More stories

  • in

    Food insecurity and health outcomes among community-dwelling middle-aged and older adults in India

    Food, Agricultural Organisation. The State of Food Security and Nutrition in the World 2019: Transforming Food Systems for Affordable Healthy Diets. Safeguarding against Economic Slowdowns and Downturns (2020). http://www.fao.org/documents/card/en/c/ca9692en (Accessed 12 June 2021).Rautela, G. et al. Prevalence and correlates of household food insecurity in Delhi and Chennai India. Food Secur. 12(2), 391–404. https://doi.org/10.1007/s12571-020-01015-0 (2020).Article 

    Google Scholar 
    Nagappa, B. et al. Prevalence of food insecurity at household level and its associated factors in rural Puducherry: A cross-sectional study. Indian J. Community Med. 45(3), 303–306. https://doi.org/10.4103/ijcm.IJCM_233_19 (2020).Article 

    Google Scholar 
    Schrock, J. M. et al. Food insecurity partially mediates associations between social disadvantage and body composition among older adults in india: Results from the study on global AGEing and adult health (SAGE). Am. J. Hum. Biol. https://doi.org/10.1002/ajhb.23033 (2017).Article 

    Google Scholar 
    Narayanan, S. Food security in India: The imperative and its challenges. Asia Pac. Policy Stud. 2, 197–209. https://doi.org/10.1002/app5.62 (2015).Article 

    Google Scholar 
    George, N. A. & McKay, F. H. The public distribution system and food security in India. Int. J. Environ. Res. Public Health 16(17), 3221. https://doi.org/10.3390/ijerph16173221 (2019).Article 

    Google Scholar 
    Global Food Security Index. India. https://impact.economist.com/sustainability/project/food-security-index/explore-countries/india (Accessed 12 November 2022).United Nations Population Fund 2017. Caring for Our Elders: Early Responses – India Ageing Report—2017. UNFPA, New Delhi, India.Arenas, D. J., Thomas, A., Wang, J. & DeLisser, H. M. A systematic review and meta-analysis of depression, anxiety, and sleep disorders in US adults with food insecurity. J. Gen. Intern. Med. 34(12), 2874–2882. https://doi.org/10.1007/s11606-019-05202-4 (2019).Article 

    Google Scholar 
    Pourmotabbed, A. et al. Food insecurity and mental health: A systematic review and meta-analysis. Public Health Nutr. 23(10), 1778–1790. https://doi.org/10.1017/S136898001900435X (2020).Article 

    Google Scholar 
    McMichael, A. J. et al. Food insecurity and brain health in adults: A systematic review. Crit. Rev. Food Sci. Nutr. 62, 1–16. https://doi.org/10.1080/10408398.2021.1932721 (2021).Article 

    Google Scholar 
    Smith, L. et al. Association between food insecurity and depression among older adults from low- and middle-income countries. Depress Anxiety 38(4), 439–446. https://doi.org/10.1002/da.23147 (2021).Article 

    Google Scholar 
    Muhammad, T., Sulaiman, K. M., Drishti, D. & Srivastava, S. Food insecurity and associated depression among older adults in India: Evidence from a population-based study. BMJ Open 12(4), e052718. https://doi.org/10.1136/bmjopen-2021-052718 (2022).Article 

    Google Scholar 
    Saha, S. K. et al. Magnitude of mental morbidity and its correlates with special reference to household food insecurity among adult slum dwellers of Bankura, India: A cross-sectional survey. Indian J. Psychol. Med. 41(1), 54–60. https://doi.org/10.4103/IJPSYM.IJPSYM_129_18 (2019).Article 

    Google Scholar 
    Frongillo, E. A., Nguyen, H. T., Smith, M. D. & Coleman-Jensen, A. Food insecurity is associated with subjective well-being among individuals from 138 countries in the 2014 Gallup World Poll. J. Nutr. 147(4), 680–687. https://doi.org/10.3945/jn.116.243642 (2017).Article 
    CAS 

    Google Scholar 
    Na, M. et al. Food insecurity and cognitive function in middle to older adulthood: A systematic review. Adv. Nutr. 11(3), 667–676. https://doi.org/10.1093/advances/nmz122 (2020).Article 

    Google Scholar 
    Srivastava, S. & Muhammad, T. Rural-urban differences in food insecurity and associated cognitive impairment among older adults: Findings from a nationally representative survey. BMC Geriatr. 22(1), 287. https://doi.org/10.1186/s12877-022-02984-x (2022).Article 

    Google Scholar 
    Miguel, E. D. S. et al. Association between food insecurity and cardiometabolic risk in adults and the elderly: A systematic review. J. Glob. Health 10(2), 020402. https://doi.org/10.7189/jogh.10.020402 (2020).Article 

    Google Scholar 
    Liu, Y. & Eicher-Miller, H. A. Food insecurity and cardiovascular disease risk. Curr. Atheroscler. Rep. 23(6), 24. https://doi.org/10.1007/s11883-021-00923-6 (2021).Article 
    CAS 

    Google Scholar 
    Beltrán, S. et al. Food insecurity and hypertension: A systematic review and meta-analysis. PLoS One 15(11), e0241628. https://doi.org/10.1371/journal.pone.0241628 (2020).Article 
    CAS 

    Google Scholar 
    Vaccaro, J. A. & Huffman, F. G. Sex and race/ethnic disparities in food security and chronic diseases in U.S. older adults. Gerontol. Geriatr. Med. 3, 2333721417718344. https://doi.org/10.1177/2333721417718344 (2017).Article 

    Google Scholar 
    Abdurahman, A. A., Chaka, E. E., Nedjat, S., Dorosty, A. R. & Majdzadeh, R. The association of household food insecurity with the risk of type 2 diabetes mellitus in adults: A systematic review and meta-analysis. Eur. J. Nutr. 58(4), 1341–1350. https://doi.org/10.1007/s00394-018-1705-2 (2019).Article 

    Google Scholar 
    Muhammad, T., Saravanakumar, P., Sharma, A., Srivastava, S. & Irshad, C. V. Association of food insecurity with physical frailty among older adults: Study based on LASI, 2017–18. Arch. Gerontol. Geriatr. 103, 104762. https://doi.org/10.1016/j.archger.2022.104762 (2022).Article 
    CAS 

    Google Scholar 
    Venci, B. J. & Lee, S. Y. Functional limitation and chronic diseases are associated with food insecurity among U.S. adults. Ann. Epidemiol. 28(3), 182–188. https://doi.org/10.1016/j.annepidem.2018.01.005 (2018).Article 

    Google Scholar 
    Kim-Mozeleski, J. E. & Pandey, R. The intersection of food insecurity and tobacco use: A scoping review. Health Promot. Pract. 21(1_suppl), 124S-138S. https://doi.org/10.1177/1524839919874054 (2020).Article 

    Google Scholar 
    Mendy, V. L. et al. Food insecurity and cardiovascular disease risk factors among mississippi adults. Int. J. Environ. Res. Public Health 15(9), 2016. https://doi.org/10.3390/ijerph15092016 (2018).Article 

    Google Scholar 
    Bergmans, R. S., Coughlin, L., Wilson, T. & Malecki, K. Cross-sectional associations of food insecurity with smoking cigarettes and heavy alcohol use in a population-based sample of adults. Drug Alcohol Depend. 205, 107646. https://doi.org/10.1016/j.drugalcdep.2019.107646 (2019).Article 

    Google Scholar 
    International Institute for Population Sciences (IIPS), NPHCE, MoHFW, Harvard T. H. Chan School of Public Health (HSPH) and the University of Southern California (USC). Longitudinal Ageing Study in India (LASI) Wave 1, 2017–18, India Report, International Institute for Population Sciences, Mumbai, 2020.Srivastava, S., Muhammad, T., Paul, R. & Thomas, A. R. Multivariate decomposition analysis of sex differences in functional difficulty among older adults based on Longitudinal Ageing Study in India, 2017–2018. BMJ Open 12(4), e054661. https://doi.org/10.1136/bmjopen-2021-054661 (2022).Article 

    Google Scholar 
    Schnittker, J. & Bacak, V. The increasing predictive validity of self-rated health. PLoS One 9(1), e84933. https://doi.org/10.1371/journal.pone.0084933 (2014).Article 
    ADS 
    CAS 

    Google Scholar 
    Cheung, F. & Lucas, R. E. Assessing the validity of single-item life satisfaction measures: Results from three large samples. Qual. Life Res. 23(10), 2809–2818. https://doi.org/10.1007/s11136-014-0726-4 (2014).Article 

    Google Scholar 
    Diener, E., Lucas, R. E. & Oishi, S. Advances and open questions in the science of subjective well-being. Collabra Psychol. 4(1), 15. https://doi.org/10.1525/collabra.115 (2018).Article 

    Google Scholar 
    Lee, J. & Smith, J. P. Regional disparities in adult height, educational attainment and gender difference in late- life cognition: Findings from the Longitudinal Aging Study in India (LASI). J. Econ. Ageing 4, 26–34. https://doi.org/10.1016/j.jeoa.2014.02.002 (2014).Article 

