Ecology

1.Lafferty, K. D., Porter, J. W. & Ford, S. E. Are diseases increasing in the ocean?. Annu. Rev. Ecol. Evol. Syst. 35, 31–54 (2004).Article 

Google Scholar 
2.Lafferty, K. D. et al. Infectious diseases affect marine fisheries and aquaculture economics. Annu. Rev. Mar. Sci. 7, 471–496 (2015).Article 
ADS 

Google Scholar 
3.Burreson, E. M., Stokes, N. A. & Friedman, C. S. Increased virulence in an introduced pathogen: Haplosporidium nelsoni (MSX) in the eastern oyster Crassostrea virginica. J. Aquat. Anim. Health 12, 1–8 (2000).CAS 
PubMed 
Article 

Google Scholar 
4.Elston, R. A., Farley, C. A. & Kent, M. L. Occurrence and significance of bonamiasis in European flat oysters Ostrea edulis in North America. Dis. Aquat. Org. 2, 49–54 (1986).Article 

Google Scholar 
5.Enzmann, P.-J., Kurath, G., Fichtner, D. & Bergmann, S. M. Infectious hematopoietic necrosis virus: Monophyletic origin of European isolates from North American genogroup M. Dis. Aquat. Org. 66, 187–195 (2005).CAS 
Article 

Google Scholar 
6.Lightner, D. V. The penaeid shrimp viral pandemics due to IHHNV, WSSV, TSV and YHV: History in the Americas and current status (Proceedings of the 32nd Joint UJNR Aquaculture Panel Symposium, Davis and Santa Barbara, California, USA, 2003).7.Sutherland, K. P., Shaban, S., Joyner, J. L., Porter, J. W. & Lipp, E. K. Human pathogen shown to cause disease in the threatened elkhorn coral Acropora palmata. PLoS ONE 6, e23468 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
8.Chang, P. et al. Herpes-like virus infection causing mortality of cultured abalone Haliotis diversicolor supertexta in Taiwan. Dis. Aquat. Org. 65, 23–27 (2005).Article 

Google Scholar 
9.Hooper, C., Hardy-Smith, P. & Handlinger, J. Ganglioneuritis causing high mortalities in farmed Australian abalone (Haliotis laevigata and Haliotis rubra). Aust. Vet. J. 85, 188–193 (2007).CAS 
PubMed 
Article 

Google Scholar 
10.Segarra, A. et al. Detection and description of a particular Ostreid herpesvirus 1 genotype associated with massive mortality outbreaks of Pacific oysters, Crassostrea gigas, in France in 2008. Virus Res. 153, 92–99 (2010).CAS 
PubMed 
Article 

Google Scholar 
11.Jenkins, C. et al. Identification and characterisation of an ostreid herpesvirus-1 microvariant (OsHV-1 μ-var) in Crassostrea gigas (Pacific oysters) in Australia. Dis. Aquat. Org. 105, 109–126 (2013).CAS 
Article 

Google Scholar 
12.Mackin, J. G. Oyster disease caused by Dermocystidium marinum and other microorganisms in Louisiana. Pub. Inst. Mar. Sci. Univ. Texas 7, 132–229 (1962).
Google Scholar 
13.Andrews, J. D. Epizootiology of the disease caused by the oyster pathogen Perkinsus marinus and its effects on the oyster industry. Am. Fish. Soc. Spec. Pub. 18, 47–63 (1988).
Google Scholar 
14.Burreson, E. M. & Andrews, J. D. Unusual intensification of Chesapeake Bay oyster diseases during recent drought conditions. In Proceeding of the Oceans ’88 Conference, Baltimore, Maryland, USA, 1988) 799–802.15.Ford, S. E. Range extension by the oyster parasite Perkinsus marinus into the northeastern United States: Response to climate change?. J. Shellfish Res. 15, 45–56 (1996).
Google Scholar 
16.Harvell, C. D. et al. Emerging marine diseases: Climate links and anthropogenic factors. Science 285, 1505–1510 (1999).CAS 
PubMed 
Article 

Google Scholar 
17.Burge, C. A. et al. Climate change influences on marine infectious diseases: Implications for management and society. Annu. Rev. Mar. Sci. 6, 249–277 (2014).Article 
ADS 

Google Scholar 
18.Cook, T., Folli, M., Klinck, J., Ford, S. & Miller, J. The relationship between increasing sea-surface temperature and the northward spread of Perkinsus marinus (Dermo) disease epizootics in oysters. Estuar. Coast. Shelf Sci. 46, 587–597 (1998).Article 
ADS 

Google Scholar 
19.Crosby, M. P. & Roberts, C. F. Seasonal infection intensity cycle of the parasite Perkinsus marinus (and an absence of Haplosporidium spp.) in oysters from a South Carolina salt marsh. Dis. Aquat. Org. 9, 149–155 (1990).Article 

Google Scholar 
20.Shearman, R. K. & Lentz, S. J. Long-term sea surface temperature variability along the U.S. East Coast. J. Phys. Oceanogr. 40, 1004–1017 (2010).Article 
ADS 

Google Scholar 
21.Ray, S. M. A culture technique for the diagnosis of infections with Dermocystidium marinus Mackin, Owen, and Collier in oysters. Science 116, 360–361 (1952).CAS 
PubMed 
Article 
ADS 

Google Scholar 
22.Carnegie, R. B., Arzul, I. & Bushek, D. Managing marine mollusc diseases in the context of regional and international commerce: Policy issues and emerging concerns. Phil. Trans. R. Soc. B 371, 20150215 (2016).PubMed 
Article 
CAS 

Google Scholar 
23.OIE Infection with Perkinsus marinus. In Manual of Diagnostic Tests for Aquatic Animals 7th edn 526–538 (OIE, Paris, 2016).
Google Scholar 
24.Mackin, J. G., Owen, H. M. & Collier, A. Preliminary note on the occurrence of a new protistan parasite, Dermocystidium marinum n sp in Crassostrea virginica (Gmelin). Science 111, 328–329 (1950).25.Perkins, F. O. Ultrastructure of vegetative stages in Labyrinthomyxa marina (Dermocystidium marinum), a commercially significant oyster pathogen. J. Invertebr. Pathol. 13, 199–222 (1969).CAS 
PubMed 
Article 

Google Scholar 
26.Gates, D. E., Valletta, J. J., Bonneaud, C. & Recker, M. Quantitative host resistance drives the evolution of increased virulence in an emerging pathogen. J. Evol. Biol. 31, 1704–1714 (2018).PubMed 
Article 

Google Scholar 
27.Moss, J. A., Burreson, E. M. & Reece, K. S. Advanced Perkinsus marinus infections in Crassostrea ariakensis maintained under laboratory conditions. J. Shellfish Res. 25, 65–72 (2006).Article 

Google Scholar 
28.Reece, K. S., Bushek, D., Hudson, K. L. & Graves, J. E. Geographic distribution of Perkinsus marinus genetic strains along the Atlantic and Gulf coasts of the USA. Mar. Biol. 139, 1047–1055 (2001).Article 

Google Scholar 
29.Thompson, P. C., Rosenthal, B. M. & Hare, M. P. Microsatellite genotypes reveal some long-distance gene flow in Perkinsus marinus, a major pathogen of the eastern oyster, Crassostrea virginica (Gmelin). J. Shellfish Res. 33, 195–206 (2014).Article 

Google Scholar 
30.Andrews, J. D. Epizootiology of diseases of oysters (Crassostrea virginica), and parasites of associated organisms in eastern North America. Helgoländer Meeresuntersuchungen 37, 149–166 (1984).Article 

Google Scholar 
31.Haven, D. S., Hargis, W. J., Jr. & Kendall, P. C. The oyster industry of Virginia: Its Status, Problems and Promise (VA Institute of Marine Science Special Papers in Marine Science No. 4, 1978).32.Andrews, J. D. Perkinsus marinus = Dermocystidium marinum (“Dermo”) in Virginia, 1950–1980 (VA Institute of Marine Science Data Report No. 16, 1980).33.Hite, J. L. & Cressler, C. E. Resource-driven changes to host population stability alter the evolution of virulence and transmission. Phil. Trans. R. Soc. B 373, 20170087 (2018).PubMed 
Article 

Google Scholar 
34.Rick, T. C. et al. Millennial-scale sustainability of the Chesapeake Bay Native American oyster fishery. Proc. Natl. Acad. Sci. USA 113, 6568–6573 (2016).CAS 
PubMed 
Article 

Google Scholar 
35.Bushek, D., Ford, S. E. & Chintala, M. M. Comparison of in vitro-cultured and wild-type Perkinsus marinus. III. Fecal elimination and its role in transmission. Dis. Aquat. Org. 51, 217–225 (2002).Article 

Google Scholar 
36.Mann, R., Southworth, M., Harding, J. M. & Wesson, J. A. Population studies of the native eastern oyster, Crassostrea virginica (Gmelin, 1791) in the James River, Virginia, USA. J. Shellfish Res. 28, 193–220 (2009).Article 

Google Scholar 
37.Andrews, J. D. Oyster mortality studies in Virginia. IV. MSX in James River public seed beds. Proc. Natl. Shellfish. Assoc. 53, 65–84 (1964).
Google Scholar 
38.Carnegie, R. B. & Burreson, E. M. Declining impact of an introduced pathogen: Haplosporidium nelsoni in the oyster Crassostrea virginica in Chesapeake Bay. Mar. Ecol. Prog. Ser. 432, 1–15 (2011).Article 
ADS 

Google Scholar 
39.Goedknegt, M. A. et al. Parasites and marine invasions: Ecological and evolutionary perspectives. J. Sea Res. 113, 11–27 (2016).Article 
ADS 

Google Scholar 
40.Kemp, W. M. et al. Eutrophication of Chesapeake Bay: Historical trends and ecological interactions. Mar. Ecol. Prog. Ser. 303, 1–29 (2005).Article 
ADS 

Google Scholar 
41.Ford, S. E. & Bushek, D. Development of resistance to an introduced marine pathogen by a native host. J. Mar. Res. 70, 205–223 (2012).Article 

Google Scholar 
42.Bobo, M. Y., Richardson, D. L., Coen, L. D. & Burrell, V. G. A Report on the Protozoan Pathogens Perkinsus marinus (dermo) and Haplosporidium nelsoni (MSX) in South Carolina shellfish populations (Tech. Rep. No. 86, SC Dept. of Natural Resources, 1997).43.Hill, K. M. et al. Observation of a Bonamia sp. infecting the oyster Ostrea stentina in Tunisia, and a consideration of its phylogenetic affinities. J. Invertebr. Pathol. 103, 179–185 (2010).PubMed 
Article 

Google Scholar 
44.Carnegie, R. B. et al. Molecular detection of the oyster parasite Mikrocytos mackini, and a preliminary phylogenetic analysis. Dis. Aquat. Org. 54, 219–227 (2003).CAS 
Article 

Google Scholar 
45.Stokes, N. A. & Burreson, E. M. A sensitive and specific DNA probe for the oyster pathogen Haplosporidium nelsoni. J. Eukaryot. Microbiol. 42, 350–357 (1995).CAS 
PubMed 
Article 

Google Scholar 
46.Reece, K. S., Dungan, C. F. & Burreson, E. M. Molecular epizootiology of Perkinsus marinus and P. chesapeaki infections among wild oysters and clams in Chesapeake Bay, USA. Dis. Aquat. Org. 82, 237–248 (2008).CAS 
Article 

Google Scholar 
47.Carnegie, R. B. Status of the Major Oyster Diseases in Virginia, 2009–2012: A Summary of the Annual Oyster Disease Monitoring Program (Virginia Institute of Marine Science, 2013).
Google Scholar 
48.Andrews, J. D. & Hewatt, W. G. Oyster mortality studies in Virginia II The fungus disease caused by Dermocystidium marinum in Chesapeake Bay. Ecol. Monogr. 27, 1–26 (1957).Article 

Google Scholar 
49.Perkins, F. O. The structure of Perkinsus marinus (Mackin, Owen and Collier, 1950) Levine, 1978 with comments on taxonomy and phylogeny of Perkinsus spp. J. Shellfish Res. 15, 67–87 (1996).
Google Scholar 
50.RStudio Team. RStudio: Integrated Development for R. (RStudio, Inc., 2019). http://www.rstudio.com/.51.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020). https://www.R-project.org/.52.Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 
ADS 

Google Scholar 
53.Wickham, H. forcats: Tools for Working with Categorical Variables (Factors). R Package Version 0.5.1. https://CRAN.R-project.org/package=forcats (2021).54.Fox, J. & Weisberg, S. An R Companion to Applied Regression 3rd edn. (Sage Publications, 2019).
Google Scholar 
55.Zar, J. H. Biostatistical Analysis 3rd edn. (Prentice Hall, 1996).
Google Scholar 
56.Ragone, L. M. & Burreson, E. M. Effect of salinity on infection progression and pathogenicity of Perkinsus marinus in the eastern oyster, Crassostrea virginica (Gmelin). J. Shellfish Res. 12, 1–7 (1993).
Google Scholar 
57.Chu, F.-L.E., Volety, A. K. & Constantin, G. A comparison of Crassostrea gigas and Crassostrea virginica: Effects of temperature and salinity on susceptibility to the protozoan parasite, Perkinsus marinus. J. Shellfish Res. 15, 375–380 (1996).
Google Scholar  More

