Ecology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Our global database was derived from studies measuring soil seed bank diversity and density of natural plant communities across all continents, albeit with a strong data availability bias towards North America, Europe, eastern Asia and Oceania as compared to elsewhere (Fig. 1). The database contains 15,698 records for soil seed banks worldwide, including 6,480 for diversity (represented here by species richness) and 9,218 for density (number of seeds per soil surface area). The database represents more than a century of research with the oldest publication dating back to 191815. This most exhaustive and comprehensive set of research data on soil seed bank to date allowed us to identify the determinants and patterns of soil seed bank at the global scale.Fig. 1: Locations of the soil seed bank studies included in our database.a Diversity; b Density.Full size imageTo make data among studies comparable, we standardized them using a three-step process. First, we identified soil seed banks that showed seasonal patterns in both diversity and density, all of which peaked slightly in winter (Supplementary Fig. 1a, b). Thus, we standardized all data (from non-(sub-)tropical regions) for other seasons to winter. Second, sampling area for soil seed bank diversity varied among studies, with 0.01 m2 being the most commonly reported (Supplementary Fig. 2), to which we standardized all data using a species-area curve (Supplementary Table 1). Third, sampling depth also varied among studies, with 0–5 cm being the most frequently reported soil depth (Supplementary Fig. 3). Therefore, 0–5 cm was chosen as the soil depth for standardization of data in various soil depths. Such standardization is needed to find the relationships of seed bank data between different soil depths. We used the upper and lower limits of soil depths (e.g., for 0–5 cm, the upper limit was 0 cm and the lower 5 cm). The log-scale regressions showed that both soil seed bank diversity and density decreased significantly with lowering upper boundaries of soil depths but increased with lower ones (Supplementary Table 2), and thus we standardized all data to 0–5 cm depth using these relationships. To account for possible variation among biomes, the second and third standardization procedures were conducted for each biome separately. The analyses during standardization confirmed the need to standardize empirical findings when comparing seed bank patterns across studies, as previously stressed in a study on grassland soil seed banks10. Our standardization procedures made all data comparable in terms of season, sampling area and soil depth.Non-parametric Kruskal–Wallis tests showed that soil seed banks differed significantly among ecosystem types. Mangroves, tundra and tropical & subtropical dry broadleaf forests had a lower diversity of soil seed banks, whereas Mediterranean forests, woodlands & scrub, tropical & subtropical moist broadleaf forests and tropical & subtropical coniferous forests had a higher diversity (Supplementary Fig. 4a). For density, mangroves and flooded grasslands & savanna had the lowest value, while temperate broadleaf & mixed forests and temperate conifer forests had the highest value (Supplementary Fig. 4b).Prior to spatial analyses, we computed semivariograms to determine whether spatial autocorrelation could affect our models. We found that there was no obvious spatial autocorrelation in the data of soil seed bank diversity or density (Supplementary Fig. 5), indicating no spatial dependence in our data. We then used the random-forest algorithm (see Methods for details) to determine the importance (as increase in node purity) of the influence of 31 variables related to climate, soil, human disturbance and spatial coordinates (Supplementary Table 3) on diversity and density of soil seed banks. These variables previously were reported to affect plant performance at the global scale16,17,18, and thus they could affect soil seed banks via their effects on seed production. Moreover, we expected that potentially these variables could affect seed longevity in the soil. Full models using all 31 predictors showed that climate and soil were important in predicting soil seed banks (Fig. 2a and Supplementary Fig. 6). Moreover, spatial coordinates (absolute latitude) were the most important predictor for diversity, i.e., diversity of soil seed banks exhibit clear spatial patterns at the global scale. Net primary productivity (NPP) and soil characteristics were important in predicting the density of soil seed banks (Fig. 2b and Supplementary Fig. 6).Fig. 2: Variable importance (increase in node purity) of random forests run with all 31 predictors.a Soil seed bank diversity; b Density. abs.latit, absolute latitude; AMT, annual mean temperature; AP, annual precipitation; ATR, annual temperature range; AVWAC, available water capacity (%); BULK, bulk density; CEC, cation exchange capacity; CLAYC, clay (mass %); diversity, plant diversity; HFP, human footprint; Isoth, isothermality; npp, plant productivity (net primary production); ORGNC, organic carbon content; PCQ, precipitation of coldest quarter; PDM, precipitation of driest month; PDQ, precipitation of driest quarter; pH, pH measured in water; Pseason, precipitation seasonality (coefficient of variation); PWeQ, precipitation of wettest quarter; PWM, precipitation of wettest month; PWQ, precipitation of warmest quarter; SANDC, sand (mass %); SILTC, silt (mass %); TCM, min temperature of coldest month; TCQ, mean temperature of coldest quarter; TDQ, mean temperature of driest quarter; TDR, mean diurnal range (mean of monthly (max temp–min temp)); Tseason, temperature seasonality (standard deviation *100); TWeQ, mean temperature of wettest quarter; TWM, max temperature of warmest month; TWQ, mean temperature of warmest quarter.Full size imageWe then built final random-forest models using the most important predictors of seed banks selected from full models: nine variables for diversity and five for density (Supplementary Fig. 7). Final models explained more of the total variance than did full models (Supplementary Table 4), and they were robust to K-fold cross-validation (Supplementary Fig. 8), indicating that a small number of variables predicted soil seed bank diversity and density. Absolute latitude (abs.latit) was the most important predictor for diversity, which varied between 0–55° and then decreased beyond this range (Fig. 3a). Five climatic variables were important for diversity. Diversity peaked at intermediate annual temperature ranges (ATR), while it was the lowest at intermediate mean temperature of driest quarter of the year (TDQ), precipitation of the coldest quarter (PCQ) and precipitation of the driest quarter (PDQ). Diversity increased with increasing annual precipitation (AP). In addition, three soil variables were important for diversity. Diversity showed a humped relationship with soil pH, with pH 6–7 having the highest diversity. Diversity increased with soil cation exchange capacity (CEC) and soil silt content (SILT). These results indicate that diversity exhibits strong spatial patterns at the global scale. However, our spatial patterns differ from those found for a specific ecosystem worldwide (e.g., grasslands), where there were only weak latitudinal gradients in seed bank diversity10. In addition, climate emerged as an important predictor for seed bank diversity, which is consistent with the report that climate acts as environmental filters affecting soil seed bank of grasslands around the world13. Our results agree with a continental study in Europe, where ATR was more important than mean annual temperature for determining seed bank richness and warmer temperatures were associated with lower seed bank richness11. Possible mechanisms by which temperature affects soil seed banks are that it (1) influences seed bank inputs via its effects on seed production; (2) cues dormancy-breaking and germination1, thus determining germinable seed output from seed banks; and (3) affects seed metabolic activity and soil fungal activity19, thereby determining seed viability and persistence in the soil. Finally, our findings of a significant effect of soil pH are supported by some regional and local studies. For instance, seed bank composition is significantly associated with soil pH at high elevations on the Tibetan Plateau20. A negative effect of low pH also has been reported in a large-scale study of acidic and calcareous grasslands in England21. Two possible mechanisms for the effects of soil pH are that (1) low pH may cause loss of seed viability due to the toxicity from aluminum or other metals that become more readily available in soils with low pH22; and (2) high pH may accelerate decomposition and promote growth of pathogens that negatively affect seed persistence23. In our study, the two mechanism may operate synchronously, thereby resulting in the highest diversity of soil seed banks at intermediate pH at the global scale. Further, our results show that soil CEC and SILT affect seed bank diversity, which agrees with a study on the Tibetan Plateau20. The physical and chemical properties of soils can affect seed bank directly by affecting seed germination and aging via regulating soil water-holding capacity24, or indirectly by affecting seed viability via controlling the activity of soil pathogens21,22,25.Fig. 3: Partial feature contributions (the marginal effect of a variable on response) of the most important variables for soil seed banks.a diversity; b density. Variable importance (inc. node) is the decrease in the residual sum of squares that results from splitting regression trees using the variable. The percentage increase in mean squared error (% inc. MSE) is the increase in model error as a result of randomly shuffling the order of values in the vector. abs.latit, absolute latitude; AP, annual precipitation; ATR, annual temperature range; BULK, bulk density; CEC, cation exchange capacity; npp, plant productivity (net primary production); PCQ, precipitation of coldest quarter; PDM, precipitation of driest month; PDQ, precipitation of driest quarter; pH, pH measured in water; SILTC, silt (mass %); TDQ, mean temperature of driest quarter; TWM, max temperature of warmest month.Full size imageFor soil seed bank density, soil bulk density (BULK) was the most important predictor; density increased below 750 g/cm3 BULK but remained stable when BULK was higher than 800 g/cm3 (Fig. 3b). Density peaked when temperature of the warmest month (TWM) was 34 °C. Density showed similar variation with NPP, precipitation of the driest quarter of the year (PDQ) and of the driest month (PDM), i.e., it peaked at intermediate values of these variables. Precipitation influences the success of sexual reproduction of plants and the size of the seed bank through seed input26, and it also affects soil pathogenic fungi, which cause seed mortality27. Therefore, precipitation has a strong effect on seed bank density, as reported for 27 alpine meadows on the Tibetan Plateau28. Our results further illustrate that PDQ and PDM are the key factors determining seed bank density worldwide, suggesting that moisture fluctuation in soils triggered by precipitation of the driest time of the year can affect seed bank density. If soil moisture fluctuations are high, seed germination will be primed by increasing moisture24.At the global scale, we mapped soil seed bank diversity and density using the final random-forest models. Mapping soil seed bank values onto global maps revealed considerable geospatial variation, the pattern of which varied between diversity and density (Fig. 4). For diversity, western North America, central South America, central Africa, central Europe, southern and eastern Asia and eastern Oceania had high values. In contrast, eastern and central North America, northern Africa and central Asia had low values (Fig. 4a). For density, northern North America, northern Europe and northern Asia had higher values than elsewhere (Fig. 4a). Our results are consistent with the reports that larger seed banks are more common in cooler temperate climates19,29. The latitudinal pattern of higher density in colder regions in the Northern Hemisphere may be driven by lower seed mortality in colder soils6, resulting in stable seed bank densities of long-lived seeds that counteract low seed production in some years at cold northern latitudes, as shown in a study of temperate forests along a 1900 km latitudinal gradient in northwestern Europe29. The latitudinal pattern highlights that particularly species rich low-latitude biomes such as tropical rainforests generally have very low seed bank densities, while their seed bank diversity does not exceed that in higher latitudes biomes. However, our global assessment should be interpreted with caution since some studies in azonal vegetation or in rare habitats in our database did not fully reflect soil seed banks in that region, and thus these data shortcomings may have induced bias in our global predictions. Moreover, data gaps in our database are also likely to have had an effect on the global predictions, i.e., fewer data available from some continents (e.g., northern Asia and Africa) could lead to less confidence for prediction in these regions. For example, Russia has very few soil seed bank data, which may have led to an inaccurate prediction for this country. Nevertheless, based on our global patterns of soil seed bank diversity and density, the latitudinal pattern strongly suggests that the biodiversity of (sub-)tropical forests is particularly vulnerable to large-scale climatic or land-use disturbances. However, in-depth investigation is needed to quantify the extent to which temporal integration of seed bank effects for long-lived trees and seed masting events may buffer the effects of low seed bank diversity and density at any given time of sampling. In contrast, the higher-latitude plant diversity, while currently low compared to that in tropical rainforest, may rely on high soil seed bank densities to boost its resilience to large-scale climate- or land-use induced disturbances. Further, our analyses suggest that the least vulnerable ecosystems in terms of hidden diversity should be those that combine high seed-bank diversity with high density; and therefore the relationships between the two variables across the global map certainly would be an interesting topic worthy of further study.Fig. 4: Extrapolated global maps of soil seed banks.a diversity in terms of number of species per 0.01 m2; b density as number of seeds per m2. In b, values are log10-transformed to facilitate viewing. The spatial resolution of grid cells is 5 arcmin-by-5 arcmin.Full size imageOur global assessment reveals that both diversity and density exhibit clear spatial patterns of soil seed banks but differ in their environmental determinants. These findings alone do not necessarily mean that this biodiversity reservoir has strong buffering capacity under climate change, because both climate and soil conditions influence seed bank diversity and density. Based on a large number and long history of studies globally, we provide quantitative evidence of how environmental conditions shape soil seed bank distributions and spatially explicit maps of this biodiversity reservoir in plant communities worldwide. Our quantification of environmental determinants and global mapping can be readily applied to dynamic global vegetation and plant diversity models to enable a more complete and accurate prediction of the impact of ongoing environmental changes on plant diversity (both above- and belowground) at the global scale. The next research challenge will be to plot current (visible) aboveground plant diversity (ideally using the available data in the studies themselves) against soil seed bank diversity under global change scenarios in order to pinpoint even more accurately which plant communities, ecosystems and biomes (and their turn-over) are most at risk of losing their diversity due to global changes. More

