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    Decadal shifts in traits of reef fish communities in marine reserves

    1.O’Leary, B. C. et al. Effective coverage targets for ocean protection. Conserv. Lett. 9, 398–404 (2016).
    Google Scholar 
    2.Edgar, G. J. et al. Global conservation outcomes depend on marine protected areas with five key features. Nature 506, 216–220 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    3.Lester, S. E. et al. Biological effects within no-take marine reserves: A global synthesis. Mar. Ecol. Prog. Ser. 384, 33–46 (2009).ADS 

    Google Scholar 
    4.Brandl, S. J., Emslie, M. J. & Ceccarelli, D. M. Habitat degradation increases functional originality in highly diverse coral reef fish assemblages. Ecosphere 7, e01557 (2016).
    Google Scholar 
    5.Ramírez-Ortiz, G. et al. Reduced fish diversity despite increased fish biomass in a Gulf of California Marine Protected Area. PeerJ 2020, e8885 (2020).
    Google Scholar 
    6.Miatta, M., Bates, A. E. & Snelgrove, P. V. R. Incorporating biological traits into conservation. Strategies https://doi.org/10.1146/annurev-marine-032320 (2021).Article 

    Google Scholar 
    7.Coleman, M. A. et al. Functional traits reveal early responses in marine reserves following protection from fishing. Divers. Distrib. 21, 876–887 (2015).ADS 

    Google Scholar 
    8.Bellwood, D. R., Streit, R. P., Brandl, S. J. & Tebbett, S. B. The meaning of the term ‘function’ in ecology: A coral reef perspective. Funct. Ecol. 33, 1365–2435. https://doi.org/10.1111/1365-2435.13265 (2019).Article 

    Google Scholar 
    9.Brandl, S. J. et al. Coral reef ecosystem functioning: Eight core processes and the role of biodiversity. Front. Ecol. Environ. https://doi.org/10.1002/fee.2088 (2019).Article 

    Google Scholar 
    10.McLean, M., Mouillot, D., Villéger, S., Graham, N. A. J. & Auber, A. Interspecific differences in environmental response blur trait dynamics in classic statistical analyses. Mar. Biol. 166, 1–10 (2019).
    Google Scholar 
    11.Hadj-Hammou, J., Mouillot, D. & Graham, N. A. J. Response and effect traits of coral reef fish. Front. Mar. Sci. 8, 640619 (2021).
    Google Scholar 
    12.Griffin-Nolan, R. J. et al. Trait selection and community weighting are key to understanding ecosystem responses to changing precipitation regimes. Funct. Ecol. 32, 1746–1756 (2018).
    Google Scholar 
    13.Lefcheck, J. S. et al. Tropical fish diversity enhances coral reef functioning across multiple scales. Sci. Adv. 5, eaav6420 (2019).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    14.McLean, M. et al. A climate-driven functional inversion of connected marine ecosystems. Curr. Biol. 28, 3654-3660.e3 (2018).CAS 
    PubMed 

    Google Scholar 
    15.Mouillot, D., Graham, N. A. J., Villéger, S., Mason, N. W. H. & Bellwood, D. R. A functional approach reveals community responses to disturbances. Trends Ecol. Evol. 28, 167–177 (2013).PubMed 

    Google Scholar 
    16.Harborne, A. R. & Mumby, P. J. Novel ecosystems: Altering fish assemblages in warming waters. Curr. Biol. 21, R822–R824 (2011).CAS 
    PubMed 

    Google Scholar 
    17.Graham, N. A. J., Cinner, J. E., Norström, A. V. & Nyström, M. Coral reefs as novel ecosystems: Embracing new futures. Curr. Opin. Environ. Sustain. 7, 9–14 (2014).
    Google Scholar 
    18.Woodhead, A. J., Hicks, C. C., Norström, A. V., Williams, G. J. & Graham, N. A. J. Coral reef ecosystem services in the Anthropocene. Funct. Ecol. 33, 1023–1034 (2019).
    Google Scholar 
    19.Munday, P. L. & Jones, G. P. The ecological implications of small body size among coral-reef fishes. Oceanogr. Mar. Biol. Annu. Rev. 36, 373–411 (1998).
    Google Scholar 
    20.Babcock, R. C. et al. Decadal trends in marine reserves reveal differential rates of change in direct and indirect effects. Proc. Natl. Acad. Sci. 107, 18256–18261 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Robinson, J. P. W. et al. Fishing degrades size structure of coral reef fish communities. Glob. Change Biol. 23, 1009–1022 (2017).ADS 

    Google Scholar 
    22.Villéger, S., Brosse, S., Mouchet, M., Mouillot, D. & Vanni, M. J. Functional ecology of fish: Current approaches and future challenges. Aquat. Sci. 79, 783–801 (2017).
    Google Scholar 
    23.Cinner, J. E. et al. Meeting fisheries, ecosystem function, and biodiversity goals in a human-dominated world. Science (80-.) 368, 307–311 (2020).ADS 
    CAS 

    Google Scholar 
    24.McClanahan, T. R. Kenyan coral reef lagoon fish: Effects of fishing, substrate complexity, and sea urchins. Coral Reefs 13, 231–241 (1994).ADS 

    Google Scholar 
    25.McClanahan, T. R. & Graham, N. A. J. Recovery trajectories of coral reef fish assemblages within Kenyan marine protected areas. Mar. Ecol. Prog. Ser. 294, 241–248 (2005).ADS 

    Google Scholar 
    26.Graham, N. A. J. et al. Changing role of coral reef marine reserves in a warming climate. Nat. Commun. 111(11), 1–8 (2020).
    Google Scholar 
    27.Greene, L. E. The use of discrete group censusing for assessment and monitoring of reef fish assemblages. PhD diss., Florida Institute of Technology, Melbourne (1990).28.McClanahan, T. R., Graham, N. A. J., Calnan, J. M. & MacNeil, M. A. Toward pristine biomass: Reef fish recovery in coral reef marine protected areas in Kenya. Ecol. Appl. 17, 1055–1067 (2007).PubMed 

    Google Scholar 
    29.McClanahan, T. R. & Humphries, A. T. Differential and slow life-history responses of fishes to coral reef closures. Mar. Ecol. Prog. Ser. 469, 121–131 (2012).ADS 

    Google Scholar 
    30.Kublicki, M. GASPAR general approach to species-abundance relationships in a context of global change, reef fish species as a model (2010).31.Froese, R. & Pauly, D. FishBase. World Wide Web Electronic Publication. (2019). Available at: http://www.fishbase.org. Accessed 23 May 2019.32.Thorson, J. T., Munch, S. B., Cope, J. M. & Gao, J. Predicting life history parameters for all fishes worldwide. Ecol. Appl. 27, 2262–2276 (2017).PubMed 

    Google Scholar 
    33.Rousseeuw, P. et al. Finding Groups in Data: Cluster Analysis Extended Rousseeuw et al. CRAN (Comprehensive R Archive Network (CRAN), 2018).34.Paradis, E. et al. Package ‘ape’: Analyses of Phylogenetics and Evolution Depends R. (2019).35.Laliberté, E., Legendre, P. & Maintainer, B. S. Package ‘FD’ Type Package Title Measuring Functional Diversity (FD) from Multiple Traits, and Other Tools for Functional Ecology (2015).36.Lavorel, S. et al. Assessing functional diversity in the field—Methodology matters!. Funct. Ecol. 22, 134–147 (2007).
    Google Scholar 
    37.Fontoura, L. et al. Climate-driven shift in coral morphological structure predicts decline of juvenile reef fishes. Glob. Change Biol. 26, 557–567 (2020).ADS 

    Google Scholar 
    38.McClanahan, T. Coral reef fish communities, diversity, and their fisheries and biodiversity status in East Africa. Mar. Ecol. Prog. Ser. 632, 175–191 (2019).ADS 

    Google Scholar 
    39.Selig, E. R., Casey, K. S. & Bruno, J. F. New insights into global patterns of ocean temperature anomalies: Implications for coral reef health and management. Glob. Ecol. Biogeogr. 19, 397–411 (2010).
    Google Scholar 
    40.Ye, H., Deyle, E. R., Gilarranz, L. J. & Sugihara, G. Distinguishing time-delayed causal interactions using convergent cross mapping. Sci. Rep. 5, 14750 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Wilson, S. K. et al. Influence of nursery microhabitats on the future abundance of a coral reef fish. Proc. R. Soc. B Biol. Sci. 283, 1–7 (2016).
    Google Scholar 
    42.McClanahan, T. R. Decadal turnover of thermally stressed coral taxa support a risk-spreading approach to marine reserve design. Coral Reefs https://doi.org/10.1007/s00338-020-01984-w (2020).Article 

    Google Scholar 
    43.Yeager, L. A., Marchand, P., Gill, D. A., Baum, J. K. & McPherson, J. M. Marine socio-environmental covariates: Queryable global layers of environmental and anthropogenic variables for marine ecosystem studies. Ecology 98, 1976 (2017).PubMed 

    Google Scholar 
    44.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (2009).45.Wood, S. N. Generalized Additive Models: An Introduction with R 2nd edn. (CRC Press, 2017).MATH 

    Google Scholar 
    46.Simpson, G. L. Modelling palaeoecological time series using generalised additive models. Front. Ecol. Evol. 6, 149 (2018).
    Google Scholar 
    47.Wood, S. N. Low-rank scale-invariant tensor product smooths for generalized additive mixed models. Biometrics 62, 1025–1036 (2006).MathSciNet 
    PubMed 
    MATH 

    Google Scholar 
    48.Pedersen, E. J., Miller, D. L., Simpson, G. L. & Ross, N. Hierarchical generalized additive models in ecology: An introduction with mgcv. PeerJ 2019, e6876 (2019).
    Google Scholar 
    49.Pecuchet, L. et al. From traits to life-history strategies: Deconstructing fish community composition across European seas. Glob. Ecol. Biogeogr. 26, 812–822 (2017).
    Google Scholar 
    50.Dormann, F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: A review. Ecography 30, 609–628 (2007).
    Google Scholar 
    51.Schulp, C. J. E., Lautenbach, S. & Verburg, P. H. Quantifying and mapping ecosystem services: Demand and supply of pollination in the European Union. Ecol. Indic. 36, 131–141 (2014).
    Google Scholar 
    52.Warton, D. I. & Hui, F. K. C. The arcsine is asinine: The analysis of proportions in ecology. Ecology 92, 3–10 (2011).PubMed 

    Google Scholar 
    53.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).54.MacNeil, M. A. et al. Recovery potential of the world’s coral reef fishes. Nature 520, 341–344 (2015).ADS 
    CAS 

    Google Scholar 
    55.McClanahan, T. R., Ateweberhan, M., Muhando, C. A., Maina, J. & Mohammed, M. S. Effects of climate and seawater temperature variation on coral bleaching and mortality. Ecol. Monogr. 77, 503–525 (2007).
    Google Scholar 
    56.Chirico, A. A. D., McClanahan, T. R. & Eklöf, J. S. Community- and government-managed marine protected areas increase fish size, biomass and potential value. PLoS ONE 12, e0182342 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    57.McClanahan, T. R., Friedlander, A. M., Graham, N. A. J., Chabanet, P. & Bruggemann, J. H. Variability in coral reef fish baseline and benchmark biomass in the central and western Indian Ocean provinces. Aquat. Conserv. Mar. Freshw. Ecosyst. https://doi.org/10.1002/aqc.3448 (2020).Article 

    Google Scholar 
    58.Mbaru, E. K., Graham, N. A. J., McClanahan, T. R. & Cinner, J. E. Functional traits illuminate the selective impacts of different fishing gears on coral reefs. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13547 (2019).Article 

    Google Scholar 
    59.Dulvy, N. K., Polunin, N. V. C., Mill, A. C. & Graham, N. A. J. Size structural change in lightly exploited coral reef fish communities: Evidence for weak indirect effects. Can. J. Fish. Aquat. Sci. 61, 466–475 (2004).
    Google Scholar 
    60.D’Agata, S. et al. Marine reserves lag behind wilderness in the conservation of key functional roles. Nat. Commun. 7, 12000 (2016).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Mbaru, E. K. & McClanahan, T. R. Escape gaps in African basket traps reduce bycatch while increasing body sizes and incomes in a heavily fished reef lagoon. Fish. Res. 148, 90–99 (2013).
    Google Scholar 
    62.Grime, J. P. Benefits of plant diversity to ecosystems: Immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
    Google Scholar 
    63.Campbell, S. J. et al. Fishing restrictions and remoteness deliver conservation outcomes for Indonesia’s coral reef fisheries. Conserv. Lett. https://doi.org/10.1111/conl.12698 (2020).Article 

    Google Scholar 
    64.Heenan, A., Williams, G. J. & Williams, I. D. Natural variation in coral reef trophic structure across environmental gradients. Front. Ecol. Environ. 18, 69–75 (2020).
    Google Scholar 
    65.Morais, R. A. & Bellwood, D. R. Pelagic subsidies underpin fish productivity on a degraded coral reef. Curr. Biol. 29, 1521-1527.e6 (2019).CAS 
    PubMed 

    Google Scholar 
    66.González-Rivero, M. et al. Linking fishes to multiple metrics of coral reef structural complexity using three-dimensional technology. Sci. Rep. 7, 1–15 (2017).
    Google Scholar 
    67.Coker, D. J., Graham, N. A. J. & Pratchett, M. S. Interactive effects of live coral and structural complexity on the recruitment of reef fishes. Coral Reefs 31, 919–927 (2012).ADS 

    Google Scholar 
    68.Benkwitt, C. E., Wilson, S. K. & Graham, N. A. J. Seabird nutrient subsidies alter patterns of algal abundance and fish biomass on coral reefs following a bleaching event. Glob. Change Biol. 25, 2619–2632 (2019).ADS 

    Google Scholar 
    69.Russ, G. R., Aller-Rojas, O. D., Rizzari, J. R. & Alcala, A. C. Off-reef planktivorous reef fishes respond positively to decadal-scale no-take marine reserve protection and negatively to benthic habitat change. Mar. Ecol. 38, e12442 (2017).ADS 

    Google Scholar 
    70.Darling, E. S., McClanahan, T. R. & Côté, I. M. Life histories predict coral community disassembly under multiple stressors. Glob. Change Biol. 19, 1930–1940 (2013).ADS 

    Google Scholar 
    71.Strain, E. M. A. et al. A global assessment of the direct and indirect benefits of marine protected areas for coral reef conservation. Divers. Distrib. 25, 9–20 (2019).
    Google Scholar 
    72.Floeter, S. R., Bender, M. G., Siqueira, A. C. & Cowman, P. F. Phylogenetic perspectives on reef fish functional traits. Biol. Rev. 93, 131–151 (2018).PubMed 

    Google Scholar 
    73.Michael, P. J., Hyndes, G. A., Vanderklift, M. A. & Vergés, A. Identity and behaviour of herbivorous fish influence large-scale spatial patterns of macroalgal herbivory in a coral reef. Mar. Ecol. Prog. Ser. 482, 227–240 (2013).ADS 

    Google Scholar 
    74.Paijmans, K. C., Booth, D. J. & Wong, M. Y. L. Predation avoidance and foraging efficiency contribute to mixed-species shoaling by tropical and temperate fishes. J. Fish Biol. 96, 806–814 (2020).PubMed 

    Google Scholar 
    75.White, J. W. & Warner, R. R. Behavioral and energetic costs of group membership in a coral reef fish. Oecologia 154, 423–433 (2007).ADS 
    PubMed 

