Philippa Kaur

Rutz, C. Predator fitness increases with selectivity for odd prey. Curr. Biol. 22, 820–824 (2012).CAS 
PubMed 

Google Scholar 
Santos, C. D. et al. Personality and morphological traits affect pigeon survival from raptor attacks. Sci. Rep. 5, 1–8 (2015).
Google Scholar 
Brown, M. B. & Wells, E. Skeletal dysplasia-like syndromes in wild giraffe. BMC Res. Notes 13, 569 (2020).PubMed 
PubMed Central 

Google Scholar 
van Grouw, H. What colour is that bird? The causes and recognition of common colour aberrations in birds. Br. Birds 106, 17–29 (2013).
Google Scholar 
Slagsvold, T., Rofstad, G. & Sandvik, J. Partial albinism and natural selection in the hooded crow Corvus corone cornix. J. Zool. 214, 157–166 (1988).
Google Scholar 
Stevens, M. et al. Revealed by conspicuousness: distractive markings reduce camouflage. Behav. Ecol. 24, 213–222 (2013).
Google Scholar 
van Grouw, H. What’s in a name? Nomenclature for colour aberrations in birds reviewed. Bull. Br. Ornithol. Club 141, 276–299 (2021).
Google Scholar 
Parsons, G. J. & Bonderup-Nielsen, S. Partial albinism in an island population of Meadow Voles, Microtus pennsylvanicus, from Nova Scotia. Can. Field-Nat. 109, 263–264 (1995).
Google Scholar 
Reis, A. da S., Zampaulo, R. de A. & Talamoni, S. A. Frequency of leucism in a colony of Anoura geoffroyi (Chiroptera: Phyllostomidae) roosting in a ferruginous cave in Brazil. Biota Neotropica 19(3): e20180676. https://doi.org/10.1590/1676-0611-BN-2018-0676 (2019).Jehl, J. R. Leucism in Eared Grebes in western north America. Condor 87, 439–441 (1985).
Google Scholar 
Forrest, S. & Naveen, R. Prevalence of leucism in Pygoscelid penguins of the Antarctic peninsula. Waterbirds 23, 283–285 (2000).
Google Scholar 
González-Ortegón, E., Drake, P., Quigley, D. T. G. & Cuesta, J. A. Leucism in the European sardine Sardina pilchardus (Clupeidae). Ecol. Indic. 117, 106544 (2020).
Google Scholar 
David, B. Z. First report of partial leucism in the poison frog Epipedobates anthonyi (Anura: Dendrobatidae) in El Oro, Ecuador. Neotrop. Biodivers. 7, 1–4 (2021).
Google Scholar 
Krecsák, L. Albinism and leucism among European Viperinae: a review. Russ. J. Herpetol. 15, 97–102 (2008).
Google Scholar 
Ritland, K., Newton, C. & Marshall, H. D. Inheritance and population structure of the white-phased “Kermode” black bear. Curr. Biol. 11, 1468–1472 (2001).CAS 
PubMed 

Google Scholar 
Galván, I., Bijlsma, R. G., Negro, J. J., Jarén, M. & Garrido-Fernández, J. Environmental constraints for plumage melanization in the northern goshawk Accipiter gentilis. J. Avian Biol. 41, 523–531 (2010).
Google Scholar 
Pijpe, A., Gardien, K. L. M., Meijeren-Hoogendoorn, R. E. van, Middelkoop, E. & Zuijlen, P. P. M. van. Scar Symptoms: Pigmentation Disorders in Textbook On Scar Management (eds. Téot, L., Mustoe, T. A., Middelkoop, E. & Gauglitz, G. G.) 109–115 (Springer, 2020).Edelaar, P. et al. Apparent selective advantage of leucism in a coastal population of Southern caracaras (Falconidae). Evol. Ecol. Res. 13, 187–196 (2011).
Google Scholar 
Ellegren, H., Lindgren, G., Primmer, C. R. & Møller, A. P. Fitness loss and germline mutations in barn swallows breeding in Chernobyl. Nature 389, 593–596 (1997).ADS 
CAS 
PubMed 

Google Scholar 
Benítez-López, A. & García-Egea, I. First record of an aberrantly colored Pin-tailed Sandgrouse (Pterocles alchata). Wilson J. Ornithol. 127, 755–759 (2015).
Google Scholar 
Zbyryt, A., Mikula, P., Ciach, M., Morelli, F. & Tryjanowski, P. A large-scale survey of bird plumage colour aberrations reveals a collection bias in Internet-mined photographs. Ibis 163, 566–578 (2020).
Google Scholar 
Bensch, S., Hansson, B., Hasselquist, D. & Nielsen, B. Partial albinism in a semi-isolated population of Great Reed Warblers. Hereditas 133, 167–170 (2000).CAS 
PubMed 

Google Scholar 
Izquierdo, L. et al. Factors associated with leucism in the common blackbird Turdus merula. J. Avian Biol. 49, e01778 (2018).
Google Scholar 
Møller, A. P. & Mousseau, T. A. Albinism and phenotype of barn swallows (Hirundo rustica) from Chernobyl. Evolution 55, 2097–2104 (2001).PubMed 

Google Scholar 
Troscianko, J., Wilson-Aggarwal, J., Stevens, M. & Spottiswoode, C. N. Camouflage predicts survival in ground-nesting birds. Sci. Rep. 6, 1–8 (2016).
Google Scholar 
Aragonés, J., Arias de Reyna, L. & Recuerda, P. Visual communication and sexual selection in a nocturnal bird species, Caprimulgus ruficollis, a balance between crypsis and conspicuousness. Wilson Bull. 111, 340–345 (1999).
Google Scholar 
Negro, J. J., Bortolotti, G. R. & Sarasola, J. H. Deceptive plumage signals in birds: manipulation of predators or prey? Biol. J. Linn. Soc. 90, 467–477 (2007).
Google Scholar 
Brooke, M. de L. Unexplained recurrent features of the plumage of birds. Ibis 152, 845–847 (2010).Forero, M. G., Tella, J. L. & García, L. Age related evolution of sexual dimorphism in the Red-necked Nightjar Caprimulgus ruficollis. J. Ornithol. 136, 447–451 (1995).
Google Scholar 
Camacho, C. Early age at first breeding and high natal philopatry in the Red-necked Nightjar Caprimulgus ruficollis. Ibis 156, 442–445 (2014).
Google Scholar 
Camacho, C. et al. The road to opportunities: landscape change promotes body-size divergence in a highly mobile species. Curr. Zool. 62, 7–14 (2016).PubMed 
PubMed Central 

Google Scholar 
Forero, M. G., Tella, J. L. & Oro, D. Annual survival rates of adult Red-necked Nightjars Caprimulgus ruficollis. Ibis 143, 273–277 (2001).
Google Scholar 
Henner, J. et al. Genetic mapping of the (G)-locus responsible for the coat color phenotype “Progressive Greying with Age” in horses (Equus caballus). Mamm. Genome 13, 535–537 (2002).CAS 
PubMed 

Google Scholar 
Edson, J. M. An epidemic of albinism. Auk 45, 377–378 (1928).
Google Scholar 
Camacho, C., Palacios, S., Sáez, P., Sánchez, S. & Potti, J. Human-induced changes in landscape configuration influence individual movement routines: lessons from a versatile, highly mobile species. PLoS ONE 9, e104974 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Enders, F. & Post, W. White-spotting in the genus Ammospiza and other grassland sparrows. Bird-Band. 42, 210–219 (1971).
Google Scholar 
Sage, B. L. Albinism and melanism in birds. Br. Birds 55, 201–225 (1962).
Google Scholar 
O’Sullivan, J. D. B. et al. The biology of human hair greying. Biol. Rev. 96, 107–128 (2021).PubMed 

Google Scholar 
Nichols, J. D., Hines, J. E. & Blums, P. Tests for senescent decline in annual survival probabilities of common pochards, Aythya ferina. Ecology 78, 1009–1018 (1997).
Google Scholar 
Owen, M. & Skimmings, P. The occurrence and performance of leucistic Barnacle Geese Branta leucopsis. Ibis 134, 22–26 (1992).
Google Scholar 
Mulder, T., Campbell, C. J. & Ruxton, G. D. Evaluation of disruptive camouflage of avian cup-nests. Ibis 163, 150–158 (2021).
Google Scholar 
Holyoak, D. Variable albinism of the flight feathers as an adaptation for recognition of individual birds in some Polynesian populations of Acrocephalus warblers. Ardea 66, 112–117 (1978).
Google Scholar 
Griffith, S. C., Parker, T. H. & Olson, V. A. Melanin- versus carotenoid-based sexual signals: is the difference really so black and red? Anim. Behav. 71, 749–763 (2006).
Google Scholar 
Galván, I., Jorge, A., Nielsen, J. T. & Møller, A. P. Pheomelanin synthesis varies with protein food abundance in developing goshawks. J. Comp. Physiol. B 189, 441–450 (2019).PubMed 

Google Scholar 
Zaragoza-Trello, C., Vilà, M., Botías, C. & Bartomeus, I. Interactions among global change pressures act in a non-additive way on bumblebee individuals and colonies. Funct. Ecol. 35, 420–434 (2021).
Google Scholar 
Rollin, N. A note on abnormally marked Song Thrushes and Blackbirds. Trans. Nat. Hist. Soc. Northumberl. Durh. Newctle upon Tyne 10, 183–184 (1953).Guerrero-Bosagna, C. et al. Transgenerational epigenetic inheritance in birds. Environ. Epigenet. 4, dvy008 (2018).Camacho, C., Negro, J. J., Redondo, I., Palacios, S. & Sáez-Gómez, P. Correlates of individual variation in the porphyrin-based fluorescence of red-necked nightjars (Caprimulgus ruficollis). Sci. Rep. 9, 1–9 (2019).
Google Scholar 
Camacho, C. Tropical phenology in temperate regions: extended breeding season in a long-distance migrant. Condor 115, 830–837 (2013).
Google Scholar 
Cleere, N. Nightjars: a guide to nightjars and related birds (A&C Black, London, 2010).
Google Scholar 
Gargallo, G. Flight feather moult in the red-necked nightjar Caprimulgus ruficollis. J. Avian Biol. 25, 119–124 (1994).
Google Scholar 
Jackson, H. D. A field survey to investigate why nightjars frequent roads at night. Ostrich 74, 97–101 (2003).
Google Scholar 
Jackson, H. D. Finding and trapping nightjars. Bokmakierie 36, 86–89 (1984).
Google Scholar 
Sénar, J. C. & Pascual, J. Keel and tarsus length may provide a good predictor of avian body size. Ardea 85, 269–274 (1997).
Google Scholar 
Svensson, L. Identification Guide To European Passerines (Lars Svensson, Cleveland, 1992).
Google Scholar 
van de Pol, M. & Wright, J. A simple method for distinguishing within-versus between-subject effects using mixed models. Anim. Behav. 77, 753–758 (2009).
Google Scholar 
Schielzeth, H. & Forstmeier, W. Conclusions beyond support: overconfident estimates in mixed models. Behav. Ecol. 20, 416–420 (2009).PubMed 

Google Scholar 
Rising, J. D. & Somers, K. M. The measurement of overall body size in birds. Auk 106, 666–674 (1989).
Google Scholar 
Magnusson, A. et al. Package “glmmTMB”. R Package Version 0.2.0. (2017).Hartig, F. DHARMa: residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.2, 4. (2019).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar 
Barton, K. MuMIn: Multi-Model inference. Model selection and model averaging based on information criteria (AICc and alike). R package version 1.43.17. (2020). More

Boutaiba, S., Hacene, H., Bidle, K. A. & Maupin-Furlow, J. A. Microbial diversity of the hypersaline Sidi Ameur and Himalatt Salt Lakes of the Algerian Sahara. J. Arid Environ. 75, 909–916. https://doi.org/10.1016/j.jaridenv.2011.04.010 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Ventosa, A. Unusual micro-organisms from unusual habitats: hypersaline environments. Symposia Society for General Microbiology (2006).Fukuchi, S., Yoshimune, K., Wakayama, M., Moriguchi, M. & Nishikawa, K. Unique amino acid composition of proteins in halophilic bacteria. J. Mol. Biol. 327, 347–357 (2003).CAS 
Article 

Google Scholar 
Pillai, S. D., Nakatsu, C. H., Miller, R. V. & Yates, M. V. Manual of environmental microbiology. Life High-Salinity Environ. https://doi.org/10.1128/9781555818821 (2015).Article 

Google Scholar 
Poli, A. et al. Microbial diversity in extreme marine habitats and their biomolecules. Microorganisms 5, 25. https://doi.org/10.3390/microorganisms5020025 (2017).CAS 
Article 
PubMed Central 

Google Scholar 
Azpiazu-Muniozguren, M., Martinez-Ballesteros, I., Gamboa, J., Seoane, S. & Bikandi, J. Altererythrobacter muriae sp. nov., isolated from hypersaline Aana Salt Valley spring water, a continental thalassohaline-type solar saltern. Int. J. Syst. Evol. Microbiol. 71, 3 (2021).
Google Scholar 
Zhang, J. et al. Bacterial diversity in Bohai Bay Solar Saltworks, China. Curr. Microbiol. 72, 55–63 (2016).CAS 
Article 

Google Scholar 
Highfield, A., Ward, A., Pipe, R. & Schroeder, D. C. Molecular and phylogenetic analysis reveals new diversity of Dunaliella salina from hypersaline environments. J. Mar. Biol. Assoc. UK 101, 27–37. https://doi.org/10.1017/s0025315420001319 (2021).CAS 
Article 

Google Scholar 
Cycil, L. M. et al. Metagenomic insights into the diversity of halophilic microorganisms indigenous to the Karak Salt Mine, Pakistan. Front. Microbiol. https://doi.org/10.3389/fmicb.2020.01567 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Jacob, J. H., Hussein, E. I., Shakhatreh, M. A. K. & Cornelison, C. T. Microbial community analysis of the hypersaline water of the Dead Sea using high-throughput amplicon sequencing. Microbiol. Open 6, e00500. https://doi.org/10.1002/mbo3.500 (2017).CAS 
Article 

Google Scholar 
Ben Abdallah, M. et al. Abundance and diversity of prokaryotes in ephemeral hypersaline lake Chott El Jerid using Illumina Miseq sequencing, DGGE and qPCR assay. Extremophiles 22, 811–823. https://doi.org/10.1007/s00792-018-1040-9 (2018).CAS 
Article 
PubMed 

Google Scholar 
Tazi, L., Breakwell, D. P., Harker, A. R. & Crandall, K. A. Life in extreme environments: Microbial diversity in Great Salt Lake, Utah. Extremophiles 18, 525–535. https://doi.org/10.1007/s00792-014-0637-x (2014).Article 
PubMed 

Google Scholar 
Kashi, F. J., Owlia, P., Amoozegar, M. A. & Kazemi, B. Halophilic prokaryotes in Urmia Salt Lake, a hypersaline environment in Iran. Curr. Microbiol. 78(8), 3230–3238 (2021).Article 

Google Scholar 
Sorokin, D. Y., Roman, P. & Kolganova, T. V. Halo(natrono)archaea from hypersaline lakes can utilize sulfoxides other than DMSO as electron acceptors for anaerobic respiration. Extremophiles 25, 173–180 (2021).CAS 
Article 

Google Scholar 
Hwang, K., Choe, H. & Kim, K. M. Complete genome of Nocardioides aquaticus KCTC 9944T isolated from meromictic and hypersaline Ekho Lake, Antarctica. Mar. Genom. 1, 100889 (2021).Article 

Google Scholar 
Didari, M. et al. Diversity of halophilic and halotolerant bacteria in the largest seasonal hypersaline lake (Aran-Bidgol-Iran). J. Environ. Health Sci. Eng. 18, 961–971. https://doi.org/10.1007/s40201-020-00519-3 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Oren, A. Diversity of halophilic microorganisms: Environments, phylogeny, physiology, and applications. J. Ind. Microbiol. Biotechnol. 28, 56–63 (2002).CAS 
Article 

Google Scholar 
Mutlu, M. B. et al. Prokaryotic diversity in Tuz Lake, a hypersaline environment in Inland Turkey. FEMS Microbiol. Ecol. 65, 474–483. https://doi.org/10.1111/j.1574-6941.2008.00510.x (2008).CAS 
Article 
PubMed 

Google Scholar 
Antón, J. et al. Distribution, abundance and diversity of the extremely halophilic bacterium Salinibacter ruber. Saline Syst. 4, 15. https://doi.org/10.1186/1746-1448-4-15 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Oren, A. Microbial life at high salt concentrations: phylogenetic and metabolic diversity. Saline Syst. 4, 2. https://doi.org/10.1186/1746-1448-4-2 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Abdeljabbar, H., Badiaa, E., Jean-Luc, C., Marie-Laure, F. & Najla, S. Prokaryotic biodiversity of halophilic microorganisms isolated from Sehline Sebkha Salt Lake (Tunisia). Afr. J. Microbiol. Res. 8, 355–367. https://doi.org/10.5897/ajmr12.1087 (2014).Article 

Google Scholar 
Najjari, A., Elshahed, M. S., Cherif, A., Youssef, N. H. & Löffler, F. E. Patterns and determinants of halophilic archaea (Class Halobacteria) diversity in Tunisian endorheic salt lakes and Sebkhet systems. Appl. Environ. Microbiol. 81, 4432–4441. https://doi.org/10.1128/aem.01097-15 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Aharon, O. The ecology of the extremely halophilic archaea. FEMS Microbiol. Rev. 1, 415–440 (1994).
Google Scholar 
Oren, A. Halophilic Archaea. FEMS Microbiol. Rev. https://doi.org/10.1016/b978-0-12-809633-8.20800-5 (2019).Article 

