More stories

  • in

    Divergent nucleic acid allocation in juvenile insects of different metamorphosis modes

    Sample collectionSamples of insects for the analyses of NAs were collected in four basins of Sierra Nevada National Park, southern Spain (Supplementary Fig. S1). Sample sites covered different spatial and temporal scales of investigation: three sampling stations across an elevational gradient and two sampling periods in spring and autumn of 2015. Given that all samples were collected from similar environments, the effect of abiotic conditions was not considered crucial for testing NAs in insects. Samples of aquatic insects were collected using a kick sampler (250 µm mesh size) by removing the substrate from at least 20 sample units (total area of 2.5 m2) taken on each station and date and distributed randomly in proportion to the occurrence of major stream habitats (i.e. rapid and slow flow, gravel, sand, zones proximal to and distant from shore and vegetated areas). All samples from each station were pooled and individuals representing six hemimetabolous and six holometabolous taxa sorted specifically for the analysis of nucleic acids (Table 1). We refer to taxa as a generic term to designate a group of one or more populations of organisms that were identified to the lowest taxonomic level possible by eye. Thus, most taxa were identified to the species (Dinocras cephalotes, and Perla marginata) or genus level (Baetis sp., Ecdyonurus sp., Epeorus sp., Rhithrogena sp., Hydropsyche sp., and Rhyacophila sp.), except for Lepidostomatidae, Limnephilidae, Brachycentridae, and Simuliidae that were identified to the family level. Although taxonomic resolution in the identification varied, taxa identified at the species and genus level represented the majority of the samples in this study. In addition, morphologically similar animals were selected for all supraspecific taxa in order to represent similar morphospecies for each taxon. When possible, up to 20 individuals per taxa that covered the full size spectrum available for each taxon were sorted into 10-mL vials containing RNAlater (Ambion Inc., Austin, Texas, USA), and transported inside a cooler to the laboratory. There, all insect samples were stored at − 80 °C until prepared for analysis. Before processing the insects, we measured body length to the nearest half millimetre under a stereoscopic microscope and verified the insect’s identity. In total, 639 individuals of 12 different taxa (six hemimetabolans and six holometabolans) were measured and analysed for NA content.Nucleic acid analysisNA analyses largely followed the methods by Wagner et al.13 with a number of recommendations by Gorokhova & Kyle25 and Bullejos et al.26. Analyses were carried out on insect legs and/or heads except for Simuliidae, where entire individuals were analysed. Preliminary analyses using legs and heads for a given individual showed that the coefficient of variation in RNA and DNA content rarely exceeded 5% (Supplementary Table S1). For the calculation of dry weight of insects where legs (one to three) were analysed, the opposite legs and the remaining body parts were separately weighed to estimate total body dry mass (total body weight = legs dry weight * 2 + remaining body parts dry weight). For the estimation of dry weight of insects where heads or the entire body were analysed, body length–weight relationships were specifically developed for each taxon in this study (Supplementary Table S2). Dry-weight was estimated by drying samples to constant weight in preweighed aluminium capsules and reweighing them with a Mettler UMT2 microbalance (± 0.1 µg; Mettler Toledo, Im Langacher, Switzerland).NAs were measured using a microplate fluorimetric high-range assay Ribo-Green assay (Initrogen, Carlsbad, California, USA) after N-laurylsarcosine extraction and RNase digestion, as described in Gorokhova & Kyle25. We used the following reagents: RiboGreenTM RNA Quantitation Kit (Invitrogen Corporation, Carlsbad, California, USA); RNase DNasefree (working solution 5 mg mL21; Q-biogen, Weston, Massachusetts, USA); N-lauroysarcosine (Sigma-Aldrich, Saint Louis, Missouri, USA); Tris-EDTA buffer (Q-biogene). Fluorescence measurements were performed using a FLUOstar Optima fluorometer (microplate reader, filters: 485 nm for excitation and 520 nm for emission; BMG Labtechnologies, Ortenberg, Germany) and black solid flat-bottom microplates (Greiner Bio-One GmbH, Frickenhausen, Germany). The plate was scanned with a 0.2-s well measurement time, making 10 measurements per well, before and after RNase digestion (30 min under dark conditions at 37 °C). Fluorescence measurements were converted into RNA and DNA concentrations (pg) by using standard curves for RNA (16S and 23S from Escherichia coli, component C of the RiboGreen Kit) and DNA (calf thymus; Sigma-Aldrich), and expressed as a percentage of body dry mass (%RNA and %DNA).Animal genome size databaseTo test the generality of our hypothesis that DNA size varied between insect metamorphosis modes across taxa and environments (terrestrial and aquatic), we incorporated the Animal Genome Size Database by Gregory16 in our analysis. The dataset covers a variety of insect groups (including 140 families and 20 orders of hemimetabolous and holometabolous insects) with a representation of most functional feeding groups, life cycles, and trait-based morphologies, comprising a total of 336 hemimetabolous and 999 holometabolous insect records.Statistical analysisTesting for differences in NAs between metamorphosis modes, we found that data were not normally distributed (Shapiro–Wilk’s W test) and could not be transformed to fit a normal distribution, so differences in NAs were tested using generalized linear mixed effects models (GLMM). Models included body length and metamorphosis mode as fixed factors, and insect taxa nested within order as random factors to account for variability within taxa subgroups. The significance of the interaction between body length and metamorphosis mode was used to test whether NA allocation to RNA differed during the ontogenetic development of animals. To examine whether insect genome size (C-value) varied between holo- and hemimetabolans using Gregory’s genome size dataset, a GLMM was also used with metamorphosis mode set as a predictor and taxa nested within order as a random variable. Before performing the models, the data were standardized (Deconstand function in R) to provide meaningful estimates of main effects in models with interaction terms27 and the best GLMM was selected according to deviance information criteria28. GLMM analyses were conducted using the ‘glmer’ function in the package ‘lme4’29. Finally, because NA data for taxon subsets were normally distributed after a log-transformation, linear-regression models were used to test the relationship between RNA and body length for each taxon. All statistical analyses were made in R30. More

  • in

    Wild Bornean orangutans experience muscle catabolism during episodes of fruit scarcity

    1.Fleming, T. H., Breitwisch, R. & Whitesides, G. Patterns of tropical vertebrate frugivore diversity. Annu. Rev. Ecol. Syst. 18, 91–109 (1987).Article 

    Google Scholar 
    2.van Schaik, C. P. & Pfannes, K. R. Tropical climates and phenology: a primate perspective. In Seasonality in Primates: Studies of Living and Extinct Human and Non-Human Primates (eds Brockman, D. K. & van Schaik, C. P.) (Cambridge University Press, 2005).
    Google Scholar 
    3.Marshall, A. J., Boyko, C. M., Feilen, K. L., Boyko, R. H. & Leighton, M. Defining fallback foods and assessing their importance in primate ecology and evolution. Am. J. Phys. Anthropol. 140, 603–614 (2009).PubMed 
    Article 

    Google Scholar 
    4.Wich, S. A. et al. Forest fruit production is higher on Sumatra Than on Borneo. PLoS ONE 6, e21278 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Meiri, S., Meijaard, E., Wich, S. A., Groves, C. P. & Helgen, K. M. Mammals of Borneo: small size on a large island. J. Biogeogr. 35, 1087–1094 (2008).Article 

    Google Scholar 
    6.Vogel, E. R. et al. Functional ecology and evolution of hominoid molar enamel thickness: Pan troglodytes schweinfurthii and Pongo pygmaeus wurmbii. J. Hum. Evol. 55, 60–74 (2008).PubMed 
    Article 

    Google Scholar 
    7.Vogel, E. R. et al. Nutritional ecology of wild Bornean orangutans (Pongo pygmaeus wurmbii) in a peat swamp habitat: effects of age, sex, and season. Am. J. Primatol. 79, e22618 (2017).Article 

    Google Scholar 
    8.Vogel, E. R. et al. Food mechanical properties, feeding ecology, and the mandibular morphology of wild orangutans. J. Hum. Evol. 75, 110–124 (2014).PubMed 
    Article 

    Google Scholar 
    9.Knott, C. D. Changes in orangutan caloric intake, energy balance, and ketones in response to fluctuating fruit availability. Int. J. Primatol. 19, 1061–1079 (1998).Article 

    Google Scholar 
    10.Harrison, M. E., Morrogh-Bernard, H. C. & Chivers, D. J. Orangutan energetics and the influence of fruit availability in the nonmasting peat-swamp forest of Sabangau, Indonesian Borneo. Int. J. Primatol. 31, 585–607 (2010).Article 

    Google Scholar 
    11.van Schaik, C. P. The socioecology of fission-fusion sociality in Orangutans. Primates 40, 69–86 (1999).PubMed 
    Article 

    Google Scholar 
    12.Taylor, A. B., Vogel, E. R. & Dominy, N. J. Food material properties and mandibular load resistance abilities in large-bodied hominoids. J. Hum. Evol. 55, 604–616 (2008).PubMed 
    Article 

    Google Scholar 
    13.Pontzer, H., Raichlen, D. A., Shumaker, R. W., Ocobock, C. & Wich, S. A. Metabolic adaptation for low energy throughput in orangutans. Proc. Natl. Acad. Sci. 107, 14048–14052 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Nie, Y. et al. Exceptionally low daily energy expenditure in the bamboo-eating giant panda. Science 349, 171–174 (2015).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Zihlman, A. L., Mcfarland, R. K. & Underwood, C. E. Functional anatomy and adaptation of male gorillas (Gorilla gorilla gorilla) with comparison to male orangutans (Pongo pygmaeus). Anat. Rec. 294, 1842–1855 (2011).Article 

    Google Scholar 
    16.Gresl, T. A., Baum, S. T. & Kemnitz, J. W. Glucose regulation in captive Pongo pygmaeus abeli, P. p. pygmaeus, and P. p. abeli x P. p. pygmaeus orangutans. Zoo Biol. 19, 193–208 (2000).CAS 
    Article 

    Google Scholar 
    17.Jones, M. L. The orang utan in captivity. In The Orang Utan: Its Biology and Conservation (ed. de Boer, L. E. M.) 17–37 (Dr. W. Junk Publishers, 1982).
    Google Scholar 
    18.Knott, C. D. Energetic responses to food availability in the great apes: implications for hominin evolution. In Seasonality in Primates: Studies of Living and Extinct Human and Non-Human Primates (eds Brockman, D. K. & van Schaik, C. P.) 351–378 (Cambridge University Press, 2005).
    Google Scholar 
    19.Knott, C. D. Reproductive, physiological and behavioral responses of orangutans in Borneo to fluctuations in food availability. Doctoral Dissertation (Harvard University, 1999).20.Emery Thompson, M. & Knott, C. D. Urinary C-peptide of insulin as a non-invasive marker of energy balance in wild orangutans. Horm. Behav. 53, 526–535 (2008).PubMed 
    Article 
    CAS 

