Ecology

NEWS
31 March 2022

Funding battles stymie ambitious plan to protect global biodiversity

Researchers are disappointed with the progress — but haven’t lost hope.

Natasha Gilbert

Natasha Gilbert

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Animals such as this orangutan in Indonesia are endangered because of illegal deforestation.Credit: Jami Tarris/Future Publishing via Getty

Scientists are frustrated with countries’ progress towards inking a new deal to protect the natural world. Government officials from around the globe met in Geneva, Switzerland, on 14–29 March to find common ground on a draft of the deal, known as the post-2020 global biodiversity framework, but discussions stalled, mostly over financing. Negotiators say they will now have to meet again before a highly anticipated United Nations biodiversity summit later this year, where the deal was to be signed.The framework so far sets out 4 broad goals, including slowing species extinction, and 21 mostly quantitative targets, such as protecting at least 30% of the world’s land and seas. It is part of an international treaty known as the UN Convention on Biological Diversity, and aims to address the global biodiversity crisis, which could see one million plant and animal species go extinct in the next few decades because of factors such as climate change, human activity and disease.
China takes centre stage in global biodiversity push
The COVID-19 pandemic has already slowed discussions of the deal. Over the past two years, countries’ negotiators met only virtually; the Geneva meeting was the first in-person gathering since the pandemic began. When it ended, Basile van Havre, one of the chairs of the framework negotiations working group, said that because negotiators couldn’t agree on goals, additional discussions will need to take place in June in Nairobi. The convention’s pivotal summit — its Conference of the Parties (COP15) — is expected to be held in Kunming, China, in August and September, but no firm date has been set.Anne Larigauderie, executive secretary of the Intergovernmental Platform on Biodiversity and Ecosystem Services in Bonn, Germany, who attended the Geneva gathering, told Nature: “We are leaving the meeting with no quantitative elements. I was hoping for more progress.”Robert Watson, a retired environmental scientist at the University of East Anglia, UK, says the quantitative targets are crucial to conserving biodiversity and monitoring progress towards that goal. He calls on governments to “bite the bullet and negotiate an appropriate deal that both protects and restores biodiversity”.Finance fightMany who were at the meeting say that disagreements over funding for biodiversity conservation were the main hold-up to negotiations. For example, the draft deal proposed that US$10 billion of funding per year should flow from developed nations to low- and middle-income countries to help them to implement the biodiversity framework. But many think this is not enough. A group of conservation organizations has called for at least $60 billion per year to flow to poorer nations.
Biodiversity moves beyond counting species
The consumption habits of wealthy nations are among the key drivers of biodiversity loss. And poorer nations are often home to areas rich in biodiversity, but have fewer means to conserve them.“The most challenging aspect is the amount of financing that wealthy nations are committing to developing nations,” says Brian O’Donnell, director of the Campaign for Nature in Washington DC, a partnership of private charities and conservation organizations advocating a deal to safeguard biodiversity. “Nations need to up their level of financing to get progress in the COP.”Other nations, particularly low-income ones, probably don’t want to agree “unless they have assurances of resources to allow them to implement the new framework”, Larigauderie says.Countries including Argentina and Brazil are largely responsible for stalling the deal, several sources close to the negotiations told Nature. They asked to remain anonymous because the negotiations are confidential.
The world’s species are playing musical chairs: how will it end?
No agreement could be reached even on targets with broad international support, such as protecting at least 30% of the world’s land and seas by 2030. O’Donnell says that just one country blocked agreement on this target, questioning its scientific basis.Van Havre downplayed the lack of progress, saying that the brinksmanship at the meeting was part of a “normal negotiating process”. He told reporters: “We are happy with the progress made.” Further delays ‘unacceptable’A bright spot in the negotiations, van Havre said, was a last-minute “major step forward” in discussions on how to fairly and equitably share the benefits of digital sequence information (DSI). DSI consists of genetic data collected from plants, animals and other organisms.
Why deforestation and extinctions make pandemics more likely
When pressed, however, van Havre admitted that the progress was simply an agreement between countries to continue discussing a way forward.Thomas Brooks, chief scientist at the International Union for Conservation of Nature in Gland, Switzerland, says that DSI discussions have actually been fraught. Communities from biodiverse-rich regions where genetic material is collected have little control over the commercialization of the data that come from it, and no way to recoup financial and other benefits, he explains.Like biodiversity financing, DSI rights could hold up negotiations on the overall framework. Low-income countries want a fair and equitable share of the benefits from genetic material that originates in their lands, but rich nations don’t want unnecessary barriers to sharing the information.“We are a long way from a watershed moment, and there remain genuine disagreements,” Brooks says. However, he is optimistic that progress will eventually be made.
The biodiversity leader who is fighting for nature amid a pandemic
Some conservation organizations take hope from new provisional language in the deal that calls for halting all human-caused species extinctions. The previous draft of the deal proposed only a reduction in the rate and risk of extinctions, says Paul Todd, an environmental lawyer at the Natural Resources Defense Council, a non-profit group based in New York City.Given how much work governments must do to reach agreement on the deal, Watson says the extra Nairobi meeting is a “logical” move. But he warns: “Any further delay would be unacceptable.”“This isn’t even the hard work,” Todd says. “Implementing the deal will be the real work.”

doi: https://doi.org/10.1038/d41586-022-00916-8

Related Articles

China takes centre stage in global biodiversity push

The biodiversity leader who is fighting for nature amid a pandemic

Why deforestation and extinctions make pandemics more likely

Biodiversity moves beyond counting species

The battle for the soul of biodiversity

The world’s species are playing musical chairs: how will it end?

Subjects

Biodiversity

Conservation biology

Climate change

Latest on:

Biodiversity

Are there limits to economic growth? It’s time to call time on a 50-year argument
Editorial 16 MAR 22

Africa: sequence 100,000 species to safeguard biodiversity
Comment 15 MAR 22

Rewilding Argentina: lessons for the 2030 biodiversity targets
Comment 07 MAR 22

Climate change

Trends in Europe storm surge extremes match the rate of sea-level rise
Article 30 MAR 22

The race to upcycle CO2 into fuels, concrete and more
News Feature 29 MAR 22

Biden bids again to boost science spending — but faces long odds
News 28 MAR 22

Jobs

wiss. Mitarbeiter/in (m/w/d)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

wiss. Mitarbeiter/in (m/w/d)

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Junior Research Group Leader on Robustness and Decision Making in Cells and Tissues

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany

Junior Research Group Leader on Physical Measurement and Manipulation of Living Systems

Technische Universität Dresden (TU Dresden)
01069 Dresden, Germany More

Moberg, F. & Folke, C. Ecological goods and services of coral reef ecosystems. Ecol. Econ. 29, 215–233 (1999).
Google Scholar 
Poloczanska, E. S. et al. Responses of marine organisms to climate change across oceans. Front. Mar. Sci. 3, 62 (2016).Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377(2017).Cornwall, C. E. et al. Resistance of corals and coralline algae to ocean acidification: physiological control of calcification under natural pH variability. Proc. R. Soc. B Biol. Sci. 285, 20181 (2018).
Google Scholar 
Schoepf, V. et al. Coral energy reserves and calcification in a high-CO2 world at two temperatures. PLoS One 8, e75049 (2013).CAS 

Google Scholar 
Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Glob. Chang. Biol. 19, 1884–1896 (2013).Heron, S. F., Maynard, J. A., Van Hooidonk, R. & Eakin, C. M. Warming trends and bleaching stress of the world’s coral reefs 1985-2012. Sci. Rep. 6, 38402, 1–14 (2016).
Google Scholar 
Marshall, A. T. & Clode, P. Calcification rate and the effect of temperature in a zooxanthellate and an azooxanthellate scleractinian reef coral. Coral Reefs. 23, 218–224 (2004).
Google Scholar 
Jokiel, P. L. & Coles, S. L. Effects of temperature on the mortality and growth of Hawaiian reef corals. Mar. Biol. 208, 201–208 (1977).
Google Scholar 
Rodolfo-Metalpa, R., Huot, Y. & Ferrier-Pagès, C. Photosynthetic response of the Mediterranean zooxanthellate coral Cladocora caespitosa to the natural range of light and temperature. J. Exp. Biol. 211, 1579–1586 (2008).CAS 

Google Scholar 
Cohen, A. L. & McConnaughey, T. A. Geochemical perspectives on coral mineralization. Rev. Mineral. Geochemistry 54, 151–187 (2003).CAS 

Google Scholar 
McCulloch, M. T., Falter, J. L., Trotter, J. & Montagna, P. Coral resilience to ocean acidification and global warming through pH up-regulation. Nat. Clim. Chang. 2, 1–5 (2012).
Google Scholar 
Venn, A. A., Tambutté, É., Holcomb, M., Allemand, D. & Tambutté, S. Live tissue imaging shows reef corals elevate pH under their calcifying tissue relative to seawater. PLoS One 6, e20013 (2011).CAS 

Google Scholar 
Cai, W.-J. et al. Microelectrode characterization of coral daytime interior pH and carbonate chemistry. Nat. Commun. 7, 11144 (2016).CAS 

