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    Socio-psychological determinants of Iranian rural households' adoption of water consumption curtailment behaviors

    Sun, C., Zhang, J., Ma, Q., Chen, Y. & Ju, H. Polycyclic aromatic hydrocarbons (PAHs) in water and sediment from a river basin: Sediment–water partitioning, source identification and environmental health risk assessment. Environ. Geochem. Health 39, 63–74 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Savari, M. & Shokati Amghani, M. Factors influencing farmers’ adaptation strategies in confronting the drought in Iran. Environ. Dev. Sustain. 2020 234 23, 4949–4972 (2020).Article 

    Google Scholar 
    Kumar Singh, P., Dey, P., Kumar Jain, S. & Mujumdar, P. P. Hydrology and water resources management in ancient India. Hydrol. Earth Syst. Sci. 24, 4691–4707 (2020).ADS 
    Article 

    Google Scholar 
    Warner, L. A. & Diaz, J. M. Amplifying the Theory of Planned behavior with connectedness to water to inform impactful water conservation program planning and evaluation. J. Agric. Educ. Ext. 27, 229–253 (2021).Article 

    Google Scholar 
    Warner, L. A. Who conserves and who approves? Predicting water conservation intentions in urban landscapes with referent groups beyond the traditional ‘important others’. Urban For. Urban Green. 60, 127070 (2021).Article 

    Google Scholar 
    Savari, M., Eskandari Damaneh, H. & Eskandari Damaneh, H. Drought vulnerability assessment: Solution for risk alleviation and drought management among Iranian farmers. Int. J. Disaster Risk Reduct. 67, 102654 (2022).Article 

    Google Scholar 
    Eskandari Damaneh, H. et al. Testing possible scenario-based responses of vegetation under expected climatic changes in Khuzestan Province. Air Soil Water Res. https://doi.org/10.1177/1178622121101333214 (2021).Article 

    Google Scholar 
    Eskandari Damaneh, H., Khosravi, H., Habashi, K., Eskandari Damaneh, H. & Tiefenbacher, J. P. The impact of land use and land cover changes on soil erosion in western Iran. Nat. Hazards 110, 2185–2205 (2022).Article 

    Google Scholar 
    Savari, M., Abdeshahi, A., Gharechaee, H. & Nasrollahian, O. Explaining farmers’ response to water crisis through theory of the norm activation model: Evidence from Iran. Int. J. Disaster Risk Reduct. 60, 102284 (2021).Article 

    Google Scholar 
    Liu, J., Scanlon, B. R., Zhuang, J. & Varis, O. Food-energy-water nexus for multi-scale sustainable development. Resour. Conserv. Recycl. 154, 104565 (2020).Article 

    Google Scholar 
    Araya, F., Osman, K. & Faust, K. M. Perceptions versus reality: Assessing residential water conservation efforts in the household. Resour. Conserv. Recycl. 162, 105020 (2020).Article 

    Google Scholar 
    Omer, A., Elagib, N. A., Zhuguo, M., Saleem, F. & Mohammed, A. Water scarcity in the Yellow River Basin under future climate change and human activities. Sci. Total Environ. 749, 141446 (2020).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Aslam, S. et al. Sustainable model: Recommendations for water conservation strategies in a developing country through a psychosocial wellness program. Water (Switzerland) 13, 1–20 (2021).
    Google Scholar 
    Diaz, J., Odera, E. & Warner, L. Delving deeper: Exploring the influence of psycho-social wellness on water conservation behavior. J. Environ. Manag. 264, 110404 (2020).Article 

    Google Scholar 
    Fader, M., Shi, S., Von Bloh, W., Bondeau, A. & Cramer, W. Mediterranean irrigation under climate change: More efficient irrigation needed to compensate for increases in irrigation water requirements. Hydrol. Earth Syst. Sci. 20, 953–973 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    Brown, T. C., Mahat, V. & Ramirez, J. A. Adaptation to future water shortages in the United States caused by population growth and climate change. Earth’s Future 7, 219–234 (2019).ADS 
    Article 

    Google Scholar 
    Lall, U., Josset, L. & Russo, T. A snapshot of the world’s groundwater challenges. Annu. Rev. Environ. Resour. 45, 171–194 (2020).Article 

    Google Scholar 
    Jin, J. et al. Impacts of climate change on hydrology in the Yellow River Source Region, China. J. Water Clim. Change 11, 916–930 (2020).Article 

    Google Scholar 
    Cochand, F., Brunner, P., Hunkeler, D., Rössler, O. & Holzkämper, A. Cross-sphere modelling to evaluate impacts of climate and land management changes on groundwater resources. Sci. Total Environ. 798, 148759 (2021).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Waha, K. et al. Climate change impacts in the Middle East and Northern Africa (MENA) region and their implications for vulnerable population groups. Reg. Environ. Change 17, 1623–1638 (2017).Article 

    Google Scholar 
    Boretti, A. & Rosa, L. Reassessing the projections of the World Water Development Report. npj Clean Water 2, 1–6 (2019).Article 

    Google Scholar 
    Fragaszy, S. R. et al. Drought monitoring in the Middle East and North Africa (MENA) region. Bull. Am. Meteorol. Soc. 101, 1148–1173 (2020).Article 

    Google Scholar 
    Tajeri moghadam, M., Raheli, H., Zarifian, S. & Yazdanpanah, M. The power of the health belief model (HBM) to predict water demand management: A case study of farmers’ water conservation in Iran. J. Environ. Manag. 263, 110388 (2020).Article 

    Google Scholar 
    Marston, L., Ao, Y., Konar, M., Mekonnen, M. M. & Hoekstra, A. Y. High-resolution water footprints of production of the United States. Water Resour. Res. 54, 2288–2316 (2018).ADS 
    Article 

    Google Scholar 
    Savari, M. & Shokati Amghani, M. SWOT-FAHP-TOWS analysis for adaptation strategies development among small-scale farmers in drought conditions. Int. J. Disaster Risk Reduct. 67, 102695 (2022).Article 

    Google Scholar 
    Savari, M. & Moradi, M. The effectiveness of drought adaptation strategies in explaining the livability of Iranian rural households. Habitat Int. 124, 102560 (2022).Article 

    Google Scholar 
    Warner, L., Chaudhary, A. K., Rumble, J., Lamm, A. & Momol, E. Using audience segmentation to tailor residential irrigation water conservation programs. J. Agric. Educ. 58, 313–333 (2017).Article 

    Google Scholar 
    Tapsuwan, S., Cook, S. & Moglia, M. Willingness to pay for rainwater tank features: A post-drought analysis of Sydney water users. Water (Switzerland) 10, 1199 (2018).
    Google Scholar 
    Chubaka, C. E., Whiley, H., Edwards, J. W. & Ross, K. E. A review of roof harvested rainwater in Australia. J. Environ. Public Health 2018, 6471324 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Smith, H. M., Brouwer, S., Jeffrey, P. & Frijns, J. Public responses to water reuse—Understanding the evidence. J. Environ. Manag. 207, 43–50 (2018).CAS 
    Article 

    Google Scholar 
    Addo, I. B., Thoms, M. C. & Parsons, M. Barriers and drivers of household water-conservation behavior: A profiling approach. Water (Switzerland) 10, 1794 (2018).
    Google Scholar 
    Jarrett, W. B. A survey of the influences on water conservation behavior in Pickens and Oconee counties (2015).Yazdanpanah, M., Forouzani, M., Abdeshahi, A. & Jafari, A. Investigating the effect of moral norm and self-identity on the intention toward water conservation among Iranian young adults. Water Policy 18, 73–90 (2016).Article 

    Google Scholar 
    Sabzali Parikhani, R., Sadighi, H. & Bijani, M. Ecological consequences of nanotechnology in agriculture: Researchers’ perspective. J. Agric. Sci. Technol. 20, 205–219 (2018).
    Google Scholar 
    Moglia, M., Cook, S. & Tapsuwan, S. Promoting water conservation: Where to from here?. Water (Switzerland) 10, 1510 (2018).
    Google Scholar 
    Savari, M. & Zhoolideh, M. The role of climate change adaptation of small-scale farmers on the households food security level in the west of Iran. Dev. Pract. 31, 650–664 (2021).Article 

    Google Scholar 
    Bennett, N. J. et al. Conservation social science: Understanding and integrating human dimensions to improve conservation. Biol. Conserv. 205, 93–108 (2017).Article 

    Google Scholar 
    Kumar Chaudhary, A., Lamm, A. & Warner, L. Using cognitive dissonance to theoretically explain water conservation intentions. J. Agric. Educ. 59, 194–210 (2018).Article 

    Google Scholar 
    Russell, S. V. & Knoeri, C. Exploring the psychosocial and behavioural determinants of household water conservation and intention. Int. J. Water Resour. Dev. 36, 940–955 (2020).Article 

    Google Scholar 
    Savari, M., Yazdanpanah, M. & Rouzaneh, D. Factors affecting the implementation of soil conservation practices among Iranian farmers. Sci. Rep. 12, 1–13 (2022).Article 
    CAS 

    Google Scholar 
    Savari, M., Zhoolideh, M. & Khosravipour, B. Explaining pro-environmental behavior of farmers: A case of rural Iran. Curr. Psychol. https://doi.org/10.1007/S12144-021-02093-9 (2021).Article 

    Google Scholar 
    Lee, M. & Tansel, B. Water conservation quantities vs customer opinion and satisfaction with water efficient appliances in Miami, Florida. J. Environ. Manag. 128, 683–689 (2013).Article 

    Google Scholar 
    Yazdanpanah, M., Klein, K., Zobeidi, T., Sieber, S. & Löhr, K. Why have economic incentives failed to convince farmers to adopt drip irrigation in southwestern Iran?. Sustainability 14, 1–15 (2022).Article 

    Google Scholar 
    Zobeidi, T., Yaghoubi, J. & Yazdanpanah, M. Developing a paradigm model for the analysis of farmers’ adaptation to water scarcity. Environ. Dev. Sustain. 24, 5400–5425 (2022).Article 

    Google Scholar 
    Russell, S. & Fielding, K. Water demand management research: A psychological perspective. Water Resour. Res. 46, 1–12 (2010).Article 

    Google Scholar 
    Shahangian, S. A., Tabesh, M., Yazdanpanah, M., Zobeidi, T. & Raoof, M. A. Promoting the adoption of residential water conservation behaviors as a preventive policy to sustainable urban water management. J. Environ. Manag. 313, 115005 (2022).Article 

    Google Scholar 
    Onwezen, M. C., Antonides, G. & Bartels, J. The Norm Activation Model: An exploration of the functions of anticipated pride and guilt in pro-environmental behaviour. J. Econ. Psychol. 39, 141–153 (2013).Article 

    Google Scholar 
    Shahangian, S. A., Tabesh, M. & Yazdanpanah, M. Psychosocial determinants of household adoption of water-efficiency behaviors in Tehran capital, Iran: Application of the social cognitive theory. Urban Clim. 39, 100935 (2021).Article 

    Google Scholar 
    Yazdanpanah, M., Feyzabad, F. R., Forouzani, M., Mohammadzadeh, S. & Burton, R. J. F. Predicting farmers’ water conservation goals and behavior in Iran: A test of social cognitive theory. Land Use Policy 47, 401–407 (2015).Article 

    Google Scholar 
    Valizadeh, N., Bijani, M., Hayati, D. & Fallah Haghighi, N. Social-cognitive conceptualization of Iranian farmers’ water conservation behavior. Hydrogeol. J. 27, 1131–1142 (2019).ADS 
    Article 

    Google Scholar 
    Greaves, M., Zibarras, L. D. & Stride, C. Using the theory of planned behavior to explore environmental behavioral intentions in the workplace. J. Environ. Psychol. 34, 109–120 (2013).Article 

    Google Scholar 
    Wang, Y. et al. Analysis of the environmental behavior of farmers for non-point source pollution control and management: An integration of the theory of planned behavior and the protection motivation theory. J. Environ. Manag. 237, 15–23 (2019).Article 

    Google Scholar 
    Savari, M. & Gharechaee, H. Application of the extended theory of planned behavior to predict Iranian farmers’ intention for safe use of chemical fertilizers. J. Clean. Prod. 263, 121512 (2020).CAS 
    Article 

    Google Scholar 
    Strydom, W. F. Applying the theory of planned behavior to recycling behavior in South Africa. Recycling 3, 43 (2018).Article 

    Google Scholar 
    Lam, S. P. Predicting intention to save water: Theory of planned behavior, response efficacy, vulnerability, and perceived efficiency of alternative solutions. J. Appl. Soc. Psychol. 36, 2803–2824 (2006).Article 

    Google Scholar 
    Abdulkarim, B., Yacob, M. R., Abdullahi, A. M. & Radam, A. Farmers’ perceptions and attitudes toward forest watershed conservation of the North Selangor Peat Swamp Forest. J. Sustain. For. 36, 309–323 (2017).
    Google Scholar 
    Yuriev, A., Dahmen, M., Paillé, P., Boiral, O. & Guillaumie, L. Pro-environmental behaviors through the lens of the theory of planned behavior: A scoping review. Resour. Conserv. Recycl. 155, 104660 (2020).Article 

    Google Scholar 
    Bosnjak, M., Ajzen, I. & Schmidt, P. Editorial the theory of planned behavior: Selected recent advances and applications (1841).Ajzen, I. Consumer attitudes and behavior: The theory of planned behavior applied to food consumption decisions. Ital. Rev. Agric. Econ. 70(2), 121–138. https://doi.org/10.13128/REA-18003 (2015).Article 

    Google Scholar 
    Soorani, F. & Ahmadvand, M. Determinants of consumers’ food management behavior: Applying and extending the theory of planned behavior. Waste Manag. 98, 151–159 (2019).PubMed 
    Article 

    Google Scholar 
    Popa, B., Niță, M. D. & Hălălișan, A. F. Intentions to engage in forest law enforcement in Romania: An application of the theory of planned behavior. For. Policy Econ. 100, 33–43 (2019).Article 

    Google Scholar 
    Tam, K. P. Understanding the psychology X politics interaction behind environmental activism: The roles of governmental trust, density of environmental NGOs, and democracy. J. Environ. Psychol. 71, 101330 (2020).Article 

    Google Scholar 
    Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 50, 179–211 (1991).Article 

    Google Scholar 
    Icek, A. From intentions to actions: A theory of planned behavior. in Action Control 11–39 (1985).Empidi, A. V. A. & Emang, D. Understanding public intentions to participate in protection initiatives for forested watershed areas using the theory of planned behavior: A case study of Cameron highlands in Pahang, Malaysia. Sustainability 13, 4399 (2021).Article 

    Google Scholar 
    Holt, J. R. et al. Using the theory of planned behavior to understand family forest owners’ intended responses to invasive forest insects. Soc. Nat. Resour. 34, 1001–1018 (2021).Article 

    Google Scholar 
    Marcos, K. J., Moersidik, S. S. & Soesilo, T. E. B. Extended theory of planned behavior on utilizing domestic rainwater harvesting in Bekasi, West Java, Indonesia. IOP Conf. Ser. Earth Environ. Sci. 716, 012054 (2021).Article 

    Google Scholar 
    Sánchez, M., López-Mosquera, N., Lera-López, F. & Faulin, J. An extended planned behavior model to explain the willingness to pay to reduce noise pollution in road transportation. J. Clean. Prod. 177, 144–154 (2018).Article 

    Google Scholar 
    Fernandez, M. E., Ruiter, R. A. C., Markham, C. M. & Kok, G. Intervention mapping: Theory-and evidence-based health promotion program planning: Perspective and examples. Front. Public Health 7, 209 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhong, F. et al. Quantifying the influence path of water conservation awareness on water-saving irrigation behavior based on the theory of planned behavior and structural equation modeling: A case study from Northwest China. Sustainability 11, 1–16 (2019).
    Google Scholar 
    Ullah, S. et al. Predicting behavioral intention of rural inhabitants toward economic incentive for deforestation in Gilgit-Baltistan, Pakistan. Sustainability 13, 1–17 (2021).
    Google Scholar 
    Koop, S. H. A., Van Dorssen, A. J. & Brouwer, S. Enhancing domestic water conservation behaviour: A review of empirical studies on influencing tactics. J. Environ. Manag. 247, 867–876 (2019).CAS 
    Article 

    Google Scholar 
    Goh, E., Ritchie, B. & Wang, J. Non-compliance in national parks: An extension of the theory of planned behaviour model with pro-environmental values. Tour. Manag. 59, 123–127 (2017).Article 

    Google Scholar 
    Liang, Y., Kee, K. F. & Henderson, L. K. Towards an integrated model of strategic environmental communication: Advancing theories of reactance and planned behavior in a water conservation context. J. Appl. Commun. Res. 46, 135–154 (2018).CAS 
    Article 