    Google Scholar 
    Lee, J., Shih, R. A., Feeney, K. C. & Langa, K. M. Cognitive Health of Older Indians: Individual and Geographic Determinants of Female Disadvantage, WR-889 (RAND Corporation, 2011).Book 

    Google Scholar 
    Ganguli, M. et al. A Hindi version of the MMSE: The development of a cognitive screening instrument for a largely illiterate rural population in India. Int. Psychogeriatr. 10, 367–377 (1995).
    Google Scholar 
    Tiwari, S. C., Tripathi, R. K. & Kumar, A. Applicability of the Mini-mental State Examination (MMSE) and the Hindi Mental State Examination (HMSE) to the urban elderly in India: A pilot study. Int. Psychogeriatr. 21(1), 123–128. https://doi.org/10.1017/S1041610208007916 (2009).Article 
    CAS 

    Google Scholar 
    Mathuranath, P. S. et al. Mini mental state examination and the Addenbrooke’s cognitive examination: Effect of education and norms for a multicultural population. Neurol. India 55(2), 106–110. https://doi.org/10.4103/0028-3886.32779 (2007).Article 
    CAS 

    Google Scholar 
    Jenkins, C. D., Stanton, B. A., Niemcryk, S. J. & Rose, R. M. A scale for the estimation of sleep problems in clinical research. J. Clin. Epidemiol. 41(4), 313–321. https://doi.org/10.1016/0895-4356(88)90138-2 (1988).Article 
    CAS 

    Google Scholar 
    Cho, E. & Chen, T. Y. The bidirectional relationships between effort-reward imbalance and sleep problems among older workers. Sleep Health 6(3), 299–305. https://doi.org/10.1016/j.sleh.2020.01.008 (2020).Article 

    Google Scholar 
    Fabbri, M. et al. Measuring subjective sleep quality: A review. Int. J. Environ. Res. Public Health 18(3), 1082. https://doi.org/10.3390/ijerph18031082 (2021).Article 

    Google Scholar 
    Andresen, E. M., Malmgren, J. A., Carter, W. B. & Patrick, D. L. Screening for depression in well older adults: Evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Am. J. Prev. Med. 10(2), 77–84 (1994).Article 
    CAS 

    Google Scholar 
    Kumar, S., Nakulan, A., Thoppil, S. P., Parassery, R. P. & Kunnukattil, S. S. Screening for depression among community-dwelling elders: Usefulness of the center for epidemiologic studies depression scale. Indian J. Psychol. Med. 38(5), 483–485. https://doi.org/10.4103/0253-7176.191380 (2016).Article 

    Google Scholar 
    Chokkanathan, S. & Mohanty, J. Factor structure of the CES-D scale among older adults in Chennai India. Aging Ment. Health 17, 517–525 (2013).Article 

    Google Scholar 
    Kessler, R. C., Andrews, A., Mroczek, D., Ustun, B. & Wittchen, H. U. The World Health Organization composite international diagnostic interview short-form (CIDI-SF). Int. J. Methods Psychiatr. Res. 7, 171–185 (1998).Article 

    Google Scholar 
    Steffick D. Documentation of affective functioning measures in the health and retirement study, 2000. http://hrsonline.isr.umich.edu/sitedocs/userg/dr-005.pdf (Accessed 2 January 2021).Trainor, K., Mallett, J. & Rushe, T. Age related differences in mental health scale scores and depression diagnosis: Adult responses to the CIDI-SF and MHI-5. J. Affect. Disord. 151(2), 639–645 (2013).Article 

    Google Scholar 
    Wen, C. P. et al. Are Asians at greater mortality risks for being overweight than Caucasians? Redefining obesity for Asians. Public Health Nutr. 12(4), 497–506. https://doi.org/10.1017/S1368980008002802 (2009).Article 

    Google Scholar 
    Dhawan, D. & Sharma, S. Abdominal Obesity, adipokines and non-communicable diseases. J. Steroid Biochem. Mol. Biol. 203, 105737. https://doi.org/10.1016/j.jsbmb.2020.105737 (2020).Article 
    CAS 

    Google Scholar 
    Rose, G. A. The diagnosis of ischaemic heart pain and intermittent claudication in field surveys. Bull. World Health Organ. 27, 645–658 (1962).CAS 

    Google Scholar 
    Achterberg, S. et al. Prognostic value of the Rose questionnaire: A validation with future coronary events in the SMART study. Eur. J. Prev. Cardiol. 19(1), 5–14. https://doi.org/10.1177/1741826710391117 (2012).Article 
    CAS 

    Google Scholar 
    Rahman, M. A. et al. Rose Angina questionnaire: Validation with cardiologists’ diagnoses to detect coronary heart disease in Bangladesh. Indian Heart J. 65(1), 30–39. https://doi.org/10.1016/j.ihj.2012.09.008 (2013).Article 

    Google Scholar 
    Chobanian, A. V. et al. Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 42(6), 1206–52. https://doi.org/10.1161/01.HYP.0000107251.49515.c2 (2003).Article 
    CAS 

    Google Scholar 
    Katz, S., Ford, A. B., Moskowitz, R. W., Jackson, B. A. & Jaffe, M. W. Studies of illness in the aged. The index of adl: A standardized measure of biological and psychosocial function. JAMA 185, 914–9. https://doi.org/10.1001/jama.1963.03060120024016 (1963).Article 
    CAS 

    Google Scholar 
    Lawton, M. P. & Brody, E. M. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 9(3), 179–186 (1969).Article 
    CAS 

    Google Scholar 
    Singh, S., Multani, S. & Verma, N. Development and validation of geriatric assessment tools: A preliminary report from Indian population. JESP 3(2), 103–110 (2007).
    Google Scholar 
    Blumberg, S. J., Bialostosky, K., Hamilton, W. L. & Briefel, R. R. The effectiveness of a short form of the household food security scale. Am. J. Public Health 89(8), 1231–1234. https://doi.org/10.2105/ajph.89.8.1231 (1999).Article 
    CAS 

    Google Scholar 
    Lee, J., Shih, R.A., Feeney, K., Langa, K.M. Cognitive health of older indians individual and geographic determinants of female disadvantage. https://www.rand.org/content/dam/rand/pubs/working_papers/2011/RAND_WR889.pdf (Accessed 5 June 2021) (2011).Coates, J. et al. Commonalities in the experience of household food insecurity across cultures: What are measures missing?. J. Nutr. 136(5), 1438S-1448S. https://doi.org/10.1093/jn/136.5.1438S (2006).Article 
    CAS 

    Google Scholar 
    Sethi, V., Maitra, C., Avula, R. & Bhalla, S. Internal validity and reliability of experience-based household food insecurity scales in Indian settings. Agric. Food Secur. 6, 21. https://doi.org/10.1186/s40066-017-0099-3 (2017).Article 

    Google Scholar 
    Berkman, L. F., Sekher, T. V., Capistrant, B. & Zheng, Y. Social networks, family, and care giving among older adults in India. In Aging in Asia: Findings From New and Emerging Data Initiatives (eds Smith, J. P. & Majmundar, M.) 261–278 (The National Academic Press, 2012).
    Google Scholar 
    Marsland, A. L., Gianaros, P. J., Abramowitch, S. M., Manuck, S. B. & Hariri, A. R. Interleukin-6 covaries inversely with hippocampal grey matter volume in middle-aged adults. Biol. Psychiatry 64(6), 484–490. https://doi.org/10.1016/j.biopsych.2008.04.016 (2008).Article 
    CAS 

    Google Scholar 
    Bruening, M., Dinour, L. M. & Chavez, J. B. R. Food insecurity and emotional health in the USA: A systematic narrative review of longitudinal research. Public Health Nutr. 20(17), 3200–3208. https://doi.org/10.1017/S1368980017002221 (2017).Article 

    Google Scholar 
    Huddleston-Casas, C., Charnigo, R. & Simmons, L. A. Food insecurity and maternal depression in rural, low-income families: A longitudinal investigation. Public Health Nutr. 12(8), 1133–1140. https://doi.org/10.1017/S1368980008003650 (2009).Article 

    Google Scholar 
    Leung, C. W., Epel, E. S., Willett, W. C., Rimm, E. B. & Laraia, B. A. Household food insecurity is positively associated with depression among low-income supplemental nutrition assistance program participants and income-eligible nonparticipants. J. Nutr. 145(3), 622–627. https://doi.org/10.3945/jn.114.199414 (2015).Article 
    CAS 

    Google Scholar 
    Laraia, B. A. Food insecurity and chronic disease. Adv. Nutr. 4(2), 203–212. https://doi.org/10.3945/an.112.003277 (2013).Article 

    Google Scholar 
    Vercammen, K. A. et al. Food security and 10-year cardiovascular disease risk among U.S. adults. Am. J. Prev. Med. 56(5), 689–697. https://doi.org/10.1016/j.amepre.2018.11.016 (2019).Article 

