More stories

  • in

    Substantial loss of isoprene in the surface ocean due to chemical and biological consumption

    Evidence for biological and chemical isoprene consumption in coastal seawaterThe time course of isoprene concentration in coastal seawater samples incubated in closed glass bottles at the in situ temperature and in the dark demonstrated sustained loss for at least 45 h (Fig. 1a). Enclosure without headspace prevented isoprene loss by ventilation, and darkness was assumed to arrest all or most of the biological production25 and any photochemical production15 or degradation. Thus, the measured loss was considered the result of microbial degradation and chemical oxidation. In most cases an exponential function fitted better the decay than a linear function (Supplementary Table 1), indicating first-order (concentration-dependent) kinetics for isoprene loss.Fig. 1: Isoprene loss in dark incubations of coastal seawater.a Time course of isoprene concentration in 2 L dark incubations of non-filtered seawater samples from the back-reef lagoon of Mo’orea in April (blue) and the coastal Mediterranean in March (red) and May (green). Filled and open symbols correspond to duplicate incubations. Exponential fits to the data are shown by lines. See Supplementary Table 1 for fit equations and metrics, water temperatures and chlorophyll a concentrations. b Time course of isoprene concentration in series of 30 mL dark incubations of coastal Mediterranean seawater. Dark blue: non-filtered; red: filtered through 0.2 µm; green: filtered + 10 µmol L−1 H2O2; purple: filtered + 0.0025 units mL−1 bromoperoxidase (BrPO); light blue: filtered + H2O2 + BrPO. Exponential fit results in Supplementary Table 2.Full size imageIncubation of microorganism-devoid (filtered through 0.2 µm) coastal seawater sampled next to seaweeds showed an isoprene loss (0.12 d−1) that was half the loss in non-filtered water (0.20 d−1; Fig. 1b and Supplementary Table 2), implying that chemical oxidation accounted for half the total loss. Oxidation by OH·, the fastest amongst isoprene reactions with oxidative transients for which reaction rate data exist19, could account for the observed chemical loss. However, the possibility of oxidation by hitherto overlooked, pervasive oxidants like H2O2 deserved consideration. The addition of unrealistically high concentrations of either H2O2 or the enzyme bromoperoxidase (BrPO), substantially speeded up the chemical loss (0.91 d−1 with 10 µmol H2O2 L−1, 0.31 d−1 with 0.0025 units BrPO mL−1; Fig. 1b and Supplementary Table 2). Isoprene could have reacted with H2O2 in seawater as it does in acidic aerosols26. Besides, should dissolved27 BrPOs from seaweeds or outer-membrane-bound28 BrPOs from phytoplankton occur, they would have reacted with added H2O2 to produce hypobromous acid (HOBr), a strong oxidant29 that would further remove isoprene. Indeed, the addition of BrPO consumed isoprene because it produced HOBr by reaction with the naturally occurring H2O2. Confirming this interpretation, large HOBr production by simultaneous addition of BrPO and H2O2 caused complete isoprene removal in less than 4 h (Fig. 1b). Therefore, the results shown in Fig. 1b indicate that isoprene is reactive to pervasive H2O2 either directly or through the formation of enzymatically derived HOBr. All in all, first-order total isoprene loss (Fig. 1a) is expected to depend on photochemically-produced oxidants30 like H2O2, OH· and 1O2 as well as on microbiota through (a) microbial uptake and catabolism11 and (b) reaction with biologically produced oxidants26,31,32 like HOBr, H2O2 or superoxide.Variability of isoprene loss rate constants in the open oceanTen of the eleven offshore experimental sites were located in the open ocean, and one was located on the Southwestern Atlantic Shelf. Altogether they covered wide ranges of latitude (40°N–61°S), sea surface temperature (−0.8–28.6 °C), daily-averaged wind speed (3–12 m s−1), fluorometric chlorophyll-a (chla) concentration (0.1–5.8 mg m−3), and isoprene concentration (4–104 nmol m−3) (Fig. 2, Table 1 and Supplementary Table 3). Unfiltered seawater samples from the surface ocean were incubated in glass bottles for 24 h, at the in situ temperature and in the dark, and first-order loss rate constants were determined from initial and final isoprene concentrations (see Methods). Note that loss was determined under the assumption that isoprene production was arrested in the dark25. There is published evidence that residual isoprene production may occur in the dark33, but in our incubations, it was insufficient to counteract loss. Thus, isoprene losses caused by processes other than ventilation may have been underestimated.Fig. 2: Geographical distribution of the offshore experiments.Location of the sampling and incubation sites are shown by circles, coloured for isoprene concentration.Full size imageTable 1 Measured biological variables and isoprene process rate constants.Full size tableLoss rate constants (kloss = kbio + kchem) varied over an order of magnitude, ranging 0.03–0.64 d−1 with a median of 0.08 d−1 (Table 1). They did not show any significant relationship to sea surface temperature (SST) (Supplementary Fig. 1) but showed proportionality to the chla concentration (Fig. 3a) that was best described by the following linear regression equation:$${k}_{{{{{{rm{loss}}}}}}}=0.10; (pm 0.01),{{{{{rm{x}}}}}}, [{{{{{rm{chl}}}}}}a]+0.05; (pm 0.01)$$
    (1)
    Fig. 3: Isoprene processes and their main drivers.a Rate constant of isoprene loss in dark incubations (kloss, considered to be microbial and chemical consumption) vs. chlorophyll-a concentration. The linear regression equation is kloss = 0.10 × [chla] + 0.05 (R2 = 0.96, p = 10−7, n = 11). The standard error of the slope is 0.01 L mg−1 d−1, and the standard error of the intercept is 0.01 d−1. Error bars represent the experimentally determined standard error of kloss. The colour scale of the circles indicates bacterial abundances. b Specific (chla-normalised) rate of isoprene production vs seawater temperature (SST) across the sample series. The dashed line is the general smoothed trend. The blue line is the exponential adjustment at SST , 1000)$$
    (2)
    Substitution in Eq. (1) results in:$${k}_{{{{{{rm{loss}}}}}}}=0.14,{{{{{rm{x}}}}}}, {[{{{{{rm{chl}}}}}}{a}_{{{{{{rm{sat}}}}}}}]}^{1.28}+0.05$$
    (3)
    which is our recommended equation for kloss prediction from satellite chla. Note that only the variable term (kbio) changes from Eq. (1), while the intercept (kchem) is maintained at 0.05 d−1.Comparison of isoprene sinks and total turnover timeThe change of isoprene concentration ([iso]) in the surface mixed layer over time can be described as the budget of sources and sinks:$$varDelta [{{{{{rm{iso}}}}}}]/varDelta {{{{{rm{t}}}}}}=[{{{{{rm{iso}}}}}}]cdot ({k}_{{{{{{rm{prod}}}}}}} – {k}_{{{{{{rm{loss}}}}}}} – {k}_{{{{{{rm{vent}}}}}}} – {k}_{{{{{{rm{mix}}}}}}})$$
    (4)
    where kprod, kvent and kmix are the rate constants of isoprene production, ventilation to the atmosphere and vertical downward mixing by turbulent diffusion, respectively.We calculated kvent from our sampling sites over a period of 24 h (Table 1). Ventilation has been considered the main isoprene sink from the upper mixed layer of the ocean18. In our sampling sites, kloss was 0.4 to 10 times the kvent (median factor: 1.2). That is, loss through microbial + chemical consumption was of the same order as ventilation, sometimes considerably faster. Vertical mixing, kmix, was estimated to be one order of magnitude lower than the other process rates (Table 1), and in all cases but one it was calculated or assumed not to be a loss term but an import term into the mixed layer, because vertical profiles generally show maximum isoprene concentrations below the mixed layer and turbulent diffusion causes upward transport14,17. Altogether, the microbial, chemical, ventilation, and, where relevant, mixing losses resulted in total turnover times (1/(kloss + kvent + kmix)) of isoprene between 1.4 and 16 days, median 5 days (Table 1).Isoprene productionAssuming steady-state for isoprene concentrations over 24 h (Supplementary Fig. 2), i.e. Δ[iso]/Δt = 0 in Eq. (4), the sum of the daily rate constants of all sinks (kloss + kvent) equals the rate constant of isoprene production (kprod), with kmix adding to either side depending on whether it is an import to or an export from the mixed layer (Table 1). Note that kprod was the highest coinciding with higher [chla]. This is consistent with a recent study44 where measurement of the net biological isoprene production (i.e. production — consumption rates) across seasons in the open ocean was attempted; net production rates increased in May, coinciding with a large increase in [chla] and phytoplankton cell abundance.The product of kprod by the isoprene concentration gives the daily isoprene production rate, which can be normalised by dividing it by the chla concentration. In our study, this specific isoprene production rate varied between 1 and 38 nmol (mg chla)−1 d−1 (Table 1), median 8 nmol (mg chla)−1 d−1. These values are within the broad range reported across phytoplankton taxa from laboratory studies with monocultures41,45 (0.3–32, median 3 nmol (mg chla)−1 d−1, n = 124). Five of the eleven sites gave values >13 nmol (mg chla)−1 d−1, i.e. in the higher end of the laboratory data range. This is not unexpected, since measurements in monoculture experiments are typically conducted before reaching nutrient limitation, below light saturation and in the absence of UV radiation, to mention three stressors commonly occurring in the surface open ocean. If isoprene biosynthesis and release is enhanced by any of these stressors, as is the case in vascular plants7,10, then monoculture-derived results will easily render underestimates of isoprene production in the open ocean. Production by heterotrophic bacteria46 could have also contributed to increase apparent specific isoprene production rates, but the occurrence and importance of this process in the marine environment is unknown.When plotted against the SST, which was also the temperature of the incubations, specific isoprene production rates increased exponentially between −0.8 and 23 °C and dropped drastically at higher SST (Fig. 3b). Several studies with phytoplankton monocultures have reported positive dependence of specific isoprene production rates on temperature45,47,48,49,50. One of these studies45 described that the increase with temperature reaches an optimum for production that varies among phytoplankton strains and with light intensity, but falls around 23–26 °C. The most detailed study47 was conducted with a Prochlorococcus strain; remarkably, the shape of the specific production rate vs. temperature curve for this cyanobacterium strain was almost identical to that of Fig. 3b, with an exponential increase until 23 °C and a drop thereafter. This is the canonical curve type of enzymatic activities, but the thermal behaviour of the enzymes for isoprene synthesis in marine unicellular algae has not yet been characterised12.Revising the magnitude and players of the marine isoprene cycleOur results allow redrawing the isoprene cycle in the surface mixed layer of the ocean. Figure 4 sketches the magnitude of the rate constants for production and sinks presented in Table 1, averaged according to a chla concentration threshold: the blue and green arrows correspond to the experiments in waters with [chla] lower and higher than 0.4 mg m−3, respectively. Isoprene production in productive (chla-richer) waters is faster than in oligotrophic (chla-poorer) waters. Vertical mixing is assumed to majorly constitute an input into the mixed layer, yet very small. Photochemical production and emission from surfactants15 in the surface microlayer of productive waters is depicted as uncertain. Among sinks, the microbiota-dependent consumption is much faster in productive waters; actually, the statistical uncertainty of Eq. (1) and the uneven distribution of incubation results along the [chla] axis hamper resolving kbio in phytoplankton-poor waters ( More

  • in

    Dynamics of actively dividing prokaryotes in the western Mediterranean Sea

    1.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).CAS 
    ADS 

    Google Scholar 
    2.Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
    PubMed 

    Google Scholar 
    3.Giovannoni, S. J. & Stingl, U. Molecular diversity and ecology of microbial plankton. Nature 437, 343–348 (2005).CAS 
    PubMed 
    ADS 

    Google Scholar 
    4.Kujawinski, E. B. The impact of microbial metabolism on marine dissolved organic matter. Ann. Rev. Mar. Sci. 3, 567–599 (2011).PubMed 

    Google Scholar 
    5.Moran, M. A. The global ocean microbiome. Science 350, aac8455 (2015).PubMed 

