Ecology

1.Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.DeLong, J. P. et al. The body size dependence of trophic cascades. Am. Nat. 185, https://doi.org/10.1086/679735 (2015).3.Ellner, S. P. et al. Habitat structure and population persistence in an experimental community. Nature 412, 538–543 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
4.Lin Jiang & Peter J., Morin Predator diet Breadth Influences the relative importance of bottom-up and top-down control of prey biomass and diversity. Am. Nat. 165, 350–363 (2005).PubMed 
Article 

Google Scholar 
5.Johnke, J. et al. Multiple micro-predators controlling bacterial communities in the environment. Curr. Opin. Biotechnol. 27, 185–190 (2014).CAS 
PubMed 
Article 

Google Scholar 
6.Suttle, C. A. Marine viruses: major players in the global ecosystem. Nat. Rev. Micro 5, 801–812 (2007).CAS 
Article 

Google Scholar 
7.Pernthaler, J. Predation on prokaryotes in the water column and its ecological implications. Nat. Rev. Micro 3, 537–546 (2005).CAS 
Article 

Google Scholar 
8.Rotem, O. et al. in The Prokaryotes: Deltaproteobacteria and 740 Epsilonproteobacteria (eds Rosenberg, R. et al.) 3–17 (Springer, 2014).9.Chen, H., Athar, R., Zheng, G. & Williams, H. N. Prey bacteria shape the community structure of their predators. ISME J. https://doi.org/10.1038/ismej.2011.4 (2011).10.Koval, S. F. et al. Bdellovibrio exovorus sp. nov., a novel predator of Caulobacter crescentus. Int. J. Syst. Evol. Microbiol 63, 146–151 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Jurkevitch, E., Minz, D., Ramati, B. & Barel, G. Prey range characterization, ribotyping, and diversity of soil and rhizosphere Bdellovibrio spp. isolated on phytopathogenic bacteria. Appl. Environ. Microbiol. 66, 2365–2371 (2000).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Kadouri, D. E., To, K., Shanks, R. M. Q. & Doi, Y. Predatory bacteria: a potential ally against multidrug-resistant gram-negative pathogens. PLoS ONE 8, e63397 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Williams, H. N. et al. Halobacteriovorax, an underestimated predator on bacteria: potential impact relative to viruses on bacterial mortality. ISME J. 10, 491–499 (2016).CAS 
PubMed 
Article 

Google Scholar 
14.Feng, S. et al. Predation by Bdellovibrio bacteriovorus significantly reduces viability and alters the microbial community composition of activated sludge flocs and granules. FEMS Microbiol. Ecol. 93, fix020–fix020 (2017).Article 
CAS 

Google Scholar 
15.Chauhan, A., Cherrier, J. & Williams, H. N. Impact of sideways and bottom-up control factors on bacterial community succession over a tidal cycle. Proc. Natl Acad. Sci. USA 106, 4301–4306 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Kandel, P. P., Pasternak, Z., van Rijn, J., Nahum, O. & Jurkevitch, E. Abundance, diversity and seasonal dynamics of predatory bacteria in aquaculture zero discharge systems. FEMS Microbiol. Ecol. 89, 149–161 (2014).CAS 
PubMed 
Article 

Google Scholar 
17.Li, N. & Williams, H. 454 Pyrosequencing reveals diversity of Bdellovibrio and like organisms in fresh and salt water. Antonie van Leeuwenhoek 107, 305–311 (2015).PubMed 
Article 

Google Scholar 
18.Daims, H., Taylor, M. W. & Wagner, M. Wastewater treatment: a model system for microbial ecology. Trends Biotechnol. 24, 483–489 (2006).CAS 
PubMed 
Article 

Google Scholar 
19.Dolinšek, J., Lagkouvardos, I., Wanek, W., Wagner, M. & Daims, H. Interactions of nitrifying bacteria and heterotrophs: identification of a Micavibrio-like putative predator of Nitrospira spp. Appl. Environ. Microbiol. 79, 2027–2037 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
20.Yu, R., Zhang, S., Chen, Z. & Li, C. Isolation and application of predatory Bdellovibrio-and-like organisms for municipal waste sludge biolysis and dewaterability enhancement. Front. Env. Sci. Eng. 11, 10 (2017).Article 
CAS 

Google Scholar 
21.Pineiro, S. et al. Niche partition of Bacteriovorax operational taxonomic units along salinity and temporal gradients in the chesapeake bay reveals distinct estuarine strains. Microb. Ecol. 65, 652–660 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Cohen, Y. et al. Bacteria and microeukaryotes are differentially segregated in sympatric wastewater microhabitats. Environ. Microbiol. 21, 1757–1770 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Mahmoud, K. K., McNeely, D., Elwood, C. & Koval, S. F. Design and performance of a 16S rRNA-targeted oligonucleotide probe for detection of members of the genus Bdellovibrio by fluorescence in situ hybridization. Appl. Environ. Microbiol. 73, 7488–7493 (2007).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Albertsen, M., Karst, S. M., Ziegler, A. S., Kirkegaard, R. H. & Nielsen, P. H. Back to basics – the influence of DNA extraction and primer choice on phylogenetic analysis of activated sludge communities. PLOS ONE 10, e0132783 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
25.Welsh, R. M. et al. Bacterial predation in a marine host-associated microbiome. ISME J. 10, 1540–1544 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
26.Chow, C.-E. T., Kim, D. Y., Sachdeva, R., Caron, D. A. & Fuhrman, J. A. Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists. ISME J. 8, 816–829 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Newman, M. E. J. Modularity and community structure in networks. Proc. Natl Acad. Sci. USA 103, 8577–8582 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Ju, F. & Zhang, T. Bacterial assembly and temporal dynamics in activated sludge of a full-scale municipal wastewater treatment plant. ISME J. 9, 683–695 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.Tudor, J. J. & Conti, S. F. Characterization of bdellocysts of Bdellovibrio sp. J. Bacteriol. 131, 314–322 (1977).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Eloe-Fadrosh, E. A., Ivanova, N. N., Woyke, T. & Kyrpides, N. C. Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nat. Microbiol. 1, 15032 (2016).CAS 
PubMed 
Article 

Google Scholar 
31.Parks, D. H. et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat. Microbiol. 2, 1533–1542 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Williams, H. N. The recovery of high numbers of bdellovibrios from the surface water microlayer. Can. J. Microbiol. 33, 572–575 (1987).Article 

Google Scholar 
33.Liu, L., Yang, J., Yu, Z. & Wilkinson, D. M. The biogeography of abundant and rare bacterioplankton in the lakes and reservoirs of China. ISME J https://doi.org/10.1038/ismej.2015.29 (2015).34.Wilén, B.-M., Jin, B. & Lant, P. The influence of key chemical constituents in activated sludge on surface and flocculating properties. Water Res. 37, 2127–2139 (2003).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
35.Phuong, K., Kakii, K. & Nikata, T. Intergeneric coaggregation of non-flocculating Acinetobacter spp. isolates with other sludge-constituting bacteria. J. Biosci. Bioeng. 107, 394–400 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
36.Kadouri, D. & O’Toole, G. A. Susceptibility of biofilms to Bdellovibrio bacteriovorus attack. Appl. Environ. Microbiol. 71, 4044–4051 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Im, H., Dwidar, M. & Mitchell, R. J. Bdellovibrio bacteriovorus HD100, a predator of Gram-negative bacteria, benefits energetically from Staphylococcus aureus biofilms without predation. ISME J. https://doi.org/10.1038/s41396-018-0154-5 (2018).38.Feng, S., Tan, C. H., Cohen, Y. & Rice, S. A. Isolation of Bdellovibrio bacteriovorus from a tropical wastewater treatment plant and predation of mixed species biofilms assembled by the native community members. Environ. Microbiol. 18, 3923–3931 (2016).CAS 
PubMed 
Article 

Google Scholar 
39.Rice, T. D., Williams, H. N. & Turng, B. F. Susceptibility of bacteria in estuarine environments to autochthonous bdellovibrios. Microb. Ecol. 35, 256–264 (1998).CAS 
PubMed 
Article 

Google Scholar 
40.Szabó, E. et al. Comparison of the bacterial community composition in the granular and the suspended phase of sequencing batch reactors. AMB Express 7, 168 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
41.Wilén, B.-M., Jin, B. & Lant, P. Impacts of structural characteristics on activated sludge floc stability. Water Res. 37, 3632–3645 (2003).PubMed 
Article 
CAS 

Google Scholar 
42.Hahn, M. W. & Hofle, M. G. Flagellate predation on a bacterial model community: interplay of size-selective grazing, specific bacterial cell size, and bacterial community composition. Appl. Environ. Microbiol. 65, 4863–4872 (1999).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Kadouri, D., Venzon, N. C. & O’Toole, G. A. Vulnerability of pathogenic biofilms to Micavibrio aeruginosavorus. Appl. Environ. Microbiol. 73, 605–614 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
44.Dashiff, A., Junka, R., Libera, M. & Kadouri, D. Predation of human pathogens by the predatory bacteria Micavibrio aeruginosavorus and Bdellovibrio bacteriovorus. J. Appl. Microbiol. https://doi.org/10.1111/j.1365-2672.2010.04900.x (2011).45.Winder, M. Photosynthetic picoplankton dynamics in Lake Tahoe: temporal and spatial niche partitioning among prokaryotic and eukaryotic cells. J. Plankton Res. 31, 1307–1320 (2009).Article 

Google Scholar 
46.Dini-Andreote, F. et al. Dynamics of bacterial community succession in a salt marsh chronosequence: evidences for temporal niche partitioning. ISME J 8, 1989 (2014).47.Kelley, J., Turng, B., Williams, H. & Baer, M. Effects of temperature, salinity, and substrate on the colonization of surfaces in situ by aquatic bdellovibrios. Appl. Environ. Microbiol. 63, 84–90 (1997).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Thingstad, T. A theoretical approach to structuring mechanisms in the pelagic food web. Hydrobiologia 363, 59–72 (1998).Article 

Google Scholar 
49.Shapiro, O. H., Kushmaro, A. & Brenner, A. Bacteriophage predation regulates microbial abundance and diversity in a full-scale bioreactor treating industrial wastewater. ISME J. 4, 327–336 (2009).PubMed 
Article 

Google Scholar 
50.Dwidar, M., Nam, D. & Mitchell, R. J. Indole negatively impacts predation by Bdellovibrio bacteriovorus and its release from the bdelloplast. Environ. Microbiol. 17, 1009–1022 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Mun, W. et al. Cyanide production by chromobacterium piscinae shields it from Bdellovibrio bacteriovorus HD100 predation. mBio https://doi.org/10.1128/mBio.01370-17 (2017).52.Sathyamoorthy, R. et al. To hunt or to rest: prey depletion induces a novel starvation survival strategy in bacterial predators. ISME J. 15, 109–123 (2021).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Winter, C., Bouvier, T., Weinbauer, M. G. & Thingstad, T. F. Trade-Offs between competition and defense specialists among unicellular planktonic organisms: the “Killing the Winner” hypothesis revisited. Microbiol. Molec. Biol. Rev. 74, 42–57 (2010).CAS 
Article 

Google Scholar 
54.Chanyi, R. M., Ward, C., Pechey, A. & Koval, S. F. To invade or not to invade: two approaches to a prokaryotic predatory life cycle. Can. J. Microbiol. 59, 273–279 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Lu, F. & Cai, J. The protective effect of Bdellovibrio-and-like organisms (BALO) on tilapia fish fillets against Salmonella enterica ssp. enterica serovar Typhimurium. Lett. Appl. Microbiol. 51, 625–631 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Peura, S., Bertilsson, S., Jones, R. I. & Eiler, A. Resistant microbial cooccurrence patterns inferred by network topology. Appl. Environ. Microbiol. 81, 2090–2097 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Meerburg, F. A. et al. High-rate activated sludge communities have a distinctly different structure compared to low-rate sludge communities, and are less sensitive towards environmental and operational variables. Water Res. 100, 137–145 (2016).CAS 
PubMed 
Article 

Google Scholar 
58.de Celis, M. et al. Tuning up microbiome analysis to monitor WWTPs’ biological reactors functioning. Sci. Rep. 10, 1–8 (2020).ADS 
Article 
CAS 

Google Scholar 
59.Hashimoto, T., Diedrich, D. L. & Conti, S. F. Isolation of a bacteriophage for Bdellovibrio bacteriovorus. J. Virol. 5, 87–98 (1970).Article 

Google Scholar 
60.Varon, M. & Levisohn, R. Three-membered parasitic systems: a bacteriophage, Bdellovibrio bacteriovorus, and Escherichia coli. J. Virol. 9, 519–525 (1972).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Johnke, J., Boen–igk, J., Harms, H. & Chatzinotas, A. Killing the killer: predation between protists and predatory bacteria. FEMS Microbiol. Lett. 364, fnx089–fnx089 (2017).Article 
CAS 

Google Scholar 
62.Johnke, J. et al. A generalist protist predator enables coexistence in multitrophic predator–prey systems containing a phage and the bacterial predator Bdellovibrio. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2017.00124 (2017).63.Berleman, J. E., Chumley, T., Cheung, P. & Kirby, J. R. Rippling is a predatory behavior in Myxococcus xanthus. J. Bacteriol. 188, 5888–5895 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Shimkets, L. J. Social and developmental biology of myxobacteria. Microbiol. Rev. 54, 473–501 (1990).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
65.Friman, V.-P. & Buckling, A. Phages can constrain protist predation-driven attenuation of Pseudomonas aeruginosa virulence in multienemy communities. ISME J. 8, 1820 (2014).66.Matassa, S., Verstraete, W., Pikaar, I. & Boon, N. Autotrophic nitrogen assimilation and carbon capture for microbial protein production by a novel enrichment of hydrogen-oxidizing bacteria. Water Res. 101, 137–146 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
67.Semblante, G. U. et al. The role of microbial diversity and composition in minimizing sludge production in the oxic-settling-anoxic process. Sci. Tot. Environ. 607–608, 558–567 (2017).Article 
CAS 

Google Scholar 
68.Xia, Y., Kong, Y., Thomsen, T. R. & Halkjær Nielsen, P. Identification and ecophysiological characterization of epiphytic protein-hydrolyzing saprospiraceae (“Candidatus Epiflobacter” spp.) in activated sludge. Appl. Environ. Microbiol. 74, 2229–2238 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
69.Niu, T. et al. Effects of dissolved oxygen on performance and microbial community structure in a micro-aerobic hydrolysis sludge in situ reduction process. Water Res. 90, 369–377 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Ju, F., Xia, Y., Guo, F., Wang, Z. & Zhang, T. Taxonomic relatedness shapes bacterial assembly in activated sludge of globally distributed wastewater treatment plants. Environ. Microbiol. 16, 2421–2432 (2014).CAS 
PubMed 
Article 

Google Scholar 
71.Günther, S. et al. Correlation of community dynamics and process parameters as a tool for the prediction of the stability of wastewater treatment. Environ. Sci. Technol. 46, 84–92 (2012).ADS 
PubMed 
Article 
CAS 

Google Scholar 
72.Nettmann, E. et al. Development of a flow-fluorescence in situ hybridization protocol for the analysis of microbial communities in anaerobic fermentation liquor. BMC Microbiol. 13, 278–278 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
73.Kim, J. M. et al. Analysis of the fine-scale population structure of “Candidatus accumulibacter phosphatis” in enhanced biological phosphorus removal sludge, using fluorescence In Situ hybridization and flow cytometric sorting. Appl. Environl. Microbiol. 76, 3825–3835 (2010).ADS 
CAS 
Article 

