, Bauer E, Strauss AS, Vogel H, Marz M, Kaltenpoth M. Vitamin supplementation by gut symbionts ensures metabolic homeostasis in an insect host. Proc R Soc B. 2014;281:1838.
Google Scholar 

46.
Duron O, Morel O, Noël V, Buysse M, Binetruy F, Lancelot R, et al. Tick-bacteria mutualism depends on B vitamin synthesis pathways. Curr Biol. 2018;28:1896–902.
CAS  PubMed  Google Scholar 

47.
Pant NC, Fraenkel G. The function of the symbiotic yeasts of two insect species, Lasioderma serricorne F. and Stegobium (Sitodrepa) paniceum L. Science. 1950;112:498–500.
CAS  PubMed  Google Scholar 

48.
Byrne DN, Bellows TS Jr. Whitefly biology. Annu Rev Entomol. 1991;36:431–57.
Google Scholar 

49.
Giorgini M, Monti MM, Caprio E, Stouthamer R, Hunter MS. Feminization and the collapse of haplodiploidy in an asexual parasitoid wasp harboring the bacterial symbiont Cardinium. Heredity. 2009;102:365–71.
CAS  PubMed  Google Scholar 

50.
Ma WJ, Pannebakker BA, van de Zande L, Schwander T, Wertheim B, Beukeboom LW. Diploid males support a two-step mechanism of endosymbiont-induced thelytoky in a parasitoid wasp. BMC Evol Biol. 2015;15:84.
PubMed  PubMed Central  Google Scholar 

51.
Sloan DB, Moran NA. The evolution of genomic instability in the obligate endosymbionts of whiteflies. Genome Biol Evol. 2013;5:783–93.
PubMed  PubMed Central  Google Scholar 

52.
Chen W, Hasegawa DK, Kaur N, Kliot A, Pinheiro PV, Luan JB, et al. The draft genome of whitefly Bemisia tabaci MEAM1, a global crop pest, provides novel insights into virus transmission, host adaptation, and insecticide resistance. BMC Biol. 2016;14:110.
PubMed  PubMed Central  Google Scholar 

53.
Luan JB, Chen W, Hasegawa DK, Simmons A, Wintermantel WM, Ling KS, et al. Metabolic coevolution in the bacterial symbiosis of whiteflies and related plant sap-feeding insects. Genome Biol Evol. 2015;7:2635–47.
CAS  PubMed  PubMed Central  Google Scholar 

54.
Russell JA, Latorre A, Sabater-Muñoz B, Moya A, Moran NA. Side-stepping secondary symbionts: widespread horizontal transfer across and beyond the Aphidoidea. Mol Ecol. 2003;12:1061–75.
CAS  PubMed  Google Scholar 

55.
Manzano-Marı́n A, Coeur d’acier A, Clamens AL, Orvain C, Cruaud C, Barbe V, et al. Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids’ di-symbiotic systems. ISME J. 2020;14:259–73.
Google Scholar 

56.
Ayoubi A, Talebi AA, Fathipour Y, Mehrabadi M. Coinfection of the secondary symbionts, Hamiltonella defensa and Arsenophonus sp. contribute to the performance of the major aphid pest, Aphis gossypii (Hemiptera: Aphididae). Insect Sci. 2020;27:86–98.
PubMed  Google Scholar 

57.
Thao MLL, Baumann P. Evidence for multiple acquisition of Arsenophonus by whitefly species (Sternorrhyncha: Aleyrodidae). Curr Microbiol. 2004;48:140–4.
CAS  PubMed  Google Scholar 

58.
Nováková E, Hypša V, Moran NA. Arsenophonus, an emerging clade of intracellular symbionts with a broad host distribution. BMC Microbiol. 2009;9:143.
PubMed  PubMed Central  Google Scholar 

59.
Nováková E, Husník F, Šochová E, Hypša V. Arsenophonus and Sodalis symbionts in louse flies: an analogy to the Wigglesworthia and Sodalis system in tsetse flies. Appl Environ Microbiol. 2015;81:6189–99.
PubMed  PubMed Central  Google Scholar 

60.
Nikoh N, Hosokawa T, Moriyama M, Oshima K, Hattori M, Fukatsu T. Evolutionary origin of insect-Wolbachia nutritional mutualism. Proc Natl Acad Sci USA. 2014;111:10257–62.
CAS  PubMed  Google Scholar 

61.
Wu D, Daugherty SC, Van Aken SE, Pai GH, Watkins KL, Khouri H, et al. Metabolic complementarity and genomics of the dual bacterial symbiosis of sharpshooters. PLoS Biol. 2006;4:e188.
PubMed  PubMed Central  Google Scholar 

62.
McCutcheon JP, Moran NA. Parallel genomic evolution and metabolic interdependence in an ancient symbiosis. Proc Natl Acad Sci USA. 2007;104:19392–7.
CAS  PubMed  Google Scholar 

63.
McCutcheon JP, McDonald BR, Moran NA. Convergent evolution of metabolic roles in bacterial co-symbionts of insects. Proc Natl Acad Sci USA. 2009;106:15394–9.
CAS  PubMed  Google Scholar 

64.
Matsuura Y, Moriyama M, Łukasik P, Vanderpool D, Tanahashi M, Meng XY, et al. Recurrent symbiont recruitment from fungal parasites in cicadas. Proc Natl Acad Sci USA. 2018;115:E5970–E5979.
CAS  PubMed  Google Scholar 

65.
Kapantaidaki DE, Ovcarenko I, Fytrou N, Knott KE, Bourtzis K, Tsagkarakou A. Low levels of mitochondrial DNA and symbiont diversity in the worldwide agricultural pest, the greenhouse whitefly Trialeurodes vaporariorum (Hemiptera: Aleyrodidae). J Hered. 2014;106:80–92.
PubMed  Google Scholar 

66.
Douglas AE. The B vitamin nutrition of insects: the contributions of diet, microbiome and horizontally acquired genes. Curr Opin Insect Sci. 2017;23:65–69.
PubMed  Google Scholar 

67.
Smykal V, Raikhel AS. Nutritional control of insect reproduction. Curr Opin Insect Sci. 2015;11:31–38.
PubMed  PubMed Central  Google Scholar 

68.
Wheeler D. The role of nourishment in oogenesis. Ann Rev Entomol. 1996;41:407–31.
CAS  Google Scholar 

69.
Himler AG, Adachi-Hagimori T, Bergen JE, Kozuch A, Kelly SE, Tabashnik BE, et al. Rapid spread of a bacterial symbiont in an invasive whitefly is driven by fitness benefits and female bias. Science. 2011;332:254–6.
CAS  PubMed  Google Scholar  More

1.
Ponomarova O, Patil KR. Metabolic interactions in microbial communities: untangling the Gordian knot. Curr Opin Microbiol. 2015;27:37–44.
PubMed  Google Scholar 
2.
D’Souza G, Shitut S, Preussger D, Yousif G, Waschina S, Kost C. Ecology and evolution of metabolic cross-feeding interactions in bacteria. Nat Prod Rep. 2018;35:455–88.
PubMed  Google Scholar 

3.
Schink B. Energetics of syntrophic cooperation in methanogenic degradation. Microbiol Mol Biol Rev. 1997;61:262–80.
CAS  PubMed  PubMed Central  Google Scholar 

4.
Pernthaler A, Dekas AE, Brown CT, Goffredi SK, Embaye T, Orphan VJ. Diverse syntrophic partnerships from deep-sea methane vents revealed by direct cell capture and metagenomics. Proc Natl Acad Sci USA. 2008;105:7052–7.
CAS  PubMed  Google Scholar 

5.
Men Y, Feil H, Verberkmoes NC, Shah MB, Johnson DR, Lee PKH, et al. Sustainable syntrophic growth of Dehalococcoides ethenogenes strain 195 with Desulfovibrio vulgaris Hildenborough and Methanobacterium congolense: global transcriptomic and proteomic analyses. ISME J. 2012;6:410–21.
CAS  PubMed  Google Scholar 

6.
Zelezniak A, Andrejev S, Ponomarova O, Mende DR, Bork P, Patil KR. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc Natl Acad Sci USA. 2015;112:6449–54.
CAS  PubMed  Google Scholar 

7.
Marchal M, Goldschmidt F, Derksen-Müller SN, Panke S, Ackermann M, Johnson DR. A passive mutualistic interaction promotes the evolution of spatial structure within microbial populations. BMC Evol Biol. 2017;17:106.
PubMed  PubMed Central  Google Scholar 

8.
Thompson AW, Foster RA, Krupke A, Carter BJ, Musat N, Vaulot D, et al. Unicellular cyanobacterium symbiotic with a single-celled eukaryotic alga. Science. 2012;337:1546–50.
CAS  PubMed  Google Scholar 

9.
Zengler K, Palsson BO. A road map for the development of community systems (CoSy) biology. Nat Rev Microbiol. 2012;10:366–72.
CAS  PubMed  Google Scholar 

10.
Sachs JL, Hollowell AC. The origins of cooperative bacterial communities. MBio. 2012;3:1–3.

11.
Johnson DR, Goldschmidt F, Lilja EE, Ackermann M. Metabolic specialization and the assembly of microbial communities. ISME J. 2012;6:1985–91.
CAS  PubMed  PubMed Central  Google Scholar 

12.
Bull JJ, Rice WR. Distinguishing mechanisms for the evolution of co-operation. J Theor Biol. 1991;149:63–74.
CAS  PubMed  Google Scholar 

13.
Foster KR, Wenseleers T. A general model for the evolution of mutualisms. J Evol Biol. 2006;19:1283–93.
CAS  PubMed  Google Scholar 

14.
Weber KA, Achenbach LA, Coates JD. Microorganisms pumping iron: anaerobic microbial iron oxidation and reduction. Nat Rev Microbiol. 2006;4:752–64.
CAS  PubMed  Google Scholar 

