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    Identifying volatile organic compounds used for olfactory navigation by homing pigeons

    Regional observations of volatile organic compounds
    Measurements were conducted in summer 2017 and spring 2018 in the area of Pisa, Italy, as part of the HOMING project (Hunting Organic Molecules In NaviGation). Measurements consisted of: (i) a pilot study (summer 2017) to investigate the volatile organic compounds (VOCs) emitted by three representative local ecosystems surrounding the bird’s home aviary; (ii) a 2-month intensive field campaign (spring 2018) at the bird’s home aviary to monitor VOCs along with meteorological variables; (iii) three flights on board of a Cessna aircraft to sample at ca. 180 m, within the birds typical flight altitude (spring 2018). Specifically, (i) was conducted to identify the chemical composition of surrounding forest sites and test whether they can be smelled distinctly from the aviary. (ii) Was performed to derive the olfactory maps developed by birds based on the assumption that birds and our analytical equipment have comparable detection thresholds. (iii) Was needed to examine any regional scale gradient used by pigeons when flying. The home aviary is operated by the University of Pisa and is located at the rural site Arnino (43°39′25.7″N 10°18′14.7″E), outside the city of Pisa (11 km North–East), close to the Tyrrenian sea (1.8 km West) and the mouth of river Arno (3.2 km North) (Fig. 1).
    Figure 1

    Map of the sampling area. The point designated as “home” refers to the birds aviary of Arnino (43°39′25.7″N 10°18′14.7″E) located in Tuscany in the area of Pisa-Livorno. Points on the map show the three sites used for releasing birds during a navigational experiment and the main biogenic and anthropogenic sites in the area. Photos show an aerial view of the Arnino field site during the airborne sampling (left) and a homing pigeon equipped with a GPS-tag before being released for testing its homing performance (right). Figure drawn with Igor WaveMetrics.

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    Arnino houses hundreds homing pigeons (Columba livia) used for navigational experiments. Volatile organic compound mixing ratios were determined with state-of-the-art on-line and off-line analytical techniques (PTR-MS and GC–MS) and speciated in their isomeric and enantiomeric forms.
    Navigational experiments entailed releasing 174 pigeons equipped with GPS loggers (N. 80/2013-A), who had never left their home before, from unfamiliar sites 50–70 km from their home (Fig. 1). For logistical reasons (power availability and shelter for technological equipment at release points), no simultaneous measurement of air composition and bird release was possible. Therefore, we used results from the most recent available flight experiments (summer 2016 and summer 2017) to determine general homing performance indicators and examined the latter in conjunction with available meteorological information, as modelled air masses trajectories.
    The daily variation of volatile organic compounds at the aviary site
    Meteorological parameters including, temperature, relative humidity, wind direction and wind speed were measured in May–June 2018 during the ground-based campaign at the home aviary (Supplementary Fig. 1). Air temperature and relative humidity ranged between 12–25 °C and 60–85%, respectively (Fig. 2 and Supplementary Fig. 1), showing the expected mirrored diel cycle, whereby temperature is highest and RH lowest by day. Wind direction and speed exhibited a repeating daily pattern during the 2 months of measurements (Fig. 2 and Supplementary Fig. 1).
    Figure 2

    Diel cycle of meteorological parameters (a,b) and measured VOCs (c–f). The volumetric mixing ratio (VMR) of the volatile organic compounds are reported as their measured mass fragment by PTR-MS: m/z 63 (c), m/z 69 (d), m/z 81 (e) and m/z 107 (f); identified respectively as dimethyl sulphide (c), isoprene (d), sum of monoterpenes (e) and sum of xylenes (f). Meteorological parameters are plotted as mean campaign values (marker), median (line) and interquartile range (shaded area). VOC box plots report mean campaign values (marker), median (line), interquartile range (box) and 10th and 90th percentiles (whiskers).

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    The proximity to the coast exposes the measurement site to influence by the local sea breeze system. Air masses reaching the aviary came from inland (mean wind angle 150°) every day until midday, and thereafter between 12:00 and 20:00–21:00 (local time) the air masses were advected from the sea (mean angle 270°). The wind speed, like the wind direction, also showed a diurnal profile. Figure 2 shows the diurnal profile of some compounds measured by proton transfer reaction mass spectrometry (PTR-MS). They have contrasting diel cycles and hereafter investigated further: dimethyl sulphide (DMS, m/z 63), isoprene (m/z 69), sum of monoterpenes (MT, m/z 81) and sum of xylenes (m/z 107). The full mass list and identities are reported in Supplementary Table 4 with other compound diel variations in Supplementary Fig. 2. We refer to the abbreviations included at the beginning of the manuscript for a reader not familiar with the terms used herein.
    Dimethyl sulphide is a biogenic compound emitted by phytoplankton activity in seawater21,22,23; it is insoluble in water, and so readily transfers to the air at a rate dependent on the temperature and wind speed. Our measurements show that DMS mixing ratios at the site remained low and constant until 12:00, possibly reflecting weak soil emissions from the land24, to increase to a maximum at 20:00 due to continued marine emission, until the reversal of the wind back to offshore. The maximum hourly averaged mixing ratio of DMS is 0.17 ± 0.8 ppbv, comparable to 0.12 ppbv measured by Derstroff et al.25 at a coastal site on Cyprus.
    Isoprene mixing ratios showed a bimodal distribution with two maxima at 11:00–12:00, and 18:00–20:00; similar to methyl ethyl ketone (MEK) and isoprene oxidation products methyl vinyl ketone, methacrolein and isoprene peroxides (MVK + MACR + ISOPOOH) and acetic acid (Supplementary Fig. 2). At both morning and afternoon maxima, the isoprene mixing ratio reached 0.7 ± 0.6 ppbv (maximum hourly average ± 1σ standard deviation). This is 10 times higher than the value observed during wintertime at a similar rural Mediterranean site in Spain26, but lower than the value measured in summertime by Steinbacher and coauthors27 at a rural site in the Po valley, Italy. Isoprene emission from terrestrial vegetation is mainly light dependent28, therefore near-source measurements typically exhibit a broad maximum around noon when irradiation is highest29,30. The two isoprene maxima observed here are the result of the sea breeze system, which brings isoprene poor marine influenced air to the site around midday.
    Monoterpenes (C10H16) are emitted from vegetation, and are to a large part responsible for the scent of forests. Their sum at the site reached 1.6 ± 1 ppbv as a maximum hourly average at 01:00. Despite monoterpene emissions being also a function of temperature they do not show the same profile as isoprene. Monoterpene mixing ratios were higher at night, decreasing slowly from 03:00 and then sharply at 08:00, and remaining low through the day before rising back to early morning levels in the late evening. While isoprene is formed in the plant and released directly, mainly in response to light, many plant species emit monoterpenes day and night from resin duct storage pools contained in the leaves or needles31. The rates of monoterpene emission therefore depend strongly on temperature, and although lower in the night, continued emission into a shallower boundary layer can generate significant mixing ratios, as has been observed elsewhere (for example in the boreal forest32). As expected, air advected from the more vegetated inland had higher monoterpene mixing ratios than air from the sea. Although, marine monoterpene and isoprene emissions have been observed previously33, in this region terrestrial vegetation emissions clearly dominate. A similar profile (higher nighttime daytime ambient mixing ratio) was reported by Davison et al.34 from the Mediterranean forest of Castelporziano, however, the average daytime and nighttime concentrations were 3 times lower than reported here.
    Xylenes (Fig. 2), benzene, toluene, and sum of trimethylbenzenes (Supplementary Fig. 2) are aromatic compounds predominantly emitted from anthropogenic sources, for example fossil fuel use. Mixing ratios increased in the morning at 8:00, probably due to traffic emissions, and decreased steadily between 9:00–12:00, as the wind direction changed. They remained low in the afternoon as the wind came from the sea, reflecting the expected absence of significant sources from this sector. The mixing ratios of all compounds increased again after 17:00, slowly for benzene and toluene and faster for xylenes and trimethylbenzene, suggesting different anthropogenic sources of such species (i.e. road traffic and industries). Although evidence for biogenic aromatic species has been documented35,36, here no evidence of natural sources of aromatic compounds was found. The maximum mixing ratios for benzene, toluene, sum of xylenes and trimethylbenzene, were 0.16 ± 0.08 ppbv, 0.32 ± 0.7 ppbv, 0.22 ± 0.09 ppbv and 0.11 ± 0.9 (hourly average maximum ± 1σ standard deviation), respectively. Similar results were found for benzene and toluene at a rural Mediterranean site in Spain (0.19 and 0.41 ppbv, respectively26).
    Chiral monoterpenes
    Many monoterpenes exist in two chiral forms, meaning they exist in nature as two non-superimposable mirror image forms (enantiomers), often with different biological activities. Insects can perceive each enantiomer differently and each may act as entirely distinct chemical signals37. We investigated the abundance of the prevalent chiral monoterpenes at the home aviary and at three ecosystems surrounding the aviary North (lake), East (mixed forest) and South (pine forest). This was to test whether unique mixtures could be ascertained at each site which is a condition of the mosaic hypothesis. At all sites, including the aviary, the (−) configuration dominated over the (+) for the monoterpene species α-pinene, β-pinene and limonene. This has been also observed in the tropical rainforest for α-pinene38. Relative ratios for the same monoterpene enantiomeric pair do not change significantly across the investigated sites, however, their absolute concentrations and relative abundance to the other terpene species do differ (see Fig. 3).
    Figure 3

    Volume mixing ratio (VMR) distribution of enantiomers from three ecosystems surrounding the birds’ aviary and at the birds’ aviary. Category plots show mean measured values with their standard deviation from three days of sampling during summer 2017. Box plots show mean campaign values (marker), median (line), interquartile range (box) and 10th and 90th percentiles (whiskers) from 2 months intensive field campaign at the birds’ aviary in spring 2018 (home) and from three flights conducted at 180 m altitude over the region during the airborne campaign. The x axes shows the retention time (min) of the chiral molecules in the gas chromatography mass spectrometer (GC–MS) and shows the good resolution achieved with the method. The left y axes indicate the volume mixing ratio of all molecules, except for (−)limonene measured at the aviary, whose VMR is indicated by the right y axes.

