More stories

  • in

    Using past interglacial temperature maxima to explore transgressions in modern Maldivian coral and Amphistegina bleaching thresholds

    Study site and target foraminiferal speciesThe Maldivian archipelago is a partially drowned carbonate platform within the central, equatorial Indian Ocean. It consists of two rows of north–south orientated atolls, which encompass an Inner Sea. The lowermost neritic carbonate unit sits upon volcanic bedrock and has been dated back to the Eocene19 with continuous drift deposition, within the Inner Sea, starting ~ 12.9 Ma at the establishment of the modern South Asian Monsoon (SAM)35,36. This seasonally reversing, major climatic system has an impact on both the regional precipitation patterns as well as physiochemical oceanographic properties (Fig. 1). The summer southwest SAM brings warm, wet conditions to the Indian subcontinent, as well as higher saline surface waters from the Arabian Sea into the Maldives region. In comparison, the winter northeast SAM results in cool, dry continental conditions and transports lower salinity water from the Bay of Bengal into the central, equatorial Indian Ocean. As a result, the Maldives seasonal salinity depth profiles can vary significantly, yet due to its tropical location the seasonal sea water temperatures are relatively stable.Three symbiont-bearing foraminiferal species are used in this study: Amphistegina lessonii, Globigerinoides ruber (white) and Trilobatus sacculifer (with sac-like final chamber):Amphistegina lessonii is a larger benthic, symbiont-bearing (diatoms) foraminiferal species. It has a shallow depth range (0–50 m)37,38,39 and is globally abundant in tropical coral reef, benthic foraminiferal shoal and general carbonate shelf settings40. Similarly to corals, amphisteginids have been shown to bleach under high temperatures/high irradiance levels with the new development of the Amphistegina Bleaching Index (ABI) as an indicator of photo-inhibitory stress in coral reef settings41,42. From ~ 30 °C this species starts showing signs of thermal stress, with bleaching and mortality reported for temperatures  > 31 °C11,12.Globigerinoides ruber (w) hosts dinoflagellate endosymbionts and is the most common planktonic foraminiferal species in tropical-subtropical waters13 state that while G. ruber (w) is generally considered one of the shallowest-dwelling species, its depth distribution does vary in relation to regional ecological conditions. It has a particular relation to the nutricline depth in less turbid, oligotrophic conditions43 which has been confirmed for the Maldives28. It is omnivorous, however in comparison to other omnivorous, symbiont-bearing species, it has demonstrated an elevated adaptation for consuming phytoplankton protein over zooplankton protein13. From culture experiments, it has a broad temperature (14–31 °C) and salinity (22–49 PSU) tolerance, and has been reported as the most tolerant species to low sea surface salinity (SSS)13. This species occurs year-round and has a fortnightly reproduction13.Trilobatus sacculifer is a planktonic foraminiferal species abundant in tropical-subtropical surface waters and as such is extensively used in paleo-reconstructions. It hosts dinoflagellate endosymbionts yet is omnivorous, feeding predominantly on calanoid copepods13. It is a euryhaline species, with a broad salinity (24–47 PSU) and temperature (14–32 °C) tolerance. Similarly to G. ruber (w), this species occurs year-round and has a monthly reproduction on a synodic lunar cycle13. While a shallow dwelling species, it is generally reported to live slightly deeper in the water column, in comparison to G. ruber (w)28,30,44.SamplingAll planktonic foraminiferal specimens (G. ruber (w) and T. sacculifer (w/s)) for the geochemical analysis (δ18Oc and Mg/Ca) originate from the International Ocean Discovery Program (IODP) Expedition 359, Site U1467 (4° 51.0274′ N, 73° 17.0223′ E) drilled in 2015 within the Inner Sea of the Maldivian archipelago at a water depth of 487 m19. The age model for these samples was adopted from a previous study45 which is based on the correlation of their long-term (0–1800 kyr) Site 359-U1467 C. mabahethi and G. ruber (w) δ18Oc records to the stacked reference curve of46. Recent surface sediment samples (mudline A and B: representing the sample from the sediment/water interface), as well as three samples from the peak of MIS9e (U1467C, 2H6, 0–1 cm; U1467C, 2H6, 15–16 cm; U1467C, 2H6, 18–19 cm) and MIS11c (U1467B, 3H2, 147–148 cm; U1467B, 3H3, 9–10 cm; U1467B, 3H3, 12–13 cm) were analysed19,28 (sample locations are shown on Fig. 3). The mudline is identified as Recent, likely representing the last few hundred years, based on the presence of Rose Bengal (1 g/L) stained ostracods and benthic foraminifera. The study by45 has verified that diagenetic influences, within this shallow, carbonate environment, are not a concern for foraminiferal geochemical compositions over the investigated time-interval (MIS1-11).Rose Bengal stained A. lessonii specimens were obtained from modern surface rubble samples collected by hand, at 10 m water depth, during the 2015 International Union for Conservation of Nature (IUCN) REGENERATE cruise47 (Supplementary Table 6). Samples were collected from the reefs of two islands, Maayafushi and Rasdhoo, both located within the central part of the Maldivian archipelago. As the foraminifera shells were stained pink, it implies they were living at the time of collection. These specimens were used for stable isotopic analysis and their reconstructed temperatures represent modern (a cumulative signal encompassing their lifespan of four to twelve months48) conditions (Supplementary Tables 5–6). A full explanation of the Rose Bengal protein stain for foraminifera is detailed in49.δ18Oc stable isotopic analysisAll samples were initially washed using a 32 μm sieve to remove the finer clay and silt fractions. Subsequently, they were air dried and sieved into discrete sizes for foraminiferal picking. To ensure enough calcite for the measurements, all specimens for Individual Foraminifera Analysis (IFA) for both G. ruber (w) and T. sacculifer (w/s) (n = 632) were picked from the 355–400 μm size fraction. In addition, traditional whole-shell (pooled) measurements for G. ruber (w) (n = 24) were conducted on specimens from the 212–400 μm fraction (2–5 pooled specimens). Trilobatus sacculifer (w/s) traditional whole-shell analysis (n = 21) was measured on specimens (2 pooled specimens) from the 300–355 μm fraction. The majority of these pooled measurements are obtained from28,45,50,51 (Supplementary Table 1). Amphistegina lessonii measurements were run on single specimens  > 250 μm in size. Prior to stable isotopic analysis, all shells were briefly cleaned (1–2 s) by ultrasonication in Milli-Q water to remove any adhering particles. All stable isotopic measurements were conducted at the School of GeoSciences at the University of Edinburgh on a Thermo Electron Delta + Advantage mass spectrometer integrated with a Kiel carbonate III automated extraction line. Samples were reacted with 100% phosphoric acid (H3PO4) at 90 °C for 15 min, with the evolved CO2 gas collected in a liquid nitrogen coldfinger and analysed compared to a reference gas. All samples are corrected using an internal laboratory standard and expressed as parts per mil (‰) relative to Vienna Pee Dee Belemnite (VPDB). Replicate measurements of the standards give the instrument an analytical precision (1σ) of ~ 0.05 ‰ for δ18O and δ13C.Mg/Ca analysisThe Mg/Ca data is obtained from28,45,50,51 (Supplementary Table 1). Each G. ruber (w) Mg/Ca analysis (n = 17; 212–250 μm in size) was conducted on 30 pooled specimens by inductively coupled plasma optical emission spectrometry (ICP-OES) on a Thermoscientific iCap 6300 (dual viewing) at the Institute of Geosciences of the Goethe-University of Frankfurt. All samples were initially cleaned (1–2 s) by ultrasonication in Milli-Q water and then the standard oxidative cleaning protocol of52 followed to prevent clay mineral contamination. The final centrifuged sample solution was diluted with an yttrium solution (1 mg/l) prior to measurement to allow for the correction of matrix effects. In addition, before each analysis five calibration solutions were measured to allow for intensity ratio calibrations. All element/Ca measurements were standardized using an internal consistency standard (ECRM 752–1, 3.761 mmol/mol Mg/Ca). Furthermore, the elements Al, Fe, and Mn were screened and blanks periodically run to monitor for further signs of contamination during the analyses.Establishment of present and past seawater temperaturesPrior to temperature calculations, we test the IFA distributions for normality using the Shapiro‐Wilk test and the Fisher–Pearson coefficient of skewness with bootstrap confidence intervals, to define the skewness of the datasets53 (Supplementary Table 3). The Recent G. ruber (w) and T. sacculifer (w/s) and MIS11c T. sacculifer are normally distributed. In the case of both MIS9e datasets and the MIS11c G. ruber population, the null hypothesis that the data are normally distributed (p ≤ 0.05) is rejected (Supplementary Table 3). Considering bioturbation within the sediment record is a possibility, we use two methods to identify and remove outliers in the IFA datasets. Firstly, the inter-quartile range (IQR) is used for each δ18Oc dataset, which defines a measurement as an outlier if it falls outside the range [Q1 − 1.5 (Q3 − Q1), Q3 + 1.5 (Q3 − Q1)], with IQR = Q3 − Q1 and Q3 and Q1 representing the third and first quartile of the dataset20. But if there is considerable reworking, the IQR method would not necessarily identify reworked glacial measurements (highest δ18Oc values) within the interglacial samples. As such, the Recent IFA datasets, which are both normally distributed, are used to further set a rudimentary cut-off point for the highest δ18Oc (= lowest temperatures) value to expect during past interglacial minima periods for both G. ruber (w) and T. sacculifer (w/s) (this is discussed further in the Supplementary Materials, Supplementary Figs. 1–3).There are innumerable analytical techniques (e.g., traditional mass spectrometry, secondary-ion mass spectrometry, laser ablation inductively coupled plasma mass spectrometry), proxies (Mg/Ca, δ18O, clumped isotopes, TEX86, Uk’37) as well as target medians (e.g., calcitic shells of foraminifera, aragonitic coral skeletons, ice, lipids, alkenones) which are used in marine paleo-temperature reconstructions. Furthermore, different methods exist in the literature to calculate temperature estimates using both planktonic foraminiferal δ18Oc and Mg/Ca measurements with innumerable species-specific δ18O-temperature and Mg/Ca-temperature equations reported20,23,30,54,55,56. Moreover, due to the exponential nature of the Mg/Ca-temperature equations, if inappropriately applied, offsets in the upper temperature range are exacerbated. Additional considerations are species-specific offsets and differential geochemical compositions within the shell (e.g., high versus low Mg banding, gametogenic calcite). Trilobatus sacculifer gametogenic calcite has been reported to be significantly enriched in Mg in comparison to the rest of the shell57. As T. sacculifer specimens selected for use in this study underwent reproduction, indicated by the presence of a sac-like final chamber58, we can expect their Mg/Ca ratios to be biased. As such, to avoid overestimates we chose to use only G ruber (w, pooled) Mg/Ca and δ18Oc data to calculate representative δ18Osw values for each time interval, for use with both the G. ruber (w) and T. sacculifer (w/s) δ18Oc IFA datasets. Considering both planktonic species are considered as shallow-dwellers with similar living depths and an affinity for the DCM, the utilisation of common δ18Osw values is applicable13,28,30.The G. ruber Mg/Ca-temperature Eq. (1) from55 (temperature calibration range: ~ 22–27 °C), similarly applied in the Maldivian study of28, was used in this study:$$Mg/Ca=0.34left(pm 0.08right)mathrm{exp}(0.102left(pm 0.010right)*T)$$
    (1)
    The applied δ18O-temperature species-specific equations (Eqs. 2 and 3) were previously utilised in the local study by28. Both the G. ruber (Eq. 2) and T. sacculifer (Eq. 3) equations are from the Indian Ocean study of59 (temperature calibration range: ~ 20–31 °C):$$T=12.75-5({delta }^{18}{O}_{c}-{delta }^{18}{O}_{sw})$$
    (2)
    $$T=11.95-5.26({delta }^{18}{O}_{c}-{delta }^{18}{O}_{sw})$$
    (3)
    Using the above equations, the range in temperature estimates are obtained as follows (Fig. 4):

