Ecology

Vega Thurber, R. et al. Deciphering coral disease dynamics: Integrating host, microbiome, and the changing environment. Front. Ecol. Evol. 2020, 8 (2020).
Google Scholar 
Groner, M. L. et al. Managing marine disease emergencies in an era of rapid change. Philos. Trans. R. Soc. Lond. B Biol. Sci. 371, 1689 (2016).
Google Scholar 
Richardson, L. L. Coral diseases: What is really known?. Trends Ecol. Evol. 13, 438–443 (1998).CAS 
PubMed 

Google Scholar 
Miller, M. W., Lohr, K. E., Cameron, C. M., Williams, D. E. & Peters, E. C. Disease dynamics and potential mitigation among restored and wild staghorn coral, Acropora cervicornis. PeerJ https://doi.org/10.7287/peerj.preprints.328 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Teplitski, M. & Ritchie, K. How feasible is the biological control of coral diseases?. Trends Ecol. Evol. 24, 378–385 (2009).PubMed 

Google Scholar 
Zhu, Y. et al. Genetic diversity and disease control in rice. Nature 406, 718–722 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Altermatt, F. & Ebert, D. Genetic diversity of Daphnia magna populations enhances resistance to parasites. Ecol. Lett. 11, 918–928 (2008).PubMed 

Google Scholar 
Aronson, R. B. & Precht, W. F. White-band disease and the changing face of Caribbean coral reefs. In (ed Porter, J. W.) The Ecology and Etiology of Newly Emerging Marine Diseases 25–38 (Springer Netherlands, 2001).Ruiz-Moreno, D. et al. Global coral disease prevalence associated with sea temperature anomalies and local factors. Dis. Aquat. Organ. 100, 249–261 (2012).PubMed 

Google Scholar 
Precht, W. F., Gintert, B. E., Robbart, M. L., Fura, R. & van Woesik, R. Unprecedented disease-related coral mortality in Southeastern Florida. Sci. Rep. 6, 31374 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gignoux-Wolfsohn, S. A., Marks, C. J. & Vollmer, S. V. White Band Disease transmission in the threatened coral, Acropora cervicornis. Sci. Rep. 2, 804 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Aronson, R., Bruckner, A., Moore, J., Precht, B. & Weil, E. Acropora cervicornis. IUCN Red List of Threatened Species https://doi.org/10.2305/iucn.uk.2008.rlts.t133381a3716457.en (2008).Alvarez-Filip, L., González-Barrios, F. J., Pérez-Cervantes, E., Molina-Hernández, A. & Estrada-Saldívar, N. Stony coral tissue loss disease decimated Caribbean coral populations and reshaped reef functionality. Commun. Biol. 5, 440 (2022).PubMed 
PubMed Central 

Google Scholar 
Heres, M. M., Farmer, B. H., Elmer, F. & Hertler, H. Ecological consequences of Stony Coral Tissue Loss Disease in the Turks and Caicos Islands. Coral Reefs 40, 609–624 (2021).
Google Scholar 
Neely, K. L., Shea, C. P., Macaulay, K. A., Hower, E. K. & Dobler, M. A. Short- and long-term effectiveness of coral disease treatments. Front. Mar. Sci. 2021, 8 (2021).
Google Scholar 
Neely, K. L., Macaulay, K. A., Hower, E. K. & Dobler, M. A. Effectiveness of topical antibiotics in treating corals affected by stony coral tissue loss disease. PeerJ 8, e9289 (2020).PubMed 
PubMed Central 

Google Scholar 
Shilling, E. N., Combs, I. R. & Voss, J. D. Assessing the effectiveness of two intervention methods for stony coral tissue loss disease on Montastraea cavernosa. Sci. Rep. 11, 8566 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Walker, B. K., Turner, N. R., Noren, H. K. G., Buckley, S. F. & Pitts, K. A. Optimizing stony coral tissue loss disease (SCTLD) intervention treatments on Montastraea cavernosa in an Endemic Zone. Front. Mar. Sci. 8, 666224 (2021).
Google Scholar 
Forrester, G. E., Arton, L., Horton, A., Nickles, K. & Forrester, L. M. Antibiotic treatment ameliorates the impact of stony coral tissue loss disease (SCTLD) on coral communities. Front. Mar. Sci. 2022, 9 (2022).
Google Scholar 
Lee-Hing, C. et al. Management responses in Belize and Honduras, as stony coral tissue loss disease expands its prevalence in the Mesoamerican reef. Front. Mar. Sci. 9, 1 (2022).ADS 

Google Scholar 
Young, C. N., Schopmeyer, S. A. & Lirman, D. A review of reef restoration and coral propagation using the threatened genus Acropora in the Caribbean and Western Atlantic. Bull. Mar. Sci. 88, 1075–1098 (2012).
Google Scholar 
Lirman, D. & Schopmeyer, S. Ecological solutions to reef degradation: Optimizing coral reef restoration in the Caribbean and Western Atlantic. PeerJ 4, e2597 (2016).PubMed 
PubMed Central 

Google Scholar 
Baums, I. B. et al. Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic. Ecol. Appl. 29, e01978 (2019).PubMed 
PubMed Central 

Google Scholar 
Rosales, S. M. et al. Microbiome differences in disease-resistant vs susceptible Acropora corals subjected to disease challenge assays. Sci. Rep. 9, 18279 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pinzón, C. J. H., Beach-Letendre, J., Weil, E. & Mydlarz, L. D. Relationship between phylogeny and immunity suggests older caribbean coral lineages are more resistant to disease. PLoS ONE 9, e104787. https://doi.org/10.1371/journal.pone.0104787 (2014).Article 
ADS 
CAS 

Google Scholar 
Drury, C. et al. Genomic patterns in Acropora cervicornis show extensive population structure and variable genetic diversity. Ecol. Evol. 7, 6188–6200 (2017).PubMed 
PubMed Central 

Google Scholar 
Maneval, P., Jacoby, C. A., Harris, H. E. & Frazer, T. K. Genotype, nursery design, and depth influence the growth of Acropora cervicornis fragments. Front. Mar. Sci. 8, 1 (2021).
Google Scholar 
Wright, R. M. et al. Intraspecific differences in molecular stress responses and coral pathobiome contribute to mortality under bacterial challenge in Acropora millepora. Sci. Rep. 7, 2609 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Vollmer, S. V. & Kline, D. I. Natural disease resistance in threatened staghorn corals. PLoS ONE 3, e3718 (2008).ADS 
PubMed 
PubMed Central 

Google Scholar 
Miller, N., Maneval, P., Manfrino, C., Frazer, T. K. & Meyer, J. L. Spatial distribution of microbial communities among colonies and genotypes in nursery-reared Acropora cervicornis. PeerJ 8, e9635 (2020).PubMed 
PubMed Central 

Google Scholar 
Klinges, G., Maher, R. L., Vega-Thurber, R. L. & Muller, E. M. Parasitic, “Candidatus Aquarickettsia rohweri” is a marker of disease susceptibility in Acropora cervicornis but is lost during thermal stress. Environ. Microbiol. 22, 5341–5355 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Miller, M. W. et al. Genotypic variation in disease susceptibility among cultured stocks of elkhorn and staghorn corals. PeerJ 7, e6751 (2019).PubMed 
PubMed Central 

Google Scholar 
Rohr, J. R. et al. Towards common ground in the biodiversity-disease debate. Nat. Ecol. Evol. 4, 24–33 (2020).PubMed 

Google Scholar 
Shearer, T. L., Porto, I. & Zubillaga, A. L. Restoration of coral populations in light of genetic diversity estimates. Coral Reefs 28, 727–733 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ostfeld, R. S. & Keesing, F. Biodiversity and disease risk: The case of lyme disease. Conserv. Biol. 14, 722–728 (2000).
Google Scholar 
Lively, C. M. The effect of host genetic diversity on disease spread. Am. Nat. 175, E149–E152 (2010).PubMed 

Google Scholar 
Ostfeld, R. S. & Keesing, F. Effects of host diversity on infectious disease. Annu. Rev. Ecol. Evol. Syst. 43, 157–182 (2012).
Google Scholar 
King, K. C. & Lively, C. M. Does genetic diversity limit disease spread in natural host populations?. Heredity 109, 199–203 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Acevedo-Whitehouse, K., Gulland, F., Greig, D. & Amos, W. Inbreeding: Disease susceptibility in California sea lions. Nature 422, 35 (2003).ADS 
CAS 
PubMed 

Google Scholar 
O’Brien, S. J. et al. Genetic basis for species vulnerability in the cheetah. Science 227, 1428–1434 (1985).ADS 
PubMed 

Google Scholar 
Pearman, P. B. & Garner, T. W. J. Susceptibility of Italian agile frog populations to an emerging strain of Ranavirus parallels population genetic diversity. Ecol. Lett. 8, 401–408 (2005).
Google Scholar 
Reber, A., Castella, G., Christe, P. & Chapuisat, M. Experimentally increased group diversity improves disease resistance in an ant species. Ecol. Lett. 11, 682–689 (2008).PubMed 

Google Scholar 
Mundt, C. C. Use of multiline cultivars and cultivar mixtures for disease management. Annu. Rev. Phytopathol. 40, 381–410 (2002).CAS 
PubMed 

Google Scholar 
Elton, C. S. The Ecology of Invasions by Animals and Plants (University of Chicago Press, 2000).
Google Scholar 
Schopmeyer, S. A. et al. Regional restoration benchmarks for Acropora cervicornis. Coral Reefs 36, 1047–1057 (2017).ADS 

Google Scholar 
Baums, I. B., Miller, M. W. & Hellberg, M. E. Geographic variation in clonal structure in a reef-building Caribbean coral, Acropora palmata. Ecol. Monogr. 76, 503–519 (2006).
Google Scholar 
Gignoux-Wolfsohn, S. A., Precht, W. F., Peters, E. C., Gintert, B. E. & Kaufman, L. S. Ecology, histopathology, and microbial ecology of a white-band disease outbreak in the threatened staghorn coral Acropora cervicornis. Dis. Aquat. Organ. 137, 217–237 (2020).PubMed 