Google Scholar 
74.Wallner, G., Erhart, R. & Amann, R. Flow cytometric analysis of activated sludge with rRNA-targeted probes. Appl. Environ. Microbiol. 61, 1859–1866 (1995).ADS 
CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
76.Spencer, S. J. et al. Massively parallel sequencing of single cells by epicPCR links functional genes with phylogenetic markers. ISME J. 10, 427–436 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Pernthaler, J. & Amann, R. Fate of heterotrophic microbes in pelagic habitats: focus on populations. Microbiol. Mol. Biol. Rev. 69, 440–461 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Jurkevitch, E. In The Ecology of Predation at the Microscale (eds Mitchell, R. J.) 37–64 (Springer, 2020).79.Delmont, T. O. et al. Accessing the soil metagenome for studies of microbial diversity. Appl. Environ. Microbiol. 77, 1315–1324 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Green, S. J., Venkatramanan, R. & Naqib, A. Deconstructing the polymerase chain reaction: understanding and correcting bias associated with primer degeneracies and primer-template mismatches. PloS ONE 10, e0128122 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
81.Schloss, P. D. et al. Introducing Mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
82.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41, D590–D596 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
83.Huse, S. M., Welch, D. M., Morrison, H. G. & Sogin, M. L. Ironing out the wrinkles in the rare biosphere through improved OTU clustering. Environ. Microbiol. 12, 1889–1898 (2010).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
85.Yarza, P. et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12, 635 (2014).CAS 
PubMed 
Article 

Google Scholar 
86.McMurdie, P. J. & Holmes, S. Waste not, want not: why rarefying microbiome data is inadmissible. PLOS Comput. Biol. 10, e1003531 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
87.Mather, P. Computational Methods of Multivariate Analysis in Physical Geography (J Wiley and Sons, 1976).88.Berry, K. J. & Mielke, P. W. Computation of exact probability values for multi-response permutation procedures (MRPP). Commun. Stat. – Simul. Comput. 13, 417–432 (1984).MathSciNet 
Article 

Google Scholar 
89.Edgar, R. C. MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinf. 5, 113 (2004).Article 
CAS 

Google Scholar 
90.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol https://doi.org/10.1093/molbev/mst197 (2013).91.Kendall, M. G. Rank Correlation Methods 2nd edn, (Hafner, 1955).92.Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. Icwsm 8, 361–362 (2009).
Google Scholar 
93.Sathyamoorthy, R. et al. Bacterial predation under changing viscosities. Environ. Microbiol. 21, 2997–3010 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Jurkevitch, E. In Current Protocols in Microbiology (ed Coico, R. et al.) (John Wiley and Sons, 2012).95.Whelan, J. A., Russell, N. B. & Whelan, M. A. A method for the absolute quantification of cDNA using real-time PCR. J. Immunol. Methods 278, 261–269 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Nakatsuji, T. et al. The microbiome extends to subepidermal compartments of normal skin. Nat. Commun. https://www.nature.com/articles/ncomms2441 (2013).97.Van Essche, M. et al. Development and performance of a quantitative PCR for the enumeration of Bdellovibrionaceae. Environ. Microbiol. Rep. 1, 228–233 (2009).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
98.Zheng, G., Wang, C., Williams, H. N. & Pineiro, S. A. Development and evaluation of a quantitative real-time PCR assay for the detection of saltwater. Bacteriovorax. Environ. Microbiol. 10, 2515–2526 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
99.Liu, Z. et al. Neutral mechanisms and niche differentiation in steady-state insular microbial communities revealed by single cell analysis. Environ. Microbiol. 21, 164–181 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.Amann, R. I. et al. Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations. Appl. Environ. Microbiol. 56, 1919–1925 (1990).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
101.Cichocki, N. et al. Bacterial mock communities as standards for reproducible cytometric microbiome analysis. Nat. Protoc. 15, 2788–2812 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Perry, A. L., Low, P. J., Ellis, J. R. & Reynolds, J. D. Climate change and distribution shifts in marine fishes. Science 308, 1912–1915 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Nye, J. A., Link, J. S., Hare, J. A. & Overholtz, W. J. Changing spatial distribution of fish stocks in relation to climate and population size on the Northeast United States continental shelf. Mar. Ecol. Prog. Ser. 393, 111–129 (2009).ADS 
Article 

Google Scholar 
3.Fodrie, F. J., Heck, K. L. Jr., Powers, S. P., Graham, W. M. & Robinson, K. L. Climate-related, decadal-scale assemblage changes of seagrass-associated fishes in the northern Gulf of Mexico. Glob. Change Biol. 16, 48–59 (2010).ADS 
Article 

Google Scholar 
4.Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
5.Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
6.Simpson, S. D. et al. Continental shelf-wide response of a fish assemblage to rapid warming of the sea. Curr. Biol. 21, 1565–1570 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Pecl, G. T. et al. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science 355 (2017).8.Poloczanska, E. S. et al. Responses of marine organisms to climate change across oceans. Front. Mar. Sci. 3, 62 (2016).Article 

Google Scholar 
9.Oswald, S. A. & Arnold, J. M. Direct impacts of climatic warming on heat stress in endothermic species: seabirds as bioindicators of changing thermoregulatory constraints. Integr. Zool. 7, 121–136 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
10.Boyles, J. G., Seebacher, F., Smit, B. & McKechnie, A. E. Adaptive thermoregulation in endotherms may alter responses to climate change. Integr. Comp. Biol. 51, 676–690 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Khaliq, I., Hof, C., Prinzinger, R., Böhning-Gaese, K. & Pfenninger, M. Global variation in thermal tolerances and vulnerability of endotherms to climate change. Proc. R. Soc. B Biol. Sci. 281, 20141097 (2014).Article 

Google Scholar 
12.Gibson-Reinemer, D. K., Sheldon, K. S. & Rahel, F. J. Climate change creates rapid species turnover in montane communities. Ecol. Evol. 5, 2340–2347 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Pörtner, H.-O. et al. Climate induced temperature effects on growth performance, fecundity and recruitment in marine fish: developing a hypothesis for cause and effect relationships in Atlantic cod (Gadus morhua) and common eelpout (Zoarces viviparus). Cont. Shelf Res. 21, 1975–1997 (2001).ADS 
Article 

Google Scholar 
14.Neuheimer, A., Thresher, R., Lyle, J. & Semmens, J. Tolerance limit for fish growth exceeded by warming waters. Nat. Clim. Chang. 1, 110–113 (2011).ADS 
Article 

Google Scholar 
15.Pörtner, H.-O. Climate variations and the physiological basis of temperature dependent biogeography: systemic to molecular hierarchy of thermal tolerance in animals. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 132, 739–761 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Buckley, L. B., Hurlbert, A. H. & Jetz, W. Broad-scale ecological implications of ectothermy and endothermy in changing environments. Glob. Ecol. Biogeogr. 21, 873–885 (2012).Article 

Google Scholar 
17.Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Burrows, M. T. et al. The pace of shifting climate in marine and terrestrial ecosystems. Science 334, 652–655 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Przeslawski, R., Falkner, I., Ashcroft, M. B. & Hutchings, P. Using rigorous selection criteria to investigate marine range shifts. Estuar. Coast. Shelf Sci. 113, 205–212 (2012).ADS 
Article 

Google Scholar 
20.Sunday, J. M. et al. Species traits and climate velocity explain geographic range shifts in an ocean-warming hotspot. Ecol. Lett. 18, 944–953 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
21.MacLeod, C. D. Global climate change, range changes and potential implications for the conservation of marine cetaceans: a review and synthesis. Endanger. Species Res. 7, 125–136 (2009).Article 

Google Scholar 
22.Sydeman, W., Poloczanska, E., Reed, T. & Thompson, S. Climate change and marine vertebrates. Science 350, 772–777 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Bowen, W. Role of marine mammals in aquatic ecosystems. Mar. Ecol. Prog. Ser. 158, 74 (1997).Article 

Google Scholar 
24.Williams, T. M., Estes, J. A., Doak, D. F. & Springer, A. M. Killer appetites: assessing the role of predators in ecological communities. Ecology 85, 3373–3384 (2004).Article 

Google Scholar 
25.Roman, J. et al. Whales as marine ecosystem engineers. Front. Ecol. Environ. 12, 377–385 (2014).Article 

Google Scholar 
26.Neutel, A.-M., Heesterbeek, J. A. & de Ruiter, P. C. Stability in real food webs: weak links in long loops. Science 296, 1120–1123 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Bascompte, J., Melián, C. J. & Sala, E. Interaction strength combinations and the overfishing of a marine food web. Proc. Natl. Acad. Sci. U.S.A. 102, 5443–5447 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Macnab, B. K. The Physiological Ecology of Vertebrates: A View from Energetics (Cornell University Press, 2002).
Google Scholar 
29.Robinson, R. A. et al. Climate change and migratory species (2005).30.Worthy, G. A. & Edwards, E. F. Morphometric and biochemical factors affecting heat loss in a small temperate cetacean (Phocoena phocoena) and a small tropical cetacean (Stenella attenuata). Physiol. Zool., 432–442 (1990).31.Koopman, H. N. Phylogenetic, ecological, and ontogenetic factors influencing the biochemical structure of the blubber of odontocetes. Mar. Biol. 151, 277–291 (2007).Article 

Google Scholar 
32.Adamczak, S. K., Pabst, D. A., McLellan, W. A. & Thorne, L. H. Do bigger bodies require bigger radiators? Insights into thermal ecology from closely related marine mammal species and implications for ecogeographic rules. J. Biogeogr. 47, 1193–1206 (2020).Article 

Google Scholar 
33.Silber, G. K. et al. Projecting marine mammal distribution in a changing climate. Front. Mar. Sci. 4, 413 (2017).Article 

Google Scholar 
34.Kaschner, K., Tittensor, D. P., Ready, J., Gerrodette, T. & Worm, B. Current and future patterns of global marine mammal biodiversity. PLoS ONE 6, e19653 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Salvadeo, C. J., Lluch-Belda, D., Gómez-Gallardo, A., Urbán-Ramírez, J. & MacLeod, C. D. Climate change and a poleward shift in the distribution of the Pacific white-sided dolphin in the northeastern Pacific. Endanger. Species Res. 11, 13–19 (2010).Article 

Google Scholar 
36.Kovacs, K. M., Lydersen, C., Overland, J. E. & Moore, S. E. Impacts of changing sea-ice conditions on Arctic marine mammals. Mar. Biodivers. 41, 181–194 (2011).Article 

Google Scholar 
37.MacLeod, C. D. et al. Climate change and the cetacean community of north-west Scotland. Biol. Cons. 124, 477–483 (2005).Article 

Google Scholar 
38.Higdon, J. W. & Ferguson, S. H. Loss of Arctic sea ice causing punctuated change in sightings of killer whales (Orcinus orca) over the past century. Ecol. Appl. 19, 1365–1375 (2009).PubMed 
Article 

Google Scholar 
39.Evans, P. G. & Hammond, P. S. Monitoring cetaceans in European waters. Mammal Rev. 34, 131–156 (2004).Article 

Google Scholar 
40.Kaschner, K., Quick, N. J., Jewell, R., Williams, R. & Harris, C. M. Global coverage of cetacean line-transect surveys: status quo, data gaps and future challenges. PLoS ONE 7, e44075 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Taylor, B. L., Martinez, M., Gerrodette, T., Barlow, J. & Hrovat, Y. N. Lessons from monitoring trends in abundance of marine mammals. Mar. Mamm. Sci. 23, 157–175 (2007).Article 

Google Scholar 
42.Pyenson, N. D. The high fidelity of the cetacean stranding record: insights into measuring diversity by integrating taphonomy and macroecology. Proc. R. Soc. B Biol. Sci. 278, 3608–3616 (2011).Article 

Google Scholar 
43.Leeney, R. H. et al. Spatio-temporal analysis of cetacean strandings and bycatch in a UK Wsheries hotspot. Biodivers. Conserv. 17, 2323–2338 (2008).Article 

Google Scholar 
44.Lambert, E. et al. Quantifying likely cetacean range shifts in response to global climatic change: implications for conservation strategies in a changing world. Endanger. Species Res. 15, 205–222 (2011).Article 

Google Scholar 
45.Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Chang. 3, 919–925 (2013).ADS 
Article 

Google Scholar 
46.Pershing, A. J. et al. Slow adaptation in the face of rapid warming leads to collapse of the Gulf of Maine cod fishery. Science 350, 809–812 (2015).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Nawojchik, R., St. Aubin, D. J. & Johnson, A. Movements and dive behavior of two stranded, rehabilitated long-finned pilot whales (Globicephala melas) in the northwest Atlantic. Mar. Mammal Sci. 19, 232–239 (2003).Article 

Google Scholar 
48.Bloch, D. et al. Short-term movements of long-finned pilot whales Globicephala melas around the Faroe Islands. Wildl. Biol. 9, 47–8 (2003).Article 

Google Scholar 
49.Hayes, S., Josephson, E., Maze‐Foley, K. & Rosel, P. US Atlantic and Gulf of Mexico marine mammal stock assessments–2019. NOAA Tech Memo NMFS‐NE 264 (2020).50.Gannon, D., Read, A., Craddock, J., Fristrup, K. & Nicolas, J. Feeding ecology of long-finned pilot whales Globicephala melas in the western North Atlantic. Mar. Ecol. Prog. Ser. Oldendorf 148, 1–10 (1997).ADS 
Article 

Google Scholar 
51.Harden Jones, F. R. In Animal migration. Soc. Exp. Biol. Sem. Ser. 13 (ed. Aidley, D. J.) 139–165 (Cambridge Univ. Press, 1981).
Google Scholar 
52.Alerstam, T., Hedenström, A. & Åkesson, S. Long-distance migration: evolution and determinants. Oikos 103, 247–260 (2003).Article 

Google Scholar 
53.Heide-Jørgensen, M. P. et al. Diving behaviour of long-finned pilot whales Globicephala melas around the Faroe Islands. Wildl. Biol. 8, 307–313 (2002).Article 

Google Scholar 
54.Baird, R. W., Borsani, J. F., Hanson, M. B. & Tyack, P. L. Diving and night-time behavior of long-finned pilot whales in the Ligurian Sea. Mar. Ecol. Prog. Ser. 237, 301–305 (2002).ADS 
Article 

Google Scholar 
55.Adamczak, S. K., McLellan, W. A., Read, A. J., Wolfe, C. L. & Thorne, L. H. The impact of temperature at depth on estimates of thermal habitat for short‐finned pilot whales. Mar. Mammal Sci. (2020).56.Jorda, G. et al. Ocean warming compresses the three-dimensional habitat of marine life. Nat. Ecol. Evolut. 4, 109–114 (2020).Article 

Google Scholar 
57.Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Chang. 9, 959–963 (2019).ADS 
Article 

Google Scholar 
58.Kleisner, K. M. et al. The effects of sub-regional climate velocity on the distribution and spatial extent of marine species assemblages. PLoS ONE 11, e0149220 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Kleisner, K. M. et al. Marine species distribution shifts on the US Northeast Continental Shelf under continued ocean warming. Prog. Oceanogr. 153, 24–36 (2017).ADS 
Article 