15.
Lüdecke C, Reiche M, Eusterhues K, Nietzsche S, Küsel K. Acid-tolerant microaerophilic Fe(II)-oxidizing bacteria promote Fe(III)-accumulation in a fen. Environ Microbiol. 2010;12:2814–25.
PubMed  Google Scholar 

16.
Emerson D, Fleming EJ, McBeth JM. Iron-oxidizing bacteria: an environmental and genomic perspective. Annu Rev Microbiol. 2010;64:561–83.
CAS  PubMed  Google Scholar 

17.
Emerson D, Field EK, Chertkov O, Davenport KW, Goodwin L, Munk C, et al. Comparative genomics of freshwater Fe-oxidizing bacteria: implications for physiology, ecology, and systematics. Front Microbiol. 2013;4:254.
PubMed  PubMed Central  Google Scholar 

18.
Fabisch M, Beulig F, Akob DM, Küsel K. Surprising abundance of Gallionella-related iron oxidizers in creek sediments at pH 4.4 or at high heavy metal concentrations. Front Microbiol. 2013;4:390.
PubMed  PubMed Central  Google Scholar 

19.
Fleming EJ, Cetinić I, Chan CS, Whitney King D, Emerson D. Ecological succession among iron-oxidizing bacteria. ISME J. 2014;8:804–15.
CAS  PubMed  Google Scholar 

20.
Byrne JM, van der Laan G, Figueroa AI, Qafoku O, Wang C, Pearce CI, et al. Size dependent microbial oxidation and reduction of magnetite nano- and micro-particles. Sci Rep. 2016;6:1–13.
Google Scholar 

21.
Byrne JM, Klueglein N, Pearce C, Rosso KM, Appel E, Kappler A. Redox cycling of Fe(II) and Fe(III) in magnetite by Fe-metabolizing bacteria. Science. 2015;347:1473–6.

22.
Braunschweig J, Bosch J, Meckenstock RU. Iron oxide nanoparticles in geomicrobiology: from biogeochemistry to bioremediation. N. Biotechnol. 2013;30:793–802.
CAS  PubMed  Google Scholar 

23.
Bosch J, Heister K, Hofmann T, Meckenstock RU. Nanosized iron oxide colloids strongly enhance microbial iron reduction. Appl Environ Microbiol. 2010;76:184–9.
CAS  PubMed  Google Scholar 

24.
Küsel K, Blöthe M, Schulz D, Reiche M, Drake HL. Microbial reduction of iron and porewater biogeochemistry in acidic peatlands. Biogeosci Discuss. 2008;5:2165–96.
Google Scholar 

25.
Marsili E, Baron DB, Shikhare ID, Coursolle D, Gralnick JA, Bond DR. Shewanella secretes flavins that mediate extracellular electron transfer. Proc Natl Acad Sci USA. 2008;105:3968–73.
CAS  PubMed  Google Scholar 

26.
Royer RA, Burgos WD, Fisher AS, Unz RF, Dempsey BA. Enhancement of biological reduction of hematite by electron shuttling and Fe(II) complexation. Environ Sci Technol. 2002;36:1939–46.
CAS  PubMed  Google Scholar 

27.
Beckwith CR, Edwards MJ, Lawes M, Shi L, Butt JN, Richardson DJ, et al. Characterization of MtoD from Sideroxydans lithotrophicus: a cytochrome c electron shuttle used in lithoautotrophic growth. Front Microbiol. 2015;6:332.
PubMed  PubMed Central  Google Scholar 

28.
Hartshorne RS, Reardon CL, Ross D, Nuester J, Clarke TA, Gates AJ, et al. Characterization of an electron conduit between bacteria and the extracellular environment. Proc Natl Acad Sci USA. 2009;106:22169–74.
CAS  PubMed  Google Scholar 

29.
White GF, Shi Z, Shi L, Wang Z, Dohnalkova AC, Marshall MJ, et al. Rapid electron exchange between surface-exposed bacterial cytochromes and Fe(III) minerals. Proc Natl Acad Sci USA. 2013;110:6346–51.
CAS  PubMed  Google Scholar 

30.
Venkateswaran K, Moser DP, Dollhopf ME, Lies DP, Saffarini DA, MacGregor BJ, et al. Polyphasic taxonomy of the genus Shewanella and description of Shewanella oneidensis sp. nov. Int J Syst Bacteriol. 1999;49:705–24.
CAS  PubMed  Google Scholar 

31.
Myers CR, Nealson KH. Bacterial manganese reduction and growth with manganese oxide as the sole electron acceptor. Science. 1988;240:1319–21.
CAS  PubMed  Google Scholar 

32.
Myers CR, Nealson KH. Respiration-linked proton translocation coupled to anaerobic reduction of manganese(IV) and iron(III) in Shewanella putrefaciensMR-1. J Bacteriol. 1990;172:6232–8.
CAS  PubMed  PubMed Central  Google Scholar 

33.
McBeth JM, Little BJ, Ray RI, Farrar KM, Emerson D. Neutrophilic iron-oxidizing ‘Zetaproteobacteria’ and mild steel corrosion in nearshore marine environments. Appl Environ Microbiol. 2011;77:1405–12.
CAS  PubMed  Google Scholar 

34.
Mori JF, Ueberschaar N, Lu S, Cooper RE, Pohnert G, Küsel K. Sticking together: inter-species aggregation of bacteria isolated from iron snow is controlled by chemical signaling. ISME J. 2017;11:1075–86.
CAS  PubMed  PubMed Central  Google Scholar 

35.
Tamura H, Goto K, Yotsuyanagi T, Nagayama M. Spectrophotometric determination of iron(II) with 1,10-phenanthroline in the presence of large amounts of iron(III). Talanta. 1974;21:314–8.
CAS  PubMed  Google Scholar 

36.
Cooper RE, Wegner C-E, McAllister SM, Shevchenko O, Chan CS, Küsel K. Draft genome sequence of Sideroxydanssp. Strain CL21, an Fe(II)-oxidizing bacterium. Microbiol Resour Announc. 2020;9:1–2.

37.
Wegner C-E, Gaspar M, Geesink P, Herrmann M, Marz M, Küsel K. Biogeochemical regimes in shallow aquifers reflect the metabolic coupling of the elements nitrogen, sulfur, and carbon. Appl Environ Microbiol. 2019;8:1–19.

38.
Andrews S. FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc..40.

39.
Bushnell B. BBMap short read aligner. https://www.sourceforge.net/projects/bbmap/..41.

40.
Kopylova E, Noé L, Touzet H, Noe L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.
CAS  PubMed  Google Scholar 

41.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.
CAS  PubMed  Google Scholar 

42.
Burge SW, Daub J, Eberhardt R, Tate J, Barquist L, Nawrocki EP, et al. Rfam 11.0: 10 years of RNA families. Nucleic Acids Res. 2013;41:D226–32.
CAS  PubMed  Google Scholar 

43.
Heidelberg JF, Paulsen IT, Nelson KE, Gaidos EJ, Nelson WC, Read TD, et al. Genome sequence of the dissimilatory metal ion-reducing bacterium Shewanella oneidensis. Nat Biotechnol. 2002;20:1118–23.
CAS  PubMed  Google Scholar 

44.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAM tools. Bioinformatics. 2009;25:2078–9.
PubMed  PubMed Central  Google Scholar 

45.
Liao Y, Smyth GK, Shi W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 2013;41:e108.
PubMed  PubMed Central  Google Scholar 

46.
Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.
CAS  Google Scholar 

47.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2018.

48.
Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26:139–40.
CAS  Google Scholar 

49.
Tautenhahn R, Patti GJ, Rinehart D, Siuzdak G. XCMS Online: a web-based platform to process untargeted metabolomic data. Anal Chem. 2012;84:5035–9.
CAS  PubMed  PubMed Central  Google Scholar 

50.
Stettin D, Poulin RX, Pohnert G. Metabolomics benefits fom orbitrap GC-MS—Comparison of low- and high-resolution GC-MS. Metabolites. 2020;10:1–16.
Google Scholar 

51.
Chong J, Yamamoto M, Xia J. MetaboAnalystR 2.0: from raw spectra to biological insights. Metabolites. 2019;9:1–10.

52.
Hummel J, Strehmel N, Bölling C, Schmidt S, Walther D, Kopka J. Mass Spectral search and analysis using the golm metabolome database. In: Weckwerth W, Kahl G (eds). The handbook of plant metabolomics. 2013. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, Germany, p. 321–43.

53.
Lueder U, Druschel G, Emerson D, Kappler A, Schmidt C. Quantitative analysis of O2 and Fe2+ profiles in gradient tubes for cultivation of microaerophilic Iron(II)-oxidizing bacteria. FEMS Microbiol Ecol. 2018;94:1–15.