Google Scholar 
Gignoux-Wolfsohn, S. A. & Vollmer, S. V. Identification of candidate coral pathogens on white band disease-infected staghorn coral. PLoS ONE 10, e0134416 (2015).PubMed 
PubMed Central 

Google Scholar 
Brooks, M. et al. GlmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Google Scholar 
Fox, J. & Weisburg, S. An R Companion to Applied Regression 3rd edn. (Sage, 2019).
Google Scholar  More

Paerl, H. W. Mitigating toxic planktonic cyanobacterial blooms in aquatic ecosystems facing increasing anthropogenic and climatic pressures. Toxins. 10, 1–16 (2018).
Google Scholar 
Harke, M. J. et al. A review of the global ecology, genomics, and biogeography of the toxic cyanobacterium Microcystis spp. Harmful Algae 54, 4–20. https://doi.org/10.1016/j.hal.2015.12.007 (2016).Article 
PubMed 

Google Scholar 
Paerl, H. W. & Barnard, M. A. Mitigating the global expansion of harmful cyanobacterial blooms: Moving targets in a human- and climatically-altered world. Harmful Algae 96, 101845. https://doi.org/10.1016/j.hal.2020.101845 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Paerl, H. W. Mitigating harmful cyanobacterial blooms in a human- and climatically-impacted world. Life. 4, 988–1012 (2014).PubMed 
PubMed Central 

Google Scholar 
Burford, M. A. et al. Perspective: Advancing the research agenda for improving understanding of cyanobacteria in a future of global change. Harmful Algae 91, 101601. https://doi.org/10.1016/j.hal.2019.04.004 (2020).Article 
CAS 
PubMed 

Google Scholar 
Havens, K. E., James, R. T., East, T. L. & Smith, V. H. N: P ratios, light limitation, and cyanobacterial dominance in a subtropical lake impacted by non-point source nutrient pollution. Environ. Pollut. 122, 379–390 (2003).CAS 
PubMed 

Google Scholar 
Bernard, C. Cyanobacteria and cyanotoxins. Rev. Franç. Lab. 2014, 53–68 (2014).
Google Scholar 
Paerl, H. W. & Otten, T. G. Harmful cyanobacterial blooms: Causes, consequences, and controls. Microb. Ecol. 65, 995–1010 (2013).CAS 
PubMed 

Google Scholar 
Dolman, A. M. et al. Cyanobacteria and cyanotoxins: The influence of nitrogen versus phosphorus. PLoS ONE 7, 38575 (2012).
Google Scholar 
Svirčev, Z. et al. Global geographical and historical overview of cyanotoxin distribution and cyanobacterial poisonings. Arch. Toxicol. https://doi.org/10.1007/s00204-019-02524-4 (2019).Article 
PubMed 

Google Scholar 
Massey, I. Y. & Yang, F. A mini review on microcystins and bacterial degradation. Toxins 12, 268 (2020).CAS 
PubMed Central 

Google Scholar 
Paerl, H. W. et al. Mitigating eutrophication and toxic cyanobacterial blooms in large lakes: The evolution of a dual nutrient (N and P) reduction paradigm. Hydrobiologia 847, 4359–4375. https://doi.org/10.1007/s10750-019-04087-y (2020).Article 
CAS 

Google Scholar 
Sapp, M. et al. Species-specific bacterial communities in the phycosphere of microalgae?. Microb. Ecol. 53, 683–699 (2007).PubMed 

Google Scholar 
Cai, H., Jiang, H., Krumholz, L. R. & Yang, Z. Bacterial community composition of size-fractioned aggregates within the phycosphere of cyanobacterial blooms in a eutrophic freshwater lake. PLoS ONE 9, 102879 (2014).ADS 

Google Scholar 
Grant, M. A. A., Kazamia, E., Cicuta, P. & Smith, A. G. Direct exchange of vitamin B 12 is demonstrated by modelling the growth dynamics of algal-bacterial cocultures. ISME J. Nat. Publ. Group 8, 1418–1427 (2014).CAS 

Google Scholar 
Shi, L., Cai, Y., Kong, F. & Yu, Y. Specific association between bacteria and buoyant Microcystis colonies compared with other bulk bacterial communities in the eutrophic Lake Taihu, China. Environ. Microbiol. Rep. 4, 669–678 (2012).CAS 
PubMed 

Google Scholar 
Brunberg, A. K. Contribution of bacteria in the mucilage of Microcystis spp (Cyanobacteria) to benthic and pelagic bacterial production in a hypereutrophic lake. FEMS Microbiol. Ecol. 29, 13–22 (1999).CAS 

Google Scholar 
Shao, K. et al. The responses of the taxa composition of particle-attached bacterial community to the decomposition of Microcystis blooms. Sci. Total. Environ. 488–489, 236–242. https://doi.org/10.1016/j.scitotenv.2014.04.101 (2014).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Jankowiak, J. G. & Gobler, C. J. The composition and function of microbiomes within microcystis colonies are significantly different than native bacterial assemblages in two North American lakes. Front. Microbiol. 11, 1–26 (2020).
Google Scholar 
Bauer, A. & Forchhammer, K. Bacterial predation on cyanobacteria. Microb. Physiol. 99, 108 (2021).
Google Scholar 
Ndlela, L. L., Oberholster, P. J., Van Wyk, J. H. & Cheng, P. H. Bacteria as biological control agents of freshwater cyanobacteria: Is it feasible beyond the laboratory?. Appl. Microbiol. Biotechnol. 102, 9911–9923 (2018).CAS 
PubMed 

Google Scholar 
Yang, C. et al. Distinct network interactions in particle-associated and free-living bacterial communities during a Microcystis aeruginosa bloom in a plateau lake. Front. Microbiol. 8, 1–15 (2017).
Google Scholar 
Xu, H. et al. Contrasting network features between free-living and particle-attached bacterial communities in Taihu Lake. Microb. Ecol. 76, 303–313 (2018).PubMed 

Google Scholar 
Liu, M. et al. Community dynamics of free-living and particle-attached bacteria following a reservoir Microcystis bloom. Sci. Total Environ. 660, 501–511. https://doi.org/10.1016/j.scitotenv.2018.12.414 (2019).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Parveen, B. et al. Bacterial communities associated with Microcystis colonies differ from free-living communities living in the same ecosystem. Environ. Microbiol. Rep. 5, 716–724 (2013).CAS 
PubMed 

Google Scholar 
Louati, I. et al. Structural diversity of bacterial communities associated with bloom-forming freshwater cyanobacteria differs according to the cyanobacterial genus. PLoS ONE 10, 0140614 (2015).
Google Scholar 
Zwirglmaier, K., Keiz, K., Engel, M., Geist, J. & Raeder, U. Seasonal and spatial patterns of microbial diversity along a trophic gradient in the interconnected lakes of the Osterseen Lake District, Bavaria. Front. Microbiol. 6, 1–18 (2015).
Google Scholar 
Scherer, P. I. et al. Temporal dynamics of the microbial community composition with a focus on toxic cyanobacteria and toxin presence during harmful algal blooms in two South German lakes. Front. Microbiol. 8, 1–17 (2017).
Google Scholar 
Kokocinski, M., Dziga, D., Antosiak, A. & Soininen, J. Are bacterio- and phytoplankton community compositions related in lakes differing in their cyanobacteria contribution and physico-chemical properties?. Genes 12, 855 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dziga, D. et al. Correlation between specific groups of heterotrophic bacteria and microcystin biodegradation in freshwater bodies of central Europe. FEMS Microbiol. Ecol. https://doi.org/10.1111/j.1574-6941.1999.tb00594.x (2019).Article 
PubMed 

Google Scholar 
Jurczak, T. et al. Elimination of microcystins by water treatment processes: Examples from Sulejow Reservoir, Poland. Water Res. 39, 2394–2406 (2005).CAS 
PubMed 

Google Scholar 
Mankiewicz-Boczek, J. et al. Detection and monitoring toxigenicity of cyanobacteria by application of molecular methods. Environ Toxicol. 21, 380–387 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Rajaniemi-Wacklin, P. et al. Correspondence between phylogeny and morphology of Snowella spp. and Woronichinia naegeliana, cyanobacteria commonly occurring in lakes. J. Phycol. 42, 226–232 (2006).
Google Scholar 
DrobacBacković, D. et al. Cyanobacteria, cyanotoxins, and their histopathological effects on fish tissues in Fehérvárcsurgó reservoir Hungary. Environ. Monit. Assess. https://doi.org/10.1007/s10661-021-09324-3 (2021).Article 

Google Scholar 
Kallscheuer, N. et al. Analysis of bacterial communities in a municipal duck pond during a phytoplankton bloom and isolation of Anatilimnocola aggregata gen. nov., sp. Nov., Lacipirellula limnantheis sp. Nov. and Urbifossiella limnaea gen. nov. sp. nov. belonging to the phylum. Environ. Microbiol. 23, 1379–1396 (2021).CAS 
PubMed 

Google Scholar 
Davis, T. W. et al. Effects of nitrogenous compounds and phosphorus on the growth of toxic and non-toxic strains of Microcystis during cyanobacterial blooms. Aquat. Microb. Ecol. 61, 149–162 (2010).
Google Scholar 
Gobler, C. J., Davis, T. W., Coyne, K. J. & Boyer, G. L. Interactive influences of nutrient loading, zooplankton grazing, and microcystin synthetase gene expression on cyanobacterial bloom dynamics in a eutrophic New York lake. Harmful Algae 6, 119–133 (2007).CAS 

Google Scholar 
Mankiewicz-Boczek, J. et al. Cyanophages infection of microcystis bloom in lowland dam reservoir of Sulejów, Poland. Microb. Ecol. 71, 315–325 (2016).CAS 
PubMed 

Google Scholar 
Davis, T. W., Berry, D. L., Boyer, G. L. & Gobler, C. J. The effects of temperature and nutrients on the growth and dynamics of toxic and non-toxic strains of Microcystis during cyanobacteria blooms. Harmful Algae 8, 715–725 (2009).CAS 