Google Scholar 
Al-Horani, F. A., Al-Moghrabi, S. M. & de Beer, D. The mechanism of calcification and its relation to photosynthesis and respiration in the scleractinian coral Galaxea fascicularis. Mar. Biol. 142, 419–426 (2003).CAS 

Google Scholar 
Holcomb, M. et al. Coral calcifying fluid pH dictates response to ocean acidification. Sci. Rep. 4, 5207 (2014).CAS 

Google Scholar 
Holcomb, M., DeCarlo, T. M., Gaetani, G. A. & McCulloch, M. T. Factors affecting B/Ca ratios in synthetic aragonite. Chem. Geol. 437, 67–76 (2016).CAS 

Google Scholar 
McCulloch, M. T., D’Olivo, J. P., Falter, J., Holcomb, M. & Trotter, J. A. Coral calcification in a changing World: the interactive dynamics of pH and DIC up-regulation. Nat. Commun. 8, 15686 (2017).Tambutté, S. et al. Calcein labelling and electrophysiology: insights on coral tissue permeability and calcification. Proc. R. Soc. B 279, 19–27 (2012).
Google Scholar 
Trotter, J. et al. Quantifying the pH ‘vital effect’ in the temperate zooxanthellate coral Cladocora caespitosa: Validation of the boron seawater pH proxy. Earth Planet. Sci. Lett. 303, 163–173 (2011).CAS 

Google Scholar 
Schoepf, V., Jury, C. P., Toonen, R. & McCulloch, M. Coral calcification mechanisms facilitate adaptive response to ocean acidification. Proc. R. Soc. B 284, 2117 (2017).
Google Scholar 
Comeau, S., Cornwall, C. E. & McCulloch, M. T. Decoupling between the response of coral calcifying fluid pH and calcification to ocean acidification. Sci. Rep. 7, 7573 (2017).CAS 

Google Scholar 
Schoepf, V., D’Olivo, J. P., Rigal, C., Jung, E. M. U. & Mcculloch, M. T. Heat stress differentially impacts key calcification mechanisms in reef-building corals. Coral Reefs. https://doi.org/10.1007/s00338-020-02038-x (2021).Article 

Google Scholar 
Schoepf, V. et al. Short-term coral bleaching is not recorded by skeletal boron isotopes. PLoS One 9, e112011 (2014).
Google Scholar 
D’Olivo, J. P. & McCulloch, M. T. Response of coral calcification and calcifying fluid composition to thermally induced bleaching stress. Sci. Rep. 7, 2207 (2017).
Google Scholar 
Dishon, G. et al. A novel paleo-bleaching proxy using boron isotopes and high-resolution laser ablation to reconstruct coral bleaching events. Biogeosciences 12, 5677–5687 (2015).
Google Scholar 
Guillermic, M. et al. Thermal stress reduces pocilloporid coral resilience to ocean acidification by impairing control over calcifying fluid chemistry. Sci. Adv. 7, 20172117(2021).Ross, C. L., Falter, J. L. & McCulloch, M. T. Active modulation of the calcifying fluid carbonate chemistry (δ11B, B/Ca) and seasonally invariant coral calcification at sub-tropical limits. Sci. Rep. 7, 1–11 (2017). 13830.
Google Scholar 
D’Olivo, J. P., Ellwood, G., Decarlo, T. M. & Mcculloch, M. T. Deconvolving the long-term impacts of ocean acidification and warming on coral biomineralisation. Earth Planet. Sci. Lett. 526, 115785 (2019).
Google Scholar 
Ross, C. L., DeCarlo, T. M. & McCulloch, M. T. Environmental and physiochemical controls on coral calcification along a latitudinal temperature gradient in Western Australia. Glob. Chang. Biol. 25, 431–447 (2019).
Google Scholar 
Guo, W. Seawater temperature and buffering capacity modulate coral calcifying pH. Sci. Rep. 9, 1–13 (2019).
Google Scholar 
Reynaud, S., Ferrier-Pagès, C., Boisson, F., Allemand, D. & Fairbanks, R. G. Effect of light and temperature on calcification and strontium uptake in the scleractinian coral Acropora verweyi. Mar. Ecol. Prog. Ser. 279, 105–112 (2004).CAS 

Google Scholar 
Dissard, D. et al. Light and temperature effects on δ11B and B/Ca ratios of the zooxanthellate coral Acropora sp.: results from culturing experiments. Biogeosciences 9, 4589–4605 (2012).CAS 

Google Scholar 
Hönisch, B. et al. Assessing scleractinian corals as recorders for paleo-pH: Empirical calibration and vital effects. Geochim. Cosmochim. Acta 68, 3675–3685 (2004).
Google Scholar 
Comeau, S. et al. Flow-driven micro-scale pH variability affects the physiology of corals and coralline algae under ocean acidification. Sci. Reports 9, 1–12 (2019). 2019 91.
Google Scholar 
DeCarlo, T. M., Ross, C. L. & McCulloch, M. T. Diurnal cycles of coral calcifying fluid aragonite saturation state. Mar. Biol. 166, 1–6 (2019).CAS 

Google Scholar 
Ross, C. L., Schoepf, V., DeCarlo, T. M. & McCulloch, M. T. Mechanisms and seasonal drivers of calcification in the temperate coral Turbinaria reniformis at its latitudinal limits. Proc. R. Soc. B 285, 20180 (2018).
Google Scholar 
Krief, S. et al. Physiological and isotopic responses of scleractinian corals to ocean acidification. Geochim. Cosmochim. Acta 74, 4988–5001 (2010).CAS 

Google Scholar 
Coles, S. L. & Jokiel, P. L. Effects of temperature on photosynthesis and respiration in hermatypic corals. Mar. Biol. 43, 209–216 (1977).CAS 

Google Scholar 
Gattuso, J.-P., Allemand, D. & Frankignoulle, M. Photosynthesis and calcification at cellular, organismal and community levels in coral reefs: a review on interactions and control by carbonate chemistry. Am. Zool. 39, 160–183 (1999).CAS 

Google Scholar 
Kajiwara, K., Nagai, A., Ueno, S. & Yokochi, H. Examination of the effect of temperature, light intensity and zooxanthellae concentration on calcification and photosynthesis of scleractinian coral Acropora pulchra. J. Sch. Mar. Sci. Technol. Tokai Univ. 40, 95–103 (1995).
Google Scholar 
Reynaud, S. et al. Interacting effects of CO2 partial pressure and temperature on photosynthesis and calcification in a scleractinian coral. Glob Chang. Biol. 9, 1660–1668 (2003).
Google Scholar 
Furla, P., Galgani, I., Durand, I. & Allemand, D. Sources and mechanisms of inorganic carbon transport for coral calcification and photosynthesis. J. Exp. Biol. 203, 3445–3457 (2000).CAS 

Google Scholar 
Zoccola, D. et al. Bicarbonate transporters in corals point towards a key step in the evolution of cnidarian calcification. Sci. Rep. 5, 9983 (2015).CAS 

Google Scholar 
Allison, N. et al. Corals concentrate dissolved inorganic carbon to facilitate calcification. Nat. Commun. 5, 5741 (2014).CAS 

Google Scholar 
Vajed Samiei, J. et al. Variation in calcification rate of Acropora downingi relative to seasonal changes in environmental conditions in the northeastern Persian Gulf. Coral Reefs. https://doi.org/10.1007/s00338-016-1464-6 (2016).Article 

Google Scholar 
Kuffner, I. B., Hickey, T. D. & Morrison, J. M. Calcification rates of the massive coral Siderastrea siderea and crustose coralline algae along the Florida Keys (USA) outer-reef tract. Coral Reefs. 32, 987–997 (2013).
Google Scholar 
Courtney, T. A. et al. Environmental controls on modern scleractinian coral and reef-scale calcification. Sci. Adv. 3, e170135 (2017).
Google Scholar 
Burton, E. A. & Walter, L. M. Relative precipitation rates of aragonite and Mg calcite from seawater: Temperature or carbonate ion control? Geology 15, 111 (1987).CAS 

Google Scholar 
Lough, J. M. & Barnes, D. Environmental controls on growth of the massive coral Porites. J. Exp. Mar. Bio. Ecol. 245, 225–243 (2000).CAS 

Google Scholar 
Fitt, W. K., Brown, B., Warner, M. E. & Dunne, R. Coral bleaching: interpretation of thermal tolerance limits and thermal thresholds in tropical corals. Coral Reefs. 20, 51–65 (2001).
Google Scholar 
Fisher, R., Bessell-Browne, P. & Jones, R. Synergistic and antagonistic impacts of suspended sediments and thermal stress on corals. Nat. Commun. 10, 1–9 (2019).CAS 

