Philippa Kaur

1.Griffiths, J. R. et al. The importance of benthic-pelagic coupling for marine ecosystem functioning in a changing world. Glob. Chang. Biol. 23, 2179–2196 (2017).ADS 
PubMed 

Google Scholar 
2.Graf, G., Bengtsson, W., Diesner, U., Schulz, R. & Theede, H. Benthic response to sedimentation of a spring phytoplankton bloom: Process and budget. Mar. Biol. 67, 201–208 (1982).
Google Scholar 
3.Campanyà-llovet, N., Snelgrove, P. V. R. & Parrish, C. C. Rethinking the importance of food quality in marine benthic food webs. Prog. Oceanogr. 156, 240–251 (2017).
Google Scholar 
4.Blomqvist, S. & Heiskanen, A.-S. The challenge of sedimentation in the Baltic Sea. In A Systems Analysis of the Baltic Sea. Ecological Studies (Analysis and Synthesis) Vol. 148 (eds Wulff, F. V. et al.) 211–227 (Springer, Berlin, 2001).
Google Scholar 
5.Elmgren, R. Trophic dynamics in the enclosed, brackish Baltic Sea. Rapp. P.-V. Réun. Cons. int. Explor. Mer. 183, 152–169 (1984).
Google Scholar 
6.Kahru, M., Elmgren, R., Di Lorenzo, E. & Savchuk, O. Unexplained interannual oscillations of cyanobacterial blooms in the Baltic Sea. Sci. Rep. 8, 6–10 (2018).ADS 

Google Scholar 
7.BACC II Author Team. Second Assessment of Climate Change for the Baltic Sea Basin. (SpringerOpen, 2015) https://doi.org/10.1007/978-3-319-16006-1.8.Spilling, K. & Lindström, M. Phytoplankton life cycle transformations lead to species-specific effects on sediment processes in the Baltic Sea. Cont. Shelf Res. 28, 2488–2495 (2008).ADS 

Google Scholar 
9.Suikkanen, S. et al. Climate change and eutrophication induced shifts in northern summer plankton communities. PLoS ONE 8, e66475 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
10.Tamelander, T., Spilling, K. & Winder, M. Organic matter export to the seafloor in the Baltic Sea: Drivers of change and future projections. Ambio 46, 842–851 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
11.Giere, O. Meiobenthology: The Microscopic Motile Fauna of Aquatic Sediments (Springer, 2009).
Google Scholar 
12.Schratzberger, M. & Ingels, J. Meiofauna matters: The roles of meiofauna in benthic ecosystems. J. Exp. Mar. Biol. Ecol. 502, 12–25 (2018).
Google Scholar 
13.Bonaglia, S., Nascimento, F. J. A., Bartoli, M., Klawonn, I. & Brüchert, V. Meiofauna increases bacterial denitrification in marine sediments. Nat. Commun. 5, 5133 (2014).ADS 
CAS 
PubMed 

Google Scholar 
14.Nascimento, F. J. A., Näslund, J. & Elmgren, R. Meiofauna enhances organic matter mineralization in soft sediment ecosystems. Limnol. Oceanogr. 57, 338–346 (2012).ADS 
CAS 

Google Scholar 
15.Nealson, K. H. Sediment bacteria: Who’s there, what are they doing, and what’s new?. Annu. Rev. Earth Planet Sci. 25, 403–434 (1997).ADS 
CAS 
PubMed 

Google Scholar 
16.Meyer-Reil, L.-A. Seasonal and spatial distribution of extracellular enzymatic activities and microbial incorporation of dissolved organic substrates in marine sediments. Appl. Environ. Microbiol. 53, 1748–1755 (1987).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Ólafsson, E. & Elmgren, R. Seasonal dynamics of sublittoral meiobenthos in relation to phytoplankton sedimentation in the Baltic Sea. Estuar. Coast. Shelf Sci. 45, 149–164 (1997).ADS 

Google Scholar 
18.Pfannkuche, O. Benthic response to the sedimentation of particulate organic matter at the BIOTRANS station, 47°N, 20°W. Deep. Res. Part II 40, 135–149 (1993).
Google Scholar 
19.Hoffmann, K., Hassenrück, C., Salman-Carvalho, V., Holtappels, M. & Bienhold, C. Response of bacterial communities to different detritus compositions in Arctic deep-sea sediments. Front. Microbiol. 8, 266 (2017).PubMed 
PubMed Central 

Google Scholar 
20.Stoeck, T., Kochems, R., Forster, D., Lejzerowicz, F. & Pawlowski, J. Metabarcoding of benthic ciliate communities shows high potential for environmental monitoring in salmon aquaculture. Ecol. Indic. 85, 153–164 (2018).
Google Scholar 
21.Rudnick, D. T. Time lags between the deposition and meiobenthic assimilation of phytodetritus. Mar. Ecol. Prog. Ser. 50, 231–240 (1989).ADS 

Google Scholar 
22.van der Heijden, L. H. et al. How do food sources drive meiofauna community structure in soft-bottom coastal food webs?. Mar. Biol. 165, 166 (2018).
Google Scholar 
23.Schratzberger, M., Forster, R. M., Goodsir, F. & Jennings, S. Nematode community dynamics over an annual production cycle in the central North Sea. Mar. Environ. Res. 66, 508–519 (2008).CAS 
PubMed 

Google Scholar 
24.Wieser, W. Die beziehung zwischen mundhöhlengestalt, ernährungsweise und vorkommen bei freilebenden marinen nematoden. Ark Zool 2, 439–484 (1953).
Google Scholar 
25.Moens, T., Van Gansbeke, D. & Vincx, M. Linking estuarine nematodes to their suspected food. A case study from the Westerschelde Estuary (south-west Netherlands). J. Mar. Biol. Assoc. UK 79, 1017–1027 (1999).
Google Scholar 
26.Nascimento, F. J. A., Karlson, A. M. L. & Elmgren, R. Settling blooms of filamentous cyanobacteria as food for meiofauna assemblages. Limnol. Oceanogr. 53, 2636–2643 (2008).ADS 

Google Scholar 
27.Nascimento, F. J. A., Karlson, A. M. L., Näslund, J. & Gorokhova, E. Settling cyanobacterial blooms do not improve growth conditions for soft bottom meiofauna. J. Exp. Mar. Biol. Ecol. 368, 138–146 (2009).
Google Scholar 
28.Groendahl, S. & Fink, P. High dietary quality of non-toxic cyanobacteria for a benthic grazer and its implications for the control of cyanobacterial biofilms. BMC Ecol. 17, 20 (2017).PubMed 
PubMed Central 

Google Scholar 
29.Broman, E. et al. Spring and late summer phytoplankton biomass impact on the coastal sediment microbial community structure. Microb. Ecol. 77, 288–303 (2019).CAS 
PubMed 

Google Scholar 
30.Fagervold, S. K. et al. River organic matter shapes microbial communities in the sediment of the Rhône prodelta. ISME J. 8, 2327–2338 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Reed, H. E. & Martiny, J. B. H. Microbial composition affects the functioning of estuarine sediments. ISME J. 7, 868–879 (2013).CAS 
PubMed 

Google Scholar 
32.Tuominen, L. et al. Nutrient fluxes, porewater profiles and denitrification in sediment influenced by algal sedimentation and bioturbation by Monoporeia affinis. Estuar. Coast. Shelf Sci. 49, 83–97 (1999).ADS 
CAS 

Google Scholar 
33.Zilius, M., De Wit, R. & Bartoli, M. Response of sedimentary processes to cyanobacteria loading. J. Limnol. 75, 236–247 (2016).
Google Scholar 
34.Blazewicz, S. J., Barnard, R. L., Daly, R. A. & Firestone, M. K. Evaluating rRNA as an indicator of microbial activity in environmental communities: Limitations and uses. ISME J. 7, 2061–2068 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Guardiola, M. et al. Spatio-temporal monitoring of deep-sea communities using metabarcoding of sediment DNA and RNA. PeerJ 4, e2807 (2016).PubMed 
PubMed Central 

Google Scholar 
36.Soto, E., Quiroga, E., Ganga, B. & Alarcón, G. Influence of organic matter inputs and grain size on soft-bottom macrobenthic biodiversity in the upwelling ecosystem of central Chile. Mar. Biodivers. 47, 433–450 (2017).
Google Scholar 
37.Broman, E., Bonaglia, S., Norkko, A., Creer, S. & Nascimento, F. J. A. High throughput shotgun sequencing of eRNA reveals taxonomic and derived functional shifts across a benthic productivity gradient. Mol. Ecol. 00, 1–17 (2020).CAS 

Google Scholar 
38.Ingels, J., Tchesunov, A. V. & Vanreusel, A. Meiofauna in the Gollum Channels and the Whittard Canyon, Celtic Margin—How local environmental conditions shape nematode structure and function. PLoS ONE 6, e20094 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Albert, S. et al. Influence of settling organic matter quantity and quality on benthic nitrogen cycling. Limnol. Oceanogr. 66, 1882–1895 (2021).ADS 
CAS 

Google Scholar 
40.Modig, H. & Ólafsson, E. Responses of Baltic benthic invertebrates to hypoxic events. J. Exp. Mar. Biol. Ecol. 229, 133–148 (1998).
Google Scholar 
41.Ankar, S. Annual dynamics of a Northern Baltic Soft Bottom. In Cyclic Phenomena in Marine Plants and Animals (eds Naylor, E. & Hartnoll, R. G.) 29–36 (Pergamon Press, 1979). https://doi.org/10.1016/b978-0-08-023217-1.50011-4.Chapter 

Google Scholar 
42.Karlson, A. M. L., Nascimento, F. J. A. & Elmgren, R. Incorporation and burial of carbon from settling cyanobacterial blooms by deposit-feeding macrofauna. Limnol. Oceanogr. 53, 2754–2758 (2008).ADS 

Google Scholar 
43.Hedberg, P., Albert, S., Nascimento, F. J. A. & Winder, M. Effects of changing phytoplankton species composition on carbon and nitrogen uptake in benthic invertebrates. Limnol. Oceanogr. 66, 469–480 (2021).ADS 
CAS 

Google Scholar 
44.Ólafsson, E., Modig, H. & van de Bund, W. J. Species specific uptake of radio-labelled phytodetritus by benthic meiofauna from the Baltic Sea. Mar. Ecol. Prog. Ser. 177, 63–72 (1999).ADS 

Google Scholar 
45.Guden, R. M., Vafeiadou, A., De Meester, N., Derycke, S. & Moens, T. Living apart-together: Microhabitat differentiation of cryptic nematode species in a saltmarsh habitat. PLoS ONE 13, e0204750 (2018).PubMed 
PubMed Central 

Google Scholar 
46.Rudnick, D. T. & Oviatt, C. A. Seasonal lags between organic carbon deposition and mineralization in marine sediments. J. Mar. Res. 44, 815–837 (1986).CAS 

Google Scholar 
47.Moens, T. et al. Diatom feeding across trophic guilds in tidal flat nematodes, and the importance of diatom cell size. J. Sea Res. 92, 125–133 (2014).ADS 

Google Scholar 
48.Schuelke, T., Pereira, T. J., Hardy, S. M. & Bik, H. M. Nematode-associated microbial taxa do not correlate with host phylogeny, geographic region or feeding morphology in marine sediment habitats. Mol. Ecol. 27, 1930–1951 (2018).PubMed 

Google Scholar 
49.Fenchel, T. & Jansson, B.-O. On the vertical distribution of the microfauna in the sediments of a brackish-water beach. Ophelia 3, 161–177 (1966).
Google Scholar 
50.Fenchel, T. The ecology of marine microbenthos II. The food of marine benthic ciliates. Ophelia 5, 73–121 (1968).
Google Scholar 
51.Shimeta, J., Starczak, V. R., Ashiru, O. M. & Zimmer, C. A. Influences of benthic boundary-layer flow on feeding rates of ciliates and flagellates at the sediment-water interface. Limnol. Oceanogr. 46, 1709–1719 (2001).ADS 

Google Scholar 
52.Nagata, T. Organic matter–bacteria interactions in seawater. In Microbial Ecology of the Oceans 2nd edn (ed. Kirchman, D. L.) 207–241 (Wiley, 2008).
Google Scholar 
53.De Mesel, I. et al. Top-down impact of bacterivorous nematodes on the bacterial community structure: A microcosm study. Environ. Microbiol. 6, 733–744 (2004).PubMed 

Google Scholar 
54.Landa, M. et al. Phylogenetic and structural response of heterotrophic bacteria to dissolved organic matter of different chemical composition in a continuous culture study. Environ. Microbiol. 16, 1668–1681 (2014).CAS 
PubMed 

Google Scholar 
55.Izabel-Shen, D., Albert, S., Winder, M., Farnelid, H. & Nascimento, F. J. A. Quality of phytoplankton deposition structures bacterial communities at the water-sediment interface. Mol. Ecol. 30, 3515–3529 (2021).CAS 
PubMed 

Google Scholar 
56.Bowen, J. L., Babbin, A. R., Kearns, P. J. & Ward, B. B. Connecting the dots: Linking nitrogen cycle gene expression to nitrogen fluxes in marine sediment mesocosms. Front. Microbiol. 5, 429 (2014).PubMed 
PubMed Central 

Google Scholar 
57.Broman, E. et al. Denitrification responses to increasing cadmium exposure in Baltic Sea sediments. Aquat. Toxicol. 217, 105328 (2019).CAS 
PubMed 

Google Scholar 
58.van der Loos, L. M. & Nijland, R. Biases in bulk: DNA metabarcoding of marine communities and the methodology involved. Mol. Ecol. 30, 3270–3288 (2021).PubMed 

Google Scholar 
59.Zinger, L. et al. DNA metabarcoding—Need for robust experimental designs to draw sound ecological conclusions. Mol. Ecol. 28, 1857–1862 (2019).PubMed 

Google Scholar 
60.Prokopowich, C. D., Gregory, T. R. & Crease, T. J. The correlation between rDNA copy number and genome size in eukaryotes. Genome 46, 48–50 (2003).CAS 

Google Scholar 
61.Nascimento, F. J. A., Lallias, D., Bik, H. M. & Creer, S. Sample size effects on the assessment of eukaryotic diversity and community structure in aquatic sediments using high-throughput sequencing. Sci. Rep. 8, 11737 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
62.Brannock, P. M. & Halanych, K. M. Meiofaunal community analysis by high-throughput sequencing: Comparison of extraction, quality filtering, and clustering methods. Mar. Genomics 23, 67–75 (2015).PubMed 