    Google Scholar 
    76.van Kooten, T., Persson, L. & de Roos, A. M. Population dynamical consequences of gregariousness in a size-structured consumer-resource interaction. J. Theor. Biol. 245, 763–774 (2007).ADS 
    MathSciNet 
    PubMed 
    MATH 

    Google Scholar 
    77.Kelley, J. L., Grierson, P. F., Collin, S. P. & Davies, P. M. Habitat disruption and the identification and management of functional trait changes. Fish Fish. 19, 716–728 (2018).
    Google Scholar 
    78.Rochet, M. Short-term effects of fishing on life history traits of fishes. ICES J. Mar. Sci. 55, 371–391 (1998).
    Google Scholar 
    79.McClanahan, T. R. et al. Global baselines and benchmarks for fish biomass: Comparing remote reefs and fisheries closures. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps12874 (2019).Article 

    Google Scholar 
    80.Jacob, U. et al. The role of body size in complex food webs: A cold case. Adv. Ecol. Res. 45, 181–223 (2011).
    Google Scholar 
    81.McClanahan, T. R. & Graham, N. A. J. Marine reserve recovery rates towards a baseline are slower for reef fish community life histories than biomass. Proc. Biol. Sci. 282, 20151938 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    82.Humphries, A. T. Algal turf consumption by sea urchins and fishes is mediated by fisheries management on coral reefs in Kenya. Coral Reefs https://doi.org/10.1007/s00338-020-01943-5 (2020).Article 

    Google Scholar 
    83.Ward, T. J., Heinemann, D. & Evans, N. The role of marine reserves as fisheries management tools. A review of concepts, evidence and international experience. Bur. Rural Sci. Aust. 192, 105 (2001).
    Google Scholar 
    84.Bergseth, B. J., Williamson, D. H., Russ, G. R., Sutton, S. G. & Cinner, J. E. A social-ecological approach to assessing and managing poaching by recreational fishers. Front. Ecol. Environ. 15, 67–73 (2017).
    Google Scholar 
    85.McClanahan, T. R. Recovery of functional groups and trophic relationships in tropical fisheries closures. Mar. Ecol. Prog. Ser. 497, 13–23 (2014).ADS 

    Google Scholar 
    86.Mcclanahan, T. R. & Omukoto, J. O. Comparison of modern and historical fish catches (AD 750–1400) to inform goals for marine protected areas and sustainable fisheries. Conserv. Biol. 25, 945–955 (2011).PubMed 

    Google Scholar 
    87.Williams, G. J. & Graham, N. A. J. Rethinking coral reef functional futures. Funct. Ecol. 33, 942–947 (2019).
    Google Scholar  More

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    Community RNA-Seq: multi-kingdom responses to living versus decaying roots in soil

    1.Swift MJ, Anderson JM, Heal OW. Decomposition in terrestrial ecosystems. Oxford: Blackwell Publishing; 1979.2.Scholes MC, Powlson D, Tian G. Input control of organic matter dynamics. Geoderma. 1997;79:25–47.CAS 

    Google Scholar 
    3.Sokol NW, Kuebbing SE, Ayala EK, Bradford MA. Evidence for the primacy of living root inputs, not root or shoot litter, in forming soil organic carbon. New Phytologist. 2019;221:233–46.CAS 

    Google Scholar 
    4.Jackson RB, Lajtha K, Crow SE, Hugelius G, Kramer MG, Piñeiro G. The ecology of soil carbon: pools, vulnerabilities, and biotic and abiotic controls. Ann Rev Ecol Evol Syst. 2017;48:419–45.
    Google Scholar 
    5.Greyston SJ, Vaughan D, Jones D. Rhizosphere carbon flow in trees, in comparison with annual plants: the importance of root exudation and its impact on microbial activity and nutrient availability. Appl Soil Ecol. 1996;5:29–56.
    Google Scholar 
    6.Schimel DS. Terrestrial biogeochemical cycles: global estimates with remote sensing. Remote Sens Environ. 1995;51:49–56.
    Google Scholar 
    7.Angst G, Mueller KE, Nierop KGJ, Simpson MJ. Plant- or microbial-derived? A review on the molecular composition of stabilized soil organic matter. Soil Biol Biochem. 2021;156:108189.CAS 

    Google Scholar 
    8.Bardgett RD. The biology of soil: a community ecosystem approach. Oxford: Oxford University Press; 2005.9.Schimel JP, Schaeffer SM. Microbial control over carbon cycling in soil. Front Microbiol. 2012;3:1–11.
    Google Scholar 
    10.Geisen S, Mitchell EAD, Wilkinson DM, Adl S, Bonkowski M, Brown MW, et al. Soil protistology rebooted: 30 fundamental questions to start with. Soil Biol Biochem. 2017;111:94–103.CAS 

    Google Scholar 
    11.Purahong W, Wubet T, Lentendu G, Schloter M, Pecyna MJ, Kapturska D, et al. Life in leaf litter: novel insights into community dynamics of bacteria and fungi during litter decomposition. Mol Ecol. 2016;25:4059–74.CAS 
    PubMed 

    Google Scholar 
    12.Osono T. Ecology of ligninolytic fungi associated with leaf litter decomposition. Ecol Res. 2007;22:955–74.
    Google Scholar 
    13.Hattenschwiler S, Tiunov AV, Scheu S. Biodiversity and litter decomposition in terrestrial ecosystems. Ann Rev Ecol Evol Syst. 2005;36:191–218.
    Google Scholar 
    14.Pugh G. Terrestrial fungi. In: Dickenson C, Pugh G, editors. Biology of plant litter decomposition. 2. London: Academic Press Inc.; 1974. p. 303–36.15.Sinsabaugh RL, Moorhead DL. Resource allocation to extracellular enzyme production: a model for nitrogen and phosphorus control of litter decomposition. Soil Biol Biochem. 1994;26:1305–11.
    Google Scholar 
    16.Geisen S, Koller R, Hünninghaus M, Dumack K, Urich T, Bonkowski M. The soil food web revisited: Diverse and widespread mycophagous soil protists. Soil Biol Biochem. 2016;94:10–8.CAS 

    Google Scholar 
    17.Chakraborty S, Old K. Ultrastructure and description of a fungus-feeding amoeba, Trichamoeba mycophaga n. sp. (Amoebidae, Amoebea), from Australia. J Eukaryot Microbiol. 1986;33:564–9.
    Google Scholar 
    18.Bjørnlund L, Rønn R. ‘David and Goliath’of the soil food web–Flagellates that kill nematodes. Soil Biol Biochem. 2008;40:2032–9.
    Google Scholar 
    19.Xiong W, Jousset A, Guo S, Karlsson I, Zhao Q, Wu H, et al. Soil protist communities form a dynamic hub in the soil microbiome. ISME J. 2018;12:634–8.PubMed 

    Google Scholar 
    20.Neher DA, Weicht TR, Barbercheck ME. Linking invertebrate communities to decomposition rate and nitrogen availability in pine forest soils. Appl Soil Ecol. 2012;54:14–23.
    Google Scholar 
    21.Bokhorst S, Wardle DA. Microclimate within litter bags of different mesh size: Implications for the ‘arthropod effect’ on litter decomposition. Soil Biol Biochem. 2013;58:147–52.CAS 

    Google Scholar 
    22.Carrillo Y, Ball BA, Bradford MA, Jordan CF, Molina M. Soil fauna alter the effects of litter composition on nitrogen cycling in a mineral soil. Soil Biol Biochem. 2011;43:1440–9.CAS 

    Google Scholar 
    23.Riutta T, Slade EM, Bebber DP, Taylor ME, Malhi Y, Riordan P, et al. Experimental evidence for the interacting effects of forest edge, moisture and soil macrofauna on leaf litter decomposition. Soil Biol Biochem. 2012;49:124–31.CAS 

    Google Scholar 
    24.Meyer WM, Ostertag R, Cowie RH. Macro-invertebrates accelerate litter decomposition and nutrient release in a Hawaiian rainforest. Soil Biol Biochem. 2011;43:206–11.CAS 

    Google Scholar 
    25.Stout JD. The Relationship between protozoan populations and biological activity in soils. Integr Comp Biol. 1973;13:193–201.
    Google Scholar 
    26.Bonkowski M, Griffiths B, Scrimgeour C. Substrate heterogeneity and microfauna in soil organic ‘hotspots’ as determinants of nitrogen capture and growth of ryegrass. Appl Soil Ecol. 2000;14:37–53.
    Google Scholar 
    27.Hünninghaus M, Dibbern D, Kramer S, Koller R, Pausch J, Schloter-Hai B, et al. Disentangling carbon flow across microbial kingdoms in the rhizosphere of maize. Soil Biol Biochem. 2019;134:122–30.
    Google Scholar 
    28.Tedersoo L, Anslan S. Towards PacBio‐based pan‐eukaryote metabarcoding using full‐length ITS sequences. Environ Microbiol Rep. 2019;11:659–68.CAS 
    PubMed 

    Google Scholar 
    29.Tedersoo L, Anslan S, Bahram M, Põlme S, Riit T, Liiv I, et al. Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. Mycokeys. 2015;10:1–43.
    Google Scholar 
    30.Guillou L, Bachar D, Audic S, Bass D, Berney C, Bittner L, et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote Small Sub-Unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 2013;41:D597–604.CAS 
    PubMed 

    Google Scholar 
    31.Baldrian P, Kolařík M, Stursová M, Kopecký J, Valášková V, Větrovský T, et al. Active and total microbial communities in forest soil are largely different and highly stratified during decomposition. ISME J. 2012;6:248–58.CAS 
    PubMed 

    Google Scholar 
    32.Poisot T, Péquin B, Gravel D. High‐throughput sequencing: a roadmap toward community ecology. Ecol Evol. 2013;3:1125–39.PubMed 
    PubMed Central 

    Google Scholar 
    33.Nguyen NH, Smith D, Peay K, Kennedy P. Parsing ecological signal from noise in next generation amplicon sequencing. New Phytol. 2015;205:1389–93.CAS 
    PubMed 

    Google Scholar 
    34.Engelbrektson A, Kunin V, Wrighton KC, Zvenigorodsky N, Chen F, Ochman H, et al. Experimental factors affecting PCR-based estimates of microbial species richness and evenness. ISME J. 2010;4:642–7.CAS 
    PubMed 

    Google Scholar 
    35.Suzuki MT, Giovannoni SJ. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl Environ Microbiol. 1996;62:625–30.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Soergel D, Dey N, Knight R, Brenner S. Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. ISME J. 2012;6:1440–4.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Nomura M, Gourse R, Baughman G. Regulation of the synthesis of ribosomes and ribosomal components. Annu Rev Biochem. 1984;53:75–117.CAS 
    PubMed 

    Google Scholar 
    38.Urich T, Lanzén A, Qi J, Huson DH, Schleper C, Schuster SC. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS ONE. 2008;3:e2527.PubMed 
    PubMed Central 

    Google Scholar 
    39.Kembel SW, Wu M, Eisen JA, Green JL. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Comp Biol. 2012;8:e1002743.CAS 

    Google Scholar 
    40.Gong W, Marchetti A. Estimation of 18S gene copy number in marine eukaryotic plankton using a next-generation sequencing approach. Front Mar Sci. 2019;6:219.
    Google Scholar 
    41.Miller CS, Baker BJ, Thomas BC, Singer SW, Banfield JF. EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data. Genome Biol. 2011;12:R44.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Xue Y, Lanzén A, Jonassen I. Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data. Bioinformatics. 2020;36:3365–71.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    43.Bang-Andreasen T, Anwar MZ, Lanzén A, Kjøller R, Rønn R, Ekelund F, et al. Total RNA-sequencing reveals multi-level microbial community changes and functional responses to wood ash application in agricultural and forest soil. FEMS Microbiol Ecol. 2020;96:fiaa016.PubMed 
    PubMed Central 

    Google Scholar 
    44.Geisen S, Tveit AT, Clark IM, Richter A, Svenning MM, Bonkowski M, et al. Metatranscriptomic census of active protists in soils. ISME J. 2015;9:2178–90.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Adl SM, Habura A, Eglit Y. Amplification primers of SSU rDNA for soil protists. Soil Biol Biochem. 2014;69:328–42.CAS 

    Google Scholar 
    46.Wagner M, Nielsen PH, Loy A, Nielsen JL, Daims H. Linking microbial community structure with function: fluorescence in situ hybridization-microautoradiography and isotope arrays. Curr Opin Biotechnol. 2006;17:83–91.CAS 
    PubMed 

    Google Scholar 
    47.Neufeld J, Wagner M, Murrell J. Who eats what, where and when? Isotope-labelling experiments are coming of age. ISME J. 2007;1:103–10.CAS 
    PubMed 

    Google Scholar 
    48.Radajewski S, Ineson P, Parekh NR, Murrell J. Stable-isotope probing as a tool in microbial ecology. Nature. 2000;403:646–9.CAS 
    PubMed 

    Google Scholar 
    49.Radajewski S, Murrell JC. Stable isotope probing for detection of methanotrophs after enrichment with 13CH4. In: de Muro MA, Rapley R, editors. Gene probes: principles and protocols. Totowa, NJ: Humana Press; 2002. p. 149–57.50.Manefield M, Whiteley AS, Griffiths R, Bailey MJ. RNA stable isotope probing, a novel means of linking microbial community function to phylogeny. Appl Environ Microbiol. 2002;68:5367–73.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Mayali X, Weber PK, Nuccio E, Lietard J, Somoza M, Blazewicz SJ, et al. Stable isotope probing, methods and protocols. Methods Mol Biol. 2019;2046:71–87.PubMed 

    Google Scholar 
    52.Mayali X, Weber PK, Brodie EL, Mabery S, Hoeprich PD, Pett-Ridge J. High-throughput isotopic analysis of RNA microarrays to quantify microbial resource use. ISME J. 2012;6:1210–21.CAS 
    PubMed 

    Google Scholar 
    53.Waldrop MP, Firestone MK. Seasonal dynamics of microbial community composition and function in oak canopy and open grassland soils. Microb Ecol. 2006;52:470–9.CAS 
    PubMed 

    Google Scholar 
    54.Shi S, Nuccio E, Herman DJ, Rijkers R, Estera K, Li J, et al. Successional trajectories of rhizosphere bacterial communities over consecutive seasons. mBio. 2015;6:e00746.PubMed 
    PubMed Central 

    Google Scholar 
    55.DeAngelis KM, Brodie EL, DeSantis TZ, Andersen GL, Lindow SE, Firestone MK. Selective progressive response of soil microbial community to wild oat. ISME J. 2009;3:168–78.CAS 
    PubMed 

    Google Scholar 
    56.Jaeger CH, Lindow SE, Miller W, Clark E, Firestone MK. Mapping of sugar and amino acid availability in soil around roots with bacterial sensors of sucrose and tryptophan. Appl Environ Microbiol. 1999;65:2685–90.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Nuccio EE, Starr E, Karaoz U, Brodie EL, Zhou J, Tringe SG, et al. Niche differentiation is spatially and temporally regulated in the rhizosphere. ISME J. 2020;269:1–16.
    Google Scholar 
    58.Griffiths RI, Whiteley AS, O’Donnell AG, Bailey M. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl Environ Microbiol. 2000;66:5488–91.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Andrews S. FastQC: a quality control tool for high throughput sequence data (Version 0.10.1) 2012; http://www.bioinformatics.babraham.ac.uk/projects/fastqc/60.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.McDonald D, Price MN, Goodrich J, Nawrocki EP, DeSantis TZ, Probst A, et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 2012;6:610–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.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:D590–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    63.Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10:R25.PubMed 
    PubMed Central 