    Google Scholar 
    21.Vogel, E. R. et al. A noninvasive method for estimating nitrogen balance in free-ranging primates. Int. J. Primatol. 33, 567–587 (2012).Article 

    Google Scholar 
    22.Vogel, E. R. et al. Bornean orangutans on the brink of protein bankruptcy. Biol. Lett. 8, 333–336 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Wheatley, B. P. The evolution of large body size in orangutans: a model for hominoid divergence. Am. J. Primatol. 13, 313–324 (1987).PubMed 
    Article 

    Google Scholar 
    24.Emery Thompson, M., Muller, M. N. & Wrangham, R. W. Technical note: variation in muscle mass in wild chimpanzees: application of a modified urinary creatinine method. Am. J. Phys. Anthropol. 149, 622–627 (2012).PubMed 
    Article 

    Google Scholar 
    25.Emery Thompson, M. et al. Evaluating the impact of physical frailty during ageing in wild chimpanzees (Pan troglodytes schweinfurthii). Philos. Trans. R. Soc. B Biol. Sci. 375, 20190607 (2020).Article 

    Google Scholar 
    26.Bergstrom, M. L., Thompson, M. E., Melin, A. D. & Fedigan, L. M. Using urinary parameters to estimate seasonal variation in the physical condition of female white-faced capuchin monkeys (Cebus capucinus imitator). Am. J. Phys. Anthropol. 163, 707–715 (2017).PubMed 
    Article 

    Google Scholar 
    27.Markham, R. & Groves, C. P. Brief communication: weights of wild orang utans. Am. J. Phys. Anthropol. 81, 1–3 (1990).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Rayadin, Y. & Spehar, S. N. Body mass of wild bornean orangutans living in human-dominated landscapes: implications for understanding their ecology and conservation. Am. J. Phys. Anthropol. 157, 339–346 (2015).PubMed 
    Article 

    Google Scholar 
    29.Vogel, E. R. et al. The power of protein: protein regulation, energetics, and health in wild Bornean orangutan (Pongo pygmaeus wurmbii). Am. J. Phys. Anthropol. 162, 397 (2017).Article 

    Google Scholar 
    30.Ashton, P. S., Givnish, T. J. & Appanah, S. Staggered flowering in the dipterocarpaceae: new insights into floral induction and the evolution of mast fruiting in the aseasonal tropics. Am. Nat. 132, 44–66 (1988).Article 

    Google Scholar 
    31.Cannon, C. H., Curran, L. M., Marshall, A. J. & Leighton, M. Long-term reproductive behaviour of woody plants across seven Bornean forest types in the Gunung Palung National Park (Indonesia): suprannual synchrony, temporal productivity and fruiting diversity. Ecol. Lett. 10, 956–969 (2007).PubMed 
    Article 

    Google Scholar 
    32.Vogel, E. R. et al. Nutritional differences between two orangutan habitats: implications for population density. PLoS ONE 10, e0138612 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    33.Wich, S. A. et al. Life history of wild Sumatran orangutans (Pongo abelii). J. Hum. Evol. 47, 385–398 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    34.van Noordwijk, M. A. et al. The slow ape: high infant survival and long interbirth intervals in wild orangutans. J. Hum. Evol. 125, 38–49 (2018).PubMed 
    Article 

    Google Scholar 
    35.Finney, H., Newman, D., Thakkar, H., Fell, J. & Price, C. Reference ranges for plasma cystatin C and creatinine measurements in premature infants, neonates, and older children. Arch. Dis. Child. 82, 71–75 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Stonestreet, B. S., Bell, E. F. & Oh, W. Validity of endogenous creatinine clearance in low birthweight infants. Pediatr. Res. 13, 1012–1014 (1979).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    37.Kingsley, S. Causes of non-breeding and the development of the secondary sexual characteristics in the male orangutan: a hormonal study. In The Orang-Utan, Its Biology and Conservation (ed. de Boer, L. E. M.) 215–229 (Dr. W. Junk Publishers, 1982).38.Maggioncalda, A. N., Sapolsky, R. M. & Czekala, N. M. Reproductive hormone profiles in captive male orangutans: implications for understanding developmental arrest. Am. J. Phys. Anthropol. 109, 19–32 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    39.Marty, P. R. et al. Endocrinological correlates of male bimaturism in wild Bornean orangutans. Am. J. Primatol. 77, 1170–1178 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Bhasin, S. et al. The effects of supraphysiologic doses of testosterone on muscle size and strength in normal men. N. Engl. J. Med. 335, 1–7 (1996).CAS 
    PubMed 
    Article 

    Google Scholar 
    41.Buckley, B. J. W. Ranging behaviour of male Orang-Utans in an unfragmented Bornean habitat and implications for mating-system mechanics. Doctoral Dissertation (Cambridge University, 2015).42.Dunkel, L. P. et al. Variation in developmental arrest among male orangutans: a comparison between a Sumatran and a Bornean population. Front. Zool. 10, 1–11 (2013).Article 

    Google Scholar 
    43.Kleiber, M. Body size and metabolic rate. Physiol. Rev. 27, 511–541 (1947).CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    44.Chapman, S. et al. Compounding impact of deforestation on Borneo’s climate during El Niño events. Environ. Res. Lett. 15, 084006 (2020).ADS 
    Article 

    Google Scholar 
    45.Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    46.R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2019).
    Google Scholar  More

  • in

    Behavioural and oceanographic isolation of an island-based jellyfish (Copula sivickisi, Class Cubozoa) population

    Mapping the populationThe geographic extent of the Copula sivickisi population inhabiting the east coast of Magnetic Island was mapped in the 2017 medusae season (September to November) with underwater Jellyfish Camera units (JCams; Fig. 1b;9). The photopositive C. sivickisi medusae were attracted to the light on each JCam and recorded by the adjacent camera in 30-min deployments. Following established methods for managing potential repeat counts25, the abundance of medusae was measured by counting the maximum number of medusae in any single frame of video during the deployment period (Nmax).Figure 1The Copula sivickisi population at Magnetic Island (MI) in the Townsville (TSV) region (star), Queensland, Australia. (a) Population structure analysis (green colour scale). The simulated export of C. sivickisi from MI, i.e., the tracks of the C. sivickisi medusae lost from MI reefs as adults during the 2017 season ( 33% substrate coverage by Sargassum sp. and coral) following9, and the presence of reefal habitat at the sites was later confirmed from the JCam footage. Each bay/reef, excluding Geoffrey Bay, was sampled two times over six non-consecutive nights from the 25th of September to the 30th of October. Two sites within Geoffrey Bay, randomly placed near the C. sivickisi hotspot identified in9, were sampled on each of the 6 trips to confirm the presence of C. sivickisi medusae throughout the sampling period. Geoffrey Bay was sampled at a lower spatial resolution compared to the other bays/reefs (2 sites compared to 6 sites) because we could be confident in detecting medusae in Geoffrey Bay given the information derived from the comprehensive sampling of the bay in previous medusae seasons (presented in9). Further, a higher temporal resolution (6 trips to Geoffrey Bay compared to two for the other bays/reefs) was required to verify that C. sivickisi were present during the entire sampling period, and this had associated logistical constraints such as the weather.Biophysical modellingHydrodynamic descriptionThe two-dimensional version of the Second-generation Louvain-la-Neuve Ice-ocean Model [SLIM; 26] was used to model the currents off the eastern coast of Magnetic Island and in the surrounding region. A detailed description of SLIM and the specifics of its application in this study is presented in the supplementary information. Magnetic Island lies in the central area of the Great Barrier Reef (GBR). The currents in the GBR system are largely driven by the jets of the South Equatorial Current (SEC) which flow westward across the Coral Sea and collide with the outer reefs of the GBR27. In the central GBR, the North Caledonian Jet (NCJ) from the SEC generally diverges around the Queensland Plateau before meeting the outer reefs27. In addition to these regional scale forcings, the waters within the GBR system are shallow (mostly  5 mm in diameter (half their maximum size6), are generally sexually mature20, and C. sivickisi medusae would take around 25 days to grow to 5 mm [unpublished data]. To generate the measure of relative connectivity, a log base 10 transformation was performed on the counts + 1 data, and the transformed data was scaled from 0 (no connections) to 1 (most connections).The potential for connectivity between the island and mainland populations, and thereby the potential for Magnetic Island to represent an isolated stock (aims 4), was assessed by tracking the positions of all adult medusae lost from habitat at/near Magnetic Island through time. The combined trajectories showed the maximum extent of the emigration plume from the Magnetic Island population.Ethics approvalThis work was supported by the ARC Centre of Excellence for Coral Reef Studies and the Australian Lions Foundation. It was conducted in accordance with James Cook University’s Animal Ethics Committee’s policies, procedures and guidelines. No specific permissions were required. More

  • in

    DOM degradation by light and microbes along the Yukon River-coastal ocean continuum

    1.Holmes, R. M. et al. Seasonal and annual fluxes of nutrients and organic matter from large rivers to the arctic ocean and surrounding seas. Estuaries Coasts 35(2), 369–382 (2011).Article 
    CAS 

    Google Scholar 
    2.Peterson, B.J., Holmes, R.M., McClelland, J.W., Vörösmarty, C.J., Lammers, R.B., Shiklomanov, A. et al. Increasing river discharge to the Arctic Ocean. Science 298, 2171-2173 (2002).
    3.McClelland, J.W., Déry, S.J., Peterson, B.J., Holmes, R.M., Wood, E.F. A pan-arctic evaluation of changes in river discharge during the latter half of the 20th century. Geophys. Res. Lett. 33(6), (2006).4.Spencer, R. G. M. et al. Detecting the signature of permafrost thaw in Arctic rivers. Geophys. Res. Lett. 42(8), 2830–2835 (2015).ADS 
    Article 

    Google Scholar 
    5.O’Donnell, J. A. et al. Dissolved organic matter composition of Arctic rivers: Linking permafrost and parent material to riverine carbon. Glob. Biogeochem. Cycles 30(12), 1811–1826 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Kirchman, D. L., Malmstrom, R. R. & Cottrell, M. T. Control of bacterial growth by temperature and organic matter in the Western Arctic. Deep Sea Res. Part II 52(24–26), 3386–3395 (2005).ADS 
    Article 