Google Scholar 
60.Kavanaugh, M. T., Rheuban, J. E., Luis, K. M. & Doney, S. C. Thirty-three years of ocean benthic warming along the US northeast continental shelf and slope: Patterns, drivers, and ecological consequences. J. Geophys. Res. Oceans 122, 9399–9414 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Grady, J. M. et al. Metabolic asymmetry and the global diversity of marine predators. Science 363 (2019).62.Williams, T. M. et al. The diving physiology of bottlenose dolphins (Tursiops truncatus). III. Thermoregulation at depth. J. Exp. Biol. 202, 2763–2769 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
63.Pabst, D. A., Rommel, S. A. & McLELLAN, W. A. The emergence of whales 379–397 (Springer, 1998).Book 

Google Scholar 
64.McNab, B. K. Short-term energy conservation in endotherms in relation to body mass, habits, and environment. J. Therm. Biol 27, 459–466 (2002).Article 

Google Scholar 
65.Yeates, L. C. & Houser, D. S. Thermal tolerance in bottlenose dolphins (Tursiops truncatus). J. Exp. Biol. 211, 3249–3257 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
66.Saba, V. S. et al. Enhanced warming of the Northwest Atlantic Ocean under climate change. J. Geophys. Res. Oceans (2016).67.Kenney, R. D., Scott, G. P., Thompson, T. J. & Winn, H. E. Estimates of prey consumption and trophic impacts of cetaceans in the USA northeast continental shelf ecosystem. J. Northwest Atl. Fish. Sci. 22, 155–171 (1997).Article 

Google Scholar 
68.Read, A. J. & Brownstein, C. R. Considering other consumers: fisheries, predators, and Atlantic herring in the Gulf of Maine. Conserv. Ecol. 7, 2 (2003).
Google Scholar 
69.Overholtz, W. & Link, J. Consumption impacts by marine mammals, fish, and seabirds on the Gulf of Maine-Georges Bank Atlantic herring (Clupea harengus) complex during the years 1977–2002. ICES J. Mar. Sci. J. Conseil 64, 83–96 (2007).Article 

Google Scholar 
70.Smith, L. A., Link, J. S., Cadrin, S. X. & Palka, D. L. Consumption by marine mammals on the Northeast US continental shelf. Ecol. Appl. 25, 373–389 (2015).PubMed 
Article 

Google Scholar 
71.Estes, J. A., Tinker, M. T., Williams, T. M. & Doak, D. F. Killer whale predation on sea otters linking oceanic and nearshore ecosystems. Science 282, 473–476 (1998).ADS 
CAS 
PubMed 
Article 

Google Scholar 
72.Pace, M. L., Cole, J. J., Carpenter, S. R. & Kitchell, J. F. Trophic cascades revealed in diverse ecosystems. Trends Ecol. Evol. 14, 483–488 (1999).CAS 
PubMed 
Article 

Google Scholar 
73.Myers, R. A., Baum, J. K., Shepherd, T. D., Powers, S. P. & Peterson, C. H. Cascading effects of the loss of apex predatory sharks from a coastal ocean. Science 315, 1846–1850 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
74.Durant, J. M., Hjermann, D. Ø., Ottersen, G. & Stenseth, N. C. Climate and the match or mismatch between predator requirements and resource availability. Clim. Res. (CR) 33, 271–283 (2007).ADS 
Article 

Google Scholar 
75.Schweiger, O., Settele, J., Kudrna, O., Klotz, S. & Kühn, I. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479 (2008).PubMed 
Article 

Google Scholar 
76.Both, C., Van Asch, M., Bijlsma, R. G., Van Den Burg, A. B. & Visser, M. E. Climate change and unequal phenological changes across four trophic levels: constraints or adaptations?. J. Anim. Ecol. 78, 73–83 (2009).PubMed 
Article 

Google Scholar 
77.Evans, K. et al. Periodic variability in cetacean strandings: links to large-scale climate events. Biol. Let. 1, 147–150 (2005).CAS 
Article 

Google Scholar 
78.Overholtz, W., Hare, J. & Keith, C. Impacts of interannual environmental forcing and climate change on the distribution of Atlantic mackerel on the US Northeast continental shelf. Mar. Coastal Fish. 3, 219–232 (2011).Article 

Google Scholar 
79.Roper, C., Lu, C. & Vecchione, M. A revision of the systematics and distribution of Illex species (Cephalopoda: Ommastrephidae). Smithsonian Contrib. Zool., 405–424 (1998).80.Brodziak, J. & Hendrickson, L. An analysis of environmental effects on survey catches of squids Loligo pealei and Illex illecebrosus in the northwest Atlantic. Fish. Bull. 97, 9–24 (1999).
Google Scholar 
81.Henderson, M. E., Mills, K. E., Thomas, A. C., Pershing, A. J. & Nye, J. A. Effects of spring onset and summer duration on fish species distribution and biomass along the Northeast United States continental shelf. Rev. Fish Biol. Fisheries 27, 411–424 (2017).Article 

Google Scholar 
82.Sosebee, K. A. & Cadrin, S. X. A historical perspective on the abundance and biomass of northeast demersal complex stocks from NMFS and Massachusetts inshore bottom trawl surveys, 1963–2002. (2006).83.Brito-Morales, I. et al. Climate velocity can inform conservation in a warming world. Trends Ecol. Evol. 33, 441–457 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
84.Burrows, M. T. et al. Geographical limits to species-range shifts are suggested by climate velocity. Nature 507, 492–495 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.García Molinos, J., Schoeman, D. S., Brown, C. J. & Burrows, M. T. VoCC: an r package for calculating the velocity of climate change and related climatic metrics. Methods Ecol. Evolut. 10, 2195–2202 (2019).Article 

Google Scholar  More

1.Huai, J. J. Dynamics of resilience of wheat to drought in Australia from 1991–2010. Sci. Rep. 7, 9532 (2017).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
2.Li, M., Peterson, C. A., Tautges, N. E., Scow, K. M. & Gaudin, A. C. M. Yields and resilience outcomes of organic cover crop, and conventional practices in a Mediterranean climate. Sci. Rep. 9, 12283 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Keersmaecker, W. D. et al. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover. Glob. Ecol. Biogeogr. 24, 539–548 (2015).Article 

Google Scholar 
4.Griffith, G. P. et al. Ecological resilience of Arctic marine food webs to climate change. Nat. Clim. Change 9, 868–872 (2019).ADS 
Article 

Google Scholar 
5.You, N. S., Meng, J. J. & Zhu, L. K. Sensitivity and resilience of ecosystems to climate variability in the semi-arid to hyper-arid areas of Northern China: a case study in the Heihe River Basin. Ecol. Res. 33, 161–174 (2018).Article 

Google Scholar 
6.Reijers, V. C. et al. Resilience of beach grasses along a biogeomorphic successive gradient: resource availability vs. clonal integration. Oceologia https://doi.org/10.1007/s00442-019-04568-w (2019).Article 

Google Scholar 
7.Chambers, J. C. et al. Resilience to stress and disturbance, and resistance to Bromus tectorum L. invasion in clod desert shrublands of western North America. Ecosystems 17, 360–375 (2014).CAS 
Article 

Google Scholar 
8.Driessen, M. M. Fire resilience of a rare, freshwater crustacean in a fire-prone ecosystem and the implications for fire management. Austral Ecol. 44, 1030–1039 (2019).Article 

Google Scholar 
9.Ren, H., Lu, H. F., Li, Y. D. & Wen, Y. G. Vegetation restoration and its research advancement in Southern China. J. Trop. Subtrop. Bot. 27(5), 469–480 (2019).
Google Scholar 
10.Yan, H. M., Zhan, J. Y. & Zhang, T. Review of ecosystem resilience research progress. Prog. Geogr. 31(3), 303–314 (2012).
Google Scholar 
11.Zhan, J. Y., Yan, H. M., Deng, X. Z. & Zhang, T. Assessment of forest ecosystem resilience in Lianhua County of Jiangxi Province. J. Nat. Resour. 27(8), 1304–1315 (2012).
Google Scholar 
12.Pérez-Girón, J. C., Álvarez-Álvarez, P., Díaz-Valera, E. R. & Lopes, D. M. M. Influence of climate variations on primary production indicators and on the resilience of forest ecosystems in a future scenario of climate change: application to sweet chestnut agroforestry systems in the Iberian Peninsula. Ecol. Indic. 113, 106199 (2020).Article 

Google Scholar 
13.Meng, Y. Y. et al. Analysis of ecological resilience to evaluate the inherent maintenance capacity of a forest ecosystem using a dense Landsat time series. Ecol. Inform. 57, 101064 (2020).Article 

Google Scholar 
14.Han, L. et al. Species composition, community structure, and floristic characteristics of desert riparian forest community along the mainstream of Tarim River. Plant Sci. J. 37(3), 324–336 (2019).
Google Scholar 
15.Zhou, H. H. et al. Climate change may accelerate the decline of desert riparian forest in the lower Tarim River, Northwestern China: evidence from tree-rings of Populus euphratica. Ecol. Indic. 111, 105997 (2020).Article 

Google Scholar 
16.Aini, A. et al. Analysis of stakeholders’ cognition on desert riparian forest ecosystem services in the lower reaches of Tarim River, China. Res. Soil Water Conserv. 23(1), 205–209 (2016).
Google Scholar 
17.Li, Y. Q., Chen, Y. N., Zhang, Y. Q. & Xia, Y. Rehabilitating China’s largest inland river. Conserv. Biol. 23(3), 531–536 (2009).PubMed 
Article 

Google Scholar 
18.Dai, J. S. Evaluation of eco-environment and socio-economic benefits on comprehensive reclamation projects on the Tarim River Basin. Doctoral Dissertation of Xinjiang Agricultural University (2015).19.Han, L., Wang, H. Z., Niu, J. L., Wang, J. Q. & Liu, W. Y. Response of Populus euphratica communities in a desert riparian forest to the groundwater level gradient in the Tarim River Basin. Acta Ecol. Sin. 37, 6836–6846 (2017).
Google Scholar 
20.Yang, G. & Guo, Y. P. The change and prospect of vegetation in the end of the lower reaches of Tarim River after ecological water delivery. J. Desert Res. 24(2), 167–172 (2004).
Google Scholar 
21.Yan, H. M., Zhan, J. Y. & Zhang, T. Resilience of forest ecosystems and its influencing factors. Procedia Environ. Sci. 10, 2201–2206 (2011).Article 

Google Scholar 
22.Abenayake, C. C., Mikami, Y., Matsuda, Y. & Jayasinghe, A. Ecosystem service-based composite indicator for assessing community resilience to floods. Environ. Dev. 27, 34–46 (2018).Article 

Google Scholar 
23.Maestas, J. D., Campbell, S. B., Chambers, J. C., Pellant, M. & Miller, R. F. Tapping soil survey information for rapid assessment of sagebrush ecosystem resilience and resistance. Rangelands 38(3), 120–128 (2016).Article 

Google Scholar 
24.Ponce-Campos, G. E. et al. Ecosystem resilience despite large-scale altered hydroclimatic conditions. Nature 494, 349–352 (2013).ADS 
CAS 
PubMed 
Article 

Google Scholar 
25.Frazier, A. E., Renschler, C. S. & Miles, S. B. Evaluating post-disaster ecosystem resilience using MODIS GPP data. Int. J. Appl. Earth Obs. Geoinform. 21, 43–52 (2013).ADS 
Article 

Google Scholar 
26.Kahiluoto, H. et al. Decline in climate resilience of European wheat. PNAS 116(1), 123–128 (2019).CAS 
PubMed 
Article 

Google Scholar 
27.Li, X. Y. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evolut. 4, 1075–1083 (2020).Article 

Google Scholar 
28.Li, C. H., Zhou, M., Wang, Y. T., Zhu, T. B., Sun, H., Yin, H. H., Cao, H. J., Han, H. Y. Inter-annual variations of vegetation net primary productivity and their spatial-temporal contribution and climate driving in arid Northwest China: a case study of Hexi Corridor. Chin. J. Ecol. (2020).29.Song, J. et al. A global database of plant production and carbon exchange from global change manipulative experiments. Sci. Data 7, 1–7 (2020).Article 
CAS 

Google Scholar 
30.Yang, G. et al. Research progress of ecosystem resilience assessment. Zhejiang Agric. Sci. 60(3), 508–513 (2019).
Google Scholar 
31.Liu, J. Z. & Chen, Y. N. Analysis on converse succession of plant communities at the lower reaches of Tarim River. Arid Land Geogr. 25(3), 231–236 (2002).
Google Scholar 
32.Chen, X., Bao, A. M., Wang, X. P., Guli, J. P. E. & Huang, Y. Recent ecological effectiveness assessment of integrated management projects in the Tarim River. Bull. Chin. Acad. Sci. 32(1), 20–28 (2017).
Google Scholar 
33.Zhao, H., Yan, L. & Ji, F. The dynamics of land utilization in the upper reaches of Tarim River. J. Arid Land Resour. Environ. 15(4), 40–43 (2001).
Google Scholar 
34.Sun, F., Wang, Y. & Chen, Y. N. Dynamics of desert-oasis ecotone and its influencing factors in the Tarim Basin. Chin. J. Ecol. 39(10), 1–11 (2020).
Google Scholar 
35.Xu, G. H. A genetic explanation of the recent changes of ecological environment in the Tarim River Basin, southern Xinjiang. Xinjiang Meteorol. 28–31 (2005).36.Kamkin, A. & Lozinsky, I. Mechanically Gated Channels and Their Regulation (Springer, 2012).Book 

Google Scholar 
37.Feyisa, K. et al. Effects of enclosure management on carbon sequestration, soil properties and vegetation attributes in East African rangelands. CATENA 159, 9–19 (2017).Article 

Google Scholar 
38.Wang, G. H., Ren, Y. J. & Gou, Q. Q. The changes of soil physical and chemical property during the enclosure process in a typical desert oasis ecotone of the Hexi Corridor in northwestern China. J. Desert Res. 40(2), 222–231 (2020).
Google Scholar 
39.Xu, H. L., Ye, M. & Li, J. M. Changes in groundwater levels and the response of natural vegetation to the transfer of water to the lower reaches of the Tarim River. J. Environ. Sci. 19(10), 1199–1207 (2007).Article 

Google Scholar 
40.Zhang, P. F., Guli, J., Bao, A. M., Meng, F. H. & Guo, H. Ecological effects evaluation for short term planning of the Tarim River. Arid Land Geogr. 40(1), 156–164 (2017).
Google Scholar 
41.Gulimire, H., Wang, G. Y., Zhang, Y., Liu, Q. Q. & Su, L. T. Influence mechanisms of intermittent ecological water conveyance on groundwater level and vegetation in arid land. Arid Land Geogr. 41(4), 726–733 (2018).
Google Scholar 
42.Guo, H. W., Xu, H. L. & Ling, H. B. Study of ecological water transfer mode and ecological compensation scheme of the Tarim River Basin in dry years. J. Nat. Resour. 32(10), 1705–1717 (2017).
Google Scholar 
43.Wu, T. Z., Ding, J., Guan, W. K., Ruan, C. J. & Guan, Y. Populus euphratica forest replacement and photosynthetic characteristics in Tarim Populus euphratica national nature reserve. Prot. For. Sci. Technol. 8, 1–4 (2020).
Google Scholar 
44.Zhu, C. G., Aikeremu, A., Li, W. H. & Zhou, H. H. Ecosystem restoration of Populus euphratica forest under the ecological water conveyance in the lower reaches of Tarim River. Arid Land Geography, 44(3), 629–636 (2021).
Google Scholar 
45.Chen, Y. N. Study on Eco-hydrological Problems of the Tarim River Basin in Xinjiang (Science Press, 2010).
Google Scholar 
46.Halik, U., Aishan, T., Betz, F., Kurban, A. & Rouzi, A. Effectiveness and challenges of ecological engineering for desert riparian forest restoration along China’s largest inland river. Ecol. Eng. 127, 11–22 (2019).Article 

