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    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

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    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

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    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

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    Contribution of tree community structure to forest productivity across a thermal gradient in eastern Asia

    Synthetic data for Fig. 1To provide examples of the proposed two hypotheses, i.e., species-response hypothesis and community structure hypothesis, for Fig. 1, we generated synthetic data assuming bivariate lognormal distributions of species relative woody productivity pi and species standing biomass Bi, where i for species identity, with log-log linear, (or power-law) correlations, ln pi = k + b ln Bi, as in left-hand panels of Fig. 1. The slope (scaling exponent) b is common at –0.15, and the constant k = –3.4 and –3.8 for tropical and temperate forests respectively for species response hypothesis (Fig. 1a), whereas k = –3.6 for both ‘tropical’ and ‘temperate’ forests for the community structure hypothesis (Fig. 1b). Mean ln Bi are –0.6 for two forests in Fig. 1a, while they are –1.0 and –0.2 for tropical and temperate forest respectively in Fig. 1b, Standard deviations of ln Bi and ln pi are 2.0 and 0.65 respectively for all forests, except those in tropical forest in Fig. 1b are 1.6 and 0.6, respectively. In the left-hand panels, the Bi axis ranges 0.005–500 (Mg C ha–1), and the pi axis ranges 0.001–0.5 (yr–1). In the right-hand panels, the axis for B = Σi Bi ranges 50–500 (Mg C ha–1) and the axis for P = Σi pi Bi ranges 0.5–20 (Mg C ha–1 yr–1).Forest plot dataWe selected 60 forest plots located in old-growth forests along the climatic gradient of insular eastern Asia, located on Java (3 plots), Kalimantan (5 plots), Peninsular Malaysia (2 plots), Taiwan (6 plots), and the Japanese archipelago (44 plots), ranging from 6.8°S to 44.4°N latitude and from 20 to 1,880 m in elevation (Supplementary Fig. 1, Supplementary Data 1). We collected climate data for all the plots for the period 1981–2010 from CHELSA version 2.139; these are the period-average annual and monthly ground surface mean temperature, precipitation, and potential evapotranspiration. The potential evapotranspiration was estimated by Hargreaves-Samani equation40 based on monthly data of these climatic variables. Supplementary Data 2 presents mean annual temperature (MAT, °C), annual precipitation (AP, mm yr–1), annual potential evapotranspiration (PET, mm yr–1), monthly-data-based Thornthwaite moisture index (TMI) and the climatic types defined by TMI26. The target region is in Asian monsoon climate41,42, and moist forest ecosystems predominate from tropics in Southeast Asia to sub-boreal biomes in northern Japan. Across 60 plots, MAT ranges from 2.0 °C to 26.6 °C, AP-PET ranges from 58.5 to 5049 mm yr–1, and plots are classified as “perhumid” or “humid” by TMI (Supplementary Data 2); the smallest TMI for the plot in cloud forest on Hahajima Island, oceanic Ogasawara Islands, where AP-PET was +217 mm yr–1 (against +58.5 by CHELSA39) based on the weather station records on the island. AP-PET sowed no correlation with MAT or with any forest structural or dynamic variable, in contrast to MAT exhibiting significant correlations to all forest variables (Supplementary Fig. 5). We therefore mainly employ MAT to quantify climatic dependence of the 60 plots. According to bioclimatic classification of the region43,44, we define forest biomes into tropical (MAT ≥ 24 °C), subtropical (20–24 °C), warm-temperate (12–20 °C), cool-temperate (5–12 °C) and sub-boreal or subalpine ( More

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    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

    Mock community as an in situ positive control for amplicon sequencing of microbiotas from the same ecosystem

    Proctor, L. Priorities for the next 10 years of human microbiome research. Nature 569(7758), 623–625 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Bahl, M. I., Bergström, A. & Licht, T. R. Freezing fecal samples prior to DNA extraction affects the Firmicutes to Bacteroidetes ratio determined by downstream quantitative PCR analysis. FEMS Microbiol. Lett. 329, 193–197 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wu, X. et al. Metagenomic insights into nitrogen and phosphorus cycling at the soil aggregate scale driven by organic material amendments. Sci. Total Environ. 785, 147329 (2021).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Singh, B. K., Millard, P., Whiteley, A. S. & Murrell, J. C. Unravelling rhizosphere-microbial interactions: Opportunities and limitations. Trends Microbiol. 12, 386–393 (2004).Article 
    CAS 
    PubMed 