Google Scholar 
Teixeira, C. D. et al. Sustained mass coral bleaching (2016–2017) in Brazilian turbid-zone reefs: taxonomic, cross-shelf and habitat-related trends. Coral Reefs. 38, 801–813 (2019).
Google Scholar 
Bonesso, J. L., Leggat, W. & Ainsworth, T. D. Exposure to elevated sea-surface temperatures below the bleaching threshold impairs coral recovery and regeneration following injury. PeerJ 5, e3719 (2017).
Google Scholar 
Ulstrup, K. E., Kühl, M., van Oppen, M. J. H., Cooper, T. F. & Ralph, P. J. Variation in photosynthesis and respiration in geographically distinct populations of two reef-building coral species. Aquat. Biol. 12, 241–248 (2011).
Google Scholar 
Lough, J. M. & Cantin, N. E. Perspectives on massive coral growth rates in a changing ocean. Biol. Bull. 226, 187–202 (2014).
Google Scholar 
Howells, E. J., Berkelmans, R., van Oppen, M. J. H., Willis, B. L. & Bay, L. K. Historical thermal regimes define limits to coral acclimatization. Ecology 94, 1078–1088 (2013).
Google Scholar 
Veron, J. E. N. Corals of the world. Townsville, Australia (Australian Institute of Marine Science, 2000).Foster, T., Short, J., Falter, J. L., Ross, C. & McCulloch, M. T. Reduced calcification in Western Australian corals during anomalously high summer water temperatures. J. Exp. Mar. Bio. Ecol. 461, 133–143 (2014).CAS 

Google Scholar 
Ross, C. L., Falter, J. L., Schoepf, V. & McCulloch, M. T. Perennial growth of hermatypic corals at Rottnest Island, Western Australia (32°S). PeerJ 3, e781 (2015).
Google Scholar 
McCulloch, M. T., Holcomb, M., Rankenburg, K. & Trotter, J. A. Rapid, high-precision measurements of boron isotopic compositions in marine carbonates. Rapid Commun. Mass Spectrom. RCM 28, 2704–2712 (2014).CAS 

Google Scholar 
Okai, T., Suzuki, A., Kawahata, H., Terashima, S. & Imai, N. Preparation of a new Geological Survey of Japan geochemical reference material: Coral JCp-1. Geostand. Newsl 26, 95–99 (2002).CAS 

Google Scholar 
Dickson, A. G. Thermodynamics of the dissociation of boric acid in synthetic seawater from 273.15 to 318.15 K. Deep Sea Res. Part A. Oceanogr. Res. Pap. 37, 755–766 (1990).CAS 

Google Scholar 
McCulloch, M. T. et al. Resilience of cold-water scleractinian corals to ocean acidification: Boron isotopic systematics of pH and saturation state up-regulation. Geochim. Cosmochim. Acta 87, 21–34 (2012).CAS 

Google Scholar 
Cornwall, C. E. & Hurd, C. L. Experimental design in ocean acidification research: problems and solutions. ICES J. Mar. Sci. 73, 572–581 (2015). More

Storage and displayAll collected datasets (directly downloadable as tabular files), the bibtex file with all references, all reports and all kinetic bioaccumulation metric estimates are publicly available on Zenodo17. An rmarkdown file18,19 was created to build the overview table with information collected from the name of the dataset and from the dataset itself (e.g., column headers, number of data, number of replicates), as well as from the bibtex file. The R package DT was additionally used20 to combine all collected information in a user-friendly manner including a convenient search tool, and the rmarkdown file was finally compiled19 in HTML format for display to the user in packs of 10 lines by default. In such a way, each new dataset added into the repository will compile the rmarkdown file automatically for update.In parallel, the database can also be accessed directly via http://lbbe-shiny.univ-lyon1.fr/mosaic-bioacc/data/database/TK_database.html, or from MOSAICbioacc clicking on the “More scientific TK data” button. An example of the output of the overview table is shown in Fig. 2, while the full table is provided in the supplementary information (Table S2). The collected raw TK data of the database consist in the time-course of several types of chemical substances bioaccumulated in various species via different exposure routes.Fig. 2Screenshot of the first page of the overview table of the database available from MOSAICbioacc.Full size imageDatasets overviewEach dataset is summarized by:

the file name (raw data directly downloadable by clicking on the file name, in text or CSV format),

the genus of the tested organism,

the category of the organism (e.g., aquatic, terrestrial, etc.),

the tested chemical substance,

the duration of the accumulation phase,

the tested exposure routes (e.g., water, sediment, food, pore water),

the total number of observations in the dataset (plus the number of replicate(s) in brackets),

the kinetic bioaccumulation metric median value with its 95% uncertainty interval,

the report which contains all the outputs from MOSAICbioacc (in PDF format),

the link to the reference or the source of the data,

some additional comments (e.g., lipid fraction, growth, biotransformation, if exposure was done for chemical mixtures or not, if total radioactivity was used or not, etc.).

A summary of all datasets is presented in Table 1. Genus were separated in 12 categories: aquatic invertebrates (n = 105), fish (n = 42), insects (n = 17), aquatic worms (n = 10), terrestrial worms (n = 16), seawater sponges (n = 2), seawater plants (n = 1), aquatic algae (n = 1), terrestrial invertebrates (n = 1), vertebrates other than fish (n = 4), marine invertebrates (n = 8), and heterotrichea (n = 4). The most represented genus in the database are Gammarus (aquatic invertebrate, n = 43) and Daphnia (aquatic invertebrate, n = 27), followed by Oncorhynchus (fish, n = 15), genus that are classically used in ecotoxicological experiments. Recommended genus by OECD guidelines for bioaccumulation tests are Eisenia and Enchytraeus for terrestrial organisms (OECD 317)21, and Tubifex or Lumbriculus for aquatic invertebrates exposed to sediment (OECD 315)22; some datasets for these specific species are available in the database (n = 24).Table 1 Summary of the collected TK datasets.Full size tableChemical substances were divided in 10 classes following at the best the nomenclature used in Standartox23: pesticides (n = 71), hydrocarbons (n = 37), metals (n = 20), nanoparticules (n = 23), polychlorobiphenyls (PCB, n = 22), flame retardants (brominated or chlorinated, n = 8), pharmaceutical products (n = 14), PFAS (n = 7), octyphenol (n = 2) and other (n = 7). Among all datasets, the majority of bioaccumulation tests were performed via spiked water (n = 137). Besides, 34 datasets account for biotransformation processes, considering from 1 to 8 metabolites.According to ECHA (2017)2, BCF below 1,000 means that the chemical substance is not bioaccumulative, whereas one ranging between 1,000 and 5,000 corresponds to a bioaccumulative chemical substance: low bioaccumulative if BCF ∈]1,000; 2,000]; mid-bioaccumulative if BCF ∈]2,000; 5,000]. If BCF is >5000, the chemical substance is classified as very bioaccumulative. These ranges are reported in Table 1, where BCF median estimates are >5000 for 25 datasets, indicating a very bioaccumulative capacity of the corresponding chemical substances for the corresponding genus. Concerning BSAF and BMF estimates, their value must be compared to threshold 1. A median BSAF estimate >1 indicates that the corresponding chemical substance can bioaccumulate from soil or sediment into organisms at the base of the non-aquatic food chain24,25; a median BMF estimate >1 indicates that the corresponding chemical substance can biomagnify in the trophic relationship under consideration26. In the database, 16 datasets in 36 led to BSAF >1, for genus Eisenia (n = 2), Enchytraeus (n = 6), Gallus (n = 1), Lumbriculus (n = 2), Metaphire (n = 2), Physa (n = 1), Radix (n = 2)), while 8 datasets in 38 led to BMF >1, for genus Gallus (n = 1), Oncorhynchus (n = 5) and Perca (n = 2). On an ecotoxicological point of view, the highest BCF estimates were obtained for genus Culex and Sialis exposed to chlorpyrifos due to a very low estimate of the elimination rate, for genus Gammarus and Calanus exposed to hydrocarbons, and several aquatic invertebrates exposed to pesticides, especially chlorpyrifos (n = 4), attesting to the potential high bioaccumulation capacity and high risk of toxicity associated with this chemical substance for aquatic organisms. Overall, aquatic invertebrates seem to be the most sensitive category of organisms in terms of bioaccumulation of chemical substances representing 20 in the 25 datasets with a BCF estimates >5000. More

Van Goethem MW, Pierneef R, Bezuidt OKI, Van De Peer Y, Cowan DA, Makhalanyane TP. A reservoir of ‘historical’ antibiotic resistance genes in remote pristine Antarctic soils. Microbiome. 2018;6:40.Article 

Google Scholar 
D’Costa VM, McGrann KM, Hughes DW, Wright GD. Sampling the antibiotic resistome. Science. 2006;311:374–7.Article 

Google Scholar 
Allen HK, Donato J, Wang HH, Cloud-Hansen KA, Davies J, Handelsman J. Call of the wild: antibiotic resistance genes in natural environments. Nat Rev Microbiol. 2010;8:251–9.CAS 
Article 

Google Scholar 
Martinez JL, Coque TM, Baquero F. What is a resistance gene? Ranking risk in resistomes. Nat Rev Microbiol. 2015;13:116–23.CAS 
Article 

Google Scholar 
Genilloud O. Actinomycetes: still a source of novel antibiotics. Nat Prod Rep. 2017;34:1203–32.CAS 
Article 

Google Scholar 
Ochoa-Hueso R, Plaza C, Moreno-Jimenez E, Delgado-Baquerizo M. Soil element coupling is driven by ecological context and atomic mass. Ecol Lett. 2021;24:319–26.Article 