    Google Scholar 
    64.Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    65.Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Wang Q, Garrity GM, Tiedje JM, Cole JR. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol. 2007;73:5261–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27:2194–200.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Miller CS, Handley KM, Wrighton KC, Frischkorn KR, Thomas BC, Banfield JF. Short-read assembly of full-length 16S amplicons reveals bacterial diversity in subsurface sediments. PLoS ONE. 2013;8:e56018.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 2005;71:8228–35.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Choi J, Kim S-H. A genome tree of life for the Fungi kingdom. Proc Natl Acad Sci USA. 2017;114:9391–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2018;47:D259–64.PubMed Central 

    Google Scholar 
    72.Adl SM, Simpson AGB, Farmer MA, Andersen RA, Anderson OR, Barta JR, et al. The new higher level classification of eukaryotes with emphasis on the taxonomy of protists. J Eukaryot Microbiol. 2005;52:399–451.
    Google Scholar 
    73.Ludwig W, Strunk O, Westram R, Richter L, Meier H, Yadhukumar, et al. ARB: a software environment for sequence data. Nucleic Acids Res. 2004;32:1363–71.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Mayali X, Weber PK, Pett-Ridge J. Taxon-specific C/N relative use efficiency for amino acids in an estuarine community. FEMS Microbiol Ecol. 2013;83:402–12.CAS 
    PubMed 

    Google Scholar 
    75.Pausch J, Kramer S, Scharroba A, Scheunemann N, Butenschoen O, Kandeler E, et al. Small but active—pool size does not matter for carbon incorporation in below‐ground food webs. Funct Ecol. 2016;30:479–89.
    Google Scholar 
    76.el Zahar Haichar F, Achouak W, Christen R. Identification of cellulolytic bacteria in soil by stable isotope probing. Environ Microbiol. 2007;9:625–34.CAS 

    Google Scholar 
    77.Ha YE, Kang CI, Joo EJ, Park SY, Kang SJ, Wi YM, et al. Bacterial populations assimilating carbon from 13C-labeled plant residue in soil: analysis by a DNA-SIP approach. Soil Biol Biochem. 2011;43:814–22.
    Google Scholar 
    78.Eichorst SA, Kuske CR. Identification of cellulose-responsive bacterial and fungal communities in geographically and edaphically different soils by using stable isotope probing. Appl Environ Microbiol. 2012;78:2316–27.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.Pepe-Ranney C, Campbell AN, Koechli CN, Berthrong S, Buckley DH. Unearthing the ecology of soil microorganisms using a high resolution DNA-SIP approach to explore cellulose and xylose metabolism in soil. Front Microbiol. 2016;7:626.
    Google Scholar 
    80.Wilhelm RC, Pepe-Ranney C, Weisenhorn P, Lipton M, Buckley DH. Competitive exclusion and metabolic dependency among microorganisms structure the cellulose economy of an agricultural soil. mBio. 2021;12:e03099-20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    81.Lehtovirta-Morley LE, Ross J, Hink L, Weber EB, Gubry-Rangin C, Thion C, et al. Isolation of ‘Candidatus Nitrosocosmicus franklandus’, a novel ureolytic soil archaeal ammonia oxidiser with tolerance to high ammonia concentration. FEMS Microbiol Ecol. 2016;92:fiw057.PubMed 
    PubMed Central 

    Google Scholar 
    82.Nuccio EE, Anderson-Furgeson J, Estera KY, Pett-Ridge J, De Valpine P, Brodie EL, et al. Climate and edaphic controllers influence rhizosphere community assembly for a wild annual grass. Ecology. 2016;97:1307–18.PubMed 

    Google Scholar 
    83.Ceja-Navarro JA, Wang Y, Arellano A, Ramanculova L, Yuan M, Byer A, et al. Protist diversity and network complexity in the rhizosphere are dynamic and changing as the plant develops. Microbiome. 2021;9. https://doi.org/10.1186/s40168-021-01042-9.
    Google Scholar 
    84.Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi S, et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470–80.CAS 
    PubMed 

    Google Scholar 
    85.Zhang L, Lueders T. Micropredator niche differentiation between bulk soil and rhizosphere of an agricultural soil depends on bacterial prey. FEMS Microbiol Ecol. 2017;93:fix103.
    Google Scholar 
    86.Gao Z, Karlsson I, Geisen S, Kowalchuk G, Jousset A. Protists: puppet masters of the rhizosphere microbiome. Trends Plant Sci. 2019;24:165–76.CAS 
    PubMed 

    Google Scholar 
    87.Rosenberg K, Bertaux J, Krome K, Hartmann A, Scheu S, Bonkowski M. Soil amoebae rapidly change bacterial community composition in the rhizosphere of Arabidopsis thaliana. ISME J. 2009;3:675–84.CAS 
    PubMed 

    Google Scholar 
    88.Zaragoza SR, Mayzlish E, Steinberger Y. Seasonal changes in free-living Amoeba species in the root canopy of Zygophyllum dumosum in the Negev Desert, Israel. Microb Ecol. 2005;49:134–41.
    Google Scholar 
    89.Baldock BM, Baker JH, Sleigh MA. Laboratory growth rates of six species of freshwater Gymnamoebia. Oecologia. 1980;47:156–9.CAS 
    PubMed 

    Google Scholar 
    90.Bates ST, Clemente JC, Flores GE, Walters WA, Parfrey LW, Knight R, et al. Global biogeography of highly diverse protistan communities in soil. ISME J. 2013;7:652–9.CAS 
    PubMed 

    Google Scholar 
    91.Cotrufo MF, Wallenstein MD, Boot CM, Denef K, Paul E. The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Global Change Biol. 2013;19:988–95.
    Google Scholar 
    92.Schmidt MW, Torn MS, Abiven S, Dittmar T, Guggenberger G, Janssens IA, et al. Persistence of soil organic matter as an ecosystem property. Nature. 2011;478:49–56.CAS 
    PubMed 

    Google Scholar 
    93.Allison SD, Martiny JB. Resistance, resilience, and redundancy in microbial communities. Proc Natl Acad Sci USA. 2008;105:11512–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    94.Wickings K, Grandy AS, Reed SC, Cleveland CC. The origin of litter chemical complexity during decomposition. Ecol Lett. 2012;15:1180–8.PubMed 

    Google Scholar 
    95.Hungate BA, Marks JC, Power ME, Schwartz E, van Groenigen KJ, Blazewicz SJ, et al. The functional significance of bacterial predators. mBio. 2021;12:e00466–21.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    96.de Ruiter PC, Neutel AM, Moore JC. Energetics, patterns of interaction strengths, and stability in real ecosystems. Science. 1995;269:1257–60.PubMed 

    Google Scholar 
    97.Glücksman E, Bell T, Griffiths RI, Bass D. Closely related protist strains have different grazing impacts on natural bacterial communities. Environ Microbiol. 2010;12:3105–13.PubMed 

    Google Scholar 
    98.Yeates GW, Bongers T, De Goede R, Freckman DW, Georgieva SS. Feeding habits in soil nematode families and genera—an outline for soil ecologists. J Nematol. 1993;25:315–31.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    99.Okada H, Harada H, Kadota I. Fungal-feeding habits of six nematode isolates in the genus Filenchus. Soil Biol Biochem. 2005;37:1113–20.CAS 

    Google Scholar 
    100.Rotem O, Pasternak Z, Jurkevitch E. Bdellovibrio and Like Organisms. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompson F, editors. The prokaryotes, deltaproteobacteria and epsilonproteobacteria. Berlin: Springer-Verlag; 2014. p. 3–17.101.Griffiths BS. Microbial-feeding nematodes and protozoa in soil: their effectson microbial activity and nitrogen mineralization in decomposition hotspots and the rhizosphere. Plant Soil. 1994;164:25–33.CAS 

    Google Scholar 
    102.Bonkowski M, Clarholm M. Stimulation of plant growth through interactions of bacteria and protozoa: testing the auxiliary microbial loop hypothesis. Acta Protozool. 2012;51:237–47.
    Google Scholar 
    103.Clarholm M. Interactions of bacteria, protozoa and plants leading to mineralization of soil nitrogen. Soil Biol Biochem. 1985;17:181–7.CAS 

    Google Scholar 
    104.Halter D, Goulhen-Chollet F, Gallien S, Casiot C, Hamelin J, Gilard F, et al. In situ proteo-metabolomics reveals metabolite secretion by the acid mine drainage bio-indicator, Euglena mutabilis. ISME J. 2012;6:1391–402.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    105.Yuan C, Lei J, Cole J, Sun Y. Reconstructing 16S rRNA genes in metagenomic data. Bioinformatics. 2015;31:i35–43.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    106.Zeng F, Wang Z, Wang Y, Zhou J, Chen T. Large-scale 16S gene assembly using metagenomics shotgun sequences. Bioinformatics. 2017;33:1447–56.CAS 
    PubMed 

    Google Scholar 
    107.Pericard P, Dufresne Y, Couderc L, Blanquart S, Touzet H. MATAM: reconstruction of phylogenetic marker genes from short sequencing reads in metagenomes. Bioinformatics. 2017;34:585–91.
    Google Scholar 
    108.Callahan BJ, Wong J, Heiner C, Oh S, Theriot CM, Gulati AS, et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res. 2019;47:e103-e.
    Google Scholar  More

  • in

    Eco-evolutionary responses of the microbial loop to surface ocean warming and consequences for primary production

    1.Falkowski PG, Fenchel T, Delong EF. The microbial engines that drive Earth’s biogeochemical cycles. Science. 2008;320:1034–9.CAS 
    PubMed 

    Google Scholar 
    2.Riebesell U, Körtzinger A, Oschlies A. Sensitivities of marine carbon fluxes to ocean change. Proc Natl Acad Sci USA. 2009;106:20602–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.Hutchins DA, Fu F. Microorganisms and ocean global change. Nat Microbiol. 2017;2:1–11.
    Google Scholar 
    4.Cavicchioli R, Ripple WJ, Timmis KN, Azam F, Bakken LR, Baylis M, et al. Scientists’ warning to humanity: microorganisms and climate change. Nat Rev Microbiol. 2019;17:569–86.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Bopp L, Resplandy L, Orr JC, Doney SC, Dunne JP, Gehlen M, et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences. 2013;10:6225–45.
    Google Scholar 
    6.Oschlies A, Brandt P, Stramma L, Schmidtko S. Drivers and mechanisms of ocean deoxygenation. Nat Geosci. 2018;11:467–73.CAS 

    Google Scholar 
    7.Cazenave A, Llovel W. Contemporary sea level rise. Ann Rev Mar Sci. 2010;2:145–73.PubMed 

    Google Scholar 
    8.Frölicher TL, Ramseyer L, Raible CC, Rodgers KB, Dunne J. Potential predictability of marine ecosystem drivers. Biogeosciences. 2020;17:2061–83.
    Google Scholar 
    9.Taucher J, Oschlies A. Can we predict the direction of marine primary production change under global warming? Geophys Res Lett. 2011;38:L02603.10.Laufkötter C, Vogt M, Gruber N, Aita-Noguchi M, Aumont O, Bopp L, et al. Drivers and uncertainties of future global marine primary production in marine ecosystem models. Biogeosciences. 2015;12:6955–84.
    Google Scholar 
    11.Azam F, Fenchel T, Field JG, Gray J, Meyer-Reil L, Thingstad F. The ecological role of water-column microbes in the sea. Mar Ecol Prog Ser. 1983:257–63.12.Fenchel T. The microbial loop–25 years later. J Exp Mar Biol Ecol. 2008;366:99–103.
    Google Scholar 
    13.Kirchman DL, Morán XAG, Ducklow H. Microbial growth in the polar oceans—role of temperature and potential impact of climate change. Nat Rev Microbiol. 2009;7:451–9.CAS 
    PubMed 

    Google Scholar 
    14.Aumont O, Éthé C, Tagliabue A, Bopp L, Gehlen M. PISCES-v2: An ocean biogeochemical model for carbon and ecosystem studies. Geosci Model Dev Discuss. 2015;8:2465–513.15.Vichi M, Masina S. Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000. Biogeosciences. 2009;6:2333–53.CAS 

    Google Scholar 
    16.Hasumi H, Nagata T. Modeling the global cycle of marine dissolved organic matter and its influence on marine productivity. Ecol Model. 2014;288:9–24.CAS 

    Google Scholar 
    17.Laufkötter C, Vogt M, Gruber N, Aumont O, Bopp L, Doney SC, et al. Projected decreases in future marine export production: the role of the carbon flux through the upper ocean ecosystem. Biogeosciences. 2016;13:4023–47.
    Google Scholar 
    18.Monroe JG, Markman DW, Beck WS, Felton AJ, Vahsen ML, Pressler Y. Ecoevolutionary dynamics of carbon cycling in the anthropocene. Trends Ecol Evol. 2018;33:213–25.PubMed 

    Google Scholar 
    19.Bennett AF, Dao KM, Lenski RE. Rapid evolution in response to high-temperature selection. Nature. 1990;346:79–81.CAS 
    PubMed 

    Google Scholar 
    20.Garud NR, Good BH, Hallatschek O, Pollard KS. Evolutionary dynamics of bacteria in the gut microbiome within and across hosts. PLoS Biol. 2019;17:e3000102.PubMed 
    PubMed Central 

    Google Scholar 
    21.Zhao S, Lieberman TD, Poyet M, Kauffman KM, Gibbons SM, Groussin M, et al. Adaptive evolution within gut microbiomes of healthy people. Cell Host Microbe. 2019;25:656–67.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Pomeroy LR, Williams PJleB, Azam F, Hobbie JE. The microbial loop. J Oceanogr. 2007;20:28–33.
    Google Scholar 
    23.Walworth NG, Zakem EJ, Dunne JP, Collins S, Levine NM. Microbial evolutionary strategies in a dynamic ocean. Proc Natl Acad Sci USA. 2020;117:5943–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Malik AA, Martiny JB, Brodie EL, Martiny AC, Treseder KK, Allison SD. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 2020;14:1–9.CAS 
    PubMed 

    Google Scholar 
    25.Saifuddin M, Bhatnagar JM, Segrè D, Finzi AC. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat Commun. 2019;10:1–10.CAS 

    Google Scholar 
    26.Muscarella ME, Howey XM, Lennon JT. Trait‐based approach to bacterial growth efficiency. Environ Microbiol. 2020;22:3494–3504.CAS 
    PubMed 

    Google Scholar 
    27.Roller BR, Stoddard SF, Schmidt TM. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat Microbiol. 2016;1:1–7.
    Google Scholar 
    28.Sarmiento JL, Gruber N. Ocean biogeochemical dynamics. Princeton University Press, 2006.29.Bendtsen J, Lundsgaard C, Middelboe M, Archer D. Influence of bacterial uptake on deep-ocean dissolved organic carbon. Glob Biogeocehm Cycles. 2002;16:74–1.
    Google Scholar 
    30.Chen B, Landry MR, Huang B, Liu H. Does warming enhance the effect of microzooplankton grazing on marine phytoplankton in the ocean? Limnol Oceanogr. 2012;57:519–26.CAS 