    Google Scholar 
    Chakraborty R, Kundu J, Jana A. Factors associated with food insecurity among older adults in India: Impacts of functional impairments and chronic diseases. Ageing International, 1–24 (2022).
    Jackson, J. A., Branscum, A., Tang, A. & Smit, E. Food insecurity and physical functioning limitations among older U.S. adults. Prev. Med. Rep. 14, 100829. https://doi.org/10.1016/j.pmedr.2019.100829 (2019).Article 

    Google Scholar 
    Sreeramareddy, C. T. & Ramakrishnareddy, N. Association of adult tobacco use with household food access insecurity: Results from Nepal demographic and health survey, 2011. BMC Public Health 18(1), 48. https://doi.org/10.1186/s12889-017-4579-y (2017).Article 

    Google Scholar 
    Mayer, M., Gueorguieva, R., Ma, X. & White, M. A. Tobacco use increases risk of food insecurity: An analysis of continuous NHANES data from 1999 to 2014. Prev. Med. 126, 105765. https://doi.org/10.1016/j.ypmed.2019.105765 (2019).Article 

    Google Scholar 
    Kim-Mozeleski, J. E., Poudel, K. C. & Tsoh, J. Y. Examining reciprocal effects of cigarette smoking, food insecurity and psychological distress in the U.S.. J. Psychoact. Drugs 53(2), 177–184. https://doi.org/10.1080/02791072.2020.1845419 (2021).Article 

    Google Scholar 
    Dewing, S., Tomlinson, M., le Roux, I. M., Chopra, M. & Tsai, A. C. Food insecurity and its association with co-occurring postnatal depression, hazardous drinking, and suicidality among women in peri-urban South Africa. J. Affect. Disord. 150(2), 460–465. https://doi.org/10.1016/j.jad.2013.04.040 (2013).Article 

    Google Scholar  More

  • in

    Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems

    Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).Article 
    CAS 

    Google Scholar 
    DeSoto, L. et al. Low growth resilience to drought is related to future mortality risk in trees. Nat. Commun. 11, 545 (2020).Article 
    CAS 

    Google Scholar 
    Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6, 1–55 (2015).Article 

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

    Google Scholar 
    IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Gazol, A. et al. Forest resilience to drought varies across biomes. Glob. Change Biol. 24, 2143–2158 (2018).Wu, X. et al. Differentiating drought legacy effects on vegetation growth over the temperate Northern Hemisphere. Glob. Change Biol. 24, 504–516 (2018).Article 

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

    Google Scholar 
    Li, X. et al. Temporal trade-off between gymnosperm resistance and resilience increases forest sensitivity to extreme drought. Nat. Ecol. Evol. 4, 1075–1083 (2020).Article 

    Google Scholar 
    Kannenberg, S. A. et al. Drought legacies are dependent on water table depth, wood anatomy and drought timing across the eastern US. Ecol. Lett. 22, 119–127 (2019).Article 

    Google Scholar 
    Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).Article 

    Google Scholar 
    Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).Article 

    Google Scholar 
    Bastos, A. et al. Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity. Sci. Adv. 6, eaba2724 (2020).Article 
    CAS 

    Google Scholar 
    Buermann, W. et al. Widespread seasonal compensation effects of spring warming on northern plant productivity. Nature 562, 110–114 (2018).Article 
    CAS 

    Google Scholar 
    Lian, X. et al. Seasonal biological carryover dominates northern vegetation growth. Nat. Commun. 12, 983 (2021).Myneni, R. B. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).Article 
    CAS 

    Google Scholar 
    Jeong, S. J. et al. Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests. Remote Sens. Environ. 190, 178–187 (2017).Article 

    Google Scholar 
    Zeng, Z. et al. Legacy effects of spring phenology on vegetation growth under preseason meteorological drought in the Northern Hemisphere. Agric. Meteorol. 310, 108630 (2021).Article 

    Google Scholar 
    Kelsey, K. C. et al. Winter snow and spring temperature have differential effects on vegetation phenology and productivity across Arctic plant communities. Glob. Change Biol. 27, 1572–1586 (2021).Article 

    Google Scholar 
    Wang, X. et al. Disentangling the mechanisms behind winter snow impact on vegetation activity in northern ecosystems. Glob. Change Biol. 24, 1651–1662 (2018).Article 

    Google Scholar 
    IPCC. Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).Article 

    Google Scholar 
    Magney, T. S. et al. Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence. Proc. Natl Acad. Sci. USA 116, 11640–11645 (2019).Article 
    CAS 

    Google Scholar 
    Zhang, Y. et al. Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl Acad. Sci. USA 117, 9216–9222 (2020).Article 
    CAS 

    Google Scholar 
    Liu, Y. Y. et al. Global long-term passive microwave satellite-based retrievals of vegetation optical depth. Geophys. Res. Lett. 38, L18402 (2011).Article 

    Google Scholar 
    Beguería, S. et al. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).Article 

    Google Scholar 
    Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).Article 
    CAS 

    Google Scholar 
    D’Andrea, E. et al. Unravelling resilience mechanisms in forests: role of non-structural carbohydrates in responding to extreme weather events. Tree Physiol. 41, 1808–1818 (2021).Article 

    Google Scholar 
    Yun, J. et al. Influence of winter precipitation on spring phenology in boreal forests. Glob. Change Biol. 24, 5176–5187 (2018).Article 

    Google Scholar 
    Xie, J. et al. Spring temperature and snow cover climatology drive the advanced springtime phenology (1991–2014) in the European Alps. J. Geophys. Res. Biogeosci. 126, e2020JG006150 (2021).Xie, J. et al. Altitude-dependent influence of snow cover on alpine land surface phenology. J. Geophys. Res. Biogeosci. 122, 1107–1122 (2017).Article 

    Google Scholar 
    Peng, S. et al. Change in winter snow depth and its impacts on vegetation in China. Glob. Change Biol. 16, 3004–3013 (2010).
    Google Scholar 
    Wu, X. et al. Uneven winter snow influence on tree growth across temperate China. Glob. Change Biol. 25, 144–154 (2019).Article 

    Google Scholar 
    Angert, A. et al. Drier summers cancel out the CO2 uptake enhancement induced by warmer springs. Proc. Natl Acad. Sci. USA 102, 10823–10827 (2005).Article 
    CAS 

    Google Scholar 
    Musselman, K. N. et al. Winter melt trends portend widespread declines in snow water resources. Nat. Clim. Change 11, 418–424 (2021).Article 

    Google Scholar 
    Kreyling, J. Winter climate change: a critical factor for temperate vegetation performance. Ecology 91, 1939–1948 (2010).Article 

    Google Scholar 
    Bose, A. K. et al. Growth and resilience responses of Scots pine to extreme droughts across Europe depend on predrought growth conditions. Glob. Change Biol. 26, 4521–4537 (2020).Article 

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

    Google Scholar 
    Klein, T. et al. Drought stress, growth and nonstructural carbohydrate dynamics of pine trees in a semi-arid forest. Tree Physiol. 34, 981–992 (2014).Article 
    CAS 

    Google Scholar 
    Kannenberg, S. A. & Phillips, R. P. Non-structural carbohydrate pools not linked to hydraulic strategies or carbon supply in tree saplings during severe drought and subsequent recovery. Tree Physiol. 40, 259–271 (2020).Article 
    CAS 

    Google Scholar 
    Karst, J. et al. Stress differentially causes roots of tree seedlings to exude carbon. Tree Physiol. 37, 154–164 (2017).CAS 

    Google Scholar 
    Chitra-Tarak, R. et al. Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. New Phytol. 231, 1798–1813 (2021).Article 

    Google Scholar 
    Jiao, W. et al. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun. 12, 3777 (2021).Wu, X. et al. Higher temperature variability reduces temperature sensitivity of vegetation growth in Northern Hemisphere. Geophys. Res. Lett. 44, 6173–6181 (2017).Article 

    Google Scholar 
    Anderegg, W. R. L. et al. Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Change Biol. 25, 3793–3802 (2019).Article 

    Google Scholar 
    Martin-Benito, D. & Pederson, N. Convergence in drought stress, but a divergence of climatic drivers across a latitudinal gradient in a temperate broadleaf forest. J. Biogeogr. 42, 925–937 (2015).Article 

    Google Scholar 
    Tucker, C. J. et al. An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. Int. J. Remote Sens. 26, 4485–4498 (2005).Article 

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

    Google Scholar 
    Zhang, W. et al. Divergent response of vegetation growth to soil water availability in dry and wet periods over Central Asia. J. Geophys. Res. Biogeosci. 126, e2020JG005912 (2021).Article 

    Google Scholar 
    Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric. For. Meteorol. 169, 156–173 (2013).Article 

    Google Scholar 
    Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).Article 

    Google Scholar 
    Liang, W. et al. Analysis of spatial and temporal patterns of net primary production and their climate controls in China from 1982 to 2010. Agric. For. Meteorol. 204, 22–36 (2015).Article 