Google Scholar 
Feng, Y. et al. The evolutionary origins of extreme halophilic archaeal lineages. Genome Biol. Evol. 13, 8. https://doi.org/10.1093/gbe/evab166 (2021).CAS 
Article 

Google Scholar 
Ventosa, A., Nieto, J. J. & Oren, A. Biology of moderately halophilic aerobic bacteria. Microbiol. Mol. Biol. Rev. 62, 504–544 (1998).CAS 
Article 

Google Scholar 
Kushner, D. J. Halophilic bacteria. Adv. Appl. Microbiol. 10, 73–99 (1968).CAS 
Article 

Google Scholar 
Ghozlan, H., Deif, H., Kandil, R. A. & Sabry, S. Biodiversity of moderately halophilic bacteria in hypersaline habitats in Egypt. J. Gen. Appl. Microbiol. 52, 63–72 (2006).CAS 
Article 

Google Scholar 
Ali, I., Prasongsuk, S., Akbar, A., Aslam, M. & Rakshit, S. K. Hypersaline habitats and halophilic microorganisms. Maejo Int. J. Sci. Technol. 10, 330–345 (2016).CAS 

Google Scholar 
Margesin, R. & Schinner, F. Biodegradation and bioremediation of hydrocarbons in extreme environments. Appl. Microbiol. Biotechnol. 56, 650–663. https://doi.org/10.1007/s002530100701 (2001).CAS 
Article 
PubMed 

Google Scholar 
Poosarla, V. G. & Ts, C. Xylanase production by halophilic bacterium Gracilibacillus sp. TSCPVG under solid state fermentation. Res. J. Biotechnol. 16, 92–100 (2021).Article 

Google Scholar 
Foti, M. et al. Diversity, activity, and abundance of sulfate-reducing bacteria in saline and hypersaline soda lakes. Appl. Environ. Microbiol. 73, 2093–2100. https://doi.org/10.1128/aem.02622-06 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Boujelben, I. et al. Spatial and seasonal prokaryotic community dynamics in ponds of increasing salinity of Sfax solar saltern in Tunisia. Antonie Van Leeuwenhoek 101, 845–857. https://doi.org/10.1007/s10482-012-9701-7 (2012).CAS 
Article 
PubMed 

Google Scholar 
García-Maldonado, J. Q., Bebout, B. M., Everroad, R. C. & López-Cortés, A. Evidence of novel phylogenetic lineages of methanogenic archaea from hypersaline microbial mats. Microb. Ecol. 69, 106–117. https://doi.org/10.1007/s00248-014-0473-7 (2014).CAS 
Article 
PubMed 

Google Scholar 
Abed, R. M. M., de Beer, D. & Stief, P. Functional-structural analysis of nitrogen-cycle bacteria in a hypersaline mat from the omani desert. Geomicrobiol. J. 32, 119–129. https://doi.org/10.1080/01490451.2014.932033 (2014).CAS 
Article 

Google Scholar 
Coban, O., Rasigraf, O., Jong, A., Spott, O. & Bebout, B. M. Quantifying potential N turnover rates in hypersaline microbial mats by 15 N tracer techniques. Appl. Environ. Microbiol. 87, 8 (2021).Article 

Google Scholar 
Rodriguez-Valera, F. Introduction to Saline Environments (Springer, 1993).
Google Scholar 
Wei, H. C., Qi-Shun, F., Fu-Yuan, A., Fa-Shou, S. & Qin, Z. J. Chemical elements in core sediments of the qarhan salt lake and palaeoclimate evolution during 94–9 ka. Acta Geosci. Sin. (2016).Yu, S., Liu, X., Tan, H. & Cao, G. Sustainable Utilization of Qarhan Salt Lake Resources 27–265 (Science Press, 2009).
Google Scholar 
Zhu, D. et al. An evaluation of the core bacterial communities associated with hypersaline environments in the Qaidam Basin, China. Arch. Microbiol. 202, 2093–2103. https://doi.org/10.1007/s00203-020-01927-7 (2020).CAS 
Article 
PubMed 

Google Scholar 
Liu, W., Jiang, H., Yang, J. & Wu, G. Gammaproteobacterial diversity and carbon utilization in response to salinity in the lakes on the qinghai-tibetan plateau. Geomicrobiol. J. 35, 392–403. https://doi.org/10.1080/01490451.2017.1378951 (2018).CAS 
Article 

Google Scholar 
Zhong, Z.-P. et al. Prokaryotic community structure driven by salinity and ionic concentrations in plateau lakes of the tibetan plateau. Appl. Environ. Microbiol. 82, 1846–1858. https://doi.org/10.1128/aem.03332-15 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
He, C. et al. Synergistic effect of magnetite and zero-valent iron on anaerobic degradation and methanogenesis of phenol. Biores. Technol. 291, 121874. https://doi.org/10.1016/j.biortech.2019.121874 (2019).CAS 
Article 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336. https://doi.org/10.1038/nmeth.f.303 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. Embnet J. 17, 10–12 (2011).Article 

Google Scholar 
Zhang, J., Kassian, K., Tomáš, F. & Alexandros, S. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614 (2014).CAS 
Article 

Google Scholar 
Schmieder, R. & Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 27, 863–864 (2011).CAS 
Article 

Google Scholar 
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–1000 (2013).CAS 
Article 

Google Scholar 
Schloss, P. D. et al. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537 (2009).ADS 
CAS 
Article 

Google Scholar 
Chen, H. & Boutros, P. C. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinform. 12, 35. https://doi.org/10.1186/1471-2105-12-35 (2011).Article 

Google Scholar 
McArdle, B. H. et al. Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82, 290–290 (2001).Article 

Google Scholar 
Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821. https://doi.org/10.1038/nbt.2676 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Louca, S. & Doebeli, M. Efficient comparative phylogenetics on large trees. Bioinformatics 34, 1–3 (2017).
Google Scholar 
Louca, S., Parfrey, L. W. & Doebeli, M. Decoupling function and taxonomy in the global ocean microbiome. Science 353, 1272 (2016).ADS 
CAS 
Article 

Google Scholar 
Junker, B. H. & Schreiber, F. Analysis of Biological Networks 283–304 (Analysis of biological networks, 2008).Book 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538–550. https://doi.org/10.1038/nrmicro2832 (2012).CAS 
Article 
PubMed 

Google Scholar 
Behzad, H., Ibarra, M. A., Mineta, K. & Gojobori, T. Metagenomic studies of the Red Sea. Gene 576, 717–723. https://doi.org/10.1016/j.gene.2015.10.034 (2016).CAS 
Article 
PubMed 

Google Scholar 
Naghoni, A. et al. Microbial diversity in the hypersaline Lake Meyghan, Iran. Sci. Rep. https://doi.org/10.1038/s41598-017-11585-3 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Kambura, A. K. et al. Bacteria and Archaea diversity within the hot springs of Lake Magadi and Little Magadi in Kenya. BMC Microbiol. https://doi.org/10.1186/s12866-016-0748-x (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Paul, D. et al. Exploration of microbial diversity and community structure of Lonar lake: The only hypersaline meteorite crater lake within basalt rock. Front. Microbiol. https://doi.org/10.3389/fmicb.2015.01553 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ventosa, A., de la Haba, R. R., Sánchez-Porro, C. & Papke, R. T. Microbial diversity of hypersaline environments: A metagenomic approach. Curr. Opin. Microbiol. 25, 80–87. https://doi.org/10.1016/j.mib.2015.05.002 (2015).CAS 
Article 
PubMed 

Google Scholar 
Liu, F. H. et al. Bacterial and archaeal assemblages in sediments of a large shallow freshwater lake, Lake Taihu, as revealed by denaturing gradient gel electrophoresis. J. Appl. Microbiol. 106, 1022–1032. https://doi.org/10.1111/j.1365-2672.2008.04069.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Song, H., Li, Z., Du, B., Wang, G. & Ding, Y. Bacterial communities in sediments of the shallow Lake Dongping in China. J. Appl. Microbiol. 112, 79–89. https://doi.org/10.1111/j.1365-2672.2011.05187.x (2012).CAS 
Article 
PubMed 

Google Scholar 
Wu, Q. L., Zwart, G., Schauer, M., Agterveld, K. V. & Hahn, M. W. Bacterioplankton community composition along a salinity gradient of sixteen high-mountain lakes located on the Tibetan Plateau, China. Appl. Environ. Microbiol. 72, 5478–5485 (2006).ADS 
CAS 
Article 

Google Scholar 
Xing, P., Hahn, M. W. & Wu, Q. L. Low taxon richness of bacterioplankton in high-altitude lakes of the Eastern Tibetan Plateau, with a predominance of bacteroidetes and Synechococcus spp. Appl. Environ. Microbiol. 75, 7017–7025. https://doi.org/10.1128/aem.01544-09 (2009).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Liu, Y. et al. Bacterial diversity of freshwater Alpine Lake Puma Yumco on the Tibetan Plateau. Geomicrobiol. J. 26, 131–145. https://doi.org/10.1080/01490450802660201 (2009).CAS 
Article 

Google Scholar 
MounÃc, S., Caumette, P., Matheron, R. & Willison, J. C. Molecular sequence analysis of prokaryotic diversity in the anoxic sediments underlying cyanobacterial mats of two hypersaline ponds in Mediterranean salterns. FEMS Microbiol. Ecol. 44, 117–130. https://doi.org/10.1016/s0168-6496(03)00017-5 (2003).Article 

Google Scholar 
Valenzuela-Encinas, C. et al. Changes in the bacterial populations of the highly alkaline saline soil of the former lake Texcoco (Mexico) following flooding. Extremophiles 13, 609–621. https://doi.org/10.1007/s00792-009-0244-4 (2009).Article 
PubMed 

Google Scholar 
Kim, T. J., Lee, E. Y., Kim, Y. J., Cho, K.-S. & Ryu, H. W. Degradation of polyaromatic hydrocarbons by Burkholderia cepacia 2A–12. World J. Microbiol. Biotechnol. 19, 411–417. https://doi.org/10.1023/A:1023998719787 (2003).CAS 
Article 

Google Scholar 
Gales, G. et al. Preservation of ancestral Cretaceous microflora recovered from a hypersaline oil reservoir. Sci. Rep. https://doi.org/10.1038/srep22960 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Kleinsteuber, S., Riis, V., Fetzer, I., Harms, H. & Müller, S. Population dynamics within a microbial consortium during growth on diesel fuel in saline environments. Appl. Environ. Microbiol. 72, 3531–3542. https://doi.org/10.1128/aem.72.5.3531-3542.2006 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Valenzuela-Encinas, C. et al. The archaeal diversity and population in a drained alkaline saline soil of the former lake Texcoco (Mexico). Geomicrobiol. J. 29, 18–22. https://doi.org/10.1080/01490451.2010.520075 (2012).Article 

Google Scholar 
He, S., Tan, J., Hu, W. & Mo, C. Diversity of archaea and its correlation with environmental factors in the Ebinur Lake Wetland. Curr. Microbiol. 76, 1417–1424. https://doi.org/10.1007/s00284-019-01768-8 (2019).CAS 
Article 
PubMed 

Google Scholar 
Sandaa, R. A., Enger, O. & Torsvik, V. Abundance and diversity of Archaea in heavy-metal-contaminated soils. Appl. Environ. Microbiol. 65, 3293–3297 (1999).ADS 
CAS 
Article 

Google Scholar 
Dave, B. P. & Soni, A. Diversity of halophilic archaea at salt pans around Bhavnagar Coast, Gujarat. Proc. Natl. Acad. Sci. India B 83, 225–232. https://doi.org/10.1007/s40011-012-0124-z (2012).Article 

Google Scholar 
Zafrilla, B., Martínez-Espinosa, R., Alonso, M. A. & Bonete, M. J. Biodiversity of Archaea and floral of two inland saltern ecosystems in the Alto Vinalopó Valley, Spain. Saline Syst. 6, 10 (2010).Article 

Google Scholar 
Costa, M., Santos, H. & Galinski, E. A. An overview of the role and diversity of compatible solutes in Bacteria and Archaea. Adv. Biochem. Eng. Biotechnol. 61, 117 (1998).PubMed 

Google Scholar 
Williams, R. J., Howe, A. & Hofmockel, K. S. Demonstrating microbial co-occurrence pattern analyses within and between ecosystems. Front. Microbiol. https://doi.org/10.3389/fmicb.2014.00358 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Schmidt, T. S. B., MatiasRodrigues, J. F. & von Mering, C. A family of interaction-adjusted indices of community similarity. ISME J. 11, 791–807. https://doi.org/10.1038/ismej.2016.139 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Oyewusi, H. A. et al. Functional profiling of bacterial communities in Lake Tuz using 16S rRNA gene sequences. Biotechnol. Biotechnol. Equip. 35, 1–10. https://doi.org/10.1080/13102818.2020.1840437 (2020).CAS 
Article 

Google Scholar  More

Taylor, M. W., Radax, R., Steger, D. & Wagner, M. Sponge-associated microorganisms: Evolution, ecology, and biotechnological potential. Microbiol. Mol. Biol. Rev. 71, 295–347 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thomas, T. et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat. Commun. 7, 11870 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Webster, N. S. et al. Deep sequencing reveals exceptional diversity and modes of transmission for bacterial sponge symbionts. Environ. Microbiol. 12, 2070–2082 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sipkema, D. et al. Similar sponge-associated bacteria can be acquired via both vertical and horizontal transmission: Microbial transmission in Petrosia ficiformis. Environ. Microbiol. 17, 3807–3821 (2015).CAS 
PubMed 

Google Scholar 
Cleary, D. F. R. et al. The sponge microbiome within the greater coral reef microbial metacommunity. Nat. Commun. 10, 1644 (2019).Björk, J. R., Díez-Vives, C., Astudillo-García, C., Archie, E. A. & Montoya, J. M. Vertical transmission of sponge microbiota is inconsistent and unfaithful. Nat. Ecol. Evol. 3, 1172–1183 (2019).PubMed 
PubMed Central 

Google Scholar 
Webster, N. S. & Taylor, M. W. Marine sponges and their microbial symbionts: Love and other relationships. Environ. Microbiol. 14, 335–346 (2012).CAS 
PubMed 

Google Scholar 
Kennedy, J. et al. Evidence of a putative deep sea specific microbiome in marine sponges. PLoS ONE 9, e91092 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Steinert, G. et al. Compositional and quantitative insights into bacterial and archaeal communities of south pacific deep-sea sponges (Demospongiae and Hexactinellida). Front. Microbiol. 11, 716 (2020).Busch, K. et al. On giant shoulders: How a seamount affects the microbial community composition of seawater and sponges. Biogeosciences 17, 3471–3486 (2020).ADS 
CAS 

Google Scholar 
Olson, J. B. & Gao, X. Characterizing the bacterial associates of three Caribbean sponges along a gradient from shallow to mesophotic depths. FEMS Microbiol. Ecol. 85, 74–84 (2013).PubMed 

Google Scholar 
Steinert, G. et al. In four shallow and mesophotic tropical reef sponges from Guam the microbial community largely depends on host identity. PeerJ 4, e1936 (2016).PubMed 
PubMed Central 

Google Scholar 
Morrow, K. M., Fiore, C. L. & Lesser, M. P. Environmental drivers of microbial community shifts in the giant barrel sponge, Xestospongia muta, over a shallow to mesophotic depth gradient. Environ. Microbiol. 18, 2025–2038 (2016).CAS 
PubMed 

Google Scholar 
Ebada, S. S. & Proksch, P. The chemistry of marine sponges. In Handbook of Marine Natural Products (eds Fattorusso, E. et al.) 191–293 (Springer, 2012). https://doi.org/10.1007/978-90-481-3834-0_4.Chapter 

Google Scholar 
Kornprobst, J.-M. Porifera (Sponges). Encyclopedia of Marine Natural Products (Wiley, 2014).
Google Scholar 
Leal, M. C., Puga, J., Serôdio, J., Gomes, N. C. M. & Calado, R. Trends in the discovery of new marine natural products from invertebrates over the last two decades—Where and what are we bioprospecting?. PLoS ONE 7, e30580 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blunt, J. W., Copp, B. R., Keyzers, R. A., Munro, M. H. G. & Prinsep, M. R. Marine natural products. Nat. Prod. Rep. 34, 235–294 (2017).CAS 
PubMed 

Google Scholar 
Unson, M. D., Holland, N. D. & Faulkner, D. J. A brominated secondary metabolite synthesized by the cyanobacterial symbiont of a marine sponge and accumulation of the crystalline metabolite in the sponge tissue. Mar. Biol. 119, 1–11 (1994).CAS 

Google Scholar 
Bewley, C. A., Holland, N. D. & Faulkner, D. J. Two classes of metabolites from Theonella swinhoei are localized in distinct populations of bacterial symbionts. Experientia 52, 716–722 (1996).CAS 
PubMed 

Google Scholar 
Wilson, M. C. et al. An environmental bacterial taxon with a large and distinct metabolic repertoire. Nature 506, 58–62 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Tianero, M. D., Balaich, J. N. & Donia, M. S. Localized production of defence chemicals by intracellular symbionts of Haliclona sponges. Nat. Microbiol. 4, 1149–1159 (2019).CAS 
PubMed 