Google Scholar 
Wardle DA, Walker LR, Bardgett RD. Ecosystem properties and forest decline in contrasting long-term chronosequences. Science. 2004;305:509–13.CAS 
Article 

Google Scholar 
Crews TE, Kitayama K, Fownes JH, Riley RH, Herbert DA, Mueller-Dombois D, et al. Changes in soil phosphorus fractions and ecosystem dynamics across a long chronosequence in Hawaii. Ecology. 1995;76:1407–24.Article 

Google Scholar 
Walker LR, Wardle DA, Bardgett RD, Clarkson BD. The use of chronosequences in studies of ecological succession and soil development. J Ecol. 2010;98:725–36.Article 

Google Scholar 
Delgado-Baquerizo M, Reich PB, Bardgett RD, Eldridge DJ, Lambers H, Wardle DA, et al. The influence of soil age on ecosystem structure and function across biomes. Nat Commun. 2020;11:4721.CAS 
Article 

Google Scholar 
Andersson DI, Hughes D. Antibiotic resistance and its cost: is it possible to reverse resistance? Nat Rev Microbiol. 2010;8:260–71.CAS 
Article 

Google Scholar 
Zhu YG, Johnson TA, Su JQ, Qiao M, Guo GX, Stedtfeld RD, et al. Diverse and abundant antibiotic resistance genes in Chinese swine farms. Proc Natl Acad Sci USA. 2013;110:3435–40.CAS 
Article 

Google Scholar 
Zhu YG, Zhao Y, Li B, Huang CL, Zhang SY, Yu S, et al. Continental-scale pollution of estuaries with antibiotic resistance genes. Nat Microbiol. 2017;2:16270.CAS 
Article 

Google Scholar 
Li J, Cao J, Zhu YG, Chen QL, Shen F, Wu Y, et al. Global survey of antibiotic resistance genes in air. Environ Sci Technol. 2018;52:10975–84.CAS 
Article 

Google Scholar 
Delgado-Baquerizo M, Bardgett RD, Vitousek PM, Maestre FT, Williams MA, Eldridge DJ, et al. Changes in belowground biodiversity during ecosystem development. Proc Natl Acad Sci USA. 2019;116:6891–6.CAS 
Article 

Google Scholar 
Ortiz-Álvarez R, Fierer N, de Los Ríos A, Casamayor EO, Barberán A. Consistent changes in the taxonomic structure and functional attributes of bacterial communities during primary succession. ISME J. 2018;12:1658–67.Article 

Google Scholar 
Shen J, Li Z-M, Hu H, Zeng J, Zhang L-M, He J, et al. Distribution and succession feature of antibiotic resistance genes along a soil development chronosequence in Urumqi No. 1 Glacier of China. Front Microbiol. 2019;10:1569.Article 

Google Scholar 
Drenovsky RE, Vo D, Graham KJ, Scow KM. Soil water content and organic carbon availability are major determinants of soil microbial community composition. Micro Ecol. 2004;48:424–30.CAS 
Article 

Google Scholar 
Bastida F, Eldridge DJ, Garcia C, Kenny Png G, Bardgett RD, Delgado-Baquerizo M. Soil microbial diversity-biomass relationships are driven by soil carbon content across global biomes. ISME J. 2021;15:2081–91.CAS 
Article 

