More stories

  • in

    Vitamin B12 is not shared by all marine prototrophic bacteria with their environment

    Vitamin B12 biosynthesis potential of different bacteriaB vitamins play a key role in complex marine microbial interactions as they are obligatory cofactors in various essential metabolic reactions in all living organism [13, 14, 39,40,41]. An exciting fact about B12 is that genes for synthesis of this complex cofactor have never made the transition to the eukaryotic kingdom, although it is required by both prokaryotes and eukaryotes. De novo synthesis is restricted to a minor fraction of bacteria and archaea, thus, suggesting that the ability to synthesise B12 is disproportionate to its demand in nature [1, 4]. This phenomenon can be observed in various habitats, for example in the soil microbiome, where the proportion of B12 producers is less than one tenth [8]. Similar findings have been shown for the microbiome on human skin, where only 1% of the core species are predicted to produce B12 de novo, while 39 % of the species are predicted to use B12 for metabolism [42]. In order to adequately answer this fundamental question regarding the balance between B12 availability and consumption, we should aim to better understand the synthesis potential of individual prototrophic prokaryotes.Here we present intra- and extracellular B12 concentrations of various B12 prototrophic, alphaproteobacterial strains. The concentration of intracellular B12 differs widely between the various heterotrophic bacteria examined. Converted, B12 molecules detected per cell ranged between 664 to 26,619 in the analysed bacterial cultures, including B12-provider and B12-retainer. Such strong variation in intracellular B12 concentrations have already been shown for a number of other prokaryotes, including Archaea, heterotrophic bacteria, and cyanobacteria [11, 34]. Also, in these studies, the detected intracellular B12 values differed up to three orders of magnitude and showed values similar to the ones we detected. Whether factors such as cell size, which we did not consider in our analysis, or the exact growth phase in which we took the samples had an influence on the strong variation cannot be clarified here. It is quite conceivable that different B12 requirements of the individual cells or different regulatory mechanisms of B12 synthesis played a decisive role for the intracellular B12 concentrations. Nevertheless, we can conclude that not only the genetic B12 biosynthetic potential within a microbial community is decisive, but rather which prokaryote is actually present is crucial for the availability of B12.The extracellular concentrations of B12 detected in M. algicola and P. inhibens were about 8 and 256 times lower than respective intracellular levels. For example, M. algicola secreted about 936 B12 molecules per cell, which was roughly 85 times more as detected for P. inhibens. On the basis of the detected B12 demand of T. pseudonana determined by the bioassay, we can calculate that the eukaryote requires roughly 135,000 B12 molecules per cell, if we base the limitation of cell number solely on B12 availability. Thus, it would take about 144 living M. algicola cells that release B12 to cover the requirements for the growth of one T. pseudonana cell. In fact, the bacterial cell numbers in the stationary phase of the B12-provider-diatom co-cultures were at least 110 times higher than the cell numbers of T. pseudonana. These calculations are all based on ideal laboratory conditions, with sufficient supply of inorganic nutrients and organic substrates and may differ in natural environments where viral infections or sloppy feeding can lead to cell disruption and subsequent release of intracellular B12 [43, 44]. Also, B12 requirement of T. pseudonana cells can vary under different growth conditions. For example, it has been shown that growth of T. pseudonana even with 1 pM of B12 can result in a significant change in the metabolite pool of the diatom, which in turn may have implications for the interaction with bacteria [45]. Nevertheless, our data give a first approximate insight into the interplay between B12-producers and -consumers in the world of microorganisms.Bacterial effects on the growth of T. pseudonana
    Growth characteristics of T. pseudonana in co-culture show not only the obligatory provision of B12 by bacteria but also other bacterial factors that influence growth. For example, we observed that Sulfitobacter litoralis, a representative of the Roseobacter group, showed inhibitory behaviour towards the diatom. Other studies have shown that Roseobacter group isolates can produce inhibitory substances, roseobacticides, which can suppress the growth of eukaryotic phototrophs [46]. The provision of B12 leads to a promotion in growth and, at the same time, growth of the diatom is inhibited. One reason for the different growth characteristics of the diatoms observed in co-culture with different bacteria could be the adaptation to different habitats where the bacterial isolates naturally occur.In contrast to these observations, Celeribacter baekdonensis DSM 27375 significantly stimulated the growth of T. pseudonana. Even though C. baekdonensis did not provide B12 despite being synthesized, its presence in co-culture with B12 addition significantly increased the growth rate and growth yield of T. pseudonana compared to the positive control of the corresponding experimental run. In previous bacterial-diatom co-culture experiments, it has been shown that the excretion of cyclic peptides, diketopiperazines, by a bacterium, significantly increased diatom cell numbers [47]. Another plausible scenario is the synthesis and excretion of indoleacetic acid (IAA) by C. baekdonensis, which is a growth-promoting hormone for diatoms [48]. A similar effect is also conceivable for C. baekdonensis and would be exciting to explore in greater depth.A finding that appears to be overlooked in the context of our actual question is the fact that the expected bacterial cell death does not necessarily lead to the release of B12, which would promote the growth of T. pseudonana, and thus promote the interaction. Even after up to six weeks in co-culture, we cannot observe significant growth of T. pseudonana despite the presence of a bacterial B12 prototroph. This fact highlights the importance of cell lysis mechanisms in nature, for example caused by viral infections or sloppy feeding. Already today, these two natural processes are considered to play a significant role in the turnover of dissolved organic matter [44, 49,50,51] and are likely to also have a decisive influence on the release of B-vitamins in marine ecosystems [23]. Additionally, T. pseudonana is known to secret a B12 binding protein under B12 deficient conditions that has an affinity constant of 2 × 1011 M−1. This protein might help them to acquire B12 from the surroundings, when it is released through bacterial cell lysis mechanism [52]. Other phytoplankton might also have a similar strategy to scavenge B12 from the environment. When intracellular B12 is considered as a reservoir for other B12 auxotrophic microorganisms, then, for example, already 19 M. algicola cells would be sufficient to enable the growth of one T. pseudonana cell.The vital cofactor B12 is not shared by all prototrophic bacteriaAbout half of the marine phytoplankton species are B12 auxotrophs and rely on prototrophic prokaryotes to obtain this essential vitamin [1, 53]. Several co-culture experiments have confirmed that individual marine bacterial isolates, mainly Alphaproteobacteria, enable phytoplankton species to overcome their auxotrophy by providing the essential cofactor [13,14,15,16, 27, 28]. In our study we hypothesised that not all B12 prototrophs share B12 with other microorganisms and to prove that we performed individual co-culture experiments between T. pseudonana and 33 B12 prototrophic bacteria. B12 prototrophy of the bacterial isolates was confirmed by their genetic ability to synthesize B12 (Supplementary table S2) and their ability to grow in B12-free medium. The results of our study support this hypothesis, as we were able to identify one group of bacteria that enables growth of T. pseudonana by the supply of the essential cofactor, B12-providers. On the other hand, we also identified a second group of B12 prototrophic bacteria that did not support the growth of the diatom, the B12-retainers. Moreover, while categorizing them into B12-providers and B12-retainers, we observed that there are species within one genus, such as P. inhibens and P. galleciensis, in which one is a B12-provider and the other is a B12-retainer, respectively, although both of them possess the necessary genes for B12 biosynthesis. Yet, the question remains why some bacteria share the cofactor, and others, despite an obligatory interaction enforced in co-culture, do not. In the following, we describe and discuss three scenarios that we consider plausible, whereby not only one scenario has to be correct, but rather all three can take place in the B12-retainer strains that we have identified.First, biosynthesis of metabolites, such as the energetically costly B12 cofactor, are subject to intracellular regulation. Transcriptional regulation of the B12 biosynthesis pathway determines whether, and in what quantity B12 is synthesised in the cell. For example, sigma factors can alter the specificity of an RNA polymerase for a particular promoter, so that gene expression is enhanced or reduced [54]. In the case of the bacterial isolate Propionibacterium strain UF1, the riboswitch cbiMCbl was identified to regulate the gene expression of the cobA operon and thus controls B12 biosynthesis [55]. It is also known that sufficient availability of B12 can repress B12 biosynthesis gene expression in bacteria [56, 57]. In gram-negative proteobacteria as well as in cyanobacteria, for example, cobalamin (pseudocobalamin, in case of some bacteria) biosynthesis and B12 transport genes are regulated by inhibition of translation initiation, whereas in some gram-positive bacteria gene regulation proceeds by transcriptional antitermination [58]. The mechanisms described above are likely to also occur in the bacterial isolates that we tested. The large difference between the detected intracellular B12 concentrations could therefore be due to differences in gene regulation of the different bacteria and may also have had an influence on the release of B12 in the co-culture with T. pseudonana.Second, cobalamin, which we referred to here as B12 for simplicity, belongs to a group of B12-like metabolites, called cobamides. Each cobamide differs in the lower ligand attached. For example, the common cobamide, cobalamin, which is bioavailable to most microorganisms, carries 5,6-dimethylbenzimidazol (DMB) as its lower ligand, whereas pseudocobalamin synthesised by cyanobacteria in high concentrations in the ocean and being less or not bioavailable to most microorganisms, has adenine attached as its lower ligand [11, 41, 59, 60]. In general, the lower ligands of cobamides can be divided into benzimidazoles, purines, and phenols, and more than a dozen cobamides and cobamide-analogs have already been discovered [61]. However, research into the synthesis and actual diversity of cobamides, especially in marine bacteria and archaea, is still in its infancy. In our study, we were unable to detect intracellular B12 in four out of eight bacterial B12-retainer strains, although the cell counts at the time of sampling should have been sufficient for its detection. However, as is generally the case, our LC-MS analysis only targets cobalamin (B12) with its different upper ligands (adenosyl-, cyano-, methyl-, and hydroxy-cobalamin). Therefore, we cannot exclude the possibility that the here studied bacteria synthesise different cobamides, which are possibly not or less bioavailable to T. pseudonana, and not covered by our analytical measurement method. This speculation was supported by the fact that one of these four B12- retainer strains, Sulfitobacter sp. DFL-23, does not possess the DMB synthesis gene bluB and there was no intracellular B12 detected in this strain (Supplementary table S2 and Table 2). Again, it is difficult to explain this phenomenon solely depending on the presence of annotated DMB synthesis gene, as for Loktanella salsilacus DSM 16199 no bluB gene was annotated, still we detected intracellular B12 in this strain using our detection method (Supplementary table S2 and Table 2).Third, the bacteria we have identified as B12-retainer simply may not have actively released the synthesised B12 into their environment. Regardless of the importance of B12 for the vast majority of living organisms on our planet, its excretion mechanisms are to our knowledge still largely unknown. Its size of more than 1,350 Dalton does not allow sufficient diffusion through the cell membrane, which would enable microbial interactions [32]. Thus, it is likely that an unknown mechanism is required for its release. This assumption is further supported by the fact that we were able to detect intracellular B12 in four of the eight B12-retainer strains and at concentrations comparable to those detected in the B12-provider strains. In addition, we could detect intracellular B12 in P. xiamenensis, but none in its exometabolome. On the other hand, presence of extracellular B12 was detected in the exometabolome of both the provider strains examined, M. algicola and P. inhibens. Our findings show that not all bacteria share the pivotal cofactor with their environment, which has an impact on our current understanding of the marine B12 cycle and presumably in other ecosystems as well. The active exchange of B12 and thus microbial interaction plays a much smaller role than previously assumed for a relatively large number of bacteria. Consequently, for some of the B12 prototrophic bacteria within a community, it is likely that the cofactor is only released upon cell lysis.B12 production in the marine ecosystem and ecological implicationsLooking at the original source of B12 in nature, namely prototrophic bacteria and archaea, the bacteria studied here show pronounced differences between the biosynthetic potentials of the cofactors and the ability to share them with their environment. Thus, the natural source of vitamin B12 within a given ecosystem does not primarily depend on the ratio of prototrophic bacteria, but even more crucially on how much of the cofactor is synthesised by the prototrophic prokaryotes within an ecosystem and is actively released. The fact that some bacteria do not voluntarily share B12 with ambient microorganisms, significantly increases the importance of processes, such as sloppy feeding by zooplankton or virus infections [44, 49,50,51], for the release of vitamins in the marine and likely also other ecosystems.Our results also contribute to the controversially discussed question of whether B12 prototrophic bacteria live in symbiosis with phototrophic microorganisms [13, 30]. Despite numerous co-cultivation experiments demonstrating the obligatory provision of B12 by individual bacteria to phototrophic microorganisms, the decisive question of the mechanism of provision has so far been overlooked [13,14,15,16, 27, 28]. In our view, however, this question is crucial when assessing whether a symbiotic interaction is taking place. Our results support the hypothesis that a bacterial mechanism for the active release is likely to exist, as our experiments distinguish between B12-provider and B12-retainer within prototrophic bacteria. Looking at the ecological niches and the isolation sites of the two respective groups, differences can be identified. Most B12-provider strains were isolated from or discovered in association with eukaryotic microorganisms, whereas most B12-retainer strains were isolated as free-living in the ocean (Supplementary table S4). Moreover, six of the tested bacterial strains were isolated from dinoflagellates and five of them were B12-provider. Since we used a diatom as a B12 auxotrophic organism in our study, it would also be interesting to know if these B12-provider strains also provide B12 to other phytoplankton, such as dinoflagellates. Also, in this study we only studied bacteria from the alphaproteobacteria class, since a large share of them are known to be B12 prototrophs and abundant in the marine ecosystem. For future studies, it would be interesting to see if a similar pattern of B12 provisioning can be observed in bacteria from other classes. Our results indicate that the B12 prototrophy of a bacterium does not necessarily indicate a mutualistic interaction with other auxotrophic microorganisms. However, the bacterial group of B12-provider in particular seems to favour living in close proximity to other microorganisms, which is why the exchange of B12 for e.g. organic compounds can establish itself as a distinct symbiotic interaction between individual microorganisms. More