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    At the aviary, (−)limonene was the most abundant species (0.89 ± 1.7 ppbv and 2.5 ± 2.7 ppbv), followed by (−)α-pinene (0.42 ± 0.57 ppbv and 1 ± 1 ppbv), (+)limonene (0.26 ± 0.29 ppbv and 0.37 ± 0.42 ppbv) and (−)β-pinene (0.17 ± 0.45 ppbv and 0.27 ± 0.49 ppbv, values indicate mean daytime and nighttime values ± 1σ standard deviation, respectively). The lake site emitted the least monoterpenes, followed by the pine forest and the mixed forest, the latter showing the highest concentration of measured terpenes. Since (−)limonene dominates the terpene blend measured at the aviary, we can surmise that the surrounding pine forest had a strong impact on the air chemical composition at the site (Fig. 3). A similar suite of biogenic molecules was found at all locations, albeit with differing ratios. In other words, there were no clear unique chemical markers for a particular area. Therefore, the source of biogenic molecules near to the aviary has the potential to interfere with or mask, odours from more distant sites. These findings are not consistent with the original “mosaic” hypothesis in which each location in the region has a unique chemical signature that can be determined from the aviary.
    Olfactory maps: spatial and temporal distribution of VOCs
    Soon after fledging, the young pigeons are housed in a loft with an associated aviary, and from the end of May are allowed to perform spontaneous flights around the loft typically until October, when the season of the experiments usually ends. During the first months after fledging39 young pigeons are exposed to the changing chemical conditions at the home site and according to the Papi olfactory hypothesis, they learn to associate wind-borne odours with wind directions thereby generating an olfactory map. It is thought that this olfactory map is continually updated throughout their life depending on conditions experienced40,41. A visual representation of this spatio-temporal information can be expressed in the form of a bivariate plot of the measured volatile organic compounds. Bivariate polar plots represent the mean campaign mixing ratio of a given compound as a function of the wind direction (angle) and wind speed (radius), similarly to a wind rose they can highlight spatial gradients in the surrounding of a measurement site. Figure 4 shows that methanol (a biogenic compound and biomass burning marker) mixing ratios were higher for higher wind speed and easterly winds (from inland). However, mixing ratios were elevated at both high and low wind speeds; indicating some methanol sources were also local. Dimethyl sulphide mixing ratios were larger for higher wind speeds originating from the North–West and South–West sectors, spanning from the mouth of the river Arno to the sea. The largest mixing ratios for isoprene came from inland, in particularly from the West–North–East for higher wind speeds and the South–East for lower ones. Smaller mixing ratios are found from the sea for large wind speed (North–West), suggestive of a weaker marine isoprene emission source. Small marine isoprene emissions have been measured previously, especially in chlorophyll rich waters12,33,42. Monoterpene mixing ratios are higher at lower wind speeds, mostly for winds coming from South–East but also East–North–East and South–West. The polar graph highlights the multiple sources of monoterpenes, and that the levels at the site are also locally influenced. The anthropogenic aromatic species xylenes and trimethylbenzene were larger when the air was transported from inland, in particular from the South-East, where heavy-traffic roads such as highway E80, the road FI-PI-LI and via AureliaSS1 are located. Additional sources of aromatics appear South of the aviary for high wind conditions, especially for trimethylbenzene. Trimethylbenzene (C6H3(CH3)3), is an aromatic hydrocarbon characterized by a strong odour, which is generally isolated from the C9 fraction of aromatics during petroleum distillation. South of the aviary, in the Stagno industrial area, there is a large petroleum refinery (Fig. 1).
    Figure 4

    Spatial distributions of measured VOCs reported as protonated masses and identified as: methanol (m/z 33), DMS (m/z 63), isoprene (m/z 69), sum of monoterpenes (m/z 81), sum of xylenes (m/z 107), trimethylbenzene (m/z 121). Mean campaign values of VOCs (colored scale) are showed as a function of wind direction (angle) and wind speed (radius). Figures drawn with R.

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    This air composition measured at the aviary is the air breathed and smelled by the birds during their first months after fledging. The birds are therefore likely to be aware of, mixed biogenic and anthropogenic sources to the South-East, a more biogenic dominated source (with differing composition) to the West-North–East, and a marine source to the West. Thus, the ground based measurement campaign mapped several chemical compounds that vary distinctly at the site according to the wind direction and time of day. Which VOCs are useful for navigation will also depend on their respective atmospheric lifetimes. Following emission to the atmosphere VOCs are oxidized mainly by OH radicals, with small contributions by O3, NO3 and Cl radicals. Methanol, the most abundant species measured, has a lifetime of 12 days (based on OH reactivity only, assuming OH radical concentration of 2 × 106 molecules cm–343). The corresponding lifetime of isoprene is 1.4 h, therefore with a typical windspeed of 2.2 m/s the concentration of isoprene will be reduced to 1/e of the initial value in 11 km, while limonene in 2 km, xylenes in 33 km and DMS in 88 km. Therefore, over distances of ca. 50–100 km, VOCs with moderate lifetimes such as DMS and the aromatic species appear capable of creating regional gradients. The more reactive VOCs are usually photochemically transformed into less reactive products which themselves, or their combination, may create regional gradients. Significantly, the three regional gradients highlighted in Fig. 4, namely DMS, the aromatics and the monoterpenes are not aligned, rather they slope in different directions. Theoretically then, by comparing the level of these compounds to that experienced at the aviary the pigeon may, with reference to the wind-odour experience at the aviary, orient homeward. For example if the DMS level is lower at the release site than at the aviary, and the bird “knows” from the olfactory map learning phase that DMS comes from the West, then the birds homeward direction will have a westerly component. Having multiple gradients available would enable the pigeon to triangulate a homeward direction.
    Regional spatial gradients
    To verify that the spatial gradients observed from the olfactory maps exist at a larger regional scale we measured the air composition at the altitudes and over distances typically flown by homing pigeons (10–300 m, 100 km). Results from a flight campaign conducted on 26/05/2018 and 27/05/2018, in conjunction with the ground based campaign, are depicted in Figs. 3, 5 and Supplementary Fig. 3. Figure 3 shows the speciation of chiral molecules from ground measurements at four distinct sites (including the aviary) and above the sampled region considering all the airborne samples taken with the three flights. Interestingly, the regionally predominant compound at 180 m is α-pinene, rather than limonene observed at the ground. This is because α-pinene has a longer atmospheric lifetime (2.6 h), in comparison to β-pinene (1.8 h) and limonene (49 min)43. Figure 5 shows the mixing ratio of a biogenic precursor compound (−)α-pinene and the common terpene photochemical oxidation product nopinone44 sampled at different locations above the home aviary, and the release sites. The terpene airborne concentrations compare well with the ground-based ones (higher above the mixed Mediterranean forest, followed by the pine forest, followed by the lake site, as reported in Fig. 3). However, as expected, the terpene airborne concentrations are smaller compared to ground concentrations (40–50 pptv and 80–208 pptv, respectively) consistent with the ground based emissions being progressively oxidized by OH radicals and ozone as well as being mixed with the relatively clean air in the free troposphere above. This means that the birds will encounter strong vertical gradients of primary emitted species during flight with lower concentrations aloft. Furthermore oxidation chemistry will generate entirely different species, complicating the mixture, weakening the primary chemical signal and possibly obfuscating the originally emitted olfactory signals. In Fig. 5 we observe during the afternoon flights (with onshore winds) the nopinone mixing ratios generally increase to the East. This demonstrates that monoterpenes emitted at ground level during the day and in onshore winds are being oxidized to (among other species) nopinone on the time and space scales of the pigeon release experiments. Therefore, spatial gradients of reactive VOC do exist for the primary emitted species and their oxidation products, at a spatial scale compatible with the distances used for navigation experiments.
    Figure 5

    Spatial distributions of measured (−)α-pinene (a) and nopinone (b) from airborne sampling during three flights conducted on 26/05/2018 and 27/05/2018. Colored scales indicate respectively volume mixing ratio and counts of chromatograms peak area. Figures drawn with Igor WaveMetrics.

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    Pigeon flight tracks and air masses trajectories analysis
    To test whether the intensity of atmospheric odour signals could potentially aid in the homing of pigeons, we provide here a first preliminary analysis based on real pigeon tracks and simulated air masses trajectories. We hypothesize that overall stronger gradients of indicative aerial chemicals enable better navigational performance in pigeons18. To do this we examine the GPS logged tracks of homing birds in relation to the general gradients of atmospheric chemicals determined in the previous sections, and to the origin of air at the release sites. Bird release experiments were conducted over six days in summer 2016 and summer 2017 from three release sites simultaneously (Fig. 1). Those experiments were chosen for being the most recent results available for indices analysis, and for being conducted on sunny days, when air temperature, humidity and atmospheric pressure were comparable to those encountered during VOCs measurements (Supplementary Fig. 4). Each release experiment involved 9 or 10 individual birds, released singly every 5–10 min, over approximately 1 h and involved 27–30 individuals released on the same day from three sites (N = 174). The total number of tracks obtained was 143, due to either the loss of the device by some birds or the misfunctioning of the GSM GPS logger. Bird flight tracks were used to determine: the pigeon initial flight direction, the homing capabilities en route with the homing efficiency index (HEI45) and for the whole track with a mean aggregate azimuth penalty (MAAP); see Methods and supplementary information. Due to lack of power and shelter at the release sites, chemical information of atmospheric composition was not available during those experiments, therefore a meteorological approach, based on modelled air trajectories, was developed in order to examine how air mass transport was related to the birds’ homing performance. Specifically, we generated forward and backward trajectories of the air masses for each release day, for the site and time of release. Forward trajectories analysis showed that the prevalent wind direction during the days and time of pigeons’ flights was from west (supplementary information). We cannot yet test the bouquet of potential chemical information, but concentrate here on one traceable chemical gradient: DMS (Dimethyl sulphide) emanating from the Tyrrhenian sea. DMS, a chemical compound of marine origin (see Supplementary Fig. 5), is here identified to be a suitable candidate for homing by olfaction. Figures 2 and 4 show that it was among the most abundant compounds measured during daytime at the aviary, it follows a regular pattern of emission, is known to be detectable by birds, and it is atmospherically stable enough to survive transport over longer distances, likely decreasing along the West–East direction. Air masses reaching the aviary diurnally during the ground campaign were found to spend considerable time ( > 20 h in most cases) in the marine boundary layer in the past 24 h (see table 1 supplementary material). For these reasons, we tested if more marine-influenced air masses can influence the pigeon initial orientation (Fig. 6) and the homing path en route (homing efficiency index, supplementary material). We examined where each air mass was located (during 24 h prior to the release) reported in latitude and longitude coordinates and determined the time the air masses spent in the marine boundary layer, and the time the air masses spent over non-marine areas. As shown in Fig. 4, pigeons at the home loft are exposed to DMS associated with westerly winds; easterly winds are unlikely to carry DMS. According to the olfactory navigation hypothesis5 it is expected that a low atmospheric level of DMS at the release site is likely to produce a bias towards west in the orientation of the birds, regardless of their ultimate home direction. To test this hypothesis, the deviation from west of the individual mean vectors computed on the initial part of each track (within 10 km from the release site) is tested against the ratio of the time the air masses spent over sea versus over land (ratio sea/land) and the opposite (ratio land/sea). The Spearman ranking test highlighted that the deviation from west is positively correlated to the sea/land ratio of the air masses trajectories (n = 135, S = 0.163, p  More

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    No protofeathers on pterosaurs

    1.
    Wellnhofer, P. The Illustrated Encyclopedia of Pterosaurs (Salamander Books, 1991).
    2.
    Unwin, D. M. The Pterosaurs from Deep Time (Pi, 2005).