1.Csiki-Sava, Z., Buffetaut, E., Ősi, A., Pereda-Suberbiola, X. & Brusatte, S. L. Island life in the Cretaceous – faunal composition, biogeography, evolution, and extinction of land-living vertebrates on the Late Cretaceous European archipelago. ZooKeys. https://doi.org/10.3897/zookeys.469.8439 (2015).2.Benton, M. J. et al. Dinosaurs and the island rule: The dwarfed dinosaurs from Haţeg Island. Palaeogeogr. Palaeoclimatol. Palaeoecol. 293, 438–454 (2010).
Google Scholar 
3.Scotese, C. R. An Atlas of phanerozoic Paleogeographic maps: The seas come in and the seas go out. Annu. Rev. Earth Planet. Sci. 49, 679–728 (2021).CAS 

Google Scholar 
4.Randazzo, V. et al. The migration path of Gondwanian dinosaurs toward Adria: New insights from the Cretaceous of NW Sicily (Italy). Cretac. Res. 126, 104919 (2021).5.Dal Sasso, C., Pierangelini, G., Famiani, F., Cau, A. & Nicosia, U. First sauropod bones from Italy offer new insights on the radiation of Titanosauria between Africa and Europe. Cretac. Res. 64, 88–109 (2016).
Google Scholar 
6.Vlahović, I., Tišljar, J., Velić, I. & Matičec, D. Evolution of the Adriatic Carbonate Platform: Palaeogeography, main events and depositional dynamics. Palaeogeogr. Palaeoclimatol. Palaeoecol. 220, 333–360 (2005).
Google Scholar 
7.Dalla Vecchia, F. M. Tethyshadros insularis, a new hadrosauroid dinosaur (Ornithischia) from the Upper Cretaceous of Italy. J. Vertebr. Paleontol. 29, 1100–1116 (2009).
Google Scholar 
8.Tarlao, A., Tentor, M., Tunis, G. & Venturini, S. Evidence of a tectonic phase in the Lower Senonian of the Villaggio del Pescatore area. Gortania Atti Museo Friulano Storia Naturale 135–142 (1993).9.Tarlao, A., Tentor, M., Tunis, G., Venturini, S. Stop 4: Villaggio del Pescatore. Atti Museo Geologico Paleontologico Monfalcone 135–142 (1995).10.Alessandro Palci. Ricostruzione Paleoambientale del Sito Fossilifero Senoniano del Villaggio del Pescatore (Trieste). (Università degli Studi di Trieste, 2003).11.Arbulla, D., Caffa, M., Cotza, F., Cucchi, F., Flora, O., Masetti, D., Pittau, P., Pugliese, N., Stenni, B., Tarlao, A., Tunisè, G. & Zini, L. The Santonina-Campanian succession of the Villaggio del Pescatore (Trieste karst) yielding the hadrosaur: palaeoecology, stratigraphy, sedimentology and geochemistry. in Sixth European workshop on vertebrate palaeontology abstracts (2001).12.Arbulla, D., Cotza, F., Cucchi, F., Dalla Vecchia, F. M., De Giusto, A., Flora, O., Masetti, D., Palci, A., Pittau, P., Pugliese, N., Stenni, B., Tarlao, A., Tunis, G. & Zini, L. La successione Santoniano–Campaniana del Villaggio del Pescatore (Carso Triestino) nel quale sono stati rinvenuti i resti di dinosauro. in Guida alle escursioni/excursions guide, Società Paleontologica Italiana 20–27 (EUT Edizioni Università di Trieste, 2006).13.Dal Sasso, C. Dinosaurs of Italy. Comptes Rendus Palevol. 2, 45–66 (2003).
Google Scholar 
14.Dal Sasso, C. & Brillante, G. Dinosaurs of Italy. (Indiana University Press, 2005).15.Dalla Vecchia, F., Tunis, G., Venturini, S. & Tarlao, A. Dinosaur track sites in the upper Cenomanian (Late Cretaceous) of Istrian Peninsula (Croatia). Boll. Della Soc. Paleontol. Ital. 40, 25–54 (2001).16.Dalla Vecchia, F. M. Observations on the presence of plant-eating dinosaurs in an oceanic carbonate platform. Nat. Nascosta 27 (2003).17.Nicosia, U., Avanzini, M., Barbera, C., Conti, M. A., Dalla Vecchia, F. M. e altri 15 coautori in ordine alfabetico. I vertebrati continentali del Paleozoico e Mesozoico. in Paleontologia dei Vertebrati in Italia vol. Memorie Museo Civico di Storia Naturale Verona 41–66 (Bonfiglio L., 2005).18.Dalla Vecchia, F. M. The impact of dinosaur palaeoichnology in palaeoenvironmental and palaeogeographic reconstructions: The case of the Periadriatic carbonate platforms. Oryctos 8, 19 (2008).
Google Scholar 
19.Delfino, M., Martin, J. E. & Buffetaut, E. A new species of Acynodon (Crocodylia) from the upper cretaceous (Santonian–Campanian) of Villaggio del Pescatore, Italy. Palaeontology 51, 1091–1106 (2008).
Google Scholar 
20.Dalla Vecchia, F. M. The unusual tail of Tethyshadros insularis (Dinosauria, Hadrosauroidea) from the Adriatic Island of the European Archipelago. Rivista Italiana di Paleontologia e Stratigrafia 126, 46 (2020).
Google Scholar 
21.Benson, R. B. J., Hunt, G., Carrano, M. T. & Campione, N. Cope’s rule and the adaptive landscape of dinosaur body size evolution. Palaeontology 61, 13–48 (2018).
Google Scholar 
22.Dalla Vecchia, F. Telmatosaurus and the other hadrosaurids of the Cretaceous European Archipelago. An update. Nat. Nascosta 39, 1–18 (2009).
Google Scholar 
23.Soul, L. & Wright, D. Phylogenetic Comparative Methods: A User’s Guide for Paleontologists. (2020) https://doi.org/10.32942/osf.io/ytm5x.24.Felsenstein, J. Phylogenies and the comparative method. Am. Nat. 125, 1–15 (1985).
Google Scholar 
25.Chiocchini, M., Farinacci, A., Mancinelli, A., Molinari, V. & Potetti, M. Biostratigrafia a foraminiferi, dasicladali e calpionelle delle successioni carbonatiche mesozoiche dell’Appennino centrale (Italia). in Biostratigrafia dell’Italia centrale. Studi Geologici Camerti vol. Biostratigrafia dell’Italia centrale 129 (Mancinelli, A, 1994).26.Chiocchini, M., Pampaloni, M. L., Pichezzi, R. M. Microfacies and microfossils of the Mesozoic carbonate successions of Latium and Abruzzi (Central Italy), Vol. 269 (Memorie per Servire alla Descrizione della Carta Geologica D’Italia, ISPRA, Dipartimento Difesa del Suolo, Roma, 2012).27.Frijia, G., Parente, M., Di Lucia, M. & Mutti, M. Carbon and strontium isotope stratigraphy of the Upper Cretaceous (Cenomanian-Campanian) shallow-water carbonates of southern Italy: Chronostratigraphic calibration of larger foraminifera biostratigraphy. Cretac. Res. 53, 110–139 (2015).
Google Scholar 
28.Steuber, T., Korbar, T., Jelaska, V. & Gušić, I. Strontium-isotope stratigraphy of Upper Cretaceous platform carbonates of the island of Brač (Adriatic Sea, Croatia): Implications for global correlation of platform evolution and biostratigraphy. Cretac. Res. 26, 741–756 (2005).
Google Scholar 
29.Schlüter, M., Steuber, T. & Parente, M. Chronostratigraphy of Campanian-Maastrichtian platform carbonates and rudist associations of Salento (Apulia, Italy). Cretac. Res. 29, 100–114 (2008).
Google Scholar 
30.Consorti, L., Frijia, G. & Caus, E. Rotaloidean foraminifera from the Upper Cretaceous carbonates of Central and Southern Italy and their chronostratigraphic age. Cretac. Res. 70, 226–243 (2017).
Google Scholar 
31.Fourcade, E. Murciella cuvillieri n. gen. n. sp. nouveau foraminifère du Sénonien supérieur du sud-est de l’Espagne. Rev. Micropaléontol. 147–155 (1966).32.Fleury, J.-J. Rhapydioninidés du Campanien-Maastrichtien en région méditerranéenne : Les genres Murciella, Sigalveolina n. gen. et Cyclopseudedomia. Carnets Géologie Noteb. Geol. 18, 233–280 (2018).33.Dercourt et al. Atlas of Peri-Tethys Palaeogeographical Maps-digital-CCGM-CGMW. https://ccgm.org/en/atlases/189-atlas-of-peri-tethys-palaeogeographical-maps-.html (2000).34.Bosellini, A. Dinosaurs, “re-write” the geodynamics of the eastern Mediterranean and the paleogeography of the Apulia Platform. Earth-Sci. Rev. 59, 211–234 (2002).ADS 

Google Scholar 
35.Zarcone, G. et al. A possible bridge between Adria and Africa: New palaeobiogeographic and stratigraphic constraints on the Mesozoic palaeogeography of the Central Mediterranean area. Earth-Sci. Rev. 103, 154–162 (2010).ADS 

Google Scholar 
36.Ustaszewski, K. et al. Late Cretaceous intra-oceanic magmatism in the internal Dinarides (northern Bosnia and Herzegovina): Implications for the collision of the Adriatic and European plates. Lithos 108, 106–125 (2009).ADS 
CAS 