    1.

    The mean G. ruber (w) Mg/Ca measurements are used together with Eq. (1) to calculate a temperature estimate for each time point (Supplementary Table 1). Since the Mg/Ca calcification temperatures are based on 30 pooled specimens, they are considered to reflect mean calcification temperatures.

    2.

    The Mg/Ca derived temperature estimates are then used together with the mean traditional (pooled) G. ruber (w) δ18Oc data and Eq. (2) to calculate representative δ18Osw values for each time point (Supplementary Table 2). As these are calculated from pooled samples, they are considered to mirror mean δ18Osw values for both the Recent and fossil populations.

    3.

    The G. ruber (w) and T. sacculifer (w/s) IFA datasets are then used, together with the relevant species-specific δ18O-temperature equations and δ18Osw values, to calculate the spread in temperature estimates (Fig. 4, Supplementary Tables 3–4).

    Trilobatus sacculifer (w/s) data from the glacial maxima of MIS12 are included in the study to illustrate the applicability of the IFA method, however, as they do not contribute to the discussion on bleaching thresholds, they are discussed further in the Supplementary Materials (Supplementary Figs. 1, 3).Finally, the temperature estimates for the shallow-dwelling symbiont-bearing benthic A. lessonii are obtained using the genus-specific δ18O-temperature equation of60 (Eq. 4) (Supplementary Tables 5–6).$$T=16.3-4.24({delta }^{18}{O}_{c}-{delta }^{18}{O}_{sw})$$
    (4)
    Considering the benthic specimens were deemed living at the time of collection (Rose Bengal stained), a mean regional surface (0 m) δ18Osw value (0.49 ‰) is used together with the δ18Oc data in the calculations (Supplementary Tables 5–6). More

  • in

    Ecological recipes for selecting community function

    1.Doolittle, W. F. & Zhaxybayeva, O. BioScience 60, 102–112 (2010).Article 

    Google Scholar 
    2.Liautaud, K., van Nes, E. H., Barbier, M., Scheffer, M. & Loreau, M. Ecol. Lett. 22, 1243–1252 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    3.Morris, A., Meyer, K. & Bohannan, B. Phil. Trans. R. Soc. B Biol. Sci. 375, 20190244 (2020).CAS 
    Article 

    Google Scholar 
    4.De Monte, S. & Rainey, P. B. J. Biosci. 39, 237–248 (2014).PubMed 

    Google Scholar 
    5.Chang, C.-Y. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01457-5 (2021).6.Goldford, J. E. et al. Science 361, 469–474 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Machado, D. et al. Nat. Ecol. Evol. 5, 195–203 (2021).PubMed 
    Article 

    Google Scholar 
    8.Ratzke, C., Barrere, J. & Gore, J. Nat. Ecol. Evol. 4, 376–383 (2020).PubMed 
    Article 

    Google Scholar 
    9.Wright, E. S. & Vetsigian, K. H. Nat. Commun. 7, 11274 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Szathmáry, E. & Demeter, L. J. Theor. Biol. 128, 463–486 (1987).PubMed 
    Article 

    Google Scholar 
    11.Xie, L., Yuan, A. E. & Shou, W. PLoS Biol. 17, e3000295 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.van Vliet, S. & Doebeli, M. Proc. Natl Acad. Sci. USA 116, 20591–20597 (2019).PubMed 
    Article 

    Google Scholar 
    13.Doulcier, G., Lambert, A., De Monte, S. & Rainey, P. B. eLife 9, e53433 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Black, A. J., Bourrat, P. & Rainey, P. B. Nat. Ecol. Evol. 4, 426–436 (2020).PubMed 
    Article 

    Google Scholar 
    15.Williams, H. T. P. & Lenton, T. M. Proc. Natl Acad. Sci. USA 104, 8918–8923 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Barbier, M., Arnoldi, J.-F., Bunin, G. & Loreau, M. Proc. Natl Acad. Sci. USA 115, 2156–2161 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    17.Pearce, M. T., Agarwala, A. & Fisher, D. S. Proc. Natl Acad. Sci. USA 117, 14572–14583 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    18.Roy, F., Barbier, M., Biroli, G. & Bunin, G. PLoS Comput. Biol. 16, e1007827 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Faster monitoring of the invasive alien species (IAS) Dreissena polymorpha in river basins through isothermal amplification

    1.EU. No 1143/2014 of the European Parliament and of the Council of 22 October 2014 on the prevention and management of the introduction and spread of invasive alien species. Off. J. Eur. Union 2014, 35–55 (2014).
    Google Scholar 
    2.Ludyanskiy, M. L., McDonald, D. & MacNeill, D. Impact of the Zebra Mussel, a Bivalve Invader. Bioscience 43, 533–544 (1993).Article 

    Google Scholar 
    3.Lalaguna, C. D. & Marco, A. A. The zebra mussel invasion in Spain and navigation rules. Aquat. Invasions 3, 315–324 (2008).Article 

    Google Scholar 
    4.Rajagopal, S. et al. Origin of spanish invasion by the zebra mussel, dreissena polymorpha (pallas, 1771) revealed by amplified fragment length polymorphism (AFLP) fingerprinting. Biol. Invasions 11, 2147–2159 (2009).Article 

    Google Scholar 
    5.CABI Invasive species Compendium: Dreissena polymorpha (zebra mussel) 2017. https://www.cabi.org/isc/datasheet/85295.6.Marescaux, J. & Van Doninck, K. Using DNA barcoding to differentiate invasive Dreissena species (Mollusca, Bivalvia). Zookeys 365, 235–244 (2013).Article 

    Google Scholar 
    7.Benson, A. J., Raikow, D., Larson, J., Fusaro, A., & Bogdanoff, A. K. Dreissena polymorpha (Pallas, 1771): U.S. Geological Survey, Nonindigenous Aquatic Species Database. Gainesville, FL (2017).8.Minchin, D., Lucy, F. & Sullivan, M. Zebra Mussel: Impacts and Spread. Invasive Aquat. Species Eur. Distrib. Impacts Manag. 135–146. © 2002 Kluwer Acad. Publ. Dordreicht, Netherlands. 135–148 (2002) doi:https://doi.org/10.1007/978-94-015-9956-6_15.9.Montero Melendez, J. Control of invasive alien species in Guadalquivir river basin. EURO-RIOB 2017. https://www.riob.org/es/node/404510.Molloy, D. P., Karatayev, A., Burlakova, L. E., Kurandina, D. P. & Laruelle, F. Natural enemies of zebra mussels: predators, parasites, and ecological competitors. Rev. Fish. Sci. 5, 27–97 (1997).Article 

    Google Scholar 
    11.Nalepa, T. F. & Schloesser, D. W. Zebra Mussels Biology, Impacts, and Control (Lewis Publishers, Boca Raton, 1993).
    Google Scholar 
    12.Birnbaum, C. NOBANIS – Invasive Alien Species Fact Sheet – Dreissena polymorpha. Accessed 2 Dec 2019. https://www.nobanis.org (Online Database of the European Network on Invasive Alien Species – NOBANIS, 2011).13.Lowe, S., Browne, M., Boudjelas, S., De Poorter, M. 100 of the World’s Worst Invasive Alien Species A selection from the Global Invasive Species
    Database. (The Invasive Species Specialist Group (ISSG), 2000).14.Glomski, L. M. Zebra Mussel Chemical Control Guide. US Army Corps of Engineers: Waterways Experiment Station. https://erdclibrary.erdc.dren.mil/jspui/bitstream/11681/6966/1/ERDC-EL-TR-15-9.pdf (2015). 15.Boelman, S. F., Neilson, F. M., Dardeau, E. A. & Cross, T. Zebra mussel (Dreissena polymorpha) control handbook for facility operators, first edition . US Army Corps of Engineers: Waterways Experiment Station. https://hdl.handle.net/11681/2966 (1997).16.Durán, C., Lanao, M., Anadón, A. & Touyá, V. Management in practice management strategies for the zebra mussel invasion in the Ebro River basin. Aquat. Invasions 5, 309–316 (2010).Article 

    Google Scholar 
    17.Bij de Vaate, A. Rajagopal, S. & van der Velde, G. The zebra mussel in Europe: summary and synthesis. in The Zebra Mussel in Europe (ed van der Velde, G.
    et al.) 415–421 (Backhuys Publishers, 2010).18.Herder, J. et al. Environmental DNA—a review of the possible applications for the detection of (invasive) species. Report 2013-104. Accessed 3 May 2019. https://www.researchgate.net/publication/283267157_Environmental_DNA_-_a_review_of_the_possible_applications_for_the_detection_of_invasive_species#fullTextFileContent (Stichting RAVON, Nijmegen, 2014).19.Xiong, W., Li, H. & Zhan, A. Early detection of invasive species in marine ecosystems using high-throughput sequencing: technical challenges and possible solutions. Mar. Biol. 163, 1–12 (2016).CAS 
    Article 

