Ecology

Dynamic in-flight flock organizationIt is commonly assumed that during flocking, flock members follow three basic interaction rules: Attraction, Repulsion and Alignment, to coordinate spatial positions between each other18. To study the spatial organization of our zebra finch flock during flight, the spatial positions of all birds in the flight section were tracked in every fifth frame (sample rate: 24 Hz (that is, frames per second)) of the synchronized footage recorded by two high-speed digital video cameras (Camera 1: centred upwind view, Fig. 1a,b; Camera 2: upturned vertical view, Fig. 1a,c) for the entire duration (51.7, 58.3, 69.2 and 127 s) of four (session 2, 5, 8 and 13) out of 13 flight sessions. Flight paths were reconstructed from the tracking data for each bird in the flock, with horizontal and vertical coordinates delivered by Camera 1 and coordinates in wind direction delivered by Camera 2. The data show that each bird mainly occupied a particular area in the flight section, and that this spatial preference was stable over different flight sessions. Bird Green, for example, was preferentially flying very low above the flight section’s floor, and bird Lilac preferred to fly at upwind positions in front of the flock (Fig. 1d, Extended Data Figs. 1 and 3 and Supplementary Information).Despite their preference in flight area, all birds constantly changed their spatial positions fast and rhythmically along the horizontal dimension of the flight section (Fig. 1e–g, Extended Data Figs. 2 and 4, Supplementary Video 1 and Supplementary Information). This behaviour is reminiscent of the flight behaviour of wild zebra finches: when being surprised in flight by a predator, zebra finches fly in a rapid zig-zag course low above the ground, heading for nearby vegetation16. Whether the sideways oscillating flight manoeuvres, which are performed by both wild birds in open space and domesticated birds in the wind tunnel’s flight section, are caused by the close proximity to the ground or are part of an escape reaction is yet unknown.From the tracking data, we further calculated the spatial distances in all three dimensions between all pairwise combinations of birds throughout the four flight sessions (sample rate: 24 Hz). When normalized to the maximum distance detected for each bird pairing, each dimension and each flight session, mean distances of bird pairings in all dimensions were narrowly distributed within a range of 27.7–38.0% of maximum distance (Fig. 1h and Supplementary Table 1). This may indicate that during flocking flight, zebra finches actively balance Attraction and Repulsion to maintain a stable 3D distance towards all other members of the flock. Owing to the spatial limitations in the wind tunnel’s flight section, we did not expect the zebra finches to perform large-scale flight manoeuvres with movements aligned between all flock members (Extended Data Fig. 5 and Supplementary Information), as can be observed, for example, in freely flying flocks of homing pigeons (Columba livia domestica)19 and white storks (Ciconia Ciconia)20.Visually guided horizontal repositioningWhen observing the dynamic spatial organization of our zebra finch flock, a question immediately arises: how do the birds prevent collisions during their frequent horizontal position changes? When considering the spatial limitation experienced by the flock of six birds during flight in the flight section and their highly dynamic flight style, collision rates seemed to be astonishingly low (median: 0.02 Hz; interquartile range (IQR): 0–0.03 Hz; n = 13 sessions) during flocking flight (in total 16 collisions in 13 min of analysed flight time). In birds, the visual system represents the main input channel for environmental information. To tackle the above question, we therefore first investigated the role of vision during flocking flight, and tested whether a bird’s viewing direction was correlated with the direction of horizontal position change. As gaze changes are governed by head movements in birds21, we used a bird’s head direction as an indicator for the orientation of its visual axis. We tracked (sample rate: 120 Hz) the position of a bird’s beak tip and neck in each frame of the footage during ten horizontal position changes (Fig. 2a and Supplementary Video 2) per bird, and found a strong interaction between a bird’s head angle relative to the wind direction and its direction of horizontal position change. During horizontal position changes, the birds always turned their heads in the direction of the position change (Fig. 2b). While the population’s median absolute angle of position change was 84.0° (IQR: 78.6–87.2°; n = 60) relative to 0° in wind direction, the population’s median absolute head turning angle was 36.0° (IQR: 26.4–42.5°; n = 60; see Supplementary Information for results on head movements during solo flight). The eyes of zebra finches are positioned laterally on their heads22 and each retina features a small region of highest ganglion cell density (fovea, that is, region of highest visual spatial resolution) at an area that receives visual input from horizontal positions at 60° relative to the midsagittal plane23. By turning their heads by about 36° during horizontal position changes, the zebra finches roughly align the foveal area in the retina of one eye with their direction of position change, and in the retina of the other eye with the wind direction (Fig. 2c,d). Thus, head turns in the direction of position change may indicate that the birds use visual cues while repositioning themselves within the flock. This hypothesis is supported by a study on zebra finch head movements performed during an obstacle avoidance task. In this study, instead of fixating on the obstacle, zebra finches turned their head in the direction of movement while navigating around the obstacle24.Fig. 2: Horizontal position changes are accompanied by head turns.a, Head and body orientation of bird Orange (ventral view) during one example of position changes to the right, tracked (sample rate: 120 Hz) in the footage of Camera 2. Circles: beak tip positions; plus signs: neck positions; upward pointing triangles: tail base positions. Cutouts of freeze frames of the footage taken with Camera 2 show the bird’s head and body posture for 11 time points during the position change. b, In all birds, the median angle of head turn during horizontal position change in flocking flight is positively correlated (linear mixed effects model (LMM), estimates ± s.e.m.: 2.05 ± 0.1, P  More

Neo, M. L., Eckman, W., Vicentuan, K., Teo, S.L.-M. & Todd, P. A. The ecological significance of giant clams in coral reef ecosystems. Biol. Conserv. 181, 111–123 (2015).Article 

Google Scholar 
Hill, R. W. et al. Acid secretion by the boring organ of the burrowing giant clam, Tridacna crocea. Biol. Lett. 14, 20180047 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Welsh, K., Elliot, M., Tudhope, A., Ayling, B. & Chappell, J. Giant bivalves (Tridacna gigas) as recorders of ENSO variability. Earth Planet. Sci. Lett. 307, 266–270 (2011).ADS 
CAS 
Article 

Google Scholar 
Elliot, M. et al. Profiles of trace elements and stable isotopes derived from giant long-lived Tridacna gigas bivalves: Potential applications in paleoclimate studies. Palaeogeogr. Palaeoclimatol. Palaeoecol. 280, 132–142 (2009).Article 

Google Scholar 
Killam, D., Thomas, R., Al-Najjar, T. & Clapham, M. Interspecific and intrashell stable isotope variation among the Red Sea giant clams. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2019GC008669 (2020).Article 

Google Scholar 
Duprey, N., Galipaud, J.-C., Cabioch, G. & Lazareth, C. E. Isotopic records from archeological giant clams reveal a variable climate during the southwestern Pacific colonization ca. 3.0ka BP. Palaeogeogr. Palaeoclimatol. Palaeoecol. 404, 97–108 (2014).Article 

Google Scholar 
Batenburg, S. J. et al. Interannual climate variability in the Miocene: High resolution trace element and stable isotope ratios in giant clams. Palaeogeogr. Palaeoclimatol. Palaeoecol. 306, 75–81 (2011).Article 

Google Scholar 
Ayling, B. F., Chappell, J., Gagan, M. K. & McCulloch, M. T. ENSO variability during MIS 11 (424–374 ka) from Tridacna gigas at Huon Peninsula, Papua New Guinea. Earth Planet. Sci. Lett. 431, 236–246 (2015).ADS 
CAS 
Article 

Google Scholar 
Yan, H., Shao, D., Wang, Y. & Sun, L. Sr/Ca profile of long-lived Tridacna gigas bivalves from South China Sea: A new high-resolution SST proxy. Geochim. Cosmochim. Acta 112, 52–65 (2013).ADS 
CAS 
Article 

Google Scholar 
Warter, V. & Müller, W. Daily growth and tidal rhythms in Miocene and modern giant clams revealed via ultra-high resolution LA-ICPMS analysis—A novel methodological approach towards improved sclerochemistry. Palaeogeogr. Palaeoclimatol. Palaeoecol. 465, 362–375 (2017).Article 

Google Scholar 
Warter, V., Erez, J. & Müller, W. Environmental and physiological controls on daily trace element incorporation in Tridacna crocea from combined laboratory culturing and ultra-high resolution LA-ICP-MS analysis. Palaeogeogr. Palaeoclimatol. Palaeoecol. 496, 32–47 (2018).Article 

Google Scholar 
Wei, G., Sun, M., Li, X. & Nie, B. Mg/Ca, Sr/Ca and U/Ca ratios of a Porites coral from Sanya Bay, Hainan Island, South China Sea and their relationships to sea surface temperature. Palaeogeogr. Palaeoclimatol. Palaeoecol. 162, 59–74 (2000).Article 

Google Scholar 
Brahmi, C. et al. Effects of elevated temperature and pCO2 on the respiration, biomineralization and photophysiology of the giant clam Tridacna maxima. Conserv. Physiol. 9, 041 (2021).Article 
CAS 

Google Scholar 
Watson, S.-A. & Neo, M. L. Conserving threatened species during rapid environmental change: Using biological responses to inform management strategies of giant clams. Conserv. Physiol. 9, 082 (2021).
Google Scholar 
Armstrong, E. J., Dubousquet, V., Mills, S. C. & Stillman, J. H. Elevated temperature, but not acidification, reduces fertilization success in the small giant clam, Tridacna maxima. Mar. Biol. 167, 8 (2020).CAS 
Article 

Google Scholar 
Leggat, W., Buck, B. H., Grice, A. & Yellowlees, D. The impact of bleaching on the metabolic contribution of dinoflagellate symbionts to their giant clam host. Plant Cell Environ. 26, 1951–1961 (2003).CAS 
Article 

Google Scholar 
Zhou, Z., Liu, Z., Wang, L., Luo, J. & Li, H. Oxidative stress, apoptosis activation and symbiosis disruption in giant clam Tridacna crocea under high temperature. Fish Shellfish Immunol. 84, 451–457 (2019).CAS 
PubMed 
Article 

Google Scholar 
Dubousquet, V. et al. Changes in fatty acid composition in the giant clam Tridacna maxima in response to thermal stress. Biol. Open 5, 1400–1407 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Blidberg, E., Elfwing, T., Plantman, P. & Tedengren, M. Water temperature influences on physiological behaviour in three species of giant clams (Tridacnidae). In Proc. 9th International Coral Reef Symposium 561–565 (2000).Junchompoo, C., Sinrapasan, N., Penpain, C. & Patsorn, P. Changing seawater temperature effects on giant clams bleaching, Mannai Island, Rayong Province, Thailand. In Proc. Design Symposium on Conservation of Ecosystem. https://doi.org/10.13140/2.1.1906.5600 (2012).Watson, S.-A., Southgate, P. C., Miller, G. M., Moorhead, J. A. & Knauer, J. Ocean acidification and warming reduce juvenile survival of the fluted giant clam, Tridacna squamosa. Molluscan Res. 32, 177–180 (2012).
Google Scholar 
Watson, S.-A. Giant clams and rising CO2: Light may ameliorate effects of ocean acidification on a solar-powered animal. PLoS ONE 10, 1–18 (2015).CAS 

Google Scholar 
Kurihara, H. & Shikota, T. Impact of increased seawater pCO2 on the host and symbiotic algae of juvenile giant clam Tridacna crocea. Galaxea J. Coral Reef Stud. 20, 19–28 (2018).Article 

Google Scholar 
Alves Monteiro, H. J. et al. Molecular mechanisms of acclimation to long-term elevated temperature exposure in marine symbioses. Glob. Change Biol. 26, 1271–1284 (2020).ADS 
Article 

Google Scholar 
Collins, M. et al. Long-term climate change: Projections, commitments and irreversibility. In Climate Change 2013—The Physical Science Basis (eds Stocker, T. F. et al.) 1029–1136 (Cambridge University Press, 2013).
Google Scholar 
Poloczanska, E. et al. Climate change and Australian marine life. Oceanogr. Mar. Biol. 45, 407 (2007).
Google Scholar 
Ganachaud, A. S. et al. Observed and expected changes to the tropical Pacific Ocean. In Vulnerability Trop. Pac. Fish. Aquac. Clim. Change Secr. Pac. Community Noumea New Caledonia 101–187 (2011).Doney, S. C., Fabry, V. J., Feely, R. A. & Kleypas, J. A. Ocean acidification: The other CO2 problem. Annu. Rev. Mar. Sci. 1, 169–192 (2009).ADS 
Article 

