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    Mild chronic exposure to pesticides alters physiological markers of honey bee health without perturbing the core gut microbiota

    European Commission. Report from the commission to the European Parliament and the council on the implementation of the measures concerning the apiculture sector of Regulation (EU) No 1308/2013 of the European Parliament and of the Council establishing a common organisation of the markets in agricultural products. p. 1–16. https://eur-lex.europa.eu/legal-content/en/ALL/?uri=CELEX:52016DC0776 (2016).Motta, E. V. S. & Moran, N. A. Impact of glyphosate on the honey bee gut microbiota: Effects of intensity, duration, and timing of exposure. msystems 5, e00268-e1220. https://doi.org/10.1128/mSystems.00268-20 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Klein, A. M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B-Biol. Sci. 274, 303–313. https://doi.org/10.1098/rspb.2006.3721 (2007).Article 

    Google Scholar 
    Ollerton, J. Pollinator diversity: Distribution, ecological function, and conservation. Annu. Rev. Ecol. Evol. Syst. 48, 353–376. https://doi.org/10.1146/annurev-ecolsys-110316-022919 (2017).Article 

    Google Scholar 
    Greenleaf, S. S. & Kremen, C. Wild bees enhance honey bees’ pollination of hybrid sunflower. PNAS 103, 13890–13895. https://doi.org/10.1073/pnas.0600929103 (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Williams, I. H. The dependence of crop production within the European Union on pollination by honey bees. Agric. Zool. Rev. 20, 20 (1994).
    Google Scholar 
    Potts, S. G. et al. Declines of managed honey bees and beekeepers in Europe. J. Apic. Res. 49, 15–22. https://doi.org/10.3896/ibra.1.49.1.02 (2010).Article 

    Google Scholar 
    Vanengelsdorp, D., Hayes, J., Underwood, R. M. & Pettis, J. A survey of honey bee colony losses in the US, fall 2007 to spring 2008. PLoS One 3, 6. https://doi.org/10.1371/journal.pone.0004071 (2008).CAS 
    Article 

    Google Scholar 
    Chagnon, M. Fédération Canadienne de la Faune (Bureau régional du Québec, 2008).
    Google Scholar 
    Schreinemachers, P. & Tipraqsa, P. Agricultural pesticides and land use intensification in high, middle and low income countries. Food Policy 37, 616–626. https://doi.org/10.1016/j.foodpol.2012.06.003 (2012).Article 

    Google Scholar 
    Haber, A. I., Steinhauer, N. A. & vanEngelsdorp, D. Use of chemical and nonchemical methods for the control of Varroa destructor (Acari: Varroidae) and associated winter colony losses in US beekeeping operations. J. Econ. Entomol. https://doi.org/10.1093/jee/toz088 (2019).Article 
    PubMed 

    Google Scholar 
    Le Conte, Y., Ellis, M. & Ritter, W. Varroa mites and honey bee health: Can Varroa explain part of the colony losses?. Apidologie 41, 353–363. https://doi.org/10.1051/apido/2010017 (2010).Article 

    Google Scholar 
    Ellis, J. D., Evans, J. D. & Pettis, J. Colony losses, managed colony population decline, and colony collapse disorder in the United States. J. Apic. Res. 49, 134–136. https://doi.org/10.3896/IBRA.1.49.1.30 (2010).Article 

    Google Scholar 
    Chauzat, M. P. et al. Influence of pesticide residues on honey bee (Hymenoptera: Apidae) colony health in France. Environ. Entomol 38, 514–523. https://doi.org/10.1603/022.038.0302 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Juan-Borras, M., Domenech, E. & Escriche, I. Mixture-risk-assessment of pesticide residues in retail polyfloral honey. Food Control 67, 127–134. https://doi.org/10.1016/j.foodcont.2016.02.051 (2016).CAS 
    Article 

    Google Scholar 
    Kasiotis, K. M., Anagnostopoulos, C., Anastasiadou, P. & Machera, K. Pesticide residues in honeybees, honey and bee pollen by LC–MS/MS screening: Reported death incidents in honeybees. Sci. Total. Environ 485–486, 633–642. https://doi.org/10.1016/j.scitotenv.2014.03.042 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Mullin, C. A. et al. High levels of miticides and agrochemicals in north american apiaries: Implications for honey bee health. PLoS One 5, 19. https://doi.org/10.1371/journal.pone.0009754 (2010).CAS 
    Article 

    Google Scholar 
    Brandt, A., Gorenflo, A., Siede, R., Meixner, M. & Buchler, R. The neonicotinoids thiacloprid, imidacloprid, and clothianidin affect the immunocompetence of honey bees (Apis mellifera L.). J. Insect. Physiol. 86, 40–47. https://doi.org/10.1016/j.jinsphys.2016.01.001 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Alptekin, S. et al. Induced thiacloprid insensitivity in honeybees (Apis mellifera L.) is associated with up-regulation of detoxification genes. Insect Mol. Biol. 25, 171–180. https://doi.org/10.1111/imb.12211 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Tesovnik, T. et al. Exposure of honey bee larvae to thiamethoxam and its interaction with Nosema ceranae infection in adult honey bees. Environ. Pollut. 256, 113443. https://doi.org/10.1016/j.envpol.2019.113443 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Gregore, A. et al. Effects of coumaphos and imidacloprid on honey bee (Hymenoptera: Apidae) lifespan and antioxidant gene regulations in laboratory experiments. Sci. Rep. https://doi.org/10.1038/s41598-018-33348-4 (2018).Article 

    Google Scholar 
    Schneider, C. W., Tautz, J., Grunewald, B. & Fuchs, S. RFID tracking of sublethal effects of two neonicotinoid insecticides on the foraging behavior of Apis mellifera. PLoS One 7, 9. https://doi.org/10.1371/journal.pone.0030023 (2012).CAS 
    Article 

    Google Scholar 
    Vazquez, D. E., Ilina, N., Pagano, E. A., Zavala, J. A. & Farina, W. M. Glyphosate affects the larval development of honey bees depending on the susceptibility of colonies. PLoS One https://doi.org/10.1371/journal.pone.0205074 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vázquez, D. E., Latorre-Estivalis, J. M., Ons, S. & Farina, W. M. Chronic exposure to glyphosate induces transcriptional changes in honey bee larva: A toxicogenomic study. Environ. Pollut. https://doi.org/10.1016/j.envpol.2020.114148 (2020).Article 
    PubMed 

    Google Scholar 
    Farina, W. M., Balbuena, M., Herbert, L. T., Mengoni Goñalons, C. & Vázquez, D. E. Effects of the herbicide glyphosate on honey bee sensory and cognitive abilities: Individual impairments with implications for the hive. Insects 10, 354. https://doi.org/10.3390/insects10100354 (2019).Article 
    PubMed Central 

    Google Scholar 
    Wang, Y. H., Zhu, Y. C. & Li, W. H. Interaction patterns and combined toxic effects of acetamiprid in combination with seven pesticides on honey bee (Apis mellifera L.). Ecotox. Environ. Safe 190, 10. https://doi.org/10.1016/j.ecoenv.2019.110100 (2020).CAS 
    Article 

    Google Scholar 
    Kretschmann, A., Gottardi, M., Dalhoff, K. & Cedergreen, N. The synergistic potential of the azole fungicides prochloraz and propiconazole toward a short α-cypermethrin pulse increases over time in Daphnia magna. Aquat. Toxicol. 162, 94–101. https://doi.org/10.1016/j.aquatox.2015.02.011 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yuan, X. et al. Gut microbiota: An underestimated and unintended recipient for pesticide-induced toxicity. Chemosphere https://doi.org/10.1016/j.chemosphere.2019.04.088 (2019).Article 
    PubMed 

    Google Scholar 
    Yang, Y. et al. Effects of three common pesticides on survival, food consumption and midgut bacterial communities of adult workers Apis cerana and Apis mellifera. Environ. Pollut. 249, 860–867. https://doi.org/10.1016/j.envpol.2019.03.077 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Martinson, V. G. et al. A simple and distinctive microbiota associated with honey bees and bumble bees. Mol. Ecol. 20, 619–628. https://doi.org/10.1111/j.1365-294X.2010.04959.x (2011).Article 
    PubMed 

    Google Scholar 
    Corby-Harris, V., Maes, P. & Anderson, K. E. The bacterial communities associated with honey bee (Apis mellifera) foragers. PLoS One 9, 13. https://doi.org/10.1371/journal.pone.0095056 (2014).CAS 
    Article 

    Google Scholar 
    Moran, N. A., Hansen, A. K., Powell, J. E. & Sabree, Z. L. Distinctive gut microbiota of honey bees assessed using deep sampling from individual worker bees. PLoS One https://doi.org/10.1371/journal.pone.0036393 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honey bee gut microbiota. Curr. Opin. Microbiol. 43, 69–76. https://doi.org/10.1016/j.mib.2017.12.009 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384. https://doi.org/10.1038/nrmicro.2016.43 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kešnerová, L. et al. Gut microbiota structure differs between honeybees in winter and summer. ISME J. 14, 801–814. https://doi.org/10.1038/s41396-019-0568-8 (2020).Article 
    PubMed 

    Google Scholar 
    Killer, J., Dubná, S., Sedláček, I. & Švec, P. Lactobacillus apis sp. Nov., from the stomach of honeybees (Apis mellifera), having an in vitro inhibitory effect on the causative agents of American and European foulbrood. Int. J. Syst. Evol. Microbiol. 64, 152–157. https://doi.org/10.1099/ijs.0.053033-0 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Forsgren, E., Olofsson, T. C., Váasquez, A. & Fries, I. Novel lactic acid bacteria inhibiting Paenibacillus larvae in honey bee larvae. Apidologie 41, 99–108. https://doi.org/10.1051/apido/2009065 (2010).Article 

    Google Scholar 
    Schwarz, R. S., Huang, Q. & Evans, J. D. Hologenome theory and the honey bee pathosphere. Curr. Opin. Insect Sci. 10, 1–7. https://doi.org/10.1016/j.cois.2015.04.006 (2015).Article 
    PubMed 

    Google Scholar 
    Engel, P., Martinson, V. G. & Moran, N. A. Functional diversity within the simple gut microbiota of the honey bee. PNAS 109, 11002–11007. https://doi.org/10.1073/pnas.1202970109 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kešnerová, L. et al. Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol. 15, 28. https://doi.org/10.1371/journal.pbio.2003467 (2017).CAS 
    Article 

    Google Scholar 
    Kwong, W. K., Engel, P., Koch, H. & Moran, N. A. Genomics and host specialization of honey bee and bumble bee gut symbionts. PNAS 111, 11509–11514. https://doi.org/10.1073/pnas.1405838111 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lee, F. J., Rusch, D. B., Stewart, F. J., Mattila, H. R. & Newton, I. L. G. Saccharide breakdown and fermentation by the honey bee gut microbiome. Environ. Microbiol. 17, 796–815. https://doi.org/10.1111/1462-2920.12526 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Motta, E. V. S., Raymann, K. & Moran, N. A. Glyphosate perturbs the gut microbiota of honey bees. PNAS 115, 10305–10310. https://doi.org/10.1073/pnas.1803880115 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Blot, N., Veillat, L., Rouze, R. & Delatte, H. Glyphosate, but not its metabolite AMPA, alters the honeybee gut microbiota. PLoS One 14, 16. https://doi.org/10.1371/journal.pone.0215466 (2019).CAS 
    Article 

    Google Scholar 
    Raymann, K. et al. Imidacloprid decreases honey bee survival rates but does not affect the gut microbiome. Appl. Environ. Microbiol. 84, 13. https://doi.org/10.1128/aem.00545-18 (2018).CAS 
    Article 

    Google Scholar 
    Rouze, R., Mone, A., Delbac, F., Belzunces, L. & Blot, N. The honeybee gut microbiota is altered after chronic exposure to different families of insecticides and infection by Nosema ceranae. Microbes Environ. 34, 226–233. https://doi.org/10.1264/jsme2.ME18169 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    DeGrandi-Hoffman, G., Corby-Harris, V., DeJong, E. W., Chambers, M. & Hidalgo, G. Honey bee gut microbial communities are robust to the fungicide PristineA (R) consumed in pollen. Apidologie 48, 340–352. https://doi.org/10.1007/s13592-016-0478-y (2017).CAS 
    Article 

    Google Scholar 
    Liu, Y. J. et al. Thiacloprid exposure perturbs the gut microbiota and reduces the survival status in honeybees. J. Hazard. Mater. 389, 11. https://doi.org/10.1016/j.jhazmat.2019.121818 (2020).CAS 
    Article 

    Google Scholar 
    Syromyatnikov, M. Y., Isuwa, M. M., Savinkova, O. V., Derevshchikova, M. I. & Popov, V. N. The effect of pesticides on the microbiome of animals. Agriculture 10, 79. https://doi.org/10.3390/agriculture10030079 (2020).CAS 
    Article 