Google Scholar 
Yoshida, M., Yoshida, T., Takashima, Y., Hosoda, N. & Hiroishi, S. Dynamics of microcystin-producing and non-microcystin-producing Microcystis populations is correlated with nitrate concentration in a Japanese lake. FEMS Microbiol. Lett. 266, 49–53 (2007).CAS 
PubMed 

Google Scholar 
Sezenna, M. L. Proteobacteria: Phylogeny, Metabolic Diversity and Ecological Effects (Nova Science Publishers, Inc., 2011).
Google Scholar 
Rilling, J. I., Acuña, J. J., Sadowsky, M. J. & Jorquera, M. A. Putative nitrogen-fixing bacteria associated with the rhizosphere and root endosphere of wheat plants grown in an andisol from southern Chile. Front. Microbiol. 9, 1–13 (2018).
Google Scholar 
Lukumbuzya, M. et al. A refined set of rRNA-targeted oligonucleotide probes for in situ detection and quantification of ammonia-oxidizing bacteria. Water Res. 186, 116375 (2020).
Google Scholar 
Prosser, J. I., Head, I. M. & Stein, L. Y. The family Nitrosomonadaceae. In The Prokaryotes: Alphaproteobacteria and Betaproteobacteria (eds Rosenberg, E. et al.) 901–918 (Springer, 2014). https://doi.org/10.1007/978-3-642-30197-1_372.Chapter 

Google Scholar 
Jia, L., Jiang, B., Huang, F. & Hu, X. Nitrogen removal mechanism and microbial community changes of bioaugmentation subsurface wastewater infiltration system. Bioresour. Technol. 294, 122140. https://doi.org/10.1016/j.biortech.2019.122140 (2019).Article 
CAS 
PubMed 

Google Scholar 
Daft, M. J. & Stewart, W. D. P. Bacterial pathogens of freshwater blue-green algae. New Phytol. 70, 819–829 (1971).
Google Scholar 
Chun, S. J. et al. Network analysis reveals succession of Microcystis genotypes accompanying distinctive microbial modules with recurrent patterns. Water Res. 170, 115326. https://doi.org/10.1016/j.watres.2019.115326 (2020).Article 
CAS 
PubMed 

Google Scholar 
Parulekar, N. N. et al. Characterization of bacterial community associated with phytoplankton bloom in a eutrophic lake in South Norway using 16S rRNA gene amplicon sequence analysis. PLoS ONE 12, 1–22 (2017).
Google Scholar 
Guedes, I. A. et al. Close link between harmful cyanobacterial dominance and associated bacterioplankton in a tropical eutrophic reservoir. Front. Microbiol. 9, 424 (2018).PubMed 
PubMed Central 

Google Scholar 
Allgaier, M. & Grossart, H. P. Seasonal dynamics and phylogenetic diversity of free-living and particle-associated bacterial communities in four lakes in northeastern Germany. Aquat. Microb. Ecol. 45, 115–128 (2006).
Google Scholar 
Chen, S. et al. Disentangling the drivers of Microcystis decomposition: Metabolic profile and co-occurrence of bacterial community. Sci. Total Environ. 739, 140062. https://doi.org/10.1016/j.scitotenv.2020.140062 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Leflaive, J. & Ten-Hage, L. Algal and cyanobacterial secondary metabolites in freshwaters: A comparison of allelopathic compounds and toxins. Freshw. Biol. 52, 199–214 (2007).CAS 

Google Scholar 
Song, H. et al. Biological and chemical factors driving the temporal distribution of cyanobacteria and heterotrophic bacteria in a eutrophic lake (West Lake, China). Appl. Microbiol. Biotechnol. 101, 1685–1696. https://doi.org/10.1007/s00253-016-7968-8 (2017).Article 
CAS 
PubMed 

Google Scholar 
Bagatini, I. L. et al. Host-specificity and dynamics in bacterial communities associated with bloom-forming freshwater phytoplankton. PLoS ONE 9, 85957 (2014).ADS 

Google Scholar 
Kohler, E. et al. Biodegradation of microcystins during gravity-driven membrane (GDM) ultrafiltration. PLoS ONE 9, 111794 (2014).ADS 

Google Scholar 
Wu, X. et al. Culturing of “unculturable” subsurface microbes: Natural organic carbon source fuels the growth of diverse and distinct bacteria from groundwater. Front. Microbiol. 11, 1–10 (2020).CAS 

Google Scholar 
Morotomi, M., Nagai, F. & Watanabe, Y. Parasutterella secunda sp. no., isolated from human faeces and proposal of Sutterellaceae fam. nov. in the order Burkholderiales. Int. J. Syst. Evol. Microbiol. 61, 637–643 (2011).CAS 
PubMed 

Google Scholar 
Kiedrzyńska, E. et al. Point sources of nutrient pollution in the lowland river catchment in the context of the baltic Sea eutrophication. Ecol. Eng. 70, 337–348 (2014).
Google Scholar 
Hwang, W. M., Ko, Y., Kim, J. H. & Kang, K. Ahniella affigens gen Nov, sp. nov., a gammaproteobacterium isolated from sandy soil near a stream. Int. J. Syst. Evol. Microbiol. 68, 2478–2484 (2018).CAS 
PubMed 

Google Scholar 
Qian, H. et al. Spatial variability of cyanobacteria and heterotrophic bacteria in Lake Taihu (China). Bull. Environ. Contam. Toxicol. 99, 380–384 (2017).CAS 
PubMed 

Google Scholar 
Humbert, J. F. et al. Comparison of the structure and composition of bacterial communities from temperate and tropical freshwater ecosystems. Environ. Microbiol. 11, 2339–2350 (2009).CAS 
PubMed 

Google Scholar 
Newton, R. J., Jones, S. E., Eiler, A., McMahon, K. D. & Bertilsson, S. A guide to the natural history of freshwater lake Bacteria. Microbiol. Mol. Biol. Rev. 1, 1–10 (2011).
Google Scholar 
Parveen, B., Mary, I., Vellet, A., Ravet, V. & Debroas, D. Temporal dynamics and phylogenetic diversity of free-living and particle-associated Verrucomicrobia communities in relation to environmental variables in a mesotrophic lake. FEMS Microbiol. Ecol. 83, 189–201 (2013).CAS 
PubMed 

Google Scholar 
Henson, M. W., Lanclos, V. C., Faircloth, B. C. & Thrash, J. C. Cultivation and genomics of the first freshwater SAR11 (LD12) isolate. ISME J. 12, 1846–1860 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Yang, C. et al. The characteristics and algicidal mechanisms of cyanobactericidal bacteria, a review. World J. Microbiol. Biotechnol. 36, 1–10. https://doi.org/10.1007/s11274-020-02965-5 (2020).Article 

Google Scholar 
Izydorczyk, K. et al. Influence of abiotic and biotic factors on microcystin content in Microcystis aeruginosa cells in a eutrophic temperate reservoir. J. Plankton Res. 30, 393–400 (2008).CAS 

Google Scholar 
Mankiewicz-Boczek, J. et al. Bacteria homologus to Aeromonas capable of microcystin degradation. Open Life Sci. 10, 106–116 (2015).CAS 

Google Scholar 
Jaskulska, A., Font Nájera, A., Czarny, P., Serwecińska, L. & Mankiewicz-boczek, J. Daily dynamic of transcripts abundance of Ma-LMM01-like cyanophages in two lowland European reservoirs. Ecohydrol. Hydrobiol. 21, 543–548 (2021).
Google Scholar 
Gągała, I. et al. Role of environmental factors and toxic genotypes in the regulation of microcystins-producing cyanobacterial blooms. Microb. Ecol. 67, 465–479 (2014).PubMed 

Google Scholar 
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, 1–11 (2013).
Google Scholar 
Illumina. 16S Metagenomic Sequencing Library Preparation. (2013). http://support.illumina.com/content/dam/illumina-support/documents/documentation/chemistry_documentation/16s/16s-metagenomic-library-prep-guide-15044223-b.pdf.Frangeul, L. et al. Highly plastic genome of Microcystis aeruginosa PCC 7806, a ubiquitous toxic freshwater cyanobacterium. BMC Genomics 9, 1–20 (2008).
Google Scholar 
Hammer, Ø., Harper, D. A. T. & Ryan, P. D. Past: Paleontological statistics software package for education and data analysis even a cursory glance at the recent paleontological literature should convince anyone tha. Palaeontol. Electron. 4, 1–9 (2001).
Google Scholar 
Suzuki, M. T., Taylor, L. T. & DeLong, E. F. Quantitative analysis of small-subunit rRNA genes in mixed microbial populations via 5’-nuclease assays. Appl. Environ. Microbiol. 66, 4605–4614. https://doi.org/10.1128/AEM.66.11.4605-4614.2000 (2000).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Neilan B. A et al. rRNA sequences and evolutionary relationships among toxic and nontoxic cyanobacteria of the genus Microcystis Int J Syst Bacteriol 47(3), 693–697, https://doi.org/10.1099/00207713-47-3-693 (1997).
Google Scholar  More