Google Scholar 
Ivanišević, J., Thomas, O. P., Lejeusne, C., Chevaldonné, P. & Pérez, T. Metabolic fingerprinting as an indicator of biodiversity: Towards understanding inter-specific relationships among Homoscleromorpha sponges. Metabolomics 7, 289–304 (2011).
Google Scholar 
Pérez, T. et al. Oscarella balibaloi, a new sponge species (Homoscleromorpha: Plakinidae) from the Western Mediterranean Sea: Cytological description, reproductive cycle and ecology: O. balibaloi: Description, reproductive cycle and ecology. Mar. Ecol. (Berl.) 32, 174–187 (2011).ADS 

Google Scholar 
Reveillaud, J. et al. Relevance of an integrative approach for taxonomic revision in sponge taxa: Case study of the shallow-water Atlanto-Mediterranean Hexadella species (Porifera: Ianthellidae: Verongida). Invertebr. Syst. 26, 230–248 (2012).
Google Scholar 
Olsen, E. K. et al. Marine AChE inhibitors isolated from Geodia barretti: Natural compounds and their synthetic analogs. Org. Biomol. Chem. 14, 1629–1640 (2016).CAS 
PubMed 

Google Scholar 
Reverter, M., Perez, T., Ereskovsky, A. V. & Banaigs, B. Secondary metabolome variability and inducible chemical defenses in the Mediterranean Sponge Aplysina cavernicola. J. Chem. Ecol. 42, 60–70 (2016).CAS 
PubMed 

Google Scholar 
Reverter, M., Tribalat, M.-A., Pérez, T. & Thomas, O. P. Metabolome variability for two Mediterranean sponge species of the genus Haliclona: Specificity, time, and space. Metabolomics 14, 114 (2018).Villegas-Plazas, M. et al. Variations in microbial diversity and metabolite profiles of the tropical marine sponge Xestospongia muta with season and depth. Microb. Ecol. 78, 243–256 (2019).CAS 
PubMed 

Google Scholar 
Mohanty, I. et al. Multi-omic profiling of Melophlus sponges reveals diverse metabolomic and microbiome architectures that are non-overlapping with ecological neighbors. Mar. Drugs 18, 124 (2020).CAS 
PubMed Central 

Google Scholar 
Bowerbank, J. S. On the anatomy and physiology of the Spongiadae. Part I. On the spicula. Philos. Trans. R. Soc. Lond. 148, 279–332 (1858).ADS 

Google Scholar 
Vosmaer, G. C. J. The sponges of the ‘Willem Barents’ expedition 1880 and 1881. Bijdragen tot de Dierkunde 12, 1–47 (1885).
Google Scholar 
Radax, R. et al. Metatranscriptomics of the marine sponge Geodia barretti: Tackling phylogeny and function of its microbial community. Environ. Microbiol. 14, 1308–1324 (2012).CAS 
PubMed 

Google Scholar 
Topsent, E. Spongiaires provenant des campagnes scientifiques de la ‘Princesse Alice’ dans les Mers du Nord (1898–1899—1906–1907). Résultats des campagnes scientifiques accomplies par le Prince Albert I. Monaco 45, 1–67 (1913).
Google Scholar 
Yashayaev, I. & Loder, J. W. Further intensification of deep convection in the Labrador Sea in 2016. Geophys. Res. Lett. 44, 1429–1438 (2017).ADS 

Google Scholar 
Gutleben, J. et al. Diversity of tryptophan halogenases in sponges of the genus Aplysina. FEMS Microbiol. Ecol. 95, fiz108 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Indraningrat, A. et al. Cultivation of sponge-associated bacteria from Agelas sventres and Xestospongia muta collected from different depths. Mar. Drugs 17, 578 (2019).CAS 
PubMed Central 

Google Scholar 
Ramiro-Garcia, J. et al. NG-Tax, a highly accurate and validated pipeline for analysis of 16S rRNA amplicons from complex biomes. F1000 Res. 5, 1791 (2018).
Google Scholar 
Yilmaz, P. et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucl. Acids Res. 42, D643–D648 (2014).CAS 
PubMed 

Google Scholar 
Erngren, I., Smit, E., Pettersson, C., Cárdenas, P. & Hedeland, M. The effects of sampling and storage conditions on the metabolite profile of the marine sponge Geodia barretti. Front. Chem. 9:662659 (2021)Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).CAS 
PubMed 

Google Scholar 
Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R. & Neumann, S. CAMERA: An integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012).CAS 
PubMed 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package (2017).Dat, T. T. H., Steinert, G., Thi Kim Cuc, N., Smidt, H. & Sipkema, D. Archaeal and bacterial diversity and community composition from 18 phylogenetically divergent sponge species in Vietnam. PeerJ 6, e4970 (2018).PubMed 
PubMed Central 

Google Scholar 
Miller, M. A., Pfeiffer, W. & Schwartz, T. Creating the CIPRES science gateway for inference of large phylogenetic trees. In 2010 Gateway Computing Environments Workshop (GCE) 1–8 (IEEE, 2010). https://doi.org/10.1109/GCE.2010.5676129.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: Recent updates and new developments. Nucl. Acids Res. 47, W256–W259 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thévenot, E. A., Roux, A., Xu, Y., Ezan, E. & Junot, C. Analysis of the human adult urinary metabolome variations with age, body mass index, and gender by implementing a comprehensive workflow for univariate and OPLS statistical analyses. J. Proteome Res. 14, 3322–3335 (2015).PubMed 

Google Scholar 
Weiss, S. et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 10, 1669–1681 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Deng, Y. et al. Molecular ecological network analyses. BMC Bioinform. 13, 113 (2012).
Google Scholar 
Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Durno, W. E., Hanson, N. W., Konwar, K. M. & Hallam, S. J. Expanding the boundaries of local similarity analysis. BMC Genom. 14, S3 (2013).
Google Scholar 
Reshef, D. N. et al. Detecting novel associations in large data sets. Science 334, 1518–1524 (2011).ADS 
CAS 
PubMed 
PubMed Central 
MATH 

Google Scholar 
Hall, M. M., Torres, D. J. & Yashayaev, I. Absolute velocity along the AR7W section in the Labrador Sea. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 72, 72–87 (2013).
Google Scholar 
Reveillaud, J. et al. Host-specificity among abundant and rare taxa in the sponge microbiome. ISME J. 8, 1198–1209 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moitinho-Silva, L. et al. Predicting the HMA-LMA status in marine sponges by machine learning. Front. Microbiol. 8, 752 (2017).Lidgren, G., Bohlin, L. & Bergman, J. Studies of Swedish marine organisms VII. A novel biologically active indole alkaloid from the sponge Geodia barretti. Tetrahedron Lett. 27, 3283–3284 (1986).CAS 

Google Scholar 
Sjögren, M. et al. Antifouling activity of brominated cyclopeptides from the marine sponge Geodia barretti. J. Nat. Prod. 67, 368–372 (2004).PubMed 

Google Scholar 
Sölter, S. Identifizierung und Synthese von Naturstoffen aus Borealen Schwämmen (Universität Hamburg, 2004).
Google Scholar 
Di, X. et al. 6-Bromoindole derivatives from the Icelandic marine sponge Geodia barretti: Isolation and anti-inflammatory activity. Mar. Drugs 16, 437 (2018).CAS 
PubMed Central 

Google Scholar 
Carstens, B. B. et al. Isolation, characterization, and synthesis of the barrettides: Disulfide-containing peptides from the marine sponge Geodia barretti. J. Nat. Prod. 78, 1886–1893 (2015).CAS 
PubMed 

Google Scholar 
Hedner, E. et al. Brominated cyclodipeptides from the marine sponge Geodia barretti as selective 5-HT ligands. J. Nat. Prod. 69, 1421–1424 (2006).CAS 
PubMed 

Google Scholar 
Hedner, E. et al. Antifouling activity of a dibrominated cyclopeptide from the marine sponge Geodia barretti. J. Nat. Prod. 71, 330–333 (2008).CAS 
PubMed 

Google Scholar 
Erwin, P. M., Pita, L., López-Legentil, S. & Turon, X. Stability of sponge-associated bacteria over large seasonal shifts in temperature and irradiance. Appl. Environ. Microbiol. 78, 7358–7368 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cárdenas, C. A., Bell, J. J., Davy, S. K., Hoggard, M. & Taylor, M. W. Influence of environmental variation on symbiotic bacterial communities of two temperate sponges. FEMS Microbiol. Ecol. 88, 516–527 (2014).PubMed 

Google Scholar 
Glasl, B., Smith, C. E., Bourne, D. G. & Webster, N. S. Exploring the diversity-stability paradigm using sponge microbial communities. Sci. Rep. 8, 8425 (2018).Schöttner, S. et al. Relationships between host phylogeny, host type and bacterial community diversity in cold-water coral reef sponges. PLoS ONE 8, e55505 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Lurgi, M., Thomas, T., Wemheuer, B., Webster, N. S. & Montoya, J. M. Modularity and predicted functions of the global sponge-microbiome network. Nat. Commun. 10, 992 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Luter, H. M. et al. Microbiome analysis of a disease affecting the deep-sea sponge Geodia barretti. FEMS Microbiol. Ecol. 93, fix074 (2017).Thistle, D. Ecosystems of the Deep Oceans (Elsevier, 2003).
Google Scholar 
Pita, L., Erwin, P. M., Turon, X. & López-Legentil, S. Till death do us part: Stable sponge-bacteria associations under thermal and food shortage stresses. PLoS ONE 8, e80307 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Webster, N. S., Cobb, R. E. & Negri, A. P. Temperature thresholds for bacterial symbiosis with a sponge. ISME J. 2, 830–842 (2008).CAS 
PubMed 

Google Scholar 
Gerringer, M. E., Drazen, J. C. & Yancey, P. H. Metabolic enzyme activities of abyssal and hadal fishes: Pressure effects and a re-evaluation of depth-related changes. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 125, 135–146 (2017).CAS 

Google Scholar 
Yashayaev, I. Hydrographic changes in the Labrador Sea, 1960–2005. Prog. Oceanogr. 73, 242–276 (2007).ADS 

Google Scholar 
Rhein, M., Steinfeldt, R., Kieke, D., Stendardo, I. & Yashayaev, I. Ventilation variability of Labrador Sea Water and its impact on oxygen and anthropogenic carbon: A review. Philos. Trans. A Math. Phys. Eng. Sci. 375, 20160321 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Galand, P. E., Potvin, M., Casamayor, E. O. & Lovejoy, C. Hydrography shapes bacterial biogeography of the deep Arctic Ocean. ISME J. 4, 564–576 (2010).PubMed 

Google Scholar 
Frank, A. H., Garcia, J. A. L., Herndl, G. J. & Reinthaler, T. Connectivity between surface and deep waters determines prokaryotic diversity in the North Atlantic Deep Water: North Atlantic dark ocean prokaryotic biogeography. Environ. Microbiol. 18, 2052–2063 (2016).PubMed 
PubMed Central 

Google Scholar 
Agogué, H., Lamy, D., Neal, P. R., Sogin, M. L. & Herndl, G. J. Water mass-specificity of bacterial communities in the North Atlantic revealed by massively parallel sequencing. Mol. Ecol. 20, 258–274 (2011).PubMed 

Google Scholar 
Djurhuus, A., Boersch-Supan, P. H., Mikalsen, S.-O. & Rogers, A. D. Microbe biogeography tracks water masses in a dynamic oceanic frontal system. R. Soc. Open Sci. 4, 170033 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Müller, O. et al. Spatiotemporal dynamics of ammonia-oxidizing Thaumarchaeota in distinct Arctic water masses. Front. Microbiol. 9, 1–13 (2018).ADS 

Google Scholar 
Kraemer, S., Ramachandran, A., Colatriano, D., Lovejoy, C. & Walsh, D. A. Diversity and biogeography of SAR11 bacteria from the Arctic Ocean. ISME J. https://doi.org/10.1038/s41396-019-0499-4 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Monier, A. et al. Upper Arctic Ocean water masses harbor distinct communities of heterotrophic flagellates. Biogeosciences 10, 4273–4286 (2013).ADS 

Google Scholar 
Monier, A. et al. Oceanographic structure drives the assembly processes of microbial eukaryotic communities. ISME J. 9, 990–1002 (2015).CAS 
PubMed 

Google Scholar 
Corrège, T. The relationship between water masses and benthic ostracod assemblages in the western Coral Sea, Southwest Pacific. Palaeogeogr. Palaeoclimatol. Palaeoecol. 105, 245–266 (1993).
Google Scholar 
Muhling, B. A., Beckley, L. E., Koslow, J. A. & Pearce, A. F. Larval fish assemblages and water mass structure off the oligotrophic south-western Australian coast: SW Australian larval fish assemblages. Fish. Oceanogr. 17, 16–31 (2007).
Google Scholar 
Eerkes-Medrano, D. et al. A community assessment of the demersal fish and benthic invertebrates of the Rosemary Bank Seamount Marine Protected Area (NE Atlantic). Deep Sea Res. Part 1 Oceanogr. Res. Pap. https://doi.org/10.1016/j.dsr.2019.103180 (2019).Article 

Google Scholar 
Puerta, P. et al. Influence of water masses on the biodiversity and biogeography of deep-sea benthic ecosystems in the North Atlantic. Front. Mar. Sci. 7, 239 (2020).Roberts, E. et al. Water masses constrain the distribution of deep-sea sponges in the North Atlantic Ocean and Nordic Seas. Mar. Ecol. Prog. Ser. 659, 75–96 (2021).ADS 

Google Scholar 
Kenchington, E. et al. Connectivity modelling of areas closed to protect vulnerable marine ecosystems in the northwest Atlantic. Deep Sea Res. Part 1 Oceanogr. Res. Pap. 143, 85–103 (2019).
Google Scholar 
Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).PubMed 

Google Scholar 
McCauley, M., Chiarello, M., Atkinson, C. L. & Jackson, C. R. Gut microbiomes of freshwater mussels (Unionidae) are taxonomically and phylogenetically variable across years but remain functionally stable. Microorganisms 9, 411 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Page, M., West, L., Northcote, P., Battershill, C. & Kelly, M. Spatial and temporal variability of cytotoxic metabolites in populations of the New Zealand Sponge Mycale hentscheli. J. Chem. Ecol. 31, 1161–1174 (2005).CAS 
PubMed 

Google Scholar 
Ternon, E., Perino, E., Manconi, R., Pronzato, R. & Thomas, O. P. How environmental factors affect the production of guanidine alkaloids by the Mediterranean sponge Crambe crambe. Mar. Drugs 15, 181 (2017).PubMed Central 

Google Scholar 
Sacristán-Soriano, O., Banaigs, B. & Becerro, M. A. Temporal trends in the secondary metabolite production of the sponge Aplysina aerophoba. Mar. Drugs 10, 677–693 (2012).PubMed 
PubMed Central 

Google Scholar 
Ivanisevic, J. et al. Biochemical trade-offs: Evidence for ecologically linked secondary metabolism of the sponge Oscarella balibaloi. PLoS ONE 6, e28059 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Burg, M. B. & Ferraris, J. D. Intracellular organic osmolytes: Function and regulation. J. Biol. Chem. 283, 7309–7313 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nau-Wagner, G., Boch, J., Le Good, J. A. & Bremer, E. High-affinity transport of choline-O-sulfate and its use as a compatible solute in Bacillus subtilis. Appl. Environ. Microbiol. 65, 560–568 (1999).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Popowich, A., Zhang, Q. & Le, X. C. Arsenobetaine: The ongoing mystery. Natl. Sci. Rev. 3, 451–458 (2016).CAS 

Google Scholar 
Connor, K. M. & Gracey, A. Y. High-resolution analysis of metabolic cycles in the intertidal mussel Mytilus californianus. Am. J. Physiol. Regul. Integr. Comp. Physiol. 302, R103–R111 (2012).CAS 
PubMed 

Google Scholar 
Cárdenas, P. Who produces Ianthelline? The Arctic sponge Stryphnus fortis or its sponge Epibiont Hexadella dedritifera: A probable case of sponge–sponge contamination. J. Chem. Ecol. 42, 339–347 (2016).PubMed 

Google Scholar 
Steffen, K. et al. Barrettides: A peptide family specifically produced by the deep-sea sponge Geodia barretti. J. Nat. Prod. 84, 3138–3146 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Abbamondi, G. R., De Rosa, S., Iodice, C. & Tommonaro, G. Cyclic dipeptides produced by marine sponge-associated bacteria as quorum sensing signals. Nat. Prod. Commun. 9, 229–232 (2014).CAS 
PubMed 

Google Scholar 
Kasheverov, I. et al. 6-Bromohypaphorine from Marine Nudibranch Mollusk Hermissenda crassicornis is an agonist of human α7 nicotinic acetylcholine receptor. Mar. Drugs 13, 1255–1266 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moitinho-Silva, L. et al. The sponge microbiome project. Gigascience 6, 1–7 (2017).CAS 
PubMed 

Google Scholar 
Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of acidobacteria: Moving beyond genes and genomes. Front. Microbiol. 7, 744 (2016).Crits-Christoph, A., Diamond, S., Butterfield, C. N., Thomas, B. C. & Banfield, J. F. Novel soil bacteria possess diverse genes for secondary metabolite biosynthesis. Nature 558, 440–444 (2018).ADS 
CAS 
PubMed 