Google Scholar 
63.Wallenstein, M. D., Myrold, D. D., Firestone, M. & Voytek, M. Environmental controls on denitrifying communities and denitrification rates: Insights from molecular methods. Ecol. Appl. 16, 2143–2152 (2006).PubMed 

Google Scholar 
64.Höglander, H., Larsson, U. & Hajdu, S. Vertical distribution and settling of spring phytoplankton in the offshore NW Baltic Sea proper. Mar. Ecol. Prog. Ser. 283, 15–27 (2004).ADS 

Google Scholar 
65.Walsby, A. E. Gas vesicles. Annu. Rev. Plant Physiol. 26, 427–439 (1975).CAS 

Google Scholar 
66.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
67.Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, 36–42 (2013).
Google Scholar 
68.Huson, D. H. et al. MEGAN community edition—Interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957 (2016).PubMed 
PubMed Central 

Google Scholar 
69.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).
Google Scholar 
70.Murali, A., Bhargava, A. & Wright, E. S. IDTAXA: A novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 1–14 (2018).
Google Scholar 
71.Urban-Malinga, B., Warzocha, J. & Zalewski, M. Effects of the invasive polychaete Marenzelleria spp. on benthic processes and meiobenthos of a species-poor brackish system. J. Sea Res. 80, 25–34 (2013).ADS 

Google Scholar 
72.McMurdie, P. J. & Holmes, S. Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Oksanen, J. et al. Vegan: Community ecology package. version 2.5-7, 1–298 (2020).74.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).MATH 

Google Scholar 
75.Alberdi, A., Aizpurua, O., Gilbert, M. T. P. & Bohmann, K. Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol. Evol. 9, 134–147 (2017).
Google Scholar 
76.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).
Google Scholar  More

AffiliationsDepartment of Integrative Biology, Oregon State University, Corvallis, OR, USABenjamin D. Dalziel & Mark NovakDepartment of Mathematics, Oregon State University, Corvallis, OR, USABenjamin D. DalzielCollege of Earth, Ocean and Atmospheric Sciences, Oregon State University, Corvallis, OR, USAJames R. WatsonDepartment of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USAStephen P. EllnerAuthorsBenjamin D. DalzielMark NovakJames R. WatsonStephen P. EllnerCorresponding authorCorrespondence to
Benjamin D. Dalziel. More

1.Manel, S., Schwartz, M. K., Luikart, G. & Taberlet, P. Landscape genetics: Combining landscape ecology and population genetics. Trends Ecol. Evol. 18, 189–197. https://doi.org/10.1016/S0169-5347(03)00008-9 (2003).Article 

Google Scholar 
2.Storfer, A., Murphy, M. A., Spear, S. F., Holderegger, R. & Waits, L. P. Landscape genetics: Where are we now?. Mol. Ecol. 19, 3496–3514. https://doi.org/10.1111/j.1365-294X.2010.04691.x (2010).Article 
PubMed 

Google Scholar 
3.Alp, M., Keller, I., Westram, A. M. & Robinson, C. T. How river structure and biological traits influence gene flow: A population genetic study of two stream invertebrates with differing dispersal abilities. Freshw. Biol. 57, 969–981. https://doi.org/10.1111/j.1365-2427.2012.02758.x (2012).Article 

Google Scholar 
4.Mamos, T., Wattier, R., Majda, A., Sket, B. & Grabowski, M. Morphological vs. molecular delineation of taxa across montane regions in Europe: The case study of Gammarus balcanicus Schäferna, 1922 (Crustacea: Amphipoda). J. Zool. Syst. Evol. Res. 52, 237–248. https://doi.org/10.1111/jzs.12062 (2014).Article 

Google Scholar 
5.Mamos, T., Wattier, R., Burzýnski, A. & Grabowski, M. The legacy of a vanished sea: A high level of diversification within a European freshwater amphipod species complex driven by 15 My of Paratethys regression. Mol. Ecol. 25, 795–810. https://doi.org/10.1111/mec.13499 (2016).Article 
PubMed 

Google Scholar 
6.Grabowski, M., Mamos, T., Bacela-Spychalska, K., Rewicz, T. & Wattier, R. A. Neogene paleogeography provides context for understanding the origin and spatial distribution of cryptic diversity in a widespread balkan freshwater amphipod. PeerJ 5, e3016. https://doi.org/10.7717/peerj.3016 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Copilaş-Ciocianu, D., Zimţa, A. A., Grabowski, M. & Petrusek, A. Survival in northern microrefugia in an endemic Carpathian gammarid (Crustacea: Amphipoda). Zool. Scr. 47, 357–372. https://doi.org/10.1111/zsc.12285 (2018).Article 

Google Scholar 
8.Copilaș-Ciocianu, D., Zimța, A. & Petrusek, A. Integrative taxonomy reveals a new Gammarus species (Crustacea, Amphipoda) surviving in a previously unknown southeast European glacial refugium. J. Zool. Syst. Evol. Res. 57, 272–297. https://doi.org/10.1111/jzs.12248 (2019).Article 

Google Scholar 
9.Wattier, R. et al. Continental-scale patterns of hyper-cryptic diversity within the freshwater model taxon Gammarus fossarum (Crustacea, Amphipoda). Sci. Rep. 10, 16536. https://doi.org/10.1111/j.1365-2699.2012.02793.x (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Neumann, K. et al. Genetic spatial structure of European common hamsters (Cricetus cricetus)—A result of repeated range expansion and demographic bottlenecks. Mol. Ecol. 14, 1473–1483. https://doi.org/10.1111/j.1365-294X.2005.02519.x (2005).CAS 
Article 
PubMed 

Google Scholar 
11.Kotlík, P. et al. A northern glacial refugium for bank voles (Clethrionomys glareolus). PNAS 103, 14860–14864. https://doi.org/10.1073/pnas.0603237103 (2006).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Theissinger, K. et al. Glacial survival and post-glacial recolonization of an arctic-alpine freshwater insect (Arcynopteryx dichroa, Plecoptera, Perlodidae) in Europe. J. Biogeogr. 40, 236–248. https://doi.org/10.1111/j.1365-2699.2012.02793.x (2012).Article 

Google Scholar 
13.Vörös, J., Mikulíček, P., Major, Á., Recuero, E. & Arntzen, J. W. Phylogeographic analysis reveals northern refugia for the riverine amphibian Triturus dobrogicus (Caudata: Salamandridae). Biol. J. Linn. Soc. 119, 974–991. https://doi.org/10.1111/bij.12866 (2016).Article 

Google Scholar 
14.Copilaș-Ciocianu, D., Rutová, T., Pařil, P. & Petrusek, A. Epigean gammarids survived millions of years of severe climatic fluctuations in high latitude refugia throughout the Western Carpathians. Mol. Phylogenet. Evol. 112, 218–229. https://doi.org/10.1016/j.ympev.2017.04.027 (2017).Article 

Google Scholar 
15.Juřičková, L. et al. Early postglacial recolonisation, refugial dynamics the origin of a major biodiversity hotspot. A case study from the Malá Fatra mountains, Western Carpathians, Slovakia. Holocene 28(4), 583–594. https://doi.org/10.1177/0959683617735592 (2017).ADS 
Article 

Google Scholar 
16.Mamos, T., Jażdżewski, K., Čiamporová-Zaťovičová, Z., Čiampor, F. & Grabowski, M. Fuzzy species borders of glacial survivalists in the Carpathian biodiversity hotspot revealed using a multimarker approach. Sci. Rep. 11, 21629. https://doi.org/10.1038/s41598-021-00320-8 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
17.Pinceel, J., Jordaens, K., Pfenninger, M. & Backeljau, T. Rangewide phylogeography of a terrestrial slug in Europe: Evidence for Alpine refugia rapid colonization after the Pleistocene glaciations. Mol. Ecol. 14, 1133–1150. https://doi.org/10.1111/j.1365-294X.2005.02479.x (2005).CAS 
Article 
PubMed 

Google Scholar 
18.Magri, D. et al. A new scenario for the Quaternary history of European beech populations: Palaeobotanical evidence genetic consequences. New Phytol. 171, 199–221. https://doi.org/10.1111/j.1469-8137.2006.01740.x (2006).CAS 
Article 
PubMed 

Google Scholar 
19.Jamrichová, E., Potůčková, A. & Horsák, M. Landscape history, calcareous fen development historical events in the Slovak Eastern Carpathians. Veg. Hist. Archaeobot. 23, 497–513. https://doi.org/10.1007/s00334-013-0416-0 (2014).Article 

Google Scholar 
20.Jamrichová, E., Petr, L. & Jiménez-Alfaro, B. Pollen-inferred millennial changes in landscape patterns at a major biogeographical interface within Europe. J. Biogeogr. 44, 2386–2397 (2017).Article 

Google Scholar 
21.Wielstra, B., Babik, W. & Arntzen, J. W. The crested newt Triturus cristatus recolonized temperate Eurasia from an extra-Mediterranean glacial refugium. Biol. J. Linn. Soc. 114, 574–587. https://doi.org/10.1111/bij.12446 (2015).Article 

Google Scholar 
22.Mráz, P. & Ronikier, M. Biogeography of the Carpathians: Evolutionary spatial facets of biodiversity. Biol. J. Linn. Soc. 119, 528–559. https://doi.org/10.1111/bij.12918 (2016).Article 

Google Scholar 
23.Pauls, S. U., Lumbsch, H. A. T. & Haase, P. Phylogeography of the montane caddisfly Drusus discolor: Evidence for multiple refugia and periglacial survival. Mol. Ecol. 15(8), 2153–2169. https://doi.org/10.1111/j.1365-294X.2006.02916.x (2006).CAS 
Article 
PubMed 

Google Scholar 
24.Pauls, S. U., Theissinger, K., Ujvarosi, L., Bálint, M. & Haase, P. Patterns of population structure in two closely related, partially sympatric caddisflies in eastern Europe: Historic introgression, limited dispersal, and cryptic diversity. J. N. Am. Benthol. Soc. 28, 517–536. https://doi.org/10.1899/08-100.1 (2009).Article 

Google Scholar 
25.Lehrian, S., Pauls, S. U. & Haase, P. Contrasting patterns of population structure in the montane caddisflies Hydropsyche tenuis and Drusus discolor in the Central European highlands. Freshw. Biol. 54, 283–295. https://doi.org/10.1111/j.1365-2427.2008.02107.x (2009).Article 

Google Scholar 
26.Lande, R. & Shannon, S. The role of genetic variation in adaptation and population persistence in a changing environment. Evolution 216, 434–437 (1996).Article 

Google Scholar 
27.Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to Conservation Genetics (Cambridge University Press, 2002).Book 

Google Scholar 
28.Robert, S. & Curtean-Bănăduc, A. Aspects concerning Târnava Mare and Târnava Mică rivers (Transylvania, Romania) caddisfly (Insecta, Trichoptera) larvae communities. Transylv. Rev. Syst. Ecol. Res. 2, 89–98 (2005).
Google Scholar 
29.Bálint, M., Ujvárosi, L., Dénes, A. L. & Octavian, P. European phylogeography of Rhyacophila tristis Pictet (Trichoptera: Rhyacophilidae): Preliminary results. Zoosymposia 5, 11–18. https://doi.org/10.11646/zoosymposia.5.1.1 (2011).Article 

Google Scholar 
30.Bielik, M. Geophysical features of the Slovak Western Carpathians. Geol. Q. 43, 251–262. https://doi.org/10.1016/j.quascirev.2008.08.019 (1999).Article 

Google Scholar 
31.Céréghino, R., Cugny, P. & Lavandier, P. Influence of intermittent hydropeaking on the longitudinal zonation patterns of benthic invertebrates in a mountain stream. Int. Rev. Hydrobiol. 87, 47–60. https://doi.org/10.1002/1522-2632(200201)87:1%3c47::AID-IROH47%3e3.0.CO;2-9 (2002).Article 

Google Scholar 
32.Sworobowicz, L., Mamos, T., Grabowski, M. & Wysocka, A. Lasting through the ice age: The role of the proglacial refugia in the maintenance of genetic diversity, population growth, and high dispersal rate in a widespread freshwater crustacean. Freshw. Biol. 65, 1028–1046. https://doi.org/10.1111/fwb.13487 (2020).CAS 
Article 

Google Scholar 
33.Rudolph, K., Coleman, C. O., Mamos, T. & Grabowski, M. Description and post-glacial demography of Gammarus jazdzewskii sp. nov. (Crustacea: Amphipoda) from Central Europe. Syst. Biodivers. 16, 587–603. https://doi.org/10.1080/14772000.2018.1470118 (2018).Article 

Google Scholar 
34.Bozáňová, J., Čiamporová-Zaťovičová, Z., Čiampor, F. Jr., Mamos, T. & Grabowski, M. The tale of springs and streams: How different aquatic ecosystems impacted the mtDNA population structure of two riffle beetles in the Western Carpathians. PeerJ 8, e10039. https://doi.org/10.7717/peerj.10039 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
35.Jedlička, L., Kúdela, M., Szemes, T. & Celec, P. Population genetic structure of Simulium degrangei (Diptera: Simuliidae) from Western Carpathians. Biologia 67, 777–787. https://doi.org/10.2478/s11756-012-0057-2 (2012).Article 

Google Scholar 
36.Hughes, J. M., Bunn, S. E., Hurwood, D. A. & Cleary, C. Dispersal and recruitment of Tasiagma ciliata (Trichoptera: Tasmiidae) in rainforest streams, south-east Queensland, Australia. Freshw. Biol. 41, 1–10 (1998).
Google Scholar 
37.Finn, D. S., Theobald, D. M., Black, W. C. & Poff, N. L. Spatial population genetic structure and limited dispersal in a Rocky Mountain alpine stream insect. Mol. Ecol. 15, 3553–3566 (2006).CAS 
Article 

Google Scholar 
38.Vuataz, L., Rutschmann, S., Monaghan, M. T. & Sartori, M. Molecular phylogeny and timing of diversification in Alpine Rhithrogena (Ephemeroptera: Heptageniidae). BMC Evol. Biol. 16, 194. https://doi.org/10.1186/s12862-016-0758-1 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Schiffers, K., Bourne, E. C., Lavergne, S., Thuiller, W. & Travis, J. M. J. Limited evolutionary rescue of locally adapted populations facing climate change. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120083. https://doi.org/10.1098/rstb.2012.0083 (2013).Article 

Google Scholar 
40.Spielman, D., Brook, B. & Frankham, R. Most species are not driven to extinction before genetic factors impact them. Proc. Natl. Acad. Sci. 101, 15261–15264 (2004).ADS 
CAS 
Article 