    Google Scholar 
    6.Pedrós-Alió, C. The rare bacterial biosphere. Ann. Rev. Mar. Sci. 4, 15.1-15.18 (2012).
    Google Scholar 
    7.Salazar, G. et al. Global diversity and biogeography of deep-sea pelagic prokaryotes. ISME J. 10, 596–608 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    8.Sogin, M. L. et al. Microbial diversity in the deep sea and the underexplored ‘rare biosphere’. Proc. Natl. Acad. Sci. 103, 12115–12120 (2006).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    9.Kirchman, D. L. Growth rates of microbes in the oceans. Ann. Rev. Mar. Sci. 8, 285–309 (2016).PubMed 

    Google Scholar 
    10.Campbell, B. J., Yu, L., Heidelberg, J. F. & Kirchman, D. L. Activity of abundant and rare bacteria in a coastal ocean. Proc. Natl. Acad. Sci. 108, 12776–12781 (2011).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    11.Salter, I. et al. Seasonal dynamics of active SAR11 ecotypes in the oligotrophic Northwest Mediterranean Sea. ISME J. 9, 347–360 (2015).CAS 
    PubMed 

    Google Scholar 
    12.Giovannoni, S. J., Cameron Thrash, J. & Temperton, B. Implications of streamlining theory for microbial ecology. ISME J. 8, 1553–1565 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    13.Våge, S., Storesund, J. E. & Thingstad, T. F. Adding a cost of resistance description extends the ability of virus-host model to explain observed patterns in structure and function of pelagic microbial communities. Environ. Microbiol. 15, 1842–1852 (2012).
    Google Scholar 
    14.Våge, S., Storesund, J. E. & Thingstad, T. F. SAR11 viruses and defensive host strains. Nature 499, 9–11 (2013).
    Google Scholar 
    15.Giovannoni, S., Temperton, B. & Zhao, Y. Giovannoni et al. reply. Nature 499, 9–11 (2013).
    Google Scholar 
    16.Zhao, Y. et al. Abundant SAR11 viruses in the ocean. Nature 494, 357–360 (2013).CAS 
    PubMed 
    ADS 

    Google Scholar 
    17.Herndl, G. J. et al. Contribution of Archaea to total prokaryotic production in the deep Atlantic Ocean. Appl. Environ. Microbiol. 71, 2303–2309 (2005).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    18.Teira, E., Lebaron, P., Van Aken, H. & Herndl, G. J. Distribution and activity of Bacteria and Archaea in the deep water masses of the North Atlantic. Limnol. Oceanogr. 51, 2131–2144 (2006).CAS 
    ADS 

    Google Scholar 
    19.Newton, R. J. & Shade, A. Lifestyles of rarity: Understanding heterotrophic strategies to inform the ecology of the microbial rare biosphere. Aquat. Microb. Ecol. 78, 51–63 (2016).
    Google Scholar 
    20.Hamasaki, K., Taniguchi, A., Tada, Y., Long, R. A. & Azam, F. Actively growing bacteria in the Inland Sea of Japan, identified by combined bromodeoxyuridine immunocapture and denaturing gradient gel electrophoresis. Appl. Environ. Microbiol. 73, 2787–2798 (2007).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    21.Tada, Y., Makabe, R., Kasamatsu-Takazawa, N., Taniguchi, A. & Hamasaki, K. Growth and distribution patterns of Roseobacter/Rhodobacter, SAR11, and Bacteroidetes lineages in the Southern Ocean. Polar Biol. 36, 691–704 (2013).
    Google Scholar 
    22.Suttle, C. A. Marine viruses—Major players in the global ecosystem. Nat. Rev. Microbiol. 5, 801–812 (2007).CAS 
    PubMed 

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

    Google Scholar 
    24.Mena, C. et al. Seasonal niche partitioning of surface temperate open ocean prokaryotic communities. Front. Microbiol. 11, 1749 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    25.Mena, C. et al. Dynamic prokaryotic communities in the dark western Mediterranean Sea. Sci. Rep. 11, 17859 (2021).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    26.Urbach, E., Vergin, K. L. & Giovannoni, S. J. Immunochemical detection and isolation of DNA from metabolically active bacteria. Appl. Environ. Microbiol. 65, 1207–1213 (1999).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    27.Hatzenpichler, R. et al. In situ visualization of newly synthesized proteins in environmental microbes using amino acid tagging and click chemistry. Environ. Microbiol. 16, 2568–2590 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Emerson, J. et al. Schrödinger’s microbes: Tools for distinguishing the living from the dead in microbial ecosystems. Micobiome 5, 86 (2017).
    Google Scholar 
    29.Smriga, S., Samo, T., Malfatti, F., Villareal, J. & Azam, F. Individual cell DNA synthesis within natural marine bacterial assemblages as detected by ‘click’ chemistry. Aquat. Microb. Ecol. 72, 269–280 (2014).
    Google Scholar 
    30.Reichart, N. et al. Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment. ISME J. 14, 2851–2861 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Bakenhus, I. et al. Composition of total and cell-proliferating bacterioplankton community in early summer in the North Sea—Roseobacters are the most active component. Front. Microbiol. 8, 1–14 (2017).
    Google Scholar 
    32.Morris, R. M. et al. SAR11 clade dominates ocean surface bacterioplankton communities. Nature 420, 806–810 (2002).CAS 
    PubMed 
    ADS 

    Google Scholar 
    33.Giovannoni, S. J. SAR11 Bacteria: The most abundant plankton in the oceans. Ann. Rev. Mar. Sci. 9, 231–255 (2017).PubMed 

    Google Scholar 
    34.Clifford, E. L. et al. Taurine is a major carbon and energy source for marine prokaryotes in the North Atlantic Ocean off the Iberian Peninsula. Microb. Ecol. 78, 299–312 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Tripp, H. J. et al. SAR11 marine bacteria require exogenous reduced sulphur for growth. Nature 452, 741–744 (2008).CAS 
    PubMed 
    ADS 

    Google Scholar 
    36.Carlson, C. A. et al. Seasonal dynamics of SAR11 populations in the euphotic and mesopelagic zones of the northwestern Sargasso Sea. ISME J. 3, 283–295 (2009).CAS 
    PubMed 

    Google Scholar 
    37.Winter, C., Bouvier, T., Weinbauer, M. G. & Thingstad, T. F. Trade-offs between competition and defense specialists among unicellular planktonic organisms: The ‘Killing the Winner’ hypothesis revisited. Microbiol. Mol. Biol. Rev. 74, 42–57 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Vergin, K. L. et al. High-resolution SAR11 ecotype dynamics at the Bermuda Atlantic Time-series Study site by phylogenetic placement of pyrosequences. ISME J. 7, 1322–1332 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Hugoni, M. et al. Structure of the rare archaeal biosphere and seasonal dynamics of active ecotypes in surface coastal waters. Proc. Natl. Acad. Sci. 110, 1–6 (2013).
    Google Scholar 
    40.Qin, W. et al. Marine ammonia-oxidizing archaeal isolates display obligate mixotrophy and wide ecotypic variation. Proc. Natl. Acad. Sci. 111, 12504–12509 (2014).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    41.Pollard, P. C. & Moriarty, D. J. W. Validity of the tritiated thymidine method for estimating bacterial growth rates: Measurement of isotope dilution during DNA synthesis. Appl. Environ. Microbiol. 48, 1076–1083 (1984).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    42.Wellsbury, P., Herbert, R. A. & John Parkes, R. Incorporation of [methyl-3H]thymidine by obligate and facultative anaerobic bacteria when grown under defined culture conditions. FEMS Microbiol. Ecol. 12, 87–95 (1993).CAS 

    Google Scholar 
    43.Clausen, A., Matakos, A., Sandrini, M. & Piskur, J. Thymidine kinases in Archaea. Nucleosides Nucleotides Nucleic Acids 25, 1159–1163 (2006).CAS 
    PubMed 

    Google Scholar 
    44.Hamasaki, K., Long, R. A. & Azam, F. Individual cell growth rates of marine bacteria, measured by bromodeoxyuridine incorporation. Aquat. Microb. Ecol. 35, 217–227 (2004).
    Google Scholar 
    45.Qin, W. et al. Influence of oxygen availability on the activities of ammonia-oxidizing Archaea. Environ. Microbiol. Rep. 9, 250–256 (2017).CAS 
    PubMed 

    Google Scholar 
    46.Reji, L., Tolar, B. B., Smith, J. M., Chavez, F. P. & Francis, C. A. Differential co-occurrence relationships shaping ecotype diversification within Thaumarchaeota populations in the coastal ocean water column. ISME J. 13, 1144–1158 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    47.Sebastián, M. et al. Deep ocean prokaryotic communities are remarkably malleable when facing long-term starvation. Environ. Microbiol. 20, 713–723 (2018).PubMed 

    Google Scholar 
    48.Vergin, K. L., Done, B., Carlson, C. A. & Giovannoni, S. J. Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean. Aquat. Microb. Ecol. 71, 1–13 (2013).
    Google Scholar 
    49.Tada, Y., Taniguchi, A., Sato-Takabe, Y. & Hamasaki, K. Growth and succession patterns of major phylogenetic groups of marine bacteria during a mesocosm diatom bloom. J. Oceanogr. 68, 509–519 (2012).
    Google Scholar 
    50.Mestre, M. et al. Sinking particles promote vertical connectivity in the ocean microbiome. Proc. Natl. Acad. Sci. 115, E6799–E6807 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Ruiz-González, C. et al. Major imprint of surface plankton on deep ocean prokaryotic structure and activity. Mol. Ecol. 29, 1820–1838 (2020).PubMed 

    Google Scholar 
    52.Chen, X., Ma, R., Yang, Y., Jiao, N. & Zhang, R. Viral regulation on bacterial community impacted by lysis-lysogeny switch: A microcosm experiment in eutrophic coastal waters. Front. Microbiol. 10, 1763 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    53.McCarren, J. et al. Microbial community transcriptomes reveal microbes and metabolic pathways associated with dissolved organic matter turnover in the sea. Proc. Natl. Acad. Sci. 107, 16420–16427 (2010).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    54.Reintjes, G., Arnosti, C., Fuchs, B. & Amann, R. Selfish, sharing and scavenging bacteria in the Atlantic Ocean: A biogeographical study of bacterial substrate utilisation. ISME J. 13, 1119–1132 (2019).CAS 
    PubMed 

    Google Scholar 
    55.Middelboe, M. Bacterial growth rate and marine virus-host dynamics. Microb. Ecol. 40, 114–124 (2000).CAS 
    PubMed 

    Google Scholar 
    56.Buchan, A., Lecleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: Features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).CAS 
    PubMed 

    Google Scholar 
    57.Mou, X. et al. Bromodeoxyuridine labelling and fluorescence-activated cell sorting of polyamine-transforming bacterioplankton in coastal seawater. Environ. Microbiol. 17, 876–888 (2014).PubMed 

    Google Scholar 
    58.Azam, F. & Malfatti, F. Microbial structuring of marine ecosystems. Nat. Rev. Microbiol. 5, 782–791 (2007).CAS 
    PubMed 

    Google Scholar 
    59.Yilmaz, P., Yarza, P., Rapp, J. Z. & Glöckner, F. O. Expanding the world of marine bacterial and archaeal clades. Front. Microbiol. 6, 1524 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    60.Coe, A. et al. Survival of Prochlorococcus in extended darkness. Limnol. Oceanogr. 61, 1375–1388 (2016).ADS 

    Google Scholar 
    61.Cottrell, M. T. & Kirchman, D. L. Photoheterotrophic microbes in the arctic ocean in summer and winter. Appl. Environ. Microbiol. 75, 4958–4966 (2009).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    62.Zeder, M., Peter, S., Shabarova, T. & Pernthaler, J. A small population of planktonic Flavobacteria with disproportionally high growth during the spring phytoplankton bloom in a prealpine lake. Environ. Microbiol. 11, 2676–2686 (2009).PubMed 

    Google Scholar 
    63.Cottrell, M. T. & Kirchman, D. L. Natural assemblages of marine proteobacteria and members of the Cytophaga-flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter. Appl. Environ. Microbiol. 66, 1692–1697 (2000).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    64.Banning, E. C., Casciotti, K. L. & Kujawinski, E. B. Novel strains isolated from a coastal aquifer suggest a predatory role for flavobacteria. FEMS Microbiol. Ecol. 73, 254–270 (2010).CAS 
    PubMed 