  • in

    Climate-driven tradeoffs between landscape connectivity and the maintenance of the coastal carbon sink

    Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 3998 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Herbert, E. R., Windham-Myers, L. & Kirwan, M. L. Sea-level rise enhances carbon accumulation in United States tidal wetlands. One Earth 4, 425–433 (2021).Article 
    ADS 

    Google Scholar 
    Rogers, K. et al. Wetland carbon storage controlled by millennial-scale variation in relative sea-level rise. Nature 567, 91–95 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Murray, N. J. et al. The global distribution and trajectory of tidal flats. Nature 565, 222–225 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Saintilan, N. et al. Thresholds of mangrove survival under rapid sea level rise. Science 368, 1118–1121 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Waycott, M. et al. Accelerating loss of seagrasses across the globe threatens coastal ecosystems. Proc. Natl Acad. Sci. USA 106, 12377–12381 (2009).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kirwan, M. L. & Gedan, K. B. Sea-level driven land conversion and the formation of ghost forests. Nat. Clim. Change 9, 450–457 (2019).Article 
    ADS 

    Google Scholar 
    Raabe, E. A. & Stumpf, R. P. Expansion of tidal marsh in response to sea-level rise: Gulf Coast of Florida, USA. Estuaries Coast. 39, 145–157 (2016).Article 

    Google Scholar 
    Ury, E. A., Yang, X., Wright, J. P. & Bernhardt, E. S. Rapid deforestation of a coastal landscape driven by sea-level rise and extreme events. Ecol. Appl. 31, e02339 (2021).Article 
    PubMed 

    Google Scholar 
    Mariotti, G. Revisiting salt marsh resilience to sea level rise: are ponds responsible for permanent land loss? J. Geophys. Res. Earth Surf. 121, 1391–1407 (2016).Article 
    ADS 

    Google Scholar 
    Schepers, L., Brennand, P., Kirwan, M. L., Guntenspergen, G. R. & Temmerman, S. Coastal marsh degradation into ponds induces irreversible elevation loss relative to sea level in a microtidal system. Geophys. Res. Lett. 47, e2020GL089121 (2020).Article 
    ADS 

    Google Scholar 
    Schieder, N. W., Walters, D. C. & Kirwan, M. L. Massive upland to wetland conversion compensated for historical marsh loss in Chesapeake Bay, USA. Estuaries Coasts 41, 940–951 (2018).Article 

    Google Scholar 
    Chmura, G. L., Anisfeld, S. C., Cahoon, D. R. & Lynch, J. C. Global carbon sequestration in tidal, saline wetland soils. Glob. Biogeochem. Cycles 17, 1111 (2003).Fourqurean, J. W. et al. Seagrass ecosystems as a globally significant carbon stock. Nat. Geosci. 5, 505–509 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

    Google Scholar 
    Smart, L. S. et al. Aboveground carbon loss associated with the spread of ghost forests as sea levels rise. Environ. Res. Lett. 15, 104028 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Smith, A. J. & Kirwan, M. L. Sea level-driven marsh migration results in rapid net loss of carbon. Geophys. Res. Lett. 48, e2021GL092420 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Phang, V. X. H., Chou, L. M. & Friess, D. A. Ecosystem carbon stocks across a tropical intertidal habitat mosaic of mangrove forest, seagrass meadow, mudflat and sandbar. Earth Surf. Process. Landf. 40, 1387–1400 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Saavedra-Hortua, D. A., Friess, D. A., Zimmer, M. & Gillis, L. G. Sources of particulate organic matter across mangrove forests and adjacent ecosystems in different geomorphic settings. Wetlands 40, 1047–1059 (2020).Article 

    Google Scholar 
    Windham-Myers, L., Crooks, S. & Troxler, T. G. A Blue Carbon Primer: The State of Coastal Wetland Carbon Science, Practice and Policy (CRC Press, 2018).Donatelli, C., Kalra, T. S., Fagherazzi, S., Zhang, X. & Leonardi, N. Dynamics of marsh-derived sediments in lagoon-type estuaries. J. Geophys. Res. Earth Surf. 125, e2020JF005751 (2020).Article 
    ADS 

    Google Scholar 
    Hopkinson, C. S., Morris, J. T., Fagherazzi, S., Wollheim, W. M. & Raymond, P. A. Lateral marsh edge erosion as a source of sediments for vertical marsh accretion. J. Geophys. Res. Biogeosci. 123, 2444–2465 (2018).Article 
    CAS 

    Google Scholar 
    Mitchell, M. G. E., Bennett, E. M. & Gonzalez, A. Linking landscape connectivity and ecosystem service provision: current knowledge and research gaps. Ecosystems 16, 894–908 (2013).Article 

    Google Scholar 
    Pearson, R. M. et al. Disturbance type determines how connectivity shapes ecosystem resilience. Sci. Rep. 11, 1188 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grande, T. O., Aguiar, L. M. S. & Machado, R. B. Heating a biodiversity hotspot: connectivity is more important than remaining habitat. Landsc. Ecol. 35, 639–657 (2020).Article 

    Google Scholar 
    Olliver, E. A. & Edmonds, D. A. Hydrological connectivity controls magnitude and distribution of sediment deposition within the Deltaic Islands of Wax Lake Delta, LA, USA. J. Geophys. Res. Earth Surf. 126, e2021JF006136 (2021).Article 
    ADS 

    Google Scholar 
    Ward, N. D. et al. Representing the function and sensitivity of coastal interfaces in Earth system models. Nat. Commun. 11, 2458 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wohl, E. et al. Connectivity as an emergent property of geomorphic systems. Earth Surf. Process. Landf. 44, 4–26 (2019).Article 
    ADS 

    Google Scholar 
    Kirwan, M. L. & Mudd, S. M. Response of salt-marsh carbon accumulation to climate change. Nature 489, 550–553 (2012).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Rietl, A. J., Megonigal, J. P., Herbert, E. R. & Kirwan, M. L. Vegetation type and decomposition priming mediate brackish marsh carbon accumulation under interacting facets of global change. Geophys. Res. Lett. 48, e2020GL092051 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Kirwan, M. L., Walters, D. C., Reay, W. G. & Carr, J. A. Sea level driven marsh expansion in a coupled model of marsh erosion and migration. Geophys. Res. Lett. 43, 4366–4373 (2016).Article 
    ADS 

    Google Scholar 
    Mariotti, G. & Fagherazzi, S. A numerical model for the coupled long-term evolution of salt marshes and tidal flats. J. Geophys. Res. Earth Surf. 115, F01004 (2010).Theuerkauf, E. J., Stephens, J. D., Ridge, J. T., Fodrie, F. J. & Rodriguez, A. B. Carbon export from fringing saltmarsh shoreline erosion overwhelms carbon storage across a critical width threshold. Estuar. Coast. Shelf Sci. 164, 367–378 (2015).Article 
    CAS 

    Google Scholar 
    Murray, A. B. Reducing model complexity for explanation and prediction. Geomorphology 90, 178–191 (2007).Article 
    ADS 

    Google Scholar 
    Murray, A. B. & Paola, C. A cellular model of braided rivers. Nature 371, 54–57 (1994).Article 
    ADS 

    Google Scholar 
    Mariotti, G. & Carr, J. Dual role of salt marsh retreat: long-term loss and short-term resilience. Water Resour. Res. 50, 2963–2974 (2014).Article 
    ADS 

    Google Scholar 
    Mudd, S. M., Howell, S. M. & Morris, J. T. Impact of dynamic feedbacks between sedimentation, sea-level rise, and biomass production on near-surface marsh stratigraphy and carbon accumulation. Estuar. Coast. Shelf Sci. 82, 377–389 (2009).Article 
    ADS 
    CAS 

    Google Scholar 
    Mudd, S. M., Fagherazzi, S., Morris, J. T. & Furbish, D. J. Flow, sedimentation, and biomass production on a vegetated salt marsh in South Carolina: toward a predictive model of marsh morphologic and ecologic evolution. Ecogeomorphology Tidal Marshes 59, 165–188 (2004).Reeves, I. R. B. et al. Impacts of seagrass dynamics on the coupled long-term evolution of barrier-marsh-bay systems. J. Geophys. Res. Biogeosci. 125, e2019JG005416 (2020).Article 
    ADS 

    Google Scholar 
    Spivak, A. C., Sanderman, J., Bowen, J. L., Canuel, E. A. & Hopkinson, C. S. Global-change controls on soil-carbon accumulation and loss in coastal vegetated ecosystems. Nat. Geosci. 12, 685–692 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    de Broek, M. V. et al. Long-term organic carbon sequestration in tidal marsh sediments is dominated by old-aged allochthonous inputs in a macrotidal estuary. Glob. Change Biol. 24, 2498–2512 (2018).Article 
    ADS 

    Google Scholar 
    Noyce, G. L., Kirwan, M. L., Rich, R. L. & Megonigal, J. P. Asynchronous nitrogen supply and demand produce nonlinear plant allocation responses to warming and elevated CO2. Proc. Natl Acad. Sci. USA 116, 21623–21628 (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Smith, A. J., Noyce, G. L., Megonigal, J. P., Guntenspergen, G. R. & Kirwan, M. L. Temperature optimum for marsh resilience and carbon accumulation revealed in a whole-ecosystem warming experiment. Glob. Change Biol. 28, 3236–3245 (2022).Article 
    CAS 

    Google Scholar 
    Guimond, J. & Tamborski, J. Salt marsh hydrogeology: a review. Water 13, 543 (2021).Article 
    CAS 

    Google Scholar 
    Xin, P. et al. Surface water and groundwater interactions in salt marshes and their impact on plant ecology and coastal biogeochemistry. Rev. Geophys. 60, e2021RG000740 (2022).Article 
    ADS 

    Google Scholar 
    Chen, Y. & Kirwan, M. L. Climate-driven decoupling of wetland and upland biomass trends on the mid-Atlantic coast. Nat. Geosci. 15, 913–918 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Rapalee, G., Trumbore, S. E., Davidson, E. A., Harden, J. W. & Veldhuis, H. Soil Carbon stocks and their rates of accumulation and loss in a boreal forest landscape. Glob. Biogeochem. Cycles 12, 687–701 (1998).Article 
    ADS 
    CAS 

    Google Scholar 
    Stewart, C. E., Paustian, K., Conant, R. T., Plante, A. F. & Six, J. Soil carbon saturation: concept, evidence and evaluation. Biogeochemistry 86, 19–31 (2007).Article 
    CAS 

    Google Scholar 
    Zhou, T. et al. Age-dependent forest carbon sink: Estimation via inverse modeling. J. Geophys. Res. Biogeosci. 120, 2473–2492 (2015).Article 
    CAS 

    Google Scholar 
    Morris, J. T., Sundareshwar, P. V., Nietch, C. T., Kjerfve, B. & Cahoon, D. R. Responses of coastal wetlands to rising sea level. Ecology 83, 2869–2877 (2002).Article 

    Google Scholar 
    Kirwan, M. L., Temmerman, S., Skeehan, E. E., Guntenspergen, G. R. & Fagherazzi, S. Overestimation of marsh vulnerability to sea level rise. Nat. Clim. Change 6, 253–260 (2016).Article 
    ADS 

    Google Scholar 
    Brinson, M. M., Christian, R. R. & Blum, L. K. Multiple states in the sea-level induced transition from terrestrial forest to estuary. Estuaries 18, 648–659 (1995).Article 
    CAS 

    Google Scholar 
    Schieder, N. W. & Kirwan, M. L. Sea-level driven acceleration in coastal forest retreat. Geology 47, 1151–1155 (2019).Article 
    ADS 

    Google Scholar 
    Leonardi, N., Ganju, N. K. & Fagherazzi, S. A linear relationship between wave power and erosion determines salt-marsh resilience to violent storms and hurricanes. Proc. Natl Acad. Sci. USA 113, 64–68 (2016).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Feagin, R. A., Martinez, M. L., Mendoza-Gonzalez, G. & Costanza, R. Salt marsh zonal migration and ecosystem service change in response to global sea level rise: a case study from an urban region. Ecol. Soc. 15, 14 (2010).Sapkota, Y. & White, J. R. Marsh edge erosion and associated carbon dynamics in coastal Louisiana: a proxy for future wetland-dominated coastlines world-wide. Estuar. Coast. Shelf Sci. 226, 106289 (2019).Article 
    CAS 

    Google Scholar 
    Smith, K. E. L., Terrano, J. F., Khan, N. S., Smith, C. G. & Pitchford, J. L. Lateral shoreline erosion and shore-proximal sediment deposition on a coastal marsh from seasonal, storm and decadal measurements. Geomorphology 389, 107829 (2021).Article 

    Google Scholar 
    Bouma, T. J. et al. Short-term mudflat dynamics drive long-term cyclic salt marsh dynamics. Limnol. Oceanogr. 61, 2261–2275 (2016).Article 
    ADS 

    Google Scholar 
    Gillis, L. G. et al. Potential for landscape-scale positive interactions among tropical marine ecosystems. Mar. Ecol. Prog. Ser. 503, 289–303 (2014).Article 
    ADS 