Google Scholar 
37.Dragičević, I. & Velić, I. The Northeastern Margin of the Adriatic Carbonate Platform. Geol. Croat. 55, 185–232 (2002).
Google Scholar 
38.Petti, F. et al. Cretaceous tetrapod tracks from Italy: A treasure trove of exceptional biodiversity. J. Mediterr. Earth Sci. 12, 167–191 (2020).
Google Scholar 
39.Blatnik, M. et al. Late Cretaceous and Paleogene Paleokarsts of the Northern Sector of the Adriatic Carbonate Platform. in Karstology in the classical Karst (eds. Knez, M., Otoničar, B., Petrič, M., Pipan, T. & Slabe, T.) 11–31 (Springer, 2020). https://doi.org/10.1007/978-3-030-26827-5_2.40.Boscarolli, D. & Dalla Vecchia, F. The Upper Hauterivian-Lower Barremian dinosaur site of Bale/valle (SW Istria, Croatia). (1999).41.Dalla Vecchia, F. M. Theropod footprints in the Cretaceous Adriatic-Dinaric Carbonate Platform (Italy and Croatia). Gaia 15, 355–367 (1998).
Google Scholar 
42.Dalla Vecchia, F. Cretaceous dinosaurs in the Adriatic-Dinaric Carbonate Platform (Italy and Croatia): Paleoenvironmental implications and paleogeographical hypotheses. Mem. Della Soc. Geol. Ital. 57, 89–100 (2002).
Google Scholar 
43.Dalla Vecchia, F., Vlahović, I., Posocco, L., Tarlao, A. & Tentor, M. Late Barremian and late Albian (Early Cretaceous) dinosaur tracksites in the main Brioni/Brijun island (SW Istria, Croatia). Nat. Nascosta 25, 1–36 (2002).44.Dalla Vecchia, F. M. et al. New dinosaur track sites in the Albian (Early Cretaceous) of the Istrian peninsula (Croatia) part I—Stratigraphy and sedimentology part II—paleontology. 69 (2000).45.Debeljak, I., Košir, A. & Otoničar, B. A preliminary note on dinosaurs and non-dinosaurian reptiles from the Upper Cretaceous carbonate platform succession at Kozina (SW Slovenia). Razpr. Slov. Akad. Znan. Umet. Razred Za Naravosl. Vede Diss. Acad. Sci. Artium Slov. Cl. IV Hist. Nat. 3–25 (1999).46.Debeljak, I., Košir, A., Buffetaut, E. & Otoničar, B. The Late Cretaceous dinosaurs and crocodiles of Kozina (SW Slovenia): A preliminary study. Mem. Della Soc. Geol. Ital. 57, 193–201 (2002).
Google Scholar 
47.Mezga, A. et al. A new dinosaur tracksite in the Cenomanian of Istria, Croatia. Riv. Ital. Paleontol. E Stratigr. 112, 435–445 (2006).
Google Scholar 
48.Mezga, A., Meyer, C. A., Tešović, B. C., Bajraktarević, Z. & Gušić, I. The first record of dinosaurs in the Dalmatian part (Croatia) of the Adriatic-Dinaric carbonate platform (ADCP). Cretac. Res. 27, 735–742 (2006).
Google Scholar 
49.Weishampel, D., Norman, D. & Grigorescu, D. Telmatosaurus transsylvanicus from the Late Cretaceous of Romania: the most basal hadrosaurid dinosaur. Palaeontology 36, 361–385 (1993).
Google Scholar 
50.Richard Owen. Report on British fossil reptiles, Part 2. in vol. 11 60–204 (1842).51.Seeley, H. G. On the classification of the fossil animals commonly named Dinosauria. Proc. R. Soc. Lond. 43, 165–171 (1887).
Google Scholar 
52.Marsh, O. C. Principal characters of American Jurassic dinosaurs, IV. Am. J. Sci. s3–21, 167–170 (1881).53.Sereno, P. C. The Origin and Evolution of Dinosaurs. Annu. Rev. Earth Planet. Sci. 25, 435–489 (1997).ADS 
CAS 

Google Scholar 
54.Cope, E. D. Synopsis of the extinct Batrachia, Reptilia, and Aves of North America. Trans. Am. Philos. Soc. 1–252 (1870).55.Madzia, D., Jagt, J. W. M. & Mulder, E. W. A. Osteology, phylogenetic affinities and taxonomic status of the enigmatic late Maastrichtian ornithopod taxon Orthomerus dolloi (Dinosauria, Ornithischia). Cretac. Res. 108, 104334 (2020).56.Norman, D. On the history, osteology, and systematic position of the Wealden (Hastings Group) dinosaur Hypselospinus fittoni (Iguanodontia: Styracosterna). Zool. J. Linn. Soc. 173, 92 (2014).57.Xing, H., Mallon, J. C. & Currie, M. L. Supplementary cranial description of the types of Edmontosaurus regalis (Ornithischia: Hadrosauridae), with comments on the phylogenetics and biogeography of Hadrosaurinae. PLOS ONE 12, e0175253 (2017).58.Sues, H.-D. & Averianov, A. A new basal hadrosauroid dinosaur from the Late Cretaceous of Uzbekistan and the early radiation of duck-billed dinosaurs. Proc. R. Soc. B Biol. Sci. 276, 2549–2555 (2009).
Google Scholar 
59.Shibata, M., Jintasakul, P., Azuma, Y. & You, H.-L. A New Basal Hadrosauroid Dinosaur from the Lower Cretaceous Khok Kruat Formation in Nakhon Ratchasima Province, Northeastern Thailand. PLOS ONE 10, e0145904 (2015).60.McDonald, A. T., Bird, J., Kirkland, J. I. & Dodson, P. Osteology of the Basal Hadrosauroid Eolambia caroljonesa (Dinosauria: Ornithopoda) from the Cedar Mountain Formation of Utah. PLoS ONE 7, e45712 (2012).61.Gates, T., Horner, J., Hanna, R. & Nelson, R. New Unadorned Hadrosaurine Hadrosaurid (Dinosauria, Ornithopoda) from the Campanian of North America. J. Vertebr. Paleontol. 31, 798–811 (2011).
Google Scholar 
62.Prieto-Marquez, A. New information on the cranium of Brachylophosaurus canadensis (Dinosauria, Hadrosauridae), with a revision of its phylogenetic position. J. Vertebr. Paleontol. 25, 144–156 (2005).
Google Scholar 
63.Gates, T. A. & Lamb, J. Redescription of Lophorhothon atopus (Ornithopoda: Dinosauria) from the Late Cretaceous of Alabama based on new material. Can. J. Earth Sci. https://doi.org/10.1139/cjes-2020-0173 (2021).Article 

Google Scholar 
64.Prieto-Márquez, A., Erickson, G. M. & Ebersole, J. A. Anatomy and osteohistology of the basal hadrosaurid dinosaur Eotrachodon from the uppermost Santonian (Cretaceous) of southern Appalachia. PeerJ 4, e1872 (2016).65.You, H.-L. & Li, D.-Q. A new basal hadrosauriform dinosaur (Ornithischia: Iguanodontia) from the Early Cretaceous of northwestern China. Can. J. Earth Sci. 46, 949–957 (2009).ADS 

Google Scholar 
66.Fowler, E. A. F. & Horner, J. R. A New Brachylophosaurin Hadrosaur (Dinosauria: Ornithischia) with an Intermediate Nasal Crest from the Campanian Judith River Formation of Northcentral Montana. PLOS ONE 10, e0141304 (2015).67.Gates, T. A., Hall, B. & Lamb, J. P. A redescription of Lophorhothon atopus (Ornithopoda: Dinosauria) from the Cretaceous of Alabama based on new material. Can. J. Earth Sci. 58, 918–935 (2021).
Google Scholar 
68.Gates, T. A., Evans, D. C. & Sertich, J. J. W. Description and rediagnosis of the crested hadrosaurid (Ornithopoda) dinosaur Parasaurolophus cyrtocristatus on the basis of new cranial remains. PeerJ 9, e10669 (2021).69.Griffin, C. T. et al. Assessing ontogenetic maturity in extinct saurian reptiles. Biol. Rev. 96, 470–525. https://doi.org/10.1111/brv.12666?af=R (2021).
Google Scholar 
70.Bailleul, A. M., Scannella, J. B., Horner, J. R. & Evans, D. C. Fusion patterns in the skulls of modern archosaurs reveal that sutures are ambiguous maturity indicators for the Dinosauria. PLOS ONE 11, e0147687 (2016).71.Francillon-Vieillot, H. et al. Microstructure and mineralization of vertebrate skeletal tissues. in Skeletal biomineralization: Patterns, processes and evolutionary trends 175–234 (American Geophysical Union (AGU), 1989). https://doi.org/10.1029/SC005p0175.72.Woodward Ballard, H., Horner, J. & Farlow, J. Osteohistological evidence for determinate growth in the American Alligator. J. Herpetol. 45, 339–342 (2011).73.Horner, J. R., Ricqlès, A. D. & Padian, K. Long bone histology of the hadrosaurid dinosaur Maiasaura peeblesorum: Growth dynamics and physiology based on an ontogenetic series of skeletal elements. J. Vertebr. Paleontol. 20, 115–129 (2000).
Google Scholar 
74.Erickson, G. M. Assessing dinosaur growth patterns: A microscopic revolution. Trends Ecol. Evol. 20, 677–684 (2005).PubMed 

Google Scholar 
75.Hone, D. W. E., Farke, A. A. & Wedel, M. J. Ontogeny and the fossil record: What, if anything, is an adult dinosaur?. Biol. Lett. 12, 20150947 (2016).PubMed 
PubMed Central 

Google Scholar 
76.Sharma, P. P., Clouse, R. M. & Wheeler, W. C. Hennig’s semaphoront concept and the use of ontogenetic stages in phylogenetic reconstruction. Cladistics 33, 93–108 (2017).PubMed 

Google Scholar 
77.Campione, N. E., Brink, K. S., Freedman, E. A., McGarrity, C. T. & Evans, D. C. ‘Glishades ericksoni’, an indeterminate juvenile hadrosaurid from the Two Medicine Formation of Montana: implications for hadrosauroid diversity in the latest Cretaceous (Campanian-Maastrichtian) of western North America. Palaeobiodivers. Palaeoenviron. 93, 65–75 (2013).
Google Scholar 
78.Kobayashi, Y., Takasaki, R., Kubota, K. & Fiorillo, A. R. A new basal hadrosaurid (Dinosauria: Ornithischia) from the latest Cretaceous Kita-ama Formation in Japan implies the origin of hadrosaurids. Sci. Rep. 11(1), 1–15. https://www.nature.com/articles/s41598-021-87719-5 (2021).79.A new brachylophosaurin (Dinosauria: Hadrosauridae) from the Upper Cretaceous Menefee Formation of New Mexico [PeerJ]. https://peerj.com/articles/11084/.80.Prieto-Marquez, A. & Carrera Farias, M. The late-surviving early diverging Ibero-Armorican ‘duck-billed’ dinosaur Fylax and the role of the Late Cretaceous European Archipelago in hadrosauroid biogeography. Acta Palaeontol. Pol. 66, 425–435 (2021).81.Prieto-Márquez, A., Dalla Vecchia, F. M., Gaete, R. & Galobart, À. Diversity, Relationships, and biogeography of the Lambeosaurine Dinosaurs from the European Archipelago, with Description of the New Aralosaurin Canardia garonnensis. PLoS ONE 8, e69835 (2013).82.Longrich, N. R., Suberbiola, X. P., Pyron, R. A. & Jalil, N.-E. The first duckbill dinosaur (Hadrosauridae: Lambeosaurinae) from Africa and the role of oceanic dispersal in dinosaur biogeography. Cretac. Res. 120, 104678 (2021).83.Brown, C. M., Evans, D. C., Campione, N. E., O’Brien, L. J. & Eberth, D. A. Evidence for taphonomic size bias in the Dinosaur Park Formation (Campanian, Alberta), a model Mesozoic terrestrial alluvial-paralic system. Palaeogeogr. Palaeoclimatol. Palaeoecol. 372, 108–122 (2013).
Google Scholar 
84.Mallon, J. C., Evans, D. C., Ryan, M. J. & Anderson, J. S. Feeding height stratification among the herbivorous dinosaurs from the Dinosaur Park Formation (upper Campanian) of Alberta, Canada. BMC Ecol. 13, 14 (2013).PubMed 
PubMed Central 