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.This is a summary of: Sauterey, B. et al. Early Mars habitability and global cooling by H2-based methanogens. Nat. Astron. https://doi.org/10.1038/s41550-022-01786-w (2022). More

npj Biodiversity aims to be a common forum where discoveries in all areas of biodiversity science can be discussed, so that the research in specific topics with broad implications for other disciplines permeates the whole community. This requires that scientific debates are made in egalitarian terms between people with different backgrounds and points of view. We will strive to provide safe spaces where all biodiversity research can be showcased without bias, and theoretical and practical advances can be subject to calm and civil debate. As journal editors we will implement measures to work towards a fairer and more inclusive science, such as giving proper recognition to all researchers involved in the research published13, or ensuring in revisions that former research made by different identity groups and local scientists is adequately acknowledged14. We will also acknowledge diversity by maintaining a diverse editorial board15 and engaging external peer-reviewers16 that represent local specialists, the diversity of approaches in each field, as well as early-career researchers across demographic groups. We will also encourage access to research and engage in the FAIR principles for data management and sharing17. Here, good practice includes making data available for reanalysis or compilation in larger databases by researchers anywhere in the world, promoting open software, and sharing reproducible code18,19. Our hope is that this extends the capacity of developing meta-analyses and macroecological and macroevolutionary research beyond the borders of high-income countries.npj Biodiversity seeks to promote scientific discussion and synthesis. As editors, we will act as guides and moderators rather than as gatekeepers that merely decide which papers are above the threshold of publication20. Thus, we encourage debate as a central part of the editorial process, allowing well-grounded and clearly-identified speculation and policy-related statements in published papers when appropriate. This may include publishing non-conventional papers that foster discussion in established topics or open new research avenues21, if and only if they are well supported by data or published evidence. In this sense, we welcome Comments on areas currently under discussion, as well as Reviews and Perspectives that allow synthesis in theoretical and practical topics that are not necessarily general, but can help advance specific subdisciplines or topics. Last but not least, we want to facilitate communication between basic research and applied practitioners through Perspectives that translate the implications of recent research for management, conservation and adaptation to global change, or that identify which theoretical advances or additional empirical evidence would be needed to tackle specific problems.Creating the appropriate publishing environment for journals to be true forums for debate and provide value to the scientific community is a challenging enterprise. Above all, it requires escaping from the haste imposed by the “publish or perish model”, and making an explicit effort to raise the quality of the editorial process. In npj Biodiversity we will seek to follow ‘slow publishing’ principles, putting emphasis on meaningful debate between authors, editors and reviewers22. Current research environments can prevent researchers from having time to think, but true advance stems from digesting ideas and discussing them with the detail, depth and time they may need (http://slow-science.org/)23,24,25. Therefore, to contribute to a healthier, gentler and more thoughtful approach to biodiversity science, we will provide thorough and thoughtful reviews. We will make editorial decisions that, when paired with equally thorough and thoughtful work by authors, can reduce the number of times a paper bounces back and forth in successive rounds of peer review and revision. Note that this does not necessarily mean longer editorial times! Paradoxically, when authors, reviewers and editors commit to these “slow” publishing principles, the publication process can speed up. And most importantly, it will promote the spirit of productive debate that we aim for in npj Biodiversity. More

To our knowledge, this is the only work done on the terrestrial biodiversity status in the direct vicinity of a limited duration volcanic eruption. In this contribution, we document and assess the impact on the main plant and animal groups within the ecosystems during a volcanic eruption (Table 1). While some groups were clearly disadvantaged: ferns and herbaceous plants as well as invertebrates and saurians (lizards and geckos); other groups such as conifers and woody shrubs showed better resilience, as did the birds.This study is particularly important because of its location in a Mediterranean biodiversity hotspot13,14, harbouring a unique ecosystem of oceanic island organisms (38% of the Canary archipelago endemicity). Islands indeed exhibit a disproportionate amount of the world’s biodiversity but unfortunately a high number of extinctions have also occurred there14. The biodiversity in the south of the island is poorer than in the north. This is probably explained in part by the relatively frequent volcanic activity featuring seven major eruptions since 1585, including this one in 2021 (see15), which led to alternating destruction and neo-colonization processes.Concerning the flora, the Canary pine forest was the most affected ecosystem and vegetation type, as it is dominant in the vicinity of the new volcanic vents. The southern slopes of this forest were the most disturbed area due to the location of the volcano, combined with the prevailing northeasterly trade winds (Fig. 1). Tephra fallout and sulphurous gases were the main factors that affected the pine forest, over a vast surface area. Furthermore, the local xerophytic and thermophilous habitats also lost much of their surface area. In contrast to the pine forest, this drastic reduction was caused by the progressive downslope expansion of the lava flows.The Canary Island pine was thus notably affected by tephra fall, sulphuric acid aerosol12, and short episodes of acid rain. However, this conifer shows high resistance to temperature, confirming its great adaptation to volcanic events16, which is probably also one of the keys to its resistance to the more frequent present-day wildfires17. This pine species has evolved among volcanoes for the last 13 My16 and has adapted successfully to high temperatures. Moreover, thunderstorms with lightning occur in the Canaries together with abundant rainfall; consequently, wild forest fires should presumably not have been so frequent in the island’s past, before human colonization. In this habitat it is also remarkable that epiphytic lichens (U. articulata) apparently resisted on the pines until the 12th week, considering their high sensitivity to anthropogenic pollution18.The life cycle of flowering plants was drastically disrupted due to all the above factors, with great impact on foliage, photosynthesis, and growth. However, soil changes due to the deposition of tephra and its lixiviation by rain is one of the most dramatic factors affecting plants and a long-term impact of volcanic eruptions19. The nearest individuals to the crater were most directly affected by intense tephra falls and concentrated volcanic gases (SO2, HCl, HF, CO2). However, plants located in the nearest 200 m to the lava flows but at more than 2 km from the crater were presumably more disturbed by the high temperature of the slow-cooling lava and its lesser gas emissions.Large woody plants exhibited a better frequency of survival than smaller ones in the face of this extreme stress (Table S1 and19). In the Hekla area (Iceland), most trees have thickened trunks, indicating that those trees that survive have had a long life subjected to frequent volcanic damage19. Secondary woodiness of island plants (sensu20) has been traditionally related to drought20,21, ecological shift22 or a counter-selection of inbreeding depression in founding island populations23. However, this adaptation also favours the resistance of many shrubby plants to high temperatures close to craters and lava flows but primarily their resistance to the intense tephra falls that affect a much larger area. In addition, plant and stem height plays a fundamental role in overcoming the deep layers of deposits. This latter effect was particularly important up to 2.5 km from the crater (tephra thickness  > 30 cm) (Figs. 1 and 2), as the herbaceous plants were completely buried, sometimes to more than 1.5 m depth. Therefore, the seed bank has also probably been rendered largely non-functional. However, deposits were recorded over almost the whole island, indicating that longer lasting or more intense eruptions would severely affect an even larger area. Such events have been hitherto ignored in the intensely discussed “island woodiness” debate21,23,24,25,26,27. We found surviving populations of endemic woody taxa heavily impacted by tephra deposits close to lava flows, across a wide range of genera such as Rumex (R. lunaria), Echium (E. brevirame), Euphorbia (E. lamarckii, E. canariensis and E. balsamifera), Aeonium (A. davidbramwellii), Rubia (R. fruticosa), Schizogyne (S. sericea), Carlina (C. falcata) or Sonchus (S. hierrensis) (Table S2), which coincide with the general list of woody Canary plants20. Most members of these genera in other ecosystems on continents are mainly herbaceous. As such eruptions and their impacts due to ash depositions are frequent events on volcanic islands, e.g. several times within a century on La Palma, this is a “frequent” selective process at evolutionary time scales.With regard to the fauna, the invertebrate community collapsed during the first two weeks (Table S2), probably due to rapid deterioration of the growth state of plants. These changes in the invertebrates were caused by the tephra contacting the cuticular lipid layer28 and water loss due to tegument abrasion29. In this period, many insect pests (especially whitefly pupae) in banana plantations (farmers’ observations) were drastically reduced. This sudden decrease in insect populations affected the whole food web and probably caused part of the ecological collapse of saurian and some passerine communities30. In the case of lizards, smaller individuals seem to resist the adverse conditions better than large ones, as observed in other eruptions3. This could be linked to their lower food requirements and greater ease in finding refuges. Loss of body condition in lizards post-eruption has been recorded and negatively affects reproduction quality31. However, some lizards have shown a good ability to find food in the tephra substrate32. We found abundant tephra particles in some vertebrate droppings (lizards, birds, and mammals) during the eruption, probably involuntarily ingested. At least in bats, ingestion during feeding produces physiological stress that is likely related to baldness, high ectoparasite loads or possible mineral deficiencies33.As described in the Canary Islands, some passerines show high fidelity to their territories (see34). During the eruption, Sardinian warblers (Curruca melanocephala) maintained their territories until the imminent arrival of lava flows. Larger birds (kestrels F. tinnunculus, ravens C. corax and buzzards B. buteo) were well able to continue flying in the areas surrounding the crater. Furthermore, some cases like F. tinnunculus showed great feeding plasticity in the first couple of weeks. At least six times, kestrels tried to catch birds (especially small passerines and doves), contrary to their usual diet based on abundant lizards and insects35. Widening of trophic niches in island organisms has traditionally been interpreted as linked to disharmony in island ecosystems36,37,38. However, this plasticity is tremendously beneficial in ecological catastrophes, where food becomes exceptionally scarce. In the case of bats, their flight is limited by the delicate structure of their patagium, which can be damaged by the frequent pyroclastic tephra fall. Furthermore, scarcity of insects in the first few kilometres from the crater probably led to their displacement to other more distant and richer food resource zones.As we learned from the movement capacity of the vertebrate animals that still inhabited the affected area, those with greater mobility, birds and bats, resisted the eruptive process much better than those with less mobility, e.g. saurians.Lastly, during this destructive event on La Palma, we had the opportunity to increase our knowledge of how ecological-evolutionary adaptations have favoured the survival of insular organisms. Such responses are traditionally mentioned in the context of island biology. As already mentioned, one of the most interesting findings verifies the remarkable adaptation of Canary Island pine trees (P. canariensis) to volcanism (see16), including extremely harsh ecological conditions. Other insular trends related to the prevalence of woodiness in insular flowering plants20,21, or the high trophic plasticity of some vertebrates on oceanic islands36, have not previously been associated with their potential evolution along with volcanic processes. However, such evolutionary adaptations most likely played an important role in the survival of plants and animals affected by the volcano. For this reason, it is worth considering and debating whether these previously mentioned evolutionary processes are in fact also linked to repeated volcanic episodes on oceanic islands. More

Delavaux, C. S., Smith‐Ramesh, L. M. & Kuebbing, S. E. Beyond nutrients: a meta‐analysis of the diverse effects of arbuscular mycorrhizal fungi on plants and soils. Ecology 98, 2111–2119 (2017).Lugtenberg, B. & Kamilova, F. Plant-growth-promoting rhizobacteria. Annu. Rev. Microbiol. 63, 541–556 (2009).Article 
CAS 
PubMed 

Google Scholar 
Franche, C., Lindström, K. & Elmerich, C. Nitrogen-fixing bacteria associated with leguminous and non-leguminous plants. Plant Soil 321, 35–59 (2009).Article 
CAS 