    Google Scholar 
    7.Mann, P.J., Davydova, A., Zimov, N., Spencer, R.G.M., Davydov, S., Bulygina, E. et al. Controls on the composition and lability of dissolved organic matter in Siberia’s Kolyma River basin. J. Geophys. Res. Biogeosci. 117(G1), (2012).8.Crump, B. C., Kling, G. W., Bahr, M. & Hobbie, J. E. Bacterioplankton community shifts in an arctic lake correlate with seasonal changes in organic matter source. Appl. Environ. Microbiol. 69(4), 2253–2268 (2003).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Docherty, K. M., Young, K. C., Maurice, P. A. & Bridgham, S. D. Dissolved organic matter concentration and quality influences upon structure and function of freshwater microbial communities. Microb. Ecol. 52(3), 378–388 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    10.Elifantz, H., Malmstrom, R. R., Cottrell, M. T. & Kirchman, D. L. Assimilation of polysaccharides and glucose by major bacterial groups in the Delaware Estuary. Appl. Environ. Microbiol. 71(12), 7799–7805 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Nalven, S. G. et al. Experimental metatranscriptomics reveals the costs and benefits of dissolved organic matter photo-alteration for freshwater microbes. Environ. Microbiol. 22(8), 3505–3521 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    12.Ward, C. P. & Cory, R. M. Complete and partial photo-oxidation of dissolved organic matter draining permafrost soils. Environ. Sci. Technol. 50(7), 3545–3553 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Ward, C. P., Nalven, S. G., Crump, B. C., Kling, G. W. & Cory, R. M. Photochemical alteration of organic carbon draining permafrost soils shifts microbial metabolic pathways and stimulates respiration. Nat. Commun. 8(1), 772 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    14.Kaiser, K., Canedo-Oropeza, M., McMahon, R. & Amon, R. M. W. Origins and transformations of dissolved organic matter in large Arctic rivers. Sci. Rep. 7(1), 13064 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Soares, A. R. A., Lapierre, J. F., Selvam, B. P., Lindstrom, G. & Berggren, M. Controls on dissolved organic carbon bioreactivity in river systems. Sci. Rep. 9(1), 14897 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    16.Pegau, W.S. Inherent optical properties of the central Arctic surface waters. J. Geophys. Res. Oceans, 107(C10), SHE-16 (2002).17.Kim, G. E., Pradal, M.-A. & Gnanadesikan, A. Increased surface ocean heating by colored detrital matter (CDM) linked to greater Northern Hemisphere ice formation in the GFDL CM2Mc ESM. J. Clim. 29(24), 9063–9076 (2016).ADS 
    Article 

    Google Scholar 
    18.Laglera, L. M. et al. First quantification of the controlling role of Humic substances in the transport of iron across the surface of the Arctic Ocean. Environ. Sci. Technol. 53(22), 13136–13145 (2019).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Charette, M.A., Kipp, L.E., Jensen, L.T., Dabrowski, J.S., Whitmore, L.M., Fitzsimmons, J.N. et al. The transpolar drift as a source of riverine and shelf‐derived trace elements to the Central Arctic Ocean. J. Geophys. Res. Oceans 125(5), (2020). https://doi.org/10.1029/2019JC015920.20.Amon, R. M. W. et al. Dissolved organic matter sources in large Arctic rivers. Geochim. Cosmochim. Acta 94, 217–237 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Spencer, R.G.M., Aiken, G.R., Wickland, K.P., Striegl, R.G., Hernes, P.J. Seasonal and spatial variability in dissolved organic matter quantity and composition from the Yukon River basin, Alaska. Glob. Biogeochem. Cycles 22(4), (2008).22.Mann, P.J., Spencer, R.G.M., Hernes, P.J., Six, J., Aiken, G.R., Tank, S.E., et al. Pan-Arctic trends in terrestrial dissolved organic matter from optical measurements. Front. Earth Sci. 4(25), (2016).23.Hernes, P. J. & Benner, R. Terrigenous organic matter sources and reactivity in the North Atlantic Ocean and a comparison to the Arctic and Pacific oceans. Mar. Chem. 100(1–2), 66–79 (2006).CAS 
    Article 

    Google Scholar 
    24.Catalá, T.S., Reche, I., Fuentes-Lema, A., Romera-Castillo, C., Nieto-Cid, M., Ortega-Retuerta, E., et al. Turnover time of fluorescent dissolved organic matter in the dark global ocean. Nature communications 6(1), 1–9 (2015).25.Colatriano, D., Tran, P.Q., Guéguen, C., Williams, W.J., Lovejoy, C., Walsh, D.A. Genomic evidence for the degradation of terrestrial organic matter by pelagic Arctic Ocean Chloroflexi bacteria. Commun. Biol. 1(1), 1–9 (2018).26.Müller, O., Seuthe, L., Bratbak, G., Paulsen, M.L. Bacterial response to permafrost derived organic matter input in an Arctic Fjord. Front. Mar. Sci. 5(263), (2018).27.Kujawinski, E.B., Longnecker, K., Barott, K.L., Weber, R.J.M., Kido Soule, M.C. Microbial community structure affects marine dissolved organic matter composition. Front. Mar. Sci. 3(45), (2016).28.Avila, M. P. et al. Linking shifts in bacterial community with changes in dissolved organic matter pool in a tropical lake. Sci. Total Environ. 672, 990–1003 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    29.Elifantz, H., Dittel, A. I., Cottrell, M. T. & Kirchman, D. L. Dissolved organic matter assimilation by heterotrophic bacterial groups in the western Arctic Ocean. Aquat. Microb. Ecol. 50, 39–49 (2007).Article 

    Google Scholar 
    30.Lee, J. et al. Latitudinal distributions and controls of bacterial community composition during the summer of 2017 in Western Arctic Surface Waters (from the Bering Strait to the Chukchi Borderland). Sci. Rep. 9(1), 16822 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    31.Fortunato, C. S. & Crump, B. C. Bacterioplankton community variation across river to ocean environmental gradients. Microb. Ecol. 62(2), 374–382 (2011).PubMed 
    Article 

    Google Scholar 
    32.Balmonte, J. P. et al. Sharp contrasts between freshwater and marine microbial enzymatic capabilities, community composition, and DOM pools in a NE Greenland fjord. Limnol. Oceanogr. 65(1), 77–95 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    33.Sipler, R. E. et al. Microbial community response to terrestrially derived dissolved organic matter in the Coastal Arctic. Front. Microbiol. 8, 1018 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Scully, N. M., Cooper, W. J. & Tranvik, L. J. Photochemical effects on microbial activity in natural waters: the interaction of reactive oxygen species and dissolved organic matter. FEMS Microbiol. Ecol. 46(3), 353–357 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Bélanger, S., Xie, H., Krotkov, N., Larouche, P., Vincent, W.F., Babin, M. Photomineralization of terrigenous dissolved organic matter in Arctic coastal waters from 1979 to 2003: Interannual variability and implications of climate change. Glob. Biogeochem. Cycles 20(4), (2006).36.Timko, S.A., Maydanov, A., Pittelli, S.L., Conte, M.H., Cooper, W.J., Koch, B.P. et al. Depth-dependent photodegradation of marine dissolved organic matter. Front. Mar. Sci. 2(66) (2015).37.Pisani, O., Yamashita, Y. & Jaffe, R. Photo-dissolution of flocculent, detrital material in aquatic environments: contributions to the dissolved organic matter pool. Water Res. 45(13), 3836–3844 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Coble, P. G. Characterization of marine and terrestrial DOM in seawater using excitation-emission matrix spectroscopy. Mar. Chem. 51, 325–346 (1996).CAS 
    Article 

    Google Scholar 
    39.Kothawala, D. N. et al. Controls of dissolved organic matter quality: Evidence from a large-scale boreal lake survey. Glob. Chang Biol. 20(4), 1101–1114 (2014).ADS 
    PubMed 
    Article 

    Google Scholar 
    40.Paerl, R. W., Claudio, I. M., Shields, M. R., Bianchi, T. S. & Osburn, C. L. Dityrosine formation via reactive oxygen consumption yields increasingly recalcitrant humic-like fluorescent organic matter in the ocean. Limnol. Oceanogr. Lett. 5(5), 337–345 (2020).CAS 
    Article 

    Google Scholar 
    41.Spencer, R.G.M., Aiken, G.R., Butler, K.D., Dornblaser, M.M., Striegl, R.G., Hernes, P.J. Utilizing chromophoric dissolved organic matter measurements to derive export and reactivity of dissolved organic carbon exported to the Arctic Ocean: A case study of the Yukon River, Alaska. Geophys. Res. Lett. 36(6), (2009).42.Maie, N., Scully, N. M., Pisani, O. & Jaffe, R. Composition of a protein-like fluorophore of dissolved organic matter in coastal wetland and estuarine ecosystems. Water Res. 41(3), 563–570 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Hernes, P.J., Bergamaschi, B.A., Eckard, R.S., Spencer, R.G.M. Fluorescence-based proxies for lignin in freshwater dissolved organic matter. J. Geophys. Res. 114(G4) (2009).44.Murphy, K. R., Stedmon, C. A., Wenig, P. & Bro, R. OpenFluor—An online spectral library of auto-fluorescence by organic compounds in the environment. Anal. Methods 6(3), 658–661 (2014).CAS 
    Article 

    Google Scholar 
    45.Lanzalunga, O. & Bietti, M. Photo- and radiation chemical induced degradation of lignin model compounds. J. Photochem. Photobiol. B 56(2–3), 85–108 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    46.Brym, A. et al. Optical and chemical characterization of base-extracted particulate organic matter in coastal marine environments. Mar. Chem. 162, 96–113 (2014).CAS 
    Article 

    Google Scholar 
    47.Tanentzap, A. J. et al. Chemical and microbial diversity covary in fresh water to influence ecosystem functioning. Proc. Natl. Acad. Sci. USA 116(49), 24689–24695 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Jørgensen, L., Stedmon, C. A., Granskog, M. A. & Middelboe, M. Tracing the long-term microbial production of recalcitrant fluorescent dissolved organic matter in seawater. Geophys. Res. Lett. 41(7), 2481–2488 (2014).ADS 
    Article 
    CAS 

    Google Scholar 
    49.McDonald, N., Achterberg, E.P., Carlson, C.A., Gledhill, M., Liu, S., Matheson-Barker, J.R. et al. The role of heterotrophic bacteria and archaea in the transformation of lignin in the open ocean. Front. Mar. Sci. 6(743), (2019).50.Zark, M. & Dittmar, T. Universal molecular structures in natural dissolved organic matter. Nat. Commun. 9(1), 3178 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    51.Harfmann, J. L. et al. Convergence of terrestrial dissolved organic matter composition and the role of microbial buffering in aquatic ecosystems. J. Geophys. Res. Biogeosci. 124(10), 3125–3142 (2019).CAS 
    Article 