1.Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Change 6, 1023–1027 (2016).CAS 
Article 
ADS 

Google Scholar 
2.Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529 (2005).CAS 
PubMed 
Article 
ADS 

Google Scholar 
3.Porporato, A., D’odorico, P., Laio, F., Ridolfi, L. & Rodriguez-Iturbe, I. Ecohydrology of water-controlled ecosystems. Adv. Water Resour. 25, 1335–1348 (2002).Article 
ADS 

Google Scholar 
4.Huang, K. et al. Enhanced peak growth of global vegetation and its key mechanisms. Nat. Ecol. Evolution 2, 1897 (2018).Article 

Google Scholar 
5.Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166 (2016).Article 
ADS 

Google Scholar 
6.Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, eaax1396 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
7.Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951 (2010).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
8.Sherwood, S. & Fu, Q. A drier future? Science 343, 737–739 (2014).CAS 
PubMed 
Article 
ADS 

Google Scholar 
9.Lucht, W. et al. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science 296, 1687–1689 (2002).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
10.Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. change 6, 791–795 (2016).CAS 
Article 
ADS 

Google Scholar 
11.Fensholt, R. et al. Greenness in semi-arid areas across the globe 1981–2007—an Earth Observing Satellite based analysis of trends and drivers. Remote Sens. Environ. 121, 144–158 (2012).Article 
ADS 

Google Scholar 
12.Fernández-Martínez, M. et al. Global trends in carbon sinks and their relationships with CO2 and temperature. Nat. Clim. change 9, 73 (2019).Article 
ADS 
CAS 

Google Scholar 
13.Dannenberg, M. P., Wise, E. K. & Smith, W. K. Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes. Sci. Adv. 5, eaaw0667 (2019).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
14.Piao, S. et al. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nat. Commun. 5, 5018 (2014).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
15.Wild, M. et al. From dimming to brightening: decadal changes in solar radiation at Earth’s surface. Science 308, 847–850 (2005).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
16.Beer, C. et al. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate. Science 329, 834–838 (2010).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
17.Forzieri, G., Alkama, R., Miralles, D. G. & Cescatti, A. Satellites reveal contrasting responses of regional climate to the widespread greening of Earth. Science 356, 1180–1184 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
19.Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538 (2018).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
20.Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324 (2013).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
21.Jiao, W., Wang, L. & McCabe, M. F. Multi-sensor remote sensing for drought characterization: current status, opportunities and a roadmap for the future. Remote Sens. Environ. 256, 112313 (2021).Article 
ADS 

Google Scholar 
22.Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science 344, 516–519 (2014).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
23.Saleska, S. R., Didan, K., Huete, A. R. & Da Rocha, H. R. Amazon forests green-up during 2005 drought. Science 318, 612–612 (2007).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
24.Chen, T., Werf, G., Jeu, R., Wang, G. & Dolman, A. A global analysis of the impact of drought on net primary productivity. Hydrol. Earth Syst. Sci. 17, 3885 (2013).Article 
ADS 

Google Scholar 
25.Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
26.Kreuzwieser, J. & Rennenberg, H. Molecular and physiological responses of trees to waterlogging stress. Plant, Cell Environ. 37, 2245–2259 (2014).CAS 

Google Scholar 
27.Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110 (2018).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
28.Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
29.Anderegg, W. R. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
30.Field, C. B., Barros, V., Stocker, T. F. & Dahe, Q. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: Special Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2012).31.Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52 (2013).Article 
ADS 

Google Scholar 
32.Trenberth, K. E. et al. Global warming and changes in drought. Nat. Clim. Change 4, 17 (2013).Article 
ADS 

Google Scholar 
33.Cook, B. I., Ault, T. R. & Smerdon, J. E. Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci. Adv. 1, e1400082 (2015).PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
34.Milly, P. C. & Dunne, K. A. Potential evapotranspiration and continental drying. Nat. Clim. Change 6, 946 (2016).Article 
ADS 

Google Scholar 
35.Xu, C. et al. Increasing impacts of extreme droughts on vegetation productivity under climate change. Nat. Clim. Change 9, 948–953 (2019).CAS 
Article 
ADS 

Google Scholar 
36.Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 11, 1–10 (2020).Article 
CAS 

Google Scholar 
37.Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evolution 1, 1438–1445 (2017).Article 

Google Scholar 
38.Humphrey, V. et al. Sensitivity of atmospheric CO2 growth rate to observed changes in terrestrial water storage. Nature 560, 628–631 (2018).CAS 
PubMed 
Article 
ADS 

Google Scholar 
39.Vicente-Serrano, S. M., Beguería, S. & López-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).Article 
ADS 

Google Scholar 
40.Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
41.Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202 (2017).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
42.Peters, W. et al. Increased water-use efficiency and reduced CO2 uptake by plants during droughts at a continental scale. Nat. Geosci. 11, 744 (2018).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
43.Hugelius, G. et al. Large stocks of peatland carbon and nitrogen are vulnerable to permafrost thaw. Proc. Natl Acad. Sci. USA 117, 20438–20446 (2020).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
44.Minasny, B. et al. Digital mapping of peatlands–A critical review. Earth-Sci. Rev. 196, 102870 (2019).CAS 
Article 

Google Scholar 
45.Cronk, J. K. & Fennessy, M. S. Wetland Plants: Biology and Ecology. (CRC press, 2016).46.Zohaib, M. & Choi, M. Satellite-based global-scale irrigation water use and its contemporary trends. Sci. Total Environ. 714, 136719 (2020).47.Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306 (2016).Article 
ADS 
CAS 

Google Scholar 
48.Abel, C. et al. The human–environment nexus and vegetation–rainfall sensitivity in tropical drylands. Nat. Sustain. 4, 25–32 (2020).49.Lu, X., Wang, L. & McCabe, M. F. Elevated CO2 as a driver of global dryland greening. Sci. Rep. 6, 20716 (2016).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
50.Oliveira, P. J., Davin, E. L., Levis, S. & Seneviratne, S. I. Vegetation‐mediated impacts of trends in global radiation on land hydrology: a global sensitivity study. Glob. Change Biol. 17, 3453–3467 (2011).Article 
ADS 

Google Scholar 
51.Grömping, U. Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 17, 1–27 (2006).Article 

Google Scholar 
52.Moesinger, L. et al. The global long-term microwave vegetation optical depth climate archive (VODCA). Earth Syst. Sci. Data 12, 177–196 (2020).Article 
ADS 

Google Scholar 
53.Li, X. & Xiao, J. A global, 0.05-degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and reanalysis data. Remote Sens. 11, 517 (2019).Article 
ADS 

Google Scholar 
54.Palmer, W. C. Meteorological Drought. Vol. 30 (Citeseer, 1965).55.Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Trabucco, A. & Zomer, R. J. Global aridity index (global-aridity) and global potential evapo-transpiration (global-PET) geospatial database. CGIAR Consortium for Spatial Information (2009).57.Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agriculture, Ecosyst. Environ. 126, 67–80 (2008).Article 

Google Scholar 
58.Gruber, A., Scanlon, T., Schalie, R. V. D., Wagner, W. & Dorigo, W. Evolution of the ESA CCI soil moisture climate data records and their underlying merging methodology. Earth Syst. Sci. Data 11, 717–739 (2019).Article 
ADS 

Google Scholar 
59.Dorigo, W. et al. ESA CCI Soil moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sens. Environ. 203, 185–215 (2017).Article 
ADS 

Google Scholar 
60.Wagner, W. et al. Fusion of active and passive microwave observations to create an essential climate variable data record on soil moisture. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (ISPRS Annals) 7, 315–321 (2012).61.Harris, I., Jones, P., Osborn, T. & Lister, D. Updated high‐resolution grids of monthly climatic observations–the CRU TS3. 10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

Google Scholar 
62.Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).Article 
ADS 

Google Scholar 
63.Tian, F. et al. Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sens. Environ. 177, 265–276 (2016).Article 
ADS 

Google Scholar 
64.Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
65.Frank, D. et al. Effects of climate extremes on the terrestrial carbon cycle: concepts, processes and potential future impacts. Glob. Change Biol. 21, 2861–2880 (2015).Article 
ADS 

Google Scholar 
66.Goetz, S. J., Bunn, A. G., Fiske, G. J. & Houghton, R. A. Satellite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance. Proc. Natl Acad. Sci. USA 102, 13521–13525 (2005).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
67.Wu, D. et al. Time‐lag effects of global vegetation responses to climate change. Glob. Change Biol. 21, 3520–3531 (2015).Article 
ADS 

Google Scholar 
68.Tei, S. & Sugimoto, A. Time lag and negative responses of forest greenness and tree growth to warming over circumboreal forests. Glob. Change Biol. 24, 4225–4237 (2018).Article 
ADS 

Google Scholar 
69.Wen, Y. et al. Cumulative effects of climatic factors on terrestrial vegetation growth. J. Geophys. Res.: Biogeosciences 124, 789–806 (2019).Article 
ADS 

Google Scholar 
70.McKee, T. B., Doesken, N. J. & Kleist, J. in Proceedings of the 8th Conference on Applied Climatology. 179-183 (American Meteorological Society Boston, MA).71.Jiao, W., Tian, C., Chang, Q., Novick, K. A. & Wang, L. A new multi-sensor integrated index for drought monitoring. Agric. For. Meteorol. 268, 74–85 (2019).Article 
ADS 