    Google Scholar 
    65.Uchimiya, M. et al. Coupled response of bacterial production to a wind-induced fall phytoplankton bloom and sediment resuspension in the chukchi sea shelf, Western Arctic Ocean. Front. Mar. Sci. 3, 1–12 (2016).
    Google Scholar 
    66.Ivancic, I. et al. Seasonal variations in extracellular enzymatic activity in marine snow-associated microbial communities and their impact on the surrounding water. FEMS Microbiol. Ecol. 94, fiy198 (2018).CAS 

    Google Scholar 
    67.Cram, J. A. et al. Seasonal and interannual variability of the marine bacterioplankton community throughout the water column over ten years. ISME J. 9, 563–580 (2015).PubMed 

    Google Scholar 
    68.Manca, B. et al. Physical and biochemical averaged vertical profiles in the Mediterranean regions: An important tool to trace the climatology of water masses and to validate incoming data from operational oceanography. J. Mar. Syst. 48, 83–116 (2004).
    Google Scholar 
    69.Puig, P. & Palanques, A. Temporal variability and composition of settling particle fluxes on the Barcelona continental margin (Northwestern Mediterranean). J. Mar. Res. 56, 639–654 (1998).
    Google Scholar 
    70.Buesseler, K. O. & Boyd, P. W. Shedding light on processes that control particle export and flux attenuation in the twilight zone of the open ocean. Limnol. Oceanogr. 54, 1210–1232 (2009).CAS 
    ADS 

    Google Scholar 
    71.Alonso-González, I. J., Arístegui, J., Lee, C. & Calafat, A. Regional and temporal variability of sinking organic matter in the subtropical northeast Atlantic Ocean: A biomarker diagnosis. Biogeosciences 7, 2101–2115 (2010).ADS 

    Google Scholar 
    72.Hunt, D. E. et al. Relationship between abundance and specific activity of bacterioplankton in open ocean surface waters. Appl. Environ. Microbiol. 79, 177–184 (2013).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    73.Campbell, B. J. & Kirchman, D. L. Bacterial diversity, community structure and potential growth rates along an estuarine salinity gradient. ISME J. 7, 210–220 (2013).CAS 
    PubMed 

    Google Scholar 
    74.Alderkamp, A. C., Sintes, E. & Herndl, G. J. Abundance and activity of major groups of prokaryotic plankton in the coastal North Sea during spring and summer. Aquat. Microb. Ecol. 45, 237–246 (2006).
    Google Scholar 
    75.De Corte, D., Sintes, E., Yokokawa, T. & Herndl, G. J. Comparison between MICRO-CARD-FISH and 16S rRNA gene clone libraries to assess the active versus total bacterial community in the coastal Arctic. Environ. Microbiol. Rep. 5, 272–281 (2013).PubMed 

    Google Scholar 
    76.Bergauer, K. et al. Organic matter processing by microbial communities throughout the Atlantic water column as revealed by metaproteomics. Proc. Natl. Acad. Sci. 115, E400–E408 (2018).CAS 
    PubMed 

    Google Scholar 
    77.Georges, A. A., El-Swais, H., Craig, S. E., Li, W. K. W. & Walsh, D. A. Metaproteomic analysis of a winter to spring succession in coastal northwest Atlantic Ocean microbial plankton. ISME J. 8, 1301–1313 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Couradeau, E. et al. Probing the active fraction of soil microbiomes using BONCAT-FACS. Nat. Commun. 10, 2770 (2019).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    79.Wu, X. et al. Culturing of ‘unculturable’ subsurface microbes: Natural organic carbon source fuels the growth of diverse and distinct bacteria from groundwater. Front. Microbiol. 11, 610001 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    80.Alonso-Sáez, L., Díaz-Pérez, L. & Morán, X. A. G. The hidden seasonality of the rare biosphere in coastal marine bacterioplankton. Environ. Microbiol. 17, 3766–3780 (2015).PubMed 

    Google Scholar 
    81.Liu, J., Meng, Z., Liu, X. & Zhang, X.-H. Microbial assembly, interaction, functioning, activity and diversification: A review derived from community compositional data. Mar. Life Sci. Technol. 1, 112–128 (2019).ADS 

    Google Scholar 
    82.Long, R. A. & Azam, F. Antagonistic interactions among marine pelagic bacteria. Appl. Environ. Microbiol. 67, 4975–4983 (2001).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    83.López-Jurado, J. L. et al. The RADMED monitoring programme as a tool for MSFD implementation: Towards an ecosystem-based approach. Ocean Sci. 11, 897–908 (2015).ADS 

    Google Scholar 
    84.Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis (Fisheries Research Board of Canada, 1968).
    Google Scholar 
    85.Grasshoff, K., Ehrhardt, M. & Kremling, K. Methods of seawater analysis (Verlag Chemie GmbH, 1983). https://doi.org/10.1002/iroh.19850700232.Book 

    Google Scholar 
    86.Murphy, J. & Riley, J. P. A modified single solution method for the determination of phosphate in natural waters. Anal. Chim. Acta 27, 31–36 (1962).CAS 

    Google Scholar 
    87.Brussaard, C. P. D. Optimization of procedures for counting viruses by flow cytometry. Appl. Environ. Microbiol. 70, 1506–1513 (2004).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    88.Gasol, J. M. & del Giorgio, P. A. Using flow cytometry for counting natural planktonic bacteria and understanding the structure of planktonic bacterial communities. Sci. Mar. 64, 197–224 (2000).
    Google Scholar 
    89.Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
    PubMed 

    Google Scholar 
    90.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    91.Callahan, B. J. et al. Dada2: High-resolution sample inference from illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    92.Katoh, K. & Standley, D. M. MAFFT Multiple sequence aligment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    93.Eren, A. M. et al. Oligotyping: Differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evol. 4, 1111–1119 (2013).PubMed Central 

    Google Scholar  More

  • in

    Energy allocation shifts from sperm production to self-maintenance at low temperatures in male bats

    1.Thomas, D. W., Fenton, M. B. & Barclay, R. M. R. Social-behavior of the little brown bat, myotis-lucifugus. 1. mating-behavior. Behav. Ecol. Sociobiol. 6, 129–136. https://doi.org/10.1007/bf00292559 (1979).Article 

    Google Scholar 
    2.Weiner, J. Physiological limits to sustainable energy budgets in birds and mammals-ecological implications. Trends Ecol. Evol. 7, 384–388. https://doi.org/10.1016/0169-5347(92)90009-z (1992).CAS 
    Article 
    PubMed 

    Google Scholar 
    3.Becker, N. I., Encarnação, J. A., Kalko, E. K. V. & Tschapka, M. The effects of reproductive state on digestive efficiency in three sympatric bat species of the same guild. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 162, 386–390. https://doi.org/10.1016/j.cbpa.2012.04.021 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    4.Becker, N. I., Encarnação, J. A., Tschapka, M. & Kalko, E. K. V. Energetics and life-history of bats in comparison to small mammals. Ecol. Res. 28, 249–258. https://doi.org/10.1007/s11284-012-1010-0 (2012).CAS 
    Article 

    Google Scholar 
    5.Ruf, T. & Bieber, C. Physiological, behavioral, and life-history adaptations to environmental fluctuations in the edible dormouse. Front. Physiol. https://doi.org/10.3389/fphys.2020.00423 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Scholander, P. F., Hock, R., Walters, V. & Irving, L. Adaptation to cold in arctic and tropical mammals and birds in relation to body temperature, insulation, and basal metabolic rate. Biol. Bull. 99, 259–271. https://doi.org/10.2307/1538742 (1950).CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Geiser, F. & Ruf, T. Hibernation versus daily torpor in mammals and birds-physiological variables and classification of torpor patterns. Physiol. Zool. 68, 935–966. https://doi.org/10.1086/physzool.68.6.30163788 (1995).Article 

    Google Scholar 
    8.Aschoff, J. Thermal conductance in mammals and birds-its dependence on body size and circadian phase. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 69, 611–619. https://doi.org/10.1016/0300-9629(81)90145-6 (1981).Article 

    Google Scholar 
    9.McNab, B. K. The economics of temperature regulation in neotropical bats. Comp. Biochem. Physiol 31, 227–268. https://doi.org/10.1016/0010-406X(69)91651-X (1969).CAS 
    Article 
    PubMed 

    Google Scholar 
    10.Speakman, J. R. & Thomas, D. W. in Bat ecology (ed Thomas H. Kunz and M. Brock Fenton) 430–490 (University of Chicago Press, 2003).11.Wang, L. C. H. & Wolowyk, M. W. Torpor in mammals and birds. Can. J. Zool.-Rev. Can. Zool. 66, 133–137. https://doi.org/10.1139/z88-017 (1988).CAS 
    Article 

    Google Scholar 
    12.Geiser, F. Metabolic rate and body temperature reduction during hibernation and daily torpor. Annu. Rev. Physiol. 66, 239–274. https://doi.org/10.1146/annurev.physiol.66.032102.115105 (2004).CAS 
    Article 
    PubMed 
    ADS 

    Google Scholar 
    13.Geiser, F. & Masters, P. Torpor in relation to reproduction in the mulgara, dasycercus-cristicauda (dasyuridae, marsupialia). J. Therm. Biol. 19, 33–40. https://doi.org/10.1016/0306-4565(94)90007-8 (1994).Article 

    Google Scholar 
    14.Wojciechowski, M. S., Jefimow, M. & Tęgowska, E. Environmental conditions, rather than season, determine torpor use and temperature selection in large mouse-eared bats (Myotis myotis). Comp. Biochem. Physiol. A Mol. Integr. Physiol. 147, 828–840. https://doi.org/10.1016/j.cbpa.2006.06.039 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Ruf, T. & Geiser, F. Daily torpor and hibernation in birds and mammals. Biol. Rev. 90, 891–926. https://doi.org/10.1111/brv.12137 (2015).Article 
    PubMed 

    Google Scholar 
    16.Tuttle, M. D. Population ecology of the gray bat (Myotis grisescens): factors Iifluencing growth and survival of newly volant young. Ecology 57, 587–595. https://doi.org/10.2307/1936443 (1976).Article 

    Google Scholar 
    17.Racey, P. A. & Swift, S. M. Variations in gestation length in a colony of Pipistrelle bats (Pipistrellus pipistrellus) from year to year. J. Reprod. Fertil. 61, 123–129. https://doi.org/10.1530/jrf.0.0610123 (1981).CAS 
    Article 
    PubMed 

    Google Scholar 
    18.Audet, D. & Fenton, M. B. Heterothermy and the use of torpor by the bat Eptesicus fuscus (Chiroptera, Vespertilionidae)-a field study. Physiol. Zool. 61, 197–204. https://doi.org/10.1086/physzool.61.3.30161232 (1988).Article 

    Google Scholar 
    19.Barnes, B. M., Kretzmann, M., Licht, P. & Zucker, I. The influence of hibernation on testis growth and spermatogenesis in the golden mantled ground squirrel, Spermophilus lateralis. Biol. Reprod. 35, 1289–1297. https://doi.org/10.1095/biolreprod35.5.1289 (1986).CAS 
    Article 
    PubMed 

    Google Scholar 
    20.Gagnon, M. F., Lafleur, C., Landry-Cuerrier, M., Humphries, M. M. & Kimmins, S. Torpor expression is associated with differential spermatogenesis in hibernating eastern chipmunks. Am. J. Physiol. Regul. Integr. Comp. Physiol. 319, R455–R465. https://doi.org/10.1152/ajpregu.00328.2019 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.McLean, J. A. & Speakman, J. R. Energy budgets of lactating and non-reproductive Brown Long-Eared Bats (Plecotus auritus) suggest females use compensation in lactation. Funct. Ecol. 13, 360–372. https://doi.org/10.1046/j.1365-2435.1999.00321.x (1999).Article 

    Google Scholar 
    22.Wilde, C. J., Knight, C. R. & Racey, P. A. Influence of torpor on milk protein composition and secretion in lactating bats. J. Exp. Zool. 284, 35–41. https://doi.org/10.1002/(sici)1097-010x(19990615)284:1%3c35::aid-jez6%3e3.0.co;2-z (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    23.Racey, P. A. The prolonged storage and survival of spermatozoa in Chiroptera. J. Reprod. Fertil. 56, 391–402. https://doi.org/10.1530/jrf.0.0560391 (1979).CAS 
    Article 
    PubMed 