Google Scholar 
41.Frankham, R. Genetics and extinction. Biol. Conserv. 126, 131–140 (2005).Article 

Google Scholar 
42.Bunn, S. E. & Hughes, J. M. Dispersal and recruitment in streams: Evidence from genetic studies. J. N. Am. Benthol. Soc. 16, 338–346. https://doi.org/10.2307/1468022 (1997).Article 

Google Scholar 
43.Barron, E. & Pollard, D. High-resolution climate simulations of oxygen isotope stage 3 in Europe. Quat. Res. 28, 296–309. https://doi.org/10.1006/qres.2002.2374 (2002).Article 

Google Scholar 
44.Bennet, K. & Provan, J. What do we mean by “refugia”? Quat. Sci. Rev. 27, 2449–2455 (2008).ADS 
Article 

Google Scholar 
45.Kondracki, J. Karpaty. Wydanie drugie i poprawione [The Carpathians. Ed. 2].—Wydawnictwa Szkolne i Pedagogiczne, Warszawa (1989).46.Grecula, P. (ed.). Geological evolution of the Western Carpathians. Monograph: Mineralia Slovaca (1997).47.Lukniš, M. The course of the last glaciation of the Western Carpathians in the relation to the Alps, to the glaciation of northern Europe, and to the division of the central European Wurm into periods. Geografický Časopis 16, 127–142 (1964).
Google Scholar 
48.Lindner, L., Dzierzek, J., Marciniak, B. & Nitychoruk, J. Outline of Quaternary glaciations in the Tatra Mts.: Their development, age and limits. Geol. Q. 47, 269–280 (2003).
Google Scholar 
49.Frost, S. Evaluation of kicking technique for sampling stream bottom fauna. Can. J. Zool. 49, 161–173. https://doi.org/10.1016/j.biocon.2005.05.002 (1971).Article 

Google Scholar 
50.Sedlák, E. Řád Chrostíci—Trichoptera. In Klíč vodních larev hmyzu (ed. Rozkošný, R.) 163–220 (ČSAV, 1980).
Google Scholar 
51.Waringer, J. & Graf, W. Atlas of Central European Trichoptera Larvae: Atlas der Mitteleuropäischen Köcherfliegenlarven (Erik Mauch, 2011).
Google Scholar 
52.Casquet, J., Thebaud, C. & Gillespie, R. G. Chelex without boiling, a rapid and easy technique to obtain stable amplifiable DNA from small amounts of ethanol-stored spiders. Mol. Ecol. Resour. 12(1), 136–141. https://doi.org/10.1111/j.1755-0998.2011.03073.x (2012).CAS 
Article 
PubMed 

Google Scholar 
53.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3(5), 294–299 (1994).CAS 
PubMed 

Google Scholar 
54.Bálint, M., Botoşaneanu, L., Ujvárosi, L. & Popescu, O. Taxonomic revision of Rhyacophila aquitanica (Trichoptera: Rhyacophilidae), based on molecular and morphological evidence and change of taxon status of Rhyacophila aquitanica ssp. carpathica to Rhyacophila carpathica stat. n. Zootaxa 2148, 39–48. https://doi.org/10.11646/zootaxa.2148.1.3 (2009).Article 

Google Scholar 
55.Simon, C. et al. Evolution, weighting and phylogenetic utility of mitochondrial gene sequences and a compilation of conserved polymerase chain reaction primers. Ann. Entomol. Soc. Am. 87, 651–701 (1994).CAS 
Article 

Google Scholar 
56.Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797. https://doi.org/10.1093/nar/gkh340 (2004).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. 33, 1870–1874. https://doi.org/10.1093/molbev/msw054 (2016).CAS 
Article 

Google Scholar 
58.Ratnasingham, S. & Hebert, P. D. N. The barcode of life data system. Mol. Ecol. Notes 7, 355–364. https://doi.org/10.1111/j.1471-8286.2007.01678.x (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
59.Puillandre, N., Brouillet, S. & Achaz, G. ASAP: Assemble species by automatic partitioning. Mol. Ecol. Resour. 21(2), 609–620. https://doi.org/10.1111/1755-0998.13281 (2021).Article 
PubMed 

Google Scholar 
60.Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25(11), 1451–1452. https://doi.org/10.1093/bioinformatics/btp187 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
61.Leigh, J. W. & Bryant, D. POPART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 6, 1110–1116. https://doi.org/10.1111/2041-210X.12410 (2015).Article 

Google Scholar 
62.Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15(4), e1006650. https://doi.org/10.1371/journal.pcbi.1006650 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
63.Bouckaert, R. R. & Drummond, A. J. bModelTest: Bayesian phylogenetic site model averaging and model comparison. BMC Evol. Biol. 17(42), 1–11. https://doi.org/10.1186/s12862-017-0890-6 (2017).Article 

Google Scholar 
64.Brower, A. V. Z. Rapid morphological radiation and convergence among races of the butterfly Heliconius erato inferred from patterns of mitochondrial DNA evolution. PNAS 91(14), 6491–6495. https://doi.org/10.1073/pnas.91.14.6491 (1994).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
65.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarisation in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67(5), 901–904. https://doi.org/10.1093/sysbio/syy032 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
66.Miller, M. P. Alleles In Space (AIS): Computer software for the joint analysis of interindividual spatial and genetic information. J. Hered. 96, 722–724. https://doi.org/10.1093/jhered/esi119 (2005).CAS 
Article 
PubMed 

Google Scholar 
67.Mantel, N. The detection of disease clustering and a generalized regression approach. Cancer Res. 27, 209–220 (1967).CAS 
PubMed 

Google Scholar 
68.Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x (2010).Article 
PubMed 

Google Scholar 
69.Tajima, F. The effect of change in population size on DNA polymorphism. Genetics 123(3), 597–601 (1989).CAS 
Article 

Google Scholar 
70.Fu, Y. X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147(2), 915–925 (1997).CAS 
Article 

Google Scholar 
71.Fu, Y. X. & Li, W. H. Statistical tests of neutrality of mutations. Genetics 14, 693–709 (1993).Article 

Google Scholar  More

Table S1 lists the data used in this paper, the instruments that it is based on, the data repositories, and in which figures the data are used.Global data setsBathymetryBathymetric data was taken from the International Bathymetric Chart of the Arctic Ocean (IBCAO 30 sec V3)64 available at https://www.ngdc.noaa.gov/mgg/bathymetry/arctic/grids/version3_0/.Sea ice concentrationWe use data derived from the Advanced Microwave Scanning Radiometer sensor AMSR-2 for the years 2013–18 processed in accordance with65 and downloaded from https://seaice.uni-bremen.de/sea-ice-concentration-amsr-eamsr2/66. At each grid point the sum of days during all April/May/June of 2013–2018 when the sea ice concentration at the grid point was >20% was divided by the total number of days with data in those months to obtain the percentage of days with ice concentration >20% (Fig. 1). For separate 7-day periods in April/May/June 2017 and 2018 the mean ice concentration over those 7 days was calculated and the 20% contour of this mean was plotted separately for each of those 7-day periods. For each mooring and each day, the ice concentration at the grid cell closest to the mooring was calculated (Fig. 4a and S1a), and if the ice concentration at the mooring was below 20%, the shortest distance to grid cells where the ice concentration exceeded 20% was calculated (Fig. 4a and S1a). If the ice concentration at the mooring exceeded 20%, the shortest distance to grid cells where the ice concentration was below 20% was calculated and the distance was defined as negative.Sea ice velocity and sea ice area exportIce area flux estimates in Fig. 2a are calculated using CERSAT (Center for Satellite Exploitation and Research, France) motion estimates together with CERSAT ice concentration information67. Fluxes are estimated along a zonal gate positioned at 82°N between 12°W and 20°E and a meridional gate at 20°E between 80.5°N and 82°N (Fig. 1) for the period 1994–2020 (January–May). The ice area flux at the gate is the integral of the product between the meridional and zonal ice drift and ice concentration. For a more detailed description we refer to ref. 68. Arctic-wide sea ice velocity anomalies (Fig. 2b, c) were computed from the OSI-405-c motion product provided by the Ocean and Sea Ice Satellite Application 635 Facility (OSISAF)69.Satellite chlorophyllSurface chlorophyll concentrations measured with the Sentinel 3 A OLCI (Ocean and Land Colour Instrument) were downloaded from https://earth.esa.int/web/sentinel/sentinel-data-access. The 8-day satellite data were averaged for the time series over grid points within boxes of 60 km by 60 km around the moorings.Atmospheric reanalysisERA-Interim reanalysis70 data at the surface on a 0.25° latitude by 0.25° longitude grid at 12 hourly resolution was downloaded from https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=sfc/. Incoming shortwave radiation (ssr) and outgoing longwave radiation (str), sensible heat flux (sshf), and latent heat flux (slhf) were extracted and averaged to daily values.Physical numerical modelsFESOMIn this study, we used model data from the Finite-Element Sea ice-Ocean Model (FESOM) version 1.471. FESOM is a sea ice-ocean model that solves the hydrostatic primitive equations for the ocean and comprises a finite element sea ice component. It uses triangular surface meshes for spatial discretization, allowing for a refined mesh in regions of interest, while keeping a coarser mesh elsewhere. In the model configuration used here, a mesh resolution of nominally 1° was applied in the global oceans. The mesh was refined to 25 km north of 40°N, and to 4.5 km in the Nordic Seas and Arctic Ocean. In the wider Fram Strait (20°W-20°E/76°N-82°30′N), the mesh was further refined to 1 km. In this region, the simulation can be considered as eddy-resolving, as the local internal Rossby radius of deformation is about 2–6 km72,73. In the vertical, the model used 47 z-levels with a resolution of 10 m in the upper 100 m, and coarser resolution with depth (with a resolution of ~100 m at 800 m depth). For bottom topography, the RTopo-2 data set was used74. The model simulation covers the period 2010–2018 and has daily model output. It was forced with atmospheric reanalysis data from Era-Interim70, and was initialized with model fields from the simulation described in ref. 75. River runoff (except for Greenland) was taken from the JRA-55 data set76, and Greenland ice-sheet runoff was taken from ref. 77. Tides were not taken into account in this simulation. Here we studied the model data of 2016 to 2018 in Fram Strait for comparison with our observations.1-dimensional mixed layer depth modelThe PWP78 1-dimensional mixed layer model simulates the response of the ocean to surface fluxes. It ignores horizontal gradients and horizontal advection. This allows to judge whether certain surface flux conditions can on their own explain observed conditions. We ran the PWP model (as implemented for Matlab by http://www.po.gso.uri.edu/rafos/research/pwp/) with four different scenarios (Fig. S6: P17-M17, P17-M18, P18-M17, P18-M18) where: P17: An idealized initial profile based on the observed profiles (Fig. 3) representing the conditions in 2017: constant temperature of 2 °C in the vertical, linear salinity gradient from 30.5 at the surface to 35 at 50 m and another linear salinity gradient from 35 at 50 m to 35.1 at 200 m. P18: An idealized initial profile based on the observed profiles (Fig. 3) representing the conditions in 2018: Same as P17 except that the surface to 50 m salinity gradient is from 34.8 to 35. M17: A time series of the the meteorological forcing (10 m wind velocity, heat fluxes, and evaporation minus precipitation) from the ERA-Interim reanalysis (Fig. 4b) at the grid point closest to mooring HG-IV for the period 15-May-2017 to 01-Aug-2017. M18: Same as M17 but for the period 15-May-2018 to 01-Aug-2018. M17 and M18 are provided in Supplementary Data 1.Shipboard CTD dataShipboard CTD casts of a standard dual sensor Seabird 911+ CTD-rosette were occupied in spatial and temporal vicinity to the moored observations (Tab. S2) on three cruises: PS107 in 2017 (https://doi.org/10.1594/PANGAEA.894189), PS114 in 2018 (https://doi.org/10.1594/PANGAEA.898694) of RV Polarstern, and JR17005 in 2018 (https://doi.org/10.5285/84988765-5fc2-5bba-e053-6c86abc05d53) of RRS James Clark Ross. The data were processed according to standard routine79. Additionally, we use underway CTD data from an OceanScience underway CTD collected during PS107 in 2017 (https://doi.org/10.1594/PANGAEA.886146) and processed according to ref. 21.Mooring dataThe mooring data discussed in this paper is from two mooring clusters in the central and eastern Fram Strait (named “HG-IV” at ~79°N 4°20’E and “F4” at ~79°N 7°E) where moorings were located as close to each other as possible (the horizontal separation was equal to the water depth) in order to enable more measurements than could be fit physically onto a single mooring. Tab. S2/S3 list the deployment and recovery details of the moorings including the exact latitudes/longitudes as well as the individual instruments on the moorings. Note that all data shown in this paper from ~30 m depth and the temperature/salinity/oxygen data from ~55 m is from the HG-IV-S-* and F4-S-* moorings, while all other data is from the HG-IV-FEVI-* and F4-* moorings. The AZFP data is from F5-17 located roughly half way between the two clusters. All sensor based mooring raw data (except for the ASL AZFP data) is available at ref. 80.It is known that conversion factors for biogeochemical sensors (e.g., chlorophyll fluorescence) change over the seasons, depths, and regions81,82. In order to make as few assumptions as possible, we used the following approach: we could have determined the conversion factors from the instance when the ship was there with the CTD-rosette, but these conversion factors might not be appropriate for the majority of the time series. Hence, simply using the manufacturers’ calibrations, as we do here, introduces fewer uncertainties. Where we have different estimates of the same parameter, we present them together and demonstrate that they agree qualitatively and also mostly quantitatively (e.g., Fig. 5b). In particular the timing of events is robust.At some locations, the target variables were not measured the whole time or the measurements failed, hence we present what is available. The vertical location of the instruments (Fig. 4c and S1c) varied substantially (intermittently up to 200 m) as a result of mooring blow downs caused by strong intermittent ocean currents. Time series have not been corrected for this vertical motion, but data are not used during blow downs in order not to bias the time series interpretation by temporal changes introduced by instruments traversing through vertical property gradients.Physical sensor measurementsThe physical sensors (for pressure, temperature, conductivity, and oxygen) were pre-cruise manufacturer calibrated and processed similar to ref. 83; the processed data is also available at ref. 80.Mixed layer depth (MLD)Since there are no autonomous vertically profiling measurements available, we can only determine the minimum value of the mixed layer depth. At each hourly time step, the potential density difference (Δσ) between the uppermost (~30 m) temperature/salinity recorder and the underlying temperature/salinity recorders is calculated. The 0.5th percentile of each Δσ time series is added to the Δσ time series for the different deployments. This fixes slight offsets in the temperature and/or conductivity calibrations which result in too negative or too positive density differences. The minimum estimate of the mixed layer depth at hourly resolution is then determined as the depth of the deepest instrument where Δσ  0.05 kg m−3 for all depths at a time step, then the minimum mixed layer depth can only be determined as 0 for that time step. Daily values of the MLD were defined as the depth at which three hourly realizations of MLD were shallower within a 24 h time span and at which the remaining 21 MLD realizations were deeper. This biases the daily MLD estimate towards situations where phytoplankton is kept in the surface ocean rather than also being mixed down for some amount of time.Stratification estimated between 30 m and 55 mBased on the temperature and salinity time series observed at ~30 m and ~55 m, we estimate the buoyancy frequency as ({N}^{2}=frac{-g}{{rho }_{0}}frac{Delta rho }{Delta z}) where g is the acceleration due to gravity, Δσ is the potential density difference over the vertical distance of Δz = 25 m, and ρ0 is the average density. The contributions to stratification due to temperature (N2T) and salinity (N2S) are estimated as ({N}_{T}^{2}=g*alpha frac{Delta T}{Delta z}) and ({N}_{S}^{2}=-g*beta frac{Delta S}{Delta z}), respectively, where ΔT/ΔS are the temperature/salinity differences and α/β are the thermal expansion/haline contraction coefficients estimated from the average temperature/salinity at the two measurement depths.Apparent oxygen utilization (AOU)Oxygen concentration from the microcats was calculated using the pre-cruise manufacturer calibrations. AOU was calculated as the atmospherically equilibrated oxygen concentration (calculated from measured pressure, temperature, and salinity with sw_satO2 from the Seawater toolbox available at http://www.cmar.csiro.au/datacentre/ext_docs/seawater.htm) minus the measured oxygen concentration.LightPolar night/polar dayThe length of day (hours per 24 h that the sun is above the horizon) was calculated from the sunrise equation as implemented for Matlab by https://de.mathworks.com/matlabcentral/fileexchange/55509-sunrise-sunset.Photosynthetically available radiation (PAR)The WetLabs Eco PAR measured PAR for 5 (in 2016–2017) or 10 (in 2017–2018) individual measurements 1 s apart from each other before it slept for 1 h before repeating the measurement cycle. These 5 or 10 individual measurements are averaged linearly to obtain hourly values at ~30 m depth (Fig. 5a blue). Values below the detection limit are set to a constant of 10−1.32 μmol m−2 s−1. Hourly values are linearly averaged to daily values (Fig. 5a black). The incoming solar shortwave radiation varies as a function of season and latitude as well as cloud cover as represented in the ERA-Interim reanalysis (parameters ssr). Its unit of W m−2 is converted to PAR assuming a constant spectral distribution as 1 W m−2 = 2.1 μmol m−2 s−184. In order to compare the PAR measured at a depth of approximately 30 m to the surface values, we approximate a spectrally averaged diffuse attenuation coefficient for PAR in clear water using the values of85 as kd = 0.02 m−1 and apply it to calculate a constant exponential extinction applied to the reanalysis surface values (Fig. 5a yellow). The average PAR available (PARavailable) to phytoplankton being moved around in the clear water mixed layer of depth MLD was calculated as the depth averaged vertical integral of the clear water extinguished PAR at the surface (PARsurf from the shortwave radiation of ERA-Interim): ({{PAR}}_{{available}}=frac{1}{{MLD}}*{int }_{z=0}^{z={MLD}}{{PAR}}_{{surf}}*{e}^{-{k}_{d}z}{dz}) (Fig. 5a red).Chlorophyll concentration and optical backscatteringChlorophyll fluorescenceThe WetLabs ECO Triplet measures fluorescence at a “chlorophyll wavelength” and at a “CDOM wavelength” as well as optical scattering at 700 nm. The conversion from fluorescence to chlorophyll a concentration (in μg l−1) follows a manufacturer determined conversion determined for a mono-culture of phytoplankton (Thalassiosira weissflogii), which typically overestimates the chlorophyll concentration. Hence, we applied the community-established calibration bias of 2 for the WetLabs ECO-series fluorometer to these in situ fluorometric chlorophyll values81. This conversion factor may be different in ocean waters of Fram Strait, but it still gives reasonable agreement with independent estimates.Optical backscatteringThe EcoTriplet measured 8 individual measurements 1 s apart from each other before it slept for 1 h before repeating the measurement cycle. For the chlorophyll fluorescence, the individual measurements are averaged to hourly values. For the scattering, times when individual 1-second measurements exceed 0.002 m−1 sr−1 are indicative of strong optical backscattering not due to small particles in the water column, but rather to larger potentially aggregated particles. The times of strong backscattering are marked individually (Fig. 5b red).NutrientsNitrate (SUNA sensor)Prior to deployment (11 and 15 days for sensors deployed at HG-IV and F4, respectively), the reference spectrum of the sensors were updated as per manufacturer specifications. We first let the sensors cool down for 24 h at 0 °C in a temperature controlled laboratory. Next, the reference spectrum update was achieved by measuring Milli-Q water (i.e., no nitrate present). To verify if this update was successful, solutions with three different nitrate concentrations (3, 7, and 14 μmol l−1) were then measured, with the output being monitored live (expected to be within ±2 μmol l−1 of each concentration). A measuring time of 20 s yields stable results and was thus applied during the deployments with an interval of 6 h. Upon recovery, SUNA data were processed using the SeaBird UCI software package version 1.2.1. Here, temperature and salinity data were used to remove the spectrum of bromide and compensate for temperature dependent absorption using an algorithm developed by ref. 86. This step yields the spectrum of nitrate only, at a precision of ±0.3 μmol l−1. The sensor is characterized by a drift of 0.3 μmol l−1 per hour lamp time. Given the deployment settings, a total operational time of about 8 h was accumulated. Therefore, a linear drift correction of 2.4 μmol l−1 (365 days)−1 was applied. Up to this point, however, accuracy remains at 2 μmol l−1 as per manufacturer specifications. Therefore, an offset correction is then applied based on the in situ concentrations observed at the beginning of the deployment as well as with the RAS (see below) where available, with outliers excluded.Inorganic nutrients from Remote Access Samplers (RAS)McLane RAS were programmed to draw two 500 ml samples (1 h apart, starting at noon) approximately every other week. Samples within the RAS were collected in sterile plastic bags and fixed with 700 μl of 50% mercuric chloride solution. Upon recovery, two samples from a given sampling date were combined to yield a volume of 1 l, required for bacterial and phytoplankton genetic analyses (see below), and a 50-ml aliquot destined for the measurement of dissolved inorganic nutrients. Aliquots for nutrient analysis were collected in PE bottles, which were then stored frozen (−20 °C) until analysis on land. Analyses for inorganic nutrients were carried out using a QuAAtro Seal Analytical segmented continuous flow autoanalyser following standard colorimetric techniques. The accuracy of the analysis was evaluated through the measurement of KANSO LTD Japan Certified Reference Materials and corrections were applied accordingly. Finally, we evaluated pressure, temperature, and salinity data from the CTD (SBE37-SMP-ODO) attached to the RAS to determine whether the two samples taken one hour apart on a given date drew water from the same depth and with consistent properties.Carbonate system
pCO
2 and pH
The calibration of SAMI pH and SAMI CO2 sensors was carried out by the manufacturer, approximately 2 months prior to deployment. The calibration certificates specify accuracy and precision of ±0.003/±0.001 pH units and ±3/ More