    Google Scholar 
    Zhang, Y. et al. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).Article 
    CAS 

    Google Scholar 
    Jones, M. O. et al. Satellite passive microwave remote sensing for monitoring global land surface phenology. Remote Sens. Environ. 115, 1102–1114 (2011).Article 

    Google Scholar 
    Konings, A. G. et al. Interannual variations of vegetation optical depth are due to both water stress and biomass changes. Geophys. Res. Lett. 48, e2021GL095267 (2021).Article 

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

    Google Scholar 
    Harris, I. et al. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).Article 

    Google Scholar 
    Barichivich, J. et al. Temperature and snow-mediated moisture controls of summer photosynthetic activity in northern terrestrial ecosystems between 1982 and 2011. Remote Sens. 6, 1390–1431 (2014).Article 

    Google Scholar 
    Vicente-Serrano, S. M., Begueria, S. & Lopez-Moreno, J. I. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Clim. 23, 1696–1718 (2010).Article 

    Google Scholar 
    Wieder, W. R. et al. Regridded Harmonized World Soil Database v1.2 (ORNL DAAC, 2014); https://doi.org/10.3334/ORNLDAAC/1247Kottek, M. et al. World map of the Koppen–Geiger climate classification updated. Meteorol. Z. 15, 259–263 (2006).Article 

    Google Scholar 
    Jakubauskas, M. E., Legates, D. R. & Kastens, J. H. Harmonic analysis of time-series AVHRR NDVI data. Photogramm. Eng. Remote Sens. 67, 461–470 (2001).
    Google Scholar 
    Liu, Q. et al. Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China. Glob. Change Biol. 22, 644–655 (2016).Article 
    CAS 

    Google Scholar 
    Fu, Y. H. et al. Recent spring phenology shifts in western Central Europe based on multiscale observations. Glob. Ecol. Biogeogr. 23, 1255–1263 (2014).Article 

    Google Scholar 
    Jiang, P. et al. Enhanced growth after extreme wetness compensates for post-drought carbon loss in dry forests. Nat. Commun. 10, 195 (2019).Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).Pham, L. T. H. & Brabyn, L. Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms. ISPRS J. Photogramm. Remote Sens. 128, 86–97 (2017).Article 

    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

    Google Scholar 
    Li, Y. Code for ‘Widespread spring phenology effects on drought recovery of Northern Hemisphere ecosystems’. GitHub https://github.com/leeyang1991/phenology-effects-on-drought-recovery (2022). More

  • in

    Spring phenology alters vegetation drought recovery

    Mishra, A. K. & Singh, V. P. J. Hydrol. 391, 202–216 (2010).Article 

    Google Scholar 
    Jiao, W. et al. Nat. Commun. 12, 3777 (2021).Article 
    CAS 

    Google Scholar 
    Gampe, D. et al. Nat. Clim. Change 11, 772–779 (2021).Article 

    Google Scholar 
    IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Schwalm, C. R. et al. Nature 548, 202–205 (2017).Article 
    CAS 

    Google Scholar 
    Li, Y. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01584-2 (2023).Fourth National Climate Assessment: Volume II—Impacts, Risks, and Adaptation in the United States (US Global Change Research Program, 2018).Daryanto, S., Wang, L. & Jacinthe, P. A. PLoS ONE 11, e0156362 (2016).Article 

    Google Scholar 
    Jiao, W. et al. J. Geophys. Res. Biogeosci. 127, e2021JG006431 (2022).Augspurger, C. K. Oecologia 156, 281–286 (2008).Article 

    Google Scholar 
    Lian, X. et al. Nat. Commun. 12, 983 (2021).Article 
    CAS 

    Google Scholar 
    Buermann, W. et al. Nature 562, 110–114 (2018).Article 
    CAS 

    Google Scholar 
    Lian, X. et al. Sci. Adv. 6, eaax0255 (2020).Article 

    Google Scholar 
    Jiao, W., Wang, L. & McCabe, M. F. Rem. Sens. Environ. 256, 112313 (2021).Article 

    Google Scholar  More

  • in

    Human fingerprint on structural density of forests globally

    Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).Article 

    Google Scholar 
    Potapov, P. et al. The last frontiers of wilderness: tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv. https://doi.org/10.1126/sciadv.1600821 (2017).Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).Article 
    CAS 

    Google Scholar 
    Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).Article 
    CAS 

    Google Scholar 
    Grantham, H. S. et al. The emerging threat of extractives sector to intact forest landscapes. Front. For. Glob. Change https://doi.org/10.3389/ffgc.2021.692338 (2021).IPBES: Summary for Policymakers. In The Global Assessment Report on Biodiversity and Ecosystem Services (eds Díaz, S. et al.) (IPBES, 2019).Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change https://doi.org/10.1038/s41558-021-01026-5 (2021).Article 

    Google Scholar 
    Maxwell, S. L. et al. Degradation and forgone removals increase the carbon impact of intact forest loss by 626%. Sci. Adv. 5, eaax2546 (2019).Article 
    CAS 

    Google Scholar 
    Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).Article 
    CAS 

    Google Scholar 
    Venter, O. et al. Targeting global protected area expansion for imperiled biodiversity. PLoS Biol. 12, e1001891 (2014).Article 

    Google Scholar 
    Laurance, W. F. et al. Averting biodiversity collapse in tropical forest protected areas. Nature 489, 290–294 (2012).Article 
    CAS 

    Google Scholar 
    Coad, L. et al. Measuring impact of protected area management interventions: current and future use of the global database of protected area management effectiveness. Phil. Trans. R. Soc. B 370, 20140281 (2015).Article 

    Google Scholar 
    Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).Article 
    CAS 

    Google Scholar 
    Ehbrecht, M. et al. Global patterns and climatic controls of forest structural complexity. Nat. Commun. 12, 519 (2021).Article 
    CAS 

    Google Scholar 
    Zhang, J., Nielsen, S. E., Mao, L., Chen, S. & Svenning, J. C. Regional and historical factors supplement current climate in shaping global forest canopy height. J. Ecol. 104, 469–478 (2016).Article 

    Google Scholar 
    Ellis, E. C. et al. People have shaped most of terrestrial nature for at least 12,000 years. Proc. Natl Acad. Sci. USA 118, e2023483118 (2021).Article 
    CAS 

    Google Scholar 
    Knight, C. A. et al. Land management explains major trends in forest structure and composition over the last millennium in California’s Klamath Mountains. Proc. Natl Acad. Sci. USA 119, e2116264119 (2022).Article 
    CAS 

    Google Scholar 
    Stephens, L. et al. Archaeological assessment reveals Earth’s early transformation through land use. Science 365, 897–902 (2019).Article 
    CAS 

    Google Scholar 
    Asner, G. P., Llactayo, W., Tupayachi, R. & Luna, E. R. Elevated rates of gold mining in the Amazon revealed through high-resolution monitoring. Proc. Natl Acad. Sci. USA 110, 18454–18459 (2013).Article 
    CAS 

    Google Scholar 
    Hoang, N. T. & Kanemoto, K. Mapping the deforestation footprint of nations reveals growing threat to tropical forests. Nat. Ecol. Evol. 5, 845–853 (2021).Article 

    Google Scholar 
    Lim, C. L., Prescott, G. W., De Alban, J. D. T., Ziegler, A. D. & Webb, E. L. Untangling the proximate causes and underlying drivers of deforestation and forest degradation in Myanmar. Conserv. Biol. 31, 1362–1372 (2017).Article 

    Google Scholar 
    Sandel, B. & Svenning, J. C. Human impacts drive a global topographic signature in tree cover. Nat Commun. https://doi.org/10.1038/ncomms3474 (2013).Potapov, P. et al. Mapping the world’s intact forest landscapes by remote sensing. Ecol. Soc. 13, 51 (2008).Article 

    Google Scholar 
    Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl Acad. Sci. USA 116, 23209–23215 (2019).Article 
    CAS 

    Google Scholar 
    Yang, H. et al. A global assessment of the impact of individual protected areas on preventing forest loss. Sci. Total Environ. 777, 145995 (2021).Article 
    CAS 

    Google Scholar 
    Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).Article 
    CAS 

    Google Scholar 
    Clerici, N. et al. Deforestation in Colombian protected areas increased during post-conflict periods. Sci. Rep. 10, 4971 (2020).Article 
    CAS 

    Google Scholar 
    Heino, M. et al. Forest loss in protected areas and intact forest landscapes: a global analysis. PLoS ONE 10, e0138918 (2015).Article 

    Google Scholar 
    Leberger, R., Rosa, I. M. D., Guerra, C. A., Wolf, F. & Pereira, H. M. Global patterns of forest loss across IUCN categories of protected areas. Biol. Conserv. 241, 108299 (2020).Article 

    Google Scholar 
    Wade, C. M. et al. What is threatening forests in protected areas? A global assessment of deforestation in protected areas, 2001–2018. Forests 11, 539 (2020).Article 