54.
Lefevre E, Bossa N, Wiesner MR, Gunsch CK. A review of the environmental implications of in situ remediation by nanoscale zero valent iron (nZVI): behavior, transport and impacts on microbial communities. Sci Total Environ. 2016;565:889–901.
CAS  PubMed  PubMed Central  Google Scholar 

55.
Kirschling TL, Gregory KB, Minkley EG Jr, Lowry GV, Tilton RD. Impact of nanoscale zero valent iron on geochemistry and microbial populations in trichloroethylene contaminated aquifer materials. Environ Sci Technol. 2010;44:3474–80.
CAS  PubMed  Google Scholar 

56.
Wu S, Cajthaml T, Semerád J, Filipová A, Klementová M, Skála R, et al. Nano zero-valent iron aging interacts with the soil microbial community: a microcosm study. Environ Sci: Nano. 2019;6:1189–206.
CAS  Google Scholar 

57.
Auffan M, Rose J, Wiesner MR, Bottero J-Y. Chemical stability of metallic nanoparticles: a parameter controlling their potential cellular toxicity in vitro. Environ Pollut. 2009;157:1127–33.
CAS  Google Scholar 

58.
Anza M, Salazar O, Epelde L, Alkorta I, Garbisu C. The application of nanoscale zero-valent iron promotes soil remediation while negatively affecting soil microbial biomass and activity. Front Environ Sci. 2019;7:1–6.
Google Scholar 

59.
Friedrich B, Magasanik B. Enzymes of agmatine degradation and the control of their synthesis in Klebsiella aerogenes. J Bacteriol. 1979;137:1127–33.
CAS  PubMed  PubMed Central  Google Scholar 

60.
Kurihara S, Oda S, Kato K, Kim HG, Koyanagi T, Kumagai H, et al. A novel putrescine utilization pathway involves gamma-glutamylated intermediates of Escherichia coli K-12. J Biol Chem. 2005;280:4602–8.
CAS  PubMed  Google Scholar 

61.
Hädrich A, Taillefert M, Akob DM, Cooper RE, Litzba U, Wagner FE, et al. Microbial Fe(II) oxidation by Sideroxydans lithotrophicus ES-1 in the presence of Schlöppnerbrunnen fen-derived humic acids. FEMS Microbiol Ecol. 2019;95:1–19.

62.
Cooper RE, Eusterhues K, Wegner C-E, Totsche KU, Küsel K. Ferrihydrite-associated organic matter (OM) stimulates reduction by Shewanella oneidensis MR-1 and a complex microbial consortia. Biogeosciences. 2017;14:5171–88.
CAS  Google Scholar 

63.
Liu J, Wang Z, Belchik SM, Edwards MJ, Liu C, Kennedy DW, et al. Identification and characterization of MtoA: a decaheme c-type cytochrome of the neutrophilic Fe(II)-oxidizing bacterium Sideroxydans lithotrophicus ES-1. Front Microbiol. 2012;3:37.
CAS  PubMed  PubMed Central  Google Scholar 

64.
DiChristina TJ, Moore CM, Haller CA. Dissimilatory Fe(III) and Mn(IV) reduction by Shewanella putrefaciens requires ferE, a homolog of the pulE (gspE) type II protein secretion gene. J Bacteriol. 2002;184:142–51.
CAS  PubMed  PubMed Central  Google Scholar 

65.
Meshulam-Simon G, Behrens S, Choo AD, Spormann AM. Hydrogen metabolism in Shewanella oneidensis MR-1. Appl Environ Microbiol. 2007;73:1153–65.
CAS  PubMed  Google Scholar 

66.
Shi L, Belchik SM, Plymale AE, Heald S, Dohnalkova AC, Sybirna K, et al. Purification and characterization of the [NiFe]-hydrogenase of Shewanella oneidensis MR-1. Appl Environ Microbiol. 2011;77:5584–90.
CAS  PubMed  PubMed Central  Google Scholar 

67.
Vignais PM, Billoud B, Meyer J. Classification and phylogeny of hydrogenases. FEMS Microbiol Rev. 2001;25:455–501.
CAS  PubMed  Google Scholar 

68.
Reiche M, Torburg G, Küsel K. Competition of Fe(III) reduction and methanogenesis in an acidic fen. FEMS Microbiol Ecol. 2008;65:88–101.
CAS  PubMed  Google Scholar 

69.
Reiche M, Haedrich A, Lischeid G, Kuesel K, Hädrich A, Lischeid G, et al. Impact of manipulated drought and heavy rainfall events on peat mineralization processes and source-sink functions of an acidic fen. J Geophys Res-Biogeosci. 2009;114:1–13.
Google Scholar 

70.
Hädrich A, Heuer VB, Herrmann M, Hinrichs K-U, Küsel K. Origin and fate of acetate in an acidic fen. FEMS Microbiol Ecol. 2012;81:339–54.
PubMed  Google Scholar 

71.
Hamberger A, Horn MA, Dumont MG, Murrell JC, Drake HL. Anaerobic consumers of monosaccharides in a moderately acidic fen. Appl Environ Microbiol. 2008;74:3112–20.
CAS  PubMed  PubMed Central  Google Scholar 

72.
Wüst PK, Horn MA, Drake HL. Trophic links between fermenters and methanogens in a moderately acidic fen soil. Environ Microbiol. 2009;11:1395–409.
PubMed  Google Scholar 

73.
Tabor CW, Tabor H. Polyamines. Annu Rev Biochem. 1984;53:749–90.
CAS  PubMed  Google Scholar 

74.
Karatan E, Watnick P. Signals, regulatory networks, and materials that build and break bacterial biofilms. Microbiol Mol Biol Rev. 2009;73:310–47.
CAS  PubMed  PubMed Central  Google Scholar 

75.
Bachrach U, Heimer YM. The physiology of polyamines. 1989. CRC Press Taylor and Francis Group, Boca Raton, FL, USA.

76.
Karatan E, Duncan TR, Watnick PI. NspS, a predicted polyamine sensor, mediates activation of Vibrio cholerae biofilm formation by norspermidine. J Bacteriol. 2005;187:7434–43.
CAS  PubMed  PubMed Central  Google Scholar 

77.
Cockerell SR, Rutkovsky AC, Zayner JP, Cooper RE, Porter LR, Pendergraft SS, et al. Vibrio cholerae NspS, a homologue of ABC-type periplasmic solute binding proteins, facilitates transduction of polyamine signals independent of their transport. Microbiology. 2014;160:832–43.
CAS  PubMed  PubMed Central  Google Scholar 

78.
Matthysse AG, Yarnall HA, Young N. Requirement for genes with homology to ABC transport systems for attachment and virulence of Agrobacterium tumefaciens. J Bacteriol. 1996;178:5302–8.
CAS  PubMed  PubMed Central  Google Scholar 

79.
Sauer K, Camper AK. Characterization of phenotypic changes in Pseudomonas putida in response to surface-associated growth. J Bacteriol. 2001;183:6579–89.
CAS  PubMed  PubMed Central  Google Scholar 

80.
Patel CN, Wortham BW, Lines JL, Fetherston JD, Perry RD, Oliveira MA. Polyamines are essential for the formation of plague biofilm. J Bacteriol. 2006;188:2355–63.
CAS  PubMed  PubMed Central  Google Scholar 

81.
Capdevila DA, Wang J, Giedroc DP. Bacterial strategies to maintain zinc metallostasis at the host–pathogen interface. J Biol Chem. 2016;291:20858–68.
CAS  PubMed  PubMed Central  Google Scholar 

82.
Schoepp-Cothenet B, van Lis R, Philippot P, Magalon A, Russell MJ, Nitschke W. The ineluctable requirement for the trans-iron elements molybdenum and/or tungsten in the origin of life. Sci Rep. 2012;2:263.
PubMed  PubMed Central  Google Scholar 

83.
Reda T, Plugge CM, Abram NJ, Hirst J. Reversible interconversion of carbon dioxide and formate by an electroactive enzyme. Proc Natl Acad Sci USA. 2008;105:10654–8.
CAS  PubMed  Google Scholar 

84.
Hartmann T, Schwanhold N, Leimkühler S. Assembly and catalysis of molybdenum or tungsten-containing formate dehydrogenases from bacteria. Biochim Biophys Acta. 2015;1854:1090–1100.
CAS  PubMed  Google Scholar 

85.
Ilbert M, Bonnefoy V. Insight into the evolution of the iron oxidation pathways. Biochim Biophys Acta. 2013;1827:161–75.
CAS  PubMed  Google Scholar 

86.
Lovley DR, Holmes DE, Nevin KP. Dissimilatory Fe(III) and Mn(IV) reduction. Adv Micro Physiol. 2004;49:219–86.
CAS  Google Scholar 

87.
Melton ED, Swanner ED, Behrens S, Schmidt C, Kappler A. The interplay of microbially mediated and abiotic reactions in the biogeochemical Fe cycle. Nat Rev Microbiol. 2014;12:797–808.
CAS  PubMed  Google Scholar 

88.
Maisch M, Lueder U, Laufer K, Scholze C, Kappler A, Schmidt C. Contribution of microaerophilic Iron(II)-oxidizers to Iron(III) mineral formation. Environ Sci Technol. 2019;53:8197–204.
CAS  PubMed  Google Scholar 

89.
Hong Y, Wu J, Wilson S, Song B. Vertical stratification of sediment microbial communities along geochemical gradients of a subterranean estuary located at the Gloucester Beach of Virginia, United States. Front Microbiol. 2018;9:3343.
PubMed  Google Scholar 

90.
Liptzin D, Silver WL. Spatial patterns in oxygen and redox sensitive biogeochemistry in tropical forest soils. Ecosphere. 2015;6:art211.
Google Scholar 

91.
Borer B, Tecon R, Or D. Spatial organization of bacterial populations in response to oxygen and carbon counter-gradients in pore networks. Nat Commun. 2018;9:769.
PubMed  PubMed Central  Google Scholar 

92.
Widder S, Allen RJ, Pfeiffer T, Curtis TP, Wiuf C, Sloan WT, et al. Challenges in microbial ecology: building predictive understanding of community function and dynamics. ISME J. 2016;10:2557–68.
PubMed  PubMed Central  Google Scholar 

93.
Frey PA, Reed GH. The ubiquity of iron. ACS Chem Biol. 2012;7:1477–81.
CAS  PubMed  Google Scholar 

94.
Edwards KJ, Bach W, McCollom TM, Rogers DR. Neutrophilic iron-oxidizing bacteria in the ocean: their habitats, diversity, and roles in mineral deposition, rock alteration, and biomass production in the deep-sea. Geomicrobiol J. 2004;21:393–404.
CAS  Google Scholar 

95.
Bondici VF, Khan NH, Swerhone GDW, Dynes JJ, Lawrence JR, Yergeau E, et al. Biogeochemical activity of microbial biofilms in the water column overlying uranium mine tailings. J Appl Microbiol. 2014;117:1079–94.
CAS  PubMed  Google Scholar 

96.
Lee AK, Newman DK. Microbial iron respiration: impacts on corrosion processes. Appl Microbiol Biotechnol. 2003;62:134–9.
CAS  PubMed  Google Scholar 

97.
Glasser NR, Saunders SH, Newman DK. The colorful world of extracellular electron shuttles. Annu Rev Microbiol. 2017;71:731–51.
CAS  PubMed  PubMed Central  Google Scholar 

98.
Philips J, Verbeeck K, Rabaey K, Arends JBA. Electron transfer mechanisms in biofilms. In: Scott K, Yu EH (eds). Microbial electrochemical and fuel cells. 2016. Woodhead Publishing, Sawston, Cambridge, United Kingdom, p. 67–113.