Google Scholar  More

Fitting tolerance time versus temperature to build a thermal death time curveThe high coefficients of determination found in the D. melanogaster TDT curves (Fig. 3A) are not uncommon and the exponential relation has consistently been found to provide a good fit of tolerance time vs. temperature in ectotherms3,15,20,22,23,24. Tolerance time vs. temperature data are also well fitted to Arrhenius plots which are based on thermodynamic principles (see for example15,36) and the absence of breakpoints in such plots provides a strong indication (but not direct proof) that the cause of coma/heat failure under the different intensities of acute heat stress is related to the same physiological process regardless whether failure occurs after 10 min or 10 h2,3 (but see “Discussion” section below). Despite the superior theoretical basis of Arrhenius analysis, we proceed with simple linear regressions of log10-transformed tcoma (TDT curve) as this analysis likewise provides a high R2 and is mathematically more straightforward. The physiological cause(s) of ectotherm heat failure are poorly understood37,38 but we argue that they are founded in a common process where heat injury accumulates at a temperature-dependent rate until a species-specific critical dose is attained (area below the curve and above Tc in Fig. 2). Thus, the organism has a fixed amount (dose) of thermally induced stress that it can tolerate before evoking the chosen endpoint. The experienced temperature of the animals then dictates the rate of which this stress is acquired, and accordingly when the endpoint is reached (Fig. 2) It is this reasoning that leads to TDT curves and explains why heat stress can be additive and thus also determines the boundaries of TDT curve modelling.Injury is additive across different stressful assay temperaturesIf heat stress acquired at intense and moderate stress within the span of the TDT curve acts through the same physiological mechanisms or converges to result in the same form of injury, then it is expected that injury is additive at different heat stress intensities. This hypothesis was tested by exposing flies sequentially to two static temperatures (different injury accumulation rates) and observe whether coma occurred as predicted from the summed injury (Fig. 2C). The accumulated heat injury at the two temperatures was found to be additive regardless of the order of temperature exposure (Fig. 3B,C). This finding is consistent with a conceptually similar study using speckled trout which also found strong support for additivity of heat stress at different stressful temperatures13. The exact physiological mechanism of heat injury accumulation is interesting to understand in this perspective, but it is not critical as long as the relation between temperature and injury accumulation rate is known.If injury accumulation is additive irrespective of the order of the heat exposure, we can extend the model to fluctuating temperature conditions. We have previously done this by accurately predicting dynamic CTmax from TDT parameters obtained from static assays for 11 Drosophila species (Fig. 6A, see “Discussion” section below and15). Here we extend this to temperature fluctuations that cannot be described by a simple mathematical ramp function. Specifically, groups of flies were subjected to randomly fluctuating temperatures and the observed tcoma was then compared to tcoma predicted using integration of heat injury based on TDT parameters (Fig. 4). The injury accumulation (Fig. 4C) was calculated by introducing the fluctuating temperature profiles in the associated R-script and the observed and predicted tcoma was found to correlate well (R2  > 0.94) across the 13 groups tested for each sex. These results further support the idea that injury is additive across a range of fluctuating and stressful temperatures and hence that similar physiological perturbations are in play during moderate and intense heat stress. It is important to note that in these experiments, temperatures fluctuated between 34.5 and 42.5 °C and accordingly the flies were never exposed to benign temperatures that could allow repair or hardening (see below).Figure 6adapted from Fig. 4b in15. (B) TDT parameters based on dCTmax from three dynamic tests were used to predict tcoma in static assays. Each point represents an observed vs. predicted value of species- and temperature-specific log10(tcoma). (Inset) Species values of the thermal sensitivity parameter z parameterized from TDT curves based on static assays (x-axis) or dynamic assays (y-axis). The dashed line represents the line of unity in all three panels.Conversion of heat tolerance measures between static and dynamic assays in Drosophila. Data from43. (A) Heat tolerance (dCTmax, d for dynamic assays) plotted against predicted dCTmax derived from species-specific TDT curves created from multiple (9–17) static assays. Data are presented for three different ramping rates (0.05, 0.1 and 0.25 °C min-1). Note that this graph is Full size imageIn conclusion, empirical data (present study;6,13,14,22) support the application of TDT curves to assess heat injury accumulation under fluctuating temperature conditions both in the lab and field for vertebrate and invertebrate ectotherms. Potential applications could be assessment of injury during foraging in extreme and fluctuating environments (e.g. ants in the desert39 or lizards in exposed habitat40) or for other animals experiencing extreme conditions41,42. The associated R-scripts allow assessment of percent lethal damage under such conditions if the model is provided with TDT parameters and information of temperature fluctuations (but see “Discussion” section of model limitations below).Model application for comparison of static versus dynamic dataThere is little consensus on the optimal protocol to assess ectotherm thermal tolerance and many different types of static or dynamic tests have been used to assess heat tolerance. TDT curves represent a mathematical and theoretical approach to reconcile different estimates of tolerance as the derived parameters can subsequently be used to assess heat injury accumulation at different rates (temperatures) and durations13,15,16. Here we provide R-scripts that enable such reconciliation and to demonstrate the ability of the TDT curves to reconcile data from static vs. dynamic assays we used published measurements of heat tolerance for 11 Drosophila species using three dynamic and 9–17 static measurements for each species43. Introducing data from only static assays we derived TDT parameters and subsequently used these to predict dynamic CTmax that were compared to empirically observed CTmax for three ramp rates (Fig. 6A). In a similar analysis, TDT parameters were derived from the three dynamic (ramp) experiments to predict tcoma at different static temperatures which were compared to empirical measures from static assays (Fig. 6B). Both analyses found good correlation between the predicted and observed values regardless whether the TDT curve was parameterized from static or dynamic experiments (Fig. 6). However, predictions from TDT curves based on three dynamic assays were characterised by more variation, particularly when used to assess tolerance time at very short or long durations. Furthermore, D. melanogaster and D. virilis which had the poorest correlation between predicted and observed tcoma in Fig. 6B had values of z from the TDT curves based on dynamic input data that were considerably different from values of z derived from TDT curves based on static assays (Fig. 6B inset). In conclusion TDT curves (and the associated R-scripts) are useful for conversion between static and dynamic assessment of tolerance. The quality of model output depends on the quality and quantity of data used as model input, and in this example the poorer model was parameterized from only three dynamic assays while the stronger model was based on 9–17 static assays (see also “Discussion” section below).Model application for comparison of published dataThermal tolerance is important for defining the fundamental niche of animals1,2,4 and the current anthropogenic changes in climate has reinvigorated the interest in comparative physiology and ecology of thermal limits in ectotherms. Meta-analyses of ectotherm heat tolerance data have provided important physiological, ecological and evolutionary insights5,44,45,46, but such studies are often challenged with comparison of tolerance estimates obtained through very different methodologies.Species tolerance is likely influenced by acclimation, age, sex, diet, etc.47 and also by the endpoint used (onset of spasms, coma, death, etc.27). Nevertheless, we expected heat tolerance of a species to be somewhat constrained45, so here we tested the model by converting literature data for nine species to a single and species-specific estimate of tolerance, sCTmax (1 h), the temperature that causes heat failure in 1 h (Fig. 5). The overwhelming result of this analysis is that TDT parameters are useful to convert static and dynamic heat tolerance measures to a single metric, and accordingly, the TDT model and R-scripts presented here have promising applications for large-scale comparative meta-analyses of ectotherm heat tolerance where a single metric allows for qualified direct comparison of results from different publications and experimental backgrounds. While this is an intriguing and powerful application, we caution that careful consideration should be put into the limitations of this model (see “Discussion” section below).Practical considerations and pitfalls for model interpretationAs shown above it is possible to convert and reconcile different types of heat tolerance measures using TDT parameters and these parameters can also be used to model heat stress under fluctuating field conditions. Modelling and discussion of TDT predictions beyond the boundaries of the input data has recently gained traction (see examples in48,49) but we caution that the potent exponential nature of the TDT curve requires careful consideration as it is both easy and enticing to misuse this model.Input dataThe quality of the model output is dictated by the input used for parameterization. Accordingly, we recommend TDT parameterization using several ( > 5) static experiments that should cover the time and temperature interval of interest, e.g. temperatures resulting in tcoma spanning 10 min to 10 h, thus covering both moderate and intense heat exposure. Such an experimental series can verify TDT curve linearity and allows modelling of temperature impacts across a broad range of temperatures and stress durations13,15,22. It is tempting to use only brief static experiments (high temperatures) for TDT parameterization, but in such cases, we recommend that the resulting TDT curve is only used to describe heat injury accumulation under severe heat stress intensities. Thus, the thermal sensitivity factor z represents a very powerful exponential factor (equivalent to Q10 = 100 to 100,000;15) which should ideally be parametrized over a broad temperature range (see below). We also include a script that allows TDT parametrization from multiple ramping experiments and again we recommend a broad span of ramping rates to cover the time/temperature interval of interest. A drawback of ramping experiments is the relatively large proportion of time spent at benign temperatures where there is no appreciable heat injury accumulation. Thus, dynamic experiments can conveniently use starting temperatures that are close to the temperature where injury accumulation rate surpasses injury repair rate (see “Discussion” section of “true” Tc below, in Supplemental Information and19 for other considerations regarding ramp experiments).A final methodological consideration relates to body-temperature in brief static experiments where the animal will spend a considerable proportion of the experiment in a state of thermal disequilibrium (i.e. it takes time to heat the animal). To avoid this, we recommend direct measurement of body temperature (large animals) or container temperature (small animals), and advise against excessive reliance on data from test temperatures that results in coma in less than 10 min.ExtrapolationMost studies of ectotherm heat tolerance include only a single measure of heat tolerance which is inadequate to parameterize a TDT curve. However, a TDT curve can still be generated from a single measure of tolerance (static or dynamic) if a value of z is assumed (see Supplemental Information). As z differs within species and between phylogenetic groups (Table S115,20), choosing the appropriate value may be difficult and discrepancies between the ‘true’ and assumed z represent a problem that should be approached with care. In Fig. 7A we illustrate this point in a constructed example where a single heat tolerance measurement is sampled from a ‘true’ TDT curve (full line; tcoma = 40 min at 37 °C). Along with this ‘true’ TDT curve we depict the consequences for model predictions if the assumed value of z is misestimated by ± 50%. Extrapolation from the original data point is necessary if an estimate of the temperature that causes coma after 1 h is desired, however due to limited extrapolation (from 40 to 60 min), estimation of sCTmax (1 h) values based on the ‘true’ and z ± 50% are not very different ( 6 h) between heat exposure disrupted additivity, suggesting that injury is repaired at benign temperature50. Injury repair rate is largely understudied but repair rate is generally increasing with temperature51,52,53. It is therefore an intriguing and promising idea to include a temperature-dependent repair function in more advanced modelling of heat injury. Until such repair processes are introduced in the model, we recommend that additivity of heat injury is evaluated critically if it involves periods at temperatures both above and below Tc (i.e. over consecutive days, see also13). An alternative, but not mutually excluding, explanation of increased heat resilience in split-dose experiments relates to the contribution of heat hardening as it is likely that the first heat exposure in a series can induce hardening responses that increase resilience (and thus change the TDT parameters) when a second heat exposure occurs. Such issues of repeated thermal stress have been discussed previously54 but for the purpose of the present study the main conclusion is that simple TDT curve modelling is not applicable to fluctuations bracketing Tc unless this is empirically validated. Future studies could address this issue as inclusion of repair functions would add further promise to the use of TDT curves in modelling of the impacts of temperature fluctuations. More