Google Scholar  More

Discovery of closely related phage sequences with the conserved genetic context of bS21Multiple phage-related sequences with a conserved genomic context were detected from several freshwater metagenome-assembled datasets (see Methods). Genes for bS21, TerL, PVP, prohead core scaffolding, and protease protein (hereafter prohead protease for short), and MCP are encoded in the genomic region. BLASTp search of the TerL sequences against the ggKbase sequences (ggkbase.berkeley.edu) obtained a total of 47 unique scaffolds with the conserved genomic region (Supplementary Table 1). Two related phages were included as outgroups for comparative analyses. The corresponding samples were collected from freshwater lakes or reservoirs (one from a wastewater treatment plant), and all but three were from the oxic layer (see Methods for details).General features of manually curated genomesAll the 49 phage sequences were manually curated to fill scaffolding gaps and fix the assembly errors, and nine of them (including one outgroup phage) were curated to completion (circular and no gaps or local assembly errors) (Supplementary Table 1). A total of 14 related phage genomes from IMG/VR were also included for further analyses. The eight bS21-encoding complete genomes had genome lengths of 293–331 kbp, GC contents of 31.0–33.7% and encoded 350–413 protein-coding genes (coding density, 91.1–94.9%), with 5–25 (average 17) tRNA genes. No alternative coding signal (i.e., stop codon reassignment) was detected in any genome. In comparison, the outgroup complete genome has a size of 308 kbp (450 protein-coding genes, 6 tRNAs, 94.7% coding density) and GC content of 27.3%.Genomic context of bS21 in phagesGenomic context analyses for bS21 genes showed a highly conserved gene architecture across phage genomes in proximity to the region encoding bS21 (see Fig. 1a for example). Specifically, we found that bS21 was consistently located in between two hypothetical protein families (positions 1 and –1 in Fig. 1b and Supplementary Table 2), with core structural proteins—including the TerL, PVP, prohead protease, and MCP—generally located within five genes in both the upstream and downstream DNA. Other hypothetical proteins were also consistently found in this region, although their positions were more variable upstream (positions –4 through –10, Fig. 1b). Importantly, the bS21 gene was consistently encoded in the reverse strand relative to the conserved hypothetical and structural protein genes (Fig. 1a and Supplementary Fig. 1).Fig. 1: Genetic context of the genes encoding bS21 in the phage genomes.a Examples of genetic context of phage genomes with and without bS21. The annotation of protein-coding genes is the same as indicated in b by different colors. Those in white are genes not shown in subfigure (b). b Summary of genetic context of all phage genomes encoding bS21. The relative position of genes near the bS21 gene is shown, and the size of circles indicates the number of phages with a gene belonging to a given protein family (annotation shown on right) at that relative position. Only the 12 most frequent families are shown. The details of the genetic context are shown in Supplementary Fig. 1.Full size imagePhylogeny of bS21-encoding phagesPhylogenetic analyses based on TerL suggested the phages belonging to several groups, we thus assigned them to clades a–e (Fig. 2 and Supplementary Table 1). Most of the phages belong to clades c, d, and e, and they have a broader environmental distribution than clades a and b. Interestingly, we found that some phages within a single clade were from distant sampling sites. Closer inspection indicated they also shared large genomic fragments with high similarity (82–98% for nucleotide sequences; Supplementary Fig. 2). Comparative genome-wide analyses of the complete genomes from the same site but sampled at different time points showed sequence variations in some genes (Supplementary Fig. 3).Fig. 2: The phylogeny of bS21 phages based on the large terminal (TerL) protein sequences.Two closely related phages without bS21 encoded were included as outgroups (shown at the top of the tree). The genomes are assigned to five clades (a, b, c, d, and e) based on the topology of the phylogenetic tree. The numbers in the brackets following the scaffold names indicate the total counts of the same scaffold detected from the corresponding sampling sites. The genomes that were manually curated to completion (circular and no gap) are indicated by squares, and the genome sizes are shown in brackets.Full size imageTerL phylogeny, constructed using sequences from this study and NCBI RefSeq sequences, indicated the most closely related classified phages belong to Caudovirales of either the Myoviridae or Ackermannviridae (Supplementary Fig. 4). A phage baseplate assembly protein was encoded in most curated genomes. This is an important building block for members of Siphoviridae and Myoviridae [8], so we concluded that the bS21-encoding phages are myoviruses.Predicted bacterial hosts of bS21-encoding phagesTo predict host-phage relationships we first used CRISPR-Cas spacers targeting. While none of the 16.5k unique spacers from the relevant metagenomes targeted any of the curated phage genomes from the same sampling sites, a single cross-site target was detected. Specifically, MIW1_072018_0_1um_scaffold_78 was targeted by a spacer (24 nt and no mismatch) from a MIW2 Flavobacterium genome (affiliation: Bacteroidetes, Flavobacteria). We then predicted the bacterial hosts based on the bacterial taxonomic affiliations of the phage gene inventories as previously described [2] (Supplementary Table 3). The results indicated that all of the phages infect members of Bacteroidetes, which were detected in 43 out of 45 samples (Fig. 3 and Supplementary Table 4). The two metagenomic samples without Bacteroidetes identified were both collected via filtering through 0.2 μm and onto 0.1 μm pore size filters. Bacteroidetes were detected in both of the corresponding 0.2 μm fraction samples (Fig. 3).Fig. 3: The relative abundance of the Bacteroidetes classes in all the analyzed samples in this study.The microbial communities were profiled based on ribosomal protein S3 (rpS3) assigned to the Bacteroidetes classes. The sampling sites were indicated by colored names, and the filter sizes used during sampling are shown by circles. The three pairs of filter samples are indicated by colored stars.Full size imageWe profiled the co-detection of phage clades and Bacteroidetes classes to test for specific connections (Supplementary Fig. 5). However, this was uninformative because most samples contained more than one class. However, phages from clades a and b are unlikely to infect class Bacteroidia members, as they did not co-occur in any sample.Comparison of bacterial and phage-encoded bS21Phylogenetic analyses revealed that bS21 protein sequences from phages (this study) and the bacterial bS21 sequences (from the corresponding samples and NCBI RefSeq) clustered separately (Supplementary Fig. 6). The bacterial bS21 sequences that are most similar to phage bS21 were from Bacteroidetes, mostly from the Flavobacteriia class (Supplementary Table 5). We aligned and compared the Bacteroidetes and phage bS21 sequences and mapped the divergent and non-divergent residues to the model of the ribosome of Flavobacterium johnsoniae (Fig. 4a). Multiple divergent positions are located at the beginning of the bS21 sequences and four residues (Arg21, Phe23, Asp25, and Thr28) were significantly divergent (Fig. 4b).Fig. 4: Conservation and differences between phage and bacterial bS21.a Location of bS21 (blue) within the 16S rRNA (green) and the ASD (magenta) of the F. johnsoniae ribosome (PDB ID: 7JIL) [9]. bS21 is in the neck region of the 16S rRNA, interacting closely with the 3’ end of the 16S rRNA, where the ASD is located. The 16S rRNA is shown from the subunit interface direction. b Zebra2 divergency results from an alignment of phage and bacterial bS21 sequences mapped on F. johnsoniae bS21. Divergent positions between phage and bacterial bS21 are shown with red. c Zebra2 conservation results from the same alignment as in (b) mapped on F. johnsoniae bS21 with conserved residues shown in yellow. The stacking interaction between Tyr54 and Adenine 1534 is indicated. d The sequence logo and consensus sequences of phage and bacterial bS21 alignments and the corresponding position of Tyr54 in F. johnsoniae bS21 in the alignment are highlighted. The C-terminal parts are highlighted with gray backgrounds.Full size imageBacteroidetes usually lack the SD sequences. It was recently reported that the bS21 Tyr54 (numbering in F. johnsoniae) is an important residue for blocking the ASD in the 16S rRNA within the ribosome [9]. Our analyses predict that all the analyzed bacterial and phage bS21 in this study have an amino acid with an aromatic ring (often Tyr54 but in a few cases His54, and in one case Phe54) at the position of Tyr54 in F. johnsoniae (Fig. 4c, d and Supplementary Fig. 6). This conservation of the aromatic property in phage bS21 should ensure stacking interaction with Adenine 1534 (numbering in F. johnsoniae 16S) from the ASD. In that way, phage bS21 mimics Bacteroidetes bS21 in the region where it binds the ribosome but differs from it in the region where the mRNA would bind.In contrast, the C-terminal regions of both the bacterial and phage bS21 sets were highly divergent (Fig. 4d). However, the phage C-terminal regions are generally conserved within the clades defined based on TerL phylogeny (Fig. 2 and Supplementary Fig. 7).Metabolic potentials of bS21-encoding phagesFunctional annotation of the predicted protein-coding genes revealed that in addition to bS21, these phages carry other genes related to protein production and stability (Supplementary Table 6). Examples include protein folding chaperones and Clp protease, suggesting the importance of controlling the proteostasis network of the cell. Interestingly, we also identified many genes involved in sugar-related chemistry and polysaccharide biosynthesis. Many of these genes were predicted to perform chemical transformations related to the biosynthesis of lipopolysaccharide, a major component of the Gram-negative bacterial outer membrane. We interpret this as a potential mechanism to remodel the cell surface and prevent superinfection by competitor phages, a strategy common to the phage lysogenic cycle. These phages lack detectable integration machinery (no gene for integrase or resolvase was detected), suggesting the possibility of a non-integrative long-term infection state such as pseudolysogeny [10].Clustering analyses of 22 phages with a minimum genome size of 100 kbp (including the two outgroup genomes) based on the presence/absence of protein families indicated they shared a total of 16 protein families (Supplementary Fig. 8 and Supplementary Table 7). Phosphate starvation-inducible protein PhoH (“fam582”) was the only predicted protein detected in all 22 phages (excluding the shared predicted proteins in the conserved rpS21-encoding region described above). Other common protein families include those related to DNA replication (e.g., DNA primase/helicase, DNA polymerase, HNH endonuclease, thymidylate synthase (EC:2.1.1.45), deoxyuridine 5’-triphosphate nucleotidohydrolase (EC:3.6.1.23)), those associated with virion assembly (e.g., a phage tail sheath protein, phage baseplate assembly protein W), and those for other functions (e.g., chaperone ATPase, alpha-amylase, DegT/DnrJ/EryC1/StrS aminotransferase).Temporal and spatial distribution and activity of bS21-encoding phages in Lake RotseeTo reveal the spatial and temporal distribution of the bS21-encoding phages, we focused on the Lake Rotsee data and profiled phage occurrence based on the sequencing coverage in the metagenomic datasets. The Lake Rotsee samples were collected from the oxic (7 samples) and anoxic (3 samples) layers of the water column. The bS21-encoding phages were readily detected in oxic samples, especially in the under-ice samples when the whole water column was oxic (Fig. 5a).Fig. 5: The spatial and temporal distribution and activity of bS21 phages at Lake Rotsee.a The sequencing coverage of each phage genome in each metagenomic dataset is shown in the heatmaps. The phages are phylogenetically clustered based on their TerL protein sequences (bootstraps shown in numbers), the colored backgrounds are the same as shown in Fig. 2 for different clades. The sampling time points and depths are shown on the left, and the oxygen conditions are indicated by colored circles on the right. Two replicates were sequenced from the 15 m sample collected in 2018. b The percentage of mapped RNA reads to the phage genomes in the corresponding samples (rows labeled in (a)). The mapped RNA reads had a minimum similarity of 98% to the phage genomes. No RNA data were generated for the three samples collected on October 10, 2017. See the figure legend for each genome in the upper right, the circular genomes have names in bold font.Full size imageRotsee Lake RNA reads were mapped to the phage genomes curated from this site to reveal the transcriptional activities of bS21-encoding phages (Fig. 5b). In general, the phages were likely to be most transcriptionally active in the oxic water columns. A total of 736 genes were transcribed in at least one sample (Supplementary Table 8), those for MCP, an AAA ATPase, tail sheath protein, bS21, FKBP-type peptidyl-prolyl cis-trans isomerase, and a methyltransferase FkbM domain protein are among the top 100 most highly transcribed. The high transcriptional activities of MCP in five phages indicated they were in the late stage of replication at the time of sampling.The transcriptional behavior of phage bS21 genesTo seek evidence of a transcriptional relationship involving bS21 and other genes we focused on the three phages that were most active based on the transcriptional level of their 19 shared single-copy genes (Fig. 6a). bS21 had very similar (but slightly lower) transcriptional activities as a neighboring gene (hereafter, bS21_CN gene) encoded on the opposite strand. The bS21_CN gene encodes a hypothetical protein (protein family: fam498) and was not detected in the two outgroup phages without bS21 (Supplementary Table 6). Interestingly, a comparison of the phylogenies of bS21 and bS21_CN showed a very similar evolutionary pattern (Supplementary Fig. 9), likely suggesting their potential functional relationship in the bS21-encoding phages.Fig. 6: The transcription levels of bS21 and core structural protein genes.a The normalized transcriptional level (NTL) of shared single-copy protein families of three phages (indicated by arrows in Fig. 5b) with ≥1000 RNA reads mapped. Two families (including MCP) are listed on a different scale due to their much higher transcription levels. Refer to Fig. 5 for shape symbols that designate phage genomes and samples. b Examples of RNA mapping profiles indicating the co-transcription of some genes neighboring bS21. Hypothetical protein genes are shown in white.Full size imageInspection of the RNA reads mapping profiles indicated that the conserved region encoding bS21 and core structural proteins was not transcribed as an operon, whereas bS21 and bS21_CN, MCP and its upstream hypothetical protein gene, and prohead protease and its downstream hypothetical protein gene may each be transcribed together (Fig. 6b). Given the observed RNA expression patterns, we conclude that the phage-encoded bS21 genes were actively transcribed during late-stage replication, along with other core structural proteins.Genomic context of bS21 genes in published phage genomesTo determine whether the phage bS21 genes are generally co-located with those for core structural proteins in diverse phages, we profiled the genomic context of bS21 in 900 published bS21-encoding phages [2, 11] (Supplementary Table 9). Functional annotations were performed for the upstream and downstream ten genes of the bS21 genes using pVOG (Supplementary Table 10). Of the 20 most abundant pVOGs, 6 were related to core structural assembly (Fig. 7a), i.e., prohead protease (n = 310), MCP (n = 154), PVP (n = 120), TerL (n = 78), neck protein (n = 70), and a tail sheath protein (n = 29). A total of 388 genomes contained at least one of these genes within ten genes of bS21, and eight had all of these six core structural proteins in close proximity. Three pVOGs were related to DNA processing, i.e., an exonuclease (n = 37), an endonuclease (n = 32), DNA helicase (n = 30). Other pVOGs included Hsp20 heat shock protein (n = 127), two ATP-dependent CLP proteases (n = 50 and 47, respectively), and lysozyme (for lysis; n = 29). Interestingly, the prohead protease and the MCP pVOG genes are very close to the bS21 gene (generally 2–4 genes; Fig. 7b), as in the bS21-encoding phage genomes analyzed in this study (2–6 genes away; Fig. 1 and Supplementary Fig. 1).Fig. 7: Neighboring genes within 10 genes of bS21 in published bS21-encoding phage genomes.a The annotation and corresponding functional category (if assigned) of the 20 most commonly detected pVOG genes and their predicted functions are shown on the left, the total number of genomes with the gene are shown on the right. b The distribution of the distance of each gene to bS21 in the genomes. The position of genes next to bS21 (thus distance = 1) is highlighted using a red dashed line. The average distance of each gene to bS21 is shown on the left. c The predicted hosts of bS21-encoding phages with the top 4 most abundant genes detected within 10 genes of bS21. The total count of hosts is shown on the right.Full size imageWe respectively predicted the hosts of the bS21-encoding phages with the four most dominant pVOGs within ten genes of bS21 (Fig. 7c and Supplementary Table 11). The bacterial hosts are diverse and include Proteobacteria, Bacteroidetes, and Firmicutes. More