    Google Scholar 
    31.Krause S, Le Roux X, Niklaus PA, Van Bodegom PM, Lennon JT, Bertilsson S, et al. Trait-based approaches for understanding microbial biodiversity and ecosystem functioning. Front Microbiol. 2014;5:251.PubMed 
    PubMed Central 

    Google Scholar 
    32.Kiørboe T, Visser A, Andersen KH. A trait-based approach to ocean ecology. ICES Int J Mar Sci. 2018;75:1849–63.
    Google Scholar 
    33.Grime JP. Evidence for the existence of three primary strategies in plants and its relevance to ecological and evolutionary theory. Am Nat. 1977;111:1169–94.
    Google Scholar 
    34.Polz MF, Cordero OX. Bacterial evolution: genomics of metabolic trade-offs. Nat Microbiol. 2016;1:1–2.
    Google Scholar 
    35.Carlson CA, Del Giorgio PA, Herndl GJ. Microbes and the dissipation of energy and respiration: from cells to ecosystems. J Oceanogr. 2007;20:89–100.
    Google Scholar 
    36.Arnosti C. Patterns of microbially driven carbon cycling in the ocean: links between extracellular enzymes and microbial communities. Adv Oceanogr. 2014;2014:706082.37.Pfeiffer T, Schuster S, Bonhoeffer S. Cooperation and competition in the evolution of ATP-producing pathways. Science. 2001;292:504–7.CAS 
    PubMed 

    Google Scholar 
    38.Button D. Biochemical basis for whole-cell uptake kinetics: specific affinity, oligotrophic capacity, and the meaning of the Michaelis constant. Appl Environ Microbiol. 1991;57:2033–8.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Metz JA, Nisbet RM, Geritz SA. How should we define ‘fitness’ for general ecological scenarios? Trends Ecol Evol. 1992;7:198–202.CAS 
    PubMed 

    Google Scholar 
    40.Geritz SA, Metz JA, Kisdi E, Meszéna G. Dynamics of adaptation and evolutionary´ branching. Phys Rev Lett. 1997;78:2024.CAS 

    Google Scholar 
    41.Abs E, Ferrière R. Modeling microbial dynamics and heterotrophic soil respiration: effect of climate change. Biogeochemical cycles: ecological drivers and environmental impact. 2020:103–29.42.Lipson DA. The complex relationship between microbial growth rate and yield and its implications for ecosystem processes. Front Microbiol. 2015;6:615.PubMed 
    PubMed Central 

    Google Scholar 
    43.Hansell DA, Carlson CA. Biogeochemistry of marine dissolved organic matter. Academic Press, 2014.44.Urban MC, De Meester L, Vellend M, Stoks R, Vanoverbeke J. A crucial step toward realism: responses to climate change from an evolving metacommunity perspective. Evol Appl. 2012;5:154–67.PubMed 

    Google Scholar 
    45.Norberg J, Urban MC, Vellend M, Klausmeier CA, Loeuille N. Eco-evolutionary responses of biodiversity to climate change. Nat Clim Change. 2012;2:747–51.
    Google Scholar 
    46.Sarmento H, Montoya JM, Vázquez-Domínguez E, Vaqué D, Gasol JM. Warming effects on marine microbial food web processes: how far can we go when it comes to predictions? Philos Trans R Soc Long B Biol Sci. 2010;365:2137–49.
    Google Scholar 
    47.Walther S, Voigt M, Thum T, Gonsamo A, Zhang Y, Köhler P, et al. Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Glob Change Biol. 2016;22:2979–96.
    Google Scholar 
    48.Williams RG, Follows MJ. Ocean dynamics and the carbon cycle: Principles and mechanisms. Cambridge University Press, 2011.49.Lewis K, Van Dijken G, Arrigo KR. Changes in phytoplankton concentration now drive increased Arctic Ocean primary production. Science. 2020;369:198–202.CAS 
    PubMed 

    Google Scholar 
    50.Ward B, Collins S, Dutkiewicz S, Gibbs S, Bown P, Ridgwell A, et al. Considering the role of adaptive evolution in models of the ocean and climate system. J Adv Model Earth Syst. 2019;11:3343–61.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Vázquez-Domínguez E, Vaque D, Gasol JM. Ocean warming enhances respiration and carbon demand of coastal microbial plankton. Glob Change Biol. 2007;13:1327–34.
    Google Scholar 
    52.López-Urrutia A, Morán XAG. Resource limitation of bacterial production distorts´ the temperature dependence of oceanic carbon cycling. Ecology. 2007;88:817–22.PubMed 

    Google Scholar 
    53.Parker GA, Smith JM. Optimality theory in evolutionary biology. Nature. 1990;348:27–33.
    Google Scholar 
    54.Hammerstein P. Darwinian adaptation, population genetics and the streetcar theory of evolution. J Math Biol. 1996;34:511–32.CAS 
    PubMed 

    Google Scholar 
    55.Eshel I, Feldman MW, Bergman A. Long-term evolution, short-term evolution, and population genetic theory. J Theor Biol. 1998;191:391–6.
    Google Scholar 
    56.Hagerty SB, Allison SD, Schimel JP. Evaluating soil microbial carbon use efficiency explicitly as a function of cellular processes: implications for measurements and models. Biogeochemistry. 2018;140:269–83.CAS 

    Google Scholar 
    57.Segre D, Vitkup D, Church GM. Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci USA. 2002;99:15112–7.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Marx CJ. Can you sequence ecology? Metagenomics of adaptive diversification. PLoS Biol. 2013;11:e1001487.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.O’Brien S, Hodgson DJ, Buckling A. The interplay between microevolution and community structure in microbial populations. Curr Opin Biotechnol. 2013;24:821–5.PubMed 

    Google Scholar 
    60.Scheuerl T, Hopkins M, Nowell RW, Rivett DW, Barraclough TG, Bell T. Bacterial adaptation is constrained in complex communities. Nat Commun. 2020;11:1–8.
    Google Scholar 
    61.Schloissnig S, Arumugam M, Sunagawa S, Mitreva M, Tap J, Zhu A, et al. Genomic variation landscape of the human gut microbiome. Nature. 2013;493:45–50.PubMed 

    Google Scholar 
    62.Boyd JA, Woodcroft BJ, Tyson GW. GraftM: a tool for scalable, phylogenetically informed classification of genes within metagenomes. Nucleic Acids Res. 2018;46:e59–9.PubMed 
    PubMed Central 

    Google Scholar 
    63.Gregory AC, Gerhardt K, Zhong ZP, Bolduc B, Temperton B, Konstantinidis KT, et al. MetaPop: a pipeline for macro-and micro-diversity analyses and visualization of microbial and viral metagenome-derived populations. bioRxiv 2020. https://doi.org/10.1101/2020.11.01.363960.64.Coles VJ, Stukel MR, Brooks MT, Burd A, Crump BC, Moran MA, et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science. 2017;358:1149–1154.CAS 
    PubMed 

    Google Scholar 
    65.Scheinin M, Riebesell U, Rynearson TA, Lohbeck KT, Collins S. Experimental evolution gone wild. J R Soc Interface. 2015;12:20150056.PubMed 
    PubMed Central 

    Google Scholar 
    66.Thomas MK, Kremer CT, Klausmeier CA, Litchman E. A global pattern of thermal adaptation in marine phytoplankton. Science. 2012;338:1085–8.CAS 
    PubMed 

    Google Scholar 
    67.Grimaud GM, Le Guennec V, Ayata SD, Mairet F, Sciandra A, Bernard O. Modelling the effect of temperature on phytoplankton growth across the global ocean. IFACPapersOnLine. 2015;48:228–33.
    Google Scholar 
    68.Sauterey B, Ward B, Rault J, Bowler C, Claessen D. The implications of ecoevolutionary processes for the emergence of marine plankton community biogeography. Am Nat. 2017;190:116–30.PubMed 

    Google Scholar 
    69.Beckmann A, Schaum CE, Hense I. Phytoplankton adaptation in ecosystem models. J Theor Biol. 2019;468:60–71.PubMed 

    Google Scholar 
    70.Wilhelm SW, Suttle CA. Viruses and nutrient cycles in the sea: viruses play critical roles in the structure and function of aquatic food webs. Bioscience. 1999;49:781–8.
    Google Scholar 
    71.Danovaro R, Corinaldesi C, Dell’Anno A, Fuhrman JA, Middelburg JJ, Noble RT, et al. Marine viruses and global climate change. FEMS Microbiol Rev. 2011;35:993–1034.CAS 
    PubMed 

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

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

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

    Google Scholar  More

  • in

    Diet-driven mercury contamination is associated with polar bear gut microbiota

    1.Evariste, L. et al. Gut microbiota of aquatic organisms: A key endpoint for ecotoxicological studies. Environ. Pollut. 248, 989–999 (2019).CAS 
    PubMed 

    Google Scholar 
    2.Guo, G., Yumvihoze, E., Poulain, A. J. & Chan, H. M. Monomethylmercury degradation by the human gut microbiota is stimulated by protein amendments. J. Toxicol. Sci. 43, 717–725 (2018).CAS 
    PubMed 

    Google Scholar 
    3.Dempsey, J. L., Little, M. & Cui, J. Y. Gut microbiome: An intermediary to neurotoxicity. Neurotoxicology 75, 41–69 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    4.Breton, J. Ô. et al. Gut microbiota limits heavy metals burden caused by chronic oral exposure. Toxicol. Lett. 222, 132–138 (2013).CAS 
    PubMed 

    Google Scholar 
    5.Claus, S. P., Guillou, H. & Ellero-Simatos, S. The gut microbiota: A major player in the toxicity of environmental pollutants?. NPJ Biofilms Microbiomes 2, 16003 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    6.Nakamura, I., Hosokawa, K., Tamura, H. & Miura, T. Reduced mercury excretion with feces in germfree mice after oral administration of methyl mercury chloride. Bull. Environ. Contam. Toxicol. 17, 528–533 (1977).CAS 
    PubMed 

    Google Scholar 
    7.Rowland, I. R., Davies, M. J. & Evans, J. G. Tissue content of mercury in rats given methylmercuric chloride orally: Influence of intestinal flora. Arch. Environ. Health 35, 155–160 (1980).CAS 
    PubMed 

    Google Scholar 
    8.Seko, Y., Miura, T., Takahashi, M. & Koyama, T. Methyl mercury decomposition in mice treated with antibiotics. Acta Pharmacol. Toxicol. (Copenh) 49, 259–265 (1981).CAS 

    Google Scholar 
    9.Lapanje, A., Zrimec, A., Drobne, D. & Rupnik, M. Long-term Hg pollution-induced structural shifts of bacterial community in the terrestrial isopod (Porcellio scaber) gut. Environ. Pollut. 158, 3186–3193 (2010).CAS 
    PubMed 

    Google Scholar 
    10.Ruan, Y. et al. High doses of copper and mercury changed cecal microbiota in female mice. Biol. Trace Elem. Res. 189, 134–144 (2019).CAS 
    PubMed 

    Google Scholar 
    11.Desforges, J.-P.W. et al. Immunotoxic effects of environmental pollutants in marine mammals. Environ. Int. 86, 126–139 (2016).CAS 
    PubMed 

    Google Scholar 
    12.Dietz, R. et al. What are the toxicological effects of mercury in Arctic biota?. Sci. Total Environ. 443, 775–790 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    13.Amstrup, S. C., Feldhamer, G. A., Thompson, B. C. & Chapman, J. A. The polar bear-Ursus maritimus biology, management, and conservation. Wild Mammals North Am. Biol. Manag. Conserv. 2, 587–610 (2003).
    Google Scholar 
    14.McKinney, M. A., Atwood, T. C., Iverson, S. J. & Peacock, E. Temporal complexity of southern Beaufort Sea polar bear diets during a period of increasing land use. Ecosphere 8, e01633 (2017).
    Google Scholar 
    15.Bourque, J., Atwood, T. C., Divoky, G. J., Stewart, C. & McKinney, M. A. Fatty acid-based diet estimates suggest ringed seal remain the main prey of southern Beaufort Sea polar bears despite recent use of onshore food resources. Ecol. Evol. 10, 2093–2103 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    16.Routti, H. et al. Contaminants in Polar Bears from the Circumpolar Arctic State of Knowledge and Further Recommendations for Monitoring and Research-Action #42 of the Circumpolar Action Plan for polar Bear Conservation (2019).17.Letcher, R. J. et al. Exposure and effects assessment of persistent organohalogen contaminants in Arctic wildlife and fish. Sci. Total Environ. 408, 2995–3043 (2010).ADS 
    CAS 
    PubMed 

    Google Scholar 
    18.Dietz, R. et al. Trends in mercury in hair of Greenlandic polar bears (Ursus maritimus) during 1892–2001. Environ. Sci. Technol. 40, 1120–1125 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    19.Borgå, K., Fisk, A. T., Hoekstra, P. F. & Muir, D. C. G. Biological and chemical factors of importance in the bioaccumulation and trophic transfer of persistent organochlorine contaminants in Arctic marine food webs. Environ. Toxicol. Chem. 23, 2367 (2004).PubMed 

    Google Scholar 
    20.Hoekstra, P. F. et al. Trophic transfer of persistent organochlorine contaminants (OCs) within an Arctic marine food web from the southern Beaufort-Chukchi Seas. Environ. Pollut. 124, 509–522 (2003).CAS 
    PubMed 

    Google Scholar 
    21.Ley, R. E. et al. Evolution of mammals and their gut microbes. Science (80–) 320, 1647–1651 (2008).ADS 
    CAS 
    PubMed Central 

    Google Scholar 
    22.Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep. 14, 1655–1661 (2016).CAS 
    PubMed 

    Google Scholar 
    23.Borbón-García, A., Reyes, A., Vives-Flórez, M. & Caballero, S. Captivity shapes the gut microbiota of Andean bears: Insights into health surveillance. Front. Microbiol. 8, 1316 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    24.Ferguson, S. H., Stirling, I. & McLoughlin, P. Climate change and ringed seal (Phoca hispida) recruitment in western Hudson Bay. Mar. Mammal Sci. 21, 121–135 (2005).
    Google Scholar 
    25.Thiemann, G., Iverson, S. & Stirling, I. Polar bear diets and arctic marine food webs: Insights from fatty acid analysis. Ecol. Monogr. 78, 591–613 (2008).
    Google Scholar 
    26.Muir, D. C., Norstrom, R. J. & Simon, M. Organochlorine contaminants in Arctic marine food chains: Accumulation of specific polychlorinated biphenyls and chlordane-related compounds. Environ. Sci. Technol. 22, 1071–1079 (1988).ADS 
    CAS 
    PubMed 

    Google Scholar 
    27.Young, B. G., Loseto, L. L. & Ferguson, S. H. Diet differences among age classes of Arctic seals: Evidence from stable isotope and mercury biomarkers. Polar Biol. 33, 153–162 (2010).
    Google Scholar 
    28.Correa, L., Castellini, J. M., Quakenbush, L. T. & O’Hara, T. M. Mercury and selenium concentrations in skeletal muscle, liver, and regions of the heart and kidney in bearded seals from Alaska, USA. Environ. Toxicol. Chem. 34, 2403–2408 (2015).CAS 
    PubMed 

    Google Scholar 
    29.Brown, T. M. et al. Mercury and cadmium in ringed seals in the Canadian Arctic: Influence of location and diet. Sci. Total Environ. 545–546, 503–511 (2016).ADS 
    PubMed 