    Google Scholar 
    Gkargkavouzi, A., Halkos, G. & Matsiori, S. Environmental behavior in a private-sphere context: Integrating theories of planned behavior and value belief norm, self-identity and habit. Resour. Conserv. Recycl. 148, 145–156 (2019).Article 

    Google Scholar 
    Vaske, J. J., Landon, A. C. & Miller, C. A. Normative influences on farmers’ intentions to practice conservation without compensation. Environ. Manag. 66, 191–201 (2020).Article 

    Google Scholar 
    Nguru, W. M., Gachene, C. K., Onyango, C. M., Ng’ang’a, S. K. & Girvetz, E. H. Factors constraining the adoption of soil organic carbon enhancing technologies among small-scale farmers in Ethiopia. Heliyon 7, e08497 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Møller, M., Haustein, S. & Bohlbro, M. S. Adolescents’ associations between travel behaviour and environmental impact: A qualitative study based on the Norm-Activation Model. Travel Behav. Soc. 11, 69–77 (2018).Article 

    Google Scholar 
    Savari, M., Naghibeiranvand, F. & Asadi, Z. Modeling environmentally responsible behaviors among rural women in the forested regions in Iran. Glob. Ecol. Conserv. 35, e02102 (2022).Article 

    Google Scholar 
    van Valkengoed, A. M. & Steg, L. Meta-analyses of factors motivating climate change adaptation behaviour. Nat. Clim. Chang. 9, 158–163 (2019).ADS 
    Article 

    Google Scholar 
    Maduku, D. K. Water conservation campaigns in an emerging economy: How effective are they?. Int. J. Advert. 40, 452–472 (2021).Article 

    Google Scholar 
    Thøgersen, J. & Grønhøj, A. Electricity saving in households—A social cognitive approach. Energy Policy 38, 7732–7743 (2010).Article 

    Google Scholar 
    Ouellette, J. A. & Wood, W. Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychol. Bull. 124, 54–74 (1998).Article 

    Google Scholar 
    Ajzen, I. The theory of planned behavior: Frequently asked questions. Hum. Behav. Emerg. Technol. 2, 314–324 (2020).Article 

    Google Scholar 
    Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W. & Schmitt, M. Working memory capacity and self-regulatory behavior: toward an individual differences perspective on behavior determination by automatic versus controlled processes. J. Pers. Soc. Psychol. 95, 962–977 (2008).PubMed 
    Article 

    Google Scholar 
    Jorgensen, B. S., Martin, J. F., Pearce, M. W. & Willis, E. M. Aligning theory and measurement in behavioral models of water conservation. Water Policy 17, 762–776 (2015).Article 

    Google Scholar 
    Barr, S. & Gilg, A. W. A conceptual framework for understanding and analyzing attitudes towards environmental behaviour. Geogr. Ann. Ser. B Hum. Geogr. 89 B, 361–379 (2007).Article 

    Google Scholar 
    Hansmann, R., Bernasconi, P., Smieszek, T., Loukopoulos, P. & Scholz, R. W. Justifications and self-organization as determinants of recycling behavior: The case of used batteries. Resour. Conserv. Recycl. 47, 133–159 (2006).Article 

    Google Scholar 
    Tang, Z., Chen, X. & Luo, J. Determining socio-psychological drivers for rural household recycling behavior in developing countries: A case study from Wugan, Hunan, China. Environ. Behav. 43, 848–877 (2011).Article 

    Google Scholar 
    Krejcie, R. V. & Morgan, W. D. (1970) “Determining sample size for research activities”, educational and psychological measurement. Int. J. Employ. Stud. 18, 89–123 (1996).
    Google Scholar 
    Gregory, G. D. & Di Leo, M. Repeated behavior and environmental psychology: The role of personal involvement and habit formation in explaining water consumption. J. Appl. Soc. Psychol. 33, 1261–1296 (2003).Article 

    Google Scholar 
    Keramitsoglou, K. M. & Tsagarakis, K. P. Raising effective awareness for domestic water saving: Evidence from an environmental educational programme in Greece. Water Policy 13, 828–844 (2011).Article 

    Google Scholar 
    Chaudhary, A. K. et al. Using the theory of planned behavior to encourage water conservation among extension clients. J. Agric. Educ. 58, 185–202 (2017).Article 

    Google Scholar 
    Pradhananga, A. K., Davenport, M. A., Fulton, D. C., Maruyama, G. M. & Current, D. An integrated moral obligation model for landowner conservation norms. Soc. Nat. Resour. 30, 212–227 (2017).Article 

    Google Scholar 
    Heath, Y. & Gifford, R. Extending the theory of planned behavior: Predicting the use of public transportation. J. Appl. Soc. Psychol. 32, 2154–2189 (2002).Article 

    Google Scholar 
    Bodimeade, H. et al. Testing the direct, indirect, and interactive roles of referent group injunctive and descriptive norms for sun protection in relation to the theory of planned behavior. J. Appl. Soc. Psychol. 44, 739–750 (2014).Article 

    Google Scholar 
    Veisi, K., Bijani, M. & Abbasi, E. A human ecological analysis of water conflict in rural areas: Evidence from Iran. Glob. Ecol. Conserv. 23, e01050 (2020).Article 

    Google Scholar 
    Botetzagias, I., Dima, A. F. & Malesios, C. Extending the Theory of Planned Behavior in the context of recycling: The role of moral norms and of demographic predictors. Resour. Conserv. Recycl. 95, 58–67 (2015).Article 

    Google Scholar 
    Martínez-Espiñeira, R., García-Valiñas, M. A. & Nauges, C. Households’ pro-environmental habits and investments in water and energy consumption: Determinants and relationships. J. Environ. Manag. 133, 174–183 (2014).Article 

    Google Scholar 
    Dolnicar, S., Hurlimann, A. & Grün, B. Water conservation behavior in Australia. J. Environ. Manag. 105, 44–52 (2012).Article 

    Google Scholar 
    Untaru, E. N., Ispas, A., Candrea, A. N., Luca, M. & Epuran, G. Predictors of individuals’ intention to conserve water in a lodging context: The application of an extended Theory of Reasoned Action. Int. J. Hosp. Manag. 59, 50–59 (2016).Article 

    Google Scholar 
    Khoshmaram, M., Shiri, N., Shinnar, R. S. & Savari, M. Environmental support and entrepreneurial behavior among Iranian farmers: The mediating roles of social and human capital. J. Small Bus. Manag. https://doi.org/10.1111/jsbm.1250158,1064-1088 (2020).Article 

    Google Scholar 
    Benitez, J., Henseler, J., Castillo, A. & Schuberth, F. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Inf. Manag. 57, 103168 (2020).Article 

    Google Scholar 
    Sarstedt, M., Ringle, C. M. & Hair, J. F. Partial least squares structural equation modeling. in Handbook of Market Research 1–47. https://doi.org/10.1007/978-3-319-05542-8_15-2 (2021).Clark, W. A. & Finley, J. C. Determinants of water conservation intention in Blagoevgrad, Bulgaria. Soc. Nat. Resour. 20, 613–627 (2007).Article 

    Google Scholar 
    De Dominicis, S., Sokoloski, R., Jaeger, C. M. & Schultz, P. W. Making the smart meter social promotes long-term energy conservation. Palgrave Commun. 5, 1–8 (2019).Article 

    Google Scholar 
    Wang, S., Hung, K. & Huang, W.-J. Motivations for entrepreneurship in the tourism and hospitality sector: A social cognitive theory perspective. Int. J. Hosp. Manag. https://doi.org/10.1016/j.ijhm.2018.11.018 (2018).Article 

    Google Scholar 
    Ramirez, E., Kulinna, P. H. & Cothran, D. Constructs of physical activity behaviour in children: The usefulness of Social Cognitive Theory. Psychol. Sport Exerc. 13, 303–310 (2012).Article 

    Google Scholar 
    Glanz, K., Rimer, B. K. & Viswanath, K. Health and Health (2002). More

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    Microbiota succession throughout life from the cradle to the grave

    Chu, D. M. et al. Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat. Med. 23, 314–326 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ward, T. L. et al. Development of the human mycobiome over the first month of life and across body sites. mSystems 3, e00140–17 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oh, J. et al. Biogeography and individuality shape function in the human skin metagenome. Nature 514, 59–64 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Abeles, S. R. et al. Human oral viruses are personal, persistent and gender-consistent. ISME J. 8, 1753–1767 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Grice, E. A. & Segre, J. A. The human microbiome: our second genome. Annu. Rev. Genomics Hum. Genet. 13, 151–170 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zengler, K. & Zaramela, L. S. The social network of microorganisms – how auxotrophies shape complex communities. Nat. Rev. Microbiol. 16, 383–390 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rasko, D. A. Changes in microbiome during and after travellers’ diarrhea: what we know and what we do not. J. Travel. Med. 24, S52–S56 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zheng, D., Liwinski, T. & Elinav, E. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492–506 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zaneveld, J. R., McMinds, R. & Vega Thurber, R. Stress and stability: applying the Anna Karenina principle to animal microbiomes. Nat. Microbiol. 2, 17121 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dini-Andreote, F., Stegen, J. C., van Elsas, J. D. & Salles, J. F. Disentangling mechanisms that mediate the balance between stochastic and deterministic processes in microbial succession. Proc. Natl Acad. Sci. USA 112, E1326–E1332 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dominguez-Bello, M. G. et al. Delivery mode shapes the acquisition and structure of the initial microbiota across multiple body habitats in newborns. Proc. Natl Acad. Sci. USA 107, 11971–11975 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yassour, M. et al. Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med. 8, 343ra81 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bokulich, N. A. et al. Antibiotics, birth mode, and diet shape microbiome maturation during early life. Sci. Transl. Med. 8, 343ra82 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    David, L. A. et al. Host lifestyle affects human microbiota on daily timescales. Genome Biol. 15, R89 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Vangay, P. et al. US immigration westernizes the human gut microbiome. Cell 175, 962–972.e10 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gregory, A. C. et al. The gut virome database reveals age-dependent patterns of virome diversity in the human gut. Cell Host Microbe 28, 724–740.e8 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Faith, J. J. et al. The long-term stability of the human gut microbiota. Science 341, 1237439 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Thaiss, C. A. et al. Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell 167, 1495–1510.e12 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zaura, E. et al. Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. mBio 6, e01693–15 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4554–4561 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hsiao, A. et al. Members of the human gut microbiota involved in recovery from Vibrio cholerae infection. Nature 515, 423–426 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chng, K. R. et al. Metagenome-wide association analysis identifies microbial determinants of post-antibiotic ecological recovery in the gut. Nat. Ecol. Evol. 4, 1256–1267 (2020).PubMed 
    Article 

    Google Scholar 
    Gibbons, S. M. Keystone taxa indispensable for microbiome recovery. Nat. Microbiol. 5, 1067–1068 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rizzatti, G., Lopetuso, L. R., Gibiino, G., Binda, C. & Gasbarrini, A. Proteobacteria: a common factor in human diseases. Biomed. Res. Int. 2017, 9351507 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Biagi, E. et al. Gut microbiota and extreme longevity. Curr. Biol. 26, 1480–1485 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lim, A. I. et al. Prenatal maternal infection promotes tissue-specific immunity and inflammation in offspring. Science 373, eabf3002 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Al Nabhani, Z. & Eberl, G. Imprinting of the immune system by the microbiota early in life. Mucosal Immunol. 13, 183–189 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lynn, M. A. et al. Early-life antibiotic-driven dysbiosis leads to dysregulated vaccine immune responses in mice. Cell Host Microbe 23, 653–660.e5 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Blaser, M. J. The theory of disappearing microbiota and the epidemics of chronic diseases. Nat. Rev. Immunol. 17, 461–463 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Thorburn, A. N. et al. Evidence that asthma is a developmental origin disease influenced by maternal diet and bacterial metabolites. Nat. Commun. 6, 7320 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gomez de Agüero, M. et al. The maternal microbiota drives early postnatal innate immune development. Science 351, 1296–1302 (2016).PubMed 
    Article 
    CAS 

    Google Scholar 
    Macpherson, A. J., de Agüero, M. G. & Ganal-Vonarburg, S. C. How nutrition and the maternal microbiota shape the neonatal immune system. Nat. Rev. Immunol. 17, 508–517 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nakajima, A. et al. Maternal high fiber diet during pregnancy and lactation influences regulatory T cell differentiation in offspring in mice. J. Immunol. 199, 3516–3524 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jamalkandi, S. A. et al. Oral and nasal probiotic administration for the prevention and alleviation of allergic diseases, asthma and chronic obstructive pulmonary disease. Nutr. Res. Rev. 34, 1–16 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Örtqvist, A. K., Lundholm, C., Halfvarson, J., Ludvigsson, J. F. & Almqvist, C. Fetal and early life antibiotics exposure and very early onset inflammatory bowel disease: a population-based study. Gut 68, 218–225 (2019).PubMed 
    Article 
    CAS 

    Google Scholar 
    Munyaka, P. M., Eissa, N., Bernstein, C. N., Khafipour, E. & Ghia, J.-E. Antepartum antibiotic treatment increases offspring susceptibility to experimental colitis: a role of the gut microbiota. PLoS ONE 10, e0142536 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kiss, E. A. et al. Natural aryl hydrocarbon receptor ligands control organogenesis of intestinal lymphoid follicles. Science 334, 1561–1565 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lee, J. S. et al. AHR drives the development of gut ILC22 cells and postnatal lymphoid tissues via pathways dependent on and independent of Notch. Nat. Immunol. 13, 144–151 (2011).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Qiu, J. et al. The aryl hydrocarbon receptor regulates gut immunity through modulation of innate lymphoid cells. Immunity 36, 92–104 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schulfer, A. F. et al. Intergenerational transfer of antibiotic-perturbed microbiota enhances colitis in susceptible mice. Nat. Microbiol. 3, 234–242 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ma, J. et al. High-fat maternal diet during pregnancy persistently alters the offspring microbiome in a primate model. Nat. Commun. 5, 3889 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Torres, J. et al. Infants born to mothers with IBD present with altered gut microbiome that transfers abnormalities of the adaptive immune system to germ-free mice. Gut 69, 42–51 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Milliken, S., Allen, R. M. & Lamont, R. F. The role of antimicrobial treatment during pregnancy on the neonatal gut microbiome and the development of atopy, asthma, allergy and obesity in childhood. Expert. Opin. Drug. Saf. 18, 173–185 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Santacruz, A. et al. Gut microbiota composition is associated with body weight, weight gain and biochemical parameters in pregnant women. Br. J. Nutr. 104, 83–92 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Trevisanuto, D. et al. Fetal placental inflammation is associated with poor neonatal growth of preterm infants: a case-control study. J. Matern. Fetal Neonatal Med. 26, 1484–1490 (2013).PubMed 
    Article 

    Google Scholar 
    Song, S. J. et al. Naturalization of the microbiota developmental trajectory of Cesarean-born neonates after vaginal seeding. Med 2, 951–964.e5 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Abu-Raya, B., Michalski, C., Sadarangani, M. & Lavoie, P. M. Maternal immunological adaptation during normal pregnancy. Front. Immunol. 11, 575197 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hanson, L. A. et al. The transfer of immunity from mother to child. Ann. NY. Acad. Sci. 987, 199–206 (2003).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dominguez-Bello, M. G. et al. Partial restoration of the microbiota of cesarean-born infants via vaginal microbial transfer. Nat. Med. 22, 250–253 (2016). This study demonstrates that ‘seeding’ infants born by caesarean delivery with the vaginal microbiota of the mother at birth partially naturalizes development of the microbial community.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ferretti, P. et al. Mother-to-infant microbial transmission from different body sites shapes the developing infant gut microbiome. Cell Host Microbe 24, 133–145.e5 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Helve, O. et al. 2843. Maternal fecal transplantation to infants born by cesarean section: safety and feasibility. Open. Forum Infect. Dis. 6, S68 (2019).PubMed Central 
    Article 

    Google Scholar 
    Subramanian, S. et al. Persistent gut microbiota immaturity in malnourished Bangladeshi children. Nature 510, 417–421 (2014). This study shows that severe acute malnutrition leads to immature microbial development and introduces a metric for the measure of microbiota maturity.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Palmer, C., Bik, E. M., DiGiulio, D. B., Relman, D. A. & Brown, P. O. Development of the human infant intestinal microbiota. PLoS Biol. 5, e177 (2007).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Groer, M. W. et al. Development of the preterm infant gut microbiome: a research priority. Microbiome 2, 38 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Henrick, B. M. et al. Bifidobacteria-mediated immune system imprinting early in life. Cell 184, 3884–3898.e11 (2021). This report describes the immune development driven by microbial interactions and the negative impact of lack of HMO-utilizing microorganisms on the immune system.CAS 
    PubMed 
    Article 