Google Scholar 
Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241 (2011).ADS 
CAS 
Article 

Google Scholar 
Pierrot, D., Lewis, E. & Wallace, D. MS Excel program developed for CO2 system calculations. In ORNLCDIAC-105a Carbon Dioxide Inf. Anal. Cent. Oak Ridge Natl. Lab. US Dep. Energy Oak Ridge Tenn. Vol. 10 (2006).Mehrbach, C., Culberson, C. H., Hawley, J. E. & Pytkowicx, R. M. Measurement of the apparent dissociation constants of carbonic acid in seawater at atmospheric pressure. Limnol. Oceanogr. 18, 897–907 (1973).ADS 
CAS 
Article 

Google Scholar 
Dickson, A. G. & Millero, F. J. A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media. Deep-Sea Res. 34, 1733–1743 (1987).ADS 
CAS 
Article 

Google Scholar 
Dickson, A. G. Standard potential of the reaction: AgCl (s) + 12H2 (g) = Ag (s) + HCl (aq), and the standard acidity constant of the ion HSO4− in synthetic sea water from 273.15 to 318.15 K. J. Chem. Thermodyn. 22, 113–127 (1990).CAS 
Article 

Google Scholar 
Wolf, R. E. & Adams, M. Multi-elemental Analysis of Aqueous Geochemical Samples by Quadrupole Inductively Coupled Plasma-Mass Spectrometry (ICP-MS) 38. http://pubs.er.usgs.gov/publication/ofr20151010, https://doi.org/10.3133/ofr20151010 (2015).Schrag, D. P. Rapid analysis of high-precision Sr/Ca ratios in corals and other marine carbonates. Paleoceanography 14, 97–102 (1999).ADS 
Article 

Google Scholar 
Howell, D. C. Permutation Tests for Factorial ANOVA Designs (2009).Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).
Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2020).Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means (2020).Navarro, D. Learning Statistics with R: A Tutorial for Psychology Students and other beginners (Version 0.5) (University of Adelaide, 2015).
Google Scholar 
Zhao, L., Schöne, B. R. & Mertz-Kraus, R. Controls on strontium and barium incorporation into freshwater bivalve shells (Corbicula fluminea). Palaeogeogr. Palaeoclimatol. Palaeoecol. 465, 386–394 (2017).Article 

Google Scholar 
Bragg, W. L. The structure of some crystals as indicated by their diffraction of X-rays. Proc. R. Soc. Lond. Ser. Contain. Pap. Math. Phys. Character 89, 248–277 (1913).ADS 
CAS 

Google Scholar 
Bragg, W. L. The structure of aragonite. Proc. R. Soc. Lond. Ser. Contain. Pap. Math. Phys. Character 105, 16–39 (1924).ADS 
CAS 

Google Scholar 
Killam, D., Al-Najjar, T. & Clapham, M. Giant clam growth in the Gulf of Aqaba is accelerated compared to fossil populations. Proc. R. Soc. B Biol. Sci. 288, 20210991 (2021).CAS 
Article 

Google Scholar 
Waters, C. G. Biological Responses of Juvenile Tridacna maxima (Mollusca: Bivalvia) to Increased pCO2 and Ocean Acidification (The Evergreen State College, 2008).
Google Scholar 
Toonen, R. J., Nakayama, T., Ogawa, T., Rossiter, A. & Delbeek, J. C. Growth of cultured giant clams (Tridacna spp.) in low pH, high-nutrient seawater: Species-specific effects of substrate and supplemental feeding under acidification. J. Mar. Biol. Assoc. U. K. 92, 731–740 (2012).CAS 
Article 

Google Scholar 
Hart, A. M., Bell, J. D. & Foyle, T. P. Growth and survival of the giant clams, Tridacna derasa, T. maxima and T. crocea, at village farms in the Solomon Islands. Aquaculture 165, 203–220 (1998).Article 

Google Scholar 
Van Wynsberge, S. et al. Growth, survival and reproduction of the giant clam Tridacna maxima (Röding 1798, Bivalvia) in two contrasting lagoons in French Polynesia. PLoS ONE 12, 1–20 (2017).
Google Scholar 
Lucas, J. S., Nash, W. J., Crawford, C. M. & Braley, R. D. Environmental influences on growth and survival during the ocean-nursery rearing of giant clams, Tridacna gigas (L.). Aquaculture 80, 45–61 (1989).Article 

Google Scholar 
Schwartzmann, C. et al. In situ giant clam growth rate behavior in relation to temperature: A one-year coupled study of high-frequency noninvasive valvometry and sclerochronology. Limnol. Oceanogr. 56, 1940–1951 (2011).ADS 
Article 

Google Scholar 
Syazili, A., Syafiuddin, N. A. & Jompa, J. Effect of ocean acidification and temperature on growth, survival, and shell performance of fluted giant clams (Tridacna squamosa). IOP Conf. Ser. Earth Environ. Sci. 473, 012141 (2020).Article 

Google Scholar 
Li, J. et al. Assessment of the juvenile vulnerability of symbiont-bearing giant clams to ocean acidification. Sci. Total Environ. 812, 152265 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Li, S. et al. Cloning and expression of a pivotal calcium metabolism regulator: Calmodulin involved in shell formation from pearl oyster (Pinctada fucata). Comp. Biochem. Physiol. B Biochem. Mol. Biol. 138, 235–243 (2004).PubMed 
Article 
CAS 

Google Scholar 
Wang, X., Li, C., Lv, Z., Zhang, Z. & Qiu, L. A calcification-related calmodulin-like protein in the oyster Crassostrea gigas mediates the enhanced calcium deposition induced by CO2 exposure. Sci. Total Environ. 833, 155114 (2022).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rees, T., Fitt, W. & Yellowlees, D. The haemolymph and its temporal relationship with zooxanthellae metabolism in the giant clam symbiosis [Conference paper]. In ACIAR Proc.-Aust. Cent. Int. Agric. Res. Aust. (1993).Leggat, W., Rees, T. A. V. & Yellowlees, D. Meeting the photosynthetic demand for inorganic carbon in an alga-invertebrate association: Preferential use of CO2 by symbionts in the giant clam Tridacna gigas. Proc. Biol. Sci. 267, 523–529 (2000).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ip, Y. K. et al. Molecular characterization, light-dependent expression, and cellular localization of a host vacuolar-type H+-ATPase (VHA) subunit A in the giant clam, Tridacna squamosa, indicate the involvement of the host VHA in the uptake of inorganic carbon and. Gene 659, 137–148 (2018).CAS 
PubMed 
Article 

Google Scholar 
Armstrong, E. J., Roa, J. N., Stillman, J. H. & Tresguerres, M. Symbiont photosynthesis in giant clams is promoted by V-type H+-ATPase from host cells. J. Exp. Biol. https://doi.org/10.1242/jeb.177220 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Sano, Y. et al. Past daily light cycle recorded in the strontium/calcium ratios of giant clam shells. Nat. Commun. 3, 761 (2012).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Adams, A. L., Needham, E. W. & Knauer, J. The effect of shade on water quality parameters and survival and growth of juvenile fluted giant clams, Tridacna squamosa, cultured in a land-based growth trial. Aquac. Int. 21, 1311–1324 (2013).CAS 
Article 

Google Scholar 
Rossbach, S., Saderne, V., Anton, A. & Duarte, C. M. Light-dependent calcification in Red Sea giant clam Tridacna maxima. Biogeosciences 16, 2635–2650 (2019).ADS 
CAS 
Article 

Google Scholar 
Ip, Y. K. et al. The whitish inner mantle of the giant clam, Tridacna squamosa, expresses an apical plasma membrane Ca2+-ATPase (PMCA) which displays light-dependent gene and protein expressions. Front. Physiol. 8, 781 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Berner, R. A. The role of magnesium in the crystal growth of calcite and aragonite from sea water. Geochim. Cosmochim. Acta 39, 489–504 (1975).ADS 
CAS 
Article 

Google Scholar 
Alibert, C. et al. Source of trace element variability in Great Barrier Reef corals affected by the Burdekin flood plumes. Geochim. Cosmochim. Acta 67, 231–246 (2003).ADS 
CAS 
Article 

Google Scholar 
McCulloch, M. et al. Coral record of increased sediment flux to the inner Great Barrier Reef since European settlement. Nature 421, 727–730 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Sinclair, D. & Mcculloch, M. Corals record low mobile barium concentrations in the Burdekin River during the 1974 flood: Evidence for limited Ba supply to rivers? Palaeogeogr. Palaeoclimatol. Palaeoecol. 214, 155–174 (2004).Article 

Google Scholar 
Fleitmann, D. et al. East African soil erosion recorded in a 300 year old coral colony from Kenya. Geophys. Res. Lett. 34, L04401 (2007).ADS 
Article 

Google Scholar 
Prouty, N. G., Field, M. E., Stock, J. D., Jupiter, S. D. & McCulloch, M. Coral Ba/Ca records of sediment input to the fringing reef of the southshore of Moloka’i, Hawai’i over the last several decades. Mar. Pollut. Bull. 60, 1822–1835 (2010).CAS 
PubMed 
Article 

Google Scholar 
Fallon, S. J., McCulloch, M. T., van Woesik, R. & Sinclair, D. J. Corals at their latitudinal limits: Laser ablation trace element systematics in Porites from Shirigai Bay, Japan. Earth Planet. Sci. Lett. 172, 221–238 (1999).ADS 
CAS 
Article 

Google Scholar 
Reuer, M. K., Boyle, E. A. & Cole, J. E. A mid-twentieth century reduction in tropical upwelling inferred from coralline trace element proxies. Earth Planet. Sci. Lett. 210, 437–452 (2003).ADS 
CAS 
Article 

Google Scholar 
Montaggioni, L. F., Le Cornec, F., Corrège, T. & Cabioch, G. Coral barium/calcium record of mid-Holocene upwelling activity in New Caledonia, South-West Pacific. Palaeogeogr. Palaeoclimatol. Palaeoecol. 237, 436–455 (2006).Article 

Google Scholar 
Ourbak, T. et al. A high-resolution investigation of temperature, salinity, and upwelling activity proxies in corals: Activity proxies in corals. Geochem. Geophys. Geosyst. 7, 1. https://doi.org/10.1029/2005GC001064 (2006).CAS 
Article 

Google Scholar 
Alibert, C. & Kinsley, L. A 170-year Sr/Ca and Ba/Ca coral record from the western Pacific warm pool: 1. What can we learn from an unusual coral record? J. Geophys. Res. 113, C04008 (2008).ADS 

Google Scholar 
Alibert, C. & Kinsley, L. A 170-year Sr/Ca and Ba/Ca coral record from the western Pacific warm pool: 2. A window into variability of the new ireland coastal undercurrent. J. Geophys. Res. 113, C06006 (2008).ADS 

Google Scholar 
Agbaje, O. B. A. et al. Architecture of crossed-lamellar bivalve shells: The southern giant clam (Tridacna derasa, Röding, 1798). R. Soc. Open Sci. 4, 170622 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Clark, M. S. et al. Deciphering mollusc shell production: The roles of genetic mechanisms through to ecology, aquaculture and biomimetics. Biol. Rev. https://doi.org/10.1111/brv.12640 (2020).Article 
PubMed 

Google Scholar 
Wilkerson, F. P. & Trench, R. K. Uptake of dissolved inorganic nitrogen by the symbiotic clam Tridacna gigas and the coral Acropora sp.. Mar. Biol. 93, 237–246 (1986).CAS 
Article 

Google Scholar 
Summons, R. E., Boag, T. S. & Osmond, C. B. The effect of ammonium on photosynthesis and the pathway of ammonium assimilation in Gymnodinium microadriaticum in vitro and in symbiosis with tridacnid clams and corals. Proc. R. Soc. Lond. B Biol. Sci. 227, 147–159 (1986).ADS 
CAS 
Article 

Google Scholar 
Onate, J. & Naguit, M. A preliminary study on the effect of increased nitrate concentration on the growth of giant clams Hippopus hippopus. In Cult. Giant Clams Bivalvia Tridacnidae Aust. Cent. Int. Agric. Res. Canberra 57–61 (1989).Hastie, L. C., Watson, T. C., Isamu, T. & Heslinga, G. A. Effect of nutrient enrichment on Tridacna derasa seed: Dissolved inorganic nitrogen increases growth rate. Aquaculture 106, 41–49 (1992).CAS 
Article 