    Google Scholar 
    52.Wünsch, U. J., Bro, R., Stedmon, C. A., Wenig, P. & Murphy, K. R. Emerging patterns in the global distribution of dissolved organic matter fluorescence. Anal. Methods 11(7), 888–893 (2019).Article 

    Google Scholar 
    53.Fitch, A., Orland, C., Willer, D., Emilson, E. J. S. & Tanentzap, A. J. Feasting on terrestrial organic matter: Dining in a dark lake changes microbial decomposition. Glob. Chang Biol. 24(11), 5110–5122 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Saw, J.H.W., Nunoura, T., Hirai, M., Takaki, Y., Parsons, R., Michelsen, M. et al. Pangenomics analysis reveals diversification of enzyme families and niche specialization in globally abundant SAR202 bacteria. mBio 11(1), (2020).55.Min, D. W. et al. Abiotic formation of humic-like substances through freezing-accelerated reaction of phenolic compounds and nitrite. Environ. Sci. Technol. 53(13), 7410–7418 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    56.Dagley, S. & Gibson, D. The bacterial degradation of catechol. Biochem. J. 95(2), 466–474 (1965).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.Kraepiel, A. M., Bellenger, J. P., Wichard, T. & Morel, F. M. Multiple roles of siderophores in free-living nitrogen-fixing bacteria. Biometals 22(4), 573–581 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    58.Stedmon, C. A. & Markager, S. Behaviour of the optical properties of coloured dissolved organic matter under conservative mixing. Estuar. Coast. Shelf Sci. 57(5–6), 973–979 (2003).ADS 
    CAS 
    Article 

    Google Scholar 
    59.Servais, P., Courties, C., Lebaron, P. & Troussellier, M. Coupling bacterial activity measurements with cell sorting by flow cytometry. Microb. Ecol. 38(2), 180–189 (1999).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Newton, R. J. & Shade, A. Lifestyles of rarity: Understanding heterotrophic strategies to inform the ecology of the microbial rare biosphere. Aquat. Microb. Ecol. 78(1), 51–63 (2016).Article 

    Google Scholar 
    61.Amado, A. M., Cotner, J. B., Cory, R. M., Edhlund, B. L. & McNeill, K. Disentangling the interactions between photochemical and bacterial degradation of dissolved organic matter: Amino acids play a central role. Microb. Ecol. 69(3), 554–566 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    62.Mestre, M., Borrull, E., Sala, M. & Gasol, J. M. Patterns of bacterial diversity in the marine planktonic particulate matter continuum. ISME J. 11(4), 999–1010 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Gundersen, K., Bratbak, G., Heldal, M. Factors influencing the loss of bacteria in preserved seawater samples. Marine ecology progress
    series 137, 305–310 (1996).64.Logozzo, L., Tzortziou, M., Neale, P., Clark, B. Photochemical and microbial degradation of chromophoric dissolved organic matter exported from tidal marshes. J. Geophys. Res. Biogeosci. 126, e2020JG005744. https://doi.org/10.1029/2020JG005744(2021).65.Tzortziou, M. et al. Tidal marshes as a source of optically and chemically distinctive colored dissolved organic matter in the Chesapeake Bay. Limnol. Oceanogr. 53(1), 148–159 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    66.Grunert B. bricegrunert/cdom: Version 1 (Version v1.0). 2020, December 23 (ed: Zenodo). https://doi.org/10.5281/zenodo.439109767.Green, S. A. & Blough, N. V. Optical absorption and fluorescence properties of chromophoric dissolved organic matter in natural waters. Limnol. Oceanogr. 39(8), 1903–1916 (1994).ADS 
    CAS 
    Article 

    Google Scholar 
    68.Weishaar, J. L. et al. Evaluation of specific ultraviolet absorbance as an indicator of the chemical composition and reactivity of dissolved organic carbon. Environ. Sci. Technol. 37(20), 4702–4708 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    69.Andersen, C. M. & Bro, R. Practical aspects of PARAFAC modeling of fluorescence excitation-emission data. J. Chemom. 17(4), 200–215 (2003).CAS 
    Article 

    Google Scholar 
    70.Bahram, M., Bro, R., Stedmon, C. & Afkhami, A. Handling of Rayleigh and Raman scatter for PARAFAC modeling of fluorescence data using interpolation. J. Chemom. 20(3–4), 99–105 (2006).CAS 
    Article 

    Google Scholar 
    71.Murphy, K.R., Stedmon, C.A., Graeber, D., Bro, R. Fluorescence spectroscopy and multi-way techniques. PARAFAC. Anal. Methods 5(23), 6557-6566 (2013). More

  • in

    Denitrifying bacteria respond to and shape microscale gradients within particulate matrices