    Google Scholar 
    Transforming Our World: The 2030 Agenda for Sustainable Development (UN DESA, 2016).Burleson, E. Paris Agreement and consensus to address climate challenge. ASIL Insight 20, 8 (2016).
    Google Scholar 
    Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 
    CAS 

    Google Scholar 
    Quegan, S. et al. The European Space Agency BIOMASS mission: measuring forest above-ground biomass from space. Remote Sens. Environ. 227, 44–60 (2019).Article 

    Google Scholar 
    Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2011JG001708 (2011).Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).Article 

    Google Scholar 
    Atkins, J. W., Fahey, R. T., Hardiman, B. S. & Gough, C. M. Forest canopy structural complexity and light absorption relationships at the subcontinental scale. J. Geophys. Res. Biogeosci. 123, 1387–1405 (2018).Article 

    Google Scholar 
    Scarth, P., Armston, J., Lucas, R. & Bunting, P. A structural classification of Australian vegetation using ICESat/GLAS, ALOS PALSAR, and Landsat sensor data. Remote Sens. 11, 147 (2019).Article 

    Google Scholar 
    Dubayah, R. et al. The global ecosystem dynamics investigation: high-resolution laser ranging of the Earth’s forests and topography. Sci. Remote Sens. 1, 100002 (2020).Article 

    Google Scholar 
    Lang, N. et al. Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sens. Environ. 268, 112760 (2022).Article 

    Google Scholar 
    Marselis, S. M., Keil, P., Chase, J. M. & Dubayah, R. The use of GEDI canopy structure for explaining variation in tree species richness in natural forests. Environ. Res. Lett. 17, 045003 (2022).Article 

    Google Scholar 
    MacArthur, R. H. & MacArthur, J. W. On bird species diversity. Ecology 42, 594–598 (1961).Article 

    Google Scholar 
    Walter, J. A., Stovall, A. E. L. & Atkins, J. W. Vegetation structural complexity and biodiversity in the Great Smoky Mountains. Ecosphere 12, e03390 (2021).Article 

    Google Scholar 
    Camps-Valls, G. et al. A unified vegetation index for quantifying the terrestrial biosphere. Sci. Adv. 7, eabc7447 (2021).Article 
    CAS 

    Google Scholar 
    Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S. & Kiesecker, J. Managing the middle: a shift in conservation priorities based on the global human modification gradient. Glob. Change Biol. 25, 811–826 (2019).Article 

    Google Scholar 
    Weiss, D. J. et al. A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336 (2018).Article 
    CAS 

    Google Scholar 
    Chazdon, R. L. et al. A policy‐driven knowledge agenda for global forest and landscape restoration. Conserv. Lett. 10, 125–132 (2017).Article 

    Google Scholar 
    Skidmore, A. K. et al. Priority list of biodiversity metrics to observe from space. Nat. Ecol. Evol. 5, 896–906 (2021).Article 

    Google Scholar 
    Schneider, F. D. et al. Mapping functional diversity from remotely sensed morphological and physiological forest traits. Nat. Commun. 8, 1441 (2017).Article 

    Google Scholar 
    Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).Article 
    CAS 

    Google Scholar 
    Ponta, N. et al. Drivers of transgression: what pushes people to enter protected areas. Biol. Conserv. 257, 109121 (2021).Article 

    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 
    Tollefson, J. Illegal mining in the Amazon hits record high amid Indigenous protests. Nature 598, 15–16 (2021).Article 
    CAS 

    Google Scholar 
    Thies, C., Rosoman, G., Cotter, J. & Meaden, S. Intact Forest Landscapes. Why It Is Crucial to Protect Them from Industrial Exploitation Technical Note Bd 5 (Greenpeace, 2011).Chazdon, R. L. Beyond deforestation: restoring forests and ecosystem services on degraded lands. Science 320, 1458–1460 (2008).Article 
    CAS 

    Google Scholar 
    Lindenmayer, D. B. et al. New policies for old trees: averting a global crisis in a keystone ecological structure. Conserv. Lett. 7, 61–69 (2014).Article 

    Google Scholar 
    Dave, R. et al. Second Bonn Challenge Progress Report: Application of the Barometer in 2018 (IUCN, 2018).Tang, H. & Armston, J. Algorithm Theoretical Basis Document (ATBD) for GEDI L2B Footprint Canopy Cover and Vertical Profile Metrics (Goddard Space Flight Center, 2019).Adam, M., Urbazaev, M., Dubois, C. & Schmullius, C. Accuracy assessment of GEDI terrain elevation and canopy height estimates in European temperate forests: influence of environmental and acquisition parameters. Remote Sens. 12, 3948 (2020).Article 

    Google Scholar 
    Dorado-Roda, I. et al. Assessing the accuracy of GEDI data for canopy height and aboveground biomass estimates in Mediterranean forests. Remote Sens. 13, 2279 (2021).Article 

    Google Scholar 
    Duncanson, L. et al. Aboveground biomass density models for NASA’s Global Ecosystem Dynamics Investigation (GEDI) lidar mission. Remote Sens. Environ. 270, 112845 (2022).Article 

    Google Scholar 
    Hofton, M., Blair, J. B., Story, S. & Yi, D. Algorithm Theoretical Basis Document (ATBD) (NASA, 2020).Dubayah, R. et al. GEDI L3 Gridded Land Surface Metrics v.2 (ORNL DAAC, 2021).Roy, D. P., Kashongwe, H. B. & Armston, J. The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring. Sci. Remote Sens. 4, 100024 (2021).Article 

    Google Scholar 
    Potapov, P., Hansen, M. C., Stehman, S. V., Loveland, T. R. & Pittman, K. Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss. Remote Sens. Environ. 112, 3708–3719 (2008).Article 

    Google Scholar 
    Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 

    Google Scholar 
    Silva, C. A. et al. rGEDI: NASA’s global ecosystem ynamics investigation (GEDI) data visualization and processing. R package version 0.1.2. (2020).The R Project for Statistical Computing (The R Foundation, 2014); https://www.R-project.org/Fischer, B., Smith, M., Pau, G., Morgan, M. & van Twisk, D. rhdf5: R interface to HDF5. R package version 2.40.0 (2022).Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).Article 

    Google Scholar 
    Giglio, L., Loboda, T., Roy, D. P., Quayle, B. & Justice, C. O. An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sens. Environ. 113, 408–420 (2009).Article 

    Google Scholar 
    Hengl, T. & Wheeler, I. Soil organic carbon content in x 5 g/kg at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution. Zenodo https://doi.org/10.5281/zenodo.1475458 (2018).Farr, T. The shuttle radar topography mission. Rev. Geophys. https://doi.org/10.1029/2005RG000183 (2007).James, G., Witten, D., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning Vol. 112 (Springer, 2013).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Bivand, R. et al. Package ‘spdep’: spatial dependence: weighting schemes, statistics version 1.2-7 (The Comprehensive R Archive Network, 2015).Bivand, R., Yu, D., Nakaya, T., Garcia-Lopez, M.-A. & Bivand, M. R. Package ‘spgwr’: geographically eighted regression. R package version 0.6-35 (2020).Fotheringham, A. S., Brunsdon, C. & Charlton, M. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (Wiley, 2003). More

  • in

    Evolutionary diversification of methanotrophic ANME-1 archaea and their expansive virome

    Sampling and incubationFour rock samples were collected from the 3.7 km-deep Auka vent field in the Southern Pescadero Basin (23.956094N, 108.86192W)20,23. Sample NA091.008 was collected in 2017 on cruise NA091 with the Eexploration vessle Nautilus and incubated as described previously34. Samples 12,019 (S0200-R1), 11,719 (S0193-R2) and 11,868 (S0197-PC1), the latter representing a lithified nodule recovered from a sediment push core, were collected with Remotely operated vehicle SuBastian and Research vessel Falkor on cruise FK181031 in November 2018. These samples were processed shipboard and stored under anoxic conditions at 4 °C for subsequent incubation in the laboratory. In the laboratory, rock samples 12,019 and 11,719 were broken into smaller pieces under sterile conditions, immersed in N2-sparged sterilized artificial sea water and incubated under anoxic conditions with methane, as described previously for NA091.008 (ref. 34). Additional sampling information can be found in Supplementary Table 1. Mineralogical analysis by X-ray Powder Diffraction (XRD) identified barite in several of these samples, collected from two locations in the Auka vent field, including on the western side of the Matterhorn vent (11,719, NA091.008), and one oil-saturated sample (12,019) recovered from the sedimented flanks from the southern side of Z vent. Our analysis also includes metagenomic data from two sediment cores from the Auka vent field (DR750-PC67 and DR750-PC80) collected in April 2015 with the ROV Doc Ricketts and R/V Western Flyer (MBARI2015), previously published (ref. 23).Fluorescence in situ hybridizationSamples were fixed shipboard using freshly prepared paraformaldehyde (2 vol% in 3× Phosphate Buffer Solution (PBS), EMS15713) at 4 °C overnight, rinsed twice using 3× PBS, and stored in ethanol (50% in 1× PBS) at −20 °C until processing. Small pieces ( More