Google Scholar 
Belda, C. A., Lucas, J. S. & Yellowlees, D. Nutrient limitation in the giant clam-zooxanthellae symbiosis: Effects of nutrient supplements on growth of the symbiotic partners. Mar. Biol. 117, 655–664 (1993).Article 

Google Scholar 
Belda-Baillie, C., Leggat, W. & Yellowlees, D. Growth and metabolic responses of the giant clam-zooxanthellae symbiosis in a reef-fertilisation experiment. Mar. Ecol. Prog. Ser. 170, 131–141 (1998).ADS 
CAS 
Article 

Google Scholar 
Calosi, P. et al. Multiple physiological responses to multiple environmental challenges: An individual approach. Integr. Comp. Biol. 53, 660–670 (2013).CAS 
PubMed 
Article 

Google Scholar 
Tanner, R. L. & Dowd, W. W. Inter-individual physiological variation in responses to environmental variation and environmental change: Integrating across traits and time. Comp. Biochem. Physiol. A. Mol. Integr. Physiol. 238, 110577 (2019).CAS 
PubMed 
Article 

Google Scholar 
Guscelli, E., Spicer, J. I. & Calosi, P. The importance of inter-individual variation in predicting species’ responses to global change drivers. Ecol. Evol. 9, 4327–4339 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Telesca, L. et al. Biomineralization plasticity and environmental heterogeneity predict geographical resilience patterns of foundation species to future change. Glob. Change Biol. 25, 4179–4193 (2019).ADS 
Article 

Google Scholar 
Yan, H., Shao, D., Wang, Y. & Sun, L. Sr/Ca differences within and among three Tridacnidae species from the South China Sea: Implication for paleoclimate reconstruction. Chem. Geol. 390, 22–31 (2014).ADS 
CAS 
Article 

Google Scholar 
Warter, V., Mueller, W., Wesselingh, F. P., Todd, J. A. & Renema, W. Late Miocene seasonal to subdecadal climate variability in the Indo-West Pacific (East Kalimantan, Indonesia) preserved in giant clams. Palaios 30, 66–82 (2015).ADS 
Article 

Google Scholar 
Gannon, M. E., Pérez-Huerta, A., Aharon, P. & Street, S. C. A biomineralization study of the Indo-Pacific giant clam Tridacna gigas. Coral Reefs 36, 503–517 (2017).ADS 
Article 

Google Scholar 
Zhao, L. et al. A review of transgenerational effects of ocean acidification on marine bivalves and their implications for sclerochronology. Estuar. Coast. Shelf Sci. 235, 106620 (2020).CAS 
Article 

Google Scholar  More

Sample collection from sea turtlesIn total, 45 serum samples from 33 juvenile green turtles (C. mydas), including 6 sea turtles with tumors, 5 juvenile hawksbill turtles (Eretmochelys imbricate), and 7 olive ridley turtles (Lepidochelys olivacea) (juvenile = 5; sub-adult = 2). All turtles were sourced from: eastern Taiwan (n = 24), southern Taiwan (n = 14), central Taiwan (n = 6), and northern Taiwan (n = 1). Among the 45 sea turtle samples, 6 green turtles developed FP (n = 1 with tumor score 1; n = 1 with tumor score 2; n = 4 with tumor score 3)32, while 39 did not have FP. FP tumor tissues were collected from 6 green turtles (from shoulder/flippers/inguinal regions) with FP during surgical procedures. Regarding the collection of normal skin tissues, one normal skin tissue (from shoulder) was collected from one necropsied dead green turtles (stranding and discovered from southern Taiwan) confirmed without FP. All tissue samples were fixed in 10% neutral buffered formalin prior to further analysis. In this study, all sea turtles were discovered and rescued through the official reporting system of the Marine Animal Rescue Network (established by the Ocean Conservation Administration) and admitted to the National Museum of Marine Biology and Aquarium (NMMBA), between 2017 and 2020.Detection of ChHV5 DNA by polymerase chain reaction (PCR)Total DNA was extracted from blood of 45 sea turtles by DNeasy blood & tissue kit (Cat. No. 69504, Qiagen, Valencia, CA, USA) following manufacturer’s instructions. Subsequently, the ChHV5 infection status all 45 sea turtles was determined by PCR using primers targeting on UL18 (capsid protein gene), UL22 (glycoprotein H gene), and UL27 (glycoprotein B gene) regions4. The sequence of primer sets are: UL18F: 5′-CACCACGAGGGGGAAAATGA, UL18R:5′-TCAAATCCCCCGTTCACTCG; UL22F: 5′-ACGGCGTTGGCTAGTGAATC, UL22R: 5′-GCAGTTCGGTACACACCTCT; UL27F: 5′-TAACAAGAAAGAACCGCGCG; UL27R: 5′-ATTTTCCCGGTCAGTGCCAA. PCR amplifications were performed in a total volume of 50 μl. The reaction included 1 μl of the template DNA, 1 μl of each primer (10 μM), 22 μl of distilled water (DDW), and 25 μl of the AmpliTaq Gold® 360 Master Mix (Cat. No. 4398876, Life Technologies, Valencia, CA, USA). The thermocycle for amplification was: Initial denaturing at 95 °C for 10 min, followed by 40 cycles of 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 60 s, and then a final extension at 72 °C for 7 min. Results were visualized by gel electrophoresis (2% agarose) with SYBR Safe DNA Gel Stain (Cat. No. S33102, Invitrogen, Carlsbad, CA, USA).Sequence optimization of the UL27 gene for expression of the ChHV5 glycoprotein protein using E. coli
To express large quantities of ChHV5 gB, we adopted the prokaryotic Escherichia coli (E. coli) expression system. The construct (namely UL27/pUC57) containing sequences of the full length UL27 fused with FLAG tag sequence (GenBank accession no. AF035003.3) was synthesized by Allbio Science Co., Ltd, Taiwan. The sequence information of the glycoprotein (gB) datasets used and analyzed for protein expression during the current study was obtained and available from the GenBank repository [https://www.ncbi.nlm.nih.gov/nuccore/AF035003.3]. Considering the difference in tRNA-codon usages between prokaryotes and eukaryotes would possibly affect subsequent protein expression, the optimized UL27 gene sequence, without altering the translated amino acid sequences, to fit the E. coli expression system was synthesized. The codon optimized UL27 gene was further used as the template for amplification of different gene fragments by Polymerase Chain Reaction (PCR).Construction of plasmids expressing partial fragments of ChHV5 gB proteinTo determine the relative antigenicity and also to increase the expression yield, plasmids expressing various regions of gB protein were constructed. Briefly, the five regions covering different fragments of the UL27 gene were amplified from plasmid UL27/pUC57 by PCR using specific primer sets with built-in restriction enzyme sequences shown as underlined in Table 1. The thermal cycling conditions were: 98 °C (5 min) followed by 35 cycles of denaturation (98 °C, 30 s), annealing (58 °C, 1 min), and extension (72 °C, 2 min), and finished with a final extension (72 °C, 10 min). PCR amplicons with expected sizes were isolated from gel and trimmed with the restriction enzymes followed by ligation with vectors either pET24a or pET32b (Novagen, Germany) linearized with the same restriction enzymes. The resulting plasmids with expected insert sizes as confirmed by restriction enzymes were sent for automated DNA sequencing (Mission Biotech, Taipei, Taiwan).Table 1 Information on the constructs expressing the UL27 fragments. The bold characters indicate sequences recognized by restriction enzymes for the ease of further cloning procedure.Full size tableExpression of recombinant gB fragments in E. coli
In the current study, the recombinant gB protein is a key reagent that served as antigen for seroprevalence of ChHV5 as well as for the generation of ChHV5 gB antibody (conducted by Yao-Hong Biotech Inc., Taiwan). The plasmids expressing individual gB fragment were transformed into E. coli host cells, strain BL21 (DE3), Rosetta. Expression of all the recombinant gB fragments was induced by 0.8 mM of isopropyl β-d-1-thiogalactopyranoside (IPTG) at 28 °C for 16 h. As all the gB fragments cloned into the pET series vectors were expressed as a fusion protein with a 6-histidine tag at C-terminus end, they could be further purified by Ni–NTA column chromatography using the chelating Sepharose Fast Flow (GE Healthcare) following the method described in one previous study33. The yield and purity of recombinant gB proteins were confirmed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE). Subsequently, 6 M urea and 0.4 M imidazole contained in the purified protein were depleted by step-wise dialysis against 1 × PBS buffer (0.02 M phosphate, 0.15 M NaCl) with gradually decreased concentrations of urea at 4 °C. The concentration of recombinant proteins were then estimated by National Institutes of Health ImageJ software (https://imagej.nih.gov/ij/, 1997–2018.) using the standard curve established by bovine serum albumin (BSA) with known concentrations42.Western blot analysisRecombinant gB fragments were separated by 12.5% or 15% SDS-PAGE and electrotransferred to PVDF membrane by using Mini Proten III apparatus (Cat. No. 165-3301, BioRad). The filters were blocked in PBS-T buffer (0.02 M phosphate, 0.15 M NaCl, 0.05% Tween-20) containing 5% skimmed milk and reacted with mouse anti-his tag antibody (1:5,000, Cat. No. GTX40628, GeneTex) at 4 °C for overnight. After six-time wash with PBS-T buffer, the PVDF filter was then incubated with the secondary antibody, 1:5000 diluted goat anti-mouse IgG conjugated with horseradish peroxidase (HRP), or 1:500 diluted Protein A/G-HRP (Cat. No. 32400, Thermo fisher scientific™, United States) for sea turtle antibody detection, at room temperature for 1 h followed by PBS-T wash to remove the unbound antibodies. Ultimately, the signal was detected by ECL reagents (Thermo Fisher Scientific, United States) and the image was acquired by ImageQuant LAS 4000 Mini (GE Healthcare).Immunohistochemical (IHC) analysisTo establish IHC protocol, normal skin tissue from PCR-negative sea turtles served as the negative control. In total, the FP on skin tissue from six individual sea turtles that were detected positive for ChHV5 DNA (positive tissue samples), and one normal tissue detected negative (the negative tissue) were included in the IHC analysis.IHC procedure was conducted as reported in our previous study34. In brief, sections of formalin-fixed and wax-embedded skin tissues of sea turtles were made using a rotary microtome (Leica RM2245, Leica Biosystems, Germany) and were further deparaffinized and rehydrated. Antigen retrieval was carried out by heat-induced epitope retrieval method: slides immersed into boiled sodium citrate buffer (0.01 M, pH 6.0), which was preheated up to 100 °C, for 20 min and cooled at room temperature for 20 min. Subsequently, the slides were incubated with peroxidase-blocking reagent (Cat. No. S200389, Dako, Denmark) for 30 min, and then treated with or without primary antibodies (the anti-gB serum prepared from this study). In each interval of the following procedures, sections were rinsed with a mixture of TBST buffer. Tissue sections were then reacted with secondary antibody (HRP anti-rabbit/mouse, DAKO, Denmark), followed by incubation of DAB and chromogen (dilution 1 μL in 100 μL) from a commercial ChemMate EnVision detection kit (Cat. No. K5007, Dako, Denmark). Ultimately, tissue sections were counterstained with Mayer’s hematoxylin reagents (Code S3309, Dako, Denmark) for 2 min followed by wash with DDW, and reacted with 37 mM ammonia water for 5 s and rinsed with DDW.Immunofluorescent assay (IFA)Human 293 T cells were transfected with plasmids expressing full-length ChHV5 gB protein fused with FLAG tag at its C-terminus. At 24 h post transfection, 293 T cells (CRL-3216, ATCC, USA) were fixed with 2% formaldehyde for 10 min, followed by permeabilization with 0.1% Triton X-100 for another 10 min. Subsequently, cells were incubated with anti-FLAG antibody (1:500) (F7425; Sigma-Aldrich), or antisera (F1, F2, F3, F2–3) at the dilution of 1:500 for 1 h at room temperature. After six times of washes with PBS containing1% bovine serum, goat anti-mouse IgG (1:2,000 fold diluted) (Cat. No. A28175, Alexa Fluor® 488, Invitrogen) was used as secondary antibody. After one-hour incubation, nuclei were stained with 4, 6-diamidino-2-phenylindole (DAPI, Cat. No. D9542, Sigma-Aldrich) for 10 min, followed by confocal microscopy (FV1000, Olympus, Tokyo, Japan) with Olympus FV10-ASW 1.3 viewer software.Statistical analysisTo evaluate the association between seropositivity and FP or viremia tested by PCR of UL27 gene, Fisher’s exact test was applied due to very limited number of sea turtles with FP. The statistical significance was determined by p  More