    3.
    Kellner, A. W. A. et al. The soft tissue of Jeholopterus (Pterosauria, Anurognathidae, Batrachognathinae) and the structure of the pterosaur wing membrane. Proc. R. Soc. B 277, 321–329 (2010).
    Article  Google Scholar 

    4.
    Witton, M. P. Pterosaurs. Natural History, Evolution, Anatomy (Princeton Univ. Press, 2013).

    5.
    Yang, Z. et al. Pterosaur integumentary structures with complex feather-like branching. Nat. Ecol. Evol. 4, 24–30 (2019).
    Article  Google Scholar 

    6.
    Xu, X. et al. An integrative approach to understanding bird origins. Science 346, 1253293 (2014).
    Article  Google Scholar 

    7.
    Benton, M. J., Dhouially, D., Jiang, B. & McNamara, M. The early origin of feathers. Trends Ecol. Evol. 34, 856–859 (2019).
    Article  Google Scholar 

    8.
    Xu, X. Feather evolution: Looking up close and through deep time. Sci. Bull. 64, 563–564 (2019).
    Article  Google Scholar 

    9.
    Unwin, D. M. & Bakhurina, N. N. Sordes pilosus and the nature of the pterosaur flight apparatus. Nature 371, 62–64 (1994).
    Article  Google Scholar 

    10.
    Frey, E., Tischlinger, H., Buchy, M. C. & Martill, D. M. in Evolution and Palaeobiology of Pterosaurs Vol. 217 (eds Buffetaut, E. & Mazin, J.-M.) 233–266 (Geological Society of London, Special Publications, 2003).

    11.
    Li, Q. et al. Melanosome evolution indicates a key physiological shift within feathered dinosaurs. Nature 507, 350–353 (2014).
    CAS  Article  Google Scholar 

    12.
    Pinheiro, F. L. et al. Chemical characterization of pterosaur melanin challenges color inferences in extinct animals. Sci. Rep. 9, 15947 (2019).
    Article  Google Scholar 

    13.
    Campione, N. E., Barrett, P. M. & Evans, D. C. in The Evolution of Feathers (eds Foth, C. & Rauhut, O. W. M.) 213–243 (Springer, 2020).

    14.
    Czerkas, S. A. & Ji, Q. in Feathered Dinosaurs and the Origin of Flight (ed. Czerkas, S. J.) 15–41 (The Dinosaur Museum, 2002).

    15.
    Ji, Q. & Yuan, C. Discovery of two kinds of protofeathered pterosaurs in the Mesozoic Daohugou biota in the Ningcheng region and its stratigraphic and biologic significances. Geol. Rev. 48, 221–224 (2002).
    Google Scholar 

    16.
    Wang, X. L., Zhou, Z. H., Zhang, F. C. & Xu, X. A nearly completely articulated rhamphorhynchoid pterosaur with exceptionally well-preserved wing membranes and ‘hairs’ from Inner Mongolia, northeast China. Chin. Sci. Bull. 47, 226–230 (2002).
    Article  Google Scholar 

    17.
    Jäger, K. R. K., Tischlinger, H., Oleschinski, G. & Sander, P. M. Goldfuß was right: soft part preservation in the Late Jurassic pterosaur Scaphognathus crassirostris revealed by reflectance transformation imaging and ultraviolet light and the auspicious beginnings of paleo-art. Palaeontol. Electron. 21.3.3T (2019).

    18.
    Lü, J. & Hone, D. W. E. A new Chinese anurognathid pterosaur and the evolution of pterosaurian tail lengths. Acta Geol. Sin. 86, 1317–1325 (2012).
    Article  Google Scholar 

    19.
    Bennett, S. C. New interpretation of the wings of the pterosaur Rhamphorhynchus muensteri based on the Zittel and Marsh specimens. J. Paleontol. 89, 845–869 (2016).
    Article  Google Scholar 

    20.
    Hone, D., Henderson, D. M., Therrien, F. & Habib, M. B. A specimen of Rhamphorhynchus with soft tissue preservation, stomach contents and a putative coprolite. PeerJ 3, e1191 (2015).
    Article  Google Scholar 

    21.
    Pan, Y. et al. Molecular evidence of keratin and melanosomes in feathers of the Early Cretaceous bird Eoconfuciusornis. Proc. Natl Acad. Sci. USA 113, E7900–E7907 (2016).
    CAS  Article  Google Scholar 

    22.
    Alibardi, L. Adaptation to the land: the skin of reptiles in comparison to that of amphibians and endotherm amniotes. J. Exp. Zool. 298B, 12–41 (2003).
    CAS  Article  Google Scholar  More

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    Vegetation traits of pre-Alpine grasslands in southern Germany

    Study area
    The study area is located in the TERENO Pre-Alpine Observatory28,29 in southern Bavaria, Germany (Fig. 2). The ten test plots are situated on three sites at different elevations: Fendt (FE), Rottenbuch (RB), and Eschenlohe (EL). Table 1 gives an overview about the main characteristics of these sites and the plots.
    Fig. 2

    Location of the study sites (pink stars). Background: Sentinel-2b (27/04/2018), true colour composite (contains modified Copernicus Sentinel data [2018], processed by ESA). Used coordinate reference system: EPSG: 25832. EL = Eschenlohe, FE = Fendt, RB = Rottenbuch.

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    Table 1 Site and plot characteristics.
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    Geologically, the plots in FE are located in the major structural unit of the molasse basin of the Bavarian Alpine foreland, while the other ones lie in the major structural unit of the Alps with the major tectonic units folded molasse (RB) and northern calcareous Alps (EL)33. Glacial erosion and Quaternary deposition processes influenced most of the molasse area. Therefore, alluvial structures and moraines largely effect soil parent material here28. The dominant soil types in the northern part of the study area are Cambisols, Luvisols, and Regosols, and in the southern part Rendzic Leptosols and Calcaric Cambisols. Gleysols and Histosols characterize areas along the course of rivers and areas of recent and paleo lakes28. According to the Köppen-Geiger climate classification the study area has a warm temperate climate without a dry season and warm summers (Cfb)34. Mean annual precipitation at the study sites varied between 1008 mm and 1419 mm35, and mean annual air temperature between 8.0 °C and 8.6 °C36 (see Table 1). The land cover around the study sites is characterized by a mix of pastures, natural grasslands, forests (needle-leaf, broad-leaf, mixed), discontinuous urban fabric, and in EL additionally peat bogs37. All ten plots are situated on managed grasslands, the dominant land use around the study sites. The management intensity of the plots range from very extensive management with only one cut and no fertilizer application per year to intensive management with five cuts and five slurry applications per year (Table 1).
    Sampling design
    The field campaign with UAS flights and vegetation sampling took place on 24–25 April 2018 at ten different grassland plots (FE1, FE2, FE3, FE4, RB1, RB2, RB3, EL1, EL2, EL3). Plots were selected by i) visual characterisation of standing biomass to select plots that differ in management as well as soil nutrient and water status (based on talks with local farmers and corresponding field visits; no specific method was applied), and that fulfil other criteria such as ii) homogeneous, flat area, iii) accessibility (including permission by farmers), and iv) proximity of the plots to ideally cover several plots with one UAS flight.
    When designing the sampling strategy, a perspective linkage of the sample data to Sentinel-2 images with a spatial resolution of 10 m × 10 m was taken into account. Therefore, we adapted the sampling strategy proposed by Baret et al.38 for the validation of medium spatial resolution land satellite products. The authors suggested relatively flat and homogeneous validation sites of 3 km × 3 km for validating data of sensors with a spatial resolution of up to 1 km × 1 km. Their validation sites were sampled at several so called elementary sampling units (ESUs, 20 m × 20 m). These ESUs were spread across the validation site using a division of the site in nine 1 km × 1 km squares (three to five ESUs per square) with a higher sampling density in the central square (five to seven ESUs)38.
    In our study, we used 30 m × 30 m plots that were ideally sampled at 12 subplots (corresponding to the ESUs of Baret et al.38) of 0.25 m × 0.25 m (Fig. 1). We divided each plot in nine equally sized squares of 10 m × 10 m, in which we randomly placed one subplot. Following the suggestions of Baret et al.38, we sampled the central square with a higher density (i.e. with four subplots). Compared to Baret et al.38 we targeted a smaller number of subplots per plot (12 instead of 30 to 50) as our plot size is notably smaller than their plot size and hence it is easier to select a homogenous area. During the sampling campaign, we needed to reduce the number of subplots in the central square for EL1 (11 subplots were sampled) and EL2 (9 subplots) due to time constraints. In all other plots, we sampled 12 subplots.
    Preparations in the field
    Some arrangements needed to be done in the field prior to sampling to prepare accompanying UAS flights. The resulting images of these UAS flights were used among others for retrieving the exact location of the subplots. After the localisation of the plots in the field (aiming for a north-orientation of one plot site), bright 0.5 m × 0.5 m flakeboards were distributed in the plots at the approximate locations of the subplots (Fig. 3). These flakeboards were used to identify the location of the subplots in the orthophotos that were generated from images of the UAS.
    Fig. 3

    Sampling design. (a) Scheme of a sampling plot with subplots. The location of subplots within a 10 m × 10 m square was chosen randomly; (b) Location of a subplot with respect to the flakeboard.