Google Scholar 
47.Xinjiang Morning News. In the past three years, the area of the Populus euphratica forest reserve in the Tarim River Basin has increased by 569.95 km2. https://www.sohu.com/a/308626663_100034331?sec=wd (2019).48.China News Service. Ecological water transfer for desert vegetation in lower reaches of Konqi River in Xinjiang. https://news.sina.com.cn/o/2020-02-22/doc-iimxyqvz4945915.shtml (2020). More

Research cruisesThis dataset consists of sequence data from 4 separate cruises: ARK-XXVII/1 (PS80)—17th June to 9th July 2012; Stratiphyt-II— April to May 2011; ANT-XXIX/1 (PS81)—1st to 24th November 2012 and ANT-XXXII/2 (PS103)—16th December 2016 to 3rd February 2017 and covers a transect of the Atlantic Ocean from Greenland to the Weddell Sea (71.36°S to 79.09°N) (Supplementary Table 1). In order to study the composition, distribution and activity of microbial communities in the upper ocean across the broadest latitudinal ranges possible, samples have been collected during four field campaigns as shown in Fig. 1A. The first collection of samples was collected in the North Atlantic Ocean from April to May 2011 by Dr. Willem van de Poll of the University of Groningen, Netherlands and Dr. Klaas Timmermans of the Royal Netherlands Institute for Sea Research. The second set of samples was collected in the Arctic Ocean from June to July 2012, and the third set of samples was collected in the South Atlantic Ocean from October to November 2012. Both of which were collected by Dr. Katrin Schmidt of the University of East Anglia. The final set of samples was collected in the Antarctic Ocean from December 2016 to January 2017 by Dr. Allison Fong of the Alfred-Wegener Institute for Polar and Marine Research, Bremerhaven, Germany.SamplingWater samples from the Arctic Ocean and South Atlantic Ocean expeditions were collected using 12 L Niskin bottles (Rosette sampler with an attached Sonde (CTD, conductivity, temperature, depth) either at the chlorophyll maximum (10–110 m) and/or upper of the ocean (0–10 m). As soon as the rosette sampler was back on board, water samples were immediately transferred into plastic containers and transported to the laboratory. All samples were accompanied by measurements on salinity, temperature, sampling depth and silicate, nitrate, phosphate concentration (Supplementary Table 1). Water samples were pre-filtered with a 100 μm mesh to remove larger organisms and subsequently filtered onto 1.2 μm polycarbonate filters (Isopore membrane, Millipore, MA, USA). All filters were snap frozen in liquid nitrogen and stored at −80 °C until further analysis.Water samples from the North Atlantic Ocean cruise were also taken with 12 L Niskin bottles attached to a Rosette sampler with a Sonde. However, these samples were filtered onto 0.2 μm polycarbonate filters (Isopore membrane, Millipore, MA, USA) without pre-filtration but snap frozen in liquid nitrogen and stored at −80 °C as the other samples.Water samples from the Southern Ocean cruise were taken with 12 L Niskin bottles attached to an SBE911plus CTD system equipped with 24 Niskin samplers. These samples were filtered onto 1.2 μm polycarbonate membrane filters (Merck Millipore, Germany) in a container cooled to 4 °C and snap frozen in liquid nitrogen and stored at −80 °C as the other samples. Environmental data recorded at the time of sampling can be found in Supplementary Table 1.DNA extractions: Arctic Ocean and South Atlantic Ocean samplesDNA was extracted with the EasyDNA Kit (Invitrogen, Carlsbad, CA, USA) with modification to optimise DNA quantity and quality. Briefly, cells were washed off the filter with pre-heated (65 °C) Solution A and the supernatant was transferred into a new tube with one small spoon of glass beads (425–600 μm, acid washed) (Sigma-Aldrich, St. Louis, MO, USA). Samples were vortexed three times in intervals of 3 s to break the cells. RNase A was added to the samples and incubated for 30 min at 65 °C. The supernatant was transferred into a new tube and Solution B was added followed by a chloroform phase separation and an ethanol precipitation step. DNA was pelleted by centrifugation and washed several times with isopropanol, air dried and suspended in 100 μL TE buffer (10 mM Tris-HCl, pH 7.5, 1 mM EDTA, pH 8.0). Samples were snap frozen in liquid nitrogen and stored at −80 °C until sequencing.DNA extractions: North Atlantic Ocean samplesNorth Atlantic Ocean samples were extracted with the ZR-Duet™DNA/RNA MiniPrep kit (Zymo Research, Irvine, USA) allowing simultaneous extraction of DNA and RNA from one sample filter. Briefly, cells were washed from the filters with DNA/RNA Lysis Buffer and one spoon of glass beads (425–600 μm, Sigma-Aldrich, MO, USA) was added. Samples were vortexed quickly and loaded onto Zymno-Spin™IIIC columns. The columns were washed several times and DNA was eluted in 60 μmL, DNase-free water. Samples were snap frozen in liquid nitrogen and stored at −80 °C until sequencing.DNA extractions: Southern Ocean samplesDNA from the Southern Ocean samples was extracted with the NucleoSpin Soil DNA extraction kit (Macherey‐Nagel) following the manufacturer’s instructions. Briefly, cells were washed from the filters with DNA Lysis Buffer and into a lysis tube containing glass beads was added. Samples were disrupted by bead beating for 2 × 30 s interrupted by 1 min cooling on ice and loaded onto the NucleoSpin columns. The columns were washed three times and DNA was eluted in 50 μL, DNase-free water. Samples were stored at −20 °C until further processing.Amplicon sequencing of 16S and 18S rDNAAll extracted DNA samples were sequenced and pre-processed by the Joint Genome Institute (JGI) (Department of Energy, Berkeley, CA, USA). iTAG amplicon sequencing was performed at JGI with primers for the V4 region of the 16S (FW(515F): GTGCCAGCMGCCGCGGTAA; RV(806R): GGACTACNVGGGTWTCTAAT)49 and 18S (FW(565F): CCAGCASCYGCGGTAATTCC; RV(948R): ACTTTCGTTCTTGATYRA)50. (Supplementary Table 6) rRNA gene (on an Illumina MiSeq instrument with a 2 × 300 base pairs (bp) read configuration51. 18S sequences were pre-processed, this consisted of scanning for contamination with the tool Duk (US Department of Energy Joint Genome Institute (JGI), 2017,a) and quality trimming of reads with cutadapt52. Paired end reads were merged using FLASH53 with a max mismatch set to 0.3 and min overlap set to 20. A total of 54 18S samples passed quality control after sequencing. After read trimming, there was an average of 142,693 read pairs per 18S sample with an average length of 367 bp and 2.8 Gb of data over all samples.16S sequences were pre-processed, this consisted of merging the overlapping read pairs using USEARCH’s merge pairs54 with the parameter minimum number of differences (merge max diff pct) set to 15.0 into unpaired consensus sequences. Any reads that could not be merged are discarded. JGI then applied the tool USEARCH’s search oligodb tool with the parameters mean length (len mean) set to 292, length standard deviation (len stdev) set to 20, primer trimmed max difference (primer trim max diffs) set to 3, a list of primers and length filter max difference (len filter max diffs) set to 2.5 to ensure the Polymerase Chain Reaction (PCR) primers were located with the correct direction and inside the expected spacing. Reads that did not pass this quality control step were discarded. With a max expected error rate (max exp err rate) set to 0.02, JGI evaluated the quality score of the reads and those with too many expected errors were discarded. Any identical sequence was de-duplicated. These are then counted and sorted alphabetically for merging with other such files later. A total of 57 × 16S samples passed quality control after sequencing. There was an average 393,247 read pairs per sample and an average base length of 253 bp for each sequence with a total of 5.6 Gb.RNA extractions: Arctic Ocean and Atlantic samplesRNA from the Arctic and Atlantic Ocean samples was extracted using the Direct-zol RNA Miniprep Kit (Zymo Research, USA). Briefly, cells were washed off the filters with Trizol into a tube with one spoon of glass beads (425–600 μm, Sigma-Aldrich, MO, USA). Filters were removed and tubes bead beaten for 3 min. An equal volume of 95% ethanol was added, and the solution was transferred onto Zymo-Spin™ IICR Column and the manufacturer instructions were followed. Samples were treated with DNAse to remove DNA impurities, snap frozen in liquid nitrogen and stored at −80 °C until sequencing.RNA extractions: Southern OceanRNA from the Southern Ocean samples was extracted using the QIAGEN RNeasy Plant Mini Kit (QIAGEN, Germany) following the manufacturer’s instructions with on-column DNA digestion. Cells were broken by bead beating like for the DNA extractions before loading samples onto the columns. Elution was performed with 30 µm RNase-free water. Extracted samples were snap frozen in liquid nitrogen and stored at −80 °C until sequencing.Metatranscriptome sequencingAll samples were sequenced and pre-processed by the U.S. Department of Energy Joint Genome Institute (JGI). Metatranscriptome sequencing was performed on an Illumina HiSeq-2000 instrument27. A total of 79 samples passed quality control after sequencing with 19.87 Gb of sequence read data over all samples for analysis. This comprised a total of 34,241,890 contigs, with an average length of 503 and an average GC% of 51%. This resulted in 36354419 of non-redundant genes detected.JGI employed their suite of tools called BBTools55 for preprocessing the sequences. First, the sequences were cleaned using Duk a tool in the BBTools suite that performs various data quality procedures such as quality trimming and filtering by kmer matching. In our dataset, Duk identified and removed adaptor sequences, and also quality trimmed the raw reads to a phred score of Q10. In Duk the parameters were; kmer-trim (ktrim) was set to r, kmer (k) was set to 25, shorter kmers (mink) set to 12, quality trimming (qtrim) was set to r, trimming phred (trimq) set to 10, average quality below (maq) set to 10, maximum Ns (maxns) set to 3, minimum read length (minlen) set to 50, the flag “tpe” was set to t, so both reads are trimmed to the same length and the “tbo” flag was set to t, so to trim adaptors based on pair overlap detection. The reads were further filtered to remove process artefacts also using Duk with the kmer (k) parameter set to 16.BBMap55 is another a tool in the BBTools suite, that performs mapping of DNA and RNA reads to a database. BBMap aligns the reads by using a multi-kmer-seed-and-extend approach. To remove ribosomal RNA reads, the reads were aligned against a trimmed version of the SILVA database using BBMap with parameters set to; minratio (minid) set to 0.90, local alignment converter flag (local) set to t and fast flag (fast) set to t. Also, any human reads identified were removed using BBMap.BBmerge56 is a tool in the BBTools suite that performs the merging of overlapping paired end reads (Bushnell, 2017). For assembling the metatranscriptome, the reads were first merged with the tool BBmerge, and then BBNorm was used to normalise the coverage so as to generate a flat coverage distribution. This type of operation can speed up assembly and can even result in an improved assembly quality.Rnnotator52 was employed for assembling the metatranscriptome samples 1–68. Rnnotator assembles the transcripts by using a de novo assembly approach of RNA-Seq data and it accomplishes this without a reference genome52. MEGAHIT57 was employed for assembling the metatranscriptome samples 69–82. The tool BBMap was used for reference mapping, the cleaned reads were mapped to metagenome/isolate reference(s) and the metatranscriptome assembly.Metatranscriptome analysisJGI performed the functional analysis on the metatranscriptomic dataset. JGI’s annotation system is called the Metagenome Annotation Pipeline (MAP) (v4.15.2)27. JGI used HMMER 3.1b258 and the Pfam v3059 database for the functional analysis of our metatranscriptomic dataset. This resulted in 11,205,641 genes assigned to one or more Pfam domain. This resulted in 8379 Pfam functional assignments and their gene counts across the 79 samples. The files were further normalised by applying hits per million.18S rDNA analysisA reference dataset of 18S rRNA gene sequences that represent algae taxa was compiled for the construction of the phylogenetic tree by retrieving sequences of algae and outgroups taxa from the SILVA database (SSUREF 115)60 and Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) database61. The algae reference database consists of 1636 species from the following groups: Opisthokonta, Cryptophyta, Glaucocystophyceae, Rhizaria, Stramenopiles, Haptophyceae, Viridiplantae, Alveolata, Amoebozoa and Rhodophyta. A diagram of the 18S classification pipeline can be found in Supplementary Fig. 1. In order to construct the algae 18S reference database, we first retrieved all eukaryotic species from the SILVA database with a sequence length of  > = 1500 base pairs (bp) and converted all base letters of U to T. Under each genus, we took the first species to represent that genus. Using a custom written script (https://github.com/SeaOfChange/SOC/blob/master/get_ref_seqs.pl), the species of interest (as stated above) were selected from the SILVA database, classified with NCBI taxa IDs and a sequence information file produced that describes each of the algae sequences by their sequence ID and NCBI species ID. Taxonomy from the NCBI database, eukaryote sequences from the SILVA database and a list of algal taxa including outgroups were used as input for the script. This information was combined with the MMETSP database excluding duplications.The algae reference database was clustered to remove closely related sequences with CD-HIT (4.6.1)62 using a similarity threshold of 97%. Using ClustalW (2.1)63 we aligned the reference sequences with the addition of the parameter iteration numbers set to 5. The alignment was examined by colour coding each species to their groups and visualising in iTOL64. It was observed that a few species were misaligning to other groups and these were then deleted using Jalview65. The resulting alignment was tidied up with TrimAL (1.1)66 by applying parameters to delete any positions in the alignment that have gaps in 10% or more of the sequence, except if this results in less than 60% of the sequence remaining. A maximum likelihood phylogenetic reference tree and statistics file based on our algae reference alignment was constructed by employing RaxML (8.0.20)67 with a general time reversible model of nucleotide substitution along with the GAMMA model of rate heterogeneity. For a description of the lineages of all species back to the root in the algae reference database, the taxa IDs were submitted for each species to extract a subset of the NCBI taxonomy with the NCBI taxtastic tool (0.8.4)68 Based on the algae reference multiple sequence alignment, with HMMER3 (3.1B1)69 a Profile HMM was created. A pplacer reference package using taxtastic was generated, which produced an organized collection of all the files and taxonomic information into one directory. With the reference package, a SQLite database was created using pplacer’s Reference Package PReparer (rppr). With hmmalign, the query sequences were aligned to the reference set and created a combined Stockholm format alignment. Pplacer (re-aligned to the reference set and created a combined Stockholm format alignment. Pplacer (1.1)70 was used to place the query sequences on the phylogenetic reference tree by means of the reference alignment according to a maximum likelihood model70 The place files were converted to CSV with pplacer’s guppy tool; in order to easily take those with a maximum likelihood score of  > = 0.5 and counted the number of reads assigned to each classification. This resulted in 6,053,291 reads that were taxonomically assigned being taken for analysis.Normalisation of 18S rDNA gene copy number18S rDNA gene copy number vary widely among eukaryotes. In order to create an estimate of abundances of the species in the samples the data had to be normalised. Previous work has explored the link between copy number and genome size71. However, there is not a single database of 18S rDNA gene copy numbers for eukaryote species. In order to address this, gene copy number and related genome sizes of 185 species across the eukaryote tree was investigated and plotted (Supplementary Fig. 2, Supplementary Table 4)68,71,72,73,74,75,76,77,78,79. Based on the log transformed data, a significant correlation with a R2 of 0.55 with a p-value  More