We surveyed three islands of Lake Chany: Uzkoredkii (54° 58′ 15′′ N, 77°27′04′′ E), Reden’kii (54° 56′ 05′′ N, 77° 22′ 27′′ 52 E), Korablik (54° 59′ 31′′ N, 77° 40′ 38′′ E). The studied intertidal habitats are rarely reached by humans.Gull nests were counted in colonies by regular surveys over eight years (1993, 1994, 1996–1998, 2001–2003) on the islands of Lake Chany. Colonies were visited daily or sometimes every other day. To minimize the disturbance caused by the investigation, the time spent working, within view of the gulls was restricted to a maximum of forty minutes per study plots. We noted nest content at every visit for the presence of eggs or chicks. In total, there were 1 164 nests under observation. Nests contained 1 (n = 140), 2 (n = 518), 3 (n = 504) or 4 (n = 2) eggs. Modal clutch size of the great black-headed gull is two or three eggs, varying seasonally. The length and width of the eggs were measured using Vernier calipers (division accuracy 0,1 mm) and numbered with a waterproof marker. Egg volumes were estimated using Hoyt’s equation: Volume = 0.51 * Length * Width * Width/100013. We determined the volume of 2117 great black-headed gull eggs.As the laying of eggs has already started by the first visit to the colony, the date of the beginning of egg laying was calculated by subtracting the average length of the incubation period of great black-headed gulls (27 days) from the hatching date of first chick in the nest (n = 559 nests). If the hatching date was not known, the clutch initiation date was determined by subtracting the number of days of incubation from the date that the nest was first discovered (n = 469 nests). The stage of incubation was estimated from the change in position of an incubated egg placed in water14,15. The technique’s accuracy varied throughout incubation and mean prediction error fall between 0–4 days. On average, egg flotation estimated an embryo’s developmental age to within 1.9 ± 1.6 days (mean ± 1 SD)16. Only 47 nests were found during egg laying. Great black-headed gulls usually laid eggs at intervals of two days. Incubation started as soon as the first egg was laid, so eggs hatched asynchronously, one or two days apart.Whenever possible, we determined the within-clutch laying sequence of eggs (1st, 2nd, 3rd, and 4th). A complete laying sequence was established by observation in 47 cases. In about 48% of clutches the position in laying sequence was established on the basis of the sequence of hatching. In other cases, if we could distinguish within-clutch distinct flotation levels of eggs, we numbered eggs according to the stage of incubation. Sometimes this technique for distinguishing egg laying order were used in other seabirds17,18.We recorded the pipping date (i.e. appearance of star-like bursts) and the actual hatching date of the individual eggs. Wet chicks were registered as hatchlings of that day; dry chicks were registered as 1 day old. Chicks older than two days left the nest and moved to a location nearby. Newly hatched gull chicks were captured by hand at nests, ringed, and measured. We determined wing, tarsus, and head length using a ruler with zero-stop and vernier calipers and body weight measured using Pesola spring balances for 747 chicks of great black-headed gulls, and 457 of them hatched from eggs that were measured. More

Turroni F, Milani C, Duranti S, Lugli GA, Bernasconi S, Margolles A, et al. The infant gut microbiome as a microbial organ influencing host well-being. Ital J Pediatr. 2020;46:1–13.Article 

Google Scholar 
Bokulich NA, Chung J, Battaglia T, Henderson N, Jay M, Li H, et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci Transl Med. 2016;8:343ra82–343ra82.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Akagawa S, Tsuji S, Onuma C, Akagawa Y, Yamaguchi T, Yamagishi M, et al. Effect of delivery mode and nutrition on gut microbiota in neonates. Ann Nutr Metab. 2019;74:132–9.CAS 
PubMed 
Article 

Google Scholar 
Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T, Zeevi D, et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 2018;555:210–5.Vellend M, Srivastava DS, Anderson KM, Brown CD, Jankowski JE, Kleynhans EJ, et al. Assessing the relative importance of neutral stochasticity in ecological communities. Oikos. 2014;123:1420–30.Article 

Google Scholar 
Fukami T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu Rev Ecol Evol Syst. 2015;46:1–23.Article 

Google Scholar 
Fukami T, Nakajima M. Community assembly: Alternative stable states or alternative transient states? Ecol Lett. 2011;14:973–84.PubMed 
PubMed Central 
Article 

Google Scholar 
Sprockett D, Fukami T, Relman DA. Role of priority effects in the early-life assembly of the gut microbiota. Nat Rev Gastroenterol Hepatol. 2018;15:197–205.PubMed 
PubMed Central 
Article 

Google Scholar 
Debray R, Herbert RA, Jaffe AL, Crits-Christoph A, Power ME, Koskella B. Priority effects in microbiome assembly. Nat Rev Microbiol. 2022;20:109–21.CAS 
PubMed 
Article 

Google Scholar 
Tannock GW, Lawley B, Munro K, Pathmanathan SG, Zhou SJ, Makrides M, et al. Comparison of the compositions of the stool microbiotas of infants fed goat milk formula, cow milk-based formula, or breast milk. Appl Environ Microbiol. 2013;79:3040–8.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Matsuki T, Yahagi K, Mori H, Matsumoto H, Hara T, Tajima S, et al. A key genetic factor for fucosyllactose utilization affects infant gut microbiota development. Nat Commun. 2016;7:11939.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sakanaka M, Hansen ME, Gotoh A, Katoh T, Yoshida K, Odamaki T, et al. Evolutionary adaptation in fucosyllactose uptake systems supports bifidobacteria-infant symbiosis. Sci Adv. 2019;5:eaaw7696.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Engfer MB, Stahl B, Finke B, Sawatzki G, Daniel H. Human milk oligosaccharides are resistant to enzymatic hydrolysis in the upper gastrointestinal tract. Am J Clin Nutr. 2000;71:1589–96.CAS 
PubMed 
Article 

Google Scholar 
Macrobal A, Sonnenburg JL. Human milk oligosaccharide consumption by intestinal microbiota. Clin Microbiol Infect. 2012;18:12–15.Article 

Google Scholar 
Macrobal A, Barboza M, Froehlich JW, Block DE, German JB, Lebrilla CB, et al. Consumption of human milk oligosaccharides by gut-related microbes. J Agric Food Chem. 2010;58:5334–40.Article 
CAS 

Google Scholar 
Sakanaka M, Gotoh A, Yoshida K, Odamaki T, Koguchi H, Xiao JZ, et al. Varied pathways of infant gut-associated Bifidobacterium to assimilate human milk oligosaccharides: prevalence of the gene set and its correlation with bifidobacteria-rich microbiota formation. Nutrients. 2020;12:71.CAS 
Article 

Google Scholar 
Katayama T. Host-derived glycans serve as selected nutrients for the gut microbe: human milk oligosaccharides and bifidobacteria. Biosci Biotechnol Biochem. 2016;80:621–32.CAS 
PubMed 
Article 

Google Scholar 
Turroni F, Foroni E, Pizzetti P, Giubellini V, Ribbera A, Merusi P, et al. Exploring the diversity of the bifidobacterial population in the human intestinal tract. Appl Environ Microbiol. 2009;75:1534–45.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gore C, Munro K, Lay C, Bibiloni R, Morris J, Woodcock A, et al. Bifidobacterium pseudocatenulatum is associated with atopic eczema: A nested case-control study investigating the fecal microbiota of infants. J Allergy Clin Immunol. 2008;121:135–40.PubMed 
Article 

Google Scholar 
Lewis ZT, Mills DA. Differential establishment of bifidobacteria in the breastfed infant gut. Nestle Nutr Inst Work Ser. 2017;88:149–59.Article 

Google Scholar 
Tannock GW, Lee PS, Wong KH, Lawley B. Why don’t all infants have bifidobacteria in their stool? Front Microbiol. 2016;7:6–10.Article 

Google Scholar 
Reyman M, van Houten MA, van Baarle D, Bosch AATM, Man WH, Chu MLJN, et al. Impact of delivery mode-associated gut microbiota dynamics on health in the first year of life. Nat Commun. 2019;10:1–12.CAS 
Article 

Google Scholar 
Underwood MA, Kalanetra KM, Bokulich NA, Lewis ZT, Mirmiran M, Tancredi DJ, et al. A comparison of two probiotic strains of bifidobacteria in preterm infants. J Pediatr. 2013;163:1585–91.CAS 
PubMed 
Article 

Google Scholar 
Plummer EL, Bulach DM, Murray GL, Jacobs SE, Tabrizi SN, Garland SM. Gut microbiota of preterm infants supplemented with probiotics: sub-study of the ProPrems trial. BMC Microbiol. 2018;18:1–8.Article 
CAS 

Google Scholar 
Kitajima H, Sumida Y, Tanaka R, Yuki N, Takayama H, Fujimura M. Early administration of Bifidobacterium breve to preterm infants: Randomised controlled trial. Arch Dis Child Fetal Neonatal Ed. 1997;76:101–7.Article 

Google Scholar 
Ojima MN, Yoshida K, Sakanaka M, Jiang L, Odamaki T, Katayama T. Ecological and molecular perspectives on responders and non-responders to probiotics and prebiotics. Curr Opin Biotechnol. 2022;73:108–20.CAS 
PubMed 
Article 

Google Scholar 
Suez J, Zmora N, Segal E, Elinav E. The pros, cons, and many unknowns of probiotics. Nat Med. 2019;25:716–29.CAS 
PubMed 
Article 

Google Scholar 
Costeloe K, Hardy P, Juszczak E, Wilks M, Millar MR. Bifidobacterium breve BBG-001 in very preterm infants: A randomised controlled phase 3 trial. Lancet. 2016;387:649–60.PubMed 
Article 

Google Scholar 
Overbeek R, Begley T, Butler RM, Choudhuri JV, Chuang HY, Cohoon M, et al. The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes. Nucleic Acids Res. 2005;33:5691–702.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Katoh T, Ojima MN, Sakanaka M, Ashida H, Gotoh A, Katayama T. Enzymatic adaptation of Bifidobacterium bifidum to host glycans, viewed from glycoside hydrolyases and carbohydrate-binding modules. Microorganisms. 2020;8:481.CAS 
PubMed Central 
Article 

Google Scholar 
Egan M, Motherway MO, Kilcoyne M, Kane M, Joshi L, Ventura M, et al. Cross-feeding by Bifidobacterium breve UCC2003 during co-cultivation with Bifidobacterium bifidum PRL2010 in a mucin-based medium. BMC Microbiol. 2014;14:1–14.Article 
CAS 

Google Scholar 
Higgins MA, Ryan KS. Generating a fucose permease deletion mutant in Bifidobacterium longum subspecies infantis ATCC 15697. Anaerobe. 2021;68:102320.CAS 
PubMed 
Article 

Google Scholar 
O’Connell Motherway M, Kinsella M, Fitzgerald GF, van Sinderen D. Transcriptional and functional characterization of genetic elements involved in galacto-oligosaccharide utilization by Bifidobacterium breve UCC2003. Micro Biotechnol. 2013;6:67–79.Article 
CAS 

Google Scholar 
Yoshida E, Sakurama H, Kiyohara M, Nakajima M, Kitaoka M, Ashida H, et al. Bifidobacterium longum subsp. infantis uses two different β-galactosidases for selectively degrading type-1 and type-2 human milk oligosaccharides. Glycobiology. 2012;22:361–8.CAS 
PubMed 
Article 

Google Scholar 
Vannette RL, Fukami T. Historical contingency in species interactions: Towards niche-based predictions. Ecol Lett. 2014;17:115–24.PubMed 
Article 

Google Scholar 
Bäckhed F, Roswall J, Peng Y, Feng Q, Jia H, Kovatcheva-Datchary P, et al. Dynamics and stabilization of the human gut microbiome during the first year of life. Cell Host Microbe. 2015;17:690–703.PubMed 
Article 
CAS 

Google Scholar 
Pu Z, Jiang L. Dispersal among local communities does not reduce historical contingencies during metacommunity assembly. Oikos. 2015;124:1327–36.Article 

Google Scholar 
Chase JM. Community assembly: when should history matter? Oecologia. 2003;136:489–98.PubMed 
Article 

Google Scholar 
Schröder A, Persson L, De Roos AM. Direct experimental evidence for alternative stable states: A review. Oikos. 2005;110:3–19.Article 