    Full size image

    Additionally, several ground control points (GCPs) were distributed in the overflight area of the UAS. The exact location of the GCPs’ centre was measured with a Global Navigation Satellite System (GNSS) receiver (Viva GNSS GS 10, Leica Geosystems AG, Switzerland) in static mode for 10 minutes. The data from the GNSS was reprocessed with Leica Geo Office 8.3 software (Leica Geosystems AG, Heerbrugg, Switzerland) utilising reference data from the satellite positioning service of the surveying administration of the federal states of Germany (SAPOS) for the real reference stations 0285-Garmisch, 0270-Bad Tölz, and 1271-Weilheim. The reference data were obtained via the SAPOS website of Bavaria (https://sapos.bayern.de/). The accuracy of the used GNSS in post-processing mode is 0.003 m in horizontal direction and 0.005 m in vertical direction39. The transformation of the corrected coordinates from ellipsoidal heights to physical (geoid-based) heights (height system: DHHN2016, EPSG 7837) was done with the online processing service “CRS-Transformation Bayern” from SAPOS (https://sapos.bayern.de/coord_tm.php). The transformation accuracy for this height transformation is 0.005 m40.
    The UAS flights were conducted after the preparation of the respective field site, followed by the field measurements and vegetation sampling. A RGB camera (Sony Cyber-shot WX 220, Sony Corp., Minato, Japan) mounted on a fixed-wing UAS (eBee, senseFly, Cheseaux-sur-Lausanne, Switzerland) was used to acquire high-resolution images of the study sites. In total, four UAS-flights were necessary to cover all ten plots – one in FE, one in RB and two in EL (EL-N, EL-S).
    Acquisition of field measurements and field samples
    The methods for acquiring in-situ data of canopy height, destructive vegetation sampling, and subsequent sample processing were adapted from the Integrated Carbon Observation System (ICOS) instructions for vegetation measurements in grasslands41,42.
    Canopy height measurements and sampling for biomass and element content measurements
    After the UAS flight, first the subplot area was identified (0.3 m south of the corresponding flakeboard, one site centred and parallel to the flakeboard, see Fig. 3b). Second, the bulk canopy height of the grassland canopy within the subplot was measured with a platemeter, which had the same area as the subplot for destructive sampling (0.25 m × 0.25 m) and was build according to the ICOS instructions41. The plate of the platemeter was constructed from acrylic glass and weighed 1680 g. Third, a metallic sampling frame (size: 0.25 m × 0.25 m × 0.03 m) was put on the subplot. After verifying that the sampling frame was not sliding on the vegetation, the vegetation within the sampling frame was clipped down to stubble height (0.03 m) with a manual grass cutter for later determination of biomass and element contents. Finally, the clipped vegetation was put in a labelled paper bag, then in an airtight plastic bag and afterwards in a cooling box until further processing in the laboratory.
    Sampling for leaf mass per area determination
    Additional samples were taken outside the subplots (within a radius of 2 m; one sample per subplot) to determine LMA. First, the area percentage of the PFTs legumes, other forbs and graminoids of the 30 m × 30 m plot was visually estimated (rough estimation based on field observations, no specific method applied). Then, the corresponding number of samples for each PFT was determined in relation to the number of subplots. That is, if the percentage of PFTs is e.g. 50% graminoids, 25% legumes, and 25% other forbs, and we have 12 subplots, there were six samples for graminoids, three for legumes and three for other forbs in this plot. At each subplot we took one LMA sample for just one specific PFT. One LMA sample consisted of fully expanded, undamaged leaves of the selected PFT originating from different individuals. The number of leaves per sample varied between one and seven depending on the leave size and the species composition of the PFT. However, the species composition could just be considered for dominant and easily distinguishable species, as the team was not specifically trained in grassland botany. Therefore, multiple species samples occurred mainly for forb samples. The clipped leaves were wrapped in humid paper. Then, the sample was put in a labelled plastic bag which was hermetically closed before it was placed in the cooling box.
    Sample processing in the laboratory
    After transportation, the samples were stored in the fridge and/or in a cooling room at 4 °C until further processing. Sample processing in the lab started within one to nine days after the sampling date.
    Samples for the determination of biomass, green area index, and element content
    The samples were removed from the plastic bags and weighed. The weight of the fresh material was multiplied by 16 to determine the fresh weight per 1 m² (Biom wm). Afterwards, the sample material was sorted into the PFT non-green vegetation (NG; representing photosynthetically inactive structures), legumes (L), non-leguminous forbs (F), and graminoids (G). Then, the fresh weight of each PFT was determined. The sorting was done either on the full sample or on a representative smaller subsample in case there was a lot of sample material. For taking a subsample, the vegetation material was thoroughly mixed and then a handful of material was taken out. The remaining material was dried and further processed/analysed for C and N content like the PFT specific samples.
    The total hemi-surface area of the green material (GA) needs to be obtained to determine the GAI of a sample. As we have flat vegetation structures, we used a planimeter (LI-3100A Area Meters, LI-COR, USA) to obtain the hemi-surface area. The hemi-surface area was measured separately for each PFT of a sample, but not for the non-green vegetation. The GAI of a certain PFT (GAIPFT) was then calculated by dividing the hemi-surface area of the PFT (GAPFT) by the area of the subplot (A; 0.25 m × 0.25 m) and in case a subsample was taken by multiplying with the ratio of the fresh weight of the sample (Biomass wmsample) to the subsample (Biomass wmsubsample):

    $$GA{I}_{PFT}=frac{G{A}_{PFT}}{A}frac{Biom,w{m}_{sample}}{Biom,w{m}_{subsample}}$$
    (1)

    The total GAI (GAItot) of the sample was calculated by summing the GAI of each PFT:

    $$GA{I}_{tot}=sum GA{I}_{PFT}=GA{I}_{L}+GA{I}_{F}+GA{I}_{G}$$
    (2)

    After the planimeter measurements the samples were dried in an oven at 65 °C until constant weight was achieved. Then, the dry weight of each PFT of each sample was measured (dmPFT). The total weight of dry biomass of a sample (Biomass dm) was obtained by summing up the dry weights of all PFTs and if applicable the dry weight of the remaining material (dmrest) of a sample and then scaled to 1 m²:

    $$Biomass,dm=16times sum d{m}_{PFT}=16times (d{m}_{NG}+d{m}_{L}+d{m}_{F}+d{m}_{G}+d{m}_{rest})$$
    (3)

    The percentage of each PFT (PPFT) with respect to the total dry biomass weight of the sorted material (either subsample or full sample) (ΣdmPFT) was calculated as follows:

    $${P}_{PFT}=frac{d{m}_{PFT}}{sum d{m}_{PFT}}times 100=frac{d{m}_{PFT}}{d{m}_{NG}+d{m}_{L}+d{m}_{F}+d{m}_{G}}times 100$$
    (4)

    The plant water content (PWC) was calculated from the weight of the fresh (Biom wm) and dry biomass (Biom dm) as follows:

    $$PWC=frac{Biom,wm-Biom,dm}{Biom,wm}times 100$$
    (5)

    Finally, the dried vegetation material was ground in a ball mill for elemental analysis of C and N.
    Samples for the determination of leaf mass per area
    After returning from the field work, the leaves for the LMA determination were rehydrated until full turgescence. When the leaves were fully expanded, the last mature leaf from each tiller (in case tillers were sampled) were separated and the petiole was recut at the base of the leave blade. Then, the hemi-surface area of each sample was determined with the planimeter. Afterwards, the samples were dried in an oven at 65 °C until constant weight was achieved, before the dry weight of the LMA samples was measured. The LMA of a sample i was calculated by the ratio of the leaf dry weight (Wi) to its fresh area (Ai):

    $$LM{A}_{i}=frac{{W}_{i}}{{A}_{i}}$$
    (6)

    Analysis of C and N content
    The C and N content of the milled vegetation samples was determined using an elemental analyser (varioMax CUBE, Elementar Analysesysteme GmbH, Germany) operated in CNS (carbon, nitrogen, sulphur) mode with the plant method and a weighted sample of 17 mg at the laboratory of the Technical University Munich, Chair of Soil Science, in Freising (Germany). The detection limits of the instrument were 0.020 wt.% for C and 0.015 wt.% for N.
    Due to the sorting of the sample in different PFTs, the C and N contents were obtained specific for each PFT. In cases where a subsample was taken for the sorting into PFT, C and N contents were additionally measured also for the mixed remaining sample material.
    Mean concentrations of C and N for each subplot (plant community C and N) were calculated as follows:

    $$bar{E}={P}_{NG}times {E}_{NG}+{P}_{L}times {E}_{L}+{P}_{F}times {E}_{F}+{P}_{G}times {E}_{G}$$
    (7)

    where (bar{E}) is the mean element content (C or N), PPFT is the percentage of the PFT (NG = non-green, L = legumes, F = non-leguminous forbs, G = graminoids), and EPFT is the element content (C or N) in the PFT.
    Retrieving the coordinates of the subplot centres
    The single images from each UAS flight were processed with the photogrammetric software PIX4D (Pix4Dmapper Pro, Pix4D S.A., Prilly, Switzerland) to obtain orthophotos. The GCPs in the orthophotos were used to georeference the orthophotos. The final spatial resolution of the orthophotos was 0.036 m (FE), 0.034 m (RB), 0.030 m (EL-N), and 0.043 m (EL-S).
    Afterwards, the coordinates of the subplots centres were manually extracted from the high-resolution georeferenced orthophotos utilizing QGIS (Version 3.0.0-Girona)43. The bright flakeboards were visually localised in the images. Then the subplots centres were identified (0.425 m perpendicular away from the middle of the southern flakeboards site) and their coordinates extracted.
    Characterisation of grassland type and plant community composition on the plot-level
    Vegetation relevés were carried out on 18–19 June 2020 for each of the ten 30 m × 30 m plots. Within each plot all vascular plant species were systematically determined and their cover was visually estimated to the nearest percentage as a proxy for abundance. Data is provided as percentage cover per plot. Plant species names were updated according to The Plant List, a working list of all known plant species, aiming to be comprehensive for all species of vascular plants, including flowering plants, conifers, ferns and their allies, and of bryophytes, including mosses and liverworts (theplantlist.org). The grassland type was classified sensu Oberdorfer (1977 and updated since)44. Please note that vegetation relevés from 2020 may slightly differ in relative coverage to actual species specific data from sampling in 2018. Nevertheless, most grassland species reach life spans of several decades and persist through time. More

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    Divergent forest sensitivity to repeated extreme droughts

    1.
    Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).
    CAS  Article  Google Scholar 
    2.
    Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).
    CAS  Article  Google Scholar 

    3.
    IPCC Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) (Cambridge Univ. Press, 2012).

    4.
    Schwalm, C. R. et al. Reduction in carbon uptake during turn of the century drought in western North America. Nat. Geosci. 5, 551–556 (2012).
    CAS  Article  Google Scholar 

    5.
    Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).
    CAS  Article  Google Scholar 

    6.
    Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 129 (2015).
    Article  Google Scholar 

    7.
    Dai, A. Drought under global warming: a review. Wiley Interdiscip. Rev. Clim. Change 2, 45–65 (2011).
    Article  Google Scholar 

    8.
    Cook, B. I., Smerdon, J. E., Seager, R. & Coats, S. Global warming and 21st century drying. Clim. Dyn. 43, 2607–2627 (2014).
    Article  Google Scholar 

    9.
    Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A. & Totterdell, I. J. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184–187 (2000).
    CAS  Article  Google Scholar 

    10.
    Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).
    Article  Google Scholar 

    11.
    Zscheischler, J. et al. A few extreme events dominate global interannual variability in gross primary production. Environ. Res. Lett. 9, 035001 (2014).
    Article  Google Scholar 

    12.
    Miao, S., Zou, C. B. & Breshears, D. D. Vegetation responses to extreme hydrological events: sequence matters. Am. Nat. 173, 113–118 (2008).
    Article  Google Scholar 

    13.
    Anderegg, W. R. L. et al. Pervasive drought legacies in forest ecosystems and their implications for carbon cycle models. Science 349, 528–532 (2015).
    CAS  Article  Google Scholar 

    14.
    Schwalm, C. R. et al. Global patterns of drought recovery. Nature 548, 202–205 (2017).
    CAS  Article  Google Scholar 

    15.
    Holling, C. S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst. 4, 1–23 (1973).
    Article  Google Scholar 

    16.
    Gunderson, L. H. et al. Ecological resilience—in theory and application. Annu. Rev. Ecol. Syst. 31, 425–439 (2000).
    Article  Google Scholar 

    17.
    Ingrisch, J. & Bahn, M. Towards a comparable quantification of resilience. Trends Ecol. Evol. 33, 251–259 (2018).
    Article  Google Scholar 

    18.
    Bartlett, M. K. et al. Global analysis of plasticity in turgor loss point, a key drought tolerance trait. Ecol. Lett. 17, 1580–1590 (2014).
    Article  Google Scholar 

    19.
    Martínez‐Vilalta, J. et al. Hydraulic adjustment of Scots pine across Europe. New Phytol. 184, 353–364 (2009).
    Article  Google Scholar 