    Google Scholar 
    Sela, D. A. & Mills, D. A. Nursing our microbiota: molecular linkages between bifidobacteria and milk oligosaccharides. Trends Microbiol. 18, 298–307 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Seppo, A. E. et al. Infant gut microbiome is enriched with Bifidobacterium longum ssp. infantis in old order mennonites with traditional farming lifestyle. Allergy 76, 3489–3503 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Triantis, V., Bode, L. & van Neerven, R. J. J. Immunological effects of human milk oligosaccharides. Front. Pediatr. 6, 190 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yu, Z.-T., Chen, C. & Newburg, D. S. Utilization of major fucosylated and sialylated human milk oligosaccharides by isolated human gut microbes. Glycobiology 23, 1281–1292 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).Article 
    CAS 

    Google Scholar 
    McDonald, D. et al. American gut: an open platform for citizen science microbiome research. mSystems 3, e00031–18 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Odamaki, T. et al. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol. 16, 90 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Schei, K. et al. Early gut mycobiota and mother-offspring transfer. Microbiome 5, 107 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Alonso, R., Pisa, D., Fernández-Fernández, A. M. & Carrasco, L. Infection of fungi and bacteria in brain tissue from elderly persons and patients with Alzheimer’s disease. Front. Aging Neurosci. 10, 159 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Nagpal, R. et al. Gut mycobiome and its interaction with diet, gut bacteria and Alzheimer’s disease markers in subjects with mild cognitive impairment: a pilot study. EBioMedicine 59, 102950 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ahmad, H. F. et al. Gut mycobiome dysbiosis is linked to hypertriglyceridemia among home dwelling elderly Danes. Preprint at bioRxiv https://doi.org/10.1101/2020.04.16.044693 (2020).Article 

    Google Scholar 
    Wampach, L. et al. Colonization and succession within the human gut microbiome by archaea, bacteria, and microeukaryotes during the first year of life. Front. Microbiol. 8, 738 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Breitbart, M. et al. Metagenomic analyses of an uncultured viral community from human feces. J. Bacteriol. 185, 6220–6223 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liang, G. et al. The stepwise assembly of the neonatal virome is modulated by breastfeeding. Nature 581, 470–474 (2020). This study describes the assembly of the human virome during development.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lim, E. S. et al. Early life dynamics of the human gut virome and bacterial microbiome in infants. Nat. Med. 21, 1228–1234 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liang, G. et al. Dynamics of the stool virome in very early-onset inflammatory bowel disease. J. Crohns. Colitis 14, 1600–1610 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Koren, O. & Rautava, S. The Human Microbiome in Early Life: Implications to Health and Disease (Academic, 2020).Reyes, A. et al. Gut DNA viromes of Malawian twins discordant for severe acute malnutrition. Proc. Natl Acad. Sci. USA 112, 11941–11946 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Liang, G. & Bushman, F. D. The human virome: assembly, composition and host interactions. Nat. Rev. Microbiol. 19, 514–527 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Oude Munnink, B. B. & van der Hoek, L. Viruses causing gastroenteritis: the known, the new and those beyond. Viruses 8, 42 (2016).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Woolhouse, M., Scott, F., Hudson, Z., Howey, R. & Chase-Topping, M. Human viruses: discovery and emergence. Phil. Trans. R. Soc. B 367, 2864–2871 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rascovan, N., Duraisamy, R. & Desnues, C. Metagenomics and the human virome in asymptomatic individuals. Annu. Rev. Microbiol. 70, 125–141 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mason, M. R., Chambers, S., Dabdoub, S. M., Thikkurissy, S. & Kumar, P. S. Characterizing oral microbial communities across dentition states and colonization niches. Microbiome 6, 67 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Dzidic, M. et al. Oral microbiome development during childhood: an ecological succession influenced by postnatal factors and associated with tooth decay. ISME J. 12, 2292–2306 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Merglova, V. & Polenik, P. Early colonization of the oral cavity in 6- and 12-month-old infants by cariogenic and periodontal pathogens: a case-control study. Folia Microbiol. 61, 423–429 (2016).CAS 
    Article 

    Google Scholar 
    Gomez, A. & Nelson, K. E. The oral microbiome of children: development, disease, and implications beyond oral health. Microb. Ecol. 73, 492–503 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Cephas, K. D. et al. Comparative analysis of salivary bacterial microbiome diversity in edentulous infants and their mothers or primary care givers using pyrosequencing. PLoS ONE 6, e23503 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Crielaard, W. et al. Exploring the oral microbiota of children at various developmental stages of their dentition in the relation to their oral health. BMC Med. Genomics 4, 22 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Darwazeh, A. M. & al-Bashir, A. Oral candidal flora in healthy infants. J. Oral. Pathol. Med. 24, 361–364 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stecksén-Blicks, C., Granström, E., Silfverdal, S. A. & West, C. E. Prevalence of oral Candida in the first year of life. Mycoses 58, 550–556 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Ghannoum, M. A. et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 6, e1000713 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Brusa, T., Conca, R., Ferrara, A., Ferrari, A. & Pecchioni, A. The presence of methanobacteria in human subgingival plaque. J. Clin. Periodontol. 14, 470–471 (1987).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ferrari, A., Brusa, T., Rutili, A., Canzi, E. & Biavati, B. Isolation and characterization ofMethanobrevibacter oralis sp. nov. Curr. Microbiol. 29, 7–12 (1994).CAS 
    Article 

    Google Scholar 
    Nguyen-Hieu, T., Khelaifia, S., Aboudharam, G. & Drancourt, M. Methanogenic archaea in subgingival sites: a review. APMIS 121, 467–477 (2013).PubMed 
    Article 

    Google Scholar 
    Abeles, S. R., Ly, M., Santiago-Rodriguez, T. M. & Pride, D. T. Effects of long term antibiotic therapy on human oral and fecal viromes. PLoS ONE 10, e0134941 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Pérez-Brocal, V. & Moya, A. The analysis of the oral DNA virome reveals which viruses are widespread and rare among healthy young adults in Valencia (Spain). PLoS ONE 13, e0191867 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Dye, B. A., Li, X. & Thornton-Evans, G. Oral health disparities as determined by selected healthy people 2020 oral health objectives for the United States, 2009–2010. NCHS Data Brief. 104, 1–8 (2012).
    Google Scholar 
    Baker, J. L., Bor, B., Agnello, M., Shi, W. & He, X. Ecology of the oral microbiome: beyond bacteria. Trends Microbiol. 25, 362–374 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gaitanis, G. et al. Variation of cultured skin microbiota in mothers and their infants during the first year postpartum. Pediatr. Dermatol. 36, 460–465 (2019).PubMed 

    Google Scholar 
    Lee, Y. W., Yim, S. M., Lim, S. H., Choe, Y. B. & Ahn, K. J. Quantitative investigation on the distribution of Malassezia species on healthy human skin in Korea. Mycoses 49, 405–410 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    Byrd, A. L., Belkaid, Y. & Segre, J. A. The human skin microbiome. Nat. Rev. Microbiol. 16, 143–155 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Sugita, T. et al. Quantitative analysis of the cutaneous Malassezia microbiota in 770 healthy Japanese by age and gender using a real-time PCR assay. Med. Mycol. 48, 229–233 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Probst, A. J., Auerbach, A. K. & Moissl-Eichinger, C. Archaea on human skin. PLoS ONE 8, e65388 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hulcr, J. et al. A jungle in there: bacteria in belly buttons are highly diverse, but predictable. PLoS ONE 7, e47712 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Caporaso, J. G. et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Moya, A. & Brocal, V. P. The Human Virome: Methods and Protocols (Springer, 2018).Foulongne, V. et al. Human skin microbiota: high diversity of DNA viruses identified on the human skin by high throughput sequencing. PLoS ONE 7, e38499 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Turnbaugh, P. J. et al. Organismal, genetic, and transcriptional variation in the deeply sequenced gut microbiomes of identical twins. Proc. Natl Acad. Sci. USA 107, 7503–7508 (2010). This study shows that cohabitating identical twins result in different microbial communities, highlighting the many unknown processes that lead to the unique human microbiota.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shao, Y. et al. Stunted microbiota and opportunistic pathogen colonization in caesarean-section birth. Nature 574, 117–121 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stewart, C. J. et al. Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature 562, 583–588 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ainonen, S. et al. Antibiotics at birth and later antibiotic courses: effects on gut microbiota. Pediatr. Res. 91, 154–162 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chen, X., Lu, Y., Chen, T. & Li, R. The female vaginal microbiome in health and bacterial vaginosis. Front. Cell. Infect. Microbiol. 11, 631972 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wells, J. S., Chandler, R., Dunn, A. & Brewster, G. The vaginal microbiome in U.S. black women: a systematic review. J. Womens Health 29, 362–375 (2020).Article 

    Google Scholar 
    Martino, C. et al. Context-aware dimensionality reduction deconvolutes gut microbial community dynamics. Nat. Biotechnol. 39, 165–168 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Furman, O. et al. Stochasticity constrained by deterministic effects of diet and age drive rumen microbiome assembly dynamics. Nat. Commun. 11, 1904 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Henderickx, J. G. E., Zwittink, R. D., van Lingen, R. A., Knol, J. & Belzer, C. The preterm gut microbiota: an inconspicuous challenge in nutritional neonatal care. Front. Cell. Infect. Microbiol. 9, 85 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Malamitsi-Puchner, A. et al. The influence of the mode of delivery on circulating cytokine concentrations in the perinatal period. Early Hum. Dev. 81, 387–392 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Stokholm, J. et al. Maturation of the gut microbiome and risk of asthma in childhood. Nat. Commun. 9, 141 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Andersen, V., Möller, S., Jensen, P. B., Møller, F. T. & Green, A. Caesarean delivery and risk of chronic inflammatory diseases (inflammatory bowel disease, rheumatoid arthritis, coeliac disease, and diabetes mellitus): a population based registry study of 2,699,479 births in Denmark during 1973–2016. Clin. Epidemiol. 12, 287–293 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Blustein, J. et al. Association of caesarean delivery with child adiposity from age 6 weeks to 15 years. Int. J. Obes. 37, 900–906 (2013).CAS 
    Article 

    Google Scholar 
    Ardic, C., Usta, O., Omar, E., Yıldız, C. & Memis, E. Caesarean delivery increases the risk of overweight or obesity in 2-year-old children. J. Obstet. Gynaecol. 41, 374–379 (2021).PubMed 
    Article 

    Google Scholar 
    Cox, L. M. et al. Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell 158, 705–721 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Martinez, K. A. 2nd et al. Increased weight gain by C-section: functional significance of the primordial microbiome. Sci. Adv. 3, eaao1874 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Olszak, T. et al. Microbial exposure during early life has persistent effects on natural killer T cell function. Science 336, 489–493 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Livanos, A. E. et al. Antibiotic-mediated gut microbiome perturbation accelerates development of type 1 diabetes in mice. Nat. Microbiol. 1, 16140 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Moya-Pérez, A. et al. Intervention strategies for cesarean section–induced alterations in the microbiota-gut-brain axis. Nutr. Rev. 75, 225–240 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Braniste, V. et al. The gut microbiota influences blood-brain barrier permeability in mice. Sci. Transl. Med. 6, 263ra158 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Forbes, J. D. et al. Association of exposure to formula in the hospital and subsequent infant feeding practices with gut microbiota and risk of overweight in the first year of life. JAMA Pediatr. 172, e181161 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shenhav, L. & Azad, M. B. Using community ecology theory and computational microbiome methods to study human milk as a biological system. mSystems 7, e01132–21 (2022).PubMed Central 
    Article 

    Google Scholar 
    Kaetzel, C. S. Cooperativity among secretory IgA, the polymeric immunoglobulin receptor, and the gut microbiota promotes host-microbial mutualism. Immunol. Lett. 162, 10–21 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Munblit, D., Verhasselt, V. & Warner, J. O. Human Milk Composition and Health Outcomes in Children (Frontiers Media, 2019).Mastromarino, P. et al. Correlation between lactoferrin and beneficial microbiota in breast milk and infant’s feces. Biometals 27, 1077–1086 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Agus, A., Planchais, J. & Sokol, H. Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host Microbe 23, 716–724 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Coats, S. R., Pham, T.-T. T., Bainbridge, B. W., Reife, R. A. & Darveau, R. P. MD-2 mediates the ability of tetra-acylated and penta-acylated lipopolysaccharides to antagonize Escherichia coli lipopolysaccharide at the TLR4 signaling complex. J. Immunol. 175, 4490–4498 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    Denou, E. et al. Defective NOD 2 peptidoglycan sensing promotes diet‐induced inflammation, dysbiosis, and insulin resistance. EMBO Mol. Med. 7, 259–274 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Quinn, R. A. et al. Global chemical effects of the microbiome include new bile-acid conjugations. Nature 579, 123–129 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rooks, M. G. & Garrett, W. S. Gut microbiota, metabolites and host immunity. Nat. Rev. Immunol. 16, 341–352 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vatanen, T. et al. Variation in microbiome LPS immunogenicity contributes to autoimmunity in humans. Cell 165, 1551 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fan, Y. & Pedersen, O. Gut microbiota in human metabolic health and disease. Nat. Rev. Microbiol. 19, 55–71 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Xiao, J., Fiscella, K. A. & Gill, S. R. Oral microbiome: possible harbinger for children’s health. Int. J. Oral. Sci. 12, 12 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Zhao, S. et al. Adaptive evolution within gut microbiomes of healthy people. Cell Host Microbe 25, 656–667.e8 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zhang, R., Lahens, N. F., Ballance, H. I., Hughes, M. E. & Hogenesch, J. B. A circadian gene expression atlas in mammals: implications for biology and medicine. Proc. Natl Acad. Sci. USA 111, 16219–16224 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Allaband, C. et al. Intermittent hypoxia and hypercapnia alter diurnal rhythms of luminal gut microbiome and metabolome. mSystems 6, e00116–e00121 (2021).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Marotz, C. et al. Quantifying live microbial load in human saliva samples over time reveals stable composition and dynamic load. mSystems 6, e01182–20 (2021).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bouslimani, A. et al. The impact of skin care products on skin chemistry and microbiome dynamics. BMC Biol. 17, 47 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009). This study demonstrates the important variability between body habitats and between individuals across the same body habitat.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kolodziejczyk, A. A., Zheng, D. & Elinav, E. Diet–microbiota interactions and personalized nutrition. Nat. Rev. Microbiol. 17, 742–753 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zaramela, L. S. et al. Gut bacteria responding to dietary change encode sialidases that exhibit preference for red meat-associated carbohydrates. Nat. Microbiol. 4, 2082–2089 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Zmora, N., Suez, J. & Elinav, E. You are what you eat: diet, health and the gut microbiota. Nat. Rev. Gastroenterol. Hepatol. 16, 35–56 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Etemadi, A. et al. Mortality from different causes associated with meat, heme iron, nitrates, and nitrites in the NIH-AARP Diet and Health Study: population based cohort study. BMJ 357, j1957 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Koeth, R. A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Durack, J. & Lynch, S. V. The gut microbiome: relationships with disease and opportunities for therapy. J. Exp. Med. 216, 20–40 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lai, Y. et al. Commensal bacteria regulate Toll-like receptor 3–dependent inflammation after skin injury. Nat. Med. 15, 1377–1382 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chng, K. R. et al. Whole metagenome profiling reveals skin microbiome-dependent susceptibility to atopic dermatitis flare. Nat. Microbiol. 1, 16106 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Li, H. et al. Skin commensal Malassezia globosa secreted protease attenuates Staphylococcus aureus biofilm formation. J. Invest. Dermatol. 138, 1137–1145 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Shirtliff, M. E., Peters, B. M. & Jabra-Rizk, M. A. Cross-kingdom interactions: Candida albicans and bacteria. FEMS Microbiol. Lett. 299, 1–8 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Santus, W., Devlin, J. R. & Behnsen, J. Crossing kingdoms: how the mycobiota and fungal-bacterial interactions impact host health and disease. Infect. Immun. 89, e00648–20 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Taur, Y. et al. Reconstitution of the gut microbiota of antibiotic-treated patients by autologous fecal microbiota transplant. Sci. Transl. Med. 10, eaap9489 (2018). This study shows that autologous faecal microbiota transplantation helps to restore the microbiota of patients who underwent antibiotic treatment.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    van Nood, E., Dijkgraaf, M. G. W. & Keller, J. J. Duodenal infusion of feces for recurrent Clostridium difficile. N. Engl. J. Med. 368, 2145 (2013).PubMed 
    Article 
    CAS 