    Google Scholar 
    Methé, B. A. et al. A framework for human microbiome research. Nature 486, 215–221 (2012).Article 
    ADS 
    PubMed Central 

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

    Google Scholar 
    Gilbert, J. A., Jansson, J. K. & Knight, R. Earth microbiome project and global systems biology. mSystems 3, e00217-17 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Trivedi, P., Leach, J. E., Tringe, S. G., Sa, T. & Singh, B. K. Plant–microbiome interactions: from community assembly to plant health. Nat. Rev. Microbiol. 18(11), 607–621 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569(7758), 655–662 (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chen, T. et al. A plant genetic network for preventing dysbiosis in the phyllosphere. Nature 580(7805), 653–657 (2020).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Holman, D. B. & Gzyl, K. E. A meta-analysis of the bovine gastrointestinal tract microbiota. FEMS Microbiol. Ecol. 95, 72 (2019).Article 

    Google Scholar 
    Chen, L. et al. Plant growth–promoting bacteria improve maize growth through reshaping the rhizobacterial community in low-nitrogen and low-phosphorus soil. Biol. Fertil. Soils 57, 1075–1088. https://doi.org/10.1007/S00374-021-01598-6 (2021).Article 
    CAS 

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

    Google Scholar 
    Hauffe, H. C. & Barelli, C. Conserve the germs: The gut microbiota and adaptive potential. Conserv. Genet. 20(1), 19–27 (2019).Article 

    Google Scholar 
    Pollock, J., Glendinning, L., Wisedchanwet, T. & Watson, M. The madness of microbiome: Attempting to find consensus ‘best practice’ for 16S microbiome studies. Appl. Environ. Microbiol. 84(7), e02627-17 (2018).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551(7681), 457–463 (2017).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Costea, P. I. et al. Towards standards for human fecal sample processing in metagenomic studies. Nat. Biotechnol. 35, 1069–1076 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: And this is not optional. Front. Microbiol. 8, 2224 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tourlousse, D. M. et al. Synthetic spike-in standards for high-throughput 16S rRNA gene Amplicon sequencing. Nucleic Acids Res. 45, e23–e23 (2017).PubMed 

    Google Scholar 
    Thissen, J. B. et al. Axiom Microbiome Array, the next generation microarray for high-throughput pathogen and microbiome analysis. PLoS ONE 14, e0212045 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ducarmon, Q. R., Hornung, B. V. H., Geelen, A. R., Kuijper, E. J. & Zwittink, R. D. Toward standards in clinical microbiota studies: Comparison of three DNA extraction methods and two bioinformatic pipelines. mSystems 5, e00547-19 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ray, T. et al. The microbiome of common bedding materials before and after use on commercial dairy farms. Anim. Microbiome 4(1), 1–21 (2022).Article 
    MathSciNet 
    CAS 

    Google Scholar 
    Akhremchuk, K. V. et al. Gut microbiome of healthy people and patients with hematological malignancies in Belarus. Microbiol. Indep. Res. J. (MIR J.) 9, 18–30 (2022).Article 

    Google Scholar 
    Smets, W. et al. A method for simultaneous measurement of soil bacterial abundances and community composition via 16S rRNA gene sequencing. Soil Biol. Biochem. 96, 145–151 (2016).Article 
    CAS 

    Google Scholar 
    Palmer, J. M., Jusino, M. A., Banik, M. T. & Lindner, D. L. Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6, e4925 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alteio, L. V. et al. A critical perspective on interpreting amplicon sequencing data in soil ecological research. Soil Biol. Biochem. 160, 108357 (2021).Article 
    CAS 

    Google Scholar 
    Stämmler, F. et al. Adjusting microbiome profiles for differences in microbial load by spike-in bacteria. Microbiome 4, 1–13 (2016).Article 