Co-culture dynamicsThis study was designed to enhance understanding of metabolite release and utilization across bloom stages in a simple community of phytoplankton and heterotrophic bacteria. The synthetic community was established with the diatom T. pseudonana and the bacterial strains R. pomeroyi DSS-3, Stenotrophomonas sp. SKA14, and P. dokdonensis MED152. These bacterial strains have high genetic similarity to isolates from phytoplankton cultures [14] and represent taxa that are common in phytoplankton blooms. Metabolites derived from the diatom were the sole source of carbon available for the bacteria, since no organic substrates were added. In addition, none of the bacteria can assimilate nitrate, and usable nitrogen was only available as diatom or bacterial extracellular products. The diatom had its highest specific growth rate of 1.65 d−1 during days 0–3, after which the rate declined (Fig. 1A). The total abundance of heterotrophic bacteria increased steadily but there was a succession that favored P. dokdonensis through day 15, and then R. pomeroyi by day 20; Stenotrophomonas disappeared from the model system by day 3 (Fig. 1B). The presence of bacteria did not affect the growth of diatoms based on comparisons of abundance in co-cultures versus axenic cultures at day 15 (Fig. 1A), as has been found previously [14, 26]. Inorganic nutrients were not limiting ( >5 μM at day 15; Table S1).Fig. 1: Time course of microbial abundances.A Cell abundance based on flow cytometric analysis for co-cultures (5 time points) and axenic cultures (day 15 only) (n = 3). The intensive sampling dates for the early and late bloom comparisons are marked with gray boxes. B Mean relative abundance of bacterial species is based on CFUs (n = 3). The day 0 samples were collected 8 h after inoculation.Full size imageDiatom endometabolite shiftsAnalyses focused on the day 3 (early bloom) and day 15 (late bloom) co-culture time points, for which a complete set of metabolomic and transcriptomic data were collected. Twenty-two diatom endometabolites that were annotated with high confidence by NMR analysis (Table S2) and quantified after normalizing to diatom cell number revealed that endometabolome composition differed substantially between bloom stages. Metabolites with significantly different cellular concentrations included nine compounds that were higher in intracellular concentration during the late bloom; these were arginine, valine, lysine, DHPS, glycerol-3-phosphate, phosphorylcholine, DMSP, glycine betaine, and homarine (T-test; P  More

Viral-like particle abundanceThe 10 sampling sites were equidistantly (average distance: 1.6 km) distributed between 1689 and 717 m above sea level in a 95.7 km2, pristine catchment and covered a flow-connected distance of 14.3 km (Fig. 1, Methods).Fig. 1: No evidence for a downstream accumulation of VLPs.Viral-like particles (VLP) were purified from 10 sites sampled during four seasons along an altitudinal gradient in an alpine stream (Vièze, Switzerland) (a). Neither VLP abundance (b) nor Virus-to-Prokaryote Ratios (VPR; (c)) showed pronounced spatial or temporal trends.Full size imageViral-like particle (VLP) counts normalized to areal coverage of the stream biofilm ranged from 2.8 × 109 to 3.4 × 1010 VLP m−2. On average, VLP abundance was highest in summer with 1.87 ± 0.75 × 1010 VLP m−2; however, there were no statistically significant seasonal differences in VLP abundance (repeated-measures ANOVA, F = 0.87, p = 0.47). VLP numbers did not exhibit a continuous spatial tendency, except during fall when VLP numbers increased significantly with downstream distance (r = 0.81, p 0.7 and/or pident >0.4). Indeed, 90 of the 203 putative viral depolymerases showed significant sequence similarity with 198 vOTU sequences (i.e., 6% of the overall vOTU diversity). We were able to obtain taxonomic classification for 80 of these 198 vOTUs, and found that all large Caudovirales families were represented (i.e., Myoviridae, n = 31, Siphoviridae, n = 17, Podoviridae, n = 15, Autographiviridae, n = 13, Ackermannviridae, n = 2, and Herelleviridae, n = 1). This suggests that depolymerase activity may be widespread among viruses infecting bacteria in stream biofilms. Although both the number of potential depolymerases included in our database and the number of classified vOTUs was limited, we observed that depolymerase-harboring Myoviridae vOTUs corresponded the expectation based on the overall relative abundance of Myoviridae, pointing toward the importance of dispersal for this important viral family. Siphoviridae, in contrast, were relatively underrepresented among depolymerase-harboring vOTUs. In combination with neutral model predictions, this may point towards a fundamental difference between Siphoviridae and Myoviridae in infecting stream biofilm bacteria. While Myoviridae may rather rely on efficiently spreading across distant biofilm patches facilitated by an ability to decompose the EPS matrix, many members of Siphoviridae seem to lack this ability.To investigate our second hypothesis, that lysogeny might be a successful viral life cycle strategy to spread locally within biofilm patches, we used BACPHLIP [36]. BACPHLIP predicted with high probability ( >75%) a lysogenic life cycle for 58 out of 256 complete viral genomes and a lytic life cycle for 177 viral genomes. For the remaining 21 complete viral genomes in our dataset, BACPHLIP did not result in sufficiently high prediction probability (i.e., More

Morton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, Vázquez-Baesa Y, et al. Balance trees reveal microbial niche differentiation. MSystems. 2017;2:e00162–16.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 
Salles JF, Poly F, Schmid B, Le Roux X. Community niche predicts the functioning of denitrifying bacterial assemblages. Ecology. 2009;90:3324–32.PubMed 

Google Scholar 
Ge X, Thorgersen MP, Poole FL, Deutschbauer AM, Chandonia J-M, Novichov PS, et al. Characterization of a metal-resistant bacillus strain with a high molybdate affinity ModA from contaminated sediments at the Oak Ridge Reservation. Front Microbiol. 2020;11:2543.
Google Scholar 
Wiedenbeck J, Cohan FM. Origins of bacterial diversity through horizontal genetic transfer and adaptation to new ecological niches. FEMS Microbiol Rev. 2011;35:957–76.CAS 
PubMed 

Google Scholar 
Moon J-W, Paradis CJ, Joyner DC, von Netzer F, Majumder EL, Dixon ER, et al. Characterization of subsurface media from locations up- and down-gradient of a uranium-contaminated aquifer. Chemosphere. 2020;255:126951.CAS 
PubMed 

Google Scholar 
Berkowitz B, Silliman SE, Dunn AM. Impact of the capillary fringe on local flow, chemical migration, and microbiology. Vadose Zo J. 2004;3:534–48.CAS 

Google Scholar 
Winter J, Ippisch O, Vogel H-J. Dynamic processes in capillary fringes. Vadose Zo J. 2015;14:1–2.Silliman SE, Berkowitz B, Simunek J, van Genuchten MT. Fluid flow and solute migration within the capillary fringe. Ground Water. 2002;40:76–84.CAS 
PubMed 

Google Scholar 
Haberer CM, Rolle M, Liu S, Cirpka OA, Prathwohl P. A high-resolution non-invasive approach to quantify oxygen transport across the capillary fringe and within the underlying groundwater. J Contam Hydrol. 2011;122:26–39.CAS 
PubMed 

Google Scholar 
Bouskill NJ, Conrad ME, Bill M, Brodie EL, Cheng Y, Hobson C, et al. Evidence for microbial mediated NO3− cycling within floodplain sediments during groundwater fluctuations. Front Earth Sci. 2019;7:189.
Google Scholar 
Rühle FA, von Netzer F, Lueders T, Stumpp C. Response of transport parameters and sediment microbiota to water table fluctuations in laboratory columns. Vadose Zo J. 2015;14:vzj2014.09.0116.Aigle A, Prosser JI, Gubry-Rangin C. The application of high-throughput sequencing technology to analysis of amoA phylogeny and environmental niche specialisation of terrestrial bacterial ammonia-oxidisers. Environ Microbiome. 2019;14:3.PubMed 
PubMed Central 