    Google Scholar 
    Schuerch, M., Dolch, T., Reise, K. & Vafeidis, A. T. Unravelling interactions between salt marsh evolution and sedimentary processes in the Wadden Sea (southeastern North Sea). Prog. Phys. Geogr. Earth Environ. 38, 691–715 (2014).Article 

    Google Scholar 
    Gonneea, M. E. et al. Salt marsh ecosystem restructuring enhances elevation resilience and carbon storage during accelerating relative sea-level rise. Estuar. Coast. Shelf Sci. 217, 56–68 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    McTigue, N. et al. Sea level rise explains changing carbon accumulation rates in a salt marsh over the past two millennia. J. Geophys. Res. Biogeosci. 124, 2945–2957 (2019).Article 
    CAS 

    Google Scholar 
    Wang, F., Lu, X., Sanders, C. J. & Tang, J. Tidal wetland resilience to sea level rise increases their carbon sequestration capacity in United States. Nat. Commun. 10, 5434 (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wang, F. et al. Global blue carbon accumulation in tidal wetlands increases with climate change. Natl Sci. Rev. 8, nwaa296 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Ganju, N. K., Defne, Z., Elsey-Quirk, T. & Moriarty, J. M. Role of tidal wetland stability in lateral fluxes of particulate organic matter and carbon. J. Geophys. Res. Biogeosci. 124, 1265–1277 (2019).Article 
    CAS 

    Google Scholar 
    Krauss, K. W. et al. The role of the upper tidal estuary in wetland blue carbon storage and flux. Glob. Biogeochem. Cycles 32, 817–839 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Baustian, M. M., Stagg, C. L., Perry, C. L., Moss, L. C. & Carruthers, T. J. B. Long-term carbon sinks in marsh soils of coastal louisiana are at risk to wetland loss. J. Geophys. Res. Biogeosci. 126, e2020JG005832 (2021).Article 
    ADS 

    Google Scholar 
    DeLaune, R. D. & White, J. R. Will coastal wetlands continue to sequester carbon in response to an increase in global sea level?: a case study of the rapidly subsiding Mississippi river deltaic plain. Clim. Change 110, 297–314 (2012).Article 
    ADS 

    Google Scholar 
    Lovelock, C. E. & Duarte, C. M. Dimensions of Blue Carbon and emerging perspectives. Biol. Lett. 15, 20180781 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lovelock, C. E. & Reef, R. Variable impacts of climate change on Blue Carbon. One Earth 3, 195–211 (2020).Article 
    ADS 

    Google Scholar 
    Bernal, B. & Mitsch, W. J. Comparing carbon sequestration in temperate freshwater wetland communities. Glob. Change Biol. 18, 1636–1647 (2012).Article 
    ADS 

    Google Scholar 
    Mack, S. K., Lane, R. R., Deng, J., Morris, J. T. & Bauer, J. J. Wetland carbon models: applications for wetland carbon commercialization. Ecol. Model. 476, 110228 (2023).Article 
    CAS 

    Google Scholar 
    Young, I. R. & Verhagen, L. A. The growth of fetch limited waves in water of finite depth. Part 1. Total energy and peak frequency. Coast. Eng. 29, 47–78 (1996).Article 

    Google Scholar 
    Mariotti, G. & Fagherazzi, S. Critical width of tidal flats triggers marsh collapse in the absence of sea-level rise. Proc. Natl Acad. Sci. USA 110, 5353–5356 (2013).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Koppel, J., van de, Wal, D., van der, Bakker, J. P. & Herman, P. M. J. Self‐organization and vegetation collapse in salt marsh ecosystems. Am. Nat. 165, E1–E12 (2005).Article 
    PubMed 

    Google Scholar 
    D’Alpaos, A., Lanzoni, S., Marani, M. & Rinaldo, A. Landscape evolution in tidal embayments: modeling the interplay of erosion, sedimentation, and vegetation dynamics. J. Geophys. Res. Earth Surf. 112, F01008 (2007).Kirwan, M. L. et al. Limits on the adaptability of coastal marshes to rising sea level. Geophys. Res. Lett. 37, L23401 (2010).Larsen, L. G. & Harvey, J. W. How vegetation and sediment transport feedbacks drive landscape change in the everglades and wetlands worldwide. Am. Nat. 176, E66–E79 (2010).Article 
    PubMed 

    Google Scholar 
    Smith, J. A. M. The role of Phragmites australis in mediating inland salt marsh migration in a Mid-Atlantic Estuary. PLoS ONE 8, e65091 (2013).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mariotti, G., Elsey-Quirk, T., Bruno, G. & Valentine, K. Mud-associated organic matter and its direct and indirect role in marsh organic matter accumulation and vertical accretion. Limnol. Oceanogr. 65, 2627–2641 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Ladd, C. J. T., Duggan-Edwards, M. F., Bouma, T. J., Pagès, J. F. & Skov, M. W. Sediment supply explains long-term and large-scale patterns in salt marsh lateral expansion and erosion. Geophys. Res. Lett. 46, 11178–11187 (2019).Article 
    ADS 

    Google Scholar 
    Törnqvist, T. E., Jankowski, K. L., Li, Y.-X. & González, J. L. Tipping points of Mississippi Delta marshes due to accelerated sea-level rise. Sci. Adv. 6, eaaz5512 (2020).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fagherazzi, S. et al. Numerical models of salt marsh evolution: ecological, geomorphic, and climatic factors. Rev. Geophys. 50, RG1002 (2012). More

  • in

    Minke whale feeding rate limitations suggest constraints on the minimum body size for engulfment filtration feeding

    Dove, A. D. & Pierce, S. J. Whale Sharks: Biology, Ecology, and Conservation (CRC Press, 2021).Friedman, M. et al. 100-million-year dynasty of giant planktivorous bony fishes in the Mesozoic seas. Science 327, 990–993 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Friedman, M. Parallel evolutionary trajectories underlie the origin of giant suspension-feeding whales and bony fishes. Proc. R. Soc. B https://doi.org/10.1098/rspb.2011.1381 (2011).Sanderson, S. L. & Wassersug, R. in The Skull: Functional and Evolutionary Mechanisms Vol. 3 (eds Hanken, J. & Hall, B. K.) 37–112 (Univ. Chicago Press, 1993).Rowat, D. & Brooks, K. A review of the biology, fisheries and conservation of the whale shark Rhincodon typus. J. Fish Biol. 80, 1019–1056 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Pimiento, C., Cantalapiedra, J. L., Shimada, K., Field, D. J. & Smaers, J. B. Evolutionary pathways toward gigantism in sharks and rays. Evolution 73, 588–599 (2019).Article 
    PubMed 

    Google Scholar 
    Stiefel, K. M. Evolutionary trends in large pelagic filter-feeders. Hist. Biol. 33, 1477–1488 (2021).Article 

    Google Scholar 
    Goldbogen, J. & Madsen, P. The largest of August Krogh animals: physiology and biomechanics of the blue whale revisited. Comp. Biochem. Physiol. A 254, 110894 (2021).Article 
    CAS 

    Google Scholar 
    Jørgensen, C. B. Quantitative aspects of filter feeding in invertebrates. Biol. Rev. 30, 391–453 (1955).Article 

    Google Scholar 
    Radke, R. J. & Kahl, U. Effects of a filter‐feeding fish [silver carp, Hypophthalmichthys molitrix (Val.)] on phyto‐and zooplankton in a mesotrophic reservoir: results from an enclosure experiment. Freshw. Biol. 47, 2337–2344 (2002).Article 

    Google Scholar 
    Schiemer, F. in Perspectives in Tropical Limnology (eds Schiemer, F. & Boland, K.T.) 65–76 (SPB Academic Publishing, 1996).Carey, N. & Goldbogen, J. A. Kinematics of ram filter feeding and beat-glide swimming in the northern anchovy Engraulis mordax. J. Exp. Biol. 220, 2717–2725 (2017).PubMed 

    Google Scholar 
    Haines, G. E. & Sanderson, S. L. Integration of swimming kinematics and ram suspension feeding in a model American paddlefish, Polyodon spathula. J. Exp. Biol. 220, 4535–4547 (2017).PubMed 

    Google Scholar 
    Paig‐Tran, E. M., Kleinteich, T. & Summers, A. P. The filter pads and filtration mechanisms of the devil rays: variation at macro and microscopic scales. J. Morphol. 274, 1026–1043 (2013).Article 
    PubMed 

    Google Scholar 
    Jacobsen, I. P. & Bennett, M. B. A comparative analysis of feeding and trophic level ecology in stingrays (Rajiformes; Myliobatoidei) and electric rays (Rajiformes: Torpedinoidei). PLoS ONE 8, e71348 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ellis, J. Occurrence of pelagic stingray Pteroplatytrygon violacea (Bonaparte, 1832) in the North Sea. J. Fish Biol. 71, 933–937 (2007).Article 

    Google Scholar 
    Werth, A. J. & Potvin, J. Baleen hydrodynamics and morphology of cross-flow filtration in balaenid whale suspension feeding. PLoS ONE 11, e0150106 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Orton, L. S. & Brodie, P. F. Engulfing mechanics of fin whales. Can. J. Zool. 65, 2898–2907 (1987).Article 

    Google Scholar 
    Shadwick, R. E., Goldbogen, J. A., Potvin, J., Pyenson, N. D. & Vogl, A. W. Novel muscle and connective tissue design enables high extensibility and controls engulfment volume in lunge-feeding rorqual whales. J. Exp. Biol. 216, 2691–2701 (2013).PubMed 

    Google Scholar 
    Shadwick, R. E., Goldbogen, J. A., Pyenson, N. D. & Whale, J. C. Structure and function in the lunge feeding apparatus: mechanical properties of the fin whale mandible. Anat. Rec. 300, 1953–1962 (2017).Article 

    Google Scholar 
    Werth, A. J., Ito, H. & Ueda, K. Multiaxial movements at the minke whale temporomandibular joint. J. Morphol. 281, 402–412 (2020).Article 
    PubMed 

    Google Scholar 
    Lambertsen, R., Ulrich, N. & Straley, J. Frontomandibular stay of Balaenopteridae: a mechanism for momentum recapture during feeding. J. Mammal. 76, 877–899 (1995).Article 

    Google Scholar 
    Pyenson, N. D. et al. Discovery of a sensory organ that coordinates lunge feeding in rorqual whales. Nature 485, 498–501 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Goldbogen, J. A. et al. How baleen whales feed: the biomechanics of engulfment and filtration. Annu. Rev. Mar. Sci. 9, 367–386 (2017).Article 
    CAS 

    Google Scholar 
    Bierlich, K. C. et al. A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones. Mar. Ecol. Prog. Ser. 673, 193–210 (2021).Article 

    Google Scholar 
    Slater, G. J., Goldbogen, J. A. & Pyenson, N. D. Independent evolution of baleen whale gigantism linked to Plio-Pleistocene ocean dynamics. Proc. R. Soc. B 284, 20170546 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lockyer, C. Growth and energy budgets of large baleen whales from the Southern Hemisphere. Food Agric. Organ. 3, 379–487 (1981).
    Google Scholar 
    Mackintosh, A. & Wheeler, J. Southern blue and fin whales. Discover. Rep. 1, 257–540 (1929).Smith, F. A. & Lyons, S. K. How big should a mammal be? A macroecological look at mammalian body size over space and time. Phil. Trans. R. Soc. B 366, 2364–2378 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gearty, W., McClain, C. R. & Payne, J. L. Energetic tradeoffs control the size distribution of aquatic mammals. Proc. Natl Acad. Sci. USA 115, 4194–4199 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lockyer, C. Body weights of some species of large whales. ICES J. Mar. Sci. 36, 259–273 (1976).Article 

    Google Scholar 
    Goldbogen, J. A. Physiological constraints on marine mammal body size. Proc. Natl Acad. Sci. USA 115, 3995–3997 (2018).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Goldbogen, J. A. et al. Why whales are big but not bigger: physiological drivers and ecological limits in the age of ocean giants. Science 366, 1367–1372 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Cade, D. E. et al. Social exploitation of extensive, ephemeral, environmentally controlled prey patches by super-groups of rorqual whales. Anim. Behav. 182, 251–266 (2021).Article 

    Google Scholar 
    Goldbogen, J. A. et al. Scaling of lunge‐feeding performance in rorqual whales: mass‐specific energy expenditure increases with body size and progressively limits diving capacity. Funct. Ecol. 26, 216–226 (2012).Article 

    Google Scholar 
    Kahane-Rapport, S. R. & Goldbogen, J. A. Allometric scaling of morphology and engulfment capacity in rorqual whales. J. Morphol. 279, 1256–1268 (2018).Article 
    PubMed 

    Google Scholar 
    Kahane-Rapport, S. R. et al. Lunge filter feeding biomechanics constrain rorqual foraging ecology across scale. J. Exp. Biol. https://doi.org/10.1242/jeb.224196 (2020).McNab, B. K. Complications inherent in scaling the basal rate of metabolism in mammals. Q. Rev. Biol. 63, 25–54 (1988).Article 
    CAS 
    PubMed 

    Google Scholar 
    Boyd, I. in Marine Mammal Biology: An Evolutionary Approach (ed. Hoelzel, A. R.) 247–277 (Blackwell Science Ltd, 2002).Kleiber, M. Body size and metabolism. Hilgardia 6, 315–353 (1932).Article 
    CAS 

    Google Scholar 
    West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).Article 
    CAS 
    PubMed 

    Google Scholar 
    Corkeron, P. J. & Connor, R. C. Why do baleen whales migrate? Mar. Mamm. Sci. 15, 1228–1245 (1999).Article 