    Google Scholar 
    Risely, A., Wilhelm, K., Clutton-Brock, T., Manser, M. B. & Sommer, S. Diurnal oscillations in gut bacterial load and composition eclipse seasonal and lifetime dynamics in wild meerkats. Nat. Commun. 12(1), 1–12 (2021).Article 

    Google Scholar 
    Risely, A., et al. Gut microbiota repeatability is contingent on temporal scale and age in wild meerkats. ecoevorxiv (2022). https://doi.org/10.32942/OSF.IO/DSQFRSzóstak, N. et al. The standardisation of the approach to metagenomic human gut analysis: From sample collection to microbiome profiling. Sci. Rep. 12(1), 1–21 (2022).Article 

    Google Scholar 
    Tourlousse, D. M. et al. Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res. 45, e23 (2017).PubMed 

    Google Scholar 
    Sheu, S. Y., Arun, A. B., Jiang, S. R., Young, C. C. & Chen, W. M. Allobacillus halotolerans gen. nov., sp. Nov. isolated from shrimp paste. Int. J. Syst. Evol. Microbiol. 61, 1023–1027 (2011).Article 
    CAS 
    PubMed 

    Google Scholar 
    Surendra, V., Bhawana, P., Suresh, K., Srinivas, T. N. R. & Anil Kumar, P. Imtechella halotolerans gen. nov., sp. nov., a member of the family Flavobacteriaceae isolated from estuarine water. Int. J. Syst. Evol. Microbiol. 62, 2624–2630 (2012).Article 
    CAS 
    PubMed 

    Google Scholar 
    Praeg, N. et al. The role of land management and elevation in shaping soil microbial communities: Insights from the Central European Alps. Soil Biol. Biochem. 150, 107951 (2020).Article 
    CAS 

    Google Scholar 
    Albonico, F. et al. Raw milk and fecal microbiota of commercial Alpine dairy cows varies with herd, fat content and diet. PLoS ONE 15, e0237262 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Watson, S. E. et al. Global change-driven use of onshore habitat impacts polar bear faecal microbiota. ISME J. https://doi.org/10.1038/s41396-019-0480-2 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Huebner, K. L. et al. Effects of a Saccharomyces cerevisiae fermentation product on liver abscesses, fecal microbiome, and resistome in feedlot cattle raised without antibiotics. Sci. Rep. 9(1), 1–11 (2019).Article 

    Google Scholar 
    Fan, P. et al. Host genetic effects upon the early gut microbiota in a bovine model with graduated spectrum of genetic variation. ISME J. 14(1), 302–317 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mtshali, K., Khumalo, Z. T. H., Kwenda, S., Arshad, I. & Thekisoe, O. M. M. Exploration and comparison of bacterial communities present in bovine faeces, milk and blood using 16S rRNA metagenomic sequencing. PLoS ONE 17, e0273799 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Johnson, J. S. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun. 10(1), 5029 (2019).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pei, A. Y. et al. Diversity of 16S rRNA genes within individual prokaryotic genomes. Appl. Environ. Microbiol. 76, 3886 (2010).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stoler, N. & Nekrutenko, A. Sequencing error profiles of Illumina sequencing instruments. NAR Genomics Bioinforma. 3, lqab019 (2021).Article 

    Google Scholar 
    Schirmer, M. et al. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res. 43, e37–e37 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    McLaren, M. R., Willis, A. D. & Callahan, B. J. Consistent and correctable bias in metagenomic sequencing experiments. Elife 8, e46923 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gonzalez, J. M., Portillo, M. C., Belda-Ferre, P. & Mira, A. Amplification by PCR artificially reduces the proportion of the rare biosphere in microbial communities. PLoS ONE 7, e29973 (2012).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gilbert, J. A., Jansson, J. K. & Knight, R. The earth microbiome project: Successes and aspirations. BMC Biol. 12, 1–4 (2014).Article 

    Google Scholar 
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, 1–8 (2011).Article 

    Google Scholar 
    McDonald, D. et al. American gut: An open platform for citizen science microbiome research. mSystems 3, e00031-18 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Illumina. IMPORTANT NOTICE This document provides information for an application for 16S Metagenomic Sequencing Library Preparation Preparing 16S Ribosomal RNA Gene Amplicons for the Illumina MiSeq System.Teng, F. et al. Impact of DNA extraction method and targeted 16S-rRNA hypervariable region on oral microbiota profiling. Sci. Rep. 8(1), 1–12 (2018).Article 
    ADS 