    Google Scholar 
    30.McKinney, M. A., Atwood, T. C., Pedro, S. & Peacock, E. Ecological change drives a decline in mercury concentrations in southern Beaufort Sea polar bears. Environ. Sci. Technol. 51, 7814–7822 (2017).ADS 
    CAS 
    PubMed 

    Google Scholar 
    31.Watson, S. E. et al. Global change-driven use of onshore habitat impacts polar bear faecal microbiota. ISME J. 20, 1–1 (2019).
    Google Scholar 
    32.Calvert, W. & Ramsay, M. A. Evaluation of age determination of polar bears by counts of cementum growth layer groups. Ursus 10, 449–453 (1998).
    Google Scholar 
    33.Cattet, M. R., Caulkett, N. A., Obbard, M. E. & Stenhouse, G. B. A body-condition index for ursids. Can. J. Zool. 80, 1156–1161 (2002).
    Google Scholar 
    34.Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
    PubMed 

    Google Scholar 
    35.Albanese, D., Fontana, P., De Filippo, C., Cavalieri, D. & Donati, C. MICCA: A complete and accurate software for taxonomic profiling of metagenomic data. Sci. Rep. 5, 9743 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    37.Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Price, M. N., Dehal, P. S. & Arkin, A. P. Fasttree: Computing large minimum evolution trees with profiles instead of a distance matrix. Mol. Biol. Evol. 26, 1641–1650 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Golzadeh, N. et al. Evaluating the concentrations of total mercury, methylmercury, selenium, and selenium:mercury molar ratios in traditional foods of the Bigstone Cree in Alberta Canada. Chemosphere 250, 20 (2020).
    Google Scholar 
    40.Iverson, S. J., Field, C., DonBowen, W. & Blanchard, W. Quantitative fatty acid signature analysis: A new method of estimating predator diets. Ecol. Monogr. 74, 211–235 (2004).
    Google Scholar 
    41.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
    Google Scholar 
    42.Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).
    Google Scholar 
    43.Grandjean, P. & Budtz-Jørgensen, E. Total imprecision of exposure biomarkers: Implications for calculating exposure limits. Am. J. Ind. Med. 50, 712–719 (2007).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Dietz, R. et al. Temporal trends and future predictions of mercury concentrations in Northwest Greenland polar bear (Ursus maritimus) hair. Environ. Sci. Technol. 45, 1458–1465 (2011).ADS 
    CAS 
    PubMed 

    Google Scholar 
    45.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    46.Foster, Z. S. L., Sharpton, T. J. & Grünwald, N. J. Metacoder: An R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput. Biol. 13, e1005404 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Xia, J. et al. Effects of short term lead exposure on gut microbiota and hepatic metabolism in adult zebrafish. Comp. Biochem. Physiol. Part C Toxicol. Pharmacol. 209, 1–8 (2018).CAS 

    Google Scholar 
    48.Rothenberg, S. E. et al. The role of gut microbiota in fetal methylmercury exposure: Insights from a pilot study. Toxicol. Lett. 242, 60–67 (2016).CAS 
    PubMed 

    Google Scholar 
    49.Wu, J. et al. Perinatal lead exposure alters gut microbiota composition and results in sex-specific bodyweight increases in adult mice. Toxicol. Sci. 151, 324–333 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    50.Pascoe, E. L., Hauffe, H. C., Marchesi, J. R. & Perkins, S. E. Network analysis of gut microbiota literature: An overview of the research landscape in non-human animal studies. ISME J. 11, 2644–2651 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    51.Gilmour, C. C. et al. Mercury methylation by novel microorganisms from new environments. Environ. Sci. Technol. 47, 11810–11820 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    52.Li, H. et al. Intestinal methylation and demethylation of mercury. Bull. Environ. Contam. Toxicol. 1025(102), 597–604 (2018).
    Google Scholar 
    53.Guo, X. et al. Metagenomic profiles and antibiotic resistance genes in gut microbiota of mice exposed to arsenic and iron. Chemosphere 112, 1–8 (2014).ADS 
    CAS 
    PubMed 

    Google Scholar 
    54.Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Ley, R. E., Turnbaugh, P. J., Klein, S. & Gordon, J. I. Microbial ecology: Human gut microbes associated with obesity. Nature 444, 1022–1023 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    56.Hollister, E. B. et al. Structure and function of the healthy pre-adolescent pediatric gut microbiome. Microbiome 3, 36 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    57.Rowland, I., Davies, M. & Grasso, P. Biosynthesis of methylmercury compounds by the intestinal flora of the rat. Arch. Environ. Health Int. J. 32, 24–28 (1977).CAS 

    Google Scholar 
    58.Paredes-Sabja, D., Setlow, P. & Sarker, M. R. Germination of spores of Bacillales and Clostridiales species: Mechanisms and proteins involved. Trends Microbiol. 19, 85–94 (2011).CAS 
    PubMed 

    Google Scholar 
    59.Setlow, P., Wang, S. & Li, Y. Q. Germination of spores of the orders Bacillales and Clostridiales. Annu. Rev. Microbiol. 71, 459–477 (2017).CAS 
    PubMed 

    Google Scholar 
    60.Ilinskaya, O. N., Ulyanova, V. V., Yarullina, D. R. & Gataullin, I. G. Secretome of intestinal bacilli: A natural guard against pathologies. Front. Microbiol. 8, 25 (2017).
    Google Scholar 
    61.Hiller-Bittrolff, K., Foreman, K., Bulseco-McKim, A. N., Benoit, J. & Bowen, J. L. Effects of mercury addition on microbial community composition and nitrate removal inside permeable reactive barriers. Environ. Pollut. 242, 797–806 (2018).CAS 
    PubMed 

    Google Scholar 
    62.Kuhn, K. A. et al. Bacteroidales recruit IL-6-producing intraepithelial lymphocytes in the colon to promote barrier integrity. Mucosal Immunol. 11, 357–368 (2018).CAS 
    PubMed 

    Google Scholar 
    63.Wei, Z. S. et al. Effect of gaseous mercury on nitric oxide removal performance and microbial community of a hybrid catalytic membrane biofilm reactor. Chem. Eng. J. 316, 584–591 (2017).CAS 

    Google Scholar 
    64.Pagano, A. M. et al. High-energy, high-fat lifestyle challenges an Arctic apex predator, the polar bear. Science (80–) 359, 568–572 (2018).ADS 
    CAS 

    Google Scholar 
    65.Van Waaij, D., Berghuis-de Vries, J. M. & Lekkerkerk-Van Der Wees, J. E. C. Colonization resistance of the digestive tract in conventional and antibiotic-treated mice. J. Hyg. (Lond.) 69, 405–411 (1971).
    Google Scholar 
    66.Girvan, M. S., Campbell, C. D., Killham, K., Prosser, J. I. & Glover, L. A. Bacterial diversity promotes community stability and functional resilience after perturbation. Environ. Microbiol. 7, 301–313 (2005).CAS 
    PubMed 

    Google Scholar 
    67.Cowan, T. E. et al. Chronic coffee consumption in the diet-induced obese rat: Impact on gut microbiota and serum metabolomics. J. Nutr. Biochem. 25, 489–495 (2014).CAS 
    PubMed 

    Google Scholar 
    68.Bishara, J. et al. Obesity as a risk factor for Clostridium difficile infection. Clin. Infect. Dis. 57, 489–493 (2013).PubMed 

    Google Scholar 
    69.Pohlner, M. et al. The majority of active Rhodobacteraceae in marine sediments belong to uncultured genera: A molecular approach to link their distribution to environmental conditions. Front. Microbiol. 10, 659 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    70.Simon, M. et al. Phylogenomics of Rhodobacteraceae reveals evolutionary adaptation to marine and non-marine habitats. ISME J. 11, 1483–1499 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    71.Castonguay-Paradis, S. et al. Dietary fatty acid intake and gut microbiota determine circulating endocannabinoidome signaling beyond the effect of body fat. Sci. Rep. 10, 1–11 (2020).
    Google Scholar  More

  • in

    Tropical bee species abundance differs within a narrow elevational gradient

    1.Galbraith, S. M., Griswold, T., Price, W. J. & Bosque-Pérez, N. A. Biodiversity and community composition of native bee populations vary among human-dominated land uses within the seasonally dry tropics. J. Insect Conserv. https://doi.org/10.1007/s10841-020-00274-8 (2020).Article 

    Google Scholar 
    2.Imbach, P. et al. Climate change, ecosystems and smallholder agriculture in Central America: An introduction to the special issue. Clim. Change 141, 1–12 (2017).
    Google Scholar 
    3.HilleRisLambers, J., Harsch, M. A., Ettinger, A. K., Ford, K. R. & Theobald, E. J. How will biotic interactions influence climate change-induced range shifts?. Ann. N. Y. Acad. Sci. 1297, 112–125 (2013).PubMed 

    Google Scholar 
    4.Butt, N. et al. Cascading effects of climate extremes on vertebrate fauna through changes to low-latitude tree flowering and fruiting phenology. Glob. Chang. Biol. 21, 3267–3277 (2015).ADS 
    PubMed 

    Google Scholar 
    5.Garibaldi, L. A. et al. Wild pollinators enhance fruit set of crops regardless of honey bee abundance. Science 339, 1608–1611 (2013).ADS 
    CAS 
    PubMed 

    Google Scholar 
    6.Orr, M. C. et al. Global patterns and drivers of bee distribution. Curr. Biol. 31, 451-458.e4 (2021).CAS 
    PubMed 

    Google Scholar 
    7.Bezerra, E. S., Lopes, A. V. & Machado, I. C. Biologia reprodutiva de Byrsonima gardnerana A. Juss. (Malpighiaceae) e interações com abelhas Centris (Centridini) no Nordeste do Brasil. Rev. Bras. Bot. 32, 95–108 (2009).
    Google Scholar 
    8.Schleuning, M. et al. Trait-based assessments of climate-change impacts on interacting species. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2019.12.010 (2020).Article 
    PubMed 

    Google Scholar 
    9.Hoiss, B., Krauss, J. & Steffan-Dewenter, I. Interactive effects of elevation, species richness and extreme climatic events on plant-pollinator networks. Glob. Chang. Biol. 21, 4086–4097 (2015).ADS 
    PubMed 

    Google Scholar 
    10.Freitas, B. M. et al. Diversity, threats and conservation of native bees in the Neotropics. Apidologie 40, 332–346 (2009).MathSciNet 

    Google Scholar 
    11.Classen, A. et al. Temperature versus resource constraints: Which factors determine bee diversity on Mount Kilimanjaro, Tanzania?. Glob. Ecol. Biogeogr. 24, 642–652 (2015).
    Google Scholar 
    12.Ramos-Jiliberto, R. et al. Topological change of Andean plant–pollinator networks along an altitudinal gradient. Ecol. Complex. 7, 86–90 (2010).
    Google Scholar 
    13.Dellinger, A. S. et al. Low bee visitation rates explain pollinator shifts to vertebrates in tropical mountains. New Phytol. https://doi.org/10.1111/nph.17390 (2021).Article 
    PubMed 

    Google Scholar 
    14.González-Vanegas, P. A., Rös, M., García-Franco, J. G. & Aguirre-Jaimes, A. Buzz-pollination in a tropical montane cloud forest: Compositional similarity and plant-pollinator interactions. Neotrop. Entomol. https://doi.org/10.1007/s13744-021-00867-1 (2021).Article 
    PubMed 

    Google Scholar 
    15.Aslan, C. E., Zavaleta, E. S., Tershy, B. & Croll, D. Mutualism disruption threatens global plant biodiversity: A systematic review. PLoS ONE 8, 1–11 (2013).
    Google Scholar 
    16.García-Robledo, C., Kuprewicz, E. K., Staines, C. L., Erwin, T. L. & Kress, W. J. Limited tolerance by insects to high temperatures across tropical elevational gradients and the implications of global warming for extinction. Proc. Natl. Acad. Sci. U. S. A. 113, 680–685 (2016).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    17.Sheldon, K. S. Climate change in the tropics: Ecological and evolutionary responses at low latitudes. Annu. Rev. Ecol. Evol. Syst. 50, 303–333 (2019).
    Google Scholar 
    18.McCain, C. M. & Colwell, R. K. Assessing the threat to montane biodiversity from discordant shifts in temperature and precipitation in a changing climate. Ecol. Lett. 14, 1236–1245 (2011).PubMed 

    Google Scholar 
    19.Aguilar, I., Herrera, E. & Zamora, G. Stingless bees of Costa Rica. Pot-Honey https://doi.org/10.1007/978-1-4614-4960-7 (2012).Article 

    Google Scholar 
    20.Köppler, K., Vorwohl, G. & Koeniger, N. Comparison of pollen spectra collected by four different subspecies of the honey bee Apis mellifera. Apidologie 38, 341–353 (2007).
    Google Scholar 
    21.Brehm, G., Colwell, R. K. & Kluge, J. The role of environment and mid-domain effect on moth species richness along a tropical elevational gradient. Glob. Ecol. Biogeogr. 16, 205–219 (2007).
    Google Scholar 
    22.Ortiz-Mora, R. A., Van Veen, J. W., Corrales, G. & Sommeijer, M. J. Influence of altitude on the distribution of stingless bees (Hymenoptera Apidae: Meliponinae). Apiacta 30, 101–105 (1995).
    Google Scholar 
    23.Michener, C. D. The Bees of the World (The Johns Hopkins University Press, 2007).
    Google Scholar 
    24.Rehan, S. M., Tierney, S. M. & Wcislo, W. T. Evidence for social nesting in Neotropical ceratinine bees. Insectes Soc. 62, 465–469 (2015).
    Google Scholar 
    25.Gonzalez, V. H. et al. Thermal tolerance varies with dim-light foraging and elevation in large carpenter bees (Hymenoptera: Apidae: Xylocopini). Ecol. Entomol. 45, 688–696 (2020).
    Google Scholar 
    26.Bolker, B. M. et al. Generalized linear mixed models: A practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

    Google Scholar 
    27.Theobald, E. J., Gabrielyan, H. & HilleRisLambers, J. Lilies at the limit: Variation in plant-pollinator interactions across an elevational range. Am. J. Bot. 103, 189–197 (2016).PubMed 

    Google Scholar 
    28.Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258–261 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    29.Bode, R. F., Linhart, R. D. & Dufresne, C. Variation in the pollinator community visiting invasive Cytisus scoparius L. Link (Fabaceae) along an elevation gradient. Arthropod. Plant. Interact. https://doi.org/10.1007/s11829-020-09755-8 (2020).Article 

    Google Scholar 
    30.Sheldon, K. S., Yang, S. & Tewksbury, J. J. Climate change and community disassembly: Impacts of warming on tropical and temperate montane community structure. Ecol. Lett. 14, 1191–1200 (2011).PubMed 

    Google Scholar 
    31.Dymond, K. et al. The role of insect pollinators in avocado production: A global review. J. Appl. Entomol. https://doi.org/10.1111/jen.12869 (2021).Article 