Google Scholar 
Asakuma S, Hatakeyama E, Urashima T, Yoshida E, Katayama T, Yamamoto K, et al. Physiology of consumption of human milk oligosaccharides by infant gut-associated bifidobacteria. J Biol Chem. 2011;286:34583–92.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gotoh A, Katoh T, Sakanaka M, Ling Y, Yamada C, Asakuma S, et al. Sharing of human milk oligosaccharides degradants within bifidobacterial communities in faecal cultures supplemented with Bifidobacterium bifidum. Sci Rep. 2018;8:13958.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ashida H, Miyake A, Kiyohara M, Wada J, Yoshida E, Kumagai H, et al. Two distinct α-l-fucosidases from Bifidobacterium bifidum are essential for the utilization of fucosylated milk oligosaccharides and glycoconjugates. Glycobiology. 2009;19:1010–7.CAS 
PubMed 
Article 

Google Scholar 
Roger LC, Costabile A, Holland DT, Hoyles L, McCartney AL. Examination of faecal Bifidobacterium populations in breast- and formula-fed infants during the first 18 months of life. Microbiology. 2010;156:3329–41.CAS 
PubMed 
Article 

Google Scholar 
Avershina E, Storrø O, Øien T, Johnsen R, Wilson R, Egeland T, et al. Bifidobacterial succession and correlation networks in a large unselected cohort of mothers and their children. Appl Environ Microbiol. 2013;79:497–507.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Turroni F, Peano C, Pass DA, Foroni E, Severgnini M, Claesson MJ, et al. Diversity of bifidobacteria within the infant gut microbiota. PLoS One. 2012;7:20–4.Article 
CAS 

Google Scholar 
James K, Bottacini F, Contreras JIS, Vigoureux M, Egan M, Motherway MO, et al. Metabolism of the predominant human milk oligosaccharide fucosyllactose by an infant gut commensal. Sci Rep. 2019;9:1–20.Article 
CAS 

Google Scholar 
Dedon LR, Özcan E, Rani A, Sela DA. Bifidobacterium infantis metabolizes 2′fucosyllactose-derived and free fucose through a common catabolic pathway resulting in 1,2-propanediol secretion. Front Nutr. 2020;7:1–16.Article 
CAS 

Google Scholar 
Sprockett D, Martin M, Costello E, Burns A, Holmes S, Gurven M, et al. Microbiota assembly, structure, and dynamics among tsimane horticulturalists of the Bolivian Amazon. Nat Commun. 2019;11:1–14.Laursen MF, Sakanaka M, von Burg N, Mörbe U, Andersen D, Moll JM, et al. Bifidobacterium species associated with breastfeeding produce aromatic lactic acids in the infant gut. Nat Microbiol. 2021;6:1367–82.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Meng D, Sommella E, Salviati E, Campiglia P, Ganguli K, Djebali K, et al. Indole-3-lactic acid, a metabolite of tryptophan, secreted by Bifidobacterium longum subspecies infantis is anti-inflammatory in the immature intestine. Pediatr Res. 2020;88:209–17.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bunesova V, Lacroix C, Schwab C. Fucosyllactose and l-fucose utilization of infant Bifidobacterium longum and Bifidobacterium kashiwanohense. BMC Microbiol. 2016;16:248.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Ruiz-Moyano S, Totten SM, Garrido D, Smilowitz JT, Bruce German J, Lebrilla CB, et al. Variation in consumption of human milk oligosaccharides by infant gut-associated strains of Bifidobacterium breve. Appl Environ Microbiol. 2013;79:6040–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lawson MAE, O’Neill IJ, Kujawska M, Gowrinadh Javvadi S, Wijeyesekera A, Flegg Z, et al. Breast milk-derived human milk oligosaccharides promote Bifidobacterium interactions within a single ecosystem. ISME J. 2020;14:635–48.CAS 
PubMed 
Article 

Google Scholar 
Schwab C, Ruscheweyh HJ, Bunesova V, Pham VT, Beerenwinkel N, Lacroix C. Trophic interactions of infant bifidobacteria and Eubacterium hallii during l-fucose and fucosyllactose degradation. Front Microbiol. 2017;8:1–14.Article 

Google Scholar 
Engels C, Ruscheweyh HJ, Beerenwinkel N, Lacroix C, Schwab C. The common gut microbe Eubacterium hallii also contributes to intestinal propionate formation. Front Microbiol. 2016;7:1–12.Article 

Google Scholar 
Marcobal A, Barboza M, Sonnenburg ED, Pudlo N, Martens EC, Desai P, et al. Bacteroides in the infant gut consume milk oligosaccharides via mucus-utilization pathways. Cell Host Microbe. 2011;10:507–14.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vatanen T, Kostic AD, D’Hennezel E, Siljander H, Franzosa EA, Yassour M, et al. Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans. Cell. 2016;165:842–53.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vatanen T, Franzosa EA, Schwager R, Tripathi S, Arthur TD, Vehik K, et al. The human gut microbiome in early-onset type 1 diabetes from the TEDDY study. Nature. 2018;562:589–94.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moossavi S, Sepehri S, Robertson B, Bode L, Goruk S, Field CJ, et al. Composition and Variation of the Human Milk Microbiota Are Influenced by Maternal and Early-Life Factors. Cell Host Microbe. 2019;25:324–335.e4.CAS 
PubMed 
Article 

Google Scholar 
Martín R, Langa S, Reviriego C, Jiménez E, Marín ML, Xaus J, et al. Human milk is a source of lactic acid bacteria for the infant gut. J Pediatr. 2003;143:754–8.PubMed 
Article 

Google Scholar 
Martín R, Jiménez E, Heilig H, Fernández L, Marín ML, Zoetendal EG, et al. Isolation of bifidobacteria from breast milk and assessment of the bifidobacterial population by PCR-denaturing gradient gel electrophoresis and quantitative real-time PCR. Appl Environ Microbiol. 2009;75:965–9.PubMed 
Article 
CAS 

Google Scholar 
Heikkilä MP, Saris PEJ. Inhibition of Staphylococcus aureus by the commensal bacteria of human milk. J Appl Microbiol. 2003;95:471–8.PubMed 
Article 
CAS 

Google Scholar 
Li Y, Shimizu T, Hosaka A, Kaneko N, Ohtsuka Y, Yamashiro Y. Effects of Bifidobacterium breve supplementation on intestinal flora of low birth weight infants. Pediatr Int. 2004;46:509–15.PubMed 
Article 

Google Scholar 
Nishimoto M, Kitaoka M. Practical preparation of lacto-N-biose I, a candidate for the bifidus factor in human milk. Biosci Biotechnol Biochem. 2007;71:2101–4.CAS 
PubMed 
Article 

Google Scholar 
Duncan SH, Hold GL, Harmsen HJM, Stewart CS, Flint HJ. Growth requirements and fermentation products of Fusobacterium prausnitzii, and a proposal to reclassify it as Faecalibacterium prausnitzii gen. nov., comb. nov. Int J Syst Evol Microbiol. 2002;52:2141–6.CAS 
PubMed 

Google Scholar 
Browne HP, Forster SC, Anonye BO, Kumar N, Neville BA, Stares MD, et al. Culturing of ‘unculturable’ human microbiota reveals novel taxa and extensive sporulation. Nature. 2016;533:543–6.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tanizawa Y, Fujisawa T, Kaminuma E, Nakamura Y. Arita M. DFAST and DAGA: Web-based integrated genome annotation tools and resources. Biosci Microbiota, Food Heal. 2016;35:173–84.CAS 
Article 

Google Scholar 
Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, et al. The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST). Nucleic Acids Res. 2014;42:206–14.Article 
CAS 

Google Scholar 
Price MN, Arkin AP. PaperBLAST: Text-mining papers for information about homologs. bioRxiv. 2017;2:1–10.
Google Scholar 
Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:490–5.Article 
CAS 

Google Scholar 
Saier MH, Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-Hagelsieb G. The Transporter Classification Database (TCDB): Recent advances. Nucleic Acids Res. 2016;44:D372–9.CAS 
PubMed 
Article 

Google Scholar 
Martínez I, Wallace G, Zhang C, Legge R, Benson AK, Carr TP, et al. Diet-induced metabolic improvements in a hamster model of hypercholesterolemia are strongly linked to alterations of the gut microbiota. Appl Environ Microbiol. 2009;75:4175–84.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Anumula KR. Advances in fluorescence derivatization methods for high-performance liquid chromatographic analysis of glycoprotein carbohydrates. Anal Biochem. 2006;350:1–23.CAS 
PubMed 
Article 

Google Scholar 
Cohenford MA, Abraham A, Abraham J, Dain JA. Colorimetric assay for free and bound l-fucose. Anal Biochem. 1989;177:172–7.CAS 
PubMed 
Article 

Google Scholar 
Kato K, Odamaki T, Mitsuyama E, Sugahara H, Xiao JZ, Osawa R. Age-related changes in the composition of gut Bifidobacterium species. Curr Microbiol. 2017;74:987–95.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. 2011;17:10–12.Article 

Google Scholar 
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:1–13.Article 
CAS 

Google Scholar 
Lu J, Breitwieser FP, Thielen P, Salzberg SL. Bracken: Estimating species abundance in metagenomics data. PeerJ Comput Sci. 2017;2017:1–17.CAS 

Google Scholar 
Milani C, Lugli GA, Fontana F, Mancabelli L, Alessandri G, Longhi G, et al. METAnnotatorX2: A comprehensive tool for deep and shallow metagenomic data set analyses. mSystems. 2021;6:1–15.Article 

Google Scholar 
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. vegan: Community Ecology Package. 2019. More

Anderson, P. K. et al. Emerging infectious diseases of plants: Pathogen pollution, climate change and agrotechnology drivers. Trends Ecol. Evol. 19, 535–544 (2004).PubMed 

Google Scholar 
Brasier, C. M. The biosecurity threat to the UK and global environment from international trade in plants. Plant Pathol. 57, 792–808 (2008).
Google Scholar 
Waage, J. K. & Mumford, J. D. Agricultural biosecurity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 363, 863–876 (2008).CAS 
PubMed 

Google Scholar 
IPPC. Surveillance guide—A guide to understand the principal requirements of surveillance programmes for national plant protection organizations. Second edition. http://www.fao.org/documents/card/en/c/cb7139en (2021) https://doi.org/10.4060/cb7139en.Parnell, S., van den Bosch, F., Gottwald, T. & Gilligan, C. A. Surveillance to inform control of emerging plant diseases: An epidemiological perspective. Annu. Rev. Phytopathol. 55, 591–610 (2017).CAS 
PubMed 

Google Scholar 
Cunniffe, N. J., Cobb, R. C., Meentemeyer, R. K., Rizzo, D. M. & Gilligan, C. A. Modeling when, where, and how to manage a forest epidemic, motivated by sudden oak death in California. Proc. Natl. Acad. Sci. 113, 5640–5645 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gottwald, T. R., Dixon, W., Parnell, S. & Riley, T. Huanglongbing: The dragon arrives in the USA. In Huanglongbing-Greening International Workshop, July 14–21 13–14 (2006).Herms, D. A., Stone, A. K. & Chatfield, J. A. Emerald ash borer: The beginning of the end of ash in North America?. Ornam. Plants Annu. Rep. Res. Rev. 2003, 62–71 (2004).
Google Scholar 
Sansford, C. E. Pest Risk Analysis for Hymenoscyphus pseudoalbidus (anamorph Chalara fraxinea) for the UK and the Republic of Ireland. https://webarchive.nationalarchives.gov.uk/ukgwa/20140904094312mp_/http://www.fera.defra.gov.uk/plants/plantHealth/pestsDiseases/documents/hymenoscyphusPseudoalbidusPRA.pdf (2013).Alonso Chavez, V., Parnell, S. & van den Bosch, F. Monitoring invasive pathogens in plant nurseries for early-detection and to minimise the probability of escape. J. Theor. Biol. 407, 290–302 (2016).ADS 
MathSciNet 
PubMed 
MATH 

Google Scholar 
Bourhis, Y., Gottwald, T. R., Lopez-Ruiz, F. J., Patarapuwadol, S. & van den Bosch, F. Sampling for disease absence-deriving informed monitoring from epidemic traits. J. Theor. Biol. 461, 8–16 (2019).ADS 
MathSciNet 
PubMed 
MATH 