    Google Scholar 
    Tariq, R., Pardi, D. S., Bartlett, M. G. & Khanna, S. Low cure rates in controlled trials of fecal microbiota transplantation for recurrent Clostridium difficile infection: a systematic review and meta-analysis. Clin. Infect. Dis. 68, 1351–1358 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Panigrahi, P. et al. Corrigendum: a randomized synbiotic trial to prevent sepsis among infants in rural India. Nature 553, 238 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Halkjær, S. I. et al. Faecal microbiota transplantation alters gut microbiota in patients with irritable bowel syndrome: results from a randomised, double-blind placebo-controlled study. Gut 67, 2107–2115 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    Korpela, K. et al. Maternal fecal microbiota transplantation in cesarean-born infants rapidly restores normal gut microbial development: a proof-of-concept study. Cell 183, 324–334.e5 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Morton, J. T. et al. Learning representations of microbe–metabolite interactions. Nat. Methods 16, 1306–1314 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Strandwitz, P. et al. GABA-modulating bacteria of the human gut microbiota. Nat. Microbiol. 4, 396–403 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rubin, B. E. et al. Species- and site-specific genome editing in complex bacterial communities. Nat. Microbiol. 7, 34–47 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zmora, N. et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell 174, 1388–1405.e21 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schooley, R. T. et al. Development and use of personalized bacteriophage-based therapeutic cocktails to treat a patient with a disseminated resistant Acinetobacter baumannii infection. Antimicrob. Agents Chemother. 61, e00954–17 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mu, A. et al. Effects on the microbiome during treatment of a staphylococcal device infection. Preprint at Research Square https://doi.org/10.21203/rs.3.rs-969336/v1 (2021).Article 

    Google Scholar 
    Claesson, M. J. et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488, 178–184 (2012). This study reports microbial community alterations between older individuals (aged 65 years and older) dependent on whether they live in the company of others or alone, the latter of which was correlated to worse outcomes (that is, frailty and co-morbidity).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wu, L. et al. A cross-sectional study of compositional and functional profiles of gut microbiota in Sardinian centenarians. mSystems 4, e00325–19 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Kong, F. et al. Gut microbiota signatures of longevity. Curr. Biol. 26, R832–R833 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Claesson, M. J. et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc. Natl Acad. Sci. USA 108 (Suppl. 1), 4586–4591 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    O’Toole, P. W. & Jeffery, I. B. Gut microbiota and aging. Science 350, 1214–1215 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Shibagaki, N. et al. Aging-related changes in the diversity of women’s skin microbiomes associated with oral bacteria. Sci. Rep. 7, 10567 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Liu, S., Wang, Y., Zhao, L., Sun, X. & Feng, Q. Microbiome succession with increasing age in three oral sites. Aging 12, 7874–7907 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schwartz, J. L. et al. Old age and other factors associated with salivary microbiome variation. BMC Oral. Health 21, 490 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Strati, F. et al. Age and gender affect the composition of fungal population of the human gastrointestinal tract. Front. Microbiol. 7, 01227 (2016).Article 

    Google Scholar 
    Wu, L. et al. Age-related variation of bacterial and fungal communities in different body habitats across the young, elderly, and centenarians in Sardinia. mSphere 5, e00558–19 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nagpal, R. et al. Gut microbiome and aging: physiological and mechanistic insights. Nutr. Healthy Aging 4, 267–285 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wilmanski, T. et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat. Metab. 3, 274–286 (2021).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Sato, Y. et al. Novel bile acid biosynthetic pathways are enriched in the microbiome of centenarians. Nature 599, 458–464 (2021). This study finds that centenarians often had high abundances of microorganisms that produced unique secondary bile acids, namely various isoforms of lithocholic acid.CAS 
    PubMed 
    Article 

    Google Scholar 
    Gill-King, H. in Forensic Taphonomy: the Postmortem Fate of Human Remains 93–108 (CRC, 1997).Janaway, R. C., Percival, S. L. & Wilson, A. S. in Microbiology and Aging (ed. Percival, S. L) 313–334 (Humana, 2009).Forbes, S. L., Perrault, K. A. & Comstock, J. L. in Taphonomy of Human Remains: Forensic Analysis of the Dead and the Depositional Environment (eds Schotsmans, E. M. J., Márquez-Grant, N. & Forbes, S. L.) 26–38 (Wiley, 2017).Heimesaat, M. M. et al. Comprehensive postmortem analyses of intestinal microbiota changes and bacterial translocation in human flora associated mice. PLoS ONE 7, e40758 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Parkinson, R. A. et al. in Criminal and Environmental Soil Forensics (eds Ritz, K., Dawson, L. & Miller, D.) 379–394 (Springer, 2009).Metcalf, J. L. et al. Microbial community assembly and metabolic function during mammalian corpse decomposition. Science 351, 158–162 (2016). This study finds that the time since death was predictable through the microbial community composition independent of the soil type and season.CAS 
    PubMed 
    Article 

    Google Scholar 
    DeBruyn, J. M. & Hauther, K. A. Postmortem succession of gut microbial communities in deceased human subjects. PeerJ 5, e3437 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pechal, J. L., Schmidt, C. J., Jordan, H. R. & Benbow, M. E. A large-scale survey of the postmortem human microbiome, and its potential to provide insight into the living health condition. Sci. Rep. 8, 5724 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kodama, W. A. et al. Trace evidence potential in postmortem skin microbiomes: from death scene to morgue. J. Forensic Sci. 64, 791–798 (2019).PubMed 
    Article 

    Google Scholar 
    Hauther, K. A., Cobaugh, K. L., Jantz, L. M., Sparer, T. E. & DeBruyn, J. M. Estimating time since death from postmortem human gut microbial communities. J. Forensic Sci. 60, 1234–1240 (2015).PubMed 
    Article 

    Google Scholar 
    Burcham, Z. M. et al. Fluorescently labeled bacteria provide insight on post-mortem microbial transmigration. Forensic Sci. Int. 264, 63–69 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Burcham, Z. M. et al. Bacterial community succession, transmigration, and differential gene transcription in a controlled vertebrate decomposition model. Front. Microbiol. 10, 745 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Balzan, S., de Almeida Quadros, C., de Cleva, R., Zilberstein, B. & Cecconello, I. Bacterial translocation: overview of mechanisms and clinical impact. J. Gastroenterol. Hepatol. 22, 464–471 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Metcalf, J. L. et al. A microbial clock provides an accurate estimate of the postmortem interval in a mouse model system. eLife 2, e01104 (2013).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hyde, E. R., Haarmann, D. P., Petrosino, J. F., Lynne, A. M. & Bucheli, S. R. Initial insights into bacterial succession during human decomposition. Int. J. Leg. Med. 129, 661–671 (2015).Article 

    Google Scholar 
    Javan, G. T., Finley, S. J., Smith, T., Miller, J. & Wilkinson, J. E. Cadaver thanatomicrobiome signatures: the ubiquitous nature of Clostridium species in human decomposition. Front. Microbiol. 8, 2096 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Johnson, H. R. et al. A machine learning approach for using the postmortem skin microbiome to estimate the postmortem interval. PLoS ONE 11, e0167370 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Belk, A. et al. Microbiome data accurately predicts the postmortem interval using random forest regression models. Genes 9, 104 (2018).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Metcalf, J. L. Estimating the postmortem interval using microbes: knowledge gaps and a path to technology adoption. Forensic Sci. Int. Genet. 38, 211–218 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Deel, H. et al. A pilot study of microbial succession in human rib skeletal remains during terrestrial decomposition. mSphere 6, e0045521 (2021).PubMed 
    Article 

    Google Scholar 
    Metcalf, J. L. et al. Microbiome tools for forensic science. Trends Biotechnol. 35, 814–823 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nguyen, T. T., Hathaway, H., Kosciolek, T., Knight, R. & Jeste, D. V. Gut microbiome in serious mental illnesses: a systematic review and critical evaluation. Schizophr. Res. 234, 24–40 (2021).PubMed 
    Article 

    Google Scholar 
    Jeste, D. V., Koh, S. & Pender, V. B. Perspective: social determinants of mental health for the new decade of healthy aging. Am. J. Geriatr. Psychiatry 30, 733–736 (2022).PubMed 
    Article 

    Google Scholar 
    Matijašić, M. et al. Gut microbiota beyond bacteria-mycobiome, virome, archaeome, and eukaryotic parasites in IBD. Int. J. Mol. Sci. 21, 2668 (2020).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Morton, J. T. et al. Establishing microbial composition measurement standards with reference frames. Nat. Commun. 10, 2719 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Gerber, G. K. The dynamic microbiome. FEBS Lett. 588, 4131–4139 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vázquez-Baeza, Y. et al. Guiding longitudinal sampling in IBD cohorts. Gut 67, 1743–1745 (2018).PubMed 
    Article 

    Google Scholar 
    Kane, P. B., Bittlinger, M. & Kimmelman, J. Individualized therapy trials: navigating patient care, research goals and ethics. Nat. Med. 27, 1679–1686 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Huang, S. et al. Human skin, oral, and gut microbiomes predict chronological age. mSystems 5, e00630–19 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Franzosa, E. A. et al. Identifying personal microbiomes using metagenomic codes. Proc. Nat. Acad. Sci. USA 112, E2930–E2938 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Vangay, P. et al. Microbiome metadata standards: report of the national microbiome data collaborative’s workshop and follow-on activities. mSystems 6, e01194–20 (2021).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Biogeographic implication of temperature-induced plant cell wall lignification

    Körner, C. The cold range limit of trees. Trends Ecol. Evo. 36, 979–989 (2021).Article 

    Google Scholar 
    Körner, C. Alpine Treelines (Springer, 2012).Miehe, G., Miehe, S., Vogel, J., Co, S. & Duo, L. Highest treeline in the northern hemisphere found in southern Tibet. Mt. Res. Dev. 27, 169–173 (2007).Article 

    Google Scholar 
    Hoch, G. & Körner, C. Growth, demography and carbon relations of Polylepis trees at the world’s highest treeline. Funct. Ecol. 19, 941–951 (2005).Article 

    Google Scholar 
    von Humboldt, A. & Bonpland, A. Ideen zu einer Geographie der Pflanzen nebst einem Naturgemälde der Tropenländer: auf Beobachtungen und Messungen gegründet, welche vom 10ten Grade nördlicher bis zum 10ten Grade südlicher Breite, in den Jahren 1799, 1800, 1801, 1802 und 1803 angestellt worden sind. Tübingen, Bey F.G. Cotta (1807).Körner, C. Climatic treelines: conventions, global patterns, causes. Erdkunde 61, 315–324 (2007).Article 

    Google Scholar 
    Piermattei, A., Crivellaro, A., Carrer, M. & Urbinati, C. The “blue ring”: anatomy and formation hypothesis of a new tree-ring anomaly in conifers. Trees Struct. Funct. 29, 613–620 (2015).CAS 
    Article 

    Google Scholar 
    Körner, C. et al. Life at 0 °C: the biology of the alpine snowbed plant Soldanella pulsatilla. Alp. Bot. 129, 63–80 (2019).Article 

    Google Scholar 
    Crivellaro, A. & Büntgen, U. New evidence of thermally-constraint plant cell wall lignification. Trends Plant Sci. 24, 322–324 (2020).Article 
    CAS 

    Google Scholar 
    Büntgen, U. et al. Temperature-induced recruitment pulses of Arctic dwarf shrub communities. J. Ecol. 103, 489–501 (2015).Article 

    Google Scholar 
    Dolezal, J. et al. Vegetation dynamics at the upper elevational limit of vascular plants in Himalaya. Sci. Rep. 6, 24881 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ryan, M. G. & Yoder, B. J. Hydraulic limits to tree height and tree growth. Biosci 47, 235–242 (1997).Article 

    Google Scholar 
    Koch, G. W., Sillett, S. C., Jennings, G. M. & Davis, S. D. The limits to tree height. Nature 428, 851–854 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems (Springer, 2003).Scherrer, D. & Körner, C. Infra-red thermometry of alpine landscapes challenges climatic warming projections. Glob. Change Biol. 16, 2602–2613 (2010).
    Google Scholar 
    Begum, S., Nakaba, S., Yamagishi, Y., Oribe, Y. & Funada, R. Regulation of cambial activity in relation to environmental conditions: understanding the role of temperature in wood formation of trees. Physiol. Planta 147, 46–54 (2013).CAS 
    Article 

    Google Scholar 
    Plomion, C., Leprovost, G. & Stokes, A. Wood formation in trees. Plant Physiol. 127, 1513–1523 (2001).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rossi, S., Deslauriers, A., Anfodillo, T. & Carraro, V. Evidence of threshold temperatures for xylogenesis in conifers at high altitudes. Oecologia 152, 1–12 (2007).PubMed 
    Article 

    Google Scholar 
    Moura, J. C. M. S., Bonine, C. A. V., Viana, J. O. F., Dornelas, M. C. & Mazzafera, P. Abiotic and biotic stresses and changes in the lignin content and composition in plants. J. Integr. Plant Biol. 52, 360–376 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Weng, J. K. & Chapple, C. The origin and evolution of lignin biosynthesis. N. Phytol. 187, 273–285 (2010).CAS 
    Article 

    Google Scholar 
    Niklas, K. J., Cobb, E. D. & Matas, A. J. The evolution of hydrophobic cell wall biopolymers: from algae to angiosperms. J. Exp. 68, 5261–5269 (2017).CAS 

    Google Scholar 
    Popper, Z. A. et al. Evolution and diversity of plant cell walls: from algae to flowering plants. Annu. Rev. Plant Biol. 62, 567–590 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    Piquemal, J. et al. Down regulation of cinnamoyl CoA reductase induces significant changes of lignin profiles in transgenic tobacco plants. Plant J. 13, 71–83 (1998).CAS 
    Article 

    Google Scholar 
    Renault, H., Werck-Reichhart, D. & Weng, J.-K. Harnessing lignin evolution for biotechnological applications. Curr. Opin. Biotechnol. 56, 105–111 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Schenk, H. J., Espino, S., Rich-Cavazos, S. M. & Jansen, S. From the sap’s perspective: The nature of vessel surfaces in angiosperm xylem. Am. J. Bot. 105, 172–185 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Polo, C. C. et al. Correlations between lignin content and structural robustness in plants revealed by X-ray ptychography. Sci. Rep. 10, 6023 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Meents, M. J., Watanabe, Y. & Samuels, A. L. The cell biology of secondary cell wall biosynthesis. Ann. Bot. 121, 1107–1125 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Campbell, M. M. & Sederoff, R. R. Variation in lignin content and composition (mechanisms of control and implications for the genetic improvement of plants). Plant Physiol. 110, 3–13 (1996).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Schweingruber, F. H. & Büntgen, U. What is ‘wood’ – An anatomical re-definition. Dendrochronologia 31, 187–191 (2013).Article 

    Google Scholar 
    Ellenberg, H. & Mueller-Dombois, D. A key to Raunkiaer plant life forms with revised subdivisions. Ber. Geobot. Inst. ETH Z.ürich. 37, 56–73 (1967).
    Google Scholar 
    Kim, W. J., Campbell, A. G. & Koch, P. Chemical variation in Lodgepole pine with latitude, elevation, and diameter class. Prod. J. 39, 7–12 (1989).CAS 

    Google Scholar 
    Gindl, W., Grabner, M. & Wimmer, R. The influence of temperature on latewood lignin content in treeline Norway spruce compared with maximum density and ring width. Trees, Struct. Funct. 14, 409–414 (2000).Article 

    Google Scholar 
    Schenker, G., Lens, A., Körner, C. & Hoch, G. Physiological minimum temperatures for root growth in seven common European broad-leaved tree species. Tree Physiol. 34, 302–313 (2014).PubMed 
    Article 

    Google Scholar 
    Nagelmüller, S., Hiltbrunner, E. & Körner, C. Low temperature limits for root growth in alpine species are set by cell differentiation. AoB Plants 9, plx054 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ji, H. et al. The Arabidopsis RCC1 family protein TCF1 regulates freezing tolerance and cold acclimation through modulating lignin biosynthesis. PLoS Gen. 11, e1005471 (2015).Article 
    CAS 

    Google Scholar 
    Büntgen, U. Re-thinking the boundaries of dendrochronology. Dendrochronologia 53, 1–4 (2019).Article 

    Google Scholar 
    Piermattei, A. et al. A millennium-long ‘Blue-Ring’ chronology from the Spanish Pyrenees reveals sever ephemeral summer cooling after volcanic eruptions. Environ. Res. Lett. 15, 124016 (2020).Article 