    Google Scholar 
    32.Giannini, T. C. et al. Identifying the areas to preserve passion fruit pollination service in Brazilian Tropical Savannas under climate change. Agric. Ecosyst. Environ. 171, 39–46 (2013).
    Google Scholar 
    33.Ashworth, L., Quesada, M., Casas, A., Aguilar, R. & Oyama, K. Pollinator-dependent food production in Mexico. Biol. Conserv. 142, 1050–1057 (2009).
    Google Scholar 
    34.Tepedino, V. J. The Pollination efficiency of the squash bee (Peponapis pruinosa) and the honey bee (Apis mellifera) on summer squash (Cucurbita pepo). J. Kansas Entomol. Soc. 54, 359–377 (1981).
    Google Scholar 
    35.Didham, R. K. et al. Interpreting insect declines: Seven challenges and a way forward. Insect Conserv. Divers. 13, 103–114 (2020).
    Google Scholar 
    36.Barrantes, G. The role of historical and local factors in determining species composition of the highland avifauna of Costa Rica and western Panamá. Rev. Biol. Trop. 57, 333–346 (2009).
    Google Scholar 
    37.Macedo, M. V. et al. Insect elevational specialization in a tropical biodiversity hotspot. Insect Conserv. Divers. 11, 240–254 (2018).
    Google Scholar 
    38.Frankie, G. W. et al. Diversity and abundance of bees visiting a mass flowering tree species in disturbed seasonal dry forest, Costa Rica. Kansas Entomol. Soc. 70, 281–296 (1997).
    Google Scholar 
    39.Heard, T. A. The role of stingless bees in crop pollination. Annu. Rev. Entomol. 44, 183–206 (1999).CAS 
    PubMed 

    Google Scholar 
    40.Abrol, D. P. Wild bees and crop pollination. In Pollination Biology: Biodiversity Conservation and Agricultural Production 111–184 (Springer, 2012).
    Google Scholar 
    41.Tucker, E. M. & Rehan, S. M. Farming for bees: Annual variation in pollinator populations across agricultural landscapes. Agric. For. Entomol. 20, 541–548 (2018).
    Google Scholar 
    42.Peters, V. E., Mordecai, R., Carroll, C. R., Cooper, R. J. & Greenberg, R. Bird community response to fruit energy. J. Anim. Ecol. 79, 824–835 (2010).PubMed 

    Google Scholar 
    43.Baker, C. P. Moon Costa Rica (Moon Travel, 2007).
    Google Scholar 
    44.Hinton, C. R. & Peters, V. E. Plant species with the trait of continuous flowering do not hold core roles in a Neotropical lowland plant-pollinating insect network. Ecol. Evol. 11, 2346–2359 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    45.Dew, R. M., Rehan, S. M. & Schwarz, M. P. Biogeography and demography of an Australian native bee Ceratina australensis (Hymenoptera, Apidae) since the last glacial maximum. J. Hymenopt. Res. 49, 25–41 (2016).
    Google Scholar 
    46.Engel, M. S. A new interpretation of the oldest fossil bee (Hymenoptera: Apidae). Am. Museum Nat. Hist. 3296, 1–11 (2000).
    Google Scholar 
    47.Calfee, E., Agra, M. N., Palacio, M. A., Ramírez, S. R. & Coop, G. Selection and hybridization shaped the Africanized honey bee invasion of the Americas. bioRxiv https://doi.org/10.1101/2020.03.17.994632 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Davila, Y. C. & Wardle, G. M. Variation in native pollinators in the absence of honeybees: Implications for reproductive success of an Australian generalist-pollinated herb Trachymene incisa (Apiaceae). Bot. J. Linn. Soc. 156, 479–490 (2008).
    Google Scholar 
    49.Chen, H., Morrell, P. L., Ashworth, V. E. T. M., De La Cruz, M. & Clegg, M. T. Tracing the geographic origins of major avocado cultivars. J. Hered. 100, 56–65 (2009).PubMed 

    Google Scholar 
    50.Bender, G. S. Avocado flowering and pollination. Avocado Prod. Calif. 1, 39–49 (2002).ADS 

    Google Scholar 
    51.Bergh, B. O. The remarkable avocado flower. Calif. Avocado Soc. Yearb. 57, 40–41 (1973).
    Google Scholar 
    52.Wilson, H. D. Gene flow in squash species. Bioscience 40, 449–455 (1990).
    Google Scholar 
    53.Hurd, P. D., Linsley, E. G. & Whitaker, T. W. Squash and gourd bees (Peponapis, Xenoglossa) and the origin of the cultivated Cucurbita. Evolution 25, 218–234 (1971).PubMed 

    Google Scholar 
    54.Willis, S. D. & Kevan, P. G. Foraging dynamics of Peponapis pruinosa (Hymenoptera: Anthophoridae) on pumpkin (Cucurbita pepo) in Southern Ontario. Can. Entomol. 127, 167–175 (1995).
    Google Scholar 
    55.Gómez-Escobar, E., Liedo, P., Montoya, P., Vandame, R. & Sánchez, D. Behavioral response of two species of stingless bees and the honey bee (Hymenoptera: Apidae) to GF-120. J. Econ. Entomol. 107, 1447–1449 (2014).PubMed 

    Google Scholar 
    56.Jarau, S. & Barth, F. G. Stingless bees of the Golfo Dulce region, Costa Rica (Hymenoptera, Apidae, Apinae, Meliponini). Stapfia 88, 267–276 (2008).
    Google Scholar 
    57.Fick, S. E. & Hijmans, R. J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
    Google Scholar 
    58.Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. Maps: Draw Geographical Maps. (2018).59.Hijmans, R. J. Raster: Geographic Data Analysis and Modeling. (2020).60.Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).MATH 

    Google Scholar 
    61.Salim, H. M. W. et al. Stingless bee (Hymenoptera: Apidae: Meliponini) diversity in dipterocarp forest reserves in Peninsular Malaysia. Raffles Bull. Zool. 60, 213–219 (2012).MathSciNet 

    Google Scholar 
    62.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
    Google Scholar 
    63.Baselga, A. et al. Partitioning Beta Diversity into Turnover and Nestedness Components (Wiley, 2021).
    Google Scholar 
    64.Oksanen, J. et al. Package ‘vegan’. Community Ecol. Packag. 2, 1–295 (2013).
    Google Scholar 
    65.Wang, Y. et al. Statistical Methods for Analysing Multivariate Abundance Data. (2021).66.Kindt, R. & Coe, R. Tree Diversity Analysis. A Manual and Software for Common Statistical Methods for Ecological and Biodiversity Studies (World Agroforestry Centre (ICRAF), 2005).
    Google Scholar  More

  • in

    Soundscape and ambient noise levels of the Arctic waters around Greenland

    1.Hildebrand, J. A. Anthropogenic and natural sources of ambient noise in the ocean. Mar. Ecol. Prog. Ser. 395, 5–20 (2009).2.Wenz, G. M. Acoustic ambient noise in the ocean: Spectra and sources. J. Acoust. Soc. Am. 34, 1936–1956 (1962).ADS 

    Google Scholar 
    3.Ross, D. Ship sources of ambient noise. IEEE J. Ocean. Eng. 30, 257–261 (2005).ADS 

    Google Scholar 
    4.Duarte, C. M. et al. The soundscape of the Anthropocene ocean. Science (80-). 371, eaba4658 (2021).CAS 
    PubMed 

    Google Scholar 
    5.Tyack, P., Frisk, G., Boyd, I., Urban, E. & Seeyave, S. (eds). An International Quiet Ocean Experiment Science Plan. Scientific Committee on Oceanic Research / Partnership for Observation of the Global Oceans (2015).6.Kaplan, M. B. & Solomon, S. A coming boom in commercial shipping? The potential for rapid growth of noise from commercial ships by 2030. Mar. Policy 73, 119–121 (2016).
    Google Scholar 
    7.McDonald, M. A., Hildebrand, J. A. & Wiggins, S. M. Increases in deep ocean ambient noise in the Northeast Pacific west of San Nicolas Island, California. J. Acoust. Soc. Am. 120, 711 (2006).ADS 
    PubMed 

    Google Scholar 
    8.Kyhn, L. A. et al. Basin-wide contributions to the underwater soundscape by multiple seismic surveys with implications for marine mammals in Baffin Bay, Greenland. Mar. Pollut. Bull. 138, 474–490 (2019).CAS 
    PubMed 

    Google Scholar 
    9.Bailey, H. et al. Assessing underwater noise levels during pile-driving at an offshore windfarm and its potential effects on marine mammals. Mar. Pollut. Bull. 60, 888–897 (2010).CAS 
    PubMed 

    Google Scholar 
    10.Nieukirk, S. L., Stafford, K. M., Mellinger, D. K., Dziak, R. P. & Fox, C. G. Low-frequency whale and seismic airgun sounds recorded in the mid-Atlantic Ocean. J. Acoust. Soc. Am. 115, 1832–1843 (2004).ADS 
    PubMed 

    Google Scholar 
    11.Guerra, M., Thode, A. M., Blackwell, S. B. & Michael Macrander, A. Quantifying seismic survey reverberation off the Alaskan North Slope. J. Acoust. Soc. Am. 130, 3046–3058 (2011).ADS 
    PubMed 

    Google Scholar 
    12.OSPAR Commission. The North-East Atlantic Environment Strategy: Strategy of the OSPAR Commission for the Protection of the Marine Environment of the North-East Atlantic 2010–2020. OSPAR Secretariat, London (2010).13.UN. General Assembly (74th sess.: 2019–2020). Oceans and the law of the sea: Resolution/adopted by the General Assembly. A/RES/74/19 (2019).14.Arctic Council. Arctic Marine Shipping Assessment 2009 Report, second printing. Arctic Council, Tromsø, Norway (2009).15.International Maritime Organization. Guidelines from the International Maritime Organization for the reduction of underwater noise from commercial shipping, to address adverse impacts on marine life. MEPC. 1/Circ. 833. IMO, London (2014).16.European Commission. Directive 2008/56/EC of the European Parliament and of the Council of 17 June 2008 establishing a framework for community action in the field of marine environmental policy (Marine Strategy Framework Directive). European Commission, Brussels (2008).17.Halliday, W. D., Pine, M. K. & Insley, S. J. Underwater noise and Arctic marine mammals: Review and policy recommendations. Environ. Rev. https://doi.org/10.1139/er-2019-0033 (2020).Article 

    Google Scholar 
    18.PAME. Underwater Noise in the Arctic: A State of Knowledge Report, Roveniemi, May 2019. Protection of the Arctic Marine Environment (PAME) Secretariat, Akureyri (2019).19.Stroeve, J. & Notz, D. Changing state of Arctic sea ice across all seasons. Environ. Res. Lett. 13, 103001 (2018).ADS 

    Google Scholar 
    20.Melia, N., Haines, K. & Hawkins, E. Sea ice decline and 21st century trans-Arctic shipping routes. Geophys. Res. Lett. 43, 9720–9728 (2016).ADS 

    Google Scholar 
    21.Smith, L. C. & Stephenson, S. R. New Trans-Arctic shipping routes navigable by midcentury. PNAS 110, E1191–E1195 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Ebinger, C. K. & Zambetakis, E. The geopolitics of Arctic melt. Int. Aff. 85, 1215–1232 (2009).
    Google Scholar 
    23.Huntington, H. P. A preliminary assessment of threats to arctic marine mammals and their conservation in the coming decades. Mar. Policy 33, 77–82 (2009).
    Google Scholar 
    24.Merchant, N. D. et al. Measuring acoustic habitats. Methods Ecol. Evol. 6, 257–265 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    25.Baumgartner, M. F., Stafford, K. M. & Latha, G. Near real-time underwater passive acoustic monitoring of natural and anthropogenic sounds. In Observing the Oceans in Real Time (eds Venkatesan, R. et al.) 203–226 (Springer Oceanography, 2018). https://doi.org/10.1007/978-3-319-66493-4_10.Chapter 

    Google Scholar 
    26.Mellinger, D. K. & Clark, C. W. Blue whale (Balaenoptera musculus) sounds from the North Atlantic. J. Acoust. Soc. Am. 114, 1108 (2003).ADS 
    PubMed 

    Google Scholar 
    27.Mustonen, M. et al. Spatial and temporal variability of ambient underwater sound in the Baltic Sea. Sci. Rep. 9, 1–13 (2019).CAS 

    Google Scholar 
    28.Pieretti, N. & Danovaro, R. Acoustic indexes for marine biodiversity trends and ecosystem health. Philos. Trans. R. Soc. B 375, 20190447 (2020).
    Google Scholar 
    29.Palmer, K. J., Brookes, K. L., Davies, I. M., Edwards, E. & Rendell, L. Habitat use of a coastal delphinid population investigated using passive acoustic monitoring. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 254–270 (2019).
    Google Scholar 
    30.Sigray, P. et al. BIAS: A regional management of underwater sound in the Baltic Sea. In The Effects of Noise on Aquatic Life II (eds. Popper A., Hawkins A.) 1015–1023. Advances in Experimental Medicine and Biology. 875. (Springer New York, 2016).31.Farcas, A., Powell, C. F., Brookes, K. L. & Merchant, N. D. Validated shipping noise maps of the Northeast Atlantic. Sci. Total Environ. 735, 139509 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    32.Davis, G. E. et al. Long-term passive acoustic recordings track the changing distribution of North Atlantic right whales (Eubalaena glacialis) from 2004 to 2014. Sci. Rep. 7, 13460 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Caruso, F. et al. Long-term monitoring of dolphin biosonar activity in deep pelagic waters of the Mediterranean Sea. Sci. Rep. 7, 1–12 (2017).CAS 

    Google Scholar 
    34.Thomas, L. et al. Last call: Passive acoustic monitoring shows continued rapid decline of critically endangered vaquita. J. Acoust. Soc. Am. 142, EL512–EL517 (2017).PubMed 

    Google Scholar 
    35.Hildebrand, J. A. et al. Passive acoustic monitoring of beaked whale densities in the Gulf of Mexico. Sci. Rep. 5, 16343 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.ANSI S1.11-2004. Specification for Octave, Half-Octave, and Third Octave Band Filters. American National Standards Institute Inc., New York (2004).37.Jakobsson, M. et al. The International Bathymetric Chart of the Arctic Ocean Version 4.0. Sci. Data 7, 1–14 (2020).
    Google Scholar 
    38.Gillespie, D. et al. PAMGUARD: Semiautomated, open source software for real-time acoustic detection and localisation of cetaceans. J. Acoust. Soc. Am. 30, 54–62 (2008).
    Google Scholar 
    39.Gillespie, D., Caillat, M., Gordon, J. & White, P. Automatic detection and classification of odontocete whistles. J. Acoust. Soc. Am. 134, 2427–2437 (2013).ADS 
    PubMed 

    Google Scholar 
    40.Mellinger, D. K. et al. Ishmael 3.0 User Manual ISHMAEL 3.O User Guide. (2018).41.Jensen, F. H., Johnson, M., Ladegaard, M., Wisniewska, D. M. & Madsen, P. T. Narrow acoustic field of view drives frequency scaling in toothed whale biosonar. Curr. Biol. 28, 3878-3885.e3 (2018).CAS 
    PubMed 