Google Scholar 
Mastin, A. J., van den Bosch, F., van den Berg, F. & Parnell, S. Quantifying the hidden costs of imperfect detection for early detection surveillance. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180261 (2019).
Google Scholar 
Mastin, A. J., van den Bosch, F., Gottwald, T. R., Alonso Chavez, V. & Parnell, S. R. A method of determining where to target surveillance efforts in heterogeneous epidemiological systems. PLoS Comput. Biol. 13, e1005712 (2017).PubMed 
PubMed Central 

Google Scholar 
Parnell, S., Gottwald, T. R., Gilks, W. R. & van den Bosch, F. Estimating the incidence of an epidemic when it is first discovered and the design of early detection monitoring. J. Theor. Biol. 305, 30–36 (2012).ADS 
MathSciNet 
CAS 
PubMed 
MATH 

Google Scholar 
Parnell, S., Gottwald, T. R., Cunniffe, N. J., Alonso Chavez, V. & van den Bosch, F. Early detection surveillance for an emerging plant pathogen: A rule of thumb to predict prevalence at first discovery. Proc. R. Soc. B Biol. Sci. 282, 20151478 (2015).
Google Scholar 
Silva, G. et al. Plant pest surveillance: From satellites to molecules. Emerg. Top. Life Sci. 5, 275–287 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mastin, A. J., Gottwald, T. R., van den Bosch, F., Cunniffe, N. J. & Parnell, S. Optimising risk-based surveillance for early detection of invasive plant pathogens. PLoS Biol. 18, e3000863 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Martelli, G. P., Boscia, D., Porcelli, F. & Saponari, M. The olive quick decline syndrome in south-east Italy: A threatening phytosanitary emergency. Eur. J. Plant Pathol. 144, 235–243 (2015).
Google Scholar 
Saponari, M., Boscia, D., Nigro, F. & Martelli, G. P. Identification of DNA sequences related to Xylella fastidiosa in oleander, almond and olive trees exhibiting leaf scorch symptoms in Apulia (southern Italy). J. Plant Pathol. 95, 668 (2013).
Google Scholar 
Ben Moussa, I. E. et al. Seasonal fluctuations of sap-feeding insect species infected by Xylella fastidiosa in Apulian olive groves of southern Italy. J. Econ. Entomol. 109, 1512–1518 (2016).PubMed 

Google Scholar 
Cornara, D. et al. Transmission of Xylella fastidiosa to grapevine by the meadow spittlebug. Phytopathology 106, 1285–1290 (2016).CAS 
PubMed 

Google Scholar 
Cornara, D. et al. Transmission of Xylella fastidiosa by naturally infected Philaenus spumarius (Hemiptera, Aphrophoridae) to different host plants. J. Appl. Entomol. 141, 80–87 (2017).
Google Scholar 
Saponari, M. et al. Infectivity and transmission of Xylella fastidiosa by Philaenus spumarius (Hemiptera: Aphrophoridae) in Apulia, Italy. J. Econ. Entomol. 107, 1316–1319 (2014).PubMed 

Google Scholar 
European Commission. Commission Implementing Regulation (EU) 2020/1201 of 14 August 2020 as regards measures to prevent the introduction into and the spread within the Union of Xylella fastidiosa (Wells et al.). (2021).EFSA et al. Guidelines for statistically sound and risk-based surveys of Xylella fastidiosa. EFSA. Support. Publ. 17, 1873 (2020).EFSA et al. General guidelines for statistically sound and risk-based surveys of plant pests. EFSA Support. Publ. 17, 1919E (2020).Bourhis, Y., Gottwald, T. & van den Bosch, F. Translating surveillance data into incidence estimates. Philos. Trans. R. Soc. B Biol. Sci. 374, 20180262 (2019).CAS 

Google Scholar 
Cornara, D. et al. Spittlebugs as vectors of Xylella fastidiosa in olive orchards in Italy. J. Pest Sci. 90, 521–530 (2017).
Google Scholar 
Cornara, D., Bosco, D. & Fereres, A. Philaenus spumarius: when an old acquaintance becomes a new threat to European agriculture. J. Pest Sci. 91, 957–972 (2018).
Google Scholar 
Almeida, R. P. P., Blua, M. J., Lopes, J. R. S. & Purcell, A. H. Vector transmission of Xylella fastidiosa: Applying fundamental knowledge to generate disease management strategies. Ann. Entomol. Soc. Am. 98, 775–786 (2005).
Google Scholar 
Purcell, A. H. & Finlay, A. H. Evidence for noncirculative transmission of Pierce’s disease bacterium by sharpshooter leafhoppers. Phytopathology 69, 393–395 (1979).
Google Scholar 
Hill, B. & Purcell, A. H. Acquisition and retention of Xylella fastidiosa by an efficient vector, Graphocephala atropunctata. Phytopathology 85, 209 (1995).
Google Scholar 
Hill, B. L. & Purcell, A. H. Multiplication and movement of Xylella fastidiosa within grapevine and four other plants. Phytopathology 85, 1368 (1995).
Google Scholar 
Huang, Q., Bentz, J. & Sherald, J. L. Fast, easy and efficient DNA extraction and one-step polymerase chain reaction for the detection of Xylella fastidiosa in potential insect vectors. J. Plant Pathol. 88, 77–81 (2006).CAS 

Google Scholar 
Harper, S. J., Ward, L. I. & Clover, G. R. G. Development of LAMP and real-time PCR methods for the rapid detection of Xylella fastidiosa for quarantine and field applications. Phytopathology 100, 1282–1288 (2010).CAS 
PubMed 

Google Scholar 
EFSA et al. Pest survey card on Xylella fastidiosa. EFSA Support. Publ. 16, (2019).Fierro, A., Liccardo, A. & Porcelli, F. A lattice model to manage the vector and the infection of the Xylella fastidiosa on olive trees. Sci. Rep. 9, 8723 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
EPPO. PM 7/24 (4) Xylella fastidiosa. EPPO Bull. 49, 175–227 (2019).Landa, B. B. et al. Emergence of a plant pathogen in Europe associated with multiple intercontinental introductions. Appl. Environ. Microbiol. 86, 1–15 (2019).
Google Scholar 
Castro, C., DiSalvo, B. & Roper, M. C. Xylella fastidiosa: A reemerging plant pathogen that threatens crops globally. PLoS Pathog. 17, e1009813 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Saponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D. & Saldarelli, P. Xylella fastidiosa in olive in Apulia: Where we stand. Phytopathology 109, 175–186 (2019).CAS 
PubMed 

Google Scholar 
Zarco-Tejada, P. J. et al. Previsual symptoms of Xylella fastidiosa infection revealed in spectral plant-trait alterations. Nat. Plants 4, 432–439 (2018).CAS 
PubMed 

Google Scholar 
Gottwald, T. et al. Canine olfactory detection of a vectored phytobacterial pathogen, Liberibacter asiaticus, and integration with disease control. Proc. Natl. Acad. Sci. 117, 3492–3501 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mendel, J., Furton, K. G. & Mills, D. An Evaluation of scent-discriminating canines for rapid response to agricultural diseases. HortTechnology 28, 102–108 (2018).
Google Scholar 
ECDC. Guidelines for the Surveillance of Invasive Mosquitoes in Europe. (2012).Kading, R. C., Golnar, A. J., Hamer, S. A. & Hamer, G. L. Advanced surveillance and preparedness to meet a new era of invasive vectors and emerging vector-borne diseases. PLoS Negl. Trop. Dis. 12, e0006761 (2018).PubMed 
PubMed Central 

Google Scholar 
Kumagai, L. B. et al. First report of Candidatus Liberibacter asiaticus associated with citrus huanglongbing in California. Plant Dis. 97, 283 (2013).CAS 
PubMed 

Google Scholar 
Ben Moussa, I. E. et al. Evaluation of “Spy Insect” approach for monitoring Xylella fastidiosa in symptomless olive orchards in the Salento peninsula (Southern Italy). IOBC WPRS Bull. 121, 77–84 (2017).
Google Scholar 
Cruaud, A. et al. Using insects to detect, monitor and predict the distribution of Xylella fastidiosa: A case study in Corsica. Sci. Rep. 8, 15628 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Yaseen, T. et al. On-site detection of Xylella fastidiosa in host plants and in “spy insects” using the real-time loop-mediated isothermal amplification method. Phytopathol. Mediterr. https://doi.org/10.14601/Phytopathol_Mediterr-15250 (2015).Article 