99.
Gao L, Lu X, Liu H, Li J, Li W, Song R, et al. Mediation of extracellular polymeric substances in microbial reduction of hematite by Shewanella oneidensis MR-1. Front Microbiol. 2019;10:575.
PubMed  PubMed Central  Google Scholar 

100.
Roden EE, McBeth JM, Blöthe M, Percak-Dennett EM, Fleming EJ, Holyoke RR, et al. The microbial ferrous wheel in a neutral pH groundwater seep. Front Microbiol. 2012;3:172.
PubMed  PubMed Central  Google Scholar  More

1.
Barbier, E. B. et al. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81, 169–193 (2011).
Article  Google Scholar 
2.
Douglas, E. J. et al. Macrofaunal functional diversity provides resilience to nutrient enrichment in coastal sediments. Ecosystems 20, 1324–1336 (2017).
CAS  Article  Google Scholar 

3.
Edgar, G. J. & Barrett, N. S. Effects of catchment activities on macrofaunal assemblages in Tasmanian estuaries. Estuar. Coast. Shelf Sci. 50, 639–654 (2000).
ADS  Article  Google Scholar 

4.
Murray, N. J. et al. The global distribution and trajectory of tidal flats. Nature 565, 222–225 (2019).
ADS  CAS  Article  Google Scholar 

5.
Hewitt, J. E., Thrush, S. F., Dayton, P. K. & Bonsdorff, E. The effect of spatial and temporal heterogeneity on the design and analysis of empirical studies of scale-dependent systems. Am. Nat. 169, 398–408 (2007).
Article  Google Scholar 

6.
Cadenasso, M. L., Pickett, S. T. A., Weathers, K. C. & Jones, C. G. A framework for a theory of ecological boundaries. Bioscience 53, 750 (2003).
Article  Google Scholar 

7.
Lohrer, A. M. et al. Biogenic habitat transitions influence facilitation in a marine soft-sediment ecosystem. Ecology 94, 136–145 (2013).
Article  Google Scholar 

8.
Schenone, S., O’Meara, T. A. & Thrush, S. F. Non-linear effects of macrofauna functional trait interactions on biogeochemical fluxes in marine sediments change with environmental stress. Mar. Ecol. Prog. Ser. 624, 13–21 (2019).
ADS  CAS  Article  Google Scholar 

9.
Thrush, S. F., Pridmore, R. D., Hewitt, J. E. & Cummings, V. J. Adult infauna as facilitators of colonization on intertidal sandflats. J. Exp. Mar. Biol. Ecol. 159, 253–265 (1992).
Article  Google Scholar 

10.
Borcard, D., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).
Article  Google Scholar 

11.
Mermillod-Blondin, F., Rosenberg, R., François-Carcaillet, F., Norling, K. & Mauclaire, L. Influence of bioturbation by three benthic infaunal species on microbial communities and biogeochemical processes in marine sediment. Aquat. Microb. Ecol. 36, 271–284 (2004).
Article  Google Scholar 

12.
Dornhoffer, T., Waldbusser, G. & Meile, C. Modeling lugworm irrigation behavior effects on sediment nitrogen cycling. Mar. Ecol. Prog. Ser. 534, 121–134 (2015).
ADS  CAS  Article  Google Scholar 

13.
Braeckman, U. et al. Role of macrofauna functional traits and density in biogeochemical fluxes and bioturbation. Mar. Ecol. Prog. Ser. 399, 173–186 (2010).
ADS  CAS  Article  Google Scholar 

14.
Banta, G. T., Holmer, M., Jensen, M. H. & Kristensen, E. Effects of two polychaete worms, Nereis diversicolor and Arenicola marina, on aerobic and anaerobic decomposition in a sandy marine sediment. Aquat. Microb. Ecol. 19, 189–204 (1999).
Article  Google Scholar 

15.
Woodin, S. A. et al. Same pattern, different mechanism: Locking onto the role of key species in seafloor ecosystem process. Sci. Rep. https://doi.org/10.1038/srep26678 (2016).
Article  PubMed  PubMed Central  Google Scholar 

16.
Woodin, S. A., Wethey, D. S., Hewitt, J. E. & Thrush, S. F. Small scale terrestrial clay deposits on intertidal sandflats: Behavioral changes and productivity reduction. J. Exp. Mar. Biol. Ecol. 413, 184–191 (2012).
Article  Google Scholar 

17.
Thrush, S. F., Hewitt, J. E. & Pridmore, R. D. Patterns in the spatial arrangements of polychaetes and bivalves in intertidal sandflats. Mar. Biol. 102, 529–535 (1989).
Article  Google Scholar 

18.
Pridmore, R. D., Thrush, S. F., Hewitt, J. E. & Roper, D. S. Macrobenthic community composition of six intertidal sandflats in Manukau Harbour, New Zealand Macrobenthic community composition of six intertidal sandflats in Manukau Harbour, New Zealand. N. Z. J. Mar. Freshw. Res. 24, 81–96 (1990).
Article  Google Scholar 

19.
Turner, S. J. et al. Are soft-sediment communities stable? An example from a windy harbour. Mar. Ecol. Prog. Ser. 120, 219–230 (1995).
ADS  Article  Google Scholar 

20.
Zajac, R. N. et al. Responses of infaunal populations to benthoscape structure and the potential importance of transition zones. Limnol. Oceanogr. 48, 829–842 (2003).
ADS  Article  Google Scholar 

21.
Kobayashi, G., Goto, R., Takano, T. & Kojima, S. Molecular phylogeny of Maldanidae (Annelida): Multiple losses of tube-capping plates and evolutionary shifts in habitat depth. Mol. Phylogenet. Evol. 127, 332–344 (2018).
Article  Google Scholar 

22.
Volkenborn, N. et al. Intermittent bioirrigation and oxygen dynamics in permeable sediments: An experimental and modeling study of three tellinid bivalves. J. Mar. Res. 70, 794–823 (2012).
CAS  Article  Google Scholar 

23.
Waldbusser, G. G., Marinelli, R. L., Whitlatch, R. B. & Visscher, P. T. The effects of infaunal biodiversity on biogeochemistry of coastal marine sediments. Limnol. Ocean. 49, 1482–1492 (2004).
CAS  Article  Google Scholar 

24.
Walker, B. H. Biodiversity and ecological redundancy. Conserv. Biol. 6, 18–23 (1992).
Article  Google Scholar 

25.
Volkenborn, N., Polerecky, L., Wethey, D. S. & Woodin, S. A. Oscillatory porewater bioadvection in marine sediments induced by hydraulic activities of Arenicola marina. Limnol. Oceanogr. 55, 1231–1247 (2010).
ADS  CAS  Article  Google Scholar 

26.
Thrush, S. F. et al. Changes in the location of biodiversity–ecosystem function hot spots across the seafloor landscape with increasing sediment nutrient loading. Proc. R. Soc. B. Biol. Sci. https://doi.org/10.1098/rspb.2016.2861 (2017).
Article  Google Scholar 

27.
O’Meara, T., Gibbs, E. & Thrush, S. F. Rapid organic matter assay of organic matter degradation across depth gradients within marine sediments. Methods Ecol. Evol. 9, 245–253 (2018).
Article  Google Scholar 

28.
Kana, T. M. et al. Membrane inlet mass spectrometer for rapid high-precision determination of N2, O2, and Ar in environmental water samples. Anal. Chem. 66, 4166–4170 (1994).
CAS  Article  Google Scholar 

29.
Thrush, S. F. et al. Changes in the location of biodiversity–ecosystem function hot spots across the seafloor landscape with increasing sediment nutrient loading. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2016.2861 (2017).
Article  Google Scholar 

30.
Legendre, P. & Legendre, L. F. J. Numerical Ecology (Elsevier, New York, 2012).
Google Scholar 

31.
Grömping, U. Relative importance for linear regression in R: The package relaimpo. J. Stat. Softw. 20, 17 (2006).
Google Scholar 

32.
Team RC. R: A language and environment for statistical computing. 2013. More

The backward and forward processes
Let us consider a branched tree, starting with (N_0) cells at time (t=0) and ending with N(t) cells at time t as shown on Fig. 1. We assume that all lineages survive up to time t, and therefore the final number N(t) of cells corresponds to the number of lineages in the tree.
The most natural way to sample the lineages is to put uniform weights on all of them. This sampling is called backward, (or retrospective) because at the end of the experiment one randomly chooses one lineage among the N(t) with a uniform probability and then one traces the history of the lineage backward in time from time t to 0, until reaching the ancestor population. The backward weight associated with a lineage l is defined as

$$begin{aligned} omega _{text {back}}(l)=N(t)^{-1} ,. end{aligned}$$
(1)

In a tree, some lineages divide more often than others, which results in an over-representation of lineages that have divided more often than the average. Therefore by choosing a lineage with uniform distribution, we are more likely to choose a lineage with more divisions than the average number of divisions in the tree.
The other way of sampling a tree is the forward (or chronological) one and consists in putting the weight

$$begin{aligned} omega _text {for}(l)= N_0^{-1} m^{-K(l)} ,, end{aligned}$$
(2)

on a lineage l with K(l) divisions, where m is the number of offspring at division. This choice of weights is called forward because one starts at time 0 by uniformly choosing one cell among the (N_0) initial cells, and one goes forward in time up to time t, by choosing one of the m offspring with equal weight 1/m at each division. The backward and forward weights are properly normalized probabilities, defined on the N(t) lineages in the tree at time t: (sum _{i=1}^{N(t)} omega _{text {back}}(l_i) = sum _{i=1}^{N(t)} omega _{text {for}}(l_i) =1).
Figure 1

Example of a tree with (N_0=1) and (N(t)=10) lineages at time t. Two lineages are highlighted, the first in blue with 2 divisions and the second in orange with 5 divisions. The forward sampling is represented with the green right arrows: it starts at time (t=0) and goes forward in time by choosing one of the two daughters lineages at each division with probability 1/2. The backward sampling is pictured by the left purple arrows: starting from time t with uniform weight on the 10 lineages it goes backward in time down to time (t=0).