    Google Scholar 
    20.Harvey, C. T., Qureshi, S. A. & MacIsaac, H. J. Detection of a colonizing, aquatic, non-indigenous species. Divers. Distrib. 15, 429–437 (2009).Article 

    Google Scholar 
    21.Jerde, C. L., Mahon, A. R., Chadderton, W. L. & Lodge, D. M. ‘Sight-unseen’ detection of rare aquatic species using environmental DNA. Conserv. Lett. 4, 150–157 (2011).Article 

    Google Scholar 
    22.Dejean, T. et al. Improved detection of an alien invasive species through environmental DNA barcoding: the example of the American bullfrog Lithobates catesbeianus. J. Appl. Ecol. 49, 953–959 (2012).Article 

    Google Scholar 
    23.Jerde, C. L. et al. Detection of Asian carp DNA as part of a Great Lakes basin-wide surveillance program. Can. J. Fish. Aquat. Sci. 70, 522–526 (2013).CAS 
    Article 

    Google Scholar 
    24.Laramie, M. B., Pilliod, D. S. & Goldberg, C. S. Characterizing the distribution of an endangered salmonid using environmental DNA analysis. Biol. Conserv. 183, 29–37 (2015).Article 

    Google Scholar 
    25.Takahara, T., Minamoto, T. & Doi, H. Using environmental DNA to estimate the distribution of an invasive fish species in ponds. PLoS ONE 8, e56584 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Gingera, T. D., Bajno, R., Docker, M. F. & Reist, J. D. Environmental DNA as a detection tool for zebra mussels Dreissena polymorpha (Pallas, 1771) at the forefront of an invasion event in Lake Winnipeg, Manitoba, Canada. Manag. Biol. Invasions 8, 287–300 (2017).Article 

    Google Scholar 
    27.Darling, J. A. & Mahon, A. R. From molecules to management: adopting DNA-based methods for monitoring biological invasions in aquatic environments. Environ. Res. 111, 978–988 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Kaprou, G. D. et al. Miniaturized devices for isothermal DNA amplification addressing DNA diagnostics. Microsyst. Technol. 22, 1529–1534 (2016).CAS 
    Article 

    Google Scholar 
    29.Mori, Y. & Notomi, T. Loop-mediated isothermal amplification (LAMP): a rapid, accurate, and cost-effective diagnostic method for infectious diseases. J. Infect. Chemother. 15, 62–69 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Fang, X., Liu, Y., Kong, J. & Jiang, X. Loop-mediated isothermal amplification integrated on microfluidic chips for point-of-care quantitative detection of pathogens. Anal. Chem. 82, 3002–3006 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    31.Rafati, A. & Gill, P. Microfluidic method for rapid turbidimetric detection of the DNA of Mycobacterium tuberculosis using loop-mediated isothermal amplification in capillary tubes. Microchim. Acta 182, 523–530 (2014).Article 
    CAS 

    Google Scholar 
    32.Notomi, T. et al. Loop-mediated isothermal amplification of DNA. Nucleic Acids Res. 28, e63 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Mori, Y., Kitao, M., Tomita, N. & Notomi, T. Real-time turbidimetry of LAMP reaction for quantifying template DNA. J. Biochem. Biophys. Methods 59, 145–157 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Tomita, N., Mori, Y., Kanda, H. & Notomi, T. Loop-mediated isothermal amplification (LAMP) of gene sequences and simple visual detection of products. Nat. Protoc. 3, 877–882 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Garrido-Maestu, A., Fuciños, P., Azinheiro, S., Carvalho, J. & Prado, M. Systematic loop-mediated isothermal amplification assays for rapid detection and characterization of Salmonella spp., Enteritidis and Typhimurium in food samples. Food Control 80, 297–306 (2017).CAS 
    Article 

    Google Scholar 
    36.Verkaar, E., Nijman, I., Boutaga, K. & Lenstra, J. Differentiation of cattle species in beef by PCR-RFLP of mitochondrial and satellite DNA. Meat Sci. 60, 365–369 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Wilson-Wilde, L., Norman, J. & Robertson, J. et al. Current issues in species identification for forensic science and the validity of using the cytochrome oxidase I
    (COI) gene. Forensic Sci. Med. Pathol. 6, 233–241. https://doi.org/10.1007/s12024-010-9172-y (2010). CAS 
    PubMed 
    Article 

    Google Scholar 
    38.Hebert, P. D. N., Cywinska, A., Ball, S. L. & Jeremy, R. Biological identifications through DNA barcodes. Proc. Biol. Sci. 270(1512), 313–321 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Staroscik, A. Copy number calculator for realtime PCR. http://www.scienceprimer.com/copy-number-calculator-for-realtime-pcr (2012).40.Ficetola, G. F., Miaud, C., Pompanon, F. & Taberlet, P. Species detection using environmental DNA from water samples. Biol. Lett. 4, 423–425 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Zanoli, L. M. & Spoto, G. Isothermal amplification methods for the detection of nucleic acids in microfluidic devices. Biosensors 3, 18–43 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    42.Egan, S. P. et al. Rapid molecular detection of invasive species in ballast and harbor water by integrating environmental DNA and light transmission spectroscopy. Environ. Sci. Technol. 49, 4113–4121 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    43.Xia, Z. et al. Early detection of a highly invasive bivalve based on environmental DNA (eDNA). Biol. Invasions 20, 437–447 (2018).Article 

    Google Scholar 
    44.Williams, M. R. et al. Isothermal amplification of environmental DNA (eDNA) for direct field-based monitoring and laboratory confirmation of Dreissena sp. PLoS ONE 12, e0186462 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    45.Frackman, B. S., Kobs, G., Simpson, D., Storts, D. & Corporation, P. Betaine and DMSO: enhancing agents for PCR. Promega Notes 65, 9–12 (1998).
    Google Scholar 
    46.Wang, D.-G., Brewster, J., Paul, M. & Tomasula, P. Two methods for increased specificity and sensitivity in loop-mediated isothermal amplification. Molecules 20, 6048–6059 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Jantz, B. & Neumann, D. Growth and reproductive cycle of the zebra mussel in the River Rhine as studied in a river bypass. Oecologia 114, 213–225 (1998).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Grigorovich, I. A., Kelly, J. R., Darling, J. A. & West, C. W. The quagga mussel invades the Lake Superior Basin. J. Great Lakes Res. 34, 342–350 (2008).Article 

    Google Scholar 
    49.Mahon, A. R. et al. Molecular detection of invasive species in heterogeneous mixtures using a microfluidic carbon nanotube platform. PLoS ONE 6, 1–5 (2011).Article 
    CAS 

    Google Scholar 
    50.PrimerExplorer. LAMP Primer Designing Software (Fujitsu Ltd, Tokyo, 2005).
    Google Scholar 
    51.Untergasser, A. et al. Primer3-new capabilities and interfaces. Nucleic Acids Res. 40, e115–e115 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar  More

  • in

    Engineering complex communities by directed evolution

    1.Mueller, U. G. & Sachs, J. L. Engineering microbiomes to improve plant and animal health. Trends Microbiol. 23, 606–617 (2015).CAS 
    Article 

    Google Scholar 
    2.Gilbert, E. S., Walker, A. W. & Keasling, J. D. A constructed microbial consortium for biodegradation of the organophosphorus insecticide parathion. Appl. Microbiol. Biotechnol. 61, 77–81 (2003).CAS 
    Article 

    Google Scholar 
    3.Yoshida, S., Ogawa, N., Fujii, T. & Tsushima, S. Enhanced biofilm formation and 3-chlorobenzoate degrading activity by the bacterial consortium of Burkholderia sp. NK8 and Pseudomonas aeruginosa PAO1. J. Appl. Microbiol. 106, 790–800 (2009).CAS 
    Article 

    Google Scholar 
    4.Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl Acad. Sci. USA 116, 15979–15984 (2019).CAS 
    Article 

    Google Scholar 
    5.Herrera Paredes, S. et al. Design of synthetic bacterial communities for predictable plant phenotypes. PLoS Biol. 16, e2003962 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    6.Minty, J. J. et al. Design and characterization of synthetic fungal-bacterial consortia for direct production of isobutanol from cellulosic biomass. Proc. Natl Acad. Sci. USA 110, 14592–14597 (2013).CAS 
    Article 

    Google Scholar 
    7.Jiang, Y., Dong, W., Xin, F. & Jiang, M. Designing synthetic microbial consortia for biofuel production. Trends Biotechnol. 38, 828–831 (2020).CAS 
    Article 

    Google Scholar 
    8.Eng, A. & Borenstein, E. Microbial community design: methods, applications, and opportunities. Curr. Opin. Biotechnol. 58, 117–128 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).CAS 
    Article 

    Google Scholar 
    10.Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F. & Sanchez, A. High-order interactions distort the functional landscape of microbial consortia. PLoS Biol. 17, e3000550 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Senay, Y., John, G., Knutie, S. A. & Brandon Ogbunugafor, C. Deconstructing higher-order interactions in the microbiota: a theoretical examination. Preprint at bioRxiv https://doi.org/10.1101/647156 (2019).12.Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl Acad. Sci. USA 115, E11951–E11960 (2018).CAS 
    Article 

    Google Scholar 
    13.Mickalide, H. & Kuehn, S. Higher-order interaction between species inhibits bacterial invasion of a phototroph-predator microbial community. Cell Syst. 9, 521–533.e10 (2019).CAS 
    Article 

    Google Scholar 
    14.Sanchez, A. Defining higher-order interactions in synthetic ecology: lessons from physics and quantitative genetics. Cell Syst. 9, 519–520 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Guo, X. & Boedicker, J. Q. The contribution of high-order metabolic interactions to the global activity of a four-species microbial community. PLoS Comput. Biol. 12, e1005079 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    16.Sundarraman, D. et al. Higher-order interactions dampen pairwise competition in the zebrafish gut microbiome. mBio 11, e01667-20 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Goldman, R. P. & Brown, S. P. Making sense of microbial consortia using ecology and evolution. Trends Biotechnol. 27, 3–4 (2009).CAS 
    Article 

    Google Scholar 
    18.Brenner, K., You, L. & Arnold, F. H. Response to Goldman and Brown: Making sense of microbial consortia using ecology and evolution. Trends Biotechnol. 27, 4 (2009).CAS 
    Article 