    Google Scholar 
    Lockyer, C. Review of baleen whale (Mysticeti) reproduction and implications for management. Rep. Int. Whal. Commn 6, 27–50 (1984).
    Google Scholar 
    Lockyer, C. All creatures great and smaller: a study in cetacean life history energetics. J. Mar. Biol. Assoc. UK 87, 1035–1045 (2007).Article 

    Google Scholar 
    Frazer, J. & Huggett, A. S. G. Specific foetal growth rates of cetaceans. J. Zool. 169, 111–126 (1973).Article 

    Google Scholar 
    Zhou, M. & Dorland, R. D. Aggregation and vertical migration behavior of Euphausia superba. Deep Sea Res. II 51, 2119–2137 (2004).Article 

    Google Scholar 
    Gough, W. T. et al. Scaling of swimming performance in baleen whales. J. Exp. Biol. 222, jeb204172 (2019).Article 
    PubMed 

    Google Scholar 
    Cade, D. E. et al. Predator-scale spatial analysis of intra-patch prey distribution reveals the energetic drivers of rorqual whale super group formation. Funct. Ecol. 35, 894–908 (2021).Article 
    CAS 

    Google Scholar 
    Gough, W. T. et al. Scaling of oscillatory kinematics and Froude efficiency in baleen whales. J. Exp. Biol. 224, jeb237586 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Croll, D. A., Kudela, R. & Tershy, B. R. in Whales, Whaling, and Ocean Ecosystems (eds Estes, J. A. et al.) Ch. 16 (Univ. California Press, 2006).Woodward, B. L., Winn, J. P. & Fish, F. E. Morphological specializations of baleen whales associated with hydrodynamic performance and ecological niche. J. Morphol. 267, 1284–1294 (2006).Article 
    PubMed 

    Google Scholar 
    Webb, P. W. & De Buffrénil, V. Locomotion in the biology of large aquatic vertebrates. Trans. Am. Fish. Soc. 119, 629–641 (1990).Article 

    Google Scholar 
    Acevedo-Gutiérrez, A., Croll, D. & Tershy, B. High feeding costs limit dive time in the largest whales. J. Exp. Biol. 205, 1747–1753 (2002).Article 
    PubMed 

    Google Scholar 
    Goldbogen, J. A. et al. Mechanics, hydrodynamics and energetics of blue whale lunge feeding: efficiency dependence on krill density. J. Exp. Biol. 214, 131–146 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Potvin, J., Cade, D. E., Werth, A. J., Shadwick, R. E. & Goldbogen, J. A. Rorqual lunge-feeding energetics near and away from the kinematic threshold of optimal efficiency. Integr. Org. Biol. 3, obab005 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pyenson, N. D. The ecological rise of whales chronicled by the fossil record. Curr. Biol. 27, R558–R564 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Williams, T. M. in Whales, Whaling, and Ocean Ecosystems (eds Estes, J. A. et al.) Ch. 15 (Univ. California Press, 2006).Tackaberry, J. E. et al. From a calf’s perspective: humpback whale nursing behavior on two US feeding grounds. PeerJ 8, e8538 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Huang, S.-L., Chou, L.-S. & Ni, I.-H. Comparable length at weaning in cetaceans. Mar. Mamm. Sci. 25, 875–887 (2009).Article 

    Google Scholar 
    Rice, D. Marine Mammals of the World: Systematics and Distribution (Society for Marine Mammalogy Special Publication, 1998).McNamara, J. M. & Houston, A. I. The effect of a change in foraging options on intake rate and predation rate. Am. Nat. 144, 978–1000 (1994).Article 

    Google Scholar 
    Mittelbach, G. G. Foraging efficiency and body size: a study of optimal diet and habitat use by bluegills. Ecology 62, 1370–1386 (1981).Article 

    Google Scholar 
    Robbins, C. T. et al. Optimizing protein intake as a foraging strategy to maximize mass gain in an omnivore. Oikos 116, 1675–1682 (2007).Article 

    Google Scholar 
    Werth, A. J. et al. Filtration area scaling and evolution in mysticetes: trophic niche partitioning and the curious cases of sei and pygmy right whales. Biol. J. Linn. Soc. 125, 264–279 (2018).Article 

    Google Scholar 
    Leslie, M. S., Peredo, C. M. & Pyenson, N. D. Norrisanima miocaena, a new generic name and redescription of a stem balaenopteroid mysticete (Mammalia, Cetacea) from the Miocene of California. PeerJ 7, e7629 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marx, F. G. & Uhen, M. D. Climate, critters, and cetaceans: Cenozoic drivers of the evolution of modern whales. Science 327, 993–996 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Perrin, W. F. Why are there so many kinds of whales and dolphins? Bioscience 41, 460–462 (1991).Article 

    Google Scholar 
    Kot, B. W., Sears, R., Zbinden, D., Borda, E. & Gordon, M. S. Rorqual whale (Balaenopteridae) surface lunge‐feeding behaviors: standardized classification, repertoire diversity, and evolutionary analyses. Mar. Mamm. Sci. 30, 1335–1357 (2014).Article 

    Google Scholar 
    Segre, P. S. et al. Scaling of maneuvering performance in baleen whales: larger whales outperform expectations. J. Exp. Biol. 225, jeb243224 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kawamura, A. A review of food of balaenopterid whales. Sci. Rep. Whales Res. Inst. 32, 155–197 (1980).
    Google Scholar 
    Iwata, T. et al. Tread-water feeding of Bryde’s whales. Curr. Biol. 27, R1154–R1155 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    McMillan, C. J., Towers, J. R. & Hildering, J. The innovation and diffusion of “trap‐feeding,” a novel humpback whale foraging strategy. Mar. Mamm. Sci. 35, 779–796 (2019).Article 

    Google Scholar 
    Robbins, J. & Mattila, D. Estimating Humpback Whale (Megaptera novaeangliae) Entanglement Rates on the Basis of Scar Evidence (Northeast Fisheries Science Center, 2004).Horwood, J. in Encyclopedia of Marine Mammals 2nd edn (eds Wursig, B et al.) 1001–1003 (Elsevier, 2009).Haug, T., Lindstrøm, U. & Nilssen, K. T. Variations in minke whale (Balaenoptera acutorostrata) diet and body condition in response to ecosystem changes in the Barents Sea. Sarsia 87, 409–422 (2002).Article 

    Google Scholar 
    García-Vernet, R., Borrell, A., Víkingsson, G., Halldórsson, S. D. & Aguilar, A. Ecological niche partitioning between baleen whales inhabiting Icelandic waters. Prog. Oceanogr. 199, 102690 (2021).Article 

    Google Scholar 
    Cade, D. E., Carey, N., Domenici, P., Potvin, J. & Goldbogen, J. A. Predator-informed looming stimulus experiments reveal how large filter feeding whales capture highly maneuverable forage fish. Proc. Natl Acad. Sci. USA 117, 472–478 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Deméré, T. A., McGowen, M. R., Berta, A. & Gatesy, J. Morphological and molecular evidence for a stepwise evolutionary transition from teeth to baleen in mysticete whales. Syst. Biol. 57, 15–37 (2008).Article 
    PubMed 

    Google Scholar 
    Stafford, K. M., Fox, C. G. & Clark, D. S. Long-range acoustic detection and localization of blue whale calls in the northeast Pacific Ocean. J. Acoust. Soc. Am. 104, 3616–3625 (1998).Article 
    CAS 
    PubMed 

    Google Scholar 
    Totterdell, J. A. et al. The first three records of killer whales (Orcinus orca) killing and eating blue whales (Balaenoptera musculus). Mar. Mamm. Sci. 38, 1286–1301 (2022).Article 

    Google Scholar 
    Cade, D. E., Friedlaender, A. S., Calambokidis, J. & Goldbogen, J. A. Kinematic diversity in rorqual whale feeding mechanisms. Curr. Biol. 26, 2617–2624 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Goldbogen, J. A. et al. Using digital tags with integrated video and inertial sensors to study moving morphology and associated function in large aquatic vertebrates. Anat. Rec. 300, 1935–1941 (2017).Article 
    CAS 

    Google Scholar 
    Bierlich, K. et al. Comparing uncertainty associated with 1-, 2-, and 3D aerial photogrammetry-based body condition measurements of baleen whales. Front. Mar. Sci. 8, 1729 (2021).Article 

    Google Scholar 
    Cade, D. E. et al. Tools for integrating inertial sensor data with video bio-loggers, including estimation of animal orientation, motion, and position. Anim. Biotelemetry https://doi.org/10.1186/s40317-021-00256-w (2021).Cade, D. E., Barr, K. R., Calambokidis, J., Friedlaender, A. S. & Goldbogen, J. A. Determining forward speed from accelerometer jiggle in aquatic environments. J. Exp. Biol. 221, jeb170449 (2018).PubMed 

    Google Scholar 
    Wilson, R. P. et al. All at sea with animal tracks; methodological and analytical solutions for the resolution of movement. Deep Sea Res. II 54, 193–210 (2007).Article 

    Google Scholar 
    Potvin, J., Cade, D. E., Werth, A. J., Shadwick, R. E. & Goldbogen, J. A. A perfectly inelastic collision: bulk prey engulfment by baleen whales and dynamical implications for the world’s largest cetaceans. Am. J. Phys. 88, 851–863 (2020).Article 

    Google Scholar 
    Torres, W. I. & Bierlich, K. MorphoMetriX: a photogrammetric measurement GUI for morphometric analysis of megafauna. J. Open Source Softw. 5, 1825 (2020).Article 

    Google Scholar 
    Suter, H. & Houston, A. I. How to model optimal group size in social carnivores. Am. Nat. 197, 473–485 (2021).Article 
    PubMed 

    Google Scholar 
    Hazen, E. L., Friedlaender, A. S. & Goldbogen, J. A. Blue whale (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Sci. Adv. 1, e1500469 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Doniol-Valcroze, T., Lesage, V., Giard, J. & Michaud, R. Optimal foraging theory predicts diving and feeding strategies of the largest marine predator. Behav. Ecol. 22, 880–888 (2011).Article 

    Google Scholar 
    Gough, W. T. et al. Fast and furious: energetic tradeoffs and scaling of high-speed foraging in rorqual whales. Integr. Org. Biol. 4, obac038 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Laws, R. M. The ecology of the Southern Ocean. Am. Sci. 73, 26–40 (1985).
    Google Scholar 
    Brown, S. & Lockyer, C. in Antarctic Ecology Vol. 2 (ed. Laws, R. M.) (Academic Press, 1984).Peters, R. H. The Ecological Implications of Body Size Vol. 2 Ch. 7 (Cambridge Univ. Press, 1986).Rall, B. C. et al. Universal temperature and body-mass scaling of feeding rates. Phil. Trans. R. Soc. B 367, 2923–2934 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Evans, E. & Miller, D. Comparative nutrition, growth and longevity. Proc. Nutr. Soc. 27, 121–129 (1968).Article 
    CAS 
    PubMed 

    Google Scholar 
    Farlow, J. O. A consideration of the trophic dynamics of a Late Cretaceous large‐dinosaur community (Oldman Formation). Ecology 57, 841–857 (1976).Article 

    Google Scholar 
    Harestad, A. S. & Bunnel, F. Home range and body weight – a reevaluation. Ecology 60, 389–402 (1979).Article 

    Google Scholar 
    Schoener, T. W. Sizes of feeding territories among birds. Ecology 49, 123–141 (1968).Article 

    Google Scholar 
    Calder, W. A. in Avian Energetics (ed. Paynter, R. A.) 86–151 (Nuttall Ornithological Club, 1974).Savage, V. M., Deeds, E. J. & Fontana, W. Sizing up allometric scaling theory. PLoS Comp. Biol. 4, e1000171 (2008).Article 

    Google Scholar 
    Kolokotrones, T., Savage, V., Deeds, E. J. & Fontana, W. Curvature in metabolic scaling. Nature 464, 753–756 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hudson, L. N., Isaac, N. J. & Reuman, D. C. The relationship between body mass and field metabolic rate among individual birds and mammals. J. Anim. Ecol. 82, 1009–1020 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Unexpected fishy microbiomes

    Authors and AffiliationsCenter for Evolutionary Hologenomics, Globe Institute, University of Copenhagen, Copenhagen, DenmarkMorten T. Limborg & Jacob A. RasmussenSanger Institute, Wellcome Trust Genome Campus, Hinxton, UKPhysilia Y. S. ChuaAuthorsMorten T. LimborgPhysilia Y. S. ChuaJacob A. RasmussenCorresponding authorsCorrespondence to
    Morten T. Limborg or Physilia Y. S. Chua. More

  • in

    Top-down and bottom-up effects modulate species co-existence in a context of top predator restoration

    Alston, J. M. et al. Reciprocity in restoration ecology: When might large carnivore reintroduction restore ecosystems?. Biol. Conserv. 234, 82–89 (2019).Article 

    Google Scholar 
    Ripple, W. J. & Beschta, R. L. Large predators limit herbivore densities in northern forest ecosystems. Eur. J. Wildl. Res. 58, 733–742 (2012).Article 

    Google Scholar 
    Estes, J. A. & Duggins, D. O. Sea otters and kelp forests in Alaska: Generality and variation in a community ecological paradigm. Ecol. Monogr. 65, 75–100 (1995).Article 

    Google Scholar 
    Schmitz, O. J., Beckerman, A. P. & O’Brien, K. M. Behaviorally mediated trophic cascades: Effects of predation risk on food web interactions. Ecology 78, 1388–1399 (1997).Article 