    Google Scholar 
    Willis, C., Desai, D. & Laroche, J. Influence of 16S rRNA variable region on perceived diversity of marine microbial communities of the Northern North Atlantic. FEMS Microbiol. Lett. 366, fnz152 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chen, Z. et al. Impact of preservation method and 16S rRNA hypervariable region on gut microbiota profiling. mSystems 4, e00271-18 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sanada, T. J. et al. Gut microbiota modification suppresses the development of pulmonary arterial hypertension in an SU5416/hypoxia rat model. Pulm. Circ. 10(3), 1–3. https://doi.org/10.1177/2045894020929147 (2020).Article 
    MathSciNet 
    CAS 

    Google Scholar 
    Praeg, N., Schwinghammer, L. & Illmer, P. Larix decidua and additional light affect the methane balance of forest soil and the abundance of methanogenic and methanotrophic microorganisms. FEMS Microbiol. Lett. 366, 259 (2019).Article 

    Google Scholar 
    Vandeputte, D. et al. Quantitative microbiome profiling links gut community variation to microbial load. Nature 551(7681), 507–511 (2017).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Sanders, H. L. Marine benthic diversity: A comparative study. Am. Nat. 102, 243–282. https://doi.org/10.1086/282541 (2015).Article 

    Google Scholar 
    Aitchison, J. The statistical analysis of compositional data. J. R. Stat. Soc. Ser. B 44, 139–160 (1982).MathSciNet 
    MATH 

    Google Scholar 
    Stanaway, I. B. et al. Human oral buccal microbiomes are associated with farmworker status and azinphos-methyl agricultural pesticide exposure. Appl. Environ. Microbiol. 83, e02149-16 (2017).Article 
    PubMed 

    Google Scholar 
    Grice, E. A. et al. A diversity profile of the human skin microbiota. Genome Res. 18, 1043–1050 (2008).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Payne, M. A. et al. Horizontal and vertical transfer of oral microbial dysbiosis and periodontal disease. J. Dent. Res. 98, 1503–1510 (2019).Article 
    CAS 
    PubMed 

    Google Scholar 
    Karasov, T. L. et al. The relationship between microbial population size and disease in the Arabidopsis thaliana phyllosphere. bioRxiv https://doi.org/10.1101/828814 (2020).Article 

    Google Scholar 
    Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6(8), 1621–1624 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

    Google Scholar 
    Albanese, D., Fontana, P., De Filippo, C., Cavalieri, D. & Donati, C. MICCA: A complete and accurate software for taxonomic profiling of metagenomic data. Sci. Rep. 5(1), 1–7 (2015).Article 

    Google Scholar 
    Edgar, R. C. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv https://doi.org/10.1101/081257 (2016).Article 

    Google Scholar 
    Team, R. C. R: A Language and Environment for Statistical Computing. (2019).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    De Mendiburu, F. Agricolae: statistical procedures for agricultural research. R package version, 1(1). https://scholar.google.com/scholar?hl=it&as_sdt=0%2C5&q=Agricolae%3A+Statistical+Procedures+for+Agricultural+Research&btnG (2014).Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5(7), 621–628 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Metsalu, T. & Vilo, J. ClustVis: A web tool for visualizing clustering of multivariate data using Principal Component Analysis and heatmap. Nucleic Acids Res. 43, W566–W570 (2015).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gloor, G. B. & Reid, G. Compositional analysis: A valid approach to analyze microbiome high-throughput sequencing data. Can. J. Microbiol. https://doi.org/10.1139/cjm-2015-082162,692-703 (2016).Article 
    PubMed 

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

    Google Scholar 
    Oksanen, J., Blanchet, F. G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P. R., O’Hara, R. B., Simpson, G. L., Solymos, P., Stevens M. H. H., Szöcs, E. & Wagner, H. vegan: Community Ecology Package. R package version 2.5-7. 2020 (2022).Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag, New York. 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

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    Interannual variability in early life phenology is driven by climate and oceanic processes in two NE Atlantic flatfishes