    Google Scholar 
    19.Escalante, A. E., Rebolleda-Gómez, M., Benítez, M. & Travisano, M. Ecological perspectives on synthetic biology: insights from microbial population biology. Front. Microbiol. 6, 143 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Gilmore, S. P. et al. Top-down enrichment guides in formation of synthetic microbial consortia for biomass degradation. ACS Synth. Biol. 8, 2174–2185 (2019).CAS 
    Article 

    Google Scholar 
    21.Cortes-Tolalpa, L., Jiménez, D. J., de Lima Brossi, M. J., Salles, J. F. & van Elsas, J. D. Different inocula produce distinctive microbial consortia with similar lignocellulose degradation capacity. Appl. Microbiol. Biotechnol. https://doi.org/10.1007/s00253-016-7516-6 (2016).22.Lee, D.-J., Show, K.-Y. & Wang, A. Unconventional approaches to isolation and enrichment of functional microbial consortium – a review. Bioresour. Technol. 136, 697–706 (2013).CAS 
    Article 

    Google Scholar 
    23.Lazuka, A., Auer, L., O’Donohue, M. & Hernandez-Raquet, G. Anaerobic lignocellulolytic microbial consortium derived from termite gut: enrichment, lignocellulose degradation and community dynamics. Biotechnol. Biofuels 11, 284 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Puentes-Téllez, P. E. & Falcao Salles, J. Construction of effective minimal active microbial consortia for lignocellulose degradation. Microb. Ecol. 76, 419–429 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    25.He, X., McLean, J. S., Guo, L., Lux, R. & Shi, W. The social structure of microbial community involved in colonization resistance. ISME J. 8, 564–574 (2014).Article 

    Google Scholar 
    26.Jung, J., Philippot, L. & Park, W. Metagenomic and functional analyses of the consequences of reduction of bacterial diversity on soil functions and bioremediation in diesel-contaminated microcosms. Sci. Rep. 6, 23012 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Franklin, R. B. & Mills, A. L. Structural and functional responses of a sewage microbial community to dilution-induced reductions in diversity. Microb. Ecol. 52, 280–288 (2006).Article 

    Google Scholar 
    28.Kang, D. et al. Enrichment and characterization of an environmental microbial consortium displaying efficient keratinolytic activity. Bioresour. Technol. 270, 303–310 (2018).CAS 
    Article 

    Google Scholar 
    29.Goodnight, C. J. Evolution in metacommunities. Phil. Trans. R. Soc. B 366, 1401–1409 (2011).Article 

    Google Scholar 
    30.Swenson, W., Wilson, D. S. & Elias, R. Artificial ecosystem selection. Proc. Natl Acad. Sci. USA 97, 9110–9114 (2000).CAS 
    Article 

    Google Scholar 
    31.Jochum, M. D., McWilliams, K. L., Pierson, E. A. & Jo, Y.-K. Host-mediated microbiome engineering (HMME) of drought tolerance in the wheat rhizosphere. PLoS ONE 14, e0225933 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Mueller, U. G. et al. Artificial microbiome-selection to engineer microbiomes that confer salt-tolerance to plants. Preprint at bioRxiv https://doi.org/10.1101/081521 (2016).33.Panke-Buisse, K., Poole, A. C., Goodrich, J. K., Ley, R. E. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9, 980–989 (2015).CAS 
    Article 

    Google Scholar 
    34.Panke-Buisse, K., Lee, S. & Kao-Kniffin, J. Cultivated sub-populations of soil microbiomes retain early flowering plant trait. Microb. Ecol. https://doi.org/10.1007/s00248-016-0846-1 (2016).35.Arora, J., Mars Brisbin, M. A. & Mikheyev, A. S. Effects of microbial evolution dominate those of experimental host-mediated indirect selection. PeerJ 8, e9350 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    36.Swenson, W., Arendt, J. & Wilson, D. S. Artificial selection of microbial ecosystems for 3-chloroaniline biodegradation. Environ. Microbiol. 2, 564–571 (2000).CAS 
    Article 

    Google Scholar 
    37.Wright, R. J., Gibson, M. I. & Christie-Oleza, J. A. Understanding microbial community dynamics to improve optimal microbiome selection. Microbiome 7, 85 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Blouin, M., Karimi, B., Mathieu, J. & Lerch, T. Z. Levels and limits in artificial selection of communities. Ecol. Lett. 18, 1040–1048 (2015).Article 

    Google Scholar 
    39.Raynaud, T., Devers, M., Spor, A. & Blouin, M. Effect of the reproduction method in an artificial selection experiment at the community level. Front. Ecol. Evol. 7, 416 (2019).Article 

    Google Scholar 
    40.Chang, C.-Y., Osborne, M. L., Bajic, D. & Sanchez, A. Artificially selecting bacterial communities using propagule strategies. Evolution https://doi.org/10.1111/evo.14092 (2020).41.Arias-Sánchez, F. I., Vessman, B. & Mitri, S. Artificially selecting microbial communities: if we can breed dogs, why not microbiomes? PLoS Biol. 17, e3000356 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    42.Day, M. D., Beck, D. & Foster, J. A. Microbial communities as experimental units. BioScience 61, 398–406 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Wade, M. J. Group selections among laboratory populations of Tribolium. Proc. Natl Acad. Sci. USA 73, 4604–4607 (1976).CAS 
    Article 

    Google Scholar 
    44.Wade, M. J. An experimental study of group selection. Evolution 31, 134–153 (1977).Article 

    Google Scholar 
    45.Wade, M. J. A critical review of the models of group selection. Q. Rev. Biol. 53, 101–114 (1978).Article 

    Google Scholar 
    46.Goodnight, C. J. Experimental studies of community evolution I: The response to selection at the community level. Evolution 44, 1614–1624 (1990).Article 

    Google Scholar 
    47.Guo, X. & Boedicker, J. High-order interactions between species strongly influence the activity of microbial communities. Biophys. J. 110, 143a (2016).Article 

    Google Scholar 
    48.Stein, R. R. et al. Computer-guided design of optimal microbial consortia for immune system modulation. eLife 7, e30916 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Arnold, F. H. Innovation by evolution: bringing new chemistry to life (Nobel lecture). Angew. Chem. Int. Ed. 58, 14420–14426 (2019).CAS 
    Article 

    Google Scholar 
    50.Tracewell, C. A. & Arnold, F. H. Directed enzyme evolution: climbing fitness peaks one amino acid at a time. Curr. Opin. Chem. Biol. 13, 3–9 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Williams, H. T. P. & Lenton, T. M. Artificial selection of simulated microbial ecosystems. Proc. Natl Acad. Sci. USA 104, 8918–8923 (2007).CAS 
    Article 

    Google Scholar 
    52.Williams, H. T. P. & Lenton, T. M. in Advances in Artificial Life ECAL 2007. Lecture Notes in Computer Science, vol. 4648 (eds Almeida e Costa, F. et al.) 93–102 (Springer, 2007).53.Doulcier, G., Lambert, A., De Monte, S. & Rainey, P. B. Eco-evolutionary dynamics of nested Darwinian populations and the emergence of community-level heredity. eLife 9, e53433 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Xie, L., Yuan, A. E. & Shou, W. Simulations reveal challenges to artificial community selection and possible strategies for success. PLoS Biol. 17, e3000295 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Wilson, D. S. Complex interactions in metacommunities, with implications for biodiversity and higher levels of selection. Ecology 73, 1984–2000 (1992).Article 

    Google Scholar 
    56.Marsland, R. III et al. Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    57.Marsland, R., Cui, W., Goldford, J. & Mehta, P. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Marsland, R. III, Cui, W. & Mehta, P. A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns. Sci. Rep. 10, 3308 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Advani, M., Bunin, G. & Mehta, P. Statistical physics of community ecology: a cavity solution to MacArthur’s consumer resource model. J. Stat. Mech. 2018, 033406 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Lu, N., Sanchez-Gorostiaga, A., Tikhonov, M. & Sanchez, A. Cohesiveness in microbial community coalescence. Preprint at bioRxiv https://doi.org/10.1101/282723 (2018).62.Faith, J. J., Ahern, P. P., Ridaura, V. K., Cheng, J. & Gordon, J. I. Identifying gut microbe-host phenotype relationships using combinatorial communities in gnotobiotic mice. Sci. Transl. Med. 6, 220ra11 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    63.Estrela, S. et al. Metabolic rules of microbial community assembly. Preprint at bioRxiv https://doi.org/10.1101/2020.03.09.984278 (2020).64.Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 0109 (2017).Article 

    Google Scholar 
    65.Venturelli, O. S. et al. Deciphering microbial interactions in synthetic human gut microbiome communities. Mol. Syst. Biol. 14, e8157 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Hall, B. G. Experimental evolution of a new enzymatic function. II. Evolution of multiple functions for ebg enzyme in E. coli. Genetics 89, 453–465 (1978).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Smith, G. P. & Petrenko, V. A. Phage display. Chem. Rev. 97, 391–410 (1997).CAS 
    Article 

    Google Scholar 
    68.Bloom, J. D. & Arnold, F. H. In the light of directed evolution: pathways of adaptive protein evolution. Proc. Natl Acad. Sci. USA 106, 9995–10000 (2009).CAS 
    Article 

    Google Scholar 
    69.Romero, P. A., Krause, A. & Arnold, F. H. Navigating the protein fitness landscape with Gaussian processes. Proc. Natl Acad. Sci. USA 110, E193–E201 (2013).CAS 
    Article 

    Google Scholar 
    70.Ho, K.-L., Lee, D.-J., Su, A. & Chang, J.-S. Biohydrogen from cellulosic feedstock: dilution-to-stimulation approach. Int. J. Hydrog. Energy 37, 15582–15587 (2012).CAS 
    Article 

    Google Scholar 
    71.Shepherd, E. S., DeLoache, W. C., Pruss, K. M., Whitaker, W. R. & Sonnenburg, J. L. An exclusive metabolic niche enables strain engraftment in the gut microbiota. Nature 557, 434–438 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Ting, S.-Y. et al. Targeted depletion of bacteria from mixed populations by programmable adhesion with antagonistic competitor cells. Cell Host Microbe https://doi.org/10.1016/j.chom.2020.05.006 (2020).73.Sheth, R. U., Cabral, V., Chen, S. P. & Wang, H. H. Manipulating bacterial communities by in situ microbiome engineering. Trends Genet. 32, 189–200 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Lemon, K. P., Armitage, G. C., Relman, D. A. & Fischbach, M. A. Microbiota-targeted therapies: an ecological perspective. Sci. Transl. Med. 4, 137rv5 (2012).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    75.Harcombe, W. R. & Bull, J. J. Impact of phages on two-species bacterial communities. Appl. Environ. Microbiol. 71, 5254–5259 (2005).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    76.Chan, B. K. et al. Phage treatment of an aortic graft infected with Pseudomonas aeruginosa. Evol. Med. Public Health 2018, 60–66 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    77.Rillig, M. C., Tsang, A. & Roy, J. Microbial community coalescence for microbiome engineering. Front. Microbiol. 7, 1967 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Sierocinski, P. et al. A single community dominates structure and function of a mixture of multiple methanogenic communities. Curr. Biol. 27, 3390–3395.e4 (2017).CAS 
    Article 

