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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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    Higher productivity in forests with mixed mycorrhizal strategies

    Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354, aaf8957 (2016).Article 
    PubMed 

    Google Scholar 
    Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Luo, S. et al. Community‐wide trait means and variations affect biomass in a biodiversity experiment with tree seedlings. Oikos 129, 799–810 (2020).Article 

    Google Scholar 
    Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Article 

    Google Scholar 
    Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, 1–10 (2020).Article 

    Google Scholar 
    Freschet, G. T. et al. Root traits as drivers of plant and ecosystem functioning: current understanding, pitfalls and future research needs. N. Phytol. 232, 1123–1158 (2021).Article 

    Google Scholar 
    Zhong, Y. et al. Arbuscular mycorrhizal trees influence the latitudinal beta-diversity gradient of tree communities in forests worldwide. Nat. Commun. 12, 1–12 (2021).Article 
    ADS 

    Google Scholar 
    Carteron, A., Vellend, M. & Laliberté, E. Mycorrhizal dominance reduces local tree species diversity across US forests. Nat. Ecol. Evol. 6, 370–374 (2022).Article 
    PubMed 

    Google Scholar 
    Phillips, R. P., Brzostek, E. & Midgley, M. G. The mycorrhizal‐associated nutrient economy: a new framework for predicting carbon–nutrient couplings in temperate forests. N. Phytol. 199, 41–51 (2013).Article 
    CAS 

    Google Scholar 
    Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505, 543–545 (2014).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Craig, M. E. et al. Tree mycorrhizal type predicts within‐site variability in the storage and distribution of soil organic matter. Glob. Chang. Biol. 24, 3317–3330 (2018).Article 
    ADS 
    PubMed 

    Google Scholar 
    van der Heijden, M. G. A. et al. Mycorrhizal fungal diversity determines plant biodiversity, ecosystem variability and productivity. Nature 396, 69–72 (1998).Article 
    ADS 

    Google Scholar 
    Klironomos, J. N., McCune, J., Hart, M. & Neville, J. The influence of arbuscular mycorrhizae on the relationship between plant diversity and productivity. Ecol. Lett. 3, 137–141 (2000).Article 

    Google Scholar 
    Wagg, C., Jansa, J., Stadler, M., Schmid, B. & Van Der Heijden, M. G. A. Mycorrhizal fungal identity and diversity relaxes plant-plant competition. Ecology 92, 1303–1313 (2011).Article 
    PubMed 

    Google Scholar 
    Luo, S., Schmid, B., De Deyn, G. B. & Yu, S. Soil microbes promote complementarity effects among co‐existing trees through soil nitrogen partitioning. Funct. Ecol. 32, 1879–1889 (2018).Article 

    Google Scholar 
    Ferlian, O. et al. Mycorrhiza in tree diversity–ecosystem function relationships: conceptual framework and experimental implementation. Ecosphere 9, e02226 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tedersoo, L. & Bahram, M. Mycorrhizal types differ in ecophysiology and alter plant nutrition and soil processes. Biol. Rev. 94, 1857–1880 (2019).Article 
    PubMed 

    Google Scholar 
    Rineau, F. et al. The ectomycorrhizal fungus Paxillus involutus converts organic matter in plant litter using a trimmed brown-rot mechanism involving Fenton chemistry. Environ. Microbiol. 14, 1477–1487 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lindahl, B. D. & Tunlid, A. Ectomycorrhizal fungi – potential organic matter decomposers, yet not saprotrophs. N. Phytol. 205, 1443–1447 (2015).Article 
    CAS 

    Google Scholar 
    Hodge, A. Arbuscular mycorrhizal fungi influence decomposition of, but not plant nutrient capture from, glycine patches in soil. N. Phytol. 151, 725–734 (2001).Article 
    CAS 

    Google Scholar 
    Read, D. J. & Perez-Moreno, J. Mycorrhizas and nutrient cycling in ecosystems – A journey towards relevance? N. Phytol. 157, 475–492 (2003).Article 
    CAS 