    Google Scholar 
    Montwé, D., Isaac-Rentin, M., Hamman, A. & Spiecker, H. Cold adaptation recorded in tree rings highlights risks associated with climate change and assisted migration. Nat. Comm. 9, 1574 (2018).Article 
    CAS 

    Google Scholar 
    Barros, J., Serk, H., Granlund, I. & Pesquet, E. The cell biology of lignification in higher plants. Ann. Bot. 115, 1053–1074 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hao, Z. & Mohnen, D. A review of xylan and lignin biosynthesis: Foundation for studying Arabidopsis irregular xylem mutants with pleiotropic phenotypes. Cri. Rev. Biochem. Mol. Biol. 49, 212–241 (2014).CAS 
    Article 

    Google Scholar 
    Liu, Q., Luo, L. & Zheng, L. Lignins: biosynthesis and biological functions in plants. Int. J. Mol. Sci. 19, 335 (2018).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Kumar, M., Campbell, L. & Turner, S. Secondary cell walls: biosynthesis and manipulation. J. Exp. Bot. 67, 515–531 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mellerowicz, E. J., Baucher, M., Sundberg, B. & Boerjan, W. Unravelling cell wall formation in the woody dicot stem. Plant Mol. Biol. 47, 239–274 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Petit, G., Anfodillo, T., Carraro, V., Grani, F. & Carrer, M. Hydraulic constraints limit height growth in trees at high altitude. N. Phytol. 189, 241–252 (2010).Article 

    Google Scholar 
    Li, L. et al. Combinatorial modification of multiple lignin traits in trees through multigene co-transformation. Proc. Natl Acad. Sci. USA 100, 4939–4944 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Baldacci-Cresp, F. et al. A rapid and quantitative safranin-based fluorescent microscopy method to evaluate cell wall lignification. Plant J. 102, 1074–1089 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Körner, C. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115, 445–459 (1998).PubMed 
    Article 

    Google Scholar 
    Landolt, E. et al. Flora indicativa: Okologische Zeigerwerte und biologische Kennzeichen zur Flora der Schweiz und der Alpen (Haupt, 2010).Büntgen, U., Psomas, A. & Schweingruber, F. H. Introducing wood anatomical and dendrochronological aspects of herbaceous plants: applications of the Xylem Database to vegetation science. J. Veg. Sci. 25, 967–977 (2014).Article 

    Google Scholar 
    Körner, C. Coldest places on earth with angiosperm plant life. Alp. Bot. 121, 11–22 (2011).Article 

    Google Scholar 
    GBIF.org. GBIF Occurrence Download. https://doi.org/10.15468/dl.ms4hjt (2018).Chamberlain, S., Ram, K. & Hart, T. Spocc: Interface to Specie Occurrence Data Sources, R package v.0.9.0. http://CRAN.R-project.org/package=spocc (2018).Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high-resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

    Google Scholar 
    Hijmans, R. J. Raster: geographic data analysis and modelling, R package v.2.2-12. http://CRAN.R-project.org/package=raster (2014).Gärtner, H. et al. A technical perspective in modern tree-ring research – How to overcome dendroecological and wood anatomical challenges. J. Vis. Exp. 97, e52337 (2015).
    Google Scholar 
    Gärtner, H. & Schweingruber, F. H. Microscopic Preparation Techniques for Plant Stem Analysis (Verlag Kessel, 2013).Ghislan, B., Engel, J. & Clair, B. Diversity of anatomical structure of tension wood among 242 tropical tree species. IAWA J. 40, 1–20 (2019).Article 

    Google Scholar 
    Schweingruber, F. H., Börner, A. & Schulze, E. D. Atlas of Stem Anatomy in Herbs, Shrubs and Trees Vol. 1 (Springer, 2011).Schweingruber, F. H., Börner, A. & Schulze, E. D. Atlas of Stem Anatomy in Herbs, Shrubs and Trees Vol. 2 (Springer, 2013).Dolezal, J., Dvorsky, M., Börner, A., Wild, J. & Schweingruber, F. H. Anatomy, Age and Ecology of High Mountain Plants in Ladakh, the Western Himalaya (Springer International Publishing, 2018).Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH image to imageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Ter Braak, C. J. F. & Šmilauer, P. Canoco Reference Manual and User’s Guide: Software 559 for Ordination, Version 5.0 (Cambridge Univ. Press, 2012).Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using Canoco 5 (Cambridge Univ. Press, 2014). More

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    Flow patterns in circular fish tanks and its relations with flow rate and nozzle features

    Rotational velocityFigure 3 shows the effect of flow rate, nozzle diameter and number of nozzles on the rotational velocity of water in a circular tank. The results indicate that the rotational velocity increases with increasing flow rates and deceasing nozzle diameter. It could be seen that, the rotational velocity decreased from 10.1 to 5.0 cm s−1, when the nozzle diameter increased from 10 to 20 mm, respectively for 5 nozzles used, and it decreased from 5.1 to 4.0 cm s−1, when the nozzle diameter increased from 10 to 15 mm, respectively, for 10 nozzles used with 5 m3 h−1 flow rate. At 15 m3 h−1, the rotational velocity was decreased from 23.5 to 17.5, 12.0 to 7.5, 10.0 to 6.9, 7.6 to 4.7 and 5.9 to 4.0 cm s−1 when the nozzle diameter increased from 10 to 20 mm, respectively, for 5, 10, 15, 20 and 25 nozzles, respectively. The results also indicate that when the nozzle diameter increased from 20 to 25 mm, the rotational velocity decreased from 19.0 to 16.5, 12.0 to 10.0 and 7.1 to 5.5 cm s−1 for 3, 6 and 9 nozzles, respectively, with 15 m3 h−1 flow rate.Figure 3Effect of flow rate, nozzle diameter and number of nozzles on the rotational velocity of water in a circular tank.Full size imageAt 30 m3 h−1 flow rate, the highest value of the rotational velocity was 33.5 cm s−1 was found for 5 nozzles and 10 mm nozzle diameter. While, the lowest value of the rotational velocity was 7.3 cm s−1 was found for 25 nozzles and 25 mm nozzle diameter. At 45 m3 h−1 flow rate, the rotational velocity ranged from 11.0 to 49.9 cm s−1 for all treatments under study.At 60 m3 h−1 flow rate, the rotational velocity deceased from 61.0 to 50.1, 47.7 to 34.0, 36.3 to 23.0, 23.5 to 17.5, 21.0 to 15.0 and 17.0 to 11.5 cm s−1 when the nozzle diameter increased from 10 to 20 mm, respectively at 5, 10, 15, 20, 25 and 30 number of nozzles. The results also indicate that, when the nozzle diameter increased from 20 to 25 mm, the rotational velocity decreased from 56.0 to 47.0, 43.0 to 33.0, 27.0 to 22.0 and 19.0 to 16.5 cm s−1 at 3, 6, 9 and 12 nozzles, respectively.At 75 m3 h−1 flow rate, the rotational velocity deceased from 60.9 to 49.1, 48.4 to 38.0, 39.0 to 30, 31.8 to 23.0, 23.5 to 17.5 and 22.0 to 15.0 cm s−1 when the nozzle diameter increased from 10 to 20 mm, respectively for 5, 10, 15, 20, 25 and 30 nozzles, respectively. The results also indicate that, when the nozzle diameter increased from 20 to 25 mm, the rotational velocity decreased from 50.48 to 43.0 to 38.5, 33.0 to 27.5 and 23.5 to 22.0 cm s−1 for 3, 6, 9 and 12 nozzles, respectively.The results also indicate that the highest values of the rotational velocities were 10.1, 23.5, 33.5, 49.9, 60.9 and 61.0 cm s−1 were found for 5 nozzles and 10 mm nozzle diameter at 5, 15, 30, 45, 60 and 75 m3 h−1 flow rate, respectively. While, the lowest values of the rotational velocities were 4.0, 7.5 and 11.5 cm s−1 for 25 nozzles and 15 mm nozzle diameter at 5, 15 and 30 m3 h−1 flow rate, respectively. They were 11.5 and 15 cm s−1 were found for 30 nozzles and 15 mm nozzle diameter at 60 and 75 m3 h−1 flow rate, respectively. The velocity of water obtained seemed to be in the recommended range of safe and proper velocity for fish according to12. Due to it is effective compromise to allow heavy solids settle rapidly, yet sufficiently fast to create “good” hydraulics. Timmons and Youngs18 mentioned that the water velocity needed to maintain self-cleaning properties ranges from 3 to 40 cm s−1 varying greatly according to the physical properties of the biosolids. When fish swims at lower speed than its optimal, a large amount of energy will be used for higher spontaneous activity such as aggression. In contrast, when fish swim at higher speed than optimal, they become stressful, unstable, increase lactate production and fatigue6.Multiple regression analysis was carried out to obtain a relationship between the rotational velocity of water as dependent variable and different both of flow rate and nozzle diameter as independent variables. The best fit for this relationship with coefficient of determination of 0.95 and an error of 1.06% is in the following form:-$$ RV = 6.97 + 0.41Q – 0.19Dquad {text{R}}^{{2}} = 0.95 $$
    (3)
    where RV is the rotational velocity of water, cm s−1, Q is the water flow rate, m3 h−1, D is the nozzle diameter, mm.This equation could be applied in the range of 5 to 75 m3 h−1 water flow rate and from 10 to 25 mm of nozzle diameter.Impulse force of waterFigure 4 shows the effect of flow rate, diameter and number of nozzles on the impulse force of water in a circular tank. The results indicate that the impulse force of water increases with increasing flow rates and deceasing nozzle diameter and number of nozzles. It could be seen that, the impulse force of water decreased from 5.1 to 1.7 N, when the number of nozzles increased from 5 to 15, respectively at 10 nozzle diameter, and it decreased from 2.3 to 1.2 N, when the number of nozzles increased from 5 to 10, respectively, at 15 diameter nozzle with 5 m3 h−1 flow rate. At 15 m3 h−1, the impulse force of water was decreased from 84.7 to 9.4 N when the number of nozzles increased from 5 to 30, respectively 10 mm diameter nozzle. The results also indicate that when the number of nozzles increased from 5 to 25, the impulse force of water decreased from 14.8 to 1.4 N at 15 mm nozzle diameter, respectively, and it decreased from 9.5 to 1.9 and 5.3 to 1.3 N at 20 and 25 mm, respectively, when the number of nozzles increased from 3 to 9.Figure 4Effect of flow rate, nozzle diameter and number of nozzles on the impulse force of water in a circular tank.Full size imageAt 30 m3 h−1 flow rate, the impulse force of water deceased from 84.7 to 46.9, 56.9 to 14.8, 28.5 to 5.3, 14.9 to 3.0 and 11.8 to 2.2 N when the nozzle diameter increased from 10 to 15 mm, respectively at 5, 10, 15, 20 and 25 nozzles. The results also indicate that, when the nozzle diameter increased from 20 to 25 mm, the impulse force of water decreased from 21.4 to 14.9, 14.8 to 5.4, 5.3 to 2.2 and 2.3 to 1.9 N for 3, 6, 9 and 12 nozzles, respectively.At 45 m3 h−1 flow rate, the impulse force of water was ranged from 2.1 to 111.2 N for all treatments under this study. Also, at 60 m3 h−1 flow rate, the impulse force of water ranged from 5.1 to 151.3 N for all treatments under this study. At 75 m3 h−1 flow rate, the highest value of the impulse force of water 211.2 N was found for 5 numbers of nozzles and 10 mm nozzle diameter, respectively. While, the lowest value of the impulse force of water was 9.1 N was found for 12 nozzles and 25 mm nozzle diameter, respectively.The results also indicate that the highest value of the impulse force of water 211.2 N was found for 5 nozzles and 10 mm nozzle diameter at 75 m3 h−1 flow rate, respectively. While, the lowest value of the impulse force of water was 1.2 N was found for 10 nozzles and 15 mm nozzle diameter at 5 m3 h−1 flow rate, respectively.The results indicated that, the relationship between the rotational velocity and impulse force of water is linear relationship at the same treatments. When the rotational velocity increased from 10.7 to 37.6, 8.1 to 28.8, 10.2 to 36.0 and 11.0 to 31.9 cm s−1, the impulse force of water increased from 3.1 to 106.6, 1.8 to 31.1, 1.3 to 32.5 and 1.4 to 22.8 N, respectively, at the same treatments. The trend of these results agreed with those obtained by19.Multiple regression analysis was carried out to obtain a relationship between the impulse force of water as dependent variable and different both of flow rate and nozzle diameter as independent variables. The best fit for this relationship with coefficient of determination of 0.88 and an error of 2.13% is in the following form:-$$ F_{i} = 38.18 + 0.67Q – 2.35Dquad {text{R}}^{{2}} = 0.88 $$
    (4)
    This equation could be applied in the range of 5 to 75 m3 h−1 water flow rate and from 10 to 25 mm of nozzle diameter.Average velocity of waterFigure 5 shows the effect of flow rate, diameter and number of nozzles on the average velocity of water in a circular tank. The results indicate that the average velocity of water increases with increasing flow rates and deceasing nozzle diameter and number of nozzles. It could be seen that, the average velocity of water decreased from 3.32 to 1.59 cm s−1, when the number of nozzles increased from 5 to 15, respectively at 10 nozzle diameter, and it decreased from 1.13 to 1.07 cm s−1, when the number of nozzles increased from 5 to 10, respectively, at 15 diameter nozzle with 5 m3 h−1 flow rate. At 15 m3 h−1, the average velocity of water was decreased from 12.03 to 4.33 cm s−1 when the number of nozzles increased from 5 to 30, respectively 10 mm diameter nozzle. The results also indicate that when the number of nozzles increased from 5 to 25, the average velocity of water decreased from 6.93 to 2.89 cm s−1 at 15 mm nozzle diameter, respectively, and it decreased from 7.55 to 4.00 and 4.89 to 2.95 cm s−1 at 20 and 25 mm, respectively, when the number of nozzles increased from 3 to 9.Figure 5Effect of flow rate, nozzle diameter and number of nozzles on the average velocity of water in a circular tank.Full size imageAt 30 m3 h−1 flow rate, the highest value of the average velocity of water 18.51 cm s−1 was found for 5 nozzles and 10 mm nozzle diameter. While, the lowest value of the average velocity of water was 4.65 cm s−1 was found for 12 nozzles and 25 mm nozzle diameter. At 45 m3 h−1 flow rate, the average velocity of water ranged from 6.66 to 23.26 for all treatments under study, also, at 60 m3 h−1 flow rate, the average velocity of water ranged from 9.23 to 34.82 for all treatments under study. At 75 m3 h−1 flow rate, the average velocity of water ranged from 10.00 to 48.76 for all treatment of this study.The results also indicate that the highest value of the average velocity of water 48.76 cm s−1 was found for 5 nozzles and 10 mm nozzle diameter at 75 m3 h−1 flow rate, respectively. While, the lowest value of the average velocity of water was 1.07 cm s−1 was found for 10 nozzles and 15 mm nozzle diameter at 5 m3 h−1 flow rate, respectively. These results agreed with those obtained by18,20. Fish distribution in the circular tank is influenced by the heterogeneity of water velocity in the area between inlet flow and the center of the tank9. Fish distribution in the circular tank is mostly concentrated in the area between high and low velocity area. The high velocity area will be avoided by most fishes as it requires high swimming energy, while dead volumes (low velocity area) are unfavorable condition for fish (low DO and higher metabolites accumulation)21.Multiple regression analysis was carried out to obtain a relationship between the average velocity of water as dependent variable and different both of flow rate and nozzle diameter as independent variables. The best fit for this relationship with coefficient of determination of 0.91 and an error of 1.48% is in the following form:$$ V_{avg} = 6.53 + 0.26Q – 0.37Dquad {text{R}}^{{2}} = 0.91 $$
    (5)
    This equation could be applied in the range of 5 to 75 m3 h−1 water flow rate and from 10 to 25 mm of nozzle diameter. More

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    Multidecadal, continent-level analysis indicates agricultural practices impact wheat aphid loads more than climate change

    El Bilali, H., Callenius, C., Strassner, C. & Probst, L. Food and nutrition security and sustainability transitions in food systems. Food Energy Secur 8, e00154 (2019).Article 

    Google Scholar 
    De Raymond, A. B. & Goulet, F. Science, technology and food security: An introduction. Sci. Technol. Soc. 25, 7–18 (2020).Article 

    Google Scholar 
    Wang, C. et al. Occurrence of crop pests and diseases has largely increased in China since 1970. Nat. Food 3, 57–65 (2022).Article 