    Google Scholar 
    Thompson, H. M. et al. Evaluating exposure and potential effects on honeybee brood (Apis mellifera) development using glyphosate as an example. Integr. Environ. Assess. Manag. 10, 463–470. https://doi.org/10.1002/ieam.1529 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Motta, E. V. S. et al. Oral and topical exposure to glyphosate in herbicide formulation impact the gut microbiota and survival rates of honey bees. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.01150-20 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Berg, C. J. et al. Glyphosate residue concentrations in honey attributed through geospatial analysis to proximity of large-scale agriculture and transfer off-site by bees. PLoS ONE 13, e0198876. https://doi.org/10.1371/journal.pone.0198876 (2018).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rubio, F., Guo, E. & Kamp, L. Survey of glyphosate residues in honey, corn, and soy products. Abstr. Pap. Am. Chem. Soc. https://doi.org/10.4172/2161-0525.1000249 (2015).Article 

    Google Scholar 
    El Agrebi, N. et al. Honeybee and consumer’s exposure and risk characterisation to glyphosate-based herbicide (GBH) and its degradation product (AMPA): Residues in beebread, wax, and honey. Sci. Total. Environ. 704, 135312. https://doi.org/10.1016/j.scitotenv.2019.135312 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kubik, M. et al. Residues of captan (contact) and difenoconazole (systemic) fungicides in bee products from an apple orchard. Apidologie 31, 531–541 (2000).CAS 
    Article 

    Google Scholar 
    Lopez, S. H., Lozano, A., Sosa, A., Hernando, M. D. & Fernandez-Alba, A. R. Screening of pesticide residues in honeybee wax comb by LC-ESI-MS/MS. A pilot study. Chemosphere 163, 44–53. https://doi.org/10.1016/j.chemosphere.2016.07.008 (2016).CAS 
    Article 

    Google Scholar 
    Pettis, J. S. et al. Crop pollination exposes honey bees to pesticides which alters their susceptibility to the gut pathogen Nosema ceranae. PLoS One 8, 9. https://doi.org/10.1371/journal.pone.0070182 (2013).CAS 
    Article 

    Google Scholar 
    Abdallah, O. I., Hanafi, A., Ghani, S. B. A., Ghisoni, S. & Lucini, L. Pesticides contamination in Egyptian honey samples. J. Consum. Prot. Food Sci. 12, 317–327. https://doi.org/10.1007/s00003-017-1133-x (2017).CAS 
    Article 

    Google Scholar 
    Blaga, G. V. et al. Antifungal residues analysis in various Romanian honey samples analysis by high resolution mass spectrometry. J. Environ. Sci. Health Part B-Pestic. Contam. Agric. Wastes https://doi.org/10.1080/03601234.2020.1724016 (2020).Article 

    Google Scholar 
    Piechowicz, B., Wos, I., Podbielska, M. & Grodzicki, P. The transfer of active ingredients of insecticides and fungicides from an orchard to beehives. J. Environ. Sci. Health Part B-Pestic. Contam. Agric. Wastes 53, 18–24. https://doi.org/10.1080/03601234.2017.1369320 (2018).CAS 
    Article 

    Google Scholar 
    Almasri, H. et al. Mixtures of an insecticide, a fungicide and a herbicide induce high toxicities and systemic physiological disturbances in winter Apis mellifera honey bees. Ecotoxicol. Environ. Saf. 203, 111013. https://doi.org/10.1016/j.ecoenv.2020.111013 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Babendreier, D., Joller, D., Romeis, J., Bigler, F. & Widmer, F. Bacterial community structures in honeybee intestines and their response to two insecticidal proteins. FEMS Microbiol. Ecol. 59, 600–610. https://doi.org/10.1111/j.1574-6941.2006.00249.x (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    Emery, O., Schmidt, K. & Engel, P. Immune system stimulation by the gut symbiont Frischella perrara in the honey bee (Apis mellifera). Mol. Ecol. 26, 2576–2590. https://doi.org/10.1111/mec.14058 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yanez, O., Gauthier, L., Chantawannakul, P. & Neumann, P. Endosymbiotic bacteria in honey bees: Arsenophonus spp. are not transmitted transovarially. FEMS Microbiol. Lett. https://doi.org/10.1093/femsle/fnw147 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tornisielo, V. L., Botelho, R. G., Alves, P. A. T., Bonfleur, E. J. & Monteiro, S. H. Pesticide tank mixes: an environmental point of view. in Herbicides-Current Research and Case Studies in Use. 473–487 (InTech, 2013).

    Google Scholar 
    Kanga, L. H., Siebert, S. C., Sheikh, M. & Legaspi, J. C. Pesticide residues in conventionally and organically managed Apiaries in South and North Florida. Curre. Investig. Agric. Curr. Res. https://doi.org/10.32474/CIACR.2019.07.000262 (2019).Article 

    Google Scholar 
    Lambert, O. et al. Widespread occurrence of chemical residues in beehive matrices from apiaries located in different landscapes of western France. PLoS One 8, 12. https://doi.org/10.1371/journal.pone.0067007 (2013).CAS 
    Article 

    Google Scholar 
    Mullins, J. W. Pest Control with Enhanced Environmental Safety, Vol 524 ACS Symposium Series, Vol. 13 183–198 (American Chemical Society, 1993).Book 

    Google Scholar 
    Nguyen, B. K. et al. Does imidacloprid seed-treated maize have an impact on honey bee mortality?. J. Econ. Entomol. 102, 616–623. https://doi.org/10.1603/029.102.0220 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pollak, P. Fine chemicals–the industry and the business. Chem. Int. 29, 22. https://doi.org/10.1515/ci.2007.29.5.22b (2007).Article 

    Google Scholar 
    Amrhein, N., Deus, B., Gehrke, P. & Steinrücken, H. C. The site of the inhibition of the shikimate pathway by glyphosate. II. Interference of glyphosate with chorismate formation in vivo and in vitro. Plant. Physiol. 66, 830–834. https://doi.org/10.1104/pp.66.5.830 (1980).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cao, G. et al. A novel 5-enolpyruvylshikimate-3-phosphate synthase shows high glyphosate tolerance in Escherichia coli and tobacco plants. PLoS One 7, e38718. https://doi.org/10.1371/journal.pone.0038718 (2012).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hitchcock, C. A., Dickinson, K., Brown, S. B., Evans, E. G. V. & Adams, D. J. Interaction of azole antifungal antibiotics with cytochrome P-450-dependent 14α-sterol demethylase purified from Candida albicans. Biochem. J. 266, 475–480. https://doi.org/10.1042/bj2660475 (1990).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Alberoni, D., Favaro, R., Baffoni, L., Angeli, S. & Di Gioia, D. Neonicotinoids in the agroecosystem: In-field long-term assessment on honeybee colony strength and microbiome. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.144116 (2021).Article 
    PubMed 

    Google Scholar 
    Xu, C. et al. Changes in gut microbiota may be early signs of liver toxicity induced by epoxiconazole in rats. Chemotherapy 60, 135–142. https://doi.org/10.1159/000371837 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yang, C., Hamel, C., Vujanovic, V. & Gan, Y. Fungicide: Modes of action and possible impact on nontarget microorganisms. ISRN Ecol. https://doi.org/10.5402/2011/130289 (2011).Article 

    Google Scholar 
    Coupe, R. H., Kalkhoff, S. J., Capel, P. D. & Gregoire, C. Fate and transport of glyphosate and aminomethylphosphonic acid in surface waters of agricultural basins. Pest Manag. Sci. 68, 16–30. https://doi.org/10.1002/ps.2212 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    Howe, C. M. et al. Toxicity of glyphosate-based pesticides to four North American frog species. Environ. Toxicol. Chem. 23, 1928–1938. https://doi.org/10.1002/etc.2268 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Wagner, N., Reichenbecher, W., Teichmann, H., Tappeser, B. & Lötters, S. Questions concerning the potential impact of glyphosate-based herbicides on amphibians. Environ. Toxicol. Chem. 32, 1688–1700. https://doi.org/10.1002/etc.2268 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pareja, L. et al. Evaluation of glyphosate and AMPA in honey by water extraction followed by ion chromatography mass spectrometry. A pilot monitoring study. Anal. Methods 11, 2123–2128. https://doi.org/10.1039/c9ay00543a (2019).CAS 
    Article 

    Google Scholar 
    Thompson, T. S., van den Heever, J. P. & Limanowka, R. E. Determination of glyphosate, AMPA, and glufosinate in honey by online solid-phase extraction-liquid chromatography-tandem mass spectrometry.. Food. Addit. Contam. Part A Chem. Anal. Control. Expo. Risk. Assess 36, 434–446. https://doi.org/10.1080/19440049.2019.1577993 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Dai, P. et al. The herbicide glyphosate negatively affects midgut bacterial communities and survival of honey bee during larvae reared in vitro. J. Agric. Food Chem. 66, 7786–7793. https://doi.org/10.1021/acs.jafc.8b02212 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zheng, H., Powell, J. E., Steele, M. I., Dietrich, C. & Moran, N. A. Honeybee gut microbiota promotes host weight gain via bacterial metabolism and hormonal signaling. PNAS 114, 4775–4780. https://doi.org/10.1073/pnas.1701819114 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    du Rand, E. E. et al. Detoxification mechanisms of honey bees (Apis mellifera) resulting in tolerance of dietary nicotine. Sci. Rep. https://doi.org/10.1038/srep11779 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Xiao, W. J. et al. Modulation of the pentose phosphate pathway alters phase I metabolism of testosterone and dextromethorphan in HepG2 cells. Acta Pharmacol. Sin. 36, 259–267. https://doi.org/10.1038/aps.2014.137 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Renzi, M. T. et al. Chronic toxicity and physiological changes induced in the honey bee by the exposure to fipronil and Bacillus thuringiensis spores alone or combined. Ecotox. Environ. Safe. 127, 205–213. https://doi.org/10.1016/j.ecoenv.2016.01.028 (2016).CAS 
    Article 

    Google Scholar 
    Singh, A., Gupta, V., Siddiqi, N., Tiwari, S. & Gopesh, A. Time course studies on impact of low temperature exposure on the levels of protein and enzymes in fifth instar larvae of Eri Silkworm, Philosamia ricini (Lepidoptera: satuniidae). Biochem. Anal. Biochem. 6, 6. https://doi.org/10.4172/2161-1009.1000321 (2017).CAS 
    Article 

    Google Scholar 
    Vlahović, M., Lazarević, J., Perić-Mataruga, V., Ilijin, L. & Mrdaković, M. Plastic responses of larval mass and alkaline phosphatase to cadmium in the gypsy moth larvae. Ecotox. Environ. Safe 72, 1148–1155. https://doi.org/10.1016/j.ecoenv.2008.03.012 (2009).CAS 
    Article 

    Google Scholar 
    Coleman, J. E. Structure and mechanism of alkaline-phosphatase. Annu. Rev. Biophys. Biomol. Struct. 21, 441–483. https://doi.org/10.1146/annurev.bb.21.060192.002301 (1992).CAS 
    Article 
    PubMed 

    Google Scholar 
    Bates, J. M., Akerlund, J., Mittge, E. & Guillemin, K. Intestinal alkaline phosphatase detoxifies lipopolysaccharide and prevents inflammation in zebrafish in response to the gut microbiota. Cell Host Microbe 2, 371–382. https://doi.org/10.1016/j.chom.2007.10.010 (2007).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kanost, M. R. & Gorman, M. J. Phenoloxidases in insect immunity. Insect Immunol. 1, 69–96. https://doi.org/10.1016/B978-012373976-6.50006-9 (2008).Article 

    Google Scholar 
    Collison, E., Hird, H., Cresswell, J. & Tyler, C. Interactive effects of pesticide exposure and pathogen infection on bee health—a critical analysis. Biol. Rev. 91, 1006–1019. https://doi.org/10.1111/brv.12206 (2016).Article 
    PubMed 

    Google Scholar 
    Helmer, S. H., Kerbaol, A., Aras, P., Jumarie, C. & Boily, M. Effects of realistic doses of atrazine, metolachlor, and glyphosate on lipid peroxidation and diet-derived antioxidants in caged honey bees (Apis mellifera). Environ. Sci. Pollut. Res. 22, 8010–8021. https://doi.org/10.1007/s11356-014-2879-7 (2015).CAS 
    Article 

    Google Scholar 
    Efferth, T., Schwarzl, S. M., Smith, J. & Osieka, R. Role of glucose-6-phosphate dehydrogenase for oxidative stress and apoptosis. Cell Death Differ. 13, 527–528. https://doi.org/10.1038/sj.cdd.4401807 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    Corona, M. & Robinson, G. E. Genes of the antioxidant system of the honey bee: Annotation and phylogeny. Insect Mol. Biol. 15, 687–701. https://doi.org/10.1111/j.1365-2583.2006.00695.x (2006).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Field, L. M., Devonshire, A. L., Ffrench-Constant, R. H. & Forde, B. G. Changes in DNA methylation are associated with loss of insecticide resistance in the peach-potato aphid Myzus persicae (Sulz.). FEBS Lett. 243, 323–327. https://doi.org/10.1016/0014-5793(89)80154-1 (1989).CAS 
    Article 