Full size image

Single lineage experiments are precisely described by a forward process since experimentally, at each division, only one of the two daughter cells is conserved while the other is eliminated (for instance flushed away in a microfluidic channel9, 10). In these experiments, a tree is generated but at each division only one of the two lineages is conserved, with probability 1/2, while the rest of the tree is eliminated. This means that single lineage observables can be measured without single lineage experiments, provided population experiments are analyzed with the correct weights on lineages.
Link with the population growth rate
Since the backward weight put on a lineage depends on the number of cells at time t, it takes into account the reproductive performance of the colony but it is unaffected by the reproductive performance of the lineage considered. On the contrary, the forward weight put on a specific lineage depends on the number of divisions of that lineage but is unaffected by the reproductive performance of other lineages in the tree. Therefore, the difference between the values of the two weights for a particular lineage informs on the difference between the reproductive performance of the lineage with respect to the colony.
We now introduce the population growth rate:

$$begin{aligned} Lambda _t=frac{1}{t} ln frac{N(t)}{N_0} ,, end{aligned}$$
(3)

which is linked to forward weights by the relation

$$begin{aligned} frac{N(t)}{N_0}=sum _{i=1}^{N(t)} m^{K_i} omega _text {for}(l_i) = langle m^K rangle _text {for} ,, end{aligned}$$
(4)

where (langle cdot rangle _text {for}) is the average over the lineages weighted by (omega _text {for}), and (K_i=K(l_i)). Combining the two equations above, we obtain19:

$$begin{aligned} Lambda _t=frac{1}{t} ln langle m^K rangle _text {for} ,, end{aligned}$$
(5)

which allows an experimental estimation of the population growth rate from the knowledge of the forward statistics only.
Equation (4) can also be re-written to express the bias between the forward and backward weights of the same lineage

$$begin{aligned} frac{omega _{text {back}}(l)}{omega _text {for}(l)}=frac{m^{K(l)}}{langle m^K rangle _text {for}} ,, end{aligned}$$
(6)

which is the reproductive performance of the lineage divided by its average in the colony with respect to (omega _text {for}).
A similar relation is derived using the relation

$$begin{aligned} frac{N_0}{N(t)}=sum _{i=1}^{N(t)} m^{-K_i} omega _text {back}(l_i) = langle m^{-K} rangle _text {back} ,. end{aligned}$$
(7)

Combining Eqs. (5) and (7) we obtain:

$$begin{aligned} Lambda _t= – frac{1}{t} ln langle m^{-K} rangle _text {back} ,. end{aligned}$$
(8)

A similar equation as Eq. (6) can be obtained in terms of the backward sampling and reads: 

$$begin{aligned} frac{omega _{text {back}}(l)}{omega _text {for}(l)}=frac{langle m^{-K} rangle _text {back}}{m^{-K(l)}} ,. end{aligned}$$
(9)

Combining Eqs. (1) to (3), we obtain the fluctuation relation13,17:

$$begin{aligned} omega _{text {back}}(l)= omega _text {for}(l) e^{K(l) ln m – t Lambda _t} ,. end{aligned}$$
(10)

If we now introduce the probability distribution of the number of divisions for the forward sampling (p_text {for}(K)=sum _l delta (K-K(l)) omega _text {for}(l)) and similarly for the backward sampling, we can also recast the above relation as a fluctuation relation for the distribution of the number of divisions:

$$begin{aligned} p_{text {back}} (K,t)=p_{text {for}} (K,t) e^{K ln m – t Lambda _t} ,. end{aligned}$$
(11)

Let us now introduce the Kullback–Leibler divergence between two probability distributions p and q, which is the non-negative number:

$$begin{aligned} {{mathscr {D}}}_{text {KL}}(p||q)=int {mathrm {d}}x , p(x) ln frac{p(x)}{q(x)} ge 0 ,. end{aligned}$$
(12)

Using Eq. (10), we obtain

$$begin{aligned} {{mathscr {D}}}_{text {KL}}(omega _{text {back}}|| omega _text {for}) = langle K rangle _{text {back}} ln m – t Lambda _t ge 0 ,. end{aligned}$$
(13)

A similar inequality follows by considering ({{mathscr {D}}}_{text {KL}}(omega _{text {for}}|| omega _text {back})). Finally we obtain

$$begin{aligned} frac{t}{langle K rangle _{text {back}}} le frac{ln m}{Lambda _t} le frac{t}{langle K rangle _text {for}} ,. end{aligned}$$
(14)

In the long time limit, (lim nolimits _{t rightarrow + infty } t/langle K rangle _{text {back}} = langle tau rangle _{text {back}}), where (tau) is the inter-division time, or generation time, defined as the time between two consecutive divisions on a lineage. The same argument goes for the forward average. In the case of cell division where each cell only gives birth to two daughter cells ((m=2)), the center term in the inequality tends to the population doubling time (T_d). Therefore, this inequality reads in the long time limit:

$$begin{aligned} langle tau rangle _{text {back}} le T_d le langle tau rangle _text {for} ,. end{aligned}$$
(15)

Let us now mention a minor but subtle point related to this long time limit. For a lineage with K divisions up to time t, we can write (t=a + sum _{i=1}^{K} tau _i), where a is the age of the cell at time t and where (tau _i) is the generation time associated with the ith division. Then (t/ K= tau _m + a/K), where (tau _m) is the mean generation time along the lineage. For finite times, all we can deduce is (t/ K ge tau _m). Therefore the left inequality of Eq. (15) always holds

$$begin{aligned} langle tau rangle _{text {back}} le frac{t}{langle K rangle _{text {back}}} le frac{ln m}{Lambda _t} ,, end{aligned}$$
(16)

while the right inequality does not necessarily hold at finite time.
Inspired by work by Powell6, the inequalities of Eq. (15) have been theoretically derived in12 for age models. In our previous work17, we have replotted the experimental data of12 which confirm theses inequalities and we have shown theoretically that the same inequalities should also hold for size models. In fact, as the present derivation shows, the relation equation (14) is very general and only depends on the branching structure of the tree, while the relation equation (15) requires in addition the existence of a steady state. These inequalities and Eq. (11) express fundamental constraints between division and growth, which should hold for any model.
Stochastic thermodynamic interpretation
The results derived above have a form similar to that found in Stochastic Thermodynamics18. According to this framework, Eq. (5) is an integral fluctuation relation (similar to Jarzynski relation) while Eq. (11) is a detailed fluctuation relation (similar to Crooks fluctuation relation). Furthermore, the inequalities equation  (14) represent a constraint equivalent to the second law of thermodynamics, which classically follows from the Jarzynski or Crooks fluctuation relations. It is known that these inequalities take a slightly different form when expressed at finite time or at steady state, which is indeed the case here when comparing Eq. (14) with Eq. (15). A difference between work fluctuation relations like Crooks or Jarzynski and equations (5) and (11), is that Crooks or Jarzynski describe non-autonomous systems which are driven out of equilibrium by the application of a time-dependent protocol, whereas the relations for cell growth derived here concern autonomous systems, in the absence of any external protocol.
One of the main applications of Jarzynski or Crooks fluctuation relations concerns the thermodynamic inference of free energies from non-equilibrium fluctuations. Similarly, Eq. (5) or Eq. (11) can be used as estimators of the population growth rate. The specific advantage of Eq. (5) with respect to Eq. (11) is that it only requires single lineage statistics, which can be obtained from mother machine experiments. Let us now show how this can be done in practice. We use the data from20, where the growth of many independent lineages of E. coli have been recorded over 70 generations in a mother machine at three different temperatures (25 °C, 27 °C, and 37 °C), precisely 65 lineages for 25 °C, 54 for 27 °C, and 160 for 37 °C. For each temperature condition, we study the convergence of the estimator of the population growth rate based on Eq. (5), which we call (Lambda _{mathrm{lin}}) as a function of the length t of the lineages for a fixed number of independent lineages L, and as a function of the number of independent lineages for a fixed observation time.
Figure 2

Estimator of the population growth rate (Lambda _{mathrm{lin}}) based on Eq. (5), (a) as function of the the length t of the lineages and (b) as function of the number L of lineages used in the estimation. In (a), the curves for the three temperatures converge to a constant value. In (b), only the curve for 37 °C is shown and the horizontal dashed line represents the quantity (ln (2)/langle tau rangle _{text {for}}), which is smaller than the limit value of (Lambda _{mathrm{lin}}), as expected from the second law-like inequality, namely Eq. (15). In the inset, the purple histogram is the distribution of the number of divisions, while the green filled histogram is the histogram deduced from it by weighting it by a factor (2^K) and normalizing. All the 160 lineages were used to plot these histograms.