  • in

    Author Correction: Measuring the world’s cropland area

    Authors and AffiliationsStatistics Division, Food and Agriculture Organization of the United Nations, Rome, ItalyFrancesco N. Tubiello, Giulia Conchedda, Leon Casse & Giorgia De SantisDigitization and Informatics Division, Food and Agriculture Organization of the United Nations, Rome, ItalyHao Pengyu & Chen ZhongxinInternational Institute for Applied Systems Analysis, Laxenburg, AustriaSteffen FritzGeospatial Unit, Land and Water Division, Food and Agriculture Organization of the United Nations, Rome, ItalyDouglas MuchoneyAuthorsFrancesco N. TubielloGiulia ConcheddaLeon CasseHao PengyuChen ZhongxinGiorgia De SantisSteffen FritzDouglas MuchoneyCorresponding authorCorrespondence to
    Francesco N. Tubiello. More

  • in

    A new technique to study nutrient flow in host-parasite systems by carbon stable isotope analysis of amino acids and glucose

    Kuris, A. M. et al. Ecosystem energetic implications of parasite and free-living biomass in three estuaries. Nature 454, 515–518. https://doi.org/10.1038/nature06970 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Dobson, A., Lafferty, K. D., Kuris, A. M., Hechinger, R. F. & Jetz, W. Homage to Linnaeus: How many parasites? How many hosts?. Proc. Natl. Acad. Sci. 105, 11482–11489 (2008).Article 
    ADS 
    CAS 

    Google Scholar 
    Lafferty, K. D., Dobson, A. & Kuris, A. M. Parasites dominate food web links. Proc. Natl. Acad. Sci. 103, 11211–11216 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Amundsen, P. A. et al. Food web topology and parasites in the pelagic zone of a subarctic lake. J. Anim. Ecol. 78, 563–572. https://doi.org/10.1111/j.1365-2656.2008.01518.x (2009).Article 

    Google Scholar 
    Thompson, R. M., Mouritsen, K. N. & Poulin, R. Importance of parasites and their life cycle characteristics in determining the structure of a large marine food web. J. Anim. Ecol. 74, 77–85. https://doi.org/10.1111/j.1365-2656.2004.00899.x (2005).Article 

    Google Scholar 
    Thieltges, D. W. et al. Parasites as prey in aquatic food webs: Implications for predator infection and parasite transmission. Oikos 122, 1473–1482. https://doi.org/10.1111/j.1600-0706.2013.00243.x (2013).Article 

    Google Scholar 
    Sato, T. et al. Nematomorph parasites drive energy flow through a riparian ecosystem. Ecology 92, 201–207 (2011).Article 

    Google Scholar 
    Lafferty, K. D. & Kuris, A. M. Trophic strategies, animal diversity and body size. Trends Ecol. Evol. 17, 507–513 (2002).Article 

    Google Scholar 
    Goedknegt, M. A. et al. Trophic relationship between the invasive parasitic copepod Mytilicola orientalis and its native blue mussel (Mytilus edulis) host. Parasitology 145, 814–821. https://doi.org/10.1017/S0031182017001779 (2018).Article 
    CAS 

    Google Scholar 
    Timi, J. T. & Poulin, R. Why ignoring parasites in fish ecology is a mistake. Int. J. Parasitol. 50, 755–761. https://doi.org/10.1016/j.ijpara.2020.04.007 (2020).Article 

    Google Scholar 
    Barber, I. & Svensson, P. A. Effects of experimental Schistocephalus solidus infections on growth, morphology and sexual development of female three-spined sticklebacks Gasterosteus aculeatus. Parasitology 126, 359–367. https://doi.org/10.1017/s0031182002002925 (2003).Article 
    CAS 

    Google Scholar 
    Scharsack, J. P., Koch, K. & Hammerschmidt, K. Who is in control of the stickleback immune system: Interactions between Schistocephalus solidus and its specific vertebrate host. Proc. Biol. Sci. 274, 3151–3158. https://doi.org/10.1098/rspb.2007.1148 (2007).Article 

    Google Scholar 
    Hopkins, C. A. Studies on cestode metabolism. I. glycogen metabolism in Schistocephalus solidus In vivo. J. Parasitol. 36, 384–390 (1950).Article 
    CAS 

    Google Scholar 
    Körting, W. & Barrett, J. Carbohydrate catabolism in the plerocercoids of Schistocephalus solidus (Cestoda: Pseudophyllidea). Int. J. Parasitol. 7, 411–417 (1977).Article 

    Google Scholar 
    Hebert, F. O., Grambauer, S., Barber, I., Landry, C. R. & Aubin-Horth, N. Major host transitions are modulated through transcriptome-wide reprogramming events in Schistocephalus solidus, a threespine stickleback parasite. Mol. Ecol. 26, 1118–1130. https://doi.org/10.1111/mec.13970 (2017).Article 
    CAS 

    Google Scholar 
    Berger, C. S. et al. The parasite Schistocephalus solidus secretes proteins with putative host manipulation functions. Parasites Vectors 14, 436. https://doi.org/10.1186/s13071-021-04933-w (2021).Article 
    CAS 

    Google Scholar 
    Jolles, J. W., Mazue, G. P. F., Davidson, J., Behrmann-Godel, J. & Couzin, I. D. Schistocephalus parasite infection alters sticklebacks’ movement ability and thereby shapes social interactions. Sci. Rep. 10, 12282. https://doi.org/10.1038/s41598-020-69057-0 (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Scharsack, J. P. et al. Climate change facilitates a parasite’s host exploitation via temperature-mediated immunometabolic processes. Glob. Change Biol. 27, 94–107. https://doi.org/10.1111/gcb.15402 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Kochneva, A., Borvinskaya, E. & Smirnov, L. Zone of interaction between the parasite and the host: Protein profile of the body cavity fluid of Gasterosteus aculeatus L. infected with the Cestode Schistocephalus solidus (Muller, 1776). Acta Parasitol. 66, 569–583. https://doi.org/10.1007/s11686-020-00318-8 (2021).Article 
    CAS 

    Google Scholar 
    Barber, I. & Scharsack, J. P. The three-spined stickleback-Schistocephalus solidus system: An experimental model for investigating host-parasite interactions in fish. Parasitology 137, 411–424. https://doi.org/10.1017/S0031182009991466 (2010).Article 
    CAS 

    Google Scholar 
    Weber, J. N., Steinel, N. C., Shim, K. C. & Bolnick, D. I. Recent evolution of extreme cestode growth suppression by a vertebrate host. Proc. Natl. Acad. Sci. U. S. A. 114, 6575–6580. https://doi.org/10.1073/pnas.1620095114 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Sabadel, A. J. M., Stumbo, A. D. & MacLeod, C. D. Stable-isotope analysis: A neglected tool for placing parasites in food webs. J. Helminthol. 93, 1–7. https://doi.org/10.1017/S0022149X17001201 (2019).Article 
    CAS 

    Google Scholar 
    Hayes, J. M. Factors controlling 13C contents of sedimentary organic compounds: Principles and evidence. Mar. Geol. 113, 111–125 (1993).Article 
    ADS 
    CAS 

    Google Scholar 
    France, R. L. Differentiation between littoral and pelagic food webs in lakes using stable carbon isotopes. Limnol. Oceanogr. 40, 1310–1313 (1995).Article 
    ADS 

    Google Scholar 
    Post, D. M. Using stable isotopes to estimate trophic position: Models, methods and assumptions. Ecology 83, 703–718 (2002).Article 

    Google Scholar 
    O’Connell, T. C. ‘Trophic’ and ‘source’ amino acids in trophic estimation: A likely metabolic explanation. Oecologia 184, 317–326. https://doi.org/10.1007/s00442-017-3881-9 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    McMahon, K. W., Fogel, M. L., Elsdon, T. S. & Thorrold, S. R. Carbon isotope fractionation of amino acids in fish muscle reflects biosynthesis and isotopic routing from dietary protein. J. Anim. Ecol. 79, 1132–1141. https://doi.org/10.1111/j.1365-2656.2010.01722.x (2010).Article 

    Google Scholar 
    Liu, H.-z, Luo, L. & Cai, D.-l. Stable carbon isotopic analysis of amino acids in a simplified food chain consisting of the green alga Chlorella spp., the calanoid copepod Calanus sinicus, and the Japanese anchovy (Engraulis japonicus). Can. J. Zool. 96, 23–30. https://doi.org/10.1139/cjz-2016-0170 (2018).Article 
    CAS 

    Google Scholar 
    Wang, Y. V. et al. Know your fish: A novel compound-specific isotope approach for tracing wild and farmed salmon. Food Chem. 256, 380–389. https://doi.org/10.1016/j.foodchem.2018.02.095 (2018).Article 
    CAS 