The absence of the first principles for biological systems in general, and in particular for vegetation populations where phenomena are interconnected makes their mathematical modelling complex. The theory of vegetation pattern formation rests on the self-organisation hypothesis and symmetry-breaking instability that provoke the fragmentation of the uniform cover. The symmetry-breaking instability takes place even if the environment is isotropic31,33,35. This instability may be an advection-induced transition that requires the pre-existence of the environment anisotropy due to the topography of the landscape34,39,40. Generally speaking, this transition requires at least two feedback mechanisms having a short-range activation and a long-range inhibition. In this respect, we consider three different vegetation models that are experimentally relevant systems: (i) the generic interaction redistribution model describing vegetation pattern formation which incorporates explicitly the facilitation, competition and seed dispersion nonlocal interactions (ii) the local nonvariational partial differential model described by a nonvariational Swift–Hohenberg type of model equation, and (iii) the reaction–diffusion system that incorporate explicetely water transport.The interaction-redistribution approachThe integrodifferential modelThis approach consists of considering a well-known logistic equation with nonlocal plant-to-plant interactions. Three types of interactions are considered: the facilitative (M_{f}(mathbf {r},t)), the competitive (M_{c}(mathbf {r},t)), and the seed dispersion (M_{d}(mathbf {r},t)) nonlocal interactions. To simplify further the mathematical modelling, we consider that the seed dispersion obeys a diffusive process (M_{d}(mathbf {r},t)approx nabla ^{2}b(mathbf {r},t)), with D the diffusion coefficient, b the biomass density, and (nabla ^{2}=partial ^2/partial x^2+partial ^2/partial y^2) is the Laplace operator acting in the (x,y) plane. The interaction-redistribution reads$$begin{aligned} M_{i}=expleft{ frac{xi _{i}}{N_{i}}int b(mathbf {r}+mathbf {r}’,t)phi _i(r,t)dmathbf {r}’right} , { text{ with } } phi _i(r,t)= exp(-r/L_{i}) end{aligned}$$
(1)
where (i=f,c). (xi _i) represents the strength of the interaction, (N_i) is a normalisation constant. We assume that their Kernels (phi _i(r,t)) are exponential functions with (L_i) the range of their interactions. The facilitative interaction (M_{f}(mathbf {r},t)) favouring vegetation development. They involve the accumulation of nutrients in the neighbourhood of plants, the reciprocal sheltering of neighbouring plants against climatic harshness which improves the water budget in the soil. The range of the facilitative interaction (L_f) operates on the crown size. The competitive interaction operates over a length (L_c) and involves the below-ground structures, i.e., the rhizosphere. In nutrient-poor or/and in water-limited territories, lateral spreading may extend beyond the radius of the crown. This extension of roots relative to their crown size is necessary for the survival and the development of the plant in order to extract enough nutrients and/or water from the soil. When incorporating these nonlocal interactions in the paradigmatic logistic equation, the spatiotemporal evolution of the normalised biomass density (b(mathbf {r}, t)) in isotropic environmental conditions reads14$$begin{aligned} partial _{t} b(mathbf {r},t)=b(mathbf {r},t)[1-b(mathbf {r},t)]M_{f}(mathbf {r},t)- mu b(mathbf {r},t)M_{c}(mathbf {r},t)+Dnabla ^{2}b(mathbf {r},t). end{aligned}$$
(2)
The normalisation is performed with respect to the total amount of biomass supported by the system. The first two terms in the logistic equation with nonlocal interaction Eq. (2) describe the biomass gains and losses, respectively. The third term models seed dispersion. The aridity parameter (mu) accounts for the biomass loss and gain ratio, which depends on water availability and nutrients soil distribution, topography, etc. The homogeneous cover solutions of Eq. (2) are: (b_{o}=0) which corresponds to the state totally devoid of vegetation, and the homogeneous cover solutions satisfy the equation$$begin{aligned} mu =(1-b)exp (Delta b), end{aligned}$$
(3)
with (Delta =xi _{f}-xi _{c}) measures the community cooperativity if (Delta >0) or anti-cooperativity when (Delta 0). The solution (u_{-}) is always unstable even in the presence of small spatial fluctuations. The linear stability analysis of vegetated cover ((u_{+})) with respect to small spatial fluctuations, yields the dispersion relation$$begin{aligned} sigma (k)=u_{+}(kappa -2u_{+})-(nu -gamma u_{+})k^{2}-alpha u_{+}k^{4}. end{aligned}$$
(8)
Imposing (partial sigma /partial k|_{k_{c}}=0) and (sigma (k_{c})=0), the critical mode can be determined$$begin{aligned} k_{c}=sqrt{frac{gamma -nu /u_{c}}{2alpha }}, end{aligned}$$
(9)
where (u_{c}) satisfies (4alpha u_{c}^2(2u_{c}-kappa )=(2gamma u_{c}-nu )^2). The corresponding aridity parameter (eta _{c}) can be calculated from Eq. (7).The reaction–diffusion approachThe second approach explicitly adds the water transport by below ground diffusion. The coupling between the water dynamics and the plant biomass involves positive feedbacks that tend to enhance water availability. Negative feedbacks allow for an increase in water consumption caused by vegetation growth, which inhibits further biomass growth.The modelling considers the coupled evolution of biomass density (b(mathbf {r},t)) and groundwater density (w(mathbf {r},t)). In its dimensionless form, this model reads33$$begin{aligned} frac{partial b}{partial t}= & {} frac{gamma w}{1+omega w}b-b^{2}-theta b+nabla ^{2}b, end{aligned}$$
(10)
$$begin{aligned} frac{partial w}{partial t}= & {} p-(1-rho b)w-w^{2}b+delta nabla ^{2}(w-beta b). end{aligned}$$
(11)
The first term in the first equation describes plant growth at a constant rate ((gamma /omega)) that grows linearly with w for dry soil. The quadratic nonlinearity (-b^{2}) accounts for saturation imposed by poor nutrients soil. The term proportional to (theta) accounts for mortality, grazing or herbivores. The mechanisms of dispersion are modelled by a simple diffusion process. The groundwater evolves due to a precipitation input p. The term ((1-rho b)w) in the second equation accounts for the evaporation and drainage, that decreases with the presence of vegetation. The term (w^{2}b) models the water uptake by the plants due to the transpiration process. The groundwater movement follows the Darcy’s law in unsaturated conditions; that is, the water flux is proportional to the gradient of the water matric potential41. The matric potential is equal to w, under the assumption that the hydraulic diffusivity is constant41. To model the suction of water by the roots, a correction to the matric potential is included; (-beta b), where (beta) is the strength of the suction. More

1.Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to conservation genetics (Cambridge university press, 2002).2.Nadachowska-Brzyska, K., Burri, R., Smeds, L. & Ellegren, H. PSMC analysis of effective population sizes in molecular ecology and its application to black-and-white Ficedula flycatchers. Mol. Ecol. 25, 1058–1072 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Martínez-Freiría, F., Velo-Antón, G. & Brito, J. C. Trapped by climate: Interglacial refuge and recent population expansion in the endemic Iberian adder Vipera seoanei. Divers. Distrib. 21, 331–344 (2015).Article 

Google Scholar 
4.Martínez-Freiría, F. et al. Integrative phylogeographical and ecological analysis reveals multiple pleistocene refugia for Mediterranean Daboia vipers in north-west Africa. Biol. J. Linn. Soc. 122, 366–384 (2017).Article 

Google Scholar 
5.Veríssimo, J. et al. Pleistocene diversification in Morocco and recent demographic expansion in the Mediterranean pond turtle Mauremys leprosa. Biol. J. Linn. Soc. 119, 943–959 (2016).Article 

Google Scholar 
6.Chattopadhyay, B., Garg, K. M., Gwee, C. Y., Edwards, S. V. & Rheindt, F. E. Gene flow during glacial habitat shifts facilitates character displacement in a Neotropical flycatcher radiation. BMC Evol. Biol. 17, 1–15 (2017).Article 

Google Scholar 
7.Garg, K. M., Chattopadhyay, B., Koane, B., Sam, K. & Rheindt, F. E. Last Glacial Maximum led to community-wide population expansion in a montane songbird radiation in highland Papua New Guinea. BMC Evol. Biol. 20, 82 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Vences, M., Wollenberg, K. C., Vieites, D. R. & Lees, D. C. Madagascar as a model region of species diversification. Trends Ecol. Evol. 24, 456–465 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Goodman, S. M., Raherilalao, M. J. & Wohlhauser, S. The Terrestrial Protected Areas of Madagascar: Their History, Description and Biota (Association Vahatra in Antananarivo, The University of Chicago Press, 2018).10.Douglass, K. The diversity of late holocene shellfish exploitation in Velondriake, Southwest Madagascar. J. Island Coast. Archaeol. 12, 333–359 (2016).11.Yoder, A. D., Campbell, C. R., Blanco, M. B., Ganzhorn, J. U. & Goodman, S. M. Geogenetic patterns in mouse lemurs (genus Microcebus) reveal the ghosts of Madagascar’s forests past. PNAS 113, 8049–8056 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L., Climate change. and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Townsend, T. M., Vieites, D. R., Glaw, F. & Vences, M. Testing species-level diversification hypotheses in Madagascar: the case of microendemic Brookesia leaf Chameleons. Syst. Biol. 58, 641–656 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Brown, J. L., Cameron, A., Yoder, A. D. & Vences, M. A necessarily complex model to explain the biogeography of the amphibians and reptiles of Madagascar. Nat. Commun. 5, 5046 (2014).15.Schüßler, D. et al. Ecology and morphology of mouse lemurs (Microcebus spp.) in a hotspot of microendemism in northeastern Madagascar, with the description of a new species. Am. J. Primatol. 82, e23180 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Chikhi, L. & Bruford, M. Mammalian population genetics and genomics. Mamm. Genome https://doi.org/10.1079/9780851999104.0539 (2005).17.Olivieri, G. L., Sousa, V., Chikhi, L. & Radespiel, U. From genetic diversity and structure to conservation: Genetic signature of recent population declines in three mouse lemur species (Microcebus spp.). Biol. Conserv. 141, 1257–1271 (2008).Article 

Google Scholar 
18.Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 5, e1000695 (2009).19.Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).21.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat Genet. 47, 555–559 (2015).22.Salmona, J., Heller, R., Lascoux, M. & Shafer, A. Inferring demographic history using genomic data. in Population Genomics 511–537 (Springer, 2017).23.Beichman, A. C., Huerta-Sanchez, E. & Lohmueller, K. E. Using genomic data to infer historic population dynamics of nonmodel organisms. Annu. Rev. Ecol. Evol. Syst. 49, 433–456 (2018).Article 

Google Scholar 
24.Sgarlata, G. M. et al. Genetic and morphological diversity of mouse lemurs (Microcebus spp.) in northern Madagascar: The discovery of a putative new species? Am. J. Primatol. 81, e23070 (2019).25.Demenocal, P. et al. Abrupt onset and termination of the African humid period:: rapid climate responses to gradual insolation forcing. Quat. Sci. Rev. 19, 347–361 (2000).Article 

Google Scholar 
26.Tierney, J. E. & DeMenocal, P. B. Abrupt shifts in Horn of Africa hydroclimate since the last glacial maximum. Science 342, 843–846 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Los, S. O. et al. Sensitivity of a tropical montane cloud forest to climate change, present, past and future: Mt. Marsabit, N. Kenya. Quat. Sci. Rev. 218, 34–48 (2019).Article 

Google Scholar 
28.Ivory, S. J. & Russell, J. Climate, herbivory, and fire controls on tropical African forest for the last 60ka. Quat. Sci. Rev. 148, 101–114 (2016).Article 

Google Scholar 
29.Conroy, J. L., Overpeck, J. T., Cole, J. E., Shanahan, T. M. & Steinitz-Kannan, M. Holocene changes in eastern tropical Pacific climate inferred from a Galápagos lake sediment record. Quat. Sci. Rev. 27, 1166–1180 (2008).Article 

Google Scholar 
30.Martin-Puertas, C., Tjallingii, R., Bloemsma, M. & Brauer, A. Varved sediment responses to early Holocene climate and environmental changes in Lake Meerfelder Maar (Germany) obtained from multivariate analyses of micro X-ray fluorescence core scanning data. J. Quat. Sci. 32, 427–436 (2017).Article 

Google Scholar 
31.Flenley, J. R. Tropical forests under the climates of the last 30,000 years. in Potential Impacts of Climate Change on Tropical Forest Ecosystems, 37–57 (Springer, 1998).32.Burrough, S. L. & Thomas, D. S. G. Central southern Africa at the time of the African humid period: a new analysis of Holocene palaeoenvironmental and palaeoclimate data. Quat. Sci. Rev. 80, 29–46 (2013).Article 

Google Scholar 
33.Ivory, S. J. & Russell, J. Lowland forest collapse and early human impacts at the end of the African humid period at Lake Edward, equatorial East. Afr. Quat. Res. 89, 7–20 (2018).Article 

Google Scholar 
34.Anderson, A. et al. New evidence of megafaunal bone damage indicates late colonization of Madagascar. PLoS ONE 13, 1–14 (2018).
Google Scholar 
35.Hansford, J. et al. Early Holocene human presence in Madagascar evidenced by exploitation of avian megafauna. Sci. Adv. 4, eaat6925 (2018).36.Burney, D. A., Robinson, G. S. & Burney, L. P. Sporormiella and the late holocene extinctions in Madagascar. Proc. Natl Acad. Sci. USA 100, 10800–10805 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Railsback, L. B. et al. Relationships between climate change, human environmental impact, and megafaunal extinction inferred from a 4000-year multi-proxy record from a stalagmite from northwestern Madagascar. Quat. Sci. Rev. 234, 106244 (2020).Article 

Google Scholar 
38.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. PNAS 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Radimilahy, C. Mahilaka: an Archaeological Investigation of an Early Town in Northwestern Madagascar. Acta Universitatis Upsaliensis (University of Uppsala, 1998).40.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat. Genet 47, 555–559 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Lapierre, M., Lambert, A. & Achaz, G. Accuracy of demographic inferences from the site frequency spectrum: the case of the yoruba population. Genetics 206, 139–449 (2017).Article 

Google Scholar 
42.Terhorst, J., Kamm, J. A. & Song, Y. S. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49, 303–309 (2017).CAS 
Article 

Google Scholar 
43.Patton, A. H. et al. Contemporary demographic reconstruction methods are robust to genome assembly quality: a case study in Tasmanian devils. Mol. Biol. Evol. 36, 2906–2921 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Mazet, O., Rodríguez, W., Grusea, S., Boitard, S. & Chikhi, L. On the importance of being structured: Instantaneous coalescence rates and human evolution-lessons for ancestral population size inference? Heredity 116, 362–371 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Orozco-terWengel, P. The devil is in the details: the effect of population structure on demographic inference. Heredity 116, 349–350 (2016).46.Mazet, O., Rodríguez, W. & Chikhi, L. Demographic inference using genetic data from a single individual: separating population size variation from population structure. Theor. Popul. Biol. 104, 46–58 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Chikhi, L. et al. The IICR (inverse instantaneous coalescence rate) as a summary of genomic diversity: Insights into demographic inference and model choice. Heredity 120, 13–24 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Simons, E. L., Godfrey, L. R., Vuillaume-Randriamanantena, M., Chatrath, P. S. & Gagnon, M. Discovery of new giant subfossil lemurs in the Ankarana Mountains of Northern Madagascar. J. Hum. Evol. 19, 311–319 (1990).Article 