Google Scholar 
85.Mallon, J. C. Competition structured a Late Cretaceous megaherbivorous dinosaur assemblage. Sci. Rep. 9, 15447 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
86.Campione, N. E. & Evans, D. C. The accuracy and precision of body mass estimation in non-avian dinosaurs. Biol. Rev. 95, 1759–1797 (2020).PubMed 

Google Scholar 
87.Benítez-López, A. et al. The island rule explains consistent patterns of body size evolution in terrestrial vertebrates. Nat. Ecol. Evol. 5, 768–786 (2021).PubMed 

Google Scholar 
88.Fabbri M. et al. A shift in ontogenetic timing produced the unique sauropod skull. Evolution 75, 819–831 (2021).PubMed 

Google Scholar 
89.Chinsamy, A. & Raath, M. A. Preparation of fossil bone for histological examination. (1992).90.Fabbri, M., Wiemann, J., Manucci, F. & Briggs, D. E. G. Three-dimensional soft tissue preservation revealed in the skin of a non-avian dinosaur. Palaeontology 63, 185–193 (2020).
Google Scholar 
91.Takasaki R., et al. Re-examination of the cranial osteology of the Arctic Alaskan hadrosaurine with implications for its taxonomic status. https://doi.org/10.1371/journal.pone.0232410 (2020).92.Raven, T. J. & Maidment, S. C. R. The systematic position of the enigmatic thyreophoran dinosaur Paranthodon africanus, and the use of basal exemplifiers in phylogenetic analysis. PeerJ 6, e4529 (2018).93.Goloboff, P. A. Extended implied weighting. Cladistics 30, 260–272 (2014).PubMed 

Google Scholar 
94.Goloboff, P. A., Farris, J. S. & Nixon, K. C. TNT, a free program for phylogenetic analysis. Cladistics 24, 774–786 (2008).
Google Scholar 
95.Herne, M. C., Nair, J. P., Evans, A. R. & Tait, A. M. New small-bodied ornithopods (Dinosauria, Neornithischia) from the Early Cretaceous Wonthaggi Formation (Strzelecki Group) of the Australian-Antarctic rift system, with revision of Qantassaurus intrepidus Rich and Vickers-Rich, 1999. J. Paleontol. 93, 543–584 (2019).
Google Scholar 
96.Madzia, D. & Cau, A. Inferring ‘weak spots’ in phylogenetic trees: application to mosasauroid nomenclature. PeerJ 5, e3782 (2017).97.Madzia, D. & Cau, A. Estimating the evolutionary rates in mosasauroids and plesiosaurs: discussion of niche occupation in Late Cretaceous seas. PeerJ 8, e8941 (2020).98.Nicholl, C. S. C., Rio, J. P., Mannion, P. D. & Delfino, M. A re-examination of the anatomy and systematics of the tomistomine crocodylians from the Miocene of Italy and Malta. J. Syst. Palaeontol. 18, 1853–1889 (2020).
Google Scholar 
99.Rio, J. P., Mannion, P. D., Tschopp, E., Martin, J. E. & Delfino, M. Reappraisal of the morphology and phylogenetic relationships of the alligatoroid crocodylian Diplocynodon hantoniensis from the late Eocene of the United Kingdom. Zool. J. Linn. Soc. 188, 579–629 (2020).
Google Scholar 
100.Groh, S. S., Upchurch, P., Barrett, P. M. & Day, J. J. The phylogenetic relationships of neosuchian crocodiles and their implications for the convergent evolution of the longirostrine condition. Zool. J. Linn. Soc. 188, 473–506 (2020).
Google Scholar 
101.Bell, M. A. & Lloyd, G. T. Strap: An R package for plotting phylogenies against stratigraphy and assessing their stratigraphic congruence. Palaeontology 58, 379–389 (2015).
Google Scholar 
102.Hansen, T. F. Stabilizing selection and the comparative analysis of adaptation. Evolution 51, 1341–1351 (1997).PubMed 

Google Scholar 
103.Butler, M. A. & King, A. A. Phylogenetic comparative analysis: A modeling approach for adaptive evolution. Am. Nat. 164, 683–695 (2004).PubMed 

Google Scholar 
104.Beaulieu, J. M., Jhwueng, D.-C., Boettiger, C. & O’Meara, B. C. Modeling stabilizing selection: Expanding the Ornstein–Uhlenbeck model of adaptive evolution. Evolution 66, 2369–2383 (2012).PubMed 

Google Scholar 
105.Hunt, G. Measuring rates of phenotypic evolution and the inseparability of tempo and mode. Paleobiology 38, 351–373 (2012).
Google Scholar 
106.Grosheny, D. et al. The Cenomanian-Turonian Boundary Event (CTBE) in northern Lebanon as compared to regional data—Another set of evidences supporting a short-lived tectonic pulse coincidental with the event?. Palaeogeogr. Palaeoclimatol. Palaeoecol. 487, 447–461 (2017).
Google Scholar 
107.Prieto-Márquez, A. Global historical biogeography of hadrosaurid dinosaurs. Zool. J. Linn. Soc. 159, 503–525 (2010).
Google Scholar 
108.Nariaki Sugiura. Further analysts of the data by akaike’ s information criterion and the finite corrections: Further analysts of the data by Akaike’s: Communications in Statistics—Theory and Methods: Vol 7, No 1. https://doi.org/10.1080/03610927808827599 (1978).109.Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach. (Springer, Berlin, 2002). https://doi.org/10.1007/b97636.110.Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
111.Gates, T. A., Organ, C. & Zanno, L. E. Bony cranial ornamentation linked to rapid evolution of gigantic theropod dinosaurs. Nat. Commun. 7, 12931 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
112.Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4, 754–759 (2013).
Google Scholar 
113.Pennell, M. W. et al. geiger v2.0: an expanded suite of methods for fitting macroevolutionary models to phylogenetic trees. Bioinformatics 30, 2216–2218 (2014). More

Alcala N, Goldberg A, Ramakrishnan U, Rosenberg NA (2019) Coalescent theory of migration network motifs. Mol Biol Evol 36:2358–2374CAS 
PubMed 
PubMed Central 

Google Scholar 
Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19:1655–1664CAS 
PubMed 
PubMed Central 

Google Scholar 
Anjana S, Sammeta SP, Das R (2020) Developing ancestry informative marker panel for Nigeria-Cameroonian chimpanzees. J Genet 99:1–7
Google Scholar 
Armstrong E, Khan A, Taylor RW, Gouy A, Greenbaum G, Thiéry A et al. (2021) Recent evolutionary history of tigers highlights contrasting roles of genetic drift and selection. Mol Biol Evol 38:2366–2379CAS 
PubMed 
PubMed Central 

Google Scholar 
Ballou JD (1992) Genetic and demographic considerations in endangered species captive breeding and reintroduction programs. In: Wildlife 2001: populations, Springer: Dordrecht, pp. 262–275Balloux F, Lugon‐Moulin N (2002) The estimation of population differentiation with microsatellite markers. Mol Ecol 11:155–165PubMed 

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

Google Scholar 
Brandies P, Peel E, Hogg CJ, Belov K (2019) The value of reference genomes in the conservation of threatened species. Genes (Basel) 10:846CAS 

Google Scholar 
Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140PubMed 
PubMed Central 

Google Scholar 
Chapman JR, Nakagawa S, Coltman DW, Slate J, Sheldon BC (2009) A quantitative review of heterozygosity–fitness correlations in animal populations. Mol Ecol 18:2746–2765CAS 
PubMed 

Google Scholar 
Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158CAS 
PubMed 
PubMed Central 

Google Scholar 
Das R, Roy R, Venkatesh N (2019) Using ancestry informative markers (AIMs) to detect fine structure within gorilla populations. Front Genet 10:43CAS 
PubMed 
PubMed Central 

Google Scholar 
Das R, Upadhyai P (2018) An ancestry informative marker set which recapitulates the known fine structure of populations in South Asia. Genome Biol Evol 10:2408–2416PubMed 
PubMed Central 

Google Scholar 
Das R, Upadhyai P (2019) Application of the geographic population genetic structure (GPS) algorithm for biogeographical analyses of wild and captive gorillas. BMC Bioinforma 20:35
Google Scholar 
Dudash MR, Fenster CB (2000) Inbreeding and outbreeding depression in fragmented populations. Conserv Biol Ser 35–54Esposito U, Das R, Syed S, Pirooznia M, Elhaik E (2018) Ancient ancestry informative markers for identifying fine-scale ancient population genetic structure in Eurasians. Genes (Basel) 9:625
Google Scholar 
Feng S, Stiller J, Deng Y, Armstrong J, Fang Q, Reeve AH et al. (2020) Dense sampling of bird diversity increases power of comparative genomics. Nature 587:252–257CAS 
PubMed 
PubMed Central 

Google Scholar 
Frantz AC, Pourtois JT, Heuertz M, Schley L, Flamand MC, Krier A et al. (2006) Genetic structure and assignment tests demonstrate illegal translocation of red deer (Cervus elaphus) into a continuous population. Mol Ecol 15:3191–3203CAS 
PubMed 

Google Scholar 
Friar EA, Boose DL, LaDoux T, Roalson EH, Robichaux RH (2001) Population genetic structure in the endangered Mauna Loa silversword, Argyroxiphium kauense (Asteraceae), and its bearing on reintroduction. Mol Ecol 10:1657–1663CAS 
PubMed 

Google Scholar 
Fuentes‐Pardo AP, Ruzzante DE (2017) Whole‐genome sequencing approaches for conservation biology: Advantages, limitations and practical recommendations. Mol Ecol 26:5369–5406PubMed 

Google Scholar 
Goodrich J, Lynam A, Miquelle D, Wibisono H, Kawanishi K, Pattanavibool A et al. (2015) Panthera tigris. In: The IUCN Red List of Threatened Species 2015, p. 15955 50659951Ivy JA, Lacy RC (2010) Using molecular methods to improve the genetic management of captive breeding programs for threatened species. In: Molecular approaches in natural resource conservation and management, pp. 267–295Jangtarwan K, Koomgun T, Prasongmaneerut T, Thongchum R, Singchat W, Tawichasri P et al. (2019) Take one step backward to move forward: Assessment of genetic diversity and population structure of captive Asian woolly-necked storks (Ciconia episcopus). PLoS One 14:e0223726CAS 
PubMed 
PubMed Central 

Google Scholar 
Jiménez‐Mena B, Schad K, Hanna N, Lacy RC (2016) Pedigree analysis for the genetic management of group‐living species. Ecol Evol 6:3067–3078PubMed 
PubMed Central 