    Google Scholar 
    24.Racey, P. A. The reproductive cycle in male noctule bats, Nyctalus noctula. J. Reprod. Fertil. 41, 169–182. https://doi.org/10.1530/jrf.0.0410169 (1974).CAS 
    Article 
    PubMed 

    Google Scholar 
    25.Gustafson, A. W. Male reproductive patterns in hibernating bats. J. Reprod. Fertil. 56, 317–0 (1979).CAS 
    Article 

    Google Scholar 
    26.Komar, E., Dechmann, D. K. N., Fasel, N. J., Zegarek, M. & Ruczyński, I. Food restriction delays seasonal sexual maturation but does not increase torpor use in male bats. J. Exp. Biol. https://doi.org/10.1242/jeb.214825 (2020).Article 
    PubMed 

    Google Scholar 
    27.Wilkinson, G. S. & McCracken, G. F. in Bat ecology (eds Thomas H. Kunz & M. Brock Fenton) 128–155 (University of Chicago Press, 2003).28.Pescovitz, O. H., Srivastava, C. H., Breyer, P. R. & Monts, B. A. Paracrine control of spermatogenesis. Trends Endocrinol. Metab. 5, 126–131. https://doi.org/10.1016/1043-2760(94)90094-9 (1994).CAS 
    Article 
    PubMed 

    Google Scholar 
    29.Sharpe, R. M., Kerr, J. B., McKinnell, C. & Millar, M. Temporal relationship between androgen-dependent changes in the volume of seminiferous tubule fluid, lumen size and seminiferous tubule protein secretion in rats. J. Reprod. Fertil. 101, 193–198 (1994).CAS 
    Article 

    Google Scholar 
    30.Becker, N. I., Tschapka, M., Kalko, E. K. V. & Encarnacao, J. A. Balancing the energy budget in free ranging male Myotis daubentonii bats. Physiol. Biochem. Zool. 86, 361–369. https://doi.org/10.1086/670527 (2013).Article 
    PubMed 

    Google Scholar 
    31.Entwistle, A. C., Racey, P. A. & Speakman, J. R. The reproductive cycle and determination of sexual maturity in male brown long eared bats, Plecotus auritus (Chiroptera: Vespertilionidae). J. Zool. 244, 63–70. https://doi.org/10.1111/j.1469-7998.1998.tb00007.x (1998).Article 

    Google Scholar 
    32.Fasel, N. J., Kołodziej-Sobocińska, M., Komar, E., Zegarek, M. & Ruczyński, I. Penis size and sperm quality, are all bats grey in the dark?. Curr. Zool. 65, 697–703. https://doi.org/10.1093/cz/zoy094 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Dietz, M. & Kalko, E. K. V. Reproduction affects flight activity in female and male Daubenton’s bats, Myotis daubentoni. Can. J. Zool.-Rev. Can. Zool. 85, 653–664. https://doi.org/10.1139/z07-045 (2007).Article 

    Google Scholar 
    34.Encarnação, J. A. Spatiotemporal pattern of local sexual segregation in a tree dwelling temperate bat Myotis daubentonii. J. Ethol. 30, 271–278. https://doi.org/10.1007/s10164-011-0323-8 (2012).Article 

    Google Scholar 
    35.Safi, K. & Kerth, G. Comparative analyses suggest that information transfer promoted sociality in male bats in the temperate zone. Am. Nat. 170, 465–472. https://doi.org/10.1086/520116 (2007).Article 
    PubMed 

    Google Scholar 
    36.Hałat, Z., Dechmann, D. K. N., Zegarek, M. & Ruczyński, I. Male bats respond to adverse conditions with larger colonies and increased torpor use during sperm production. Mamm. Biol. 22, 2109 (2020).
    Google Scholar 
    37.Dietz, M. & Horig, A. Thermoregulation of tree dwelling temperate bats-a behavioural adaptation to force live history strategy. Folia Zool. 60, 5–16. https://doi.org/10.25225/fozo.v60.i1.a2.2011 (2011).Article 

    Google Scholar 
    38.Ruczyński, I., Zahorowicz, P., Borowik, T. & Hałat, Z. Activity patterns of two syntopic and closely related aerial-hawking bat species during breeding season in Bialowieza Primaeval Forest. Mammal Res. 62, 65–73. https://doi.org/10.1007/s13364-016-0298-5 (2017).Article 

    Google Scholar 
    39.Jolly, S. E. & Blackshaw, A. W. Prolonged epididymal sperm storage, and the temporal dissociation of testicular and accessory gland activity in the common sheath-tail bat, Taphozous georgianus, of tropical Australia. J. Reprod. Fertil. 81, 205–211. https://doi.org/10.1530/jrf.0.0810205 (1987).CAS 
    Article 
    PubMed 

    Google Scholar 
    40.Boyles, J. G., Dunbar, M. B., Storm, J. J. & Brack, V. Energy availability influences microclimate selection of hibernating bats. J. Exp. Biol. 210, 4345–4350. https://doi.org/10.1242/jeb.007294 (2007).Article 
    PubMed 

    Google Scholar 
    41.Ruczyński, I., Hałat, Z., Zegarek, M., Borowik, T. & Dechmann, D. K. N. Camera transects as a method to monitor high temporal and spatial ephemerality of flying nocturnal insects. Methods Ecol. Evol. https://doi.org/10.1111/2041-210x.13339 (2020).Article 

    Google Scholar 
    42.Safi, K. Social bats: the males’ perspective. J. Mammal. 89, 1342–1350. https://doi.org/10.1644/08-mamm-s-058.1 (2008).Article 

    Google Scholar 
    43.Webb, P. I., Speakman, J. R. & Racey, P. A. The implication of small reductions in body temperature for radiant and convective heat loss in resting endothermic brown long eared bats (Pecotus auritus). J. Therm. Biol. 18, 131–135. https://doi.org/10.1016/0306-4565(93)90026-p (1993).Article 

    Google Scholar 
    44.Boratyński, J. S., Iwińska, K. & Bogdanowicz, W. An intrapopulation heterothermy continuum: notable repeatability of body temperature variation in food deprived yellow necked mice. J. Exp. Biol. 222, 197152. https://doi.org/10.1242/jeb.197152 (2019).Article 

    Google Scholar 
    45.Christian, N. & Geiser, F. To use or not to use torpor? Activity and body temperature as predictors. Naturwissenschaften 94, 483–487. https://doi.org/10.1007/s00114-007-0215-5 (2007).CAS 
    Article 
    PubMed 
    ADS 

    Google Scholar 
    46.Smith, L. B. & Walker, W. H. The regulation of spermatogenesis by androgens. Semin. Cell Dev. Biol. 30, 2–13. https://doi.org/10.1016/j.semcdb.2014.02.012 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    47.Macdonald, J. & Harrison, R. G. Effect of low temperatures on rat spermatogenesis. Fertil. Steril. 5, 205–216 (1954).CAS 
    Article 

    Google Scholar 
    48.Fowler, P. A. & Racey, P. A. Relationship between body and testis temperatures in the European hedgehog, Erinaceus europaeus, during hibernation and sexual reactivation. Reproduction 81, 567. https://doi.org/10.1530/jrf.0.0810567 (1987).CAS 
    Article 

    Google Scholar 
    49.Davis, J. R., Firlit, C. F. & Hollinger, M. A. Effect of temperature on incorporation of l-lysine-U-C14 into testicular proteins. Am. J. Physiol. 204, 696–698. https://doi.org/10.1152/ajplegacy.1963.204.4.696 (1963).CAS 
    Article 
    PubMed 

    Google Scholar 
    50.LeVier, R. R. & Spaziani, E. The influence of temperature on steroidogenesis in the rat testis. J. Exp. Zool. 169, 113–120. https://doi.org/10.1002/jez.1401690113 (1968).CAS 
    Article 
    PubMed 

    Google Scholar 
    51.Geiser, F. & Brigham, R. M. in Living in a seasonal world (eds Thomas Ruf, Claudia Bieber, Walter Arnold, & Eva Millesi) 109–121 (Springer, 2012).52.Safi, K. Die Zweifarbfledermaus in der Schweiz: Status und Grundlagen zum Schutz. (Haupt Verlag, 2006).53.Hałat, Z., Dechmann, D. K. N., Zegarek, M., Visser, A. F. J. & Ruczyński, I. Sociality and insect abundance affect duration of nocturnal activity of male parti-colored bats. J. Mammal. 99, 1503–1509. https://doi.org/10.1093/jmammal/gyy141 (2018).Article 

    Google Scholar 
    54.Ruczyński, I. Influence of temperature on maternity roost selection by noctule bats (Nyctalus noctula) and Leisler’s bats (N-leisleri) in Biaowieza Primeval Forest, Poland. Can. J. Zool. 84, 900–907. https://doi.org/10.1139/z06-060 (2006).Article 

    Google Scholar 
    55.Ruczyński, I. & Bartoń, K. A. Seasonal changes and the influence of tree species and ambient temperature on the fission-fusion dynamics of tree-roosting bats. Behav. Ecol. Sociobiol. 74, 63. https://doi.org/10.1007/s00265-020-02840-1 (2020).Article 

    Google Scholar 
    56.Linton, D. M. & Macdonald, D. W. Phenology of reproductive condition varies with age and spring weather conditions in male Myotis daubentonii and Myotis nattereri (Chiroptera: Vespertilionidae). Sci. Rep. 10, 6664. https://doi.org/10.1038/s41598-020-63538-y (2020).CAS 
    Article 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    57.Dammhahn, M., Landry-Cuerrier, M., Reale, D., Garant, D. & Humphries, M. M. Individual variation in energy-saving heterothermy affects survival and reproductive success. Funct. Ecol. 31, 866–875. https://doi.org/10.1111/1365-2435.12797 (2017).Article 

    Google Scholar 
    58.Boyles, J. G., Johnson, J. S., Blomberg, A. & Lilley, T. M. Optimal hibernation theory. Mammal. Rev. 50, 91–100. https://doi.org/10.1111/mam.12181 (2020).Article 

    Google Scholar 
    59.Boratyński, J. S., Willis, C. K. R., Jefimow, M. & Wojciechowski, M. S. Huddling reduces evaporative water loss in torpid Natterer’s bats, Myotis nattereri. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 179, 125–132. https://doi.org/10.1016/j.cbpa.2014.09.035 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Ruczyński, I., Kalko, E. K. V. & Siemers, B. M. The sensory basis of roost finding in a forest bat, Nyctalus noctula. J. Exp. Biol. 210, 3607–3615. https://doi.org/10.1242/jeb.009837 (2007).Article 
    PubMed 

    Google Scholar 
    61.Lovegrove, B. G. Modification and miniaturization of Thermochron iButtons for surgical implantation into small animals. J. Comp. Physiol. B 179, 451–458. https://doi.org/10.1007/s00360-008-0329-x (2009).Article 
    PubMed 

    Google Scholar 
    62.Willis, C. K. R., Lane, J. E., Liknes, E. T., Swanson, D. L. & Brigham, R. M. Thermal energetics of female big brown bats (Eptesicus fuscus). Can. J. Zool. 83, 871–879. https://doi.org/10.1139/z05-074 (2005).Article 

    Google Scholar 
    63.Willis, C. K. R. An energy-based body temperature threshold between torpor and normothermia for small mammals. Physiol. Biochem. Zool. 80, 643–651. https://doi.org/10.1086/521085 (2007).Article 
    PubMed 

    Google Scholar 
    64.Krutzsch, P. H. in Reproductive Biology of Bats (ed Academic Press) 91–155 (2000).65.Wood, S. N. Generalized Additive Models: An Introduction With R. Vol. 66 (2006).66.Jackman, S. Bayesian Analysis for the Social Sciences. (Wiley, 2009).67.Brooks, S. P. & Gelman, A. General methods for monitoring convergence of iterative simulations. J. Comput. Graph. Stat. 7, 434–455. https://doi.org/10.2307/1390675 (1998).MathSciNet 
    Article 

    Google Scholar 
    68.Kellner, K. jagsUI: A Wrapper Around ‘rjags’ to Streamline ‘JAGS’ Analyses. v.R package version 1.5.1. (2019). More

  • in

    Consider fungal friends

    Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
    the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
    Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
    and JavaScript. More