    Google Scholar 
    Ma, M. et al. Isolation of carboxylesterase (esterase FE4) from Apis cerana cerana and its role in oxidative resistance during adverse environmental stress. Biochimie 144, 85–97. https://doi.org/10.1016/j.biochi.2017.10.022 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zou, F., Guo, Q., Shen, B. & Zhu, C. A cluster of CYP6 gene family associated with the major quantitative trait locus is responsible for the pyrethroid resistance in Culex pipiens pallen. Insect Mol. Biol. 28, 528–536. https://doi.org/10.1111/imb.12571 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lang, M. L., Braun, C. L., Kanost, M. R. & Gorman, M. J. Multicopper oxidase-1 is a ferroxidase essential for iron homeostasis in Drosophila melanogaster. PNAS 109, 13337–13342. https://doi.org/10.1073/pnas.1208703109 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Habineza, P. et al. The promoting effect of gut microbiota on growth and development of Red Palm Weevil, Rhynchophorus ferrugineus (Olivier) (Coleoptera: Dryophthoridae) by modulating its nutritional metabolism. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.01212 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kwong, W. K., Mancenido, A. L. & Moran, N. A. Immune system stimulation by the native gut microbiota of honey bees. R. Soc. Open Sci. 4, 170003. https://doi.org/10.1098/rsos.170003 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Paradis, D., Berail, G., Bonmatin, J. M. & Belzunces, L. P. Sensitive analytical methods for 22 relevant insecticides of 3 chemical families in honey by GC-MS/MS and LC-MS/MS. Anal. Bioanal. Chem 406, 621–633. https://doi.org/10.1007/s00216-013-7483-z (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    Wiest, L. et al. Multi-residue analysis of 80 environmental contaminants in honeys, honeybees and pollens by one extraction procedure followed by liquid and gas chromatography coupled with mass spectrometric detection. J. Chromatogr. A 1218, 5743–5756. https://doi.org/10.1016/j.chroma.2011.06.079 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zufelato, M. S., Lourenco, A. P., Simoes, Z. L. P., Jorge, J. A. & Bitondi, M. M. G. Phenoloxidase activity in Apis mellifera honey bee pupae, and ecdysteroid-dependent expression of the prophenoloxidase mRNA. Insect Biochem. Mol. Biol. 34, 1257–1268. https://doi.org/10.1016/j.ibmb.2004.08.005 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Gallup, J. M. qPCR inhibition and amplification of difficult templates. in PCR troubleshooting and optimization: the essential guide. 23–65 (Horizon Scientific Press, 2011).
    Google Scholar 
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. PNAS 108, 4516–4522. https://doi.org/10.1073/pnas.1000080107 (2011).Article 
    PubMed 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 226. https://doi.org/10.1186/s40168-018-0605-2 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schliep, K. P. phangorn: Phylogenetic analysis in R. Bioinformatics 27, 592–593. https://doi.org/10.1093/bioinformatics/btq706 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363. https://doi.org/10.1002/bimj.200810425 (2008).MathSciNet 
    Article 
    PubMed 
    MATH 

    Google Scholar 
    Belzunces, L. P., Theveniau, M., Masson, P. & Bounias, M. Membrane acetylcholinesterase from Apis mellifera head solubilized by phosphatidylinositol-specific phospholipase-C interacts with an anti-CRD antibody. Comp. Biochem. Physiol. B-Biochem. Mol. Biol. 95, 609–612. https://doi.org/10.1016/0305-0491(90)90029-s (1990).Article 

    Google Scholar 
    Bergmeyer, H. U. & Gawehn, K. Principles of Enzymatic Analysis (Verlag Chemie, 1978).
    Google Scholar 
    Al-Lawati, H., Kamp, G. & Bienefeld, K. Characteristics of the spermathecal contents of old and young honeybee queens. J. Insect Physiol. 55, 117–122. https://doi.org/10.1016/j.jinsphys.2008.10.010 (2009).CAS 
    Article 

    Google Scholar 
    Habig, W. H., Pabst, M. J. & Jakoby, W. B. Glutathione s-transferases—first enzymatic step in mercapturic acid formation. J. Biol. Chem. 249, 7130–7139 (1974).CAS 
    Article 

    Google Scholar 
    Bounias, M., Kruk, I., Nectoux, M. & Popeskovic, D. Toxicology of cupric salts on honeybees. V. Gluconate and sulfate action on gut alkaline and acid phosphatases. Ecotox. Envirom. Safe 35, 67–76. https://doi.org/10.1006/eesa.1996.0082 (1996).CAS 
    Article 

    Google Scholar 
    Alaux, C. et al. Interactions between Nosema microspores and a neonicotinoid weaken honeybees (Apis mellifera). Environ. Microbiol. 12, 774–782. https://doi.org/10.1111/j.1462-2920.2009.02123.x (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Therneau, T. “Survival”: A Package for Survival Analysis in S. R package version 2.38. https://CRAN.R-project.org/package=survival. (2015).Kassambara, A. & Kosinski, M. “Survminer”: Drawing Survival Curves using “ggplot2”. R package version 0.4.2. https://CRAN.R-project.org/package=survminer. (2018).de Mendiburu, F. Statistical Procedures for Agricultural Research. Package “Agricolae” Version 1.44. Comprehensive R Archive Network. Institute for Statistics and Mathematics, Vienna, Austria. http://cran.r-project.org/web/packages/agricolae/agricolae.pdf (2013).Caraux, G. & Pinloche, S. PermutMatrix: A graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 21, 1280–1281. https://doi.org/10.1093/bioinformatics/bti141 (2004).Article 
    PubMed 

    Google Scholar  More

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    Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015–2019

    Daily change in primary pollutantsTo elucidate the change trend of primary pollutants under the 13th Five-Year Plan, we calculated the daily primary pollutants in 2015 and 2019 based on formula (1) and formula (2). Such diurnal comparisons can reduce the effects of seasonal weather to some extent. From the 19 UAs (224 prefecture-level cities), the heat diagram of the daily change transfer matrix of primary pollutants from 2015 to 2019 is shown in Fig. 2, including six primary pollutants and clean day conditions.Figure 2Transfer change matrix heatmap of primary pollutants from 2015 to 2019.Full size imageFrom the sum of the diagonal numbers, 37% of the primary pollutants had no shift during the 13th Five-Year Plan period. PM2.5, PM10 and O3 were the main primary pollutants, especially PM2.5. More primary pollutants were diverted to ozone pollution, indicating that the proportion of O3 as the primary pollutant is gradually increasing. In addition, the proportion of clean air has increased significantly, which shows that pollution control has been effectively reflected during the 13th Five-Year Plan period. However, the proportion of NO2 before and after metastasis was approximately the same, with approximately 5% NO2 pollution. This may imply that the governance of NO2 pollution was rendered nonsignificant. It is noteworthy that ozone pollution in China has become an increasingly prominent task in recent years. Similar to Xiao’s16 research on ozone pollution, they argue that present-day ozone levels in major Chinese cities are comparable to or even higher than the 1980 levels in the United States. Taken together, ozone and PM2.5 have become the top two air pollution pollutants in China.Monthly distribution of primary pollutantsTo further explore the spatiotemporal distribution of the primary pollutants across the UAs, we obtained the most primary pollutants per month by dividing the number of days with the most pollutants by the number of cities in each UA from the 2019 data. In Fig. 3, the UAs location was plotted on the abscissa, and the monthly variance of the primary pollutant was plotted on the ordinate. As shown in Fig. 3, PM2.5 appeared as dark green, PM10 appeared as light green, O3 appeared as orange, NO2 appeared as yellow, and clean days appear as dark blue. The main pollutants in the 19 UAs are PM2.5, PM10 and O3. NO2, as the primary pollutant, only appeared in the HBOY UA in January. Ordos, located in HBOY, possess rich oil and coal resources, with coal mining as its leading industry38. According to the China Energy Statistical Yearbook 2019, nearly 250 million tons of raw coal were used for thermal power generation in Inner Mongolia Autonomous Region, making it the region with the largest amount of raw coal for thermal power generation in China39. To a certain extent, the increase of heating40 and the imperfect denitration technology41 are both contributing to the increase of NO2 pollution in the atmosphere. CO and SO2 did not become major pollutants. Clean days (where AQI  More

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    The accumulation of microplastic pollution in a commercially important fishing ground

    PlasticsEurope. Plastics – the Facts 2019, Avenue E. van Nieuwenhuyse 4/3, 1160 Brussels. Belgium: PlasticsEurope. https://www.plasticseurope.org/de/resources/publications/4312-plastics-facts-2020 (2020).Mattsson, K., Jocic, S., Doverbratt, I. & Hansson, L. A. In Nanoplastics in the Aquatic Environment: Microplastic Contamination in Aquatic Environments (ed. Zheng, E. Y.) 379–399 (Elsevier, 2018).Chapter 

    Google Scholar 
    Lusher, A. L., Tirelli, V., O’Connor, I. & Officer, R. Microplastics in Arctic polar waters: The first reported values of particles in surface and sub-surface samples. Sci. Rep. 5, 14947. https://doi.org/10.1038/srep14947 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Waller, C. L. et al. Microplastics in the Antarctic marine system: An emerging area of research. Sci. Total. Environ. 598, 220–227. https://doi.org/10.1016/j.scitotenv.2017.03.283 (2017).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Thompson, R. C. et al. Lost at sea: where is all the plastic?. Sci. 304, 838–838 (2004).CAS 
    Article 

    Google Scholar 
    Gall, S. C. & Thompson, R. C. The impact of debris on marine life. Mar. Pollut. Bull. 92, 170–179. https://doi.org/10.1016/j.marpolbul.2014.12.041 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kroon, F. J., Motti, C. E., Jensen, L. H. & Berry, K. L. Classification of marine microdebris: A review and case study on fish from the Great Barrier Reef, Australia. Sci. Rep. 8, 1–15. https://doi.org/10.1038/s41598-018-34590-6 (2018).CAS 
    Article 

    Google Scholar 
    Cunningham, E. M. & Sigwart, J. D. Environmentally accurate microplastic levels and their absence from exposure studies. Integr. Comp. Biol. 59, 1485–1496. https://doi.org/10.1093/icb/icz068 (2019).Article 
    PubMed 

    Google Scholar 
    Welden, N. A. & Cowie, P. R. Long-term microplastic retention causes reduced body condition in the langoustine Nephrops norvegicus. Environ. Pollut. 218, 895–900. https://doi.org/10.1016/j.envpol.2016.08.020 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Green, D. S., Colgan, T. J., Thompson, R. C. & Carolan, J. C. Exposure to microplastics reduces attachment strength and alters the haemolymph proteome of blue mussels (Mytilus edulis). Environ. Pollut. 246, 423–434. https://doi.org/10.1016/j.envpol.2018.12.017 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Schéré, C. M., Dawson, T. P. & Schreckenberg, K. Multiple conservation designations: what impact on the effectiveness of marine protected areas in the Irish Sea?. Int. J. Sustain. Dev. 27, 596–610. https://doi.org/10.1080/13504509.2019.1706058 (2020).Article 

    Google Scholar 
    Ungfors, A. et al. Nephrops fisheries in European waters. In Advances in Marine Biology 247–314 (Academic Press, 2013).
    Google Scholar 
    ICES. Celtic Seas Ecosystem—Fisheries Overview. In Report of the ICES Advisory Committee, 2019. ICES Advice 2019, Section 7.2. 40 pp https://doi.org/10.17895/ices.advice.5708. (2019).Becker, C., Dick, J. T., Cunningham, E. M., Schmitt, C. & Sigwart, J. D. The crustacean cuticle does not record chronological age: New evidence from the gastric mill ossicles. Arthropod. Struct. Dev. 47, 498–512. https://doi.org/10.1016/j.asd.2018.07.002 (2018).Article 
    PubMed 

    Google Scholar 
    Woodall, L. C. et al. The deep sea is a major sink for microplastic debris. R. Soc. Open Sci. 1, 140317. https://doi.org/10.1098/rsos.140317 (2014).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Yin, J., Li, J. Y., Craig, N. J. & Su, L. Microplastic pollution in wild populations of decapod crustaceans: A review. Chemosphere https://doi.org/10.1016/j.chemosphere.2021.132985 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Cau, A. et al. Benthic crustacean digestion can modulate the environmental fate of microplastics in the deep sea. Environ. Sci. Technol. 54, 4886–4892. https://doi.org/10.1021/acs.est.9b07705 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hara, J., Frias, J. & Nash, R. Quantification of microplastic ingestion by the decapod crustacean Nephrops norvegicus from Irish waters. Mar. Pollut. Bull. 152, 110905. https://doi.org/10.1016/j.marpolbul.2020.110905 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Hill, A. E., Durazo, R. & Smeed, D. A. Observations of a cyclonic gyre in the western Irish Sea. Cont. Shelf Res. 14, 479–490. https://doi.org/10.1016/0278-4343(94)90099-X (1994).ADS 
    Article 

    Google Scholar 
    Horsburgh, K. J. & Hill, A. E. A three-dimensional model of density-driven circulation in the Irish Sea. J. Phys. Oceanogr. 33, 343–365. https://doi.org/10.1175/1520-0485(2003)033%3c0343:ATDMOD%3e2.0.CO;2 (2003).ADS 
    Article 