Google Scholar  More

To our knowledge, this study provides the first recorded information on the active swimming of Japanese eels and on their transport by currents in the open ocean. Specifically, the strong flow of the KC largely dominated the movements of the eels and transported them northeastward while they swam mainly southward, and active swimming contributed a little to their travel trajectories. In contrast, the swimming of eels made a relatively higher contribution to their travel trajectories in the TS area.Our in situ estimates of the mean swimming speeds of Japanese eels (26–41 cm/s) were similar or slightly lower than those of European eels. In the acoustic tracking experiment of European eels considering environmental current vectors, their swimming speeds were 35–58 cm/s in the coastal midwater26. In a laboratory experiment using stamina tunnels with stable temperatures, the optimal swimming speeds of European eels were estimated to be 61–68 cm/s (0.74–1.02 BL/s)56, which were higher than the in situ estimates. The minimum swimming speed of European eels is considered to be 40 cm/s if they will arrive at their spawning area in the Sargasso Sea (distance of 5500 km) in 6 months, and their optimal swimming speeds were sufficient to migrate over the long distance in time for the near-spawning period after escape from their growth habitats56. However, field studies using PSAT tagging also reported that in situ migration speeds (including transport by currents) were less than the optimal swimming speeds and suggested that some European eels could reach their spawning area within the near-spawning periods and that others only arrive in time for the following spawning season19.Our estimated effective swimming speed of Japanese eels, all day and all night over the tracking periods, ranged from 3 to 30 cm/s with individual variations. These estimates were consistent with the swimming speeds (excluding transport by currents) of 2.2–15.1 km/day (2–18 cm/s) estimated in the PSAT study of Japanese eels14. Silver-phase Japanese eels start migrating from their coastal growth habitats in Japan primarily in October to December57, 58, and spawning near the West Mariana Ridge occurs in April to August33, 35. Numerical models assuming that migrating eels use true navigation (readjusted compass) or a constant compass heading (fixed compass from the departure place to the spawning site) indicate that the minimal swimming speed required to arrive at the spawning area within 8 months is 10–12 cm/s37. Our estimated effective swimming speeds of five out of ten eels during the day and eight out of ten eels during the night were similar or higher than these minimal speeds. The low effective swimming speeds frequently observed during the day might be due to the relatively low values observed in the swimming speed at 10 min intervals and the swimming directions often varying during the day. When eels swim with stable orientation, as observed in three of the eels (WE2999_TS, WE3001_TS, and WE3002_TS) during the night, the effective swimming speeds exceeded 25 cm/s. If such a stable orientation is maintained and compensate the low speeds during the day, the eels that leave during autumn and winter will be able to arrive at the spawning area during the next spring to summer.It should also be noted that the swimming speeds in body length per second were significantly higher in shallow water during the night than in deep water during the day. In the open ocean, anguillid species exhibit DVMs during oceanic migration, swimming at depth during the day and in the shallows during the night9,10,11,12,13,14,15,16,17,18,19,20,21,22. These DVMs are likely related to the possible avoidance from visual predators under light conditions19 or maturation control59. Essentially, through the DVMs, the eels encounter low temperatures ( 20 °C) during the same day. Generally, the swimming speeds of fishes are restricted by the ambient water temperature60, and the water temperature encountered through DVMs might influence the horizontal-swimming speeds of Japanese eels.Other factors besides swimming speed are important for the success of eel migrations, such as adapting to mesopelagic zones that silver eels undergo during their spawning migrations. The most important and obvious morphological adaptation in mesopelagic fish is their well-developed eyes, and migrating eels also seem to use this strategy. These fish often have relatively large pupils61, high photosensitive structures, such as tubular eyes62, a pure rod multibank retina63, and maximum rhodopsin absorption to adapt to the blue-green light in the deep sea64. The eyes of catadromous eels displayed enlargement during their transformation into migrating silver-phase eels65, 66 and potentially increase their retinal surface area, which results in the possibility of increased photon capture. In addition, the rhodopsins in the eyes change from a freshwater type with a maximum absorption of ~ 500 nm to a deep-sea type with a maximum absorption of ~ 480 nm67,68,69. Their extreme sensitivity to light is evident through their DVM in mesopelagic water, where the timing of a large descent and ascent in the DVM demonstrated by migrating catadromous eels is precisely synchronized with sunrise and sunset. Furthermore, eels alter their swimming depth in response to the phase of the Moon9, 15, 20, 21, appearing to be capable of perceiving extremely low-intensity moonlight.This study showed that three eels released in the TS area (mainly 300–400-m depth) and one eel in the KC area (near surface) were found to change their swimming direction around the time of the solar culmination when the Sun’s bearing changed. The clockwise and counterclockwise trajectories of these eels corresponded to whether the Sun moved from the east to west in the southern and northern sky, suggesting that they demonstrated horizontal negative phototaxis swimming to avoid sunlight. They might move to avoid high-intensity sunlight horizontally, not vertically, as they gradually increase the swimming depths possibly due to acclimation to cold deep water after release. The daytime swimming depths of the eels became deeper day-by-day after their release (Fig. 4); a similar phenomenon was observed in European eels12, American eels17, and long fin eels13. Recently, Higuchi et al.20 observed that the daytime swimming depths of Japanese eels released in the TS area gradually became deeper until 13 days after their release. These facts indicate that they gradually acclimate to the cold water at the deep depths after release. Since this tracking study was conducted 2–8 days after their release, the daytime swimming depth of eels would not have reached a steady state yet. The relatively high intensity from sunlight at the shallow depths where eels swam immediately after release in the TS area might cause horizontal avoidance behavior from the light.In other cases, many eels, especially those released in the KC area, did not demonstrate the rotational behavior. The eels in the KC area mostly stayed deeper (500–800 m) during the day than the eels in the TS area (stayed at depths of 300–600 m) even during the periods shortly after their release. This is possibly due to higher water temperatures even at the deeper depths in the KC area (Fig. 4). The eels in the TS area did not demonstrate clear rotational behavior at depths of more than 400 m. The PSAT studies have reported that the steady swimming depths during the day were 500–800 m14, 20. Therefore, it was assumed that the rotational behavior observed in some eels was not a regular behavior during their migration. However, the rotational behavior observed in this study suggests that they surely perceive the horizontal direction of Sun’s bearing at 400 m depths at least. Generally, they exhibit DVM precisely synchronizing with sunrise and sunset and surely perceive the change in sunlight intensity at deeper depths9,10,11,12,13,14,15,16,17,18,19,20,21,22. Even though the rotational behavior were not observed below 400 m, it remains unknown whether the eels could not perceive the Sun’s bearing from the light penetrated at depth; thus, further investigation of response to underwater light is required in future.While possible negative phototaxis behaviors were observed in some eels after release around the time of solar culmination, the trajectories of ten eels during the entire period of tracking experiments implied that each eel tended to swim meridionally toward the bearing of the Sun at culmination. We observed that eels released at middle (20°–34° N) and low (12°–13°N) latitudes tended to swim southward and northward in the meridional direction, respectively (Fig. 6A, B). The tendency to move in a north–south swimming direction corresponded to whether the Sun culminated to the north or south: eels swam southward if the culmination occurred in the southern sky, but they swam northward if it occurred in the northern sky (Fig. 6). In the KC area (33°–35° N), the Sun rose in the southeast, passed celestial meridian in the southern sky, and set in the southwest (Fig. 6C). At 20° N in the summer time when the tracking study was conducted, the Sun also passed a celestial meridian in the southern sky, but rose in the northeast and set in the northwest (Fig. 6C). When Sun culmination occurred in the southern sky, the meridional swimming directions tended to be southward (Fig. 6A). However, at 12° to 13° N in the summer time, the Sun rose in the northeast, passed the celestial meridian in the northern sky, and set in the northwest (Fig. 6D). When the Sun at culmination appeared in the northern sky, the meridional swimming directions tended to be northward (Fig. 6B). Furthermore, the swimming behavior by one eel (WE4264_TS) that was released slightly south (14° 15′ N) from the latitude with the Sun passing through the zenith was also indicative of the meridional swimming traits. This eel moved in a northerly direction on the first day, but then it lost its north–south bias in swimming around 14° 30′ N, where the Sun nearly passed through the zenith (Figs. 1 and 6D). These observations imply that the eels might move toward the latitude with the Sun passing through the zenith.Figure 6Swimming trajectories of eels and solar paths in the celestial sphere viewed from east during each tracking period. Swimming trajectories of eels released at (A) 20°N in the tropical–subtropical area and the Kuroshio Current area, and (B) 12°–14°15′N in the tropical–subtropical area. Solar paths through the north (N)–south (S) axis and the zenith at the time of tracking in (C) 20°N in the tropical–subtropical area and the Kuroshio Current area, and (D) 12°–14°15′N in the tropical–subtropical area.Full size imageTheoretically, it is possible for mesopelagic animals to use solar cues for navigation at depths shallower than the asymptotic depth, below which penetrating light rays are symmetrical around the vertical axis and the polarization plane becomes horizontal. For example, the Sargasso Sea, where the two Atlantic catadromous eels spawn1, 3, has extremely transparent water70, and the major axis of radiance distribution still remains tilted in the mesopelagic zone. The angle of maximum radiance of sunlight at 475 nm was 13° at depths of 400 m when the Sun’s elevation was 60° (Fig. 7)52, 53. In highly transparent water, the asymptotic depth could be as high as 1000–1200 m, and the depths below this cannot be utilized for compass use53. Currently, it is not possible to verify whether the Sun culminating to north or south caused the meridional swimming tendencies of eels in this study. Potentially, these meridional swimming tendencies could be due to other orientation clues, such as the geomagnetic field, as discussed for temperate anguillid eels17, 45. Nevertheless, in future studies, it would be worthwhile considering solar cues as a possible candidate factor in the orientation of eels, even when under faint underwater light conditions.Figure 7Optical features of underwater sunlight. (A) Schematic diagram of sunlight penetrating the deep ocean at 90° to the solar bearing. The line of arrows indicates the major axis of the incident beam in a vertical plane perpendicular to the Sun’s bearing. Blue light (around 475 nm) reaches the lowest depths. With increasing depth, the light field alters its character into a less directed distribution and a lower energetic level through scattering and absorption processes. Penetrating light rays are symmetrical around the region below the asymptotic depth. (B) An example of spectral radiance distribution (e. g. 475 nm) at a certain depth. The radiance distribution is shown by an ellipsoid and the major axis is drawn by a line with arrow. The refracted angular deviation (a) of the major axis of underwater radiance distribution from the vertical axis equals the tilt of the electric vector (ee bar) from the horizontal axis53. When the Sun’ s elevation was 60° in the Sargasso Sea, the radiance distributions were measured at three different depths and the tilt of the electric vector were estimated to be 24° at depths of 100 m and 200 m and 13° at depth of 400 m52, 53.Full size imageGiven that eels might be able to use the Sun’s bearing at culmination to orient their meridional swimming direction, this orientation scheme could support a clockwise eel migration route following a partial subtropical gyre2, 37. Japanese eels that departed from the nursery area first transported northeastward via the strong KC. Maintaining southward swimming in the current, they eventually crossed the current and shifted to the southward migration course. When they enter the KC, movement to the left of the bearing of the Sun at culmination (i.e., south) is the typical pattern for the early migration of eels from Japan. The movements of eels observed in the KC were consistent with the expected route; however, eels released at low latitudes of the TS area often swam northward but also westward, which resulted in their traveling an unreasonable distance from the spawning area. This might be due to their behavior during early migration. In this study, eels were transported from Japan and released into the open ocean at low latitudes. They might have swum toward the expected bearing of the Sun at culmination as if they were in the north and moved to the left of the Sun’s bearing along with the North Equatorial Current, which would mimic the early migration of eels leaving Japan and moving along the KC.Among the eels tracked in this study were individuals with impaired swim bladders, yellow-phase eels in the process of hormone-treatment maturation, and silver-phase eels collected from different rivers in different years. Despite these variations, the swimming characteristics of the eels did not differ in terms of their DVM behavior16 and swimming speed. Nevertheless, confirmation of our results using samples with a uniform status in future research would be highly desirable. In this study, the tracked eel position was assumed to be identical to that of the tracking ship and the errors between these two positions could not be evaluated; thus, the positioning of tracked fish also may need to be improved in future studies. Experimental studies, such as tracking of blind, magnetically disturbed, or olfactory-blocked eels, could help obtain or eliminate alternative candidate clues and enhance our understanding of the navigational system of anguillid eels. Controlled laboratory experiments are required to directly quantify the ability of eels to perceive radiance distribution or polarization, along with any associated behaviors. In addition, the internal clock of eels required to perform celestial navigation should be investigated. Meanwhile, the results obtained from this study can enhance our knowledge of the mechanisms underlying the migratory behaviors of eels in the open ocean. More

The full-length sequences of PacBio SMRT sequencingBased on PacBio SMRT sequencing, 3,751,089, 3,434,452, 3,900,180, 8,535,019, and 4,435,846 subreads were generated for root, stem, leaf, flower, and seed, with a N50 of 3040, 3367, 2611, 2198, and 4584 bp, respectively (Table S1; Fig. S1). Subreads were processed to generate circular consensus sequences (CCSs). By detecting the primers and poly(A) tail, 238,196, 232,290, 211,535, 257,905, and 231,877 full-length non-chimeric (FLNC) reads were identified for root, stem, leaf, flower, and seed, with a mean length of 2633, 3070, 2561, 1746, and 3762 bp, respectively (Table S2; Fig. S2). After Iterative Clustering for Error Correction (ICE) clustering, polishing, base correction, de-redundancy, and non-plant sequences filtering, 37,789, 34,034, 38,100, 54,937, and 53,906 unigenes were retained for root, stem, leaf, flower, and seed, respectively, with an average unigene length of 1802–3786 bp and N50 of 2238–4707 bp (Table S2). The length of most unigenes from five organs exceeded 2000 bp, accounting for 68.88% of the total number (Table S3; Fig. 1A). Based on Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment, about 88.1% (single-copy: 353; duplicated: 916) of the 1440 core embryophyte genes were found to be complete (90.6% were present when counting fragmented genes), suggesting the high integrity of the M. micrantha transcriptome (Fig. S3).Figure 1Length distribution of unigenes from PacBio SMRT sequencing (A) and Illumina RNA-Seq (B) across five organs.Full size imageDe novo assembly of Illumina RNA-Seq dataBased on Illumina RNA-Seq, 43.23, 40.27, 41.01, 65.85, and 41.09 million clean reads were obtained for root, stem, leaf, flower, and seed, respectively, with Q20 exceeding 96.72%. Using Trinity software, clean reads were de novo assembled into 124,238, 60,232, 63,370, 93,229, and 66,411 unigenes for root, stem, leaf, flower, and seed. After filtering non-plant sequences, 124,233, 60,232, 63,370, 93,228, and 66,410 unigenes were finally retained for the five organs, respectively (Table S4). The length of most unigenes (84.70%) was shorter than 2000 bp (Table S3). In addition, the average length and N50 of unigenes generated by Illumina RNA-Seq were 1067–1312 bp and 1336–1685 bp, respectively, which were shorter than that from PacBio SMRT sequencing (Table S4; Fig. 1B).Functional annotationTo obtain a comprehensive functional annotation of M. micrantha transcriptome, unigenes generated by PacBio SMRT sequencing were annotated in seven public databases, including NCBI non-redundant nucleotide sequences (NT), NCBI non-redundant protein sequences (NR), Gene Ontology (GO), Eukaryotic Orthologous Groups (KOG), Kyoto Encyclopedia of Genes and Genomes (KEGG), Swiss-Prot, and Pfam protein families. For root, stem, leaf, flower, and seed, 35,714 (94.51%), 32,614 (95.83%), 36,134 (94.84%), 49,197 (89.55%), and 50,962 (94.54%) unigenes were annotated to at least one database, respectively, suggesting that our transcriptome is well annotated and that most of unigenes may be functional (Table 1).Table 1 Statistics of annotation of full-length transcripts from five M. micrantha organs in seven databases.Full size tableBased on NR database annotation, the top three homologous species for the five organs were Cynara cardunculus, Vitis vinifera, and Daucus carota (Fig. S4). The top homologous species was a plant of the Asteraceae family. For the GO function annotation, “binding”, “catalytic activities”, “metabolic process”, “cellular process”, “cell”, and “cell part” were functional categories with the most abundant unigenes (Fig. S5). In addition, numerous unigenes were assigned to “response to stimulus”, “response to biotic stimulus”, and “response to oxidative stress” category (Table S5). Positive response to stress stimuli is an important strategy for invasive plants to adapt to the environment. In the KEGG annotation, the top two pathways with the most abundant unigenes were “carbohydrate metabolism” and “translation”. Furthermore, “energy metabolism” and “environmental adaptation” were also worthy of attention, which are important pathways responsible for energy supply and stress responses (Fig. S6).TFs identification and AS analysisUsing the iTAK pipeline, 1776 (root), 1293 (stem), 1627 (leaf), 2529 (flower), and 1733 (seed) unigenes were identified as TFs, which were classified into 68 families (Table S6). C3H (884), C2H2 (525), and bHLH (501) were the most abundant TF families (Fig. S7A). In addition, MYB (333) TFs were also found in the five organs. The differential expression levels of the top 15 TF families were further characterized. We found that the top 15 TF families had a certain amount of expression in the five organs of M. micrantha (Fig. S7B).For root, stem, leaf, flower, and seed, 3300, 2324, 3219, 4730, and 3740 unique transcript models (UniTransModels) were constructed, among which the UniTransModels containing two isoforms were the most common (Fig. S8A). There were 329, 270, 358, 336, and 537 AS events identified in root, stem, leaf, flower, and seed, respectively. Retained introns (RIs) were detected as the most abundant AS event in all five organs, followed by alternative 3′ splice sites (A3) and alternative 5′ splice sites (A5). Mutually exclusive exons (MX) were the least frequent event (Fig. S8B).Gene expression analysisThe number of unigenes in different expression level intervals was similar across the five organs (Fig. 2A). Using FPKM  > 0.3 as the threshold for unigene expression, the total number of unigenes expressed in the five organs was 102,464 (Fig. 2B). Among them, 39,227 unigenes were co-expressed in all five organs. The information of differentially expressed genes (DEGs) identified in pairwise comparisons among the five organs is listed in Table S7. In total, 21,161 DEGs were identified among the five organs (Fig. S9). The number of DEGs between the five organs varied from 3469 (root vs stem) to 10,716 (leaf vs seed) (Fig. 2C). Notably, 933, 428, 1410, 1018, and 1292 DEGs showed significant higher expression in root, stem, leaf, flower, and seed, respectively (Figs. S10 and S11).Figure 2Gene expression patterns in five M. micrantha organs. (A) The FPKM interval distribution in the five organs. (B) Venn diagram of the number of unigenes expressed in five organs. (C) Number of differentially expressed genes in each pairwise comparison of five organs.Full size imageKEGG enrichment of unigenes with higher expression in each organAccording to the KEGG enrichment analysis results, there were obvious differences in enriched pathways in the five organs (Table S8; Fig. 3). The unigenes with higher expression in root were mainly enriched to defense response and protein processing pathways, such as “plant-pathogen interaction” and “protein processing in endoplasmic reticulum”. In stem, unigenes with higher expression were predominantly enriched to pathways related to the secondary metabolite, sugar, and terpenoid biosynthesis, such as “phenylpropanoid biosynthesis”, “starch and sucrose metabolism”, and “diterpenoid biosynthesis”. In flower, unigenes with higher expression were mainly related to “starch and sucrose metabolism”, “phenylpropanoid biosynthesis”, and “cutin, suberine, and wax biosynthesis”. The unigenes with higher expression in seed were mainly enriched in three fatty acid and sugar metabolism pathways, namely “biosynthesis of unsaturated fatty acids”, “galactose metabolism”, and “amino sugar and nucleotide sugar metabolism”. The unigenes with higher expression in leaf were significantly enriched in photosynthesis pathways, including “photosynthesis-antenna proteins”, “photosynthesis”, “porphyrin and chlorophyll metabolism”, and “carbon fixation in photosynthetic organisms”, which are important for the photosynthesis of M. micrantha.Figure 3The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of unigenes with higher expression in each organ. The significantly enriched pathways with corrected p-value (q value)  More

Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
PubMed 

Google Scholar 
Orgiazzi, A. et al. Global Soil Biodiversity Atlas (European Commission, Publications Office of the European Union, 2016).Tecon, R. & Or, D. Biophysical processes supporting the diversity of microbial life in soil. FEMS Microbiol. Rev. 41, 599–623 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Williamson, K. E., Fuhrmann, J. J., Wommack, K. E. & Radosevich, M. Viruses in soil ecosystems: an unknown quantity within an unexplored territory. Annu. Rev. Virol. 4, 201–219 (2017). This Review provides a comprehensive overview of methods and technologies used to study soil viruses alongside a guide of metrics describing soil viruses across diverse soil ecosystems.CAS 
PubMed 

Google Scholar 
Stefan, G., Cornelia, B., Jörg, R. & Michael, B. Soil water availability strongly alters the community composition of soil protists. Pedobiologia 57, 205–213 (2014).
Google Scholar 
Leake, J. et al. Networks of power and influence: the role of mycorrhizal mycelium in controlling plant communities and agroecosystem functioning. Can. J. Bot. 82, 1016–1045 (2004).
Google Scholar 
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018). This study compiled metagenomic and metabarcoding data from 189 sites to demonstrate global patterns in the structure and function of soil microbial communities as well as the widespread prevalence of bacterial–fungal antagonism as an important structuring force of microbial communities.CAS 
PubMed 

Google Scholar 
He, L. et al. Global biogeography of fungal and bacterial biomass carbon in topsoil. Soil Biol. Biochem. 151, 108024 (2020).CAS 

Google Scholar 
Bach, E. M., Williams, R. J., Hargreaves, S. K., Yang, F. & Hofmockel, K. S. Greatest soil microbial diversity found in micro-habitats. Soil Biol. Biochem. 118, 217–226 (2018).CAS 

Google Scholar 
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).CAS 
PubMed 

Google Scholar 
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).CAS 
PubMed 

Google Scholar 
Delgado-Baquerizo, M. et al. Multiple elements of soil biodiversity drive ecosystem functions across biomes. Nat. Ecol. Evol. 4, 210–220 (2020).PubMed 

Google Scholar 
Crowther, T. W. et al. The global soil community and its influence on biogeochemistry. Science 365, eaav0550 (2019).CAS 
PubMed 

Google Scholar 
Liang, C., Amelung, W., Lehmann, J. & Kästner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Change Biol. 25, 3578–3590 (2019). This article estimates that more than 50% of SOM may be derived from microbial necromass in grassland and agricultural ecosystems based on extrapolations from amino sugar biomarker data.
Google Scholar 
Angst, G., Mueller, K. E., Nierop, K. G. J. & Simpson, M. J. Plant- or microbial-derived? A review on the molecular composition of stabilized soil organic matter. Soil Biol. Biochem. 156, 108189 (2021).CAS 

Google Scholar 
Ludwig, M. et al. Microbial contribution to SOM quantity and quality in density fractions of temperate arable soils. Soil Biol. Biochem. 81, 311–322 (2015). This study uses lipid biomarkers to estimate that at least 50% of SOM may be derived from microbial necromass.CAS 

Google Scholar 
Simpson, A. J., Simpson, M. J., Smith, E. & Kelleher, B. P. Microbially derived inputs to soil organic matter: are current estimates too low? Environ. Sci. Technol. 41, 8070–8076 (2007).CAS 
PubMed 

Google Scholar 
Blazewicz, S. J. et al. Taxon-specific microbial growth and mortality patterns reveal distinct temporal population responses to rewetting in a California grassland soil. ISME J. 14, 1520–1532 (2020). This study used quantitative stable isotope probing to calculate growth and mortality rates of bacteria following the rewetting of a dry Mediterranean soil, and demonstrated that bacterial growth was density independent whereas bacterial mortality was density dependent.PubMed 
PubMed Central 

Google Scholar 
Vieira, S. et al. Drivers of the composition of active rhizosphere bacterial communities in temperate grasslands. ISME J. 14, 463–475 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nuccio, E. E. et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 14, 999–1014 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S. et al. Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio 6, e00746 (2015).PubMed 
PubMed Central 

Google Scholar 
Bastian, F., Bouziri, L., Nicolardot, B. & Ranjard, L. Impact of wheat straw decomposition on successional patterns of soil microbial community structure. Soil Biol. Biochem. 41, 262–275 (2009).CAS 

Google Scholar 
Whitman, T. et al. Microbial community assembly differs across minerals in a rhizosphere microcosm. Environ. Microbiol. 20, 4444–4460 (2018).CAS 
PubMed 

Google Scholar 
Maynard, D. S., Crowther, T. W. & Bradford, M. A. Fungal interactions reduce carbon use efficiency. Ecol. Lett. 20, 1034–1042 (2017). This study demonstrated that antagonistic interactions between wood-decay fungi can reduce CUE of the fungal community.PubMed 

Google Scholar 
Crowther, T. W. et al. Environmental stress response limits microbial necromass contributions to soil organic carbon. Soil Biol. Biochem. 85, 153–161 (2015).CAS 

Google Scholar 
Hu, Y., Zheng, Q., Noll, L., Zhang, S. & Wanek, W. Direct measurement of the in situ decomposition of microbial-derived soil organic matter. Soil Biol. Biochem. 141, 107660 (2020).CAS 

Google Scholar 
Fernandez, C. W., Langley, J. A., Chapman, S., McCormack, M. L. & Koide, R. T. The decomposition of ectomycorrhizal fungal necromass. Soil Biol. Biochem. 93, 38–49 (2016). This review article summarizes how the stoichiometry, morphology and chemistry of microbial necromass affects its decomposition rate in soil.CAS 

Google Scholar 
Buckeridge, K. M. et al. Sticky dead microbes: rapid abiotic retention of microbial necromass in soil. Soil Biol. Biochem. 149, 107929 (2020).CAS 

Google Scholar 
Creamer, C. A. et al. Mineralogy dictates the initial mechanism of microbial necromass association. Geochim. Cosmochim. Acta 260, 161–176 (2019). This study used Raman microspectroscopy and 13C-labelled necromass to demonstrate that different mineral types retained microbial necromass through different mechanisms and with different strengths.CAS 

Google Scholar 
Schurig, C. et al. Microbial cell-envelope fragments and the formation of soil organic matter: a case study from a glacier forefield. Biogeochemistry 113, 595–612 (2013).CAS 

Google Scholar 
Kopittke, P. M. et al. Nitrogen-rich microbial products provide new organo-mineral associations for the stabilization of soil organic matter. Glob. Change Biol. 24, 1762–1770 (2018).
Google Scholar 
Miltner, A., Bombach, P., Schmidt-Brücken, B. & Kästner, M. SOM genesis: microbial biomass as a significant source. Biogeochemistry 111, 41–55 (2012).CAS 

Google Scholar 
Kleber, M. et al. Dynamic interactions at the mineral–organic matter interface. Nat. Rev. Earth Environ. 2, 402–421 (2021).
Google Scholar 
Blagodatskaya, E. & Kuzyakov, Y. Active microorganisms in soil: critical review of estimation criteria and approaches. Soil Biol. Biochem. 67, 192–211 (2013).CAS 

Google Scholar 
Or, D., Smets, B. F., Wraith, J. M., Dechesne, A. & Friedman, S. P. Physical constraints affecting bacterial habitats and activity in unsaturated porous media–a review. Adv. Water Resour. 30, 1505–1527 (2007).
Google Scholar 
Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 

Google Scholar 
Finzi, A. C. et al. Rhizosphere processes are quantitatively important components of terrestrial carbon and nutrient cycles. Glob. Change Biol. 21, 2082–2094 (2015).
Google Scholar 
Yuan, M. M. et al. Fungal-bacterial cooccurrence patterns differ between arbuscular mycorrhizal fungi and nonmycorrhizal fungi across soil niches. mBio 12, e03509-20 (2015).
Google Scholar 
Zhang, L. & Lueders, T. Micropredator niche differentiation between bulk soil and rhizosphere of an agricultural soil depends on bacterial prey. FEMS Microbiol. Ecol. 93, fix103 (2017).
Google Scholar 
Sokol, N. W. & Bradford, M. A. Microbial formation of stable soil carbon is more efficient from belowground than aboveground input. Nat. Geosci. 12, 46–53 (2019).CAS 

Google Scholar 
Kallenbach, C. M., Frey, S. D. & Grandy, A. S. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun. 7, 13630 (2016). This study used artificial soils to provide empirical evidence that SOM can be entirely microbially derived, and also demonstrated a positive relationship between CUE and SOM formation.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wood, J. L., Tang, C. & Franks, A. E. Competitive traits are more important than stress-tolerance traits in a cadmium-contaminated rhizosphere: a role for trait theory in microbial ecology. Front. Microbiol. 9, 121 (2018).PubMed 
PubMed Central 

Google Scholar 
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Google Scholar 
Madin, J. S. et al. A synthesis of bacterial and archaeal phenotypic trait data. Sci. Data 7, 170 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shaffer, M. et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 48, 8883–8900 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown, C. T., Olm, M. R., Thomas, B. C. & Banfield, J. F. Measurement of bacterial replication rates in microbial communities. Nat. Biotechnol. 34, 1256–1263 (2016). This study developed an algorithm, iRep, that uses draft-quality genome sequences and single time-point metagenome sequencing to infer microbial population replication rates.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nayfach, S. & Pollard, K. S. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 16, 51 (2015).PubMed 
PubMed Central 

Google Scholar 
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vieira-Silva, S. & Rocha, E. P. C. The systemic imprint of growth and its uses in ecological (meta)genomics. PLoS Genet. 6, e1000808 (2010).PubMed 
PubMed Central 

Google Scholar 
Hasby, F. A., Barbi, F., Manzoni, S. & Lindahl, B. D. Transcriptomic markers of fungal growth, respiration and carbon-use efficiency. FEMS Microbiol. Lett. 368, fnab100 (2021).PubMed 
PubMed Central 

Google Scholar 
Maillard, F., Schilling, J., Andrews, E., Schreiner, K. M. & Kennedy, P. Functional convergence in the decomposition of fungal necromass in soil and wood. FEMS Microbiol. Ecol. 96, fiz209 (2020).CAS 
PubMed 

Google Scholar 
Clemmensen, K. E. et al. Carbon sequestration is related to mycorrhizal fungal community shifts during long-term succession in boreal forests. N. Phytol. 205, 1525–1536 (2015).CAS 

Google Scholar 
Olivelli, M. S. et al. Unraveling mechanisms behind biomass–clay interactions using comprehensive multiphase nuclear magnetic resonance (NMR) Spectroscopy. ACS Earth Space Chem. 4, 2061–2072 (2020).CAS 

Google Scholar 
Achtenhagen, J., Goebel, M.-O., Miltner, A., Woche, S. K. & Kästner, M. Bacterial impact on the wetting properties of soil minerals. Biogeochemistry 122, 269–280 (2015).CAS 

Google Scholar 
Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534 (2020).CAS 

Google Scholar 
Ahmed, E. & Holmström, S. J. M. Microbe–mineral interactions: The impact of surface attachment on mineral weathering and element selectivity by microorganisms. Chem. Geol. 403, 13–23 (2015).CAS 

Google Scholar 
Chenu, C. Clay- or sand-polysaccharide associations as models for the interface between micro-organisms and soil: water related properties and microstructure. Geoderma 56, 143–156 (1993).CAS 

Google Scholar 
Sher, Y. et al. Microbial extracellular polysaccharide production and aggregate stability controlled by switchgrass (Panicum virgatum) root biomass and soil water potential. Soil Biol. Biochem. 143, 107742 (2020).CAS 

Google Scholar 
Lybrand, R. A. et al. A coupled microscopy approach to assess the nano-landscape of weathering. Sci. Rep. 9, 5377 (2019).PubMed 
PubMed Central 

Google Scholar 
Prommer, J. et al. Increased microbial growth, biomass, and turnover drive soil organic carbon accumulation at higher plant diversity. Glob. Change Biol. 26, 669–681 (2020).
Google Scholar 
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).
Google Scholar 
Liang, C., Schimel, J. P. & Jastrow, J. D. The importance of anabolism in microbial control over soil carbon storage. Nat. Microbiol. 2, 17105 (2017).CAS 
PubMed 

Google Scholar 
Geyer, K. M., Kyker-Snowman, E., Grandy, A. S. & Frey, S. D. Microbial carbon use efficiency: accounting for population, community, and ecosystem-scale controls over the fate of metabolized organic matter. Biogeochemistry 127, 173–188 (2016).CAS 

Google Scholar 
Kallenbach, C. M., Grandy, A. S., Frey, S. D. & Diefendorf, A. F. Microbial physiology and necromass regulate agricultural soil carbon accumulation. Soil Biol. Biochem. 91, 279–290 (2015).CAS 

Google Scholar 
Buckeridge, K. M. et al. Environmental and microbial controls on microbial necromass recycling, an important precursor for soil carbon stabilization. Commun. Earth Env. 1, 36 (2020).
Google Scholar 
Saifuddin, M., Bhatnagar, J. M., Segrè, D. & Finzi, A. C. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat. Commun. 10, 3568 (2019).PubMed 
PubMed Central 

Google Scholar 
Schimel, J., Balser, T. C. & Wallenstein, M. Microbial stress-response physiology and its implications for ecosystem function. Ecology 88, 1386–1394 (2007).PubMed 

Google Scholar 
Mason‐Jones, K., Banfield, C. C. & Dippold, M. A. Compound-specific 13C stable isotope probing confirms synthesis of polyhydroxybutyrate by soil bacteria. Rapid Commun. Mass. Spectrom. 33, 795–802 (2019).PubMed 

Google Scholar 
Bååth, E. The use of neutral lipid fatty acids to indicate the physiological conditions of soil fungi. Microb. Ecol. 45, 373–383 (2003).PubMed 

Google Scholar 
Slessarev, E. W. et al. Cellular and extracellular C contributions to respiration after wetting dry soil. Biogeochemistry 147, 307–324 (2020).CAS 

Google Scholar 
Slessarev, E. W. & Schimel, J. P. Partitioning sources of CO2 emission after soil wetting using high-resolution observations and minimal models. Soil Biol. Biochem. 143, 107753 (2020).CAS 

Google Scholar 
Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9, 119–130 (2011).CAS 
PubMed 

Google Scholar 
Brangarí, A. C., Manzoni, S. & Rousk, J. A soil microbial model to analyze decoupled microbial growth and respiration during soil drying and rewetting. Soil Biol. Biochem. 148, 107871 (2020).
Google Scholar 
Zha, J. & Zhuang, Q. Microbial dormancy and its impacts on northern temperate and boreal terrestrial ecosystem carbon budget. Biogeosciences 17, 4591–4610 (2020).CAS 

Google Scholar 
Anderson, T.-H. Microbial eco-physiological indicators to asses soil quality. Agric. Ecosyst. Environ. 98, 285–293 (2003).
Google Scholar 
Geyer, K., Schnecker, J., Grandy, A. S., Richter, A. & Frey, S. Assessing microbial residues in soil as a potential carbon sink and moderator of carbon use efficiency. Biogeochemistry 151, 237–249 (2020).CAS 