  • in

    Pantanal fires

    Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
    the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
    Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
    and JavaScript. More

  • in

    Giant sponge grounds of Central Arctic seamounts are associated with extinct seep life

    1.Maldonado, M. et al. in Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds. Rossi, S., Bramanti, L., Gori, A. & del Valle, C.) (Springer, 2016).2.de Goeij, J. M. et al. Surviving in a marine desert: the sponge loop retains resources within coral reefs. Science 342, 108–110 (2013).ADS 
    PubMed 

    Google Scholar 
    3.Beazley, L., Kenchington, E., Yashayaev, I. & Murillo, F. J. Drivers of epibenthic megafaunal composition in the sponge grounds of the Sackville Spur, northwest. Atl. Deep. Res. Part I 98, 102–114 (2015).
    Google Scholar 
    4.Klitgaard, A. B. & Tendal, O. S. Progress in oceanography distribution and species composition of mass occurrences of large-sized sponges in the northeast Atlantic. Prog. Oceanogr. 61, 57–98 (2004).ADS 

    Google Scholar 
    5.Kazanidis, G. et al. Distribution of deep-sea sponge aggregations in an area of multisectoral activities and changing oceanic conditions. Front. Mar. Sci. 6, 163 (2019).
    Google Scholar 
    6.Hanz, U., Roberts, E. M., Duineveld, G., Davies, A. & Rapp, H. T. Long – term observations reveal environmental conditions and food supply mechanisms at an Arctic deep-sea sponge ground. J. Geophisical. Res. 126, 1–18 (2021).
    Google Scholar 
    7.Roberts, E. et al. Water masses constrain the distribution of deep-sea sponges in the North Atlantic Ocean and Nordic Seas. Mar. Ecol. Prog. Ser. 659, 75–96 (2021).ADS 

    Google Scholar 
    8.Cathalot, C. et al. Cold-water coral reefs and adjacent sponge grounds: hotspots of benthic respiration and organic carbon cycling in the deep sea. Front. Mar. Sci. 2, 37 (2015).
    Google Scholar 
    9.Kahn, A. S., Yahel, G., Chu, J. W. F., Tunnicliffe, V. & Leys, S. P. Benthic grazing and carbon sequestration by deep-water glass sponge reefs. Limnol. Oceanogr. 60, 78–88 (2015).ADS 

    Google Scholar 
    10.Morganti, T., Coma, R., Yahel, G. & Ribes, M. Trophic niche separation that facilitates co-existence of high and low microbial abundance sponges is revealed by in situ study of carbon and nitrogen fluxes. Limnol. Oceanogr. 62, 1963–1983 (2017).ADS 
    CAS 

    Google Scholar 
    11.Kutti, T., Bannister, R. J. & Fosså, J. H. Community structure and ecological function of deep-water sponge grounds in the Traenadypet MPA — Northern Norwegian continental shelf. Cont. Shelf Res. 69, 21–30 (2013).ADS 

    Google Scholar 
    12.Bart, M. C. et al. Dissolved organic carbon (DOC) is essential to balance the metabolic demands of four dominant North-Atlantic deep-sea sponges. Limnol. Oceanogr. 9999, 1–14 (2020).
    Google Scholar 
    13.Gloeckner, V. et al. The HMA-LMA dichotomy revisited: an electron microscopical survey of 56 sponge species. Biol. Bull. 227, 78–88 (2014).PubMed 

    Google Scholar 
    14.Bruck, T. B., Self, W. T., Reed, J. K., Nitecki, S. S. & McCarthy, P. J. Comparison of the anaerobic microbiota of deep-water Geodia spp. and sandy sediments in the Straits of Florida. ISME J. 4, 686–699 (2010).PubMed 

    Google Scholar 
    15.Schottner, S. et al. Relationships between host phylogeny, host type and bacterial community diversity in cold-water coral reef sponges. PLoS ONE 8, 1–11 (2013).
    Google Scholar 
    16.Hoffmann, F. et al. An anaerobic world in sponges. Geomicrobiol. J. 22, 1–10 (2005).
    Google Scholar 
    17.Schlindwein, V. & Schmid, F. Mid-ocean-ridge seismicity reveals extreme types of ocean lithosphere. Nature 535, 276–279 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    18.Cochran, J. R. Seamount volcanism along the Gakkel Ridge. Arct. Ocean. Geophys. J. Int. 174, 1153–1173 (2008).ADS 

    Google Scholar 
    19.Arrigo, K. R., van Dijken, G. & Pabi, S. Impact of a shrinking Arctic ice cover on marine primary production. Geophys. Res. Lett. 35, L19603 (2008).ADS 

    Google Scholar 
    20.Wassmann, P., Slagstad, D. & Ellingsen, I. Primary production and climatic variability in the European sector of the Arctic Ocean prior to 2007: preliminary results. Polar Biol. 33, 1641–1650 (2010).
    Google Scholar 
    21.Wiedmann, I. et al. What feeds the Benthos in the Arctic Basins? Assembling a carbon budget for the deep Arctic Ocean. Front. Mar. Sci. 7, 224 (2020).
    Google Scholar 
    22.Boetius, A. & Purser, A. The Expedition PS101 of the Research Vessel POLARSTERN to the Arctic Ocean in 2016, Berichte zur Polar- und Meeresforschung = Reports on polar and marine research, Bremerhaven, Alfred Wegener Institute for Polar and Marine Research. (2017).23.Alvizu, A., Xavier, J. R. & Rapp, H. T. Description of new chiactine-bearing sponges provides insights into the higher classification of Calcaronea (Porifera: Calcarea). Zootaxa 4615, 201–251 (2019).
    Google Scholar 
    24.Rybakova, E., Kremenetskaia, A., Vedenin, A., Boetius, A. & Gebruk, A. Deep-sea megabenthos communities of the Eurasian Central Arctic are influenced by ice-cover and sea-ice algal falls. PLoS ONE 14, 1–27 (2019).
    Google Scholar 
    25.Astrom, E. K. L. et al. Methane cold seeps as biological oases in the high-Arctic deep sea. Limnol. Oceanogr. 63, 209–231 (2018).
    Google Scholar 
    26.Sen, A., Didriksen, A., Hourdez, S., Svenning, M. M. & Rasmussen, T. L. Frenulate siboglinids at high Arctic methane seeps and insight into high latitude frenulate distribution. Ecol. Evol. 10, 1339–1351 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    27.Henrich, R. et al. Facies belts and communities of the arctic Vesterisbanken Seamount (Central Greenland Sea). Facies 27, 71 (1992).
    Google Scholar 
    28.Leys, S. P., Kahn, A. S., Fang, J. K. H., Kutti, T. & Bannister, R. J. Phagocytosis of microbial symbionts balances the carbon and nitrogen budget for the deep-water boreal sponge Geodia barretti. Limnol. Oceanogr. 63, 187–202 (2018).ADS 
    CAS 

    Google Scholar 
    29.Druffel, E. R. M., Griffin, S., Glynn, C. S., Benner, R. & Walker, B. D. Radiocarbon in dissolved organic and inorganic carbon of the Arctic Ocean. Geophys. Res. Lett. 44, 2369–2376 (2017).ADS 
    CAS 

    Google Scholar 
    30.Mehrshad, M., Rodriguez-Valera, F., Amoozegar, M. A., López-García, P. & Ghai, R. The enigmatic SAR202 cluster up close: shedding light on a globally distributed dark ocean lineage involved in sulfur cycling. ISME J. 12, 655–668 (2018).CAS 
    PubMed 

    Google Scholar 
    31.Petersen, J. M., Wentrup, C., Verna, C., Knittel, K. & Dubilier, N. Origins and evolutionary flexibility of chemosynthetic symbionts from deep-sea animals. Biol. Bull. 223, 123–137 (2012).CAS 
    PubMed 

    Google Scholar 
    32.Rubin-Blum, M. et al. Fueled by methane: deep-sea sponges from asphalt seeps gain their nutrition from methane-oxidizing symbionts. ISME J. 13, 1209–1225 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Bayer, K., Jahn, M. T., Slaby, B. M., Moitinho-Silva, L. & Hentschel, U. Marine sponges as chloroflexi hot spots: genomic insights and high-resolution visualization of an abundant and diverse symbiotic clade. mSystems 3, e00150–18 (2018).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Kamke, J. et al. Single-cell genomics reveals complex carbohydrate degradation patterns in poribacterial symbionts of marine sponges. ISME J. 7, 2287–2300 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Bayer, K. et al. Microbial strategies for survival in the glass sponge Vazella pourtalesii. mSystems 5, e00473–20 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Van Duyl, F. C., Hegeman, J., Hoogstraten, A. & Maier, C. Dissolved carbon fixation by sponge-microbe consortia of deep water coral mounds in the northeastern Atlantic Ocean. Mar. Ecol. Prog. Ser. 358, 137–150 (2008).ADS 

    Google Scholar 
    37.Leitner, A. B., Neuheimer, A. B. & Drazen, J. C. Evidence for long-term seamount-induced chlorophyll enhancements. Sci. Rep. 10, 12729 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.von Appen, W.-J., Latarius, K. & Kanzow, T. Physical oceanography and current meter data from mooring F6-17. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven (2017). https://doi.org/10.1594/PANGAEA.870845.39.Woodgate, R. Arctic ocean circulation: going around at the top of the world. Nat. Educ. Knowl. 4, 8 (2013).
    Google Scholar 
    40.White, M., Bashmachnikov, I., Arístegui, J. & Martins, A. in Seamounts: Ecology, Fisheries & Conservation (eds Pitcher, T. J. et al.) Ch. 4 (Wiley, 2007).41.Buchs, D. M., Hoernle, K. & Grevemeyer, I. In Encyclopedia of Marine Geosciences (eds Harff, J., Meschede, M., Petersen, S. & Thiede, J.) (Springer, Dordrecht, 2015). https://doi.org/10.1007/978-94-007-6644-0_34-2.42.Emerson, D. & Moyer, C. Microbiology of seamounts: common patterns observed in community structure. Oceanography 23, 148–163 (2010).
    Google Scholar 
    43.Rimskaya-Korsakova, N. N. et al. First discovery of pogonophora (Annelida, Siboglinidae) in the Kara Sea coincide with the area of high methane concentration. Dokl. Biol. Sci. 490, 25–27 (2020).CAS 
    PubMed 

    Google Scholar 
    44.Cardenas, P. & Rapp, H. T. Demosponges from the Northern mid-Atlantic ridge shed more light on the diversity and biogeography of North Atlantic deep-sea sponges. J. Mar. Biol. Assoc. U. Kindom 95, 1475–1516 (2015).
    Google Scholar 
    45.Meyer, H. K., Roberts, E. M., Rapp, H. T. & Davies, A. J. Spatial patterns of arctic sponge ground fauna and demersal fish are detectable in autonomous underwater vehicle (AUV) imagery. Deep. Res. Part I Oceanogr. Res. Pap. 153, 103137 (2019).
    Google Scholar 
    46.Grebmeier, J. M. et al. Ecosystem characteristics and processes facilitating persistent macrobenthic biomass hotspots and associated benthivory in the Pacific Arctic. Prog. Oceanogr. 136, 92–114 (2015).ADS 

    Google Scholar 
    47.Oevelen, D. Van et al. The cold-water coral community as a hot spot for carbon cycling on continental margins: a food-web analysis from Rockall Bank (northeast Atlantic). Limnol. Oceanogr. 54, 1829–1844 (2009).ADS 

    Google Scholar 
    48.Hammel, J. U., Herzen, J., Beckmann, F. & Nickel, M. Sponge budding is a spatiotemporal morphological patterning process: insights from synchrotron radiation-based x-ray microtomography into the asexual reproduction of Tethya wilhelma. Front. Zool. 6, 19 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    49.Witte, U. & Graf, G. Metabolism of deep-sea sponges in the Greenland- Norwegian Sea. Mar. Biol. 198, 223–235 (1996).
    Google Scholar 
    50.Rovelli, L. et al. Benthic O2 uptake of two cold-water coral communities estimated with the non-invasive eddy correlation technique. Mar. Ecol. Prog. Ser. 525, 97–104 (2015).ADS 