Physical modelThe physical part of the model is a global Oceanic General Circulation Model, Meteorological Research Institute Community Ocean Model version 3 (MRI.COM3)40. The model has horizontal resolutions of 1° in longitude and 0.5° in latitude south of 64° N, and tripolar coordinates are applied north of 64° N. The model is discretized in 51 vertical layers. In the upper 160 m, tracers are calculated at depths of 2.0, 6.5, 12.25, 19.25, 27.5, 37.75, 50.5, 65.5, 82.25, 100.0, 118.2, 137.5, and 157.75 m, and therefore vertical variation in chlorophyll concentration below the grid-scale is not represented in our model. The model was forced with realistic wind stress, surface heat and freshwater fluxes40.Marine ecosystem modelWe developed a marine ecosystem model composed of phytoplankton, zooplankton, nitrate, ammonia, particulate organic nitrogen, dissolved organic nitrogen, dissolved iron (Fed), and particulate iron. Our model is a 3D version of the FlexPFT model27 and is called the FlexPFT-3D model. The main changes of the FlexPFT-3D from original FlexPFT model are the introduction of iron limitation and substitution of the carbon-based phytoplankton biomass in the original with nitrogen-based biomass herein. The iron cycle is based on the nitrogen-, silicon- and iron-regulated Marine Ecosystem Model41 including the process of scavenging and iron input from dust and sediment. Dissolved iron starts from the distribution calculated by the Biological Elemental Cycling model in Misumi et al.42. Nitrate starts from the distribution of World Ocean Database 199843. After the connection of the physical model, a 20 years of historical simulation (1985–2004) is performed. In addition to the standard case with the chlorophyll-specific initial slope of growth versus irradiance, aI, of 0.35 m2 E−1 mol C (g chl)−1, the case studies with aI of 0.5 and 1.0 m2 E−1 mol C (g chl)−1 were implemented. The case studies are calculated from 2003 to 2004, starting from the distributions of biological variables at the end of 2002 in the standard case.Phytoplankton growthThe procedures of numerical integration of phytoplankton concentration are described here. Readers can construct a numerical model using the following equations. The derivations of the following equations from theories are presented by Smith et al.27 (hereafter Smith2016). Values of biological parameters are described in Supplementary Table 1.In accordance with Pahlow’s resource allocation theory28, the FlexPFT model assumes that resources are allocated among structural material, nutrient uptake and, light harvesting (Supplementary Fig. 1a). The fraction of structural material is assumed to be Qs/Q, where Q is the nitrogen cell quota, which is the intracellular nitrogen to carbon ratio (mol N mol C−1), and Qs is the structural cell quota (mol N mol C−1) given as a fixed parameter. The fraction of nutrient uptake is defined as fV (non-dimensional), so that the residual fraction available for light-harvesting is equal to ((1-frac{{Q}_{{rm{s}}}}{Q}-{f}_{{rm{v}}})). Optimal uptake kinetics further sub-divides the resources allocated to nutrient uptake between surface uptake sites (affinity) and enzymes for assimilation (maximum uptake rate), the fraction of which is given by fA and (1 − fA), respectively. Under nutrient-deficient conditions, the number of surface uptake sites (and hence affinity) increases, while enzyme concentration (hence, maximum uptake rate) decreases. The FlexPFT model assumes instantaneous resource allocation, which means that resource allocation tracks temporal environmental change with no lag time. It has elsewhere been demonstrated that an instantaneous acclimation model provides an accurate approximation of a fully dynamic acclimation model44.We assume that acclimation responds to daily-averaged environmental conditions, which are used to calculate the optimal values of fV, fA, and Q as ({f}_{V}^{o}), ({f}_{A}^{o}), and ({Q}^{o}). The optimal values are estimated at the beginning of a day and are retained for the following 24 h. The daily-averaged environmental variables of the seawater temperature, T (°C), intensity of photosynthetically active radiation, I, nitrogen concentration, [N], which is the sum of nitrate and ammonia concentrations, and dissolved iron concentration, [Fed] are defined as (bar{T}), (bar{I}), ([bar{{rm{N}}}]), and ([{overline{{rm{Fe}}}}_{{rm{d}}}]), respectively. Based on the assumption that diurnal variation of temperature and nutrient are very small, T, [N] and [Fed] at the beginning of a day are used as (bar{T}), ([bar{{rm{N}}}]), and ([{overline{{rm{Fe}}}}_{{rm{d}}}]), respectively. For (bar{I}), we use the average in sunshine duration in a day, which is slightly modified from the daily average in Smith2016.Phytoplankton growth rate per unit carbon biomass (day−1), μ, is given by$$mu ={hat{mu }}^{I}left(1-frac{{Q}_{{rm{s}}}}{{Q}^{o}}-{f}_{V}^{o}right)-{zeta }^{N}{f}_{V}^{o}{hat{V}}^{N},$$
(1)
where ({hat{mu }}^{I}) is the potential carbon fixation rate per unit carbon biomass (day−1), ({zeta }^{N}) is the energetic respiratory cost of assimilating inorganic nitrogen (0.6 mol C mol N−1), and ({hat{V}}^{N}) is the potential nitrogen uptake rate per unit carbon biomass (mol N mol C−1 day−1). Equation (1) represents the balance of net carbon fixation and respiration costs of nitrogen uptake, which are proportional to the fraction of resource allocation. ({hat{V}}^{N}([bar{{rm{N}}}],,bar{T})) is$${hat{V}}^{N}([bar{{rm{N}}}],bar{T})=frac{{hat{V}}_{0}[bar{{rm{N}}}]}{(frac{{hat{V}}_{0}}{{hat{A}}_{0}})+2sqrt{frac{{hat{V}}_{0}[bar{{rm{N}}}]}{{hat{A}}_{0}}}+[bar{{rm{N}}}]},$$
(2)
where ({hat{A}}_{0}) and ({hat{V}}_{0}) are the maximum value of affinity and maximum nitrogen uptake rate.From here, we will explain how the optimized values such as ({f}_{V}^{o}), ({f}_{A}^{o}), and ({Q}^{o}) are calculated. The optimal fraction of resource allocation to affinity, ({f}_{A}^{o}), is given by$${f}_{A}^{o}={[1+sqrt{frac{{hat{A}}_{0}[bar{{rm{N}}}]}{F(bar{T}){hat{V}}_{0}}}]}^{-1},$$
(3)
which is derived by substituting Eqs. (18) and (19) in Smith2016 into Eq. (17). (F(bar{T})) is temperature dependence, defined as$$F(bar{T})=exp {-frac{{E}_{a}}{R}[frac{1}{bar{T}+298}-frac{1}{{T}_{{rm{ref}}}+298}],},$$
(4)
where Ea is the parameter of the activation energy of 4.8 × 104 J mol−1, R is the gas constant of 8.3145 J (mol K)−1, and Tref is the reference temperature of 20 °C.Optimization for light-harvesting is described below. The potential carbon fixation rate per unit carbon biomass (day−1), ({hat{mu }}^{I},)(day−1), in Eq. (1) is$${hat{mu }}^{I}(bar{I},bar{T},[{overline{{rm{Fe}}}}_{{rm{d}}}])={hat{mu }}_{0}frac{[{overline{{rm{Fe}}}}_{{rm{d}}}]}{[{overline{{rm{Fe}}}}_{{rm{d}}}]+{k}_{{rm{Fe}}}}S(bar{I},bar{T},[{overline{{rm{Fe}}}}_{{rm{d}}}])F(bar{T}),$$
(5)
where ({hat{mu }}_{0}) and kFe are the maximum carbon fixation rate and half saturation constant for iron, respectively. S specifies the dependence of light. Defining ({hat{mu }}_{0}^{{rm{limFe}}}={hat{mu }}_{0}frac{[{overline{{rm{Fe}}}}_{{rm{d}}}]}{[{overline{{rm{Fe}}}}_{{rm{d}}}]+{k}_{{rm{Fe}}}}),$${hat{mu }}^{I}(bar{I},bar{T},[{overline{{rm{Fe}}}}_{{rm{d}}}])={hat{mu }}_{0}^{{rm{limFe}}}S(bar{I},bar{T},[{overline{{rm{Fe}}}}_{{rm{d}}}],)F(bar{T}).,$$
(6)
Iron limitation is imposed by substituting ({hat{mu }}_{0}) to ({hat{mu }}_{0}^{{rm{limFe}}}) in all equations in Smith2016. S is defined as$$S(bar{I},bar{T},[{overline{{rm{Fe}}}}_{{rm{d}}}],)=1-exp {frac{-{a}_{I}{hat{Theta }}^{o}bar{I}}{{hat{mu }}_{0}^{{rm{limFe}}}F(bar{T})}},$$
(7)
where ({a}_{I}) is the chlorophyll-specific initial slope of growth versus irradiance. ({hat{Theta }}^{o}), optimal chloroplast chl:phyC (g chl (mol C)−1), is$${hat{Theta }}^{o} = ; frac{1}{{zeta }^{{rm{chl}}}}+frac{{hat{mu }}_{0}^{{rm{limFe}}}}{{a}_{I}bar{I}}{1-{W}_{0}[(1+frac{{R}_{M}^{{rm{chl}}}}{{L}_{{rm{d}}}{hat{mu }}_{0}^{{rm{limFe}}}})exp (1+frac{{a}_{I}bar{I}}{{zeta }^{{rm{chl}}}{hat{mu }}_{0}^{{rm{limFe}}}}),],},(bar{I} > {I}_{0})\ {hat{Theta }}^{o} = ; 0,(bar{I}le {I}_{0}),$$
(8)
where constant parameters ({{rm{zeta }}}^{{rm{chl}}}) and ({R}_{M}^{{rm{chl}}}) are the respiratory cost of photosynthesis (mol C (g chl)−1) and the loss rate of chlorophyll (day−1), respectively. Ld is the fractional day length in 24 h. W0 is the zero-branch of Lambert’s W function. I0 is the threshold irradiance below which the respiratory costs overweight the benefits of producing chlorophyll:$${I}_{0}=frac{{zeta }^{{rm{chl}}}{R}_{M}^{{rm{chl}}}}{{L}_{{rm{d}}}{a}_{I}}.,$$
(9)
The optimal fraction of resource allocation to nutrient uptake, ({f}_{V}^{o}), is$${f}_{V}^{o}=frac{{hat{mu }}^{I}(bar{I},bar{T},[{overline{{rm{Fe}}}}_{{rm{d}}}]){Q}_{{rm{s}}}}{{hat{V}}^{N}([bar{{rm{N}}}],bar{T})}[-1+sqrt{{[{Q}_{{rm{s}}}(frac{{hat{mu }}^{I}(bar{I},bar{T},[{overline{{rm{Fe}}}}_{{rm{d}}}])}{{hat{V}}^{N}([bar{{rm{N}}}],bar{T})}+{zeta }^{N})]}^{-1}+1},]$$
(10)
The optimal nitrogen cell quota, ({Q}^{o}) is$${Q}^{o}={Q}_{{rm{s}}}[1+sqrt{1+{[{Q}_{{rm{s}}}(frac{{hat{mu }}^{I}(bar{I},bar{T},[{overline{{rm{Fe}}}}_{{rm{d}}}])}{{hat{V}}^{N}([bar{{rm{N}}}],bar{T})}+{zeta }^{N})]}^{-1}},]$$
(11)
Optimal cellular chl:phyC (g chl (mol C)−1), ({Theta }^{o}), is$${Theta }^{o}=(1-frac{{Q}_{{rm{s}}}}{{Q}^{o}}-{f}_{V}^{o}){hat{Theta }}^{o}$$
(12)
which is the multiplication of the fraction of resource allocation to light-harvesting and optimal chloroplast chl:phyC. The cellular chl:phyC and chloroplast chl:phyC in Figs. 1 and 2 are optimal cellular chl:phyC, ({Theta }^{o}), and optimal chloroplast chl:phyC, ({hat{Theta }}^{o}), respectively. The relation in Eq. (12) is displayed in Fig. 1i-n. If we artificially turn off the optimization of resource allocation by applying the constant ({Q}^{o}) and ({f}_{V}^{o}) to the all grid points, optimal cellular chl:phyC (Fig. 1i,j) only depends on optimal chloroplast chl:phyC (Fig. 1k, l), and therefore significant variation of SCM depth across the equatorial, subtropical, and subpolar regions is not reproduced.In the above equations, Eqs. (3), (8), (10), (11), and (12), optimized values related to acclimation processes are obtained and then used in calculating the phytoplankton growth rate. Phytoplankton growth rate per unit carbon biomass (day−1), (mu), in Eq. (1) is calculated at each time step:$$mu (I,T,[{rm{N}}],[{{rm{Fe}}}_{{rm{d}}}])=frac{{hat{mu }}^{I}(I,T,[{{rm{Fe}}}_{{rm{d}}}]){f}_{V}^{o}(1-{f}_{A}^{o}){hat{V}}_{0}{f}_{A}^{o}{hat{A}}_{0}[{rm{N}}]}{{hat{mu }}^{I}(I,T,[{{rm{Fe}}}_{{rm{d}}}]){Q}_{0}(1-{f}_{A}^{o}){hat{V}}_{0}+({hat{mu }}^{I}(I,T,[{{rm{Fe}}}_{{rm{d}}}]){Q}_{0}+{f}_{V}^{o}(1-{f}_{A}^{o}){hat{V}}_{0}){f}_{A}^{o}{hat{A}}_{0}[{rm{N}}]},$$
(13)
where ({hat{mu }}^{I}(I,,T,,[{{rm{Fe}}}_{{rm{d}}}])) is obtained by substituting I, T, and [Fed] for (bar{I}), (bar{{rm{T}}}), and ([{overline{{rm{Fe}}}}_{{rm{d}}}]) in Eq. (5), respectively. Note that the model calculates circadian variation in solar irradiance, I, and therefore the phytoplankton growth rate, μ, reaches its maximum at noon local time and is zero during night. On the other hand, phytoplankton optimization is assumed to respond to daily-averaged conditions. The FlexPFT model introduces phytoplankton respiration proportional to chlorophyll content, which is another important originality of Pahlow’s resource allocation theory30,33.The carbon biomass-specific respiratory costs of maintaining chlorophyll, Rchl, is$${R}^{{rm{chl}}}(I,T,[{rm{N}}],[{{rm{Fe}}}_{{rm{d}}}])=({hat{mu }}^{I}(I,T,[{{rm{Fe}}}_{{rm{d}}}])+{R}_{M}^{{rm{chl}}}){{rm{zeta }}}^{{rm{chl}}}{Theta }^{o}.,$$
(14)
The growth rate per unit nitrogen biomass, ({mu }_{{rm{N}}}), is equal to that per unit carbon biomass, μ. Instantaneous acclimation assumes that the quota of nitrogen to carbon biomass obtained by phytoplankton growth is equal to the nitrogen quota in a cell: (frac{{mu }_{{rm{N}}}[{{rm{p}}}^{{rm{N}}}]}{mu [{{rm{p}}}^{{rm{C}}}]}={Q}^{o}), where [pC] and [pN] are phytoplankton carbon and nitrogen concentration in a cell, respectively. Since (frac{[{{rm{p}}}^{{rm{N}}}]}{[{{rm{p}}}^{C}]}={Q}^{o}), ({mu }_{{rm{N}}}=mu). When temporal ({Q}^{o}) change occurs, to satisfy the mass conservation, carbon or nitrogen biomass is adjusted with the other fixed. The FlexPFT fixes carbon biomass, while the FlexPFT-3D fixes nitrogen biomass to the temporal ({Q}^{o}) change.The rate of change in the phytoplankton nitrogen concentration, [pN], except for the advection and diffusion terms is given by the following equation:$$frac{partial [{{rm{p}}}^{{rm{N}}}]}{partial t}=mu [{{rm{p}}}^{{rm{N}}}]-({R}^{{rm{chl}}}+{R}^{{rm{cnst}}})[{{rm{p}}}^{{rm{N}}}]-{M}_{{rm{p}}}{[{{rm{p}}}^{{rm{N}}}]}^{2}-({rm{extracellular}},{rm{excretion}})-({rm{grazing}}),$$
(15)
where Rcnst and Mp are the coefficient of respiration not related to chlorophyll concentration and mortality rate coefficient, respectively. The extracellular excretion is$$({rm{extracellular}},{rm{excretion}})={gamma }_{{rm{ex}}}[(mu -{R}^{{rm{chl}}})[{{rm{p}}}^{{rm{N}}}]],$$
(16)
where ({gamma }_{{rm{ex}}}) is the coefficient of extracellular excretion. The grazing term is represented by$$({rm{grazing}})={G}_{20deg }F(T)[{{rm{z}}}^{{rm{N}}}]frac{{[{{rm{p}}}^{{rm{N}}}]}^{{a}_{{rm{H}}}}}{{({k}_{{rm{H}}})}^{{a}_{{rm{H}}}}+{[{{rm{p}}}^{{rm{N}}}]}^{{a}_{{rm{H}}}}},$$
(17)
where G20deg is the maximum grazing rate at 20 °C, and [zN] is zooplankton concentration. Temperature dependency, F(T), is obtained by substituting T for (bar{T}) in Eq. (4). ({a}_{{rm{H}}}) is the parameter controlling Holling-type grazing, which takes a value from 1 to 2. kH is the grazing coefficient in Holling-type grazing.Once [pN] is calculated, phytoplankton carbon concentration (mol C L−1), and chlorophyll concentration (g chl L−1) are uniquely determined in an environmental condition, without prognostic calculation. Therefore, an instantaneous acclimation model can represent stoichiometric flexibility with lower computational costs compared with a dynamic acclimation model44.Model validationThe spatial pattern of simulated annually mean chlorophyll at the ocean surface agrees with that of satellite observation45 (Supplementary Fig. 3). The model reproduced the contrast of the surface chlorophyll concentration between subtropical and subpolar regions, although simulated surface chlorophyll concentration in subtropical regions is lower than that of the observation partly due to the lack of nitrogen fixers. Nitrogen fixation is estimated to support about 30–50% of carbon export in subtropical regions46,47. Simulated surface chlorophyll distribution in the Pacific equatorial region is close to the observed.Our model properly simulates the meridional distribution of nitrate compared with that of observations48 (Supplementary Fig. 4). The simulated horizontal distribution of primary production is consistent with that estimated by satellite data9,49 (Supplementary Fig. 5), although simulated primary production is underestimated in subtropical regions, associated with the underestimation of surface chlorophyll in these regions (Supplementary Fig. 3). More