    Google Scholar 
    Toju, H., Kishida, O., Katayama, N. & Takagi, K. Networks depicting the fine-scale co-occurrences of fungi in soil horizons. PLoS ONE 11, 1–18 (2016).Article 

    Google Scholar 
    Taylor, D. L. et al. A first comprehensive census of fungi in soil reveals both hyperdiversity and fine-scale niche partitioning. Ecol. Monogr. 84, 3–20 (2014).Article 

    Google Scholar 
    Chen, W. et al. Root morphology and mycorrhizal symbioses together shape nutrient foraging strategies of temperate trees. Proc. Natl Acad. Sci. USA 113, 8741–8746 (2016).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Liu, X. et al. Partitioning of soil phosphorus among arbuscular and ectomycorrhizal trees in tropical and subtropical forests. Ecol. Lett. 21, 713–723 (2018).Article 
    PubMed 

    Google Scholar 
    Averill, C., Bhatnagar, J. M., Dietze, M. C., Pearse, W. D. & Kivlin, S. N. Global imprint of mycorrhizal fungi on whole-plant nutrient economics. Proc. Natl Acad. Sci. USA 116, 23163–23168 (2019).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dietrich, P. et al. Tree diversity effects on productivity depend on mycorrhizae and life strategies in a temperate forest experiment. Ecology 104, e3896 https://doi.org/10.1002/ecy.3896 (2022).Averill, C., Dietze, M. C. & Bhatnagar, J. M. Continental-scale nitrogen pollution is shifting forest mycorrhizal associations and soil carbon stocks. Glob. Chang. Biol. 24, 4544–4553 (2018).Article 
    ADS 
    PubMed 

    Google Scholar 
    Jo, I., Fei, S., Oswalt, C. M., Domke, G. M. & Phillips, R. P. Shifts in dominant tree mycorrhizal associations in response to anthropogenic impacts. Sci. Adv. 5, eaav6358, (2019).Fei, S. et al. Impacts of climate on the biodiversity-productivity relationship in natural forests. Nat. Commun. 9, 5436 (2018).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bongers, F. J. et al. Functional diversity effects on productivity increase with age in a forest biodiversity experiment. Nat. Ecol. Evol. 5, 1594–1603 (2021).Article 
    PubMed 

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

    Google Scholar 
    Tilman, D., Lehman, C. L. & Thomson, K. T. Plant diversity and ecosystem productivity: theoretical considerations. Proc. Natl Acad. Sci. USA 94, 1857–1861 (1997).Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schwilk, D. W. & Ackerly, D. D. Limiting similarity and functional diversity along environmental gradients. Ecol. Lett. 8, 272–281 (2005).Article 

    Google Scholar 
    Wagg, C., Jansa, J., Schmid, B. & van der Heijden, M. G. A. Belowground biodiversity effects of plant symbionts support aboveground productivity. Ecol. Lett. 14, 1001–1009 (2011).Article 
    PubMed 

    Google Scholar 
    Agerer, R. Exploration types of ectomycorrhizae: a proposal to classify ectomycorrhizal mycelial systems according to their patterns of differentiation and putative ecological importance. Mycorrhiza 11, 107–114 (2001).Article 

    Google Scholar 
    Cheng, L. et al. Mycorrhizal fungi and roots are complementary in foraging within nutrient patches. Ecology 97, 2815–2823 (2016).Article 
    PubMed 

    Google Scholar 
    Wambsganss, J. et al. Tree species mixing causes a shift in fine-root soil exploitation strategies across European forests. Funct. Ecol. 35, 1886–1902 (2021).Article 
    CAS 

    Google Scholar 
    Gerz, M., Guillermo Bueno, C., Ozinga, W. A., Zobel, M. & Moora, M. Niche differentiation and expansion of plant species are associated with mycorrhizal symbiosis. J. Ecol. 106, 254–264 (2018).Article 
    CAS 