    Cheung, W. W. L. et al. Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. Nat. Clim. Change 3, 1–5 (2012).
    Google Scholar 
    Pilotto, F. et al. Meta-analysis of multidecadal biodiversity trends in Europe. Nat. Commun. 11, 3486 (2010).Article 
    ADS 

    Google Scholar 
    Ong, J. J. L. et al. Contrasting environmental drivers of adult and Juvenile growth in a marine fish: Implications for the effects of climate change. Sci. Rep. 5, 10859 (2015).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rijnsdorp, A. D., Peck, M. A., Engelhard, G. H., Moellmann, C. & Pinnegar, J. K. Resolving the effect of climate change on fish populations. ICES J. Mar. Sci. 66(7), 1570–1583 (2009).Article 

    Google Scholar 
    Pankhurst, N. W. & Munday, P. L. Effects of climate change on fish reproduction and early life history stages. Mar. Freshw. Res. 62(9), 1015 (2011).Article 
    CAS 

    Google Scholar 
    Ainsworth, C. H. et al. Potential impacts of climate change on Northeast Pacific marine foodwebs and fisheries. ICES J. Mar. Sci. 68, 1217–1229 (2011).Article 

    Google Scholar 
    Morrongiello, J. R., Horn, P. L., Ó Maolagáin, C. & Sutton, P. J. H. Synergistic effects of harvest and climate drive synchronous somatic growth within key New Zealand fisheries. Glob. Change Biol. 27(7), 1470–1484 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Ottersen, G., Hjermann, D. O. & Stensenth, N. C. Changes in spawning stocks structure strengthen the link between climate and recruitment in a heavily fished cod (Gadus morhua) stock. Fish. Oceanogr. 15(3), 230–243 (2006).Article 

    Google Scholar 
    Cheung, W. W. L. & Oyinlola, M. A. Vulnerability of flatfish and their fisheries to climate change. J. Sea Res. 140, 1–10 (2018).Article 
    ADS 

    Google Scholar 
    Fedewa, E. J., Miller, J. A. & Hurst, T. P. Pre-settlement process of northern rock sole (Lepidopsetta polyxystra) in relation to interannual variability in the Gulf of Alaska. J. Sea Res. 111, 25–36 (2016).Article 
    ADS 

    Google Scholar 
    Cabral, H. N. et al. Relative importance of estuarine flatfish nurseries along the Portuguese coast. J. Sea Res. 57, 209–217 (2007).Article 
    ADS 

    Google Scholar 
    Martinho, F., van der Veer, H. W., Cabral, H. N. & Pardal, M. A. Juvenile nursery colonization patterns for the European flounder (Platichthys flesus): A latitudinal approach. J. Sea Res. 84, 61–69 (2013).Article 
    ADS 

    Google Scholar 
    Primo, A. L. et al. Contrasting links between growth and survival in the early life stages of two flatfish species. Estuar. Coast. Shelf Sci. 254, 107314 (2021).Article 

    Google Scholar 
    Vaz, A., Scarcella, G., Pardal, M. A. & Martinho, F. Water temperature gradients drive early life-history patterns of the common sole (Solea solea L.) in the Northeast Atlantic and Mediterranean. Aquat. Ecol. 53(5) (2019).Geffen, A., van der Veer, H. W. & Nash, R. The cost of metamorphosis in flatfishes. J. Sea Res. 58(1), 35–45 (2007).Article 
    ADS 

    Google Scholar 
    Cowen, R. K., Lwiza, K. M. M., Sponaugle, S., Paris, C. B. & Olson, D. B. Connectivity in marine populations: Open or closed?. Science 287, 857–859 (2000).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gillanders, B. M., Black, B. A., Meekan, M. G. & Morrison, M. A. Climatic effects on the growth of a temperate reef fish from the Southern Hemisphere: a biochronological approach. Mar. Biol. 159, 1327–1333 (2012).Article 

    Google Scholar 
    Treml, E. A., Ford, J. R., Black, K. P. & Swearer, S. E. Identifying the key biophysical drivers, connectivity outcomes, and metapopulation consequences of larval dispersal in the sea. Mov. Ecol. 3(1), 345 (2015).Article 