eCIS are encoded by 1.9% and 1.2% of sequenced bacteria and archaea, respectively, with a highly biased taxonomic distributionFirst, we were interested in identifying all eCIS loci in a large genomic dataset. We compiled a set of 64,756 microbial isolate genomes retrieved from Integrated Microbial Genomes (Supplementary Data 1)16. To identify core component homologs from known systems, we searched for genes with known eCIS-associated pfam annotations (Supplementary Table 1). To supplement this, we also annotated homologous genes ourselves by searching using the Hidden Markov Model (HMM) profiles from a recent publication1,17. We defined putative eCIS operons as gene cassettes that included these multiple eCIS core genes in close proximity and were not bacteriophage, T6SS, or R-type pyocins (Supplementary Table 1, Methods). Overall, we identified eCIS operons encoded in 1230 (1.9%) bacteria and 19 (1.2%) archaea from our genomic repository (Supplementary Data 2–3). We identified two core genes, Afp8 and Afp11, that co-occur in eCIS operons across 98.7% of loci and used their protein sequences to construct an eCIS phylogenetic gene tree (Fig. 1a, Supplementary Figs. 2–3, Supplementary Data 4). Afp8 and Afp11 alone resulted in phylogenetically similar trees (Supplementary Fig. 4) and the trees agree with eCIS division into subtype I and II that were defined in a previous eCIS analysis17 (Supplementary Fig. 5). eCIS is scattered across the prokaryotic diversity with presence in 14 bacterial phyla and one archaeal phylum. The incongruence between this tree and the genomic phylogeny suggests that eCIS undergo HGT frequently, as was proposed before1,17. The previously experimentally characterized eCISs are located within a narrow clade on the eCIS tree, pointing to the possibility that other eCIS particles may play more diverse ecological roles (Fig. 1a, Supplementary Fig. 2).Fig. 1: Taxonomic Distribution of eCIS-encoding microbes.a A phylogenetic tree of eCIS across the microbial world. eCIS core genes Afp8 and Afp11 from each operon were concatenated, aligned, and used to construct the phylogenetic tree. The Domain and Phylum corresponding to each leaf are indicated in the inner and outer rings, respectively. Scaffolds encoding eCIS that have been predicted to be plasmids using Deeplasmid were marked with black triangles. Previously experimentally investigated eCIS are marked on their respective leaves (2 o’clock). Within the tree MACS, AFP, and PVC are abbreviations for Metamorphosis-associated Contractile Structures, Antifeeding Prophage, and Photorhabdus Virulence Cassettes. b eCIS distribution in different genera. We calculated the eCIS distribution across genera using a Fisher exact test. The Odds Ratio represents the enrichment or depletion magnitude, with hotter colors representing enrichment, and colder colors representing depletion. Calculated p values were corrected for multiple testing using FDR to yield minus log10 q values, shown in shades of gray. Only selected Genera are shown. Source data are provided in Supplementary Data 1–2,5–6.Full size imageNext, we looked for genetic mechanisms that may mediate the eCIS HGT. Using Deeplasmid, a new plasmid prediction tool that we developed18, we identified that 7.6% of eCIS are likely plasmid-borne (Fig. 1a and Supplementary Fig. 6, Supplementary Data 5, Methods). In other cases, we found a clear signature of eCIS operon integration into a specific bacterial chromosome (Supplementary Fig. 7). For example, we identified a likely homologous recombination event between identical tRNA genes, a classical integration site19 (Supplementary Fig. 7b). These genomic integration events and the plasmid-borne eCIS operons shed light on the mechanisms through which eCIS loci have been horizontally propagated in microbial genomes.eCIS displays a highly biased taxonomic distributionGiven the propensity of eCIS to transfer between microbes as phylogenetically distant as bacteria and archaea, we were surprised by its scarcity in microbial genomes. We tested if eCIS loci are homogeneously distributed across microbial taxa and found that eCIS are mostly constrained to particular taxa (Fig. 1b, Supplementary Data 6). Strikingly, we found that it is present in 100% (18/18) of Photorhabdus genomes in our dataset (Fisher exact test, odds ratio = infinity, q value = 2.97e−28), 89% of sequenced Chitinophaga (odds ratio = 276, q value = 1.69e−35), 86% of sequenced Dickeya (odds ratio = 211, q value = 3.78e−18), and 69% of sequenced Algoriphagus (odds ratio = 73, q value = 1.99e−24). These genera are known as environmental microbes; Photorhabdus is a commensal of entomopathogenic nematodes20, Chitinophaga is a soil microbe and a fungal endosymbiont21, Dickeya is a plant and pea aphid pathogen22,23, and Algoriphagus is an aquatic or terrestrial microbe24,25,26,27,28. In contrast, eCIS is strongly depleted from the most cultured and sequenced genera of Gram-positive and negative human pathogens, including Staphylococcus, Escherichia, Salmonella, Streptococcus, Acinetobacter, and Klebsiella. Strikingly, within these genera, for which our repository had 18,355 genomes, eCIS was totally absent (odds ratio = 0, q value ≤ 3.86E-16 for each one of these genera), suggesting a very potent purifying selection acting against eCIS integration into these microbial genomes, despite the eCIS operons’ tendency for extensive lateral transfer and its presence in other host-associated systems. Interestingly, 146 genomes, mostly from Photorhabdus, Dickeya, Actinokineospora, Streptomyces, Algoriphagus, Chitinophaga, Flavobacterium, and Calothrix genera, were found to contain more than one eCIS operon, ranging from 2 to 5 copies per genomes (Supplementary Data 7).eCIS presence is highly correlated with specific ecosystems, microbial lifestyles, and microbial hostsGiven the strong eCIS taxonomic bias we identified, we were curious to know if we could further associate eCIS with specific ecological features. To this end, we retrieved metadata available for all sequenced genomes in our repository (Methods). These traits include the microbial isolation site, ecosystem and habitat, microbial lifestyle and physiology, and the organisms hosting the microbes (Supplementary Data 8). We calculated the correlation of eCIS presence with certain microbial traits to identify significant enrichment and depletion patterns. This was done using a naïve enrichment test (Fisher exact test) together with a phylogeny-aware test, Scoary29, which is used to correct for the phylogenetic bias of the isolate genomes. Using this test we quantify to what extent the eCIS presence in a genome correlates with a certain trait, independently of the microbial phylogeny (Fig. 2, Supplementary Fig. 8). Notably, eCIS is positively correlated with terrestrial and aquatic environments, such as soil, sediments, lakes, and coasts, but is depleted from food production venues. In terms of microbial lifestyle and physiology, eCISs are enriched in environmental microbes, mostly symbiotic, and are depleted from pathogens (the vast majority of which were isolated from humans). eCISs are enriched in aerobic microbes that dwell in mild and cold temperatures. In general, the eCIS-encoding microbes tend to associate with terrestrial hosts including insects, nematodes, annelids, protists, fungi, and plants, and in aquatic hosts such as fish, sponges, and molluscs. Intriguingly, we detected a strong depletion from bacteria that were isolated from birds and mammals, including humans. We did find some eCIS isolated from bacteria associated with humans, but sparse and statistically depleted (Supplementary Fig. 8). Looking closer we also see that the operon is depleted from all tissues in which the human microbiome is abundant: oral and digestive systems, skin, and the urogenital tract. However, we detected a mild eCIS enrichment in the human gut commensal Bacteroides (Fig. 1b) and Parabacteroidetes genera. Bacteroides was recently reported by the Shikuma group as being eCIS-rich30.Fig. 2: eCIS-encoding microbes’ lifestyle and isolation.A Fisher exact test combined with a modified version of Scoary was used to perform a phylogeny-aware analysis of eCIS-encoding microbes’ metadata. The Odds Ratio represents the enrichment or depletion magnitude, with hotter colors representing enrichment, and colder colors representing depletion. The negative log10 of the q-values, shown in shades of gray, are corrected for multiple hypothesis testing. One q-value corresponds to the statistical significance of a two-sided Fisher exact test, and the other represents the same for the Scoary pairwise comparison test. Source Data are provided in Supplementary Data 8.Full size imageWe also see that eCIS is clearly associated with larger bacterial genomes in five bacterial phyla (Supplementary Fig. 9), although small genome endosymbionts are found to contain eCIS as well, for example, the Candidatus Regiella insecticola LSR1, which harbours an eCIS even though its genome size is ~2 Mbps and it contains 10 is defined “Core”, 4–10 is “Shell”, More

1.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: an ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Sinn, D. L., Apiolaza, L. A. & Moltschaniwskyj, N. A. Heritability and fitness-related consequences of squid personality traits. J. Evol. Biol. 19, 1437–1447 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Dochtermann, N. A., Schwab, T. & Sih, A. The contribution of additive genetic variation to personality variation: heritability of personality. Proc. R. Soc. B Biol. Sci. 282, 20142201 (2015).Article 

Google Scholar 
4.Hensley, N. M., Cook, T. C., Lang, M., Petelle, M. B. & Blumstein, D. T. Personality and habitat segregation in giant sea anemones (Condylactis gigantea). J. Exp. Mar. Biol. Ecol. 426–427, 1–4 (2012).Article 

Google Scholar 
5.Holtmann, B., Santos, E. S. A., Lara, C. E. & Nakagawa, S. Personality-matching habitat choice, rather than behavioural plasticity, is a likely driver of a phenotype‒environment covariance. Proc. R. Soc. B Biol. Sci. 284, 20170943 (2017).6.Boyer, N., Réale, D., Marmet, J., Pisanu, B. & Chapuis, J.-L. Personality, space use and tick load in an introduced population of Siberian chipmunks Tamias sibiricus. J. Anim. Ecol. 79, 538–547 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
7.Schindler, D. E. et al. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–612 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Cockrem, J. F. Corticosterone responses and personality in birds: Individual variation and the ability to cope with environmental changes due to climate change. Gen. Comp. Endocrinol. 190, 156–163 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Villegas‐Ríos, D., Réale, D., Freitas, C., Moland, E. & Olsen, E. M. Personalities influence spatial responses to environmental fluctuations in wild fish. J. Anim. Ecol. 87, 1309–1319 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
10.Trnka, A., Požgayová, M., Samaš, P. & Honza, M. Repeatability of host female and male aggression towards a brood parasite. Ethology 119, 907–917 (2013).Article 

Google Scholar 
11.Bohn, S. J. et al. Personality predicts ectoparasite abundance in an asocial sciurid. Ethology 123, 761–771 (2017).Article 

Google Scholar 
12.Ballew, N. G., Mittelbach, G. G. & Scribner, K. T. Fitness consequences of boldness in juvenile and adult largemouth bass. Am. Nat. 189, 396–406 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Seyfarth, R. M., Silk, J. B. & Cheney, D. L. Variation in personality and fitness in wild female baboons. Proc. Natl Acad. Sci. 109, 16980–16985 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Twiss, S. D., Cairns, C., Culloch, R. M., Richards, S. A. & Pomeroy, P. P. Variation in female grey seal (Halichoerus grypus) reproductive performance correlates to proactive-reactive behavioural types. PLoS ONE 7, e49598 (2012).15.Sinn, D. L., Gosling, S. D. & Moltschaniwskyj, N. A. Development of shy/bold behaviour in squid: context-specific phenotypes associated with developmental plasticity. Anim. Behav. 75, 433–442 (2008).Article 

Google Scholar 
16.Monceau, K. et al. Larval personality does not predict adult personality in a holometabolous insect. Biol. J. Linn. Soc. 120, 869–878 (2017).Article 

Google Scholar 
17.Wilson, A. D. M. & Krause, J. Personality and metamorphosis: is behavioral variation consistent across ontogenetic niche shifts? Behav. Ecol. 23, 1316–1323 (2012).Article 

Google Scholar 
18.Brodin, T. Behavioral syndrome over the boundaries of life—carryovers from larvae to adult damselfly. Behav. Ecol. 20, 30–37 (2009).Article 

Google Scholar 
19.Cabrera, D., Nilsson, J. R. & Griffen, B. D. The development of animal personality across ontogeny: a cross-species review. Anim. Behav. 173, 137–144 (2021).Article 

Google Scholar 
20.Brommer, J. E. & Class, B. The importance of genotype-by-age interactions for the development of repeatable behavior and correlated behaviors over lifetime. Front. Zool. 12, S2 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: a meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
22.López, B. D. When personality matters: personality and social structure in wild bottlenose dolphins, Tursiops truncatus. Anim. Behav. 163, 73–84 (2020).Article 