Full size image

Firstly, for each temperature, we take into account all the lineages available and truncate them at an arbitrary time t smaller than the length of the shortest lineage of the set. On these portions of lineages of length t, we compute (Lambda _{mathrm{lin}}) versus the time t as shown in Fig. 2a. We see that the estimator (Lambda _{mathrm{lin}}) starts from zero, increases and eventually converges rather quickly towards a limiting value. The limit we found agree with the independent analysis carried out in19, with only one caveat, these authors reported that their estimator started at high values and then decreased towards the limit, while in our case, the estimator starts at zero and later increases towards the limit. In our case, the estimator needs to be zero at short times, before the first divisions occur.
Secondly, we truncate all the lineages at a fixed time equal to the length of the shortest lineage of the set, and compute (Lambda _{mathrm{lin}}) versus the number L of lineages considered for the estimation, which have been randomly selected from the ensemble of available lineages. As shown in Fig. 2b for the case at (37^{,circ } hbox {C}) (curves for the other temperatures look exactly the same), the convergence is also excellent in that case. Although the value of the population growth rate which is obtained in this way can not be measured independently from the evolution of the population in the mother machine setup, this convergence is indicative of the success of the method. The figure also confirms that the value of the population growth rate deduced from the estimator (Lambda _{mathrm{lin}}) is larger than (ln (2)/langle tau rangle _{text {for}}), as predicted by the right inequality of Eq. (15).
Here, the estimator is found to provide an excellent estimation, but this is not always so. For instance, for the inference of free energies from non-equilibrium work measurements, the exponential average of the estimator is often dominated by rare values, which are not accessible or not well sampled21. To understand why this problem does not arise here, we show in inset of Fig. 2b, the distribution P(K) of the number of divisions together with the same distribution weighted by the factor (2^K) and normalized. The peak of that modified distribution informs on the dominant values in the estimator21. Here, we observe that both distributions have a narrow support and are close to each other. The weighted distribution is peaked at (K=67) while P(K) is peaked at (K=66), therefore typical and dominating values are very close, which explains why the estimator is good.
Let us now further develop the Stochastic Thermodynamic interpretation of our results by analyzing the implications of the previous fluctuation relations when dynamical variables are introduced on the branched tree of the population. Let us introduce M variables labeled ((y_1,y_2, ldots ,y_M)) to describe a dynamical state of the system, then a path is fully determined by the values of these variables at division, and the times of each division. We call ({mathbf {y}}(t)=(y_1(t),y_2(t), ldots ,y_M(t))) a vector state at time t and ({{mathbf {y}}}={{mathbf {y}}(t_j)}_{j=1}^{K}) a path with K divisions. For cell growth models, the variables (y_i) can typically be the size and age of the cell, or the concentration of a key protein.
The probability ({{mathscr {P}}}) of path ({{mathbf {y}}}) is defined as the sum over all lineages of the weights of the lineages that follow the path ({{mathbf {y}}}):

$$begin{aligned} {{mathscr {P}}}({{mathbf {y}}},K,t)=sum _{i=1}^{N(t)} omega (l_i) , delta (K-K_i) delta ({{mathbf {y}}} – {{mathbf {y}}}_i) ,, end{aligned}$$
(17)

where ({{mathbf {y}}}_i) is the path followed by lineage (l_i). Using the normalization of the weights (omega) on the lineages, we show that ({{mathscr {P}}}) is properly normalized: (int mathrm {d}{{mathbf {y}}} sum _K {{mathscr {P}}}({{mathbf {y}}},K,t) = 1). We then define the number (n({{mathbf {y}}},K,t)) of lineages in the tree at time t that follow the path ({{mathbf {y}}}) with K divisions:

$$begin{aligned} n({{mathbf {y}}},K,t)=sum _{i=1}^{N(t)} delta (K-K_i) delta ({{mathbf {y}}} – {{mathbf {y}}}_i) ,. end{aligned}$$
(18)

This number of lineages is normalized as (int mathrm {d}{{mathbf {y}}} sum _K n({{mathbf {y}}},K,t) = N(t)). Then, the path probability can be re-written as

$$begin{aligned} {{mathscr {P}}}({{mathbf {y}}},K,t) = n({{mathbf {y}}},K,t) cdot omega (l) ,. end{aligned}$$
(19)

Since (n({{mathbf {y}}},K,t)) is independent of a particular choice of lineage weighting, we obtain

$$begin{aligned} frac{{{mathscr {P}}}_{text {back}}({{mathbf {y}}},K,t)}{{{mathscr {P}}}_text {for} ({{mathbf {y}}},K,t)}=frac{omega _{text {back}}(l)}{omega _text {for}(l)}= e^{K ln m – t Lambda _t} , , end{aligned}$$
(20)

which generalizes Eq. (11). In our previous work17, we have derived this relation for size models with individual growth rate fluctuations (i.e. ({mathbf {y}}=(x,nu ))) but we were not aware of the weighting method introduced by13, and for this reason, we used the term ‘tree’ to denote the backward sampling, and the term ‘lineage’ to denote the forward sampling.
This relation has a familiar form in Stochastic Thermodynamics. The central quantity called entropy production can indeed be expressed similarly as the relative entropy between probability distributions associated with a forward and a backward evolution. In this analogy, ({{mathbf {y}}}) is analog to the trajectory and (t Lambda _t – K ln m) is analog to the entropy production. Then, the equivalent of a reversible trajectory for which the entropy production is null is a lineage for which the number K of divisions is equal to (t Lambda _t / ln m), that is, a lineage having the same reproductive performance as that of the colony. When all the lineages in a tree have this property, there is no variability of the number of divisions among them. In that case, the forward and backward distributions are identical, and the cost function (t Lambda _t – K ln m) vanishes for all lineages. More