    Google Scholar 
    79.Tilman, D. The ecological consequences of changes in biodiversity: a search for general principles. Ecology 80, 1455–1474 (1999).
    Google Scholar 
    80.Shade, A. et al. Fundamentals of microbial community resistance and resilience. Front. Microbiol. 3, 417 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    81.Kang, D. et al. Construction of simplified microbial consortia to degrade recalcitrant materials based on enrichment and dilution-to-extinction cultures. Front. Microbiol. 10, 3010 (2019).Article 

    Google Scholar 
    82.Zanaroli, G. et al. Characterization of two diesel fuel degrading microbial consortia enriched from a non acclimated, complex source of microorganisms. Microb. Cell Factories 9, 10 (2010).Article 
    CAS 

    Google Scholar 
    83.Peter, H. et al. Function-specific response to depletion of microbial diversity. ISME J. 5, 351–361 (2011).CAS 
    Article 

    Google Scholar 
    84.Pacheco, A. R., Moel, M. & Segrè, D. Costless metabolic secretions as drivers of interspecies interactions in microbial ecosystems. Nat. Commun. 10, 103 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    85.West, S. A., Griffin, A. S., Gardner, A. & Diggle, S. P. Social evolution theory for microorganisms. Nat. Rev. Microbiol. 4, 597–607 (2006).CAS 
    Article 

    Google Scholar 
    86.Scheuerl, T. et al. Bacterial adaptation is constrained in complex communities. Nat. Commun. 11, 754 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    87.Lewontin, R. C. The units of selection. Annu. Rev. Ecol. Syst. 1, 1–18 (1970).Article 

    Google Scholar 
    88.Marsland, R., Cui, W., Goldford, J. & Mehta, P. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Shoemaker, W. R., Locey, K. J. & Lennon, J. T. A macroecological theory of microbial biodiversity. Nat. Ecol. Evol. 1, 0107 (2017).Article 

    Google Scholar 
    90.Degnan, P. H., Taga, M. E. & Goodman, A. L. Vitamin B12 as a modulator of gut microbial ecology. Cell Metab. 20, 769–778 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    91.Degnan, P. H., Barry, N. A., Mok, K. C., Taga, M. E. & Goodman, A. L. Human gut microbes use multiple transporters to distinguish vitamin B12 analogs and compete in the gut. Cell Host Microbe 15, 47–57 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Reduced resilience of terrestrial ecosystems locally is not reflected on a global scale

    1.Smol, J. P. et al. Climate-driven regime shifts in the biological communities of arctic lakes. Proc. Natl Acad. Sci. USA 102, 4397–4402 (2005).CAS 
    Article 

    Google Scholar 
    2.Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).CAS 
    Article 

    Google Scholar 
    3.Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).CAS 
    Article 

    Google Scholar 
    4.Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of savanna and forest as alternative biome states. Science 334, 230–232 (2011).CAS 
    Article 

    Google Scholar 
    5.Su, H. et al. Long‐term empirical evidence, early warning signals and multiple drivers of regime shifts in a lake ecosystem. J. Ecol. https://doi.org/10.1111/1365-2745.13544 (2020).6.Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 486, 52–58 (2012).CAS 
    Article 

    Google Scholar 
    7.Steffen, W. et al. Trajectories of the Earth system in the Anthropocene. Proc. Natl Acad. Sci. 115, 8252–8259 (2018).CAS 
    Article 

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

    Google Scholar 
    9.Ratajczak, Z. et al. Abrupt change in ecological systems: inference and diagnosis. Trends Ecol. Evol. 33, 513–526 (2018).Article 

    Google Scholar 
    10.Pimm, S. L. The complexity and stability of ecosystems. Nature 307, 321–326 (1984).Article 

    Google Scholar 
    11.Holling, C. S. Engineering resilience versus ecological resilience. Eng. Ecol.Constraints 31, 32 (1996).
    Google Scholar 
    12.Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 4, 1075–1083 (2020).Article 

    Google Scholar 
    13.Carpenter, S. R. & Brock, W. A. Rising variance: a leading indicator of ecological transition. Ecol. Lett. 9, 311–318 (2006).CAS 
    Article 

    Google Scholar 
    14.Dakos, V. et al. Slowing down as an early warning signal for abrupt climate change. Proc. Natl Acad. Sci. USA 105, 14308–14312 (2008).CAS 
    Article 

    Google Scholar 
    15.Guttal, V. & Jayaprakash, C. Changing skewness: an early warning signal of regime shifts in ecosystems. Ecol. Lett. 11, 450–460 (2008).Article 

    Google Scholar 
    16.Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).CAS 
    Article 

    Google Scholar 
    17.Drake, J. M. & Griffen, B. D. Early warning signals of extinction in deteriorating environments. Nature 467, 456 (2010).CAS 
    Article 

    Google Scholar 
    18.Wang, R. et al. Flickering gives early warning signals of a critical transition to a eutrophic lake state. Nature 492, 419–422 (2012).Article 
    CAS 

    Google Scholar 
    19.Clements, C. F. & Ozgul, A. Including trait-based early warning signals helps predict population collapse. Nat. Commun. 7, 10984 (2016).CAS 
    Article 

    Google Scholar 
    20.Chevalier, M. & Grenouillet, G. Global assessment of early warning signs that temperature could undergo regime shifts. Sci. Rep. 8, 10058 (2018).Article 
    CAS 

    Google Scholar 
    21.Cole, L. E., Bhagwat, S. A. & Willis, K. J. Recovery and resilience of tropical forests after disturbance. Nat. Commun. 5, 3906 (2014).CAS 
    Article 

    Google Scholar 
    22.Willis, K. J., Jeffers, E. S. & Tovar, C. What makes a terrestrial ecosystem resilient? Science 359, 988–989 (2018).CAS 
    Article 

    Google Scholar 
    23.Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).CAS 
    Article 

    Google Scholar 
    24.Seddon, A. W., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).CAS 
    Article 

    Google Scholar 
    25.Ehleringer, J. R., Cerling, T. E. & Helliker, B. R. C4 photosynthesis, atmospheric CO2, and climate. Oecologia 112, 285–299 (1997).Article 

    Google Scholar 
    26.Higgins, S. I. & Scheiter, S. Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally. Nature 488, 209 (2012).CAS 
    Article 

    Google Scholar 
    27.Holmgren, M., Hirota, M., Van Nes, E. H. & Scheffer, M. Effects of interannual climate variability on tropical tree cover. Nat. Clim. Chang. 3, 755–758 (2013).Article 

    Google Scholar 
    28.Thornton, P. K., Ericksen, P. J., Herrero, M. & Challinor, A. J. Climate variability and vulnerability to climate change: a review. Glob. Change Biol. 20, 3313–3328 (2014).Article 

    Google Scholar 
    29.Ray, D. K., Gerber, J. S., MacDonald, G. K. & West, P. C. Climate variation explains a third of global crop yield variability. Nat. Commun. 6, 5989 (2015).CAS 
    Article 

    Google Scholar 
    30.Jha, S., Das, J. & Goyal, M. K. Assessment of risk and resilience of terrestrial ecosystem productivity under the influence of extreme climatic conditions over India. Sci. Rep. 9, 18923 (2019).CAS 
    Article 

    Google Scholar 
    31.Li, D., Wu, S., Liu, L., Zhang, Y. & Li, S. Vulnerability of the global terrestrial ecosystems to climate change. Glob. Change Biol. 24, 4095–4106 (2018).Article 

    Google Scholar 
    32.Gonzalez, P., Neilson, R. P., Lenihan, J. M. & Drapek, R. J. Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Glob. Ecol. Biogeogr. 19, 755–768 (2010).Article 

    Google Scholar 
    33.Wang, S. & Loreau, M. Ecosystem stability in space: α, β and γ variability. Ecol. Lett. 17, 891–901 (2014).Article 

    Google Scholar 
    34.Stenseth, N. C. et al. The effect of climatic forcing on population synchrony and genetic structuring of the Canadian lynx. Proc. Natl Acad. Sci. USA 101, 6056–6061 (2004).CAS 
    Article 

    Google Scholar 
    35.Koenig, W. D. & Liebhold, A. M. Temporally increasing spatial synchrony of North American temperature and bird populations. Nat. Clim. Chang. 6, 614–617 (2016).Article 

    Google Scholar 
    36.Sheppard, L. W., Bell, J. R., Harrington, R. & Reuman, D. C. Changes in large-scale climate alter spatial synchrony of aphid pests. Nat. Clim. Chang. 6, 610–613 (2016).Article 

    Google Scholar 
    37.Dakos, V., van Nes, E. H., Donangelo, R., Fort, H. & Scheffer, M. Spatial correlation as leading indicator of catastrophic shifts. Theor. Ecol. 3, 163–174 (2010).Article 

    Google Scholar 
    38.Paruelo, J. M., Epstein, H. E., Lauenroth, W. K. & Burke, I. C. ANPP estimates from NDVI for the central grassland region of the United States. Ecology 78, 953–958 (1997).Article 

    Google Scholar 
    39.Piao, S., Fang, J., Zhou, L., Tan, K. & Tao, S. Changes in biomass carbon stocks in China’s grasslands between 1982 and 1999. Global Biogeochem. Cycles 21, 2 (2007).
    Google Scholar 
    40.Maurer, G. E., Hallmark, A. J., Brown, R. F., Sala, O. E. & Collins, S. L. Sensitivity of primary production to precipitation across the United States. Ecol. Lett. 23, 527–536 (2020).Article 

    Google Scholar 
    41.Brown, J. H. & Kodric-Brown, A. Turnover rates in insular biogeography: effect of immigration on extinction. Ecology 58, 445–449 (1977).Article 

    Google Scholar 
    42.Earn, D. J., Levin, S. A. & Rohani, P. Coherence and conservation. Science 290, 1360–1364 (2000).CAS 
    Article 