    20.
    Hacke, U. G., Stiller, V., Sperry, J. S., Pittermann, J. & McCulloh, K. A. Cavitation fatigue. Embolism and refilling cycles can weaken the cavitation resistance of xylem. Plant Physiol. 125, 779–786 (2001).
    CAS  Article  Google Scholar 

    21.
    Sala, A., Woodruff, D. R. & Meinzer, F. C. Carbon dynamics in trees: feast or famine? Tree Physiol. 32, 764–775 (2012).
    CAS  Article  Google Scholar 

    22.
    Schymanski, S. J., Roderick, M. L., Sivapalan, M., Hutley, L. B. & Beringer, J. A canopy-scale test of the optimal water-use hypothesis. Plant Cell Environ. 31, 97–111 (2008).
    Google Scholar 

    23.
    Zhang, T., Niinemets, Ü., Sheffield, J. & Lichstein, J. W. Shifts in tree functional composition amplify the response of forest biomass to climate. Nature 556, 99–102 (2018).
    CAS  Article  Google Scholar 

    24.
    Anderegg, W. R. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).
    CAS  Article  Google Scholar 

    25.
    Royer, P. D. et al. Extreme climatic event-triggered overstorey vegetation loss increases understorey solar input regionally: primary and secondary ecological implications. J. Ecol. 99, 714–723 (2011).
    Article  Google Scholar 

    26.
    Raffa, K. F. et al. Cross-scale drivers of natural disturbances prone to anthropogenic amplification: the dynamics of bark beetle eruptions. BioScience 58, 501–517 (2008).
    Article  Google Scholar 

    27.
    Anderegg, W. R., Trugman, A. T., Bowling, D. R., Salvucci, G. & Tuttle, S. E. Plant functional traits and climate influence drought intensification and land–atmosphere feedbacks. Proc. Natl Acad. Sci. USA 116, 14071–14076 (2019).

    28.
    Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Change Biol. 23, 1675–1690 (2017).
    Article  Google Scholar 

    29.
    Camarero, J. J., Gazol, A., Sangüesa-Barreda, G., Oliva, J. & Vicente-Serrano, S. M. To die or not to die: early warnings of tree dieback in response to a severe drought. J. Ecol. 103, 44–57 (2015).
    CAS  Article  Google Scholar 

    30.
    Jump, A. S. et al. Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol. 23, 3742–3757 (2017).
    Article  Google Scholar 

    31.
    Saatchi, S. et al. Persistent effects of a severe drought on Amazonian forest canopy. Proc. Natl Acad. Sci. USA 110, 565–570 (2013).
    CAS  Article  Google Scholar 

    32.
    Konings, A. G., Williams, A. P. & Gentine, P. Sensitivity of grassland productivity to aridity controlled by stomatal and xylem regulation. Nat. Geosci. 10, 284–288 (2017).
    CAS  Article  Google Scholar 

    33.
    Carnicer, J. et al. Widespread crown condition decline, food web disruption, and amplified tree mortality with increased climate change-type drought. Proc. Natl Acad. Sci. USA 108, 1474–1478 (2011).
    CAS  Article  Google Scholar 

    34.
    Brienen, R. J. W. et al. Long-term decline of the Amazon carbon sink. Nature 519, 344–348 (2015).
    CAS  Article  Google Scholar 

    35.
    Lenton, T. M. et al. Tipping elements in the Earth’s climate system. Proc. Natl Acad. Sci. USA 105, 1786–1793 (2008).
    CAS  Article  Google Scholar 

    36.
    Duffy, P. B., Brando, P., Asner, G. P. & Field, C. B. Projections of future meteorological drought and wet periods in the Amazon. Proc. Natl Acad. Sci. USA 112, 13172–13177 (2015).
    CAS  Article  Google Scholar 

    37.
    Johnson, D. M., McCulloh, K. A., Woodruff, D. R. & Meinzer, F. C. Hydraulic safety margins and embolism reversal in stems and leaves: why are conifers and angiosperms so different? Plant Sci. 196, 48–53 (2012).
    Article  CAS  Google Scholar 

    38.
    Morris, H. et al. A global analysis of parenchyma tissue fractions in secondary xylem of seed plants. New Phytol. 209, 1553–1565 (2016).
    CAS  Article  Google Scholar 

    39.
    DeSoto, L. et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 11, 1–9 (2020).
    Article  CAS  Google Scholar 

    40.
    Fisher, R. A. et al. Vegetation demographics in Earth system models: a review of progress and priorities. Glob. Change Biol. 24, 35–54 (2018).
    Article  Google Scholar 

    41.
    Kennedy, D. et al. Implementing plant hydraulics in the Community Land Model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).
    Article  Google Scholar 

    42.
    Trugman, A. T. et al. Tree carbon allocation explains forest drought-kill and recovery patterns. Ecol. Lett. 21, 1552–1560 (2018).
    CAS  Article  Google Scholar 

    43.
    Trugman, A. T. et al. Climate and plant trait strategies determine tree carbon allocation to leaves and mediate future forest productivity. Glob. Change Biol. 25, 3395–3405 (2019).
    Article  Google Scholar 

    44.
    Scheiter, S., Langan, L. & Higgins, S. I. Next-generation dynamic global vegetation models: learning from community ecology. New Phytol. 198, 957–969 (2013).
    Article  Google Scholar 

    45.
    Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., Angulo, M. & El Kenawy, A. A new global 0.5 gridded dataset (1901–2006) of a multiscalar drought index: comparison with current drought index datasets based on the Palmer Drought Severity Index. J. Hydrometeorol. 11, 1033–1043 (2010).
    Article  Google Scholar 

    46.
    Beguería, S., Vicente-Serrano, S. M. & Angulo-Martínez, M. A multiscalar global drought dataset: the SPEIbase: a new gridded product for the analysis of drought variability and impacts. Bull. Am. Meteorol. Soc. 91, 1351–1356 (2010).
    Article  Google Scholar 

    47.
    Beguería, S., Vicente-Serrano, S. M., Reig, F. & Latorre, B. Standardized Precipitation Evapotranspiration Index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).
    Article  Google Scholar 

    48.
    Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).
    CAS  Article  Google Scholar 

    49.
    Gazol, A., Camarero, J. J., Anderegg, W. R. L. & Vicente-Serrano, S. M. Impacts of droughts on the growth resilience of Northern Hemisphere forests. Glob. Ecol. Biogeogr. 26, 166–176 (2017).
    Article  Google Scholar 

    50.
    Klesse, S. et al. Sampling bias overestimates climate change impacts on forest growth in the southwestern United States. Nat. Commun. 9, 5336 (2018).
    CAS  Article  Google Scholar 

    51.
    Bechtold, W. A. & Patterson, P. L. The Enhanced Forest Inventory and Analysis Program—National Sampling Design and Estimation Procedures General Technical Report SRS-80 (USDA, 2005).

    52.
    Bechtold, W. & Scott, C. T. in The Enhanced Forest Inventory and Analysis Program—National Sampling Design and Estimation Procedures General Technical Report SRS-80 (eds Bechtold, W. A. & Patterson, P. L.) 37–52 (USDA, 2005).

    53.
    Woudenberg, S. W. et al. The Forest Inventory and Analysis Database: Database Description and Users Manual Version 4.0 for Phase 2 General Technical Report RMRS-GTR-245 (USDA, 2010).

    54.
    Jacobi, W. R., Kearns, H. S. J. & Johnson, D. W. Persistence of pinyon pine snags and logs in southwestern Colorado. West. J. Appl. For. 20, 247–252 (2005).
    Article  Google Scholar 

    55.
    Shaw, J. D. et al. Arizona’s Forest Resources, 2001–2014 Resource Bulletin RMRS-RB-25 (USDA, 2018).

    56.
    Shaw, J. D., Steed, B. E. & DeBlander, L. T. Forest inventory and analysis (FIA) annual inventory answers the question: what is happening to pinyon-juniper woodlands? J. For. 103, 280–285 (2005).
    Google Scholar 

    57.
    Breshears, D. D. et al. Regional vegetation die-off in response to global-change-type drought. Proc. Natl Acad. Sci. USA 102, 15144–15148 (2005).
    CAS  Article  Google Scholar 

    58.
    Williams, A. P. et al. Forest responses to increasing aridity and warmth in the southwestern United States. Proc. Natl Acad. Sci. USA 107, 21289–21294 (2010).
    CAS  Article  Google Scholar 

    59.
    Anderegg, W. R. et al. Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol. 208, 674–683 (2015).
    Article  Google Scholar 

    60.
    Jackson, T. J. & Schmugge, T. J. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 36, 203–212 (1991).
    Article  Google Scholar 

    61.
    Tian, F. et al. Remote sensing of vegetation dynamics in drylands: evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sens. Environ. 177, 265–276 (2016).
    Article  Google Scholar 

    62.
    Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).
    Article  Google Scholar 

    63.
    Momen, M. et al. Interacting effects of leaf water potential and biomass on vegetation optical depth. J. Geophys. Res. Biogeosci. 122, 3031–3046 (2017).
    Article  Google Scholar 

    64.
    Konings, A. G. & Gentine, P. Global variations in ecosystem-scale isohydricity. Glob. Change Biol. 23, 891–905 (2017).
    Article  Google Scholar 

    65.
    Van de Griend, A. A. & Wigneron, J.-P. The b-factor as a function of frequency and canopy type at H-polarization. IEEE Trans. Geosci. Remote Sens. 42, 786–794 (2004).
    Article  Google Scholar 

    66.
    Konings, A. G., Rao, K. & Steele-Dunne, S. C. Macro to micro: microwave remote sensing of plant water content for physiology and ecology. New Phytol. https://doi.org/10.1111/nph.15808 (2019).

    67.
    Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave earth observations. Earth Syst. Sci. Data Discuss. https://doi.org/10.5194/essd-2017-27 (2017).

    68.
    Du, J., Kimball, J. S., Jones, L. A. & Member, S. Passive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR-E. IEEE Trans. Geosci. Remote Sens. 54, 597–608 (2015).
    Article  Google Scholar 

    69.
    Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth Syst. Sci. Data 9, 791–808 (2017).
    Article  Google Scholar 

    70.
    Jones, L. A. et al. Satellite microwave remote sensing of daily land surface air temperature minima and maxima from AMSR-E. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 3, 111–123 (2010).
    Article  Google Scholar 

    71.
    Olson, D. M. et al. Terrestrial Ecoregions of the World: A New Map of Life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).
    Article  Google Scholar 

    72.
    Zar, J. H. in Biostatistical Analysis 1st edn, 185–205 (Prentice-Hall International, 1984).

    73.
    Fox, J. et al. Package ‘car’ (R Foundation for Staistical Computing, 2012).

    74.
    Fox, J., Friendly, M. & Weisberg, S. Hypothesis tests for multivariate linear models using the car package. R J. 5, 39–52 (2013).
    Article  Google Scholar 

    75.
    Dormann, C. F. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).
    Article  Google Scholar 

    76.
    Pinheiro, J. et al. nlme: linear and nonlinear mixed effects models. R package v.3.1-117 (R Foundation for Statistical Computing, 2014).