    Google Scholar 
    Power, M. E. Top-down and bottom-up forces in food webs: Do plants have primacy. Ecology 73, 733–746 (1992).Article 

    Google Scholar 
    Travers, T., Lea, M. A., Alderman, R., Terauds, A. & Shaw, J. Bottom-up effect of eradications: The unintended consequences for top-order predators when eradicating invasive prey. J. Appl. Ecol. 58, 801–811 (2021).Article 

    Google Scholar 
    Stoessel, M., Elmhagen, B., Vinka, M., Hellström, P. & Angerbjörn, A. The fluctuating world of a tundra predator guild: bottom-up constraints overrule top-down species interactions in winter. Ecography (Cop.) 42, 488–499 (2019).Article 

    Google Scholar 
    Wolf, C. & Ripple, W. J. Rewilding the world ’s large carnivores. R. Soc. Open Sci. 5, 172235 (2018).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Krofel, M. & Jerina, K. Mind the cat: Conservation management of a protected dominant scavenger indirectly affects an endangered apex predator. Biol. Conserv. 197, 40–46 (2016).Article 

    Google Scholar 
    Prugh, L. R. & Sivy, K. J. Enemies with benefits: Integrating positive and negative interactions among terrestrial carnivores. Ecol. Lett. https://doi.org/10.1111/ele.13489 (2020).Article 
    PubMed 

    Google Scholar 
    Caro, T. M. & Stoner, C. J. The potential for interspecific competition among African carnivores. Biol. Conserv. 110, 67–75 (2003).Article 

    Google Scholar 
    Linnell, J. D. C. & Strand, O. Interference interactions, co-existence and conservation of mammalian carnivores. Divers. Distrib. 6, 169–176 (2000).Article 

    Google Scholar 
    Newsome, T. M. et al. Top predators constrain mesopredator distributions. Nat. Commun. 8, 15469 (2017).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Crooks, K. & Soulé, M. Mesopredator release and avifaunal extinctions in a fragmented system. Nature 400, 563–566 (1999).Article 
    ADS 
    CAS 

    Google Scholar 
    Schoener, T. W. Resource partitioning in ecological communities. Science 185, 27–39 (1974).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Fedriani, J. M., Fuller, T. K., Sauvajot, R. M. & York, E. C. Competition and intraguild predation among three sympatric carnivores. Oecologia 125, 258–270 (2000).Article 
    ADS 
    PubMed 

    Google Scholar 
    Monterroso, P., Díaz-Ruiz, F., Lukacs, P. M., Alves, P. C. & Ferreras, P. Ecological traits and the spatial structure of competitive coexistence among carnivores. Ecology 101, 1–16 (2020).Article 

    Google Scholar 
    Karanth, K. U. et al. Spatio-temporal interactions facilitate large carnivore sympatry across a resource gradient. Proc. R. Soc. B Biol. Sci. 284, 20161860 (2017).Article 

    Google Scholar 
    Ferreiro-Arias, I., Isla, J., Jordano, P. & Benítez-López, A. Fine-scale coexistence between Mediterranean mesocarnivores is mediated by spatial, temporal, and trophic resource partitioning. Ecol. Evol. 11, 15520–15533 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Di Bitetti, M. S., De Angelo, C. D., Di Blanco, Y. E. & Paviolo, A. Niche partitioning and species coexistence in a Neotropical felid assemblage. Acta Oecol. 36, 403–412 (2010).Article 
    ADS 

    Google Scholar 
    Carvalho, J. C. & Gomes, P. Feeding resource partitioning among four sympatric carnivores in the Peneda-Gerês National Park (Portugal). J. Zool. 263, 275–283 (2004).Article 

    Google Scholar 
    Gil-Sánchez, J. M., Mañá-Varela, B., Herrera-Sánchez, F. J. & Urios, V. Spatio-temporal ecology of a carnivore community in middle atlas NW of Morocco. Zoology 146, 125904 (2021).Article 
    PubMed 

    Google Scholar 
    Monterroso, P., Alves, P. C. & Ferreras, P. Plasticity in circadian activity patterns of mesocarnivores in Southwestern Europe: Implications for species coexistence. Behav. Ecol. Sociobiol. 68, 1403–1417 (2014).Article 

    Google Scholar 
    Gallagher, A. J., Creel, S., Wilson, R. P. & Cooke, S. J. Energy landscapes and the landscape of fear. Trends Ecol. Evol. 32, 88–96 (2017).Article 
    PubMed 

    Google Scholar 
    Sergio, F. & Hiraldo, F. Intraguild predation in raptor assemblages: A review. Ibis 150, 132–145 (2008).Article 

    Google Scholar 
    Jiménez, J. et al. Restoring apex predators can reduce mesopredator abundances. Biol. Conserv. 238, 108234 (2019).Article 

    Google Scholar 
    Palomares, F., Ferreras, P., Fedriani, J. M. & Delibes, M. Spatial relationships between Iberian lynx and other carnivores in an area of south-western Spain. J. Appl. Ecol. 33, 5–13 (1996).Article 

    Google Scholar 
    Wooster, E. I. F., Ramp, D., Lundgren, E. J., O’Neill, A. J. & Wallach, A. D. Red foxes avoid apex predation without increasing fear. Behav. Ecol. 32, 895–902 (2021).Article 

    Google Scholar 
    Santos, F. et al. Prey availability and temporal partitioning modulate felid coexistence in Neotropical forests. PLoS ONE 14, 1–23 (2019).Article 

    Google Scholar 
    Barrientos, R. & Virgós, E. Reduction of potential food interference in two sympatric carnivores by sequential use of shared resources. Acta Oecol. 30, 107–116 (2006).Article 
    ADS 

    Google Scholar 
    MacArthur, R. H. & Pianka, E. R. On optimal use of a patchy environment. Am. Nat. 100, 603–609 (1966).Article 

    Google Scholar 
    López-Martín, J. M. Comparison of feeding behaviour between stone marten and common genet: living in coexistence. Martes Carniv. Communities 137–155 (2006).Sarmento, P. et al. Adapt or perish: How the Iberian lynx reintroduction affects fox abundance and behaviour. Hystrix Ital. J. Mammal. 32, 48–54 (2021).
    Google Scholar 
    Forsyth, D. M., Ramsey, D. S. L. & Woodford, L. P. Estimating abundances, densities, and interspecific associations in a carnivore community. J. Wildl. Manag. 83, 1090–1102 (2019).Article 

    Google Scholar 
    Monterroso, P. et al. Disease-mediated bottom-up regulation: An emergent virus affects a keystone prey, and alters the dynamics of trophic webs. Sci. Rep. 6, 1–9 (2016).Article 

    Google Scholar 
    Ritchie, E. G. et al. Ecosystem restoration with teeth: What role for predators?. Trends Ecol. Evol. 27, 265–271 (2012).Article 
    PubMed 

    Google Scholar 
    Santos-Reis, M. et al. Relationships between stone martens, genets and cork oak woodlands in Portugal. Martens Fish. Hum.-Altered Environ. Int. Perspect. https://doi.org/10.1007/0-387-22691-5_7 (2004).Article 

    Google Scholar 
    Goszczyński, J., Posłuszny, M., Pilot, M. & Gralak, B. Patterns of winter locomotion and foraging in two sympatric marten species: Martes martes and Martes foina. Can. J. Zool. 85, 239–249 (2007).Article 
    ADS 

    Google Scholar 
    Díaz-Ruiz, F., Caro, J., Delibes-Mateos, M., Arroyo, B. & Ferreras, P. Drivers of red fox (Vulpes vulpes) daily activity: Prey availability, human disturbance or habitat structure?. J. Zool. 298, 128–138 (2016).Article 

    Google Scholar 
    Zanón Martínez, J. I., Seoane, J., Kelly, M. J., Sarasola, J. H. & Travaini, A. Assessing carnivore spatial co-occurrence and temporal overlap in the face of human interference in a semi-arid forest. Ecol. Appl. https://doi.org/10.1002/eap.2482 (2021).Article 
    PubMed 

    Google Scholar 
    Allen, M. L., Sibarani, M. C., Utoyo, L. & Krofel, M. Terrestrial mammal community richness and temporal overlap between tigers and other carnivores in Bukit Barisan Selatan National Park Sumatra. Anim. Biodivers. Conserv. 1, 97–107 (2020).Article 

    Google Scholar 
    Vilella, M., Ferrandiz-Rovira, M. & Sayol, F. Coexistence of predators in time: Effects of season and prey availability on species activity within a Mediterranean carnivore guild. Ecol. Evol. 10, 11408–11422 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Santos, N. et al. Protein metabolism and physical fitness are physiological determinants of body condition in Southern European carnivores. Sci. Rep. 10, 1–11 (2020).Article 

    Google Scholar 
    Ferreras, P., Travaini, A., Cristina Zapata, S. & Delibes, M. Short-term responses of mammalian carnivores to a sudden collapse of rabbits in Mediterranean Spain. Basic Appl. Ecol. 12, 116–124 (2011).Article 

    Google Scholar 
    Moreno, S. Reproduction of Garden Dormouse Eliomys quercinus lusitanicus, in southwest Spain. Mammalia 52, 401–408 (1988).Article 

    Google Scholar 
    Bakaloudis, D. E., Vlachos, C. G., Papakosta, M. A., Bontzorlos, V. A. & Chatzinikos, E. N. Diet composition and feeding strategies of the stone marten (Martes foina) in a typical mediterranean ecosystem. Sci. World J. 2012, 1–11 (2012).Article 

    Google Scholar 
    Pereira, L. M., Owen-Smith, N. & Moleón, M. Facultative predation and scavenging by mammalian carnivores: Seasonal, regional and intra-guild comparisons. Mamm. Rev. 44, 44–55 (2014).Article 

    Google Scholar 
    Gil-Sánchez, J. M., Ballesteros-Duperón, E. & Bueno-Segura, J. F. Feed ing ecology of the Iberian lynx Lynx pardinus in east ern. Acta Theriol. (Warsz) 51, 85–90 (2006).Article 

    Google Scholar 
    Krofel, M., Huber, D. & Kos, I. Diet of Eurasian lynx Lynx lynx in the northern Dinaric Mountains (Slovenia and Croatia). Acta Theriol. (Warsz) 56, 315–322 (2011).Article 

    Google Scholar 
    Virgós, E., Baniandrés, N., Burgos, T. & Recio, M. R. Intraguild predation by the eagle owl determines the space use of a mesopredator carnivore. Diversity 12, 13–15 (2020).Article 

    Google Scholar 
    Gordon, C. E., Feit, A., Grüber, J. & Letnic, M. Mesopredator suppression by an apex predator alleviates the risk of predation perceived by small prey. Proc. R. Soc. B Biol. Sci. 282, 20142870 (2015).Article 

    Google Scholar 
    Draper, J. P., Young, J. K., Schupp, E. W., Beckman, N. G. & Atwood, T. B. Frugivory and seed dispersal by carnivorans. Front. Ecol. Evol. 10, 864864 (2022).Article 

    Google Scholar 
    González-Varo, J. P., López-Bao, J. V. & Guitián, J. Functional diversity among seed dispersal kernels generated by carnivorous mammals. J. Anim. Ecol. 82, 562–571 (2013).Article 
    PubMed 

    Google Scholar 
    Virgós, E., Llorente, M. & Cortés, Y. Geographical variation in genet (Genetta genetta L.) diet: A literature review. Mamm. Rev. 29, 117–126 (1999).Article 

    Google Scholar 
    Fedriani, J. M., Ayllón, D., Wiegand, T. & Grimm, V. Intertwined effects of defaunation, increased tree mortality and density compensation on seed dispersal. Ecography (Cop.) 43, 1352–1363 (2020).Article 

    Google Scholar 
    Burgos, T. et al. Predation risk can modify the foraging behaviour of frugivorous carnivores: Implications of rewilding apex predators for plant–animal mutualisms. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13682 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Escribano-Ávila, G. et al. Spanish juniper gain expansion opportunities by counting on a functionally diverse dispersal assemblage community. Ecol. Evol. 3, 3751–3763 (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gazzola, A. & Balestrieri, A. Nutritional ecology provides insights into competitive interactions between closely related Martes species. Mamm. Rev. 50, 82–90 (2020).Article 

    Google Scholar 
    Simón, M. A. et al. Diez años de conservación del lince ibérico, 326 (2012).Royle, J. A., Chandler, R. B., Sollmann, R. & Gardner, B. Spatial Capture-Recapture (Elsevier, 2014).
    Google Scholar 
    Rodríguez, A. & Calzada, J. Lynx pardinus (errata version published in 2020). The IUCN Red List of Threatened Species 2015. https://doi.org/10.2305/IUCN.UK.2015-2.RLTS.T12520A174111773.en (Accessed 27 January 2023) (2015).Gil-Sánchez, J. M. et al. The use of camera trapping for estimating Iberian lynx (Lynx pardinus) home ranges. Eur. J. Wildl. Res. 57, 1203–1211 (2011).Article 

    Google Scholar 
    Gerber, B. D., Karpanty, S. M. & Kelly, M. J. Evaluating the potential biases in carnivore capture-recapture studies associated with the use of lure and varying density estimation techniques using photographic-sampling data of the Malagasy civet. Popul. Ecol. 54, 43–54 (2012).Article 

    Google Scholar 
    Jiménez, J., Díaz-Ruiz, F., Monterroso, P., Tobajas, J. & Ferreras, P. Occupancy data improves parameter precision in spatial capture–recapture models. Ecol. Evol. https://doi.org/10.1002/ece3.9250 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ferreras, P., DÍaz-Ruiz, F. & Monterroso, P. Improving mesocarnivore detectability with lures in camera-trapping studies. Wildl. Res. 45, 505–517 (2018).Article 