    Google Scholar 
    Hill, A.E., Brown, J., & Fernand, L. The western Irish Sea gyre: a retention system for Norway lobster (Nephrops norvegicus)? Oceanol. Acta. 19, 357–368. (1996). https://archimer.ifremer.fr/doc/00094/20493/Lebreton, L. et al. Evidence that the great pacific garbage patch is rapidly accumulating plastic. Sci. Rep. 8, 1–15. https://doi.org/10.1038/s41598-018-22939-w (2018).CAS 
    Article 

    Google Scholar 
    Charlesworth, M., Mitchell, S. H. & Oliver, W. T. Metals in surficial sediments of the north-west Irish Sea. Bull. Environ. Contam. Toxicol. 62, 40–47. https://doi.org/10.1007/s001289900839 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    Charlesworth, M. E., Service, M. & Gibson, C. E. The distribution and transport of Sellafield derived 137Cs and 241Am to western Irish Sea sediments. Sci. Total. Environ. 354, 83–92. https://doi.org/10.1016/j.scitotenv.2004.12.062 (2006).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Global Monitoring and Forecasting Center. Atlantic-European North West Shelf – Ocean Physics Analysis and Forecast, E.U Copernicus Marine Service Information . Available at: https://resources.marine.copernicus.eu/product-detail/NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013/INFORMATION (Accessed: 8th December 2021).Cunningham, E. M. et al. High abundances of microplastic pollution in deep-sea sediments: Evidence from antarctica and the Southern Ocean. Environ. Sci. Technol. 54, 13661–13671. https://doi.org/10.1021/acs.est.0c03441 (2020).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhang, S. et al. A simple method for the extraction and identification of light density microplastics from soil. Sci. Total. Environ. 616, 1056–1065. https://doi.org/10.1016/j.scitotenv.2017.10.213 (2018).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Martin, J., Lusher, A., Thompson, R. C. & Morley, A. The deposition and accumulation of microplastics in marine sediments and bottom water from the Irish continental shelf. Sci. Rep. 7, 10772. https://doi.org/10.1038/s41598-017-11079-2 (2017).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Development Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).
    Google Scholar 
    Nor, N. H. M. & Obbard, J. P. Microplastics in Singapore’s coastal mangrove ecosystems. Mar. Pollut. Bullet. 79, 278–283. https://doi.org/10.1016/j.marpolbul.2013.11.025 (2014).CAS 
    Article 

    Google Scholar 
    Lacerda, A. L. D. F. et al. Plastics in sea surface waters around the Antarctic Peninsula. Sci. Rep. 9, 1–12. https://doi.org/10.1038/s41598-019-40311-4 (2019).MathSciNet 
    CAS 
    Article 

    Google Scholar 
    Tata, T., Belabed, B. E., Bououdina, M. & Bellucci, S. Occurrence and characterization of surface sediment microplastics and litter from North African coasts of Mediterranean Sea: Preliminary research and first evidence. Sci. Total. Environ. 713, 136664. https://doi.org/10.1016/j.scitotenv.2020.136664 (2020).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Lorenz, C. et al. Spatial distribution of microplastics in sediments and surface waters of the southern North Sea. Environ. Pollut. 252, 1719–1729 (2019).CAS 
    Article 

    Google Scholar 
    Chouchene, K. et al. Microplastics on Barra beach sediments in Aveiro, Portgal. Mar. Pollut. Bull. 167, 112264. https://doi.org/10.1016/j.marpolbul.2021.112264 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kane, I. A. et al. Seafloor microplastic hotspots controlled by deep-sea circulation. Science 368, 1140–1145. https://doi.org/10.1126/science.aba5899 (2020).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    Gaylarde, C. C., Neto, J. A. B. & da Fonseca, E. M. Paint fragments as polluting microplastics: A brief review. Mar. Pollut. Bull. 162, 111847. https://doi.org/10.1016/j.marpolbul.2020.111847 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    Sait, S. T. L. et al. Microplastic fibres from synthetic textiles: Environmental degradation and additive chemical content. Environ. Pollut. 268, 115745. https://doi.org/10.1016/j.envpol.2020.115745 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    Chen, Q. et al. Bioassay guided analysis coupled with non-target chemical screening in polyethylene plastic shopping bag fragments after exposure to simulated gastric juice of Fish. J. Hazard. Mater. 401, 123421. https://doi.org/10.1016/j.jhazmat.2020.123421 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    Wu, X. et al. Photo aging and fragmentation of polypropylene food packaging materials in artificial seawater. Water. Res. 188, 116456. https://doi.org/10.1016/j.watres.2020.116456 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zabaniotou, A. & Kassidi, E. Life cycle assessment applied to egg packaging made from polystyrene and recycled paper. J. Clean. Prod. 11, 549–559. https://doi.org/10.1016/S0959-6526(02)00076-8 (2003).Article 

    Google Scholar 
    Tanaka, K. & Takada, H. Microplastic fragments and microbeads in digestive tracts of planktivorous fish from urban coastal waters. Sci. Rep. 6, 34351. https://doi.org/10.1038/srep34351 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Biamis, C., O’Driscoll, K. & Hardiman, G. Microplastic toxicity: A review of the role of marine sentinel species in assessing the environmental and public health impacts. CSCEE. https://doi.org/10.1016/j.cscee.2020.100073 (2020).Article 

    Google Scholar 
    Bakir, A., Rowland, S. J. & Thompson, R. C. Transport of persistent organic pollutants by microplastics in estuarine conditions. Estuar. Coast. 140, 14–21. https://doi.org/10.1016/j.ecss.2014.01.004 (2014).CAS 
    Article 

    Google Scholar 
    Nelson, A. M. & Long, T. E. A perspective on emerging polymer technologies for bisphenol-A replacement. Polym. Int. 61, 1485–1491. https://doi.org/10.1002/pi.4323 (2012).CAS 
    Article 

    Google Scholar 
    Le Bihanic, F. et al. Organic contaminants sorbed to microplastics affect marine medaka fish early life stages development. Mar. Pollut. Bull. 154, 111059. https://doi.org/10.1016/j.marpolbul.2020.111059 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Murray, F. & Cowie, P. R. Plastic contamination in the decapod crustacean Nephrops norvegicus (Linnaeus, 1758). Mar. Pollut. Bull. 62, 1207–1217. https://doi.org/10.1016/j.marpolbul.2011.03.032 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    Cobb, J. S. & Phillips, B. F. (eds) The Biology and Management of Lobsters, Physiology and Behaviour 2–61 (Academic Press Inc., 1980).
    Google Scholar 
    Quintana, M. M., Motova, A., Wilkie, O., Patience, N. Seafish: Economics of the UK fishing fleet 2020. Seafish Report No. SR758. Edinburgh, UK. https://www.seafish.org/document/?id=d9e7982d-e374-4de7-85a4-ca80c35f5666 (2021). More

  • in

    Relationship between bacterial phylotype and specialized metabolite production in the culturable microbiome of two freshwater sponges

    Mehbub MF, Lei J, Franco C, Zhang W. Marine sponge derived natural products between 2001 and 2010: trends and opportunities for discovery of bioactives. Mar Drugs. 2014;12:4539–77.PubMed 
    PubMed Central 

    Google Scholar 
    Sipkema D, Franssen MCR, Osinga R, Tramper J, Wijffels RH. Marine sponges as pharmacy. Mar Biotechnol. 2005;7:142–62.CAS 

    Google Scholar 
    Dobson CM. Chemical space and biology. Nature. 2004;432:824–8.CAS 
    PubMed 

    Google Scholar 
    Indraningrat AAG, Micheller S, Runderkamp M, Sauerland I, Becking LE, Smidt H, et al. Cultivation of sponge-associated bacteria from Agelas sventres and Xestospongia muta collected from different depths. Mar Drugs. 2019;17:578.CAS 
    PubMed Central 

    Google Scholar 
    Piel J. Metabolites from symbiotic bacteria. Nat Prod Rep. 2009;26:338–62.CAS 
    PubMed 

    Google Scholar 
    Webster NS, Thomas T. The sponge hologenome. mBio. 2016;7:e00135–16.PubMed 
    PubMed Central 

    Google Scholar 
    de Oliveira MRF, de Maringá UE, da Costa C, Benedito E. Trends and gaps in scientific production on freshwater sponges. Oecologia Austrlis. 2020;24:61–75.
    Google Scholar 
    Manconi R, Pronzato R. How to survive and persist in temporary freshwater? Adaptive traits of sponges (Porifera: Spongillida): a review. Hydrobiologia. 2016;782:11–22.
    Google Scholar 
    Manconi R, Pronzato R. Chapter 8 – Phylum Porifera. In: Thorp JH, Rogers DC, editors. Ecology and general biology. Thorp and Covich’s freshwater invertebrates. vol 1 (4th ed.) New York: Academic Press; 2015. p. 133–157.Manconi R, Pronzato R. Chapter 3 – Phylum Porifera. In: Thorp JH, Rogers DC, editors. Keys to Nearctic fauna. Thorp and Covich’s freshwater invertebrates vol 2(4th ed.) San Diego: Academic Press, Elsevier; 2016. p. 39–83.Leidy J. On Spongilla. In: Proceedings of the Academy of Natural Sciences of Philadelphia. Philadelphia: Academy of Natural Sciences of Philadelphia; 1850. p. 278.Smith F. Distribution of the fresh-water sponges of North America. INHS Bull. 1921;14:9–22.
    Google Scholar 
    Old MC. Environmental selection of the fresh-water sponges (Spongillidae) of Michigan. Trans Am Microsc Soc. 1932;51:129–36.CAS 

    Google Scholar 
    Ashley JM. Fresh water sponges of Illinois and Michigan. Urbana-Champaign: Master of Arts, University of Illinois; 1913.Jewell ME. An ecological study of the fresh-water sponges of northeastern Wisconsin. Ecol Monogr. 1935;5:461–504.CAS 

    Google Scholar 
    Kolomyjec SH, Willford RA. The fall 2019 genetics class. Phylogenetic analysis of Michigan’s freshwater sponges (Porifera, Spongillidae) using extended COI mtDNA sequences. bioRxiv. 2020; https://doi.org/10.1101/2020.04.26.062448.Copeland J, Kunigelis S, Tussing J, Jett T, Rich C. Freshwater sponges (Porifera: Spongillida) of Tennessee. Am Midl Nat. 2019;181:310–26.
    Google Scholar 
    Lauer TE, Spacie A. An association between freshwater sponges and the zebra mussel in a southern Lake Michigan harbor. J Freshw Ecol. 2004;19:631–7.
    Google Scholar 
    Skelton J, Strand M. Trophic ecology of a freshwater sponge (Spongilla lacustris) revealed by stable isotope analysis. Hydrobiologia. 2013;709:227–35.CAS 

    Google Scholar 
    Early TA, Glonek T. Zebra mussel destruction by a Lake Michigan sponge: populations, in vivo 31P nuclear magnetic resonance, and phospholipid profiling. Environ Sci Technol. 1999;33:1957–62.CAS 

    Google Scholar 
    Early TA, Kundrat JT, Schorp T, Glonek T. Lake Michigan sponge phospholipid variations with habitat: A 31P nuclear magnetic resonance study. Comp Biochem Physiol. 1996;114:77–89.
    Google Scholar 
    Dembitsky VM, Rezanka T, Srebnik M. Lipid compounds of freshwater sponges: family Spongillidae, class Demospongiae. Chem Phys Lipids. 2003;123:117–55.CAS 
    PubMed 

    Google Scholar 
    Řezanka T, Sigler K, Dembitsky VM. Syriacin, a novel unusual sulfated ceramide glycoside from the freshwater sponge Ephydatia syriaca (Porifera, Demospongiae, Spongillidae). Tetrahedron. 2006;62:5937–43.
    Google Scholar 
    Radnaeva LD, Bazarsadueva SV, Taraskin VV, Tulokhonov AK. First data on lipids and microorganisms of deepwater endemic sponge Baikalospongia intermedia and sediments from hydrothermal discharge area of the Frolikha Bay (North Baikal, Siberia). J Great Lakes Res. 2020;46:67–74.CAS 

    Google Scholar 
    Manconi R, Piccialli V, Pronzato R, Sica D. Steroids in porifera, sterols from freshwater sponges Ephydatia fluviatilis (L.) and Spongilla lacustris (L.). Comp Biochem Physiol. 1988;91:237–45.
    Google Scholar 
    Belikov S, Belkova N, Butina T, Chernogor L, Kley AM-V, Nalian A, et al. Diversity and shifts of the bacterial community associated with Baikal sponge mass mortalities. PLoS ONE. 2019;14:e0213926.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Costa R, Keller-Costa T, Gomes NCM, da Rocha UN, van Overbeek L, van Elsas JD. Evidence for selective bacterial community structuring in the freshwater sponge Ephydatia fluviatilis. Microb Ecol. 2013;65:232–44.PubMed 

    Google Scholar 
    Laport MS, Pinheiro U, Rachid CTCC. Freshwater sponge Tubella variabilis presents richer microbiota than marine sponge species. Front Microbiol. 2019;10:2799.PubMed 
    PubMed Central 

    Google Scholar 
    Kenny NJ, Plese B, Riesgo A, Itskovich VB. Symbiosis, selection, and novelty: freshwater adaptation in the unique sponges of Lake Baikal. Mol Biol Evol. 2019;36:2462–80.CAS 
    PubMed Central 