    Google Scholar 
    51.De Clippele, L. H. et al. Mapping cold-water coral biomass: an approach to derive ecosystem functions. Coral Reefs 40, 215–231 (2021).
    Google Scholar 
    52.de Kluijver, A. et al. An integrative model of carbon and nitrogen metabolism in a common deep-sea sponge (Geodia barretti). Front. Mar. Sci. 7, 1–18 (2021).
    Google Scholar 
    53.Lalande, C., Nothig, E.-M. & Fortier, L. Algal export in the Arctic ocean in times of global warming. Geophys. Res. Lett. 46, 1–9 (2019).
    Google Scholar 
    54.Boetius, A. et al. Export of algal biomass from the melting Arctic sea ice. Science 339, 1430–1433 (2013).55.Maier, S. R. et al. Survival under conditions of variable food availability: Resource utilization and storage in the cold-water coral Lophelia pertusa. Limnol. Oceanogr. 64, 1651–1671 (2019).ADS 
    CAS 

    Google Scholar 
    56.Rix, L. et al. Heterotrophy in the earliest gut: a single-cell view of heterotrophic carbon and nitrogen assimilation in sponge-microbe symbioses. ISME J. 14, 2554–2567 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Hansell, D. A. Recalcitrant dissolved organic carbon fractions. Ann. Rev. Mar. Sci. 5, 421–445 (2013).PubMed 

    Google Scholar 
    58.Bart, M. C. et al. Differential processing of dissolved and particulate organic matter by deep-sea sponges and their microbial symbionts. Sci. Rep. 10, 1–13 (2020).
    Google Scholar 
    59.Anderson, L. G. & Amon, R. M. W. DOM in the Arctic Ocean. In Biogeochemistry of Marine Dissolved Organic Matter (eds Hansell, D. A. & Carlson, C. A.) Ch. 14 (Academic Press, 2015).60.Rossel, P. E., Bienhold, C., Boetius, A. & Dittmar, T. Dissolved organic matter in pore water of Arctic Ocean sediments: environmental influence on molecular composition. Org. Geochem. 97, 41–52 (2016).CAS 

    Google Scholar 
    61.Landry, Z., Swan, B. K., Herndl, G. J., Stepanauskas, R. & Giovannoni, S. J. SAR202 genomes from the dark ocean predict pathways for the oxidation of recalcitrant dissolved organic matter. MBio 8, e00413–e00417 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Radax, R. et al. Metatranscriptomics of the marine sponge Geodia barretti: tackling phylogeny and function of its microbial community. Environ. Microbiol. 14, 1308–1324 (2012).63.Busch, K. et al. Chloroflexi dominate the deep-sea golf ball sponges Craniella zetlandica and Craniella infrequens throughout different life stages. Front. Mar. Sci. 7, 1–13 (2020).
    Google Scholar 
    64.Raimundo, I. et al. Functional metagenomics reveals differential chitin degradation and utilization features across free-living and host-associated marine microbiomes. Microbiome 9, 1–18 (2021).
    Google Scholar 
    65.Hoffmann, F. et al. Complex nitrogen cycling in the sponge Geodia barretti. Environ. Microbiol. 11, 2228–2243 (2009).CAS 
    PubMed 

    Google Scholar 
    66.Radax, R., Hoffmann, F., Rapp, H. T., Leininger, S. & Schleper, C. Ammonia-oxidizing archaea as main drivers of nitrification in cold-water sponges. Environ. Microbiol. 14, 909–923 (2012).CAS 
    PubMed 

    Google Scholar 
    67.Kahn, A. S., Chu, J. W. F. & Leys, S. P. Trophic ecology of glass sponge reefs in the Strait of Georgia, British Columbia. Sci. Rep. 8, 756 (2018).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Thiel, V. et al. Mid-chain branched alkanoic acids from “living fossil” demosponges: a link to ancient sedimentary lipids? Org. Geochem. 30, 1–14 (1999).CAS 

    Google Scholar 
    69.de Kluijver, A. et al. Bacterial precursors and unsaturated long-chain fatty acids are biomarkers of North-Atlantic deep-sea demosponges. PLoS ONE 16, 1–18 (2021).
    Google Scholar 
    70.Parnell, A. C., Inger, R., Bearhop, S. & Jackson, A. L. Source partitioning using stable isotopes: coping with too much variation. PLoS ONE 5, e9672 (2010).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    71.Freeman, C. J. et al. Microbial symbionts and ecological divergence of Caribbean sponges: a new perspective on an ancient association. ISME J. 14, 1571–1583 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    72.Middelburg, J. J. Stable isotopes dissect aquatic food webs from the top to the bottom. Biogeosciences 11, 2357–2371 (2014).ADS 

    Google Scholar 
    73.Åström, E. et al. Chemosynthesis influences food web and community structure in high-Arctic benthos. Mar. Ecol. Prog. Ser. 629, 19–42 (2019).ADS 

    Google Scholar 
    74.Ravaux, J. et al. Comparative degradation rates of chitinous exoskeletons from deep-sea environments. Mar. Biol. 143, 405–412 (2003).CAS 

    Google Scholar 
    75.Gooday, G. W. The Ecology of Chitin Degradation. In Advances in Microbial Ecology, (ed. Marshall, K. C.) vol 11. Springer, Boston, MA. https://doi.org/10.1007/978-1-4684-7612-5_10.76.Schwarz, J. R., Yayanos, A. A. & Colwell, R. R. Metabolic activities of the intestinal microflora of a deep-sea invertebrate. Appl. Environ. Microbiol. 31, 46 LP–46 48 (1976).ADS 

    Google Scholar 
    77.Godefroy, N. et al. Sponge digestive system diversity and evolution: filter feeding to carnivory. Cell Tissue Res. 377, 341–351 (2019).PubMed 

    Google Scholar 
    78.Ehrlich, H. et al. First evidence of chitin as a component of the skeletal fibers of marine sponges. Part I. Verongidae (demospongia: Porifera). J. Exp. Zool. Part B Mol. Dev. Evol. 308B, 347–356 (2007).CAS 

    Google Scholar 
    79.Bowden, D. A. et al. Cold seep epifaunal communities on the Hikurangi Margin, New Zealand: composition, succession, and vulnerability to human activities. PLoS ONE 8, e76869 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    80.Georgieva, M. N. et al. Identification of fossil worm tubes from Phanerozoic hydrothermal vents and cold seeps. J. Syst. Palaeontol. 17, 287–329 (2017).
    Google Scholar 
    81.Morganti, T. M. et al. In situ observation of sponge trails suggests common sponge locomotion in the deep central Arctic. Curr. Biol. 31, R368–R370 (2021).CAS 
    PubMed 

    Google Scholar 
    82.Maldonado, M. An experimental approach to the ecological significance of microhabitat-scale movement in an encrusting sponge. Mar. Ecol. Prog. Ser. 185, 239–255 (1999).ADS 

    Google Scholar 
    83.Rice, A. L., Thurston, M. H. & New, A. L. Dense aggregations of a hexactinellid sponge, Pheronema carpenteri, in the Porcupine Seabight (northeast Atlantic Ocean), and possible causes. Prog. Oceanogr. 24, 179–196 (1990).ADS 

    Google Scholar 
    84.Roberts, E. M. et al. Oceanographic setting and short-timescale environmental variability at an Arctic seamount sponge ground. Deep. Res. Part I Oceanogr. Res. Pap. 138, 98–113 (2018).ADS 

    Google Scholar 
    85.Purser, A. et al. Ocean floor observation and bathymetry system (OFOBS): a new towed camera/sonar system for deep-sea habitat surveys. IEEE J. Ocean. Eng. 44, 1–13 (2019).
    Google Scholar 
    86.Marcon, Y. & Purser, A. PAPARA(ZZ)I: an open-source software interface for annotating photographs of the deep-sea. SoftwareX 6, 69–80 (2017).ADS 

    Google Scholar 
    87.Morganti, T. M., Ribes, M., Yahel, G. & Coma, R. Size is the major determinant of pumping rates in marine sponges. Front. Physiol. 10, 1474 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    88.Zelles, L. Phospholipid fatty acid profiles in selected members of soil microbial communities. Chemosphere 35, 275–294 (1997).ADS 
    CAS 
    PubMed 

    Google Scholar 
    89.Volkman, J. K., Jeffrey, S. W., Nichols, P. D., Rogers, G. I. & Garland, C. D. Fatty acid and lipid composition of 10 species of microalgae used in mariculture. J. Exp. Mar. Bio. Ecol. 128, 219–240 (1989).CAS 

    Google Scholar 
    90.Koopmans, M. et al. Seasonal variation of fatty acids and stable carbon isotopes in sponges as indicators for nutrition: biomarkers in sponges identified. Mar. Biotechnol. 17, 43–54 (2015).CAS 

    Google Scholar 
    91.Mollenhauer, G., Grotheer, H., Gentz, T., Bonk, E. & Hefter, J. Standard operation procedures and performance of the MICADAS radiocarbon laboratory at Alfred Wegener Institute (AWI). Ger. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. At. 496, 45–51 (2021).ADS 
    CAS 

    Google Scholar 
    92.Fallon, S. J., James, K., Norman, R., Kelly, M. & Ellwood, M. J. A simple radiocarbon dating method for determining the age and growth rate of deep-sea sponges. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. At. 268, 1241–1243 (2010).ADS 
    CAS 

    Google Scholar 
    93.Griffith, D. R. et al. Carbon dynamics in the western Arctic Ocean: insights from full-depth carbon isotope profiles of DIC, DOC, and POC. Biogeosciences 9, 1217–1224 (2012).ADS 
    CAS 

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

    Google Scholar 
    95.Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    96.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    97.Li, D. et al. MEGAHIT v1.0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods 102, 3–11 (2016).CAS 
    PubMed 

    Google Scholar 
    98.Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    99.Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    100.Finn, R. D., Clements, J. & Eddy, S. R. HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    101.Finn, R. D. et al. Pfam: the protein families database. Nucleic Acids Res. 42, D222–D230 (2014).CAS 
    PubMed 

    Google Scholar 
    102.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    103.De Anda, V. et al. MEBS, a software platform to evaluate large (meta)genomic collections according to their metabolic machinery: unraveling the sulfur cycle. Gigascience 6, 1–17 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    104.Benner, R., Benitez-Nelson, B., Kaiser, K. & Amon, R. M. W. Export of young terrigenous dissolved organic carbon from rivers to the Arctic Ocean. Geophys. Res. Lett. 31, 10–13 (2004).
    Google Scholar 
    105.Thibodeau, B., Bauch, D. & Voss, M. Nitrogen dynamic in Eurasian coastal Arctic ecosystem: Insight from nitrogen isotope. Glob. Biogeochem. Cycles 31, 836–849 (2017).ADS 
    CAS 

    Google Scholar 
    106.Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER – Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011). More

  • in

    Higher temperature extremes exacerbate negative disease effects in a social mammal

    1.Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).
    Google Scholar 
    2.Fuller, A. et al. Physiological mechanisms in coping with climate change. Physiol. Biochem. Zool. 83, 713–720 (2010).
    Google Scholar 
    3.Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).CAS 

    Google Scholar 
    4.Brawn, J. D., Benson, T. J., Stager, M., Sly, N. D. & Tarwater, C. E. Impacts of changing rainfall regime on the demography of tropical birds. Nat. Clim. Change 7, 133–136 (2016).
    Google Scholar 
    5.Summers, B. A. Climate change and animal disease. Vet. Pathol. 46, 1185–1186 (2009).CAS 

    Google Scholar 
    6.Randall, C. J. & van Woesik, R. Contemporary white-band disease in Caribbean corals driven by climate change. Nat. Clim. Change 5, 375–379 (2015).
    Google Scholar 
    7.Munson, L. et al. Climate extremes promote fatal co-infections during canine distemper epidemics in African lions. PLoS ONE 3, e2545 (2008).
    Google Scholar 
    8.Rohr, J. R. et al. Frontiers in climate change–disease research. Trends Ecol. Evol. 26, 270–277 (2011).
    Google Scholar 
    9.Zarnetske, P. L., Skelly, D. K. & Urban, M. C. Biotic multipliers of climate change. Science 336, 1516–1518 (2012).CAS 

    Google Scholar 
    10.Cohen, J. M., Sauer, E. L., Santiago, O., Spencer, S. & Rohr, J. R. Divergent impacts of warming weather on wildlife disease risk across climates. Science 370, eabb1702 (2020).CAS 