Google Scholar 
49.Jungers, W. L., Godfrey, L. R., Simons, E. L. & Chatrath, P. S. Subfossil Indri indri from the Ankarana Massif of northern Madagascar. Am. J. Phys. Anthropol. 97, 357–366 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Wilson, J. M., Stewart, P. D. & Fowler, S. V. Ankarana — a rediscovered nature reserve in northern Madagascar. Oryx 22, 163–171 (1988).Article 

Google Scholar 
51.Everson, K. M., Jansa, S. A., Goodman, S. M. & Olson, L. E. Montane regions shape patterns of diversification in small mammals and reptiles from Madagascar’s moist evergreen forest. J. Biogeogr. 47, 2059–2072 (2020).Article 

Google Scholar 
52.Douglass, K., Hixon, S., Wright, H. T., Godfrey, L. R. & Crowley, B. E. A critical review of radiocarbon dates clarifies the human settlement of Madagascar. Quat. Sci. Rev. 221, 105878 (2019).53.Orozco-Terwengel, P., Andreone, F., Louis, E. & Vences, M. Mitochondrial introgressive hybridization following a demographic expansion in the tomato frogs of Madagascar, genus. Dyscophus. Mol. Ecol. 22, 6074–6090 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Johnson, J. A. et al. Long-term survival despite low genetic diversity in the critically endangered Madagascar fish-eagle. Mol. Ecol. 18, 54–63 (2009).PubMed 
PubMed Central 

Google Scholar 
55.Sommer, S. Effects of habitat fragmentation and changes of dispersal behaviour after a recent population decline on the genetic variability of noncoding and coding DNA of a monogamous Malagasy rodent. Mol. Ecol. 12, 2845–2851 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Craul, M. et al. Influence of forest fragmentation on an endangered large-bodied lemur in northwestern Madagascar. Biol. Conserv. 142, 2862–2871 (2009).Article 

Google Scholar 
57.Parga, J. A., Sauther, M. L., Cuozzo, F. P., Jacky, I. A. Y. & Lawler, R. R. Evaluating ring-tailed lemurs (Lemur catta) from southwestern Madagascar for a genetic population bottleneck. Am. J. Phys. Anthropol. 147, 21–29 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. Proc. Natl Acad. Sci. USA 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Schüler, L. & Hemp, A. Atlas of pollen and spores and their parent taxa of Mt. Kilimanjaro and tropical East Africa. Quat. Int. 425, 301–386 (2016).Article 

Google Scholar 
60.Du Puy, D. J. & Moat, J. Vegetation mapping and classification in Madagascar (using GIS): implications and recommendations for the conservation of biodiversity. in Chorology, Taxonomy and Ecology of the floras of Africa and Madagascar, 97–117 (1998, in press).61.Guillaumet, J.-L., Betsch, J.-M. & Callmander, M. W. Renaud Paulian et le programme du CNRS sur les hautes montagnes à Madagascar: étage vs domaine. Zoosystema 30, 723 (2008).PubMed 
PubMed Central 

Google Scholar 
62.Weisrock, D. W. et al. Delimiting species without nuclear monophyly in Madagascar’s mouse lemurs. PLoS ONE 5, e9883 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Croudace, I. W., Rindby, A. & Rothwell, R. G. ITRAX: description and evaluation of a new multi-function X-ray core scanner. Geol. Soc. Lond. Spec. Publ. 267, 51–63 (2006).CAS 
Article 

Google Scholar 
64.Blott, S. J. & Pye, K. GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf. Process. Landforms 26, 1237–1248 (2001).Article 

Google Scholar 
65.Hogg, A. G. et al. SHCal13 Southern Hemisphere calibration, 0–50,000 years cal BP. Radiocarbon 55, 1889–1903 (2013).CAS 
Article 

Google Scholar 
66.Rina Evasoa, M. et al. Sources of variation in social tolerance in mouse lemurs (Microcebus spp.). BMC Ecol. 19, 1–16 (2019).CAS 
Article 

Google Scholar 
67.Aleixo-Pais, I. et al. The genetic structure of a mouse lemur living in a fragmented habitat in Northern Madagascar. Conserv. Genet. 20, 229–243 (2019).Article 

Google Scholar 
68.Radespiel, U., Jurić, M. & Zimmermann, E. Sociogenetic structures, dispersal and the risk of inbreeding in a small nocturnal lemur, the golden-brown mouse lemur (Microcebus ravelobensis). Behaviour 146, 607–628 (2009).Article 

Google Scholar 
69.Radespiel, U., Ehresmann, P. & Zimmermann, E. Species-specific usage of sleeping sites in two sympatric mouse lemur species (Microcebus murinus and M. ravelobensis) in northwestern Madagascar. Am. J. Primatol. 59, 139–151 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Larsen, P. A. et al. Hybrid de novo genome assembly and centromere characterization of the gray mouse lemur (Microcebus murinus). BMC Biol. 15, 1–17 (2017).Article 
CAS 

Google Scholar 
71.Metzker, M. L. Sequencing technologies — the next generation. Nat. Rev. Genet. 11, 31 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing. Data 195, 693–702 (2013).CAS 

Google Scholar 
73.Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 1–13 (2014).Article 

Google Scholar 
74.Korneliussen, T. S. & Moltke, I. Sequence analysis NgsRelate: a software tool for estimating pairwise relatedness from next-generation sequencing data. Bioinformatics 31, 4009–4011 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Soraggi, S., Wiuf, C. & Albrechtsen, A. Powerful inference with the D-Statistic on low-coverage whole-genome data. G3 8, 551–566 (2017).PubMed Central 
Article 

Google Scholar 
76.Chikhi, L., Sousa, V. C., Luisi, P., Goossens, B. & Beaumont, M. A. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186, 983–995 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
77.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L. Climate change and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Schneider, N., Chikhi, L., Currat, M. & Radespiel, U. Signals of recent spatial expansions in the grey mouse lemur (Microcebus murinus). BMC Evol. Biol. 10, 105 (2010).81.Radespiel, U., Lutermann, H., Schmelting, B. & Zimmermann, E. An empirical estimate of the generation time of mouse lemurs. Am. J. Primatol. 81, 1–8 (2019).Article 

Google Scholar 
82.Hawkins, M. T. R. et al. Genome sequence and population declines in the critically endangered greater bamboo lemur (Prolemur simus) and implications for conservation. BMC Genomics 19, 1–15 (2018).Article 
CAS 

Google Scholar 
83.Poelstra, J. et al. Cryptic patterns of speciation in cryptic primates: microendemic mouse lemurs and the multispecies coalescent. Syst. Biol. https://doi.org/10.1093/sysbio/syaa053 (2020).84.Campbell, C. R. et al. Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur. Heredity 127.2, 233–244 (2021).Article 

Google Scholar 
85.Hudson, R. R. Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Fredsted, T., Pertoldi, C., Schierup, M. H. & Kappeler, P. M. Microsatellite analyses reveal fine-scale genetic structure in grey mouse lemurs (Microcebus murinus). Mol. Ecol. 14, 2363–2372 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Radespiel, U., Schulte, J., Burke, R. J. & Lehman, S. M. Molecular edge effects in the endangered golden-brown mouse lemur Microcebus ravelobensis. Oryx 53, 716–726 (2019).Article 

Google Scholar 
88.Radespiel, U., Lutermann, H., Schmelting, B., Bruford, M. W. & Zimmermann, E. Patterns and dynamics of sex-biased dispersal in a nocturnal primate, the grey mouse lemur, Microcebus murinus. Anim. Behav. 65, 709–719 (2003).Article 

Google Scholar 
89.Radespiel, U., Rakotondravony, R. & Chikhi, L. Natural and anthropogenic determinants of genetic structure in the largest remaining population of the endangered golden-brown mouse lemur, Microcebus ravelobensis. Am. J. Primatol. 70, 860–870 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Schliehe-Diecks, S., Eberle, M. & Kappeler, P. M. Walk the line-dispersal movements of gray mouse lemurs (Microcebus murinus). Behav. Ecol. Sociobiol. 66, 1175–1185 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
91.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).92.Beerli, P. Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations. Mol. Ecol. 13, 827–836 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
93.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Control 19, 716–723 (1974).Article 

Google Scholar 
94.Bagley, R. K., Sousa, V. C., Niemiller, M. L. & Linnen, C. R. History, geography and host use shape genomewide patterns of genetic variation in the redheaded pine sawfly (Neodiprion lecontei). Mol. Ecol. 26, 1022–1044 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Milner AM, Khamis K, Battin TJ, Brittain JE, Barrand NE, Füreder L, et al. Glacier shrinkage driving global changes in downstream systems. Proc Nat Acad Sci USA. 2017;114:9770.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Battin TJ, Wille A, Sattler B, Psenner R. Phylogenetic and functional heterogeneity of sediment biofilms along environmental gradients in a glacial stream. Appl Environ Microbiol. 2001;67:799–807.CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Wilhelm L, Singer GA, Fasching C, Battin TJ, Besemer K. Microbial biodiversity in glacier-fed streams. ISME J. 2013;7:1651.CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Ren Z, Gao H, Elser JJ, Zhao Q. Microbial functional genes elucidate environmental drivers of biofilm metabolism in glacier-fed streams. Sci Rep. 2017;7:12668.PubMed 
PubMed Central 

Google Scholar 
5.Dini-Andreote F, Stegen JC, van Elsas JD, Salles JF. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc Nat Acad Sci USA. 2015;112:1326.
Google Scholar 
6.Stegen JC, Lin X, Fredrickson JK, Chen X, Kennedy DW, Murray CJ, et al. Quantifying community assembly processes and identifying features that impose them. ISME J. 2013;7:2069–79.PubMed 
PubMed Central 

Google Scholar 
7.Stegen JC, Lin X, Fredrickson JK, Konopka AE. Estimating and mapping ecological processes influencing microbial community assembly. Front Microbiol. 2015;6:370.8.Allen R, Hoffmann LJ, Larcombe MJ, Louisson Z, Summerfield TC. Homogeneous environmental selection dominates microbial community assembly in the oligotrophic South Pacific Gyre. Mol Ecol. 2020;29:4680–91.CAS 
PubMed 

Google Scholar 
9.Li Y, Gao Y, Zhang W, Wang C, Wang P, Niu L, et al. Homogeneous selection dominates the microbial community assembly in the sediment of the Three Gorges Reservoir. Sci Tot Environ. 2019;690:50–60.CAS 

Google Scholar 
10.Zhang K, Shi Y, Cui X, Yue P, Li K, Liu X, et al. Salinity is a key determinant for soil microbial communities in a desert ecosystem. mSystems. 2019;4:e00225–18.11.Thrash CJ, Temperton B, Swan BK, Landry ZC, Woyke T, DeLong EF, et al. Single-cell enabled comparative genomics of a deep ocean SAR11 bathytype. ISME J. 2014;8:1440–51.PubMed 

Google Scholar 
12.Hunt DE, David LA, Gevers D, Preheim SP, Alm EJ, Polz MF. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science. 2008;320:1081.CAS 
PubMed 

Google Scholar 
13.Kent AG, Baer SE, Mouginot C, Huang JS, Larkin AA, Lomas MW, et al. Parallel phylogeography of Prochlorococcus and Synechococcus. ISME J. 2019;13:430–41.PubMed 

Google Scholar 
14.Brown MV, Furham JA. Marine bacterial microdiversity as revealed by internal transcribed spacer analysis. Aquat Microb Ecol. 2005;41:15–23.
Google Scholar 
15.Scanlan DJ, Ostrowski M, Mazard S, Dufresne A, Garczarek L, Hess WR, et al. Ecological genomics of marine picocyanobacteria. Microbiol Mol Biol Rev. 2009;73:249.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Yung C-M, Vereen MK, Herbert A, Davis KM, Yang J, Kantorowska A, et al. Thermally adaptive tradeoffs in closely related marine bacterial strains. Environ Microbiol. 2015;17:2421–9.PubMed 

Google Scholar 
17.Props R, Denef VJ. Temperature and nutrient levels correspond with lineage-specific microdiversification in the ubiquitous and abundant freshwater genus. Limnohabitans Appl Environ Microbiol. 2020;86:e00140–00120.CAS 
PubMed 

Google Scholar 
18.Chase AB, Karaoz U, Brodie EL, Gomez-Lunar Z, Martiny AC, Martiny JBH. Microdiversity of an abundant terrestrial bacterium encompasses extensive variation in ecologically relevant traits. mBio. 2017;8:e01809–17.19.Choudoir MJ, Buckley DH. Phylogenetic conservatism of thermal traits explains dispersal limitation and genomic differentiation of Streptomyces sister-taxa. ISME J. 2018;12:2176–86.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Cohan FM. Bacterial species and speciation. Syst Biol. 2001;50:513–24.CAS 
PubMed 

Google Scholar 
21.Cohan FM, Koeppel AF. The origins of ecological diversity in prokaryotes. Curr Biol. 2008;18:R1024–34.CAS 
PubMed 

Google Scholar 
22.Larkin AA, Martiny AC. Microdiversity shapes the traits, niche space, and biogeography of microbial taxa. Environ Microbiol Rep. 2017;9:55–70.CAS 
PubMed 

Google Scholar 
23.Fodelianakis S, Lorz A, Valenzuela-Cuevas A, Barozzi A, Booth JM, Daffonchio D. Dispersal homogenizes communities via immigration even at low rates in a simplified synthetic bacterial metacommunity. Nat Commun. 2019;10:1314.PubMed 
PubMed Central 

Google Scholar 
24.Duarte CM, Røstad A, Michoud G, Barozzi A, Merlino G, Delgado-Huertas A, et al. Discovery of Afifi, the shallowest and southernmost brine pool reported in the Red Sea. Sci Rep. 2020;10:910.CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Kohler TJ, Peter H, Fodelianakis S, Pramateftaki P, Styllas M, Tolosano M, et al. Patterns and drivers of extracellular enzyme activity in New Zealand glacier-fed streams. Front Microbiol. 2020;11:2922.
Google Scholar 
26.Amalfitano S, Fazi S. Recovery and quantification of bacterial cells associated with streambed sediments. J Microbiol Methods. 2008;75:237–43.CAS 
PubMed 

Google Scholar 
27.Hammes F, Berney M, Wang Y, Vital M, Köster O, Egli T. Flow-cytometric total bacterial cell counts as a descriptive microbiological parameter for drinking water treatment processes. Water Res. 2008;42:269–77.CAS 
PubMed 

Google Scholar 
28.Busi SB, Pramateftaki P, Brandani J, Fodelianakis S, Peter H, Halder R, et al. Optimised biomolecular extraction for metagenomic analysis of microbial biofilms from high-mountain streams. PeerJ. 2020;8:e9973.PubMed 
PubMed Central 

Google Scholar 
29.Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013;41:e1.CAS 
PubMed 

Google Scholar 
30.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotech. 2019;37:852–7.CAS 

Google Scholar 
32.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Meth. 2016;13:581–3.CAS 

Google Scholar 
33.Props R, Kerckhof F-M, Rubbens P, De Vrieze J, Hernandez-Sanabria E, Waegeman W, et al. Absolute quantification of microbial taxon abundances. ISME J. 2017;11:584–7.PubMed 