Google Scholar 
Almeida EL, Carrillo Rincón AF, Jackson SA, Dobson ADW. Comparative genomics of marine sponge-derived Streptomyces spp. isolates SM17 and SM18 with their closest terrestrial relatives provides novel insights into environmental niche adaptations and secondary metabolite biosynthesis potential. Front Microbiol. 2019;10:1713.PubMed 
PubMed Central 

Google Scholar 
Scheuerl T, Hopkins M, Nowell RW, Rivett DW, Barraclough TG, Bell T, et al. Bacterial adaptation is constrained in complex communities. Nat Commun. 2020;11:754.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bellanger X, Payot S, Leblond-Bourget N, Guédon G. Conjugative and mobilizable genomic islands in bacteria: evolution and diversity. FEMS Microbiol Rev. 2014;38:720–60.CAS 
PubMed 

Google Scholar 
Harrison E, Brockhurst MA. Plasmid-mediated horizontal gene transfer is a coevolutionary process. Trends Microbiol. 2012;20:262–7.CAS 
PubMed 

Google Scholar 
Wisniewski-Dyé F, Lozano L, Acosta-Cruz E, Borland S, Drogue B, Prigent-Combaret C, et al. Genome sequence of Azospirillum brasilense CBG497 and comparative analyses of Azospirillum core and accessory genomes provide insight into niche adaptation. Genes. 2012;3:576–602.Conn HJ, Dimmick I. Soil bacteria similar in morphology to Mycobacterium and Corynebacterium. J Bacteriol. 1947;54:291–303.CAS 
PubMed 
PubMed Central 

Google Scholar 
Boivin-Jahns V, Bianchi A, Ruimy R, Garcin J, Daumas S, Cristen R, et al. Comparison of phenotypical and molecular methods for the identification of bacterial strains isolated from a deep subsurface environment. Appl Environ Microbiol. 1995;61:3400–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Rusterholtz KJ, Mallory LM. Density, activity, and diversity of bacteria indigenous to a karstic aquifer. Microb Ecol. 1994;28:79–99.CAS 
PubMed 

Google Scholar 
Eschbach M, Möbitz H, Rompf A, Jahn D. Members of the genus Arthrobacter grow anaerobically using nitrate ammonification and fermentative processes: anaerobic adaptation of aerobic bacteria abundant in soil. FEMS Microbiol Lett. 2003;223:227–30.CAS 
PubMed 

Google Scholar 
Banerjee S, Palit R, Sengupta C, Standing D. Stress induced phosphate solubilization by ’Arthrobacter’ Sp. and ’Bacillus’ sp. isolated from tomato rhizosphere. Aust J Crop Sci. 2010;4:378–83.CAS 

Google Scholar 
Keddie RM, Collins D, Jones D. Genus Arthrobacter. In: Sneath PHA, Mair NS, Sharpe ME, Holt JG, editors. Bergey’s manual of systematic bacteriology. Vol 2. Williams and Wilkins: New York, NY. 1986. p. 1288–301.Crocker FH, Fredrickson JK, White DC, Ringelberg DB, Balkwill DL. Phylogenetic and physiological diversity of Arthrobacter strains isolated from unconsolidated subsurface sediments. Microbiology. 2000;146:1295–310.CAS 
PubMed 

Google Scholar 
Baran R, Brodie EL, Mayberry-Lewis J, Hummel E, Da Rocha UN, Chakraborty R, et al. Exometabolite niche partitioning among sympatric soil bacteria. Nat Commun. 2015;6:8289.CAS 
PubMed 

Google Scholar 
Wu X, Spencer S, Gushgari-Doyle S, Yee MO, Voriskova J, Li Y, et al. Culturing of “unculturable” subsurface microbes: natural organic carbon source fuels the growth of diverse and distinct bacteria from groundwater. Front Microbiol. 2020;11:3171.
Google Scholar 
Watson DB, Kostka JE, Fields MW, Jardine PM. The Oak Ridge Field Research Center conceptual model. NABIR F. Res. Center: Oak Ridge, TN; 2004.Moon J, Roh Y, Phelps TJ, Phillips DH, Watson DB, Kim Y-J, et al. Physicochemical and mineralogical characterization of soil–saprolite cores from a field research site, Tennessee. J Environ Qual. 2006;35:1731–41.CAS 
PubMed 

Google Scholar 
Wu X, Wu L, Liu Y, Zhang P, Li Q, Zhou J, et al. Microbial interactions with dissolved organic matter drive carbon dynamics and community succession. Front Microbiol. 2018;9:1234.PubMed 
PubMed Central 

Google Scholar 
Chakraborty R, Woo H, Dehal P, Walker R, Zemla M, Auer M, et al. Complete genome sequence of Pseudomonas stutzeri strain RCH2 isolated from a Hexavalent Chromium [Cr(VI)] contaminated site. Stand Genomic Sci. 2017;12:23.PubMed 
PubMed Central 

Google Scholar 
Guttenberger M, Hampp R. Ectomycorrhizins—symbiosis-specific or artifactual polypeptides from ectomycorrhizas? Planta. 1992;188:129–36.CAS 
PubMed 

Google Scholar 
Wick RR, Judd LM, Gorrie CL, Holt KE. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput Biol. 2017;13:e1005595.PubMed 
PubMed Central 

Google Scholar 
Hyatt D, Chen G, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.PubMed 
PubMed Central 

Google Scholar 
Cantalapiedra CP, Hernández-Plaza A, Letunic I, Bork P, Huerta-Cepas J. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol Biol Evol. 2021;38:5825–9.Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics. 2013;14:60.PubMed 
PubMed Central 

Google Scholar 
Meier-Kolthoff JP, Carbasse JS, Peinado-Olarte RL, Göker M. TYGS and LPSN: a database tandem for fast and reliable genome-based classification and nomenclature of prokaryotes. Nucleic Acids Res. 2022;50:D801–D807.CAS 
PubMed 

Google Scholar 
Price MN, Deutschbauer AM, Arkin AP. GapMind: automated annotation of amino acid biosynthesis. mSystems. 2020;5:e00291–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–W101.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bertelli C, Laird MR, Wiliams KP, Lau BY, Hoad G, Winsor GL, et al. IslandViewer 4: expanded prediction of genomic islands for larger-scale datasets. Nucleic Acids Res. 2017;45:W30–W35.CAS 
PubMed 
PubMed Central 

Google Scholar 
Trifinopoulos J, Nguyen L-T, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44:W232–W235.CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh K, Misawa K, Kuma K, Miyata T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002;30:3059–66.CAS 
PubMed 
PubMed Central 

Google Scholar 
Procter JB, Carstairs GM, Soares B, Mourão K, Ofoegbu TC, Barton D, et al. Alignment of biological sequences with Jalview. In: Katoh K Editor. Multiple sequence alignment. Springer, Humana Press: New York, NY. 2021. p. 203–24.Letunic I, Bork P. Interactive Tree of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6. https://doi.org/10.1093/nar/gkab301.Eren AM, Esen O, Quince C, Vines JH, Horrison HG, Sogin ML, et al. Anvi’o: an advanced analysis and visualization platform for ’omics data. PeerJ. 2015;3:e1319.PubMed 
PubMed Central 

Google Scholar 
Qiong W, Garrity GM, Tiedge JM, Cole JR. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.
Google Scholar 
Liao J, Guo X, Weller DL, Pollak S, Buckley DH, Wiedmann M, et al. Nationwide genomic atlas of soil-dwelling Listeria reveals effects of selection and population ecology on pangenome evolution. Nat Microbiol. 2021;6:1021–30.CAS 
PubMed 

Google Scholar 
Schwyn B, Neilands JB. Universal chemical assay for detection and determination of siderophores. Anal Biochem. 1987;160:47–56.CAS 
PubMed 

Google Scholar 
Pérez-Miranda S, Cabirol N, George-Téllez R, Zamudio-Rivera LS, Fernandez FJ. O-CAS, a fast and universal method for siderophore detection. J Microbiol Methods. 2007;70:127–31.PubMed 

Google Scholar 
Nyyssönen M, Tran HM, Karaoz U, Weihe C, Hadi MZ, Martiny JBH, et al. Coupled high-throughput functional screening and next generation sequencing for identification of plant polymer decomposing enzymes in metagenomic libraries. Front Microbiol. 2013;4:282PubMed 
PubMed Central 

Google Scholar 
Rousk J, Bååth E, Brookes PC, Lauber CL, Lozupone C, Gregory Caporaso J, et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 2010;4:1340–51.PubMed 

Google Scholar 
Oliveira PL, de, Duarte MCT, Ponezi AN, Durrant LR. Purification and partial characterization of manganese peroxidase from Bacillus pumilus and Paenibacillus sp. Braz J Microbiol. 2009;40:818–26.PubMed 
PubMed Central 

Google Scholar 
Varrot A, Yip VLY, Li Y, Rajan SS, Yang X, Anderson WF, et al. NAD+ and metal-ion dependent hydrolysis by family 4 glycosidases: structural insight into specificity for phospho-β-D-glucosides. J Mol Biol.2005;346:423–35.CAS 
PubMed 