    Google Scholar 
    Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337 (2009).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Jiménez, J. et al. Estimating carnivore community structures. Sci. Rep. https://doi.org/10.1038/srep41036 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Genovesi, P., Sinibaldi, I. & Boitani, L. Spacing patterns and territoriality of the stone marten. Can. J. Zool. 75, 1966–1971 (1997).Article 

    Google Scholar 
    Royle, J. A. & Converse, S. J. Hierarchical spatial capture-recapture models: Modelling population density in stratified populations. Methods Ecol. Evol. 5, 37–43 (2014).Article 

    Google Scholar 
    Palomares, F. & Delibes, M. Spatio-temporal ecology and behavior of European genets in southwestern Spain. J. Mammal. 75, 714–724 (1994).Article 

    Google Scholar 
    Camps, D. Jineta – Genetta genetta. En Encicl. Virtual los Vertebr. Españoles. Salvador. A., Barja, I. (Eds.). Mus. Nac. Ciencias Nat. Madrid. https://www.vertebradosibericos.org/ (2017).Efford, M. Density estimation in live-trapping studies. Oikos 106, 598–610 (2004).Article 

    Google Scholar 
    de Valpine, P. et al. Programming with models: Writing statistical algorithms for general model structures with NIMBLE. J. Comput. Graph. Stat. 26, 403–413 (2017).Article 
    MathSciNet 

    Google Scholar 
    NIMBLE Development Team. NIMBLE user manual (2017).Morin, D. J., Waits, L. P., McNitt, D. C. & Kelly, M. J. Efficient single-survey estimation of carnivore density using fecal DNA and spatial capture-recapture: A bobcat case study. Popul. Ecol. 60, 197–209 (2018).Article 

    Google Scholar 
    Gelman, A. et al. Bayesian Data Analysis (CRC Press, 2013).Book 

    Google Scholar 
    Weitzman, M. S. Measure of the Overlap of Income Distribution of White and Negro Families in the United States. Technical report No 22 (1970).Jammalamadaka, S. R. & Sengupta, A. Topics in Circular Statistics. Series on Multivariate Analyisis Vol. 5 (World Scientific, 2001).Book 

    Google Scholar 
    Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (CRC Press, 1994).Book 
    MATH 

    Google Scholar 
    Mielke, P. W., Berry, K. J. & Johnson, E. S. Multi-response permutation proccedures for a priori classifications. Commun. Stat. Theory Methods 5, 1409–1424 (1976).Article 
    MATH 

    Google Scholar 
    Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. lme4: Linear mixed-effects models. R Packag. version 1.1.21 (2020).Barton, K. Package “MuMIn: Multi-model inference” for R. R Packag. Version 1.9.5 45 (2013). More

  • in

    Abiotic selection of microbial genome size in the global ocean

    Non-prokaryotic metagenomic sequences confound average genome size estimationsIn this work, we employed MicrobeCensus22 for de novo estimation of the average genome size (AGS) of microorganisms captured in shotgun metagenome sequences (Fig. 1a; Supplementary Data 1). Briefly, MicrobeCensus optimally aligns metagenomic reads to a set of 30 conserved single-copy gene (CSCG) families found in prokaryotes 22. Based on these mappings, the relative abundance of each CSCG is then computed and used to estimate AGS based on the proportionality principle—that is, the AGS of the community is inversely proportional to the relative abundance of each marker genes22. Finally, a weighted average AGS is calculated that excludes outliers to obtain a robust AGS estimate for a given metagenomic sample22.Fig. 1: Eukaryotic and viral metagenomic reads bias AGS estimates in marine microbial metagenomes.a Schematic workflow of procedures used for estimating AGS in metagenomic samples. AGS is estimated based directly on preprocessed high-quality metagenomic reads (AGS1) and after three iterative steps to remove potential eukaryotic reads (AGS2) and viral reads detected based on the RefSeq viral genome database (AGS3) or de novo (AGS4). See the “Methods” section for more details. b Relationship between depth and proportion of total putative eukaryotic and viral sequences in marine metagenomic collections. The blue line indicates the fitted one-tailed Spearman correlation (r), with the corresponding 95% confidence intervals for the curve indicated by grey bands. The density distribution of the estimated proportion of contaminants is shown in green, with the corresponding median values (µ) highlighted. Values in parenthesis denote the filter size range of sampled metagenomes. c The fraction of ‘contaminating’ reads is highest in the epipelagic ocean relative to other ocean depth layers. EPI Epipelagic (~3–200 m), MES Mesopelagic (200–1000 m), BAT Bathypelagic (1000–4000 m). Values in parenthesis indicate the number of metagenomes. Only the results from the Malaspina Vertical Profiles (MProfile) metagenomes are shown as they cover greater depths of the global ocean (mean 1114 m; Supplementary Data 1). d Eukaryotic and viral metagenomic sequences significantly increase AGS estimates for prokaryotic plankton in marine metagenomes. Values in parenthesis show number of metagenomes for AGS1 and AGS2. e AGS estimates decreased in most metagenomic samples (85%; n = 220) after decontamination compared to predictions directly from preprocessed metagenomes by 1–19% (n = 39). Boxplots (c–e) show the median as middle horizontal (c, d) or vertical (e) lines and interquartile ranges as boxes (whiskers extend no further than 1.5 times the interquartile ranges). Data are shown as circular symbols, while mean values are shown as white colored diamonds. Values at the top (c, d) indicate the adjusted significant P-values of the unpaired (c) and paired (d) two-sided Wilcoxon test with Benjamini-Hochberg correction. Source data are provided as a Source Data file.Full size imageOf note, the AGS of complete prokaryotic genomes increases with the cumulative number of associated phages and other mobile genetic elements37. Similarly, AGS estimates derived from metagenomic sequences of uncultured “free-living” microbes (captured in 0.1–3 µm-size filters) may also be affected by putative phage and eukaryotic microbiomes sequenced concurrently in fractionated seawater samples (see,8,22). To evaluate this possibility in our AGS predictions, we compared AGS estimates obtained directly from quality-controlled metagenomes with estimates from the same metagenomes iteratively subjected to three (de novo) decontamination procedures to filter out potential eukaryotic and viral sequence reads (Fig. 1a; see details in the “Methods” section). Overall, putatively ‘contaminating’ viral and eukaryotic reads accounted for 1% to 20% (average 7.5%) of the high-quality trimmed sequences in the four microbial metagenome collections (Fig. 1b; Supplementary Data 1). As expected, the average proportion of contaminating sequences in metagenomes from large (0.2–3.0 µm) and small (0.1–1.2 µm) size fraction filters were the highest (~11%) and lowest (~5%), respectively (Fig. 1b). In addition, the proportion of contaminating reads was significantly dependent on the depth layer of the ocean (Kruskal-Wallis χ2 = 32.40, df = 2, p  200–1000 m), and bathypelagic (BAT,  > 1000–4000 m). c AGS estimates in the “free-living” (0.2–0.8 µm) and particle-associated (0.8–20 µm) bathypelagic microbiome sampled latitudinally at 4000 m depth during the Malaspina expedition. Boxplots show the median as middle horizontal line and interquartile ranges as boxes (whiskers extend no further than 1.5 times the interquartile ranges). Data are shown as circular symbols, while mean values are shown as white colored diamonds. Values at the top indicate the adjusted significant P-values of the unpaired (b) and paired (c) two-sided Wilcoxon test with Benjamini-Hochberg correction. The number of metagenomes analyzed is indicated in parentheses in all three panels. Source data are provided as a Source Data file.Full size imageThe median AGS estimate range of 2.2 to ~3.0 Mbp in the sampled free-living (0.1–3 µm in size) marine prokaryotic communities (n = 209 metagenomes) is consistent with other large-scale metagenome sequence-based estimates and the sizes of metagenome-assembled prokaryotic genomes (MAGs; in 0.22–3 µm filters) from the photic ocean (surface to mesopelagic) based on the Tara Oceans Expedition (1.5–2.3 Mbp)15,16. Overall, our metagenome sequence-based AGS estimates support the unimodal distribution of prokaryotic genome sizes recently demonstrated in environmental genomes in several biomes38 and on cultured isolates (including marine bacterioplankton)14,39. However, estimates from isolates are likely biased since current cultivation approaches tend to favor copiotrophs (see, ref. 3).We next tested whether the derived AGS estimates depended on microbial cell size by analyzing 25 paired bathypelagic metagenomes (MDeep; Supplementary Data 1) sampled during the global Malaspina Expedition40 in which both prokaryotic life strategies, free-living (0.2–0.8 µm size) and particle-associated (0.8–20 µm size), were sampled simultaneously35. The analyzed metagenomes (MDeep) were from the Atlantic, Pacific, and Indian Ocean provinces and cover a spatial distance of 9437 km with an average depth (± SD) of 3688 ± 526 m at the tropical and subtropical latitudes (–33.55°N to 32.0788°N). These microbial metagenomes were also screened for contaminating eukaryotic and viral sequences as indicated in Fig. 1a (see details in the “Methods” section and Supplementary Data 1). The genomes of bathypelagic prokaryotes associated with marine particles (5.6 ± 0.97 Mbp) were twice as large (paired two-sided Wilcoxon test, p  3 µm) prokaryotes, respectively (Supplementary Data 3). These estimates are also consistent with those of MAGs reconstructed from the same metagenomes in the Challenger Deep (Mariana Trench)43. Overall, this reinforces the patterns of larger AGS in particle-associated compared to free-living bathypelagic prokaryotes, and larger microbial genomes in the deep ocean compared to the upper ocean.AGS patterns are not geographically constrainedExamination of the geographic patterns of AGS estimates showed that AGS distribution was independent of geographic distance in both the regional (Red Sea, Mantel statistic r = 0.01824, p = 0.2971) and global (MProfile, r = –0.01413, p = 0.7924) ocean metagenomes. Furthermore, AGS estimates in the vertically profiled global Malaspina metagenomes (MProfile, n = 81) were significantly independent of the Longhurst biogeochemical province sampled (n = 9; Kruskal-Wallis χ2 = 1.0006, df = 8, p = 0.9982; Supplementary Data 1). The lack of covariance between the patterns of AGS estimates and geographic distance or Longhurst province sampled may reflect the high connectivity of microbial communities throughout the global ocean, particularly the redistributive effects of circulation by ocean currents and other transport processes, as well as the enormous population sizes of plankton that allow dispersal constraints to be overcome44,45. This is consistent with the relatively small differences in microbial assemblages recently found in different ocean basins23,46. Another possible explanation is the effect of seasonality, which can cause selection of different taxa, resulting in the succession of microbial communities and affecting their distribution (see, ref. 47), and thus influence AGS patterns.An assessment of the relationship between AGS and measured environmental variables (Supplementary Fig. S1; Data 1)—separately for the Red Sea metagenomes (regional scale) and Malaspina Vertical Profiles metagenomes (global scale), showed that the cumulative effect of temperature, salinity, dissolved oxygen, and depth on AGS patterns was significant at both the regional scale (n = 45; Mantel statistic r = 0.1944, p = 0.0057) and the global scale (n = 81; Mantel statistic r = 0.1779, p = 1 × 10–4). This result suggests that environmental conditions are a driving force behind predicted AGS patterns in the marine microbiome. While no significant interaction effect was evident between many environmental variables (i.e., salinity, depth, oxygen, nitrate, and phosphate) in controlling AGS patterns (one-way ANOVA, p  More

  • in

    Predicting metabolomic profiles from microbial composition through neural ordinary differential equations

    Donia, M. S. & Fischbach, M. A. Small molecules from the human microbiota. Science 349, 1254766 (2015).Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).Article 

    Google Scholar 
    Koppel, N., Rekdal, V. M. & Balskus, E. P. Chemical transformation of xenobiotics by the human gut microbiota. Science 356, eaag2770 (2017).Myhrstad, M. C., Tunsjø, H., Charnock, C. & Telle-Hansen, V. H. Dietary fiber, gut microbiota, and metabolic regulation—current status in human randomized trials. Nutrients 12, 859 (2020).Article 

    Google Scholar 
    Lin, R., Liu, W., Piao, M. & Zhu, H. A review of the relationship between the gut microbiota and amino acid metabolism. Amino Acids 49, 2083–2090 (2017).Article 

    Google Scholar 
    Tyson, G. W. et al. Community structure and metabolism through reconstruction of microbial genomes from the environment. Nature 428, 37–43 (2004).Article 

    Google Scholar 
    Flint, H. J., Scott, K. P., Louis, P. & Duncan, S. H. The role of the gut microbiota in nutrition and health. Nat. Rev. Gastroenterol. Hepatol. 9, 577–589 (2012).Article 

    Google Scholar 
    Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).Article 

    Google Scholar 
    Yang, Q. et al. Metabolomics biotechnology, applications, and future trends: a systematic review. RSC Adv. 9, 37245–37257 (2019).Article 

    Google Scholar 
    Castelli, F. A. et al. Metabolomics for personalized medicine: the input of analytical chemistry from biomarker discovery to point-of-care tests. Anal. Bioanal. Chem. 414, 759–789 (2022).Article 

    Google Scholar 
    Dias-Audibert, F. L. et al. Combining machine learning and metabolomics to identify weight gain biomarkers. Front. Bioeng. Biotechnol. 8, 6 (2020).Article 