Google Scholar 
Kanthaswamy S, Johnson Z, Trask JS, Smith DG, Ramakrishnan R, Bahk J et al. (2014) Development and validation of a SNP‐based assay for inferring the genetic ancestry of rhesus macaques (Macaca mulatta). Am J Primatol 76:1105–1113CAS 
PubMed 
PubMed Central 

Google Scholar 
Khan A, Patel K, Bhattacharjee S, Sharma S, Chugani AN, Sivaraman K et al. (2020) Are shed hair genomes the most effective noninvasive resource for estimating relationships in the wild? Ecol Evol 10:4583–4594PubMed 
PubMed Central 

Google Scholar 
Khan A, Patel K, Shukla H, Viswanathan A, van der Valk T, Borthakur U, et al. (2021). Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. bioRxivKhan A, Tyagi A (2021) Considerations for initiating a wildlife genomics research project in South and South-East Asia. J Indian Inst Sci 101:243–256. https://doi.org/10.1007/s41745-021-00243-3Article 

Google Scholar 
Kirk H, Freeland JR (2011) Applications and implications of neutral versus non-neutral markers in molecular ecology. Int J Mol Sci 12:3966–3988PubMed 
PubMed Central 

Google Scholar 
Kolipakam V, Singh S, Pant B, Qureshi Q, Jhala YV (2019) Genetic structure of tigers (Panthera tigris tigris) in India and its implications for conservation. Glob. Ecol Conserv 20:710
Google Scholar 
Kunde MN, Martins RF, Premier J, Fickel J, Förster DW (2020) Population and landscape genetic analysis of the Malayan sun bear Helarctos malayanus. Conserv Genet 21:123–135CAS 

Google Scholar 
Laikre L, Palm S, Ryman N (2005) Genetic population genetic structure of fishes: implications for coastal zone management. AMBIO A J Hum Environ 34:111–119
Google Scholar 
Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357CAS 
PubMed 
PubMed Central 

Google Scholar 
Liang Z, Bu L, Qin Y, Peng Y, Yang R, Zhao Y (2019) Selection of optimal ancestry informative markersfor classification and ancestry proportion estimation in pigs. Front genet 10:183PubMed 
PubMed Central 

Google Scholar 
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The sequence alignment/map format and SAMtools. Bioinformatics 25:2078–2079PubMed 
PubMed Central 

Google Scholar 
Lott MJ, Wright BR, Kemp LF, Johnson RN, Hogg CJ (2020) Genetic Management of Captive and Reintroduced Bilby Populations. J Wildl Manag 84:20–32
Google Scholar 
Luo SJ, Johnson WE, Martenson J, Antunes A, Martelli P, Uphyrkina O et al. (2008) Subspecies genetic assignments of worldwide captive tigers increase conservation value of captive populations. Curr Biol 18:592–596CAS 
PubMed 

Google Scholar 
Miller W, Hayes VM, Ratan A, Petersen DC, Wittekindt NE, Miller J et al. (2011) Genetic diversity and population structure of the endangered marsupial Sarcophilus harrisii (Tasmanian devil) Proc Natl Acad Sci 108:12348–12353CAS 
PubMed 
PubMed Central 

Google Scholar 
Mondol S, Bruford MW, Ramakrishnan U (2013) Demographic loss, genetic structure and the conservation implications for Indian tigers. Proc R Soc B Biol Sci 280:20130496
Google Scholar 
Muñoz I, Henriques D, Johnston JS, Chávez-Galarza J, Kryger P, Pinto MA (2015) Reduced SNP Panels for Genetic Identification and Introgression Analysis in the Dark Honey Bee (Apis mellifera mellifera) PLOS One 10:e0124365PubMed 
PubMed Central 

Google Scholar 
Nassir R, Kosoy R, Tian C, White PA, Butler LM, Silva G et al. (2009) An ancestry informative marker set for determining continental origin: validation and extension using human genome diversity panels. BMC Genet 10:39PubMed 
PubMed Central 

Google Scholar 
Natesh M, Atla G, Nigam P, Jhala YV, Zachariah A, Borthakur U et al. (2017) Conservation priorities for endangered Indian tigers through a genomic lens. Sci Rep. 7:1–11
Google Scholar 
Natesh M, Taylor RW, Truelove NK, Hadly EA, Palumbi SR, Petrov DA et al. (2019) Empowering conservation practice with efficient and economical genotyping from poor quality samples. Methods Ecol evolution 10:853–859
Google Scholar 
Patterson N, Price AL, Reich D (2006) Population genetic structure and eigenanalysis. PLoS Genet 2:190
Google Scholar 
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904–909CAS 
PubMed 

Google Scholar 
Pritchard JK, Stephens M, Donnelly P (2000) Inference of population genetic structure using multilocus genotype data. Genetics 155:945–959CAS 
PubMed 
PubMed Central 

Google Scholar 
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559–575CAS 
PubMed 
PubMed Central 

Google Scholar 
Putnam AS, Ivy JA (2014) Kinship-based management strategies for captive breeding programs when pedigrees are unknown or uncertain. J Hered 105:303–311PubMed 

Google Scholar 
Rosenberg NA, Li LM, Ward R, Pritchard JK (2003) Informativeness of genetic markers for inference of ancestry. Am J Hum Genet 73:1402–1422CAS 
PubMed 
PubMed Central 

Google Scholar 
Sagar V, Kaelin CB, Natesh M, Reddy PA, Mohapatra RK, Chhattani H et al. (2021) High frequency of an otherwise rare phenotype in a small and isolatedtiger population. Proc Natl Acad Sci p. 118Saunders CT, Wong WS, Swamy S, Becq J, Murray LJ, Cheetham RK (2012) Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs. Bioinformatics 28:1811–1817CAS 
PubMed 

Google Scholar 
Schlaepfer DR, Braschler B, Rusterholz HP, Baur B (2018) Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta‐analysis. Ecosphere 9:2488
Google Scholar 
Shriver MD, Parra EJ, Dios S, Bonilla C, Norton H, Jovel C et al. (2003) Skin pigmentation, biogeographical ancestry and admixture mapping. Hum Genet 112:387–399PubMed 

Google Scholar 
Somenzi E, Ajmone-Marsan P, Barbato M (2020) Identification of ancestry informative marker (AIM) panels to assess hybridisation between feral and domestic sheep. Animals 10:582PubMed Central 

Google Scholar 
Supple MA, Shapiro B (2018) Conservation of biodiversity in the genomics era. Genome Biol 19:1–12
Google Scholar 
Svardal H, Jasinska AJ, Apetrei C, Coppola G, Huang Y, Schmitt CA et al. (2017) Ancient hybridization and strong adaptation to viruses across African vervet monkey populations. Nat genet 49:1705–1713CAS 
PubMed 
PubMed Central 

Google Scholar 
Vongpaisarnsin K, Listman JB, Malison RT, Gelernter J (2015) Ancestry informative markers for distinguishing between Thai populations based on genome-wide association datasets. Leg Med 17:245–250CAS 

Google Scholar 
Wasser SK, Brown L, Mailand C, Mondol S, Clark W, Laurie C et al. (2015) Genetic assignment of large seizures of elephant ivory reveals Africa’s major poaching hotspots. Science (80-) 349:84–87CAS 

Google Scholar 
Wilkinson S, Wiener P, Archibald AL, Law A, Schnabel RD, McKay SD et al. (2011) Evaluation of approaches for identifying population informative markers from high density SNP chips. BMC genetics 12:1–14Wright S (1969) Evolution and the genetics of populationsWultsch C, Caragiulo A, Dias-Freedman I, Quigley H, Rabinowitz S, Amato G (2016) Genetic diversity and population genetic structure of Mesoamerican jaguars (Panthera onca): implications for conservation and management. PLoS One 11:162377
Google Scholar  More

1.Thompson JR, Rivera HE, Closek CJ, Medina M. Microbes in the coral holobiont: partners through evolution, development, and ecological interactions. Front Cell Infect Microbiol. 2014;4:176.PubMed 

Google Scholar 
2.Pogoreutz C, Voolstra CR, Rädecker N, Weis V, Cardenas A, Raina J-B. The coral holobiont highlights the dependence of cnidarian animal hosts on their associated microbes. In: Bosch TCG, Hadfield MG, editors. Cellular dialogues in the holobiont. CRC Press; 2020. p. 91–118.3.Stanley GD, van de Schootbrugge B. The evolution of the coral–algal symbiosis. In: van Oppen MJH, Lough JM, editors. Coral bleaching: patterns, processes, causes and consequences. Berlin, Heidelberg: Springer; 2009. p. 7–19.4.LaJeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. Systematic revision of Symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr Biol. 2018;28:2570–80.e6.CAS 
PubMed 

Google Scholar 
5.Muscatine L. The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reefs. 1990;25:75–87.
Google Scholar 
6.Stolarski J, Kitahara MV, Miller DJ, Cairns SD, Mazur M, Meibom A. The ancient evolutionary origins of Scleractinia revealed by azooxanthellate corals. BMC Evol Biol. 2011;11:316.PubMed 
PubMed Central 

Google Scholar 
7.Frankowiak K, Wang XT, Sigman DM, Gothmann AM, Kitahara MV, Mazur M, et al. Photosymbiosis and the expansion of shallow-water corals. Sci Adv. 2016;2:e1601122.PubMed 
PubMed Central 

Google Scholar 
8.Wild C, Hoegh-Guldberg O, Naumann MS, Colombo-Pallotta MF, Ateweberhan M, Fitt WK, et al. Climate change impedes scleractinian corals as primary reef ecosystem engineers. Mar Freshw Res. 2011;62:205–15.CAS 

Google Scholar 
9.Hughes TP, Barnes ML, Bellwood DR, Cinner JE, Cumming GS, Jackson JBC. et al. Coral reefs in the Anthropocene. Nature. 2017;546:82–90.CAS 
PubMed 

Google Scholar 
10.Kleypas J, Allemand D, Anthony K, Baker AC, Beck MW, Hale LZ, et al. Designing a blueprint for coral reef survival. Biol Conserv. 2021;257:109107.
Google Scholar 
11.Anthony KRN, Hoogenboom MO, Maynard JA, Grottoli AG, Middlebrook R. Energetics approach to predicting mortality risk from environmental stress: a case study of coral bleaching. Funct Ecol. 2009;23:539–50.
Google Scholar 
12.Hughes TP, Anderson KD, Connolly SR, Heron SF, Kerry JT, Lough JM. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science. 2018;359:80–83.CAS 
PubMed 

Google Scholar 
13.Voolstra CR, Suggett DJ, Peixoto RS, John E, Parkinson KM, Quigley CB, Silveira M, et al. Extending the natural adaptive capacity of coral holobionts. Nat Rev Earth Environ. 2021;2:747–62; https://doi.org/10.1038/s43017-021-00214-3.Article 

Google Scholar 
14.Suggett DJ, Smith DJ. Coral bleaching patterns are the outcome of complex biological and environmental networking. Glob Chang Biol. 2020;26:68–79.PubMed 

Google Scholar 
15.Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F, Bougoure J, et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci USA. 2021;118:e2022653118.PubMed 
PubMed Central 

Google Scholar 
16.Morris LA, Voolstra CR, Quigley KM, Bourne DG, Bay LK. Nutrient availability and metabolism affect the stability of coral–Symbiodiniaceae symbioses. Trends Microbiol. 2019;27:678–89.CAS 
PubMed 