Google Scholar 
López-Mercadal, J. et al. Collection of data and information in Balearic Islands on biology of vectors and potential vectors of Xylella fastidiosa (GP/EFSA/ALPHA/017/01). EFSA Support. Publ. 18, 6925E (2021).
Google Scholar 
Cunty, A. Detection, identification and surveillance of Xylella fastidiosa on vectors in France https://zenodo.org/record/3551122#.XjGqBs77SUl. (2019) https://doi.org/10.5281/zenodo.3551122.Kottelenberg, D., Hemerik, L., Saponari, M. & van der Werf, W. Shape and rate of movement of the invasion front of Xylella fastidiosa spp. pauca in Puglia. Sci. Rep. 11, 1061 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Sampling, DNA preparation and sequencingStockholmSamples LOW002, LOW003, LOW006, LOW007, LOW008 and PON012 were processed at the Archaeological Research Laboratory at Stockholm University, Sweden, following methods previously described8. In brief, this involved extracting DNA by incubating the bone powder for 24 h at 37 °C in 1.5 ml of digestion buffer (0.45 M EDTA (pH 8.0) and 0.25 mg ml–1 proteinase K), concentrating supernatant on Amicon Ultra-4 (30-kDa molecular weight cut-off (MWCO)) filter columns (MerckMillipore) and purifying on Qiagen MinElute columns. Double-stranded Illumina libraries were prepared using the protocol outlined in ref. 48, with the inclusion of USER enzyme and the modifications described in ref. 49.Samples 367, PDM100, Taimyr-1 and Yana-1 were processed at the Swedish Museum of Natural History in Stockholm, Sweden, following previously described methods8. In brief, this involved extracting DNA using a silica-based method with concentration on Vivaspin filters (Sartorius), according to a protocol optimized for recovery of ancient DNA50. Double-stranded Illumina libraries were prepared using the protocol outlined in ref. 48, with the inclusion of USER enzyme.Samples ALAS_024, VAL_033, ALAS_016, VAL_008, HMNH_007, HMNH_011, VAL_050, VAL_005, DS04, VAL_037, VAL_012, VAL_011, VAL_18A, IN18_016 and IN18_005 were processed at the Swedish Museum of Natural History in Stockholm, Sweden, following previously described methods for permafrost bone and tooth samples51. In brief, this involved DNA extraction using the methodology of ref. 52 and double-stranded Illumina library preparation as described in ref. 48, with dual unique indexes and the inclusion of USER enzyme. Between eight and ten separate PCR reactions with unique indexes were carried out for each sample to maximize library complexity. The libraries were sequenced alongside samples HOV4, AL2242, AL2370, AL2893, AL3272 and AL3284 across three Illumina NovaSeq 6000 lanes with an S4 100-bp paired-end set-up at SciLifeLab in Stockholm.PotsdamSamples JAL48, JAL65, JAL69, JAL358, AH574, AH575 and AH577 were processed at the University of Potsdam. Pre-amplification steps (DNA extraction and library preparation) were conducted in separated laboratory rooms specially equipped for the processing of ancient DNA. Amplification and post-amplification steps were performed in different laboratory rooms. DNA was extracted from bone powder (29–54 mg) following a protocol specially adapted to recover short DNA fragments52. Single-stranded double-indexed libraries were built from 20 µl of DNA extract according to the protocol in ref. 53. The libraries were sequenced on an HiSeq X platform at SciLifeLab in Stockholm.Tübingen/JenaSamples JK2174, JK2175, JK2179, JK2181, JK2183, TU144, TU148, TU839 and TU840 were processed at the University of Tübingen, with DNA extraction and pre-amplification steps undertaken in clean room facilities and post-amplification steps performed in a separate DNA laboratory. Both laboratories fulfil standards for work with ancient DNA54,55. All surfaces of tooth and bone samples were initially UV irradiated for 30 min, to minimize the potential risk of modern DNA contamination. Subsequently, DNA was extracted by applying a well-established guanidine silica-based protocol for ancient samples52. Illumina sequencing libraries were prepared by using 20 µl of DNA extract per library48; afterwards, dual barcodes (indexes) were chemically added to the prime ends of the libraries56. For the samples from Auneau (TU839 and TU840), five sequencing libraries each were prepared; for all other samples processed in Tübingen, three sequencing libraries each were prepared. To detect potential contamination of the chemicals, negative controls were conducted for extraction and library preparation. After preparation of the sequencing libraries, DNA concentration was measured with qPCR (Roche LightCycler) using corresponding primers48. The DNA concentration was given by the copy number of the DNA fragments in 1 µl of the sample.Amplification of the indexed sequencing libraries was performed using Herculase II Fusion under the following conditions: 1× Herculase II buffer, 0.4 µM IS5 primer and 0.4 µM IS6 primer48, Herculase II Fusion DNA polymerase (Agilent Technologies), 0.25 mM dNTPs (100 mM; 25 mM each dNTP) and 0.5–4 µl barcoded library as template in a total reaction volume of 100 µl. The applied amplification thermal profile was processed as follows: initial denaturation for 2 min at 95 °C; denaturation for 30 s at 95 °C, annealing for 30 s at 60 °C and elongation for 30 s at 72 °C for 3 to 20 cycles; and a final elongation step for 5 min at 72 °C. Thereafter, the amplified DNA was purified using a MinElute purification step and DNA was eluted in 20 µl TET. The concentration of the amplified DNA sequencing libraries was measured using a Bioanalyzer (Agilent Technologies) and a DNA1000 lab chip from Agilent Technologies.The sequencing libraries were sequenced on an Illumina HiSeq 4000 platform at the Max Planck Institute for Science of Human History in Jena. The samples from Auneau (TU839 and TU840) were paired-end sequenced applying 2 × 50 + 8 + 8 cycles. All other libraries prepared in Tübingen were single-end sequenced using 75 + 8 + 8 cycles.OxfordSamples AL2657, AL2541, AL2741, AL2744, AL3185, AL2350, CH1109, AL2370, AL3272 and AL3284 were processed at the dedicated ancient DNA facility at the PalaeoBARN laboratory at the University of Oxford, following methods described previously8. In brief, double-stranded libraries were constructed following the protocol in ref. 48. These libraries were sequenced on a HiSeq 2500 (AL2657, AL2541, AL2741, AL2744) or a HiSeq 4000 (AL3185, AL2350, CH1109) instrument at the Danish National Sequencing Center or on a NextSeq 550 instrument (AL2741) at the Natural History Museum of London. For samples AL2370, AL3272 and AL3284, between six and eight separate PCR reactions with unique indexes were carried out on their libraries and they were sequenced alongside samples HOV4, VAL_18A and IN18_016 on an Illumina NovaSeq 6000 lane with an S4 100-bp paired-end set-up at SciLifeLab in Stockholm.CopenhagenSamples CGG13, CGG17, CGG19, CGG20, CGG21, CGG25, CGG26, CGG27, CGG28, CGG34, Tumat1 and IRK were processed at the GLOBE Institute, University of Copenhagen. All pre-PCR work was performed in ancient DNA facilities following ancient DNA guidelines57. The details of extraction, library construction and sequencing for the samples with CGG codes are described in ref. 21, in relation to the publication of mitochondrial data from these specimens. The Tumat1 sample was processed following the exact same protocol. In brief, DNA extraction was performed using a buffer containing urea, EDTA and proteinase K50, double-stranded libraries were prepared with NEBNext DNA Sample Prep Master MixSet 2 (E6070S, New England Biolabs) and Illumina-specific adaptors48, and sequencing was performed on an Illumina HiSeq 2500 platform using 100-bp single-read chemistry. For the IRK sample, DNA was extracted from three subsamples and purified as described in ref. 21. The three DNA extracts and the purified pre-digest of one subsample were incorporated into double-stranded libraries following the BEST protocol58, with the modifications described in ref. 59, and sequenced on a BGISEQ-500 platform using 100-bp single-read chemistry.Santa CruzSamples SC19.MCJ017, SC19.MCJ015, SC19.MCJ010 and SC19.MCJ014 were processed at the UCSC Paleogenomics Lab and were provided by the Yukon Government Paleontology program. All pre-PCR work was performed in a dedicated ancient DNA facility at the University of California, Santa Cruz, following standard ancient DNA methods60. Subsamples (250–350 mg) were sent to the UCI KECK AMS facility for radiocarbon dating, and the remaining amounts were powdered in a Retsch MM400 for extraction. For each sample, ~100 mg of powder was treated with a 0.5% sodium hypochlorite solution before extraction to remove surface contaminants61 and then combined with 1 ml lysis buffer for extraction, following the protocol in ref. 52. Samples were processed in parallel with a negative control. We quantified the extracts using a Qubit 1× dsDNA HS Assay kit (Q33231) before preparing libraries. We prepared single-stranded libraries following the protocol in ref. 62 and amplified the libraries for 9–16 cycles as informed by qPCR. After amplification, we cleaned the libraries using a 1.2× SPRI bead solution and pooled them to an equimolar ratio for in-house shallow quality-control sequencing on a NextSeq 550 paired-end 75-bp run. We then sent the libraries to Fulgent Genetics for deeper sequencing on two paired-end 150-bp lanes on a HiSeq X instrument.ViennaSample HOV4 was processed at the Department of Anthropology, University of Vienna. The sample is a canine tooth, which after sequencing was determined to derive from a dhole (Cuon alpinus). DNA was extracted from its cementum using the methods described in ref. 63 with a modified incubation time of ~18 h. The library was prepared according to the protocol in ref. 48 with the modifications from ref. 64. Five separate PCR reactions with unique indexes were carried out on the library and were sequenced alongside samples VAL_18A, IN18_016, AL2242, AL2370, AL2893, AL3272 and AL3284 on an Illumina NovaSeq 6000 lane with an S4 100-bp paired-end set-up at SciLifeLab in Stockholm.An overview of all samples and their associated metadata is available in Supplementary Data 1.Genome sequence data processingFor paired-end data, read pairs were merged and adaptors were trimmed using SeqPrep (https://github.com/jstjohn/SeqPrep), discarding reads that could not be successfully merged. Reads were mapped to the dog reference genome canFam3.1 using BWA aln (v.0.7.17)65 with permissive parameters, including a disabled seed (-l 16500 -n 0.01 -o 2). Duplicates were removed by keeping only one read from any set of reads that had the same orientation, length and start and end coordinates. For sample Taimyr-1, previously published data13 were merged with newly generated data. Data from samples processed in Copenhagen were processed as described previously66 except that they were also mapped to canFam3.1. Post-mortem damage was quantified using PMDtools (v0.60)67 with the ‘–first’ and ‘–CpG’ arguments.Genotyping and integration with previously published genomesTo construct a comparative dataset for population genetic analyses, we started from a published variant call set compiling 722 modern dog, wolf and other canid genomes from multiple previous studies (NCBI BioProject accession PRJNA448733)40. To this, we added additional modern whole genomes from other studies: 4 African golden wolves and 15 Nigerian village dogs (Genome Sequence Archive (http://gsa.big.ac.cn/), accession PRJCA000335)68, 12 Scandinavian wolves (European Nucleotide Archive accession PRJEB20635)69, 9 North American wolves and coyotes (European Nucleotide Archive accession PRJNA496590)25 and 8 other canids (African hunting dog, dhole, Ethiopian wolf, golden jackal, Middle Eastern grey wolves) (European Nucleotide Archive accession PRJNA494815)22. Reads from these genomes were mapped to the dog reference genome using bwa mem (version 0.7.15)70, marked for duplicates using Picard Tools (v2.21.4) (http://broadinstitute.github.io/picard), genotyped at the sites present in the above dataset using GATK HaplotypeCaller (v3.6)71 with the ‘-gt_mode GENOTYPE_GIVEN_ALLELES’ argument and then merged into the dataset using bcftools merge (http://www.htslib.org/). The following filters were then applied to sites and genotypes across the full dataset: sites with excess heterozygosity (bcftools fill-tags ‘ExcHet’ P value 5.8×. For divergence time analyses, haploid X chromosomes from two different male genomes were combined and the point at which the inferred effective population size for this ‘pseudodiploid’ chromosome increased sharply upwards was taken to correspond to a population divergence. Results were scaled using a mutation rate of 0.4 × 10−8 mutations per site per generation13,87 (with a 25% lower rate for X-chromosome analyses) and a mean generational interval of 3 years13. For effective population size inferences, transition variants were ignored and results were scaled using a transversions-only mutation rate inferred from results on modern genomes. For more details on the MSMC2 analyses, see Supplementary Information section 3.Selection analysesSelection analysis was performed using PLINK (v1.90b5.2)88. This analysis used the 72 ancient wolf genomes and 68 modern wolf genomes (with the latter including a historical Japanese wolf genome73 treated as ancient for analysis purposes, with its age set to 200 bp). A list of the genomes used for this analysis is available in Supplementary Data 2 (“Used for selection scan” column). All SNPs, not only transversions, were used for this analysis. The age of each wolf was set as the phenotype, with values of 0 for modern wolves, and the ‘–linear’ argument was used to test for an association between SNP genotypes and age, also applying the ‘–adjust’ argument to correct P values using genomic control. The application of genomic control34 here aimed to use the magnitude of temporal allele frequency variance observed across the genome to account for what was observed from genetic drift alone given wolf demographic history. Only results for the following sets of sites were retained and included in the Manhattan plot: sites where at least 40 ancient genomes had a genotype call, sites with a minor allele frequency among the ancient wolves of ≥5% and sites that had at least 7 neighbouring sites within a 50-kb window with a P value that was at least 90% as large (on a log10 scale) as the P value of the site itself. The last ‘neighbourhood filter’ aimed to reduce false positives by requiring similar evidence across multiple nearby sites. As a P-value significance cut-off to correct for the genome-wide testing, we used 5 × 10−8, which is commonly used in genome-wide association studies in humans and also in dogs89. We excluded 15 regions where only a single variant reached significance. A detailed table with the 24 detected regions is available in Supplementary Data 3. To test the robustness of this analysis to false positives arising from genetic drift alone, we applied the same analysis to data from neutral coalescent simulations generated using ms90 and found no false positives. For more details, see Supplementary Information section 4.Ancestry modelling with qpAdm and qpWaveWe used the qpAdm and qpWave methods43 from ADMIXTOOLS (v5.0)84 to test ancestry models for wolf and dog targets postdating 23 ka. For the primary analyses, we used the following set of candidate source populations (age estimate in brackets, years bp): Armenia_Hovk1.HOV4 (ancient dhole), Siberia_UlakhanSular.LOW008 (70,772), Germany_Aufhausener.AH575 (57,233), Siberia_BungeToll.CGG29 (48,210), Germany_HohleFels.JK2183 (32,366), Siberia_BelayaGora.IN18_016 (32,020), Yukon_QuartzCreek.SC19.MCJ010 (29,943), Altai_Razboinichya.AL2744 (28,345), Siberia_BelayaGora.IN18_005 (18,148) and Germany_HohleFels.JK2179 (13,229). We used a rotating approach in which, for each target, we tested all possible one-, two- and three-source models that could be enumerated from the above set. Individuals from the set that were not used as a source in a given model served as thereference set (or the ‘right’ population in the qpAdm framework). This means that, in every model, each of the above individuals was always either in the source list or in the reference list. We ranked models on the basis of their P values, but prioritized models with fewer sources using a P-value threshold of 0.01: if a simpler model (meaning a model with fewer sources) had a P value above this threshold, it ranked above a more complex model (meaning a model with more sources) regardless of the P value of the latter. We also failed models with inferred ancestry proportions larger than 1.1 or smaller than −0.1. For single-source models, qpWave was run instead of qpAdm. Both programs were run with the ‘allsnps: YES’ option (without this option, there was very little power to reject models). We describe ancestry assigned to the ancient dhole source (Armenia_Hovk1.HOV4) as ‘unsampled’ ancestry; note that this does not imply that such ancestry is of non-wolf origin, only that it is not represented by (that is, diverged early from and lacks shared genetic drift with) the ancient wolf genomes in the reference set.To test whether any post-23 ka or modern wolf genome available might be a good proxy for the western Eurasian wolf-related ancestry identified in Near Eastern and African dogs, we added the 9,500-year-old Zhokhov dog17 to the rotating set of candidate source populations. Chosen for its high coverage, early date and easterly location, this makes the assumption that the Zhokhov dog is a good representative for the eastern dog ancestry component. Using the African Basenji dog as a target, models involving the Zhokhov dog plus another given wolf thus allowed us to test whether that wolf was a good match for the additional component of ancestry. For more details on the qpAdm and qpWave analyses, see Supplementary Information sections 2 (wolf targets) and  5 (dog targets).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this paper. More