    Google Scholar 
    Zheng, C., Zhang, S., Ragg, S., Raftery, D. & Vitek, O. Identification and quantification of metabolites in 1H NMR spectra by Bayesian model selection. Bioinformatics 27, 1637–1644 (2011).Article 

    Google Scholar 
    Information Resources Management Association. Bioinformatics: Concepts, Methodologies, Tools, and Applications (IGI Global, 2013).Johnson, C. H. & Gonzalez, F. J. Challenges and opportunities of metabolomics. J. Cell. Physiol. 227, 2975–2981 (2012).Article 

    Google Scholar 
    Ayling, M., Clark, M. D. & Leggett, R. M. New approaches for metagenome assembly with short reads. Brief. Bioinform. 21, 584–594 (2020).Article 

    Google Scholar 
    Brumfield, K. D., Huq, A., Colwell, R. R., Olds, J. L. & Leddy, M. B. Microbial resolution of whole genome shotgun and 16S amplicon metagenomic sequencing using publicly available neon data. PLoS ONE 15, e0228899 (2020).Article 

    Google Scholar 
    Garza, D. R., van Verk, M. C., Huynen, M. A. & Dutilh, B. E. Towards predicting the environmental metabolome from metagenomics with a mechanistic model. Nat. Microbiol. 3, 456–460 (2018).Article 

    Google Scholar 
    Noecker, C. et al. Metabolic model-based integration of microbiome taxonomic and metabolomic profiles elucidates mechanistic links between ecological and metabolic variation. MSystems 1, e00013–15 (2016).Article 

    Google Scholar 
    Yin, X. et al. A comparative evaluation of tools to predict metabolite profiles from microbiome sequencing data. Front. Microbiol. 11, 3132 (2020).Article 

    Google Scholar 
    Kettle, H., Louis, P., Holtrop, G., Duncan, S. H. & Flint, H. J. Modelling the emergent dynamics and major metabolites of the human colonic microbiota. Environ. Microbiol. 17, 1615–1630 (2015).Article 

    Google Scholar 
    Quinn, R. A. et al. Niche partitioning of a pathogenic microbiome driven by chemical gradients. Sci. Adv. 4, eaau1908 (2018).Article 

    Google Scholar 
    Wang, T., Goyal, A., Dubinkina, V. & Maslov, S. Evidence for a multi-level trophic organization of the human gut microbiome. PLoS Comput. Biol. 15, e1007524 (2019).Article 

    Google Scholar 
    Goyal, A., Wang, T., Dubinkina, V. & Maslov, S. Ecology-guided prediction of cross-feeding interactions in the human gut microbiome. Nat. Commun. 12, 1335 (2021).Mallick, H. et al. Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences. Nat. Commun. 10, 3136 (2019).Article 

    Google Scholar 
    Le, V., Quinn, T. P., Tran, T. & Venkatesh, S. Deep in the bowel: highly interpretable neural encoder–decoder networks predict gut metabolites from gut microbiome. BMC Genom. 21, 256 (2020).Reiman, D., Layden, B. T. & Dai, Y. MiMeNet: exploring microbiome–metabolome relationships using neural networks. PLoS Comput. Biol. 17, e1009021 (2021).Article 

    Google Scholar 
    Morton, J. T. et al. Learning representations of microbe–metabolite interactions. Nat. Methods 16, 1306–1314 (2019).Article 

    Google Scholar 
    Chen, R. T., Rubanova, Y., Bettencourt, J. & Duvenaud, D. Neural ordinary differential equations. In Advances in Neural Information Processing Systems 31, 6572–6583 (NeurIPS, 2018).Lu, Y., Zhong, A., Li, Q. & Dong, B. Beyond finite layer neural networks: bridging deep architectures and numerical differential equations. In International Conference on Machine Learning 3276–3285 (PMLR, 2018).Qiu, C., Bendickson, A., Kalyanapu, J. & Yan, J. Accuracy and architecture studies of residual neural network solving ordinary differential equations. Preprint at arXiv https://doi.org/10.48550/arXiv.2101.03583 (2021).Dutta, S., Rivera-Casillas, P. & Farthing, M. W. Neural ordinary differential equations for data-driven reduced order modeling of environmental hydrodynamics. Preprint at https://doi.org/10.48550/arXiv.2104.13962 (2021).Marsland III, R. et al. Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).Article 

    Google Scholar 
    Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).Article 

    Google Scholar 
    Swenson, T. L., Karaoz, U., Swenson, J. M., Bowen, B. P. & Northen, T. R. Linking soil biology and chemistry in biological soil crust using isolate exometabolomics. Nat. Commun. 9, 19 (2018).Article 

    Google Scholar 
    Litonjua, A. A. et al. Effect of prenatal supplementation with vitamin D on asthma or recurrent wheezing in offspring by age 3 years: the VDAART randomized clinical trial. JAMA 315, 362–370 (2016).Article 

    Google Scholar 
    Litonjua, A. A. et al. Six-year follow-up of a trial of antenatal vitamin D for asthma reduction. N. Engl. J. Med. 382, 525–533 (2020).Article 

    Google Scholar 
    Lee-Sarwar, K. A. et al. Integrative analysis of the intestinal metabolome of childhood asthma. J. Allergy Clin. Immunol. 144, 442–454 (2019).Article 

    Google Scholar 
    Lee-Sarwar, K. et al. Association of the gut microbiome and metabolome with wheeze frequency in childhood asthma. J. Allergy Clin. Immunol. 147, AB53 (2021).Article 

    Google Scholar 
    Harvard Willett Food Frequency Questionnaire (T.H. Chan School of Public Health, Department of Nutrition, Harvard Univ., 2015).Plan and Operation of the Third National Health and Nutrition Examination Survey, 1988–94 (National Centre for Health Statistics, 1994).Nelson, K. M., Reiber, G. & Boyko, E. J. Diet and exercise among adults with type 2 diabetes: findings from the third National Health and Nutrition Examination Survey (NHANES III). Diabetes Care 25, 1722–1728 (2002).Article 

    Google Scholar 
    Marriott, B. P., Olsho, L., Hadden, L. & Connor, P. Intake of added sugars and selected nutrients in the United States, National Health and Nutrition Examination Survey (NHANES) 2003-2006. Crit. Rev. Food Sci. Nutr. 50, 228–258 (2010).Article 

    Google Scholar 
    Moshfegh, A. Food and Nutrient Database for Dietary Studies (US Department of Agriculture, Agricultural Research Service, Food Surveys Research Group, 2022); http://www.ars.usda.gov/nea/bhnrc/fsrgRidlon, J. M., Kang, D.-J. & Hylemon, P. B. Bile salt biotransformations by human intestinal bacteria. J. Lipid Res. 47, 241–259 (2006).Article 

    Google Scholar 
    Bachmann, V. et al. Bile salts modulate the mucin-activated type VI secretion system of pandemic Vibrio cholerae. PLoS Negl. Trop. Dis. 9, e0004031 (2015).Article 

    Google Scholar 
    Ramírez-Pérez, O., Cruz-Ramón, V., Chinchilla-López, P. & Méndez-Sánchez, N. The role of the gut microbiota in bile acid metabolism. Ann. Hepatol. 16, 21–26 (2018).Article 

    Google Scholar 
    Jia, W., Xie, G. & Jia, W. Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis. Nat. Rev. Gastroenterol. Hepatol. 15, 111–128 (2018).Article 

    Google Scholar 
    Heinken, A. et al. Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease. Microbiome 7, 75 (2019).Duboc, H. et al. Connecting dysbiosis, bile-acid dysmetabolism and gut inflammation in inflammatory bowel diseases. Gut 62, 531–539 (2013).Article 

    Google Scholar 
    Thomas, J. P., Modos, D., Rushbrook, S. M., Powell, N. & Korcsmaros, T. The emerging role of bile acids in the pathogenesis of inflammatory bowel disease. Front. Immunol. 13, 246 (2022).Kristal, A. R., Peters, U. & Potter, J. D. Is it time to abandon the food frequency questionnaire? Cancer Epidemiol. Biomarkers Prev. 14, 2826–2828 (2005).Article 

    Google Scholar 
    Scalbert, A. et al. The food metabolome: a window over dietary exposure. Am. J. Clin. Nutr. 99, 1286–1308 (2014).Article 

    Google Scholar 
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).Article 

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

    Google Scholar 
    Evans, A. M. et al. High resolution mass spectrometry improves data quantity and quality as compared to unit mass resolution mass spectrometry in high-throughput profiling metabolomics. Metabolomics 4, 1 (2014).
    Google Scholar 
    Blum, R. E. et al. Validation of a food frequency questionnaire in Native American and Caucasian children 1 to 5 years of age. Matern. Child Health J. 3, 167–172 (1999).Article 

    Google Scholar 
    Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://doi.org/10.48550/arXiv.1412.6980 (2014).Wang, T. wt1005203/mnode: initial release. Zenodo https://doi.org/10.5281/zenodo.7602940 (2023). More

  • in

    Pan-Arctic marine biodiversity and species co-occurrence patterns under recent climate

    Randelhoff, A. et al. Pan-Arctic ocean primary production constrained by turbulent nitrate fluxes. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00150 (2020).Article 

    Google Scholar 
    Wegner, C. et al. Variability in transport of terrigenous material on the shelves and the deep Arctic Ocean during the Holocene. Polar Res. https://doi.org/10.3402/polar.v%v.24964 (2015).Article 

    Google Scholar 
    Arrigo, K. R. & van Dijken, G. L. Continued increases in Arctic Ocean primary production. Prog. Oceanogr. 136, 60–70. https://doi.org/10.1016/j.pocean.2015.05.002 (2015).Article 
    ADS 

    Google Scholar 
    Lewis, K. M., van Dijken, G. L. & Arrigo, K. R. Changes in phytoplankton concentration now drive increased Arctic Ocean primary production. Science 369, 198–202. https://doi.org/10.1126/science.aay8380 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Mueter, F. J. et al. Possible future scenarios in the gateways to the Arctic for Subarctic and Arctic marine systems: II. Prey resources, food webs, fish, and fisheries. ICES J. Mar. Sci. 78, 3017–3045. https://doi.org/10.1093/icesjms/fsab122 (2021).Article 

    Google Scholar 
    Alabia, I. D. et al. Multiple facets of marine biodiversity in the Pacific Arctic under future climate. Sci. Total Environ. 744, 140913. https://doi.org/10.1016/j.scitotenv.2020.140913 (2020).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    CAFF. Arctic Biodiversity Assessment. Status and trends in Arctic biodiversity. (Conservation of Arctic Flora and Fauna, Akureyri, Iceland, 2013).Stafford, K. M., Farley, E. V., Ferguson, M., Kuletz, K. J. & Levine, R. Northward range expansion of subarctic upper trophic level animals into the Pacific Arctic Region. Oceanography. 35, 158–166. https://doi.org/10.5670/oceanog.2022.101 (2022).Csapó, H. K., Grabowski, M. & Węsławski, J. M. Coming home—Boreal ecosystem claims Atlantic sector of the Arctic. Sci. Total Environ. 771, 144817. https://doi.org/10.1016/j.scitotenv.2020.144817 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Frainer, A. et al. Climate-driven changes in functional biogeography of Arctic marine fish communities. Proc. Natl. Acad. Sci. 114, 12202–12207. https://doi.org/10.1073/pnas.1706080114 (2017).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gordó-Vilaseca, C., Stephenson, F., Coll, M., Lavin, C. & Costello, M. J. Three decades of increasing fish biodiversity across the northeast Atlantic and the Arctic Ocean. Proc. Natl. Acad. Sci. 120, e2120869120. https://doi.org/10.1073/pnas.2120869120 (2023).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kalenitchenko, D., Joli, N., Potvin, M., Tremblay, J. -É. & Lovejoy, C. Biodiversity and species change in the arctic ocean: A view through the lens of nares strait. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00479 (2019).Article 

    Google Scholar 
    Michel, C. et al. Arctic Ocean outflow shelves in the changing Arctic: A review and perspectives. Prog. Oceanogr. 139, 66–88. https://doi.org/10.1016/j.pocean.2015.08.007 (2015).Article 
    ADS 

    Google Scholar 
    Ribeiro, S. et al. Vulnerability of the North Water ecosystem to climate change. Nat. Commun. 12, 4475. https://doi.org/10.1038/s41467-021-24742-0 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Poisot, T., Stouffer, D. B. & Gravel, D. Beyond species: Why ecological interaction networks vary through space and time. Oikos 124, 243–251. https://doi.org/10.1111/oik.01719 (2015).Article 

    Google Scholar 
    Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evolut. 4, 376–383. https://doi.org/10.1038/s41559-020-1099-4 (2020).Article 

    Google Scholar 
    Blanchet, F. G., Cazelles, K. & Gravel, D. Co-occurrence is not evidence of ecological interactions. Ecol. Lett. 23, 1050–1063. https://doi.org/10.1111/ele.13525 (2020).Article 
    PubMed 

    Google Scholar 
    Michael, E. L. Marine ecology and the coefficient of association: A plea in behalf of quantitative biology. J. Ecol. 8, 54–59. https://doi.org/10.2307/2255213 (1920).Article 

    Google Scholar 
    Gotelli, N. J., Graves, G. R. & Rahbek, C. Macroecological signals of species interactions in the Danish avifauna. Proc. Natl. Acad. Sci. 107, 5030–5035. https://doi.org/10.1073/pnas.0914089107 (2010).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gotelli, N. J. & McCabe, D. J. Species co-occurrence: A meta-analysis of J. M. Diamond’s assembly rules model. Ecology 83, 2091–2096. https://doi.org/10.1890/0012-9658(2002)083[2091:SCOAMA]2.0.CO;2 (2002).Article 