    Google Scholar 
    Gaikwad S, Shouche YS, Gade WN. Microbial community structure of two freshwater sponges using Illumina MiSeq sequencing revealed high microbial diversity. AMB Express. 2016;6:40.PubMed 
    PubMed Central 

    Google Scholar 
    Gernert C, Glöckner FO, Krohne G, Hentschel U. Microbial diversity of the freshwater sponge Spongilla lacustris. Microb Ecol. 2005;50:206–12.CAS 
    PubMed 

    Google Scholar 
    Hernandez A, Nguyen LT, Dhakal R, Murphy BT. The need to innovate sample collection and library generation in microbial drug discovery: a focus on academia. Nat Prod Rep. 2021;38:292–300.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li C-Q, Liu W-C, Zhu P, Yang J-L, Cheng K-D. Phylogenetic diversity of bacteria associated with the marine sponge Gelliodes carnosa collected from the Hainan Island coastal waters of the South China Sea. Microb Ecol. 2011;62:800–12.PubMed 

    Google Scholar 
    Sipkema D, Schippers K, Maalcke WJ, Yang Y, Salim S, Blanch HW. Multiple approaches to enhance the cultivability of bacteria associated with the marine sponge Haliclona (gellius) sp. Appl Environ Microbiol. 2011;77:2130–40.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Montalvo NF, Davis J, Vicente J, Pittiglio R, Ravel J, Hill RT. Integration of culture-based and molecular analysis of a complex sponge-associated bacterial community. PLoS ONE. 2014;9:e90517.PubMed 
    PubMed Central 

    Google Scholar 
    Elfeki M, Alanjary M, Green SJ, Ziemert N, Murphy BT. Assessing the efficiency of cultivation techniques to recover natural product biosynthetic gene populations from sediment. ACS Chem Biol. 2018;13:2074–81.CAS 
    PubMed 

    Google Scholar 
    Dieckmann R, Graeber I, Kaesler I, Szewzyk U, von Döhren H. Rapid screening and dereplication of bacterial isolates from marine sponges of the Sula Ridge by intact-cell-MALDI-TOF mass spectrometry (ICM-MS). Appl Microbiol Biotechnol. 2005;67:539–48.CAS 
    PubMed 

    Google Scholar 
    Costa MS, Clark CM, Ómarsdóttir S, Sanchez LM, Murphy BT. Minimizing taxonomic and natural product redundancy in microbial libraries using MALDI-TOF MS and the bioinformatics pipeline IDBac. J Nat Prod. 2019;82:2167–73.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Clark CM, Costa MS, Sanchez LM, Murphy BT. Coupling MALDI-TOF mass spectrometry protein and specialized metabolite analyses to rapidly discriminate bacterial function. Proc Natl Acad Sci USA. 2018;115:4981–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Clark CM, Costa MS, Conley E, Li E, Sanchez LM, Murphy BT. Using the open-source MALDI TOF-MS IDBac pipeline for analysis of microbial protein and specialized metabolite data. J Vis Exp. 2019;147:e59219.
    Google Scholar 
    Ryzhov V, Fenselau C. Characterization of the protein subset desorbed by MALDI from whole bacterial cells. Anal Chem. 2001;73:746–50.CAS 
    PubMed 

    Google Scholar 
    Welker M, Moore ERB. Applications of whole-cell matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry in systematic microbiology. Syst Appl Microbiol. 2011;34:2–11.CAS 
    PubMed 

    Google Scholar 
    Sandrin TR, Goldstein JE, Schumaker S. MALDI TOF MS profiling of bacteria at the strain level: a review. Mass Spectrom Rev. 2013;32:188–217.CAS 
    PubMed 

    Google Scholar 
    Seuylemezian A, Aronson HS, Tan J, Lin M, Schubert W, Vaishampayan P. Development of a custom MALDI-TOF MS database for species-level identification of bacterial isolates collected from spacecraft and associated surfaces. Front Microbiol. 2018;9:780.PubMed 
    PubMed Central 

    Google Scholar 
    Strejcek M, Smrhova T, Junkova P, Uhlik O. Whole-cell MALDI-TOF MS versus 16S rRNA gene analysis for identification and dereplication of recurrent bacterial isolates. Front Microbiol. 2018;9:1294.PubMed 
    PubMed Central 

    Google Scholar 
    Giraud-Gatineau A, Texier G, Garnotel E, Raoult D, Chaudet H. Insights into subspecies discrimination potentiality from bacteria MALDI-TOF mass spectra by using data mining and diversity studies. Front Microbiol. 2020;11:1931.PubMed 
    PubMed Central 

    Google Scholar 
    LaMontagne MG, Tran PL, Benavidez A, Morano LD. Development of an inexpensive matrix-assisted laser desorption-time of flight mass spectrometry method for the identification of endophytes and rhizobacteria cultured from the microbiome associated with maize. PeerJ. 2021;9:e11359.PubMed 
    PubMed Central 

    Google Scholar 
    Freiwald A, Sauer S. Phylogenetic classification and identification of bacteria by mass spectrometry. Nat Protoc. 2009;4:732–42.CAS 
    PubMed 

    Google Scholar 
    Croxatto A, Prod’hom G, Greub G. Applications of MALDI-TOF mass spectrometry in clinical diagnostic microbiology. FEMS Microbiol Rev. 2012;36:380–407.CAS 
    PubMed 

    Google Scholar 
    Rodríguez-Sánchez B, Cercenado E, Coste AT, Greub G. Review of the impact of MALDI-TOF MS in public health and hospital hygiene, 2018. Eurosurveillance. 2019;24:1800193. PubMed Central 

    Google Scholar 
    Rahi P, Vaishampayan P. MALDI-TOF MS application in microbial ecology studies. Front Microbiol. 2019;10:2954.PubMed 

    Google Scholar 
    Popović NT, Kazazić SP, Strunjak-Perović I, Čož-Rakovac R. Differentiation of environmental aquatic bacterial isolates by MALDI-TOF MS. Environ Res. 2017;152:7–16.PubMed 

    Google Scholar 
    Rahi P, Prakash O, Shouche YS. Matrix-assisted laser desorption/ionization Time-of-Flight mass-spectrometry (MALDI-TOF MS) based microbial identifications: challenges and scopes for microbial ecologists. Front Microbiol. 2016;7:1359.PubMed 
    PubMed Central 

    Google Scholar 
    Schumann P, Maier T. Chapter 13 – MALDI-TOF mass spectrometry applied to classification and identification of bacteria. In: Methods in microbiology, vol 41, ISSN 0580-9517. Goodfellow M, Sutcliffe I, Chun J, editors. Academic Press; 2014. p. 275–306.Murtagh F, Legendre P. Ward’s hierarchical agglomerative clustering method: which algorithms implement Ward’s criterion? J Classif. 2014;31:274–95.
    Google Scholar 
    Batagelj V. Generalized Ward and related clustering problems. In: Bock HH, editor. North Holland, Amsterdam: Proceedings of the First Conference of the International Federation of Classification Societies; 1988. p. 67–74.van Santen JA, Jacob G, Singh AL, Aniebok V, Balunas MJ, Bunsko D, et al. The natural products atlas: an open access knowledge base for microbial natural products discovery. ACS Cent Sci. 2019;5:1824–33.PubMed 
    PubMed Central 

    Google Scholar 
    Ghyselinck J, Van Hoorde K, Hoste B, Heylen K, De Vos P. Evaluation of MALDI-TOF MS as a tool for high-throughput dereplication. J Microbiol Meth. 2011;86:327–36.CAS 

    Google Scholar 
    Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Henson MW, Lanclos VC, Pitre DM, Weckhorst JL, Lucchesi AM, Cheng C, et al. Expanding the diversity of bacterioplankton isolates and modeling isolation efficacy with large-scale dilution-to-extinction cultivation. Appl Environ Microbiol. 2020;86:e00943–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hoffmann T, Krug D, Bozkurt N, Duddela S, Jansen R, Garcia R, et al. Correlating chemical diversity with taxonomic distance for discovery of natural products in myxobacteria. Nat Commun. 2018;9:1–10.CAS 

    Google Scholar 
    Jensen PR, Williams PG, Oh D-C, Zeigler L, Fenical W. Species-specific secondary metabolite production in marine actinomycetes of the genus Salinispora. Appl Environ Microbiol. 2007;73:1146–52.CAS 
    PubMed 

    Google Scholar 
    Ziemert N, Lechner A, Wietz M, Millán-Aguiñaga N, Chavarria KL, Jensen PR. Diversity and evolution of secondary metabolism in the marine actinomycete genus Salinispora. Proc Natl Acad Sci USA. 2014;111:E1130–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bruns H, Crüsemann M, Letzel A-C, Alanjary M, McInerney JO, Jensen PR, et al. Function-related replacement of bacterial siderophore pathways. ISME J. 2018;12:320–9.CAS 
    PubMed 

    Google Scholar 
    Chase AB, Sweeney D, Muskat MN, Guillén-Matus DG, Jensen PR. Vertical inheritance facilitates interspecies diversification in biosynthetic gene clusters and specialized metabolites. MBio. 2021;12:e0270021.PubMed 

    Google Scholar 
    Covington BC, Xu F, Seyedsayamdost MR. A natural product chemist’s guide to unlocking silent biosynthetic gene clusters. Annu Rev Biochem. 2021;90:763–88.CAS 
    PubMed 

    Google Scholar 
    Adamek M, Alanjary M, Sales-Ortells H, Goodfellow M, Bull AT, Winkler A, et al. Comparative genomics reveals phylogenetic distribution patterns of secondary metabolites in Amycolatopsis species. BMC Genomics. 2018;19:426.PubMed 
    PubMed Central 

    Google Scholar 
    Chevrette MG, Currie CR. Emerging evolutionary paradigms in antibiotic discovery. J Ind Microbiol Biotechnol. 2019;46:257–71.CAS 
    PubMed 

    Google Scholar 
    Zdouc MM, Iorio M, Maffioli SI, Crüsemann M, Donadio S, Sosio M. Planomonospora: a metabolomics perspective on an underexplored Actinobacteria genus. J Nat Prod. 2021;84:204–19.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kang D, Shoaie S, Jacquiod S, Sørensen SJ, Ledesma-Amaro R. Comparative genomics analysis of keratin-degrading Chryseobacterium species reveals their keratinolytic potential for secondary metabolite production. Microorganisms. 2021;9:1042.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Han S, Van Treuren W, Fischer CR, Merrill BD, DeFelice BC, Sanchez JM, et al. A metabolomics pipeline for the mechanistic interrogation of the gut microbiome. Nature. 2021;595:415–20.CAS 
    PubMed 

    Google Scholar 
    Newman DJ, Cragg GM. Natural products as sources of new drugs over the nearly four decades from 01/1981 to 09/2019. J Nat Prod. 2020;83:770–803.CAS 
    PubMed 

    Google Scholar 
    Demain AL, Sanchez S. Microbial drug discovery: 80 years of progress. J Antibiot. 2009;62:5–16.CAS 

    Google Scholar 
    Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol. 2012;30:918–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gibb S, Strimmer K. Mass spectrometry analysis using MALDIquant. In: Datta S, Mertens BJA, editors. Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry. Cham: Springer International Publishing; 2017. p. 101–24.Gibb S, Strimmer K. MALDIquant: a versatile R package for the analysis of mass spectrometry data. Bioinformatics. 2012;28:2270–1.CAS 
    PubMed 

    Google Scholar 
    Weisburg WG, Barns SM, Pelletier DA, Lane DJ. 16S ribosomal DNA amplification for phylogenetic study. J Bacteriol. 1991;173:697–703.CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Macroalgae and interspecific alarm cues regulate behavioral interactions between sea urchins and sea cucumbers

    Lawrence, J.M. Sea urchins: biology and ecology. Amsterdam, The Netherlands: Elsevier B.V. (2020)Purcell, S.W., Samyn, Y. & Conand, C. Commercially important sea cucumbers of the world. Rome, Italy: FAO. (2012)Yorke, C. E., Page, H. M. & Miller, R. J. Sea urchins mediate the availability of kelp detritus to benthic consumers. Proc. R. Soc. B. 286(1906), 20190846 (2019).CAS 
    Article 

    Google Scholar 
    Dethier, M. N. et al. Feces as food: The nutritional value of urchin feces and implications for benthic food webs. J. Exp. Mar. Biol. Ecol. 514, 95–102 (2019).Article 

    Google Scholar 
    Purcell, S. W. et al. Ecological roles of exploited sea cucumbers. Oceanogr. Mar. Biol. 54, 367–386 (2017).
    Google Scholar 
    Hamel, J. F. & Mercier, A. Early development, settlement, growth, and spatial distribution of the sea cucumber Cucumaria frondosa (Echinodermata: Holothuroidea). Can. J. Fish. Aquat. Sci. 53(2), 253–271 (1996).Article 

    Google Scholar 
    Grosso, L. et al. Integrated Multi-Trophic Aquaculture (IMTA) system combining the sea urchin Paracentrotus lividus, as primary species, and the sea cucumber Holothuria tubulosa as extractive species. Aquaculture 534, 736268 (2021)Gabara, S.S., Konar, B.H. & Edwards, M.S. Biodiversity loss leads to reductions in community-wide trophic complexity. Ecosphere 12(2), e03361 (2021)Duffy, J. E. et al. The functional role of biodiversity in ecosystems: Incorporating trophic complexity. Ecol. Lett. 10(6), 522–538 (2010).ADS 
    Article 