    Google Scholar 
    Gibson, R. N. Behaviour and the distribution of flatfishes. J. Sea Res. 37(1997), 241–256 (1997).Article 
    ADS 

    Google Scholar 
    Mellado-Cano, J., Barriopedro, D., García-Herrera, R., Trigo, R. M. & Hernández, A. Examining the North Atlantic Oscillation, East Atlantic Pattern, and jet variability since 1685. J. Clim. 32, 6285–6298 (2019).Article 
    ADS 

    Google Scholar 
    Tanner, S. E. et al. Marine regime shifts impact synchrony of deep-sea fish growth in the northeast Atlantic. Oikos 129(12), 1781–1794 (2020).Article 

    Google Scholar 
    Trigo, R. M., Osborn, T. J. & Corte-Real, J. M. The North Atlantic Oscillation influence on Europe: Climate impacts and associated physical mechanisms. Clim. Res. 20, 9–17 (2002).Article 

    Google Scholar 
    Leis, J. M. et al. Does fish larval dispersal differ between high and low latitudes?. Proc. R. Soc. B Biol. Sci. 280(1759), 20130327 (2013).Article 

    Google Scholar 
    Raventos, N., Torrado, H., Arthur, R., Alcoverro, T. & Macpherson, E. Temperature reduces fish dispersal as larvae grow faster to their settlement size. J. Anim. Ecol. 90(6), 1419–1432 (2021).Article 
    PubMed 

    Google Scholar 
    Santos, A. M. P. et al. Physical-biological interactions in the life history of small Pelagic Fish in the Western Iberia upwelling ecosystem. Prog. Oceanogr. 74(2), 192–209 (2007).Article 
    ADS 

    Google Scholar 
    Le Pape, O. & Bonhommeau, S. The food limitation hypothesis for juvenile marine fish. Fish Fish. 16(3), 373–398 (2015).Article 

    Google Scholar 
    Fox, C. et al. Birth-date selection in early life stage of plaice Pleuronectes platessa in the eastern Irish Sea (British Isles). Mar. Ecol. Prog. Ser. 345, 255–269 (2007).Article 
    ADS 

    Google Scholar 
    Joh, M. & Wada, A. Inter-annual and spatial difference in hatch date and settlement date distribution and planktonic larval duration in yellow striped flounder Pseudopleuronectes Herzensteini. J. Sea Res. 137, 26–34 (2018).Article 
    ADS 

    Google Scholar 
    Pinto, M. et al. Influence of oceanic and climate conditions on the early life history of European seabass Dicentrarchus labrax. Mar. Environ. Res. 169, 105362 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Morais, P., Dias, E., Babaluk, J. & Antunes, C. The migration patterns of the European flounder Platichthys flesus (Linnaeus, 1758) (Pleuronectidae, Pisces) at the southern limit of its distribution range: Ecological implications and fishery management. J. Sea Res. 65, 235–246 (2011).Article 
    ADS 

    Google Scholar 
    Lacroix, G., Maes, G. E., Bolle, L. J. & Volckaert, F. Modelling dispersal dynamics of the early life stages of a marine flatfish (Solea Solea L.). J. Sea Res. 84(C), 13–25 (2013).Article 
    ADS 

    Google Scholar 
    Tanner, S. E., Teles-Machado, A., Martinho, F., Peliz, A. & Cabral, H. N. Modelling larval dispersal Dynamics of common sole (Solea solea) along the western Iberian coast. Prog. Oceanogr. 156, 78–90 (2017).Article 
    ADS 

    Google Scholar 
    Amorim, E., Ramos, S., Elliott, M. & Bordalo, A. A. Immigration and early life stages recruitment of the European flounder (Platichthys flesus) to an estuarine nursery: The influence of environmental factors. J. Sea Res. 107(Part 1), 56–66 (2016).Article 
    ADS 

    Google Scholar 
    Vasconcelos, R. P., Reis-Santos, P., Costa, M. J. & Cabral, H. N. Connectivity between estuaries and marine environment: Integrating metrics to assess estuarine nursery function. Ecol. Indic. 11(5), 1123–1133 (2011).Article 