1.Brewer TE, Aronson EL, Arogyaswamy K, Billings SA, Botthoff JK, Campbell AN, et al. Ecological and genomic attributes of novel bacterial taxa that thrive in subsurface soil horizons. mBio. 2019;10:e01318–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
2.Brubaker SC, Jones AJ, Lewis DT, Frank K. Soil properties associated with landscape position. Soil Sci Soc Am J. 1993;57:235–9.
Google Scholar 
3.Richter DD, Markewitz D. How deep is soil? BioScience. 1995;45:600–9.
Google Scholar 
4.Rumpel C, Kögel-Knabner I. Deep soil organic matter—a key but poorly understood component of terrestrial C cycle. Plant Soil. 2010;338:143–58.
Google Scholar 
5.Dove NC, Arogyaswamy K, Billings SA, Botthoff JK, Carey CJ, Cisco C, et al. Continental-scale patterns of extracellular enzyme activity in the subsoil: an overlooked reservoir of microbial activity. Environ Res Lett. 2020;15:1040a1.CAS 

Google Scholar 
6.Sinsabaugh RL, Lauber CL, Weintraub MN, Ahmed B, Allison SD, Crenshaw C, et al. Stoichiometry of soil enzyme activity at global scale. Ecol Lett. 2008;11:1252–64.PubMed 

Google Scholar 
7.Tedersoo L, Bahram M, Põlme S, Kõljalg U, Yorou NS, Wijesundera R, et al. Global diversity and geography of soil fungi. Science. 2014;346:6213.
Google Scholar 
8.Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Jobbágy EG, Jackson RB. The vertical distribution of soil organic carbon and its relation to climate and vegetation. Ecol Appl. 2000;10:423–36.
Google Scholar 
10.Chapin FS, Matson PA, Vitousek P. Principles of terrestrial ecosystem ecology. New York: Springer Science & Business Media; 2011.
Google Scholar 
11.Bárcenas‐Moreno G, Gómez‐Brandón M, Rousk J, Bååth E. Adaptation of soil microbial communities to temperature: comparison of fungi and bacteria in a laboratory experiment. Glob Change Biol. 2009;15:2950–7.
Google Scholar 
12.Wallenstein M, Allison SD, Ernakovich J, Steinweg JM, Sinsabaugh R. Controls on the temperature sensitivity of soil enzymes: a key driver of in situ enzyme activity rates. In: Shukla G, Varma A, editors. Soil enzymology. Berlin, Heidelberg: Springer; 2011. p. 245–58.13.German DP, Marcelo KRB, Stone MM, Allison SD. The Michaelis–Menten kinetics of soil extracellular enzymes in response to temperature: a cross-latitudinal study. Glob Change Biol. 2012;18:1468–79.
Google Scholar 
14.Oliverio AM, Bradford MA, Fierer N. Identifying the microbial taxa that consistently respond to soil warming across time and space. Glob Change Biol. 2017;23:2117–29.
Google Scholar 
15.Jenny H. Factors of soil formation. New York: McGraw-Hill; 1941.
Google Scholar 
16.Parton WJ, Scurlock JMO, Ojima DS, Schimel DS, Hall DO. Impact of climate change on grassland production and soil carbon worldwide. Glob Change Biol. 1995;1:13–22.
Google Scholar 
17.Jenny H. The soil resource: origin and behavior. Berlin: Springer Science & Business Media; 1980.18.Fierer N, Jackson RB. The diversity and biogeography of soil bacterial communities. PNAS. 2006;103:626–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Lauber CL, Hamady M, Knight R, Fierer N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl Environ Microbiol. 2009;75:5111–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
20.Slessarev EW, Lin Y, Bingham NL, Johnson JE, Dai Y, Schimel JP, et al. Water balance creates a threshold in soil pH at the global scale. Nature. 2016;540:567–9.CAS 
PubMed 

Google Scholar 
21.Brovkin V. Climate-vegetation interaction. J Phys IV France. 2002;12:57–72.
Google Scholar 
22.Aerts R. Climate, leaf litter chemistry and leaf litter decomposition in terrestrial ecosystems: a triangular relationship. Oikos. 1997;79:439–49.
Google Scholar 
23.Djukic I, Kepfer-Rojas S, Schmidt IK, Larsen KS, Beier C, Berg B, et al. Early stage litter decomposition across biomes. Sci Total Environ. 2018;628–9:1369–94.
Google Scholar 
24.Shiozawa S, Campbell GS. Soil thermal conductivity. Remote Sens Rev. 1990;5:301–10.
Google Scholar 
25.Verhoef A, Fernández-Gálvez J, Diaz-Espejo A, Main BE, El-Bishti M. The diurnal course of soil moisture as measured by various dielectric sensors: effects of soil temperature and the implications for evaporation estimates. J Hydrol. 2006;321:147–62.
Google Scholar 
26.Dove NC, Torn MS, Hart SC, Taş N. Metabolic capabilities mute positive response to direct and indirect impacts of warming throughout the soil profile. Nat Commun. 2021;12:2089.CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Bai W, Wang G, Xi J, Liu Y, Yin P. Short-term responses of ecosystem respiration to warming and nitrogen addition in an alpine swamp meadow. Eur J Soil Biol. 2019;92:16–23.CAS 

Google Scholar 
28.Yost JL, Hartemink AE. How deep is the soil studied—an analysis of four soil science journals. Plant Soil. 2020;425:5–18.
Google Scholar 
29.Hicks Pries CE, Castanha C, Porras RC, Torn MS. The whole-soil carbon flux in response to warming. Science. 2017;355:1420–3.CAS 
PubMed 

Google Scholar 
30.Jones DL, Magthab EA, Gleeson DB, Hill PW, Sánchez-Rodríguez AR, Roberts P, et al. Microbial competition for nitrogen and carbon is as intense in the subsoil as in the topsoil. Soil Biol Biochem. 2018;117:72–82.CAS 

Google Scholar 
31.Ofiti NOE, Zosso CU, Soong JL, Solly EF, Torn MS, Wiesenberg GLB, et al. Warming promotes loss of subsoil carbon through accelerated degradation of plant-derived organic matter. Soil Biol Biochem. 2021;156:108185.CAS 

Google Scholar 
32.Soong JL, Castanha C, Pries CEH, Ofiti N, Porras RC, Riley WJ, et al. Five years of whole-soil warming led to loss of subsoil carbon stocks and increased CO2 efflux. Sci Adv. 2021;7:eabd1343.CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Nottingham AT, Fierer N, Turner BL, Whitaker J, Ostle NJ, McNamara NP, et al. Microbes follow Humboldt: temperature drives plant and soil microbial diversity patterns from the Amazon to the Andes. Ecology. 2018;99:2455–66.PubMed 