    Google Scholar 
    Niklaus, P. A., Baruffol, M., He, J. S., Ma, K. & Schmid, B. Can niche plasticity promote biodiversity–productivity relationships through increased complementarity? Ecology 98, 1104–1116 (2017).Article 
    PubMed 

    Google Scholar 
    Barry, K. E. et al. The future of complementarity: disentangling causes from consequences. Trends Ecol. Evol. 34, 167–180 (2019).Article 
    PubMed 

    Google Scholar 
    Jacobs, L. M., Sulman, B. N., Brzostek, E. R., Feighery, J. J. & Phillips, R. P. Interactions among decaying leaf litter, root litter and soil organic matter vary with mycorrhizal type. J. Ecol. 106, 502–513 (2018).Article 
    CAS 

    Google Scholar 
    Midgley, M. G., Brzostek, E. & Phillips, R. P. Decay rates of leaf litters from arbuscular mycorrhizal trees are more sensitive to soil effects than litters from ectomycorrhizal trees. J. Ecol. 103, 1454–1463 (2015).Article 

    Google Scholar 
    Kumar, A., Phillips, R. P., Scheibe, A., Klink, S. & Pausch, J. Organic matter priming by invasive plants depends on dominant mycorrhizal association. Soil Biol. Biochem. 140, 107645 (2020).Article 
    CAS 

    Google Scholar 
    Tedersoo, L., Bahram, M. & Zobel, M. How mycorrhizal associations drive plant population and community biology. Science 367, eaba1223 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Kitajima, K. & Poorter, L. Functional basis for resource niche partitioning by tropical trees. Trop. For. community Ecol. 1936, 160–181 (2008).MacArthur, R. H. Patterns of species diverstiy. Biol. Rev. 40, 510–533 (1965).Article 

    Google Scholar 
    Pellissier, V., Barnagaud, J. Y., Kissling, W. D., Şekercioğlu, Ç. & Svenning, J. C. Niche packing and expansion account for species richness–productivity relationships in global bird assemblages. Glob. Ecol. Biogeogr. 27, 604–615 (2018).Article 

    Google Scholar 
    Huang, Y. et al. Effects of enemy exclusion on biodiversity–productivity relationships in a subtropical forest experiment. J. Ecol. 110, 2167–2178. https://doi.org/10.1111/1365-2745.13940 (2022).Tilman, D. Community invasibility, recruitment limitation, and grassland biodiversity. Ecology 78, 81–92 (1997).Article 

    Google Scholar 
    Feng, Y. et al. Multispecies forest plantations outyield monocultures across a broad range of conditions. Science 376, 865–868 (2022).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Harper, J. L. Population biology of plants. (1977).Ewel, J. J. Designing agricultural ecosystems for the humid tropics. Annu. Rev. Ecol. Syst. 17, 245–271 (1986).Article 

    Google Scholar 
    Grossiord, C. Having the right neighbors: how tree species diversity modulates drought impacts on forests. N. Phytol. 228, 42–49 (2020).Article 

    Google Scholar 
    Allen, M. F. Mycorrhizal fungi: highways for water and nutrients in arid soils. Vadose Zo. J. 6, 291–297 (2007).Article 

    Google Scholar 
    Brzostek, E. R. et al. Chronic water stress reduces tree growth and the carbon sink of deciduous hardwood forests. Glob. Chang. Biol. 20, 2531–2539 (2014).Article 
    ADS 
    PubMed 

    Google Scholar 
    Liese, R., Lübbe, T., Albers, N. W. & Meier, I. C. The mycorrhizal type governs root exudation and nitrogen uptake of temperate tree species. Tree Physiol. 38, 83–95 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Linton, M. J., Sperry, J. S. & Williams, D. G. Limits to water transport in Juniperus osteosperma and Pinus edulis: Implications for drought tolerance and regulation of transpiration. Funct. Ecol. 12, 906–911 (1998).Article 