    Google Scholar 
    Miller, R. J. et al. Giant kelp, Macrocystis pyrifera, increases faunal diversity through physical engineering. Proc. R. Soc. B. 285(1874), 20172571 (2018).Article 

    Google Scholar 
    Soulsby, P. G., Lowthion, D. & Houston, M. Effects of macroalgal mats on the ecology of intertidal mudflats. Mar. Pollut. Bull. 13(5), 162–166 (1982).Article 

    Google Scholar 
    Filbee-Dexter, K. & Scheibling, R.E. Sea urchin barrens as alternative stable states of collapsed kelp ecosystems. Mar. Ecol.: Prog. Ser. 495(1), 1–25 (2014)Hendler, G., Miller, J. E., Pawson, D. L. & Kier, P. M. Sea stars, sea urchins and allies: echinoderms of Florida and the Caribbean (Smithsonian Institution Press, 1995).
    Google Scholar 
    James, D. B. Sea cucumber and sea urchin resources. CMFRI Bull. 34, 85–93 (1983).
    Google Scholar 
    Muthiga, N.A. & Kawaka, J.A. The effects of temperature and light on the gametogenesis and spawning of four sea urchin and one sea cucumber species on coral reefs in Kenya. Proceedings of the 11th international coral reef symposium. Fort Lauderdale, Florida pp 356–360 (2008)Byrnes, J., Cardinale, B. & Reed, D. Interactions between sea urchin grazing and prey diversity on temperate rocky reef communities. Ecology 94(7), 1636–1646 (2013).Article 

    Google Scholar 
    Vanderklift, M.A. & Kendrick, G.A. Contrasting influence of sea urchins on attached and drift macroalgae. Mar. Ecol.: Prog. Ser. 299, 101–110 (2005)Duggins, D. O. Interspecific facilitation in a guild of benthic marine herbivores. Oecologia 48(2), 157–163 (1981).ADS 
    Article 

    Google Scholar 
    Bonaviri, C. et al. Fish versus starfish predation in controlling sea urchin populations in Mediterranean rocky shores. Mar. Ecol.: Prog. Ser. 382(1), 129–138 (2009)Purcell, S. W. & Simutoga, M. Spatio-temporal and size-dependent variation in the success of releasing cultured sea cucumbers in the wild. Rev. Fish. Sci. 16, 204–214 (2008).Article 

    Google Scholar 
    Scheibling, R. E. & Robinson, M. C. Settlement behaviour and early post-settlement predation of the sea urchin Strongylocentrotus droebachiensis. J. Exp. Mar. Biol. Ecol. 365(1), 59–66 (2008).Article 

    Google Scholar 
    Francour, P. Predation on holothurians: a literature review. Invertebr. Biol. 116(1), 52–60 (1997).Article 

    Google Scholar 
    Scheibling, R. E. & Hamm, J. Interactions between sea urchins (Strongylocentrotus droebachiensis) and their predators in field and laboratory experiments. Mar. Biol. 110(1), 105–116 (1991).Article 

    Google Scholar 
    Bartumeus, F., Romero, J. & Alcoverro, T. The scent of fear makes sea urchins go ballistic. Mov. Ecol. 9(1), 1–12 (2021).Article 

    Google Scholar 
    Campbell, A.C. & Coppard, S., Tudor-Thomas CD. Escape and aggregation responses of three echinoderms to conspecific stimuli. Biol. Bull. 201(2), 175–185 (2001)Chi, X. et al. Conspecific alarm cues are a potential effective barrier to regulate foraging behavior of the sea urchin Mesocentrotus nudus. Mar. Environ. Res. 171(8), 105476 (2021)Chi, X. et al. Foraging behavior of the sea urchin Mesocentrotus nudus exposed to conspecific alarm cues in various conditions. Sci. Rep. 11(1), 1–6 (2021).Article 

    Google Scholar 
    Zhadan, P.M. & Vaschenko, M.A. Long-term study of behaviors of two cohabiting sea urchin species, Mesocentrotus nudus and Strongylocentrotus intermedius, under conditions of high food quantity and predation risk in situ. PeerJ 7(1), e8087 (2019)Bshary, R. & Noë, R. Red colobus and Diana monkeys provide mutual protection against predators. Anim. Behav. 54(6), 1461–1474 (1997).CAS 
    Article 

    Google Scholar 
    Peres, C. A. Anti-predation benefits in a mixed-species group of Amazonian tamarins. Folia Primatol. 61(2), 61–76 (1993).CAS 
    Article 

    Google Scholar 
    Fuji, A. Ecological studies on the growth and food consumption of Japanese common littoral sea urchin, Strongylocentrotus intermedius (A. Agassiz). Mem. Fac. Fish. Hokkaido Univ. 15(2), 83–160 (1967)Chang, Y., Ding, J., Song, J. & Yang, W. Biology and aquaculture of sea cucumbers and sea urchins (Ocean Press, 2004).
    Google Scholar 
    Yang, H., Hamel, J. F. & Mercier, A. The sea cucumber Apostichopus japonicus: history, biology and aquaculture (Elsevier Inc., 2015).
    Google Scholar 
    Zhao, C. et al. Carryover effects of short-term UV-B radiation on fitness related traits of the sea urchin Strongylocentrotus intermedius. Ecotoxicol. Environ. Saf. 164, 659–664 (2018).CAS 
    Article 

    Google Scholar 
    Zhang, L. et al. Effects of long-term elevated temperature on covering, sheltering and righting behaviors of the sea urchin Strongylocentrotus intermedius. PeerJ 5, e3122 (2017)Zhao, C. et al. Effects of covering behavior and exposure to a predatory crab Charybdis japonica on survival and HSP70 expression of juvenile sea urchins Strongylocentrotus intermedius. PloS One 9(5), e97840 (2014)Kawai, T. & Agatsuma, Y. Predators on released seed of the sea urchin Strongylocentrotus intermedius at Shiribeshi, Hokkaido, Japan. Fish. Sci. (Tokyo, Jpn.) 62(2), 317–318 (1996)Hatanaka, H. Experimental studies on the predation of juvenile sea cucumber, Stichopus japonicus by sea star Asterina pectinifera. Aquacult. Sci. 42(4), 563–566 (1994).
    Google Scholar 
    Guidetti, P. & Mori, M. Morpho-functional defences of Mediterranean sea urchins, Paracentrotus lividus and Arbacia lixula, against fish predators. Mar. Biol. 147(3), 797–802 (2005).Article 

    Google Scholar 
    Moitoza, D.J & Phillips, D.W. Prey defense, predator preference, and nonrandom diet: the interactions between Pycnopodia helianthoides and two species of sea urchins. Mar. Biol. 53(4), 299–304 (1979)Williams, J.P. et al. Sea urchin mass mortality rapidly restores kelp forest communities. Mar. Ecol.: Prog. Ser. 664, 117–131 (2021)Pearse, J. Ecological role of purple sea urchins. Science 314(5801), 940–941 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    Vadas, R. L. Preferential feeding: an optimization strategy in sea urchins. Ecol. Monogr. 47(4), 337–371 (1977).Article 

    Google Scholar 
    Lowe, A. T. et al. Sedentary urchins influence benthic community composition below the macroalgal zone. Mar. Biol. 36(2), 129–140 (2015).
    Google Scholar 
    Layton, C. et al. Kelp Forest Restoration in Australia. Front. Mar. Sci. 7(74) (2020)Eger, A.M. et al. Global Kelp forest restoration: Past lessons, status, and future goals. Preprint. EcoEvoRxiv. https://doi.org/10.32942/osf.io/emaz2 (2021)Ritson-Williams, R. & Paul, V. J. Marine benthic invertebrates use multimodal cues for defense against reef fish. Mar. Ecol. Prog. Ser. 340, 29–39 (2007).ADS 
    Article 

    Google Scholar 
    Hu, F. et al. Effects of artificial reefs on selectivity and behaviors of the sea cucumber Apostichopus japonicas: New insights into the pond culture. Aquacult. Rep. 21(3), 100842 (2021)Sun, J. et al. Light intensity regulates phototaxis, foraging and righting behaviors of the sea urchin Strongylocentrotus intermedius. PeerJ 7, e8001 (2019)Bi, S., Shi, J. & Liu, A. Exploitation and utilization of Ulva lactuca L. Mod. Fish. Inf. 11, 21–23 (1993).
    Google Scholar 
    Chang, Y. Q., Wang, Z. C. & Wang, G. J. Effect of temperature and algae on feeding and growth in sea urchin Strongylocentrotus intermedius. J. Fish. China 23(1), 69–76 (1999).
    Google Scholar 
    Dumont, C., Himmelman, J.H. & Russell, M.P. Size-specific movement of green sea urchins Strongylocentrotus droebachiensis on urchin barrens in eastern Canada. Mar. Ecol.: Prog. Ser. 276, 93–101 (2004)Sun, J. et al. Interaction among sea urchins in response to food cues. Sci. Rep. 11(1), 1–9 (2021).ADS 
    Article 

    Google Scholar 
    Węglarczyk, S. Kernel density estimation and its application. ITM Web Conf. 23(2), 00037 (2018).Article 

    Google Scholar  More

  • in

    Physiological acclimatization in Hawaiian corals following a 22-month shift in baseline seawater temperature and pH

    Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science (80- ). 359, 80–83 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Eakin, C. M., Sweatman, H. P. A. & Brainard, R. E. The 2014–2017 global-scale coral bleaching event: Insights and impacts. Coral Reefs 38, 539–545 (2019).ADS 

    Google Scholar 
    Glynn. Coral reef bleaching: Facts, hypotheses and implications. Glob. Chang. Biol. 2, 495–509 (1996).ADS 

    Google Scholar 
    Brown, B. E. Coral bleaching: Causes and consequences. Coral Reefs 16, 129–138 (1997).
    Google Scholar 
    Maynard, J. A. et al. Projections of climate conditions that increase coral disease susceptibility and pathogen abundance and virulence. Nat. Clim. Chang. 5, 688–694 (2015).ADS 

    Google Scholar 
    Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S. & Hoegh-Guldberg, O. Ocean acidification causes bleaching and productivity loss in coral reef builders. Proc. Natl. Acad. Sci. U. S. A. 105, 17442–17446 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Huang, H. et al. Positive and negative responses of coral calcification to elevated pCO2: Case studies of two coral species and the implications of their responses. Mar. Ecol. Prog. Ser. 502, 145–156 (2014).ADS 
    CAS 

    Google Scholar 
    Hoadley, K. D. et al. Physiological response to elevated temperature and pCO2 varies across four Pacific coral species: Understanding the unique host + symbiont response. Sci. Rep. 5, 1–15 (2015).
    Google Scholar 
    Schoepf, V. et al. Coral energy reserves and calcification in a high-CO2 world at two temperatures. PLoS One. 8, e75049 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    IPCC. In IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, (eds. Pörtner, H.-O. et al.) 1–36 (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2019).Bahr, K. D., Jokiel, P. L. & Rodgers, K. S. Relative sensitivity of five Hawaiian coral species to high temperature under high-pCO2 conditions. Coral Reefs 35, 729–738 (2016).ADS 

    Google Scholar 
    Dove, S. G., Brown, K. T., Van Den Heuvel, A., Chai, A. & Hoegh-Guldberg, O. Ocean warming and acidification uncouple calcification from calcifier biomass which accelerates coral reef decline. Commun. Earth Environ. 1, 1–9 (2020).
    Google Scholar 
    Chow, M. H., Tsang, R. H. L., Lam, E. K. Y. & Ang, P. O. Quantifying the degree of coral bleaching using digital photographic technique. J. Exp. Mar. Bio. Ecol. 479, 60–68 (2016).
    Google Scholar 
    Amid, C. et al. Additive effects of the herbicide glyphosate and elevated temperature on the branched coral Acropora formosa in Nha Trang, Vietnam. Environ. Sci. Pollut. Res. 25, 13360–13372 (2018).CAS 

    Google Scholar 
    Anthony, K. R. N., Connolly, S. R. & Willis, B. L. Comparative analysis of energy allocation to tissue and skeletal growth in corals. Limnol. Oceanogr. 47, 1417–1429 (2002).ADS 

    Google Scholar 
    Edmunds, P. J. & Davies, P. S. An energy budget for Porites porites (Scleractinia). Mar. Biol. 92, 339–347 (1986).
    Google Scholar 
    Stimson, J. S. Location, quantity and rate of change in quantity of lipids in tissue of Hawaiian hermatypic corals. Bull. Mar. Sci. 41, 889–904 (1987).ADS 

    Google Scholar 
    Harland, A. D., Navarro, J. C., Spencer Davies, P. & Fixter, L. M. Lipids of some Caribbean and Red Sea corals: Total lipid, wax esters, triglycerides and fatty acids. Mar. Biol. 117, 113–117 (1993).CAS 