    Google Scholar 
    Deutsch, C. A. et al. Increase in crop losses to insect pests in a warming climate. Science 361, 916–919 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Verger, P. J. P. & Boobis, A. R. Reevaluate pesticides for food security and safety. Science 341, 717–718 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Humann‐Guilleminot, S. et al. A nation‐wide survey of neonicotinoid insecticides in agricultural land with implications for agri‐environment schemes. J. Appl. Ecol. 56, 1502–1514 (2019).Article 
    CAS 

    Google Scholar 
    Haynes, K. J., Allstadt, A. J. & Klimetzek, D. Forest defoliator outbreaks under climate change: Effects on the frequency and severity of outbreaks of five pine insect pests. Glob. Change Biol. 20, 2004–2018 (2014).Article 

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

    Google Scholar 
    Skendžić, S. et al. The impact of climate change on agricultural insect pests. Insects 12, 440 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    WASDE. World Agricultural Supply and Demand Estimates 1554–9089 (World Agricultural Outlook Board, 2012).FAOSTAT. Food and agriculture organisation of the United Nations. http://faostat.fao.org/ (2018).Bellard, C. et al. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bebber, D. P. Range-expanding pests and pathogens in a warming world. Annu. Rev. Phytopathol. 53, 335–356 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Jactel, H., Koricheva, J. & Castagneyrol, B. Responses of forest insect pests to climate change: Not so simple. Curr. Opin. Insect Sci. 35, 103–108 (2019).PubMed 
    Article 

    Google Scholar 
    Stephane, A. P., Derocles, D. H., Lunt Sophie, C. F. & Moss., B. Climate warming alters the structure of farmland tritrophic ecological networks and reduces crop yield. Mol. Ecol. 27, 4931–4946 (2018).Article 

    Google Scholar 
    Nechols, J. R. The potential impact of climate change on non-target risks from imported generalist natural enemies and on biological control. Bio. Control 66, 37–44 (2021).
    Google Scholar 
    Tian, B. et al. Elevated temperature reduces wheat grain yield by increasing pests and decreasing soil mutualists. Pest Manag. Sci. 75, 466–475 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lehmann, P. et al. Complex responses of global insect pests to climate warming. Front. Ecol. Environ. 18, 141–150 (2020).Article 

    Google Scholar 
    Zhao, F., Zhang, W., Hoffmann, A. A. & Ma, C. Night warming on hot days produces novel impacts on development, survival, and reproduction in a small arthropod. J. Anim. Ecol. 83, 769–778 (2014).PubMed 
    Article 

    Google Scholar 
    Marini, L. et al. Climate drivers of bark beetle outbreak dynamics in Norway spruce forests. Ecography 40, 1426–1435 (2017).Article 

    Google Scholar 
    Bale, J. S. et al. Herbivory in global climate change research: Direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).Article 

    Google Scholar 
    Jamieson, M. A., Trowbridge, A. M., Raffa, K. F. & Lindroth, R. L. Consequences of climate warming and altered precipitation patterns for plant-insect and multitrophic interactions. Plant Physiol. 160, 1719–1727 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gagic, V. et al. Better outcomes for pest pressure, insecticide use, and yield in less intensive agricultural landscapes. Proc. Natl Acad. Sci. USA 118, 1–6 (2021).Article 
    CAS 

    Google Scholar 
    Paredes, D. et al. Landscape simplification increases vineyard pest outbreaks and insecticide use. Ecol. Lett. 24, 73–83 (2021).PubMed 
    Article 

    Google Scholar 
    Brattsten, L. B., Holyoke, C. W., Leeper, J. R. & Raffa, K. F. Insecticide resistance: Challenge to pest management and basic research. Science 231, 1255–1260 (1986).CAS 
    PubMed 
    Article 

    Google Scholar 
    Haddi, K. et al. Rethinking biorational insecticides for pest management: Unintended effects and consequences. Pest Manag. Sci. 76, 2286–2293 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gould, F., Brown, Z. S. & Kuzma, J. Wicked evolution: Can we address the sociobiological dilemma of pesticide resistance? Science 360, 728–732 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wei, N. et al. Transcriptome analysis and identification of insecticide tolerance-related genes after exposure to insecticide in Sitobion avenae. Genes 1012, 951 (2019).Article 
    CAS 

    Google Scholar 
    Gong, X. et al. Feasibility of reinforced post-endogenous denitrification coupling with synchronous nitritation, denitrification and phosphorus removal for high-nitrate sewage treatment using limited carbon source in municipal wastewater. Chemosphere 269, 128687 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tilman, D. et al. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Geiger, F. et al. Persistent negative effects of pesticides on biodiversity and biological control potential on European farmland. Basic Appl. Ecol. 11, 97–105 (2010).CAS 
    Article 

    Google Scholar 
    Muneret, L. et al. Evidence that organic farming promotes pest control. Nat. Sustain 1, 361–368 (2018).Article 

    Google Scholar 
    Lu, Y. et al. Widespread adoption of Bt cotton and insecticide decrease promotes biocontrol services. Nature 487, 362–365 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chaplin‐Kramer, R., O’Rourke, M. E., Blitzer, E. J. & Kremen, C. A meta‐analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932 (2011).PubMed 
    Article 

    Google Scholar 
    Baillod, A. B., Tscharntke, T., Clough, Y. & Batary, P. Landscape‐scale interactions of spatial and temporal cropland heterogeneity drive biological control of cereal aphids. J. Appl. Ecol. 54, 1804–1813 (2017).Article 

    Google Scholar 
    Gagic, V. et al. Combined effects of agrochemicals and ecosystem services on crop yield across Europe. Ecol. Lett. 20, 1427–1436 (2017).PubMed 
    Article 

    Google Scholar 
    Zhang, W. et al. Multidecadal, county-level analysis of the effects of land use, Bt cotton, and weather on cotton pests in China. Proc. Natl Acad. Sci. USA 115, 700–7709 (2018).
    Google Scholar 
    Horgan, F. G. et al. Population development of rice black bug, Scotinophara latiuscula (Breddin), under varying nitrogen in a field experiment. Entomol. Gen. 37, 19–33 (2018).Article 

    Google Scholar 
    Butler, J., Garratt, M., & Leather, S. Fertilisers and insect herbivores: A meta‐analysis. Ann. Appl. Biol. 161, 223–233 (2012).Article 

    Google Scholar 
    Aqueel, M. A. et al. Effect of plant nutrition on aphid size, prey consumption, and life history characteristics of green lacewing. Insect Sci. 21, 74–82 (2014).PubMed 
    Article 

    Google Scholar 
    Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: Is habitat heterogeneity the key? Trends Ecol. Evol. 18, 182–188 (2003).Article 

    Google Scholar 
    Winqvist, C. et al. Mixed effects of organic farming and landscape complexity on farmland biodiversity and biological control potential across Europe. J. Appl. Ecol. 48, 570–579 (2011).Article 

    Google Scholar 
    Tscharntke, T. et al. Landscape perspectives on agricultural intensification and biodiversity-ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

    Google Scholar 
    Meehan, T. D., Werling, B. P., Landis, D. A. & Gratton, C. Agricultural landscape simplification and insecticide use in the Midwestern United States. Proc. Natl Acad. Sci. USA 108, 11500–11505 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Macfadyen, S. et al. Do differences in food web structure between organic and conventional farms affect the ecosystem service of pest control? Ecol. Lett. 12, 229–238 (2009).PubMed 
    Article 

    Google Scholar 
    Liu, J., Ning, J., Kuang, W. & Xu, X. Spatio-temporal patterns and characteristics of land-use change in China during 2010-2015. J. Geogr. Sci. 73, 789–802 (2018).
    Google Scholar 
    Ma, C., Ma, G. & Zhao, F. Impact of global warming on cereal aphids. Chin. J. Appl. Entomol. 51, 1435–1443 (2014).
    Google Scholar 
    Han, Z. et al. Effects of simulated climate warming on the population dynamics of Sitobion avenae (Fabricius) and its parasitoids in wheat fields. Pest Manag. Sci. 75, 3252–3259 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Meisner, M. H., Harmon, J. P. & Ives, A. R. Temperature effects on long‐term population dynamics in a parasitoid-host system. Ecol. Monogr. 84, 457–476 (2014).Article 

    Google Scholar 
    Xiao, H. et al. Exposure to mild temperatures decreases overwintering larval survival and post-diapause reproductive potential in the rice stem borer Chilo suppressalis. J. Pest Sci. 90, 117–125 (2017).Article 

    Google Scholar 
    Senior, V. L. et al. Phenological responses in a sycamore-aphid-parasitoid system and consequences for aphid population dynamics: A 20 year case study. Glob. Change Biol. 26, 2814–2828 (2020).Article 

    Google Scholar 
    Chiu, M. C., Chen, Y. H. & Kuo, M. H. The effect of experimental warming on a low‐latitude aphid, Myzus varians. Entomol. Exp. Appl. 142, 216–222 (2012).Article 

    Google Scholar 
    Adler, L. S., De Valpine, P., Harte, J. & Call, J. Effects of long-term experimental warming on aphid density in the field. J. Kans. Entomol. Soc. 80, 156–169 (2007).Article 

    Google Scholar 
    Clement, S. L., Husebye, D. S. & Eigenbrode, S. D. Aphid Biodiversity under Environmental Change 107–129 (Springer, 2010).Van der Putten, W. H., Macel, M. & Visser, M. E. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels. Philos. T. Roy. Soc. B. 365, 2025–2034 (2010).Article 

    Google Scholar 
    Evans, E. W. Multitrophic interactions among plants, aphids, alternate prey and shared natural enemies—a review. Eur. J. Entomol. 105, 369–380 (2013).Article 

    Google Scholar 
    Sigsgaard, L. A survey of aphids and aphid parasitoids in cereal fields in Denmark, and the parasitoids’ role in biological control. J. Appl. Entomol. 126, 101–107 (2002).Article 

    Google Scholar 
    Diehl, E., Sereda, E., Wolters, V. & Birkhofer, K. Effects of predator specialization, host plant and climate on biological control of aphids by natural enemies: a meta‐analysis. J. Appl. Ecol. 50, 262–270 (2013).Article 

    Google Scholar 
    Hopper, K. R. et al. Natural enemy impact on the abundance of Diuraphis noxia (Homoptera: Aphididae) in wheat in Southern France. Environ. Entomol. 24, 402–408 (1995).Article 

    Google Scholar 
    Latham, D. R. & Mills, N. J. Quantifying aphid predation: The mealy plum aphid Hyalopterus pruni in California as a case study. J. Appl. Ecol. 47, 200–208 (2010).Article 

    Google Scholar 
    Östman, Ö., Ekbom, B. & Bengtsson, J. Yield increase attributable to aphid predation by ground-living polyphagous natural enemies in spring barley in Sweden. Ecol. Econ. 45, 149–158 (2003).Article 

    Google Scholar 
    Snyder, W. E. & Ives, A. R. Interactions between specialist and generalist natural enemies: Parasitoids, predators, and pea aphid control. Ecology 84, 91–107 (2003).Article 

    Google Scholar 
    Freier, B., Triltsch, H., Möwes, M. & Moll, E. The potential of predators in natural control of aphids in wheat: results of a ten-year field study in two German landscapes. Biocontrology 52, 775–788 (2007).Article 

    Google Scholar 
    Barczak, T., Dębek-Jankowska, A. & Bennewicz, J. Primary parasitoid and hyperparasitoid guilds (Hymenoptera) of grain aphid (Sitobion avenae F.) in northern Poland. Arch. Biol. Sci. 66, 1141–1148 (2014).Article 

    Google Scholar 
    Sánchez-Bayo, F. & Wyckhuys, K. A. G. Worldwide decline of the entomofauna: A review of its drivers. Biol. Conserv. 232, 8–27 (2019).Article 

    Google Scholar 
    Seibold, S. et al. Arthropod decline in grasslands and forests is associated with landscape-level drivers. Nature 574, 671–674 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang, W., Jiang, F. & Ou, J. Global pesticide consumption and pollution: With China as a focus. P. Intern. Acad. Ecol. Environ. Sci. 1, 125–144 (2011).CAS 

    Google Scholar 
    El-Wakeil, N., Gaafar, N., Sallam, A. & Volkmar, C. Side Effects of Insecticides on Natural Enemies and Possibility of their Integration in Plant Protection Strategies. Insecticides: Development of Safer and More Effective Technologies Agricultural and Biological Sciences (ed Trdan, S.) 1–56 (Intech Open Access Publisher, 2013).Peshin, R. & Dhawan, A. K. Integrated Pest Management: Innovation-Development Process (Springer Science & Business Media, 2009).Jia, B., Hong, S., Zhang, Y. & Cao, Y. Toxicity and safety of 12 insecticides to Diadegma semiclausum. J. Shanxi Agric. Sci. 43, 999–1002 (2015).
    Google Scholar 
    Emery, S. E. et al. High agricultural intensity at the landscape scale benefits pests, but low intensity practices at the local scale can mitigate these effects. Agric. Ecosyst. Environ. 306, 107199 (2021).Article 

    Google Scholar 
    Aqueel, M. A. & Leather, S. R. Effect of nitrogen fertilizer on the growth and survival of Rhopalosiphum padi (L.) and Sitobion avenae (F.)(Homoptera: Aphididae) on different wheat cultivars. Crop. Prot. 30, 216–221 (2011).Article 

    Google Scholar 
    Gao, J., Guo, H. J., Sun, Y. C. & Ge, F. Juvenile hormone mediates the positive effects of nitrogen fertilization on weight and reproduction in pea aphid. Pest Manag. Sci. 74, 2511–2519 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Barnett, K. L. & Facey, S. L. Grasslands, invertebrates, and precipitation: A review of the effects of climate change. Front. Plant. Sci. 7, 1196 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yu, X. et al. Engineering plants for aphid resistance: Current status and future perspectives. Theor. Appl. Genet. 127, 2065–2083 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Martin, E. A. et al. The interplay of landscape composition and configuration: New pathways to manage functional biodiversity and agroecosystem services across Europe. Ecol. Lett. 22, 1083–1094 (2019).PubMed 
    Article 

    Google Scholar 
    Steckel, J. et al. Landscape composition and configuration differently affect trap-nesting bees, wasps and their antagonists. Biol. Conserv. 172, 56–64 (2014).Article 

    Google Scholar 
    Lu, Y. H. et al. Major ecosystems in China: Dynamics and challenges for sustainable management. Environ. Manag. 48, 13–27 (2011).Article 

    Google Scholar 
    Wood, G. A. et al. Real-time measures of canopy size as a basis for spatially varying nitroge applications to winter wheat sown at different seed rates. Biosyst. Eng. 84, 513–531 (2003).Article 

    Google Scholar 
    NOAA. https://www.ncdc.noaa.gov/cdo-web/ (2018).WORLD BANK GROUP. https://climateknowledgeportal.worldbank.org/download-data (2018). More

  • in

    Strategic planning to mitigate mining impacts on protected areas in the Brazilian Amazon

    Adams, V. M., Iacona, G. D. & Possingham, H. P. Weighing the benefits of expanding protected areas versus managing existing ones. Nat. Sustain. 2, 404–411 (2019).Article 

    Google Scholar 
    Blicharska, M. et al. Biodiversity’s contributions to sustainable development. Nat. Sustain. 2, 1083–1093 (2019).Article 

    Google Scholar 
    Hanson, J. O. et al. Global conservation of species’ niches. Nature 580, 232–234 (2020).CAS 
    Article 

    Google Scholar 
    Sonter, L. J., Barrett, D. J., Soares-filho, B. S. & Moran, C. J. Global demand for steel drives extensive land-use change in Brazil’ s Iron Quadrangle. Glob. Environ. Change 26, 63–72 (2014).Article 

    Google Scholar 
    Siqueira-Gay, J., Soares-Filho, B., Sánchez, L. E., Oviedo, A. & Sonter, L. J. Proposed legislation to mine Brazil’s Indigenous lands will threaten Amazon forests and their valuable ecosystem services. One Earth 3, 356–362 (2020).Article 

    Google Scholar 
    El Bizri, H. R., Macedo, J. C. B. M., Plaglia, A. P. & Morcatty, T. Q. Mining undermining Brazil’s environment. Science 353, 2–3 (2016).Article 

    Google Scholar 
    Ferreira, J. et al. Brazil’s environmental leadership at risk. Science 346, 706–707 (2014).CAS 
    Article 

    Google Scholar 
    Rudke, A. P. et al. Impact of mining activities on areas of environmental protection in the southwest of the Amazon: a GIS- and remote sensing-based assessment. J. Environ. Manage. 263, 110392 (2020).Article 

    Google Scholar 
    Naughton-Treves, L. & Holland, M. B. Losing ground in protected areas? Science 364, 832–833 (2019).CAS 
    Article 