Google Scholar 
34.O‘Geen A (Toby), Safeeq M, Wagenbrenner J, Stacy E, Hartsough P, Devine S., et al. Southern Sierra Critical Zone Observatory and Kings River Experimental Watersheds: a synthesis of measurements, new insights, and future directions. Vadose Zone J. 2018;17:180081.35.Frisbie JA. Soil organic carbon storage and aggregate stability in an arid mountain range, White Mountains, CA. UC Riverside Master’s Thesis. 2014. https://escholarship.org/uc/item/4rn6j9rq.36.Marchand DE. Soil contamination in the White Mountains, Eastern California. GSA Bull. 1970;81:2497–506.
Google Scholar 
37.Aciego SM, Riebe CS, Hart SC, Blakowski MA, Carey CJ, Aarons SM, et al. Dust outpaces bedrock in nutrient supply to montane forest ecosystems. Nat Commun. 2017;8:14800.CAS 
PubMed 
PubMed Central 

Google Scholar 
38.Dove NC, Safford HD, Bohlman GN, Estes BL, Hart SC. High-severity wildfire leads to multi-decadal impacts on soil biogeochemistry in mixed-conifer forests. Ecol Appl. 2020;30:e02072.PubMed 

Google Scholar 
39.USDA-NRCS. Soil taxonomy: a basic system of soil classification for making and interpreting soil surveys. U.S. Department of Agriculture Handbook 436. 2nd edition. Washington D.C.: Natural Resources Conservation Service; 1999.40.Lajtha K, Driscoll CT, Jarrell WM, Elliot ET. Phosphorus characterization and total element analysis. In: Robertson GP, Coleman DC, Bledsoe CS, Sollins P, editors. Standard soil methods for long-term ecological research. New York: Oxford University Press; 1999. p. 115–42.41.Harris D, Horwáth WR, vanKessel C. Acid fumigation of soils to remove carbonates prior to total organic carbon or CARBON-13 isotopic analysis. Soil Sci Soc Am J. 2001;65:1853–6.CAS 

Google Scholar 
42.Thomas GW, Soil pH and soil acidity. In: Sparks DL, Page AL, Helmke PA, Loeppert RH, Soltanpour PN, Tabatabai MA, et al., editors. Methods of soil analysis, part 3: chemical methods. Madison, WI, USA: Soil Science Society of America, American Society of Agronomy; 1996, p. 475–90.43.Vance ED, Brookes PC, Jenkinson DS. An extraction method for measuring soil microbial biomass C. Soil Biol Biochem. 1987;19:703–7.CAS 

Google Scholar 
44.Hart SC, Firestone MK. Forest floor-mineral soil interactions in the internal nitrogen cycle of an old-growth forest. Biogeochemistry. 1991;12:103–27.CAS 

Google Scholar 
45.Haubensak KA, Hart SC, Stark JM. Influences of chloroform exposure time and soil water content on C and N release in forest soils. Soil Biol Biochem. 2002;34:1549–62.CAS 

Google Scholar 
46.Stenberg B, Johansson M, Pell M, Sjödahl-Svensson K, Stenström J, Torstensson L. Microbial biomass and activities in soil as affected by frozen and cold storage. Soil Biol Biochem. 1998;30:393–402.CAS 

Google Scholar 
47.Bell CW, Fricks BE, Rocca JD, Steinweg JM, McMahon SK, Wallenstein MD. High-throughput fluorometric measurement of potential soil extracellular enzyme activities. J Vis Exp. 2013;81:e50961.
Google Scholar 
48.Parada AE, Needham DM, Fuhrman JA. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol. 2016;18:1403–14.CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Ihrmark K, Bödeker ITM, Cruz-Martinez K, Friberg H, Kubartova A, Schenck J, et al. New primers to amplify the fungal ITS2 region – evaluation by 454-sequencing of artificial and natural communities. FEMS Microbiol Ecol. 2012;82:666–77.CAS 

Google Scholar 
50.Smith DP, Peay KG. Sequence depth, not PCR replication, improves ecological inference from next generation DNA sequencing. PLoS ONE. 2014;9:e90234.PubMed 
PubMed Central 

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

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

Google Scholar 
53.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 
54.Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinform. 2009;10:421.
Google Scholar 
55.Abarenkov K, Nilsson RH, Larsson K-H, Alexander IJ, Eberhardt U, Erland S, et al. The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytologist. 2010;186:281–5.
Google Scholar 
56.Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016;20:241–8.
Google Scholar 
57.R Development Core Team. R: a language and environment for statistical computing. 2008. Vienna, Australia: R Foundation for Statistical Computing. http://www.R-project.org.58.Cribari-Neto F, Zeileis A. Beta regression in R. J Statl Softw. 2010;34:1–24.
Google Scholar 
59.Fox J, Weisberg S. An {R} companion to applied regression, Second. 2011. Thousand Oaks, CA: Sage.60.Bates D, Maechler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67:1–48.
Google Scholar 
61.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Oksanen J, Blanchet FG, Kindt R, Legendre P, Simpson GL, Minchin PR, et al. vegan: community Ecology Package. 2013 http://CRAN.R-project.org/package=vegan.63.Anderson MJ. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 2001;26:32–46.
Google Scholar 
64.Anderson MJ, Ellingsen KE, McArdle BH. Multivariate dispersion as a measure of beta diversity. Ecol Lett. 2006;9:683–93.PubMed 

Google Scholar 
65.Legendre P, Cáceres MD. Beta diversity as the variance of community data: dissimilarity coefficients and partitioning. Ecol Lett. 2013;16:951–63.PubMed 

Google Scholar 
66.Legendre P, Anderson MJ. Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecol Monogr. 1999;69:1–24.
Google Scholar 
67.Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:3514.CAS 
PubMed 
PubMed Central 

Google Scholar 
68.Delgado-Baquerizo M, Oliverio AM, Brewer TE, Benavent-González A, Eldridge DJ, Bardgett RD, et al. A global atlas of the dominant bacteria found in soil. Science. 2018;359:320–5.CAS 
PubMed 

Google Scholar 
69.Kanzaki Y, Takemoto K. Diversity of dominant soil bacteria increases with warming velocity at the global scale. Diversity. 2021;13:120.
Google Scholar 
70.Russell NJ, Harrisson P, Johnston IA, Jaenicke R, Zuber M, Franks F, et al. Cold adaptation of microorganisms. Philos Trans R Soc Lond B Biol Sci. 1990;326:595–611.CAS 
PubMed 

Google Scholar 
71.Chanal A, Chapon V, Benzerara K, Barakat M, Christen R, Achouak W, et al. The desert of Tataouine: an extreme environment that hosts a wide diversity of microorganisms and radiotolerant bacteria. Environ Microbiol. 2006;8:514–25.CAS 
PubMed 

Google Scholar 
72.Jobbágy EG, Jackson RB. The distribution of soil nutrients with depth: global patterns and the imprint of plants. Biogeochemistry. 2001;53:51–77.
Google Scholar 
73.Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.PubMed 

Google Scholar 
74.Offre P, Spang A, Schleper C. Archaea in biogeochemical cycles. Ann Rev Microbiol. 2013;67:437–57.CAS 

Google Scholar 
75.Stegen JC, Bottos EM, Jansson JK. A unified conceptual framework for prediction and control of microbiomes. Curr Opin in Microbiol. 2018;44:20–27.
Google Scholar 
76.Xiong J, Sun H, Peng F, Zhang H, Xue X, Gibbons SM, et al. Characterizing changes in soil bacterial community structure in response to short-term warming. FEMS Microbiol Ecol. 2014;89:281–92.CAS 
PubMed 

Google Scholar 
77.DeAngelis KM, Pold G, Topçuoğlu BD, van Diepen LTA, Varney RM, Blanchard JL, et al. Long-term forest soil warming alters microbial communities in temperate forest soils. Front Microbiol. 2015;6:104.PubMed 
PubMed Central 

Google Scholar 
78.Hayden HL, Mele PM, Bougoure DS, Allan CY, Norng S, Piceno YM, et al. Changes in the microbial community structure of bacteria, archaea and fungi in response to elevated CO2 and warming in an Australian native grassland soil. Environ Microbiol. 2012;14:3081–96.CAS 
PubMed 

Google Scholar 
79.Johnston ER, Hatt JK, He Z, Wu L, Guo X, Luo Y, et al. Responses of tundra soil microbial communities to half a decade of experimental warming at two critical depths. PNAS. 2019;116:15096–105.CAS 
PubMed 
PubMed Central 

Google Scholar 
80.Větrovský T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE. 2013;8:e57923.PubMed 
PubMed Central 

Google Scholar 
81.Sarkar JM, Leonowicz A, Bollag J-M. Immobilization of enzymes on clays and soils. Soil Biol Biochem. 1989;21:223–30.CAS 

Google Scholar 
82.Burns RG, DeForest JL, Marxsen J, Sinsabaugh RL, Stromberger ME, Wallenstein MD, et al. Soil enzymes in a changing environment: Current knowledge and future directions. Soil Biol Biochem. 2013;58:216–34.CAS 

Google Scholar 
83.Eilers KG, Debenport S, Anderson S, Fierer N. Digging deeper to find unique microbial communities: The strong effect of depth on the structure of bacterial and archaeal communities in soil. Soil Biol Biochem. 2012;50:58–65.CAS 

Google Scholar 
84.Kellogg CA, Griffin DW. Aerobiology and the global transport of desert dust. Trends Ecol Evol. 2006;21:638–44.PubMed 

Google Scholar 
85.Martiny JBH, Bohannan BJM, Brown JH, Colwell RK, Fuhrman JA, Green JL, et al. Microbial biogeography: putting microorganisms on the map. Nat Rev Microbiol. 2006;4:102–12.CAS 
PubMed 

Google Scholar 
86.Du X, Deng Y, Li S, Escalas A, Feng K, He Q, et al. Steeper spatial scaling patterns of subsoil microbiota are shaped by deterministic assembly process. Mol Ecol. 2021;30:1072–85.CAS 
PubMed 

Google Scholar 
87.Fanning DS, Fanning MCB. Soil morphology, genesis and classification. New York: John Wiley & Sons; 1989.88.IPCC. Climate Change 2014: synthesis report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. 2014. Geneva, Switzerland: IPCC.89.Bradford MA, Davies CA, Frey SD, Maddox TR, Melillo JM, Mohan JE, et al. Thermal adaptation of soil microbial respiration to elevated temperature. Ecol Lett. 2008;11:1316–27.PubMed 

Google Scholar  More

1.Frank, S. A. Models of parasite virulence. Q. Rev. Biol.  https://doi.org/10.1086/419267 (1996).Article 
PubMed 

Google Scholar 
2.Dobson, A. P. The population dynamics of competition between parasites. Parasitology https://doi.org/10.1017/S0031182000057401 (1985).Article 
PubMed 

Google Scholar 
3.Haelewaters, D. et al. Mortality of native and invasive ladybirds co-infected by ectoparasitic and entomopathogenic fungi. PeerJ https://doi.org/10.7717/peerj.10110 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
4.Shapiro-Ilan, D. I., Bruck, D. J. & Lacey, L. A. Principles of Epizootiology and Microbial Control. In Insect Pathology 29–72 (Elsevier, 2012). https://doi.org/10.1016/B978-0-12-384984-7.00003-8.5.Renkema, J. M. & Cuthbertson, A. G. S. Impact of multiple natural enemies on immature Drosophila suzukii in strawberries and blueberries. Biocontrol https://doi.org/10.1007/s10526-018-9874-8 (2018).Article 

Google Scholar 
6.Furlong, M. & Pell, J. Interactions between entomopathogenic fungi and other arthropods natural enemies. In Insect-Fungal Associations, Ecology and Evolution (eds Vega, F. & Blackwell, M.) 51–73 (Oxford University Press, 2005).
Google Scholar 
7.Lafferty, K. D. Interacting parasites. Science https://doi.org/10.1126/science.1196915 (2010).Article 
PubMed 

Google Scholar 
8.Price, S. L. et al. Recent findings in fungus-growing ants: evolution, ecology, and behavior of a complex microbial symbiosis. In Genes, Behaviors and Evolution of Social Insects (eds Azuma, N. & Higashi, S.) 255–280 (Hokkaido University Press, 2003).
Google Scholar 
9.Telfer, S. et al. Species interactions in a parasite community drive infection risk in a wildlife population. Science https://doi.org/10.1126/science.1190333 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
10.Carlson, C. J. et al. A global parasite conservation plan. Biol. Conserv. https://doi.org/10.1016/j.biocon.2020.108596 (2020).Article 

Google Scholar 
11.Colwell, R. K., Dunn, R. R. & Harris, N. C. Coextinction and persistence of dependent species in a changing world. Annu. Rev. Ecol. Evol. Syst. https://doi.org/10.1146/annurev-ecolsys-110411-160304 (2012).Article 

Google Scholar 
12.Gagne, R. B. et al. Parasites as conservation tools. Conserv. Biol. https://doi.org/10.1111/cobi.13719 (2021).Article 
PubMed 

Google Scholar 
13.Csősz, S. & Majoros, G. Ontogenetic origin of mermithogenic Myrmica phenotypes (Hymenoptera, Formicidae). Insectes Soc.  https://doi.org/10.1007/s00040-008-1040-3 (2009).Article 

Google Scholar 
14.Csata, E. et al. Lock-picks: fungal infection facilitates the intrusion of strangers into ant colonies. Sci. Rep. https://doi.org/10.1038/srep46323 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
15.Pearson, B. & Raybould, A. F. The effects of antibiotics on the development of larvae and the possible role of bacterial load in caste determination and diapause in Myrmica rubra (Hymenoptera: Formicidae). Sociobiology 31, 77–90 (1998).
Google Scholar 
16.Schmid Hempel, P. Evolutionary Parasitology—The Integrated Study of Infections, Immunology, Ecology, and Genetics (Oxford University Press, 2011).
Google Scholar 
17.Donisthorpe, J. K. The Guests of British Ants—Their Habits and Life Histories (George Routledge And Sons, Limited, 1927).
Google Scholar 
18.Hölldobler, B. E. & Wilson, E. O. The Ants (The Belknap Press of Harvard University Press, 1990).Book 

Google Scholar 
19.Buschinger, A. Social parasitism among ants: A review (Hymenoptera: Formicidae). Myrmecol. News 12, 219–235 (2009).
Google Scholar 
20.Quevillon, L. E. The Ecology, Epidemiology, and Evolution of Parasites Infecting Ants (Hymenoptera: Formicidae) (Pennsylvania State University, 2018).
Google Scholar 
21.Quevillon, L. E. & Hughes, D. P. Pathogens, parasites, and parasitoids of ants: a synthesis of parasite biodiversity and epide-miological traits. BioRxiv https://doi.org/10.1101/384495 (2018).Article 