    Google Scholar 
    11.Cornwallis, C. K. et al. Cooperation facilitates the colonization of harsh environments. Nat. Ecol. Evol. 1, 0057 (2017).
    Google Scholar 
    12.Koenig, W. D. & Dickinson, J. L. (eds) Cooperative Breeding in Vertebrates: Studies of Ecology, Evolution, and Behavior (Cambridge Univ. Press, 2016).13.Groenewoud, F. & Clutton-Brock, T. Meerkat helpers buffer the detrimental effects of adverse environmental conditions on fecundity, growth and survival. J. Anim. Ecol. 90, 641–652 (2020).
    Google Scholar 
    14.Langwig, K. E. et al. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol. Lett. 15, 1050–1057 (2012).
    Google Scholar 
    15.Vicente, J., Delahay, R. J., Walker, N. J. & Cheeseman, C. L. Social organization and movement influence the incidence of bovine tuberculosis in an undisturbed high-density badger Meles meles population. J. Anim. Ecol. 76, 348–360 (2007).CAS 

    Google Scholar 
    16.Bermejo, M. et al. Ebola outbreak killed 5000 gorillas. Science 314, 1564 (2006).CAS 

    Google Scholar 
    17.Hanya, G. et al. Mass mortality of Japanese macaques in a western coastal forest of Yakushima. Ecol. Res. 19, 179–188 (2004).
    Google Scholar 
    18.Angulo, E. et al. Allee effects in social species. J. Anim. Ecol. 87, 47–58 (2018).
    Google Scholar 
    19.Woodroffe, R., Groom, R. & McNutt, J. W. Hot dogs: high ambient temperatures impact reproductive success in a tropical carnivore. J. Anim. Ecol. 86, 1329–1338 (2017).
    Google Scholar 
    20.Brandell, E. E., Dobson, A. P., Hudson, P. J., Cross, P. C. & Smith, D. W. A metapopulation model of social group dynamics and disease applied to Yellowstone wolves. Proc. Natl Acad. Sci. USA 118, 33649227 (2021).
    Google Scholar 
    21.Clutton-Brock, T. H. & Manser, M. in Cooperative Breeding in Vertebrates: Studies of Ecology, Evolution, and Behavior (eds Koenig, W. D. & Dickinson, J. L.) 294–317 (Cambridge Univ. Press, 2016).22.Drewe, J. A. Who infects whom? Social networks and tuberculosis transmission in wild meerkats. Proc. R. Soc. B 277, 633–642 (2010).
    Google Scholar 
    23.Parsons, S. D. C., Drewe, J. A., van Pittius, N. C. G., Warren, R. M. & van Helden, P. D. Novel cause of tuberculosis in meerkats, South Africa. Emerg. Infect. Dis. 19, 2004–2007 (2013).
    Google Scholar 
    24.Duncan, C., Manser, M., & Clutton-Brock, T. H. Decline and fall: the causes of group failure in cooperatively breeding meerkats. Ecol. Evol. https://doi.org/10.1002/ece3.7655 (2021).25.Drewe, J. A., Foote, A. K., Sutcliffe, R. L. & Pearce, G. P. Pathology of Mycobacterium bovis infection in wild meerkats (Suricata suricatta). J. Comp. Pathol. 140, 12–24 (2009).CAS 

    Google Scholar 
    26.van Wilgen, N. J., Goodall, V. & Holness, S. Rising temperatures and changing rainfall patterns in South Africa’s national parks. Aquat. Microb. Ecol. 36, 706–721 (2016).
    Google Scholar 
    27.Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).CAS 

    Google Scholar 
    28.Fischer, E. M., Beyerle, U. & Knutti, R. Robust spatially aggregated projections of climate extremes. Nat. Clim. Change 3, 1033–1038 (2013).
    Google Scholar 
    29.Bourne, A. R., Cunningham, S. J., Spottiswoode, C. N. & Ridley, A. R. Hot droughts compromise interannual survival across all group sizes in a cooperatively breeding bird. Ecol. Lett. 23, 1776–1788 (2020).
    Google Scholar 
    30.Van de Ven, T. M. F. N., Fuller, A. & Clutton‐Brock, T. H. Effects of climate change on pup growth and survival in a cooperative mammal, the meerkat. Funct. Ecol. 34, 194–202 (2020).
    Google Scholar 
    31.Katale, B. Z. et al. Prevalence and risk factors for infection of bovine tuberculosis in indigenous cattle in the Serengeti ecosystem, Tanzania. BMC Vet. Res. 9, 267 (2013).
    Google Scholar 
    32.Paniw, M., Maag, N., Cozzi, G., Clutton-Brock, T. & Ozgul, A. Life history responses of meerkats to seasonal changes in extreme environments. Science 363, 631–635 (2019).CAS 

    Google Scholar 
    33.Dwyer, R. A., Witte, C., Buss, P., Goosen, W. J. & Miller, M. Epidemiology of tuberculosis in multi-host wildlife systems: implications for black (Diceros bicornis) and white (Ceratotherium simum) rhinoceros. Front. Vet. Sci. 7, 580476 (2020).
    Google Scholar 
    34.Patterson, S., Drewe, J. A., Pfeiffer, D. U. & Clutton-Brock, T. H. Social and environmental factors affect tuberculosis related mortality in wild meerkats. J. Anim. Ecol. 86, 442–450 (2017).
    Google Scholar 
    35.Dubuc, C. et al. Increased food availability raises eviction rate in a cooperative breeding mammal. Biol. Lett. 13, 20160961 (2017).
    Google Scholar 
    36.Maag, N., Cozzi, G., Clutton-Brock, T. H. & Ozgul, A. Density‐dependent dispersal strategies in a cooperative breeder. Ecology 99, 1932–1941 (2018).
    Google Scholar 
    37.Ekernas, L. S. & Cords, M. Social and environmental factors influencing natal dispersal in blue monkeys, Cercopithecus mitis stuhlmanni. Anim. Behav. 73, 1009–1020 (2007).
    Google Scholar 
    38.Ozgul, A., Bateman, A. W., English, S., Coulson, T. & Clutton-Brock, T. H. Linking body mass and group dynamics in an obligate cooperative breeder. J. Anim. Ecol. 83, 1357–1366 (2014).
    Google Scholar 
    39.Tomlinson, A. J., Chambers, M. A., Wilson, G. J., McDonald, R. A. & Delahay, R. J. Sex-related heterogeneity in the life-history correlates of Mycobacterium bovis infection in European badgers (Meles meles). Transbound. Emerg. Dis. 60, 37–45 (2013).
    Google Scholar 
    40.Courchamp, F., Grenfell, B. & Clutton-Brock, T. H. Population dynamics of obligate cooperators. Proc. R. Soc. B 266, 557–563 (1999).
    Google Scholar 
    41.Lerch, B. A., Nolting, B. C. & Abbott, K. C. Why are demographic Allee effects so rarely seen in social animals? J. Anim. Ecol. 87, 1547–1559 (2018).
    Google Scholar 
    42.Borg, B. L., Brainerd, S. M., Meier, T. J. & Prugh, L. R. Impacts of breeder loss on social structure, reproduction and population growth in a social canid. J. Anim. Ecol. 84, 177–187 (2015).
    Google Scholar 
    43.Brown, P. T. & Caldeira, K. Greater future global warming inferred from Earth’s recent energy budget. Nature 552, 45–50 (2017).CAS 

    Google Scholar 
    44.Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).
    Google Scholar 
    45.Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).CAS 

    Google Scholar 
    46.Blackwood, J. C., Streicker, D. G., Altizer, S. & Rohani, P. Resolving the roles of immunity, pathogenesis, and immigration for rabies persistence in vampire bats. Proc. Natl Acad. Sci. USA 110, 20837–20842 (2013).CAS 

    Google Scholar 
    47.Fenner, A. L., Godfrey, S. S. & Michael Bull, C. Using social networks to deduce whether residents or dispersers spread parasites in a lizard population. J. Anim. Ecol. 80, 835–843 (2011).
    Google Scholar 
    48.Paniw, M. et al. The myriad of complex demographic responses of terrestrial mammals to climate change and gaps of knowledge: a global analysis. J. Anim. Ecol. 90, 1398–1407 (2021).
    Google Scholar 
    49.McDonald, J. L. et al. Demographic buffering and compensatory recruitment promotes the persistence of disease in a wildlife population. Ecol. Lett. 19, 443–449 (2016).
    Google Scholar 
    50.Plowright, R. K., Sokolow, S. H., Gorman, M. E., Daszak, P. & Foley, J. E. Causal inference in disease ecology: investigating ecological drivers of disease emergence. Front. Ecol. Environ. 6, 420–429 (2008).
    Google Scholar 
    51.Russell, R., DiRenzo, G. V., Szymanski, J., Alger, K. & Grant, E. H. C. Principles and mechanisms of wildlife population persistence in the face of disease. Front. Ecol. Evol. 8, 344 (2020).
    Google Scholar 
    52.Baudouin, A. et al. Disease avoidance, and breeding group age and size condition the dispersal patterns of western lowland gorilla females. Ecology 100, e02786 (2019).
    Google Scholar 
    53.Townsend, A. K., Hawley, D. M., Stephenson, J. F. & Williams, K. E. G. Emerging infectious disease and the challenges of social distancing in human and non-human animals. Proc. R. Soc. B 287, 20201039 (2020).CAS 

    Google Scholar 
    54.Schisler, G. J., Bergersen, E. P. & Walker, P. G. Effects of multiple stressors on morbidity and mortality of fingerling rainbow trout infected with Myxobolus cerebralis. Trans. Am. Fish. Soc. 129, 859–865 (2000).
    Google Scholar 
    55.Härkönen, T., Harding, K., Rasmussen, T. D., Teilmann, J. & Dietz, R. Age- and sex-specific mortality patterns in an emerging wildlife epidemic: the phocine distemper in European harbour seals. PLoS ONE 2, e887 (2007).
    Google Scholar 
    56.Clutton-Brock, T. H. et al. Reproduction and survival of suricates (Suricata suricatta) in the southern Kalahari. Afr. J. Ecol. 37, 69–80 (1999).
    Google Scholar 
    57.Clutton-Brock, T. H., Hodge, S. J. & Flower, T. P. Group size and the suppression of subordinate reproduction in Kalahari meerkats. Anim. Behav. 76, 689–700 (2008).
    Google Scholar 
    58.Bateman, A. W., Ozgul, A., Coulson, T. & Clutton-Brock, T. H. Density dependence in group dynamics of a highly social mongoose, Suricata suricatta. J. Anim. Ecol. 81, 628–639 (2012).
    Google Scholar 
    59.Adler, R. F. et al. The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9, 138 (2018).
    Google Scholar 
    60.Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).CAS 

    Google Scholar 
    61.Parding, K. M. et al. GCMeval – an interactive tool for evaluation and selection of climate model ensembles. Clim. Serv. 18, 100167 (2020).
    Google Scholar 
    62.Delahay, R. J., Langton, S., Smith, G. C., Clifton-Hadley, R. S. & Cheeseman, C. L. The spatio-temporal distribution of Mycobacterium bovis (bovine tuberculosis) infection in a high-density badger population. J. Anim. Ecol. 69, 428–441 (2000).
    Google Scholar 
    63.Delahay, R. J. et al. Long-term temporal trends and estimated transmission rates for Mycobacterium bovis infection in an undisturbed high-density badger (Meles meles) population. Epidemiol. Infect. 141, 1445–1456 (2013).CAS 

    Google Scholar 
    64.Buzdugan, S. N., Chambers, M. A., Delahay, R. J. & Drewe, J. A. Diagnosis of tuberculosis in groups of badgers: an exploration of the impact of trapping efficiency, infection prevalence and the use of multiple tests. Epidemiol. Infect. 144, 1717–1727 (2016).CAS 

    Google Scholar 
    65.Akaike, H. in Selected Papers of Hirotugu Akaike (eds Parzen, E. et al.) 199–213 (Springer, 1998).66.Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B Stat. 73, 3–36 (2011).
    Google Scholar 
    67.Grimm, V. et al. The ODD protocol: a review and first update. Ecol. Model. 221, 2760–2768 (2010).
    Google Scholar 
    68.Wood, S. N. Statistical inference for noisy nonlinear ecological dynamic systems. Nature 466, 1102–1104 (2010).CAS 