    Google Scholar 
    43.Hodgson, D., McDonald, J. L. & Hosken, D. J. What do you mean,‘resilient’? Trends Ecol. Evol. 30, 503–506 (2015).Article 

    Google Scholar 
    44.Seidl, R. et al. Forest disturbances under climate change. Nat. Clim. Chang. 7, 395–402 (2017).Article 

    Google Scholar 
    45.Bernstein, L. et al. IPCC, 2007: Climate Change 2007: Synthesis Report. (IPCC, Geneva, 2008)46.Myers-Smith, I. H. et al. Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities. Environ. Res. Lett. 6, 045509 (2011).Article 

    Google Scholar 
    47.Myers-Smith, I. H. et al. Climate sensitivity of shrub growth across the tundra biome. Nat. Clim. Chang. 5, 887–891 (2015).Article 

    Google Scholar 
    48.Thompson, I., Mackey, B., McNulty, S. & Mosseler, A. Forest resilience, biodiversity, and climate change. In Secretariat of the Convention on Biological Diversity, Montreal. Technical Series 43, 1–67 (2009).
    Google Scholar 
    49.Carpenter, S. R. et al. Early warnings of regime shifts: a whole-ecosystem experiment. Science 332, 1079–1082 (2011).CAS 
    Article 

    Google Scholar 
    50.Gsell, A. S. et al. Evaluating early-warning indicators of critical transitions in natural aquatic ecosystems. Proc. Natl Acad. Sci. USA 113, E8089–E8095 (2016).CAS 
    Article 

    Google Scholar 
    51.Clements, C. F., Blanchard, J. L., Nash, K. L., Hindell, M. A. & Ozgul, A. Body size shifts and early warning signals precede the historic collapse of whale stocks. Nat. Ecol. Evol. 1, 0188 (2017).Article 

    Google Scholar 
    52.Dakos, V., Carpenter, S. R., van Nes, E. H. & Scheffer, M. Resilience indicators: prospects and limitations for early warnings of regime shifts. Philos. Trans. R. Soc. B, Biol. Sci. 370, 20130263 (2015).Article 

    Google Scholar 
    53.Zemp, D. C. et al. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8, 14681 (2017).CAS 
    Article 

    Google Scholar 
    54.Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Chang. 8, 539–543 (2018).Article 

    Google Scholar 
    55.Poorter, L. et al. Biomass resilience of Neotropical secondary forests. Nature 530, 211–214 (2016).CAS 
    Article 

    Google Scholar 
    56.Locosselli, G. M. et al. Global tree-ring analysis reveals rapid decrease in tropical tree longevity with temperature. Proc. Natl Acad. Sci. USA 117, 33358–33364 (2020).CAS 
    Article 

    Google Scholar 
    57.Ruiz-Pérez, G. & Vico, G. Effects of temperature and water availability on Northern European boreal forests. Front. For. Glob.Change 3, 34 (2020).Article 

    Google Scholar 
    58.Kitzberger, T., Aráoz, E., Gowda, J. H., Mermoz, M. & Morales, J. M. Decreases in fire spread probability with forest age promotes alternative community states, reduced resilience to climate variability and large fire regime shifts. Ecosystems 15, 97–112 (2012).Article 

    Google Scholar 
    59.Scheffer, M., Hirota, M., Holmgren, M., Van Nes, E. H. & Chapin, F. S. Thresholds for boreal biome transitions. Proc. Natl Acad. Sci. USA 109, 21384–21389 (2012).CAS 
    Article 

    Google Scholar 
    60.Newbold, T. et al. Climate and land-use change homogenise terrestrial biodiversity, with consequences for ecosystem functioning and human well-being. Emerg. Top. Life Sci. 3, 207–219 (2019).Article 

    Google Scholar 
    61.Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).Article 

    Google Scholar 
    62.Wang, S. et al. An invariability-area relationship sheds new light on the spatial scaling of ecological stability. Nat. Commun. 8, 1–8 (2017).Article 
    CAS 

    Google Scholar 
    63.Mehrabi, Z. & Ramankutty, N. Synchronized failure of global crop production. Nat. Ecol. Evol. 3, 780–786 (2019).Article 

    Google Scholar 
    64.Post, E. & Forchhammer, M. C. Spatial synchrony of local populations has increased in association with the recent Northern Hemisphere climate trend. Proc. Natl Acad. Sci. 101, 9286–9290 (2004).CAS 
    Article 

    Google Scholar 
    65.Ripa, J. Analysing the Moran effect and dispersal: their significance and interaction in synchronous population dynamics. Oikos 89, 175–187 (2000).Article 

    Google Scholar 
    66.Peterson, G., Allen, C. R. & Holling, C. S. Ecological resilience, biodiversity, and scale. Ecosystems 1, 6–18 (1998).Article 

    Google Scholar 
    67.Wang, S. & Loreau, M. Biodiversity and ecosystem stability across scales in metacommunities. Ecol. Lett. 19, 510–518 (2016).Article 

    Google Scholar 
    68.Dakos, V. et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PloS ONE 7, e41010 (2012).CAS 
    Article 

    Google Scholar 
    69.R core team. R: a language and environment for statistical computing. R Foundation for Statistical Computing https://www.R-project.org/ (2019).70.Bivand, R., Keitt, T. & Rowlingson, B. rgdal: bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.5-16 https://CRAN.R-project.org/package=rgdal (2020).71.Tucker, C. J. et al. An extended AVHRR 8‐km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498 (2005).Article 

    Google Scholar 
    72.Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).Article 

    Google Scholar 
    73.Holben, B. N. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 7, 1417–1434 (1986).Article 

    Google Scholar 
    74.Piao, S. et al. Changes in vegetation net primary productivity from 1982 to 1999 in China. Global Biogeochem. Cycles 19, 2 (2005).Article 
    CAS 

    Google Scholar 
    75.Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).Article 

    Google Scholar 
    76.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 1–18. (2020).Article 

    Google Scholar 
    77.Mitchell, A. The ESRI Guide to GIS Analysis: Spatial Measurements and Statistics (Environmental System Research Institute Press, 2005).78.Fang, J., Piao, S., He, J. & Ma, W. Increasing terrestrial vegetation activity in China, 1982–1999. Sci. China C Life Sci. 47, 229–240 (2004).
    Google Scholar 
    79.Peng, S. et al. Recent change of vegetation growth trend in China. Environ. Res. Lett. 6, 044027 (2011).Article 

    Google Scholar 
    80.Thenkabail, P. S. & Lyon, J. G. Hyperspectral Remote Sensing of Vegetation (CRC press, 2016).81.Feng, Y. et al. Changes in the trends of vegetation net primary productivity in China between 1982 and 2015. Environ. Res. Lett. 14, 124009 (2019).Article 

    Google Scholar 
    82.He, H. et al. Altered trends in carbon uptake in China’s terrestrial ecosystems under the enhanced summer monsoon and warming hiatus. Natl Sci. Rev. 6, 505–514 (2019).CAS 
    Article 

    Google Scholar  More

  • in

    Strong variations in urban allergenicity riskscapes due to poor knowledge of tree pollen allergenic potential

    1.Traidl-Hoffmann, C. et al. Impact of pollen on human health: More than allergen carriers?. Int. Arch. Allergy Immunol. 131, 1–13 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    2.Nicolaou, N., Siddique, N. & Custovic, A. Allergic disease in urban and rural populations: Increasing prevalence with increasing urbanization. Allergy Eur. J. Allergy Clin. Immunol. 60, 1357–1360 (2005).CAS 
    Article 

    Google Scholar 
    3.Biedermann, T. et al. Birch pollen allergy in Europe. Allergy Eur. J. Allergy Clin. Immunol. 74, 1237–1248 (2019).CAS 

    Google Scholar 
    4.D’Amato, G. et al. Meteorological conditions, climate change, new emerging factors, and asthma and related allergic disorders. A statement of the World Allergy Organization. World Allergy Organ. J. 8, 1–52 (2015).Article 
    CAS 

    Google Scholar 
    5.Braun-Fahrländer, C. et al. No further increase in asthma, hay fever and atopic sensitisation in adolescents living in Switzerland. Eur. Respir. J. 23, 407–413 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Ziska, L. H. et al. Temperature-related changes in airborne allergenic pollen abundance and seasonality across the northern hemisphere: A retrospective data analysis. Lancet Planet. Health 3, e124–e131 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    7.Ziello, C. et al. Changes to airborne pollen counts across Europe. PLoS ONE 7, e34076 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Wolkovich, E. M. et al. Warming experiments underpredict plant phenological responses to climate change. Nature 485, 494–497 (2012).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Sierra-Heredia, C. et al. Aeroallergens in Canada: Distribution, public health impacts, and opportunities for prevention. Int. J. Environ. Res. Public Health 15, 1577 (2018).PubMed Central 
    Article 

    Google Scholar 
    10.Reed, S. D., Lee, T. A. & McCrory, D. C. The economic burden of allergic rhinitis: A critical evaluation of the literature. Pharmacoeconomics 22, 345–361 (2004).PubMed 
    Article 

    Google Scholar 
    11.Ziter, C. D., Pedersen, E. J., Kucharik, C. J. & Turner, M. G. Scale-dependent interactions between tree canopy cover and impervious surfaces reduce daytime urban heat during summer. Proc. Natl. Acad. Sci. U. S. A. 116, 7575–7580 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Akbari, H. & Konopacki, S. Calculating energy-saving potentials of heat-island reduction strategies. Energy Policy 33, 721–756 (2005).Article 

    Google Scholar 
    13.World Health Organization. Urban Green Spaces and Health: a Review of Evidence. (2016).14.Keddem, S. et al. Mapping the urban asthma experience: Using qualitative GIS to understand contextual factors affecting asthma control. Soc. Sci. Med. 140, 9–17 (2015).PubMed 
    Article 

    Google Scholar 
    15.Lovasi, G. S. et al. Urban tree canopy and asthma, wheeze, rhinitis, and allergic sensitization to tree pollen in a New York city birth cohort. Environ. Health Perspect. 121, 494–500 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Buters, J. T. M. et al. Release of Bet v 1 from birch pollen from 5 European countries. Results from the HIALINE study. Atmos. Environ. 55, 496–505 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    17.Asam, C., Hofer, H., Wolf, M., Aglas, L. & Wallner, M. Tree pollen allergens—An update from a molecular perspective. Allergy Eur. J. Allergy Clin. Immunol. 70, 1201–1211 (2015).CAS 
    Article 