Google Scholar 
Razanajatovo, M. et al. Autofertility and self‐compatibility moderately benefit island colonization of plants. Glob. Ecol. Biogeogr. 28, 341–352 (2019).Article 

Google Scholar 
Schrader, J., Wright, I. J., Kreft, H. & Westoby, M. A roadmap to plant functional island biogeography. Biol. Rev. (2021).Herridge, D. F., Peoples, M. B. & Boddey, R. M. Global inputs of biological nitrogen fixation in agricultural systems. Plant Soil 311, 1–18 (2008).Article 
CAS 

Google Scholar 
Vitousek, P. Nutrient cycling and limitation: Hawai’i as a model ecosystem. (Princeton Univ. Press, Princeton, NJ, 2004). Nutrient cycling and limitation: Hawai’i as a model ecosystem. Princeton Univ. Press, Princeton, NJ.Book 

Google Scholar 
Becking, L. G. M. B. Geobiologie of inleiding tot de milieukunde. (WP Van Stockum & Zoon, 1934).Peay, K. G. & Bruns, T. D. Spore dispersal of basidiomycete fungi at the landscape scale is driven by stochastic and deterministic processes and generates variability in plant–fungal interactions. N. Phytol. 204, 180–191 (2014).Article 

Google Scholar 
Delavaux, C. S. et al. Mycorrhizal fungi influence global plant biogeography. Nat. Ecol. Evol. 3, 424 (2019).Article 
PubMed 

Google Scholar 
Duchicela, J., Bever, J. D. & Schultz, P. A. Symbionts as Filters of Plant Colonization of Islands: Tests of Expected Patterns and Environmental Consequences in the Galapagos. Plants 9, 74 (2020).Article 
CAS 
PubMed Central 

Google Scholar 
Delavaux, C. S. et al. Mycorrhizal types influence island biogeography of plants. Commun. Biol. 4, 1–8 (2021).Article 

Google Scholar 
Simonsen, A. K., Dinnage, R., Barrett, L. G., Prober, S. M. & Thrall, P. H. Symbiosis limits establishment of legumes outside their native range at a global scale. Nat. Commun. 8, 1–9 (2017).Article 

Google Scholar 
Poole, P., Ramachandran, V. & Terpolilli, J. Rhizobia: from saprophytes to endosymbionts. Nat. Rev. Microbiol. 16, 291–303 (2018).Article 
CAS 
PubMed 

Google Scholar 
Sprent, J. I., Ardley, J. & James, E. K. Biogeography of nodulated legumes and their nitrogen‐fixing symbionts. N. Phytol. 215, 40–56 (2017).Article 
CAS 

Google Scholar 
Menge, D. N. Hedin, L. O. & Pacala, S. W. Nitrogen and phosphorus limitation over long-term ecosystem development in terrestrial ecosystems. (2012).Lambers, H., Raven, J. A., Shaver, G. R. & Smith, S. E. Plant nutrient-acquisition strategies change with soil age. Trends Ecol. evolution 23, 95–103 (2008).Article 

Google Scholar 
Walker, T. & Syers, J. K. The fate of phosphorus during pedogenesis. Geoderma 15, 1–19 (1976).Article 
CAS 

Google Scholar 
Jin, L. et al. Synergistic interactions of arbuscular mycorrhizal fungi and rhizobia promoted the growth of Lathyrus sativus under sulphate salt stress. Symbiosis 50, 157–164 (2010).Article 
CAS 

Google Scholar 
Afkhami, M. E. & Stinchcombe, J. R. Multiple mutualist effects on genomewide expression in the tripartite association between Medicago truncatula, nitrogen‐fixing bacteria and mycorrhizal fungi. Mol. Ecol. 25, 4946–4962 (2016).Article 
CAS 
PubMed 

Google Scholar 
Larimer, A. L., Clay, K. & Bever, J. D. Synergism and context dependency of interactions between arbuscular mycorrhizal fungi and rhizobia with a prairie legume. Ecology 95, 1045–1054 (2014).Article 
PubMed 

Google Scholar 
Primieri, S., Magnoli, S. M., Koffel, T. S., Stürmer, S. L. & Bever, J. D. Perennial, but not annual legumes synergistically benefit from infection with arbuscular mycorrhizal fungi and rhizobia: a meta‐analysis. N. Phytol. 233, 505-514 (2021).Larimer, A. L., Bever, J. D. & Clay, K. The interactive effects of plant microbial symbionts: a review and meta-analysis. Symbiosis 51, 139–148 (2010).Article 

Google Scholar 
Werner, G. D., Cornwell, W. K., Sprent, J. I., Kattge, J. & Kiers, E. T. A single evolutionary innovation drives the deep evolution of symbiotic N 2-fixation in angiosperms. Nat. Commun. 5, 1–9 (2014).Article 

Google Scholar 
Weigelt, P., König, C. & Kreft, H. GIFT- A global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

Google Scholar 
Werner, G. D. et al. Symbiont switching and alternative resource acquisition strategies drive mutualism breakdown. Proc. Natl Acad. Sci. 115, 5229–5234 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bamba, M. et al. Wide distribution range of rhizobial symbionts associated with pantropical sea-dispersed legumes. Antonie van. Leeuwenhoek 109, 1605–1614 (2016).Article 
PubMed 

Google Scholar 
Chen, W.-M., Lee, T.-M., Lan, C.-C. & Cheng, C.-P. Characterization of halotolerant rhizobia isolated from root nodules of Canavalia rosea from seaside areas. FEMS Microbiol. Ecol. 34, 9–16 (2000).Article 
CAS 
PubMed 

Google Scholar 
Toma, M. A. et al. Tripartite symbiosis of Sophora tomentosa, rhizobia and arbuscular mycorhizal fungi. Braz. J. Microbiol. 48, 680–688 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Elanchezhian, R., Rajalakshmi, S. & Jayakumar, V. Salt tolerance characteristics of rhizobium species associated with Vigna marina. Indian J. Agric. Sci. 79, 980–985 (2009).CAS 

Google Scholar 
Chapin, F. S., Matson, P. A., Mooney, H. A. & Vitousek, P. M. Principles of Terrestrial Ecosystem Ecology (Springer, 2002).Vitousek, P. M., Walker, L. R., Whiteaker, L. D. & Matson, P. A. Nutrient limitations to plant growth during primary succession in Hawaii Volcanoes National Park. Biogeochemistry 23, 197–215 (1993).Article 

Google Scholar 
Liao, C. et al. Altered ecosystem carbon and nitrogen cycles by plant invasion: a meta-analysis. N. Phytologist 177, 706–714 (2008).Article 
CAS 

Google Scholar 
Woodward, S. A. et al. Use of the Exotic Tree Myrica Faya by Native and Exotic Birds in Hawai’i Volcanoes National Park (University of Hawaii Press, 1990).Vitousek, P. M., Walker, L. R., Whiteaker, L. D., Mueller-Dombois, D. & Matson, P. A. Biological invasion by Myrica faya alters ecosystem development in Hawaii. Science 238, 802–804 (1987).Article 
CAS 
PubMed 

Google Scholar 
Theoharides, K. A. & Dukes, J. S. Plant invasion across space and time: factors affecting nonindigenous species success during four stages of invasion. N. phytologist 176, 256–273 (2007).Article 

Google Scholar 
Kalwij, J. M. Review of ‘The Plant List, a working list of all plant species’. J. Vegetation Sci. 23, 998–1002 (2012).Article 

Google Scholar 
Byng, J. W. et al. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Botanical J. Linn. Soc. 181, 1–20 (2016).Article 

Google Scholar 
Soudzilovskaia, N. A. et al. FungalRoot: Global online database of plant mycorrhizal associations. N. Phytol. 227, 955–966 (2020).Article 

Google Scholar 
Weigelt, P., König, C. & Kreft, H. GIFT–A global inventory of floras and traits for macroecology and biogeography. J. Biogeogr. 47, 16–43 (2020).Article 

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

Google Scholar 
Danielson, J. J. & Gesch, D. B. “Global multi-resolution terrain elevation data 2010 (GMTED2010),” (US Geological Survey, 2011).Weigelt, P. & Kreft, H. Quantifying island isolation–insights from global patterns of insular plant species richness. Ecography 36, 417–429 (2013).Article 

Google Scholar 
Kreft, H., Jetz, W., Mutke, J., Kier, G. & Barthlott, W. Global diversity of island floras from a macroecological perspective. Ecol. Lett. 11, 116–127 (2008).PubMed 

Google Scholar 
Triantis, K. A., Economo, E. P., Guilhaumon, F. & Ricklefs, R. E. Diversity regulation at macro‐scales: species richness on oceanic archipelagos. Glob. Ecol. Biogeogr. 24, 594–605 (2015).Article 

Google Scholar 
Crase, B., Liedloff, A. C. & Wintle, B. A. A new method for dealing with residual spatial autocorrelation in species distribution models. Ecography 35, 879–888 (2012).Article 

Google Scholar 
Bivand, R. R packages for analyzing spatial data: a comparative case study with areal data. Geogr. Anal. 54, 488–518 (2022).Article 

Google Scholar 
R. C. Team, R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2019).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar  More

Suttle CA. Marine viruses-major players in the global ecosystem. Nat Rev Microbiol. 2007;5:801–12.CAS 
PubMed 

Google Scholar 
Fuhrman JA. Marine viruses and their biogeochemical and ecological effects. Nature 1999;399:541–8.CAS 
PubMed 

Google Scholar 
Rohwer F, Thurber RV. Viruses manipulate the marine environment. Nature 2009;459:207–12.CAS 
PubMed 

Google Scholar 
Breitbart M, Bonnain C, Malki K, Sawaya NA. Phage puppet masters of the marine microbial realm. Nat Microbiol. 2018;3:754–66.CAS 
PubMed 

Google Scholar 
Zimmerman AE, Howard-Varona C, Needham DM, John SG, Worden AZ, Sullivan MB, et al. Metabolic and biogeochemical consequences of viral infection in aquatic ecosystems. Nat Rev Microbiol. 2020;18:21–34.CAS 
PubMed 