    Google Scholar 
    69.Fronzek, S., Carter, T. R., Räisänen, J., Ruokolainen, L. & Luoto, M. Applying probabilistic projections of climate change with impact models: a case study for sub-Arctic palsa mires in Fennoscandia. Clim. Change 99, 515–534 (2010).
    Google Scholar  More

  • in

    Changes in the acoustic activity of beaked whales and sperm whales recorded during a naval training exercise off eastern Canada

    We observed a clear reduction in the acoustic activity of sperm whales and beaked whales during the period when sonar signals were recorded at Station 5, indicating that whales ceased foraging in this area while military sonars were in use. The acoustic detection rate of sperm whales returned to pre-exercise baseline levels within the days following the CF16 exercise, while the observed reduction in beaked whale acoustic activity was more prolonged. Detection rates of Cuvier’s beaked whale clicks remained low throughout the 8-day period immediately following the exercise, and UMBW clicks were largely absent during this period. This study is observational and limited to showing correlation rather than cause and effect; nonetheless, these results are consistent with previous experimental research on the responses of beaked whales to simulated and real military sonars and suggest that whales were disturbed from normal foraging behaviour and likely displaced from the affected area during the CF16 exercise.The scale and duration of sonar use recorded during this study provides important context for the observed results. Much of the experimental work conducted to date on the responses of beaked whales and other odontocetes to sonar has involved controlled exposure experiments using animal-borne tags to record the fine-scale movements and acoustic behavior of individuals, allowing responses to be examined on the scale of minutes to hours e.g.,7,8,10. Experimental exposures to simulated sonar signals lasting approximately 15–30 min have elicited pronounced avoidance responses in Blainville’s beaked whales7, Cuvier’s beaked whales8, Baird’s beaked whales16, and northern bottlenose whales9,10. Generally, these studies were focused on the onset of the response and did not always assess the duration over which altered behaviour continued. However, the absence of foraging behaviour for several hours following exposure was noted in some cases, and focal animals performed sustained directed movement away from the exposure location during this time, covering distances of up to tens of kilometers10. In broader-scale studies examining responses of Blainville’s beaked whales to real multi-ship naval training operations on the Atlantic Undersea Test and Evaluation Center (AUTEC) in the Bahamas, displacements of up to 68 km were observed, lasting 2–4 days before whales returned to foraging in the area where they were exposed7,28. In the present study, the duration of naval sonar activity recorded during the CF16 exercise was considerably more prolonged, with bouts of sonar continuing for up to 13 consecutive hours and occurring repeatedly over an 8-day period. Although we can only make inference on species-level rather than individual-level responses based on the absence of clicks in our recordings, it is plausible that military sonar activity at this scale led to wide spatial avoidance of the affected area over an extended period.The absence of sperm whale click detections in the Station 5 recordings for 6 consecutive days during the CF16 exercise is notable; few prior studies have demonstrated sustained changes in foraging behaviour or substantial displacement of sperm whales following sonar exposure. Behavioural response studies conducted in northern Norway using controlled experimental exposures showed varying responses by sperm whales, which included changes in orientation and direction of horizontal movement, changes in acoustic behaviour, and altered dive profiles23. Exposure to lower frequency sonar signals in the 1–2 kHz range generally prompted stronger responses, including a reduction in foraging effort or transition from a foraging to non-foraging state, while exposure to higher frequency sonar signals in the range of 6–7 kHz did not appear to trigger changes in foraging behaviour21,29. More recently, Isojunno et al.30 quantified the responses of sperm whales to continuous and pulsed active sonars, and found that sound exposure level was more important than amplitude in predicting a change in foraging effort. We were not able to investigate differential responses to frequency or other sonar characteristics in this study, due to the observational nature of the study and the absence of sperm whale clicks throughout most of the exercise period. Likewise, we cannot exclude the physical presence of ships, aircraft, and submarines in the area or additional types of noise produced during maneuvers as potential factors contributing to the cessation of sperm whale and beaked whale click production and foraging behaviour.The observed changes in acoustic activity were more easily quantified for sperm whales than for beaked whales, due to higher baseline hourly presence of sperm whale clicks in the recordings. Sperm whales produce powerful echolocation clicks throughout their foraging dives, which can be recorded at ranges of 16 km or more31, and a single individual foraging in the vicinity of a hydrophone may be detected continuously throughout multiple dive cycles. Our analysis was based on sperm whale click detections that met a threshold signal-to-noise ratio (SNR), and the results therefore provide a minimum estimate of sperm whale presence in the vicinity of the recorder. Reporting results at the level of hourly presence rather than the number of individual click detections largely mitigated the effects of excluding low-SNR clicks recorded at greater distances from the hydrophone or during higher ambient noise conditions. Likewise, the presence of sperm whales on an hourly time scale is not likely to be substantially underestimated when recordings are collected using a low duty cycle32. By contrast, beaked whales produce echolocation clicks at higher frequencies and lower source levels, with highly directional beam patterns33. These clicks are likely only detected at ranges of up to approximately 4 km when the whale is oriented toward the hydrophone, and at lesser distances when clicks are received off-axis34. As a result, there is greater variability and lower baseline detection rates of beaked whale clicks on fixed passive acoustic recorders, which reduces statistical power to assess temporal changes in acoustic activity. Moreover, the duty-cycled recording schedule used at Station 5 provided only 65 s of high-frequency data 3 times per hour, and the presence of beaked whales is likely to be underestimated by this duty cycle, with potentially greater underestimation of Mesoplodont species compared to Cuvier’s beaked whales35.Continuous recordings were collected at the East Gully and Central Gully recording sites, but included only partial temporal coverage of the exercise period and no pre-exercise baseline data. No comparable recordings were available from these locations in a prior or subsequent year to form a control dataset. As a result, we were not able to use these datasets to assess changes in acoustic activity associated with the CF16 exercise. A slight decrease in hourly presence of northern bottlenose whale clicks in the Central Gully recordings occurred on September 19th–20th, 2016; however, we are aware that an oceanographic research vessel was coincidentally in the area deploying scientific instrumentation in close proximity to the Central Gully recording site on these dates, creating an additional source of potential disturbance. Despite these limitations, we included an analysis of the recordings collected at the East and Central Gully sites for two reasons: first, to provide perspective on the geographic extent over which activities associated with the CF16 exercise occurred; and second, to illustrate the diversity in beaked whale species composition at different locations across the region. Analysis of the recordings for sonar signals revealed that higher levels of sonar activity occurred near the Station 5 recording site than near the East or Central Gully locations. Due to the distance between recording sites and the timing of the sonar signals recorded, it appears that the recorded sonar signals came from multiple source locations over the duration of the exercise. Recordings from Central Gully contained the fewest sonar signals and lowest measured received levels, likely due to the deliberate avoidance of the Gully MPA and surrounding area by exercise participants during CF16. The Gully was established as an MPA in 2004, and is one of three adjacent canyons on the eastern Scotian Shelf currently designated as critical habitat areas for the endangered Scotian Shelf population of northern bottlenose whales36. The Station 5 recording site was located approximately 300 km to the southwest, and experienced higher levels of naval sonar activity during CF16. However, none of the locations were chosen specifically to monitor CF16, and we do not have access to information on the general exercise areas used, specific locations of naval vessels, submarines, or aircraft participating in the CF16 exercise, or the source levels of transmitted sonar signals. Due to the opportunistic nature of the recordings, the received levels of sonar signals measured at Station 5 likely do not represent the highest sound levels introduced into the marine environment during the CF16 exercise.Unlike many areas where behavioural responses to sonar are commonly studied, there are no instrumented naval training ranges off eastern Canada, and cetaceans inhabiting this region are unlikely to be accustomed to regularly hearing naval active sonars. Other than during the CF16 exercise, sonar signals were not noted during a large-scale analysis of cetacean call occurrence and soundscape characterization in 2 years of recordings collected at Station 5 and numerous other passive acoustic monitoring sites off eastern Canada26. Exposure context and familiarity with a signal may be important factors influencing an individual’s response to acoustic disturbance15. Experimental research on Cuvier’s beaked whales near a U.S. naval training range located off southern California demonstrated possible distance-mediated effects of sonar exposure, with more pronounced behavioural responses occurring with closer source proximity, even when received levels from the closer source were likely lower than those from more distant, high-powered sonar transmissions, which did not elicit as strong a response15. The movement and predictability of the sound source as well as the timing and duration of sonar transmissions may also be important factors influencing the behavioural response15. Whales inhabiting waters off southern California are likely habituated to hearing distant sonar due to routine naval training activities occurring on the range. Conversely, Wensveen et al.10 found that northern bottlenose whales in the eastern North Atlantic exhibited similar responses to simulated sonar signals played at various distances up to 28 km, suggesting that they perceived this novel stimuli as a potential threat even from a distance and at relatively low received levels. Bernaldo de Quiros et al.5 hypothesized that beaked whales not regularly exposed to active sonar signals may respond more strongly, both physiologically and behaviourally, which poses a concern for a region where military training activities involving the use of sonar are relatively infrequent, but occur periodically in the form of large-scale exercises involving the extensive use of active sonars and creating significant potential for acoustic disturbance.Behavioural disturbance due to anthropogenic noise may have energetic, health, and fitness consequences for deep-diving odontocete species. Disruption of normal diving patterns creates energetic costs due to the significant investment in each dive and the reduction of time available for prey intake when foraging dives are interrupted. Recent studies on the functional relationship between beaked whales and deep-sea prey resources suggest that certain characteristics of prey, including minimum size and density thresholds, are required for beaked whales to successfully meet their energetic needs12,37. While the distribution and characteristics of deep-sea prey are challenging to study and largely unknown in most regions, considerable environmental heterogeneity may be present, causing the quality of foraging habitat to vary significantly over even small horizontal scales12,37. This patchiness in habitat quality has important implications for behavioural disturbance, as even short-term displacement from high-quality habitat areas can affect the fitness of individuals and potentially lead to population-level consequences13.In addition to the consequences of sublethal disturbance, it is important to note that the likelihood of observing more acute impacts of exposure to naval active sonar, including injuries or fatalities, is extremely low in offshore regions. Individual and mass strandings of beaked whales and other cetaceans associated with military activities have typically been documented on oceanic islands with populated coastlines1,3,6. Factors affecting the probability that cetacean carcasses will wash ashore include buoyancy and decomposition rates in local water conditions, oceanic surface currents, the topography of coastlines, and the location of habitat relative to shore6. Off Nova Scotia, potential beaked whale and sperm whale habitat (consisting of water depths greater than 500 m) is located more than 100 km from the coastline, and injuries or fatalities occurring in deep water habitat in this region are unlikely to result in observed strandings. Stranding incidents involving sperm whales and beaked whales have been reported in Nova Scotia, but the cause of mortality is usually unknown38. Cetacean mortality is highly underestimated even in the aftermath of catastrophic events such as large oil spills39, and a lack of observed injuries or mortalities following offshore military activities should not be construed as evidence that no direct or immediate harm was caused.This study offered a unique opportunity to use existing passive acoustic monitoring (PAM) data to assess disturbance of poorly-known odontocete species during a real-world, large-scale military sonar exercise in a region where military sonar use at this scale is relatively uncommon. Ideally, a PAM study designed to examine disturbance in this context would collect continuous rather than duty-cycled recordings, and include ample baseline data surrounding the period of interest as well as in prior and subsequent years. Additionally, multiple acoustic sensors arranged in a dense array surrounding exercise locations would provide further insight into the spatial context of exposure and patterns of disturbance. Despite the data limitations in the present study, our results demonstrate that changes in odontocete foraging behaviour associated with acute, large-scale disturbance may be evident in PAM data even at low duty cycles. The nature of the observed effect (e.g., temporary disruption of foraging, spatial displacement, or more acute injury or distress) remains unknown, as do the number of individuals affected and the longer-term health and fitness implications. Broader baseline data on species occurrence and an improved understanding of species’ ecology and habitat use in the region are necessary for making informed mitigation decisions, allowing key habitat areas to be avoided, and understanding the impacts of naval active sonar exposure in this region on individuals and populations. More