    Google Scholar 
    Kroner, R. E. G. et al. The uncertain future of protected lands and waters. Science 364, 881–886 (2019).Article 
    CAS 

    Google Scholar 
    Pack, S. M. et al. Protected area downgrading, downsizing, and degazettement (PADDD) in the Amazon. Biol. Conserv. 197, 32–39 (2016).Article 

    Google Scholar 
    PADDDtracker.org Data Release Version 2.0 (Conservation International and World Wildlife Fund, 2019); https://doi.org/10.5281/zenodo.3371733Bebbington, A. J., Humphreys, D., Aileen, L., Rogan, J. & Agrawal, S. Resource extraction and infrastructure threaten forest cover and community rights. Proc. Natl Acad. Sci. USA 115, 13164–13173 (2018).CAS 
    Article 

    Google Scholar 
    Paiva, P. F. P. R. et al. Deforestation in protect areas in the Amazon: a threat to biodiversity. Biodivers. Conserv. 29, 19–38 (2020).Article 

    Google Scholar 
    Boldy, R., Santini, T., Annandale, M., Erskine, P. D. & Sonter, L. J. Understanding the impacts of mining on ecosystem services through a systematic review. Extr. Ind. Soc. https://doi.org/10.1016/j.exis.2020.12.005 (2020).Murguía, D. I., Bringezu, S. & Schaldach, R. Global direct pressures on biodiversity by large-scale metal mining: spatial distribution and implications for conservation. J. Environ. Manage. 180, 409–420 (2016).Article 

    Google Scholar 
    Kobayashi, H., Watando, H. & Kakimoto, M. A global extent site-level analysis of land cover and protected area overlap with mining activities as an indicator of biodiversity pressure. J. Clean. Prod. 84, 459–468 (2014).Article 

    Google Scholar 
    Craig, M. D., White, D. A., Stokes, V. L. & Prince, J. Can postmining revegetation create habitat for a threatened mammal? Ecol. Manage. Restor. 18, 149–155 (2017).Article 

    Google Scholar 
    Sonter, L. J. et al. Mining drives extensive deforestation in the Brazilian Amazon. Nat. Commun. 8, 1013 (2017).Article 
    CAS 

    Google Scholar 
    Siqueira-Gay, J., Sonter, L. J. & Sánchez, L. E. Exploring potential impacts of mining on forest loss and fragmentation within a biodiverse region of Brazil’s northeastern Amazon. Resour. Policy 67, 101662 (2020).Article 

    Google Scholar 
    Siqueira-Gay, J. & Sánchez, L. E. Keep the Amazon niobium in the ground. Environ. Sci. Policy 111, 1–6 (2020).CAS 
    Article 

    Google Scholar 
    Mascia, M. B. & Pailler, S. Protected area downgrading, downsizing, and degazettement (PADDD) and its conservation implications. Conserv. Lett. 4, 9–20 (2011).Article 

    Google Scholar 
    Raiter, K. G., Possingham, H. P., Prober, S. M. & Hobbs, R. J. Under the radar: mitigating enigmatic ecological impacts. Trends Ecol. Evol. 29, 635–644 (2014).Article 

    Google Scholar 
    Whitehead, A. L., Kujala, H. & Wintle, B. A. Dealing with cumulative biodiversity impacts in strategic environmental assessment: a new frontier for conservation planning. Conserv. Lett. 10, 195–204 (2017).Article 

    Google Scholar 
    Jenner, N. Making Mining ‘Forest-Smart’: Executive Summary Report (World Bank, 2019); http://documents.worldbank.org/curated/en/369711560319906622/Making-Mining-Forest-Smart-Executive-Summary-ReportRenca: Situação legal dos direitos minerários da reserva nacional do cobre (WWF, 2017).Soares-Filho, B. S., Cerqueira, G. C. & Pennachin, C. L. DINAMICA—a stochastic cellular automata model designed to simulate the landscape dynamics in an Amazonian colonization frontier. Ecol. Modell. 154, 217–235 (2002).Article 

    Google Scholar 
    Strand, J. et al. Spatially explicit valuation of the Brazilian Amazon forest’s ecosystem services. Nat. Sustain. 1, 657–664 (2018).Article 

    Google Scholar 
    Barber, C. P., Cochrane, M. A., Souza, C. M. & Laurance, W. F. Roads, deforestation, and the mitigating effect of protected areas in the Amazon. Biol. Conserv. 177, 203–209 (2014).Article 

    Google Scholar 
    Rorato, A. C. et al. Brazilian Amazon Indigenous peoples threatened by mining bill. Environ. Res. Lett. 15, 1040a3 (2020).Article 

    Google Scholar 
    Villén-Pérez, S., Anaya-Valenzuela, L., Conrado da Cruz, D. & Fearnside, P. M. Mining threatens isolated Indigenous peoples in the Brazilian Amazon. Glob. Environ. Change 72, (2022).Siqueira-Gay, J. & Sánchez, L. E. The outbreak of illegal gold mining in the Brazilian Amazon boosts deforestation. Reg. Environ. Change 21, 28 (2021).Article 

    Google Scholar 
    Sonter, L. J., Dade, M. C., Watson, J. E. M. & Valenta, R. K. Renewable energy production will exacerbate mining threats to biodiversity. Nat. Commun. 11, 4174 (2020).CAS 
    Article 

    Google Scholar 
    Tallis, H., Kennedy, C. M., Ruckelshaus, M., Goldstein, J. & Kiesecker, J. M. Mitigation for one & all: an integrated framework for mitigation of development impacts on biodiversity and ecosystem services. Environ. Impact Assess. Rev. 55, 21–34 (2015).Article 

    Google Scholar 
    Bull, J. W. et al. Quantifying the “avoided” biodiversity impacts associated with economic development. Front. Ecol. Environ. https://doi.org/10.1002/fee.2496 (2022).Gastauer, M. et al. Mine land rehabilitation: modern ecological approaches for more sustainable mining. J. Clean. Prod. 172, 1409–1422 (2018).Article 

    Google Scholar 
    Souza, B. A., Rosa, J. C. S., Siqueira-Gay, J. & Sánchez, L. E. Mitigating impacts on ecosystem services requires more than biodiversity offsets. Land Use Policy 105, 105393 (2021).Article 

    Google Scholar 
    Ritter, C. D. et al. Environmental impact assessment in Brazilian Amazonia: challenges and prospects to assess biodiversity. Biol. Conserv. 206, 161–168 (2017).Article 

    Google Scholar 
    Good Practice Handbook: Cumulative Impact Assessment and Management, Guidance for the Private Sector in Emerging Markets (IFC, 2013).Gunn, J. H. & Noble, B. F. Integrating cumulative effects in regional strategic environmental assessment frameworks: lessons from practice. J. Environ. Assess. Policy Manage. 11, 267–290 (2009).Article 

    Google Scholar 
    Ferrante, L. & Fearnside, P. M. The Amazon’ s road to deforestation. Science 20, 20–22 (2020).
    Google Scholar 
    Runge, C. A., Tulloch, A. I. T., Gordon, A. & Rhodes, J. R. Quantifying the conservation gains from shared access to linear infrastructure. Conserv. Biol. 31, 1428–1438 (2017).Article 

    Google Scholar 
    Kiesecker, J. M., Copeland, H., Pocewicz, A. & McKenney, B. Development by design: blending landscape-level planning with the mitigation hierarchy. Front. Ecol. Environ. 8, 261–266 (2010).Article 

    Google Scholar 
    Heiner, M. et al. Moving from reactive to proactive development planning to conserve Indigenous community and biodiversity values. Environ. Impact Assess. Rev. 74, 1–13 (2019).Article 

    Google Scholar 
    Pfaff, A., Robalino, J., Herrera, D. & Sandoval, C. Protected areas’ impacts on Brazilian Amazon deforestation: examining conservation–development interactions to inform planning. PLoS ONE 10, 1–17 (2015).Article 
    CAS 

    Google Scholar 
    Almeida, C. A. et al. High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5 / TM and MODIS data. Acta Amazon. 46, 291–302 (2008).Article 

    Google Scholar 
    Asner, G. P. & Tupayachi, R. Accelerated losses of protected forests from gold mining in the Peruvian Amazon. Environ. Res. Lett. 12, 094004 (2016).Article 

    Google Scholar 
    Boham-Carter, G. F. Geographic Information Systems for Geoscientists: Modelling with GIS (Elsevier, 1994).Soares-Filho, B., Rodrigues, H. & Follador, M. A hybrid analytical–heuristic method for calibrating land-use change models. Environ. Model. Softw. 43, 80–87 (2013).Article 

    Google Scholar 
    INPE. TerraClass https://www.terraclass.gov.br/geoportal-aml/ (2021).INPE. Slope http://www.dsr.inpe.br/topodata/acesso.php (2020).Ministério do Meio Ambiente (MMA). Conservation units http://mapas.mma.gov.br/i3geo/datadownload.htm (2022).Fundação Nacional do Índio (FUNAI). Indigenous lands http://www.funai.gov.br/index.php/shape (2021).Leite-Filho, A., Soares-filho, B. S., Davis, J. & Rodrigues, H. Dinamica EGO Guidebook (Centro de Sensoriamento Remoto, UFMG, 2020).Serviço Geológico do Brasil. Mineral deposits https://geosgb.cprm.gov.br/ (2020).Soares-Filho, B. et al. Simulating the response of land-cover changes to road paving and governance along a major Amazon highway: the Santarém-Cuiabá corridor. Glob. Change Biol. 10, 745–764 (2004).Article 

    Google Scholar 
    Centro de Sensoriamento Remoto. Biodiversity https://csr.ufmg.br/amazones/biodiversity/ (2021).Fahrig, L. Ecological responses to habitat fragmentation per se. Annu. Rev. Ecol. Evol. Syst. 48, 1–23 (2017).Pardini, R., de Bueno, A. A., Gardner, T. A., Prado, P. I. & Metzger, J. P. Beyond the fragmentation threshold hypothesis: regime shifts in biodiversity across fragmented landscapes. PLoS ONE 5, e13666 (2010).Montibeller, B., Kmoch, A., Virro, H., Mander, Ü. & Uuemaa, E. Increasing fragmentation of forest cover in Brazil’s Legal Amazon from 2001 to 2017. Sci. Rep. 10, 5803 (2020).CAS 
    Article 

    Google Scholar 
    Cabral, A. I. R., Saito, C., Pereira, H. & Laques, A. E. Deforestation pattern dynamics in protected areas of the Brazilian Legal Amazon using remote sensing data. Appl. Geogr. 100, 101–115 (2018).Article 

    Google Scholar 
    Colson, F., Bogaert, J. & Ceulemans, R. Fragmentation in the Legal Amazon, Brazil: can landscape metrics indicate agricultural policy differences? Ecol. Indic. 11, 1467–1471 (2011).Article 

    Google Scholar 
    Monmonier, M. S. Measures of pattern complexity for choroplethic maps. Am. Cartogr. 1, 159–169 (1974).Article 

    Google Scholar 
    Werner, T. T. et al. Global-scale remote sensing of mine areas and analysis of factors explaining their extent. Glob. Environ. Change 60, 102007 (2020).Article 

    Google Scholar 
    Soares-Filho, B. et al. Roads, http://maps.csr.ufmg.br/ (2016). More

  • in

    Increased incompatibility of heterologous algal symbionts under thermal stress in the cnidarian-dinoflagellate model Aiptasia

    Sylvan, J. How to protect a coral reef: the public trust doctrine and the law of the sea recommended citation. Sustain. Dev. Law Policy 7, 12 (2006).
    Google Scholar 
    LaJeunesse, T. C. et al. Systematic revision of symbiodiniaceae highlights the antiquity and diversity of coral endosymbionts. Curr. Biol. 28, 2570–2580.e6 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kopp, C. et al. Highly dynamic cellular-level response of symbiotic coral to a sudden increase in environmental nitrogen. mBio 4, e00052–13 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Muscatine, L. The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reef. 25, 75–87 (1990).
    Google Scholar 
    Dubinsky, Z. & Stambler, N. Coral reefs: an ecosystem in transition. (Springer, 2011).Wiedenmann, J. et al. Nutrient enrichment can increase the susceptibility of reef corals to bleaching. https://doi.org/10.1038/NCLIMATE1661 (2012).Suggett, D. J., Warner, M. E. & Leggat, W. Symbiotic dinoflagellate functional diversity mediates coral survival under ecological crisis. Trends Ecol. Evolution 32, 735–745 (2017).Article 

    Google Scholar 
    Morris, L. A., Voolstra, C. R., Quigley, K. M., Bourne, D. G. & Bay, L. K. Nutrient availability and metabolism affect the stability of coral–symbiodiniaceae symbioses. Trends Microbiol. 27, 678–689 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Lehnert, E. M. et al. Extensive differences in gene expression between symbiotic and aposymbiotic cnidarians. G3 (Bethesda) 4, 277–95 (2014).CAS 
    Article 

    Google Scholar 
    Dubinsky, Z. & Berman-Frank, I. Uncoupling primary production from population growth in photosynthesizing organisms in aquatic ecosystems. in. Aquat. Sci. 63, 4–17 (2001).CAS 
    Article 

    Google Scholar 
    Burriesci, M. S., Raab, T. K. & Pringle, J. R. Evidence that glucose is the major transferred metabolite in dinoflagellate–cnidarian symbiosis. J. Exp. Biol. 215, 3467–3477 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Davy, S. K., Allemand, D. & Weis, V. M. Cell biology of cnidarian-dinoflagellate symbiosis. Microbiol. Mol. Biol. Rev. 76, 229–61 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rädecker, N., Pogoreutz, C., Voolstra, C. R., Wiedenmann, J. & Wild, C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 23, 490–497 (2015).PubMed 
    Article 
    CAS 

    Google Scholar 
    Cui, G. et al. Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia. PLOS Genet. 15, e1008189 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rädecker, N. et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc. Natl Acad. Sci. USA 118, https://doi.org/10.1073/pnas.2022653118 (2021).Weis, V. M. Cellular mechanisms of Cnidarian bleaching: stress causes the collapse of symbiosis. J. Exp. Biol. 211, 3059–3066 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wooldridge, S. A. Breakdown of the coral-algae symbiosis: towards formalising a linkage between warm-water bleaching thresholds and the growth rate of the intracellular zooxanthellae. Biogeosciences Discuss. 9, 8111–8139 (2012).
    Google Scholar 
    Cziesielski, M. J., Schmidt‐Roach, S. & Aranda, M. The past, present, and future of coral heat stress studies. Ecol. Evol. https://doi.org/10.1002/ece3.5576 (2019).Leggat, W. et al. Differential responses of the coral host and their algal symbiont to thermal stress. PLoS ONE 6, e26687 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Pinzón, J. H. et al. Whole transcriptome analysis reveals changes in expression of immune-related genes during and after bleaching in a reef-building coral. R. Soc. Open Sci. 2, 140214 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Ziegler, M., Seneca, F. O., Yum, L. K., Palumbi, S. R. & Voolstra, C. R. Bacterial community dynamics are linked to patterns of coral heat tolerance. Nat. Commun. 8, 14213 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bang, C. et al. Metaorganisms in extreme environments: do microbes play a role in organismal adaptation? Zoology 127, 1–19 (2018).PubMed 
    Article 

    Google Scholar 
    Berkelmans, R. & van Oppen, M. J. H. The role of zooxanthellae in the thermal tolerance of corals: a “nugget of hope” for coral reefs in an era of climate change. Proc. Biol. Sci./R. Soc. 273, 2305–12 (2006).
    Google Scholar 
    Sampayo, E. M., Ridgway, T., Bongaerts, P. & Hoegh-Guldberg, O. Bleaching susceptibility and mortality of corals are determined by fine-scale differences in symbiont type. Proc. Natl Acad. Sci. 105, 10444–10449 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Howells, E. J. et al. Coral thermal tolerance shaped by local adaptation of photosymbionts. Nat. Clim. Change https://doi.org/10.1038/nclimate1330 (2011).Cziesielski, M. J. et al. Multi-omics analysis of thermal stress response in a zooxanthellate cnidarian reveals the importance of associating with thermotolerant symbionts. Proc. Biol. Sci. 285, 20172654 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Baker, A. C., Starger, C. J., McClanahan, T. R. & Glynn, P. W. Corals’ adaptive response to climate change. Nature 430, 741–741 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    Thornhill, D. J., LaJeunesse, T. C., Kemp, D. W., Fitt, W. K. & Schmidt, G. W. Multi-year, seasonal genotypic surveys of coral-algal symbioses reveal prevalent stability or post-bleaching reversion. Mar. Biol. 148, 711–722 (2006).Article 