Ethics statement
This study did not involve endangered or protected species and was carried out in accordance with the guidelines of the French Polynesia code for animal ethics and scientific research (https://www.service-public.pf/diren/partager/code/). All protocols and experiments were approved by the CRIOBE-IRCP animal ethics committee (DL-20150214).
Model species
The convict surgeonfish Acanthurus triostegus (Linnaeus, 1758) is an abundant coral-reef-associated species found throughout the Indo-Pacific60, including around Moorea Island, French Polynesia11. It has a pelagic larval duration of ~53 days60 after which larvae move back to the reef61. A. triostegus is a well-studied species with regards to metamorphosis, with a well-defined developmental sequence11. The recruitment of A. triostegus larvae to reef habitats coincides with a true metamorphosis into juveniles, with this full process taking around 1 week11. Its metamorphosis is controlled by thyroid hormones (TH, the precursor thyroxine (T4), and the active hormone triiodothyronine (T3)), which is the same as other teleosts and other metamorphosing vertebrates such as amphibians11. TH signaling in larval A. triostegus is vulnerable to disruption by anthropogenic stressors, including the waterborne pesticide chlorpyrifos and artificial light at night11,17. Given the importance of TH during teleost metamorphosis10, and as metamorphosis coincides with recruitment in a taxonomically diverse range of coral-reef fishes (i.e., Acanthuridae, Apogonidae, Balistidae, Chaetodontidae, and Pomacentridae)11, we consider A. triostegus a representative teleost model for examining the effects of anthropogenic stressors on fish recruitment via their impacts on metamorphic processes.
Study period and site
This study was conducted from February 2015 to June 2018, at Moorea Island, French Polynesia (S17°32′16.4589″, W149°49′48.3018″). Sampling for the examination of sensory development under pharmacological treatments was conducted in 2015. Sampling for the investigation of behavioral preferences and survival to predation under pharmacological treatments, as well as sensory development under anthropogenic stressor exposure, were conducted in 2016. Sampling for the examination of survival to predation under anthropogenic stressors exposure, and TH levels under co-exposure to anthropogenic stressors were conducted in 2018. Sampling for the investigation of TH levels under single anthropogenic stressors was conducted in both 2016 and 2018.
Fish sampling
Settlement-stage A. triostegus (i.e., fully transparent individuals11, here define as day 0 (d0) individuals), were collected on the north–east coast of the island (S17°29′49.7362″, W149°45′13.899″) at night using a crest net11, as they transitioned from the ocean to the reef. Fish were then transferred to either in situ cages (for thyroid hormone signaling experiments) where they remained until d8, or to aquaria at the CRIOBE Marine Research Station (for increased temperature and chlorpyrifos exposure experiments) where they remained until d5.
Thyroid hormone signaling experiment: control, T3- and N3 treatments
To test the role of TH on sensory development, behavior (response to sensory cues), and survival (predation test) in metamorphosing A. triostegus, their TH pathway was pharmacologically manipulated. Fish were injected, at d0 immediately following capture, in their ventral cavity with 20 µl of a pharmacological treatment: (i) T3 + iopanoic acid (IOP) both at 10−6 M (T3 treatment), or (ii) NH3 at 10−6 M (N3 treatment). IOP was used as an inhibitor of deiodinase enzymes, following comparable work in mammals and amphibians62, and as routinely used in fish to prevent the immediate degradation of injected T348. The T3 treatment was therefore applied to promote TH signaling. NH3 is a known antagonist of TH receptors (TR) in vertebrates63 and in A. triostegus in particular11. NH3 prevents the binding of TH such as T3 to TR, therefore impairing the binding of transcriptional coactivators to TR, which therefore remain in an inactive and repressive conformation63,64. The N3 treatment was thus applied to repress TH signaling by disrupting the TH pathway. T3 and NH3 were initially suspended in dimethyl sulfoxide (DMSO), at 10−2 M, and then diluted in phosphate buffered saline (PBS) 1× to reach 10−6 M. Control fish were injected with 20 µL of DMSO diluted 10.000 times in PBS 1× to control for the effect of the solvent and injection. DMSO and NH3 non-toxicity has been previously determined11. Until subsequent sampling, all fish were re-treated each morning (i.e., at d2, d3, and d4 for fish sampled at d5) to maintain pharmacological activity11.
Thyroid hormone signaling experiment: fish husbandry
Following collection and subsequent treatment, larvae were transferred to a nursery area on the north coast of the island (S17°29′26.5378″, W149°53′29.2252″) where they were raised in in situ cages (cylindrical cages, diameter: 30 cm, height: 50 cm, 15 fish per cage). This allowed them to develop in in situ conditions11. As A. triostegus feeds on algal turf following settlement11,60, cages were stocked with a supply of turf-covered coral rubble that was replaced daily, ensuring both shelter and constant food availability. Fish were subsequently sampled on d2, d5, and d8, post collection to examine sensory development, behavior (response to sensory cues), and survival (predation test). Twelve in situ cages were used, and the use of any given cage was randomized prior each experiment.
Increased temperature and chlorpyrifos (CPF) exposures: fish husbandry and treatments
Following collection, d0 fish were maintained in groups of ten individuals at the CRIOBE Marine Research Station. Each group was held in a 30 L × 20 W × 20 H-cm aquaria containing 12 L of filtered (1-µm filter) seawater. All tanks were subject to a 12:12 h light–dark cycle (06:00–18:00 light period) and oxygenated with an air stone. Twelve aquaria were used (six for exposures to increased temperatures only, and six for exposures with CPF), and the use of any given aquarium was randomized prior each experiment.
For increased temperature treatments, seawater was in an open system, and water temperature was maintained at either 28.5 °C, 30.0 °C or 31.5 °C. 28.5 °C was chosen as the basal temperature as this was the mean temperature in the Moorea lagoon at the time of the study, and corresponds to the mean annual lagoon temperature in this region (http://observatoire.criobe.pf). Subsequent increases of +1.5 °C and +3.0 °C were selected, as these are in line with end-of-century projections for tropical Pacific sea surface temperatures37. Fresh coral rubble was added to the tanks and replaced each day to provide both food and shelter. Heaters controlled by thermostats were used to maintain the temperature treatments. Before the experiment, each tank was in open circuit with temperature maintained at 28.5 °C. At the beginning of the experiment, fish were introduced in the aquarium, and the thermostat temperature was then set up to 28.5 °C, 30.0 °C or 31.5 °C according to the treatment. The temperature of interest was reached within 2 h. Temperature in each tank was then visually checked (on the thermostat controller) at least five times per day, and never differed from the target temperature by more than 0.2 °C.
For CPF exposure, five different treatments were applied: unaltered seawater (control), seawater with acetone at a final concentration of 1:1.000.000 (CPF0, solvent control treatment, as CPF was made soluble using acetone), or seawater with CPF at a nominal concentration of either 1, 5, or 30 μg L−1 (CPF1, CPF5, and CPF30 treatments), based on the findings of recent studies of reef fishes exposed to CPF11,14,65. CPF was spiked in each tank from dilutions that were prepared in advance: 1 µg µL−1, 5 µg µL−1, and 30 µg µL−1. From these dilutions, 12 µL were pipetted and spiked in the 12-L exposure tanks, therefore reaching nominal concentrations of 1 µg L−1, 5 µg L−1, and 30 µg L−1. Similarly, 12 µL of acetone was spiked in the tank for the CPF0 condition. Spike was allowed to mix for 2 min (water mixing due to the air stone) before fish were introduced in the tank. At the end of the 32-h exposure, CPF concentrations in the water or in the fish tissues were not evaluated, as we were only interested in the effects of CPF spikes on fish metamorphic processes. Nevertheless, a previous study using similar methods and nominal concentrations of similar magnitude (i.e., ranging from 4 to 64 µg L−1) measured CPF levels corresponding to 80% of nominal concentrations after 24 h66, therefore suggesting a good stability of CPF levels in the condition of our study. Environmentally, these nominal concentrations represent high (CPF1) to extremely high (CPF30) exposures, as recorded contamination concentration in Australian and North American surface waters are generally below 1 µg L−1, with a few high outliers reaching up to 26 µg L−153. However, this shows that concentrations of up to 30 µg L−1 are possible on a short timescale (such as the 32-h exposure of this study), in particular in coastal and shallow areas such as fish nurseries. Aquaria used for CPF exposure treatments and associated controls were not equipped with coral rubble to prevent potential interaction with the pesticide11. This may explain the delay in trunk canal development observed in control fish from the CPF exposure treatments compared with control fish from the increased temperature treatments and control fish from the TH treatments (Figs. 1g and  3e). Water was replaced each day to ensure the maintenance of CPF concentrations11.
For combined increased temperature and CPF exposure treatments, fish were also maintained without coral rubble with water replaced daily.
For treatments where fish were exposed to an anthropogenic stressor and provided with supplemental T3, fish were maintained in either the elevated temperature or CPF exposure treatments as described above, but were also injected with either T3 (T3 treatment) or with DMSO (control, to control for solvent and injection). These injections were done as described above (thyroid hormone signaling experiment: control, T3- and N3-treatments).
Sample preparation and fish measurements
For thyroid hormone quantifications and histological analyses, fish were first euthanized in freshly prepared MS222 at 0.4 mg ml−1 in filtered seawater at 4 °C, and instantly placed on ice. Following euthanasia, all fish except those for histological analyses were weighed (W) then placed on a scale bar and photographed for standardized measurements (e.g., height (H) and standard length (SL)). These measurements were used to calculate Fulton’s condition factor K = 100*(W/SL3) (with SL, in cm, and W, in g) for each individual67. Weight measurements were performed using a precision balance (Ohaus Adventurer Precision), and length measurements were taken using the imageJ software.
Retinas
The retina is the light-sensitive tissue within the eye, and is composed of different cell layers organized ventrally and dorsally around the optic nerve (Fig. 1c). To analyze the retina’s cell layers, we removed the head region of euthanized fish in a way that allowed us to distinguish the right retina from the left retina, and the dorsal and ventral sides of the retina. This region was then fixed in Bouin solution before being embedded in paraffin for microtome sectioning (5 µm). Sections were then stained using hematoxylin and eosin. We chose cross-sections of the retina that were done at similar depth by selecting sections at the level of the optical nerve (Supplementary Fig. 5a). These sections enabled us to identify different cell segments, types, and layers, among which (i) photoreceptor external segments (perceiving light signals), (ii) photoreceptor nuclei, (iii) bipolar cells (which integrate the synaptic signals originating from the photoreceptors), and (iv) ganglion cells (which integrate signals from bipolar cells and create action potential toward the optic nerve) were easily distinguishable (Supplementary Fig. 5b). We only investigated the right eye of A. triostegus fish, as evidence suggests this species is visually lateralized at recruitment, and predominantly uses its right eye to examine visual predator stimuli14. Also, we only examined the dorsal side of the retina, as the ventral side (vs) was shown to not undergo change at metamorphosis in another coral-reef fish species, the goatfish Upeneus tragula13 (Supplementary Fig. 5a). To compare the developmental state of the retina between treatments, we looked at the peripheral area of the dorsal side (see the dotted square in Supplementary Fig. 5a, magnified in Supplementary Fig. 5b) as settlement-stage individuals in another acanthurid species (Naso brevirostris) showed weak if any spatial specialization of the retina, in particular on this axis68. We prioritized the examination of bipolar cell densities as important changes in bipolar cell density was observed during U. tragula metamorphosis13. However, we also examined the densities of photoreceptor external segments, photoreceptor nuclei, and ganglion cells along metamorphosis and across TH treatments, revealing for photoreceptor external segments (Supplementary Fig. 6) and nuclei (Supplementary Fig. 7) a TH-dependent density increase at metamorphosis, while ganglion cells showed no density variation during metamorphosis (Supplementary Fig. 8). Cell counting was performed in a 50-µm wide area perpendicular to the retina cell layers (Supplementary Fig. 5b). The measure of bipolar cell density corresponds to the number of bipolar cells per 0.001 mm² in the inner-nuclear cell layer (INL), after measuring the mean thickness of this INL (average of three measurements at random non-overlapping locations within the 50-µm wide area) (Supplementary Fig. 5b). The prn and ggc densities were obtained in a similar manner after measuring the mean width of their respective cell layer. Pes density corresponds to the exact number of pes in the 50-µm wide area as the pes cell layer is monocellular (Supplementary Fig. 5b). Cells that were located on the edges of the 50-µm wide area were included in the counts.