    Nitrate (Nar) and nitrite (Nir) reductase expression in PAO1 were quantified with fluorescently tagged promoter fusions (Supplementary Fig. 1), green for the NarK subunit (NarK-GFP) and red for the NirS subunit (NirS-dsRed)35. As the first two steps in the denitrification pathway, expression of these genes indicates a metabolic switch from aerobic respiration with O2 to anaerobic respiration via denitrification. These PAO1 reporter strains were grown embedded within 3 mm diameter14 agarose particle discs held within a custom-built gastight millifluidic device (Fig. 1; Supplementary Methods), permitting continual lateral nutrient supply of rich nutrient media (with O2 and NO3–) from the bulk media via diffusion while maintaining a constant boundary condition at the particle periphery (Fig. 1a, b). This created an analog of a particle replete with dissolved organic nutrients whereby the agarose acted as an inert polymeric matrix rather than as a carbon source. Over ~24 h of growth in Luria-Bertani Broth (LB) media supplemented with NO3–, PAO1 formed densely packed stationary microcolonies (Supplementary Video 1), similar to its growth morphology in model alginate beads15,36 and agar blocks34. Seeding bacterial cells within a 1% agarose matrix allows colony expansion due only to growth (passive movement) and prevents active aerotactic and chemotactic movement driven by flagellar motility37. A subset of agarose particles contained biocompatible O2 nanosensors that could faithfully report O2 conditions varying dynamically over the scale of minutes (Supplementary Fig. 2).Fig. 1: Denitrifiers create anoxia within particles in fully aerated fluid.Agarose disc particles seeded with P. aeruginosa PAO1 were incubated with media in glass devices that permit only lateral diffusion into particles. a Topview and b sideview illustrating nutrient diffusion into particles as occurred in c, a millifluidic device with directional flow of air-saturated fluid, or a ‘domino’ device held within sealed glass bottles. Scale bar ~1.5 cm. a inset, PAO1 cells grew as dense microcolonies. d Air saturation across four particles as determined from microscopic signal of fluorescent oxygen nanosensors. Despite aerobic bulk fluid surrounding the particles, cell growth created anoxia and its onset depended on PAO1 seeding density, ranging 104–106 cells mL−1 (70–7000 cells particle−1; the 105 mL−1 cell density was tested for duplicate particles). Gray shading indicates time range shown in e and asterisks coincide with noted timepoints in e. e The air saturation for radial profiles across a single particle (105 cells mL−1 seeding) during the cellular respiration-driven transition from air-saturated to anoxic conditions. Suboxia first developed in the core (x  > 1000 μm) then spread to the particle periphery (x 103 microcolonies within each particle (e.g., Fig. 2). Both Nar and Nir expression were skewed toward the particle edge for lower bulk NO3– concentrations (Fig. 2b, c, Supplementary Fig. 4), indicative of a low NO3– flux reaching microcolonies in the particle core. In contrast, homogenous Nar expression occurred across all radial distances with 4 mM bulk NO3– (Fig. 2b), which suggests NO3– was nonlimiting for expression throughout the particle at such high bulk NO3–. Meanwhile, reduced Nir expression across the particle at 4 mM NO3– compared with lower NO3– treatments (Fig. 2c) indicates PAO1 preferentially reduced NO3– over NO2– throughout the particle under such NO3–-replete conditions. Bulk NO3– also influenced microcolony size distributions (Fig. 2b, c), with larger microcolonies manifesting near the periphery closer to the NO3– supply from the bulk fluid. Higher bulk NO3– stimulated greater microcolony growth in the particle core, i.e., at the center, the mean microcolony radius r was 6.4 ± 0.1 µm (sd) at 40 µM bulk NO3– but 12.7 ± 0.1 µm (sd) at 4 mM. Notably, under 4 mM bulk NO3–, size was skewed toward the particle periphery even while Nar expression was not, suggesting maximum cell-specific Nar expression rates occurred across all radial locations despite biomass production in the particle core remaining NO3– limited.Fig. 2: Denitrification gene expression and microcolony size within particles in fully anoxic fluid.a Example images showing PAO1 microcolonies expressing NarK-GFP (nitrate reductase) after 40 h of growth in bulk anoxic media amended with 3 nitrate concentrations. Scale bar = 700 μm. b Mean relative expression of NarK-GFP for microcolonies, and mean radii of those microcolonies, shown in relationship to the radial distance to the nearest particle edge. The mean NarK-GFP expression for microcolonies in each particle (3–4 replicates) is indicated by a thin green line and the mean for all particles by a thick line. Similarly, an exponential model fit of microcolony radii for each particle is indicated by a thin black line and the mean for all particles by a thick line inset, Results for one example particle in a, illustrating the data for n microcolonies relative to the respective fits for those data. c Same as b but for reporter strain NirS-dsRed (nitrite reductase) expression in separate particles incubated in parallel.Full size imageNot only did particle denitrification readily occur in anoxic bulk fluid as expected, it was also prevalent among particles in oxygenated bulk fluid. Moreover, the spatiotemporal expression of Nar and Nir coincided with the development and microscale distribution of suboxic conditions. In these experiments, agarose particles were embedded with either NarK-GFP or NirS-dsRed PAO1 (106 cells mL−1) as previously, then incubated in partially oxygenated LB media (50% air saturation) containing NO3– (40 µM). A subset of particles was co-embedded with oxygen nanosensors to enable imaging of the O2 landscape. Minimal Nar and Nir expression were detected for the first 14 h of incubation. Strikingly, Nar and Nir activation then occurred as a wave that traced the initiation and expansion of suboxia (Fig. 3a–c). At the earliest stage of the wave (following 16–18 h of growth), expression increased substantially in the vicinity of the particle core (Fig. 3d), crafting a transition zone of only 390 ± 50 µm (sd) width that differentiated low- from high-expression microcolonies (Fig. 3e, Supplementary Fig. 5, Supplementary Fig. 6). This upregulation of expression coincided spatially with the development of anoxia in the core (Fig. 4, Supplementary Fig. 7; (x) > 1000 µm). During the next stage of the wave (t = 20–22 h), expression near the core elevated further while also initiating farther afield, widening the transition zone and reflecting expansion of suboxia toward the particle periphery. As the wave progressed (t = 24–28 h), expression diminished near the core, and consequently, the transition zone width contracted (200 ± 15 µm (sd)) even while its outermost edge neared to within 200 µm of the particle edge, where O2 conditions had become anoxic (Fig. 4). Finally, in the final stage of the wave (exemplified at t = 40 h), high expression was confined to a fine band distantly from the core, yet not directly at the edge, as evidenced by a very narrow transition zone (57 ± 13 µm (sd) width). Notably, at the last stage, O2 throughout the particle was elevated above the minimal observed levels, in contrast to previous stages (t = 24–28 h; Fig. 4, Supplementary Fig. 7). This may reflect anoxic acclimation by PAO1 to preferentially perform denitrification over oxidative respiration thereby permitting O2 to diffuse more readily through the particle matrix.Fig. 3: Radial migration of denitrification expression within particles in partially aerated fluid.Particles seeded with cells were incubated in LB media saturated with 50% air and supplemented with 40 μM NO3–, then stopped at various timepoints. a Example images showing PAO1 microcolonies expressing NarK-GFP (nitrate reductase). Scale bar = 700 μm and applies to all images. Separate particles with the reporter strain for NirS-dsRed (nitrite reductase) were incubated in parallel. b Relative expression of NarK-GFP and c NirS-dsRed in particles. All microcolonies from 3–4 replicate particles per timepoint are represented. Nar expression initiates at the particle core while Nir expression initiates just proximal to it. For both Nar and Nir, maximal expression migrates outward creating a wave over subsequent timepoints. d Relative microcolony expression (as in b and c) shown as a microcolony’s radial location within the particle. An expression intensity fit was calculated as mean fits of each strain at each timepoint (Supplementary Note). Here the maximum fit value for each reporter strain at t = 24 h was set equal to 1, and shown are the expression values for all microcolonies relative to 1. e The radial location and range of the transition zone for each timepoint, approximated as the sloped region closest to the particle edge in the expression profile for NarK-GFP (green) and NirS-dsRed (red) (see also Supplementary Fig. 6). The midpoint of the slope (white diamonds) and the inflection point of the slope (circles) are indicated. NarK-GFP shows significantly higher expression between timepoints for midpoints (one-way ANOVA; F 1,35 = 27.9, p = 4.0 × 10−11) and for inflection points (one-way ANOVA; F 1,35 = 17.2, p = 9.5 × 10−9). NirS-dsRed also showed significantly higher expression between timepoints for midpoints (one-way ANOVA; F 1,35 = 14.9, p = 4.5 × 10−8) and for inflection points (one-way ANOVA; F 1,35 = 21.0, p = 1.0 × 10−9).Full size imageFig. 4: Evolution of anoxia within particles in partially aerated fluid.Two-dimensional profiles of air saturation were generated from particles co-seeded with the NarK-GFP reporter strain (nitrate reductase) and oxygen nanosensors. Scale bar = 700 μm. Separate analogous particles with the NirS-dsRed reporter strain (nitrite reductase) were incubated in parallel (Supplementary Fig. 7). For both, suboxic conditions develop in the particle core and then migrate outward toward the particle periphery over time.Full size imageThroughout the evolution of particle suboxia, Nar and Nir activity corresponded with the estimated spatiotemporal availability of O2 and NO3– in particles. Curve fits for microcolony fluorescence signal data were generated by assuming that O2 and NO3– concentrations at each radial location were the only controlling factors on expression, wherein O2 inhibits expression exponentially and NO3– has a directly proportional relationship, i.e., (Epropto {e}^{-k[{{mathrm{O}}}_{2}]}times [{mathrm{N}}{mathrm{{O}}}_{3}^{-}]). Approximating the distributions of O2 and NO3– in the particle as simple logistic functions, denitrification gene expression, E, is governed by the following relationship:$$E=alpha times {e}^{-beta left[2{[{{mathrm{O}}}_{2}]}_{{mathrm{bulk}}}left(1-frac{1}{1+{e}^{-gamma x}}right)right]}times {[{mathrm{N}}{{mathrm{O}}}_{3}^{-}]}_{{mathrm{bulk}}}left(1-frac{1}{1+{e}^{-delta (x-varepsilon )}}right)$$whereby α, β, γ, δ, and ε are fitting parameters and x is the distance to the particle edge. (Supplementary Note). The shape of this fit prediction matches the observed empirical data remarkably well (Fig. 3d, Supplementary Fig. 5), and the midpoints and inflection points from one timepoint to the next were significantly different (one-way ANOVA, F 1,8 = 27.9, p = 4.0 (times) 10−11 for Nar midpoints, F 1,8 = 14.9, p = 4.5 (times) 10−8 for Nir midpoints, F 1,8 = 17.2, p = 9.5 (times) 10−9 for Nar inflection points, and F 1,8 = 21.0, p = 1.0 (times) 10−9 for Nir inflection points). These fits illustrate how the response of PAO1 nar and nir gene expression reflects the balance between bacterial consumption and diffusion of O2 and NO3– from bulk surrounding fluid. In this manner, the fluorescence signal diminishes after achieving its peak and microcolonies remain small behind the advancing fluorescence wave while colonies continue to expand ahead of it (Supplementary Fig. 5, Supplementary Fig. 8). Nar was downregulated in the wake of the wave, causing microcolony expansion to slow or cease in the absence of respiration. Putatively, continued O2 and then NO3– uptake by large microcolonies at the periphery created growth limitation for microcolonies in their shadows farther from the bulk fluid source. Nir expression also advanced as a wave but interestingly created an annulus of maximal expression bounded by lower expression toward both the periphery and center of the particle. This expression pattern likely reflects localized production and utilization of NO2– within the particle interior. Exterior to the ring, ample NO3– from the bulk favored nitrate reductase, but interior to the ring, NO3– and NO2– were diffusion-limited.Importantly, the heterogeneous distribution of Nar and Nir expression across the particle also manifested among PAO1 cells at the scale of individual microcolonies. High magnification colony-scale images near the particle periphery and in the transition zone revealed a common phenotype reflecting the overall expression across the particle whereby the core of a single colony is expressive but the outer margin is not. As quantified for >103 microcolonies per particle, the subregion expressing Nar or Nir (i.e., “on”) varied with distance from the particle edge (Fig. 5). A thin radial zone within the particle (distance from the particle edge, x ~90–240 µm) harbored high heterogeneity with colonies ranging from 0–100% as shown (Fig. 5b, c). This narrow transition zone aligned with that quantified at lower magnification (Figs. 3, 4), indicative of a sharp transition from O2 to nitrate- and nitrite-driven respiration. In the flanking region exterior to this zone (x  240 µm), Nar was predominately on (median colony fraction expression = 0.78 ± 0.40 interquartile range; Fig. 5b) and Nir was almost completely on (median colony fraction expression = 0.95 ± 0.25 interquartile range; Fig. 5c). These expression characteristics resulted in a significantly stronger population bimodality for Nir than for Nar (Fig. 5d); Kolmogorov–Smirnov nonparametric test for probability distribution similarity, n1 = 1554, n2 = 1354, p = 1.2 (times) 10−40, Dn = 0.25. Akin to the annular feature observed at lower magnification (Fig. 3), this binary Nir expression likely reflects localized endogenous production and utilization of NO2–. Since NO2– is not continuously supplied from bulk media via lateral external diffusion like NO3–, microcolonies in the interior use Nar to produce NO2–, which is then preferentially consumed via Nir within each microcolony as the next most available oxidant for generating energy.Fig. 5: Heterogenous denitrification gene expression within individual microcolonies.a PAO1 NarK-GFP (top) or NirS-dsRed (bottom) were grown in separate particles in LB media saturated with 50% air and supplemented with 40 μM NO3–. Arrows indicate the particle edge; scale bars = 100 μm. For hundreds of microcolonies, the fraction expressing either NarK or NirS was quantified, and example microcolonies (right) illustrate ‘on’ fractions ranging from 0 to 0.90, with ‘on’ subregions noted by blue dotted lines. b Microcolony fraction expression for NarK-GFP and c NirS-dsRed, as a function of distance to the nearest particle edge. Shown are the median (blue crosses) and quartiles binned over 25 μm of radial particle space. Colonies were primarily ‘off’ in the aerated zone nearest bulk fluid (x  250 μm). In the aerobic zone, the median expression fraction of NarK-GFP is 3.1 × 10−5 (interquartile range 5.5 × 10−5) whereas the NirS-dsRed expression fraction is 2.7 × 10−5 (interquartile range 1.5 × 10−5). Expression of both genes are not significantly different from each other (Wilcoxon rank sum; p = 0.1). In the anaerobic zone, the median expression fraction of NarK-GFP and NirS-dsRed are 0.70 (interquartile 0.39) and 0.94, (interquartile 0.24), respectively. These anoxic fractional expressions are significantly different from each other (n1 = 1160, n2 = 1078, Wilcoxon rank sum; p = 2.2 × 10−32, w = 2724). The occurrence of heterogenous partially-on microcolonies reflects a sharp transition zone between presumptive aerobic and anoxic conditions (x ~ 100–250 μm). d Probability density functions for each reporter strain indicate stronger bimodality and higher binary expression for NirS-dsRed than for NarK-GFP. The distribution of NarK v. NirS expression are significantly different from each other (two sample Kolmogorov–Smirnov test; n1 = 1554, n2 = 1354 p = 1.2 × 10−40, Dn = 0.25) with significantly different medians (n1 = 1554, n2 = 1354, Wilcoxon rank sum; p = 1.9 × 10−33, w = 1.9 × 106).Full size imageRespiratory shading by exterior colonies and cells was a key emergent feature among microcolonies within the agarose particles, occurring in a fractal-like geometry. At the particle-scale ((R) ~1500 µm), nutrient consumption by microcolonies at the periphery prevented uptake by those at the interior; similarly, at the microcolony-scale (r ~25 µm), cells at the margin prevented uptake by those at the center, with consumption generating gradients of uptake flux at both scales. Shading did not occur for microcolonies in the particle core through the early stages of suboxia, as Nar and Nir expression were absent and microcolony size distribution was uniform over the first 14 h (Fig. 3, Supplementary Fig. 8). Since microcolonies were small over this stage, the O2 flux to the center outpaced aerobic respiration, and cell biomass at the particle-scale had not yet substantially diminished diffusive O2 availability. Then, owing to cell growth and the onset of shading, the O2 concentrations decreased rapidly over ~2 h (Fig. 4). As such, respiratory shading should be diminished when the density of microcolonies is lower. We tested this hypothesis with particles seeded at very low density (~102 cells mL−1) resulting in 1–6 microcolonies particle−1. Indeed, after 40 h of growth, the resultant microcolonies were quite large (r = 62 ± 11 µm (sd)); Supplementary Fig. 9) regardless of radial location within the particle, reflecting low intercolony competition for oxidants that readily diffused throughout the particle. While here respiratory shading across scales occurred for a clonal population, natural multispecies communities may additionally distribute functional roles among diverse taxa, e.g., anammox aggregates spatially differentiate such that aerotolerant species encase the outer perimeter to respire O2, shielding Planctomycetes to perform oxygen-inhibited anammox in the interior10. More