Google Scholar 
17.Muscatine L, Porter JW. Reef Corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience. 1977;27:454–60.
Google Scholar 
18.Falkowski PG, Dubinsky Z, Muscatine L, Porter JW. Light and the bioenergetics of a symbiotic coral. Bioscience. 1984;34:705–9.CAS 

Google Scholar 
19.Falkowski PG, Dubinsky Z, Muscatine L, McCloskey L. Population control in symbiotic corals. Bioscience. 1993;43:606–11.
Google Scholar 
20.Peng S-E, Chen C-S, Song Y-F, Huang H-T, Jiang P-L, Chen W-NU, et al. Assessment of metabolic modulation in free-living versus endosymbiotic Symbiodinium using synchrotron radiation-based infrared microspectroscopy. Biol Lett. 2012;8:434–7.PubMed 

Google Scholar 
21.Krueger T, Horwitz N, Bodin J, Giovani M-E, Escrig S, Fine M, et al. Intracellular competition for nitrogen controls dinoflagellate population density in corals. Proc R Soc B. 2020;287:20200049.CAS 
PubMed 
PubMed Central 

Google Scholar 
22.Roberty S, Béraud E, Grover R, Ferrier-Pagès C. Coral croductivity is co-limited by bicarbonate and ammonium availability. Microorganisms. 2020;8:640CAS 
PubMed Central 

Google Scholar 
23.Baker DM, Freeman CJ, Wong JCY, Fogel ML, Knowlton N. Climate change promotes parasitism in a coral symbiosis. ISME J. 2018;12:921–30.CAS 
PubMed 
PubMed Central 

Google Scholar 
24.Cunning R, Muller EB, Gates RD, Nisbet RM. A dynamic bioenergetic model for coral- Symbiodinium symbioses and coral bleaching as an alternate stable state. J Theor Biol. 2017;431:49–62.CAS 
PubMed 

Google Scholar 
25.Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 2015;23:490–7.PubMed 

Google Scholar 
26.Wiedenmann J, D’Angelo C, Smith EG, Hunt AN, Legiret F-E, Postle AD, et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. Nat Clim Chang. 2012;3:160–4.
Google Scholar 
27.Houlbrèque F, Ferrier-Pagès C. Heterotrophy in tropical scleractinian corals. Biol Rev Camb Philos Soc. 2009;84:1–17.PubMed 

Google Scholar 
28.Fiore CL, Jarett JK, Olson ND, Lesser MP. Nitrogen fixation and nitrogen transformations in marine symbioses. Trends Microbiol. 2010;18:455–63.CAS 
PubMed 

Google Scholar 
29.Grover R, Maguer J-F, Allemand D, Ferrier-Pagès C. Uptake of dissolved free amino acids by the scleractinian coral Stylophora pistillata. J Exp Biol. 2008;211:860–5.CAS 
PubMed 

Google Scholar 
30.Lema KA, Willis BL, Bourne DG. Corals form characteristic associations with symbiotic nitrogen-fixing bacteria. Appl Environ Microbiol. 2012;78:3136–44.CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Nitrogen fixation aligns with nifH abundance and expression in two coral trophic functional groups. Front Microbiol. 2017;8:1187.PubMed 
PubMed Central 

Google Scholar 
32.Olson ND, Lesser MP. Diazotrophic diversity in the Caribbean coral, Montastraea cavernosa. Arch Microbiol. 2013;195:853–9.CAS 
PubMed 

Google Scholar 
33.Lesser MP, Morrow KM, Pankey SM, Noonan SHC. Diazotroph diversity and nitrogen fixation in the coral Stylophora pistillata from the Great Barrier Reef. ISME J. 2018;12:813–24.CAS 
PubMed 

Google Scholar 
34.Moynihan MA, Goodkin NF, Morgan KM, Kho PYY, dos Santos AL, Lauro FM, et al. Coral-associated nitrogen fixation rates and diazotrophic diversity on a nutrient-replete equatorial reef. ISME J. 2021; https://doi.org/10.1038/s41396-021-01054-1.35.Tilstra A, Pogoreutz C, Rädecker N, Ziegler M, Wild C, Voolstra CR. Relative diazotroph abundance in symbiotic Red Sea corals decreases with water depth. Front Mar Sci. 2019;6:372.
Google Scholar 
36.Bednarz VN, Cardini U, van Hoytema N, Al-Rshaidat MMD, Wild C. Seasonal variation in dinitrogen fixation and oxygen fluxes associated with two dominant zooxanthellate soft corals from the northern Red Sea. Mar Ecol Prog Ser. 2015;519:141–52.
Google Scholar 
37.Rädecker N, Meyer FW, Bednarz VN, Cardini U, Wild C. Ocean acidification rapidly reduces dinitrogen fixation associated with the hermatypic coral Seriatopora hystrix. Mar Ecol Prog Ser. 2014;511:297–302.
Google Scholar 
38.Cardini U, Bednarz VN, Naumann MS, van Hoytema N, Rix L, Foster RA, et al. Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc R Soc B. 2015;282:20152257.PubMed 
PubMed Central 

Google Scholar 
39.Bednarz VN, van de Water JAJM, Grover R, Maguer J-F, Fine M, Ferrier-Pagès C. Unravelling the importance of diazotrophy in corals – combined assessment of nitrogen assimilation, diazotrophic community and natural stable isotope signatures. Front Microbiol. 2021;12:1638.
Google Scholar 
40.Santos HF, Carmo FL, Duarte G, Dini-Andreote F, Castro CB, Rosado AS, et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 2014;8:2272–9.PubMed 
PubMed Central 

Google Scholar 
41.Cardini U, van Hoytema N, Bednarz VN, Rix L, Foster RA, Al-Rshaidat MMD, et al. Microbial dinitrogen fixation in coral holobionts exposed to thermal stress and bleaching. Environ Microbiol. 2016;18:2620–33.CAS 
PubMed 

Google Scholar 
42.Bednarz VN, van de Water JAJM, Rabouille S, Maguer J-F, Grover R, Ferrier-Pagès C. Diazotrophic community and associated dinitrogen fixation within the temperate coral Oculina patagonica. Environ Microbiol. 2019;21:480–95.CAS 
PubMed 

Google Scholar 
43.Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Voolstra CR, Wild C. Sugar enrichment provides evidence for a role of nitrogen fixation in coral bleaching. Glob Chang Biol. 2017;23:3838–48.PubMed 

Google Scholar 
44.Bednarz VN, Grover R, Maguer J-F, Fine M, Ferrier-Pagès C. The assimilation of diazotroph-derived nitrogen by scleractinian corals depends on their metabolic status. mBio. 2017;8:e02058–16.CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Petrou K, Nunn BL, Padula MP, Miller DJ, Nielsen DA. Broad scale proteomic analysis of heat-destabilised symbiosis in the hard coral Acropora millepora. Sci Rep. 2021;11:19061.CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Roth F, Rädecker N, Carvalho S, Duarte CM, Saderne V, Anton A, et al. High summer temperatures amplify functional differences between coral‐ and algae‐dominated reef communities. Ecology. 2021;102:e03226.PubMed 

Google Scholar 
47.Andersson AF, Lindberg M, Jakobsson H, Bäckhed F, Nyrén P, Engstrand L. Comparative analysis of human gut microbiota by barcoded pyrosequencing. PLoS ONE. 2008;3:e2836.PubMed 
PubMed Central 

Google Scholar 
48.Bayer T, Neave MJ, Alsheikh-Hussain A, Aranda M, Yum LK, Mincer T, et al. The microbiome of the Red Sea coral Stylophora pistillata is dominated by tissue-associated Endozoicomonas bacteria. Appl Environ Microbiol. 2013;79:4759–62.CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Gaby JC, Buckley DH. A comprehensive evaluation of PCR primers to amplify the nifH gene of nitrogenase. PLoS ONE. 2012;7:e42149.CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
51.R Core Team. R: a language and environment for statistical computing computer program. Vienna, Austria: R Foundation for Statistical Computing; 2021.52.Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2013;41:D590–6.CAS 
PubMed 

Google Scholar 
53.McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
54.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Community Ecol Package. 2007;10:631–7.
Google Scholar 
55.Roesch LFW, Dobbler PT, Pylro VS, Kolaczkowski B, Drew JC, Triplett EW. pime: A package for discovery of novel differences among microbial communities. Mol Ecol Resour. 2020;20:415–28.CAS 
PubMed 

Google Scholar 
56.Guo K. microbial: do 16s data analysis and generate figures. R package version 1.14.4. 2021.57.Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.
Google Scholar 
58.Wang Q, Quensen JF, 3rd, Fish JA, Lee TK, Sun Y, Tiedje JM. et al. Ecological patterns of nifH genes in four terrestrial climatic zones explored with targeted metagenomics using FrameBot, a new informatics tool. mBio. 2013;4:e00592–13.PubMed 
PubMed Central 

Google Scholar 
59.Meunier V, Geissler L, Bonnet S, Rädecker N, Perna G, Grosso O, et al. Microbes support enhanced nitrogen requirements of coral holobionts in a high CO2 environment. Mol Ecol. 2021;30:5888–99 .60.Angel R, Nepel M, Panhölzl C, Schmidt H, Herbold CW, Eichorst SA, et al. Evaluation of primers targeting the diazotroph functional gene and development of NifMAP – a bioinformatics pipeline for analyzing nifH amplicon data. Front Microbiol. 2018;9:703.PubMed 
PubMed Central 

Google Scholar 
61.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 

Google Scholar 
62.Frank IE, Turk-Kubo KA, Zehr JP. Rapid annotation of nifH gene sequences using classification and regression trees facilitates environmental functional gene analysis. Environ Microbiol Rep. 2016;8:905–16.PubMed 

Google Scholar 
63.Berger SA, Krompass D, Stamatakis A. Performance, accuracy, and web server for evolutionary placement of short sequence reads under maximum likelihood. Syst Biol. 2011;60:291–302.PubMed 
PubMed Central 

Google Scholar 
64.Hardy RW, Holsten RD, Jackson EK, Burns RC. The acetylene-ethylene assay for N2 fixation: laboratory and field evaluation. Plant Physiol. 1968;43:1185–207.CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Breitbarth E, Mills MM, Friedrichs G, LaRoche J. The Bunsen gas solubility coefficient of ethylene as a function of temperature and salinity and its importance for nitrogen fixation assays: Bunsen coefficient and N2fixation. Limnol Oceanogr Methods 2004;2:282–8.
Google Scholar 
66.Zehr JP, Montoya JP. Measuring N2 fixation in the field. In: Bothe H, Ferguson SJ, Newton WE, editors. Biology of the nitrogen cycle. Elsevier; 2007. p. 193–205.67.Soper FM, Simon C, Jauss V. Measuring nitrogen fixation by the acetylene reduction assay (ARA): is 3 the magic ratio?. Biogeochemistry. 2021;152:345–51.CAS 

Google Scholar 
68.Lavy A, Eyal G, Neal B, Keren R, Loya Y, Ilan M. A quick, easy and non‐intrusive method for underwater volume and surface area evaluation of benthic organisms by 3D computer modelling. Methods Ecol Evol. 2015;6:521–31.
Google Scholar 
69.Wilson ST, Böttjer D, Church MJ, Karl DM. Comparative assessment of nitrogen fixation methodologies, conducted in the oligotrophic North Pacific Ocean. Appl Environ Microbiol. 2012;78:6516–23.CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Hoppe P, Cohen S, Meibom A. NanoSIMS: Technical aspects and applications in cosmochemistry and biological geochemistry. Geostand Geoanal Res. 2013;37:111–54.CAS 