    Google Scholar 
    Johnson, D. M. et al. Co-occurring woody species have diverse hydraulic strategies and mortality rates during an extreme drought. Plant. Cell Environ. 41, 576–588 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Lin, G. et al. Mycorrhizal associations of tree species influence soil nitrogen dynamics via effects on soil acid–base chemistry. Glob. Ecol. Biogeogr. 31, 168–182 (2022).Article 

    Google Scholar 
    Read, D. J. Mycorrhizas in ecosystems. Experientia 47, 376–391 (1991).Article 

    Google Scholar 
    Hobbie, S. E. Plant species effects on nutrient cycling: revisiting litter feedbacks. Trends Ecol. Evol. 30, 357–363 (2015).Article 
    PubMed 

    Google Scholar 
    De Schrijver, A. et al. Tree species traits cause divergence in soil acidification during four decades of postagricultural forest development. Glob. Chang. Biol. 18, 1127–1140 (2012).Article 
    ADS 

    Google Scholar 
    Loreau, M. & Hector, A. Partitioning selection and complementarity in biodiversity experiments. Nature 412, 72–76 (2001).Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar 
    Braghiere, R. K. et al. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. 14, 1–23 (2022).Article 

    Google Scholar 
    Eisenhauer, N. et al. Biotic interactions as mediators of context-dependent biodiversity-ecosystem functioning relationships. Res. Ideas Outcomes 8, e85873 (2022).Article 

    Google Scholar 
    Fisher, J. B. et al. Tree-mycorrhizal associations detected remotely from canopy spectral properties. Glob. Chang. Biol. 22, 2596–2607 (2016).Article 
    ADS 
    PubMed 

    Google Scholar 
    Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 1–10 (2019).Article 
    CAS 

    Google Scholar 
    Burrill, E. A. et al. The forest inventory and analysis database. USDA . Serv. 2, 1026 (2015).
    Google Scholar 
    Chao, A., Chiu, C.-H. & Jost, L. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through hill numbers. Annu. Rev. Ecol. Evol. Syst. 45, 297–324 (2014).Article 

    Google Scholar 
    Cleland, D. T. et al. Ecological subregions: Sections and subsections for the conterminous United States. Gen. Tech. Rep. WO-76D (2007).Soudzilovskaia, N. A. et al. FungalRoot: global online database of plant mycorrhizal associations. N. Phytol. 227, 955–966 (2020).Article 

    Google Scholar 
    Gallion, J. et al. Indiana DNR State Forest Properties Report of Continuous Forest Inventory (CFI) Summary of years 2015–2019. 1–25 (2020).Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).Article 

    Google Scholar 
    Craven, D. et al. A cross-scale assessment of productivity–diversity relationships. Glob. Ecol. Biogeogr. 29, 1940–1955 (2020).Article 

    Google Scholar 
    Paquette, A. & Messier, C. The effect of biodiversity on tree productivity: from temperate to boreal forests. Glob. Ecol. Biogeogr. 20, 170–180 (2011).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2020).Dowle, M. & Srinivasan, A. data.table: Extension of ‘data.frame‘. R package version 1.14.2 (2021).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.Kassambara, A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. R package version 0.4.0 (2020).Dunnington, D. ggspatial: Spatial Data Framework for ggplot2. R package version 1.1.5 (2021).Robert, J. Hijmans. raster: Geographic Data Analysis and Modeling. R package version 3.5-2 (2021).Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. R package version 1.0.8 (2022).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Lefcheck, J. S. piecewiseSEM: piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

    Google Scholar 
    Luo, S. et al. High productivity in forests with mixed mycorrhizal strategies. Figshare https://doi.org/10.6084/m9.figshare.22060238. (2023). More

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    Impact of Pacific Ocean heatwaves on phytoplankton community composition

    Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Change 6, 1042–1047 (2016).Article 

    Google Scholar 
    Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 42, 3414–3420 (2015).Article 

    Google Scholar 
    Blunden, J. & Arndt, D. S. State of the Climate in 2015. Bull. Am. Meteorol. Soc. 97, s1–s275 (2016).Article 

    Google Scholar 
    Santoso, A., Mcphaden, M. J. & Cai, W. The Defining Characteristics of ENSO Extremes and the Strong 2015/2016 El Niño. Rev. Geophys. 55, 1079–1129 (2017).Article 

    Google Scholar 
    Amaya, D. J., Miller, A. J., Xie, S.-P. & Kosaka, Y. Physical drivers of the summer 2019 North Pacific marine heatwave. Nat. Commun. 11. https://doi.org/10.1038/s41467-020-15820-w (2020).Frölicher, T. L., Fischer, E. M. & Gruber, N. Marine heatwaves under global warming. Nature 560, 360–364 (2018).Laufkötter, C., Zscheischler, J. & Frölicher, T. L. High-impact marine heatwaves attributable to human-induced global warming. Science 369, 1621–1625 (2020).Article 
    PubMed 

    Google Scholar 
    Piatt, J. F. et al. Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016. PLOS ONE 15, 1–32 (2020).Article 

    Google Scholar 
    Savage, K. Alaska and British Columbia large whale unusual mortality event summary report. NOAA Affiliate Protected Resources Division, NOAA Fisheries Juneau, AK. https://repository.library.noaa.gov/view/noaa/17715 (2017).Cavole, L. M. et al. Biological Impacts of the 2013-2015 Warm-Water Anomaly in the Northeast Pacific: Winners, Losers, and the Future. Oceanography 29, 273–285 (2016).Article 

    Google Scholar 
    Barbeaux, S. et al. Chapter 2: assessment of the pacific cod stock in the Gulf of Alaska. North Pacific Fish Manag. Counc. Gulf Alaska Stock Assess. Fish Eval. Rep. 140. https://archive.afsc.noaa.gov/refm/docs/2019/GOApcod.pdf (2019).Arimitsu, M. L. et al. Heatwave-induced synchrony within forage fish portfolio disrupts energy flow to top pelagic predators. Glob. Change Biol. 27, 1859–1878 (2021).Article 
    CAS 

    Google Scholar 
    Leising, A. W. et al. State of the California Current 2014-15: Impacts of the Warm-Water “Blob”. CalCOFI Rep. 56, 31–68 (2015).
    Google Scholar 
    Chandler, P. & Yoo, S. Marine Ecosystems of the North Pacific Ocean 2009-2016: Synthesis Report. PICES Spec. Publ. 7, 1–82 (2021).
    Google Scholar 
    Peterson, W. et al. Ocean Ecosystem Indicators of Salmon Marine Survival in the Northern California Current. NOAA Northwest Fishery Science Center1-94. http://www.nwfsc.noaa.gov/research/divisions/fe/estuarine/oeip/documents/Peterson_etal_2015.pdf (2015).Volk, T. & Hoffert, M. Ocean carbon pumps: analysis of relative strengths and efficiencies in ocean-driven atmospheric CO2 changes. In Sundquist, E. & Broecker, W. (eds.) The carbon cycle and atmospheric CO2 : natural variations Archean to present. Chapman conference papers, 1984, 99–110 (American Geophysical Union; Geophysical Monograph 32, 1985).Whitney, F. A. Anomalous winter winds decrease 2014 transition zone productivity in the NE Pacific. Geophys. Res. Lett. 42, 428–431 (2015).Article 

    Google Scholar 
    McCabe, R. M. et al. An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys. Res. Lett. 43, 10366–10376 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Du, X., Peterson, W., Fisher, J., Hunter, M. & Peterson, J. Initiation and Development of a Toxic and Persistent Pseudo-nitzschia Bloom off the Oregon Coast in Spring/Summer 2015. PLOS ONE 11, 1–17 (2016).Article 