Google Scholar 
34.Bokulich NA, Kaehler BD, Rideout JR, Dillon M, Bolyen E, Knight R, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:90.PubMed 
PubMed Central 

Google Scholar 
35.DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol. 2006;72:5069–72.CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Singer E, Bushnell B, Coleman-Derr D, Bowman B, Bowers RM, Levy A, et al. High-resolution phylogenetic microbial community profiling. ISME J. 2016;10:2020–32.PubMed 
PubMed Central 

Google Scholar 
37.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011;7:539–9.PubMed 
PubMed Central 

Google Scholar 
40.Foster ZSL, Sharpton TJ, Grünwald NJ. Metacoder: an R package for visualization and manipulation of community taxonomic diversity data. PLOS Comput Biol. 2017;13:e1005404.PubMed 
PubMed Central 

Google Scholar 
41.R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2014.42.Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community ecology package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan.43.Fodelianakis S, Moustakas A, Papageorgiou N, Manoli O, Tsikopoulou I, Michoud G, et al. Modified niche optima and breadths explain the historical contingency of bacterial community responses to eutrophication in coastal sediments. Mol Ecol. 2017;26:2006–18.CAS 
PubMed 

Google Scholar 
44.Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, et al. Picante: R tools for integrating phylogenies and ecology. Bioinformatics. 2010;26:1463–4.CAS 
PubMed 

Google Scholar 
45.Washburne AD, Silverman JD, Leff JW, Bennett DJ, Darcy JL, Mukherjee S, et al. Phylogenetic factorization of compositional data yields lineage-level associations in microbiome datasets. PeerJ. 2017;5:e2969.PubMed 
PubMed Central 

Google Scholar 
46.Washburne AD, Silverman JD, Morton JT, Becker DJ, Crowley D, Mukherjee S, et al. Phylofactorization: a graph partitioning algorithm to identify phylogenetic scales of ecological data. Ecol Monogr. 2019;89:e01353.
Google Scholar 
47.Gawor J, Grzesiak J, Sasin-Kurowska J, Borsuk P, Gromadka R, Górniak D, et al. Evidence of adaptation, niche separation and microevolution within the genus Polaromonas on Arctic and Antarctic glacial surfaces. Extremophiles. 2016;20:403–13.PubMed 
PubMed Central 

Google Scholar 
48.Sohm JA, Ahlgren NA, Thomson ZJ, Williams C, Moffett JW, Saito MA, et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 2016;10:333–45.CAS 
PubMed 

Google Scholar 
49.Tromas N, Taranu ZE, Castelli M, Pimentel JSM, Pereira DA, Marcoz R, et al. The evolution of realized niches within freshwater. Synechococcus Environ Microbiol. 2020;22:1238–50.PubMed 

Google Scholar 
50.Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.CAS 
PubMed 

Google Scholar 
51.Cerqueira T, Barroso C, Froufe H, Egas C, Bettencourt R. Metagenomic signatures of microbial communities in deep-sea hydrothermal sediments of Azores Vent Fields. Microb Ecol. 2018;76:387–403.CAS 
PubMed 

Google Scholar 
52.Osburn MR, LaRowe DE, Momper LM, Amend JP. Chemolithotrophy in the continental deep subsurface: Sanford underground research facility (SURF), USA. Front Microbiol. 2014;5:610.53.Tran P, Ramachandran A, Khawasik O, Beisner BE, Rautio M, Huot Y, et al. Microbial life under ice: Metagenome diversity and in situ activity of Verrucomicrobia in seasonally ice-covered Lakes. Environ Microbiol. 2018;20:2568–84.CAS 
PubMed 

Google Scholar 
54.Vick-Majors TJ, Priscu JC, Amaral-Zettler LA. Modular community structure suggests metabolic plasticity during the transition to polar night in ice-covered Antarctic lakes. ISME J. 2014;8:778–89.CAS 
PubMed 

Google Scholar 
55.Darcy JL, Lynch RC, King AJ, Robeson MS, Schmidt SK. Global distribution of Polaromonas phylotypes – evidence for a highly successful dispersal capacity. PloS ONE. 2011;6:e23742.CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Smith HJ, Foreman CM, Ramaraj T. Draft genome sequence of a metabolically diverse Antarctic supraglacial stream organism, Polaromonas sp. strain CG9_12, determined using Pacific Biosciences single-molecule real-time sequencing technology. Genome Announc. 2014;2:e01242–01214.PubMed 
PubMed Central 

Google Scholar 
57.Rime T, Hartmann M, Frey B. Potential sources of microbial colonizers in an initial soil ecosystem after retreat of an alpine glacier. ISME J. 2016;10:1625–41.CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Liu Q, Zhou Y-G, Xin Y-H. High diversity and distinctive community structure of bacteria on glaciers in China revealed by 454 pyrosequencing. Syst Appl Microbiol. 2015;38:578–85.PubMed 

Google Scholar 
59.Kalyuzhnaya MG, Bowerman S, Lara JC, Lidstrom ME, Chistoserdova L. Methylotenera mobilis gen. nov., sp. nov., an obligately methylamine-utilizing bacterium within the family Methylophilaceae. Int J Syst Evol Microbiol. 2006;56:2819–23.CAS 
PubMed 

Google Scholar 
60.Kane SR, Chakicherla AY, Chain PSG, Schmidt R, Shin MW, Legler TC, et al. Whole-genome analysis of the methyl tert-butyl ether-degrading Beta-Proteobacterium Methylibium petroleiphilum PM1. J Bacteriol. 2007;189:1931.CAS 
PubMed 

Google Scholar 
61.Martineau C, Mauffrey F, Villemur R, Müller V. Comparative analysis of denitrifying activities of Hyphomicrobium nitrativorans, Hyphomicrobium denitrificans, and Hyphomicrobium zavarzinii. Appl Environ Microbiol. 2015;81:5003–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Dieser M, Broemsen ELJE, Cameron KA, King GM, Achberger A, Choquette K, et al. Molecular and biogeochemical evidence for methane cycling beneath the western margin of the Greenland Ice Sheet. ISME J. 2014;8:2305–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Michaud AB, Dore JE, Achberger AM, Christner BC, Mitchell AC, Skidmore ML, et al. Microbial oxidation as a methane sink beneath the West Antarctic Ice Sheet. Nat Geosci. 2017;10:582–6.CAS 

Google Scholar 
64.Bendall ML, Stevens SLR, Chan L-K, Malfatti S, Schwientek P, Tremblay J, et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 2016;10:1589–601.PubMed 
PubMed Central 

Google Scholar 
65.Baker JM, Riester CJ, Skinner BM, Newell AW, Swingley WD, Madigan MT, et al. Genome sequence of Rhodoferax antarcticus ANT.BRT; a psychrophilic purple nonsulfur bacterium from an Antarctic microbial mat. Microorganisms. 2017;5:8.66.Crisafi F, Giuliano L, Yakimov MM, Azzaro M, Denaro R. Isolation and degradation potential of a cold-adapted oil/PAH-degrading marine bacterial consortium from Kongsfjorden (Arctic region). Rendiconti Lincei. 2016;27:261–70.
Google Scholar 
67.Zhong Z-P, Solonenko NE, Gazitúa MC, Kenny DV, Mosley-Thompson E, Rich VI, et al. Clean low-biomass procedures and their application to ancient ice core microorganisms. Front Microbiol. 2018;9:1094.68.Bai Y, Huang X, Zhou X, Xiang Q, Zhao K, Yu X, et al. Variation in denitrifying bacterial communities along a primary succession in the Hailuogou Glacier retreat area, China. PeerJ. 2019;7:e7356.PubMed 
PubMed Central 

Google Scholar 
69.Garcia-Lopez E, Rodriguez-Lorente I, Alcazar P, Cid C. Microbial communities in coastal glaciers and tidewater tongues of Svalbard archipelago, Norway. Front Mar Sci. 2019;5:512.70.Liu S, Wang H, Chen L, Wang J, Zheng M, Liu S, et al. Comammox Nitrospira within the Yangtze River continuum: community, biogeography, and ecological drivers. ISME J. 2020;14:2488–504.CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Harrold ZR, Skidmore ML, Hamilton TL, Desch L, Amada K, van Gelder W, et al. Aerobic and anaerobic thiosulfate oxidation by a cold-adapted, subglacial chemoautotroph. Appl Environ Microbiol. 2016;82:1486–95.CAS 
PubMed Central 

Google Scholar 
72.Franzetti A, Pittino F, Gandolfi I, Azzoni RS, Diolaiuti G, Smiraglia C, et al. Early ecological succession patterns of bacterial, fungal and plant communities along a chronosequence in a recently deglaciated area of the Italian Alps. FEMS Microbiol Ecol. 2020;96:10.73.Kohler TJ, Van Horn DJ, Darling JP, Takacs-Vesbach CD, McKnight DM. Nutrient treatments alter microbial mat colonization in two glacial meltwater streams from the McMurdo Dry Valleys, Antarctica. FEMS Microbiol Ecol. 2016;92:4.
Google Scholar 
74.Sawayama M, Suzuki T, Hashimoto H, Kasai T, Furutani M, Miyata N, et al. Isolation of a Leptothrix strain, OUMS1, from ocherous deposits in groundwater. Cur Microbiol. 2011;63:173–80.CAS 

Google Scholar 
75.Li Y, Cha Q-Q, Dang Y-R, Chen X-L, Wang M, McMinn A, et al. Reconstruction of the functional ecosystem in the high light, low temperature union glacier region, Antarctica. Front Microbiol. 2019;10.76.Cauvy-Fraunié S, Dangles O. A global synthesis of biodiversity responses to glacier retreat. Nat Ecol Evol. 2019;3:1675–85.PubMed 

Google Scholar 
77.Jorquera MA, Graether SP, Maruyama F. Editorial: bioprospecting and biotechnology of extremophiles. Front Bioeng Biotech. 2019;7:204.
Google Scholar 
78.Thompson JR, Pacocha S, Pharino C, Klepac-Ceraj V, Hunt DE, Benoit J, et al. Genotypic diversity within a natural coastal bacterioplankton population. Science. 2005;307:1311.CAS 
PubMed 

Google Scholar 
79.Chase AB, Gomez-Lunar Z, Lopez AE, Li J, Allison SD, Martiny AC, et al. Emergence of soil bacterial ecotypes along a climate gradient. Environ Microbiol. 2018;11:4112–26.
Google Scholar 
80.Chafee M, Fernàndez-Guerra A, Buttigieg PL, Gerdts G, Eren AM, Teeling H, et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 2018;12:237–52.PubMed 

Google Scholar 
81.Needham DM, Sachdeva R, Fuhrman JA. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 2017;11:1614–29.PubMed 
PubMed Central 

Google Scholar 
82.Garcia-Garcia N, Tamames J, Linz AM, Pedros-Alio C, Puente-Sanchez F. Microdiversity ensures the maintenance of functional microbial communities under changing environmental conditions. ISME J. 2019;13:2969–83.CAS 
PubMed 
PubMed Central 

Google Scholar 
83.Becraft ED, Wood JM, Rusch DB, Kühl M, Jensen SI, Bryant DA, et al. The molecular dimension of microbial species: 1. Ecological distinctions among, and homogeneity within, putative ecotypes of Synechococcus inhabiting the cyanobacterial mat of Mushroom Spring, Yellowstone National Park. Front Microbiol. 2015;6:590.PubMed 
PubMed Central 

Google Scholar 
84.Becraft ED, Cohan FM, Kühl M, Jensen SI, Ward DM. Fine-scale distribution patterns of Synechococcus ecological diversity in microbial mats of Mushroom Spring, Yellowstone National Park. Appl Environ Microbiol. 2011;77:7689–97.CAS 
PubMed 
PubMed Central 

Google Scholar 
85.Koeppel A, Perry EB, Sikorski J, Krizanc D, Warner A, Ward DM, et al. Identifying the fundamental units of bacterial diversity: a paradigm shift to incorporate ecology into bacterial systematics. Proc Nat Acad Sci USA. 2008;105:2504.CAS 
PubMed 
PubMed Central 

Google Scholar 
86.Stegen JC, Lin X, Konopka AE, Fredrickson JK. Stochastic and deterministic assembly processes in subsurface microbial communities. ISME J. 2012;6:1653–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Zhou J, Ning D. Stochastic community assembly: does it matter in microbial ecology? Microbiol Mol Biol Rev. 2017;81:e00002–17.88.Ning D, Deng Y, Tiedje JM, Zhou J. A general framework for quantitatively assessing ecological stochasticity. Proc Nat Acad Sci USA. 2019;116:16892–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
89.Zhou J, Deng Y, Zhang P, Xue K, Liang Y, Van Nostrand JD, et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proc Nat Acad Sci USA. 2014;111:E836–45.CAS 
PubMed 
PubMed Central 

Google Scholar 
90.Evans S, Martiny JBH, Allison SD. Effects of dispersal and selection on stochastic assembly in microbial communities. ISME J. 2017;11:176–85.PubMed 

Google Scholar 
91.Ning D, Yuan M, Wu L, Zhang Y, Guo X, Zhou X, et al. A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nat Commun. 2020;11:4717.CAS 
PubMed 
PubMed Central 

Google Scholar 
92.Cohan FM. Systematics: the cohesive nature of bacterial species taxa. Curr Biol. 2019;29:169–72.
Google Scholar 
93.Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 

Google Scholar 
94.Callahan BJ, Grinevich D, Thakur S, Balamotis MA, Yehezkel TB. Ultra-accurate microbial amplicon sequencing with synthetic long reads. Microbiome. 2021;9:130.PubMed 
PubMed Central 

Google Scholar 
95.Matsuo Y, Komiya S, Yasumizu Y, Yasuoka Y, Mizushima K, Takagi T, et al. Full-length 16S rRNA gene amplicon analysis of human gut microbiota using MinION™ nanopore sequencing confers species-level resolution. BMC Microbiol. 2021;21:35.CAS 
PubMed 
PubMed Central 

Google Scholar 
96.Nygaard AB, Tunsjø HS, Meisal R, Charnock C. A preliminary study on the potential of Nanopore MinION and Illumina MiSeq 16S rRNA gene sequencing to characterize building-dust microbiomes. Sci Rep. 2020;10:3209.CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).ADS 

Google Scholar 
2.Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).ADS 

Google Scholar 
3.Huang, Y., Wu, S. & Kaplan, J. O. Sensitivity of global wildfire occurrences to various factors in the context of global change. Atmos. Environ. 121, 86–92 (2015).ADS 
CAS 

Google Scholar 
4.van Oldenborgh, G. J. et al. Attribution of the Australian bushfire risk to anthropogenic climate change. Nat. Hazards Earth Syst. Sci. 21, 941–960 (2021).ADS 

Google Scholar 
5.Ward, M. et al. Impact of 2019–2020 mega-fires on Australian fauna habitat. Nat. Ecol. Evol. 4, 1321–1326 (2020).
Google Scholar 
6.Kablick III, G. P., Allen, D. R., Fromm, M. D. & Nedoluha, G. E. Australian PyroCb smoke generates synoptic-scale stratospheric anticyclones. Geophys. Res. Lett. 47, e2020GL088101 (2020).ADS 

Google Scholar 
7.Hirsch, E. & Koren, I. Record-breaking aerosol levels explained by smoke injection into the stratosphere. Science 371, 1269–1274 (2021).ADS 
CAS 