    Google Scholar 
    Whiteman, J. P., Kim, S. L., McMahon, K. W., Koch, P. L. & Newsome, S. D. Amino acid isotope discrimination factors for a carnivore: Physiological insights from leopard sharks and their diet. Oecologia 188, 977–989. https://doi.org/10.1007/s00442-018-4276-2 (2018).Article 
    ADS 

    Google Scholar 
    Rogers, M., Bare, R., Gray, A., Scott-Moelder, T. & Heintz, R. Assessment of two feeds on survival, proximate composition, and amino acid carbon isotope discrimination in hatchery-reared Chinook salmon. Fish. Res. 219, 105303. https://doi.org/10.1016/j.fishres.2019.06.001 (2019).Article 

    Google Scholar 
    Choy, K., Smith, C. I., Fuller, B. T. & Richards, M. P. Investigation of amino acid δ13C signatures in bone collagen to reconstruct human palaeodiets using liquid chromatography–isotope ratio mass spectrometry. Geochim. Cosmochim. Acta 74, 6093–6111. https://doi.org/10.1016/j.gca.2010.07.025 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Newsome, S. D., Clementz, M. T. & Koch, P. L. Using stable isotope biogeochemistry to study marine mammal ecology. Mar. Mamm. Sci. 26, 509–572. https://doi.org/10.1111/j.1748-7692.2009.00354.x (2010).Article 
    CAS 

    Google Scholar 
    Raghavan, M., McCullagh, J. S., Lynnerup, N. & Hedges, R. E. Amino acid delta13C analysis of hair proteins and bone collagen using liquid chromatography/isotope ratio mass spectrometry: Paleodietary implications from intra-individual comparisons. Rapid Commun. Mass Spectrom. 24, 541–548. https://doi.org/10.1002/rcm.4398 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Honch, N. V., McCullagh, J. S. & Hedges, R. E. Variation of bone collagen amino acid delta13C values in archaeological humans and fauna with different dietary regimes: Developing frameworks of dietary discrimination. Am. J. Phys. Anthropol. 148, 495–511. https://doi.org/10.1002/ajpa.22065 (2012).Article 

    Google Scholar 
    Mora, A. et al. High-resolution palaeodietary reconstruction: Amino acid δ 13 C analysis of keratin from single hairs of mummified human individuals. Quatern. Int. 436, 96–113. https://doi.org/10.1016/j.quaint.2016.10.018 (2017).Article 

    Google Scholar 
    Matos, M. P. V., Konstantynova, K. I., Mohr, R. M. & Jackson, G. P. Analysis of the (13)C isotope ratios of amino acids in the larvae, pupae and adult stages of Calliphora vicina blow flies and their carrion food sources. Anal. Bioanal. Chem. 410, 7943–7954. https://doi.org/10.1007/s00216-018-1416-9 (2018).Article 
    CAS 

    Google Scholar 
    Bontempo, L. et al. Bulk and compound-specific stable isotope ratio analysis for authenticity testing of organically grown tomatoes. Food Chem. 318, 126426. https://doi.org/10.1016/j.foodchem.2020.126426 (2020).Article 
    CAS 

    Google Scholar 
    Gaye-Siessegger, J., McCullagh, J. S. & Focken, U. The effect of dietary amino acid abundance and isotopic composition on the growth rate, metabolism and tissue delta13C of rainbow trout. Br. J. Nutr. 105, 1764–1771. https://doi.org/10.1017/S0007114510005696 (2011).Article 
    CAS 

    Google Scholar 
    Newsome, S. D., Fogel, M. L., Kelly, L. & del Rio, C. M. Contributions of direct incorporation from diet and microbial amino acids to protein synthesis in Nile tilapia. Funct. Ecol. 25, 1051–1062. https://doi.org/10.1111/j.1365-2435.2011.01866.x (2011).Article 

    Google Scholar 
    Larsen, T. et al. Tracing carbon sources through aquatic and terrestrial food webs using amino acid stable isotope fingerprinting. PLoS ONE 8, e73441. https://doi.org/10.1371/journal.pone.0073441 (2013).Article 
    ADS 
    CAS 

    Google Scholar 
    Thieltges, D. W., Goedknegt, M. A., O’Dwyer, K., Senior, A. M. & Kamiya, T. Parasites and stable isotopes: A comparative analysis of isotopic discrimination in parasitic trophic interactions. Oikos 128, 1329–1339. https://doi.org/10.1111/oik.06086 (2019).Article 

    Google Scholar 
    Layman, C. A. et al. Applying stable isotopes to examine food-web structure: An overview of analytical tools. Biol. Rev. Camb. Philos. Soc. 87, 545–562. https://doi.org/10.1111/j.1469-185X.2011.00208.x (2011).Article 

    Google Scholar 
    Wang, Y. V., Wan, A. H. L., Krogdahl, A., Johnson, M. & Larsen, T. (13)C values of glycolytic amino acids as indicators of carbohydrate utilization in carnivorous fish. PeerJ 7, e7701. https://doi.org/10.7717/peerj.7701 (2019).Article 

    Google Scholar 
    Hesse, T. et al. Insights into amino acid fractionation and incorporation by compound-specific carbon isotope analysis of three-spined sticklebacks. Sci. Rep. 12, 11690. https://doi.org/10.1038/s41598-022-15704-7 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Riekenberg, P. M. et al. Stable nitrogen isotope analysis of amino acids as a new tool to clarify complex parasite–host interactions within food webs. Oikos 130, 1650–1664. https://doi.org/10.1111/oik.08450 (2021).Article 
    CAS 

    Google Scholar 
    Carleton, S. A. & Del Rio, C. M. Growth and catabolism in isotopic incorporation: A new formulation and experimental data. Funct. Ecol. 24, 805–812. https://doi.org/10.1111/j.1365-2435.2010.01700.x (2010).Article 

    Google Scholar 
    Perga, M. E. & Gerdeaux, D. ‘Are fish what they eat’ all year round?. Oecologia 144, 598–606. https://doi.org/10.1007/s00442-005-0069-5 (2005).Article 
    ADS 
    CAS 

    Google Scholar 
    Grey, J. Trophic fractionation and the effects of diet switch on the carbon stable isotopic ‘signatures’ of pelagic consumers. SIL Proc. 1922–2010(27), 3187–3191. https://doi.org/10.1080/03680770.1998.11898266 (2000).Article 

    Google Scholar 
    Danfaer, A. Nutrient metabolism and utilization in the liver. Livest. Prod. Sci. 39, 115–127 (1994).Article 

    Google Scholar 
    Read, C. P. & Simmons, J. E. Biochemistry and physiology of tapeworms. Physiol. Rev. 43, 263–305 (1963).Article 
    CAS 

    Google Scholar 
    Nachev, M. et al. Understanding trophic interactions in host-parasite associations using stable isotopes of carbon and nitrogen. Parasites Vectors 10, 1–9. https://doi.org/10.1186/s13071-017-2030-y (2017).Article 
    CAS 

    Google Scholar 
    Kanaya, G. et al. Application of stable isotopic analyses for fish host–parasite systems: An evaluation tool for parasite-mediated material flow in aquatic ecosystems. Aquat. Ecol. 53, 217–232. https://doi.org/10.1007/s10452-019-09684-6 (2019).Article 
    CAS 

    Google Scholar 
    Gilbert, B. M. et al. You are how you eat: differences in trophic position of two parasite species infecting a single host according to stable isotopes. Parasitol. Res. 119, 1393–1400. https://doi.org/10.1007/s00436-020-06619-1 (2020).Article 

    Google Scholar 
    Gilbert, B. M. et al. Stable isotope analysis spills the beans about spatial variance in trophic structure in a fish host—Parasite system from the Vaal River System, South Africa. Int. J. Parasitol. Parasites Wildl. 12, 134–141. https://doi.org/10.1016/j.ijppaw.2020.05.011 (2020).Article 

    Google Scholar 
    Felig, P. The glucose-alanine cycle. Metabolism 22, 179–207 (1973).Article 
    CAS 

    Google Scholar 
    Dale, R. A. Catabolism of threonine in mammals by coupling of L-threonine 3-dehydrogenase with 2-amino-3-oxobutyrate-CoA ligase. Biochem. Biophys. Acta. 544, 496–503 (1978).Article 
    CAS 

    Google Scholar 
    Jordan, P. M. & Akhtar, M. The mechanism of action of serine Transhydroxymethylase. Biochem. J. 116, 277–286 (1970).Article 
    CAS 

    Google Scholar 
    Linstead, D. J., Klein, R. A. & Cross, G. A. M. Threonine catabolism in Trypanosoma brucei. J. Gen. Microbiol. 101, 243–251 (1977).Article 
    CAS 

    Google Scholar 
    Hare, P. E., Fogel, M. L., Stafford, T. W. Jr., Mitchell, A. D. & Hoering, T. C. The isotopic composition of carbon and nitrogen in individual amino acids isolated from modern and fossil proteins. J. Archaeol. Sci. 18, 277–292 (1991).Article 