Google Scholar 
Rosenwasser S, Ziv C, Creveld SGV, Vardi A. Virocell metabolism: metabolic innovations during host-virus interactions in the ocean. Trends Microbiol. 2016;24:821–32.CAS 
PubMed 

Google Scholar 
Fuchsman CA, Carlson MCG, Garcia Prieto D, Hays MD, Rocap G. Cyanophage host-derived genes reflect contrasting selective pressures with depth in the oxic and anoxic water column of the Eastern Tropical North Pacific. Environ Microbiol. 2021;23:2782–2800.CAS 
PubMed 

Google Scholar 
Roux S, Brum JR, Dutilh BE, Sunagawa S, Duhaime MB, Loy A, et al. Ecogenomics and potential biogeochemical impacts of globally abundant ocean viruses. Nature 2016;537:689–93.CAS 
PubMed 

Google Scholar 
Gregory AC, Zayed AA, Conceição-Neto N, Temperton B, Bolduc B, Alberti A, et al. Marine DNA viral macro-and microdiversity from pole to pole. Cell 2019;177:1109–23.CAS 
PubMed 
PubMed Central 

Google Scholar 
Brum JR, Ignacio-Espinoza JC, Roux S, Doulcier G, Acinas SG, Alberti A, et al. Patterns and ecological drivers of ocean viral communities. Science 2015;348:1261498.PubMed 

Google Scholar 
Dion MB, Oechslin F, Moineau S. Phage diversity, genomics and phylogeny. Nat Rev Microbiol. 2020;18:125–38.CAS 
PubMed 

Google Scholar 
Sullivan MB, Waterbury JB, Chisholm SW. Cyanophages infecting the oceanic cyanobacterium Prochlorococcus. Nature 2003;424:1047–51.CAS 
PubMed 

Google Scholar 
Mann NH. Phages of the marine cyanobacterial picophytoplankton. FEMS Microbiol Rev. 2003;27:17–34.CAS 
PubMed 

Google Scholar 
Ni T, Zeng Q. Diel infection of cyanobacteria by cyanophages. Front Mar Sci. 2016;2:123.
Google Scholar 
Flombaum P, Gallegos JL, Gordillo RA, Rincon J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci USA 2013;110:9824–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Biller SJ, Berube PM, Lindell D, Chisholm SW. Prochlorococcus: the structure and function of collective diversity. Nat Rev Microbiol 2015;13:13–27.CAS 
PubMed 

Google Scholar 
Proctor LM, Fuhrman JA. Viral mortality of marine-bacteria and cyanobacteria. Nature 1990;343:60–62.
Google Scholar 
Carlson MCG, Ribalet F, Maidanik I, Durham BP, Hulata Y, Ferron S, et al. Viruses affect picocyanobacterial abundance and biogeography in the North Pacific Ocean. Nat Microbiol 2022;7:570–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Matteson AR, Loar SN, Pickmere S, DeBruyn JM, Ellwood MJ, Boyd PW, et al. Production of viruses during a spring phytoplankton bloom in the South Pacific Ocean near of New Zealand. FEMS Microbiol Ecol 2012;79:709–19.CAS 
PubMed 

Google Scholar 
Ribalet F, Swalwell J, Clayton S, Jimenez V, Sudek S, Lin Y, et al. Light-driven synchrony of Prochlorococcus growth and mortality in the subtropical Pacific gyre. Proc Natl Acad Sci USA. 2015;112:8008–12.CAS 
PubMed 
PubMed Central 

Google Scholar 
Demory D, Liu R, Chen Y, Zhao F, Coenen AR, Zeng Q, et al. Linking light-dependent life history traits with population dynamics for Prochlorococcus and cyanophage. mSystems 2020;5:e00586–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Avrani S, Wurtzel O, Sharon I, Sorek R, Lindell D. Genomic island variability facilitates Prochlorococcus-virus coexistence. Nature 2011;474:604–8.CAS 
PubMed 

Google Scholar 
Marston MF, Pierciey FJ Jr, Shepard A, Gearin G, Qi J, Yandava C, et al. Rapid diversification of coevolving marine Synechococcus and a virus. Proc Natl Acad Sci USA 2012;109:4544–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiao X, Guo W, Li X, Wang C, Chen X, Lin X, et al. Viral lysis alters the optical properties and biological availability of dissolved organic matter derived from Prochlorococcus picocyanobacteria. Appl Environ Microbiol. 2021;87:e02271–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiao X, Zeng Q, Zhang R, Jiao N. Prochlorococcus viruses—From biodiversity to biogeochemical cycles. Sci China Earth Sci. 2018;61:1728–36.
Google Scholar 
Jover LF, Effler TC, Buchan A, Wilhelm SW, Weitz JS. The elemental composition of virus particles: implications for marine biogeochemical cycles. Nat Rev Microbiol. 2014;12:519–28.CAS 
PubMed 

Google Scholar 
Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Viruses inhibit CO2 fixation in the most abundant phototrophs on earth. Curr Biol 2016;26:1585–9.CAS 
PubMed 

Google Scholar 
Weitz JS, Stock CA, Wilhelm SW, Bourouiba L, Coleman ML, Buchan A, et al. A multitrophic model to quantify the effects of marine viruses on microbial food webs and ecosystem processes. ISME J. 2015;9:1352–64.PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Coleman ML, Weigele P, Rohwer F, Chisholm SW. Three Prochlorococcus cyanophage genomes: signature features and ecological interpretations. PLoS Biol. 2005;3:e144.PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Krastins B, Hughes JL, Kelly L, Chase M, Sarracino D, et al. The genome and structural proteome of an ocean siphovirus: a new window into the cyanobacterial ‘mobilome’. Environ Microbiol. 2009;11:2935–51.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sullivan MB, Huang KH, Ignacio-Espinoza JC, Berlin AM, Kelly L, Weigele PR, et al. Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments. Environ Microbiol. 2010;12:3035–56.CAS 
PubMed 
PubMed Central 

Google Scholar 
Sabehi G, Shaulov L, Silver DH, Yanai I, Harel A, Lindell D. A novel lineage of myoviruses infecting cyanobacteria is widespread in the oceans. Proc Natl Acad Sci USA 2012;109:2037–42.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Wang K, Jiao N, Chen F. Genome sequences of siphoviruses infecting marine Synechococcus unveil a diverse cyanophage group and extensive phage-host genetic exchanges. Environ Microbiol. 2012;14:540–58.CAS 
PubMed 

Google Scholar 
Labrie SJ, Frois-Moniz K, Osburne MS, Kelly L, Roggensack SE, Sullivan MB, et al. Genomes of marine cyanopodoviruses reveal multiple origins of diversity. Environ Microbiol. 2013;15:1356–76.CAS 
PubMed 

Google Scholar 
Dekel-Bird NP, Avrani S, Sabehi G, Pekarsky I, Marston MF, Kirzner S, et al. Diversity and evolutionary relationships of T7-like podoviruses infecting marine cyanobacteria. Environ Microbiol. 2013;15:1476–91.CAS 
PubMed 

Google Scholar 
Huang S, Wilhelm SW, Jiao N, Chen F. Ubiquitous cyanobacterial podoviruses in the global oceans unveiled through viral DNA polymerase gene sequences. ISME J. 2010;4:1243–51.PubMed 

Google Scholar 
Baran N, Goldin S, Maidanik I, Lindell D. Quantification of diverse virus populations in the environment using the polony method. Nat Microbiol. 2018;3:62–72.CAS 
PubMed 

Google Scholar 
Chow C-ET, Suttle CA. Biogeography of viruses in the sea. Annu Rev Virol. 2015;2:41–66.CAS 
PubMed 

Google Scholar 
Chen F, Lu JR. Genomic sequence and evolution of marine cyanophage P60: a new insight on lytic and lysogenic phages. Appl Environ Microbiol. 2002;68:2589–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Zhang S, Jiao N, Chen F. Comparative genomic and phylogenomic analyses reveal a conserved core genome shared by estuarine and oceanic cyanopodoviruses. PLoS One. 2015;10:e0142962.PubMed 
PubMed Central 

Google Scholar 
Pope WH, Weigele PR, Chang J, Pedulla ML, Ford ME, Houtz JM, et al. Genome sequence, structural proteins, and capsid organization of the cyanophage Syn5: A “horned’ bacteriophage of marine Synechococcus. J Mol Biol. 2007;368:966–81.CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang S, Sun Y, Zhang S, Long L. Temporal transcriptomes of a marine cyanopodovirus and its Synechococcus host during infection. Microbiologyopen 2021;10:e1150.CAS 
PubMed 

Google Scholar 
Wang K, Chen F. Prevalence of highly host-specific cyanophages in the estuarine environment. Environ Microbiol. 2008;10:300–12.CAS 
PubMed 

Google Scholar 
Chen F, Wang K, Huang S, Cai H, Zhao M, Jiao N, et al. Diverse and dynamic populations of cyanobacterial podoviruses in the Chesapeake Bay unveiled through DNA polymerase gene sequences. Environ Microbiol. 2009;11:2884–92.PubMed 

Google Scholar 
Goldin S, Hulata Y, Baran N, Lindell D. Quantification of T4-like and T7-like cyanophages using the polony method show they are significant members of the virioplankton in the North Pacific Subtropical Gyre. Front Microbiol. 2020;11:1210.PubMed 
PubMed Central 

Google Scholar 
Nasko DJ, Chopyk J, Sakowski EG, Ferrell BD, Polson SW, Wommack KE. Family A DNA polymerase phylogeny uncovers diversity and replication gene organization in the virioplankton. Front Microbiol. 2018;9:3053.PubMed 
PubMed Central 

Google Scholar 
Dekel-Bird NP, Sabehi G, Mosevitzky B, Lindell D. Host-dependent differences in abundance, composition and host range of cyanophages from the Red Sea. Environ Microbiol. 2015;17:1286–99.CAS 
PubMed 

Google Scholar 
Hanson CA, Marston MF, Martiny JBH. Biogeographic variation in host range phenotypes and taxonomic composition of marine cyanophage isolates. Front Microbiol. 2016;7:983.PubMed 
PubMed Central 