    Google Scholar 
    Orio, A. et al. Spatial contraction of demersal fish populations in a large marine ecosystem. J. Biogeogr. 46(3), 633–645 (2019).Article 

    Google Scholar 
    Peliz, A., Rosa, T. L., Santos, A. M. P. & Pissarra, J. L. Fronts, jets, and counter-flows in the Western Iberian upwelling system. J. Mar. Syst. 35, 61–77 (2002).Article 

    Google Scholar 
    Teles-Machado, A., Peliz, A., McWilliams, J. C., Dubert, J. & Le Cann, B. Circulation on the Northwestern Iberian Margin: Swoddies. Prog. Oceanogr 140, 116–133 (2016).Article 
    ADS 

    Google Scholar 
    Primo, A. L. et al. Colonization and nursery habitat use patterns of larval and juvenile flatfish species in a small temperate estuary. J. Sea. Res. 76(C), 126–134 (2013).Article 
    ADS 

    Google Scholar 
    Vasconcelos, R. P. et al. Evidence of estuarine nursery origin of five coastal fish species along the Portuguese coast through otolith elemental fingerprints. Estuar. Coast. Shelf Sci. 79, 317–327 (2008).Article 
    ADS 

    Google Scholar 
    du Sert, N. P. et al. The ARRIVAGE guidelines 2.0: updated guidelines for reporting animal research. J. Physiol. Lond. 598(18), 3793–3801 (2020).Article 

    Google Scholar 
    Trigo, R. M. et al. The impact of north atlantic wind and cyclone trends on European precipitation and significant wave height in the Atlantic. Ann. N. Y. Acad. Sci. 1146(1), 212–234 (2008).Article 
    ADS 
    PubMed 

    Google Scholar 
    Murase, H., Nagashima, H., Yonezaki, S., Matsukura, R. & Kitakado, T. Application of a generalized additive model (GAM) to reveal relationships between environmental factors and distributions of Pelagic Fish and Krill: a Case Study in Senday Bay, Japan. ICES J. Mar. Sci. 66(6), 1417–1424 (2009).Article 

    Google Scholar 
    Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B Stat. Methodol. 73(1), 3–36 (2011).Article 
    MathSciNet 
    MATH 

    Google Scholar 
    Tanner, S. E. et al. Regional climate, primary productivity and fish biomass drive growth cariation and population resilience in a small pelagic fish. Ecol. Indic. 103, 530–541 (2019).Article 

    Google Scholar 
    Almeida, J. R., Gravato, C. & Guilermino, L. Effects of temperature in juvenile Seabass (Dicentrarchus labrax L.) biomarker responses and behaviour: implications for environmental monitoring. Estuaries Coasts 38, 45–55 (2015).Article 
    CAS 

    Google Scholar 
    Sims, D. W., Wearmouth, V. J., Genner, M. J., Southward, A. J. & Hawkins, S. J. Low-temperature-driven early spawning migration of a temperate marine fish. J. Anim. Ecol. 73(2), 333–341 (2004).Article 

    Google Scholar 
    Faria, A. M., Muha, T., Morote, R. & Chicharro, M. A. Influence of starvation on the critical swimming behaviour of the Senegalensis sole (Solea senegalensis) and its relationship with RNA/DNA ratios during ontogeny. Sci. Mar. 75(1), 87–94 (2011).Article 
    CAS 

    Google Scholar 
    Downie, A. T., Illing, B., Faria, A. M. & Rummer, J. L. Swimming performance of marine fish larvae: review of a universal trait under ecological and environmental pressure. Rev. Fish Biol. Fish. 30, 93–108 (2020).Article 

    Google Scholar 
    Durant, J. M. et al. Contrasting effects of rising temperatures on trophic interactions in marine ecosystems. Na. Sci. Rep. 9(1), 15213 (2019).Article 
    ADS 

    Google Scholar 
    Stenseth, N. C. et al. Ecological effects of climate fluctuations. Science 297(5585), 1292–1296 (2002).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Harrington, A. M., Clark, K. F. & Hamlin, H. J. Expected ocean warming conditions significantly alter the transcriptone of developing postlarval American lobsters (Homarus americanus): Implications for energetic trade-offs. Comp. Biochem. Physiol. D Genom. Proteom. 36, 100716 (2020).CAS 