    Google Scholar 
    Petzke, K. J., Boeing, H., Klaus, S. & Metges, C. C. Carbon and nitrogen stable isotopic composition of hair protein and amino acids can be used as biomarkers for animal-derived dietary protein intake in humans. J. Nutr. 135, 1515–1520 (2005).Article 
    CAS 

    Google Scholar 
    McMahon, K. W., Polito, M. J., Abel, S., McCarthy, M. D. & Thorrold, S. R. Carbon and nitrogen isotope fractionation of amino acids in an avian marine predator, the gentoo penguin (Pygoscelis papua). Ecol. Evol. 5, 1278–1290. https://doi.org/10.1002/ece3.1437 (2015).Article 

    Google Scholar 
    Fuller, B. T. & Petzke, K. J. The dietary protein paradox and threonine (15) N-depletion: Pyridoxal-5’-phosphate enzyme activity as a mechanism for the delta (15) N trophic level effect. Rapid Commun. Mass Spectrom. 31, 705–718. https://doi.org/10.1002/rcm.7835 (2017).Article 
    ADS 
    CAS 

    Google Scholar 
    Bowyer, A. et al. Structure and function of the l-threonine dehydrogenase (TkTDH) from the hyperthermophilic archaeon Thermococcus kodakaraensis. J. Struct. Biol. 168, 294–304. https://doi.org/10.1016/j.jsb.2009.07.011 (2009).Article 
    CAS 

    Google Scholar 
    Kikuchi, G., Motokawa, Y., Yoshida, T. & Hiraga, K. Glycine cleavage system: Reaction mechanism, physiological significance and hyperglycinemia. Proc. Jpn. Acad. https://doi.org/10.2183/pjab/84.246 (2008).Article 

    Google Scholar 
    Locasale, J. W. Serine, glycine and one-carbon units: Cancer metabolism in full circle. Nat. Rev. Cancer 13, 572–583. https://doi.org/10.1038/nrc3557 (2013).Article 
    CAS 

    Google Scholar 
    Kalhan, S. C. & Hanson, R. W. Resurgence of serine: An often neglected but indispensable amino Acid. J. Biol. Chem. 287, 19786–19791. https://doi.org/10.1074/jbc.R112.357194 (2012).Article 
    CAS 

    Google Scholar 
    Larsen, T., Wang, Y. V. & Wan, A. H. L. Tracing the Trophic fate of aquafeed macronutrients with carbon isotope ratios of amino acids. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.813961 (2022).Article 

    Google Scholar 
    Sweeting, C. J., Polunin, N. V. & Jennings, S. Effects of chemical lipid extraction and arithmetic lipid correction on stable isotope ratios of fish tissues. Rapid Commun. Mass Spectrom. 20, 595–601. https://doi.org/10.1002/rcm.2347 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Tarallo, A., Bailey, C., Agnisola, C. & D’Onofrio, G. A theoretical evaluation of the respiration rate partition in the Gasterosteus aculeatus-Schistocephalus solidus host-parasite system. Int. Aquat. Res. 13, 185. https://doi.org/10.22034/IAR.2021.1924974.1142 (2021).Article 

    Google Scholar 
    Takizawa, Y. et al. A new insight into isotopic fractionation associated with decarboxylation in organisms: Implications for amino acid isotope approaches in biogeoscience. Progress Earth Planet. Sci. https://doi.org/10.1186/s40645-020-00364-w (2020).Article 

    Google Scholar 
    Ron-Harel, N. et al. T cell activation depends on extracellular alanine. Cell Rep. 28, 3011-3021.e4. https://doi.org/10.1016/j.celrep.2019.08.034 (2019).Article 
    CAS 

    Google Scholar 
    Wang, W. et al. Glycine metabolism in animals and humans: Implications for nutrition and health. Amino Acids 45, 463–477. https://doi.org/10.1007/s00726-013-1493-1 (2013).Article 
    CAS 

    Google Scholar 
    Mathis, D. & Shoelson, S. E. Immunometabolism: An emerging frontier. Nat. Rev. Immunol. 11, 81. https://doi.org/10.1038/nri2922 (2011).Article 
    CAS 

    Google Scholar 
    Guo, C. et al. Live Edwardsiella tarda vaccine enhances innate immunity by metabolic modulation in zebrafish. Fish Shellfish Immunol. 47, 664–673. https://doi.org/10.1016/j.fsi.2015.09.034 (2015).Article 
    CAS 

    Google Scholar 
    Peuss, R. et al. Adaptation to low parasite abundance affects immune investment and immunopathological responses of cavefish. Nat. Ecol. Evol. 4, 1416–1430. https://doi.org/10.1038/s41559-020-1234-2 (2020).Article 

    Google Scholar 
    Smyth, J. D. Fertilization of Schistocephalus solidus in vitro. Exp. Parasitol. 3, 64–71 (1954).Article 
    CAS 

    Google Scholar 
    Schärer, L. & Wedekind, C. Lifetime reproductive output in a hermaphrodite cestode when reproducing alone or in pairs. Evol. Ecol. 13, 381–394 (1999).Article 

    Google Scholar 
    McCullagh, J. S. Mixed-mode chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 24, 483–494. https://doi.org/10.1002/rcm.4322 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Dunn, P. J., Honch, N. V. & Evershed, R. P. Comparison of liquid chromatography-isotope ratio mass spectrometry (LC/IRMS) and gas chromatography-combustion-isotope ratio mass spectrometry (GC/C/IRMS) for the determination of collagen amino acid delta13C values for palaeodietary and palaeoecological reconstruction. Rapid Commun. Mass Spectrom. 25, 2995–3011. https://doi.org/10.1002/rcm.5174 (2011).Article 
    ADS 
    CAS 

    Google Scholar 
    Fry, B., Carter, J. F., Yamada, K., Yoshida, N. & Juchelka, D. Position-specific (13) C/(12) C analysis of amino acid carboxyl groups—Automated flow-injection-analysis based on reaction with ninhydrin. Rapid Commun. Mass Spectrom. https://doi.org/10.1002/rcm.8126 (2018).Article 

    Google Scholar 
    Marks, R. G. H., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. How to couple LC-IRMS with HRMS─A proof-of-concept study. Anal Chem 94, 2981–2987 (2022).Article 
    CAS 

    Google Scholar 
    Sun, Y. et al. A method for stable carbon isotope measurement of underivatized individual amino acids by multi-dimensional high-performance liquid chromatography and elemental analyzer/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 34, e8885. https://doi.org/10.1002/rcm.8885 (2020).Article 
    CAS 

    Google Scholar 
    Werner, R. A. & Brand, W. A. Referencing strategies and techniques in stable isotope ratio analysis. Rapid Commun. Mass Spectrom. 15, 501–519. https://doi.org/10.1002/rcm.258 (2001).Article 
    ADS 
    CAS 

    Google Scholar 
    Köster, D., Villalobos, I. M. S., Jochmann, M. A., Brand, W. A. & Schmidt, T. C. New concepts for the determination of oxidation efficiencies in liquid chromatography-isotope ratio mass spectrometry. Anal. Chem. 91, 5067–5073. https://doi.org/10.1021/acs.analchem.8b05315 (2019).Article 
    CAS 

    Google Scholar 
    Boschker, H. T., Moerdijk-Poortvliet, T. C., van Breugel, P., Houtekamer, M. & Middelburg, J. J. A versatile method for stable carbon isotope analysis of carbohydrates by high-performance liquid chromatography/isotope ratio mass spectrometry. Rapid Commun. Mass Spectrom. 22, 3902–3908. https://doi.org/10.1002/rcm.3804 (2008).Article 
    ADS 
    CAS 

    Google Scholar  More

  • in

    The vulnerability of global forests to human and climate impacts

    Duke, N. C. et al. Mar. Freshw. Res. 68, 1816–1829 (2017).Article 

    Google Scholar 
    Li, W. et al. Nat. Sustain. https://doi.org/10.1038/s41893-022-01020-5 (2023).Article 

    Google Scholar 
    Potapov, P. et al. Ecol. Soc. 13, 51 (2008).Article 

    Google Scholar 
    Hancock, S. et al. Earth Space Sci. 6, 294–310 (2019).Article 

    Google Scholar 
    Wade, C. M. et al. Forests 11, 539 (2020).Article 

    Google Scholar 
    Abhilash, P. C. Land 10, 201 (2021).Article 

    Google Scholar 
    Biermann, F., Kanie, N. & Kim, R. E. Curr. Opin. Environ. Sustain. 26–27, 26–31 (2017).Article 

    Google Scholar 
    den Elzen, M. et al. Energy Policy 126, 238–250 (2019).Article 

    Google Scholar 
    Betts, M. G. et al. Nature 547, 441–444 (2017).Article 
    CAS 

    Google Scholar 
    Watson, J. E. M. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-018-0490-x (2018).Article 

    Google Scholar  More