    Google Scholar 
    Grottoli, A. G., Tchernov, D. & Winters, G. Physiological and biogeochemical responses of super-corals to thermal stress from the northern gulf of Aqaba, Red Sea. Front. Mar. Sci. 4, 1–12 (2017).
    Google Scholar 
    Rodrigues, L. J. & Grottoli, A. G. Energy reserves and metabolism as indicators of coral recovery from bleaching. Limnol. Oceanogr. 52, 1874–1882 (2007).ADS 

    Google Scholar 
    Anthony, K. R. N., Hoogenboom, M. O., Maynard, J. A., Grottoli, A. G. & Middlebrook, R. Energetics approach to predicting mortality risk from environmental stress: A case study of coral bleaching. Funct. Ecol. 23, 539–550 (2009).
    Google Scholar 
    Baumann, J. H., Grottoli, A. G., Hughes, A. D. & Matsui, Y. Photoautotrophic and heterotrophic carbon in bleached and non-bleached coral lipid acquisition and storage. J. Exp. Mar. Bio. Ecol. 461, 469–478 (2014).CAS 

    Google Scholar 
    Hughes, A. D. & Grottoli, A. G. Heterotrophic compensation: A possible mechanism for resilience of coral reefs to global warming or a sign of prolonged stress?. PLoS ONE 8, 1–10 (2013).
    Google Scholar 
    Grottoli, A. G. et al. The cumulative impact of annual coral bleaching can turn some coral species winners into losers. Glob. Chang. Biol. 20, 3823–3833 (2014).ADS 
    PubMed 

    Google Scholar 
    Grottoli, A. G., Rodrigues, L. J. & Palardy, J. E. Heterotrophic plasticity and resilience in bleached corals. Nature 440, 1186–1189 (2006).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Levas, S. J. et al. Can heterotrophic uptake of dissolved organic carbon and zooplankton mitigate carbon budget deficits in annually bleached corals?. Coral Reefs 35, 495–506 (2016).ADS 

    Google Scholar 
    Jury, C. P., Delano, M. N. & Toonen, R. J. High heritability of coral calcification rates and evolutionary potential under ocean acidification. Sci. Rep. 9, 1–13 (2019).
    Google Scholar 
    Jury, C. P. & Toonen, R. J. Adaptive responses and local stressor mitigation drive coral resilience in warmer, more acidic oceans. Proc. R. Soc. B Biol. Sci. 286, 20190614 (2019).
    Google Scholar 
    Concepcion, G. T., Polato, N. R., Baums, I. B. & Toonen, R. J. Development of microsatellite markers from four Hawaiian corals: Acropora cytherea, Fungia scutaria, Montipora capitata and Porites lobata. Conserv. Genet. Resour. 2, 11–15 (2010).

    Google Scholar 
    Gorospe, K. D. & Karl, S. A. Genetic relatedness does not retain spatial pattern across multiple spatial scales: Dispersal and colonization in the coral, Pocillopora damicornis. Mol. Ecol. 22, 3721–3736 (2013).PubMed 

    Google Scholar 
    Wall, C. B., Ritson-Williams, R., Popp, B. N. & Gates, R. D. Spatial variation in the biochemical and isotopic composition of corals during bleaching and recovery. Limnol. Oceanogr. 64, 2011–2028 (2019).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bahr, K. D., Tran, T., Jury, C. P. & Toonen, R. J. Abundance, size, and survival of recruits of the reef coral Pocillopora acuta under ocean warming and acidification. PLoS ONE 15, 1–13 (2020).
    Google Scholar 
    Rogelj, J. et al. Paris agreement climate proposals need a boost to keep warming well below 2 °C. Nature 534, 631–639 (2016).ADS 
    CAS 
    PubMed 

    Google Scholar 
    McLachlan, R. H., Price, J. T., Solomon, S. L. & Grottoli, A. G. Thirty years of coral heat-stress experiments: A review of methods. Coral Reefs 39, 885–902 (2020).
    Google Scholar 
    Grottoli, A. G. et al. Increasing comparability among coral bleaching experiments. Ecol. Appl. 31, e02262 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grottoli, A. G. Variability of stable isotopes and maximum linear extension in reef-coral skeletons at Kaneohe Bay, Hawaii. Mar. Biol. 135, 437–449 (1999).
    Google Scholar 
    McLachlan, R. H., Dobson, K. L., Grottoli, A. G. Quantification of Total Biomass in Ground Coral Samples. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bdyai7se.McLachlan, R. H., Muñoz-Garcia, A., Grottoli, A. G. Extraction of Total Soluble Lipid from Ground Coral Samples. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bc4qiyvw.McLachlan, R. H., Price, J. T., Dobson, K. L., Weisleder, N. & Grottoli, A. G. Microplate Assay for Quantification of Soluble Protein in Ground Coral Samples. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bdc8i2zw.McLachlan, R. H., Juracka, C. & Grottoli, A. G. Symbiodiniaceae Enumeration in Ground Coral Samples Using Countess™ II FL Automated Cell Counter. Protocols.io (2020). https://doi.org/10.17504/protocols.io.bdc5i2y6.McLachlan, R. H. & Grottoli, A. G. Geometric Method for Estimating Coral Surface Area Using Image Analysis. Protocols.io https://doi.org/10.17504/protocols.io.bdyai7se(2021).Muscatine, L., McCloskey, L. R. & Marian, R. E. Estimating the daily contribution of carbon from zooxanthellae to coral animal respiration. Limnol. Oceanogr. 26, 601–611 (1981).ADS 
    CAS 

    Google Scholar 
    Levas, S. J. et al. Organic carbon fluxes mediated by corals at elevated pCO2 and temperature. Mar. Ecol. Prog. Ser. 519, 153–164 (2015).ADS 
    CAS 

    Google Scholar 
    Perry, C. T. et al. Loss of coral reef growth capacity to track future increases in sea level. Nature 558, 396–400 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Woodley, C. M., Burnett, A. & Downs, C. A. Epidemiological Assessment of Reproductive Condition of ESA Priority Coral (2013).Logan, C. A., Dunne, J. P., Eakin, C. M. & Donner, S. D. Incorporating adaptive responses into future projections of coral bleaching. Glob. Chang. Biol. 20, 125–139 (2014).ADS 
    PubMed 

    Google Scholar 
    Rodrigues, L. J., Grottoli, A. G. & Lesser, M. P. Long-term changes in the chlorophyll fluorescence of bleached and recovering corals from Hawaii. J. Exp. Biol. 211, 2502–2509 (2008).PubMed 

    Google Scholar 
    Rowan, H. et al. Environmental gradients drive physiological variation in Hawaiian corals. Coral Reefs 40(5), 1505–1523. https://doi.org/10.1007/s00338-021-02140-8 (2021).Article 

    Google Scholar 
    Houlbrèque, F. & Ferrier-Pagès, C. Heterotrophy in tropical scleractinian corals. Biol. Rev. 84, 1–17 (2009).PubMed 

    Google Scholar 
    J. T. Price, thesis, The Ohio State University (2020). More

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    Field experiments underestimate aboveground biomass response to drought

    Literature search and study selectionA systematic literature search was conducted in the ISI Web of Science database for observational and experimental studies published from 1975 to 13 January 2020 using the following search terms: TOPIC: (grassland* OR prairie* OR steppe* OR shrubland* OR scrubland* OR bushland*) AND TOPIC: (drought* OR ‘dry period*’ OR ‘dry condition*’ OR ‘dry year*’ OR ‘dry spell*’) AND TOPIC: (product* OR biomass OR cover OR abundance* OR phytomass). The search was refined to include the subject categories Ecology, Environmental Sciences, Plant Sciences, Biodiversity Conservation, Multidisciplinary Sciences and Biology, and the document types Article, Review and Letter. This yielded a total of 2,187 peer-reviewed papers (Supplementary Fig. 1). At first, these papers were screened by title and abstract, which resulted in 197 potentially relevant full-text articles. We then examined the full text of these papers for eligibility and selected 87 studies (43 experimental, 43 observational and 1 that included both types) on the basis of the following criteria:

    (1)

    The research was conducted in the field, in natural or semi-natural grasslands or shrublands (for example, artificially constructed (seeded or planted) plant communities or studies using monolith transplants were excluded). We used this restriction because most reports on observational droughts are from intact ecosystems, and experiments in disturbed sites or using artificial communities would thus not be comparable to observational drought studies.

    (2)

    In the case of observational studies, the drought year or a multi-year drought was clearly specified by the authors (that is, we did not arbitrarily extract dry years from a long-term dataset). Please note that some observational data points are from control plots of experiments (of any kind), where the authors reported that a drought had occurred during the study period. We did not involve gradient studies that compare sites of different climates, which are sometimes referred to as ‘observational studies’.

    (3)

    The paper reported the amount or proportion of change in annual or growing-season precipitation (GSP) compared with control conditions. We consistently use the term ‘control’ for normal precipitation (non-drought) year or years in observational studies and for ambient precipitation (no treatment) in experimental studies hereafter. Similarly, we use the term ‘drought’ for both drought year or years in observational studies and drought treatment in experimental studies. In the case of multi-factor experiments, where precipitation reduction was combined with any other treatment (for example, warming), data from the plots receiving drought only and data from the control plots were used.

    (4)

    The paper contained raw data on plant production under both control and drought conditions, expressed in any of the following variables: ANPP, aboveground plant biomass (in grassland studies only) or percentage plant cover. In 79% of the studies that used ANPP as a production variable, ANPP was estimated by harvesting peak or end-of-season AGB. We therefore did not distinguish between ANPP and AGB, which are referred to as ‘biomass’ hereafter. We included the papers that reported the production of the whole plant community, or at least that of the dominant species or functional groups approximating the abundance of the whole community.

    (5)

    When multiple papers were published on the same experiment or natural drought event at the same study site, the most long-term study including the largest number of drought years was chosen.

    In addition to the systematic literature search, we included 27 studies (9 experimental, 17 observational and 1 that included both types) meeting the above criteria from the cited references of the Web of Science records selected for our meta-analyses, and from previous meta-analyses and reviews on the topic. In total, this resulted in 114 studies (52 experimental, 60 observational and 2 that included both types; Supplementary Note 9, Supplementary Fig. 2 and ref. 25).Data compilationData were extracted from the text or tables, or were read from the figures using Web Plot Digitizer26. For each study, we collected the study site, latitude, longitude, mean annual temperature (MAT) and precipitation (MAP), study type (experimental or observational), and drought length (the number of consecutive drought years). When MAT or MAP was not documented in the paper, it was extracted from another published study conducted at the same study site (identified by site names and geographic coordinates) or from an online climate database cited in the respective paper. We also collected vegetation type—that is, grassland when it was dominated by grasses, or shrubland when the dominant species included one or more shrub species (involving communities co-dominated by grasses and shrubs). Data from the same study (that is, paper) but from different geographic locations or environmental conditions (for example, soil types, land uses or multiple levels of experimental drought) were collected as distinct data points (but see ‘Statistical analysis’ for how these points were handled). As a result, the 114 published papers provided 239 data points (112 experimental and 127 observational)25.For the observational studies, normal precipitation year or years specified by the authors was used as the control. If it was not specified in the paper, the year immediately preceding the drought year(s) was chosen as the control. When no data from the pre-drought year were available, the year immediately following the drought year(s) (14 data points) or a multi-year period given in the paper (22 data points) was used as the control. For the experimental studies, we also collected treatment size (that is, rainout shelter area or, if it was not reported in the paper, the experimental plot size).For the calculation of drought severity, we used yearly precipitation (YP), which was reported in a much higher number of studies than GSP. We extracted YP for both control (YPcontrol) and drought (YPdrought). For the observational studies, when a multi-year period was used as the control or the natural drought lasted for more than one year, precipitation values were averaged across the control or drought years, respectively. Consistently, in the case of multi-year drought experiments, YPcontrol and YPdrought were averaged across the treatment years. When only GSP was published in the paper (63 of 239 data points), we used this to obtain YP data as follows: we regarded MAP as YPcontrol, and YPdrought was calculated as YPdrought = MAP − (GSPcontrol − GSPdrought). From YPcontrol and YPdrought data, we calculated drought severity as follows: (YPdrought − YPcontrol)/YPcontrol × 100.For production, we compiled the mean, replication (N) and, if the study reported it, a variance estimate (s.d., s.e.m. or 95% CI) for both control and drought. In the case of multi-year droughts, data only from the last drought year were extracted, except in five studies (17 data points) where production data were given as an average for the drought years. When both biomass and cover data were presented in the paper, we chose biomass. For each study, we consistently considered replication as the number of the smallest independent study unit. When only the range of replications was reported in a study, we chose the smallest number.To quantify climatic aridity for each study site, we used an aridity index (AI), calculated as the ratio of MAP and mean annual PET (AI = MAP/PET). This is a frequently used index in recent climate change research27,28. AI values were extracted from the Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v.2 for the period of 1970–2000 (aggregated on annual basis)29.Because we wanted to prevent our analysis from being distorted by a strongly unequal distribution of studies between the two study types regarding some potentially important explanatory variables, we left out studies from our focal meta-analysis in three steps. First, we left out studies that were conducted at wet sites—that is, where site AI exceeded 1. The value of 1 was chosen for two reasons: above this value, the distribution of studies between the two study types was extremely uneven (22 experimental versus 2 observational data points with AI  > 1)25, and the AI value of 1 is a bioclimatically meaningful threshold, where MAP equals PET. Second, we left out shrublands, because we had only 14 shrubland studies (out of 105 studies with AI  More