  • in

    Crop origins explain variation in global agricultural relevance

    1.FAOSTAT: Crops (FAO, 2109); http://www.fao.org/faostat/en/#data/QC2.Mottet, A. et al. Livestock: on our plates or eating at our table? A new analysis of the feed/food debate. Glob. Food Sec. 14, 1–8 (2017).Article 

    Google Scholar 
    3.Prescott-Allen, R. & Prescott-Allen, C. How many plants feed the world? Conserv. Biol. 4, 365–374 (1990).Article 

    Google Scholar 
    4.Crittenden, A. N. & Schnorr, S. L. Current views on hunter-gatherer nutrition and the evolution of the human diet. Am. J. Phys. Anthropol. 162, 84–109 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Khoury, C. K. et al. Origins of food crops connect countries worldwide. Proc. R. Soc. B 283, 20160792 (2016).Article 

    Google Scholar 
    6.Poisot, T., Canard, E., Mouquet, N. & Hochberg, M. E. A comparative study of ecological specialization estimators. Methods Ecol. Evol. 3, 537–544 (2012).Article 

    Google Scholar 
    7.Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8, e66428 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Khoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl Acad. Sci. USA 111, 4001–4006 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Newton, A. C., Johnson, S. N. & Gregory, P. J. Implications of climate change for diseases, crop yields and food security. Euphytica 179, 3–18 (2011).Article 

    Google Scholar 
    11.Hawkesworth, S. et al. Feeding the world healthily: the challenge of measuring the effects of agriculture on health. Philos. Trans. R. Soc. B 365, 3083–3097 (2010).Article 

    Google Scholar 
    12.Popkin, B. M. Technology, transport, globalization and the nutrition transition food policy. Food Policy 31, 554–569 (2006).Article 

    Google Scholar 
    13.Spengler III, R. N. Fruit from the Sands: The Silk Road Origins of the Foods We Eat (Univ. of California Press, 2019).14.Vaughan, J. & Geissler, C. The New Oxford Book of Food Plants (Oxford Univ. Press, 2009).15.Purugganan, M. D. & Fuller, D. Q. The nature of selection during plant domestication. Nature 457, 843–848 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Wang, L. et al. The interplay of demography and selection during maize domestication and expansion. Genome Biol. 18, 215 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    17.Milla, R., Bastida, J. M., Turcotte, M. M. & Al, E. Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food. Nat. Ecol. Evol. 2, 1808–1817 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Ellis, E. C., Klein Goldewijk, K., Siebert, S., Lightman, D. & Ramankutty, N. Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr. 19, 589–606 (2010).
    Google Scholar 
    19.Xu, C., Kohler, T. A., Lenton, T. M., Svenning, J.-C. & Scheffer, M. Future of the human climate niche. Proc. Natl Acad. Sci. USA 117, 11350–11355 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Harlan, J. R. Crops and Man (ASA, 1992).21.Blumler, M. A. et al. in The Origins and Spread of Agriculture and Pastoralism in Eurasia (ed. Harris, D. R.) 25–50 (Smithsonian Institution Press, 1996).22.Hancock, J. F. Plant Evolution and the Origin of Crop Species (CABI, 2012).23.Harlan, J. R. The Living Fields: Our Agricultural Heritage (Cambridge Univ. Press, 1998).24.Lombardo, U. et al. Early Holocene crop cultivation and landscape modification in Amazonia. Nature 581, 190–193 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Denham, T. et al. The domestication syndrome in vegetatively propagated field crops. Ann. Bot. 125, 581–597 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Meyer, R. S., DuVal, A. E. & Jensen, H. R. Patterns and processes in crop domestication: an historical review and quantitative analysis of 203 global food crops. New Phytol. 196, 29–48 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Milla, R. Crop origins and phylo food: a database and a phylogenetic tree to stimulate comparative analyses on the origins of food crops. Glob. Ecol. Biogeogr. 29, 606–614 (2020).Article 

    Google Scholar 
    28.Larson, G. et al. Current perspectives and the future of domestication studies. Proc. Natl Acad. Sci. USA 111, 6139–6146 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Esquinas-Alcázar, J. Protecting crop genetic diversity for food security: political, ethical and technical challenges. Nat. Rev. Genet. 6, 946–953 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Clement, C. R. 1492 and the loss of Amazonian crop genetic resources. I. The relation between domestication and human population decline. Econ. Bot. 53, 188–202 (1999).Article 

    Google Scholar 
    31.Webb, C. O., Ackerly, D. D., McPeek, M. A. & Donoghue, M. J. Phylogenies and community ecology. Annu. Rev. Ecol. Syst. 33, 475–505 (2002).Article 

    Google Scholar 
    32.Tauger, M. B. Agriculture in World History (Routledge, 2013).33.Futuyma, D. J. & Moreno, G. The evolution of ecological specialization. Annu. Rev. Ecol. Syst. 19, 207–233 (1988).Article 

    Google Scholar 
    34.Forister, M. L., Dyer, L. A., Singer, M. S., Stireman, J. O. III & Lill, J. T. Revisiting the evolution of ecological specialization, with emphasis on insect–plant interactions. Ecology 93, 981–991 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    35.Colles, A., Liow, L. H. & Prinzing, A. Are specialists at risk under environmental change? Neoecological, paleoecological and phylogenetic approaches. Ecol. Lett. 12, 849–863 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Richerson, P. J., Boyd, R. & Bettinger, R. L. Was agriculture impossible during the Pleistocene but mandatory during the Holocene? A climate change hypothesis. Am. Antiq. 66, 387–411 (2001).Article 

    Google Scholar 
    38.Mueller, U. G. & Rabeling, C. A breakthrough innovation in animal evolution. Proc. Natl Acad. Sci. USA 105, 5287–5288 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Schultz, T. R. & Brady, S. G. Major evolutionary transitions in ant agriculture. Proc. Natl Acad. Sci. USA 105, 5435–5440 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Mueller, U. G., Scott, J. J., Ishak, H. D., Cooper, M. & Rodrigues, A. Monoculture of leafcutter ant gardens. PLoS ONE 5, e12668 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    41.Kingsbury, N. Hybrid, the History and Science of Plant Breeding (Univ. of Chicago Press, 2009).42.Food Outlook—Biannual Report on Global Food Markets: June 2020 (FAO, 2020).43.van Kleunen, M. et al. Economic use of plants is key to their naturalization success. Nat. Commun. 11, 3201 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    44.Li, T. et al. Domestication of wild tomato is accelerated by genome editing. Nat. Biotechnol. 36, 1160–1163 (2018).CAS 
    Article 

    Google Scholar 
    45.Siddique, K. H. M., Li, X. & Gruber, K. Rediscovering Asia’s forgotten crops to fight chronic and hidden hunger. Nat. Plants 7, 116–122 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Lancaster, L. T. Host use diversification during range shifts shapes global variation in Lepidopteran dietary breadth. Nat. Ecol. Evol. 4, 963–969 (2020).47.Milla, R. Crop Origins and Phylo Food (GitHub, accessed 1 December 2020); https://github.com/rubenmilla/Crop_Origins_Phylo48.Global Biodiversity Information Facility (GBIF, 2018); https://www.gbif.org49.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

    Google Scholar 
    50.Paradis, E., Claude, J. & Strimmer, K. {APE}: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
    Article 

    Google Scholar 
    51.Martin, A. R. et al. Regional and global shifts in crop diversity through the Anthropocene. PLoS ONE 14, e0209788 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.The Plant List Version 2 (2013); http://www.theplantlist.org/53.Cayuela, L., la Cerda, Í. G., Albuquerque, F. S. & Golicher, D. J. taxonstand: an R package for species names standardisation in vegetation databases. Methods Ecol. Evol. 3, 1078–1083 (2012).Article 

    Google Scholar 
    54.Beres, B. L. et al. A systematic review of durum wheat: enhancing production systems by exploring genotype, environment, and management (Gx Ex M) synergies. Front. Plant. Sci. 11, 568657 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Paradis, E. in Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 3–18 (Springer, 2014).56.Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2011).Article 

    Google Scholar 
    58.de Villemereuil, P. & Nakagawa, S. in Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 287–304 (Springer, 2014).59.Keck, F., Rimet, F., Bouchez, A. & Franc, A. phylosignal: an R package to measure, test, and explore the phylogenetic signal. Ecol. Evol. 6, 2774–2780 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Bush, S. E. et al. Unlocking the black box of feather louse diversity: a molecular phylogeny of the hyper-diverse genus Brueelia. Mol. Phylogenet. Evol. 94, 737–751 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).62.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2019).63.Grafen, A. & Hamilton, W. D. The phylogenetic regression. Philos. Trans. R. Soc. Lond. B 326, 119–157 (1989).CAS 
    Article 

    Google Scholar 
    64.Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Development Core Team. nlme: Linear and nonlinear mixed effects models. R package version 3.1-142 (2020).65.Ives, A. R. & Garland, T. Jr. Phylogenetic logistic regression for binary dependent variables. Syst. Biol. 59, 9–26 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Earliest evidence of marine habitat use by mammals

    1.McCrea, R. T., Pemberton, S. G. & Currie, P. J. New ichnotaxa of mammal and reptile tracks from the Upper Paleocene of Alberta. Ichnos 11, 323–339 (2004).Article 

    Google Scholar 
    2.Henderson, D. M. A wide-gauge, large-mammal trackway from the upper Paleocene of Alberta Canada. Can. J. Earth Sci. 52, 696–700 (2015).ADS 
    Article 

    Google Scholar 
    3.Lüthje, C. J., Milàn, J. & Hurum, J. H. Paleocene tracks of the mammal pantodont genus Titanoides in coal-bearing strata, Svalbard, Arctic Norway. J. Vertebr. Paleontol. 30, 521–527 (2010).Article 