    Google Scholar 
    42.Madsen, P. T., Wahlberg, M. & Møhl, B. Male sperm whale (Physeter macrocephalus) acoustics in a high-latitude habitat: Implications for echolocation and communication. Behav. Ecol. Sociobiol. 53, 31–41 (2002).
    Google Scholar 
    43.Zahn, M. J., Laidre, K. L., Stilz, P., Rasmussen, M. H. & Koblitz, J. C. Vertical sonar beam width and scanning behavior of wild belugas (Delphinapterus leucas) in West Greenland. PLoS ONE 16, e0257054 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Frouin-Mouy, H., Kowarski, K., Martin, B. & Bröker, K. Seasonal trends in acoustic detection of marine mammals in Baffin Bay and Melville Bay, Northwest Greenland. Source Arct. 70, 59–76 (2017).
    Google Scholar 
    45.Commission, E. Commission Decision (EU) 2017/848 of 17 May 2017 laying down criteria and methodological standards on good environmental status of marine waters and specifications and standardised methods for monitoring and assessment, and repealing Decision 2010/477/EU. Off. J. Eur. Union 125, 43–74 (2017).
    Google Scholar 
    46.Diachok, O. I. Effects of sea-ice ridges on sound propagation in the Arctic Ocean. J. Acoust. Soc. Am. 59, 1110 (1998).ADS 

    Google Scholar 
    47.McGrath, J. R. Depth and Seasonal Dependence of Ambient Sea Noise Near the Marginal Ice Zone of the Greenland Sea. Naval Research Laboratory. Washington DC (1976).48.Ahonen, H. et al. The underwater soundscape in western Fram Strait: Breeding ground of Spitsbergen’s endangered bowhead whales. Mar. Pollut. Bull. 123, 97–112 (2017).CAS 
    PubMed 

    Google Scholar 
    49.Merchant, N. D. et al. Underwater noise levels in UK waters. Sci. Rep. 6, 36942, (2016).50.Urick, R. J. Ambient Noise in the Sea (Undersea Warfare Technology Office, Naval Sea Systems Command, Department of the Navy, 1984).
    Google Scholar 
    51.Kinda, G. B., Simard, Y., Gervaise, C., Mars, J. I. & Fortier, L. Arctic underwater noise transients from sea ice deformation: Characteristics, annual time series, and forcing in Beaufort Sea. J. Acoust. Soc. Am. 138, 2034 (2015).ADS 
    PubMed 

    Google Scholar 
    52.Urick, R. J. The noise of melting icebergs. J. Acoust. Soc. Am. 50, 337–341 (1971).ADS 

    Google Scholar 
    53.Roth, E. H., Hildebrand, J. A., Wiggins, S. M. & Ross, D. Underwater ambient noise on the Chukchi Sea continental slope from 2006–2009. J. Acoust. Soc. Am. 131, 104–110 (2012).ADS 
    PubMed 

    Google Scholar 
    54.Tervo, O. M., Parks, S. E. & Miller, L. A. Seasonal changes in the vocal behavior of bowhead whales (Balaena mysticetus) in Disko Bay, Western-Greenland. J. Acoust. Soc. Am. 126, 1570–1580 (2009).ADS 
    PubMed 

    Google Scholar 
    55.Boye, T. K., Simon, M. J., Laidre, K. L., Rigét, F. & Stafford, K. M. Seasonal detections of bearded seal (Erignathus barbatus) vocalizations in Baffin Bay and Davis Strait in relation to sea ice concentration. Polar Biol. 43, 1493–1502 (2020).
    Google Scholar 
    56.De Vreese, S. et al. Marine mammal acoustic detections in the Greenland and Barents Sea, 2013–2014 seasons. Sci. Rep. 8, 1–14 (2018).
    Google Scholar 
    57.Simon, M., Stafford, K. M., Beedholm, K., Lee, C. M. & Madsen, P. T. Singing behavior of fin whales in the Davis Strait with implications for mating, migration and foraging. J. Acoust. Soc. Am. 128, 3200–3210 (2010).ADS 
    PubMed 

    Google Scholar 
    58.Meire, L. et al. Marine-terminating glaciers sustain high productivity in Greenland fjords. Glob. Chang. Biol. 23, 5344–5357 (2017).ADS 
    PubMed 

    Google Scholar 
    59.Møhl, B. Masking effects of noise: their distribution in time and space. In The question of sound from icebreaker operations: The proceedings of a workshop (ed. Peterson, N. M.) 259–266. Arctic Pilot Project. Calgary, AB (1981).60.Erbe, C. & Farmer, D. M. Masked hearing thresholds of a beluga whale (Delphinapterus leucas) in icebreaker noise. Deep Sea Res. Part II Top. Stud. Oceanogr. 45, 1373–1388 (1998).ADS 

    Google Scholar 
    61.Gordon, J. C. D. et al. A review of the effects of seismic survey on marine mammals. Mar. Technol. Soc. J. 37, 14–32 (2004).
    Google Scholar 
    62.Nowacek, D. P., Thorne, L. H., Johnston, D. W. & Tyack, P. L. Responses of cetaceans to anthropogenic noise. Mamm. Rev. 37, 81–115 (2007).
    Google Scholar 
    63.Southall, B. L. et al. Marine mammal noise exposure criteria: Updated scientific recommendations for residual hearing effects. Aquat. Mamm. 45, 125–232 (2019).
    Google Scholar 
    64.Frid, A. & Dill, L. Human-caused Disturbance Stimuli as a Form of Predation Risk. Conserv. Ecol. 6, 11 (2002). More

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    Millimeter-scale vertical partitioning of nitrogen cycling in hypersaline mats reveals prominence of genes encoding multi-heme and prismane proteins

    Porewater concentrations of dissolved oxygen and nutrientsThe sampling location and appearance of the microbial mats used in this study in cross section are shown in Fig. 1. Profound changes in dissolved oxygen concentration were observed over the diel cycle because of high rates of oxygenic photosynthesis in the daytime and oxygen-requiring respiration at night (Table 1). Briefly, Layer 1 was characterized by oxygen concentration fluctuations in the range of 200–800 µM. Layers 2 and 3 ranged from 0–1200 µM and 0–200 µM, respectively. Mat Layer 4 (3–4 mm below the surface) may contain some dissolved oxygen near noon on days when there is high solar irradiance but stays anoxic for most hours of most days. Layers 5–7 (4–7 mm from the surface) remain anoxic.Table 1 Oxygen concentrations throughout the first 4 mm of the mat measured at 100 µm resolution using microsensors, measured on 22 August, 2019.Full size tableConcentrations of ammonium (Table 1) reveal a pattern of increasing concentration with depth (34–124 µM) through the layers examined here. Nitrate concentrations ranged between 26–33 µM, with low variation across depths. The concentration of phosphate ranged between 3–6 µM, with the highest concentration detected in Layer 1 (0–1 mm from surface) at 5.5 µM.Analysis of genes and transcripts in mat layers by qPCR and RT-qPCRGene-copy number ranges for both DNA and cDNA across all layers for all genes examined are summarized as follows: Bacteria, 104−1010 per g mat and 101−105, per ng nucleic acid; Archaea, 106−108 and 102−104; nifH, 108−1011 and 104−107; archaeal-amoA, 104−105 and 2–3; bacterial-amoA, 104−107 and 3–335; Nitrospira-nxrB, 105−107 and 27–372; nosZ, 103−105 and 2–10; nirS, 105−107 and 33–1941; Planctomycetes-16S rRNA gene and cDNA of transcripts, 104−106 and 6–66 (Fig. 2, S1).Fig. 2: Vertical patterns in the abundance (DNA) and expression (cDNA) of Bacterial and Archaeal ribosomal and nitrogen cycling genes.Number of copies of DNA and cDNA genes recovered for Bacteria (A), Archaea (B), nifH (C), Archaeal-amoA (D), Bacterial-amoA (E), Nitrospira-nxrB (F), nosZ (G), nirS (H) and Planctomycetes-16S rRNA gene marker (anammox proxy) (I), per g of microbial mat, quantified by qPCR and RT-qPCR in hypersaline microbial mat profiles from different depths. P-values from Kruskal–Wallis test are overlain on each, and different letters indicate significantly different values for the given gene based on a Conover-Iman test p-value of  0.8, Table 2).Fig. 4: Non-metric multidimensional scaling (NMDS) plots of quantification of all nitrogen genes across all layers examined in this study.Genes associated with the following nitrogen transformations were examined: nitrogen fixation (nifH), nitrification (Bacterial-amoA, Archaeal-amoA, Nitrospira-nxrB), denitrification (nosZ, nirS) and Planctomycetes-16S rRNA gene marker (anammox proxy). The biotic data was standardized, and a sample resemblance matrix was generated using Bray-Curtis coefficient of similarity. In order to analyze the influence of abiotic variables (porewater nutrient and oxygen concentration) on the patterns of the biotic data, monotonic correlations of the abiotic variables were performed. In the plots, the distance between the samples’ points reflects their relative similarity, according to Bray-Curtis similarity matrices based on cDNA/DNA ratios of nitrogen genes examined. The vectors in panel A represent the cDNA/DNA ratios of nitrogen gene examined. In panel B, the vectors represent the environmental variables.Full size imageTable 2 (A) Spearman correlations coefficient (r) between the ratios of cDNA/DNA of nitrogen fixation (nifH), nitrification (Bacterial-amoA, Archaeal-amoA, Nitrospira-nxrB), denitrification (nosZ, nirS) and Planctomycetes-16S rRNA gene marker (anammox proxy) and oxygen, ammonium, nitrate and phosphate concentrations. (B) Spearman correlation p-value.Full size tablenifH, Bacterial-amoA and Archaeal-amoA were positively correlated with oxygen concentration (r ≥ 0.22, Table 2), while Nitrospira-nxrB was negatively correlated with oxygen (r = −0.68, Table 2). Denitrification genes (nosZ, nirS) and Planctomycetes-16S rRNA genes were all positively correlated with ammonium (r ≥ 0.5) and orthophosphate (r ≥ 0.13) and negatively correlated with oxygen (r  > −0.70).Metagenome analysis of nitrogen cyclingA total number of 922 324 genes were identified; 1305 of these genes were annotated with KOs that are part of KEGG’s Nitrogen Metabolism pathway (Table S2, S3). A dendrogram based on Bray-Curtis similarities of normalized coverages of all recovered nitrogen metabolism genes is shown in Fig. 5A. Overall, the similarity between the layers was >75%. According to SIMPROF analysis, there was a significant difference in the N-related gene coverages (based on an alpha value of 0.05) between Layers 1-Layer 2, Layer 3, and Layer 4 (p = 0.001) and Layer 2-Layer 3, and Layer 4 (p = 0.001), but not between Layers 3 and Layer 4 (p = 1), where the similarity was >90%.Fig. 5: Functional nitrogen gene distribution based on metagenome analysis.A Cluster analysis illustrating the similarity of normalized coverages of all recovered nitrogen metabolism genes across the uppers 4 layers examined [(Layer 1 (0–1 mm from surface), Layer 2 (1–2 mm from surface), Layer 3 (2–3 mm from surface), Layer 4 (3–4 mm from surface)]. Red lines show non-significant differences, according to SIMPROF analysis (p  > 0.05). B The bar plots show the genes of the metabolic pathways in the nitrogen cycle identified in the mat, according metagenome analysis, with relative coverage of each nitrogen cycling gene across depths examined (Fraction of Depth Integrated Coverage, FDIC). 355 unique genes were recovered from KEGG’s Nitrogen Metabolism pathway: 60 annotated as involved in nitrogen fixation, 15 in assimilatory nitrate reduction, 38 in dissimilatory nitrate reduction to ammonia (DNRA), 52 in hydroxylamine dehydrogenase EC 1.7.2.6, 121 in hydroxylamine reductase, 69 in denitrification pathway. C Values of Nitrogen-focused Coverage per Million (N-CPM). The following enzymes perform nitrogen transformation in the mat: nitrogenase molybdenum-iron protein alpha chain (nifD), nitrogenase iron protein NifH, nitrogenase molybdenum-iron protein beta chain (nifK), hydroxylamine dehydrogenase EC 1.7.2.6 (hao), hydroxylamine reductase (hcp), nitrate reductase/nitrite oxidoreductase, alpha subunit (narG, narZ, nxrA), nitrate reductase/nitrite oxidoreductase, beta subunit (narH, narY, nxrB), nitrate reductase (cytochrome) (napA), nitrate reductase (cytochrome), electron transfer subunit (napB), nitrite reductase (NO-forming) / hydroxylamine reductase (nirS), nitrogenase molybdenum-iron protein beta chain (nirK), nitric oxide reductase subunit B (norB), nitric oxide reductase subunit C (norC), nitrous-oxide reductase (nosZ), nitrate reductase gamma subunit (narI, narV), cytochrome c nitrite reductase small subunit (nrfH), nitrite reductase (cytochrome c-552) (nrfA), ferredoxin-nitrite reductase (nirA), ferredoxin-nitrate reductase (narB), MFS transporter, NNP family, nitrate/nitrite transporter (NRT, nark, nrtP, nasA). D Nitrogen cycling genes recovered in this study and the transformation that they catalyze.Full size imageThe nitrogen fixation pathway was identified with nifD, nifH, and nifK genes (Fig. 5B, C, Table S4). Of the 60 genes detected in this metabolic pathway 17 genes were annotated as nifD, 22 genes as nifH, and 21 genes as nifK. The normalized coverage of these genes showed a decreasing trend with depth. Layer 1 was characterized by the highest values of Nitrogen-focused coverage per million (N-CPM, see Supplementary Text 1) of nifD, nifH, and nifK genes: 56264.7, 54934.2 and 60059.2, respectively. On average, the three genes involved in nitrogen fixation, nifD, nifH, and nifK, decreased with depth, (2.7-fold from Layer 1 to Layer 4, with a nearly 2-fold difference solely between Layer 1 and Layer 2).Genes involved in nitrate assimilation, annotated as nirA and narB which code for ferredoxin nitrate reductase, were 3 times as abundant in Layer 1 than Layer 2, but decreased less markedly from Layer 2 to Layers 3 and 4.Genes for dissimilatory nitrite reduction (nrfA, and nrfH) were 4 and 16 times more abundant in Layer 4 than Layer 1. Similarly, the nitrate/nitrite regulator protein genes narl and narV displayed a nearly inverse pattern, with Layer 1 having the least proportion of genes, a large increase from Layer 1 to Layer 2, and additional increases from Layer 2 to Layers 3 and Layer 4 (Fig. 5B, C, Table S4).Genes associated with nitrification were very poorly represented in the metagenome. No genes associated with ammonia oxidation (amoA) were detected. Genes associated with nitrite oxidation (nrxA, nrxB) that were detected are so closely related to denitrifier genes (narG, narZ, narH, narY) as to be annotated with the same KEGG KO models (K00370 representing narG, narZ, nxrA; and K00371 representing narH, narY, nxrB).The following genes involved in denitrification were detected: napA, napB, narG, narZ, narH, narY, narI, narV, nirK, nirS, norB, norC, and nosZ (Fig. 5B, C). The nitrate reduction metabolic pathway was represented by 4 genes encoding the nitrate reductase-nitrite oxidoreductase-alpha subunit (narG, narZ, nxrA genes), 6 genes encoding the nitrate reductase-nitrite oxidoreductase-beta subunit (narH, narY, nxrB genes), 31 genes encoding the nitrate reductase gamma subunit (narI, narV), 5 genes encoding the nitrate reductase -cytochrome electron transfer subunit (napB) and 7 genes encoding the nitrate reductase -cytochrome (napA) (Table S4). The N-CPM of nitrate reductase increased with depth, but with a similar proportion of those genes in Layers 3 and 4. With respect to nitrite reductase (nirk and nirS genes, 2 and 1 genes, respectively), no nirK genes were detected in Layer 1, where the highest N-CPM of nirS was recovered (Fig. 5B). In contrast, Layer 3 had no detected nirS and the highest N-CPM of nirK. Regarding nitric oxide reductase (norB and norC genes, 6 and 1 genes, respectively), the highest normalized coverage of norB was detected in Layer 3, while highest for norC was in Layer 1. Finally, nosZ (6 genes) was detected in all the layers, steadily decreasing in normalized coverage from the top layer to the deepest (Fig. 5B, C; Table S4).DNRA metabolism was represented by nrfA (26 genes) and nrfH (12 genes), and by narI, narV (31). Layer 1 was characterized by the lowest normalized coverage of narI, narV, nrfA, and nrfH genes (6880.2, 3724.6, and 284.6 N-CPM, respectively), while Layer 3 was characterized by the greatest coverage of narI, narV, nrfA, and nrfH genes (32760.5, 14417.9 and 4504.1, respectively; Fig. 5B, C; Table S4).Genes for hydroxylamine dehydrogenase EC 1.7.2.6 and hydroxylamine reductase (hao and hcp, respectively) were the most abundant nitrogen metabolism genes in the mat: hao having a cumulative N-CPM of ~150000 and hcp having a cumulative N-CPM of nearly 350,000 across the 4 depths (Fig. 5C). Both genes increased in abundance with depth; hcp increased two-fold between Layer 1 and Layer 2, and more gradually in Layer 3 and Layer 4. Hao exhibited a three-fold increase in relative abundance from Layer 1 to Layer 2 and remained relatively constant through Layer 3 and Layer 4 (Fig. 5B, C; Table S4). More

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    Evidence of unidirectional gene flow in a fragmented population of Salmo trutta L.