    77.
    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2012). More

  • in

    Mapping the bacterial metabolic niche space

    1.
    Hutchinson, G. E. Cold Spring Harbor symposium on quantitative biology. Concluding Remarks 22, 415–427 (1957).
    Google Scholar 
    2.
    MacArthur, R. H. In Challenging Biological Problems: Directions Toward Their Solution (ed. Behnke, J. A.) pp. 253–259 (Oxford University Press, 1972).

    3.
    Chase, J. M. & Leibold, M. A. Ecological Niches: Linking Classical and Contemporary Approaches (University of Chicago Press, 2003).

    4.
    Holt, R. D. Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives. Proc. Natl Acad. Sci. USA 106, 19659–19665 (2009).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Winemiller, K. O., Fitzgerald, D. B., Bower, L. M. & Pianka, E. R. Functional traits, convergent evolution, and periodic tables of niches. Ecol. Lett. 18, 737–751 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    6.
    Pianka, E. R., Vitt, L. J., Pelegrin, N., Fitzgerald, D. B. & Winemiller, K. O. Toward a periodic table of niches, or exploring the lizard niche hypervolume. Am. Naturalist 190, 601–616 (2017).
    Article  Google Scholar 

    7.
    Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n-dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).
    Article  Google Scholar 

    8.
    Hoogenboom, M. O. & Connolly, S. R. Defining fundamental niche dimensions of corals: synergistic effects of colony size, light, and flow. Ecology 90, 767–780 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    9.
    Porter, W. P. & Kearney, M. Size, shape, and the thermal niche of endotherms. Proc. Natl Acad. Sci. USA 106, 19666–19672 (2009).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    10.
    Kraft, N. J. B., Godoy, O. & Levine, J. M. Plant functional traits and the multidimensional nature of species coexistence. Proc. Natl Acad. Sci. USA 112, 797–802 (2015).
    ADS  CAS  PubMed  Article  Google Scholar 

    11.
    Benjamin, B. Hypervolume concepts in niche-and trait-based ecology. Ecography 41, 1441–1455 (2018).
    Article  Google Scholar 

    12.
    González, A. L., Dézerald, O., Marquet, P. A., Romero, G. Q. & Srivastava, D. S. The multidimensional stoichiometric niche. Front. Ecol. Evol. 5, 110 (2017).
    Article  Google Scholar 

    13.
    Stevenson, B. G. The Hutchinsonian niche: multivariate statistical analysis of dung beetle niches. Coleopter. Bull. 36, 246–249 (1982).

    14.
    Inward, D. J. G., Davies, R. G., Pergande, C., Denham, A. J. & Vogler, A. P. Local and regional ecological morphology of dung beetle assemblages across four biogeographic regions. J. Biogeogr. 38, 1668–1682 (2011).
    Article  Google Scholar 

    15.
    Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
    ADS  PubMed  Article  CAS  Google Scholar 

    16.
    Green, J. L., Bohannan, B. J. M. & Whitaker, R. J. Microbial biogeography: from taxonomy to traits. science 320, 1039–1043 (2008).
    ADS  CAS  PubMed  Article  Google Scholar 

    17.
    Noah, F., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
    Article  Google Scholar 

    18.
    Claire Horner-Devine, M. & Bohannan, B. J. M. Phylogenetic clustering and overdispersion in bacterial communities. Ecology 87, S100–S108 (2006).
    PubMed  Article  Google Scholar 

    19.
    Lennon, J. T., Aanderud, Z. T., Lehmkuhl, B. K. & Schoolmaster Jr, D. R. Mapping the niche space of soil microorganisms using taxonomy and traits. Ecology 93, 1867–1879 (2012).
    PubMed  Article  Google Scholar 

    20.
    Fisher, C. K., Thierry, M. & Walczak, A. M. Variable habitat conditions drive species covariation in the human microbiota. PLoS Comput. Biol. 13, e1005435 (2017).

    21.
    Prosser, J. I. et al. The role of ecological theory in microbial ecology. Nat. Rev. Microbiol. 5, 384–392 (2007).
    CAS  PubMed  Article  Google Scholar 

    22.
    Elhanan, B., Martin, K., Feldman, M. W. & Ruppin, E. Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proc. Natl Acad. Sci. USA 105, 14482–14487 (2008).
    ADS  Article  Google Scholar 

    23.
    Humphries, M. M. & McCann, K. S. Metabolic ecology. J. Anim. Ecol. 83, 7–19 (2014).
    PubMed  Article  Google Scholar 

    24.
    Chase, J. M. In The theory of ecology (eds Scheiner, S. M. and Willig, M. R.) pp. 93–107 (2011).

    25.
    D’Andrea, R. & Ostling, A. Challenges in linking trait patterns to niche differentiation. Oikos 125, 1369–1385 (2016).
    Article  Google Scholar 

    26.
    Barter, E. & Gross, T. Manifold cities: Social variables of urban areas in the uk. Proc. R. Soc. A 475, 20180615 (2019).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Coifman, R. R. et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl Acad. Sci. USA 102, 7426–7431 (2005).
    ADS  CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

    28.
    Coifman, R. R. & Lafon, S. Diffusion maps. Appl. Comput. Harmonic Anal. 21, 5–30 (2006).
    MathSciNet  MATH  Article  Google Scholar 

    29.
    Kac, M. Can one hear the shape of a drum? Am. Math. Monthly 73, 1–23 (1966).
    MathSciNet  MATH  Article  Google Scholar 

    30.
    Boaz, N., Stephane, L., Ioannis, K. & Coifman, R. R. Diffusion maps, spectral clustering and eigenfunctions of fokker-planck operators. In Advances in Neural Information Processing Systems 955–962 (2006).

    31.
    Jones, P. W., Mauro, M. & Schul, R. Manifold parametrizations by eigenfunctions of the laplacian and heat kernels. Proc. Natl Acad. Sci. USA 105, 1803–1808 (2008).
    ADS  MathSciNet  CAS  PubMed  MATH  Article  PubMed Central  Google Scholar 

    32.
    Daniel, M., Sergej, A., Melanie, T. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).
    Article  CAS  Google Scholar 

    33.
    Pruitt, K. D., Tatiana, T. & Maglott, D. R. Ncbi reference sequences (refseq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res. 35, D61–D65 (2006).
    PubMed  PubMed Central  Article  Google Scholar 

    34.
    Mendes-Soares, H., Michael, M., Soares, L. M. & Chia, N. Mminte: an application for predicting metabolic interactions among the microbial species in a community. BMC Bioinforma. 17, 343 (2016).
    Article  Google Scholar 

    35.
    Boaz, N., Stephane, L., Ronald, C. & Kevrekidis, I. G. In Principal Manifolds For Data Visualization and Dimension Reduction pp. 238–260 (Springer, 2008).

    36.
    Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    37.
    Marion, E. et al. The photorespiratory glycolate metabolism is essential for cyanobacteria and might have been conveyed endosymbiontically to plants. Proc. Natl Acad. Sci. USA 105, 17199–17204 (2008).
    Article  Google Scholar 

    38.
    Watzer, B. & Forchhammer, K. Cyanophycin synthesis optimizes nitrogen utilization in the unicellular cyanobacterium synechocystis sp. strain pcc 6803. Appl. Environ. Microbiol. 84, e01298–18 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    39.
    Sonia, F., Lunn, J. E., Franck, B. & Ferrer, J.-L. The structure of a cyanobacterial sucrose-phosphatase reveals the sugar tongs that release free sucrose in the cell. Plant Cell 17, 2049–2058 (2005).
    Article  CAS  Google Scholar 

    40.
    Amy, N., Thilo, G. & Bassler, K. E. Mesoscopic structures and the laplacian spectra of random geometric graphs. J. Complex Netw. 3, 543–551 (2015).
    MathSciNet  Article  Google Scholar 

    41.
    Komagata, K., Iino, T., Yamada, Y. The Family Acetobacteraceae. In The Prokaryotes (eds Rosenberg, E., DeLong, E. F., Lory, S., Stackebrandt, E., Thompson, F.) pp. 3–78 (Springer, Berlin, Heidelberg, 2014).

    42.
    Meadows, J. A. & Wargo, M. J. Carnitine in bacterial physiology and metabolism. Microbiology 161, 1161 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    43.
    Kämpfer, P., Svenja, M. & Müller, H. E. Characterization of buttiauxella and kluyvera species by analysis of whole cell fatty acid patterns. Syst. Appl. Microbiol. 20, 566–571 (1997).
    Article  Google Scholar 

    44.
    Parsons, J. B. & Rock, C. O. Bacterial lipids: metabolism and membrane homeostasis. Prog. Lipid Res. 52, 249–276 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    45.
    Foster, D. B. et al. Phosphatidylethanolamine recognition promotes enteropathogenic E. coli and enterohemorrhagic E. coli host cell attachment. Microb. Pathogenesis 27, 289–301 (1999).
    Article  CAS  Google Scholar 

    46.
    Mayer, C. & Boos, W. Hexose/pentose and hexitol/pentitol metabolism. EcoSal Plus 1 (2005).

    47.
    Reimer, L. C. et al. Bac dive in 2019: bacterial phenotypic data for high-throughput biodiversity analysis. Nucleic Acids Res. 47, D631–D636 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    48.
    Devinder, K., Brennan, P. J. & Crick, D. C. Decaprenyl diphosphate synthesis in mycobacterium tuberculosis. J. Bacteriol. 186, 7564–7570 (2004).
    Article  CAS  Google Scholar 

    49.
    Newton, G. L., Nancy, B. & Fahey, R. C. Biosynthesis and functions of mycothiol, the unique protective thiol of Actinobacteria. Microbiol. Mol. Biol. Rev. 72, 471–494 (2008).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    50.
    Yaozhu, W., Xiaofei, Z., Sixue, Z. & Tan, X. Structural and functional insights into corrinoid iron-sulfur protein from human pathogen Clostridium difficile. J. Inorg. Biochem. 170, 26–33 (2017).
    Article  CAS  Google Scholar 

    51.
    Charles, D., Plants-Paris, K., Dayna, B. & DuPont, H. L. Clostridium difficile modulates the gut microbiota by inducing the production of indole, an interkingdom signaling and antimicrobial molecule. mSystems 4, e00346–18 (2019).
    Google Scholar 

    52.
    Luo, H. & Moran, M. A. How do divergent ecological strategies emerge among marine bacterioplankton lineages? Trends Microbiol. 23, 577–584 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    53.
    Kanehisa, M. & Goto, S. Kegg: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    54.
    Neshich, I. A. P., Eduardo, K. & Arruda, P. Genome-wide analysis of lysine catabolism in bacteria reveals new connections with osmotic stress resistance. ISME J. 7, 2400–2410 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    55.
    Chang, H.-H. et al. Complete genome sequence of ?candidatus sulcia muelleri? ml, an obligate nutritional symbiont of maize leafhopper (dalbulus maidis). Genome Announc. 3, e01483–14 (2015).
    PubMed  PubMed Central  Google Scholar 

    56.
    López-Madrigal, S., Amparo, L., Andres, M. & Gil, R. The link between independent acquisition of intracellular gamma-endosymbionts and concerted evolution in tremblaya princeps. Front. Microbiol. 6, 642 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    57.
    Dale, C. & Moran, N. A. Molecular interactions between bacterial symbionts and their hosts. Cell 126, 453–465 (2006).
    CAS  PubMed  Article  Google Scholar 

    58.
    Langille, M. G. I. et al. Predictive functional profiling of microbial communities using 16s rrna marker gene sequences. Nat. Biotechnol. 31, 814 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    59.
    Stilianos, L. et al. Function and functional redundancy in microbial systems. Nat. Ecol. Evol. 2, 936 (2018).
    Article  Google Scholar 

    60.
    Douglas, G. M. et al. Picrust2: an improved and extensible approach for metagenome inference. BioRxiv https://www.biorxiv.org/content/10.1101/672295v2 (2019).