Google Scholar 
23.Yoshida, K. C. S., Meter, P. E. V. & Holekamp, K. E. Variation among free-living spotted hyenas in three personality traits. Behaviour 153, 1665–1722 (2016).Article 

Google Scholar 
24.Kulahci, I. G., Ghazanfar, A. A. & Rubenstein, D. I. Consistent individual variation across interaction networks indicates social personalities in lemurs. Anim. Behav. 136, 217–226 (2018).Article 

Google Scholar 
25.Dingemanse, N. J., Both, C., Drent, P. J. & Tinbergen, J. M. Fitness consequences of avian personalities in a fluctuating environment. Proc. R. Soc. Lond. B Biol. Sci. 271, 847–852 (2004).Article 

Google Scholar 
26.Polverino, G., Cigliano, C., Nakayama, S. & Mehner, T. Emergence and development of personality over the ontogeny of fish in absence of environmental stress factors. Behav. Ecol. Sociobiol. 70, 2027–2037 (2016).Article 

Google Scholar 
27.Gyuris, E., Feró, O. & Barta, Z. Personality traits across ontogeny in firebugs, Pyrrhocoris apterus. Anim. Behav. 84, 103–109 (2012).Article 

Google Scholar 
28.Stanley, C. R., Mettke-Hofmann, C. & Preziosi, R. F. Personality in the cockroach Diploptera punctata: Evidence for stability across developmental stages despite age effects on boldness. PLOS ONE 12, e0176564 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
29.Stamps, J. & Groothuis, T. G. G. The development of animal personality: relevance, concepts and perspectives. Biol. Rev. 85, 301–325 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Groothuis, T. G. G. & Trillmich, F. Unfolding personalities: the importance of studying ontogeny. Dev. Psychobiol. 53, 641–655 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Wuerz, Y. & Krüger, O. Personality over ontogeny in zebra finches: long-term repeatable traits but unstable behavioural syndromes. Front. Zool. 12, S9 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Petelle, M. B., McCoy, D. E., Alejandro, V., Martin, J. G. A. & Blumstein, D. T. Development of boldness and docility in yellow-bellied marmots. Anim. Behav. 86, 1147–1154 (2013).Article 

Google Scholar 
33.Urszán, T. J., Török, J., Hettyey, A., Garamszegi, L. Z. & Herczeg, G. Behavioural consistency and life history of Rana dalmatina tadpoles. Oecologia 178, 129–140 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Suomi, S. J., Novak, M. A. & Well, A. Aging in rhesus monkeys: Different windows on behavioral continuity and change. Dev. Psychol. 32, 1116 (1996).Article 

Google Scholar 
35.Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Cote, J. & Clobert, J. Social personalities influence natal dispersal in a lizard. Proc. R. Soc. B Biol. Sci. 274, 383–390 (2007).CAS 
Article 

Google Scholar 
37.Johnson, J. C. & Sih, A. Precopulatory sexual cannibalism in fishing spiders (Dolomedes triton): a role for behavioral syndromes. Behav. Ecol. Sociobiol. 58, 390–396 (2005).Article 

Google Scholar 
38.Spiegel, O., Leu, S. T., Bull, C. M. & Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 20, 3–18 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Brent, L. J. N. et al. Genetic origins of social networks in rhesus macaques. Sci. Rep. 3, 1042 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
40.Ellis, S., Snyder-Mackler, N., Ruiz-Lambides, A., Platt, M. L. & Brent, L. J. N. Deconstructing sociality: the types of social connections that predict longevity in a group-living primate. Proc. R. Soc. B Biol. Sci. 286, 20191991 (2019).Article 

Google Scholar 
41.Stanton, M. A. & Mann, J. Early social networks predict survival in wild bottlenose dolphins. PLoS ONE 7, e47508 (2012).42.Silk, J. B., Alberts, S. C. & Altmann, J. Social relationships among adult female baboons (Papio cynocephalus) II. Variation in the quality and stability of social bonds. Behav. Ecol. Sociobiol. 61, 197–204 (2006).Article 

Google Scholar 
43.Seyfarth, R. M. & Cheney, D. L. The evolutionary origins of friendship. Annu. Rev. Psychol. 63, 153–177 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Aplin, L. M. et al. Consistent individual differences in the social phenotypes of wild great tits, Parus major. Anim. Behav. 108, 117–127 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Wilson, A. D. M., Krause, S., Dingemanse, N. J. & Krause, J. Network position: a key component in the characterization of social personality types. Behav. Ecol. Sociobiol. 67, 163–173 (2013).Article 

Google Scholar 
46.Formica, V., Wood, C., Cook, P. & Brodie, E. Consistency of animal social networks after disturbance. Behav. Ecol. 28, 85–93 (2017).Article 

Google Scholar 
47.Rankin, R. W. et al. The role of weighted and topological network information to understand animal social networks: a null model approach. Anim. Behav. 113, 215–228 (2016).Article 

Google Scholar 
48.Strickland, K. & Frère, C. H. Predictable males and unpredictable females: repeatability of sociability in eastern water dragons. Behav. Ecol. 29, 236–243 (2018).Article 

Google Scholar 
49.Karniski, C., Krzyszczyk, E. & Mann, J. Senescence impacts reproduction and maternal investment in bottlenose dolphins. Proc. R. Soc. B Biol. Sci. 285, 20181123 (2018).Article 

Google Scholar 
50.Mann, J. Maternal Care and Offspring Development in Odontocetes. In: Ethology and Behavioral Ecology of Odontocetes (ed. Würsig, B.) 95–116 (Springer International Publishing, 2019). https://doi.org/10.1007/978-3-030-16663-2_5.51.Stanton, M. A. & Mann, J. Shark Bay Bottlenose Dolphins: A Case Study for Defining and Measuring Sociality. In: Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies (eds. Yamagiwa, J. & Karczmarski, L.) 115–126 (Springer, 2014). https://doi.org/10.1007/978-4-431-54523-1_6.52.Frère, C. H. et al. Social and genetic interactions drive fitness variation in a free-living dolphin population. Proc. Natl Acad. Sci. 107, 19949–19954 (2010).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
53.Frère, C. H. et al. Home range overlap, matrilineal and biparental kinship drive female associations in bottlenose dolphins. Anim. Behav. 80, 481–486 (2010).Article 

Google Scholar 
54.Tsai, Y.-J. J. & Mann, J. Dispersal, philopatry, and the role of fission-fusion dynamics in bottlenose dolphins. Mar. Mammal. Sci. 29, 261–279 (2013).Article 

Google Scholar 
55.Galezo, A. A., Krzyszczyk, E. & Mann, J. Sexual segregation in Indo-Pacific bottlenose dolphins is driven by female avoidance of males. Behav. Ecol. 29, 377–386 (2018).Article 

Google Scholar 
56.Smith, J. E., Memenis, S. K. & Holekamp, K. E. Rank-related partner choice in the fission–fusion society of the spotted hyena (Crocuta crocuta). Behav. Ecol. Sociobiol. 61, 753–765 (2007).Article 

Google Scholar 
57.Connor, R. C., Smolker, R. A. & Richards, A. F. Two levels of alliance formation among male bottlenose dolphins (Tursiops sp.). Proc. Natl Acad. Sci. 89, 987–990 (1992).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Smolker, R. A., Richards, A. F., Connor, R. C. & Pepper, J. W. Sex differences in patterns of association among indian ocean bottlenose dolphins. Behaviour 123, 38–69 (1992).Article 

Google Scholar 
59.Gibson, Q. A. & Mann, J. The size, composition and function of wild bottlenose dolphin (Tursiops sp.) mother–calf groups in Shark Bay, Australia. Anim. Behav. 76, 389–405 (2008).Article 

Google Scholar 
60.Mann, J., Stanton, M. A., Patterson, E. M., Bienenstock, E. J. & Singh, L. O. Social networks reveal cultural. Behav. tool.-using dolphins. Nat. Commun. 3, 980 (2012).
Google Scholar 
61.Wolak, M. E., Fairbairn, D. J. & Paulsen, Y. R. Guidelines for estimating repeatability. Methods Ecol. Evol. 3, 129–137 (2012).Article 

Google Scholar 
62.Villemereuil, P., de, Gimenez, O. & Doligez, B. Comparing parent–offspring regression with frequentist and Bayesian animal models to estimate heritability in wild populations: a simulation study for Gaussian and binary traits. Methods Ecol. Evol. 4, 260–275 (2013).Article 

Google Scholar 
63.Mann, J. Establishing trust: Sociosexual behaviour among Indian Ocean bottlenose dolphins and the development of male-male bonds. In Homosexual Behaviour in Animals: An Evolutionary Perspective (eds Vasey, P. & Sommer, V.) Chapter 4, pp. 107–130 (Cambridge University Press, 2006).64.Carter, K. D., Seddon, J. M., Frère, C. H., Carter, J. K. & Goldizen, A. W. Fission–fusion dynamics in wild giraffes may be driven by kinship, spatial overlap and individual social preferences. Anim. Behav. 85, 385–394 (2013).Article 

Google Scholar 
65.Murphy, D., Mumby, H. S. & Henley, M. D. Age differences in the temporal stability of a male African elephant (Loxodonta africana) social network. Behav. Ecol. 31, 21–31 (2020).
Google Scholar 
66.Bruck, J. N. Decades-long social memory in bottlenose dolphins. Proc. R. Soc. B Biol. Sci. 280, 20131726 (2013).Article 

Google Scholar 
67.Lukas, D. & Clutton‐Brock, T. Social complexity and kinship in animal societies. Ecol. Lett. 21, 1129–1134 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Schradin, C. Intraspecific variation in social organization by genetic variation, developmental plasticity, social flexibility or entirely extrinsic factors. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120346 (2013).Article 

Google Scholar 
69.Kappeler, P. M. & van Schaik, C. P. Evolution of primate social systems. Int. J. Primatol. 23, 707–740 (2002).Article 

Google Scholar 
70.Roberts, B. W. & DelVecchio, W. F. The rank-order consistency of personality traits from childhood to old age: a quantitative review of longitudinal studies. Psychol. Bull. 126, 3–25 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Terracciano, A., Costa, P. T. & McCrae, R. R. Personality plasticity after age 30. Pers. Soc. Psychol. Bull. 32, 999–1009 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
72.Costa, P. T., McCrae, R. R. & Löckenhoff, C. E. Personality across the life span. Annu. Rev. Psychol. 70, 423–448 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
73.Harris, M. A. Personality stability from age 14 to age 77 years. Psychol. Aging 31, 862 (2016).74.Trochet, A. et al. Evolution of sex-biased dispersal. Q. Rev. Biol. 91, 297–320 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
75.Turner, J. W., Bills, P. S. & Holekamp, K. E. Ontogenetic change in determinants of social network position in the spotted hyena. Behav. Ecol. Sociobiol. 72, 10 (2018).Article 

Google Scholar 
76.Brent, L. J. et al. Personality traits in rhesus macaques (Macaca mulatta) are heritable but do not predict reproductive output. Int. J. Primatol. 35, 188–209 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
77.Koski, S. E. Social personality traits in chimpanzees: temporal stability and structure of behaviourally assessed personality traits in three captive populations. Behav. Ecol. Sociobiol. 65, 2161–2174 (2011).Article 

Google Scholar 
78.Mann, J. & Sargeant, B. Like mother, like calf: The ontogeny of foraging traditions in wild Indian Ocean bottlenose dolphins (Tursiops sp.). In The Biology of Traditions: Models and Evidence (eds Fragaszy, D. & Perry, S.) pp. 236–266 (Cambridge University Press, 2003).79.Strickland, K., Mann, J., Foroughirad, V., Levengood, A. L. & Frère, C. H. Maternal effects and fitness consequences of individual variation in bottlenose dolphins’ ecological niche. J. Anim. Ecol. n/a, (2021).80.von Merten, S., Zwolak, R. & Rychlik, L. Social personality: a more social shrew species exhibits stronger differences in personality types. Anim. Behav. 127, 125–134 (2017).Article 

Google Scholar 
81.Réale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).Article 

Google Scholar 
82.Hall, M. L. et al. Animal personality and pace-of-life syndromes: do fast-exploring fairy-wrens die young? Front. Ecol. Evol. 3, 28 (2015).83.Wolf, M. & Weissing, F. J. An explanatory framework for adaptive personality differences. Philos. Trans. R. Soc. B Biol. Sci. 365, 3959–3968 (2010).Article 

Google Scholar 
84.Dingemanse, N. J. & Wolf, M. Recent models for adaptive personality differences: a review. Philos. Trans. R. Soc. B Biol. Sci. 365, 3947–3958 (2010).Article 

Google Scholar 
85.Highfill, L. E. & Kuczaj, S. A. Do bottlenose dolphins (Tursiops truncatus) have distinct and stable personalities? Aquat. Mamm. 33, 380 (2007).Article 

Google Scholar 
86.Kuczaj II, S. A., Highfill, L. & Byerly, H. The importance of considering context in the assessment of personality characteristics: evidence from ratings of dolphin personality. Int. J. Comp. Psychol. 25, 309–329 (2012).87.Mann, J., Connor, R. C., Barre, L. M. & Heithaus, M. R. Female reproductive success in bottlenose dolphins (Tursiops sp.): life history, habitat, provisioning, and group-size effects. Behav. Ecol. 11, 210–219 (2000).Article 