Google Scholar 
71.Neave MJ, Rachmawati R, Xun L, Michell CT, Bourne DG, Apprill A, et al. Differential specificity between closely related corals and abundant Endozoicomonas endosymbionts across global scales. ISME J. 2017;11:186–200.PubMed 

Google Scholar 
72.Savary R, Barshis DJ, Voolstra CR, Cárdenas A, Evensen NR, Banc-Prandi G, et al. Fast and pervasive transcriptomic resilience and acclimation of extremely heat tolerant coral holobionts from the northern Red Sea. Proc Natl Acad Sci USA. 2021;118:e2023298118.CAS 
PubMed 
PubMed Central 

Google Scholar 
73.Lesser MP, Mazel CH, Gorbunov MY, Falkowski PG. Discovery of symbiotic nitrogen-fixing cyanobacteria in corals. Science. 2004;305:997–1000.CAS 
PubMed 

Google Scholar 
74.Lesser MP, Falcón LI, Rodríguez-Román A, Enríquez S, Hoegh-Guldberg O, Iglesias-Prieto R. Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar Ecol Prog Ser. 2007;346:143–52.CAS 

Google Scholar 
75.Tilstra A, El-Khaled YC, Roth F, Rädecker N, Pogoreutz C, Voolstra CR, et al. Denitrification aligns with N2 fixation in Red Sea corals. Sci Rep. 2019;9:19460.PubMed 
PubMed Central 

Google Scholar 
76.Rädecker N, Pogoreutz C, Ziegler M, Ashok A, Barreto MM, Chaidez V, et al. Assessing the effects of iron enrichment across holobiont compartments reveals reduced microbial nitrogen fixation in the Red Sea coral Pocillopora verrucosa. Ecol Evol. 2017;7:6614–21.PubMed 
PubMed Central 

Google Scholar 
77.Ainsworth TD, Fine M, Blackall LL, Hoegh-Guldberg O. Fluorescence in situ hybridization and spectral imaging of coral-associated bacterial communities. Appl Environ Microbiol. 2006;72:3016–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
78.van de Water JAJM, Ainsworth TD, Leggat W, Bourne DG, Willis BL, van Oppen MJH. The coral immune response facilitates protection against microbes during tissue regeneration. Mol Ecol. 2015;24:3390–404.PubMed 

Google Scholar 
79.Wada N, Ishimochi M, Matsui T, Pollock FJ, Tang S-L, Ainsworth TD, et al. Characterization of coral-associated microbial aggregates (CAMAs) within tissues of the coral Acropora hyacinthus. Sci Rep. 2019;9:14662.PubMed 
PubMed Central 

Google Scholar 
80.Udvardi M, Poole PS. Transport and metabolism in legume-rhizobia symbioses. Annu Rev Plant Biol. 2013;64:781–805.CAS 
PubMed 

Google Scholar 
81.Pernice M, Meibom A, Van Den Heuvel A, Kopp C, Domart-Coulon I, Hoegh-Guldberg O, et al. A single-cell view of ammonium assimilation in coral–dinoflagellate symbiosis. ISME J. 2012;6:1314–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
82.Shashar N, Cohen Y, Loya Y, Sar N. Nitrogen fixation (acetylene reduction) in stony corals: evidence for coral-bacteria interactions. Mar Ecol Prog Ser. 1994;111:259–64.CAS 

Google Scholar 
83.Inomura K, Bragg J, Riemann L, Follows MJ. A quantitative model of nitrogen fixation in the presence of ammonium. PLoS ONE. 2018;13:e0208282.PubMed 
PubMed Central 

Google Scholar 
84.Agawin NSR, Rabouille S, Veldhuis MJW, Servatius L, Hol S, van Overzee HMJ, et al. Competition and facilitation between unicellular nitrogen-fixing cyanobacteria and non-nitrogen-fixing phytoplankton species. Limnol Oceanogr. 2007;52:2233–48.CAS 

Google Scholar 
85.El-Khaled YC, Roth F, Tilstra A, Rädecker N, Karcher DB, Kürten B, et al. In situ eutrophication stimulates dinitrogen fixation, denitrification, and productivity in Red Sea coral reefs. Mar Ecol Prog Ser. 2020;645:55–66.CAS 

Google Scholar 
86.Fine M, Loya Y. Endolithic algae: an alternative source of photoassimilates during coral bleaching. Proc R Soc B. 2002;269:1205–10.PubMed 
PubMed Central 

Google Scholar 
87.Sangsawang L, Casareto BE, Ohba H, Vu HM, Meekaew A, Suzuki T, et al. 13C and 15N assimilation and organic matter translocation by the endolithic community in the massive coral Porites lutea. R Soc Open Sci. 2017;4:171201.PubMed 
PubMed Central 

Google Scholar 
88.Fine M, Roff G, Ainsworth TD, Hoegh-Guldberg O. Phototrophic microendoliths bloom during coral “white syndrome. Coral Reefs. 2006;25:577–81.
Google Scholar 
89.Fine M, Steindler L, Loya Y. Endolithic algae photoacclimate to increased irradiance during coral bleaching. Mar Freshw Res. 2004;55:115–21.CAS 

Google Scholar 
90.Meunier V, Bonnet S, Benavides M, Ravache A, Grosso O, Lambert C, et al. Diazotroph-derived nitrogen assimilation strategies differ by scleractinian coral species. Front Mar Sci. 2021;8:1018.
Google Scholar  More

Probing directional interactions of OMM12 strains using spent culture mediaTo characterize directional interactions of the OMM12 consortium members, we chose an in vitro approach to explore how the bacterial strains alter their chemical environment by growth to late stationary phase.Growth of the individual monocultures in a rich culture medium that supports growth of all members (AF medium, Methods, Table S1) was monitored over time (Fig. S1; SI data table 1) and growth rates (Table S2) were determined. Strains were grouped by growth rate (GR) into fast growing strains (GR  > 1.5 h–1, E. faecalis KB1, B. animalis YL2, C. innocuum I46 and B. coccoides YL58), strains with intermediate growth rate (GR  > 1 h–1, M. intestinale YL27, F. plautii YL31, E. clostridioformis YL32, B. caecimuris I48 and L.reuteri I49) and slow growing strains (GR  More

1.Food and Agriculture Organization & World Health Organization. Health and nutrition properties of probiotics in food including powder milk with live lactic acid bacteria. (FAO food and nutrition paper, 85, 2001).2.Barba-Vidal, E., Martín-Orúe, S. M. & Castillejos, L. Review: Are we using probiotics correctly in post-weaning piglets?. Animal 12, 2489–2498 (2018).CAS 
PubMed 

Google Scholar 
3.Bernardeau, M., Lehtinen, M. J., Forssten, S. D. & Nurminen, P. Importance of the gastrointestinal life cycle of Bacillus for probiotic functionality. J. Food Sci. Technol. 54, 2570–2584 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Hong, H. A., Duc, L. H. & Cutting, S. M. The use of bacterial spore formers as probiotics. FEMS Microbiol. Rev. 29, 813–835 (2005).CAS 
PubMed 

Google Scholar 
5.Duc, L. H., Hong, H. A. & Cutting, S. M. Germination of the spore in the gastrointestinal tract provides a novel route for heterologous antigen delivery. Vaccine 21, 4215–4224 (2003).CAS 

Google Scholar 
6.Leser, T. D., Knarreborg, A. & Worm, J. Germination and outgrowth of Bacillus subtilis and Bacillus licheniformis spores in the gastrointestinal tract of pigs. J. Appl. Microbiol. 104, 1025–1033 (2008).CAS 
PubMed 

Google Scholar 
7.Cutting, S. M. Bacillus probiotics. Food Microbiol. 28, 214–220 (2011).PubMed 

Google Scholar 
8.Prieto, M. L. et al. Assessment of the bacteriocinogenic potential of marine bacteria reveals lichenicidin production by seaweed-derived Bacillus spp. Mar. Drugs 10, 2280–2299 (2012).CAS 
PubMed 

Google Scholar 
9.Prieto, M. L. et al. In vitro assessment of marine Bacillus for use as livestock probiotics. Mar. Drugs 12, 2422–2445 (2014).PubMed 
PubMed Central 

Google Scholar 
10.Prieto, M. L. et al. Evaluation of the efficacy and safety of a marine-derived Bacillus strain for use as an in-feed probiotic for newly weaned pigs. PLoS ONE 9, e88599 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
11.National Research Council. Nutrient Requirements of Swine (The National Academies Press, 2012).
12.Berends, B. R., Urlings, H. A. P., Snijders, J. M. A. & Van Knapen, F. Identification and quantification of risk factors in animal management and transport regarding Salmonella spp. in pigs. Int. J. Food Microbiol. 30, 37–53. https://doi.org/10.1016/0168-1605(96)00990-7 (1996).CAS 
Article 
PubMed 

Google Scholar 
13.Miller, M. F., Carr, M. A., Bawcom, D. B., Ramsey, C. B. & Thompson, L. D. Microbiology of pork carcasses from pigs with differing origins and feed withdrawal times†. J. Food Prot. 60, 242–245. https://doi.org/10.4315/0362-028x-60.3.242 (1997).Article 
PubMed 

Google Scholar 
14.Adewole, D. I., Kim, I. H. & Nyachoti, C. M. Gut health of pigs: Challenge models and response criteria with a critical analysis of the effectiveness of selected feed additives—A review. Asian-Austr. J. Anim. Sci. 29, 909–924 (2016).CAS 

Google Scholar 
15.Department of Agriculture and Food and Rural Development. European communities (pig carcase (grading)) (amendment) regulations. (S.I. No. 413/2001, 2001).16.Gardiner, G. E. et al. Relative ability of orally administered Lactobacillus murinus to predominate and persist in the porcine gastrointestinal tract. Appl. Environ. Microbiol. 70, 1895–1906 (2004).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
17.McCormack, U. M. et al. Exploring a possible link between the intestinal microbiota and feed efficiency in pigs. Appl. Environ. Microbiol. 83, e00380-e417 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
18.Buzoianu, S. G. et al. High-throughput sequence-based analysis of the intestinal microbiota of weanling pigs fed genetically modified MON810 maize expressing Bacillus thuringiensis Cry1Ab (Bt maize) for 31 days. Appl. Environ. Microbiol. 78, 4217–4224 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Andrews, S. FastQC: A quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (2010).20.Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
21.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
23.R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).24.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).MATH 

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

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

Google Scholar 
27.Jadamus, A., Vahjen, W. & Simon, O. Growth behaviour of a spore forming probiotic strain in the gastrointestinal tract of broiler chicken and piglets. Arch. Tierernahr. 54, 1–17 (2001).CAS 
PubMed 

Google Scholar 
28.Duc, L. H., Hong, H. A., Barbosa, T. M., Henriques, A. O. & Cutting, S. M. Characterization of Bacillus probiotics available for human use. Appl. Environ. Microbiol. 70, 2161–2171 (2004).ADS 
CAS 
PubMed Central 