    61.
    Cooley, S. M., Timothy, H., Deeds, E. J. & Ray, J. C. J. A novel metric reveals previously unrecognized distortion in dimensionality reduction of scRNA-seq data. BioRxiv https://www.biorxiv.org/content/10.1101/689851v3 (2019).

    62.
    Thompson, L. R. et al. A communal catalogue reveals earth’s multiscale microbial diversity. Nature 551, 457 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    63.
    Lozupone, C. & Knight, R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    64.
    Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    65.
    King, Z. A. et al. Bigg models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44, D515–D522 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    66.
    Lee, M. D. GtoTree: a user-friendly workflow for phylogenomics. Bioinformatics 1, 3 (2019).
    Google Scholar 

    67.
    Hug, L. A. et al. A new view of the tree of life. Nat. Microbiol. 1, 16048 (2016).
    CAS  PubMed  Article  Google Scholar 

    68.
    Eddy, S. R. Accelerated profile hmm searches. PLoS Comput. Biol. 7, e1002195 (2011).
    ADS  MathSciNet  CAS  PubMed  PubMed Central  Article  Google Scholar 

    69.
    Edgar, R. C. Muscle: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Capella-Gutiérrez, S., Silla-Martínez, J. M. & Gabaldón, T. trimal: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics 25, 1972–1973 (2009).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    71.
    Price, M. N., Dehal, P. S. & Arkin, A. P. Fasttree 2–approximately maximum-likelihood trees for large alignments. PloS ONE 5, e9490 (2010).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    72.
    Letunic, I. & Bork, P. Interactive tree of life (iTol) v4: recent updates and new developments. Nucleic Acids Res. 47, 256–259 (2019).

    73.
    Aravind, S. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
    Article  CAS  Google Scholar 

    74.
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2019).

    75.
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).
    MathSciNet  MATH  Google Scholar 

    76.
    Altschul, S. F., Warren, G., Webb, M., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    77.
    Wright, E. S. Using DECIPHER v2.0 to analyze big biological sequence data in R. R. J. 8, 352–359 (2016).

    78.
    Ward Jr, J. H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963).
    MathSciNet  Article  Google Scholar  More

  • in

    Climate change disturbs wildlife microbiomes

    1.
    Baquero, F. & Nombela, C. Clin. Microbiol. Infec. 18(Suppl. 4), 2–4 (2012).
    2.
    Roughgarden, J., Gilbert, S. F., Rosenberg, E., Zilber-Rosenberg, I. & Lloyd, E. A. Biol. Theory 13, 44–65 (2018).
    Article  Google Scholar 

    3.
    Greenspan, S. E. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-020-0899-5 (2020).

    4.
    Caporaso, J. G. et al. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).
    CAS  Article  Google Scholar 

    5.
    Cho, I. & Blaser, M. J. Nat. Rev. Genet. 13, 260–270 (2012).
    CAS  Article  Google Scholar 

    6.
    Walke, J. B. et al. ISME J. 8, 2207–2217 (2014).
    CAS  Article  Google Scholar 

    7.
    Antwis, R. E. et al. PLoS ONE 9, e85563 (2014).
    Article  Google Scholar 

    8.
    Heiman, M. L. & Greenway, F. L. Mol. Metab. 5, 317–320 (2016).
    CAS  Article  Google Scholar 

    9.
    Romero, G. Q. et al. Nat. Commun. 11, 3215 (2020).
    CAS  Article  Google Scholar 

    10.
    Sabagh, L. T. et al. Copeia 2012, 683–689 (2012).
    Article  Google Scholar  More

  • in

    Winter in a warming Arctic

    1.
    Cooper, E. J. Annu. Rev. Ecol. Evol. S. 45, 271–295 (2014).
    Article  Google Scholar 
    2.
    Rapacz, M. et al. Plant Sci. 225, 34–44 (2014).
    CAS  Article  Google Scholar 

    3.
    Bokhorst, S., Bjerke, J. W., Tømmervik, H., Preece, C. & Phoenix, G. K. Ambio 41, 246–255 (2012).
    Article  Google Scholar 

    4.
    Niitynen, P. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-020-00916-4 (2020).

    5.
    Blok, D. et al. Environ. Res. Lett. 6, 035502 (2011).
    Article  Google Scholar 

    6.
    Elmendorf, S. C. et al. Nat. Clim. Change 2, 453–457 (2012).
    Article  Google Scholar 

    7.
    Myers-Smith, I. H. et al. Nat. Clim. Change 5, 887–891 (2015).
    Article  Google Scholar 

    8.
    Snow, Water, Ice and Permafrost in the Arctic (SWIPA) (Arctic Monitoring and Assessment Programme (AMAP), 2017).

    9.
    Zhu, L. K., Ives, A. R., Zhang, C., Guo, Y. Y. & Radeloff, V. C. Nat. Clim. Change 9, 886–893 (2019).
    Article  Google Scholar  More

  • in

    A framework for in situ molecular characterization of coral holobionts using nanopore sequencing

    1.
    Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377. https://doi.org/10.1038/nature21707 (2017).
    ADS  CAS  Article  PubMed  Google Scholar 
    2.
    LaJeunesse, T. C. et al. Systematic revision of symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580. https://doi.org/10.1016/j.cub.2018.07.008 (2018).
    CAS  Article  PubMed  Google Scholar 

    3.
    Bourne, D. G., Morrow, K. M. & Webster, N. S. Insights into the coral microbiome: underpinning the health and resilience of reef ecosystems. Annu. Rev. Microbiol. 70, 317–340. https://doi.org/10.1146/annurev-micro-102215-095440 (2016).
    CAS  Article  PubMed  Google Scholar 

    4.
    Peixoto, R. S., Rosado, P. M., Leite, D. C. D., Rosado, A. S. & Bourne, D. G. Beneficial microorganisms for corals (BMC): proposed mechanisms for coral health and resilience. Front. Microbiol. https://doi.org/10.3389/Fmicb.2017.00341 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    5.
    Reshef, L., Koren, O., Loya, Y., Zilber-Rosenberg, I. & Rosenberg, E. The coral probiotic hypothesis. Environ. Microbiol. 8, 2068–2073. https://doi.org/10.1111/j.1462-2920.2006.01148.x (2006).
    CAS  Article  PubMed  Google Scholar 

    6.
    Lesser, M. P. et al. Nitrogen fixation by symbiotic cyanobacteria provides a source of nitrogen for the scleractinian coral Montastraea cavernosa. Mar. Ecol. Prog. Ser. 346, 143–152. https://doi.org/10.3354/meps07008 (2007).
    ADS  CAS  Article  Google Scholar 

    7.
    Ben-Haim, Y. et al. Vibrio coralliilyticus sp. nov., a temperature-dependent pathogen of the coral Pocillopora damicornis. Int. J. System. Evol. Microbiol. 53, 309–315. https://doi.org/10.1099/ijs.0.02402-0 (2003).
    CAS  Article  Google Scholar 

    8.
    Johnston, E. C. et al. A genomic glance through the fog of plasticity and diversification in Pocillopora. Sci. Rep. https://doi.org/10.1038/S41598-017-06085-3 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    9.
    Shearer, T. L., Van Oppen, M. J., Romano, S. L. & Worheide, G. Slow mitochondrial DNA sequence evolution in the Anthozoa (Cnidaria). Mol. Ecol. 11, 2475–2487 (2002).
    CAS  Article  Google Scholar 

    10.
    Hellberg, M. E. No variation and low synonymous substitution rates in coral mtDNA despite high nuclear variation. BMC Evol. Biol. 6, 24. https://doi.org/10.1186/1471-2148-6-24 (2006).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    11.
    Wares, J. P. Mitochondrial cytochrome b sequence data are not an improvement for species identification in scleractinian corals. PeerJ 2, e564. https://doi.org/10.7717/peerj.564 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    12.
    Arrigoni, R. et al. A new sequence data set of SSU rRNA gene for Scleractinia and its phylogenetic and ecological applications. Mol. Ecol. Resour. 17, 1054–1071. https://doi.org/10.1111/1755-0998.12640 (2017).
    CAS  Article  PubMed  Google Scholar 

    13.
    Suzuki, G. & Nomura, K. Species boundaries of Astreopora corals (Scleractinia, Acroporidae) inferred by mitochondrial and nuclear molecular markers. Zool. Sci. 30, 626–632. https://doi.org/10.2108/zsj.30.626 (2013).
    CAS  Article  PubMed  Google Scholar 

    14.
    Gelin, P., Postaire, B., Fauvelot, C. & Magalon, H. Reevaluating species number, distribution and endemism of the coral genus Pocillopora Lamarck, 1816 using species delimitation methods and microsatellites. Mol. Phylogenet. Evol. 109, 430–446. https://doi.org/10.1016/j.ympev.2017.01.018 (2017).
    CAS  Article  PubMed  Google Scholar 

    15.
    LaJeunesse, T. C. Investigating the biodiversity, ecology, and phylogeny of endosymbiotic dinoflagellates in the genus Symbiodinium using the its region: In search of a “species” level marker. J. Phycol. 37, 866–880. https://doi.org/10.1046/j.1529-8817.2001.01031.x (2001).
    CAS  Article  Google Scholar 

    16.
    Hume, B. C. C. et al. An improved primer set and amplification protocol with increased specificity and sensitivity targeting the Symbiodinium ITS2 region. PeerJ 6, e4816. https://doi.org/10.7717/peerj.4816 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    17.
    Hume, B. C. C. et al. SymPortal: A novel analytical framework and platform for coral algal symbiont next-generation sequencing ITS2 profiling. Mol. Ecol. Resour. 19, 1063–1080. https://doi.org/10.1111/1755-0998.13004 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    18.
    Arif, C. et al. Assessing Symbiodinium diversity in scleractinian corals via next-generation sequencing-based genotyping of the ITS2 rDNA region. Mol. Ecol. 23, 4418–4433. https://doi.org/10.1111/mec.12869 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    19.
    Smith, E. G., Ketchum, R. N. & Burt, J. A. Host specificity of Symbiodinium variants revealed by an ITS2 metahaplotype approach. Isme J. 11, 1500–1503. https://doi.org/10.1038/ismej.2016.206 (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    20.
    Ziegler, M. et al. Biogeography and molecular diversity of coral symbionts in the genus Symbiodinium around the Arabian Peninsula. J. Biogeogr. 44, 674–686. https://doi.org/10.1111/jbi.12913 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    21.
    Mouchka, M. E., Hewson, I. & Harvell, C. D. Coral-associated bacterial assemblages: current knowledge and the potential for climate-driven impacts. Integr. Comp. Biol. 50, 662–674. https://doi.org/10.1093/icb/icq061 (2010).
    Article  PubMed  Google Scholar 