Google Scholar 
22.Di Salvo, M. et al. The microbiome of the Maculinea-Myrmica host-parasite interaction. Sci. Rep. https://doi.org/10.1038/s41598-019-44514-7 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
23.Witek, M., Barbero, F. & Markó, B. Myrmica ants host highly diverse parasitic communities: from social parasites to microbes. Insectes Soc. https://doi.org/10.1007/s00040-014-0362-6 (2014).Article 

Google Scholar 
24.Witek, M. et al. Interspecific relationships in co-occurring populations of social parasites and their host ants. Biol. J. Linn. Soc. https://doi.org/10.1111/bij.12074 (2013).Article 

Google Scholar 
25.Tartally, A. et al. Patterns of host use by brood parasitic Maculinea butterflies across Europe. Philos. Trans. R Soc. B Biol. Sci. https://doi.org/10.1098/rstb.2018.0202 (2019).Article 

Google Scholar 
26.Wardlaw, J. C., Thomas, J. A. & Elmes, G. W. Do Maculinea rebeli caterpillars provide vestigial mutualistic benefits to ants when living as social parasites inside Myrmica ant nests? Entomol. Exp. Appl. https://doi.org/10.1046/j.1570-7458.2000.00646.x (2000).Article 

Google Scholar 
27.Thomas, J. A. & Wardlaw, J. C. The capacity of a Myrmica ant nest to support a predacious species of Maculinea butterfly. Oecologia https://doi.org/10.1007/BF00317247 (1992).Article 
PubMed 

Google Scholar 
28.Csata, E., Billen, J., Bernadou, A., Heinze, J. & Markó, B. Infection-related variation in cuticle thickness in the ant Myrmica scabrinodis (Hymenoptera: Formicidae). Insectes Soc. https://doi.org/10.1007/s00040-018-0628-5 (2018).Article 

Google Scholar 
29.Csősz, S., Rádai, Z., Tartally, A., Ballai, L. E. & Báthori, F. Ectoparasitic fungi Rickia wasmannii infection is associated with smaller body size in Myrmica ants. Sci. Rep. https://doi.org/10.1038/s41598-021-93583-0 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Csata, E., Erős, K. & Markó, B. Effects of the ectoparasitic fungus Rickia wasmannii on its ant host Myrmica scabrinodis: Changes in host mortality and behavior. Insectes Soc. https://doi.org/10.1007/s00040-014-0349-3 (2014).Article 

Google Scholar 
31.Báthori, F., Rádai, Z. & Tartally, A. The effect of Rickia wasmannii (Ascomycota, Laboulbeniales) on the aggression and boldness of Myrmica scabrinodis (Hymenoptera, Formicidae). J. Hymenopt. Res. https://doi.org/10.3897/jhr.58.13253 (2017).Article 

Google Scholar 
32.Báthori, F., Csata, E. & Tartally, A. Rickia wasmannii increases the need for water in Myrmica scabrinodis (Ascomycota: Laboulbeniales; Hymenoptera: Formicidae). J. Invertebr. Pathol. https://doi.org/10.1016/j.jip.2015.01.005 (2015).Article 
PubMed 

Google Scholar 
33.Tartally, A. Myrmecophily of Maculinea Butterflies in the Carpathian Basin (Lepidoptera: Lycaenidae), PhD thesis, https://dea.lib.unideb.hu/dea/handle/2437/78921 (University of Debrecen, Hungary, 2008)
Google Scholar 
34.Elmes, G. W., Wardlaw, J. C., Schönrogge, K., Thomas, J. A. & Clarke, R. T. Food stress causes differential survival of socially parasitic caterpillars of Maculinea rebeli integrated in colonies of host and non-host Myrmica ant species. Entomol. Exp. Appl. https://doi.org/10.1111/j.0013-8703.2004.00121.x (2004).Article 

Google Scholar 
35.Nash, D. R., Als, T. D. & Boomsma, J. J. Survival and growth of parasitic Maculinea alcon caterpillars (Lepidoptera, Lycaenidae) in laboratory nests of three Myrmica ant species. Insectes Soc. https://doi.org/10.1007/s00040-011-0157-y (2011).Article 

Google Scholar 
36.Wilson, K., Grenfell, B. T. & Shaw, D. J. Analysis of aggregated parasite distributions: a comparison of methods. Funct. Ecol. https://doi.org/10.2307/2390169 (1996).Article 

Google Scholar 
37.Tartally, A., Nash, D. R., Varga, Z. & Lengyel, S. Changes in host ant communities of Alcon Blue butterflies in abandoned mountain hay meadows. Insect Conserv. Divers. https://doi.org/10.1111/icad.12369 (2019).Article 

Google Scholar 
38.Csata, E., Bernadou, A., Rákosy-Tican, E., Heinze, J. & Markó, B. The effects of fungal infection and physiological condition on the locomotory behaviour of the ant Myrmica scabrinodis. J. Insect Physiol. https://doi.org/10.1016/j.jinsphys.2017.01.004 (2017).Article 
PubMed 

Google Scholar 
39.Baylis, M. & Pierce, N. E. Lack of compensation by final instar larvae of the myrmecophilous lycaenid butterfly, Jalmenus evagoras, for the loss of nutrients to ants. Physiol. Entomol. https://doi.org/10.1111/j.1365-3032.1992.tb01186.x (1992).Article 

Google Scholar 
40.Elgar, M. A. & Pierce, N. E. Mating success and fecundity in an ant-tended lycaenid butterfly. In Reproductive Success: Studies of Individual Variation in Contrasting Breeding Systems 59–75 (Chicago University Press, 1988).41.Thomas, J. A., Elmes, G. W. & Wardlaw, J. C. Contest competition among Maculinea rebeli butterfly larvae in ant nests. Ecol. Entomol. https://doi.org/10.1111/j.1365-2311.1993.tb01082.x (1993).Article 

Google Scholar 
42.Nash, D. R., Als, T. D., Maile, R., Jones, G. R. & Boomsma, J. J. A mosaic of chemical coevolution in a large blue butterfly. Science https://doi.org/10.1126/science.1149180 (2008).Article 
PubMed 

Google Scholar 
43.Schlick-Steiner, B. C. et al. A butterfly’s chemical key to various ant forts: intersection-odour or aggregate-odour multi-host mimicry? Naturwissenschaften https://doi.org/10.1007/s00114-004-0518-8 (2004).Article 
PubMed 

Google Scholar 
44.Schönrogge, K. et al. Changes in chemical signature and host specificity from larval retrieval to full social integration in the myrmecophilous butterfly Maculinea rebeli. J. Chem. Ecol.  https://doi.org/10.1023/B:JOEC.0000013184.18176.a9 (2004).Article 
PubMed 

Google Scholar 
45.Als, T. D., Nash, D. R. & Boomsma, J. J. Geographical variation in host-ant specificity of the parasitic butterfly Maculinea alcon in Denmark. Ecol. Entomol. https://doi.org/10.1046/j.1365-2311.2002.00427.x (2002).Article 

Google Scholar 
46.Als, T. D., Nash, D. R. & Boomsma, J. J. Adoption of parasitic Maculinea alcon caterpillars (Lepidoptera: Lycaenidae) by three Myrmica ant species. Anim. Behav. https://doi.org/10.1006/anbe.2001.1716 (2001).Article 

Google Scholar 
47.Tartally, A., Somogyi, A. Á., Révész, T. & Nash, D. R. Host ant change of a socially parasitic butterfly (Phengaris alcon) through host nest take-over. Insects https://doi.org/10.3390/insects11090556 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
48.Thomas, J. A., Elmes, G. W., Schönrogge, K., Simcox, D. J. & Settele, J. Primary hosts, secondary hosts and ‘non-hosts’: common confusions in the interpretation of host specificity in Maculinea butterflies and other social parasites of ants. In Studies on the Ecology and Conservation of Butterflies in Europe (eds. Settele, J., Kühn, E. & Thomas, J. A.) vol. 2 99–104 (Pensoft, 2005).49.Thomas, J. A. et al. Mimetic host shifts in an endangered social parasite of ants. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2012.2336 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Fürst, M. A., Durey, M. & Nash, D. R. Testing the adjustable threshold model for intruder recognition on Myrmica ants in the context of a social parasite. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2011.0581 (2012).Article 

Google Scholar 
51.Maák, I. et al. Habitat features and colony characteristics influencing ant personality and its fitness consequences. Behav. Ecol. https://doi.org/10.1093/beheco/araa112 (2021).Article 
PubMed 

Google Scholar 
52.Chapman, B. B., Thain, H., Coughlin, J. & Hughes, W. O. H. Behavioural syndromes at multiple scales in Myrmica ants. Anim. Behav. https://doi.org/10.1016/j.anbehav.2011.05.019 (2011).Article 

Google Scholar 
53.Martin, S. J., Helanterä, H. & Drijfhout, F. P. Is parasite pressure a driver of chemical cue diversity in ants? Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2010.1047 (2011).Article 

Google Scholar 
54.Nehring, V., Evison, S. E. F., Santorelli, L. A., D’Ettorre, P. & Hughes, W. O. H. Kin-informative recognition cues in ants. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2010.2295 (2011).Article 

Google Scholar 
55.Van Zweden, J. S. et al. Blending of heritable recognition cues among ant nestmates creates distinct colony gestalt odours but prevents within-colony nepotism. J. Evol. Biol. https://doi.org/10.1111/j.1420-9101.2010.02020.x (2010).Article 
PubMed 

Google Scholar 
56.Nash, D. R. & Andersen, A. Maculinea-sommerfugle og stikmyrer på danske heder—coevolution i tid og rum. Flora og Fauna 121, 133–141 (2015).
Google Scholar 
57.Haelewaters, D., Boer, P., Gort, G. & Noordijk, J. Studies of Laboulbeniales (Fungi, Ascomycota) on Myrmica ants (II): variation of infection by Rickia wasmannii over habitats and time. Anim. Biol. https://doi.org/10.1163/15707563-00002472 (2015).Article 

Google Scholar 
58.Dallas, T. A., Laine, A.-L. & Ovaskainen, O. Detecting parasite associations within multi-species host and parasite communities. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2019.1109 (2019).Article 

Google Scholar 
59.Herczeg, D., Ujszegi, J., Kásler, A., Holly, D. & Hettyey, A. Host–multiparasite interactions in amphibians: a review. Parasit. Vectors https://doi.org/10.1186/s13071-021-04796-1 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
60.Bronstein, J. L. Conditional outcomes in mutualistic interactions. Trends Ecol. Evol. https://doi.org/10.1016/0169-5347(94)90246-1 (1994).Article 
PubMed 

Google Scholar 
61.Zhang, Z., Yan, C. & Zhang, H. Mutualism between antagonists: Its ecological and evolutionary implications. Integr. Zool. https://doi.org/10.1111/1749-4877.12487 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
62.Rogalski, M. A., Stewart Merrill, T., Gowler, C. D., Cáceres, C. E. & Duffy, M. A. Context-dependent host-symbiont interactions: Shifts along the parasitism-mutualism continuum. Am. Nat. https://doi.org/10.1086/716635 (2021).Article 
PubMed 

Google Scholar 
63.Pfliegler, W. P., Báthori, F., Haelewaters, D. & Tartally, A. Studies of Laboulbeniales on Myrmica ants (III): myrmecophilous arthropods as alternative hosts of Rickia wasmannii. Parasite https://doi.org/10.1051/parasite/2016060 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
64.Chouvenc, T., Efstathion, C. A., Elliott, M. L. & Su, N.-Y. Resource competition between two fungal parasites in subterranean termites. Naturwissenschaften https://doi.org/10.1007/s00114-012-0977-2 (2012).Article 
PubMed 

Google Scholar 
65.Lawton, J. H. & Hassell, M. P. Asymmetrical competition in insects. Nature https://doi.org/10.1038/289793a0 (1981).Article 

Google Scholar 
66.Price, P. W. Evolutionary Biology of Parasites (Princeton University Press, 1980).
Google Scholar 
67.Nash, D. R. & Boomsma, J. J. Communication between hosts and social parasites. In Sociobiology of Communication (eds D’Ettorre, P. & Hughes, D. P.) 55–80 (Oxford University Press, 2008).Chapter 

Google Scholar 
68.Tartally, A., Szűcs, B. & Ebsen, J. R. The first records of Rickia wasmannii Cavara, 1899, a myrmecophilous fungus, and its Myrmica Latreille, 1804 host ants in Hungary and Romania (Ascomycetes: Laboulbeniales; Hymenoptera: Formicidae). Myrmecol. News 10, 123 (2007).
Google Scholar 
69.Radchenko, A. G. & Elmes, G. W. Myrmica (Hymenoptera: Formicidae) ants of the Old World. vol. 6 (Fauna Mundi 3, 2010).70.Tragust, S., Tartally, A., Espadaler, X. & Billen, J. Histopathology of Laboulbeniales (Ascomycota: Laboulbeniales): ectoparasitic fungi on ants (Hymenoptera: Formicidae). Myrmecol. News 23, 81–89 (2016).
Google Scholar 
71.Haelewaters, D., Boer, P. & Noordijk, J. Studies of Laboulbeniales (Fungi, Ascomycota) on Myrmica ants: Rickia wasmannii in the Netherlands. J. Hymenopt. Res. https://doi.org/10.3897/JHR.44.4951 (2015).Article 

Google Scholar 
72.Espadaler, X. & Santamaria, S. Ecto- and endoparasitic fungi on ants from the Holarctic Region. Psyche, 2012, 168478. https://doi.org/10.1155/2012/168478 (2012).Article 

Google Scholar 
73.Báthori, F., Pfliegler, W. P., Zimmerman, C.-U. & Tartally, A. Online image databases as multi-purpose resources: discovery of a new host ant of Rickia wasmannii Cavara (Ascomycota, Laboulbeniales) by screening AntWeb.org. J. Hymenopt. Res, 61, 85-94. https://doi.org/10.3897/jhr.61.20255 (2017).Article 

Google Scholar 
74.Riddick, E. W. Ectoparasitic mite and fungus on an invasive lady beetle: parasite coexistence and influence on host survival. Bull. Insectol. 63, 13–20 (2010).
Google Scholar 
75.Konrad, M., Grasse, A. V, Tragust, S. & Cremer, S. Anti-pathogen protection versus survival costs mediated by an ectosymbiont in an ant host. Proc. Biol. Sci. https://doi.org/10.1098/rspb.2014.197620141976 (2015).76.De Kesel, A., Haelewaters, D. & Dekoninck, W. Myrmecophilous Laboulbeniales Ascomycota in Belgium. Sterbeeckia 34, 3–6 (2016).
Google Scholar 
77.Haelewaters, D. The first record of Laboulbeniales (Fungi, Ascomycota) on Ants (Hymenoptera, Formicidae) in The Netherlands. Ascomycete.org 4, 65-69 (2012).78.van Swaay, C. et al. European Red List of Butterflies (Publications Office of the European Union, 2010).
Google Scholar 
79.Gergely, P. & Hudák, T. Revision of threatened butterfly species in Hungary (Lepidoptera: Rhopalocera). Lepidopterol. Hungarica https://doi.org/10.24386/lephung.2021.17.1.27 (2021).Article 