    Google Scholar 
    Pörtner, H. O. & Farrell, A. P. Ecology. Physiol. Clim. Change. Sci. 322(5902), 690–692 (2008).
    Google Scholar 
    Drinkwater, K. F. et al. On the processes linking climate to ecosystem changes. J. Mar. Syst. 79, 374–388 (2010).Article 

    Google Scholar 
    Alix, M., Kjesbu, O. S. & Anderson, K. C. From Gametogenesis to spawning: How climate-driven warming affects teleost reproductive biology. J. Fish Biol. 97(3), 607–632 (2020).Article 
    PubMed 

    Google Scholar 
    Conover, D. O. & Present, T. M. C. Countergradient variation in growth rate: compensation for length of the growing season among Atlantic silversides from different latitudes. Oceanologia 83, 316–324 (1990).ADS 

    Google Scholar 
    van de Wolfshaar, K. E., Barbut, L. & Lacroix, G. From spawning to first-year recruitment: the fate of Juvenile Sole Growth and survival under future climate conditions in the North Sea. ICES J. Mar. Sci. (2021).Cabral, H. et al. Contrasting impacts of climate change on connectivity and larval recruitment to estuarine nursery areas. Prog. Oceanogr. 196, 102608 (2011).Article 

    Google Scholar 
    Iglesias, I., Lorenzo, M. N. & Taboada, J. J. Seasonal predictability of the East Atlantic Pattern from sea surface temperatures. PLoS ONE 9(1), 86439–86448 (2014).Article 
    ADS 

    Google Scholar 
    Rodríguez-Puebla, C., Encinas, A. H., García-Casado, L. A. & Nieto, S. Trends in warm days and cold nights over the Iberian Peninsula: relationships to large-scale variables. Clim. Change 100(3), 667–684 (2010).Article 
    ADS 

    Google Scholar 
    Hurrell, J. W. & Van Loon, H. Decadal variations in climate associated with the North Atlantic oscillation. Clim. Change 36, 301–326 (1997).Article 

    Google Scholar 
    Henderson, P. A. & Seaby, R. M. The role of climate in determining the temporal variation in abundance, recruitment and growth of sole Solea solea in the Bristol Channel. JMBA 85, 197–204 (2005).
    Google Scholar 
    Rodwell, M. J., Rowell, D. P. & Folland, C. K. Oceanic forcing of the wintertime North Atlantic Oscillation and European Climate. Letters to Nature 398, 320–323 (1999).Article 
    ADS 
    CAS 

    Google Scholar 
    Hurrell, J. W. Decadal trends in the North Atlantic oscillation: Regional temperatures and precipitation. Sci. 269, 676–679 (1995).Article 
    ADS 
    CAS 

    Google Scholar 
    Avalos, M. R. et al. Comparing the foraging strategies of a seabird predator when recovering from drastic climatic event. Mar. Biol. 164, 48 (2017).Article 

    Google Scholar 
    Wang, C., Liu, H. & Lee, S. K. The record-breaking cold temperatures during the winter of 2009/2010 in the Northern Hemisphere. Atmos. Sci. Lett. 11(3), 161–168 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Rodrigo, F. S. Exploring combined influences of Seasonal East Atlantic (EA) and North Atlantic Oscillation (NAO) on the temperature-precipitation relationship in the Iberian Peninsula. Geosciences 11(5), 211 (2021).Article 
    ADS 

    Google Scholar 
    Alvarez, I., Gommez-Gesteira, M., Decastro, M. & Dias, J. M. Spatiotemporal evolution of upwelling regime along the western coast of the Iberian Peninsula. J. Geophys. Res. Oceans 113(C7), C07020 (2008).Article 
    ADS 

    Google Scholar 
    Demarcq, H. Trends in primary production, Sea surface temperature and wind in upwelling systems (1998–2007). Prog. Oceanogr. 83(1), 376–385 (2009).Article 
    ADS 

    Google Scholar 
    Thorrold, S. R., Latkoczy, C., Swart, P. K. & Jones, C. M. Natal homing in a marine fish metapopulation. Science 291, 297–299 (2001).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar  More