    Google Scholar 
    Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to environmental stress,making its relative ability to acclimate or adapt extremely important to the to future climate change. Science 344, 895–898 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Herrera, M. et al. Temperature transcends partner specificity in the symbiosis establishment of a cnidarian. ISME J. 15, 141–153 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Howells, E. J. et al. Corals in the hottest reefs in the world exhibit symbiont fidelity not flexibility. Mol. Ecol. 29, 899–911 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hume, B. C. C., Mejia-Restrepo, A., Voolstra, C. R. & Berumen, M. L. Fine-scale delineation of Symbiodiniaceae genotypes on a previously bleached central Red Sea reef system demonstrates a prevalence of coral host-specific associations. Coral Reefs 1–19 https://doi.org/10.1007/s00338-020-01917-7 (2020).Perez, S. F., Cook, C. B. & Brooks, W. R. The role of symbiotic dinoflagellates in the temperature-induced bleaching response of the subtropical sea anemone Aiptasia pallida. J. Exp. Mar. Biol. Ecol. 256, 1–14 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mieog, J. C. et al. The roles and interactions of symbiont, host and environment in defining coral fitness. PLoS ONE 4, e6364 (2009).Cantin, N. E., van Oppen, M. J. H., Willis, B. L., Mieog, J. C. & Negri, A. P. Juvenile corals can acquire more carbon from high-performance algal symbionts. Coral Reefs 28, 405–414 (2009).Article 

    Google Scholar 
    Herrera, M. et al. Unfamiliar partnerships limit cnidarian holobiont acclimation to warming. Glob. Change Biol. 26, 5539–5553 (2020).Article 

    Google Scholar 
    LaJeunesse, T. et al. Closely related Symbiodinium spp. differ in relative dominance in coral reef host communities across environmental, latitudinal and biogeographic gradients. Mar. Ecol. Prog. Ser. 284, 147–161 (2004).Article 

    Google Scholar 
    Parkinson, J. E. & Baums, I. B. The extended phenotypes of marine symbioses: ecological and evolutionary consequences of intraspecific genetic diversity in coral-algal associations. Front. Microbiol. 5, 445 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Coffroth, M. A., Poland, D. M., Petrou, E. L., Brazeau, D. A. & Holmberg, J. C. Environmental symbiont acquisition may not be the solution to warming seas for reef-building corals. PLoS ONE 5, e13258 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Bellantuono, A. J., Granados-Cifuentes, C., Miller, D. J., Hoegh-Guldberg, O. & Rodriguez-Lanetty, M. Coral thermal tolerance: tuning gene expression to resist thermal stress. PLoS ONE 7, e50685 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sunagawa, S. et al. Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genomics 10, 258 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Baumgarten, S. et al. The genome of Aiptasia, a sea anemone model for coral symbiosis. Proc. Natl Acad. Sci. 112, 201513318 (2015).
    Google Scholar 
    Matthews, J. L. et al. Menthol-induced bleaching rapidly and effectively provides experimental aposymbiotic sea anemones (Aiptasia sp.) for symbiosis investigations. J. Exp. Biol. jeb.128934 https://doi.org/10.1242/JEB.128934 (2015).Kenkel, C. D. et al. Evidence for a host role in thermotolerance divergence between populations of the mustard hill coral (Porites astreoides) from different reef environments. Mol. Ecol. 22, 4335–4348 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Polato, N. R., Altman, N. S. & Baums, I. B. Variation in the transcriptional response of threatened coral larvae to elevated temperatures. Mol. Ecol. 22, 1366–1382 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    DeSalvo, M., Sunagawa, S., Voolstra, C. R. & Medina, M. Transcriptomic resonses to heat stress and bleaching in the elkhorn coral Acropora palmata. Mar. Ecol. Prog. Ser. 402, 97–113 (2010).CAS 
    Article 

    Google Scholar 
    Maor-Landaw, K. & Levy, O. Gene expression profiles during short-term heat stress; branching vs. massive Scleractinian corals of the Red Sea. PeerJ 4, e1814 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Yamamoto, K. et al. Control of the heat stress-induced alternative splicing of a subset of genes by hnRNP K. Genes Cells 21, 1006–1014 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Seneca, F. O. & Palumbi, S. R. The role of transcriptome resilience in resistance of corals to bleaching. Mol. Ecol. 24, 1467–1484 (2015).PubMed 
    Article 

    Google Scholar 
    Meyer, E. & Weis, V. M. Study of cnidarian-algal symbiosis in the “omics” age. Biol. Bull. 223, 44–65 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Oakley, C. A. et al. Thermal shock induces host proteostasis disruption and endoplasmic reticulum stress in the model symbiotic Cnidarian Aiptasia. J. Proteome Res. 16, 2121–2134 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Robbart, M. L., Peckol, P., Scordilis, S. P., Curran, H. A. & Brown-Saracino, J. Population recovery and differential heat shock protein expression for the corals Agaricia agaricites and A-tenuifolia in Belize. Mar. Ecol. Prog. Ser. 283, 151–160 (2004).Article 

    Google Scholar 
    Barshis, D. J. et al. Genomic basis for coral resilience to climate change. Proc. Natl Acad. Sci. 110, 1387–1392 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Traylor-Knowles, N., Rose, N. H. & Palumbi, S. R. The cell specificity of gene expression in the response to heat stress in corals. J. Exp. Biol. 220, 1837–1845 (2017).PubMed 

    Google Scholar 
    Benchimol, S. p53-dependent pathways of apoptosis. Cell Death Differ. 8, 1049–1051 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Moya, A. et al. Functional conservation of the apoptotic machinery from coral to man: The diverse and complex Bcl-2 and caspase repertoires of Acropora millepora. BMC Genomics 17, 62 (2016).Elmore, S. Apoptosis: a review of programmed cell death. Toxicol. Pathol. 35, 495–516 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Karim, W., Nakaema, S. & Hidaka, M. Temperature effects on the growth rates and photosynthetic activities of symbiodinium cells. J. Mar. Sci. Eng. 3, 368–381 (2015).Article 

    Google Scholar 
    Cunning, R. & Baker, A. C. Excess algal symbionts increase the susceptibility of reef corals to bleaching. Nat. Clim. Change 3, 259–262 (2013).Article 

    Google Scholar 
    Rehman, A. U. et al. Symbiodinium sp. cells produce light-induced intra- and extracellular singlet oxygen, which mediates photodamage of the photosynthetic apparatus and has the potential to interact with the animal host in coral symbiosis. N. Phytologist 212, 472–484 (2016).CAS 
    Article 

    Google Scholar 
    Lesser, K. B. & Garcia, F. A. Association between polycystic ovary syndrome and glucose intolerance during pregnancy. J. Matern. Fetal Med. 6, 303–307 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    Dunn, S. R., Schnitzler, C. E. & Weis, V. M. Apoptosis and autophagy as mechanisms of dinoflagellate symbiont release during cnidarian bleaching: every which way you lose. Proc. R. Soc. Lond. B: Biol. Sci. 274, 3079–3085 (2007).
    Google Scholar 
    DeSalvo, M. K. et al. Coral host transcriptomic states are correlated with Symbiodinium genotypes. Mol. Ecol. 19, 1174–1186 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Levin, R. A. et al. Engineering strategies to decode and enhance the genomes of coral symbionts. Front. Microbiol. https://doi.org/10.3389/fmicb.2017.01220 (2017).Yuyama, I., Ishikawa, M., Nozawa, M., Yoshida, M. & Ikeo, K. Transcriptomic changes with increasing algal symbiont reveal the detailed process underlying establishment of coral-algal symbiosis. Sci. Rep. 8, 16802 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Sproles, A. E. et al. Sub-cellular imaging shows reduced photosynthetic carbon and increased nitrogen assimilation by the non-native endosymbiont Durusdinium trenchii in the model cnidarian Aiptasia. Environ. Microbiol. 22, 3741–3753 (2020).CAS 
    PubMed 
    Article 

    Google Scholar 
    Rädecker, N. et al. Using Aiptasia as a model to study metabolic interactions in Cnidarian-Symbiodinium symbioses. Front. Physiol. 9, 214 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Falkowski, P. G., Dubinsky, Z., Muscatine, L. & McCloskey, L. Population control in symbiotic corals. BioScience 43, 606–611 (1993).Article 

    Google Scholar 
    Wang & Douglas. Nitrogen recycling or nitrogen conservation in an alga-invertebrate symbiosis? J. Exp. Biol. 201, 2445–53 (1998).Loram, J. E., Trapido-Rosenthal, H. G. & Douglas, A. E. Functional significance of genetically different symbiotic algae Symbiodinium in a coral reef symbiosis. Mol. Ecol. 16, 4849–4857 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    Karako-Lampert, S. et al. Transcriptome analysis of the scleractinian coral Stylophora pistillata. PLoS One 9, e88615 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Hillyer, K. E., Tumanov, S., Villas-Bôas, S. & Davy, S. K. Metabolite profiling of symbiont and host during thermal stress and bleaching in a model cnidarian-dinoflagellate symbiosis. J. Exp. Biol. 219, 516–27 (2016).PubMed 

    Google Scholar 
    Bertucci, A., Forêt, S., Ball, E. E. & Miller, D. J. Transcriptomic differences between day and night in Acropora millepora provide new insights into metabolite exchange and light-enhanced calcification in corals. Mol. Ecol. 24, 4489–4504 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Matthews, J. L. et al. Optimal nutrient exchange and immune responses operate in partner specificity in the cnidarian-dinoflagellate symbiosis. Proc. Natl Acad. Sci. 114, 13194–13199 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lin, M.-F., Takahashi, S., Forêt, S., Davy, S. K. & Miller, D. J. Transcriptomic analyses highlight the likely metabolic consequences of colonization of a cnidarian host by native or non-native Symbiodinium species. Biol. Open 8, bio038281 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Medrano, E., Merselis, D. G., Bellantuono, A. J. & Rodriguez-Lanetty, M. Proteomic Basis of Symbiosis: A Heterologous Partner Fails to Duplicate Homologous Colonization in a Novel Cnidarian– Symbiodiniaceae Mutualism. Front. Microbiol. 10, 1153 (2019).Schoepf, V., Stat, M., Falter, J. L. & McCulloch, M. T. Limits to the thermal tolerance of corals adapted to a highly fluctuating, naturally extreme temperature environment. Sci. Rep. 5, 17639 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Xiang, T., Hambleton, E. A., DeNofrio, J. C., Pringle, J. R. & Grossman, A. R. Isolation of clonal axenic strains of the symbiotic dinoflagellate Symbiodinium and their growth and host specificity1. J. Phycol. 49, 447–458 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pimentel, H., Bray, N. L., Puente, S., Melsted, P. & Pachter, L. Differential analysis of RNA-seq incorporating quantification uncertainty. Nat. Methods 14, 687–690 (2017).CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    RNA viromes from terrestrial sites across China expand environmental viral diversity

    Shi, M. et al. Redefining the invertebrate RNA virosphere. Nature 540, 539–543 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    Zhang, Y.-Z., Shi, M. & Holmes, E. C. Using metagenomics to characterize an expanding virosphere. Cell 172, 1168–1172 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Li, C.-X. et al. Unprecedented genomic diversity of RNA viruses in arthropods reveals the ancestry of negative-sense RNA viruses. eLife 4, e05378 (2015).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Starr, E. P., Nuccio, E. E., Pett-Ridge, J., Banfield, J. F. & Firestone, M. K. Metatranscriptomic reconstruction reveals RNA viruses with the potential to shape carbon cycling in soil. Proc. Natl Acad. Sci. USA 116, 25900–25908 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wolf, Y. I. et al. Doubling of the known set of RNA viruses by metagenomic analysis of an aquatic virome. Nat. Microbiol. 5, 1262–1270 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zayed, A. A. et al. Cryptic and abundant marine viruses at the evolutionary origins of Earth’s RNA virome. Science 376, 156–162 (2022).CAS 
    PubMed 
    Article 

    Google Scholar 
    Simmonds, P. et al. Virus taxonomy in the age of metagenomics. Nat. Rev. Microbiol. 15, 161–168 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Trubl, G., Hyman, P., Roux, S. & Abedon, S. T. Coming-of-age characterization of soil viruses: a user’s guide to virus isolation, detection within metagenomes, and viromics. Soil Syst. 4, 23 (2020).CAS 
    Article 

    Google Scholar 
    Jin, M. et al. Diversities and potential biogeochemical impacts of mangrove soil viruses. Microbiome 7, 58 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Trubl, G. et al. Soil viruses are underexplored players in ecosystem carbon processing. mSystems 3, e00076-18 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Steward, G. F. et al. Are we missing half of the viruses in the ocean? ISME J. 7, 672–679 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Paul, K. I., Scott Black, A. & Conyers, M. K. in Advances in Agronomy. Sparks, D.L., Vol. 78 187–214 (Elsevier, 2003).Urayama, S., Takaki, Y. & Nunoura, T. FLDS: a comprehensive dsRNA sequencing method for intracellular RNA virus surveillance. Microbes Environ. 31, 33–40 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Armbrust, E. V. The life of diatoms in the world’s oceans. Nature 459, 185–192 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wu, W., Jin, Y., Bai, F. & Jin, S. in Molecular Medical Microbiology. Tang, Y.W., Liu, D., Schwartzman, J., Sussman, M., Poxton, I., 753–767 (Elsevier, 2015).Cooney, S., O’Brien, S., Iversen, C. & Fanning, S. in Encyclopedia of Food Safety. Motarjemi, Y., 433–441 (Elsevier, 2014).Geoghegan, J. L. et al. Hidden diversity and evolution of viruses in market fish. Virus Evol. 4, vey031 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lauber, C. et al. Deciphering the origin and evolution of hepatitis B viruses by means of a family of non-enveloped fish viruses. Cell Host Microbe 22, 387–399.e6 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Shi, M., Zhang, Y.-Z. & Holmes, E. C. Meta-transcriptomics and the evolutionary biology of RNA viruses. Virus Res. 243, 83–90 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Turnbull, O. M. H. et al. Meta-transcriptomic identification of divergent Amnoonviridae in Fish. Viruses 12, 1254 (2020).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    Bauermann, F. V., Hause, B., Buysse, A. R., Joshi, L. R. & Diel, D. G. Identification and genetic characterization of a porcine hepe-astrovirus (bastrovirus) in the United States. Arch. Virol. 164, 2321–2326 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Oude Munnink, B. B. et al. A novel astrovirus-like RNA virus detected in human stool. Virus Evol. 2, vew005 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Williamson, K. E. et al. Estimates of viral abundance in soils are strongly influenced by extraction and enumeration methods. Biol. Fertil. Soils 49, 857–869 (2013).Article 

    Google Scholar 
    Wang, C., Liu, D. & Bai, E. Decreasing soil microbial diversity is associated with decreasing microbial biomass under nitrogen addition. Soil Biol. Biochem. 120, 126–133 (2018).CAS 
    Article 

    Google Scholar 
    Wang, Q. et al. Effects of nitrogen and phosphorus inputs on soil bacterial abundance, diversity, and community composition in Chinese fir plantations. Front. Microbiol. 9, 1543 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Payne, S. in Viruses. Payne, S., 219–226 (Elsevier, 2017).Hillman, B. I. & Cai, G. The family Narnaviridae. Adv. Virus Res. 86, 149–176 (2013).PubMed 
    Article 

    Google Scholar 
    Wolf, Y. I. et al. Origins and evolution of the global RNA virome. mBio 9, e02329-18 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Wu, F. et al. A new coronavirus associated with human respiratory disease in China. Nature 579, 265–269 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analysis in R. Bioinformatics 35, 526–528 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T. ggtree: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).Article 

    Google Scholar 
    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Nayfach, S. et al. CheckV assesses the quality and completeness of metagenome-assembled viral genomes. Nat. Biotechnol. 39, 578–585 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Almagro Armenteros, J. J. et al. SignalP 5.0 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    Gupta, R., Jung, E. & Brunak, S. NetNGlyc 1.0 Server (2017). DTU Health Tech. http://www.cbs.dtu.dk/services/NetNGlyc/Mirdita, M. et al. Uniclust databases of clustered and deeply annotated protein sequences and alignments. Nucleic Acids Res. 45, D170–D176 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    Remmert, M., Biegert, A., Hauser, A. & Söding, J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods 9, 173–175 (2012).CAS 
    Article 

    Google Scholar 
    Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2012).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lagkouvardos, I., Fischer, S., Kumar, N. & Clavel, T. Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons. PeerJ 5, e2836 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    McLeod, A., Xu, C. & Lai, Y. Package ‘bestglm’. CRAN. (2020).Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More