Google Scholar 
Sepehrnia, N. et al. Transport, retention, and release of Escherichia coli and Rhodococcus erythropolis through dry natural soils as affected by water repellency. Sci. Total Environ. 694, 133666 (2019).CAS 
PubMed 

Google Scholar 
Boeddinghaus, R. S. et al. The mineralosphere — interactive zone of microbial colonization and carbon use in grassland soils. Biol. Fertil. Soils 57, 587–601 (2021).CAS 

Google Scholar 
Vieira, S. et al. Bacterial colonization of minerals in grassland soils is selective and highly dynamic. Environ. Microbiol. 22, 917–933 (2020).CAS 
PubMed 

Google Scholar 
Ma, T. et al. Divergent accumulation of microbial necromass and plant lignin components in grassland soils. Nat. Commun. 9, 3480 (2018).PubMed 
PubMed Central 

Google Scholar 
Blazewicz, S. J., Schwartz, E. & Firestone, M. K. Growth and death of bacteria and fungi underlie rainfall-induced carbon dioxide pulses from seasonally dried soil. Ecology 95, 1162–1172 (2014).PubMed 

Google Scholar 
Ceja-Navarro, J. A. et al. Protist diversity and community complexity in the rhizosphere of switchgrass are dynamic as plants develop. Microbiome 9, 96 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Starr, E. P., Nuccio, E. E., Pett-Ridge, J., Banfield, J. F. & Firestone, M. K. Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proc. Natl Acad. Sci. USA 116, 25900–25908 (2019). This comprehensive study of RNA viruses detectable in a grassland soil showed how these viruses are shaped by the presence of plant roots and litter.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, S. et al. The interconnected rhizosphere: high network complexity dominates rhizosphere assemblages. Ecol. Lett. 19, 926–936 (2016).PubMed 

Google Scholar 
Yan, Y., Kuramae, E. E., de Hollander, M., Klinkhamer, P. G. L. & van Veen, J. A. Functional traits dominate the diversity-related selection of bacterial communities in the rhizosphere. ISME J. 11, 56–66 (2017).PubMed 

Google Scholar 
Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470 (2018).CAS 
PubMed 

Google Scholar 
Pett-Ridge, J. et al. in Rhizosphere Biology: Interactions Between Microbes and Plants (eds Gupta, V. V. S. R. & Sharma, A. K.) 51–73 (Springer, 2021).Poll, C., Marhan, S., Ingwersen, J. & Kandeler, E. Dynamics of litter carbon turnover and microbial abundance in a rye detritusphere. Soil Biol. Biochem. 40, 1306–1321 (2008).CAS 

Google Scholar 
Buchkowski, R. W., Bradford, M. A., Grandy, A. S., Schmitz, O. J. & Wieder, W. R. Applying population and community ecology theory to advance understanding of belowground biogeochemistry. Ecol. Lett. 20, 231–245 (2017).PubMed 

Google Scholar 
Erktan, A., Or, D. & Scheu, S. The physical structure of soil: determinant and consequence of trophic interactions. Soil Biol. Biochem. 148, 107876 (2020).CAS 

Google Scholar 
Roesch, L. F. W. et al. Pyrosequencing enumerates and contrasts soil microbial diversity. ISME J. 1, 283–290 (2007).CAS 
PubMed 

Google Scholar 
Carson, J. K. et al. Low pore connectivity increases bacterial diversity in soil. Appl. Environ. Microbiol. 76, 3936–3942 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Raynaud, X. & Nunan, N. Spatial ecology of bacteria at the microscale in soil. PLoS ONE 9, e87217 (2014).PubMed 
PubMed Central 

Google Scholar 
Ekelund, F., Rønn, R. & Christensen, S. Distribution with depth of protozoa, bacteria and fungi in soil profiles from three Danish forest sites. Soil Biol. Biochem. 33, 475–481 (2001).CAS 

Google Scholar 
Sharrar, A. M. et al. Bacterial secondary metabolite biosynthetic potential in soil varies with phylum, depth, and vegetation type. mBio 11, e00416-20 (2020).PubMed 
PubMed Central 

Google Scholar 
Georgiou, K., Abramoff, R. Z., Harte, J., Riley, W. J. & Torn, M. S. Microbial community-level regulation explains soil carbon responses to long-term litter manipulations. Nat. Commun. 8, 1223 (2017). This modelling study demonstrated that including a density-dependent microbial mortality term can reduce the oscillatory behaviour of soil carbon models.PubMed 
PubMed Central 

Google Scholar 
Thakur, M. P. & Geisen, S. Trophic regulations of the soil microbiome. Trends Microbiol. 27, 771–780 (2019).CAS 
PubMed 

Google Scholar 
Fanin, N. et al. The ratio of Gram-positive to Gram-negative bacterial PLFA markers as an indicator of carbon availability in organic soils. Soil Biol. Biochem. 128, 111–114 (2019).CAS 

Google Scholar 
Wang, W. et al. Predatory Myxococcales are widely distributed in and closely correlated with the bacterial community structure of agricultural land. Appl. Soil Ecol. 146, 103365 (2020).
Google Scholar 
Hungate, B. A. et al. The functional significance of bacterial predators. mBio 12, e00466-21 (2021).PubMed 
PubMed Central 

Google Scholar 
Jover, L. F., Effler, T. C., Buchan, A., Wilhelm, S. W. & Weitz, J. S. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat. Rev. Microbiol. 12, 519–528 (2014).CAS 
PubMed 

Google Scholar 
Emerson, J. B. et al. Host-linked soil viral ecology along a permafrost thaw gradient. Nat. Microbiol. 3, 870–880 (2018). This study identified novel viral genomes from metagenomes and linked many of these viruses in silico to bacterial hosts and carbon metabolisms across the spatial gradient of permafrost thaw.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ren, D., Madsen, J. S., Sørensen, S. J. & Burmølle, M. High prevalence of biofilm synergy among bacterial soil isolates in cocultures indicates bacterial interspecific cooperation. ISME J. 9, 81–89 (2015).CAS 
PubMed 

Google Scholar 
Lee, K. W. K. et al. Biofilm development and enhanced stress resistance of a model, mixed-species community biofilm. ISME J. 8, 894–907 (2014).CAS 
PubMed 

Google Scholar 
Witzgall, K. et al. Particulate organic matter as a functional soil component for persistent soil organic carbon. Nat. Commun. 12, 4115 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Frey, S. D. Mycorrhizal fungi as mediators of soil organic matter dynamics. Annu. Rev. Ecol. Evol. Syst. 50, 237–259 (2019).
Google Scholar 
Drigo, B. et al. Shifting carbon flow from roots into associated microbial communities in response to elevated atmospheric CO2. Proc. Natl Acad. Sci. USA 107, 10938–10942 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kaiser, C. et al. Exploring the transfer of recent plant photosynthates to soil microbes: mycorrhizal pathway vs direct root exudation. N. Phytol. 205, 1537–1551 (2015).CAS 

Google Scholar 
Shah, F. et al. Ectomycorrhizal fungi decompose soil organic matter using oxidative mechanisms adapted from saprotrophic ancestors. N. Phytol. 209, 1705–1719 (2016).CAS 

Google Scholar 
Tisserant, E. et al. Genome of an arbuscular mycorrhizal fungus provides insight into the oldest plant symbiosis. Proc. Natl Acad. Sci. USA 110, 20117–20122 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hestrin, R., Hammer, E. C., Mueller, C. W. & Lehmann, J. Synergies between mycorrhizal fungi and soil microbial communities increase plant nitrogen acquisition. Commun. Biol. 2, 233 (2019).PubMed 
PubMed Central 

Google Scholar 
Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543–545 (2014).CAS 
PubMed 

Google Scholar 
Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).PubMed 

Google Scholar 
Craig, M. E. et al. Tree mycorrhizal type predicts within-site variability in the storage and distribution of soil organic matter. Glob. Change Biol. 24, 3317–3330 (2018).
Google Scholar 
See, C. R. et al. Hyphae move matter and microbes to mineral microsites: Integrating the hyphosphere into conceptual models of soil organic matter stabilization. Glob. Change Biol. https://doi.org/10.1111/gcb.16073 (2022).Article 

Google Scholar 
Adamczyk, B., Sietiö, O.-M., Biasi, C. & Heinonsalo, J. Interaction between tannins and fungal necromass stabilizes fungal residues in boreal forest soils. N. Phytol. 223, 16–21 (2019).
Google Scholar 
Vidal, A. et al. Visualizing the transfer of organic matter from decaying plant residues to soil mineral surfaces controlled by microorganisms. Soil Biol. Biochem. 160, 108347 (2021).CAS 

Google Scholar 
Kallenbach, C. M., Wallenstein, M. D., Schipanksi, M. E. & Grandy, A. S. Managing agroecosystems for soil microbial carbon use efficiency: ecological unknowns, potential outcomes, and a path forward. Front. Microbiol. 10, 1146 (2019).PubMed 
PubMed Central 

Google Scholar 
Blagodatskaya, E., Blagodatsky, S., Anderson, T.-H. & Kuzyakov, Y. microbial growth and carbon use efficiency in the rhizosphere and root-free soil. PLoS ONE 9, e93282 (2014).PubMed 
PubMed Central 

Google Scholar 
Domeignoz-Horta, L. A. et al. Microbial diversity drives carbon use efficiency in a model soil. Nat. Commun. 11, 3684 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fernandez, C. W. & Kennedy, P. G. Revisiting the ‘Gadgil effect’: do interguild fungal interactions control carbon cycling in forest soils? N. Phytol. 209, 1382–1394 (2016).CAS 

Google Scholar 
Nicolas, A. M. et al. Soil candidate phyla radiation bacteria encode components of aerobic metabolism and co-occur with nanoarchaea in the rare biosphere of rhizosphere grassland communities. mSystems 6, e0120520 (2021).PubMed 

Google Scholar 
Starr, E. P. et al. Stable isotope informed genome-resolved metagenomics reveals that Saccharibacteria utilize microbially-processed plant-derived carbon. Microbiome 6, 122 (2018).PubMed 
PubMed Central 

Google Scholar 
Pace, M. L. Bacterial mortality and the fate of bacterial production. Hydrobiologia 159, 41–49 (1988).
Google Scholar 
Cram, J. A., Parada, A. E. & Fuhrman, J. A. Dilution reveals how viral lysis and grazing shape microbial communities. Limnol. Oceanogr. 61, 889–905 (2016).
Google Scholar 
Ankrah, N. Y. D. et al. Phage infection of an environmentally relevant marine bacterium alters host metabolism and lysate composition. ISME J. 8, 1089–1100 (2014). This study demonstrated that in a marine environment, the mechanism of death (that is, phage infection) altered the biochemistry of microbial necromass relative to uninfected cells.CAS 
PubMed 

Google Scholar 
Lindeman, R. L. The trophic-dynamic aspect of ecology. Ecology 23, 399–417 (1942).
Google Scholar 
Clarholm, M. Interactions of bacteria, protozoa and plants leading to mineralization of soil nitrogen. Soil Biol. Biochem. 17, 181–187 (1985).CAS 

Google Scholar 
Pasternak, Z. et al. In and out: an analysis of epibiotic vs periplasmic bacterial predators. ISME J. 8, 625–635 (2014).CAS 
PubMed 

Google Scholar 
Lee, X., Wu, H.-J., Sigler, J., Oishi, C. & Siccama, T. Rapid and transient response of soil respiration to rain. Glob. Change Biol. 10, 1017–1026 (2004).
Google Scholar 
Schimel, J. P. Life in dry soils: effects of drought on soil microbial communities and processes. Annu. Rev. Ecol. Evol. Syst. 49, 409–432 (2018).
Google Scholar 
Granato, E. T., Meiller-Legrand, T. A. & Foster, K. R. The evolution and ecology of bacterial warfare. Curr. Biol. 29, R521–R537 (2019).CAS 
PubMed 

Google Scholar 
Bradford, M. A. et al. Managing uncertainty in soil carbon feedbacks to climate change. Nat. Clim. Change 6, 751–758 (2016).
Google Scholar 
Sierra, C. A. & Müller, M. A general mathematical framework for representing soil organic matter dynamics. Ecol. Monogr. 85, 505–524 (2015).
Google Scholar 
Wang, G. et al. Microbial dormancy improves development and experimental validation of ecosystem model. ISME J. 9, 226–237 (2015).CAS 
PubMed 

Google Scholar 
Wieder, W., Grandy, S., Kallenbach, M. & Bonan, B. Integrating microbial physiology and physio-chemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model. Biogeosciences 11, 3899–3917 (2014).
Google Scholar 
Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012). This paper described one of the first trait-based modelling approaches to link microbial community composition with physiological and enzymatic traits to predict litter decomposition in soil.CAS 
PubMed 

Google Scholar 
Kaiser, C., Franklin, O., Dieckmann, U. & Richter, A. Microbial community dynamics alleviate stoichiometric constraints during litter decay. Ecol. Lett. 17, 680–690 (2014).PubMed 
PubMed Central 

Google Scholar 
Ebrahimi, A. & Or, D. Microbial community dynamics in soil aggregates shape biogeochemical gas fluxes from soil profiles – upscaling an aggregate biophysical model. Glob. Change Biol. 22, 3141–3156 (2016). This paper presented a demonstration of how to upscale results from a mechanistic model of microbial activity in soil aggregates to scales of practical interest for hydrological and climate models.
Google Scholar 
Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019). This opinion article discussed trait-based approaches in microbial ecology with a focus on utilization of large-scale datasets for improved ecological understanding.CAS 
PubMed 

Google Scholar 
Wang, G., Post, W. M. & Mayes, M. A. Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses. Ecol. Appl. 23, 255–272 (2013).PubMed 

Google Scholar 
Moorhead, D. L. & Sinsabaugh, R. L. A theoretical model of litter decay and microbial interaction. Ecol. Monogr. 76, 151–174 (2006).
Google Scholar 
Kooijman, S. A. L. M., Muller, E. B. & Stouthamer, A. H. Microbial growth dynamics on the basis of individual budgets. Antonie Van Leeuwenhoek 60, 159–174 (1991).CAS 
PubMed 

Google Scholar 
Evans, S., Dieckmann, U., Franklin, O. & Kaiser, C. Synergistic effects of diffusion and microbial physiology reproduce the Birch effect in a micro-scale model. Soil Biol. Biochem. 93, 28–37 (2016).CAS 

Google Scholar 
Allison, S. D. Modeling adaptation of carbon use efficiency in microbial communities. Front. Microbiol. 5, 571 (2014).PubMed 
PubMed Central 

Google Scholar 
Hawkes, C. V. & Keitt, T. H. Resilience vs. historical contingency in microbial responses to environmental change. Ecol. Lett. 18, 612–625 (2015).PubMed 

Google Scholar 
Tang, J. & Riley, W. J. Weaker soil carbon–climate feedbacks resulting from microbial and abiotic interactions. Nat. Clim. Change 5, 56–60 (2015).CAS 

Google Scholar 
Zhang, Y. et al. Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically-defined MEMS 2.0 model. Biogeosciences 18, 3147–3171 (2021).CAS 

Google Scholar 
Blankinship, J. C. et al. Improving understanding of soil organic matter dynamics by triangulating theories, measurements, and models. Biogeochemistry 140, 1–13 (2018).CAS 

Google Scholar 
Ebrahimi, A. N. & Or, D. Microbial dispersal in unsaturated porous media: Characteristics of motile bacterial cell motions in unsaturated angular pore networks. Water Resour. Res. 50, 7406–7429 (2014).
Google Scholar 
Tang, J. & Riley, W. J. A theory of effective microbial substrate affinity parameters in variably saturated soils and an example application to aerobic soil heterotrophic respiration. J. Geophys. Res. Biogeosci. 124, 918–940 (2019).
Google Scholar 
Manzoni, S., Schaeffer, S. M., Katul, G., Porporato, A. & Schimel, J. P. A theoretical analysis of microbial eco-physiological and diffusion limitations to carbon cycling in drying soils. Soil Biol. Biochem. 73, 69–83 (2014).CAS 

Google Scholar 
Brangarí, A. C., Fernàndez-Garcia, D., Sanchez-Vila, X. & Manzoni, S. Ecological and soil hydraulic implications of microbial responses to stress – a modeling analysis. Adv. Water Resour. 116, 178–194 (2018).
Google Scholar 
Alster, C. J., Weller, Z. D. & von Fischer, J. C. A meta-analysis of temperature sensitivity as a microbial trait. Glob. Change Biol. 24, 4211–4224 (2018).
Google Scholar 
Wang, G., Li, W., Wang, K. & Huang, W. Uncertainty quantification of the soil moisture response functions for microbial dormancy and resuscitation. Soil Biol. Biochem. 160, 108337 (2021).CAS 

Google Scholar 
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S. & Janssens, I. Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture. J. Adv. Model. Earth Syst. 7, 335–356 (2015).
Google Scholar 
Nunan, N., Schmidt, H. & Raynaud, X. The ecology of heterogeneity: soil bacterial communities and C dynamics. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190249 (2020).CAS 

Google Scholar 
Kaiser, C., Franklin, O., Richter, A. & Dieckmann, U. Social dynamics within decomposer communities lead to nitrogen retention and organic matter build-up in soils. Nat. Commun. 6, 8960 (2015).CAS 
PubMed 

Google Scholar 
Craig, M. E., Mayes, M. A., Sulman, B. N. & Walker, A. P. Biological mechanisms may contribute to soil carbon saturation patterns. Glob. Change Biol. 27, 2633–2644 (2021).
Google Scholar 
Fan, X. et al. Improved model simulation of soil carbon cycling by representing the microbially derived organic carbon pool. ISME J. 15, 2248–2263 (2021).CAS 
PubMed 