    Google Scholar 
    Ulrich, W. Species co-occurrences and neutral models: Reassessing J. M. Diamond’s Assembly Rules. Oikos 107, 603–609 (2004).Article 

    Google Scholar 
    Kraan, C., Thrush, S. F. & Dormann, C. F. Co-occurrence patterns and the large-scale spatial structure of benthic communities in seagrass meadows and bare sand. BMC Ecol. 20, 37. https://doi.org/10.1186/s12898-020-00308-4 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tulloch, A. I. T., Chadès, I. & Lindenmayer, D. B. Species co-occurrence analysis predicts management outcomes for multiple threats. Nat. Ecol. Evolut. 2, 465–474. https://doi.org/10.1038/s41559-017-0457-3 (2018).Article 

    Google Scholar 
    Drinkwater, K. F. et al. Possible future scenarios for two major Arctic Gateways connecting Subarctic and Arctic marine systems: I. Climate and physical–chemical oceanography. ICES J. Mar. Sci. 78, 3046–3065. https://doi.org/10.1093/icesjms/fsab182 (2021).Article 

    Google Scholar 
    Pilfold, N. W., McCall, A., Derocher, A. E., Lunn, N. J. & Richardson, E. Migratory response of polar bears to sea ice loss: To swim or not to swim. Ecography 40, 189–199. https://doi.org/10.1111/ecog.02109 (2017).Article 

    Google Scholar 
    Chambault, P. et al. The impact of rising sea temperatures on an Arctic top predator, the narwhal. Sci. Rep. 10, 18678. https://doi.org/10.1038/s41598-020-75658-6 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Perovich, D. et al. Arctic Report Card 2020: Sea Ice. https://doi.org/10.25923/n170-9h57 (2020).Post, E. et al. Ecological dynamics across the arctic associated with recent climate change. Science 325, 1355–1358. https://doi.org/10.1126/science.1173113 (2009).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Post, E. et al. Ecological consequences of sea-ice decline. Science 341, 519–524. https://doi.org/10.1126/science.1235225 (2013).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bienhold, C. et al. Effects of sea ice retreat and ocean warming on the Laptev Sea continental slope ecosystem (1993 vs 2012). Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.1004959 (2022).Article 

    Google Scholar 
    Olafsdottir, A. H. et al. Geographical expansion of Northeast Atlantic mackerel (Scomber scombrus) in the Nordic Seas from 2007 to 2016 was primarily driven by stock size and constrained by low temperatures. Deep Sea Res. Part II 159, 152–168. https://doi.org/10.1016/j.dsr2.2018.05.023 (2019).Article 

    Google Scholar 
    MacKenzie, B. R., Payne, M. R., Boje, J., Høyer, J. L. & Siegstad, H. A cascade of warming impacts brings bluefin tuna to Greenland waters. Glob. Change Biol. 20, 2484–2491. https://doi.org/10.1111/gcb.12597 (2014).Article 
    ADS 

    Google Scholar 
    Alabia, I. D. et al. Distribution shifts of marine taxa in the Pacific Arctic under contemporary climate changes. Divers. Distrib. 24, 1583–1597. https://doi.org/10.1111/ddi.12788 (2018).Article 

    Google Scholar 
    Stewart, D. B. & Barber, D. G. in A Little Less Arctic: Top Predators in the World’s Largest Northern Inland Sea, Hudson Bay (eds Steven H. Ferguson, Lisa L. Loseto, & Mark L. Mallory) 1–38 (Springer Netherlands, 2010).Ferland, J., Gosselin, M. & Starr, M. Environmental control of summer primary production in the Hudson Bay system: The role of stratification. J. Mar. Syst. 88, 385–400. https://doi.org/10.1016/j.jmarsys.2011.03.015 (2011).Article 

    Google Scholar 
    Peacock, E., Derocher, A. E., Lunn, N. J. & Obbard, M. E. in A Little Less Arctic: Top Predators in the World’s Largest Northern Inland Sea, Hudson Bay (eds Steven H. Ferguson, Lisa L. Loseto, & Mark L. Mallory) 93–116 (Springer Netherlands, 2010).Chambellant, M. in A Little Less Arctic: Top Predators in the World’s Largest Northern Inland Sea, Hudson Bay (eds Steven H. Ferguson, Lisa L. Loseto, & Mark L. Mallory) 137–158 (Springer Netherlands, 2010).Mallory, M. L., Gaston, A. J., Gilchrist, H. G., Robertson, G. J. & Braune, B. M. in A Little Less Arctic: Top Predators in the World’s Largest Northern Inland Sea, Hudson Bay (eds Steven H. Ferguson, Lisa L. Loseto, & Mark L. Mallory) 179–195 (Springer Netherlands, 2010).Lone, K., Hamilton, C. D., Aars, J., Lydersen, C. & Kovacs, K. M. Summer habitat selection by ringed seals (Pusa hispida) in the drifting sea ice of the northern Barents Sea. Polar Res. https://doi.org/10.33265/polar.v38.3483 (2019).Article 

    Google Scholar 
    Jackson, R. et al. Holocene polynya dynamics and their interaction with oceanic heat transport in northernmost Baffin Bay. Sci. Rep. 11, 10095. https://doi.org/10.1038/s41598-021-88517-9 (2021).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stafford, K. M. et al. Beluga whales in the western Beaufort Sea: Current state of knowledge on timing, distribution, habitat use and environmental drivers. Deep Sea Res. Part II 152, 182–194. https://doi.org/10.1016/j.dsr2.2016.11.017 (2018).Article 

    Google Scholar 
    Kuletz, K. J. et al. Seasonal spatial patterns in seabird and marine mammal distribution in the eastern Chukchi and western Beaufort seas: Identifying biologically important pelagic areas. Prog. Oceanogr. 136, 175–200. https://doi.org/10.1016/j.pocean.2015.05.012 (2015).Article 
    ADS 

    Google Scholar 
    Polyakov, I. V. et al. Borealization of the Arctic Ocean in response to anomalous advection from sub-arctic seas. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00491 (2020).Article 

    Google Scholar 
    Fossheim, M. et al. Recent warming leads to a rapid borealization of fish communities in the Arctic. Nat. Clim. Change 5, 673–677. https://doi.org/10.1038/nclimate2647 (2015).Article 
    ADS 

    Google Scholar 
    Ardyna, M. et al. Recent Arctic Ocean sea ice loss triggers novel fall phytoplankton blooms. Geophys. Res. Lett. 41, 6207–6212. https://doi.org/10.1002/2014GL061047 (2014).Article 
    ADS 

    Google Scholar 
    Randelhoff, A. & Sundfjord, A. Short commentary on marine productivity at Arctic shelf breaks: Upwelling, advection and vertical mixing. Ocean Sci. 14, 293–300. https://doi.org/10.5194/os-14-293-2018 (2018).Article 
    ADS 

    Google Scholar 
    Bluhm, B. A. et al. The Pan-Arctic continental slope: sharp gradients of physical processes affect pelagic and benthic ecosystems. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.544386 (2020).Article 

    Google Scholar 
    Daase, M., Berge, J., Søreide, J. E. & Falk-Petersen, S. in Arctic Ecology (ed David N. Thomas) Ch. 9, 219–259 (Wiley, 2021).McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185. https://doi.org/10.1016/j.tree.2006.02.002 (2006).Article 
    PubMed 

    Google Scholar 
    Young, K. A. Asymmetric competition, habitat selection, and niche overlap in Juvenile Salmonids. Ecology 85, 134–149 (2004).Article 

    Google Scholar 
    Aguilera, M. A., Valdivia, N., Broitman, B. R., Jenkins, S. R. & Navarrete, S. A. Novel co-occurrence of functionally redundant consumers induced by range expansion alters community structure. Ecology 101, e03150. https://doi.org/10.1002/ecy.3150 (2020).Article 
    PubMed 

    Google Scholar 
    Usinowicz, J. & Levine, J. M. Species persistence under climate change: A geographical scale coexistence problem. Ecol. Lett. 21, 1589–1603. https://doi.org/10.1111/ele.13108 (2018).Article 
    PubMed 

    Google Scholar 
    Durant, J. M. et al. Contrasting effects of rising temperatures on trophic interactions in marine ecosystems. Sci. Rep. 9, 15213. https://doi.org/10.1038/s41598-019-51607-w (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    García-Baquero, G. & Crujeiras, R. M. Can environmental constraints determine random patterns of plant species co-occurrence?. Ecol. Evol. 5, 1088–1099. https://doi.org/10.1002/ece3.1349 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bar-Massada, A. Complex relationships between species niches and environmental heterogeneity affect species co-occurrence patterns in modelled and real communities. Proc. R. Soc. B Biol. Sci. 282, 20150927. https://doi.org/10.1098/rspb.2015.0927 (2015).Article 

    Google Scholar 
    Overland, J. E., Wang, M., Walsh, J. E. & Stroeve, J. C. Future Arctic climate changes: Adaptation and mitigation time scales. Earth’s Future 2, 68–74. https://doi.org/10.1002/2013EF000162 (2014).Article 
    ADS 

    Google Scholar 
    Hirawake, T. et al. Response and biodiversity of Arctic ecosystems to environmental change: Findings from the ArCS project. Polar Sci. https://doi.org/10.1016/j.polar.2020.100533 (2020).Article 

    Google Scholar 
    Solan, M., Archambault, P., Renaud, P. E. & März, C. The changing Arctic Ocean: Consequences for biological communities, biogeochemical processes and ecosystem functioning. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 378, 20200266. https://doi.org/10.1098/rsta.2020.0266 (2020).Article 
    ADS 

    Google Scholar 
    Timmermans, M.-L. & Marshall, J. Understanding Arctic Ocean circulation: A review of ocean dynamics in a changing climate. J. Geophys. Res. Oceans. 125, e2018JC014378. https://doi.org/10.1029/2018JC014378 (2020).Article 
    ADS 

    Google Scholar 
    Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496. https://doi.org/10.1175/2007JCLI1824.1 (2007).Article 
    ADS 

    Google Scholar 
    Amante, C. & Eakins, B. W. ETOPO1 1 Arc-Minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24. National Geophysical Data Center, NOAA. https://doi.org/10.7289/V5C8276M (2009).Lehodey, P., Murtugudde, R. & Senina, I. Bridging the gap from ocean models to population dynamics of large marine predators: A model of mid-trophic functional groups. Prog. Oceanogr. 84, 69–84. https://doi.org/10.1016/j.pocean.2009.09.008 (2010).Article 
    ADS 

    Google Scholar 
    Green, D. B. et al. Modelled mid-trophic pelagic prey fields improve understanding of marine predator foraging behaviour. Ecography 43, 1014–1026. https://doi.org/10.1111/ecog.04939 (2020).Article 

    Google Scholar 
    Pérez-Jorge, S. et al. Environmental drivers of large-scale movements of baleen whales in the mid-North Atlantic Ocean. Divers. Distrib. 26, 683–698. https://doi.org/10.1111/ddi.13038 (2020).Article 

    Google Scholar 
    Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. spThin: An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545. https://doi.org/10.1111/ecog.01132 (2015).Article 

    Google Scholar 
    Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338. https://doi.org/10.1111/j.2041-210X.2011.00172.x (2012).Article 

    Google Scholar 
    Thuiller, W., Georges D., Gueguen, M., Engler, R., & Breiner, F. biomod2: Ensemble Platform for species Distribution Modeling. R package version 3.5.1. http://CRAN.R-project.org/package=biomod2 (2021). Accessed on 15 January 2022.
    Baselga, A. & Orme, C. D. L. betapart: An R package for the study of beta diversity. Methods Ecol. Evol. 3, 808–812. https://doi.org/10.1111/j.2041-210X.2012.00224.x (2012).Article 

    Google Scholar 
    Griffith, D. M., Veech, J. A. & Marsh, C. J. cooccur: Probabilistic species co-occurrence analysis in R. J. Stat. Softw. Code Snippets 69, 1–17. https://doi.org/10.18637/jss.v069.c02 (2016).Article 

    Google Scholar 
    Veech, J. A. A probabilistic model for analysing species co-occurrence. Glob. Ecol. Biogeogr. 22, 252–260. https://doi.org/10.1111/j.1466-8238.2012.00789.x (2013).Article 

    Google Scholar 
    Abdi, A. M. et al. First assessment of the plant phenology index (PPI) for estimating gross primary productivity in African semi-arid ecosystems. Int. J. Appl. Earth Obs. Geoinf. 78, 249–260. https://doi.org/10.1016/j.jag.2019.01.018 (2019).Article 
    ADS 

    Google Scholar 
    Ban, S. S., Alidina, H. M., Okey, T. A., Gregg, R. M. & Ban, N. C. Identifying potential marine climate change Refugia: A case study in Canada’s Pacific marine ecosystems. Glob. Ecol. Conserv. 8, 41–54. https://doi.org/10.1016/j.gecco.2016.07.004 (2016).Article 

    Google Scholar 
    Alabia, I. D. et al. Marine biodiversity Refugia in a climate-sensitive subarctic shelf. Glob. Change Biol. 27, 3299–3311. https://doi.org/10.1111/gcb.15632 (2021).Article 

    Google Scholar 
    Alabia, I. D., Saitoh, S.-I., Igarashi, H., Ishikawa, Y. & Imamura, Y. Spatial habitat shifts of oceanic cephalopod (Ommastrephes bartramii) in oscillating climate. Remote Sensing. https://doi.org/10.3390/rs12030521 (2020).Article 

    Google Scholar  More