    22.
    Hernandez-Agreda, A., Leggat, W., Bongaerts, P. & Ainsworth, T. D. The microbial signature provides insight into the mechanistic basis of coral success across reef habitats. mBio https://doi.org/10.1128/mBio.00560-16 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    23.
    Neave, M. J., Apprill, A., Ferrier-Pages, C. & Voolstra, C. R. Diversity and function of prevalent symbiotic marine bacteria in the genus Endozoicomonas. Appl. Microbiol. Biotechnol. 100, 8315–8324. https://doi.org/10.1007/s00253-016-7777-0 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    24.
    Hernandez-Agreda, A., Gates, R. D. & Ainsworth, T. D. Defining the Core Microbiome in Corals’ Microbial Soup. Trends Microbiol. 25, 125–140. https://doi.org/10.1016/j.tim.2016.11.003 (2017).
    CAS  Article  PubMed  Google Scholar 

    25.
    Roder, C., Bayer, T., Aranda, M., Kruse, M. & Voolstra, C. R. Microbiome structure of the fungid coral Ctenactis echinata aligns with environmental differences. Mol. Ecol. 24, 3501–3511. https://doi.org/10.1111/mec.13251 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    26.
    Pogoreutz, C. et al. Dominance of Endozoicomonas bacteria throughout coral bleaching and mortality suggests structural inflexibility of the Pocillopora verrucosa microbiome. Ecol. Evol. 8, 2240–2252. https://doi.org/10.1002/ece3.3830 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    27.
    Neave, M. J. et al. Differential specificity between closely related corals and abundant Endozoicomonas endosymbionts across global scales. Isme J. 11, 186–200. https://doi.org/10.1038/ismej.2016.95 (2017).
    Article  PubMed  Google Scholar 

    28.
    Menegon, M. et al. On site DNA barcoding by nanopore sequencing. PLoS ONE 12, e0184741. https://doi.org/10.1371/journal.pone.0184741 (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    29.
    Parker, J., Helmstetter, A. J., Devey, D., Wilkinson, T. & Papadopulos, A. S. T. Field-based species identification of closely-related plants using real-time nanopore sequencing. Sci. Rep. 7, 8345. https://doi.org/10.1038/s41598-017-08461-5 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    30.
    Pomerantz, A. et al. Real-time DNA barcoding in a rainforest using nanopore sequencing: opportunities for rapid biodiversity assessments and local capacity building. Gigascience https://doi.org/10.1093/gigascience/giy033 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    31.
    Santos, A., van Aerle, R., Barrientos, L. & Martinez-Urtaza, J. Computational methods for 16S metabarcoding studies using Nanopore sequencing data. Comput. Struct. Biotechnol. J. 18, 296–305. https://doi.org/10.1016/j.csbj.2020.01.005 (2020).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    32.
    Berntson, E. A., Bayer, F. M., McArthur, A. G. & France, S. C. Phylogenetic relationships within the Octocorallia (Cnidaria:Anthozoa) based on nuclear 18S rRNA sequences. Mar. Biol. 138, 235–246. https://doi.org/10.1007/s002270000457 (2001).
    CAS  Article  Google Scholar 

    33.
    Pootakham, W. et al. High resolution profiling of coral-associated bacterial communities using full-length 16S rRNA sequence data from PacBio SMRT sequencing system. Sci. Rep. 7, 2774. https://doi.org/10.1038/s41598-017-03139-4 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    34.
    Hume, B. et al. Corals from the Persian/Arabian Gulf as models for thermotolerant reef-builders: prevalence of clade C3 Symbiodinium, host fluorescence and ex situ temperature tolerance. Mar. Pollut. Bull. 72, 313–322. https://doi.org/10.1016/j.marpolbul.2012.11.032 (2013).
    CAS  Article  PubMed  Google Scholar 

    35.
    Hume, B. C. et al. Symbiodinium thermophilum sp. nov., a thermotolerant symbiotic alga prevalent in corals of the world’s hottest sea, the Persian/Arabian Gulf. Sci. Rep. 5, 8562. https://doi.org/10.1038/srep08562 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    36.
    Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100. https://doi.org/10.1093/bioinformatics/bty191 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    37.
    Noonan, S. H. C., Fabricius, K. E. & Humphrey, C. Symbiodinium community composition in scleractinian corals is not affected by life-long exposure to elevated carbon dioxide. PLoS ONE https://doi.org/10.1371/journal.pone.0063985 (2013).
    Article  PubMed  PubMed Central  Google Scholar 

    38.
    Bayer, T. et al. Bacteria of the genus Endozoicomonas dominate the microbiome of the Mediterranean gorgonian coral Eunicella cavolini. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps10197 (2013).
    Article  Google Scholar 

    39.
    Glasl, B., Herndl, G. J. & Frade, P. R. The microbiome of coral surface mucus has a key role in mediating holobiont health and survival upon disturbance. Isme J. 10, 2280–2292. https://doi.org/10.1038/ismej.2016.9 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    40.
    Morrow, K. M. et al. Natural volcanic CO2 seeps reveal future trajectories for host-microbial associations in corals and sponges. Isme J. 9, 894–908. https://doi.org/10.1038/ismej.2014.188 (2015).
    CAS  Article  PubMed  Google Scholar 

    41.
    Morrow, K. M., Bromhall, K., Motti, C. A., Munn, C. B. & Bourne, D. G. Allelochemicals produced by brown macroalgae of the lobophora genus are active against coral larvae and associated bacteria, supporting pathogenic shifts to vibrio dominance. Appl. Environ. Microb. https://doi.org/10.1128/AEM.02391-16 (2017).
    Article  Google Scholar 

    42.
    Neave, M. J., Michell, C. T., Apprill, A. & Voolstra, C. R. Endozoicomonas genomes reveal functional adaptation and plasticity in bacterial strains symbiotically associated with diverse marine hosts. Sci. Rep. 7, 40579. https://doi.org/10.1038/srep40579 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    43.
    Cardenas, A. et al. Excess labile carbon promotes the expression of virulence factors in coral reef bacterioplankton. Isme J. 12, 59–76. https://doi.org/10.1038/ismej.2017.142 (2018).
    CAS  Article  PubMed  Google Scholar 

    44.
    Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. https://doi.org/10.1038/S41467-018-07275-X (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    45.
    Cardenas, A., Rodriguez, L. M., Pizarro, V., Cadavid, L. F. & Arevalo-Ferro, C. Shifts in bacterial communities of two caribbean reef-building coral species affected by white plague disease. Isme J. 6, 502–512. https://doi.org/10.1038/ismej.2011.123 (2012).
    CAS  Article  PubMed  Google Scholar 

    46.
    Gajigan, A. P., Diaz, L. A. & Conaco, C. Resilience of the prokaryotic microbial community of Acropora digitifera to elevated temperature. Microbiologyopen https://doi.org/10.1002/mbo3.478 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    47.
    Shnit-Orland, M., Sivan, A. & Kushmaro, A. Shewanella corallii sp. nov., a marine bacterium isolated from a Red Sea coral. Int. J. System. Evol. Microbiol. 60, 2293–2297. https://doi.org/10.1099/ijs.0.015768-0 (2010).
    CAS  Article  Google Scholar 

    48.
    Ziegler, M. et al. Coral microbial community dynamics in response to anthropogenic impacts near a major city in the central Red Sea. Mar. Pollut. Bull. 105, 629–640. https://doi.org/10.1016/j.marpolbul.2015.12.045 (2016).
    CAS  Article  PubMed  Google Scholar 

    49.
    Paramasivam, N. et al. Bacterial Consortium of Millepora dichotoma exhibiting unusual multifocal lesion event in the gulf of Eilat Red Sea. Microb Ecol 65, 50–59. https://doi.org/10.1007/s00248-012-0097-8 (2013).
    Article  PubMed  Google Scholar 

    50.
    Paramasivam, N., Ben-Dov, E., Arotsker, L. & Kushmaro, A. Eilatimonas milleporae gen. nov., sp. nov., a marine bacterium isolated from the hydrocoral Millepora dichotoma. Int. J. Syst. Evol. Microbiol. 63, 1880–1884. https://doi.org/10.1099/ijs.0.043976-0 (2013).
    CAS  Article  PubMed  Google Scholar 

    51.
    Spring, S., Lunsdorf, H., Fuchs, B. M. & Tindall, B. J. The photosynthetic apparatus and its regulation in the aerobic Gammaproteobacterium Congregibacter litoralis gen. nov., sp nov. PLoS ONE https://doi.org/10.1371/journal.pone.0004866 (2009).
    Article  PubMed  PubMed Central  Google Scholar 

    52.
    Roder, C. et al. Bacterial profiling of White Plague Disease in a comparative coral species framework. Isme J. 8, 31–39. https://doi.org/10.1038/ismej.2013.127 (2014).
    CAS  Article  PubMed  Google Scholar 

    53.
    Sekar, R., Mills, D. K., Remily, E. R., Voss, J. D. & Richardson, L. L. Microbial communities in the surface mucopolysaccharide layer and the black band microbial mat of black band-diseased Siderastrea siderea. Appl. Environ. Microbiol. 72, 5963–5973. https://doi.org/10.1128/AEM.00843-06 (2006).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    54.
    Blackall, L. L., Wilson, B. & van Oppen, M. J. Coral-the world’s most diverse symbiotic ecosystem. Mol. Ecol. 24, 5330–5347. https://doi.org/10.1111/mec.13400 (2015).
    Article  PubMed  Google Scholar 

    55.
    LaJeunesse, T. C. “Species” radiations of symbiotic Dinoflagellates in the Atlantic and Indo-Pacific since the Miocene-Pliocene transition (vol 22, pg 570, 2005). Mol. Biol. Evol. 22, 1158–1158. https://doi.org/10.1093/molbev/msi042 (2005).
    CAS  Article  Google Scholar 

    56.
    Hume, B. C. et al. Ancestral genetic diversity associated with the rapid spread of stress-tolerant coral symbionts in response to Holocene climate change. Proc. Natl. Acad. Sci. USA 113, 4416–4421. https://doi.org/10.1073/pnas.1601910113 (2016).
    ADS  CAS  Article  PubMed  Google Scholar 

    57.
    Thornhill, D. J., Lewis, A. M., Wham, D. C. & LaJeunesse, T. C. Host-specialist lineages dominate the adaptive radiation of reef coral endosymbionts. Evolution 68, 352–367. https://doi.org/10.1111/evo.12270 (2014).
    CAS  Article  PubMed  Google Scholar  More