Google Scholar 
29.Tam, N. K. M. et al. The intestinal life cycle of Bacillus subtilis and close relatives. J. Bacteriol. 188, 2692–2700 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
30.Casula, G. & Cutting, S. M. Bacillus Probiotics: Spore germination in the gastrointestinal tract. Appl. Environ. Microbiol. 68, 2344–2352 (2002).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
31.Kidder, D. E. & Manners, M. J. Digestion in the pig. (Scientechnica, 1978).32.Crespo-Piazuelo, D. et al. Maternal supplementation with Bacillus altitudinis spores improves porcine offspring growth performance and carcass weight. Br. J. Nutr. https://doi.org/10.1017/S0007114521001203 (2021).Article 
PubMed 

Google Scholar 
33.Zhou, H., Wang, C., Ye, J., Chen, H. & Tao, R. Effects of dietary supplementation of fermented Ginkgo biloba L. residues on growth performance, nutrient digestibility, serum biochemical parameters and immune function in weaned piglets. Anim. Sci. J. 86, 790–799 (2015).CAS 
PubMed 

Google Scholar 
34.Kim, S. J., Kwon, C. H., Park, B. C., Lee, C. Y. & Han, J. H. Effects of a lipid-encapsulated zinc oxide dietary supplement, on growth parameters and intestinal morphology in weanling pigs artificially infected with enterotoxigenic Escherichia coli. J. Anim. Sci. Technol. 57, 4 (2015).PubMed 
PubMed Central 

Google Scholar 
35.Pérez, V. G. et al. Additivity of effects from dietary copper and zinc on growth performance and fecal microbiota of pigs after weaning. J. Anim. Sci. 89, 414–425 (2011).PubMed 

Google Scholar 
36.Ventrella, D. et al. The biomedical piglet: establishing reference intervals for haematology and clinical chemistry parameters of two age groups with and without iron supplementation. BMC Vet. Res. 13, 23 (2017).PubMed 
PubMed Central 

Google Scholar 
37.Thorn, C. E. Hematology of the pig. in Schalm’s Veterinary Hematology, 6th edition (eds. Weiss, D. J. & Wardrop, K. J.) 843–851 (2010). https://doi.org/10.1111/j.1939-165X.2011.00324.x38.Morrow-Tesch, J. L., McGlone, J. J. & Salak-Johnson, J. L. Heat and social stress effects on pig immune measures. J. Anim. Sci. 72, 2599–2609 (1994).CAS 
PubMed 

Google Scholar 
39.Schmid, L., Heit, W. & Flury, R. Agranulocytosis associated with semisynthetic penicillins and cephalosporins. Report of 7 cases. Blut 48, 11–18 (1984).CAS 
PubMed 

Google Scholar 
40.Kloubert, V. et al. Influence of zinc supplementation on immune parameters in weaned pigs. J. Trace Elem. Med. Biol. 49, 231–240 (2018).CAS 
PubMed 

Google Scholar 
41.The European Agency for the Evaluation of Medicinal Products (EMEA). Commitee for veterinary medicinal products: Apramycin. (1999).42.Frese, S. A., Parker, K., Calvert, C. C. & Mills, D. A. Diet shapes the gut microbiome of pigs during nursing and weaning. Microbiome 3, 28 (2015).PubMed 
PubMed Central 

Google Scholar 
43.Slifierz, M. J., Friendship, R. M. & Weese, J. S. Longitudinal study of the early-life fecal and nasal microbiotas of the domestic pig. BMC Microbiol. 15, 184 (2015).PubMed 
PubMed Central 

Google Scholar 
44.Ivarsson, E., Roos, S., Liu, H. Y. & Lindberg, J. E. Fermentable non-starch polysaccharides increases the abundance of Bacteroides-Prevotella-Porphyromonas in ileal microbial community of growing pigs. Animal 8, 1777–1787 (2014).CAS 
PubMed 

Google Scholar 
45.Pajarillo, E. A. B., Chae, J.-P., Balolong, M. P., Bum Kim, H. & Kang, D.-K. Assessment of fecal bacterial diversity among healthy piglets during the weaning transition. J. Gen. Appl. Microbiol. 60, 140–146 (2014).CAS 

Google Scholar 
46.Yu, T. et al. Low-molecular-weight chitosan supplementation increases the population of Prevotella in the cecal contents of weanling pigs. Front. Microbiol. 8, 1–9 (2017).
Google Scholar 
47.Shen, J. et al. Coated zinc oxide improves intestinal immunity function and regulates microbiota composition in weaned piglets. Br. J. Nutr. 111, 2123–2134 (2014).CAS 
PubMed 

Google Scholar 
48.Rattigan, R., Sweeney, T., Vigors, S., Rajauria, G. & O’Doherty, J. V. Effects of reducing dietary crude protein concentration and supplementation with laminarin or zinc oxide on the faecal scores and colonic microbiota in newly weaned pigs. J. Anim. Physiol. Anim. Nutr. (Berl) 104, 1471–1483 (2020).CAS 

Google Scholar 
49.López-Colom, P., Estellé, J., Bonet, J., Coma, J. & Martín-Orúe, S. M. Applicability of an unmedicated feeding program aimed to reduce the use of antimicrobials in nursery piglets: Impact on performance and fecal microbiota. Animals 10, 242 (2020).PubMed Central 

Google Scholar 
50.Wei, X. et al. ZnO modulates swine gut microbiota and improves growth performance of nursery pigs when combined with peptide cocktail. Microorganisms 8, 146 (2020).CAS 
PubMed Central 

Google Scholar 
51.Vahjen, W., Pieper, R. & Zentek, J. Increased dietary zinc oxide changes the bacterial core and enterobacterial composition in the ileum of piglets. J. Anim. Sci. 89, 2430–2439 (2011).CAS 
PubMed 

Google Scholar 
52.Pieper, R., Vahjen, W., Neumann, K., Van Kessel, A. G. & Zentek, J. Dose-dependent effects of dietary zinc oxide on bacterial communities and metabolic profiles in the ileum of weaned pigs. J. Anim. Physiol. Anim. Nutr. (Berl) 96, 825–833 (2012).CAS 

Google Scholar 
53.Yu, T. et al. Dietary high zinc oxide modulates the microbiome of ileum and colon in weaned piglets. Front. Microbiol. 8, 1–12 (2017).
Google Scholar 
54.Xia, T. et al. Dietary ZnO nanoparticles alters intestinal microbiota and inflammation response in weaned piglets. Oncotarget 8, 64878–64891 (2017).PubMed 
PubMed Central 

Google Scholar 
55.Poulsen, A.-S.R. et al. Impact of Bacillus spp. spores and gentamicin on the gastrointestinal microbiota of suckling and newly weaned piglets. PLoS ONE 13, e0207382 (2018).PubMed 
PubMed Central 

Google Scholar 
56.Hagerty, S. L., Hutchison, K. E., Lowry, C. A. & Bryan, A. D. An empirically derived method for measuring human gut microbiome alpha diversity: Demonstrated utility in predicting health-related outcomes among a human clinical sample. PLoS ONE 15, e0229204 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
57.Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).ADS 

Google Scholar 
58.Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Ryden, R. & Moore, B. J. The in vitro activity of apramycin, a new aminocyditol antibiotic. J. Antimicrob. Chemother. 3, 609–613 (1977).CAS 
PubMed 

Google Scholar 
60.Jones, N., Ray, B., Ranjit, K. T. & Manna, A. C. Antibacterial activity of ZnO nanoparticle suspensions on a broad spectrum of microorganisms. FEMS Microbiol. Lett. 279, 71–76 (2008).CAS 
PubMed 

Google Scholar 
61.Gardiner, G. E., Metzler-Zebeli, B. U. & Lawlor, P. G. Impact of intestinal microbiota on growth and feed efficiency in pigs: A review. Microorganisms 8, 1886 (2020).CAS 
PubMed Central 

Google Scholar 
62.Ghanbari, M., Klose, V., Crispie, F. & Cotter, P. D. The dynamics of the antibiotic resistome in the feces of freshly weaned pigs following therapeutic administration of oxytetracycline. Sci. Rep. 9, 4062 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
63.Zeineldin, M., Aldridge, B. & Lowe, J. Antimicrobial effects on swine gastrointestinal microbiota and their accompanying antibiotic resistome. Front. Microbiol. 10, 1035 (2019).PubMed 
PubMed Central 

Google Scholar 
64.On, S. L. W. Identification methods for campylobacters, helicobacters, and related organisms. Clin. Microbiol. Rev. 9, 405–422 (1996).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Bergström, S., Garon, C. F., Barbour, A. G. & MacDougall, J. Extrachromosomal elements of spirochetes. Res. Microbiol. 143, 623–628 (1992).PubMed 

Google Scholar 
66.Oh, J. K. et al. Association between the body weight of growing pigs and the functional capacity of their gut microbiota. Anim. Sci. J. 91, e13418 (2020).CAS 
PubMed 

Google Scholar 
67.Ruiz, V. L. A. et al. Case–control study of pathogens involved in piglet diarrhea. BMC Res. Notes 9, 22 (2016).PubMed 
PubMed Central 

Google Scholar 
68.Yang, Q. et al. Longitudinal development of the gut microbiota in healthy and diarrheic piglets induced by age-related dietary changes. Microbiologyopen 8, e923 (2019).PubMed 
PubMed Central 

Google Scholar 
69.Wang, S. et al. Combined supplementation of Lactobacillus fermentum and Pediococcus acidilactici promoted growth performance, alleviated inflammation, and modulated intestinal microbiota in weaned pigs. BMC Vet. Res. 15, 239 (2019).PubMed 
PubMed Central 

Google Scholar 
70.Looft, T. et al. Bacteria, phages and pigs: The effects of in-feed antibiotics on the microbiome at different gut locations. ISME J. 8, 1566–1576 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Quan, J. et al. A global comparison of the microbiome compositions of three gut locations in commercial pigs with extreme feed conversion ratios. Sci. Rep. 8, 4536 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
72.Ramayo-Caldas, Y. et al. Phylogenetic network analysis applied to pig gut microbiota identifies an ecosystem structure linked with growth traits. ISME J. 10, 2973–2977 (2016).PubMed 
PubMed Central 

Google Scholar 
73.Che, L. et al. Inter-correlated gut microbiota and SCFAs changes upon antibiotics exposure links with rapid body-mass gain in weaned piglet model. J. Nutr. Biochem. 74, 108246 (2019).CAS 
PubMed 

Google Scholar 
74.Machiels, K. et al. A decrease of the butyrate-producing species Roseburia hominis and Faecalibacterium prausnitzii defines dysbiosis in patients with ulcerative colitis. Gut 63, 1275–1283 (2014).CAS 
PubMed 

Google Scholar 
75.Segain, J. P. et al. Butyrate inhibits inflammatory responses through NFkappaB inhibition: Implications for Crohn’s disease. Gut 47, 397–403 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Roediger, W. E. The colonic epithelium in ulcerative colitis: An energy-deficiency disease?. Lancet (London, England) 2, 712–715 (1980).CAS 

Google Scholar 
77.Jewell, K. A., Scott, J. J., Adams, S. M. & Suen, G. A phylogenetic analysis of the phylum Fibrobacteres. Syst. Appl. Microbiol. 36, 376–382. https://doi.org/10.1016/j.syapm.2013.04.002 (2013).CAS 
Article 
PubMed 

Google Scholar 
78.Abdul Rahman, N. et al. A phylogenomic analysis of the bacterial phylum Fibrobacteres. Front. Microbiol. 6, 1469–1469. https://doi.org/10.3389/fmicb.2015.01469 (2016).Article 
PubMed 
PubMed Central 

Google Scholar  More