    1.Klemetsen, A. et al. Atlantic salmon Salmo salar L., brown trout Salmo trutta L. and Arctic charr Salvelinus alpinus (L.): A review of aspects of their life histories. Ecol. Freshw. Fish. 12, 1–59. https://doi.org/10.1034/j.1600-0633.2003.00010.x (2003).Article 

    Google Scholar 
    2.Elliott, J. M. Quantitative Ecology and the Brown Trout (Oxford University Press, 1994).
    Google Scholar 
    3.ICES. Baltic Salmon and Trout Assessment Working Group (WGBAST). ICES Sci. Rep. 2(22), 261. https://doi.org/10.17895/ices.pub.5974 (2020).Article 

    Google Scholar 
    4.Berrebi, P., Horvath, Á., Splendiani, A., Palm, S. & Bernaś, R. Genetic diversity of domestic brown trout stocks in Europe. Aquaculture 544, 737043. https://doi.org/10.1016/j.aquaculture.2021.737043 (2021).CAS 
    Article 

    Google Scholar 
    5.Jonsson, B. & Jonsson, N. Partial migration: Niche shift versus sexual maturation in fishes. Rev. Fish Biol. Fish. 3, 348–365. https://doi.org/10.1007/BF00043384 (1993).Article 

    Google Scholar 
    6.Jonsson, B. Diadromous and resident Trout Salmo Trutta: Is their difference due to genetics?. Oikos 38, 297–300. https://doi.org/10.2307/3544668 (1982).Article 

    Google Scholar 
    7.Olsson, I. C., Greenberg, L. A., Bergman, E. & Wysujack, K. Environmentally induced migration: The importance of food. Ecol. Lett. 9, 45–51. https://doi.org/10.1111/j.1461-0248.2006.00909.x (2006).Article 

    Google Scholar 
    8.Wysujack, K., Greenberg, L. A., Bergman, E. & Olsson, I. C. The role of the environment in partial migration: Food availability affects the adoption of a migratory tactic in brown trout Salmo trutta. Ecol. Freshw. Fish. 18, 52–59. https://doi.org/10.1111/j.1600-0633.2008.00322.x (2009).Article 

    Google Scholar 
    9.Charles, K., Roussel, J. M. & Cunjak, R. A. Estimating the contribution of sympatric anadromous and freshwater resident brown trout to juvenile production. Mar. Freshw. Res. 55, 185–191. https://doi.org/10.1071/MF03173 (2004).CAS 
    Article 

    Google Scholar 
    10.Youngson, A. F., Mitchell, A. I., Noack, P. T. & Laird, L. M. Carotenoid pigment profiles distinguish anadromous and nonanadromous brown trout (Salmo trutta). Can. J. Fish. Aquat. Sci. 54, 1064–1066. https://doi.org/10.1139/f97-023 (1997).CAS 
    Article 

    Google Scholar 
    11.Eek, D. & Bohlin, T. Strontium in scales verifies that sympatric sea-run and stream-resident brown trout can be distinguished by coloration. J. Fish Biol. 51, 659–661. https://doi.org/10.1111/j.1095-8649.1997.tb01522.x (1997).Article 

    Google Scholar 
    12.Veinott, G., Northcote, T., Rosenau, M. & Evans, R. D. Concentrations of strontium in the pectoral fin rays of the white sturgeon (Acipenser transmontanus) by laser ablation sampling—inductively coupled plasma—mass spectrometry as an indicator of marine migrations. Can. J. Fish. Aquat. Sci. 56, 1981–1990. https://doi.org/10.1139/f99-120 (1999).CAS 
    Article 

    Google Scholar 
    13.Jardine, T. D., Cartwright, D. F., Dietrich, J. P. & Cunjak, R. A. Resource use by salmonids in riverine, lacustrine and marine environments: Evidence from stable isotope analysis. Environ. Biol. Fishes. 73, 309–319. https://doi.org/10.1007/s10641-005-2259-8 (2005).Article 

    Google Scholar 
    14.Jones, A. G. & Ardren, W. R. Methods of parentage analysis in natural populations. Mol. Ecol. 12, 2511–2523. https://doi.org/10.1046/j.1365-294X.2003.01928.x (2003).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Goodwin, J. C. A., King, R. A., Jones, J. I., Ibbotson, A. & Stevens, J. R. A small number of anadromous females drive reproduction in a brown trout (Salmo trutta) population in an English chalk stream. Freshw. Biol. 61, 1075–1089. https://doi.org/10.1111/fwb.12768 (2016).Article 

    Google Scholar 
    16.Charles, K., Guyomard, R., Hoyheim, B., Ombredane, D. & Baglinière, J.-L. Lack of genetic differentiation between anadromous and resident sympatric brown trout (Salmo trutta) in a Normandy population. Aquat. Living Resour. 18, 65–69. https://doi.org/10.1051/alr:2005006 (2005).CAS 
    Article 

    Google Scholar 
    17.Charles, K., Roussel, J.-M., Lebel, J.-M., Bagliniere, J.-L. & Ombredane, D. Genetic differentiation between anadromous and freshwater resident brown trout (Salmo trutta L.): Insights obtained from stable isotope analysis. Ecol. Freshw. Fish. 15, 255–263. https://doi.org/10.1111/j.1600-0633.2006.00149.x (2006).Article 

    Google Scholar 
    18.Jarry, M. et al. Sea trout (Salmo trutta L.) growth patterns during early steps of invasion in the Kerguelen Islands. Polar Biol. 41, 925–934. https://doi.org/10.1007/s00300-018-2253-1 (2018).Article 

    Google Scholar 
    19.Brauer, C. J. & Beheregaray, L. B. Recent and rapid anthropogenic habitat fragmentation increases extinction risk for freshwater biodiversity. Evol. Appl. 13, 2857–2869. https://doi.org/10.1111/eva.13128 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    20.Griffiths, A. M., Koizumi, I., Bright, D. & Stevens, J. R. A case of isolation by distance and shortterm temporal stability of population structure in brown trout (Salmo trutta) within the River Dart, southwest England. Evol. Appl. 2, 537–554. https://doi.org/10.1111/j.1752-4571.2009.00092.x (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.HELCOM. Sea Trout and Salmon Populations and Rivers in Poland—HELCOM Assessment of Salmon (Salmo salar) and Sea Trout (Salmo trutta) Populations and Habitats in Rivers Flowing to the Baltic Sea. Balt. Sea Environ. Proc. No. 126B. 2011.22.Dębowski, P. Fish assemblages in the Parsęta River drainage basin. Pol. Arch. Hydrobiol. 46, 161–172 (1999).
    Google Scholar 
    23.Kuligowski, D. R., Ford, M. J. & Berejikian, B. A. Breeding structure of steelhead inferred from patterns of genetic relatedness among nests. Trans. Am. Fish. Soc. 134, 1202–2121. https://doi.org/10.1577/T04-187.1 (2005).Article 

    Google Scholar 
    24.Dauphin, G., Prévost, E., Adams, C. E. & Boylan, P. Using redd counts to estimate salmonids spawner abundances: A Bayesian modelling approach. Fish. Res. 106, 32–40. https://doi.org/10.1016/j.fishres.2010.06.014 (2010).Article 

    Google Scholar 
    25.Cairney, M., Taggart, J. B. & Hoyheim, B. Characterization of microsatellite and minisatellite loci in Atlantic salmon (Salmo salar L.) and cross-species amplification in other salmonids. Mol. Ecol. 9, 2175–2178. https://doi.org/10.1046/j.1365-294X.2000.105312.x (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    26.Estoup, A., Presa, P., Krieg, F., Vaiman, D. & Guyomard, R. (CT)n and (GT)n microsatellites: A new class of genetic markers for Salmo trutta L. brown trout. Heredity 71, 488–496. https://doi.org/10.1038/hdy.1993.167 (1993).CAS 
    Article 
    PubMed 

    Google Scholar 
    27.O’Reilly, P. T., Hamilton, L. C., McConnell, S. K. & Wright, J. M. Rapid analysis of genetic variation in Atlantic salmon (Salmo salar) by PCR multiplexing of dinucleotide and tetranucleotide microsatellites. Can. J. Fish. Aquat. Sci. 53, 2292–2298. https://doi.org/10.1139/f96-192 (1996).Article 

    Google Scholar 
    28.Poteaux, C., Bonhomme, F. & Berrebi, P. Microsatellite polymorphism and genetic impact of restocking in Mediterranean brown trout (Salmo trutta L.). Heredity 82, 645–653. https://doi.org/10.1046/j.1365-2540.1999.00519.x (1999).Article 
    PubMed 

    Google Scholar 
    29.Presa, P. & Guyomard, R. Conservation of microsatellites in three species of salmonids. J. Fish Biol. 49, 1326–1329. https://doi.org/10.1111/j.1095-8649.1996.tb01800.x (1996).Article 

    Google Scholar 
    30.Scribner, K. T., Gust, J. R. & Fields, R. L. Isolation and characterization of novel salmon microsatellite loci: Cross species amplification and population genetics applications. Can. J. Fish. Aquat. Sci. 53, 833–841. https://doi.org/10.1139/cjfas-53-4-833 (1996).CAS 
    Article 

    Google Scholar 
    31.Slettan, A., Olsaker, I. & Lie, O. Atlantic salmon, Salmo salar, microsatellites at the SSOSL25, SSOSL85, SSOSL311, SSOSL417 loci. Anim. Genet. 26, 281–282. https://doi.org/10.1111/j.1365-2052.1995.tb03262.x (1995).CAS 
    Article 
    PubMed 

    Google Scholar 
    32.Slettan, A., Olsaker, I. & Lie, O. Polymorphic Atlantic salmon, Salmo salar L., microsatellites at the SSOSL438, SSOSL429 and SSOSL444 loci. Anim. Genet. 27, 57–58 (1996).CAS 
    Article 

    Google Scholar 
    33.Linløkken, A. N., Haugen, T. O., Kent, M. P. & Lien, S. Genetic differences between wild and hatchery-bred brown trout (Salmo trutta L.) in single nucleotide polymorphisms linked to selective traits. Ecol. Evol. 7, 4963–4972. https://doi.org/10.1002/ece3.3070 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Bernaś, R. et al. Genetic differentiation in hatchery and stocked populations of sea trout in the Southern Baltic: Selection evidence at SNP loci. Genes 11, 184. https://doi.org/10.3390/genes11020184 (2020).CAS 
    Article 
    PubMed Central 

    Google Scholar 
    35.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 35: 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 
    36.Peakall, R. & Smouse, P. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1093/bioinformatics/bts460 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Kalinowski, S. T. hp-rare 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes 5, 187–189. https://doi.org/10.1111/j.1471-8286.2004.00845.x (2005).CAS 
    Article 

    Google Scholar 
    38.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    Article 

    Google Scholar 
    39.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    40.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191. https://doi.org/10.1111/1755-0998.12387 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223–225. https://doi.org/10.1111/j.1558-5646.1989.tb04220.x (1989).Article 
    PubMed 

    Google Scholar 
    42.Bernaś, R., Burzyński, A., Dębowski, P., Poćwierz-Kotus, A. & Wenne, R. Genetic diversity within sea trout population from an intensively stocked southern Baltic river, based on microsatellite DNA analysis. Fish. Manage. Ecol. 21, 398–409. https://doi.org/10.1111/fme.12090 (2014).Article 

    Google Scholar 
    43.Bernaś, R. & Wąs-Barcz, A. Genetic structure of important resident brown trout breeding lines in Poland. J. Appl. Genet. 61, 239–247. https://doi.org/10.1007/s13353-020-00548-6 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Ostergren, J. & Nilsson, J. Importance of life-history and landscape characteristics for genetic structure and genetic diversity of brown trout (Salmo trutta L.). Ecol. Freshw. Fish. 21, 119–133 (2012).Article 

    Google Scholar 
    45.Lehtonen, P. K., Tonteri, A., Sendek, D., Titov, S. & Primmer, C. R. Spatio-temporal genetic structuring of brown trout (Salmo trutta L.) populations within the River Luga, northwest Russia. Conserv. Genet. 10, 281–289. https://doi.org/10.1007/s10592-008-9577-2 (2009).Article 

    Google Scholar 
    46.Cross, T. F., Mills, C. P. R. & de CourcyWilliams, M. An intensive study of allozyme variation in freshwater resident and anadromous trout, Salmo trutta L., in western Ireland. J. Fish Biol. 40, 25–32. https://doi.org/10.1111/j.1095-8649.1992.tb02550.x (1992).CAS 
    Article 

    Google Scholar 
    47.Stelkens, R., Jaffuel, G., Escher, M. & Wedekind, C. Genetic and phenotypic population divergence on a microgeographic scale in brown trout. Mol. Ecol. 21, 2896–2915. https://doi.org/10.1111/j.1365-294X.2012.05581.x (2012).Article 
    PubMed 

    Google Scholar 
    48.Hansen, M. M., Limborg, M. T., Ferchaud, A.-L. & Pujolar, J.-M. The effects of Medieval dams on genetic divergence and demographic history in brown trout populations. BMC Evol. Biol. 14, 122. https://doi.org/10.1186/1471-2148-14-122 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    49.Kohlmann, K. & Wüstemann, O. Tracing the genetic origin of brown trout (Salmo trutta) re-colonizing the Ecker reservoir in the Harz National Park, Germany. Environ. Biotechnol. 8, 39–44 (2012).
    Google Scholar 
    50.Dellefors, C. & Faremo, U. Early sexual maturation in males of wild sea trout, Salmo trutta L. inhibits smoltification. J. Fish Biol. 33, 741–749. https://doi.org/10.1111/j.1095-8649.1988.tb05519.x (1988).Article 

    Google Scholar 
    51.Jonsson, B. & Jonsson, N. Differences in growth between offspring of anadromous and freshwater brown trout Salmo trutta. J. Fish Biol. 20, 1–7. https://doi.org/10.1111/jfb.14693 (2021).Article 

    Google Scholar  More