Olfactory organ lamellae
In fish, the olfactory organ is a fluid-filled blind sac that contains the ciliated sensory epithelium, which forms a rosette comprise of a number of folds, i.e., lamellae44. In A. triostegus, the left and right olfactory organs can be found in two cavities on the dorsal surface, between the eye and the snout edge, with water moving through each cavity from the anterior nostril to the posterior nostril (Supplementary Fig. 3). Pictures (e.g., Supplementary Fig. 3) were obtained using SEM microscopy following specialized tissue preparation involving the dissection of the olfactory organ (removal of the thin skin layer between the two nostrils) and fixation in a sodium cacodylate + glutaraldehyde solution (2.5% glutaraldehyde in 1 M sucrose and 0.1 M sodium cacodylate, pH 7.4) for a week at 4 °C. Samples were then washed (10% sucrose in 0.1 M sodium cacodylate solution, pH 7.4) 15 × 15 min at room temperature (RT). Samples were then dehydrated using successive ethanol (EtOH) baths (30%, 50%, 70%, 85%, 95%, 100%, 100%, 100%, each time during 15 min at RT). Samples were then placed in EtOH:HMDS (1:1) 15 min at RT, in 100% HMDS for 15 min at RT, and lastly in 100% HMDS for 30 min at RT. HMDS was used to replace critical point drying. Samples were not metalized. SEM pictures were obtained using a MiniMEB Hitachi TM3030 (University of French Polynesia). Given that lamellae are well formed and have a macroscopic size in A. triostegus at recruitment, lamellae counting was performed on tissues samples identically prepared, but observed under a simple binocular stereomicroscope. Lamellae counting was performed on both left and right olfactory organs to then have a measure of the average number of lamellae per olfactory organ. In the case where one of the left or right olfactory organ was damaged and thus accurate lamellae counts were not possible, we used the count from the other olfactory organ as the average number, as no significant difference was observed between the number of lamellae in the left and right olfactory organs in our preliminary observations.
Lateral line trunk canal pores
The lateral line system enables fish to detect water motions and pressure gradients, such as those caused by other fish (e.g., movement from other fish in the shoal or predator strikes). It is composed of superficial and canal neuromast receptor organs, which are the functional units of the lateral line system and are ciliary sensory organs, composed of hair cells, like those in the inner ear, located either on the skin or embedded in lateral line canals43. Canal neuromasts are found in the epithelium lining the bottom of the lateral line canals, and one canal neuromast is usually found within the short canal segments between two adjacent canal pores43. The development of neuromasts and the morphogenesis of lateral line canals and their pores initiate in late-stage larvae and continue through metamorphosis43. Counting the number of pores on the trunk canal is thus an appropriate way to rapidly characterize the development of the lateral line system when one cannot perform more advanced histological analyzes of the neuromasts. In d0 to d8 A. triostegus at recruitment, a fully formed trunk canal corresponding to a complete arched canal can be observed on each of the fish body flanks (Supplementary Fig. 4a). At this stage, A. triostegus does not exhibit scales69, and the trunk canal is therefore only composed of soft tissue (Supplementary Fig. 4a). Following the same preparation protocol as used for olfactory organs, we investigated the development of the lateral line system of A. triostegus by counting the number of pores on the trunk canal (Supplementary Fig. 4a-b). Counting was performed either on the left or the right side of the body, depending on tissue preservation. Indeed, as fish body frequently curved when dried to a critical point in HMDS, only one side of the body was generally accessible for counting trunk canal pores. We did not consider this an issue, as preliminary observations revealed no significant difference between the numbers of trunk canal pores present on the left or right flanks of the body. Variation in the number of pores cannot be attributed to variation in the length of the fully formed trunk canal (measured from the vicinity of the head to the base of the tail) as it does not change in A. triostegus during metamorphosis (Supplementary Fig. 9).
Thyroid hormone quantification
TH was extracted from frozen fish, following an extraction protocol adapted from previous studies70,71,72, including on coral-reef fishes11. Fish were individually crushed in 500 µl of methanol using a Minilys and glass beads (3*30 s, 5.000 rpm), centrifuged at 4 °C (10 min, 12.000 rpm), and supernatant reserved. These operations were performed twice, then fish were crushed one last time with 400 µl of methanol, 100 µl of chloroform, and 100 µl of barbital buffer (3*30 s, 5.000 rpm, RT), centrifuged at 4 °C (10 min, 12.000 rpm), and supernatant reserved. Pooled supernatants were then dried at 70 °C for 2 h. Dried pellets were re-suspended in 400 µl of methanol, 100 µl of chloroform, and 100 µl of barbital buffer, centrifuged at 4 °C (20 min, 12.000 rpm), and supernatant reserved. The same operation was again performed on the pellets. Pooled supernatants were once again dried out at 70 °C for 2 h with the final extract reconstituted in 2 ml of PBS 1× for quantification (Roche Elica kit on a Cobas analyzer, following the manufacturer’s standardized method). TH levels in pg g−1 of fish were then transformed into relative levels by selecting the respective control fish as standards in the increased temperature and CPF exposure treatment experiments.
Behavioral tests
A two-channel chemical choice flume73,74,75 was used to assess the responses of A. triostegus towards chemical predator cues (Supplementary Fig. 10). Each trial presented an individual fish with two water sources: control seawater (Ø = UV-sterilized and 1-µm filtered seawater from the collection site) vs “seawater containing chemical cues from predator” (P = UV-sterilized and 1-µm filtered seawater from the collection site in which five Lutjanus fulvus predators were soaked, into a 125-L tank, for 2 h prior to the experiment). Water was fed into the flume through two water inlets, with equivalent flow rates of 100 ml min−1 maintained using flow meters (MM Minimaster, Admi-France). This rate ensured laminar flow of each water source was maintained in parallel while allowing fish to swim naturally between the two water sources. Fine mesh and collimators also helped to ensure laminar flow of each water source was maintained75. Preliminary experiments were conducted to ensure the absence of unanticipated biased behavior within the two-channel flume (e.g., preference for one side of the flume over the other, irrespectively of the water sources, or preference for the drain area over the choice area). Prior to each trial, dye tests were conducted to confirm laminar flow, without eddies or areas of water mixing, within the choice area. After releasing the fish in the middle of the choice area, a 2-min acclimation period was observed, then fish position (left or right part of the choice area, or drain area) was recorded every 2 s for 5 min (Supplementary Fig. 10), using a camera (GoPro Hero 2) located above the edge of the flume tank. Water inlets were then switched (to account for any side preference due to fish’s immobility) followed by another 2-min acclimation period. A second 5-min test period followed, during which fish position was again assessed every 2 s. Preference or avoidance of water sources, and the absence of a side preference, were confirmed by comparing responses during the first and second 5-min test periods. These experiments were performed in the dark with red light, to limit potential visual perturbations such as the presence of an observer and to allow comparison between d2 and d0 fish, as d0 fish were tested immediately after collection (i.e., at night) as this is when they are actively moving from the ocean to the reef, and is thus the most biologically relevant time to do so. Fish that did not swim actively during the first acclimation period were removed from the analysis (n = 3) to prevent side preference bias. However, all remaining fish swam actively between the two water sources after water inlets were switched (either during the second acclimation period or during the second test period), ensuring no continuous side bias due to immobility. Fish that spent more than 50% of the test time in the drain area (i.e., where the two water sources mix) were also removed from the analysis (n = 4) as we considered that they did not show any particular preference or avoidance for any of the two water sources. Fish that did not make a clear choice between the two water sources but spent more than 50% of the time in the choice area were included in the analysis. This was done as we wanted to assess fish preference as well as the absence of preference.
A double-choice tank was used to assess the responses of A. triostegus in the presence of a visual predator cues. Each trial presented an individual fish with a choice of two visual stimuli, each contained in a separate aquarium placed at the end of the central rectangular choice tank. These were an aquarium, containing a 10-cm (standard length) L. fulvus (P condition) vs an empty aquarium, equipped with an air stone (Ø condition) (Supplementary Fig. 11). Fish were first placed into the central “no choice” area of the choice tank for a 2-min acclimation period. During this time, fish were not able to see the contents of either adjacent aquaria or access the “choice” areas of the choice tank as opaque panels were positioned at the edges of the “no choice” area (see the dotted lines in Supplementary Fig. 11). Following the acclimation period, the opaque panels were removed, allowing fish to observe the visual stimuli in the adjacent aquaria through the transparent walls of the choice tank and to access the choice areas. Fish position (i.e., choice area 1, no choice area, choice area 2; Supplementary Fig. 11) was then assessed every 2 s over a 10 min, using a camera (GoPro Hero 2) to limit any external visual disturbances such as an observer’s presence. The camera was located above the choice tank. Location of visual stimulus (left or right side of the double-choice tank) was switched between each fish to ensure the absence of a side preference. Fish that remained immobile and spent >50% of the test time in the “no choice” area were removed from the analysis (n = 11), as we considered that they did not show any particular preference or avoidance for any of the two visual stimuli.
Survival arena
The survival tests presented in Figs. 2c, 3f were conducted in an arena set up in situ in a nursery lagoon area of the north coast (S17°29’7.0272″, W149°49’51.1166″). The arena consisted of a 1-m3 cage with a hard bottom covered with sand and coral rubble and four lateral walls made from 5-mm fine mesh (Supplementary Fig. 12). Each trial consisted of 45 fishes, with 15 from each treatment group. Fish from each treatment group were tagged with a specific color using visible implant fluorescent filament (Northwest Marine Technology) 2 h prior being released into the arena. For each trial, all fishes were released simultaneously and allowed to acclimate for 30 min before the introduction of six L. fulvus (15–20 cm SL). After 2 h, predators were removed, and surviving A. triostegus was identified to treatment using their color tag. Color tags attributed to each fish group were randomly switched between each replicate to ensure no predation bias based on tag color. Differences in survival during this predation test cannot be attributed to variation in fish size or condition between treatments, as these did not differ significantly between groups (Supplementary Fig. 13).
Statistical analyses
All statistical analyses were conducted using R version 3.5.376. Conway–Maxwell–Poisson (COM-Poisson) generalized linear models (GLM) were used to assess if pharmacological and anthropogenic stressor treatments influenced the number of lamellae, the number of trunk canal pores, and the number of survivors to the predation experiment77. COM-Poisson GLM were also used to assess if pharmacological treatments influenced the number of photoreceptor external segments. Linear models (LM) were used to assess if pharmacological treatments influenced the bipolar cell, ganglion cell, and photoreceptor nuclei densities, and if pharmacological and anthropogenic stressor treatments influenced fish Fulton’s K condition factor. LM were also used to assess if the trunk canal length varied with age. Gamma generalized linear mixed-effect models (GLMEM) were used to assess if anthropogenic stressor exposures influenced TH levels and T3/T4 ratios78. TH level or T3/T4 ratios were used as the dependent variable, and replicate was included as a random factor to account for differences in TH levels only due to the two different Cobas analyzers that were used in the two different years. As preliminary experiments provided no evidence that season and lunar phase affected T3 levels in metamorphosing A. triostegus, we did not include them in our analyses (Gamma GLMEM, Supplementary Fig. 14). For each model, diagnostic plots were examined and outputs compared with raw data to confirm goodness-of-fit and residual homoscedasticity, and, when applicable, residual normality was assessed using Shapiro–Wilk normality test. Paired t tests or Wilcoxon signed-rank tests were used to assess whether fish spent more time in the no cue choice area vs predator-cue choice area, depending on residual normality (Shapiro–Wilk normality test).
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More