    Google Scholar 
    4.Davydenko, S., Laime, M. J. & Gol’din, P. The earliest record of a marine mammal (Cetacea: Basilosauridae) from the Eocene of Amazonia. J. Vertebr. Paleontol. 38, e1549060 (2019).Article 

    Google Scholar 
    5.Hansen, D.E. Laramide tectonics and deposition of the Ferris and Hanna Formations, south-central Wyoming in Paleotectonics and sedimentation in the Rocky Mountain Region, United States: American Association of Petroleum Geologists Memoir 41 (ed. Peterson, J.A.) 481–495 (AAPG, 1986).6.Dechesne, M. et al. A new stratigraphic framework and constraints for the position of the Paleocene-Eocene boundary in the rapidly subsiding Hanna Basin, Wyoming. Geosphere 16, 594–618 (2020).ADS 
    Article 

    Google Scholar 
    7.Hasiotis, S. T. & Honey, J. G. Paleohydrologic and stratigraphic significance of crayfish burrows in continental deposits: examples from several Paleocene Laramide basins in the Rocky Mountains. J. Sediment. Res. 70, 127–139 (2000).ADS 
    CAS 
    Article 

    Google Scholar 
    8.Gingras, M. K., Pemberton, S. G., Saunders, T. D. A. & Clifton, H. E. The ichnology of modern and Pleistocene brackish-water deposits at Willapa Bay, Washington: variability in estuarine settings. Palaios 14, 352–374 (1999).ADS 
    Article 

    Google Scholar 
    9.Gingras, M. K., Hubbard, S. M., Pemberton, S. G. & Saunders, T. The significance of Pleistocene Psilonichnus at Willapa Bay, Washington. Palaios 15, 142–151 (2000).ADS 
    Article 

    Google Scholar 
    10.Gingras, M.K., MacEachern, J.A., Dashtgard, S.E., Zonneveld, J.-P., Schoengut, J., Ranger, M.J., & Pemberton, G. Estuaries. in Trace fossils as indicators of sedimentary environments. Developments in sedimentology, Volume 64 (eds. Knaust, D. &Bromley, R.G.) 463–507 (Elsevier, 2012).11.Gingras, M. K., MacEachern, J. A., Dashtgard, S. E., Ranger, M. J. & Pemberton, S. G. The significance of trace fossils in the McMurray Formation, Alberta, Canada. Bull. Can. Pet. Geol. 64, 233–250 (2016).Article 

    Google Scholar 
    12.MacEachern, J.A., Bann, K.L., Bhattacharya, J., & Howell, C.D. Ichnology of deltas: organism responses to the dynamic interplay of rivers, waves, storms, and tides. in River deltas-concepts, models, and examples, Volume 51, SEPM Special Publication (eds. Giosan, L., & Bhattacharya, J.P.) 49–85 (SEPM, 2005).13.Hauk, T. E., Dashtgard, S. E. & Pemberton, S. G. Brackish-water ichnological trends in a microtidal barrier island–embayment system, Kouchibouguac National Park, New Brunswick, Canada. Palaios 24, 478–496 (2011).ADS 
    Article 

    Google Scholar 
    14.Pemberton, S.G., & Wightman, D.M. Ichnological characteristics of brackish water deposits. in Applications of ichnology to petroleum exploration. Volume 17, SEPM Core Workshops, (ed. Pemberton, S.G.) 141–167 (SEPM, 1992).15.Hubbard, S. M., Gingras, M. K. & Pemberton, S. G. Palaeoenvironmental implications of trace fossils in estuary deposits of the Cretaceous Bluesky Formation, Cadotte region, Alberta, Canada. Fossils Strata 51, 68–87 (2004).
    Google Scholar 
    16.Xing, L. et al. Dinosaur natural track casts from the Lower Cretaceous Hekou Group in the Lanzhou-Minhe Basin, Gansu, Northwest China: Ichnology, track formation, and distribution. Cretac. Res. 52, 194–205 (2015).Article 

    Google Scholar 
    17.Elbroch, M. Mammal tracks and sign (Stackpole Books, 2003).
    Google Scholar 
    18.Osborn, H. F. Evolution of the Amblypoda. Part I. Taligrada and Pantodonta. Bull. Am. Mus. Nat. Hist. Bull. 10, 1–50 (1898).
    Google Scholar 
    19.Simons, E. L. The Paleocene Pantodonta. Trans. Am. Philos. Soc. 50, 3–99 (1960).Article 

    Google Scholar 
    20.Bennett, M. R., Morse, S. A. & Falkingham, P. L. Tracks made by swimming Hippopotami: an example from Koobi Fora (Turkana Basin, Kenya). Palaeogeogr. Palaeoclimatol. Palaeoecol. 409, 9–23 (2014).Article 

    Google Scholar 
    21.Clementz, M. T., Holroyd, P. A. & Koch, P. L. Identifying aquatic habits of herbivorous mammals through stable isotope analysis. Palaios 23, 574–585 (2008).ADS 
    Article 

    Google Scholar 
    22.Uhen, M. D. & Gingerich, P. D. Evolution of Coryphodon (Mammalia, Pantodonta) in the late Paleocene and early Eocene of northwestern Wyoming. Contrib. Mus. Paleontol. Univ. Michigan 29, 259–289 (1995).
    Google Scholar 
    23.Hasiotis, S. T. Reconnaissance of Upper Jurassic Morrison Formation ichnofossils, Rocky Mountain Region, USA: paleoenvironmental, stratigraphic, and paleoclimatic significance of terrestrial and freshwater ichnocoenoses. Sed. Geol. 167, 177–268 (2004).Article 

    Google Scholar 
    24.Bordy, E. M., Bumby, A. J., Catuneanu, O. & Eriksson, P. G. Possible trace fossils of putative termite origin in the Lower Jurassic (Karoo Supergroup) of South Africa and Lesotho. S. Afr. J. Sci. 105, 356–362 (2009).
    Google Scholar 
    25.Bromley, R. G. et al. Comments on the paper “Reconnaissance of Upper Jurassic Morrison Formation ichnofossils, Rocky Mountain Region, USA: Paleoenvironmental, stratigraphic, and paleoclimatic significance of terrestrial and freshwater ichnocoenoses” by Stephen T. Hasiotis. Sed. Geol. 200, 141–150 (2007).Article 

    Google Scholar 
    26.Eberle, J. J. A new ‘tapir’ from Ellesmere Island, Arctic Canada-implications for northern high latitude palaeobiogeography and tapir palaeobiology. Palaeogeogr. Palaeoclimatol. Palaeoecol. 227, 311–322 (2004).Article 

    Google Scholar 
    27.Halliday, T. J. D., Upchurch, P. & Goswami, A. Resolving the relationships of Paleocene placental mammals. Biol. Rev. 92, 521–550 (2017).Article 

    Google Scholar 
    28.Zurano, J. P. et al. Cetrtiodactyla: updating a time-calibrated molecular phylogeny. Mol. Phylogenet. Evol. 133, 256–262 (2019).Article 

    Google Scholar 
    29.Knaust, D. Atlas of trace fossils in well core: appearance, taxonomy and interpretation (Springer, 2017).Book 

    Google Scholar 
    30.Bingham, B. L., Freytes, I., Emery, M., Dimond, J. & Muller-Parker, G. Aerial exposure and body temperature of the intertidal sea anemone Anthopleura elegantissima. Invertebr. Biol. 130, 291–301 (2011).Article 

    Google Scholar 
    31.Jayewardene, J. The elephant in Sri Lanka. Wildlife Heritage Trust of Sri Lanka, Sri Lanka (1994).32.Miller, F.L. Inter-island water crossings by Peary caribou, south-central Queen Elizabeth Islands. Arctic, 8–12 (1995).33.Harveson, P. M., Grant, W. E., Lopez, R. R., Silvy, N. J. & Frank, P. A. The role of dispersal in Florida Key deer metapopulation dynamics. Ecol. Model. 195, 393–401 (2006).Article 

    Google Scholar 
    34.Quigley, D. T. G. & Moffatt, S. Sika-like deer Cervus nippon Temminck, 1838 observed swimming out to sea at Greystones, Co., Wicklow: increasing deer population pressure?. Bull. Ir. Biogeogr. Soc. 38, 251–261 (2014).
    Google Scholar 
    35.Castelló, J. R. Bovids of the world: Antelopes, gazelles, cattle, goas, sheep, and relatives (Princeton University Press, 2016).Book 

    Google Scholar 
    36.Naranjo, E.J. Tapirs of the Neotropics. in Ecology and conservation of tropical ungulates in Latin America (ed. Gallina-Tessaro, S.) 439–451(Springer, 2019).37.Kavčić, K., Corlatti, L., Rodriguez, O., Kavčić, B. & Šprem, N. From the mountains to the sea! Unusual swimming behavior in chamois Rupicapra spp. Ethol. Ecol. Evol. 32, 402–408 (2020).Article 

    Google Scholar 
    38.Roth, H. H., Hoppe-Dominik, B., Mühlenberg, M., Steinhauer-Burkart, B. & Fischer, F. Distribution and status of the hippopotamids in the Ivory Coast. Afr. Zool. 39, 211–224 (2004).Article 

    Google Scholar 
    39.Pilfold, N. W., McCall, A., Derocher, A. E., Lunn, N. J. & Richardson, E. Migratory response of polar bears to sea ice loss: to swim or not to swim. Ecography 40, 189–199 (2017).Article 

    Google Scholar 
    40.Smith, T. S. & Partridge, S. T. Dynamics of intertidal foraging by coastal brown bears in southwestern Alaska. J. Wildl. Manag. 68, 233–240 (2004).Article 

    Google Scholar 
    41.Lewis, T. M. & Lafferty, D. J. Brown bears and wolves scavenge humpback whale carcass in Alaska. Ursus 25, 8–13 (2014).Article 

    Google Scholar 
    42.Morgan, B. J. & Lee, P. C. Forest elephant group composition, frugivory and coastal use in the Réserve de Faune du Petit Loango, Gabon. Afr. J. Ecol. 45, 519–526 (2007).Article 

    Google Scholar 
    43.Prinsloo, A. S., Pillay, D. & O’Riain, M. J. Multiscale drivers of hippopotamus distribution in the St Lucia Estuary, South Africa. Afr. Zool. 55, 127–140 (2020).Article 

    Google Scholar 
    44.Boonratana, R. A statewide survey to estimate the distribution and density of the Sumatran rhinoceros, Asian elephant and banteng in Sabah, Malaysia. Wildlife Conservation Society, New York (1997). More

  • in

    Global option space for organic agriculture is delimited by nitrogen availability