Google Scholar 
88.Bichell, L. M. V., Krzyszczyk, E., Patterson, E. M. & Mann, J. The reliability of pigment pattern-based identification of wild bottlenose dolphins. Mar. Mammal. Sci. 34, 113–124 (2018).Article 

Google Scholar 
89.Krützen, M. et al. A biopsy system for small cetaceans: darting success and wound healing in Tursiops spp. Mar. Mammal. Sci. 18, 863–878 (2002).Article 

Google Scholar 
90.Krzyszczyk, E. & Mann, J. Why become speckled? Ontogeny and function of speckling in Shark Bay bottlenose dolphins (Tursiops sp.). Mar. Mammal. Sci. 28, 295–307 (2012).Article 

Google Scholar 
91.Karniski, C. et al. A comparison of survey and focal follow methods for estimating individual activity budgets of cetaceans. Mar. Mammal. Sci. 31, 839–852 (2015).Article 

Google Scholar 
92.Stanton, M. A. Social networks and fitness consequences of early sociality in wild bottlenose dolphins (Tursiops sp.). (Georgetown University, 2011).93.Singer, J. D., Willett, J. B., Willett, C. W. E. P. J. B. & Willett, J. B. Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. (Oxford University Press, 2003).94.Hadfield, J. D. MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package. J. Stat. Softw. 33, 1–22 (2010).95.R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).96.Houslay, T. M. & Wilson, A. J. Avoiding the misuse of BLUP in behavioural ecology. Behav. Ecol. 28, 948–952 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
97.Revelle, W. & Revelle, M. W. Package ‘psych’. Compr. R. Arch. Netw. 337, 338 (2015).
Google Scholar 
98.Wei, T. et al. Package ‘corrplot’. Statistician 56, e24 (2017).
Google Scholar 
99.Thys, B. et al. Exploration and sociability in a highly gregarious bird are repeatable across seasons and in the long term but are unrelated. Anim. Behav. 123, 339–348 (2017).Article 

Google Scholar 
100.McCrae, R. R. et al. Nature over nurture: temperament, personality, and life span development. J. Pers. Soc. Psychol. 78, 173–186 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
101.Lee, K. & Ashton, M. C. Psychometric properties of the HEXACO personality inventory. Multivar. Behav. Res. 39, 329–358 (2004).Article 

Google Scholar 
102.McCrae, R. R. & Costa, P. T., Jr The five-factor theory of personality. in Handbook of personality: Theory and research, 3rd ed 159–181 (The Guilford Press, 2008).103.Dominey, W. J. Alternative mating tactics and evolutionarily stable strategies. Am. Zool. 24, 385–396 (1984).Article 

Google Scholar  More

1.Morris, J. L. et al. The timescale of early land plant evolution. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1719588115 (2018).Article 
PubMed 

Google Scholar 
2.Gibling, M. R. & Davies, N. S. Palaeozoic landscapes shaped by plant evolution. Nat. Geosci. 5, 99–105 (2012).ADS 
CAS 
Article 

Google Scholar 
3.Gibling, M. R. et al. Palaeozoic co-evolution of rivers and vegetation: A synthesis of current knowledge. Proc. Geol. Assoc. 125, 524–533 (2014).Article 

Google Scholar 
4.Mitchell, R. L. et al. Mineral weathering and soil development in the earliest land plant ecosystems. Geology 44, 1007–1010 (2016).ADS 
CAS 
Article 

Google Scholar 
5.Mergelov, N. et al. Alteration of rocks by endolithic organisms is one of the pathways for the beginning of soils on Earth. Sci. Rep. 8, 1–15 (2018).CAS 
Article 

Google Scholar 
6.McMahon, W. J. & Davies, N. S. Evolution of alluvial mudrock forced by early land plants. Science 359, 1022–1024 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
7.Field, K. J. et al. Functional analysis of liverworts in dual symbiosis with Glomeromycota and Mucoromycotina fungi under a simulated Palaeozoic CO2 decline. ISME J. 10, 1514–1526 (2016).CAS 
PubMed 
Article 

Google Scholar 
8.Mills, B., Watson, A. J., Goldblatt, C., Boyle, R. & Lenton, T. M. Timing of Neoproterozoic glaciations linked to transport-limited global weathering. Nat. Geosci. 4, 861–864 (2011).ADS 
CAS 
Article 

Google Scholar 
9.Porada, P., Weber, B., Elbert, W., Pöschl, U. & Kleidon, A. Estimating impacts of lichens and bryophytes on global biogeochemical cycles. Global Biogeochem. Cycles 28, 71–85 (2014).ADS 
CAS 
Article 

Google Scholar 
10.Edwards, D., Cherns, L. & Raven, J. A. Could land-based early photosynthesizing ecosystems have bioengineered the planet in mid-Palaeozoic times?. Palaeontology 58, 803–837 (2015).Article 

Google Scholar 
11.Williams, A. J., Buck, B. J. & Beyene, M. A. Biological soil crusts in the mojave desert, USA: micromorphology and pedogenesis. Soil Sci. Soc. Am. J. 76, 1685 (2012).ADS 
CAS 
Article 

Google Scholar 
12.Belnap, J. & Lange, O. L. Biological Soil Crusts: Structure, Function, and Management (Springer, 2001).
Google Scholar 
13.Mitchell, R. L. et al. Cryptogamic ground covers as analogues for early terrestrial biospheres: Initiation and evolution of biologically mediated soils. Geobiology 00, 1–15 (2021).
Google Scholar 
14.Kenrick, P., Wellman, C. H., Schneider, H. & Edgecombe, G. D. A timeline for terrestrialization: Consequences for the carbon cycle in the Palaeozoic. Philos. Trans. R. Soc. B Biol. Sci. 367, 519–536 (2012).Article 

Google Scholar 
15.Strullu-Derrien, C., Wawrzyniak, Z., Goral, T. & Kenrick, P. Fungal colonization of the rooting system of the early land plant Asteroxylon mackiei from the 407-Myr-old Rhynie Chert (Scotland, UK). Bot. J. Linn. Soc. 179, 201–213 (2015).Article 

Google Scholar 
16.Krings, M., Kerp, H., Hass, H., Taylor, T. N. & Dotzler, N. A filamentous cyanobacterium showing structured colonial growth from the Early Devonian Rhynie chert. Rev. Palaeobot. Palynol. 146, 265–276 (2007).Article 

Google Scholar 
17.Remy, W., Taylort, T. N., Hass, H. & Kerp, H. Four Hundred-million-year-old Vesicular Arbuscular Mycorrhizae (Endomycorrhiae/symbiosis/fossil fungi/mutualims). Proc. Natl. Acad. Sci. United States Am. 91, 11841–11843 (1994).ADS 
CAS 
Article 

Google Scholar 
18.Field, K. J. et al. Contrasting arbuscular mycorrhizal responses of vascular and non-vascular plants to a simulated Palaeozoic CO2 decline. Nat. Commun. 3, 1–8 (2012).Article 
CAS 

Google Scholar 
19.Lenton, T. M., Crouch, M., Johnson, M., Pires, N. & Dolan, L. First plants cooled the Ordovician. Nat. Geosci. 5, 86–89 (2012).ADS 
CAS 
Article 

Google Scholar 
20.Mills, B. J. W., Batterman, S. A. & Field, K. J. Nutrient acquisition by symbiotic fungi governs Palaeozoic climate transition. Phil. Trans. R. Soc. B 373, 20160503 (2017).21.Mitchell, R. L., Strullu-Derrien, C. & Kenrick, P. Biologically mediated weathering in modern cryptogamic ground covers and the early paleozoic fossil record. J. Geol. Soc. London. 176, 430–439 (2019).CAS 
Article 

Google Scholar 
22.Furnes, H. et al. Comparing petrographic signatures of bioalteration in recent to Mesoarchean pillow lavas: Tracing subsurface life in oceanic igneous rocks. Precambrian Res. 158, 156–176 (2007).ADS 
CAS 
Article 

Google Scholar 
23.Smits, M. M. et al. Plant-driven fungal weathering: Early stages of mineral alteration at the nanometer scale. Geology 37, 615–618 (2009).ADS 
Article 
CAS 

Google Scholar 
24.Bonneville, S. et al. Tree-mycorrhiza symbiosis accelerate mineral weathering: Evidences from nanometer-scale elemental fluxes at the hypha-mineral interface. Geochim. Cosmochim. Acta 75, 6988–7005 (2011).ADS 
CAS 
Article 

Google Scholar 
25.McLoughlin, N. Fungal origins?. Nat. Ecol. Evol. 1, 1–2 (2017).Article 

Google Scholar 
26.Ivarsson, M. et al. Intricate tunnels in garnets from soils and river sediments in Thailand-Possible endolithic microborings. PLoS ONE 13, 0200351 (2018).Article 
CAS 

Google Scholar 
27.Hoffland, E. et al. The role of fungi in weathering. Front. Ecol. Environ. 2, 258–264 (2004).Article 

Google Scholar 
28.McLoughlin, N., Furnes, H., Banerjee, N. R., Muehlenbachs, K. & Staudigel, H. Ichnotaxonomy of microbial trace fossils in volcanic glass. J. Geol. Soc. London. 166, 159–169 (2009).Article 

Google Scholar 
29.Berner, R. A. & Cochran, M. F. Plant-induced weathering of Hawaiian basalts. J. Sediment. Res. 68, 723–726 (1998).ADS 
CAS 
Article 

Google Scholar 
30.Landeweert, R., Hoffland, E., Finlay, R. D., Kuyper, T. W. & Van Breemen, N. Linking plants to rocks: Ectomycorrhizal fungi mobilize nutrients from minerals. Trends Ecol. Evol. 16, 248–254 (2001).CAS 
PubMed 
Article 

Google Scholar 
31.Van Schöll, L. et al. Rock-eating mycorrhizas: Their role in plant nutrition and biogeochemical cycles. Plant Soil 303, 35–47 (2008).Article 
CAS 

Google Scholar 
32.Quirk, J. et al. Evolution of trees and mycorrhizal fungi intensifies silicate mineral weathering. Biol. Lett. 8, 1006–1011 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
33.Daly, M. et al. A multi-scale correlative investigation of ductile fracture. Acta Mater. 130, 56–68 (2017).ADS 
CAS 
Article 

Google Scholar 
34.Gelb, J., Finegan, D. P., Brett, D. J. L. & Shearing, P. R. Multi-scale 3D investigations of a commercial 18650 Li-ion battery with correlative electron- and X-ray microscopy. J. Power Sour. 357, 77–86 (2017).ADS 
CAS 
Article 

Google Scholar 
35.Slater, T. J. A. et al. Multiscale correlative tomography: An investigation of creep cavitation in 316 stainless steel. Sci. Rep. 7, 1–10 (2017).CAS 
Article 

Google Scholar 
36.Burnett, T. L. & Withers, P. J. Completing the picture through correlative characterization. Nat. Mater. 18, 1041–1049 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
37.Mitchell, R. L. et al. Macro-to-nanoscale investigation of wall-plate joints in the acorn barnacle Semibalanus balanoides: correlative imaging, biological form and function, and bioinspiration. J. R. Soc. Interface 16, 20190218 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.Bradley, R. S. & Withers, P. J. Correlative multiscale tomography of biological materials. MRS Bull. 41, 549–556 (2016).ADS 
Article 

Google Scholar 
39.Ferstl, S. et al. Nanoscopic X-ray tomography for correlative microscopy of a small meiofaunal sea-cucumber. Sci. Rep. 10, 1–12 (2020).Article 
CAS 

Google Scholar 
40.O’Sullivan, J. D. B., Cruickshank, S. M., Starborg, T., Withers, P. J. & Else, K. J. Characterisation of cuticular inflation development and ultrastructure in Trichuris muris using correlative X-ray computed tomography and electron microscopy. Sci. Rep. 10, 1–9 (2020).Article 
CAS 

Google Scholar 
41.Goral, J., Walton, I., Andrew, M. & Deo, M. Pore system characterization of organic-rich shales using nanoscale- resolution 3D imaging. Fuel 258, 116049 (2019).CAS 
Article 

Google Scholar 
42.Andrew, M. Comparing organic-hosted and intergranular pore networks: topography and topology in grains, gaps and bubbles. Geol. Soc. Lond. Spec. Publ. 484, 4844 (2018).
Google Scholar 
43.Ma, L. et al. Correlative multi-scale imaging of shales: a review and future perspectives. Geol. Soc. Lond. Spec. Publ. 454, 175–199 (2017).ADS 
Article 

Google Scholar 
44.Schlüter, S., Eickhorst, T. & Mueller, C. W. Correlative imaging reveals holistic view of soil microenvironments. Environ. Sci. Technol. 53, 829–837 (2019).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
45.Bandara, C. D. et al. High-Resolution Chemical Mapping and Microbial Identification of Rhizosphere using Correlative Microscopy. bioRxiv 1–26 (2021).46.Spruzeniece, L., Piazolo, S., Daczko, N. R., Kilburn, M. R. & Putnis, A. Symplectite formation in the presence of a reactive fluid: insights from hydrothermal experiments. J. Metamorph. Geol. 35, 281–299 (2017).ADS 
CAS 
Article 