Google Scholar 
8.Schlosser, J. S. et al. Analysis of aerosol composition data for western United States wildfires between 2005 and 2015: Dust emissions, chloride depletion, and most enhanced aerosol constituents. J. Geophys. Res. Atmos. 122, 8951–8966 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Barkley, A. E. et al. African biomass burning is a substantial source of phosphorus deposition to the Amazon, Tropical Atlantic Ocean, and Southern Ocean. Proc. Natl Acad. Sci. USA 116, 16216–16221 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Guieu, C., Bonnet, S., Wagener, T. & Loÿe-Pilot, M.-D. Biomass burning as a source of dissolved iron to the open ocean? Geophys. Res. Lett. 32, L19608 (2005).ADS 

Google Scholar 
11.Ito, A. Mega fire emissions in Siberia: potential supply of bioavailable iron from forests to the ocean. Biogeosciences 8, 1679–1697 (2011).ADS 
CAS 

Google Scholar 
12.Abram, N. J., Gagan, M. K., McCulloch, M. T., Chappell, J. & Hantoro, W. S. Coral reef death during the 1997 Indian Ocean Dipole linked to Indonesian wildfires. Science 301, 952–955 (2003).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
13.Ito, A. et al. Pyrogenic iron: the missing link to high iron solubility in aerosols. Sci. Adv. 5, eaau7671 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Jia, G. et al. in Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems Ch. 2 (IPCC, in the press).15.Jiang, Y. et al. Impacts of wildfire aerosols on global energy budget and climate: the role of climate feedbacks. J. Clim. 33, 3351–3366 (2020).ADS 

Google Scholar 
16.Bowman, D. et al. Wildfires: Australia needs national monitoring agency. Nature 584, 188–191 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
17.New WWF report: 3 billion animals impacted by Australia’s bushfire crisis. WWF https://www.wwf.org.au/news/news/2020/3-billion-animals-impacted-by-australia-bushfire-crisis#gs.ebzve2 (2020).18.van der Velde, I. R. et al. Vast CO2 release from Australian fires in 2019–2020 constrained by satellite. Nature https://doi.org/10.1038/s41586-021-03712-y (2021).19.National Greenhouse Gas Inventory Report: 2018 (Australian Government, 2020); https://www.industry.gov.au/data-and-publications/national-greenhouse-gas-inventory-report-2018.20.Mahowald, N. M. et al. Aerosol impacts on climate and biogeochemistry. Annu. Rev. Environ. Res. 36, 45–74 (2011).
Google Scholar 
21.Boyd, P. W. et al. Mesoscale iron enrichment experiments 1993–2005: synthesis and future directions. Science 315, 612–617 (2007).ADS 
CAS 

Google Scholar 
22.Jickells, T. et al. Global iron connections between desert dust, ocean biogeochemistry, and climate. Science 308, 67–71 (2005).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
23.Martin, J. H. Glacial‐interglacial CO2 change: the iron hypothesis. Paleoceanography 5, 1–13 (1990).ADS 

Google Scholar 
24.Tagliabue, A. et al. Surface-water iron supplies in the Southern Ocean sustained by deep winter mixing. Nat. Geosci. 7, 314–320 (2014).ADS 
CAS 

Google Scholar 
25.Cassar, N. et al. The Southern Ocean biological response to aeolian iron deposition. Science 317, 1067–1070 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
26.Gabric, A. J., Cropp, R., Ayers, G. P., McTainsh, G. & Braddock, R. Coupling between cycles of phytoplankton biomass and aerosol optical depth as derived from SeaWiFS time series in the Subantarctic Southern Ocean. Geophys. Res. Lett. 29, 16-11–16-14 (2002).
Google Scholar 
27.Ardyna, M. et al. Hydrothermal vents trigger massive phytoplankton blooms in the Southern Ocean. Nat. Commun. 10, 2451 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
28.Duprat, L. P. A. M., Bigg, G. R. & Wilton, D. J. Enhanced Southern Ocean marine productivity due to fertilization by giant icebergs. Nat. Geosci. 9, 219–221 (2016).ADS 
CAS 

Google Scholar 
29.Bixby, R. J. et al. Fire effects on aquatic ecosystems: an assessment of the current state of the science. Freshwater Sci. 34, 1340–1350 (2015).
Google Scholar 
30.Inness, A. et al. The CAMS reanalysis of atmospheric composition. Atmos. Chem. Phys. 19, 3515–3556 (2019).ADS 
CAS 

Google Scholar 
31.Shafeeque, M., Sathyendranath, S., George, G., Balchand, A. N. & Platt, T. Comparison of seasonal cycles of phytoplankton chlorophyll, aerosols, winds and sea-surface temperature off Somalia. Front. Marine Sci. 4, 384 (2017).
Google Scholar 
32.Cassar, N. et al. The influence of iron and light on net community production in the Subantarctic and Polar Frontal zones. Biogeosciences 8, 227–237 (2011).ADS 
CAS 

Google Scholar 
33.Mitchell, B. G. & Holm-Hansen, O. Observations of modeling of the Antartic phytoplankton crop in relation to mixing depth. Deep Sea Res. Part A 38, 981–1007 (1991).ADS 
CAS 

Google Scholar 
34.Longo, A. F. et al. Influence of atmospheric processes on the solubility and composition of iron in Saharan dust. Environ. Sci. Technol. 50, 6912–6920 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Meskhidze, N., Nenes, A., Chameides, W. L., Luo, C. & Mahowald, N. Atlantic Southern Ocean productivity: fertilization from above or below? Global Biogeochem. Cycles 21, GB2006 (2007).ADS 

Google Scholar 
36.Sarmiento, J. L., Slater, R. D., Dunne, J., Gnanadesikan, A. & Hiscock, M. R. Efficiency of small scale carbon mitigation by patch iron fertilization. Biogeosciences 7, 3593–3624 (2010).ADS 
CAS 

Google Scholar 
37.Brzezinski, M. A., Jones, J. L. & Demarest, M. S. Control of silica production by iron and silicic acid during the Southern Ocean Iron Experiment (SOFeX). Limnol. Oceanogr. 50, 810–824 (2005).ADS 
CAS 

Google Scholar 
38.Lovenduski, N. S. & Gruber, N. Impact of the Southern Annular Mode on Southern Ocean circulation and biology. Geophys. Res. Lett. 32, L11603 (2005).ADS 

Google Scholar 
39.Cai, W., Cowan, T. & Raupach, M. Positive Indian Ocean Dipole events precondition southeast Australia bushfires. Geophys. Res. Lett. 36, L19710 (2009).ADS 

Google Scholar 
40.Chen, Y. et al. A pan-tropical cascade of fire driven by El Niño/Southern Oscillation. Nat. Climate Change 7, 906–911 (2017).ADS 
CAS 

Google Scholar 
41.Lim, E.-P. et al. Australian hot and dry extremes induced by weakenings of the stratospheric polar vortex. Nat. Geosci. 12, 896–901 (2019).ADS 
CAS 

Google Scholar 
42.Cai, W. et al. Increased frequency of extreme Indian Ocean Dipole events due to greenhouse warming. Nature 510, 254–258 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
43.Cropp, R. A. et al. The likelihood of observing dust-stimulated phytoplankton growth in waters proximal to the Australian continent. J. Mar. Syst. 117–118, 43–52 (2013).
Google Scholar 
44.Hamilton, D. S. et al. Impact of changes to the atmospheric soluble iron deposition flux on ocean biogeochemical cycles in the anthropocene. Global Biogeochem. Cycles 34, e2019GB006448 (2020).ADS 
CAS 

Google Scholar 
45.Duce, R. et al. Impacts of atmospheric anthropogenic nitrogen on the open ocean. Science 320, 893–897 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Han, Y. et al. Asian inland wildfires driven by glacial-interglacial climate change. Proc. Natl Acad. Sci. USA 117, 5184–5189 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
47.van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Sys. Sci. Data 9, 697–720 (2017).ADS 

Google Scholar 
48.Orsi, A. H., Whitworth, T. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep Sea Res. Part I 42, 641–673 (1995).
Google Scholar 
49.Sathyendranath, S. et al. An ocean-colour time series for use in climate studies: the experience of the Ocean-Colour Climate Change Initiative (OC-CCI). Sensors 19, 4285 (2019).ADS 
CAS 

Google Scholar 
50.Morcrette, J.-J. et al. Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: forward modeling. J. Geophys. Res. Atmospheres 114, D06206 (2009).ADS 

Google Scholar 
51.Levy, R. C. et al. Exploring systematic offsets between aerosol products from the two MODIS sensors. Atmos. Meas. Tech. 11, 4073–4092 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
52.Benedetti, A. et al. Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation. J. Geophys. Res. 114, D13 (2009).
Google Scholar 
53.Kaiser, J. W. et al. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527–554 (2012).ADS 
CAS 

Google Scholar 
54.Y. Bennouna et al. Validation Report of the CAMS Global Reanalysis of Aerosols and Reactive Gases, Years 2003–2019 (Copernicus Atmosphere Monitoring Service, 2020).55.Ito, A. et al. Evaluation of aerosol iron solubility over Australian coastal regions based on inverse modeling: implications of bushfires on bioaccessible iron concentrations in the Southern Hemisphere. Prog. Earth Planet. Sci. 7, 42 (2020).ADS 

Google Scholar 
56.Khaykin, S. et al. The 2019/20 Australian wildfires generated a persistent smoke-charged vortex rising up to 35 km altitude. Commun. Earth Environ. 1, 22 (2020).57.Haëntjens, N., Boss, E. & Talley, L. D. Revisiting Ocean Color algorithms for chlorophyll a and particulate organic carbon in the Southern Ocean using biogeochemical floats. J. Geophys. Res. Oceans 122, 6583–6593 (2017).ADS 

Google Scholar 
58.Boss, E. et al. The characteristics of particulate absorption, scattering and attenuation coefficients in the surface ocean; contribution of the Tara Oceans expedition. Methods Oceanogr. 7, 52–62 (2013).
Google Scholar 
59.de Boyer Montégut, C., Madec, G., Fischer, A. S., Lazar, A. & Iudicone, D. Mixed layer depth over the global ocean: an examination of profile data and a profile‐based climatology. J. Geophys. Res. Oceans 109, C12003 (2004).ADS 

Google Scholar 
60.Dong, S., Sprintall, J., Gille, S. T. & Talley, L. Southern Ocean mixed-layer depth from Argo float profiles. J. Geophys. Res. Oceans 113, C06013 (2008).ADS 

Google Scholar 
61.Cutter, G. A. et al. Sampling and Sample-handling Protocols for GEOTRACES Cruises, version 3.0 (2017).
Google Scholar 
62.Morton, P. L. et al. Methods for the sampling and analysis of marine aerosols: results from the 2008 GEOTRACES aerosol intercalibration experiment. Limnol. Oceanogr. Methods 11, 62–78 (2013).CAS 

Google Scholar 
63.Perron, M. M. G. et al. Assessment of leaching protocols to determine the solubility of trace metals in aerosols. Talanta 208, 120377 (2020).CAS 

Google Scholar 
64.Shelley, R. U., Landing, W. M., Ussher, S. J., Planquette, H. & Sarthou, G. Regional trends in the fractional solubility of Fe and other metals from North Atlantic aerosols (GEOTRACES cruises GA01 and GA03) following a two-stage leach. Biogeosciences 15, 2271–2288 (2018).ADS 
CAS 

Google Scholar 
65.Sanz Rodriguez, E. et al. Analysis of levoglucosan and its isomers in atmospheric samples by ion chromatography with electrospray lithium cationisation—triple quadrupole tandem mass spectrometry. J. Chromatogr. A 1610, 460557 (2020).CAS 

Google Scholar 
66.McLennan, S. M. Relationships between the trace element composition of sedimentary rocks and upper continental crust. Geochem. Geophys. Geosyst. 2, 1201 (2001).
Google Scholar 
67.Shelley, R. U. et al. Quantification of trace element atmospheric deposition fluxes to the Atlantic Ocean ( >40°N; GEOVIDE, GEOTRACES GA01) during spring 2014. Deep Sea Res. Part I 119, 34–49 (2017).CAS 

Google Scholar 
68.Sholkovitz, E. R., Sedwick, P. N., Church, T. M., Baker, A. R. & Powell, C. F. Fractional solubility of aerosol iron: synthesis of a global-scale data set. Geochim. Cosmochim. Acta 89, 173–189 (2012).ADS 
CAS 

Google Scholar 
69.Stein, A. F. et al. NOAA’s HYSPLIT atmospheric transport and dispersion modeling system. Bull. Am. Meteorol. Soc. 96, 2059–2077 (2016).ADS 

Google Scholar 
70.Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–471 (1996).ADS 

Google Scholar 
71.Tatlhego, M., Bhattachan, A., Okin, G. S. & D’Odorico, P. Mapping areas of the Southern Ocean where productivity likely depends on dust‐delivered Iron. J. Geophys. Res. Atmospheres 125, e2019JD030926 (2020).ADS 
CAS 

Google Scholar 
72.Stein, A. F., Rolph, G. D., Draxler, R. R., Stunder, B. & Ruminski, M. Verification of the NOAA smoke forecasting system: model sensitivity to the injection height. Weather Forecast. 24, 379–394 (2009).ADS 

Google Scholar 
73.Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite‐based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).ADS 
CAS 

Google Scholar 
74.Behrenfeld, M. J., Boss, E., Siegel, D. A. & Shea, D. M. Carbon-based ocean productivity and phytoplankton physiology from space. Global Biogeochem. Cycles 19, GB1006 (2005).ADS 

Google Scholar 
75.Westberry, T., Behrenfeld, M. J., Siegel, D. A. & Boss, E. Carbon-based primary productivity modeling with vertically resolved photoacclimation. Global Biogeochem. Cycles 22, GB2024 (2008).ADS 

Google Scholar 
76.Silsbe, G. M., Behrenfeld, M. J., Halsey, K. H., Milligan, A. J. & Westberry, T. K. The CAFE model: a net production model for global ocean phytoplankton. Global Biogeochem. Cycles 30, 1756–1777 (2016).ADS 
CAS 

Google Scholar 
77.Laws, E. A., D’Sa, E. & Naik, P. Simple equations to estimate ratios of new or export production to total production from satellite‐derived estimates of sea surface temperature and primary production. Limnol. Oceanogr. Methods 9, 593–601 (2011).
Google Scholar 
78.Dunne, J. P., Armstrong, R. A., Gnanadesikan, A. & Sarmiento, J. L. Empirical and mechanistic models for the particle export ratio. Global Biogeochem. Cycles 19, GB4026 (2005).ADS 

Google Scholar 
79.Li, Z. & Cassar, N. Satellite estimates of net community production based on O2/Ar observations and comparison to other estimates. Global Biogeochem. Cycles 30, 735–752 (2016).ADS 
CAS 

Google Scholar 
80.Siegel, D. A. et al. Global assessment of ocean carbon export by combining satellite observations and food‐web models. Global Biogeochem. Cycles 28, 181–196 (2014).ADS 
CAS 

Google Scholar 
81.Marshall, G. J. Trends in the Southern Annular Mode from observations and reanalyses. J. Climate 16, 4134–4143 (2003).ADS 

Google Scholar 
82.Saji, N. H. & Yamagata, T. Possible impacts of Indian Ocean Dipole mode events on global climate. Climate Res. 25, 151–169 (2003).ADS 

Google Scholar  More