    Google Scholar 
    Du, X. & Peterson, W. T. Phytoplankton community structure in 2011-2013 compared to the extratropical warming event of 2014-2015. Geophys. Res. Lett. 45, 1534–1540 (2018).Article 

    Google Scholar 
    Peña, M. AandNemcek,NandRobert,M. Phytoplankton responses to the 2014-2016 warming anomaly in the northeast subarctic Pacific Ocean. Limnol. Oceanogr. 64, 515–525 (2019).Article 

    Google Scholar 
    Barth, A., Walter, R. K., Robbins, I. & Pasulka, A. Seasonal and interannual variability of phytoplankton abundance and community composition on the Central Coast of California. Mar. Ecol. Prog. Ser. 637, 29–43 (2020).Article 
    CAS 

    Google Scholar 
    Batten, S. D., Ostle, C., Hélaouët, P. & Walne, A. W. Responses of Gulf of Alaska plankton communities to a marine heat wave. Deep Sea Res. Part II: Topical Stud. Oceanogr. 195, 105002 (2022).Article 

    Google Scholar 
    Johnstone, J. A. & Mantua, N. J. Atmospheric controls on northeast Pacific temperature variability and change, 1900–2012. Proc. Natl Acad. Sci. 111, 14360–14365 (2014).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gregg, W. W. & Rousseaux, C. S. Global ocean primary production trends in the modern ocean color satellite record (1998–2015). Environ. Res. Lett. 14, 124011 (2019).Article 
    CAS 

    Google Scholar 
    Hamme, R. C. et al. Volcanic ash fuels anomalous plankton bloom in subarctic northeast Pacific. Geophys. Res. Lett. 37. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2010GL044629. https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2010GL044629 (2010).Rousseaux, C. S. & Gregg, W. W. Climate variability and phytoplankton composition in the Pacific Ocean. J. Geophys. Res. Oceans 117. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2012JC008083. https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2012JC008083 (2012).Lewing, J. Silicification. In Lewin, R. (ed.) Physiology and biochemistry of algae, 445 – 455 (Academic Press, New York, 1962).Pančić, M., Torres, R. R., Almeda, R. & Kiørboe, T. Silicified cell walls as a defensive trait in diatoms. Proc. R. Soc. B: Biol. Sci. 286, 20190184 (2019).Article 

    Google Scholar 
    Kröger, N. & Poulsen, N. Diatoms—from cell wall biogenesis to nanotechnology. Annu. Rev. Genet. 42, 83–107 (2008).Article 
    PubMed 

    Google Scholar 
    Miklasz, K. A. & Denny, M. W. Diatom sinkings speeds: Improved predictions and insight from a modified stokes’ law. Limnol. Oceanogr. 55, 2513–2525 (2010).Article 

    Google Scholar 
    Nishioka, J. et al. Subpolar marginal seas fuel the North Pacific through the intermediate water at the termination of the global ocean circulation. Proc. Natl Acad. Sci. 117, 12665–12673 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nishioka, J. et al. A review: iron and nutrient supply in the subarctic Pacific and its impact on phytoplankton production. J. Oceanogr. 77, 561–587 (2021).Article 
    CAS 

    Google Scholar 
    Dave, A. C. & Lozier, M. S. The impact of advection on stratification and chlorophyll variability in the equatorial Pacific. Geophys. Res. Lett. 42, 4523–4531 (2015).Article 

    Google Scholar 
    JA, B. Atmospheric teleconnections from the equatorial Pacific. Monthly Weather Rev. 97, 163–172 (1969).Article 

    Google Scholar 
    Martin, J. H. & Fitzwater, S. E. Iron deficiency limits phytoplankton growth in the north-east Pacific subarctic. Nature 331, 341 – 343 (1988).Article 