Google Scholar 
80.Wallis de Vries, M. Code rood voor het gentiaanblauwtje. Vlinders 4, 5–8 (2017).
Google Scholar 
81.Barbero, F., Thomas, J. A., Bonelli, S., Balletto, E. & Schönrogge, K. Queen ants make distinctive sounds that are mimicked by a butterfly social parasite. Science https://doi.org/10.1126/science.1163583 (2009).Article 
PubMed 

Google Scholar 
82.Thomas, J. A., Elmes, G. W., Wardlaw, J. C. & Woyciechowski, M. Host specificity among Maculinea butterflies in Myrmica ant nests. Oecologia https://doi.org/10.1007/BF00378660 (1989).Article 
PubMed 

Google Scholar 
83.Elmes, G. W. et al. The ecology of Myrmica ants in relation to the conservation of Maculinea butterflies. J. Insect Conserv. https://doi.org/10.1023/A:1009696823965 (1998).Article 

Google Scholar 
84.Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods https://doi.org/10.1038/nmeth.2019 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
85.Cammaerts-Tricot, M.-C. Ontogenesis of the defence reactions in the workers of Myrmica rubra L. (Hymenoptera: Formicidae). Anim. Behav. https://doi.org/10.1016/0003-3472(75)90058-5 (1975).Article 

Google Scholar 
86.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015). More

1.Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 

Google Scholar 
2.Zou, Y. et al. 1,520 reference genomes from cultivated human gut bacteria enable functional microbiome analyses. Nat. Biotechnol. 37, 179–185 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Mohammad, B. F. et al. Structure and function of the global topsoil microbiome. Nature 560 233–237 (2018).4.Qin, J. et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Xiao, L. et al. A catalog of the mouse gut metagenome. Nat. Biotechnol. 33, 1103–1108 (2015).CAS 
PubMed 

Google Scholar 
6.Coelho, L. P. et al. Similarity of the dog and human gut microbiomes in gene content and response to diet. Microbiome 6, 72 (2018).PubMed 
PubMed Central 

Google Scholar 
7.Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography, and lifestyle. Cell 176, 649–662.e20 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Partridge, S. R., Kwong, S. M., Firth, N. & Jensen, S. O. Mobile genetic elements associated with antimicrobial resistance. Clin. Microbiol. Rev. 31, (2018).9.Mende, D. R. et al. ProGenomes2: An improved database for accurate and consistent habitat, taxonomic and functional annotations of prokaryotic genomes. Nucleic Acids Res. 48, D621–D625 (2020).CAS 
PubMed 

Google Scholar 
10.Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
11.Steinegger, M. & Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35, 1026–1028 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Daniel H. et al. RefSeq: an update on prokaryotic genome annotation and curation. Nuc. Acids Res. 46, D851–D860 (2018).13.Mering, C. von et al. Quantitative phylogenetic assessment of microbial communities in diverse environments. Science 315, 1126–1130 (2007).ADS 

Google Scholar 
14.Richardson, E. J. et al. Gene exchange drives the ecological success of a multi-host bacterial pathogen. Nat. Ecol. Evol. 2, 1468–1478 (2018).PubMed 
PubMed Central 

Google Scholar 
15.Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).CAS 
PubMed 

Google Scholar 
16.Mende, D. R., Sunagawa, S., Zeller, G. & Bork, P. Accurate and universal delineation of prokaryotic species. Nat. Methods 10, 881–884 (2013).CAS 
PubMed 

Google Scholar 
17.Huerta-Cepas, J. et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper. Mol. Biol. Evol. 34, 2115–2122 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Louca, S. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936–943 (2018).PubMed 

Google Scholar 
19.Maistrenko, O. M. et al. Disentangling the impact of environmental and phylogenetic constraints on prokaryotic within-species diversity. ISME J. 14, 1247–1259 (2020).PubMed 
PubMed Central 

Google Scholar 
20.Baumdicker, F., Hess, W. R. & Pfaffelhuber, P. The diversity of a distributed genome in bacterial populations. Ann. Appl. Probab. 20, 1567–1606 (2010).MathSciNet 
MATH 

Google Scholar 
21.Sela, I., Wolf, Y. I. & Koonin, E. V. Theory of prokaryotic genome evolution. Proc. Natl Acad. Sci. USA 113, 11399–11407 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Dandekar, T., Snel, B., Huynen, M. & Bork, P. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci. 23, 324–328 (1998).CAS 
PubMed 

Google Scholar 
23.Nei, M., Suzuki, Y. & Nozawa, M. The neutral theory of molecular evolution in the genomic era. Annu. Rev. Genomics Hum. Genet. 11, 265–289 (2010).CAS 
PubMed 

Google Scholar 
24.Iranzo, J., Cuesta, J. A., Manrubia, S., Katsnelson, M. I. & Koonin, E. V. Disentangling the effects of selection and loss bias on gene dynamics. Proc. Natl Acad. Sci. USA 114, E5616–E5624 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
25.Wolf, Y. I., Makarova, K. S., Lobkovsky, A. E. & Koonin, E. V. Two fundamentally different classes of microbial genes. Nat. Microbiol. 2, 16208 (2016).CAS 
PubMed 

Google Scholar 
26.Rasko, D. A. et al. The pangenome structure of Escherichia coli: comparative genomic analysis of E. coli commensal and pathogenic isolates. J. Bacteriol. 190, 6881–6893 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
27.Koskella, B., Hall, L. J. & Metcalf, C. J. E. The microbiome beyond the horizon of ecological and evolutionary theory. Nat. Ecol. Evol. 1, 1606–1615 (2017).PubMed 

Google Scholar 
28.Liu, R. et al. Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention. Nat. Med. 23, 859–868 (2017).CAS 
PubMed 

Google Scholar 
29.Metcalf, J. L. et al. Microbial community assembly and metabolic function during mammalian corpse decomposition. Science 351, 158–162 (2015).ADS 
PubMed 

Google Scholar 
30.Vincent, C. et al. Bloom and bust: intestinal microbiota dynamics in response to hospital exposures and Clostridium difficile colonization or infection. Microbiome 4, 12 (2016).PubMed 
PubMed Central 

Google Scholar 
31.Zeller, G. et al. Potential of fecal microbiota for early‐stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766 (2014).PubMed 
PubMed Central 

Google Scholar 
32.Gibson, M. K. et al. Developmental dynamics of the preterm infant gut microbiota and antibiotic resistome. Nat. Microbiol. 1, 16024 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Zhang, X. et al. The oral and gut microbiomes are perturbed in rheumatoid arthritis and partly normalized after treatment. Nat. Med. 21, 895–905 (2015).CAS 
PubMed 

Google Scholar 
34.Brito, I. L. et al. Mobile genes in the human microbiome are structured from global to individual scales. Nature 535, 435–439 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
35.Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 842–853 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
36.Turnbaugh, P. J. et al. The human microbiome project. Nature 449, 804–810 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Hannigan, G. D. et al. The human skin double-stranded DNA virome: topographical and temporal diversity, genetic enrichment, and dynamic associations with the host microbiome. MBio 6, e01578-15 (2015).PubMed 
PubMed Central 

Google Scholar 
38.Taft, D. H. et al. Intestinal microbiota of preterm infants differ over time and between hospitals. Microbiome 2, 36 (2014).PubMed 
PubMed Central 

Google Scholar 
39.Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).CAS 
PubMed 

Google Scholar 
40.Wilhelm, R. C. et al. Biogeography and organic matter removal shape long-term effects of timber harvesting on forest soil microbial communities. ISME J. 11, 2552–2568 (2017).PubMed 
PubMed Central 

Google Scholar 
41.Xie, H. et al. Shotgun metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome. Cell Syst. 3, 572–584.e3 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
42.The MetaSUB International Consortium. The metagenomics and metadesign of the subways and urban biomes (metasub) international consortium inaugural meeting report. Microbiome 4, 24 (2016).
Google Scholar 
43.Chatelier, E. L. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).PubMed 

Google Scholar 
44.Li, J. et al. Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome 5, (2017).45.Pehrsson, E. C. et al. Interconnected microbiomes and resistomes in low-income human habitats. Nature 533, 212–216 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).CAS 
PubMed 

Google Scholar 
47.Feng, Q. et al. Gut microbiome development along the colorectal adenoma–carcinoma sequence. Nat. Commun. 6, 6528 (2015).ADS 
CAS 
PubMed 

Google Scholar 
48.Gu, Y. et al. Analyses of gut microbiota and plasma bile acids enable stratification of patients for antidiabetic treatment. Nat. Commun. 8, 1785 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
49.Karlsson, F. H. et al. Gut metagenome in european women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).ADS 
CAS 
PubMed 

Google Scholar 
50.Yu, J. et al. Metagenomic analysis of faecal microbiome as a tool towards targeted non-invasive biomarkers for colorectal cancer. Gut 66, 70–78 (2017).CAS 
PubMed 

Google Scholar 
51.Youngster, I. et al. Fecal microbiota transplant for relapsing clostridium difficile infection using a frozen inoculum from unrelated donors: a randomized, open-label, controlled pilot study. Clin. Infect. Dis. 58, 1515–1522 (2014).PubMed 
PubMed Central 

Google Scholar 
52.Guittar, J., Shade, A. & Litchman, E. Trait-based community assembly and succession of the infant gut microbiome. Nat. Commun. 10, 512 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Vogtmann, E. et al. Colorectal cancer and the human gut microbiome: reproducibility with whole-genome shotgun sequencing. PLoS ONE 11, e0155362 (2016).PubMed 
PubMed Central 

Google Scholar 
54.Chng, K. R. et al. Whole metagenome profiling reveals skin microbiome-dependent susceptibility to atopic dermatitis flare. Nat Microbiol 1, 16106 (2016).CAS 
PubMed 

Google Scholar 
55.Chu, D. M. et al. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat. Med. 23, 314–326 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
56.Van Rossum, T. et al. Spatiotemporal dynamics of river viruses, bacteria and microeukaryotes. Preprint at https://doi.org/10.1101/259861 (2018).57.Feng, Q. et al. Integrated metabolomics and metagenomics analysis of plasma and urine identified microbial metabolites associated with coronary heart disease. Sci. Rep. 6, 22525 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Oh, J., Byrd, A. L., Park, M., Kong, H. H. & Segre, J. A. Temporal stability of the human skin microbiome. Cell 165, 854–866 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Xiao, L. et al. A reference gene catalogue of the pig gut microbiome. Nat. Microbiol. 1, 16161 (2016).CAS 
PubMed 

Google Scholar 
60.R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2014).61.Coelho, L. P. et al. NG-meta-profiler: Fast processing of metagenomes using ngless, a domain-specific language. Microbiome 7, 84 (2019).PubMed 
PubMed Central 

Google Scholar 
62.Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct De Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
63.Besemer, J. & Borodovsky, M. GeneMark: web software for gene finding in prokaryotes, eukaryotes and viruses. Nucleic Acids Res. 33, W451–W454 (2005).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Coelho, L. P. Jug: Software for parallel reproducible computation in Python. J. Open Res. Softw. 5, 30 (2017).
Google Scholar 
65.Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using diamond. Nat. Methods 12, 59–60 (2015).CAS 
PubMed 

Google Scholar 
66.Eberhardt, R. Y. et al. AntiFam: A tool to help identify spurious ORFs in protein annotation. Database 2012, bas003 (2012).PubMed 
PubMed Central 

Google Scholar 
67.Kang, D. et al. MetaBAT 2: An adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).PubMed 
PubMed Central 

Google Scholar 
68.Li, H. Aligning sequence reads, clone sequences and assembly contigs with bwa-mem. Preprint at https://arxiv.org/abs/1303.3997 (2013).69.Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Bowers, R. M. et al. Minimum information about a single amplified genome (MISAG) and a metagenome-assembled genome (MIMAG) of bacteria and archaea. Nat. Biotechnol. 35, 725–731 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Zhou, W., Gay, N. & Oh, J. ReprDB and panDB: minimalist databases with maximal microbial representation. Microbiome 6, 15 (2018).PubMed 
PubMed Central 

Google Scholar 
72.Hingamp, P. et al. Exploring nucleo-cytoplasmic large DNA viruses in tara oceans microbial metagenomes. ISME J. 7, 1678–1695 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).CAS 
PubMed 

Google Scholar 
74.Huerta-Cepas, J. et al. eggNOG 5.0: A hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47, D309–D314 (2019).CAS 
PubMed 

Google Scholar 
75.Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Smyshlyaev, G., Barabas, O. & Bateman, A. Sequence analysis allows functional annotation of tyrosine recombinases in prokaryotic genomes. Mol. Syst. Biol. 17, e9880 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Jia, B. et al. CARD 2017: Expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res. 45, D566–D573 (2017).CAS 
PubMed 

Google Scholar 
78.Gibson, M. K., Forsberg, K. J. & Dantas, G. Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J. 9, 207–216 (2015).CAS 
PubMed 

Google Scholar 
79.Li, T., Fan, K., Wang, J. & Wang, W. Reduction of protein sequence complexity by residue grouping. Protein Eng. 16, 323–330 (2003).CAS 
PubMed 

Google Scholar 
80.Zhao, M., Lee, W.-P., Garrison, E. P. & Marth, G. T. SSW library: an SIMD Smith–Waterman C/C++ library for use in genomic applications. PLoS ONE 8, e82138 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
81.Li, H. Minimap2: Pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2017).
Google Scholar 
82.Milanese, A. et al. Microbial abundance, activity and population genomic profiling with mOTUs2. Nat. Commun. 10, 1014 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
83.Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).PubMed 
PubMed Central 

Google Scholar 
84.Kumar, R., Acharya, V., Singh, D. & Kumar, S. Strategies for high-altitude adaptation revealed from high-quality draft genome of non-violacein producing Janthinobacterium lividum ERGS5:01. Stand. Genomic Sci. 13, 11 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
85.Patijanasoontorn, B. et al. Hospital acquired Janthinobacterium lividum septicemia in srinagarind hospital. J. Med. Assoc. Thai. 75 Suppl 2, 6–10 (1992).PubMed 

Google Scholar 
86.Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
87.Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
88.Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).MathSciNet 
MATH 