Misra, V. K. & Draper, D. E. On the role of magnesium ions in RNA stability. Biopolymers 48, 113–135 (1998).CAS 
PubMed 
Article 

Google Scholar 
Apell, H.-J., Hitzler, T. & Schreiber, G. Modulation of the Na, K-ATPase by magnesium ions. Biochemistry 56, 1005–1016 (2017).CAS 
PubMed 
Article 

Google Scholar 
Iseri, L. T. & French, J. H. Magnesium: Nature’s physiologic calcium blocker. Am. Heart J. 108, 188–193 (1984).CAS 
PubMed 
Article 

Google Scholar 
Rubin, H. Central role for magnesium in coordinate control of metabolism and growth in animal cells. Proc. Natl. Acad. Sci. USA 72, 3551–3555 (1975).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
de Baaij, J. H. F., Hoenderop, J. G. J. & Bindels, R. J. M. Magnesium in man: Implications for health and disease. Physiol. Rev. 95, 1–46 (2015).PubMed 
Article 
CAS 

Google Scholar 
Association, A. D. Diagnosis and classification of diabetes mellitus. Diabetes Care 37, S81–S90 (2014).Article 

Google Scholar 
Chatterjee, S., Khunti, K. & Davies, M. J. Type 2 diabetes. Lancet 389, 2239–2251 (2017).CAS 
PubMed 
Article 

Google Scholar 
Gommers, L. M. M., Hoenderop, J. G. J., Bindels, R. J. M. & de Baaij, J. H. F. Hypomagnesemia in type 2 diabetes: A vicious circle?. Diabetes 65, 3–13 (2016).CAS 
PubMed 
Article 

Google Scholar 
Pham, P.-C.T., Pham, P.-M.T., Pham, S. V., Miller, J. M. & Pham, P.-T.T. Hypomagnesemia in patients with type 2 diabetes. Clin. J. Am. Soc. Nephrol. 2, 366–373 (2007).CAS 
PubMed 
Article 

Google Scholar 
Mather, H. M. et al. Hypomagnesaemia in diabetes. Clin. Chim. Acta 95, 235–242 (1979).CAS 
PubMed 
Article 

Google Scholar 
Hubbard, S. R. Crystal structure of the activated insulin receptor tyrosine kinase in complex with peptide substrate and ATP analog. EMBO J. 16, 5572–5581 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kurstjens, S. et al. Determinants of hypomagnesemia in patients with type 2 diabetes mellitus. Eur. J. Endocrinol. 176, 11–19 (2017).CAS 
PubMed 
Article 

Google Scholar 
Viering, D. H. H. M., de Baaij, J. H. F., Walsh, S. B., Kleta, R. & Bockenhauer, D. Genetic causes of hypomagnesemia, a clinical overview. Pediatr. Nephrol. 32, 1123–1135 (2017).PubMed 
Article 

Google Scholar 
Peacock, J. M. et al. Serum magnesium and risk of sudden cardiac death in the Atherosclerosis Risk in Communities (ARIC) Study. Am. Heart J. 160, 464–470 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Veronese, N. et al. Effect of magnesium supplementation on glucose metabolism in people with or at risk of diabetes: A systematic review and meta-analysis of double-blind randomized controlled trials. Eur. J. Clin. Nutr. 70, 1354–1359 (2016).CAS 
PubMed 
Article 

Google Scholar 
Rodríguez-Morán, M., Simental-Mendía, L. E., Gamboa-Gómez, C. I. & Guerrero-Romero, F. Oral magnesium supplementation and metabolic syndrome: A randomized double-blind placebo-controlled clinical trial. Adv. Chronic Kidney Dis. 25, 261–266 (2018).PubMed 
Article 

Google Scholar 
Grigoryan, R. et al. Multi-collector ICP-mass spectrometry reveals changes in the serum Mg isotopic composition in diabetes type I patients. J. Anal. At. Spectrom. 34, 1514–1521 (2019).CAS 
Article 

Google Scholar 
Bigeleisen, J. & Mayer, M. G. Calculation of equilibrium constants for isotopic exchange reactions. J. Chem. Phys. 15, 261–267 (1947).ADS 
CAS 
Article 

Google Scholar 
Bigeleisen, J. The relative reaction velocities of isotopic molecules. J. Chem. Phys. 17, 675–678 (1949).ADS 
CAS 
Article 

Google Scholar 
McKeegan, K. D. et al. Isotopic compositions of cometary matter returned by stardust. Science 314, 1724–1728 (2006).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Jouzel, J. et al. Vostok ice core: A continuous isotope temperature record over the last climatic cycle (160,000 years). Nature 329, 403–408 (1987).ADS 
CAS 
Article 

Google Scholar 
Albarède, F., Télouk, P. & Balter, V. Medical applications of isotope metallomics. Rev. Mineral. Geochem. 82, 851–885 (2017).Article 
CAS 

Google Scholar 
Balter, V. et al. Natural variations of copper and sulfur stable isotopes in blood of hepatocellular carcinoma patients. PNAS 112, 982–985 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Télouk, P. et al. Copper isotope effect in serum of cancer patients. A pilot study. Metallomics 7, 299–308 (2015).PubMed 
Article 
CAS 

Google Scholar 
Lobo, L. et al. Elemental and isotopic analysis of oral squamous cell carcinoma tissues using sector-field and multi-collector ICP-mass spectrometry. Talanta 165, 92–97 (2017).CAS 
PubMed 
Article 

Google Scholar 
Costas-Rodríguez, M. et al. Body distribution of stable copper isotopes during the progression of cholestatic liver disease induced by common bile duct ligation in mice. Metallomics 11, 1093–1103 (2019).PubMed 
Article 
CAS 

Google Scholar 
Lamboux, A. et al. The blood copper isotopic composition is a prognostic indicator of the hepatic injury in Wilson disease. Metallomics 12, 1781–1790 (2020).CAS 
PubMed 
Article 

Google Scholar 
Moynier, F., Creech, J., Dallas, J. & Le Borgne, M. Serum and brain natural copper stable isotopes in a mouse model of Alzheimer’s disease. Sci. Rep. 9, 11894 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sauzéat, L. et al. Isotopic evidence for disrupted copper metabolism in amyotrophic lateral sclerosis. iScience 6, 264–271 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Krayenbuehl, P.-A., Walczyk, T., Schoenberg, R., von Blanckenburg, F. & Schulthess, G. Hereditary hemochromatosis is reflected in the iron isotope composition of blood. Blood 105, 3812–3816 (2005).CAS 
PubMed 
Article 

Google Scholar 
Anoshkina, Y. et al. Iron isotopic composition of blood serum in anemia of chronic kidney disease. Metallomics 9, 517–524 (2017).CAS 
PubMed 
Article 

Google Scholar 
Morgan, J. L. L. et al. Rapidly assessing changes in bone mineral balance using natural stable calcium isotopes. Proc. Natl. Acad. Sci. USA 109, 9989–9994 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eisenhauer, A. et al. Calcium isotope ratios in blood and urine: A new biomarker for the diagnosis of osteoporosis. Bone Rep. 10, 100200 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Isaji, Y. et al. Magnesium isotope fractionation during synthesis of chlorophyll a and bacteriochlorophyll a of benthic phototrophs in hypersaline environments. ACS Earth Space Chem. 3, 1073–1079 (2019).ADS 
CAS 
Article 

Google Scholar 
Pokharel, R. et al. Magnesium stable isotope fractionation on a cellular level explored by cyanobacteria and black fungi with implications for higher plants. Environ. Sci. Technol. 52, 12216–12224 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Bolou-Bi, E. B., Poszwa, A., Leyval, C. & Vigier, N. Experimental determination of magnesium isotope fractionation during higher plant growth. Geochim. Cosmochim. Acta 74, 2523–2537 (2010).ADS 
CAS 
Article 

Google Scholar 
Wang, Y. et al. Magnesium isotope fractionation reflects plant response to magnesium deficiency in magnesium uptake and allocation: A greenhouse study with wheat. Plant Soil 455, 93–105 (2020).CAS 
Article 

Google Scholar 
Martin, J. E., Vance, D. & Balter, V. Natural variation of magnesium isotopes in mammal bones and teeth from two South African trophic chains. Geochim. Cosmochim. Acta 130, 12–20 (2014).ADS 
CAS 
Article 

Google Scholar 
Martin, J. E., Vance, D. & Balter, V. Magnesium stable isotope ecology using mammal tooth enamel. PNAS 112, 430–435 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
DeFronzo, R. A., Tobin, J. D. & Andres, R. Glucose clamp technique: A method for quantifying insulin secretion and resistance. Am. J. Physiol.-Endocrinol. Metab. 237, E214 (1979).CAS 
Article 

Google Scholar 
Kim, J. K. Hyperinsulinemic-euglycemic clamp to assess insulin sensitivity in vivo. In Type 2 Diabetes: Methods and Protocols, Methods in Molecular Biology (ed. Stocker, C.) 221–238 (Humana Press, 2009).Chapter 

Google Scholar 
DeFronzo, R. A., Hendler, R. & Simonson, D. Insulin resistance is a prominent feature of insulin-dependent diabetes. Diabetes 31, 795–801 (1982).CAS 
PubMed 
Article 

Google Scholar 
Balter, V. et al. Contrasting Cu, Fe, and Zn isotopic patterns in organs and body fluids of mice and sheep, with emphasis on cellular fractionation. Metallomics 5, 1470–1482 (2013).CAS 
PubMed 
Article 

Google Scholar 
Zhang, B., Podolskiy, D. I., Mariotti, M., Seravalli, J. & Gladyshev, V. N. Systematic age-, organ-, and diet-associated ionome remodeling and the development of ionomic aging clocks. Aging Cell 19, e13119 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Morel, J.-D. et al. The mouse metallomic landscape of aging and metabolism. Nat. Commun. 13, 607 (2022).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Grigoryan, R., Costas-Rodríguez, M., Vandenbroucke, R. E. & Vanhaecke, F. High-precision isotopic analysis of Mg and Ca in biological samples using multi-collector ICP-mass spectrometry after their sequential chromatographic isolation—Application to the characterization of the body distribution of Mg and Ca isotopes in mice. Anal. Chim. Acta 1130, 137–145 (2020).CAS 
PubMed 
Article 

Google Scholar 
Goff, S. L., Albalat, E., Dosseto, A., Godin, J.-P. & Balter, V. Determination of magnesium isotopic ratios of biological reference materials via multi-collector inductively coupled plasma mass spectrometry. Rapid Commun. Mass Spectrom. 35, e9074 (2021).PubMed 

Google Scholar 
DeRocher, K. A. et al. Chemical gradients in human enamel crystallites. Nature 583, 66–71 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Johansen, T., Hansen, H. S., Richelsen, B. & Malmlöf, K. The obese Göttingen minipig as a model of the metabolic syndrome: dietary effects on obesity, insulin sensitivity, and growth hormone profile. Comp. Med. 51, 150–155 (2001).CAS 
PubMed 

Google Scholar 
Coelho, P. G. et al. Effect of obesity or metabolic syndrome and diabetes on osseointegration of dental implants in a miniature swine model: A pilot study. J. Oral Maxillofac. Surg. 76, 1677–1687 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Saltiel, A. R. & Kahn, C. R. Insulin signalling and the regulation of glucose and lipid metabolism. Nature 414, 799–806 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Elin, R. J. Assessment of magnesium status. Clin. Chem. 33, 1965–1970 (1987).CAS 
PubMed 
Article 

Google Scholar 
Koopmans, S. J., van der Meulen, J., Dekker, R., Corbijn, H. & Mroz, Z. Diurnal variation in insulin-stimulated systemic glucose and amino acid utilization in pigs fed with identical meals at 12-hour intervals. Horm. Metab. Res. 38, 607–613 (2006).CAS 
PubMed 
Article 

Google Scholar 
Koopmans, S. J., Maassen, J. A., Radder, J. K. & Frölich, M. In vivo insulin responsiveness for glucose uptake and production at eu- and hyperglycemic levels in normal and diabetic rats. Biochimica et Biophysica Acta (BBA) General Subjects 1115, 230–238 (1992).CAS 
Article 

Google Scholar 
Koopmans, S. J. et al. Association of insulin resistance with hyperglycemia in streptozotocin-diabetic pigs: Effects of metformin at isoenergetic feeding in a type 2–like diabetic pig model. Metabolism 55, 960–971 (2006).CAS 
PubMed 
Article 

Google Scholar  More