Google Scholar 
Lambers H. Introduction: dryland salinity: a key environmental issue in southern Australia. Plant Soil. 2003;257:v–vii.Galinski EA, Trüper HG. Microbial behaviour in salt-stressed ecosystems. FEMS Microbiol Rev. 1994;15:95–108.CAS 

Google Scholar 
Korom SF. Natural denitrification in the saturated zone: a review. Water Resour Res. 1992;28:1657–68.CAS 

Google Scholar 
Niewerth H, Schuldes J, Parschat K, Kiefer P, Vorholt JA, Daniel R, et al. Complete genome sequence and metabolic potential of the quinaldine-degrading bacterium Arthrobacter sp. Rue61a. BMC Genomics. 2012;13:1–19.
Google Scholar 
See-Too W-S, Ee R, Lim Y-L, Convey P, Pearce DA, Mohidin TBM, et al. Complete genome of Arthrobacter alpinus strain R3. 8, bioremediation potential unraveled with genomic analysis. Stand Genomic Sci. 2017;12:1–7.
Google Scholar 
Bazhanov DP, Li C, Li H, Li J, Zhang X, Chen X, et al. Occurrence, diversity and community structure of culturable atrazine degraders in industrial and agricultural soils exposed to the herbicide in Shandong Province, PR China. BMC Microbiol. 2016;16:1–21.
Google Scholar 
Fan X, Nie MQ, Wang Y, Diwu ZJ, Liu L, Liu Y. Characteristics of the co-metabolism of 1-naphthol by Arthrobacter crystallopoietes NT16 and symbiotic Bacillus NG16. Acta Sci Circumstantiae. 2019;39:1482–8.CAS 

Google Scholar 
Nakatsu CH, Barabote R, Thompson S, Bruce D, Detter C, Brettin T, et al. Complete genome sequence of Arthrobacter sp. strain FB24. Stand Genomic Sci. 2013;9:106–16.PubMed 
PubMed Central 

Google Scholar 
Shimasaki T, Masuda S, Garrido-Oter R, Kawasaki T, Aoki Y, Shibata A, et al. Tobacco root endophytic Arthrobacter harbors genomic features enabling the catabolism of host-specific plant specialized metabolites. MBio. 2021;12:e00846–21.CAS 
PubMed Central 

Google Scholar 
Kumar R, Singh D, Swarnkar MK, Singh AK, Kumar S. Complete genome sequence of Arthrobacter alpinus ERGS4: 06, a yellow pigmented bacterium tolerant to cold and radiations isolated from Sikkim Himalaya. J Biotechnol. 2016;220:86–87.CAS 
PubMed 

Google Scholar 
Russell DA, Hatfull GF. Complete genome sequence of Arthrobacter sp. ATCC 21022, a host for bacteriophage discovery. Genome Announc. 2016;4:e00168–16.PubMed 
PubMed Central 

Google Scholar 
Fomenkov A, Akimov VN, Vasilyeva LV, Andersen DT, Vincze T, Roberts RJ, et al. Complete genome and methylome analysis of psychrotrophic bacterial isolates from Lake Untersee in Antarctica. Genome Announc. 2017;5:e01753–16.PubMed 
PubMed Central 

Google Scholar 
Hiraoka S, Machiyama A, Ijichi M, Inoue K, Oshima K, Hattori M, et al. Genomic and metagenomic analysis of microbes in a soil environment affected by the 2011 Great East Japan Earthquake tsunami. BMC Genomics. 2016;17:1–13.
Google Scholar 
Han S-R, Kim B, Jang JH, Park H, Oh T-J. Complete genome sequence of Arthrobacter sp. PAMC25564 and its comparative genome analysis for elucidating the role of CAZymes in cold adaptation. BMC Genomics. 2021;22:1–14.
Google Scholar 
Koh H-W, Kang M, Lee K, Lee E, Kim H, Park SJ. Arthrobacter dokdonellae sp. nov., isolated from a plant of the genus Campanula. J Microbiol. 2019;57:732–7.CAS 
PubMed 

Google Scholar 
Xu X, Xu M, Zhao Q, Xia Y, Chen C, Shen Z. Complete genome sequence of Cd (II)-resistant Arthrobacter sp. PGP41, a plant growth-promoting bacterium with potential in microbe-assisted phytoremediation. Curr Microbiol. 2018;75:1231–9.CAS 
PubMed 

Google Scholar 
Lee GLY, Ahmad SA, Yasid NA, Zulkharnain A, Convey P, Johari WLW, et al. Biodegradation of phenol by cold-adapted bacteria from Antarctic soils. Polar Biol. 2018;41:553–62.
Google Scholar 
Stockdale A, Davison W, Zhang H. Micro-scale biogeochemical heterogeneity in sediments: a review of available technology and observed evidence. Earth-Science Rev. 2009;92:81–97.CAS 

Google Scholar 
Whiting AK, Boldt YR, Hendrich MP, Wackett LP, Que L. Manganese (II)-dependent extradiol-cleaving catechol dioxygenase from Arthrobacter globiformis CM-2. Biochemistry. 1996;35:160–70.CAS 
PubMed 

Google Scholar 
Jeng W-Y, Wang M, Lin N, Lin C, Liaw Y, Cheng W, et al. Structural and functional analysis of three β-glucosidases from bacterium Clostridium cellulovorans, fungus Trichoderma reesei and termite Neotermes koshunensis. J Struct Biol. 2011;173:46–56.CAS 
PubMed 

Google Scholar 
Stevenson IL. Utilization of aromatic hydrocarbons by Arthrobacter spp. Can J Microbiol. 1967;13:205–11.CAS 
PubMed 

Google Scholar 
Dsouza M, Taylor MW, Turner SJ, Aislabie J. Genomic and phenotypic insights into the ecology of Arthrobacter from Antarctic soils. BMC Genomics. 2015;16:36.PubMed 
PubMed Central 

Google Scholar 
Taylor R, Cronin A, Pedley S, Barker J, Atkinson T. The implications of groundwater velocity variations on microbial transport and wellhead protection–review of field evidence. FEMS Microbiol Ecol. 2004;49:17–26.CAS 
PubMed 

Google Scholar 
Zhang X, Liu X, Yang F, Chen L. Pan-genome analysis links the hereditary variation of leptospirillum ferriphilum with its evolutionary adaptation. Front Microbiol. 2018;9:577.PubMed 
PubMed Central 

Google Scholar 
Broadbent JR, Neeno-Eckwall EC, Stahl B, Tandee K, Cai H, Morovic W, et al. Analysis of the Lactobacillus casei supragenome and its influence in species evolution and lifestyle adaptation. BMC Genomics. 2012;13:533.CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang Y, Sievert S. Pan-genome analyses identify lineage- and niche-specific markers of evolution and adaptation in Epsilonproteobacteria. Front Microbiol. 2014;5:110.PubMed 
PubMed Central 

Google Scholar 
Aminov R. Horizontal gene exchange in environmental microbiota. Front Microbiol. 2011;2:158.PubMed 
PubMed Central 

Google Scholar 
Kothari A, Wu Y, Chandonia J-M, Charrier M, Rajiv L, Rocha AM, et al. Large circular plasmids from groundwater plasmidomes span multiple incompatibility groups and are enriched in multimetal resistance genes. MBio. 2019;10:e02899–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Penn K, Jenkins C, Nett M, Udwary DW, Gontang EA, McGlinchey RP, et al. Genomic islands link secondary metabolism to functional adaptation in marine Actinobacteria. ISME J. 2009;3:1193–203.CAS 
PubMed 

Google Scholar 
Wu X, Kazakov AE, Gushgari-Doyle S, Yu X, Trotter V, Stuart RK, et al. Comparative genomics reveals insights into induction of violacein biosynthesis and adaptive evolution in Janthinobacterium. Microbiol Spectr. 2022;9:e01414–e01421.
Google Scholar 
Jonkheer EM, Brankovics B, Houwers IM, van der Wolf JM, Bonants PJM, Vreeburg RAM, et al. The Pectobacterium pangenome, with a focus on Pectobacterium brasiliense, shows a robust core and extensive exchange of genes from a shared gene pool. BMC Genomics. 2021;22:265.CAS 
PubMed 
PubMed Central 

Google Scholar 
Abdel-Glil MY, Rischer U, Steinhagen D, McCarthy U, Neubauer H, Sprague LD. Phylogenetic relatedness and genome structure of Yersinia ruckeri revealed by whole genome sequencing and a comparative analysis. Front Microbiol. 2021;12:782415.González-Dominici LI, Saati-Santamaría Z, García-Fraile P. Genome analysis and genomic comparison of the novel species Arthrobacter ipsi reveal its potential protective role in its bark beetle host. Microb Ecol. 2021;81:471–82.PubMed 

Google Scholar 
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–D596.CAS 
PubMed 

Google Scholar 
Herrick JB, Stuart-Keil KG, Ghiorse WC, Madsen EL. Natural horizontal transfer of a naphthalene dioxygenase gene between bacteria native to a coal tar-contaminated field site. Appl Environ Microbiol. 1997;63:2330–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Griebler C, Lueders T. Microbial biodiversity in groundwater ecosystems. Freshw Biol. 2009;54:649–77.
Google Scholar  More