Google Scholar 
89.Collins, R. E. & Higgs, P. G. Testing the infinitely many genes model for the evolution of the bacterial core genome and pangenome. Mol. Biol. Evol. 29, 3413–3425 (2012).CAS 
PubMed 

Google Scholar 
90.Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega. Mol. Syst. Biol. 7, 539 (2011).PubMed 
PubMed Central 

Google Scholar 
91.Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
92.Huerta-Cepas, J., Serra, F. & Bork, P. ETE 3: reconstruction, analysis, and visualization of phylogenomic data. Mol. Biol. Evol. 33, 1635–1638 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
93.Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
PubMed 
PubMed Central 

Google Scholar 
94.Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 34, W609–12 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
95.Murrell, B. et al. FUBAR: a fast, unconstrained Bayesian approximation for inferring selection. Mol. Biol. Evol. 30, 1196–1205 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
96.Smith, M. D. et al. Less is more: an adaptive branch-site random effects model for efficient detection of episodic diversifying selection. Mol. Biol. Evol. 32, 1342–1353 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
97.Washietl, S. et al. RNAcode: robust discrimination of coding and noncoding regions in comparative sequence data. RNA 17, 578–594 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Mercury additions to the study catchmentMETAALICUS was conducted on the Lake 658 catchment at the Experimental Lakes Area (ELA; now IISD-ELA), a remote area in the Precambrian Shield of northwestern Ontario, Canada (49° 43′ 95″ N, 93° 44′ 20″ W) set aside for whole-ecosystem research31. The Lake 658 catchment includes upland (41.2 ha), wetland (1.7 ha) and lake surface (8.4 ha) areas. Lake 658 is a double basin (13 m depth), circumneutral, headwater lake, with a fish community consisting of forage (yellow perch (P. flavescens) and blacknose shiner (Notropis heterolepis)), benthivorous (lake whitefish (C. clupeaformis) and white sucker (Catostomus commersonii)), and piscivorous (northern pike (E. lucius)) fishes. The lake is closed to fishing.Hg addition methods used in METAALICUS have been described in detail elsewhere19,32,33. In brief, three Hg spikes, each enriched with a different stable Hg isotope, were applied separately to the lake surface, upland and wetland areas. Upland and wetland spikes were applied once per year (when possible; Fig. 1a) by fixed-wing aircraft (Cessna 188 AGtruck). Mercury spikes (as HgNO3) were diluted in acidified water (pH 4) in a 500 l fiberglass tank and sprayed with a stainless-steel boom on upland (approximately 79.9% 200Hg) and wetland (approximately 90.1% 198Hg) areas. Spraying was completed during or immediately before a rain event, with wind speeds less than 15 km h−1 to minimize drift of spike Hg outside of target areas. Aerial spraying of upland and wetland areas left a 20-m buffer to the shoreline, which was sprayed by hand with a gas-powered pump and fire hose to within about 5 m of the lake32. Average net application rates of isotopically labelled Hg to the upland and wetland areas were 18.5 μg m−2 yr−1 and 17.8 μg m−2 yr−1, respectively.The average net application rate for lake spike Hg was 22.0 μg m−2 yr−1. For each lake addition, inorganic Hg enriched with approximately 89.7% 202Hg was added as HgNO3 from four 20-l carboys filled with acidified lake water (pH 4). Nine lake additions were conducted bi-weekly at dusk over an 18-week (wk) period during the open-water season of each year (2001–2007) by injecting at 70-cm depth into the propeller wash of trolling electric motors of two boats crisscrossing each basin of the lake32,33. It was previously demonstrated with 14C additions to an ELA lake that this approach evenly distributed spike added in the evening by the next morning34.We did not attempt to simulate Hg in rainfall for isotopic lake additions because it is impossible to simulate natural rainfall concentrations (about 10 ng l−1) in the 20-l carboys used for additions. Instead, our starting point for the experiment was to ensure that the spike was behaving as closely as possible to ambient surface water Hg very soon after it entered the lake. Several factors support this assertion. By the next morning each spike addition had increased epilimnetic Hg concentrations by only 1 ng l−1 202Hg. Average ambient concentrations were 2 ng l−1. Thus, while the Hg concentrations in the carboys were high (2.6 mg l−1), the receiving waters were soon at trace levels. Furthermore, we investigated if the additions altered the degree of bioavailability or photoreactivity of Hg(ii) in the receiving surface water. We examined the bioavailability of spike Hg(ii) as compared to ambient Hg in the lake itself using a genetically engineered bioreporter bacterium35. On seven occasions, epilimnetic samples were collected on the day before and within 12 h of spike additions. The spike was added to the lake as Hg(NO3)2, which is bioavailable to the bioreporter bacterium (detection limit = 0.1 ng Hg(ii) l−1), but we never saw bioavailable ambient or spike Hg(ii) in the lake, presumably because it was quickly bound to dissolved organic carbon (DOC). This indicates that, in terms of bioavailability, the spike Hg was behaving like ambient Hg soon after additions. Photoreactivity in the surface water was examined on seven occasions, by measuring the % of total Hg(ii) that was dissolved gaseous Hg for spike and ambient Hg, either 24 h or 48 h after the lake was spiked36. There was no significant difference (paired t-test, P > 0.05), demonstrating that by then the lake spike was behaving in the same way as ambient Hg during gaseous Hg production.Lake, food web and fish samplingWater samples were collected from May to October every four weeks at the deepest point of Lake 658. Water was pumped from six depths through acid-cleaned Teflon tubing into acid-cleaned Teflon or glass bottles. Water samples were filtered in-line using pre-ashed quartz fibre filters (Whatman GFQ, 0.7 µm). Subsequently, Hg species were measured in the filtered water samples (dissolved Hg and MeHg) and in particles collected on the quartz fibre filter (particulate Hg and MeHg).From 2001 to 2012, Lake 658 sediments were sampled at 4 fixed sites up to 5 times per year. Sampling frequency was highest in 2001, with monthly sampling from May to September, and declined over the course of the study. Fixed sites were located at depths of 0.5, 2, 3 and 7 m. A sediment survey of up to 12 additional sites was also conducted once or twice each year. Survey sites were selected to represent the full range of water depths in both basins. Cores were collected by hand by divers, or by subsampling sediments collected using a small box corer. Cores were capped and returned to the field station for processing within a few hours. For each site, three separate cores were sectioned and composited in zipper lock bags for a 0- to 2-cm depth sampling horizon, and then frozen at −20 °C.Bulk zooplankton and Chaoborus samples were collected from Lake 658 for MeHg analysis. Zooplankton were collected during the day from May to October (bi-weekly: 2001–2007; monthly: 2008–2015). A plankton net (150 μm, 0.5 m diameter) was towed vertically through the water column from 1 m above the lake bottom at the deepest point to the surface of the lake. Samples were frozen in plastic Whirl-Pak bags after removal of any Chaoborus using acid-washed tweezers. Dominant zooplankton taxa in Lake 658 included calanoid copepods (Diaptomus oregonensis) and Cladocera (Holopedium glacialis, Daphnia pulicaria and Daphnia mendotae). Chaoborus samples were collected monthly in the same manner at least 1 h after sunset. After collection, Chaoborus were picked from the sample using forceps and frozen in Whirl-Pak bags. Chaoborus were not separated by species for MeHg analyses, but both C. flavicans and C. punctipennis occur in the lake. Profundal chironomids were sampled at the deepest part of the lake using a standard Ekman grab sampler. Grab material was washed using water from a nearby lake and individual chironomids were picked by hand.All work with vertebrate animals was approved by Animal Care Committees (ACC) through the Canadian Council on Animal Care (Freshwater Institute ACC for Fisheries and Oceans Canada, 2001–2013; University of Manitoba ACC for IISD-ELA, 2014–2015). Licenses to Collect Fish for Scientific Purposes were granted annually by the Ontario Ministry of Natural Resources and Forestry. Prior to any Hg additions, a small-mesh fence was installed at the outlet of Lake 658 to the downstream lake to prevent movement of fish between lakes. Sampling for determination of MeHg concentrations (measured as total mercury (THg), see below) occurred each autumn (August–October; that is, the end of the growing season in north temperate lakes) for all fish species in Lake 658, and for northern pike and yellow perch in nearby reference Lake 240 (Extended Data Tables 2, 3). Fish collections occurred randomly throughout the lakes. Forage fish (YOY and 1+ yellow perch, and blacknose shiner) were captured using small mesh gillnets (6–10 mm) set for 90% of the Hg in muscle tissue from yellow perch in Lake 658 is MeHg40,41, here we report fish mercury data as MeHg.THg concentrations (ambient, lake spike, upland spike and wetland spike) in fish muscle samples were quantified by ICP-MS39. Samples were digested with HNO3/H2SO4 (7:3 v/v) and heated at 80 °C until brown NOx gases no longer formed. The THg in sample digests was reduced by SnCl2 to Hg0 which was then quantified by ICP-MS (Thermo-Finnigan Element2) using a continuous flow cold vapour generation technique41. To correct for procedural recoveries, all samples were spiked with 201HgCl2 prior to sample analysis. Samples of CRMs (DORM2 (2001–2011), DORM3 (2012–2013), DORM4 (2014–2015); National Research Council of Canada) were submitted to the same procedures; measured THg concentrations in the reference materials were not statistically different from certified values (P > 0.05). Detection limit for each of the spikes was 0.5% of ambient Hg.Calculations and statistical methodsAnalyses were completed with Statistica (6.1, Statsoft) and Sigmaplot (11.0, Systat Software). We present wet weight (w.w.) MeHg concentrations for all samples, except sediments which are dry weight (d.w.) concentrations. For zooplankton, Chaoborus, and profundal chironomids, d.w. MeHg concentrations were multiplied by a standard proportion (0.15) to yield w.w. concentrations for each sample42. The resulting w.w. concentrations were averaged over each open water season to determine annual means. For fish muscle biopsies, d.w. MeHg concentrations were multiplied by individual d.w. proportions to yield w.w. MeHg concentrations for each sample. To avoid any size-related biases, we calculated standardized annual MeHg concentrations (ambient and lake spike) for northern pike and lake whitefish by determining best-fit relationships between FL and MeHg concentrations for each year (quadratic polynomial, except for a linear fit for lake whitefish in 2004), and using the resulting regression equations to estimate MeHg concentrations at a standard FL43 (the mean FL of all fish sampled for each species: northern pike, 475 mm; lake whitefish, 530 mm). Square root transformation of raw northern pike data was required to satisfy assumptions of normality and homoscedasticity prior to standardization. The resulting data represent standardized concentrations of lake spike and ambient MeHg for each species each year.We used the ratio of lake spike and ambient Hg in each sample as a measure of the amount by which Hg concentrations were changed with the addition of isotopically enriched Hg:$${rm{P}}{rm{e}}{rm{r}}{rm{c}}{rm{e}}{rm{n}}{rm{t}},{rm{i}}{rm{n}}{rm{c}}{rm{r}}{rm{e}}{rm{a}}{rm{s}}{rm{e}}={[{rm{l}}{rm{a}}{rm{k}}{rm{e}}{rm{s}}{rm{p}}{rm{i}}{rm{k}}{rm{e}}{rm{H}}{rm{g}}]}_{i}/{[{rm{a}}{rm{m}}{rm{b}}{rm{i}}{rm{e}}{rm{n}}{rm{t}}{rm{H}}{rm{g}}]}_{i}times 100$$
(1)
where [lake spike Hg]i is the concentration of lake spike MeHg in sample i, and [ambient Hg]i is the concentration of ambient MeHg in sample i. For northern pike and lake whitefish, we calculated the mean annual relative increase from all individuals (not the size-standardized concentration data).Biomagnification factors (BMF) were calculated to describe differences in Hg concentrations between predator and prey5:$${rm{BMF}}={log }_{10}({[{rm{MeHg}}]}_{{rm{p}}{rm{r}}{rm{e}}{rm{d}}{rm{a}}{rm{t}}{rm{o}}{rm{r}}}/{[{rm{MeHg}}]}_{{rm{p}}{rm{r}}{rm{e}}{rm{y}}})$$
(2)
where [MeHg]predator is the mean (forage fish) or standardized (large-bodied fish) concentration of MeHg in the predator (ng g−1 w.w.) and [MeHg]prey is the mean concentration of MeHg in the prey (ng g−1 w.w.). MeHg concentration of prey items were averaged from samples collected throughout the open-water season immediately prior to autumn sampling of fish species to represent an integrated exposure for calculation of BMF. We used a dominant prey item to represent the diet of each fish species. For age 1+ yellow perch, northern pike, and lake whitefish, dominant prey items were zooplankton, forage fishes (YOY and 1+ yellow perch, and blacknose shiner) and Chaoborus, respectively.To assess loss of lake spike MeHg by northern pike during the recovery period (2008–2015), we calculated28 whole body burdens (in μg) of lake spike MeHg for the standardized population and for individuals that had been sampled in autumn 2007 (t0 is the final time spike Hg was added to the lake) and again in at least one subsequent year during annual autumn sampling (n = 16 fish, of which 1–9 individuals were recaptured annually from 2008–2015). This calculation of MeHg burden is a relative measure of whole fish Hg content because MeHg is higher in muscle tissue than in other tissue types28,40. For the standardized population data, we used best-fit relationships between FL (in mm) and body weight (in g; quadratic polynomial) to determine body weight at the standard FL. We multiplied this body weight by standard ambient and spike MeHg concentrations (in ng g−1 w.w.) in muscle tissue for each year to determine body burdens over time (in ng). For individual fish, we multiplied spike MeHg concentration (in ng g−1 w.w.) by body weight (in g) to yield individual body burdens (in ng). To account for differences among individuals and between individuals and the population, we normalized the data to examine the mean proportion of original (t0) lake spike MeHg burden present in northern pike each year of the recovery period (2008–2015).$${rm{change}},{rm{in}},{rm{burden}},{rm{from}},{t}_{0}={{rm{burden}}}_{{rm{tx}}}/{{rm{burden}}}_{{rm{t}}0}$$
(3)
We used a best fit regression (exponential decay, beginning in the second year of recovery) to estimate the half-life (50% of original burden) of lake spike MeHg for the population.Northern pike and lake whitefish ages were determined by cleithra and otoliths, respectively, if mortality had occurred, but most ages were quantified using fin rays collected from live fish44 (K. H. Mills, DFO or North/South Consultants). Northern pike of the sizes selected for biopsy sampling had a median age of 3 years (range: 2–12 years; n = 305); the median age of lake whitefish was 17 years (range: 3–38 years; n = 86).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this paper. More