    Google Scholar 
    Ryther, J. H. Photosynthesis and fish production in the sea. Science 166, 72–76 (1969).Article 
    CAS 
    PubMed 

    Google Scholar 
    Smetacek, V. Diatoms and the ocean carbon cycle. Protist 150, 25–32 (1999).Article 
    CAS 
    PubMed 

    Google Scholar 
    Chavez, F. P., Buck, K. R. & Barber, R. T. Phytoplankton taxa in relation to primary production in the equatorial Pacific. Deep Sea Res. Part A Oceanogr. Res. Pap. 37, 1733–1752 (1990).Article 

    Google Scholar 
    Uitz, J., Claustre, H., Gentili, B. & Stramski, D. Phytoplankton class-specific primary production in the world’s oceans: Seasonal and interannual variability from satellite observations. Glob. Biogeochem. Cycles 24. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2009GB003680 (2010).Strutton, P. G. & Chavez, F. P. Primary productivity in the equatorial Pacific during the 1997-1998 El Niño. J. Geophys. Res. Oceans 105, 26089–26101 (2000).Article 

    Google Scholar 
    Ondrusek, M. E., Bidigare, R. R., Sweet, S. T., Defreitas, D. A. & Brooks, J. M. Distribution of phytoplankton pigments in the north pacific ocean in relation to physical and optical variability. Deep Sea Res. Part A. Oceanogr. Res. Pap. 38, 243–266 (1991).Article 
    CAS 

    Google Scholar 
    Behrenfeld, M. J., Bale, A. J., Kolber, Z. S., Aiken, J. & Falkowski, P. G. Confirmation of iron limitation of phytoplankton in the equatorial Pacific Ocean. Nature 383, 508–511 (1996).Article 
    CAS 

    Google Scholar 
    Barber, R. T. & Chavez, F. P. Regulation of primary productivity rate in the equatorial Pacific. Limnol. Oceanogr. 36, 1803–1815 (1991).Article 

    Google Scholar 
    Coale, K. H., Fitzwater, S. E., Gordon, R. M., Johnson, K. S. & Barber, R. T. Control of community growth and export production by upwelled iron in the equatorial Pacific Ocean. Nature 379, 621–624 (1996).Article 
    CAS 

    Google Scholar 
    Dugdale, R. C. & Wilkerson, F. P. Silicate regulation of new production in the equatorial Pacific upwelling. Nature 391, 270–273 (1998).Article 
    CAS 

    Google Scholar 
    Le Grix, N., Zscheischler, J., Laufkötter, C., Rousseaux, C. S. & Frölicher, T. L. Compound high-temperature and low-chlorophyll extremes in the ocean over the satellite period. Biogeosciences 18, 2119–2137 (2021).Article 

    Google Scholar 
    Behrenfeld, M. J. & Boss, E. S. Resurrecting the ecological underpinnings of ocean plankton blooms. Annu. Rev. Mar. Sci. 6, 167–194 (2014).Article 

    Google Scholar 
    Gregg, W. W. & Casey, N. W. Modeling coccolithophores in the global oceans. Deep Sea Res. Part II: Topical Stud. Oceanogr. 54, 447–477 (2007). The Role of Marine Organic Carbon and Calcite Fluxes in Driving Global Climate Change, Past and Future.Article 

    Google Scholar 
    Wang, B. et al. Historical change of El Niño properties sheds light on future changes of extreme El Niño. Proc. Natl Acad. Sci. 116, 22512–22517 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014).Article 

    Google Scholar 
    Jackson, T., Bouman, H. A., Sathyendranath, S. & Devred, E. Regional-scale changes in diatom distribution in the Humboldt upwelling system as revealed by remote sensing: implications for fisheries. ICES J. Mar. Sci. 68, 729–736 (2011).Article 