    Google Scholar 
    18.D’Amato, G. et al. Allergenic pollen and pollen allergy in Europe. Allergy Eur. J. Allergy Clin. Immunol. 62, 976–990 (2007).Article 
    CAS 

    Google Scholar 
    19.Weber, R. W. Patterns of pollen cross-allergenicity. J. Allergy Clin. Immunol. 112, 229–239 (2003).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Damialis, A., Traidl-Hoffmann, C. & Treudler, R. Climate Change and Pollen Allergies. In Biodiversity and Health in the Face of Climate Change (eds. Marselle, M., Stadler, J., Korn, H., Irvine, K. & Bonn, A.) 47–66 (Springer, Cham, 2019).21.Sousa-Silva, R., Smargiassi, A., Paquette, A., Kaiser, D. & Kneeshaw, D. Exactly what do we know about tree pollen allergenicity?. Lancet Respir. Med. 6, 1869–1876 (2020).
    Google Scholar 
    22.Radauer, C. & Breiteneder, H. Pollen allergens are restricted to few protein families and show distinct patterns of species distribution. J. Allergy Clin. Immunol. 117, 141–147 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Gassner, M., Schmid-Grendelmeier, P. & Clot, B. Ash pollen allergy and aerobiology. Allergo J. Int. 28, 289–298 (2019).Article 

    Google Scholar 
    24.Imhof, K., Probst, E., Seifert, B., Regenass, S. & Schmid-Grendelmeier, P. Ash pollen allergy: Reliable detection of sensitization on the basis of IgE to Ole e 1. Allergo J. Int. 23, 78–83 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Lin, R. Y., Clauss, A. E. & Bennett, E. S. Hypersensitivity to common tree pollens in New York city patients. Allergy Asthma Proc. 23, 253–258 (2002).PubMed 

    Google Scholar 
    26.Ribeiro, H. et al. Pollen allergenic potential nature of some trees species: A multidisciplinary approach using aerobiological, immunochemical and hospital admissions data. Environ. Res. 109, 328–333 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    27.Yun, Y. Y., Ko, S. H., Park, J. W. & Hong, C. S. IgE immune response to Ginkgo biloba pollen. Ann. Allergy Asthma Immunol. 85, 298–302 (2000).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Hruska, K. Assessment of urban allergophytes using an allergen index. Aerobiologia 19, 107–111 (2003).Article 

    Google Scholar 
    29.Cariñanos, P., Casares-Porcel, M. & Quesada-Rubio, J. M. Estimating the allergenic potential of urban green spaces: A case-study in Granada, Spain. Landsc. Urban Plan. 123, 134–144 (2014).Article 

    Google Scholar 
    30.Friedman, J. & Barrett, S. C. H. Wind of change: New insights on the ecology and evolution of pollination and mating in wind-pollinated plants. Ann. Bot. 103, 1515–1527 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Kasprzyk, I., Wójcik, T., Cariñanos, P., Borycka, K. & Ćwik, A. Evaluation of the allergenicity of various types of urban parks in a warm temperate climate zone. Aerobiologia 35, 57–71 (2019).Article 

    Google Scholar 
    32.Jochner-Oette, S., Stitz, T., Jetschni, J. & Cariñanos, P. The influence of individual-specific plant parameters and species composition on the allergenic potential of urban green spaces. Forests 9, 284 (2018).Article 

    Google Scholar 
    33.Zong, H., Yao, M., Tang, Y. & Chen, H. Assessing the composition, diversity, and allergenic risk of street trees in Qingyang District of Chengdu City. Urban For. Urban Green. 54, 126747 (2020).Article 

    Google Scholar 
    34.Cariñanos, P., Adinolfi, C., DíazdelaGuardia, C., De Linares, C. & Casares-Porcel, M. Characterization of allergen emission sources in urban areas. J. Environ. Qual. 45, 244–252 (2016).PubMed 
    Article 
    CAS 

    Google Scholar 
    35.Lai, Y. & Kontokosta, C. E. The impact of urban street tree species on air quality and respiratory illness: A spatial analysis of large-scale, high-resolution urban data. Health Place 56, 80–87 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Baxi, S. N. & Phipatanakul, W. The role of allergen exposure and avoidance in asthma. Adolesc. Med. State Art Rev. 21, 57–71 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    37.Cariñanos, P. & Casares-Porcel, M. Urban green zones and related pollen allergy: A review. Some guidelines for designing spaces with low allergy impact. Landsc. Urban Plan. 101, 205–214 (2011).Article 

    Google Scholar 
    38.Lara, B. et al. Impact of plane tree abundance on temporal and spatial variations in pollen concentration. Forests 11, 817 (2020).Article 

    Google Scholar 
    39.Alcázar, P. et al. Airborne plane-tree (Platanus hispanica) pollen distribution in the city of Córdoba, South-western Spain, and possible implications on pollen allergy. J. Investig. Allergol. Clin. Immunol. 14, 238–243 (2004).PubMed 
    PubMed Central 

    Google Scholar 
    40.AAAAI. Allergies & Gardening. (2020). https://www.aaaai.org/conditions-and-treatments/library/allergy-library/allergy-friendly-gardening. Accessed 11th Oct 2020.41.Ortolani, C. et al. Allergenicità delle piante arboree e arbustive destinate al verde urbano italiano. Revisione Sistematica e Raccomandazioni basate sull’evidenza. Eur. J. Aerobiol. Environ. Med. XI, (2015).42.Cecchi, L. Introduction. In Allergenic Pollen: A Review of the Production, Release, Distribution and Health Impacts, Vol. 9789400748, 1–7 (Springer Netherlands, 2013).43.Mossabeb, R., Kraft, D. & Valenta, R. Evaluation of the allergenic potential of Ginkgo biloba extracts. Wien. Klin. Wochenschr. 113, 580–587 (2001).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.Sercombe, J. K. et al. London Plane Tree bioaerosol exposure and allergic sensitization in Sydney. Australia. Ann. Allergy Asthma Immunol. 107, 493–500 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Lo, F., Bitz, C. M., Battisti, D. S. & Hess, J. J. Pollen calendars and maps of allergenic pollen in North America. Aerobiologia 35, 613–633 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Hanski, I. et al. Environmental biodiversity, human microbiota, and allergy are interrelated. Proc. Natl. Acad. Sci. USA. 109, 8334–8339 (2012).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Haahtela, T. A biodiversity hypothesis. Allergy Eur. J. Allergy Clin. Immunol. 74, 1445–1456 (2019).
    Google Scholar 
    48.CDC & National Center for Health Statistics. FastStats – Allergies and Hay Fever. U.S. Department of Health & Human Services (2014). https://www.cdc.gov/nchs/fastats/allergies.htm. Accessed: 27th April 2020.49.American College of Allergy Asthma and Immunology. Allergy Facts. (2018).50.Bousquet, J. et al. Allergic rhinitis and its impact on asthma (ARIA) 2008 update (in collaboration with the World Health Organization, GA2LEN and AllerGen). Allergy Eur. J. Allergy Clin. Immunol. 63, 8–160 (2008).Article 

    Google Scholar 
    51.Bastl, K., Berger, M., Bergmann, K. C., Kmenta, M. & Berger, U. The medical and scientific responsibility of pollen information services. Wien. Klin. Wochenschr. 129, 70–74 (2017).PubMed 
    Article 

    Google Scholar 
    52.van Velthoven, M. H. & Smith, C. Some considerations on digital health validation. npj Digit. Med. 2, 1–2 (2019).Article 

    Google Scholar 
    53.Roman, L. A. et al. Beyond ‘trees are good’: Disservices, management costs, and tradeoffs in urban forestry. Ambio 50, 615–630 (2021).Article 
    PubMed 

    Google Scholar 
    54.Vogt, J. et al. Citree: A database supporting tree selection for urban areas in temperate climate. Landsc. Urban Plan. 157, 14–25 (2017).Article 

    Google Scholar 
    55.Eisenman, T. S. et al. Urban trees, air quality, and asthma: An interdisciplinary review. Landsc. Urban Plan. 187, 47–59 (2019).Article 

    Google Scholar 
    56.van Dorn, A. Urban planning and respiratory health. Lancet Respir. Med. 5, 781–782 (2017).PubMed 
    Article 

    Google Scholar 
    57.Poland, T. M. & McCullough, D. G. Emerald ash borer: Invasion of the urban forest and the threat to North America’s ash resource. J. For. 104, 118–124 (2006).
    Google Scholar 
    58.Ahern, J. From fail-safe to safe-to-fail: Sustainability and resilience in the new urban world. Landsc. Urban Plan. 100, 341–343 (2011).Article 

    Google Scholar 
    59.Conway, T. M. & Vander Vecht, J. Growing a diverse urban forest: Species selection decisions by practitioners planting and supplying trees. Landsc. Urban Plan. 138, 1–10 (2015).Article 

    Google Scholar 
    60.Konijnendijk, C. C., Ricard, R. M., Kenney, A. & Randrup, T. B. Defining urban forestry—A comparative perspective of North America and Europe. Urban For. Urban Green. 4, 93–103 (2006).Article 

    Google Scholar 
    61.Katz, D. S. W. & Batterman, S. A. Urban-scale variation in pollen concentrations: A single station is insufficient to characterize daily exposure. Aerobiologia (Bologna). 36, 417–431 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Weinberger, K. R. et al. Levels and determinants of tree pollen in New York City. J. Expo. Sci. Environ. Epidemiol. 28, 119–124 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Zimmermann, B. & Kohler, A. Infrared spectroscopy of pollen identifies plant species and genus as well as environmental conditions. PLoS ONE 9, e95417 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Bell, K. L. et al. Pollen DNA barcoding: Current applications and future prospects. Genome 59, 629–640 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.California Department of Public Health (CDPH). Strategic Plan for Asthma in California, 2015–2019. (2015).66.Giguère, M. Mesures de lutte aux îlots de chaleur urbains. Revue de littérature. (2009).67.Ogren, T. L. The Allergy-Fighting Garden: Stop Asthma and Allergies with Smart Landscaping. (2015).68.RNSA. Guide d’information de la végétation en ville. (2016).69.Pollen.com. About Pollen.com. (2020). https://www.pollen.com/help/about. Accessed 20 Apr 2020.70.Nock, C. A., Paquette, A., Follett, M., Nowak, D. J. & Messier, C. Effects of urbanization on tree species functional diversity in eastern North America. Ecosystems 16, 1487–1497 (2013).Article 