    1.Muller, A. et al. Strategies for feeding the world more sustainably with organic agriculture. Nat. Commun. 8, 1290 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    2.Foley, J. A. et al. Solutions for a cultivated planet. Nature 478, 337–342 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    3.Mäder, P. et al. Soil fertility and biodiversity in organic farming. Science 296, 1694–1697 (2002).ADS 
    PubMed 
    Article 

    Google Scholar 
    4.Bergström, L. & Kirchmann, H. Are the claimed benefits of organic agriculture justified? Nat. Plants 2, 16099 (2016).PubMed 
    Article 

    Google Scholar 
    5.Connor, D. J. Organic agriculture and food security: a decade of unreason finally implodes. Field Crops Res. 225, 128–129 (2018).Article 

    Google Scholar 
    6.Connor, D. J. Organic agriculture cannot feed the world. Field Crops Res. 106, 187–190 (2008).Article 

    Google Scholar 
    7.Erb, K. et al. Exploring the biophysical option space for feeding the world without deforestation. Nat. Commun. 7, 11382 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Nowak, B., Nesme, T., David, C. & Pellerin, S. Disentangling the drivers of fertilising material inflows in organic farming. Nutr. Cycl. Agroecosyst. 96, 79–91 (2013).Article 

    Google Scholar 
    9.Oelofse, M., Jensen, L. S. & Magid, J. The implications of phasing out conventional nutrient supply in organic agriculture: Denmark as a case. Organ. Agric. 3, 41–55 (2013).Article 

    Google Scholar 
    10.Tayleur, C. & Phalan, B. Organic farming and deforestation. Nat. Plants 2, 16098 (2016).PubMed 
    Article 

    Google Scholar 
    11.Principles of Organic Agriculture (IFOAM, 2018); https://www.ifoam.bio/en/organic-landmarks/principles-organic-agriculture12.European Commission Commission Regulation (EC) No 889/2008. Official Journal of the European Union L 250/1 (2008).13.Yussefi-Menzler, M., Willer, H. & Sorensen, N. The World of Organic Agriculture. Statistics and Emerging Trends 2019 (Routledge, 2019); https://doi.org/10.4324/978184977599114.Barbieri, P., Pellerin, S. & Nesme, T. Comparing crop rotations between organic and conventional farming. Sci. Rep. 7, 13761 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.McKenzie, F. C. & Williams, J. Sustainable food production: constraints, challenges and choices by 2050. Food Security https://doi.org/10.1007/s12571-015-0441-1 (2015).16.Rigby, D. & Cáceres, D. Organic farming and the sustainability of agricultural systems. Agric. Syst. 68, 21–40 (2001).Article 

    Google Scholar 
    17.Barbieri, P., Pellerin, S., Seufert, V. & Nesme, T. Changes in crop rotations would impact food production in an organically farmed world. Nat. Sustain. 2, 378–385 (2019).Article 

    Google Scholar 
    18.Baudry, J. et al. Improvement of diet sustainability with increased level of organic food in the diet: findings from the BioNutriNet cohort. Am. J. Clin. Nutr. 109, 1173–1188 (2019).PubMed 
    Article 

    Google Scholar 
    19.Chaudhary, A., Gustafson, D. & Mathys, A. Multi-indicator sustainability assessment of global food systems. Nat. Commun. 9, 848 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    20.Smith, L. C. & Haddad, L. Reducing child undernutrition: past drivers and priorities for the post-MDG era. World Dev. 68, 180–204 (2015).Article 

    Google Scholar 
    21.Gibson, R. S. & Hotz, C. Dietary diversification/modification strategies to enhance micronutrient content and bioavailability of diets in developing countries. Br. J. Nutr. 85, S159 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    22.Mie, A. et al. Human health implications of organic food and organic agriculture: a comprehensive review. Environ. Health 16, 1–22 (2017).Article 
    CAS 

    Google Scholar 
    23.Van Zanten, H. H. E. et al. Defining a land boundary for sustainable livestock consumption. Glob. Change Biol. 24, 4185–4194 (2018).ADS 
    Article 

    Google Scholar 
    24.White, R. R. & Hall, M. B. Nutritional and greenhouse gas impacts of removing animals from US agriculture. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1707322114 (2017).25.Soussana, J. F. & Lemaire, G. Coupling carbon and nitrogen cycles for environmentally sustainable intensification of grasslands and crop-livestock systems. Agr. Ecosyst. Environ. 190, 9–17 (2014).CAS 
    Article 

    Google Scholar 
    26.Schader, C. et al. Impacts of feeding less food-competing feedstuffs to livestock on global food system sustainability. J. R. Soc. Interface 12, 20150891 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    27.Persson, U. M., Johansson, D. J. A., Cederberg, C., Hedenus, F. & Bryngelsson, D. Climate metrics and the carbon footprint of livestock products: where’s the beef? Environ. Res. Lett. 10, 034005 (2015).ADS 
    Article 
    CAS 

    Google Scholar 
    28.Mehrabi, Z., Ellis, E. C. & Ramankutty, N. The challenge of feeding the world while conserving half the planet. Nat. Sustain. 1, 409–412 (2018).Article 

    Google Scholar 
    29.Eyhorn, F. et al. Sustainability in global agriculture driven by organic farming. Nat. Sustain. 2, 253–255 (2019).Article 

    Google Scholar 
    30.Badgley, M. C. et al. Organic agriculture and the global food supply. Renew. Agr. Food Syst. 22, 86–108 (2007).Article 

    Google Scholar 
    31.Karlsson, J. O. & Röös, E. Resource-efficient use of land and animals—environmental impacts of food systems based on organic cropping and avoided food-feed competition. Land Use Policy 85, 63–72 (2019).Article 

    Google Scholar 
    32.Watson, C. A. et al. A review of farm-scale nutrient budgets for organic farms as a tool for management of soil fertility. Soil Use Manage. 18, 264–273 (2002).Article 

    Google Scholar 
    33.Nowak, B., Nesme, T., David, C. & Pellerin, S. To what extent does organic farming rely on nutrient inflows from conventional farming? Environ. Res. Lett. 8, 044045 (2013).ADS 
    Article 

    Google Scholar 
    34.Feuerbacher, A., Luckmann, J., Boysen, O., Zikeli, S. & Grethe, H. Is Bhutan destined for 100% organic? Assessing the economy-wide effects of a large-scale conversion policy. PLoS ONE 13, e0199025 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    35.Ponisio, L. C. et al. Diversification practices reduce organic to conventional yield gap. Proc. R. Soc. B https://doi.org/10.1098/rspb.2014.1396 (2015).36.Trimmer, J. T. & Guest, J. S. Recirculation of human-derived nutrients from cities to agriculture across six continents. Nat. Sustain. 1, 427–435 (2018).Article 

    Google Scholar 
    37.Hoornweg, D. & Bhada-Tata, P. What a Waste. A Global Review of Solid Waste Management (World Bank, 2012).38.Reganold, J. P. & Wachter, J. M. Organic agriculture in the twenty-first century. Nat. Plants 2, 15221 (2016).PubMed 
    Article 

    Google Scholar 
    39.Tuomisto, H. L., Hodge, I. D., Riordan, P. & Macdonald, D. W. Does organic farming reduce environmental impacts? A meta-analysis of European research. J. Environ. Manage. 112, 309–320 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    40.Crowder, D. W. & Reganold, J. P. Financial competitiveness of organic agriculture on a global scale. Proc. Natl Acad. Sci. USA 112, 7611–7616 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    41.Bartelt, K. D. & Bland, W. L. Theoretical analysis of manure transport distance as a function of herd size and landscape fragmentation. J. Soil Water Conserv. 62, 345–352 (2007).
    Google Scholar 
    42.De Klein, C. et al. in IPCC Guidelines for National Greenhouse Gas Inventories (eds Buendia, L. & Eggleston, S.) Ch. 11 (IPCC, 2006).43.Godard, C., Roger-Estrade, J., Jayet, P. A., Brisson, N. & Le Bas, C. Use of available information at a European level to construct crop nitrogen response curves for the regions of the EU. Agric. Syst. 97, 68–82 (2008).Article 

    Google Scholar 
    44.Sheldrick, W., Syers, J. K. & Lingard, J. Contribution of livestock excreta to nutrient balances. Nutr. Cycling Agroecosyst. 66, 119–131 (2003).Article 

    Google Scholar 
    45.Dong, H. et al. in IPCC Guidelines for National Greenhouse Gas Inventories (eds Buendia, L. & Eggleston, S.) Ch. 10 (IPCC, 2006).46.Hogh-Jensen, H., Loges, R., Jorgensen, F. V., Vinther, F. P. & Jensen, E. S. An empirical model for quantification of symbiotic nitrogen fixation in grass-clover mixtures. Agric. Syst. 82, 181–194 (2004).Article 

    Google Scholar 
    47.Liu, J. et al. A high-resolution assessment on global nitrogen flows in cropland. Proc. Natl Acad. Sci. USA 107, 8035–8040 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Dentener, F. et al. Nitrogen and sulfur deposition on regional and global scales: a multimodel evaluation. Glob. Biogeochem. Cycles 20, GB4003 (2006).ADS 
    Article 
    CAS 

    Google Scholar 
    49.Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).ADS 
    Article 
    CAS 

    Google Scholar 
    50.Licker, R. et al. Mind the gap: How do climate and agricultural management explain the ‘yield gap’ of croplands around the world? Glob. Ecol. Biogeogr. 19, 769–782 (2010).Article 

    Google Scholar 
    51.Srednicka-Tober, D. et al. Composition differences between organic and conventional meat: a systematic literature review and meta-analysis. Br. J. Nutr. 115, 994–1011 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Herrero, M. et al. Biomass use, production, feed efficiencies, and greenhouse gas emissions from global livestock systems. Proc. Natl Acad. Sci. USA 110, 20888–20893 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Van Drecht, G., Bouwman, A. F., Harrison, J. & Knoop, J. M. Global nitrogen and phosphate in urban wastewater for the period 1970 to 2050. Glob. Biogeochem. Cycles 23, 1–19 (2009).
    Google Scholar 
    54.World Population Prospects 2015—Data Booklet (United Nations, 2015); https://doi.org/ST/ESA/SER.A/37755.Ahmed, S. & Blumberg, J. Dietary guidelines for Americans, 2010. Nutr. Rev. https://doi.org/10.1016/S0300-7073(05)71075-6 (2009).56.Gerten, D. et al. Feeding ten billion people is possible within four terrestrial planetary boundaries. Nat. Sustain 3, 200–208 (2020).Article 

    Google Scholar 
    57.Fetzel, T. et al. Quantification of uncertainties in global grazing systems assessment. Glob. Biogeochem. Cycles 31, 1089–1102 (2017).ADS 
    CAS 
    Article 

    Google Scholar  More