Google Scholar 
Sulman, B. N. et al. Multiple models and experiments underscore large uncertainty in soil carbon dynamics. Biogeochemistry 141, 109–123 (2018). This paper addressed key uncertainties in the representation of microbial degradation and mineral stabilization in five microbially explicit soil carbon models.CAS 

Google Scholar 
Marschmann, G. L., Pagel, H., Kügler, P. & Streck, T. Equifinality, sloppiness, and emergent structures of mechanistic soil biogeochemical models. Environ. Model. Softw. 122, 104518 (2019).
Google Scholar 
Martiny, J. B. H., Jones, S. E., Lennon, J. T. & Martiny, A. C. Microbiomes in light of traits: a phylogenetic perspective. Science 350, aac9323 (2015).PubMed 

Google Scholar 
Malik, A. A., Thomson, B. C., Whiteley, A. S., Bailey, M. & Griffiths, R. I. Bacterial physiological adaptations to contrasting edaphic conditions identified using landscape scale metagenomics. mBio 8, e00799-17 (2017).PubMed 
PubMed Central 

Google Scholar 
Westoby, M. et al. Trait dimensions in bacteria and archaea compared to vascular plants. Ecol. Lett. 24, 1487–1504 (2021).PubMed 

Google Scholar 
Jung, M.-Y. et al. Ammonia-oxidizing archaea possess a wide range of cellular ammonia affinities. ISME J. 16, 272–283 (2022).CAS 
PubMed 

Google Scholar 
Kempes, C. P., Wang, L., Amend, J. P., Doyle, J. & Hoehler, T. Evolutionary tradeoffs in cellular composition across diverse bacteria. ISME J. 10, 2145–2157 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dethlefsen, L. & Schmidt, T. M. Performance of the translational apparatus varies with the ecological strategies of bacteria. J. Bacteriol. 189, 3237–3245 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Andersen, K. H. et al. Characteristic sizes of life in the oceans, from bacteria to whales. Annu. Rev. Mar. Sci. 8, 217–241 (2016).CAS 

Google Scholar 
Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).CAS 
PubMed 

Google Scholar 
Weissman, J. L., Hou, S. & Fuhrman, J. A. Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns. Proc. Natl Acad. Sci. USA 118, e2016810118 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, G., Rabe, K. S., Nielsen, J. & Engqvist, M. K. M. Machine learning applied to predicting microorganism growth temperatures and enzyme catalytic optima. ACS Synth. Biol. 8, 1411–1420 (2019).CAS 
PubMed 

Google Scholar 
Hungate, B. A. et al. Quantitative microbial ecology through stable isotope probing. Appl. Environ. Microbiol. 81, 7570–7581 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Couradeau, E. et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat. Commun. 10, 2770 (2019).PubMed 
PubMed Central 

Google Scholar 
Starr, E. P. et al. Stable-isotope-informed, genome-resolved metagenomics uncovers potential cross-kingdom interactions in rhizosphere soil. mSphere 6, e0008521 (2021).PubMed 

Google Scholar 
Rousk, J. & Bååth, E. Fungal and bacterial growth in soil with plant materials of different C/N ratios. FEMS Microbiol. Ecol. 62, 258–267 (2007).CAS 
PubMed 

Google Scholar 
Koechli, C., Campbell, A. N., Pepe-Ranney, C. & Buckley, D. H. Assessing fungal contributions to cellulose degradation in soil by using high-throughput stable isotope probing. Soil Biol. Biochem. 130, 150–158 (2019).CAS 

Google Scholar 
Wilhelm, R. C., Singh, R., Eltis, L. D. & Mohn, W. W. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 13, 413–429 (2019).CAS 
PubMed 

Google Scholar 
Neurath, R. A. et al. Root carbon interaction with soil minerals is dynamic, leaving a legacy of microbially derived residues. Environ. Sci. Technol. 55, 13345–13355 (2021).CAS 
PubMed 

Google Scholar 
Luo, Y. et al. Rice rhizodeposition promotes the build-up of organic carbon in soil via fungal necromass. Soil Biol. Biochem. 160, 108345 (2021).CAS 

Google Scholar 
Carini, P. et al. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2, 16242 (2016).PubMed 

Google Scholar 
Sharma, K., Palatinszky, M., Nikolov, G., Berry, D. & Shank, E. A. Transparent soil microcosms for live-cell imaging and non-destructive stable isotope probing of soil microorganisms. eLife 9, e56275 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Arellano-Caicedo, C., Ohlsson, P., Bengtsson, M., Beech, J. P. & Hammer, E. C. Habitat geometry in artificial microstructure affects bacterial and fungal growth, interactions, and substrate degradation. Commun. Biol. 4, 1226 (2021).PubMed 
PubMed Central 

Google Scholar 
Jansson, J. K. & Hofmockel, K. S. Soil microbiomes and climate change. Nat. Rev. Microbiol. 18, 35–46 (2020).CAS 
PubMed 

Google Scholar 
García-Palacios, P. et al. Evidence for large microbial-mediated losses of soil carbon under anthropogenic warming. Nat. Rev. Earth Env. 2, 507–517 (2021).
Google Scholar 
Schulz, F. et al. Hidden diversity of soil giant viruses. Nat. Commun. 9, 4881 (2018).PubMed 
PubMed Central 

Google Scholar 
Trubl, G. et al. Towards optimized viral metagenomes for double-stranded and single-stranded DNA viruses from challenging soils. PeerJ 7, e7265 (2019).PubMed 
PubMed Central 

Google Scholar 
Guo, J. et al. VirSorter2: a multi-classifier, expert-guided approach to detect diverse DNA and RNA viruses. Microbiome 9, 37 (2021).PubMed 
PubMed Central 

Google Scholar 
Sommers, P., Chatterjee, A., Varsani, A. & Trubl, G. Integrating viral metagenomics into an ecological framework. Annu. Rev. Virol. 8, 133–158 (2021).PubMed 

Google Scholar 
Pratama, A. A. & van Elsas, J. D. The ‘neglected’ soil virome–potential role and impact. Trends Microbiol. 26, 649–662 (2018).CAS 
PubMed 

Google Scholar 
Ghosh, D. et al. Prevalence of lysogeny among soil bacteria and presence of 16S rRNA and trzN genes in viral-community DNA. Appl. Environ. Microbiol. 74, 495–502 (2008).CAS 
PubMed 

Google Scholar 
Roux, S. et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 537, 689–693 (2016).CAS 
PubMed 

Google Scholar 
Howard-Varona, C. et al. Phage-specific metabolic reprogramming of virocells. ISME J. 14, 881–895 (2020).PubMed 
PubMed Central 

Google Scholar 
Howard-Varona, C. et al. Multiple mechanisms drive phage infection efficiency in nearly identical hosts. ISME J. 12, 1605–1618 (2018).PubMed 
PubMed Central 

Google Scholar 
Van Goethem, M. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. mBio 10, e02287-19 (2019).PubMed 
PubMed Central 

Google Scholar 
Trubl, G. et al. Active virus-host interactions at sub-freezing temperatures in Arctic peat soil. Microbiome 9, 208 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lee, S. et al. Methane-derived carbon flows into host–virus networks at different trophic levels in soil. Proc. Natl Acad. Sci. USA 118, e2105124118 (2021). This study used stable isotope probing metagenomics to connect, in situ, active virus–host infections with the biogeochemical process of methane oxidation in soil.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolduc, B., Youens-Clark, K., Roux, S., Hurwitz, B. L. & Sullivan, M. B. iVirus: facilitating new insights in viral ecology with software and community data sets imbedded in a cyberinfrastructure. ISME J. 11, 7–14 (2017).PubMed 

Google Scholar  More

Lombard, M. et al. South African and Lesotho Stone Age sequence updated. S. Afr. Archaeol. Bull. 67, 120–144 (2012).
Google Scholar 
Kandel, A. W. et al. Increasing behavioral flexibility? An integrative macro-scale approach to understanding the Middle Stone Age of southern Africa. J. Archaeol. Method Theory 23, 623–628 (2015).
Google Scholar 
Porraz, G. et al. Experimentation preceding innovation in a MIS5 Pre-Still Bay layer from Diepkloof Rock Shelter (South Africa): emerging technologies and symbols. Preprint at EcoEvoRxiv https://ecoevorxiv.org/ch53r/ (2020).Texier, P. J. et al. A Howiesons Poort tradition of engraving ostrich eggshell containers dated to 60,000 years ago at Diepkloof Rock Shelter, South Africa. Proc. Natl Acad. Sci. USA 107, 6180–6185 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
Henshilwood, C. S. et al. Klipdrift Shelter, southern Cape, South Africa: preliminary report on the Howiesons Poort layers. J. Archaeol. Sci. 45, 284–303 (2014).
Google Scholar 
Powell, A., Shennan, S. & Thomas, M. G. Late Pleistocene demography and the appearance of modern human behavior. Science 324, 1298–1301 (2009).CAS 
PubMed 

Google Scholar 
Marean, C. W. The transition to foraging for dense and predictable resources and its impact on the evolution of modern humans. Phil. Trans. R. Soc. B 371, 20150239 (2016).PubMed 
PubMed Central 

Google Scholar 
Mackay, A., Stewart, B. A. & Chase, B. M. Coalescence and fragmentation in the late Pleistocene archaeology of southernmost Africa. J. Hum. Evol. 72, 26–51 (2014).PubMed 

Google Scholar 
Wilkins, J. et al. Innovative Homo sapiens behaviours 105,000 years ago in a wetter Kalahari. Nature 592, 248–252 (2021).CAS 
PubMed 

Google Scholar 
Dewar, G. & Stewart, B. A. Preliminary results of excavations at Spitzkloof Rockshelter, Richtersveld, South Africa. Quat. Int. 270, 30–39 (2012).
Google Scholar 
Cowling, R. M. & Pierce, S. Namaqualand: A Succulent Desert (Fernwood Press, 1999).Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Google Scholar 
Mucina, L. et al. in The Vegetation of South Africa, Lesotho and Swaziland (eds Mucina, L. & Rutherford, M. C.) 221–299 (SANBI, 2006).Rebelo, A. G., Boucher, C., Helme, N., Mucina, L. & Rutherford, M. C. in The Vegetation of South Africa, Lesotho and Swaziland (eds Mucina, L. & Rutherford, M. C.) 53–219 (SANBI, 2006).Marean, C. W. et al. in Fynbos: Ecology, Evolution, and Conservation of a Megadiverse Region (eds Allsopp, N. et al.) 164–199 (Oxford Univ. Press, 2014).Carr, A. S., Chase, B. M. & Mackay, A. in Africa from MIS 6-2: Population Dynamics and Paleoenvironments (eds Jones, S. & Stewart, B. A.) 23–47 (Springer, 2016).Chase, B. M. & Meadows, M. E. Late Quaternary dynamics of southern Africa’s winter rainfall zone. Earth Sci. Rev. 84, 103–138 (2007).
Google Scholar 
Steele, T. E. et al. Varsche Rivier 003: a Middle and Later Stone Age site with Still Bay and Howiesons Poort assemblages in southern Namaqualand, South Africa. Paleoanthropology 2016, 100–163 (2016).
Google Scholar 
Sharp, W. D. et al. 230Th/U burial dating of ostrich eggshell. Quat. Sci. Rev. 219, 263–276 (2019).
Google Scholar 
Chase, B. M. et al. South African speleothems reveal influence of high- and low-latitude forcing over the last 113.5 kyr. Geology 49, 1353–1357 (2021).CAS 

Google Scholar 
Chase, B. M. et al. Influence of tropical easterlies in southern Africa’s winter rainfall zone during the Holocene. Quat. Sci. Rev. 107, 138–148 (2015).
Google Scholar 
Manning, J. Namaqualand (Briza, 2008).Skinner, J. D. & Chimimba, C. T. The Mammals of the Southern African Subregion 3rd edn (Cambridge Univ. Press, 2005).Skead, C. J. Historical Mammal Incidence in the Cape Province Vol 1: The Western and Northern Cape (Cape Town Department of Nature and Environmental Conservation, 1980).Churcher, C. S. Distribution and history of the Cape zebra (Equus capensis) in the Quaternary of Africa. Trans. R. Soc. S. Afr. 61, 89–95 (2006).
Google Scholar 
Spratt, R. M. & Lisiecki, L. E. A Late Pleistocene sea level stack. Climate 12, 1079–1092 (2016).
Google Scholar 
De Wet, W. Bathymetry of the South African Continental Shelf. MSc thesis, Univ. Cape Town (2013).Jerardino, A. & Marean, C. W. Shellfish gathering, marine paleoecology and modern human behavior: perspectives from Cave PP13B, Pinnacle Point, South Africa. J. Hum. Evol. 59, 412–424 (2010).PubMed 

Google Scholar 
Marean, C. W. Pinnacle Point Cave 13B (Western Cape Province, South Africa) in context: the Cape Floral kingdom, shellfish, and modern human origins. J. Hum. Evol. 59, 425–443 (2010).PubMed 

Google Scholar 
Kandel, A. W. Modification of ostrich eggs by carnivores and its bearing on the interpretation of archaeological and paleontological find. J. Archaeol. Sci. 31, 377–391 (2004).
Google Scholar 
Steele, T. E. & Klein, R. G. The Middle and Later Stone Age faunal remains from Diepkloof Rock Shelter, Western Cape, South Africa. J. Archaeol. Sci. 40, 3453–3462 (2013).
Google Scholar 
Klein, R. G. et al. The Ysterfontein 1 Middle Stone Age site, South Africa, and early human exploitation of coastal resources. Proc. Natl Acad. Sci. USA 101, 5708–5715 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vogelsang, R. et al. New excavations of Middle Stone Age deposits at Apollo 11 Rockshelter, Namibia: stratigraphy, archaeology, chronology and past environments. J. Afr. Archaeol. 8, 185–218 (2010).Marean, C. W. et al. Early human use of marine resources and pigment in South Africa during the Middle Pleistocene. Nature 449, 905–908 (2007).CAS 
PubMed 

Google Scholar 
Schmidt, I. et al. New investigations at the Middle Stone Age site of Pockenbank Rockshelter, Namibia. Antiquity 90, e2 (2016).
Google Scholar 
Vogelsang, R. Middle Stone Age Fundstellen in Südwest-Namibia, Africa (Heinrich-Barth-Institut, 1998).Plug, I. Aquatic animals and their associates from the Middle Stone Age levels at Sibudu. South. Afr. Humanit. 18, 289–299 (2006).
Google Scholar 
Wurz, S. Technological trends in the Middle Stone Age of South Africa between MIS 7 and MIS 3. Curr. Anthropol. 54, S305–S319 (2013).
Google Scholar 
Volman, T. P. The Middle Stone Age in the Southern Cape. PhD thesis, Univ. Chicago (1981).Schmidt, P. & Mackay, A. Why was silcrete heat-treated in the Middle Stone Age? An early transformative technology in the context of raw material use at Mertenhof Rock Shelter, South Africa. PloS ONE 11, e0149243 (2016).PubMed 
PubMed Central 

Google Scholar 
Porraz, G. et al. Technological successions in the Middle Stone Age sequence of Diepkloof Rock Shelter, Western Cape, South Africa. J. Archaeol. Sci. 40, 3376–3400 (2013).
Google Scholar 
Schmid, V., Conard, N. J., Parkington, J., Texier, P. J. & Porraz, G. The ‘MSA 1’ of Elands Bay Cave (South Africa) in the context of the southern African early MSA technologies. South. Afr. Humanit. 29, 153–201 (2016).
Google Scholar 
Evans, U. Hollow Rock Shelter, a Middle Stone Age site in the Cederberg. South. Afr. Field Archaeol. 3, 63–73 (1994).
Google Scholar 
Mackay, A., Jacobs, Z. & Steele, T. E. Pleistocene archaeology and chronology of Putslaagte 8 (PL8) rockshelter, Western Cape, South Africa. J. Afr. Archaeol. 13, 71–98 (2015).
Google Scholar 
Thompson, J. C. et al. Ecological risk, demography and technological complexity in the Late Pleistocene of northern Malawi: implications for geographical patterning in the Middle Stone Age. J. Quat. Sci. 33, 261–284 (2018).
Google Scholar 
Vaesen, K. & Houkes, W. Is human culture cumulative? Curr. Anthropol. 62, 218–238 (2021).
Google Scholar 
Boyd, R., Richerson, P. J. & Henrich, J. The cultural niche: why social learning is essential for human adaptation. Proc. Natl Acad. Sci. USA 108, 10918–10925 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sterelny, K. From hominins to humans: how sapiens became behaviourally modern. Phil. Trans. R. Soc. B 366, 809–822 (2011).PubMed 
PubMed Central 

Google Scholar 
Gärdenfors, P. & Högberg, A. The archaeology of teaching and the evolution of Homo docens. Curr. Anthropol. 58, 188–208 (2017).
Google Scholar 
Marwick, B. Pleistocene exchange networks as evidence for the evolution of language. Camb. Archaeol. J. 13, 67–81 (2003).
Google Scholar 
Blegen, N. The earliest long-distance obsidian transport: evidence from the ∼200 ka Middle Stone Age Sibilo School Road Site, Baringo, Kenya. J. Hum. Evol. 103, 1–19 (2017).PubMed 

Google Scholar 
McBrearty, S. & Brooks, A. S. The revolution that wasn’t: a new interpretation of the origin of modern human behavior. J. Hum. Evol. 39, 453–563 (2000).CAS 
PubMed 