Google Scholar 
47.Stefánsson, A. et al. Major impact of volcanic gases on the chemical composition of precipitation in Iceland during the 2014–2015 Holuhraun eruption. J. Geophys. Res. Atmos. Geophys. Res. Atmos. 122, 1971–1982 (2017).ADS 
Article 
CAS 

Google Scholar 
48.Gadd, G. M. Metals, minerals and microbes: Geomicrobiology and bioremediation. Microbiology 156, 609–643 (2010).CAS 
PubMed 
Article 

Google Scholar 
49.Jongmans, A. G. et al. Rock-eating fungi. 389, 682–683 (1997).CAS 

Google Scholar 
50.Gadd, G. M. Fungi, rocks, and minerals. Elements 13, 171–176 (2017).Article 

Google Scholar 
51.Warscheid, T. & Braams, J. Biodeterioration of stone: a review. Int. Biodeterior. Biodegredation 46, 343–368 (2000).CAS 
Article 

Google Scholar 
52.Burghelea, C. et al. Mineral nutrient mobilization by plants from rock: influence of rock type and arbuscular mycorrhiza. Biogeochemistry 124, 187–203 (2015).CAS 
Article 

Google Scholar 
53.Mcloughlin, N., Staudigel, H., Furnes, H., Eickmann, B. & Ivarsson, M. Mechanisms of microtunneling in rock substrates: Distinguishing endolithic biosignatures from abiotic microtunnels. Geobiology 8, 245–255 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Hoffland, E., Giesler, R., Jongmans, T. & Van Breemen, N. Increasing feldspar tunneling by fungi across a North Sweden podzol chronosequence. Ecosystems 5, 11–22 (2002).Article 

Google Scholar 
55.Wierzchos, J. & delos Ríos A, Ascaso C, ,. Microorganisms in desert rocks: The edge of life on Earth. Int. Microbiol. 15, 173–183 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Ascaso, C. & Wierzchos, J. New approaches to the study of Antarctic lithobiontic microorganisms and their inorganic traces, and their application in the detection of life in Martian rocks. Int. Microbiol. 5, 215–222 (2003).
Google Scholar 
57.Gorbushina, A. A., Boettcher, M., Brumsack, H. J., Krumbein, W. E. & Vendrell-Saz, M. Biogenic forsterite and opal as a product of biodeterioration and lichen stromatolite formation in table mountain systems (Tepuis) of Venezuela. Geomicrobiol. J. 18, 117–132 (2001).CAS 
Article 

Google Scholar 
58.Adamo, P. & Violante, P. Weathering of rocks and neogenesis of minerals associated with lichen activity. Appl. Clay Sci. 16, 229–256 (2000).CAS 
Article 

Google Scholar 
59.Oggerin, M., Tornos, F., Rodriguez, N., Pascual, L. & Amils, R. Fungal iron biomineralization in Río Tinto. Minerals 6, 37 (2016).Article 
CAS 

Google Scholar 
60.Akhtar, M. E. & Kelso, W. I. Electron microscopic characterisation of iron and manganese oxide/hydroxide precipitates from agricultural field drains 1. Biol. Fertil. Soils 16, 305–312 (1993).CAS 
Article 

Google Scholar 
61.Gadd, G. M. Fungal production of citric and oxalic acid: importance in metal speciation, physiology and biogeochemical processes. Adv. Microb. Physiol. 41, 47–92 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Napieralski, S. A. et al. Microbial chemolithotrophy mediates oxidative weathering of granitic bedrock. Proc. Natl. Acad. Sci. U. S. A. 116, 26394–26401 (2019).ADS 
CAS 
PubMed Central 
Article 

Google Scholar 
63.Dorn, R. I., Mahaney, W. C. & Krinsley, D. H. Case hardening: turning weathering rinds into protective shells. Elements 13, 165–169 (2017).Article 

Google Scholar 
64.Schreiber, H. D. Experimental studies of nickel and chromium partitioning into olivine from synthetic basaltic melts. in Lunar and Planetary Science Conference, 10th, Houston, Texas, Proceedings Volume 1 509–516 (1979).65.Burford, E. P., Kierans, M. & Gadd, G. M. Geomycology: Fungi in mineral substrata. Mycologist 17, 98–107 (2003).Article 

Google Scholar 
66.Dorn, R. I., Gordon, S. J., Krinsley, D. & Langworthy, K. Nanoscale: Mineral Weathering Boundary. In: Treatise on Geomorphology (eds. Shroder, J., Pope, G. A.), vol. 4, 44–69 (2013).67.Smits, M. Mineral tunneling by fungi. in Fungi in Biogeochemical cycles (ed. Gadd, G. M.) 311–327 (Cambridge University Press, 2006).68.Gorbushina, A. A. Life on the rocks. Environ. Microbiol. 9, 1613–1631 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Gadd, G. M. Geomycology: biogeochemical transformations of rocks, minerals, metals and radionuclides by fungi, bioweathering and bioremediation. Mycol. Res. 111, 3–49 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Arocena, J. M., Zhu, L. P. & Hall, K. Mineral accumulations induced by biological activity on granitic rocks in Qinghai Plateau China. Earth Surf. Process. Landforms 28, 1429–1437 (2003).ADS 
CAS 
Article 

Google Scholar 
71.Krumbein, W. E. & Jens, K. Biogenic rock varnishes of the Negev desert (Israel) an ecological study of iron and manganese transformation by cyanobacteria and fungi. Oecologia 50, 25–38 (1981).ADS 
CAS 
PubMed 
Article 

Google Scholar 
72.Gadd, G. M. Microbial formation and transformation of organometallic and organometalloid compounds. FEMS Microbiol. Rev. 11, 297–316 (1993).CAS 
Article 

Google Scholar 
73.Mitchell, R. L. et al. What lies beneath: 3d vs 2d correlative imaging challenges and how to overcome them. Microsc. Microanal. 25, 416–417 (2019).Article 

Google Scholar 
74.Thévenaz, P., Ruttimann, U. E. & Unser, M. A pyramid approach to subpixel registration based on intensity. IEEE Trans. Image Process. 7, 27–41 (1998).ADS 
PubMed 
Article 

Google Scholar  More

A single long day induces the photoperiodic molecular responseFigure 1c shows results from the experiment 1, as evidenced from the qPCR measurement of mRNA expression of genes of known biological functions in the blood and hypothalamus. Clearly, the exposure to extended light period induced a molecular response by hour 18 of the first long day, as shown by change in mRNA levels of candidate genes in both central (hypothalamus) peripheral (blood) tissues of photosensitive buntings. Blood mRNA levels of peroxiredoxin 4 (prdx4) were significantly lower at hour 18 mimicking a long 18 h photoperiod than those at hour 10 mimicking a short 10 h photoperiod (p = 0.002, t = 5.18, n = 4/time point). Paradoxically, this indicated a reduced cellular response against oxidative stress in the otherwise photo stimulated birds on the first long day. We speculate that prdx4 expression pattern would be inversed (i.e. increased prdx4 mRNA levels) after several long days when birds show photoperiodically stimulated hyperphagia (increased food intake) and lipogenesis (fat accumulation). Intriguingly, however, blood mRNA levels of gpx1 (p = 0.399, t = 0.91, n = 4/time point) and sod1 (p = 0.845, t = 0.20, n = 4/time point) genes were not different between hours 10 and 18 (Student’s t-test, Fig. 1c(a–c)). Taken together differences in the expression pattern of these enzymes, we speculate differential activation of the enzymatic pathways that are probably involved in the oxidative cellular response when migratory birds are exposed to an acute change in their photoperiodic environment.On the other hand, blood il1β mRNA levels were significantly higher at hour 18 than the hour 10 (p = 0.041, t = 2.58, n = 4/time point; Student’s t-test, Fig. 1c(d)). It is consistent with the known role of il1β-encoded interleukin 1β, as a crucial mediator of the inflammation and a marker of the innate immune system22,23. Increased il1β mRNA expression on the first long day is consistent with the idea of parallel photoperiodic induction of multiple biological processes, including those associated with the innate immune response, body fattening and gonadal maturation in migratory songbirds28; however, the possibility that an upregulated interleukin was an indicative a stress response cannot be excluded at this time.Changes in hypothalamic gene expressions further confirm a rapid molecular response to the extended light period when it surpasses the threshold photoperiod, i.e. acts as the stimulatory long day. Reciprocal switching of genes involved in the thyroid hormone responsive pathway at hour 18 particularly evidences this. Hypothalamic mRNA levels of tshβ (p = 0.033, t = 2.75, n = 4/time point) and dio2 (p = 0.0004, t = 7.14, n = 4/time point) genes were higher, and that of dio3 gene expression was lower at hour 18 than the hour 10 (p = 0.036, t = 2.68, n = 4/time point). This is also in agreement with the rapid photoperiodic response found on the first long day in plasma LH secretion, and in hypothalamic expressions of Fos-immunoreactivity and thyroid hormone responsive genes in blackheaded buntings14,33 and other photoperiodic birds15,17,19,32,34,35,36,37,38. However, gnrh mRNA levels were not found significantly different between hours 10 and 18 of the first long day (p = 0.324, t = 1.07, n = 4/time point; Student’s t-test, Fig. 1c(e–h) indicating that hour 18 was probably too early a time for an upregulated gnrh expression on the first long day37,38,39.RNA-Seq reveals differences in time course of the photoperiodic responseTable S2 summarizes the primary statistics used for RNA-Seq results. Using only transcripts with non-zero abundance, we compared the time course of transcriptome-wide response in the hypothalamus both as the function of time (within photosensitive or photorefractory state) and LHS (photosensitive vs. photorefractory state; n = 2/time point/state except at hour 22 in photorefractory state which had n = 1 sample size). Further, to show a functional linkage of differentially expressed genes (DEGs), we performed STRING analysis that predicts the protein–protein interaction (see methods for details).Results on hypothalamic gene expressions suggest that buntings react to the acute photoperiodic change in photorefractory state almost as they do in the photosensitive state. However, the comparison of the overall RNASeq data from both states revealed LHS-dependent pattern in the time course of transcriptional response, with differences in the number and functions of DEGs and associated physiological pathways.Within state differences in time course of transcriptional responseWe examined the time course of response on the first long day, by comparing gene expressions at the hours 14, 18 and 22 of the extended light period that mimicked 14 h, 18 h and 22 h long photoperiods, respectively, with those at hour 10 that mimicked a 10 h short photoperiod.Photosensitive stateAt hour 14, we found 10 differentially expressed genes (DEGs) with 4 upregulated and 6 downregulated genes (Figs. 2a, 3a, Table S3). Of the 10 DEGs, atp6v1e1, atp6v1b2, uqcrc1 and pgam1 genes enriched the oxidative phosphorylation, metabolic pathways, phagosome and mTOR signalling pathways (Table 1). The oxidative phosphorylation and metabolic pathways were upregulated at hour 10, while the phagosome and mTOR signalling pathways were enriched by two genes that were opposite in the expression trend: atp6v1e1 was upregulated while atp6v1b2 was downregulated at hour 14. The STRING analysis showed a significant interaction of atp6v1e1 and atp6v1b2 encoded proteins (ATP6V1E1 and ATP6V1B2). These proteins are the components of vacuolar ATPase enzyme that mediates the acidification of eukaryotic intracellular organelles necessary for protein sorting and zymogen activation. Further, at hour 14, ttr gene that codes for transthyretin (a preferential T3 binder) and pomc gene that codes for the proopiomelanocortin receptor had significantly lower expressions. Whereas, low ttr gene expression, as in photostimulated redheaded buntings40, might indicate a reduced trafficking of thyroid hormones via ttr-encoded transthyretins in the photosensitive state, the low pomc gene expression might suggest the removal of inhibitory effects of the opioids (e.g. β-endorphin, a pomc-encoded proopiomelanocortin product) on hypothalamic GnRH and, in turn, pituitary LH secretion41,42.Figure 2Top panel: Volcano plots showing results of differential gene expression analysis (− log10 padj. vs. log2 fold change values) in the hypothalamus within the photosensitive (a–c) and photorefractory states (e–g). The comparison protocol is shown on the left. In each state, the comparisons were done with respect to the hour 10 value (akin to short day control). Venn diagram shows common and unique DEGs in photosensitive (d) and photorefractory states (h). Bottom panel: Volcano plots showing results of differential gene expression analysis (− log10 padj. vs. log2 fold change values) between the photosensitive and photorefractory states. The pairwise comparisons were made at all the four time points (hours 10 (i), 14 (j), 18 (k) and 22 (l)). Venn diagram shows common and unique DEGs between states at hours 10, 14, 18 and 22 (m). Genes in a volcano plot with log2 fold change  > 2 are marked by green colour, and those with log2 fold change  > 2 and p value (padj.)  More