    Google Scholar 
    71.Tuomisto, H. Commentary: Do we have a consistent terminology for species diversity? Yes, if we choose to use it. Oecologia 167, 903–911 (2011).ADS 
    Article 

    Google Scholar 
    72.R Core Team. R: A Language and Environment for Statistical Computing. (2019).73.Chamberlain, S. A. & Szöcs, E. Taxize: Taxonomic search and retrieval in R. 2, (2013).74.Oksanen, J. et al. vegan: Community Ecology Package. (2017).75.Green, B. J. et al. Landscape plant selection criteria for the allergic patient. J. Allergy Clin. Immunol. Pract. 6, 1869–1876 (2018).PubMed 
    Article 

    Google Scholar 
    76.Asselin, S., Bachand, S., Christin, C. & Bonvalot, Y. Liens entre les pollens allergènes, leur mesure et les symptômes ressentis. (1998). More

  • 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

    Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales

    Strains and mediaThe set of 16 strains used in this experiment contains environmental isolates along with strains from the ATCC collection (Supplementary Table 1). The strains were chosen based on two criteria: a distinct colony morphology that would enable visual identification when plated on an NB agar plate; and ability to coexist for ~60 generations with at least two other strains in our collection.All cultures were grown in M9 minimal salts media containing 1X M9 salts, 2 mM MgSO4, 0.1 mM CaCl2, 1X trace metal solution (Teknova), supplemented with 3 mM galacturonic acid (Sigma), 6.1 mM Serine (Sigma), and 9.1 mM sodium acetate as carbon sources, which correspond to 16.67 mM carbon atoms for each compound and 50 mM overall. We chose a combination of carbon sources representing three chemical groups—a carbohydrate, an amino acid, and a carboxylic acid—in order to promote the survival and coexistence of a diverse set of species. The media was prepared on the day of each transfer. A carbon source mixture was prepared ahead at 10X, and was kept in aliquots at 4 °C for up to four weeks.Evolution experimentFrozen stocks of individual species were streaked out on nutrient agar Petri plates and grown at 28 °C. After 48 h single colonies were picked and inoculated into 15 ml falcon tubes containing 3 ml nutrient broth (5 g/L peptone BD difco, BD Bioscience; 3 g/L yeast extract BD difco, BD Bioscience), and were grown overnight at 28 °C shaken at 250 rpm. Initial mixtures were prepared by diluting each species separately to an OD of ({10}^{-2}) and mixing the normalized cultures at equal volumes. OD measurements were done using a Epoch2 microplate reader (BioTek) and were recorded using the Gen5 v3.09 software (BioTek). After mixing, the cocultures were aliquoted to replicates and further diluted to a final OD of ({10}^{-4}), at which the evolutionary experiment was initialized. The number of replicates for each community varied between 3 and 18 (Supplementary Data 1).Communities were grown in 96-well plates containing 200 µl M9 at 28°C and were shaken at 900 rpm. Every 48 h cultures were diluted by a factor of 1500 into fresh M9 media, and OD600 was measured. For this dilution factor, each cycle corresponds to ~10.5 generations. As 1 OD600 ~ of ({10}^{9}) C.F.U/ml, and communities reached ~ 0.5 OD600 and were grown in 200 µl and was diluted by 1500, ~({10}^{5}) cells were transferred each dilution. To avoid cross contaminations, cultures were grown in a checkerboard formation, meaning that each community was surrounded by wells containing media but no bacteria.At transfers 0, 2, 5, 7, 10, 14, 19, 30, and 38 community composition was measured by plating on nutrient agar plates (5 g/L peptone BD difco, BD Bioscience; 3 g/L yeast extract BD difco, BD Bioscience, 15 g/L agar Bacto, BD Bioscience) and counting colonies. For that, the cultures were diluted to an OD of (2.4* {10}^{-8})− (1* {10}^{-8}) and 100 µl of the diluted culture was plated on NB plates and spread using glass beads. Plates were incubated at 28 °C for 48 h and colonies were counted manually. The distribution of the number of colonies counted at each plate to infer community composition is found in Supplementary Fig. 11.We chose the communities based on a preliminary experiment that was conducted by the same protocol for six transfers. In this experiment, 114 of 171 possible pairs of a set of 19 strains (3 strains were not included in the evolution experiment) were cocultured. Pairs that had coexisted for the duration of this experiment, and were confidently distinguishable by colony morphology, and trios that are composed of these pairs, were used for the coevolutionary experiment. We started the evolutionary experiment with 51 pairs and 51 trios, and removed communities that did not coexist for the first ~70 from the final analysis. If a replicate was suspected to be contaminated it was also excluded from further analysis.Ecological experimentsWe supplemented the data of the evolutionary experiment with two ecological competition experiments with the same experimental condition. In order to assess whether communities typically reach an ecological equilibrium within ~50–70 generations (Supplementary Fig. 3), we cultured eight of the pairs that were used in the evolutionary experiment. This experiment was initiated in the same way as the evolutionary experiment, only that after the species’ starters were normalized they were inoculated at the varying initial fractions – 9:1, 5:5, 1:9. Because the normalization depended on optical density, there is a variation in the actual initial fractions between different pairs. Community composition was then measured on six transfers during this experiment: 0, 1, 2, 4, 5, and 6.In order to assess whether changes in composition are due to heritable changes in species’ phenotypes, we used strains that were re-isolated from 31 evolved pairs, and 13 pairs of ancestral strains (Supplementary Figs. 6, 7). Strains were replicated from glycerol stocks into the experimental media and grown for 24 h. The starters were normalized to initiate the competition assay at ({rm{OD}}={10}^{-4}) in fresh M9 media. Species were mixed at equal volume and were propagated for five cycles. community composition was measured at initial conditions, and at the end of the final cycle (5).Quantification of repeatabilityIn order to quantify the qualitative repeatability of different replicate communities we first identified which species was the maximally increasing member at each replicate, that is, which species had increased its abundance by the largest factor between generation 70 and 400. Then, we quantified the frequency of the replicates that had the same maximally increasing member for each community. This measure always produces a value between 1 and 1/n where n is the number of species in the community. We checked the distribution of the repeatability scores against the null hypothesis that the factor by which a species’ abundance increases during evolution is independent of the species or the community. For this, we shuffled the factor of change in relative abundance across all samples, for pairs and trios separately, and quantified the new repeatability scores of the shuffled data. Data of the null hypothesis were generated over 2000 times, and the p value was given by the probability to get a mean equal or above the real data mean.We used the average Euclidean distance of replicates from the median replicate in order to quantify the variability between replicate communities. In order to check whether the distribution of variabilities is similar to what can be expected of random communities, in which each species in the community is just as likely to have any relative abundance, we replaced the real fractions with fractions drawn from a uniform Dirichlet distribution with (underline{{boldsymbol{alpha }}}=underline{1}). We then checked the statistical difference between the two distributions using one-sided Mann–Whitney U test.Trio composition predictionsWe used the formerly established method for predicting the composition of trios from the composition of pairs that was developed by Abreu et al.14 In this approach the fraction of a species when grown in a multispecies community is predicted as the weighted geometric mean of the fraction of the species in all pairwise cultures. The accuracy of the predictions was measured as the Euclidean distance between the prediction and the mean composition of the observed trio, normalized to the largest possible distance between each two communities, (sqrt{n}), where n is the number of species.We used the factors by which species increased their abundance during coevolution in pairs (between generations ~70 and ~400) to predict which species would increase by the largest factor in trios. The maximally increasing member in a given community was assigned to be the one that was the maximally increasing member in the most replicates of that community. If the same species was the maximally increasing member in both pairs it was a member of, then this species was predicted to be the maximally increasing member of the trio. If in every pair a different species was the maximally increasing member, then we predicted that the maximally increasing member of the trio would be the one with the highest mean increase. Only two trios had such transient topology, where in each pair a different species increases, thus we are unable to determine the general utility of the latter approach.Re-isolationEach ~50 generations all communities were frozen at −80 °C with 50% glycerol in a 96-deep well plate. In order to re-isolate strains, stocks were inoculated to a 96-well plate containing the experimental media using a 96-pin replicator, and grown for 24 h at 28 °C. After growth, cultures were diluted by a factor of (2.4* {10}^{-8}) and 100 µl were spread on a nutrient agar plate using glass beads. Plates were kept at room temperature for at least two days and no longer than a week before re-isolations. 5-15 colonies of each strain were picked using a sterile toothpick, and pooled together into 200 µl M9. Re-isolated strains were incubated at 28 °C and shaken at 900 rpm for 24 h and kept in 50% glycerol stock at −80 °C until further use.Growth rates and carrying capacities of individually evolved strainsRe-isolated strains were replicated from glycerol stocks into the experimental media and grown for 24 h. The starters were normalized to initiate the growth assay at (OD={10}^{-4}) in fresh M9 media. The optical density was measured in two automated plate readers simultaneously, Epoch2 microplate reader (BioTek) and Synergy microplate reader (BioTek), and was recorded using Gen5 v3.09 software (BioTek). Plates were incubated at 28 °C with a 1 °C gradient to avoid condensation on the lid, and were shaken at 250 cpm. OD was measured every 10 min. Each strain was measured in four technical replicates, evenly distributed between the two plates, and 2–3 evolutionary replicates were measured for each species (replicates that evolved separately for the duration of the experiment). Growth rates were quantified as the number of divisions it takes a strain to grow from the initial OD of ({10}^{-4}) to an OD of (8* {10}^{-2}) (({log }_{2}frac{0.08}{{10}^{-4}})) divided by the time it took the strain to reach this OD. This measure gives the average doubling time during the initial growth and also accounts for the lag times of the strain. The growth rates of evolutionary replicates were averaged after averaging technical replicates.Carrying capacity was defined as the OD a monoculture reached at the end of each growth cycle of the evolutionary experiment averaged across replicates. These measurements were done in an Epoch2 microplate reader (BioTek). In order to reduce noise, the trajectories of OD measurements were smoothed for each well using moving mean with an averaging window of three.Carrying capacities of coevolved strainsRe-isolated strains were replicated from glycerol stocks into the experimental media and grown for 48 h in M9 media at 28 °C. Cultures were then diluted by 1500 into 3 technical replicates in fresh M9-media, and were given another 48 h to reach carrying capacity. The strains used in this experiment were isolated from 1-3 evolutionary replicates (Supplementary Data 2).Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More