Google Scholar 
Rocap G, Larimer FW, Lamerdin J, Malfatti S, Chain P, Ahlgren NA, et al. Genome divergence in two Prochlorococcus ecotypes reflects oceanic niche differentiation. Nature 2003;424:1042–7.CAS 
PubMed 

Google Scholar 
Chen B, Wang L, Song S, Huang B, Sun J, Liu H. Comparisons of picophytoplankton abundance, size, and fluorescence between summer and winter in northern South China Sea. Cont Shelf Res. 2011;31:1527–40.
Google Scholar 
Lindell D, Jaffe JD, Coleman ML, Futschik ME, Axmann IM, Rector T, et al. Genome-wide expression dynamics of a marine virus and host reveal features of co-evolution. Nature 2007;449:83–86.CAS 
PubMed 

Google Scholar 
Zhao Y, Qin F, Zhang R, Giovannoni SJ, Zhang Z, Sun J, et al. Pelagiphages in the Podoviridae family integrate into host genomes. Environ Microbiol. 2019;21:1989–2001.CAS 
PubMed 

Google Scholar 
Leptihn S, Gottschalk J, Kuhn A. T7 ejectosome assembly: A story unfolds. Bacteriophage 2016;6:e1128513.CAS 
PubMed 
PubMed Central 

Google Scholar 
Thompson LR, Zeng Q, Kelly L, Huang KH, Singer AU, Stubbe J, et al. Phage auxiliary metabolic genes and the redirection of cyanobacterial host carbon metabolism. Proc Natl Acad Sci USA 2011;108:E757–64.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zeng Q, Chisholm SW. Marine viruses exploit their host’s two-component regulatory system in response to resource limitation. Curr Biol 2012;22:124–8.CAS 
PubMed 

Google Scholar 
Zeng Q, Bonocora RP, Shub DA. A free-standing homing endonuclease targets an intron insertion site in the psbA gene of cyanophages. Curr Biol. 2009;19:218–22.CAS 
PubMed 

Google Scholar 
Lindell D, Jaffe JD, Johnson ZI, Church GM, Chisholm SW. Photosynthesis genes in marine viruses yield proteins during host infection. Nature 2005;438:86–89.CAS 
PubMed 

Google Scholar 
Breitbart M, Thompson LR, Suttle CA, Sullivan MB. Exploring the vast diversity of marine viruses. Oceanography. 2007;20:135–9.
Google Scholar 
Kazlauskas D, Venclovas C. Computational analysis of DNA replicases in double-stranded DNA viruses: relationship with the genome size. Nucleic Acids Res. 2011;39:8291–305.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu X, Zhang Q, Murata K, Baker ML, Sullivan MB, Fu C, et al. Structural changes in a marine podovirus associated with release of its genome into Prochlorococcus. Nat Struct Mol Biol. 2010;17:830–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Dai W, Fu C, Raytcheva D, Flanagan J, Khant HA, Liu XG, et al. Visualizing virus assembly intermediates inside marine cyanobacteria. Nature 2013;502:707–10.CAS 
PubMed 
PubMed Central 

Google Scholar 
Liu R, Liu Y, Chen Y, Zhan Y, Zeng Q. Cyanobacterial viruses exhibit diurnal rhythms during infection. Proc Natl Acad Sci USA 2019;116:14077–82.CAS 
PubMed 
PubMed Central 

Google Scholar 
Maidanik I, Kirzner S, Pekarski I, Arsenieff L, Tahan R, Carlson MCG, et al. Cyanophages from a less virulent clade dominate over their sister clade in global oceans. ISME J. 2022;16:2169–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
Shitrit D, Hackl T, Laurenceau R, Raho N, Carlson MCG, Sabehi G, et al. Genetic engineering of marine cyanophages reveals integration but not lysogeny in T7-like cyanophages. ISME J. 2022;16:488–99.CAS 
PubMed 

Google Scholar 
Liang Y, Wang L, Wang Z, Zhao J, Yang Q, Wang M, et al. Metagenomic analysis of the diversity of DNA viruses in the surface and deep sea of the South China Sea. Front Microbiol. 2019;10:1951.PubMed 
PubMed Central 

Google Scholar 
Pedrós-Alió C, Potvin M, Lovejoy C. Diversity of planktonic microorganisms in the Arctic Ocean. Prog Oceanogr. 2015;139:233–43.
Google Scholar 
Luo E, Eppley JM, Romano AE, Mende DR, DeLong EF. Double-stranded DNA virioplankton dynamics and reproductive strategies in the oligotrophic open ocean water column. ISME J. 2020;14:1304–15.CAS 
PubMed 
PubMed Central 

Google Scholar 
Steidinger BS, Crowther TW, Liang J, Van Nuland ME, Werner GDA, Reich PB, et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 2019;569:404–8.CAS 
PubMed 

Google Scholar 
Xie X, Wu T, Zhu M, Jiang G, Xu Y, Wang X, et al. Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecol Indic. 2021;120:106925.CAS 

Google Scholar 
Lee SJ, Richardson CC. Choreography of bacteriophage T7 DNA replication. Curr Opin Chem Biol. 2011;15:580–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kulczyk AW, Richardson CC. The replication system of bacteriophage T7. Enzymes. 2016;39:89–136.CAS 
PubMed 

Google Scholar 
Benkovic SJ, Valentine AM, Salinas F. Replisome-mediated DNA replication. Annu Rev Biochem. 2001;70:181–208.CAS 
PubMed 

Google Scholar 
Johnson A, O’Donnell M. Cellular DNA replicases: components and dynamics at the replication fork. Annu Rev Biochem. 2005;74:283–315.CAS 
PubMed 

Google Scholar 
Seco EM, Zinder JC, Manhart CM, Lo Piano A, McHenry CS, Ayora S. Bacteriophage SPP1 DNA replication strategies promote viral and disable host replication in vitro. Nucleic Acids Res. 2013;41:1711–21.CAS 
PubMed 

Google Scholar 
Mruwat N, Carlson MCG, Goldin S, Ribalet F, Kirzner S, Hulata Y, et al. A single-cell polony method reveals low levels of infected Prochlorococcus in oligotrophic waters despite high cyanophage abundances. ISME J. 2021;15:41–54.CAS 
PubMed 

Google Scholar 
Moore LR, Rocap G, Chisholm SW. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 1998;393:464–7.CAS 
PubMed 

Google Scholar 
Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Shedding new light on viral photosynthesis. Photosynth Res. 2015;126:71–97.CAS 
PubMed 

Google Scholar 
Edwards KF, Steward GF, Schvarcz CR. Making sense of virus size and the tradeoffs shaping viral fitness. Ecol Lett. 2021;24:363–73.PubMed 

Google Scholar 
Moore LR, Coe A, Zinser ER, Saito MA, Sullivan MB, Lindell D, et al. Culturing the marine cyanobacterium Prochlorococcus. Limnol Oceanogr Methods. 2007;5:353–62.CAS 

Google Scholar 
Hyman P, Abedon ST. Bacteriophage host range and bacterial resistance. Adv Appl Microbiol. 2010;70:217–48.CAS 
PubMed 

Google Scholar 
Fridman S, Flores-Uribe J, Larom S, Alalouf O, Liran O, Yacoby I, et al. A myovirus encoding both photosystem I and II proteins enhances cyclic electron flow in infected Prochlorococcus cells. Nat Microbiol. 2017;2:1350–7.CAS 
PubMed 

Google Scholar 
Fang X, Liu Y, Zhao Y, Chen Y, Liu R, Qin QL, et al. Transcriptomic responses of the marine cyanobacterium Prochlorococcus to viral lysis products. Environ Microbiol. 2019;21:2015–28.CAS 
PubMed 

Google Scholar 
John SG, Mendez CB, Deng L, Poulos B, Kauffman AK, Kern S, et al. A simple and efficient method for concentration of ocean viruses by chemical flocculation. Environ Microbiol Rep. 2011;3:195–202.CAS 
PubMed 
PubMed Central 

Google Scholar 
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 
Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 2011;27:863–4.CAS 
PubMed 
PubMed Central 

Google Scholar 
Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:1–10.
Google Scholar 
Peng Y, Leung HC, Yiu SM, Chin FY. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 2012;28:1420–8.CAS 
PubMed 

Google Scholar 
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014;30:2068–9.CAS 
PubMed 

Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic Inference in the genomic era. Mol Biol Evol. 2020;37:2461–2461.PubMed 
PubMed Central 

Google Scholar 
Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 

Google Scholar 
Martinez-Hernandez F, Fornas O, Lluesma Gomez M, Bolduc B, de la Cruz Pena MJ, Martinez JM, et al. Single-virus genomics reveals hidden cosmopolitan and abundant viruses. Nat Commun. 2017;8:15892.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang Z, Qin F, Chen F, Chu X, Luo H, Zhang R, et al. Culturing novel and abundant pelagiphages in the ocean. Environ Microbiol 2021;23:1145–61.CAS 
PubMed 

Google Scholar 
Buchholz HH, Michelsen ML, Bolanos LM, Browne E, Allen MJ, Temperton B. Efficient dilution-to-extinction isolation of novel virus-host model systems for fastidious heterotrophic bacteria. ISME J. 2021;15:1585–98.CAS 
PubMed 
PubMed Central 

Google Scholar 
Qin F, Du S, Zhang Z, Ying H, Wu Y, Zhao G, et al. Newly identified HMO-2011-type phages reveal genomic diversity and biogeographic distributions of this marine viral group. ISME J. 2022;16:1363–75.CAS 
PubMed 
PubMed Central 