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    Climate-change-driven growth decline of European beech forests

    IPCC. IPCC Fifth Assessment Report (AR5). 10–12 (IPCC, 2014).Cailleret, M. et al. A synthesis of radial growth patterns preceding tree mortality. Glob. Chang. Biol. 23, 1675–1690 (2017).PubMed 

    Google Scholar 
    Forzieri, G. et al. Emergent vulnerability to climate-driven disturbances in European forests. Nat. Commun. 12, 1–12 (2021).
    Google Scholar 
    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science https://doi.org/10.1126/science.1155121 (2008).Article 
    PubMed 

    Google Scholar 
    Buras, A. & Menzel, A. Projecting tree species composition changes of European forests for 2061–2090 under RCP 4.5 and RCP 8.5 scenarios. Front. Plant Sci. 9, 1–13 (2019).
    Google Scholar 
    van der Maaten, E. et al. Species distribution models predict temporal but not spatial variation in forest growth. Ecol. Evol. 7, 2585–2594 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Lebaube, S., Le Goff, N. L., Ottorini, J. M. & Granier, A. Carbon balance and tree growth in a Fagus sylvatica stand. Ann. Sci. 57, 49–61 (2000).
    Google Scholar 
    Dobbertin, M. Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur. J. For. Res. 124, 319–333 (2005).
    Google Scholar 
    Büntgen, U. Re-thinking the boundaries of dendrochronology. Dendrochronologia 53, 1–4 (2019).
    Google Scholar 
    Klesse, S. et al. Continental-scale tree-ring-based projection of Douglas-fir growth: Testing the limits of space-for-time substitution. Glob. Chang. Biol. 26, 5146–5163 (2020).PubMed 

    Google Scholar 
    Zhao, S. et al. The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity. J. Biogeogr. 46, 355–368 (2019).
    Google Scholar 
    Babst, F. et al. When tree rings go global: challenges and opportunities for retro- and prospective insight. Quat. Sci. Rev. 197, 1–20 (2018).
    Google Scholar 
    Klesse, S. et al. Sampling bias overestimates climate change impacts on forest growth in the southwestern United States. Nat. Commun. 9, 1–9 (2018).
    Google Scholar 
    Yousefpour, R. et al. Realizing mitigation efficiency of European commercial forests by climate smart forestry. Sci. Rep. 8, 1–11 (2018).CAS 

    Google Scholar 
    Giesecke, T., Hickler, T., Kunkel, T., Sykes, M. T. & Bradshaw, R. H. W. Towards an understanding of the Holocene distribution of Fagus sylvatica L. J. Biogeogr. 34, 118–131 (2007).
    Google Scholar 
    Fang, J. & Lechowicz, M. J. Climatic limits for the present distribution of beech (Fagus L.) species in the world. J. Biogeogr. 33, 1804–1819 (2006).
    Google Scholar 
    Luterbacher, J., Dietrich, D., Xoplaki, E., Grosjean, M. & Wanner, H. European seasonal and annual temperature variability, trends, and extremes since 1500. Science 303, 1499–1503 (2004).CAS 
    PubMed 

    Google Scholar 
    Luterbacher, J. et al. European summer temperatures since Roman times. Environ. Res. Lett. 11, 24001 (2016).Nabuurs, G. J. et al. By 2050 the mitigation effects of EU forests could nearly double through climate smart forestry. Forests 8, 1–14 (2017).
    Google Scholar 
    Walentowski, H. et al. Assessing future suitability of tree species under climate change by multiple methods: a case study in southern Germany. Ann. Res. 60, 101–126 (2017).
    Google Scholar 
    Mäkelä, A. et al. Process-based models for forest ecosystem management: current state of the art and challenges for practical implementation. Tree Physiol. 20, 289–298 (2000).PubMed 

    Google Scholar 
    Leech, S. M., Almuedo, P. L. & Neill, G. O. Assisted migration: adapting forest management to a changing climate. BC J. Ecosyst. Manag. 12, 18–34 (2011).
    Google Scholar 
    Sass-Klaassen, U. G. W. et al. A tree-centered approach to assess impacts of extreme climatic events on forests. Front. Plant Sci. 7, 1069 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Bowman, D. M. J. S., Brienen, R. J. W., Gloor, E., Phillips, O. L. & Prior, L. D. Detecting trends in tree growth: not so simple. Trends Plant Sci. 18, 11–17 (2013).CAS 
    PubMed 

    Google Scholar 
    Hacket-Pain, A. J. et al. Climatically controlled reproduction drives interannual growth variability in a temperate tree species. Ecol. Lett. 21, 1833–1844 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Dorji, Y., Annighöfer, P., Ammer, C. & Seidel, D. Response of beech (Fagus sylvatica L.) trees to competition-new insights from using fractal analysis. Remote Sens. 11, 2656 (2019).Petit-Cailleux, C. et al. Combining statistical and mechanistic models to unravel the drivers of mortality within a rear-edge beech population. bioRxiv https://doi.org/10.1101/645747 (2019).Weigel, R., Gilles, J., Klisz, M., Manthey, M. & Kreyling, J. Forest understory vegetation is more related to soil than to climate towards the cold distribution margin of European beech. J. Veg. Sci. 30, 746–755 (2019).
    Google Scholar 
    Etzold, S. et al. Nitrogen deposition is the most important environmental driver of growth of pure, even-aged and managed European forests. Forest Ecol. Manag. 458, 117762 (2020).
    Google Scholar 
    Martínez-Sancho, E. et al. The GenTree dendroecological collection, tree-ring and wood density data from seven tree species across Europe. Sci. Data 7, 1–7 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Hartl-Meier, C., Dittmar, C., Zang, C. & Rothe, A. Mountain forest growth response to climate change in the Northern Limestone Alps. Trees 28, 819–829 (2014).
    Google Scholar 
    Way, D. A. & Montgomery, R. A. Photoperiod constraints on tree phenology, performance and migration in a warming world. Plant Cell Environ. 38, 1725–1736 (2015).PubMed 

    Google Scholar 
    Martínez del Castillo, E. et al. Spatial patterns of climate – growth relationships across species distribution as a forest management tool in Moncayo Natural Park (Spain). Eur. J. Res. 138, 299 (2019).
    Google Scholar 
    Hacket-Pain, A. J., Cavin, L., Friend, A. D. & Jump, A. S. Consistent limitation of growth by high temperature and low precipitation from range core to southern edge of European beech indicates widespread vulnerability to changing climate. Eur. J. Res. 135, 897–909 (2016).
    Google Scholar 
    van der Maaten, E. Climate sensitivity of radial growth in European beech (Fagus sylvatica L.) at different aspects in southwestern Germany. Trees 26, 777–788 (2012).
    Google Scholar 
    Decuyper, M. et al. Spatio-temporal assessment of beech growth in relation to climate extremes in Slovenia – an integrated approach using remote sensing and tree-ring data. Agric. Meteorol. 287, 107925 (2020).
    Google Scholar 
    Kraus, C., Zang, C. & Menzel, A. Elevational response in leaf and xylem phenology reveals different prolongation of growing period of common beech and Norway spruce under warming conditions in the Bavarian Alps. Eur. J. Res. 135, 1011–1023 (2016).
    Google Scholar 
    Martínez del Castillo, E. et al. Living on the edge: contrasted wood-formation dynamics in Fagus sylvatica and Pinus sylvestris under mediterranean conditions. Front. Plant Sci. 7, 370 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Čufar, K. et al. Temporal shifts in leaf phenology of beech (Fagus sylvatica) depend on elevation. Trees 26, 1091–1100 (2012).
    Google Scholar 
    Bontemps, J. D., Hervé, J. C. & Dhôte, J. F. Dominant radial and height growth reveal comparable historical variations for common beech in north-eastern France. Forest Ecol. Manag. 259, 1455–1463 (2010).
    Google Scholar 
    Latte, N., Lebourgeois, F. & Claessens, H. Increased tree-growth synchronization of beech (Fagus sylvatica L.) in response to climate change in northwestern Europe. Dendrochronologia 33, 69–77 (2015).
    Google Scholar 
    Zimmermann, J., Hauck, M., Dulamsuren, C. & Leuschner, C. Climate warming-related growth decline affects Fagus sylvatica, but not other broad-leaved tree species in central european mixed forests. Ecosystems 18, 560–572 (2015).CAS 

    Google Scholar 
    Tegel, W. et al. A recent growth increase of European beech (Fagus sylvatica L.) at its Mediterranean distribution limit contradicts drought stress. Eur. J. Res. 133, 61–71 (2014).
    Google Scholar 
    Hacket-Pain, A. J. & Friend, A. D. Increased growth and reduced summer drought limitation at the southern limit of Fagus sylvatica L., despite regionally warmer and drier conditions. Dendrochronologia 44, 22–30 (2017).
    Google Scholar 
    Dulamsuren, C., Hauck, M., Kopp, G., Ruff, M. & Leuschner, C. European beech responds to climate change with growth decline at lower, and growth increase at higher elevations in the center of its distribution range (SW Germany). Trees 31, 673–686 (2017).
    Google Scholar 
    Spiecker, H., Mielikäinen, K., Köhl, M. & Skovsgaard, J. P. Growth trends in European forests: studies from 12 countries. European Forest Institute Research Report (1996).Cavin, L. & Jump, A. S. Highest drought sensitivity and lowest resistance to growth suppression are found in the range core of the tree Fagus sylvatica L. not the equatorial range edge. Glob. Chang. Biol. 23, 1–18 (2016).
    Google Scholar 
    Mette, T. et al. Climatic turning point for beech and oak under climate change in Central Europe. Ecosphere 4, 1–19 (2013).
    Google Scholar 
    Michelot, A., Simard, S., Rathgeber, C. B. K., Dufrêne, E. & Damesin, C. Comparing the intra-annual wood formation of three European species (Fagus sylvatica, Quercus petraea and Pinus sylvestris) as related to leaf phenology and non-structural carbohydrate dynamics. Tree Physiol. 32, 1033–1045 (2012).PubMed 

    Google Scholar 
    Meier, I. C. & Leuschner, C. Belowground drought response of European beech: Fine root biomass and carbon partitioning in 14 mature stands across a precipitation gradient. Glob. Chang. Biol. 14, 2081–2095 (2008).
    Google Scholar 
    Leuschner, C. & Ellenberg, H. Ecology of Central European Forests. Vegetation Ecology of Central Europe. Vol. I (Springer, 2017).Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere. 6, 1–55 (2015).
    Google Scholar 
    Pechanec, V., Purkyt, J., Benc, A., Nwaogu, C. & Lenka, Š. Ecological Informatics Modelling of the carbon sequestration and its prediction under climate change. https://doi.org/10.1016/j.ecoinf.2017.08.006 (2017).Speer, J. H. Fundamentals of Tree-Ring Research (University of Arizona Press, 2010).Biondi, F. & Qeadan, F. A theory-driven approach to tree-ring standardization: defining the biological trend from expected basal area increment. Tree-Ring Res. 64, 81–96 (2008).
    Google Scholar 
    Biondi, F. & Qeadan, F. Removing the tree-ring width biological trend using expected basal area increment. in USDA Forest Service RMRS-P-55 124–131 (2008).Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 1–20 (2017).
    Google Scholar 
    De Martonne, E. Une nouvelle fonction climatologique: L’indice d’aridité. La Meteorol. 2, 449–458 (1926).Martínez del Castillo, E., Longares, L. A., Serrano-Notivoli, R. & de Luis, M. Modeling tree-growth: assessing climate suitability of temperate forests growing in Moncayo Natural Park (Spain). Ecol. Manag. 435, 128–137 (2019).
    Google Scholar 
    Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2009).PubMed 

    Google Scholar 
    Calcagno, V. & Mazancourt, C. De. glmulti: an R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34, 1–29 (2010).
    Google Scholar 
    Detry, M. A. & Ma, Y. Analyzing repeated measurements using mixed models. JAMA J. Am. Med. Assoc. 315, 407 (2016).CAS 

    Google Scholar 
    Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 2018, 1–32 (2018).
    Google Scholar 
    Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).PubMed 

    Google Scholar 
    Caudullo, G., Welk, E. & San-Miguel-Ayanz, J. Chorological maps for the main European woody species. Data Brief 12, 662–666 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Meinshausen, M. et al. The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. Geosci. Model Dev. 13, 3571–3605 (2020).CAS 

    Google Scholar 
    Karger, D. N. & Zimmermann, N. E. CHELSAcruts – High Resolution Temperature And Precipitation Timeseries For The 20th Century And Beyond. https://doi.org/10.16904/envidat.159 (2018).Norinder, U., Rybacka, A. & Andersson, P. L. Conformal prediction to define applicability domain – a case study on predicting ER and AR binding. SAR QSAR Environ. Res. 27, 303–316 (2016).CAS 
    PubMed 

    Google Scholar 
    Metzger, M. J., Bunce, R. G. H., Jongman, R. H. G., Mücher, C. A. & Watkins, J. W. A climatic stratification of the environment of Europe. Glob. Ecol. Biogeogr. 14, 549–563 (2005).
    Google Scholar  More