Google Scholar 
Klein, R. G. Archeology and the evolution of human behavior. Evol. Anthropol. 9, 17–36 (2000).
Google Scholar 
Wynn, T. & Coolidge, F. L. Archeological insights into hominin cognitive evolution. Evol. Anthropol. 25, 200–213 (2016).PubMed 

Google Scholar 
Derex, M. & Mesoudi, A. Cumulative cultural evolution within evolving population structures. Trends Cogn. Sci. 24, 654–667 (2020).PubMed 

Google Scholar 
Sterelny, K. Adaptable individuals and innovative lineages. Phil. Trans. R. Soc. B 371, 20150196 (2016).PubMed 
PubMed Central 

Google Scholar 
Henshilwood, C. S. & Marean, C. W. The origin of modern human behavior: critique of the models and their test implications. Curr. Anthropol. 44, 627–651 (2003).PubMed 

Google Scholar 
Stoops, G., Marcelino, V. & Mees, F. (eds) Interpretation of Micromorphological Features of Soils and Regoliths (Elsevier, 2010).Stoops, G. Guidelines for Analysis and Description of Soil and Regolith Thin Sections (Soil Science Society of America, 2003).McPherron, S. P. Additional statistical and graphical methods for analyzing site formation processes using artifact orientations. PLoS ONE 13, e0190195 (2018).PubMed 
PubMed Central 

Google Scholar 
Thomsen, K. J., Murray, A. S., Jain, M. & Bøtter-Jensen, L. Laboratory fading rates of various luminescence signals from feldspar-rich sediment extracts. Radiat. Meas. 43, 1474–1486 (2008).CAS 

Google Scholar 
Armitage, S. J. & Bailey, R. M. The measured dependence of laboratory beta dose rates on sample grain size. Radiat. Meas. 39, 123–127 (2005).CAS 

Google Scholar 
Huntley, D. J. & Lamothe, M. Ubiquity of anomalous fading in K-feldspars and the measurement and correction for it in optical dating. Can. J. Earth Sci. 38, 1093–1106 (2001).CAS 

Google Scholar 
Jaffey, A. H., Flynn, K. F., Glendenin, L. E. & Essling, A. M. Precision measurement of half-lives and specific activities of 235U and 238U. Phys. Rev. C 4, 1889–1906 (1971).
Google Scholar 
Cheng, H. et al. Improvements in 230Th dating, 230Th and 234U half-life values, and U–Th isotopic measurements by multi-collector inductively coupled plasma mass spectrometry. Earth Planet. Sci. Lett. 371–372, 82–91 (2013).
Google Scholar 
Holden, N. E. Total half-lives for selected nuclides. Pure Appl. Chem. 62, 941–958 (1990).CAS 

Google Scholar 
Ludwig, K. R. Isoplot/Ex Version 3.75: A Geochronological Toolkit for Microsoft Excel (Berkeley Geochronology Center Special Publication, 2010).Collins, B. & Steele, T. E. An often overlooked resource: ostrich (Struthio spp.) eggshell in the archaeological record. J. Archaeol. Sci. Rep. 13, 121–131 (2017).
Google Scholar 
Schmidt, P. How reliable is the visual identification of heat treatment on silcrete? A quantitative verification with a new method. Archaeol. Anthropol. Sci. 11, 713–726 (2017).
Google Scholar 
Roberts, D. L. Age, Genesis and Significance of South African Coastal Belt Silcretes (Council for Geoscience, South Africa, 2003).Schmidt, P. et al. A previously undescribed organic residue sheds light on heat treatment in the Middle Stone Age. J. Hum. Evol. 85, 22–34 (2015).PubMed 

Google Scholar 
Trabucco, A. & Zomer, R. Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2 (figshare, 2019); https://doi.org/10.6084/m9.figshare.7504448.v3World Atlas of Desertification 2nd edn (UNEP, 1997).Mucina, L. & Rutherford, M. C. The Vegetation of South Africa, Lesotho and Swaziland (South African National Biodiversity Institute, 2006).Cordova, C. E. C3 Poaceae and Restionaceae phytoliths as potential proxies for reconstructing winter rainfall in South Africa. Quat. Int. 287, 121–140 (2013).
Google Scholar 
Esteban, I. et al. Modern soil phytolith assemblages used as proxies for paleoscape reconstruction on the south coast of South Africa. Quat. Int. 434, 160–179 (2017).
Google Scholar 
Laskar, J. et al. A long-term numerical solution for the insolation quantities of the Earth. Astron. Astrophys. 428, 261–285 (2004).
Google Scholar 
Chase, B. M. et al. Orbital controls on Namib Desert hydroclimate over the past 50,000 years. Geology 47, 867–871 (2019).
Google Scholar 
Farmer, E. C., deMenocal, P. B. & Marchitto, T. M. Holocene and deglacial ocean temperature variability in the Benguela upwelling region: implications for low‐latitude atmospheric circulation. Paleoceanography 20, PA2018 (2005).
Google Scholar 
Pichevin, L., Cremer, M., Giraudeau, J. & Bertrand, P. A 190 kyr record of lithogenic grain size on the Namibian slope: forging a tight link between past wind‐strength and coastal upwelling dynamics. Mar. Geol. 218, 81–96 (2005).
Google Scholar 
Little, M. G. et al. Trade wind forcing of upwelling, seasonality, and Heinrich events as a response to sub‐Milankovitch climate variability. Paleoceanography 12, 568–576 (2005).
Google Scholar 
Stuut, J.-B. et al. A 300‐kyr record of aridity and wind strength in southwestern Africa: inferences from grain‐size distributions of sediments on Walvis Ridge, SE Atlantic. Mar. Geol. 180, 221–233 (2002).
Google Scholar 
Kandel, A. W. & Conard, N. J. Production sequences of ostrich eggshell beads and settlement dynamics in the Geelbek Dunes of the Western Cape, South Africa. J. Archaeol. Sci. 32, 1711–1721 (2005).
Google Scholar  More

Trenberth, K. E. & Jones, P. D. in Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) 235–335 (Cambridge Univ. Press, 2007).Linderholm, H. W. Growing season changes in the last century. Agr. For. Meteorol. 137, 1–14 (2006).
Google Scholar 
Yang, B. et al. New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data. Proc. Natl Acad. Sci. USA 114, 6966–6971 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Shen, M., Tang, Y., Chen, J. & Yang, W. Specification of thermal growing season in temperate China from 1960 to 2009. Clim. Change 114, 783–798 (2012).
Google Scholar 
Zhou, B., Zhai, P., Chen, Y. & Yu, R. Projected changes of thermal growing season over Northern Eurasia in a 1.5 °C and 2 °C warming world. Environ. Res. Lett. 13, 35004 (2018).
Google Scholar 
Barichivich, J., Briffa, K. R., Osborn, T. J., Melvin, T. M. & Caesar, J. Thermal growing season and timing of biospheric carbon uptake across the Northern Hemisphere. Glob. Biogeochem. Cycles 26, B4015 (2012).
Google Scholar 
Buitenwerf, R., Rose, L. & Higgins, S. I. Three decades of multi-dimensional change in global leaf phenology. Nat. Clim. Change 5, 364–368 (2015).
Google Scholar 
Gonsamo, A., Chen, J. M. & Ooi, Y. W. Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems. Glob. Change Biol. 24, 2117–2128 (2018).
Google Scholar 
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).
Google Scholar 
Montgomery, R. A., Rice, K. E., Stefanski, A., Rich, R. L. & Reich, P. B. Phenological responses of temperate and boreal trees to warming depend on ambient spring temperatures, leaf habit, and geographic range. Proc. Natl Acad. Sci. USA 117, 10397–10405 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Piao, S. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).CAS 
PubMed 

Google Scholar 
Barichivich, J. et al. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Glob. Change Biol. 19, 3167–3183 (2013).
Google Scholar 
Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).PubMed 

Google Scholar 
Piao, S. et al. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nat. Clim. Change 7, 359–363 (2017).CAS 

Google Scholar 
Richardson, A. D. et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B 365, 3227–3246 (2010).
Google Scholar 
Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).CAS 
PubMed 

Google Scholar 
Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).
Google Scholar 
Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).CAS 
PubMed 

Google Scholar 
Park, T. et al. Changes in timing of seasonal peak photosynthetic activity in northern ecosystems. Glob. Change Biol. 25, 2382–2395 (2019).
Google Scholar 
Xu, C., Liu, H., Williams, A. P., Yin, Y. & Wu, X. Trends toward an earlier peak of the growing season in Northern Hemisphere mid-latitudes. Glob. Change Biol. 22, 2852–2860 (2016).
Google Scholar 
Wang, X. et al. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun. 10, 2389 (2019).PubMed 
PubMed Central 

Google Scholar 
Piao, S., Friedlingstein, P., Ciais, P., Viovy, N. & Demarty, J. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, B3018 (2007).
Google Scholar 
Buermann, W., Bikash, P. R., Jung, M., Burn, D. H. & Reichstein, M. Earlier springs decrease peak summer productivity in North American boreal forests. Environ. Res. Lett. 8, 24027 (2013).
Google Scholar 
Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).CAS 
PubMed 

Google Scholar 
Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).PubMed 
PubMed Central 

Google Scholar 
Piao, S. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 49–52 (2008).CAS 
PubMed 

Google Scholar 
Wang, H. et al. Alpine grassland plants grow earlier and faster but biomass remains unchanged over 35 years of climate change. Ecol. Lett. 23, 701–710 (2020).PubMed 
PubMed Central 

Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 
PubMed 

Google Scholar 
Delpierre, N. et al. Temperate and boreal forest tree phenology: from organ-scale processes to terrestrial ecosystem models. Ann. For. Sci. 73, 5–25 (2016).
Google Scholar 
Huang, J. et al. Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers. Proc. Natl Acad. Sci. USA 117, 20645–20652 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, X. et al. Critical minimum temperature limits xylogenesis and maintains treelines on the southeastern Tibetan Plateau. Sci. Bull. 62, 804–812 (2017).
Google Scholar 
Rossi, S. et al. Critical temperatures for xylogenesis in conifers of cold climates. Glob. Ecol. Biogeogr. 17, 696–707 (2008).
Google Scholar 
Lenz, A., Vitasse, Y., Hoch, G. & Körner, C. Growth and carbon relations of temperate deciduous tree species at their upper elevation range limit. J. Ecol. 102, 1537–1548 (2014).
Google Scholar 
Zeng, Q., Rossi, S., Yang, B., Qin, C. & Li, G. Environmental drivers for cambial reactivation of Qilian junipers (Juniperus przewalskii) in a semi-arid region of northwestern China. Atmosphere 11, 232 (2020).
Google Scholar 
Ren, P. et al. Growth rate rather than growing season length determines wood biomass in dry environments. Agr. For. Meteorol. 271, 46–53 (2019).
Google Scholar 
Sanginés De Cárcer, P. et al. Vapor-pressure deficit and extreme climatic variables limit tree growth. Glob. Change Biol. 24, 1108–1122 (2017).
Google Scholar 
Zhang, J. et al. Drought limits wood production of Juniperus przewalskii even as growing seasons lengthens in a cold and arid environment. Catena 196, 104936 (2021).
Google Scholar 
Huang, J., Deslauriers, A. & Rossi, S. Xylem formation can be modeled statistically as a function of primary growth and cambium activity. New Phytol. 203, 831–841 (2014).CAS 
PubMed 

Google Scholar 
Rossi, S., Morin, H. & Deslauriers, A. Causes and correlations in cambium phenology: towards an integrated framework of xylogenesis. J. Exp. Bot. 63, 2117–2126 (2012).CAS 
PubMed 

Google Scholar 
Rossi, S., Girard, M. J. & Morin, H. Lengthening of the duration of xylogenesis engenders disproportionate increases in xylem production. Glob. Change Biol. 20, 2261–2271 (2014).
Google Scholar 
Cuny, H. E. et al. Woody biomass production lags stem-girth increase by over one month in coniferous forests. Nat. Plants 1, 15160 (2015).CAS 
PubMed 

Google Scholar 
Pasho, E., Camarero, J. J. & Vicente-Serrano, S. M. Climatic impacts and drought control of radial growth and seasonal wood formation in Pinus halepensis. Trees 26, 1875–1886 (2012).
Google Scholar 
Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 4, 598–604 (2014).CAS 

Google Scholar 
Chen, L. et al. Leaf senescence exhibits stronger climatic responses during warm than during cold autumns. Nat. Clim. Change 10, 777–780 (2020).CAS 

Google Scholar 
Körner, C. Paradigm shift in plant growth control. Curr. Opin. Plant Biol. 25, 107–114 (2015).PubMed 

Google Scholar 
Muller, B. et al. Water deficits uncouple growth from photosynthesis, increase C content, and modify the relationships between C and growth in sink organs. J. Exp. Bot. 62, 1715–1729 (2011).CAS 
PubMed 

Google Scholar 
Charney, N. D. et al. Observed forest sensitivity to climate implies large changes in 21st century North American forest growth. Ecol. Lett. 19, 1119–1128 (2016).PubMed 

Google Scholar 
Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).PubMed 
PubMed Central 

Google Scholar 
Deslauriers, A. & Morin, H. Intra-annual tracheid production in balsam fir stems and the effect of meteorological variables. Trees 19, 402–408 (2005).
Google Scholar 
Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).
Google Scholar 
Huang, M. et al. Air temperature optima of vegetation productivity across global biomes. Nat. Ecol. Evol. 3, 772–779 (2019).PubMed 
PubMed Central 

Google Scholar 
Keenan, T. F. & Riley, W. J. Greening of the land surface in the world’s cold regions consistent with recent warming. Nat. Clim. Change 8, 825–828 (2018).CAS 

Google Scholar 
Camarero, J. J., Olano, J. M. & Parras, A. Plastic bimodal xylogenesis in conifers from continental Mediterranean climates. New Phytol. 185, 471–480 (2010).PubMed 

Google Scholar 
Fu, Y. H. et al. Unexpected role of winter precipitation in determining heat requirement for spring vegetation green-up at northern middle and high latitudes. Glob. Change Biol. 20, 3743–3755 (2014).
Google Scholar 
Wu, X. et al. Uneven winter snow influence on tree growth across temperate China. Glob. Change Biol. 25, 144–154 (2018).
Google Scholar 
Wang, X. et al. Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems. Glob. Change Biol. 24, 1651–1662 (2018).
Google Scholar 
Adams, H. D. et al. Experimental drought and heat can delay phenological development and reduce foliar and shoot growth in semiarid trees. Glob. Change Biol. 21, 4210–4220 (2015).
Google Scholar 
He, W., Liu, H., Qi, Y., Liu, F. & Zhu, X. Patterns in nonstructural carbohydrate contents at the tree organ level in response to drought duration. Glob. Change Biol. 26, 3627–3638 (2020).
Google Scholar 
Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Change 3, 292–297 (2012).
Google Scholar 
Vitasse, Y. et al. Contrasting resistance and resilience to extreme drought and late spring frost in five major European tree species. Glob. Change Biol. 25, 3781–3792 (2019).
Google Scholar 
Zhao, S. et al. The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).
Google Scholar 
Babst, F., Poulter, B., Bodesheim, P., Mahecha, M. D. & Frank, D. C. Improved tree-ring archives will support earth-system science. Nat. Ecol. Evol. 1, 8 (2017).PubMed 

Google Scholar 
Elmore, A. J., Guinn, S. M., Minsley, B. J. & Richardson, A. D. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob. Change Biol. 18, 656–674 (2012).
Google Scholar 
Kannenberg, S. A. et al. Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US. Ecol. Lett. 22, 119–127 (2018).PubMed 

Google Scholar 
Rossi, S., Deslauriers, A., Anfodillo, T. & Carraro, V. Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia 152, 1–12 (2007).PubMed 

Google Scholar 
Gao, S. et al. Dynamic responses of tree-ring growth to multiple dimensions of drought. Glob. Change Biol. 24, 5380–5390 (2018).
Google Scholar 
Peltier, D. M. P. & Ogle, K. Tree growth sensitivity to climate is temporally variable. Ecol. Lett. 23, 1561–1572 (2020).PubMed 

Google Scholar 
Wilmking, M. et al. Global assessment of relationships between climate and tree growth. Glob. Change Biol. 26, 3212–3220 (2020).
Google Scholar 
Seftigen, K., Frank, D. C., Björklund, J., Babst, F. & Poulter, B. The climatic drivers of normalized difference vegetation index and tree-ring-based estimates of forest productivity are spatially coherent but temporally decoupled in Northern Hemispheric forests. Glob. Ecol. Biogeogr. 27, 1352–1365 (2018).
Google Scholar 
Bunn, A. G. A dendrochronology program library in R (dplR). Dendrochronologia 26, 115–124 (2008).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.R-project.org/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).
Google Scholar 
Frich, P. L. et al. Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19, 193–212 (2002).
Google Scholar 
Selyaninov, G. T. About climate agricultural estimation (in Russian). Proc. Agric. Meteorol. 20, 165–177 (1928).
Google Scholar 
Streiner, D. L. Finding our way: an introduction to path analysis. Can. J. Psychiatry 50, 115–122 (2005).PubMed 

Google Scholar 
Fox, J., Nie, Z. & Byrnes, J. sem: Structural equation models. R package version 3.1-9 https://CRAN.R-project.org/package=sem (2017).Iturbide, M. et al. An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets. Earth Syst. Sci. Data 12, 2959–2970 (2020).
Google Scholar 
Bagozzi, R. P. & Yi, Y. Specification, evaluation, and interpretation of structural equation models. J. Acad. Mark. Sci. 40, 8–34 (2012).
Google Scholar  More