Google Scholar  More

Data processingIn 2019, the CETUS data spanning between 2012 and 2017 was published open access through the Flanders Marine Institute (VLIZ) IPT portal and distributed by EMODnet and OBIS, in a first version of the CETUS dataset9. The data collected between 2018 and 2019 was prepared as the 2012–2017 data9. Methods for photographic verification/validation and to evaluate the MMOs experience were applied (see below), in order to include new variables on data quality in an updated version of the dataset. Currently, the CETUS dataset is updated, with a 2nd version available10. It comprises data from 2012 to 2017, with the following two new columns on the observers’ experience: “most experienced observer” and “least experienced observer”; and a new column associated with validation of the sightings’ identifications: “photographic validation”. The results here presented correspond to the analysis of the data from 2012 to 2019, and the open-access dataset will soon be further updated with the 2018–2019 data.Photographic verificationAll the former MMOs who have integrated the CETUS Project, between 2012 and 2019, were contacted and asked to provide any available photographic or video records of cetaceans collected during their on board periods. The collection of sighting’s images was not a requirement of the CETUS protocol, and so these records were obtained opportunistically, with availability and quality depending on several factors: observers on board having personal cameras, camera quality, intention of the observer taking the photograph (e.g., for aesthetic or identification purposes).The images obtained were organized in a folder hierarchy from the year to the day of recording. However, not all the images had metadata up to the day of recording, so these were inserted into the most appropriate hierarchy-level of the folder organization. For each set of records corresponding to a single-taxon sighting, the photos/videos with the better quality or framing (i.e., that allowed for an easier species identification) were selected for that sighting. The remaining photos/videos were only consulted in case of doubt (e.g., to look for additional details that could help with the identification).Verification consisted of the process of matching the photographic/video records with the dataset sighting registers. Whenever possible and ideally, the file metadata was used for the process. However, often, the date and/or time of the file metadata were wrong, non-existent, or in different time zones. In these cases, a conservative methodology was applied using all available information to match as many sightings as possible. An estimation of time lag was attempted (based on, at least, two obvious matches between photographs/videos and dataset registers, e.g., unique sighting of the day, close to the boat, easy/obvious identification). When not possible, further evaluation consisted in assessing whether the sighting and image record was too obvious, and accounting for unique complementary information on the sighting (e.g., the number of animals or the side of the sighting were unique for that day and/or for that species/group).Photographic validationAfter the verification process, the validation of the matched records was carried out, to confirm or correct the species identification of sightings in the 1st version of the CETUS dataset (i.e., reported by the MMOs on board). The validation approach involved, for more dubious identification through the photo/video records, the discussion between four experienced observers of the CETUS team. In cases where no consensual agreement was achieved, an external expert on cetacean identification was also consulted. Identifications made through the photographic/video records required 100% certainty, and these were then compared with the cetacean identifications provided in the 1st version of the CETUS dataset. Then, the occurrence records with originally misidentifications of cetaceans, as well as those records where validation allowed to achieve an identification to a lower taxon, were corrected in the 2nd version of the dataset (i.e., a delphinid sighting validated as common dolphin, will now appear as common dolphin). A new column “photographic validation” was added to the dataset with the following categories: “yes” (i.e., validated with photograph/video), “no” (i.e., not validated with photograph/video), and “to the family” (i.e., validation only to the family taxon).For further analysis, specifically for the model process on the identification success (see below), registers were considered “completely validated” if it was possible to complete the photographic/video identification process up to the species level (then, differentiating if the original identification from the MMOs was or not correct). For Globicephala sp. and Kogia sp., validation to the genus was considered complete, since the species from both genera are visually hardly differentiated, especially at sea.Creating a data quality criteria: evaluating MMOs experienceQuality criteria were created to evaluate the MMOs experience based on the information collected from their curricula vitae (CVs) (alumni MMOs provided as many CVs as the years of their participation in CETUS). The following quality criteria were considered: (i) the experience at sea, (ii) the experience with cetaceans’ ID, (iii) the number of species they have worked with, and (iv) the experience working with the CETUS Project protocol. Each of these quality criteria was ranked from 0 to 5, and then these were summed to generate an evaluation score, on a scale of 0 to 20, attributed to each MMO (Table 4).Table 4 Quality criteria for MMOs evaluation.Full size tableThe MMOs evaluations were computed for each cruise (i.e., the trip from one port to another), considering the experience of the MMOs based on the CV obtained for that year, plus the experience acquired during CETUS participation in previous cruises that year. Since most of the times, the team of observers on board each cruise was constituted by two MMOs, two final evaluation scores were attributed to each cruise in the 2nd version of the CETUS dataset, into two new columns: “most experienced observer” and “least experienced observer”. On rare occasions where there is only one observer on board that cruise, only the evaluation of the single observer was included under the column “most experienced observer”, leaving the column “least experienced observer” as “NULL”. To investigate the experience of MMOs on board, both individually and cumulative (LEO + MEO), the combination of the score values was computed by cruise. These were then trimmed to unique combinations of evaluation scores.The names of observers, previously presented in the online dataset for each cruise, were removed for anonymity purposes, as there is now ancillary information regarding their experience.Model fittingTwo Generalized Additive Models (GAM) were fitted to assess bias on the number of sightings recorded per survey and on the identification success of cetacean species. Details for each model are presented below. Both models were fitted in R (Version 4.1.0). Prior to modelling, Pearson correlations were calculated between all pairs of explanatory variables, considered for each model (see below), to exclude highly correlated variables, considering a threshold of 0.7524,25,28. Since the variables regarding MMOs’ experience were correlated (LEO or MEO correlated with cumulative and mean experience; and cumulative experience correlated with mean experience – Supplementary Fig. S3), these variables were not included in the first fitting stage (backward selection) but included later through forward selection (see below). Multicollinearity among explanatory variables was measured through the Variance Inflation Factor (VIF), with a threshold of 3 (Supplementary Tables S4)24,25,29. After removing the MMOs evaluation scores, no multicollinearity was observed, so all the other variables were kept for the first fitting stage.For model selection, a backward selection was applied to oversaturated models18,24,25,30,31. The Akaike Information Criterion (AIC) was used as a measure of adequation of fitness, choosing the model with the lowest AIC value at each step of the model fitting process, i.e., comparing nested models (larger model incorporating one more explanatory variable compared with the smaller model). If the AIC-difference between the two models was less than 2, an Analysis of Variance (ANOVA), through chi-square test, was used to check if the AIC-difference was significant24,25,32. If this difference was not statistically significant (p  > 0.05), the simplest model (smaller model) was kept. Through a forward selection process, the variables regarding the MMOs evaluation scores were added, one at a time, to the best model obtained in the previous backward selection. After comparing the models with each other (separate variables for LEO + MEO vs. Cumulative Evaluation vs Mean Evaluation), the best model, considering the AIC value, was kept. A final backward selection process was then applied.All GAMs were fitted with the “mgcv” package (https://cran.r-project.org/web/packages/mgcv) and a maximum of four splines (k = 4) was chosen to limit the complexity of smoothers describing the effects of the explanatory variables25,31. If a spline was close to linear (with estimated degrees of freedom of ~1), the smooth term was removed, and a linear function was fitted. To check for model quality, the “gam.check” function was used to verify the diagnostic plots and the adequacy of the number of splines (Supplementary Figs. S5 and S6). Existence of influential data points was assessed (with the threshold of 0.25 for the Hat values), as well as the correlation between model residuals and explanatory variables. In both final models, number of splines was adequate and there were no influential data points or clear correlation between residuals and explanatory variables (Supplementary Figs. S7 and S8)24,32.Bias modelling of number of sightingsTo assess the bias parameters on the number of sightings recorded per survey (i.e., a full day monitoring, from sunrise to sunset), the following detectability factors were considered as explanatory variables: weather conditions (i.e., the minimums and maximums of the sea state, wind state, and visibility), the experience of MMOs (i.e., the evaluation scores of the least and the most experienced observers, as well as the mean and cumulative evaluations of the MMOs experience) and kilometres sampled “on-effort” (i.e., periods of active survey). Sampling periods were divided into “On-effort” and “Off-effort” conditions, based on four meteorological variables: sea state (Douglas scale), wind state (Beaufort scale), visibility (measured in a categorical scale ranging from 0–10 and estimated from the distance to the horizon line and possible reference points at a known range, e.g., ships with an automatic identification system,  > 1000 km), and the occurrence of rain (see Supplementary Table S9)10. For the model fitting, only “on-effort” periods of sampling were considered. Given that the response variable was count data, a Poisson distribution was tested (with a log link function). Then, the resulting first oversaturated model was checked for overdispersion, through a Pearson estimator. Since it tested positive for overdispersion (φ = 1.99), a negative binomial distribution (with a log link function) was fitted.Bias modelling of identification successA binary response variable, based on the success in the species identification for each sighting, was generated, and a model with binomial distribution (with a logit link function) was fitted. As in the previous model, only “on-effort” records were used. The total number of non-successful identifications across the dataset (the 0 s of the model) was extrapolated from the proportion of wrong identifications obtained in the validation process. To calculate this proportion, only complete validated sightings registered “on-effort” were used. Proportions were computed and extrapolated to Odontoceti and Mysticeti, separately. This resulted in 78 non-successful identifications in delphinids, plus 17 misidentifications in baleen whales, i.e., a total of 95 “on-effort” sightings randomly selected from the dataset were defined as unsuccessful identifications (0 s in the response variable for model fitting). The remaining records were considered successful identifications (1 s in the response variable for model fitting). To assess the bias parameters on the identification success, the following independent variables were considered in the analysis: the group (i.e., Group A: Odontoceti sightings, excluding sperm whale (Physeter macrocephalus); and Group B: Mysticeti sightings, plus sperm whale), the size of the group (i.e., the best estimate of the number of animals in a sighting, from the observer’s perspective), sighting distance (i.e., a relative measure according to the scale of the binoculars), weather conditions (i.e., the sea state, wind state, and visibility at the time of each sighting), the experience of MMOs (i.e., the evaluation scores of least and most experienced observers, as well as the mean and cumulative scores of the MMOs teams). Group A and B were settled according to cetacean morphology. However, since sperm whales have closer similarities with Mysticeti species, they were also included in Group B21,33. This categorization was mostly based on body size, as this is likely the main factor, regarding the species morphology, influencing the identification. Group A is constituted by species with a medium length of less than 10 meters, while Group B includes the larger species over 10 meters (Mysticeti plus P. macrocephalus)33. Since in the CETUS Project, different binoculars have been used – with two different reticle scales – it was necessary to standardize binocular distances to the same scale. More