    Google Scholar 
    Suryan, R. M. et al. Ecosystem response persists after a prolonged marine heatwave. Sci. Rep. 11, 6235–6252 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Glantz, M. H. Currents of change: impacts of El Ninño and La Ninña on climate and society. (Cambridge University Press, Cambridge, United Kingdom, 2001).
    Google Scholar 
    Arteaga, L. A., Boss, E., Behrenfeld, M. J., Westberry, T. K. & Sarmiento, J. L. Seasonal modulation of phytoplankton biomass in the Southern Ocean. Nat. Commun. 11, 5364 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Behrenfeld, M. J., Doney, S. C., Lima, I., Boss, E. S. & Siegel, D. A. Annual cycles of ecological disturbance and recovery underlying the subarctic Atlantic spring plankton bloom. Glob. Biogeochem. Cycles 27, 526–540 (2013).Article 
    CAS 

    Google Scholar 
    Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).Article 

    Google Scholar 
    Schopf, P. S. & Loughe, A. A Reduced-Gravity Isopycnal Ocean Model: Hindcasts of El Niño. Monthly Weather Rev. 123, 2839–2863 (1995).Article 

    Google Scholar 
    Rienecker, M. M. et al. MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Clim. 24, 3624–3648 (2011).Article 

    Google Scholar 
    Gregg, W. W. & Casey, N. W. Skill assessment of a spectral ocean-atmosphere radiative model. J. Mar. Syst. 76, 49–63 (2009). Skill assessment for coupled biological/physical models of marine systems.Article 

    Google Scholar 
    Eppley, R. W. Temperature and phytoplankton growth in the sea. Fish. Bull. 70, 1063–1085 (1972).
    Google Scholar 
    Csanady, G. T. Mass transfer to and from small particles in the sea. Limnol. Oceanogr. 31, 237–248 (1986).Article 
    CAS 

    Google Scholar 
    McGillicuddy, D. J., McCarthy, J. J. & Robinson, A. R. Coupled physical and biological modeling of the spring bloom in the North Atlantic (I): Model formulation and one dimensional bloom processes. Deep-Sea Res. 42, 1313–1357 (1995).Article 
    CAS 

    Google Scholar 
    Greene, C. A. et al. The climate data toolbox for matlab. Geochem. Geophys. Geosyst. 20, 3774–3781 (2019).Article 

    Google Scholar 
    Morel, A. et al. Examining the consistency of products derived from various ocean color sensors in open ocean (case 1) waters in the perspective of a multi-sensor approach. Remote Sens. Environ. 111, 69 – 88 (2007).Article 

    Google Scholar 
    Gregg, W. W. Assimilation of seawifs ocean chlorophyll data into a three-dimensional global ocean model. J. Mar. Syst. 69, 205–225 (2008). Physical-Biological Interactions in the Upper Ocean.Article 

    Google Scholar 
    Conkright, M. E. et al. World Ocean Atlas 2001. Volume 4, Nutrients. In NOAA atlas NESDIS ; 52, vol. 4, 392 (US Government Printing Office, Washington, DC, 2002). https://repository.library.noaa.gov/view/noaa/1102.Fung, I. Y. et al. Iron supply and demand in the upper ocean. Glob. Biogeochemical Cycles 14, 281–295 (2000).Article 
    CAS 

    Google Scholar 
    Gregg, W. W., Ginoux, P., Schopf, P. S. & Casey, N. W. Phytoplankton and iron: validation of a global three-dimensional ocean biogeochemical model. Deep Sea Res. Part II: Topical Stud. Oceanogr. 50, 3143–3169 (2003). The US JGOFS Synthesis and Modeling Project: Phase II.Article 
    CAS 

    Google Scholar 
    Rousseaux, C. S. & Gregg, W. W. Recent decadal trends in global phytoplankton composition. Glob. Biogeochem. Cycles 29, 1674–1688 (2015).Article 
    CAS 

    Google Scholar  More

  • in

    Unexpected fishy microbiomes

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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    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

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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

  • 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